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Item 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, MuhammadCybersecurity 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.Item 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, MuhammadItem 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, ImranMachine 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.Item Application of Higher Order Ordinary Differential Equation Model in Financial Investment Stock Price Forecast(Sciendo, 2022-01-01) Zhang, Liqin; Tian, Xiaojing; Chabani, ZakariyaIn 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.Item Application of Higher-Order Ordinary Differential Equation Model in Financial Investment Stock Price Forecast(Sciendo, 2022) Zhang, Liqin; Tian, Xiaojing; Chabani, ZakariyaIn 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.Item 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, LiaqatItem 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, TauqeerItem 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, LiaqatSecurity 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.Item Dealing with randomness and concept drift in large datasets(MDPI AG, 2021-07) Mwitondi, Kassim S.; Said, Raed A.Data-driven solutions to societal challenges continue to bring new dimensions to our daily lives. For example, while good-quality education is a well-acknowledged foundation of sustainable development, innovation and creativity, variations in student attainment and general performance remain commonplace. Developing data-driven solutions hinges on two fronts-technical and appli-cation. The former relates to the modelling perspective, where two of the major challenges are the impact of data randomness and general variations in definitions, typically referred to as concept drift in machine learning. The latter relates to devising data-driven solutions to address real-life challenges such as identifying potential triggers of pedagogical performance, which aligns with the Sustainable Development Goal (SDG) #4-Quality Education. A total of 3145 pedagogical data points were obtained from the central data collection platform for the United Arab Emirates (UAE) Ministry of Education (MoE). Using simple data visualisation and machine learning techniques via a generic algorithm for sampling, measuring and assessing, the paper highlights research pathways for educa-tionists and data scientists to attain unified goals in an interdisciplinary context. Its novelty derives from embedded capacity to address data randomness and concept drift by minimising modelling variations and yielding consistent results across samples. Results show that intricate relationships among data attributes describe the invariant conditions that practitioners in the two overlapping fields of data science and education must identify. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.Item 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, MunirSkin 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.Item 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, MuhammadThe 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.Item 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, MuhammadOne 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.Item Edge AI-Based Automated Detection and Classification of Road Anomalies in VANET Using Deep Learning(Hindawi Limited, 2021) Bibi, Rozi; Saeed, Yousaf; Zeb, Asim; Ghazal, Taher M.; Rahman, Taj; Said, Raed A.; Abbas, Sagheer; Ahmad, Munir; Khan, Muhammad AdnanRoad surface defects are crucial problems for safe and smooth traffic flow. Due to climate changes, low quality of construction material, large flow of traffic, and heavy vehicles, road surface anomalies are increasing rapidly. Detection and repairing of these defects are necessary for the safety of drivers, passengers, and vehicles from mechanical faults. In this modern era, autonomous vehicles are an active research area that controls itself with the help of in-vehicle sensors without human commands, especially after the emergence of deep learning (DNN) techniques. A combination of sensors and DNN techniques can be useful for unmanned vehicles for the perception of their surroundings for the detection of tracks and obstacles for smooth traveling based on the deployment of artificial intelligence in vehicles. One of the biggest challenges for autonomous vehicles is to avoid the critical road defects that may lead to dangerous situations. To solve the accident issues and share emergency information, the Intelligent Transportation System (ITS) introduced the concept of vehicular network termed as vehicular ad hoc network (VANET) for achieving security and safety in a traffic flow. A novel mechanism is proposed for the automatic detection of road anomalies by autonomous vehicles and providing road information to upcoming vehicles based on Edge AI and VANET. Road images captured via camera and deployment of the trained model for road anomaly detection in a vehicle could help to reduce the accident rate and risk of hazards on poor road conditions. The techniques Residual Convolutional Neural Network (ResNet-18) and Visual Geometry Group (VGG-11) are applied for the automatic detection and classification of the road with anomalies such as a pothole, bump, crack, and plain roads without anomalies using the dataset from different online sources. The results show that the applied models performed well than other techniques used for road anomalies identification. © 2021 Rozi Bibi et al.Item Energy demand forecasting using fused machine learning approaches(Tech Science Press, 2022) Ghazal, Taher M.; Noreen, Sajida; Said, Raed A.; Khan, Muhammad Adnan; Siddiqui, Shahan Yamin; Abbas, Sagheer; Aftab, Shabib; Ahmad, MunirThe usage of IoT-based smart meter in electric power consumption shows a significant role in helping the users to manage and control their electric power consumption. It produces smooth communication to build equitable electric power distribution for users and improved management of the entire electric system for providers. Machine learning predicting algorithms have been worked to apply the electric efficiency and response of progressive energy creation, trans-mission, and consumption. In the proposed model, an IoT-based smart meter uses a support vector machine and deep extreme machine learning techniques for professional energy management. A deep extreme machine learning approach applied to feature-based data provided a better result. Lastly, decision-based fusion applied to both datasets to predict power consumption through smart meters and get better results than previous techniques. The established model smart meter with automatic load control increases the effectiveness of energy management. The proposed EDF-FMLA model achieved 90.70 accuracy for predicting energy consumption with a smart meter which is better than the existing approaches. © 2022, Tech Science Press. All rights reserved.Item Energy-efficiency model for residential buildings using supervised machine learning algorithm(Tech Science Press, 2021) Aslam, Muhammad Shoukat; Ghazal, Taher M.; Fatima, Areej; Said, Raed A.; Abbas, Sagheer; Khan, Muhammad Adnan; Siddiqui, Shahan Yamin; Ahmad, MunirThe real-time management and control of heating-system networks in residential buildings has tremendous energy-saving potential, and accurate load prediction is the basis for system monitoring. In this regard, selecting the appro-priate input parameters is the key to accurate heating-load forecasting. In existing models for forecasting heating loads and selecting input parameters, with an increase in the length of the prediction cycle, the heating-load rate gradually decreases, and the influence of the outside temperature gradually increases. In view of different types of solutions for improving buildings’ energy efficiency, this study proposed a Energy-efficiency model for residential buildings based on gradient descent optimization (E2B-GDO). This model can predict a building’s heating-load conservation based on a building energy performance dataset. The input layer includes area (distribution of the glazing area, wall area, and surface area), relative density, and overall elevation. The proposed E2B-GDO model achieved an accuracy of 99.98% for training and 98.00% for validation. © 2021, Tech Science Press. All rights reserved.Item A framework for data-driven solutions with covid-19 illustrations(Ubiquity Press, 2021) Mwitondi, Kassim S.; Said, Raed A.Data–driven solutions have long been keenly sought after as tools for driving the world’s fast changing business environment, with business leaders seeking to enhance decision making processes within their organisations. In the current era of Big Data, applications of data tools in addressing global, regional and national challenges have steadily grown in almost all fields across the globe. However, working in silos has continued to impede research progress, creating knowledge gaps and challenges across geographical borders, legislations, sectors and fields. There are many examples of the challenges the world faces in tackling global issues, including the complex interactions of the 17 Sustainable Development Goals (SDG) and the spatio–temporal variations of the impact of the on-going COVID–19 pandemic. Both challenges can be seen as non–orthogonal, strongly correlated and requiring an interdisciplinary approach to address. We present a generic framework for filling such gaps, based on two data-driven algorithms that combine data, machine learning and interdisciplinarity to bridge societal knowledge gaps. The novelty of the algorithms derives from their robust built–in mechanics for handling data randomness. Animation applications on structured COVID–19 related data obtained from the European Centre for Disease Prevention and Control (ECDC) and the UK Office of National Statistics exhibit great potentials for decision-support systems. Predictive findings are based on unstructured data–a large COVID–19 X–Ray data, 3181 image files, obtained from GitHub and Kaggle. Our results exhibit consistent performance across samples, resonating with cross-disciplinary discussions on novel paths for data-driven interdisciplinary research. © 2021, Ubiquity Press. All rights reserved.Item The Impact of Entrepreneurial Culture on Economy Competitiveness in the Arab Region(Allied Business Academies, 2021) Chabani, ZakariyaThe main emphasis of this study was assessing to what scope entrepreneurial culture among Arab counties affects the overall regional competitiveness of the MENA region. Data from each country were first solely analyzed then grouped by its subsequent trading blocks. An average assertion of the combined data was granted in the bid of assessing any entrepreneurial cultures. The entrepreneurial culture analysis of the countries and regions within the study were based on the time span through which the referenced entrepreneurial activity has been dominant and the education level of stakeholder. Most Arab countries have the same entrepreneurial structure and economic competitiveness despite various external factors affecting them. The results also revealed that the relationship between each country's internal economic competitiveness and the overall posture of the Arab region is positive. Besides, the relationship between dominant entrepreneurial cultures and the overall economic posture of the Arab region is also statistically confirmed. Much of the past studies failed to include and relate the external factors like the growing entrepreneurial cultures among states and regions. Basing conclusions on one region while others could convey disparities was regraded rather impractical. As such, to affirm the validity of conclusions; The relationship between the Economic Community of West Africa (ECOWAS) and North African countries from one part, and the Association of Southern Asian Nations (ASEAN) and the other Arab countries from another hand, had to be taken into consideration. © 2021. All Rights Reserved.Item 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, ZakariyaItem 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, SanjayTeamwork 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.Item An iomt-enabled smart healthcare model to monitor elderly people using machine learning technique(Hindawi Limited, 2021) Khan, Muhammad Farrukh; Ghazal, Taher M.; Said, Raed A.; Fatima, Areej; Abbas, Sagheer; Khan, M.A.; Issa, Ghassan F.; Ahmad, Munir; Khan, Muhammad AdnanThe Internet of Medical Things (IoMT) enables digital devices to gather, infer, and broadcast health data via the cloud platform. The phenomenal growth of the IoMT is fueled by many factors, including the widespread and growing availability of wearables and the ever-decreasing cost of sensor-based technology. The cost of related healthcare will rise as the global population of elderly people grows in parallel with an overall life expectancy that demands affordable healthcare services, solutions, and developments. IoMT may bring revolution in the medical sciences in terms of the quality of healthcare of elderly people while entangled with machine learning (ML) algorithms. The effectiveness of the smart healthcare (SHC) model to monitor elderly people was observed by performing tests on IoMT datasets. For evaluation, the precision, recall, fscore, accuracy, and ROC values are computed. The authors also compare the results of the SHC model with different conventional popular ML techniques, e.g., support vector machine (SVM), K-nearest neighbor (KNN), and decision tree (DT), to analyze the effectiveness of the result. © 2021 Muhammad Farrukh Khan et al.