Open Access. Powered by Scholars. Published by Universities.®
Social and Behavioral Sciences Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Institution
- Publication Year
- Publication
- Publication Type
Articles 1 - 30 of 36
Full-Text Articles in Social and Behavioral Sciences
A Bibliometric Analysis Of Plant Disease Classification With Artificial Intelligence Using Convolutional Neural Network, Sumit Kumar, Rutuja Rajendra Patil, Vasu Kumawat, Yashovardhan Rai, Navaneeth Krishnan, Shubham Kumar Singh
A Bibliometric Analysis Of Plant Disease Classification With Artificial Intelligence Using Convolutional Neural Network, Sumit Kumar, Rutuja Rajendra Patil, Vasu Kumawat, Yashovardhan Rai, Navaneeth Krishnan, Shubham Kumar Singh
Library Philosophy and Practice (e-journal)
In 2021 and the modern future which everyone is going to be a part of, Artificial intelligence is going to be the biggest part of our livelihood. In the future there is going to be a huge expansion of population especially at the rate right now which we are moving but the biggest problem which everyone should be concerned about is the food supply as many of the nations would not be able to feed and make survive their population as even now, there is scarcity of it. Currently in the world the people revolving around the artificial intelligence are …
Application Of The Cluster Classification Data Mining Method To Child Illiteracy In Indonesia, Muhammad Arifin, Gita Widi Bhawika, M.A. Muazar Habibi, Winci Firdaus, Danu Eko Agustinova, Robbi Rahim
Application Of The Cluster Classification Data Mining Method To Child Illiteracy In Indonesia, Muhammad Arifin, Gita Widi Bhawika, M.A. Muazar Habibi, Winci Firdaus, Danu Eko Agustinova, Robbi Rahim
Library Philosophy and Practice (e-journal)
The objective of this study is to cluster and classify data using a combination of the k-means and C4.5 methods. The process involves clustering and subsequent classification. The classification process uses k-folds = 10 and samples = stratified sampling. In this study, analphabets in Indonesia of a minimum age of 15 years (15+) were evaluated. The data are the percentage of analogs between 2017 and 2019. The dataset was obtained from https://www.bps.go.id and is accessible at https://osf.io/crwug. In this study, the Davies Bouldin index (DBI) was used to determine the number of clusters with an optimal DBI value of k …
A Bibliometric Analysis Of Plant Disease Classification With Artificial Intelligence Based On Scopus And Wos, Shivali Amit Wagle, Harikrishnan R
A Bibliometric Analysis Of Plant Disease Classification With Artificial Intelligence Based On Scopus And Wos, Shivali Amit Wagle, Harikrishnan R
Library Philosophy and Practice (e-journal)
The maneuver of Artificial Intelligence (AI) techniques in the field of agriculture help in the classification of diseases. Early prediction of the disease benefits in taking relevant management steps. This is an important step towards controlling the disease growth that will yield good quality products to fulfill the global food demand. The main objective of this paper is to study the extent of research work done in this area of plant disease classification. The paper discusses the bibliometric analysis of plant disease classification with AI in Scopus and Web of Science core collection (WOS) database in analyzing the research by …
A Review Paper: Analysis Of Weka Data Mining Techniques For Heart Disease Prediction System, Basma Jumaa Saleh, Ahmed Yousif Falih Saedi, Ali Talib Qasim Al-Aqbi, Lamees Abdalhasan Salman
A Review Paper: Analysis Of Weka Data Mining Techniques For Heart Disease Prediction System, Basma Jumaa Saleh, Ahmed Yousif Falih Saedi, Ali Talib Qasim Al-Aqbi, Lamees Abdalhasan Salman
Library Philosophy and Practice (e-journal)
Data mining is characterized as searching for useful information through very large data sets. Some of the key and most common techniques for data mining are association rules, classification, clustering, prediction, and sequential models. For a wide range of applications, data mining techniques are used. Data mining plays a significant role in disease detection in the health care industry. The patient should be needed to detect a number of tests for the disease. However, the number of tests should be reduced by using data mining techniques. In time and performance, this reduced test plays an important role. Heart disease is …
Using Cost-Sensitive Learning And Feature Selection Algorithms To Improve The Performance Of Imbalanced Classification, Fang Feng, Kuan-Ching Li, Jun Shen, Qingguo Zhou, Xuhui Yang
Using Cost-Sensitive Learning And Feature Selection Algorithms To Improve The Performance Of Imbalanced Classification, Fang Feng, Kuan-Ching Li, Jun Shen, Qingguo Zhou, Xuhui Yang
Faculty of Engineering and Information Sciences - Papers: Part A
Imbalanced data problem is widely present in network intrusion detection, spam filtering, biomedical engineering, finance, science, being a challenge in many real-life data-intensive applications. Classifier bias occurs when traditional classification algorithms are used to deal with imbalanced data. As already known, the General Vector Machine (GVM) algorithm has good generalization ability, though it does not work well for the imbalanced classification. Additionally, the state-of-the-art Binary Ant Lion Optimizer (BALO) algorithm has high exploitability and fast convergence rate. Based on these facts, we have proposed in this paper a Cost-sensitive Feature selection General Vector Machine (CFGVM) algorithm based on GVM and …
Disaster Damage Categorization Applying Satellite Images And Machine Learning Algorithm, Farinaz Sabz Ali Pour, Adrian Gheorghe
Disaster Damage Categorization Applying Satellite Images And Machine Learning Algorithm, Farinaz Sabz Ali Pour, Adrian Gheorghe
Engineering Management & Systems Engineering Faculty Publications
Special information has a significant role in disaster management. Land cover mapping can detect short- and long-term changes and monitor the vulnerable habitats. It is an effective evaluation to be included in the disaster management system to protect the conservation areas. The critical visual and statistical information presented to the decision-makers can help in mitigation or adaption before crossing a threshold. This paper aims to contribute in the academic and the practice aspects by offering a potential solution to enhance the disaster data source effectiveness. The key research question that the authors try to answer in this paper is how …
Finding Truth In Fake News: Reverse Plagiarism And Other Models Of Classification, Matthew Przybyla, David Tran, Amber Whelpley, Daniel W. Engels
Finding Truth In Fake News: Reverse Plagiarism And Other Models Of Classification, Matthew Przybyla, David Tran, Amber Whelpley, Daniel W. Engels
SMU Data Science Review
As the digital age creates new ways of spreading news, fake stories are propagated to widen audiences. A majority of people obtain both fake and truthful news without knowing which is which. There is not currently a reliable and efficient method to identify “fake news”. Several ways of detecting fake news have been produced, but the various algorithms have low accuracy of detection and the definition of what makes a news item ‘fake’ remains unclear. In this paper, we propose a new method of detecting on of fake news through comparison to other news items on the same topic, as …
A Survey Of Attention Deficit Hyperactivity Disorder Identification Using Psychophysiological Data, S. De Silva, S. Dayarathna, G. Ariyarathne, D. Meedeniya, Sampath Jayarathna
A Survey Of Attention Deficit Hyperactivity Disorder Identification Using Psychophysiological Data, S. De Silva, S. Dayarathna, G. Ariyarathne, D. Meedeniya, Sampath Jayarathna
Computer Science Faculty Publications
Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common neurological disorders among children, that affects different areas in the brain that allows executing certain functionalities. This may lead to a variety of impairments such as difficulties in paying attention or focusing, controlling impulsive behaviours and overreacting. The continuous symptoms may have a severe impact in the long-term. This paper explores the ADHD identification studies using eye movement data and functional Magnetic Resonance Imaging (fMRI). This study discusses different machine learning techniques, existing models and analyses the existing literature. We have identified the current challenges and possible future directions …
The Tao Of The Dao: Taxing An Entity That Lives On A Blockchain, David J. Shakow
The Tao Of The Dao: Taxing An Entity That Lives On A Blockchain, David J. Shakow
All Faculty Scholarship
In this report, Shakow explains how a decentralized autonomous organization functions and interacts with the U.S. tax system and presents the many tax issues that these structures raise. The possibility of using smart contracts to allow an entity to operate totally autonomously on a blockchain platform seems attractive. However, little thought has been given to how such an entity can comply with the requirements of a tax system. The DAO, the first major attempt to create such an organization, failed because of a programming error. If successful examples proliferate in the future, tax authorities will face significant problems in getting …
From Creativity To Classification: A Logical Approach To Patent Searching, Marian G. Armour-Gemmen
From Creativity To Classification: A Logical Approach To Patent Searching, Marian G. Armour-Gemmen
Faculty & Staff Scholarship
Engineering students and professors need to understand and search intellectual property. In the past, librarians have instructed them on using the United States Patent Classification (USPC). In 2015, after a period of transition, the United States Patent and Trademark Office phased out the USPC and began exclusively classifying in the Cooperative Patent Classification (CPC). This adoption presented librarians a challenge of instructing students and professors in the easiest and most effective patent search. By tying patent searching to an example and presenting classification in an understandable fashion using CPC in conjunction with USPC, this writer presents a logical directed search …
A Comparison Study For Supervised Machine Learning Models In Cancer Classification, Huaming Chen, Hong Zhao, Lei Wang, Jiangning Song, Jun Shen
A Comparison Study For Supervised Machine Learning Models In Cancer Classification, Huaming Chen, Hong Zhao, Lei Wang, Jiangning Song, Jun Shen
Faculty of Engineering and Information Sciences - Papers: Part B
No abstract provided.
Vehicle Classification By Estimation Of The Direction Angle In A Mixed Traffic Flow, Nguyen Viet Hung, Nguyen Hoang Dung, Le Chung Tran, Thang Manh Hoang, Nguyen Tien Dzung
Vehicle Classification By Estimation Of The Direction Angle In A Mixed Traffic Flow, Nguyen Viet Hung, Nguyen Hoang Dung, Le Chung Tran, Thang Manh Hoang, Nguyen Tien Dzung
Faculty of Engineering and Information Sciences - Papers: Part A
The application of Intelligent Transportation System (ITS) is very important in developing societies nowadays. Vehicle monitoring is one of the primary tasks of ITS, where vehicles are classified by lanes for traffic management, especially in case of a mixed flow of motorcycles and other automobiles in the transport system of Vietnam. This paper proposes a new approach in vehicle classification, which is based on evaluation of the direction angle of the first primary axis of each coming vehicle detected in the captured video sequence and map into the predetermined database to mark it as motorcycle or automobiles instead of consideration …
A New Approach For Classification And Characterization Of Voltage Dips And Swells Using 3d Polarization Ellipse Parameters, Mollah R. Alam, Kashem M. Muttaqi, Abdesselam Bouzerdoum
A New Approach For Classification And Characterization Of Voltage Dips And Swells Using 3d Polarization Ellipse Parameters, Mollah R. Alam, Kashem M. Muttaqi, Abdesselam Bouzerdoum
Faculty of Engineering and Information Sciences - Papers: Part A
This paper presents a new method for classification and characterization of voltage dips and swells in electricity networks. The proposed method exploits unique signatures and parameters of three phase voltage signals extracted from the polarization ellipse in three-dimensional (3D) co-ordinates. Five ellipse parameters, which include azimuthal angle, elevation, tilt, semi-minor axis and semi-major axis, are used to classify and characterize voltage dips and swells. Seven types of voltage dips, which include a total of 19 groups of dips incorporating different kinds of balanced (three-phase dips) and unbalanced (single-phase or double-phase) dips, are identified and successfully classified using the 3D polarization …
Functional Brain Network Classification With Compact Representation Of Sice Matrices, Jianjia Zhang, Luping Zhou, Lei Wang, Wanqing Li
Functional Brain Network Classification With Compact Representation Of Sice Matrices, Jianjia Zhang, Luping Zhou, Lei Wang, Wanqing Li
Faculty of Engineering and Information Sciences - Papers: Part A
Recently, sparse inverse covariance estimation (SICE) technique has been employed to model functional brain connectivity. The inverse covariance matrix (SICE matrix in short) estimated for each subject is used as a representation of brain connectivity to discriminate Alzheimers disease from normal controls. However, we observed that direct use of the SICE matrix does not necessarily give satisfying discrimination, due to its high dimensionality and the scarcity of training subjects. Looking into this problem, we argue that the intrinsic dimensionality of these SICE matrices shall be much lower, considering i) an SICE matrix resides on a Riemannian manifold of symmetric positive …
Classification Of Micro-Doppler Signatures Of Human Motions Using Log-Gabor Filters, Fok Hing Chi Tivive, Son Lam Phung, Abdesselam Bouzerdoum
Classification Of Micro-Doppler Signatures Of Human Motions Using Log-Gabor Filters, Fok Hing Chi Tivive, Son Lam Phung, Abdesselam Bouzerdoum
Faculty of Engineering and Information Sciences - Papers: Part A
In recent years, Doppler radar has been used as a sensing modality for human gait recognition, due to its ability to operate in adverse weather and penetrate opaque obstacles. Doppler radar captures not only the speed of the target, but also the micro-motions of its moving parts. These micro-motions induce frequency modulations that can be used to characterise the target movements. However, a major challenge in Doppler signal processing is to extract discriminative features from the radar returns for target classification. This study presents a feature extraction method for classification of human motions from the micro-Doppler radar signal. The proposed …
Video Classification Based On Spatial Gradient And Optical Flow Descriptors, Xiaolin Tang, Abdesselam Bouzerdoum, Son Lam Phung
Video Classification Based On Spatial Gradient And Optical Flow Descriptors, Xiaolin Tang, Abdesselam Bouzerdoum, Son Lam Phung
Faculty of Engineering and Information Sciences - Papers: Part A
Feature point detection and local feature extraction are the two critical steps in trajectory-based methods for video classification. This paper proposes to detect trajectories by tracking the spatiotemporal feature points in salient regions instead of the entire frame. This strategy significantly reduces noisy feature points in the background region, and leads to lower computational cost and higher discriminative power of the feature set. Two new spatiotemporal descriptors, namely the STOH and RISTOH are proposed to describe the spatiotemporal characteristics of the moving object. The proposed method for feature point detection and local feature extraction is applied for human action recognition. …
Multiple Kernel Learning In The Primal For Multimodal Alzheimer's Disease Classification, Fayao Liu, Luping Zhou, Chunhua Shen, Jianping Yin
Multiple Kernel Learning In The Primal For Multimodal Alzheimer's Disease Classification, Fayao Liu, Luping Zhou, Chunhua Shen, Jianping Yin
Faculty of Engineering and Information Sciences - Papers: Part A
To achieve effective and efficient detection of Alzheimer's disease (AD), many machine learning methods have been introduced into this realm. However, the general case of limited training samples, as well as different feature representations typically makes this problem challenging. In this work, we propose a novel multiple kernel learning framework to combine multi-modal features for AD classification, which is scalable and easy to implement. Contrary to the usual way of solving the problem in the dual, we look at the optimization from a new perspective. By conducting Fourier transform on the Gaussian kernel, we explicitly compute the mapping function, which …
Hep-2 Cell Image Classification With Multiple Linear Descriptors, Lingqiao Liu, Lei Wang
Hep-2 Cell Image Classification With Multiple Linear Descriptors, Lingqiao Liu, Lei Wang
Faculty of Engineering and Information Sciences - Papers: Part A
The automatic classification of the HEp-2 cell stain patterns from indirect immunofluorescence images has attracted much attention recently. As an image classification problem, it can be well solved by the state-of-the-art bag-of-features (BoF) model as long as a suitable local descriptor is known. Unfortunately, for this special task, we have very limited knowledge of such a descriptor. In this paper, we explore the possibility of automatically learning the descriptor from the image data itself. Specifically, we assume that a local patch can be well described by a set of linear projections performed on its pixel values. Based on this assumption, …
Discriminative Sparse Inverse Covariance Matrix: Application In Brain Functional Network Classification, Luping Zhou, Lei Wang, Philip O. Ogunbona
Discriminative Sparse Inverse Covariance Matrix: Application In Brain Functional Network Classification, Luping Zhou, Lei Wang, Philip O. Ogunbona
Faculty of Engineering and Information Sciences - Papers: Part A
Recent studies show that mental disorders change the functional organization of the brain, which could be investigated via various imaging techniques. Analyzing such changes is becoming critical as it could provide new biomarkers for diagnosing and monitoring the progression of the diseases. Functional connectivity analysis studies the covary activity of neuronal populations in different brain regions. The sparse inverse covariance estimation (SICE), also known as graphical LASSO, is one of the most important tools for functional connectivity analysis, which estimates the interregional partial correlations of the brain. Although being increasingly used for predicting mental disorders, SICE is basically a generative …
Neural Network Classification And Prior Class Probabilities, Steve Lawrence, Ian Burns, Andrew Back, Ah Chung Tsoi, C Lee Giles
Neural Network Classification And Prior Class Probabilities, Steve Lawrence, Ian Burns, Andrew Back, Ah Chung Tsoi, C Lee Giles
Ah Chung Tsoi
A commonly encountered problem in MLP (multi-layer perceptron) classification problems is related to the prior probabilities of the individual classes - if the number of training examples that correspond to each class varies significantly between the classes, then it may be harder for the network to learn the rarer classes in some cases. Such practical experience does not match theoretical results which show that MLPs approximate Bayesian a posteriori probabilities (independent of the prior class probabilities). Our investigation of the problem shows that the difference between the theoretical and practical results lies with the assumptions made in the theory (accurate …
Automated Authorship Attribution Using Advanced Signal Classification Techniques, Maryam Ebrahimpour, Talis J. Putnins, Matthew J. Berryman, Andrew Allison, Brian W-H Ng, Derek Abbott
Automated Authorship Attribution Using Advanced Signal Classification Techniques, Maryam Ebrahimpour, Talis J. Putnins, Matthew J. Berryman, Andrew Allison, Brian W-H Ng, Derek Abbott
Faculty of Engineering and Information Sciences - Papers: Part A
In this paper, we develop two automated authorship attribution schemes, one based on Multiple Discriminant Analysis (MDA) and the other based on a Support Vector Machine (SVM). The classification features we exploit are based on word frequencies in the text. We adopt an approach of preprocessing each text by stripping it of all characters except a-z and space. This is in order to increase the portability of the software to different types of texts. We test the methodology on a corpus of undisputed English texts, and use leave-one-out cross validation to demonstrate classification accuracies in excess of 90%. We further …
Influence Of Rock Depth On Seismic Site Classification For Shallow Bedrock Regions, P Anbazhagan, M. Neaz Sheikh, Aditya Parihar
Influence Of Rock Depth On Seismic Site Classification For Shallow Bedrock Regions, P Anbazhagan, M. Neaz Sheikh, Aditya Parihar
Faculty of Engineering and Information Sciences - Papers: Part A
Seismic site classifications are used to represent site effects for estimating hazard parameters (response spectral ordinates) at the soil surface. Seismic site classifications have generally been carried out using average shear wave velocity and/or standard penetration test n-values of top 30-m soil layers, according to the recommendations of the National Earthquake Hazards Reduction Program (NEHRP) or the International Building Code (IBC). The site classification system in the NEHRP and the IBC is based on the studies carried out in the United States where soil layers extend up to several hundred meters before reaching any distinct soil-bedrock interface and may not …
Sparse Representation Of Gpr Traces With Application To Signal Classification, Wenbin Shao, Abdesselam Bouzerdoum, Son Lam Phung
Sparse Representation Of Gpr Traces With Application To Signal Classification, Wenbin Shao, Abdesselam Bouzerdoum, Son Lam Phung
Faculty of Engineering and Information Sciences - Papers: Part A
Sparse representation (SR) models a signal with a small number of elementary waves using an overcomplete dictionary. It has been employed for a wide range of signal and image processing applications, including denoising, deblurring, and compression. In this paper, we present an adaptive SR method for modeling and classifying ground penetrating radar (GPR) signals. The proposed method decomposes each GPR trace into elementary waves using an adaptive Gabor dictionary. The sparse decomposition is used to extract salient features for SR and classification of GPR signals. Experimental results on real-world data show that the proposed sparse decomposition achieves efficient signal representation …
A Classification Theorem For Helfrich Surfaces, James Mccoy, Glen Wheeler
A Classification Theorem For Helfrich Surfaces, James Mccoy, Glen Wheeler
Faculty of Engineering and Information Sciences - Papers: Part A
In this paper we study the functional W , which is the the sum of the Willmore energy, weighted surface area, and weighted volume, for surfaces immersed in R^3. This coincides with the Helfrich functional with zero `spontaneous curvature'. Our main result is a complete classification of all smooth immersed critical points of the functional with nonnegative surface area weight and small L^2 norm of tracefree curvature. In particular we prove the non-existence of critical points of the functional for which the surface area and enclosed volume are positively weighted.
An Image-Based Approach For Classification Of Human Micro-Doppler Radar Signatures, Fok Hing Chi Tivive, Son Lam Phung, Abdesselam Bouzerdoum
An Image-Based Approach For Classification Of Human Micro-Doppler Radar Signatures, Fok Hing Chi Tivive, Son Lam Phung, Abdesselam Bouzerdoum
Faculty of Engineering and Information Sciences - Papers: Part A
With the advances in radar technology, there is an increasing interest in automatic radar-based human gait identification. This is because radar signals can penetrate through most dielectric materials. In this paper, an image-based approach is proposed for classifying human micro-Doppler radar signatures. The time-varying radar signal is first converted into a time-frequency representation, which is then cast as a two-dimensional image. A descriptor is developed to extract micro-Doppler features from local time-frequency patches centered along the torso Doppler frequency. Experimental results based on real data collected from a 24-GHz Doppler radar showed that the proposed approach achieves promising classification performance.
Computer Aided Decision Support System For Cervical Cancer Classification, - Rahmadwati, Golshah Naghdy, Montserrat B. Ros, Catherine Todd
Computer Aided Decision Support System For Cervical Cancer Classification, - Rahmadwati, Golshah Naghdy, Montserrat B. Ros, Catherine Todd
Faculty of Engineering and Information Sciences - Papers: Part A
Conventional analysis of a cervical histology image, such a pap smear or a biopsy sample, is performed by an expert pathologist manually. This involves inspecting the sample for cellular level abnormalities and determining the spread of the abnormalities. Cancer is graded based on the spread of the abnormal cells. This is a tedious, subjective and timeconsuming process with considerable variations in diagnosis between the experts. This paper presents a computer aided decision support system (CADSS) tool to help the pathologists in their examination of the cervical cancer biopsies. The main aim of the proposed CADSS system is to identify abnormalities …
Scene Segmentation And Pedestrian Classification From 3-D Range And Intensity Images, Xue Wei, Son Lam Phung, Abdesselam Bouzerdoum
Scene Segmentation And Pedestrian Classification From 3-D Range And Intensity Images, Xue Wei, Son Lam Phung, Abdesselam Bouzerdoum
Faculty of Engineering and Information Sciences - Papers: Part A
This paper proposes a new approach to classify obstacles using a time-of-flight camera, for applications in assistive navigation of the visually impaired. Combining range and intensity images enables fast and accurate object segmentation, and provides useful navigation cues such as distances to the nearby obstacles and obstacle types. In the proposed approach, a 3-D range image is first segmented using histogram thresholding and mean-shift grouping. Then Fourier and GIST descriptors are applied on each segmented object to extract shape and texture features. Finally, support vector machines are used to recognize the obstacles. This paper focuses on classifying pedestrian and non-pedestrian …
A Kernel Fuzzy C-Means Clustering-Based Fuzzy Support Vector Machine Algorithm For Classification Problems With Outliers Or Noises, Xiaowei Yang, Guangquan Zhang, Jie Lu, Jun Ma
A Kernel Fuzzy C-Means Clustering-Based Fuzzy Support Vector Machine Algorithm For Classification Problems With Outliers Or Noises, Xiaowei Yang, Guangquan Zhang, Jie Lu, Jun Ma
Faculty of Engineering and Information Sciences - Papers: Part A
The support vector machine (SVM) has provided higher performance than traditional learning machines and has been widely applied in real-world classification problems and nonlinear function estimation problems. Unfortunately, the training process of the SVM is sensitive to the outliers or noises in the training set. In this paper, a common misunderstanding of Gaussian-function-based kernel fuzzy clustering is corrected, and a kernel fuzzy c-means clustering-based fuzzy SVM algorithm (KFCM-FSVM) is developed to deal with the classification problems with outliers or noises. In the KFCM-FSVM algorithm, we first use the FCM clustering to cluster each of two classes from the training set …
Hippocampal Shape Classification Using Redundancy Constrained Feature Selection, Luping Zhou, Lei Wang, Chunhua Shen, Nick Barnes
Hippocampal Shape Classification Using Redundancy Constrained Feature Selection, Luping Zhou, Lei Wang, Chunhua Shen, Nick Barnes
Faculty of Engineering and Information Sciences - Papers: Part A
Landmark-based 3D hippocampal shape classification involves high-dimensional descriptor space, many noisy and redundant features, and a very small number of training samples. Feature selection becomes critical in this situation, because it not only improves classification performance, but also identifies the regions that contribute more to shape discrimination. This work identifies the drawbacks of SVM-RFE, and proposes a novel class-separability-based feature selection approach to overcome them. We formulate feature selection as a constrained integer optimization and develop a new algorithm to efficiently and optimally solve this problem. Theoretical analysis and experimental study on both synthetic data and real hippocampus data demonstrate …
Vegetation Identification Based On Satellite Imagery, Vamsi K.R. Mantena, Ramu Pedada, Srinivas Jakkula, Yuzhong Shen, Jiang Li, Hamid R. Arabnia (Ed.)
Vegetation Identification Based On Satellite Imagery, Vamsi K.R. Mantena, Ramu Pedada, Srinivas Jakkula, Yuzhong Shen, Jiang Li, Hamid R. Arabnia (Ed.)
Electrical & Computer Engineering Faculty Publications
Automatic vegetation identification plays an important role in many applications including remote sensing and high performance flight simulations. This paper presents a method to automatically identify vegetation based upon satellite imagery. First, we utilize the ISODATA algorithm to cluster pixels in the images where the number of clusters is determined by the algorithm. We then apply morphological operations to the clustered images to smooth the boundaries between clusters and to fill holes inside clusters. After that, we compute six features for each cluster. These six features then go through a feature selection algorithm and three of them are determined to …