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Sustainable energy management in the AI era: a comprehensive analysis of ML and DL approaches

Computing 2025年第6期107卷 1-64页

作者： Javed, Haseeb Eid, Fatma El-Sappagh, Shaker Abuhmed, Tamer Department of Computer Science and Engineering College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Technology Management Stony Brook University NY11794 United States Applied Artificial Intelligence College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Faculty of Computer Science and Engineering Galala University Suez435611 Egypt Information Systems Department Faculty of Computers and Artificial Intelligence Benha University Banha13518 Egypt

This study comprehensively analyzes the application of innovative deep learning (DL) and machine learning (ML) techniques in smart energy management systems (EMSs), with an emphasis on load forecasting, demand response, and the development of smart energy sectors. The application of various ML and DL models were examined in over 200 studies from 2014 to 2024 in an electrical network's EMS to highlight the key benefits and advances made by each technology for the sustainable management systems in energy sector. The findings emphasize DL and ML models’ enhanced precision and predictive capabilities in load forecasting, their efficacy in enabling efficient demand response mechanisms, and their significance in supporting the development of smart energy sectors. Furthermore, recommendations are made based on the survey results to assist in incorporating these techniques into EMS frameworks, such as investment in data infrastructure, model training and validation, and collaboration between researchers, industry experts, and policymakers. The study also discusses the limitations identified in the literature, such as limited real-world implementations, challenges regarding quality and data availability, and the need for enhanced ML and DL model interpretability. Addressing these limitations can assist in increasing the application and efficacy of ML and DL techniques in EMSs, enabling a more efficient and sustainable energy landscape. Finally, this study facilitates researchers' exploration of ML and DL in energy management, highlighting relevant limitations, strengths, and alternative approaches associated with sustainable energy management. It also indicates potential future research directions for further investigation. © The Author(s) 2025.

关键词： Power management (telecommunication)

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Collision avoidance and routing based on location access (CARLA) of mobile robots

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Neural Computing and Applications 2025年第9期37卷 6401-6430页

作者： ElSayyad, Shimaa Ezzat Saleh, Ahmed I. Ali, Hesham A. Saraya, M.S. Rabie, Asmaa H. Abdelsalam, Mohamed M. Computers Engineering Department MISR Higher Institute for Engineering and Technology Mansoura Round Road Mansoura City35516 Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt Dean of Faculty of Artificial Intelligence Delta University for Science and Technology Gamasa Mansoura Egypt Faculty of Engineering Mansoura National University Gamasa Mansoura Egypt

The paper introduces a new path-planning robotic system methodology called Collision Avoidance and Routing based on Location Access (CARLA) for use in critical environments such as hospitals and crises where quick action and saving human lives are vital. The main focus of our framework is on accuracy and fast responses, such as delivering tools or items in a specific area while avoiding collisions with other robots and obstacles. CARLA is designed to provide quick responses during emergencies, unlike most existing algorithms that are integrated into site control units or distributed among mobile robots on-site. By being loaded onto a remote server node rather than individual robots, CARLA helps to conserve the robots' capabilities, hardware resources, and power consumption. Additionally, our system utilizes cloud computing and Fog servers technology to improve data transmission times between the cloud and smart devices, especially for applications with strict timing requirements like emergency response. The Fog platform is also leveraged to enhance on-site access to real-time interaction and location-based services by bringing processing power closer to the robots from far-off Cloud servers. CARLA has various applications, such as in factories and warehouses, where mobile robots need to be selected and directed by a central control system remotely. The proposed framework consists of three main modules: Robot Knowledge Module, Robot Selection Module, and Route Reservation Module, which will all be discussed in detail in this paper. The results of simulations using this framework show that the robots have improved flexibility and efficiency in terms of computing paths and successfully fulfiling requests without colliding, compared to traditional methods used in similar scenarios. © The Author(s) 2025.

关键词： Mobile robots

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Attention Markets of Blockchain-Based Decentralized Autonomous Organizations

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1370-1380页

作者： Juanjuan Li Rui Qin Sangtian Guan Wenwen Ding Fei Lin Fei-Yue Wang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China IEEE

The attention is a scarce resource in decentralized autonomous organizations(DAOs),as their self-governance relies heavily on the attention-intensive decision-making process of“proposal and voting”.To prevent the negative effects of pro-posers’attention-capturing strategies that contribute to the“tragedy of the commons”and ensure an efficient distribution of attention among multiple proposals,it is necessary to establish a market-driven allocation scheme for DAOs’***,the Harberger tax-based attention markets are designed to facilitate its allocation via continuous and automated trading,where the individualized Harberger tax rate(HTR)determined by the pro-posers’reputation is ***,the Stackelberg game model is formulated in these markets,casting attention to owners in the role of leaders and other competitive proposers as *** equilibrium trading strategies are also discussed to unravel the intricate dynamics of attention ***,utilizing the single-round Stackelberg game as an illustrative example,the existence of Nash equilibrium trading strategies is ***,the impact of individualized HTR on trading strategies is investigated,and results suggest that it has a negative correlation with leaders’self-accessed prices and ownership duration,but its effect on their revenues varies under different *** study is expected to provide valuable insights into leveraging attention resources to improve DAOs’governance and decision-making process.

关键词： Attention decentralized autonomous organizations Harberger tax Stackelberg game.

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Development of a Smart Car Parking System

Development of a Smart Car Parking System

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artificial intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Pawan Dilip Chaudhari Mayur Muneshwar Tilak Kale Chatianya Meghe Shital Hajare Faculty of Engineering and Technology(DMIHER) Artificial Intelligence and Data Science Wardha

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

This paper discusses how the Internet of Things (IoT) can help solve parking problems in urban areas by proposing a comprehensive smart parking approach comprising three major components: a parking management application, customer-managing systems, and an Internet-of-Things enabling platform. To detect vacancies and alert drivers to them[1]. The system reduces idle time spent looking for free spaces during peak seasons or holidays by offering real-time updates, making driving around town less confusing and time-consuming. As smart cities continue to expand, managing this growth is no simple task. It is already evident that they need smart parking systems more than ever before. Thus data exchange between sensor equipment and portable devices takes place through collaboration among hardware with software resources within a single unit or module [5]. The abstract emphasizes the significance of IoT-based friendly smart ensemble systems to help improve parking administration processes, lower transportation delays on the roadways plus enhance user experience behind the wheel substantially within growing metropolis areas like these.

关键词： Pollution Smart cities Wheels Medical services Automated parking User experience Real-time systems Software Internet of Things Vehicles

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Analysis and Classification of Fake News Using Sequential Pattern Mining

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Big data Mining and Analytics 2024年第3期7卷 942-963页

作者： M.Zohaib Nawaz M.Saqib Nawaz Philippe Fournier-Viger Yulin He College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060 Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) Shenzhen 518107China Department of Computer Science Faculty of Computing and Information TechnologyUnivesity of SargodhaSargodha 40100Pakistan

Disinformation,often known as fake news,is a major issue that has received a lot of attention *** researchers have proposed effective means of detecting and addressing *** machine and deep learning based methodologies for classification/detection of fake news are content-based,network(propagation)based,or multimodal methods that combine both textual and visual *** introduce here a framework,called FNACSPM,based on sequential pattern mining(SPM),for fake news analysis and *** this framework,six publicly available datasets,containing a diverse range of fake and real news,and their combination,are first transformed into a proper ***,algorithms for SPM are applied to the transformed datasets to extract frequent patterns(and rules)of words,phrases,or linguistic *** obtained patterns capture distinctive characteristics associated with fake or real news content,providing valuable insights into the underlying structures and commonalities of ***,the discovered frequent patterns are used as features for fake news *** framework is evaluated with eight classifiers,and their performance is assessed with various *** experiments were performed and obtained results show that FNACSPM outperformed other state-of-the-art approaches for fake news classification,and that it expedites the classification task with high accuracy.

关键词： disinformation fake news sequential pattern mining(SPM) frequent patterns classification

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Community Clustering on Fraud Transactions Applied the Louvain-Coloring Algorithm

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International Journal of Electronics and Telecommunications 2023年第3期69卷 593-598页

作者： Mardiansyah, Heru Suwilo, Saib Nababan, Erna Budiarti Efendi, Syahril Department Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department Mathematics Faculty of Mathematics and Natural Sciences Universitas Sumatera Utara Medan Indonesia Department Data Science and Artificial Intelligence Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia

The contribution main from this research is modularity and better processing time in detecting community by using K-1 coloring. Testing performed on transaction datasets remittance on P2P platforms where the Louvain Coloring algorithm is better in comparison to Louvain Algorithm data used is data transfer transactions made by customers on the P2P Online platform. The data is the User data that has information transfer transactions, Card data that has information card, IP data that has IP information, and Device data that has information device. Every user owns unique 128-bit identification, and other nodes representing card, device, and IP are assigned a random UUID. The Device node has the guide, and device properties. IP nodes only have property guide and node User has property fraud Money Transfer, guide, money Transfer Error Cancel Amount, first Charge back Date. Each node has a unique 128-bit guide, with the amount whole of as many as 789,856 nodes. Application technique K-1 staining on Louvain algorithm shows enhancement value modularity and better processing time for detecting community on the network large scale. Through a series of exercises and tests carried out in various scenarios, it shows that the experiments carried out in this paper, namely the Louvain Coloring algorithm, are more effective and efficient than the Louvain algorithm in scenario 1,3, and 5 meanwhile For Scenarios 2 and 4 Louvain Algorithm is better. © 2023 Polish Academy of sciences. All rights reserved.

关键词： Clustering algorithms

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Cloud Storage and Secure File Sharing Using Blockchain with Xchacha20 and Shake Cryptography 16

Cloud Storage and Secure File Sharing Using Blockchain with ...

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16th IEEE International Conference on Computational intelligence and Communication Networks, CICN 2024

作者： Jayachitra, J. Arshad, H. Muthukumaran, R. Ifet College of Engineering Villupuram India Information Technology Ifet College of Engineering Villupuram India Artificial Intelligence and Data Science Ifet College of Engineering Villupuram India

ISBN: (纸本)9798331505264

Blockchain technology revolutionized record keeping for secure transactions but is not naturally file storage-oriented. This means that to share files securely and without central control systems, their secured decentralized sharing system needs to be developed. we propose an efficient file-sharing and storage system based on cryptographic hash function SHAKE-256 and end-to-end encryption using XChaCha20. The system highlights emphasis on privacy and integrity of data based on flexible access control policies for authorized users to access specific groups of data. Architecture will eliminate dependence on central storage solutions that are prime to vulnerabilities and single points of failure. Our solution offers a scalable and secure environment for applications in need of decentralized file storage by combining blockchain with more enhanced cryptographic techniques. Experimental results validate that the system is effective in keeping data confidential, integrity, and accessibility so it is eligible for privacy-sensitive use cases. © 2024 IEEE.

关键词： Blockchain Cryptography Decentralized Storage Secure File Sharing SHAKE-256 XChaCha20

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Early Landslide Detection Using Multi-Sensor data and IoT-Based Alert Mechanism 8

Early Landslide Detection Using Multi-Sensor Data and IoT-Ba...

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8th International Conference on Electronics, Communication and Aerospace technology, ICECA 2024

作者： Rajas, Neha Kandare, Kalash Bhadre, Ajay Talele, Ekant Gurav, Aarti Wani, Himanshu Vishwakarma Institute of Technology Artificial Intelligence and Data Science Pune India Vishwakarma Institute of Technology Mechanical Engineering Pune India ReLVishwakarma Institute of Technology Pune India

ISBN: (纸本)9798350367904

The 'Early Landslide Detection System' utilizes IoT to monitor and detect landslide risks in real-time. An artificial rain setup simulates rainfall on a model mountain, with a rain sensor, soil moisture sensors, and vibration sensors tracking rainfall, soil saturation, and ground movement. data is logged on ThingSpeak, and once significant vibrations are detected, the system alerts users through the Blynk app via mobile notifications and emails. This system provides an efficient, cost-effective solution for early landslide detection, enhancing disaster preparedness and minimizing potential damage. © 2024 IEEE.

关键词： Rain

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Cardiovascular Disease Prediction with Machine Learning Algorithms and Interpretation using Explainable AI methods: LIME & SHAP 3

Cardiovascular Disease Prediction with Machine Learning Algo...

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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Pratheek, N. Balaji Yashas, D.H. Bhanu Prakash, Y.R. Bharadwaj, Anirudh M Kodapalli, Ashwini Rao, Trupthi Global Academy of Technology Dept. of Information Science & Engineering Karnataka Bengaluru India Global Academy of Technology Dept of Artificial Intelligence & Data Science Karnataka Bengaluru India

ISBN: (纸本)9798350354171

Every year a third of the population dies due to cardiovascular disease and these numbers grow exponentially which is one of the leading cause of deaths. Cardiovascular disease is a group of disorders that affect the heart and blood vessels. These disorders often result from high cholesterol, high blood pressure, smoking, obesity, and many other medical attributes. Therefore, early intervention and diagnosis of cardiovascular diseases improves the patient's quality of life. In this era of AI and ML, the ML techniques integrated with explainable AI are a promising tool for implementing and getting improved accuracies in the detection of cardiovascular disease. With the usage of Binary classification dataset on heart disease this paper explored a comparative study with various machine learning algorithms which includes single classifiers (LR, KNN, DT, SVM), bagging(RF), and boosting(XG, ADA, CAT, GRADIENT), ensemble machine learning model using RF, GRAD, ADA, XG, CAT helped in achieving exceptional accuracy of 89.13%, ensemble model with hyperparameter tuning techniques (GridsearchCV and RandomsearchCV) and implementation XAI methods(lime and sharp). In a comparative analysis of boosting algorithms specifically CAT and ADA boost outperformed other algorithms in identifying cardiovascular disease. The hyperparameter tuning techniques help improve the accuracy of Random forest classifier method to an impressive 88.26%. Additionally, the use of XAI methods such as LIME and SHARP provides global and local insights into model behavior and feature importance and represents it graphically which helps in better *** use of these machine learning models in remote areas where diagnosis and specialized treatment of heart-related disease is limited this model plays a pivotal role by helping in early intervention and diagnosis of cardiovascular disease. © 2024 IEEE.

关键词： Diagnosis

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Pneumonia Detection from X- Ray Images using Convolutional Neural Networks 8

Pneumonia Detection from X- Ray Images using Convolutional N...

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8th IEEE International Conference on science, technology, engineering and Mathematics, ICONSTEM 2023

作者： Josephin Shermila, P. Anusha, Alapati Devi Akila, M. Abirami, S. R.M.K. College of Engineering and Technology Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798350347791

Pneumonia is a condition in which the air sacs in the lungs, known as alveoli, become inflamed due to an infection. This inflammation causes the affected air sacs to fill up with fluid or pus. Pneumonia is a condition in which the air sacs in the lungs, known as alveoli, become inflamed due to an infection. This inflammation causes the affected air sacs to fill up with fluid or *** to severe symptoms might range from having a cough that produces mucus (a sticky substance), to having a fever, chills, and difficulty breathing. Your age, general health, and the cause of your illness all affect how serious your case of pneumonia *** 2020,COVID pandemic, pneumonia had been a life-threatening disease. Many people have expired during the COVID crisis. However, 50% of the people full of COVID were recovered by medication. On the opposite hand people faced with pneumonia, if tormented by COVID -19 hopelessly lost their lives. The patients littered with pneumonia were 54.64% among severe COVID-19 cases and 5% mediocre COVID-19 *** project requires the proper x-ray image of the patient. The preference is on x-ray since it's affordable for people too. X-ray is cost-efficient in comparison to CT-SCAN. © 2023 IEEE.

关键词： Air

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