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21st International system-on-Chip Design Conference, ISOCC 2024

作者： Lee, Wilaiporn Suwanasri, Cattareeya Pipanmekaporn, Luepol Srisomboon, Kanabadee King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Computer and Information Sciences Bangkok Thailand

ISBN: (纸本)9798350377088

In this research, we propose a low-cost indoor localization technique using the CSI. By using CSI signal as input data, different locations and human activities are classified effectively using machine learning models. According to a low-cost constraint where a single transmitter (Tx) and receiver (Rx) are utilized, we investigate the appropriate transmitter location for achieving accurate position estimation in an indoor environment under two patterns of room environment. Moreover, an appropriate machine learning model for position estimation is examined where decision tree, KNN, Naïve bayes, and ANN are considered. The experimental results demonstrate that the highest accuracy can be achieved using the ANN technique. The shortest distance between a Tx and Rx provides the highest precision for determining the human location in an empty room, whereas placing the Tx and Rx in opposing positions is advantageous for a section with items positioned against the wall. When determining an average accuracy, placing the Tx and Rx in opposing positions shows the highest accuracy among others. © 2024 IEEE.

关键词： Adversarial machine learning

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Virtuous, multi-level integration of research and education within the project "european Digital Dynamic Mapping" (EDDY) Excellent applied research as an enabler for excellent education in the context of Project-Based Learning in Higher Education Institutions

Virtuous, multi-level integration of research and education ...

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2023 Designing Futures: Zukunfte gestalten, INFORMATIK 2023 - 2023 Designing Futures: Shaping the Future, INFORMATIK 2023

作者： Rettig, Rasmus Weltz, Maximilian Hamburg University of Applied Sciences Faculty of Engineering and Computer Science Department of Information and Electrical Engineering Berliner Tor 7 Hamburg20099 Germany

ISBN: (纸本)9783885797319

Project-and Problem-Based Learning has been subject to research and implementation in engineering and computer science curricula for about the last 20 years. However, the projects and problems focused on in education have hardly been questioned for their relevance and effect. Considering current global and financial challenges, the authors propose to revise project-and problem-based approaches in education in the context of relevance and excellence in applied research. Besides coping with current cost-cutting, existing control mechanisms could ensure both relevance and excellence. Example projects based on more than 10 years of hands-on experience in Project-Based Learning in the context of externally funded research, for various classroom situations are included. © 2023 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Autonomous vehicles

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Not Necessarily Relaxed: How Work Interruptions Affect Users’ Perception of Stress in Remote Work Situations 3rd

Not Necessarily Relaxed: How Work Interruptions Affect Users...

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3rd International Conference on AI-assisted Solutions for COVID-19 and Biometrical Applications in Smart Cities, AISCOVID-19 2022

作者： Metzger, Lukas Kutzner, Aaron Huffstadt, Karsten University of Applied Sciences Würzburg-Schweinfurt Faculty of Computer Science and Business Information Systems Sanderheinrichsleitenweg 20 Würzburg97074 Germany

ISBN: (纸本)9783031382031

Remote work was ubiquitous during the COVID-19 pandemic to minimize the spread of the virus. However, working away from the actual workplace also presented new challenges. In a study conducted in 2020, a research team from Switzerland examined the effects of acute work stress in a group office setting. Part of the study included examining the effects of work interruptions. We took this as an opportunity to conduct an exploratory study of the subjective perception of work interruptions during remote work regarding stress. Using eye-tracking technology, we investigated the visual attention of subjects within a laboratory experiment while they were repeatedly interrupted while performing a work task. We then processed the experiences in qualitative interviews to reconstruct the social reality of the effects of remote work interruptions. According to our results, we assume that the more personal standards cannot be met in case of interruptions, the stronger the subjective stress experience. This should be further explored in quantitative studies. We were able to draw up further findings in the form of recommendations to managers. For example, unnecessary interruptions should be minimized, or better yet, eliminated. Alternatively, necessary work interruptions should be directed to employees who appear to have more capacity to respect and protect the concentration of busy employees. We hope that further research will follow up on this topic in the future. In this way, robust approaches that promote the mental health of employees in the home office can be formulated to avoid unnecessary stressors, and contribute to effectiveness, efficiency, and well-being. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Eye tracking

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MQDD: Pre-training of Multimodal Question Duplicity Detection for Software Engineering Domain

MQDD: Pre-training of Multimodal Question Duplicity Detectio...

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2023 International Conference Recent Advances in Natural Language Processing: Large Language Models for Natural Language Processing, RANLP 2023

作者： Pašek, Jan Sido, Jakub Konopík, Miloslav Pražák, Ondřej NTIS - New Technologies for the Information Society Department of Computer Science and Engineering Faculty of Applied Sciences University of West Bohemia Plzeň Czech Republic

ISBN: (纸本)9789544520922

This work proposes a new pipeline for leveraging data collected on the Stack Overflow website for pre-training a multimodal model for searching duplicates on question answering websites. Our multimodal model is trained on question descriptions and source codes in multiple programming languages. We design two new learning objectives to improve duplicate detection capabilities. The result of this work is a mature, fine-tuned Multimodal Question Duplicity Detection (MQDD) model, ready to be integrated into a Stack Overflow search system, where it can help users find answers for already answered questions. Alongside the MQDD model, we release two datasets related to the software engineering domain. The first Stack Overflow Dataset (SOD) represents a massive corpus of paired questions and answers. The second Stack Overflow Duplicity Dataset (SODD) contains data for training duplicate detection models. © 2023 Incoma Ltd. All rights reserved.

关键词： Websites

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From Social Media to Ballot Box:Leveraging Location-Aware Sentiment Analysis for Election Predictions

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computers, Materials & Continua 2023年第12期77卷 3037-3055页

作者： Asif Khan Nada Boudjellal Huaping Zhang Arshad Ahmad Maqbool Khan School of Computer Science and Technology Beijing Institute of TechnologyBeijing100081China The Faculty of New Information and Communication Technologies University Abdel-Hamid Mehri Constantine 2Constantine25000Algeria Department of IT and Computer Science Pak-Austria Fachhochschule:Institute of Applied Sciences and TechnologyHaripur22620Pakistan

Predicting election outcomes is a crucial undertaking,and various methods are employed for this purpose,such as traditional opinion polling,and social media ***,traditional polling approaches often struggle to capture the intricate nuances of voter sentiment at local levels,resulting in a limited depth of analysis and *** light of this challenge,this study focuses on predicting elections at the state/regional level along with the country level,intending to offer a comprehensive analysis and deeper insights into the electoral *** achieve this,the study introduces the Location-Based Election Prediction Model(LEPM),which utilizes social media data,specifically Twitter,and integrates location-aware sentiment analysis techniques at both the state/region and country *** predicts the support and opposing strength of each political party/*** determine the location of users/voters who have not disclosed their location information in tweets,the model utilizes a Voter Location Detection(VotLocaDetect)approach,which leverages recent tweets/*** sentiment analysis techniques employed in this study include rule-based sentiment analysis,Valence Aware Dictionary and Sentiment Reasoner(VADER)as well as transformers-based sentiment analysis such as Bidirectional Encoder Representations from Transformers(BERT),BERTweet,and Election based BERT(ElecBERT).This study uses the 2020 United States(US)Presidential Election as a case *** applying the LEPM model to the election,the study demonstrates its ability to accurately predict outcomes in forty-one states,achieving an 0.84 accuracy rate at the state ***,at the country level,the LEPM model outperforms traditional polling *** a low Mean Absolute Error(MAE)of 0.87,the model exhibits more precise predictions and serves as a successful alternative to conventional polls and other *** the extensive social media data,the LEPM model provides nuanc

关键词： Sentiment analysis big data machine learning election predictions social media analysis

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Improving Sentiment Analysis in Election-Based Conversations on Twitter with ElecBERT Language Model

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computers, Materials & Continua 2023年第9期76卷 3345-3361页

作者： Asif Khan Huaping Zhang Nada Boudjellal Arshad Ahmad Maqbool Khan School of Computer Science and Technology Beijing Institute of TechnologyBeijing100081China The Faculty of New Information and Communication Technologies University Abdel-Hamid Mehri Constantine 2Constantine25000Algeria Department of IT and Computer Science Pak-Austria Fachhochschule:Institute of Applied Sciences and TechnologyHaripur22620Pakistan

Sentiment analysis plays a vital role in understanding public opinions and sentiments toward various *** recent years,the rise of social media platforms(SMPs)has provided a rich source of data for analyzing public opinions,particularly in the context of election-related ***,sentiment analysis of electionrelated tweets presents unique challenges due to the complex language used,including figurative expressions,sarcasm,and the spread of *** address these challenges,this paper proposes Election-focused Bidirectional Encoder Representations from Transformers(ElecBERT),a new model for sentiment analysis in the context of election-related ***-related tweets pose unique challenges for sentiment analysis due to their complex language,sarcasm,*** is based on the Bidirectional Encoder Representations from Transformers(BERT)language model and is fine-tuned on two datasets:Election-Related Sentiment-Annotated Tweets(ElecSent)-Multi-Languages,containing 5.31 million labeled tweets in multiple languages,and ElecSent-English,containing 4.75million labeled tweets in *** outperforms othermachine learning models such as Support Vector Machines(SVM),Na飗e Bayes(NB),and eXtreme Gradient Boosting(XGBoost),with an accuracy of 0.9905 and F1-score of 0.9816 on ElecSent-Multi-Languages,and an accuracy of 0.9930 and F1-score of 0.9899 on *** performance of differentmodels was compared using the 2020 United States(US)Presidential Election as a case *** ElecBERT-English and ElecBERT-Multi-Languages models outperformed BERTweet,with the ElecBERT-English model achieving aMean Absolute Error(MAE)of *** paper presents a valuable contribution to sentiment analysis in the context of election-related tweets,with potential applications in political analysis,social media management,and policymaking.

关键词： Sentiment analysis social media election prediction machine learning transformers

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The Role of the Internet of Things in Monitoring Air Pollution-A Survey 11

The Role of the Internet of Things in Monitoring Air Polluti...

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11th International Conference on information Technology, ICIT 2023

作者： Abusukhon, Ahmad Alghannam, Bareeq Al-Zaytoonah University of Jordan Faculty of Science and Information Technology Computer Science Dep. Amman Jordan College of Business Studies the Public Authority for Applied Education and Training Department of Computer Science and Information System Kuwait City Kuwait

ISBN: (纸本)9798350320060

Air pollution is one of the most common problems that the world is facing today. In fact, there are numerous causes of air pollution, including the large number of industries and automobiles that emit carbon dioxide (CO2) and carbon monoxide. Pollution is increasing on a daily basis, making air quality monitoring and management a major challenge for governments in both developed and developing countries. Air pollution affects human health as well as the atmosphere, causing climate change. One of the techniques used for monitoring air pollution is the Internet of Things (IoT). In this paper, we demonstrate various techniques for monitoring air quality using IoT and make a comparison based on various factors elicited from the previous work. © 2023 IEEE.

关键词： Internet of things

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Exploring advanced feature selection techniques: an application to dialectal Arabic data

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International Journal of information Technology (Singapore) 2024年第7期16卷 4637-4649页

作者： Abdellah, Ait Elouli Ouahi, Hassan Cherrat, El Mehdi Bekkar, Abdellatif Department of Computer Science Laboratory of Innovation in Mathematics and Intelligent Systems Faculty of Applied Sciences Ibn Zohr University Ait Melloul Morocco Laboratory of Innovation in Mathematics and Intelligent SystemsFaculty of Applied Sciences Ibn ZohrUniversity Ait Melloul Morocco Laboratory of Systems Engineering and Information Technology National School of Applied Sciences Ibn Zohr University Agadir Morocco Department of Computer ScienceLIM LaboratoryFaculty of Sciences and Technics Hassan II University of Casablanca Mohammedia Casablanca Morocco

In the field of automated language processing, distinguishing between Moroccan Arabic (Darija) in multilingual contexts is a major challenge. This study addresses this challenge by exploiting feature selection techniques to improve the accuracy of tongue detection. Using a comprehensive methodology integrating various feature selection methods, including TF-idf, CBOW, Word2Vec for feature extraction, LASSO regression, decision trees for machine learning techniques, and statistical feature selection such as ANOVA, Pearson correlation coefficient, and Mutual information, alongside semantic techniques by developing our semantic encoders adapted to Arabic data, we strive to uncover the most important, relevant language features while attenuating the noise. As a result, we have obtained robust results by applying the famous XGBOOST, with conventional extraction methods and SVD as a dimensional reduction method with a very reasonable execution time. Our experimental results highlight the effectiveness of feature selection techniques to reinforce supervised learning tasks. This research not only advances the field of natural language processing but also highlights the central role of feature selection in deciphering complex linguistic landscapes. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Comparative Analysis Dialect Identification Dimensionality Reduction Moroccan Dialect Arabic NLP Standard Arabic

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A Hybrid Neural Network and Box-Jenkins Models for Time Series Forecasting

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computers, Materials & Continua 2022年第3期70卷 4829-4845页

作者： Mohammad Hadwan Basheer M.Al-Maqaleh Fuad N.Al-Badani Rehan Ullah Khan Mohammed A.Al-Hagery Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computer Science College of Applied SciencesTaiz UniversityTaizYemen Intelligent Analytics Group(IAG) College of ComputerQassim UniversityBuraydahSaudi Arabia Faculty of Computer Sciences and Information Systems Thamar UniversityThamarYemen Faculty of Administration Science Ibb UniversityIbbYemen Department of Computer Science College of ComputerQassim UniversityBuraydahSaudi Arabia

Time series forecasting plays a significant role in numerous applications,including but not limited to,industrial planning,water consumption,medical domains,exchange rates and consumer price *** main problem is insufficient forecasting *** present study proposes a hybrid forecastingmethods to address this *** proposed method includes three *** first model is based on the autoregressive integrated moving average(ARIMA)statistical model;the second model is a back propagation neural network(BPNN)with adaptive slope and momentum parameters;and the thirdmodel is a hybridization between ARIMA and BPNN(ARIMA/BPNN)and artificial neural networks and ARIMA(ARIMA/ANN)to gain the benefits of linear and *** forecasting models proposed in this study are used to predict the indices of the consumer price index(CPI),and predict the expected number of cancer patients in the Ibb Province in *** standard measures used to evaluate the proposed method include(i)mean square error,(ii)mean absolute error,(iii)root mean square error,and(iv)mean absolute percentage *** on the computational results,the improvement rate of forecasting the CPI dataset was 5%,71%,and 4%for ARIMA/BPNN model,ARIMA/ANN model,and BPNN model respectively;while the result for cancer patients’dataset was 7%,200%,and 19%for ARIMA/BPNNmodel,ARIMA/ANN model,and BPNNmodel ***,it is obvious that the proposed method reduced the randomness degree,and the alterations affected the time series with data *** ARIMA/ANN model outperformed each of its components when it was applied separately in terms of increasing the accuracy of forecasting and decreasing the overall errors of forecasting.

关键词： Hybrid model forecasting non-linear data time series models cancer patients neural networks box-jenkins consumer price index

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Deep Transfer Learning Driven Oral Cancer Detection and Classification Model

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computers, Materials & Continua 2022年第11期73卷 3905-3920页

作者： Radwa Marzouk Eatedal Alabdulkreem Sami Dhahbi Mohamed K.Nour Mesfer Al Duhayyim Mahmoud Othman Manar Ahmed Hamza Abdelwahed Motwakel Ishfaq Yaseen Mohammed Rizwanullah Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam Bin Abdulaziz UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

Oral cancer is the most commonly occurring‘head and neck cancers’across the *** of the oral cancer cases are diagnosed at later stages due to absence of awareness among *** earlier identification of disease is essential for improved outcomes,Artificial Intelligence(AI)and Machine Learning(ML)models are used in this *** this background,the current study introduces Artificial Intelligence with Deep Transfer Learning driven Oral Cancer detection and Classification Model(AIDTLOCCM).The primary goal of the proposed AIDTL-OCCM model is to diagnose oral cancer using AI and image processing *** proposed AIDTL-OCCM model involves fuzzy-based contrast enhancement approach to perform data *** by,the densely-connected networks(DenseNet-169)model is employed to produce a useful set of deep ***,Chimp Optimization Algorithm(COA)with Autoencoder(AE)model is applied for oral cancer detection and ***,COA is employed to determine optimal parameters involved in AE model.A wide range of experimental analyses was conducted on benchmark datasets and the results were investigated under several *** extensive experimental analysis outcomes established the enhanced performance of AIDTLOCCM model compared to other approaches with a maximum accuracy of 90.08%.

关键词： Oral cancer detection image classification artificial intelligence machine learning deep learning

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