Journals / IASC / Vol.30, No.1

  • ARTICLE

    A Hybrid Deep Learning Intrusion Detection Model for Fog Computing Environment

    K. Kalaivani*, M. Chinnadurai
    Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 1-15, 2021, DOI:10.32604/iasc.2021.017515
    Abstract Fog computing extends the concept of cloud computing by providing the services of computing, storage, and networking connectivity at the edge between data centers in cloud computing environments and end devices. Having the intelligence at the edge enables faster real-time decision-making and reduces the amount of data forwarded to the cloud. When enhanced by fog computing, the Internet of Things (IoT) brings low latency and improves real time and quality of service (QoS) in IoT applications of augmented reality, smart grids, smart vehicles, and healthcare. However, both cloud and fog computing environments are vulnerable to several kinds of attacks that… More >

  • ARTICLE

    Expert System for Stable Power Generation Prediction in Microbial Fuel Cell

    Kathiravan Srinivasan1, Lalit Garg2,*, Bor-Yann Chen3, Abdulellah A. Alaboudi4, N. Z. Jhanjhi5, Chang-Tang Chang6, B. Prabadevi1, N. Deepa1
    Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 17-30, 2021, DOI:10.32604/iasc.2021.018380
    Abstract Expert Systems are interactive and reliable computer-based decision-making systems that use both facts and heuristics for solving complex decision-making problems. Generally, the cyclic voltammetry (CV) experiments are executed a random number of times (cycles) to get a stable production of power. However, presently there are not many algorithms or models for predicting the power generation stable criteria in microbial fuel cells. For stability analysis of Medicinal herbs’ CV profiles, an expert system driven by the augmented K-means clustering algorithm is proposed. Our approach requires a dataset that contains voltage-current relationships from CV experiments on the related subjects (plants/herbs). This new… More >

  • ARTICLE

    A Shadowed Rough-fuzzy Clustering Algorithm Based on Mahalanobis Distance for Intrusion Detection

    Lina Wang1,2,*, Jie Wang3, Yongjun Ren4, Zimeng Xing1, Tao Li1, Jinyue Xia5
    Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 31-47, 2021, DOI:10.32604/iasc.2021.018577
    Abstract Intrusion detection has been widely used in many application domains; thus, it has caught significant attention in academic fields these years. Assembled with more and more sub-systems, the network is more vulnerable to multiple attacks aiming at the network security. Compared with the other issues such as complex environment and resources-constrained devices, network security has been the biggest challenge for Internet construction. To deal with this problem, a fundamental measure for safeguarding network security is to select an intrusion detection algorithm. As is known, it is less effective to determine the abnormal behavior as an intrusion and learn the entire… More >

  • ARTICLE

    Research and Development of a Brain-Controlled Wheelchair for Paralyzed Patients

    Mohammad Monirujjaman Khan1,*, Shamsun Nahar Safa1, Minhazul Hoque Ashik1, Mehedi Masud2, Mohammed A. AlZain3
    Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 49-64, 2021, DOI:10.32604/iasc.2021.016077
    Abstract Smart wheelchairs play a significant role in supporting disabled people. Individuals with motor function impairments due to some disorders such as strokes or multiple sclerosis face frequent moving difficulties. Hence, they need constant support from an assistant. This paper presents a brain-controlled wheelchair model to assist disabled and paralyzed patients. The wheelchair is controlled by interpreting Electroencephalogram (EEG) signals, also known as brain waves. In the EEG technique, an electrode cap is positioned on the user’s scalp to receive EEG signals, which are detected and transformed by the Arduino microcontroller into motion commands, which drive the wheelchair. The proposed wheelchair… More >

  • ARTICLE

    Intrusion Detection Using a New Hybrid Feature Selection Model

    Adel Hamdan Mohammad*
    Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 65-80, 2021, DOI:10.32604/iasc.2021.016140
    Abstract Intrusion detection is an important topic that aims at protecting computer systems. Besides, feature selection is crucial for increasing the performance of intrusion detection. This paper employs a new hybrid feature selection model for intrusion detection. The implemented model uses Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) algorithms in a new manner. In addition, this study introduces two new models called (PSO-GWO-NB) and (PSO-GWO-ANN) for feature selection and intrusion detection. PSO and GWO show emergent results in feature selection for several purposes and applications. This paper uses PSO and GWO to select features for the intrusion detection system.… More >

  • ARTICLE

    Research on College English Teaching Model Based on Decision Trees

    Hao Wu1,*, B. Nagaraj2
    Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 81-95, 2021, DOI:10.32604/iasc.2021.017654
    Abstract English teaching has always attracted much attention. However, the processes of its transmission and acquirement is often divided into two separate parts, which seriously hinders the effective implementation of its objectives. Teachers attach particular importance to the choice of the curriculum structure and teaching material. Students are busy comprehending the assignments their teachers deem important. Under such a scenario, the effective acquisition of knowledge and the development of sustainable comprehensive abilities are ignored. The random forest algorithm in machine learning applications could play important role improving on the current English teaching system. A random forest model is constructed using a… More >

  • ARTICLE

    Utilization of Artificial Intelligence in Medical Image Analysis for COVID-19 Patients Detection

    Mohammed Baz1,*, Hatem Zaini1, Hala S. El-sayed2, Matokah AbuAlNaja3, Heba M. El-Hoseny4, Osama S. Faragallah5
    Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 97-111, 2021, DOI:10.32604/iasc.2021.018265
    Abstract In the era of medical technology, automatic scan detection can be considered a charming tool in medical diagnosis, especially with rapidly spreading diseases. In light of the prevalence of the current Coronavirus disease (COVID-19), which is characterized as highly contagious and very complicated, it is urgent and necessary to find a quick way that can be practically implemented for diagnosing COVID-19. The danger of the virus lies in the fact that patients can spread the disease without showing any symptoms. Moreover, several vaccines have been produced and vaccinated in large numbers but, the outbreak does not stop. Therefore, it is… More >

  • ARTICLE

    Research on Detection Method of Interest Flooding Attack in Named Data Networking

    Yabin Xu1,2,*, Peiyuan Gu2, Xiaowei Xu3
    Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 113-127, 2021, DOI:10.32604/iasc.2021.018895
    Abstract In order to effectively detect interest flooding attack (IFA) in Named Data Networking (NDN), this paper proposes a detection method of interest flooding attack based on chi-square test and similarity test. Firstly, it determines the detection window size based on the distribution of information name prefixes (that is information entropy) in the current network traffic. The attackers may append arbitrary random suffix to a certain prefix in the network traffic, and then send a large number of interest packets that cannot get the response. Targeted at this problem, the sensitivity of chi-square test is used to detect the change of… More >

  • ARTICLE

    Resource Management and Task Offloading Issues in the Edge–Cloud Environment

    Jaber Almutairi1, Mohammad Aldossary2,*
    Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 129-145, 2021, DOI:10.32604/iasc.2021.018480
    Abstract With the increasing number of Internet of Things (IoT) devices connected to the internet, a platform is required to support the enormous amount of data they generate. Since cloud computing is far away from the connected IoT devices, applications that require low-latency, real-time interaction and high quality of service (QoS) may suffer network delay in using the Cloud. Consequently, the concept of edge computing has appeared to complement cloud services, working as an intermediate layer with computation capabilities between the Cloud and IoT devices, to overcome these limitations. Although edge computing is a promising enabler for issues related to latency… More >

  • ARTICLE

    An Intelligent Business Model for Product Price Prediction Using Machine Learning Approach

    Naeem Ahmed Mahoto1, Rabia Iftikhar1, Asadullah Shaikh2,*, Yousef Asiri2, Abdullah Alghamdi2, Khairan Rajab2,3
    Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 147-159, 2021, DOI:10.32604/iasc.2021.018944
    Abstract The price of a product plays a vital role in its market share. Customers usually buy a product when it fits their needs and budget. Therefore, it is an essential area in the business to make decisions about prices for each product. The major portion of the business profit is directly connected with the percentage of the sale, which relies on certain factors of customers including customers’ behavior and market competitors. It has been observed in the past that machine learning algorithms have made the decision-making process more effective and profitable in businesses. The fusion of machine learning with business… More >

  • ARTICLE

    Exploiting Rich Event Representation to Improve Event Causality Recognition

    Gaigai Jin1, Junsheng Zhou1,*, Weiguang Qu1, Yunfei Long2, Yanhui Gu1
    Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 161-173, 2021, DOI:10.32604/iasc.2021.017440
    Abstract Event causality identification is an essential task for information extraction that has attracted growing attention. Early researchers were accustomed to combining the convolutional neural network or recurrent neural network models with external causal knowledge, but these methods ignore the importance of rich semantic representation of the event. The event is more structured, so it has more abundant semantic representation. We argue that the elements of the event, the interaction of the two events, and the context between the two events can enrich the event’s semantic representation and help identify event causality. Therefore, the effective semantic representation of events in event… More >

  • ARTICLE

    Semantic Analysis of Urdu English Tweets Empowered by Machine Learning

    Nadia Tabassum1, Tahir Alyas2, Muhammad Hamid3,*, Muhammad Saleem4, Saadia Malik5, Zain Ali2, Umer Farooq2
    Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 175-186, 2021, DOI:10.32604/iasc.2021.018998
    Abstract Development in the field of opinion mining and sentiment analysis has been rapid and aims to explore views or texts on various social media sites through machine-learning techniques with the sentiment, subjectivity analysis and calculations of polarity. Sentiment analysis is a natural language processing strategy used to decide if the information is positive, negative, or neutral and it is frequently performed on literature information to help organizations screen brand, item sentiment in client input, and comprehend client needs. In this paper, two strategies for sentiment analysis is proposed for word embedding and a bag of words on Urdu and English… More >

  • ARTICLE

    A Smart Comparative Analysis for Secure Electronic Websites

    Sobia Wassan1, Chen Xi1,*, Nz Jhanjhi2, Hassan Raza3
    Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 187-199, 2021, DOI:10.32604/iasc.2021.015859
    Abstract Online banking is an ideal method for conducting financial transactions such as e-commerce, e-banking, and e-payments. The growing popularity of online payment services and payroll systems, however, has opened new pathways for hackers to steal consumers’ information and money, a risk which poses significant danger to the users of e-commerce and e-banking websites. This study uses the selection method of the entire e-commerce and e-banking website dataset (Chi-Squared, Gini index, and main learning algorithm). The results of the analysis suggest the identification and comparison of machine learning and deep learning algorithm performance on binary category labels (legal, fraudulent) between similar… More >

  • ARTICLE

    Improving the Power Quality of Smart Microgrid Based Solar Photovoltaic Systems

    Emad H. El-Zohri1, Hegazy Rezk2,3,*, Basem Alamri4, Hamdy A. Ziedan5
    Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 201-213, 2021, DOI:10.32604/iasc.2021.018700
    (This article belongs to this Special Issue: Artificial Techniques: Application, Challenges, Performance Improvement of Smart Grid and Renewable Energy Systems)
    Abstract Microgrids are hybrid power systems that consist of several distributed generation resources and local loads that can supply electrical power to remote or specific areas. The integration of microgrids with the utility network is one of the most recent technologies developed in countries like Egypt. One area of study is how the integration of smart microgrids and utility systems can be used to solve power quality problems such as voltage sags, increased use of distributed generators, deep energy, and power loss. This paper is aimed at investigating a possible solution to some common and dangerous power quality issues associated with… More >

  • ARTICLE

    Liver Lesions and Acute Intracerebral Hemorrhage Detection Using Multimodal Fusion

    Osama S. Faragallah1,*, Abdullah N. Muhammed2, Taha S. Taha3, Gamal G. N. Geweid4,5
    Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 215-225, 2021, DOI:10.32604/iasc.2021.019058
    Abstract Medical image fusion is designed to help physicians in their decisions by providing them with a preclinical image with enough information. Accurate assessment and effective treatment of the disease reduce the time it takes to relieve the symptoms of the disease. This article utilizes an effective data fusion approach to work on two different imaging modalities; computed tomography (CT) and magnetic resonance imaging (MRI). The data fusion approach is based on the combination of singular value decomposition (SVD) and the Fast Discrete Curvelet Transform (FDCT) techniques to reduce processing time during the fusion process. The SVD-FDCT data fusion approach is… More >

  • ARTICLE

    Short Text Entity Disambiguation Algorithm Based on Multi-Word Vector Ensemble

    Qin Zhang1, Xuyu Xiang1,*, Jiaohua Qin1, Yun Tan1, Qiang Liu1, Neal N. Xiong2
    Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 227-241, 2021, DOI:10.32604/iasc.2021.017648
    Abstract With the rapid development of network media, the short text has become the main cover of information dissemination by quickly disseminating relevant entity information. However, the lack of context in the short text can easily lead to ambiguity, which will greatly reduce the efficiency of obtaining information and seriously affect the user’s experience, especially in the financial field. This paper proposed an entity disambiguation algorithm based on multi-word vector ensemble and decision to eliminate the ambiguity of entities and purify text information in information processing. First of all, we integrate a variety of unsupervised pre-trained word vector models as vector… More >

  • ARTICLE

    Modelling Supply Chain Information Collaboration Empowered with Machine Learning Technique

    Naeem Ali1,*, Alia Ahmed1, Leena Anum2, Taher M. Ghazal3,4, Sagheer Abbas5, Muhammad Adnan Khan6,7, Haitham M. Alzoubi8, Munir Ahmad5
    Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 243-257, 2021, DOI:10.32604/iasc.2021.018983
    Abstract Information Collaboration of the supply chain is the domination and control of product flow information from the producer to the customer. The data information flow is correlated with demand fill-up, a role delivering service, and feedback. The collaboration of supply chain information is a complex contrivance that impeccably manages the efficiency flow and focuses on its vulnerable area. As there is always room for growth in the current century, major companies have shown a growing tendency to improve their supply chain’s productivity and sustainability to increase customer consumption in complying with environmental regulations. Therefore, in supply chain collaboration, it is… More >

  • ARTICLE

    A Multi-Task Network for Cardiac Magnetic Resonance Image Segmentation and Classification

    Jing Peng1,2,4, Chaoyang Xia2, Yuanwei Xu3, Xiaojie Li2, Xi Wu2, Xiao Han1,4, Xinlai Chen5, Yucheng Chen3, Zhe Cui1,4,*
    Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 259-272, 2021, DOI:10.32604/iasc.2021.016749
    Abstract Cardiomyopathy is a group of diseases that affect the heart and can cause serious health problems. Segmentation and classification are important for automating the clinical diagnosis and treatment planning for cardiomyopathy. However, this automation is difficult because of the poor quality of cardiac magnetic resonance (CMR) imaging data and varying dimensions caused by movement of the ventricle. To address these problems, a deep multi-task framework based on a convolutional neural network (CNN) is proposed to segment the left ventricle (LV) myocardium and classify cardiopathy simultaneously. The proposed model consists of a longitudinal encoder–decoder structure that obtains high- and low-level features… More >

  • ARTICLE

    Measurement-based Quantum Repeater Network Coding

    Si-Yi Chen1, Gang Xu2, Xiu-Bo Chen1, Haseeb Ahmad3, Yu-Ling Chen4,*
    Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 273-284, 2021, DOI:10.32604/iasc.2021.018120
    Abstract Quantum network coding can effectively improve the aggregate throughput of quantum networks and alleviate bottlenecks caused by topological constraints. Most of previous schemes are dedicated to the efficient teleportation of unknown quantum states in a quantum network. Herein a proposal for transmission of deterministic known states over quantum repeater network based on quantum measurements. We show that the new protocol offers advantages over three aspects. Firstly, the senders in our protocol obtain the knowledge of the quantum state to be transmitted, which enables the autonomy of quantum network transmission. Secondly, we study the quantum repeater network coding for long-distance deterministic… More >

  • ARTICLE

    Blind and Visually Impaired User Interface to Solve Accessibility Problems

    Azeem Shera1, Muhammad Waseem Iqbal2,*, Syed Khuram Shahzad3, Madeeha Gul1, Natash Ali Mian4, Muhammad Raza Naqvi5, Babar Ayub Khan1
    Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 285-301, 2021, DOI:10.32604/iasc.2021.018009
    Abstract Blind and visually impaired (BVI) users often have interface accessibility problems while using mobile applications. This study was conducted to reduce the cognitive effort required for interface navigation by identifying the accessibility issues according to the user’s mental model. The study evaluated the accessibility of smartphone screens to solve organizational, presentation, and behavioral (OPB) problems of using mobile applications. Usability evaluation of an application was conducted and validated with a specific focus on BVI user experience. A total of 56 BVI participants were included in the evaluation. Overall, four tasks to assess organization, avoidance of redundant information, serialization of content,… More >

  • ARTICLE

    Main Factor Selection Algorithm and Stability Analysis of Regional FDI Statistics

    Juan Huang1, Bifang Zhou1, Huajun Huang2,*, Dingwen Qing1, Neal N. Xiong3
    Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 303-318, 2021, DOI:10.32604/iasc.2021.016953
    Abstract There are various influencing factors in regional FDI (foreign direct investment) and it is difficult to identify the main influencing factors. For this reason, a main factor selection algorithm is proposed in this article for the main factors affecting regional FDI statistics by analyzing the regional economic characteristics and the possible influencing factors in the regional FDI. Then, an example is used to illustrate its effectiveness and its stability. Firstly, the characteristics of regional economy and the regional FDI data are introduced to develop the main factor selection algorithm based on the adaptive Lasso problem for the regional FDI and… More >

  • ARTICLE

    Intelligent Nutrition Diet Recommender System for Diabetic’s Patients

    Nadia Tabassum1, Abdul Rehman2, Muhammad Hamid3,*, Muhammad Saleem4, Saadia Malik5, Tahir Alyas2
    Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 319-335, 2021, DOI:10.32604/iasc.2021.018870
    Abstract Diabetes is one of the ever-increasing menace crippling millions of people worldwide. It is an independent risk factor for many cardiovascular diseases including medium and small vessels and results in heart attack, stroke, kidney failure, blindness, and lower-limb amputations. According to a World Health Organization (WHO) report estimated 1.6 million deaths were the direct result of diabetes. Nutrition plays a vital role in diabetes management alongside physical activity, drugs, and insulin. Weight management can help to avert or delay at pre-diabetic stages. This research work explains the features of the Nutrition Diet Expert System (NDES), which will preferably be used… More >

  • ARTICLE

    Deep Learning Anomaly Detection Based on Hierarchical Status-Connection Features in Networked Control Systems

    Jianming Zhao1,2,3,4, Peng Zeng1,2,3,4,*, Chunyu Chen1,2,3,4, Zhiwei Dong5, Jongho Han6
    Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 337-350, 2021, DOI:10.32604/iasc.2021.016966
    Abstract As networked control systems continue to be widely used in large-scale industrial productions, industrial cyber-attacks have become an inevitable problem that can cause serious damage to critical infrastructures. In practice, industrial intrusion detection has been widely acknowledged to detect abnormal communication behaviors. However, unlike traditional IT systems, networked control systems have their own communication characteristics due to specific industrial communication protocols. Thus, simple cyber-attack modeling is inadequate and impractical for high-efficiency intrusion detection because the characteristics of network control systems are less considered. Based on the status information and transmission connection in industrial communication data payloads, which can properly express… More >

  • ARTICLE

    Strategies for Reducing the Spread of COVID-19 Based on an Ant-Inspired Framework

    Ghassan Ahmed Ali*
    Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 351-360, 2021, DOI:10.32604/iasc.2021.017453
    Abstract Many living organisms respond to pandemics using strategies such as isolation. This is true, for example, of social insects, for whom the spread of disease can pose a high risk to colony survival. In light of such behaviors, the present study investigated a different way of developing strategies to mitigate the effects of the coronavirus pandemic. Specifically, we considered the strategies ants use to handle epidemics and limit disease spread within colonies. To enhance our understanding of these strategies, we explored ants’ social systems and how they specifically respond to infectious diseases. The early warning threshold system reflects the importance… More >

  • ARTICLE

    Robust Sound Source Localization Using Convolutional Neural Network Based on Microphone Array

    Xiaoyan Zhao1,*, Lin Zhou2, Ying Tong1, Yuxiao Qi1, Jingang Shi3
    Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 361-371, 2021, DOI:10.32604/iasc.2021.018823
    Abstract In order to improve the performance of microphone array-based sound source localization (SSL), a robust SSL algorithm using convolutional neural network (CNN) is proposed in this paper. The Gammatone sub-band steered response power-phase transform (SRP-PHAT) spatial spectrum is adopted as the localization cue due to its feature correlation of consecutive sub-bands. Since CNN has the “weight sharing” characteristics and the advantage of processing tensor data, it is adopted to extract spatial location information from the localization cues. The Gammatone sub-band SRP-PHAT spatial spectrum are calculated through the microphone signals decomposed in frequency domain by Gammatone filters bank. The proposed algorithm… More >

  • ARTICLE

    Ontology-Based System for Educational Program Counseling

    Mamoona Majid1, Muhammad Faisal Hayat2, Farrukh Zeeshan Khan3, Muneer Ahmad4,*, NZ Jhanjhi5, Mohammad Arif Sobhan Bhuiyan6, Mehedi Masud7, Mohammed A. AlZain8
    Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 373-386, 2021, DOI:10.32604/iasc.2021.017840
    Abstract Choosing the right university program can be very challenging for students. This is especially the case in developing countries such as India and Pakistan, where university admission depends on not only the program of interest but also other factors such as the candidate’s financial standing. Since information on the Internet can be highly scattered, university candidates often need counseling from qualified people to decide their educational programs. Traditional database systems cannot effectively organize the large unstructured data related to university programs. It is challenging, then, for prospective students to acquire the information needed to make good decisions to consider factors… More >

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