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Machine Learning Stroke Prediction in Smart Healthcare:Integrating Fuzzy K-Nearest Neighbor and Artificial Neural Networks with Feature Selection Techniques

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computers, Materials & Continua 2025年第3期82卷 5115-5134页

作者： Abdul Ahad Ira Puspitasari Jiangbin Zheng Shamsher Ullah Farhan Ullah Sheikh Tahir Bakhsh Ivan Miguel Pires Department Information System Study Program Faculty of Science and TechnologyUniversitas AirlanggaSurabaya60286Indonesia School of Software Northwestern Polytechnical UniversityXi’an710072China Research Center for Quantum Engineering Design Faculty of Science and TechnologyUniversitas AirlanggaSurabaya60286Indonesia School of Computer Science and Software Engineering Shenzhen UniversityShenzhen518061China Cybersecurity Center Prince Mohammad Bin Fahd University617Al JawharahKhobarDhahran34754Saudi Arabia Cardiff School of Technologies Cardiff Metropolitan UniversityWestern AvenueCardiffCF52YBUK Instituto de Telecomunicacoes Escola Superior de Tecnologia e Gestao de AguedaUniversidade de Aveiroágueda3750-127Portugal

This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and Best First Search(BFS).The study demonstrates that BFS significantly enhances the performance of both *** BFS preprocessing,the ANN model achieved an impressive accuracy of 97.5%,precision and recall of 97.5%,and an Receiver Operating Characteristics(ROC)area of 97.9%,outperforming the Chi-Square-based ANN,which recorded an accuracy of 91.4%.Similarly,the F-KNN model with BFS achieved an accuracy of 96.3%,precision and recall of 96.3%,and a Receiver Operating Characteristics(ROC)area of 96.2%,surpassing the performance of the Chi-Square F-KNN model,which showed an accuracy of 95%.These results highlight that BFS improves the ability to select the most relevant features,contributing to more reliable and accurate stroke *** findings underscore the importance of using advanced feature selection methods like BFS to enhance the performance of machine learning models in healthcare applications,leading to better stroke risk management and improved patient outcomes.

关键词： Fuzzy K-nearest neighbor artificial neural network accuracy precision recall F-measure Chi-Square best search first heart stroke

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EBCFL: An Efficient BlockChain-based Federated Learning Framework for Internet of Vehicles

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IEEE Network 2025年

作者： Cao, Dun Liao, Yonghui Alqahtani, Fayez Wang, Jin School of Computer and Communication Engineering Changsha University of Science and Technology China Changsha University of Science and Technology China Software Engineering Department College of Computer and Information Sciences King Saud University Saudi Arabia Hunan University of Science and Technology China

Federated Learning (FL) can train a shared model while protecting vehicle data privacy, which has significant advantages in improving driving safety and efficiency. However, there are still many challenges in applying FL to Internet of Vehicles (IoV). First, the varying computing capabilities of moving vehicles result in slow convergence speed due to underperforming vehicles. Second, malicious vehicles may tamper with model parameters before uploading them to the central server, which is itself vulnerable to single-point failure. To solve the above challenges, we design an efficient and decentralized blockchain FL framework (Efficient BlockChain-based Federated Learning Framework, EBCFL). This framework effectively prevents possible malicious behaviors in the vehicular network nodes and improves the efficiency of FL through a well-designed security verification mechanism. The experimental results show that EBCFL outperforms other schemes in terms of average test accuracy and average total time cost, indicating that EBCFL significantly improves the learning efficiency while bearing an acceptable additional time cost for blockchain communication, demonstrating its strong ability to guard against malicious behaviours and ensure robustness. © 1986-2012 IEEE.

关键词： Blockchain Federated learning IoV Security verification

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WQuatNet: Wide range quaternion-based head pose estimation

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Journal of King Saud University - computer and Information sciences 2025年第3期37卷 1-14页

作者： Algabri, Redhwan Shin, Hyunsoo Abdu, Ahmed Bae, Ji-Hun Lee, Sungon Dept. of Computer Science & Engineering College of AI Convergence Sejong University Seoul 05006 South Korea Research Institute of Engineering and Technology Hanyang University Ansan 15588 South Korea School of Software Northwestern Polytechnical University Xi’an 710072 China Department of Robotics Hanyang University Ansan 15588 South Korea

Head pose estimation (HPE) is a critical task for numerous applications ranging from human-computer interaction, healthcare, and robotics, to surveillance. Most existing methods employ Euler angles as a representation, which often face challenges such as a gimbal lock, especially in full-range rotation scenarios or rotation matrices that require nine parameters. This study introduces WQuatNet, a novel deep learning-based model that leverages the quaternion representation, which uses only four parameters, to avoid this challenge. WQuatNet was designed based on a landmark-free HPE method to predict head poses across the full-range angles of 360∘ from images. Landmark-free methods bypass the need for explicit detection of facial landmarks;instead, they leverage the entire image to estimate the head orientation. The model incorporates a RepVGG-D2se backbone for robust feature extraction and introduces two loss functions tailored for quaternion predictions. Our experimental results on multiple HPE datasets covering both narrow- and full-range angles demonstrate that WQuatNet outperforms the state-of-the-art (SOTA) approaches in terms of accuracy. The performance of the proposed HPE was evaluated using the CMU, AGORA, BIWI, AFLW2000, and 300W-LP datasets. We also perform ablation studies and error analyses to validate the significance of each component of the model. © The Author(s) 2025.

关键词： Deep neural network Full range of rotation Head pose estimation Quaternion

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ViT Enhanced Privacy-Preserving Secure Medical Data Sharing and Classification

ViT Enhanced Privacy-Preserving Secure Medical Data Sharing ...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Al Amin Kamrul Hasan Sharif Ullah M. Shamim Hossain Department of Electrical & Computer Engineering College of Engineering Tennessee State University TN USA Department of Computer Science & Engineering College of Science and Engineering University of Central Arkansas Conway AR USA Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

Privacy-preserving and secure data sharing are critical for medical image analysis while maintaining accuracy and minimizing computational overhead are also crucial. Applying existing deep neural networks (DNNs) to encrypted medical data is not always easy and often compromises performance and security. To address these limitations, this research introduces a secure framework consisting of a learnable encryption method based on block-pixel operation to encrypt the data and subsequently integrate it with the Vision Transformer (ViT). The proposed framework ensures data privacy and security by creating unique scrambling patterns per key, providing robust performance against leading bit attacks and minimum difference attacks.

关键词： Data privacy computer vision Image analysis Accuracy Artificial neural networks Transformers Robustness Encryption Biomedical imaging

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POS tagging of low-resource Pashto language: annotated corpus and BERT-based model

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Language Resources and Evaluation 2025年 1-23页

作者： Haq, Ijazul Zhang, Yingjie Qadri, Intakhab Alam Shien-Ming Wu School of Intelligent Manufacturing South China University of Technology Guangzhou China Artificial Intelligence Department Guangdong CAS Angels Biotechnology Co. Ltd. Foshan China College of Computer Science and Software Engineering Shenzhen University Shenzhen 518060 China

This paper presents the development of a comprehensive part-of-speech (POS) annotated corpus for the low-resource Pashto language, along with a deep learning model for automatic POS tagging. The corpus comprises approximately 700K words (30K sentences), labeled for word boundaries, considering Pashto lacks explicit delimiters for word segmentation. The corpus was then annotated for POS information using a concise and pragmatic tagset of 36 grammatical categories. We utilized this corpus to train a supervised POS tagging model. For the model development, we employed a multilingual BERT (Bidirectional Encoder Representations from Transformers), fine-tuning it for this specific task. The BERT-based model performance was evaluated against an RNN-based model that employs the BiLSTM-CRF network and word embeddings (Word2Vec, fastText, and GloVe). Experimental results demonstrated that the BERT-based model achieved the best performance, attaining an accuracy of 96.24% and an F1-score (weighted average) of 96.22%. The model’s performance is highly satisfactory, making it useful in practical applications. Furthermore, the applications of the annotated corpus are not limited to this study only;it can be employed in various NLP applications, including named entity recognition (NER), text proofing, and constituency and dependency parsing. © The Author(s), under exclusive licence to Springer Nature B.V. 2025.

关键词： Artificial Intelligence BERT Corpus Linguistics LLMs Low-resource Languages Machine Learning NLP Pashto POS Tagging Transformers

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User Experience (UX) Enrichment Through Digital Branding 1st

User Experience (UX) Enrichment Through Digital Branding

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1st International Conference on Computing and Emerging Technologies, ICCET 2023

作者： Zia, Saira Iqbal, Muhammad Waseem Noor, Misbah Aqeel, Muhammad Hamid, Khalid Department of Computer Science Superior University Lahore Lahore Pakistan Department of Software Engineering Superior University Lahore Lahore Pakistan

ISBN: (纸本)9783031776168

The change in digital marketing from desktop to mobile contexts (e.g., browser) is getting increasingly pronounced (e.g., within mobile applications). Banners and videos, on the other hand, remain mostly unchanged as ad distribution mechanisms. We look at how to adapt ad distribution methods to the mobile environment. We demonstrate how demanding gentle contact with the ad – while lowering available screen space – might improve users’ perceptions of remote marketing. Business remember may be improved by more encounters with the commercial distribution device. A holistic knowledge of customer experience necessitates examining in marketing and branding, user experience is important. This study’s aim is to offer a theoretical background for finer comprehension the implications of user experience (UX), client experience (CX), and corporate reputation (BE) are all terms used to describe how a user, a customer, or a brand (BE) on brand equity (BX). As a mediator with a high path weight, customer experience has a major impact on brand equity. Employing UX techniques that connect with management plans can help companies reach a high level of customer satisfaction and brand value perception by consumers. This study’s findings and analyses can assist business in developing a strategy. For understanding which components of User Experiences are important for brand image and the customer satisfaction. Given the increased financial significance of the "user experience," more gamification should be used. As an outcome, the study’s goal is to look into the causes and the consequences of an online experience, in the area of internet gamification, it also partially mediates the relationship. According to the findings, mobility as it is perceived practical and hedonistic features, as well as perceived mobility characteristics, influence experience design, which in turn drives perceived advantages, perceived value categories, and product equity. This study also confirms the role of use

关键词： Gamification

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Harnessing the Power of AI with Deep Learning: Innovations and Implications

Harnessing the Power of AI with Deep Learning: Innovations a...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Upender, Talluri Sathiyamoorthy, C. Udayakumar, K. Kirubakaran, D. Purushothaman, V. Raja, Kiruba Thangam CMR College of Engineering and Technology Department of CSE Medchal Telangana Hyderabad India Department of Commerce Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of CSE Tamil Nadu Chennai India R.M.K. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India VIT University Department of Software and Systems Engineering Tamil Nadu Vellore India

ISBN: (纸本)9798331512965

The rapid advancements in artificial intelligence (AI) and deep learning have revolutionized various sectors, enabling unprecedented levels of innovation and efficiency. This paper delves into the transformative impact of deep learning across multiple domains, highlighting key innovations and their broader implications. By examining cutting-edge applications in healthcare, finance, autonomous systems, and natural language processing, the study elucidates how deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are pushing the boundaries of what's possible. Moreover, the paper explores the integration of deep learning with emerging technologies such as the Internet of Things (IoT), edge computing, and quantum computing, underscoring the synergistic potential of these combinations. The role of deep learning in predictive analytics and personalized solutions is discussed, showcasing its ability to enhance decision-making processes and improve user experiences. Furthermore, the paper discusses the ethical considerations, challenges, and future prospects associated with the widespread adoption of deep learning technologies. The insights provided aim to foster a deeper understanding of how harnessing AI with deep learning can drive progress while addressing critical societal and technological issues. © 2025 IEEE.

关键词： Convolutional neural networks

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Usability Approach of Distributed Database in Health Care System 1st

Usability Approach of Distributed Database in Health Care Sy...

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1st International Conference on Computing and Emerging Technologies, ICCET 2023

作者： Mazhar, Sana Iqbal, Muhammad Waseem Irshad, Muhammad Ahmad Department of Software Engineering Superior University Lahore54000 Pakistan Department of Computer Science Superior University Lahore54000 Pakistan

ISBN: (纸本)9783031776168

The blockchain is safe mechanism for store and distributed data due to its transparency. Every block of the chain serves as both an individual component that has its own data as well as a dependent connection inside the shared network, so this duality provides a networks organized by users who keep and share data instead of third party. Blockchain has several uses in healthcare, including the improvement of applications for mobile health monitoring-devices, the distribution and store of electrical-medical-records, clinical research data, as well as the storage of insurance information. Blockchain analysis throughout insurance is currently limited, but blockchain is at the frontlines of trying to transform the medical system;throughout its distributed concepts, blockchain could even keep improving patient records availability and privacy. This article presents a solution for managing identity and access in a digital system that makes use of blockchain technology to help with entity authentication and permission. Using the Permissioned Blockchain architecture, a prototype illustrates the use of blockchain in access and identity management. It gives a proof-of-concept centered on some use case involving Electronic-Health-Records from the health sector in which an immutable and traceability record for information related to patients is desired. Blockchain has the implicit to meet specific unique criteria, similar as confidentiality and invariability, and so might be employed in a wide range of operations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electronic health record

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An Efficient Deep Learning-Based Framework for Predicting Cyber Violence in Social Networks

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Complexity 2025年第1期2025卷

作者： Fayand Fathabad, Younes Balafar, Mohammad Ali Golzari Oskouei, Amin Koohi, Kamal Department of Computer Engineering Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran Faculty of IT and Computer Engineering Urmia University of Technology Urmia Iran Department of Software Engineering Faculty of Engineering and Natural Science İstinye University Istanbul Turkey Department of Social Sciences Faculty of Law and Social Sciences University of Tabriz Tabriz Iran

The widespread use of the internet has led to the rapid expansion of social networks, making it easier for individuals to share content online. However, this has also increased the prevalence of cyber violence, necessitating the development of automated detection methods. Deep learning-based algorithms have proven effective in identifying violent content, yet existing models often struggle with understanding contextual nuances and implicit forms of cyber violence. To address this limitation, we propose a novel deep multi-input recurrent neural network architecture that incorporates neighborhood-based contextual information during training. The Jaccard similarity metric is employed to construct neighborhoods of input texts, allowing the model to leverage surrounding context for improved feature extraction. The proposed model combines Bi-LSTM and GRU networks to capture both sequential dependencies and contextual relationships effectively. The proposed model was evaluated on a real-world cyber violence dataset, achieving an accuracy of 94.29%, recall of 81%, precision of 72%, and an F1-score of 76.23% when incorporating neighborhood-based learning. Without contextual information, the model attained an accuracy of 89.15%, recall of 72.00%, precision of 71.5%, and an F1-score of 71.74%. These results demonstrate that neighborhood-based learning contributed to an average improvement of 5.14% in accuracy and 4.49% in F1-score, underscoring the importance of contextual awareness in cyber violence detection. These results highlight the significance of contextual awareness in deep learning-based text classification and underscore the potential of our approach for real-world applications. Copyright © 2025 Younes Fayand Fathabad et al. Complexity published by John Wiley & Sons Ltd.

关键词： Recurrent neural networks

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A Convolutional Neural Network Approach for Mood Classification in Short Texts Using Character-Level Details and Local Text Structure 1st

A Convolutional Neural Network Approach for Mood Classificat...

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1st International Conference on Computing and Emerging Technologies, ICCET 2023

作者： Hussain, Moodser Jameel, Muhammad Ullah, Muhammad Farhat Jabbar, Taimoor Hassan Irfan, Roha Iqbal, Muhammad Waseem Department of Information Technology University of the Punjab Gujranwala Campus Gujranwala Pakistan Department of Computer Science Superior University Lahore Lahore Pakistan Department of Software Engineering University of Lahore Lahore Pakistan Department of Computer Science BZU Multan Pakistan Department of Computer Science University of Central Punjab Lahore Lahore Pakistan Department of Software Engineering Superior University Lahore Lahore Pakistan

ISBN: (纸本)9783031776168

Mood classification is a crucial and complex task in the fields of psychology and Natural Language Processing (NLP). It is particularly challenging to assess an individual’s mood based on their chats or comments on social media platforms. This study introduces a Convolutional Neural Network (CNN) for mood classification and evaluates its efficacy on the ISEAR dataset. The dataset comprises 7,652 short texts labeled with seven different moods including joy, anger, fear, guilt, disgust, sadness, and shame. Our proposed model achieves an impressive F1 score of 94.59%, surpassing existing statistical and deep learning models. The superior performance can be attributed to two key features of our approach. Firstly, our model incorporates the local structure of the text by leveraging relationships between adjacent word embeddings, allowing it to capture the subtle nuances of language. Secondly, it captures character-level details within the input text, allowing for a more comprehensive understanding of the data. Overall, our study demonstrates the effectiveness of the CNN-based approach for mood classification in NLP, with potential implications for developing automated systems that can accurately recognize and respond to people's moods in various applications, such as mental health and customer service. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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