Relevant bibliographies by topics / Classification: Advanced Methods

Academic literature on the topic 'Classification: Advanced Methods'

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Published: 27 July 2024
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Journal articles on the topic "Classification: Advanced Methods"

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A, Shruti. "Comparative Study of Advanced Classification Methods." International Journal on Recent and Innovation Trends in Computing and Communication 3, no. 3 (2015): 1216–20. http://dx.doi.org/10.17762/ijritcc2321-8169.150371.

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Gola, Jessica, Dominik Britz, Thorsten Staudt, Marc Winter, Andreas Simon Schneider, Marc Ludovici, and Frank Mücklich. "Advanced microstructure classification by data mining methods." Computational Materials Science 148 (June 2018): 324–35. http://dx.doi.org/10.1016/j.commatsci.2018.03.004.

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Wei, Chien-Hung, Cheng-Chih Chang, and Sheng-Shih Wang. "Vehicle Classification Using Advanced Technologies." Transportation Research Record: Journal of the Transportation Research Board 1551, no. 1 (January 1996): 45–50. http://dx.doi.org/10.1177/0361198196155100106.

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Applying advanced technologies to existing problem domains is a highly desirable approach in many research areas. Among these techniques, image processing has been shown useful in transportation fields for such tasks as traffic pattern recognition, data collection, accident detection, and pavement evaluation. The integrated model with artificial neural networks (ANNs) has promising potential applications. The image processing and ANN model are combined to explore the feasibility of vehicle classification in real-world situations. Three methods were developed during the research process: ground segmentation, background subtraction, and window segmentation. The first two methods were used to separate the objects of scene and nonscene from the actual traffic image. To reduce the complexity of neural networks, the image was divided into 16 windows and three characteristics (occupation rates of vehicles, of horizontal image lines, and of vertical image lines) of each window were extracted to generate 48 factors as the input units of the neural network. The backpropagation ANN model with one hidden layer is employed. The experiments show that the accurate recognition rates of heavy vehicles, small cars, and motorcycles are 98.5, 96.92, and 91.94 percent, respectively. The result implies the remarkable applicability of the proposed methods in transportation areas.
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Katona, Tamás, Gábor Tóth, Mátyás Petró, and Balázs Harangi. "Advanced Multi-Label Image Classification Techniques Using Ensemble Methods." Machine Learning and Knowledge Extraction 6, no. 2 (June 7, 2024): 1281–97. http://dx.doi.org/10.3390/make6020060.

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Chest X-rays are vital in healthcare for diagnosing various conditions due to their low Radiation exposure, widespread availability, and rapid interpretation. However, their interpretation requires specialized expertise, which can limit scalability and delay diagnoses. This study addresses the multi-label classification challenge of chest X-ray images using the Chest X-ray14 dataset. We propose a novel online ensemble technique that differs from previous penalty-based methods by focusing on combining individual model losses with the overall ensemble loss. This approach enhances interaction and feedback among models during training. Our method integrates multiple pre-trained CNNs using strategies like combining CNNs through an additional fully connected layer and employing a label-weighted average for outputs. This multi-layered approach leverages the strengths of each model component, improving classification accuracy and generalization. By focusing solely on image data, our ensemble model addresses the challenges posed by null vectors and diverse pathologies, advancing computer-aided radiology.
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Jonáková, Lenka, and Ivan Nagy. "Power purchase strategy of retail customers utilizing advanced classification methods." Neural Network World 31, no. 2 (2021): 89–107. http://dx.doi.org/10.14311/nnw.2021.31.005.

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Powell, Jade, Daniele Trifirò, Elena Cuoco, Ik Siong Heng, and Marco Cavaglià. "Classification methods for noise transients in advanced gravitational-wave detectors." Classical and Quantum Gravity 32, no. 21 (October 9, 2015): 215012. http://dx.doi.org/10.1088/0264-9381/32/21/215012.

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Guizani, Douraied, Erika Buday-Bódi, János Tamás, and Attila Nagy. "An advanced classification method for urban land cover classification." Acta Agraria Debreceniensis, no. 1 (June 3, 2024): 51–57. http://dx.doi.org/10.34101/actaagrar/1/13652.

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This manuscript presents a detailed comparative analysis of three advanced classification techniques that were used between 2018 and 2020 to classify land cover using Landsat8 imagery, namely Support Vector Machine (SVM), Maximum Likelihood Classification (MLSC), and Random Forests (RF). The study focuses on evaluating the accuracy of these methods by comparing the classified maps with a higher-resolution ground truth map, utilising 500 randomly selected points for assessment. The obtained results show that, compared to MLSC and RT, the Support Vector Machine (SVM) approach performs better. The SVM model demonstrates enhanced precision in land cover classification, showcasing its effectiveness in discerning subtle differences in landscape features. Furthermore, using the precise classification results produced by the SVM method, this study examines the temporal variations in land cover between 2018 and 2020. The results provide insight into dynamic land cover changes and highlight the significance of applying reliable classification techniques for thorough temporal analysis with Landsat8 images.
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Taherian, Hessam, and Robert W. Peters. "Advanced Active and Passive Methods in Residential Energy Efficiency." Energies 16, no. 9 (May 5, 2023): 3905. http://dx.doi.org/10.3390/en16093905.

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Energy efficiency in buildings is very important since it contributes significantly to fossil fuel consumption and consequently climate change. Several approaches have been taken by researchers and the industry to address the issue. These approaches are classified as either passive or active approaches. The purpose of this review article is to summarize a number of the technologies that have been investigated and/or developed. In this technical review paper, the more commonly used active and passive building energy conservation techniques are described and discussed. The pros and cons of both the active and passive energy techniques are described with appropriate reference citations provided. This review article provides a description to give an understanding of building conservation approaches. In the active classification, several methods have been reviewed that include earth-to-air heat exchangers, ground-source and hybrid heat pumps, and the use of new refrigerants, among other methods. In the passive classification, methods such as vegetated roofs, solar chimneys, natural ventilation, and more are discussed. Often, in a building, multiple passive and active methods can be employed simultaneously.
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Kabakchieva, Dorina. "Predicting Student Performance by Using Data Mining Methods for Classification." Cybernetics and Information Technologies 13, no. 1 (March 1, 2013): 61–72. http://dx.doi.org/10.2478/cait-2013-0006.

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Abstract Data mining methods are often implemented at advanced universities today for analyzing available data and extracting information and knowledge to support decision-making. This paper presents the initial results from a data mining research project implemented at a Bulgarian university, aimed at revealing the high potential of data mining applications for university management.
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G. Syam Kumar. "Sports Videos Classification using Advanced Deep Neural Networks." International Transactions on Electrical Engineering and Computer Science 3, no. 2 (June 30, 2024): 92–100. http://dx.doi.org/10.62760/iteecs.3.2.2024.92.

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The field of digital content is experiencing a meteoric rise in popularity as a direct result of the rapid development of information technology. When it comes to the archiving of digital content on the assistant, the segregation in sports videos is of an extremely important part. Consequently, the utilization of deep-neural-network algorithm (DNN), convolutional-neural-network (CNN), and deliver learning allows for the correct segregation of sports video classification to be achieved. There are two methods that have been proposed: block-brightness-comparison-coding (BICC) cum block colour histogram. Both of these methods analyze the contrast relationship among various parts of a video cum the colour matter that is present in a sector. In order to accomplish the goal of transfer learning, the maximum-mean-difference (MMD) procedure is utilized. Obtaining characteristics in sports video pictures is the foundation for the sports video image segregation approach that is dependent on deep-learning-coding model. This method is utilized in order that accomplish task of sports video segregation. As a consequence of the findings, it is clear that the overall segregation reaction of this procedure is significantly superior to that of other sports video classification methods that are currently in use. This results in a significant improvement in the classification effect of sports videos.
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Dissertations / Theses on the topic "Classification: Advanced Methods"

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Zeggada, Abdallah. "Advanced classification methods for UAV imagery." Doctoral thesis, Università degli studi di Trento, 2018. https://hdl.handle.net/11572/367947.

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The rapid technological advancement manifested lately in the remote sensing acquisition platforms has triggered many benefits in favor of automated territory control and monitoring. In particular, unmanned aerial vehicles (UAVs) technology has drawn a lot of attention, providing an efficient solution especially in real-time applications. This is mainly motivated by their capacity to collect extremely high resolution (EHR) data over inaccessible areas and limited coverage zones, thanks to their small size and rapidly deployable flight capability, notwithstanding their ease of use and affordability. The very high level of details of the data acquired via UAVs, however, in order to be properly availed, requires further treatment through suitable image processing and analysis approaches. In this respect, the proposed methodological contributions in this thesis include: i) a complete processing chain which assists the Avalanche Search and Rescue (SAR) operations by scanning the UAV acquired images over the avalanche debris in order to detect victims buried under snow and their related objects in real time; ii) two multilabel deep learning strategies for coarsely describing extremely high resolution images in urban scenarios; iii) a novel multilabel conditional random fields classification framework that exploits simultaneously spatial contextual information and cross-correlation between labels; iv) a novel spatial and structured support vector machine for multilabel image classification by adding to the cost function of the structured support vector machine a term that enhances spatial smoothness within a one-step process. Conducted experiments on real UAV images are reported and discussed alongside suggestions for potential future improvements and research lines.
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Zeggada, Abdallah. "Advanced classification methods for UAV imagery." Doctoral thesis, University of Trento, 2018. http://eprints-phd.biblio.unitn.it/2943/1/thesis_disclaimer.pdf.

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The rapid technological advancement manifested lately in the remote sensing acquisition platforms has triggered many benefits in favor of automated territory control and monitoring. In particular, unmanned aerial vehicles (UAVs) technology has drawn a lot of attention, providing an efficient solution especially in real-time applications. This is mainly motivated by their capacity to collect extremely high resolution (EHR) data over inaccessible areas and limited coverage zones, thanks to their small size and rapidly deployable flight capability, notwithstanding their ease of use and affordability. The very high level of details of the data acquired via UAVs, however, in order to be properly availed, requires further treatment through suitable image processing and analysis approaches. In this respect, the proposed methodological contributions in this thesis include: i) a complete processing chain which assists the Avalanche Search and Rescue (SAR) operations by scanning the UAV acquired images over the avalanche debris in order to detect victims buried under snow and their related objects in real time; ii) two multilabel deep learning strategies for coarsely describing extremely high resolution images in urban scenarios; iii) a novel multilabel conditional random fields classification framework that exploits simultaneously spatial contextual information and cross-correlation between labels; iv) a novel spatial and structured support vector machine for multilabel image classification by adding to the cost function of the structured support vector machine a term that enhances spatial smoothness within a one-step process. Conducted experiments on real UAV images are reported and discussed alongside suggestions for potential future improvements and research lines.
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Villa, Alberto. "Advanced spectral unmixing and classification methods for hyperspectral remote sensing data." Phd thesis, Université de Grenoble, 2011. http://tel.archives-ouvertes.fr/tel-00767250.

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La thèse propose des nouvelles techniques pour la classification et le démelange spectraldes images obtenus par télédétection iperspectrale. Les problèmes liées au données (notammenttrès grande dimensionalité, présence de mélanges des pixels) ont été considerés et destechniques innovantes pour résoudre ces problèmes. Nouvelles méthodes de classi_cationavancées basées sur l'utilisation des méthodes traditionnel de réduction des dimension etl'integration de l'information spatiale ont été développés. De plus, les méthodes de démelangespectral ont été utilisés conjointement pour ameliorer la classification obtenu avec lesméthodes traditionnel, donnant la possibilité d'obtenir aussi une amélioration de la résolutionspatial des maps de classification grace à l'utilisation de l'information à niveau sous-pixel.Les travaux ont suivi une progression logique, avec les étapes suivantes:1. Constat de base: pour améliorer la classification d'imagerie hyperspectrale, il fautconsidérer les problèmes liées au données : très grande dimensionalité, presence demélanges des pixels.2. Peut-on développer méthodes de classi_cation avancées basées sur l'utilisation des méthodestraditionnel de réduction des dimension (ICA ou autre)?3. Comment utiliser les differents types d'information contextuel typique des imagés satellitaires?4. Peut-on utiliser l'information données par les méthodes de démelange spectral pourproposer nouvelles chaines de réduction des dimension?5. Est-ce qu'on peut utiliser conjointement les méthodes de démelange spectral pour ameliorerla classification obtenu avec les méthodes traditionnel?6. Peut-on obtenir une amélioration de la résolution spatial des maps de classi_cationgrace à l'utilisation de l'information à niveau sous-pixel?Les différents méthodes proposées ont été testées sur plusieurs jeux de données réelles, montrantresultats comparable ou meilleurs de la plus part des methodes presentés dans la litterature.
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Bergamasco, Luca. "Advanced Deep-Learning Methods For Automatic Change Detection and Classification of Multitemporal Remote-Sensing Images." Doctoral thesis, Università degli studi di Trento, 2022. http://hdl.handle.net/11572/342100.

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Deep-Learning (DL) methods have been widely used for Remote Sensing (RS) applications in the last few years, and they allow improving the analysis of the temporal information in bi-temporal and multi-temporal RS images. DL methods use RS data to classify geographical areas or find changes occurring over time. DL methods exploit multi-sensor or multi-temporal data to retrieve results more accurately than single-source or single-date processing. However, the State-of-the-Art DL methods exploit the heterogeneous information provided by these data by focusing the analysis either on the spatial information of multi-sensor multi-resolution images using multi-scale approaches or on the time component of the image time series. Most of the DL RS methods are supervised, so they require a large number of labeled data that is challenging to gather. Nowadays, we have access to many unlabeled RS data, so the creation of long image time series is feasible. However, supervised methods require labeled data that are expensive to gather over image time series. Hence multi-temporal RS methods usually follow unsupervised approaches. In this thesis, we propose DL methodologies that handle these open issues. We propose unsupervised DL methods that exploit multi-resolution deep feature maps derived by a Convolutional Autoencoder (CAE). These DL models automatically learn spatial features from the input during the training phase without any labeled data. We then exploit the high temporal resolution of image time series with the high spatial information of Very-High-Resolution (VHR) images to perform a multi-temporal and multi-scale analysis of the scene. We merge the information provided by the geometrical details of VHR images with the temporal information of the image time series to improve the RS application tasks. We tested the proposed methods to detect changes over bi-temporal RS images acquired by various sensors, such as Landsat-5, Landsat-8, and Sentinel-2, representing burned and deforested areas, and kinds of pasture impurities using VHR orthophotos and Sentinel-2 image time series. The results proved the effectiveness of the proposed methods.
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Mehner, Henny. "The potential of high spatial resolution remote sensing for mapping upland vegetation using advanced classification methods." Thesis, University of Newcastle Upon Tyne, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.417524.

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Verzotto, Davide. "Advanced Computational Methods for Massive Biological Sequence Analysis." Doctoral thesis, Università degli studi di Padova, 2011. http://hdl.handle.net/11577/3426282.

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With the advent of modern sequencing technologies massive amounts of biological data, from protein sequences to entire genomes, are becoming increasingly available. This poses the need for the automatic analysis and classification of such a huge collection of data, in order to enhance knowledge in the Life Sciences. Although many research efforts have been made to mathematically model this information, for example finding patterns and similarities among protein or genome sequences, these approaches often lack structures that address specific biological issues. In this thesis, we present novel computational methods for three fundamental problems in molecular biology: the detection of remote evolutionary relationships among protein sequences, the identification of subtle biological signals in related genome or protein functional sites, and the phylogeny reconstruction by means of whole-genome comparisons. The main contribution is given by a systematic analysis of patterns that may affect these tasks, leading to the design of practical and efficient new pattern discovery tools. We thus introduce two advanced paradigms of pattern discovery and filtering based on the insight that functional and conserved biological motifs, or patterns, should lie in different sites of sequences. This enables to carry out space-conscious approaches that avoid a multiple counting of the same patterns. The first paradigm considered, namely irredundant common motifs, concerns the discovery of common patterns, for two sequences, that have occurrences not covered by other patterns, whose coverage is defined by means of specificity and extension. The second paradigm, namely underlying motifs, concerns the filtering of patterns, from a given set, that have occurrences not overlapping other patterns with higher priority, where priority is defined by lexicographic properties of patterns on the boundary between pattern matching and statistical analysis. We develop three practical methods directly based on these advanced paradigms. Experimental results indicate that we are able to identify subtle similarities among biological sequences, using the same type of information only once. In particular, we employ the irredundant common motifs and the statistics based on these patterns to solve the remote protein homology detection problem. Results show that our approach, called Irredundant Class, outperforms the state-of-the-art methods in a challenging benchmark for protein analysis. Afterwards, we establish how to compare and filter a large number of complex motifs (e.g., degenerate motifs) obtained from modern motif discovery tools, in order to identify subtle signals in different biological contexts. In this case we employ the notion of underlying motifs. Tests on large protein families indicate that we drastically reduce the number of motifs that scientists should manually inspect, further highlighting the actual functional motifs. Finally, we combine the two proposed paradigms to allow the comparison of whole genomes, and thus the construction of a novel and practical distance function. With our method, called Unic Subword Approach, we relate to each other the regions of two genome sequences by selecting conserved motifs during evolution. Experimental results show that our approach achieves better performance than other state-of-the-art methods in the whole-genome phylogeny reconstruction of viruses, prokaryotes, and unicellular eukaryotes, further identifying the major clades of these organisms.
Con l'avvento delle moderne tecnologie di sequenziamento, massive quantità di dati biologici, da sequenze proteiche fino a interi genomi, sono disponibili per la ricerca. Questo progresso richiede l'analisi e la classificazione automatica di tali collezioni di dati, al fine di migliorare la conoscenza nel campo delle Scienze della Vita. Nonostante finora siano stati proposti molti approcci per modellare matematicamente le sequenze biologiche, ad esempio cercando pattern e similarità tra sequenze genomiche o proteiche, questi metodi spesso mancano di strutture in grado di indirizzare specifiche questioni biologiche. In questa tesi, presentiamo nuovi metodi computazionali per tre problemi fondamentali della biologia molecolare: la scoperta di relazioni evolutive remote tra sequenze proteiche, l'individuazione di segnali biologici complessi in siti funzionali tra loro correlati, e la ricostruzione della filogenesi di un insieme di organismi, attraverso la comparazione di interi genomi. Il principale contributo è dato dall'analisi sistematica dei pattern che possono interessare questi problemi, portando alla progettazione di nuovi strumenti computazionali efficaci ed efficienti. Vengono introdotti così due paradigmi avanzati per la scoperta e il filtraggio di pattern, basati sull'osservazione che i motivi biologici funzionali, o pattern, sono localizzati in differenti regioni delle sequenze in esame. Questa osservazione consente di realizzare approcci parsimoniosi in grado di evitare un conteggio multiplo degli stessi pattern. Il primo paradigma considerato, ovvero irredundant common motifs, riguarda la scoperta di pattern comuni a coppie di sequenze che hanno occorrenze non coperte da altri pattern, la cui copertura è definita da una maggiore specificità e/o possibile estensione dei pattern. Il secondo paradigma, ovvero underlying motifs, riguarda il filtraggio di pattern che hanno occorrenze non sovrapposte a quelle di altri pattern con maggiore priorità, dove la priorità è definita da proprietà lessicografiche dei pattern al confine tra pattern matching e analisi statistica. Sono stati sviluppati tre metodi computazionali basati su questi paradigmi avanzati. I risultati sperimentali indicano che i nostri metodi sono in grado di identificare le principali similitudini tra sequenze biologiche, utilizzando l'informazione presente in maniera non ridondante. In particolare, impiegando gli irredundant common motifs e le statistiche basate su questi pattern risolviamo il problema della rilevazione di omologie remote tra proteine. I risultati evidenziano che il nostro approccio, chiamato Irredundant Class, ottiene ottime prestazioni su un benchmark impegnativo, e migliora i metodi allo stato dell'arte. Inoltre, per individuare segnali biologici complessi utilizziamo la nozione di underlying motifs, definendo così alcune modalità per il confronto e il filtraggio di motivi degenerati ottenuti tramite moderni strumenti di pattern discovery. Esperimenti su grandi famiglie proteiche dimostrano che il nostro metodo riduce drasticamente il numero di motivi che gli scienziati dovrebbero altrimenti ispezionare manualmente, mettendo in luce inoltre i motivi funzionali identificati in letteratura. Infine, combinando i due paradigmi proposti presentiamo una nuova e pratica funzione di distanza tra interi genomi. Con il nostro metodo, chiamato Unic Subword Approach, relazioniamo tra loro le diverse regioni di due sequenze genomiche, selezionando i motivi conservati durante l'evoluzione. I risultati sperimentali evidenziano che il nostro approccio offre migliori prestazioni rispetto ad altri metodi allo stato dell'arte nella ricostruzione della filogenesi di organismi quali virus, procarioti ed eucarioti unicellulari, identificando inoltre le sottoclassi principali di queste specie.
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Preethy, Byju Akshara. "Advanced Methods for Content Based Image Retrieval and Scene Classification in JPEG 2000 Compressed Remote Sensing Image Archives." Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/281771.

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Recent advances in satellite imaging technologies have paved its way to the RS big data era. Efficient storage, management and utilization of massive amounts of data is one of the major challenges faced by the remote sensing (RS) community. To minimize the storage requirements and speed up the transmission rate, RS images are compressed before archiving. Accordingly, developing efficient Content Based Image Retrieval (CBIR) and scene classification techniques to effectively utilize these huge volume of data is one among the most researched areas in RS. With the continual growth in the volume of compressed RS data, the dominant aspect that plays a key role in the development of these techniques is the decompression time required by these images. Existing CBIR and scene classification methods in RS require fully decompressed RS images as input, which is a computationally complex and time consuming task to perform. Among several compression algorithms introduced to RS, JPEG 2000 is the most widely used in operational satellites due to its multiresolution paradigm, scalability and high compression ratio. In light of this, the goal of this thesis is to develop novel methods to achieve image retrieval and scene classification for JPEG 2000 compressed RS image archives. The first contribution of the thesis addresses the possibility of performing CBIR directly on compressed RS images. The aim of the proposed method is to achieve efficient image characterization and retrieval within the JPEG 2000 compressed domain. The proposed progressive image retrieval approach achieves a coarse to fine image description and retrieval in the partially decoded JPEG 2000 compressed domain. Its aims to reduce the computational time required by the CBIR system for compressed RS image archives. The second contribution of the thesis concerns the possibility of achieving scene classification for JPEG 2000 compressed RS image archives. Recently, deep learning methods have demonstrated a cutting edge improvement in scene classification performance in large-scale RS image archives. In view of this, the proposed method is based on deep learning and aims to achieve maximum scene classification accuracy with minimal decoding. The proposed approximation approach learns the high-level hierarchical image description in a partially decoded domain thereby avoiding the requirement to fully decode the images from the archive before any scene classification is performed. Quantitative as well as qualitative experimental results demonstrate the efficiency of the proposed methods, which show significant improvements over state-of-the-art methods.
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Preethy, Byju Akshara. "Advanced Methods for Content Based Image Retrieval and Scene Classification in JPEG 2000 Compressed Remote Sensing Image Archives." Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/281771.

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Recent advances in satellite imaging technologies have paved its way to the RS big data era. Efficient storage, management and utilization of massive amounts of data is one of the major challenges faced by the remote sensing (RS) community. To minimize the storage requirements and speed up the transmission rate, RS images are compressed before archiving. Accordingly, developing efficient Content Based Image Retrieval (CBIR) and scene classification techniques to effectively utilize these huge volume of data is one among the most researched areas in RS. With the continual growth in the volume of compressed RS data, the dominant aspect that plays a key role in the development of these techniques is the decompression time required by these images. Existing CBIR and scene classification methods in RS require fully decompressed RS images as input, which is a computationally complex and time consuming task to perform. Among several compression algorithms introduced to RS, JPEG 2000 is the most widely used in operational satellites due to its multiresolution paradigm, scalability and high compression ratio. In light of this, the goal of this thesis is to develop novel methods to achieve image retrieval and scene classification for JPEG 2000 compressed RS image archives. The first contribution of the thesis addresses the possibility of performing CBIR directly on compressed RS images. The aim of the proposed method is to achieve efficient image characterization and retrieval within the JPEG 2000 compressed domain. The proposed progressive image retrieval approach achieves a coarse to fine image description and retrieval in the partially decoded JPEG 2000 compressed domain. Its aims to reduce the computational time required by the CBIR system for compressed RS image archives. The second contribution of the thesis concerns the possibility of achieving scene classification for JPEG 2000 compressed RS image archives. Recently, deep learning methods have demonstrated a cutting edge improvement in scene classification performance in large-scale RS image archives. In view of this, the proposed method is based on deep learning and aims to achieve maximum scene classification accuracy with minimal decoding. The proposed approximation approach learns the high-level hierarchical image description in a partially decoded domain thereby avoiding the requirement to fully decode the images from the archive before any scene classification is performed. Quantitative as well as qualitative experimental results demonstrate the efficiency of the proposed methods, which show significant improvements over state-of-the-art methods.
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Harikumar, Aravind. "Advanced methods for tree species classification and biophysical parameter estimation using crown geometric information in high density LiDAR data." Doctoral thesis, Università degli studi di Trento, 2019. https://hdl.handle.net/11572/369121.

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The ecological, climatic and economic influence of forests makes them an essential natural resource to be studied, preserved, and managed. Forest inventorying using single sensor data has a huge economic advantage over multi-sensor data. Remote sensing of forests using high density multi-return small footprint Light Detection and Ranging (LiDAR) data is becoming a cost-effective method to automatic estimation of forest parameters at the Individual Tree Crown (ITC) level. Individual tree detection and delineation techniques form the basis for ITC level parameter estimation. However SoA techniques often fail to exploit the huge amount of three dimensional (3D) structural information in the high density LiDAR data to achieve accurate detection and delineation of the 3D crown in dense forests, and thus, the first contribution of the thesis is a technique that detects and delineates both dominant and subdominant trees in dense multilayered forests. The proposed method uses novel two dimensional (2D) and 3D features to achieve this goal. Species knowledge at individual tree level is relevant for accurate forest parameter estimation. Most state-of-the-art techniques use features that represent the distribution of data points within the crown to achieve species classification. However, the performance of such methods is low when the trees belong to the same taxonomic class (e.g., the conifer class). High density LiDAR data contain a huge amount of fine structural information of individual tree crowns. Thus, the second contribution of the thesis is on novel methods for classifying conifer species using both the branch level and the crown level geometric characteristics. Accurate localization of trees is fundamental to calibrate the individual tree level inventory data, as it allows to match reference to LiDAR data. An important biophysical parameter for precision forestry applications is the Diameter at Breast Height (DBH). SoA methods locate the stem directly below the tree top, and indirectly estimate DBH using species-specific allometric models. Both approaches tend to be inaccurate and depend on the forest type. Thus, in this thesis, a method for accurate stem localization and DBH measurement is proposed. This is the third contribution of the thesis. Qualitative and quantitative results of the experiments confirm the effectiveness of the proposed methods over the SoA ones.
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Harikumar, Aravind. "Advanced methods for tree species classification and biophysical parameter estimation using crown geometric information in high density LiDAR data." Doctoral thesis, University of Trento, 2019. http://eprints-phd.biblio.unitn.it/3782/1/PhD_Thesis_Harikumar.pdf.

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The ecological, climatic and economic influence of forests makes them an essential natural resource to be studied, preserved, and managed. Forest inventorying using single sensor data has a huge economic advantage over multi-sensor data. Remote sensing of forests using high density multi-return small footprint Light Detection and Ranging (LiDAR) data is becoming a cost-effective method to automatic estimation of forest parameters at the Individual Tree Crown (ITC) level. Individual tree detection and delineation techniques form the basis for ITC level parameter estimation. However SoA techniques often fail to exploit the huge amount of three dimensional (3D) structural information in the high density LiDAR data to achieve accurate detection and delineation of the 3D crown in dense forests, and thus, the first contribution of the thesis is a technique that detects and delineates both dominant and subdominant trees in dense multilayered forests. The proposed method uses novel two dimensional (2D) and 3D features to achieve this goal. Species knowledge at individual tree level is relevant for accurate forest parameter estimation. Most state-of-the-art techniques use features that represent the distribution of data points within the crown to achieve species classification. However, the performance of such methods is low when the trees belong to the same taxonomic class (e.g., the conifer class). High density LiDAR data contain a huge amount of fine structural information of individual tree crowns. Thus, the second contribution of the thesis is on novel methods for classifying conifer species using both the branch level and the crown level geometric characteristics. Accurate localization of trees is fundamental to calibrate the individual tree level inventory data, as it allows to match reference to LiDAR data. An important biophysical parameter for precision forestry applications is the Diameter at Breast Height (DBH). SoA methods locate the stem directly below the tree top, and indirectly estimate DBH using species-specific allometric models. Both approaches tend to be inaccurate and depend on the forest type. Thus, in this thesis, a method for accurate stem localization and DBH measurement is proposed. This is the third contribution of the thesis. Qualitative and quantitative results of the experiments confirm the effectiveness of the proposed methods over the SoA ones.
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Books on the topic "Classification: Advanced Methods"

1

Buck, Carol J. The next step: Advanced medical coding. 2nd ed. St. Louis, Mo: Elsevier/Saunders, 2012.

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1930-, Marcus Leslie Floyd, North Atlantic Treaty Organization. Scientific Affairs Division., and NATO Advanced Study Institute on Advances in Morphometrics (1993 : Il Ciocco, Italy), eds. Advances in morphometrics. New York: Plenum Press, 1996.

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C, Choi Sung, ed. Statistical methods of discrimination and classification: Advances in theory and applications. New York: Pergamon Press, 1986.

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Kashlev, Sergey. Interactive learning technology. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1033836.

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The educational and methodological manual examines interactive methods, the technology of interactive learning as an innovative pedagogical phenomenon, defines the features, content and structure of interactive methods, justifies the classification of interactive teaching methods, reveals the theoretical and methodological foundations of the use of interactive teaching methods in the pedagogical process, provides characteristics of about 70 individual interactive teaching methods, as well as the content of interactive classes, scientific and methodological seminars on pedagogy with students, listeners of the system of additional adult education. Meets the requirements of the federal state educational standards of higher education of the latest generation. For teachers and heads of institutions of secondary general education, additional education of children and youth, social educators, practical psychologists, students and teachers of pedagogical specialties of universities, undergraduates, postgraduates of psychological and pedagogical specialties, students of the system of advanced training and retraining of educational specialists, methodologists of educational institutions.
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Renaud, Fortuner, ed. Advances in computer methods for systematic biology: Artificial intelligence, databases, computer vision. Baltimore: Johns Hopkins University Press, 1993.

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Sassine, Youssef Najib, ed. Mushrooms: Agaricus bisporus. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781800620414.0000.

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Abstract This book, which is selected in nature to Agaricus bisporus, presents fundamental guidelines for mushroom production together with the advances in research in this field. The first chapter presents the history of button mushroom cultivation, mushroom classification, distribution, and nutritional and medicinal value. The methods of composting for substrate preparation, their basics, application, and innovation are discussed in Chapter 2. The basic principles and methods to improve compost quality are shown in the third chapter. Moving to the fourth chapter, the genetics, breeding approaches, and selection of new mushroom strains are discussed in detail. Chapter 5 addresses the stages of casing and cropping by focusing on the tools and methods to optimize production during these stages. Chapter 6 details the management of pests and control of diseases at a mushroom farm, with a special focus on the ideal farm design to avoid the spread of infesting agents. The last chapter of the book shows the advances in harvest and postharvest technologies, applied to maximize the postharvest benefits from button mushroom cultivation.
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International Conference on p-Adic Functional Analysis (11th 2010 Université Blaise Pascal). Advances in non-Archimedean analysis: Eleventh International Conference on p-Adic Functional Analysis, July 5-9 2010, Université Blaise Pascal, Clermont-Ferrand, France. Edited by Araujo-Gomez Jesus 1965-, Diarra B. (Bertin) 1944-, and Escassut Alain. Providence, R.I: American Mathematical Society, 2011.

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Konrad, Paul Markus. Calibration of Rating Models: Estimation of the Probability of Default Based on Advanced Pattern Classification Methods. Tectum Verlag, 2014.

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Breit, Alfred, P. Lukas, and A. Heuck. Tumor Response Monitoring and Treatment Planning: Advanced Radiation Therapy. Springer, 2012.

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Breit, Alfred. Tumor Response Monitoring and Treatment Planning: Advanced Radiation Therapy. Springer-Verlag Telos, 1992.

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Book chapters on the topic "Classification: Advanced Methods"

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Lencevicius, Raimondas. "Query Analysis and Classification." In Advanced Debugging Methods, 101–25. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4419-8774-7_5.

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Honerkamp, Josef. "Statistical Tests and Classification Methods." In Advanced Texts in Physics, 445–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-662-04763-7_13.

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Pascualvaca, José Manuel Sánchez, Carlos Fernandes, Alberto Guillén, Antonio M. Mora, Rogerio Largo, Agostinho C. Rosa, and Luis Javier Herrera. "Sleep Stage Classification Using Advanced Intelligent Methods." In Advances in Computational Intelligence, 604–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38679-4_61.

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Upadhyay, Prashant, and Pradeep Tomar. "Alzheimer’s Disease Classification Using Ensemble Methods." In Advanced IoT Sensors, Networks and Systems, 3–15. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1312-1_1.

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Wang, Jinlong, Ke Gao, Yang Jiao, and Gang Li. "Study on Ensemble Classification Methods towards Spam Filtering." In Advanced Data Mining and Applications, 314–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03348-3_31.

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Paoletti, Matteo, and Carlo Marchesi. "Interpretation and Classification of Patient Status Patterns." In Advanced Methods of Biomedical Signal Processing, 551–70. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118007747.ch22.

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Serramazza, Davide Italo, Thu Trang Nguyen, Thach Le Nguyen, and Georgiana Ifrim. "Evaluating Explanation Methods for Multivariate Time Series Classification." In Advanced Analytics and Learning on Temporal Data, 159–75. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-49896-1_11.

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Bellazzi, Riccardo, Silvio Bicciato, Claudio Cobelli, Barbara Di Camillo, Fulvia Ferrazzi, Paolo Magni, Lucia Sacchi, and Gianna Toffolo. "Microarray Data Analysis: General Concepts, Gene Selection, and Classification." In Advanced Methods of Biomedical Signal Processing, 443–71. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118007747.ch18.

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Hachaj, Tomasz. "Pattern Classification Methods for Analysis and Visualization of Brain Perfusion CT Maps." In Computational Intelligence Paradigms in Advanced Pattern Classification, 145–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-24049-2_8.

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Pham, Thi-Ngan, Quang-Thuy Ha, Minh-Chau Nguyen, and Tri-Thanh Nguyen. "A Probability-Based Close Domain Metric in Lifelong Learning for Multi-label Classification." In Advanced Computational Methods for Knowledge Engineering, 143–49. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-38364-0_13.

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Conference papers on the topic "Classification: Advanced Methods"

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Rabadi, Dima, and Sin G. Teo. "Advanced Windows Methods on Malware Detection and Classification." In ACSAC '20: Annual Computer Security Applications Conference. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3427228.3427242.

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Beauxis-Aussalet, Emma, and Lynda Hardman. "Extended Methods to Handle Classification Biases." In 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 2017. http://dx.doi.org/10.1109/dsaa.2017.52.

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Valenzuela Rubilar, Joan Manuel, Josep Domenech, and Ana Pont. "Changes in corporate websites and business activity: automatic classification of corporate webpages." In CARMA 2022 - 4th International Conference on Advanced Research Methods and Analytics. valencia: Universitat Politècnica de València, 2022. http://dx.doi.org/10.4995/carma2022.2022.15090.

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Every time a firm or institution performs an activity on the Web, this is registered, leaving a "digital footprint”. Part this digital footprint is reflected on their websites as these officially represent them on the Web. We plan to automatically monitor the changes that periodically occur in a website to relate them with the business activity. The aim of this paper is to propose a theoretical classification of corporate webpages to associate changes that occur on them with the regular activity of the firms, and to evaluate the possibility of an automatic categorization using classification models. To generate the classification of corporate webpages, a significant number of today corporate webpages were analyzed and observed, distinguishing four theoretical types of corporate webpages. To evaluate the automatic categorization of corporate webpages, a dataset of 1005 today corporate pages was generated by manually labeling them and evaluating their automatic categorization using classification models.
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Wlodarski, M., K. Kopczyński, M. Kaliszewski, M. Kwaśny, M. Mularczyk-Oliwa, and M. Kastek. "Application of advanced optical methods for classification of air contaminants." In AIR POLLUTION 2009. Southampton, UK: WIT Press, 2009. http://dx.doi.org/10.2495/air090221.

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Vlasenko, Nataliia, and Olena Peredrii. "DATA HASHING IN VISUAL OBJECTS STRUCTURAL CLASSIFICATION METHODS." In ADVANCED DISCOVERIES OF MODERN SCIENCE: EXPERIENCE, APPROACHES AND INNOVATIONS. European Scientific Platform, 2021. http://dx.doi.org/10.36074/logos-09.04.2021.v1.46.

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Sekhar, C. Chandra, Sasi Kumar, Madhan Subhas, and Raj Kumar Buyya. "Kernel methods based approaches to image classification and retrieval." In 2012 Fourth International Conference on Advanced Computing (ICoAC). IEEE, 2012. http://dx.doi.org/10.1109/icoac.2012.6416867.

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Wan, Shaohua. "Analyzing Microarray Data with Classification and Clustering Methods." In 2015 Third International Conference on Advanced Cloud and Big Data (CBD). IEEE, 2015. http://dx.doi.org/10.1109/cbd.2015.36.

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Yang, Yuexiang, Shouhui Pan, Bo Xu, Yiyang Wang, and Chao Lei. "Web text classification methods research of product quality and safety." In 5th International Conference on Advanced Computer Control. Southampton, UK: WIT Press, 2014. http://dx.doi.org/10.2495/icacc130071.

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Tientcheu, Rostand Tcheumeleu, and David Pouhe. "Analysis of methods for classification of intentional electromagnetic environments." In 2015 International Conference on Electromagnetics in Advanced Applications (ICEAA). IEEE, 2015. http://dx.doi.org/10.1109/iceaa.2015.7297344.

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Valentour, Nicholas, and Steve Saville. "Alternative positioning methods for advanced geophysical classification in GPS-denied environments." In Symposium on the Application of Geophysics to Engineering and Environmental Problems 2021. Society of Exploration Geophysicists and Environment and Engineering Geophysical Society, 2021. http://dx.doi.org/10.4133/sageep.33-119.

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Reports on the topic "Classification: Advanced Methods"

1

Klay, Jonathan, David K. Mellinger, David J. Moretti, Steve W. Martin, and Marie A. Roch. Advanced Methods for Passive Acoustic Detection, Classification, and Localization of Marine Mammals. Fort Belvoir, VA: Defense Technical Information Center, September 2012. http://dx.doi.org/10.21236/ada573543.

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Klay, Jonathan, David K. Mellinger, David J. Moretti, Steve W. Martin, and Marie A. Roch. Advanced Methods for Passive Acoustic Detection, Classification, and Localization of Marine Mammals. Fort Belvoir, VA: Defense Technical Information Center, September 2014. http://dx.doi.org/10.21236/ada616403.

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Zhao, George, Grang Mei, Bulent Ayhan, Chiman Kwan, and Venu Varma. DTRS57-04-C-10053 Wave Electromagnetic Acoustic Transducer for ILI of Pipelines. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), March 2005. http://dx.doi.org/10.55274/r0012049.

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In this project, Intelligent Automation, Incorporated (IAI) and Oak Ridge National Lab (ORNL) propose a novel and integrated approach to inspect the mechanical dents and metal loss in pipelines. It combines the state-of-the-art SH wave Electromagnetic Acoustic Transducer (EMAT) technique, through detailed numerical modeling, data collection instrumentation, and advanced signal processing and pattern classifications, to detect and characterize mechanical defects in the underground pipeline transportation infrastructures. The technique has four components: (1) thorough guided wave modal analysis, (2) recently developed three-dimensional (3-D) Boundary Element Method (BEM) for best operational condition selection and defect feature extraction, (3) ultrasonic Shear Horizontal (SH) waves EMAT sensor design and data collection, and (4) advanced signal processing algorithm like a nonlinear split-spectrum filter, Principal Component Analysis (PCA) and Discriminant Analysis (DA) for signal-to-noise-ratio enhancement, crack signature extraction, and pattern classification. This technology not only can effectively address the problems with the existing methods, i.e., to detect the mechanical dents and metal loss in the pipelines consistently and reliably but also it is able to determine the defect shape and size to a certain extent.
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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, July 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detection, refractometer and a scale (mass). Data were analyzed and provided input for five classification models. Chlorophyll from fluorescence was found to give the best estimation for ripeness stage while the combination of machine vision and firmness from impact performed best for quality sorting. A new algorithm was developed to estimate and minimize training size for supervised classification. A new criteria was established to choose a training set such that a recurrent auto-associative memory neural network is stabilized. Moreover, this method provides for rapid and accurate updating of the classifier over growing seasons, production environments and cultivars. Different classification approaches (parametric and non-parametric) for grading were examined. Statistical methods were found to be as accurate as neural networks in grading. Classification models by voting did not enhance the classification significantly. A hybrid model that incorporated heuristic rules and either a numerical classifier or neural network was found to be superior in classification accuracy with half the required processing of solely the numerical classifier or neural network. In Israel: A multi-sensing approach utilizing non-destructive sensors was developed. Shape, color, stem identification, surface defects and bruises were measured using a color image processing system. Flavor parameters (sugar, acidity, volatiles) and ripeness were measured using a near-infrared system and an electronic sniffer. Mechanical properties were measured using three sensors: drop impact, resonance frequency and cyclic deformation. Classification algorithms for quality sorting of fruit based on multi-sensory data were developed and implemented. The algorithms included a dynamic artificial neural network, a back propagation neural network and multiple linear regression. Results indicated that classification based on multiple sensors may be applied in real-time sorting and can improve overall classification. Advanced image processing algorithms were developed for shape determination, bruise and stem identification and general color and color homogeneity. An unsupervised method was developed to extract necessary vision features. The primary advantage of the algorithms developed is their ability to learn to determine the visual quality of almost any fruit or vegetable with no need for specific modification and no a-priori knowledge. Moreover, since there is no assumption as to the type of blemish to be characterized, the algorithm is capable of distinguishing between stems and bruises. This enables sorting of fruit without knowing the fruits' orientation. A new algorithm for on-line clustering of data was developed. The algorithm's adaptability is designed to overcome some of the difficulties encountered when incrementally clustering sparse data and preserves information even with memory constraints. Large quantities of data (many images) of high dimensionality (due to multiple sensors) and new information arriving incrementally (a function of the temporal dynamics of any natural process) can now be processed. Furhermore, since the learning is done on-line, it can be implemented in real-time. The methodology developed was tested to determine external quality of tomatoes based on visual information. An improved model for color sorting which is stable and does not require recalibration for each season was developed for color determination. Excellent classification results were obtained for both color and firmness classification. Results indicted that maturity classification can be obtained using a drop-impact and a vision sensor in order to predict the storability and marketing of harvested fruits. In conclusion: We have been able to define quantitatively the critical parameters in the quality sorting and grading of both fresh market cantaloupes and tomatoes. We have been able to accomplish this using nondestructive measurements and in a manner consistent with expert human grading and in accordance with market acceptance. This research constructed and used large databases of both commodities, for comparative evaluation and optimization of expert system, statistical and/or neural network models. The models developed in this research were successfully tested, and should be applicable to a wide range of other fruits and vegetables. These findings are valuable for the development of on-line grading and sorting of agricultural produce through the incorporation of multiple measurement inputs that rapidly define quality in an automated manner, and in a manner consistent with the human graders and inspectors.
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Qamer, Faisal M., Sravan Shrestha, Kiran Shakya, Birendra Bajracharya, Shib Nandan Shah, Ram Krishna Regmi, Salik Paudel, et al. Operational in-season rice area estimation through Earth observation data in Nepal - working paper. International Centre for Integrated Mountain Development (ICIMOD), March 2023. http://dx.doi.org/10.53055/icimod.1017.

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In an effort to adopt emerging technologies in food security assessment through a codevelopment approach, the Government of Nepal’s Ministry of Agriculture and Livestock Development (MoALD) and the International Centre for Integrated Mountain Development’s (ICIMOD) SERVIR-HKH Initiative undertook a pilot study in Chitwan District in 2019 to jointly develop methods for satellite remote sensing and machine learning-based in-season crop assessment. MoALD experts and relevant stakeholders thoroughly reviewed the approach before the honourable minister approved it for formal use in the national-level assessment for 2020 and onwards. For wider adoption of the advanced data science methods established in the pilot study, we customised the technology by developing a digital suite of software, including GeoFairy (a mobile app to facilitate field data collection by field extension professionals at the district level) and RiceMapEngine (a simplified platform for machine learning-based crop classification to facilitate crop area map production by MoALD’s GIS Section). In the current federal governance structure of Nepal, high-quality crop maps and yield estimates will not only bridge information needs among the federal and subnational institutions but also provide a means for consistent cross-country crop status assessments and communication.
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Desa, Hazry, and Muhammad Azizi Azizan. OPTIMIZING STOCKPILE MANAGEMENT THROUGH DRONE MAPPING FOR VOLUMETRIC CALCULATION. Penerbit Universiti Malaysia Perlis, 2023. http://dx.doi.org/10.58915/techrpt2023.004.

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Stockpile volumetric calculation is an important aspect in many industries, including construction, mining, and agriculture. Accurate calculation of stockpile volumes is essential for efficient inventory management, logistics planning, and quality control. Traditionally, stockpile volumetric calculation is done using ground-based survey methods, which can be time-consuming, labour-intensive, and often inaccurate. However, with the recent advancements in drone technology, it has become possible to use drones for stockpile volumetric calculation, providing a faster, safer, and more accurate solution. The duration of this project is one year, from May 1st, 2019, until April 30th, 2020, and is comprised of two primary research components: analyzing the properties and classification of limestone and conducting digital aerial mapping to calculate stockpile volumetrics. The scope of this technical report is specifically limited to the aerial mapping aspect of the project, which was carried out using drones. The project involved two phases, with drone flights taking place during each phase, spaced about six months apart. The first drone flight for data collection occurred on July 12th, 2019, while the second took place on December 15th, 2020. The project aims to utilize drone technology for stockpile volumetric calculation, providing a more efficient and cost-effective solution. The project will involve the use of advanced drone sensors and imaging technology to capture high-resolution data of the stockpile area. The data will then be processed using sophisticated software algorithms to generate accurate 3D models and volumetric calculations of the stockpile.
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Sikora, Yaroslava B., Olena Yu Usata, Oleksandr O. Mosiiuk, Dmytrii S. Verbivskyi, and Ekaterina O. Shmeltser. Approaches to the choice of tools for adaptive learning based on highlighted selection criteria. [б. в.], June 2021. http://dx.doi.org/10.31812/123456789/4447.

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The article substantiates the relevance of adaptive learning of students in the modern information society, reveals the essence of such concepts as “adaptability” and “adaptive learning system”. It is determined that a necessary condition for adaptive education is the criterion of an adaptive learning environment that provides opportunities for advanced education, development of key competencies, formation of a flexible personality that is able to respond to different changes, effectively solve different problems and achieve results. The authors focus on the technical aspect of adaptive learning. Different classifications of adaptability are analyzed. The approach to the choice of adaptive learning tools based on the characteristics of the product quality model stated by the standard ISO / IEC 25010 is described. The following criteria for the selecting adaptive learning tools are functional compliance, compatibility, practicality, and support. By means of expert assessment method there were identified and selected the most important tools of adaptive learning, namely: Acrobatiq, Fishtree, Knewton (now Wiliy), Lumen, Realize it, Smart Sparrow (now Pearson). Comparative tables for each of the selected tools of adaptive learning according to the indicators of certain criteria are given.
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