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Visual Analytics and Data Visualization (ICVADV), International Conference on

作者： Y M Blessy Raveendra N Amarnath D. Antony Joseph Rajan R Dillibai. S.T Aarthy. T. R. GaneshBabu Department of Electornics and Communication Engineering R.M.K. Engineering College Chennai Tamil Nadu India Mphasis Campbell Dr Melissa Texas USA Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Computer Science and Engineering Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

In this research, we provide a new real-time method for managing vehicle utilization patterns using cloud-enabled Bayesian networks (BN). Traditional solutions often employ static models and batch processing to account for the ever-changing nature of vehicle utilization. The objective is to use cloud-enabled BN to improve real-time vehicle utilisation monitoring, optimise fleet management, and make better decisions. To improve the precision and responsiveness of consumption predictions, businesses are increasingly turning to cloud computing, enabling continuous, real-time data stream processing. To weather, traffic patterns, and historical data, among other elements that impact vehicle utilization, BN provide a strong foundation for modelling complicated connections. To increase the accuracy of its adaptive use pattern forecasts, the system updates probabilistic associations in real time using incoming data. Fleet managers may optimize resource allocation, improve service delivery, and achieve overall operational efficiency via incorporating cloud computing, enabling scalable and efficient processing. In metropolitan areas, where use patterns might change quickly, this method helps manage vehicle fleets more adaptively and responsively. It showcases the practicality and advantages of our method in simulations and case studies, showing how it can change the game for fleet management techniques. It highlights the revolutionary power of BN offered by the cloud in improving the efficiency and responsiveness of fleet operations.

关键词： Cloud computing Adaptation models Adaptive systems Computational modeling Biological system modeling Transportation Real-time systems Bayes methods Resource management Vehicle dynamics

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A Novel Deep Learning Approach of BH & SH on MANET basis Traffic Prediction with Big Data 3

A Novel Deep Learning Approach of BH & SH on MANET basis Tra...

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3rd International Conference on Intelligent engineering and Management, ICIEM 2022

作者： Narasima Venkatesh, A. Mahaboob John, Y.M. Ananthi, P. Adusumalli, Balaji Manikumar, T. Pant, Bhasker ISBR Business School HRM and General Management Karnataka Bangalore India Mahendra College of Engineering Department of Electronics & Communication Engineering Tamil Nadu Salem India Kongu Engineering College Department of Computer Technology Tamil Nadu Erode India Guntur Engineering College Department of Computer Science & Engineering Andhra Pradesh Guntur India Madanapalle Institute of Technology & Science Department of MCA AP India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (数字)9781665467568

ISBN: (纸本)9781665467568

Intelligent transportation systems can't function well without reliable data on current traffic conditions. As the volume of traffic data has increased in the last several years, we've entered the era of big data in transportation. Current methods for estimating traffic flow have been let down many real-world applications, which depend mostly on shallow traffic prediction models. Therefore, we'll review the difficulty of predicting traffic flow using detailed architectural models and a vast quantity of actual traffic data. Methods for traffic flow forecasting that consider both spatial and temporal correlations are given in this article. A layered auto encoder model is used to teach traffic flow characteristics since it builds up greedy layer by layer. Deep architecture models for traffic flow prediction have never before used auto encoders as building blocks. Researchers have also shown that the recommended method for estimating traffic flow is more accurate. © 2022 IEEE.

关键词： Big data

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Improved ant colony algorithm based on multi-objective optimization control 10

Improved ant colony algorithm based on multi-objective optim...

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10th IEEE Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2022

作者： Li, Zhang Luo, Yixiang Qin, Jian Zhong, Yubin School of Mathematics and Information Science Guangzhou University Guangzhou China School of Computer Science and Network Engineering Guangzhou University Guangzhou China Guangzhou University Department of Electronics and Communication Engineering Guangzhou China Guangzhou University Department of Mathematics and Information Science Guangzhou China

ISBN: (数字)9781665422079

ISBN: (纸本)9781665422079

Aiming how to achieve a high-precision furnace temperature curve in the production process of electronic products, a double-objective optimization model is established, which combines heat conduction and heat convection, and transforms it into a single objective to solve the optimal furnace temperature curve and important parameters through dynamic comprehensive weighting. In this paper, an improved ant colony algorithm has been proposed to address the issue. The population initialization method based on good points is set to make the initial population distribution more uniform. The nonlinear control parameter is utilized to balance the global search and local development ability during different iteration periods. The Markov process is executed to get a wider range of solutions. Compared with other intelligent algorithms, the results show that the improved ant colony algorithm avoids local premature phenomenon, has higher convergence, and the obtained results are more accurate. Using this algorithm to solve the problem can better face the field of precise temperature control. © 2022 IEEE.

关键词： Temperature control

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Comparative Analysis of Dual-edge Triggered and Sense Amplifier Based Flip-flops in 32 nm CMOS Regime

Comparative Analysis of Dual-edge Triggered and Sense Amplif...

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Advances in Power, Signal, and Information Technology (APSIT), International Conference in

作者： Owais Ahmad Shah Shivangi Bansal Prashant Kumar Mavi Anupam Yadav Satvik Vats Zaiba Ishrat Department of Electronics & Communication Engineering Noida International University Gautam Budh Nagar India Department of Electronics & Communication Engineering IIMT College of Engineering Greater Noida India Department of Computer Science & Engineering Graphic Era Hill University Dehradun India

The article summarizes the outcomes of extensive research carried out on dual-edge triggered flip-flops (DETFFs) and sense amplifier based flip-flops (SAFFs) in relation to their power consumption, delay measurements and area requirements. The research focused on four of the most advanced flip-flops (FFs) currently available, and simulations were conducted in SPICE using 32 nm CMOS technology. The results showed that Lee's FF had better power efficiency at data activities of greater than 25% whereas Lapshev's FF could be used for circuits with less data activity. Jeong's FF had better speed efficiency at higher voltages. Lee's FF also had the best power delay product (PDP) followed closely by Jeong's FF at nominal conditions. Additionally, Jeong's FF outperformed other designs in terms of area overhead.

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Cloud-Based IoT Solutions for Smart Grids: Advancing Smart Technologies in Energy Management 3

Cloud-Based IoT Solutions for Smart Grids: Advancing Smart T...

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3rd IEEE International Conference on Electrical Power and Energy Systems, ICEPES 2024

作者： Antonius Alijoyo, Franciskus Vidhate, Deepak A. Kaur, Chamandeep Nivedan, V. Rani, V. Kanpur Balakumar, A. School of Business and Information Technology STMIK LIKMI Bandung Indonesia Dr. Vithalrao Vikhe Patil College of Engineering Department of Information Technology Maharashtra Ahmednagar India Jazan University Department of Computer Science Jazan Saudi Arabia Sri Ramakrishna College of Arts & Science Department of Electronics and Communication System Coimbatore India Hindustan Institute of Technology and Science Chennai India K.Ramakrishnan College of Engineering Department of ECE Trichy India

ISBN: (纸本)9798350390728

The incorporation of Internet of Things (IoT) cloud computing solutions with smart grids signifies a significant advancement in energy management. Traditional methods, though beneficial, often lack scalability and real-time capabilities, limiting their effectiveness in managing the complexity of modern energy systems. In this paper, a novel approach cloud-based IoT technologies to address these shortcomings and enhance smart grid functionalities. Existing energy management methods heavily rely on localized monitoring and control systems, constraining scalability and real-time responsiveness. Additionally, these methods struggle to handle the immense data generated by IoT devices dispersed throughout the grid. The proposed approach tackles these challenges by utilizing cloud computing to store, process, and analyze IoT sensor data in real-time. The novelty of this method lies in seamlessly integrating cloud-based platforms with IoT devices across the smart grid. Through utilising cloud resources, the limitations of traditional approaches are overcome, enabling scalable, real-time oversight and management of grid assets. The methodology involves deploying IoT sensors across the grid to compile information about energy usage, generation, and distribution, which is then transmitted to cloud servers for analysis and decision-making. Through simulations and real-world experiments, the effectiveness of this approach in optimizing grid performance, improving reliability, and facilitating demand-response programs is demonstrated. The results exhibit significant enhancements in grid efficiency and resilience, showcasing the potential of cloud-based IoT solutions to transform energy management in smart grids. Overall, this proposed method presents a promising pathway towards establishing more efficient, sustainable, and resilient energy infrastructure for the future. © 2024 IEEE.

关键词： Cloud platforms

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Cognitive Radio Networks: An AI Enabled Approach 9

Cognitive Radio Networks: An AI Enabled Approach

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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Prema, S. Solaimalai, Gautam Lalithambikai, S. Priya, S. Gayathri Sowmya, P.D. Thanikodi, Sathish Mahendra Engineering College Department of Information Technology Tamil Nadu Namakkal637503 India U.S. Bank GA30041 United States Knowledge Institute of Technology Department of Information Technology Tamil Nadu Salem637504 India R.M.D. Engineering College Department of Electronics And Communication Engineering Tamil Nadu Kavaraipettai601206 India Reva University Department of Computer Science & Applications Karnataka Bengaluru560064 India Saveetha School of Engineering Simats Department of Mechanical Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350365092

As the need for data transfer in wireless networks grows, cognitive radios are likely to meet a large part of that need. Cognitive radio nodes can sense what's going on around them, figure out what's best for using the radio spectrum by allocating resources in terms of time, frequency, and space, and then act on that information. AI and machine learning are often combined with cognitive radio to create smart and flexible sharing, which makes real-time operations more effective. This research talks about cognitive radio networks and their goals, problems, resources, and limits. There is a research into how learning affects cognitive radios and methods for AI and machine learning are shown. This section gives an outline of the newest research on how machine learning can be used in cognitive radios. A number of AI techniques are used to organize the literature review, such as neural networks, fuzzy logic, case-based reasoning, entropy, Bayesian analysis, Markov models, multi-agent systems, and artificial bee colony algorithms. Cognitive radio uses and possible learning barriers are also talked about. © 2024 IEEE.

关键词： Cognitive radio

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Sv-net: A deep learning approach to video based human activity recognition 11th

Sv-net: A deep learning approach to video based human activi...

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11th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2019, and 11th World Congress on Nature and Biologically Inspired Computing, NaBIC 2019

作者： Bhattacharya, Sukrit Shaw, Vaibhav Singh, Pawan Kumar Sarkar, Ram Bhattacharjee, Debotosh Department of Electronics and Communication Engineering SRM Institute of Science and Technology Chennai India Department of Computer Science and Engineering Jalpaiguri Government Engineering College Kolkata India Department of Computer Science and Engineering Jadavpur University Kolkata India

ISBN: (纸本)9783030493448

The automatic identification of physical activities performed by human beings is referred to as Human Activity Recognition (HAR). It aims to infer the actions of one or more persons from a set of observations captured by sensors, videos or still images. Recognizing human activities from video sequences is a much challenging task due to problems such as background clutter, partial occlusion, changes in scale, viewpoint, lighting, and appearance etc. In this paper, we propose a Convolutional Neural Network (CNN) model named as SV-NET, in order to classify human activities obtained directly from RGB videos. The proposed model has been tested on three benchmark video datasets namely, KTH, UCF11 and HMDB51. The results of the proposed model demonstrate improved performance over some existing deep learning based models. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Convolution

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An Intelligent Hybrid Algorithm for Smooth AUV Path Generation

An Intelligent Hybrid Algorithm for Smooth AUV Path Generati...

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Industrial electronics: Developments & Applications (ICIDeA), IEEE International Conference on-

作者： Paramjeet Singh Bhaskar Jyoti Talukdar Rudra Narayan Dash Bikramaditya Das Dept. of Computer Science and Engineering BPUT Rourkela Odisha India Dept. of Electronics and Telecommunication Engineering VSSUT Burla Odisha India School of Electrical Engineering KIIT Deemed to be University Bhubaneswar India Dept. of Electronics and Communication Engineering BPUT Rourkela Odisha India

ISBN: (数字)9798331533205

ISBN: (纸本)9798331533212

The development of effective multi-objective optimization strategies for Autonomous Underwater Vehicles (AUVs) operating in uncharted territories constitutes a considerable challenge. This endeavor necessitates the simultaneous minimization of travel time, reduction of path length, enhancement of navigational safety, and smoothing of trajectories. In light of these challenges, this research presents an innovative real-time path planning framework that seamlessly integrates Particle Swarm Optimization (PSO) with Simulated Annealing (SA), thereby enhancing AUV navigation within static environments. The PSO algorithm is utilized to generate optimal temporary waypoints across the scenarios examined, by balancing the crucial optimization parameters. Subsequently, an optimal path is formulated, informed by both the initial and terminal points. The efficacy of the proposed methodology is assessed through simulations conducted across three distinct maps. Each obstacle map demonstrates varying densities of obstacles. The outcomes of these simulations emphasize the robustness and adaptability of the proposed strategy, highlighting its ability to formulate optimal paths within complex underwater environments, irrespective of the arrangement of obstacles. The AUVs display remarkable agility in maneuvering through intricate underwater landscapes, adeptly avoiding debris and obstacles while preserving operational efficiency. This enhanced path planning technique not only elevates the responsiveness of the AUVs during critical missions—such as search and rescue operations and structural assessments—but also significantly bolsters their overall navigational safety. Through this research, we introduce a formidable tool for enhancing AUV capabilities in practical applications, ultimately facilitating more efficient underwater exploration and intervention.

关键词： Adaptation models Smoothing methods Navigation Simulation Urban areas Simulated annealing Trajectory Scattering parameters Collision avoidance Optimization

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Retraction Note: Design of precoder for a MIMO–NOMA system using Gaussian mixture modelling

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Optical and Quantum electronics 2024年第10期56卷 1-2页

作者： Markkandan, S Aggarwal, Kapil Ashok, K. Selvakumarasamy, K Kaushal, Rajanish Kumar Jadhav, Makarand Mohan School of Electronics Engineering (SENSE) Vellore Institute of Technology Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur India Department of Computer Science and Engineering New Horizon College of Engineering Bangalore India Department of Electronics and Communication Engineering Saveetha School of Engineering (SIMATS) Chennai India Department of Electrical Engineering Chandigarh University Mohali India Department of E & T C N B N Sinhgad Technical Institutes Campus Pune India

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Automated Classification of Chest CT Scans for Accurate Diagnosis and Risk Stratification: A Deep Learning Approach

Automated Classification of Chest CT Scans for Accurate Diag...

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computer, electronics & Electrical engineering & their Applications (IC2E3), International Conference on

作者： Deepak Upadhyay Mridul Gupta Devansh Yadav Abhay Upadhyay Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era deemed to be University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798350388534

ISBN: (纸本)9798350388541

The performance assessment metrics of the topic of classification models, such as recall, accuracy, and F1-score were considered in this work. We examined how many types of events our model categorizes well and view the more detailed metrics for every class. A special account of the precision-recall trade-off even shows that there is an undeniable advantage, and there may be a negative side. The outcomes also show how good the model is. For example, a perfect accuracy score in the 2nd Class demonstrates that the algorithm can differentiate occurrences of such a category. The weighted average metrics consider the fundamental dissimilarity in a group better. The model has an average 0.91 F1-score, indicating that it makes a balanced decision between accuracy and recall. While I can be proud of these results, I understand that the presented weaknesses realized that additional study of potential feature engineering and other model architectures remain. This experiment will be valuable for future research and usage in real-life.

关键词： Measurement Deep learning Accuracy Computed tomography Classification algorithms

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