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2022 IEEE Congress on Cybermatics: 15th IEEE International Conferences on Internet of Things, iThings 2022, 18th IEEE International Conferences on Green Computing and Communications, GreenCom 2022, 2022 IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2022 and 8th IEEE International Conference on Smart Data, SmartData 2022

作者： Li, Liying Cong, Peijin Zhou, Junlong Gu, Zonghua Li, Keqin School of Computer Science and Engineering Nanjing University of Science and Technology China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China Department of Applied Physics and Electronics Ume˚a University Sweden Department of Computer Science State University of New York United States

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

As the backbone of Industry 4.0, Cyber-Physical Systems (CPSs) have attracted extensive attention from industry, academia, and government. Missing data is a common problem in CPS data processing and may cause incorrect results and eventually serious malfunction. Existing data availability optimization methods either rely on a large amount of complete training data or suffer from poor performance. To solve these problems, this paper proposes an iterative data availability optimization method for CPSs. Specifically, the proposed method first pre-processes the raw dataset by using a Singular Value Decomposition-based feature selection approach to identify crucial features and reduce computation overheads. It then makes an initial guess for missing values via a designed K-Means-based imputation approach. The appropriate initial estimation decreases the probability of the proposed method falling into the local optimum. Finally, the proposed method iteratively estimates missing data based on the Orthogonal Matching Pursuit algorithm. The proposed method optimizes data availability by accurately imputing missing values. Simulation results on two datasets demonstrate that compared to multiple state-of-the-art approaches, the proposed data availability optimization method can reduce imputation error by up to 99.65%. © 2022 IEEE.

关键词： Iterative methods

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A new clustering-based semi-supervised method to restrict the users from anomalous electricity consumption: supporting urbanization

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Electrical Engineering 2024年第5期106卷 6431-6448页

作者： Aslam, Zeeshan Javaid, Nadeem Javed, Muhammad Umar Aslam, Muhammad Aldegheishem, Abdulaziz Alrajeh, Nabil Bredesen Center for Interdisciplinary Research and Graduate Education The University of Tennessee TN37996 United States Department of Computer Science COMSATS University Islamabad Islamabad44000 Pakistan International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Yunlin64002 Taiwan Department of Artificial Intelligence Sejong University Seoul05006 Korea Republic of Department of Urban Planning College of Architecture and Planning King Saud University Riyadh11574 Saudi Arabia Department of Biomedical Technology College of Applied Medical Sciences King Saud University Riyadh11633 Saudi Arabia

One of the crucial issues for power grids in strengthening the urbanization around the world is imbalance between supply and demand, which leads the users to consume electricity in an anomalous manner without paying for it. Electricity theft plays a pivotal role in cutting down on the electricity bills. The existing data-oriented approaches for electricity theft detection (ETD) in the smart cities have limited ability to handle noisy high-dimensional data and features’ associations. These limitations raise the misclassification rate, which makes some of the approaches unacceptable for electric utilities. A new twofold end-to-end methodology is proposed for ETD. In the first fold, it groups the similar electricity consumption (EC) cases through grey wolf optimization (GWO)-based clustering mechanism;clustering by fast search and find of density peaks (CFSFDP), we named it GC. In the second fold, a new relational stacked denoising autoencoder (RSDAE)-based semi-supervised generative adversarial network (GAN), termed as RGAN, is used for ETD. The combined methodology is named as GC-RGAN. In the methodology, RSDAE acts as both feature extraction technique and generator sub-model of the proposed RGAN. The proposed methodology utilizes the advantages of clustering, adversarial learning and semi-supervised EC data. Besides, to validate the effectiveness of the proposed solution, extensive simulations are performed using smart meter data. Simulation results validate the excellent ETD performance of the proposed GC-RGAN against existing ETD schemes, such as random forest and semi-supervised support vector machine. In comparison, GC-RGAN covers the ETD score of 98% that shows its suitability for real-world scenarios. The proposed solution has extraordinary performance for ETD as compared to traditional solutions, which shows its superiority and usefulness for real-world applications. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Na

关键词： Generative adversarial networks

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A New Belief-Based Incomplete Pattern Unsupervised Classification Method : Extended Abstract 39

A New Belief-Based Incomplete Pattern Unsupervised Classific...

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39th IEEE International Conference on Data Engineering, ICDE 2023

作者： Zhang, Zuowei Liu, Zhe Ma, Zongfang Zhang, Yiru Wang, Hao University of Rennes 1 Institut de Recherche en Informatique et Systèmes Aléatoires France Xi'an University of Architecture and Technology School of Information and Control Engineering China Norwegian University of Science and Technology Department of Computer Science Norway

ISBN: (纸本)9798350322279

Imputing the incomplete patterns in clustering tasks is a common but risky procedure, because the estimated values may affect the real distribution of the data and deteriorate the results. To address this problem, a new belief-based incomplete pattern unsupervised classification method (BPC) with uncertainty and imprecision reasoning is proposed in this paper. First, the complete patterns are grouped into a few clusters to obtain the corresponding reliable centers, and thereby are divided into reliable patterns and unreliable ones by an optimization method. Second, a basic classifier trained by reliable patterns is employed to classify unreliable patterns and incomplete patterns edited by the neighbors. Finally, some imprecise patterns are carefully reassigned again by a new distance-based rule depending on the obtained reliable centers and belief functions theory. The simulation results show that the BPC has the potential to deal with real datasets. © 2023 IEEE.

关键词： Uncertainty analysis

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A novel Cross-Layer Protocols for Resource Allocation in Mobile Networks 15

A novel Cross-Layer Protocols for Resource Allocation in Mob...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Giri, Rakesh Kumar Vittapu, Mani Sarma Dev, Anoop Shewale, Chaitali R. Majid, Mohd Aliff Abdul Kulkarni, Nandini Sunrise University Alwar Department of Computer Science and Engineering India Mallareddy Engineering College and Management Sciences Department of Computer Science and Engineering Kistapur Village Medchal Telangana Hyderabad India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura India Vishwakarma Institute of Technology Department of Information Technology Pune India University Teknology MARA Faculty of Hotel and Tourism Management Department of Foodservice Management Malaysia Symbiosis International University Department of Symbiosis School of Planning Architecture and Design Nagpur Nagpur India

ISBN: (纸本)9798350370249

Cross-layer Protocols for proper resource Allocation in Mobile Networks are a singular technique to enhance the efficiency of wireless networks. The method utilizes multiple layers of the radio protocol stack better to coordinate cell node scheduling and resource allocation. Using combining communication, routing, and scheduling functionalities in an unmarried protocol, pass-Layer Protocols for proper resource Allocation can offer a sizable will increase in the number of facts that may be transferred and the overall latency and throughput of the community. This technique can also enhance the radio interface's operation by lowering interference and encouraging collaborative communications. Eventually, by using facts and managing records from the communique and routing layers, go-Layer Protocols for proper resource Allocation can reduce the complexity of the community, even offering greater scalability. This paper affords unique cross-Layer Protocols for resource Allocation in Cellular Networks. The proposed protocol permits integrated resource allocation in the cell network environment, considering communication and computation layers. © 2024 IEEE.

关键词： Network layers

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Evaluating Machine Learning Classifiers for Predicting Liver Disease Outcomes

Evaluating Machine Learning Classifiers for Predicting Liver...

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Decision Aid sciences and Application (DASA), International Conference on

作者： Khaled Mohamad AlMustafa Juliano Katrib Department of Electrical and Computer Engineering College of Engineering and Architecture Gulf University for Science and Technology (GUST)

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

The increasing prevalence of liver disease necessitates the development of accurate predictive models to aid in early diagnosis and treatment. This study investigates the effectiveness of several machine learning classifiers, Random Forest, Gradient Boosting, AdaBoost, Support Vector Machine (SVM), and Logistic Regression, in predicting liver disease outcomes based on clinical and biochemical features. Each model's performance was rigorously evaluated using multiple metrics, including F1-score, accuracy, root mean square error (RMSE), mean absolute error (MAE), precision, recall, and cross-validation ROC-AUC scores. The Random Forest Classifier emerged as the most effective model, achieving a perfect accuracy of 100% and an F1-score of 1.00, suggesting its robustness in handling complex, high-dimensional data. The Gradient Boosting Classifier also demonstrated commendable performance with an accuracy of 89% and an F1-score of 0.85, indicating its efficacy in capturing the underlying patterns of liver disease. Conversely, Logistic Regression and SVM exhibited lower performance metrics, pointing to potential limitations in their ability to model the intricacies of liver disease data effectively. The insights from this study not only enhance our understanding of liver disease prediction using machine learning but also emphasize the need for further research to validate these models in diverse clinical populations. This study contributes to the growing body of literature advocating for the integration of machine learning techniques in clinical decision-making processes, ultimately aiming to improve patient outcomes in liver disease management.

关键词： Support vector machines Measurement Logistic regression Accuracy Liver diseases Biological system modeling Predictive models Boosting Root mean square Random forests

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An Efficient Intrusion Detection System using Advanced Machine Learning Techniques in SDN for Healthcare System

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Asif, Muhammad Waseem Aqdus, Aqsa Amin, Rashid Chaudhry, Shehzad Ashraf Alsubaei, Faisal S. Iqbal, Sajid Department of Computer Science and IT The University of Lahore Lahore54000 Pakistan Department of Computer Science University of Chakwal Chakwal48800 Pakistan Department of Computer Science and Information Technology College of Engineering Abu Dhabi University Abu Dhabi United Arab Emirates Department of Software Engineering Faculty of Engineering and Architecture Nisantasi University Istanbul Turkey The Department of Cybersecurity College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia

The quick advancement of healthcare systems necessitates robust and efficient network security keys to defend sensitive patient records and guarantee uninterrupted service delivery. The current IDS has many challenges, such as a high false positive rate, poor accuracy of detection, slow response to threats, and inability to scale well. This paper proposes an efficient and real-time intrusion detection system (IDS) using advanced machine learning techniques within a software-defined networking (SDN) framework specifically tailored for healthcare systems. The proposed architecture implements a Machine Learning (ML) model that combines the SVM and KNN to better identify malicious activities. Full sets of detection and mitigation capabilities are implemented to address different types of traffic in the network with the least interference. Through the different evaluation measures, the efficiency of the proposed model is assured. Network performance is determined by success rate queries, packet losses in each domain path, and the CPU being used by the system. Responsiveness is measured through delay metrics grounded on end-to-end delay, hop-to-hop packet delay, latency rate, and propagation delay. Moreover, model accuracy fidelity is reviewed via precision assessment, alpha (α) affecting the accuracy of the model, and confusion matrix with different techniques with the proposed hybrid SVM-KNN model. Last of all, a comparison of the security of the models in question strengthens the argument in favor of the proposed model. More specifically, flow and network topology diagrams are included to show how integration may be accomplished in linkage or merger with existing healthcare networks. The results also present a 30% overall advancement in detection and mitigation by presenting the hybrid SVM-KNN model to overcome other traditional models. This proposed model shows significant improvements not less than 20-30% improvement in CPU use, 30-50% reduction in end-to-end delay,

关键词： Decision trees

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Language Models for Automatic Distribution of Review Notes in Movie Production 24th

Language Models for Automatic Distribution of Review Notes...

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24th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2023

作者： Garcés, Diego Santos, Matilde Fernández-Llorca, David Computer Science Faculty Complutense University of Madrid Madrid Spain Skydance Animation Madrid Madrid Spain Institute of Knowledge Technology Complutense University of Madrid Madrid Spain Computer Engineering Department University of Alcalá de Henares Madrid Spain European Commission Joint Research Centre Seville Spain

ISBN: (纸本)9783031482311

During the several years of production of an animated movie, review meetings take place daily, where supervisors and directors generate text notes about fixes needed for the movie. These notes are manually assigned to artistic departments for them to fixed. Being manual, many notes are not properly assigned and are never fixed, lowering the quality of the final movie. This paper presents a proposal for automating the distribution of these notes using multi-label text classification techniques. The comparison of the results obtained by fine-tuning several transformer-based language models is presented. A highest mean accuracy of 0.776 is achieved assigning several departments to each of the review notes in the test set with a BERT Multilingual model. A mean accuracy of 0.762 was reached in just 10 epochs and 10 min of training on an RTX-3090 with a DistilBERT transformer model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Classification (of information)

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Text Classification for Automatic Distribution of Review Notes in Movie Production 18th

Text Classification for Automatic Distribution of Review N...

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Proceedings of the 18th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2023

ISBN: (纸本)9783031425288

During movie production many review sessions take place in which many text notes on the film are generated. These notes are manually distributed among the studio departments by the production team. This is a time-consuming and error prone process and many notes are not addressed because they do not reach the appropriate departments. In this paper, a multi-label text classification system is presented to automatically distribute these notes. By combining a DistilBERT transformer model and a Logistic Regression classifier, a mean accuracy of 0.68 is achieved when assigning the review notes to the appropriate departments. The resulting deep learning approach is compared against a standard classifier showing an improvement in the results. The outcome shows that it is possible to automatically distribute movie review notes to the interested departments in an automatic way, to improve the movie production process. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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Wearable and interactive multicolored photochromic fiber display

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Light(science & Applications) 2024年第3期13卷 454-465页

作者： Pan Li Yuwei Wang Xiaoxian He Yuyang Cui Jingyu Ouyang Ju Ouyang Zicheng He Jiayu Hu Xiaojuan Liu Hang Wei Yu Wang Xiaoling Lu Qian Ji Xinyuan Cai Li Liu Chong Hou Ning Zhou Shaowu Pan Xiangru Wang Huamin Zhou Cheng-Wei Qiu Yan-Qing Lu Guangming Tao State Key Laboratory of Material Processing and Die&Mould Technology School of Materials Science and Engineering and Wuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan 430074China Key Laboratory of Vascular Aging(HUST) Ministry of EducationWuhan 430030China School of Optoelectronic Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore National Laboratory of Solid State Microstructures Key Laboratory of Intelligent Optical Sensing and ManipulationCollege of Engineering and Applied Sciencesand Collaborative Innovation Center of Advanced MicrostructuresNanjing UniversityNanjing 210023China School of Performing Arts Wuhan Conservatory of MusicWuhan 430060China School of Mechanical Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China School of Architecture and Urban Planning Huazhong University of Science and TechnologyWuhan 430074China School of Fashion Beijing Institute of Fashion TechnologyBeijing 100029China School of Optical and Electronic Information Huazhong University of Science and TechnologyWuhan 430074China Tongji Medical College Huazhong University of Science and TechnologyWuhan 430074China State Key Laboratory for Modification of Chemical Fibers and Polymer Materials College of Materials Science and EngineeringDonghua UniversityShanghai 201620China

Endowing flexible and adaptable fiber devices with light-emitting capabilities has the potential to revolutionize the current design philosophy of intelligent,wearable interactive ***,significant challenges remain in developing fiber devices when it comes to achieving uniform and customizable light effects while utilizing lightweight ***,we introduce a mass-produced,wearable,and interactive photochromic fiber that provides uniform multicolored light *** designed independent waveguides inside the fiber to maintain total internal reflection of light as it traverses the *** impact of excessive light leakage on the overall illuminance can be reduced by utilizing the saturable absorption effect of fluorescent materials to ensure light emission uniformity along the transmission *** addition,we coupled various fluorescent composite materials inside the fiber to achieve artificially controllable spectral radiation of multiple color systems in a single *** prepared fibers on mass-produced kilometer-long using the thermal drawing *** fibers can be directly integrated into daily wearable devices or clothing in various patterns and combined with other signal input components to control and display patterns as *** work provides a new perspective and inspiration to the existing field of fiber display interaction,paving the way for future human–machine integration.

关键词： fiber interactive colored

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Integrating environmental sustainability in construction Time-Cost trade-off for decision-making using hybrid NSGA-III and MOPSO approach

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Asian Journal of Civil Engineering 2025年第4期26卷 1527-1542页

作者： Nagayo, Analene Montesines Singh, Rekha Dhawan, Amit Manjunath, T.C. Qasem, Altayeb Sethi, Krushna Chandra Sharma, Kamal Department of Engineering and Technology University of Technology and Applied Sciences-Al Musanna Al Muladdah Oman Department of Civil Engineering Raj Kumar Goel Institute of Technology Ghaziabad India Department of Civil Engineering Dr. Bhim Rao Ambedkar Polytechnic College Gwalior India Computer Science Engineering Department Rajarajeswari College of Engineering Bangalore India Department of Building Engineering College of Architecture and Planning Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Department of Civil Engineering Centurion University of Technology and Management Gajapati India Department of Civil Engineering Madhav Institute of Technology and Science Gwalior India

Construction project management often involves optimizing time and cost while ensuring minimal environmental impact. This study presents an innovative hybrid approach combining non-dominated sorting genetic algorithm III (NSGA-III) and multi-objective particle swarm optimization (MOPSO) to address the time-cost-environmental sustainability trade-off (TCEST) in construction projects. The proposed model aims to minimize project completion time and cost while maximizing environmental sustainability. A case study is conducted to validate the model, incorporating diverse construction activities and their respective time, cost, and environmental sustainability metrics. The results reveal Pareto-optimal solutions demonstrating significant trade-offs among the three objectives. The hybrid approach outperforms standalone algorithms in terms of solution diversity, convergence, and hypervolume indicators. Weighted sum methods are employed to select the most suitable solution from the Pareto front based on project priorities. Correlation analysis further explores interdependencies among objectives, emphasizing the feasibility of balancing these critical factors. The study contributes a robust decision-support tool for sustainable project planning, facilitating informed decision-making in modern construction management. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Hybrid Optimization MOPSO NSGA-III Sustainable Construction Time–Cost-Environmental Trade-off

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