Search results for: Image classification.

Authors: Seung You Na, Joo Hyun Jung, Jin Young Kim, Mohammad AhangarKiasari

Abstract:

There are various kinds of medical equipment which requires relatively accurate positional adjustments for successful treatment. However, patients tend to move without notice during a certain span of operations. Therefore, it is common practice that accompanying operators adjust the focus of the equipment. In this paper, tracking controllers for medical equipment are suggested to replace the operators. The tracking controllers use AHRS sensor information to recognize the movements of patients. Sensor fusion is applied to reducing the error magnitudes through linear Kalman filters. The image processing of optical markers is included to adjust the accumulation errors of gyroscope sensor data especially for yaw angles. The tracking controller reduces the positional errors between the current focus of a device and the target position on the body of a patient. Since the sensing frequencies of AHRS sensors are very high compared to the physical movements, the control performance is satisfactory. The typical applications are, for example, ESWT or rTMS, which have the error ranges of a few centimeters.

Keywords: AHRS, Sensor fusion, Tracking control, Position and posture.

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Authors: K. Bommanna Raja, M.Madheswaran, K.Thyagarajah

Abstract:

A higher order spline interpolated contour obtained with up-sampling of homogenously distributed coordinates for segmentation of kidney region in different classes of ultrasound kidney images has been developed and presented in this paper. The performance of the proposed method is measured and compared with modified snake model contour, Markov random field contour and expert outlined contour. The validation of the method is made in correspondence with expert outlined contour using maximum coordinate distance, Hausdorff distance and mean radial distance metrics. The results obtained reveal that proposed scheme provides optimum contour that agrees well with expert outlined contour. Moreover this technique helps to preserve the pixels-of-interest which in specific defines the functional characteristic of kidney. This explores various possibilities in implementing computer-aided diagnosis system exclusively for US kidney images.

Keywords: Ultrasound Kidney Image – Kidney Segmentation –Active Contour – Markov Random Field – Higher Order SplineInterpolation

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Authors: Yuan Zheng

Abstract:

3D model-based vehicle matching provides a new way for vehicle recognition, localization and tracking. Its key is to construct an evaluation function, also called fitness function, to measure the degree of vehicle matching. The existing fitness functions often poorly perform when the clutter and occlusion exist in traffic scenarios. In this paper, we present a practical and efficient fitness function. Unlike the existing evaluation functions, the proposed fitness function is to study the vehicle matching problem from both local and global perspectives, which exploits the pixel gradient information as well as the silhouette information. In view of the discrepancy between 3D vehicle model and real vehicle, a weighting strategy is introduced to differently treat the fitting of the model’s wireframes. Additionally, a normalization operation for the model’s projection is performed to improve the accuracy of the matching. Experimental results on real traffic videos reveal that the proposed fitness function is efficient and robust to the cluttered background and partial occlusion.

Keywords: 3D-2D matching, fitness function, 3D vehicle model, local image gradient, silhouette information.

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Authors: Sanath Kumar Shetty, Sanya Sinha, K. Kamalakanth Shenoy

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The posterior reference for the ala tragal line is a cause of confusion, with different authors suggesting different locations as to the superior, middle or inferior part of the tragus. This study was conducted on 200 subjects to evaluate if any correlation exists between the variation of angulation of palatal throat form and the relative parallelism of occlusal plane to ala-tragal line at different tragal levels. A custom made Occlusal Plane Analyzer was used to check the parallelism between the ala-tragal line and occlusal plane. A lateral cephalogram was shot for each subject to measure the angulation of the palatal throat form. Fisher’s exact test was used to evaluate the correlation between the angulation of the palatal throat form and the relative parallelism of occlusal plane to the ala tragal line. Also, a classification was formulated for the palatal throat form, based on confidence interval. From the results of the study, the inferior part, middle part and superior part of the tragus were seen as the reference points in 49.5%, 32% and 18.5% of the subjects respectively. Class I palatal throat form (41degree-50 degree), Class II palatal throat form (below 41 degree) and Class III palatal throat form (above 50 degree) were seen in 42%, 43% and 15% of the subjects respectively. It was also concluded that there is no significant correlation between the variation in the angulations of the palatal throat form and the relative parallelism of occlusal plane to the ala-tragal line.

Keywords: Ala-tragal line, occlusal plane, palatal throat form, cephalometry.

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Authors: C. Caçador, M. J. Reis Lima, J. Oliveira, M. J. Veiga, M. Teixeira Veríssimo, F. Ramos, M. C. Castilho, E. Teixeira-Lemos

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In the present study we evaluated the nutritional status of 214 institutionalized elderly residents of both genders, aged 65 years and older of 11 care homes located in the district of Viseu (center of Portugal). The evaluation was based on anthropometric measurements and the Mini Nutritional Assessment (MNA) score.

The mean age of the subjects was 82.3 ± 6.1 years-old. Most of the elderly residents were female (72.0%). The majority had 4 years of formal education (51.9%) and was widowed (74.3%) or married (14.0%).

Men presented a mean age of 81.2±8.5 years-old, weight 69.3±14.5 kg and BMI 25.33±6.5 kg/m². In women, the mean age was 84.5±8.2 years-old, weight 61.2±14.7 kg and BMI 27.43±5.6 kg/m².

The evaluation of the nutritional status using the MNA score showed that 24.0% of the residents show a risk of undernutrition and 76.0% of them were well nourished.

There was a high prevalence of obese (24.8%) and overweight residents (33.2%) according to the BMI. 7.5% were considered underweight.

We also found that according to their waist circumference measurements 88.3% of the residents were at risk for cardiovascular disease (CVD) and 64.0% of them presented very high risk for CVD (WC≥88 cm for women and WC ≥102 cm for men).

The present study revealed the coexistence of a dual form of malnutrition (undernourished and overweight) among the institutionalized Portuguese concomitantly with an excess of abdominal adiposity. The high prevalence of residents at high risk for CVD should not be overlooked.

Given the vulnerability of the group of institutionalized elderly, our study highlights the importance of the classification of nutritional status based on both instruments: the BMI and the MNA.

Keywords: Nutritional status, MNA, BMI, elderly.

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Authors: Marcell S. A. Martins, Benedito S. R. Neto, Gerson L. Serejo, Carlos G. R. Santos

Abstract:

Indoor positioning systems use sensors such as Bluetooth, ZigBee, and Wi-Fi, as well as cameras for image capture, which can be fixed or mobile. These computer vision-based positioning approaches are low-cost to implement, mainly when it uses a mobile camera. The present study aims to create a design of a fiducial marker for a low-cost indoor localization system. The marker is tape-shaped to perform a continuous reading employing two detection algorithms, one for greater distances and another for smaller distances. Therefore, the location service is always operational, even with variations in capture distance. A minimal localization and reading algorithm was implemented for the proposed marker design, aiming to validate it. The accuracy tests consider readings varying the capture distance between [0.5, 10] meters, comparing the proposed marker with others. The tests showed that the proposed marker has a broader capture range than the ArUco and QRCode, maintaining the same size. Therefore, reducing the visual pollution and maximizing the tracking since the ambient can be covered entirely.

Keywords: Multiscale recognition, indoor localization, tape-shaped marker, Fiducial Marker.

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Authors: A. Bumbová, M. Čáslavský, F. Božek, J. Dvořák, E. Bakoš

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The paper presents the case study of hazard identification and sensitivity of potential resource of emergency water supply as part of the application of methodology classifying the resources of drinking water for emergency supply of population. The case study has been carried out on a selected resource of emergency water supply in one region of the Czech Republic. The hazard identification and sensitivity of potential resource of emergency water supply is based on a unique procedure and developed general registers of selected types of hazards and sensitivities. The registers have been developed with the help of the “Fault Tree Analysis” method in combination with the “What if method”. The identified hazards for the assessed resource include hailstorms and torrential rains, drought, soil erosion, accidents of farm machinery, and agricultural production. The developed registers of hazards and vulnerabilities and a semi-quantitative assessment of hazards for individual parts of hydrological structure and technological elements of presented drilled wells are the basis for a semi-quantitative risk assessment of potential resource of emergency supply of population and the subsequent classification of such resource within the system of crisis planning.

Keywords: Hazard identification, register of hazards, sensitivity identification, register of sensitivity, emergency water supply, state of crisis, resource of emergency water supply, ground water.

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Authors: Javad Rahimipour Anaraki, Saeed Samet, Mahdi Eftekhari, Chang Wook Ahn

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Feature selection and attribute reduction are crucial problems, and widely used techniques in the field of machine learning, data mining and pattern recognition to overcome the well-known phenomenon of the Curse of Dimensionality. This paper presents a feature selection method that efficiently carries out attribute reduction, thereby selecting the most informative features of a dataset. It consists of two components: 1) a measure for feature subset evaluation, and 2) a search strategy. For the evaluation measure, we have employed the fuzzy-rough dependency degree (FRFDD) of the lower approximation-based fuzzy-rough feature selection (L-FRFS) due to its effectiveness in feature selection. As for the search strategy, a modified version of a binary shuffled frog leaping algorithm is proposed (B-SFLA). The proposed feature selection method is obtained by hybridizing the B-SFLA with the FRDD. Nine classifiers have been employed to compare the proposed approach with several existing methods over twenty two datasets, including nine high dimensional and large ones, from the UCI repository. The experimental results demonstrate that the B-SFLA approach significantly outperforms other metaheuristic methods in terms of the number of selected features and the classification accuracy.

Keywords: Binary shuffled frog leaping algorithm, feature selection, fuzzy-rough set, minimal reduct.

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Authors: Seongcheol Kwon, Jeongmin Kim, Kwang Ryel Ryu

Abstract:

We assume an IoT-based smart-home environment where the on-off status of each of the electrical appliances including the room lights can be recognized in a real time by monitoring and analyzing the smart meter data. At any moment in such an environment, we can recognize what the household or the user is doing by referring to the status data of the appliances. In this paper, we focus on a smart-home service that is to activate a robot vacuum cleaner at right time by recognizing the user situation, which requires a situation-aware model that can distinguish the situations that allow vacuum cleaning (Yes) from those that do not (No). We learn as our candidate models a few classifiers such as naïve Bayes, decision tree, and logistic regression that can map the appliance-status data into Yes and No situations. Our training and test data are obtained from simulations of user behaviors, in which a sequence of user situations such as cooking, eating, dish washing, and so on is generated with the status of the relevant appliances changed in accordance with the situation changes. During the simulation, both the situation transition and the resulting appliance status are determined stochastically. To compare the performances of the aforementioned classifiers we obtain their learning curves for different types of users through simulations. The result of our empirical study reveals that naïve Bayes achieves a slightly better classification accuracy than the other compared classifiers.

Keywords: Situation-awareness, Smart home, IoT, Machine learning, Classifier.

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Authors: Tung-Liang Chen, Ming - Yi Huang, Tchiu-Hui Su

Abstract:

This study investigates the relationships between Work Motivation, Work Stress, and Job Satisfaction toward cross-strait employees. The target subjects are three manufacturing firms in Mainland China and Taiwan. Out of 450 distributed surveys, 352 valid surveys were obtained with the response rate of 78.22%.The findings have addressed three main pull factors toward cross-strait employees in choosing jobs, which are (1) high level of firm stability, (2) good firm image, and (3) good employee benefits. In addition, various employee attributes exert different impacts on Work Motivation, Work Stress, and Job Satisfaction. The comparison between expected and actual perceived Job Satisfaction toward cross-strait employees shows that “salary" ranks highest regarding expected Job Satisfaction whereas “co-worker relationship" ranks highest regarding actual perceived Job Satisfaction, which implies actual perceived Job Satisfaction do not match employee expectations. Therefore, this research further concludes that there exists differences between employees- expected and actual perceived Job Satisfaction.

Keywords: Cross-strait, job satisfaction, work motivation, work stress

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Authors: G. M. Ozkan, H. Akilli

Abstract:

The aim of present study is to control the unsteady flow structure downstream of a circular cylinder by use of attached permeable plates. Particle image velocimetry (PIV) technique and dye visualization experiments were performed in deep water and the flow characteristics were evaluated by means of time-averaged streamlines, Reynolds Shear Stress and Turbulent Kinetic Energy concentrations. The permeable plate was made of a chrome-nickel screen having a porosity value of β=0.6 and it was attached on the cylinder surface along its midspan. Five different angles were given to the plate (θ=0^o, 15^o, 30^o, 45^o, 60^o) with respect to the centerline of the cylinder in order to examine its effect on the flow control. It was shown that the permeable plate is effective on elongating the vortex formation length and reducing the fluctuations in the wake region. Compared to the plain cylinder, the reductions in the values of maximum Reynolds shear stress and Turbulent Kinetic Energy were evaluated as 72.5% and 66%, respectively for the plate angles of θ=45^oand 60^o which were also found to be suggested for applications concerning the vortex shedding and consequent Vortex-Induced Vibrations.

Keywords: Bluff body, flow control, permeable plate, PIV, VIV, vortex shedding.

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Authors: Jin-Tae Lee, Jung-Hoon Suh, Sang-Soo Jang, Jae-Il Lee

Abstract:

Information and communication service providers (ICSP) that are significant in size and provide Internet-based services take administrative, technical, and physical protection measures via the information security check service (ISCS). These protection measures are the minimum action necessary to secure the stability and continuity of the information and communication services (ICS) that they provide. Thus, information assets are essential to providing ICS, and deciding the relative importance of target assets for protection is a critical procedure. The risk analysis model designed to decide the relative importance of information assets, which is described in this study, evaluates information assets from many angles, in order to choose which ones should be given priority when it comes to protection. Many-sided risk analysis (MSRS) grades the importance of information assets, based on evaluation of major security check items, evaluation of the dependency on the information and communication facility (ICF) and influence on potential incidents, and evaluation of major items according to their service classification, in order to identify the ISCS target. MSRS could be an efficient risk analysis model to help ICSPs to identify their core information assets and take information protection measures first, so that stability of the ICS can be ensured.

Keywords: Information Asset, Information CommunicationFacility, Evaluation, ISCS (Information Security Check Service), Evaluation, Grade.

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Authors: Hyun June Kim, Tailong Shi, Seden Akdagli, Sam Most, Yuling Yan

Abstract:

Quantitative analyses of whisker movements provide a means to study functional recovery and regeneration of mouse facial nerve after an injury. However, accurate tracking of the mouse whisker movement is challenging. Most methods for whisker tracking require manual intervention, e.g. fixing the head of the mouse during a study. Here we describe a semi-automated image processing method, which is applied to high-speed video recordings of free-moving mice to track the whisker movements. We first track the head movement of a mouse by delineating the lower head contour frame-by-frame that allows for detection of the location and orientation of the head. Then, a region of interest is identified for each frame; the subsequent application of a mask and the Hough transform detects the selected whiskers on each side of the head. Our approach is used to examine the functional recovery of damaged facial nerves in mice over a course of 21 days.

Keywords: Mystacial macrovibrissae, whisker tracking, head tracking, facial nerve recovery.

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Authors: I. Yalcin Enis, J. Vojtech, T. Gok Sadikoglu

Abstract:

In this study, polycaprolactone (PCL) was dissolved in chloroform:ethanol solvent system at a concentration of 18 w/v %. 1, 2, 4, and 6 droplets of formic acid were added to the prepared 10ml PCL-chloroform:ethanol solutions separately. Fibrous webs were produced by electrospinning technique based on the horizontal working principle. Morphology of the webs was investigated by using scanning electron microscopy (SEM) whereas fiber diameters were measured by Image J Software System. The effect of formic acid addition to the mostly used chloroform solvent on fiber morphology was examined. Results indicate that there is a distinct fall in fiber diameter with the addition of formic acid drops. The average fiber diameter was measured as 2.22μm in PCL /chloroform:ethanol solution system. On the other hand, 328nm and 256 nm average fiber diameters were measured for the samples of 4 drops and 6 drops formic acid added. This study offers alternative solvent systems to produce nanoscaled, nontoxic PCL fibrous webs by electrospinning technique.

Keywords: Chloroform, electrospinning, formic acid polycaprolactone.

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Authors: Dimitrios Zarpalas, Anastasios Zafeiropoulos, Petros Daras, Nicos Maglaveras

Abstract:

Segmentation techniques based on Active Contour Models have been strongly benefited from the use of prior information during their evolution. Shape prior information is captured from a training set and is introduced in the optimization procedure to restrict the evolution into allowable shapes. In this way, the evolution converges onto regions even with weak boundaries. Although significant effort has been devoted on different ways of capturing and analyzing prior information, very little thought has been devoted on the way of combining image information with prior information. This paper focuses on a more natural way of incorporating the prior information in the level set framework. For proof of concept the method is applied on hippocampus segmentation in T1-MR images. Hippocampus segmentation is a very challenging task, due to the multivariate surrounding region and the missing boundary with the neighboring amygdala, whose intensities are identical. The proposed method, mimics the human segmentation way and thus shows enhancements in the segmentation accuracy.

Keywords: Medical imaging & processing, Brain MRI segmentation, hippocampus segmentation, hippocampus-amygdala missingboundary, weak boundary segmentation, region based segmentation, prior information, local weighting scheme in level sets, spatialdistribution of labels, gradient distribution on boundary.

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Authors: Bireswar Paul, Amitava Datta

Abstract:

Indoor air environment is a big concern in the last few decades in the developing countries, with increased focus on monitoring the air quality. In this work, an experimental study has been conducted to establish the existence of carbon nanoparticles below the size range of 10 nm in the non-sooting zone of a LPG/air partially premixed flame. Mainly, four optical techniques, UV absorption spectroscopy, fluorescence spectroscopy, dynamic light scattering and TEM have been used to characterize and measure the size of carbon nanoparticles in the sampled materials collected from the inner surface of the flame front. The existence of the carbon nanoparticles in the sampled material has been confirmed with the typical nature of the absorption and fluorescence spectra already reported in the literature. The band gap energy shows that the particles are made up of three to six aromatic rings. The size measurement by DLS technique also shows that the particles below the size range of 10 nm. The results of DLS are also corroborated by the TEM image of the same material. 

Keywords: Indoor air, carbon nanoparticles, LPG, partially premixed flame, optical techniques.

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Authors: V. Emery David Jr., J. Wenchao, D. Mmereki, Y. John, F. Heriniaina

Abstract:

Healthcare waste management continues to present an array of challenges for developing countries, and Liberia is of no exception. There is insufficient information available regarding the generation, handling, and disposal of health care waste. This face serves as an impediment to healthcare management schemes. The specific objective of this study is to present an evaluation of the current health care management practices in Liberia. It also presented procedures, techniques used, methods of handling, transportation, and disposal methods of wastes as well as the quantity and composition of health care waste. This study was conducted as an investigative case study, covering three different health care facilities; a hospital, a health center, and a clinic in Monrovia, Montserrado County. The average waste generation was found to be 0-7kg per day at the clinic and health center and 8-15kg per/day at the hospital. The composition of the waste includes hazardous and non-hazardous waste i.e. plastic, papers, sharps, and pathological elements etc. Nevertheless, the investigation showed that the healthcare waste generated by the surveyed healthcare facilities were not properly handled because of insufficient guidelines for separate collection, and classification, and adequate methods for storage and proper disposal of generated wastes. This therefore indicates that there is a need for improvement within the healthcare waste management system to improve the existing situation.

Keywords: Disposal, Healthcare waste, management, Montserrado County, Monrovia.

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Authors: Ahmad R. Naghsh-Nilchi, Mohammad Roshanzamir

Abstract:

One of the popular methods for recognition of facial expressions such as happiness, sadness and surprise is based on deformation of facial features. Motion vectors which show these deformations can be specified by the optical flow. In this method, for detecting emotions, the resulted set of motion vectors are compared with standard deformation template that caused by facial expressions. In this paper, a new method is introduced to compute the quantity of likeness in order to make decision based on the importance of obtained vectors from an optical flow approach. For finding the vectors, one of the efficient optical flow method developed by Gautama and VanHulle[17] is used. The suggested method has been examined over Cohn-Kanade AU-Coded Facial Expression Database, one of the most comprehensive collections of test images available. The experimental results show that our method could correctly recognize the facial expressions in 94% of case studies. The results also show that only a few number of image frames (three frames) are sufficient to detect facial expressions with rate of success of about 83.3%. This is a significant improvement over the available methods.

Keywords: Facial expression, Facial features, Optical flow, Motion vectors.

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Authors: Lewis E. Hibell, Honghai Liu, David J. Brown

Abstract:

This paper presents the results of enhancing images from a left and right stereo pair in order to increase the resolution of a 3D representation of a scene generated from that same pair. A new neural network structure known as a Self Delaying Dynamic Network (SDN) has been used to perform the enhancement. The advantage of SDNs over existing techniques such as bicubic interpolation is their ability to cope with motion and noise effects. SDNs are used to generate two high resolution images, one based on frames taken from the left view of the subject, and one based on the frames from the right. This new high resolution stereo pair is then processed by a disparity map generator. The disparity map generated is compared to two other disparity maps generated from the same scene. The first is a map generated from an original high resolution stereo pair and the second is a map generated using a stereo pair which has been enhanced using bicubic interpolation. The maps generated using the SDN enhanced pairs match more closely the target maps. The addition of extra noise into the input images is less problematic for the SDN system which is still able to out perform bicubic interpolation.

Keywords: Genetic Evolution, Image Enhancement, Neuron Networks, Stereo Vision

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Authors: A. Bahar Ganiyeva, M. Sabuhi Tanriverdiyev

Abstract:

The article analyzes state support and policy mechanisms aimed at driving tourism as one of the vibrant and rapidly developing economies. State programs and long-range strategic roadmaps and previous programs execution, results and their impact on the particular countries economy have been raised during the research. This theme provides a useful framework for discussions with a wider range of stakeholders as the implications arising are of importance both for academics and practitioners engaged in hospitality and tourism development and research. The impact that tourism has on sustainable regional development in emerging markets is highly substantial. For Azerbaijan, Turkey, Georgia, and Russia, with their rich natural resources and cultural heritage, tourism can be an important basis for economic expansion, and a way to form an acceptable image of the countries as safe, open, hospitable, and complex.

Keywords: Sustainable tourism, hospitality, destination, strategic roadmap, tourism, economy, growth, state support, mechanism, policy formation, state program.

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Authors: Morteza Zahedi, Philippe Dreuw, Thomas Deselaers, Hermann Ney

Abstract:

Sign language is used by the deaf and hard of hearing people for communication. Automatic sign language recognition is a challenging research area since sign language often is the only way of communication for the deaf people. Sign language includes different components of visual actions made by the signer using the hands, the face, and the torso, to convey his/her meaning. To use different aspects of signs, we combine the different groups of features which have been extracted from the image frames recorded directly by a stationary camera. We combine the features in two levels by employing three techniques. At the feature level, an early feature combination can be performed by concatenating and weighting different feature groups, or by concatenating feature groups over time and using LDA to choose the most discriminant elements. At the model level, a late fusion of differently trained models can be carried out by a log-linear model combination. In this paper, we investigate these three combination techniques in an automatic sign language recognition system and show that the recognition rate can be significantly improved.

Keywords: American sign language, appearance-based features, Feature combination, Sign language recognition

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Authors: Angelica S. Valeriani, Lorenzo Biasiolo

Abstract:

In recent years, the scale of the impact of climate change and its related side effects has become ever more massive and devastating. Sustainable Development Goals (SDGs), promoted by United Nations, aim to front issues related to climate change, among others. In particular, the project CROWD4SDG focuses on a bunch of SDGs, since it promotes environmental activities and climate-related issues. In this context, we developed a prototype of an application, under advanced development considering web design, that focuses on SDG 13 (SDG on climate action) by providing users with useful instruments to face environmental crises and climate-related disasters. Our prototype is thought and structured for both web and mobile development. The main goal of the application, POLI4SDG, is to help users to get through emergency services. To this extent, an organized overview and classification prove to be very effective and helpful to people in need. A careful analysis of data related to environmental crises prompted us to integrate the user contribution, i.e. exploiting a core principle of Citizen Science, into the realization of a public catalog, available for consulting and organized according to typology and specific features. In addition, gender equality and opportunity features are considered in the prototype, in order to allow women, often the most vulnerable category, to have direct support. The overall description of the application functionalities is detailed. Moreover, implementation features and properties of the prototype are discussed.

Keywords: Crowdsourcing, social media, SDG, climate change, natural disasters, gender equality.

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Authors: Kamal Amarouche, Houda Benbrahim, Ismail Kassou

Abstract:

Nowadays, e-commerce shopping websites have experienced noticeable growth. These websites have gained consumers’ trust. After purchasing a product, many consumers share comments where opinions are usually embedded about the given product. Research on the automatic management of opinions that gives suggestions to potential consumers and portrays an image of the product to manufactures has been growing recently. After launching the product in the market, the reviews generated around it do not usually contain helpful information or generic opinions about this product (e.g. telephone: great phone...); in the sense that the product is still in the launching phase in the market. Within time, the product becomes old. Therefore, consumers perceive the advantages/ disadvantages about each specific product feature. Therefore, they will generate comments that contain their sentiments about these features. In this paper, we present an unsupervised method to extract different product features hidden in the opinions which influence its purchase, and that combines Time Weighting (TW) which depends on the time opinions were expressed with Term Frequency-Inverse Document Frequency (TF-IDF). We conduct several experiments using two different datasets about cell phones and hotels. The results show the effectiveness of our automatic feature extraction, as well as its domain independent characteristic.

Keywords: Opinion mining, product feature extraction, sentiment analysis, SentiWordNet.

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Authors: Fattah Alizadeh, Saeed Nalousi, Chiman Savari

Abstract:

Because of increasing demands for security in today-s society and also due to paying much more attention to machine vision, biometric researches, pattern recognition and data retrieval in color images, face detection has got more application. In this article we present a scientific approach for modeling human skin color, and also offer an algorithm that tries to detect faces within color images by combination of skin features and determined threshold in the model. Proposed model is based on statistical data in different color spaces. Offered algorithm, using some specified color threshold, first, divides image pixels into two groups: skin pixel group and non-skin pixel group and then based on some geometric features of face decides which area belongs to face. Two main results that we received from this research are as follow: first, proposed model can be applied easily on different databases and color spaces to establish proper threshold. Second, our algorithm can adapt itself with runtime condition and its results demonstrate desirable progress in comparison with similar cases.

Keywords: face detection, skin color modeling, color, colorfulimages, face recognition.

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Authors: Mallikarjuna Rao Gundavarapu, Ch. Mallikarjuna Rao, K. Anuradha Bai

Abstract:

Real Time Video Tracking is a challenging task for computing professionals. The performance of video tracking techniques is greatly affected by background detection and elimination process. Local regions of the image frame contain vital information of background and foreground. However, pixel-level processing of local regions consumes a good amount of computational time and memory space by traditional approaches. In our approach we have explored the concurrent computational ability of General Purpose Graphic Processing Units (GPGPU) to address this problem. The Gaussian Mixture Model (GMM) with adaptive weighted kernels is used for detecting the background. The weights of the kernel are influenced by local regions and are updated by inter-frame variations of these corresponding regions. The proposed system has been tested with GPU devices such as GeForce GTX 280, GeForce GTX 280 and Quadro K2000. The results are encouraging with maximum speed up 10X compared to sequential approach.

Keywords: Connected components, Embrace threads, Local weighted kernel, Structuring element.

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Authors: Mahmoud Elmezain, Samar El-shinawy

Abstract:

Many approaches to pattern recognition are founded on probability theory, and can be broadly characterized as either generative or discriminative according to whether or not the distribution of the image features. Generative and discriminative models have very different characteristics, as well as complementary strengths and weaknesses. In this paper, we study these models to recognize the patterns of alphabet characters (A-Z) and numbers (0-9). To handle isolated pattern, generative model as Hidden Markov Model (HMM) and discriminative models like Conditional Random Field (CRF), Hidden Conditional Random Field (HCRF) and Latent-Dynamic Conditional Random Field (LDCRF) with different number of window size are applied on extracted pattern features. The gesture recognition rate is improved initially as the window size increase, but degrades as window size increase further. Experimental results show that the LDCRF is the best in terms of results than CRF, HCRF and HMM at window size equal 4. Additionally, our results show that; an overall recognition rates are 91.52%, 95.28%, 96.94% and 98.05% for CRF, HCRF, HMM and LDCRF respectively.

Keywords: Statistical Pattern Recognition, Generative Model, Discriminative Model, Human Computer Interaction.

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Authors: Rujijan Vichivanives, Kittiya Poonsilp, Canasanan Wanavijit

Abstract:

This research aims to create mobile tools to analyze rice disease quickly and easily. The principle of object-oriented software engineering and objective-C language were used for software development methodology and the principle of decision tree technique was used for analysis method. Application users can select the features of rice disease or the color appears on the rice leaves for recognition analysis results on iOS mobile screen. After completing the software development, unit testing and integrating testing method were used to check for program validity. In addition, three plant experts and forty farmers have been assessed for usability and benefit of this system. The overall of users’ satisfaction was found in a good level, 57%. The plant experts give a comment on the addition of various disease symptoms in the database for more precise results of the analysis. For further research, it is suggested that image processing system should be developed as a tool that allows users search and analyze for rice diseases more convenient with great accuracy.

Keywords: Rice disease, analysis system, mobile application, iOS operating system.

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Authors: Rodolfo Lorbieski, Silvia Modesto Nassar

Abstract:

Many supervised machine learning tasks require decision making across numerous different classes. Multi-class classification has several applications, such as face recognition, text recognition and medical diagnostics. The objective of this article is to analyze an adapted method of Stacking in multi-class problems, which combines ensembles within the ensemble itself. For this purpose, a training similar to Stacking was used, but with three levels, where the final decision-maker (level 2) performs its training by combining outputs from the tree-based pair of meta-classifiers (level 1) from Bayesian families. These are in turn trained by pairs of base classifiers (level 0) of the same family. This strategy seeks to promote diversity among the ensembles forming the meta-classifier level 2. Three performance measures were used: (1) accuracy, (2) area under the ROC curve, and (3) time for three factors: (a) datasets, (b) experiments and (c) levels. To compare the factors, ANOVA three-way test was executed for each performance measure, considering 5 datasets by 25 experiments by 3 levels. A triple interaction between factors was observed only in time. The accuracy and area under the ROC curve presented similar results, showing a double interaction between level and experiment, as well as for the dataset factor. It was concluded that level 2 had an average performance above the other levels and that the proposed method is especially efficient for multi-class problems when compared to binary problems.

Keywords: Stacking, multi-layers, ensemble, multi-class.

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Authors: Mohd. Noor Md. Sap, A. Majid Awan

Abstract:

Intelligent systems based on machine learning techniques, such as classification, clustering, are gaining wide spread popularity in real world applications. This paper presents work on developing a software system for predicting crop yield, for example oil-palm yield, from climate and plantation data. At the core of our system is a method for unsupervised partitioning of data for finding spatio-temporal patterns in climate data using kernel methods which offer strength to deal with complex data. This work gets inspiration from the notion that a non-linear data transformation into some high dimensional feature space increases the possibility of linear separability of the patterns in the transformed space. Therefore, it simplifies exploration of the associated structure in the data. Kernel methods implicitly perform a non-linear mapping of the input data into a high dimensional feature space by replacing the inner products with an appropriate positive definite function. In this paper we present a robust weighted kernel k-means algorithm incorporating spatial constraints for clustering the data. The proposed algorithm can effectively handle noise, outliers and auto-correlation in the spatial data, for effective and efficient data analysis by exploring patterns and structures in the data, and thus can be used for predicting oil-palm yield by analyzing various factors affecting the yield.

Keywords: Pattern analysis, clustering, kernel methods, spatial data, crop yield

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Authors: Lars Steiner, Zahra Ahmadi

Abstract:

Public housing is a vital factor in community development. Successful city, housing and eco system regeneration design is essential in providing positive community development. This concerns work places, nice dwellings, providing premises for child care, care of the elderly, providing qualitative premises for different kinds of commercial service, providing a nice built environment and housing areas and not the least activating tenants. The public housing companies give value to society by stimulating people, renovating socially and economically sustainable as well as being partners to local business and authorities. By their activities the housing companies contribute to sustainable local and regional growth and the identity and reputation of cities. A Social, Economic and Ecological Reputation Effect (SEERE) model for actions to promote housing and community reputation is presented. The model emphasizes regenerative actions to restore natural eco systems as part of housing renewal strategies and to strengthen municipality reputation.

Keywords: Community Development, Image and Reputation, Public Housing, Renewal Strategies.

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