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2017

Feature extraction

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Full-Text Articles in Physical Sciences and Mathematics

Unobtrusive Monitoring To Detect Depression For Elderly With Chronic Illnesses, Jung-Yoon Kim, Na Liu, Hwee Xian Tan, Chao-Hsien Chu Sep 2017

Unobtrusive Monitoring To Detect Depression For Elderly With Chronic Illnesses, Jung-Yoon Kim, Na Liu, Hwee Xian Tan, Chao-Hsien Chu

Research Collection School Of Computing and Information Systems

Mental health related disorders are common diseases, especially among the elder. Among the various mental health diseases, one potential threat to ageing-in-place is the risk of depression. In this paper, we propose a simple unobtrusive sensing system using passive infra-red motion sensors to monitor the activities of daily living of elderly, who are living alone. A feature extraction module comprising of three layers-states, events, and activities, and the corresponding algorithms are proposed to extract features. Four popular classification models-neural network, C4.5 decision tree, Bayesian network, and support vector machine are then applied to detect the severity of depression. We implement …

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Who Will Leave The Company?: A Large-Scale Industry Study Of Developer Turnover By Mining Monthly Work Report, Lingfeng Bao, Zhenchang Xing, Xin Xia, David Lo, Shanping Li May 2017

Who Will Leave The Company?: A Large-Scale Industry Study Of Developer Turnover By Mining Monthly Work Report, Lingfeng Bao, Zhenchang Xing, Xin Xia, David Lo, Shanping Li

Research Collection School Of Computing and Information Systems

Software developer turnover has become a big challenge for information technology (IT) companies. The departure of key software developers might cause big loss to an IT company since they also depart with important business knowledge and critical technical skills. Understanding developer turnover is very important for IT companies to retain talented developers and reduce the loss due to developers' departure. Previous studies mainly perform qualitative observations or simple statistical analysis of developers' activity data to understand developer turnover. In this paper, we investigate whether we can predict the turnover of software developers in non-open source companies by automatically analyzing monthly …

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Plant Leaf Image Detection Method Using A Midpoint Circle Algorithm For Shape-Based Feature Extraction, B. Vijaya Lakshmi, V. Mohan May 2017

Plant Leaf Image Detection Method Using A Midpoint Circle Algorithm For Shape-Based Feature Extraction, B. Vijaya Lakshmi, V. Mohan

Journal of Modern Applied Statistical Methods

Shape-based feature extraction in content-based image retrieval is an important research area at present. An algorithm is presented, based on shape features, to enhance the set of features useful in a leaf identification system.

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A Comparison Of Feature Extraction Techniques For Malware Analysis, Mohammad Imran, Muhammad Tanvir Afzal, Muhammad Abdul Qadir Jan 2017

A Comparison Of Feature Extraction Techniques For Malware Analysis, Mohammad Imran, Muhammad Tanvir Afzal, Muhammad Abdul Qadir

Turkish Journal of Electrical Engineering and Computer Sciences

The manifold growth of malware in recent years has resulted in extensive research being conducted in the domain of malware analysis and detection, and theories from a wide variety of scientific knowledge domains have been applied to solve this problem. The algorithms from the machine learning paradigm have been particularly explored, and many feature extraction methods have been proposed in the literature for representing malware as feature vectors to be used in machine learning algorithms. In this paper we present a comparison of several feature extraction techniques by first applying them on system call logs of real malware, and then …

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Late Fusion Of Facial Dynamics For Automatic Expression Recognition, Alessandra Bandrabur, Laura Florea, Cornel Florea, Matei Mancas Jan 2017

Late Fusion Of Facial Dynamics For Automatic Expression Recognition, Alessandra Bandrabur, Laura Florea, Cornel Florea, Matei Mancas

Turkish Journal of Electrical Engineering and Computer Sciences

Installment of a facial expression is associated with contractions and extensions of specific facial muscles. Noting that expression is about changes, we present a model for expression classification based on facial landmarks dynamics. Our model isolates the trajectory of facial fiducial points by wrapping them up in relevant features and discriminating among various alternatives with a machine learning classification system. The used features are geometric and temporal-based and the classification system is represented by a late fusion framework that combines several neural networks with binary responses. The proposed method is robust, being able to handle complex expression classes.

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Multiclass Semantic Segmentation Of Faces Using Crfs, Khalil Khan, Nasir Ahmad, Khalil Ullah, Irfanud Din Jan 2017

Multiclass Semantic Segmentation Of Faces Using Crfs, Khalil Khan, Nasir Ahmad, Khalil Ullah, Irfanud Din

Turkish Journal of Electrical Engineering and Computer Sciences

Multiclass semantic image segmentation is widely used in a variety of computer vision tasks, such as object segmentation and complex scene understanding. As it decomposes an image into semantically relevant regions, it can be applied in segmentation of face images. In this paper, an algorithm based on multiclass semantic segmentation of faces is proposed using conditional random fields. In the proposed model, each node corresponds to a superpixel, while the neighboring superpixels are connected to nodes through edges. Unlike previous approaches, which rely on three or four classes, the label set is extended here to six classes, i.e. hair, eyes, …

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Classification Of Eeg Signals Of Familiar And Unfamiliar Face Stimuli Exploiting Most Discriminative Channels, Abdurrahman Özbeyaz, Sami̇ Arica Jan 2017

Classification Of Eeg Signals Of Familiar And Unfamiliar Face Stimuli Exploiting Most Discriminative Channels, Abdurrahman Özbeyaz, Sami̇ Arica

Turkish Journal of Electrical Engineering and Computer Sciences

The objective of the study is to classify electroencephalogram signals recorded in a familiar and unfamiliar face recognition experiment. Frontal views of familiar and unfamiliar face images were shown to 10 volunteers in different sessions. In contrast to previous studies, no marker button was used during the experiment. Participants had to decide whether the displayed face was familiar or unfamiliar at the instant of stimulus presentation. The signals were analyzed in the preprocessing, channel selection, feature extraction, and classification stages. The novel two-feature extraction and eight-channel selection methods were applied to the analyses. Sixteen classification results were compared and the …

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Capia: Cloud Assisted Privacy-Preserving Image Annotation, Yifan Tian, Yantian Hou, Jiawei Yuan Jan 2017

Capia: Cloud Assisted Privacy-Preserving Image Annotation, Yifan Tian, Yantian Hou, Jiawei Yuan

Computer Science Faculty Publications and Presentations

Using public cloud for image storage has become a prevalent trend with the rapidly increasing number of pictures generated by various devices. For example, today's most smartphones and tablets synchronize photo albums with cloud storage platforms. However, as many images contain sensitive information, such as personal identities and financial data, it is concerning to upload images to cloud storage. To eliminate such privacy concerns in cloud storage while keeping decent data management and search features, a spectrum of keywords-based searchable encryption (SE) schemes have been proposed in the past decade. Unfortunately, there is a fundamental gap remains open for their …

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