Facial Expression Recognition (FER) has been an interesting area of research in places where there is human-computer interaction. Human psychology, emotions and behaviors can be analyzed in FER. Classifiers used in FER have been perfect on normal faces but have been found to be constrained in occluded faces. Recently, Deep Learning Techniques (DLT) have gained popularity in applications of real-world problems including recognition of human emotions. The human face reflects emotional states and human intentions. An expression is the most natural and powerful way of communicating non-verbally. Systems which form communications between the two are termed Human Machine Interaction (HMI) systems. FER can improve HMI systems as human expressions convey useful information to an observer. This paper proposes a FER scheme called EECNN (Enhanced Convolution Neural Network with Attention mechanism) to recognize seven types of human emotions with satisfying results in its experiments. Proposed EECNN achieved 89.8% accuracy in classifying the images.

No abstract

Knowledge sharing among peers plays a vital role in students' learning process and an Effective and inclusive Knowledge Sharing is an integral part of successful and practical university learning. This study aims to investigate the knowledge-sharing behavior of undergraduate students of the Faculty of Management Studies andCommerce students at the University of Jaffna. The present study involved a quantitative approach concerning the design of surveys and the population to reach that purpose. The researcher used SPSS software version 20.0 for data analysis purposes. It was found that, generally, students displayed a positive attitude towards knowledge sharing and were appreciative of its importance in peer learning. It was found that, generally, students displayed a positive attitude towards knowledge sharing and were appreciative of its importance in peer learning. Further, the study found that technological availability, social media ties, self-efficacy, and experience were the significant determinants of knowledge-sharing behavior among students. Final recommendations are given by the author; it also provides more support and direction to future work.

Employee engagement can be regarded as the emotional connection an employee feels toward his or her employment organization, which tends to influence his or her behaviors and level of effort in work related activities. The objective of the study is to determine the work antecedents of employee engagement. The work antecedents considered in the study are perceived role benefits, job autonomy and strategic attention. Sample comprises of 129 employees of it industry. Simple linear regression was used to analyze the data. The results prove that all the three work antecedents were proved to positively predict employee engagement. Perceived role benefit and job autonomy are the major predictors of employee engagement. Hence the organization needs to emphasis on providing a growth focused work environment for the employees to become more engaged.

The aim of home automation is to make our lives easier and to improve the quality of life. The concept of Smart Homes builds on the progressing maturity of areas such as Artificial Intelligence and Natural Language Processing. Here, natural language processing (NLP) plays a vital role since it acts as an interface between human interaction and machines. Through NLP users can either command or control devices at home even though disabled persons command or request varies from presets. An application area of AI is Natural Language Processing (NLP). Voice assistants incorporate AI using cloud computing and can communicate with the users in natural language. Voice assistants are easy to use and thus there are millions of devices that incorporate them in households nowadays. Our project aims at providing a fully automated voice based solution that our users can rely on, to perform more than just switching on/off the appliances. The user sends a command through speech to the mobile device, which interprets the message and sends the appropriate command to the specific appliance. The primary objective is to construct a useful voice-based system that utilizes AI and NLP to control all domestic applications and services and also learn the user preferences over time using machine learning algorithms.