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    Innovation in knowledge management on employee’s productivity in the voluntary sector
    (Inderscience Publishers, 2023) Hymavathi, E.; Koneru, Kalpana; Chabani, Zakariya; Othman, Bestoon; Pham, Long Tam; Rizal, Sanjay
    Teamwork is one of the most important factors for corporate success in the current world. Almost every business sector is paying more attention to teams now that it has been established that cooperation enhances individual, collective, and even corporate performance. Information and abilities are seldom frequently or freely exchanged amongst co-workers at work or with other volunteers for non-profit organisations. If information is not shared, it can lead to unproductive learning cycles and potentially serious organisational failures. A lack of information sharing may occur from associates not knowing each other’s strengths, not knowing how to get specialised knowledge, or from an organisational behaviour that discourages or does not facilitate the exchange of information and skills. This study explores team collaboration and how it affects internal and external employees. The authors studied which characteristics of effective teamwork are associated with long-term sustainability. Individual and group performance indexes were used to foster teamwork. Comparing individual and group ratings reveals differences between employees. This strategy used cooperative gaming. Copyright © 2023 Inderscience Enterprises Ltd.
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    The New Normal: The Challenges and Opportunities of Freelancing and Remote Work for HR Departments
    (Springer Science and Business Media Deutschland GmbH, 2023) Chabani, Zakariya; Sergio, Rommel; Hoffman, Ettiene Paul
    The aim of this paper is to explore the impact of freelancing and remote work on HR departments, specifically in recruitment, training and development, and employee engagement. The discussion highlights the innovative strategies and tools HR departments can use to manage a decentralized workforce, including online recruitment platforms, virtual training programs, and collaboration tools, as well as recognizing the challenges associated with these strategies, such as the lack of face-to-face interaction and engagement. The paper emphasizes the importance of adapting to a more decentralized workforce and the development of new strategies to remain competitive in the evolving business landscape. The paper concludes by highlighting the theoretical and practical implications of the growth of freelancing and remote work for companies such as the need for companies to adapt and innovate in their recruitment, training and development, and management strategies to remain competitive. Additionally, the need for HR departments to find practical ways to keep remote workers engaged and connected to the company culture while also considering legal and ethical implications. The paper suggests that companies must be proactive in addressing these challenges, as failure to do so may result in the loss of the best talent. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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    A Roadmap for SMEs to Adopt an AI Based Cyber Threat Intelligence
    (Springer Science and Business Media Deutschland GmbH, 2023) Varma, Abhilash J.; Taleb, Nasser; Said, Raed A.; Ghazal, Taher M.; Ahmad, Munir; Alzoubi, Haitham M.; Alshurideh, Muhammad
    Cybersecurity has started to become the most significant concern among organizations as the number of threats and criminal activities in the past decade has increased exponentially. Cybercriminals and their attacking techniques have become increasingly sophisticated over the past couple of years. Conventional security measures will no longer be able to detect and mitigate the propagation of such advanced attacking trends. More and more hackers have started focusing on Small and medium-sized enterprises (SMEs) taking advantage of their limited resources. Therefore, SMEs will have to quickly adopt Artificial Intelligence (AI) based cybersecurity system in their infrastructure to defend themselves effectively and efficiently. It is currently forecasted that by 2021, 75% of all organizations will use AI and Machine learning (ML) applications in their security architecture to protect against all cyber threats. In this paper, the researchers identify the various challenges faced by SMEs in adopting an AI based cybersecurity due to their knowledge gap and lack of expertise. The researcher intends to provide a good background on AI, Cyber Threat Intelligence (CTI) and highlight some of the significant benefits provided by an AI based CTI system. A simple roadmap is developed using a qualitative research methodology to help SMEs effectively implement an AI based Cyber Threat Intelligent system in their infrastructure. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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    Software Testing Issues Improvement in Quality Assurance
    (Institute of Electrical and Electronics Engineers Inc., 2023) Salahat, Mohammed; Said, Raed A.; Hamid, Khalid; Haseeb, Usama; Abdel Maguid Abdel Ghani, Elsaid; Abualkishik, Abedallah; Iqbal, Muhammad Waseem; Inairat, Mohammad
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    Development of Data Mining Expert System Using Naïve Bayes
    (Springer Science and Business Media Deutschland GmbH, 2023) Salahat, Mohammed; Al-Dmour, Nidal A.; Said, Raed A.; Alzoubi, Haitham M.; Alshurideh, Muhammad
    The consumer spectrum consists of a wide range, including the affluent, middle-income, and low-income. This consumer shows different behaviors or motivations towards choosing clothes. We want to develop a framework for a Sale Recommendation System. These expert System can be helpful for sale persons, fashion designer, promoter, brand manager as well as sponsor of Recommendation System. The study implemented the Data Science approach and techniques to see how reliable Recommendation Systems are and in our selected dataset we have applied different modelling techniques such as KNN, SVM, Bayes Naïve and Decision Tree and fond the NB as the most suitable and practical method of modelling in regard to the accuracy, recall and runtime. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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    Development of Data Mining Framework Cardiovascular Disease Prediction
    (Springer Science and Business Media Deutschland GmbH, 2023) Said, Raed A.; Al-Dmour, Nidal A.; Salahat, Mohammed; Issa, Ghassan F.; Alzoubi, Haitham M.; Alshurideh, Muhammad
    One of the highest shares of data-driven technology of health sector happens for private insurance stakeholders. It is therefore clear that private insurance companies can only survive being competitive in covering different medical stages such as surgery, intervention and other clinical trials in a high-risk environment. Estimation of expected costs and coverage is also important for both patient and insurer. In this case study we as a Data Mining and Artificial Business consultant want to explore different techniques of data mining to find out business risks for patients. We have asked the insurer to provide us a sizable medical history to watch those features. We would like to predict if given biographical profile of the patient along with exam results can predict CVD so he can cover his costs with this Insurer. On the other hand, in case of higher error of misclassified CVD what kind of decision should be taken by risk holder and insurer. Which one of these attributes causing this cost and what other stakeholders like target group of patients can be suffered from the loss? The ultimate goal is to develop a model that can predict the gap between those patients’ perception of their disease and their real disease. This can further help stakeholders to develop specific insurance policy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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    Linear Discrimination Analysis Using Image Processing Optimization
    (Springer Science and Business Media Deutschland GmbH, 2023) Said, Raed A.; Al-Dmour, Nidal A.; Ali, Liaqat; Alzoubi, Haitham M.; Alshurideh, Muhammad; Salahat, Mohammed
    When we talk about Machinery Vision and Deep Learning, we often talk about algorithms. In fact, mathematical models with computer knowledge are the basis of how we deal with graphical data to process the Image and make decision. Machine learning can play an important role in determining agricultural plant type in order to optimize the harvesting steps in an automated way. How to process and introduce the products to the market often requires detailed information about the stages of planting and harvesting. In addition, by using this method, sophisticated research can be designed in plant genetics and effect of environmental variables on the end product. The ultimate goal of this work is to use Linear Discrimination Analysis for the Image Processing and classification of harvested wheat grain which are belonged to different types of grain namely Rosa, Kama and Canadian. The above discovery has proved with the statistics to have with more than 94% of accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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    Machine Learning-Based Intrusion Detection Approaches for Secured Internet of Things
    (Springer Science and Business Media Deutschland GmbH, 2023) Ghazal, Taher M.; Hasan, Mohammad Kamrul; Abdullah, Siti Norul Huda Sheikh; Bakar, Khairul Azmi Abu; Al-Dmour, Nidal A.; Said, Raed A.; Abdellatif, Tamer Mohamed; Moubayed, Abdallah; Alzoubi, Haitham M.; Alshurideh, Muhammad; Alomoush, Waleed
    Nowadays, protecting communication and information for Internet of Things (IOT) has emerged as a critical challenge. Existing systems use firewalls to ensure that they are safe from any unexpected occurrences that may disrupt the desired systems and applications. Intrusion detection systems (IDSs) are an acceptable second line of defence for IOT applications. IDS play a crucial role ensuring that it enhances the IOT security level maintaining sophisticated framework. Attackers have continuously been attempting to determine novel ways to circumnavigate security frameworks that prevent the structures. This paper reviews the security advances, threats and countermeasures for the IOT applications. A state of art review has accomplished using the references from 2009 to 2020 to encompass the real demography of the IOT security research data. This work also highlights the deep learning-based intrusion detection approaches for Internet of Things (IOT) security. With the systematic literature review approach, the review suggests that implementing existing security measures, such as encryption, authentication, access control, network and application security for IoT systems and their intrinsic amenability is ineffective for the IOT systems. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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    DDoS Intrusion Detection with Ensemble Stream Mining for IoT Smart Sensing Devices
    (Springer Science and Business Media Deutschland GmbH, 2023) Ghazal, Taher M.; Al-Dmour, Nidal A.; Said, Raed A.; Omidvar, Alireza; Khan, Urooj Yousuf; Soomro, Tariq Rahim; Soomro, Tariq Rahim; Alshurideh, Muhammad; Abdellatif, Tamer Mohamed; Moubayed, Abdullah; Ali, Liaqat
    Security threats in the Smart City Systems are becoming a challenge. These Smart City Systems, generating Big Data, are a revolutionizing application of the Internet of Things(IoT). Data Stream Mining, which is an efficient way of handling Big Data, is now of great concern. The acquired information is computationally expensive to process in terms of efficiency and runtime. Detection of suspicious activities on decentralized servers, generating and computing massive data streams requires time. Moreover, several stakeholders should be engaged to train the heterogenous malware data streams in the level of service application. Small experiments can be performed on the functionality of Batch ML on IoT datasets with available heap size resources. Among these candidate datasets, a little contribution has been already represented on the Mirai Attack. This research aims at the study of Data Stream Mining algorithms. Owing to the accuracy and interferences of the measurement, these algorithms are able to handle the non-hierarchical and unbalanced datasets similar to the Mirai Attacks. No single method can solely improve these critical standpoints. Thus, an Ensemble technique should be implemented. According to our study, a pool of meta or selective classifiers that interact based on the temporal Data Mining swiftly can outperform others. The maintainability and security concerns of such applications can be best fulfilled in meta-heuristics with the one-time scanning network approach for the recognition of the most frequent attacking pattern with the on-the-fly scheme. These are implemented in Create, Read, Update and Delete (CRUD) operations of the Big Data Systems. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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    Breast Cancer Prediction Using Machine Learning and Image Processing Optimization
    (Springer Science and Business Media Deutschland GmbH, 2023) Al-Dmour, Nidal A.; Said, Raed A.; Alzoubi, Haitham M.; Alshurideh, Muhammad; Ali, Liaqat
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    Information Systems Solutions for the Database Problems
    (Springer Science and Business Media Deutschland GmbH, 2023) Al-Dmour, Nidal A.; Ali, Liaqat; Salahat, Mohammed; Alzoubi, Haitham M.; Alshurideh, Muhammad; Chabani, Zakariya
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    IT Governance and Control: Mitigation and Disaster Preparedness of Organizations in the UAE
    (Springer Science and Business Media Deutschland GmbH, 2023) Al Blooshi, Ismail Ali; Alamim, Abdulazez Salem; Said, Raed A.; Taleb, Nasser; Ghazal, Taher M.; Ahmad, Munir; Alzoubi, Haitham M.; Alshurideh, Muhammad
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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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    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
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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.