The increasing number of diabetes individuals in the globe has alarmed the medical sector to seek alternatives to improve their medical technologies. Machine learning and deep learning approaches are active research in developing intelligent and efficient diabetes detection systems. This study profoundly investigates and discusses the impacts of the latest machine learning and deep learning approaches in diabetes identification/classifications. It is observed that diabetes data are limited in availability. Available databases comprise lab-based and invasive test measurements. Investigating anthropometric measurements and non-invasive tests must be performed to create a cost-effective yet high-performance solution. Several findings showed the possibility of reconstructing the detection models based on anthropometric measurements and non-invasive medical indicators. This study investigated the consequences of oversampling techniques and data dimensionality reduction through feature selection approaches. The future direction is highlighted in the research of feature selection approaches to improve the accuracy and reliability of diabetes identifications.

Remote photoplethysmography (rPPG) gains recent great interest due to its potential in contactless heart rate measurement using consumer-level cameras. This paper presents a detailed review of rPPG measurement using computer vision and deep learning techniques for heart rate estimation. Several common gaps and difficulties of rPPG development are highlighted for the feasibility study in real-world applications. Numerous computer vision and deep learning methods are reviewed to mitigate crucial issues such as motion artifact and illumination variation. In comparison, deep learning approaches are proven more accurate than conventional computer vision methods due to their adaptive pattern learning and generalization characteristics. An increasing trend of applying deep learning techniques in rPPG can improve effective heart rate estimation and artifact removal. To consider more realistic disturbances into account, additional vital signs and large training datasets are crucial to improve the accuracy of heart rate estimations. By taking the benefit of contactless and accurate estimation, the application of rPPG can be greatly adopted in real-world activities, especially in precision sports.

Traffic sign recognition system is an important subsystem in advanced driver assistance systems (ADAS) that assisting a driver to detect a critical driving scenario and subsequently making an immediate decision. Recently, deep architecture neural network is popular because it adapts well in various kind of scenarios, even those which were not used during training. Therefore, a deep architecture neural network is implemented to perform traffic sign classification in order to improve the traffic sign recognition rate. A comparative study for a deep and shallow architecture neural network is presented in this paper. Deep and shallow architecture neural network refer to convolutional neural network (CNN) and radial basis function neural network (RBFNN) respectively. In the simulation result, two types of training modes had been compared i.e. incremental training and batch training. Experimental results show that incremental training mode trains faster than batch training mode. The performance of the convolutional neural network is evaluated with the Malaysian traffic sign database and achieves 99% of the recognition rate.

Deep Belief Network (DBN) is made up of stacked Restricted Boltzmann Machine layers associated with global weight fine-tuning for pattern recognition. However, DBN suffers from vanishing gradient problem due to the saturation characteristic of activation function. Therefore, the selection of activation function in DBN is critical to reduce the network complexity and improve performance of pattern recognition. Unsaturated activation functions such as rectified linear unit and leaky rectified linear unit were recently proposed to avoid the effect of vanishing gradient for a deep learning neural network. In this paper, we investigated the network performance with both saturated and unsaturated activation functions. Besides that, the randomization of training samples would significantly improve the performance of DBN. The experimental results showed that hyperbolic tangent activation function achieved the lowest error rate which is 1.99% on MNIST handwritten digit dataset.