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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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Bibliometric Analysis of Intelligent Techniques for Obstetric Complication Prediction in the Last 20 Years

Bibliometric Analysis of Intelligent Techniques for Obstetri...

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Dian Lestari Fairuz Iqbal Maulana Puput Dani Prasetyo Adi Anita Rahayu Reny Nadlifatin Vandha Pradwiyasma Widartha Faculty of Medicine Airlangga University Surabaya Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Research Center for Telecommunication National Research and Innovation Agency Bandung Indonesia Department of Information System Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Information System Pukyong National University Busan South Korea

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

This study conducts a bibliometric analysis of intelligent techniques in predicting obstetric complications over the past 20 years. These complications significantly impact maternal and neonatal health, necessitating early prediction and management. The study highlights the growing use of machine learning (ML) and artificial intelligence (AI) in this field. By reviewing literature from 2004 to 2023, it identifies a gap in research focused on using intelligent techniques for predicting obstetric complications. Keyword co-occurrence analysis reveals significant research clusters in areas like diabetes mellitus, biomarkers, and treatment management. The study emphasizes the need for practical ML-based applications to aid clinical decision-making and suggests that trend data should inform curriculum development and clinical practice guidelines in obstetrics.

关键词： Pediatrics Technological innovation Bibliometrics Collaboration Machine learning Predictive models Obstetrics

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Mudahnya BM: A Context-Aware Mobile Cloud Learning Application Using Semantic-Based Approach

Mudahnya BM: A Context-Aware Mobile Cloud Learning Applicati...

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7th International Conference on Advances in Visual Informatics, IVIC 2021

作者： Muhammad, Sufri Admodisastro, Novia Osman, Hafeez Mohd Ali, Norhayati Faculty of Computer Science and Information Technology Department of Software Engineering and Information System Universiti Putra Malaysia Seri Kembangan Selangor43400 Malaysia

ISBN: (纸本)9783030902346

A great potential of various learning environment in mobile learning application can clearly be seen in current pandemic situation. The accessibility of the learning resources needs to be available from anywhere anytime despite having strong or poor internet connection. It has motivated researchers to imply context-aware capability in improving the accessibility of the learning resources. This paper presents a development process of Mudahnya BM mobile application that follows a fundamental concept of Mobile Cloud Learning (MCL). Mudahnya BM is an application to learn basic Malay language for learner 7–12 years old. The injection of extrinsic and intrinsic context-aware help the application to improve the reasoning process for finding available learning resources from service providers. Semantic-based approach is applied in the reasoning process. This study involved the end users for evaluation purposes. 33 randomized scenarios have been tested using One-Sample Wilcoxon Signed Rank test. The result shows a positive impact to the population. © 2021, Springer Nature Switzerland AG.

关键词： Semantics

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Dt2f-Tlnet: A Novel Text-Independent Writer Identification Model Using a Combination of Deep Type-2 Fuzzy Architecture and Transfer Learning Networks Based on Handwriting Data

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SSRN 2023年

作者： Yang, Jing Shokouhifar, Mohammad Yee, Por Lip Khan, Abdullah Ayub Awais, Muhammad Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur50603 Malaysia Department of Electrical & Computer Engineering Shahid Beheshti University Tehran*** Iran Department of Computer Science and Information Technology Benazir Bhutto Shaheed University Lyari Karachi75660 Pakistan COMSATS University Islamabad Pakistan

Identifying and verifying the identity of people based on scanned images of handwritten documents is an applicable biometric modality with applications in forensic and historic document investigation, and it is an important study area within the research field of behavioral biometrics. Despite this, there are few studies in this field. Furthermore, there are very few standard datasets for identifying and verify handwritten documents. Also, handwritten documents lose their character during time because of ink spread and drying. Therefore, it is necessary to provide a method that can identify and verify handwritten documents under various uncertainties. In this study, a text-independent writer identification and verification model in offline state under different experimental conditions is developed using a combination of Deep Type-2 Fuzzy architecture and Transfer Learning networks (DT2F-TLNet). So, a right-to-left dataset has been collected. The proposed DT2F-TLNet model is validated using both the designed dataset and other benchmark datasets. The proposed model is distinguished by the fact that it is developed to be independent of the textual content of the handwritten cases and can be used for various languages. The study's findings show that the developed DT2F-TLNet model can learn properties from heterogeneous handwriting data and results in higher accuracy than other comparable approaches. © 2023, The Authors. All rights reserved.

关键词： Biometrics

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A Comparative Analysis on Three Duplication Elements in Copy-Move Forgery using PatchMatch-Based Detection Method

A Comparative Analysis on Three Duplication Elements in Copy...

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International Conference on Software Engineering and computer systems (ICSECS)

作者： Nur Izzati Nor Azaimi Nor Bakiah Abd Warif Nor-Syahidatul N Ismail Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Johor Malaysia Faculty of Computer Science and Information Technology Centre for Information Security Research Universiti Tun Hussein Onn Malaysia Johor Malaysia Department of Computer System and Networking Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah Pekan Pahang Malaysia

Image forgery is the alteration of a digital image to hide some of the important and useful information. Copy-move forgery (CMF) is one of the most difficult to detect because the copied part of the image has the same characteristics as the original image. Most of the existing datasets only highlight additional attacks in the copied part. Since there are no categories of duplication elements in the datasets, this research analyzed three categories of duplication elements in CMF which are animals, food and non-living things using DEFACTO and CoMo3Dataset. The analysis is performed on PatchMatch-based detection method and the results show that the method able to maintain at least 83% for all duplication elements in both DEFACTO and CoMo3Dataset. Furthermore, the method is able to detect a minimum 92% score for the food category in both datasets.

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Blockchain Technology Supports Employee Career Management to Increase Transparency of Employee Career Achievements

Blockchain Technology Supports Employee Career Management to...

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International Conference on Cybernetics and Intelligent system (ICORIS)

作者： Inayatulloh Yasri Ahmad Fathurrozi R Muhammad Yasir Fried Sinlae Tyastuti Sri Lestari Wowon Priatna Indra Kusumadi Hartono Achmad Noe'man Information Systems Department School of Information system Bina Nusantara University Jakarta Indonesia Computers Science Department School of Compters Science Bina Nusantara University Jakarta Indonesia Faculty of Computer Science Bhayangkara Jakarta Raya University Jakarta Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

Career planning for employees is an important part for employees to increase motivation and work performance. On the other hand, career management for companies plays an important role in meeting employee needs in the future. The problem is that employees cannot get real-time and valid information about their career progress based on objective company assessments. Many old employees are not promoted to higher positions without an accurate explanation from the company. Thus, a system is needed that can help employees monitor their career progress. So the aim of this research is to build an employee career management model using blockchain technology to increase the transparency of employee career paths in the company. The research uses a qualitative approach through a literature review to obtain information about employee management phenomena and problems. The literature review is also used to study blockchain technology as an alternative solution to career management problems. The research output is a blockchain technology adoption model for corporate career management and simulation with the Hyperledger framework.

关键词： Accuracy Engineering profession Smart contracts Companies Real-time systems Blockchains Planning Monitoring systematic literature review Qualifications

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Digital Technology in Health Education Trend Mapping through Bibliometrics Analysis 4

Digital Technology in Health Education Trend Mapping through...

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4th International Conference on Informatics, multimedia, Cyber and information system, ICIMCIS 2022

作者： Maulana, Fairuz Iqbal Lestari, Dian Widartha, Vandha Pradwiyasma Haidar, Nirwana Dirgantara, Wahyu School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Airlangga University Faculty of Medicine Surabaya60132 Indonesia Telkom University Information System of Industrial Engineering Faculty Bandung Indonesia University of Madura Informatics Engineering Study Program Pamekasan Indonesia University of Merdeka Malang Department of Electrical Engineering Malang Indonesia

ISBN: (纸本)9781665473279

This study examines the mapping of research data on digital technology in the field of health education using bibliometric analysis method. Data was collected by identifying keywords in the Scopus database and sorting them out to sort the studies from the last 10 years (2012-2021). Through this step, a total of 1482 documents were obtained (articles, journals, proceedings, books, and others). The data is then processed using the VOSViewer instrument to obtain a visualization of the mapping analysis. This study also analyzes the network types of authors and co-authors through the VOSViewer instrument. The result of this study indicates that the most document types published within ten years are Articles (66.5%), Review (22.9%), Conference Paper (4.2%), and others. The most studied subjects are Medicine (55.7%), Nursing (9.0%), Health Professions (8.2%), Social sciences (7.8%), Engineering (3.9%), computer science (2.6%), Environmental science (2.6%), Biochemistry-Genetics and Molecular Biology (2.2%), Psychology (1.6%), and Dentistry (1.3%). This study offers a written communication process and the nature and direction of developing descriptive means of counting and analyzing the various phases of communication as well as recognizing the authorship and direction of its symptoms in documents on the subject of digital technology in the health sector. © 2022 IEEE.

关键词： Mapping

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Detection of Student Sleepiness Using U-Net and Conv-LSTM Methods to Improve Learning Quality

Detection of Student Sleepiness Using U-Net and Conv-LSTM Me...

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Technology, Engineering, and Computing Applications (ICTECA), International Conference on

作者： Ben Rahman Septi Andryana Aris Gunaryati Teddy Mantoro Department of Information System Faculty of Information and Communication Technology Universitas Nasional Jakarta Indonesia Department of Information Technology Faculty of Information and Communication Technology Universitas Nasional Jakarta Indonesia Department of Computer Science Faculty of Science and Technology Sampoerna University Jakarta Selatan Jakarta Indonesia

In the context of monitoring student engagement during learning sessions, the ability to accurately detect sleepiness can be a valuable asset. Early detection of sleepiness is critical to evaluate the need for intervention to ensure effective learning outcomes. Prolonged student sleepiness can negatively impact learning effectiveness and may result in academic challenges. This research used a semantic segmentation network to monitor and detect the level of sleepiness in students during learning sessions by modifying the U-Net architecture, which is enhanced with Convolutional Long Short-Term Memory (Conv-LSTM) blocks. Conv-LSTM blocks enable efficient feature sharing between network layers and improve accuracy in detecting sleepiness. In this study, experiments were carried out and the proposed method for monitoring students in the classroom was evaluated. The results of this research showed that the percentage of students who made movements detected in the video and extracted in image detection was 92% accurate and not sleepy.

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Deep learning frequent pattern mining on static semi structured data streams for improving fast speed and complex data streams 7

Deep learning frequent pattern mining on static semi structu...

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7th International Conference on Optimization and Applications, ICOA 2021

作者： Suseendran, G. Balaganesh, D. Akila, D. Pal, Souvik Technology Advanced Studies Vels Institute of Science Department of Information Technology Chennai India Lincoln University College Faculty of Computer Science and Multimedia Malaysia Global Institute of Management and Technology Department of Computer Science and Engineering India

ISBN: (纸本)9781665441032

This paper illustrated deep learning Semi-structured Tree Miner for Data Stream's algorithm focused on frequent pattern mining used in data streams on semi-structured data. In the target time, the algorithm obtains a frequent pattern. The Semistructured Tree Miner for Data Streams algorithm can handle sliding windows use the Time attenuation model to minimise historical data on the mining process relative to traditional algorithms, following the concept of inclusion and exclusion conservation at the same time and full real-time mining operations. And it is effective. © 2021 IEEE.

关键词： Miners

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Optimizing Multi-Media Database Performance: A Comprehensive Review of Fragmentation Methods and Dynamic Access Patterns

Optimizing Multi-Media Database Performance: A Comprehensive...

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IEEE International Conference on Intelligent Computing and information systems (ICICIS)

作者： Shymaa H. Mahmoud Ahmed E. Abdelraouf Nagwa L. Badr Mohamed I. Ali Museums and Archaeology Sites Management Department Faculty of Archeology Ain Shams University Cairo Egypt Information System Department Faculty of Computer and Information Science Ain Shams University Cairo Egypt

Large databases with a lot of data and many users can be difficult to manage and slow to query. Fragmentation is a technique that can be used to improve performance by dividing the database into smaller pieces that can be stored and accessed more efficiently. This research provides a systematic overview of the literature on fragmentation methods for databases. We performed a comprehensive literature review on fragmentation techniques proposed in articles published between 2012 and 2022, aiming to gather a systematic understanding of the subject. We examined the articles to determine if they took into account multimedia data or another data type, as well as other criteria such as the use of a cost model, ease of implementation, completeness, different algorithms applied to databases, and partitioning schemes. We also investigated whether the methods were dynamic, meaning that they could be adapted to changes in the database. Our investigation discovered a significant finding about the underutilization of dynamic fragmentation approaches and cost models in the context of multi-media databases. By discovering this gap, we have thrown light on an underutilized area of database management, providing opportunities for increased efficiency and cost-effectiveness. Furthermore, we stress the critical need of addressing the integration of a heritage database into existing infrastructure, which poses a substantial difficulty in the overall database management process. This discovery necessitates additional investigation and integration of dynamic fragmentation algorithms and cost models, which will result in improved performance, scalability, and resource allocation for multi-media databases.

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