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Amgad Muneer - received his B. Eng degree (with honors) in Mechatronic Engineering from Asia Pacific University of Technology and Innovation (APU), Malaysia in 2018. Currently, pursuing Master's in Information Technology and working as a research officer in the Department of Computer and Information Sciences in Universiti Teknologi PETRONAS, Malaysia. He has authored several ISI and Scopus journal/conference papers. He is a reviewer in some international impact-factor journals such as the Journal of Combinatorial Optimization and several IGI global journals.
Big data is rapidly being seen as a new frontier for improving organizational performance. However, it is still in its early phases of implementation in developing countries’ healthcare organizations. As data-driven insights become critical competitive advantages, it is critical to ascertain which elements influence an organization’s decision to adopt big data. The aim of this study is to propose and empirically test a theoretical framework based on technology–organization–environment (TOE) factors to identify the level of readiness of big data adoption in developing countries’ healthcare organizations. The framework empirically tested 302 Malaysian healthcare employees. The structural equation modeling was used to analyze the collected data. The results of the study demonstrated that technology, organization, and environment factors can significantly contribute towards big data adoption in healthcare organizations. However, the complexity of technology factors has shown less support for the notion. For technology practitioners, this study showed how to enhance big data adoption in healthcare organizations through TOE factors.
Ebrahim Ghaleb; P. Dominic; Suliman Fati; Amgad Muneer; Rao Ali. The Assessment of Big Data Adoption Readiness with a Technology–Organization–Environment Framework: A Perspective towards Healthcare Employees. Sustainability 2021, 13, 8379 .
AMA StyleEbrahim Ghaleb, P. Dominic, Suliman Fati, Amgad Muneer, Rao Ali. The Assessment of Big Data Adoption Readiness with a Technology–Organization–Environment Framework: A Perspective towards Healthcare Employees. Sustainability. 2021; 13 (15):8379.
Chicago/Turabian StyleEbrahim Ghaleb; P. Dominic; Suliman Fati; Amgad Muneer; Rao Ali. 2021. "The Assessment of Big Data Adoption Readiness with a Technology–Organization–Environment Framework: A Perspective towards Healthcare Employees." Sustainability 13, no. 15: 8379.
In biological systems, Nitration is a crucial post-translational modification which occurs on various amino acids. Nitration of Tyrosine is regarded as nitorsative stress biomarker resulting in the formation of peroxynitrite and other reactive and harmful nitrogen species. NitroTyrosine is closely related to Carcinogenesis, tumor growth progression and other major pathological conditions including systemic autoimmune diseases, inflammation, neurodegeneration and cardiovascular disorders. Additionally, the alteration in Nitrotyrosine profile occurs well before appearance of any symptoms of aforementioned diseases making nitrotyrosine a biomarker and potential target for early prognosis of aforementioned diseases. The wet lab identification of potential nitrotyrosine sites is laborious, time-taking and costly due to challenges of in vitro, ex vivo and in vivo identification processes. To supplement wet lab identification of nitrotyrosine, we proposed, implemented and evaluated a different approach to develop tyrosine nitration site predictors using pseudo amino acid compositions (PseAAC) and deep neural networks (DNNs). Proposed approach does not require any feature extraction and uses DNNs for learning a feature representation of peptide sequences and classification thereof. Validation of proposed approach is done using well-known model evaluation measures. Among different deep neural networks, convolutional neural network-based predictor achieved best scores on independent dataset with accuracy of 87.2%, matthew’s correlation coefficient score of 0.74 and AuC score of 0.91 which outperforms the previous reported scores of Nitrotyrosine predictors.
Sheraz Naseer; Rao Faizan Ali; Suliman Mohamed Fati; Amgad Muneer. iNitroY-Deep: Computational Identification of Nitrotyrosine Sites to Supplement Carcinogenesis Studies Using Deep Learning. IEEE Access 2021, 9, 73624 -73640.
AMA StyleSheraz Naseer, Rao Faizan Ali, Suliman Mohamed Fati, Amgad Muneer. iNitroY-Deep: Computational Identification of Nitrotyrosine Sites to Supplement Carcinogenesis Studies Using Deep Learning. IEEE Access. 2021; 9 (99):73624-73640.
Chicago/Turabian StyleSheraz Naseer; Rao Faizan Ali; Suliman Mohamed Fati; Amgad Muneer. 2021. "iNitroY-Deep: Computational Identification of Nitrotyrosine Sites to Supplement Carcinogenesis Studies Using Deep Learning." IEEE Access 9, no. 99: 73624-73640.
High blood pressure (BP) may lead to further health complications if not monitored and controlled, especially for critically ill patients. Particularly, there are two types of blood pressure monitoring, invasive measurement, whereby a central line is inserted into the patient’s body, which is associated with infection risks. The second measurement is cuff-based that monitors BP by detecting the blood volume change at the skin surface using a pulse oximeter or wearable devices such as a smartwatch. This paper aims to estimate the blood pressure using machine learning from photoplethysmogram (PPG) signals, which is obtained from cuff-based monitoring. To avoid the issues associated with machine learning such as improperly choosing the classifiers and/or not selecting the best features, this paper utilized the tree-based pipeline optimization tool (TPOT) to automate the machine learning pipeline to select the best regression models for estimating both systolic BP (SBP) and diastolic BP (DBP) separately. As a pre-processing stage, notch filter, band-pass filter, and zero phase filtering were applied by TPOT to eliminate any potential noise inherent in the signal. Then, the automated feature selection was performed to select the best features to estimate the BP, including SBP and DBP features, which are extracted using random forest (RF) and k-nearest neighbors (KNN), respectively. To train and test the model, the PhysioNet global dataset was used, which contains 32.061 million samples for 1000 subjects. Finally, the proposed approach was evaluated and validated using the mean absolute error (MAE). The results obtained were 6.52 mmHg for SBS and 4.19 mmHg for DBP, which show the superiority of the proposed model over the related works.
Suliman Fati; Amgad Muneer; Nur Akbar; Shakirah Taib. A Continuous Cuffless Blood Pressure Estimation Using Tree-Based Pipeline Optimization Tool. Symmetry 2021, 13, 686 .
AMA StyleSuliman Fati, Amgad Muneer, Nur Akbar, Shakirah Taib. A Continuous Cuffless Blood Pressure Estimation Using Tree-Based Pipeline Optimization Tool. Symmetry. 2021; 13 (4):686.
Chicago/Turabian StyleSuliman Fati; Amgad Muneer; Nur Akbar; Shakirah Taib. 2021. "A Continuous Cuffless Blood Pressure Estimation Using Tree-Based Pipeline Optimization Tool." Symmetry 13, no. 4: 686.
Amidation is an important post translational modification where a peptide ends with an amide group (–NH2) rather than carboxyl group (–COOH). These amidated peptides are less sensitive to proteolytic degradation with extended half-life in the bloodstream. Amides are used in different industries like pharmaceuticals, natural products, and biologically active compounds. The in-vivo, ex-vivo, and in-vitro identification of amidation sites is a costly and time-consuming but important task to study the physiochemical properties of amidated peptides. A less costly and efficient alternative is to supplement wet lab experiments with accurate computational models. Hence, an urgent need exists for efficient and accurate computational models to easily identify amidated sites in peptides. In this study, we present a new predictor, based on deep neural networks (DNN) and Pseudo Amino Acid Compositions (PseAAC), to learn efficient, task-specific, and effective representations for valine amidation site identification. Well-known DNN architectures are used in this contribution to learn peptide sequence representations and classify peptide chains. Of all the different DNN based predictors developed in this study, Convolutional neural network-based model showed the best performance surpassing all other DNN based models and reported literature contributions. The proposed model will supplement in-vivo methods and help scientists to determine valine amidation very efficiently and accurately, which in turn will enhance understanding of the valine amidation in different biological processes.
Sheraz Naseer; Rao Ali; Amgad Muneer; Suliman Fati. iAmideV-Deep: Valine Amidation Site Prediction in Proteins Using Deep Learning and Pseudo Amino Acid Compositions. Symmetry 2021, 13, 560 .
AMA StyleSheraz Naseer, Rao Ali, Amgad Muneer, Suliman Fati. iAmideV-Deep: Valine Amidation Site Prediction in Proteins Using Deep Learning and Pseudo Amino Acid Compositions. Symmetry. 2021; 13 (4):560.
Chicago/Turabian StyleSheraz Naseer; Rao Ali; Amgad Muneer; Suliman Fati. 2021. "iAmideV-Deep: Valine Amidation Site Prediction in Proteins Using Deep Learning and Pseudo Amino Acid Compositions." Symmetry 13, no. 4: 560.
The mining industry has been recognized as one of the most hazardous industry. With the growth of the industry, the number of human casualties has increased. The mines can be very lethal for workers or in the event of an accident or with high humidity and temperature leading to workers fainting. Some of the problems that besets are of air blast, ground movement, dust explosion, inundation etc. This study reviews the common problems associated with Carbon Monoxide content with temperature and humidity. In this paper, we assemble an automatic robotic scanning and inspection mechanism for mines that is designed and assembled to recognize Carbon Monoxide (CO), humidity and temperature variance inside the mines. The proposed system employees a mobile robot that can be manually controlled by a self-developed mobile application and an Internet of Things (IoT) system. The sensors included take the input from the air and transmit them to the mobile application using Bluetooth module. The experimental results show that the IoT achieved an accuracy of 97.5 % for the mobile robot and the sensor system.
Ibrahim Qadri; Amgad Muneer; Suliman Mohamed Fati. Automatic robotic scanning and inspection mechanism for mines using IoT. IOP Conference Series: Materials Science and Engineering 2021, 1045, 012001 .
AMA StyleIbrahim Qadri, Amgad Muneer, Suliman Mohamed Fati. Automatic robotic scanning and inspection mechanism for mines using IoT. IOP Conference Series: Materials Science and Engineering. 2021; 1045 (1):012001.
Chicago/Turabian StyleIbrahim Qadri; Amgad Muneer; Suliman Mohamed Fati. 2021. "Automatic robotic scanning and inspection mechanism for mines using IoT." IOP Conference Series: Materials Science and Engineering 1045, no. 1: 012001.
The advent of social media, particularly Twitter, raises many issues due to a misunderstanding regarding the concept of freedom of speech. One of these issues is cyberbullying, which is a critical global issue that affects both individual victims and societies. Many attempts have been introduced in the literature to intervene in, prevent, or mitigate cyberbullying; however, because these attempts rely on the victims’ interactions, they are not practical. Therefore, detection of cyberbullying without the involvement of the victims is necessary. In this study, we attempted to explore this issue by compiling a global dataset of 37,373 unique tweets from Twitter. Moreover, seven machine learning classifiers were used, namely, Logistic Regression (LR), Light Gradient Boosting Machine (LGBM), Stochastic Gradient Descent (SGD), Random Forest (RF), AdaBoost (ADB), Naive Bayes (NB), and Support Vector Machine (SVM). Each of these algorithms was evaluated using accuracy, precision, recall, and F1 score as the performance metrics to determine the classifiers’ recognition rates applied to the global dataset. The experimental results show the superiority of LR, which achieved a median accuracy of around 90.57%. Among the classifiers, logistic regression achieved the best F1 score (0.928), SGD achieved the best precision (0.968), and SVM achieved the best recall (1.00).
Amgad Muneer; Suliman Mohamed Fati. A Comparative Analysis of Machine Learning Techniques for Cyberbullying Detection on Twitter. Future Internet 2020, 12, 187 .
AMA StyleAmgad Muneer, Suliman Mohamed Fati. A Comparative Analysis of Machine Learning Techniques for Cyberbullying Detection on Twitter. Future Internet. 2020; 12 (11):187.
Chicago/Turabian StyleAmgad Muneer; Suliman Mohamed Fati. 2020. "A Comparative Analysis of Machine Learning Techniques for Cyberbullying Detection on Twitter." Future Internet 12, no. 11: 187.
Recognizing the desired herb among thousands of herbs is an exhausting and time-consuming practice. Hence, herbs identification via a vision system is beneficial since the pharmacist and botanic need not to collect them through traditional ways. Thus, this paper proposed an efficient and automatic classification system to recognize Malaysian herbs that would be used in medical or cooking areas. As per the authors’ knowledge, there is no evidence for similar studies on medical herbs in Malaysia. In the proposed system, we have investigated different classifiers to build an efficient classifier; then, the classifier was integrated with a mobile app to ease the real-time classification. The proposed system employed two classifiers, namely Support Vector Machine (SVM) and Deep Learning Neural Network (DLNN). The two models have been tested on our own dataset, which contains 1000 leaves. The experimental results showed that SVM achieved 74.63% recognition accuracy and DLNN achieved 93% recognition accuracy for both of the experimental model and the developed mobile app. Furthermore, the processing time was 4 seconds for SVM and 5 seconds for DLNN classifier, while the processing time using the mobile app was 2 seconds only.
Amgad Muneer; Suliman Mohamed Fati. Efficient and Automated Herbs Classification Approach Based on Shape and Texture Features using Deep Learning. IEEE Access 2020, 8, 1 -18.
AMA StyleAmgad Muneer, Suliman Mohamed Fati. Efficient and Automated Herbs Classification Approach Based on Shape and Texture Features using Deep Learning. IEEE Access. 2020; 8 ():1-18.
Chicago/Turabian StyleAmgad Muneer; Suliman Mohamed Fati. 2020. "Efficient and Automated Herbs Classification Approach Based on Shape and Texture Features using Deep Learning." IEEE Access 8, no. : 1-18.
Amgad Muneer; Suliman Mohamed Fati; Saddam Fuddah. Smart health monitoring system using IoT based smart fitness mirror. TELKOMNIKA (Telecommunication Computing Electronics and Control) 2020, 18, 317 .
AMA StyleAmgad Muneer, Suliman Mohamed Fati, Saddam Fuddah. Smart health monitoring system using IoT based smart fitness mirror. TELKOMNIKA (Telecommunication Computing Electronics and Control). 2020; 18 (1):317.
Chicago/Turabian StyleAmgad Muneer; Suliman Mohamed Fati; Saddam Fuddah. 2020. "Smart health monitoring system using IoT based smart fitness mirror." TELKOMNIKA (Telecommunication Computing Electronics and Control) 18, no. 1: 317.
Coma or unconsciousness is a state wherein the patient cannot respond to any internal or external stimulus. In this situation, the patient has no physical control over his entire body. Such cases require a serious attention and continuous monitoring to save patient's life. Currently, monitoring coma patients critically is very expensive and needs more manpower. Besides, such continuous intensive care by a paramedical assistant are error-prone, which may lead to further complications. Thus, the need for automated healthcare systems still exist. These automated systems help in continuously monitoring and recording all the vital information of a particular subject by maintaining all the comatose records. In this article, a health monitoring system for the coma patient based on the global system for mobile (GSM) and the Internet of Things (IoT) is proposed. IoT as a new technology which facilitates the process of extracting, analyzing and sending data with high efficiency. In this proposed system, four health parameters, temperature, heartbeat, accelerometer and eye blinks are monitored. By integrating these four parameters with live monitoring module and/or a GSM module, the need for clinical staff and accompanying persons will be less as the systems allows relatives and staff to monitor the coma patient online via mobile phones or receive notification based on the patient's status changes. The results achieved by the system shows real time reading of body temperature and the heartbeat. Finally, the results obtained by the MPU-6050 gyroscope and the eye blink sensor were very satisfactory.
Amgad Muneer; Suliman Fati. Automated Health Monitoring System Using Advanced Technology. Journal of Information Technology Research 2019, 12, 104 -132.
AMA StyleAmgad Muneer, Suliman Fati. Automated Health Monitoring System Using Advanced Technology. Journal of Information Technology Research. 2019; 12 (3):104-132.
Chicago/Turabian StyleAmgad Muneer; Suliman Fati. 2019. "Automated Health Monitoring System Using Advanced Technology." Journal of Information Technology Research 12, no. 3: 104-132.
Amgad Muneer. Automated Library System Using SMS Based Pick and Place Robot. International Journal of Computing and Digital Systemss 2019, 8, 535 -544.
AMA StyleAmgad Muneer. Automated Library System Using SMS Based Pick and Place Robot. International Journal of Computing and Digital Systemss. 2019; 8 (6):535-544.
Chicago/Turabian StyleAmgad Muneer. 2019. "Automated Library System Using SMS Based Pick and Place Robot." International Journal of Computing and Digital Systemss 8, no. 6: 535-544.
In this paper, we construct an automatic classification vision system that is designed to recognize Malaysian herbs that are typically used for medical or culinary purposes. The proposed system employs two classifiers, Support Vector machine (SVM) and Deep Neural Network (DNN). The two classifiers have been implemented using OpenCV-Python. For the training test SVM achieved 86.63% recognition accuracy and DNN (TensorFlow) achieved 98% recognition accuracy. For the real life testing SVM achieved 74.63% recognition accuracy and DNN achieved 93% recognition accuracy. In the proposed system a total of 1000 leaves were used. A total of 50 samples of herbs were collected for each class and they were divided into two datasets. The first dataset which consisted 60% of the herbs samples were used for the training purpose and the other dataset with 40% of the herbs samples were used for the testing purpose. The time taken for each recognition process was 4 seconds for SVM and 5 seconds for DNN classifier. Also, the proposed system is capable of identifying the herbs leaves even though they are wet, dried and deformed with a recognition accuracy of 52.50%. Finally, based on the experiments that were done, the system proved to be very efficient and accurate with the highest recognition rate being 98%. The results indicate that the techniques used in the proposed system are significantly efficient when compared to the various techniques employed in the existing literature.
Vickneswari Durairajah; Suresh Gobee; Amgad Muneer. Automatic Vision Based Classification System Using DNN and SVM Classifiers. 2018 3rd International Conference on Control, Robotics and Cybernetics (CRC) 2018, 6 -14.
AMA StyleVickneswari Durairajah, Suresh Gobee, Amgad Muneer. Automatic Vision Based Classification System Using DNN and SVM Classifiers. 2018 3rd International Conference on Control, Robotics and Cybernetics (CRC). 2018; ():6-14.
Chicago/Turabian StyleVickneswari Durairajah; Suresh Gobee; Amgad Muneer. 2018. "Automatic Vision Based Classification System Using DNN and SVM Classifiers." 2018 3rd International Conference on Control, Robotics and Cybernetics (CRC) , no. : 6-14.
This paper proposes a health monitoring system for the patient in a coma based on GSM and Internet of Things (IoT). In this proposed system, the four health parameters implemented are LM35 temperature sensor, heartbeat sensor, accelerometer sensor and eye blink sensor. Using these four parameters simultaneously, it has been monitored the coma patient's condition. Hence ruling out the use of the thermometer and other devices to check the condition of the patient. Consequently, there is no need for a lot of clinical staff nor accompanying persons to be physically present to check the condition of the patient because there is GSM module to send SMS message to the mobile phone of the person in charge (nurses/doctor) in case there is any abnormalities in health parameters. Moreover, this system uses Wi-Fi for IoT wherein “Thingspeak” is used to monitor the coma patient online via mobile phone. Alternatively, the patient's relative can log in easily to the system to check the patient statues. The IoT shows the results online of body temperature, heartbeat, body movement and eye blink along with the time and the date for each parameter. Additionally, the results achieved for the body temperature and the heartbeat are 360° and 87 BPM respectively. Finally, the results obtained by the MPU-6050 gyroscope and the eye blink sensor were very satisfactory.
Suliman Mohamed Fati; Amgad Muneer; Dheeraj Mungur; Ahmad Badawi. Integrated Health Monitoring System using GSM and IoT. 2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE) 2018, 1 -7.
AMA StyleSuliman Mohamed Fati, Amgad Muneer, Dheeraj Mungur, Ahmad Badawi. Integrated Health Monitoring System using GSM and IoT. 2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE). 2018; ():1-7.
Chicago/Turabian StyleSuliman Mohamed Fati; Amgad Muneer; Dheeraj Mungur; Ahmad Badawi. 2018. "Integrated Health Monitoring System using GSM and IoT." 2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE) , no. : 1-7.