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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.
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    Machine Learning Models for the Classification of Skin Cancer
    (Institute of Electrical and Electronics Engineers Inc., 2022) Arooj, Sahar; Khan, Muhammad Farhan; Khan, Muhammad Adnan; Khan, Muhammad Saleem; Taleb, Nasser
    Skin cancer is a serious illness that requires early identification in order to improve survival rates. Deep learning algorithms for computerized skin cancer detection have now become popular in recent years. These models may increase their performance by having access to additional data, and their prime objective is image categorization. This activity is extremely useful in the realm of health since it may help physicians and experts make the best decisions and accurately assess a patient's condition. Early detection of skin cancer helps patients to receive appropriate treatment and so enhance their survival rate. This proposed methodology is generated to detect and classify skin cancers. In this study, we employed four pre-trained deep learning models (Squeeze net, Alex net, Res net 101, VGG 19) for the classification of four types of skin cancers in more than 6000 skin images including actinic keratoses, intraepithelial carcinoma Bowen's disease (akiec), basal cell carcinoma (bcc), benign keratosis-like lesions (bkl) and melanocytic nevi (nv). The objective was the identification of the best model in the classification of these breast cancer images with highest accuracy. Experimental results reveal that the Squeeze net model achieved an accuracy of 92.5% which is highest when compared with all other models while Alex net, Res net 101, VGG 19 acquired 91.1%, 83.2%, and 90.4% respectively. © 2022 IEEE.
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    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.