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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Nanyonga, Aziida Wasswa, Hassan Turhan, Ugur Joiner, Keith Wild, Graham University of New South Wales School of Engineering and Technology Canberra Australia University of New South Wales School of Systems and Computing Canberra Australia University of New South Wales School of Science Canberra Australia

ISBN: (纸本)9798350381931

Improvements in aviation safety analysis call for innovative techniques to extract valuable insights from the abundance of textual data available in accident reports. This paper explores the application of four prominent topic modelling techniques, namely Probabilistic Latent Semantic Analysis (pLSA), Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA), and Non-negative Matrix Factorization (NMF), to dissect aviation incident narratives using the Australian Transport Safety Bureau (ATSB) dataset. The study examines each technique's ability to unveil latent thematic structures within the data, providing safety professionals with a systematic approach to gain actionable insights. Through a comparative analysis, this research not only showcases the potential of these methods in aviation safety but also elucidates their distinct advantages and limitations. © 2024 IEEE.

关键词： Natural language processing systems

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The Effects of Anthropomorphism and Explanation Types on User Perception and Acceptance: Implications for Explainable AI 44

The Effects of Anthropomorphism and Explanation Types on Use...

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44th International Conference on Information systems: Rising like a Phoenix: Emerging from the Pandemic and Reshaping Human Endeavors with Digital Technologies, ICIS 2023

作者： Tong, Jiawei Cui, Tingru Turel, Ofir Du, Bo Cheng, Huaihui School of Computing and Information Systems University of Melbourne Melbourne Australia Faculty of Engineering and Information Sciences University of Wollongong Wollongong Australia

ISBN: (纸本)9781713893622

Explainable Artificial Intelligence (XAI) applications are widely used in interactions with end users. However, there remains a lack of understanding of how the different characteristics of these systems, particularly the anthropomorphic design and the type of explanations provided interact to affect user reactions to AI. We address this research gap by building on social response theory (SRT), prior XAI and anthropomorphic design literature, to investigate how anthropomorphic design (human-like vs. machine-like) and types of explanations (consensual, expert, internal, empirical validation-based explanations) affect user reactions to AI (perceived trust and persuasiveness) and acceptance of AI systems. We will evaluate the proposed research model by conducting a 2 × 4 between-subjects experiment. This study will enrich the theoretical landscape of anthropomorphic design and human-AI interaction (HAII), offering actionable insights into user perception and acceptance for XAI practitioners. Copyright © 2023 Tong et al.

关键词： Artificial intelligence

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Comparison of Segmentation Algorithms for Extraction of Stone from Kidney X-ray Image 4th

Comparison of Segmentation Algorithms for Extraction of Ston...

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4th International Conference on Smart IoT systems: Innovations and computing, SSIC 2023

作者： Nisha, N. Dhaka, Vijaypal Singh Sinwar, Deepak Department of Computer and Communication Engineering Manipal University Jaipur Jaipur India Director of School of Computing and Intelligent Systems Department of Computer and Communication Engineering Manipal University Jaipur Jaipur India Department of IoT and Intelligent Systems Manipal University Jaipur Jaipur India

ISBN: (纸本)9789819751990

Healthcare systems are gaining popularity because of the advancement of new technology aiding healthcare professional for diagnosis of disease from medical modalities. The detection and classification of disease using AI and its technologies are active research areas in the last decade. Radiologist usually detects stone from the kidney using different imaging like CT scan, MRI scan, and X-ray. For the detection of stone in kidney X-ray images, various segmentation and classification methods are used. These different classification and segmentation methods are used to reduce errors and time of healthcare professional in diagnosis of stone in X-ray image. In this paper, comparison of different algorithms like thresholding algorithm, contour-based algorithm, deep learning algorithms like U-Net, U-Net++, V-Net, V-Net++, and Transformer Net are used for segmentation of stone from kidney X-ray images. The thorough comparison of different deep learning architectures in diagnosing stone from kidney X-ray images is also made in this paper. Most of the papers work on classification of stone from different medical modalities before segmentation which cause data leakage problem. In this research paper, comparisons of different segmentation algorithms help in achieving higher accuracy and reduce the data leakage problem. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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Using Smart Wearable Technology Acceptance Model for Health Monitoring Technology 7

Using Smart Wearable Technology Acceptance Model for Health ...

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7th International Conference on Cloud computing and Big Data Analytics, ICCCBDA 2022

作者： Yang, Chai Wei Lee, Angela Siew-Hoong School of Engineering and Technology Sunway Unversitiy Department of Computing and Information Systems Malaysia

ISBN: (纸本)9781665487115

The increasing of aging population has caused the rising need for healthcare and assistance through smart wearables health monitoring systems in order to attend impairments in cognitive psychosocial functioning of the elderly, as well as their ability and mobility. To facilitate and encourage the use of smart wearable systems among older adults, this study investigated the factors that may have contributed to the acceptance of such systems, and smart wearables acceptance model for elderly people will be developed by using structural equation modeling. The model will be validated with 250 samples. Validation and reliability of the constructs in the model will be tested through Partial Least Square method. The results will indicate what are the factors that have significant positive effect on the intention of the elderly people in using smart wearable systems. © 2022 IEEE.

关键词： Technology acceptance model Wearable computers Sociology Medical services Mathematical models Reliability Older adults

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Byte-Sized Reviewers: Exploring Review Generation through LNRG-S 3

Byte-Sized Reviewers: Exploring Review Generation through LN...

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2023 IEEE 3rd International Conference on Data Science and Computer Application, ICDSCA 2023

作者： Li, Yiting Fu, Min Sun Yat-sen University School of Intelligent Systems Engineering Shenzhen China Macquarie University Department of Computing Sydney Australia

ISBN: (纸本)9798350341546

The development of network technology has led to an explosive growth of user-generated comments. These contents are based on users' experiences after using services and products, reflecting users' evaluations. Automatically generating high-quality and controllable sentiment comments can help creators improve work quality, help service providers enhance service levels, and improve consumers' consumption experience. However, current methods have some limitations. Seq2seq models have difficulty maintaining semantic consistency and generating long texts, LSTM can maintain a memory state and generate coherent text, but is prone to overfitting, generating repetitive text, and having difficulty establishing local coherence. N-gram models text locally but cannot understand the global context. This paper proposes a hybrid architecture, called LNRG-S (LSTM N-gram Reviews Generator with Sentiments), which combines LSTM and N-gram to model both global and local dependencies, and trains separate LSTM generators for positive and negative samples to control sentiment polarity of generated comments. We computed the perplexity of comments generated by different models. LNRG-S has 43.93% and 18.89% lower perplexity than LSTM and n-gram respectively, demonstrating that LNRG-S generates more fluent and coherent text. © 2023 IEEE.

关键词： LSTM n-gram Perplexity Sentiment control User-generated reviews

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Comparative Study of Deep Learning Architectures for Textual Damage Level Classification 11

Comparative Study of Deep Learning Architectures for Textual...

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11th IEEE International Conference on Signal Processing and Integrated Networks, SPIN 2024

作者： Nanyonga, Aziida Wasswa, Hassan Wild, Graham University of New South Wales School of Engineering and Technology Canberra Australia University of New South Wales School of Systems and Computing Canberra Australia University of New South Wales School of Science Canberra Australia

ISBN: (纸本)9798350308433

Given the paramount importance of safety in the aviation industry, even minor operational anomalies can have significant consequences. Comprehensive documentation of incidents and accidents serves to identify root causes and propose safety measures. However, the unstructured nature of incident event narratives poses a challenge for computer systems to interpret. Our study aimed to leverage Natural Language Processing (NLP) and deep learning models to analyze these narratives and classify the aircraft damage level incurred during safety occurrences. Through the implementation of LSTM, BLSTM, GRU, and sRNN deep learning models, our research yielded promising results, with all models showcasing competitive performance, achieving an accuracy of over 88% significantly surpassing the 25% random guess threshold for a four-class classification problem. Notably, the sRNN model emerged as the top performer in terms of recall and accuracy, boasting a remarkable 89%. These findings underscore the potential of NLP and deep learning models in extracting actionable insights from unstructured text narratives, particularly in evaluating the extent of aircraft damage within the realm of aviation safety occurrences. © 2024 IEEE.

关键词： Aircraft

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Automated face recognition using deep learning technique and center symmetric multivariant local binary pattern

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Neural computing and Applications 2025年第1期37卷 263-281页

作者： Sekhar, J.C. Josephson, P. Joel Chinnasamy, A. Maheswari, M. Sankar, S. Kalangi, Ruth Ramya Department of Computer Science and Engineering NRI Institute of Technology Andhra Pradesh Guntur India Department of Electronics and Communication Engineering Malla Reddy Engineering College Telangana Hyderabad India Department of Data Science and Business Systems School of Computing SRMIST Kattankulathur Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vijayawada India

Researchers have recently created several deep learning strategies for various tasks, and facial recognition has made remarkable progress in employing these techniques. Face recognition is a noncontact, nonobligatory, acceptable, and harmonious biometric recognition method with a promising national and social security future. The purpose of this paper is to improve the existing face recognition algorithm, investigate extensive data-driven face recognition methods, and propose a unique automated face recognition methodology based on generative adversarial networks (GANs) and the center symmetric multivariable local binary pattern (CS-MLBP). To begin, this paper employs the center symmetric multivariant local binary pattern (CS-MLBP) algorithm to extract the texture features of the face, addressing the issue that C2DPCA (column-based two-dimensional principle component analysis) does an excellent job of removing the global characteristics of the face but struggles to process the local features of the face under large samples. The extracted texture features are combined with the international features retrieved using C2DPCA to generate a multifeatured face. The proposed method, GAN-CS-MLBP, syndicates the power of GAN with the robustness of CS-MLBP, resulting in an accurate and efficient face recognition system. Deep learning algorithms, mainly neural networks, automatically extract discriminative properties from facial images. The learned features capture low-level information and high-level meanings, permitting the model to distinguish among dissimilar persons more successfully. To assess the proposed technique’s GAN-CS-MLBP performance, extensive experiments are performed on benchmark face recognition datasets such as LFW, YTF, and CASIA-WebFace. Giving to the findings, our method exceeds state-of-the-art facial recognition systems in terms of recognition accuracy and resilience. The proposed automatic face recognition system GAN-CS-MLBP provides a solid basis for a

关键词： Principal component analysis

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A Pilot Study on the User Experience of Different Upper Body Extended Reality Physiotherapy Interventions 3

A Pilot Study on the User Experience of Different Upper Body...

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3rd IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural engineering, MetroXRAINE 2024

作者： Brennan, Alex Rankin, Debbie Charles, Therese Blackburn, Nicole McCreadie, Karl Condell, Joan School of Computing Engineering and Intelligent Systems Ulster University Londonderry United Kingdom School of Arts and Humanities Ulster University Londonderry United Kingdom School of Health Sciences Ulster University Londonderry United Kingdom

ISBN: (纸本)9798350378009

The integration of Extended Reality (XR) technology in clinical contexts has shown promising outcomes for diverse vulnerable populations. This paper presents a multistage study conducted to assess user experience during physiotherapy within an XR nature environment, aiming to inform future work in this field. While prior research highlighted the positive impact of XR rehabilitation, comparative analysis between different XR experiences is lacking. This study involved six participants, guided through three stages. Stage 1 used a computer monitor displaying an instructional physiotherapy video, while stages 2 and 3 incorporated XR experiences using a Meta Quest 3 headset. Stage 3 introduced visual and auditory feedback upon completion of exercises. Data collection included pre-study questionnaires, think-aloud interviews and assessments using the NASA Task Load Index (NASA TLX) and the Game User Experience Satisfaction Scale (GUESS-18). Findings revealed that stage 3 caused the highest cognitive workload but also the highest user experience satisfaction. Participants expressed a preference for immersive XR environments, citing enhanced focus and enjoyment. Overall, participants demonstrated a willingness to return to all three stages, further supporting the potential of XR physiotherapy for improving engagement and outcomes in rehabilitation. Further research will explore optimisation strategies regarding workload and user experience and assess long-term impacts on patient outcomes. © 2024 IEEE.

关键词： Extended Reality (XR) Physiotherapy Rehabilitation User Experience Virtual Reality (VR)

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Online Shopping Behavior during Pandemic 2022

Online Shopping Behavior during Pandemic

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6th International Conference on Compute and Data Analysis, ICCDA 2022

作者： Teh, Phoey Lee Siew Hoong Lee, Angela Teo, Jie Ying Department of Computing and Information Systems School of Engineering and Technology Sunway University Malaysia

ISBN: (纸本)9781450395472

Many industries have been impacted, and some have been force to shut down as a result of the pandemic outbreak, . Meanwhile, e-commerce has survived and thrived. Since then, online shopping grown in popularity. Consumers are encouraged to stay at home and order their daily necessities online to reduce the spread of the virus. This paper aims to revisit online shopping behaviour by investigating financial risk, convenience risk, privacy risk, delivery risk, environmental risk, and information trust towards shopping behaviour. We collected a total of 250 questionnaires during the lockdown period in the year 2020. Findings shown that participants agree that information trust, privacy risk, convenient risk, and delivery risk can influence their online shopping behaviour, but the only not is environmental risk. © 2022 ACM.

关键词： Electronic commerce

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Sentiment Analysis and Student Emotions: Improving Satisfaction in Online Learning Platforms 9

Sentiment Analysis and Student Emotions: Improving Satisfact...

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9th IEEE International Smart Cities Conference, ISC2 2023

作者： Anwar, Aamir Ur Rehman, Ikram Nasralla, Moustafa M. Bin Altaf Khattak, Sohaib Khilji, Nasrullah School of Computing & Engineering University of West London London United Kingdom Prince Sultan University Smart Systems Engineering Lab Riyadh Saudi Arabia

ISBN: (纸本)9798350397758

Human emotion recognition using artificial intelligence is among the most prominent research areas. Human-Computer Interaction (HCI) and Sentiment Analysis (SA) are extensively used to detect human emotions. The role of students' sentiments and emotions becomes vital while using online learning platforms due to the need for physical interaction between instructor and learner to understand each other compared to face-to-face learning. This study identifies challenges and issues of online learning and student satisfaction while using online learning platforms. In addition, we analysed the importance of sentiment analysis for students' satisfaction while using online learning platforms. This study retrieved 112 research articles/reports using keywords such as 'Online Learning', 'Online Learning Post-COVID-19', 'Student Satisfaction', 'Student Sentiment Analysis', 'Learning Environments' and 'Student Performance in Online Learning'. Among 112 research articles, 37 were identified after excluding duplicate and irrelevant research articles based on defined elimination criteria, such as studies on student emotions related to non-educational purposes. For systematic literature, the study has been divided into two research questions, i.e., (i) exploring key challenges of online learning and (ii) the role of sentiment analysis during online learning. These two questions have been answered based on arguments and evidence from the existing literature. © 2023 IEEE.

关键词： Emotion Recognition

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