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    Proposed Model of Work Ethics in Artificial Intelligence and Emerging Digital Technologies
    (Institute of Electrical and Electronics Engineers Inc., 2022) Aldulaimi, Saeed Hameed; Abdeldayem, Marwan M.; Abo Keir, Mohammed Yousif; Abdelhakim, Mohamed
    The digital revolution witnessed by human civilization and technological development increases the degree of complexity and takes global dimensions, with many challenges and ethical problems. This research deals with digital ethics from a philosophical side, which must be taken into consideration in light of the reign of the digital age in order to solve and avoid emerging ethical problems. The research draws attention to the need to think beyond the facts and theories related to the current standards for the formation of moral awareness. That calls for and emphasizes the need to spread the culture of information and ethics. The findings of this paper led to propose a practical model to imagine the formation of the ethical model in the digital era consisting of several factors. © 2022 IEEE.
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    Information Technology Adoption Barriers in Public Sector
    (Institute of Electrical and Electronics Engineers Inc., 2022) Abdelhakim, Mohamed; Abdeldayem, Marwan M.; Aldulaimi, Saeed Hameed
    The utilization of Information Technology (IT) in the public sector became a strategic goal for many organizations to achieve their strategic goals and improve competitive advantages. The implementation of digital services on a national scale is a complex challenge requiring substantial investment in and development of Information and Telecommunication Technology (ICT) resources. The problem of integrating technology is a complex problem. In the public sector, extracting value from big investment in technology and successful implementation is challenging. Although there has been considerable research in the area of the design, development and implementation of computerized systems, there has not been an equivalent research effort in terms of investigating and exploring what are information technology adoption barriers in the public sector. This research used secondary data from published articles and internet. It is concluded that main barriers are: lack of top management support, lack of IT project management, lack of resources, lack of user's involvement, lack of awareness and training, change resistance, culture and structure changes. Main proposed change management strategies for successful IT projects adoption are top management support, more IT resources, more user's involvement in IT projects development to capture user requirements, more motivation, better interactive two ways communication, better understanding, sharing and implementation of the corporate strategy and making sure that departments strategies are aligned with corporate strategy. It is recommended to manage required structure and culture change for successful IT project implementation. © 2022 IEEE.
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    Feature optimization and identification of ovarian cancer using internet of medical things
    (John Wiley and Sons Inc, 2022) Ghazal, Taher M.; Taleb, Nasser
    Ovarian 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.
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    Ovary Cancer Diagnosing Empowered with Machine Learning
    (Institute of Electrical and Electronics Engineers Inc., 2022) Taleb, Nasser; Mehmood, Shahid; Zubair, Muhammad; Naseer, Iftikhar; Mago, Beenu Skyline University Colleg; Nasir, Muhammad Umar
    A high mortality rate is associated with ovarian cancer, one of the most common types of cancers in women. Ovarian cancer refers to a group of disorders that develop in the ovaries and spread to the fallopian tubes and peritoneum. Treatment is most effective when ovarian cancer is discovered in its early stages. Machine learning has recently demonstrated that it is capable of better identifying ovarian cancer and its stages. Most modern research studies on ovarian cancer use a single classification model, leading to poor performance in diagnosis. For the detection of ovarian cancer, the highly sophisticated and efficient machine learning algorithms Support vector machine (SVM) and K-Nearest Neighbor (KNN) are employed in this study. Before diagnosing illness, the suggested approach can optimize and standardize data. Experimental results show that SVM has outperformed KNN in both training and validation performance and achieved an accuracy of 98.1% 97.16% for training and validation respectively. If used in medical diagnosis systems, the proposed model can significantly improve the accuracy of ovarian cancer detection leading to effective treatment and an increase in patient survival rates. © 2022 IEEE.
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    A Proposed Architecture for Traffic Monitoring Control System via LiFi Technology in Smart Homes
    (Institute of Electrical and Electronics Engineers Inc., 2022) Asif, Muhammad; Khan, Tahir Abbas; Taleb, Nasser; Said, Raed A.; Siddiqui, Shahan Yamin; Batool, Ghanwa
    Realizing secure and fast communication in smart homes is still a challenging task due to slow and imperfect information reception from end nodes for proper traffic control. The image data captured through high-resolution cameras demand high-speed data link nodes having a capacity of more than 10 Gbps. The advancement in technologies makes it possible to send accurate and timely images data to store in clouds. LiFi is a wireless technology that uses lights for data transmission between devices. The speed of LiFi is 100 times greater than Conventional WiFi Technology. A Proposed Architecture for Traffic Monitoring Control systems via LiFi Technology in Smart Homes is presented in this paper. The proposed model is the multi-purposed model like we can control, manage and identify traffic vehicles on time. © 2022 IEEE.