Article
Mathematics
Tariq Ahamed Ahanger, Usman Tariq, Atef Ibrahim, Imdad Ullah, Yassine Bouteraa, Fayez Gebali
Summary: The Internet of Things (IoT) is an interconnected network of computing nodes that can send and receive data without human participation. With advancements in software and communication technology, IoT devices have significantly increased. However, the rapid expansion has raised security concerns and scalability challenges in processing IoT data on the cloud. Fog computing brings computation to the network edge to address these difficulties, and current research covers IoT evolution, Fog computing, and machine learning (ML) strategies.
Review
Computer Science, Artificial Intelligence
Bahareh Rezazadeh, Parvaneh Asghari, Amir Masoud Rahmani
Summary: The infectious disease Covid-19 has caused significant global impacts since 2019, leading to social, economic, and humanitarian crises. Countries have adopted different strategies based on their capabilities and technological infrastructure to combat the virus. An intelligent and automatic healthcare system is crucial for controlling such a massive epidemic. Initially, the focus was on lockdown measures and disease diagnosis, but now research has shifted towards computer-aided methods for monitoring, tracking, detecting, and treating individuals affected by Covid-19, as well as providing services to citizens. This article surveys computer-based approaches in prevention, detection, and service provision for combating Covid-19, analyzing current methods, providing a technical taxonomy, and exploring future challenges and opportunities.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Theory & Methods
Hongling Jiang, Jinzhi Lin, Haiyan Kang
Summary: Due to the increasing reliance on machine learning models for network intrusion detectors in the Internet of Things (IoT), attacks against these detectors are also on the rise. Existing solutions for adversarial attacks against IoT networks are rare and have limitations. To address this problem, we propose an algorithm to generate adversarial samples and evaluate the performance of the IoT network intrusion detector. We also present a novel framework, FGMD (Feature Grouping and Multi-model fusion Detector), that can defend against adversarial attacks through feature grouping and multi model fusion. Experimental results demonstrate the effectiveness of FGMD against adversarial attacks.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2022)
Article
Computer Science, Information Systems
Alireza Souri, Monire Norouzi, Yousef Alsenani
Summary: With the rapid development of artificial intelligence methods, the Industrial Internet of Things (IIoT) has become highly developed in tracing industrial communications and optimizing manufacturing processes. However, with the increasing data communication in IIoT environments, the security and safety of hyper-automation process face challenges from cyber-attacks and abnormal activities. Traditional cyber-attack detection systems have critical problems in handling massive data and accurate classification models. This paper proposes a cloud-based cyber-attack detection architecture based on Ensemble Bagged Trees Detection (EBTD) algorithm to predict malicious behaviors and types of cyber-attacks in the IIoT.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2023)
Review
Chemistry, Analytical
Nancy A. Angel, Dakshanamoorthy Ravindran, P. M. Durai Raj Vincent, Kathiravan Srinivasan, Yuh-Chung Hu
Summary: Cloud computing is important due to the expanding IoT network, but it has limitations when it comes to processing vast data from novel IoT applications. The recent trend is to move computational and storage resources to the network edge to overcome these limitations and optimize computing applications and services.
Article
Computer Science, Information Systems
Ananya Chakraborty, Mohit Kumar, Nisha Chaurasia
Summary: In this article, a lightweight secure framework is proposed using Deep Learning techniques to detect security attacks and monitor network traffic for IoT applications. The framework is trained and deployed over a cloud platform while the detection mechanism is implemented on fog Nodes to reduce vulnerabilities and delayed execution. The proposed framework achieves higher accuracy, precision, and lower False Alarm rate compared to baseline algorithms.
JOURNAL OF INFORMATION SECURITY AND APPLICATIONS
(2023)
Article
Automation & Control Systems
Aaisha Makkar, Sahil (GE) Garg, Neeraj Kumar, M. Shamim Hossain, Ahmed Ghoneim, Mubarak Alrashoud
Summary: The Internet of Things (IoT) consists of millions of devices connected through wired or wireless channels for data transmission, with data volume expected to grow rapidly in the coming years. Machine learning algorithms play a key role in enhancing security and usability of IoT systems, while also being exploited by attackers to target vulnerabilities. This article proposes a spam detection method using machine learning for IoT devices, which has been validated and proven effective compared to existing schemes.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Chemistry, Analytical
Nazik Alturki, Turki Aljrees, Muhammad Umer, Abid Ishaq, Shtwai Alsubai, Oumaima Saidani, Sirojiddin Djuraev, Imran Ashraf
Summary: This research paper investigates the latest trends in safety, security, and privacy related to drones and highlights the importance of secure drone networks. The proposed framework incorporates intelligent machine learning models into the design and structure of IoT-aided drones, rendering adaptable and secure technology to mitigate cyber-security threats.
Article
Engineering, Multidisciplinary
M. M. Kamruzzaman, Saad Alanazi, Madallah Alruwaili, Ibrahim Alrashdi, Yousef Alhwaiti, Nasser Alshammari
Summary: This paper presents an F-AMLF that combines fuzzy-assisted machine learning framework with fog computing to improve the effectiveness of health care monitoring systems. By using fuzzy logic to calculate the required computing capacity, device resource cost reduction is achieved while maintaining efficiency, enabling accurate monitoring and prediction of health data.
Review
Computer Science, Hardware & Architecture
Mohammad Nikravan, Mostafa Haghi Kashani
Summary: This paper presents a systematic review of trust management in Fog and Edge Computing (FEC), categorizing and comparing different trust management approaches. It also discusses evaluation techniques, tools and simulation environments, and important trust metrics. The paper highlights open issues and future trends for further studies in this field.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Souradip Roy, Juan Li, Yan Bai
Summary: In this paper, the authors investigate intrusion detection techniques for IoT networks and propose a machine learning-based two-layer hierarchical intrusion detection mechanism. The proposed approach outperforms existing methods in terms of accuracy and performance, and offers advantages in improving service time, reducing delay, and optimizing energy utilization.
INTERNET OF THINGS
(2022)
Article
Construction & Building Technology
Xiaopeng Zhu, Yuanyuan Zhu, Lei Li, Sian Pan, Muhammad Usman Tariq, Mian Ahmad Jan
Summary: The widespread use of sensor-based applications in healthcare has led to the development of Internet of Health Things (IoHT) that improves patient safety, staff morale, and operational efficiency. Edge-fog computing has made significant progress recently, but still faces challenges in handling various IoHT settings. The proposed edge-fog computing framework efficiently manages real-time data related to glioma and automatic detection of diseases related to glial cells surrounding nerve cells.
SUSTAINABLE CITIES AND SOCIETY
(2021)
Article
Chemistry, Analytical
Muhammad Umar Nasir, Safiullah Khan, Shahid Mehmood, Muhammad Adnan Khan, Muhammad Zubair, Seong Oun Hwang
Summary: This study presents a framework that utilizes machine learning techniques to analyze and detect meddling in real-time network data, and it successfully identifies various meddling patterns. The proposed framework achieves excellent results in meddling detection, making it highly beneficial for various communication and transaction processes.
Article
Computer Science, Information Systems
Prabhat Kumar, Govind P. Gupta, Rakesh Tripathi
Summary: Internet of Medical Things (IoMT), a branch of Internet of Things (IoT), provides unparalleled benefits in improving patient care quality and treatment efficiency. However, the increasing severity and frequency of cyber-attacks in IoMT environments pose significant challenges.
COMPUTER COMMUNICATIONS
(2021)
Article
Computer Science, Information Systems
Ali Kadhum Idrees, Sara Kadhum Idrees, Raphael Couturier, Tara Ali-Yahiya
Summary: This article proposes an edge-fog computing-enabled lossless electroencephalogram (EEG) data compression with epileptic seizure detection method in Internet of Medical Things (IoMT) networks. The method aims to improve smart health systems for monitoring the health situation of patients. It reduces data transmission volume and improves the accuracy of epileptic seizure detection, thus enhancing system efficiency.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Meeting Abstract
Biophysics
Meng Huang, Shailendra Rathore, Manfred Lindau
BIOPHYSICAL JOURNAL
(2019)
Article
Computer Science, Hardware & Architecture
Shailendra Rathore, Jung Hyun Ryu, Pradip Kumar Sharma, Jong Hyuk Park
Article
Computer Science, Hardware & Architecture
Mikail Mohammed Salim, Shailendra Rathore, Jong Hyuk Park
JOURNAL OF SUPERCOMPUTING
(2020)
Article
Computer Science, Information Systems
Baraka William Nyamtiga, Jose Costa Sapalo Sicato, Shailendra Rathore, Yunsick Sung, Jong Hyuk Park
Article
Computer Science, Theory & Methods
Sushil Kumar Singh, Shailendra Rathore, Jong Hyuk Park
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2020)
Article
Green & Sustainable Science & Technology
Shailendra Rathore, Yi Pan, Jong Hyuk Park
Article
Computer Science, Hardware & Architecture
Shailendra Rathore, Byung Wook Kwon, Jong Hyuk Park
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2019)
Article
Computer Science, Hardware & Architecture
Shailendra Rathore, Jong Hyuk Park
IEEE CONSUMER ELECTRONICS MAGAZINE
(2020)
Article
Computer Science, Information Systems
Younghun Lee, Shailendra Rathore, Jin Ho Park, Jong Hyuk Park
HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES
(2020)
Article
Computer Science, Artificial Intelligence
Heena Rathore, Amr Mohamed, Mohsen Guizani, Shailendra Rathore
Summary: This paper introduces a machine learning approach called NueroFATH for the physical assessment of athletes. It uses neural networks and fuzzy c-means techniques to predict the potential of athletes winning medals. The study also identifies important physical characteristics related to the assessment results.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Automation & Control Systems
Shailendra Rathore, Jong Hyuk Park
Summary: The development of IoT in CPS, such as autonomous driving, has led to a significant need for big data analysis with high accuracy and low latency. Deep learning plays a key role in efficient data analysis, but current research faces challenges like centralized control, adversarial attacks, security, and privacy. A secure DL approach using blockchain for decentralized DL operations among edge nodes has been proposed to address these challenges and achieve higher accuracy.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Computer Science, Theory & Methods
Saurabh Singh, Shailendra Rathore, Osama Alfarraj, Amr Tolba, Byungun Yoon
Summary: This article proposes a secure architecture for privacy-preserving in smart healthcare through the use of blockchain and federated learning. It focuses on the adoption of privacy-preserving federated learning technology and the use of a blockchain-based IoT cloud platform for security and privacy. This technology provides a scalable solution for healthcare machine learning applications in a smart city environment.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2022)
Article
Computer Science, Information Systems
Shailendra Rathore, Jong Hyuk Park, Hangbae Chang
Summary: The increasing number of IoT applications with 5G technology requires more intelligent data analytics and security measures that traditional methods cannot meet. Therefore, a framework utilizing Deep Learning and blockchain technology has been proposed, demonstrating its validity in practical applications through simulations and analysis.
Article
Computer Science, Information Systems
Jin Ho Park, Shailendra Rathore, Sushil Kumar Singh, Mikail Mohammed Salim, Abir El Azzaoui, Tae Woo Kim, Yi Pan, Jong Hyuk Park
Summary: 5G communication, as an emerging medium, provides high speed, low latency and massive connectivity, but also brings new security requirements and challenges due to the introduction of new technologies and advanced features. This paper surveys various threats and solutions in the field of 5G security and privacy, discusses the application of emerging technologies in 5G, and summarizes the challenges and future directions of 5G wireless security.
HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES
(2021)
Article
Computer Science, Information Systems
Jae Dong Lee, Hyo Soung Cha, Shailendra Rathore, Jong Hyuk Park
Summary: In recent years, the growth of smart city applications in healthcare via IoT systems has led to new advanced network intrusions, prompting the development of a Multi-class Classification based Intrusion Detection Model using real device data and convolutional neural networks for improved threat detection performance.
CMC-COMPUTERS MATERIALS & CONTINUA
(2021)
Article
Computer Science, Artificial Intelligence
Jin Zhang, Zekang Bian, Shitong Wang
Summary: This study proposes a novel style linear k-nearest neighbor method to extract stylistic features using matrix expressions and improve the generalizability of the predictor through style membership vectors.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qifeng Wan, Xuanhua Xu, Jing Han
Summary: In this study, we propose an innovative approach for dimensionality reduction in large-scale group decision-making scenarios that targets linguistic preferences. The method combines TF-IDF feature similarity and information loss entropy to address challenges in decision-making with a large number of decision makers.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Hegui Zhu, Yuchen Ren, Chong Liu, Xiaoyan Sui, Libo Zhang
Summary: This paper proposes an adversarial attack method based on frequency information, which optimizes the imperceptibility and transferability of adversarial examples in white-box and black-box scenarios respectively. Experimental results validate the superiority of the proposed method and its application in real-world online model evaluation reveals their vulnerability.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jing Tang, Xinwang Liu, Weizhong Wang
Summary: This paper proposes a hybrid generalized TODIM approach in the Fine-Kinney framework to evaluate occupational health and safety hazards. The approach integrates CRP, dynamic SIN, and PLTSs to handle opinion interactions and incomplete opinions among decision makers. The efficiency and rationality of the proposed approach are demonstrated through a numerical example, comparison, and sensitivity studies.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Shigen Shen, Chenpeng Cai, Zhenwei Li, Yizhou Shen, Guowen Wu, Shui Yu
Summary: To address the damage caused by zero-day attacks on SIoT systems, researchers propose a heuristic learning intrusion detection system named DQN-HIDS. By integrating Deep Q-Networks (DQN) into the system, DQN-HIDS gradually improves its ability to identify malicious traffic and reduces resource workloads. Experiments demonstrate the superior performance of DQN-HIDS in terms of workload, delayed sample queue, rewards, and classifier accuracy.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Song Deng, Qianliang Li, Renjie Dai, Siming Wei, Di Wu, Yi He, Xindong Wu
Summary: In this paper, we propose a Chinese text classification algorithm based on deep active learning for the power system, which addresses the challenge of specialized text classification. By applying a hierarchical confidence strategy, our model achieves higher classification accuracy with fewer labeled training data.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Kaan Deveci, Onder Guler
Summary: This study proves the lack of robustness in nonlinear IF distance functions for ranking intuitionistic fuzzy sets (IFS) and proposes an alternative ranking method based on hypervolume metric. Additionally, the suggested method is extended as a new multi-criteria decision making method called HEART, which is applied to evaluate Turkey's energy alternatives.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Fu-Wing Yu, Wai-Tung Ho, Chak-Fung Jeff Wong
Summary: This research aims to enhance the energy management in commercial building air-conditioning systems, specifically focusing on chillers. Ridge regression is found to outperform lasso and elastic net regression when optimized with the appropriate hyperparameter, making it the most suitable method for modeling the system coefficient of performance (SCOP). The key variables that strongly influence SCOP include part load ratios, the operating numbers of chillers and pumps, and the temperatures of chilled water and condenser water. Additionally, July is identified as the month with the highest potential for performance improvement. This study introduces a novel approach that balances feature selection, model accuracy, and optimal tuning of hyperparameters, highlighting the significance of a generic and simplified chiller system model in evaluating energy management opportunities for sustainable operation. The findings from this research can guide future efforts towards more energy-efficient and sustainable operations in commercial buildings.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Xiaoyan Chen, Yilin Sun, Qiuju Zhang, Xuesong Dai, Shen Tian, Yongxin Guo
Summary: In this study, a method for dynamically non-destructive grasping of thin-skinned fruits is proposed. It utilizes a multi-modal depth fusion convolutional neural network for image processing and segmentation, and combines the evaluation mechanism of optimal grasping stability and the forward-looking non-destructive grasp control algorithm. The proposed method greatly improves the comprehensive performance of grasping delicate fruits using flexible hands.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Yuxuan Yang, Siyuan Zhou, He Weng, Dongjing Wang, Xin Zhang, Dongjin Yu, Shuiguang Deng
Summary: The study proposes a novel model, POIGDE, which addresses the challenges of data sparsity and elusive motives by solving graph differential equations to capture continuous variation of users' interests. The model learns interest transference dynamics using a time-serial graph and an interval-aware attention mechanism, and applies Siamese learning to directly learn from label representations for predicting future POI visits. The model outperforms state-of-the-art models on real-world datasets, showing potential in the POI recommendation domain.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
S. Karthika, P. Rathika
Summary: The widespread development of monitoring devices in the power system has generated a large amount of power consumption data. Storing and transmitting this data has become a significant challenge. This paper proposes an adaptive data compression algorithm based on the discrete wavelet transform (DWT) for power system applications. It utilizes multi-objective particle swarm optimization (MO-PSO) to select the optimal threshold. The algorithm has been tested and outperforms other existing algorithms.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jiaqi Guo, Haiyan Wu, Xiaolei Chen, Weiguo Lin
Summary: In this study, an adaptive SV-Borderline SMOTE-SVM algorithm is proposed to address the challenge of imbalanced data classification. The algorithm maps the data into kernel space using SVM and identifies support vectors, then generates new samples based on the neighbors of these support vectors. Extensive experiments show that this method is more effective than other approaches in imbalanced data classification.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qiumei Zheng, Linkang Xu, Fenghua Wang, Yongqi Xu, Chao Lin, Guoqiang Zhang
Summary: This paper proposes a new semantic segmentation network model called HilbertSCNet, which combines the Hilbert curve traversal and the dual pathway idea to design a new spatial computation module to address the problem of loss of information for small targets in high-resolution images. The experiments show that the proposed network performs well in the segmentation of small targets in high-resolution maps such as drone aerial photography.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Mojtaba Ashour, Amir Mahdiyar
Summary: Analytic Hierarchy Process (AHP) is a widely applied technique in multi-criteria decision-making problems, but the sheer number of AHP methods presents challenges for scholars and practitioners in selecting the most suitable method. This paper reviews articles published between 2010 and 2023 proposing hybrid, improved, or modified AHP methods, classifies them based on their contributions, and provides a comprehensive summary table and roadmap to guide the method selection process.
APPLIED SOFT COMPUTING
(2024)
Review
Computer Science, Artificial Intelligence
Gerardo Humberto Valencia-Rivera, Maria Torcoroma Benavides-Robles, Alonso Vela Morales, Ivan Amaya, Jorge M. Cruz-Duarte, Jose Carlos Ortiz-Bayliss, Juan Gabriel Avina-Cervantes
Summary: Electric power system applications are complex optimization problems. Most literature reviews focus on studying electrical paradigms using different optimization techniques, but there is a lack of review on Metaheuristics (MHs) in these applications. Our work provides an overview of the paradigms underlying such applications and analyzes the most commonly used MHs and their search operators. We also discover a strong synergy between the Renewable Energies paradigm and other paradigms, and a significant interest in Load-Forecasting optimization problems. Based on our findings, we provide helpful recommendations for current challenges and potential research paths to support further development in this field.
APPLIED SOFT COMPUTING
(2024)