Relevant bibliographies by topics / Train pattern recognition

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Train pattern recognition'

Author: Grafiati
Published: 10 January 2023
Last updated: 28 January 2023

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Journal articles on the topic "Train pattern recognition"

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Ahmadian, Kushan, and Marina Gavrilova. "Chaotic Neural Network for Biometric Pattern Recognition." Advances in Artificial Intelligence 2012 (August 30, 2012): 1–9. http://dx.doi.org/10.1155/2012/124176.

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Biometric pattern recognition emerged as one of the predominant research directions in modern security systems. It plays a crucial role in authentication of both real-world and virtual reality entities to allow system to make an informed decision on granting access privileges or providing specialized services. The major issues tackled by the researchers are arising from the ever-growing demands on precision and performance of security systems and at the same time increasing complexity of data and/or behavioral patterns to be recognized. In this paper, we propose to deal with both issues by introducing the new approach to biometric pattern recognition, based on chaotic neural network (CNN). The proposed method allows learning the complex data patterns easily while concentrating on the most important for correct authentication features and employs a unique method to train different classifiers based on each feature set. The aggregation result depicts the final decision over the recognized identity. In order to train accurate set of classifiers, the subspace clustering method has been used to overcome the problem of high dimensionality of the feature space. The experimental results show the superior performance of the proposed method.
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Wang, Yi, and Wei Lian Qu. "Multi-Axle Moving Train Loads Identification by Using Fuzzy Pattern Recognition Technique." Applied Mechanics and Materials 29-32 (August 2010): 1307–12. http://dx.doi.org/10.4028/www.scientific.net/amm.29-32.1307.

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Identification of multi-axle moving loads on bridge is very important for bridge design, construction, and maintenance in engineering field. It is complicated and time consuming to identify the multi-axle moving train loads with general identification methods and far away from practical practice. Based on the theory of fuzzy pattern recognition, the fuzzy pattern recognition method for multi-axle moving train loads identification on bridge is presented in this paper. The multi-axle moving loads pattern library on a simply supported bridge is established with numerical methods. Effect of measurement noise on the proposed method is investigated in three situations. The results show that the proposed identification method has a certain resistance to measurement noise and can realize moving train loads identification with high accuracy.
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Joshila Grace, L. K., K. Rahul, and P. S. Sidharth. "An Efficient Action Detection Model Using Deep Belief Networks." Journal of Computational and Theoretical Nanoscience 16, no. 8 (August 1, 2019): 3232–36. http://dx.doi.org/10.1166/jctn.2019.8168.

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Computer Vision and image processing have gained an enormous advance in the field of machine learning techniques. Some of the major research areas within machine learning are Action detection and Pattern Recognition. Action recognition is a new advancement of pattern recognition approaches where the actions performed by any action or living being is tracked and monitored. Action recognition still encounters some challenges that needs to be looked upon and perform recognize the actions is a very minimal time. Networks like SVM and Neural Networks are used to train the network in such a way they are able to detect a pattern of an action when a new frame is given. In this paper, we have proposed a model which detects patterns of actions from a video or an image. Bounding boxes are used to detect the actions and localize it. Deep Belief Network is used to train the model where numerous images having actions are given as the training set. The performance evaluation was done on the model and it is observed that it detects the actions very accurately when a new image is given to the network.
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Li, Lu, Guo Qing Jiang, Tian Ye Niu, Yi Wang, Yong Lu, Qi Lan, Li Chang, Ya Lin Liu, and Chao Chen. "High Voltage Equipment PD Pattern Recognition Based on BP Classifier." Applied Mechanics and Materials 734 (February 2015): 99–103. http://dx.doi.org/10.4028/www.scientific.net/amm.734.99.

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The corresponding discharge waveforms were detected by ultrasonic sensor. The dimension of feature vectors extracted from discharge waveforms were reduced by local linear embedding algorithm. The processed vectors were used as input to train and test BP_Adaboost classifier. Recognition results show that, high voltage reactor insulating defects recognition with this method can reduce the calculation and maintain a high recognition rate at the same time. This shows its effectiveness in the application of partial discharge pattern recognition.
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Manzi, Daniel, Bruno Brentan, Gustavo Meirelles, Joaquín Izquierdo, and Edevar Luvizotto. "Pattern Recognition and Clustering of Transient Pressure Signals for Burst Location." Water 11, no. 11 (October 30, 2019): 2279. http://dx.doi.org/10.3390/w11112279.

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A large volume of the water produced for public supply is lost in the systems between sources and consumers. An important—in many cases the greatest—fraction of these losses are physical losses, mainly related to leaks and bursts in pipes and in consumer connections. Fast detection and location of bursts plays an important role in the design of operation strategies for water loss control, since this helps reduce the volume lost from the instant the event occurs until its effective repair (run time). The transient pressure signals caused by bursts contain important information about their location and magnitude, and stamp on any of these events a specific "hydraulic signature". The present work proposes and evaluates three methods to disaggregate transient signals, which are used afterwards to train artificial neural networks (ANNs) to identify burst locations and calculate the leaked flow. In addition, a clustering process is also used to group similar signals, and then train specific ANNs for each group, thus improving both the computational efficiency and the location accuracy. The proposed methods are applied to two real distribution networks, and the results show good accuracy in burst location and characterization.
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Yuan, Jiaxin, and Zhe Kan. "Research and Implementation of Flow Pattern Recognition for Gas-liquid Two-phase Flows Based on GoogLeNet." Journal of Physics: Conference Series 2224, no. 1 (April 1, 2022): 012021. http://dx.doi.org/10.1088/1742-6596/2224/1/012021.

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Abstract In order to solve the flow pattern recognition problem of gas-liquid two-phase flow in pipelines, this paper uses high-speed photography to sample the flow patterns of transparent pipe sections and combines the GoogLeNet convolutional neural network model under migration learning to implement a flow pattern recognition method with small samples. In this paper, the GoogLeNet Inception V1 network is used, and the convolutional layer and the pooling layer weights parameters obtained from its training on the imageNet dataset are retained, and the flow pattern samples obtained on the gas-liquid two-phase flow experimental platform are used to train the network model. The recognition accuracy was 98.37% with a training set of 400 and a test set of 100 samples of each flow type. The convolutional neural network directly uses images as data input without operations such as image pre-processing and feature extraction, and its unique fine-grained feature extraction enables the recognition of images by convolutional neural networks at a nearly human level.
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Cerreto, Fabrizio, Bo Friis Nielsen, Otto Anker Nielsen, and Steven S. Harrod. "Application of Data Clustering to Railway Delay Pattern Recognition." Journal of Advanced Transportation 2018 (2018): 1–18. http://dx.doi.org/10.1155/2018/6164534.

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K-means clustering is employed to identify recurrent delay patterns on a high traffic railway line north of Copenhagen, Denmark. The clusters identify behavioral patterns in the very large (“big data”) datasets generated automatically and continuously by the railway signal system. The results reveal the conditions where corrective actions are necessary, showing the cases where recurrent delay patterns take place. Delay profiles and delay change profiles are generated from timestamps to compare different train runs and to partition the set of observations into groups of similar elements. K-means clustering can identify and discriminate different patterns affecting the same stations, which is otherwise difficult in previous approaches based on visual inspection. Classical methods of univariate analysis do not reveal these patterns. The demonstrated methodology is scalable and can be applied to any system of transport.
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Hu, Ke Dong, Yi Xuan Ji, and Da Peng Tan. "Pattern Recognition of the Soft Abrasive Flow Based on Wavelet Packet." Advanced Materials Research 588-589 (November 2012): 756–60. http://dx.doi.org/10.4028/www.scientific.net/amr.588-589.756.

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A Pattern Recognition method based on wavelet packet and artificial neural is proposed for solid-liquid two-phase flow characteristic parameters and the non-linear relationship between flow pattern. This method firstly established the physical and dynamic model, then set a monitoring point. To get the optimum wavelet tree and its information entropy, six floors of wavelet packet was used to decompose the collected velocity fluctuation signal. Transported the proper vector which is component by information entropy into Back Propagation neural network to train and identify. The recognition results show that this method can effectively overcome the subjectivity of traditional identification methods. It has good recognition effect, thus provide an effective choice for solid-liquid two-phase flow pattern recognition.
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Adeyemi, Oladimeji, Martins Irhebhude, and Adeola Kolawole. "Speed Breakers, Road Marking Detection and Recognition Using Image Processing Techniques." Advances in Image and Video Processing 7, no. 5 (November 8, 2019): 30–42. http://dx.doi.org/10.14738/aivp.75.7205.

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This paper presents a image processing technique for speed breaker, road marking detection and recognition. An Optical Character Recognition (OCR) algorithm was used to recognize traffic signs such as “STOP” markings and a Hough transform was used to detect line markings which serves as a pre-processing stage to determine when the proposed technique does OCR or speed breaker recognition. The stopline inclusion serves as a pre-processing stage that tells the system when to perform stop marking recognition or speed breaker recognition. Image processing techniques was used for the processing of features from the images. Local Binary Pattern (LBP) was extracted as features and employed to train the Support Vector Machine (SVM) classifier for speed breaker recognition. Experimental results shows 79%, 100% “STOP” sign and speed breaker recognitions respectively. The proposed system goes very well for the roads which are constructed with proper painting irrespective of their dimension.
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Fang, Huijuan, Yongji Wang, Jiping He, and Shan Liu. "Temporal pattern recognition using spiking neural networks for cortical neuronal spike train decoding." IFAC Proceedings Volumes 41, no. 2 (2008): 5203–8. http://dx.doi.org/10.3182/20080706-5-kr-1001.00874.

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Dissertations / Theses on the topic "Train pattern recognition"

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Sammouri, Wissam. "Data mining of temporal sequences for the prediction of infrequent failure events : application on floating train data for predictive maintenance." Thesis, Paris Est, 2014. http://www.theses.fr/2014PEST1041/document.

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De nos jours, afin de répondre aux exigences économiques et sociales, les systèmes de transport ferroviaire ont la nécessité d'être exploités avec un haut niveau de sécurité et de fiabilité. On constate notamment un besoin croissant en termes d'outils de surveillance et d'aide à la maintenance de manière à anticiper les défaillances des composants du matériel roulant ferroviaire. Pour mettre au point de tels outils, les trains commerciaux sont équipés de capteurs intelligents envoyant des informations en temps réel sur l'état de divers sous-systèmes. Ces informations se présentent sous la forme de longues séquences temporelles constituées d'une succession d'événements. Le développement d'outils d'analyse automatique de ces séquences permettra d'identifier des associations significatives entre événements dans un but de prédiction d'événement signant l'apparition de défaillance grave. Cette thèse aborde la problématique de la fouille de séquences temporelles pour la prédiction d'événements rares et s'inscrit dans un contexte global de développement d'outils d'aide à la décision. Nous visons à étudier et développer diverses méthodes pour découvrir les règles d'association entre événements d'une part et à construire des modèles de classification d'autre part. Ces règles et/ou ces classifieurs peuvent ensuite être exploités pour analyser en ligne un flux d'événements entrants dans le but de prédire l'apparition d'événements cibles correspondant à des défaillances. Deux méthodologies sont considérées dans ce travail de thèse: La première est basée sur la recherche des règles d'association, qui est une approche temporelle et une approche à base de reconnaissance de formes. Les principaux défis auxquels est confronté ce travail sont principalement liés à la rareté des événements cibles à prédire, la redondance importante de certains événements et à la présence très fréquente de "bursts". Les résultats obtenus sur des données réelles recueillies par des capteurs embarqués sur une flotte de trains commerciaux permettent de mettre en évidence l'efficacité des approches proposées
In order to meet the mounting social and economic demands, railway operators and manufacturers are striving for a longer availability and a better reliability of railway transportation systems. Commercial trains are being equipped with state-of-the-art onboard intelligent sensors monitoring various subsystems all over the train. These sensors provide real-time flow of data, called floating train data, consisting of georeferenced events, along with their spatial and temporal coordinates. Once ordered with respect to time, these events can be considered as long temporal sequences which can be mined for possible relationships. This has created a neccessity for sequential data mining techniques in order to derive meaningful associations rules or classification models from these data. Once discovered, these rules and models can then be used to perform an on-line analysis of the incoming event stream in order to predict the occurrence of target events, i.e, severe failures that require immediate corrective maintenance actions. The work in this thesis tackles the above mentioned data mining task. We aim to investigate and develop various methodologies to discover association rules and classification models which can help predict rare tilt and traction failures in sequences using past events that are less critical. The investigated techniques constitute two major axes: Association analysis, which is temporal and Classification techniques, which is not temporal. The main challenges confronting the data mining task and increasing its complexity are mainly the rarity of the target events to be predicted in addition to the heavy redundancy of some events and the frequent occurrence of data bursts. The results obtained on real datasets collected from a fleet of trains allows to highlight the effectiveness of the approaches and methodologies used
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Landes, Pierre-Edouard. "Extraction d'information pour l'édition et la synthèse par l'exemple en rendu expressif." Phd thesis, Université de Grenoble, 2011. http://tel.archives-ouvertes.fr/tel-00637651.

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Cette thèse prend pour cadre la synthèse par l'exemple et l'édition de contenu graphique en infographie et propose une réflexion sur les possibles sources d'information utiles à ces fins. Contrairement aux techniques "procédurales", l'approche par l'exemple se distingue par sa grande simplicité d'utilisation : reviennent en effet à l'algorithme de synthèse l'identification, analyse et reproduction des éléments caractéristiques des exemples fournis en entrée par l'utilisateur. Ce mode de création de même que les techniques approfondies d'édition ont grandement contribué à la facilitation de la production à grande échelle de contenus graphiques convaincants et ainsi participé à l'adoption par la communauté des artistes des outils proposés par le support numérique. Mais pour être ainsi exploitées, celles-ci doivent également être hautement contrôlables tout en évitant l'écueil de n'être que le simple prolongement de la main de l'artiste. Nous explorons ici cette thématique dans le cadre de la création de rendus dits expressifs et étudions les interactions (collaboratives ou concurrentielles) entre les différentes sources d'information au cœur de ce processus. Ces dernières sont à notre sens au nombre de trois : l'analyse automatique des données d'entrée avant rendu ou traitement ; l'utilisation de modèles a priori en vue de leur compréhension ; et enfin le contrôle explicite par l'utilisateur. En les combinant au plus juste, nous proposons des techniques nouvelles dans divers domaines de la synthèse en rendu expressif. Au delà du réalisme photographique, le rendu expressif se caractérise par sa poursuite de critères plus difficilement quantifiables tels la facilité de compréhension ou le caractère artistique de ses résultats. La subjectivité de tels objectifs nous force donc ici plus qu'ailleurs à estimer avec soin les sources d'information à privilégier, le niveau d'implication à accorder à l'utilisateur (sans que ce choix ne s'opère au détriment de la qualité théorique de la méthode), ainsi que le possible recours à des modèles d'analyse (sans en compromettre la généralité). Trois principales instances de synthèse sont ici détaillés : la génération de textures, la désaturation d'images, et la représentation de maillages par le dessin au trait. La grande variété des données d'entrée (textures matricielles ou vectorielles, images complexes, géométries 3d), des modalités de synthèse (imitation, conversion, représentation alternative) et d'objectifs (reproduction de la signature visuelle d'une texture, restitution crédible de contrastes chromatiques, génération de dessins conformes au style de l'utilisateur) permettent l'examen de divers équilibres entre ces sources d'information et l'exploration de degrés plus ou moins élevés d'interaction avec l'utilisateur.
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Smit, Willem Jacobus. "Sparse coding for speech recognition." Thesis, 2008. http://upetd.up.ac.za/thesis/available/etd-11112008-151309/.

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Le, Noury Peter. "It’s out of this world: exploring the use of virtual reality technology for enhancing perceptual-cognitive skill in tennis." Thesis, 2021. https://vuir.vu.edu.au/42798/.

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The aim of this thesis was to increase our understanding of how virtual reality (VR) can be applied to assess and train pattern recognition and decision-making skill in sport, specifically the sport of tennis. There has been a growing interest in using VR for training perceptual- cognitive skill in sport; however, for VR training to effectively simulate real-world performance, it must recreate the contextual information and movement behaviours present in the real-world environment. Although it is well established that skilled performers can effectively use prior sources of contextual information to enhance anticipation performance compared to lesser skilled performers, little is known about the relative difficulty of identifying different types of contextual information and the requisite regularity of patterns to influence anticipation. Moreover, there is a lack of research assessing the effect of using more representative experimental tasks on anticipation and decision-making behaviour. Therefore, study one of this thesis assessed the representativeness of VR for simulation of tennis performance. Participants included 28 skilled tennis players aged between 12 to 17 years (M = 14.4, SD = 1.6). Participants sense of presence was assessed VR, and participants movement behaviours were compared when playing tennis in VR and real-world environments. The results showed that when performing groundstrokes, participants frequently used the same stance in VR as they did in the real-world condition and experienced a high sense of presence. Study two of this thesis used VR to assess the ability of 28 skilled tennis players aged between 13 and 18 years (M = 15.7, SD = 1.4) to identify two specific serving patterns being used by opponents. These serving patterns related to the opponent’s action tendencies, with a wide serve pattern connected to the side of the court the point started from (advantage side), and a tee serve pattern connected to the point score in the game (0-0). Participants were assessed on their ability to identify serving patterns by controlling how frequently patterns occurred during matches. Results revealed that patterns need to occur at high frequencies (100% of the time) during matches for skilled juniors to utilise this information to inform their anticipation responses. Study three of this thesis used VR to train 5 skilled tennis players aged between 14 and 18 years (M = 16, SD = 1.67) to utilise patterns of play when they occur at lower frequencies (80% of the time). Additionally, the influence of explicit instructions and no-instruction on learning and performance under pressure was assessed. It was found that exposure to patterns coupled with explicit instructions resulted in faster changes to response time and response accuracy performance, compared to no-instruction learning. Furthermore, instructions during training did not affect performance under pressure conditions. Overall, this thesis extends the perceptual-cognitive skill literature through its use of VR technology and methods of assessing task representativeness. Moreover, this thesis helps guide the design of future perceptual-cognitive skill research through the manipulation of contextual information in the VR environment and use of more implicit and explicit instructional methods to train decision-making performance.
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Books on the topic "Train pattern recognition"

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Fanelli, Anna Maria. Fuzzy Logic and Applications: 9th International Workshop, WILF 2011, Trani, Italy, August 29-31,2011. Proceedings. Berlin, Heidelberg: Springer-Verlag GmbH Berlin Heidelberg, 2011.

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Petrosino, Alfredo, Anna Maria Fanelli, and Witold Pedrycz. Fuzzy Logic and Applications: 9th International Workshop, WILF 2011, Trani, Italy, August 29-31, 2011, Proceedings. Springer, 2012.

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Book chapters on the topic "Train pattern recognition"

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Ramos-Pollán, Raúl, Miguel Ángel Guevara-López, and Eugénio Oliveira. "Introducing ROC Curves as Error Measure Functions: A New Approach to Train ANN-Based Biomedical Data Classifiers." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 517–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16687-7_68.

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Pearson, Caroline, Susan J. Simmons, Karl Ricanek, and Edward L. Boone. "Comparative Analysis of a Hierarchical Bayesian Method for Quantitative Trait Loci Analysis for the Arabidopsis Thaliana." In Pattern Recognition in Bioinformatics, 60–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-75286-8_7.

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Kumar, Sachin, and Marina I. Nezhurina. "Sentiment Analysis on Tweets for Trains Using Machine Learning." In Proceedings of the Tenth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2018), 94–104. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17065-3_10.

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Naik, Ganesh, Dinesh Kant Kumar, and Sridhar Arjunan. "ICA as Pattern Recognition Technique for Gesture Identification." In Cross-Disciplinary Applications of Artificial Intelligence and Pattern Recognition, 367–87. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-61350-429-1.ch020.

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In recent times there is an urgent need for a simple yet robust system to identify natural hand actions and gestures for controlling prostheses and other computer assisted devices. Surface Electromyogram (sEMG) is a non-invasive measure of the muscle activities but is not reliable because there are multiple simultaneously active muscles. This research first establishes the conditions for the applicability of Independent Component Analysis (ICA) pattern recognition techniques for sEMG. Shortcomings related to order and magnitude ambiguity have been identified and a mitigation strategy has been developed by using a set of unmixing matrix and neural network weight matrix corresponding to the specific user. The experimental results demonstrate a marked improvement in the accuracy. The other advantages of this system are that it is suitable for real time operations and it is easy to train by a lay user.
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Bhattacharyya, Siddhartha. "Neural Networks." In Cross-Disciplinary Applications of Artificial Intelligence and Pattern Recognition, 450–98. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-61350-429-1.ch024.

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These networks generally operate in two different modes, viz., supervised and unsupervised modes. The supervised mode of operation requires a supervisor to train the network with a training set of data. Networks operating in unsupervised mode apply topology preservation techniques so as to learn inputs. Representative examples of networks following either of these two modes are presented with reference to their topologies, configurations, types of input-output data and functional characteristics. Recent trends in this computing paradigm are also reported with due regards to the application perspectives.
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Liu, Chaoran, and Wei Qi Yan. "Gait Recognition Using Deep Learning." In Handbook of Research on Multimedia Cyber Security, 214–26. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2701-6.ch011.

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Gait recognition mainly uses different postures of each individual to perform identity authentication. In the existing methods, the full-cycle gait images are used for feature extraction, but there are problems such as occlusion and frame loss in the actual scene. It is not easy to obtain a full-cycle gait image. Therefore, how to construct a highly efficient gait recognition algorithm framework based on a small number of gait images to improve the efficiency and accuracy of recognition has become the focus of gait recognition research. In this chapter, deep neural network CRBM+FC is created. Based on the characteristics of Local Binary Pattern (LBP) and Histogram of Oriented Gradient (HOG) fusion, a method of learning gait recognition from GEI to output is proposed. A brand-new gait recognition algorithm based on layered fu-sion of LBP and HOG is proposed. This chapter also proposes a feature learning network, which uses an unsupervised convolutionally constrained Boltzmann machine to train the Gait Energy Images (GEI).
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Phinyomark, Angkoon, Franck Quaine, and Yann Laurillau. "The Relationship Between Anthropometric Variables and Features of Electromyography Signal for Human–Computer Interface." In Computer Vision, 2234–68. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5204-8.ch098.

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Muscle-computer interfaces (MCIs) based on surface electromyography (EMG) pattern recognition have been developed based on two consecutive components: feature extraction and classification algorithms. Many features and classifiers are proposed and evaluated, which yield the high classification accuracy and the high number of discriminated motions under a single-session experimental condition. However, there are many limitations to use MCIs in the real-world contexts, such as the robustness over time, noise, or low-level EMG activities. Although the selection of the suitable robust features can solve such problems, EMG pattern recognition has to design and train for a particular individual user to reach high accuracy. Due to different body compositions across users, a feasibility to use anthropometric variables to calibrate EMG recognition system automatically/semi-automatically is proposed. This chapter presents the relationships between robust features extracted from actions associated with surface EMG signals and twelve related anthropometric variables. The strong and significant associations presented in this chapter could benefit a further design of the MCIs based on EMG pattern recognition.
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Kar, Pushpendu, and Anusua Das. "Artificial Neural Networks and Learning Techniques." In Advances in Computer and Electrical Engineering, 227–51. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9479-8.ch009.

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The recent craze for artificial neural networks has spread its roots towards the development of neuroscience, pattern recognition, machine learning and artificial intelligence. The theoretical neuroscience is basically converging towards the basic concept that the brain acts as a complex and decentralized computer which can perform rigorous calculations in a different approach compared to the conventional digital computers. The motivation behind the study of neural networks is due to their similarity in the structure of the human central nervous system. The elementary processing component of an Artificial Neural Network (ANN) is called as ‘Neuron'. A large number of neurons interconnected with each other mimic the biological neural network and form an ANN. Learning is an inevitable process that can be used to train an ANN. We can only transfer knowledge to the neural network by the learning procedure. This chapter presents the detailed concepts of artificial neural networks in addition to some significant aspects on the present research work.
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Saraswal, Ashish, and Urvashi Rahul Saxena. "Analysis and Recognition of Handwriting Patterns for Personality Trait Prediction Using Unsupervised Machine Learning Approach." In Advances in Transdisciplinary Engineering. IOS Press, 2022. http://dx.doi.org/10.3233/atde220778.

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The study of the link between handwriting and personality is known as handwriting analysis. It is a specific manner of using handwriting to convey one’s cognitive process. That’s why it’s also known as “brain writing”. Handwriting, like a person’s fingerprint, is a unique trait that cannot be duplicated by two distinct people. It also aids us in determining a person’s emotional state. Handwriting recognition is usually done in a traditional manner, in which a professional handwriting expert examines a person’s handwriting samples for various personality features, which takes a lot of time and effort. As a result, the focus of this study is on developing an automated system that can predict personality characteristics utilizing the machine learning algorithm KNN to identify a certain individual based on their handwriting sample. In today’s modern society, predicting personality traits using face, voice, handwriting, and other techniques is becoming increasingly essential. To develop this approach, we look at a document’s many features such as letter size, word spacing, and slant of words, pen pressure, and baseline, and so on to anticipate the writer’s personality.
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Conference papers on the topic "Train pattern recognition"

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Zhao, Gangming, Zhaoxiang Zhang, He Guan, Peng Tang, and Jingdong Wang. "Rethinking ReLU to Train Better CNNs." In 2018 24th International Conference on Pattern Recognition (ICPR). IEEE, 2018. http://dx.doi.org/10.1109/icpr.2018.8545612.

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Raptis, Michalis, Kamil Wnuk, and Stefano Soatto. "Spike train driven dynamical models for human actions." In 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2010. http://dx.doi.org/10.1109/cvpr.2010.5539885.

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De Souza, Cesar Roberto, Adrien Gaidon, Yohann Cabon, and Antonio Manuel Lopez. "Procedural Generation of Videos to Train Deep Action Recognition Networks." In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2017. http://dx.doi.org/10.1109/cvpr.2017.278.

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Xu, Yihong, Aljosa sep, Yutong Ban, Radu Horaud, Laura Leal-Taixe, and Xavier Alameda-Pineda. "How to Train Your Deep Multi-Object Tracker." In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020. http://dx.doi.org/10.1109/cvpr42600.2020.00682.

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Ozer, Burak, and Marilyn Wolf. "A Train Station Surveillance System: Challenges and Solutions." In 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2014. http://dx.doi.org/10.1109/cvprw.2014.99.

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Belloc, M., S. A. Velastin, R. Fernandez, and M. Jara. "Detection of People Boarding/Alighting a Metropolitan Train using Computer Vision." In 9th International Conference on Pattern Recognition Systems (ICPRS 2018). Institution of Engineering and Technology, 2018. http://dx.doi.org/10.1049/cp.2018.1281.

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Smith, Leslie N., Emily M. Hand, and Timothy Doster. "Gradual DropIn of Layers to Train Very Deep Neural Networks." In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2016. http://dx.doi.org/10.1109/cvpr.2016.515.

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Vemparala, Manoj-Rohit, Nael Fasfous, Alexander Frickenstein, Sreetama Sarkar, Qi Zhao, Sabine Kuhn, Lukas Frickenstein, et al. "Adversarial Robust Model Compression using In-Train Pruning." In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2021. http://dx.doi.org/10.1109/cvprw53098.2021.00016.

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Gupta, Sonal, and Raymond J. Mooney. "Using closed captions to train activity recognizers that improve video retrieval." In 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2009. http://dx.doi.org/10.1109/cvprw.2009.5204202.

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Alwala, Kalyan Vasudev, Abhinav Gupta, and Shubham Tulsiani. "Pretrain, Self-train, Distill: A simple recipe for Supersizing 3D Reconstruction." In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2022. http://dx.doi.org/10.1109/cvpr52688.2022.00375.

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