Pub Date : 2012-11-01DOI: 10.1109/ISDA.2012.6416661
N. Zamani, A. D. M. Zahamdin, S. Abdullah, M. J. Nordin
The enhancement analysis in video forensics is used to enhance the clarity of video frames of a video exhibit. The enhanced version of these video frames is important as to assist law enforcement agency for investigation or to be tended as evidence in court. The most significant problem observed in the analysis is the enhancement of objects under probe in video. In many cases, the probes appeared to be in low-resolution and degraded with noise, lens blur and compression artifacts. The enhancement of these low quality probes via conventional method of denoising and resizing has proven to further degrade the quality of the prober The objective of this paper is to propose an enhancement analysis algorithm based on super-resolution. Hence, we present an solution which is a single-frame solution for super-resolution. For that purpose, our proposed method incorporates sparse coding with Non-Negative Matrix Factorization in order to improve hallucination of probes in video. Sparse coding is employed in learning a localized part-based subspace which synthesizes higher resolution with respect to overcomplete patch dictionaries. We test our proposed method and compare with state-of-the-art methods namely resampling and super-resolution method, by enhancing probes in exhibit videos. We measure the image quality using peak-signal-to-noise-ratio. The result shows that our proposed method outperforms state-of the-art methods after enhancing probes in exhibit videos.
{"title":"Sparse representation super-resolution method for enhancement analysis in video forensics","authors":"N. Zamani, A. D. M. Zahamdin, S. Abdullah, M. J. Nordin","doi":"10.1109/ISDA.2012.6416661","DOIUrl":"https://doi.org/10.1109/ISDA.2012.6416661","url":null,"abstract":"The enhancement analysis in video forensics is used to enhance the clarity of video frames of a video exhibit. The enhanced version of these video frames is important as to assist law enforcement agency for investigation or to be tended as evidence in court. The most significant problem observed in the analysis is the enhancement of objects under probe in video. In many cases, the probes appeared to be in low-resolution and degraded with noise, lens blur and compression artifacts. The enhancement of these low quality probes via conventional method of denoising and resizing has proven to further degrade the quality of the prober The objective of this paper is to propose an enhancement analysis algorithm based on super-resolution. Hence, we present an solution which is a single-frame solution for super-resolution. For that purpose, our proposed method incorporates sparse coding with Non-Negative Matrix Factorization in order to improve hallucination of probes in video. Sparse coding is employed in learning a localized part-based subspace which synthesizes higher resolution with respect to overcomplete patch dictionaries. We test our proposed method and compare with state-of-the-art methods namely resampling and super-resolution method, by enhancing probes in exhibit videos. We measure the image quality using peak-signal-to-noise-ratio. The result shows that our proposed method outperforms state-of the-art methods after enhancing probes in exhibit videos.","PeriodicalId":370150,"journal":{"name":"2012 12th International Conference on Intelligent Systems Design and Applications (ISDA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132632547","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}
Pub Date : 2012-11-01DOI: 10.1109/ISDA.2012.6416547
N. Lakshmiprabha, S. Majumder
Facial plastic surgery changes facial features to large extend and thus creating a major problem to face recognition system. This paper proposes a new face recognition system using novel shape local binary texture (SLBT) feature from face images cascaded with periocular feature for plastic surgery invariant face recognition. In-spite of many uniqueness and advantages, the existing feature extraction methods are capable of extracting either shape or texture feature. A method which can extract both shape and texture feature is more attractive. The proposed SLBT can extract global shape, local shape and texture information from a face image by extracting local binary pattern (LBP) instead of direct intensity values from shape free patch of active appearance model (AAM). The experiments conducted using MUCT and plastic surgery face database shows that the SLBT feature performs better than AAM and LBP features. Further increase in recognition rate is achieved by cascading SLBT features from face with LBP features from periocular regions. The result from surgical and non-surgical face database shows that the proposed face recognition system can easily tackle illumination, pose, expression, occlusion and plastic surgery variations in face images.
{"title":"Face recognition system invariant to plastic surgery","authors":"N. Lakshmiprabha, S. Majumder","doi":"10.1109/ISDA.2012.6416547","DOIUrl":"https://doi.org/10.1109/ISDA.2012.6416547","url":null,"abstract":"Facial plastic surgery changes facial features to large extend and thus creating a major problem to face recognition system. This paper proposes a new face recognition system using novel shape local binary texture (SLBT) feature from face images cascaded with periocular feature for plastic surgery invariant face recognition. In-spite of many uniqueness and advantages, the existing feature extraction methods are capable of extracting either shape or texture feature. A method which can extract both shape and texture feature is more attractive. The proposed SLBT can extract global shape, local shape and texture information from a face image by extracting local binary pattern (LBP) instead of direct intensity values from shape free patch of active appearance model (AAM). The experiments conducted using MUCT and plastic surgery face database shows that the SLBT feature performs better than AAM and LBP features. Further increase in recognition rate is achieved by cascading SLBT features from face with LBP features from periocular regions. The result from surgical and non-surgical face database shows that the proposed face recognition system can easily tackle illumination, pose, expression, occlusion and plastic surgery variations in face images.","PeriodicalId":370150,"journal":{"name":"2012 12th International Conference on Intelligent Systems Design and Applications (ISDA)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125301814","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}
Pub Date : 2012-11-01DOI: 10.1109/ISDA.2012.6416610
M. Jabbar, P. Chandra, B. Deekshatulu
Medical data mining is the search for relationships and patterns within the medical data that could provide useful knowledge for effective medical diagnosis. Extracting useful information from these data bases can lead to discovery of rules for later diagnosis tools. Generally medical data bases are highly voluminous in nature. If a training data set contains irrelevant and redundant features classification may produce less accurate results. Feature selection as a pre-processing step in used to reduce dimensionality, removing irrelevant data and increasing accuracy and improves comprehensibility. Associative classification is a recent and rewarding technique that applies the methodology of association into classification and achieves high classification accuracy. Most associative classification algorithms adopt exhaustive search algorithms like in Apriori, and generate huge no. of rules from which a set of high quality of rules are chosen to construct efficient classifier. Hence generating a small set of high quality rules to build classifier is a challenging task. Cardiovascular diseases are the leading cause of death globally and in India more deaths are due to CHD. Cardiovascular disease is an increasingly an important cause of death in Andhra Pradesh. Hence there is an urgent need to develop a system to predict the heart disease of people. This paper discusses prediction of risk score for heart disease in Andhra Pradesh. We generated class association rules using feature subset selection. These generated rules will help physicians to predict the heart disease of a patient.
{"title":"Prediction of risk score for heart disease using associative classification and hybrid feature subset selection","authors":"M. Jabbar, P. Chandra, B. Deekshatulu","doi":"10.1109/ISDA.2012.6416610","DOIUrl":"https://doi.org/10.1109/ISDA.2012.6416610","url":null,"abstract":"Medical data mining is the search for relationships and patterns within the medical data that could provide useful knowledge for effective medical diagnosis. Extracting useful information from these data bases can lead to discovery of rules for later diagnosis tools. Generally medical data bases are highly voluminous in nature. If a training data set contains irrelevant and redundant features classification may produce less accurate results. Feature selection as a pre-processing step in used to reduce dimensionality, removing irrelevant data and increasing accuracy and improves comprehensibility. Associative classification is a recent and rewarding technique that applies the methodology of association into classification and achieves high classification accuracy. Most associative classification algorithms adopt exhaustive search algorithms like in Apriori, and generate huge no. of rules from which a set of high quality of rules are chosen to construct efficient classifier. Hence generating a small set of high quality rules to build classifier is a challenging task. Cardiovascular diseases are the leading cause of death globally and in India more deaths are due to CHD. Cardiovascular disease is an increasingly an important cause of death in Andhra Pradesh. Hence there is an urgent need to develop a system to predict the heart disease of people. This paper discusses prediction of risk score for heart disease in Andhra Pradesh. We generated class association rules using feature subset selection. These generated rules will help physicians to predict the heart disease of a patient.","PeriodicalId":370150,"journal":{"name":"2012 12th International Conference on Intelligent Systems Design and Applications (ISDA)","volume":" 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113947504","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}
Pub Date : 2012-11-01DOI: 10.1109/ISDA.2012.6416665
A. Gopalai, S. M. N. Arosha Senanayake
During rehabilitation routines for postural control, clinician use proprioception training involving wobble boards to help strengthen the proprioception. Wobble board routines are carried out for at least six weeks, subjects are required to perform certain motions on the boards which are targeted to improve proprioception. Subjects perform this tasks without (or with minimal) real-time feedback. A real-time system to monitor proprioception training, using a wobble board, was designed and tested. This work presents a force sensing platform, equipped with soft-computing methods to measure effects of destabilizing postural perturbations. Experiments were conducted to verify the system's ability to monitor and gauge subject's postural control via proprioception. The experimental set-up was observed at a frequency of three times a week for a duration of six weeks. Fuzzy clustering and area of sway analysis was used to determine the effects of training on subjects' postural control in Eyes Open (EO) and Eyes Close (EC) conditions. All data was tabulated and compared using one-way ANOVA to determine its statistical significance, with a false rejection ratio α = 0.05. The results of the experiment supported the suitability of the system for clinical applications pertaining to postural control improvements.
{"title":"Force sensing resistors for monitoring proprioception response in rehabilitation routines","authors":"A. Gopalai, S. M. N. Arosha Senanayake","doi":"10.1109/ISDA.2012.6416665","DOIUrl":"https://doi.org/10.1109/ISDA.2012.6416665","url":null,"abstract":"During rehabilitation routines for postural control, clinician use proprioception training involving wobble boards to help strengthen the proprioception. Wobble board routines are carried out for at least six weeks, subjects are required to perform certain motions on the boards which are targeted to improve proprioception. Subjects perform this tasks without (or with minimal) real-time feedback. A real-time system to monitor proprioception training, using a wobble board, was designed and tested. This work presents a force sensing platform, equipped with soft-computing methods to measure effects of destabilizing postural perturbations. Experiments were conducted to verify the system's ability to monitor and gauge subject's postural control via proprioception. The experimental set-up was observed at a frequency of three times a week for a duration of six weeks. Fuzzy clustering and area of sway analysis was used to determine the effects of training on subjects' postural control in Eyes Open (EO) and Eyes Close (EC) conditions. All data was tabulated and compared using one-way ANOVA to determine its statistical significance, with a false rejection ratio α = 0.05. The results of the experiment supported the suitability of the system for clinical applications pertaining to postural control improvements.","PeriodicalId":370150,"journal":{"name":"2012 12th International Conference on Intelligent Systems Design and Applications (ISDA)","volume":"180 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121627047","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}
Pub Date : 2012-11-01DOI: 10.1109/ISDA.2012.6416617
S. Acharjee, N. Dey, D. Biswas, P. Das, S. S. Chaudhuri
The most computationally expensive operation in entire video compression process is Motion Estimation. The challenge is to reduce the computational complexity and time of Exhaustive Search Algorithm without losing too much quality at the output. The proposed work is to implement a novel block matching algorithm for Motion Vector Estimation which performs better than other conventional Block Matching Algorithms such as Three Step Search (TSS), New Three Step Search (NTSS), and Four Step Search (FSS) etc.
{"title":"A novel Block Matching Algorithmic Approach with smaller block size for motion vector estimation in video compression","authors":"S. Acharjee, N. Dey, D. Biswas, P. Das, S. S. Chaudhuri","doi":"10.1109/ISDA.2012.6416617","DOIUrl":"https://doi.org/10.1109/ISDA.2012.6416617","url":null,"abstract":"The most computationally expensive operation in entire video compression process is Motion Estimation. The challenge is to reduce the computational complexity and time of Exhaustive Search Algorithm without losing too much quality at the output. The proposed work is to implement a novel block matching algorithm for Motion Vector Estimation which performs better than other conventional Block Matching Algorithms such as Three Step Search (TSS), New Three Step Search (NTSS), and Four Step Search (FSS) etc.","PeriodicalId":370150,"journal":{"name":"2012 12th International Conference on Intelligent Systems Design and Applications (ISDA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122093892","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}
Pub Date : 2012-11-01DOI: 10.1109/ISDA.2012.6416608
T. Nogueira, S. O. Rezende, H. Camargo
System flexibility means the ability of a system to manage imprecise and/or uncertain information. There are two ways to address the Information Retrieval Systems (IRS) flexibility: through methods that improve the query formulation and through methods that improve the document organization. Since the query formulation has obtained more attention in retrieval process, we aim to investigate the flexibility in document organization. When a document organization is carried out using fuzzy clustering, the documents can belong to more than one cluster simultaneously with different membership degrees, allowing the management of imprecise and/or uncertain information in the collection organization. Clusters represent topics and are identified by one or more descriptors. In this work we use an unsupervised method to extract cluster descriptors for a specific database and investigate whether the quality of the fuzzy cluster descriptors improves the flexible organization of documents.
{"title":"Fuzzy cluster descriptors improve flexible organization of documents","authors":"T. Nogueira, S. O. Rezende, H. Camargo","doi":"10.1109/ISDA.2012.6416608","DOIUrl":"https://doi.org/10.1109/ISDA.2012.6416608","url":null,"abstract":"System flexibility means the ability of a system to manage imprecise and/or uncertain information. There are two ways to address the Information Retrieval Systems (IRS) flexibility: through methods that improve the query formulation and through methods that improve the document organization. Since the query formulation has obtained more attention in retrieval process, we aim to investigate the flexibility in document organization. When a document organization is carried out using fuzzy clustering, the documents can belong to more than one cluster simultaneously with different membership degrees, allowing the management of imprecise and/or uncertain information in the collection organization. Clusters represent topics and are identified by one or more descriptors. In this work we use an unsupervised method to extract cluster descriptors for a specific database and investigate whether the quality of the fuzzy cluster descriptors improves the flexible organization of documents.","PeriodicalId":370150,"journal":{"name":"2012 12th International Conference on Intelligent Systems Design and Applications (ISDA)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116620619","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}
Pub Date : 2012-11-01DOI: 10.1109/ISDA.2012.6416536
Yoisel Campos, Carlos Morell, F. Ferri
Accurate machine learning with high-dimensional data is affected by phenomena known as the “curse” of dimensionality. One of the main strategies explored in the last decade to deal with this problem is the use of multi-classifier systems. Several of such approaches are inspired by the Random Subspace Method for the construction of decision forests. Furthermore, other studies rely on estimations of the individual classifiers' competence, to enhance the combination in the multi-classifier and improve the accuracy. We propose a competence estimate which is based on local complexity measurements, to perform a weighted average combination of the decision forest. Experimental results show how this idea significantly outperforms the standard non-weighted average combination and also the renowned Classifier Local Accuracy competence estimate, while consuming significantly less time.
{"title":"A local complexity based combination method for decision forests trained with high-dimensional data","authors":"Yoisel Campos, Carlos Morell, F. Ferri","doi":"10.1109/ISDA.2012.6416536","DOIUrl":"https://doi.org/10.1109/ISDA.2012.6416536","url":null,"abstract":"Accurate machine learning with high-dimensional data is affected by phenomena known as the “curse” of dimensionality. One of the main strategies explored in the last decade to deal with this problem is the use of multi-classifier systems. Several of such approaches are inspired by the Random Subspace Method for the construction of decision forests. Furthermore, other studies rely on estimations of the individual classifiers' competence, to enhance the combination in the multi-classifier and improve the accuracy. We propose a competence estimate which is based on local complexity measurements, to perform a weighted average combination of the decision forest. Experimental results show how this idea significantly outperforms the standard non-weighted average combination and also the renowned Classifier Local Accuracy competence estimate, while consuming significantly less time.","PeriodicalId":370150,"journal":{"name":"2012 12th International Conference on Intelligent Systems Design and Applications (ISDA)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116907038","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}
Pub Date : 2012-11-01DOI: 10.1109/ISDA.2012.6416541
R. Visakh
Clustering is a predominant data mining task which attempts to partition a group of unlabelled data instances into distinct clusters. The clusters so obtained will have maximum intra-cluster similarity and minimum inter-cluster similarity. Several clustering techniques have been proposed in literature, which includes stand-alone as well as ensemble clustering techniques. Most of them lack robustness and suffer from an important drawback that they cannot effectively visualize clustering results to help knowledge discovery and constructive learning. Recently, clustering techniques via visualization of data have been proposed. These rely on building a Self Organizing Map (SOM) originally proposed by Kohonen. Even though Kohonen SOM preserves topology of the input data, it is widely observed that the clustering accuracy achieved by SOM is poor. To perform robust and accurate clustering using SOM, a cluster ensemble framework based on input constraints is proposed in this paper. Cluster ensemble is a set of clustering solutions obtained as a result of individual clustering on subsets of the original high-dimensional data. The final consensus matrix is fed to a neural network which transforms the input data to a lower-dimensional output map. The map clearly depicts the distribution of input data instances into clusters.
{"title":"Using Self-Organizing Maps in constrained ensemble clustering framework","authors":"R. Visakh","doi":"10.1109/ISDA.2012.6416541","DOIUrl":"https://doi.org/10.1109/ISDA.2012.6416541","url":null,"abstract":"Clustering is a predominant data mining task which attempts to partition a group of unlabelled data instances into distinct clusters. The clusters so obtained will have maximum intra-cluster similarity and minimum inter-cluster similarity. Several clustering techniques have been proposed in literature, which includes stand-alone as well as ensemble clustering techniques. Most of them lack robustness and suffer from an important drawback that they cannot effectively visualize clustering results to help knowledge discovery and constructive learning. Recently, clustering techniques via visualization of data have been proposed. These rely on building a Self Organizing Map (SOM) originally proposed by Kohonen. Even though Kohonen SOM preserves topology of the input data, it is widely observed that the clustering accuracy achieved by SOM is poor. To perform robust and accurate clustering using SOM, a cluster ensemble framework based on input constraints is proposed in this paper. Cluster ensemble is a set of clustering solutions obtained as a result of individual clustering on subsets of the original high-dimensional data. The final consensus matrix is fed to a neural network which transforms the input data to a lower-dimensional output map. The map clearly depicts the distribution of input data instances into clusters.","PeriodicalId":370150,"journal":{"name":"2012 12th International Conference on Intelligent Systems Design and Applications (ISDA)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117148295","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}
Pub Date : 2012-11-01DOI: 10.1109/ISDA.2012.6416563
R. Preetha, G. Suresh
In MRI images, the boundary of an encephalic tissue is highly curved and irregular. Three dimensional reconstruction of such encephalic tissue is complicated. The surface reconstruction is the sub-field of Medical imaging which provides an effective way to investigate and determine brain related diseases in an efficient and effective manner. The basic purpose of 3-D surface reconstruction is to analyze the brain images precisely in order to effectively diagnose and examine the diseases for surgical planning and tumor localization. Reconstruction of tumor images is the goal in dealing with these images. In this paper, a brief overview is given on the advantages and disadvantages of existing surface reconstruction methods in clinical applications. The traditional cube based algorithms extracts the surface by forming imaginary cube and then determines the polygons needed to represent the part of the isosurface that passes through this cube. But, it requires post processing and needs more Computational time for reconstruction. Also they cannot provide the proof of correctness. The vector machine based algorithms like Immune Sphere Shaped Support Vector Machine (ISSSVM) transforms the highly irregular object into the high dimensional feature space and construct the hyper-sphere as compact as possible which encloses almost all the target object. This paper concludes that ISSSVM can outperform the cube based algorithm by reconstructing the irregular boundaries of the encephalic tissue efficiently without post processing. It can also provide its proof of correctness with greater accuracy.
{"title":"Performance analysis on three dimensional surface reconstruction of head magnetic resonance images","authors":"R. Preetha, G. Suresh","doi":"10.1109/ISDA.2012.6416563","DOIUrl":"https://doi.org/10.1109/ISDA.2012.6416563","url":null,"abstract":"In MRI images, the boundary of an encephalic tissue is highly curved and irregular. Three dimensional reconstruction of such encephalic tissue is complicated. The surface reconstruction is the sub-field of Medical imaging which provides an effective way to investigate and determine brain related diseases in an efficient and effective manner. The basic purpose of 3-D surface reconstruction is to analyze the brain images precisely in order to effectively diagnose and examine the diseases for surgical planning and tumor localization. Reconstruction of tumor images is the goal in dealing with these images. In this paper, a brief overview is given on the advantages and disadvantages of existing surface reconstruction methods in clinical applications. The traditional cube based algorithms extracts the surface by forming imaginary cube and then determines the polygons needed to represent the part of the isosurface that passes through this cube. But, it requires post processing and needs more Computational time for reconstruction. Also they cannot provide the proof of correctness. The vector machine based algorithms like Immune Sphere Shaped Support Vector Machine (ISSSVM) transforms the highly irregular object into the high dimensional feature space and construct the hyper-sphere as compact as possible which encloses almost all the target object. This paper concludes that ISSSVM can outperform the cube based algorithm by reconstructing the irregular boundaries of the encephalic tissue efficiently without post processing. It can also provide its proof of correctness with greater accuracy.","PeriodicalId":370150,"journal":{"name":"2012 12th International Conference on Intelligent Systems Design and Applications (ISDA)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129419686","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}
Pub Date : 2012-11-01DOI: 10.1109/ISDA.2012.6416630
V. B. Singh, K. K. Chaturvedi
Predicting software defects is one of the key areas of research in software engineering. Researchers have devised and implemented a plethora of defect/bug prediction approaches namely code churn, past bugs, refactoring, number of authors, file size and age, etc by measuring the performance in terms of accuracy and complexity. Different mathematical models have also been developed in the literature to monitor the bug occurrence and fixing process. These existing mathematical models named software reliability growth models are either calendar time or testing effort dependent. The occurrence of bugs in the software is mainly due to the continuous changes in the software code. The continuous changes in the software code make the code complex. The complexity of the code changes have already been quantified in terms of entropy as follows in Hassan [9]. In the available literature, few authors have proposed entropy based bug prediction using conventional simple linear regression (SLR) method. In this paper, we have proposed an entropy based bug prediction approach using support vector regression (SVR). We have compared the results of proposed models with the existing one in the literature and have found that the proposed models are good bug predictor as they have shown the significant improvement in their performance.
{"title":"Entropy based bug prediction using support vector regression","authors":"V. B. Singh, K. K. Chaturvedi","doi":"10.1109/ISDA.2012.6416630","DOIUrl":"https://doi.org/10.1109/ISDA.2012.6416630","url":null,"abstract":"Predicting software defects is one of the key areas of research in software engineering. Researchers have devised and implemented a plethora of defect/bug prediction approaches namely code churn, past bugs, refactoring, number of authors, file size and age, etc by measuring the performance in terms of accuracy and complexity. Different mathematical models have also been developed in the literature to monitor the bug occurrence and fixing process. These existing mathematical models named software reliability growth models are either calendar time or testing effort dependent. The occurrence of bugs in the software is mainly due to the continuous changes in the software code. The continuous changes in the software code make the code complex. The complexity of the code changes have already been quantified in terms of entropy as follows in Hassan [9]. In the available literature, few authors have proposed entropy based bug prediction using conventional simple linear regression (SLR) method. In this paper, we have proposed an entropy based bug prediction approach using support vector regression (SVR). We have compared the results of proposed models with the existing one in the literature and have found that the proposed models are good bug predictor as they have shown the significant improvement in their performance.","PeriodicalId":370150,"journal":{"name":"2012 12th International Conference on Intelligent Systems Design and Applications (ISDA)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125990788","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}
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