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Browsing MITGov by Author "Ghazal, Taher M."
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Item A Comprehensive Review on Big Data Challenges(Institute of Electrical and Electronics Engineers Inc., 2023) Bharany, Salil; Taleb, Nasser; Sadiq, M. Tariq; Kanwal, Nayab; Abdelhakim, Mohamed; Ghazal, Taher M.; Pradhan, Manas; Rehman, Ateeq UrItem An Integrated Cloud and Blockchain Enabled Platforms for Biomedical Research(Springer Science and Business Media Deutschland GmbH, 2023) Ghazal, Taher M.; Hasan, Mohammad Kamrul; Abdullah, Siti Norul Huda Sheikh; Bakar, Khairul Azmi Abu; Taleb, Nasser; Al-Dmour, Nidal A.; Yafi, Eiad; Chauhan, Ritu; Alzoubi, Haitham M.; Alshurideh, MuhammadIn the current pandemic scenario, healthcare data tends to be an important asset among organizations. The major challenge is to handle the data effectively while maintaining the privacy and security of the data. In a real-world, context healthcare data proves to be heterogeneous. Hence, managing such significance to big data has ardently laid numerous challenges among researchers and scientists around the globe. Cloud environment and blockchain technology can be discussed as usable platforms which can deliver a comprehensive centralized data privacy system. In the current approach study, we have integrated both technologies to provide usability in medical systems. Further, we have also proposed and implemented a blockchain application with an integrated cloud-based environment regarding heterogeneous medical databases. The study is proposed in 2 phases to maintain the privacy and the accessibility of the data. The double-spending problem is also presented, as mentioned above, using Blockchain’s consensus process. Each network node independently verifies the validity of individual transactions and entire blocks. As a result, there is no need to put faith in a single entity or other nodes. As a result, third parties are no longer required for network actions or blockchain management. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item Feature optimization and identification of ovarian cancer using internet of medical things(John Wiley and Sons Inc, 2022-11) Ghazal, Taher M.; Taleb, NasserOvarian cancer (OC) is one kind of tumour that impacts women's ovaries and is hard to diagnose in the initial phase as a primary cause of cancer death. The ovarian cancer information generated by the Clinical Network has been used, and the Self Organizing Map (SOM) and Optimized Neural Networks have suggested a new method for the distinction between ovarian cancer and remaining cancer. Feature optimization and identification of the ovarian cancer (FOI-OV) framework are proposed in this research. The SOM algorithm has also been used separately to improve the functional subset, with understandable and intriguing information from participants' health information steps. The SOM-based collection appears to be tolerable in guided learning strategies due to the lack of different classifiers, which would direct the quest for knowledge specific to the classification algorithm. The classification technique will classify data from ovarian cancer as benign/malignant. By optimizing Neural Network configuration, Advanced Harmony Searching Optimization (AHSO) can enhance the ovarian cancer detection method compared with other methods. This research's suggested model can also diagnose cancer with high precision, and low root means square error (RMSE) early. With 94% precision and 0.029%, RMSE, SOM, and NN techniques have shown identification and precision in ovarian cancer. Optimization (AHSO) has provided an efficient classification approach with a better failure rate. © 2022 John Wiley & Sons Ltd.Item Software defect prediction using ensemble learning: A systematic literature review(Institute of Electrical and Electronics Engineers Inc., 2021) Matloob, Faseeha; Ghazal, Taher M.; Taleb, Nasser; Aftab, Shabib; Ahmad, Munir; Khan, Muhammad AdnanRecent advances in the domain of software defect prediction (SDP) include the integration of multiple classification techniques to create an ensemble or hybrid approach. This technique was introduced to improve the prediction performance by overcoming the limitations of any single classification technique. This research provides a systematic literature review on the use of the ensemble learning approach for software defect prediction. The review is conducted after critically analyzing research papers published since 2012 in four well-known online libraries: ACM, IEEE, Springer Link, and Science Direct. In this study, five research questions covering the different aspects of research progress on the use of ensemble learning for software defect prediction are addressed. To extract the answers to identified questions, 46 most relevant papers are shortlisted after a thorough systematic research process. This study will provide compact information regarding the latest trends and advances in ensemble learning for software defect prediction and provide a baseline for future innovations and further reviews. Through our study, we discovered that frequently employed ensemble methods by researchers are the random forest, boosting, and bagging. Less frequently employed methods include stacking, voting and Extra Trees. Researchers proposed many promising frameworks, such as EMKCA, SMOTE-Ensemble, MKEL, SDAEsTSE, TLEL, and LRCR, using ensemble learning methods. The AUC, accuracy, F-measure, Recall, Precision, and MCC were mostly utilized to measure the prediction performance of models. WEKA was widely adopted as a platform for machine learning. Many researchers showed through empirical analysis that features selection, and data sampling was necessary pre-processing steps that improve the performance of ensemble classifiers. © 2013 IEEE.Item Using blockchain to ensure trust between donor agencies and ngos in under-developed countries(MDPI AG, 2021-08) Rehman, Ehsan; Khan, Muhammad Asghar; Soomro, Tariq Rahim; Taleb, Nasser; Afifi, Mohammad A.; Ghazal, Taher M.Non-governmental organizations (NGOs) in under-developed countries are receiving funds from donor agencies for various purposes, including relief from natural disasters and other emergencies, promoting education, women empowerment, economic development, and many more. Some donor agencies have lost their trust in NGOs in under-developed countries, as some NGOs have been involved in the misuse of funds. This is evident from irregularities in the records. For instance, in education funds, on some occasions, the same student has appeared in the records of multiple NGOs as a beneficiary, when in fact, a maximum of one NGO could be paying for a particular beneficiary. Therefore, the number of actual beneficiaries would be smaller than the number of claimed beneficiaries. This research proposes a blockchain-based solution to ensure trust between donor agencies from all over the world, and NGOs in under-developed countries. The list of National IDs along with other keys would be available publicly on a blockchain. The distributed software would ensure that the same set of keys are not entered twice in this blockchain, preventing the problem highlighted above. The details of the fund provided to the student would also be available on the blockchain and would be encrypted and digitally signed by the NGOs. In the case that a record inserted into this blockchain is discovered to be fake, this research provides a way to cancel that record. A cancellation record is inserted, only if it is digitally signed by the relevant donor agency. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.