Journal articles: 'Pattern recognition systems – Evaluation'

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Elmqvist, Niklas, and Ji Soo Yi. "Patterns for visualization evaluation." Information Visualization 14, no. 3 (December 10, 2013): 250–69. http://dx.doi.org/10.1177/1473871613513228.

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We propose a pattern-based approach to evaluating data visualization: a set of general and reusable solutions to commonly occurring problems in evaluating visualization tools, techniques, and systems. Patterns have had significant impact in a wide array of disciplines, particularly software engineering, and we believe that they provide a powerful lens for characterizing visualization evaluation practices by offering practical, tried-and-tested tips, and tricks that can be adopted immediately. The 20 patterns presented here have also been added to a freely editable Wiki repository. The motivation for creating this evaluation pattern language is to (a) capture and formalize “dark” practices for visualization evaluation not currently recorded in the literature, (b) disseminate these hard-won experiences to researchers and practitioners alike, (c) provide a standardized vocabulary for designing visualization evaluation, and (d) invite the community to add new evaluation patterns to a growing repository of patterns.

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Dobler, Lorenz, Oganowski Marek, Eckard Rolf, Günsel Andreas, Müller Antje, Kemper Fritz Hubertus, and Wiesmüller Gerhard Andreas. "Rapid Evaluation of Human Biomonitoring Data Using Pattern Recognition Systems." Journal of Toxicology and Environmental Health, Part A 71, no. 11-12 (June 2, 2008): 816–26. http://dx.doi.org/10.1080/15287390801985778.

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Cao, Runsheng, and Thomas McAvoy. "Evaluation of a pattern recognition adaptive PID controller." Automatica 26, no. 4 (July 1990): 797–801. http://dx.doi.org/10.1016/0005-1098(90)90055-m.

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Nagaraju, C., D. Sharadamani, C. Maheswari, and D. Vishnu Vardhan. "Evaluation of LBP-Based Facial Emotions Recognition Techniques to Make Consistent Decisions." International Journal of Pattern Recognition and Artificial Intelligence 29, no. 06 (August 12, 2015): 1556008. http://dx.doi.org/10.1142/s021800141556008x.

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Decision making is one of the smouldering problems in day to day works. Human emotions play crucial role in decision-making systems. While person is in high emotion he cannot make proper decision. Robust local binary pattern (RLBP) operator is more powerful to recognize the emotions and extends the features of local binary pattern (LBP). However, there are some precincts like discriminating bright faces against dark features and vice versa and intra-class variances increase. The RLBP solves this problem by finding minimum of LBP codes and their complements. However, it miss the mark for different local structures a similar feature is obtained, weak contrast local patterns and similar strong contrast local patterns. Hence, the discriminative robust local binary pattern (DRLBP) method is proposed to retain the contrast information of image patterns next to considering both edge and texture information. Nevertheless, LBP family methods are highly sensitive to noise. To trounce these drawbacks this paper extends fuzzy rule-based DRLBP which is more robust to noise, low contrasted, uneven lighting conditions, variations in expressions and rotation variant images.

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Chang, C. S. "Online transient stability evaluation of interconnected power systems using pattern recognition strategy." IEE Proceedings C Generation, Transmission and Distribution 140, no. 2 (1993): 115. http://dx.doi.org/10.1049/ip-c.1993.0016.

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Bennett, Laura F. "Knowledge-based evaluation of the segmentation component in automatic pattern recognition systems." Optical Engineering 30, no. 2 (February 1, 1991): 154. http://dx.doi.org/10.1117/12.55782.

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Fang, Chi, Changsong Liu, Liangrui Peng, and Xiaoqing Ding. "Automatic performance evaluation of printed Chinese character recognition systems." International Journal on Document Analysis and Recognition 4, no. 3 (March 1, 2002): 177–82. http://dx.doi.org/10.1007/s100320200068.

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Kavdır, İ., and D. E. Guyer. "Evaluation of different pattern recognition techniques for apple sorting." Biosystems Engineering 99, no. 2 (February 2008): 211–19. http://dx.doi.org/10.1016/j.biosystemseng.2007.09.019.

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Gao, Yuanheng, Leilei Wang, and Heqing Zhang. "Intelligent urban ecological suitability system based on pattern recognition." Journal of Intelligent & Fuzzy Systems 39, no. 4 (October 21, 2020): 5009–16. http://dx.doi.org/10.3233/jifs-179986.

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Today, with the rapid development of urbanization, the ecological and environmental problems of the city have become increasingly serious and have become the focus of the world. The most important issue facing the majority of ecological workers is how to apply the theory of ecology to solve today’s problems. The various environmental problems faced in urban life and the sustainable development of the city’s ecological civilization. How ecological planning is used to coordinate the relationship between people and the natural environment and natural resources is increasingly gaining attention and expanding the range and scope of its applications. However, as an ecological suitability analysis based on ecological planning, many analytical methods and systems are still being explored and developed due to the geographical complexity and factor diversity involved. In recent years, with the rapid development of computer hardware and software technology, pattern recognition has received more and more attention, pattern recognition and image processing technology has become more and more perfect, and has been successfully applied in more and more fields. This thesis begins to focus on the urban ecological suitability content based on pattern recognition technology and image processing. The main contents of this thesis include: introducing the background of urban ecological suitability and the status quo of ecological suitability analysis and existing research methods. According to the structure of the urban ecosystem and the national standards for the construction of ecological systems and ecological cities, an indicator system for ecological suitability evaluation is established. A pattern recognition system and common pattern recognition and image processing methods are introduced. Based on some common evaluation methods and models, the pattern recognition technology theory and image processing technology are introduced into the urban ecological suitability analysis. Based on the image system theory and vector projection principle, the ecological suitability analysis is established. Associated projection model. The model considers the evaluation sample and the quality standards at each level as vectors, and respectively projects the same vector ideal. Based on establishing the ecological suitability evaluation index system and standards, the ecological suitability was evaluated by using the model.

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CAPORASO, J. GREGORY, WILLIAM A. BAUMGARTNER, DAVID A. RANDOLPH, K. BRETONNEL COHEN, and LAWRENCE HUNTER. "RAPID PATTERN DEVELOPMENT FOR CONCEPT RECOGNITION SYSTEMS: APPLICATION TO POINT MUTATIONS." Journal of Bioinformatics and Computational Biology 05, no. 06 (December 2007): 1233–59. http://dx.doi.org/10.1142/s0219720007003144.

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The primary biomedical literature is being generated at an unprecedented rate, and researchers cannot keep abreast of new developments in their fields. Biomedical natural language processing is being developed to address this issue, but building reliable systems often requires many expert-hours. We present an approach for automatically developing collections of regular expressions to drive high-performance concept recognition systems with minimal human interaction. We applied our approach to develop MutationFinder, a system for automatically extracting mentions of point mutations from the text. MutationFinder achieves performance equivalent to or better than manually developed mutation recognition systems, but the generation of its 759 patterns has required only 5.5 expert-hours. We also discuss the development and evaluation of our recently published high-quality, human-annotated gold standard corpus, which contains 1,515 complete point mutation mentions annotated in 813 abstracts. Both MutationFinder and the complete corpus are publicly available at .

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Shamim, Azra, Vimala Balakrishnan, and Muhammad Tahir. "Evaluation of opinion visualization techniques." Information Visualization 14, no. 4 (September 24, 2014): 339–58. http://dx.doi.org/10.1177/1473871614550537.

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In this article, we are reporting the findings of a usability study of opinion mining systems’ visualizations. The objectives of this study are to first to rank the visualizations of the opinion mining systems and second to identify important visualization metrics. A questionnaire survey was designed to ask users their level of agreement or disagreement about the 11 selected visualizations against a set of information visualization metrics on a Likert scale. The data were collected by conducting seminars and using a web-based online questionnaire (N = 146). The collected data were analyzed using descriptive statistics and an independent sample t-test to rank the visualizations and investigate differences between perceptions of the two groups of respondents (the participants of the seminars and the online questionnaire), respectively. The results revealed that simple, eye pleasing, easy to understand, user-friendly visualizations with less pre-knowledge required rated higher than others. It is concluded that the participants of the online questionnaire mostly required more pre-knowledge to comprehend the visualizations as compared to the participants of the seminars. The important information visualization metrics are eye pleasing, easy to understand, user-friendly, informative design, usefulness, and representation style. The results of this study could aid in the design and development of visualizations for opinion mining systems.

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Saraiya, Purvi, Chris North, and Karen Duca. "Visualizing Biological Pathways: Requirements Analysis, Systems Evaluation and Research Agenda." Information Visualization 4, no. 3 (June 23, 2005): 191–205. http://dx.doi.org/10.1057/palgrave.ivs.9500102.

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Pathway diagrams are used by life scientists to represent complex interactions at the molecular level in living cells. The recent shift towards data-intensive bioinformatics and systems-level science has created a strong need for advanced pathway visualizations that support exploratory analysis. This paper presents a comprehensive list of requirements for pathway visualization systems, based on interviews conducted to understand life scientists' needs for pathway analysis. A variety of existing pathway visualization systems are examined, to analyze common approaches by which the contemporary systems address these requirements. A heuristic evaluation, by biology domain experts, of five popular pathway visualization systems is conducted to analyze the end-user perception of these systems. Based on these studies, a research agenda is presented concerning five critical requirements for pathway visualization systems. If addressed effectively, these requirements can prove to be most helpful in supporting exploratory pathway analysis. These include: (1) automated construction and updating of pathways by searching literature databases, (2) overlaying information on pathways in a biologically relevant format, (3) linking pathways to multi-dimensional data from high-throughput experiments such as microarrays, (4) overviewing multiple pathways simultaneously with interconnections between them, (5) scaling pathways to higher levels of abstraction to analyze effects of complex molecular interactions at higher levels of biological organization.

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Wang, Xiangdong, Ying Yang, Hong Liu, and Yueliang Qian. "The PIC-TDD Framework of Test Data Design for Pattern Recognition Systems." International Journal of Advanced Pervasive and Ubiquitous Computing 6, no. 4 (October 2014): 43–62. http://dx.doi.org/10.4018/ijapuc.2014100104.

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In this paper, a new approach is proposed for the design of test data for pattern recognition systems. In the theoretical framework put forward, performance on the population of data is viewed as expectation of a random variable, and the purpose of test is to estimate the parameter. While the most popular method of test data design is random sampling, a novel approach based on performance influencing classes is proposed, which can achieve unbiased estimation and the variance of estimation is much lower than that from random sample. The method is applied to the evaluation of systems for broadcasting news segmentation, and experimental results show the advantages over the random sampling approach.

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Ltifi, Hela, Saber Amri, and Mounir Ben Ayed. "Fuzzy logic–based evaluation of visualizations generated by intelligent decision support systems." Information Visualization 17, no. 1 (November 7, 2016): 3–21. http://dx.doi.org/10.1177/1473871616674046.

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The development of intelligent decision support systems requires much research effort to solve decision-making problems’ complexity. In fact, the combination of both intelligent components and visualization aspects in intelligent decision support system required a lot of efforts in order to develop advanced information visualization schemes for decision-making processes. For this, an efficient evaluation of these systems has become a major concern for applications in multiple fields. The reports of the existing usability evaluation studies are helpful to verify the potential and the limitations of these tools. However, it is important to integrate more relevant metrics for visual analytics tasks in dynamic intelligent decision support system. The proposed method consists of a questionnaire that is given to the users and a subsequent analysis of the resulting data using fuzzy logic. The advantage of the fuzzy model is its ability to transform the input survey scores into linguistic variables, as well as linguistic evaluation of the overall intelligent decision support system visualization tool. With this approach, it is possible to model the vagueness in the ordinal judgments obtained from the users’ evaluation about the visualizations of intelligent decision support system and to support uncertainty in such evaluation.

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Bours, Patrick, and Soumik Mondal. "Performance evaluation of continuous authentication systems." IET Biometrics 4, no. 4 (December 2015): 220–26. http://dx.doi.org/10.1049/iet-bmt.2014.0070.

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Hilgurt, S. Ya. "A Method of Accelerated Quantitative Evaluation of Components of FPGA-based Security Systems." Èlektronnoe modelirovanie 44, no. 5 (July 10, 2022): 03–24. http://dx.doi.org/10.15407/emodel.44.05.003.

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Recently, various approaches have been successfully used in information security tools to detect harmful activity, including artificial intelligence technologies. But only the signature approach can completely eliminate recognition errors. That is especially important for critical infrastructure objects. One of the main disadvantages of signature tools is the high computational complexity. Therefore, the developers of such systems turn to hardware implementation, primarily on a reconfigurable platform, that is, using FPGAs. The ability to quickly reprogram FPGAs gives reconfigurable security systems unprecedented flexibility and adaptive possibilities. There are many different approaches to the construction of hardware pattern matching circuits (that are parts of signatures). Choosing the optimal technical solution for recognizing a specific set of patterns is a non-trivial task. For a more efficient distribution of patterns between components, it is necessary to solve an optimization task, the objective function of which includes the quantitative technical characteristics of hardware recognition schemes. Finding these values at each step of the algorithm by performing the full digital circuit synthesis procedure by the CAD is an unacceptably slow approach. The method proposed in this study for the accelerated quantitative evaluation of components of reconfigurable signature-based security systems, based on the use of the so-called evaluation functions, allows solving the problem.

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Naijit, Kittimasak. "Intelligent Face Tracking for Collaborative Synchronous e-Learning using Pattern Recognition Model." International Journal of Computers 15 (November 24, 2021): 105–9. http://dx.doi.org/10.46300/9108.2021.15.16.

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Intelligent Face Tracking for Collaborative Synchronous e-Learning using Pattern Recognition Model can provide high levels of interaction for distance learning initiatives. With the rapid evolution of technology, face recognition login and tracking, continuous product evaluation is necessary to ensure optimal methods and resources for connecting students, instructors, and educational content in rich, online learning communities. This article presents the analysis of online, synchronous learning solutions. Focusing on their abilities to meet technical and pedagogical needs in higher education. To make a solid comparison, the systems were examined in online classrooms with instructors, guest speakers, and students. Relative to usability, instructional needs, technical aspects and compatibility are outlined for systems.

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AL-ALAWI, RAIDA. "PERFORMANCE EVALUATION OF FUZZY SINGLE LAYER WEIGHTLESS NEURAL NETWORK." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 15, no. 03 (June 2007): 381–93. http://dx.doi.org/10.1142/s021848850700473x.

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The paper evaluates the performance of a neuro-fuzzy pattern classification system based on the weightless neural network architecture. The system utilizes a Single Layer Weightless Neural Network (SLWNN) to extract the features vector that measures the similarity of the input pattern to the different classification groups. In contrast to the traditional crisp Winner-Takes-All (WTA) classification scheme used by SLWNN, our system uses a Fuzzy Inference System (FIS) for classification. The network is trained by a hybrid learning scheme that combines a single pass learning phase for training the SLWNN followed by a supervised learning phase for extracting a set of fuzzy rules suitable to classify the training set. The FIS learns fuzzy rules from the feature vectors generated by the SLWNN for the set of training patterns. The recognition of handwritten numerals is employed as a test-bed to demonstrate the effectiveness of the proposed neuro-fuzzy system. Experimental results show that the performance of the proposed system surpasses the performance of the traditional SLWNN.

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Song, Guanggang, Bin Li, and Yuqing He. "Container Terminal Logistics Generalized Computing Architecture and Green Initiative Computational Pattern Performance Evaluation." Information 10, no. 12 (December 5, 2019): 383. http://dx.doi.org/10.3390/info10120383.

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Container terminals are the typical representatives of complex supply chain logistics hubs with multiple compound attributes and multiple coupling constraints, and their operations are provided with the strong characteristics of dynamicity, nonlinearity, coupling, and complexity (DNCC). From the perspective of computational logistics, we propose the container terminal logistics generalized computing architecture (CTL-GCA) by the migration, integration, and fusion of the abstract hierarchy, design philosophy, execution mechanism, and automatic principles of computer organization, computing architecture, and operating system. The CTL-GCA is supposed to provide the problem-oriented exploration and exploitation elementary frameworks for the abstraction, automation, and analysis of green production at container terminals. The CTL-GCA is intended to construct, evaluate, and improve the solution to planning, scheduling, and decision at container terminals, which all are nondeterministic polynomial hard problems. Subsequently, the logistics generalized computational pattern recognition and performance evaluation of a practical container terminal service case study is launched by the qualitative and quantitative approach from the sustainable perspective of green production. The case study demonstrates the application, utilization, exploitation, and exploration of CTL-GCA preliminarily, and finds the unsustainable patterns of production at the container terminal. From the above, we can draw the following conclusions. For one thing, the CTL-GCA makes a definition of the abstract and automatic running architecture of logistics generalized computation for container terminals (LGC-CT), which provides an original framework for the design and implementation of control and decision mechanism and algorithm. For another, the CTL-GCA can help us to investigate the roots of DNCC thoroughly, and then the CTL-GCA makes for conducting the efficient and sustainable running pattern recognition of LGC-CT. It is supposed to provide a favorable guidance and supplement to define, design, and implement the agile, efficient, sustainable, and robust task scheduling and resource allocation for container terminals by computational logistics whether in the strategy level or the tactical one.

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Yang, Chengdong, Wenyin Zhang, Jilin Zou, Shunbo Hu, and Jianlong Qiu. "Feature Selection in Decision Systems: A Mean-Variance Approach." Mathematical Problems in Engineering 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/268063.

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Uncertainty measure is an important implement for characterizing the degree of uncertainty. It has been extensively applied in pattern recognition and data clustering. Because of instability of traditional uncertainty measures, mean-variance measure (MVM) is utilized to perform feature selection, which could depress disturbances and noises effectively. Thereby, a novel evaluation function based on MVM is designed. The forward greedy search algorithm (FGSA) with the proposed evaluation function is exploited to perform feature selection. Experiment analysis shows the validity and effectiveness of MVM.

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Vildjiounaite, Elena, Vesa Kyllönen, and Heikki Ailisto. "Empirical evaluation of combining unobtrusiveness and security requirements in multimodal biometric systems." Image and Vision Computing 27, no. 3 (February 2009): 279–92. http://dx.doi.org/10.1016/j.imavis.2007.12.001.

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Jywe, Wen-Yuh, Tung-Hsien Hsieh, Po-Yu Chen, Ming-Shi Wang, and Yu-Tso Lin. "Evaluation of tool scraping wear conditions by image pattern recognition system." International Journal of Advanced Manufacturing Technology 105, no. 1-4 (October 26, 2019): 1791–99. http://dx.doi.org/10.1007/s00170-019-04360-3.

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Liu, Yujie, Scott Barlowe, Yaqin Feng, Jing Yang, and Min Jiang. "Evaluating exploratory visualization systems: A user study on how clustering-based visualization systems support information seeking from large document collections." Information Visualization 12, no. 1 (October 8, 2012): 25–43. http://dx.doi.org/10.1177/1473871612459995.

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Iterative, opportunistic and evolving visual sense-making has been an important research topic as it assists users in overcoming ever-increasing information overload. Exploratory visualization systems (EVSs) maximize the amount of information users can gain through learning and have been widely used in scientific discovery and decision-making contexts. Although many EVSs have been developed recently, there is a lack of general guidance on how to evaluate such systems. Researchers face challenges such as understanding the cognitive learning process supported by these systems. In this paper, we present a formal user study on Newdle, a clustering-based EVS for large news collections, shedding light on a general methodology for EVS evaluation. Our approach is built upon cognitive load theory, which takes the user as well as the system as the focus of evaluation. The carefully designed procedures allow us to thoroughly examine the user’s cognitive process as well as control the variability among human subjects. Through this study, we analyse how and why clustering-based EVSs benefit (or hinder) users in a variety of information-seeking tasks. We also summarize leverage points for designing clustering-based EVSs.

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Sain, Kunal, Abhishek Dasgupta, and Utpal Garain. "EMERS: a tree matching–based performance evaluation of mathematical expression recognition systems." International Journal on Document Analysis and Recognition (IJDAR) 14, no. 1 (June 1, 2010): 75–85. http://dx.doi.org/10.1007/s10032-010-0121-9.

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Setlur, Srirangaraj, Alfred Lawson, Venugopal Govindaraju, and Sargur Srihari. "Large scale address recognition systems Truthing, testing, tools, and other evaluation issues." International Journal on Document Analysis and Recognition 4, no. 3 (March 1, 2002): 154–69. http://dx.doi.org/10.1007/s100320200069.

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Moreira, Alexandre C., Helmo K. M. Paredes, Wesley A. de Souza, Pedro H. J. Nardelli, Fernando P. Marafão, and Luiz C. P. da Silva. "Evaluation of Pattern Recognition Algorithms for Applications on Power Factor Compensation." Journal of Control, Automation and Electrical Systems 29, no. 1 (December 5, 2017): 75–90. http://dx.doi.org/10.1007/s40313-017-0352-9.

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Boluma Mangata, Bopatriciat, Dominique Ilunga Nakashama, Donatien Kadima Muamba, and Parfum Bukanga Christian. "Implementation of an access control system based on bimodal biometrics with fusion of global decisions: Application to facial recognition and fingerprints." Journal of Computing Research and Innovation 7, no. 2 (October 4, 2022): 43–53. http://dx.doi.org/10.24191/jcrinn.v7i2.289.

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Single-mode biometric systems suffer from several problems that make them unsuitable for current biometric applications that require high levels of reliability and security. These problems include the use of a single biometric trait that is prone to noise, poor capture, lack of biometric points, and deterioration of biometric input quality. In this paper, we are interested in decision fusion access control on a biometric bimodal pattern recognition system based on fingerprints and facial recognition. To realize this access control system based on facial recognition and fingerprints, we used an embedded system under Arduino, we programmed electronic systems for the automatic opening of doors without human action being. The performance evaluation of decision fusion access control on a biometric bimodal pattern recognition system is realized by means of the confusion matrix, the calculations of the evaluation parameters (Sensitivity, Specificity, Positive Predictive Value, Negative Predictive Value and False Negative). From a sample of 500 individuals, 250 of whom were registered and 250 non-registered, our access control system obtained the results of 248 true positives, 2 false negative, 1 false positive and 249 true negatives which constitute our confusion matrix. However, from the set of tests performed we can conclude that by taking advantage of the fusion of these two modalities, we increase the verification performance of system as the verification performance of bimodal system (fingerprint decision fusion and facial recognition) is applied to give even better results compared to single mode systems.

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Scholtz, Jean. "Developing guidelines for assessing visual analytics environments." Information Visualization 10, no. 3 (July 2011): 212–31. http://dx.doi.org/10.1177/1473871611407399.

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In this article, we develop guidelines for evaluating visual analytics environments based on a synthesis of reviews for the entries to the 2009 Visual Analytics Science and Technology (VAST) Symposium Challenge and from a user study with professional intelligence analysts. By analyzing the 2009 VAST Challenge reviews, we gained a better understanding of what is important to our reviewers, both visualization researchers and professional analysts. We also report on a small user study with professional analysts to determine the important factors that they use in evaluating visual analysis systems. We also looked at guidelines developed by researchers in various domains and synthesized the results from these three efforts into an initial set for use by others in the community. One challenge for future visual analytics systems is to help in the generation of reports. In our user study, we also worked with analysts to understand the criteria they used to evaluate the quality of analytic reports. We propose that this knowledge will be useful as researchers look at systems to automate some of the report generation.1 From these two efforts, we produced some initial guidelines for evaluating visual analytics environments and for the evaluation of analytic reports. It is important to understand that these guidelines are initial drafts and are limited in scope as the visual analytics systems we evaluated were used in specific tasks. We propose these guidelines as a starting point for the Visual Analytics Community.

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Clemens, Elene A., Davide Angeletti, Beth C. Holbrook, Scott Tyler Aycock, Jonathan W. Yewdell, and Martha A. Alexander-Miller. "Evaluation of epitope specificity in the immune response to PR8 H1N1 influenza virus infection in neonatal and adult African Green monkeys." Journal of Immunology 202, no. 1_Supplement (May 1, 2019): 74.13. http://dx.doi.org/10.4049/jimmunol.202.supp.74.13.

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Abstract Generation of effective antibody responses following virus infection is challenging in neonates as a result of impairments in the innate and adaptive immune systems that characterize early life. This leaves them particularly susceptible to severe disease following virus infection, e.g. influenza A virus. A potential contributor to the efficacy of the immune response generated following infection is the epitope specificity of the elicited antibodies. Previous studies in adult mice have reported a defined and reproducible pattern of immunodominance among antibodies directed to the neutralizing epitopes on the head of the influenza hemagglutinin (HA) molecule. The impact of age on the immunodominance pattern of HA-specific antibodies has not been explored. To address this, epitope recognition was quantified in plasma collected from newborn and adult African Green monkeys on d14 following PR8 influenza virus infection. For these analyses we used HA molecules engineered to singly express each of the neutralizing epitopes identified on the HA head. In addition, we used a construct that restricts recognition to the stem region of PR8 HA. Our analyses revealed a similar pattern of recognition by HA-specific IgG between infants and adults in the 14 day period following infection. In contrast, the HA-specific IgM pools exhibited distinct binding patterns in these two groups. The most striking of these differences was in the production of antibodies capable of recognizing the stem portion of HA. These data suggest antibody immunodominance patterns may be modulated with age and sheds new light on the regulation of potentially broadly protective stem responses to influenza virus.

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Mišković, Dragiša, Milan Gnjatović, Perica Štrbac, Branimir Trenkić, Nikša Jakovljević, and Vlado Delić. "Hybrid methodological approach to context-dependent speech recognition." International Journal of Advanced Robotic Systems 14, no. 1 (January 1, 2017): 172988141668713. http://dx.doi.org/10.1177/1729881416687131.

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Although the importance of contextual information in speech recognition has been acknowledged for a long time now, it has remained clearly underutilized even in state-of-the-art speech recognition systems. This article introduces a novel, methodologically hybrid approach to the research question of context-dependent speech recognition in human–machine interaction. To the extent that it is hybrid, the approach integrates aspects of both statistical and representational paradigms. We extend the standard statistical pattern-matching approach with a cognitively inspired and analytically tractable model with explanatory power. This methodological extension allows for accounting for contextual information which is otherwise unavailable in speech recognition systems, and using it to improve post-processing of recognition hypotheses. The article introduces an algorithm for evaluation of recognition hypotheses, illustrates it for concrete interaction domains, and discusses its implementation within two prototype conversational agents.

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Nejadeh, Mohammad, Peyman Bayat, Jalal Kheirkhah, and Hassan Moladoust. "Evaluation of Pattern Recognition Techniques in Response to Cardiac Resynchronization Therapy (CRT)." Journal of Information Systems and Telecommunication (JIST) 8, no. 31 (November 14, 2020): 197–206. http://dx.doi.org/10.29252/jist.8.31.1197.

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Nejadeh, Mohammad, Peyman Bayat, Jalal Kheirkhah, and Hassan Moladoust. "Evaluation of Pattern Recognition Techniques in Response to Cardiac Resynchronization Therapy (CRT)." Journal of Information Systems and Telecommunication 8, no. 31 (July 22, 2020): 197–206. http://dx.doi.org/10.52547/jist.8.31.197.

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Gatc, Jullend. "Image Segmentation of Cattle Muzzle Using Region Merging Statistical Technic." ComTech: Computer, Mathematics and Engineering Applications 6, no. 4 (December 1, 2015): 555. http://dx.doi.org/10.21512/comtech.v6i4.2189.

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Making an identification system that able to assist in obtaining, recording and organizing information is the first step in developing any kind of recording system. Nowadays, many recording systems were developed with artificial markers although it has been proved that it has many limitations. Biometrics use of animals provides a solution to these restrictions. On a cattle, biometric features contained in the cattle muzzle that can be used as a pattern recognition sample. Pattern recognition methods can be used for the development of cattle identification system utilizing biometric found on the cattle muzzle using digital image processing techniques. In this study, we proposed cattle muzzle identification method using segmentation Statistical Region Merging (SRM). This method aims to identify specific patterns found on the cattle muzzle by separating the object pattern (foreground) from unnecessary information (background) This method is able to identified individual cattle based on the pattern of it muzzle. Based on our evaluation, this method can provide good performance results. This method good performance can be seen from the precision and recall : 87% and the value of ROC : 0.976. Hopefully this research can be used to help identify cattle accurately on the recording process.

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Siemiatkowski, Mieczyslaw. "Application of Syntactic Pattern Recognition Approach in Design and Optimisation of Group Machining Systems." Solid State Phenomena 165 (June 2010): 342–47. http://dx.doi.org/10.4028/www.scientific.net/ssp.165.342.

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The focus of this paper is on planning applications of group technology (GT) and the design of related layouts for multi-assortment cellular manufacturing (CM) of mechanical parts. A methodical approach is developed to optimally solve cell formation (CF) problems with CM systems design, which consists in the identification of machine cells and corresponding part families. The approach involves the use of syntactic pattern recognition concepts from the field of artificial intelligence (AI). It is based on methods of strings matching and clustering, applied extensively in genetics, molecular chemistry and biological sciences. The CF strategy followed implies clustering character strings that denote machine sequences in process routings. Numerical quantification of dissimilarity between part routings by a specific distance measure and the concept of average linkage clustering algorithm (ALCA) are at the core of the clustering procedure. The use of the approach is studied numerically with regard to a real industrial case and diverse layouts of cellular system are considered, including those with machine sharing. Group process alternatives with given system layouts and workflows prototyped by definite job sequencing rules, are simulated using programmed models. Generated design solutions are subjected to further analysis and quantitative evaluation by assumed measures of their operational performance.

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ZHAO, FENG. "MACHINE RECOGNITION AS REPRESENTATION AND SEARCH — A SURVEY." International Journal of Pattern Recognition and Artificial Intelligence 05, no. 05 (December 1991): 715–47. http://dx.doi.org/10.1142/s0218001491000429.

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Generality, representation, and control have been the central issues in machine recognition. Model-based recognition is the search for consistent matches of model and image features. We present a comparative framework for the evaluation of different approaches, particularly those of ACRONYM, RAF, and Ikeuchi et al. The strengths and weaknesses of these approaches are discussed and compared, and remedies are suggested. Various trade-offs made in the implementations are analyzed with respect to the systems' intended task domains. The requirements for a versatile recognition system are motivated. Several directions for future research are pointed out.

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DASARATHY, BELUR V. "CORPS_F: A NEW TOOL FOR FEATURE ASSESSMENT IN IMPRECISELY SUPERVISED OR FUZZY ENVIRONMENTS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 03, no. 04 (December 1995): 451–64. http://dx.doi.org/10.1142/s0218488595000232.

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This study presents an effective approach to the hitherto little addressed problem of feature assessment and selection for pattern recognition in imprecisely supervised environments. Unlike in classical supervised environments wherein the representative training samples have crisp class labels, here the samples have fuzzy memberships in several of the different pattern classes in the environment. The new methodology reported here is an outgrowth of a recently developed tool CORPS—Class Overlap Region Partitioning Scheme initially designed for operation in supervised environments and extended later for operation in imperfectly supervised environments. The emphasis here has been the development of a computationally efficient scheme capable of evaluating as rapidly as practical a large number of features individually as to their discrimination potential based on which a smaller subset may be selected, if so desired, for more detailed evaluation in different combinations by other tools.

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Biggio, B., Z. Akhtar, G. Fumera, G. L. Marcialis, and F. Roli. "Security evaluation of biometric authentication systems under real spoofing attacks." IET Biometrics 1, no. 1 (2012): 11. http://dx.doi.org/10.1049/iet-bmt.2011.0012.

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CHEN, QINGCAI, XIAOLONG WANG, PENGFEI SU, and YI YAO. "AUTO ADAPTED ENGLISH PRONUNCIATION EVALUATION: A FUZZY INTEGRAL APPROACH." International Journal of Pattern Recognition and Artificial Intelligence 22, no. 01 (February 2008): 153–68. http://dx.doi.org/10.1142/s0218001408006090.

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To evaluate the pronunciation skills of spoken English is one of the key tasks for computer-aided spoken language learning (CALL). While most of the researchers focus on improving the speech recognition techniques to build a reliable evaluation system, another important aspect of this task has been ignored, i.e. the pronunciation evaluation model that integrates both the reliabilities of existing speech processing systems and the learner's pronunciation personalities. To take this aspect into consideration, a Sugeno integral-based evaluation model is introduced in this paper. At first, the English phonemes that are hard to be distinguished (HDP) for Chinese language learners are grouped into different HDP sets. Then, the system reliabilities for distinguishing the phonemes within a HDP set are computed from the standard speech corpus and are integrated with the phoneme recognition results under the Sugeno integral framework. The fuzzy measures are given for each subset of speech segments that contains n occurrences of phonemes within a HDP set. Rather than providing a quantity of scores, the linguistic descriptions of evaluation results are given by the model, which is more helpful for the users to improve their spoken language skills. To get a better performance, generic algorithm (GA)-based parameter optimization is also applied to optimize the model parameters. Experiments are conducted on the Sphinx-4 speech recognition platform. They show that, with 84.7% of average recognition rate of the SR system on standard speech corpus, our pronunciation evaluation model has got reasonable and reliable results for three kinds of test corpora.

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Shyam, Radhey, and Yogendra Narain Singh. "Multialgorithmic Frameworks for Human Face Recognition." Journal of Electrical and Computer Engineering 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/4645971.

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This paper presents a critical evaluation of multialgorithmic face recognition systems for human authentication in unconstrained environment. We propose different frameworks of multialgorithmic face recognition system combining holistic and texture methods. Our aim is to combine the uncorrelated methods of the face recognition that supplement each other and to produce a comprehensive representation of the biometric cue to achieve optimum recognition performance. The multialgorithmic frameworks are designed to combine different face recognition methods such as (i) Eigenfaces and local binary pattern (LBP), (ii) Fisherfaces and LBP, (iii) Eigenfaces and augmented local binary pattern (A-LBP), and (iv) Fisherfaces and A-LBP. The matching scores of these multialgorithmic frameworks are processed using different normalization techniques whereas their performance is evaluated using different fusion strategies. The robustness of proposed multialgorithmic frameworks of face recognition system is tested on publicly available databases, for example, AT & T (ORL) and Labeled Faces in the Wild (LFW). The experimental results show a significant improvement in recognition accuracies of the proposed frameworks of face recognition system in comparison to their individual methods. In particular, the performance of the multialgorithmic frameworks combining face recognition methods with the devised face recognition method such as A-LBP improves significantly.

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Ottley, Alvitta, R. Jordan Crouser, Caroline Ziemkiewicz, and Remco Chang. "Manipulating and controlling for personality effects on visualization tasks." Information Visualization 14, no. 3 (December 4, 2013): 223–33. http://dx.doi.org/10.1177/1473871613513227.

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Researchers in human–computer interaction and visualization have recently been challenged to develop a better understanding of users’ underlying cognitive processes in order to improve system design and evaluation. While existing studies lay a critical foundation for understanding the role of cognitive processes and individual differences in visualization, concretizing the intuition that each user experiences a visual interface through an individual cognitive lens is only half the battle. In this article, we investigate the impact of manipulating users’ personality on observed behavior when using a visualization. In a targeted study, we demonstrate that personality priming can result in changes in behavior when interacting with visualizations. We then discuss how this and similar techniques could be used to control for personality effects when designing and evaluating visualizations systems.

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Chen, Biao, Chaoyang Chen, Jie Hu, Zain Sayeed, Jin Qi, Hussein F. Darwiche, Bryan E. Little, et al. "Computer Vision and Machine Learning-Based Gait Pattern Recognition for Flat Fall Prediction." Sensors 22, no. 20 (October 19, 2022): 7960. http://dx.doi.org/10.3390/s22207960.

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Background: Gait recognition has been applied in the prediction of the probability of elderly flat ground fall, functional evaluation during rehabilitation, and the training of patients with lower extremity motor dysfunction. Gait distinguishing between seemingly similar kinematic patterns associated with different pathological entities is a challenge for the clinician. How to realize automatic identification and judgment of abnormal gait is a significant challenge in clinical practice. The long-term goal of our study is to develop a gait recognition computer vision system using artificial intelligence (AI) and machine learning (ML) computing. This study aims to find an optimal ML algorithm using computer vision techniques and measure variables from lower limbs to classify gait patterns in healthy people. The purpose of this study is to determine the feasibility of computer vision and machine learning (ML) computing in discriminating different gait patterns associated with flat-ground falls. Methods: We used the Kinect® Motion system to capture the spatiotemporal gait data from seven healthy subjects in three walking trials, including normal gait, pelvic-obliquity-gait, and knee-hyperextension-gait walking. Four different classification methods including convolutional neural network (CNN), support vector machine (SVM), K-nearest neighbors (KNN), and long short-term memory (LSTM) neural networks were used to automatically classify three gait patterns. Overall, 750 sets of data were collected, and the dataset was divided into 80% for algorithm training and 20% for evaluation. Results: The SVM and KNN had a higher accuracy than CNN and LSTM. The SVM (94.9 ± 3.36%) had the highest accuracy in the classification of gait patterns, followed by KNN (94.0 ± 4.22%). The accuracy of CNN was 87.6 ± 7.50% and that of LSTM 83.6 ± 5.35%. Conclusions: This study revealed that the proposed AI machine learning (ML) techniques can be used to design gait biometric systems and machine vision for gait pattern recognition. Potentially, this method can be used to remotely evaluate elderly patients and help clinicians make decisions regarding disposition, follow-up, and treatment.

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BERTOLAMI, ROMAN, and HORST BUNKE. "INTEGRATION OF n-GRAM LANGUAGE MODELS IN MULTIPLE CLASSIFIER SYSTEMS FOR OFFLINE HANDWRITTEN TEXT LINE RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 22, no. 07 (November 2008): 1301–21. http://dx.doi.org/10.1142/s0218001408006855.

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Current multiple classifier systems for unconstrained handwritten text recognition do not provide a straightforward way to utilize language model information. In this paper, we describe a generic method to integrate a statistical n-gram language model into the combination of multiple offline handwritten text line recognizers. The proposed method first builds a word transition network and then rescores this network with an n-gram language model. Experimental evaluation conducted on a large dataset of offline handwritten text lines shows that the proposed approach improves the recognition accuracy over a reference system as well as over the original combination method that does not include a language model.

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Rusiñol, Marçal, and Josep Lladós. "A performance evaluation protocol for symbol spotting systems in terms of recognition and location indices." International Journal on Document Analysis and Recognition (IJDAR) 12, no. 2 (March 19, 2009): 83–96. http://dx.doi.org/10.1007/s10032-009-0083-y.

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Blasch, Gerald, Zhenhai Li, and James A. Taylor. "Multi-temporal yield pattern analysis method for deriving yield zones in crop production systems." Precision Agriculture 21, no. 6 (May 6, 2020): 1263–90. http://dx.doi.org/10.1007/s11119-020-09719-1.

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Abstract Easy-to-use tools using modern data analysis techniques are needed to handle spatio-temporal agri-data. This research proposes a novel pattern recognition-based method, Multi-temporal Yield Pattern Analysis (MYPA), to reveal long-term (> 10 years) spatio-temporal variations in multi-temporal yield data. The specific objectives are: i) synthesis of information within multiple yield maps into a single understandable and interpretable layer that is indicative of the variability and stability in yield over a 10 + years period, and ii) evaluation of the hypothesis that the MYPA enhances multi-temporal yield interpretation compared to commonly-used statistical approaches. The MYPA method automatically identifies potential erroneous yield maps; detects yield patterns using principal component analysis; evaluates temporal yield pattern stability using a per-pixel analysis; and generates productivity-stability units based on k-means clustering and zonal statistics. The MYPA method was applied to two commercial cereal fields in Australian dryland systems and two commercial fields in a UK cool-climate system. To evaluate the MYPA, its output was compared to results from a classic, statistical yield analysis on the same data sets. The MYPA explained more of the variance in the yield data and generated larger and more coherent yield zones that are more amenable to site-specific management. Detected yield patterns were associated with varying production conditions, such as soil properties, precipitation patterns and management decisions. The MYPA was demonstrated as a robust approach that can be encoded into an easy-to-use tool to produce information layers from a time-series of yield data to support management.

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Liu, Qing, and Jing Zhou. "Analysis of Distributed English Chunk Recognition under Correlation Evaluation Based on Deep Belief Network Model." Wireless Communications and Mobile Computing 2022 (March 15, 2022): 1–7. http://dx.doi.org/10.1155/2022/5303847.

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Most of the traditional methods of English text chunk recognition are solved by setting the corresponding phrase identifier numbers and eventually transforming the chunk recognition problem into a lexical annotation problem. In language recognition, the traditional MFCC features are easily contaminated by noise and have weak noise immunity due to the insufficient amount of information on each frame of the signal. At the same time, SDC feature extraction methods commonly used today require artificial settings in parameter selection, which increases the uncertainty of recognition results. The method of identifying English text chunks by association evaluation of central word extensions identifies English text chunks from a different perspective. It has the following features: (i) each phase is considered as a cluster with the central word as the core, and the internal composition pattern of each phrase is fully considered; (ii) the results are dynamically evaluated using association and confidence. The results show that the proposed method can achieve higher recognition rate than traditional feature extraction methods. The recognition rate is faster, and the F -measure value of English block recognition reaches 94.05%, which is comparable to the best results so far.

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Oberti, Franco, Elena Stringa, and Gianni Vernazza. "Performance Evaluation Criterion for Characterizing Video-Surveillance Systems." Real-Time Imaging 7, no. 5 (October 2001): 457–71. http://dx.doi.org/10.1006/rtim.2000.0213.

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Chang, C. S., D. Srinivasan, and A. C. Liew. "A hybrid model for transient stability evaluation of interconnected longitudinal power systems using neural network/pattern recognition approach." IEEE Transactions on Power Systems 9, no. 1 (1994): 85–92. http://dx.doi.org/10.1109/59.317554.

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Ogiela, Urszula, and Václav Snášel. "Predictive intelligence in evaluation of visual perception thresholds for visual pattern recognition and understanding." Information Processing & Management 59, no. 2 (March 2022): 102865. http://dx.doi.org/10.1016/j.ipm.2022.102865.

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Abbaspour, Sara, Maria Lindén, Hamid Gholamhosseini, Autumn Naber, and Max Ortiz-Catalan. "Evaluation of surface EMG-based recognition algorithms for decoding hand movements." Medical & Biological Engineering & Computing 58, no. 1 (November 21, 2019): 83–100. http://dx.doi.org/10.1007/s11517-019-02073-z.

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AbstractMyoelectric pattern recognition (MPR) to decode limb movements is an important advancement regarding the control of powered prostheses. However, this technology is not yet in wide clinical use. Improvements in MPR could potentially increase the functionality of powered prostheses. To this purpose, offline accuracy and processing time were measured over 44 features using six classifiers with the aim of determining new configurations of features and classifiers to improve the accuracy and response time of prosthetics control. An efficient feature set (FS: waveform length, correlation coefficient, Hjorth Parameters) was found to improve the motion recognition accuracy. Using the proposed FS significantly increased the performance of linear discriminant analysis, K-nearest neighbor, maximum likelihood estimation (MLE), and support vector machine by 5.5%, 5.7%, 6.3%, and 6.2%, respectively, when compared with the Hudgins’ set. Using the FS with MLE provided the largest improvement in offline accuracy over the Hudgins feature set, with minimal effect on the processing time. Among the 44 features tested, logarithmic root mean square and normalized logarithmic energy yielded the highest recognition rates (above 95%). We anticipate that this work will contribute to the development of more accurate surface EMG-based motor decoding systems for the control prosthetic hands.

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Alqaralleh, Esraa, and Önsen Toygar. "Ear Recognition Based on Fusion of Ear and Tragus Under Different Challenges." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 09 (May 27, 2018): 1856009. http://dx.doi.org/10.1142/s0218001418560098.

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This paper proposes a 2D ear recognition approach that is based on the fusion of ear and tragus using score-level fusion strategy. An attempt to overcome the effect of partial occlusion, pose variation and weak illumination challenges is done since the accuracy of ear recognition may be reduced if one or more of these challenges are available. In this study, the effect of the aforementioned challenges is estimated separately, and many samples of ear that are affected by two different challenges concurrently are also considered. The tragus is used as a biometric trait because it is often free from occlusion; it also provides discriminative features even in different poses and illuminations. The features are extracted using local binary patterns and the evaluation has been done on three datasets of USTB database. It has been observed that the fusion of ear and tragus can improve the recognition performance compared to the unimodal systems. Experimental results show that the proposed method enhances the recognition rates by fusion of parts that are nonoccluded with tragus in the cases of partial occlusion, pose variation and weak illumination. It is observed that the proposed method performs better than feature-level fusion methods and most of the state-of-the-art ear recognition systems.

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Journal articles on the topic 'Pattern recognition systems – Evaluation'

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