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Optimizing solar radiation prediction: a novel SVM approach for renewable energy systems

International Journal of Information Technology (Singapore) 2025年 1-13页

作者： Basha, S. Sharief Rao, A. Nagaraja Department of Mathematics School of Advanced Science Vellore Institute of Technology Tamil Nadu Vellore 632014 India Department of Computational Intelligence School of Computer Science & Engineering Vellore Institute of Technology Tamil Nadu Vellore 632014 India

The global transition toward renewable energy sources, driven by fossil fuel depletion and environmental imperatives, has positioned solar power as a crucial sustainable alternative. This study presents an advanced methodological framework for solar radiation (SR) prediction utilizing Support Vector Machine (SVM) techniques. The research analyzes comprehensive solar radiation data collected from India’s Vellore district throughout 2023, implementing a novel Support Vector Regression (SVR) approach with both polynomial and radial basis function (RBF) kernels. While conventional methodologies predominantly focus on minimizing training errors, our approach emphasizes reducing the generalization error bound to enhance predictive performance. The proposed model incorporates diverse meteorological parameters for SR prediction, demonstrating superior generalization capabilities compared to traditional approaches. Empirical results indicate that the SVR_rbf model significantly outperformed both SVR_poly and artificial neural network (ANN) models in SR prediction accuracy. The model's efficacy is validated through rigorous performance metrics: RMSE (10.3032), MAE (4.2728), MAPE (0.0152), and R² (0.9795). This research advances the understanding of machine learning applications in renewable energy forecasting, particularly for regions with limited meteorological data availability. Research findings enhance solar energy utilization and contribute to the transition towards sustainable energy. Future research directions may explore the integration of additional environmental parameters and the model’s adaptability across diverse geographical contexts. © Bharati Vidyapeeth's institute of computer Applications and Management 2025.

关键词： Artificial neural network Predictive models Solar radiation Support vector regression

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Design of a Recommendation System for an Efficient and Optimized (RSU) Usage and Protect from External Influences

Design of a Recommendation System for an Efficient and Optim...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： J Chitra S.K PiramuPreethika Department of Computer Science Vels Institute of Science Technology & Advanced Studies (VISTAS)

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

In recent years, recommendation systems have become prevalent in various domains, such as multimedia streaming, e-commerce and social media, enhancing user experience through personalized content delivery. However, these systems are vulnerable to external influences that can distort recommendations and lead to biased results. This paper presents a novel recommendation system that optimizes the usage of Resource Sharing Units (RSUs) while protecting against external manipulation. The proposed model benefits both content providers and users by delivering accurate, preference-aligned recommendations without succumbing to external bias, thanks to advanced filtering mechanisms that detect and block potential interference. Furthermore, the system is optimized for efficient memory utilization, reducing computational load and enhancing performance. Extensive testing demonstrates its ability to resist external manipulation while maintaining optimal RSU usage offering a robust solution for personalized content delivery in manipulation-prone environments.

关键词： Filtering Social networking (online) Memory management Resists Streaming media User experience Security Resource management Recommender systems Testing

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Active pixel image sensor array for dual vision using large-area bilayer WS_(2)

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InfoMat 2024年第4期6卷 68-82页

作者： Arindam Bala Mayuri Sritharan Na Liu Muhammad Naqi Anamika Sen Gyuchull Han Hyun Yeol Rho Youngki Yoon Sunkook Kim School of Advanced Materials Science and Engineering Sungkyunkwan UniversitySuwonRepublic of Korea Waterloo Institute for Nanotechnology(WIN) University of WaterlooWaterlooOntarioCanada Department of Electrical and Computer Engineering University of WaterlooWaterlooOntarioCanada

Transition metal dichalcogenides(TMDs)are a promising candidate for developing advanced sensors,particularly for day and night vision systems in vehicles,drones,and security *** traditional systems rely on separate sensors for different lighting conditions,TMDs can absorb light across a broad-spectrum *** this study,a dual vision active pixel image sensor array based on bilayer WS2 phototransistors was *** bilayer WS2 film was synthesized using a combined process of radio-frequency sputtering and chemical vapor *** WS2-based thin-film transistors(TFTs)exhibit high average mobility,excellent Ion/Ioff,and uniform electrical *** optoelectronic properties of the TFTs array exhibited consistent behavior and can detect visible to near-infrared light with the highest responsivity of 1821 A W1(at a wavelength of 405 nm)owing to the photogating ***,red,green,blue,and near-infrared image sensing capabilities of active pixel image sensor array utilizing light stencil projection were *** proposed image sensor array utilizing WS2 phototransistors has the potential to revolutionize the field of vision sensing,which could lead to a range of new opportunities in various applications,including night vision,pedestrian detection,various surveillance,and security systems.

关键词： dual vision image sensors infrared wavelength detection phototransistors thin-film transistor arrays transition metal dichalcogenides

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Recognition of Deep Fake Voice Acoustic using Ensemble Bagging Model

Recognition of Deep Fake Voice Acoustic using Ensemble Baggi...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： Shaik Abdul Yunus Basha K.Ulaga Priya Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

As artificial speech generates technological evolution; fake voice information has become an increasingly prevalent avenue for fraud. Numerous investigations have been carried out into machine learning techniques, signal processing techniques, and the Automatic Speaker Verification System to address such issues. Since there are increasing methods to create fake information, techniques must be developed to determine when audio recordings employed as electronic proof are corrupted. Such recordings contain the potential to be manipulated. Hence, this research work identified the data regarding audio systems as either fake or real by i) extracting features from audio file metadata using oversampling techniques and ii) Developing an Ensemble Bagging Model appropriate for analyzing input audio signal followed by classification of fake (fraudulent) and real (authentic) audio. Our experimental results reveal that the ensemble bagging model attained an accuracy of 99.5%, precision of 99%, 99.6% recall, and 99.9% Fl-measure in the prediction of the audio system compared with traditional approaches.

关键词： Deepfakes Accuracy Audio systems Speech recognition Metadata Feature extraction Audio recording Real-time systems Fraud Bagging

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Deep Learning-Based Image Analysis to Track Vehicles 2

Deep Learning-Based Image Analysis to Track Vehicles

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Prabakaran, P. Jayasudha, R. Sandhu, Mukta Gupta, Sandeep School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Department of Computer Applications Chennai India Dr.N.G.P.Institute of Technology Department of Mathematics Coimbatore India Shri Vishwakarma Skill University Skill Department of Computer Science and Engineering Haryana Palwal India Department of Electrical Engineering Dehradun India

ISBN: (纸本)9798350371055

There are many ways to track the traffic conditions on roads. With the rise of AI-based image processing technology, there has been a surge in interest in developing traffic monitoring systems that rely on camera vision data. An improved road monitoring system may be derived from the data collected in this research by placing a camera at a junction. The technique detects and classifies vehicles using a deep-learning-based methodology (YOLOv4). By comparing the positions of the vehicle with the map HD, lane-by-lane trajectories of the vehicle may be approximated. The predicted vehicle trajectories are used to calculate each lane's wait lengths and traffic volumes, broken down by travel direction. This technique was evaluated using five distinct metrics: vehicle detection rates, trajectory prediction, type of vehicle categorization, traffic volume estimate, and queue length assessment. Thousands of samples were used to assess its performance. Based on the findings, it is acceptable to assume that the system can identify vehicles with a 98% success rate and an error rate of less than 25% regarding lane-by-lane trip volume estimation and vehicle type classification. Therefore, this study's approach reveals the viability of collecting comprehensive traffic information from a camera set up at a junction. Current traffic monitoring schemes have a reasonable likelihood of being developed by this study's significant contribution, which is a strategy that combines AI with HD map approaches. © 2024 IEEE.

关键词： Artificial Intelligence HD Map Traffic Monitoring System Vehicle Type Classification YOLOv4

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Tensorial Evolutionary Computation for Spatial Optimization Problems

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第1期5卷 154-166页

作者： Si-Chao, Lei Xiaolin, Xiao Yue-Jiao, Gong Yun, Li Jun, Zhang the School of Computer Science and Engineering South China University of Technology Guangzhou510006 China the Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen610056 China the School of Computer Science South China Normal University Guangzhou510006 China the Department of Electrical and Electronic Engineering Hanyang University Seoul15588 Korea Republic of

—Spatial optimization problems (SOPs) refer to a class of problems where the decision variables require spatial organization. Existing methods based on evolutionary algorithms (EAs) fit conventional evolutionary operators by flattening the spatial representations of SOPs to one dimension, which hence loses crucial spatial structures of the potential solutions. To address this issue, this article proposes a tensorial evolutionary algorithm (TEA) by formulating a tensor space of the SOPs, where the population of candidate solutions is represented as a third-order tensor. Accordingly, we design a tensor-product crossover operator and a two-phase mutation consisting of a local convolutional mutation operator and a global Gaussian mutation operator in the tensor space. These operators are all implemented based on tensor operations. Under the consideration of spatial context on individual solutions, we devise three types of evolutionary tensors, namely, the crossover tensor, the convolutional mutant tensor, and the Gaussian mutant tensor for the three tensorial evolutionary operators correspondingly. The resultant TEA is tested using two typical real-world SOPs, namely, the facility layout problem and the image steganography problem. Comparative experimental studies validate the effectiveness and robustness of TEA. © 2024 IEEE.

关键词： Evolutionary algorithms

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Financial Time Series Prediction on Apple Stocks Using Machine and Deep Learning Models 4

Financial Time Series Prediction on Apple Stocks Using Machi...

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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Saini, Agampreet Singh, Nisha Singh, Rahul Kumar Sachan, Manoj Kumar School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun India Sant Longowal Institute of Engineering and Technology Department of Computer Engineering Punjab Longowal India

ISBN: (纸本)9798350395914

The scope of financial time series prediction has evolved significantly, driven by the surging availability of data and computational advancements. This transition has seen the traditional statistical methods give way to present-day machine learning and deep learning techniques. Despite these strides, challenges continue to exist, with market unpredictability, dimensionality issues, and overfitting in high-frequency data standing as primary obstacles. Newer machine learning models are better than old ones at predicting stocks. When electronic trading and yield optimisation come together, they make these models even better. Deep learning has completely changed machine learning, especially in forecasting financial trends over time. Complex deep learning models lack interpretability, hindering real-world adoption. Considering this we have used two deep learning models which are Long Short-Term Memory and Convolutional Neural Network and trained these on apple stock market data over the years along with one machine learning model i.e. Linear Regression. Then we have compared the accuracy of all the models based on their R-squared Score value. © 2024 IEEE.

关键词： Adversarial machine learning

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State of the art object detection: A comparative study of YOLO and ViT

State of the art object detection: A comparative study of YO...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Rajput, Lucky Tyagi, Neha Tyagi, Shobha Tyagi, Dheerendra Kumar Amity University Department of Computer Science and Engineering Noida India Manav Rachna International Institute of Research and Studies Department of Computer Science and Engineering Faridabad India Gl Bajaj Institute of Technology and Management Department of Information Technology Greater Noida India

ISBN: (纸本)9798350375237

As trivial as it is for humans to identify, locate, and distinguish objects from their backgrounds and other objects, for computer systems, it is not so trivial. In computer science domain, the topic of Object detection addresses this issue. Object detection is one of the core tasks of computer vision, which lays the foundation for instance segmentation, semantic segmentation, panoptic segmentation etc. The last two decades have seen the tremendous evolution of object detection methods, from the Voila Jones detector to deep learning models to transformer-based models. The recent trends in the field are YOLO models and transformer-based models (such as DETR). We aim to explore object detection using YOLO and pure transformer ViT, do a comparative study, and furthermore, explore the theoretical approach of the hybrid model of YOLO and ViT. © 2024 IEEE.

关键词： Object detection

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SVM-RideNN: hybrid support vector machine-rider neural network based sentiment analysis using product review

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Australian Journal of Electrical and Electronics Engineering 2025年

作者： A S, Sridhar Nagasundaram, S. Department of Computer Applications Vels Institute of Science Technology and Advanced Studies Tamil Nadu Chennai India

Nowadays, shopping on online sites has become most mainstream for users. Moreover, the social media comments and reviews serve a huge role in online shopping. The satisfactions of users have also been improved with the utilization of sentiment analysis (SA). In addition, the sentiment analysis determines the digital text, in which the messages are positive, negative, or neutral. SA has difficulty in examining sentiments that are expressed in different languages, dialects, or colloquialisms. Moreover, it faces trouble with slang, abbreviations, and emojis. To bridge this gap, a hybrid Support Vector Machine Rider Optimization Neural Network (SVM-RideNN)-based sentimental analysis is proposed in this research. This method aims to examine the type and identification of text representation, and performance of sentiment analysis. The input product reviews are obtained from the acquired dataset, and the essential features are refined. From the refined features, the sentimental analysis process is done using the SVM-RideNN. In addition, the metrics, namely precision, recall, F1-score, and rouge, are employed to validate the performance of the model, and it has attained the finest output of 91.8%, 94.9%, 93.3%, and 86.9%. © 2025 Engineers Australia.

关键词： Support vector machines

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Enhancing Exercise Form: A Pose Estimation Approach with Body Landmark Detection 11

Enhancing Exercise Form: A Pose Estimation Approach with Bod...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Rishabh, K. Parasher, Jai Singh, Sanskar Sharma, Vaishali Dabas, Pratibha Vashisht, Saransh Dr. Akhilesh das Gupta Institute of Professional Studies New Delhi Department of Computer Science and Engineering India

ISBN: (纸本)9798350350357

As the integration of technology in fitness becomes increasingly prevalent, our study addresses the vital aspect of exercise form through the lens of pose estimation. Employing a body landmark detection model, we devised an algorithm to accurately identify key body landmarks and their correctness in posture during exercises. This paper presents a comprehensive exploration of our methodology, emphasizing the algorithmic estimation of user poses based on the detected landmarks. Our approach enables real-time monitoring of exercise forms and provides instant critique to users on the correctness of their posture. The applications extend beyond mere tracking, fostering an interactive environment where users actively engage in perfecting their form throughout their workout sessions. This paper culminates with discerning observations into the potential future developments, emphasizing the need for more detailed landmark detection models to identify intuitive landmarks. This research contributes to the evolving landscape of technology-assisted fitness, offering a promising avenue for personalized and effective exercise routines. © 2024 IEEE.

关键词： Sports

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