The uniqueness of an individual can be identified according to one’s behavior, qualities, skills, characteristics, social connectivity, knowledge which all resemble to a single term called, “Personality.” There are ‘n’ number of ways to identify personality of an individual like judging the face gesture, way of speaking or communication, dressing style, questionnaire format, public behavior, etc. To identify what kind of a personality an individual has, traits can be classified based on individual’s handwriting. And this can be identified using the field ‘Graphology’. Graphological tests are the most effective method to measure (a candidate’s) ‘fit’ or match, for a position being applied for. According to research, it is proven that person’s brain is only 15-20% active while communicating whereas the same brain is active around 85-90% when writing. Hence, analysis of handwriting stands at par to describe and analyze the personality traits of individuals. The paper showcases a landscape of handwriting recognition strategies deployed for personality analysis, some of which involve the use of traditional neural networks like Convolution Neural Network and Recurrent Neural Network. The results presented in this paper obtained by implementing the neural networks such as a Long Short-Term Memory or a Fuzzy Logic along with possible classifier sets are significant enough in terms of higher accuracy and recognition.

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.