Article
Computer Science, Information Systems
Ghulam Abbas, Ziaul Haq Abbas, Zaiwar Ali, Muhammad Shahwar Asad, Uttam Ghosh, Muhammad Bilal
Summary: This study introduces a deep learning-based stochastic routing approach, SSR, for achieving reliable data transmission with low latency and energy efficiency in high-speed networks. By generating a dataset through a mathematical model and training a deep neural network, SSR predicts the best routing path and demonstrates effectiveness in reducing energy consumption and expected delivery delay compared to conventional stochastic routing methods.
COMPUTER COMMUNICATIONS
(2021)
Article
Automation & Control Systems
Chunyong Yin, Sun Zhang, Jin Wang, Neal N. Xiong
Summary: This article proposes an integrated model for anomaly detection, using convolutional neural network (CNN) and recurrent autoencoder as the basis, and extracting features through two-stage sliding window data preprocessing. Empirical results show that the proposed model performs better on multiple classification metrics and achieves excellent results in anomaly detection.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Engineering, Multidisciplinary
P. Manickam, M. Girija, S. Sathish, Khasim Vali Dudekula, Ashit Kumar Dutta, Yasir A. M. Eltahir, Nazik M. A. Zakari, Rafiulla Gilkaramenthi
Summary: Internet of Things (IoT) technology is widely used in smart cities, but security and privacy concerns arise as the usage increases. This paper proposes a new BBODL-ADC technique for anomaly detection and classification using deep learning, with remarkable results in experiments.
ALEXANDRIA ENGINEERING JOURNAL
(2023)
Review
Chemistry, Multidisciplinary
Redhwan Al-amri, Raja Kumar Murugesan, Mustafa Man, Alaa Fareed Abdulateef, Mohammed A. Al-Sharafi, Ammar Ahmed Alkahtani
Summary: The paper discusses the importance and challenges of anomaly detection in IoT data processing, summarizing the various aspects of issues and core challenges in IoT data processing, including data characteristics, anomaly types, learning modes, window models, and more.
APPLIED SCIENCES-BASEL
(2021)
Article
Chemistry, Analytical
Abebe Diro, Naveen Chilamkurti, Van-Doan Nguyen, Will Heyne
Summary: The Internet of Things (IoT) is made up of a massive number of smart devices capable of data collection, storage, processing, and communication, which provides tremendous innovation opportunities but also raises concerns due to inherent vulnerabilities. The challenging IoT environment requires anomaly detection monitoring systems for better security, and blockchain-based systems can collaboratively learn to detect anomalies effectively.
Article
Automation & Control Systems
Jun Liu, Jingpan Bai, Huahua Li, Bo Sun
Summary: This article highlights the importance of real-time monitoring and correction of massive data streams in the Internet of Things (IoT) and proposes a recurrent neural network model based on LSTM+ that can reduce regression errors, detect and correct abnormal data, ensuring stability and reliability in network predictions.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Computer Science, Information Systems
Yi Li, Zhangbing Zhou, Xiao Xue, Deng Zhao, Patrick C. K. Hung
Summary: This article proposes an accurate anomaly detection mechanism with energy efficiency in three-tier IoT-edge-cloud collaborative networks. It filters anomaly-relevant sensory data at the edge tier to decrease network traffic. The boundary of anomaly is determined using the Kriging spatial interpolation algorithm at the cloud tier and refined using mobile sensing nodes at edge networks.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Engineering, Electrical & Electronic
Hongda Wu, Ali Nasehzadeh, Ping Wang
Summary: The Internet of Things has been continuously growing in the past few years, with its potential becoming more apparent. An efficient caching policy and the use of deep reinforcement learning algorithms can help address issues such as transient data generation and limited energy resources while developing effective caching schemes without prior knowledge.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2022)
Article
Computer Science, Information Systems
Weiping Ding, Mohamed Abdel-Basset, Reda Mohamed
Summary: Our daily lives have been greatly influenced by the Internet of Things (IoT) in recent years. While IoT brings convenience and efficiency to our lives, it also exposes devices to cyberattacks due to weak security mechanisms. This paper introduces DeepAK-IoT, a deep learning model designed to detect cyberattacks against IoT devices. It utilizes three blocks – RSR, TRB, and DB – to extract features, learn temporal representations, and classify input records. Experimental results on three public datasets demonstrate DeepAK-IoT's high accuracy in detecting cyber threats in IoT systems, making it a powerful alternative model for managing cybersecurity in IoT networks.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Yi Liu, Sahil Garg, Jiangtian Nie, Yang Zhang, Zehui Xiong, Jiawen Kang, M. Shamim Hossain
Summary: This article proposes a communication-efficient on-device federated learning (FL)-based deep anomaly detection framework for sensing time-series data in IIoT. The framework includes an FL framework, AMCNN-LSTM model, and gradient compression mechanism, which can improve generalization ability, accurately detect anomalies, and enhance communication efficiency.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Computer Science, Information Systems
Xiaoding Wang, Sahil Garg, Hui Lin, Jia Hu, Georges Kaddoum, Md Jalil Piran, M. Shamim Hossain
Summary: This paper proposes a reliable anomaly detection strategy for Industrial Internet of Things (IIoT) using federated learning. By training local models with deep reinforcement learning algorithm and introducing privacy leakage degree and action relation, the detection accuracy can be greatly improved, achieving privacy preservation.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Engineering, Multidisciplinary
Manal M. Khayyat
Summary: This article introduces an Improved Bacterial Foraging Optimization with optimum deep learning for Anomaly Detection (IBFO-ODLAD) technique in IoT network, which shows advantages in data normalization, feature selection, intrusion detection, and classification, and achieves good performance in experiments.
ALEXANDRIA ENGINEERING JOURNAL
(2023)
Article
Telecommunications
Brett Weinger, Jinoh Kim, Alex Sim, Makiya Nakashima, Nour Moustafa, K. John Wu
Summary: Federated Learning (FL) with mobile computing and the Internet of Things (IoT) is an effective cooperative learning approach. This study investigates how data augmentation can be applied to improve detection performance in an anomaly detection task using IoT datasets.
DIGITAL COMMUNICATIONS AND NETWORKS
(2022)
Article
Computer Science, Information Systems
Nenavath Chander, Mummadi Upendra Kumar
Summary: The Industrial Internet of Things (IIoT) is the essential part of the transition towards Industry 4.0 for conventional industries. By integrating instruments, sensors, and other industry devices with the Internet, IIoT enables data analysis, acquisition, and automated control, ultimately enhancing the production and performance of IIoT systems. In this study, a novel metaheuristic feature selection with deep learning enabled anomaly detection technique, named MFSDL-ADIIoT, is developed to effectively identify and classify anomalies in the IIoT environment. The MFSDL-ADIIoT model utilizes a deer hunting optimization algorithm based feature selection technique and cascaded recurrent neural network (CRNN) system for anomaly detection, with the parameters of the CRNN model optimized using sparrow search algorithm. Extensive simulations demonstrate the better performance of the MFSDL-ADIIoT model compared to other recent approaches.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Hardware & Architecture
Mengmeng Ge, Naeem Firdous Syed, Xiping Fu, Zubair Baig, Antonio Robles-Kelly
Summary: The Internet of Things brings benefits to humanity, but also vulnerabilities to cyber attacks. This paper proposes a novel intrusion detection approach for the IoT using deep learning techniques, achieving high classification accuracy.
Article
Statistics & Probability
Denis A. Pustokhin, Irina V. Pustokhina, Phuoc Nguyen Dinh, Son Van Phan, Gia Nhu Nguyen, Gyanendra Prasad Joshi, K. Shankar
Summary: This paper presents a new RCAL-BiLSTM model based on ResNet and Class Attention Layer for COVID-19 diagnosis. The model incorporates bilateral filtering preprocessing, feature extraction using ResNet and Bi-LSTM, and softmax-based classification. Experimental results on the Chest-X-Ray dataset demonstrate the superior performance of the RCAL-BiLSTM model.
JOURNAL OF APPLIED STATISTICS
(2023)
Article
Computer Science, Information Systems
Romany F. Mansour, S. Abdel-Khalek, Ines Hilali-Jaghdam, Jamel Nebhen, Woong Cho, Gyanendra Prasad Joshi
Summary: This paper designs an intelligent outlier detection with machine learning empowered big data analytics (IODML-BDA) model for mobile edge computing (MEC). The model utilizes adaptive synthetic sampling-based outlier detection techniques and oppositional swallow swarm optimization-based feature selection techniques. Experimental analysis on various datasets confirms the higher accuracy of the proposed model.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Hardware & Architecture
R. Bhaskaran, P. S. Sujith Kumar, G. Shanthi, L. Raja, Gyanendra Prasad Joshi, Woong Cho
Summary: This study presents an Improved Metaheuristics based Energy Efficient Clustering with Node Localization (IM-EECNL) approach for real-time wireless networks. The proposed approach involves node localization and clustering to improve network performance and achieve high energy efficiency.
COMPUTER SYSTEMS SCIENCE AND ENGINEERING
(2023)
Retraction
Computer Science, Artificial Intelligence
Sudan Jha, Eunmok Yang, Alaa Omran Almagrabi, Ali Kashif Bashir, Gyanendra Prasad Joshi
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Chemistry, Analytical
Shippu Sachdeva, Simarpreet Kaur, Romisha Arora, Manoj Sindhwani, Krishan Arora, Woong Cho, Gyanendra Prasad Joshi, Ill Chul Doo
Summary: This paper presents a 4.8 Tbps ultra-high capacity optical satellite communication system using polarization division multiplexing and twisted light beams. The system incorporates Laguerre Gaussian transverse mode profiles for OAM multiplexing and investigates the effects of receiver's digital signal processing module. The experimental results demonstrate the successful performance of the proposed system over a distance of 22,000 km, with the fundamental mode LG00 showing excellent performance.
Article
Green & Sustainable Science & Technology
Thavavel Vaiyapuri, Sharath Kumar Jagannathan, Mohammed Altaf Ahmed, K. C. Ramya, Gyanendra Prasad Joshi, Soojeong Lee, Gangseong Lee
Summary: The COVID-19 outbreak has caused psychological problems and led to public expression of sentiments on social networking platforms. This study presents a Marine Predator Optimization with Natural Language Processing model for sentiment analysis of Twitter data during the pandemic. The model utilizes data preprocessing, word vectors from the BERT model, and a bidirectional recurrent neural network for sentiment detection and classification, improving classification performance.
Article
Medicine, General & Internal
Soojeong Lee, Gyanendra Prasad Joshi, Chang-Hwan Son, Gangseong Lee
Summary: Noninvasive blood pressure estimation is crucial for cardiovascular and hypertension patients. This paper proposes a new methodology that combines the Gaussian process with hybrid optimal feature decision (HOFD) in cuffless blood pressure estimation. The experimental results show that the proposed algorithm is very effective, with lower root mean square errors (RMSEs) for SBP and DBP compared to conventional algorithms.
Article
Mathematics
Bhargav Bhatt, Himanshu Sharma, Krishan Arora, Gyanendra Prasad Joshi, Bhanu Shrestha
Summary: Optimization is a broad field where researchers develop new algorithms to solve various problems. Grey wolf optimization is an efficient and easy-to-use algorithm, but it has drawbacks such as being stuck in local optima and having poor exploration. This paper discusses strategies to overcome these drawbacks and proposes a novel algorithm to improve the convergence rate and exploration capability.
Article
Mathematics
Pradeep Singh, Krishan Arora, Umesh C. Rathore, Eunmok Yang, Gyanendra Prasad Joshi, Kwang Chul Son
Summary: This paper reports the effectiveness of a grid-connected doubly fed induction generator (DFIG)-based wind energy conversion system (WECS) with a battery energy storage system (BESS) under variable wind conditions. A rotor side converter (RSC) is controlled to achieve the optimal torque for a given maximal wind power. A new fraction order proportional integral derivative (FOPID) controller is introduced, and the performance of the system is observed. A proportional integral (PI) controller is introduced to control the BESS and increase the charging and discharging rates. Two models are developed in MATLAB/Simulink to validate the effectiveness of the proposed PI-controller-equipped BESS at improving the overall performance of the WECS system under study.
Article
Mathematics
Subramanian Selvakumar, Kathirvel Jeganathan, Krishnasamy Srinivasan, Neelamegam Anbazhagan, Soojeong Lee, Gyanendra Prasad Joshi, Ill Chul Doo
Summary: This study presents and discusses the home delivery services in stochastic queuing-inventory modeling (SQIM). The system consists of two servers: one manages the inventory sales processes, and the other provides home delivery services. The study examines the scheduling, delivery options, replenishment process, and system performance measures of the home delivery system.
Article
Chemistry, Analytical
Srinivasagam Solaiappan, Bharathi Ramesh Kumar, N. Anbazhagan, Yooseung Song, Gyanendra Prasad Joshi, Woong Cho
Summary: The real-time vehicular traffic system is an essential part of the urban vehicular traffic system, providing effective traffic signal control for a complex traffic network. Coordinating vehicular traffic allows for parallel vehicle movements without accidents. This study examines vehicular traffic flow and proposes an algorithm to estimate vehicle waiting time. The effectiveness of the proposed system is verified by comparing it with a real-time vehicular traffic system experimentally and numerically.
Article
Mathematics
N. Nithya, N. Anbazhagan, S. Amutha, K. Jeganathan, Gi-Cheon Park, Gyanendra Prasad Joshi, Woong Cho
Summary: Recently, we have faced new situations that restrict our ability to visit public places. These changes have impacted various aspects of our lives, including limited access to essential establishments. In response, we have developed a queueing-inventory system that features a single server and controlled customer arrivals, taking into account factors such as arrival process, service time, inventory replenishment, and the possibility of emergency interruptions and vacations for the server.
Article
Mathematics, Applied
P. Thanalakshmi, N. Anbazhagan, Gyanendra Prasad Joshi, Eunmok Yang
Summary: Steinfeld et al. introduced the concept of Universal Designated Verifier Signature (UDVS) to ensure the designated person can verify the signer's signature on the message. Baek et al. proposed Universal Designated Verifier Signature Proof (UDVSP) that does not require the verifier's public key for verification. Existing UDVSP constructions are based on a vulnerable discrete logarithm problem, but an efficient quantum resistant UDVSP is suggested by NIST reports.
Article
Computer Science, Information Systems
A. Arokiaraj Jovith, S. Rama Sree, Gudikandhula Narasimha Rao, K. Vijaya Kumar, Woong Cho, Gyanendra Prasad Joshi, Sung Won Kim
Summary: This article introduces a DNA Computing with Water Strider Algorithm based Vector Quantization (DNAC-WSAVQ) technique for Data Storage Systems, which encodes data using DNA computing and compresses it for effective data storage. The Water Strider algorithm with Linde-Buzo-Gray (WSA-LBG) model is applied for compression and generating an optimal codebook. The performance validation of the DNAC-WSAVQ model shows improved outcomes compared to existing methods.
CMC-COMPUTERS MATERIALS & CONTINUA
(2023)
Article
Computer Science, Information Systems
R. Poonguzhali, Sultan Ahmad, P. Thiruvannamalai Sivasankar, S. Anantha Babu, Pranav Joshi, Gyanendra Prasad Joshi, Sung Won Kim
Summary: This study introduces an Automated Deep Residual U-Net Segmentation with Classification model (ADRU-SCM) for Brain Tumor Diagnosis. The ADRU-SCM model is able to accurately locate and classify brain tumors through preprocessing, segmentation, and classification steps, and performs better than other methods in terms of performance.
CMC-COMPUTERS MATERIALS & CONTINUA
(2023)
