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A novel hybrid dwarf-based Archimedes optimization (HDAO) algorithm for preserving secure data in a cloud computing environment

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Soft Computing 2024年第23期28卷 13371-13387页

作者： Alandjani, Gasim Computer Science and Engineering Department Yanbu Industrial College Yanbu Saudi Arabia

In general, cloud security is capable of providing various information, applications, services, etc. using extensive policies and progressive technologies. On the other hand, loss of data, confidentiality breaches, and loss of control are the major challenges in cloud security and they need to be solved. Therefore, this paper proposes a novel hybrid dwarf-based Archimedes optimization (HDAO) algorithm for optimal key generation and to solve both data privacy and integrity issues. The HDAO is the integration of both the dwarf optimization algorithm and Archimedes optimization algorithm. The main goal of the optimization algorithm is to solve the problem and find the optimal solutions by maximizing the objective function which makes them suitable for privacy-preserving tasks. One of the main weaknesses of the AOA algorithm is getting stuck in the local optima due to the usage of gradient-based methods. This issue is overcome using the dwarf optimization algorithm via randomization and local search strategy to complement the gradient-based approach. By employing the HDAO algorithm, the data sanitization and restoration process is effectively executed using the cloud data. In addition to this, the key extraction plays a vital role in the process of data sanitization and restoration process in which the optimization is carried out by the proposed HDAO algorithm. This paper employed five different types of datasets namely the genome, protein, general text, English text, and rand128 to evaluate the effectiveness of the proposed approach. Also, the comparative analysis is made for various parameters namely encryption time, decryption time, and avalanche effect, which is evaluated to determine the efficiency of the system. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Cloud security

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Segmentation Based Real Time Anomaly Detection and Tracking Model for Pedestrian Walkways

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Intelligent Automation & Soft Computing 2023年第6期36卷 2491-2504页

作者： B.Sophia D.Chitra Department of Computer Science and Engineering Sri Krishna College of Engineering and TechnologyCoimbatoreTamilnaduIndia Department of Computer Science and Engineering P.A College of Engineering and TechnologyPollachiTamilnaduIndia

Presently,video surveillance is commonly employed to ensure security in public places such as traffic signals,malls,railway stations,etc.A major chal-lenge in video surveillance is the identification of anomalies that exist in it such as crimes,thefts,and so ***,the anomaly detection in pedestrian walkways has gained significant attention among the computer vision communities to enhance pedestrian *** recent advances of Deep Learning(DL)models have received considerable attention in different processes such as object detec-tion,image classification,*** this aspect,this article designs a new Panoptic Feature Pyramid Network based Anomaly Detection and Tracking(PFPN-ADT)model for pedestrian *** proposed model majorly aims to the recognition and classification of different anomalies present in the pedestrian walkway like vehicles,skaters,*** proposed model involves panoptic seg-mentation model,called Panoptic Feature Pyramid Network(PFPN)is employed for the object recognition *** object classification,Compact Bat Algo-rithm(CBA)with Stacked Auto Encoder(SAE)is applied for the classification of recognized *** ensuring the enhanced results better anomaly detection performance of the PFPN-ADT technique,a comparison study is made using Uni-versity of California San Diego(UCSD)Anomaly data and other benchmark data-sets(such as Cityscapes,ADE20K,COCO),and the outcomes are compared with the Mask Recurrent Convolutional Neural Network(RCNN)and Faster Convolu-tional Neural Network(CNN)*** simulation outcome demonstrated the enhanced performance of the PFPN-ADT technique over the other methods.

关键词： Panoptic segmentation object detection deep learning tracking model anomaly detection pedestrian walkway

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Arrhythmia Detection by Using Chaos Theory with Machine Learning Algorithms

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computers, Materials & Continua 2024年第6期79卷 3855-3875页

作者： Maie Aboghazalah Passent El-kafrawy Abdelmoty M.Ahmed Rasha Elnemr Belgacem Bouallegue Ayman El-sayed Math and Computer Science Department Faculty of ScienceMenoufia UniversityShebin El-komEgypt College of Engineering Computer Science DepartmentEffat UniversityJeddahKingdom of Saudi Arabia Department of Computer Engineering College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia Computer Science and Engineering Department Faculty of Electronic EngineeringMenoufia UniversityShebin El-komEgypt Climate Change Information Center and Expert Systems Agriculture Research CenterGizaEgypt

Heart monitoring improves life ***(ECGs or EKGs)detect heart *** learning algorithms can create a few ECG diagnosis processing *** first method uses raw ECG and time-series *** second method classifies the ECG by patient *** third technique translates ECG impulses into Q waves,R waves and S waves(QRS)features using richer *** ECG signals vary naturally between humans and activities,we will combine the three feature selection methods to improve classification accuracy and *** using all three approaches have not been examined till *** researchers found that Machine Learning(ML)techniques can improve ECG *** study will compare popular machine learning techniques to evaluate ECG *** algorithms—Support Vector Machine(SVM),Decision Tree,Naive Bayes,and Neural Network—compare categorization *** plus prior knowledge has the highest accuracy(99%)of the four ML *** characteristics failed to identify signals without chaos *** 99.8%classification accuracy,the Decision Tree technique outperformed all previous experiments.

关键词： ECG extraction ECG leads time series prior knowledge and arrhythmia chaos theory QRS complex analysis machine learning ECG classification

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Early-stage stroke prediction based on Parkinson and wrinkles using deep learning

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Neural Computing and Applications 2024年第30期36卷 18781-18805页

作者： Haritha, T. Babu, A. V. Santhosh Department of Information Technology Vivekanandha College of Engineering for Women Tamilnadu Tiruchengode India Department of Computer Science and Engineering Vivekanandha College of Engineering for Women Tamilnadu Tiruchengode India

The application of noninvasive methods to enhance healthcare systems has been facilitated by the development of new technology. Among the four major cardiovascular diseases, stroke is one of the deadliest and potentially fatal, but, if detected early enough, a patient's life may be spared. Most stroke research has centered on MRI and CT scans for uncomplicated categorization. This medical approach (imaging) is costly, time-consuming and needs the utilization of complex technology. To make up for these shortcomings, however, there has been a lot of interest in adopting noninvasive, measurable EEGs. Nevertheless, the raw data should be classified before the proper characteristics can be formed, both the forecasting algorithms and the analytical techniques demand time. As a result, this work proposes a deep learning-based model that aims to predict the chance of stroke at an early stage utilizing Parkinson's disease and wrinkles as markers. A patient may have a stroke disease if they are diagnosed with both Parkinson's disease and wrinkles. To the best of our knowledge, this research is the first to use these biomarkers to predict the risk of having a stroke. The proposed model achieves a higher accuracy of 94.7% on the considered dataset. Additionally, the recommended model was evaluated and tested in terms of loss, training time, accuracy, recall, and F1-score versus the other existing models. With less price and pain than present testing approaches, these discoveries are predicted to result in major enhancements in the early detection of strokes. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Forecasting

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An optimized deep network-based intermediate code generation for the mathematical expression

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Multimedia Tools and Applications 2024年第17期84卷 1-24页

作者： Sharada, H.N. Anami, Basavaraj Allagi, Shridhar Department of Computer Science & amp Engineering SDM College of Engineering and Technology Karnataka Dharwad580002 India Department of Computer Science & amp Engineering KLE Institute of Technology Karnataka Hubli580030 India

In recent times, the system's mathematical expression and operation have gained greater reach in engineering and mathematics. It is vital to solving more complex expressions and equations in a short time. The most existing methodology was degraded in the recognition performance and provided inaccurate output. Therefore, a novel Squirrel-based Spiking Neural Model (SbSNM) was presented for the intermediate code generation from the mathematical expression relation data. Initially, the input ME relation data was preprocessed to remove noise and tokenized by the spiking neural features. The tokenized expressions were arranged into a syntax tree structure, and then intermediate code was generated by the squirrel functions. Finally, the differentiation was calculated and given as output. Subsequently, the performance parameters were evaluated for the presented SbSNM. The overall accuracy obtained by the presented model is 99.25% which is higher than the other existing techniques and also brought a higher precision and recall rate and f1-score. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Neural networks

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Container Based Nomadic Vehicular Cloud Using Cell Transmission Model

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computer Systems science & engineering 2023年第1期44卷 423-440页

作者： Devakirubai Navulkumar Menakadevi Thangavelu Department of Computer Science and Engineering Jayam College of Engineering and TechnologyDharmapuri636813India Department of Electronics and Communication Engineering Adhiyaman College of EngineeringHosur635109India

Nomadic Vehicular Cloud(NVC)is envisaged in this *** predo-minant aspects of NVC is,it moves along with the vehicle that initiates it and functions only with the resources of moving vehicles on the heavy traffic road without relying on any of the static infrastructure and NVC decides the initiation time of container migration using cell transmission model(CTM).Containers are used in the place of Virtual Machines(VM),as containers’features are very apt to NVC’s dynamic *** specifications of 5G NR V2X PC5 interface are applied to NVC,for the feature of not relying on the network ***-days,the peak traffic on the road and the bottlenecks due to it are inevitable,which are seen here as the benefits for VC in terms of resource availability and residual in-network *** speed range of high-end vehicles poses the issue of dis-connectivity among VC participants,that results the container migration *** the entire VC participants are on the move,to maintain proximity of the containers hosted by them,estimating their movements plays a vital *** infer the vehicle movements on the road stretch and initiate the container migration prior enough to avoid the migration failure due to vehicles dynamicity,this paper proposes to apply the CTM to the container based and 5G NR V2X enabled *** simulation results show that there is a significant increase in the success rate of vehicular cloud in terms of successful container migrations.

关键词： Vehicular cloud container migration cell transmission model 5G NR V2X PC5 interface

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Deep Learning Based Side-Channel Attack Detection for Mobile Devices Security in 5G Networks

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1012-1026页

作者： Amjed A.Ahmed Mohammad Kamrul Hasan Ali Alqahtani Shayla Islam Bishwajeet Pandey Leila Rzayeva Huda Saleh Abbas Azana Hafizah Mohd Aman Nayef Alqahtani Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Imam Alkadhim University College Department of Computer Techniques EngineeringBaghdad 10066Iraq Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Department of Networks and Communications Engineering College of Computer Science and Information SystemsNajran UniversityNajran 61441Saudi Arabia Institute of Computer Science and Digital Innovation UCSI UniversityKuala Lumpur 56000Malaysia Department of Intelligent Systems and Cyber Security Astana IT UniversityAstana 20000Kazakstan Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinah 42353Saudi Arabia Department of Electrical Engineering College of EngineeringKing Faisal UniversityAl-Hofuf 31982Saudi Arabia

Mobile devices within Fifth Generation(5G)networks,typically equipped with Android systems,serve as a bridge to connect digital gadgets such as global positioning system,mobile devices,and wireless routers,which are vital in facilitating end-user communication ***,the security of Android systems has been challenged by the sensitive data involved,leading to vulnerabilities in mobile devices used in 5G *** vulnerabilities expose mobile devices to cyber-attacks,primarily resulting from security ***-permission apps in Android can exploit these channels to access sensitive information,including user identities,login credentials,and geolocation *** such attack leverages"zero-permission"sensors like accelerometers and gyroscopes,enabling attackers to gather information about the smartphone's *** underscores the importance of fortifying mobile devices against potential future *** research focuses on a new recurrent neural network prediction model,which has proved highly effective for detecting side-channel attacks in mobile devices in 5G *** conducted state-of-the-art comparative studies to validate our experimental *** results demonstrate that even a small amount of training data can accurately recognize 37.5％of previously unseen user-typed ***,our tap detection mechanism achieves a 92％accuracy rate,a crucial factor for text *** findings have significant practical implications,as they reinforce mobile device security in 5G networks,enhancing user privacy,and data protection.

关键词： Fifth Generation(5G)networks smartphone information leakage Side-Channel Attack(SCA) deep learning

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An Optimized Approach to Deep Learning for Botnet Detection and Classification for Cybersecurity in Internet of Things Environment

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computers, Materials & Continua 2024年第8期80卷 2331-2349页

作者： Abdulrahman Alzahrani Department of Computer Science and Engineering College of Computer Science and EngineeringUniversity of Hafr Al BatinP.O.Box 1803Hafr Al Batin39524Saudi Arabia

The recent development of the Internet of Things(IoTs)resulted in the growth of IoT-based DDoS *** detection of Botnet in IoT systems implements advanced cybersecurity measures to detect and reduce malevolent botnets in interconnected *** detection models evaluate transmission patterns,network traffic,and device behaviour to detect deviations from usual *** learning(ML)techniques detect patterns signalling botnet activity,namely sudden traffic increase,unusual command and control patterns,or irregular device *** addition,intrusion detection systems(IDSs)and signature-based techniques are applied to recognize known malware signatures related to *** ML and deep learning(DL)techniques have been developed to detect botnet attacks in IoT *** overcome security issues in an IoT environment,this article designs a gorilla troops optimizer with DL-enabled botnet attack detection and classification(GTODL-BADC)*** GTODL-BADC technique follows feature selection(FS)with optimal DL-based classification for accomplishing security in an IoT *** data preprocessing,the min-max data normalization approach is primarily *** GTODL-BADC technique uses the GTO algorithm to select features and elect optimal feature ***,the multi-head attention-based long short-term memory(MHA-LSTM)technique was applied for botnet ***,the tree seed algorithm(TSA)was used to select the optimum hyperparameter for the MHA-LSTM *** experimental validation of the GTODL-BADC technique can be tested on a benchmark *** simulation results highlighted that the GTODL-BADC technique demonstrates promising performance in the botnet detection process.

关键词： Botnet detection internet of things gorilla troops optimizer hyperparameter tuning intrusion detection system

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An Efficient Framework for Utilizing Underloaded Servers in Compute Cloud

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computer Systems science & engineering 2023年第1期44卷 143-156页

作者： M.Hema S.Kanaga Suba Raja Department of Information Technology Easwari Engineering CollegeChennai600089India Department of Computer Science and Engineering Easwari Engineering CollegeChennai600089India

In cloud data centers,the consolidation of workload is one of the phases during which the available hosts are allocated *** phenomenon ensures that the least possible number of hosts is used without compromise in meeting the Service Level Agreement(SLA).To consolidate the workloads,the hosts are segregated into three categories:normal hosts,under-loaded hosts,and over-loaded hosts based on their *** is to be noted that the identification of an extensively used host or underloaded host is challenging to ***-old values were proposed in the literature to detect this *** current study aims to improve the existing methods that choose the underloaded hosts,get rid of Virtual Machines(VMs)from them,andfinally place them in some other *** researcher proposes a Host Resource Utilization Aware(HRUAA)Algorithm to detect those underloaded and place its virtual machines on different hosts in a vibrant Cloud *** mechanism presented in this study is contrasted with existing mechanisms *** results attained from the study estab-lish that numerous hosts can be shut down,while at the same time,the user's workload requirement can also be *** proposed method is energy-efficient in workload consolidation,saves cost and time,and leverages active hosts.

关键词： Workload consolidation energy consumption under load server resource utilization

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An implementation of personalized therapy in Clinical Decision Support System using adaptive transformer and hybrid deep learning network

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Australian Journal of Electrical and Electronics engineering 2025年第1期22卷 105-120页

作者： Praveena Rachel Kamala, S. T, Saraswathi. Kaliraj, V. Nithya, A. Hema, R. Ramyadevi, R. Department of Information Technology Easwari Engineering college Tamil Nadu Chennai India Department of CSE S.A Engineering College Tamil Nadu Chennai India Department of Computer Science and Engineering St.Joseph’s College of Engineering Tamil Nadu Chennai India Department of Electronics and Communication Engineering Easwari Engineering College Tamil Nadu Chennai India Department of Computer Science and Applications SRM Institute of Science and Technology Tamil Nadu Chennai India

This paper suggests a new mechanism from deep learning concept for personalised therapy in Clinical Decision Support Systems (CDSS). Basically, the texts used for the observation are acquired from the standard data sources and then forwarded to the text preprocessing task. In the pre-processing phase, the punctuation and special character removal, stop word removal, and stemming process are applied to remove noise and help to eliminate the redundant information in order to improve the data quality. Further, the pre-processed text is applied to the Adaptive Transformer Net (ATN) for the feature extraction purpose, where the attributes in this task are optimally determined with the aid of the Adaptive Walrus Optimization Algorithm (AWOA). Finally, the resultant text is subjected to the Hybrid Deep Learning Network (HDLNet). The HDLNet model is implemented by integrating the ‘Residual Long Short-Term Memory (Residual LSTM) with Dilated Recurrent Neural Network (Dilated RNN)’. From the results, the sensitivity analysis performed in the implemented technique secured 3.7% more efficient than LSTM, 7.76% improved than MobileNet, 6.7% superior to residual LSTM, and 0.90% effective than dilated RNN in dataset 1. Throughout the validation, the conventional techniques are evaluated with the suggested personalised therapy in CDSS to prove its efficacy. ©, Engineers Australia.

关键词： Long short-term memory

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