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Rock Mechanics Bulletin 2024年第3期3卷 1-13页

作者： Yu Zhang Kongyi Fang Manchao He Dongqiao Liu Junchao Wang Zhengjia Guo School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing100044China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and ArchitectureBeijing100044China State Key Laboratory for Geomechanics and Deep Underground Engineering China University of Mining and TechnologyBeijing100083China Thomas Lord Department of Computer Science University of Southern CaliforniaLos Angeles90007CAUSA

Rockburst is a phenomenon where sudden,catastrophic failure of the rock mass occurs in underground deep regions or areas with high tectonic stress during the excavation *** disasters endanger the safety of people's lives and property,national energy security,and social interests,so it is very important to accurately predict *** rockburst prediction has not been able to find an effective prediction method,and the study of the rockburst mechanism is facing a *** the development of artificial intelligence(AI)techniques in recent years,more and more experts and scholars have begun to introduce AI techniques into the study of the rockburst *** previous research,several scholars have attempted to summarize the application of AI techniques in rockburst ***,these studies either are not specifically focused on reviews of the application of AI techniques in rockburst prediction,or they do not provide a comprehensive *** on the advantages of extensive interdisciplinary research and a deep understanding of AI techniques,this paper conducts a comprehensive review of rockburst prediction methods leveraging AI ***,pertinent definitions of rockburst and its associated hazards are ***,the applications of both traditional prediction methods and those rooted in AI techniques for rockburst prediction are summarized,with emphasis placed on the respective advantages and disadvantages of each ***,the strengths and weaknesses of prediction methods leveraging AI are summarized,alongside forecasting future research trends to address existing challenges,while simultaneously proposing directions for improvement to advance the field and meet emerging demands effectively.

关键词： Rockburst Rockburst prediction Artificial intelligence techniques

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Global scale solar energy harnessing: An advanced intra-hourly diffuse solar irradiance predicting framework for solar energy projects

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Neural Computing and Applications 2024年第18期36卷 10585-10598页

作者： El-kenawy, El-Sayed M. Bailek, Nadjem Bouchouicha, Kada Zerouali, Bilel Hassan, Muhammed A. Kuriqi, Alban Jamil, Basharat Colak, Ilhami Khalil, Adel Ibrahim, Abdelhameed Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Mansoura35111 Egypt Laboratory of Mathematics Modeling and Applications Department of Mathematics and Computer Science Faculty of Sciences and Technology Ahmed Draia University of Adrar Adrar01000 Algeria Energies and Materials Research Laboratory Faculty of Sciences and Technology University of Tamanghasset Tamanrasset10034 Algeria MEU Research Unit Middle East University Amman Jordan Adrar01000 Algeria Vegetal Chemistry-Water-Energy Laboratory Faculty of Civil Engineering and Architecture Department of Hydraulic Hassiba Benbouali University of Chlef Ouled Fares Chlef02180 Algeria Mechanical Power Engineering Department Faculty of Engineering Cairo University Giza12613 Egypt School of Architecture University of Waterloo 7 Melville St S CambridgeONN1S 2H4 Canada CERIS Instituto Superior Tecnico Universidade de Lisboa Lisbon Portugal Civil Engineering Department University for Business and Technology PristinaKosovo10000 High Temperature Processes Unit IMDEA Energy Institute Av. Ramón de La Sagra 3 Móstoles Madrid28935 Spain Faculty of Engineering and Architectures Nisantasi University Istanbul Turkey Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura35516 Egypt

Diffuse horizontal irradiance (DHI) forecasts are critical for adopting solar photovoltaic technology. Yet, they can lack reliability given the limited and uncertain meteorological data available for desert areas. This research develops a new sine cosine dipper throated optimization (SCDTO) technique, targeting DHI prediction even given such data constraints. SCDTO uniquely hybridizes sine cosine metaheuristics, adept at exploration, with dipper throat optimization, providing focused exploitation. This hybridization aims to create a robust ensemble model capable of delivering reliable DHI predictions despite climatic uncertainty. The ensemble model, employing various input combinations, was rigorously evaluated across multiple meteorological stations. The SCDTO algorithm exhibited remarkable performance improvements, yielding substantial reductions exceeding 93% in root mean squared error and 98% in mean absolute error at key stations in Morocco, including Tan-Tan, Zagora, Erfoud, and Oujda. Comparative analyses against established optimization algorithms consistently underscored the superior predictive capabilities of SCDTO. Visualizations, including box plots and histograms, demonstrated SCDTO’s efficacy in minimizing prediction errors, particularly for Tan-Tan, Zagora, and Erfoud stations. Also, statistical validation through one-way analysis of variance (ANOVA) further affirmed the significance of the proposed SCDTO method. Therefore, this hybrid metaheuristic optimization enables more accurate DHI predictions from limited meteorological data. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Solar energy

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A Survey on Mapping of Urban Green Spaces within Remote Sensing Data Using Machine Learning & Deep Learning Techniques

A Survey on Mapping of Urban Green Spaces within Remote Sens...

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International Conference on computer and Automation Engineering, ICCAE

作者： Smita Sunil Burrewar Mazharul Haque Tanwir Uddin Haider Architecture & Planning Department National Institute of Technology Patna Patna Bihar Computer Science & Engineering Department National Institute of Technology Patna Patna Bihar

For environmental protection, urban planning, monitoring, and management of the urban ecosystem, mapping urban green spaces is a crucial undertaking. A vital source of information for United Nations Sustainable Development Goal 11.7 could come from urban green space mapping. The standard method of mapping urban green spaces requires field measurements and takes a lot of time. It is also important to update urban green space maps periodically because urban green spaces can change quickly over time due to development. With the advent of high-resolution satellite sensors like Sentinel-1 and Sentinel-2, a large number of remote sensing images may be gathered, providing quick and precise information over urban areas. This work intends to offer a new perspective on how crowd sourced geospatial big data and remote sensing may be combined to enhance the mapping of urban green spaces, including time optimization and accurate information through machine learning and deep learning. For the revitalization of cities, this data will be valuable. Remote sensing imagery data can be classified using machine learning techniques like Support vector machines (SVM), Random forests (RF), and Naive Bayes (NB). In deep learning techniques such as Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), K Nearest Neighbor (KNN), Generative Adversarial Networks (GAN), and Recurrent Neural Networks (RNN) can be used to classify remote sensing images.

关键词： Deep learning Support vector machines Recurrent neural networks Green products Urban planning Generative adversarial networks Time measurement

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The Impact of Class Weight Optimization on Improving Machine Learning Outcomes in Identifying COVID-19 Specific ECG Patterns

The Impact of Class Weight Optimization on Improving Machine...

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17th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2024

作者： Khan, Sara Ismail, Walaa N. Alsalamah, Shada Mohamed, Ebtesam Alsalamah, Hessah A. Department of Information Systems and Technology Management George Washington University WashingtonDC United States Management Information Systems Department College of Business Al Yamamah University Riyadh Saudi Arabia Information Systems Department King Saud University Riyadh Saudi Arabia Faculty of Computer Science Minia University Minia Egypt Computer Engineering Department College of Engineering and Architecture Al Yamamah University Riyadh Saudi Arabia

The Covid-19 pandemic has resulted in 550 million cases and 6.3 million fatalities, with the virus severely affecting the lungs and cardiovascular system. A study utilizes a VGG16 model adapted for a 12-Lead ECG Image database to assess the disease's impact on cardiovascular health. The research addresses the challenge of data imbalance by experimenting with different training approaches: using balanced datasets, imbalanced datasets, and class weight adjustments for imbalanced datasets. These models are designed for a three-class multiclass classification of ECG images: Abnormal, Covid-19, and Normal categories. Performance evaluations, including accuracy scores, confusion matrices, and classification reports, show promising results. The model trained on a balanced dataset achieved a 90% accuracy rate. When trained on an imbalanced dataset, the accuracy dropped to 82%. However, with class weight adjustments, the accuracy rebounded to 87%. The study proves that the adapted VGG16 model can effectively handle both balanced and imbalanced datasets. Further testing and enhancements can be carried out using additional datasets, making it a valuable tool for understanding the cardiovascular implications of Covid-19. © 2024 by SCITEPRESS–Science and technology Publications, Lda.

关键词： COVID-19

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Module-Based Approach for Detecting Performance Bugs in Java Applications at Scale

Module-Based Approach for Detecting Performance Bugs in Java...

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Annual Conference of Industrial Electronics Society

作者： Yingying Wen Guanjie Cheng Department of Architecture and Technique Innovation Huawei Cloud Hangzhou China Department of Computer Science and Technology Zhejiang University Hangzhou China

Hundreds of thousands of Java applications have been deployed in data centers at our production Cloud to support burst peak traffic. However, detecting performance bugs in Java can be difficult as they may not prevent the applications from running correctly, and may not even manifest at low loads. Profiling data collected from production provides insight into the actual running states of applications. In this paper, we aim to identify hot spots for further performance debugging by analyzing profiling data from tens of thousands of machines in the data center using a module-based approach. We present our practical experience with module classification, which allows for filtering of out-of-range modules and long-duration modules of high utilization. Our study proposes a heuristic solution to detect performance bugs in Java at scale.

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A research infrastructure for generating and sharing diversity-aware data

arXiv

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arXiv 2023年

作者： Busso, Matteo Abente Acosta, Ronald Chenu De Götzen, Amalia Department of Information Engineering and Computer Science University of Trento Italy Department of Architecture Design and Media Technology Aalborg University Denmark

The intensive flow of personal data associated with the trend of computerizing aspects of people’s diversity in their daily lives is associated with issues concerning not only people protection and their trust in new technologies, but also bias in the analysis of data and problems in their management and reuse. Faced with a complex problem, the strategies adopted, including technologies and services, often focus on individual aspects, which are difficult to integrate into a broader framework, which can be of effective support for researchers and developers. Therefore, we argue for the development of an end-to-end research infrastructure (RI) that enables trustworthy diversity-aware data within a citizen science community. © 2023, CC BY-NC-ND.

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Achieving concurrency in cloud-orchestrated Internet of Things for resource sharing through multiple concurrent access

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Computational Intelligence 2024年第1期40卷 e12630-e12630页

作者： Xu, Xuedong Sun, Wei Vivekananda, G.N. Shankar, Achyut Department of Mechanical Engineering School of Mechanical and Electrical Engineering Changchun Institute of Technology Changchun China School of Architecture and Design Changchun Institute of Technology Changchun China Department of CSE Madanapalle Institute of Technology & Science India Department of computer science and Engineering ASET Amity University Noida India

Cloud-orchestrated Internet of Things (IoT) facilitates proper utilization of network resources and placating user demands in smart communications. Multiple concurrent access (MCA) techniques designed for cloud-assisted communication helps to achieve better resource sharing features with fault tolerance ability. A multi-objective resource allocation and sharing (RAS) for balancing MCA in cloud-orchestrated IoT is presented in this article. The RAS constraints are modeled through linear programming (LP) as an optimization approach. The constraints are resolved using genetic representations (GR) for reducing the unserviced requests and failed resource allocations. Conventional genetic stages are inherited by the LP model to solve resource allocation and access issues reducing latency. The combined LP and GR jointly resolve resource allocation and MCA stagnation in cloud network. A fair outcome of LP-GR is estimation using the metrics response latency, resource utilization, request handled, and average latency. © 2020 Wiley Periodicals LLC.

关键词： Linear programming

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Integrating AEC Domain-Specific Multidisciplinary Knowledge for Informed and Interactive Feedback in Early Design Stages 1

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19th International Conference on Computing in Civil and Building Engineering, ICCCBE 2022

作者： Reisinger, Julia Rasoulzadeh, Shervin Kovacs, Balint Istvan Ferschin, Peter Vasylevska, Khrystyna Hensel, Michael Kovacic, Iva Wimmer, Michael Department for Integrated Planning and Industrial Building TU Wien Vienna Austria Department for Digital Architecture and Planning TU Wien Vienna Austria Research Unit of Computer Graphics TU Wien Vienna Austria Institute of Visual Computing & Human-Centered Technology TU Wien Vienna Austria

ISBN: (数字)9783031325151

ISBN: (纸本)9783031325144

In the context of digitalization in the industry, a variety of technologies has been developed for system integration and enhanced team collaboration in the architecture, Engineering and Construction (AEC) industry. Multidisciplinary design requirements are characterized by a high degree of complexity. Early design methods often rely on implicit or experiential design knowledge, whereas contemporary digital design tools mostly reflect domain-specific silo thinking with time-consuming iterative design processes. Yet, the early design stages hold the greatest potential for design optimization. This paper presents a framework of a multidisciplinary computational integration platform for early design stages that enables integration of AEC domain-specific methods from architecture, engineering, mathematics and computer science. The platform couples a semantic integrative mixed reality sketching application to a shape inference machine-learning based algorithm to link methods for different computation, simulation and digital fabrication tasks. A proof of concept of the proposed framework is presented for the use case of a freeform geometry wall. Future research will explore the potential of the framework to be extended to larger building projects with the aim to connect the method into BIM-processes. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Iterative methods

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DESIGNING AN ONLINE TRAINING MODULE TO DEVELOP VIRTUAL AND BLENDED INTERNATIONAL MODALITIES IN HIGHER EDUCATION 21

DESIGNING AN ONLINE TRAINING MODULE TO DEVELOP VIRTUAL AND B...

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21st International Conferences on Mobile Learning, ML 2025 and 10th International Conference on Educational Technologies, ICEduTech 2025

作者： Barana, Alice Chatzea, Vasiliki Eirini Henao, Kelly Hildebrandt, Ania Maria Conte, Marina Marchisio Samoilovich, Daniel Triantafyllidis, Georgios Vidakis, Nikolas Department of Molecular Biotechnology and Health Sciences University of Turin Piazza Nizza 44 Torino10153 Italy Department of Electrical & Computer Engineering Hellenic Mediterranean University Estavromenos Heraklion71410 Greece Columbus Association 1 rue Moillis Paris75015 France Department of Architecture Design and Media Technology Aalborg University A. C. Meyers Vænge Copenhagen152450 Denmark

ISBN: (纸本)9789898704665

Virtual and blended international collaboration activities, including Collaborative Online International Learning and Blended Intensive Programmes, offer inclusive and sustainable alternatives to traditional mobility programmes. Effective training programmes for teachers and administrative staff are indispensable to enhance the higher education institutions’ capability to implement similar activities. This paper introduces an online training module aimed at providing higher education teachers and staff with competencies to implement virtual and blended modalities of international collaboration. The module is designed according to the principles of the Self-Determination Theory and the Analysis, Design, Development, Implementation and Evaluation (ADDIE) model. The paper explores whether the design of the module is effective in creating a valuable learning experience and what its strengths and areas for improvement are. Results were derived from 150 responses to the final survey questions completed by participants who completed the online training module, analysed qualitatively and quantitatively. Findings show that the autonomy, structure, and involvement support were perceived as effective in achieving the desired learning outcomes. The results offer valuable insights into the design of training modules for higher education staff to develop international collaboration modalities. © 2025 Proceedings of the International Conferences on Mobile Learning 2025 and Educational Technologies 2025. All rights reserved.

关键词： Virtual environments

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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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