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Energy Theft Detection in Smart Grids with Genetic Algorithm-Based Feature Selection

Computers, Materials & Continua 2023年第3期74卷 5431-5446页

作者： Muhammad Umair Zafar Saeed Faisal Saeed Hiba Ishtiaq Muhammad Zubair Hala Abdel Hameed Faculty of Information Technology University of Central PunjabLahore54590Pakistan DAAI Research Group Department of Computing and Data ScienceSchool of Computing and Digital TechnologyBirmingham City UniversityBirmingham B47XGUK Faculty of Computer and Information Systems Fayoum University63514Egypt Khaybar Applied College Taibah UniversitySaudi Arabia

As big data,its technologies,and application continue to advance,the Smart Grid(SG)has become one of the most successful pervasive and fixed computing platforms that efficiently uses a data-driven approach and employs efficient information and communication technology(ICT)and cloud *** a result of the complicated architecture of cloud computing,the distinctive working of advanced metering infrastructures(AMI),and the use of sensitive data,it has become challenging tomake the SG *** of the SG are categorized into two main categories,Technical Losses(TLs)and Non-Technical Losses(NTLs).Hardware failure,communication issues,ohmic losses,and energy burnout during transmission and propagation of energy are ***’s are human-induced errors for malicious purposes such as attacking sensitive data and electricity theft,along with tampering with AMI for bill reduction by fraudulent *** research proposes a data-driven methodology based on principles of computational intelligence as well as big data analysis to identify fraudulent customers based on their load *** our proposed methodology,a hybrid Genetic Algorithm and Support Vector Machine(GA-SVM)model has been used to extract the relevant subset of feature data from a large and unsupervised public smart grid project dataset in London,UK,for theft detection.A subset of 26 out of 71 features is obtained with a classification accuracy of 96.6%,compared to studies conducted on small and limited datasets.

关键词： Big data data analysis feature engineering genetic algorithm machine learning

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Predicting software vulnerability based on software metrics: a deep learning approach

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Iran Journal of Computer Science 2024年第4期7卷 801-812页

作者： Agbenyegah, Francis Kwadzo Asante, Micheal Chen, Jinfu Akpaku, Ernest Department of Computer Science Kwame Nkrumah University of Science and Technology Kumasi Ghana School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China Department of Information Technology Faculty of Computing and Information Systems Ghana Communication Technology University Accra Ghana

The security of IT systems is the topmost priority of software developers. software vulnerabilities undermine the security of computer systems. Lately, there have been a lot of reported issues of software vulnerabilities recorded in individuals and corporate systems. software metrics-based techniques have been used and found to be effective due to low false positives and false negatives. However, there are software metrics that have not been investigated and could have an impact on the performance of the predictive model. In this study, we developed an ensemble deep learning algorithm made of LSTM, CNN, and MLP models with the non-investigated metrics as a feature to learn the latent representation of code metrics to predict anomalies in source code effectively. We trained the proposed model on the SySeVR datasets and compared the model's performance against five deep learning algorithms and four state-of-the-art SVPs. The proposed model performs better than the other deep learning with a classification accuracy of 96.17%, and a precision of 97.72%. The model's classification accuracy is 4.98% higher than that of the five deep learning models and 1.71% lower in precision than the state-of-the-art SVP. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Deep learning Machine learning software metrics software security software vulnerabilities

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Enhancing Performance of Machine Learning Models in Healthcare: An Analytical Framework for Assessing and Improving Data Quality 5th

Enhancing Performance of Machine Learning Models in Healthca...

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5th International Conference on Advances in information Communication technology and computing, AICTC 2024

作者： Al-Hgaish, Areen Atan, Rodziah Yaakob, Razali Osman, Mohd Hafeez Department of Software Engineering and Information Systems Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang44330 Malaysia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang44330 Malaysia

ISBN: (纸本)9789819761050

The utilization of Data-Driven Machine Learning (DDML) models in the healthcare sector poses unique challenges due to the crucial nature of clinical decision-making and its impact on patient outcomes. A primary concern in this domain is the data quality that forms the basis for these models, which is essential for their effectiveness. While numerous studies are dedicated to leveraging Machine Learning (ML) to enhance patient care, a substantial gap exists in understanding and evaluating the quality of training data before training ML models. This issue is exacerbated by the lack of well-defined standards to ensure that data characteristics align with the specific goals of ML in healthcare. To address this, our study introduces a conceptual three-dimensional framework focusing on data accuracy, completeness, and consistency aimed at assessing and improving the quality of training data. This framework is designed to enhance the performance, predictability, and interpretability of healthcare-specific ML models and reduce biases, thus bridging current research gaps and laying the groundwork for future developments in ML-driven healthcare systems. This paper presents the initial step for our proposed Data Quality Framework (DQF), which addresses the three dimensions mentioned above. It discusses its potential implications and applications in healthcare data, and its main goal is to provide a comprehensive overview of the proposed DQF, illustrating its significance in advancing healthcare data quality and ultimately improving patient outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data accuracy

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IOT & Machine Learning Models to Develop The Architecture For Assessment Of Security Threats.

IOT & Machine Learning Models to Develop The Architecture Fo...

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2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023

作者： Alotibi, Mohammed Masaad Rajeyyagari, Sivaram Al-Omari, Omaia Mohammed Alymani, Mofadal Planning and Digital Transformation Ministry of Health Riyadh Saudi Arabia College of Computing and Information Technology Department of Computer Engineering Shaqra Saudi Arabia College of Computing and Information Technology Department of Information Systems Shaqra Saudi Arabia

ISBN: (纸本)9798350317060

The Internet of Things (IoT) and other recent advancements in communication and information technology have profoundly impacted and enhanced people's quality of life. IoT systems are vulnerable to previously unanticipated cyber threats such as denial of service, jamming, phishing, obfuscations eavesdropping, spoofing and invasions because of the widespread availability and rising demand for connected devices. The emerging cyber-physical security problem is difficult to avoid and guard against using conventional methods. Securing IoT systems calls for robust, dynamic, and current security mechanisms. When addressing emerging security concerns in cyber-physical systems (CPS), machine learning (ML) technology is often regarded as the most cutting- edge and promising option. This literature review explains how IoTs are built, looks into the many threats they face, and discusses the most up-to-date efforts being made to ensure their security using machine learning. In addition, it covers some of the research obstacles that may arise in the future while trying to implement security measures in IoT infrastructure. © 2023 IEEE.

关键词： Internet of things

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Computational Experiments for Complex Social systems:Experiment Design and Generative Explanation

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 1022-1038页

作者： Xiao Xue Deyu Zhou Xiangning Yu Gang Wang Juanjuan Li Xia Xie Lizhen Cui Fei-Yue Wang the School of Computer Software College of Intelligence and ComputingTianjin UniversityTianjin 300350China the School of Software and the Joint SDUNTU Centre for Artificial Intelligence Research(C-FAIR) Shandong UniversityJinan 250101China the Information School Xi’an University of Financial and EconomicsXi’an 710100China the State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Hainan University Haikou 570228China the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 1001901China

Powered by advanced information technology,more and more complex systems are exhibiting characteristics of the cyber-physical-social systems(CPSS).In this context,computational experiments method has emerged as a novel approach for the design,analysis,management,control,and integration of CPSS,which can realize the causal analysis of complex systems by means of“algorithmization”of“counterfactuals”.However,because CPSS involve human and social factors(e.g.,autonomy,initiative,and sociality),it is difficult for traditional design of experiment(DOE)methods to achieve the generative explanation of system *** address this challenge,this paper proposes an integrated approach to the design of computational experiments,incorporating three key modules:1)Descriptive module:Determining the influencing factors and response variables of the system by means of the modeling of an artificial society;2)Interpretative module:Selecting factorial experimental design solution to identify the relationship between influencing factors and macro phenomena;3)Predictive module:Building a meta-model that is equivalent to artificial society to explore its operating ***,a case study of crowd-sourcing platforms is presented to illustrate the application process and effectiveness of the proposed approach,which can reveal the social impact of algorithmic behavior on“rider race”.

关键词： Agent-based modeling computational experiments cyber-physical-social systems(CPSS) generative deduction generative experiments meta model

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An Automata-Based Framework for Verification and Bug Hunting in Quantum Circuits

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Communications of the ACM 2025年第6期68卷 85-93页

作者： Chen, Yu-Fang Chung, Kai-Min Lengál, Ondřej Lin, Jyun-Ao Tsai, Wei-Lun Yen, Di-De Institute of Information Science Academia Sinica Taipei Taiwan Brno University of Technology Brno Czech Republic National Taipei University of Technology Taipei Taiwan Max Planck Institute for Software Systems Saarbrücken Germany

As quantum computing hardware advances, the demand for scalable, precise, and fully automated verification techniques for quantum circuits grows. This paper introduces a novel automata-based framework tailored for the verification of quantum circuits. In our approach, the problem is framed as a triple {P}C{S} and the question is whether, given a set P of quantum states on the input of a circuit C, the set of quantum states on the output is equal to (or included in) a set S. Our framework leverages tree automata to compactly represent sets of quantum states and we develop transformers to implement the semantics of quantum gates over this representation. We implemented the proposed approach in a prototype tool and evaluated its performance against various benchmarks from the literature. The evaluation shows that our approach is quite scalable, for example, we managed to verify a large circuit with 40 qubits and 141,527 gates, or catch bugs injected into a circuit with 320 qubits and 1,758 gates, where all tools we compared with failed. Additionally, our work bridges quantum program verification and automata, opening new possibilities to exploit the richness of automata theory in quantum computing. © 2025 Association for computing Machinery. All rights reserved.

关键词： Semantics

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Evaluation of Factors that Impede the Use of Construction 4.0 Technologies for Construction Safety Management

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Journal of Engineering, Project, and Production Management 2025年第1期15卷 1-14页

作者： Desai, Vimlesh Prabhu D’souza, Lysette Singh, Anil Bhadauria, Vikram Management Studies Department Agnel Institute of Technology and Design Goa Assagao403507 India V M Salgaocar Institute of International Hospitality Education Manora – Raia Goa Salcete 403720 India Department of Information Systems and Operation Management College of Business University of Texas ArlingtonTX76019 United States Department of Information Systems and Computing Technologies College of Business Engineering and Technology Texas A&M University TexarkanaTX75503 United States

The safety performance of the construction sector remains subpar despite the utilization of both conventional and new methodologies. Consequently, there is a decline in the standard of work, the efficiency of employees, and an escalation in project expenses and duration. In order to enhance safety, it is imperative for the industry to use cutting-edge digital technologies at every stage of the project’s lifespan. The utilization of these technologies is not yet prevalent and, by far, may be a factor in the poor safety record in the construction industry. This study aims to identify and evaluate factors that impede the use of such technology to improve health and safety in construction management. Literature review and interviews with a few experts were able to garner 18 factors, which were evaluated by experienced industry professionals on a 5-point Likert scale using a questionnaire survey. The responses were analyzed using the relative importance index (RII) and factor analysis. The analyses indicated ‘Employees’ resistance and reluctance to change’, ‘high upfront investment’, ‘lack of awareness about digital technologies and their benefits’, ‘limited trained workforce to work on digital technologies’, and ‘poor data communication infrastructure facilities’ as the top five barriers. Factor analyses churned out six groups: ‘Organization readiness’, ‘Industry readiness’, ‘Country readiness’, ‘technology related’, ‘Data related’, and ‘Investment related’ on the basis of latent characteristics. The findings will aid firms, the government, and academia in directing resources and planning strategies to improve the usage of C4.0 technologies in safety and health management in the construction industry. Copyright © Journal of Engineering, Project, and Production Management (EPPM-Journal).

关键词： Construction industry

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Transaction transmission model for blockchain channels based on non-cooperative games

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Science China(information Sciences) 2023年第1期66卷 97-114页

作者： Peiyun ZHANG Chenxi LI Mengchu ZHOU Wenjun HUANG Abdullah ABUSORRAH Omaimah O.BAMASAG Engineering Research Center of Digital Forensics of Ministry of Education School of Computer and SoftwareNanjing University of Information Science and Technology School of Computer and Information Anhui Normal University Department of Electrical and Computer Engineering New Jersey Institute of Technology Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer Engineering Faculty of Engineering and K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University Department of Computer Science Faculty of Computing and Information Technology Center of Excellence in Smart Environment Research King Abdulaziz University

In blockchain networks, transactions can be transmitted through channels. The existing transmission methods depend on their routing information. If a node randomly chooses a channel to transmit a transaction, the transmission may be aborted due to insufficient funds(also called balance) or a low transmission rate. To increase the success rate and reduce transmission delay across all transactions, this work proposes a transaction transmission model for blockchain channels based on non-cooperative game *** balance, channel states, and transmission probability are fully considered. This work then presents an optimized channel transaction transmission algorithm. First, channel balances are analyzed and suitable channels are selected if their balance is sufficient. Second, a Nash equilibrium point is found by using an iterative sub-gradient method and its related channels are then used to transmit transactions. The proposed method is compared with two state-of-the-art approaches: Silent Whispers and Speedy Murmurs. Experimental results show that the proposed method improves transmission success rate, reduces transmission delay,and effectively decreases transmission overhead in comparison with its two competitive peers.

关键词： Blockchain channel transaction transmission channel balance channel state channel transmission probability non-cooperative game

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Organizational Elements that Shape Construction Employees’ Approach and Behavior Towards OSH

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Journal of Engineering, Project, and Production Management 2025年第1期15卷 1-11页

作者： Desai, Vimlesh Prabhu D’souza, Lysette Singh, Anil Bhadauria, Vikram Management Studies Department Agnel Institute of Technology and Design Goa Assagao403507 India V M Salgaocar Institute of International Hospitality Education Manora – Raia Salcete Goa 403720 India Department of Information systems and Operation Management College of Business University of Texas ArlingtonTX76019 United States Department of Information systems and computing technologies College of Business Engineering & Technology Texas A&M University TexarkanaTX75503 United States

The construction industry has one of the highest known rates of occupational incidents. Construction organizations are increasingly recognizing the significance of occupational safety and health (OSH) problems as a prominent area of concern. Though numerous studies have shown that employees’ behavior and approach towards OSH are impacted by Environmental (such as governmental regulations), Personal and Organizational factors, there is a lack of consensus on the organizational elements. Hence, the primary objective of this study is to ascertain the organizational elements that influence employees’ adherence to OSH and suggest improvement needs, if any. To achieve the objective, the study adopted a qualitative research approach and conducted semi-structured interviews with fifteen highly experienced professionals in the construction industry. Using manual thematic analysis, the interview transcripts were analyzed to identify and extract major themes based on commonalities. Six themes were recognized: Empowerment, communication, knowledge management and learning and development, leadership and top management commitment, internal systems and processes, and work culture and spirituality. The findings of this study will assist the industry in developing a framework of effective solutions for OSH management. These themes can form the basis of a good approach to an integrated OSH strategy. Copyright © Journal of Engineering, Project, and Production Management (EPPM-Journal).

关键词： Construction industry

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Pyramid Spatial Feature Transform and Shared-Offsets Deformable Alignment Based Convolutional Network for HDR Imaging 48

Pyramid Spatial Feature Transform and Shared-Offsets Deforma...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Liao, Junda Liu, Qin Ikenaga, Takeshi Nanjing University National Key Laboratory for Novel Software Technology Software Institute China Waseda University Graduate School of Information Production and Systems Japan

ISBN: (纸本)9781728163277

To generate ghost-free high dynamic range (HDR) images by merging multiple differently exposed low dynamic range (LDR) images, the key is to handle ill-exposed areas in the input LDR images and misalignment among them. In this paper, a Pyramid Spatial Feature Transform and shared-offsets Deformable convolutional Network (PSFTDNet) is proposed to achieve this target. The pyramid spatial feature transform module tackles ill-exposed areas, which modulates the features to exploit complementary information of them in a coarse-to-fine manner. The shared-offsets deformable alignment handles misalignment among the input images, which applies the offsets used to align exposure-aligned images to align all the features. Experiments on the NTIRE HDR challenge dataset and Kalantari dataset show that the proposed PSFTDNet outperforms all the conventional methods with PSNR-L scores of 42.31 dB and 41.54 dB, and PSNR-T scores of 34.78 dB and 43.56 dB. © 2023 IEEE.

关键词： Alignment

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