Journals / CMC / Vol.70, No.3
Table of Content


  • ARTICLE

    Polygonal Finite Element for Two-Dimensional Lid-Driven Cavity Flow

    T. Vu-Huu1, C. Le-Thanh2, H. Nguyen-Xuan3,4, M. Abdel-Wahab3,5,*
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4217-4239, 2022, DOI:10.32604/cmc.2022.020889
    Abstract This paper investigates a polygonal finite element (PFE) to solve a two-dimensional (2D) incompressible steady fluid problem in a cavity square. It is a well-known standard benchmark (i.e., lid-driven cavity flow)-to evaluate the numerical methods in solving fluid problems controlled by the Navier–Stokes (N–S) equation system. The approximation solutions provided in this research are based on our developed equal-order mixed PFE, called Pe1Pe1. It is an exciting development based on constructing the mixed scheme method of two equal-order discretisation spaces for both fluid pressure and velocity fields of flows and our proposed stabilisation technique. In this research, to handle the… More >

  • ARTICLE

    Multi-Step Detection of Simplex and Duplex Wormhole Attacks over Wireless Sensor Networks

    Abrar M. Alajlan*
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4241-4259, 2022, DOI:10.32604/cmc.2022.020585
    Abstract Detection of the wormhole attacks is a cumbersome process, particularly simplex and duplex over the wireless sensor networks (WSNs). Wormhole attacks are characterized as distributed passive attacks that can destabilize or disable WSNs. The distributed passive nature of these attacks makes them enormously challenging to detect. The main objective is to find all the possible ways in which how the wireless sensor network’s broadcasting character and transmission medium allows the attacker to interrupt network within the distributed environment. And further to detect the serious routing-disruption attack “Wormhole Attack” step by step through the different network mechanisms. In this paper, a… More >

  • ARTICLE

    Fuzzy Based Latent Dirichlet Allocation for Intrusion Detection in Cloud Using ML

    S. Ranjithkumar1,*, S. Chenthur Pandian2
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4261-4277, 2022, DOI:10.32604/cmc.2022.019031
    Abstract The growth of cloud in modern technology is drastic by provisioning services to various industries where data security is considered to be common issue that influences the intrusion detection system (IDS). IDS are considered as an essential factor to fulfill security requirements. Recently, there are diverse Machine Learning (ML) approaches that are used for modeling effectual IDS. Most IDS are based on ML techniques and categorized as supervised and unsupervised. However, IDS with supervised learning is based on labeled data. This is considered as a common drawback and it fails to identify the attack patterns. Similarly, unsupervised learning fails to… More >

  • ARTICLE

    Automatic Detection and Classification of Human Knee Osteoarthritis Using Convolutional Neural Networks

    Mohamed Yacin Sikkandar1,*, S. Sabarunisha Begum2, Abdulaziz A. Alkathiry3, Mashhor Shlwan N. Alotaibi1, Md Dilsad Manzar4
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4279-4291, 2022, DOI:10.32604/cmc.2022.020571
    Abstract Knee Osteoarthritis (KOA) is a degenerative knee joint disease caused by ‘wear and tear’ of ligaments between the femur and tibial bones. Clinically, KOA is classified into four grades ranging from 1 to 4 based on the degradation of the ligament in between these two bones and causes suffering from impaired movement. Identifying this space between bones through the anterior view of a knee X-ray image is solely subjective and challenging. Automatic classification of this process helps in the selection of suitable treatment processes and customized knee implants. In this research, a new automatic classification of KOA images based on… More >

  • ARTICLE

    An Efficient Proxy Blind Signcryption Scheme for IoT

    Aamer Khan1, Insaf Ullah2,*, Fahad Algarni3, Muhammad Naeem1, M. Irfan Uddin4, Muhammad Asghar Khan2
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4293-4306, 2022, DOI:10.32604/cmc.2022.017318
    (This article belongs to this Special Issue: Deep Learning and Parallel Computing for Intelligent and Efficient IoT)
    Abstract Recent years have witnessed growing scientific research interest in the Internet of Things (IoT) technologies, which supports the development of a variety of applications such as health care, Industry 4.0, agriculture, ecological data management, and other various domains. IoT utilizes the Internet as a prime medium of communication for both single documents as well as multi-digital messages. However, due to the wide-open nature of the Internet, it is important to ensure the anonymity, untraceably, confidentiality, and unforgeability of communication with efficient computational complexity and low bandwidth. We designed a light weight and secure proxy blind signcryption for multi-digital messages based… More >

  • An Access Control Scheme Using Heterogeneous Signcryption for IoT Environments

    Insaf Ullah1,*, Hira Zahid2 , Fahad Algarni3, Muhammad Asghar Khan1
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4307-4321, 2022, DOI:10.32604/cmc.2022.017380
    (This article belongs to this Special Issue: Machine Learning for Data Analytics)
    Abstract

    When the Wireless Sensor Network (WSN) is combined with the Internet of Things (IoT), it can be employed in a wide range of applications, such as agriculture, industry 4.0, health care, smart homes, among others. Accessing the big data generated by these applications in Cloud Servers (CSs), requires higher levels of authenticity and confidentiality during communication conducted through the Internet. Signcryption is one of the most promising approaches nowadays for overcoming such obstacles, due to its combined nature, i.e., signature and encryption. A number of researchers have developed schemes to address issues related to access control in the IoT literature,… More >

  • ARTICLE

    An Intent-Driven Closed-Loop Platform for 5G Network Service Orchestration

    Talha Ahmed Khan, Khizar Abbas, Afaq Muhammad, Wang-Cheol Song*
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4323-4340, 2022, DOI:10.32604/cmc.2022.017118
    (This article belongs to this Special Issue: Intelligent Software-defined Networking (SDN) Technologies for Future Generation Networks)
    Abstract The scope of the 5G network is not only limited to the enhancements in the form of the quality of service (QoS), but it also includes a wide range of services with various requirements. Besides this, many approaches and platforms are under the umbrella of 5G to achieve the goals of end-to-end service provisioning. However, the management of multiple services over heterogeneous platforms is a complex task. Each platform and service have various requirements to be handled by domain experts. Still, if the next-generation network management is dependent on manual updates, it will become impossible to provide seamless service provisioning… More >

  • ARTICLE

    Simulation of Non-Isothermal Turbulent Flows Through Circular Rings of Steel

    Abid. A. Memon1, M. Asif Memon1, Kaleemullah Bhatti1, Kamsing Nonlaopon2,*, Ilyas Khan3
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4341-4355, 2022, DOI:10.32604/cmc.2022.019407
    (This article belongs to this Special Issue: Role of Computer in Modelling & Solving Real-World Problems)
    Abstract This article is intended to examine the fluid flow patterns and heat transfer in a rectangular channel embedded with three semi-circular cylinders comprised of steel at the boundaries. Such an organization is used to generate the heat exchangers with tube and shell because of the production of more turbulence due to zigzag path which is in favor of rapid heat transformation. Because of little maintenance, the heat exchanger of such type is extensively used. Here, we generate simulation of flow and heat transfer using non-isothermal flow interface in the Comsol multiphysics 5.4 which executes the Reynolds averaged Navier stokes equation… More >

  • ARTICLE

    Ensembles of Deep Learning Framework for Stomach Abnormalities Classification

    Talha Saeed, Chu Kiong Loo*, Muhammad Shahreeza Safiruz Kassim
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4357-4372, 2022, DOI:10.32604/cmc.2022.019076
    (This article belongs to this Special Issue: Recent Advances in Deep Learning for Medical Image Analysis)
    Abstract

    Abnormalities of the gastrointestinal tract are widespread worldwide today. Generally, an effective way to diagnose these life-threatening diseases is based on endoscopy, which comprises a vast number of images. However, the main challenge in this area is that the process is time-consuming and fatiguing for a gastroenterologist to examine every image in the set. Thus, this led to the rise of studies on designing AI-based systems to assist physicians in the diagnosis. In several medical imaging tasks, deep learning methods, especially convolutional neural networks (CNNs), have contributed to the state-of-the-art outcomes, where the complicated nonlinear relation between target classes and… More >

  • ARTICLE

    Efficient Deep CNN Model for COVID-19 Classification

    Walid El-Shafai1,2,*, Amira A. Mahmoud1, El-Sayed M. El-Rabaie1, Taha E. Taha1, Osama F. Zahran1, Adel S. El-Fishawy1, Mohammed Abd-Elnaby3, Fathi E. Abd El-Samie1,4
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4373-4391, 2022, DOI:10.32604/cmc.2022.019354
    Abstract Coronavirus (COVID-19) infection was initially acknowledged as a global pandemic in Wuhan in China. World Health Organization (WHO) stated that the COVID-19 is an epidemic that causes a 3.4% death rate. Chest X-Ray (CXR) and Computerized Tomography (CT) screening of infected persons are essential in diagnosis applications. There are numerous ways to identify positive COVID-19 cases. One of the fundamental ways is radiology imaging through CXR, or CT images. The comparison of CT and CXR scans revealed that CT scans are more effective in the diagnosis process due to their high quality. Hence, automated classification techniques are required to facilitate… More >

  • ARTICLE

    An Efficient CNN-Based Hybrid Classification and Segmentation Approach for COVID-19 Detection

    Abeer D. Algarni1,*, Walid El-Shafai2, Ghada M. El Banby3, Fathi E. Abd El-Samie1,2, Naglaa F. Soliman1,4
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4393-4410, 2022, DOI:10.32604/cmc.2022.020265
    Abstract COVID-19 remains to proliferate precipitously in the world. It has significantly influenced public health, the world economy, and the persons’ lives. Hence, there is a need to speed up the diagnosis and precautions to deal with COVID-19 patients. With this explosion of this pandemic, there is a need for automated diagnosis tools to help specialists based on medical images. This paper presents a hybrid Convolutional Neural Network (CNN)-based classification and segmentation approach for COVID-19 detection from Computed Tomography (CT) images. The proposed approach is employed to classify and segment the COVID-19, pneumonia, and normal CT images. The classification stage is… More >

  • ARTICLE

    Prediction of Extremist Behaviour and Suicide Bombing from Terrorism Contents Using Supervised Learning

    Nasir Mahmood*, Muhammad Usman Ghani Khan
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4411-4428, 2022, DOI:10.32604/cmc.2022.013956
    (This article belongs to this Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)
    Abstract This study proposes an architecture for the prediction of extremist human behaviour from projected suicide bombings. By linking ‘dots’ of police data comprising scattered information of people, groups, logistics, locations, communication, and spatiotemporal characters on different social media groups, the proposed architecture will spawn beneficial information. This useful information will, in turn, help the police both in predicting potential terrorist events and in investigating previous events. Furthermore, this architecture will aid in the identification of criminals and their associates and handlers. Terrorism is psychological warfare, which, in the broadest sense, can be defined as the utilisation of deliberate violence for… More >

  • ARTICLE

    Blockchain and Machine Learning for Intelligent Multiple Factor-Based Ride-Hailing Services

    Zeinab Shahbazi, Yung-Cheol Byun*
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4429-4446, 2022, DOI:10.32604/cmc.2022.019755
    (This article belongs to this Special Issue: Advancements in Lightweight AI for Constrained Internet of Things Devices for Smart Cities)
    Abstract One of the common transportation systems in Korea is calling taxis through online applications, which is more convenient for passengers and drivers in the modern area. However, the driver's passenger taxi request can be rejected based on the driver's location and distance. Therefore, there is a need to specify driver's acceptance and rejection of the received request. The security of this system is another main core to save the transaction information and safety of passengers and drivers. In this study, the origin and destination of the Jeju island South Korea were captured from T-map and processed based on machine learning… More >

  • ARTICLE

    A Hybrid Model for Reliability Aware and Energy-Efficiency in Multicore Systems

    Samar Nour1,*, Sameh A. Salem1,2, Shahira M. Habashy1
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4447-4466, 2022, DOI:10.32604/cmc.2022.020775
    Abstract Recently, Multicore systems use Dynamic Voltage/Frequency Scaling (DV/FS) technology to allow the cores to operate with various voltage and/or frequencies than other cores to save power and enhance the performance. In this paper, an effective and reliable hybrid model to reduce the energy and makespan in multicore systems is proposed. The proposed hybrid model enhances and integrates the greedy approach with dynamic programming to achieve optimal Voltage/Frequency (Vmin/F) levels. Then, the allocation process is applied based on the available workloads. The hybrid model consists of three stages. The first stage gets the optimum safe voltage while the second stage sets… More >

  • ARTICLE

    Deep Neural Artificial Intelligence for IoT Based Tele Health Data Analytics

    Nithya Rekha Sivakumar1,*, Ahmed Zohair Ibrahim2
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4467-4483, 2022, DOI:10.32604/cmc.2022.019041
    Abstract

    Tele health utilizes information and communication mechanisms to convey medical information for providing clinical and educational assistances. It makes an effort to get the better of issues of health service delivery involving time factor, space and laborious terrains, validating cost-efficiency and finer ingress in both developed and developing countries. Tele health has been categorized into either real-time electronic communication, or store-and-forward communication. In recent years, a third-class has been perceived as remote healthcare monitoring or tele health, presuming data obtained via Internet of Things (IOT). Although, tele health data analytics and machine learning have been researched in great depth, there… More >

  • ARTICLE

    Distributed Secure Storage Scheme Based on Sharding Blockchain

    Jin Wang1,2, Chenchen Han1, Xiaofeng Yu3,*, Yongjun Ren4, R. Simon Sherratt5
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4485-4502, 2022, DOI:10.32604/cmc.2022.020648
    Abstract Distributed storage can store data in multiple devices or servers to improve data security. However, in today's explosive growth of network data, traditional distributed storage scheme is faced with some severe challenges such as insufficient performance, data tampering, and data lose. A distributed storage scheme based on blockchain has been proposed to improve security and efficiency of traditional distributed storage. Under this scheme, the following improvements have been made in this paper. This paper first analyzes the problems faced by distributed storage. Then proposed to build a new distributed storage blockchain scheme with sharding blockchain. The proposed scheme realizes the… More >

  • A Global Training Model for Beat Classification Using Basic Electrocardiogram Morphological Features

    Shubha Sumesh1, John Yearwood1, Shamsul Huda1 and Shafiq Ahmad2,*
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4503-4521, 2022, DOI:10.32604/cmc.2022.015474
    (This article belongs to this Special Issue: Deep Learning and Parallel Computing for Intelligent and Efficient IoT)
    Abstract

    Clinical Study and automatic diagnosis of electrocardiogram (ECG) data always remain a challenge in diagnosing cardiovascular activities. The analysis of ECG data relies on various factors like morphological features, classification techniques, methods or models used to diagnose and its performance improvement. Another crucial factor in the methodology is how to train the model for each patient. Existing approaches use standard training model which faces challenges when training data has variation due to individual patient characteristics resulting in a lower detection accuracy. This paper proposes an adaptive approach to identify performance improvement in building a training model that analyze global training… More >

  • ARTICLE

    Medical Data Clustering and Classification Using TLBO and Machine Learning Algorithms

    Ashutosh Kumar Dubey1,*, Umesh Gupta2, Sonal Jain2
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4523-4543, 2022, DOI:10.32604/cmc.2022.021148
    (This article belongs to this Special Issue: Role of Machine Learning and Evolutionary Algorithms for Cancer Detection and Prediction)
    Abstract This study aims to empirically analyze teaching-learning-based optimization (TLBO) and machine learning algorithms using k-means and fuzzy c-means (FCM) algorithms for their individual performance evaluation in terms of clustering and classification. In the first phase, the clustering (k-means and FCM) algorithms were employed independently and the clustering accuracy was evaluated using different computational measures. During the second phase, the non-clustered data obtained from the first phase were preprocessed with TLBO. TLBO was performed using k-means (TLBO-KM) and FCM (TLBO-FCM) (TLBO-KM/FCM) algorithms. The objective function was determined by considering both minimization and maximization criteria. Non-clustered data obtained from the first phase… More >

  • ARTICLE

    Novel Quantum Algorithms to Minimize Switching Functions Based on Graph Partitions

    Peng Gao*, Marek Perkowski, Yiwei Li, Xiaoyu Song
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4545-4561, 2022, DOI:10.32604/cmc.2022.020483
    Abstract After Google reported its realization of quantum supremacy, Solving the classical problems with quantum computing is becoming a valuable research topic. Switching function minimization is an important problem in Electronic Design Automation (EDA) and logic synthesis, most of the solutions are based on heuristic algorithms with a classical computer, it is a good practice to solve this problem with a quantum processer. In this paper, we introduce a new hybrid classic quantum algorithm using Grover’s algorithm and symmetric functions to minimize small Disjoint Sum of Product (DSOP) and Sum of Product (SOP) for Boolean switching functions. Our method is based… More >

  • ARTICLE

    Convolutional Neural Network Based Intelligent Handwritten Document Recognition

    Sagheer Abbas1, Yousef Alhwaiti2, Areej Fatima3, Muhammad A. Khan4, Muhammad Adnan Khan5,*, Taher M. Ghazal6,7, Asma Kanwal1,8, Munir Ahmad1, Nouh Sabri Elmitwally2,9
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4563-4581, 2022, DOI:10.32604/cmc.2022.021102
    Abstract This paper presents a handwritten document recognition system based on the convolutional neural network technique. In today’s world, handwritten document recognition is rapidly attaining the attention of researchers due to its promising behavior as assisting technology for visually impaired users. This technology is also helpful for the automatic data entry system. In the proposed system prepared a dataset of English language handwritten character images. The proposed system has been trained for the large set of sample data and tested on the sample images of user-defined handwritten documents. In this research, multiple experiments get very worthy recognition results. The proposed system… More >

  • ARTICLE

    Dual-Port Content Addressable Memory for Cache Memory Applications

    Allam Abumwais1,*, Adil Amirjanov1, Kaan Uyar1, Mujahed Eleyat2
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4583-4597, 2022, DOI:10.32604/cmc.2022.020529
    (This article belongs to this Special Issue: Application of Big Data Analytics in the Management of Business)
    Abstract Multicore systems oftentimes use multiple levels of cache to bridge the gap between processor and memory speed. This paper presents a new design of a dedicated pipeline cache memory for multicore processors called dual port content addressable memory (DPCAM). In addition, it proposes a new replacement algorithm based on hardware which is called a near-far access replacement algorithm (NFRA) to reduce the cost overhead of the cache controller and improve the cache access latency. The experimental results indicated that the latency for write and read operations are significantly less in comparison with a set-associative cache memory. Moreover, it was shown… More >

  • ARTICLE

    Deep Learning Based Modeling of Groundwater Storage Change

    Mohd Anul Haq1,*, Abdul Khadar Jilani1, P. Prabu2
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4599-4617, 2022, DOI:10.32604/cmc.2022.020495
    Abstract The understanding of water resource changes and a proper projection of their future availability are necessary elements of sustainable water planning. Monitoring GWS change and future water resource availability are crucial, especially under changing climatic conditions. Traditional methods for in situ groundwater well measurement are a significant challenge due to data unavailability. The present investigation utilized the Long Short Term Memory (LSTM) networks to monitor and forecast Terrestrial Water Storage Change (TWSC) and Ground Water Storage Change (GWSC) based on Gravity Recovery and Climate Experiment (GRACE) datasets from 2003–2025 for five basins of Saudi Arabia. An attempt has been made… More >

  • ARTICLE

    EEG-Based Neonatal Sleep Stage Classification Using Ensemble Learning

    Saadullah Farooq Abbasi1,2, Harun Jamil3, Wei Chen2,*
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4619-4633, 2022, DOI:10.32604/cmc.2022.020318
    (This article belongs to this Special Issue: Advancements in Lightweight AI for Constrained Internet of Things Devices for Smart Cities)
    Abstract Sleep stage classification can provide important information regarding neonatal brain development and maturation. Visual annotation, using polysomnography (PSG), is considered as a gold standard for neonatal sleep stage classification. However, visual annotation is time consuming and needs professional neurologists. For this reason, an internet of things and ensemble-based automatic sleep stage classification has been proposed in this study. 12 EEG features, from 9 bipolar channels, were used to train and test the base classifiers including convolutional neural network, support vector machine, and multilayer perceptron. Bagging and stacking ensembles are then used to combine the outputs for final classification. The proposed… More >

  • ARTICLE

    Mechanical Properties of All MoS2 Monolayer Heterostructures: Crack Propagation and Existing Notch Study

    Reza Khademi Zahedi1, Naif Alajlan2, Hooman Khademi Zahedi3, Timon Rabczuk2,*
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4635-4655, 2022, DOI:10.32604/cmc.2022.017682
    Abstract The outstanding thermal, optical, electrical and mechanical properties of molybdenum disolphide (MoS2) heterostructures make them exceptional candidates for an extensive area of applications. Nevertheless, despite considerable technological and academic interest, there is presently a few information regarding the mechanical properties of these novel two-dimensional (2D) materials in the presence of the defects. In this manuscript, we performed extensive molecular dynamics simulations on pre-cracked and pre-notched all-molybdenum disolphide (MoS2) heterostructure systems using ReaxFF force field. Therefore, we study the influence of several central-crack lengths and notch diameters on the mechanical response of 2H phase, 1T phase and composite 2H /1T MoS2More >

  • ARTICLE

    Analytic Beta-Wavelet Transform-Based Digital Image Watermarking for Secure Transmission

    Hesham Alhumyani1,*, Ibrahim Alrube1, Sameer Alsharif1, Ashraf Afifi1, Chokri Ben Amar1, Hala S. El-Sayed2, Osama S. Faragallah3
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4657-4673, 2022, DOI:10.32604/cmc.2022.020338
    Abstract The rapid development in the information technology field has introduced digital watermark technologies as a solution to prevent unauthorized copying and redistribution of data. This article introduces a self-embedded image verification and integrity scheme. The images are firstly split into dedicated segments of the same block sizes. Then, different Analytic Beta-Wavelet (ABW) orthogonal filters are utilized for embedding a self-segment watermark for image segment using a predefined method. ABW orthogonal filter coefficients are estimated to improve image reconstruction under different block sizes. We conduct a comparative study comparing the watermarked images using three kinds of ABW filters for block sizes… More >

  • ARTICLE

    Dynamic Hand Gesture Recognition Using 3D-CNN and LSTM Networks

    Muneeb Ur Rehman1, Fawad Ahmed1, Muhammad Attique Khan2, Usman Tariq3, Faisal Abdulaziz Alfouzan4, Nouf M. Alzahrani5, Jawad Ahmad6,*
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4675-4690, 2022, DOI:10.32604/cmc.2022.019586
    (This article belongs to this Special Issue: Recent Advances in Deep Learning, Information Fusion, and Features Selection for Video Surveillance Application)
    Abstract Recognition of dynamic hand gestures in real-time is a difficult task because the system can never know when or from where the gesture starts and ends in a video stream. Many researchers have been working on vision-based gesture recognition due to its various applications. This paper proposes a deep learning architecture based on the combination of a 3D Convolutional Neural Network (3D-CNN) and a Long Short-Term Memory (LSTM) network. The proposed architecture extracts spatial-temporal information from video sequences input while avoiding extensive computation. The 3D-CNN is used for the extraction of spectral and spatial features which are then given to… More >

  • ARTICLE

    Position Control of Flexible Joint Carts Using Adaptive Generalized Dynamics Inversion

    Ibrahim M. Mehedi1,2,*, Mohd Heidir Mohd Shah1 , Soon Xin Ng3 , Abdulah Jeza Aljohani1,2, Mohammed El-Hajjar3, Muhammad Moinuddin1,2
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4691-4705, 2022, DOI:10.32604/cmc.2022.020954
    (This article belongs to this Special Issue: Recent Advances in Metaheuristic Techniques and Their Real-World Applications)
    Abstract

    This paper presents the design and implementation of Adaptive Generalized Dynamic Inversion (AGDI) to track the position of a Linear Flexible Joint Cart (LFJC) system along with vibration suppression of the flexible joint. The proposed AGDI control law will be comprised of two control elements. The baseline (continuous) control law is based on principle of conventional GDI approach and is established by prescribing the constraint dynamics of controlled state variables that reflect the control objectives. The control law is realized by inverting the prescribed dynamics using dynamically scaled Moore-Penrose generalized inversion. To boost the robust attributes against system nonlinearities, parametric… More >

  • ARTICLE

    Ontology Based Ocean Knowledge Representation for Semantic Information Retrieval

    Anitha Velu*, Menakadevi Thangavelu
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4707-4724, 2022, DOI:10.32604/cmc.2022.020095
    Abstract The drastic growth of coastal observation sensors results in copious data that provide weather information. The intricacies in sensor-generated big data are heterogeneity and interpretation, driving high-end Information Retrieval (IR) systems. The Semantic Web (SW) can solve this issue by integrating data into a single platform for information exchange and knowledge retrieval. This paper focuses on exploiting the SW base system to provide interoperability through ontologies by combining the data concepts with ontology classes. This paper presents a 4-phase weather data model: data processing, ontology creation, SW processing, and query engine. The developed Oceanographic Weather Ontology helps to enhance data… More >

  • ARTICLE

    A Non-Destructive Time Series Model for the Estimation of Cherry Coffee Production

    Jhonn Pablo Rodríguez1,*, David Camilo Corrales1,2, David Griol3, Zoraida Callejas3, Juan Carlos Corrales1
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4725-4743, 2022, DOI:10.32604/cmc.2022.019135
    (This article belongs to this Special Issue: Artificial Intelligence based Smart precision agriculture with analytic pattern in sustainable environments using IoT)
    Abstract Coffee plays a key role in the generation of rural employment in Colombia. More than 785,000 workers are directly employed in this activity, which represents the 26% of all jobs in the agricultural sector. Colombian coffee growers estimate the production of cherry coffee with the main aim of planning the required activities, and resources (number of workers, required infrastructures), anticipating negotiations, estimating, price, and foreseeing losses of coffee production in a specific territory. These important processes can be affected by several factors that are not easy to predict (e.g., weather variability, diseases, or plagues.). In this paper, we propose a… More >

  • ARTICLE

    Reactions’ Descriptors Selection and Yield Estimation Using Metaheuristic Algorithms and Voting Ensemble

    Olutomilayo Olayemi Petinrin1, Faisal Saeed2, Xiangtao Li1, Fahad Ghabban2, Ka-Chun Wong1,3,*
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4745-4762, 2022, DOI:10.32604/cmc.2022.020523
    (This article belongs to this Special Issue: Recent Advances in Metaheuristic Techniques and Their Real-World Applications)
    Abstract Bioactive compounds in plants, which can be synthesized using N-arylation methods such as the Buchwald-Hartwig reaction, are essential in drug discovery for their pharmacological effects. Important descriptors are necessary for the estimation of yields in these reactions. This study explores ten metaheuristic algorithms for descriptor selection and model a voting ensemble for evaluation. The algorithms were evaluated based on computational time and the number of selected descriptors. Analyses show that robust performance is obtained with more descriptors, compared to cases where fewer descriptors are selected. The essential descriptor was deduced based on the frequency of occurrence within the 50 extracted… More >

  • ARTICLE

    BERT for Conversational Question Answering Systems Using Semantic Similarity Estimation

    Abdulaziz Al-Besher1, Kailash Kumar1, M. Sangeetha2,*, Tinashe Butsa3
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4763-4780, 2022, DOI:10.32604/cmc.2022.021033
    Abstract Most of the questions from users lack the context needed to thoroughly understand the problem at hand, thus making the questions impossible to answer. Semantic Similarity Estimation is based on relating user’s questions to the context from previous Conversational Search Systems (CSS) to provide answers without requesting the user's context. It imposes constraints on the time needed to produce an answer for the user. The proposed model enables the use of contextual data associated with previous Conversational Searches (CS). While receiving a question in a new conversational search, the model determines the question that refers to more past CS. The… More >

  • ARTICLE

    Error Detection and Pattern Prediction Through Phase II Process Monitoring

    Azam Zaka1, Riffat Jabeen2,*, Kanwal Iqbal Khan3
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4781-4802, 2022, DOI:10.32604/cmc.2022.020316
    (This article belongs to this Special Issue: Emerging Trends and Real-World Applications of Intelligent Computing Techniques)
    Abstract The continuous monitoring of the machine is beneficial in improving its process reliability through reflected power function distribution. It is substantial for identifying and removing errors at the early stages of production that ultimately benefit the firms in cost-saving and quality improvement. The current study introduces control charts that help the manufacturing concerns to keep the production process in control. It presents an exponentially weighted moving average and extended exponentially weighted moving average and then compared their performance. The percentiles estimator and the modified maximum likelihood estimator are used to constructing the control charts. The findings suggest that an extended… More >

  • ARTICLE

    IRKO: An Improved Runge-Kutta Optimization Algorithm for Global Optimization Problems

    R. Manjula Devi1, M. Premkumar2, Pradeep Jangir3, Mohamed Abdelghany Elkotb4,5, Rajvikram Madurai Elavarasan6, Kottakkaran Sooppy Nisar7,*
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4803-4827, 2022, DOI:10.32604/cmc.2022.020847
    Abstract Optimization is a key technique for maximizing or minimizing functions and achieving optimal cost, gains, energy, mass, and so on. In order to solve optimization problems, metaheuristic algorithms are essential. Most of these techniques are influenced by collective knowledge and natural foraging. There is no such thing as the best or worst algorithm; instead, there are more effective algorithms for certain problems. Therefore, in this paper, a new improved variant of a recently proposed metaphorless Runge-Kutta Optimization (RKO) algorithm, called Improved Runge-Kutta Optimization (IRKO) algorithm, is suggested for solving optimization problems. The IRKO is formulated using the basic RKO and… More >

  • A Hybrid Neural Network and Box-Jenkins Models for Time Series Forecasting

    Mohammad Hadwan1,2,3,*, Basheer M. Al-Maqaleh4 , Fuad N. Al-Badani5 , Rehan Ullah Khan1,3, Mohammed A. Al-Hagery6
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4829-4845, 2022, DOI:10.32604/cmc.2022.017824
    Abstract

    Time series forecasting plays a significant role in numerous applications, including but not limited to, industrial planning, water consumption, medical domains, exchange rates and consumer price index. The main problem is insufficient forecasting accuracy. The present study proposes a hybrid forecasting methods to address this need. The proposed method includes three models. The first model is based on the autoregressive integrated moving average (ARIMA) statistical model; the second model is a back propagation neural network (BPNN) with adaptive slope and momentum parameters; and the third model is a hybridization between ARIMA and BPNN (ARIMA/BPNN) and artificial neural networks and ARIMA… More >

  • ARTICLE

    Improved Software Implementation for Montgomery Elliptic Curve Cryptosystem

    Mohammad Al-Khatib*, Wafaa Saif
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4847-4865, 2022, DOI:10.32604/cmc.2022.021483
    Abstract The last decade witnessed rapid increase in multimedia and other applications that require transmitting and protecting huge amount of data streams simultaneously. For such applications, a high-performance cryptosystem is compulsory to provide necessary security services. Elliptic curve cryptosystem (ECC) has been introduced as a considerable option. However, the usual sequential implementation of ECC and the standard elliptic curve (EC) form cannot achieve required performance level. Moreover, the widely used Hardware implementation of ECC is costly option and may be not affordable. This research aims to develop a high-performance parallel software implementation for ECC. To achieve this, many experiments were performed… More >

  • ARTICLE

    Defocus Blur Segmentation Using Genetic Programming and Adaptive Threshold

    Muhammad Tariq Mahmood*
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4867-4882, 2022, DOI:10.32604/cmc.2022.019544
    (This article belongs to this Special Issue: Security and Privacy issues for various Emerging Technologies and Future Trends)
    Abstract Detection and classification of the blurred and the non-blurred regions in images is a challenging task due to the limited available information about blur type, scenarios and level of blurriness. In this paper, we propose an effective method for blur detection and segmentation based on transfer learning concept. The proposed method consists of two separate steps. In the first step, genetic programming (GP) model is developed that quantify the amount of blur for each pixel in the image. The GP model method uses the multi-resolution features of the image and it provides an improved blur map. In the second phase,… More >

  • ARTICLE

    Optimal Confidential Mechanisms in Smart City Healthcare

    R. Gopi1,*, P. Muthusamy2, P. Suresh3, C. G. Gabriel Santhosh Kumar4, Irina V. Pustokhina5, Denis A. Pustokhin6, K. Shankar7
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4883-4896, 2022, DOI:10.32604/cmc.2022.019442
    Abstract Smart City Healthcare (SHC2) system is applied in monitoring the patient at home while it is also expected to react to their needs in a timely manner. The system also concedes the freedom of a patient. IoT is a part of this system and it helps in providing care to the patients. IoT-based healthcare devices are trustworthy since it almost certainly recognizes the potential intensifications at very early stage and alerts the patients and medical experts to such an extent that they are provided with immediate care. Existing methodologies exhibit few shortcomings in terms of computational complexity, cost and data… More >

  • ARTICLE

    Towards Securing Machine Learning Models Against Membership Inference Attacks

    Sana Ben Hamida1,2, Hichem Mrabet3,4, Sana Belguith5,*, Adeeb Alhomoud6, Abderrazak Jemai7
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4897-4919, 2022, DOI:10.32604/cmc.2022.019709
    (This article belongs to this Special Issue: AI for Wearable Sensing--Smartphone / Smartwatch User Identification / Authentication)
    Abstract From fraud detection to speech recognition, including price prediction, Machine Learning (ML) applications are manifold and can significantly improve different areas. Nevertheless, machine learning models are vulnerable and are exposed to different security and privacy attacks. Hence, these issues should be addressed while using ML models to preserve the security and privacy of the data used. There is a need to secure ML models, especially in the training phase to preserve the privacy of the training datasets and to minimise the information leakage. In this paper, we present an overview of ML threats and vulnerabilities, and we highlight current progress… More >

  • ARTICLE

    Machine Learning Approaches to Detect DoS and Their Effect on WSNs Lifetime

    Raniyah Wazirali1, Rami Ahmad2,*
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4922-4946, 2022, DOI:10.32604/cmc.2022.020044
    (This article belongs to this Special Issue: Advanced IoT Industrial Solutions and Cyber Security Threats in Communication Networks)
    Abstract Energy and security remain the main two challenges in Wireless Sensor Networks (WSNs). Therefore, protecting these WSN networks from Denial of Service (DoS) and Distributed DoS (DDoS) is one of the WSN networks security tasks. Traditional packet deep scan systems that rely on open field inspection in transport layer security packets and the open field encryption trend are making machine learning-based systems the only viable choice for these types of attacks. This paper contributes to the evaluation of the use machine learning algorithms in WSN nodes traffic and their effect on WSN network life time. We examined the performance metrics… More >

  • ARTICLE

    Personality Detection Using Context Based Emotions in Cognitive Agents

    Nouh Sabri Elmitwally1,2, Asma Kanwal3,4, Sagheer Abbas3, Muhammad A. Khan5, Muhammad Adnan Khan6,*, Munir Ahmad3, Saad Alanazi1
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4947-4964, 2022, DOI:10.32604/cmc.2022.021104
    Abstract Detection of personality using emotions is a research domain in artificial intelligence. At present, some agents can keep the human’s profile for interaction and adapts themselves according to their preferences. However, the effective method for interaction is to detect the person’s personality by understanding the emotions and context of the subject. The idea behind adding personality in cognitive agents begins an attempt to maximize adaptability on the basis of behavior. In our daily life, humans socially interact with each other by analyzing the emotions and context of interaction from audio or visual input. This paper presents a conceptual personality model… More >

  • ARTICLE

    Hybrid Sensor Selection Technique for Lifetime Extension of Wireless Sensor Networks

    Khaled M. Fouad1 , Basma M. Hassan2,*, Omar M. Salim3
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4965-4985, 2022, DOI:10.32604/cmc.2022.020926
    (This article belongs to this Special Issue: Role of Computer in Modelling & Solving Real-World Problems)
    Abstract

    Energy conservation is a crucial issue to extend the lifetime of wireless sensor networks (WSNs) where the battery capacity and energy sources are very restricted. Intelligent energy-saving techniques can help designers overcome this issue by reducing the number of selected sensors that report environmental measurements by eliminating all replicated and unrelated features. This paper suggests a Hybrid Sensor Selection (HSS) technique that combines filter-wrapper method to acquire a rich-informational subset of sensors in a reasonable time. HSS aims to increase the lifetime of WSNs by using the optimal number of sensors. At the same time, HSS maintains the desired level… More >

  • ARTICLE

    A Novel Auto-Annotation Technique for Aspect Level Sentiment Analysis

    Muhammad Aasim Qureshi1,*, Muhammad Asif1, Mohd Fadzil Hassan2, Ghulam Mustafa1, Muhammad Khurram Ehsan1, Aasim Ali1, Unaza Sajid1
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4987-5004, 2022, DOI:10.32604/cmc.2022.020544
    (This article belongs to this Special Issue: Machine Learning Empowered Secure Computing for Intelligent Systems)
    Abstract In machine learning, sentiment analysis is a technique to find and analyze the sentiments hidden in the text. For sentiment analysis, annotated data is a basic requirement. Generally, this data is manually annotated. Manual annotation is time consuming, costly and laborious process. To overcome these resource constraints this research has proposed a fully automated annotation technique for aspect level sentiment analysis. Dataset is created from the reviews of ten most popular songs on YouTube. Reviews of five aspects—voice, video, music, lyrics and song, are extracted. An N-Gram based technique is proposed. Complete dataset consists of 369436 reviews that took 173.53… More >

  • ARTICLE

    Alzheimer Disease Detection Empowered with Transfer Learning

    Taher M. Ghazal1,2, Sagheer Abbas3, Sundus Munir3,4, M. A. Khan5, Munir Ahmad3, Ghassan F. Issa2, Syeda Binish Zahra4, Muhammad Adnan Khan6,*, Mohammad Kamrul Hasan1
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5005-5019, 2022, DOI:10.32604/cmc.2022.020866
    Abstract Alzheimer's disease is a severe neuron disease that damages brain cells which leads to permanent loss of memory also called dementia. Many people die due to this disease every year because this is not curable but early detection of this disease can help restrain the spread. Alzheimer's is most common in elderly people in the age bracket of 65 and above. An automated system is required for early detection of disease that can detect and classify the disease into multiple Alzheimer classes. Deep learning and machine learning techniques are used to solve many medical problems like this. The proposed system… More >

  • ARTICLE

    Semantic Information Extraction from Multi-Corpora Using Deep Learning

    Sunil Kumar1, Hanumat G. Sastry1, Venkatadri Marriboyina2, Hammam Alshazly3,*, Sahar Ahmed Idris4, Madhushi Verma5, Manjit Kaur5
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5021-5038, 2022, DOI:10.32604/cmc.2022.021149
    (This article belongs to this Special Issue: Recent Advances in Metaheuristic Techniques and Their Real-World Applications)
    Abstract Information extraction plays a vital role in natural language processing, to extract named entities and events from unstructured data. Due to the exponential data growth in the agricultural sector, extracting significant information has become a challenging task. Though existing deep learning-based techniques have been applied in smart agriculture for crop cultivation, crop disease detection, weed removal, and yield production, still it is difficult to find the semantics between extracted information due to unswerving effects of weather, soil, pest, and fertilizer data. This paper consists of two parts. An initial phase, which proposes a data preprocessing technique for removal of ambiguity… More >

  • ARTICLE

    Local-Tetra-Patterns for Face Recognition Encoded on Spatial Pyramid Matching

    Khuram Nawaz Khayam1, Zahid Mehmood2,*, Hassan Nazeer Chaudhry3, Muhammad Usman Ashraf4, Usman Tariq5, Mohammed Nawaf Altouri6, Khalid Alsubhi7
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5039-5058, 2022, DOI:10.32604/cmc.2022.019975
    Abstract Face recognition is a big challenge in the research field with a lot of problems like misalignment, illumination changes, pose variations, occlusion, and expressions. Providing a single solution to solve all these problems at a time is a challenging task. We have put some effort to provide a solution to solving all these issues by introducing a face recognition model based on local tetra patterns and spatial pyramid matching. The technique is based on a procedure where the input image is passed through an algorithm that extracts local features by using spatial pyramid matching and max-pooling. Finally, the input image… More >

  • ARTICLE

    Convolutional Neural Network-Based Regression for Predicting the Chloride Ion Diffusion Coefficient of Concrete

    Hyun Kyu Shin1, Ha Young Kim2, Sang Hyo Lee3,*
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5059-5071, 2022, DOI:10.32604/cmc.2022.017262
    Abstract The durability performance of reinforced concrete (RC) building structures is significantly affected by the corrosion of the steel reinforcement due to chloride penetration, thus, the chloride ion diffusion coefficient should be investigated through experiments or theoretical equations to assess the durability of an RC structure. This study aims to predict the chloride ion diffusion coefficient of concrete, a heterogeneous material. A convolutional neural network (CNN)-based regression model that learns the condition of the concrete surface through deep learning, is developed to efficiently obtain the chloride ion diffusion coefficient. For the model implementation to determine the chloride ion diffusion coefficient, concrete… More >

  • ARTICLE

    Controller Placement in Software Defined Internet of Things Using Optimization Algorithm

    Sikander Hans1, Smarajit Ghosh1, Aman Kataria2, Vinod Karar2,*, Sarika Sharma3
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5073-5089, 2022, DOI:10.32604/cmc.2022.019971
    Abstract The current and future status of the internet is represented by the upcoming Internet of Things (IoT). The internet can connect the huge amount of data, which contains lot of processing operations and efforts to transfer the pieces of information. The emerging IoT technology in which the smart ecosystem is enabled by the physical object fixed with software electronics, sensors and network connectivity. Nowadays, there are two trending technologies that take the platform i.e., Software Defined Network (SDN) and IoT (SD-IoT). The main aim of the IoT network is to connect and organize different objects with the internet, which is… More >

  • ARTICLE

    Forecasting E-Commerce Adoption Based on Bidirectional Recurrent Neural Networks

    Abdullah Ali Salamai1,*, Ather Abdulrahman Ageeli1, El-Sayed M. El-kenawy2
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5091-5106, 2022, DOI:10.32604/cmc.2022.021268
    (This article belongs to this Special Issue: Artificial Intelligence and Machine Learning Algorithms in Real-World Applications and Theories)
    Abstract E-commerce refers to a system that allows individuals to purchase and sell things online. The primary goal of e-commerce is to offer customers the convenience of not going to a physical store to make a purchase. They will purchase the item online and have it delivered to their home within a few days. The goal of this research was to develop machine learning algorithms that might predict e-commerce platform sales. A case study has been designed in this paper based on a proposed continuous Stochastic Fractal Search (SFS) based on a Guided Whale Optimization Algorithm (WOA) to optimize the parameter… More >

  • ARTICLE

    Multi-View Multi-Modal Head-Gaze Estimation for Advanced Indoor User Interaction

    Jung-Hwa Kim1, Jin-Woo Jeong2,*
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5107-5132, 2022, DOI:10.32604/cmc.2022.021107
    Abstract Gaze estimation is one of the most promising technologies for supporting indoor monitoring and interaction systems. However, previous gaze estimation techniques generally work only in a controlled laboratory environment because they require a number of high-resolution eye images. This makes them unsuitable for welfare and healthcare facilities with the following challenging characteristics: 1) users’ continuous movements, 2) various lighting conditions, and 3) a limited amount of available data. To address these issues, we introduce a multi-view multi-modal head-gaze estimation system that translates the user’s head orientation into the gaze direction. The proposed system captures the user using multiple cameras with… More >

  • ARTICLE

    Fractional Order Linear Active Disturbance Rejection Control for Linear Flexible Joint System

    Ibrahim M. Mehedi1,2,*, Rachid Mansouri3, Ubaid M. Al-Saggaf1,2, Ahmed I. M. Iskanderani1, Maamar Bettayeb4, Abdulah Jeza Aljohani1,2, Thangam Palaniswamy1, Shaikh Abdul Latif5, Abdul Latif6
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5133-5142, 2022, DOI:10.32604/cmc.2022.021018
    (This article belongs to this Special Issue: Recent Advances in Metaheuristic Techniques and Their Real-World Applications)
    Abstract A linear flexible joint system using fractional order linear active disturbance rejection control is studied in this paper. With this control scheme, the performance against disturbances, uncertainties, and attenuation is enhanced. Linear active disturbance rejection control (LADRC) is mainly based on an extended state observer (ESO) technology. A fractional integral (FOI) action is combined with the LADRC technique which proposes a hybrid control scheme like FO-LADRC. Incorporating this FOI action improves the robustness of the standard LADRC. The set-point tracking of the proposed FO-LADRC scheme is designed by Bode's ideal transfer function (BITF) based robust closed-loop concept, an appropriate pole… More >

  • ARTICLE

    Accurate Multi-Site Daily-Ahead Multi-Step PM2.5 Concentrations Forecasting Using Space-Shared CNN-LSTM

    Xiaorui Shao, Chang Soo Kim*
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5143-5160, 2022, DOI:10.32604/cmc.2022.020689
    Abstract

    Accurate multi-step PM2.5 (particulate matter with diameters 2.5um) concentration prediction is critical for humankinds’ health and air population management because it could provide strong evidence for decision-making. However, it is very challenging due to its randomness and variability. This paper proposed a novel method based on convolutional neural network (CNN) and long-short-term memory (LSTM) with a space-shared mechanism, named space-shared CNN-LSTM (SCNN-LSTM) for multi-site daily-ahead multi-step PM2.5 forecasting with self-historical series. The proposed SCNN-LSTM contains multi-channel inputs, each channel corresponding to one-site historical PM2.5 concentration series. In which, CNN and LSTM are used to extract each… More >

  • ARTICLE

    IoT-Cloud Empowered Aerial Scene Classification for Unmanned Aerial Vehicles

    K. R. Uthayan1,*, G. Lakshmi Vara Prasad2, V. Mohan3, C. Bharatiraja4, Irina V. Pustokhina5, Denis A. Pustokhin6, Vicente García Díaz7
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5161-5177, 2022, DOI:10.32604/cmc.2022.021300
    Abstract Recent trends in communication technologies and unmanned aerial vehicles (UAVs) find its application in several areas such as healthcare, surveillance, transportation, etc. Besides, the integration of Internet of things (IoT) with cloud computing environment offers several benefits for the UAV communication. At the same time, aerial scene classification is one of the major research areas in UAV-enabled MEC systems. In UAV aerial imagery, efficient image representation is crucial for the purpose of scene classification. The existing scene classification techniques generate mid-level image features with limited representation capabilities that often end up in producing average results. Therefore, the current research work… More >

  • ARTICLE

    Leveraging Active Decremental TTL Measuring for Flexible and Efficient NAT identification

    Tao Yang1, Chengyu Wang1, Tongqing Zhou1, Zhiping Cai1,*, Kui Wu2, Bingnan Hou1
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5179-5198, 2022, DOI:10.32604/cmc.2022.021626
    Abstract Malicious attacks can be launched by misusing the network address translation technique as a camouflage. To mitigate such threats, network address translation identification is investigated to identify network address translation devices and detect abnormal behaviors. However, existing methods in this field are mainly developed for relatively small-scale networks and work in an offline manner, which cannot adapt to the real-time inference requirements in high-speed network scenarios. In this paper, we propose a flexible and efficient network address translation identification scheme based on actively measuring the distance of a round trip to a target with decremental time-to-live values. The basic intuition… More >

  • ARTICLE

    Predicting Resource Availability in Local Mobile Crowd Computing Using Convolutional GRU

    Pijush Kanti Dutta Pramanik1, Nilanjan Sinhababu2, Anand Nayyar3,4,*, Mehedi Masud5, Prasenjit Choudhury1
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5199-5212, 2022, DOI:10.32604/cmc.2022.019630
    Abstract In mobile crowd computing (MCC), people’s smart mobile devices (SMDs) are utilized as computing resources. Considering the ever-growing computing capabilities of today’s SMDs, a collection of them can offer significantly high-performance computing services. In a local MCC, the SMDs are typically connected to a local Wi-Fi network. Organizations and institutions can leverage the SMDs available within the campus to form local MCCs to cater to their computing needs without any financial and operational burden. Though it offers an economical and sustainable computing solution, users’ mobility poses a serious issue in the QoS of MCC. To address this, before submitting a… More >

  • ARTICLE

    Wi-Fi Positioning Dataset with Multiusers and Multidevices Considering Spatio-Temporal Variations

    Imran Ashraf, Sadia Din, Soojung Hur, Yongwan Park*
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5213-5232, 2022, DOI:10.32604/cmc.2022.018707
    Abstract Precise information on indoor positioning provides a foundation for position-related customer services. Despite the emergence of several indoor positioning technologies such as ultrawideband, infrared, radio frequency identification, Bluetooth beacons, pedestrian dead reckoning, and magnetic field, Wi-Fi is one of the most widely used technologies. Predominantly, Wi-Fi fingerprinting is the most popular method and has been researched over the past two decades. Wi-Fi positioning faces three core problems: device heterogeneity, robustness to signal changes caused by human mobility, and device attitude, i.e., varying orientations. The existing methods do not cover these aspects owing to the unavailability of publicly available datasets. This… More >

  • ARTICLE

    Efficient Morphological Segmentation of Brain Hemorrhage Stroke Lesion Through MultiResUNet

    R. Shijitha1,*, P. Karthigaikumar2, A. Stanly Paul2
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5233-5249, 2022, DOI:10.32604/cmc.2022.020227
    Abstract Brain Hemorrhagic stroke is a serious malady that is caused by the drop in blood flow through the brain and causes the brain to malfunction. Precise segmentation of brain hemorrhage is crucial, so an enhanced segmentation is carried out in this research work. The brain image of various patients has taken using an MRI scanner by the utilization of T1, T2, and FLAIR sequence. This work aims to segment the Brain Hemorrhagic stroke using deep learning-based Multi-resolution UNet (multires UNet) through morphological operations. It is hard to precisely segment the brain lesions to extract the existing region of stroke. This… More >

  • ARTICLE

    Deep Neural Network Driven Automated Underwater Object Detection

    Ajisha Mathias1, Samiappan Dhanalakshmi1,*, R. Kumar1, R. Narayanamoorthi2
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5251-5267, 2022, DOI:10.32604/cmc.2022.021168
    Abstract Object recognition and computer vision techniques for automated object identification are attracting marine biologist's interest as a quicker and easier tool for estimating the fish abundance in marine environments. However, the biggest problem posed by unrestricted aquatic imaging is low luminance, turbidity, background ambiguity, and context camouflage, which make traditional approaches rely on their efficiency due to inaccurate detection or elevated false-positive rates. To address these challenges, we suggest a systemic approach to merge visual features and Gaussian mixture models with You Only Look Once (YOLOv3) deep network, a coherent strategy for recognizing fish in challenging underwater images. As an… More >

  • ARTICLE

    Intelligent Multilevel Node Authentication in Mobile Computing Using Clone Node

    Neha Malhotra1,2,*, Manju Bala3
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5269-5284, 2022, DOI:10.32604/cmc.2022.020920
    (This article belongs to this Special Issue: Emerging Applications of Artificial Intelligence, Machine learning and Data Science)
    Abstract Nodes in a mobile computing system are vulnerable to clone attacks due to their mobility. In such attacks, an adversary accesses a few network nodes, generates replication, then inserts this replication into the network, potentially resulting in numerous internal network attacks. Most existing techniques use a central base station, which introduces several difficulties into the system due to the network's reliance on a single point, while other ways generate more overhead while jeopardising network lifetime. In this research, an intelligent double hashing-based clone node identification scheme was used, which reduces communication and memory costs while performing the clone detection procedure.… More >

  • ARTICLE

    An Auction-Based Recommender System for Over-The-Top Platform

    Hameed AlQaheri1,*, Anjan Bandyopadhay2, Debolina Nath2, Shreyanta Kar2, Arunangshu Banerjee2
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5285-5304, 2022, DOI:10.32604/cmc.2022.021631
    Abstract In this era of digital domination, it is fit to say that individuals are more inclined towards viewership on online platforms due to the wide variety and the scope of individual preferences it provides. In the past few years, there has been a massive growth in the popularity of Over-The-Top platforms, with an increasing number of consumers adapting to them. The Covid-19 pandemic has also caused the proliferation of these services as people are restricted to their homes. Consumers are often in a dilemma about which subscription plan to choose, and this is where a recommendation system makes their task… More >

  • ARTICLE

    Disease Diagnosis System Using IoT Empowered with Fuzzy Inference System

    Talha Mahboob Alam1,*, Kamran Shaukat2,6, Adel Khelifi3, Wasim Ahmad Khan4, Hafiz Muhammad Ehtisham Raza5, Muhammad Idrees6, Suhuai Luo2, Ibrahim A. Hameed7
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5305-5319, 2022, DOI:10.32604/cmc.2022.020344
    (This article belongs to this Special Issue: Machine Learning Applications in Medical, Finance, Education and Cyber Security)
    Abstract Disease diagnosis is a challenging task due to a large number of associated factors. Uncertainty in the diagnosis process arises from inaccuracy in patient attributes, missing data, and limitation in the medical expert's ability to define cause and effect relationships when there are multiple interrelated variables. This paper aims to demonstrate an integrated view of deploying smart disease diagnosis using the Internet of Things (IoT) empowered by the fuzzy inference system (FIS) to diagnose various diseases. The Fuzzy System is one of the best systems to diagnose medical conditions because every disease diagnosis involves many uncertainties, and fuzzy logic is… More >

  • ARTICLE

    Automatic Detection of Nephrops Norvegicus Burrows from Underwater Imagery Using Deep Learning

    Atif Naseer1,*, Enrique Nava Baro1, Sultan Daud Khan2, Yolanda Vila3, Jennifer Doyle4
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5321-5344, 2022, DOI:10.32604/cmc.2022.020886
    Abstract The Norway lobster, Nephrops norvegicus, is one of the main commercial crustacean fisheries in Europe. The abundance of Nephrops norvegicus stocks is assessed based on identifying and counting the burrows where they live from underwater videos collected by camera systems mounted on sledges. The Spanish Oceanographic Institute (IEO) and Marine Institute Ireland (MI-Ireland) conducts annual underwater television surveys (UWTV) to estimate the total abundance of Nephrops within the specified area, with a coefficient of variation (CV) or relative standard error of less than 20%. Currently, the identification and counting of the Nephrops burrows are carried out manually by the marine… More >

  • ARTICLE

    Using Capsule Networks for Android Malware Detection Through Orientation-Based Features

    Sohail Khan1,*, Mohammad Nauman2, Suleiman Ali Alsaif1, Toqeer Ali Syed3, Hassan Ahmad Eleraky1
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5345-5362, 2022, DOI:10.32604/cmc.2022.021271
    Abstract Mobile phones are an essential part of modern life. The two popular mobile phone platforms, Android and iPhone Operating System (iOS), have an immense impact on the lives of millions of people. Among these two, Android currently boasts more than 84% market share. Thus, any personal data put on it are at great risk if not properly protected. On the other hand, more than a million pieces of malware have been reported on Android in just 2021 till date. Detecting and mitigating all this malware is extremely difficult for any set of human experts. Due to this reason, machine learning–and… More >

  • ARTICLE

    A Hybrid Deep Learning-Based Unsupervised Anomaly Detection in High Dimensional Data

    Amgad Muneer1,2,*, Shakirah Mohd Taib1,2, Suliman Mohamed Fati3, Abdullateef O. Balogun1, Izzatdin Abdul Aziz1,2
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5363-5381, 2022, DOI:10.32604/cmc.2022.021113
    (This article belongs to this Special Issue: Applications of Machine Learning for Big Data)
    Abstract Anomaly detection in high dimensional data is a critical research issue with serious implication in the real-world problems. Many issues in this field still unsolved, so several modern anomaly detection methods struggle to maintain adequate accuracy due to the highly descriptive nature of big data. Such a phenomenon is referred to as the “curse of dimensionality” that affects traditional techniques in terms of both accuracy and performance. Thus, this research proposed a hybrid model based on Deep Autoencoder Neural Network (DANN) with five layers to reduce the difference between the input and output. The proposed model was applied to a… More >

  • ARTICLE

    Beamforming Performance Analysis of Millimeter-Wave 5G Wireless Networks

    Omar A. Saraereh*, Ashraf Ali
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5383-5397, 2022, DOI:10.32604/cmc.2022.021724
    (This article belongs to this Special Issue: Advances in 5G Antenna Designs and Systems)
    Abstract With the rapid growth in the number of mobile devices and user connectivity, the demand for higher system capacity and improved quality-of-service is required. As the demand for high-speed wireless communication grows, numerous modulation techniques in the frequency, temporal, and spatial domains, such as orthogonal frequency division multiplexing (OFDM), time division multiple access (TDMA), space division multiple access (SDMA), and multiple-input multiple-output (MIMO), are being developed. Along with those approaches, electromagnetic waves’ orbital angular momentum (OAM) is attracting attention because it has the potential to boost the wireless communication capacity. Antenna electromagnetic radiation can be described by a sum of… More >

  • ARTICLE

    Sentiment Analysis on Social Media Using Genetic Algorithm with CNN

    Dharmendra Dangi*, Amit Bhagat, Dheeraj Kumar Dixit
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5399-5419, 2022, DOI:10.32604/cmc.2022.020431
    (This article belongs to this Special Issue: Emerging Applications of Artificial Intelligence, Machine learning and Data Science)
    Abstract There are various intense forces causing customers to use evaluated data when using social media platforms and microblogging sites. Today, customers throughout the world share their points of view on all kinds of topics through these sources. The massive volume of data created by these customers makes it impossible to analyze such data manually. Therefore, an efficient and intelligent method for evaluating social media data and their divergence needs to be developed. Today, various types of equipment and techniques are available for automatically estimating the classification of sentiments. Sentiment analysis involves determining people's emotions using facial expressions. Sentiment analysis can… More >

  • ARTICLE

    Indoor Electromagnetic Radiation Intensity Relationship to Total Energy of Household Appliances

    Murad A.A. Almekhlafi1, Lamia Osman Widaa2, Fahd N. Al-Wesabi3,*, Mohammad Alamgeer4, Anwer Mustafa Hilal5, Manar Ahmed Hamza5, Abu Sarwar Zamani5, Mohammed Rizwanullah5
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5421-5435, 2022, DOI:10.32604/cmc.2022.019823
    Abstract The rapid technological developments in the modern era have led to increased electrical equipment in our daily lives, work, and homes. From this standpoint, the main objective of this study is to evaluate the potential relationship between the intensity of electromagnetic radiation and the total energy of household appliances in the living environment within the building by measuring and analyzing the strength of the electric field and the entire electromagnetic radiation flux density of electrical devices operating at frequencies (5 Hz to 1 kHz). The living room was chosen as a center for measurement at 15 homes in three different… More >

  • ARTICLE

    RSS-Based Indoor Localization System with Single Base Station

    Samir Salem Al-Bawri1,*, Mohammad Tariqul Islam2, Mandeep Jit Singh1,2, Mohd Faizal Jamlos3, Adam Narbudowicz4, Max J. Ammann4, Dominique M. M. P. Schreurs5
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5437-5452, 2022, DOI:10.32604/cmc.2022.020781
    (This article belongs to this Special Issue: Intelligent Computing Techniques for Communication Systems)
    Abstract The paper proposes an Indoor Localization System (ILS) which uses only one fixed Base Station (BS) with simple non-reconfigurable antennas. The proposed algorithm measures Received Signal Strength (RSS) and maps it to the location in the room by estimating signal strength of a direct line of sight (LOS) signal and signal of the first order reflection from the wall. The algorithm is evaluated through both simulations and empirical measurements in a furnished open space office, sampling 21 different locations in the room. It is demonstrated the system can identify user’s real-time location with a maximum estimation error below 0.7 m… More >

  • ARTICLE

    Suggestion of Maintenance Criteria for Electric Railroad Facilities Based on Fuzzy TOPSIS

    Sunwoo Hwang1, Joouk Kim1, Hagseoung Kim1, Hyungchul Kim2, Youngmin Kim3,*
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5453-5466, 2022, DOI:10.32604/cmc.2022.021057
    (This article belongs to this Special Issue: Analysis, Processing, and Applications of Fuzzy System and Deep Learning)
    Abstract This paper is on the suggestion of maintenance items for electric railway facility systems. With the recent increase in the use of electric locomotives, the utilization and importance of railroad electrical facility systems are also increasing, but the railroad electrical facility system in Korea is rapidly aging. To solve this problem, various methodologies are applied to ensure operational reliability and stability for railroad electrical facility systems, but there is a lack of detailed evaluation criteria for railroad electrical facility system maintenance. Also, maintenance items must be selected in a scientific and systematic method. Therefore, railroad electrical facility systems are selected… More >

  • ARTICLE

    Deep Stacked Ensemble Learning Model for COVID-19 Classification

    G. Madhu1, B. Lalith Bharadwaj1, Rohit Boddeda2, Sai Vardhan1, K. Sandeep Kautish3, Khalid Alnowibet4, Adel F. Alrasheedi4, Ali Wagdy Mohamed5,6,*
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5467-5469, 2022, DOI:10.32604/cmc.2022.020455
    (This article belongs to this Special Issue: Role of Computer in Modelling & Solving Real-World Problems)
    Abstract COVID-19 is a growing problem worldwide with a high mortality rate. As a result, the World Health Organization (WHO) declared it a pandemic. In order to limit the spread of the disease, a fast and accurate diagnosis is required. A reverse transcript polymerase chain reaction (RT-PCR) test is often used to detect the disease. However, since this test is time-consuming, a chest computed tomography (CT) or plain chest X-ray (CXR) is sometimes indicated. The value of automated diagnosis is that it saves time and money by minimizing human effort. Three significant contributions are made by our research. Its initial purpose… More >

  • ARTICLE

    Two-Mode Biomedical Sensor Build-up: Characterization of Optical Amplifier

    Usman Masud1,2,*, Fathe Jeribi3, Mohammed Alhameed3, Faraz Akram4, Ali Tahir3, Mohammad Yousaf Naudhani5
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5487-5489, 2022, DOI:10.32604/cmc.2022.020417
    (This article belongs to this Special Issue: Role of Computer in Modelling & Solving Real-World Problems)
    Abstract Intracavity absorption spectroscopy is a strikingly sensitive technique that has been integrated with a two-wavelength setup to develop a sensor for human breath. Various factors are considered in such a scenario, out of which Relative Intensity Noise (RIN) has been exploited as an important parameter to characterize and calibrate the said setup. During the performance of an electrical based assessment arrangement which has been developed in the laboratory as an alternative to the expensive Agilent setup, the optical amplifier plays a pivotal role in its development and operation, along with other components and their significance. Therefore, the investigation and technical… More >

  • ARTICLE

    Fusion of Infrared and Visible Images Using Fuzzy Based Siamese Convolutional Network

    Kanika Bhalla1, Deepika Koundal2,*, Surbhi Bhatia3, Mohammad Khalid Imam Rahmani4, Muhammad Tahir4
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5503-5518, 2022, DOI:10.32604/cmc.2022.021125
    (This article belongs to this Special Issue: Innovations in Artificial Intelligence using Data Mining and Big Data)
    Abstract Traditional techniques based on image fusion are arduous in integrating complementary or heterogeneous infrared (IR)/visible (VS) images. Dissimilarities in various kind of features in these images are vital to preserve in the single fused image. Hence, simultaneous preservation of both the aspects at the same time is a challenging task. However, most of the existing methods utilize the manual extraction of features; and manual complicated designing of fusion rules resulted in a blurry artifact in the fused image. Therefore, this study has proposed a hybrid algorithm for the integration of multi-features among two heterogeneous images. Firstly, fuzzification of two IR/VS… More >

  • ARTICLE

    Robust Length of Stay Prediction Model for Indoor Patients

    Ayesha Siddiqa1, Syed Abbas Zilqurnain Naqvi1, Muhammad Ahsan1, Allah Ditta2, Hani Alquhayz3, M. A. Khan4, Muhammad Adnan Khan5,*
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5519-5536, 2022, DOI:10.32604/cmc.2022.021666
    Abstract Due to unforeseen climate change, complicated chronic diseases, and mutation of viruses’ hospital administration’s top challenge is to know about the Length of stay (LOS) of different diseased patients in the hospitals. Hospital management does not exactly know when the existing patient leaves the hospital; this information could be crucial for hospital management. It could allow them to take more patients for admission. As a result, hospitals face many problems managing available resources and new patients in getting entries for their prompt treatment. Therefore, a robust model needs to be designed to help hospital administration predict patients’ LOS to resolve… More >

  • ARTICLE

    An OWL-Based Specification of Database Management Systems

    Sabin C. Buraga1,*, Daniel Amariei1, Octavian Dospinescu2
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5537-5550, 2022, DOI:10.32604/cmc.2022.021714
    Abstract In the context of a proliferation of Database Management Systems (DBMSs), we have envisioned and produced an OWL 2 ontology able to provide a high-level machine-processable description of the DBMSs domain. This conceptualization aims to facilitate a proper execution of various software engineering processes and database-focused administration tasks. Also, it can be used to improve the decision-making process for determining/selecting the appropriate DBMS, subject to specific requirements. The proposed model describes the most important features and aspects regarding the DBMS domain, including the support for various paradigms (relational, graph-based, key-value, tree-like, etc.), query languages, platforms (servers), plus running environments (desktop,… More >

  • Preserving Privacy of User Identity Based on Pseudonym Variable in 5G

    Mamoon M. Saeed1, Mohammad Kamrul Hasan2,*, Rosilah Hassan2 , Rania Mokhtar3 , Rashid A. Saeed3,4, Elsadig Saeid1, Manoj Gupta5
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5551-5568, 2022, DOI:10.32604/cmc.2022.017338
    Abstract

    The fifth generation (5G) system is the forthcoming generation of the mobile communication system. It has numerous additional features and offers an extensively high data rate, more capacity, and low latency. However, these features and applications have many problems and issues in terms of security, which has become a great challenge in the telecommunication industry. This paper aimed to propose a solution to preserve the user identity privacy in the 5G system that can identify permanent identity by using Variable Mobile Subscriber Identity, which randomly changes and does not use the permanent identity between the user equipment and home network.… More >

  • ARTICLE

    Aeroelastic Optimization of the High Aspect Ratio Wing with Aileron

    Mohammad Ghalandari1, Ibrahim Mahariq2, Farhad Ghadak3, Oussama Accouche2, Fahd Jarad4,5,*
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5569-5581, 2022, DOI:10.32604/cmc.2022.020884
    Abstract In aircraft wings, aileron mass parameter presents a tremendous effect on the velocity and frequency of the flutter problem. For that purpose, we present the optimization of a composite design wing with an aileron, using machine-learning approach. Mass properties and its distribution have a great influence on the multi-variate optimization procedure, based on speed and frequency of flutter. First, flutter speed was obtained to estimate aileron impact. Additionally mass-equilibrated and other features were investigated. It can deduced that changing the position and mass properties of the aileron are tangible following the speed and frequency of the wing flutter. Based on… More >

  • ARTICLE

    An Optimal Text Watermarking Method for Sensitive Detecting of Illegal Tampering Attacks

    Anwer Mustafa Hilal1,*, Fahd N. Al-Wesabi2,3, Mohammed Alamgeer4, Manar Ahmed Hamza1, Mohammad Mahzari5, Murad A. Almekhlafi6
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5583-5600, 2022, DOI:10.32604/cmc.2022.019686
    Abstract Due to the rapid increase in the exchange of text information via internet networks, the security and authenticity of digital content have become a major research issue. The main challenges faced by researchers are how to hide the information within the text to use it later for authentication and attacks tampering detection without effects on the meaning and size of the given digital text. In this paper, an efficient text-based watermarking method has been proposed for detecting the illegal tampering attacks on the Arabic text transmitted online via an Internet network. Towards this purpose, the accuracy of tampering detection and… More >

  • ARTICLE

    Fuzzy-Based Automatic Epileptic Seizure Detection Framework

    Aayesha1, Muhammad Bilal Qureshi2, Muhammad Afzaal3, Muhammad Shuaib Qureshi4, Jeonghwan Gwak5,6,7,8,*
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5601-5630, 2022, DOI:10.32604/cmc.2022.020348
    (This article belongs to this Special Issue: Advancements in Lightweight AI for Constrained Internet of Things Devices for Smart Cities)
    Abstract Detection of epileptic seizures on the basis of Electroencephalogram (EEG) recordings is a challenging task due to the complex, non-stationary and non-linear nature of these biomedical signals. In the existing literature, a number of automatic epileptic seizure detection methods have been proposed that extract useful features from EEG segments and classify them using machine learning algorithms. Some characterizing features of epileptic and non-epileptic EEG signals overlap; therefore, it requires that analysis of signals must be performed from diverse perspectives. Few studies analyzed these signals in diverse domains to identify distinguishing characteristics of epileptic EEG signals. To pose the challenge mentioned… More >

  • ARTICLE

    MLA: A New Mutated Leader Algorithm for Solving Optimization Problems

    Fatemeh Ahmadi Zeidabadi1, Sajjad Amiri Doumari1, Mohammad Dehghani2, Zeinab Montazeri3, Pavel Trojovský4,*, Gaurav Dhiman5
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5631-5649, 2022, DOI:10.32604/cmc.2022.021072
    (This article belongs to this Special Issue: Recent Advances in Metaheuristic Techniques and Their Real-World Applications)
    Abstract Optimization plays an effective role in various disciplines of science and engineering. Optimization problems should either be optimized using the appropriate method (i.e., minimization or maximization). Optimization algorithms are one of the efficient and effective methods in providing quasi-optimal solutions for these type of problems. In this study, a new algorithm called the Mutated Leader Algorithm (MLA) is presented. The main idea in the proposed MLA is to update the members of the algorithm population in the search space based on the guidance of a mutated leader. In addition to information about the best member of the population, the mutated… More >

  • ARTICLE

    SDN Based DDos Mitigating Approach Using Traffic Entropy for IoT Network

    Muhammad Ibrahim1, Muhammad Hanif2, Shabir Ahmad3, Faisal Jamil1, Tayyaba Sehar2, YunJung Lee4, DoHyeun Kim1,*
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5651-5665, 2022, DOI:10.32604/cmc.2022.017772
    (This article belongs to this Special Issue: Intelligent Software-defined Networking (SDN) Technologies for Future Generation Networks)
    Abstract The Internet of Things (IoT) has been widely adopted in various domains including smart cities, healthcare, smart factories, etc. In the last few years, the fitness industry has been reshaped by the introduction of smart fitness solutions for individuals as well as fitness gyms. The IoT fitness devices collect trainee data that is being used for various decision-making. However, it will face numerous security and privacy issues towards its realization. This work focuses on IoT security, especially DoS/DDoS attacks. In this paper, we have proposed a novel blockchain-enabled protocol (BEP) that uses the notion of a self-exposing node (SEN) approach… More >

  • ARTICLE

    Intelligent Deep Learning Based Disease Diagnosis Using Biomedical Tongue Images

    V. Thanikachalam1,*, S. Shanthi2, K. Kalirajan3, Sayed Abdel-Khalek4,5, Mohamed Omri6, Lotfi M. Ladhar7
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5667-5681, 2022, DOI:10.32604/cmc.2022.020965
    Abstract The rapid development of biomedical imaging modalities led to its wide application in disease diagnosis. Tongue-based diagnostic procedures are proficient and non-invasive in nature to carry out secondary diagnostic processes ubiquitously. Traditionally, physicians examine the characteristics of tongue prior to decision-making. In this scenario, to get rid of qualitative aspects, tongue images can be quantitatively inspected for which a new disease diagnosis model is proposed. This model can reduce the physical harm made to the patients. Several tongue image analytical methodologies have been proposed earlier. However, there is a need exists to design an intelligent Deep Learning (DL) based disease… More >

  • ARTICLE

    Estimating Usable-Security Through Hesitant Fuzzy Linguistic Term Sets Based Technique

    Abdulaziz Attaallah1, Raees Ahmad Khan2,*
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5683-5705, 2022, DOI:10.32604/cmc.2022.021643
    (This article belongs to this Special Issue: Emerging Applications of Artificial Intelligence, Machine learning and Data Science)
    Abstract The apparent contradiction between usability and security has been discussed in the literature for several years. This continuous trade-off requires be acknowledging and handling whenever security solutions are introduced. However, some progressive analysts point out that present security solutions are usually very difficult for several users, and they have expressed a willingness to simplify the security product user experience. Usable security is still mostly unexplored territory in computer science. Which we are all aware with security and usability on many levels, usable security has received little operational attention. Companies have recently focused primarily on usable security. As consumers prefer to… More >

  • ARTICLE

    Graph Transformer for Communities Detection in Social Networks

    G. Naga Chandrika1, Khalid Alnowibet2, K. Sandeep Kautish3, E. Sreenivasa Reddy4, Adel F. Alrasheedi2, Ali Wagdy Mohamed5,6,*
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5707-5720, 2022, DOI:10.32604/cmc.2022.021186
    (This article belongs to this Special Issue: Artificial Intelligence and Machine Learning Algorithms in Real-World Applications and Theories)
    Abstract Graphs are used in various disciplines such as telecommunication, biological networks, as well as social networks. In large-scale networks, it is challenging to detect the communities by learning the distinct properties of the graph. As deep learning has made contributions in a variety of domains, we try to use deep learning techniques to mine the knowledge from large-scale graph networks. In this paper, we aim to provide a strategy for detecting communities using deep autoencoders and obtain generic neural attention to graphs. The advantages of neural attention are widely seen in the field of NLP and computer vision, which has… More >

  • ARTICLE

    Handover Mechanism Based on Underwater Hybrid Software-Defined Modem in Advanced Diver Networks

    K. M. Delphin Raj1, Sun-Ho Yum1, Jinyoung Lee2, Eunbi Ko3, Soo-Yong Shin2, Soo-Hyun Park3,*
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5721-5743, 2022, DOI:10.32604/cmc.2022.020870
    (This article belongs to this Special Issue: Future Generation of Artificial Intelligence and Intelligent Internet of Things)
    Abstract For the past few decades, the internet of underwater things (IoUT) obtained a lot of attention in mobile aquatic applications such as oceanography, diver network monitoring, unmanned underwater exploration, underwater surveillance, location tracking system, etc. Most of the IoUT applications rely on acoustic medium. The current IoUT applications face difficulty in delivering a reliable communication system due to the various technical limitations of IoUT environment such as low data rate, attenuation, limited bandwidth, limited battery, limited memory, connectivity problem, etc. One of the significant applications of IoUT include monitoring underwater diver networks. In order to perform a reliable and energy-efficient… More >

  • ARTICLE

    2D Finite Element Analysis of Asynchronous Machine Influenced Under Power Quality Perturbations

    Jasmin Pamela S1 , R. Saranya1 , V. Indragandhi1 , R. Raja Singh1 , V. Subramaniyaswamy2, Yuvaraja Teekaraman3 , Shabana Urooj4,*, Norah Alwadai5
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5745-5763, 2022, DOI:10.32604/cmc.2022.020093
    Abstract

    Asynchronous machines are predominantly preferred in industrial sectors for its reliability. Power quality perturbations have a greater impact on industries; among the different power quality events, voltage fluctuations are the most common and that may cause adverse effect on machine's operation since they are longer enduring. The article discusses a numerical technique for evaluating asynchronous motors while taking into account magnetic saturation, losses, leakage flux, and voltage drop. A 2D linear analysis involving a multi-slice time stepping finite element model is used to predict the end effects. As an outcome, the magnetic saturation and losses are estimated using a modified… More >

  • ARTICLE

    Deep Q-Learning Based Optimal Query Routing Approach for Unstructured P2P Network

    Mohammad Shoab, Abdullah Shawan Alotaibi*
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5765-5781, 2022, DOI:10.32604/cmc.2022.021941
    (This article belongs to this Special Issue: Emerging Trends in Software-Defined Networking for Industry 4.0)
    Abstract Deep Reinforcement Learning (DRL) is a class of Machine Learning (ML) that combines Deep Learning with Reinforcement Learning and provides a framework by which a system can learn from its previous actions in an environment to select its efforts in the future efficiently. DRL has been used in many application fields, including games, robots, networks, etc. for creating autonomous systems that improve themselves with experience. It is well acknowledged that DRL is well suited to solve optimization problems in distributed systems in general and network routing especially. Therefore, a novel query routing approach called Deep Reinforcement Learning based Route Selection… More >

  • ARTICLE

    Intelligent Integrated Model for Improving Performance in Power Plants

    Ahmed Ali Ajmi1,2, Noor Shakir Mahmood1,2, Khairur Rijal Jamaludin1,*, Hayati Habibah Abdul Talib1, Shamsul Sarip1, Hazilah Mad Kaidi1
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5783-5801, 2022, DOI:10.32604/cmc.2022.021885
    (This article belongs to this Special Issue: Application of Artificial Intelligence, Internet of Things, and Learning Approach for Learning Process in COVID-19/Industrial Revolution 4.0)
    Abstract Industry 4.0 is expected to play a crucial role in improving energy management and personnel performance in power plants. Poor performance problem in maintaining power plants is the result of both human errors, human factors and the poor implementation of automation in energy management. This problem can potentially be solved using artificial intelligence (AI) and an integrated management system (IMS). This article investigates the current challenges to improving personnel and energy management performance in power plants, identifies the critical success factors (CSFs) for an integrated intelligent framework, and develops an intelligent framework that enables power plants to improve performance. The… More >

  • ARTICLE

    Machine Learning Based Depression, Anxiety, and Stress Predictive Model During COVID-19 Crisis

    Fahd N. Al-Wesabi1,2,*, Hadeel Alsolai3, Anwer Mustafa Hilal4, Manar Ahmed Hamza4, Mesfer Al Duhayyim5, Noha Negm6,7
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5803-5820, 2022, DOI:10.32604/cmc.2022.021195
    Abstract Corona Virus Disease-2019 (COVID-19) was reported at first in Wuhan city, China by December 2019. World Health Organization (WHO) declared COVID-19 as a pandemic i.e., global health crisis on March 11, 2020. The outbreak of COVID-19 pandemic and subsequent lockdowns to curb the spread, not only affected the economic status of a number of countries, but it also resulted in increased levels of Depression, Anxiety, and Stress (DAS) among people. Therefore, there is a need exists to comprehend the relationship among psycho-social factors in a country that is hypothetically affected by high levels of stress and fear; with tremendously-limiting measures… More >

  • ARTICLE

    Comparative Study of Transfer Learning Models for Retinal Disease Diagnosis from Fundus Images

    Kuntha Pin1, Jee Ho Chang2, Yunyoung Nam3,*
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5821-5834, 2022, DOI:10.32604/cmc.2022.021943
    (This article belongs to this Special Issue: Future Generation of Artificial Intelligence and Intelligent Internet of Things)
    Abstract While the usage of digital ocular fundus image has been widespread in ophthalmology practice, the interpretation of the image has been still on the hands of the ophthalmologists which are quite costly. We explored a robust deep learning system that detects three major ocular diseases: diabetic retinopathy (DR), glaucoma (GLC), and age-related macular degeneration (AMD). The proposed method is composed of two steps. First, an initial quality evaluation in the classification system is proposed to filter out poor-quality images to enhance its performance, a technique that has not been explored previously. Second, the transfer learning technique is used with various… More >

  • ARTICLE

    Artificial Intelligence Based Clustering with Routing Protocol for Internet of Vehicles

    Manar Ahmed Hamza1,*, Haya Mesfer Alshahrani2, Fahd N. Al-Wesabi3,4, Mesfer Al Duhayyim5, Anwer Mustafa Hilal1, Hany Mahgoub3
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5835-5853, 2022, DOI:10.32604/cmc.2022.021059
    Abstract With recent advances made in Internet of Vehicles (IoV) and Cloud Computing (CC), the Intelligent Transportation Systems (ITS) find it advantageous in terms of improvement in quality and interactivity of urban transportation service, mitigation of costs incurred, reduction in resource utilization, and improvement in traffic management capabilities. Many traffic-related problems in future smart cities can be sorted out with the incorporation of IoV in transportation. IoV communication enables the collection and distribution of real-time essential data regarding road network condition. In this scenario, energy-efficient and reliable intercommunication routes are essential among vehicular nodes in sustainable urban computing. With this motivation,… More >

  • ARTICLE

    Ontology Driven Testing Strategies for IoT Applications

    Muhammad Raza Naqvi1, Muhammad Waseem Iqbal2, Muhammad Usman Ashraf3, Shafiq Ahmad4, Ahmed T. Soliman4, Shahzada Khurram5, Muhammad Shafiq6,*, Jin-Ghoo Choi6
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5855-5869, 2022, DOI:10.32604/cmc.2022.019188
    Abstract Internet-of-Things (IoT) has attained a major share in embedded software development. The new era of specialized intelligent systems requires adaptation of customized software engineering approaches. Currently, software engineering has merged the development phases with the technologies provided by industrial automation. The improvements are still required in testing phase for the software developed to IoT solutions. This research aims to assist in developing the testing strategies for IoT applications, therein ontology has been adopted as a knowledge representation technique to different software engineering processes. The proposed ontological model renders 101 methodology by using Protégé. After completion, the ontology was evaluated in… More >

  • ARTICLE

    Intelligent Deep Learning Based Automated Fish Detection Model for UWSN

    Mesfer Al Duhayyim1, Haya Mesfer Alshahrani2, Fahd N. Al-Wesabi3, Mohammed Alamgeer4, Anwer Mustafa Hilal5,*, Manar Ahmed Hamza5
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5871-5887, 2022, DOI:10.32604/cmc.2022.021093
    Abstract An exponential growth in advanced technologies has resulted in the exploration of Ocean spaces. It has paved the way for new opportunities that can address questions relevant to diversity, uniqueness, and difficulty of marine life. Underwater Wireless Sensor Networks (UWSNs) are widely used to leverage such opportunities while these networks include a set of vehicles and sensors to monitor the environmental conditions. In this scenario, it is fascinating to design an automated fish detection technique with the help of underwater videos and computer vision techniques so as to estimate and monitor fish biomass in water bodies. Several models have been… More >

  • ARTICLE

    Energy Efficiency Trade-off with Spectral Efficiency in MIMO Systems

    Rao Muhammad Asif1, Mustafa Shakir1, Jamel Nebhen2, Ateeq Ur Rehman3, Muhammad Shafiq4,*, Jin-Ghoo Choi4
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5889-5905, 2022, DOI:10.32604/cmc.2022.020777
    Abstract 5G technology can greatly improve spectral efficiency (SE) and throughput of wireless communications. In this regard, multiple input multiple output (MIMO) technology has become the most influential technology using huge antennas and user equipment (UE). However, the use of MIMO in 5G wireless technology will increase circuit power consumption and reduce energy efficiency (EE). In this regard, this article proposes an optimal solution for weighing SE and throughput tradeoff with energy efficiency. The research work is based on the Wyner model of uplink (UL) and downlink (DL) transmission under the multi-cell model scenario. The SE-EE trade-off is carried out by… More >

  • ARTICLE

    An Improved Convolutional Neural Network Model for DNA Classification

    Naglaa. F. Soliman1,*, Samia M. Abd-Alhalem2 , Walid El-Shafai2 , Salah Eldin S. E. Abdulrahman3, N. Ismaiel3 , El-Sayed M. El-Rabaie2 , Abeer D. Algarni1, Fathi E. Abd El-Samie1,2
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5907-5927, 2022, DOI:10.32604/cmc.2022.018860
    Abstract

    Recently, deep learning (DL) became one of the essential tools in bioinformatics. A modified convolutional neural network (CNN) is employed in this paper for building an integrated model for deoxyribonucleic acid (DNA) classification. In any CNN model, convolutional layers are used to extract features followed by max-pooling layers to reduce the dimensionality of features. A novel method based on downsampling and CNNs is introduced for feature reduction. The downsampling is an improved form of the existing pooling layer to obtain better classification accuracy. The two-dimensional discrete transform (2D DT) and two-dimensional random projection (2D RP) methods are applied for downsampling.… More >

  • ARTICLE

    An Energy-Efficient Mobile Agent-Based Data Aggregation Scheme for Wireless Body Area Networks

    Gulzar Mehmood1, Muhammad Zahid Khan1, Muhammad Fayaz2, Mohammad Faisal1, Haseeb Ur Rahman1, Jeonghwan Gwak3,4,5,6,*
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5929-5948, 2022, DOI:10.32604/cmc.2022.020546
    (This article belongs to this Special Issue: Advancements in Lightweight AI for Constrained Internet of Things Devices for Smart Cities)
    Abstract Due to the advancement in wireless technology and miniaturization, Wireless Body Area Networks (WBANs) have gained enormous popularity, having various applications, especially in the healthcare sector. WBANs are intrinsically resource-constrained; therefore, they have specific design and development requirements. One such highly desirable requirement is an energy-efficient and reliable Data Aggregation (DA) mechanism for WBANs. The efficient and reliable DA may ultimately push the network to operate without much human intervention and further extend the network lifetime. The conventional client-server DA paradigm becomes unsuitable and inefficient for WBANs when a large amount of data is generated in the network. Similarly, in… More >

  • ARTICLE

    Improved Dragonfly Optimizer for Intrusion Detection Using Deep Clustering CNN-PSO Classifier

    K. S. Bhuvaneshwari1, K. Venkatachalam2, S. Hubálovský3,*, P. Trojovský4, P. Prabu5
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5949-5965, 2022, DOI:10.32604/cmc.2022.020769
    Abstract With the rapid growth of internet based services and the data generated on these services are attracted by the attackers to intrude the networking services and information. Based on the characteristics of these intruders, many researchers attempted to aim to detect the intrusion with the help of automating process. Since, the large volume of data is generated and transferred through network, the security and performance are remained an issue. IDS (Intrusion Detection System) was developed to detect and prevent the intruders and secure the network systems. The performance and loss are still an issue because of the features space grows… More >

  • ARTICLE

    Laboratory Evaluation of Fiber-Modified Asphalt Mixtures Incorporating Steel Slag Aggregates

    Adham Mohammed Alnadish1,*, Mohamad Yusri Aman1, Herda Yati Binti Katman2, Mohd Rasdan Ibrahim3
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5967-5990, 2022, DOI:10.32604/cmc.2022.017387
    Abstract Vigorous and continued efforts by researchers and engineers have contributed towards maintaining environmental sustainability through the utilization of waste materials in civil engineering applications as an alternative to natural sources. In this study, granite aggregates in asphaltic mixes were replaced by electric arc furnace (EAF) steel slag aggregates with different proportions to identify the best combination in terms of superior performance. Asphalt mixtures showing the best performance were further reinforced with polyvinyl alcohol (PVA), acrylic, and polyester fibers at the dosages of 0.05%, 0.15%, and 0.3% by weight of the aggregates. The performance tests of this study were resilient modulus,… More >

  • ARTICLE

    Primary User-Awareness-Based Energy-Efficient Duty-Cycle Scheme in Cognitive Radio Networks

    Zilong Jin1,2, Chengbo Zhang1 , Kan Yao3 , Dun Cao4 , Seokhoon Kim5, Yuanfeng Jin6,*
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5991-6005, 2022, DOI:10.32604/cmc.2022.021498
    Abstract

    Cognitive radio devices can utilize the licensed channels in an opportunistic manner to solve the spectrum scarcity issue occurring in the unlicensed spectrum. However, these cognitive radio devices (secondary users) are greatly affected by the original users (primary users) of licensed channels. Cognitive users have to adjust operation parameters frequently to adapt to the dynamic network environment, which causes extra energy consumption. Energy consumption can be reduced by predicting the future activity of primary users. However, the traditional prediction-based algorithms require large historical data to achieve a satisfying precision accuracy which will consume a lot of time and memory space.… More >

  • ARTICLE

    SutteARIMA: A Novel Method for Forecasting the Infant Mortality Rate in Indonesia

    Ansari Saleh Ahmar1,2,*, Eva Boj del Val3, M. A. El Safty4, Samirah AlZahrani4, Hamed El-Khawaga5,6
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6007-6022, 2022, DOI:10.32604/cmc.2022.021382
    (This article belongs to this Special Issue: Application of Artificial Intelligence, Internet of Things, and Learning Approach for Learning Process in COVID-19/Industrial Revolution 4.0)
    Abstract This study focuses on the novel forecasting method (SutteARIMA) and its application in predicting Infant Mortality Rate data in Indonesia. It undertakes a comparison of the most popular and widely used four forecasting methods: ARIMA, Neural Networks Time Series (NNAR), Holt-Winters, and SutteARIMA. The data used were obtained from the website of the World Bank. The data consisted of the annual infant mortality rate (per 1000 live births) from 1991 to 2019. To determine a suitable and best method for predicting Infant Mortality rate, the forecasting results of these four methods were compared based on the mean absolute percentage error… More >

  • ARTICLE

    Malaria Parasite Detection Using a Quantum-Convolutional Network

    Javaria Amin1 , Muhammad Almas Anjum2 , Abida Sharif3 , Mudassar Raza4 , Seifedine Kadry5, Yunyoung Nam6,*
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6023-6039, 2022, DOI:10.32604/cmc.2022.019115
    (This article belongs to this Special Issue: Recent Advances in Deep Learning for Medical Image Analysis)
    Abstract

    Malaria is a severe illness triggered by parasites that spreads via mosquito bites. In underdeveloped nations, malaria is one of the top causes of mortality, and it is mainly diagnosed through microscopy. Computer-assisted malaria diagnosis is difficult owing to the fine-grained differences throughout the presentation of some uninfected and infected groups. Therefore, in this study, we present a new idea based on the ensemble quantum-classical framework for malaria classification. The methods comprise three core steps: localization, segmentation, and classification. In the first core step, an improved FRCNN model is proposed for the localization of the infected malaria cells. Then, the… More >

  • ARTICLE

    An Intelligent Forecasting Model for Disease Prediction Using Stack Ensembling Approach

    Shobhit Verma1 , Nonita Sharma1 , Aman Singh2 , Abdullah Alharbi3 , Wael Alosaimi3 , Hashem Alyami4, Deepali Gupta5, Nitin Goyal5 ,*
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6041-6055, 2022, DOI:10.32604/cmc.2022.021747
    Abstract This research work proposes a new stack-based generalization ensemble model to forecast the number of incidences of conjunctivitis disease. In addition to forecasting the occurrences of conjunctivitis incidences, the proposed model also improves performance by using the ensemble model. Weekly rate of acute Conjunctivitis per 1000 for Hong Kong is collected for the duration of the first week of January 2010 to the last week of December 2019. Pre-processing techniques such as imputation of missing values and logarithmic transformation are applied to pre-process the data sets. A stacked generalization ensemble model based on Auto-ARIMA (Autoregressive Integrated Moving Average), NNAR (Neural… More >

  • ARTICLE

    Design of Latency-Aware IoT Modules in Heterogeneous Fog-Cloud Computing Networks

    Syed Rizwan Hassan1, Ishtiaq Ahmad1, Jamel Nebhen2, Ateeq Ur Rehman3, Muhammad Shafiq4, Jin-Ghoo Choi4,*
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6057-6072, 2022, DOI:10.32604/cmc.2022.020428
    Abstract The modern paradigm of the Internet of Things (IoT) has led to a significant increase in demand for latency-sensitive applications in Fog-based cloud computing. However, such applications cannot meet strict quality of service (QoS) requirements. The large-scale deployment of IoT requires more effective use of network infrastructure to ensure QoS when processing big data. Generally, cloud-centric IoT application deployment involves different modules running on terminal devices and cloud servers. Fog devices with different computing capabilities must process the data generated by the end device, so deploying latency-sensitive applications in a heterogeneous fog computing environment is a difficult task. In addition,… More >

  • ARTICLE

    Analysis of Pneumonia Model via Efficient Computing Techniques

    Kamaledin Abodayeh1, Ali Raza2,3,*, Muhammad Rafiq4, Muhammad Shoaib Arif5, Muhammad Naveed5, Zunir Zeb3, Syed Zaheer Abbas3, Kiran Shahzadi3, Sana Sarwar3, Qasim Naveed3, Badar Ul Zaman3, Muhammad Mohsin6
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6073-6088, 2022, DOI:10.32604/cmc.2022.020732
    (This article belongs to this Special Issue: Emerging Trends and Real-World Applications of Intelligent Computing Techniques)
    Abstract Pneumonia is a highly transmissible disease in children. According to the World Health Organization (WHO), the most affected regions include south Asia and sub-Saharan Africa. Worldwide, 15% of pediatric deaths can be attributed to pneumonia. Computing techniques have a significant role in science, engineering, and many other fields. In this study, we focused on the efficiency of numerical techniques via computer programs. We studied the dynamics of the pneumonia-like infections of epidemic models using numerical techniques. We discuss two types of analysis: dynamical and numerical. The dynamical analysis included positivity, boundedness, local stability, reproduction number, and equilibria of the model.… More >

  • ARTICLE

    Incentive-Driven Approach for Misbehavior Avoidance in Vehicular Networks

    Shahid Sultan1, Qaisar Javaid1, Eid Rehman2,*, Ahmad Aziz Alahmadi3, Nasim Ullah3, Wakeel Khan4
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6089-6106, 2022, DOI:10.32604/cmc.2022.021374
    Abstract For efficient and robust information exchange in the vehicular ad-hoc network, a secure and trusted incentive reward is needed to avoid and reduce the intensity of misbehaving nodes and congestion especially in the case where the periodic beacons exploit the channel. In addition, we cannot be sure that all vehicular nodes eagerly share their communication assets to the system for message dissemination without any rewards. Unfortunately, there may be some misbehaving nodes and due to their selfish and greedy approach, these nodes may not help others on the network. To deal with this challenge, trust-based misbehavior avoidance schemes are generally… More >

  • ARTICLE

    Efficient Deep-Learning-Based Autoencoder Denoising Approach for Medical Image Diagnosis

    Walid El-Shafai1, Samy Abd El-Nabi1,2, El-Sayed M. El-Rabaie1, Anas M. Ali1,2, Naglaa F. Soliman3,*, Abeer D. Algarni3, Fathi E. Abd El-Samie1,3
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6107-6125, 2022, DOI:10.32604/cmc.2022.020698
    Abstract Effective medical diagnosis is dramatically expensive, especially in third-world countries. One of the common diseases is pneumonia, and because of the remarkable similarity between its types and the limited number of medical images for recent diseases related to pneumonia, the medical diagnosis of these diseases is a significant challenge. Hence, transfer learning represents a promising solution in transferring knowledge from generic tasks to specific tasks. Unfortunately, experimentation and utilization of different models of transfer learning do not achieve satisfactory results. In this study, we suggest the implementation of an automatic detection model, namely CADTra, to efficiently diagnose pneumonia-related diseases. This… More >

  • ARTICLE

    Wireless Sensor Networks Routing Attacks Prevention with Blockchain and Deep Neural Network

    Mohamed Ali1, Ibrahim A. Abd El-Moghith2, Mohamed N. El-Derini3, Saad M. Darwish2,*
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6127-6140, 2022, DOI:10.32604/cmc.2022.021305
    Abstract Routing is a key function in Wireless Sensor Networks (WSNs) since it facilitates data transfer to base stations. Routing attacks have the potential to destroy and degrade the functionality of WSNs. A trustworthy routing system is essential for routing security and WSN efficiency. Numerous methods have been implemented to build trust between routing nodes, including the use of cryptographic methods and centralized routing. Nonetheless, the majority of routing techniques are unworkable in reality due to the difficulty of properly identifying untrusted routing node activities. At the moment, there is no effective way to avoid malicious node attacks. As a consequence… More >

  • ARTICLE

    A Secure Key Agreement Scheme for Unmanned Aerial Vehicles-Based Crowd Monitoring System

    Bander Alzahrani1, Ahmed Barnawi1, Azeem Irshad2, Areej Alhothali1, Reem Alotaibi1, Muhammad Shafiq3,*
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6141-6158, 2022, DOI:10.32604/cmc.2022.020774
    Abstract Unmanned aerial vehicles (UAVs) have recently attracted widespread attention in civil and commercial applications. For example, UAVs (or drone) technology is increasingly used in crowd monitoring solutions due to its wider air footprint and the ability to capture data in real time. However, due to the open atmosphere, drones can easily be lost or captured by attackers when reporting information to the crowd management center. In addition, the attackers may initiate malicious detection to disrupt the crowd-sensing communication network. Therefore, security and privacy are one of the most significant challenges faced by drones or the Internet of Drones (IoD) that… More >

  • ARTICLE

    Data Wipe-Off Technique for Tracking Weak GPS Signals

    Dah-Jing Jwo*, Sheng-Feng Chiu
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6159-6176, 2022, DOI:10.32604/cmc.2022.020793
    Abstract In this paper, the data wipe-off (DWO) algorithm is incorporated into the vector tracking loop of the Global Positioning System (GPS) receiver for improving signal tracking performance. The navigation data, which contains information that is necessary to perform navigation computations, are binary phase-shift keying (BPSK) modulated onto the GPS carrier phase with the bit duration of 20 ms (i.e., 50 bits per second). To continuously track the satellite’s signal in weak signal environment, the DWO algorithm on the basis of pre-detection method is adopted to detect data bit sign reversal every 20 ms. Tracking accuracy of a weak GPS signal… More >

  • ARTICLE

    Ultra-wideband Frequency Selective Surface for Communication Applications

    Shahid Habib1, Ghaffer Iqbal Kiani2, Muhammad Fasih Uddin Butt1,3,*, Syed Muzahir Abbas4,5, Abdulah Jeza Aljohani2, Soon Xin Ng3
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6177-6187, 2022, DOI:10.32604/cmc.2022.021644
    (This article belongs to this Special Issue: Advances in 5G Antenna Designs and Systems)
    Abstract A low-profile ultra-wideband (UWB) band-stop frequency selective surface (FSS) is designed for S-, C-, X- and Ku-bands communication applications. The FSS is constructed by using square and circular loop elements printed on the top and bottom sides of the RO3210 substrate. The FSS has been designed to reduce the electromagnetic interference (EMI) as well as to mitigate the harmful effects of electromagnetic radiation on the human body caused by different radio devices. The dimension and size of the UWB FSS have been reduced to 0.12 λ × 0.12 λ and 90%, respectively, as compared to the reported literature. The other… More >

  • ARTICLE

    Estimating Fuel-Efficient Air Plane Trajectories Using Machine Learning

    Jaiteg Singh1, Gaurav Goyal1, Farman Ali2, Babar Shah3, Sangheon Pack4,*
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6189-6204, 2022, DOI:10.32604/cmc.2022.021657
    (This article belongs to this Special Issue: Machine Learning Empowered Secure Computing for Intelligent Systems)
    Abstract Airline industry has witnessed a tremendous growth in the recent past. Percentage of people choosing air travel as first choice to commute is continuously increasing. Highly demanding and congested air routes are resulting in inadvertent delays, additional fuel consumption and high emission of greenhouse gases. Trajectory planning involves creation identification of cost-effective flight plans for optimal utilization of fuel and time. This situation warrants the need of an intelligent system for dynamic planning of optimized flight trajectories with least human intervention required. In this paper, an algorithm for dynamic planning of optimized flight trajectories has been proposed. The proposed algorithm… More >

  • ARTICLE

    Educational Videos Subtitles’ Summarization Using Latent Dirichlet Allocation and Length Enhancement

    Sarah S. Alrumiah*, Amal A. Al-Shargabi
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6205-6221, 2022, DOI:10.32604/cmc.2022.021780
    (This article belongs to this Special Issue: Machine Learning Applications in Medical, Finance, Education and Cyber Security)
    Abstract Nowadays, people use online resources such as educational videos and courses. However, such videos and courses are mostly long and thus, summarizing them will be valuable. The video contents (visual, audio, and subtitles) could be analyzed to generate textual summaries, i.e., notes. Videos’ subtitles contain significant information. Therefore, summarizing subtitles is effective to concentrate on the necessary details. Most of the existing studies used Term Frequency–Inverse Document Frequency (TF-IDF) and Latent Semantic Analysis (LSA) models to create lectures’ summaries. This study takes another approach and applies Latent Dirichlet Allocation (LDA), which proved its effectiveness in document summarization. Specifically, the proposed… More >

  • ARTICLE

    Artificial Intelligence Enabled Apple Leaf Disease Classification for Precision Agriculture

    Fahd N. Al-Wesabi1,2,*, Amani Abdulrahman Albraikan3, Anwer Mustafa Hilal4, Majdy M. Eltahir1, Manar Ahmed Hamza4, Abu Sarwar Zamani4
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6223-6238, 2022, DOI:10.32604/cmc.2022.021299
    Abstract Precision agriculture enables the recent technological advancements in farming sector to observe, measure, and analyze the requirements of individual fields and crops. The recent developments of computer vision and artificial intelligence (AI) techniques find a way for effective detection of plants, diseases, weeds, pests, etc. On the other hand, the detection of plant diseases, particularly apple leaf diseases using AI techniques can improve productivity and reduce crop loss. Besides, earlier and precise apple leaf disease detection can minimize the spread of the disease. Earlier works make use of traditional image processing techniques which cannot assure high detection rate on apple… More >

  • ARTICLE

    A Novel Binary Emperor Penguin Optimizer for Feature Selection Tasks

    Minakshi Kalra1, Vijay Kumar2, Manjit Kaur3, Sahar Ahmed Idris4, Şaban Öztürk5, Hammam Alshazly6,*
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6239-6255, 2022, DOI:10.32604/cmc.2022.020682
    Abstract Nowadays, due to the increase in information resources, the number of parameters and complexity of feature vectors increases. Optimization methods offer more practical solutions instead of exact solutions for the solution of this problem. The Emperor Penguin Optimizer (EPO) is one of the highest performing meta-heuristic algorithms of recent times that imposed the gathering behavior of emperor penguins. It shows the superiority of its performance over a wide range of optimization problems thanks to its equal chance to each penguin and its fast convergence features. Although traditional EPO overcomes the optimization problems in continuous search space, many problems today shift… More >

  • ARTICLE

    Deep Learning Empowered Cybersecurity Spam Bot Detection for Online Social Networks

    Mesfer Al Duhayyim1, Haya Mesfer Alshahrani2, Fahd N. Al-Wesabi3, Mohammed Alamgeer4, Anwer Mustafa Hilal5,*, Mohammed Rizwanullah5
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6257-6270, 2022, DOI:10.32604/cmc.2022.021212
    Abstract Cybersecurity encompasses various elements such as strategies, policies, processes, and techniques to accomplish availability, confidentiality, and integrity of resource processing, network, software, and data from attacks. In this scenario, the rising popularity of Online Social Networks (OSN) is under threat from spammers for which effective spam bot detection approaches should be developed. Earlier studies have developed different approaches for the detection of spam bots in OSN. But those techniques primarily concentrated on hand-crafted features to capture the features of malicious users while the application of Deep Learning (DL) models needs to be explored. With this motivation, the current research article… More >

  • ARTICLE

    ATS: A Novel Time-Sharing CPU Scheduling Algorithm Based on Features Similarities

    Samih M. Mostafa1,*, Sahar Ahmed Idris2, Manjit Kaur3
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6271-6288, 2022, DOI:10.32604/cmc.2022.021978
    (This article belongs to this Special Issue: Recent Advances in Metaheuristic Techniques and Their Real-World Applications)
    Abstract Minimizing time cost in time-shared operating systems is considered basic and essential task, and it is the most significant goal for the researchers who interested in CPU scheduling algorithms. Waiting time, turnaround time, and number of context switches are the most time cost criteria used to compare between CPU scheduling algorithms. CPU scheduling algorithms are divided into non-preemptive and preemptive. Round Robin (RR) algorithm is the most famous as it is the basis for all the algorithms used in time-sharing. In this paper, the authors proposed a novel CPU scheduling algorithm based on RR. The proposed algorithm is called Adjustable… More >

  • ARTICLE

    Artificial Intelligence Based Optimal Functional Link Neural Network for Financial Data Science

    Anwer Mustafa Hilal1, Hadeel Alsolai2, Fahd N. Al-Wesabi3, Mohammed Abdullah Al-Hagery4, Manar Ahmed Hamza1,*, Mesfer Al Duhayyim5
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6289-6304, 2022, DOI:10.32604/cmc.2022.021522
    Abstract In present digital era, data science techniques exploit artificial intelligence (AI) techniques who start and run small and medium-sized enterprises (SMEs) to have an impact and develop their businesses. Data science integrates the conventions of econometrics with the technological elements of data science. It make use of machine learning (ML), predictive and prescriptive analytics to effectively understand financial data and solve related problems. Smart technologies for SMEs enable allows the firm to get smarter with their processes and offers efficient operations. At the same time, it is needed to develop an effective tool which can assist small to medium sized… More >

  • ARTICLE

    Extremal Coalitions for Influence Games Through Swarm Intelligence-Based Methods

    Fabián Riquelme1,*, Rodrigo Olivares1, Francisco Muñoz1, Xavier Molinero3, Maria Serna2
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6305-6321, 2022, DOI:10.32604/cmc.2022.021804
    (This article belongs to this Special Issue: Recent Advances in Metaheuristic Techniques and Their Real-World Applications)
    Abstract An influence game is a simple game represented over an influence graph (i.e., a labeled, weighted graph) on which the influence spread phenomenon is exerted. Influence games allow applying different properties and parameters coming from cooperative game theory to the contexts of social network analysis, decision-systems, voting systems, and collective behavior. The exact calculation of several of these properties and parameters is computationally hard, even for a small number of players. Two examples of these parameters are the length and the width of a game. The length of a game is the size of its smaller winning coalition, while the… More >

  • ARTICLE

    Deep Learning Based Intelligent Industrial Fault Diagnosis Model

    R. Surendran1,*, Osamah Ibrahim Khalaf2, Carlos Andres Tavera Romero3
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6323-6338, 2022, DOI:10.32604/cmc.2022.021716
    Abstract In the present industrial revolution era, the industrial mechanical system becomes incessantly highly intelligent and composite. So, it is necessary to develop data-driven and monitoring approaches for achieving quick, trustable, and high-quality analysis in an automated way. Fault diagnosis is an essential process to verify the safety and reliability operations of rotating machinery. The advent of deep learning (DL) methods employed to diagnose faults in rotating machinery by extracting a set of feature vectors from the vibration signals. This paper presents an Intelligent Industrial Fault Diagnosis using Sailfish Optimized Inception with Residual Network (IIFD-SOIR) Model. The proposed model operates on… More >

  • ARTICLE

    Swarm-Based Extreme Learning Machine Models for Global Optimization

    Mustafa Abdul Salam1,*, Ahmad Taher Azar2, Rana Hussien2
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6339-6363, 2022, DOI:10.32604/cmc.2022.020583
    (This article belongs to this Special Issue: Role of Machine Learning and Evolutionary Algorithms for Cancer Detection and Prediction)
    Abstract Extreme Learning Machine (ELM) is popular in batch learning, sequential learning, and progressive learning, due to its speed, easy integration, and generalization ability. While, Traditional ELM cannot train massive data rapidly and efficiently due to its memory residence, high time and space complexity. In ELM, the hidden layer typically necessitates a huge number of nodes. Furthermore, there is no certainty that the arrangement of weights and biases within the hidden layer is optimal. To solve this problem, the traditional ELM has been hybridized with swarm intelligence optimization techniques. This paper displays five proposed hybrid Algorithms “Salp Swarm Algorithm (SSA-ELM), Grasshopper… More >

  • ARTICLE

    An Optimized Deep Learning Model for Emotion Classification in Tweets

    Chinu Singla1, Fahd N. Al-Wesabi2,3, Yash Singh Pathania1, Badria Sulaiman Alfurhood4, Anwer Mustafa Hilal5,*, Mohammed Rizwanullah5, Manar Ahmed Hamza5, Mohammad Mahzari6
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6365-6380, 2022, DOI:10.32604/cmc.2022.020480
    Abstract The task of automatically analyzing sentiments from a tweet has more use now than ever due to the spectrum of emotions expressed from national leaders to the average man. Analyzing this data can be critical for any organization. Sentiments are often expressed with different intensity and topics which can provide great insight into how something affects society. Sentiment analysis in Twitter mitigates the various issues of analyzing the tweets in terms of views expressed and several approaches have already been proposed for sentiment analysis in twitter. Resources used for analyzing tweet emotions are also briefly presented in literature survey section.… More >

  • ARTICLE

    Spark Spectrum Allocation for D2D Communication in Cellular Networks

    Tanveer Ahmad1, Imran Khan2, Azeem Irshad3, Shafiq Ahmad4, Ahmed T. Soliman4, Akber Abid Gardezi5, Muhammad Shafiq6,*, Jin-Ghoo Choi6
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6381-6394, 2022, DOI:10.32604/cmc.2022.019787
    Abstract The device-to-device (D2D) technology performs explicit communication between the terminal and the base station (BS) terminal, so there is no need to transmit data through the BS system. The establishment of a short-distance D2D communication link can greatly reduce the burden on the BS server. At present, D2D is one of the key technologies in 5G technology and has been studied in depth. D2D communication reuses the resources of cellular users to improve system key parameters like utilization and throughput. However, repeated use of the spectrum and coexistence of cellular users can cause co-channel interference. Aiming at the interference problem… More >

Share Link

WeChat scan