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Machine learning assisted screening of binary alloys for metal-based anode materials

Journal of Energy Chemistry 2025年第5期104卷 62-68页

作者： Xingyue Shi Linming Zhou Yuhui Huang Chaohui Wu Yongjun Wu Juan Li Zijian Hong State Key Laboratory of Silicon and Advanced Semiconductor Materials School of Materials Science and EngineeringZhejiang UniversityHangzhou 310058ZhejiangChina Nanhu Brain-Computer Interface Institute Hangzhou 311100ZhejiangChina Zhejiang Key Laboratory of Advanced Solid State Energy Storage Technology and Applications Research Institute of Zhejiang University-TaizhouTaizhou 318000ZhejiangChina Inner Mongolia Electric Power Economic and Technological Research Institute Hohhot 010010Inner MongoliaChina College of Materials Science and Engineering Zhejiang University of TechnologyHangzhou 310014ZhejiangChina School of Engineering Hangzhou City UniversityHangzhou 310015ZhejiangChina Institute of Fundamental and Transdisciplinary Research Zhejiang UniversityHangzhou 311121China

Metal alloy anode materials with high specific capacity and low voltage have recently gained significant attention due to their excellent electrochemical performance and the ability to suppress dendrite ***,experimental investigations of metal alloys can be time-consuming and expensive,often requiring extensive experimental design and *** this study,we developed a machine learning model based on the Crystal Graph Convolutional Neural Network(CGCNN)to screen alloy anode materials for seven battery systems,including lithium(Li),sodium(Na),potassium(K),zinc(Zn),magnesium(Mg),calcium(Ca),and aluminum(Al).We utilized data with tens of thousands of alloy materials from the Materials Project(MP)and Automatic FLOW for Materials Discovery(AFLOW)*** any experimental voltage input,we identified over 30 alloy systems that have been experimentally validated with good ***,we predicted over 100 alloy anodes with low potential and high specific *** hope this work to spur further interest in employing advanced machine learning models for the design of battery materials.

关键词： CGCNN Alloy anodes Machine learning Metal-based batteries

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Enhancing Internet of Vehicles Security: Advanced Intrusion Detection for Threat Detection and Mitigation 1st

Enhancing Internet of Vehicles Security: Advanced Intrusion ...

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1st International Conference on Intelligent Systems, Blockchain, and Communication Technologies, ISBCom 2024

作者： Ali, Maged El-Badawy, Hesham Bahaa-Eldin, Ayman Sobh, Mohamed Giza12577 Egypt Cairo Egypt Elsewedy University of Technology Ain Shams University Cairo Egypt Department of Computer and System Engineering Ain Shams University Cairo Egypt

ISBN: (纸本)9783031823763

In this paper, a comprehensive approach to network intrusion detection data using Python is presented. Various techniques including XGB and DQN are discussed, necessitating a preprocessing phase at the beginning. This phase involves fixing data types, handling null and infinite values, dropping unnecessary columns, creating binary labels for threat detection, reducing memory usage, and balancing the dataset. Additionally, feature importance analysis and correlation visualization are explored to identify and drop highly correlated columns. Furthermore, machine learning processes, including data splitting, scaling, and computing class weights, are discussed, followed by the implementation of two machine learning methods: XGBoost and Deep Q-Network (DQN). For XGBoost, performance metrics such as F1-score, recall, precision and support are evaluated to assess model performance. For DQN, insights into its architecture, training objectives, and integration with Gym environment for reinforcement learning tasks are provided. The paper offers a comprehensive guide to preprocessing and machine learning approaches for network intrusion detection datasets, contributing to the advancement of cybersecurity research and applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Network intrusion

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A Multimodal Deep Learning Approach for Predicting Dementia’s Disease Progression Using MRI Clinical Data

A Multimodal Deep Learning Approach for Predicting Dementia...

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Data Science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： M. Shamini S. K Muthusundar K. Selvamani S. Kanimozhi Chandrasekaran N Antony Doss A Department of Computer Science and Engineering Chennai Institute Of Technology Chennai India Department of Artificial Intelligence and Data Sciences Chennai Institute of Technology Chennai India Department of Computer Science and Engineering Anna University Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of CSBS MNM Jain Engineering College Chennai India Department of CSBS S.A Engineering College Chennai India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Dementia’s disease (DD) is a progressive neurological disease that primarily affects the elderly. Its primary symptom is cognitive impairment. There is currently no known cure; the only available treatments are meant to improve the patient’s quality of life. Given the challenges in treating DD, predicting its trajectory can be crucial in guiding management and treatment strategies. In this work, we propose a multimodal approach that uses MRI scans and clinical data to predict the phases of DD. The VGG19 deep learning model is utilized to interpret MRI images for image classification, and LightGBM (LGBMClassifier) is used to evaluate patient clinical data in CSV format. A multimodal deep learning technique is then used to combine the predictions from both modalities in order to increase the overall prediction accuracy. The integrated model aims to provide a comprehensive and accurate assessment of DD’s progression. This multimodal method offers a promising step toward a better understanding and management of Dementia’s disease by providing doctors with improved prediction insights into the development of the disease.

关键词： Deep learning Neurological diseases Accuracy Magnetic resonance imaging Medical services Predictive models Trajectory Older adults Image classification Dementia

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Energy-Efficiency Optimization in IoT-Based Machine Learning for Smart Environmental Monitoring 6th

Energy-Efficiency Optimization in IoT-Based Machine Learning...

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6th International Conference on Computational Intelligence in Communications and Business Analytics, CICBA 2024

作者： Anand, Nishant Parwekar, Pritee Bali, Vikram Department of Computer Science and Engineering SRM Institute of Science and Technology SRM University Delhi-Meerut Road UP Ghaziabad India Department of Computer Science and Engineering IMS Engineering College UP Ghaziabad India

ISBN: (纸本)9783031813351

The rapid proliferation of the Internet of Things (IoT) technology has paved the way for a more sustainable and efficient approach to environmental monitoring. In this research paper, a comprehensive comparison-based review of energy-efficient strategies in IoT-based machine learning for smart environmental monitoring is presented. This study addresses the need for optimizing energy consumption while maintaining the accuracy and reliability of data collection and analysis in smart environmental monitoring systems. Starting by discussing the evolution of IoT-based environmental monitoring and its significance in the context of creating a smarter and more energy-efficient society. The paper examines a wide range of machine-learning techniques and algorithms commonly employed for environmental data analysis and their respective energy consumption profiles. This findings reveal that, by carefully selecting and implementing energy-efficient machine learning algorithms, it is possible to significantly reduce the power consumption of IoT devices, extending their operational lifespans and reducing the need for frequent battery replacements or recharging. Additionally, the discussion is done on the impact of data compression and transfer techniques, as well as low-power communication protocols, in further enhancing energy efficiency within IoT systems. Also delve into various case studies and practical implementations, highlighting successful approaches and their outcomes in terms of energy efficiency and data quality. The paper concludes by emphasizing the critical importance of continuous research and development in the realm of energy-efficient IoT-based machine learning for smart environmental monitoring, to achieve a more sustainable and eco-friendly smart society. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Environmental monitoring

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Modern Encryption Techniques for Wireless Body Area Networks in E-healthcare: A survey

Modern Encryption Techniques for Wireless Body Area Networks...

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2025 International Conference on Ambient Intelligence in Health Care, ICAIHC 2025

作者： Singh, Pooja Dasgupta, Mou National Institute of Technology Department of Computer Applications Raipur India

ISBN: (纸本)9798331530259

Healthcare has grown increasingly dependent on Wireless Body Area Networks (WBANs) for continuous monitoring of patient health metrics and real-time data transmission. Though wearable technology has limited resources and also medical data is sensitive, the integration of WBANs into healthcare systems raises serious security challenges, which require strong encryption methods to safeguard data integrity and restrict unauthorized access to sensitive medical health data. This survey presents a broad overview of modern encryption techniques used in WBANs, particularly as they pertain to e-healthcare applications. To provide secure data access in e-healthcare applications, we explore both conventional and advanced cryptographic techniques, such as lightweight encryption algorithms, identity-based encryption (IBE), attribute-based encryption (ABE), and hybrid encryption techniques. The paper also highlights ECC which works well in resource-constraint environments, making it suitable for WBAN applications. By examining modern issues like computational overhead, latency, and energy efficiency, the paper highlights the significance of a balance between security and performance when implementing encryption for WBANs. This survey aims to give a comprehensive assessment of the current state of encryption techniques used in e-healthcare, as well as suggestions for future research directions that could improve the security of next-generation healthcare systems. The ultimate goal is to protect patient data without sacrificing the effectiveness of medical services. © 2025 IEEE.

关键词： mHealth

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Banana Disease and Deficiency Detection Using YOLOv8 3rd

Banana Disease and Deficiency Detection Using YOLOv8

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3rd International Conference on Machine Vision and Augmented Intelligence, MAI 2023

作者： Vinta, Surendra Reddy Dhanalaxmi, B. Lavanya, M. Thotapalli, Aruna Raju, Yeligeti School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India Telangana Hyderabad India Telangana Hyderabad India Department of Information Technology CVR College of Engineering Telangana Hyderabad India

ISBN: (纸本)9789819743582

Banana farms are under risk from a variety of diseases, including Black Sigatoka and Panama sickness, as well as fertilizer shortages. Accurate and prompt identification of these issues is necessary to halt financial losses and promote sustainable practices. For the identification and classification of leaf diseases and their deficiencies based on the visual cues automatically, YOLOV8 is one of the latest detection models to detect the illness of banana leaves and improve the yielding capacity. Yolov8 is the state-of-the-art model to detect diseases very accurately and efficiently. It helps to prevent the illness of plants, disease detection, and improve the yielding capacity. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Fruits

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Analysis of Challenges of IoT Adoption for Smart City Application 5th

Analysis of Challenges of IoT Adoption for Smart City Applic...

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5th International Conference on Innovative Product Design and Intelligent Manufacturing Systems, ICIPDIMS 2023

作者： Das, Rashmi Prava Muduli, Debendra Luhach, Ashish Kumar Department of Computer Science and Engineering C V Raman Global University Bhubaneswar752054 India School of Electrical and Communication Engineering Papua New Guinea University of Technology Lae411 Papua New Guinea

ISBN: (纸本)9789819767311

The Internet of Things (IoT) is an architecture for a network that processes and analyzes sensitive data in order to deliver a variety of services to a large number of users. IoT devices are capable of delivering a wide variety of value-added services in a variety of smart city applications. Nevertheless, there have been a lot of worries voiced concerning data privacy, confidentiality, and security in relation to the IoT, as well as consumer resistance to adopting such innovative technology. This article examines difficulties linked to the expansion of the IoTs and their adoption by users in smart city applications using Step-wise Weight Assessment Ratio Analysis (SWARA) technique. A total of 30 factors that inhibit IoT adoption were analyzed and categorized under 5 major factors. It revealed that, functional and technological barriers offer more resistance to IoT adoption for smart city application in India. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： IoT Optimization Smart city SWARA

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Ontology-Based Semantic Reasoning for Multi-Source Heterogeneous Industrial Devices Using OPC UA

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IEEE Internet of Things Journal 2025年

作者： Bi, Jing Wu, Rina Yuan, Haitao Wang, Ziqi Zhang, Jia Zhou, MengChu Beijing University of Technology College of Computer Science Beijing100124 China Beihang University School of Automation Science and Electrical Engineering Beijing China Southern Methodist University Department of Computer Science Lyle School of Engineering DallasTX75205 United States New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

The advent of smart manufacturing in Industry 4.0 signifies the era of connections. As a communication protocol, Object linking and embedding for Process Control Unified Architecture (OPC UA) can address most semantic heterogeneity issues. However, its semantics are not formally defined at the application layer. To address the information silo problem caused by semantic heterogeneity, an integration framework named Querying of Ontology Mapping-based OPC UA (QOMOU) is proposed. QOMOU extracts information models of OPC UA servers into resource description framework triples and utilizes web ontology language for semantic enrichment and inference. Then, an Event Class Semantic Similarity Calculation (ECSSC) method is proposed for device type identification, enabling the classification of semantically heterogeneous OPC UA devices. The effectiveness of ECSSC is validated through queries with the RDF query language (SPARQL) protocol in Apache Jena. Experimental results demonstrate that ECSSC improves the accuracy of device identification by approximately 7% compared to benchmark device identification models. Specifically, compared with graph embedding-based methods, QOMOU’s query performance is approximately 13% higher, and its query efficiency is 5% higher on average compared to both structured query and extensible markup languages. Moreover, by employing a keyword-matching algorithm, the query accuracy of the existing heterogeneous data integration scheme is improved by 4% on average. This enhancement can boost the operational efficiency of Internet of Things systems based on the OPC UA architecture. © 2014 IEEE.

关键词： Structured Query Language

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A Comparison of Deep Learning and Machine Learning Methods for Water Quality Prediction

A Comparison of Deep Learning and Machine Learning Methods f...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： N. Srinivasa Gupta B. Valarmathi Ishaan Hiwanj Department of Manufacturing Engineering School of Mechanical Engineering Vellore Institute of Technology Vellore Tamil Nadu India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Tamil Nadu India Department of Information Technology School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Tamil Nadu India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Predicting water quality is essential to preserving human health and environmental sustain ability. Traditional water quality assessment methods often face scalability and real-time monitoring limitations. With accuracies of 62%, 72 %, 83 %, 69%, 63 %, 66%, 71 %, 63 %, and 64%, respectively, the current techniques utilized were Logistic Regression, Decision Trees, Random Forest Regressor, Extreme Gradient Boosting, Naive Bayes, K-nearest neighbors, Support Vector Machine, AdaBoost, and Bagging [9]. This study addresses these challenges by leveraging Adaptive Synthetic Sampling (ADASYN) to balance the dataset and evaluating model performance on datasets of 5,000 and 10,000 entries per class. A robust dataset obtained from Kaggle was used, with five models - Long Short-Term Memory (LSTM), Feed Forward Neural Network (FFNN), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Random Forest - evaluated and compared. The proposed methods demonstrate significant improvements in accuracy, with XGBoost achieving the highest accuracy of 95.53%, followed by Random Forest at 93.98%. This work underscores the importance of advanced machine learning techniques in addressing the limitations of traditional methods, enhancing accuracy, scalability, and adaptability in water quality prediction. These findings contribute to advancing environmental monitoring and management practices with reliable, data-driven insights.

关键词： Support vector machines Adaptation models Accuracy Scalability Water quality Boosting Reliability Random forests Long short term memory Regression tree analysis

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ACDV: Adaptive Content Delivery for Vehicular Digital Twin Networks

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IEEE Transactions on Vehicular technology 2025年第5期74卷 7084-7098页

作者： Zheng, Jinkai Luan, Tom H. Li, Guanjie Yin, Zhisheng Wu, Yuan Dong, Mianxiong Dongguan University of Technology School of Electrical Engineering and Intelligentization Dongguan523808 China Xi'an Jiaotong University School of Cyber Science and Engineering Xi'an710049 China Xidian University School of Cyber Engineering Xi'an710071 China Singapore University of Technology and Design Information Systems Technology and Design Pillar 487372 Singapore Xidian University State Key Laboratory of Isn Xi'an710071 China University of Macau State Key Laboratory of Internet of Things for Smart City 999078 China University of Macau Department of Computer Information Science 999078 China Muroran Institute of Technology Department of Sciences and Informatics Muroran05-08585 Japan

Digital Twins (DTs), serving personalized cloud-based digital assistants, hold great promise for supporting infotainment applications of the Internet of Vehicles (IoVs), in which personalized content such as high-definition maps, system updates, and social video streaming can be delivered from individualized DTs to vehicular users (VUEs) to enhance the intelligence of IoV services and improve their driving experience. This paper proposes a novel content caching framework tailored to DT-enabled IoVs, where DTs selectively cache on-demand contents on edge devices (i.e., cellular base stations and roadside units) managed by the edge service manager (ESM) along the VUE's trip to avoid backbone congestion yet save download time. However, ESMs are inherently selfish and reluctant to contribute resources to cache contents without benefits. Furthermore, considering the diverse trajectories of vehicles, it is inefficient to cache data along a single path. To address these challenges, we propose ACDV, which first models the interactions between the ESMs and DTs as a Stackelberg game to incentivize ESMs to actively participate in the content caching process. We then deploy a Markov model to predict the VUE distribution, which enables DTs to cache contents with a focus on predicted trajectories. To save storage costs, we derive an upper bound of content sizes that the VUE can download within a limited network connection time. Considering the lack of network information and users' private utility model in practical scenarios, we further develop a learning-based algorithm to find the optimal pricing scheme and content size strategies of ESMs and DTs. Through extensive simulations, we show that our proposal can effectively find the optimal strategy and achieve a fast convergence speed and high-level performance compared to the baselines. © 1967-2012 IEEE.

关键词： Markov processes

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