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Now showing 1 - 5 of 24
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    Skin Cancer Detection and Classification Based on Deep Learning
    (Institute of Electrical and Electronics Engineers Inc., 2022) Said, Raed A.; Raza, Hammad; Muneer, Salman; Amjad, Kamran; Mohammed, Abdul Salam; Akbar, Syed Shehryar; Zonain, Muhammad; Aslam, Muhammad Arslan
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    Classification of Skin Cancer empowered with convolutional neural network
    (Institute of Electrical and Electronics Engineers Inc., 2022) Atta, Ayesha; Khan, Muhammad Adnan; Asif, Muhammad; Issa, Ghassan F.; Said, Raed A.; Faiz, Tauqeer
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    Application of Higher-Order Ordinary Differential Equation Model in Financial Investment Stock Price Forecast
    (Sciendo, 2022) Zhang, Liqin; Tian, Xiaojing; Chabani, Zakariya
    In order to improve the efficiency of dynamic system prediction modelling, this paper proposes a predictive model based on high-order normal differential equations to obtain an explicit model. The high-order constant differential equation model is reduced, and the numerical method is used to solve the predictive value. The results show that the method achieves the synchronisation of model establishment and parameter optimisation, in addition to greatly enhancing the modelling efficiency. © 2021 Zhang et al., published by Sciendo.
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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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    (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.