It is highly important to give social care on the personal information that is collected by mobile health applications. There has been a rise in the mobile applications which are applied in almost all the departments and this is as a result of the high technological advancement globally. The developers of these applications need to be somehow reluctant in maintaining the privacy of information collected through the applications because many release insecure apps. The aim of this report is to analyze the status of privacy and security in relation to mobile health. The analysis or the review has been done through academic literature review, a study of the laws which regulate mobile health in the EU and USA. Also, lastly, giving a recommendation for the mobile application developers, on how to maintain privacy and security. As a result, other certifications and standards will be proposed for app developers and another guide for the researchers and developers as well.

Generalized formalization of recognition algorithm for specialized computer systems is presented in this paper. The structure features of image recognition methods, which have to be taken into account when developing classifiers for object recognition in specialized computer systems, are described. The fundamental types of images characteristics-features, which are used in various methods of image recognition, are discussed. Approaches to development of classifiers for recognizing robotization objects, which are implemented on basis of Haar classifiers, are discussed. The issues of using machine learning algorithm of adaptive gain AdaBoost for development of such classifiers are also considered. Utilities have been developed for implementation of classifiers for object recognition in specialized computer systems.

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Centre of attraction of paper is on the main complication on classification of Big Data on network encroachment on traffic. It also explains the disputes this system faces that is bestowed by the Big Data difficulties that are correlate with the network interruption forecast. Forecasting of an attainable interruption in a network entails a prolonged accumulation of traffic information or data and being able to get the concept on their features on motion. The constant accumulation in the network of traffic data thereafter ends with Big Data difficulties that as a result of the large amount, change and possessions of Big Data. In order to learn the features of a network, one needs to have the skills in the machine techniques that are always able to capture world skills and knowledge of the traffic to be in order. The properties of Big Data will always end to an important system disputes to be able to apply machine learning foundation. The paper also discusses the disputes and problems in the way of taking care of Big Data categorization representing geometric techniques of learning along with the existing technologies of Big networking. The study particularly explains challenges that have a relationship with the combined directed by the techniques one learns, machine long learning techniques, and representation-learning techniques and technologies that are related to Big Data for example Hive, Hadoop and Cloud that are basics that enhances problem-solving that gives relevant solutions to classification problems in traffic networking.

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