Big data have become an important asset due to its immense power hidden in analytics. Every organization is inundated with colossal amount of data generated with high speed, requiring high-performance resources for storage and processing, special skills and technologies to get value out of it. Sources of big data may be either internal or external to organization, and big data may reside in structured, semi-structured or unstructured form. Artificial intelligence, Internet of Things, and social media are contributing to the growth of big data. Analytics is the use of statistics, maths, and machine learning to derive meaningful insights from data to make timely decisions and enable data-driven organization of the future. This paper sheds light upon big data, taxonomy of data, and hierarchical journey of data from its original form to the high level understanding in terms of wisdom. The paper also focuses on key characteristics of big data and challenges of handling big data. In addition, big data storage systems have also been briefly covered to get the idea on how storage systems help to accommodate the requirements of big data. This paper scrupulously articulates the eras of evolution of analytics varying from descriptive, predictive and prescriptive analytics. Process models used for inferring information from data have been compared and their applicability for analyzing big data has also been sought. Finally, recent developments carried in the domain of big data and analytics are compared based on the state-of-the-art approaches.

Cloud Computing is an innovation ideas that helps in reducing the computing cost. Cloud Computing offers better computing through improved utilization and reduced administration and infrastructure costs. Cloud computing is the long-held dream of computing as a utility. Cloud Computing is the combination of Software as a Service (SaaS) and Utility Computing. Cloud computing shares characteristics with autonomic computing, peer to peer, grid computing, client server model, mainframe computer and utility computing. It has various open source resources which gives different platform for better computing utilization. Cloud computing are managed by Cloud Management tools, loaded and tested by various other software testing tools. Cloud computing modelling and simulation is done by CloudSim or SPECI or GroundSim or DCSim on the basis of testing benchmark. The application of Cloud Computing is discussed.

In recent technology the popularity and demand of image processing is increasing due to its immense number of application in various fields. Most of these are related to biometric science like face recognitions, fingerprint recognition, iris scan, and speech recognition. Among them face detection is a very powerful tool for video surveillance, human computer interface, face recognition, and image database management. There are a different number of works on this subject. Face recognition is a rapidly evolving technology, which has been widely used in forensics such as criminal identification, secured access, and prison security. In this paper we had gone through different survey and technical papers of this field and list out the different techniques like Linear discriminant analysis, Viola and Jones classification and adaboost learning curvature analysis and discuss about their advantages and disadvantages also describe some of the detection and recognition algorithms, mention some application domain along with different challenges in this field.. We had proposed a classification of detection techniques and discuss all the recognition methods also. .

The availability of computing resources and generation of large scale data emanating from Artificial Intelligence, Internet of Things and social media platforms have resulted into resurgence of deep learning technology. Deep learning architectures have been successfully adopted to solve the problems arising in variety of domains such as computer vision, information retrieval, robotics, and natural language processing, etc. Due to inherent ability of deep architectures to extract hierarchical structures from complex multimedia data, they have been widely used for the tasks of classification, regression and prediction. Motivated by the same, this paper addresses the problem of identifying the subjective information from text documents and predicting the sentiments at sentence level using deep feedforward neural network with global average pooling and long short term memory model with dense layers. The experimentation details state that both models are on par and provide good accuracy on the benchmarked dataset of sentiment classification.