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Journal articles on the topic 'Classification correctness'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Yaroshynskyi, M. S., O. V. Sirotkin, D. P. Sinko, S. B. Hunko, and D. O. Manoliuk. "Correctness of Flat Classification." Èlektronnoe modelirovanie 45, no. 2 (2023): 34–43. http://dx.doi.org/10.15407/emodel.45.02.034.

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Classifications are widely used in semantic networks and decision support systems based on formal knowledge and are part of computer ontologies. Classifications and computer ontologies built on them are the result of the work of one or more experts. As a result, such classifications reflect the subjective view of the author or authors on the world and the relationship between the classes (concepts) of the created classification. In the work, the authors propose an approach that will allow assessing how correctly the classification is constructed.

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KRAVTSOV, H. A., and V. I. KOSHEL. "Classification Calculus. The Classification Correctness." Èlektronnoe modelirovanie 39, no. 5 (2017): 59–70. http://dx.doi.org/10.15407/emodel.39.05.059.

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Kopp, Andrii, Dmytro Orlovskyi, and Uliya Litvinova. "DEVELOPMENT AND RESEARCH OF SOFTWARE SOLUTION FOR BUSINESS PROCESS MODEL CORRECTNESS ANALYSIS USING MACHINE LEARNING." Bulletin of National Technical University "KhPI". Series: System Analysis, Control and Information Technologies, no. 1 (11) (July 30, 2024): 39–46. http://dx.doi.org/10.20998/2079-0023.2024.01.06.

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Poorly designed business process models are a source of errors and the subsequent costs associated with these errors, such as monetary costs, lost time, or even some harmful impact on people or the environment if the erroneous business process models are associated with critical industries. The BPM (Business Process Management) lifecycle usually consists of designing, implementing, monitoring, and controlling the business process execution, but it lacks continuous quality control of the created BPMN (Business Process Model and Notation) models. Thus, this paper considers the problem of busines

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Matsumoto, Kazuma, Takato Tatsumi, Hiroyuki Sato, Tim Kovacs, and Keiki Takadama. "XCSR Learning from Compressed Data Acquired by Deep Neural Network." Journal of Advanced Computational Intelligence and Intelligent Informatics 21, no. 5 (2017): 856–67. http://dx.doi.org/10.20965/jaciii.2017.p0856.

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The correctness rate of classification of neural networks is improved by deep learning, which is machine learning of neural networks, and its accuracy is higher than the human brain in some fields. This paper proposes the hybrid system of the neural network and the Learning Classifier System (LCS). LCS is evolutionary rule-based machine learning using reinforcement learning. To increase the correctness rate of classification, we combine the neural network and the LCS. This paper conducted benchmark experiments to verify the proposed system. The experiment revealed that: 1) the correctness rate

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Pelánek, Radek, and Tomáš Effenberger. "Beyond binary correctness: Classification of students’ answers in learning systems." User Modeling and User-Adapted Interaction 30, no. 5 (2020): 867–93. http://dx.doi.org/10.1007/s11257-020-09265-5.

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Mrowicka, Blanka, Magdalena Kosińska, and Szczepan Cofta. "Morphological classification of patients with sleep obstructive disturbances." Anthropological Review 66 (June 30, 2003): 87–93. http://dx.doi.org/10.18778/1898-6773.66.07.

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It has been suggested that enlarged values of morphological measurements manifesting obesity intensify the risk of sleep disturbances. The main goal of this work was to find a set of predictor morphometric variables that best distinguish between the mutually exclusive groups of interest, the well and the ill – i.e., those who suffer from obstructive sleep apnoea (OSA) syndrome, and healthy patients. Somatic measurements of 96 males were taken. To analyze the dependencies between the degree of intensity of the studied pathology (oxygen desaturation index) and the values of the somatic measureme

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Zahoor, Kanwal, Narmeen Zakaria Bawany, and Tehreem Qamar. "Evaluating text classification with explainable artificial intelligence." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 278. http://dx.doi.org/10.11591/ijai.v13.i1.pp278-286.

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<span lang="EN-US">Nowadays, artificial intelligence (AI) in general and machine learning techniques in particular has been widely employed in automated systems. Increasing complexity of these machine learning based systems have consequently given rise to blackbox models that are typically not understandable or explainable by humans. There is a need to understand the logic and reason behind these automated decision-making black box models as they are involved in our day-to-day activities such as driving, facial recognition identity systems, online recruitment. Explainable artificial inte

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Zahoor, Kanwal, Narmeen Zakaria Bawany, and Tehreem Qamar. "Evaluating text classification with explainable artificial intelligence." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 278–86. https://doi.org/10.11591/ijai.v13.i1.pp278-286.

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Nowadays, artificial intelligence (AI) in general and machine learning techniques in particular has been widely employed in automated systems. Increasing complexity of these machine learning based systems have consequently given rise to blackbox models that are typically not understandable or explainable by humans. There is a need to understand the logic and reason behind these automated decision-making black box models as they are involved in our day-to-day activities such as driving, facial recognition identity systems, online recruitment. Explainable artificial intelligence (XAI) is an evol

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Ruah, Carlos B., David Cohen, and Jacob Sadé. "Eustachian tube teratoma and its terminological correctness." Journal of Laryngology & Otology 113, no. 3 (1999): 271–74. http://dx.doi.org/10.1017/s0022215100143774.

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AbstractTeratomas are germ cell tumours usually found in the young and are characteristically composed of tissue foreign to the place where they arise. Two teratomas of the middle ear were first described in 1866 and since then, and to the best of our knowledge, 19 additional cases have been reported in the literature under different terms such as hairy congenital polyps, epidermoid cysts, dermoid cysts, hamartoma, cutaneous teratoma and teratomas. The difficulty in classifying germ cell tumours may explain the different terminologies encountered in the literature. The authors describe a case

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Tran, G., D. Nguyen, M. Milenkovic, and N. Pfeifer. "POTENTIAL OF FULL WAVEFORM AIRBORNE LASER SCANNING DATA FOR URBAN AREA CLASSIFICATION – TRANSFER OF CLASSIFICATION APPROACHES BETWEEN MISSIONS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W3 (April 30, 2015): 1317–23. http://dx.doi.org/10.5194/isprsarchives-xl-7-w3-1317-2015.

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Full-waveform (FWF) LiDAR (Light Detection and Ranging) systems have their advantage in recording the entire backscattered signal of each emitted laser pulse compared to conventional airborne discrete-return laser scanner systems. The FWF systems can provide point clouds which contain extra attributes like amplitude and echo width, etc. In this study, a FWF data collected in 2010 for Eisenstadt, a city in the eastern part of Austria was used to classify four main classes: buildings, trees, waterbody and ground by employing a decision tree. Point density, echo ratio, echo width, normalised digi

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Witteman, Cilia L. M., and John H. L. van den Bercken. "Intermediate Effects in Psychodiagnostic Classification." European Journal of Psychological Assessment 23, no. 1 (2007): 56–61. http://dx.doi.org/10.1027/1015-5759.23.1.56.

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Most comparative studies of performance in psychodiagnostic classification contrast novices with very experienced clinicians, and do not include a group with an intermediate level of experience. We describe an empirical study involving 41 clinicians with three levels of experience. We asked novices, intermediates, and very experienced psychodiagnosticians to give the diagnostic (DSM) classification for 10 written cases. We found an intermediate effect in diagnosticians' decision time and correctness: Intermediates performed worse and faster than both novices and very experienced clinicians. In

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Adeleke, Oluwamayowa Ade, Nicholas Bastin, and Deniz Gurkan. "Network Traffic Generation: A Survey and Methodology." ACM Computing Surveys 55, no. 2 (2023): 1–23. http://dx.doi.org/10.1145/3488375.

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Network traffic workloads are widely utilized in applied research to verify correctness and to measure the impact of novel algorithms, protocols, and network functions. We provide a comprehensive survey of traffic generators referenced by researchers over the last 13 years, providing in-depth classification of the functional behaviors of the most frequently cited generators. These classifications are then used as a critical component of a methodology presented to aid in the selection of generators derived from the workload requirements of future research.

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Patanavijit, Vorapoj, and Kornkamol Thakulsukanant. "The novel noise classification techniques found on quadruple threshold statistical detection filter under fix intensity impulse outlier environment." Bulletin of Electrical Engineering and Informatics 10, no. 5 (2021): 2520–29. http://dx.doi.org/10.11591/eei.v10i5.3105.

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Because of the enormous necessity of contemporary noise suppressing algorithms, this article proposes the novel noise classification technique found on QTSD filter improved from the TTSD filter. The four thresholds for each auxiliary situations are incorporated into the proposed QTSD framework for dealing with the limitation of the earlier noise classification technique. The mathematical pattern is modeled by each photograph elements and is investigated in contradiction to the 1st threshold for analyzing whether it is non-noise or noise photograph elements. Subsequently, the calculated photogr

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Pokussov, V. V. "Formalization and determination of the protocol correctness for information and technical interaction (on the example of integrated information security system)." Informatization and communication, no. 2 (February 16, 2021): 55–68. http://dx.doi.org/10.34219/2078-8320-2021-12-2-55-68.

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The article deals with the problem of designing protocols for information and technical interaction, which have a sign of correctness. For this, its model is built in graphical and analytical form, as well as using a modified adjacency matrix. A classification of modules participating in the interaction is proposed, namely the following: terminal sensor and executor, degenerate handler, object storage, intermediate handler. Five correctness requirements for the protocol are introduced: the need and sufficiency of exchange objects, input and output terminality of modules, and their involvement.

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Ibrhim, Hamada, Hesham Hassan, and Emad Nabil. "A conflicts’ classification for IoT-based services: a comparative survey." PeerJ Computer Science 7 (April 27, 2021): e480. http://dx.doi.org/10.7717/peerj-cs.480.

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Recently, Internet of Things (IoT)-based systems, especially automation systems, have become an indispensable part of modern-day lives to support the controlling of the networked devices and providing context-aware and intelligent environments. IoT-based services/apps developed by the end-users interact with each other and share concurrent access to devices according to their preferences, which increases safety, security, and correctness issues in IoT systems. Due to the critical impacts resulting from these issues, IoT-based apps require a customized type of compilers or checking tools that c

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Kowalski, Piotr A., and Piotr Kulczycki. "A Complete Algorithm for the Reduction of Pattern Data in the Classification of Interval Information." International Journal of Computational Methods 13, no. 03 (2016): 1650018. http://dx.doi.org/10.1142/s0219876216500183.

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The aim of this paper is to present a novel method of data sample reduction that can be applied, in particular, to the classification of interval type imprecise information. Its concept is based on the sensitivity method, inspired by artificial neural networks, while the goal is to increase the number of apposite classifications, and, consequently, to increase calculation speed. As evident in this paper, the use of reduction algorithm eliminates the particular elements of all data sample patterns which have insignificant or negative influence on the correctness of classification. The methodolo

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Nowosad, Piotr, and Małgorzata Charytanowicz. "Object classification using X-ray images." Journal of Computer Sciences Institute 15 (June 30, 2020): 206–13. http://dx.doi.org/10.35784/jcsi.1720.

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The main aim of the presented research was to assess the possibility of utilizing geometric features in object classification.Studies were conducted using X-ray images of kernels belonging to three different wheat varieties: Kama, Canadian andRosa. As a part of the work, image processing methods were used to determine the main geometric grain parameters,including the kernel area, kernel perimeter, kernel length and kernel width. The results indicate significant differencesbetween wheat varieties, and demonstrates the importance of their size and shape parameters in the classification process.T

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Weili, Zhao. "Research on Hotel Customer Relationship Management System Based on the Classification Algorithm." International Journal of Information Systems and Supply Chain Management 12, no. 2 (2019): 68–75. http://dx.doi.org/10.4018/ijisscm.2019040105.

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The hotel management relationship is a good business strategy for hotels, which can promote the development of a hotel, when a classification algorithm is applied to customer relationship management system. First, the classification algorithm is based on a support vector machine is studied, the nearest neighbor sample density is used, and the corresponding mathematical model is constructed. Second, the procedure of a classification algorithm based on fuzzy support vector machine is designed. Third, a customer acquisition plan based on a classification algorithm is analyzed. Finally, a hotel is

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Karachun, Yuliia G., and Nataliia V. Davydenko. "POLITICAL CORRECTNESS IN POLITICAL DISCOURSE: THEORY OF IDEOLOGICAL ASPECT." Alfred Nobel University Journal of Philology 2, no. 26/2 (2023): 139–50. http://dx.doi.org/10.32342/2523-4463-2023-2-26/2-9.

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The article aims to highlight the ideological aspect of functioning politically correct vocabulary in today’s political discourse. Realization of the set goal requires solving the following objectives: 1) to expand scholarly ideas about the interpretation of the linguistic phenomenon of political correctness as sociocultural and linguistic-behavioral ideology in political discourse; 2) based on the introduction of the concept “ideologeme”, to consider the classification of politically correct lexical units, used in political discourse. The study employs general scientific methods (analysis, ge

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Qi, Teng, Miyuki Iwamoto, Dongeun Choi, Noriyuki Kida, and Noriaki Kuwahara. "Frailty-Focused Movement Monitoring: A Single-Camera System Using Joint Angles for Assessing Chair-Based Exercise Quality." Sensors 25, no. 13 (2025): 3907. https://doi.org/10.3390/s25133907.

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Ensuring that older adults perform chair-based exercises (CBEs) correctly is essential for improving physical outcomes and reducing the risk of injury, particularly in home and community rehabilitation settings. However, evaluating the correctness of movements accurately and objectively outside clinical environments remains challenging. In this study, camera-based methods have been used to evaluate practical exercise quality. A single-camera system utilizing MediaPipe pose estimation was used to capture joint angle data as twenty older adults performed eight CBEs. Simultaneously, surface elect

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Gärtner, Felix. "Transformational Approaches to the Specification and Verification of Fault-Tolerant Systems: Formal Background and Classification." JUCS - Journal of Universal Computer Science 5, no. (10) (1999): 668–92. https://doi.org/10.3217/jucs-005-10-0668.

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Proving that a program suits its specification and thus can be called correct has been a research subject for many years resulting in a wide range of methods and formalisms. However, it is a common experience that even systems which have been proven correct can fail due to physical faults occurring in the system. As computer programs control an increasing part of todays critical infrastructure, the notion of correctness has been extended to fault tolerance, meaning correctness in the presence of a certain amount of faulty behavior of the environment. Formalisms to verify fault-tolerant systems

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Li, Yan Kun, and Shuo Yang. "Research on Serum Spectrum Analysis Model Applied to Pathema Identification." Advanced Materials Research 864-867 (December 2013): 486–89. http://dx.doi.org/10.4028/www.scientific.net/amr.864-867.486.

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The SELDI-TOF MS serum peptide profiles of normal and malignant tumor samples were studied by pattern recognition method. In this study, Partial Least Squares-Discriminate Analysis (PLS-DA) combined with consensus classification model was constructed to predict practical serum samples and compared with the results of principal component analysis (PCA) method. The correctness of consensus PLS-DA classification model for normal and malignant samples was 90% and 84% respectively. So the approach proposed was proved to be a reliable and practicable method for cancer identification.

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Yu, Yongquan, Chao Lin, and Ping Xu. "Pattern Classification and Gear Design of Spatial Noncircular Gear Continuously Variable Transmission." Applied Sciences 12, no. 5 (2022): 2715. http://dx.doi.org/10.3390/app12052715.

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Noncircular gear and curve face gear are collectively referred to as spatial noncircular gear. Combined with the transmission characteristics of spatial noncircular gear, a new classification design method of spatial noncircular gear continuously variable transmission (CVT) pattern was proposed, including two categories named addition type and multiplication type, with a total of five subcategories. Through the combination of the transmission ratio changing mechanism and transmission selection mechanism, it can realize the CVT. The overall transmission ratio depends on the phase angle between

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Cho, Du Hyung, and Seok Lyong Lee. "Defect Identification and Classification for Plasma Display Panels." Advanced Materials Research 694-697 (May 2013): 1197–201. http://dx.doi.org/10.4028/www.scientific.net/amr.694-697.1197.

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The defect inspection is a crucial process for the plasma display panel (PDP) production that significantly influences the quality of final products. In this paper, we propose a defect identification and classification method that extracts and classifies defects using various image analysis techniques. First, we identify defects through binarization of images using Gaussian filter. Then, those defects are classified into seven different types by analyzing geometric characteristics of defects and utilizing a support vector machine (SVM) classifier. The experimental results using separate sets o

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Lopes Queiroz, Gustavo, Gregory J. McDermid, Guillermo Castilla, Julia Linke, and Mir Mustafizur Rahman. "Mapping Coarse Woody Debris with Random Forest Classification of Centimetric Aerial Imagery." Forests 10, no. 6 (2019): 471. http://dx.doi.org/10.3390/f10060471.

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Coarse woody debris (CWD; large parts of dead trees) is a vital element of forest ecosystems, playing an important role in nutrient cycling, carbon storage, fire fuel, microhabitats, and overall forest structure. However, there is a lack of effective tools for identifying and mapping both standing (snags) and downed (logs) CWD in complex natural settings. We applied a random forest machine learning classifier to detect CWD in centimetric aerial imagery acquired over a 270-hectare study area in the boreal forest of Alberta, Canada. We used a geographic object-based image analysis (GEOBIA) appro

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Petrova, Desislava. "CLASSIFICATION OF MICROMECHANICAL ELEMENTS AND FABRICATION OF BEAM-TYPE MICROMECHANICAL ELEMENTS." ENVIRONMENT. TECHNOLOGY. RESOURCES. Proceedings of the International Scientific and Practical Conference 4 (June 8, 2025): 289–96. https://doi.org/10.17770/etr2025vol4.8396.

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This article presents the developed classification of micromechanical elements, as well as the calculations and fabrication of micromechanical elements of the beam type, using the theory of elasticity and the Finite Element Method (FEM). The principle of etching a conventional angle and partial etching of freely vibrating components is considered. The technical model of the clamped beam, physical and mathematical modeling, as well as simulation and verification of correctness are described. The relationship between the design and entry into the database of the automated design system "MICROSYS

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Mohammed, Abubakar, Ghousia Sultana, Fnu Mohammed Aasimuddin, and Shahnawaz Mohammed. "Leveraging Natural Language Processing for Trade Exception Classification and Resolution in Capital Markets: A Comprehensive Study." Journal of Cognitive Computing and Cybernetic Innovations(JCCCI) 1, no. 1 (2025): 14–18. https://doi.org/10.21276/jccci/2025.v1.i1.3.

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In capital markets, trade exceptions pose serious problems that frequently lead to financial losses and operational inefficiencies. This study explores how to effectively classify and manage trade exceptions using Natural Language Processing (NLP). Our goal is to use machine learning models and sophisticated natural language processing techniques to automate exception management and enhance decision-making in trade operations. Data preparation, feature extraction, model training, and evaluation make up our methodology. According to experimental data, manual interventions have significantly dec

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Yehoshkin, Danylo, and Natalia Guk. "Automatic construction of a fuzzy system with a matrix representation of rules and a correct knowledge base." Eastern-European Journal of Enterprise Technologies 6, no. 4 (120) (2022): 14–22. http://dx.doi.org/10.15587/1729-4061.2022.268908.

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The object of this study is the process of automatic formation of fuzzy production rules on the basis of a training sample for solving the classification problem. The problem of automatically creating and then checking the correctness of a fuzzy inference model for a classification task is solved. The result is an automatically constructed correct database of rules for solving the classification problem. Analysis of the correctness of the knowledge base is carried out using the criteria of completeness, minimality, coherence, and consistency. To prove the completeness of the rule base, Hoare l

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Danylo, Yehoshkin, and Guk Natalia. "Automatic construction of a fuzzy system with a matrix representation of rules and a correct knowledge base." Eastern-European Journal of Enterprise Technologies 6, no. 4 (120) (2022): 14–22. https://doi.org/10.15587/1729-4061.2022.268908.

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The object of this study is the process of automatic formation of fuzzy production rules on the basis of a training sample for solving the classification problem. The problem of automatically creating and then checking the correctness of a fuzzy inference model for a classification task is solved. The result is an automatically constructed correct database of rules for solving the classification problem. Analysis of the correctness of the knowledge base is carried out using the criteria of completeness, minimality, coherence, and consistency. To prove the completeness of the rule base, Hoare l

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Katarzyna and Paweł. "A Vision-Based Method Utilizing Deep Convolutional Neural Networks for Fruit Variety Classification in Uncertainty Conditions of Retail Sales." Applied Sciences 9, no. 19 (2019): 3971. http://dx.doi.org/10.3390/app9193971.

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This study proposes a double-track method for the classification of fruit varieties for application in retail sales. The method uses two nine-layer Convolutional Neural Networks (CNNs) with the same architecture, but different weight matrices. The first network classifies fruits according to images of fruits with a background, and the second network classifies based on images with the ROI (Region Of Interest, a single fruit). The results are aggregated with the proposed values of weights (importance). Consequently, the method returns the predicted class membership with the Certainty Factor (CF

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Thompson, Valerie A. "Normativism versus mechanism." Behavioral and Brain Sciences 34, no. 5 (2011): 272–73. http://dx.doi.org/10.1017/s0140525x11000574.

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AbstractUsing normative correctness as a diagnostic tool reduces the outcome of complex cognitive functions to a binary classification (normative or non-normative). It also focuses attention on outcomes, rather than processes, impeding the development of good cognitive theories. Given that both normative and non-normative responses may be produced by the same process, normativity is a poor indicator of underlying processes.

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Nivetha, S., and H. Hannah Inbarani. "Novel Architecture for Image Classification Based on Rough Set." International Journal of Service Science, Management, Engineering, and Technology 14, no. 1 (2023): 1–38. http://dx.doi.org/10.4018/ijssmet.323452.

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The Computed Tomography (CT) scan images classification problem is one of the most challenging problems in recent years. Different medical treatments have been developed based on the correctness of CT scan images classification. In this work, a novel deep learning architecture is proposed to correctly diagnose COVID-19 patients using CT scan images. In fact, a new classifier based on rough set theory is suggested. Extensive experiments showed that the novel deep learning architecture provides a significant improvement over well-known classifier. The new classifier produces 95% efficiency and a

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Mercier, Dominique, Andreas Dengel, and Sheraz Ahmed. "TimeREISE: Time Series Randomized Evolving Input Sample Explanation." Sensors 22, no. 11 (2022): 4084. http://dx.doi.org/10.3390/s22114084.

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Deep neural networks are one of the most successful classifiers across different domains. However, their use is limited in safety-critical areas due to their limitations concerning interpretability. The research field of explainable artificial intelligence addresses this problem. However, most interpretability methods align to the imaging modality by design. The paper introduces TimeREISE, a model agnostic attribution method that shows success in the context of time series classification. The method applies perturbations to the input and considers different attribution map characteristics such

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FLORINDO, JOÃO BATISTA, MÁRIO DE CASTRO, and ODEMIR MARTINEZ BRUNO. "ENHANCING VOLUMETRIC BOULIGAND–MINKOWSKI FRACTAL DESCRIPTORS BY USING FUNCTIONAL DATA ANALYSIS." International Journal of Modern Physics C 22, no. 09 (2011): 929–52. http://dx.doi.org/10.1142/s0129183111016701.

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This work proposes and studies the concept of Functional Data Analysis transform, applying it to the performance improving of volumetric Bouligand–Minkowski fractal descriptors. The proposed transform consists essentially in changing the descriptors originally defined in the space of the calculus of fractal dimension into the space of coefficients used in the functional data representation of these descriptors. The transformed descriptors are used here in texture classification problems. The enhancement provided by the FDA transform is measured by comparing the transformed to the original desc

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Amitani, Reishi, Kazuyuki Matsumoto, Minoru Yoshida, and Kenji Kita. "Buzz Tweet Classification Based on Text and Image Features of Tweets Using Multi-Task Learning." Applied Sciences 11, no. 22 (2021): 10567. http://dx.doi.org/10.3390/app112210567.

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This study investigates social media trends and proposes a buzz tweet classification method to explore the factors causing the buzz phenomenon on Twitter. It is difficult to identify the causes of the buzz phenomenon based solely on texts posted on Twitter. It is expected that by limiting the tweets to those with attached images and using the characteristics of the images and the relationships between the text and images, a more detailed analysis than that of with text-only tweets can be conducted. Therefore, an analysis method was devised based on a multi-task neural network that uses both th

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Gao, Fan, Fan Li, Zhifei Wang, Wenqi Ge, and Xinqin Li. "Research on Multilevel Classification of High-Speed Railway Signal Equipment Fault Based on Text Mining." Journal of Electrical and Computer Engineering 2021 (May 19, 2021): 1–11. http://dx.doi.org/10.1155/2021/7146435.

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In this paper, the multilevel classification model of high-speed railway signal equipment fault based on text mining technology is proposed for the data of high-speed railway signal fault. An improved feature representation method of TF-IDF is proposed to extract the feature of fault text data of signal equipment. In the multilevel classification model, the single-layer classification model was designed based on stacking integrated learning idea; the recurrent neural network BiGRU and BiLSTM were used as primary learners, and the weight combination calculation method was designed for secondary

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Wang, Yue, Xiaochen Meng, and Lianqing Zhu. "Cell Group Recognition Method Based on Adaptive Mutation PSO-SVM." Cells 7, no. 9 (2018): 135. http://dx.doi.org/10.3390/cells7090135.

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The increased volume and complexity of flow cytometry (FCM) data resulting from the increased throughput greatly boosts the demand for reliable statistical methods for the analysis of multidimensional data. The Support Vector Machines (SVM) model can be used for classification recognition. However, the selection of penalty factor c and kernel parameter g in the model has a great influence on the correctness of clustering. To solve the problem of parameter optimization of the SVM model, a support vector machine algorithm of particle swarm optimization (PSO-SVM) based on adaptive mutation is pro

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Přibil, Jiří, Anna Přibilová, and Daniela Ďuračkoá. "EVALUATION OF SPECTRAL AND PROSODIC FEATURES OF SPEECH AFFECTED BY ORTHODONTIC APPLIANCES USING THE GMM CLASSIFIER." Journal of Electrical Engineering 65, no. 1 (2014): 30–36. http://dx.doi.org/10.2478/jee-2014-0004.

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Abstract The paper describes our experiment with using the Gaussian mixture models (GMM) for classification of speech uttered by a person wearing orthodontic appliances. For the GMM classification, the input feature vectors comprise the basic and the complementary spectral properties as well as the supra-segmental parameters. Dependence of classification correctness on the number of the parameters in the input feature vector and on the computation complexity is also evaluated. In addition, an influence of the initial setting of the parameters for GMM training process was analyzed. Obtained rec

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Zyryanova, Irina Petrovna. "Political correctness in public speeches of politician and statesman Donald Trump." Филология: научные исследования, no. 4 (April 2025): 249–65. https://doi.org/10.7256/2454-0749.2025.4.70696.

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This work is devoted to the study of the category of political correctness, its social and political origin. This issue is of wide interest among domestic and foreign linguists, which determines the relevance of this research area. The object of this work is the category of political correctness, which is understood as a cultural, behavioral and linguistic tendency designed to find new lexical expressions in order to avoid creating conflict and offending the feelings of different social groups. The article analyzes the features of this category in the speeches of the American politician and st

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Mõisja, Kiira, Tõnu Oja, Evelyn Uuemaa, and Jordan T. Hastings. "Completeness and classification correctness of features on topographic maps: An analysis of the estonian basic map." Transactions in GIS 21, no. 5 (2016): 954–68. http://dx.doi.org/10.1111/tgis.12257.

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Wójcik, Beata, Michał Błaszczykowski, and Edmund Wąsik. "Human movement monitoring system for classification of strength exercises and verification of their execution technique." Journal of Modern Science 57, no. 3 (2024): 823–38. http://dx.doi.org/10.13166/jms/191431.

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Human movement analysis is critical to optimizing sports training and influencing exercise intensity and effectiveness. In the age of modern technology, more and more advanced systems are emerging to support coaches and expand the range of analysis performed. This article aims to verify that artificial intelligence, together with machine learning algorithms, can accurately classify exercises in a dynamic gym environment and effectively assess the correctness of their performance. For the initial analysis of movement, the Google MediaPipe Pose model was used, which was responsible for detecting

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AMPUŁA, Dariusz. "Prediction of Post-Diagnostic Decisions for Tested Hand Grenades’ Fuzes Using Decision Trees." Problems of Mechatronics Armament Aviation Safety Engineering 12, no. 2 (2021): 39–54. http://dx.doi.org/10.5604/01.3001.0014.9332.

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The article presents a brief history of creation of decision trees and defines the purpose of the undertaken works. The process of building a classification tree, according to the CHAID method, is shown paying particular attention to the disadvantages, advantages, and characteristics features of this method, as well as to the formal requirements that are necessary to build this model. The tree’s building method for UZRGM (Universal Modernised Fuze of Hand Grenades) fuzes was characterized, specifying the features of the tested hand grenade fuzes and the predictors used that are necessary to cr

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Cheng, Xiao Jun, Kai Tan, and Qi Ye Lou. "Relations of the Laser Intensity Value, the Laser Ranging Value and the Laser Incident Angle." Applied Mechanics and Materials 239-240 (December 2012): 198–201. http://dx.doi.org/10.4028/www.scientific.net/amm.239-240.198.

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Firstly, the functional relationships of the laser intensity value, the target reflectance, the laser ranging value and the laser incidence angle was derived theoretically in this paper, and a new method of target classification based on the laser intensity value and the target reflectance was proposed. Then, the experimental data was analyzed to verify the correctness of the theoretical formula. Finally, the work done in this paper was summarized.

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Zhang, Guo Dong, and Zhong Liu. "A Target Selection Algorithm of Seeker Based on the Prior Information and SVM." Advanced Materials Research 734-737 (August 2013): 3071–74. http://dx.doi.org/10.4028/www.scientific.net/amr.734-737.3071.

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Aiming at the phenomenon that the chaff and corner reflector released by surface ship can influence the selection of missile seeker, this paper proposed a multi-target selection method based on the prior information of false targets distribution and Support Vector Machine (SVM). By analyzing the false targets distribution law we obtain two classification principles, which are used to train the SVM studies the true and false target characteristics. The trained SVM is applied to the seeker in the target selection. This method has advantages of simple programming and high classification accuracy,

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Kelebercová, Lívia, Michal Munk, and František Forgáč. "Could You Understand Me? The Relationship among Method Complexity, Preprocessing Complexity, Interpretability, and Accuracy." Mathematics 11, no. 13 (2023): 2922. http://dx.doi.org/10.3390/math11132922.

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The need to train experts who will be able to apply machine learning methods for knowledge discovery is increasing. Building an effective machine learning model requires understanding the principle of operation of the individual methods and their requirements in terms of data pre-preparation, and it is also important to be able to interpret the acquired knowledge. This article presents an experiment comparing the opinion of the 42 students of the course called Introduction to Machine Learning on the complexity of the method, preprocessing, and interpretability of symbolic, subsymbolic and stat

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Khapugin, Anatoliy A. "Environment Status Estimation of the Forest Communities Based on Floristic Surveys in the Mordovia State Nature Reserve, Russia." Forests 12, no. 11 (2021): 1475. http://dx.doi.org/10.3390/f12111475.

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Environmental scales include species responsive to changes in environmental conditions. The present paper aims to apply floristic survey data to reveal environmental conditions in habitats studied in the Mordovia State Nature Reserve, European Russia. In total, 161 square plots were established within a selected forest area. In each plot, all species were registered to conduct a further analysis. Then, average values of six environmental factors were calculated based on the Tsyganov environmental scale. Contour maps were created for four factors to demonstrate spatial changes through the study

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Liu, (Freddie), Gizem Karagoz, and Nirvana Meratnia. "Analyzing the Impact of Data Augmentation on the Explainability of Deep Learning-Based Medical Image Classification." Machine Learning and Knowledge Extraction 7, no. 1 (2024): 1. https://doi.org/10.3390/make7010001.

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Deep learning models are widely used for medical image analysis and require large datasets, while sufficient high-quality medical data for training are scarce. Data augmentation has been used to improve the performance of these models. The lack of transparency of complex deep-learning models raises ethical and judicial concerns inducing a lack of trust by both medical experts and patients. In this paper, we focus on evaluating the impact of different data augmentation methods on the explainability of deep learning models used for medical image classification. We investigated the performance of

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Witharana, Chandi, Md Abul Ehsan Bhuiyan, Anna K. Liljedahl, et al. "An Object-Based Approach for Mapping Tundra Ice-Wedge Polygon Troughs from Very High Spatial Resolution Optical Satellite Imagery." Remote Sensing 13, no. 4 (2021): 558. http://dx.doi.org/10.3390/rs13040558.

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Very high spatial resolution commercial satellite imagery can inform observation, mapping, and documentation of micro-topographic transitions across large tundra regions. The bridging of fine-scale field studies with pan-Arctic system assessments has until now been constrained by a lack of overlap in spatial resolution and geographical coverage. This likely introduced biases in climate impacts on, and feedback from the Arctic region to the global climate system. The central objective of this exploratory study is to develop an object-based image analysis workflow to automatically extract ice-we

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Vijayaraj, A., P. T. Vasanth Raj, R. Jebakumar, et al. "Deep Learning Image Classification for Fashion Design." Wireless Communications and Mobile Computing 2022 (June 14, 2022): 1–13. http://dx.doi.org/10.1155/2022/7549397.

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Fashion has always been an essential feature in our daily routine. It also plays a significant role in everyone’s lives. In this research, convolutional neural networks (CNN) were used to train images of different fashion styles, which were attempted to be predicted with a high success rate. Deep learning has been widely applied in a variety of fields recently. A CNN is a deep neural network that delivers the most accurate answers when tackling real-world situations. Apparel manufacturers have employed CNN to tackle various difficulties on their e-commerce sites, including clothing recognition

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Ku, Yuedong, Jianhong Yang, Huaiying Fang, Jiangteng Zhuang, and Wen Xiao. "Experimental Research on Construction-Waste-Classification Algorithms for a Hyperspectral-Camera System." Journal of Solid Waste Technology and Management 46, no. 1 (2020): 5–14. http://dx.doi.org/10.5276/jswtm/2020.5.

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A large proportion of construction waste has a high recovery value, and some of the existing recycling-classification methods rely mainly on physical properties for vibration screening. In order to effectively recover construction waste, an industrial near-infrared hyperspectral camera is proposed in this paper to distinguish the spectral characteristics of the objects. The testing results were verified using an extreme learning machine, an adaptive-learning multilayer perceptron, and a one-dimensional convolutional neural network classification model. By establishing several different models

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