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Journal of Network Intelligence 2024年第1期9卷 613-628页

作者： Sitenda, Amos Song, Pei-Cheng Chu, Shu-Chuan Chen, Shi-Huang College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Department of Computer Science and Information Engineering Shu-Te UniversityKaohsiung Kaohsiung824 Taiwan

Differential Evolution (DE) is a potent stochastic evolutionary optimization algorithm garnering increasing research attention. Over the years, it has been found applicable in solving diverse real-world problems. DE employs mutation, crossover, and selection operators to guide populations toward optimal or nearly optimal solutions. However, the standard DE mutation strategies have found limitations in balancing exploration and exploitation effectively, thus prompting research into possible improvements. This study introduces a novel mutation strategy named Chebyshev Infused Chaos Mutation Strategy (CICMS). where a chaotic sequence partially guides the process of donor vector generation. Rigorous evaluations were conducted, comparing our modified DE against the standard DE and seven other metaheuristic algorithms, including Genetic Algorithm, Particle Swarm Optimization and Gravitational Search Algorithm. Experiments were performed using the challenging CEC 2014 benchmark functions, consisting of 30 objective functions. Results indicate substantial improvements in convergence speed and solution quality, highlighting the potential of our novel mutation strategy to enhance DE’s practicality in addressing complex optimization problems. This research contributes valuable insights to the dynamic field of optimization algorithms with implications for a wide range of applications. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Chaos theory

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A Comprehensive Review of Modern Methods for Load Prediction in the Smart Grid

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Recent Patents on engineering 2024年第4期18卷 40-51页

作者： Attiwal, Pushpa Indora, Sanjeev Department of Computer Science and Engineering Deenbandhu Chhotu Ram University of Science and Technology Sonepat India

Load forecasting plays a crucial role in mitigating risks for utilities by predicting future usage of commodity markets transmission or supplied by the utility. To achieve this, various techniques such as price elastic demand, climate and consumer response, load analysis, and sustainable energy generation predictive modelling are used. As both supply and demand fluctuate, and weather and power prices can rise significantly during peak periods, accurate load forecasting becomes critical for utilities. By providing brief demand forecasts, load forecasting can assist in estimating load flows and making decisions that prevent overloading. Therefore, load forecasting is crucial in helping electric utilities make informed decisions related to power, load switching, voltage regulation, switching, and infrastructure development. Forecasting is a methodology used by electricity companies to forecast the amount of electricity or power production needed to maintain constant supply as well as load demand balance. It is required for the electrical industry to function properly. The smart grid is a new system that enables electricity providers and customers to communicate in real-time. The precise energy consumption sequence of the consumers is required to enhance the demand schedule. This is where predicting the future comes into play. Forecasting future power system load (electricity consumption) is a critical task in providing intelligence to the power grid. Accurate forecasting allows utility companies to allocate resources and assume system control in order to balance the same demand and availability for electricity. In this article, a study on load forecasting algorithms based on deep learning, machine learning, hybrid methods, bio-inspired techniques, and other techniques is carried out. Many other algorithms based on load forecasting are discussed in this study. Different methods of load forecasting were compared using three performance indices: RMSE (Root Mean Square Err

关键词： Electric power transmission

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Automated Gas Leakage Detection and Control System

Automated Gas Leakage Detection and Control System

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2024 International Conference on Emerging Innovations and Advanced Computing, INNOCOMP 2024

作者： Dange, Varsha Bagde, Samarth Bagul, Siddhi Barsude, Shreya Bhanji, Omkar Vishwakarma Institute of Technology Department of Information Technology Pune India Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350376470

In order to enhance kitchen safety, this system introduces an innovative approach that addresses and prevents potential threats resulting from gas leaks.. Using a Node MCU, MQ5 gas sensor, relay module, and servo motors, we take all the necessary safety measures when a gas leak is detected such as cutting off the electricity by tripping MCB using a relay module, opening the window flaps for air ventilation and turning off the gas knob to stop further diffusion of gas, using servo motors. We ensure that the gas leak is controlled in time and causes minimal or zero damage. Our system is integrated with a mobile application named 'GasGuard' developed in Flutter. This is a real-time mobile application that notifies the user about gas leakage detection. The app ensures remote monitoring of the concentration of the gas effused and provides features such as controlling all devices like opening and closing of windows, turning a gas knob on and off, and switching the electricity on and off remotely. This system ensures full-fledged safety and avoids unattended gas leaks. © 2024 IEEE.

关键词： Sensor nodes

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Curriculum Guided Domain Adaptation in the Dark

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2604-2614页

作者： Jahan, Chowdhury Sadman Savakis, Andreas Rochester Institute of Technology Center for Imaging Science RochesterNY14623 United States Kate Gleason College of Engineering Rochester Institute of Technology Department of Computer Engineering RochesterNY14623 United States

Addressing the rising concerns of privacy and security, domain adaptation in the dark aims to adapt a black-box source trained model to an unlabeled target domain without access to any source data or source model parameters. The need for domain adaptation of black-box predictors becomes even more pronounced to protect intellectual property as deep learning based solutions are becoming increasingly commercialized. Current methods distill noisy predictions on the target data obtained from the source model to the target model, and/or separate clean/noisy target samples before adapting using traditional noisy label learning algorithms. However, these methods do not utilize the easy-to-hard learning nature of the clean/noisy data splits. Also, none of the existing methods are end-to-end, and require a separate fine-tuning stage and an initial warmup stage. In this work, we present Curriculum Adaptation for Black-Box (CABB) which provides a curriculum guided adaptation approach to gradually train the target model, first on target data with high confidence (clean) labels, and later on target data with noisy labels. CABB utilizes Jensen-Shannon divergence as a better criterion for clean-noisy sample separation, compared to the traditional criterion of cross-entropy loss. Our method utilizes cotraining of a dual-branch network to suppress error accumulation resulting from confirmation bias. The proposed approach is end-to-end trainable and does not require any extra fine-tuning stage, unlike existing methods. Empirical results on standard domain adaptation datasets show that CABB outperforms existing state-of-the-art black-box DA models and is comparable to white-box domain adaptation models. © 2020 IEEE.

关键词： Curricula

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Heterostructure interfaces and dimensionally transformed GaO/ZnO nanostructures for room-temperature hydrogen gas sensors

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International Journal of Hydrogen Energy 2024年 64卷 889-895页

作者： Huang, Bohr-Ran Kathiravan, Deepa Hsieh, Hsin-Rong Saravanan, Adhimoorthy Graduate Institute of Electro-Optical Engineering and Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei106 Taiwan

This work demonstrates the significant prospective of GaO/ZnO hybrid nanostructures for high-performance hydrogen gas sensing. The wavy grains of GaO support the structural transformation of ZnO nanorod into nanosheets by providing appropriate nucleation sites for ZnO growth. Compared to pure ZnO and GaO, GaO/ZnO exhibited a sixfold increase in H2 response (60% at 500 ppm), excellent selectivity towards H2 among other interfering gases, and outstanding long-term stability (retaining 98% of its initial response after 36 days). This greater performance rises from the unique morphology and different surface interactions within the GaO/ZnO hybrid nanostructure, assisting multiple adsorption sites and various hydrogen interaction pathways. Alloying effects, internal strain, and the presence of both GaO and ZnO regions within the nanosheets further contribute to enhanced sensitivity, selectivity, and stability. © 2024 Hydrogen Energy Publications LLC

关键词： Zinc oxide

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Integrating a Rule-Based Approach to Malware Detection with an LSTM-Based Feature Selection Technique

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SN computer Science 2023年第6期4卷 737页

作者： Bhardwaj, Sonam Dave, Mayank Department of Computer Engineering National Institute of Technology Kurukshetra Kurukshetra India

technology has amplified malware activity, affecting network and users. Before being forwarded to the next host, network traffic must be dynamically analysed for malware. By exploiting network vulnerabilities, attackers gain control of the system and implement their own network rules to enable malicious traffic. Yet, another recursive acronym (YARA) rules are effective string and pattern-matching malware analysis approaches. The quality and amount of YARA rules utilized in analysis determine its effectiveness. YARA rules focus on whether to activate a rule for a suspicious sample after examining its rule condition. YARA rules rely on binary conclusion on malware analysis, which may limit its use and results. Thus, the proposed approach selects malware features using the ML-based LSTM model. Rule-based traffic analysis and long-short term memory (LSTM)-based feature selection strengthen the malware detection model in the proposed approach. By comparing performance results with and without LSTM-based feature (parameter) selection, this research assesses model integrity. Due to LSTM-based feature selection, the model achieved its best accuracy of 97%, proving its suitability for malware detection on diverse datasets belonging to different network environments. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Feature importance Feature selection LSTM Malware detection Rule-based YARA rules

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AquaMap: Empowering Communities to Report and Map Water-Related Issues in Real-Time with Deep Learning 5th

AquaMap: Empowering Communities to Report and Map Water-Rela...

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5th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Nalla, Harshitha Lakshmi Durga Bhuchupalli, Anusha Addala, Tejasree Sabbineni, Yasasri Geetha, Koppisetti Sravya Aasha, Ghantasala Bonthu, Sridevi Department of Computer Science and Engineering Vishnu Institute of Technology Andhra Pradesh Bhimavaram India

ISBN: (纸本)9783031770746

In today’s era of rapid urbanization and environmental challenges, effective disaster and crisis management demand innovative solutions. This paper presents a novel approach focusing on community-level water-related issues through an intelligently designed application. The primary objective is to develop a user-friendly platform facilitating the reporting of water-related problems by both social media posts and the general public, subsequently enabling prompt action by relevant government authorities. Leveraging deep learning techniques and user-generated data, our solution introduces real-time detection and classification of six distinct water-related problems. A custom dataset is curated to train a ResNet-18 model, achieving an impressive accuracy of 73%. The application, developed with a React-based frontend and Flask-powered backend, acts as a centralized hub for reporting and managing water issues. Notably, it employs user-inputted data to accurately pinpoint problem locations, thereby enhancing the precision of reporting. By presenting a holistic approach, this research significantly contributes to the development of efficient crisis management and response strategies for water-related disasters. © ICST institute for computer Sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Budget control

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Optimizing Time Series Denoising with an Improved Singular Spectrum Analysis Approach

Optimizing Time Series Denoising with an Improved Singular S...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2025

作者： Gupta, Muktesh Maulana Azad National Institute of Technology Department of Computer Science and Engineering Bhopal India

ISBN: (纸本)9798331529833

A time series is a two-dimensional dataset with an ordered sequence and a time. It represents the essential properties of the process and is applicable across various domains. Time series captured by sensors often include random oscillations that obscure the valuable insights. Denoising the time series aims to remove fluctuations and outliers. However, there are many challenges, as noise frequently overlaps with meaningful patterns. Existing approaches often fail to accomplish this task efficiently due to the non-stationary nature of the time series. Based on singular spectrum analysis, the proposed methodology successfully isolates signals from noise. The methodology breaks down time series into discrete components and selects only noise-free components. The proposed method is well-suited to complicated and dynamic data. When tested on two synthetic datasets, the proposed strategy outperforms existing approaches, keeping essential patterns while minimizing noise. © 2025 IEEE.

关键词： Time series analysis

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Advanced Fraud Detection in Smart Grids Using Deep Neural Networks

Advanced Fraud Detection in Smart Grids Using Deep Neural Ne...

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2025 International Conference on Advances in Renewable Energy and Electric Vehicles, AREEV 2025

作者： Teja, Pallapothu Sasank Kumar, Pane Pavan Sankari, M. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai600119 India

ISBN: (纸本)9798331529574

Fraud detection in smart grids is critical to ensure reliable and efficient energy distribution, preventing significant financial losses and maintaining grid stability. This project presents an advanced fraud detection system utilizing deep neural networks (DNN) to automatically identify and prevent fraudulent activities such as energy theft, meter tampering, and unauthorized energy consumption. The DNN model processes vast amounts of data generated by smart meters and other grid sensors, learning complex patterns and anomalies that indicate potential fraud. By integrating feature extraction techniques and real-time monitoring, the system is designed for scalability and accuracy, capable of handling high-dimensional data from modern energy grids. Additionally, this solution improves the grid's resilience and operational efficiency by enabling proactive fraud detection and reducing false positives, helping utility companies safeguard their assets. © 2025 IEEE.

关键词： Smart power grids

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A Comprehensive Review Analysis on Osteoarthritis Using Deep Learning and Machine Learning Technologies for Arthritis Detection 7

A Comprehensive Review Analysis on Osteoarthritis Using Deep...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Thomas, Tinu Prince Mary, S. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India St. Joseph's College of Engineering and Technology Department of Electronics and Computer Engineering Palai India

ISBN: (纸本)9798350372816

Osteoarthritis is a prevalent degenerative knee joint disease, causing considerable pain and mobility issues, thus impacting the independence and quality of life of millions. Deep learning and Machine Learning techniques have emerged as promising tools for enhancing the diagnosis, prognosis, and treatment of osteoarthritis (OA). These technologies allow for the extraction of valuable insights from medical images, clinical data, and genetic information, thereby improving patient care and management strategies. By employing advanced algorithms, healthcare professionals can improve early detection, and treatment plans, and accurately predict disease progression, ultimately leading to better outcomes for OA patients. This paper proposes a comprehensive review of various DL-based and ML-based methods for OA, spanning the years 2020 to 2024. It aims to provide valuable insights for researchers interested in exploring different models and potentially inspiring the development of integrated approaches for even more effective OA detection. © 2024 IEEE.

关键词： Joints (anatomy)

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