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    Single and Mitochondrial Gene Inheritance Disorder Prediction Using Machine Learning
    (Tech Science Press, 2022) Nasir, Muhammad Umar; Khan, Muhammad Adnan; Zubair, Muhammad; Ghazal, Taher M.; Said, Raed A.; Hamadi, Hussam Al
    One of the most difficult jobs in the post-genomic age is identifying a genetic disease from a massive amount of genetic data. Furthermore, the complicated genetic disease has a very diverse genotype, making it challenging to find genetic markers. This is a challenging process since it must be completed effectively and efficiently. This research article focuses largely on which patients are more likely to have a genetic disorder based on numerous medical parameters. Using the patient’s medical history, we used a genetic disease prediction algorithm that predicts if the patient is likely to be diagnosed with a genetic disorder. To predict and categorize the patient with a genetic disease, we utilize several deep and machine learning techniques such as Artificial neural network (ANN), K-nearest neighbors (KNN), and Support vector machine (SVM). To enhance the accuracy of predicting the genetic disease in any patient, a highly efficient approach was utilized to control how the model can be used. To predict genetic disease, deep and machine learning approaches are performed. The most productive tool model provides more precise efficiency. The simulation results demonstrate that by using the proposed model with the ANN, we achieve the highest model performance of 85.7%, 84.9%, 84.3% accuracy of training, testing and validation respectively. This approach will undoubtedly transform genetic disorder prediction and give a real competitive strategy to save patients’ lives. © 2022 Tech Science Press. All rights reserved.
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    THE CHALLENGES FACING PUBLIC ORGANIZATIONS TO IMPLEMENT HUMAN RESOURCES INFORMATION SYSTEMS: A CASE STUDY OF ALGERIA
    (Allied Business Academies, 2020-09) Chabani, Zakariya
    The purpose of this research is to identify the managerial purposes, benefits and challenges of HRIS implementation within public organizations to better understand how such technologies affect the capacity of the HR department to conduct effective and efficient work. This research is part of a constructivist epistemological approach. To achieve the main goal of the study, data were collected primarily via a semi-directive interview conducted at one of the biggest public companies in Algeria in 2018. In addition to the questionnaire, some of the organization's documents were used in combination with observations that allowed the researcher to confirm some of the data collected from the questionnaire. The management of the case study organization well understands the importance of HRISs. Thus, the management decided to implement the system. Although the implementation was successful, the management faced some problems, such as resistance to change from users in various divisions. Therefore, we can conclude that HRISs may contribute to enhancing the performance of HR departments and improving management processes. However, to ensure performance, the implementation must take place under the best possible conditions. Otherwise, the HRIS is no longer be an advantage but rather a significant cost. This is one of the few studies investigating the role of HRIS implementation in improving the HR department and management processes within a public organization. Due to the very specific characteristics of such organizations, which vary with regard to flexibility, bureaucratic attitude, resistance to change, etc., implementing HRISs can be very challenging. © 2020. All Rights Reserved.
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    Adaptively Intelligent Meta-search Engine with Minimum Edit Distance
    (Institute of Electrical and Electronics Engineers Inc., 2022) Kanwal, Asma; Septyanto, Arif Wicaksono; Muhammad, Muhammad Hassan Ghulam; Said, Raed A.; Farrukh, Muhammad; Ibrahim, Muhammad
    In current era retrieval of information has attained high demand due to spectra from websites has abundantly increased. Search Engines are basic tools to get information from the web of data and show irrelevant data causing wastage of time. Considering the fact that time is a precious commodity and is the hallmark of everything around us. To overcome the wastage of time and for its optimum utilization meta-search engines are design. Meta-Search Engine use to fetch relevant data. Existing meta-search engine shows their relevant data based on keywords as well as a semantic query. Semantic query-based results still have some irrelevancy in the results. In this paper, we analyze the semantic query based on machine learning algorithms. This paper hypothesizes improved results through the query expansion mechanism. Author also remove duplicated URLs that come from multiple search engines. Minimum Edit Distance algorithm is used to measure the similarity between titles, snippets and if measuring similarity is more than 0.6 then it must remove that title and snippet. Ranking process, generated retrieval of the relevant document at the top relevant document. Comparative analysis of proposed work is done with existing meta-search engines, overall performance of Intelligent Meta-Search Engine (IMSE) remains 74.17%. © 2022 IEEE.
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    Application of Computational Intelligence and Machine Learning to Conventional Operational Research Methods
    (Institute of Electrical and Electronics Engineers Inc., 2022) Ali, Atif; Said, Raed A.; Rizwan, Hafiz Muhammad Amir; Shehzad, Khurram; Naz, Imran
    Machine learning and computational intelligence are two methods for achieving this (CI); traditional operational research methods are combined with machine learning-based computational techniques (OR). Students can handle complex decision-making problems thanks to the synergy between those methods and techniques. This research's primary goal is to present and demonstrate potential connections amid the two computational arenas. Using applications, we show how machine learning techniques like fuzzy logic, neural networks and reinforcement learning can be combined to provide a simpler solution to more complex problems than traditional OR methods., which is a research contribution in and of itself. © 2022 IEEE.
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    Detection of Benign and Malignant Tumors in Skin Empowered with Transfer Learning
    (Hindawi Limited, 2022) Ghazal, Taher M.; Hussain, Sajid; Khan, Muhammad Farhan; Khan, Muhammad Adnan; Said, Raed A. T.; Ahmad, Munir
    Skin cancer is a major type of cancer with rapidly increasing victims all over the world. It is very much important to detect skin cancer in the early stages. Computer-developed diagnosis systems helped the physicians to diagnose disease, which allows appropriate treatment and increases the survival ratio of patients. In the proposed system, the classification problem of skin disease is tackled. An automated and reliable system for the classification of malignant and benign tumors is developed. In this system, a customized pretrained Deep Convolutional Neural Network (DCNN) is implemented. The pretrained AlexNet model is customized by replacing the last layers according to the proposed system problem. The softmax layer is modified according to binary classification detection. The proposed system model is well trained on malignant and benign tumors skin cancer dataset of 1920 images, where each class contains 960 images. After good training, the proposed system model is validated on 480 images, where the size of images of each class is 240. The proposed system model is analyzed using the following parameters: accuracy, sensitivity, specificity, Positive Predicted Values (PPV), Negative Predicted Value (NPV), False Positive Ratio (FPR), False Negative Ratio (FNR), Likelihood Ratio Positive (LRP), and Likelihood Ratio Negative (LRN). The accuracy achieved through the proposed system model is 87.1%, which is higher than traditional methods of classification. © 2022 Taher M.