Home based Telehealth is a combination of communications, imaging, sensing and human computer interaction technologies targeted at diagnosis, treatment and monitoring patients without disturbing the quality of lifestyle. This paper proposes development of a low cost medical sensing, communication and analytics device that is real-time monitoring internet enabled patients physiological conditions. Internet of Things (IoT) network will provide active and real-time engagement of patient, hospitals, caretaker and doctors. Massaging and synchronising the system has been the based focus in this paper, where it applies the suggested algorithm to predict the minimum time period that separates two consecutive bursts of messages and measures the minimum queue sizes for the health care personals nods, to manage the traffic and avoid the dropping of messages. NS2 simulator was employed to simulate the Telehealth environment algorithm.

Recently, the need for a real-time healthcare monitoring system that able to offer remote and personal health-care services has increased. The patients with Sickle Cell Disease (SCD) require continuous services of testing, following-up and monitoring. Offering these services to patients smoothly at any time needs to integrated healthcare system. The recent development in information systems and technologies facilitate introducing such healthcare systems. This paper proposed an integrated system model, which offers the services of testing, following-up and monitoring patients with (SCD). The proposed system uses support vector machine SVM, which is supervised machine learning approach to analyze the collected data of a specific patient and takes the appropriate action such as send alert message to the healthcare staff. To perform the classification process, four methods are applied with SVM algorithm, which are Sequential Minimal Optimization SMO, Rules JRIP, Tree Decision Stump and Naive Bays for comparative analysis. In this paper, many experiments were implemented based on the four machine learning algorithms to determine patients of SCD from normal patients. The results were promising as they show 99% classifications were accurate when using SMO algorithm.

This paper developed Mushroom Diagnosis Assistance System (MDAS) that can be used on a mobile phone. Two classifiers are used which are Naive Bays and Decision Tree to classify the mushroom types. The proposed approach selects the most effective of the already known mushroom attributes, and then specify the mushroom type. The use of specific features in mushroom determination process achieved very accurate results.

Multiple input multiple output (MIMO) systems, which are suitable for mmWave communications, facilitate building large arrays of communication channel with Reasonable form factors indoor and outdoor communications. In both, indoors and outdoors communications a reasonable coverage can be achieved by the high gains of array. Deploying MIMO systems in frequency-division duplex mode faces the issue of high feedback overhead of channel state information (CSI) to the transmitter. This issue is attributed to the used multiplexing algorithm. From other hand, it is expected that by 2020, all new 5G technologies will be in operation. Hence, it is crucial to investigate and analyze using the MIMO system with orthogonal frequency-division multiplexing OFDM modulation in 5G technology. This research sheds light on the potential challenges of implementing MIMO-OFDM in 5G technology. It proposed four models with different number of transmitter and receiver antenna elements in the array. All proposed model used 2 elements per row in transmitter and receiver antenna. The four models are implemented in two cases Line of sight LOS and Non Line of Sight NLOS. The channel rank and condition number of channel matrix are regarded as significant and accurate mathematical indicators which can be used to evaluation MIMO channel technology. The condition number is calculated from the obtained results of four models with rank of channel metrics.