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11th International Conference on Robotics, Vision, Signal Processing and Power Applications, RoViSP 2021

作者： Alnagrat, Ahmed Jamah Ahmed Ismail, Rizalafande Che Idrus, Syed Zulkarnain Syed Faculty of Applied and Human Sciences Universiti Malaysia Perlis Perlis Arau Malaysia Department of Computer Science and Information Technology Higher Institute of Science and Technology Wadi al-Shati Fezzan Libya School of Computing and Informatics Albukhary International University Kedah Alor Setar Malaysia

ISBN: (纸本)9789811681288

Due to the possibilities provided by such technologies to provide people with live immersive virtual worlds, Extended Reality (XR) technologies such as virtual (VR), augmented (AR), and mixed reality (MR) have grown. The role of XR has a highly effective and efficient technology in virtual laboratories (VL) as experiments can be performed almost anywhere. During the laboratory activities, students are facing the issue of dealing with dangerous equipment such as Semiconductor fabrication furnace, which has a high temperature. In recent years, modern sensors such as Leap Motion Controller can track human movements and analyses the accuracy. This work presents a novelty approach of design training that can tracks body movements. Moreover, it provides warring alerts to trainer when it comes closer to dangerous places. While she/he is making wrong hand/body movement. When user do the experiment a semiconductor furnace training is operation in a VL. To illustrate more, this training enables the trainee to prevent his/her burning during the experiment according to safety and social distancing standards as well. Thus, Unity 3D has the capability to measure the following: accuracy, quality and efficiency of the system. XR training is significantly considering as safe technology to avoid user’s accidents. To conclude, in the field of Human-computer interaction (HCI), the analysis has been improved the training movement in terms of accuracy and interactivity respectively. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Human computer interaction

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A Netnographic-Based Semantic Analysis of Tweet Contents for Stress Management

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computers, Materials & Continua 2022年第1期70卷 1845-1856页

作者： Jari Jussila Eman Alkhammash Norah Saleh Alghamdi Prashanth Madhala Mohammad Ayoub Khan HAMK Smart Research Unit Häme University of Applied SciencesHämeenlinna13100Finland Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia College of Computer and Information Sciences Princess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Faculty of Management and Business Tampere UniversityTampere33720Finland College of Computing and Information Technologies University of BishaBisha67714Saudi Arabia

Social media platforms provide new value for markets and research *** article explores the use of social media data to enhance customer value *** case study involves a company that develops wearable Internet of Things(IoT)devices and services for stress *** and semantic annotation for recognizing and categorizing the context of tweets are conducted to gain a better understanding of users’stress management *** aim is to analyze the tweets about stress management practices and to identify the context from the ***,we map the tweets on pleasure and arousal to elicit customer *** analyzed a case study of a marketing strategy on the Twitter *** in the marketing campaign shared photos and texts about their stress management *** learning techniques were used to evaluate and estimate the emotions and contexts of the tweets posted by the campaign *** computational semantic analysis of the tweets was compared to the text analysis of the *** content analysis of only tweet images resulted in 96%accuracy in detecting tweet context,while that of the textual content of tweets yielded an accuracy of 91%.Semantic tagging by Ontotext was able to detect correct tweet context with an accuracy of 50%.

关键词： Social media stress semantic analysis Twitter context recognition

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A Two-level GPU-Accelerated Incomplete LU Preconditioner for General Sparse Linear Systems

arXiv

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arXiv 2023年

作者： Xu, Tianshi Li, Ruipeng Osei-Kuffuor, Daniel Department of Computer Science and Engineering University of Minnesota Minnesota United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory CA United States

This paper presents a parallel preconditioning approach based on incomplete LU (ILU) factorizations in the framework of Domain Decomposition (DD) for general sparse linear systems. We focus on distributed memory parallel architectures, specifically, those that are equipped with graphic processing units (GPUs). In addition to block Jacobi, we present general purpose two-level ILU Schur complement-based approaches, where different strategies are presented to solve the coarse-level reduced system. These strategies are combined with modified ILU methods in the construction of the coarse-level operator, in order to effectively remove smooth errors. We leverage available GPU-based sparse matrix kernels to accelerate the setup and the solve phases of the proposed ILU preconditioner. We evaluate the efficiency of the proposed methods as a smoother for algebraic multigrid (AMG) and as a preconditioner for Krylov subspace methods, on challenging anisotropic diffusion problems and a collection of general sparse *** Codes G.1.3 Copyright © 2023, The Authors. All rights reserved.

关键词： Graphics processing unit

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AI-Driven Network Forensics Using Hybrid Anomaly Detection with Enhanced Transformer Based ISO-Encoder and LSTM-CNN Fusion

AI-Driven Network Forensics Using Hybrid Anomaly Detection w...

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Artificial Intelligence, Applications, Innovation and Ethics (AI2E), International Conference for

作者： Abisha B. Sheeja Kumari V. Wilfred Blessing N. R Sutherlin Subitha G. Seldev Christopher C. Wasim Haidar SK Department of Computational Intelligence Saveetha School of Engineering SIMATS University Chennai India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Sultanate of Oman Department of Computer Science and Engineering Stella Mary's College of Engineering Affiliated to Anna University Tamil Nadu India Department of Computer Science and Engineering St. Xavier's Catholic College of Engineering Nagercoil Tamil Nadu India College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Sultanate of Oman

ISBN: (数字)9798331513733

ISBN: (纸本)9798331513740

Traditional network forensic techniques frequently find it difficult to identify and look into sophisticated cybercrimes in today's quickly changing cyber scene. A Transformer-based Iso-Encoder and a Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) fusion model are combined in this research to present an AI-driven method that makes use of a novel hybrid anomaly detection model. The LSTM-CNN fusion model performs exceptionally well in temporal and spatial feature extraction, while the Iso-Encoder is improved to capture complex patterns in network traffic. This hybrid approach is intended to accurately detect known and undiscovered anomalies. By offering practical insights into the nature of the dangers, the suggested framework not only helps with the comprehensive investigation of cyber incidents but also identifies anomalies in real-time. The model is a powerful tool for cybersecurity experts since it incorporates cutting-edge AI techniques to guarantee that it adjusts to changing cyberthreats. According to experimental data, the hybrid model maintains a low false positive rate and processing time of 0.5 ms/packet while outperforming current techniques in terms of detection accuracy (98.7%), precision (98.4%), recall (97.8%), and F1-score (98.1%). Traditional models with somewhat poorer performance include Random Forest (94.5% accuracy), Support Vector Machine (SVM) (93.7% accuracy), and LSTM (96.2 % accuracy). This research paves the way for more resilient and intelligent network forensic solutions, enhancing the ability to counteract and investigate complex cyber threats effectively.

关键词： Support vector machines Accuracy Forensics Telecommunication traffic Transformers Real-time systems computer crime Long short term memory Anomaly detection Random forests

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Empowering Donors: How Blockchain Technology Can Help Ensure Their Contributions Reach the Right Recipients

Empowering Donors: How Blockchain Technology Can Help Ensure...

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Business Analytics for Technology and Security (ICBATS), International Conference on

作者： Sahar Ajmal Muteeb Bin Muzammil Muhammad Shoaib Muhammad Hassaan Mehmood Department of Software Engineering National University of Computer and Emmerging Sciences Chiniot-Faisalabad Pakistan College of Computing Riphah International University Faisalabad Pakistan Department of Computer Science University of Engineering and Technology Lahore Pakistan School of Computer Science NCBA&E Lahore Pakistan Applied School Research Center Applied Science Private University Amman Jordan

Charity giving is a fundamental aspect of society, but there are concerns about the accountability and transparency of the donation process. Blockchain technology has emerged as a potential solution, offering a secure and auditable platform for managing charitable contributions. In this paper, we explore the concept of empowering donors through blockchain technology and examine how it can help ensure that contributions reach the right recipients. We review existing literature on the topic and identify key areas for future research, including the development of hybrid blockchain solutions along with the implementation of novel consensus mechanisms. Moreover, providing the idea of decentralized autonomous organizations, self-executing smart contracts, and integration of blockchain with artificial intelligence and Internet of things. By providing a comprehensive overview of the current state of the field and outlining a roadmap for future research, we hope to stimulate further exploration of the potential of blockchain technology to revolutionize charity giving, and create a more accountable and transparent system for managing charitable funds. Some research questions are designed for each novel idea. Our analysis suggests that blockchain has the potential to empower donors and ensure that their contributions make a positive impact in the world, but further research is needed to fully realize its potential.

关键词： Decentralized autonomous organization Smart contracts Transforms Blockchains Consensus protocol Internet of Things Security

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Machine Learning and Deep Learning Methods for the Detection of Schizophrenia Using Magnetic Resonance Images and EEG Signals: An Overview of the Recent Advancements 7th

Machine Learning and Deep Learning Methods for the Detection...

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7th International Conference on Information and Communication Technology for Competitive Strategies , ICTCS 2022

作者： Shaffi, Noushath Mahmud, Mufti Hajamohideen, Faizal Subramanian, Karthikeyan Shamim Kaiser, M. College of Computing and Information Science University of Technology and Applied Sciences Sohar Oman Department of Computer Science Nottingham Trent University NottinghamNG11 8NS United Kingdom CIRC and MTIF Nottingham Trent University NottinghamNG11 8NS United Kingdom Institute of Information Technology Jahangirnagar University Dhaka1342 Bangladesh

ISBN: (纸本)9789811996375

The availability of large-scale datasets, massively parallelizable GPUs and a wide spectrum of open-source tools have immensely capacitated Artificial Intelligence (AI) field in accurate analytics of medical data. Although it is a challenging task, the application of powerful AI-based computer-aided diagnosis has enabled health practitioners to predict Neurodegenerative diseases more accurately than ever before. This paper presents a brief review of methodological research papers dealing with the prediction of Schizophrenia disease based on machine learning and deep learning techniques using MRI and EEG signals data. The result shows that these techniques have permeated the Schizophrenia disease prediction/classification task with significantly higher accuracy. The paper concludes by providing future avenues based on challenges that are currently faced by this sector. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Magnetic resonance imaging

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Fit and Forecasting Indonesia Banking Stock Price During Covid-19 Pandemic Based on Logistic Differential Equation System 4

Fit and Forecasting Indonesia Banking Stock Price During Cov...

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2022 4th International Conference on E-Business and E-Commerce Engineering, EBEE 2022

作者： Noviantri, Viska Chandra, Andree Sulistio Yusof, Yuhani Mathematics Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Centre for Mathematical Sciences College of Computing and Applied Sciences Universiti Malaysia Pahang Persiaran Tun Khalil Yaakob Gambang Pahang Kuantan26300 Malaysia

ISBN: (纸本)9798400700071

Banks are financial service companies that play an important role in channeling finances to folks who want capital for micro, small and medium establishments. The increase in bankrupt companies and unemployment in many sectors due to the uncertain condition during the Covid-19 pandemic will affect bank performance which may change their stock price dynamics. This study analyzes the stock price fluctuation in banking companies using a logistic differential equation model since the fluctuation changes rapidly and has a nonlinear pattern. This model was finished by the fit and forecasting process;and completed by Mean Average Percentage Error (MAPE) value to see how well the model captures the stock price fluctuation. Furthermore, the closing stock price during the pandemic of the four banks with the largest number of assets in Indonesia's banking sectors is applied here to complete the fit and forecasting simulation. The results show that the MAPE average value of this model in the fit process is 6.331%. In other words, the logistic equation accurately represents banking stock price fluctuation. Next, the forecast process conducts to predict the stock price fluctuation for some periods. Thus, the fluctuation of stock prices in Indonesian banking during the Covid-19 pandemic can analyze further. © 2022 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Forecasting

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A Dynamic Approach for Handling UAV in Crowded Environment Using Deep Q-Networks

A Dynamic Approach for Handling UAV in Crowded Environment U...

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Global Conference for Advancement in Technology (GCAT)

作者： Yash Upadhyay Muskan Gahlawat S. Nivetha Saurabh Rana B Natarajan Pratik Gupta Department of Computer Science and Engineering (AIE) Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Computer Science and Engineering RVS College of Engineering & Technology Coimbatore Department of Mathematics Chandigarh University Department of Computer Science and Engineering (AIE) Amrita Vishwa Vidyapeetham Chennai India Department of Applied Mathematics Gautam Buddha University Noida

The demand for wireless connections is rising, and the advent of mobile internet has sparked a hunt for fresh, creative ways to expand the capacity and reach of cellular networks. One strategy to provide ground mobile users with long-term communication coverage is to employ Unmanned Aerial vehicles (UAV) as mobile base stations. The proposed method incorporates a Deep Reinforcement Learning (DRL) method to address the challenges that persist with traditional approaches. We introduced Deep Q-Learning with an attention mechanism-based multi-agent model to develop an energy-efficient control system for UAVs in cellular networks. Due to the proposed strategy’s dynamic nature, it efficiently handles the crowded environment by resolving scalability and mobility issues. The researchers evaluated the proposed model using the benchmark dataset, producing promising results. Further, the researchers tested the proposed model using the sum rate metric to analyze the capability of the proposed algorithm to provide a reasonable throughput rate.

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Developer-Centric Task Allocation: An Intelligent Approach for Enhanced Software Productivity

Developer-Centric Task Allocation: An Intelligent Approach f...

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International Conference on Information, Intelligence, Systems and Applications (IISA)

作者： Evandro L. R. Fensterseifer Filho Joaquim V. C. Assunção Lisandra M. Fontoura Graduate Program in Computer Science Federal University of Santa Maria - UFSM Santa Maria – RS Brazil Applied Computing Department Federal University of Santa Maria - UFSM Santa Maria – RS Brazil

ISBN: (数字)9798350368833

ISBN: (纸本)9798350368840

Software projects are affected by technical knowledge as well as the personality of the team. Such factors can reduce or increase the software quality and development speed. For successful task allocation, it is essential to consider the skills and profile of each developer, thus maximizing their productivity. In projects with large teams, task allocation can be challenging, and the help of tools can facilitate its execution. In this work, we propose an intelligent approach for allocating software development tasks suitable to the profile of developers. From the literature, we define the appropriate skills and technical profiles for a development team, and the assessment is based on the developer completing a questionnaire. We developed a recommendation system to suggest tasks to be allocated to developers, employing text processing techniques. For validation, 495 tasks were used from an actual project developing simulator software for military training. The recommended allocations were evaluated by the project developers and used to improve the system. Validations showed that the developed approach makes consistent and coherent task recommendations to developers according to the developer profile, as participants agree with the recommendation for 76% of tasks.

关键词： Training Productivity Databases Mathematical models Classification algorithms Resource management Data mining Recommender systems Text processing Software development management

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Detecting Information on the Spread of Dengue on Twitter Using Articial Neural Networks

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computers, Materials & Continua 2021年第4期67卷 1317-1332页

作者： Samina Amin M.Irfan Uddin M.Ali Zeb Ala Abdulsalam Alarood Marwan Mahmoud Monagi H.Alkinani Institute of Computing Kohat University of Science and TechnologyKohat26000Pakistan College of Computer Science and Engineering University of JeddahJeddah21959Saudi Arabia Faculty of Applied Studies King Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Articial Intelligence College of Computer Sciences and EngineeringUniversity of JeddahJeddahSaudi Arabi

Social media platforms have lately emerged as a promising tool for predicting the outbreak of epidemics by analyzing information on them with the help of machine learning *** analytical and statistical models are available to infer a variety of user sentiments in posts on social *** amount of data generated by social media platforms,such as Twitter,that can be used to track diseases is increasing *** paper proposes a method for the classication of tweets related to the outbreak of dengue using machine learning *** articial neural network(ANN)-based method is developed using Global Vector(GloVe)embedding to use the data in tweets for the automatic and efcient identication and classication of *** proposed method classies tweets related to the outbreak of dengue into positives and *** were conducted to assess the proposed ANN model based on performance evaluation matrices(confusion matrices).The results show that the GloVe vectors can efciently capture a sufcient amount of information for the classier to accurately identify and classify tweets as relevant or irrelevant to dengue *** proposed method can help healthcare professionals and researchers track and analyze epidemic outbreaks through social media in real time.

关键词： Articial neural network classication social media GloVe social networking sites

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