International Journal of Computer (IJC)

Survey on Emotion Recognition Using Facial Expression

Moe Moe Htay, Zin Mar Win — Tue, 16 Apr 2019 19:28:20 +0000

Automatic recognition of human affects has become more interesting and challenging problem in artificial intelligence, human-computer interaction and computer vision fields. Facial Expression (FE) is the one of the most significant features to recognize the emotion of human in daily human interaction. FE Recognition (FER) has received important interest from psychologists and computer scientists for the applications of health care assessment, human affect analysis, and human computer interaction. Human express their emotions in a number of ways including body gesture, word, vocal and facial expressions. Expression is the important channel to convey emotion information of different people because face can express mainly human emotion. This paper surveys the current research works related to facial expression recognition. The study attends to explored details of the facial datasets, feature extraction methods, the comparison results and futures studies of the facial emotion system.

Smart Touch Attendance Management System Using NFC Tag: Improving Learning Outcomes

Thu, 18 Apr 2019 09:08:57 +0000

Given the need to maximize information technology to optimize attendance management systems, based on its apparent shortcomings, the aim of the study is to develop, test, appraise and analyze a NFC tag automated attendance system in an educational learning environment to improve learning outcomes. We used unified modeling language, PHP and Aptana Studio to develop a system based on the Radio Frequency Identification (RFID) technology. It operates at 13.56 MHz and relies on ISO14443 and ISO 18092 for low level data exchange between two NFC devices to automate attendance, which can be used by educational institutions and other organizations for efficient, effective and sustainable attendance database management. It was discovered that variables such as time, cost, energy inherent in the use of traditional systems were not significant factors as they were optimized to add value to the organization. This work adds value to the management of organizations’ processes, people and product affording them opportunity to maximize time, cost, effort and energy.

Comparative Study for Text Document Classification Using Different Machine Learning Algorithms

Yin Min Tun, Phyu Hnin Myint — Fri, 19 Apr 2019 20:41:32 +0000

Classification is a supervised learning method: the goal is finding the labels of the unknown object. In the real world, the tedious amounts of manual works are required to label the unknown documents. The system is initially trained by labeled documents by using one of the supervise machine learning algorithm and then applied trained model to predict the label of the unknown documents. The framework of text document classification consists of: input text document, pre-processing, feature extraction and classification. The analysis four common classification methods are performed: Naïve Bayes, Decision Tree, Support Vector Machine and K-nearest neighbors for text document classification. The main focus of this paper is to present comparative study of different exiting classification methods for text document classification. The experiment performed different classification methods on the Enron Email Dataset and measure classification accuracy, true positive, true negative, false positive and false negative to compare the performance of different classification methods.

Automatic Plant Detection Using HOG and LBP Features With SVM

Mohammad Aminul Islam, Md. Sayeed Iftekhar Yousuf, M. M. Billah — Sun, 28 Apr 2019 13:50:06 +0000

Plants play a vital role in the cycle of nature. Plants are the only organisms which produce food by converting light energy from the sun. They also help in maintaining oxygen balance on earth by emitting oxygen and taking carbon dioxide. They have plenty of use in medicine and industry. But plant species are vast in number. To identify this large number of existing plant species in the world is a tedious and time-consuming task for a human. Hence, an automatic plant identification tool is very useful even for experienced botanists to identify the vast number of plants. In this paper, we proposed a technique to identify the plant leaf images. For training and testing, we used a publicly available dataset called Flavia leaf dataset. Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP) are used to extract features and multiclass Support Vector Machine (SVM) is applied to classify the leaf images. We observed that the accuracy of HOG+SVM with HOG feature extraction using cells size of 2 x 2, 4 x 4 and 8 x 8 are 77.5%, 81.25% and 85.31 respectively. The accuracy of LBP+ SVM is 40.6% and the combination of HOG and LBP based features with SVM achieved 91.25% accuracy. The experimental results indicate the effectiveness of HOG+LBP with SVM over HOG+SVM and LBP+SVM techniques.

Review on Blockchain Technology for Healthcare Records

Ekram Ahmed Ali Mirdah, Sokchoo Ng, Khiam Ping Chih — Mon, 06 May 2019 08:30:35 +0000

For a long time, the healthcare system has been facing many problems such as incomprehensible doctor’s hand-writing, problematic retrieval of patient information and patients are unable to monitor their own data. In addition to that, there are problems afflicting the medical record systems such as how to share the medical data for various purposes without risking data privacy and security. Due to technology advancements, it is now possible to improve the current situations and to minimise these problems by providing more personalised services to the patients. This paper explains the interesting area of blockchain in healthcare, evaluating the blockchain technology from the multiple perspectives around healthcare data and explaining some platforms which could be used for the blockchain system development. Finally, the challenges of implementing the blockchain technology are deliberated.

A Synthesis Survey of Ontology Evaluation Tools, Applications and Methods to Propose a Novel Branch in Evaluating the Structure of Ontologies: Graph-Independent Approach

Maziar Amirhosseini, Juhana Salim — Thu, 16 May 2019 07:31:07 +0000

Diverse tools, application and methods can logically be organized in clear categories (i.e., Gold standard, Application, Data-driven and Human assessment) or their dimensions (i.e., Functionality (task-based), Usability based and Structural evaluation). This paper attempts to propose a novel branch in structural analysis of ontology through analyzing current methods. Structural dimensions can be involved in evaluating ontologies when the research attempts to analyze the graph representation based on Conceptual Graph (CG). Two types of nodes (i.e., concepts and conceptual relations) can be merely linked with one another via logical conjunction. When logical conjunction between concepts and conceptual relations were removed, the remaining components would be independent domains which would no longer bear the meaning of graph. The separate concepts and conceptual relations cannot be involved in the notion of the graph-dependent approach. Thus, there is the lack of a novel branch in structural analysis which is called Graph-independent approach.

Swarm Intelligence Based Feature Selection for High Dimensional Classification: A Literature Survey

Thinzar Saw, Phyu Hnin Myint — Thu, 16 May 2019 07:52:23 +0000

Feature selection is an important and challenging task in machine learning and data mining techniques to avoid the curse of dimensionality and maximize the classification accuracy. Moreover, feature selection helps to reduce computational complexity of learning algorithm, improve prediction performance, better data understanding and reduce data storage space. Swarm intelligence based feature selection approach enables to find an optimal feature subset from an extremely large dimensionality of features for building the most accurate classifier model. There is still a type of researches that is not done yet in data mining. In this paper, the utilization of swarm intelligence algorithms for feature selection process in high dimensional data focusing on medical data classification is form the subject matter. The results shows that swarm intelligence algorithms reviewed based on state-of-the-art literature have a promising capability that can be applied in feature selections techniques. The significance of this work is to present the comparison and various alternatives of swarm algorithms to be applied in feature selections for high dimensional classification.

A Study on Image Forgery Detection Techniques

Shijo Easow, Dr. L. C. Manikandan — Fri, 24 May 2019 08:06:06 +0000

In this contemporary world, digital image plays a vital role in several application areas. Image forgery means that handling of the digital image to hide some significant or helpful information of the image. The aim of this study is to provide the knowledge of image forgery and its detection techniques for the new researchers.