A physiological-based driver monitoring system (DMS) has attracted research interest and has great potential for providing more accurate and reliable monitoring of the driver's state during a driving experience. Many driving monitoring systems are driver behavior-based or vehicle-based. When these non-physiological based DMS are coupled with physiological-based data analysis from electroencephalography (EEG), electrooculography (EOG), electrocardiography (ECG), and electromyography (EMG), the physical and emotional state of the driver may also be assessed. Drivers' wellness can also be monitored, and hence, traffic collisions can be avoided. This paper highlights work that has been published in the past five years related to physiological-based DMS. Specifically, we focused on the physiological indicators applied in DMS design and development. Work utilizing key physiological indicators related to driver identification, driver alertness, driver drowsiness, driver fatigue, and drunk driver is identified and described based on the PRISMA Extension for Scoping Reviews (PRISMA-Sc) Framework. The relationship between selected papers is visualized using keyword co-occurrence. Findings were presented using a narrative review approach based on classifications of DMS. Finally, the challenges of physiological-based DMS are highlighted in the conclusion. Doi: 10.28991/CEJ-2022-08-12-020 Full Text: PDF

The transformative integration of artificial intelligence (AI) into educational settings, exemplified by ChatGPT, presents a myriad of ethical considerations that extend beyond conventional risk assessments. This study employs a pioneering framework encapsulating risk, reward, and resilience (RRR) dynamics to explore the ethical landscape of ChatGPT utilization in education. Drawing on an extensive literature review and a robust conceptual framework, the research identifies and categorizes ethical concerns associated with ChatGPT, offering decision-makers a structured approach to navigate this intricate terrain. Through the Analytic Hierarchy Process (AHP), the study prioritizes ethical themes based on global weights. The findings underscore the paramount importance of resilience elements such as solidifying ethical values, higher-level reasoning skills, and transforming educative systems. Privacy and confidentiality emerge as critical risk concerns, along with safety and security concerns. This work also highlights reward elements, including increasing productivity, personalized learning, and streamlining workflows. This study not only addresses immediate practical implications but also establishes a theoretical foundation for future AI ethics research in education.

Heart Rate Variability (HRV) may be used as a psychological marker to assess drivers’ states from physiological signals such as an electrocardiogram (ECG), electroencephalogram (EEG), and photoplethysmography (PPG). This paper reviews HRV acquisition methods from drivers and machine learning approaches for driver cardiac health based on HRV classification. The study examines four publicly available ECG datasets and analyzes their HRV features, including time domain, frequency domain, short-term measures, and a combination of time and frequency domains. Eight machine learning classifiers, namely K-Nearest Neighbor, Decision Tree, Naive Bayes, Linear Discriminant Analysis, Support Vector Machine, Random Forest, Gradient Boost, and Adaboost, were used to determine whether the driver's state is normal or abnormal. The results show that K-Nearest Neighbor and Decision Tree classifiers had the highest accuracy at 92.86%. The study concludes by assessing the performance of machine learning algorithms in classifying HRV for the driver's physiological condition using the Man-Whitney U test in terms of accuracy and F1 score. We have statistical evidence to support that the prediction quality is different when HRV analysis applies these three sets: (i) time domain measures or frequency domain measures; (ii) frequency domain measures or short-term measures; and (iii) combining time and frequency domains or only frequency domains. Doi: 10.28991/CEJ-2023-09-09-013 Full Text: PDF

Due to the limited availability of parking slots in parking areas, drivers often have difficulty finding an empty parking slot. The number of parking slots available at a particular location is usually less than the number of vehicles. Hence, drivers spend a lot of time looking for vacant parking slots, which eventually delays the completion of their tasks, such as paying bills, attending a meeting, or visiting a patient at the hospital, etc. There are a couple of parking guidance systems that have been highlighted by the other researchers, but most of them lack real-time, convenient guidance. This research proposed a smart parking guidance system made of an IoT Raspberry Pi combined with an Android application that makes use of the weighted k nearest neighbours for positioning the vehicle. This was achieved through the use of Wi-Fi signal strength indicator fingerprinting, allowing for real-time navigation and parking detection. In order to achieve real-time parking over the internet, Raspberry Pi hardware and the ThingSpeak IoT cloud with ultrasonic sensors are used in the proposed method. An Android application was involved in this parking detection system, which adopted IoT approaches to estimate the location of users in real-time and provide routes using route-finding techniques to assist drivers in finding their desired parking slots. Data from the sensors was processed and translated into the Raspberry Pi using the Python programming language. They were sent using the Message Telemetry Transport protocol to send parking data to the ThingSpeak IoT cloud in real-time. This data was displayed via the Android app. The user is then able to view each available parking slot, acquire the route, and be directed with high accuracy to the parking slots of their choice. In this study, advanced sensing and communication technologies were used together with the weighted k nearest neighbours algorithm for positioning and wayfinding in order to improve parking guidance accuracy. Based on the experimental results, the proposed system showed a lower average error rate of 1.5 metres in comparison to other positioning techniques, such as GPS, or other similar algorithms for positioning, such as maximum a posteriori, which have shown average errors of 2.3 metres and 3.55 metres, respectively, a potential increase of more than 35% from the previous error rate. Doi: 10.28991/CEJ-2023-09-08-012 Full Text: PDF

The oil palm industry is one of the main contributors to the Malaysian national economy. However, the industry faces the challenge of Ganoderma infection, a destructive disease affecting oil palm trees that causes base stem rot (BSR). To address this issue, a novel method is proposed for detecting Ganoderma disease in oil palm trees by combining hyperspectral imaging and machine learning techniques. In this paper, four different classifiers, such as Support Vector Machine (SVM), Random Forest (RF), Multilayer Perceptron (MLP), and Decision Tree (DT), were trained on a tabular dataset generated by clustering spectral signatures of each pixel in the hyperspectral image. The evaluation results show that the MLP model showed 100% accuracy, sensitivity, and specificity in distinguishing between healthy and infected trees. SVM with radial basis function (RBF) and polynomial kernels show the second highest performance, attaining 91.67% accuracy, 83.33% sensitivity, and 100% specificity. Before training on classifiers, a feature-based band alignment technique is developed to overcome the hyperspectral image misalignment issue and ensure the accuracy of the spectral data. The developed band alignment significantly improved spectral coherence across the adjacent bands, as proved by enhanced Root Mean Squared Error (RMSE), Pearson Correlation Coefficient (PCC), and Normalized Mutual Information (NMI) values. Additionally, the developed background subtraction method effectively extracted the tree pixels from the background, resulting in a high precision score of 93.99%. Future research will focus on collecting more data, incorporating temporal information for disease stage classification, and implementing a more robust machine learning (ML) model for performance enhancement. Overall, this research shows the great potential of hyperspectral imaging for accurate detection of Ganodermadisease in oil palm plantations. Doi: 10.28991/HEF-2025-06-01-05 Full Text: PDF