Gotowe bibliografie tematyczne / Classification: Advanced Methods

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  1. Artykuły w czasopismach
  2. Rozprawy doktorskie
  3. Książki
  4. Części książek
  5. Streszczenia konferencji
  6. Raporty organizacyjne

Gotowa bibliografia na temat „Classification: Advanced Methods”

Autor: Grafiati
Data publikacji: 27 lipca 2024
Data aktualizacji: 19 lipca 2025

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Artykuły w czasopismach na temat "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, et al. "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 (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
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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 (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
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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 (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. Th
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Taherian, Hessam, and Robert W. Peters. "Advanced Active and Passive Methods in Residential Energy Efficiency." Energies 16, no. 9 (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
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Chelak, Viktor, Oleksii Hornostal, Yehor Chelak, and Svitlana Gavrylenko. "ADVANCED METHODS FOR CLASSIFICATION QUALITY ASSESSMENT LEVERAGING ROC ANALYSIS AND MULTIDIMENSIONAL CONFUSION MATRIX." Advanced Information Systems 9, no. 1 (2025): 24–34. https://doi.org/10.20998/2522-9052.2025.1.03.

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The object of the study is the process of classifying objects in scientific problems. The subject of the study is methods aimed at assessing the effectiveness of multiclass classification. The goal of the study is to study the classification process and develop a classifier evaluation module to increase the speed of such evaluation and reduce the time to build complex machine learning classifiers. Methods used: methods for evaluating machine learning classifiers, methods for constructing ROC curves, principles of parallel and distributed computing. Results obtained: an analytical review of the
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Lokman, Amar, Wan Zakiah Wan Ismail, and Nor Azlina Ab Aziz. "Fuzzy Frameworks: Advanced Models for Water Quality Classification." Semarak Proceedings of Applied Sciences and Engineering Technology 1, no. 1 (2025): 158–64. https://doi.org/10.37934/spaset.1.1.158164a.

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Monitoring water quality is essential for safeguarding the health and safety of aquatic ecosystems and public water sources. Conventional water quality classification techniques frequently encounter challenges due to the intrinsic uncertainty and imprecision present in environmental data. This study presents an innovative fuzzy logic framework, enhanced by a Genetic Algorithm (GA), to address these complexities. The GA optimizes membership functions within the fuzzy framework, refining the classification process and improving adaptability. The proposed method integrates fuzzy set theory with m
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Rozprawy doktorskie na temat "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 affordabil
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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 affordabil
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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 class
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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 inf
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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 n
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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 o
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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 o
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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 techniqu
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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 techniq
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Książki na temat "Classification: Advanced Methods"

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Buck, Carol J. The next step: Advanced medical coding. 2nd ed. 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. Plenum Press, 1996.

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

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Kashlev, Sergey. Interactive learning technology. 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, l
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Renaud, Fortuner, ed. Advances in computer methods for systematic biology: Artificial intelligence, databases, computer vision. Johns Hopkins University Press, 1993.

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Sassine, Youssef Najib, ed. Mushrooms: Agaricus bisporus. 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 appro
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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. 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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Części książek na temat "Classification: Advanced Methods"

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Lencevicius, Raimondas. "Query Analysis and Classification." In Advanced Debugging Methods. 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. 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, et al. "Sleep Stage Classification Using Advanced Intelligent Methods." In Advances in Computational Intelligence. 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. 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. 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. John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118007747.ch22.

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Menezes, José Antonio Alves de, Juliana Carneiro Gomes, Vitor de Carvalho Hazin, et al. "Classification of Motor Imagery EEG Signals Based on Sparse Representations of Empirical Mode Decomposition Features." In Advanced Electroencephalography Analytical Methods. CRC Press, 2024. http://dx.doi.org/10.1201/9781003252092-13.

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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. 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, et al. "Microarray Data Analysis: General Concepts, Gene Selection, and Classification." In Advanced Methods of Biomedical Signal Processing. 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. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-24049-2_8.

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Streszczenia konferencji na temat "Classification: Advanced Methods"

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Patil, Rahul Prashant, Rohit Shetty, Raghav N., et al. "Advanced classification of Keratoconus using machine learning and polarization-sensitive OCT." In Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXIX, edited by Rainer A. Leitgeb and Yoshiaki Yasuno. SPIE, 2025. https://doi.org/10.1117/12.3042827.

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Attiah, Ameera, Mawaddah Alagha, Lujain Bukassim, and Passent El-Kafrawy. "Revolutionizing Diabetes Prediction Models: Leveraging Advanced Feature Reduction and Classification Methods." In 2025 22nd International Learning and Technology Conference (L&T). IEEE, 2025. https://doi.org/10.1109/lt64002.2025.10940224.

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Wang, Minjia, Fangzhou Liu, Xiuxing Li, et al. "Bio-RFX: Refining Biomedical Extraction via Advanced Relation Classification and Structural Constraints." In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.emnlp-main.588.

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Wang, Qiyuan, Lingbao Kong, and Huixin Song. "Data graph classification and super-resolution reconstruction methods based on deep learning." In Second Conference on Advanced Optical Manufacturing Technologies and Applications & Fourth Forum of Young Scientists on Advanced Optical Manufacturing, edited by Lingbao Kong, Guoyan Sun, Guofeng Zhang, and Jiang Guo. SPIE, 2024. http://dx.doi.org/10.1117/12.3048240.

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Kaur, Jaspreet, Prabhdeep Singh, and Amit Kumar. "Advanced Hacker Forum Use Collection and Classification Methods for Preventive Cyber Threat Intelligence." In 2024 4th International Conference on Technological Advancements in Computational Sciences (ICTACS). IEEE, 2024. https://doi.org/10.1109/ictacs62700.2024.10841221.

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Handa, Keshav, Viabhav Sharma, and Meenakshi N. "Advanced Deep Learning Methods for Early Detection and Progressive Classification of Cervical Cancer." In 2024 4th International Conference on Artificial Intelligence and Signal Processing (AISP). IEEE, 2024. https://doi.org/10.1109/aisp61711.2024.10870716.

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Mekni, Amal, Jyotindra Narayan, and Hassène Gritli. "Leveraging Machine Learning for Gait Phase Classification with Varied Training Methods." In 2024 IEEE 7th International Conference on Advanced Technologies, Signal and Image Processing (ATSIP). IEEE, 2024. http://dx.doi.org/10.1109/atsip62566.2024.10638961.

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Kawano, Tomoka, Masahiro Migita, Kaito Kamimura, et al. "A study on detection and classification methods for red tide phytoplankton using deep learning." In International Workshop on Advanced Imaging Technology (IWAIT) 2025, edited by Jae-Gon Kim, Chia-Hung Yeh, Kemao Qian, Masayuki Nakajima, Chuan-Yu Chang, and Phooi Yee Lau. SPIE, 2025. https://doi.org/10.1117/12.3058042.

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Aljoboury, Tania, Ahmed Aldhahab, and Hanaa Mohsin Al Abboodi. "Review of Advanced Machine Learning Methods for Lung Disease Diagnoses: Pre-processing, Features Extraction, Classification." In 2024 3rd International Conference on Advances in Engineering Science and Technology (AEST). IEEE, 2024. https://doi.org/10.1109/aest63017.2024.10959878.

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Nabil, Bourenane Mohamed, Kermani Mohamed Hachem, and Boufaida Zizette. "A Comprehensive Review of Experimental and Computational Methods for Protein Structure Prediction and Classification." In 2024 International Conference on Advanced Aspects of Software Engineering (ICAASE). IEEE, 2024. https://doi.org/10.1109/icaase64542.2024.10851085.

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Raporty organizacyjne na temat "Classification: Advanced Methods"

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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. Defense Technical Information Center, 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. Defense Technical Information Center, 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. Pipeline Research Council International, Inc. (PRCI), 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
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Qamer, Faisal M., Sravan Shrestha, Kiran Shakya, et al. Operational in-season rice area estimation through Earth observation data in Nepal - working paper. International Centre for Integrated Mountain Development (ICIMOD), 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-lev
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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, 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 detecti
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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 solu
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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.], 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 learni
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