Stress can be recognised by observing changes in physiological responses on the human body. Wearable sensors for stress detection are becoming more prominent in recent years due to their functionality and non-intrusive nature. By utilising data from wearable sensors, we have developed a personalized stress detection system. Our system performs classification on stress level using multimodal data from wrist-worn device Empatica E4 wearable sensor. We implemented three different classification algorithms: Logistic Regression, Decision Tree, and Random Forest and used four-class classification conditions: baseline, stress, amusement, and meditation. By evaluating the performance of the system, we demonstrate that our system can perform the best and consistent personalized stress detection using Random Forest classifier with the accuracy of 88%-99% on 15 subjects.

Abstract-Leather craft products, such as belt, gloves, shoes, bag, and wallet are mainly originated from cow, crocodile, lizard, goat, sheep, buffalo, and stingray skin. Before the skins are used as leather craft materials, they go through a tanning process. With the rapid development of leather craft industry, an automation system for leather tanning factories is important to achieve large scale production in order to meet the demand of leather craft materials. The challenges in automatic leather grading system based on type and quality of leather are the skin color and texture after tanning process will have a large variety within the same skin category and have high similarity with the other skin categories. Furthermore, skin from different part of animal body may have different color and texture. Therefore, a leather classification method on tanning leather image is proposed. The method uses pre-trained deep convolution neural network (CNN) to extract rich features from tanning leather image and Support Vector Machine (SVM) to classify the features into several types of leather. Performance evaluation shows that the proposed method can classify various types of leather with good accuracy and superior to other state-of-the-art leather classification method in terms of accuracy and computational time.

Abstract-One of the problems in community health center or health clinic is documenting the toddlers' data. The numbers of malnutrition cases in developing country are quite high. If the problem of malnutrition is not resolved, it can disrupt the country's economic development. This study identifies malnutrition status of toddlers based on the context data from community health center (PUSKESMAS) in Jogjakarta, Indonesia. Currently, the patients' data cannot directly map into appropriate groups of toddlers' malnutrition status. Therefore, data mining concept with k-means clustering is used to map the data into several malnutrition status categories. The aim of this study is building software that can be used to assist the Indonesian government in making decisions to take preventive action against malnutrition.

This research work proposes a novel method to improve quality of animal leather images using digital image processing approach. In this work, piecewise linear contrast stretch based on unsharp masking algorithm is employed for image enhancement. The proposed method minimizes contrast problem. Experiments had been done on four categories of animal leather images namely crocodile leather, monitor lizard leather, cow leather and goat leather. The proposed method was then compared with other piecewise linear transforms based image enhancement techniques including intensity level slicing, bit plane slicing and contrast stretching methods. PSNR, MSE and SSIM values were obtained by using our proposed method and our proposed method produced better result. The values of PSNR when using piecewise linear contrast stretch unsharp masking (PLCSUS) respectively for crocodile leather, monitor lizard leather, cow leather and goat leather are 30.06 dB, 18.97 dB, 20.66 dB and 14.73 dB. This value is higher when compared to using other methods on the same image. Experiments show that our proposed method is better compared to conventional methods with respect to special characteristics of animal leather to be used as raw materials of artworks.

The development of science in the field of health clinical pharmacy grows rapidly in recent years. Based on the data from information was obtained that needs to be done a reparation a learning process in clinical pharmacy to produce them who as requested by users pharmaceutical graduates. According to the results of the information there is a problem that in conducting the process of determining the pharmacys drug it can be made a mistake, especially in patients who have complications disease. The process of checking conducted repeatedly to make sure a medicine that is concocted in accordance with a list of the acts of treat a patient, while patient data not yet integrated into a system that could help them in analysis and determine a drug that in accordance. Notification system that developed using android platform this, the hope can become the tools in the form of a system that can give notification to the farmasis easily accessible at any time through gadgets. Based on the results of testing with the methods alpha test can be concluded the number of feasibility this system reached 88.75%. Thus notification system in the determination of medicine patients rule based as a medium learn students pharmaceutical clinic worthy to used.