Sultan Noman Qasem, Ph.D

Al Imam Mohammad Ibn Saud Islamic University (IMSIU), Computer Science, Faculty Member

Universiti Teknologi Malaysia - UTM, Faculty Computer Science, Faculty Member

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Sultan Noman Qasem is Professor at Imam Mohammad Ibn Saud Islamic University, College of Computer and Information Sciences, Department of Computer Science. He received B.Sc. with honors in 2002 from Mustansiriyah University, Faculty of Science, Computer Science Department, Baghdad, Iraq, He received M.Sc. degree in 2008, Ph.D. degree in 2011 both from University Technology Malaysia (UTM), Faculty of Computing, Johor, Malaysia. He has authored/coauthored over 85 research publications in peer-reviewed reputed journals, book chapters and conference proceedings. He has served as the program committee member of various international conferences and reviewer for various international journals. He has received of many awards, including Microsoft’s award for best paper presented during the Annual Computer Science Postgraduate Annual Research Seminar in 2010, the best student award from the school of graduate studies in UTM in 2011, the Chancellor’ award for excellent academic achievement in UTM in 2011 and Excellent researcher awards for International Publication from Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia from 2013 to 2021. He has directed many funded research projects. He is the technical editor for more journals. His research interests include, Applied Artificial Intelligence, Data Science, Multi-Objective Machine Learning, Object Recognition and Health Informatics.
Phone: +966535289679
Address: 6989, Al Mathnab
3387, Al Falah Dist.
Riyadh
13314
Kingdom of Saudi Arabia

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Papers by Sultan Noman Qasem, Ph.D

Integrating the pressure-state-response model with the extension catastrophe progression for flood risk and resilience assessment

by Sultan Noman Qasem, Ph.D and Amir Mosavi

Environmental and Sustainability Indicators, 2025

Despite the devastating effects of floods, the concept of resilience is still not fully considere... more Despite the devastating effects of floods, the concept of resilience is still not fully considered in the assessment and management of flood risk. To study how resilience can lower the risk of floods and further enhance disaster response, this research aims to close this knowledge gap. With a focus on the Kashkan watershed in Iran, the study combines the extended catastrophe progression method with the pressure-state-response model. Three catastrophe models, namely the cusp, swallowtail, and butterfly, are applied. According to the findings, southern regions, i.e., Pol-Dokhtar city, have the highest risk of floods and the lowest resilience. Resilience and flood risk have a complementary relationship, according to the analysis, and resilience is a helpful metric for risk assessment. The results emphasize the necessity to incorporate resilience-focused pre-disruption planning and post-disaster recovery into flood risk management strategy. This work offers a foundation to incorporate resilience into future flood policies and strategies.

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A deep reinforcement learning approach for wind speed forecasting

by Sultan Noman Qasem, Ph.D and Amir Mosavi

ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS, 2025

The conventional wind forecasting methods often struggle to handle the non-stationary and inconsi... more The conventional wind forecasting methods often struggle to handle the non-stationary and inconsistent wind patterns. This paper presents a hybrid method of Empirical Wavelet Transform (EWT) and Deep Reinforcement Learning (DRL) for wind speed modeling to overcome the forecasting challenges. The EWT method transforms the original wind speed series into several independent modes and a residual series. In addition, the DRL method is utilised to optimise the weights associated with three distinct supervised deep learning models, i.e., Long Short-Term Memory (LSTM), Convolutional Neural Networks with LSTM (CNN-LSTM), and CNN with Gated Recurrent Units (CNN-GRU). The performance of the proposed EWT-DRL is evaluated against deep learning models, including LSTM, CNN-LSTM, CNN-GRU, and their coupling with EWT. The combination of EWT and the DRL (EWT-DRL) method achieves a Mean Absolute Error (MAE) of 0.151, a Mean Squared Error (MSE) of 0.060, a Root Mean Squared Error (RMSE) of 0.192, and a correlation coefficient (R) of 0.9913. These results indicate the effectiveness of EWT-DRL in improving accuracy for wind speed modeling.

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Machine Learning-Based Predictive Models for Detection of Cardiovascular Diseases

Diagnostics, Jan 7, 2024

This article is an open access article distributed under the terms and conditions of the Creative... more

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Security in wireless body area networks via anonymous authentication: Comprehensive literature review, scheme classification, and future challenges

Ad Hoc Networks

An Effective Forecasting Approach of Temperature Enabling Climate Change Analysis in Saudi Arabia

International journal of advanced computer science and applications/International journal of advanced computer science & applications, 2024

Climate change is a global issue with far-reaching consequences, and understanding regional tempe... more Climate change is a global issue with far-reaching consequences, and understanding regional temperature patterns is critical for effective climate change analysis. In this context, accurate forecasting of temperature is critical for mitigating and understanding its impact. This study proposes an effective temperature forecasting approach in Saudi Arabia, a region highly vulnerable to climate change's effects, particularly rising temperatures. The approach uses advanced neural networks models such as the Long Short-Term Memory (LSTM), Gate Recurrent Unit (GRU), and Bidirectional LSTM (BiLSTM) model. A comparative analysis of these models is also introduced to determine the most effective model for forecasting the mean values of temperatures in the following years, understanding climate variability, and informing sustainable adaptation strategies. Several experiments are conducted to train and evaluate the models on a time series data of temperatures in Saudi Arabia, taken from a public dataset of countries' historical global average land temperatures. Performance metrics such as Mean Absolute Error (MAE), Mean Relative Error (MRE), Root Mean Squared Error (RMSE), and coefficient of determination (R-squared) are employed to measure the accuracy and reliability of each model. Experimental results show the models' ability to capture short-term fluctuations and long-term trends in temperature patterns. The findings contribute to the advancement of climate modeling methodologies and offer a basis for selecting a suitable model in similar environmental contexts.

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ECG Signals-Based Security and Steganography Approaches in WBANs: A Comprehensive Survey and Taxonomy

Sustainable Computing: Informatics and Systems

High-performance computing-enabled probabilistic framework for migration from monolithic to microservices architecture using genetic algorithms

Soft Computing

DenseNet-201 and Xception Pre-Trained Deep Learning Models for Fruit Recognition

Electronics

With the dramatic increase of the global population and with food insecurity increasing, it has b... more With the dramatic increase of the global population and with food insecurity increasing, it has become a major concern for both individuals and governments to fulfill the need for foods such as vegetables and fruits. Moreover, the desire for the consumption of healthy food, including fruit, has increased the need for applications in the field of agriculture that help to achieve better methods for fruit sorting and fruit disease prediction and classification. Automated fruit recognition is a potential solution to reduce the time and labor required to identify different fruits in situations such as retail stores during checkout, fruit processing centers during sorting, and orchards during harvest. Automating these processes reduces the need for human intervention, making them cheaper, faster, and immune to human error and biases. Past research in the field has focused mainly on the size, shape, and color features of fruits or employed convolutional neural networks (CNNs) for their cla...

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An Adaptive Early Stopping Technique for DenseNet169-Based Knee Osteoarthritis Detection Model

Diagnostics

Knee osteoarthritis (OA) detection is an important area of research in health informatics that ai... more Knee osteoarthritis (OA) detection is an important area of research in health informatics that aims to improve the accuracy of diagnosing this debilitating condition. In this paper, we investigate the ability of DenseNet169, a deep convolutional neural network architecture, for knee osteoarthritis detection using X-ray images. We focus on the use of the DenseNet169 architecture and propose an adaptive early stopping technique that utilizes gradual cross-entropy loss estimation. The proposed approach allows for the efficient selection of the optimal number of training epochs, thus preventing overfitting. To achieve the goal of this study, the adaptive early stopping mechanism that observes the validation accuracy as a threshold was designed. Then, the gradual cross-entropy (GCE) loss estimation technique was developed and integrated to the epoch training mechanism. Both adaptive early stopping and GCE were incorporated into the DenseNet169 for the OA detection model. The performance ...

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Deep Learning-based Method for Enhancing the Detection of Arabic Authorship Attribution using Acoustic and Textual-based Features

International Journal of Advanced Computer Science and Applications

Authorship attribution (AA) is defined as the identification of the original author of an unseen ... more Authorship attribution (AA) is defined as the identification of the original author of an unseen text. It is found that the style of the author's writing can change from one topic to another, but the author's habits are still the same in different texts. The authorship attribution has been extensively studied for texts written in different languages such as English. However, few studies investigated the Arabic authorship attribution (AAA) due to the special challenges faced with the Arabic scripts. Additionally, there is a need to identify the authors of texts extracted from livestream broadcasting and the recorded speeches to protect the intellectual property of these authors. This paper aims to enhance the detection of Arabic authorship attribution by extracting different features and fusing the outputs of two deep learning models. The dataset used in this study was collected from the weekly livestream and recorded Arabic sermons that are available publicly on the official website of Al-Haramain in Saudi Arabia. The acoustic, textual and stylometric features were extracted for five authors. Then, the data were pre-processed and fed into the deep learning-based models (CNN architecture and its pre-trained ResNet34). After that the hard and soft voting ensemble methods were applied for combining the outputs of the applied models and improve the overall performance. The experimental results showed that the use of CNN with textual data obtained an acceptable performance using all evaluation metrics. Then, the performance of ResNet34 model with acoustic features outperformed the other models and obtained the accuracy of 90.34%. Finally, the results showed that the soft voting ensemble method enhanced the performance of AAA and outperformed the other method in terms of accuracy and precision, which obtained 93.19% and 0.9311 respectively.

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A Review on Neural Network Based Models for Short Term Solar Irradiance Forecasting

Applied Sciences

The accuracy of solar energy forecasting is critical for power system planning, management, and o... more The accuracy of solar energy forecasting is critical for power system planning, management, and operation in the global electric energy grid. Therefore, it is crucial to ensure a constant and sustainable power supply to consumers. However, existing statistical and machine learning algorithms are not reliable for forecasting due to the sporadic nature of solar energy data. Several factors influence the performance of solar irradiance, such as forecasting horizon, weather classification, and performance evaluation metrics. Therefore, we provide a review paper on deep learning-based solar irradiance forecasting models. These models include Long Short-Term Memory (LTSM), Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), Generative Adversarial Networks (GAN), Attention Mechanism (AM), and other existing hybrid models. Based on our analysis, deep learning models perform better than conventional models in solar forecasting applications, especia...

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A Novel Honey Badger Algorithm with Multilayer Perceptron for Forecasting COVID-19 Time Series Data

Research Square (Research Square), Apr 18, 2023

Prediction of the SARS-CoV-2 Derived T-Cell Epitopes’ Response Against COVID Variants

Computers, Materials & Continua

To Cluster or Not to Cluster: The Impact of Clustering on the Performance of Aspect-Based Collaborative Filtering

IEEE Access

Collaborative filtering (CF) is one of the most widely utilised approaches in recommendation tech... more Collaborative filtering (CF) is one of the most widely utilised approaches in recommendation techniques. It suggests items to users based on the ratings of other users who share their preferences. Thus, one of the aims of CF is to find reliable neighbours. Typically, CF produces a sparse user-item rating matrix, when relying only on the ratings to identify the precise neighbours, resulting in poor performance. User reviews can be essential in overcoming those situations because of the diverse elements available in reviews. The most popular element is aspects, which can provide a fine-grained analysis of users' behaviours, thus improving personalised recommendations. However, increasing the number of aspects also results in sparsity, therefore may deteriorate the recommendation performance. As a result, clustering of aspects may lessen this sparsity, but it is yet unclear how much this would affect the performance of CF systems. This study proposes a CF approach based on aspect clustering that addresses the above issue in terms of rating prediction. The approach aims to reduce the sparseness in the multi-criteria rating matrix by grouping aspects into clusters based on their semantic similarity, which will be less expensive and require less memory to discover the neighbourhood set. Our approach extracts aspects and represents them using Google's pretrained Word2vec model. Then, aspects are organised into clusters using the K-means clustering algorithm. Multi-dimensional Euclidean distance is used as a similarity measure for finding the appropriate neighbours and predicted ratings of unseen items are then made using the kNN algorithm. This study also identifies the number of aspects that significantly impacts CF performance. Experiments are carried out using a real large-scale dataset: the Amazon movie dataset. Evaluation is also performed by comparing CF performance of the proposed approach with three different baseline approaches. Results show that the proposed approach improves CF performance compared to other approaches in terms of three predictive accuracy metrics.

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Hybrib NSGA-II optimization for improving the three-term BP network for multiclass classification problems

Recently, hybrid algorithms have received considerable attention from a number of researchers. Th... more Recently, hybrid algorithms have received considerable attention from a number of researchers. This paper presents a hybrid of the multiobjective evolutionary algorithm to gain a better accuracy of the fi nal solutions.The aim of using the hybrid algorithm is to improve the multiobjective evolutionary algorithm performance in terms of the enhancement of all the individuals in the population and increase the quality of the Pareto optimal solutions.The multiobjective evolutionary algorithm used in this study is a nondominated sorting genetic algorithm-II (NSGA-II) together with its hybrid, the backpropagation algorithm (BP), which is used as a local search algorithm to optimize the accuracy and complexity of the three-term backpropagation (TBP) network. The outcome positively demonstrates that the hybrid algorithm is able to improve the classification performance with a smaller number of hidden nodes and is effective in multiclass classifi cation problems.Furthermore, the results indi...

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Cluster ensemble of valid small clusters

Journal of Intelligent & Fuzzy Systems, 2020

During the last decade, ensemble clustering has been the subject of many researches in data minin... more During the last decade, ensemble clustering has been the subject of many researches in data mining. In ensemble clustering, several basic partitions are first generated and then a function is used for the clustering aggregation in order to create a final partition that is similar to all of the basic partitions as much as possible. Ensemble clustering has been proposed to enhance efficiency, strength, reliability, and stability of the clustering. A common slogan concerning the ensemble clustering techniques is that "the model combining several poorer models is better than a stronger model". Here at this paper, an ensemble clustering method is proposed using the basic k-means clustering method as its base clustering algorithm. Also, this study could raise the diversity of consensus by adopting some measures. Although our clustering ensemble approach has the strengths of kmeans, such as its efficacy and low complexity, it lacks the drawbacks which the kmeans suffers from; such as its problem in detection of clusters that are not uniformly distributed or in the circular shape. In the empirical studies, we test the proposed ensemble clustering algorithm as well as the other up-to-date cluster ensembles on different data-sets. Based on the experimental results, our cluster ensemble method is stronger than the recent competitor cluster ensemble algorithms and is the most up-to-date clustering method available.

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Smart Home Privacy Protection Methods against a Passive Wireless Snooping Side-Channel Attack

Sensors

Smart home technologies have attracted more users in recent years due to significant advancements... more Smart home technologies have attracted more users in recent years due to significant advancements in their underlying enabler components, such as sensors, actuators, and processors, which are spreading in various domains and have become more affordable. However, these IoT-based solutions are prone to data leakage; this privacy issue has motivated researchers to seek a secure solution to overcome this challenge. In this regard, wireless signal eavesdropping is one of the most severe threats that enables attackers to obtain residents’ sensitive information. Even if the system encrypts all communications, some cyber attacks can still steal information by interpreting the contextual data related to the transmitted signals. For example, a “fingerprint and timing-based snooping (FATS)” attack is a side-channel attack (SCA) developed to infer in-home activities passively from a remote location near the targeted house. An SCA is a sort of cyber attack that extracts valuable information from...

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Augmenting IoT Intrusion Detection System Performance Using Deep Neural燦etwork

Computers, Materials & Continua

Due to their low power consumption and limited computing power, Internet of Things (IoT) devices ... more Due to their low power consumption and limited computing power, Internet of Things (IoT) devices are difficult to secure. Moreover, the rapid growth of IoT devices in homes increases the risk of cyber-attacks. Intrusion detection systems (IDS) are commonly employed to prevent cyberattacks. These systems detect incoming attacks and instantly notify users to allow for the implementation of appropriate countermeasures. Attempts have been made in the past to detect new attacks using machine learning and deep learning techniques, however, these efforts have been unsuccessful. In this paper, we propose two deep learning models to automatically detect various types of intrusion attacks in IoT networks. Specifically, we experimentally evaluate the use of two Convolutional Neural Networks (CNN) to detect nine distinct types of attacks listed in the NF-UNSW-NB15-v2 dataset. To accomplish this goal, the network stream data were initially converted to twodimensional images, which were then used to train the neural network models. We also propose two baseline models to demonstrate the performance of the proposed models. Generally, both models achieve high accuracy in detecting the majority of these nine attacks.

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Analisis Kepatuhan Dokter Dalam Mengisi Formulir Care Plan Pada Pasien Rawat Inap DI Rsup Fatmawati Tahun 2016 Analysis of Doctor S Adherence to Fill Care Plan Form in Fatmawati Hospital 2016

$Research paper thumbnail of Testing the equality of several independent stationary and non-stationary time series models with fractional Brownian motion errors$

Testing the equality of several independent stationary and non-stationary time series models with fractional Brownian motion errors

Alexandria Engineering Journal, 2021

Abstract This work is devoted to apply the parametric and nonparametric techniques to construct t... more Abstract This work is devoted to apply the parametric and nonparametric techniques to construct test of hypothesis about the equality of the probabilistic behaviors of several time series models with fractional Brownian motion errors fitted on several independent datasets. The accuracy and power of the introduced method are studied using the simulated and real datasets. The results indicate that the introduced approach is more powerful than other alternative approaches, in non-stationary cases.

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