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A Proposed Architecture for Local-Host and AWS with Multiagent System

Intelligent Automation & Soft Computing 2023年第6期36卷 2787-2802页

作者： Jaspreet Chawla Anil Kr Ahlawat Department of Computer Science&Engineering JSS Academy of Technical EducationNoidaAffiliated to AKTULucknow201301India Department of Computer Science&Engineering KIET Group of InstitutionsGhaziabadAffiliated to AKTULucknow201001India

Cloud computing has become one of the leading technologies in the world *** benefits of cloud computing affect end users *** are several cloud computing frameworks,and each has ways of monitoring and providing *** computing eliminates customer requirements such as expensive system configuration and massive infrastructure while improving dependability and *** the user’s perspective,cloud computing makes it easy to upload multiagents and operate on different web *** this paper,the authors used a restful web service and an agent system to discuss,deployments,and analysis of load performance parameters like memory use,cen-tral processing unit(CPU)utilization,network latency,etc.,both on localhost and an Amazon Web Service Elastic Cloud Computing(AWS-EC2)*** Java Agent Development Environment(JADE)tool has been used to propose an archi-tecture and conduct a comparative study on both local and remote *** is an open-source tool for maintaining applications on AWS *** focus of the study should be to reduce the complexity and time of load perfor-mance parameters by using an agent system on a cloud server instead of establish-ing a massive infrastructure on a local system,even for a small application.

关键词： Amazon web service jade local host rest web service

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A Novel Insertion Solution for the Travelling Salesman Problem

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computers, Materials & Continua 2024年第4期79卷 1581-1597页

作者： Emmanuel Oluwatobi Asani Aderemi Elisha Okeyinka Sunday Adeola Ajagbe Ayodele Ariyo Adebiyi Roseline Oluwaseun Ogundokun Temitope Samson Adekunle Pragasen Mudali Matthew Olusegun Adigun Department of Computer Science Landmark UniversityOmu Aran251103Nigeria SDG 11 Group Landmark UniversityOmu Aran251103Nigeria Department of Computing MiVA UniversityAbuja900211Nigeria Department of Computer Science Ibrahim Badamasi Babangida UniversityLapai911101Nigeria Department of Computer Science University of ZululandKwadlangezwa3886South Africa Department of Computer&Industrial Production Engineering First Technical UniversityIbadan200243Nigeria Department of Multimedia Engineering Kaunas University of TechnologyKaunasLT-44249Lithuania Department of Computer Science Colorado State UniversityFort Collins80523USA

The studypresents theHalfMax InsertionHeuristic (HMIH) as a novel approach to solving theTravelling SalesmanProblem (TSP). The goal is to outperform existing techniques such as the Farthest Insertion Heuristic (FIH) andNearest Neighbour Heuristic (NNH). The paper discusses the limitations of current construction tour heuristics,focusing particularly on the significant margin of error in FIH. It then proposes HMIH as an alternative thatminimizes the increase in tour distance and includes more nodes. HMIH improves tour quality by starting withan initial tour consisting of a ‘minimum’ polygon and iteratively adding nodes using our novel Half Max *** paper thoroughly examines and compares HMIH with FIH and NNH via rigorous testing on standard TSPbenchmarks. The results indicate that HMIH consistently delivers superior performance, particularly with respectto tour cost and computational efficiency. HMIH’s tours were sometimes 16% shorter than those generated by FIHand NNH, showcasing its potential and value as a novel benchmark for TSP solutions. The study used statisticalmethods, including Friedman’s Non-parametric Test, to validate the performance of HMIH over FIH and *** guarantees that the identified advantages are statistically significant and consistent in various situations. Thiscomprehensive analysis emphasizes the reliability and efficiency of the heuristic, making a compelling case for itsuse in solving TSP issues. The research shows that, in general, HMIH fared better than FIH in all cases studied,except for a few instances (pr439, eil51, and eil101) where FIH either performed equally or slightly better thanHMIH. HMIH’s efficiency is shown by its improvements in error percentage (δ) and goodness values (g) comparedto FIH and NNH. In the att48 instance, HMIH had an error rate of 6.3%, whereas FIH had 14.6% and NNH had20.9%, indicating that HMIH was closer to the optimal solution. HMIH consistently showed superior performanceacross many benchmarks,

关键词： Nearest neighbour heuristic farthest insertion heuristic half max insertion heuristic tour construction travelling salesman problem

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Estimating Risk-aware Flexibility Areas for Electric Vehicle Charging Pools via AC Stochastic Optimal Power Flow

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Journal of Modern Power Systems and Clean Energy 2023年第4期11卷 1247-1256页

作者： Juan S.Giraldo Nataly Bañol Arias Pedro P.Vergara Maria Vlasiou Gerwin Hoogsteen Johann L.Hurink Energy Transition Studies Group Netherlands Organisation for Applied Scientific Research(TNO)Amsterdam1043 NTThe Netherlands Department of Electrical Engineering Mathematics and Computer ScienceUniversity of TwenteEnschede7522 NBThe Netherlands Intelligent Electrical Power Grids Group Electrical EngineeringMathematics and Computer ScienceDelft University of TechnologyDelft2628 CDThe Netherlands

This paper introduces an AC stochastic optimal power flow(SOPF)for the flexibility management of electric vehicle(EV)charging pools in distribution networks under *** AC SOPF considers discrete utility functions from charging pools as a compensation mechanism for eventual energy not served to their charging *** application of the AC SOPF is described where a distribution system operator(DSO)requires flexibility to each charging pool in a day-ahead time frame,minimizing the cost for flexibility while guaranteeing technical *** areas are defined for each charging pool and calculated as a function of a risk parameter involving the uncertainty of the *** show that all players can benefit from this approach,i.e.,the DSO obtains a riskaware solution,while charging pools/tasks perceive a reduction in the total energy payment due to flexibility services.

关键词： Electric vehicle flexibility management stochastic optimal power flow(SOPF) risk awareness compensation mechanism

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A Content-Based Filtering Approach for Personalized Movie Recommendations 2

A Content-Based Filtering Approach for Personalized Movie Re...

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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Vashisht, Rohit Sharma, Rahul Kumar Pant, Maneesh Thakral, Gagan Naresh, Prashant Department of Computer Science & Information Technology KIET Group of Institutions Ghaziabad India Department of Computer Science & Engineering KIET Group of Institutions Ghaziabad India Department of Information Technology JSS Academy of Technical Education Noida India

ISBN: (纸本)9798331530389

The recommendation system of today has made it simple to locate the necessities. Movie recommendation systems are designed to assist movie buffs by making recommendations for films to see, saving them the trouble and effort of having to sort through hundreds or even millions of options. The Recommender System has become a major area of research because it helps people find things online by making suggestions that are similar to what they are looking for. In this paper, we introduce a system called MOVIEHOPE for recommending movies. It uses a content-based filtering method that uses the user's preferences and the item's description to make a profile. In CBF, we use keywords to describe items instead of the user's profile to show what they like or do not like. In plain English, the CBF algorithm likes or recommends things that were liked in the past. It looks at how items have been rated before and suggests the best item that fits. © 2025 IEEE.

关键词： Collaborative filtering

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Attention-based Vision Knowledge Adaptation for Constrained Continual Learning

Attention-based Vision Knowledge Adaptation for Constrained ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Guo, Bicheng Zhou, Conghao Liu, Haoyu He, Shibo Chen, Jiming Shen, Xuemin Zhejiang University College of Control Science and Engineering China University of Waterloo Department of Electrical and Computer Engineering Canada

ISBN: (纸本)9798350351255

The demand for continual machine learning in the context of limited computational resources and data availability is critical in the evolving landscape of the connected digital world. Current network applications predominantly rely on deep learning models that require labor/computation-intensive training processes. These models often struggle to effectively adapt to new data while preserving performance on previously acquired knowledge. In this paper, we introduce a lightweight framework for continual knowledge adaptation and learning designed to address these challenges. To prevent disruption of existing services, we propose an attention-based adapter that integrates seamlessly with the existing vision model to encode new incoming data. The weights of the original model are kept fixed during the adaptation process, ensuring the preservation of previously learned knowledge. Furthermore, to enhance learning efficiency and accelerate convergence with new data, we implement a knowledge fusion mechanism that facilitates interaction between existing knowledge and information from new data. Our framework is modular, enabling flexible deployment across distributed devices. The adapter and knowledge fusion module are implemented at each stage with minimal trainable parameters, optimizing resource usage. Extensive experiments and ablation studies validate the effectiveness of the proposed framework. © 2024 IEEE.

关键词： Adversarial machine learning

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African Bison Optimization Algorithm:A New Bio-Inspired Optimizer with engineering Applications

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computers, Materials & Continua 2024年第10期81卷 603-623页

作者： Jian Zhao Kang Wang Jiacun Wang Xiwang Guo Liang Qi School of Science University of Science and Technology LiaoningAnshan114051China Team of Artificial Intelligence Theory and Application University of Science and Technology LiaoningAnshan114051China Department of Computer Science and Software Engineering Monmouth UniversityWest Long BranchNJ 07764USA Information and Control Engineering College Liaoning Petrochemical UniversityFushun113000China Department of Computer Science and Technology Shandong University of Science and TechnologyQingdao266590China

This paper introduces the African Bison Optimization(ABO)algorithm,which is based on biological *** is inspired by the survival behaviors of the African bison,including foraging,bathing,jousting,mating,and *** foraging behavior prompts the bison to seek a richer food source for *** bison find a food source,they stick around for a while by bathing *** jousting behavior makes bison stand out in the population,then the winner gets the chance to produce offspring in the mating *** eliminating behavior causes the old or injured bison to be weeded out from the herd,thus maintaining the excellent *** above behaviors are translated into ABO by mathematical *** assess the reliability and performance of ABO,it is evaluated on a diverse set of 23 benchmark functions and applied to solve five practical engineering problems with *** findings from the simulation demonstrate that ABO exhibits superior and more competitive performance by effectively managing the trade-off between exploration and exploitation when compared with the other nine popular metaheuristics algorithms.

关键词： Optimization metaheuristics African bison optimization engineering problems

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A Comprehensive Analysis of Techniques and Applications in Multimodal Deep Learning 1

A Comprehensive Analysis of Techniques and Applications in M...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Tomar, Vivek Sharma, Swati Arora, Sangeeta Tyagi, Aditya Dayal University Computer Science & Engineering Department Dehradun India KIET Group of Institutions Delhi-NCR Computer Science & Engineering Department Ghaziabad India KIET Group of Institutions Delhi-NCR Computer Applications Department Ghaziabad India Sharda University CSA Department Greater Noida India

ISBN: (纸本)9798350353648

In recent years, deep learning has gained popularity and been used in many different applications. Models that can process and link data utilizing several modalities are the aim of multimodal deep learning (MMDL). Even with all the advancements made, unimodal learning is still unable to fully capture the entirety of human learning. Multimodal learning facilitates a better understanding and analysis of situations in which information is processed through multiple senses. The various modalities covered in this article include picture, video, text, audio, flow, RGB, stance, depth, face expressions, body motions, and point clouds. There has been a thorough examination of the most current developments in multimodal deep learning applications in the last few years, as well as a detailed investigation of the baseline methodologies. A detailed taxonomy of distinct multimodal deep learning techniques is presented, providing further details on specific applications. Finally, key issues are outlined for each domain separately, along with potential avenues for further research. © 2024 IEEE.

关键词： Artificial Intelligence Deep Learning Machine Learning Multimodal Natural Language Processing (NLP) UniModal

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A Novel Drone-Station Matching model in Smart Cities based on Strict Preferences

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无人系统（英文） 2023年第3期11卷 261-271页

作者： Debolina Nath Anjan Bandyopadhyay Ankit Rana Tarek Gaber Aboul Ella Hassanien Department of Computer Science and Engineering Amity University Kolkata KolkataWest BengalIndia Department of Computer Science and Engineering Gitam University BangaloreKarnatakaIndia School of Science Engineeringand Environment University of SalfordUK Faculty of Computers and Informatics Suez Canal UniversityIsmailiaEgypt Scientific Research Group in Egypt(SRGE) Egypt Faculty of Computer and Artificial Intelligence Cairo UniversityCairoEgypt Scientific Research Group in Egypt(SRGE) Egypt

There has been a considerable increase in the use of drones,or unmanned aerial vehicles(UAVs),in recent times,for a wide variety of purposes such as security,surveillance,delivery,search and rescue operations,penetration of inaccessible or unsafe areas,*** increasing number of drones working in an area poses a challenge to finding a suitable charging or resting station for each drone after completing its task or when it goes low on its *** classical methodology followed by drones is to return to their pre-assigned charging station every time it requires a *** approach is found to be inefficient as it leads to an unnecessary waste of time as well as power,which could be easily saved if the drone is allotted a nearby charging station that is ***,we propose a drone-allocation model based on a preference matching algorithm where the drones will be allotted the nearest available station to land if the station is *** problem is modeled as three entities:Drones,system controllers and charging *** matching algorithm was then used to design a Drone-Station Matching *** simulation results of our proposed model showed that there would be considerably less power consumption and more time saving over the conventional *** would save its travel time and power and ensure more efficient use of the drone.

关键词： Unmanned Aerial Vehicle(UAV) stable matching resource allocation smart city matching algorithm

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Analyzing the Effects of Alpha Variation in Exponential Linear Unit Activation Function for Deep Neural Networks 15

Analyzing the Effects of Alpha Variation in Exponential Line...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Singh, Mahesh Kumar Mishra, Shreyansh Tiwari, Utkarsh Patel, Vikas Kumar Maurya, Sahil Department of Computer Science and Engineering Dronacharya Group of Institutions Uttar Pradesh GreaterNoida201306 India

ISBN: (纸本)9798350370249

This research paper immerses into the subtle effects of varying values within the Exponential Linear Unit (ELU) activation function, altering from 1 to 0.10. Activation functions take an important role in deep learning models, impacting their training dynamics, convergence behavior, and stereotypical capabilities. Focusing specifically on ELU, which describes advantages over conventional activation functions like ReLU, this study aims to interpret the inference of adjusting its alpha parameter. Through a meticulous relative investigation, this paper presents a comprehensive analysis of ELUactivation function with varying alpha values. Firstly, we demonstrate the relative performance of ELU activation with varying alpha values against other conventional activation functions. Secondly, we analyze the relationship between alpha values and training loss, manifesting how habituation in alpha influences the convergence behaviorof neural networks. Thirdly, we analyze the concussion onvalidation loss to determine the effect of alpha values on the model’s ability to establish unseen data. Moreover, we analyze the correlation between alpha values and all-inclusive model performance, considering measures such as accuracy, precision, and recall b y comprehensively. ©2024 IEEE.

关键词： Deep neural networks

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Anomaly Detection Classifiers for Detecting Credit Card Fraudulent Transactions 4

Anomaly Detection Classifiers for Detecting Credit Card Frau...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Singh, Prerna Singla, Khyati Piyush, Prince Chugh, Bharti Delhi-NCR Kiet Group of Institutions Department of Computer Science and Engineering Ghaziabad201206 India

ISBN: (纸本)9798350343670

The internet and e-commerce have grown quickly, which has increased credit card use but also, regrettably, increased credit card fraud. To address this, Anomaly Detection has emerged as a crucial method for identifying unusual events and data in datasets. It uses advanced algorithms to detect deviations from normal patterns, helping authorities proactively combat fraudulent activities. While digital advancements offer convenience, they also expose vulnerabilities. Anomaly Detection offers a modern defense, safeguarding financial systems by early spotting of anomalies. In this study, we employed two algorithms - the Isolation Forest (IF) and the Local Outlier Factor (LOF) for identification of anomalies. To improve the performance of these models, we also employed a variety of resampling strategies. Specifically, we used techniques like Random Undersampling, AllKNN, Synthetic Minority Oversampling Technique (SMOTE), and Synthetic Minority Oversampling Technique - Edited Nearest Neighbor (SMOTE-ENN) to balance the European Credit Card Fraudulent transactions dataset and the German Credit Card fraud dataset. Out of the different configurations, the Isolation Forest classifier demonstrated the highest accuracy, reaching 99.81%, when applied to the initially imbalanced European credit card fraudulence dataset. On the other hand, the German credit card dataset achieved a remarkable accuracy of 70.60% through the implementation of the LOF classifier, coupled with the Random Undersampling technique to address its imbalanced nature. © 2024 IEEE.

关键词： AllKNN Anomaly Detection Credit Card Fraud IF LOF Random Undersampling SMOTE SMOTE-ENN

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