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Generator Retirement Optimization for New York State Energy Transition: Balancing Cost, Reliability, and Emissions

Generator Retirement Optimization for New York State Energy ...

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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Ahmadi, Seyed Ehsan Kabir, Elnaz Fattahi, Mohammad Marzband, Mousa Newcastle Business School Northumbria University Newcastle upon Tyne United Kingdom Texas A&M University Dept. of Engineering Technology & Industrial Distribution TX United States King Abdulaziz University Dept. of Electrical & Computer Engineering Jeddah Saudi Arabia

ISBN: (纸本)9798331541125

New York State (NYS) is shifting towards renewable energy, targeting 70 % renewable generation by 2030 and a net-zero carbon power grid by 2040. This transition necessitates the systematic retirement of fossil fuel generators (FFGs), which have long supported grid stability but contribute significantly to CO2 emissions. However, FFG retirement introduces chal-lenges related to grid reliability, energy curtailment, transmission congestion, and peak load management, given the intermittent nature of renewable energy sources (RES). This paper presents an optimization model designed to develop an effective FFG retirement schedule that considers these operational complexities alongside RES integration and transmission constraints. Utilizing historical data on demand and weather patterns, the model evaluates multiple scenarios to balance cost, emissions reduction, and grid stability. The findings indicate that strategic retire-ment of 22 GW generation capacity of high-emission inefficient FFGs, combined with targeted transmission upgrades and RES expansion, can support NYS's energy targets by 2030. This framework offers valuable insights for policymakers aiming to achieve decarbonization while preserving grid resilience. © 2025 IEEE.

关键词： Low emission

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Homomorphic Encryption in Quantum Computing

Homomorphic Encryption in Quantum Computing

引用

Devices for Integrated Circuit (DevIC)

作者： Surojit Mondal Bikash Debnath Jadav Chandra Das Debashis De Dept. of Computer Science & Engineering Jadavpur University Kolkata India Dept. of Computer Science & Engineering Asansol Engineering College West Bengal India Dept. of Information Technology MAKAUT Kolkata India Dept. of Computer Science and Engineering MAKAUT Kolkata India

ISBN: (数字)9798350391107

ISBN: (纸本)9798350391114

Homomorphic encryption (HE) is a groundbreaking cryptographic method that allows calculations on encrypted data without the need for decryption, thus safeguarding privacy and security. This article investigates how to implement homomorphic encryption with QISKIT, a free-to-use quantum computing framework. The research investigates the theoretical underpinnings of HE, its use in quantum computing, and its importance for privacy-preserving calculations. Moreover, the study covers adder circuits based on quantum logic gates, their realization with QISKIT, and the prospects for improving computational security in cloud environments. The results stress the practical viability of HE in quantum environments, underscoring its possible uses in secure data processing and privacy-enhancing technologies.

关键词： Data privacy Cloud computing Quantum computing technology Quantum mechanics Logic gates Cryptography Homomorphic encryption Adders Stress

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LexChain: A Blockchain-based Solution to Safeguard Electronic Evidence in Legal Systems

LexChain: A Blockchain-based Solution to Safeguard Electroni...

引用

International Symposium on Digital Forensic and Security (ISDFS)

作者： Neha Adhikari Manan Chhajed Divya Raichura Prasenjit Bhavathankar Dept. of Computer Science and Engineering(AIML) Sardar Patel Institute of Technology Mumbai India Dept. of Computer Engineering(AIML) Sardar Patel Institute of Technology Mumbai India

ISBN: (数字)9798331509934

ISBN: (纸本)9798331509941

The adoption of digitization has revolutionized data storage technology, resulting in improved accessibility, efficiency, collaboration, and reduced storage costs. A significant impact of this can be seen in legal systems such as case management tools and e-evidence in litigation. However, ensuring the integrity and authenticity of digital evidence poses major challenges. Forensic evidence is the most sensitive and tangible part of legal investigations and traditional methods for storing physical records and standard digital practices for document storage and forensic evidence preservation are at risk of manipulation and unauthorized access. Such issues raise concerns about the integrity of evidence in legal cases. Beyond the security concerns, manual processes such as storage, management and authentication of evidence are expensive and suffer from delays. The proposed system addresses these challenges using blockchain technology to build a robust system that securely stores all legal documents and forensic evidence. Utilizing a blockchain framework such as Ethereum can boost the security of the legal system while also automating the legal process and ensuring compliance with data privacy regulations through smart contracts, thus improving efficiency, reducing costs, and ensuring reliability and transparency.

关键词： Costs Digital forensics Storage management Smart contracts Memory Regulation Blockchains Safety Reliability Standards

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Hybrid Feature engineering and Sampling Strategies for Enhanced Diabetic Retinopathy Classification 3

Hybrid Feature Engineering and Sampling Strategies for Enhan...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Vanlalnunpuia, C. Lalhmingliana Ambeth Kumar, V.D. Vanlalawmpuia, R. Khiangte, David Lalmuanpuia Kumar Khan, Ajoy Mizoram University Dept. of Computer Engineering Aizawl India Mizoram University Dept. of Information Technology Aizawl India Eye Care Hospital Ophthalmologist Aizawl India

ISBN: (纸本)9798331523893

This study presents a machine learning approach for Diabetic Retinopathy (DR) classification, integrating advanced preprocessing, feature extraction, and adaptive sampling. Preprocessing techniques, including CLAHE, gamma correction, and noise reduction, standardize image quality while preserving anatomical details. Class imbalance is addressed with ADASYN, generating synthetic samples in sparse regions for balanced class representation. Feature extraction focuses on clinical markers like vascular density, microaneurysms, exudates, and texture descriptors such as GLCM and LBP. Recursive Feature Elimination (RFE) refines features for better interpretability and reduced computational load. This binary classification framework achieves 92.91% accuracy on test data and a cross-validated ROC AUC of 0.994, demonstrating the effectiveness of hybrid feature engineering and intelligent sampling in DR detection. © 2025 IEEE.

关键词： Diabetic Retinopathy Fundus Image Image Processing Machine Learning Medical Image Diagnostics

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Robust Multiclass Brain Tumor Classification: Leveraging Swin Transformer and Feature Optimization with Ensemble Learning

Robust Multiclass Brain Tumor Classification: Leveraging Swi...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Rahad Khan Rafiqul Islam Dept. of Computer Science and Engineering Dhaka University of Engineering & Technology Gazipur United International University Dhaka Bangladesh Dept. of Computer Science and Engineering Dhaka University of Engineering & Technology Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Brain tumor is one of the most serious health problems, and early discovery is crucial to improving patient outcomes. Magnetic resonance imaging (MRI) is vital in early detection, diagnosis, and timely treatment. Due to the complexity and diversity of multiclass brain tumors, feature extraction becomes more difficult because it requires distinguishing between tumor categories, which typically overlap features in MRI. Traditional diagnostic approaches, while effective, frequently have limits in terms of accuracy, speed, and usability. This work presents a voting-based ensemble framework for multiclass brain tumor classification, which incorporates three variations of a pre-trained Swin Transformer for feature extraction. Among these features, we used only refined features to train machine learning models by applying several feature removal processes. Numerous experiments were conducted to evaluate the performance of the suggested model and compare the outcomes to existing transformer-based models, such as the Vision Transformer and the Swin Transformer. The experimental findings revealed that the model outperformed the other models, with a maximum accuracy of 97.56%.

关键词： Analytical models Accuracy Magnetic resonance imaging Computational modeling Brain tumors Predictive models Brain modeling Transformers Feature extraction Data models

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Comparing Deep Reinforcement Learning Methods for HVAC System Optimization with Forecast Parameters Over Various Time Periods

Comparing Deep Reinforcement Learning Methods for HVAC Syste...

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Interdisciplinary Approaches in technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Prema Arokia Mary G Hema M S S Bama Nageswara Guptha Dept. of Computer Science and Engineering Sri Venkateshwara College of Engineering Bengaluru India Dept. of Computer Science and Engineering RV Institute of Technology and Management Bengaluru India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Buildings are significant contributors to global energy consumption. Maintaining comfortable indoor temperatures while reducing energy consumption are conflicting objectives. Deep Reinforcement Learning (DRL) is a promising area of research for building Heating, Ventilation and Air Conditioning (HVAC) system optimization. In this study an open-source framework Building Optimization Testing Framework (BOPTEST), which is a virtual testbed that help comparison different control strategies for evaluation of DRL control methods is used. A Proportional-Integral (PI) controller is used to benchmark the DRL methods. A single zone residential building of 192 m 2 with a radial heating system and a heat pump in a climate zone with high heating requirement with dynamic electricity prices with prices varying every 15 min based on demand is chosen for implementing different control strategies. On comparing Deep Deterministic Policy Gradient (DDPG), Proximal Policy Optimization (PPO) and Twin Delay DDPG (TD3) based DRL controllers and the baseline controller, the DDPG based controller reduced energy consumption by 97.3 % and operating cost by 17.7 % during the peak heating period with reference to baseline method. Then on analyzing the impact of inclusion of forecast parameters occupancy, solar irradiance, and electricity prices over the period 3, 6 and 12 hours in DDPG based controller. The prediction for 3 hours gave the greatest reduction in thermal discomfort of 99.7 % and prediction for 12 hours gave maximum reduction in cost by 30.4 % but resulted in only 82% reduction in thermal comfort when compared with baseline method indicating that longer prediction horizon is not necessarily results in better performance.

关键词： Energy consumption HVAC Costs PI control Heat pumps Electricity Buildings Deep reinforcement learning Optimization Resistance heating

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Industrial IoT Smart Manufacturing Systems based on Cloud Computing Energy Efficiency Analysis using Machine Learning Algorithms 3

Industrial IoT Smart Manufacturing Systems based on Cloud Co...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Chinnasamy, P. Khan, Patan Feridoz Sasikala, C. Swathi, Mavalluru Kumar, M. Prema Yadav, Ajay Kumar Kalasalingam Academy of Research and Education School of Computing Department of Computer Science and Engineering Tamilnadu India Mohan Babu University School of Computing Department of Computer Science and Engineering Andhra Pradesh Tirupati India Dept of Cse Andhra Pradesh Ananthapuramu India Audisankara College of Engineering and Technology Andhra Pradesh India Dept. of Ece Andhra Pradesh Bhimavaram India United Institute of Management Department of Computer Application Prayagraj India

ISBN: (纸本)9798331509675

With open connectivity and emergent computing, the Industrial Internet of Things (IIoT) combined with cloud computing resources provides significant breakthroughs in the field of industrial automation. These developments lead to the evolution of supplemental services, which improve automation in the production and industrial sectors. This research proposes novel technique in smart manufacturing system based on cloud computing with IIoT in energy efficiency using machine learning. Here the smart manufacturing process has been analysed based on IIoT with cloud computing network, where the energy efficiency is analysed using virtual machine-based reinforcement markov chain based linear regression. Experimental analysis has been carried out in terms of latency, energy efficiency, task response time, accuracy. proposed technique obtained 97% of Latency, 95% of Energy efficiency, 98% of Accuracy, 94% of Task response time. © 2025 IEEE.

关键词： Smart manufacturing

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Identification of Candidate Genes for Colorectal Cancer Using Machine Learning and Bioinformatics Approaches

Identification of Candidate Genes for Colorectal Cancer Usin...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Humayan Kabir Rana Boshir Ahmed Dept. of Computer Science and Engineering Rajshahi University of Engineering & Technology (RUET) Rajshahi Bangladesh Dept. of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Colorectal cancer (CRC) stands as the second most prevalent cause of cancer deaths, and its incidence is rising over time. Identifying key genes is essential for diagnosing and developing effective therapeutic strategies for cancer. Several studies were conducted to determine the candidate genes of CRC but this is still not sufficient and further research is needed in this area. Thus, we aimed to identify the key candidate genes of CRC using The Cancer Genome Atlas (TCGA) dataset, applying the Kruskal-Wallis test and Bonferroni correction machine learning techniques. We successfully identified 9 candidate genes, including CALB2, GRP, KRAS, MLH1, NPR3, PPFIA4, SOX11, STAC2, and TRPA1, from 20518 genes of CRC using our model. In addition, we used bioinformatics frameworks to identify signaling pathways, gene ontological pathways, and PPI networks that reflect the functions of these candidate genes. We found 9 significant signaling pathways, 12 ontological pathways, and 3 hub genes for CRC. The diagnostic effectiveness of the candidate genes was evaluated through the receiver operating characteristic (ROC) analysis, and all candidate genes showed good performance according to area under the curve (AUC) values. Notably, the gene MLH1 demonstrated the highest AUC of .91. In fine, the findings of this study may play a role in disease management and offer a foundation for further laboratory investigations to uncover potential therapeutic targets for CRC treatment.

关键词： Proteins Electric potential Biological system modeling Genomics Machine learning Colorectal cancer Receivers Bioinformatics

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Hardware Acceleration of Scalable Artificial Neural Network Inference on FPGA

Hardware Acceleration of Scalable Artificial Neural Network ...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Nahin Ul Sadad Md. Nazrul Islam Mondal Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Artificial intelligence (AI) relies on tasks like pattern recognition and object detection, but software-based implementations often struggle to meet real-time requirements. Field-Programmable Gate Arrays (FPGAs) have become a promising solution for hardware acceleration, offering parallelism, energy efficiency, and adaptability, making them ideal for accelerating Artificial Neural Networks (ANNs) in edge and low-power environments. This paper implements a scalable and efficient FPGA-based ANN inference model, incorporating a fully connected layer with sigmoid activation for non-linear processing. The design is evaluated for FPGA resource utilization and benchmarked against CPU and GPU implementations for computational time. Results show that the FPGA implementation achieves a 34,000x speedup over CPU-based inference and a 2,000x improvement over GPUs on computational time while consuming significantly fewer resources on FPGA (except IO).

关键词： Computational modeling Scalability Graphics processing units computer architecture Parallel processing Real-time systems Resource management Artificial intelligence Field programmable gate arrays Hardware acceleration

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Beach Litter Detection in Coastal Environments Using High Resolution Images

Beach Litter Detection in Coastal Environments Using High Re...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Swathi Panchireddy Pavan Sai Sambangi Eswari Yegireddi Prudhviraj Satapathi Firose Shaik Satish Muppidi Dept. Computer Science and Engineering GMR Institution of Technology Vizianagaram India Computer Science and Engineering GMR Institution of Technology Vizianagaram India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Marine debris, such as discarded fishing gear, plastic waste, and chemical pollutants, contributes to beach litter and posesa major threat to coastal ecosystems, with increasing coastal populations worsening pollution and its harmful effects on marine life. Effectively addressing the issue is challenging because to the slowness and labor-intensiveness of traditional litter detection techniques, such as manual surveys. High-resolution photos and deep learning technology-more especially, the YOLO (You Only Look Once) model-are used in this study to suggest a solution. employing high-resolution photos, which the YOLO algorithm analyzes to effectively and accurately detect and categorize litter. Because of its single-pass structure, YOLO operates more quickly than more traditional techniques like CNN and R-CNN, which makes it perfect for real-time applications. This approach helps to prevent further deterioration of marine ecosystems by reducing the need for manual monitoring, saving time, and more accurately identifying pollution hotspots. This invention uses cutting-edge technology to provide a quick, effective, and flexible solution to the growing problem of marine litter globally.

关键词： YOLO Deep learning Training Surveys Technological innovation Pollution Image resolution Sea measurements Real-time systems Context modeling

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