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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Sivakumar, S. Yadav, Mali Ram Bharathi, A. Akila, D. Dineshkumar, P. Banupriya, V. Vel Tech Rangarajan Dr.Sagunthala R And D Institute Of Science And Technology Department Of Electrical And Electronics Engineering Chennai India Lovely Professional University Department Of Physical Education Punjab India Vels Institute Of Science Technology And Advanced Studies Department Of Information Technology Pallavaram India Saveetha College Of Liberal Arts And Sciences Simats Department Of Computer Applications Chennai India Sona College Of Technology Department Of Information Technology Tamil Nadu Salem India M. Kumarasamy College Of Engineering Department Of Computer Science And Business Systems Karur India

ISBN: (纸本)9798350371567

This study investigates the use of RL-driven smart charging algorithms, particularly the Deep Deterministic Policy Gradients (DDPG) model, to maximise grid sustainability and battery lifetime. The current state of this field's study is lacking in real-time adaptation to different settings and in handling the ever-changing nature of charging profiles. By comparison, the suggested approach demonstrates a significant efficiency boost through the use of RL principles, which allow the model to learn and adjust its charging tactics. In specifically, the strategy contributes to a 10% improvement in total grid efficiency while cutting peak demand by 25%, minimising charge-discharge cycles by 20%, and reducing battery degradation rates by 15%. Not only do these measurements show that the suggested method works, but they also show how unique it is in offering a smart charging solution that is both sensitive and adaptive. This research fills in the gaps left by other studies and proposes an optimisation technique that is more dynamic and works in real-time. Its influence on the health of individual batteries and the sustainability of the grid as a whole is noteworthy. © 2024 IEEE.

关键词： Reinforcement learning

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Smartphone recommendation using popularity and collaborative filtering-based models

Smartphone recommendation using popularity and collaborative...

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International Conference on Recent Trends in Advance Computing (ICRTAC)

作者： Aniket Kumar Paul Trilok Nath Pandey Aswathi Manoj Kumar Savio Sajan Moloparambil Surya Kartheek Suraj Singh School of Computer Science and Engineering Vellore Institute of Technology Chennai Tamil Nadu India

With so many options available in the smartphone market, the question of which model is to be purchased, arises. The model proposed in our article will be trained with large datasets which can then suggest the perfect model with a given set of inputs without any hassle to search for hours on the internet in order to decide which phone is best suited to the customer in accordance with his/her needs. In this paper, it should be assumed that "Users" and "Reviewers" are used interchangeably in this paper as it is assumed that phone reviewers and users are the same. Therefore, our smartphone's recommendation system is designed to create a system that can suggest the best suited smartphones based on the choices or behaviors of the consumers.

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BER Improvement of Cooperative Spectrum Sharing of NOMA in 5G Network 3

BER Improvement of Cooperative Spectrum Sharing of NOMA in 5...

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3rd IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer engineering, MI-STA 2023

作者： Hassan, Mohamed Saeed, Rashid A. Mokhtar, Rania A. Hamid, Khalid Ali, Elmustafa Sayed Odeh, Nidhal Singh, Manwinder Khalifa, Othman O. Islam, Shayla Lovely Professional University Department of Wireless Communication Punjab Phagwara144001 India Taif University Department of Computer Engineering College of Computers and Information Technology Taif21944 Saudi Arabia University of Science & Technology Department of Communication Systems Engineering Omdurman Sudan Department of Electronics Engineering Khartoum Sudan University of Wollongong School of Electrical Computer and Telecommunications Engineering Australia International Islamic University Malaysia Department of Electrical and Computer Engineering Gombak Malaysia Libyan Center for Engineering Resarch and Information Technology Bani Waleed Libya Institute of Computer Science and Digital Innovation Ucsi University Malaysia Department of Computer Science Kuala Lumpur56000 Malaysia

ISBN: (纸本)9798350319897

Non-orthogonal multiple access is one of the best methods for addressing the needs of 5G wireless services (NOMA). The proliferation of mobile devices in recent years has increased the importance of cooperative spectrum sharing and utilization in wireless communication due to increases in the bit error rate (BER) brought on by collisions and interference. In this study, MIMO and Massive MIMO (M-MIMO) in the downlink (DL) NOMA power domain (PD) in conjunction with the Cooperative Cognitive Radio Network are proposed as two new ways for improving and evaluating BER in the 5G network (CCRN). Customers of NOMA compete for available channels on the CCRN in this first strategy. The second approach creates a dedicated channel for NOMA users. Three scenarios with varying distances, power location coefficients, and transmission power are used to evaluate the proposed methods in the MATLAB software program. It is assumed that four users will share a 90 MHz BW using QPSK modulation in all three scenarios. When evaluating the performance of the suggested system under the presumption of frequency-selective Rayleigh fading, concurrent interference cancellation (SIC) and unstable channel conditions are also considered. © 2023 IEEE.

关键词： 5G mobile communication systems

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Machine learning tool for exploring sentiment analysis on twitter data

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Materials Today: Proceedings 2022年 56卷 1927-1934页

作者： Biradar, Shanta H. Gorabal, J.V. Gupta, Gaurav Department of Information Science & Engineering Sir M Visvesvaraya Institute of Technology Bengaluru562157 India Department of Computer Science & Engineering ATME College of Engineering Mysore570028 India Yogananda School of AI Computing and Data Science Shoolini University Himachal Pradesh Solan India

In social media, micro-blogging is a common routine of most people around the world. Social media analytics gathering structure and unstructured Big-Data from various social sites and analyzing to make business decisions using Apache Hadoop and Apache Hive respectively. The objective of the research work was to develop big data technology used for gathering and handling large unstructured data from real time social media for sentiment analysis for identifying the brand and services. The methodology devised an algorithm based on sentimental analysis using customers review classification, which dealt with prepossessing the datasets, clustering of the data based on the specific domains, feature vector using n-gram models and tf-idf vectors extracts synonyms and classification sentiment analysis. The result shows that applied sentimental analysis with unsupervised clustering of data into specific domains and supervised machine learning techniques handle large amounts of twitter data in an efficient way. The developed tool 1.5 times faster than that of traditional database to Hadoop cluster and also the accuracy is nearly 80 %, which helps the user in computing, analyzing and interpreting interaction and associations between people, topics and ideas. © 2021

关键词： Sentiment analysis

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PlantEIRNet: Plant Leaf Disease Detection Deep Neural Networks Using Farmland Images

PlantEIRNet: Plant Leaf Disease Detection Deep Neural Networ...

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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： S. Geetha S. Anubha Pearline Karmabir Chakarborty School of Computer Science and Engineering Vellore Institute of Technology Chennai campus Chennai India

The productivity and quality of crops are greatly influenced by various diseases and pests. These are one of a kind of natural disasters which affect the natural growth of plants and can even cause the death of the plants ultimately affecting the yield of the crops. Detection of these diseases is henceforth really important. Such plant disease detected via image processing or object detection techniques. Deep Learning techniques are highly used for these kinds of tasks since they significantly outperform the conventional approaches. This research paper proposed an enhanced Inception ResNet-V2 for detecting plant diseases and is called as PlantEIRNet. Also, various state-of-the-art convolutional neural networks such as InceptionV3, ResNet-50, VGG-16, AlexNet, MobileNet-V3 are compared with the proposed method. Plant diseases are detected inspite of real-world settings such as illumination, occlusion and varied viewpoints. Three datasets namely, PlantLD-11 dataset, PlantVillage dataset and FusedPD dataset are assessed for their performance using PlantEIRNet. The proposed enhanced Inception ResNet-V2 outperformed compared to other Convolutional Neural Networks such as Inception V3, ResNet50, VGG-16 and Inception ResNet-V2.

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Wireless Federated Learning With Hybrid Local and Centralized Training: A Latency Minimization Design

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IEEE Journal on Selected Topics in Signal Processing 2023年第1期17卷 248-263页

作者： Huang, Ning Dai, Minghui Wu, Yuan Quek, Tony Q. S. Shen, Xuemin State Key Lab of Internet of Things for Smart City China University of Macau Department of Computer and Information Science China University of Macau State Key Laboratory of Internet of Things for Smart City The Department of Computer and Information Science China Zhuhai Um Science and Technology Research Institute Zhuhai519031 China Singapore University of Technology and Design Information Systems Technology and Design Pillar 487372 Singapore National Cheng Kung University Taiwan University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

Wireless federated learning (FL) is a collaborative machine learning (ML) framework in which wireless client-devices independently train their ML models and send the locally trained models to the FL server for aggregation. In this paper, we consider the coexistence of privacy-sensitive client-devices and privacy-insensitive yet computing-resource constrained client-devices, and propose an FL framework with a hybrid centralized training and local training. Specifically, the privacy-sensitive client-devices perform local ML model training and send their local models to the FL server. Each privacy-insensitive client-device can have two options, i.e., (i) conducting a local training and then sending its local model to the FL server, and (ii) directly sending its local data to the FL server for the centralized training. The FL server, after collecting the data from the privacy-insensitive client-devices (which choose to upload the local data), conducts a centralized training with the received datasets. The global model is then generated by aggregating (i) the local models uploaded by the client-devices and (ii) the model trained by the FL server centrally. Focusing on this hybrid FL framework, we firstly analyze its convergence feature with respect to the client-devices' selections of local training or centralized training. We then formulate a joint optimization of client-devices' selections of the local training or centralized training, the FL training configuration (i.e., the number of the local iterations and the number of the global iterations), and the bandwidth allocations to the client-devices, with the objective of minimizing the overall latency for reaching the FL convergence. Despite the non-convexity of the joint optimization problem, we identify its layered structure and propose an efficient algorithm to solve it. Numerical results demonstrate the advantage of our proposed FL framework with the hybrid local and centralized training as well as our proposed alg

关键词： information management

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Enhancing Credit Card Application Approval through Data Scaling in Machine Learning Algorithms

Enhancing Credit Card Application Approval through Data Scal...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： Rajeev Sekar Christopher Columbus C School of Computer Science and Engineering Vellore Institute of Technology Chennai Tamil Nadu India

In today's fast-paced world, financial transactions have undergone a remarkable transformation, and at the forefront of this revolution stand the credit card. Over the past few years, the usage of credit cards has become ingrained in our day-to-day lives. Many financial institutions, such as banks, issue credit cards in response to applications. However, these applications need to undergo verification before approval to prevent fraudulence and potential losses for the institutions. Automation of the credit card application approval process is being increasingly adopted by many financial institutions as it saves time and mitigates the involvement of human bias. Nevertheless, challenges related to the dataset, such as missing values, inconsistency and numerical noise persists. If not adequately addressed, these issues can adversely impact the performance of Machine Learning (ML) algorithms. Data preprocessing plays a crucial role in enhancing the model's performance and involves essential steps like data conversion, feature scaling, feature selection and reduction. These steps significantly contribute to increasing the prediction accuracy. The proposed work aims to compare various traditional ML algorithms concerning different scaling methods. The results demonstrate that the proposed scaling methods (MinMaxScaler, MaxAbsScaler, StandardScaler, RobustScaler) outperform existing approaches, showing promise for the automated credit card application approval process.

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Enhanced Metaheuristics with Trust Aware Route Selection for Wireless Sensor Networks

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computer systems science & engineering 2023年第8期46卷 1431-1445页

作者： A.Francis Saviour Devaraj T.Satyanarayana Murthy Fayadh Alenezi E.Laxmi Lydia Mohamad Adzhar Md Zawawi Mohamad Khairi Ishak Department of IT The University of Technology and Applied Sciences-IbriPO Box:466IbriPostal Code:516Sultanate of Oman Chaitanya Bharathi Institute of Technology HyderabadTelanganaIndia Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains MalaysiaNibong Tebal14300PenangMalaysia

Recently,a trust system was introduced to enhance security and cooperation between nodes in wireless sensor networks(WSN).In routing,the trust system includes or avoids nodes related to the estimated trust values in the routing *** article introduces Enhanced Metaheuristics with Trust Aware Secure Route Selection Protocol(EMTA-SRSP)for *** presented EMTA-SRSP technique majorly involves the optimal selection of routes in *** accomplish this,the EMTA-SRSP technique involves the design of an oppositional Aquila optimization algorithm to choose safe routes for data *** the clustering process,the nodes with maximum residual energy will be considered cluster heads(CHs).In addition,the OAOA technique gets executed to choose optimal routes based on objective functions with multiple parameters such as energy,distance,and trust *** experimental validation of the EMTA-SRSP technique is tested,and the results exhibited a better performance of the EMTA-SRSP technique over other approaches.

关键词： Security wireless sensor networks trust factor routing protocol privacy

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Framework for Integration of Generative AI into Metaverse Asset Creation

Framework for Integration of Generative AI into Metaverse As...

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Intelligent Metaverse Technologies & Applications (iMETA), International Conference on

作者： Rio Mukhtarom Paweroi Mario Köppen Department of Computer Science and Networks Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Japan

ISBN: (数字)9798350351514

ISBN: (纸本)9798350351521

Metaverse, a virtual world, is developing rapidly and is widely used in multi-sector. The number of users is projected to increase year over year. Due to the development of the metaverse platform, digital asset creation is demanding. Creating high-quality and diverse 3D digital objects is challenging. This study proposes the frameworks for integrating generative AI to create diverse 3D assets into the metaverse. We study different approaches for asset creation, i.e., generative 3D model-based, generative image projection-based, and generative language script-based. Creators can use this workflow to optimize the creation of 3D assets. Moreover, this study compares the results of generative AI and procedural generation on generating diverse 3D objects. The result shows that generative AI can simplify 3D creation and generate more diverse objects.

关键词： Solid modeling Three-dimensional displays Procedural generation Generative AI Metaverse Complexity theory Usability

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Self-Optimizing Distributed Cloud Computing with Dynamic Neural Resource Allocation and Fault-Tolerant Multi-Agent systems 4

Self-Optimizing Distributed Cloud Computing with Dynamic Neu...

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4th International Conference on Ubiquitous Computing and Intelligent information systems, ICUIS 2024

作者： Tripura, N. Divya, P. Chaganti, Koushik Reddy Rao, Kota Venkateswara Rajyalakshmi, P. Naresh, P. Vignan Institute of Technology And Science Dept of Mba Hyderabad India University of the Cumberlands Dept. of Computer and Information Systems KY United States Andhra Loyola Institute of Engineering and Technology Dept of Cse Vijayawada India Tkr College of Engineering and Technology Dept of Cse Hyderabad India Dayananda Sagar University Dept of Cse Bangalore India

ISBN: (纸本)9798331529635

The exponential growth of distributed cloud systems necessitates an intelligent form of workload management that ensures optimum performance, scalability, and reliability. Traditional approaches based on static or heuristic resource allocation and fault-tolerance mechanisms cannot capture the dynamic nature of modern workloads and heterogeneous infrastructures. Drawing from this source of inspiration, this work develops a holistic framework integrating machine learning models that facilitate self-optimizing distributed cloud computing for dynamic workload management. Three components are significant in the proposed framework: (1) DNRA;this uses a hybrid GNN and RL architecture for real-time resource allocation, offering up to 25% improvement in resource utilization and a 30% reduction in task latency. (2) Workload-Aware Transfer Learning Scheduler (WATLS), leveraging transfer learning to predict workload dynamics across heterogeneous nodes, achieving 20% higher task throughput and 15% energy savings through proactive scheduling. (3) Multi-Agent Predictive Fault-Tolerant System (MAP-FTS), combining multi-agent systems with predictive fault detection to preemptively redistribute tasks and schedule maintenance, reducing system downtime by 50% and enhancing fault detection accuracy to 90%. The framework integrates novel methodologies towards answering the main challenges of resource allocation, workload prediction, and fault tolerance. The amount of progress achieved through cumulative contributions includes improvement in system reliability and scalability up to 5x their nodes and respective scalable energy efficiency without any degradation of performance. This work paves the pathway for an adaptive and intelligent next generation of distributed cloud systems. © 2024 IEEE.

关键词： Distributed cloud

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