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A CNN_LSTM_KAN Based Genetic Algorithm for Photovoltaic Power Generation Revenue Prediction 25th

A CNN_LSTM_KAN Based Genetic Algorithm for Photovoltaic Pow...

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25th international conference on Parallel and Distributed computing, Applications and technologies, PDCAT 2024

作者： Ma, Ruikang Lin, Guanyu Dong, De Zhou, Rong Fan, Jianping Duan, Keliang Zhang, Jun Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China University of Chinese Academy of Sciences Beijing China Southern University of Science and Technology Guangdong Shenzhen China National Supercomputing Center in Shenzhen Guangdong Shenzhen China Beijing JH Eco-Energy Technology Co. Ltd. Beijing100160 China Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of Sciences Ürümqi China

ISBN: (纸本)9789819642069

In order to improve the economic benefits of photovoltaic power generation systems, accurate prediction of photovoltaic power generation revenue has become particularly important. However, traditional prediction algorithms such as CNN and LSTM suffer from overfitting issues, while the latest emerging KAN neural network also has problems with parameter tuning difficulties and sensitivity to data quality. Based on these issues, this paper proposes a CNN_LSTM_KAN photovoltaic power generation revenue prediction algorithm based on genetic algorithm. The experimental results show that the algorithm outperforms traditional methods in both prediction accuracy and robustness, providing an effective solution for predicting the revenue of photovoltaic power generation systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： genetic algorithm photovoltaics predictive algorithm

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Ensuring data Integrity in Multi-Tenant Cloud Environments Through Advanced Testing

Ensuring Data Integrity in Multi-Tenant Cloud Environments T...

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advances in Computer science, Electrical, Electronics, and Communication technologies (CE2CT), international conference on

作者： Vinay Kumar Gali Sansar Singh Chauhan Gopal Kumar Gupta Padmini Rajendra Bulani Vardhansinh Yogendrasinnh Ravalji Kumaresan Durvas Jayaraman Nagarjuna University Guntur Andhra Pradesh India Department of Computer Science and Engineering G.L. Bajaj Institute of Technology and Management Greater Noida India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Institute of Chartered Accountants of India Northeastern University Boston MA USA Bharathidasan University Tiruchirappalli Tamil Nadu India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

fortunately, multi-tenant cloud environments offer a more cost-effective provisioning model that consumes infrastructure to store and access data. As the volume of sensitive data processed and stored in the cloud continues to grow, ensuring data integrity has become a top concern for organizations. data integrity is the information that is correct, consistent, and reliable is stored in the cloud. To address this issue, we also present a solution that allows us to maintain integrity in a multi-tenant cloud environment using advanced testing. The standard approach to testing (functional and performance testing) does not identify and will enable us to prevent data integrity problems. That's because these approaches describe the system's functionality and performance rather than the accuracy of the system's data. They proposed a solution that used techniques like anomaly detection, data profiling, and data quality checks to discover possible integrity issues in the dataset. The new techniques work within the testing process, while all data is assessed for integrity before being stored in the cloud.

关键词： Access control Cloud computing data integrity Disasters Standards organizations Authentication Organizations Cryptography Reliability Testing

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Perception-Centric Explainable AI: Bridging Cognitive Theories and HCI Design for Enhanced User Experience 16

Perception-Centric Explainable AI: Bridging Cognitive Theori...

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16th international conference on Ambient Systems, Networks and technologies Networks, ANT 2025 / 8th international conference on Emerging data and Industry 4.0, EDI40 2025

作者： Alhasan, Sara Alnanih, Reem Computer Science Department Faculty of Computing and Information Technology King Abdulaziz University Jeddah 21589 Saudi Arabia

Artificial Intelligence (AI) systems increasingly influence high-stakes decision-making, necessitating explainability tailored to non-technical users. This research investigates integrating cognitive theories, Human-Computer Interaction (HCI) principles, and Explainable AI (XAI) techniques to enhance user perception, a core cognitive function. A perception-focused framework was developed and tested using a medical symptom checker tool. The study involved 20 participants divided into control and experimental groups. The experimental group used an enhanced tool with features like visual hierarchy and consistency, while the control group used a basic version with minimal explainability. Participants completed pre-test, testing, and post-test phases, including surveys and objective tasks measuring recognition and recall. Results showed that 90% of the experimental group achieved "Good Performance" in recognition tasks compared to 20% in the control group. Subjective feedback indicated significantly higher un-derstandability, with an average rating of 4.8 out of 5 for the enhanced tool versus 3.1 for the control. These findings underscore the effectiveness of combining HCI principles and cognitive theories, such as Gestalt and Feature Integration, in reducing cognitive load and improving clarity. This multidisciplinary approach advances the design of intuitive, user-centered AI systems, fostering trust and accessibility. © 2025 Elsevier B.V.. All rights reserved.

关键词： Cognitive Functions Cognitive Theories Explainable Artificial Intelligence (XAI) Human-Centered XAI Human-Computer Interaction (HCI) Medical Diagnosis Tool

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Integrating Big data Analytics in Soil Quality Assessment: Techniques, Challenges, and Applications

Integrating Big Data Analytics in Soil Quality Assessment: T...

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Intelligent and Innovative technologies in computing, Electrical and Electronics (IITCEE), international conference

作者： Shehu Mohammed Arun Singh Md Abu Hanif Department of Computer Applications Lovely Professional University Phagwara India Department of Computer Science and Engineering Lovely Professional University Phagwara India Department of Computer Science Graduate Center The City of New York (CUNY) New York NY USA

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

This review paper investigates the application of Big data analytics, focusing on soil quality assessment, with an emphasis on the innovative benefits offered by modern data-driven techniques for improved soil management. With world food demands rising, the importance for real-time and exact soil quality monitoring has increased. Although still useful, traditional approaches to modeling are limited in their ability to capture the spatial and temporal variability of soil properties. Big data analytics sits as the answer to the above by facilitating extensive and large volume data processing resulting in better, on-demand and scalable soil quality assessment. In the paper, we describe machine learning algorithms, remote sensing and geospatial analysis as key techniques to mine soil data at scales and resolutions unprecedented so far. We also critically evaluate the challenge of data integration (heterogeneity, scalability and multi-disciplinarity). These analytics are scalable for assessment and decision-making in precision agriculture, sustainable land management and environmental conservation. Thus, this paper synthesizes current research and practical implications in support of the need for Big data analytics within soils and soil sciences to cover also future challenges. Future research directions are also highlighted, including the importance of improved data acquisition and algorithmic advances as well greater synergy between Big data and conventional soil science practices..

关键词： Precision agriculture Technological innovation Scalability Decision making Transforms Soil Big data Quality assessment Environmental management Remote sensing

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Skin Cancer Detection Using A Multi-Scale AI Deep Learning Approach

Skin Cancer Detection Using A Multi-Scale AI Deep Learning A...

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advances in Electrical, computing, Communication and Sustainable technologies (ICAECT), international conference on

作者： Narenthirakumar Appavu Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamilnadu India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

Skin cancer occurrences are increasing, and melanoma is the deadliest type of the disease. Preventing the illness from spreading to other regions of the body requires early identification. In this work, an automated technique for identifying skin cancer without the need for physician assistance is presented. The suggested method for detecting melanoma lesions makes use of transfer learning. To reduce lighting effects and noise, the acquired photos are first pre-processed. By creating more training examples, image augmentation—a crucial deep learning technique—is then used to improve the dataset and raise the training process’ efficacy. Then, using transfer learning, a Convolutional Neural Network (CNN) is built using pre-learnt weights from the ImageNet dataset. The network is then adjusted to prioritise the detection of melanoma and differentiate it from benign tumours. Each of the three main phases in the method's processing of image data focusses on the central area of the picture where the suspicious lesion is located. The results from these processes are aggregated and used to train a fully connected neural network. The suggested solution outperformed other cutting-edge techniques, as seen by the 90.09% accuracy attained in testing on a collection of photos. Through concentrating on the questionable skin region at different phases, the method offers a more precise and trustworthy diagnosis.

关键词： Deep learning Training Accuracy Transfer learning Melanoma data augmentation Skin Lesions Convolutional neural networks Testing

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Cricket Shot Classification from Video Using Body Pose Landmarks

Cricket Shot Classification from Video Using Body Pose Landm...

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advances in Electrical, computing, Communication and Sustainable technologies (ICAECT), international conference on

作者： Chava Govardhana Rao N Sai Harshith Varma Kashish K C Priyangshu Mazumder Prema Ramasamy Dept of Computer Science PES University Bengaluru India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

The primary focus of this study is to use deep learning techniques to classify cricket shots. data acquisition begins by gathering video clips of different cricket shots and manually annotating them. To address imbalance in class, generated synthetic examples are used to ensure each shot type has equal representation. Advanced techniques for handling missing data points using combined object detection and 3D landmark extraction to track the batsman's move-ments are employed for efficiency. To enhance accuracy of the analysis, complex methods are used to deal with missing data. By integrating object detection methods with 3D land-mark extraction, effective tracking of batsman movements is captured. Tests with multiple machine learning models such as simple neural networks and more complicated models which could capture movement sequence over time are tested on the data. The most successful results were achieved with systems that could process video frames in both forward and reverse directions, leading to a more precise identification of cricket shots. This research illustrates the potential of machine learning techniques and how advanced models can be used to analyze and classify sports activities in video data. We contribute to the growing community and knowledge base of automated sports analytics, paving the way for future innovations in real-time action recognition and commentary generation.

关键词： Deep learning Analytical models Technological innovation Solid modeling Three-dimensional displays Tracking Object detection Streaming media data models Sports

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CorLMI-FSA: An Efficient Feature Selection Approach for Stress Classification Using Physiological Signals

CorLMI-FSA: An Efficient Feature Selection Approach for Stre...

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advances in Electrical, computing, Communication and Sustainable technologies (ICAECT), international conference on

作者： Shikha Shikha Divyashikha Sethia S. Indu Department of Computer Science and Engineering Delhi Technological University New Delhi India Department of Software Engineering Delhi Technological University New Delhi India Department of Electronics and Communication Delhi Technological University New Delhi India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

Mental stress in academic settings significantly impacts students' health and performance. Physiological signals such as Electrodermal Activity (EDA) and Heart Rate Variability (HRV) offer valuable insights for stress classification. However, the multidomain nature of these signals often introduces redundancy and irrelevant data, reducing both classification accuracy and computational efficiency [1]. Hence, this paper proposes an efficient feature selection algorithm, CorLMI-FSA (Correlation-Logistic Mutual Information Feature Selection Algorithm), which dynamically adjusts redundancy weights through a logistic function to capture nonlinear dependencies among features. The proposed algorithm achieves accuracies of 96.82% and 95.84% for binary and three-level stress classification tasks using Gradient Boosting (GB), enhancing computational efficiency by selecting only the most relevant features. These results underscore CorLMI-FSA's potential as a low-complexity solution for realtime stress monitoring in wearable devices, facilitating timely intervention.

关键词： Accuracy Heuristic algorithms Redundancy Human factors Feature extraction Classification algorithms Computational efficiency Biomedical monitoring Wearable devices Stress

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Consumer Behavior in the Post-Pandemic Era: Insights from Machine Learning and AI-Driven Analytics

Consumer Behavior in the Post-Pandemic Era: Insights from Ma...

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advances in Electrical, computing, Communication and Sustainable technologies (ICAECT), international conference on

作者： Divya Nimma Ch. Anudeep Tripti Sahu Nusrat Hamid Shah Lubna Hamid Shah B Kiran Bala PhD in Computational Science University of Southern Mississippi Data Analyst in UMMC USA Department of Management studies Shri Vishnu Engineering college for women (A) Bhimavaram India MIMA Institute of Management Pune Maharashtra India College of Engineering and Computer Science Jazan University Kingdom of Saudi Arabia Department of Management and Marketing College of Business Jazan University Kingdom of Saudi Arabia Department of AI & DS K.Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

COVID-19 has been the disruptor in the world, and the society has been changed from one perspective of the consumer behavior in terms of digital platforms and their preferences for safety, convenience and sustainability. It is therefore very important for any business to acquaint itself with these changes so that may be in a position to effectively compete for the market within the current ever-changing business environment. Conventional consumer behavior models, on the other hand, rely on fragmented data sources and analytical tools that may not reflect the current post-pandemic consumption patterns. Such models are also incapable of incorporating multiple data types and can also fail to address changing social factors. In response, a Multi-Modal Consumer Behavior Prediction Framework (MCBP) that considers a vast array of data sources is introduced. BERT and GNNs are used to give a detailed, real-time interpretation of consumer behaviour accomplished by the MCBP framework. It uses BERT to extract and analyze sentiment and topics of the textual data and GNNs to capture influence and peer effects on purchase decisions. The findings of the study show the effectiveness of the proposed framework with an accuracy of 99%. 5% and the implementation is done using Python. The high level of accuracy shown assures the success of the MCBP model in measuring and forecasting consumers' behaviors in the post-pandemic world.

关键词： Consumer behavior Accuracy Soft sensors Predictive models data models Social factors Real-time systems Safety Sustainable development Business

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Applying Machine Learning Tools for Urban Resilience Against Floods

Applying Machine Learning Tools for Urban Resilience Against...

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advances in Electrical, computing, Communication and Sustainable technologies (ICAECT), international conference on

作者： Mahla Ardebili Pour Mohammad B. Ghiasi Ali Karkehabadi Civil and Environmental Engineering University of California Davis Davis USA Electrical and Computer Engineering University of California Davis Davis USA

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

Floods are among the most prevalent and destructive natural disasters, often leading to severe social and economic impacts in urban areas due to the high concentration of assets and population density. In Iran, particularly in Tehran, recurring flood events underscore the urgent need for robust urban resilience strategies. This paper explores flood resilience models to identify the most effective approach for District 6 in Tehran. Through an extensive literature review, various resilience models were analyzed, with the Climate Disaster Resilience Index (CDRI) emerging as the most suitable model for this district due to its comprehensive resilience dimensions: Physical, Social, Economic, Organizational, and Natural/Health resilience. Although the CDRI model provides a structured approach to resilience measurement, it remains a static model focused on spatial characteristics and lacks temporal adaptability. An extensive literature review enhances the CDRI model by integrating data from 2013 to 2022 in three-year intervals and applying machine learning techniques to predict resilience dimensions for 2025. This integration enables a dynamic resilience model that can accommodate temporal changes, providing a more adaptable and data-driven foundation for urban flood resilience planning. By employing artificial intelligence to reflect evolving urban conditions, this model offers valuable insights for policymakers and urban planners to enhance flood resilience in Tehran's district 6.

关键词： Adaptation models Biological system modeling Disasters Machine learning Predictive models data models Floods Resilience Systematic literature review Meteorology

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Cloud Based Automated Control System Workshops and Rooms for Controlling Parameters

Cloud Based Automated Control System Workshops and Rooms for...

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advances in Computer science, Electrical, Electronics, and Communication technologies (CE2CT), international conference on

作者： Dinesh Kumar Ravinder Singh Kuntal Pragya Deep Anup Shekhar Chamoli Pinki Singh Rishabh Mandal Department of Computer Science and Engineering SRM Institute of Science & Technology Ghaziabad UP Department of Mathematics Nitte Meenakshi Institute of Technology Bangalore Karnataka India Department of AI & DS IIMT College of Engineering Greater Noida India Uttaranchal School of Computing Sciences Uttaranchal University Dehradun India Department of Computer Science and Engineering Accurate College of Engineering & Management Greator Noida India Department of Computer Science and Engineering Tula's Institute Dehradun UK India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Across all industries, cloud-based automated control systems have completely changed how operational and environmental parameters are tracked and controlled. With cloud-integrated systems for real-time data collection, processing, and control, this concept presents intelligent, remote-controlled workshops and control rooms. To guarantee accurate control of crucial variables like temperature, humidity, pressure, and equipment efficiency, these systems make use of IoT devices, AI techniques, and big data analytics. Through sophisticated data perspectives, the cloud-based strategy enables predictive maintenance, improves flexibility, and lowers operating expenses. Stakeholders may make prompt, data-driven choices from anywhere with remote access to control rooms. These seminars provide practical experience with cloud-controlled settings and function as training and development centres. The system's flexibility to meet a range of industrial demands is further enhanced by its compatibility with external tools and equipment. The design, deployment, and possible uses of cloud-based control systems are covered in this paper, with an emphasis on how they might promote operational excellence, energy efficiency, and smart manufacturing.

关键词： Knowledge engineering Machine control Clouds Control systems Software Machine tools Security Time factors Personnel Standards

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