Search results for: mutual recognition
1922 Impact of Behavioral Biases on Indian Investors: Case Analysis of a Mutual Fund Investment Company
Authors: Priyal Motwani, Garvit Goel
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In this study, we have studied and analysed the transaction data of investors of a mutual fund investment company based in India. Based on the data available, we have identified the top four biases that affect the investors of the emerging market economies through regression analysis and three uniquely defined ratios. We found that the four most prominent biases that affected the investment making decisions in India are– Chauffer Knowledge, investors tend to make ambitious decisions about sectors they know little about; Bandwagon effect – the response of the market indices to macroeconomic events are more profound and seem to last longer compared to western markets; base-rate neglect – judgement about stocks are too much based on the most recent development ignoring the long-term fundamentals of the stock; availability bias – lack of proper communication channels of market information lead people to be too reliant on limited information they already have. After segregating the investors into six groups, the results have further been studied to identify a correlation among the demographics, gender and unique cultural identity of the derived groups and the corresponding prevalent biases. On the basis of the results obtained from the derived groups, our study recommends six methods, specific to each group, to educate the investors about the prevalent biases and their role in investment decision making.Keywords: Bandwagon effect, behavioural biases, Chauffeur knowledge, demographics, investor literacy, mutual funds
Procedia PDF Downloads 2271921 Parents' View of Children's Preschool Education in Slovakia
Authors: Sona Lorencova
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The practice of kindergartens shows that the communication and cooperation of teachers and directors of kindergartens with parents, who come with different expectations and ideas about preschool education, is becoming an increasing problem, which leads to many misunderstandings in their mutual functioning. In practice, little attention is paid to communication and cooperation between kindergartens and families, whether in the professional development of educators, in professional articles and publications, or in the field of research. The approach of teachers to parents whose children attend kindergarten requires more and more skills on the part of educators, which is also related to the introduction of the institute of compulsory preschool education. Thus, the discrepancy in mutual expectations and requirements for children's preschool education on the part of the kindergarten and the family is increasing. The aim of the research was to look into the world of parents and learn about their subjective experience with preschool education of children in kindergarten. The findings could be useful in the search for appropriate strategies for mutual communication and cooperation between kindergartens and families in order to achieve more effective progress for children in education. The data were collected through the method of a constellation with figures, semi-structured interviews, and the use of a research diary. Through an interpretive phenomenological analysis, it was found that the parents' view of preschool education in kindergarten is connected to 6 basic topics - parenting with a preschooler, adaptation to kindergarten, professionalism of teachers, cooperation with kindergarten, and parents' satisfaction with preschool education. The conducted research also revealed that the determinants at the level of microsystem, mesosystem, exosystem, macrosystem, and chronosystem influenced parents' view of children's preschool education in kindergarten.Keywords: preschool education, parents, kindergarten, interpretive phenomenological analysis, subjective experience
Procedia PDF Downloads 961920 Defect Localization and Interaction on Surfaces with Projection Mapping and Gesture Recognition
Authors: Qiang Wang, Hongyang Yu, MingRong Lai, Miao Luo
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This paper presents a method for accurately localizing and interacting with known surface defects by overlaying patterns onto real-world surfaces using a projection system. Given the world coordinates of the defects, we project corresponding patterns onto the surfaces, providing an intuitive visualization of the specific defect locations. To enable users to interact with and retrieve more information about individual defects, we implement a gesture recognition system based on a pruned and optimized version of YOLOv6. This lightweight model achieves an accuracy of 82.8% and is suitable for deployment on low-performance devices. Our approach demonstrates the potential for enhancing defect identification, inspection processes, and user interaction in various applications.Keywords: defect localization, projection mapping, gesture recognition, YOLOv6
Procedia PDF Downloads 791919 SCNet: A Vehicle Color Classification Network Based on Spatial Cluster Loss and Channel Attention Mechanism
Authors: Fei Gao, Xinyang Dong, Yisu Ge, Shufang Lu, Libo Weng
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Vehicle color recognition plays an important role in traffic accident investigation. However, due to the influence of illumination, weather, and noise, vehicle color recognition still faces challenges. In this paper, a vehicle color classification network based on spatial cluster loss and channel attention mechanism (SCNet) is proposed for vehicle color recognition. A channel attention module is applied to extract the features of vehicle color representative regions and reduce the weight of nonrepresentative color regions in the channel. The proposed loss function, called spatial clustering loss (SC-loss), consists of two channel-specific components, such as a concentration component and a diversity component. The concentration component forces all feature channels belonging to the same class to be concentrated through the channel cluster. The diversity components impose additional constraints on the channels through the mean distance coefficient, making them mutually exclusive in spatial dimensions. In the comparison experiments, the proposed method can achieve state-of-the-art performance on the public datasets, VCD, and VeRi, which are 96.1% and 96.2%, respectively. In addition, the ablation experiment further proves that SC-loss can effectively improve the accuracy of vehicle color recognition.Keywords: feature extraction, convolutional neural networks, intelligent transportation, vehicle color recognition
Procedia PDF Downloads 1691918 Analyzing the Use of Augmented Reality and Image Recognition in Cultural Education: Use Case of Sintra Palace Treasure Hunt Application
Authors: Marek Maruszczak
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Gamified applications have been used successfully in education for years. The rapid development of technologies such as augmented reality and image recognition increases their availability and reduces their prices. Thus, there is an increasing possibility and need for a wide use of such applications in education. The main purpose of this article is to present the effects of work on a mobile application with augmented reality, the aim of which is to motivate tourists to pay more attention to the attractions and increase the likelihood of moving from one attraction to the next while visiting the Palácio Nacional de Sintra in Portugal. Work on the application was carried out together with the employees of Parques de Sintra from 2019 to 2021. Their effect was the preparation of a mobile application using augmented reality and image recognition. The application was tested on the palace premises by both Parques de Sintra employees and tourists visiting Palácio Nacional de Sintra. The collected conclusions allowed for the formulation of good practices and guidelines that can be used when designing gamified apps for the purpose of cultural education.Keywords: augmented reality, cultural education, gamification, image recognition, mobile games
Procedia PDF Downloads 1861917 The Effect of Experimentally Induced Stress on Facial Recognition Ability of Security Personnel’s
Authors: Zunjarrao Kadam, Vikas Minchekar
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The facial recognition is an important task in criminal investigation procedure. The security guards-constantly watching the persons-can help to identify the suspected accused. The forensic psychologists are tackled such cases in the criminal justice system. The security personnel may loss their ability to correctly identify the persons due to constant stress while performing the duty. The present study aimed at to identify the effect of experimentally induced stress on facial recognition ability of security personnel’s. For this study 50, security guards from Sangli, Miraj & Jaysingpur city of the Maharashtra States of India were recruited in the experimental study. The randomized two group design was employed to carry out the research. In the initial condition twenty identity card size photographs were shown to both groups. Afterward, artificial stress was induced in the experimental group through the difficultpuzzle-solvingtask in a limited period. In the second condition, both groups were presented earlier photographs with another additional thirty new photographs. The subjects were asked to recognize the photographs which are shown earliest. The analyzed data revealed that control group has ahighest mean score of facial recognition than experimental group. The results were discussed in the present research.Keywords: experimentally induced stress, facial recognition, cognition, security personnel
Procedia PDF Downloads 2491916 Optimized Dynamic Bayesian Networks and Neural Verifier Test Applied to On-Line Isolated Characters Recognition
Authors: Redouane Tlemsani, Redouane, Belkacem Kouninef, Abdelkader Benyettou
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In this paper, our system is a Markovien system which we can see it like a Dynamic Bayesian Networks. One of the major interests of these systems resides in the complete training of the models (topology and parameters) starting from training data. The Bayesian Networks are representing models of dubious knowledge on complex phenomena. They are a union between the theory of probability and the graph theory in order to give effective tools to represent a joined probability distribution on a set of random variables. The representation of knowledge bases on description, by graphs, relations of causality existing between the variables defining the field of study. The theory of Dynamic Bayesian Networks is a generalization of the Bayesians networks to the dynamic processes. Our objective amounts finding the better structure which represents the relationships (dependencies) between the variables of a dynamic bayesian network. In applications in pattern recognition, one will carry out the fixing of the structure which obliges us to admit some strong assumptions (for example independence between some variables).Keywords: Arabic on line character recognition, dynamic Bayesian network, pattern recognition, networks
Procedia PDF Downloads 6071915 Size-Reduction Strategies for Iris Codes
Authors: Jutta Hämmerle-Uhl, Georg Penn, Gerhard Pötzelsberger, Andreas Uhl
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Iris codes contain bits with different entropy. This work investigates different strategies to reduce the size of iris code templates with the aim of reducing storage requirements and computational demand in the matching process. Besides simple sub-sampling schemes, also a binary multi-resolution representation as used in the JBIG hierarchical coding mode is assessed. We find that iris code template size can be reduced significantly while maintaining recognition accuracy. Besides, we propose a two stage identification approach, using small-sized iris code templates in a pre-selection satge, and full resolution templates for final identification, which shows promising recognition behaviour.Keywords: iris recognition, compact iris code, fast matching, best bits, pre-selection identification, two-stage identification
Procedia PDF Downloads 4351914 Static and Dynamic Hand Gesture Recognition Using Convolutional Neural Network Models
Authors: Keyi Wang
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Similar to the touchscreen, hand gesture based human-computer interaction (HCI) is a technology that could allow people to perform a variety of tasks faster and more conveniently. This paper proposes a training method of an image-based hand gesture image and video clip recognition system using a CNN (Convolutional Neural Network) with a dataset. A dataset containing 6 hand gesture images is used to train a 2D CNN model. ~98% accuracy is achieved. Furthermore, a 3D CNN model is trained on a dataset containing 4 hand gesture video clips resulting in ~83% accuracy. It is demonstrated that a Cozmo robot loaded with pre-trained models is able to recognize static and dynamic hand gestures.Keywords: deep learning, hand gesture recognition, computer vision, image processing
Procedia PDF Downloads 1311913 Features Reduction Using Bat Algorithm for Identification and Recognition of Parkinson Disease
Authors: P. Shrivastava, A. Shukla, K. Verma, S. Rungta
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Parkinson's disease is a chronic neurological disorder that directly affects human gait. It leads to slowness of movement, causes muscle rigidity and tremors. Gait serve as a primary outcome measure for studies aiming at early recognition of disease. Using gait techniques, this paper implements efficient binary bat algorithm for an early detection of Parkinson's disease by selecting optimal features required for classification of affected patients from others. The data of 166 people, both fit and affected is collected and optimal feature selection is done using PSO and Bat algorithm. The reduced dataset is then classified using neural network. The experiments indicate that binary bat algorithm outperforms traditional PSO and genetic algorithm and gives a fairly good recognition rate even with the reduced dataset.Keywords: parkinson, gait, feature selection, bat algorithm
Procedia PDF Downloads 5351912 Efficient and Timely Mutual Authentication Scheme for RFID Systems
Authors: Hesham A. El Zouka, Mustafa M. Hosni ka
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The Radio Frequency Identification (RFID) technology has a diverse base of applications, but it is also prone to security threats. There are different types of security attacks that limit the range of the RFID applications. For example, deploying the RFID networks in insecure environments could make the RFID system vulnerable to many types of attacks such as spoofing attack, location traceability attack, physical attack and many more. Therefore, security is often an important requirement for RFID systems. In this paper, RFID mutual authentication protocol is implemented based on mobile agent technology and timestamp, which are used to provide strong authentication and integrity assurances to both the RFID readers and their corresponding RFID tags. The integration of mobile agent technology and timestamp provides promising results towards achieving this goal and towards reducing the security threats in RFID systems.Keywords: RFID, security, authentication protocols, privacy, agent-based architecture, time-stamp, digital signature
Procedia PDF Downloads 2561911 KSVD-SVM Approach for Spontaneous Facial Expression Recognition
Authors: Dawood Al Chanti, Alice Caplier
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Sparse representations of signals have received a great deal of attention in recent years. In this paper, the interest of using sparse representation as a mean for performing sparse discriminative analysis between spontaneous facial expressions is demonstrated. An automatic facial expressions recognition system is presented. It uses a KSVD-SVM approach which is made of three main stages: A pre-processing and feature extraction stage, which solves the problem of shared subspace distribution based on the random projection theory, to obtain low dimensional discriminative and reconstructive features; A dictionary learning and sparse coding stage, which uses the KSVD model to learn discriminative under or over dictionaries for sparse coding; Finally a classification stage, which uses a SVM classifier for facial expressions recognition. Our main concern is to be able to recognize non-basic affective states and non-acted expressions. Extensive experiments on the JAFFE static acted facial expressions database but also on the DynEmo dynamic spontaneous facial expressions database exhibit very good recognition rates.Keywords: dictionary learning, random projection, pose and spontaneous facial expression, sparse representation
Procedia PDF Downloads 2971910 Implementation of a Multimodal Biometrics Recognition System with Combined Palm Print and Iris Features
Authors: Rabab M. Ramadan, Elaraby A. Elgallad
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With extensive application, the performance of unimodal biometrics systems has to face a diversity of problems such as signal and background noise, distortion, and environment differences. Therefore, multimodal biometric systems are proposed to solve the above stated problems. This paper introduces a bimodal biometric recognition system based on the extracted features of the human palm print and iris. Palm print biometric is fairly a new evolving technology that is used to identify people by their palm features. The iris is a strong competitor together with face and fingerprints for presence in multimodal recognition systems. In this research, we introduced an algorithm to the combination of the palm and iris-extracted features using a texture-based descriptor, the Scale Invariant Feature Transform (SIFT). Since the feature sets are non-homogeneous as features of different biometric modalities are used, these features will be concatenated to form a single feature vector. Particle swarm optimization (PSO) is used as a feature selection technique to reduce the dimensionality of the feature. The proposed algorithm will be applied to the Institute of Technology of Delhi (IITD) database and its performance will be compared with various iris recognition algorithms found in the literature.Keywords: iris recognition, particle swarm optimization, feature extraction, feature selection, palm print, the Scale Invariant Feature Transform (SIFT)
Procedia PDF Downloads 2311909 Hand Gesture Detection via EmguCV Canny Pruning
Authors: N. N. Mosola, S. J. Molete, L. S. Masoebe, M. Letsae
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Hand gesture recognition is a technique used to locate, detect, and recognize a hand gesture. Detection and recognition are concepts of Artificial Intelligence (AI). AI concepts are applicable in Human Computer Interaction (HCI), Expert systems (ES), etc. Hand gesture recognition can be used in sign language interpretation. Sign language is a visual communication tool. This tool is used mostly by deaf societies and those with speech disorder. Communication barriers exist when societies with speech disorder interact with others. This research aims to build a hand recognition system for Lesotho’s Sesotho and English language interpretation. The system will help to bridge the communication problems encountered by the mentioned societies. The system has various processing modules. The modules consist of a hand detection engine, image processing engine, feature extraction, and sign recognition. Detection is a process of identifying an object. The proposed system uses Canny pruning Haar and Haarcascade detection algorithms. Canny pruning implements the Canny edge detection. This is an optimal image processing algorithm. It is used to detect edges of an object. The system employs a skin detection algorithm. The skin detection performs background subtraction, computes the convex hull, and the centroid to assist in the detection process. Recognition is a process of gesture classification. Template matching classifies each hand gesture in real-time. The system was tested using various experiments. The results obtained show that time, distance, and light are factors that affect the rate of detection and ultimately recognition. Detection rate is directly proportional to the distance of the hand from the camera. Different lighting conditions were considered. The more the light intensity, the faster the detection rate. Based on the results obtained from this research, the applied methodologies are efficient and provide a plausible solution towards a light-weight, inexpensive system which can be used for sign language interpretation.Keywords: canny pruning, hand recognition, machine learning, skin tracking
Procedia PDF Downloads 1771908 Arabic Character Recognition Using Regression Curves with the Expectation Maximization Algorithm
Authors: Abdullah A. AlShaher
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In this paper, we demonstrate how regression curves can be used to recognize 2D non-rigid handwritten shapes. Each shape is represented by a set of non-overlapping uniformly distributed landmarks. The underlying models utilize 2nd order of polynomials to model shapes within a training set. To estimate the regression models, we need to extract the required coefficients which describe the variations for a set of shape class. Hence, a least square method is used to estimate such modes. We then proceed by training these coefficients using the apparatus Expectation Maximization algorithm. Recognition is carried out by finding the least error landmarks displacement with respect to the model curves. Handwritten isolated Arabic characters are used to evaluate our approach.Keywords: character recognition, regression curves, handwritten Arabic letters, expectation maximization algorithm
Procedia PDF Downloads 1371907 History, Challenges and Solutions for Social Work Education and Recognition in Vietnam
Authors: Thuy Bui Anh, Ngan Nguyen Thi Thanh
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Currently, social work in Vietnam is entering the first step in the development process to become a true profession with a strong position in society. However, Spirit of helping and sharing of social work has already existed in the daily life of Vietnamese people for a very long time, becoming a precious heritage passed down from ancestors to the next generations while expanding the territory, building and defending for the country. Following the stream of history, charity work in Vietnam has gradually transformed itself towards a more professional work, especially in the last 2 decades. Accordingly, more than 50 universities and educational institutions in Vietnam have been licensed to train social work, ensuring a stronger foundation on human resources working in this field. Despite the strong growth, social work profession, social work education and the recognition of the role of the social workers still need to be fueled to develop, responded to the increasing demand of Vietnam society.Keywords: education, history, recognition, social work, Vietnam
Procedia PDF Downloads 3121906 Recognition of Gene Names from Gene Pathway Figures Using Siamese Network
Authors: Muhammad Azam, Micheal Olaolu Arowolo, Fei He, Mihail Popescu, Dong Xu
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The number of biological papers is growing quickly, which means that the number of biological pathway figures in those papers is also increasing quickly. Each pathway figure shows extensive biological information, like the names of genes and how the genes are related. However, manually annotating pathway figures takes a lot of time and work. Even though using advanced image understanding models could speed up the process of curation, these models still need to be made more accurate. To improve gene name recognition from pathway figures, we applied a Siamese network to map image segments to a library of pictures containing known genes in a similar way to person recognition from photos in many photo applications. We used a triple loss function and a triplet spatial pyramid pooling network by combining the triplet convolution neural network and the spatial pyramid pooling (TSPP-Net). We compared VGG19 and VGG16 as the Siamese network model. VGG16 achieved better performance with an accuracy of 93%, which is much higher than OCR results.Keywords: biological pathway, image understanding, gene name recognition, object detection, Siamese network, VGG
Procedia PDF Downloads 2801905 Classifications of Images for the Recognition of People’s Behaviors by SIFT and SVM
Authors: Henni Sid Ahmed, Belbachir Mohamed Faouzi, Jean Caelen
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Behavior recognition has been studied for realizing drivers assisting system and automated navigation and is an important studied field in the intelligent Building. In this paper, a recognition method of behavior recognition separated from a real image was studied. Images were divided into several categories according to the actual weather, distance and angle of view etc. SIFT was firstly used to detect key points and describe them because the SIFT (Scale Invariant Feature Transform) features were invariant to image scale and rotation and were robust to changes in the viewpoint and illumination. My goal is to develop a robust and reliable system which is composed of two fixed cameras in every room of intelligent building which are connected to a computer for acquisition of video sequences, with a program using these video sequences as inputs, we use SIFT represented different images of video sequences, and SVM (support vector machine) Lights as a programming tool for classification of images in order to classify people’s behaviors in the intelligent building in order to give maximum comfort with optimized energy consumption.Keywords: video analysis, people behavior, intelligent building, classification
Procedia PDF Downloads 3711904 A Unified Deep Framework for Joint 3d Pose Estimation and Action Recognition from a Single Color Camera
Authors: Huy Hieu Pham, Houssam Salmane, Louahdi Khoudour, Alain Crouzil, Pablo Zegers, Sergio Velastin
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We present a deep learning-based multitask framework for joint 3D human pose estimation and action recognition from color video sequences. Our approach proceeds along two stages. In the first, we run a real-time 2D pose detector to determine the precise pixel location of important key points of the body. A two-stream neural network is then designed and trained to map detected 2D keypoints into 3D poses. In the second, we deploy the Efficient Neural Architecture Search (ENAS) algorithm to find an optimal network architecture that is used for modeling the Spatio-temporal evolution of the estimated 3D poses via an image-based intermediate representation and performing action recognition. Experiments on Human3.6M, Microsoft Research Redmond (MSR) Action3D, and Stony Brook University (SBU) Kinect Interaction datasets verify the effectiveness of the proposed method on the targeted tasks. Moreover, we show that our method requires a low computational budget for training and inference.Keywords: human action recognition, pose estimation, D-CNN, deep learning
Procedia PDF Downloads 1381903 Automatic Detection of Suicidal Behaviors Using an RGB-D Camera: Azure Kinect
Authors: Maha Jazouli
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Suicide is one of the most important causes of death in the prison environment, both in Canada and internationally. Rates of attempts of suicide and self-harm have been on the rise in recent years, with hangings being the most frequent method resorted to. The objective of this article is to propose a method to automatically detect in real time suicidal behaviors. We present a gesture recognition system that consists of three modules: model-based movement tracking, feature extraction, and gesture recognition using machine learning algorithms (MLA). Our proposed system gives us satisfactory results. This smart video surveillance system can help assist staff responsible for the safety and health of inmates by alerting them when suicidal behavior is detected, which helps reduce mortality rates and save lives.Keywords: suicide detection, Kinect azure, RGB-D camera, SVM, machine learning, gesture recognition
Procedia PDF Downloads 1801902 “Octopub”: Geographical Sentiment Analysis Using Named Entity Recognition from Social Networks for Geo-Targeted Billboard Advertising
Authors: Oussama Hafferssas, Hiba Benyahia, Amina Madani, Nassima Zeriri
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Although data nowadays has multiple forms; from text to images, and from audio to videos, yet text is still the most used one at a public level. At an academical and research level, and unlike other forms, text can be considered as the easiest form to process. Therefore, a brunch of Data Mining researches has been always under its shadow, called "Text Mining". Its concept is just like data mining’s, finding valuable patterns in data, from large collections and tremendous volumes of data, in this case: Text. Named entity recognition (NER) is one of Text Mining’s disciplines, it aims to extract and classify references such as proper names, locations, expressions of time and dates, organizations and more in a given text. Our approach "Octopub" does not aim to find new ways to improve named entity recognition process, rather than that it’s about finding a new, and yet smart way, to use NER in a way that we can extract sentiments of millions of people using Social Networks as a limitless information source, and Marketing for product promotion as the main domain of application.Keywords: textmining, named entity recognition(NER), sentiment analysis, social media networks (SN, SMN), business intelligence(BI), marketing
Procedia PDF Downloads 5811901 Environmental Effects of Interactions of Industry and Residence in District No 21 Tehran Municipality (Iran)
Authors: Farideh Gheitasi, Zahra Mirzaei Pour
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Considering mutual interactions of industry and residence in an environmental point of view is essential in effective urban management and all the policies must be made based on the results of these interactions. So, District No 21 of Tehran Municipality (Iran) which is almost industrial combined with some residential area was selected to study the consequences of industrial and residential area neighborhood. In this paper the results of two observations conducted in two timescales in 2010 and 2015, according to completed questionnaires by residents of an industrial region in the study area, were analyzed. The questionnaires contain 19 different environmental parameters considering positive and negative effects of industry on human life. The results of this study show that 96% of the target group in 2010 believed if the companies regard the law, mutual coexistence of industry and residency is possible. While this number decreased to 21% in 2015, which indicates that the problem got worse and people became more unsatisfied with the situation.Keywords: environment, industry, residence, pollution
Procedia PDF Downloads 3041900 Logistic Model Tree and Expectation-Maximization for Pollen Recognition and Grouping
Authors: Endrick Barnacin, Jean-Luc Henry, Jack Molinié, Jimmy Nagau, Hélène Delatte, Gérard Lebreton
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Palynology is a field of interest for many disciplines. It has multiple applications such as chronological dating, climatology, allergy treatment, and even honey characterization. Unfortunately, the analysis of a pollen slide is a complicated and time-consuming task that requires the intervention of experts in the field, which is becoming increasingly rare due to economic and social conditions. So, the automation of this task is a necessity. Pollen slides analysis is mainly a visual process as it is carried out with the naked eye. That is the reason why a primary method to automate palynology is the use of digital image processing. This method presents the lowest cost and has relatively good accuracy in pollen retrieval. In this work, we propose a system combining recognition and grouping of pollen. It consists of using a Logistic Model Tree to classify pollen already known by the proposed system while detecting any unknown species. Then, the unknown pollen species are divided using a cluster-based approach. Success rates for the recognition of known species have been achieved, and automated clustering seems to be a promising approach.Keywords: pollen recognition, logistic model tree, expectation-maximization, local binary pattern
Procedia PDF Downloads 1731899 Using Augmented Reality to Enhance Doctor Patient Communication
Authors: Rutusha Bhutada, Gaurav Chavan, Sarvesh Kasat, Varsha Mujumdar
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This software system will be an Augmented Reality application designed to maximize the doctor’s productivity by providing tools to assist in automating the patient recognition and updating patient’s records using face and voice recognition features, which would otherwise have to be performed manually. By maximizing the doctor’s work efficiency and production, the application will meet the doctor’s needs while remaining easy to understand and use. More specifically, this application is designed to allow a doctor to manage his productive time in handling the patient without losing eye-contact with him and communicate with a group of other doctors for consultation, for in-place treatments through video streaming, as a video study. The system also contains a relational database containing a list of doctor, patient and display techniques.Keywords: augmented reality, hand-held devices, head-mounted devices, marker based systems, speech recognition, face detection
Procedia PDF Downloads 4291898 Tetracycline as Chemosensor for Simultaneous Recognition of Al³⁺: Application to Bio-Imaging for Living Cells
Authors: Jesus Alfredo Ortega Granados, Pandiyan Thangarasu
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Antibiotic tetracycline presents as a micro-contaminant in fresh water, wastewater and soils, causing environmental and health problems. In this work, tetracycline (TC) has been employed as chemo-sensor for the recognition of Al³⁺ without interring other ions, and the results show that it enhances the fluorescence intensity for Al³⁺ and there is no interference from other coexisting cation ions (Cd²⁺, Ni²⁺, Co²⁺, Sr²⁺, Mg²⁺, Fe³⁺, K⁺, Sm³⁺, Ag⁺, Na⁺, Ba²⁺, Zn²⁺, and Mn²⁺). For the addition of Cu²⁺ to [TET-Al³⁺], it appears that the intensity of fluorescence has been quenched. Other combinations of metal ions in addition to TC do not change the fluorescence behavior. The stoichiometry determined by Job´s plot for the interaction of TC with Al³⁺ was found to be 1:1. Importantly, the detection of Al³⁺⁺ successfully employed in the real samples like living cells, and it was found that TC efficiently performs as a fluorescent probe for Al³⁺ ion in living systems, especially in Saccharomyces cerevisiae; this is confirmed by confocal laser scanning microscopy.Keywords: chemo-sensor, recognition of Al³⁺ ion, Saccharomyces cerevisiae, tetracycline,
Procedia PDF Downloads 1771897 Recognition of Objects in a Maritime Environment Using a Combination of Pre- and Post-Processing of the Polynomial Fit Method
Authors: R. R. Hordijk, O. J. G. Somsen
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Traditionally, radar systems are the eyes and ears of a ship. However, these systems have their drawbacks and nowadays they are extended with systems that work with video and photos. Processing of data from these videos and photos is however very labour-intensive and efforts are being made to automate this process. A major problem when trying to recognize objects in water is that the 'background' is not homogeneous so that traditional image recognition technics do not work well. Main question is, can a method be developed which automate this recognition process. There are a large number of parameters involved to facilitate the identification of objects on such images. One is varying the resolution. In this research, the resolution of some images has been reduced to the extreme value of 1% of the original to reduce clutter before the polynomial fit (pre-processing). It turned out that the searched object was clearly recognizable as its grey value was well above the average. Another approach is to take two images of the same scene shortly after each other and compare the result. Because the water (waves) fluctuates much faster than an object floating in the water one can expect that the object is the only stable item in the two images. Both these methods (pre-processing and comparing two images of the same scene) delivered useful results. Though it is too early to conclude that with these methods all image problems can be solved they are certainly worthwhile for further research.Keywords: image processing, image recognition, polynomial fit, water
Procedia PDF Downloads 5291896 Stereotypical Motor Movement Recognition Using Microsoft Kinect with Artificial Neural Network
Authors: M. Jazouli, S. Elhoufi, A. Majda, A. Zarghili, R. Aalouane
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Autism spectrum disorder is a complex developmental disability. It is defined by a certain set of behaviors. Persons with Autism Spectrum Disorders (ASD) frequently engage in stereotyped and repetitive motor movements. The objective of this article is to propose a method to automatically detect this unusual behavior. Our study provides a clinical tool which facilitates for doctors the diagnosis of ASD. We focus on automatic identification of five repetitive gestures among autistic children in real time: body rocking, hand flapping, fingers flapping, hand on the face and hands behind back. In this paper, we present a gesture recognition system for children with autism, which consists of three modules: model-based movement tracking, feature extraction, and gesture recognition using artificial neural network (ANN). The first one uses the Microsoft Kinect sensor, the second one chooses points of interest from the 3D skeleton to characterize the gestures, and the last one proposes a neural connectionist model to perform the supervised classification of data. The experimental results show that our system can achieve above 93.3% recognition rate.Keywords: ASD, artificial neural network, kinect, stereotypical motor movements
Procedia PDF Downloads 3011895 Perceiving Casual Speech: A Gating Experiment with French Listeners of L2 English
Authors: Naouel Zoghlami
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Spoken-word recognition involves the simultaneous activation of potential word candidates which compete with each other for final correct recognition. In continuous speech, the activation-competition process gets more complicated due to speech reductions existing at word boundaries. Lexical processing is more difficult in L2 than in L1 because L2 listeners often lack phonetic, lexico-semantic, syntactic, and prosodic knowledge in the target language. In this study, we investigate the on-line lexical segmentation hypotheses that French listeners of L2 English form and then revise as subsequent perceptual evidence is revealed. Our purpose is to shed further light on the processes of L2 spoken-word recognition in context and better understand L2 listening difficulties through a comparison of skilled and unskilled reactions at the point where their working hypothesis is rejected. We use a variant of the gating experiment in which subjects transcribe an English sentence presented in increments of progressively greater duration. The spoken sentence was “And this amazing athlete has just broken another world record”, chosen mainly because it included common reductions and phonetic features in English, such as elision and assimilation. Our preliminary results show that there is an important difference in the manner in which proficient and less-proficient L2 listeners handle connected speech. Less-proficient listeners delay recognition of words as they wait for lexical and syntactic evidence to appear in the gates. Further statistical results are currently being undertaken.Keywords: gating paradigm, spoken word recognition, online lexical segmentation, L2 listening
Procedia PDF Downloads 4581894 Accuracy Improvement of Traffic Participant Classification Using Millimeter-Wave Radar by Leveraging Simulator Based on Domain Adaptation
Authors: Tokihiko Akita, Seiichi Mita
Abstract:
A millimeter-wave radar is the most robust against adverse environments, making it an essential environment recognition sensor for automated driving. However, the reflection signal is sparse and unstable, so it is difficult to obtain the high recognition accuracy. Deep learning provides high accuracy even for them in recognition, but requires large scale datasets with ground truth. Specially, it takes a lot of cost to annotate for a millimeter-wave radar. For the solution, utilizing a simulator that can generate an annotated huge dataset is effective. Simulation of the radar is more difficult to match with real world data than camera image, and recognition by deep learning with higher-order features using the simulator causes further deviation. We have challenged to improve the accuracy of traffic participant classification by fusing simulator and real-world data with domain adaptation technique. Experimental results with the domain adaptation network created by us show that classification accuracy can be improved even with a few real-world data.Keywords: millimeter-wave radar, object classification, deep learning, simulation, domain adaptation
Procedia PDF Downloads 871893 On the Network Packet Loss Tolerance of SVM Based Activity Recognition
Authors: Gamze Uslu, Sebnem Baydere, Alper K. Demir
Abstract:
In this study, data loss tolerance of Support Vector Machines (SVM) based activity recognition model and multi activity classification performance when data are received over a lossy wireless sensor network is examined. Initially, the classification algorithm we use is evaluated in terms of resilience to random data loss with 3D acceleration sensor data for sitting, lying, walking and standing actions. The results show that the proposed classification method can recognize these activities successfully despite high data loss. Secondly, the effect of differentiated quality of service performance on activity recognition success is measured with activity data acquired from a multi hop wireless sensor network, which introduces high data loss. The effect of number of nodes on the reliability and multi activity classification success is demonstrated in simulation environment. To the best of our knowledge, the effect of data loss in a wireless sensor network on activity detection success rate of an SVM based classification algorithm has not been studied before.Keywords: activity recognition, support vector machines, acceleration sensor, wireless sensor networks, packet loss
Procedia PDF Downloads 469