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An evaluation of lightweight deep learning techniques in medical imaging for high precision COVID-19 diagnostics

Healthcare Analytics 2022年 2卷 100096-100096页

作者： Ukwandu, Ogechukwu Hindy, Hanan Ukwandu, Elochukwu Department of Computer Communication and Information Systems School of Engineering and Built Environment Glasgow Caledonian University United Kingdom Computer Science Department Faculty of Computer and Information Sciences Ain Shams University Cairo 11566 Egypt Department of Applied Computing and Engineering Cardiff School of Technologies Cardiff Metropolitan University Cardiff CF5 2YB United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom

Timely and rapid diagnoses are core to informing on optimum interventions that curb the spread of COVID-19. The use of medical images such as chest X-rays and CTs has been advocated to supplement the Reverse-Transcription Polymerase Chain Reaction (RT-PCR) test, which in turn has stimulated the application of deep learning techniques in the development of automated systems for the detection of infections. Decision support systems relax the challenges inherent to the physical examination of images, which is both time consuming and requires interpretation by highly trained clinicians. A review of relevant reported studies to date shows that most deep learning algorithms utilised approaches are not amenable to implementation on resource-constrained devices. Given the rate of infections is increasing, rapid, trusted diagnoses are a central tool in the management of the spread, mandating a need for a low-cost and mobile point-of-care detection systems, especially for middle- and low-income nations. The paper presents the development and evaluation of the performance of lightweight deep learning technique for the detection of COVID-19 using the MobileNetV2 model. Results demonstrate that the performance of the lightweight deep learning model is competitive with respect to heavyweight models but delivers a significant increase in the efficiency of deployment, notably in the lowering of the cost and memory requirements of computing resources. © 2022 The Author(s)

关键词： COVID-19 detection Diagnostic analytics Lightweight deep learning techniques Machine learning Point-of-care Resource-constrained devices

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Chatbots and Customer Service: Enhancing Marketing Management with AI Technologies

Chatbots and Customer Service: Enhancing Marketing Managemen...

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Pervasive Computational Technologies (ICPCT), International Conference on

作者： Rajchandar K Somanchi Hari Krishna Showkat Khalil Wani Supriya Pathak Amit Kumar Jain C M Naveen Kumar School of CS & AI SR University Warangal Telangana India Department of Business Management Vignana Bharathi Institute of Technology Telangana State India Department of Business Administration College of Administrative and Financial Sciences Saudi Electronic University Riyadh Saudi Arabia Faculty of Management and Commerce Oriental University Indore Madhya Pradesh Department of Electrical & Electronics Engineering Poornima University Jaipur Rajasthan India Department of Computer Science & Business Systems Malnad College of Engineering Hassan Karnataka India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

By increasing engagement, expediting client contacts, and boosting satisfaction through AI-driven solutions, chatbot integration in customer service has revolutionised marketing management. With an emphasis on how model assessment techniques—such as accuracy, precision, recall, and F1 score—can be applied to enhance chatbot efficacy in customer service scenarios, this paper investigates the role of machine learning models in optimising chatbot performance. In order to appropriately meet a variety of consumer demands, this research offers perspectives into the selection, training, and evaluation of chatbot models by analysing a range of AI technologies, such as natural language processing (NLP) and predictive analytics. The influence of chatbots on customer loyalty and retention is highlighted in this article, which also explores the benefits and limitations of utilising AI in real-time consumer interactions. According to research, well-considered ML models can produce more precise and sympathetic answers, which enhances brand perception and opens up new channels for CRM in marketing plans.

关键词： Industries Training Support vector machines Ethics Customer services Companies Chatbots Marketing management Artificial intelligence Business

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Beyond Usability: A Comprehensive Ux Taxonomy for Crafting High-Quality Mobile Apps

SSRN

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SSRN 2025年

作者： Alshammare, Haifa Alshayeb, Mohammad Baslyman, Malak Information and Computer Science Department KFUPM Dhahran31261 Saudi Arabia Digital Technical College for Girls Technical and Vocational Training Corporation Al-Ahsa Saudi Arabia Interdisciplinary Research Center for Intelligent Secure Systems Dhahran31261 Saudi Arabia Interdisciplinary Research Center for Finance and Digital Economy Dhahran31261 Saudi Arabia

The rapid growth of the mobile app market has emphasized the need for a deep understanding of User Experience (UX). Modern UX goes beyond basic usability to consider the complex needs of users, incorporating emotional, motivational, and contextual factors across various devices and platforms. Therefore, this study aims to introduce a comprehensive taxonomy to guide the design and evaluation of UX in mobile applications, covering different aspects, attributes, and evaluation methods. Developing the taxonomy involved a blend of systematic mapping study methodology and qualitative content analysis. Moreover, experts rigorously validated the taxonomy to ensure its robustness and practical applicability. This resulted in a novel taxonomy, "mUX Aspects," which includes 27 UX aspects, 81 attributes, and several evaluation methods tailored for mobile apps. This framework will guide developers in designing and evaluating mobile apps' UX by focusing on crucial UX factors and utilizing recommended evaluation methods, which will help enhance the overall quality of mobile *** CONCEPTS• Human-centered computing→Human computer interaction (HCI)→HCI theory, concepts and models. © 2025, The Authors. All rights reserved.

关键词： Taxonomies

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Achieving Dexterous Bidirectional Interaction in Uncertain Conditions for Medical Robotics

arXiv

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arXiv 2022年

作者： Tiseo, Carlo Rouxel, Quentin Asenov, Martin Babarahmati, Keyhan Kouhkiloui Ramamoorthy, Subramanian Li, Zhibin Mistry, Michael Institute for Perception Action and Behaviour School of Informatics University of Edinburgh United Kingdom Department of Computer Science University College London United Kingdom School of Engineering and Informatics University of Sussex United Kingdom QR Inria CNRS Université de Lorraine France

Medical robotics can help improve the reach of healthcare services. A challenge for medical robots is their complex physical interaction. This work evaluates a recently introduced control architecture based on Fractal Impedance Control (FIC) in medical applications. The deployed FIC architecture is robust to delay between the master and the replica robots and can switch online between an admittance and impedance behaviour. Our experiments analyse three scenarios: teleoperated surgery, rehabilitation, and remote ultrasound scan. The experiments did not require any adjustment of the robot tuning, which is essential in medical applications where the operators do not have an engineering background. Our results show that it is possible to teleoperate the robot to perform remote occupational therapy, operate a scalpel, and use an ultrasound scan. However, our experiments also highlighted the need for a better robot embodiment to control the system precisely in 3D dynamic tasks. © 2022, CC BY.

关键词： Medical robotics

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Fractional-order Fixed-time Non-singular Sliding Mode Control for Nonlinear Robotic Manipulators

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IFAC-PapersOnLine 2023年第2期56卷 8105-8110页

作者： Saim Ahmed Ahmad Taher Azar Ibrahim A. Hameed Automated Systems and Soft Computing Lab (ASSCL) College of Computer and Information Sciences Prince Sultan University Riyadh 11586 Saudi Arabia Faculty of Computers and Artificial Intelligence Benha University Benha 13518 Egypt Department of ICT and Natural Sciences Norwegian University of Science and Technology Larsgårdsve-gen 2 6009 Ålesund Norway

In this study, fractional-order non-singular fixed-time terminal sliding mode control (FoFxNTSM) for nonlinear robotic manipulators in the existence of uncertainties and external disturbances is examined. To begin, the concept of fractional-order fixed-time non-singular terminal sliding mode control is introduced. This method combines the benefits of NTSM (which provides fast convergence speed, smooth and singularity-free control inputs) with the advantages of a fractional-order constants (which improves position tracking effectiveness). Lyapunov analysis yields the fixed-time stability of the closed-loop system. To evaluate and illustrate the performance of the proposed strategy, the relevant simulation results are presented.

关键词： Fixed-time SMC nonlinear robotic manipulators fractional-order control fast convergence

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Integrating educational theories with virtual reality: Enhancing engineering education and VR laboratories

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Social sciences and Humanities Open 2024年 10卷

作者： Abbas Shah, Syed Faisal Mazhar, Tehseen Shahzad, Tariq khan, Muhammad Amir Ghadi, Yazeed Yasin Hamam, Habib Department of Computer Science & Information Technology Virtual University of Pakistan Lahore 54000 Pakistan Department of Computer Science and information Technology School Education Department Government of Punjab Layyah 31200 Pakistan Department of Computer Science COMSATS University Islamabad Sahiwal Campus Sahiwal 57000 Pakistan School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Shah Alam 40450 Malaysia Department of Computer Science and Software Engineering Al Ain University Abu Dhabi 12555 United Arab Emirates Faculty of Engineering University de Moncton Moncton E1A3E9 NB Canada Department of Electrical and Electronic Engineering Sciences University of Johannesburg Johannesburg 2006 South Africa International Institute of Technology and Management (IITG) Av. Grandes Ecoles BP Libreville 1989 Gabon Bridges for Academic Excellence - Spectrum Tunis Tunisia

Immersive technologies, including virtual reality (VR), augmented reality (AR), mixed reality (MR), and extended reality (XR), create realistic digital experiences by overlaying digital elements. This study aims to highlight the benefits of utilizing VR technology in education, particularly in universities and higher education institutions, by integrating VR with educational theories to create engaging and interactive learning experiences that enhance understanding and retention. This review includes a comprehensive analysis of 103 works, synthesizing existing literature to evaluate the suitability of various educational approaches and theories for implementation in VR-based educational applications. The findings emphasize VR's potential to transform learning by providing immersive experiences that bridge different domains and encourage engagement. By integrating learning theory into VR development, meaningful learning environments can be created, improving student understanding and retention across diverse learning settings. The study concludes that the combination of technology and educational theories provides a novel approach to motivate students and foster deep learning in VR laboratories. © 2024 The Authors

关键词： Augmented reality Educational theories Engineering education Immersive technology Interactive learning VR laboratories

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Interpretable machine learning text classification for clinical computed tomography reports – a case study of temporal bone fracture

引用

computer Methods and Programs in Biomedicine Update 2023年 3卷

作者： Ling, Tong Jake, Luo Adams, Jazzmyne Osinski, Kristen Liu, Xiaoyu Friedland, David Department of Health Informatics and Administration University of Wisconsin-Milwaukee Milwaukee United States Department of Otolaryngology and Communication Sciences Medical College of Wisconsin Milwaukee WI United States Medical College of Wisconsin Clinical and Translational Science Institute of Southeastern Wisconsin United States Department of Electrical Engineering and Computer Science University of Wisconsin–Milwaukee Milwaukee WI United States

Background: Machine learning (ML) has demonstrated success in classifying patients’ diagnostic outcomes in free-text clinical notes. However, due to the machine learning model's complexity, interpreting the mechanism behind classification results remains difficult. Methods: We investigated interpretable representations of text-based machine learning classification models. We created machine learning models to classify temporal bone fractures based on 164 temporal bone computed tomography (CT) text reports. We adopted the XGBoost, Support Vector Machine, Logistic Regression, and Random Forest algorithms. To interpret models, we used two major methodologies: (1) We calculated the average word frequency score (WFS) for keywords. The word frequency score shows the frequency gap between positively and negatively classified cases. (2) We used Local Interpretable Model-Agnostic Explanations (LIME) to show the word-level contribution to bone fracture classification. Results: In temporal bone fracture classification, the random forest model achieved an average F1-score of 0.93. WFS revealed a difference in keyword usage between fracture and non-fracture cases. Additionally, LIME visualized the keywords' contributions to the classification results. The evaluation of LIME-based interpretation achieved the highest interpreting accuracy of 0.97. Conclusion: The interpretable text explainer can improve physicians' understanding of machine learning predictions. By providing simple visualization, our model can increase the trust of computerized models. Our model supports more transparent computerized decision-making in clinical settings. © 2023

关键词： Artificial intelligence Bone fracture Computed tomography Interpretable machine learning Text classification

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Facial Expression Recognition in the Wild using Artificial Rabbits Optimizer based Residual Neural Network

Facial Expression Recognition in the Wild using Artificial R...

引用

Advances in Computation, Communication and Information Technology (ICAICCIT), International Conference on

作者： P S Metkewar N Uma Rajesh Kumar Dhanaraj Balasubramanian Prabhu Kavin Malathy Sathyamoorthy Symbiosis Institute of Computer Studies and Research (SICSR) Symbiosis International (Deemed University) (SIU) Pune Maharashtra India New Horizon College of Engineering Bengaluru Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chengalpattu India Department of Information Technology KPR Institute of Engineering and Technology Coimbatore India

In affective computing, emotion acknowledgement in the wild is a much-studied area. Although there have been advancements, the difficulty of emotion acknowledgement in the wild due to head movement, face deformation, illumination fluctuation, etc. remains an open subject. To improve the model's simplification ability and, by extension, its performance across a variety of learning tasks, it is crucial that a wide variety of features be extracted by the deep neural network. Interest in facial expression detection in natural settings has grown in recent years despite the difficulty of obtaining discriminative and informative characteristics from partially obscured photos. In the first stage of this paper's pre-processing, the noisy pictures are enhanced by Contrast Limited Adaptive Histogram Equalization (CLAHE). After that, Residual Neural Network (ResNet) features extraction is used, and the same method serves as an emotion classifier. An iterative technique called stochastic gradient descent (SGD) is utilized to refine the ResNet model's objective function. Finally, the Artificial Rabbits Optimization Algorithm (ARO) is used to choose the best value for, thereby enhancing the reliability of the categorization. The suggested method is effective, as evidenced by experimental findings on three popular facial expression recognition in-the-wild datasets: AffectNet, AFEW Dataset, and RAF-DB, where it accomplishes state-of-the-art presentation with 96% accuracy and improves upon existing models by roughly 5% to 8%.

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Hydrodynamic bound states of rotating micro-cylinders in a confining geometry

arXiv

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arXiv 2023年

作者： Guo, Hanliang Man, Yi Zhu, Hai Department of Mathematics and Computer Science Ohio Wesleyan University DelawareOH43015 United States Department of Mechanics and Engineering Science College of Engineering State Key Laboratory for Turbulence and Complex Systems Peking University Beijing100871 China Center for Computational Mathematics Flatiron Institute New YorkNY10010 United States

Many micro-swimmers propel themselves by rotating micro-cylindrical organelles such as flagella or cilia. These cylindrical organelles almost never live in free space, yet their motions in a confining geometry can be counter-intuitive. For example, one of the intriguing yet classical results in this regard is that a rotating cylinder next to a plane wall does not generate any net force in Newtonian fluids and therefore does not translate. In this work, we employ analytical and numerical tools to investigate the motions of micro-cylinders under prescribed torques in a confining geometry. We show that a cylinder pair can form four non-trivial hydrodynamic bound states depending on the relative position within the confinement. Our analysis shows that the distinct states are the results of competing effects of the hydrodynamic interactions within the cylinder pair and between the active cylinders and the confinement. © 2023, CC BY.

关键词： Cylinders (shapes)

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A Hybrid Framework with Large Language Models for Rare Disease Phenotyping

arXiv

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arXiv 2024年

作者： Wu, Jinge Dong, Hang Li, Zexi Wang, Haowei Li, Runci Patra, Arijit Dai, Chengliang Ali, Waqar Scordis, Phil Wu, Honghan Institute of Health Informatics University College London London United Kingdom Department of Computer Science University of Exeter Exeter United Kingdom The Nuffield Department of Surgical Sciences University of Oxford Oxford United Kingdom Division of Medicine University College London London United Kingdom EGA- Institute for Women‘s Health University College London London United Kingdom UCB Pharma UK Slough United Kingdom School of Health and Wellbeing University of Glasgow Glasgow United Kingdom

Purpose: Rare diseases pose significant challenges in diagnosis and treatment due to their low prevalence and heterogeneous clinical presentations. Unstructured clinical notes contain valuable information for identifying rare diseases, but manual curation is time-consuming and prone to subjectivity. This study aims to develop a hybrid approach combining dictionary-based natural language processing (NLP) tools with large language models (LLMs) to improve rare disease identification from unstructured clinical reports. Methods: We propose a novel hybrid framework that integrates the Orphanet Rare Disease Ontology (ORDO) and the Unified Medical Language System (UMLS) to create a comprehensive rare disease vocabulary. SemEHR, a dictionary-based NLP tool, is employed to extract rare disease mentions from clinical notes. To refine the results and improve accuracy, we leverage various LLMs, including LLaMA3, Phi3-mini, and domain-specific models like OpenBioLLM and BioMistral. Different prompting strategies, such as zero-shot, few-shot, and knowledge-augmented generation, are explored to optimize the LLMs’ performance. Results: The proposed hybrid approach demonstrates superior performance compared to traditional NLP systems and standalone LLMs. LLaMA3 and Phi3-mini achieve the highest F1 scores in rare disease identification. Few-shot prompting with 1-3 examples yields the best results, while knowledge-augmented generation shows limited improvement. Notably, the approach uncovers a significant number of potential rare disease cases not documented in structured diagnostic records, highlighting its ability to identify previously unrecognized patients. Conclusion: The hybrid approach combining dictionary-based NLP tools with LLMs shows great promise for improving rare disease identification from unstructured clinical reports. By leveraging the strengths of both techniques, the method demonstrates superior performance and the potential to uncover hidden rare disease cases. Fur

关键词： Electronic health record

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