Reinforcement Learning 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 Reinforcement Learning in DASH are selected and their performances compared against the Conventional DASH player. It is shown that SDP works well for various network conditions.

Population counting and distribution is vital to planning and resources allocation of the nation. The birth and death registration is a good starting point for population counting and archive. Accurate record of birth and death is vital to population estimate of a country. In this modern day of ICT and computer proliferation it is sadden that Nigeria and other African countries are still registering birth and death manually hence excluded from benefit accrue to proper birth and death registration. This work proposed real-time online birth and death registration system for Nigerian citizen. The choice of programming language shall be PHP (Hypertext preprocessor) for server-side scripting while action script and Macromedia-Flash for media content authoring and MySQL shall be used as the back-end database.

Due to the Era of Big Data with the large amount of distributed databases in the web and the rapid growth in the smart systems a rapid growth happening in database models and the relational database fails to dealing with such a big amount of data and have many limitations the need to new technologies comes up, which makes DBMS developers move towards column oriented NOSQL database. The main goal of this paper is to provide a survey on NOSQL Model especiallya column oriented NOSQL database, providing the user with the benefit of using NOSQL database, Instead of using the (row database) relational to overcome the drawbacks of the relational database Model.

Use python web crawler to collect data from Trade website. The collected data is down jacket information. The fields are shell material, structure type, filling material, process information and style information. This information can be used for data mining, using clustering algorithms, correlation algorithms, etc. to identify potential value, providing decision-making reference for the management of textile and garment enterprises, with strong practical value. This paper provides a new idea for the development of textile and garment enterprises. The employees of the company screen, deal with the missing data and standardize the data, and then conduct data mining. The management of the enterprise makes decisions based on the results of data mining to improve decision-making basis and correctness.

Gene is a basic component of DNA located in the nucleus of Human cell. Currently data mining technique has huge impact in fields of human genetic science and gene sequence data analysis. Gene sequence analysis is a method of subjecting DNA sequence to systematic methods in order to know the genes character, configuration, nature and characteristics. CBC and MNBC applied to gene sequence data analysis, aims to segregate diseased diabetic genes from a vast stream of DNA gene sequence elements present in group of copious statistical data. This techniques attempts to approve, determine methods and tools for analyzing diseased gene sequences. It also helps in classification and interpretation of results accurately and meaningfully. This study is a combination of supervised and unsupervised machine learning technique for data analysis. The clustering is done by CBC whereas classification done by MNBC techniques. It recognizes gene expressions by framing association rules in accordance with support measure and confidence measure on the input data set.It will extract and filter required data into clusters based on CBC technique thereby drafting association rules. These are then applied on testing dataset to filter required (diseased) gene sequences. Finally MLRC algorithm is applied as classification algorithm to identify class labels of test genes sequences in a big dataset. In medical diagnosis gene data mining techniques through gene discretization models helps to identify various associations between the DNA genes based progressions and inconsistency in disease infections transformations. Above all it overcomes the limitation of existing Support Vector Machine Classification technology which incurs high computational cost and increased iterations.

Recently, psychologist has experienced drastic development using statistical methods to analyze the interactions of humans. The intention of past decades of psychological studies is to model how individuals learn elements and types. The scientific validation of such studies is often based on straightforward illustrations of artificial stimuli. Recently, in activities such as recognizing items in natural pictures, strong neural networks have reached or exceeded human precision. In this paper, we present Relevance Networks (RNs) as a basic plug-and-play application with Covolutionary Neural Network (CNN) to address issues that are essentially related to reasoning. Thus our proposed network performs visual answering the questions, super-human performance and text based answering. All of these have been accomplished by complex reasoning on diverse physical systems. Thus, by simply increasing convolutions, (Long Short Term Memory) LSTMs, and (Multi-Layer Perceptron)MLPs with RNs, we can remove the computational burden from network components that are unsuitable for handling relational reasoning, reduce the overall complexity of the network, and gain a general ability to reason about the relationships between entities and their properties.