International Journal of Computer Science and Technology
IJCST Vol 10 Issue 1 (Jan-March 2019)

S.No. Research Topic Paper ID Download

Class Room Attendance System Using KNN

S. Sai Kumar, S. Adithya Varun, Dr. P. K. Sahoo, K. Eswaran


Marking the attendance in schools and colleges is a key activity by the teachers. They face problems when there are a large number of students in a class. These difficulties can be overcome by using computer aided face recognition techniques. In this paper we use the KNN algorithm and compare our results with the LDA (linear discriminant analysis).
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Efficient System for Heart Disease Prediction by applying Logistic Regression

S.Adithya Varun, G.Mounika, Dr. P.K. Sahoo, K. Eswaran


In today’s modern lifestyle people are effecting by different health issues, one among them is heart disease which may be incipient from a very early age. Cardiovascular disease remains as the number one cause of death globally. The main objective of this paper is to identify the presence or absence of heart disease for an individual. In the healthcare industry, it is very difficult to discover whether an individual is affected by heart disease or not by a physician. It requires a careful understanding of patient’s data, and the identification of those parameters which cause the disease all of this is considered as a difficult task. Additional tools are required for making the clinical decision of heart disease. For the implementation of this work we referred to the Kaggle dataset1, which comprises 14 features (attributes) with class label, are identified as a cause for heart disease. In this paper logistic regression algorithm is applied for the Heart Disease prediction in order to improve the system’s efficiency when compared with Naïve Bayes (NB) and Random Forest (RF).
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Sensor Occupancy Detection Using XG Boost Algorithm

G. Mounika, S. Sai Kumar, Dr. P. K. Sahoo, K. Eswaran


A room in a smart home is fixed with environmental sensors for sensing of the indoor air quality. Environmental sensors can be any sensor from simple air temperature sensor to an indoor air quality measurement system, which holds different types of sensors or a networked sensor. Purpose of these sensors is to determine the indoor air quality and their potential in incorporating occupancy detection is largely unused. Occupancy detection is a technique used to detect the presence of living and non-living things. There are many environmental sensors which are used to detect different kinds of gases, namely CO2 (carbon dioxide) and TVOC (total volatile organic compounds) sensors which are used here to detect the gases that resides in a room. By detecting the indoor gases we can improve the quality of the air. CO2 sensor is used for detection of carbon dioxide composition, where as TVOC is internally built with CO2 sensor and it will detect other gases too. There are many machine learning algorithms that are used to classify the occupancy detection. In previous studies, naive Bayes classifier is used for detecting occupants using Weka tool. Now In this paper XGBoost, a machine learning algorithm is used for detecting occupants.
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SIFT: Scale Invarient Feature Transforms for Efficient Iris Recognition

Payal Garg, Deepika Arora


Iris recognition is an automated method of biometric identification that uses mathematical pattern-recognition techniques. The purpose of ‘Iris Recognition’, a biometrical based technology for personal identification and verification, is to recognize a person from his/her iris prints. In fact, iris patterns are characterized by high level of stability and distinctiveness. Each individual has a unique iris. Not even one egged twins or a future clone of a person will have the same iris patterns. It is stable over time even though the person ages. Iris recognition is the most precise and fastest of the biometric authentication methods. The iris is an externally visible, yet protected organ whose unique epigenetic pattern remains stable throughout adult life. These characteristics make it very attractive for use as a biometric for identifying individuals. The purpose of ‘Iris Recognition’, a biometrical based technology for personal identification and verification, is to recognize a person from his/her iris prints. Image matching is a fundamental aspect of many problems in computer vision, including object or scene recognition, solving for structure from multiple images, stereo correspondence, and motion tracking. The features are invariant to image scaling and rotation, and partially invariant to change in illumination and camera viewpoint. They are well localized in both the spatial and frequency domains, reducing the probability of disruption by occlusion, clutter, or noise. Large numbers of features can be extracted from typical images with efficient algorithms. In addition, the features are highly distinctive, which allows a single feature to be correctly matched with high probability against a large database of features, providing a basis for object and scene recognition. In this paper we propose SIFT based iris recognition method, For iris matching and recognition, SIFT features are first extracted from a set of reference images and stored in a database. A new image is matched by individually comparing each feature from the new image to this previous database and finding candidate matching features based on Euclidean distance of their feature vectors. The keypoint descriptors are highly distinctive, which allows a single feature to find its correct match with good probability in a large database of features. However, in a clutteredimage, many features from the background will not have any correct match in the database, giving rise to many false matches in addition to the correct ones. The correct matches can be filtered from the full set of matches by identifying subsets of keypoints that agree on the object and its location, scale, and orientation in the new image.
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