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Early Prediction and Diagnosis Cardiovascular Disease Using Deep Learning Models

Early Prediction and Diagnosis Cardiovascular Disease Using ...

2024 IEEE International Conference on Emerging Technologies in computer science for Interdisciplinary Applications, ICETCS 2024

作者： Rani, Rudra Yamini Prasad, Mudarakola Lakshmi Reddy, Induru Sunnihith Tayubi, Iftikhar Aslam Sultan, Ghazala Reddy, Pundru Chandra Shaker VNR Vignana Jyothi Institute of Engineering and Technology Department of Information Technology Telangana Hyderabad India Computer Science and Engineering Institute of Aeronautical Engineering Telangana Dundigal India Computer Science and Business Systems RMK Engineering College Tamilnadu Tiruvallur India King Abdulaziz University Faculty of Computing and Information Technology Rabigh Jeddah Saudi Arabia Aligarh Muslim University Department of Computer Science Uttar Pradesh Aligarh India SR University School of Computer Science and Artificial Intelligence Telangana Warangal506371 India

ISBN: (纸本)9798350372502

Cardiovascular disease (CVD) risk assessment and prognosis in otherwise healthy people is an important part of disease management. Early detection and diagnosis of CVD, supported by the extensive health data on the condition already stored in hospital systems, has the ability to greatly improve disease outcomes. Clinical practice for the management of Cardiovascular Diseases (CVDs) stands to benefit greatly from the use of machine learning approaches. Patients and the healthcare system can save money thanks to these methods, which can be used to create evidence-based clinical guidelines and management algorithms. This will cut down on expensive and time-consuming clinical and laboratory examinations. This study suggests creating new deep learning algorithms that can automatically identify important variables and detect early-stage heart disease;these algorithms should be resilient, effective, efficient, and unique in order to improve early anticipation and interference for CVDs. An F1-score of around 89% is produced by the suggested Catboost model. Hence, it outperformed numerous other state-of-the-art methods by maximizing classification efficiency with better correctness and precision. © 2024 IEEE.

关键词： Hospitals

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Privacy-Preserving Collaborative AI for Distributed Deep Learning with Cross-sectional Data 5

Privacy-Preserving Collaborative AI for Distributed Deep Lea...

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5th International Conference on Bio-engineering for Smart Technologies, BioSMART 2023

作者： Iqbal, Saeed Qureshi, Adnan N. Aurangzeb, Khursheed Javeed, Khalid University of Central Punjab Faculty of Information Technology & Computer Science Department of Computer Science Lahore Pakistan King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh Saudi Arabia University of Sharjah College of Computing and Informatics Department of Computer Engineering Sharjah United Arab Emirates

ISBN: (纸本)9798350338492

The article discusses the challenges of using deep learning in healthcare due to the lack of extensive medical datasets and concerns about confidentiality and privacy. The article then focuses on the analysis of skin lesion images, which are complex and difficult to classify. The article presents a unique CNN model (skin-net) using a progressively private federated learning architecture to potentially address these issues. Recent advancements in medical image analysis using convolutional neural networks and federated learning techniques have shown promising results (96.19 accuracy, 96.37 sensitivity and 96.53 specificity) in accurately classifying skin diseases while maintaining data privacy. Using these methods offers a potentially effective way to examine human skin while improving the model's generalization performance through image augmentation. © 2023 IEEE.

关键词： Deep learning

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A Production and Marketing Platform Model Based on the AIoT and Blockchain system for Aquaculture

A Production and Marketing Platform Model Based on the AIoT ...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Huang, Xiang-Rui Su, Chen-Wei Chen, Liang-Bi National Penghu University of Science and Technology Department of Computer Science and Information Engineering Penghu Taiwan

ISBN: (纸本)9798350324174

This article proposes a production and marketing platform model based on the AIoT and blockchain system for aquaculture, which is different from the general aquaculture market transaction mode, removes the centralization of the previous aquaculture transaction mode, and the transaction process and content are relatively safe. One-to-one direct transaction or one-to-many transaction, block each transaction, the block content is encrypted multiple transaction data, and integrates the characteristics of the AIoT to refine each aquatic product, allowing consumers to eat safely when buying, and the act of encrypting the transaction content improves the credibility of the transaction, replacing the center (bank) required for traditional transactions. Traditional transactions centralize transactions to maintain security and fairness. Nevertheless, regional decentralization of block training technology can also lead to safe and fair dealings, allowing consumers to trade quickly and safely. The proposed model architecture is expected to bring greater breakthroughs to the aquaculture industry. © 2023 IEEE.

关键词： Blockchain

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Blockchain-based Patient Recommendation system for Smart Healthcare

Blockchain-based Patient Recommendation System for Smart Hea...

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2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Mehta, Raj Mehta, Mahek Kakkar, Riya Oza, Parita Agrawal, Smita Tanwar, Sudeep Dutta, Ashit Kumar Rodrigues, Joel J.P.C. Department of Computer Science and Engineering Institute of Technology Nirma University Gujarat 382481 India Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Saudi Arabia

ISBN: (纸本)9798350350548

In recent times data breaches in various sectors of industry have become a common threat. It has become very crucial to secure patient data in the health industry. The upcoming Healthcare 4.0 techniques can play an important role in this. We have implemented these techniques in our proposed model to protect personalised health information of the individual health profiles of the patient using blockchain. The proposed model is also a recommending system with the aim to offer relevant advice to the patients to keep a check on the various health parameters like blood pressure, body temperature, blood sugar etc. © 2024 IEEE.

关键词： Electronic health record

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WiMA:Towards a Multi-Criterion Association in Software Defined Wi-Fi Networks

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computers, Materials & Continua 2023年第5期75卷 2347-2363页

作者： Sohaib Manzoor Hira Manzoor Saddaf Rubab Muhammad Attique Khan Majed Alhaisoni Abdullah Alqahtani Ye Jin Kim Byoungchol Chang Department of Electrical Engineering Mirpur University of Science and Technology(MUST)Mirpur-10250(AJK)Pakistan Faculty of Electronics Telecommunications and InformaticsGdansk University of Technology80-233 GdanskPoland Department of Computer Engineering College of Computing and InformaticsUniversity of SharjahSharjah27272United Arab Emirates National University of Sciences and Technology(NUST) Islamabad44000Pakistan Department of Computer Science HITEC UniversityTaxilaPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea Center for Computational Social Science Hanyang UniversitySeoul04763Korea

Despite the planned installation and operations of the traditional IEEE 802.11 networks,they still experience degraded performance due to the number of *** of the main reasons is the received signal strength indicator(RSSI)association problem,in which the user remains connected to the access point(AP)unless the RSSI becomes too *** this paper,we propose a multi-criterion association(WiMA)scheme based on software defined networking(SDN)in Wi-Fi *** association solution based on multi-criterion such as AP load,RSSI,and channel occupancy is proposed to satisfy the quality of service(QoS).SDNhaving an overall view of the network takes the association and reassociation decisions making the handoffs smooth in throughput *** implementWiMA extensive simulations runs are carried out on Mininet-NS3-Wi-Fi network *** performance evaluation shows that the WiMA significantly reduces the average number of retransmissions by 5%–30%and enhances the throughput by 20%–50%,hence maintaining user fairness and accommodating more wireless devices and traffic load in the network,when compared to traditional client-driven(CD)approach and state of the art Wi-Balance approach.

关键词： Association multi-criterion SDN Wi-Fi

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Wearing Mask Detection Based on T-shaped Face

Wearing Mask Detection Based on T-shaped Face

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Wang, Jing-Wein Wang, Chih-Chiang Lu, Cunwei National Kaohsiung University of Science and Technology Institute of Photonics Taiwan Taiwan National Kaohsiung University of Science and Technology Department of Computer Science and Computer Science and Information Engineering Taiwan Taiwan Fukuoka Institute of Technology Department of Electronics and Information Engineering Fukuoka Japan

ISBN: (纸本)9798350324174

This study proposes an intelligent mask detection system. During the continuous epidemic situation, it is necessary to detect whether visitors are wearing masks. When the body temperature sensor sounds, if the face detection system detects a face, it indicates that the visitor is not wearing a mask as required;otherwise, it means that the face is wearing a mask. After many live simulation tests by the students of our laboratory, it was found that this research can indeed achieve the expected goal with a 98.6 % recognition rate and effectively remind visitors to wear their masks. © 2023 IEEE.

关键词： Face recognition

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Integrating Machine Learning-Powered Smart Agents into Cyber Honeypots: Enhancing Security Frameworks 9

Integrating Machine Learning-Powered Smart Agents into Cyber...

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9th IEEE International Conference for Convergence in technology, I2CT 2024

作者： Siddique, Muhammad Raza Hussain, Muhammad Zunnurain Hasan, Muhammad Zulkifl Nosheen, Summaira Qureshi, Ali Moiz Ahmad Siddiqui, Adeel Mubarak, Zaima Chuhan, Saad Hussain Mustafa, Muzzamil Yaqub, Muhammad Atif Bilal, Afshan Bahria University Lahore Department of Computer Science Lahore54000 Pakistan University of Central Punjab Faculty of Information Technology Lahore54000 Pakistan National College of Business Administration And Economics Department of Computer Science Lahore54660 Pakistan

ISBN: (纸本)9798350394474

Software systems are installed and configured by default;there are security loopholes that could be exploited even though the vast majority of internet users do not yet have a strong sense of security even though more. As the number of nodes with Internet access increases, more and more data is being generated on the Internet itself and among those nodes. Because of the emergence of new methods for attacking networks, every host on the Internet say now be considered a potential target. Because of this, the issue of the security of the information sent across networks cannot be overlooked. The honeypot approach can not only be used passively as an information system to cope with zero-day and future assaults. Still, it can also be used actively to strengthen old systems against attacks in the future. As a consequence of this, we use machine learning to build a Cyber Security Honeypot-Based Smart Agent. In this, we integrate several different machine learning approaches with honeypot mechanisms so that we may anticipate the attacker's profile and stop them. In this section, we compare the two findings achieved with and obtained without the assistance of the honeypot and depict the comparison results. © 2024 IEEE.

关键词： Decision trees

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A Comparative Study of Machine Learning Models for Human Activity Recognition Using Signal Feature Extraction 21st

A Comparative Study of Machine Learning Models for Human Act...

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21st International Conference on Computing and information technology, IC2IT 2025

作者： Visutsak, Porawat Lording, Arzeezar Ngampromwong, Teerapong Maichum, Natdanai Wongsanit, Anusorn Suriyachay, Earn Sathitanon, Nontakorn Department of Computer and Information Science Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (纸本)9783031902949

This study explores the application of machine learning techniques to accurately classify human activities using sensor data. The research focuses on extracting meaningful features from accelerometer signals and training various machine learning models to recognize activities such as walking, sitting, lying, and standing. The process involves collecting accelerometer data from smartphones, preprocessing the data, and extracting relevant features. These features are then used to train four different machine learning templates: Naive Bayes classifier, K-Nearest Neighbors classifier (KNN), Decision Tree, and Discriminant Analysis classifier. The performance of each model is evaluated based on its accuracy in classifying human activities. The results show that the Naive Bayes model achieves an accuracy of 92.6%, KNN achieves 89.8%, Decision Tree achieves 95.9%, and Discriminant Analysis achieves 85.4%. The Decision Tree model achieved the highest accuracy (95.9%) in recognizing activities. The findings of this study demonstrate the effectiveness of machine learning in recognizing human activities using sensor data. The results have implications for various applications, including health monitoring, fitness tracking, and smart home automation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Feature Selection

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Automated Machine Learning Enabled Cybersecurity Threat Detection in Internet of Things Environment

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computer systems science & Engineering 2023年第4期45卷 687-700页

作者： Fadwa Alrowais Sami Althahabi Saud S.Alotaibi Abdullah Mohamed Manar Ahmed Hamza Radwa Marzouk Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Research Centre Future University in EgyptNew Cairo11745Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Mathematics Faculty of ScienceCairo UniversityGiza12613Egypt

Recently,Internet of Things(IoT)devices produces massive quantity of data from distinct sources that get transmitted over public *** becomes a challenging issue in the IoT environment where the existence of cyber threats needs to be *** development of automated tools for cyber threat detection and classification using machine learning(ML)and artificial intelligence(AI)tools become essential to accomplish security in the IoT *** is needed to minimize security issues related to IoT gadgets ***,this article introduces a new Mayfly optimization(MFO)with regularized extreme learning machine(RELM)model,named MFO-RELM for Cybersecurity Threat Detection and classification in IoT *** presented MFORELM technique accomplishes the effectual identification of cybersecurity threats that exist in the IoT *** accomplishing this,the MFO-RELM model pre-processes the actual IoT data into a meaningful *** addition,the RELM model receives the pre-processed data and carries out the classification *** order to boost the performance of the RELM model,the MFO algorithm has been employed to *** performance validation of the MFO-RELM model is tested using standard datasets and the results highlighted the better outcomes of the MFO-RELM model under distinct aspects.

关键词： Cybersecurity threats classification internet of things machine learning parameter optimization

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Sailfish Optimizer with Deep Transfer Learning-Enabled Arabic Handwriting Character Recognition

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computers, Materials & Continua 2023年第3期74卷 5467-5482页

作者： Mohammed Maray Badriyya B.Al-onazi Jaber S.Alzahrani Saeed Masoud Alshahrani Najm Alotaibi Sana Alazwari Mahmoud Othman Manar Ahmed Hamza Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Prince Saud AlFaisal Institute for Diplomatic Studies RiyadhSaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The recognition of the Arabic characters is a crucial task incomputer vision and Natural Language Processing fields. Some major complicationsin recognizing handwritten texts include distortion and patternvariabilities. So, the feature extraction process is a significant task in NLPmodels. If the features are automatically selected, it might result in theunavailability of adequate data for accurately forecasting the character ***, many features usually create difficulties due to high dimensionality *** this background, the current study develops a Sailfish Optimizer withDeep Transfer Learning-Enabled Arabic Handwriting Character Recognition(SFODTL-AHCR) model. The projected SFODTL-AHCR model primarilyfocuses on identifying the handwritten Arabic characters in the inputimage. The proposed SFODTL-AHCR model pre-processes the input imageby following the Histogram Equalization approach to attain this *** Inception with ResNet-v2 model examines the pre-processed image toproduce the feature vectors. The Deep Wavelet Neural Network (DWNN)model is utilized to recognize the handwritten Arabic characters. At last,the SFO algorithm is utilized for fine-tuning the parameters involved in theDWNNmodel to attain better performance. The performance of the proposedSFODTL-AHCR model was validated using a series of images. Extensivecomparative analyses were conducted. The proposed method achieved a maximum accuracy of 99.73%. The outcomes inferred the supremacy of theproposed SFODTL-AHCR model over other approaches.

关键词： Arabic language handwritten character recognition deep learning feature extraction hyperparameter tuning

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