Yazar "Alrifaie, Mohammed F." seçeneğine göre listele

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    Privacy as a Lifestyle: Empowering assistive technologies for people with disabilities, challenges and future directions
    (Elsevier, 2024) Habbal, Adib; Hamouda, Hassen; Alnajim, Abdullah M.; Khan, Sheroz; Alrifaie, Mohammed F.
    Between the changing Industry 4.0 landscape and the rise of Industry 5.0, where human intelligence and intelligent machines work together, vast amounts of privacy-sensitive data are generated, processed, and exchanged, making them attractive targets of various attacks. Hence, privacy protection has become a major human concern. Due to its importance, this paper investigates the state-of-the-art research efforts directed toward privacy preservation. Firstly, this paper examines the main privacy requirements and identifies the key privacy-related threats and attacks for systems integrating blockchain and AI. Secondly, we study Blockchainbased privacy preservation solutions, and we devise a taxonomy to classify them based on privacy of data and privacy of network. Thirdly, AI-based privacy-preserving methods are discussed and categorized into privacypreserving data, privacy-preserving model, and privacy-preserving service. Moreover, this paper adds value by analyzing the impact of privacy from technical aspects, human and social-economic aspects, and ethical and legal considerations. Additionally, it sheds light on the role of privacy as a lifestyle, which is crucial not only in mainstream sectors but also for people with disabilities who depend on technological advancements and privacy safeguards for their empowerment. The paper concludes by addressing open challenges in privacy preservation, paving the way for future research directions vital for fostering a privacy-centric evolution in the dynamic Industry 4.0 and beyond.
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    A User-Preference-Based Charging Station Recommendation for Electric Vehicles
    (Ieee-Inst Electrical Electronics Engineers Inc, 2024) Habbal, Adib; Alrifaie, Mohammed F.
    The popularity of electric vehicles (EVs) is increasing, leading to higher demand for electric vehicle charging stations (EVCS). It is crucial to select an appropriate charging station based on user preferences; however, current selection solutions are limited and primarily focus on proximity or price. Such an approach neglects other significant factors of interest to EV users, namely charging time, waiting time, charging cost, and available facilities near the EVCS. To address this issue, this paper proposes a novel recommendation scheme, the User-Preferences based Charging Station Recommendation Scheme (UPCSRS), which integrates user preferences with Multiple Attribute Decision Making (MADM) theory to suggest the best available charging stations for EV users. UPCSRS consists of two parts: adopting Analytical Hierarchical Process (AHP) for weighting the importance of each selection criterion and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for ranking available charging stations. A mathematical model of the proposed scheme was developed, and then the effectiveness and accuracy were evaluated using MATLAB and a real dataset from the US Department of Energy website. Results showed that this proposed scheme provides more precise and personalized recommendations for users compared to current solutions that only consider the nearest or cheapest option. By enhancing the overall user experience through a more customized and efficient charging station selection process, this proposed scheme has the potential to contribute to more EVs adoption.
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    Using Machine Learning Technologies to Classify and Predict Heart Disease
    (Science & Information Sai Organization Ltd, 2021) Alrifaie, Mohammed F.; Ahmed, Zakir Hussain; Hameed, Asaad Shakir; Mutar, Modhi Lafta
    The techniques of data mining are used widely in the healthcare sector to predict and diagnose various diseases. Diagnosis of heart disease is considered as one of the very important applications of these systems. Data is being collected today in a large amount where people need to rely on the device. In recent years, heart disease has increased excessively and heart disease has become one of the deadliest diseases in many countries. Most data sets often suffer from extreme values that reduce the accuracy percentage in classification. Extreme values are defined in terms of irrelevant or incorrect data, missing values, and the incorrect values of the dataset. Data conversion is another very important way to preconfigure the process of converting data into suitable mining models by acting assembly or assembly and filtering methods such as eliminating duplicate features by using the link and one of the wrap methods, and applying the repeated discrimination feature. This process is performed, dealing with lost values through the Remove with values methods and methods of estimating the layer. Classification methods like Naive Bayes (NB) and Random Forest (RF) are applied to the original datasets and data sets with the feature of selection methods too. All of these operations are implemented on three various sets of heart disease data for the analysis of pre-treatment effect in terms of accuracy.