The development of the Internet of Things for medical use has helped many medical practitioners to perform treatments remotely. Insomnia or sleep disorder is one of the most common medical conditions that could happen to the person who is living in the urban or remote area. This condition requires a clinical test through a monitoring process that includes EEG, BP, Pulse and Stress status. Due to limited sleep laboratory facilities and medical equipment for insomnia. This paper proposes an initial Tele-insomnia framework that enables the rural patients to be tested for sleep disorder remotely. The framework is tested through a well-developed tele-insomnia proto-type by way of pilot test through case study patients in three hospitals of Jakarta. The physicians and patients were interviewed regarding diagnose time, accuracy and conveniences. The proposed framework provides convenience for the patient as they did not need to commute from home to the hospital and takes up less time to complete the diagnosis.

Heart Disease are among the leading cause of death worldwide. The application of artificial neural network as decision support tool for heart disease detection. However, artificial neural network required multitude of parameter setting in order to find the optimum parameter setting that produce the best performance. This paper proposed the parameter tuning framework for artificial neural network. Statlog heart disease dataset and Cleveland heart disease dataset is used to evaluate the performance of the proposed framework. The results show that the proposed framework able to produce high classification accuracy where the overall classification accuracy for Cleveland dataset is 90.9% and 90% for Statlog dataset.

Insomnia is a common health problem in medical field as well as in psychiatry. The measurement of those factors could be collected by using polysomnography as one of the current standards. However, due to the routine of clinical assessment, the polysomnography is impractical and limited to be used in certain place. The rapid progress of electronic sensors to support IoT in health telemonitoring should provide the real time diagnosis of patient at home too. In this research, the development of centralized insomnia system for recording and analysis of patient with chronicinsomnia data is proposed. The system is composed from multi body sensors that connected to main IOT server. The test has been done for 5 patients and the result has been successfully retrieved in real time.

We present a study on Covid-19 detection using deep learning algorithms that help predict and detect Covid-19. Chest X-ray images were used as the input dataset to prepare and train the proposed model. In this context, deep learning architecture (DLA) and optimisation strategies have been proposed and explored to support the automated detection of Covid-19. A model based on a convolutional neural network was proposed to extract features of images for the feature-learning phase. Data augmentation and fine-tuning with deep-feature-based methods were applied to improve the model. Image enhancement and saliency maps were used to enhance visualisation and estimate the disease severity level based on two parameters; degree of opacity and geographic extent. Contrast-limited adaptive histogram equalisation and Otsu thresholding were employed with several parameters to investigate the effects on the visualisation results. An experimental investigation was performed between the proposed method and other pretrained DLAs. The proposed work obtained excellent classification accuracy and sensitivity of 97.36% and 95.24% respectively. In addition, the input parameters for image enhancement significantly affected the results. The overall performance metrics were perfect for DenseNet and adequately high for the proposed work which is comparable to other models. Data augmentation and fine-tuning successfully handed the networks to enhance the overall performance, especially in our case with limited datasets.

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