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Traffic violations occur due to driving or behavioral issues that result in traffic offense and violate the law. Traffic violations such as running red lights, speeding, and reckless driving are translated to millions of traffic infractions every year. This paper proposes a machine learning-based data fusion (MLDF) model for online traffic violations analysis (OTVA) system. The MLDF model is set to perform cumulative traffic analysis by using a software agent (SA) for decision making and Gradient Boosted Trees (GBT), Naive Bayes (NB), and Random Forest (RF) algorithms for classification. The MLDF model is incorporated in the OTVA system for categorizing traffic violation types online. The performance of the MLDF model that includes the SA and the NB, GBT, and RF algorithms is measured and compared in terms of accuracy, recall, precision, and f-measure. The results show that the MLDF model outperforms the single NB and RF algorithms in which GBT achieves 69.86% (±1 .28%) accuracy, NB achieves 66.02% (± 3.38%) accuracy, RF achieves 69.36% (± 0.84%) accuracy, and MLDF achieves 71.88% (± 1.23%) accuracy scores. It is hoped that the results of this paper can serve as a baseline for investigations related to the use of advanced models to automate the detection of traffic violations.
Salama A. Mostafa; Aida Mustapha; Azizul Azhar Ramli; Mohd Farhan M. D. Fudzee; David Lim; Shafiza Ariffin Kashinath. A Machine Learning-Based Data Fusion Model for Online Traffic Violations Analysis. Advances in Intelligent Systems and Computing 2021, 847 -857.
AMA StyleSalama A. Mostafa, Aida Mustapha, Azizul Azhar Ramli, Mohd Farhan M. D. Fudzee, David Lim, Shafiza Ariffin Kashinath. A Machine Learning-Based Data Fusion Model for Online Traffic Violations Analysis. Advances in Intelligent Systems and Computing. 2021; ():847-857.
Chicago/Turabian StyleSalama A. Mostafa; Aida Mustapha; Azizul Azhar Ramli; Mohd Farhan M. D. Fudzee; David Lim; Shafiza Ariffin Kashinath. 2021. "A Machine Learning-Based Data Fusion Model for Online Traffic Violations Analysis." Advances in Intelligent Systems and Computing , no. : 847-857.
With the exponential growth of science, Internet of Things (IoT) innovation, and expanding significance in renewable energy, Smart Grid has become an advanced innovative thought universally as a solution for the power demand increase around the world. The smart grid is the most practical trend of effective transmission of present-day power assets. The paper aims to survey the present literature concerning predictive maintenance and different types of faults that could be detected within the smart grid. Four databases (Scopus, ScienceDirect, IEEE Xplore, and Web of Science) were searched between 2012 and 2020. Sixty-five (n = 65) were chosen based on specified exclusion and inclusion criteria. Fifty-seven percent (n = 37/65) of the studies analyzed the issues from predictive maintenance perspectives, while about 18% (n = 12/65) focused on factors-related review studies on the smart grid and about 15% (n = 10/65) focused on factors related to the experimental study. The remaining 9% (n = 6/65) concentrated on fields related to the challenges and benefits of the study. The significance of predictive maintenance has been developing over time in connection with Industry 4.0 revolution. The paper’s fundamental commitment is the outline and overview of faults in the smart grid such as fault location and detection. Therefore, advanced methods of applying Artificial Intelligence (AI) techniques can enhance and improve the reliability and resilience of smart grid systems. For future direction, we aim to supply a deep understanding of Smart meters to detect or monitor faults in the smart grid as it is the primary IoT sensor in an AMI.
Moamin A. Mahmoud; Naziffa Raha Md Nasir; Mathuri Gurunathan; Preveena Raj; Salama A. Mostafa. The Current State of the Art in Research on Predictive Maintenance in Smart Grid Distribution Network: Fault’s Types, Causes, and Prediction Methods—A Systematic Review. Energies 2021, 14, 5078 .
AMA StyleMoamin A. Mahmoud, Naziffa Raha Md Nasir, Mathuri Gurunathan, Preveena Raj, Salama A. Mostafa. The Current State of the Art in Research on Predictive Maintenance in Smart Grid Distribution Network: Fault’s Types, Causes, and Prediction Methods—A Systematic Review. Energies. 2021; 14 (16):5078.
Chicago/Turabian StyleMoamin A. Mahmoud; Naziffa Raha Md Nasir; Mathuri Gurunathan; Preveena Raj; Salama A. Mostafa. 2021. "The Current State of the Art in Research on Predictive Maintenance in Smart Grid Distribution Network: Fault’s Types, Causes, and Prediction Methods—A Systematic Review." Energies 14, no. 16: 5078.
Mechanization in agriculture involves all stages of cultivating and preparing innovations, from basic and essential hand devices and tools to more complex and mechanized implements. Fundamentally, such devices and tools enable agricultural activities to initiate different crop yields in many different neighborhoods in the world's ecological system. Due to the unstable nature of fuel prices, market demands, and damaging effects of tillage machinery vibrations on the operator, exploring efficient methods of preventing these situations has become indispensable. There are many existing methods related to tillage optimization, including fast, high quality, and cost-effective tilling. However, none of the existing methods perform real-time and dynamic recommendations due to the inability to provide real-time tillage data. Another issue is the unavailability of a tillage recommendation model that acts based on the changes of a fixed set of tillage parameters and provides the necessary guidance to the tractor driver during the tillage operation. This paper proposes a Tillage Operations Quality Optimization (TOQO) model that aims to improve the efficiency of tillage operations. The model provides real-time tillage data through integrating the Internet of Things (IoT) technology and a dynamic Decision Support System (DSS) for tillage machinery operation and process optimization. The proposed model has been successfully implemented, tested, and evaluated in real-world tillage operations. The results of this TOQO study clearly show improvements in the tillage operations. This is because the TOQO gives online recommendations in real-time based on dynamic measurements of six tillage performance evaluation parameters. The parameters are vibration, bulk density, slippage ratio, fuel consumption, real tillage depth, and field efficiency. The TOQO model successfully handles more than one tillage machinery tractor by using cloud computing services. Vibration increases when using the system on the X-axis at 46.63843 rms, Y-axis at 23.23612 rms, and the Z-axis at 51.47240 rms. Vibration fractures the soil, reducing soil cohesiveness when using an oscillating tillage tool and producing smaller soil aggregates than a non-oscillating one. When using the system, the value of the virtual density increases by 0.23821 g/cm3. Most of the bulk density values for the optimized phase are within the recommended or required growth range. There is a slight increase in depth by 0.4279 cm and a decrease in slippage ratio by 0.64364%. Tractor fuel consumption decreases by 1.2169 L per hour, indicating a reduction in tractor wheel slippage. Reduced fuel consumption and wheel slippage of tillage operation are essential parameters to increase field efficiency and reduce tillage operation costs.
Haider Fawzi; Salama A. Mostafa; Desa Ahmed; Nayef Alduais; Mazin Abed Mohammed; Mohamed Elhoseny. TOQO: A new Tillage Operations Quality Optimization model based on parallel and dynamic Decision Support System. Journal of Cleaner Production 2021, 316, 128263 .
AMA StyleHaider Fawzi, Salama A. Mostafa, Desa Ahmed, Nayef Alduais, Mazin Abed Mohammed, Mohamed Elhoseny. TOQO: A new Tillage Operations Quality Optimization model based on parallel and dynamic Decision Support System. Journal of Cleaner Production. 2021; 316 ():128263.
Chicago/Turabian StyleHaider Fawzi; Salama A. Mostafa; Desa Ahmed; Nayef Alduais; Mazin Abed Mohammed; Mohamed Elhoseny. 2021. "TOQO: A new Tillage Operations Quality Optimization model based on parallel and dynamic Decision Support System." Journal of Cleaner Production 316, no. : 128263.
Waste generation is a continuous process that needs to be managed effectively to ensure environmental safety and public health. The recent circular economy (CE) practices have brought a new shape for the waste management industry, creating value from the generated waste. The shift to a CE represents one of the most significant challenges, particularly in sorting and classifying generated waste. Addressing these challenges would facilitate the recycling industry and helps in promoting remanufacturing. But in the COVID times, most of the generated waste is getting mixed with conventional waste types, especially in the global south. The pandemic has resulted in colossal infectious waste generation. Its handling became the most significant challenge raising fears and concerns over sorting and classifying. Hence, this study proposes an Artificial Intelligence (AI) based automated solution for sorting COVID related medical waste streams from other waste types and, at the same time, ensures data-driven decisions for recycling in the context of CE. Metal, paper, glass waste categories, including the polyethylene terephthalate (PET) waste from the pandemic, are considered. The waste type classification is done based on the image-texture-dependent features, which provided an accurate sorting and classification before the recycling process starts. The features are fused using the proposed decision-level feature fusion scheme. The classification model based on the support vector machine (SVM) classifier performs best (with 96.5 % accuracy, 95.3 % sensitivity, and 95.9 % specificity) in classifying waste types in the context of circular manufacturing and exhibiting the abilities to manage the COVID related medical waste mixed.
Nallapaneni Manoj Kumar; Mazin Abed Mohammed; Karrar Hameed Abdulkareem; Robertas Damasevicius; Salama A. Mostafa; Mashael S. Maashi; Shauhrat S. Chopra. Artificial Intelligence-based Solution for Sorting COVID Related Medical Waste Streams and Supporting Data-driven Decisions for Smart Circular Economy Practice. Process Safety and Environmental Protection 2021, 152, 482 -494.
AMA StyleNallapaneni Manoj Kumar, Mazin Abed Mohammed, Karrar Hameed Abdulkareem, Robertas Damasevicius, Salama A. Mostafa, Mashael S. Maashi, Shauhrat S. Chopra. Artificial Intelligence-based Solution for Sorting COVID Related Medical Waste Streams and Supporting Data-driven Decisions for Smart Circular Economy Practice. Process Safety and Environmental Protection. 2021; 152 ():482-494.
Chicago/Turabian StyleNallapaneni Manoj Kumar; Mazin Abed Mohammed; Karrar Hameed Abdulkareem; Robertas Damasevicius; Salama A. Mostafa; Mashael S. Maashi; Shauhrat S. Chopra. 2021. "Artificial Intelligence-based Solution for Sorting COVID Related Medical Waste Streams and Supporting Data-driven Decisions for Smart Circular Economy Practice." Process Safety and Environmental Protection 152, no. : 482-494.
There are several types of neural networks (NNs) that are widely used for data classification tasks. The supervised learning NN is an advanced network with a training algorithm for setting the weights and biases of the network in its training phase. However, traditional training algorithms such as backpropagation have some drawbacks, such as slow convergence speed and falling into local minima, which reduces the performance of the classifier. Therefore, different nature-inspired metaheuristic algorithms are integrated with the NN training algorithms to provide derivative-free solutions for complex classification problems. Consequently, this paper proposes the integration of a particle swarm optimization (PSO) algorithm with an improved Elman recurrent neural network (ERNN) to form a PSO-ERNN metaheuristic model. The key contribution of this study is the development of a new dimensional equation for ERNN architecture and the integration of PSO in ERNN learning to produce the PSO-ERNN model. The PSO is constructed to train the NN and ERNN models to achieve a fast convergence rate and avoid local minima problems. The PSO-ERNN model is validated by comparing it against the standard PSO-NN metaheuristic model and similar models from the literature. The PSO-NN and PSO-ERNN models are tested and evaluated using ten benchmark classification problems of breast cancer, heart, hepatitis, liver, wine, iris, lung cancer, yeast, Pima Indians diabetes, and ionosphere datasets. In the training phase, the results show that the PSO-ERNN model performs better than the PSO-NN model when the training set has a bigger size of samples. In the testing phase, the PSO-ERNN model outperforms the PSO-NN model for all the tested datasets except the lung cancer and yeast datasets, in which the accuracy percentage slightly decreases. In the validation phase, the PSO-ERNN model shows better performance quality in terms of accuracy percentage in six of the tested datasets. The average percentage of the training, testing, and validation accumulation show that the PSO-NN performs better than the PSO-ERNN in the lung cancer (87.27, 83.32), and heart (73.56, 70.64) datasets. On the other hand, the PSO-ERNN performs better than the PSO-NN in the iris (88.18, 86.74), hepatitis (88.60, 87.93), wine (89.16, 86.08), liver (73.56, 70.64), ionosphere (83.98, 78.94), and breast cancer (94.84, 91.17). PSO-NN and PSO-ERNN produce the same average results in the Pima Indians diabetes (84.00, 84.00) and yeast (91.31, 91.30) dataset. These results show clearly that the PSO-ERNN generally outperforms the PSO-NN when considering the overall results of the ten datasets. Nevertheless, the combinations of the PSO-NN and PSO-ERNN are proven to represent consistent and robust classification methods. The computational efficiencies of the training processes in both the PSO-NN and PSO-ERNN models are highly improved when coupled with the PSO.
Mohamad Firdaus Ab Aziz; Salama A Mostafa; Cik Feresa Mohd. Foozy; Mazin Abed Mohammed; Mohamed Elhoseny; Abedallah Zaid Abualkishik. Integrating Elman recurrent neural network with particle swarm optimization algorithms for an improved hybrid training of multidisciplinary datasets. Expert Systems with Applications 2021, 183, 115441 .
AMA StyleMohamad Firdaus Ab Aziz, Salama A Mostafa, Cik Feresa Mohd. Foozy, Mazin Abed Mohammed, Mohamed Elhoseny, Abedallah Zaid Abualkishik. Integrating Elman recurrent neural network with particle swarm optimization algorithms for an improved hybrid training of multidisciplinary datasets. Expert Systems with Applications. 2021; 183 ():115441.
Chicago/Turabian StyleMohamad Firdaus Ab Aziz; Salama A Mostafa; Cik Feresa Mohd. Foozy; Mazin Abed Mohammed; Mohamed Elhoseny; Abedallah Zaid Abualkishik. 2021. "Integrating Elman recurrent neural network with particle swarm optimization algorithms for an improved hybrid training of multidisciplinary datasets." Expert Systems with Applications 183, no. : 115441.
Physicians depend on their insight and experience and on a fundamentally indicative or symptomatic approach to decide on the possible ailment of a patient. However, numerous phases of problem identification and longer strategies can prompt a longer time for consulting and can subsequently cause other patients that require attention to wait for longer. This can bring about pressure and tension concerning those patients. In this study, we focus on developing a decision-support system for diagnosing the symptoms as a result of hearing loss. The model is implemented by utilizing machine learning techniques. The Frequent Pattern Growth (FP-Growth) algorithm is used as a feature transformation method and the multivariate Bernoulli naĂŻve Bayes classification model as the classifier. To find the correlation that exists between the hearing thresholds and symptoms of hearing loss, the FP-Growth and association rule algorithms were first used to experiment with small sample and large sample datasets. The result of these two experiments showed the existence of this relationship, and that the performance of the hybrid of the FP-Growth and naĂŻve Bayes algorithms in identifying hearing-loss symptoms was found to be efficient, with a very small error rate. The average accuracy rate and average error rate for the multivariate Bernoulli model with FP-Growth feature transformation, using five training sets, are 98.25% and 1.73%, respectively.
Mohd Abd Ghani; Nasir Noma; Mazin Mohammed; Karrar Abdulkareem; Begonya Garcia-Zapirain; Mashael Maashi; Salama Mostafa. Innovative Artificial Intelligence Approach for Hearing-Loss Symptoms Identification Model Using Machine Learning Techniques. Sustainability 2021, 13, 5406 .
AMA StyleMohd Abd Ghani, Nasir Noma, Mazin Mohammed, Karrar Abdulkareem, Begonya Garcia-Zapirain, Mashael Maashi, Salama Mostafa. Innovative Artificial Intelligence Approach for Hearing-Loss Symptoms Identification Model Using Machine Learning Techniques. Sustainability. 2021; 13 (10):5406.
Chicago/Turabian StyleMohd Abd Ghani; Nasir Noma; Mazin Mohammed; Karrar Abdulkareem; Begonya Garcia-Zapirain; Mashael Maashi; Salama Mostafa. 2021. "Innovative Artificial Intelligence Approach for Hearing-Loss Symptoms Identification Model Using Machine Learning Techniques." Sustainability 13, no. 10: 5406.
Currently, online organizational resources and assets are potential targets of several types of attack, the most common being flooding attacks. We consider the Distributed Denial of Service (DDoS) as the most dangerous type of flooding attack that could target those resources. The DDoS attack consumes network available resources such as bandwidth, processing power, and memory, thereby limiting or withholding accessibility to users. The Flash Crowd (FC) is quite similar to the DDoS attack whereby many legitimate users concurrently access a particular service, the number of which results in the denial of service. Researchers have proposed many different models to eliminate the risk of DDoS attacks, but only few efforts have been made to differentiate it from FC flooding as FC flooding also causes the denial of service and usually misleads the detection of the DDoS attacks. In this paper, an adaptive agent-based model, known as an Adaptive Protection of Flooding Attacks (APFA) model, is proposed to protect the Network Application Layer (NAL) against DDoS flooding attacks and FC flooding traffics. The APFA model, with the aid of an adaptive analyst agent, distinguishes between DDoS and FC abnormal traffics. It then separates DDoS botnet from Demons and Zombies to apply suitable attack handling methodology. There are three parameters on which the agent relies, normal traffic intensity, traffic attack behavior, and IP address history log, to decide on the operation of two traffic filters. We test and evaluate the APFA model via a simulation system using CIDDS as a standard dataset. The model successfully adapts to the simulated attack scenarios’ changes and determines 303,024 request conditions for the tested 135,583 IP addresses. It achieves an accuracy of 0.9964, a precision of 0.9962, and a sensitivity of 0.9996, and outperforms three tested similar models. In addition, the APFA model contributes to identifying and handling the actual trigger of DDoS attack and differentiates it from FC flooding, which is rarely implemented in one model.
Bashar Ahmad Khalaf; Salama A. Mostafa; Aida Mustapha; Mazin Abed Mohammed; Moamin A. Mahmoud; Bander Ali Saleh Al-Rimy; Shukor Abd Razak; Mohamed Elhoseny; Adam Marks. An Adaptive Protection of Flooding Attacks Model for Complex Network Environments. Security and Communication Networks 2021, 2021, 1 -17.
AMA StyleBashar Ahmad Khalaf, Salama A. Mostafa, Aida Mustapha, Mazin Abed Mohammed, Moamin A. Mahmoud, Bander Ali Saleh Al-Rimy, Shukor Abd Razak, Mohamed Elhoseny, Adam Marks. An Adaptive Protection of Flooding Attacks Model for Complex Network Environments. Security and Communication Networks. 2021; 2021 ():1-17.
Chicago/Turabian StyleBashar Ahmad Khalaf; Salama A. Mostafa; Aida Mustapha; Mazin Abed Mohammed; Moamin A. Mahmoud; Bander Ali Saleh Al-Rimy; Shukor Abd Razak; Mohamed Elhoseny; Adam Marks. 2021. "An Adaptive Protection of Flooding Attacks Model for Complex Network Environments." Security and Communication Networks 2021, no. : 1-17.
Recently, development in intelligent transportation systems (ITS) requires the input of various kinds of data in real-time and from multiple sources, which imposes additional research and application challenges. Ongoing studies on Data Fusion (DF) have produced significant improvement in ITS and manifested an enormous impact on its growth. This paper reviews the implementation of DF methods in ITS to facilitate traffic flow analysis (TFA) and solutions that entail the prediction of various traffic variables such as driving behavior, travel time, speed, density, incident, and traffic flow. It attempts to identify and discuss real-time and multi-sensor data sources that are used for various traffic domains, including road/highway management, traffic states estimation, and traffic controller optimization. Moreover, it attempts to associate abstractions of data level fusion, feature level fusion, and decision level fusion on DF methods to better understand the role of DF in TFA and ITS. Consequently, the main objective of this paper is to review DF methods used for real-time and multi-sensor (heterogeneous) TFA studies. The review outcomes are (i) a guideline of constructing DF methods which involve preprocessing, filtering, decision, and evaluation as core steps, (ii) a description of the recent DF algorithms or methods that adopt real-time and multi-sensor sources data and the impact of these data sources on the improvement of TFA, (iii) an examination of the testing and evaluation methodologies and the popular datasets and (iv) an identification of several research gaps, some current challenges, and new research trends.
Shafiza Ariffin Kashinath; Salama A. Mostafa; Aida Mustapha; Hairulnizam Mahdin; David Lim; Moamin A. Mahmoud; Mazin Abed Mohammed; Bander Ali Saleh Al-Rimy; Mohd Farhan Md Fudzee; Tan Jhon Yang. Review of Data Fusion Methods for Real-Time and Multi-Sensor Traffic Flow Analysis. IEEE Access 2021, 9, 51258 -51276.
AMA StyleShafiza Ariffin Kashinath, Salama A. Mostafa, Aida Mustapha, Hairulnizam Mahdin, David Lim, Moamin A. Mahmoud, Mazin Abed Mohammed, Bander Ali Saleh Al-Rimy, Mohd Farhan Md Fudzee, Tan Jhon Yang. Review of Data Fusion Methods for Real-Time and Multi-Sensor Traffic Flow Analysis. IEEE Access. 2021; 9 (99):51258-51276.
Chicago/Turabian StyleShafiza Ariffin Kashinath; Salama A. Mostafa; Aida Mustapha; Hairulnizam Mahdin; David Lim; Moamin A. Mahmoud; Mazin Abed Mohammed; Bander Ali Saleh Al-Rimy; Mohd Farhan Md Fudzee; Tan Jhon Yang. 2021. "Review of Data Fusion Methods for Real-Time and Multi-Sensor Traffic Flow Analysis." IEEE Access 9, no. 99: 51258-51276.
Recently, there has been an advancement in the development of innovative computer-aided techniques for the segmentation and classification of retinal vessels, the application of which is predominant in clinical applications. Consequently, this study aims to provide a detailed overview of the techniques available for segmentation and classification of retinal vessels. Initially, retinal fundus photography and retinal image patterns are briefly introduced. Then, an introduction to the pre-processing operations and advanced methods of identifying retinal vessels is deliberated. In addition, a discussion on the validation stage and assessment of the outcomes of retinal vessels segmentation is presented. In this paper, the proposed methods of classifying arteries and veins in fundus images are extensively reviewed, which are categorized into automatic and semi-automatic categories. There are some challenges associated with the classification of vessels in images of the retinal fundus, which include the low contrast accompanying the fundus image and the inhomogeneity of the background lighting. The inhomogeneity occurs as a result of the process of imaging, whereas the low contrast which accompanies the image is caused by the variation between the background and the contrast of the various blood vessels. This means that the contrast of thicker vessels is higher than those that are thinner. Another challenge is related to the color changes that occur in the retina from different subjects, which are rooted in biological features. Most of the techniques used for the classification of the retinal vessels are based on geometric and visual characteristics that set the veins apart from the arteries. In this study, different major contributions are summarized as review studies that adopted deep learning approaches and machine learning techniques to address each of the limitations and problems in retinal blood vessel segmentation and classification techniques. We also review the current challenges, knowledge gaps and open issues, limitations and problems in retinal blood vessel segmentation and classification techniques.
Aws A. Abdulsahib; Moamin A. Mahmoud; Mazin Abed Mohammed; Hind Hameed Rasheed; Salama A. Mostafa; Mashael S. Maashi. Comprehensive review of retinal blood vessel segmentation and classification techniques: intelligent solutions for green computing in medical images, current challenges, open issues, and knowledge gaps in fundus medical images. Network Modeling Analysis in Health Informatics and Bioinformatics 2021, 10, 1 -32.
AMA StyleAws A. Abdulsahib, Moamin A. Mahmoud, Mazin Abed Mohammed, Hind Hameed Rasheed, Salama A. Mostafa, Mashael S. Maashi. Comprehensive review of retinal blood vessel segmentation and classification techniques: intelligent solutions for green computing in medical images, current challenges, open issues, and knowledge gaps in fundus medical images. Network Modeling Analysis in Health Informatics and Bioinformatics. 2021; 10 (1):1-32.
Chicago/Turabian StyleAws A. Abdulsahib; Moamin A. Mahmoud; Mazin Abed Mohammed; Hind Hameed Rasheed; Salama A. Mostafa; Mashael S. Maashi. 2021. "Comprehensive review of retinal blood vessel segmentation and classification techniques: intelligent solutions for green computing in medical images, current challenges, open issues, and knowledge gaps in fundus medical images." Network Modeling Analysis in Health Informatics and Bioinformatics 10, no. 1: 1-32.
Automatic information extraction from online published scientific documents is useful in various applications such as tagging, web indexing and search engine optimization. As a result, automatic information extraction has become among the hottest areas of research in text mining. Although various information extraction techniques have been proposed in the literature, their efficiency demands domain specific documents with static and well-defined format. Furthermore, their accuracy is challenged with a slight modification in the format. To overcome these issues, a novel ontological framework for information extraction (OFIE) using fuzzy rule-base (FRB) and word sense disambiguation (WSD) is proposed. The proposed approach is validated with a significantly wider document domains sourced from well-known publishing services such as IEEE, ACM, Elsevier, and Springer. We have also compared the proposed information extraction approach against state-of-the-art techniques. The results of the experiment show that the proposed approach is less sensitive to changes in the document format and has a significantly better average accuracy of 89.14% and F-score as 89%.
Gohar Zaman; Hairulnizam Mahdin; Khalid Hussain; Atta- Ur- Rahman; Jemal Abawajy; Salama A. Mostafa. An Ontological Framework for Information Extraction From Diverse Scientific Sources. IEEE Access 2021, 9, 42111 -42124.
AMA StyleGohar Zaman, Hairulnizam Mahdin, Khalid Hussain, Atta- Ur- Rahman, Jemal Abawajy, Salama A. Mostafa. An Ontological Framework for Information Extraction From Diverse Scientific Sources. IEEE Access. 2021; 9 ():42111-42124.
Chicago/Turabian StyleGohar Zaman; Hairulnizam Mahdin; Khalid Hussain; Atta- Ur- Rahman; Jemal Abawajy; Salama A. Mostafa. 2021. "An Ontological Framework for Information Extraction From Diverse Scientific Sources." IEEE Access 9, no. : 42111-42124.
One of the major challenges in designing an autonomous agent system is to achieve the objective of recreating human-like cognition by exploiting the growing pragmatic architectures that act intelligently and intuitively in vital fields. Consequently, this research addresses the general problem of designing an agent-based autonomous flight control (AFC) architecture of a UAV to facilitate autonomous routing/navigation in uncharted and unascertained environments of organized foyer surroundings. The specific problem of this research is the indoor environment because of the perplexing characteristics of the required flight mechanics. We design the AFC agent architecture to consist of data acquisition, perception, localization, mapping, control, and planning modules. The AFC agent performs search and survey missions that entail commanding the UAV while performing object classifications and recognition tasks. The agent implements several image handling algorithms to detect and identify objects from their colors and shapes. It captures the video images acquired from a solitary onboard, front-facing camera which are handled off-board on a computer. We conduct tests on the AFC agent, and the results show that the agent successfully controls the UAV in three performed test cases and a total of nine implemented missions. The AFC agent detects and identifies all the assigned objects with a recall score of 1.00, a precision score of 0.9563, an accuracy score of 0.9573, an F1 score of 0.9776, an efficiency score of 0.5239, a detection total time score of 225.5 s, and an identification total time of 275 s and outperforms a human operator.
Salama A. Mostafa; Aida Mustapha; Saraswathy Shamini Gunasekaran; Mohd Sharifuddin Ahmad; Mazin Abed Mohammed; Pritee Parwekar; Seifedine Kadry. An agent architecture for autonomous UAV flight control in object classification and recognition missions. Soft Computing 2021, 1 -14.
AMA StyleSalama A. Mostafa, Aida Mustapha, Saraswathy Shamini Gunasekaran, Mohd Sharifuddin Ahmad, Mazin Abed Mohammed, Pritee Parwekar, Seifedine Kadry. An agent architecture for autonomous UAV flight control in object classification and recognition missions. Soft Computing. 2021; ():1-14.
Chicago/Turabian StyleSalama A. Mostafa; Aida Mustapha; Saraswathy Shamini Gunasekaran; Mohd Sharifuddin Ahmad; Mazin Abed Mohammed; Pritee Parwekar; Seifedine Kadry. 2021. "An agent architecture for autonomous UAV flight control in object classification and recognition missions." Soft Computing , no. : 1-14.
Intrusion Detection Systems (IDS) effort to detect intrusion and misuse attack computer systems by assembling and examining data of computer networks. The IDS is usually examining huge traffic data based on Machine Learning (ML) algorithms to identify harmful changes or attacks, however, which algorithm can manifest the best performance is an issue to be investigated. ML-IDS requires to decrease false alarm and increase true alarm rates. In this work, three tree-based ML algorithms which are Decision Tree (DT), Decision Jungle (DJ), and Decision Forest (DF) have been tested and evaluated in an IDS model. The main objective of this work is to compare the performance of the three algorithms based on accuracy, precision and recall evaluation criteria. The Knowledge Discovery in Databases (KDD) methodology and Kaggle intrusion detection dataset are used in the testing. The results show that the DF achieves the highest overall accuracy of 99.83%, the DJ achieves the second highest overall accuracy of 99.74% and the DT achieves the lowest overall accuracy of 95.59%. The obtained results can serve as a benchmark in the evaluation of advanced IDS.
Amir Zulhilmi; Salama A. Mostafa; Bashar Ahmed Khalaf; Aida Mustapha; Siti Solehah Tenah. A Comparison of Three Machine Learning Algorithms in the Classification of Network Intrusion. Communications in Computer and Information Science 2021, 313 -324.
AMA StyleAmir Zulhilmi, Salama A. Mostafa, Bashar Ahmed Khalaf, Aida Mustapha, Siti Solehah Tenah. A Comparison of Three Machine Learning Algorithms in the Classification of Network Intrusion. Communications in Computer and Information Science. 2021; ():313-324.
Chicago/Turabian StyleAmir Zulhilmi; Salama A. Mostafa; Bashar Ahmed Khalaf; Aida Mustapha; Siti Solehah Tenah. 2021. "A Comparison of Three Machine Learning Algorithms in the Classification of Network Intrusion." Communications in Computer and Information Science , no. : 313-324.
An intrusion detection system (IDS) is an important protection instrument for detecting complex network attacks. Various machine learning (ML) or deep learning (DL) algorithms have been proposed for implementing anomaly-based IDS (AIDS). Our review of the AIDS literature identifies some issues in related work, including the randomness of the selected algorithms, parameters, and testing criteria, the application of old datasets, or shallow analyses and validation of the results. This paper comprehensively reviews previous studies on AIDS by using a set of criteria with different datasets and types of attacks to set benchmarking outcomes that can reveal the suitable AIDS algorithms, parameters, and testing criteria. Specifically, this paper applies 10 popular supervised and unsupervised ML algorithms for identifying effective and efficient ML–AIDS of networks and computers. These supervised ML algorithms include the artificial neural network (ANN), decision tree (DT), k-nearest neighbor (k-NN), naive Bayes (NB), random forest (RF), support vector machine (SVM), and convolutional neural network (CNN) algorithms, whereas the unsupervised ML algorithms include the expectation-maximization (EM), k-means, and self-organizing maps (SOM) algorithms. Several models of these algorithms are introduced, and the turning and training parameters of each algorithm are examined to achieve an optimal classifier evaluation. Unlike previous studies, this study evaluates the performance of AIDS by measuring the true positive and negative rates, accuracy, precision, recall, and F-Score of 31 ML-AIDS models. The training and testing time for ML-AIDS models are also considered in measuring their performance efficiency given that time complexity is an important factor in AIDSs. The ML-AIDS models are tested by using a recent and highly unbalanced multiclass CICIDS2017 dataset that involves real-world network attacks. In general, the k-NN-AIDS, DT-AIDS, and NB-AIDS models obtain the best results and show a greater capability in detecting web attacks compared with other models that demonstrate irregular and inferior results.
Ziadoon Kamil Maseer; Robiah Yusof; Nazrulazhar Bahaman; Salama A. Mostafa; Cik Feresa Mohd Foozy. Benchmarking of Machine Learning for Anomaly Based Intrusion Detection Systems in the CICIDS2017 Dataset. IEEE Access 2021, 9, 22351 -22370.
AMA StyleZiadoon Kamil Maseer, Robiah Yusof, Nazrulazhar Bahaman, Salama A. Mostafa, Cik Feresa Mohd Foozy. Benchmarking of Machine Learning for Anomaly Based Intrusion Detection Systems in the CICIDS2017 Dataset. IEEE Access. 2021; 9 ():22351-22370.
Chicago/Turabian StyleZiadoon Kamil Maseer; Robiah Yusof; Nazrulazhar Bahaman; Salama A. Mostafa; Cik Feresa Mohd Foozy. 2021. "Benchmarking of Machine Learning for Anomaly Based Intrusion Detection Systems in the CICIDS2017 Dataset." IEEE Access 9, no. : 22351-22370.
Assessment the quality of medical services is a crucial strategy to identify the strengths and weaknesses providing actionable insights to improve the healthcare services provided to cancer patients. In Libya, the challenge of medical facilities and treatment in healthcare systems of cancer patients is of high importance. This study investigates the relationships between nurse care, attitude of patient, and nurse with cancer patient satisfaction using the quantitative methodology. In this study, a total of 217 identified cancer patients at the National Cancer Institute of Misurata, Libya was assessed for their satisfaction with the medical services provided. The study scale was adopted and adapted from that used by previous researchers to measure nurse care, nurse attitude, patient attitude, hospital service quality, and patient satisfaction on a 5-point Likert scale. A comprehensive approach has been used, where a framework that relates nurse care, nurse attitude of patients and nurse with cancer patient satisfaction while controlling the effects of hospital service quality and patients’ characteristics has been conceptualized to assess the service quality offered by the said hospital. Descriptive statistics, correlation, and multiple regression were applied in the analyses. It pointed to several important areas to enhance the satisfaction of cancer patients by analyzing the level of nurse care, nurse attitude, and patient attitude. The result shows a significant relationship between cancer patient satisfaction and patient attitude, while only possible interactions between cancer patient satisfaction and nurse attitude. On an overall basis, it can be concluded that to increase the satisfaction level of cancer patients, the management should focus on improving the level of nurse care and nurse attitude and carefully monitors patient attitude.
Ng Kim-Soon; Alyaa Idrees Abdulmaged; Salama A. Mostafa; Mazin Abed Mohammed; Fadia Abdalla Musbah; Rabei Raad Ali; Oana Geman. A framework for analyzing the relationships between cancer patient satisfaction, nurse care, patient attitude, and nurse attitude in healthcare systems. Journal of Ambient Intelligence and Humanized Computing 2021, 1 -18.
AMA StyleNg Kim-Soon, Alyaa Idrees Abdulmaged, Salama A. Mostafa, Mazin Abed Mohammed, Fadia Abdalla Musbah, Rabei Raad Ali, Oana Geman. A framework for analyzing the relationships between cancer patient satisfaction, nurse care, patient attitude, and nurse attitude in healthcare systems. Journal of Ambient Intelligence and Humanized Computing. 2021; ():1-18.
Chicago/Turabian StyleNg Kim-Soon; Alyaa Idrees Abdulmaged; Salama A. Mostafa; Mazin Abed Mohammed; Fadia Abdalla Musbah; Rabei Raad Ali; Oana Geman. 2021. "A framework for analyzing the relationships between cancer patient satisfaction, nurse care, patient attitude, and nurse attitude in healthcare systems." Journal of Ambient Intelligence and Humanized Computing , no. : 1-18.
The outbreaks of Coronavirus (COVID-19) epidemic have increased the pressure on healthcare and medical systems worldwide. The timely diagnosis of infected patients is a critical step to limit the spread of the COVID-19 epidemic. The chest radiography imaging has shown to be an effective screening technique in diagnosing the COVID-19 epidemic. To reduce the pressure on radiologists and control of the epidemic, fast and accurate a hybrid deep learning framework for diagnosing COVID-19 virus in chest X-ray images is developed and termed as the COVID-CheXNet system. First, the contrast of the X-ray image was enhanced and the noise level was reduced using the contrast-limited adaptive histogram equalization and Butterworth bandpass filter, respectively. This was followed by fusing the results obtained from two different pre-trained deep learning models based on the incorporation of a ResNet34 and high-resolution network model trained using a large-scale dataset. Herein, the parallel architecture was considered, which provides radiologists with a high degree of confidence to discriminate between the healthy and COVID-19 infected people. The proposed COVID-CheXNet system has managed to correctly and accurately diagnose the COVID-19 patients with a detection accuracy rate of 99.99%, sensitivity of 99.98%, specificity of 100%, precision of 100%, F1-score of 99.99%, MSE of 0.011%, and RMSE of 0.012% using the weighted sum rule at the score-level. The efficiency and usefulness of the proposed COVID-CheXNet system are established along with the possibility of using it in real clinical centers for fast diagnosis and treatment supplement, with less than 2 s per image to get the prediction result.
Alaa S. Al-Waisy; Shumoos Al-Fahdawi; Mazin Abed Mohammed; Karrar Hameed Abdulkareem; Salama A. Mostafa; Mashael S. Maashi; Muhammad Arif; Begonya Garcia-Zapirain. COVID-CheXNet: hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images. Soft Computing 2020, 1 -16.
AMA StyleAlaa S. Al-Waisy, Shumoos Al-Fahdawi, Mazin Abed Mohammed, Karrar Hameed Abdulkareem, Salama A. Mostafa, Mashael S. Maashi, Muhammad Arif, Begonya Garcia-Zapirain. COVID-CheXNet: hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images. Soft Computing. 2020; ():1-16.
Chicago/Turabian StyleAlaa S. Al-Waisy; Shumoos Al-Fahdawi; Mazin Abed Mohammed; Karrar Hameed Abdulkareem; Salama A. Mostafa; Mashael S. Maashi; Muhammad Arif; Begonya Garcia-Zapirain. 2020. "COVID-CheXNet: hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images." Soft Computing , no. : 1-16.
Developing an efficient and quality Software Requirements Specification (SRS) is based on software quality characteristics assessment such as completeness, consistency, feasibility and testability. These characteristics or attributes provide reasonably accurate predictions about system-free bias requirements and hidden assumptions and limit subsequent redesign. They additionally give realistic estimates for costs, risks, and timing of the product. This paper aims to identify possible rules and methods for measuring SRS quality in order to help the engineers to improve the quality of their SRS. The impact of these rules and methods on the software development lifecycle is also reviewed. In this paper, some methods of SRS quality assessment were analyzed from the literature and how to measure the impact of these SRS quality assessment methods on the software development lifecycle are also presented.
Samah W. G. AbuSalim; Rosziati Ibrahim; Salama A. Mostafa; Jahari Abdul Wahab. Analyzing the Impact of Assessing Requirements Specifications on the Software Development Life Cycle. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 12254, 632 -648.
AMA StyleSamah W. G. AbuSalim, Rosziati Ibrahim, Salama A. Mostafa, Jahari Abdul Wahab. Analyzing the Impact of Assessing Requirements Specifications on the Software Development Life Cycle. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; 12254 ():632-648.
Chicago/Turabian StyleSamah W. G. AbuSalim; Rosziati Ibrahim; Salama A. Mostafa; Jahari Abdul Wahab. 2020. "Analyzing the Impact of Assessing Requirements Specifications on the Software Development Life Cycle." Transactions on Petri Nets and Other Models of Concurrency XV 12254, no. : 632-648.
Quality management (QM) has been intensively studied from the perspective of quality management practices (QMP) and market performance in the food manufacturing industry. However, in Asian countries, studies as regards to the sizes of food manufacturing companies are being neglected. Hence, this quantitative study investigates several aspects and focuses on the extent and level of QMP implementation among small, medium, and large food manufacturing companies in Malaysia. A survey questionnaire has been used to collect the data. In general, the results show that the components and types of QMP have the highest impact on large companies and medium companies than the smallest companies. It was found that QMP significantly related to the operational performance and market performance of the food manufacturing companies in Malaysia. Moreover, the verified QMP was particularly important to improve the effectiveness of resource control of small-sized and medium-sized enterprises. The outcome of this study serves as a framework to bring an understanding of QMP and promote continuous QM improvement means to the food manufacturing industries in Malaysia and other countries of the region.
Ng Kim-Soon; Salama Mostafa; Mohammad Nurunnabi; Lim Chin; Nallapaneni Kumar; Rabei Ali; Umashankar Subramaniam. Quality Management Practices of Food Manufacturers: A Comparative Study between Small, Medium and Large Companies in Malaysia. Sustainability 2020, 12, 7725 .
AMA StyleNg Kim-Soon, Salama Mostafa, Mohammad Nurunnabi, Lim Chin, Nallapaneni Kumar, Rabei Ali, Umashankar Subramaniam. Quality Management Practices of Food Manufacturers: A Comparative Study between Small, Medium and Large Companies in Malaysia. Sustainability. 2020; 12 (18):7725.
Chicago/Turabian StyleNg Kim-Soon; Salama Mostafa; Mohammad Nurunnabi; Lim Chin; Nallapaneni Kumar; Rabei Ali; Umashankar Subramaniam. 2020. "Quality Management Practices of Food Manufacturers: A Comparative Study between Small, Medium and Large Companies in Malaysia." Sustainability 12, no. 18: 7725.
Visual cryptography is a cryptographic technique that allows visual information to be encrypted so that the human optical system can perform the decryption without any cryptographic computation. The halftone visual cryptography scheme (HVCS) is a type of visual cryptography (VC) that encodes the secret image into halftone images to produce secure and meaningful shares. However, the HVC scheme has many unsolved problems, such as pixel expansion, low contrast, cross-interference problem, and difficulty in managing share images. This article aims to enhance the visual quality and avoid the problems of cross-interference and pixel expansion of the share images. It introduces a novel optimization of color halftone visual cryptography (OCHVC) scheme by using two proposed techniques: hash codebook and construction techniques. The new techniques distribute the information pixels of a secret image into a halftone cover image randomly based on a bat optimization algorithm. The results show that these techniques have enhanced security levels and make the proposed OCHVC scheme more robust against different attacks. The OCHVC scheme achieves mean squared error (MSE) of 95.0%, peak signal-to-noise ratio (PSNR) of 28.3%, normalized cross correlation (NCC) of 99.4%, and universal quality index (UQI) of 99.3% on average for the six shares. Subsequently, the experiment results based on image quality metrics show improvement in size, visual quality, and security for retrieved secret images and meaningful share images of the OCHVC scheme. Comparing the proposed OCHVC with some related works shows that the OCHVC scheme is more effective and secure.
Firas Mohammed Aswad; Ihsan Salman; Salama A. Mostafa. An optimization of color halftone visual cryptography scheme based on Bat algorithm. Journal of Intelligent Systems 2020, 30, 816 -835.
AMA StyleFiras Mohammed Aswad, Ihsan Salman, Salama A. Mostafa. An optimization of color halftone visual cryptography scheme based on Bat algorithm. Journal of Intelligent Systems. 2020; 30 (1):816-835.
Chicago/Turabian StyleFiras Mohammed Aswad; Ihsan Salman; Salama A. Mostafa. 2020. "An optimization of color halftone visual cryptography scheme based on Bat algorithm." Journal of Intelligent Systems 30, no. 1: 816-835.
Gradually, Mobile Ad-hoc Networks (MANETs) play an important role in the construction of smart organization, resident, campus, search/rescue region and battlefield. MANETs are suitable for providing communication support where no fixed infrastructure exists due to conventional networks neither feasible nor economically profitable. These networks are essentially important in the case of a disaster or natural calamities situations for establishing urgent communication among rescue members. The MANET relies on routing protocols to adapt to the dynamic changes in its topology and maintain the supply of routing information to the nodes. This paper provides a comparative analysis to the most popular routing protocols in MANET environments namely, Destination-Sequenced Distance-Vector (DSDV), Ad-hoc On-demand Distance Vector (AODV) and Ad-hoc On-demand Multipath Distance Vector (AOMDV). The compression covers the single-path and multi-path mechanisms, and reactive and proactive behaviors of the protocols in time-critical events of search and rescue missions. The NS2 simulator is used to test and evaluate the performance of these protocols based on throughput (TP), packet delivery ratio (PDR) and packet loss ratios (PLR), and end-to-end delay (E2E delay). The results show that the most suitable MANET routing protocol for time-critical events of search and rescue missions is the AOMDV.
Salama A. Mostafa; Aida Mustapha; Azizul Azhar Ramli; Mohammed Ahmed Jubair; Mustafa Hamid Hassan; Ali Hashim Abbas. Comparative Analysis to the Performance of Three Mobile Ad-Hoc Network Routing Protocols in Time-Critical Events of Search and Rescue Missions. Advances in Intelligent Systems and Computing 2020, 117 -123.
AMA StyleSalama A. Mostafa, Aida Mustapha, Azizul Azhar Ramli, Mohammed Ahmed Jubair, Mustafa Hamid Hassan, Ali Hashim Abbas. Comparative Analysis to the Performance of Three Mobile Ad-Hoc Network Routing Protocols in Time-Critical Events of Search and Rescue Missions. Advances in Intelligent Systems and Computing. 2020; ():117-123.
Chicago/Turabian StyleSalama A. Mostafa; Aida Mustapha; Azizul Azhar Ramli; Mohammed Ahmed Jubair; Mustafa Hamid Hassan; Ali Hashim Abbas. 2020. "Comparative Analysis to the Performance of Three Mobile Ad-Hoc Network Routing Protocols in Time-Critical Events of Search and Rescue Missions." Advances in Intelligent Systems and Computing , no. : 117-123.
The optimal generation scheduling (OGS) of hydropower units holds an important position in electric power systems, which is significantly investigated as a research issue. Hydropower has a slight social and ecological effect when compared with other types of sustainable power source. The target of long-, mid-, and short-term hydro scheduling (LMSTHS) problems is to optimize the power generation schedule of the accessible hydropower units, which generate maximum energy by utilizing the available potential during a specific period. Numerous traditional optimization procedures are first presented for making a solution to the LMSTHS problem. Lately, various optimization approaches, which have been assigned as a procedure based on experiences, have been executed to get the optimal solution of the generation scheduling of hydro systems. This article offers a complete survey of the implementation of various methods to get the OGS of hydro systems by examining the executed methods from various perspectives. Optimal solutions obtained by a collection of meta-heuristic optimization methods for various experience cases are established, and the presented methods are compared according to the case study, limitation of parameters, optimization techniques, and consideration of the main goal. Previous studies are mostly focused on hydro scheduling that is based on a reservoir of hydropower plants. Future study aspects are also considered, which are presented as the key issue surrounding the LMSTHS problem.
Ali Thaeer Hammid; Omar I. Awad; Mohd Herwan Sulaiman; Saraswathy Shamini Gunasekaran; Salama A. Mostafa; Nallapaneni Manoj Kumar; Bashar Ahmad Khalaf; Yasir Amer Al-Jawhar; Raed Abdulkareem Abdulhasan. A Review of Optimization Algorithms in Solving Hydro Generation Scheduling Problems. Energies 2020, 13, 2787 .
AMA StyleAli Thaeer Hammid, Omar I. Awad, Mohd Herwan Sulaiman, Saraswathy Shamini Gunasekaran, Salama A. Mostafa, Nallapaneni Manoj Kumar, Bashar Ahmad Khalaf, Yasir Amer Al-Jawhar, Raed Abdulkareem Abdulhasan. A Review of Optimization Algorithms in Solving Hydro Generation Scheduling Problems. Energies. 2020; 13 (11):2787.
Chicago/Turabian StyleAli Thaeer Hammid; Omar I. Awad; Mohd Herwan Sulaiman; Saraswathy Shamini Gunasekaran; Salama A. Mostafa; Nallapaneni Manoj Kumar; Bashar Ahmad Khalaf; Yasir Amer Al-Jawhar; Raed Abdulkareem Abdulhasan. 2020. "A Review of Optimization Algorithms in Solving Hydro Generation Scheduling Problems." Energies 13, no. 11: 2787.