Stochastic Dynamic Programming (SDP) is an important class of optimization which has recently been used in the area of dynamic adaptive streaming over HTTP (DASH). Though DASH is very popular method of video delivery in recent years it is plagued with problems when multiple players share a bottleneck link. Thus, this area has become a very active area of research. Two works which implement SDP in DASH are selected and their performances compared against the Conventional DASH player. It is shown that SDP works well for various network conditions.

Machine Learning has become an important tool in day to day life or we can say it’s a powerful tool in most of the fields which we want to automate. Machine Learning is used to develop algorithms which can learn from the data, which is either labeled, unlabeled or learn from the environment. Machine Learning is used in most of the fields and Especially in health care sector it takes much more benefits through proper decision and prediction techniques. Machine Learning in health care is a scientific study, so we have to store, retrieve and proper use of information, data and provide knowledge to the problems facing in the healthcare sector and also knowledge for the proper decision making. Due to these technologies there is a huge development in health care sectors over the years. For analysis of medical data, medical experts use the machine learning tools and techniques to identify the risks and to provide proper diagnosis and treatment. The paper is based on survey in terms of health care management system using different machine learning approaches and techniques.

Nowadays there are serious global problems, among them, the bad air quality in urban zones stands out, and also the lack of healthy food to guarantee quality feeding. According with some information of the United Nations Organization for agriculture and feeding, the global population is expected to increase to reach 8300 millions, and for that reason objectives for the sustainable development were fixed in the 2030 Agenda. In Mexico, the 2013-2018 Development Plan in the goal National IV Mexico Prospero is looking to guarantee the security in feeding, that is why an opportunity area has been identified in which we can set innovations and new technology. The main objective of this project was to develop an Autonomous Urban Garden, which allows to plant and care for different daily consumption foods on the roofs or roofs of houses or buildings through the Arduino platform and various sensors (soil moisture, rain, humidity and temperature), they can convert a large part of the carbon dioxide into oxygen, they will provide food to a family, ensuring that they are consumable since they themselves would be in charge of planting and harvesting.

Authentication with textual password has several limitations: passwords have low entropy in practice, are often difficult to remember, are vulnerable "shoulder surfing". Biometric system does not meet requirement as well. It relies upon unchanging features that have a lifetime as long as the individual. To avoid this limitation, we start to authenticate with thinking pass thought. User performs one mental task such as thinking of a word or phrase. In this study, Electroencephalography (EEG) was used as method for monitoring and recording the electrical activity of the brain. These signals can be captured and processed to get the useful information that can be used in pas-thoughts authentication system. Suitable analysis is essential for EEG to differentiate between best and worst tasks used for authentication. This study focuses on usefulness of EEG signal to identify best tasks suitable for the pass-thoughts authentication system. Artificial neural network (ANN) is used to train the data set. Then tests are conducted on the testing data of EEG signal to identify best and worst tasks suitable for authentication. Finally, the system performance was evaluated by computing the accuracy and therefore promising results were obtained.

Visual disability is a condition that affects 285 million people around the world, this sector of the society has a lot of tools for displacement, since a simple white cane that is the first thing that comes to our mind talking about visual disability to some technologic devices that assists them, nevertheless, these devices attack different difficulties, such as the detection of objects in front or the reading of signs in the public thoroughfare. However, there is a difficulty that has not been addressed; the detection of cavities or subsidence in the ground, this section must be taken into account as it can cause very serious accidents and for this reason a device designed to overcome this need is designed.

Oral or mouth neoplasm is the type of head & neck cancers. This type of cancer starts in the throat or mouth due to uncontrollable growth of tissues, and it looks like a lump or bump. In the pre- processing step, anisotropic diffusion filter used to filter unwanted distortions from MRI image. Next, the lesion separated from MRI image using a hybrid approach KFCM clustering in segmentation and features extracted using Intensity of Histogram, GLCM & GLRLM. The comparison between these three algorithms is performed to obtain the best feature extraction technique. Next, SVM classifier used to classify the lesion. Classification accuracy obtained for the developed system is 98.04% using GLRLM feature extraction technique.

Abnormal temperature of human body is a natural extensive indicator of illness. Infrared thermography (IRT) is a fast, non-invasive, non-contact and passive substitution to ordinary medical thermometers for monitoring and observation human body temperature. Aside from, IRT is able to chart body surface heat remotely. Last five decades testified a stationary development in thermal imaging cameras utilization to obtain relations between the thermal physiology and surface temperature. IRT has effectively used in diagnosis and detection of breast cancer, diabetes neuropathy and peripheral vascular disorders. It has been employed to detect issues related to gynecology, dermatology, heart, neonatal physiology, and brain imaging. With the advent of modern infrared cameras, data acquisition and processing techniques, it is now possible to have real time high resolution thermographic images, which is likely to surge further research in this field. The emergent technology known as the Internet of Things (IoT) has guided practitioners, physicians and researchers to design innovative solutions in different environments, particularly in medical and healthcare using smart sensors, computer networks and a remote server. This paper aims to propose IoT-enabled medical system enables diagnostics and detection for several medical anomalies remotely; in real-time and simultaneous depend on combination of IoT and Thermal Infrared imaging techniques. It will detect and diagnostics any abnormal and alert the user through IoT remotely and in real-time.

Mobile Ad hoc Network (MANET) is a developing area in the existing mobile environment. Its standards are defined by IETF. MANETs consists of several characteristics like dynamic topology, easy deployment and robustness which make them as a striking topic for the research community. Routing, enhancing the QoS is a challenging issue in MANETs. In this paper, one of the best proactive routing protocols ie Source Tree Adaptive Routing (STAR) protocol has been chosen. The selection of the stable configurable values in protocol will affect QoS performance. In MANETs, Time to Live (TTL) is a constant value and it has to be configured with network size accordingly. The impact of TTL value on the performance of STAR routing protocol has been analyzed. Simulation results suggest that the impact of TTL shows significant change on the performance of STAR at all network sizes with lower mobility. This paper shows that static values like TTL are not suitable for dynamic environment in protocol configuration and suggests that the TTL value should be a varied with the networksize and mobility speed accordingly to achieve better performance.

The research on group mobility in the opportunity network concerns what nodes’ mobility may bring about, in contrast to the preciously-proposed group mobility approaches that are unilateral in the thoughts of the constitution of groups by nodes, resulting in, the distortion of reality to reflect group behavior characteristics that are revealed in the situations of their application. Based on the Spectral Clustering algorithm, this paper proposes the group mobility model (SCM), which takes the advantage of node’s own properties, such as its spatial position, to set up node’s affinity matrix, extract main characteristics from arbitrarily distributed nodes with the aid of spectral clustering algorithm, and regarding the needs of a task assigned cluster nodes dynamically in a fixed time. The model depicts truly and effectively the group characteristics of node’s mobility, accomplish the conducts of group mobility and clustering well.