Browsing by Author "Issa, Ghassan F."
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Item Analysis of Issues Affecting IoT, AI, and Blockchain Convergence(Springer Science and Business Media Deutschland GmbH, 2023) Taleb, Nasser; Al-Dmour, Nidal A.; Issa, Ghassan F.; Abdellatif, Tamer Mohamed; Alzoubi, Haitham M.; Alshurideh, Muhammad; Salahat, MohammedThe purpose of this project was to appraise the integration or convergence issues influencing the mutual functioning of blockchain, AI, and IoT. The study argued that the recent developments in the field of IoT and blockchain prediction have involved the integration of innumerable classification schemes to establish a hybrid model. The introduction of the hybrid technique relies on the prediction performance that strives to override the limitations of any available architectural scheme. This study offers a comprehensive exploratory appraisal of the issues influencing the successful integration of IoT and blockchain in regards to functionality and effectiveness of security, trust, and flawless communication issues. The exploratory research methodology was used in analyzing the issues affecting the integration of blockchain, artificial intelligence (AI), and the internet of things (IoT). The findings indicated that the integration challenges influencing the effective operations of blockchain, AI, and IoT as a single system involve security, scalability, accountability, and trust of communications. The study recommends that successful and effective integration will enhance the development of new business models as well as the digital transformation of market corporations. Accordingly, new approaches to convergence should ensure that executives address the new technology demands to obtain significant gains in efficiency. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item Classification of Skin Cancer empowered with convolutional neural network(Institute of Electrical and Electronics Engineers Inc., 2022) Atta, Ayesha; Khan, Muhammad Adnan; Asif, Muhammad; Issa, Ghassan F.; Said, Raed A.; Faiz, TauqeerItem Development of Data Mining Framework Cardiovascular Disease Prediction(Springer Science and Business Media Deutschland GmbH, 2023) Said, Raed A.; Al-Dmour, Nidal A.; Salahat, Mohammed; Issa, Ghassan F.; Alzoubi, Haitham M.; Alshurideh, MuhammadOne of the highest shares of data-driven technology of health sector happens for private insurance stakeholders. It is therefore clear that private insurance companies can only survive being competitive in covering different medical stages such as surgery, intervention and other clinical trials in a high-risk environment. Estimation of expected costs and coverage is also important for both patient and insurer. In this case study we as a Data Mining and Artificial Business consultant want to explore different techniques of data mining to find out business risks for patients. We have asked the insurer to provide us a sizable medical history to watch those features. We would like to predict if given biographical profile of the patient along with exam results can predict CVD so he can cover his costs with this Insurer. On the other hand, in case of higher error of misclassified CVD what kind of decision should be taken by risk holder and insurer. Which one of these attributes causing this cost and what other stakeholders like target group of patients can be suffered from the loss? The ultimate goal is to develop a model that can predict the gap between those patients’ perception of their disease and their real disease. This can further help stakeholders to develop specific insurance policy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item An iomt-enabled smart healthcare model to monitor elderly people using machine learning technique(Hindawi Limited, 2021) Khan, Muhammad Farrukh; Ghazal, Taher M.; Said, Raed A.; Fatima, Areej; Abbas, Sagheer; Khan, M.A.; Issa, Ghassan F.; Ahmad, Munir; Khan, Muhammad AdnanThe Internet of Medical Things (IoMT) enables digital devices to gather, infer, and broadcast health data via the cloud platform. The phenomenal growth of the IoMT is fueled by many factors, including the widespread and growing availability of wearables and the ever-decreasing cost of sensor-based technology. The cost of related healthcare will rise as the global population of elderly people grows in parallel with an overall life expectancy that demands affordable healthcare services, solutions, and developments. IoMT may bring revolution in the medical sciences in terms of the quality of healthcare of elderly people while entangled with machine learning (ML) algorithms. The effectiveness of the smart healthcare (SHC) model to monitor elderly people was observed by performing tests on IoMT datasets. For evaluation, the precision, recall, fscore, accuracy, and ROC values are computed. The authors also compare the results of the SHC model with different conventional popular ML techniques, e.g., support vector machine (SVM), K-nearest neighbor (KNN), and decision tree (DT), to analyze the effectiveness of the result. © 2021 Muhammad Farrukh Khan et al.Item Prediction of diabetes empowered with fused machine learning(Institute of Electrical and Electronics Engineers Inc., 2022) Ahmed, Usama; Issa, Ghassan F.; Khan, Muhammad Adnan; Aftab, Shabib; Khan, Muhammad Farhan; Said, Raed A. T.; Ghazal, Taher M.; Ahmad, MunirIn the medical field, it is essential to predict diseases early to prevent them. Diabetes is one of the most dangerous diseases all over the world. In modern lifestyles, sugar and fat are typically present in our dietary habits, which have increased the risk of diabetes. To predict the disease, it is extremely important to understand its symptoms. Currently, machine-learning (ML) algorithms are valuable for disease detection. This article presents a model using a fused machine learning approach for diabetes prediction. The conceptual framework consists of two types of models: Support Vector Machine (SVM) and Artificial Neural Network (ANN) models. These models analyze the dataset to determine whether a diabetes diagnosis is positive or negative. The dataset used in this research is divided into training data and testing data with a ratio of 70:30 respectively. The output of these models becomes the input membership function for the fuzzy model, whereas the fuzzy logic finally determines whether a diabetes diagnosis is positive or negative. A cloud storage system stores the fused models for future use. Based on the patient’s real-time medical record, the fused model predicts whether the patient is diabetic or not. The proposed fused ML model has a prediction accuracy of 94.87, which is higher than the previously published methods. Author