Social networks have become a part of human life. Starting from sharing information like text, photos, messages, many have started share latest news, and news related pictures in the Media domain, question papers, assignments, and workshops in Education domain, online survey, marketing, and targeting customers in Business domain, and jokes, music, and videos in Entertainment domain. Because of its usage by Internet surfers in all possible ways, even wee would mention the social networking media as the current Internet culture. While enjoying the information sharing on Social Medias, yet it requires a great deal for security and privacy. The users’ information that are to be kept undisclosed, should be made private

Sentiment analysis, a specialization of natural language processing (NLP), has witnessed significant progress since its emergence in the late 1990s, owing to the swift advances in deep learning techniques and the abundance of vast digital datasets. Though sentiment analysis has reached a relatively advanced stage in the area of NLP, it is erroneously assumed that sentiment analysis has reached its pinnacle, leaving no room for further improvement. However, it is important to acknowledge that numerous challenges that require attention persist. This survey paper provides a comprehensive overview of sentiment analysis, including its applications, approaches to sentiment classification, and commonly used evaluation metrics. The survey primarily focuses on the challenges associated with different types of data for sentiment classification, namely cross-domain data, multimodal data, cross-lingual data, and small-scale data, and provides a review of the state-of-the-art in sentiment analysis to address these challenges. The paper also addresses the challenges faced during sentiment classification irrespective of the type of data available. It aims at a better understanding of sentiment analysis to enable practitioners and researchers select suitable methods for sentiment classification depending on the type of data being analyzed.

Assistive technologies powered by machine learning are transforming the way sensory impairments are addressed, offering innovative solutions for individuals with hearing and visual disabilities. This paper provides a comprehensive review of machine learning algorithms designed to enhance accessibility for these groups. For hearing impairments, the analysis focuses on advanced models such as Support Vector Machines (SVM), Random Forests (RF), and Multi-Layer Perceptrons (MLP), examining their effectiveness in auditory assistive applications. In the context of visual impairments, state-of-the-art object detection frameworks like You Only Look Once (YOLO), Single Shot MultiBox Detector (SSD), and RetinaNet are evaluated for their capability to enable real-time object recognition and navigation aids. The study also reviews the Generative Artificial Intelligence based applications for visual and hearing impaired use cases. The study addresses the unique challenges and requirements associated with each type of sensory impairment, with particular emphasis on the customization and fine-tuning of machine learning models for personalized, effective solutions. Additionally, it highlights the transformative potential of deep learning models in advancing assistive technologies, ultimately aiming to enhance the quality of life for individuals with sensory disabilities. By advocating for the development and integration of such technologies, this paper underscores the importance of inclusivity and empowerment in creating a more equitable society.

Data generated by various real time applications, social networking sites and sensor devices is of very huge amount and unstructured, which makes it difficult for Relational database management systems to handle the data. Data is very precious component of any application and needs to be analysed after arranging it in some structure. Relational databases are only able to deal with structured data, so there is need of NoSQL Database management System which can deal with semi -structured data also. Relational database provides the easiest way to manage the data but as the use of NoSQL is increasing it is becoming necessary to migrate the data from Relational to NoSQL databases. Various frameworks has been proposed previously which provides mechanisms for migration of data stored at warehouses in SQL, middle layer solutions which can provide facility of data to be stored in NoSQL databases to handle data which is not structured. This paper provides a literature review of some of the recent approaches proposed by various researchers to migrate data from relational to NoSQL databases. Some researchers proposed mechanisms for the co-existence of NoSQL and Relational databases together. This paper provides a summary of mechanisms which can be used for mapping data stored in Relational databases to NoSQL databases. Various techniques for data transformation and middle layer solutions are summarised in the paper.

Traditional information retrieval systems lack consistent semantic description of information i.e. they fail to meet users' need due to lack of applying semantic identification to extract the information from the available information. Use of semantic equivalent of the user query will improve the efficiency of the search. In this paper, we propose a framework for semantic based information retrieval. Here we find the concepts that user specify in their query by analyzing the semantic equivalencies. The result which is a set of alternate queries to the main search query is then compared with the existing keyword based system's result. Then, according to the alternate queries' search results, the main queries result gets rearranged by assigning new weights. We further personalize the search and then re-rank the results on user preference. The proposed semantic retrieval model is combined with keyword based model to achieve completeness of the knowledge base. The model which we propose is helping to project the most relevant result URLs to the higher ranks.