Computer Sciences Commons^™

Gender And Ethnicity Classification Using Partial Face In Biometric Applications, Jamie Lyle

All Dissertations

As the number of biometric applications increases, the use of non-ideal information such as images which are not strictly controlled, images taken covertly, or images where the main interest is partially occluded, also increases. Face images are a specific example of this. In these non-ideal instances, other information, such as gender and ethnicity, can be determined to narrow the search space and/or improve the recognition results. Some research exists for gender classification using partial-face images, but there is little research involving ethnic classifications on such images. Few datasets have had the ethnic diversity needed and sufficient subjects for each ethnicity …

Distributed Owl El Reasoning: The Story So Far, Raghava Mutharaju, Pascal Hitzler, Prabhaker Mateti

Computer Science and Engineering Faculty Publications

Automated generation of axioms from streaming data, such as traffic and text, can result in very large ontologies that single machine reasoners cannot handle. Reasoning with large ontologies requires distributed solutions. Scalable reasoning techniques for RDFS, OWL Horst and OWL 2 RL now exist. For OWL 2 EL, several distributed reasoning approaches have been tried, but are all perceived to be inefficient. We analyze this perception. We analyze completion rule based distributed approaches, using different characteristics, such as dependency among the rules, implementation optimizations, how axioms and rules are distributed. We also present a distributed queue approach for the classification …

Collaborative Online Multitask Learning, Guangxia Li, Steven C. H. Hoi, Kuiyu Chang, Wenting Liu, Ramesh Jain

Research Collection School Of Computing and Information Systems

We study the problem of online multitask learning for solving multiple related classification tasks in parallel, aiming at classifying every sequence of data received by each task accurately and efficiently. One practical example of online multitask learning is the micro-blog sentiment detection on a group of users, which classifies micro-blog posts generated by each user into emotional or non-emotional categories. This particular online learning task is challenging for a number of reasons. First of all, to meet the critical requirements of online applications, a highly efficient and scalable classification solution that can make immediate predictions with low learning cost is …

Better Physical Activity Classification Using Smartphone Acceleration Sensor, Muhammad Arif, Mohsin Bilal, Ahmed Kattan, Sheikh Iqbal Ahamed

Mathematics, Statistics and Computer Science Faculty Research and Publications

Obesity is becoming one of the serious problems for the health of worldwide population. Social interactions on mobile phones and computers via internet through social e-networks are one of the major causes of lack of physical activities. For the health specialist, it is important to track the record of physical activities of the obese or overweight patients to supervise weight loss control. In this study, acceleration sensor present in the smartphone is used to monitor the physical activity of the user. Physical activities including Walking, Jogging, Sitting, Standing, Walking upstairs and Walking downstairs are classified. Time domain features are extracted …

Sentiment Classification Through Semantic Orientation Using Sentiwordnet, Dr. Muhammad Zubair Asghar, Dr, Auranzeb Khan

Dr. Muhammad Zubair Asghar

Sentiment analysis is the procedure by which information is extracted from the opinions, appraisals and emotions of people in regards to entities, events and their attributes. In decision making, the opinions of others have a significant effect on customers ease in making choices regards to online shopping, choosing events, products, entities. In this paper, a rule based domain independent sentiment analysis method is proposed. The proposed method classifies subjective and objective sentences from reviews and blog comments. The semantic score of subjective sentences is extracted from SentiWordNet to calculate their polarity as positive, negative or neutral based on the contextual …

Aidr: Artificial Intelligence For Disaster Response, Muhammad Imran, Carlos Castillo, Ji Lucas, Patrick Meier, Sarah Vieweg

Muhammad Imran

We present AIDR (Artificial Intelligence for Disaster Response), a platform designed to perform automatic classification of crisis-related microblog communications. AIDR enables humans and machines to work together to apply human intelligence to large-scale data at high speed. The objective of AIDR is to classify messages that people post during disasters into a set of user-defined categories of information (e.g., "needs", "damage", etc.) For this purpose, the system continuously ingests data from Twitter, processes it (i.e., using machine learning classification techniques) and leverages human-participation (through crowdsourcing) in real-time. AIDR has been successfully tested to classify informative vs. non-informative tweets posted during …

Identification Of Biomarkers That Distinguish Chemical Contaminants Based On Gene Expression Profiles, Xiaomou Wei, Junmei Ai, Youping Deng, Xin Guan, David R. Johnson, Choo Y. Ang, Chaoyang Zhang, Edward J. Perkins

Faculty Publications

Background: High throughput transcriptomics profiles such as those generated using microarrays have been useful in identifying biomarkers for different classification and toxicity prediction purposes. Here, we investigated the use of microarrays to predict chemical toxicants and their possible mechanisms of action.

Results: In this study, in vitro cultures of primary rat hepatocytes were exposed to 105 chemicals and vehicle controls, representing 14 compound classes. We comprehensively compared various normalization of gene expression profiles, feature selection and classification algorithms for the classification of these 105 chemicals into14 compound classes. We found that normalization had little effect on the averaged …

On Predicting User Affiliations Using Social Features In Online Social Networks, Minh Thap Nguyen

Dissertations and Theses Collection (Open Access)

User profiling such as user affiliation prediction in online social network is a challenging task, with many important applications in targeted marketing and personalized recommendation. The research task here is to predict some user affiliation attributes that suggest user participation in different social groups.

Online Feature Selection And Its Applications, Jialei Wang, Peilin Zhao, Steven C. H. Hoi, Rong Jin

Research Collection School Of Computing and Information Systems

Feature selection is an important technique for data mining. Despite its importance, most studies of feature selection are restricted to batch learning. Unlike traditional batch learning methods, online learning represents a promising family of efficient and scalable machine learning algorithms for large-scale applications. Most existing studies of online learning require accessing all the attributes/features of training instances. Such a classical setting is not always appropriate for real-world applications when data instances are of high dimensionality or it is expensive to acquire the full set of attributes/features. To address this limitation, we investigate the problem of online feature selection (OFS) in …

Sketchart: A Pen-Based Tool For Chart Generation And Interaction., Andres Vargas Gonzalez

Electronic Theses and Dissertations

It has been shown that representing data with the right visualization increases the understanding of qualitative and quantitative information encoded in documents. However, current tools for generating such visualizations involve the use of traditional WIMP techniques, which perhaps makes free interaction and direct manipulation of the content harder. In this thesis, we present a pen-based prototype for data visualization using 10 different types of bar based charts. The prototype lets users sketch a chart and interact with the information once the drawing is identified. The prototype's user interface consists of an area to sketch and touch based elements that will …

A Reduced Probabilistic Neural Network For The Classification Of Large Databases, Abdelhadi Lotfi, Abdelkader Benyettou

Turkish Journal of Electrical Engineering and Computer Sciences

The probabilistic neural network (PNN) is a special type of radial basis neural network used mainly for classification problems. Due to the size of the network after training, this type of network is usually used for problems with a small-sized training dataset. In this paper, a new training algorithm is presented for use with large training databases. Application to the handwritten digit database shows that the reduced PNN performs better than the standard PNN for all of the studied cases with a big gain in size and processing speed. This new type of neural network can be used easily for …

Online Feature Selection And Classification With Incomplete Data, Habi̇l Kalkan

Turkish Journal of Electrical Engineering and Computer Sciences

This paper presents a classification system in which learning, feature selection, and classification for incomplete data are simultaneously carried out in an online manner. Learning is conducted on a predefined model including the class-dependent mean vectors and correlation coefficients, which are obtained by incrementally processing the incoming observations with missing features. A nearest neighbor with a Gaussian mixture model, whose parameters are also estimated from the trained model, is used for classification. When a testing observation is received, the algorithm discards the missing attributes on the observation and ranks the available features by performing feature selection on the model that …

A Penalty Function Method For Designing Efficient Robust Classifiers With Input Space Optimal Separating Surfaces, Ayşegül Uçar, Yakup Demi̇r, Cüneyt Güzeli̇ş

Turkish Journal of Electrical Engineering and Computer Sciences

This paper considers robust classification as a constrained optimization problem. Where the constraints are nonlinear, inequalities defining separating surfaces, whose half spaces include or exclude the data depending on their classes and the cost, are used for attaining robustness and providing the minimum volume regions specified by the half spaces of the surfaces. The constraints are added to the cost using penalty functions to get an unconstrained problem for which the gradient descent method can be used. The separating surfaces, which are aimed to be found in this way, are optimal in the input data space in contrast to the …

Feature Selection On Single-Lead Ecg For Obstructive Sleep Apnea Diagnosis, Hüseyi̇n Gürüler, Mesut Şahi̇n, Abdullah Feri̇koğlu

Turkish Journal of Electrical Engineering and Computer Sciences

Many articles that appeared in the literature agreed upon the feasibility of diagnosing obstructive sleep apnea (OSA) with a single-lead electrocardiogram. Although high accuracies have been achieved in detection of apneic episodes and classification into apnea/hypopnea, there has not been a consensus on the best method of selecting the feature parameters. This study presents a classification scheme for OSA using common features belonging to the time domain, frequency domain, and nonlinear calculations of heart rate variability analysis, and then proposes a method of feature selection based on correlation matrices (CMs). The results show that the CMs can be utilized in …

Comparison Of Different Methods For Determining Diabetes, Mehmet Recep Bozkurt, Ni̇lüfer Yurtay, Zi̇ynet Yilmaz, Cengi̇z Sertkaya

Turkish Journal of Electrical Engineering and Computer Sciences

In this study, the Pima Indian Diabetes dataset was categorized with 8 different classifiers. The data were taken from the University of California Irvine Machine Learning Repository's web site. As a classifier, 6 different neural networks [probabilistic neural network (PNN), learning vector quantization, feedforward networks, cascade-forward networks, distributed time delay networks (DTDN), and time delay networks], the artificial immune system, and the Gini algorithm from decision trees were used. The classifier's performance ratios were studied separately as accuracy, sensitivity, and specificity and the success rates of all of the classifiers are presented. Among these 8 classifiers, the best accuracy and …

Automated Classification Of Malignant Melanoma Based On Detection Of Atypical Pigment Network In Dermoscopy Images Of Skin Lesions, Nabin K. Mishra

Doctoral Dissertations

“Melanoma causes more deaths than any other form of skin cancer. Early melanoma detection is important to prevent progression to a more deadly stage. Automated computer-based identification of melanoma from dermoscopic images of skin lesions is the most efficient method in early diagnosis. An automated melanoma identification system must include multiple steps, involving lesion segmentation, feature extraction, feature combination and classification. In this research, a classifier-based approach for automatically selecting a lesion border mask for segmentation of dermoscopic skin lesion images is presented. A logistic regression based model selects a single lesion border mask from multiple border masks generated by …