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Towards a Deep Learning Pain-Level Detection Deployment at UAE for Patient-Centric-Pain Management and Diagnosis Support: Framework and Performance Evaluation 14

Towards a Deep Learning Pain-Level Detection Deployment at U...

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14th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023

作者： Ismail, Leila Waseem, Muhammad Danish Lab School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne Australia Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates

The outbreak of the COVID-19 pandemic revealed the criticality of timely intervention in a situation exacerbated by a shortage in medical staff and equipment. Pain-level screening is the initial step toward identifying the severity of patient conditions. Automatic recognition of state and feelings help in identifying patient symptoms to take immediate adequate action and providing a patient-centric medical plan tailored to a patient's state. In this paper, we propose a framework for pain-level detection for deployment in the United Arab Emirates and assess its performance using the most used approaches in the literature. Our results show that a deployment of a pain-level deep learning detection framework is promising in identifying the pain level accurately. © 2023 Elsevier B.V.. All rights reserved.

关键词： Computer vision

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Novel Approach for Enhancing Mental Well-Being Through Machine Learning Techniques

Novel Approach for Enhancing Mental Well-Being Through Machi...

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Advancements in computing (ICAC), International Conference on

作者： O.V.D.E Roshani H.M.K.J.J Ayuwardhana P.H.M.S Rodrigo R.U Hewageegana Harinda Fernando Dilshan I. de Silva Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

Frequently, individuals undergo specific episodes of mental health challenges throughout their lifetime. But the COVID pandemic has triggered a surge in mental health disorders arising from isolation, monotonous routines, demanding workloads, financial disparities, and disruptions to daily schedules. Furthermore, the global pandemic has induced constant anxiety and stress. Beyond the pandemic, the competition and intense pressure of the modern world impact mental health. Access to advanced mental health solutions and the necessary familiarity remain limited for most of the population. Given the integration of technology into daily life, diverse remedies, including mobile and web applications, have emerged to tackle the escalating challenge of mental health disorders. This study proposes an accessible and cost-effective approach that employs machine learning to detect stress levels and discern user emotions from journal entries and facial expressions while integrating self-journaling, video recommendations, and visual content generation to stimulate positive emotions and relieve stress.

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The Impact of Transformer Models and Regression Algorithms on Automated Short Answer Grading 5

The Impact of Transformer Models and Regression Algorithms o...

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5th IEEE International Conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Nampoothiri, Abhijith. N. Thushara, M.G. Pradeesh, N. Anand Krishna, K.R. Santhosh, Abhai Abhijith Kumar, S. Kumar, Suryan Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Applications Amritapuri India Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India Omnex Software Solutions Pvt. Ltd Thoraipakkam Chennai India

ISBN: (纸本)9798350356236

Grading short answer questions in higher education often poses significant challenges due to its subjective nature and time-consuming process. As educational institutions expand, the need for efficient and objective grading solutions becomes critical. This paper investigates the use of machine learning and natural language processing to automate the evaluation of short answer responses. Our study employs a two-phase methodology: first, several transformer models - including BERT, Sentence-BERT, T5, Genism, and XLNet - are used to transform text into numerical representations. In the second phase, various regression techniques, such as Linear Regression, Support Vector Machine (SVM), Random Forest, and AdaBoost, are applied to these representations to estimate scores. Our findings indicate that the combination of the BERT model with Random Forest performs the best, demonstrating superior accuracy with the lowest error metrics. This optimal model pairing is proposed for integration into the Ample Learning Management System (LMS) to enhance grading efficiency and provide quicker, more reliable feedback. The significant implications of this research provides teachers with a robust tool for automated grading, which can enhance the educational experience by delivering timely and accurate evaluations. © 2025 IEEE.

关键词： Teaching

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Approximate k-Nearest Neighbor Query over Spatial Data Federation 28th

Approximate k-Nearest Neighbor Query over Spatial Data Fede...

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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Zhang, Kaining Tong, Yongxin Shi, Yexuan Zeng, Yuxiang Xu, Yi Chen, Lei Zhou, Zimu Xu, Ke Lv, Weifeng Zheng, Zhiming State Key Laboratory of Software Development Environment Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing School of Computer Science and Engineering and Institute of Artificial Intelligence Beihang University Beijing China Department of Computer Science and Engineering HKUST Hong Kong Guangzhou China City University of Hong Kong Hong Kong

ISBN: (纸本)9783031306365

Approximate nearest neighbor query is a fundamental spatial query widely applied in many real-world applications. In the big data era, there is an increasing demand to scale these queries over a spatial data federation, which consists of multiple data owners, each holding a private, disjoint partition of the entire spatial dataset. However, it is non-trivial to enable approximate k-nearest neighbor query over a spatial data federation. This is because stringent security constraints are often imposed to protect the sensitive, privately owned data partitions, whereas naively extending prior secure query processing solutions leads to high inefficiency (e.g., 100 s per query). In this paper, we propose two novel algorithms for efficient and secure approximate k-nearest neighbor query over a spatial data federation. We theoretically analyze their communication cost and time complexity, and further prove their security guarantees and approximation bounds. Extensive experiments show that our algorithms outperform the state-of-the-art solutions with respect to the query efficiency and often yield a higher accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Nearest neighbor search

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Finite-Time Probabilistic Impulsive Synchronization for Fuzzy Multiplicative Stochastic Coupled Memristive Neural Networks

SSRN

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SSRN 2024年

作者： Yang, Nijing Peng, Hong Lu, Xiang Wang, Xiangxiang Deng, Ping School of Computer and Software Engineering Xihua University Chengdu610039 China Ascend Computing Product Department Huawei Technologies Co Ltd Chengdu610041 China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China

In this paper, the finite-time synchronization (FTS) of T-S fuzzy multiplicative stochastic coupled memristive neural networks (CMNNs) with probabilistic delayed impulsive effects is investigated. First, a novel CMNNs model is designed, in which mismatched parameters and adjustable coupling strength are considered to ensure accuracy of the CMNNs. Meanwhile, because the multiplicative noise, which is different from external input noise, is inevitable in information transmission. The multiplicative noise is considered in the CMNNs. Then, the impulsive controller with fuzzy impulsive strength is introduced to enhance the robustness of the system. To be more practical, the probabilistic time delays are analyzed in the controller. Moreover, finite-time stability theory and impulsive control theory are utilized to establish some sufficient conditions for FTS with synchronizing impulses and desynchronizing impulses on CMNNs. Further more, the upper bounds for settling time of synchronization are effectively estimated. Finally, a numerical example is provided to illustrate the effectiveness of the main results. © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

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Automated Test Case Generation from Requirements: A Systematic Literature Review

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Computers, Materials & Continua 2021年第5期67卷 1819-1833页

作者： Ahmad Mustafa Wan M.N.Wan-Kadir Noraini Ibrahim Muhammad Arif Shah Muhammad Younas Atif Khan Mahdi Zareei Faisal Alanazi Department of Software Engineering School of ComputingUniversiti Teknologi Malaysia Johor BahruJohor81310Malaysia Department of Computer Science Government College UniversityFaisalabad38000Pakistan Department of Software Engineering Pak-Austria Fachhochschule Institute of Applied Sciences and TechnologyHaripur22620Pakistan Department of Computer Science Islamia College PeshawarPeshawarPakistan Tecnologico de Monterrey School of Engineering and SciencesZapopan45201Mexico Department of Electrical Engineering Prince Sattam Bin Abdulaziz UniversityAl Kharj11942Saudi Arabia

software testing is an important and cost intensive activity in software *** major contribution in cost is due to test case ***-based testing is an approach in which test cases are derivative from requirements without considering the implementation’s internal ***-based testing includes functional and nonfunctional *** objective of this study is to explore the approaches that generate test cases from requirements.A systematic literature review based on two research questions and extensive quality assessment criteria includes *** study identies 30 primary studies from 410 studies spanned from 2000 to *** review’s nding shows that 53%of journal papers,42%of conference papers,and 5%of book chapters’address requirementsbased *** of the studies use UML,activity,and use case diagrams for test case generation from *** of the signicant lessons learned is that most software testing errors are traced back to errors in natural language requirements.A substantial amount of work focuses on UML diagrams for test case generations,which cannot capture all the system’s developed ***,there is a lack of UML-based models that can generate test cases from natural language requirements by rening them in *** criteria indicate how efciently the testing has been performed 12.37%of studies use requirements coverage,20%of studies cover path coverage,and 17%study basic coverage.

关键词： Test case generation functional testing techniques requirementsbased test case generation system testing natural language requirement requirements tractability coverage criteria

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Smart Photo Editor for Differently-abled People using Assistive Technology 6

Smart Photo Editor for Differently-abled People using Assist...

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6th International Conference on Information Technology Research, ICITR 2021

作者： Buddhika, A.S. Wijesekera, P.K. Kavindu, D. G. Hasintha Dinusha, Dilshan S Arachchillage, Udara Srimath S. Samaratunge Kuruppu, Thilmi Anuththara Faculty of Computing Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9781665420006

Differently-abled people are significant minority groups, have many limitations to live everyday life, are starved of services, and are mostly ignored by society due to many types of disabilities. This problem may lead to massive frustrations and disappointments among themselves. Assistive technology may easily fill this gap with the enhanced different aiding applications. It helped in various ways, such as helping them to interact, communicate with people and allow people to do basic daily tasks by themselves. Nevertheless, there were fewer applications developed to entertain their leisure time. Therefore, the proposed solution provides a Smart Photo Editor, a hand-free mobile application with attractive photo editing features. This application focused on people having hand motor disabilities. Users can interact with the application using simple head gestures, eye gaze, and eye blinks. Head gestures and eye blinks are used as commands for application operations, while eye gaze is used for cursor enabling, which is helpful for navigation on application screens. Machine Learning (ML) and image processing techniques are used for real-time gesture recognition and eye gaze detection, using the input video frames captured by the inbuilt front-facing camera of the mobile phone. Moreover, in addition to the image processing techniques, Mask Recurrent-Convolutional Neural Network (R-CNN) is used to develop enhanced image adjusting features and customization of backgrounds based on object detection. © 2021 IEEE.

关键词： Image enhancement

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Automatic Detection and Characterization of Human Veins Using Infra-Red Image Processing

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Journal of Computer and Communications 2024年第9期12卷 141-159页

作者： Jean Ndoumbe Brice Ekobo Akoa Gaelle Patricia Talotsing Frederic Franck Kounga Samuel Kaissassou Bertin Chouanmo Njo Laboratory of Computer Engineering Data Science and Artificial Intelligence Department of Computer & Telecommunications Engineering National Higher Polytechnic School Douala Cameroon Laboratory of Electrical Engineering Mechatronics and Signal Processing Department of Electrical & Telecommunications Engineering National Advanced School of Engineering Yaound Cameroon Mobile Computing and Networking Research Laboratory Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada DigiPlus SARL Bonamoussadi Douala Cameroon

The detection and characterization of human veins using infrared (IR) image processing have gained significant attention due to its potential applications in biometric identification, medical diagnostics, and vein-based authentication systems. This paper presents a low-cost approach for automatic detection and characterization of human veins from IR images. The proposed method uses image processing techniques including segmentation, feature extraction, and, pattern recognition algorithms. Initially, the IR images are preprocessed to enhance vein structures and reduce noise. Subsequently, a CLAHE algorithm is employed to extract vein regions based on their unique IR absorption properties. Features such as vein thickness, orientation, and branching patterns are extracted using mathematical morphology and directional filters. Finally, a classification framework is implemented to categorize veins and distinguish them from surrounding tissues or artifacts. A setup based on Raspberry Pi was used. Experimental results of IR images demonstrate the effectiveness and robustness of the proposed approach in accurately detecting and characterizing human. The developed system shows promising for integration into applications requiring reliable and secure identification based on vein patterns. Our work provides an effective and low-cost solution for nursing staff in low and middle-income countries to perform a safe and accurate venipuncture.

关键词： Vein Detection Blood Radiation Infrared Image CLAHE Algorithm Raspberry Pi

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Learning Order Forest for Qualitative-Attribute Data Clustering 27

Learning Order Forest for Qualitative-Attribute Data Cluster...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Zhao, Mingjie Feng, Sen Zhang, Yiqun Li, Mengke Lu, Yang Cheung, Yiu-Ming School of Computer Science and Technology Guangdong University of Technology Guangzhou China Shenzhen China School of Computer Science and Software Engineering Shenzhen University Shenzhen China Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen University China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University China Department of Computer Science Hong Kong Baptist University Hong Kong

ISBN: (纸本)9781643685489

Clustering is a fundamental approach to understanding data patterns, wherein the intuitive Euclidean distance space is commonly adopted. However, this is not the case for implicit cluster distributions reflected by qualitative attribute values, e.g., the nominal values of attributes like symptoms, marital status, etc. This paper, therefore, discovered a tree-like distance structure to flexibly represent the local order relationship among intra-attribute qualitative values. That is, treating a value as the vertex of the tree allows to capture rich order relationships among the vertex value and the others. To obtain the trees in a clustering-friendly form, a joint learning mechanism is proposed to iteratively obtain more appropriate tree structures and clusters. It turns out that the latent distance space of the whole dataset can be well-represented by a forest consisting of the learned trees. Extensive experiments demonstrate that the joint learning adapts the forest to the clustering task to yield accurate results. Comparisons of 10 counterparts on 12 real benchmark datasets with significance tests verify the superiority of the proposed method. Source code of the proposed method is available at [39]. © 2024 The Authors.

关键词： Adversarial machine learning

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Energy-Efficient Client Sampling for Federated Learning in Heterogeneous Mobile Edge computing Networks

Energy-Efficient Client Sampling for Federated Learning in H...

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IEEE International Conference on Communications (ICC)

作者： Jian Tang Xiuhua Li Hui Li Min Xiong Xiaofei Wang Victor C. M. Leung School of Big Data & Software Engineering Chongqing University Chongqing China TKLAN College of Intelligence & Computing Tianjin University Tianjin China College of Computer Science & Software Engineering Shenzhen University Shenzhen China Department of Electrical & Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

To address network congestion and data privacy concerns, federated learning (FL) that combines multiple clients and a parameter server has been widely used in mobile edge computing (MEC) networks to process the abundant data generated by mobile clients. However, the existing client sampling methods do not adequately consider the data heterogeneity and system heterogeneity. Parameter server selects inappropriate clients to participate in the FL training process. This inevitably leads to slower convergence of the global model and higher energy consumption. In this paper, we design a client sampling model with the goal of selecting suitable clients to improve the energy efficiency of FL in heterogeneous MEC networks. Then we propose an energy-efficient client sampling strategy by quantifying the communication capability, computation capability and data quality of clients. Based on the quantization results, clients are assigned with a corresponding sampled probability. Simulation results show that our proposed strategy can effectively accelerate the convergence of the global model and reduce the energy consumption compared with the baseline schemes.

关键词： Training Energy consumption Multi-access edge computing Federated learning Computational modeling Simulation Sampling methods

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