Yazar "Jasim, Abdulrahman Ahmed" seçeneğine göre listele

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    A systematic review on the roles of remote diagnosis in telemedicine system: Coherent taxonomy, insights, recommendations, and open research directions for intelligent healthcare solutions
    (2025) Mohsin, Sura Saad; Salman, Omar H.; Jasim, Abdulrahman Ahmed; Al-Nouman, Mohammed A.; Kairaldeen, Ammar Riadh
    Background: The term 'remote diagnosis' in telemedicine describes the procedure wherein medical practitioners diagnose patients remotely by using telecommunications technology. With this method, patients can obtain medical care without having to physically visit a hospital, which can be helpful for people who live in distant places or have restricted mobility. When people in the past had health issues, they were usually sent to the hospital, where they received clinical examinations, diagnoses, and treatment at the facility. Thus, hospitals were overcrowded because of the increase in the number of patients or in the death of some very ill patients given that the completion of medical operations required a significant amount of time. Objective: This research aims to provide a literature review study and an in-depth analysis to (1) investigate the procedure and roles of remote diagnosis in telemedicine; (2) review the technical tools and technologies used in remote diagnosis; (3) review the diseases diagnosed remotely in telemedicine; (4) compose a crossover taxonomy among diseases, technologies, and telemedicine; (5) present lists of input variables, vital signs, data and output decisions already applied in remote diagnosis; (6) Summarize the performance assessment measures utilized to assess and validate remote diagnosis models; and (7) identify and categorize open research issues while providing recommendations for future advancements in intelligent remote diagnosis within telemedicine systems. Methods: A systematic search was conducted using online libraries for articles published from 1 January 2016 to 13 September 2023 in IEEE, PubMed, Science Direct, Springer, and Web of Science. Notably, searches were limited to articles in the English language. The papers examine remote diagnosis in telemedicine, the technologies employed for this function, and the ramifications of diagnosing patients outside hospital settings. Each selected study was synthesized to furnish proof about the implementation of remote diagnostics in telemedicine. Results: A new crossover taxonomy between the most important diagnosed diseases and technologies used for this purpose and their relationship with telemedicine tiers is proposed. The functions executed at each tier are elucidated. Additionally, a compilation of diagnostic technologies is provided. Additionally, open research difficulties, advantages of remote diagnosis in telemedicine, and suggestions for future research prospects that require attention are systematically organized and presented. Conclusions: This study reviews the role of remote diagnosis in telemedicine, with a focus on key technologies and current approaches. This study highlights research challenges, provides recommendations for future directions, and addresses research gaps and limitations to provide a clear vision of remote diagnosis in telemedicine. This study emphasizes the advantages of existing research and opens the possibility for new directions and smart healthcare solutions.
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    AI-Driven Triage: A Graph Neural Network Approach for Prehospital Emergency Triage Patients in IoMT-Based Telemedicine Systems
    (Institute of Electrical and Electronics Engineers Inc., 2024) Jasim, Abdulrahman Ahmed; Ata, Oguz; Salman, Omar Hussein
    Improper triage, overcrowding in the emergency department (ED), and long wait times for patients are some of the problems that arise from emergency triage due to a lack of medical and human resources. In emergency departments, proper patient triage is crucial for determining the urgency and appropriateness of treatment by evaluating the severity of each patient's condition. In most cases, a human operator will use the knowledge and data gathered from patient management to perform triage tactics. Hence, it is a method with the possibility of causing problems in high-priority interconnections. Currently, typical triage systems primarily rely on human recommendations, which are susceptible to personal convictions and error. Methods that maximize data collection and remove mistakes in patient triage processing are being developed with an increasing amount of interest in utilizing artificial intelligence (AI). We develop and deploy an AI module to control the distribution of emergency triage patients in EDs. We use data from past emergency room visits to educate the medical decision-making process. Patients are properly sorted into triage groups because of data including vital signs, symptoms, and an ECG. Test results have shown that the proposed algorithm is better than the currently used state-of-the-art triage techniques. In our opinion, the methods used will help healthcare professionals forecast the severity index, which in turn will help them direct the proper patient management protocols and allocate resources. © 2024 IEEE.
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    e-Diagnostic system for diabetes disease prediction on an IoMT environment-based hyper AdaBoost machine learning model
    (Springer, 2024) Jasim, Abdulrahman Ahmed; Hazim, Layth Rafea; Mohammedqasim, Hayder; Mohammedqasem, Roa’a; Ata, Oğuz; Salman, Omar Hussein
    One of the most fatal and serious diseases that humans have encountered is diabetes, an illness affecting thousands of individuals yearly. In this era of digital systems, diabetes prediction based on machine learning (ML) is gaining high momentum. One of the benefits of treating patients early in the course of their noncommunicable diseases (NCDs) is that they can avoid costly therapies when the illness worsens later in life. Incidentally, diabetes is complicated by the dearth of medical professionals in underserved areas, such as distant rural communities. In these situations, the Internet of Medical Things and machine learning (ML) models can be used to offer healthcare practitioners the necessary prediction tools to more effectively and timely make decisions, thus assisting the early identification and diagnosis of NCDs. In this study, four conventional and hyper-AdaBoost ML models were trained and tested on the PIMA Indian Diabetes dataset. Patients with diabetes were classified on the basis of laboratory findings. Pre-processing tasks, such as the handling of imbalanced data and missing values, were performed prior to feature importance and normalisation activities. The algorithm with the best performance was examined using precision, accuracy, F1, recall and area under the curve metrics. Then, all ML models were hyper parametrically tuned via grid search to optimise their performance and reduce their error times. The decision process was also evaluated to further enhance the models. The AdaBoost-ET model performed even when features were not selected for binary classification. The model proposed in this study can predict diabetes with unprecedented high accuracy compared with the models in previous studies.
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    Enhancing IIOT security with machine learning and deep learning for intrusion detection
    (University Of Malaya, 2024) Awad, Omer Fawzi; Hazim, Laytha Rafea; Jasim, Abdulrahman Ahmed; Ata, Oğuz
    The rapid growth of the Internet of Things (IoT) and digital industrial devices has significantly impacted various aspects of life, underscoring the importance of the Industrial Internet of Things (IIoT). Given its importance in industrial contexts that affect human life, the IIoT represents a key subset of the broader IoT landscape. Due to the proliferation of sensors in smart devices, which are viewed as points of contact, as the gathering of data and information regarding the IIoT systems and devices operating on the IoT, there is an urgent requirement for developing effective security methods to counter such threats as well as protecting IIoT systems. In this study, we develop and evaluate a well -optimized intrusion detection system (IDS) based on deep learning (DL) and machine learning (ML) techniques to enhance IIoT security. Leveraging the Edge-IIoTset dataset, specifically designed for IIoT cybersecurity evaluations, we focus on detecting and mitigating 14 distinct attack types targeting IIoT and IoT protocols. These attacks are categorized into five threat groups: information collection, malware, DDoS, manin -the -middle attacks, and injection attacks. We conducted experiments using machine learning algorithms (knearest neighbors, decision tree) and a neural network on the KNIME platform, achieving a remarkable 100% accuracy with the decision tree model. This high accuracy demonstrates the effectiveness of our approach in protecting industrial IoT networks.
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    ENHANCING PREDICTIVE PERFORMANCE IN COVID-19 HEALTHCARE DATASETS: A CASE STUDY BASED ON HYPER ADASYN OVER-SAMPLING AND GENETIC FEATURE SELECTION
    (Taylors Univ Sdn Bhd, 2024) Mohammedqasim, Hayder; Jasim, Abdulrahman Ahmed; Mohammedqasem, Roa'a; Ata, Oguz
    Predictive analytics is paramount in the health industry, where it finds its wide application, in that it helps increase the forecast's accuracy level based on big data. Most of the time, there is a tendency toward the imbalance of the datasets in healthcare. In this study, two COVID-19 datasets from Kaggle were used as a case study of dataset imbalance. In such scenarios of imbalanced datasets like COVID-19, conventional sampling methods like ADASYN (Adaptive Synthetic Sampling Approach for Imbalanced Learning) tend to yield only modest accuracy levels. To address another problem like finding the optimal features, this study proposes a novel approach that combines oversampling techniques with genetic feature selection (GFs) using laboratory data. This innovative method aims to construct machine -learning clinical prediction models for the identification of COVID-19 infected patients, leveraging two widely recognized datasets by using hyper ADASYN over -sampling and genetic feature selection, stands out for its unprecedented precision in identifying relevant features crucial for accurate predictions. Unlike the traditional approach, it can solve the class imbalance problem and tune the feature space to bring about a dramatic increase in accuracy, precision, recall, and overall predictive performance by using our hypermodel. Our approach significantly enhanced the performance of the classifier, and the Random Forest (RF) model with n trees classifies accurately to the limit of 99%, with precision 99%, recall 99%, and F1 -score 99% for each of the datasets. Decision Tree (DT) model achieved 92% with all metrics for Dataset I, and 95% with all metrics for Dataset II. Multilayer Perceptron (MLP) achieved 99% with all metrics, respectively, for both datasets. Gradient Boosting (XGB) achieved 97% for all metrics with dataset I and 98% with all metrics for dataset II. These results underscore the efficacy of our proposed method in balancing COVID-19 datasets and enhancing predictive accuracy.
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    Enhancing predictive performance in Covid-19 healthcare datasets: A case study based on hyper adasyn over-samplingand genetic feature selection
    (2024) Mohammedqasim, Hayder; Jasim, Abdulrahman Ahmed; Mohammedqasem, Roa'a; Ata, Oğuz
    Predictive analytics is paramount in the health industry, where it finds its wide application, in that it helps increase the forecast's accuracy level based on big data. Most of the time, there is a tendency toward the imbalance of the datasets in healthcare. In this study, two COVID-19 datasets from Kaggle were used as a case study of dataset imbalance. In such scenarios of imbalanced datasets like COVID-19, conventional sampling methods like ADASYN (Adaptive Synthetic Sampling Approach for Imbalanced Learning) tend to yield only modest accuracy levels. To address another problem like finding the optimal features, this study proposes a novel approach that combines oversampling techniques with genetic feature selection (GFs) using laboratory data. This innovative method aims to construct machine -learning clinical prediction models for the identification of COVID-19 infected patients, leveraging two widely recognized datasets by using hyper ADASYN over -sampling and genetic feature selection, stands out for its unprecedented precision in identifying relevant features crucial for accurate predictions. Unlike the traditional approach, it can solve the class imbalance problem and tune the feature space to bring about a dramatic increase in accuracy, precision, recall, and overall predictive performance by using our hypermodel. Our approach significantly enhanced the performance of the classifier, and the Random Forest (RF) model with "n" trees classifies accurately to the limit of 99%, with precision 99%, recall 99%, and F1 -score 99% for each of the datasets. Decision Tree (DT) model achieved 92% with all metrics for Dataset I, and 95% with all metrics for Dataset II. Multilayer Perceptron (MLP) achieved 99% with all metrics, respectively, for both datasets. Gradient Boosting (XGB) achieved 97% for all metrics with dataset I and 98% with all metrics for dataset II. These results underscore the efficacy of our proposed method in balancing COVID-19 datasets and enhancing predictive accuracy.
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    Intrusion Detection System (IDS) of multiclassification IoT by using pipelining and an efficient machine learning
    (Institute of Electrical and Electronics Engineers Inc., 2023) Hazim, Layth Rafea; Jasim, Abdulrahman Ahmed; Ata, Oğuz; Ilyas, Muhammad
    The Internet of Things (IoT) has quickly advanced and been incorporated into many different fields. With the use of IoT technology, gadgets can receive, process, and send data automatically. IoT has been rapidly accepted in many important fields since it makes life easier and increases service quality, yet it still faces significant privacy and security problems. An Intrusion Detection System (IDS) could be implemented as a security feature to protect IoT networks from a variety of cyberattacks. This study suggests using IDS to defend against a wide range of cyberattacks on IoT systems. The suggested approach makes use of the Multi-layer Perceptron (MLP) as well as Extra Trees (ExT) as efficient algorithms of classification. Also, the study uses the pipeline to put together several cross-validated phases while selecting various parameters to increase the detection rate. One dataset is utilized for evaluating and analyzing the performance outcomes so as to validate the efficiency of the suggested IDS approach. The evaluation findings show that the suggested IDS methods may greatly increase detection performance results concerning accuracy rate, precision, F1-score, and recall while also improving detection efficiency.
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    Multisource data framework for prehospital emergency triage in real-time IoMT-based telemedicine systems
    (2024) Jasim, Abdulrahman Ahmed; Ata, Oğuz; Salman, Omar Hussein
    Background and objective: The Internet of Medical Things (IoMT) has revolutionized telemedicine by enabling the remote monitoring and management of patient care. Nevertheless, the process of regeneration presents the difficulty of effectively prioritizing the information of emergency patients in light of the extensive amount of data generated by several integrated health care devices. The main goal of this study is to be improving the procedure of prioritizing emergency patients by implementing the Real-time Triage Optimization Framework (RTOF), an innovative method that utilizes diverse data from the Internet of Medical Things (IoMT). Methods: The study's methodology utilized a variety of Internet of Medical Things (IoMT) data, such as sensor data and texts derived from electronic medical records. Tier 1 supplies sensor and textual data, and Tier 3 imports textual data from electronic medical records. We employed our methodologies to handle and examine data from a sample of 100,000 patients afflicted with hypertension and heart disease, employing artificial intelligence algorithms. We utilized five machine-learning algorithms to enhance the accuracy of triage. Results: The RTOF approach has remarkable efficacy in a simulated telemedicine environment, with a triage accuracy rate of 98%. The Random Forest algorithm exhibited superior performance compared to the other approaches under scrutiny. The performance characteristics attained were an accuracy rate of 98%, a precision rate of 99%, a sensitivity rate of 98%, and a specificity rate of 100%. The findings show a significant improvement compared to the present triage methods. Conclusions: The efficiency of RTOF surpasses that of existing triage frameworks, showcasing its significant ability to enhance the quality and efficacy of telemedicine solutions. This work showcases substantial enhancements compared to existing triage approaches, while also providing a scalable approach to tackle hospital congestion and optimize resource allocation in real-time. The results of our study emphasize the capacity of RTOF to mitigate hospital overcrowding, expedite medical intervention, and enable the creation of adaptable telemedicine networks. This study highlights potential avenues for further investigation into the integration of the Internet of Medical Things (IoMT) with machine learning to develop cutting-edge medical technologies.