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2024 International Conference on IT and Industrial Technologies, ICIT 2024

作者： Ali, Noor Ali, Khadija Ali, Fatimah Ali, Aaminah Ali, Nisar Ali, Raja Hashim A-levels Lahore Grammer School 55 Main Gulberg Lahore54660 Pakistan Department of Software Engineering NUST College of Electrical and Mechanical Engineering Islamabad43701 Pakistan Department of Artificial Intelligence Faculty of Computer Science & Engineering GIK Institute of Engineering Sciences & Technology Topi23460 Pakistan Department of Business University of Europe for Applied Sciences Potsdam14469 Germany Faculty of Engineering and Applied Science University of Regina ReginaS4S0A2 Canada

ISBN: (纸本)9798331504465

Voice recognition systems are crucial because they allow seamless human-computer interaction and improve accessibility for users of all abilities. The use of these technologies in hands-free control, language translation, virtual assistants, transcription services, and hands-free control is revolutionising how we engage with technology and enhancing convenience and productivity in general. Several attendance systems based on voice recognition exist, but we wanted to deploy an attendance system with a good graphical user interface specifically for students of GIK Institute. For this purpose, we wanted to make a user-friendly and accurate voice recognition system based and trained on self-provided data of ten students. This study introduces an AI-driven attendance system, which demonstrates high efficiency and accuracy in identifying students’ daily class attendance. To achieve this, the Gaussian Mixture Model approach was employed. The paper also delves into the utilization of libraries and methods, encompassing the training and validation of well-known machine learning models. Additionally, the system’s performance, its strengths, weaknesses and potential areas for improvement are also discussed in the study. © 2024 IEEE.

关键词： Students

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Multi-View People Detection in Large Scenes via Supervised View-Wise Contribution Weighting

arXiv

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arXiv 2024年

作者： Zhang, Qi Gong, Yunfei Chen, Daijie Chan, Antoni B. Huang, Hui College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong

Recent deep learning-based multi-view people detection (MVD) methods have shown promising results on existing datasets. However, current methods are mainly trained and evaluated on small, single scenes with a limited number of multi-view frames and fixed camera views. As a result, these methods may not be practical for detecting people in larger, more complex scenes with severe occlusions and camera calibration errors. This paper focuses on improving multi-view people detection by developing a supervised view-wise contribution weighting approach that better fuses multi-camera information under large scenes. Besides, a large synthetic dataset is adopted to enhance the model’s generalization ability and enable more practical evaluation and comparison. The model’s performance on new testing scenes is further improved with a simple domain adaptation technique. Experimental results demonstrate the effectiveness of our approach in achieving promising cross-scene multi-view people detection performance. See code here: https://***/research/2024/MVD. © 2024, CC BY.

关键词： Cameras

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A Curious Case of Searching for the Correlation between Training Data and Adversarial Robustness of Transformer Textual Models

arXiv

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arXiv 2024年

作者： Dang, Cuong Le, Dung D. Le, Thai FPT Software AI Center Viet Nam College of Engineering and Computer Science VinUniversity Viet Nam Department of Computer Science Indiana University United States

Existing works have shown that fine-tuned textual transformer models achieve state-of-the-art prediction performances but are also vulnerable to adversarial text perturbations. Traditional adversarial evaluation is often done only after fine-tuning the models and ignoring the training data. In this paper, we want to prove that there is also a strong correlation between training data and model robustness. To this end, we extract 13 different features representing a wide range of input fine-tuning corpora properties and use them to predict the adversarial robustness of the fine-tuned models. Focusing mostly on encoder-only transformer models BERT and RoBERTa with additional results for BART, ELECTRA, and GPT2, we provide diverse evidence to support our argument. First, empirical analyses show that (a) extracted features can be used with a lightweight classifier such as Random Forest to predict the attack success rate effectively, and (b) features with the most influence on the model robustness have a clear correlation with the robustness. Second, our framework can be used as a fast and effective additional tool for robustness evaluation since it (a) saves 30x-193x runtime compared to the traditional technique, (b) is transferable across models, (c) can be used under adversarial training, and (d) robust to statistical randomness. Our code is publicly available at https://***/CaptainCuong/RobustText_ACL2024. © 2024, CC BY-NC-ND.

关键词： Adversarial machine learning

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Virtual emotional gestures to assist in the examination of the mental health of the deaf-mutes

Virtual emotional gestures to assist in the examination of t...

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作者： Liang, Hui Xu, Haoming Wang, Yi Pan, Junjun Fu, Jialin Pang, Xiangwen College of Software Engineering Zhengzhou University of Light Industry Zhengzhou China Department of Geriatric General Hospital of Pingmei Shenma Group China School of Computer Science and Engineering Beihang University Beijing China College of Economics and Management Zhengzhou University of Light Industry Zhengzhou China

The particular characteristics of deaf-mutes make them more likely to have mental health problems. Due to their particular way of communication, it is more difficult for them to deal with mental health problems than ordinary people. Nowadays, those psychologists who are good at sign language are in short supply, and remote assistance cannot achieve satisfactory results. Therefore, a library of virtual emotional gestures based on electroencephalogram (EEG) was established and a prototype system for mental health examination of deaf-mutes was proposed, which help deaf-mutes identify their psychological problems in time and assist medical staff to examine the psychological problems encountered by deaf-mutes. In addition, the virtual library of emotional gestures is established with the assistance of the chief physician from a 3A hospital in Henan Province. More importantly, the later experiments demonstrate the applicability of this virtual system. © 2023 John Wiley & Sons Ltd.

关键词： Electroencephalography

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Interval Type-2 Fuzzy Model for Intelligent Fire Intensity Detection Algorithm with Decision Making in Low-Power Devices

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Intelligent Automation & Soft Computing 2023年第10期38卷 57-81页

作者： Emmanuel Lule Chomora Mikeka Alexander Ngenzi Didacienne Mukanyiligira African Center of Excellence in Internet of Things(ACEIoT) College of Science&TechnologyUniversity of RwandaNyarugenge-Kigali3900Rwanda Department of Computer Science College of Computing&Information SciencesMakerere UniversityKampala7062Uganda Directorate of Science Technology&Innovation(DSTI)Ministry of EducationMalawiLilongwe328Malawi Department of Computer&Software Engineering College of Science&TechnologyUniversity of RwandaNyarugenge-Kigali3900Rwanda National Council for Science&Technology(NCST) Government of RwandaKigali2285Rwanda

Local markets in East Africa have been destroyed by raging fires,leading to the loss of life and property in the nearby *** circuits,arson,and neglected charcoal stoves are the major causes of these *** methods,i.e.,satellites,are expensive to maintain and cause unnecessary ***,unit-smoke detectors are highly prone to false *** this paper,an Interval Type-2 TSK fuzzy model for an intelligent lightweight fire intensity detection algorithm with decision-making in low-power devices is proposed using a sparse inference rules approach.A free open–source MATLAB/Simulink fuzzy toolbox integrated into MATLAB 2018a is used to investigate the performance of the Interval Type-2 fuzzy *** crisp input parameters,namely:FIT and FIG��are *** show that the Interval Type-2 model achieved an accuracy value of FIO�=98.2%,MAE=1.3010,MSE=1.6938 and RMSE=1.3015 using regression *** study shall assist the firefighting personnel in fully understanding and mitigating the current level of fire *** a result,the proposed solution can be fully implemented in low-cost,low-power fire detection systems to monitor the state of fire with improved accuracy and reduced false *** informed decision-making in low-cost fire detection devices,early warning notifications can be provided to aid in the rapid evacuation of people,thereby improving fire safety surveillance,management,and protection for the market community.

关键词： Interval type-2(IT2)fuzzy systems mean absolute error(MAE) mean square error(MSE) root mean square

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Sentiment analysis of COVID-19 tweets from selected hashtags in Nigeria using VADER and Text Blob analyser

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Journal of Electrical Systems and Information Technology 2023年第1期10卷 1-20页

作者： Abiola, Odeyinka Abayomi-Alli, Adebayo Tale, Oluwasefunmi Arogundade Misra, Sanjay Abayomi-Alli, Olusola Department of Computer Science Federal University of Agriculture Abeokuta Nigeria Department of Computer Science and Communication Østfold University College Halden Norway Department of Software Engineering Kaunas University of Technology Kaunas Lithuania

Information is essential for growth; without it, little can be accomplished. Data gathering has seen significant changes throughout the previous few centuries because of the certain transitory medium. The look and style of information transference are affected by the employment of new and emerging technologies, some of which are efficient, others are reliable, and many more are quick and effective, but a few were disappointing for various reasons. This study aims at using TextBlob and VADER analyser with historical tweets, to analyse emotional responses to the coronavirus pandemic (COVID-19). It shows us how much of a sociological, environmental, and economic impact it has in Nigeria, among other things. This study would be a tremendous step forward for students, researchers, and scholars who want to advance in fields like data science, machine learning, and deep learning. The hashtag ‘COVID-19' was used to collect 1,048,575 tweets from Twitter. The tweets were pre-processed with a Twitter tokenizer, while TextBlob and Valence Aware Dictionary for Sentiment Reasoning (VADER) were used for text mining and sentiment analysis, respectively. Topic modelling was done with Latent Dirichlet Allocation and visualized with Multidimensional scaling. The result of the VADER sentiment returned 39.8%, 31.3%, and 28.9%, positive, neutral, and negative sentiment, respectively, while the result of the TextBlob sentiment returned 46.0%, 36.7%, and 17.3%, neutral, positive, and negative sentiment, respectively. With all of this, information from social media may be used to help organizations, governments, and nations around the world make smart and effective decisions about how to restrict and limit the negative effects of COVID-19. Also, know the opinion and challenges of people, then deal with the problem of misinformation. It is concluded that with popular belief a significant number of the populace regards COVID-19 as a virus that has come to stay, some believe it will eventually

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TASRI: Toward Traffic-Aware, Sustainable and Reliable ISL Provisioning for LEO Satellite Constellation Networking

TASRI: Toward Traffic-Aware, Sustainable and Reliable ISL Pr...

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International Workshop on Quality of Service

作者： Long Chen Yi Ching Chou Hengzhi Wang Feng Wang Haoyuan Zhao Hao Fang Sami Ma Feilong Tang Linghe Kong Jiangchuan Liu School of Computing Science Simon Fraser University Canada College of Computer Science and Software Engineering Shenzhen University China Department of Computer and Information Science The University of Mississippi USA Department of Computer Science and Engineering Shanghai Jiao Tong University China

ISBN: (数字)9798350350128

ISBN: (纸本)9798350350135

Inter-Satellite Links (ISLs) are key for worldwide communication and efficient use of space networks in the future 6G network. However, they face challenges in sustainability and reliability. Reducing ISLs saves energy and extends battery life, which is critical since satellite batteries are hard to replace. More ISLs, however, can make the system more reliable but at the cost of higher energy use, especially problematic when traffic is uneven, speeding up battery wear. To tackle this dilemma, we for the first time develop a Traffic-Aware, Sustainable and Reliable ISL provisioning (TASRI) framework for LEO satellite constellation networks. In TASRI, ISLs can be flexibly switched on and off to better accommodate various traffic conditions as well as reliability and sustainability. We formulate the ISL provisioning problem based on the sustainability-oriented weight model and then propose an on-demand topology evolving algorithm. Extensive real-world deployment-based simulation results show that, compared to the state-of-the-art, our TASRI can substantially reduce battery life consumption, while achieving comparable reliability with considerably fewer ISLs.

关键词： Satellite constellations Satellites Network topology Simulation Low earth orbit satellites Switches Batteries

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Suggestion Mining from Opinionated Text of Big Social Media Data

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computers, Materials & Continua 2021年第9期68卷 3323-3338页

作者： Youseef Alotaibi Muhammad Noman Malik Huma Hayat Khan Anab Batool Saif ul Islam Abdulmajeed Alsufyani Saleh Alghamdi Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversitySaudi Arabia Department of Computer Science Faculty of Engineering and Computer SciencesNational University of Modern LanguagesIslamabadPakistan Department of Software Engineering Faculty of Engineering and Computer SciencesNational University of Modern LanguagesIslamabadPakistan Department of Computer Sciences Institute of Space TechnologyIslamabadPakistan Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia

:Social media data are rapidly increasing and constitute a source of user opinions and tips on a wide range of products and *** increasing availability of such big data on biased reviews and blogs creates challenges for customers and businesses in reviewing all content in their decision-making *** overcome this challenge,extracting suggestions from opinionated text is a possible *** this study,the characteristics of suggestions are analyzed and a suggestion mining extraction process is presented for classifying suggestive sentences from online customers’reviews.A classification using a word-embedding approach is used via the XGBoost *** two datasets used in this experiment relate to online hotel reviews and Microsoft Windows App Studio discussion reviews.F1,precision,recall,and accuracy scores are *** results demonstrated that the XGBoost classifier outperforms—with an accuracy of more than 80%.Moreover,the results revealed that suggestion keywords and phrases are the predominant features for suggestion ***,this study contributes to knowledge and practice by comparing feature extraction classifiers and identifying XGBoost as a better suggestion mining process for identifying online reviews.

关键词： Suggestion mining word embedding Naïve Bayes random forest XGBoost dataset

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Micro Animation Design Based on New Media App Interaction

Micro Animation Design Based on New Media App Interaction

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作者： Hu, Ping Fu, Ronghui Department of Games Software Engineering Institute Guangdong Guangzhou510900 China College of Computer Science Neijiang Normal University Sichuan Neijiang641100 China

In this paper, the interaction strategy of micro animation frame prediction is used to learn and complement each other between different modes of micro animation image sequence, so as to improve the performance of the model and its generalization ability. This paper proposes a micro animation recognition method based on dual flow network information interaction. The experiment uses three micro animation databases animebase, move and move II to evaluate the effect of the method. This paper uses 3d-cnn as the basic network, and uses optical flow sequence diagram to assist training. The research shows that although the flow visualization application in the two-dimensional display interactive environment also provides interactive controls, due to the lack of depth information, users need to use the mouse to drag repeatedly to adjust the angle of view in order to put the interactive controls in the correct position. Therefore, the interaction simulation in the new media app can better increase the rendering effect of micro animation. For example, the range of seed location selection and data cutting can be expanded by 35%, the fluency of interaction can be improved by 18%, and the demand for hardware can be reduced by 5%–10%. The system can provide efficient and natural interaction for users in typical flow visualization scenarios. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Animation

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Pretrained Model with CNN for Lung Cancer Identification and Classification 7

Pretrained Model with CNN for Lung Cancer Identification and...

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7th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Kanwal, Shaista Ali, Sajid Alahmadi, Tahani Jaser Assam, Muhammad Ansari, Ghulam Jillani Bhatti, Uzair Aslam University of Education Department of Information Sciences Lahore Pakistan College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Department of Information Systems P.O. Box 84428 Riyadh Saudi Arabia University of Science and Technology Department of Software Engineering KPK Bannu Pakistan School of Information and Communication Engineering Hainan University China

ISBN: (纸本)9798350350890

Lung cancer impacts both genders, and its early detection is key to lowering death rates. Current deep learning techniques for automated identification and classification of lung carcinoma face issues with interpretability, data variability handling, and broader applicability. In our research, we propose a novel method that merges a convolutional neural network (CNN) with a streamlined RESNET18 framework. This approach is designed for lung cancer detection and categorization from CT scans, involving image preprocessing like resizing and rescaling. It effectively differentiates between healthy and cancerous tissues, further classifying them as either Normal, Benign, or Malignant, including variants such as adenocarcinoma and squamous cell carcinoma. Our CNN+RESNET18 model boasts an impressive 99% accuracy, supporting radiologists in the intricate task of image analysis, reducing risks of incorrect or delayed diagnosis, and fostering prompt treatment for improved patient outcomes. © 2024 IEEE.

关键词： Lung cancer

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