Search results for: Internal Features

Authors: Anežka Hamranová, Blandína Šramová, Katarína Fichnová

Abstract:

We presented results of research aimed on findings influence of social - psychological training (realized with students of Constantine the Philosopher University- future teachers within their undergraduate preparation) on the choice of intrapersonal and interpersonal features. After social- psychological training using Interpersonal Check List (ICL) we found out shift of behavior to more adaptive forms in categories, which are characterized by extroversive friendly behavior, willingness to cooperation, conformity regard to social situation, responsible and regardful behavior. Using State-Trait Anxiety Inventory (STAI) we found out the cut down of state anxiety and of trait anxiety. The report was processed within grants KEGA 3/5269/07 and VEGA 1/3675/06.

Keywords: Intrapersonal and interpersonal features, social -psychological training, social competences.

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Authors: Siamak Feizi, Gunvor Baardvik

Abstract:

Soil erosion has special consequences for landfills that are more serious than those found at conventional construction sites. Different potential heads between two sides of a landfill and the subsequent movement of water through pores within the soil body could trigger the soil erosion and construction instability. Such condition was encountered in a landfill project in the southern part of Norway. To check the risk of internal erosion due changes in the groundwater level (because of seasonal flooding in the river), a series of numerical simulations by means of Geo-Seep software were conducted. Output of this study provides a total picture of the landfill stability, possibilities of erosions and necessary measures to prevent or reduce the risk for the landfill operator.

Keywords: Erosion, seepage, landfill, stability.

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Authors: Mojtaba Ghodsi, Hamidreza Ziaifar, Morteza Mohammadzaheri, Payam Soltani

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This article presents an analytical model to estimate the harvested power from a Magnetostrictive cantilevered beam with tip excitation. Furthermore, the effects of internal and external damping on harvested power are investigated. The magnetostrictive material in this harvester is Galfenol. In comparison to other popular smart materials like Terfenol-D, Galfenol has higher strength and machinability. In this article, first, a mechanical model of the Euler-Bernoulli beam is employed to calculate the deflection of the harvester. Then, the magneto-mechanical equation of Galfenol is combined with Faraday's law to calculate the generated voltage of the Magnetostrictive cantilevered beam harvester. Finally, the beam model is incorporated in the aforementioned combination. The results show that a 30×8.5×1 mm Galfenol cantilever beam harvester with 80 turn pickup coil can generate up to 3.7 mV and 9 mW. Furthermore, sensitivity analysis made by Response Surface Method (RSM) shows that the harvested power is only sensitive to the internal damping coefficient.

Keywords: Internal damping coefficient, external damping coefficient, Euler-Bernoulli, energy harvester, Galfenol, magnetostrictive, response surface method.

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Authors: Mahdieh Khalilinezhad, Silvana Dellepiane, Gianni Vernazza

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The aim of this work is to build a model based on tissue characterization that is able to discriminate pathological and non-pathological regions from three-phasic CT images. With our research and based on a feature selection in different phases, we are trying to design a neural network system with an optimal neuron number in a hidden layer. Our approach consists of three steps: feature selection, feature reduction, and classification. For each region of interest (ROI), 6 distinct sets of texture features are extracted such as: first order histogram parameters, absolute gradient, run-length matrix, co-occurrence matrix, autoregressive model, and wavelet, for a total of 270 texture features. When analyzing more phases, we show that the injection of liquid cause changes to the high relevant features in each region. Our results demonstrate that for detecting HCC tumor phase 3 is the best one in most of the features that we apply to the classification algorithm. The percentage of detection between pathology and healthy classes, according to our method, relates to first order histogram parameters with accuracy of 85% in phase 1, 95% in phase 2, and 95% in phase 3.

Keywords: Feature selection, Multi-phasic liver images, Neural network, Texture analysis.

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Authors: E. Pyle

Abstract:

This paper shows that the economy of any country can be presented as three different shells such as: economic shell of a big, a medium and a small business. The new concepts were introduced such as: volume of an economic shell, coefficient of shell-s expansion (compression) etc. These shells can expansion or compress under action by internal or external powers and when shell expansions - it means the rising of a business activity and compression shows us that economy goes on recession. This process of an expansion or a compression can develop in the various ways like linear, logarithm or any other mathematical laws.

Keywords: Different kinds of deformation of macroeconomics'shells, shells of a big or a medium and a small business.

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Authors: Jiqing Han, Rongchun Gao

Abstract:

One major source of performance decline in speaker recognition system is channel mismatch between training and testing. This paper focuses on improving channel robustness of speaker recognition system in two aspects of channel compensation technique and channel robust features. The system is text-independent speaker identification system based on two-stage recognition. In the aspect of channel compensation technique, this paper applies MAP (Maximum A Posterior Probability) channel compensation technique, which was used in speech recognition, to speaker recognition system. In the aspect of channel robust features, this paper introduces pitch-dependent features and pitch-dependent speaker model for the second stage recognition. Based on the first stage recognition to testing speech using GMM (Gaussian Mixture Model), the system uses GMM scores to decide if it needs to be recognized again. If it needs to, the system selects a few speakers from all of the speakers who participate in the first stage recognition for the second stage recognition. For each selected speaker, the system obtains 3 pitch-dependent results from his pitch-dependent speaker model, and then uses ANN (Artificial Neural Network) to unite the 3 pitch-dependent results and 1 GMM score for getting a fused result. The system makes the second stage recognition based on these fused results. The experiments show that the correct rate of two-stage recognition system based on MAP channel compensation technique and pitch-dependent features is 41.7% better than the baseline system for closed-set test.

Keywords: Channel Compensation, Channel Robustness, MAP, Speaker Identification

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Authors: María Fernández-Ropero, Ricardo Pérez-Castillo, Ismael Caballero, Mario Piattini

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Appropriate description of business processes through standard notations has become one of the most important assets for organizations. Organizations must therefore deal with quality faults in business process models such as the lack of understandability and modifiability. These quality faults may be exacerbated if business process models are mined by reverse engineering, e.g., from existing information systems that support those business processes. Hence, business process refactoring is often used, which change the internal structure of business processes whilst its external behavior is preserved. This paper aims to choose the most appropriate set of refactoring operators through the quality assessment concerning understandability and modifiability. These quality features are assessed through well-proven measures proposed in the literature. Additionally, a set of measure thresholds are heuristically established for applying the most promising refactoring operators, i.e., those that achieve the highest quality improvement according to the selected measures in each case.

Keywords: business process model, modifiability, refactoring, understandability

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Authors: Shima Soleimani, Steven Eckels

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One area of special importance for the surface-level study of heat exchangers is tubes with internal micro-fins (< 0.5 mm tall). Micro-finned surfaces are a kind of extended solid surface in which energy is exchanged with water that acts as the source or sink of energy. Significant performance gains are possible for either shell, tube, or double pipe heat exchangers if the best surfaces are identified. The parametric studies of micro-finned tubes that have appeared in the literature left some key parameters unexplored. Specifically, they ignored three-dimensional (3D) micro-fin configurations, conduction heat transfer in the fins, and conduction in the solid surface below the micro-fins. Thus, this study aimed at implementing a parametric study of 3D micro-finned tubes that considered micro-fine height and discontinuity features. A 3D conductive and convective heat-transfer simulation through coupled solid and periodic fluid domains is applied in a commercial package, ANSYS Fluent 19.1. The simulation is steady-state with turbulent water flow cooling the inner wall of a tube with micro-fins. The simulation utilizes a constant and uniform temperature on the tube outer wall. Performance is mapped for 18 different simulation cases, including a smooth tube using a realizable k-ε turbulence model at a Reynolds number of 48,928. Results compared the performance of 3D tubes with results for the similar two-dimensional (2D) one. Results showed that the micro-fine height has a greater impact on performance factors than discontinuity features in 3D micro-fin tubes. A transformed 3D micro-fin tube can enhance heat transfer, and pressure drops up to 21% and 56% compared to a 2D one, respectfully.

Keywords: Three-dimensional micro-fin tube, heat transfer, friction factor, heat exchanger.

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Authors: Songtao Wu, Yuesheng Zhu, Ziqiang Sun

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Region covariance (RC) descriptor is an effective and efficient feature for visual tracking. Current RC-based tracking algorithms use the whole RC matrix to track the target in video directly. However, there exist some issues for these whole RCbased algorithms. If some features are contaminated, the whole RC will become unreliable, which results in lost object-tracking. In addition, if some features are very discriminative to the background, other features are still processed and thus reduce the efficiency. In this paper a new robust tracking method is proposed, in which the whole RC matrix is decomposed into several low rank matrices. Those matrices are dynamically chosen and processed so as to achieve a good tradeoff between discriminability and complexity. Experimental results have shown that our method is more robust to complex environment changes, especially either when occlusion happens or when the background is similar to the target compared to other RC-based methods.

Keywords: Visual tracking, region covariance descriptor, lowrankregion covariance

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Authors: R. Ziaie Moayed, E. Khavaninzadeh, M. Ghorbani Tochaee

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Direct shear test is widely used in soil mechanics experiment to determine the shear strength parameters of granular soils. For analysis of soil stability problems such as bearing capacity, slope stability and lateral pressure on soil retaining structures, the shear strength parameters must be known well. In the present study, shear strength parameters are determined in silty-sand mixtures. Direct shear tests are performed on 161 Firoozkooh sand with different silt content at a relative density of 70% in three vertical stress of 100, 150, and 200 kPa. Wet tamping method is used for soil sample preparation, and the results include diagrams of shear stress versus shear deformation and sample height changes against shear deformation. Accordingly, in different silt percent, the shear strength parameters of the soil such as internal friction angle and dilation angle are calculated and compared. According to the results, when the sample contains up to 10% silt, peak shear strength and internal friction angle have an upward trend. However, if the sample contains 10% to 50% of silt a downward trend is seen in peak shear strength and internal friction angle.

Keywords: Shear strength parameters, direct shear test, silty sand, shear stress, shear deformation.

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Authors: W. Y. Leng, S. M. Shamsuddin

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Fingerprint based identification system; one of a well known biometric system in the area of pattern recognition and has always been under study through its important role in forensic science that could help government criminal justice community. In this paper, we proposed an identification framework of individuals by means of fingerprint. Different from the most conventional fingerprint identification frameworks the extracted Geometrical element features (GEFs) will go through a Discretization process. The intention of Discretization in this study is to attain individual unique features that could reflect the individual varianceness in order to discriminate one person from another. Previously, Discretization has been shown a particularly efficient identification on English handwriting with accuracy of 99.9% and on discrimination of twins- handwriting with accuracy of 98%. Due to its high discriminative power, this method is adopted into this framework as an independent based method to seek for the accuracy of fingerprint identification. Finally the experimental result shows that the accuracy rate of identification of the proposed system using Discretization is 100% for FVC2000, 93% for FVC2002 and 89.7% for FVC2004 which is much better than the conventional or the existing fingerprint identification system (72% for FVC2000, 26% for FVC2002 and 32.8% for FVC2004). The result indicates that Discretization approach manages to boost up the classification effectively, and therefore prove to be suitable for other biometric features besides handwriting and fingerprint.

Keywords: Discretization, fingerprint identification, geometrical features, pattern recognition

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Authors: Hendry Muljadi, Hideaki Takeda, Koichi Ando

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A manufacturing feature can be defined simply as a geometric shape and its manufacturing information to create the shape. In a feature-based process planning system, feature library that consists of pre-defined manufacturing features and the manufacturing information to create the shape of the features, plays an important role in the extraction of manufacturing features with their proper manufacturing information. However, to manage the manufacturing information flexibly, it is important to build a feature library that can be easily modified. In this paper, the implementation of Semantic Wiki for the development of the feature library is proposed.

Keywords: Manufacturing feature, feature library, feature ontology, process planning, Wiki, MediaWiki, Semantic Wiki.

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Authors: Zainah Abdullah, Aznur Hajar Abdullah

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This paper examines the impact of information and communication technology (ICT) usage, internal relationship, supplier-retailer relationship, logistics services and inventory management on convenience store suppliers- performance. Data was collected from 275 convenience store managers in Malaysia using a set of questionnaire. The multiple linear regression results indicate that inventory management, supplier-retailer relationship, logistics services and internal relationship are predictors of supplier performance as perceived by convenience store managers. However, ICT usage is not a predictor of supplier performance. The study focuses only on convenience stores and petrol station convenience stores and concentrates only on managers. The results provide insights to suppliers who serve convenience stores and possibly similar retail format on factors to consider in improving their service to retailers. The results also provide insights to government in its aspiration to improve business operations of convenience store to consider ways to enhance the adoption of ICT by retailers and suppliers.

Keywords: Information and communication technology (ICT), internal relationship, inventory management, logistics services, supplier performance, supplier-retailer relationship.

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Authors: M. V. Sudhamani, C. R. Venugopal

Abstract:

Content-based Image Retrieval (CBIR) aims at searching image databases for specific images that are similar to a given query image based on matching of features derived from the image content. This paper focuses on a low-dimensional color based indexing technique for achieving efficient and effective retrieval performance. In our approach, the color features are extracted using the mean shift algorithm, a robust clustering technique. Then the cluster (region) mode is used as representative of the image in 3-D color space. The feature descriptor consists of the representative color of a region and is indexed using a spatial indexing method that uses *R -tree thus avoiding the high-dimensional indexing problems associated with the traditional color histogram. Alternatively, the images in the database are clustered based on region feature similarity using Euclidian distance. Only representative (centroids) features of these clusters are indexed using *R -tree thus improving the efficiency. For similarity retrieval, each representative color in the query image or region is used independently to find regions containing that color. The results of these methods are compared. A JAVA based query engine supporting query-by- example is built to retrieve images by color.

Keywords: Content-based, indexing, cluster, region.

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Authors: B. Leela Devi, A. Sankar

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The web’s increased popularity has included a huge amount of information, due to which automated web page classification systems are essential to improve search engines’ performance. Web pages have many features like HTML or XML tags, hyperlinks, URLs and text contents which can be considered during an automated classification process. It is known that Webpage classification is enhanced by hyperlinks as it reflects Web page linkages. The aim of this study is to reduce the number of features to be used to improve the accuracy of the classification of web pages. In this paper, a novel feature selection method using an improved Particle Swarm Optimization (PSO) using principle of evolution is proposed. The extracted features were tested on the WebKB dataset using a parallel Neural Network to reduce the computational cost.

Keywords: Web page classification, WebKB Dataset, Term Frequency-Inverse Document Frequency (TF-IDF), Particle Swarm Optimization (PSO).

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Authors: A. Mohammadi Torkashvand, N. Haghighat

Abstract:

Some methodologies were compared in providing erosion maps of surface, rill and gully and erosion features, in research which took place in the Varamin sub-basin, north-east Tehran, Iran. A photomorphic unit map was produced from processed satellite images, and four other maps were prepared by the integration of different data layers, including slope, plant cover, geology, land use, rocks erodibility and land units. Comparison of ground truth maps of erosion types and working unit maps indicated that the integration of land use, land units and rocks erodibility layers with satellite image photomorphic units maps provide the best methods in producing erosion types maps.

Keywords: Erosion Features, Geographic Information System, Remote Sensing.

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Authors: Mangesh S. Deshpande, Raghunath S. Holambe

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Mel Frequency Cepstral Coefficient (MFCC) features are widely used as acoustic features for speech recognition as well as speaker recognition. In MFCC feature representation, the Mel frequency scale is used to get a high resolution in low frequency region, and a low resolution in high frequency region. This kind of processing is good for obtaining stable phonetic information, but not suitable for speaker features that are located in high frequency regions. The speaker individual information, which is non-uniformly distributed in the high frequencies, is equally important for speaker recognition. Based on this fact we proposed an admissible wavelet packet based filter structure for speaker identification. Multiresolution capabilities of wavelet packet transform are used to derive the new features. The proposed scheme differs from previous wavelet based works, mainly in designing the filter structure. Unlike others, the proposed filter structure does not follow Mel scale. The closed-set speaker identification experiments performed on the TIMIT database shows improved identification performance compared to other commonly used Mel scale based filter structures using wavelets.

Keywords: Speaker identification, Wavelet transform, Feature extraction, MFCC, GMM.

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Authors: Abdulaziz Abdulsaleh

Abstract:

This study aims at identifying the practices that should be taken into consideration by audit committees as a tool of corporate governance in Libyan commercial banks by investigating various perceptions on this topic. The study is based on a questionnaire submitted to audit committees ‘members at Libyan commercial banks, directors of internal audit departments as well as members of board of directors at these banks in addition to a number of external auditors and academic staff from Libyan universities. The study reveals that the role of audit committees has to be shifted from traditional areas of accounting to a broader role including functions related to financial reporting, audit planning, support the independence of internal and external auditors, acting as a channel of communication between external auditors and board of directors, reviewing external audit, and evaluating internal control systems. Although the study is a starting point in developing a framework of good audit committees’ practices in Libya, it is believed that the adoption of its results can result in enhancing the corporate governance practices not only in the banking sector but also in the entire corporate sector in Libya.

Keywords: Audit committees, Corporate Governance, Commercial Banks, Libya.

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Authors: Maisara Mohyeldin Gasim, A. K. Amirruddin, A. Shahrani

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This paper investigates the effect of replacing crankshaft with cam on the indicated torque during compression and power strokes in internal combustion engines. A Cycloidal cam profile was used in Revetec engine to calculate and compare the torque to a conventional engine, using a computational method. Firstly, the cylinder pressure was calculated using Ferguson equation, and then the torque calculated depending on cylinder pressure values in every crank angle. the results showed that by using Cycloidal cam profile in Revetec engine the torque can increased by 14% compared with conventional engines, which means an increase in engine efficiency.

Keywords: Revetec engine, indicated torque, cam profile.

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Authors: M. V. Sudhamani, C. R. Venugopal

Abstract:

This paper deals with the application for contentbased image retrieval to extract color feature from natural images stored in the image database by segmenting the image through clustering. We employ a class of nonparametric techniques in which the data points are regarded as samples from an unknown probability density. Explicit computation of the density is avoided by using the mean shift procedure, a robust clustering technique, which does not require prior knowledge of the number of clusters, and does not constrain the shape of the clusters. A non-parametric technique for the recovery of significant image features is presented and segmentation module is developed using the mean shift algorithm to segment each image. In these algorithms, the only user set parameter is the resolution of the analysis and either gray level or color images are accepted as inputs. Extensive experimental results illustrate excellent performance.

Keywords: Segmentation, Clustering, Image Retrieval, Features.

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Authors: Feifei Lee, Qiu Chen, Koji Kotani, Tadahiro Ohmi

Abstract:

In this paper, we propose an improved fast search algorithm using combined histogram features and temporal division method for short MPEG video clips from large video database. There are two types of histogram features used to generate more robust features. The first one is based on the adjacent pixel intensity difference quantization (APIDQ) algorithm, which had been reliably applied to human face recognition previously. An APIDQ histogram is utilized as the feature vector of the frame image. Another one is ordinal feature which is robust to color distortion. Combined with active search [4], a temporal pruning algorithm, fast and robust video search can be realized. The proposed search algorithm has been evaluated by 6 hours of video to search for given 200 MPEG video clips which each length is 30 seconds. Experimental results show the proposed algorithm can detect the similar video clip in merely 120ms, and Equal Error Rate (ERR) of 1% is achieved, which is more accurately and robust than conventional fast video search algorithm.

Keywords: Fast search, Adjacent pixel intensity differencequantization (APIDQ), DC image, Histogram feature.

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Authors: N. Ghatwary, A. Ahmed, H. Jalab

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In this paper, an approach for the liver tumor detection in computed tomography (CT) images is represented. The detection process is based on classifying the features of target liver cell to either tumor or non-tumor. Fractional differential (FD) is applied for enhancement of Liver CT images, with the aim of enhancing texture and edge features. Later on, a fusion method is applied to merge between the various enhanced images and produce a variety of feature improvement, which will increase the accuracy of classification. Each image is divided into NxN non-overlapping blocks, to extract the desired features. Support vector machines (SVM) classifier is trained later on a supplied dataset different from the tested one. Finally, the block cells are identified whether they are classified as tumor or not. Our approach is validated on a group of patients’ CT liver tumor datasets. The experiment results demonstrated the efficiency of detection in the proposed technique.

Keywords: Fractional differential (FD), Computed Tomography (CT), fusion.

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Authors: Mir Hadi Seyedafsari, Iraj Hasanzadeh

Abstract:

Like other external sorting algorithms, the presented algorithm is a two step algorithm including internal and external steps. The first part of the algorithm is like the other similar algorithms but second part of that is including a new easy implementing method which has reduced the vast number of inputoutput operations saliently. As decreasing processor operating time does not have any effect on main algorithm speed, any improvement in it should be done through decreasing the number of input-output operations. This paper propose an easy algorithm for choose the correct record location of the final list. This decreases the time complexity and makes the algorithm faster.

Keywords: External sorting algorithm, internal sortingalgorithm, fast sorting, robust algorithm.

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Authors: Lavanya Srinivasan

Abstract:

Biometric identification is to identify unique features in a person like fingerprints, iris, ear, and voice recognition that need the subject's permission and physical contact. Gait biometric is used to identify the unique gait of the person by extracting moving features. The main advantage of gait biometric to identify the gait of a person at a distance, without any physical contact. In this work, the gait biometric is used for person re-identification. The person walking naturally compared with the same person walking with bag, coat and case recorded using long wave infrared, short wave infrared, medium wave infrared and visible cameras. The videos are recorded in rural and in urban environments. The pre-processing technique includes human identified using You Only Look Once, background subtraction, silhouettes extraction and synthesis Gait Entropy Image by averaging the silhouettes. The moving features are extracted from the Gait Entropy Energy Image. The extracted features are dimensionality reduced by the Principal Component Analysis and recognized using different classifiers. The comparative results with the different classifier show that Linear Discriminant Analysis outperform other classifiers with 95.8% for visible in the rural dataset and 94.8% for longwave infrared in the urban dataset.

Keywords: biometric, gait, silhouettes, You Only Look Once

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Authors: Chan Weng Howe, Mohd Saberi Mohamad

Abstract:

Protein residue contact map is a compact representation of secondary structure of protein. Due to the information hold in the contact map, attentions from researchers in related field were drawn and plenty of works have been done throughout the past decade. Artificial intelligence approaches have been widely adapted in related works such as neural networks, genetic programming, and Hidden Markov model as well as support vector machine. However, the performance of the prediction was not generalized which probably depends on the data used to train and generate the prediction model. This situation shown the importance of the features or information used in affecting the prediction performance. In this research, support vector machine was used to predict protein residue contact map on different combination of features in order to show and analyze the effectiveness of the features.

Keywords: contact map, protein residue contact, support vector machine, protein structure prediction

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Authors: Baljit Singh Khehra, Amar Partap Singh Pharwaha

Abstract:

Clusters of Microcalcifications (MCCs) are most frequent symptoms of Ductal Carcinoma in Situ (DCIS) recognized by mammography. Least-Square Support Vector Machine (LS-SVM) is a variant of the standard SVM. In the paper, LS-SVM is proposed as a classifier for classifying MCCs as benign or malignant based on relevant extracted features from enhanced mammogram. To establish the credibility of LS-SVM classifier for classifying MCCs, a comparative evaluation of the relative performance of LS-SVM classifier for different kernel functions is made. For comparative evaluation, confusion matrix and ROC analysis are used. Experiments are performed on data extracted from mammogram images of DDSM database. A total of 380 suspicious areas are collected, which contain 235 malignant and 145 benign samples, from mammogram images of DDSM database. A set of 50 features is calculated for each suspicious area. After this, an optimal subset of 23 most suitable features is selected from 50 features by Particle Swarm Optimization (PSO). The results of proposed study are quite promising.

Keywords: Clusters of Microcalcifications, Ductal Carcinoma in Situ, Least-Square Support Vector Machine, Particle Swarm Optimization.

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Authors: Essam Al-Daoud

Abstract:

A New features are extracted and compared to improve the prediction of protein-protein interactions. The basic idea is to select and use the best set of features from the Tensor matrices that are produced by the frequency vectors of the protein sequences. Three set of features are compared, the first set is based on the indices that are the most common in the interacting proteins, the second set is based on the indices that tend to be common in the interacting and non-interacting proteins, and the third set is constructed by using random indices. Moreover, three encoding strategies are compared; that are based on the amino asides polarity, structure, and chemical properties. The experimental results indicate that the highest accuracy can be obtained by using random indices with chemical properties encoding strategy and support vector machine.

Keywords: protein-protein interactions, random indices, encoding strategies, support vector machine.

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Authors: N. Valliammal, S.N. Geethalakshmi

Abstract:

This paper describes an optimal approach for feature subset selection to classify the leaves based on Genetic Algorithm (GA) and Kernel Based Principle Component Analysis (KPCA). Due to high complexity in the selection of the optimal features, the classification has become a critical task to analyse the leaf image data. Initially the shape, texture and colour features are extracted from the leaf images. These extracted features are optimized through the separate functioning of GA and KPCA. This approach performs an intersection operation over the subsets obtained from the optimization process. Finally, the most common matching subset is forwarded to train the Support Vector Machine (SVM). Our experimental results successfully prove that the application of GA and KPCA for feature subset selection using SVM as a classifier is computationally effective and improves the accuracy of the classifier.

Keywords: Optimization, Feature extraction, Feature subset, Classification, GA, KPCA, SVM and Computation

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Authors: M.Ghazvini, S. A. Monadjemi, N. Movahhedinia, K. Jamshidi

Abstract:

In this article, a method has been offered to classify normal and defective tiles using wavelet transform and artificial neural networks. The proposed algorithm calculates max and min medians as well as the standard deviation and average of detail images obtained from wavelet filters, then comes by feature vectors and attempts to classify the given tile using a Perceptron neural network with a single hidden layer. In this study along with the proposal of using median of optimum points as the basic feature and its comparison with the rest of the statistical features in the wavelet field, the relational advantages of Haar wavelet is investigated. This method has been experimented on a number of various tile designs and in average, it has been valid for over 90% of the cases. Amongst the other advantages, high speed and low calculating load are prominent.

Keywords: Defect detection, tile and ceramic quality inspection, wavelet transform, classification, neural networks, statistical features.

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Authors: Tomyslav Sledevič, Artūras Serackis, Gintautas Tamulevičius, Dalius Navakauskas

Abstract:

Paper presents an comparative evaluation of features extraction algorithm for a real-time isolated word recognition system based on FPGA. The Mel-frequency cepstral, linear frequency cepstral, linear predictive and their cepstral coefficients were implemented in hardware/software design. The proposed system was investigated in speaker dependent mode for 100 different Lithuanian words. The robustness of features extraction algorithms was tested recognizing the speech records at different signal to noise rates. The experiments on clean records show highest accuracy for Mel-frequency cepstral and linear frequency cepstral coefficients. For records with 15 dB signal to noise rate the linear predictive cepstral coefficients gives best result. The hard and soft part of the system is clocked on 50 MHz and 100 MHz accordingly. For the classification purpose the pipelined dynamic time warping core was implemented. The proposed word recognition system satisfy the real-time requirements and is suitable for applications in embedded systems.

Keywords: Isolated word recognition, features extraction, MFCC, LFCC, LPCC, LPC, FPGA, DTW.

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