The neuropsychology assessment for identifying dementia in parkinson’s disease patients using a deep neural network

Parkinson’s Disease (PD) patients have a high risk of developing dementia at least a year after the diagnosis. PD-Dementia affects both the physical and mental function that can gradually worsen the condition of the patients over time. This work proposed a framework for detecting dementia among PD patients based on neuropsychological assessment. This work classifies samples using the Montreal Cognitive Assessment (MoCA) scores as a guideline. It is classified into three categories, which are No Dementia, PD-MCI, and PD-Dementia. The work continues with designing a Deep Neural Network (DNN) architecture specific for analyzing electronic health records for PDDementia detection. Then, it compares the proposed model with the other five baseline methods. The experiment results present that the proposed DNN presents the highest result of 97.5%. This result shows that this proposed model is able to identify early dementia in PD patients from non-motor symptoms

The identification of RFID signal using k-means for pallet-level tagging

Radio Frequency Identification (RFID) applications are becoming increasingly popular in a myriad of areas, and therefore, an effective RFID technology-based location would offer a much-needed additional in tracking system. This research focuses on the identification of the location of passive RFID at the pallet-level, which uses the RFID signal strength to cluster the pallet level tagging through k-means. A comparison between the actual and the predicted level attained via the k-means clustering is evaluated through a multi-class performance metrics. It was demonstrated from the investigation that the k-means model is capable of achieving a classification accuracy of 69% and 67% for the train and test data, respectively

Effect of image compression using fast fourier transformation and discrete wavelet transformation on transfer learning wafer defect image classification

Automated inspection machines for wafer defects usually captured thousands of images on a large scale to preserve the detail of defect features. However, most transfer learning architecture requires smaller images as input images. Thus, proper compression is required to preserve the defect features whilst maintaining an acceptable classification accuracy. This paper reports on the effect of image compression using Fast Fourier Transformation and Discrete Wavelet Transformation on transfer learning wafer defect image classification. A total of 500 images with 5 classes with 4 defect classes and 1 non-defect class were split to 60:20:20 ratio for training, validating and testing using InceptionV3 and Logistic Regression classifier. However, the input images were compressed using Fast Fourier Transformation and Discrete Wavelet Transformation using 4 level decomposition and Debauchies 4 wavelet family. The images were compressed by 50%, 75%, 90%, 95%, and 99%. As a result, the Fast Fourier Transformation compression show an increase from 89% to 94% in classification accuracy up to 95% compression, while Discrete Wavelet Transformation shows consistent classification accuracy throughout albeit diminishing image quality. From the experiment, it can be concluded that FFT and DWT image compression can be a reliable method for image compression for grayscale image classification as the image memory space drop 56.1% while classification accuracy increased by 5.6% with 95% FFT compression and memory space drop 55.6% while classification accuracy increased 2.2% with 50% DWT compression

The condition based monitoring for bearing health

Bearing is a small component that widely uses in industries, either in rotary machines or shafts. Faulty in bearing might cause massive downtime in the industries, which lead to loss of revenue. This paper intends to find the consequential statistical time-domain-based features that can be used in classification from accelerometry signals for the bearing condition. An accelerometer was used as the data logger device to attain the condition signals from the bearing. Machinery Failure Prevention Technology (MFPT) online dataset has three different bearing conditions: baseline condition, inner faulty condition, and outer faulty condition. Extraction of eight statistical time-domain features was done, which is root-mean-square (RMS), minimum (Min), maximum (Max), mean, median, standard deviation, variance, and skewness. The identification of informative attributes was made using a filter-based method, in which the scoring is done by using the Information gain ratio. For the extracted features, the data splitting of training data to testing data was set to the ratio of 70% and 30%, respectively. The selected feature for classification is then fed into various types of classifiers to observe the effect of this feature selection method on the classification performance. From this research, six features were identified as the significant features: variance, standard deviation, Min, Max, mean, and RMS. It is said that the classification accuracy of the training data and the testing data using the filter-based feature selection method is equivalent to the classification accuracy of all the features selected

Estimation of electric vehicle turning radius through machine learning for roundabout cornering

This paper presents an alternative approach for estimating the turning radius using machine learning technique. While on-board sensors are unable to offer adequate information on vehicle states to the algorithm, vehicle states other than those directly detected by on-board sensors can be inferred using machine learning (ML) approaches based on the collected data. A compact electric vehicle model is used to obtain data and measurements of the vehicle states for different sets of road radius. The augmented basic measurements is fed to an Extra Tree Regression to predict the turning radius of the vehicle. The feasibility of the developed algorithm was tested and validated using performance metrics. The results show that the regression accuracy for the turning radius is 99% and can be obtained with sufficient vehicle dynamics information

Identifying PTSD symptoms using machine learning techniques on social media

Post-traumatic stress disorder (PTSD) is a mental health illness brought on by watching or experiencing a horrific incident. Flashbacks, nightmares, acute anxiety, and uncontrolled thoughts about the unforgettable incident are the possible symptoms faced by PTSD sufferers. The PTSD diagnosis is usually done by a mental health specialist based on the symptoms that the person has, and the task is very time-consuming. Due to the widespread use of social media in recent years, it has opened up the opportunity to explore PTSD signs in users' postings on Twitter. The content-sharing feature available on this platform has allowed its users to share personal experiences, thoughts, and feelings that could reflect their psychological status. Thus, the goal of this work is to identify the PTSD symptom from text posting on Twitter. The crawled text posting is filtered and trained on selected machine learning and deep learning methods. The experiment results show that the support vector machine performed the best with 91% accuracy compared to others. This extracted model could be used in identifying PTSD symptoms on social media

Efficiency and accuracy of scheduling algorithms for final year project evaluation management system

Scheduling algorithms play a crucial role in optimizing the efficiency and precision of scheduling tasks, finding applications across various domains to enhance work productivity, reduce costs, and save time. This research paper conducts a comparative analysis of three algorithms: genetic algorithm, hill climbing algorithm, and particle swarm optimization algorithm, with a focus on evaluating their performance in scheduling presentations. The primary goal of this study is to assess the effectiveness of these algorithms and identify the most efficient one for handling presentation scheduling tasks, thereby minimizing the system's response time for generating schedules. The research takes into account various constraints, including evaluator availability, student and evaluator affiliations within research groups, and student-evaluator relationships where a student cannot be supervised by one of the evaluators. Considering these critical parameters and constraints, the algorithm assigns presentation slots, venues, and two evaluators to each student without encountering scheduling conflicts, ultimately producing a schedule based on the allocated slots for both students and evaluators

Deep learning based human presence detection

Human detection and tracking have been progressively demanded in various industries. The concern over human safety has inhibited the deployment of advanced and collaborative robotics, mainly attributed to the dimensionality limitation of present safety sensing. This study entails developing a deep learning-based human presence detector for deployment in smart factory environments to overcome dimensionality limitations. The objective is to develop a suitable human presence detector based on state-of-the-art YOLO variation to achieve real-time detection with high inference accuracy for feasible deployment at TT Vision Holdings Berhad. It will cover the fundamentals of modern deep learning based object detectors and the methods to accomplish the human presence detection task. The YOLO family of object detectors have truly revolutionized the Computer Vision and object detection industry and have continuously evolved since its development. At present, the most recent variation of YOLO includes YOLOv4 and YOLOv4 - Tiny. These models are acquired and pre-evaluated on the public CrowdHuman benchmark dataset. These algorithms mentioned are pre-trained on the CrowdHuman models and benchmarked at the preliminary stage. YOLOv4 and YOLOv4 – Tiny are trained on the CrowdHuman dataset for 4000 iterations and achieved a mean Average Precision of 78.21% at 25FPS and 55.59% 80FPS, respectively. The models are further fine-tuned on a Custom CCTV dataset and achieved significant precision improvements up to 88.08% at 25 FPS and 77.70% at 80FPS, respectively. The final evaluation justified YOLOv4 as the most feasible model for deployment

Normal forces effects of a two in-wheel electric vehicle towards the human body

Traditionally, in order to comprehend the impact of vibration on human and vehicle ride comfort, past research often models the human biodynamic and vehicle models individually. Recent trends suggest that a better understanding of the behaviour could be achieved by fusing the models instead of analysing it separately. The present study evaluates the impact of the normal forces on specific parts of the human body. A human biodynamic model with five degrees of freedom is modelled together with a two in-wheel electric car model travelling at a speed of 10 km/h to investigate the effect of the normal forces. From the present investigation, it could be observed that the proposed model could highlight the impact of the normal forces on the body parts when the car is travelling either on a straight path or in taking corners

Vision-based human detection by fine-tuned SSD models

Human-robot interaction (HRI) and human-robot collaboration (HRC) has become more popular as the industries are taking initiative to idealize the era of automation and digitalization. Introduction of robots are often considered as a risk due to the fact that robots do not own the intelligent as human does. However, the literature that uses deep learning technologies as the base to improve HRI safety are limited, not to mention transfer learning approach. Hence, this study intended to empirically examine the efficacy of transfer learning approach in human detection task by fine-tuning the SSD models. A custom image dataset is developed by using the surveillance system in TT Vision Holdings Berhad and annotated accordingly. Thereafter, the dataset is partitioned into the train, validation, and test set by a ratio of 70:20:10. The learning behaviour of the models was monitored throughout the fine-tuning process via total loss graph. The result reveals that the SSD fine-tuned model with MobileNetV1 achieved 87.20% test AP, which is 6.1% higher than the SSD fine-tuned model with MobileNetV2. As a trade-off, the SSD fine-tuned model with MobileNetV1 attained 46.2 ms inference time on RTX 3070, which is 9.6 ms slower as compared to SSD fine-tuned model with MobileNetV2. Taking test AP as the key metric, SSD fine-tuned model with MobileNetV1 is considered as the best fine-tuned model in this study. In conclusion, it has shown that the transfer learning approach within the deep learning domain can help to protect human from the risk by detecting human at the first place