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Designing a Model for Predicting Asthma in Adolescent Using Map Reduce and Federated Learning

Designing a Model for Predicting Asthma in Adolescent Using ...

2024 International Conference on Science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Ekpo, Raphael Henshaw Chukwudi Osamor, Victor Azeta, Ambrose A. Akindeji, Kayode Orelaja, Adeyinka Elizabeth Covenant University Department of Computer and Information Sciences Km 10 Idiroko Road Ogun State Ota Nigeria Namibia University of Science and Technology Department of Software Engineering Windhoek Namibia Durban University of Technology Faculty of Engineering and the Built Environment Department of Electrical Power Engineering South Africa Department of Computer Science and Quantitative Methods ClarksvilleTN United States

ISBN: (纸本)9798350358155

This study aims at designing Map Reduce and federated learning-based asthma prediction model for adolescent to provide answers to the problems associated with the existing asthma prediction model for adolescent. This study leveraged on two diverse datasets: the Nigerian Hospital Asthma dataset and the National Survey of Children Health dataset for benchmarking purposes. Symmetrical uncertainty and normalization interaction were employed for feature selection. The model was trained using Federated Artificial Intelligence (AI) Technology Enabler (FATE) and complemented with XGBoost model and one central server for federated algorithm averaging. The study was implemented with python programming language on Google Collaboratory environment. The results of the analysis showed considerable high accuracy of 0.98, precision (0.98%), recall (0.98%) and F1-socre (0.99%) for the asthmatic class and precision of 0.98%, recall of 0.98%, as well as F1-score of 0.99% for the non-asthmatic class. The implemented model was benchmark with the existing asthma prediction models in the literature. Simulated attack was also performed using implemented model with and without Map Reduce. This study concludes that the researchers' model has the potentials to outperform some of the existing machine learning models on asthma prediction in adolescent, thereby facilitating collaborative sharing of the model with other hospitals, protect the patient information from leakage, meeting the criteria of healthcare data protection regulation as well as beneficial for enhancing the security of the dataset. © 2024 IEEE.

关键词： Federated learning

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EdgeAuth: An intelligent token-based collaborative authentication scheme

EdgeAuth: An intelligent token-based collaborative authentic...

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作者： Jiang, Xutong Dou, Ruihan He, Qiang Zhang, Xuyun Dou, Wanchun State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Faculty of Mathematics University of Waterloo Waterloo Canada Department of Computer Science and Software Engineering Swinburne University of Technology Melbourne Australia Department of Computing Macquarie University Sydney Australia College of Big Data and Intelligent Engineering Southwest Forestry University China

Edge computing is regarded as an extension of cloud computing that brings computing and storage resources to the network edge. For some Industrial Internet of Things (IIoT) applications such as supply-chain supervision and collaboration, Internet of Vehicles, real-time video analysis and so forth, users should be authenticated before visiting the geographically distributed edge servers. Limited by the considerable latency between the cloud and edge servers, and the limited capacity of edge servers, it is infeasible to copy the authentication method from cloud servers when users are authenticated in edge servers. In view of this challenge, this paper proposes a novel token-based authentication scheme, named EdgeAuth, that enables fast edge user authentication through collaboration among cloud servers and edge servers. Under the EdgeAuth scheme, edge servers can rapidly verify the credentials of users who have been authenticated by the cloud server. EdgeAuth can also protect users from a series of authentication attacks, for example, the replay attack, DoS attack and man-in-the-middle attack. The results of experiments conducted on a simulated edge computing environment validate the usefulness of EdgeAuth through a comparison in latency and throughput against two baseline schemes. © 2023 John Wiley & Sons Ltd.

关键词： Authentication

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A Novel Multiobjective Fireworks Algorithm and Its Applications to Imbalanced Distance Minimization Problems

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IEEE/CAA Journal of Automatica Sinica 2022年第8期9卷 1476-1489页

作者： Shoufei Han Kun Zhu MengChu Zhou Xiaojing Liu Haoyue Liu Yusuf Al-Turki Abdullah Abusorrah College of Computer Science and Technology Nanjing University of Aeronautics and Astronauticsand also with the Collaborative Innovation Center of Novel Software Technology and IndustrializationNanjing 211106China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of Engineeringand also with the K.A.CARE Energy Research and Innovation CenterKing Abdulaziz UniversityJeddah 21589Saudi Arabia

Recently,multimodal multiobjective optimization problems(MMOPs)have received increasing *** goal is to find a Pareto front and as many equivalent Pareto optimal solutions as *** some evolutionary algorithms for them have been proposed,they mainly focus on the convergence rate in the decision space while ignoring solutions *** this paper,we propose a new multiobjective fireworks algorithm for them,which is able to balance exploitation and exploration in the decision *** first extend a latest single-objective fireworks algorithm to handle *** we make improvements by incorporating an adaptive strategy and special archive guidance into it,where special archives are established for each firework,and two strategies(i.e.,explosion and random strategies)are adaptively selected to update the positions of sparks generated by fireworks with the guidance of special ***,we compare the proposed algorithm with eight state-of-the-art multimodal multiobjective algorithms on all 22 MMOPs from CEC2019 and several imbalanced distance minimization *** results show that the proposed algorithm is superior to compared algorithms in solving ***,its runtime is less than its peers'.

关键词： Adaptive strategy fireworks algorithm multimodal multiobjective optimization problems(MMOP)

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Machine Learning Methods of Symptom Extraction from Medical Notes

Machine Learning Methods of Symptom Extraction from Medical ...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Olesia Galai Dmitrii Kurdiumov Alena Rusak Faculty of Software Engineering and Computer Systems ITMO University Saint-Petersburg Russia

Medical notes contain valuable information about patient conditions, treatments, and progress. Extracting symptoms from these unstructured notes is crucial for clinical research, population health analysis, and decision support systems. Traditional manual methods are time-consuming, but recent advances in natural language processing (NLP) and machine learning offer automated solutions. This article presents a novel approach that combines NLP techniques, such as conditional random fields (CRF) and transformer-based architectures. The proposed method demonstrates effective symptom extraction from medical notes, overcoming challenges such as varied terminologies and linguistic nuances. The study utilizes a dataset of Russian medical records, transforming it into a tabular format for training and employing unique tokenization algorithms for different models. Among the evaluated models, RuBERT achieved the highest accuracy of 91%, indicating its strong performance on the test dataset. SBERT exhibited the highest precision and F1 score, suggesting its effectiveness in accurately identifying specific sequence labels.

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Evaluating Different Hybrid Learning Algorithms Based Grid Search Algorithm for Epileptic Seizure Zone Detection

SSRN

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SSRN 2025年

作者： Özer, Ezgi Kinataş, Ahmet Furkan YıĞit, Emine Demir, Hamza Bedirhan Birinci, Alper Piri Reis University Engineering Faculty Computer Engineering Department İstanbul34940 Turkey İstinye University Engineering and Natural Science Faculty Software Engineering Department İstanbul34396 Turkey

Background and objective: Epilepsy is among the most prevalent illnesses of the central nervous system. This condition results in frequent, uncontrolled seizures that happen suddenly and are caused by a variety of trigger factors, including brain injury, physiological, genetic, etc. Involuntary spasms or distraction during seizures can cause severe bodily harm or even death for epileptics. In this paper, an effective method for accurately classifying Electroencephalogram (EEG) data for the early identification of epileptic seizures is ***: The suggested process essentially hybridizes several statistical data, discrete wavelet transformations (DWT), machine learning algorithms, and feature selection techniques independently. Through the use of DWT, the automated multi-resolution signal processing approach decomposes EEG signals into detail and approximation coefficients after splitting them into detailed parts with varying window sizes to guarantee an accurate classification performance. Statistical latent features are extracted from these coefficients that describe the nonlinear and dynamical patterns in the signals. Feature selection techniques were used to reduce the dimension of the feature matrix while highlighting the important elements. Different classifier structures were developed to classify input matrices. For all classifiers, the optimal hyperparameters were found using grid search techniques. Performance metrics for classification were calculated to assess the model's ***: In the analysis, to compare the proposed procedure with the other approaches in terms of detecting the epileptic behaviors correctly, a benchmark data set from the University of Bonn database was used. The results showed that the proposed approach can estimate more robust models concerning performance metrics and information criteria in classifying EEG signals. Also, the most important frequency bands were detected to distinguish EEG ***: Th

关键词： Discrete wavelet transforms

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Unlocking the potential of RNN and CNN models for accurate rehabilitation exercise classification on multi-datasets

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Multimedia Tools and Applications 2025年第3期84卷 1261-1301页

作者： Zaher, Moamen Ghoneim, Amr S. Abdelhamid, Laila Atia, Ayman Software Engineering Program Faculty of Computers & Artificial Intelligence—Helwan University Cairo Ain Helwan11795 Egypt Computer Science Department Faculty of Computers & Artificial Intelligence—Helwan University Cairo Ain Helwan11795 Egypt Information System Department Faculty of Computers & Artificial Intelligence—Helwan University Cairo Ain Helwan11795 Egypt Cairo Ain Helwan11795 Egypt Giza October12451 Egypt

Physical rehabilitation is crucial in healthcare, facilitating recovery from injuries or illnesses and improving overall health. However, a notable global challenge stems from the shortage of professional physiotherapists, particularly acute in some developing countries, where the ratio can be as low as one physiotherapist per 100,000 individuals. To address these challenges and elevate patient care, the field of physical rehabilitation is progressively integrating computer Vision and Human Activity Recognition (HAR) techniques. Numerous research efforts aim to explore methodologies that assist in rehabilitation exercises and evaluate patient movements, which is crucial as incorrect exercises can potentially worsen conditions. This study investigates applying various deep-learning models for classifying exercises using the benchmark KIMORE and UI-PRMD datasets. Employing Bi-LSTM, LSTM, CNN, and CNN-LSTM, alongside a Random Search for architectural design and Hyper-parameter tuning, our investigation reveals the (CNN) model as the top performer. After applying cross-validation, the technique achieves remarkable mean testing accuracy rates of 93.08% on the KIMORE dataset and 99.7% on the UI-PRMD dataset. This marks a slight improvement of 0.75% and 0.1%, respectively, compared to previous techniques. In addition, expanding beyond exercise classification, this study explores the KIMORE dataset’s utility for disease identification, where the (CNN) model consistently demonstrates an outstanding accuracy of 89.87%, indicating its promising role in both exercises and disease identification within the context of physical rehabilitation. © The Author(s) 2024.

关键词： Classification (of information)

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Machine learning and deep learning algorithms in detecting COVID-19 utilizing medical images: a comprehensive review

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Iran Journal of computer Science 2024年第3期7卷 699-721页

作者： Nurjahan Mahbub-Or-Rashid, Md. Satu, Md. Shahriare Tammim, Sanjana Ruhani Sunny, Farhana Akter Moni, Mohammad Ali Department of IoT and Robotics Engineering Bangabandhu Sheikh Mujibur Rahman Digital University Kaliakair Bangladesh Department of Computer Science and Engineering Bangladesh University of Business and Technology Mirpur Bangladesh Department of Management Information Systems Noakhali Science and Technology University Noakhali Bangladesh Department of Computer Science and Software Engineering Auburn University Auburn AL United States Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh Faculty of Health and Behavioural Sciences School of Health and Rehabilitation Sciences The University of Queensland St Lucia Australia

The public’s health is seriously at risk from the coronavirus pandemic. Millions of people have already died as a result of this devastating illness, which affects countless people daily worldwide. Unfortunately, no specific therapeutic drugs or vaccines are available to cure patients completely. Therefore, early identification of infected individuals and their isolation can help reduce community transmission of COVID-19. Despite being commonly used, the reverse transcription polymerase chain reaction (RT-PCR) test is costly, time-consuming, and requires suitable kits, which are not always readily available. An alternative solution for the traditional RT-PCR test is machine learning and deep learning-based COVID-19 detection that utilizes clinical features of chest X-rays and computed tomography (CT) images. In this study, we conducted a detailed review of more than 100 recently published works to detect COVID-19-infected patients. Thus, different data preprocessing, data augmentation, image enhancement, feature extraction, machine learning, deep learning, Explainable Artificial Intelligence (AI) methods, etc. were explored to understand how these techniques were used to process images for identifying COVID-19. Additionally, we identified the current challenges in this field and suggested further research directions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Chest X-ray Computed tomography COVID-19 Deep learning Machine learning

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Preliminary Exploration on Node-To-Node Fault Tolerance Coordination in Distributed System

Preliminary Exploration on Node-To-Node Fault Tolerance Coor...

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2023 IEEE International Conference on Computing, ICOCO 2023

作者： Saadoon, Muntadher Hamid, Siti Hafizah Ab. Sofian, Hazrina Altarturi, Hamza Nasuha, Nur Azizul, Zati Hakim Khalid, Haqi Universiti Malaya Faculty of Computer Science and Information Technology Department of Software Engineering Kuala Lumpur50603 Malaysia Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia Universiti Malaya Faculty of Computer Science and Information Technology Department of Artificial Intelligence Kuala Lumpur50603 Malaysia Universiti Putra Malaysia Faculty of Engineering Department of Computer and Communication Selangor 43400 Malaysia

ISBN: (纸本)9798350302684

Internet of Things, edge computing devices, the widespread use of artificial intelligence and machine learning applications, and the extensive adoption of cloud computing pose significant challenges to maintaining fault tolerance in distributed systems. As the volume and complexity of systems continue to grow, the probability of system failures also grows, leading to downtime and poor performance. Fault tolerance is pivotal in modern computing systems by preventing downtime and service disruptions, even during failures. However, adopting fault tolerance comes with additional resource consumption, such as memory, CPU, disk, and network bandwidth usage, impacting system performance. Balancing fault tolerance with system performance is crucial to optimize resource utilization while minimizing the costs associated with detecting and recovering from faults. This paper introduces a node-to-node fault tolerance coordination mechanism for distributed systems to facilitate the tradeoff between fault tolerance and the cost of performance penalties and resource consumption. The proposed mechanism is preliminarily implemented on HDFS cluster. Our proposal enables opportunities to minimize the cost of the fault tolerance impact on performance and resource usage compared to the existing fault tolerance coordination mechanism. © 2023 IEEE.

关键词： Fault tolerance

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Anomaly-Based Intrusion DetectionModel Using Deep Learning for IoT Networks

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computer Modeling in engineering & Sciences 2024年第10期141卷 823-845页

作者： Muaadh A.Alsoufi Maheyzah Md Siraj Fuad A.Ghaleb Muna Al-Razgan Mahfoudh Saeed Al-Asaly Taha Alfakih Faisal Saeed Faculty of Computing Universiti Teknologi Malaysia(UTM)Johor Bahru81310Malaysia DAAI Research Group College of Computing and Digital TechnologyBirmingham City UniversityBirminghamB47XGUK Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11345Saudi Arabia

The rapid growth of Internet of Things(IoT)devices has brought numerous benefits to the interconnected ***,the ubiquitous nature of IoT networks exposes them to various security threats,including anomaly intrusion *** addition,IoT devices generate a high volume of unstructured *** intrusion detection systems often struggle to cope with the unique characteristics of IoT networks,such as resource constraints and heterogeneous data *** the unpredictable nature of network technologies and diverse intrusion methods,conventional machine-learning approaches seem to lack *** numerous research domains,deep learning techniques have demonstrated their capability to precisely detect *** study designs and enhances a novel anomaly-based intrusion detection system(AIDS)for IoT ***,a Sparse Autoencoder(SAE)is applied to reduce the high dimension and get a significant data representation by calculating the reconstructed ***,the Convolutional Neural Network(CNN)technique is employed to create a binary classification *** proposed SAE-CNN approach is validated using the Bot-IoT *** proposed models exceed the performance of the existing deep learning approach in the literature with an accuracy of 99.9%,precision of 99.9%,recall of 100%,F1 of 99.9%,False Positive Rate(FPR)of 0.0003,and True Positive Rate(TPR)of *** addition,alternative metrics,such as training and testing durations,indicated that SAE-CNN performs better.

关键词： IoT anomaly intrusion detection deep learning sparse autoencoder convolutional neural network

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Phishing Website Detection with and Without Proper Feature Selection Techniques: Machine Learning Approach

Phishing Website Detection with and Without Proper Feature S...

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作者： Adane, Kibreab Beyene, Berhanu Faculty of Computing and Software Engineering Arba Minch University Arba Minch Ethiopia Computer Science Department Ethiopian Civil Service University Addis Ababa Ethiopia

Indeed, successful phishing website attempts could result in catastrophic data loss, login credential compromise, ransomware infection, and financial loss. It also significantly hampers the competitiveness and productivity of online users and internet-dependent organizations unless an intelligent anti-phishing solution is devised. Due to detecting fresh phishing website attacks with maximum accuracy by discovering hidden patterns from complex datasets is shown to be an intrinsic property of M- Learning approaches, the study conducted rigorous experiments on four purposely selected efficient supervised M-Learning algorithms before and after applying five widely used proper feature selection techniques such as Recursive Feature Elimination, Pierson Correlation Coefficient, Principal Component Analysis, Uni-variate Feature Selection, and Mutual Information. The proposed study was conducted to balance the research gaps and scientific disputes in the rigorously reviewed studies. The study’s final outcome is a proposal for an intelligent phishing website model that yields higher accuracy, faster response times, and fewer average misclassification rates. The study also explored the feature selection techniques that had more, less, and no contributions to enhancing each classifier's accuracy. As compared to the remaining classifiers, the Cat-Boost Classifier attained superior phishing website detection accuracy (97.46%), F1-score (97.49%), a lower average misclassification rate (2.54%), and acceptable train-test computational time (7 s) after using the UFS technique. On the other hand, the PCA technique failed to enhance the accuracy of the Cat-Boost, Gradient-Boost, and Random Forest Classifiers due to scoring less accuracy than the accuracy reached before using proper feature selection techniques. To obtain more promising results, in future work, phishing website detection is expected to be carried out using a Hybrid proper feature selection technique, huge datasets, prop

关键词： Feature Selection

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