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Short-term renewable energy uncertainties and inertia consideration in power system generation expansion planning

Energy Reports 2025年 13卷 5978-5992页

作者： Ayamolowo, Oladimeji Joseph Kusakana, K. Dept. of Electrical Electronic and Computer Engineering Central University of Technology Free State South Africa

In modern power systems, renewable energy (RE) sources are increasingly integrated to reduce reliance on fossil-fuel-based generation. However, the uncertainties and lack of rotational inertia associated with renewable energy generators (REGs) pose challenges to grid stability. This paper proposes a novel mixed-integer linear programming (MILP) model that optimizes energy costs while enhancing system inertia in the presence of RE uncertainties. The model incorporates both REG uncertainties and inertia requirements into the planning process. To validate its effectiveness, RE uncertainty data from Mangaung Municipality, Free State Province, South Africa, is used for analysis, and the model is tested on the IEEE 6-bus test system. Simulation results demonstrate that the proposed approach improves system inertia from 5.875 s to 6.304 s while reducing energy costs from $1752.88/MWh to $1614.50/MWh in Case 3, where both RE uncertainties and system inertia are considered. These results show a clear advantage over Case 1 (cost minimization only) and Case 2 (inertia maximization only), highlighting the model's ability to balance economic and stability objectives in renewable-integrated power grids. © 2025 The Authors

关键词： Linear programming

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Post-Quantum Cryptography Integration to O-RAN 22

Post-Quantum Cryptography Integration to O-RAN

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22nd IEEE Consumer Communications and Networking Conference, CCNC 2025

作者： Aydeger, Abdullah Zeydan, Engin Mangues, Josep Florida Institute of Technology Dept. of Electrical Engineering and Computer Science MelbourneFL United States Barcelona08860 Spain

ISBN: (纸本)9798331508050

With its disaggregated and open interfaces, the Open Radio Access Network (O-RAN) architecture promises flexibility, innovation, and cost-effectiveness for future wireless networks. However, the increased reliance on software and open interfaces also introduces new security challenges. As the threat of quantum computing looms, the traditional cryptographic algorithms used in O-RAN will become vulnerable to potential attacks. This paper proposes the integration of Post-Quantum Cryptography (PQC) into O-RAN to enhance its security against post-quantum adversaries. We discuss the specific vulnerabilities of current O-RAN security mechanisms to quantum attacks and identify key areas where PQC can be applied, such as key exchange, authentication, and data protection. We present a framework for evaluating the suitability of different PQC algorithms for O-RAN and outline potential challenges and implementation considerations. By proactively integrating PQC into O-RAN, we aim to ensure the long-term security and resilience of this emerging network architecture in the era of quantum computing. © 2025 IEEE.

关键词： Quantum cryptography

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Fish Detection using Dual Branch Image Enhanced Pipeline with YOLOv8

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Procedia computer Science 2025年 258卷 1972-1979页

作者： Vivek Kumar Kumaran P. Dept. of Computer Science Engineering National Institute of Technology-Puducherry Karaikal-609609 UT of Puducherry

Fish species identification plays a crucial role in the aquaculture industry. Current methods such as using sensors, acoustic devices and density map regression, face multiple challenges related to costs, computational complexity and corrosion or malfunctioning of devices in underwater environment. To address these limitations we use YOLOv8(You Only Look Once) for the identification of fish species using visual perspective through the camera. The model is trained using a dataset of fish aquarium images to provide ground truth labels for training and evaluation purposes. Two species were observed in this research: Iridescent Shark Catfish (Pangasianodon hypophthalmus) and Goldfish (Carassius auratus). Our study achieved a notable breakthrough in identifying all the two species within a comprehensive dataset comprising 173 images. The top-performing model demonstrated exceptional accuracy on the test dataset with splitting ratios of 70-20-10 for training, validation, and testing, respectively. The custom model attained a mAP which is mean Average Precision, of 92% at an IoU threshold of 0.5, underscoring its impressive capabilities to detect across all classes. To remove the turbidity, a dual branch image enhancement pipeline is used with yolov8, which gives the best confidence score of 66% in detecting the fish species.

关键词： Object detection Yolov8s Underwater image enhancement turbidity visibility

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Sustainable Agriculture in Food Security Integrating Satellite Data Risk Assessment by Cyberattack Detection: AI Applications

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Remote Sensing in Earth Systems Sciences 2025年第2期8卷 435-443页

作者： Kumar, M. Prema Krishnammal, N. Gupta, Manu Begum, Mairaj Unisa Sultana, Sameera Degala, Divya Priya Dept. of ECE Shri Vishnu Engineering College for Women (A) Andhra Pradesh Bhimavaram 534202 India Department of Computer Science and Engineering Dr. N.G.P. Institute of Technology Tamilnadu Coimbatore India Department of Electronics and Communication Engineering Sreenidhi Institute of Science and Technology Telangana Hyderabad India Department of CSE-AIML MALLAREDDY UNIVERSITY Telangana Hyderabad India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Globally, chronic food insecurity persists and is made worse by shocks brought on by climate change, like floods and droughts. In order to guarantee prompt assistance delivery, humanitarian programming prioritizes predicting levels of food insecurity and identifying homes that are at risk. Using complicated and diverse data, machine learning (ML) models have been widely used to promote food security. This research proposes a novel technique in sustainable agriculture-based food security analysis and satellite data cyberattack detection using machine learning models. Here, the input is collected based on satellite data, and its cyber detection has been analyzed using a convolutional spatio adversarial encoder neural network. Then, from this satellite data, the food security analysis data has been collected and processed for noise removal and normalization. Processed data has been classified using the fuzzy Q-Boltzmann ResNet transfer neural network. Experimental analysis is carried out for the food security analysis dataset in terms of data integrity, detection accuracy, average precision, F-1 score, and recall. The proposed approach yielded 97% detection accuracy, data integrity of 98%, 96% average precision, 93% recall, and 94% F-1 score. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Cyberattack detection Food security analysis Machine learning models Satellite data Sustainable agriculture

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Egyptian Hieroglyphics Handwriting Character Classification Using CNN with Explainable AI: SHAP and LIME

Egyptian Hieroglyphics Handwriting Character Classification ...

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

作者： Md. Ismiel Hossen Abir M D. Shaek Ahmed Nurul Bashar Hasibul Islam Peyal Dept. of Computer Science & Engineering International Standard University Dhaka Bangladesh Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Egyptian hieroglyphics, one of the oldest writing and human communication systems. In our modern life, it's challenging to interpret this language due to its complex visual symbols. In this study, we propose a Convolutional Neural Network (CNN) model for classifying Egyptian hieroglyphic handwriting characters. The dataset provides 18 different class of handwritten hieroglyph character images. To enhance model interpretability, we apply explainable AI techniques, specifically SHAP and LIME, to identify regions that influence model predictions. The results that our custom CNN model achieves train accuracy 90.62%, validation accuracy 88.25%, and test accuracy of 84.5%, with specific characters showing high classification performance. Also, AUC score reflects 0.99 to 1.00 for each of the classes.

关键词： Support vector machines Visualization Accuracy Translation Explainable AI Computational modeling Symbols Writing Predictive models Convolutional neural networks

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Face Sketch Construction and Recognition

Face Sketch Construction and Recognition

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Cognitive Computing in engineering, Communications, Sciences and Biomedical Health informatics (IC3ECSBHI), International Conference on

作者： Shaik Yacoob B. Sujatha Tamarana Anudeep Bozzam Eesha Sharvi Gande Veera Venkata Siva Sai Koppula Charles Antony Dept. of Computer Science and Engineering Godavari Global University Rajamahendravaram India Dept. of Computer Science and Engineering Godavari Institute of Engineering & Technology (A) Rajamahendravaram India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

As the crime rate increases rapidly, law enforcement organizations are facing the urgent task of quickly and properly identifying suspects. This paper proposes an innovative way to improve crime detection using new facial recognition technologies by describing a newly designed technology that allows users to create precise facial drawings using a user- friendly drag-and-drop interface. In which it will completely eliminate the need for expert forensic artists. This paper allows to create face drawings that can be effortlessly matched to huge police databases by utilizing the powerful deep learning algorithms and cloud infrastructure. By combining these modern technologies this tool speeds up the identification process by overcoming the problems and issues of typical handdrawn drawings. It will not only enhance the speed and accuracy of suspect identification, but it will also give law enforcement officers an efficient resource thereby, it will enhance the overall investigative effectiveness and responsiveness.

关键词： Deep learning computer vision Accuracy Law enforcement Databases Face recognition Forensics Organizations Cognitive systems Bioinformatics

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XGBClassifier and Explainable AI for Disease Classification from Patient Symptom and Demographic Data with Late Fusion

XGBClassifier and Explainable AI for Disease Classification ...

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

作者： Md. Ismiel Hossen Abir Nurul Bashar Md. Shaek Ahmed Hasibul Islam Peyal Dept. of Computer Science & Engineering International Standard University Dhaka Bangladesh Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

The accurate identification of diseases based on patient symptoms and demographic data is a critical area of healthcare research, with the potential to significantly improve patient outcomes. In this study, we employed machine learning algorithms, specifically the XGBClassifier, to classify diseases based on a dataset containing patient symptoms and demographic information such as age, gender, fever, cough, fatigue, and other health indicators. To ensure model interpretability and transparency, we incorporated Explainable AI (XAI) techniques like SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations), which allow us to understand and interpret the feature contributions to the model’s predictions. The results demonstrated that the XGBClassifier achieved an accuracy of 81.42%, outperforming other machine learning models tested. This study emphasizes the importance of combining XAI with machine learning for disease classification, offering greater transparency in the decision-making process, which is vital in healthcare settings.

关键词： Support vector machines Electric potential Accuracy Machine learning algorithms Explainable AI Medical services Predictive models Fatigue Convolutional neural networks Diseases

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Artificial Intelligence Techniques for the Ransomware Detection: Current Methods and Challenges 1

Artificial Intelligence Techniques for the Ransomware Detect...

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

作者： Rakibe, Priya Jawale, Madhuri Computer Engineering Research Centre Sanjivani College of Engineering Kopargaon Savitribai Phule Pune University Pune India Sanjivani College of Engineering Kopargaon Savitribai Phule Pune University Dept. of Information Technology Pune India

ISBN: (纸本)9798350355611

Recently, Artificial Intelligence (AI) Techniques have received significant interest across the different fields for data analysis and decision-making. Ransomware detection using AI has been a challenging research problem for the last decade in hybrid communication systems like blockchainintegrated Internet of Things (IoT) architectures. Ransomware is a form of malicious software that restricts access to a victim's resources by altering system login credentials or encrypting digital data. Consequently, typical approaches used to identify other types of threats are inadequate for recognizing ransomware. Different AI techniques recently have been used for the detection of such threats, but an appropriate level of accuracy and minimum false rate with automation are vital challenges. This study presents a systematic review of the current methods for ransomware detection using different AI techniques. The ransomware detection methods under categories such as rule-based, machine learning-based, and deep learning-based are reviewed in this study. The comparative analysis of these methods is provided quantitatively and qualitatively. As per the recent progress in the domain of ransomware detection, the current research problems are identified and discussed as the outcome of the study. The roadmap for future work is described as the research gap analysis output. © 2025 IEEE.

关键词： Adversarial machine learning

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Building Resilient CICD Pipelines: A DevOps Security-First Framework

Building Resilient CICD Pipelines: A DevOps Security-First F...

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Computational, Communication and Information technology (ICCCIT), International Conference on

作者： Rashmi Ashtagi Chirag Belani Prasanna Bhalerao Yash Bhalerao Atharva Chawle Dept. of Computer Engineering Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

Continuous Integration and Continuous Deployment are now integrated in automated systems that enhance code stability, and accelerate the deployment process as well as improving monitoring of the whole system in the software development field. This document deals with setting up of a strong CI/CD process involving AWS cloud services and DevOps. The infrastructure is built with the help of Terraform which ensures the scalability and repeatability applying the Infrastructure as Code (IaC) approach. A safe Virtual Private Cloud (VPC) is designed consisting of public and private subnets, load balancing for traffic distribution, and CloudFront CDN to improve geographical distribution. Jenkins is used in CI process to perform disk analysis, OWASP and Trivy vulnerability scanning, and making Docker images. The CD phase use ArgoCD for updates on Kubernetes and integrates Monitoring and observability using Prometheus and Grafana. Notifications are produced to JIRA and email communication for improvement. With the help of the offered cloud services and DevOps tool it is possible to consolidate the SDLC and bring effects in code safety, testing and utilization. This document also provides an overview of methods, instruments, and outcomes, demonstrating how the CI/CD automation influences the development and operation positively.

关键词： Cloud computing DevOps Codes Scalability Pipelines Continuous integration Security Observability Monitoring Testing

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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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