SOURCE: Diagnostics

AUTHOR: TAN XIAO JIAN (Main Author) 

RESEARCH CENTRE: CMSP

SDG: 3,9

CITATION: Tan, X.J.; Cheor, W.L.; Lim, L.L.; Ab Rahman, K.S.; Bakrin, I.H. Artificial Intelligence (AI) in Breast Imaging: A Scientometric Umbrella Review. Diagnostics 2022, 12, 3111. https://doi.org/10.3390/diagnostics12123111

ABSTRACT: Artificial intelligence (AI), a rousing advancement disrupting a wide spectrum of applications with remarkable betterment, has continued to gain momentum over the past decades. Within breast imaging, AI, especially machine learning and deep learning, honed with unlimited cross-data/case referencing, has found great utility encompassing four facets: screening and detection, diagnosis, disease monitoring, and data management as a whole. Over the years, breast cancer has been the apex of the cancer cumulative risk ranking for women across the six continents, existing in variegated forms and offering a complicated context in medical decisions. Realizing the ever-increasing demand for quality healthcare, contemporary AI has been envisioned to make great strides in clinical data management and perception, with the capability to detect indeterminate significance, predict prognostication, and correlate available data into a meaningful clinical endpoint. Here, the authors captured the review works over the past decades, focusing on AI in breast imaging, and systematized the included works into one usable document, which is termed an umbrella review. The present study aims to provide a panoramic view of how AI is poised to enhance breast imaging procedures. Evidence-based scientometric analysis was performed in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guideline, resulting in 71 included review works. This study aims to synthesize, collate, and correlate the included review works, thereby identifying the patterns, trends, quality, and types of the included works, captured by the structured search strategy. The present study is intended to serve as a “one-stop center” synthesis and provide a holistic bird’s eye view to readers, ranging from newcomers to existing researchers and relevant stakeholders, on the topic of interest.

SOURCE: IEEE ACCESS

AUTHOR: Dr Yip Sook Yee (Main-Author) 

RESEARCH CENTRE: CMSP

SDG: 9

CITATION: S. Y. Yip, D. W. Yong, K. H. Yiauw, X. J. Tan and J. Y. R. Wong, "Model Predictive Direct Torque With Fault Tolerance Control for a Permanent Magnet Synchronous Generator Based on Vienna Rectifier," in IEEE Access, vol. 10, pp. 94998-95007, 2022, doi: 10.1109/ACCESS.2022.3204809. 

ABSTRACT: This paper presents the fault tolerance of model predictive direct torque control for a permanent magnet synchronous generator under a faulty Vienna rectifier. The fault applied includes open-switch and short-switch fault in a particular active switching device of the Vienna rectifier. The measured input current is used in the proposed fault diagnosis approach to detect the switch fault’s position without any additional hardware being implemented. Whenever a switching fault occurs at any phase of the Vienna rectifier, the available switching vectors for prediction control are reduced from five to four. The feasibility and effectiveness of the proposed fault tolerance model predictive direct torque control under a faulty Vienna rectifier are demonstrated and investigated through MATLAB/Simulink. The results show that it is feasible for the proposed method to be operated under a short-switch fault with slightly higher total harmonics distortion of the input current but out of control under an open-switch fault.

SOURCE: MDPI sensors

AUTHOR: GOH KAM MENG (Co-Author) 

RESEARCH CENTRE: CMSP

SDG: 9

CITATION: Cheng, X.; Chaw, J.K.; Goh, K.M.; Ting, T.T.; Sahrani, S.; Ahmad, M.N.; Abdul Kadir, R.; Ang, M.C. Systematic Literature Review on Visual Analytics of Predictive Maintenance in the Manufacturing Industry. Sensors 2022, 22, 6321. https://doi.org/10.3390/s22176321 link

ABSTRACT: The widespread adoption of cyber-physical systems and other cutting-edge digital technology in manufacturing industry production facilities may motivate stakeholders to embrace the idea of Industry 4.0. Some industrial companies already have different sensors installed on their machines; however, without proper analysis, the data collected is not useful. This systematic review’s main goal is to synthesize the existing evidence on the application of predictive maintenance (PdM) with visual aids and to identify the key knowledge gaps in areas including utilities, power generation, industry, and energy consumption. After a thorough search and evaluation for relevancy, 37 documents were identified. Moreover, we identified the visual analytics of PdM, including anomaly detection, planning/scheduling, exploratory data analysis (EDA), and explainable artificial intelligence (XAI). The findings revealed that anomaly detection was a major domain in PdM-related works. We conclude that most of the literature lacks depth in terms of an overall framework that combines data-driven and knowledge-driven techniques of PdM in the manufacturing industry. Some works that utilized both techniques indicated promising results, but there is insufficient research on involving maintenance personnel’s feedback in the latter stage of PdM architecture. Thus, there are still pertinent issues that need to be investigated and limitations that need to be overcome before PdM is deployed with minimal human involvement.

SOURCE: Journal of King Saud University - Computer and Information Sciences

AUTHOR: TAN XIAO JIAN (Main Author) 

RESEARCH CENTRE: CMSP

SDG: 9,17

CITATION: Tan, X. J., Cheor, W. L., Yeo, K. S., & Leow, W. Z. (2022). Expert systems in oil palm precision agriculture: A decade systematic review. Journal of King Saud University-Computer and Information Sciences, 34(4), 1569-1594. (link)

ABSTRACT: Oil palm (Elaeis guineensis Jacq.) is of the most profitable and widespread commercial high tree crops in the tropical world, typically in Southeastern Asia. The present study aims to provide a brief but broad overview of different applications of expert systems (ESs) in oil palm precision agriculture (PA), focusing on the three main generic categories: crop, water, and soil management. This study is meant to review research articles from the past decade: 2011–2020. Based on the search strategy alongside the inclusion criteria, 108 articles were included for synthesis activity. The findings of the study reveal patterns, networks, relationships, and trends in the application of ESs in oil palm PA in the past decade. The broad insight obtained from the synthesis activity was used to identify the possible roads ahead in oil palm PA. The findings of this study could be useful and beneficial to the research community and stakeholders in identifying the progress and trends of ESs in oil palm PA in the past decade, help to gain a holistic view on research gaps, potential markets, relevant advantages, the roads ahead, and contributing to further systematic research (deepen or broaden) in this topic.

SOURCE: Multimedia Tools and Applications

AUTHOR: TAN XIAO JIAN (Main Author)         

RESEARCH CENTRE: CMSP

SDG: 3, 9

CITATION: Tan, X. J., Mustafa, N., Mashor, M. Y., & Ab Rahman, K. S. (2021). Spatial neighborhood intensity constraint (SNIC) clustering framework for tumor region in breast histopathology images. Link

ABSTRACT: Precise segmentation of tumor regions plays prominent role in the grading of breast carcinoma using the Nottingham Histological Grading (NHG) system. A robust segmentation framework is expected to produce cost-effective, repeatable, and reproducible quantitative outputs. In this study, a spatial neighborhood intensity constraint (SNIC) clustering framework for tumor region in breast histopathology images is presented. The proposed framework consists of five main stages:(1) color normalization,(2) segmentation and removal of nucleus cells,(3) SNIC,(4) FCM with knowledge-based initial centroids selection, and (5) post-processing. The novelty of the proposed framework lies within its simple but powerful in clustering tumor regions precisely in a heterogenous environment. The SNIC is implemented to remove and replace the intensity of the nucleus cells based on the spatial constraints. Also, a knowledge-based initial centroids selection method is implemented to ease the FCM clustering algorithm. Both of these methods are posited to facilitate the clustering stage producing complementary results. To validate the hypothesis, careful justifications are performed to evaluate the role of SNIC and knowledge-based initial centroids selection. These methods are found plausible by achieving positive results in\(Acc\),\(F1\),\(AOM\), and\(CEI\) of 91.2%, 92.1%, 85.7%, and 90.1%, respectively. To further demonstrate the applicability of the proposed framework, four recent works are included for benchmarking purposes. The proposed framework found outperformed these methods with the lowest percentages in over-segmentation and under-segmentation: 8.7% and 6.6%, respectively.

SOURCE: Alexandria Engineering Journal

AUTHOR: LIM LI LI (Co-Author) 

RESEARCH CENTRE: CMSP

SDG: 9

CITATION: Geok, T. K., Hossain, F., Rahim, S. K. A., Elijah, O., Eteng, A. A., Loh, C. T., ... & Hindia, M. N. (2022). 3D RT adaptive path sensing Method: RSSI modelling validation at 4.5 GHz, 28 GHz, and 38 GHz. Alexandria Engineering Journal, 61(12), 11041-11061. Link

ABSTRACT: This paper explains a new Adaptive Path Sensing Method (APSM) for indoor radio wave propagation prediction. Measurement campaigns, which cover indoor line-of-sight (LoS), non-line-of-sight (NLoS) and different room scenarios, are conducted at the new Wireless Communication Centre (WCC) block P15a) of Universiti Teknologi Malaysia (UTM), Johor, Malaysia. The proposed APSM is evaluated through a computerized modelling tool by comparing the Received Signal Strength Indicator (RSSI) with measurement data and the conventional Shooting-Bouncing Ray Tracing (SBRT) method. Simulations of the APSM and SBRT are performed with the same layout of the new WCC block P15a by using the exact building dimensions. The results demonstrate that the proposed method achieves a better agreement with measured data, compared to the conventional SBRT outputs. The reduced computational time and resources required are also important milestones to ray tracing technology. The proposed APSM method can assist engineers and researchers to reduce the time required in modelling and optimizing reliable radio propagation in an indoor environment.

SOURCE: Alexandria Engineering Journal

AUTHOR: LIM LI LI (Co-Author) 

RESEARCH CENTRE: CMSP

SDG: 9, 7

CITATION: Faiq, M., Tan, K. G., Liew, C. P., Hossain, F., Tso, C. P., Lim, L. L., ... & Shah, Z. M. (2023). Prediction of energy consumption in campus buildings using long short-term memory. Alexandria Engineering Journal, 67, 65-76. Link

ABSTRACT: In this paper, Long Short-Term Memory (LSTM) was proposed to predict the energy consumption of an institutional building. A novel energy usage prediction method was demonstrated for daily day-ahead energy consumption by using forecasted weather data. It used weather forecasting data from a local meteorological organization, the Malaysian Meteorological Department (MET). The predictive model was trained by considering the dependencies between energy usage and weather data. The performance of the model was compared with Support Vector Regression (SVR) and Gaussian Process Regression (GPR). The experimental results with a dataset obtained from a building in Multimedia University, Malacca Campus from January 2018 to July 2021 outperformed the SVR and GPR. The proposed model achieved the best RMSE scores (561.692–592.319) when compared to SVR (3135.590–3472.765) and GPR (1243.307–1334.919). Through experimentation and research, the dropout method reduced overfitting significantly. Furthermore, feature analysis was done with SHapley Additive exPlanation to identify the most important weather variables. The results showed that temperature, wind speed, rainfall duration and the amount had a positive effect on the model. Thus, the proposed approach could aid in the implementation of energy policies because accurate predictions of energy consumption could serve as system fault detection and diagnosis for buildings.

SOURCE: Sensors

AUTHOR: GOH KAM MENG (Co-Author) 

RESEARCH CENTRE: CMSP

SDG: 9

CITATION: Cheng, X., Chaw, J. K., Goh, K. M., Ting, T. T., Sahrani, S., Ahmad, M. N., ... & Ang, M. C. (2022). Systematic literature review on visual analytics of predictive maintenance in the manufacturing industry. Sensors, 22(17), 6321. Link

ABSTRACT: The widespread adoption of cyber-physical systems and other cutting-edge digital technology in manufacturing industry production facilities may motivate stakeholders to embrace the idea of Industry 4.0. Some industrial companies already have different sensors installed on their machines; however, without proper analysis, the data collected is not useful. This systematic review’s main goal is to synthesize the existing evidence on the application of predictive maintenance (PdM) with visual aids and to identify the key knowledge gaps in areas including utilities, power generation, industry, and energy consumption. After a thorough search and evaluation for relevancy, 37 documents were identified. Moreover, we identified the visual analytics of PdM, including anomaly detection, planning/scheduling, exploratory data analysis (EDA), and explainable artificial intelligence (XAI). The findings revealed that anomaly detection was a major domain in PdM-related works. We conclude that most of the literature lacks depth in terms of an overall framework that combines data-driven and knowledge-driven techniques of PdM in the manufacturing industry. Some works that utilized both techniques indicated promising results, but there is insufficient research on involving maintenance personnel’s feedback in the latter stage of PdM architecture. Thus, there are still pertinent issues that need to be investigated, and limitations that need to be overcome before PdM is deployed with minimal human involvement.