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2nd International Conference on Technological Advancements in Computational Sciences, ICTACS 2022

作者： Razauddin Madhuri G, Sindhu Oberoi, Ashish Vats, Aman Sivasangari, A. Siwach, Kuldeep Gulf Colleges Department of Computer and Information Sciences Saudi Arabia Faculty of Engineering & Technology Department of CSE Karnataka Bengaluru India Rimt University Department of CSE Gobindgarh Mandi India Manav Rachna International Institute of Research and Studies Faculty of Media Studies and Humanities Department of Journalism and Mass Communication Haryana Faridabad India Sathyabama Institute of Science and Technology Department of Ut Tamilnadu Chennai India School of Media and Entertainment SoME Gd Goenka University Gurgaon India

ISBN: (数字)9781665476577

ISBN: (纸本)9781665476577

Software Defect prediction results provide a list of source code artifacts that are prone to defects. Quality assurance teams can effectively devote more energy and allocate limited resources to defect-prone source code verification software products. A module that identifies defect prediction methods for frequent defects before the start of the testing phase. Measurement-based defect-prone modules improve software quality and reduce costs, leading to effective resource allocation. The previous method doesn't analyze the defect pattern, and it has less performance during software development. This work introduces a deep learning-based Pattern-based Modified Hidden Markova Fault Tree (PMHMFT) framework to extract the hidden fault analysis during cross-project validation. The proposed Modified Hidden Markova Fault Tree algorithm constructs the defect fault tree to analyze the cross-project code defect. To prevent defect based on software metrics software prediction model are used. Hidden Markova Fault tree-based classification categorize component as defective and non-defective. Using a Levy flight, optimize the method to search the fault classes efficiently compared to another method. The Markova Fault Tree model construct fault tree based given data;it is easy to identify the fault in software platform. The proposed PMHMFT to implement evaluate the performance using k-fold validation. Thus, the proposed work on software defect prediction achieves higher accuracy in true classification and prediction with less error rate. The software defects are predicted, and these predicted defects are optimized by using Levy flight optimization. Our proposed PMHMFT technique is very useful technique for predicting software defect and gives the better prediction rates in effective manner. © 2022 IEEE.

关键词： Defects

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Brain Tumor Classification System Using Decision Tree Algorithm

Brain Tumor Classification System Using Decision Tree Algori...

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2025 IEEE International Conference on Cybernetics and Innovations, ICCI 2025

作者： Thaiparnit, Sattarpoom Janchidfah, Thanakrit Farooq, Adil Rajamangala University of Technology Suvarnabhumi Faculty of Business Administration Dep. Of Information System and Computer Business Suphan Buri72130 Thailand Muban Chombueng Rajabhat University dep. Of Digital Media Technology Ratchaburi70150 Thailand Maynooth University Department of Electronic Engineering Ireland

ISBN: (纸本)9798331533267

This research aims to study, design, and develop a brain tumor classification system using artificial intelligence, specifically decision tree algorithms. The system's primary objective is to assist medical personnel in making decisions, as distinguishing between two types of brain tumors-benign and malignant (cancerous)-can be challenging, especially for less experienced medical staff. Due to the similarities between these two types of tumors, it may be difficult for medical practitioners with limited expertise to differentiate between them in a timely manner. The proposed brain tumor classification system is designed to address this challenge. The research team conducted the study over approximately seven months, starting from the second semester of the 2023 academic year through the first semester of the 2024 academic year. Experimental results revealed that among the tree-based algorithms, Random Forest achieved the highest accuracy, followed by M5P. The best average accuracy from the experiments was obtained using a 70% training and 30% testing split, yielding an accuracy of 96.83%. Therefore, the research team selected the 70% training and 30% testing split for model development, as it provided the highest accuracy compared to the 60% training and 40% testing split, and the 80% training and 20% testing split, which showed lower accuracy. The results demonstrate that the 70% training and 30% testing approach is optimal for the brain tumor classification system, ensuring high accuracy in classifying brain tumors. © 2025 IEEE.

关键词： Random forests

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Safe Barrier-Constrained Control of Uncertain Systems via Event-triggered Learning

arXiv

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arXiv 2024年

作者： Lederer, Armin Begzadić, Azra Hirche, Sandra Cortés, Jorge Herbert, Sylvia The Learning and Adaptive Systems Group Department of Computer Science ETH Zurich Switzerland The Department of Mechanical and Aerospace Engineering University of California San Diego United States The Chair of Information-oriented Control TUM School of Computation Information and Technology Technical University of Munich Germany

While control barrier functions are employed in addressing safety, control synthesis methods based on them generally rely on accurate system dynamics. This is a critical limitation, since the dynamics of complex systems are often not fully known. Supervised machine learning techniques hold great promise for alleviating this weakness by inferring models from data. We propose a novel control barrier function-based framework for safe control through event-triggered learning, which switches between prioritizing control performance and improving model accuracy based on the uncertainty of the learned model. By updating a Gaussian process model with training points gathered online, the approach guarantees the feasibility of control barrier function conditions with high probability, such that safety can be ensured in a data-efficient manner. Furthermore, we establish the absence of Zeno behavior in the triggering scheme, and extend the algorithm to sampled-data realizations by accounting for inter-sampling effects. The effectiveness of the proposed approach and theory is demonstrated in simulations. Copyright © 2024, The Authors. All rights reserved.

关键词： Gaussian distribution

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RDVC: Efficient Deep Video Compression with Regulable Rate and Complexity Optimization

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IEEE Transactions on Multimedia 2025年

作者： Wei, Xiaojie Lin, Jielian Xu, Jiawei Gao, Wei Zhao, Tiesong Putian University School of Mechanical Electrical & Information Engineering PutianFujian China Shenzhen Graduate School Peking University School of Electronic and Computer Engineering Shenzhen518055 China Fuzhou University Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou350108 China Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China Fuzhou350108 China Tsinghua University Department of Electronic Engineering Beijing100084 China

Deep video coding has paved a way to break through the performance bottleneck of reigning hybrid video coding. However, unlike hybrid video codecs, existing deep video codecs cannot offer both flexible rates and regulable complexities within one single codec, which limits their applications. In this paper, we propose a Regulable Deep Video Codec (RDVC) to address the above issue. First, we propose an Adaptive Feature Compression (AFC) network that generates variable rates while ensuring Rate-Distortion (RD) performance. The network introduces a two-stage coarse-to-fine rate adjustment that can be controlled by a user-specified rate level. Second, we propose a Spatio-Temporal Feature Propagation (STFP) mechanism to provide high-quality reference information for AFC process. Third, we also utilize slimmable convolutional components in our framework to adjust decoding complexity constrained by user configuration. Experimental results demonstrate that RDVC can adjust the codec structure flexibly according to different user configurations while maintaining advanced performance. On average, it reduces the bit-per-pixel (bpp) by 9.35%/58.12% while maintaining the same PSNR/MS-SSIM as the reference software VTM-13.2. Sourcecode is available at: https://***/WXJ0001/RDVC. © 2025 IEEE.

关键词： Signal distortion

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Enhancing Security and Scalability of Metaverse with Blockchain-based Consensus Mechanisms 15

Enhancing Security and Scalability of Metaverse with Blockch...

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15th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2023

作者： Rajawat, Anand Singh Goyal, S.B. Goyal, Aarti Rajawat, Kavita Raboaca, Maria Simona Verma, Chaman Mihaltan, Traian Candin School of Computer Science & Engineering Sandip University Nashik India City University Faculty of Information Technology Petaling Jaya Malaysia Mind Intellect Technology Indore India National Research and Development Institute for Cryogenics and Isotopic Technologies Icsi Energy Department Ramnicu Valcea Romania Eötvös Loránd University Faculty of Informatics Department of Media and Educational Informatics Budapest Hungary Technical University of Cluj-Napoca Faculty of Building Services Cluj-Napoca Romania

ISBN: (纸本)9798350321388

This study examines how blockchain technology and consensus mechanisms can safeguard and grow the metaverse ecosystem. Blockchain is transparent and decentralized. It is unchangeable, cryptographically verified, and runs without a central authority. Blockchain technology can secure the metaverse and users' data. This study compares blockchain-based consensus algorithms such as POW(PoW), PoS, and PBFT to ensure metaverse safety. These consensus mechanisms provide safe, decentralized transaction verification, waste reduction, and network consensus. Studying blockchain technology's metaverse scaling restrictions and scaling solutions, including sharding, sidechains, and off-chain protocols. These methods increase throughput, parallel computing, and transaction speed. They improve the metaverse's eco-system. Quantitative analysis, simulations, and experiments determine the security and scalability of blockchain-based metaverse consensus processes. Changes will be quantified by measuring transaction speed, network throughput, and resource consumption. This study enables safe, scalable metaverse environments. Blockchain-based consensus mechanisms allow the metaverse to retain user data safely, halt undesirable conduct, and allow users to join without trusting each other. Scalability solutions declutter networks, enabling the metaverse to handle more individuals and transactions. Blockchain-based consensus mechanisms could make the metaverse more secure, private, and scalable. Users will trust each other, create new apps, and be more open to this groundbreaking digital world. © 2023 IEEE.

关键词： Scalability

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A Toolbox of Feminist Wonder: Theories and methods that can make a difference 26

A Toolbox of Feminist Wonder: Theories and methods that can ...

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26th ACM Conference on computer-Supported Cooperative Work and Social Computing, CSCW 2023

作者： Hansson, Karin Bardzell, Shaowen Bhandari, Aparajita Boulicault, Marion Doyle, Dylan Thomas Erete, Sheena Cerratto Pargman, Teresa Lazem, Shaimaa Muller, Michael Normark, Maria Petterson, Adrian Rosén, Anton Poikolainen Taylor, Alex S. Thomas, Jakita O. Watson, Julia Södertörn University The School of Natural Sciences Technology and Environmental Studies Sweden Georgia Institute of Technology The School of Interactive Computing United States Cornell University Department of Communication United States University of Edinburgh Department of Philosophy United States University of Colorado Boulder Department of Information Science United States University of Maryland College of Information United States Stockholm University The Department of Computer and Systems Sciences Sweden City for Scientific Research and Technological Applications The Informatics Research Institute Egypt Ibm Research Human Centered Ai United States Uppsala University Department of Informatics and Media Sweden University of Toronto Faculty of Information Canada Aalto University The School of Arts Design and Architecture Finland City University of London The Centre for Human Computer Interaction Design United Kingdom Auburn University Department of Computer Science and Software Engineering United States University of Toronto Department of Computer Science Canada

ISBN: (纸本)9798400701290

This one-day hybrid workshop builds on previous feminist CSCW workshops to explore feminist theoretical and methodological approaches that have provided us with useful tools to see things differently and make space for change. Since its inception over a decade ago, feminist HCI has progressed from the margins to mainstream HCI, with numerous references in the literature. Feminist HCI has also evolved to incorporate other critical HCI practices such as Queer HCI, participatory design, and speculative design. While feminist approaches have grown in popularity and become mainstream, it is getting more difficult to distinguish the feminist emancipatory core from other attempts of developing and improving society in various ways. In this workshop, we therefore want to revisit our feminist roots, where theory is a liberatory and creative practice, motivated by affect, curiosity, and wonder. From this standpoint, we consider which of our feminist tools can make a significant difference today, in a highly datafied world. The goal of this workshop is to;1) create an inventory of feminist theories and concepts that have had an impact on our work as designers, educators, researchers, and activists;2) develop a feminist toolbox for the CSCW community to strengthen our feminist literacy. © 2023 Owner/Author.

关键词： Computation theory

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360ORB-SLAM: A Visual SLAM System for Panoramic Images with Depth Completion Network

360ORB-SLAM: A Visual SLAM System for Panoramic Images with ...

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International Conference on computer Supported Cooperative Work in Design

作者： Yichen Chen Yuqi Pan Ruyu Liu Haoyu Zhang Guodao Zhang Bo Sun Jianhua Zhang School of Information Science and Technology Hangzhou Normal University Hangzhou China Haixi Institutes Chinese Academy of Sciences Quanzhou Institute of Equipment Manufacturing Quanzhou China Department of Digital Media Technology Hangzhou Dianzi University Hangzhou China School of Computer Science and Engineering Tianjin University of Technology Tianjin China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

With the advent of the Industry 4.0 era and the increasing performance requirements for AR/VR applications and vision assistance and inspection systems in recent years, visual simultaneous localization and mapping (vSLAM) is a fundamental task in computer vision and robotics. However, traditional vSLAM systems are limited by the camera’s narrow field-of-view, resulting in challenges such as sparse feature distribution and lack of dense depth information. To overcome these limitations, this paper proposes a 360ORB-SLAM system for panoramic images that combines with a depth completion network. The system extracts feature points from the panoramic image, utilizes a panoramic triangulation module to generate sparse depth information, and employs a depth completion network to obtain a dense panoramic depth map. Experimental results on our novel panoramic dataset constructed based on Carla demonstrate that the proposed method achieves superior scale accuracy compared to existing monocular SLAM methods and effectively addresses the challenges of feature association and scale ambiguity. The integration of the depth completion network enhances system stability and mitigates the impact of dynamic elements on SLAM performance.

关键词： Visualization Simultaneous localization and mapping Accuracy Three-dimensional displays Service robots Pose estimation Inspection

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A Novel Network Security Data Resource Description Standard

Journal of Network Intelligence

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Journal of Network Intelligence 2022年第2期7卷 295-305页

作者： Han, Wei Sun, Xiu-Yan He, Chen Tang, Lin-Lin Kumari, Saru Department of Electronic and Information Engineering Laiwu Vocational and Technical College No. 1 Shancai Street Shandong Jinan China Department of Mechanical and Electrical Engineering Laiwu Vocational and Technical College No. 1 Shancai Street Shandong Jinan China Media and Communications College WeiFang University No. 5147 Dongfeng East Street Shandong Weifang China Department of Computer Science and Engineering Harbin Institute of Technology Shenzhen Taoyuan Street Shenzhen China Department of Mathematics Chaudhary Charan Singh University Uttar Pradesh Meerut250004 India

Current network security situational awareness field has problems of poor communication and low efficiency of data resources and information. Therefore, in order to solve these two problems, this paper has established a better network security data resource description standard (metadata standard) for communication of data resource information, so that communication of data resource information has a unified standard basis. Then it provides a more efficient channel for circulation of data resource information, and improve efficiency of data resource information flow through publishing and subscription technology. Finally, a data resource sharing platform was developed, which applied the above metadata standards. © 2022, Taiwan Ubiquitous information CO LTD. All rights reserved.

关键词： Network security

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Real-Time-Capable Muscle Force Estimation for Monitoring Robotic Rehabilitation Therapy in the Intensive Care Unit

Real-Time-Capable Muscle Force Estimation for Monitoring Rob...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Kim K. Peper Alexander Aasmann Elisabeth R. Jensen Sami Haddadin Technical University of Munich Germany Department of Computer Engineering Chair of Robotics and Systems Intelligence TUM School of Computation Information and Technology Munich Institute of Robotics and Machine Intelligence (MIRMI)

In this paper, a method is proposed to enable real-time monitoring of muscle forces during robotic rehabilitation therapy in the ICU. This method is solely based on sensor information provided by the rehabilitation robot. In current clinical practice, monitoring primarily takes place in the later stages of rehabilitation, but it would also be highly beneficial during early stages. Musculoskeletal models have large, mostly unrealized potential to support and improve patient monitoring. The method presented in this paper is based on a state-of-the-art muscle-tendon path model, which is applied to the use case of the robotic rehabilitation device VEMOTION. The muscle force estimation is validated against surface electromyography measurements of lower limb muscles from 12 healthy volunteers The results show an overall correlation of R = 0.70 0.25 for the single-joint muscle m. iliopsoas, which has a ±major contribution to hip flexion. Given this correlation, the proposed model could be used for real-time monitoring of active patient participation.

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Machine Learning Based Cybersecurity Threat Detection for Secure IoT Assisted Cloud Environment

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computer Systems Science & engineering 2023年第10期47卷 855-871页

作者： Z.Faizal Khan Saeed M.Alshahrani Abdulrahman Alghamdi Someah Alangari Nouf Ibrahim Altamami Khalid A.Alissa Sana Alazwari Mesfer Al Duhayyim Fahd N.Al-Wesabi Department of Computer Engineering College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Computer Science College of Science and Humanities DawadmiShaqra UniversityShaqraSaudi Arabia Department of Mathematics College of EducationShaqra UniversityShaqraSaudi Arabia Saudi Aramco Cybersecurity Chair Networks and Communications DepartmentCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAlaflaj16828Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia

The Internet of Things(IoT)is determine enormous economic openings for industries and allow stimulating innovation which obtain between domains in childcare for eldercare,in health service to energy,and in developed to *** develops a difficult problem in IoT platform whereas the presence of cyber-attack requires that *** progress of automatic devices for cyber-attack classifier and detection employing Artificial Intelligence(AI)andMachine Learning(ML)devices are crucial fact to realize security in IoT *** can be required for minimizing the issues of security based on IoT devices ***,this research proposal establishes novel mayfly optimized with Regularized Extreme Learning Machine technique called as MFO-RELM model for Cybersecurity Threat classification and detection fromthe cloud and IoT *** proposed MFORELM model provides the effective detection of cybersecurity threat which occur in the cloud and IoT *** accomplish this,the MFO-RELM technique pre-processed the actual cloud and IoT data as to meaningful ***,the proposed models will receive the pre-processing data and carry out the classifier *** boosting the efficiency of the proposed models,theMFOtechnique was utilized to *** experiential outcome of the proposed technique was tested utilizing the standard CICIDS 2017 dataset,and the outcomes are examined under distinct aspects.

关键词： Mayfly optimization machine learning artificial intelligence cybersecurity threat detection

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