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Intelligent computing and Networking - Proceedings of IC-ICN 2023

作者： Surbakti, Herison Kumar, Athmakuri Naveen Tara, Saikumar Keshta, Ismail khare, Amit Prasad, G.N.R. Faculty of Computing and Digital Technology HELP University Kuala Lumpur Malaysia Senior Software Engineer Glosoft Technologies Pvt Ltd Chennai India Department of ECE BVRIT Hyderabad College of Engineering for Women Hyderabad India Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Department of Technology JIET Institute of Design and Technology Rajasthan Jodhpur India Telangana Hyderabad500091 India

ISBN: (纸本)9789819931767

In recent years, support vector machine has become one of the most important classification techniques in pattern recognition, machine learning, and data mining due to its superior classification effect and solid theoretical base. However, its training time will increase dramatically as the number of samples increases, and training will become more sophisticated when dealing with problems involving multiple classifications. A quick training data reduction approach MOIS appropriate for multi-classification tasks is presented as a solution for the aforementioned issues. While eliminating redundant training samples, the boundary samples that play a vital role are chosen in order to considerably reduce training data and the problem of unequal distribution between categories. The experimental results demonstrate that MOIS may maintain or even improve the classification performance of support vector machines while substantially enhancing training efficiency. On the Opt digit dataset, the suggested method improves classification accuracy from 98.94% to 99.05%, while training time is reduced to 15% of the original;in HCL2000, the proposed method improves classification accuracy from 98.94% to 99.05%. When the accuracy rate is marginally increased (from 99.29% to 99.30%) on the first 100 categories dataset, the training time is dramatically reduced to less than 6% of the original. Additionally, MOIS has a high operational efficiency. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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Location Anonymous Query Algorithm based on Road Networks

Location Anonymous Query Algorithm based on Road Networks

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Augmented Intelligence and Sustainable Systems (ICAISS), International Conference on

作者： Qing Haohua Roliana Ibrahim Hui Wen Nies Department of Applied Computing and Artificial Intelligence Faculty of Computing Universiti Teknologi Malaysia Johor Malaysia Guangzhou College of Technology and Business Guangzhou Guangdong China

ISBN: (纸本)9781665489638

In this paper, we study the location anonymous query algorithm based on the Voronoi graph division of the road networks. The Voronoi diagram spatial analysis model has unique functions and application potential in the description of the location problems, the spatial partitioning of the landmarks based on the Voronoi diagram, on the one hand, it achieves the division of the spatial extent covered by the POI under the principle of shortest distance, and on the other hand, it also deals with the problem of the spatial partitioning. Hence, we propose the novel ideas of improving the division performance and also the query algorithm enhancement. Through the simulation, we select the Foshan city data as the target to evaluate the proposed model.

关键词： Analytical models Roads Urban areas Data models Partitioning algorithms

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Cutting-Edge Predictive Methods for Enhancing Short-Term Load Prediction

Cutting-Edge Predictive Methods for Enhancing Short-Term Loa...

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2024 International Conference on Electrical and Computer Engineering Researches, ICECER 2024

作者： Ghazal, Taher M. Janjua, Jamshaid Iqbal Abushiba, Walid Ahmad, Munir Abbas, Tahir Ateeq, Karamath College of Arts & Science Applied Science University P.O.Box 5055 Manama Bahrain Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Selangor Malaysia Lahore Pakistan College of Engineering Applied Science University Bahrain College of Informatics Korea University Seoul02841 Korea Republic of TIMES Institute Department of Computer Science Multan Pakistan School of Computing Skyline University College University City Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9798331539733

Efficient management and cost-related factors in the power sector call for accurate short-term load forecasting as it enables better planning with the electric grid and achieving stability within it. This paper looks into the state-of-the-art forecasting strategies that enhance load forecasts made in shorter time frames. Such techniques include but are not limited to machine learning applications, effective data models, and multi-dimensional modeling leveraging historical, meteorological, and economic data. Their review is carried out in detail with their advantages and disadvantages and their application in practice. Furthermore, this study strives to understand the barriers that need to be overcome in the quest to increase the accuracy of the forecasting models especially concerning renewable energy sources. We illustrate how new trends relate to future developments of predictive models about supporting the decision-making in operating power systems and improving the reliability and sustainability of energy systems. © 2024 IEEE.

关键词： Prediction models

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Marie's ChemLab: A Mobile Augmented Reality Game to Teach Basic Chemistry to Children 16

Marie's ChemLab: A Mobile Augmented Reality Game to Teach Ba...

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16th European Conference on Games Based Learning, ECGBL 2022

作者： Arztmann, Michaela Alfaro, Jessica Lizeth Domínguez Blattgerste, Jonas Jeuring, Johan Van Puyvelde, Peter Department of Education Faculty of Social and Behavioral Sciences Utrecht University Utrecht Netherlands Department of Chemical Engineering Faculty of Engineering KU Leuven Leuven Belgium Mixed Reality Lab University of Applied Sciences Emden/Leer Electrical Engineering and Information Technology Emden Germany Department of Information and Computing Sciences Faculty of Science Utrecht University Utrecht Netherlands

ISBN: (纸本)9781914587511

Digital games are widely used in education to motivate students for science. In parallel, the role of augmented reality (AR) in education is increasing. This paper introduces Marie's ChemLab: a marker-less handheld AR application in which K-12 students learn about science. In Marie's ChemLab, users perform interactions in their physical environment using virtual AR objects displayed on a handheld device. The utilized interaction concept is based on the TrainAR framework (Blattgerste et al, 2021), which originally was envisioned for procedural AR training and layered feedback mechanisms. The original framework has been enhanced to support gamified interactions with virtual objects, specifically for K-12 students. In total, 239 early secondary school students played the final application as part of a larger study. The usability score indicated marginally acceptable usability, and the application was successfully implemented in a classroom setting. © 2022 Dechema e.V.. All rights reserved.

关键词： Augmented reality

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Machine Learning and Network Traffic to Distinguish Between Malware and Benign Applications 1st

Machine Learning and Network Traffic to Distinguish Between ...

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1st International Conference on Pervasive Knowledge and Collective Intelligence on Web and Social Media, PerSOM 2022

作者： Abualigah, Laith Abualigah, Sayel Almahmoud, Mothanna Forestiero, Agostino Sachdeva, Gagan Hanandeh, Essam S. Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Faculty of Information Technology Middle East University Amman Jordan Applied Science Research Center Applied Science Private University Amman Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang George Town Malaysia Computer Science Department Prince Hussein Bin Abdullah Faculty for Information Technology Al Al-Bayt University Mafraq25113 Jordan Department of Computer Information Systems Jordan University of Science and Technology Ar-Ramtha Jordan Performance Computing and Networking National Research Council of Italy Rende87036 Italy First Abu Dhabi Bank Abu Dhabi United Arab Emirates Department of Computer Information System Zarqa University Zarqa Jordan

ISBN: (纸本)9783031314681

Virus detection software is widely used for servers, systems, and devices that seek to maintain security and reliability. Although these programs provide an excellent safety level, the traditional defense methods fail to detect new Malware. The more advanced approach relies on predicting malicious behavior with dynamic analysis of the process executed. This paper presents a new method for detecting malware using machine learning algorithms applied to data obtained from the Cuckoo sandbox. The Cuckoo sandbox isolates the file being analyzed, providing detailed dynamic analysis reports. The machine learning algorithms were compared and the most important features were identified. The results were obtained using six popular classifiers, including SVM, Random Forest, and LightGBM, and the XGBOOST algorithm had the highest accuracy, at an average of 97%. However, the research on machine learning-based malware analysis is limited in terms of computational complexity and detection accuracy. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Software reliability

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Obfuscated Malware Detection: Impacts on Detection Methods 15th

Obfuscated Malware Detection: Impacts on Detection Methods

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15th International scientific conferences on research and applications in the field of intelligent information and database systems, ACIIDS 2023

作者： Gorment, Nor Zakiah Selamat, Ali Krejcar, Ondrej Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia Jalan Sultan Yahya Petra Kuala Lumpur54100 Malaysia School of Computing Faculty of Engineering Universiti Teknologi Malaysia Johor Bahru Johor81310 Malaysia Universiti Teknologi Malaysia Johor Bahru Johor81310 Malaysia College of Computing and Informatics Universiti Tenaga Nasional Jalan IKRAM-UNITEN Selangor Kajang43000 Malaysia Center for Basic and Applied Research Faculty of Informatics and Management University of Hradec Kralove Hradec Kralove50003 Czech Republic

ISBN: (纸本)9783031424298

Obfuscated malware poses a challenge to traditional malware detection methods as it uses various techniques to disguise its behavior and evade detection. This paper focuses on the impacts of obfuscated malware detection techniques using a variety of detection methods. Furthermore, this paper discusses the current state of obfuscated malware, the methods used to detect it, and the limitations of those methods. The impact of obfuscation on the effectiveness of detection methods is also discussed. An approach for the creation of advanced detection techniques based on machine learning algorithms is offered, along with an empirical examination of malware detection performance assessment to battle obfuscated malware. Overall, this paper highlights the importance of staying ahead of the constantly evolving threat landscape to safeguard computer networks and systems. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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Opposition flow control for reducing skin-friction drag of a turbulent boundary layer

arXiv

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arXiv 2023年

作者： Dacome, Giulio Mörsch, Robin Kotsonis, Marios Baars, Woutijn J. Faculty of Aerospace Engineering Delft University of Technology Delft2629 HS Netherlands Institute of Applied Mathematics and Scientific Computing University of the Bundeswehr Munich Neubiberg85577 Germany

This work explores the dynamic response of a turbulent boundary layer to large-scale reactive opposition control, at a friction Reynolds number of Reτ ≈ 2 240. A surface-mounted hot-film is employed as the input sensor, capturing large-scale fluctuations in the wall-shear stress, and actuation is performed with a single on/off wall-normal blowing jet positioned 2.4δ downstream of the input sensor, operating with an exit velocity of vj = 0.4U∞. Our control efforts follow the work by Abbassi et al. [Int. J. Heat Fluid Fl. 67, 30-41, 2017;1], but includes a control-calibration experiment and a performance assessment using PIV- and PTV-based flow field analyses. With the control-off calibration-experiment conducted a-priori, a transfer kernel is identified so that the velocity fluctuations targeted by control are estimated based on the upstream hot-film’s signal. The controller targets large-scale high-speed zones when operating in "opposing" mode and low-speed zones in the "reinforcing" mode. A desynchronised mode was tested for reference and consisted of a statistically similar control mode, but without synchronization to the incoming velocity fluctuations. An energy-attenuation of about 40 % is observed for the opposing control mode in the frequency band corresponding to the passage of large-scale motions. This proves the effectiveness of the control in targeting large-scale motions, since an energy-intensification of roughly 45 % occurs for the reinforcing control mode instead. And relatively no change in energy, within the wall-normal range targeted, appears with the desynchronized control mode. Skin friction coefficients are inferred from PTV data to yield a direct measurement of the wall-shear stress. Results indicate that the opposing control logic is able to lower the wall-shear stress by about 3 % with respect to desynchronised control, and by roughly 10 % with respect to the uncontrolled flow. A FIK-decomposition of the skin-friction coefficient was performed

关键词： Reynolds number

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EmoNet: Unveiling Affective States Through Convolutional Neural Networks in Textual Emotion Classification 5

EmoNet: Unveiling Affective States Through Convolutional Neu...

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5th International Conference on Intelligent computing, Communication, Networking and Services, ICCNS 2024

作者： Salloum, Said Tahat, Khalaf Mansoori, Ahmed Alfaisal, Raghad Tahat, Dina School of Science Engineering and Environment University of Salford Manchester United Kingdom United Arab Emirates University and Yarmouk University Media and Creative Industries Jordan United Arab Emirates University Media and Creative Industries Department United Arab Emirates Universiti Pendidikan Faculty of Computing and Meta-Technology Tanjung Malim Malaysia Al Ain University Applied Sociology Department United Arab Emirates

ISBN: (纸本)9798350354690

In the burgeoning field of natural language processing, emotion classification from textual data has emerged as a critical task with applications ranging from sentiment analysis to mental health assessment. This paper explores the utilization of Convolutional Neural Networks (CNNs), traditionally dominant in image processing, for classifying emotions in text. Our proposed CNN model leverages the inherent hierarchical structure of language to identify and learn emotion-specific features, with an emphasis on capturing contextual n-grams through convolutional filters. The approach is substantiated by a comprehensive dataset, subjected to rigorous preprocessing and vectorization via TF-IDF to convert text into a numerical format suitable for deep learning. The model's architecture is meticulously crafted, incorporating convolutional layers followed by global max pooling and dense layers, culminating in a softmax activation function tailored for multi-class classification. Our findings demonstrate the model's robustness, achieving a notable accuracy of 96.08% on the test set. This high level of precision is further corroborated by the Receiver Operating Characteristic (ROC) analysis, revealing exceptional area under the curve (AUC) values across various emotion categories. The results suggest that CNNs hold significant promise for emotion recognition tasks in textual data, providing an effective framework for future explorations in the domain. © 2024 IEEE.

关键词： Convolutional neural networks

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Message Verification Protocol Based on Bilinear Pairings and Elliptic Curves for Enhanced Security in Vehicular Ad Hoc Networks

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Computers, Materials & Continua 2024年第10期81卷 1029-1057页

作者： Vincent Omollo Nyangaresi Arkan A.Ghaib Hend Muslim Jasim Zaid Ameen Abduljabbar Junchao Ma Mustafa A.Al Sibahee Abdulla J.Y.Aldarwish Ali Hasan Ali Husam A.Neamah Department of Computer Science and Software Engineering Jaramogi Oginga Odinga University of Science and TechnologyBondo40601Kenya Department of Applied Electronics Saveetha School of EngineeringSIMATSChennaiTamilnadu602105India Information Technology Department Management Technical CollegeSouthern Technical UniversityBasrah61004Iraq Department of Computer Science College of Education for Pure SciencesUniversity of BasrahBasrah61004Iraq College of Big Data and Internet Shenzhen Technology UniversityShenzhen518118China Huazhong University of Science and Technology Shenzhen InstituteShenzhen518000China National Engineering Laboratory for Big Data System Computing Technology Shenzhen UniversityShenzhen518060China Computer Technology Engineering Department Iraq University CollegeBasrah61004Iraq Department of Mathematics College of Education for Pure SciencesUniversity of BasrahBasrah61004Iraq Institute of Mathematics University of DebrecenDebrecenH-4002Hungary Department of Electrical Engineering and Mechatronics Faculty of EngineeringUniversity of DebrecenDebrecen4028Hungary

Vehicular ad hoc networks(VANETs)provide intelligent navigation and efficient route management,resulting in time savings and cost reductions in the transportation ***,the exchange of beacons and messages over public channels among vehicles and roadside units renders these networks vulnerable to numerous attacks and privacy *** address these challenges,several privacy and security preservation protocols based on blockchain and public key cryptography have been proposed ***,most of these schemes are limited by a long execution time and massive communication costs,which make them inefficient for on-board units(OBUs).Additionally,some of them are still susceptible to many *** such,this study presents a novel protocol based on the fusion of elliptic curve cryptography(ECC)and bilinear pairing(BP)*** formal security analysis is accomplished using the Burrows–Abadi–Needham(BAN)logic,demonstrating that our scheme is verifiably *** proposed scheme’s informal security assessment also shows that it provides salient security features,such as non-repudiation,anonymity,and ***,the scheme is shown to be resilient against attacks,such as packet replays,forgeries,message falsifications,and *** the performance perspective,this protocol yields a 37.88%reduction in communication overheads and a 44.44%improvement in the supported security ***,the proposed scheme can be deployed in VANETs to provide robust security at low overheads.

关键词： Attacks bilinear elliptic curve cryptography(ECC) privacy security vehicular ad hoc network(VANET)

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The Terms Estimation of S Robotics using MTRS as well as FEM

The Terms Estimation of S Robotics using MTRS as well as FEM

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Advance computing and Innovative Technologies in Engineering (ICACITE), International Conference on

作者： R. Gopal Mustafa Mudhafar Amjad Hashem Venkadeshan Ramalingam Dibyahash Bordoloi Muhammad Numan Ali Khan Myasar Mundher Adnan Zahraa N. Abdulhussain Assistant Professor Information and communication Engineering College of Engineering University of Buraimi Sultanate of Oman Department of Medical Physics Faculty of Medical Applied Sciences University of Kerbala Karbala Iraq Computer Engineer Department Al-kunooze University College Basra Iraq Network Computing College of Computing and Information Sciences University of Technology and Applied Sciences– Shinas Sultanate of Oman Dept of CSE Graphic Era Hill University Adjunct professor Graphic Era Deemed to be University Dehradun Uttarakhand Faculty – Network Computing College of Computing and Information Sciences University of Technology and Applied Sciences – Shinas Sultanate of Oman Department of computers Techniques engineering College of technical engineering The Islamic university Najaf Iraq College of Technical Engineering National University of Science and TechnologyDhi Qar Iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

This look at addresses the critical project of reaching dependable and precise proprioception in soft robots, it really is essential for his or her effective deployment in complicated environments. By integrating multi-tap resistive sensors in the robot’s form, our technique lets in for the distinct sensing of nearby deformations at various factors throughout the robotics’ frame, moving beyond traditional techniques that anticipate steady curvature. We beautify the decision of these deformations and couple the sensory facts with a strong finite detail technique (FEM) model. This included framework allows the correct estimation of the smooth robot’s form and the significance of outside forces applied to it, attaining a mean relative inaccuracy of about 4%. Additionally, our technique proves its software program in tender manipulation structures, in which it achieves thirteen% relative mistakes in external force estimation inside diverse zero to five N. The approach gives a promising direction for enhancing robotic interactions in sensitive and dynamic responsibilities, providing massive enhancements in robotic proprioception without reliance on outdoor monitoring generation.

关键词： Deformable models Deformation Force Estimation Soft robotics Robot sensing systems Software

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