Browsing by Author "Said, Raed A."
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Item 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, GhanwaItem 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 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 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 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.Item 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, MuhammadItem 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, MohammedWhen 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.Item 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, WaleedNowadays, 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.Item Modeling habit patterns using conditional reflexes in agency(Tech Science Press, 2021) Khan, Qura-Tul-Ain; Ghazal, Taher M.; Abbas, Sagheer; Khan, Wasim Ahmad; Khan, Muhammad Adnan; Said, Raed A.; Ahmad, Munir; Asif, MuhammadFor decision-making and behavior dynamics in humans, the principal focus is on cognition. Cognition can be described using cognitive behavior, which has multiple states. This cognitive behavior can be incorporated with one of the internal mental states’ help, which includes desires, beliefs, emotions, intentions, different levels of knowledge, goals, skills, etc. That leads to habit development. Habits are highly refined patterns formed in the unconscious that evolve from conscious skill patterns in the human, and the same process can be implemented in the agency. These habit patterns are the outcomes of many internal values that may vary due to variations in parameter values forming these patterns. Fluctuations in the individual agent’s conditional reflexes may subject to strong habit patterns and leads to rationality. This paper presents the modeling of habit patterns in agency using conditional reflexes. Learning patterns, limited reflex patterns, skill patterns are working as main parameters for generating habit patterns. These input and output parameters will be validated using a scenario by applying fuzzy logic cascade techniques in which validation occurs at two levels. At the first level, conditional reflexes and initial patterns are applied, which form the output’s skill patterns. Then these skill patterns are interconnected with each other to form habit patterns. © 2021, Tech Science Press. All rights reserved.Item Performances of k-means clustering algorithm with different distance metrics(Tech Science Press, 2021) Ghazal, Taher M.; Hussain, Muhammad Zahid; Said, Raed A.; Nadeem, Afrozah; Hasan, Mohammad Kamrul; Ahmad, Munir; Khan, Muhammad Adnan; Naseem, Muhammad TahirClustering is the process of grouping the data based on their similar properties. Meanwhile, it is the categorization of a set of data into similar groups (clusters), and the elements in each cluster share similarities, where the similarity between elements in the same cluster must be smaller enough to the similarity between elements of different clusters. Hence, this similarity can be considered as a distance measure. One of the most popular clustering algorithms is K-means, where distance is measured between every point of the dataset and centroids of clusters to find similar data objects and assign them to the nearest cluster. Further, there are a series of distance metrics that can be applied to calculate point-to-point distances. In this research, the K-means clustering algorithm is evaluated with three different mathematical metrics in terms of execution time with different datasets and different numbers of clusters. The results indicate that the implementation of Manhattan distance measure metrics achieves the best results in most cases. These results also demonstrate that distance metrics can affect the execution time and the number of clusters created by the K-means algorithm. © 2021, Tech Science Press. All rights reserved.