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arXiv 2022年

作者： Rajbahadur, Gopi Krishnan Wang, Shaowei Oliva, Gustavo A. Kamei, Yasutaka Hassan, Ahmed E. The Centre for Software Excellence Huawei Canada School of Computing Queen’s University Canada The department of computer science University of Manitoba Canada Lab Graduate School and Faulty of Information Science and Electrical Engineering Kyushu University Japan

Classifier specific (CS) and classifier agnostic (CA) feature importance methods are widely used (often interchangeably) by prior studies to derive feature importance ranks from a defect classifier. However, different feature importance methods are likely to compute different feature importance ranks even for the same dataset and classifier. Hence such interchangeable use of feature importance methods can lead to conclusion instabilities unless there is a strong agreement among different methods. Therefore, in this paper, we evaluate the agreement between the feature importance ranks associated with the studied classifiers through a case study of 18 software projects and six commonly used classifiers. We find that: 1) The computed feature importance ranks by CA and CS methods do not always strongly agree with each other. 2) The computed feature importance ranks by the studied CA methods exhibit a strong agreement including the features reported at top-1 and top-3 ranks for a given dataset and classifier, while even the commonly used CS methods yield vastly different feature importance ranks. Such findings raise concerns about the stability of conclusions across replicated studies. We further observe that the commonly used defect datasets are rife with feature interactions and these feature interactions impact the computed feature importance ranks of the CS methods (not the CA methods). We demonstrate that removing these feature interactions, even with simple methods like CFS improves agreement between the computed feature importance ranks of CA and CS methods. In light of our findings, we provide guidelines for stakeholders and practitioners when performing model interpretation and directions for future research, e.g., future research is needed to investigate the impact of advanced feature interaction removal methods on computed feature importance ranks of different CS methods. © 2022, CC BY-NC-ND.

关键词： Defects

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Deep Ensemble Learning Approach for Multimodal Emotion Recognition 3rd

Deep Ensemble Learning Approach for Multimodal Emotion Recog...

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3rd International Conference on Trends in Electronics and Health Informatics, TEHI 2023

作者： Dave, Maheak Mukherjee, Shivesh Krishna Singh, Pawan Kumar Mahmud, Mufti Department of Computer Science and Engineering Techno India University West Bengal Kolkata700091 India Department of Information Technology Jadavpur University Jadavpur University Second Campus Salt Lake City Plot No. 8 Salt Lake Bypass LB Block Sector III West Bengal Kolkata700106 India School of Science and Technology Nottingham Trent University Clifton NottinghamNG11 8NS United Kingdom Department of Computer Science Nottingham Trent University Clifton NottinghamNG11 8NS United Kingdom Medical Technologies Innovation Facility Nottingham Trent University NottinghamNG11 8 NS United Kingdom Computing and Informatics Research Centre Nottingham Trent University NottinghamNG11 8 NS United Kingdom

ISBN: (纸本)9789819739363

In this research initiative, our central objective is the development of an emotion recognition system through the utilization of cutting-edge deep learning methodologies. Our methodology involves adopting a subject-independent approach to classify emotions based on Electroencephalogram (EEG) signals sourced from a standard benchmark DEAP dataset. To ensure data quality and reliability, we initiate our process by meticulously preprocessing the raw signal data, which includes the application of Normalization and Common Average Reference (CAR) techniques. Subsequently, we employ Discrete Wavelet Transform (DWT) technique to extract salient features from the cleaned EEG data. These extracted features serve as the foundation for training three distinct deep learning models: the CNN-LSTM, CNN-GRU, and 2D-CNN models. To consolidate their predictive capabilities, we employ a Majority voting algorithm, effectively combining the strengths of these models. Notably, our proposed deep ensemble learning approach yields an impressive accuracy rate of 88% when evaluated on the challenging DEAP dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Discrete wavelet transforms

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Software Based Self Determined Welfare Scheme

Software Based Self Determined Welfare Scheme

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BIT Conference (BITCON), International

作者： Ajmeera Kiran M Rama Krishna C Sudheer Kumar N. Kumareshan Talapa Reddy Suman Kumar P. Chinnasamy Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Marri Laxman Reddy Institute of Technology and Management Dundigal Hyderabad Telangana India Dept of CSE Srinivasa Ramanujan Institute of Technology (Autonomous) Ananthapuramu Andhra Pradesh Department of ECE Centre for Advances in Signal Processing and Sensor Networks Sri Eshwar College of Engineering Coimbatore Civil Engineering Aditya University Andhra Pradesh India Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamilnadu India

ISBN: (数字)9798331518394

ISBN: (纸本)9798331518400

The innovative web program known as the Software-Based Self-Determined Welfare Scheme aims to completely transform the welfare benefit application process. Without the need for laborious paperwork or drawn-out bureaucratic processes, users can easily register online and ask for social aid directly through this site. In order to expedite the application process and guarantee that aid reaches the most deserving recipients, users of the website can register and provide vital personal information as well as financial situation details. This innovative web application empowers users by putting them in control of their welfare journey, offering a user-friendly interface to navigate through the application process with ease. With secure authentication protocols in place, users can trust that their information is kept confidential throughout the registration and application process. By leveraging the power of technology, this software-based welfare scheme not only enhances accessibility and efficiency but also promotes transparency and accountability in the distribution of welfare benefits, ultimately empowering individuals to take charge of their own welfare need.

关键词： Surveys Protocols Costs Navigation Government Buildings Process control Transforms Software Registers

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Cognitive-inspired Post-processing of optical character recognition for Swedish addresses

Cognitive-inspired Post-processing of optical character reco...

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IEEE International Conference on Cognitive Informatics

作者： Moa Andersson Summrina Kanwal Faiza Khan School of Electrical Engineering and Computer Science Kth Royal Institute of Technology in Stockholm Sweden School of Electrical Engineering and Computer Science Kth Royal Institute of Technology in Stockholm Centre for Applied Intelligent Systems Research Halmstad University Sweden Faculty of Computing Riphah International University Islamabad Pakistan

Optical character recognition (OCR) has many ap-plications, such as digitizing historical documents, automating processes, and helping visually impaired people read. However, extracting text from images into a digital format is not an easy problem to solve, and the outputs from the OCR frameworks often include errors. The complexity comes from the many variations in (digital) fonts, handwriting, lighting, etc. To tackle this problem, this thesis investigates two different methods for correcting the errors in OCR output. The used dataset consists of Swedish addresses. The methods are therefore applied to postal automation to investigate the usage of these methods for further automating postal work by automatically reading addresses on parcels using OCR. The first method, the lexical implementation, uses a dataset of Swedish addresses so that any valid address should be in this dataset (hence there is a known and limited vocabulary), and misspelled addresses are corrected to the address in the lexicon with the smallest Levenshtein distance. The second approach uses the same dataset, but with artificial errors, or artificial noise, added. The addresses with this artificial noise are then used together with their correct spelling to train a machine learning model based on Neural machine translation (NMT) to automatically correct errors in OCR read addresses. The results from this study could contribute by defining in what direction future work connected to OCR and postal addresses should go. The results were that the lexical implementation outperformed the NMT model. However, more experiments including real data would be required to draw definitive conclusions as to how the methods would work in real-life applications.

关键词： Vocabulary Automation Computational modeling Optical character recognition Lighting Machine learning Complexity theory

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Distributed optimization for IoT attack detection using federated learning and Siberian Tiger optimizer

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ICT Express 2025年第3期11卷 542-546页

作者： Gupta, Brij B. Gaurav, Akshat Alhalabi, Wadee Arya, Varsha Alharbi, Eman Chui, Kwok Tai Department of Computer Science and Information Engineering Asia University Taichung 413 Taiwan Department of Medical Research China Medical University Hospital China Medical University Taichung Taiwan Symbiosis Centre for Information Technology (SCIT) Symbiosis International University Pune India School of Cybersecurity Korea University Seoul South Korea Kyung Hee University 26 Kyungheedae-ro Dongdaemun-gu Seoul South Korea Ronin Institute Montclair NJ United States International Center for AI and Cyber Security Research and Innovations (CCRI) Asia University Taichung 413 Taiwan Department of Computer Science Immersive Virtual Reality Research Group King Abdulaziz University Jeddah Saudi Arabia Hong Kong Metropolitan University Hong Kong SAR China Center for Interdisciplinary Research University of Petroleum and Energy Studies (UPES) Dehradun India Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

The rapid growth of IoT devices has heightened the risk of botnet attacks, calling for scalable and distributed detection solutions. In this context, this study proposes a distributed optimization system for IoT attack detection using CNN model utilizing federated learning. After optimizing the hyperparameters of the model at the server, the Siberian Tiger Optimization (STO) method distributes these values to clients for dispersed training. Our model achieves accuracy, recall, and precision of 0.89978, 0.94355, and 0.94455, respectively, using the N-BaIoT dataset. These findings show, in spite of latency issues, the efficiency of federated learning in distributed IoT security systems. © 2025

关键词： CNN model Distributed optimization Federated learning IoT attack detection Siberian Tiger Optimization (STO)

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Toward AI-Powered Edge Intelligence for Object Detection in Self-Driving Cars: Enhancing IoV Efficiency and Safety

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IEEE Internet of Things Journal 2025年第11期12卷 16990-16997页

作者： Imran Ahmed Misbah Ahmad Muftooh Ur Rehman Siddiqi Abdellah Chehri Gwangill Jeon School of Computing and Information Science Anglia Ruskin University Cambridge U.K. Centre for Machine Vision Bristol Robotics Laboratory University of the West of England Bristol U.K. Department of Animal and Agriculture Hartpury University Gloucester U.K. College of Engineering and Physical Sciences Aston University Birmingham U.K. Department of Mathematics and Computer Science Royal Military College of Canada Kingston ON Canada Department of Embedded Systems Engineering Incheon National University Incheon South Korea

In the rapidly advancing field of intelligent transportation systems, integrating artificial intelligence (AI) with edge computing presents a promising way to enhance the safety and efficiency of the Internet of Vehicles (IoV). This study explores and presents a deep learning-based object detection model within an edge computing framework which aims to facilitate real time object detection in self driving cars. Using an urban traffic scenarios-based dataset, our research shows the ability of the model to accurately detect and classify various objects important for autonomous driving. The YOLOv8 model is used in this work due to its optimal balance between accuracy and computational efficiency. This model has also demonstrated its worth by achieving good performance results, including an average precision of 0.79, a recall of 0.62, and an F1-score of 0.69. The results are demonstrated by a detailed confusion matrix, highlighting the model’s effectiveness in complex driving environments and underscoring its reliability for in-vehicle deployment. By implementing AI directly on edge devices within vehicles, our approach might be helpful in significantly reducing latency, boosting decision-making speed, and enhancing data privacy by minimising dependence on cloud processing. The findings not only support the model’s capabilities but also illustrate the practical benefits of edge intelligence in autonomous vehicles. These benefits, such as faster decision making and improved data privacy, contribute effectively to the IoV infrastructure. This study marks a substantial step toward recognizing the possibility of AI-enhanced edge computing in driving the next generation of autonomous vehicle technology.

关键词： Computational modeling Object detection Edge computing Image edge detection Artificial intelligence computer architecture Data models Accuracy Servers Safety

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Dynamic Modeling of Patient Vital Signs: Leveraging Markov Chain Principles with Neural Networks for Irregular Time-Series Prediction

Dynamic Modeling of Patient Vital Signs: Leveraging Markov C...

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International Joint Conference on Neural Networks (IJCNN)

作者： Xin Yun Choy Li Rong Wang Thomas C. Henderson Kelvin Li Yih Yng Ng Xiuyi Fan School of Computer Science and Engineering Nanyang Technological University Singapore Singapore School of Computing University of Utah Salt Lake City USA Department of Ophthalmology Tan Tock Seng Hospital Singapore Singapore Digital and Smart Health Office Centre of Healthcare Innovation Singapore Singapore Lee Kong Chian School of Medicine Nanyang Technological University Singapore Singapore

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Analyzing patient health through irregular time series vital sign data demands innovative methods beyond conventional imputation techniques. This study introduces a novel approach diverging from prevailing attention-based models to explicitly capture temporal patient evolution. We adopt a paradigm where patients are viewed as dynamic systems evolving over time, with their vital signs encapsulating the system’s states. Our conceptual framework draws parallels to a Markov chain, exploring the transitions between states within a unit of time. To navigate the challenge of a vast state space, we employ a neural network to model expected transitions. Our method portrays the patient’s progression within one unit of time as the system evolves from one state to another, and forecasts states into the future. We outline the training process using irregular time series data and demonstrate its efficacy through analysis on two large vital sign data sets. Comparative analysis against attention-based models emphasizes the effectiveness and efficiency of our approach. This research heralds a promising avenue for patient vital sign analysis, providing insights into temporal patient evolution without relying on imputation methods, thereby enhancing predictive accuracy and interpretability of models.

关键词： Training Analytical models Accuracy Navigation Time series analysis Neural networks Predictive models

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A Framework for Tele-rehabilitation Gaming System

A Framework for Tele-rehabilitation Gaming System

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Applied Computational Intelligence in Information Systems (ACIIS), International Conference on

作者： Ahmed M. S. Elaklouk Ratna Zuarni Ramli Ibrahim Edris Nena Padilla-Valdez Adel Al Jumaily School of Computing and Informatics Universiti Teknologi Brunei Bandar Seri Begawan Brunei Darussalam College of Computing Informatics and Mathematics Universiti Teknologi MARA Negeri Seremban Malaysia Centre for Communication Universiti Teknologi Brunei Bandar Seri Begawan Brunei Darussalam School of Computing University of Technology Brunei Department of Computer Science & Software Engineering University of Western Australia and ENSTA Bretagne University France

Brain damage resulting from stroke and traumatic brain injury is the primary cause of complicated disability and one of the major causes of mortality in most countries, burdening healthcare systems owing to the growth in the number of brain injury victims. Rehabilitation is required to deal with the brain damage. With fewer therapists available, it is very challenging to manage patients and even motivate them to participate actively in their rehabilitation. Patients have frequently complained that many traditional rehabilitation systems are monotonous and uninteresting. The study’s objective is to propose a telerehabilitation gaming system framework that can serve as guidance for developing an affordable rehabilitation gaming system that can motivate and engage patients and increase rehabilitation effectiveness. The research methodology is based on synthesizing related research and currently available technologies. The proposed framework includes the therapist and patient attached with vital constructs: tailoring tools, instructional content, game characteristics, tailored games, and performance. It is an internet-based communication structure that connects the Rehabilitation Gaming System to the hospital and other care provider networks. The proposed framework is evaluated by a panel of five experts. The results reveal that all experts believe that the proposed framework can serve as a useful guide for creating gaming interventions that can boost the patient’s motivation and engagement and increase the effectiveness of rehabilitation.

关键词：

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Unveiling the Capabilities of Latent Diffusion Models for Classification of Lung Diseases in Chest X-Rays 22

Unveiling the Capabilities of Latent Diffusion Models for Cl...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Ning, Junzhi Xing, Xiaodan Zhang, Sheng Ma, Xiao Yang, Guang Department of Computing School of Engineering Imperial College London London SW7 2AZ United Kingdom Bioengineering Department and Imperial-X Imperial College London London W12 7SL United Kingdom National Heart and Lung Institute Imperial College London London SW7 2AZ United Kingdom School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 China Cardiovascular Research Centre Royal Brompton Hospital London SW3 6NP United Kingdom School of Biomedical Engineering & Imaging Sciences King's College London WC2R 2LS United Kingdom

ISBN: (纸本)9798331520526

Diffusion models have demonstrated remarkable ability in synthesizing chest X-ray (CXR) images, particularly by generating high-quality samples to address the scarcity and imbalance of annotated CXR datasets. While these models excel in generating realistic samples-suggesting that they contain rich discriminative information effectively harnessing this capability for disease classification and decomposition remains a challenge. This study investigates an approach that leverages latent conditional diffusion models, which are conditioned on corresponding radiology reports, for lung disease classification in CXRs. Specifically, we employ a pre-trained latent conditional diffusion model for CXRs to predict noise estimates for a noisy input lung CXR under various disease conditions. By comparing the noise estimation errors associated with different class prompts, we determine the most probable disease classification based on the minimal error. Through the experiments, we demonstrate that the CXR diffusion-based classifier not only achieves zero-shot classification performance comparable to existing models but also reveals lesion regions aligning with ground truth lesion areas in CXRs. © 2025 IEEE.

关键词： CXRs Lung Disease Classification Diffusion Classifier Latent Diffusion Model

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Pre-Processing Investigation For Brain Abnormality Detection And Analysis Through MRI of Brain

Pre-Processing Investigation For Brain Abnormality Detection...

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Innovations in High Speed Communication and Signal Processing (IHCSP), International Conference on

作者： Satyajee Srivastava Mohd. Arfat Sunita Pachar Rama Krishna Yellapragada Priya Jyotiyana School of Computing University of Engineering and Technology Roorkee Uttarakhand India Department of Para Medical Sciences Medical Imaging Technology School of Nursing Sciences & Allied Health (SNSAH) Jamia Hamdard New Delhi India IBM Department GLA UNIVERSITY Mathura India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Green Fields Guntur District A.P India Computer Science and engineering Jaipur engineering college and research centre India

Both men and women face equal risk from conditions such brain tumours, strokes, blood clots, and multiple sclerosis (MS). The most frequent and prevalent of these brain illnesses is a brain tumour. The prognosis and therapy for a brain lesion depend on how quickly and precisely it is diagnosed. However, neuroradiology experts are the only ones qualified to make the diagnosis, as it is extremely difficult. Each medical report from an imaging investigation must be reviewed and confirmed by at least two physicians. If there's a problem, it could mean a biopsy or even surgery. This study describes a fully automated procedure for removing artefacts and the skull from magnetic resonance (MR) pictures of the brain by utilising computational geometry, wavelet decomposition, and thresholding. The intermediate step of correct preprocessing of MR images is thresholding using the standard deviation method. For various MR images, the suggested preprocessing approach achieves a high degree of accuracy with a small error rate. This preliminary processing is employed to lessen the likelihood of erroneous detection. As a result, the accuracy with which diseases are diagnosed using brain MRI by a computer system is improved.

关键词： Brain Computational geometry Technological innovation Thresholding (Imaging) Error analysis Magnetic resonance imaging Magnetic resonance

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