Informatics Publishing Limited

Susanta Mitra ¹, Somsubhra Gupta ¹

Affiliations
1 Amity University, Kolkata, IN

Abstract

Mobile learning under cloud environment, an amalgamation between mobile cloud computing and mobile learning, has gained wide academic and commercial recognition during last few years. Though researches on MOOC and other means of Digital Learning is widely circulated, however, research to enhance traditional mobile learning using newer types of ubiquitous and pervasive devices (e.g. Modular Object Oriented Dynamic Learning Environment or MOODLE) for collecting resources is yet to be widely circulated in the literature. The proposed work is expected to make learning more cost-effective, collaborative and practical for the learners using Personal Cloud environment. This solution can be beneficial for mass learners including poor and under privileged and will help in getting rid of digital divide.

Keywords

Adaptive Learning, BYOD, Digital Divide, Flipped Classroom, Mobile Learning, Wearable Learning.

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Manoj Kumar Srivastav ¹, Somsubhra Gupta ¹

Affiliations
1 School of Computer Science, Swami Vivekananda University, Barrackpore, West Bengal, IN

Abstract

Social networks like Facebook, LinkedIn, Twitter, WhatsApp, etc. have appeared more often these days, and their importance and influence in human life are growing rapidly. A social network is useful for connecting people to communicate and share information with one another in a virtual setting. A member of a social network can establish relationships with individual members or groups of members in that network. Utilizing networks and graph theory to construct social structures is the process known as social network analysis (SNA). The mapping and measurement of relationships and flows between individuals, groups, organizations, and so on is known as social network analysis (SNA). Social networks are represented using the concept of graph theory, where members of a social network are represented as nodes, and relationships between members are represented by edges, representing links, connections, and so on. The nodes represent the members of the social network, and the relationships between them are formed by the concept that there is either a direct or indirect path between them. Generally, members of social networks can establish relationships with each other using a one-to-one or one-to-many concept. The manner in which two or more members of a social network communicate or act toward one another constitutes relationships. The representation of social networks using graph theory helps to understand the trends in research on the relationships between different types of nodes and to predict the behaviour of nodes in social network analysis. The natural process of learning by doing is the subject of a subfield of computer science called machine learning (ML). ML assists in identifying patterns and the structure of node-to-node relationships in social networks. This paper attempts to study relationships among members of social networks using mathematical graph theory and machine learning.

Keywords

Social Networks, Relationship, Trends, Machine Learning.

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Annwesha Banerjee Majumder ¹, Somsubhra Gupta ², Dharmpal Singh ³, Sourav Majumder ⁴

Affiliations
1 Information Technology name of organization JIS College of Engineering, Kalyani, IN
2 Computer Science and Engineering, Swami Vivekananda University, Barrackpore, IN
3 Computer Science and Engineering, JIS College of Engineering, Kalyani, IN
4 Capgemini, Kolkata, IN

Abstract

Cardiovascular disease is one the major cause of death around the world. Even while medical science continues to assist efforts to save lives, qualified medical professionals are still in limited. Accurate diagnosis at the right time is crucial in cardiovascular disease cases, as patients might live a long life with the right medical care. Machine learning and artificial intelligence have a significant impact on the early and precise prediction of cardiovascular disease. In this paper a machine learning based model for cardiovascular disease prediction has been proposed applying Logistics Regression, Naïve Bayes, K-Nearest Neighbor, Support Vector machine, Kernel SVM, Decision Tree classifier, Random Forest and Artificial Neural network with model explanation using Explainable AI. Based on the precision, specificity, and sensitivity scores of each method, the most effective one has been selected. Local Interpretable Model Agnostic Explanation (LIME) and Shapely Value (SHAP) have been used for model explanation.

Keywords

Artificial Neural Network; Decision Tree, Explainable AI; Kernel SVM; K-Nearest Neighbor; Local Interpretable Model Agnostic Explanation Logistic Regression; Random Forest; Shapely Value; Support Vector Machine.

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