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Design and Develop Function for Research Based Application of Intelligent Internet-of-Vehicles Model Based on Fog Computing

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computers, Materials & Continua 2024年第12期81卷 3805-3824页

作者： Abduladheem Fadhil Khudhur Ayca Kurnaz Türkben Sefer Kurnaz Department of Electrical and Computer Engineering Altinbas UniversityIstanbul34000Turkey

The fast growth in Internet-of-Vehicles(IoV)applications is rendering energy efficiency management of vehicular networks a highly important *** of the existing models are failing to handle the demand for energy conservation in large-scale heterogeneous *** on Large Energy-Aware Fog(LEAF)computing,this paper proposes a new model to overcome energy-inefficient vehicular networks by simulating large-scale network *** main inspiration for this work is the ever-growing demand for energy efficiency in IoV-most particularly with the volume of generated data and connected *** proposed LEAF model enables researchers to perform simulations of thousands of streaming applications over distributed and heterogeneous *** the possible reasons is that it provides a realistic simulation environment in which compute nodes can dynamically join and leave,while different kinds of networking protocols-wired and wireless-can also be *** novelty of this work is threefold:for the first time,the LEAF model integrates online decision-making algorithms for energy-aware task placement and routing strategies that leverage power usage traces with efficiency optimization in *** existing fog computing simulators,data flows and power consumption are modeled as parameterizable mathematical equations in LEAF to ensure scalability and ease of analysis across a wide range of devices and *** results of evaluation show that LEAF can cover up to 98.75%of the distance,with devices ranging between 1 and 1000,showing significant energy-saving potential through A wide-area network(WAN)usage *** findings indicate great promise for fog computing in the future-in particular,models like LEAF for planning energy-efficient IoV infrastructures.

关键词： Fog computing internet of vehicles LEAF segmentation distance power consumption cloud vehicle nodes wireless

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Enhanced prediction of agricultural CO2 emission using ensemble machine learning-based imputation approach

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Neural Computing and Applications 2024年第36期36卷 22867-22887页

作者： Thendral, M. Sathya Abinaya, S. Devi, M. K. Kavitha Department of Computer Science and Engineering Thiagarajar College of Engineering Madurai625015 India School of Computer Science and Engineering Vellore Institute of Technology Chennai600127 India

The agricultural sector contributes significantly to greenhouse gas emissions, which cause global warming and climate change. Numerous mathematical models have been developed to predict the greenhouse gas emissions from agriculture. However, the database utilized for prediction has thousands of missing values and imbalanced data records due to various factors, such as environmental disasters, sensor failure, maintenance issues, and budgetary constraints. Many researchers have either completely discarded records with missing values or have only partially addressed the issue, leading to less precise predictions. This study proposes a machine learning-based ensemble approach that uses multiple imputation by chained equations (MICE), K-nearest neighbors (KNN), and MissForest techniques to impute the missing values by reducing uncertainties and improves robustness in predictions. Furthermore, Synthetic Minority Oversampling Technique (SMOTE R) addresses data imbalance in CO2 emissions prediction by generating synthetic data to balance the target variable's distribution in the regression problem. Thereby the proposed SMOTE R and ensemble imputation approach aims to improve prediction accuracy and reliability by tackling data completeness and distribution issues simultaneously. The experiments are conducted on the GRACEnet database shows that the proposed approach outperforms the existing work in measure of R2, MAE, and RMSE metrics. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Greenhouse gas emissions

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Classified VPN Network Traffic Flow Using Time Related to Artificial Neural Network

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computers, Materials & Continua 2024年第7期80卷 819-841页

作者： Saad Abdalla Agaili Mohamed Sefer Kurnaz Department of Electrical and Computer Engineering Altinbas UniversityIstanbul34000Turkey

VPNs are vital for safeguarding communication routes in the continually changing cybersecurity ***,increasing network attack complexity and variety require increasingly advanced algorithms to recognize and categorizeVPNnetwork *** present a novelVPNnetwork traffic flowclassificationmethod utilizing Artificial Neural Networks(ANN).This paper aims to provide a reliable system that can identify a virtual private network(VPN)traffic fromintrusion attempts,data exfiltration,and denial-of-service *** compile a broad dataset of labeled VPN traffic flows from various apps and usage ***,we create an ANN architecture that can handle encrypted communication and distinguish benign from dangerous *** effectively process and categorize encrypted packets,the neural network model has input,hidden,and output *** use advanced feature extraction approaches to improve the ANN’s classification accuracy by leveraging network traffic’s statistical and behavioral *** also use cutting-edge optimizationmethods to optimize network characteristics and *** suggested ANN-based categorization method is extensively tested and *** show the model effectively classifies VPN traffic *** also show that our ANN-based technique outperforms other approaches in precision,recall,and F1-score with 98.79%*** study improves VPN security and protects against new *** VPNtraffic flows effectively helps enterprises protect sensitive data,maintain network integrity,and respond quickly to security *** study advances network security and lays the groundwork for ANN-based cybersecurity solutions.

关键词： VPN network traffic flow ANN classification intrusion detection data exfiltration encrypted traffic feature extraction network security

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Hybrid Local-global Power-sharing Scheme for Droop-free Controlled Microgrids

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Journal of Modern Power Systems and Clean Energy 2024年第5期12卷 1520-1534页

作者： Kunyu Zuo Lei Wu Electrical and Computer Engineering Department Stevens Institute of TechnologyHobokenNJ 07030USA

The droop-free control adopted in microgrids has been designed to cope with global power-sharing goals,i.e.,sharing disturbance mitigation among all controllable assets to even their ***,limited by neighboring communication,the time-consuming peer-to-peer coordination of the droopfree control slows down the nodal convergence to global consensus,reducing the power-sharing efficiency as the number of nodes *** this end,this paper first proposes a local power-sharing droop-free control scheme to contain disturbances within nearby nodes,in order to reduce the number of nodes involved in the coordination and accelerate the convergence speed.A hybrid local-global power-sharing scheme is then put forward to leverage the merits of both schemes,which also enables the autonomous switching between local and global power-sharing modes according to the system *** guidance for key control parameter designs is derived via the optimal control methods,by optimizing the power-sharing distributions at the steady-state consensus as well as along the dynamic trajectory to *** system stability of the hybrid scheme is proved by the eigenvalue analysis and Lyapunov direct ***,simulation results validate that the proposed hybrid local-global power-sharing scheme performs stably against disturbances and achieves the expected control performance in local and global power-sharing modes as well as mode ***,compared with the classical global power-sharing scheme,the proposed scheme presents promising benefits in convergence speed and scalability.

关键词： Droop-free control power-sharing optimal control Lyapunov stability

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Utilization of Two Cross Two Switch for Bitonic Sorting Circuit

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Micro and Nanosystems 2025年第1期17卷 45-57页

作者： Bhattacharjee, Subhasree Sarkar, Soumyadip Das, Kunal Sadhu, Arindam Sarkar, Bikramjit Department of Computer Application Narula Institute of Technology Kolkata India Department of Computer Science Acharya Prafulla Chandra College Kolkata India Department of Electronics & Communication Engineering Greater Kolkata College of Engineering and Management West Bengal India Department of Computer Science & Engineering JIS College of Engineering West Bengal India

Background: The synthesis of reversible logic has gained prominence as a crucial research area, particularly in the context of post-CMOS computing devices, notably quantum computing. Objective: To implement the bitonic sorting circuit, 2x2 switch, is used. The overarching objective of implementing a reversible sorting circuit is to mitigate power consumption while promising secure communication. The successful implementation of the bitonic sorting circuit utilizing the proposed 2x2 switch is validated through simulations conducted in IBM Qiskit. Methods: This study begins by employing the control swap gate to implement a 2x2 switch, aimed at enabling concurrent computing operations within multiprocessor systems to enhance throughput. Subsequently, following the successful implementation of the 2x2 switch, the study proceeds to design a bitonic sorting circuit. Results: Bitonic sort using a 2x2 switch on the IBM Quantum Experience (IBMQ) platform via the QISKit SDK in Python 3.6. Specifically, Qiskit v0.29.0 is utilized, with OpenQASM. OpenQASM serves as the quantum assembly and instruction language. Conclusion: This work presents a methodology for constructing a 2x2 switch, utilizing a reversible control swap gate as its core component. Additionally, it demonstrates the intricate development and implementation of a bitonic sorting circuit, capitalizing on this innovative switch architecture. © 2025 Bentham Science Publishers.

关键词： Quantum computers

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Blockchain Enabled Metaheuristic Cluster Based Routing Model for Wireless Networks

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computer Systems Science & engineering 2023年第2期44卷 1233-1250页

作者： R.M.Bhavadharini S.Karthik Department of Computer Science and Engineering Easwari Engineering CollegeChennai600089India Department of Computer Science and Engineering SNS College of TechnologyCoimbatore641035India

With recent advancements made in wireless communication techniques,wireless sensors have become an essential component in both data collection as well as tracking *** Sensor Network(WSN)is an integral part of Internet of Things(IoT)and it encounters different kinds of security *** is designed as a game changer for highly secure and effective digital ***,the current research paper focuses on the design of Metaheuristic-based Clustering with Routing Protocol for Blockchain-enabled WSN abbreviated as *** proposed MCRP-BWSN technique aims at deriving a shared memory scheme using blockchain technology and determine the optimal paths to reach the destination in clustered *** MCRP-BWSN technique,Chimp Optimization Algorithm(COA)-based clustering technique is designed to elect a proper set of Cluster Heads(CHs)and organize the selected *** addition,Horse Optimization Algorithm(HOA)-based routing technique is also presented to optimally select the routes based onﬁtness ***,HOA-based routing technique utilizes blockchain technology to avail the shared mem-ory among nodes in the *** nodes are treated as coins whereas the ownership handles the sensor nodes and Base Station(BS).In order to validate the enhanced performance of the proposed MCRP-BWSN technique,a wide range of simulations was conducted and the results were examined under different *** on the performance exhibited in simulation outcomes,the pro-posed MCRP-BWSN technique has been established as a promising candidate over other existing techniques.

关键词： Wireless networks blockchain routing protocol clustering security metaheuristics

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AI-Driven Agronomy: Enhancing Crop Yield and Market Profits 4

AI-Driven Agronomy: Enhancing Crop Yield and Market Profits

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4th International Conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Mourya, Kona Chandra, Sai Reddy, N Abhinay Kumar, Aiswarya S Department of Computer Science and Engineering Department of Computer Science and Engineering

ISBN: (纸本)9798350307757

The agriculture sector plays an important role to the nation's economy, contributing significantly to GDP and employing a sizable section of the labour force. Nonetheless, precisely projecting food production and demand remains a difficult task. Traditional prediction approaches are time-consuming and prone to errors, resulting in inefficient resource allocation and financial losses. To solve these issues, we offer an innovative platform that uses machine learning algorithms to predict the best crops for production based on environmental conditions, as well as forecast market demand and pricing patterns for those *** comparing the effectiveness of various crop forecast classification models, the Voting Classifier, which incorporates numerous models, scored an amazing accuracy rate of 0.986. This predictive power can help farmers and stakeholders make more informed decisions, resulting to better profitability and resource efficiency. Furthermore, for price estimate, we used a Stacking Regressor model, which combines the top two models, the Random Forest Regressor and the XGBoosting Regressor. The Stacking Regressor performed better than the individual regression models, earning an optimised MSE of 277.58. This ensemble approach greatly improved price estimation accuracy, allowing stakeholders to make more informed decisions and increase profitability in the agriculture industry. Finally, the suggested intelligent platform offers a promising option for precise crop forecast and price estimation, benefiting farmers and stakeholders by optimising resource allocation, increasing profitability, and contributing to the agriculture sector's longterm growth. © 2024 IEEE.

关键词： Forecasting

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Dynamic Hand Gesture-Based Person Identification Using Leap Motion and Machine Learning Approaches

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computers, Materials & Continua 2024年第4期79卷 1205-1222页

作者： Jungpil Shin Md.AlMehedi Hasan Md.Maniruzzaman Taiki Watanabe Issei Jozume School of Computer Science and Engineering The University of AizuAizuwakamatsuFukushima965-8580Japan Department of Computer Science&Engineering Rajshahi University of Engineering&TechnologyRajshahi6204Bangladesh

Person identification is one of the most vital tasks for network security. People are more concerned about theirsecurity due to traditional passwords becoming weaker or leaking in various attacks. In recent decades, fingerprintsand faces have been widely used for person identification, which has the risk of information leakage as a resultof reproducing fingers or faces by taking a snapshot. Recently, people have focused on creating an identifiablepattern, which will not be reproducible falsely by capturing psychological and behavioral information of a personusing vision and sensor-based techniques. In existing studies, most of the researchers used very complex patternsin this direction, which need special training and attention to remember the patterns and failed to capturethe psychological and behavioral information of a person properly. To overcome these problems, this researchdevised a novel dynamic hand gesture-based person identification system using a Leap Motion sensor. Thisstudy developed two hand gesture-based pattern datasets for performing the experiments, which contained morethan 500 samples, collected from 25 subjects. Various static and dynamic features were extracted from the handgeometry. Randomforest was used to measure feature importance using the Gini Index. Finally, the support vectormachinewas implemented for person identification and evaluate its performance using identification accuracy. Theexperimental results showed that the proposed system produced an identification accuracy of 99.8% for arbitraryhand gesture-based patterns and 99.6% for the same dynamic hand gesture-based patterns. This result indicatedthat the proposed system can be used for person identification in the field of security.

关键词： Person identification leap motion hand gesture random forest support vector machine

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Automated face recognition using deep learning technique and center symmetric multivariant local binary pattern

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Neural Computing and Applications 2025年第1期37卷 263-281页

作者： Sekhar, J.C. Josephson, P. Joel Chinnasamy, A. Maheswari, M. Sankar, S. Kalangi, Ruth Ramya Department of Computer Science and Engineering NRI Institute of Technology Andhra Pradesh Guntur India Department of Electronics and Communication Engineering Malla Reddy Engineering College Telangana Hyderabad India Department of Data Science and Business Systems School of Computing SRMIST Kattankulathur Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vijayawada India

Researchers have recently created several deep learning strategies for various tasks, and facial recognition has made remarkable progress in employing these techniques. Face recognition is a noncontact, nonobligatory, acceptable, and harmonious biometric recognition method with a promising national and social security future. The purpose of this paper is to improve the existing face recognition algorithm, investigate extensive data-driven face recognition methods, and propose a unique automated face recognition methodology based on generative adversarial networks (GANs) and the center symmetric multivariable local binary pattern (CS-MLBP). To begin, this paper employs the center symmetric multivariant local binary pattern (CS-MLBP) algorithm to extract the texture features of the face, addressing the issue that C2DPCA (column-based two-dimensional principle component analysis) does an excellent job of removing the global characteristics of the face but struggles to process the local features of the face under large samples. The extracted texture features are combined with the international features retrieved using C2DPCA to generate a multifeatured face. The proposed method, GAN-CS-MLBP, syndicates the power of GAN with the robustness of CS-MLBP, resulting in an accurate and efficient face recognition system. Deep learning algorithms, mainly neural networks, automatically extract discriminative properties from facial images. The learned features capture low-level information and high-level meanings, permitting the model to distinguish among dissimilar persons more successfully. To assess the proposed technique’s GAN-CS-MLBP performance, extensive experiments are performed on benchmark face recognition datasets such as LFW, YTF, and CASIA-WebFace. Giving to the findings, our method exceeds state-of-the-art facial recognition systems in terms of recognition accuracy and resilience. The proposed automatic face recognition system GAN-CS-MLBP provides a solid basis for a

关键词： Principal component analysis

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A Novel Optimized Deep Convolutional Neural Network for Efficient Seizure Stage Classification

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computers, Materials & Continua 2024年第12期81卷 3903-3926页

作者： Umapathi Krishnamoorthy Shanmugam Jagan Mohammed Zakariah Abdulaziz S.Almazyad K.Gurunathan Department of Biomedical Engineering KIT-Kalaignarkarunanidhi Institute of TechnologyCoimbatore641402India Department of Computer Sciences and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai600062India Department of Computer Sciences and Engineering College of Applied ScienceKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Science and Engineering Mount Zion College of Engineering and TechnologyPudukkottai622507India

Brain signal analysis from electroencephalogram(EEG)recordings is the gold standard for diagnosing various neural disorders especially epileptic *** signals are highly chaotic compared to normal brain signals and thus can be identified from EEG *** the current seizure detection and classification landscape,most models primarily focus on binary classification—distinguishing between seizure and non-seizure *** effective for basic detection,these models fail to address the nuanced stages of seizures and the intervals between *** identification of per-seizure or interictal stages and the timing between seizures is crucial for an effective seizure alert *** granularity is essential for improving patient-specific interventions and developing proactive seizure management *** study addresses this gap by proposing a novel AI-based approach for seizure stage classification using a Deep Convolutional Neural Network(DCNN).The developed model goes beyond traditional binary classification by categorizing EEG recordings into three distinct classes,thus providing a more detailed analysis of seizure *** enhance the model’s performance,we have optimized the DCNN using two advanced techniques:the Stochastic Gradient Algorithm(SGA)and the evolutionary Genetic Algorithm(GA).These optimization strategies are designed to fine-tune the model’s accuracy and ***,k-fold cross-validation ensures the model’s reliability and generalizability across different data *** and validated on the Bonn EEG data sets,the proposed optimized DCNN model achieved a test accuracy of 93.2%,demonstrating its ability to accurately classify EEG *** summary,the key advancement of the present research lies in addressing the limitations of existing models by providing a more detailed seizure classification system,thus potentially enhancing the effectiveness of real-time seizure prediction and management systems in clinic

关键词： Bonn EEG dataset cross-validation genetic algorithm batch normalization seizure classification stochastic gradient

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