Journals / IASC / Vol.31, No.2

  • ARTICLE

    A Deep Learning to Distinguish COVID-19 from Others Pneumonia Cases

    Sami Gazzah1,*, Rida Bayi2, Soulaimane Kaloun2, Omar Bencharef2
    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 677-692, 2022, DOI:10.32604/iasc.2022.019360
    Abstract A new virus called SARS-CoV-2 appeared at the end of space 2019 in Wuhan, China. This virus immediately spread throughout the world due to its highly contagious nature. Moreover, SARS-CoV-2 has changed the way of our life and has caused a huge economic and public health disaster. Therefore, it is urgent to identify positive cases as soon as possible and treat them as isolated. Automatic detection of viruses using computer vision and machine learning will be a valuable contribution to detecting and limiting the spread of this epidemic. The delay introduction of X-ray technology as diagnostic tool limited our ability… More >

  • ARTICLE

    Intelligent Audio Signal Processing for Detecting Rainforest Species Using Deep Learning

    Rakesh Kumar1, Meenu Gupta1, Shakeel Ahmed2,*, Abdulaziz Alhumam2, Tushar Aggarwal1
    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 693-706, 2022, DOI:10.32604/iasc.2022.019811
    Abstract Hearing a species in a tropical rainforest is much easier than seeing them. If someone is in the forest, he might not be able to look around and see every type of bird and frog that are there but they can be heard. A forest ranger might know what to do in these situations and he/she might be an expert in recognizing the different type of insects and dangerous species that are out there in the forest but if a common person travels to a rain forest for an adventure, he might not even know how to recognize these species,… More >

  • ARTICLE

    Analyzing the Data of Software Security Life-Span: Quantum Computing Era

    Hashem Alyami1, Mohd Nadeem2, Wael Alosaimi3, Abdullah Alharbi3, Rajeev Kumar4,*, Bineet Kumar Gupta4, Alka Agrawal2, Raees Ahmad Khan2
    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 707-716, 2022, DOI:10.32604/iasc.2022.020780
    Abstract Software or web application security is the main objective in the era of Information Technology (IT) and Artificial Intelligence (AI). Distinguishing proof of security at the initial stage produces significant results to comprehend the administration of security relics for best potential outcomes. A security alternative gives several methods and algorithms to ensure the software security. Security estimation is the vital factor in assessing, administrating, controlling security to improve the nature of security. It is to be realized that assessment of security at early stage of development helps in identifying distinctive worms, dangers, weaknesses and threats. This paper will talk about… More >

  • ARTICLE

    CNN Based Driver Drowsiness Detection System Using Emotion Analysis

    H. Varun Chand*, J. Karthikeyan
    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 717-728, 2022, DOI:10.32604/iasc.2022.020008
    Abstract

    The drowsiness of the driver and rash driving are the major causes of road accidents, which result in loss of valuable life, and deteriorate the safety in the road traffic. Reliable and precise driver drowsiness systems are required to prevent road accidents and to improve road traffic safety. Various driver drowsiness detection systems have been designed with different technologies which have an affinity towards the unique parameter of detecting the drowsiness of the driver. This paper proposes a novel model of multi-level distribution of detecting the driver drowsiness using the Convolution Neural Networks (CNN) followed by the emotion analysis. The… More >

  • ARTICLE

    Using Mobile Technology to Construct a Network Medical Health Care System

    Sung-Jung Hsiao1, Wen-Tsai Sung2,*
    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 729-748, 2022, DOI:10.32604/iasc.2022.020332
    Abstract In this study, a multisensory physiological measurement system was built with wireless transmission technology, using a DSPIC30F4011 as the master control center and equipped with physiological signal acquisition modules such as an electrocardiogram module, blood pressure module, blood oxygen concentration module, and respiratory rate module. The physiological data were transmitted wirelessly to Android-based mobile applications via the TCP/IP or Bluetooth serial ports of Wi-Fi. The Android applications displayed the acquired physiological signals in real time and performed a preliminary abnormity diagnosis based on the measured physiological data and built-in index diagnostic data provided by doctors, such as blood oxygen concentration,… More >

  • ARTICLE

    CGraM: Enhanced Algorithm for Community Detection in Social Networks

    Kalaichelvi Nallusamy*, K. S. Easwarakumar
    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 749-765, 2022, DOI:10.32604/iasc.2022.020189
    Abstract Community Detection is used to discover a non-trivial organization of the network and to extract the special relations among the nodes which can help in understanding the structure and the function of the networks. However, community detection in social networks is a vast and challenging task, in terms of detected communities accuracy and computational overheads. In this paper, we propose a new algorithm Enhanced Algorithm for Community Detection in Social Networks – CGraM, for community detection using the graph measures eccentricity, harmonic centrality and modularity. First, the centre nodes are identified by using the eccentricity and harmonic centrality, next a… More >

  • REVIEW

    A Review on Privacy Preservation of Location-Based Services in Internet of Things

    Raniyah Wazirali*
    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 767-779, 2022, DOI:10.32604/iasc.2022.019243
    Abstract Internet of Things (IoT) has become popular with the rapid development of sensing devices, and it offers a large number of services. Location data is one of the most important information required for IoT systems. With the widespread of Location Based Services (LBS) applications, the privacy and security threats are also emerging. Recently, a large number of studies focused on localization and positioning functionalities, however, the risk associated with user privacy has not been sufficiently addressed so far. Therefore, privacy and security of device location in IoT systems is an active area of research. Since LBS is often exposed to… More >

  • ARTICLE

    Multi-Objective Adapted Binary Bat for Test Suite Reduction

    Nagwa Reda1, Abeer Hamdy2,*, Essam A. Rashed1,3
    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 781-797, 2022, DOI:10.32604/iasc.2022.019669
    (This article belongs to this Special Issue: Soft Computing Methods for Innovative Software Practices)
    Abstract Regression testing is an essential quality test technique during the maintenance phase of the software. It is executed to ensure the validity of the software after any modification. As software evolves, the test suite expands and may become too large to be executed entirely within a limited testing budget and/or time. So, to reduce the cost of regression testing, it is mandatory to reduce the size of the test suite by discarding the redundant test cases and selecting the most representative ones that do not compromise the effectiveness of the test suite in terms of some predefined criteria such as… More >

  • ARTICLE

    Design of Optimal Controllers for Automatic Voltage Regulation Using Archimedes Optimizer

    Ahmed Agwa1,2,*, Salah Elsayed3, Mahrous Ahmed3
    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 799-815, 2022, DOI:10.32604/iasc.2022.019887
    Abstract Automatic voltage regulators (AVRs) in electrical grids preserve the voltage at its nominal value. Regulating the parameters of proportional–integral–derivative (PID) controllers used for AVRs is a nonlinear optimization issue. The objective function is designed to minimize the settling time, rise time, and overshoot of step response of resultant voltage with subjugation to constraints of PID controller parameters. In this study, we suggest using an Archimedes optimization algorithm (AOA) to tune the parameters of the PID controllers for AVRs. In addition, using an AOA to optimize the parameters of a fractional-order PID (FOPID) controller and a PID plus second-order derivative (PIDD2)… More >

  • ARTICLE

    Blood Group Classification System Based on Image Processing Techniques

    S. A. Shaban*, D. L. Elsheweikh
    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 817-834, 2022, DOI:10.32604/iasc.2022.019500
    Abstract The present paper proposes a novel system that automatically classifies the eight different blood groups according to the ABO and Rh blood group systems. The proposed system is developed by applying MATLAB’s image processing techniques on the blood sample images. These images are acquired from the laboratory using the slide test. It utilizes a mean filter for removing noise from blood sample images. In addition, the Contrast Limited Adaptive Histogram Equalization (CLAHE) is used for enhancing the image characteristics analysis. The proposed system also utilizes the automated threshold strategy (Otsu’s approach) for obtaining the blood samples binary images. Since, adding… More >

  • ARTICLE

    Arrhythmia and Disease Classification Based on Deep Learning Techniques

    Ramya G. Franklin1,*, B. Muthukumar2
    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 835-851, 2022, DOI:10.32604/iasc.2022.019877
    Abstract Electrocardiography (ECG) is a method for monitoring the human heart’s electrical activity. ECG signal is often used by clinical experts in the collected time arrangement for the evaluation of any rhythmic circumstances of a topic. The research was carried to make the assignment computerized by displaying the problem with encoder-decoder methods, by using misfortune appropriation to predict standard or anomalous information. The two Convolutional Neural Networks (CNNs) and the Long Short-Term Memory (LSTM) fully connected layer (FCL) have shown improved levels over deep learning networks (DLNs) across a wide range of applications such as speech recognition, prediction etc., As CNNs… More >

  • ARTICLE

    Optimization Based Vector Quantization for Data Reduction in Multimedia Applications

    V. R. Kavitha1,*, M. Kanchana2, B. Gobinathan3, K. R. Sekar4, Mohamed Yacin Sikkandar5
    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 853-867, 2022, DOI:10.32604/iasc.2022.018358
    Abstract Data reduction and image compression techniques in the present Internet and multi-media age are essential to increase image and video capacity in relation to memory, network bandwidth use and safe data transmission. There have been a different variety of image compression models with varying compression efficiency and visual image quality in the literature. Vector Quantization (VQ) is a widely used image coding scheme that is designed to generate an efficient coding book that includes a list of codewords that assign the input image vector to a minimum distance of Euclidea. The Linde–Buzo–Gray (LBG) historically widely used model produces the local… More >

  • ARTICLE

    Investigation of Techniques for VoIP Frame Aggregation Over A-MPDU 802.11n

    Qasem M. Kharma*, Abdelrahman H. Hussein, Faris M. Taweel, Mosleh M. Abualhaj, Qusai Y. Shambour
    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 869-883, 2022, DOI:10.32604/iasc.2022.020415
    Abstract The widespread and desirable features of IP and IEEE 802.11 networks have made these technologies a suitable medium for carrying voice over IP (VoIP). However, a bandwidth (BW) exploitation obstacle emerges when 802.11 networks are used to carry VoIP traffic. This BW exploitation obstacle is caused by the large 80-byte preamble size of the VoIP packet and a waiting time of 765 μs for each layer 2 VoIP frame. As a solution, IEEE 802.11n was consequently designed with a built-in layer 2 frame aggregation feature, but the adverse impact on the VoIP performance still needed to be addressed. Subsequent VoIP… More >

  • ARTICLE

    A Time-Efficient and Exploratory Algorithm for the Rectangle Packing Problem

    Mohammad Bozorgi1, Morteza Mohammadi Zanjireh1,*, Mahdi Bahaghighat1, Qin Xin2
    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 885-898, 2022, DOI:10.32604/iasc.2022.016075
    Abstract Today, resource waste is considered as one of the most important challenges in different industries. In this regard, the Rectangle Packing Problem (RPP) can affect noticeably both time and design issues in businesses. In this study, the main objective is to create a set of non-overlapping rectangles so that they have specific dimensions within a rectangular plate with a specified width and an unlimited height. The ensued challenge is an NP-complete problem. NP-complete problem, any of a class of computational problems that still there are no efficient solution for them. Most substantial computer-science problems such as the traveling salesman problem,… More >

  • ARTICLE

    Optimal Control and Spectral Collocation Method for Solving Smoking Models

    Amr M. S. Mahdy1,*, Mohamed S. Mohamed1, Ahoud Y. Al Amiri2, Khaled A. Gepreel1
    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 899-915, 2022, DOI:10.32604/iasc.2022.017801
    (This article belongs to this Special Issue: Recent Trends in Computational Methods for Differential Equations)
    Abstract In this manuscript, we solve the ordinary model of nonlinear smoking mathematically by using the second kind of shifted Chebyshev polynomials. The stability of the equilibrium point is calculated. The schematic of the model illustrates our proposition. We discuss the optimal control of this model, and formularize the optimal control smoking work through the necessary optimality cases. A numerical technique for the simulation of the control problem is adopted. Moreover, a numerical method is presented, and its stability analysis discussed. Numerical simulation then demonstrates our idea. Optimal control for the model is further discussed by clarifying the optimal control through… More >

  • ARTICLE

    ResNet CNN with LSTM Based Tamil Text Detection from Video Frames

    I. Muthumani1,*, N. Malmurugan2, L. Ganesan3
    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 917-928, 2022, DOI:10.32604/iasc.2022.018030
    Abstract Text content in videos includes applications such as library video retrievals, live-streaming advertisements, opinion mining, and video synthesis. The key components of such systems include video text detection and acknowledgments. This paper provides a framework to detect and accept text video frames, aiming specifically at the cursive script of Tamil text. The model consists of a text detector, script identifier, and text recognizer. The identification in video frames of textual regions is performed using deep neural networks as object detectors. Textual script content is associated with convolutional neural networks (CNNs) and recognized by combining ResNet CNNs with long short-term memory… More >

  • ARTICLE

    Severity Grade Recognition for Nasal Cavity Tumours Using Décor CNN

    Prabhakaran Mathialagan*, Malathy Chidambaranathan
    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 929-946, 2022, DOI:10.32604/iasc.2022.020163
    Abstract Nasal cavity and paranasal sinus tumours that occur in the respiratory tract are the most life-threatening disease in the world. The human respiratory tract has many sites which has different mucosal lining like frontal, parred, sphenoid and ethmoid sinuses. Nasal cavity tumours can occur at any different mucosal linings and chances of prognosis possibility from one nasal cavity site to another site is very high. The paranasal sinus tumours can metastases to oral cavity and digestive tracts may lead to excessive survival complications. Grading the respiratory tract tumours with dysplasia cases are more challenging using manual pathological procedures. Manual microscopic… More >

  • ARTICLE

    Selecting Dominant Features for the Prediction of Early-Stage Chronic Kidney Disease

    Vinothini Arumugam*, S. Baghavathi Priya
    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 947-959, 2022, DOI:10.32604/iasc.2022.018654
    Abstract Nowadays, Chronic Kidney Disease (CKD) is one of the vigorous public health diseases. Hence, early detection of the disease may reduce the severity of its consequences. Besides, medical databases of any disease diagnosis may be collected from the blood test, urine test, and patient history. Nevertheless, medical information retrieved from various sources is diverse. Therefore, it is unadaptable to evaluate numerical and nominal features using the same feature selection algorithm, which may lead to fallacious analysis. Applying machine learning techniques over the medical database is a common way to help feature identification for CKD prediction. In this paper, a novel… More >

  • ARTICLE

    Computation of Aortic Geometry Using MR and CT 3D Images

    Maryam Altalhi1, Sami Ur Rehman2, Fakhre Alam2, Ala Abdulsalam Alarood3, Amin ur Rehman2, M. Irfan Uddin4,*
    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 961-969, 2022, DOI:10.32604/iasc.2022.020607
    Abstract The proper computation of geometric parameters of the aorta and coronary arteries are very important for surgery planning, disease diagnoses, and age-related changes observation in the vessels. The accurate knowledge about the geometry of aorta and coronary arteries is required for the proper investigation of heart related diseases. The geometry of aorta and coronary arteries includes the diameter of the ascending and descending aorta and coronary arteries, length of the coronary arteries, branching angles of the coronary arteries and branching points. These geometric parameters from arteries can be computed from the 3D image data. In this paper, we propose an… More >

  • ARTICLE

    An Optimized Scale-Invariant Feature Transform Using Chamfer Distance in Image Matching

    Tamara A. Al-Shurbaji1, Khalid A. AlKaabneh2, Issam Alhadid3,*, Ra’ed Masa’deh4
    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 971-985, 2022, DOI:10.32604/iasc.2022.019654
    (This article belongs to this Special Issue: Recent Trends in Computational Methods for Differential Equations)
    Abstract Scale-Invariant Feature Transform is an image matching algorithm used to match objects of two images by extracting the feature points of target objects in each image. Scale-Invariant Feature Transform suffers from long processing time due to embedded calculations which reduces the overall speed of the technique. This research aims to enhance SIFT processing time by imbedding Chamfer Distance Algorithm to find the distance between image descriptors instead of using Euclidian Distance Algorithm used in SIFT. Chamfer Distance Algorithm requires less computational time than Euclidian Distance Algorithm because it selects the shortest path between any two points when the distance is… More >

  • ARTICLE

    Employing a Fuzzy Approach for Monitoring Fish Pond Culture Environment

    Wen-Tsai Sung1, Sung-Jung Hsiao2,*
    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 987-1006, 2022, DOI:10.32604/iasc.2022.019098
    Abstract This study builds an automatic monitoring system for the fish pond culture environment. The purpose of this study is to reduce culture costs, including those resulting from labor costs and natural disasters, and make it easier for culturists to manage their fish ponds. With the proposed system, physical indicators of water quality are extracted by temperature, dissolved oxygen, and pH sensing modules; the heater, submerged motor pump, air pump, feeding trough, and LED illuminating lamp are controlled to improve the water quality and reduce labor. The wireless sensor network (WSN) is used as the signal transmission architecture between the sensor… More >

  • ARTICLE

    Detecting Lung Cancer Using Machine Learning Techniques

    Ashit Kumar Dutta*
    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1007-1023, 2022, DOI:10.32604/iasc.2022.019778
    Abstract In recent days, Internet of Things (IoT) based image classification technique in the healthcare services is becoming a familiar concept that supports the process of detecting cancers with Computer Tomography (CT) images. Lung cancer is one of the perilous diseases that increases the mortality rate exponentially. IoT based image classifiers have the ability to detect cancer at an early stage and increases the life span of a patient. It supports oncologist to monitor and evaluate the health condition of a patient. Also, it can decipher cancer risk marker and act upon them. The process of feature extraction and selection from… More >

  • ARTICLE

    Implementation of a High-Speed and High-Throughput Advanced Encryption Standard

    T. Manoj Kumar1,*, P. Karthigaikumar2
    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1025-1036, 2022, DOI:10.32604/iasc.2022.020090
    Abstract

    Data security is an essential aspect of data communication and data storage. To provide high-level security against all kinds of unauthorized accesses, cryptographic algorithms have been applied to various fields such as medical and military applications. Advanced Encryption Standard (AES), a symmetric cryptographic algorithm, is acknowledged as the most secure algorithm for the cryptographic process globally. Several modifications have been made to the original architecture after it was proposed by two Belgian researchers, Joan Daemen and Vincent Rijment, at the third AES candidate Conference in 2000. The existing modifications aim to increase security and speed. This paper proposes an efficient… More >

  • ARTICLE

    User Interaction Based Recommender System Using Machine Learning

    R. Sabitha1, S. Vaishnavi2,*, S. Karthik1, R. M. Bhavadharini3
    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1037-1049, 2022, DOI:10.32604/iasc.2022.018985
    Abstract In the present scenario of electronic commerce (E-Commerce), the in-depth knowledge of user interaction with resources has become a significant research concern that impacts more on analytical evaluations of recommender systems. For staying in aggressive E-Commerce, various products and services regarding distinctive requirements must be provided on time. Moreover, because of the large amount of product information available online, Recommender Systems (RS) are required to analyze the availability of consumers, which improves the decision-making of customers with detailed product knowledge and reduces time consumption. With that note, this paper derives a new model called User Interaction based Recommender System (UI-RS)… More >

  • ARTICLE

    PCN2: Parallel CNN to Diagnose COVID-19 from Radiographs and Metadata

    Abdullah Baz1, Mohammed Baz2,*
    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1051-1069, 2022, DOI:10.32604/iasc.2022.020304
    Abstract COVID-19 constitutes one of the devastating pandemics plaguing humanity throughout the centuries; within about 18 months since its appearing, the cumulative confirmed cases hit 173 million, whereas the death toll approaches 3.72 million. Although several vaccines became available for the public worldwide, the speed with which COVID-19 is spread, and its different mutant strains hinder stopping its outbreak. This, in turn, prompting the desperate need for devising fast, cheap and accurate tools via which the disease can be diagnosed in its early stage. Reverse Transcription Polymerase Chain Reaction (RTPCR) test is the mainstay tool used to detect the COVID-19 symptoms.… More >

  • ARTICLE

    Multivariate Outlier Detection for Forest Fire Data Aggregation Accuracy

    Ahmad A. A. Alkhatib*, Qusai Abed-Al
    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1071-1087, 2022, DOI:10.32604/iasc.2022.020461
    Abstract Wireless sensor networks have been a very important means in forest monitoring applications. A clustered sensor network comprises a set of cluster members and one cluster head. The cluster members are normally located close to each other, with overlaps among their sensing coverage within the cluster. The cluster members concurrently detect the same event to send to the Cluster Head node. This is where data aggregation is deployed to remove redundant data at the cost of data accuracy, where some data generated by the sensing process might be an outlier. Thus, it is important to conserve the aggregated data’s accuracy… More >

  • ARTICLE

    Unsupervised Semantic Segmentation Method of User Interface Component of Games

    Shinjin Kang1, Jongin Choi2,*
    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1089-1105, 2022, DOI:10.32604/iasc.2022.019979
    Abstract The game user interface (UI) provides a large volume of information necessary to analyze the game screen. The availability of such information can be functional in vision-based machine learning algorithms. With this, there will be an enhancement in the application power of vision deep learning neural networks. Therefore, this paper proposes a game UI segmentation technique based on unsupervised learning. We developed synthetic labeling created on the game engine, image-to-image translation and segmented UI components in the game. The network learned in this manner can segment the target UI area in the target game regardless of the location of the… More >

  • ARTICLE

    Ferroresonance Overvoltage Mitigation Using Surge Arrester for Grid-Connected Wind Farm

    Nehmdoh A. Sabiha*, Hend I. Alkhammash
    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1107-1118, 2022, DOI:10.32604/iasc.2022.020070
    Abstract Ferroresonance occurrence represents a very dangerous phenomenon to electric power systems. Concerning the recent trend of the applications of grid-connected wind farms, this phenomenon can lead to undesired overvoltages stressing the wind farm components. In this paper, the ferroresonance overvoltages are studied and mitigated for the grid-connected wind farm. Single-pole switching of the breaker is considered, where it is the most famous reason behind the ferroresonance transient events in the electric power systems. During the ferroresonance period, the transient voltage of the network is increased to more than three times the voltage level and associated with harmonics. Surge arrester is… More >

  • ARTICLE

    Design of Virtual Reality System for Organic Chemistry

    Kalaphath Kounlaxay1, Dexiang Yao1, Min Woo Ha2,3, Soo Kyun Kim4,*
    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1119-1130, 2022, DOI:10.32604/iasc.2022.020151
    Abstract Virtual reality (VR) is an advanced technology widely used in many fields. Education is essential for human resources development, and the use of technology in education can enhance teaching and learning methods. This study aims to present new methods and tools for visual and interactive education in organic chemistry. The experimental design and chemical equipment used in this research are based on the basic theory of organic chemistry, and the related materials are simulated as three-dimensional (3D) models to perform the experiments in a VR system. Chemical reactions are simulated by mixing the chemicals, and the students can observe the… More >

  • ARTICLE

    Competitive Risk Aware Algorithm for k-min Search Problem

    Iftikhar Ahmad1,*, Abdulwahab Ali Almazroi2, Mohammed A. Alqarni3, Muhammad Kashif Nawaz1
    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1131-1142, 2022, DOI:10.32604/iasc.2022.020715
    Abstract In a classical k-min search problem, an online player wants to buy k units of an asset with the objective of minimizing the total buying cost. The problem setting allows the online player to view only a single price quotation at each time step. A price quotation is the price of one unit of an asset. After receiving the price quotation, the online player has to decide on the number of units to buy. The objective of the online player is to buy the required k units in a fixed length investment horizon. Online algorithms are proposed in the literature… More >

  • ARTICLE

    Automated Deep Learning of COVID-19 and Pneumonia Detection Using Google AutoML

    Saiful Izzuan Hussain*, Nadiah Ruza
    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1143-1156, 2022, DOI:10.32604/iasc.2022.020508
    (This article belongs to this Special Issue: New Trends in Artificial Intelligence and Deep learning for Instrumentation, Sensors, and Robotics)
    Abstract Coronavirus (COVID-19) is a pandemic disease classified by the World Health Organization. This virus triggers several coughing problems (e.g., flu) that include symptoms of fever, cough, and pneumonia, in extreme cases. The human sputum or blood samples are used to detect this virus, and the result is normally available within a few hours or at most days. In this research, we suggest the implementation of automated deep learning without require handcrafted expertise of data scientist. The model developed aims to give radiologists a second-opinion interpretation and to minimize clinicians’ workload substantially and help them diagnose correctly. We employed automated deep… More >

  • ARTICLE

    A Deep Learning-Based Novel Approach for Weed Growth Estimation

    Anand Muni Mishra1, Shilpi Harnal1, Khalid Mohiuddin2, Vinay Gautam1, Osman A. Nasr2, Nitin Goyal1, Mamdooh Alwetaishi3, Aman Singh4,*
    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1157-1173, 2022, DOI:10.32604/iasc.2022.020174
    Abstract Automation of agricultural food production is growing in popularity in scientific communities and industry. The main goal of automation is to identify and detect weeds in the crop. Weed intervention for the duration of crop establishment is a serious difficulty for wheat in North India. The soil nutrient is important for crop production. Weeds usually compete for light, water and air of nutrients and space from the target crop. This research paper assesses the growth rate of weeds due to macronutrients (nitrogen, phosphorus and potassium) absorbed from various soils (fertile, clay and loamy) in the rabi crop field. The weed… More >

  • ARTICLE

    Computational Approach via Half-Sweep and Preconditioned AOR for Fractional Diffusion

    Andang Sunarto1,*, Praveen Agarwal2,3,4, Jumat Sulaiman5, Jackel Vui Lung Chew6
    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1173-1184, 2022, DOI:10.32604/iasc.2022.020542
    Abstract Solving time-fractional diffusion equation using a numerical method has become a research trend nowadays since analytical approaches are quite limited. There is increasing usage of the finite difference method, but the efficiency of the scheme still needs to be explored. A half-sweep finite difference scheme is well-known as a computational complexity reduction approach. Therefore, the present paper applied an unconditionally stable half-sweep finite difference scheme to solve the time-fractional diffusion equation in a one-dimensional model. Throughout this paper, a Caputo fractional operator is used to substitute the time-fractional derivative term approximately. Then, the stability of the difference scheme combining the… More >

  • ARTICLE

    Machine Learning Privacy Aware Anonymization Using MapReduce Based Neural Network

    U. Selvi*, S. Pushpa
    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1185-1196, 2022, DOI:10.32604/iasc.2022.020164
    Abstract Due to the recent advancement in technologies, a huge amount of data is generated where individual private information needs to be preserved. A proper Anonymization algorithm with increased Data utility is required to protect individual privacy. However, preserving privacy of individuals whileprocessing huge amount of data is a challenging task, as the data contains certain sensitive information. Moreover, scalability issue in handling a large dataset is found in using existing framework. Many an Anonymization algorithm for Big Data have been developed and under research. We propose a method of applying Machine Learning techniques to protect and preserve the personal identities… More >

  • ARTICLE

    An Optimal Anchor Placement Method for Localization in Large-Scale Wireless Sensor Networks

    Tuğrul Çavdar1, Faruk Baturalp Günay2,*, Nader Ebrahimpour1, Muhammet Talha Kakız3
    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1197-1222, 2022, DOI:10.32604/iasc.2022.020127
    Abstract Localization is an essential task in Wireless Sensor Networks (WSN) for various use cases such as target tracking and object monitoring. Anchor nodes play a critical role in this task since they can find their location via GPS signals or manual setup mechanisms and help other nodes in the network determine their locations. Therefore, the optimal placement of anchor nodes in a WSN is of particular interest for reducing the energy consumption while yielding better accuracy at finding locations of the nodes. In this paper, we propose a novel approach for finding the optimal number of anchor nodes and an… More >

  • ARTICLE

    Constructing a Deep Image Analysis System Based on Self-Driving and AIoT

    Wen-Tsai Sung1, Sung-Jung Hsiao2,*, Chung-Yen Hsiao1
    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1223-1240, 2022, DOI:10.32604/iasc.2022.020746
    Abstract This research is based on the system architecture of Edge Computing in the AIoT (Artificial Intelligence & Internet of Things) field. In terms of receiving data, the authors proposed approach employed the camera module as the video source, the ultrasound module as the distance measurement source, and then compile C++ with Raspberry Pi 4B for image lane analysis, while Jetson Nano uses the YOLOv3 algorithm for image object detection. The analysis results of the two single-board computers are transmitted to Motoduino U1 in binary form via GPIO, which is used for data integration and load driving. The load drive has… More >

  • ARTICLE

    Big Data Analytics with OENN Based Clinical Decision Support System

    Thejovathi Murari1, L. Prathiba2, Kranthi Kumar Singamaneni3,*, D. Venu4, Vinay Kumar Nassa5, Rachna Kohar6, Satyajit Sidheshwar Uparkar7
    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1241-1256, 2022, DOI:10.32604/iasc.2022.020203
    (This article belongs to this Special Issue: Soft Computing Methods for Intelligent Automation Systems)
    Abstract In recent times, big data analytics using Machine Learning (ML) possesses several merits for assimilation and validation of massive quantity of complicated healthcare data. ML models are found to be scalable and flexible over conventional statistical tools, which makes them suitable for risk stratification, diagnosis, classification and survival prediction. In spite of these benefits, the utilization of ML in healthcare sector faces challenges which necessitate massive training data, data preprocessing, model training and parameter optimization based on the clinical problem. To resolve these issues, this paper presents new Big Data Analytics with Optimal Elman Neural network (BDA-OENN) for clinical decision… More >

  • ARTICLE

    Deep Transfer Learning Based Rice Plant Disease Detection Model

    R. P. Narmadha1,*, N. Sengottaiyan2, R. J. Kavitha3
    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1257-1271, 2022, DOI:10.32604/iasc.2022.020679
    Abstract In agriculture, plant diseases are mainly accountable for reduction in productivity and leads to huge economic loss. Rice is the essential food crop in Asian countries and it gets easily affected by different kinds of diseases. Because of the advent of computer vision and deep learning (DL) techniques, the rice plant diseases can be detected and reduce the burden of the farmers to save the crops. To achieve this, a new DL based rice plant disease diagnosis is developed using Densely Convolution Neural Network (DenseNet) with multilayer perceptron (MLP), called DenseNet169-MLP. The proposed model aims to classify the rice plant… More >

  • ARTICLE

    Breast Cancer Detection Through Feature Clustering and Deep Learning

    Hanan A. Hosni Mahmoud, Amal H. Alharbi, Norah S. Alghamdi*
    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1273-1286, 2022, DOI:10.32604/iasc.2022.020662
    Abstract In this paper we propose a computerized breast cancer detection and breast masses classification system utilizing mammograms. The motivation of the proposed method is to detect breast cancer tumors in early stages with more accuracy and less negative false cases. Our proposed method utilizes clustering of different features by segmenting the breast mammogram and then extracts deep features using the presented Convolution Neural Network (CNN). The extracted features are then combined with subjective features such as shape, texture and density. The combined features are then utilized by the Extreme Learning Machine Clustering (ELMC) algorithm to combine segments together to identify… More >

  • ARTICLE

    Machine Learning Empowered Software Defect Prediction System

    Mohammad Sh. Daoud1, Shabib Aftab2,3, Munir Ahmad2, Muhammad Adnan Khan4,5,*, Ahmed Iqbal3, Sagheer Abbas2, Muhammad Iqbal2, Baha Ihnaini6,7
    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1287-1300, 2022, DOI:10.32604/iasc.2022.020362
    Abstract Production of high-quality software at lower cost has always been the main concern of developers. However, due to exponential increases in size and complexity, the development of qualitative software with lower costs is almost impossible. This issue can be resolved by identifying defects at the early stages of the development lifecycle. As a significant amount of resources are consumed in testing activities, if only those software modules are shortlisted for testing that is identified as defective, then the overall cost of development can be reduced with the assurance of high quality. An artificial neural network is considered as one of… More >

  • ARTICLE

    Recommendation Learning System Model for Children with Autism

    V. Balaji*, S. Kanaga Suba Raja
    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1301-1315, 2022, DOI:10.32604/iasc.2022.020287
    Abstract Autism spectrum disorder (ASD), is a neurological developmental disorder. It affects how people communicate and interact with others, as well as how they behave and learn. The symptoms and signs appear when a child is very young. Derived with increased usage of machine learning procedure in the medicinal analysis investigations. In this paper, our objective is to find out the most significant attributes and automate the process using classification techniques and pattern clustering using K-means clustering. We have analyzed ASD datasets of children towards determining the best performance of classifier for these binary datasets considering recall, precision, accuracy and classification… More >

  • ARTICLE

    Multi-Model Detection of Lung Cancer Using Unsupervised Diffusion Classification Algorithm

    N. Jayanthi1,*, D. Manohari2, Mohamed Yacin Sikkandar3, Mohamed Abdelkader Aboamer3, Mohamed Ibrahim Waly3, C. Bharatiraja4
    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1317-1329, 2022, DOI:10.32604/iasc.2022.018974
    Abstract Lung cancer is a curable disease if detected early, and its mortality rate decreases with forwarding treatment measures. At first, an easy and accurate way to detect is by using image processing techniques on the cancer-affected images captured from the patients. This paper proposes a novel lung cancer detection method. Firstly, an adaptive median filter algorithm (AMF) is applied to preprocess those images for improving the quality of the affected area. Then, a supervised image edge detection algorithm (SIED) is presented to segment those images. Then, feature extraction is employed to extract the mean, standard deviation, energy, contrast, etc., of… More >

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