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Journal articles on the topic 'Information-extreme algorithm'

Author: Grafiati

Published: 28 May 2022

Last updated: 31 July 2025

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1

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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Van Dalen, Arjen. "When algorithms steer the news: YouTube recommendations during the 2019 national elections." Politica 53, no. 2 (2021): 189–90. http://dx.doi.org/10.7146/politica.v53i2.130390.

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What people see and read in the media is no longer only determined by journalists, but increasingly by algorithms. These algorithms select, sort, and prioritize our information. Automatic processes like YouTube’s recommendation algorithm influence our views of the world. An important democratic question is whether YouTube’s recommendation algorithm incidentally exposes the audience to political information after watching entertainment content and whether the algorithm creates a filter bubble by primarily recommending content with a similar political perspective. During the Danish parliamentary

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Dovbysh, Anatoliy S., Nikolay N. Budnyk, and Vyacheslav V. Moskalenko. "Information-Extreme Algorithm for Optimizing Parameters of Hyperellipsoidal Containers of Recognition Classes." Journal of Automation and Information Sciences 44, no. 10 (2012): 35–44. http://dx.doi.org/10.1615/jautomatinfscien.v44.i10.30.

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Yang, Ling, Na Lv, and Zhen Xing Xu. "Spectrum Prediction for Cognitive Radio System Based on Optimally Pruned Extreme Learning Machine." Applied Mechanics and Materials 536-537 (April 2014): 430–36. http://dx.doi.org/10.4028/www.scientific.net/amm.536-537.430.

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The Cognitive Radio (CR) technology is an efficient solution to spectrum scarcity by share the spectrum with the secondary users on a non-interfering basis. The spectrum prediction can rationalize the spectrum allocation based on previous information about the spectrum evolution in time. Against previous spectrum prediction algorithm lack of timeliness and accuracy, this paper proposes a novel approach for spectrum prediction based on Optimally Pruned Extreme Learning Machine (OP-ELM) which improved the original Extreme Learning Machine (ELM) algorithm. This method not only takes the advantage

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Wang, Jing, Mengli Zhao, Xiao Xie, Li Zhang, and Wenbo Zhu. "Fusion of Bilateral 2DPCA Information for Image Reconstruction and Recognition." Applied Sciences 12, no. 24 (2022): 12913. http://dx.doi.org/10.3390/app122412913.

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Being an efficient image reconstruction and recognition algorithm, two-dimensional PCA (2DPCA) has an obvious disadvantage in that it treats the rows and columns of images unequally. To exploit the other lateral information of images, alternative 2DPCA (A2DPCA) and a series of bilateral 2DPCA algorithms have been proposed. This paper proposes a new algorithm named direct bilateral 2DPCA (DB2DPCA) by fusing bilateral information from images directly—that is, we concatenate the projection results of 2DPCA and A2DPCA together as the projection result of DB2DPCA and we average between the reconstr

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Zhang, Guo Dong, Xiao Hu Xue, and Wei Guo. "Effect of Measure and Interpolation Methods for Intensity-Based Medical Image Registration." Advanced Materials Research 647 (January 2013): 612–17. http://dx.doi.org/10.4028/www.scientific.net/amr.647.612.

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The local extreme is main reason to hamper the optimization process and influence the registration accuracy in medical image registration algorithm. In general, the accuracy of image registration based on mutual information is afforded by interpolation methods. In this paper, we analyze the effect of the measure and interpolation methods for medical image registration and present a medical image registration algorithm using mutual strictly concave function measure and partial volume (PV) interpolation methods. The experiment results show that for images with low local correlation the algorithm

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Tao, Jianfeng, Chengjin Qin, Weixing Li, and Chengliang Liu. "Intelligent Fault Diagnosis of Diesel Engines via Extreme Gradient Boosting and High-Accuracy Time–Frequency Information of Vibration Signals." Sensors 19, no. 15 (2019): 3280. http://dx.doi.org/10.3390/s19153280.

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Accurate and timely misfire fault diagnosis is of vital significance for diesel engines. However, existing algorithms are prone to fall into model over-fitting and adopt low energy-concentrated features. This paper presents a novel extreme gradient boosting-based misfire fault diagnosis approach utilizing the high-accuracy time–frequency information of vibration signals. First, diesel engine misfire tests were conducted under different spindle speeds, and the corresponding vibration signals were acquired via a triaxial accelerometer. The time-domain features of signals were extracted by using

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Gao, Yuehua, and Tianyi Chen. "An Efficient Extreme Learning Machine Based on Fuzzy Information Granulation." International Journal of Emerging Technologies in Learning (iJET) 12, no. 06 (2017): 161. http://dx.doi.org/10.3991/ijet.v12i06.7089.

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In order to improve learning efficiency and generalization ability of extreme learning machine (ELM), an efficient extreme learning machine based on fuzzy information granulation (FIG) is put forward. The new approach not only improves the speed of basic ELM algorithm that contains many hidden nodes, but also overcomes the weakness of basic ELM of low learning efficiency and generalization ability by getting rid of redundant information in the observed values. The experimental results show that the proposed method is effective and can produce desirable generalization performance in most cases

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Hu, Kai, Zhaodi Zhou, Liguo Weng, et al. "An Optimization Strategy for Weighted Extreme Learning Machine based on PSO." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 01 (2017): 1751001. http://dx.doi.org/10.1142/s0218001417510016.

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Machine learning is a subfield of artificial intelligence concerned with techniques that allow computers to improve their outputs based on previous experiences. Among numerous machine learning algorithms, Weighted Extreme Learning Machine (WELM) is one of the famous cases recently. It not only has Extreme Learning Machine (ELM)’s extremely fast training speed and better generalization performance than traditional Neuron Network (NN), but also has the merit in handling imbalance data by assigning more weight to minority class and less weight to majority class. But it still has the limitation of

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Dovbysh, Anatoliy S., Anna A. Stadnyk, and Stanislav A. Holubnychyi. "Hierarchical Information-Extreme Learning Algorithm for a Diagnostic System with Redundant Training Matrix." Journal of Automation and Information Sciences 47, no. 4 (2015): 54–63. http://dx.doi.org/10.1615/jautomatinfscien.v47.i4.60.

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Papchenko, Oleksandr, Borys Kuzikov, and Oksana Shovkoplias. "Enhancing functional efficiency in information-extreme machine learning with logistic regression ensembles." Radioelectronic and Computer Systems, no. 4 (December 6, 2023): 65–74. http://dx.doi.org/10.32620/reks.2023.4.06.

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The subject matter of the article is the application of supervised machine learning for the task of object class recognition. The goal is enhancing functional efficiency in information-extreme technology (IET) for object class recognition. The tasks to be solved are: to analyse possible ways of increasing the functional efficiency IET approach; implement an ensemble of models that include logistic regression for prioritizing recognition features and an IEI learning algorithm; compare the functional efficiency of the resulting ensemble of models on well-known dataset with classic approach and r

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LV, Xia-fu, Jun-peng CHEN, Lei LIU, Bo-hua WANG, and Yong WANG. "An Efficient Extreme Learning Machine Based on Fuzzy Information Granulation." International Journal of Online Engineering (iJOE) 11, no. 8 (2015): 42. http://dx.doi.org/10.3991/ijoe.v11i8.4884.

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In order to improve learning efficiency and generalization ability of extreme learning machine (ELM), an efficient extreme learning machine based on fuzzy information granulation (FIG) is put forward. Firstly, using FIG to get rid of redundant information in the original data set and then ELM is used to do train granulated data for prediction. This method not only improves the speed of basic ELM algorithm that contains many hidden nodes, but also overcomes the weakness of basic ELM of low learning efficiency and generalization ability by getting rid of redundant information in the observed val

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Buzdalov, Maxim, Benjamin Doerr, and Mikhail Kever. "The Unrestricted Black-Box Complexity of Jump Functions." Evolutionary Computation 24, no. 4 (2016): 719–44. http://dx.doi.org/10.1162/evco_a_00185.

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We analyze the unrestricted black-box complexity of the Jump function classes for different jump sizes. For upper bounds, we present three algorithms for small, medium, and extreme jump sizes. We prove a matrix lower bound theorem which is capable of giving better lower bounds than the classic information theory approach. Using this theorem, we prove lower bounds that almost match the upper bounds. For the case of extreme jump functions, which apart from the optimum reveal only the middle fitness value(s), we use an additional lower bound argument to show that any black-box algorithm does not

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Qin, Ningning, and Ken Chen. "A wireless sensor network location algorithm based on insufficient fingerprint information." Modern Physics Letters B 32, no. 34n36 (2018): 1840093. http://dx.doi.org/10.1142/s0217984918400936.

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To realize sensor network-based positioning, the use of conventional techniques based on fingerprints has considerable cost due to the need of establishing in an offline manner a fingerprint database. Besides, it is also time-consuming when searching the database for calculating the localization solution. To address these drawbacks, we first propose to utilize the inverse distance weighted (IDW) interpolation method to improve the spatial resolution of the fingerprint database. The genetic algorithm learning machine (GAELM) is introduced to speed up the database lookup while enhancing the posi

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Yang, Yutu, Xiaolin Zhou, Ying Liu, Zhongkang Hu, and Fenglong Ding. "Wood Defect Detection Based on Depth Extreme Learning Machine." Applied Sciences 10, no. 21 (2020): 7488. http://dx.doi.org/10.3390/app10217488.

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The deep learning feature extraction method and extreme learning machine (ELM) classification method are combined to establish a depth extreme learning machine model for wood image defect detection. The convolution neural network (CNN) algorithm alone tends to provide inaccurate defect locations, incomplete defect contour and boundary information, and inaccurate recognition of defect types. The nonsubsampled shearlet transform (NSST) is used here to preprocess the wood images, which reduces the complexity and computation of the image processing. CNN is then applied to manage the deep algorithm

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Yan Yan, Yan Yan, Hongzhong Ma Yan Yan, Dongdong Song Hongzhong Ma, Yang Feng Dongdong Song, and Dawei Duan Yang Feng. "OLTC Fault Diagnosis Method Based on Time Domain Analysis and Kernel Extreme Learning Machine." 電腦學刊 33, no. 6 (2022): 091–106. http://dx.doi.org/10.53106/199115992022123306008.

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<p>Aiming at the problems of limited feature information and low diagnosis accuracy of traditional on-load tap changers (OLTCs), an OLTC fault diagnosis method based on time-domain analysis and kernel extreme learning machine (KELM) is proposed in this paper. Firstly, the time-frequency analysis method is used to analyze the collected OLTC vibration signal, extract the feature information and form the feature matrix; Then, the PCA algorithm is used to select effective features to build the initial optimal feature matrix; Finally, a kernel extreme learning machine optimized by improved gr

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Jiakang Tang, Jiakang Tang, Lin Cui Jiakang Tang, and Zhiwei Zhang Lin Cui. "Face Image Recognition Algorithm Based on Label Complementation." 網際網路技術學刊 24, no. 6 (2023): 1241–48. http://dx.doi.org/10.53106/160792642023112406007.

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<p>In face image recognition, labels play a fairly important role in recognition and classification, and rich and perfect labels can greatly improve the accuracy rate. However, it is almost impossible for the labels in the image to be recognized to describe the image completely and accurately. At the same time, the data obtained when feature extraction is performed on an image inevitably extracts a large amount of redundant and useless information at the same time, which affects the generalization performance of the model. Accordingly, we propose a face image recognition algorithm based

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Lin, Qiuzhen, Xiaozhou Wang, Bishan Hu, et al. "Multiobjective Personalized Recommendation Algorithm Using Extreme Point Guided Evolutionary Computation." Complexity 2018 (November 12, 2018): 1–18. http://dx.doi.org/10.1155/2018/1716352.

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Recommender systems suggest items to users based on their potential interests, and they are important to alleviate the search and selection pressures induced by the increasing item information. Classical recommender systems mainly focus on the accuracy of recommendation. However, with the increase of the diversified demands of users, multiple metrics which may conflict with each other have to be considered in modern recommender systems, especially for the personalized recommender system. In this paper, we design a personalized recommendation system considering the three conflicting objectives,

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., Mehvish, and Ravinder Pal Singh. "Random Forest and Extreme Learning Machine Algorithms for High Accuracy Credit Card Fraud Detection." International Journal for Research in Applied Science and Engineering Technology 11, no. 9 (2023): 892–95. http://dx.doi.org/10.22214/ijraset.2023.55752.

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Abstract: The banking sector is facing a huge issue with credit card fraud, and research has shown that machine learning algorithms are a useful tool for identifying fraudulent actions of this kind. In this investigation, we offer a method for detecting fraudulent use of credit cards that makes use of a hybrid of two machine learning algorithms known as Random Forest (RF) and Extreme Learning Machine (ELM). We compiled a dataset using information obtained from a wide variety of sources, and then we preprocessed it to eliminate any inconsistencies and errors. Following this, the RF and ELM algo

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Fang, YunFeng, Qingfang Yang, Lili Zheng, Xiangyu Zhou, and Bo Peng. "A Deep Cycle Limit Learning Machine Method for Urban Expressway Traffic Incident Detection." Mathematical Problems in Engineering 2020 (July 15, 2020): 1–11. http://dx.doi.org/10.1155/2020/5965089.

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In Beijing, Shanghai, Hangzhou, and other cities in China, traffic congestion caused by traffic incidents also accounts for 50% to 75% of the total traffic congestion on expressways. Therefore, it is of great significance to study an accurate and timely automatic traffic incident detection algorithm for ensuring the operation efficiency of expressways and improving the level of road safety. At present, many effective automatic event detection algorithms have been proposed, but the existing algorithms usually take the original traffic flow parameters as input variables, ignoring the constructio

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Xu, Ke Hu, Jin Yu Chen, De Peng Kong, and Pu Fan. "Research on Vague Set of Target Threat Information Fusion Algorithm." Applied Mechanics and Materials 599-601 (August 2014): 1671–78. http://dx.doi.org/10.4028/www.scientific.net/amm.599-601.1671.

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Information fusion technology is one of the most active research areas currently, which is affected by the fact that the computer can only handle quantitative information and the result cannot reflect the actual feature of the target sometimes. This paper makes use of advantages of the vague set in dealing with uncertain information process to establish the target threat sequencing model based on vague set and take both quantitative and qualitative information of target into account. Using the improved scoring function, this paper comes up with the target threat sequence steps based on extreme

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Qu, Jingyi, Bo Chen, Chang Liu, and Jinfeng Wang. "Flight Delay Prediction Model Based on Lightweight Network ECA-MobileNetV3." Electronics 12, no. 6 (2023): 1434. http://dx.doi.org/10.3390/electronics12061434.

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In exploring the flight delay problem, traditional deep learning algorithms suffer from low accuracy and extreme computational complexity; therefore, the deep flight delay prediction algorithm is difficult to directly deploy to the mobile terminal. In this paper, a flight delay prediction model based on the lightweight network ECA-MobileNetV3 algorithm is proposed. The algorithm first preprocesses the data with real flight information and weather information. Then, in order to increase the accuracy of the model without increasing the computational complexity too much, feature extraction is per

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Liu, Shuai, Fan Ren, Ping Li, Zhijie Li, Hao Lv, and Yonggang Liu. "Testing Scenario Identification for Automated Vehicles Based on Deep Unsupervised Learning." World Electric Vehicle Journal 14, no. 8 (2023): 208. http://dx.doi.org/10.3390/wevj14080208.

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Naturalistic driving data (NDD) are valuable for testing autonomous driving systems under various driving conditions. Automatically identifying scenes from high-dimensional and unlabeled NDD remains a challenging task. This paper presents a novel approach for automatically identifying test scenarios for autonomous driving through deep unsupervised learning. Firstly, US DAS2 NDD are leveraged, and the selection of data variables representing the vehicle state and surrounding environment is conducted to formulate the segmentation criterion. The isolation forest (IF) algorithm is then employed to

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Li, Wei, Kai Xian, Jiateng Yin, and Dewang Chen. "Developing Train Station Parking Algorithms: New Frameworks Based on Fuzzy Reinforcement Learning." Journal of Advanced Transportation 2019 (August 4, 2019): 1–9. http://dx.doi.org/10.1155/2019/3072495.

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Train station parking (TSP) accuracy is important to enhance the efficiency of train operation and the safety of passengers for urban rail transit. However, TSP is always subject to a series of uncertain factors such as extreme weather and uncertain conditions of rail track resistances. To increase the parking accuracy, robustness, and self-learning ability, we propose new train station parking frameworks by using the reinforcement learning (RL) theory combined with the information of balises. Three algorithms were developed, involving a stochastic optimal selection algorithm (SOSA), a Q-learn

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Liouane, Oumaima, Smain Femmam, Toufik Bakir, and Abdessalem Ben Abdelali. "Novel DV-hop algorithm-based machines learning technics for node localization in rang-free wireless sensor networks." International Journal of Informatics and Communication Technology (IJ-ICT) 12, no. 2 (2023): 140. http://dx.doi.org/10.11591/ijict.v12i2.pp140-149.

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Localization is a critical concern in many wireless sensor network (WSN) applications. Furthermore, correct information regarding the geographic placements of nodes (sensors) is critical for making the collected data valuable and relevant. Because of their benefits, such as simplicity and acceptable accuracy, the based connectivity algorithms attempt to localize multi-hop WSN. However, due to environmental factors, the precision of localisation may be rather low. This publication describes an Extreme Learning Machine (ELM) technique for minimizing localization error in range-free WSN. In this

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Thanos, Aristotelis E., Nurcin Celik, and Juan P. Sáenz. "An Evolutionary Sequential Sampling Algorithm for Multi-Objective Optimization." Asia-Pacific Journal of Operational Research 33, no. 01 (2016): 1650006. http://dx.doi.org/10.1142/s0217595916500068.

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In this paper, we present a novel sequential sampling methodology for solving multi-objective optimization problems. Random sequential sampling is performed using the information from within the non-dominated solution set generated by the algorithm, while resampling is performed using the extreme points of the non-dominated solution set. The proposed approach has been benchmarked against well-known multi-objective optimization algorithms that exist in the literature through a series of problem instances. The proposed algorithm has been demonstrated to perform at least as good as the alternativ

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HADDAD, BASHAR, and AMIN JARRAH. "SEMI-AUTOMATIC CRACKS CORRECTION BASED ON SEAM PROCESSING, STOCHASTIC ANALYSIS AND LEARNING PROCESS." International Journal of Image and Graphics 13, no. 04 (2013): 1350020. http://dx.doi.org/10.1142/s0219467813500204.

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The restoration process of cracked images is a challenge and an important field of image and video processing. Old images that experienced bad treatment and environmental conditions, or images of old buildings and statues have the problem of cracks. This problem restricts the ability of extracting information and processing of the image. Many algorithms have been proposed to restore cracked images but most of them failed to remove these cracks efficiently based on realistic assumptions. So, we developed and implemented a new algorithm in order to repair cracked images efficiently by proposing

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Zhang, Xiaofei, Hongbin Ma, and Huaqing Zhang. "Data-Driven Control Based on Information Concentration Estimator and Regularized Online Sequential Extreme Learning Machine." Symmetry 16, no. 1 (2024): 88. http://dx.doi.org/10.3390/sym16010088.

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Due to the complexity of digital equipment and systems, it is quite difficult to obtain a precise mechanism model in practice. For an unknown discrete-time nonlinear system, in this paper, a semi-parametric model is used to describe this discrete-time nonlinear system, and this semi-parametric model contains a parametric uncertainty part and a nonparametric uncertainty part. Based on this semi-parametric model, a novel data-driven control algorithm based on an information concentration estimator and regularized online sequential extreme learning machine (ReOS-ELM) is designed. The information

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Wang, Xiu Chen. "Local Extreme Extraction of Fabric Gray Feature Wave." Key Engineering Materials 460-461 (January 2011): 621–24. http://dx.doi.org/10.4028/www.scientific.net/kem.460-461.621.

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Aiming at lack of effective algorithm for image gray feature wave analysis, this paper proposes a new local extreme extraction for image gray feature, which can establish full information of image feature wave for continue analysis. Firstly, the definition of image gray feature wave is given. Secondly, methods of local extreme extraction of feature wave are detail described, and specific calculation formulas are given. Finally, some examples of local extreme extraction of different fabric images are listed. Results show that this method is fast and easy, can exactly judge local extreme of feat

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Kokasih, Marco Febriadi, and Adi Suryaputra Paramita. "Property Rental Price Prediction Using the Extreme Gradient Boosting Algorithm." IJIIS: International Journal of Informatics and Information Systems 3, no. 2 (2020): 54–59. http://dx.doi.org/10.47738/ijiis.v3i2.65.

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Online marketplace in the field of property renting like Airbnb is growing. Many property owners have begun renting out their properties to fulfil this demand. Determining a fair price for both property owners and tourists is a challenge. Therefore, this study aims to create a software that can create a prediction model for property rent price. Variable that will be used for this study is listing feature, neighbourhood, review, date and host information. Prediction model is created based on the dataset given by the user and processed with Extreme Gradient Boosting algorithm which then will be

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Kolisetty, Soma Sekhar, and Battula Srinivasa Rao. "Efficient failure detection and consensus at extreme-scale systems." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 5 (2022): 5339. http://dx.doi.org/10.11591/ijece.v12i5.pp5339-5347.

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<span>Distributed systems and extreme-scale systems are ubiquitous in recent years and have seen throughout academia organizations, business, home, and government sectors. Peer-to-peer (P2P) technology is a typical distributed system model that is gaining popularity for delivering computing resources and services. Distributed systems try to increase its availability in the event of frequent component failures and functioning the system in such scenario is notoriously difficult. In order to identify component failures in the system and achieve global agreement (consensus) among failed com

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Soma, Sekhar Kolisetty, and Srinivasa Rao Battula. "Efficient failure detection and consensus at extreme-scale systems." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 5 (2022): 5339–47. https://doi.org/10.11591/ijece.v12i5.pp5339-5347.

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Distributed systems and extreme-scale systems are ubiquitous in recent years and have seen throughout academia organizations, business, home, and government sectors. Peer-to-peer (P2P) technology is a typical distributed system model that is gaining popularity for delivering computing resources and services. Distributed systems try to increase its availability in the event of frequent component failures and functioning the system in such scenario is notoriously difficult. In order to identify component failures in the system and achieve global agreement (consensus) among failed components, thi

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Sudha, M. "Classifying the Fault Type in Underground Distribution System Based on Fuzzy Logic Algorithm." Indonesian Journal of Electrical Engineering and Computer Science 8, no. 2 (2017): 557. http://dx.doi.org/10.11591/ijeecs.v8.i2.pp557-560.

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This paper exhibits the best possible information example of fluffy rationale calculation for blame sort characterization in underground link. The proposed calculation utilizing mix of discrete wavelet changes (DWT) and fluffy rationale. The DWT is connected to concentrate high recurrence segment from blame current waveform utilizing mother wavelet daubechies4 (db4). The most extreme coefficients detail of DWT and greatest proportion of DWT, acquired from stage A, B, C and zero succession of blame current waveforms have been utilized as an info factors for choice calculation. The acquired outc

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Malloy, Colin. "Timbral effects the Paulstretch audio time-stretching algorithm." Journal of the Acoustical Society of America 151, no. 4 (2022): A158. http://dx.doi.org/10.1121/10.0010964.

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The Paulstretch algorithm is a procedure for achieving aurally pleasing extreme time-stretches while avoiding the “phasiness” issues common to phase-vocoder-based implementations. The algorithm accomplishes this by randomizing the phase information where a typical phase-vocoder approach would perform phase-unwrapping or another method to preserve phase alignment. When performed without time-stretching, phase randomization is often referred to as whisperization. This allows for more extreme time-stretches than other approaches. Where most audio time-stretching is usually used for small adjustme

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Li, Zhenhua, Xingxin Chen, Lin Wu, et al. "Error Analysis of Air-Core Coil Current Transformer Based on Stacking Model Fusion." Energies 14, no. 7 (2021): 1912. http://dx.doi.org/10.3390/en14071912.

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Air-core coil current transformer is a key piece of equipment in the digital substation development. However, it is more vulnerable to various faults when compared with the traditional electromagnetic current transformer. Aiming at understanding the effect of various parameters on the performance of the air-core coil current transformer, this paper investigates the influence of these factors using the maximum information coefficient. The interference mechanism of influencing factors on the transformer error is also analyzed. Finally, the Stacking model fusion algorithm is used to predict trans

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Zhang, Fan, Jiabin Liu, Bo Wang, Zhiquan Qi, and Yong Shi. "A Fast Algorithm for Multi-Class Learning from Label Proportions." Electronics 8, no. 6 (2019): 609. http://dx.doi.org/10.3390/electronics8060609.

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Learning from label proportions (LLP) is a new kind of learning problem which has attracted wide interest in machine learning. Different from the well-known supervised learning, the training data of LLP is in the form of bags and only the proportion of each class in each bag is available. Actually, many modern applications can be successfully abstracted to this problem such as modeling voting behaviors and spam filtering. However, time-consuming training is still a challenge for LLP, which becomes a bottleneck especially when addressing large bags and bag sizes. In this paper, we propose a fas

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Styve, Lise, Carlo Navarra, Julie Maria Petersen, Tina-Simone Neset, and Katerina Vrotsou. "A Visual Analytics Pipeline for the Identification and Exploration of Extreme Weather Events from Social Media Data." Climate 10, no. 11 (2022): 174. http://dx.doi.org/10.3390/cli10110174.

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Extreme weather events are expected to increase in frequency and intensity due to global warming. During disaster events, up-to-date relevant information is crucial for early detection and response. Recently, Twitter emerged as a potentially important source of volunteered geographic information of key value for global monitoring systems and increasing situational awareness. While research on the use of machine learning approaches to automatically detect disaster events from social media is increasing, the visualization and exploration of the identified events and their contextual data are oft

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Zhai, Junhai, Qingyan Shao, and Xizhao Wang. "Improvements for P-ELM1 and P-ELM2 Pruning Algorithms in Extreme Learning Machines." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 24, no. 03 (2016): 327–45. http://dx.doi.org/10.1142/s0218488516500161.

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Extreme learning machine (ELM) is an efficient training algorithm for single-hidden layer feed-forward neural networks (SLFNs). Two pruned-ELM named P-ELM1 and P-ELM2 are proposed by Rong et al. P-ELM1 and P-ELM2 employ [Formula: see text] and information gain to measure the association between the class labels and individual hidden node respectively. But for the continuous value data sets, it is inevitable for P-ELM1 and P-ELM2 to evaluate the probability distributions of the data sets with discretization methods for calculating [Formula: see text] and information gain, while the discretizati

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Gao, Ning, Yuanbo Hong, Junfei Chen, and Chonghao Pang. "Regional Expressway Freight Volume Prediction Algorithm Based on Meteorological Information." Promet - Traffic&Transportation 35, no. 2 (2023): 195–211. http://dx.doi.org/10.7307/ptt.v35i2.40.

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In the post-epidemic era, dynamic monitoring of expressway road freight volume is an important task. To accurately predict the daily freight volume of urban expressway, meteorological and other information are considered. Four commonly used algorithms, a random forest (RF), extreme gradient boosting (XGBoost), long short-term memory (LSTM) and K-nearest neighbour (KNN), are employed to predict freight volume based on expressway toll data sets, and a ridge regression method is used to fuse each algorithm. Nanjing and Suzhou in China are taken as a case study, using the meteorological data and f

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Ahmad, Abdulwahab, and Poom Kumam. "Strong Convergence Accelerated Alternated Inertial Relaxed Algorithm for Split Feasibilities with Applications in Breast Cancer Detection." Bangmod International Journal of Mathematical and Computational Science 11 (April 30, 2025): 109–34. https://doi.org/10.58715/bangmodjmcs.2025.11.6.

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In this article, we construct an accelerated relaxed algorithm with an alternating inertial extrapolation step. The proposed algorithm uses a three-term conjugate gradient-like direction, which helps to fasten the sequence of its iterates to a point in a solution set. The algorithm employs a self-adaptive step-length criterion that does not require any information related to the norm of the operator or the use of a line-search procedure. Moreover, we formulate and prove a strong convergence theorem for the algorithm to a minimum-norm solution of a split feasibility problem in infinite-dimensio

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Fu, Yanhua, Hongfei Xie, Yachun Mao, Tao Ren, and Dong Xiao. "Copper Content Inversion of Copper Ore Based on Reflectance Spectra and the VTELM Algorithm." Sensors 20, no. 23 (2020): 6780. http://dx.doi.org/10.3390/s20236780.

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Copper is an important national resource, which is widely used in various sectors of the national economy. The traditional detection of copper content in copper ore has the disadvantages of being time-consuming and high cost. Due to the many drawbacks of traditional detection methods, this paper proposes a new method for detecting copper content in copper ore, that is, through the spectral information of copper ore content detection method. First of all, we use chemical methods to analyze the copper content in a batch of copper ores, and accurately obtain the copper content in those ores. Then

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