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IEEE Workshop on High Performance Switching and Routing

作者： Xin Liu Yangming Zhao Adel Sadek Chunming Qiao School of Engineering and Applied Sciences State University of New York at Buffalo School of Computer Science and Technology University of Science and Technology of China

We envision a wide deployment of battery-operated edge devices such as data kiosk, to provide pervasive data collection and dissemination services. Although these data kiosks can be powered by green energy sources, their batteries may deplete overtime, and have to recharged frequently to prevent power outages and potential loss of critical data. In this paper, we propose an edge device recharging system using autonomous electric vehicles as Mobile Chargers (MCs). First, we determine the numbers and locations of Dispatching Centers (DCs) for these MCs, each of which will be responsible for recharging some edge devices in a surrounding area. Then, we plan optimal routes of the MCs in order to minimize the number of MCs needed to recharge all edge devices before a deadline. To reduce the time complexity involved in optimizing the routes, we cluster the edge devices and propose efficient algorithms to plan the route of MCs for each cluster based on the relaxation and rounding of a Mixed Integer Linear Programming (MILP) model. Extensive simulations show that our approach can reduce the number of MCs required to recharge all edge devices before a deadline by up to 71.93% compared with greedy-based heuristic algorithms.

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Challenges in Recycling Lead Acid Battery and Lithium-Ion Battery: A Comprehensive Review

Challenges in Recycling Lead Acid Battery and Lithium-Ion Ba...

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Smart Systems for Electrical, Electronics, Communication and computer Engineering (ICSSEECC), International Conference on

作者： S Anuradha R. Manimegalai M. Devasena Department of Computer Science PSG Institute of Technology and Applied Research Coimbatore India Global Sanitation Centre of Excellence Indian Institute of Technology Palakkad India

ISBN: (数字)9798350378177

ISBN: (纸本)9798350378184

Battery recycling has become essential due to the rising demand for batteries and the associated risk to the environment caused by the disposal of batteries in various industries. It has become a global concern to recover significant minerals and metals from discarded batteries. The rapid expansion of the electric vehicle market causes a significant challenge in managing battery waste where Lithium-ion batteries (LIBs) are used. Lithium-ion batteries are the best alternative to energy storage devices, which encourages the development of electric vehicles, which in turn reduces greenhouse gas emissions (GHG) pollution in the transportation sector. 28 % of greenhouse gas emissions and 24% of the world's energy consumption are due to the transportation industry globally. The increasing usage of LIBs can result in the accumulation of spent LIBs. To prevent improper disposal of battery waste, recycling approaches can be adopted, which can in turn help reduce landfill waste and enhance environmental quality. This study compares the difficulties of recycling Lead Acid Battery (LAB) and Lithium-Ion Battery (LIB) wastes, emphasizing the need to implement efficient battery recycling procedures towards a circular economy.

关键词： Lithium-ion batteries Lead acid batteries Wind energy Transportation industry Transportation Recycling Batteries

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Digital Sovereignty in the Era of Industry 5.0: Challenges and Opportunities

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Procedia computer science 2025年 254卷 108-117页

作者： Sanjay Misra Kousik Barik Petter Kvalvik Department of Applied Data Science Institute for Energy Technology Halden Norway Department of Computer Sciences Universidad de Alcalá Alcalá de Henares Madrid Spain Digital Systems Institute for Energy Technology Halden Norway

This paper examines the core concept of digital sovereignty within the Industry 5.0 framework, highlighting the importance of sovereign control over data, digital infrastructure, and digital artifacts such as algorithms, models, and visualizations. Trustworthy, autonomous, distributed architectures are essential to building resilience and independence in a multipolar world. Through a comprehensive analysis of existing literature, this paper identifies key challenges and opportunities in achieving digital sovereignty, using a PRISMA chart to select relevant studies from six databases systematically. The article offers strategic insights on extending digital technologies to ensure digital sovereignty while encouraging independent and ethical innovation. By identifying research gaps and proposing future study areas, this work aims to inspire further exploration and development in securing digital sovereignty, supporting resilient digital value chains, and advancing the goals of Industry 5.0.

关键词： Industry 5.0 Digital Sovereignty Privacy Cybersecurity AI tools

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A Real-Time Privacy System for Electric Vehicles using Blockchain technology 5

A Real-Time Privacy System for Electric Vehicles using Block...

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5th International Conference on Information technology for Education and Development, ITED 2022

作者： Omaji, Samuel Acheme, Ijegwa David Makinde, Ayodeji Samuel Akogwu, Blessing Omojo Yahaya, Adamu Sani Alhakami, Hosam Alhakami, Wajdi Edo State University Uzairue Department of Computer Science Edo State Nigeria Advanced Research Centre SHESTCO Applied Mathematics and Simulation Abuja Nigeria Bayero University Department of Information Technology Kano Nigeria College of Computer and Information Systems Umm Al-Qura University Department of Computer Science Makkah Saudi Arabia College of Computers and Information Technology Taif University Department of Information Technology Saudi Arabia

ISBN: (纸本)9781509064229

In a vehicular energy network (VEN), an efficient transfer of energy among vehicles is realized while increasing the mobility of vehicles in a large geographic location. However, the security and privacy of vehicle owners are not fully explored in the existing literature. Today, because of the exponential rise in the number of vehicle owners in VEN, the problems of traffic congestion, energy consumption, etc., are created. The issues can be alleviated if certain information about vehicles such as the speed, energy consumption price, and location, is efficiently collected. Besides, effective communication is required for ensuring proper and authentic dissemination of traffic information among vehicles while preserving their data privacy. As a consequence, our study suggests a blockchain-based system for privacy preservation. In the proposed system, trust among vehicles is achieved using the Nash bargaining optimization method. The method is employed to maximize the payoffs of vehicles. Additionally, an improved super-increasing weighted sequence is used to preserve the privacy of vehicles by considering two essential parameters: energy consumption and price. Furthermore, the Paillier encryption mechanism is employed to securely transmit vehicles' information across the network. The proposed system has undergone a security study, which reveals that it is resistant to privacy and security-related threats. The performance of the proposed system shows that the system is efficient and reliable. © 2022 IEEE.

关键词： Blockchain

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A New Multi-Agent Feature Wrapper Machine Learning Approach for Heart Disease Diagnosis

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computers, Materials & Continua 2021年第4期67卷 51-71页

作者： Mohamed Elhoseny Mazin Abed Mohammed Salama A.Mostafa Karrar Hameed Abdulkareem Mashael S.Maashi Begonya Garcia-Zapirain Ammar Awad Mutlag Marwah Suliman Maashi Department of Computer Science College of Computer Information TechnologyAmerican University in the Emirates503000United Arab Emirates College of Computer Science and Information Technology University of AnbarRamadi31001Iraq Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn MalaysiaJohor86400Malaysia College of Agriculture Al-Muthanna UniversitySamawah66001Iraq Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11451Saudi Arabia eVIDA Lab University of DeustoBilbao48007Spain Department of Pure Science Ministry of EducationGeneral Directorate of CurriculaBaghdadIraq Department of Medical Laboratory Science Faculty of Applied Medical ScienceKing Abdulaziz UniversityJeddah21589Saudi Arabia

Heart disease(HD)is a serious widespread life-threatening *** heart of patients with HD fails to pump sufcient amounts of blood to the entire *** the occurrence of HD early and efciently may prevent the manifestation of the debilitating effects of this disease and aid in its effective *** methods for diagnosing HD are sometimes unreliable and insufcient in analyzing the related *** an alternative,noninvasive medical procedures based on machine learning(ML)methods provide reliable HD diagnosis and efcient prediction of HD ***,the existing models of automated ML-based HD diagnostic methods cannot satisfy clinical evaluation criteria because of their inability to recognize anomalies in extracted symptoms represented as classication features from patients with *** this study,we propose an automated heart disease diagnosis(AHDD)system that integrates a binary convolutional neural network(CNN)with a new multi-agent feature wrapper(MAFW)*** MAFW model consists of four software agents that operate a genetic algorithm(GA),a support vector machine(SVM),and Naïve Bayes(NB).The agents instruct the GA to perform a global search on HD features and adjust the weights of SVM and BN during initial classication.A nal tuning to CNN is then performed to ensure that the best set of features are included in HD *** CNN consists of ve layers that categorize patients as healthy or with HD according to the analysis of optimized HD *** evaluate the classication performance of the proposed AHDD system via 12 common ML techniques and conventional CNN models by using across-validation technique and by assessing six evaluation *** AHDD system achieves the highest accuracy of 90.1%,whereas the other ML and conventional CNN models attain only 72.3%–83.8%accuracy on ***,the AHDD system proposed herein has the highest capability to identify patients with *** system can be used by medical

关键词： Heart disease machine learning multi-agent feature wrapper model heart disease diagnosis HD cleveland datasets convolutional neural network

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SEMU5G: an Emulator for Improving Model Accuracy and Performance Evaluation of 5G Network Core Architectures based on SDN 10

SEMU5G: an Emulator for Improving Model Accuracy and Perform...

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10th International Conference on Wireless and Telematics, ICWT 2024

作者： Zaki Hamidi, Eki Ahmad Lazuardi, Arga Valen Rahmansyah, Muhammad Litfan Novel Bajri, Salman Alfarizi Tulloh, Rohmat Negara, Ridha Muldina Ahdan, Syaiful Jupriyadi UIN Sunan Gunung Djati Bandung Electrical Engineering Department Bandung Indonesia Telkom University Telecommunication Technology School of Applied Science Bandung Indonesia The University Center of Excellence Intelligent Sensing-IoT Telkom University Indonesia Telkom University School of Electrical Engineering Bandung Indonesia Universitas Teknokrat Indonesia Faculty of Engineering & Computer Science Indonesia Institut Teknologi Bandung School of Electrical Engineering and Informatics Bandung Indonesia Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Bandarlampung Indonesia

ISBN: (纸本)9798350377057

The rapid advancement of 5G Radio Access Network (RAN) architecture is facilitating the construction of 5G networks, marking a significant milestone in telecommunications evolution. Given the complexity of the 5G core architecture, traditional simulation methods are insufficient, necessitating novel approaches. Emulator systems are crucial for creating dynamic, controlled environments that enable exploration of real-world scenarios without physical constraints. SEMU5G, a 5G core emulator, SDN controller, and RAN simulator, utilizes Open5GS implemented in Docker Container for test system flexibility and isolation. Additionally, an SDN controller is integrated to monitor data flows in the User Plane Function (UPF) and gNB simulated by UERANSIM in Mininet-WiFi. This comprehensive integration facilitates effective and flexible real-world exploration, providing a dynamic and controlled test environment for 5G core research. Scenario testing comprises two stages: firstly, a fixed network topology is employed to compare resource usage and confirm successful SEMU5G integration. Secondly, a mobile network topology is utilized to implement a mobile device scenario and compare the Quality of Service (QoS) of SEMU5G with other available emulators. These stages ensure thorough evaluation of SEMU5G's performance and its comparative advantage over existing solutions. © 2024 IEEE.

关键词： Wi-Fi

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A New Rhythm in A1: Convolutional Neural Networks for Music Genre Classification 18

A New Rhythm in A1: Convolutional Neural Networks for Music ...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Javed, Huzaifa Arshad, Usama Abideen, Zain Ul Ali, Raja Hashim BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany

ISBN: (纸本)9798350328172

Despite the significance of music genre classification in audio identification, it remains under-explored within AI research. This tool is crucial for personalized music recommendations and similar music detection. We have developed an efficient AI model that leverages Convolutional Neural Networks (CNNs), offering high-precision genre identification when integrated into a graphical user interface. The model effectively extracts audio features like Mel Frequency Cepstral Coefficients (MFCCs), zero-crossing rate, and tempo. Testing results reveal strong performance in genre prediction across diverse tracks, affirming the model's ability to discern unique characteristics of various music genres. This performance not only attests to the model's capability in discerning the unique characteristics inherent to different music genres but also suggests that it can effectively generalize to novel, unseen data. Our model lays the groundwork for future enhancements and demonstrates the potential of AI in transforming the music industry - from personalized music playlists to exploratory recommendation systems. The success of this model paves the way for more intricate applications of AI within music analysis. © 2023 IEEE.

关键词： Convolution

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Optimized efficient attention-based network for facial expressions analysis in neurological health care

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computers in Biology and Medicine 2024年 179卷 108822-108822页

作者： Munsif, Muhammad Sajjad, Muhammad Ullah, Mohib Tarekegn, Adane Nega Cheikh, Faouzi Alaya Tsakanikas, Panagiotis Muhammad, Khan Sejong University Seoul143-747 Korea Republic of Digital Image Processing Lab Department of Computer Science Islamia College Peshawar25000 Pakistan Department of Computer Science Norwegian University for Science and Technology Gjøvik2815 Norway Department of Computer Science Norwegian University for Science and Technology Gjøvik2815 Norway Institute of Communication and Computer Systems National Technical University of Athens Athens15773 Greece Department of Applied Artificial Intelligence School of Convergence College of Computing and Informatics Sungkyunkwan University Seoul03063 Korea Republic of

Facial Expression Analysis (FEA) plays a vital role in diagnosing and treating early-stage neurological disorders (NDs) like Alzheimer's and Parkinson's. Manual FEA is hindered by expertise, time, and training requirements, while automatic methods confront difficulties with real patient data unavailability, high computations, and irrelevant feature extraction. To address these challenges, this paper proposes a novel approach: an efficient, lightweight convolutional block attention module (CBAM) based deep learning network (DLN) to aid doctors in diagnosing ND patients. The method comprises two stages: data collection of real ND patients, and pre-processing, involving face detection and an attention-enhanced DLN for feature extraction and refinement. Extensive experiments with validation on real patient data showcase compelling performance, achieving an accuracy of up to 73.2%. Despite its efficacy, the proposed model is lightweight, occupying only 3MB, making it suitable for deployment on resource-constrained mobile healthcare devices. Moreover, the method exhibits significant advancements over existing FEA approaches, holding tremendous promise in effectively diagnosing and treating ND patients. By accurately recognizing emotions and extracting relevant features, this approach empowers medical professionals in early ND detection and management, overcoming the challenges of manual analysis and heavy models. In conclusion, this research presents a significant leap in FEA, promising to enhance ND diagnosis and *** code and data used in this work are available at: https://***/munsif200/Neurological-Health-Care. © 2024 Elsevier Ltd

关键词： Feature extraction

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Optimizing Wastewater Treatment Plant Operations: A Machine Learning Approach for Energy Consumption and Climate Dynamics Analysis 8

Optimizing Wastewater Treatment Plant Operations: A Machine ...

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8th IET Smart Cities Symposium, SCS 2024

作者： Elshabrawy, Mahmoud Rizk, Faris H. Zaki, Ahmed Mohamed Eid, Marwa M. Takieldeen, Ali E. Ibrahim, Abdelhameed Abdelhamid, Abdelaziz A. Abualigah, Laith El-Kenawy, El-Sayed M. Computer Science and Intelligent Systems Research Center Blacksburg 24060 VA United States Faculty of Artificial Intelligence Delta University for Science and Technology Mansoura 35111 Egypt School of ICT Faculty of Engineering Design and Information & Communications Technology (EDICT) Bahrain Polytechnic PO Box 33349 Isa Town Bahrain Department of Computer Science College of Computing and Information Technology Shaqra University Shaqra 11961 Saudi Arabia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan Jadara University Research Center Jadara University Jordan Applied Science Research Center Applied Science Private University Amman Jordan

ISBN: (纸本)9781837242672

Treatment plant optimization plays an important role in the implementation of eco-problem solutions. This article is going to cover how machine learning algorithms can determine energy consumption, climate, and wastewater features of electricity at a wastewater treatment plant in eastern Melbourne from 2014 to 2019. This study uses data obtained from the Melbourne Water and Airport weather station, which are freely available to carry out MLPRegressor, SVM, Linear Regression, Decision Tree Regression, random forest regression, and Nearest Neighbor. Through customary measures like MSE, RMSE, EVS, and others, the best model is adequately identified, which is the Random Forest Regressor model, which has 0.889285. (Lowest MSE) With these results, the plant treatment optimization processes, such as sampling for environmental analysis and the control of energy consumption, have improved. © The Institution of Engineering & technology 2024.

关键词： Climate dynamics Data analysis Energy consumption Wastewater characteristics Wastewater treatment

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Prognostics of rolling element bearing based on combined SSA-VMD and CNN-BiGRU models

Prognostics of rolling element bearing based on combined SSA...

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2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2023

作者： Zhang, Bangcheng Song, Jingyuan Li, Bo Gao, Zhi Changchun Institute of Engineering Changchun University of Technology School of Mechatronics Changchun China Jilin Emergency Management Institute Changchun Institute of Engineering Changchun China Changchun Institute of Engineering School of Computer Science and Computing Changchun China Changchun University of Technology School of Applied Technology Changchun China

ISBN: (纸本)9798350337754

In industrial production, rolling element bearings are key rotating components in a wide range of applications. By predicting rolling element bearings, the failure of bearings as well as problems can be detected in time and preventive measures can be taken to improve production efficiency before the failure occurs. For the prediction problem of rolling element bearing, a hybrid model of rolling bearing prediction based on variable mode decomposition optimized by sparrow search algorithm and bidirectional recurrent neural network of convolutional neural network based on attention mechanism is proposed. Firstly, the initial signal is preprocessed with data using variational modal decomposition. Secondly, the signal with threshold value more than 0.5 is selected as the input of convolutional neural network to perform feature extraction. Then, the bidirectional recurrent neural network structure is used to perform the degradation prediction of rolling bearings. Finally, the proposed method is validated using the XJTU-SY dataset and compared with gated recurrent unit, bidirectional recurrent neural network, bidirectional recurrent neural network based on attention and attention-based convolutional neural network, bidirectional recurrent neural network combination model. The experimental results show that the used model has about 30% higher prediction accuracy than the above four models and has some application val. © 2023 IEEE.

关键词： Roller bearings

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