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5th International Conference on Trends in Electronics and Informatics, ICOEI 2021

作者： Anandkumar, V. Diwakaran, M. Sivabalan, K.N. Balakrishna, R. Sri Krishna College of Engineering and Technology Department of Information Technology Coimbatore India Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering India

ISBN: (纸本)9781665415712

The internet has become an integral part of our daily lives, and it is the medium that connects everyone on the planet, allowing individuals to exchange their opinions, knowledge, and experience. Because this entire network of individuals is connected via the internet, they might be referred to as the 'internet of people'. What if the diurnal devices use by humans can also share their experiences with other things, communicate with us and work according to our commands using the internet as the medium. This whole idea is being realized under Internet of Things (IOT) technology. So, all the IOT devices or networks are developed to attain the above functionality and they are also equipped with three different layers of technologies namely Sensor, Communication and computational or processing technologies. This integrated network of various technologies is being developed to sort out the key human adversities such as water wastage, pollution, depletion of energy resources, health issues etc., and this paves way to establish a balance between the growing population and resources. © 2021 IEEE.

关键词： Internet of things

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Federated Learning With Selective Knowledge Distillation Over Bandwidth-constrained Wireless Networks

Federated Learning With Selective Knowledge Distillation Ove...

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IEEE International Conference on Communications (ICC)

作者： Gad Gad Zubair Md Fadlullah Mostafa M. Fouda Mohamed I. Ibrahem Nei Kato Department of Computer Science Western University London ON Canada Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA Center for Advanced Energy Studies (CAES) Idaho Falls ID USA School of Computer and Cyber sciences Augusta University Augusta GA USA Graduate School of Information Sciences Tohoku University Sendai Japan

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Artificial Intelligence (AI) applications on Internet of Things (IoT) networks often involve relaying generated data to a server for deep learning training, which poses security risks to users' data. Federated Learning (FL) offers a distributed model training paradigm in which local data are kept at the edge and locally trained models are exchanged and aggregated by a server over several rounds to produce a global model. While successful, standard FL algorithms do not support heterogeneous local model design, an essential requirement, especially for resource-limited edge devices. Recently, Knowledge Distillation-based FL algorithms have provided model-agnostic FL to enable clients to independently design their local model and share soft labels instead of model parameters. KD-based FL algorithms are computationally expensive due to additional distillation training. We propose Federated Learning with Selective Knowledge Distillation (FedSKD) to address the limitations of system heterogeneity; and computation and communication demands. We evaluate different aspects of the proposed algorithm relative to baseline FL algorithms. Results show that FedSKD incurs significantly less per-round computation time and communication overhead relative to the considered model-based and KD-based FL algorithms.

关键词： Training Machine learning algorithms Federated learning Computational modeling Wireless networks Data models Servers

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Robust Domain Generalization for Multi-modal Object Recognition

arXiv

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arXiv 2024年

作者： Qiao, Yuxin Li, Keqin Lin, Junhong Wei, Rong Jiang, Chufeng Luo, Yang Yang, Haoyu Department of Computer Information Technology Northern Arizona University FlagstaffAZ United States Department of Computer Science AMA University Quezon Philippines Electrical Engineering & Computer Science Department Massachusetts Institute of Technology CambridgeMA United States Academy for Advanced Interdisciplinary Studies Peking University Beijing China Department of Computer Science The University of Texas at Austin AustinTX United States Department of Computer Science University of Southern California Los AngelesCA United States College of Computing Georgia Institute of Technology AtlantaGA United States

In multi-label classification, machine learning encounters the challenge of domain generalization when handling tasks with distributions differing from the training data. Existing approaches primarily focus on vision object recognition and neglect the integration of natural language. Recent advancements in vision-language pre-training leverages supervision from extensive visual-language pairs. This allows learning across diverse domains and enhances recognition in multi-modal scenarios, showcasing superior transfer learning capabilities in methods like CLIPood. However, CLIPood has several limitations: differences in the utilized loss, loss of generality in evaluating only a single backbone, and neglect of class-aware visual fusion. To address these, we propose this paper that infers the actual loss based on the implementation, broadens evaluations to larger vision-language backbones, and introduces Mixup-CLIPood with a novel mix-up loss for enhanced class-aware visual fusion. Copyright © 2024, The Authors. All rights reserved.

关键词： Visual languages

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Enhanced Prediction of Rheumatoid Arthritis Flares Through Ensemble Learning Methods

Enhanced Prediction of Rheumatoid Arthritis Flares Through E...

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International Conference on Advances in Information Technology (ICAIT)

作者： K. Priyadharshini S. Vinitha Sivaprakash S S. Kayalvili Thilakavathy. P M. Vasavi Department of Computing Technologies School of Computing and SRM Institute of Science and Technology Kattankulathur Campus Chennai India Department of Artificial Intelligence and Machine Learning Jerusalem College of Engineering Chennai School of Computer Science and Engineering Vellore Institute of Technology Vellore Department of Artificial Intelligence Kongu Engineering College Erode Computer Science and engineering Vels Institute of Science Technology and Advanced Studies Chennai ET department CVR College of Engineering Hyderabad

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

RA is an inflammatory, autoimmune disease with an unpredictable course, during which patients experience exacerbations (increases in symptoms) and its associated complications, such as deterioration of the joint and decreased quality of life. Tipping the exact point when remission is reached or RA flares is most essential for devising optimal treatment and enhancing patient's well-being. In this study, we put forward the usage of various types of ensemble learning algorithms aimed at improved the predictive power of RA flares. We use diverse data sources, including clinical data, medical imagery, genetic profiles and patient-reported data, and ensemble methods like Random Forest, Gradient Boosting, AdaBoost and Stacking processors to help us identify the patterns automatically. Thus ensembles of models are shown to be leading in performance, reaching superior accuracy, accuracy precision, recall rate, F1-score and area under the receiver operating characteristic curve (AUC-ROC). Feature importance analysis highlights the significant impact of clinical features which include the levels of CRP, ESR, age of the patient, duration of the disease on the likelihood of RA flares among the individual variants. The sensitivity analysis shows how well the ensemble model behaves under the generation of different model parameters and input data. The presented study emphasizes the immense facilitative ability of the ensemble methods of learning as predicting RA flares and this can also provide great information about the disease process and personalized medical decision-making. The areas for future research could be improved ensembles models, identification of novel feature selection approaches and validation of model performance in a variety of clinical settings. This will give the researchers an opportunity to implement the models in practice and make clinics more effective.

关键词： Analytical models Accuracy Sensitivity analysis Stacking Receivers Predictive models Prediction algorithms Data models Arthritis Ensemble learning

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Effect of Parasitic Leakage Currents Associated with the Regrown Aperture of AlGaN/GaN Current Aperture Vertical Electron Transistors (CAVETs)

Effect of Parasitic Leakage Currents Associated with the Reg...

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IEEE Electron Devices Technology and Manufacturing Conference (EDTM)

作者： Sara Pouladi Jae Hyun Ryou Department of Mechanical Engineering Texas Center for Superconductivity at UH Advanced Manufacturing Institute University of Houston USA Department of Electrical and Computer Engineering Materials Science and Engineering Program University of Houston USA

We present a numerical simulation on the effect of leakage paths in the regrown GaN layer in the aperture and above the current blocking layer (CBL) of current aperture vertical electron transistor (CAVET) devices. Here, a 2D TCAD modeling is employed to simulate a CAVET device structure considering two main origins of parasitic leakage current from CBL/regrown-GaN interface and gate/regrown-GaN bulk and their degree of detrimental effect on the characteristics of AlGaN/GaN CAVETs.

关键词： Performance evaluation Apertures Logic gates Numerical simulation Wide band gap semiconductors Numerical models Leakage currents

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Optimal Logistic Map with DNA Computing Model for Image Encryption

Optimal Logistic Map with DNA Computing Model for Image Encr...

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Computing and Data Analytics (ICCDA), International Conference on

作者： Shiny K.V Wilfred Blessing N. R Jino R Suresh Palarimath Mathumitha V Sutherlin Subitha G Department of Computer Science and Engineering Bharath Institute of Higher Education and Research Chennai India College of Computing and Information Sciences University of Technology and Applied Sciences-Ibri Ibri Sultanate of Oman Department of Civil Engineering Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai India Department of Computer Science and Engineering Stella Mary's College of Engineering Tamil nadu India

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542580

The logistic map with optimization with a novel Deoxyribonucleic Acid (DNA) sequence operation-based novel image encryption scheme is generated. The best mask is obtained by enhancing the excellence of DNA which is the significant advantage of this approach. According to the image decryption and encryption process, correct errors and the similarity of DNA sequences are decreased by the DNA cryptogram becoming an input pace for DNA calculation. The maximum PSNR of encrypted images is obtained by the logistic map function to improve the performance of image security Ant Lion optimization (ALO). The image encryption and decryption process generate optima point DNA sequence rules. DNA coding: utilizing the complementary rule, each nucleotide is then changed into its base pair for a random time or times; the times are produced by Chebyshev maps. The experimental investigations provided better UACI, Number of Pixel Exchange Rate (NPCR), and Peak Signal Noise Ratio (PSNR). This strategy is significant because it increases entropy, which is the fundamental property of randomness, withstands multiple statistical and differential attacks, and produces positive experimental outcomes.

关键词： Resistance Ciphers PSNR Noise Reliability theory Encoding Matrices Encryption Optimization Logistics

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What is Convergence?: A Systematic Review of the Definition of and Aspects of Convergent Work

What is Convergence?: A Systematic Review of the Definition ...

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Frontiers in Education (FIE) Conference

作者： Michael S. Thompson Alan Cheville Rebecca Thomas Sarah Applehans Stewart J. Thomas Robert Nickel Philip Asare Department of Electrical and Computer Engineering Bucknell University Lewisburg PA USA Department of Engineering Studies Lafayette University Easton PA USA Institute for Studies in Transdisciplinary Engineering Education and Practice University of Toronto Toronto ON CA

Wicked problems, the National Academy of engineering's Grand Challenges, the United Nations' Sustainability Goals, and similar complex, global-scale endeavors fall under the broad umbrella of “convergent” work. Over the past two decades there has been an increase in interest and funding for work in this space. The NSF has two programs focused in this area, Growing Convergence Research and the Convergence Accelerator. Boston University's College of engineering recently announced a focus on convergent projects and work. The National Academic of engineering also has the Grand Challenge Scholars program with over 100 participating schools. The list continues to grow. The broad concept of convergence seems to be quite simple: combine the ideas, skills, and/or methods of multiple disciplines to create something new. More specific definitions vary and while the interest in convergence and convergent problems continues to increase, there is no easily operational definition of convergence. This is especially true with respect to undergraduate-level education where students have limited experience and knowledge to carry out such efforts. To better understand the variation that exists within the literature on convergence we conducted a systematic review to explore how convergence is defined in scholarly literature. We have identified a small number of categories within the definition space and conducted a thematic analysis of the aspects of each. The results show that there is a fairly consistent focus on the work being socially-relevant and on creating something new such as an idea, method, product, or process to address desired needs. Additionally, doing convergent work requires the integration of aspects of multiple disciplines and is conducted by diverse teams. Lastly, the disciplinary backgrounds of those teams almost always includes the natural and biological sciences with a subset the following disciplines: information or computing sciences, engineering, social sciences

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Enhanced Demand-Side Management for Electric Vehicle Charging Stations Using Pyramidal Dilation Attention Neural Networks

Enhanced Demand-Side Management for Electric Vehicle Chargin...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： N. Venkateswaran Ramprabu J Ramaprabha Marimuthu Nagella Venkata Ramana Mahesh Kotha Balasubbareddy Mallala Department of Master of Business Administration Panimalar Engineering College Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Kumaraguru College of Technology Coimbatore Tamil Nadu India Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Management Studies Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh India Department of Computer Science and Engineering (AIML) CMR Technical Campus Hyderabad Telangana India Department of Electrical and Electronics Engineering Chaitanya Bharathi Institute of Technology Hyderabad Telangana India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

With the improvement in EV's popularity, controlling the load demand of EVCS's has become an essential task to keep the grid balanced and efficient. Conventional DSM techniques tend to rely on historic patterns to predict charging trends, hence cannot serve as viable solutions to peak load problems. In order to improve charging demand forecasting, this research presents a Pyramidal Dilation Attention Convolutional Neural Network (PDACNN) for the DSM of EV charging stations. By using precise forecasting and ideal charging schedule, this study aims to increase the effectiveness of DSM in EV charging stations. The PDACNN is used to predict the charging load for EV stations, allowing for optimized scheduling of charging sessions that align with grid capacity and energy availability. By then, the proposed method is implemented on MATLAB platform and evaluated with other existing methods such as Artificial Neural Network (ANN), Deep Reinforcement Learning (DRL), and Genetic Algorithm (GA). The proposed PDACNN method achieves an RMSE of 0.0753 and an accuracy of 98.9%, demonstrating its effectiveness in EV charging load forecasting and demand-side management.

关键词： Training Accuracy Demand side management Scalability Transportation Electric vehicle charging Mathematical models Convolutional neural networks Vehicle dynamics Genetic algorithms

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Adaptive Vehicle Control System for Improved Safety With Image Processing

Adaptive Vehicle Control System for Improved Safety With Ima...

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IEEE International Conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： N. Vijayalakshmi L. Priya S. Ramaraj Menakadevi Nanjundan Satheesh Kumar Palanisamy Ayodeji Olalekan Salau Sepiribo Lucky Braide Department of Computer Science and Engineering Alliance School of Advanced Compuitng Alliance University Bangalore Department of ECE Sri Eshwar College of Engineering (Autonomous) Kinathukadavu Coimbatore Tamilnadu India Department of Computer Science and Engineering Karpagam College of Engineering India Department of ECE Karpagam Institute of Technology Coimbatore Department of ECE BMS Institute of Technology and Management Bengaluru Karnataka India Department of Electrical/Electronics and Computer Engineering Afe Babalola University Ado Ekiti Nigeria Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electrical and Electronics Engineering Rivers State University Port Harcourt Nigeria

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

We propose the development of an Adaptive Vehicle Control (AVC) system using readily available components such as a Raspberry Pi microcontroller, a motor, a motor driver, and a Raspberry Pi camera. This system aims to enhance safety and convenience by automatically adjusting vehicle speed based on surrounding traffic conditions and recognizing relevant road signs like school zones, speed limits, and hospital zones. The process begins with real-time image capture using the Raspberry Pi camera module. These images are then analyzed using Python-based computer vision algorithms, primarily leveraging OpenCV. The image processing pipeline employs techniques such as object detection and template matching to identify specific road signs within the captured frames. Upon sign detection, the system interprets their meanings and determines the appropriate speed limits associated with them. Using the information from the detected signs and their corresponding speed limits, the system regulates the motor connected to the vehicle's throttle through GPIO pins on the Raspberry Pi. By adjusting the motor's speed, the AVC system ensures compliance with the specified speed limits across various road conditions. Safety measures are paramount, with fail-safe mechanisms implemented to prevent accidents in case of system errors or malfunctions. This AVC system offers a cost-effective and adaptable solution for integrating intelligent cruise control capabilities into vehicles. Leveraging the computational power of the Raspberry Pi and the versatility of image processing techniques, the system can adapt to diverse driving environments, thereby enhancing overall driving safety. This project contributes to the progression of smart transportation systems and lays the foundation for further advancements in autonomous driving technologies.

关键词： Adaptive systems Roads Pipelines Motors Cameras Real-time systems Safety Smart transportation Cruise control Vehicles

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Short-Term Load Forecasting Using GRU-LGBM Fusion

Short-Term Load Forecasting Using GRU-LGBM Fusion

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International Conference on Smart Communications and Networking (SmartNets)

作者： Shijon Das Mostafa M. Fouda Mohammad G. Abdo Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA Center for Advanced Energy Studies (CAES) Idaho Falls ID USA Idaho National Laboratory Idaho Falls ID USA

ISBN: (数字)9798350385328

ISBN: (纸本)9798350385335

The paper proposes an ensemble model of Light Gradient Boosting Machines (LGBM) and Gated Recurrent Units (GRU) for Short-Term Load Forecasting (STLF). The utilization of GRU's sequential learning combined with the ensemble power of LGBM is seen to outperform the traditional load forecasting approaches, which are not capable of capturing complicated load patterns. For predicting the load of the chosen geographic area, the time series of the load data is utilized along with the hourly weather data. The proposed ensemble model is applied to the hourly load data of the Western region from January 1, 2012, to December 31, 2015, by the Electric Reliability Council of Texas (ERCOT). The accuracy of the forecasting algorithm is seen to increase significantly after the addition of a holiday flag, day of the week, and time of the day index as categorical feature vectors.

关键词： Accuracy Recurrent neural networks Load forecasting Time series analysis Predictive models Data models Vectors

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