2016 Second International Conference on Cognitive Computing and Information Processing (CCIP)最新文献

{"title":"Real time access to online immunization records and its impact on tetanus immunization coverage in the ED","authors":"Prabhu R. V. Shankar, M. Kelleman, C. McCracken, C. Morris, H. Simon","doi":"10.1109/CCIP.2016.7802850","DOIUrl":"https://doi.org/10.1109/CCIP.2016.7802850","url":null,"abstract":"The objective of this study was to evaluate the impact of online access to the state Immunization Information Systems (IIS) on the immunization practices of emergency department (ED) providers in a pediatric academic tertiary care center. Interoperability between Health Information Systems (HIS) such as Electronic Health Records (EHRs), Laboratory Information Systems (LIS), and health registries, maintained by various care providers (e.g., primary/specialty care, ED, inpatient hospital systems) and public health departments (e.g., IIS, formerly referred to as immunization registries) are required for full realization of healthcare reform, set forth by the Affordable Care Act [1], [2]. Pediatric immunization is mainly covered by the primary care providers, supplemented in certain circumstances by alternative settings such as the ED and hospitals. It is critical that updated immunization records of individual patients are available at the Point-Of-Care (POC), to help decide the need for an immunization, such as tetanus vaccination in trauma patients, and prevent over or under immunization. To provide up-to-date information about immunization administered to individual patients by all care providers, with a view to improve immunization coverage and reduce unnecessary and duplicate immunization, the Georgia Department of Public Health (DPH) created a population based IIS (Georgia Registry of Immunization Transactions and Services or GRITS) [3]. Children's Healthcare of Atlanta worked with the DPH to establish an interface between their EHR system and GRITS so that the updated immunization records could be accessed online within the EHR at POC. This online access could also be viewed as a golden opportunity to improve the Center for Disease Control (CDC) recommended Tetanus, Diphtheria and Pertussis (Tdap) coverage for the 11 to 19 year-old children, known to be difficult population to reach out to, to improve Tdap coverage targets. We compared the immunization coverage practices, based on CDC recommendations, in patients 10 to 20 years of age, presenting with trauma where tetanus immunization was indicated, pre-post availability of GRITS via EHR. At implementation onset, there was a significant increase in vaccination rates (2.3%, p=0.01), but, the increase was not sustained and the pre-implementation downward trend continued (p=0.91). There were only 4 patients who were seen more than once for trauma and ordered tetanus immunization (combined) twice; 2 patients before and 2 after the implementation. Both the pre-implementation patients were vaccinated twice, whereas only 1 patient was vaccinated twice in the post-implementation phase and the other patient's order was discontinued. While showing a short-term increase in ED based immunization post-implementation of GRITS, real time access to updated immunization records did not impact the overall long- term rates of updating Tdap immunization in the ED. As with many Quality Initiative (QI) efforts, im","PeriodicalId":354589,"journal":{"name":"2016 Second International Conference on Cognitive Computing and Information Processing (CCIP)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116870603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recognition and classification of animals based on texture features through parallel computing","authors":"N. Manohar, S. Subrahmanya, R. Bharathi, Sharath Kumar Y. H, Hemantha Kumar G","doi":"10.1109/CCIP.2016.7802872","DOIUrl":"https://doi.org/10.1109/CCIP.2016.7802872","url":null,"abstract":"In this work, we proposed an efficient system for animal recognition and classification based on texture features which are obtained from the local appearance and texture of animals. The classification of animals are done by training and subsequently testing two different machine learning techniques, namely k-Nearest Neighbors (k-NN) and Support Vector Machines (SVM). Computer-assisted technique when applied through parallel computing makes the work efficient by reducing the time taken for the task of animal recognition and classification. Here we propose a parallel algorithm for the same. Experimentation is done for about 30 different classes of animals containing more than 3000 images. Among the different classifiers, k-Nearest Neighbor classifiers have achieved a better accuracy.","PeriodicalId":354589,"journal":{"name":"2016 Second International Conference on Cognitive Computing and Information Processing (CCIP)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116900937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Integrated curvature and Convex Hull based concave point detection in Histopathological images","authors":"A. Ulle, T. N. Nagabhushan, N. Manoli, V. Basavaraj","doi":"10.1109/CCIP.2016.7802857","DOIUrl":"https://doi.org/10.1109/CCIP.2016.7802857","url":null,"abstract":"We propose a simple approach to identify concave points in Histopathological Images. Identification of all valid concave points play an important role in separating irregular shaped cells forming clumps in the Histopathological images. There exists several studies for accurate identification of concave points. Our experimental analysis reveal that existing methods fail to identify both deep, and shallow concave points. In this paper we propose an integrated curvature and convex hull based concave points detection in Histopathological images. The proposed method performed well when compared to the existing methods.","PeriodicalId":354589,"journal":{"name":"2016 Second International Conference on Cognitive Computing and Information Processing (CCIP)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114808144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Frame clustering technique towards single video summarization","authors":"Priyamvada R. Sachan, Keshaveni","doi":"10.1109/CCIP.2016.7802877","DOIUrl":"https://doi.org/10.1109/CCIP.2016.7802877","url":null,"abstract":"Recent advances in technology, multimedia and social networking sites have led to a massive growth in web video content available for the general population. This results in information overload and management problem of the same. In this context, video summarization plays an important role as it aims to reduce the content size of video and yet present the important semantic concepts in the video. This gives an opportunity to reorganize video content in most succinct form for efficient and on- demand user consumption. Video summarization in its true sense is a hard problem as it involves domain specific semantic understanding of video content and user expectations. Most of the existing approaches relies on segmenting video into contiguous shots & selecting one or more keyframes from each shot and present these keyframes as summary. Such approaches may work well if independent concepts in video appear only once. However, in videos where same concepts are repeated multiple times, these existing approaches may pick repeating summary frames belonging to same concepts. In this paper, we present a novel frame clustering approach for generating very concise summaries by grouping all frames of similar concepts together irrespective of their occurrence sequence. The proposed approach is aimed towards large videos in domains like travel guide, documentaries, dramas where video revolves around few repeating concepts. The approach utilizes multiple video features in a generic way for frame-similarity determination and is extensible for multi-video summarization. Experimental comparative results substantiate the efficiency of the proposed approach in generating concise video summaries on videos with repeating concepts.","PeriodicalId":354589,"journal":{"name":"2016 Second International Conference on Cognitive Computing and Information Processing (CCIP)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125613087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Disordered voice classification using SVM and feature selection using GA","authors":"Seema Firdos, K. Umarani","doi":"10.1109/CCIP.2016.7802868","DOIUrl":"https://doi.org/10.1109/CCIP.2016.7802868","url":null,"abstract":"Many individuals are subjected to the risk of voice disorders which may be characterized by hoarseness, vocal fatigue,periodic loss of voice or inappropriate pitch or loudness. These disordered voice cause changes in the acoustic characteristics. Therefore, the voice signal is used as an important measure to diagnose them. This paper deals the classification of normal and two disordered voices using support vector machine (SVM). For this classification the voice signal which is recorded from the patients is used. The mel frequency cepstral coefficients (MFCC), delta and double delta coefficients are extracted as features from the voice signal. For best feature selection, genetic algorithm (GA) is used. The performance of the classifier is enhanced after applying GA.","PeriodicalId":354589,"journal":{"name":"2016 Second International Conference on Cognitive Computing and Information Processing (CCIP)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132799418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fully complex-valued radial basis function networks for prediction of wind force and moment co-efficients on marine structures","authors":"K. Kumar.N, R. Savitha, A. Al Mamun","doi":"10.1109/CCIP.2016.7802885","DOIUrl":"https://doi.org/10.1109/CCIP.2016.7802885","url":null,"abstract":"In this paper, a Fully Complex-Valued Radial Basis Function (FC-RBF) network is used to predict the wind force and moment co-efficients of marine structures. The paper aims to provide an universal approach to study the wind force and moments on the ships. The force and moment co-efficient estimated in literature using regression analysis involves the ship dimensions. These dimensions can be represented in complex-valued number format, which makes it an ideal approximation problem from FC-RBF. The study considers various types of marine vessels at different loading conditions, with a total of 22 marine vessels. Of these, 18 are used to train FC-RBF. The network thus developed is tested for generalization on 2 new type of vessels at 2 different loading conditions. Thus, the developed model is capable of predicting the wind force and moment coefficients, irrespective of the type of vessel used. Performance study to predict the wind force and moment coefficients of marine vessels show that the FC-RBF has superior prediction performance, compared to state of the art results for this problem.","PeriodicalId":354589,"journal":{"name":"2016 Second International Conference on Cognitive Computing and Information Processing (CCIP)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131653714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Undecimated Complex Wavelet Transform based bleeding detection for endoscopic images","authors":"K. R. Reeha, K. Shailaja, V. Gopi","doi":"10.1109/CCIP.2016.7802888","DOIUrl":"https://doi.org/10.1109/CCIP.2016.7802888","url":null,"abstract":"Wireless Capsule Endoscopy is a methodology to detect abnormalities in the Gastrointestinal (GI) tract, mostly the internal regions of small intestine. It is a non-invasive process. In this work, Undecimated Double Density Dual Tree Discrete Wavelet Transform (UDDT-DWT) is considered in detecting bleeding WCE images. Four statistical parameters such as contrast, entropy, cluster shade and cluster prominence are calculated from Gray Level Co-occurrence Matrix (GLCM) of each sub images obtained after applying UDDDT-DWT. These features are used for the classification of WCE images. For the detection of blood in images, endoscopic image is converted to HSV colour space and several classifiers are considered. Experiments show that the proposed method provides a high accuracy rate of 99.5%, sensitivity of 99% and specificity of 100% for Random Forest and Random Tree classifier when compared with the existing methods.","PeriodicalId":354589,"journal":{"name":"2016 Second International Conference on Cognitive Computing and Information Processing (CCIP)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126488116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comparative study of feature extraction methods in defect classification of mangoes using neural network","authors":"V. Ashok, Vinod DS","doi":"10.1109/CCIP.2016.7802873","DOIUrl":"https://doi.org/10.1109/CCIP.2016.7802873","url":null,"abstract":"The “king of fruits” Mango (Mangifera indica L.) is the most sought after fruit for both direct and indirect consumption across the globe. Since it has very high export value, there is a need to develop a technique that is capable of classifying the defects of mangoes objectively. Any classifier performance is dependent on the features extracted from the region of interest of the sample. In this paper, a comparative study of feature extraction methods is made to classify the visible defects of Mangoes. “Alphonso” mango cultivar was chosen for the experimentation. 1766 color images with different quality classes were acquired, pre-processed and textural features were extracted considering one feature at a time and also in combination for each color image. Hence, we obtained 9 different cases of different textural features combination. Furthermore, most relevant features were selected from each case using sequential forward selection algorithm. The textural features like statistical, LBP and filter banks were found to be effective in designing neural network (NN) using generalized linear model classifier with cross validated performance accuracy of 90.09%, 90.26% and 90.26% for linear, logistic and softmax activation functions respectively.","PeriodicalId":354589,"journal":{"name":"2016 Second International Conference on Cognitive Computing and Information Processing (CCIP)","volume":"25 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132835640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Human activity recognition in cognitive environments using sequential ELM","authors":"R. C. Kumar, S. S. Bharadwaj, B. N. Sumukha, K. George","doi":"10.1109/CCIP.2016.7802880","DOIUrl":"https://doi.org/10.1109/CCIP.2016.7802880","url":null,"abstract":"Human activity recognition (HAR) and Extreme Learning Machines (ELM) are emerging fields of research. HAR investigates the behavioural attributes of humans and integrates that to an electronic system. An ELM is a fast learning algorithm, and overcomes the fundamental issue of slow training-error convergence that other algorithms such as the back propagation algorithm suffer. In this paper, we present the blend of the two fields by classifying the behavioural attributes of humans using Artificial Neural Networks (ANN) trained by Sequential Extreme Learning Algorithm (SELA). The algorithm is efficacious with a remarkable accuracy despite circumventing the vital job of pre-processing and feature extraction from signals that have been acquired from sensors.","PeriodicalId":354589,"journal":{"name":"2016 Second International Conference on Cognitive Computing and Information Processing (CCIP)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124459676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Facial emotion recognition for human-machine interaction using hybrid DWT-SFET feature extraction technique","authors":"Shoaib Kamal, Farrukh Sayeed, Mohammed Rafeeq, M. Zakir","doi":"10.1109/CCIP.2016.7802853","DOIUrl":"https://doi.org/10.1109/CCIP.2016.7802853","url":null,"abstract":"Facial emotion recognition is the most significant parameter for an efficacious Human Machine Interaction (HMI). It plays a crucial role in interpreting and communicating with the people who have speaking impairments as well as a medium to understand and communicate with infants who cannot emote their feelings verbally. In this paper, we propose a hybrid feature extraction technique consisting of Discrete Wavelet Transform (DWT) accompanied by Shape Feature Extraction Technique (SFET).The features extracted were tested on standard classifiers such as Support Vector Machine (SVM) and K-Nearest Neighbourhood (KNN) classifiers. Facial images from JAFFE and Cohn-Kennedy databases were utilized for training as well as testing. The work shows a very high facial emotion recognition rate of 93.94% and 91.8% with the proposed method for JAFFE and Cohn-Kanade databases respectively.","PeriodicalId":354589,"journal":{"name":"2016 Second International Conference on Cognitive Computing and Information Processing (CCIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123190993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}