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Nowadays, smartphone has been shifted as a primary needs for majority of people. This trend is making people do activities on their smartphone such as exchange information, playing games, paying bills, discussing ideas, etc. In almost every smartphone, security features (i.e. password, pin, face unlock) are already built in which are intended to protect the data inside the smartphone. Nevertheless, those security features are not perfect and have flaws either technically or by human errors. This research aim to provide an application that uses people’ facial expressions to improve the security of smartphone, namely Android. We implement face detection and expression recognition in this application as an improvement from the existing unlock screen, which is face unlock. The results that are achieved from this research is an unlock screen application on Android devices that utilizes people’ facial expression changes to open their smartphone. This application can eliminates the technical errors from existing face unlock screen application which can be deceived by photos.

Emotions hold a paramount role in the conversation, as it expresses context to the conversation. Text/word in conversation consists of lexical and contextual meanings. Extracting emotions from text has been an interesting work recent thees years. With the advancement of machine learning techniques and hardware to support the machine learning process, recognising emotions from a text with machine learning provides promising and significant results. This research aims to explore several popular machine learning to recognise emotions from a conversation in social media. The algorithms proposed in this research are ranged from traditional machine learning to deep learning techniques. The dataset used in this paper is provided by AffectiveTweets, with a baseline of F1Score of 0.71 with word N-grams and SentiStrength. The research contributes extensive explorations in a number of machine learning algorithms, resulting in a total of 2302 features sets were explored, where each features sets has 100-1000 features extracted from the text. The results demonstrate Generalised Linear Model provides the best Accuracy score (0.92), Recall (0.902), Precision (0.902), F1 score (0.901) with standard deviation of accuracy of ±1,2%. [ABSTRACT FROM AUTHOR]

This research was conducted with the aim of utilizing the human hands as an object to operate computers. It is intended to support and use technologies especially in the field of education for helping teachers to do presentation with ease. The program is developed by using a science field of computer vision, as well as additional libraries which are: FLTK, OpenGL, and OpenCV. In order to use this program, the presenters need to have a webcam and a projector. The webcam will be used to recognize the shape and the pattern of the presenters’ hands. Furthermore, the program will send signals to the computers based on the recognized pattern. The result of this research is a program that can improve the teaching process of teaching/presentation. [ABSTRACT FROM AUTHOR]