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Adaptive solar power generation forecasting using enhanced neural network with weather modulation

Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 10955-10968页

作者： Sujeeth, T. Ramesh, C. Palwe, Sushila Ramu, Gandikota Basha, Shaik Johny Upadhyay, Deepak Chanthirasekaran, K. Sivasankari, K. Rajaram, A. Department of CSE Siddartha Educational Academy Group of Institutions Andhra Pradesh Tirupati India Department of Mechanical Engineering M. Kumarasamy College of Engineering Karur India Department of Computer Engineering and Technology MIT-WPU Maharashtra India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Telangana Hyderabad India Andhra Pradesh Mylavaram India Department of Computer Science and Engineering Graphic Era hill univeristy Dehradun India Department of Electronics and Communication Engineering Saveetha Engineering College Chennai India Department of Electronics and Communication Engineering Akshaya College of Engineering and Technology Coimbatore India Department of Electronics and Communication Engineering E.G.S. Pillay Engineering College Nagapattinam India

Solar power generation forecasting plays a vital role in optimizing grid management and stability, particularly in renewable energy-integrated power systems. This research paper presents a comprehensive study on solar power generation forecasting, evaluating traditional and advanced machine learning methods, including ARIMA, Exponential Smoothing, Support Vector Regression, Random Forest, Gradient Boosting, and Physics-based Models. Moreover, we propose an innovative Enhanced Artificial Neural Network (ANN) model, which incorporates Weather Modulation and Leveraging Prior Forecasts to enhance prediction accuracy. The proposed model is evaluated using real-world solar power generation data, and the results demonstrate its superior performance compared to traditional methods and other machine learning approaches. The Enhanced ANN model achieves an impressive Root Mean Square Error (RMSE) of 0.116 and a Mean Absolute Percentage Error (MAPE) of 36.26%. The integration of Weather Modulation allows the model to adapt to changing weather conditions, ensuring reliable forecasts even during adverse scenarios. Leveraging Prior Forecasts enables the model to capture short-term trends, reducing forecasting errors arising from abrupt weather changes. The proposed Enhanced ANN model showcases its potential as a promising tool for precise and reliable solar power generation forecasting, contributing to the efficient integration of solar energy into the power grid and advancing sustainable energy practices. © 2024 – IOS Press.

关键词： Solar power generation

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Artificial intelligence in physiological characteristics recognition for internet of things authentication

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Digital communications and Networks 2024年第3期10卷 740-755页

作者： Zhimin Zhang Huansheng Ning Fadi Farha Jianguo Ding Kim-Kwang Raymond Choo School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing100083China Faculty of Informatics Engineering Aleppo UniversityAleppoSyria Department of Computer Science Blekinge Institute of Technology37179KarlskronaSweden Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Effective user authentication is key to ensuring equipment security,data privacy,and personalized services in Internet of Things(IoT)***,conventional mode-based authentication methods(e.g.,passwords and smart cards)may be vulnerable to a broad range of attacks(e.g.,eavesdropping and side-channel attacks).Hence,there have been attempts to design biometric-based authentication solutions,which rely on physiological and behavioral *** characteristics need continuous monitoring and specific environmental settings,which can be challenging to implement in ***,we can also leverage Artificial Intelligence(AI)in the extraction and classification of physiological characteristics from IoT devices processing to facilitate ***,we review the literature on the use of AI in physiological characteristics recognition pub-lished after *** use the three-layer architecture of the IoT(i.e.,sensing layer,feature layer,and algorithm layer)to guide the discussion of existing approaches and their *** also identify a number of future research opportunities,which will hopefully guide the design of next generation solutions.

关键词： Physiological characteristics recognition Artificial intelligence Internet of things Biological-driven authentication

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A Deep Analysis on Security Aspects of Cloud Computing Ontologies 5

A Deep Analysis on Security Aspects of Cloud Computing Ontol...

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5th IEEE International Conference on Computing, Power, and communication Technologies, IC2PCT 2024

作者： Bisht, Girish Singh Gobi, N. Kapse, Vinod Mansiram Tula's Institute Department of Computer Science and Engineering Dehradun India Department of Computer Science and Information Technology Bangalore India Noida Institute of Engineering and Technology Department of Electronics and Communication Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350383522

The primary objective of this research is to get a comprehensive understanding of Cloud Computing ontologies, their applications, and their specific areas of interest. The proposed research methodology for this systematic review comprises a well-defined research topic, criteria for including and excluding studies, and a comprehensive search strategy to identify relevant source papers for the review. The chosen research is categorized into four main areas: elucidation of cloud resources and services, elaboration of cloud security, clarity of cloud interoperability, and understanding of cloud services discovery and selection. Utilizing ontologies to improve the security and smooth integration of cloud computing services uncovers various challenges and identifies areas for more research. © 2024 IEEE.

关键词： Cloud Computing Cloud service ontology systematic review

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Optimized ANN for LiFePO_(4) battery charge estimation using principal components based feature generation

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Green Energy and Intelligent Transportation 2024年第4期3卷 59-71页

作者： Chaitali Mehta Amit V.Sant Paawan Sharma Department of Computer Science and Engineering School of TechnologyPandit Deendayal Energy UniversityRaysanGandhinagar 382007India Department of Electrical Engineering School of Energy TechnologyPandit Deendayal Energy UniversityRaysanGandhinagar 382007India Department of Information and Communication Technology School of TechnologyPandit Deendayal Energy UniversityRaysanGandhinagar 382007India

Electric vehicles(EVs)have gained prominence in the present energy transition *** adoption of EVs necessitates an accurate State of Charge estimation(SoC)*** predictive SoC estimations with smart charging strategies not only optimizes charging efficiency and grid reliability but also extends battery lifespan while continuously enhancing the accuracy of SoC predictions,marking a crucial milestone in sustainable electric vehicle *** this research study,machine learning methods,particularly Artificial Neural Networks(ANN),are employed for SoC estimation of LiFePO4 batteries,resulting in efficient and accurate estimation *** investigation first focuses on developing a custom-designed battery pack with 12V,4 Ah capacity with a facility for real-time data collection through a dedicated hardware *** voltage,current and open-circuit voltage of the battery are monitored with computerized battery *** battery temperature is sensed with a DHT22 temperature sensor interfaced with Raspberry *** components are derived for the collected battery data set and analyzed for feature *** principal components were generated as input parameters for the developed *** Stopping for the ANN was also implemented to achieve faster convergence of the *** considering eleven combinations for ten different optimizers loss function is *** analysis of hyperparameter tuning and optimizer selection revealed that the Adafactor optimizer with specific settings produced the best results with an RMSE value of 0.4083 and an R2 Score of *** proposed algorithm was also implemented for two different types of datasets,a UDDS drive cycle and a standard cell-level *** results obtained were in line with the results obtained with the ANN model developed based on the data collected from the developed experimental setup.

关键词： Battery Feature engineering Principal component analysis Artificial neural networks Optimizers

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A Hybrid Deep Learning Model for Real Time Hand Gestures Recognition

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Intelligent Automation & Soft Computing 2023年第4期36卷 1105-1119页

作者： S.Gnanapriya K.Rahimunnisa Department of Information Technology Easwari Engineering CollegeChennai600089India Department of Electronics and Communication Engineering Easwari Engineering CollegeChennai600089India

The performance of Hand Gesture Recognition(HGR)depends on the hand *** helps in the recognition of hand gestures for more accuracy and improves the overall performance compared to other existing deep neural *** crucial segmentation task is extremely complicated because of the background complexity,variation in illumination *** proposed mod-iﬁed UNET and ensemble model of Convolutional Neural Networks(CNN)undergoes a two stage process and results in proper hand gesture ***ﬁrst stage is segmenting the regions of the hand and the second stage is ges-ture identiﬁ*** modiﬁed UNET segmentation model is trained using resized images to generate a cost effective semantic segmentation *** Central Processing Unit(CPU)utilization and training time taken by these models with respect to three public benchmark datasets are also *** is carried out with the ensemble learning model consisting of EfﬁcientNet B0,Efﬁ-cientNet B4 and ResNet *** on NUS hand posture dataset-II,OUHANDS and HGRI benchmark datasets show that our architecture achieves a maximum recognition rate of 99.07%through semantic segmentation and the Ensemble learning model.

关键词： Convolutional neural networks EfﬁcientNet ensemble learning ResNet semantic segmentation UNet

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Defaulter detection based on Spearman correlation with hyper tuned SVM classification for preventing non-performing assets occurring in bank

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Multimedia Tools and Applications 2024年 1-23页

作者： Mallik, Rajib Puri, Shalini Bhansali, Ashok Manikandan, R. Rahunathan, L. Agartala799046 India Department of Information Technology Manipal University Jaipur Rajasthan303007 India Department of Computer Engineering and Applications GLA University Uttar Pradesh Mathura281406 India Department of Electronics and Communication Engineering Panimalar Engineering College Tamil Nadu Chennai600123 India Department of Computer Applications Kongu Engineering College Tamil Nadu Perundurai Erode638060 India

A common problem in the banking industry is loan default prediction, which may help identify defaulters who are most likely to stop making payments on their loan payments. Additionally, this information may be used to change the terms and conditions of loans, set aside additional money to guard against potential losses, or even refuse or restrict loans to clients that present a high risk. Although there are numerous conventional techniques for extracting data from loan applications, the majority of these techniques appear to be underperforming, which results in numerous problematic loans. In this proposed model, the detection of defaulter based on Spearman rank correlation with hyper tuned SVM classification is designed for preventing non-performing assets occurring in the bank. For novelty of the model, the missing value replacement is done with the Classification and Regression Tree (CART), which is an imputation as well as classification model that can impute values at missing column based on replacing the different values place at missing place by attaining the best classification accuracy. Then the Z-score normalization and Spearman rank correlation coefficient has been used for normalizing the data and correlation based feature reduction helps to classify then model with low computational complexity. In this hyper tuned SVM model, the traditional SVM model based on grid search cross validation is used for tuning the SVM parameters to accurately detect the defaulter from preventing the non-performing assets occurring in the bank. For evaluating the performance of the proposed loan prediction model, the precision, specificity, accuracy, recall, and Error are 95%, 97%, 96%, 96% and 4%. According to this evaluations, the proposed design performs better than existing model. Thus, the loan defaulter detection using Spearman’s correlation and hyper tuned SVM classification prevents the Non-Performing Assets (NPA) occurring in the bank. © The Author(s), under exclusiv

关键词： CART Defaulter Hyper tuned SVM Loan Spearman rank correlation Z-score

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Evaluation of Reliability in Request-segregated Clouds through Remiadiations Predicting Hardware Failure Scheme

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Recent Patents on engineering 2025年第1期19卷 112-127页

作者： Sharma, Rohit Yadav, Vibhash Singh, Raghuraj Department of Computer Science and Engineering Dr. Ambedkar Institute of Technology for Handicapped U.P. Kanpur208024 India Department of Information Technology Rajkiya Engineering College Banda210201 India Department of Computer Science and Engineering Harcourt Butler Technical University Kanpur208002 India

Background: Cloud services have become a popular approach for offering efficient services for a wide range of activities. Predicting hardware failures in a cloud data center can minimize downtime and make the system more reliable and fault-tolerant. Objective: This research aims to analyze a predictive hardware failure model based on machine learning that anticipates the required remediations for undiagnosed failures in a cloud computing system serving multiclass requests. Methods: The model is tested on a carefully designed cloud data center that categorizes incoming requests as web, compute, storage, and dedicated server requests. To demonstrate improved reliability, a carefully designed test case is run on ReliaCloud-NS, which is a simulator for creating a CCS and computing its reliability. Results: The work found that using this model considerably enhanced the reliability of cloud computing systems when compared to not using the model. Conclusion: Although various estimation methods are patented to evaluate the system reliability of a cloud computing network, the emphasis of this study was mostly on improving the reliability of request-segregated clouds upon failing hardware resources like CPU, memory, bandwidth, and hard disc. Moreover, the prediction model might potentially be expanded to other system resources such as GPUs, software, and database packages. © 2025 Bentham Science Publishers.

关键词： Cloud platforms

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Enhancing Accuracy of Handwritten Character Segmentation Using Region-Based Convolutional Neural Networks (R-CNN) 1

Enhancing Accuracy of Handwritten Character Segmentation Usi...

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1st International Conference on AIML-Applications for engineering and technology, ICAET 2025

作者： Varpe, Kanchan Sakhare, Sachin Joshi, Rahul Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Department of Computer Science and Engineering Pune India

ISBN: (纸本)9798350355611

Handwritten character segmentation plays a pivotal role in the performance of Optical Character Recognition (OCR) systems. This paper introduces an innovative approach to enhancing segmentation accuracy using Region-Based Convolutional Neural Networks (R-CNN). Traditional methods often struggle with issues such as varying character sizes, overlapping, and inconsistent writing styles, leading to suboptimal recognition results. The proposed R-CNN model addresses these challenges by effectively localizing and segmenting individual characters from handwritten text images. The methodology involves training the R-CNN on a comprehensive dataset of handwritten characters, annotated to capture diverse styles and complexities. The model leverages deep learning's feature extraction capabilities and region-based localization to accurately segment characters, even in challenging scenarios like connected or touching characters. Comparative experiments show that the proposed approach outperforms conventional techniques in terms of segmentation accuracy and robustness. Results demonstrate significant improvements, with the R-CNN model achieving higher precision and recall rates across multiple handwritten datasets, including different languages and scripts. This advancement holds promise for enhancing OCR systems used in applications such as automated document processing and digital archiving. Future work will focus on integrating this segmentation method with advanced recognition models to further boost overall handwritten text recognition performance. © 2025 IEEE.

关键词： Convolutional neural networks

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Electricity Consumption Classification using Various Machine Learning Models

EAI Endorsed Transactions on Energy Web

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EAI Endorsed Transactions on Energy Web 2024年 11卷 1-6页

作者： Paikaray, Bijay Kumar Jena, Swarna Prabha Mondal, Jayanta Van Thuan, Nguyen Tung, Nguyen Trong Mallick, Chandrakant Bhubaneswar India Department of Electronics and Communication Engineering Centurion of University of Technology and Management Odisha India School of Computer Engineering KIIT Deemed to be University Bhubaneswar India Faculty of Engineering Technology Hung Vuong University Viet Nam Quality Assurance Dong A University Viet Nam Department of Computer Science & Information Technology GITA Autonomous College Odisha Bhubaneswar India

INTRODUCTION: As population has increased over successive generations, human dependency on electricity has increased to the point where it has become a norm and indispensable, and the idea of living without it has become unthinkable. OBJECTIVES: Machine learning is emerging as a fundamental method for performing tasks autonomously without human intervention. Forecasting electricity consumption is challenging due to the many factors that influence it;embracing modern technology with its heavy focus on machine learning and artificial intelligence is a potential solution. METHODS: This study employs various machine learning algorithms to forecast power usage and determine which method performs best in predicting the dataset based on different variables. RESULTS: Eight models were tested, including Linear Regression, DT Classifier, RF Classifier, KNN, DT Regression, SVM, Logistic Regression, and GNB Classifier. The Decision Tree model had the greatest accuracy of 98.3%. CONCLUSION: The Decision Tree model’s accuracy can facilitate efficient use of electricity, leading to both conservation of electricity and cost savings, and be a guiding light in future planning. Copyright © 2024 Paikaray, B.K. et al., licensed to EAI. This open-access article is distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transforming, and building upon the material in any medium so long as the original work is properly cited.

关键词： Forecasting

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Comparison of Geographically Weighted Regression (GWR) and Mixed Geographically Weighted Regression (MGWR) Models on the Poverty Levels in Central Java in 2023

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IAENG International Journal of Applied Mathematics 2024年第12期54卷 2746-2757页

作者： Alya, Najma Attaqiya Almaulidiyah, Qothrotunnidha Farouk, Bailey Reshad Rantini, Dwi Ramadan, Arip Othman, Fazidah Engineering Department Data Science Technology Study Program Universitas Airlangga Indonesia Engineering Department Data Science Technology Study Program Faculty of Advanced Technology and Multi-discipline Universitas Airlangga Indonesia Information System Study Program Department of Industrial and System Engineering Telkom University Surabaya Campus Indonesia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Malaysia

Poverty is considered a serious global issue that must be immediately eradicated by Sustainable Development Goals (SDGs) 1, namely ending poverty anywhere and in any form. As a developing country, poverty is a complex issue faced by Indonesia. Tackling poverty in Indonesia has become a key focus of the government’s strategic priorities. This issue of poverty is experienced by all provinces in Indonesia, including Central Java. The poverty rate in Central Java stands at 10.77% or 3.79 million people. The causes of poverty are explained by several factors. Using these various factors, a mapping model can be conducted to determine the number of poor populations in Central Java. This research employs Geographically Weighted Regression (GWR) and Mixed Geographically Weighted Regression (MGWR) approaches to compare the effectiveness of these models in analyzing the number of poor populations in Central Java in 2023. The kernel function weighting used in this study is Fixed Gaussian. The results showed that the MGWR model outperformed both the OLS regression and GWR models, achieving an Akaike information Criterion (AIC) of 62.766, an R2 of 82.3%, and a Mean Squared Error (MSE) of 0.177. Consequently, it can be inferred that the MGWR model was more suitable for explaining the poverty levels in Central Java. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Logistic regression

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