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Smart and Sustainable Developments in Electrical engineering (SSDEE), IEEE International Conference on

作者： Shreyas Rajendra Hole Vinothkumar Kolluru Shreekant Salotagi Yagnesh Challagundla Sudeep Mungara Sriramkumar R Computer Science & Engineering AI & ML Dayananda Sagar University Bangalore India Department of data science Stevens Institute of technology Hoboken NJ U.S.A. Computer Science & Engineering Dayananda Sagar University Bagalore India Department of Computer science Herbert Wertheim College of Engineering University of Florida Gainesville Florida USA Masters in Information Systems Stevens Institute of Technology Hoboken NJ U.S.A.

ISBN: (数字)9798331542108

ISBN: (纸本)9798331542115

The latest evolution in power systems, is ‘smart grids'that offers real time monitoring, control features as well as effective management of renewable energy sources. Nonetheless, with the increase in the system complexity, the chances for insta- bility also increase and thus it is crucial to forecast the stability accurately for the safety of the grid. Weather it is in terms of Gradient Boosting Tree Algorithm or any other traditional algorithm, these machine learning models are successful in achieving the objectives of the task but they lack a forthcoming metric to assist in high stake situations, uncertainty quantification. The paper is devoted to the analysis of the reliability of smart grid systems based on Bayesian Neural Networks (BNN) taking into account both the reliability of predictive modeling and forecasting uncertainty. The smart grid stability augmented dataset was used to benchmark the efficacy of using BNNs or BNNs combined hybrid models, one with XGBoost, for feature extraction and the other with LightGBM. The present study shows that the BNN used with the above mentioned smart grid system achieved an accuracy of 92% with precision of 0.89, recall of 0.91 and F1 of 0.90. These metrics are appending with the help of the additional features of BNN where they are capable of addressing the key questions around uncertainty and thus the metrics confirming the predictions are quite strong in terms of real-time grid stability evaluation. This article encapsulates a detailed study of BNN architecture, the role of uncertainty in machine learning models and the natural application of such models in the design of dependable and fault-tolerant smart grids.

关键词： Measurement Renewable energy sources Uncertainty Neural networks Power system stability Predictive models Stability analysis Real-time systems Smart grids Bayes methods

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Three-stage data generation algorithm for multiclass network intrusion detection with highly imbalanced dataset

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International Journal of Intelligent Networks 2023年第1期4卷 202-210页

作者： Chui, Kwok Tai Gupta, Brij B. Chaurasia, Priyanka Arya, Varsha Almomani, Ammar Alhalabi, Wadee Department of Electronic Engineering and Computer Science School of Science and Technology Hong Kong Metropolitan University Hong Kong Taichung 413 Taiwan Department of Computer Science and Information Engineering Asia University Taichung 413 Taiwan School of Computing Engineering and Intelligent Systems Ulster University United Kingdom Department of Business Administration Asia University Taiwan School of Information Technology Skyline University College SharjahP.O. Box 1797 United Arab Emirates Al- Balqa Applied University Jordan Immersive Virtual Reality Research Group King Abdulaziz University Jeddah21589 Saudi Arabia Symbiosis International University Pune India Lebanese American University Beirut1102 Lebanon Birkbeck University of London United Kingdom Chandigarh University Chandigarh India

The Internet plays a crucial role in our daily routines. Ensuring cybersecurity to Internet users will provide a safe online environment. Automatic network intrusion detection (NID) using machine learning algorithms has recently received increased attention recently. The NID model is prone to bias towards the classes with more training samples due to highly imbalanced datasets across different types of attacks. The challenge in generating additional training data for minority classes is the generation of insufficient data. The study's purpose is to address this challenge, which extends the data generation ability by proposing a three-stage data generation algorithm using the synthetic minority over-sampling technique, a generative adversarial network (GAN), and a variational autoencoder. A convolutional neural network is employed to extract the representative features from the data, which were fed into a support vector machine with a customised kernel function. An ablation study evaluated the effectiveness of the three-stage data generation, feature extraction, and customised kernel. This was followed by a performance comparison between our study and existing studies. The findings revealed that the proposed NID model achieved an accuracy of 91.9%–96.2% in the four benchmark datasets. In addition, it outperformed existing methods such as GAN-based deep neural networks, conditional Wasserstein GAN-based stacked autoencoder, synthesised minority oversampling technique-based random forest, and variational autoencoder-based deep neural network, by 1.51%–28.4%. © 2023 The Authors

关键词： Support vector machines

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Closing the Intent-to-Reality Gap via Fulfillment Priority Logic

arXiv

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arXiv 2025年

作者： El Mabsout, Bassel Abdelgawad, Abdelrahman Mancuso, Renato Department of Computer Science Boston University BostonMA United States Systems Engineering Division Boston University BostonMA United States

Practitioners designing reinforcement learning policies face a fundamental challenge: translating intended behavioral objectives into representative reward functions. This challenge stems from behavioral intent requiring simultaneous achievement of multiple competing objectives, typically addressed through labor-intensive linear reward composition that yields brittle results. Consider the ubiquitous robotics scenario where performance maximization directly conflicts with energy conservation. Such competitive dynamics are resistant to simple linear reward combinations. In this paper, we present the concept of objective fulfillment upon which we build Fulfillment Priority Logic (FPL). FPL allows practioners to define logical formula representing their intentions and priorities within multiobjective reinforcement learning. Our novel Balanced Policy Gradient algorithm leverages FPL specifications to achieve up to 500% better sample efficiency compared to Soft Actor Critic. Notably, this work constitutes the first implementation of nonlinear utility scalarization design, specifically for continuous control problems. © 2025, CC BY.

关键词： Reinforcement learning

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Participants Evaluation of computer-Based Writing Exam Eligibility in High School Using Simple Additive Weighting Method 7

Participants Evaluation of Computer-Based Writing Exam Eligi...

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7th IEEE International Conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2021

作者： Negoro, Wahyu Saptha Nasution, M. Irfan Aldy Ramadhanu, Agung Agustin, Fhery Muhatrii, Muhatri Amri, Syahputra Universitas Potensi Utama Faculty of Engineering and Computer Science Medan Indonesia Universitas Putra Indonesia 'YPTK' Faculty of Computer Science Information Systems Padang Indonesia

ISBN: (纸本)9781728175225

Admission of New Students at the Joint Selection of State Universities (SBMPTN) is a selection process for entering State Universities (PTN), where the selection uses a computer-based written exam (UTBK). The test results are the highest scores that become a reference for PTNs and the Ministry of Research, Technology and Higher Education sets the SBMPTN quota of 40% in each department. This is a big competition for participants to graduate in the desired major. As well as the obstacles faced by participants in the form of not knowing whether the results of the UTBK scores obtained were feasible or not at PTN. The method used in this study is Simple Additive Weighting (SAW), where the calculation provides certain weighting criteria so that each total weight value and result will be a recommendation for SBMPTN participants at SMAN 2 West Sumatra. The data used is divided into 2 groups, namely the IPA and Soshum group data. So that the recommended results are feasible in this study from the science and technology test group as many as 23 participants because they got a total average score of >0.80 and Soshum as many as 7 participants. The data of participants in the study were 40 students, the total recommended eligibility for the SBMPTN pathway was 30 participants and the results of comparison with the actual data contained 2 data processing results that had differences. So it can be explained that there are 38 participants from 40 test data who have the same decision, between the decisions made by the system and the decisions of the SBMPTN selection party, which is 95%. The calculation of this method can be applied in every UTBK selection on the SBMPTN pathway which is the reference for participants to enter higher education. © 2021 IEEE.

关键词： Decision support systems

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PEMT-CoSim: A Co-Simulation Platform for Packetized Energy Management and Trading in Distributed Energy systems

PEMT-CoSim: A Co-Simulation Platform for Packetized Energy M...

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2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2022

作者： Li, Yuanliang Hou, Luyang Du, Hang Yan, Jun Liu, Yuhong Ghafouri, Mohsen Zhang, Peng Concordia Institute for Information Systems Engineering Concordia University MontrealQC Canada School of Computer Science Beijing University of Posts and Telecommunications Beijing China Santa Clara University Department of Computer Engineering Santa ClaraCA United States College of Information Engineering Shenzhen University Shenzhen China

ISBN: (数字)9781665432542

ISBN: (纸本)9781665432542

The integration of Internet-of-Things, artificial intelligence, and other emerging technologies is driving the transition from traditional electricity consumers to energy prosumers in smart grids. To encourage flexible participation of prosumers in decentralized/distributed power management and trading, packetized energy (PE) technology is developed to manage prosumers' load and distributed energy resources in a request-reply way by encapsulating the energy into modulated, routable, and trackable electric packets of fixed duration and/or power for PE management (PEM) and trading (PET). To facilitate the research on PE and investigate PE-related problems in real-world environments, this paper proposes a co-simulation platform called PEMT-CoSim based on the Transactive Energy Simulation Platform (TESP) for PEM and PET, which integrates PE into the management for prosumers in distribution systems by developing a dedicated Application Programming Interfaces (API) for PEM and PET. We validate our simulation platform through PEM and PET case studies to showcase how to manage the power packages with prosumers' active participation in distributed energy systems. We also make the proposed platform open-source to encourage researches in PE for smart grids. © 2022 IEEE.

关键词： Simulation platform

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Secure Resource Management in Cloud Computing: Challenges, Strategies and Meta-Analysis

arXiv

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arXiv 2025年

作者： Saxena, Deepika Swain, Smruti Rekha Kumar, Jatinder Patni, Sakshi Gupta, Kishu Singh, Ashutosh Kumar Lindenstruth, Volker Division of Information Systems University of Aizu Aizuwakamatsu9650006 Japan Department of Computer Science The University of Economics and Human Sciences Warsaw01-043 Poland Department of Computer Applications National Institute of Technology Kurukshetra Kurukshetra136119 India Department of Computing Gachon University Seongnam461701 Korea Republic of Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung804 Taiwan Department of Computer Science and Engineering Indian Institute of Information Technology Bhopal Bhopal462003 India Department of Computer Science and Engineering Goethe University Frankfurt60438 Germany

Secure resource management (SRM) within a cloud computing environment is a critical yet infrequently studied research topic. This paper provides a comprehensive survey and comparative performance evaluation of potential cyber threat countermeasure strategies that address security challenges during cloud workload execution and resource management. Cybersecurity is explored specifically in the context of cloud resource management, with an emphasis on identifying the associated challenges. The cyber threat countermeasure methods are categorized into three classes: defensive strategies, mitigating strategies, and hybrid strategies. The existing countermeasure strategies belonging to each class are thoroughly discussed and compared. In addition to conceptual and theoretical analysis, the leading countermeasure strategies within these categories are implemented on a common platform and examined using two real-world virtual machine (VM) data traces. Based on this comprehensive study and performance evaluation, the paper discusses the trade-offs among these countermeasure strategies and their utility, providing imperative concluding remarks on the holistic study of cloud cyber threat countermeasures and secure resource management. Furthermore, the study suggests future methodologies that could effectively address the emerging challenges of secure cloud resource management. © 2025, CC BY-SA.

关键词： Cloud security

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Development of a Wireless CPR Assistance Glove to Monitor and Guide Rescuers in Performing Cardiopulmonary Resuscitation 16

Development of a Wireless CPR Assistance Glove to Monitor an...

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16th IEEE Malaysia International Conference on Communication, MICC 2023

作者： Chang, Kai Han Chong, Kim Soon Tiang, Sew Sun Lim, Wei Hong Mokayef, Mastaneh Tan, Bee Sian Technology & Built Environment UCSI University Faculty of Engineering Kuala Lumpur Malaysia UCSI University Department Electrical & Electronic Engineering Kuala Lumpur Malaysia Tunku Abdul Rahman University of Management and Technology Department of Computer Science and Embedded Systems Kuala Lumpur Malaysia

ISBN: (纸本)9798350304343

Cardiopulmonary resuscitation (CPR) assistance glove represents a valuable tool for monitoring chest compressions during CPR, which is crucial for effective cardiac arrest response. By incorporating an MPU6050 accelerometer sensor and a force sensing resistor, the glove enables accurate measurement of the chest compression rate and force exerted. Preliminary testing demonstrated the glove's effectiveness in simulating chest compressions, with data collected and transmitted wirelessly to MATLAB software. Manual counting and MATLAB analysis validated the accuracy of compression rate calculations, while force sensing resistor voltage readings provided insights into the force exerted during compressions. These results highlight the glove's potential to significantly enhance the precision and quality of CPR administration, improving outcomes for cardiac arrest patients. © 2023 IEEE.

关键词： Resuscitation

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An Advanced Intrusion Detection Solution for Networks based on Honeypot Servers

An Advanced Intrusion Detection Solution for Networks based ...

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International Conference on Inventive Computation Technologies (ICICT)

作者： K. Veena K. Meena Mercy Theresa M Hemalatha C D. Rajalakshmi Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Department of Computer Science and Engineering GITAM School of Technology GITAM- Bangalore India Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India Computer Science and Engineering Sri Sairam Institute of Technology Chennai

Network security is a critical issue in modern technology. Honey pot-based intrusion detection methods provide an additional layer of security and enhance network performance by analyzing hacker behaviour and detecting unauthorized clients. IP validation and vulnerability detection are used to ensure authorized customer access and prevent illegal client activity. A voice recognition system detects malicious attacks in stage two. If authorized, packets are passed to the server, otherwise, they are delivered to honeypot servers. Using Naive Bayes algorithm, the detection rate is 85.06% with an accuracy of 98.18% and a false alarm rate of 0.15 seconds.

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Systematic Review: Predictive Models for the Winning Team of Super Leagues (SL) 2

Systematic Review: Predictive Models for the Winning Team of...

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Muneer, Salman Alvi, Muhammad Bux Al Sakhnani, Mahmoud Raza, Hamamd Ghazal, Taher M. Ahmad, Munir NCBA&E School of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan The Islamia University of Bahawalpur Department of Computer Systems Engineering Bahawalpur Pakistan University City Sharjah School of Business Skyline University College Sharjah1797 United Arab Emirates National College of Business Administration & Economics School of Computer Science Lahore Pakistan Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia School of Information Technology Skyline University College University City Sharjah 1797 United Arab Emirates

ISBN: (纸本)9798350335644

The Pakistan Super League (PSL) is one of the most popular cricket leagues in the world, attracting millions of fans and spectators each year. With the 2023 edition of the league just around the corner, the use of predictive models to determine the winning team has gained much attention. This systematic review analyzed the existing literature on predictive models for determining the winning team of PSL 2023. The study examined multiple approaches, including machine learning, statistical modeling, and expert opinion. The results of this review suggest that machine learning models, particularly ensemble models, are the most accurate predictors of PSL match outcomes. These models typically use a combination of features, such as team rankings, player performance statistics, match-winning ratio, and previous match outcomes, to predict the result of a match. This paper discovered that expert opinion-based models could provide valuable insights into the likely winner of a match. These models typically use input from cricket experts and analysts to predict the outcome of a match. © 2023 IEEE.

关键词： Machine learning

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Neurosymbolic Architectural Reasoning: Towards Formal Analysis through Neural Software Architecture Inference

Neurosymbolic Architectural Reasoning: Towards Formal Analys...

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Neuro-Symbolic Software engineering (NSE), IEEE/ACM International Workshop on

作者： Steffen Herbold Christoph Knieke Andreas Rausch Christian Schindler Faculty of Computer Science and Mathematics Universität Passau Passau Germany Institute for Software and Systems Engineering Technische Universität Clausthal Germany

ISBN: (数字)9798331514600

ISBN: (纸本)9798331514617

Formal analysis to ensure adherence of software to defined architectural constraints is not yet broadly used within software development, due to the effort involved in defining formal architecture models. Within this paper, we outline neural architecture inference to solve the problem of having a formal architecture definition for subsequent symbolic reasoning over these architectures, enabling neurosymbolic architectural reasoning. We discuss how this approach works in general and outline a research agenda based on six general research question that need to be addressed, to achieve this vision.

关键词： Codes Software architecture Large language models Conferences Neural networks computer architecture Cognition Software Distance measurement Software development management

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