Browsing by Author "Muhammad Mokhzaini Azizan"
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Publication Artificial Intelligence Enabled Personalised Assistive Tools to Enhance Education of Children with Neurodevelopmental Disorders—A Review(MDPI, 2022) ;Prabal Datta Barua ;Jahmunah Vicnesh ;Raj Gururajan ;Shu Lih Oh ;Elizabeth Palmer ;Muhammad Mokhzaini Azizan ;Nahrizul Adib KadriU Rajendra AcharyaMental disorders (MDs) with onset in childhood or adolescence include neurodevelopmental disorders (NDDs) (intellectual disability and specific learning disabilities, such as dyslexia, attention deficit disorder (ADHD), and autism spectrum disorders (ASD)), as well as a broad range of mental health disorders (MHDs), including anxiety, depressive, stress-related and psychotic disorders. There is a high co-morbidity of NDDs and MHDs. Globally, there have been dramatic increases in the diagnosis of childhood-onset mental disorders, with a 2- to 3-fold rise in prevalence for several MHDs in the US over the past 20 years. Depending on the type of MD, children often grapple with social and communication deficits and difficulties adapting to changes in their environment, which can impact their ability to learn effectively. To improve outcomes for children, it is important to provide timely and effective interventions. This review summarises the range and effectiveness of AI-assisted tools, developed using machine learning models, which have been applied to address learning challenges in students with a range of NDDs. Our review summarises the evidence that AI tools can be successfully used to improve social interaction and supportive education. Based on the limitations of existing AI tools, we provide recommendations for the development of future AI tools with a focus on providing personalised learning for individuals with NDDs - Some of the metrics are blocked by yourconsent settings
Publication Artificial Intelligence-assisted Air Quality Monitoring For Smart City Management(PeerJ Publishing, 2023) ;En Xin Neo ;Khairunnisa Hasikin ;Khin Wee Lai ;Mohd Istajib Mokhtar ;Muhammad Mokhzaini Azizan ;Hanee Farzana Hizaddin ;Sarah Abdul RazakYantoBackground. The environment has been significantly impacted by rapid urbaniza- tion, leading to a need for changes in climate change and pollution indicators. The 4IR offers a potential solution to efficiently manage these impacts. Smart city ecosys- tems can provide well-designed, sustainable, and safe cities that enable holistic climate change and global warming solutions through various community-centred initiatives. These include smart planning techniques, smart environment monitoring, and smart governance. An air quality intelligence platform, which operates as a complete mea- surement site for monitoring and governing air quality, has shown promising results in providing actionable insights. This article aims to highlight the potential of ma- chine learning models in predicting air quality, providing data-driven strategic and sustainable solutions for smart cities. Methods. This study proposed an end-to-end air quality predictive model for smart city applications, utilizing four machine learning techniques and two deep learning techniques. These include Ada Boost, SVR, RF, KNN, MLP regressor and LSTM. The study was conducted in four different urban cities in Selangor, Malaysia, including Petaling Jaya, Banting, Klang, and Shah Alam. The model considered the air qual- ity data of various pollution markers such as PM2.5, PM10, O3, and CO. Additionally, meteorological data including wind speed and wind direction were also considered, and their interactions with the pollutant markers were quantified. The study aimed to determine the correlation variance of the dependent variable in predicting air pol- lution and proposed a feature optimization process to reduce dimensionality and re- move irrelevant features to enhance the prediction of PM2.5, improving the existing LSTM model. The study estimates the concentration of pollutants in the air based on training and highlights the contribution of feature optimization in air quality predic- tions through feature dimension reductions. Results. In this section, the results of predicting the concentration of pollutants (PM2.5, PM10, O3, and CO) in the air are presented in R2 and RMSE. In predicting the PM10 and PM2.5 concentration, LSTM performed the best overall high R2 values in the four study areas with the R2 values of 0.998, 0.995, 0.918, and 0.993 in Banting, How to cite this article Neo EX, Hasikin K, Lai KW, Mokhtar MI, Azizan MM, Hizaddin HF, Razak SA, Yanto . 2023. Artificial intelligence-assisted air quality monitoring for smart city management. PeerJ Comput. Sci. 9:e1306 http://doi.org/10.7717/peerj-cs.1306 Petaling, Klang and Shah Alam stations, respectively. The study indicated that among the studied pollution markers, PM2.5, PM10, NO2, wind speed and humidity are the most important elements to monitor. By reducing the number of features used in the model the proposed feature optimization process can make the model more interpretable and provide insights into the most critical factor affecting air quality. Findings from this study can aid policymakers in understanding the underlying causes of air pollution and develop more effective smart strategies for reducing pollution levels. - Some of the metrics are blocked by yourconsent settings
Publication Assessment Of Renewable Energy Sources To Generate Electricity For Remote Areas, South Iraq(Institute of Advanced Engineering and Science (IAES), 2022) ;Ali Hadi Kother ;Zaidoon Waleed AL-Shammari Waleed Jawad ;Safaa Kother ;Ihsan Ahmed Taha ;Hussam Almukhtar ;Muhammad Mokhzaini Azizan ;Ahmad Shukri Fazil RahmanKhairunnisa HasikinWith the rising need for utilizing renewable energy instead of traditional energies in electricity generation across the world, a broad assessment of these energies' performance is required to make the most of them everywhere. This paper looks at the techno-economics of renewable energy resources for a distant health clinic in a rural location of southern Iraq. Cost, dependability, and availability are the parameters that were considered in this study, which took into consider the power load in this scenario. Because of its efficacy, the particle swarm optimization (PSO) technique was chosen for the suggested study. Results showed that the respective optimal values for number of photovoltaics (NPV) equal to (10), number of wind turbines (NWT) equal to (5), and number of batteries (NBT) of (33), cost of energy (COE) of (0.518 US$/kWh), loss power supply probability (LPSP) of (0.073%), reliability (REL) of (99.927%) and renewable factors (RF) of (100%) with (66 %) solar energy penetration, and (34%) wind energy penetration. Finally, it was discovered that implementing a hybrid renewable energy system (HRES) is an effective way to address the electrical demands of remote rural regions in Iraq and other developing countries with similar climates. - Some of the metrics are blocked by yourconsent settings
Publication An automated solid waste detection using the optimized YOLO model for riverine management(FRONTIERS, 2022) ;Nur Athirah Zailan ;Muhammad Mokhzaini Azizan ;Khairunnisa Hasikin ;Anis Salwa Mohd KhairuddinUswah KhairuddinDue to urbanization, solid waste pollution is an increasing concern for rivers, possibly threatening human health, ecological integrity, and ecosystem services. Riverine management in urban landscapes requires best management practices since the river is a vital component in urban ecological civilization, and it is very imperative to synchronize the connection between urban development and river protection. Thus, the implementation of proper and innovative measures is vital to control garbage pollution in the rivers. A robot that cleans the waste autonomously can be a good solution to manage river pollution efficiently. Identifying and obtaining precise positions of garbage are the most crucial parts of the visual system for a cleaning robot. Computer vision has paved a way for computers to understand and interpret the surrounding objects. The development of an accurate computer vision system is a vital step toward a robotic platform since this is the front-end observation system before consequent manipulation and grasping systems. The scope of this work is to acquire visual information about floating garbage on the river, which is vital in building a robotic platform for river cleaning robots. In this paper, an automated detection system based on the improved You Only Look Once (YOLO) model is developed to detect floating garbage under various conditions, such as fluctuating illumination, complex background, and occlusion. The proposed object detection model has been shown to promote rapid convergence which improves the training time duration. In addition, the proposed object detection model has been shown to improve detection accuracy by strengthening the non-linear feature extraction process. The results showed that the proposed model achieved a mean average precision (mAP) value of 89%. Hence, the proposed model is considered feasible for identifying five classes of garbage, such as plastic bottles, aluminum cans, plastic bags, styrofoam, and plastic containers. - Some of the metrics are blocked by yourconsent settings
Publication Brain Tumour Temporal Monitoring Of Interval Change Using Digital Image Subtraction Technique(Frontiers Media SA, 2021) ;Azira Binti Khalil ;Aisyah Rahimi ;Aida Luthfi ;Muhammad Mokhzaini Azizan ;Suresh Chandra Satapathy ;Khairunnisa HasikinKhin Wee LaiA process that involves the registration of two brain Magnetic Resonance Imaging (MRI) acquisitions is proposed for the subtraction between previous and current images at two different follow-up (FU) time points. Brain tumours can be non-cancerous (benign) or cancerous (malignant). Treatment choices for these conditions rely on the type of brain tumour as well as its size and location. Brain cancer is a fast-spreading tumour that must be treated in time. MRI is commonly used in the detection of early signs of abnormality in the brain area because it provides clear details. Abnormalities include the presence of cysts, haematomas or tumour cells. A sequence of images can be used to detect the progression of such abnormalities. A previous study on conventional (CONV) visual reading reported low accuracy and speed in the early detection of abnormalities, specifically in brain images. It can affect the proper diagnosis and treatment of the patient. A digital subtraction technique that involves two images acquired at two interval time points and their subtraction for the detection of the progression of abnormalities in the brain image was proposed in this study. MRI datasets of five patients, including a series of brain images, were retrieved retrospectively in this study. All methods were carried out using the MATLAB programming platform. ROI volume and diameter for both regions were recorded to analyse progression details, location, shape variations and size alteration of tumours. This study promotes the use of digital subtraction techniques on brain MRIs to track any abnormality and achieve early diagnosis and accuracy whilst reducing reading time. Thus, improving the diagnostic information for physicians can enhance the treatment plan for patients. - Some of the metrics are blocked by yourconsent settings
Publication Comparison Techniques for Optimization Switching Frequency in Energy Encryption of Wireless Power Transfer System(Semarak Ilmu Publishing, 2022) ;Nur Hazwani Hussin ;Muhammad Mokhzaini Azizan ;Azuwa Ali ;Norhidayu Rameli ;Nur Hazirah ZainiShahnurriman Abdul RahmanThis paper reviews the techniques for optimization switching frequency used in Wireless power transfer (WPT). WPT is one of the most useful ways to transfer power. Based on distances power transfer, the WPT system can be divided into three categories, namely, near, medium, and far fields. Inductive coupling and capacitive coupling contactless techniques are used in the near-field WPT. Magnetic resonant coupling technique is used in the medium-field WPT. Electromagnetic radiation is used in the far-field WPT. From the comparison in this paper, a model and design algorithm to optimize switching frequency for energy encryption of medium field wireless power transfer system are purposed. The maximum value of the performance are suitable to compute the optimal circuit parameter by algorithm itself. - Some of the metrics are blocked by yourconsent settings
Publication COVID-19 Isolation Control Proposal via UAV and UGV for Crowded Indoor Environments: Assistive Robots in the Shopping Malls(FRONTIERS, 2022) ;Muhammet Fatih Aslan ;Khairunnisa Hasikin ;Abdullah Yusefi ;Akif Durdu ;Kadir SabanciMuhammad Mokhzaini AzizanArtificial intelligence researchers conducted different studies to reduce the spread of COVID-19. Unlike other studies, this paper isn't for early infection diagnosis, but for preventing the transmission of COVID-19 in social environments. Among the studies on this is regarding social distancing, as this method is proven to prevent COVID-19 to be transmitted from one to another. In the study, Robot Operating System (ROS) simulates a shopping mall using Gazebo, and customers are monitored by Turtlebot and Unmanned Aerial Vehicle (UAV, DJI Tello). Through frames analysis captured by Turtlebot, a particular person is identified and followed at the shopping mall. Turtlebot is a wheeled robot that follows people without contact and is used as a shopping cart. Therefore, a customer doesn't touch the shopping cart that someone else comes into contact with, and also makes his/her shopping easier. The UAV detects people from above and determines the distance between people. In this way, a warning system can be created by detecting places where social distance is neglected. Histogram of Oriented-Gradients (HOG)-Support Vector Machine (SVM) is applied by Turtlebot to detect humans, and Kalman-Filter is used for human tracking. SegNet is performed for semantically detecting people and measuring distance via UAV. This paper proposes a new robotic study to prevent the infection and proved that this system is feasible. - Some of the metrics are blocked by yourconsent settings
Publication Deep Eutectic Solvents for the Removal of Lead Contaminants in Mangrove Soil(Sciencedirect, 2022) ;Shahidah Nusailah Rashid ;Hanee F. Hizaddin ;Adeeb Hayyan ;Khairunnisa Hasikin ;Sarah Abdul Razak ;Mohd Istajib MokhtarMuhammad Mokhzaini AzizanThe remediation of heavy metal contaminated mangrove soil with proper washing agents is critical for reducing the harmfulness of heavy metal contaminated soil to the environment. This work is aimed to investigate the potential of natural-based deep eutectic solvents as soil washing agents. Choline chloride (ChCl) and two organic acids, lactic acid and levulinic acid, are used as degradable and natural washing agents to remove Pb in the soil. Results verified that ChCl: Lactic Acid (1:2) [ChCl-LacA] and ChCl: Levulinic Acid (1:2) [ChCl-LevA] were found to be suitable for the removal of Pb from the contaminated soils, which significantly lowers the total Pb concentration in the soil. The high distribution ratio from the Conductor-like Screening Model for Real Solvents (COSMO-RS) screening gave an early indication that natural acid DESs are effective soil washing agents. The effect of washing agent concentration, soil to liquid ratio, contact time, and the interaction between acid DESs and metal ions was evaluated. ChCl-LacA and ChCl-LevA remove lead to 97% and 99%, respectively, under optimum conditions. ICP-MS, XRD, and FTIR spectroscopy analysis clarified the interaction between DES and metal ions. As a washing agent, green and natural DESs provide an environmentally sustainable alternative. Employing soil washing to remove heavy metals is a simple, rapid, and cheap remediation technology that could potentially be used in mitigating soil pollution. - Some of the metrics are blocked by yourconsent settings
Publication Flexible Sector Detector-based Mismatch Supply Voltage In Direct Torque Control Doubly Fed Induction Machine: An Experimental Validation(Elsevier, 2023) ;Muhammad Zaid Aihsan ;Auzani Jidin ;Muhammad Mokhzaini Azizan ;Muhammad Izuan FahmiKhairunnisa HasikinDirect Torque Control (DTC) of Induction Motors (IMs) is popular in motor drive applications because of its robust and simple control structure. The IM winding can be controlled on both sides using dual inverter technique which more effective for Electric Vehicle (EV) with a greater number of voltage vectors. However, the battery performance of the dual inverter will deteriorate unevenly on both sides, resulting in fluctuating voltages for the EV system. This will lead to the generation of distorted stator currents and a significant droop in the stator flux, which in turn can increase the total harmonic distortion (THD) in the system.Additionally, the performance of torque may not be able to regulate effectively. This paper examines the effect of unstable voltage on voltage vector mapping performance with tilted angles and proposes new sector definitions based on voltage ratio conditions. Moreover, the proposed sector for each predefined voltage ratio is tested under three-speed conditions. The proposed technique effectiveness is validated through hardware experiments using a dSPACE 1104 controller and retuning the stator current for proper waveform. This approach improves the stator current waveform, improves stator flux droop, enhances torque regulation and minimizes the THD in the DTC system. - Some of the metrics are blocked by yourconsent settings
Publication An Implementation Framework Overview of Non-Intrusive Load Monitoring(SDEWES Centre, 2023) ;Omar Al-Khadher ;Azharudin Mukhtaruddin ;Fakroul Ridzuan Hashim ;Muhammad Mokhzaini AzizanHussin MamatThe implementation of non-intrusive load monitoring has gained significant attention as a promising solution for disaggregating and identifying individual appliances' energy consumption within households and commercial buildings. The issue at the core of non-intrusive load monitoring is the accurate separation of the aggregated energy consumption signal into the energy contributions of distinct appliances without requiring additional sensors or invasive measurements. The erroneous implementation of non-intrusive load monitoring model leads to poor results and a waste of time where the criteria for the implementation are necessary. This paper presents an overview of non-intrusive load monitoring's implementation, addressing the stages of non-intrusive load monitoring and advancements in the field. The overview explores the various techniques, methodologies, and algorithms employed in non-intrusive load monitoring systems to achieve load identification and energy disaggregation. By addressing the problem statement and reviewing the state of the art, this paper sets the stage for understanding the significance of non-intrusive load monitoring in commercial buildings and modern energy-efficient households. Moreover, the limitations, applications, and recommended criteria for the implementation of non-intrusive load monitoring are highlighted as a contribution to the field of load disaggregation. Future research should address scalability, privacy concerns, and the integration of NILM with smart grids to propel energy disaggregation towards more efficient, accessible, and sustainable energy management systems. We envision that this endeavour can serve as a guiding beacon, offering a roadmap for researchers and professionals. - Some of the metrics are blocked by yourconsent settings
Publication Machine Learning Approach Regarding The Classification And Prediction Of Dog Sounds: A Case Study Of South Indian Breeds(MDPI, 2022) ;Prabu Mohandas ;Jerline Sheebha Anni ;Khairunnisa Hasikin ;Dhayalan Velauthapillai ;Veena Raj ;Thanihaichelvan Murugathas ;Muhammad Mokhzaini AzizanRajkumar ThanasekaranBarking is a form of vocal communication made by dogs. Each type of bark made by dogs has a distinct context. The classification of dog bark pattern will aid in the understanding of barking action. In this study, a machine learning algorithm is used to analyze the pattern of barking from two different dog species: Rajapalayam Hound and Kombai Hound. The objective is to find the context of the dog barking pattern based on various real-time scenarios, including whether the dogs are alone, looking at strangers, or showing an eagerness to fight. The barks of the dogs were recorded inside the house under different scenarios, such as while identifying the owner or strangers. Machine learning algorithms, such as the reinforcement learning method, were used in predicting and classifying the dog sounds. Q-learning is a reinforcement learning that will generate the next best action for the given state. It is a model-free learning used to find the best course of dog action for the given current state of the dog. The Q-learning algorithm had been used in improving the prediction of dog sounds by updating the values of learning, where the values with the highest reward were taken into consideration. In total, 6171 barks were collected from the dogs chosen for study, and the proposed approach achieved a correct prediction accuracy of 85.19% of the dog sounds. - Some of the metrics are blocked by yourconsent settings
Publication Microcalcification Discrimination in Mammography Using Deep Convolutional Neural Network: Towards Rapid and Early Breast Cancer Diagnosis(FRONTIERS, 2022) ;Yew Sum Leong ;Khairunnisa Hasikin ;Khin Wee Lai ;Norita Mohd ZainMuhammad Mokhzaini AzizanBreast cancer is among the most common types of cancer in women and under the cases of misdiagnosed, or delayed in treatment, the mortality risk is high. The existence of breast microcalcifications is common in breast cancer patients and they are an effective indicator for early sign of breast cancer. However, microcalcifications are often missed and wrongly classified during screening due to their small sizes and indirect scattering in mammogram images. Motivated by this issue, this project proposes an adaptive transfer learning deep convolutional neural network in segmenting breast mammogram images with calcifications cases for early breast cancer diagnosis and intervention. Mammogram images of breast microcalcifications are utilized to train several deep neural network models and their performance is compared. Image filtering of the region of interest images was conducted to remove possible artifacts and noises to enhance the quality of the images before the training. Different hyperparameters such as epoch, batch size, etc were tuned to obtain the best possible result. In addition, the performance of the proposed fine-tuned hyperparameter of ResNet50 is compared with another state-of-the-art machine learning network such as ResNet34, VGG16, and AlexNet. Confusion matrices were utilized for comparison. The result from this study shows that the proposed ResNet50 achieves the highest accuracy with a value of 97.58%, followed by ResNet34 of 97.35%, VGG16 96.97%, and finally AlexNet of 83.06%. - Some of the metrics are blocked by yourconsent settings
Publication Multistage Optimization Using a Modified Gaussian Mixture Model in Sperm Motility Tracking(Hindawi Publishing, 2021) ;Mohammed Alameri ;Khairunnisa Hasikin ;Nahrizul Adib Kadri ;Nashrul Fazli Mohd Nasir ;Prabu Mohandas ;Jerline Sheeba AnniMuhammad Mokhzaini AzizanInfertility is a condition whereby pregnancy does not occur despite having unprotected sexual intercourse for at least one year. The main reason could originate from either the male or the female, and sometimes, both contribute to the fertility disorder. For the male, sperm disorder was found to be the most common reason for infertility. In this paper, we proposed male infertility analysis based on automated sperm motility tracking. The proposed method worked in multistages, where the first stage focused on the sperm detection process using an improved Gaussian Mixture Model. A new optimization protocol was proposed to accurately detect the motile sperms prior to the sperm tracking process. Since the optimization protocol was imposed in the proposed system, the sperm tracking and velocity estimation processes are improved. The proposed method attained the highest average accuracy, sensitivity, and specificity of 92.3%, 96.3%, and 72.4%, respectively, when tested on 10 different samples. Our proposed method depicted better sperm detection quality when qualitatively observed as compared to other state-of-the-art techniques. - Some of the metrics are blocked by yourconsent settings
Publication Object Detection and Movement Tracking Using Tubelets and Faster RCNN Algorithm with Anchor Generation(Hindawi Limited, 2021) ;Prabu Mohandas ;Jerline Sheebha Anni ;Rajkumar Thanasekaran ;Khairunnisa HasikinMuhammad Mokhzaini AzizanObject detection in images and videos has become an important task in computer vision. It has been a challenging task due to misclassification and localization errors. The proposed approach explored the feasibility of automated detection and tracking of elephant intrusion along forest border areas. Due to an alarming increase in crop damages resulted from movements of elephant herds, combined with high risk of elephant extinction due to human activities, this paper looked into an efficient solution through elephant’s tracking. The convolutional neural network with transfer learning is used as the model for object classification and feature extraction. A new tracking system using automated tubelet generation and anchor generation methods in combination with faster RCNN was developed and tested on 5,482 video sequences. Real-time video taken for analysis consisted of heavily occluded objects such as trees and animals. Tubelet generated from each video sequence with intersection over union (IoU) thresholds have been effective in tracking the elephant object movement in the forest areas. The proposed work has been compared with other state-of-the-art techniques, namely, faster RCNN, YOLO v3, and HyperNet. Experimental results on the real-time dataset show that the proposed work achieves an improved performance of 73.9% in detecting and tracking of objects, which outperformed the existing approaches. - Some of the metrics are blocked by yourconsent settings
Publication Optimal Sizing of a Hybrid System Through Particle Swarm Optimization for Rural Areas in Iraq(Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU)., 2022) ;Zaidoon Waleed Al-Shammari ;Ali Hadi Algeboory ;Safaa Hadi Al-Jebory ;Ihsan Ahmed Taha ;Hussam Almukhtar ;Muhammad Mokhzaini Azizan ;Rahman Ahmad Shukri FazilKhairunnisa HasikinIn today’s modern world, any community anywhere has the right to have access to a basic level of electricity. It is with this in mind that efforts are being made to provide electric power to even the most remote locations. Solar and wind energy are examples of renewable energy sources that are both clean and versatile. For a distant rural school in south-eastern Iraq, this research presents Particle Swarm Optimization (PSO) to reduce the Cost of Energy (COE) according to maximum dependability of Hybrid Renewable Energy System (HRES), by utilizing an integrated electrical generation system. The suggested hybrid system consists of PV, WT, and BT, all of which are subject to a certain investment restriction. Results showed that, the optimal sizing of the Number of Photovoltaics (NPV) equal to (9), Number of Wind turbines (NWT) equal to (6), Number of the batteries (NBT) of (29), Cost of energy (0.536 US$/kwh), Loss power supply probability (0.091 %), reliability (99.909 %) and renewable factors (100 %) with (59 %) PV penetration, and (41 %) WT penetration. As a result, the use of the hybrid system is justified from a technological, economic, and humanitarian standpoint. - Some of the metrics are blocked by yourconsent settings
Publication Optimal Sizing of Standalone for Hybrid Renewable Energy System by Using Pso Optimization Technique(Institute of Advanced Engineering and Science (IAES), 2023) ;Zaidoon Waleed Jawad Al-Shammari ;Muhammad Mokhzaini Azizan ;Ahmad Shukri Fazil RahmanKhairunnisa HasikinProviding electricity to rural regions is difficult for developing countries, such as Iraq, particularly in remote parts without grid connections. The electrical demands of Zerbattiya, a community in southern Iraq near the Iranian border, are discussed in this paper. The proposed system includes wind turbines, solar panels, diesel engines, batteries, etc. This study suggests a techno-economic viable and optimal size for each component to generate electricity for this area. This research uses particle swarm optimization techniques (PSO). The best hybrid renewable energy system (HRES) design is achieved by balancing the lowest possible cost of energy (COE) with the lowest possible loss of power supply probability (LPSP) and the greatest possible reliability factor value. As a result of the findings, the respective ideal values of number of photovoltaics (NPV), number of wind turbines (NWT), number of diesel generator (NDG), number of batteries (NBT), COE, LPSP, and reliability are 138, 43, 2, 324, US$/KWh 0.129, 0.0508%, and 99.9492%, respectively. Finally, it was discovered that implementing a HRES is an effective way to address the electrical demands of remote rural regions in Iraq and other developing countries with similar climates. - Some of the metrics are blocked by yourconsent settings
Publication Predicting Occupational Injury Causal Factors Using Text-based Analytics: A Systematic Review(FRONTIERS, 2022) ;Mohamed Zul Fadhli Khairuddin ;Khairunnisa Hasikin ;Nasrul Anuar Abd Razak ;Khin Wee Lai ;Mohd Zamri Osman ;Muhammet Fatih Aslan ;Kadir Sabanci ;Muhammad Mokhzaini Azizan ;Suresh Chandra SatapathyXiang WuWorkplace accidents can cause a catastrophic loss to the company including human injuries and fatalities. Occupational injury reports may provide a detailed description of how the incidents occurred. Thus, the narrative is a useful information to extract, classify and analyze occupational injury. This study provides a systematic review of text mining and Natural Language Processing (NLP) applications to extract text narratives from occupational injury reports. A systematic search was conducted through multiple databases including Scopus, PubMed, and Science Direct. Only original studies that examined the application of machine and deep learning-based Natural Language Processing models for occupational injury analysis were incorporated in this study. A total of 27, out of 210 articles were reviewed in this study by adopting the Preferred Reporting Items for Systematic Review (PRISMA). This review highlighted that various machine and deep learning-based NLP models such as K-means, Naïve Bayes, Support Vector Machine, Decision Tree, and K-Nearest Neighbors were applied to predict occupational injury. On top of these models, deep neural networks are also included in classifying the type of accidents and identifying the causal factors. However, there is a paucity in using the deep learning models in extracting the occupational injury reports. This is due to these techniques are pretty much very recent and making inroads into decision-making in occupational safety and health as a whole. Despite that, this paper believed that there is a huge and promising potential to explore the application of NLP and text-based analytics in this occupational injury research field. Therefore, the improvement of data balancing techniques and the development of an automated decision-making support system for occupational injury by applying the deep learning-based NLP models are the recommendations given for future research. - Some of the metrics are blocked by yourconsent settings
Publication Prioritisation Assessment and Robust Predictive System for Medical Equipment: A Comprehensive Strategic Maintenance Management(Frontiers Media SA, 2021) ;Aizat Hilmi Zamzam ;Ayman Khallel Ibrahim Al-Ani ;Ahmad Khairi Abdul Wahab ;Khin Wee Lai ;Suresh Chandra Satapathy ;Azira Khalil ;Muhammad Mokhzaini AzizanKhairunnisa HasikinThe advancement of technology in medical equipment has significantly improved healthcare services. However, failures in upkeeping reliability, availability, and safety affect the healthcare services quality and significant impact can be observed in operations’ expenses. The effective and comprehensive medical equipment assessment and monitoring throughout the maintenance phase of the asset life cycle can enhance the equipment reliability, availability, and safety. The study aims to develop the prioritisation assessment and predictive systems that measure the priority of medical equipment’s preventive maintenance, corrective maintenance, and replacement programmes. The proposed predictive model is constructed by analysing features of 13,352 medical equipment used in public healthcare clinics in Malaysia. The proposed system comprises three stages: prioritisation analysis, model training, and predictive model development. In this study, we proposed 16 combinations of novel features to be used for prioritisation assessment and prediction of preventive maintenance, corrective maintenance, and replacement programme. The modified k-Means algorithm is proposed during the prioritisation analysis to automatically distinguish raw data into three main clusters of prioritisation assessment. Subsequently, these clusters are fed into and tested with six machine learning algorithms for the predictive prioritisation system. The best predictive models for medical equipment’s preventive maintenance, corrective maintenance, and replacement programmes are selected among the tested machine learning algorithms. Findings indicate that the Support Vector Machine performs the best in preventive maintenance and replacement programme prioritisation predictive systems with the highest accuracy of 99.42 and 99.80%, respectively. Meanwhile, K-Nearest Neighbour yielded the highest accuracy in corrective maintenance prioritisation predictive systems with 98.93%. Based on the promising results, clinical engineers and healthcare providers can widely adopt the proposed prioritisation assessment and predictive systems in managing expenses, reporting, scheduling, materials, and workforce. - Some of the metrics are blocked by yourconsent settings
Publication Prostate Cancer Monitoring using MRI Monomodal Feature-Based Registration(MALAYSIAN INSTITUTE OF PHYSICS, 2022) ;Aisyah Rahimi ;Azira Khalil ;Farisha Noordin ;Shahrina Ismail ;Muhammad Mokhzaini AzizanKhin Wee LaiImage registration approaches based on standard information criteria were widely employed, showing promising results in the registration of medical monomodal images. Feature-based registering is an effective clinical application technique since computational costs can be reduced significantly. The following four steps are generally used for most registration methods: detection of features, extraction of features, matching features and transformation determination. The accuracy of the registration procedure is dependent on matching a feature and detecting control points (CP). Thus, this paper supports this feature for monomodal registration of magnetic resonance imaging (MRI). MRI is gold-standard imaging to detect prostate cancer progression as it provides better information for soft tissue visualization. In this study, registration of MRI images taken from different time frames is developed to ease the physician in integrating the information about the evolution of the tumour. The accuracy of the registration process depends on matching features and CP detection by calculating the iterative closest point (ICP). The prostate volumes are calculated, and the result shows minimal errors. This registration method has been applied in coronal, sagittal and axial views from five patient datasets. The accuracy of automatic registration results is 0.1mm (axial), 1.1mm (coronal) and 0.2mm (sagittal). These accuracies are comparable to gold-standard manual registration by the experts. There was no significant difference between the automatically and manually registered MRI monomodal. Thus, the proposed method enables the physician to diagnose prostate cancer as it can provide important information about the disease progression and decide the necessary therapies regarding the patient’s condition - Some of the metrics are blocked by yourconsent settings
Publication RLMD-PA: A Reinforcement Learning-Based Myocarditis Diagnosis Combined with a Population-Based Algorithm for Pretraining Weights(WILEY - HINDAWI, 2022) ;Seyed Vahid Moravvej ;Roohallah Alizadehsani ;Sadia Khanam ;Zahra Sobhaninia ;Afshin Shoeibi ;Fahime Khozeimeh ;Zahra Alizadeh Sani ;Ru-San Tan ;Abbas Khosravi ;Saeid Nahavandi ;Nahrizul Adib Kadri ;Muhammad Mokhzaini Azizan ;N. ArunkumarU.Rajendra AcharyaMyocarditis is heart muscle inflammation that is becoming more prevalent these days, especially with the prevalence of COVID-19. Noninvasive imaging cardiac magnetic resonance (CMR) can be used to diagnose myocarditis, but the interpretation is time-consuming and requires expert physicians. Computer-aided diagnostic systems can facilitate the automatic screening of CMR images for triage. This paper presents an automatic model for myocarditis classification based on a deep reinforcement learning approach called as reinforcement learning-based myocarditis diagnosis combined with population-based algorithm (RLMD-PA) that we evaluated using the Z-Alizadeh Sani myocarditis dataset of CMR images prospectively acquired at Omid Hospital, Tehran. This model addresses the imbalanced classification problem inherent to the CMR dataset and formulates the classification problem as a sequential decision-making process. The policy of architecture is based on convolutional neural network (CNN). To implement this model, we first apply the artificial bee colony (ABC) algorithm to obtain initial values for RLMD-PA weights. Next, the agent receives a sample at each step and classifies it. For each classification act, the agent gets a reward from the environment in which the reward of the minority class is greater than the reward of the majority class. Eventually, the agent finds an optimal policy under the guidance of a particular reward function and a helpful learning environment. Experimental results based on standard performance metrics show that RLMD-PA has achieved high accuracy for myocarditis classification, indicating that the proposed model is suitable for myocarditis diagnosis.