Browsing by Author "Mohammed Imran Basheer Ahmed"

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A Novel Ensemble-Based Technique for the Preemptive Diagnosis of Rheumatoid Arthritis Disease in the Eastern Province of Saudi Arabia Using Clinical Data
(2022) Aisha Alansari; Heba Alkhorasani; Alkhorasani, Heba; Alsubaii, Meelaf; Sakloua, Rasha; Alzahrani, Reem; Alsaleem, Yasmeen; Reem Alassaf; Alhamad, Nada; Mohammed Imran Basheer Ahmed; Alshobbar, Zainab; Alassaf, Reem; Farooqui, Mehwash; Ahmed, Mohammed Imran Basheer
Rheumatoid arthritis (RA) is a chronic inflammatory disease caused by numerous genetic and environmental factors leading to musculoskeletal system pain. RA may damage other tissues and organs, causing complications that severely reduce patients’ quality of life. According to the World Health Organization (WHO), over 1.71 billion individuals worldwide had musculoskeletal problems in 2021. Rheumatologists face challenges in the early detection of RA since its symptoms are similar to other illnesses, and there is no definitive test to diagnose the disease. Accordingly, it is preferable to profit from the power of computational intelligence techniques that can identify hidden patterns to diagnose RA early. Although multiple studies were conducted to diagnose RA early, they showed unsatisfactory performance, with the highest accuracy of 87.5% using imaging data. Yet, imaging data requires diagnostic tools that are challenging to collect and examine and are more costly. Recent studies indicated that neither a blood test nor a physical finding could early confirm the diagnosis. Therefore, this study proposes a novel ensemble technique for the preemptive prediction of RA and investigates the possibility of diagnosing the disease using clinical data before the symptoms appear. Two datasets were obtained from King Fahad University Hospital (KFUH), Dammam, Saudi Arabia, including 446 patients, with 251 positive cases of RA and 195 negative cases of RA. Two experiments were conducted where the former was developed without upsampling the dataset, and the latter was carried out using an upsampled dataset. Multiple machine learning (ML) algorithms were utilized to assemble the novel voting ensemble, including support vector machine (SVM), logistic regression (LR), and adaptive boosting (Adaboost). The results indicated that clinical laboratory tests fed to the proposed voting ensemble technique could accurately diagnose RA preemptively with an accuracy, recall, and precision of 94.03%, 96.00%, and 93.51%, respectively, with 30 clinical features when utilizing the original data and sequential forward feature selection (SFFS) technique. It is concluded that deploying the proposed model in local hospitals can contribute to introducing a method that aids medical specialists in preemptively diagnosing RA and stopping or delaying the course using clinical laboratory tests.
A Novel Metadata Based Multi-Label Document Classification Technique
(2023) Reem Alzaher; Ahmad, Munir; Rahman, Atta-ur; Zaman, Gohar; Ahmed, Mohammed-Salih; Mohammed Salih Ahmed; Ahmed, Mohammed-Imran-B.; Krishnasamy, Gomathi; Gomathi Krishnasamy; Alkharraa, Mariam; AlKhulaifi, Dania; Linah Saraireh; Salam, Asiya-A.; Saraireh, Linah; Gollapalli, Mohammed; Ahmed, Rashad
From the beginning, the process of research and its publication is an ever-growing phenomenon and with the emergence of web technologies, its growth rate is overwhelming. On a rough estimate, more than thirty thousand research journals have been issuing around four million papers annually on average. Search engines, indexing services, and digital libraries have been searching for such publications over the web. Nevertheless, getting the most relevant articles against the user requests is yet a fantasy. It is mainly because the articles are not appropriately indexed based on the hierarchies of granular subject classification. To overcome this issue, researchers are striving to investigate new techniques for the classification of the research articles especially, when the complete article text is not available (a case of non-open access articles). The proposed study aims to investigate the multilabel classification over the available metadata in the best possible way and to assess, “to what extent metadata-based features can perform in contrast to content-based approaches.” In this regard, novel techniques for investigating multilabel classification have been proposed, developed, and evaluated on metadata such as the Title and Keywords of the articles. The proposed technique has been assessed for two diverse datasets, namely, from the Journal of universal computer science (J.UCS) and the benchmark dataset comprises of the articles published by the Association for computing machinery (ACM). The proposed technique yields encouraging results in contrast to the state-of-the-art techniques in the literature.
A Real-Time Computer Vision Based Approach to Detection and Classification of Traffic Incidents
(2023) Basheer Ahmed, Mohammed Imran; Sarah Alsharif; Ahmed, Mohammed Salih; Bashayr Adnan Albin Saad; Alsharif, Sarah; Mohammed Imran Basheer Ahmed; Albin Saad, Bashayr Adnan; Alsabt, Reema; Atta-ur-Rahman; Gomathi Krishnasamy
To constructively ameliorate and enhance traffic safety measures in Saudi Arabia, a prolific number of AI (Artificial Intelligence) traffic surveillance technologies have emerged, including Saher, throughout the past years. However, rapidly detecting a vehicle incident can play a cardinal role in ameliorating the response speed of incident management, which in turn minimizes road injuries that have been induced by the accident’s occurrence. To attain a permeating effect in increasing the entailed demand for road traffic security and safety, this paper presents a real-time traffic incident detection and alert system that is based on a computer vision approach. The proposed framework consists of three models, each of which is integrated within a prototype interface to fully visualize the system’s overall architecture. To begin, the vehicle detection and tracking model utilized the YOLOv5 object detector with the DeepSORT tracker to detect and track the vehicles’ movements by allocating a unique identification number (ID) to each vehicle. This model attained a mean average precision (mAP) of 99.2%. Second, a traffic accident and severity classification model attained a mAP of 83.3% while utilizing the YOLOv5 algorithm to accurately detect and classify an accident’s severity level, sending an immediate alert message to the nearest hospital if a severe accident has taken place. Finally, the ResNet152 algorithm was utilized to detect the ignition of a fire following the accident’s occurrence; this model achieved an accuracy rate of 98.9%, with an automated alert being sent to the fire station if this perilous event occurred. This study employed an innovative parallel computing technique for reducing the overall complexity and inference time of the AI-based system to run the proposed system in a concurrent and parallel manner.
Assessment of Information Extraction Techniques, Models and Systems
(2022) Rahman, Atta; Musleh, Dhiaa; Bou Zain Edden, Majed; Alubaidan, Haya; Dhiaa Abdulrab Ali Musleh; Krishna, Gomathi; Gomathi Krishnasamy; Mohammad Aftab Alam Khan; Mehwash Farooqui; Basheer Ahmed, Mohammed; Ahmed, Mohammed; Mahmud, Maqsood
The present article aims to review and evaluate the practiced and classical techniques, tools, models, and systems concerning automatic information extraction (IE) from published scientific documents like research articles, patents, theses, technical reports, and case studies etc. IE is performed for various reasons such as better indexing, archiving, searching, and retrieving. That is mainly used by the search engines and the indexing services as well the digital libraries and semantic web. In this regard, several studies have been conducted targeting various nature of documents. The study pays special consideration to the successful IE models, algorithms and approaches applied to structural IE from published documents. To grasp this, the paper is classified into several segments and each segment covers a significant aspect of IE. Furthermore, to validate their benefits and drawbacks, a comparative study of all the approaches have been conducted in terms of various performance factors like precision, accuracy, recall and F-score. Potential areas of improvement have been emphasized as research gap for the scholars in the closely related areas. Ultimately, a comprehensive summary of the evaluation is presented in tabular form and review is concluded. It was observed that the hybrid methods outperform the other methods due to their versatile nature to address various document formats.
Comprehensible Machine-Learning-Based Models for the Pre-Emptive Diagnosis of Multiple Sclerosis Using Clinical Data: A Retrospective Study in the Eastern Province of Saudi Arabia
(2023) Olatunji, Sunday O.; Alsheikh, Nawal; Lujain Alnajrani; Alhatoon Alanazy; Meshael Almusairii; Salam Alshammasi; Alansari, Aisha; Alaa Alahmadi; Rim Zaghdoud; Mohammed Salih Ahmed; Mohammed Imran Basheer Ahmed; Alhiyafi, Jamal
Multiple Sclerosis (MS) is characterized by chronic deterioration of the nervous system, mainly the brain and the spinal cord. An individual with MS develops the condition when the immune system begins attacking nerve fibers and the myelin sheathing that covers them, affecting the communication between the brain and the rest of the body and eventually causing permanent damage to the nerve. Patients with MS (pwMS) might experience different symptoms depending on which nerve was damaged and how much damage it has sustained. Currently, there is no cure for MS; however, there are clinical guidelines that help control the disease and its accompanying symptoms. Additionally, no specific laboratory biomarker can precisely identify the presence of MS, leaving specialists with a differential diagnosis that relies on ruling out other possible diseases with similar symptoms. Since the emergence of Machine Learning (ML) in the healthcare industry, it has become an effective tool for uncovering hidden patterns that aid in diagnosing several ailments. Several studies have been conducted to diagnose MS using ML and Deep Learning (DL) models trained using MRI images, achieving promising results. However, complex and expensive diagnostic tools are needed to collect and examine imaging data. Thus, the intention of this study is to implement a cost-effective, clinical data-driven model that is capable of diagnosing pwMS. The dataset was obtained from King Fahad Specialty Hospital (KFSH) in Dammam, Saudi Arabia. Several ML algorithms were compared, namely Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), and Extra Trees (ET). The results indicated that the ET model outpaced the rest with an accuracy of 94.74%, recall of 97.26%, and precision of 94.67%.
Early Identification of COVID-19 Using Dynamic Fuzzy Rule Based System
(2021) Fatimah Alamoudi; Raghad Baageel; Amjad Alqarni; Mohammed Imran Basheer Ahmed; Atta-ur-Rahman; Mehwash Farooqui
The undergoing research aims to address the problem of COVID-19 which has turned out to be a global pandemic. Despite developing some successful vaccines, the pace has not overcome so far. Several studies have been proposed in the literature in this regard, the present study is unique in terms of its dynamic nature to adapt the rules by reconfigurable fuzzy membership function. Based on patient’s symptoms (fever, dry cough etc.) and history related to travelling, diseases/medications and interactions with confirmed patients, the proposed dynamic fuzzy rule-based system (FRBS) identifies the presence/absence of the disease. This can greatly help the healthcare professionals as well as laymen in terms of disease identification. The main motivation of this paper is to reduce the pressure on the health services due to frequent test assessment requests, in which patients can do the test anytime without the need to make reservations. The main findings are that there is a relationship between the disease and the symptoms in which some symptoms can indicate the probability of the presence of the disease such as high difficulty of breathing, cough, sore throat, and so many more. By knowing the common symptoms, we developed membership functions for these symptoms, and a model generated to distinguish between infected and non-infected people with the help of survey data collected. The model gave an accuracy of 88.78%, precision of 72.22%, sensitivity of 68.42%, specificity of 93.67%, and an f1-score of 69.28%.
Ensemble Machine Learning Based Identification of Adult Epilepsy
(2023) Basheer Ahmed, Mohammed; Zaghdoud, Rim; Rayan Altamimi; Abdullah Alshammari; Abdulaziz Noaman; Mariam Alkharraa; Noaman, Abdulaziz; Ahmed, Mohammed; Alshamrani, Rahaf; Alkharraa, Mariam; Rahman, Atta; Krishna, Gomathi
Epilepsy is a chronic non-communicable illness that affects brain individuals and impacts more than 50 million people globally. To predict epileptic seizures, we proposed machine learning-based ensemble learning technique in this study. In the pre-processed stage, we applied some important techniques such as Power line noise reduction and dividing the record into windows of 5 seconds. The project is created by the help of ensemble machine learning technique, which employs several machine learning algorithms, we used the following algorithms: decision tree, support vector machine, artificial neural networks, and convolutional neural networks. We used a dataset from PhysioNet website that contains adult EEG signals. Several convolutional layers were used to extract features from the EEG signals, after that, the feature set is utilized to train a classifier model, which combines the results. Our approach successfully reached 91% accuracy while 91% sensitivity and 91% specificity, respectively.
Intelligent Directional Survey Data Analysis to Improve Directional Data Acquisition
(2023) Moosa Alrabeea; Gomathi Krishnasamy; Abdullah Alturkey, Mohammed Imran Basheer Ahmed; Rim Ali Zaghdoud; Mohammed Salih Ahmed; Mousa Alrabeea; Abdullatif Alsuwaiti; Rim Zaghdoud; Ahmed Alyousef; Mohammad Aftab Alam Khan; Sghaier Chabani; Sghaier
Many countries rely on oil and gas production as it is an essential part of the global economy. As a result, various challenges may thrive from the process of extracting oil and gas from the ground that may affect the operational aspects of the construction process. So, it is important to maintain the production and Health, Safety & Environment (HSE). The project aims to automate the process of the Directional Survey Data (DSD) in a way that can be cost-effective for the operational process and more stable for future use. DSD relates to the process of horizontal directional drilling (HDD) and raw data obtained from the surveys using survey stations on the way to bore hole like azimuth and inclination etc. In this work, we propose a fully automatic Directional Survey Data Analysis system based on the recognition patterns. The dataset comprised of 34069 real-time instances has been used. Two machine learning algorithms and four deep learning algorithms were investigated in this regard. For the deep learning approach RNN, LSTM, BI-LSTM, and Extreme Learning Machine (ELM) were used, and for the machine learning approach SVM and Naïve Bayes have been investigated. Selection of these candidate approaches was based on their promising nature in the related fields of study in terms of accuracy and precision. The experimental result demonstrated that Naïve Bays got 100% accuracy, ANN, LSTM and GRU managed to get 100% accuracy, BI-LSTM had a slightly lower accuracy achieving 98.7%, Simple RNN was lower than BI-LSTM achieving 82% accuracy, SVM got 81.1% accuracy, while ELM had the lowest performance receiving 55.3% accuracy. Overall, the scheme outperforms state-of-the-art techniques in the literature.
Network Anomaly Detection in 5G Networks
(2022) Dakheel Almogbil; Mahmud, Maqsood; Maqsood Mahmud; Tahir Iqbal Linah Saraireh; Kholidy, Hisham; Dhiaa Abdulrab Ali Musleh; Fahd Abdulsalam Alhaidari; Mohammed Imran Basheer Ahmed; Almoqbil, Dakheel; Basheer Ahmed, Mohammed
Preemptive Diagnosis of Alzheimer’s Disease in the Eastern Province of Saudi Arabia Using Computational Intelligence Techniques
(2022) Olatunji, Sunday O.; Alansari, Aisha; Alkhorasani, Heba; Mohammed Imran Basheer Ahmed; Sakloua, Rasha; Heba Alkhorasani; Meelaf Alsubaii; Rasha Sakloua; Farooqui, Mehwash; Yasmeen Alsaleem; Alhiyafi, Jamal
Alzheimer’s Disease (AD) is a silent disease that causes the brain cells to die progressively, influencing consciousness, behavior, planning ability, and language to name a few. AD increases exponentially with aging, where it doubles every 5-6 years, causing profound implications, such as swallowing difficulties and losing the ability to speak before death. According to the Ministry of Health in Saudi Arabia, AD patients will triple by 2060 to reach 14 million patients worldwide. The rapid rise of patients is caused by the silent progress of the disease, leading to late diagnosis as the symptoms will not be distinguished from normal aging affect. Moreover, with the current medical capabilities, it is impossible to confirm AD with 100% certainty via specific medical examinations. The literature review revealed that most recent publications used images to diagnose AD, which is insufficient for local hospitals with limited imaging capabilities. Other studies that used clinical and demographical data failed to achieve adequate results. Consequently, this study aims to preemptively predict AD in Saudi Arabia by employing machine learning (ML) techniques. The dataset was acquired from King Fahad Specialist Hospital (KFSH) in Dammam, Saudi Arabia, containing standard clinical tests for 152 patients. Four ML algorithms, namely, support vector machine (SVM), k-nearest neighbors (k-NN), Adaptive Boosting (AdaBoost), and eXtreme Gradient Boosting (XGBoost), were employed to preemptively diagnose the disease. The empirical results demonstrated the robustness of SVM in the pre-emptive diagnosis of AD with accuracy, precision, recall, and area under the receiver operating characteristics (AUROC) of 95.56%, 94.70%, 97.78%, and 0.97, respectively, with 13 features after applying the sequential forward feature selection technique. This model can assist the medical staff in controlling the progression of the disease at low costs.
Single vs. Multi-Label: The Issues, Challenges and Insights of Contemporary Classification Schemes
(2023) Sajid, Naseer Ahmed; Rahman, Atta; Dhiaa Musleh; Mohammed Imran Basheer Ahmed; Reem Alassaf; Alassaf, Reem; Chabani, Sghaier; Asiya Abdus Salam; Salam, Asiya Abdus; AlKhulaifi, Dania
Over the decades, a tremendous increase has been witnessed in the production of documents available in digital form. The increased production of documents has gained so much momentum that their rate of production jumps two-fold every five years. These articles are searched over the internet via search engines, digital libraries, and citation indexes. However, the retrieval of relevant research papers for user queries is still a pipedream. This is because scientific documents are not indexed based on some subject classification hierarchies. Hence, the classification of these documents becomes a challenging task for the researchers. Classification of the documents can be two-fold: one way is to assign a single label to each document and the other is to assign multi-labels to each document based on its belonging domains. Classification of the documents can be performed by using either the available metadata or the whole content of the documents. While performing classification, there are many challenges which may belong to the dataset, feature selection technique, preprocessing methodology, and which classification model is suitable for the classification of the documents. This paper highlights the issues for single-label and multi-label classification by using either metadata or content of the documents and why metadata-based approaches are better than content-based approaches in terms of feasibility.