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Efficient Energy Distribution Through Integration of Demand Response System and Renewable Energy Sources in Smart Grid 1

Efficient Energy Distribution Through Integration of Demand ...

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1st international conference on advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Govekar, Navnath Sopan Dhaygude, Priti Shivaji Pachghare, Ayesha Dhananjay Navsahyadri Group of Institutions Department of Electrical Engineering Maharashtra Pune India Navsahyadri Group of Institutions Department of Electronics and Telecommunication Engineering Maharashtra Pune India

ISBN: (纸本)9798331518578

Customers can now configure their household devices to react to demand response systems (DRSs) provided by distribution system managers (DSMs) that are connected with renewable energy resources (RERs) due to the development of smart grids (SGs). A likelihood density functioning is suggested to forecast the actions of solar and wind power resources to attain optimal outcomes. DRSs are suggested with the participation of households, businesses, and commercial customers to address the ambiguity in energy generated by solar and wind RERs. This study implemented incentive-based compensation as price-based packets in this approach to implement DRSs. Operational expense and carbon emission distribution simulated are broken down into 3 stages: (i) application of the multifaceted genetic algorithm (MFGA) to solve the distribution issues;(ii) efficiency of operational expenses and carbon emission without DRSs;and (iii) efficiency of functioning expense and carbon emission with DRSs. To support the use of the suggested MFGA-based optimizing methodology, a smart grid that serves businesses, factories, and household customers is used. The present approach, which depends on the multifaceted particle swarm optimizing (MFPSO) method, is also contrasted with the suggested MFGA-based optimal system in terms of carbon emissions and operating costs. Compared to the current paradigm depending on the MFPSO method, the suggested MFGA-based optimizer functions better in terms of carbon emissions and operating costs. According to empirical findings, MFGA reduces carbon emissions and operating costs by 29% and 25%, respectively, with and without DRS involvement. © 2025 IEEE.

关键词： Operating costs

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Computational Approaches to Molecular Dynamics Simulation for Drug Design and Discovery 1

Computational Approaches to Molecular Dynamics Simulation fo...

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1st international conference on advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Rabeek, S. Mohamed Belagalla, Ningaraj Khare, Kanchan Kodadi, Shirish Kumar Reddy, Jonnala Subba Shaik, Mohsina Bano Affiliated to Bharathidasan University PG and Research Department of Chemistry Tamil Nadu India School of Agriculture SR University Department of Entomology Ananthasagar Telangana Warangal India Jabalpur Engineering College Madhya Pradesh India St. PETER'S ENGINEERING COLLEGE Department of Chemistry Telangana Hyderabad India Mylavaram521230 India College of Clinical Pharmacy Imaam Abdulrahman Bin Faisal University Department of Pharmaceutical Chemistry Dammam31441 Saudi Arabia

ISBN: (纸本)9798331518578

Since they offer profound insights into the atomic-level interactions between drug candidates and their biological targets, molecular dynamics (MD) simulations have become extremely effective computational tools for drug design and discovery. MD simulations allow for the estimation of binding affinities, conformational modifications, and the investigation of possible binding sites by simulating the dynamic behaviour of biomolecules over time. These simulations now run much faster and are more accurate thanks to recent improvements in computing power, improved algorithms, and machine learning incorporation. Methods like adaptive sampling and accelerated MD further maximise resource utilisation, making it easier to simulate intricate biological processes in realistic timescales. Furthermore, opportunities for accurate electronic structure analysis have been created by combining MD with quantum mechanical techniques. This presentation highlights how computational methods in MD simulations for drug design can revolutionise the process by supplying crucial information for structure-based drug discovery and promoting the creation of new therapeutic agents with improved efficacy and selectivity. © 2025 IEEE.

关键词： Molecular docking

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AI-based model for securing cognitive IoT devices in advance communication systems

International Journal of Cognitive Computing in Engineering

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international Journal of Cognitive Computing in engineering 2025年 6卷 351-359页

作者： Gaurav, Akshat Arya, Varsha Chui, Kwok Tai Gupta, Brij B. Ronin Institute MontclairNJ United States Asia University Taichung 413 Taiwan Dehradun India UCRD Chandigarh University Chandigarh India Hong Kong Metropolitan University Hong Kong SAR China Department of Computer Science and Information Engineering Asia University Taichung Taiwan University of Economics and Human Science Warsaw Poland Symbiosis International University Pune India School of Cybersecurity Korea University Seoul Korea Republic of

The pervasive integration of the Cognitive Internet of Things (CIoT) in advanced communication systems has significantly heightened security concerns due to inherent vulnerabilities that cyber attackers exploit. These security challenges are exacerbated by IoT devices’ limited computational resources and storage capacities, rendering traditional security protocols ineffective. This paper introduces a AI- based security model tailored explicitly for IoT environments within advanced communication systems. Our approach leverages AI techniques for cyber attack detection and mitigation. The effectiveness of our model is demonstrated through its capability to identify different types of cyberattacks with a precision of more than 99%. This study advances the security mechanisms for IoT devices and offers a scalable and efficient solution to safeguard user privacy and system integrity in sophisticated communication networks. © 2025 The Authors

关键词： Cyber attacks

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Blockchain Technology and its Implications for Financial Services: A Techno-Financial Perspective 1

Blockchain Technology and its Implications for Financial Ser...

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1st international conference on advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Shankar, Uma Govindasamy, P. Radhakrishnan, G.V. Ramcharan School of Leadership MIT World Peace University Pune India Adhiparasakthi College of Engineering Department of Management Studies Kalavai Ranipet Tamilnadu Vellore India Kalinga School of Management Bhubaneswar India

ISBN: (纸本)9798331518578

A blockchain is an ordered, decentralized, unchangeable ledger that makes it possible to record electronic transactions. Blockchain has become an innovation that may be used in many different fields especially in financial services over the past decade. It has been emphasized as an innovation that will make financial transactions easy, efficient, secure, and affordable. Bitcoin has gained popularity during the past ten years. Millions of people around the world are drawn to bitcoins because of their unknown and unpredictable qualities. The objective of the bitcoin research is to identify the ether and calculate its digital price using the prior increases in the digital price of bitcoin. Since digital pricing is predicted instantly, previous methods of digital price estimation do not have enough information or ways to forecast changes in digital pricing. Based on the difficulties identified in Bitcoin digital pricing estimation, this study proposed a machine learning-based approach to digital pricing estimation for financial services. The proposed framework incorporates a Reinforcement Learning method for digital pricing evaluation and blockchain for protected financial transaction setup. The results show that the given architecture operates better based on digital pricing forecasting precision than alternative cutting-edge techniques. © 2025 IEEE.

关键词： Decentralized finance

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HingeRLC-GAN: Combatting Mode Collapse with Hinge Loss and RLC Regularization 27th

HingeRLC-GAN: Combatting Mode Collapse with Hinge Loss and R...

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27th international conference on Pattern Recognition, ICPR 2024

作者： Goni, Osman Arka, Himadri Saha Halder, Mithun Shibly, Mir Moynuddin Ahmed Shatabda, Swakkhar Department of Computer Science and Engineering United International University United City Madani Avenue Dhaka1212 Bangladesh Department of Computer Science and Engineering BRAC University Kha 224 Bir Uttam Rafiqul Islam Avenue. Merul Badda Dhaka1212 Bangladesh

ISBN: (纸本)9783031783883

Recent advances in Generative Adversarial Networks (GANs) have demonstrated their capability for producing high-quality images. However, a significant challenge remains mode collapse, which occurs when the generator produces a limited number of data patterns that do not reflect the diversity of the training dataset. This study addresses this issue by proposing a number of architectural changes aimed at increasing the diversity and stability of GAN models. We start by improving the loss function with Wasserstein loss and Gradient Penalty to better capture the full range of data variations. We also investigate various network architectures and conclude that ResNet significantly contributes to increased diversity. Building on these findings, we introduce HingeRLC-GAN, a novel approach that combines RLC Regularization and the Hinge loss function. With a FID Score of 18 and a KID Score of 0.001, our approach outperforms existing methods by effectively balancing training stability and increased diversity. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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Credit scoring model based on SyMProD-ENN and logistic regression 24

Credit scoring model based on SyMProD-ENN and logistic regre...

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8th international conference on Electronic Information Technology and computer engineering, EITCE 2024

作者： Feng, Jiangang Jiang, Yuwen Bo, Chen Jiang, Chengxuan School of Information Engineering Hainan Vocational University of Science and Technology Hainan Haikou China School of Computer Science and Engineering Guangzhou Institute of Science and Technology GuangDong Guangzhou China School of Mathematics and Statistics Shenzhen University Guangdong Shenzhen China International Course Center of GuangZhou No.6 Middle School Guangdong Guangzhou China

ISBN: (纸本)9798400710094

Unbalanced data distribution is a common problem in the development process of credit scoring cards. This paper proposes a new method of mixed sampling, SyMProD-ENN, to improve the problem of developing effective credit scoring cards on imbalanced datasets. The experimental results show that the SyMProD-ENN method can effectively improve the classification performance of the credit scoring model constructed based on the logistic regression methods, especially suitable for credit data with imbalance category, high-dimensional characteristics, non-normal distribution and outliers. © 2024 Copyright held by the owner/author(s).

关键词： Logistic regression

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Assessing the Effect of AI-Powered HR Analytics on Electronics Manufacturing Decision-Making 1

Assessing the Effect of AI-Powered HR Analytics on Electroni...

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1st international conference on advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Gupta, Ronak Kumar, Sanchita Karad, Sunita Singh, Vivek Lenka, Reena Nikam, Poonam University Pune India MITADT University Pune India MITADT University Department of Engineering and Management Pune India Gulf University Bahrain IIEBM - Indus Business School Pune India

ISBN: (纸本)9798331518578

A progressive pattern in the hardware producing area is the joining of Human Asset (HR) examination driven by Man-made brainpower (computer-based intelligence) into dynamic methods. This exploration investigates the impact of these complex innovations on authoritative independent direction, with an accentuation on work the board, functional effectiveness, and key arrangement. To address the consistently changing prerequisites of the hardware business, HR examination can further develop labor force arranging, upgrade ability obtaining, and spot patterns in worker execution by using computer-based intelligence's prescient attributes. This exploration examines what computer-based intelligence driven experiences mean for decisions about staffing levels, preparing drives, and representative maintenance strategies utilizing a blended strategies strategy that consolidates quantitative information examination and subjective meetings. The concentrate additionally stresses how these devices help to make information driven societies, which enable leaders to pursue choices in light of constant measurements. In any case, the concentrate additionally calls attention to potential disadvantages, for example, worker hesitance to change, information assurance issues, and moral issues with carrying out artificial intelligence in HR. Settling these issues is fundamental to upgrading the benefits of man-made intelligence-controlled investigation while protecting transparency and certainty inside the organization. This report features how man-made intelligence-controlled HR examination might further develop dynamic in the gadgets fabricating industry and gives a guide to organizations hoping to utilize innovation to acquire an upper hand. The outcomes add to the growing corpus of examination on how simulated intelligence will impact authoritative design and HR in cutting edge areas. © 2025 IEEE.

关键词： Decision making

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Optimizing Household Energy Consumption: A Web-Based Tool for Real-Time Carbon Footprint Calculation and Sustainable Benchmarking 1

Optimizing Household Energy Consumption: A Web-Based Tool fo...

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1st international conference on advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Raja, Harsh Kishore, Jayaditya Kalaivani, J. Angayarkanni, V. Rama, P. Srm Institute Of Science And Technology Faculty Of Engineering And Technology Department Of Computing Technologies Kattankulathur Chengalpattu District Tamil Nadu 603203 India

ISBN: (纸本)9798331518578

This is an innovative project from an energy-saving perspective. This project ensures that users become in control of their energy consumption and able to take an actively reducing their carbon footprints. It provides users with an interface to input details about household appliances and usage patterns, through which a correct calculation of the carbon footprint could be done. Compared against standard norms, it would help the users identify certain areas where improvement is possible and adopt more sustainable use of energy. Apart from personalized energy assessments, it has now become the central source that connects users to local schemes and initiatives on sustainable energy, including information on resources and programs into waste energy and carbon emission reduction. This integration, therefore, promotes more engagement and involvement within the community as users are nudged to join in the efforts that are being made for the goal of energy conservation. The platform educates its users about energy consumption and aids them in making green choices in data;however, with a holistic approach in carbon footprint management and education pertaining to sustainable long-term behavior, the process takes place through integrated accurate energy tracking, fine recommendations, and community-driven action. © 2025 IEEE.

关键词： Carbon sequestration

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Enhancing Academic Feedback Systems with Deep Learning and Ensemble Methods 1

Enhancing Academic Feedback Systems with Deep Learning and E...

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1st international conference on advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Sharma, Hemant Joshi, Hiren Gujarat University Dept. Computer Science Ahmedabad India

ISBN: (纸本)9798331518578

This paper attempts to improve the academic feedback systems via complex deep learning and ensemble methods. Leveraging the NIRF Rankings 2022 dataset that provides radical performance metrics of Indian academic institutions, the project would attempt to develop trustworthy models to monitor and facilitate improvement in the academic feedback system. The techniques used in data preparation prevent missing values, outliers, and categorical data in using advanced machine learning models. In order to construct the model and capture fine-grained patterns linked to academic achievement, 1D Convolutional Neural Networks (CNNs) along with Multi-Layer Perceptrons (MLPs), both individually and in combination, are employed extrapolated from the National Institutional Ranking Framework to obtain data. Precision, accuracy, recall, along with the F1-score will be used to evaluate these models' performance. By the time these findings are finished, they should be able to provide practical advice on how to improve academic evaluations' effectiveness and advance educational institutions' more flexible feedback systems. © 2025 IEEE.

关键词： Contrastive Learning

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Evaluating the Contribution of AI-Powered Knowledge and Digital Literacy to the Improvement of Fintech Operations in the Electronics Sector 1

Evaluating the Contribution of AI-Powered Knowledge and Digi...

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1st international conference on advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Choudhari, Amey Adinath Moholkar, Nilambari Hanmant Ghadage, Avinash Bhoite, Dashrath Kumari, Sujata Patil, Mangalgouri S JSPM's Rajashi Shahu College of Engineering Pune India Dr. D.Y. Patil Institute of Technology Pimpri Pune India D.Y. Patil Vidyapeeth's Global Business School and Research Centre Pune India International School of Management and Research Pune India Shri Balaji University Pune India Pune India

ISBN: (纸本)9798331518578

The financial services sector is undergoing a change because to artificial intelligence (AI) particularly in the field of financial technology (FinTech). FinTech's incorporation of artificial intelligence (AI) has fundamentally altered traditional financial services by increasing security, accuracy, and efficiency. Artificial intelligence (AI) technology, financial organizations can benefit from technologies like ML algorithms, natural language processing (NLP), including predictive analytics in making better decisions, streamlining operations, and offering customized customer experiences. While cutting edge capability and advanced education enables both staff and purchasers to capably connect with fintech courses of action, recreated insight-controlled data helps associations with overhauling heading, further foster client experiences, and limit money related risks. Since technology has made it easier to share information and lower transaction costs, financial services production could be broken down. Because specialized players offer unbundled financial services, customers can locate and put together the product suites that they want. This study sees how cutting-edge capability and man-made brainpower-controlled data could chip away at financial undertakings, advance safe trades, and addition proficiency in the contraptions business. This study assesses how these advances add to the augmentation and goodness of fintech applications in equipment by dissecting context-oriented examinations, ongoing crazes, and inconveniences. © 2025 IEEE.

关键词： Sales

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