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Securing IIoT operations with recurrent federated network-based enhanced local search grasshopper

Neural computing and Applications 2024年第25期36卷 15893-15906页

作者： Alassafi, Madini O. Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

The integration of the Industrial Internet of Things (IIoT) brings about a significant improvement in the efficiency and productivity of industrial processes. The speed and accuracy of various tasks have been greatly enhanced with the utilization of IIoT which allows smoother operations and cost-effective solutions, to protect uninterrupted intrusions and avoid potential threats. This article proposes a novel recurrent federated network-based enhanced local search grasshopper (RFN-ELG) algorithm. Datasets like UNSW-NB15 and MQTT-IoT-IDS2020 datasets are employed to determine the performances of IIoT via two diverse phases, namely the data preprocessing phase as well as attack detection phase. The recurrent neural networks (RNNs) are integrated with federated learning (FL) to overcome the gradient issues during the training process. In addition to this, the hyperparameters of RNN-FL are tuned via a grasshopper optimization algorithm with a local search strategy. Accuracy, precision, recall, F1-score, and false alarm rate (FAR) are the performance metrics taken for attack detection, where accuracy attains the value of 98.1%, precision attains the value of 97.4%, recall attains the value of 96.1%, F1-score attains the value of 97.2%, and FAR attains the value of 96.1%, respectively. Therefore, the proposed RFN-ELG algorithm attains high performance in detecting the attacks in IIoT. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Internet of things

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A hybrid memory architecture supporting fine-grained data migration

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Frontiers of Computer Science 2024年第2期18卷 31-41页

作者： Ye CHI Jianhui YUE Xiaofei LIAO Haikun LIU Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Department of Computer Science Michigan Technological UniversityMichigan 49931USA

Hybrid memory systems composed of dynamic random access memory(DRAM)and Non-volatile memory(NVM)often exploit page migration technologies to fully take the advantages of different memory *** previous proposals usually migrate data at a granularity of 4 KB pages,and thus waste memory bandwidth and DRAM *** this paper,we propose Mocha,a non-hierarchical architecture that organizes DRAM and NVM in a flat address space physically,but manages them in a cache/memory *** the commercial NVM device-Intel Optane DC Persistent Memory Modules(DCPMM)actually access the physical media at a granularity of 256 bytes(an Optane block),we manage the DRAM cache at the 256-byte size to adapt to this feature of *** design not only enables fine-grained data migration and management for the DRAM cache,but also avoids write amplification for Intel Optane *** also create an Indirect Address Cache(IAC)in Hybrid Memory Controller(HMC)and propose a reverse address mapping table in the DRAM to speed up address translation and cache ***,we exploit a utility-based caching mechanism to filter cold blocks in the NVM,and further improve the efficiency of the DRAM *** implement Mocha in an architectural *** results show that Mocha can improve application performance by 8.2%on average(up to 24.6%),reduce 6.9%energy consumption and 25.9%data migration traffic on average,compared with a typical hybrid memory architecture-HSCC.

关键词： non-volatile memory hybrid memory system data migration fine-grained caching

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A reinforcement learning based backup queue strategy for reliable and fault tolerant scheduling

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International Journal of Information technology (Singapore) 2025年第5期17卷 2613-2618页

作者： Archana, R. Kumar, K. Pradeep Mohan Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India

Fog computing has the capability to perform tasks in the local distributed environment within the expected time period. The approaches used for managing the faults in a fog computing environment are not competent to resolve the issues of latency and energy consumption in the network. Allocating and executing all the tasks regardless of how they scale up and completing them within the allotted time frame is crucial for achieving a higher throughput and a lower drop rate. In order to achieve higher scalability, timely execution, and dependability, the given paper introduces a reinforcement learning (RL) backup queue strategy (SEEDBACK_RL) that ensures every task is executed irrespective of its size. The proposed strategy is enhanced to manage the scalability and reliability by identifying the best nodes depending on the type of task. With the use of a simulative environment, the algorithm's efficacy is assessed, and the results show a notable improvement in load balancing, reliability, and network efficiency. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.

关键词： Asynchronous advantage actor critic (A3C) Distributed reinforcement learning Fog computing Internet of things (IoT) Load balancing

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A novel approach on heart disease prediction using optimized hybrid deep learning approach

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Multimedia Tools and Applications 2025年 1-26页

作者： C, Adline Sherifa Kanisha, B. Department of Computing Technologies Faculty of Engg and Technology SRM Institute of Science and Technology Kattankulathur Chennai603 203 India

The condition known as Cardio Vascular Disease can result in heart attacks, Angina, and brain assaults due to the restriction of blood flow to the myocardium. Among the most important causes of death and mortality worldwide, heart disease is also one of the main sources of disability for both young and old people. Moreover, it is one of the reasons behind weariness and mortality on Earth. Interruption can prompt benevolence, adjustments in lifestyle, and tailored therapy interventions, which ultimately lead to peaceful outcomes and reduced healthcare expenses. The data gathered in this question about work is processed beforehand using information cleaning and information standardization techniques. Measurable highlights, higher-order measurable highlights (for example, why measure weights are not measured), correlation-based IDM, weight standard deviation-related entropy, and the degree of relationship between the data and homogeneity, which in turn are removed. From the extricated features, the foremost ideal highlights are chosen via the relief-f demonstration. By selecting the most suitable features, one can prepare the crossover classifier that generates the heart illness prediction. The crossover classifier could be a combination of MLP and O-RBM. To advance the expectation exactness of the anticipated heart malady location show, the weight of Limited Boltzmann Machines is fine-tuned through the Self-Adaptive TLBO (SA- TLBO) demonstration. The result from the crossover classifier is the identified result. This SA-TLBO show is an extended version of the standard TLBO show. By analyzing the "Heart Malady Dataset" of the Kaggle store, it is established that the proposed show has 95% precision and 96% accuracy with an FNR of 0.05. This demonstration is then tested against the real models to show whether a model matches those of the proposed one. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Demonstrations

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Xception Spiking Fractional Neural Network for Oral Squamous Cell Carcinoma Classification Based on Histopathological Images

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IEEE Access 2025年 13卷 98574-98591页

作者： Ramya, Singaraju Minu, R.I. Magesh, K.T. SRM Institute of Science and Technology School of Computing Department of Computing Technologies Kattankulathur Chennai India SRM Institute of Science and Technology SRM Kattankulathur Dental College & Hospital Department of Oral & Maxillofacial Pathology Kattankulathur Chennai India

Oral Squamous Cell Carcinoma (OSCC) is the leading cancer caused due to the immense growth of malignant cells in different regions of the oral cavity particularly in the area of the neck and head. The habits of smoking, betel nuts, and chewing tobacco that mainly affect the pharynx, nasopharynx, viral infection, and oral cavity regions are the major causes of OSCC. OSCC has to be detected at the initial stages to provide accurate treatment and to assess the severity. Thus, various screening and detection models are used to accurately detect OSCC with great significance for reducing morbidity and mortality. In this research, a novel deep learning model Xception Spiking Fractional Neural Network (XSFN-Net) is introduced to classify OSCC from the histopathology images. Here, the histopathology images are enhanced initially using the Medav filter. The pixel-wise image segmentation is performed using Parallel Reverse Attention Network (PraNet) along with the designed loss function from the enhanced image. Later, the extraction of relevant features from the segmented output and the OSCC is finally classified using XSFN-Net from the resultant extracted features. The XSFN-Net attained high performance with True Positive Rate (TPR) of 96.604%, accuracy of 97.538%, and True Negative Rate (TNR) of 95.545%. © 2013 IEEE.

关键词： Image segmentation

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Managing the surge:A comprehensive review of the entire disposal framework for retired lithium-ion batteries from electric vehicles

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Journal of Energy Chemistry 2024年第5期92卷 648-680页

作者： Ruohan Guo Feng Wang M.Akbar Rhamdhani Yiming Xu Weixiang Shen Department of Engineering Technologies School of ScienceComputing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia Department of Chemistry and Biotechnology School of ScienceComputing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia Department Mechanical and Product Design Engineering School of EngineeringSwinburne University of TechnologyMelbourne VIC 3122Australia

Anticipating the imminent surge of retired lithium-ion batteries(R-LIBs)from electric vehicles,the need for safe,cost-effective and environmentally friendly disposal technologies has *** paper seeks to offer a comprehensive overview of the entire disposal framework for R-LIBs,encompassing a broad spectrum of activities,including screening,repurposing and ***,we delve deeply into a thorough examination of current screening technologies,shifting the focus from a mere enumeration of screening methods to the exploration of the strategies for enhancing screening ***,we outline battery repurposing with associated key factors,summarizing stationary applications and sizing methods for R-LIBs in their second life.A particular light is shed on available reconditioning solutions,demonstrating their great potential in facilitating battery safety and lifetime in repurposing scenarios and identifying their techno-economic *** the realm of battery recycling,we present an extensive survey of pre-treatment options and subsequent material recovery ***,we introduce several global leading recyclers to illustrate their industrial processes and technical ***,relevant challenges and evolving trends are investigated in pursuit of a sustainable end-of-life management and disposal *** hope that this study can serve as a valuable resource for researchers,industry professionals and policymakers in this field,ultimately facilitating the adoption of proper disposal practices.

关键词： Lithium-ion battery Battery reproposing and recycling Miaieiials recovery technologies Techno-economic issues End-of-life management Disposal framework

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TRS Scheduling for Improved QoS Performance in Cloud System

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Computers, Materials & Continua 2023年第4期75卷 1547-1559页

作者： G.John Samuel Babu M.Baskar Department of Computer Science and Engineering School of ComputingCollege of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathurChengalpattuTamilnadu603203India Department of Computing Technologies School of ComputingCollege of Engineering andTechnologySRM Institute of Science and TechnologyKattankulathurChengalpattuTamilnadu603203India

Numerous methods are analysed in detail to improve task schedulingand data security performance in the cloud environment. The methodsinvolve scheduling according to the factors like makespan, waiting time,cost, deadline, and popularity. However, the methods are inappropriate forachieving higher scheduling performance. Regarding data security, existingmethods use various encryption schemes but introduce significant serviceinterruption. This article sketches a practical Real-time Application CentricTRS (Throughput-Resource utilization–Success) Scheduling with Data Security(RATRSDS) model by considering all these issues in task scheduling anddata security. The method identifies the required resource and their claim timeby receiving the service requests. Further, for the list of resources as services,the method computes throughput support (Thrs) according to the number ofstatements executed and the complete statements of the service. Similarly, themethod computes Resource utilization support (Ruts) according to the idletime on any duty cycle and total servicing time. Also, the method computesthe value of Success support (Sus) according to the number of completions forthe number of allocations. The method estimates the TRS score (ThroughputResource utilization Success) for different resources using all these supportmeasures. According to the value of the TRS score, the services are rankedand scheduled. On the other side, based on the requirement of service requests,the method computes Requirement Support (RS). The selection of service isperformed and allocated. Similarly, choosing the route according to the RouteSupport Measure (RSM) enforced route security. Finally, data security hasgets implemented with a service-based encryption technique. The RATRSDSscheme has claimed higher performance in data security and scheduling.

关键词： Cloud task scheduling TRS quality of service RSM route security data security SDE RATRSDS

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Anomaly graph: leveraging dynamic graph convolutional networks for enhanced video anomaly detection in surveillance and security applications

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Neural computing and Applications 2024年第20期36卷 12011-12028页

作者： Chiranjeevi, V. Rahul Malathi, D. Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chennai603203 India

Video abnormality behavior identification plays a pivotal role in improving the safety and security of surveillance systems by identifying unusual events within video streams. However, existing methods face challenges in capturing complex spatiotemporal anomalies effectively. To address this, we propose an anomaly graph approach that leverages dynamic graph representations and dynamic graph convolutional networks (GCN) for video anomaly detection. Anomaly graph constructs dynamic graphs where nodes represent objects or regions of interest within video frames while the edges encode spatial and temporal relationships. The GCN architecture extracts spatiotemporal embeddings from the dynamic graphs and allows the model to identify anomalies involving both spatial and temporal cues. Anomaly graph introduces uniqueness scores to quantify frame distinctiveness for precise anomaly detection while it employs adaptive reconstruction errors to pinpoint spatially localized anomalies. Real-time alerts are generated for detected anomalies to ensure timely responses to security incidents, and online fine-tuning (OLT) is incorporated as a dynamic learning mechanism to adapt to evolving anomaly patterns existing in dynamic environments. The extensive experimentation on benchmark datasets including UCSD Ped2 and CUHK Avenue is carried out for diverse performance measures, and the anomaly graph exhibits enhanced performance in the detection of anomalous events by achieving a detection accuracy of 98.72% and precision of 97.18%. Moreover, it is found to have achieved better values compared to other related methods for video anomaly detection. Overall, the anomaly graph introduces a robust video anomaly detection framework that excels in identifying complex spatiotemporal anomalies and empowers surveillance and security systems to detect anomalies more effectively. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Anomaly detection

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Adaptive pelican optimization with optimized mask RCNN for automatic lung cancer detection

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Multimedia Tools and Applications 2025年 1-21页

作者： Sudha, R. Maheswari, K.M. Uma Department of Computer Science and Engineering SRM Institute of Science and Technology Chengalpattu India Department of Computing Technologies SRM Institute of Science and Technology Chengalpattu India

Lung cancer is a prevalent and deadly disease worldwide, necessitating accurate and timely detection methods for effective treatment. Deep learning-based approaches have emerged as promising solutions for automated medical image analysis. This study proposes an enhanced Mask R-CNN framework tailored specifically for the automatic detection and severity analysis of lung cancer from CT images. The proposed approach consists of three stages namely, pre-processing, lung nodule detection, and segmentation using enhanced Mask R-CNN and severity analysis. Our framework employs a deep convolutional neural network architecture trained on a comprehensive dataset of annotated lung images. By incorporating a region-based convolutional neural network (R-CNN) with a mask prediction branch, our model accurately localizes lung tumors while providing precise pixel-level segmentation masks. To enhance the performance of mask RCNN, the parameter present in the classifier is optimally selected using the adaptive pelican optimization (APO) algorithm. The proposed framework detects lung tumors and provides a comprehensive severity analysis, enabling clinicians to assess cancer stage and progression accurately. Evaluation on a benchmark dataset demonstrates superior detection accuracy and robustness compared to existing methods. Our enhanced Mask R-CNN approach shows promise as a valuable tool for early diagnosis and severity assessment of lung cancer, potentially improving patient outcomes and healthcare efficiency. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Lung cancer

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Priority Detector and Classifier Techniques Based on ML for the IoMT

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Computers, Materials & Continua 2023年第8期76卷 1853-1870页

作者： Rayan A.Alsemmeari Mohamed Yehia Dahab Badraddin Alturki Abdulaziz A.Alsulami Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

Emerging telemedicine trends,such as the Internet of Medical Things(IoMT),facilitate regular and efficient interactions between medical devices and computing *** importance of IoMT comes from the need to continuously monitor patients’health conditions in real-time during normal daily activities,which is realized with the help of various wearable devices and *** major health problem is workplace stress,which can lead to cardiovascular disease or psychiatric ***,real-time monitoring of employees’stress in the workplace is *** levels and the source of stress could be detected early in the fog layer so that the negative consequences can be mitigated ***,overwhelming the fog layer with extensive data will increase the load on fog nodes,leading to computational *** study aims to reduce fog computation by proposing machine learning(ML)models with two *** first phase of theMLmodel assesses the priority of the situation based on the stress *** the second phase,a classifier determines the cause of stress,which was either interruptions or time pressure while completing a *** approach reduced the computation cost for the fog node,as only high-priority records were transferred to the ***-priority records were forwarded to the *** MLapproaches were compared in terms of accuracy and prediction speed:Knearest neighbors(KNN),a support vector machine(SVM),a bagged tree(BT),and an artificial neural network(ANN).In our experiments,ANN performed best in both phases because it scored an F1 score of 99.97% and had the highest prediction speed compared with KNN,SVM,and BT.

关键词： Machine learning priority detector Internet of Medical Things IoMT fog computing cloud computing

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