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Relevant bibliographies by topics / Information-extreme algorithm

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Academic literature on the topic 'Information-extreme algorithm'

Author: Grafiati

Published: 28 May 2022

Last updated: 31 July 2025

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Journal articles on the topic "Information-extreme algorithm"

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Dovbysh, Anatoliy S., Sergey S. Martynenko, Alexander S. Kovalenko, and Nikolay N. Budnyk. "Information-extreme Algorithm for Recognizing Current Distribution Maps in Magnetocardiography." Journal of Automation and Information Sciences 43, no. 2 (2011): 63–70. http://dx.doi.org/10.1615/jautomatinfscien.v43.i2.60.

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GAO, Zihang. "A Novel Fault Diagnosis Method for Acceleration Sensor Utilizing IEM-Based LLE and WKELM." Mechanics 31, no. 1 (2025): 67–72. https://doi.org/10.5755/j02.mech.38258.

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In this study, fault diagnosis method for acceleration sensor utilizing information entropy measurement-based LLE and weighted kernel extreme learning machine (IEMLLE-WKELM) is proposed for fault diagnosis for acceleration sensor. First of all, this article proposes an information entropy measurement-based locally linear embedding (IEMLLE) algorithm to reduce the features of acceleration sensor data. The IEMLLE algorithm is a dimensionality reduction algorithm based on information entropy measurement. The discrimination of the distribution of sample data of the different classes based on IEMLL

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Moskalenko, V. V., A. S. Moskalenko, A. G. Korobov, and M. O. Zaretskiy. "Aerial compact drone autonomous navigation system – the model and training algorithms." Bionics of Intelligence 2, no. 91 (2018): 16–24. https://doi.org/10.30837/bi.2018.2(91).03.

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The paper presents a novel model of convolutional neural network for visual feature extraction, extreme-learning machine for position displacement estimation and boosted information-extreme classifier for obstacle prediction with new training algorithms to build decision rules of autonomous navigation system for compact drones are presented in the paper. Growing sparse-coding neural gas algorithm for unsupervised training of the convolution filters, supervised learning algorithms for training decision rules and simulated annealing search algorithm for fine tuning are proposed. The complex crit

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Yang, Shuhan, and Shufang Tian. "Extraction of Alteration Information from Hyperspectral Data Base on Kernel Extreme Learning Machine." Remote Sensing 16, no. 19 (2024): 3646. http://dx.doi.org/10.3390/rs16193646.

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Machine learning, as an increasingly prominent method in recent years, has introduced new methodologies and perspectives for extracting geological alteration information. To enhance the accuracy of remote-sensing-alteration mineral information, this study focuses on the extraction of alteration information from hyperspectral remote sensing data using the Kernel-Based Extreme Learning Machine (KELM) optimized with the Sparrow Search Algorithm (SSA). The ideal parameters of the Kernel Extreme Learning Machine model were successfully acquired by utilizing the sparrow optimization method for conti

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Shi, Jianting, Jian Yang, and Yingtao Zhang. "Research on Steel Surface Defect Detection Based on YOLOv5 with Attention Mechanism." Electronics 11, no. 22 (2022): 3735. http://dx.doi.org/10.3390/electronics11223735.

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Due to the irresistible factors of material properties and processing technology in the steel production, there may be different types of defects on the steel surface, such as rolling scale, patches and so on, which seriously affect the quality of steel, and thus have a negative impact on the economic efficiency of the enterprises. Different from the general target detection tasks, the defect detection tasks have small targets and extreme aspect ratio targets. The contradiction of high positioning accuracy for targets and their inconspicuous features makes the defect detection tasks difficult.

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Suo, Limin, Hailong Liu, Jin Ni, Zhaowei Wang, and Rui Zhao. "Sunflower Origin Identification Based on Multi-Source Information Fusion Technique of Kernel Extreme Learning Machine." Agronomy 14, no. 6 (2024): 1320. http://dx.doi.org/10.3390/agronomy14061320.

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This study constructs a model for the rapid identification of the origins of edible sunflower (Helianthus) using Kernel Extreme Learning Machine (KELM) with multi-source information fusion technology. Near-infrared spectroscopy (NIRS) and nuclear magnetic resonance spectroscopy (NMRS) were utilized to analyze 180 sunflower samples from the Xinjiang, Heilongjiang, and Inner Mongolia regions. Initially, the identification models for the origin of sunflowers using NIR and NMR data were compared between two algorithms: the Extreme Learning Machine (ELM) and KELM, combined with various spectral pre

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Anurag, Rawal, Chhikara Gaurav, Kaur Gaganjot, and Khanna Hitesh. "Cryptography Algorithm." Journal of Analog and Digital Communications 4, no. 1 (2019): 31–38. https://doi.org/10.5281/zenodo.2606230.

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With the arrival and outbreak of high speed internet, www (World Wide Web) and growth of social media, online transaction, application and business, organization across the globe produce too much amount of data every second.  Data Security is the most basic and extreme measures to ensure secure passage of information through internet. As the number of user’s increases rapidly throughout the globe tremendously which directly entice cracker for doing cyber-attack. Moreover data security is becoming necessity as we are heading towards digital globalization. In this paper we will discus

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Savchenko, Taras, Ruslana Lakhtaryna, Anastasiia Denysenko, Anatoliy Dovbysh, Sarah E. Coupland, and Roman Moskalenko. "An Information-Extreme Algorithm for Universal Nuclear Feature-Driven Automated Classification of Breast Cancer Cells." Diagnostics 15, no. 11 (2025): 1389. https://doi.org/10.3390/diagnostics15111389.

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Background/Objectives: Breast cancer diagnosis heavily relies on histopathological assessment, which is prone to subjectivity and inefficiency, especially with whole-slide imaging (WSI). This study addressed these limitations by developing an automated breast cancer cell classification algorithm using an information-extreme machine learning approach and universal cytological features, aiming for objective and generalized histopathological diagnosis. Methods: Digitized histological images were processed to identify hyperchromatic cells. A set of 21 cytological features (10 geometric and 11 text

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Wang, Daolei, Tianyu Zhang, Rui Zhu, Mingshan Li, and Jiajun Sun. "Extreme Image Classification Algorithm Based on Multicore Dense Connection Network." Mathematical Problems in Engineering 2021 (June 16, 2021): 1–11. http://dx.doi.org/10.1155/2021/6616325.

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Extreme images refer to low-quality images taken under extreme environmental conditions such as haze, heavy rain, strong light, and shaking, which will lead to the failure of the visual system to effectively recognize the target. Most of the existing extreme image restoration algorithms only handle the restoration work of a certain type of image; how to effectively recognize all kinds of extreme images is still a challenge. Therefore, this paper proposes a classification and restoration algorithm for extreme images. Due to the large differences in the features on extreme images, it is difficul

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Kurniawan, Anang, and Fauziah. "SIMPEL (Sistem Informasi Manajemen Pelatihan) Internal BRI Menggunakan Metode Agile dengan Model Extreme Programming dan Algoritma Brute Force." Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) 7, no. 2 (2023): 270–79. http://dx.doi.org/10.35870/jtik.v7i2.754.

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This research develops SIMPEL (Training Management Information System) Internal BRI using Agile Methods, especially the Extreme Programming (XP) Method, and the Brute force Algorithm as a tool. XP is used because it is known as a method that is fast and flexible in accommodating changing user needs. The Brute force algorithm is implemented to help users search for training or similar information. White Box Testing is carried out to test the algorithm and shows that the time needed to find a participant data value is 0.43 ms. The results of this study indicate that the use of Agile Methods and

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Dissertations / Theses on the topic "Information-extreme algorithm"

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Москаленко, В`ячеслав Васильович, Вячеслав Васильевич Москаленко, Viacheslav Vasylovych Moskalenko, A. G. Korobov, and R. S. Prihodchenko. "Information-Extreme Algorithm of Learning for System Identification of Objects on the Terrain." Thesis, Sumy State University, 2016. http://essuir.sumdu.edu.ua/handle/123456789/47276.

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Проскочило, В. А. "Інтелектуальна система класифікації трьох класів розпізнавання". Thesis, Видавництво СумДУ, 2011. http://essuir.sumdu.edu.ua/handle/123456789/9853.

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Закалюжна, Наталія Валеріївна, Наталья Валерьевна Закалюжная, and Nataliia Valeriivna Zakaliuzhna. "Application of Genetic Algorithm in the System of Recognition Electro." Thesis, Sumy State University, 2016. http://essuir.sumdu.edu.ua/handle/123456789/47130.

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Радченко, Г. О. "Інформаційно-екстремальний кластер-аналіз в інтелектуальних системах керування дистанційним навчанням". Thesis, Сумський державний університет, 2014. http://essuir.sumdu.edu.ua/handle/123456789/39132.

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Об’єктом дослідження є слабоформалізований процес оцінювання рівня знань студентів. Робота присвячена пошуку способів підвищення адаптивності СК, які синтезуються за методами ІЕІ -технології. У результаті роботи було показано, що існує можливість створити нові ефективні алгоритми кластер-аналізу вхідних даних і таким чином синтезувати адаптивну систему керування.

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Nordén, Frans, and Reis Marlevi Filip von. "A Comparative Analysis of Machine Learning Algorithms in Binary Facial Expression Recognition." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254259.

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In this paper an analysis is conducted regarding whether a higher classification accuracy of facial expressions are possible. The approach used is that the seven basic emotional states are combined into a binary classification problem. Five different machine learning algorithms are implemented: Support vector machines, Extreme learning Machine and three different Convolutional Neural Networks (CNN). The utilized CNN:S were one conventional, one based on VGG16 and transfer learning and one based on residual theory known as RESNET50. The experiment was conducted on two datasets, one small contai

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Books on the topic "Information-extreme algorithm"

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Ohlin, Jens David, and Duncan B. Hollis, eds. Defending Democracies. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780197556979.001.0001.

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Evidence of election interference by foreign states or their proxies has become a regular feature of national elections and is likely to get worse in the near future. Information and communication technologies afford those who would interfere with new tools that can operate in ways previously unimaginable: Twitter bots, Facebook advertisements, closed social media platforms, algorithms that prioritize extreme views, disinformation, misinformation, and malware that steals secret campaign communications. Defending Democracies: Combating Foreign Election Interference in a Digital Age tackles the

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Villez, Kris, Daniel Aguado, Janelcy Alferes, Queralt Plana, Maria Victoria Ruano, and Oscar Samuelsson, eds. Metadata Collection and Organization in Wastewater Treatment and Wastewater Resource Recovery Systems. IWA Publishing, 2024. http://dx.doi.org/10.2166/9781789061154.

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In recent years, the wastewater treatment field has undergone an instrumentation revolution. Thanks to increased efficiency of communication networks and extreme reductions in data storage costs, wastewater plants have entered the era of big data. Meanwhile, artificial intelligence and machine learning tools have enabled the extraction of valuable information from large-scale datasets. Despite this potential, the successful deployment of AI and automation depends on the quality of the data produced and the ability to analyze it usefully in large quantities. Metadata, including a quantification

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Book chapters on the topic "Information-extreme algorithm"

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Jażdżewski, Tomasz, Krzysztof Regulski, Adam Bułka, Pawel Malara, Adrian Czeszkiewicz, and Marcin Trajer. "Information Extraction from Time Series in the EDM Drilling Process." In Lecture Notes in Mechanical Engineering. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-58006-2_12.

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AbstractElectrical discharge machining (EDM) allows to obtain small holes with the high efficiency and high quality. Such features are most common in jet engine turbine airfoils. The main problem of the analysis is detection of a moment when the machine should stop the drilling process—the breakthrough detection. Machine learning applications requires that data and models to be prepared by specialists that can extract the most important information from an input data and choose most suitable Artificial Intelligence (AI) algorithm for particular case. This Article describes an experiment on how

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Han, Fei, Tat-Ming Lok, and Michael R. Lyu. "A New Learning Algorithm for Function Approximation Incorporating A Priori Information into Extreme Learning Machine." In Advances in Neural Networks - ISNN 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11759966_93.

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Zhang, Hao, Dingyi Zhang, and Tao Ku. "Multi-objective Artificial Bee Colony Algorithm with Information Learning for Model Optimization of Extreme Learning Machine." In Proceedings in Adaptation, Learning and Optimization. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23307-5_29.

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Li, Meiyi, Weibiao Cai, and Xingwang Liu. "Improved Ensemble Extreme Learning Machine Regression Algorithm." In IFIP Advances in Information and Communication Technology. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00828-4_2.

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Zhang, Yongshan, Jia Wu, Zhihua Cai, and Siwei Jiang. "A Brain Network Inspired Algorithm: Pre-trained Extreme Learning Machine." In Neural Information Processing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70139-4_2.

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Jia, Xibin, Xiaobo Li, Hua Du, and Bir Bhanu. "Local Invariance Representation Learning Algorithm with Multi-layer Extreme Learning Machine." In Neural Information Processing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46681-1_60.

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Zhang, Yongshan, Jia Wu, Zhihua Cai, and Siwei Jiang. "Erratum to: A Brain Network Inspired Algorithm: Pre-trained Extreme Learning Machine." In Neural Information Processing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70139-4_94.

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Cao, Ruimin, Fengli Wang, and Lina Hao. "Extreme Learning Machine Based Modified Deep Auto-Encoder Network Classifier Algorithm." In Communications in Computer and Information Science. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5230-9_19.

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Zhao, Yile, and Zhihai Lu. "Ford Vehicle Classification Based on Extreme Learning Machine Optimized by Bat Algorithm." In Communications in Computer and Information Science. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-1925-3_26.

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Çatak, Ferhat Özgür. "Secure Multi-party Computation Based Privacy Preserving Extreme Learning Machine Algorithm Over Vertically Distributed Data." In Neural Information Processing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26535-3_39.

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Conference papers on the topic "Information-extreme algorithm"

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Aboagye, Emmanuel A., Austin L. Lehr, Ethan Shumaker, et al. "Machine Learning Methods for the Forecasting of Environmental Impacts in Early-stage Process Design." In Foundations of Computer-Aided Process Design. PSE Press, 2024. http://dx.doi.org/10.69997/sct.141240.

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Initial design stages are inherently complex and often lack comprehensive information, posing challenges in evaluating sustainability metrics. Machine Learning (ML) emerges as a valuable solution to address these challenges. ML algorithms, particularly effective in predicting environmental impacts of new chemicals with limited data, enable more informed decisions in sustainable design. This study focuses on employing ML for predicting the environmental impacts related to human health, ecosystem quality, climate change, and resource utilization to aid in early-stage environmental impact assessm

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Sakanti, Maria Mahardini, Viacheslav Siniaev, Aurelia Amaris, Win-Jet Luo, Suhartono, and C. Bambang Dwi Kuncoro. "Psychological Stress Classification Using Extreme Gradient Boosting Algorithm." In 2024 15th International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 2024. https://doi.org/10.1109/ictc62082.2024.10827020.

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Liu, Taiye, Chengpo Mu, Yang He, and Nan Wu. "Optimization Calculation Algorithm for Radiation Parameters Based on Extreme Learning Machine." In 2024 5th International Conference on Information Science, Parallel and Distributed Systems (ISPDS). IEEE, 2024. http://dx.doi.org/10.1109/ispds62779.2024.10667499.

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Guan, Xiaofeng, Fan Wu, and Pengtao Xu. "Research on Electricity Load Forecasting Based on Improved Extreme Learning Machine of Artificial Intelligence Algorithm." In 2024 International Conference on Computers, Information Processing and Advanced Education (CIPAE). IEEE, 2024. https://doi.org/10.1109/cipae64326.2024.00131.

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Li, Jixing, Xiaoxiang Zhang, and Limin Zhao. "Coal and Gangue Recognition in Fully Mechanized Coal Mining Face using Extreme Boosting Algorithm- Ant Colony Optimization." In 2024 First International Conference on Software, Systems and Information Technology (SSITCON). IEEE, 2024. https://doi.org/10.1109/ssitcon62437.2024.10797020.

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Zhou, Quan, Yuru Pan, Shanchi Zhai, and Ziqing Ye. "Wind turbine bearing fault diagnosis method based on energy valley optimization algorithm optimization multi kernel extreme learning machine." In 2025 7th International Conference on Information Science, Electrical and Automation Engineering (ISEAE). IEEE, 2025. https://doi.org/10.1109/iseae64934.2025.11041807.

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Vieira, Ronald E., Farzin Darihaki, Jamie Li, and Siamack A. Shirazi. "Application of Machine Learning Techniques for Sand Erosion Prediction for Elbows in Multiphase Flow." In CONFERENCE 2023. AMPP, 2023. https://doi.org/10.5006/c2023-18995.

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Abstract The aim of this work is to define, implement, test, and validate an AI methodology using existing machine learning (ML) algorithms to predict sand erosion in 90° elbows for a broad range of multiphase operating conditions. Based on information obtained from the experimental UT wall thickness loss data collected for different flow regimes (gas-sand, liquid-sand, dispersed-bubble, churn, annular, and low liquid loading multiphase flows), the methodology has been developed to predict the maximum erosion magnitudes in standard metallic elbows. In order to expand the range of application o

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Yan, Liping, Xuezhi Dong, Hualiang Zhang, and Haisheng Chen. "A Feature Weighted Kernel Extreme Learning Machine Ensemble Method for Gas Turbine Fault Diagnosis." In ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/gt2020-14188.

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Abstract Fault diagnosis is a very important section of gas turbine maintenance. Kernel extreme learning machine (KELM), a novel artificial intelligence algorithm, is a potentially effective diagnosis technology. The existing KELMs are all assumed that there is the same influence to the optimal separating hyperplane from all features, which reduces its generalization performance. In this study, a feature weighted kernel extreme learning machine ensemble method (FWKELM-RF) is developed for application in the field of gas turbine fault diagnosis. First, information gain ratio is introduced to as

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Du, Yuxuan, Tongliang Liu, Yinan Li, Runyao Duan, and Dacheng Tao. "Quantum Divide-and-Conquer Anchoring for Separable Non-negative Matrix Factorization." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/289.

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It is NP-complete to find non-negative factors W and H with fixed rank r from a non-negative matrix X by minimizing ||X-WH^Τ ||^2. Although the separability assumption (all data points are in the conical hull of the extreme rows) enables polynomial-time algorithms, the computational cost is not affordable for big data. This paper investigates how the power of quantum computation can be capitalized to solve the non-negative matrix factorization with the separability assumption (SNMF) by devising a quantum algorithm based on the divide-and-conquer anchoring (DCA) scheme [Zhou et al., 2013]. The

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Silva, Lucas Barth, Roberto Zanetti Freire, and Osíris Canciglieri Junior. "Spot Energy Price Forecasting Using Wavelet Transform and Extreme Learning Machine." In Congresso Brasileiro de Inteligência Computacional. SBIC, 2021. http://dx.doi.org/10.21528/cbic2021-62.

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Given the social importance of energy, there is a concern to promote the sustainable development of the sector. Aiming at this evolution, from the 90s onwards, a wave of liberalization in the sector began to emerge in various parts of the world. These measures promoted an increase in the dynamism of commercial transactions and the transformation of electricity into a commodity. Consequently, futures, short-term, and spot markets were created. In this context, and due to the volatility of energy prices, the forecast of monetary values has become strategic for traders. This work aims to apply a

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Reports on the topic "Information-extreme algorithm"

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Perdigão, Rui A. P. Information physics and quantum space technologies for natural hazard sensing, modelling and prediction. Meteoceanics, 2021. http://dx.doi.org/10.46337/210930.

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Disruptive socio-natural transformations and climatic change, where system invariants and symmetries break down, defy the traditional complexity paradigms such as machine learning and artificial intelligence. In order to overcome this, we introduced non-ergodic Information Physics, bringing physical meaning to inferential metrics, and a coevolving flexibility to the metrics of information transfer, resulting in new methods for causal discovery and attribution. With this in hand, we develop novel dynamic models and analysis algorithms natively built for quantum information technological platfor

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