Evaluation of geological engineering factors for productivity of deep CBM well after fracturing based on grey correlation method by KONG Xiangwei,XIE Xin,WANG Cunwu,SHI Xian

Subjects: “…|coal-bed methane|grey correlation method|fracturing effect|k-means clustering algorithm|optimization of fracturing parameters…”
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An Inverse Modeling Multi-Objective Optimization Technique Based on Incremental Learning and Fuzzy Clustering by Gadallah Mohamed Abd Elaziz, Yasmine Abouelseoud, Sara H. Kamel

Subjects: “…Evolutionary algorithm…”
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A Study on the Vehicle Routing Planning Method for Fresh Food Distribution by Yuxuan Wang, Yajun Wang, Junyu Leng

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Comparison of Doubling the Size of Image Algorithms by S. E. Vaganov, S. I. Khashin

“…For each method of upscaling to twice optimal coefficients of kernel convolutions for different down-scale to twice algorithms were found. Various methods for reducing the image size by half were considered the mean value over 4 nearest points and the weighted value of 16 nearest points with optimal coefficients. …”
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Improved PESA algorithm based on comentropy by Kun WANG, Lin-lin WANG, Yan LIU, Yu-hua ZHANG, Meng WU

“…Aiming at the issue that the computational effort the complexity and the running time of PESA algorithm are increasing rapidly with the growth of the solutions set number, a comentropy-based PESA algorithm (C-PESA) by merg-ing the entropy value metric into PESA algorithm was proposed. …”
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PERFORMANCE PREDICTION OF ROADHEADERS USING SUPPORT VECTOR MACHINE (SVM), FIREFLY ALGORITHM (FA) AND BAT ALGORITHM (BA) by Arash Ebrahimabadi, Alireza Afradi

“…Additionally, this study employed Firefly Algorithm (FA), Bat Algorithm (BA) and Support Vector Machine (SVM), which were assessed using coefficient of determination (R²), root mean square error (RMSE), mean squared error (MSE) and mean absolute error (MAE).The obtained results for Firefly Algorithm (FA) are found to be as R2 = 0.9104, RMSE = 0.0658, MSE= 0.0043 and MAE= 0.0039, for Bat Algorithm (BA) are found to be as R2 = 0.9421, RMSE = 0.0528, MSE= 0.0027 and MAE= 0.0024, and for Support Vector Machine (SVM) are found to be as R2 = 0.8795, RMSE = 0.0762, MSE= 0.0058 and MAE= 0.0052, respectively. …”
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Fast blind detection of short-wave frequency hopping signal based on MeanShift by Zhengyu ZHU, Yu LIN, Zixuan WANG, Kexian GONG, Pengfei CHEN, Zhongyong WANG, Jing LIANG

“…In the complex short-wave channel environment, combined with time-frequency analysis technology, a fast blind detection algorithm of the connected domain labeled frequency hopping signals based on MeanShift algorithm was proposed to reduce the influence of various interference signals and noises on frequency hopping signals and realize blind detection of frequency hopping signals under low signal-to-noise ratio.Firstly, the channel environment gray-scale time-frequency map was filtered by the secondary gray-scale morphology to obtain the binary time-frequency map.Secondly, the maximum duration of the signal was calculated by the connected domain labeling algorithm.Then, the MeanShift algorithm was used to cluster the maximum duration of the signal.Finally, the clustering result was made a second judgment by combining with the adaptive double threshold.The simulation results show that the proposed algorithm can quickly separate various interference signals and sharp noise under low signal-to-noise ratio, and realize fast blind detection of frequency hopping signals without any prior information.It has high detection probability, strong anti-interference ability in short-wave channel environment, low computational complexity and high engineering practical value.…”
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Modified Bancroft Algorithm for Multilateration Systems by A. A. Monakov

“…The algorithm allows to obtain the location estimation by means of direct method and does not require significant computing costs. …”
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The Support Splitting Algorithm for Induced Codes by Yury V. Kosolapov, Aleksey N. Shigaev

“…This suggests a potentially high resistance of the McEliece-type cryptosystem on the code Flq ⊗ C. The algorithm for splitting the support for the code Flq ⊗ C is constructed and the efficiency of this algorithm is compared with the existing attack on the key of the McElice type cryptosystem based on the code Flq ⊗ C.…”
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Knowledge-based Algorithms for BDI-agents by Nikolay Vyacheslavovich Shilov, Natalia Olegovna Garanina

“…Multiagent algorithm is a knowledge-based distributed algorithm that solves some problems by means of cooperative work of agents. …”
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Technologies and Algorithms for Building the Augmented Reality by I. A. Blagoveshchenskiy, N. A. Demyankov

“…In order to analyze video stream and recognize known objects in it, algorithms of the Computer Vision are used. The authors give a short description and the main characteristics only of two of them: genetic algorithms and feature detection & description. …”
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Quantum algorithms for enhanced educational technologies by Basil Hanafi, Mohammad Ali, Devyaani Singh

“…Modern quantum algorithms like Grover’s Algorithm and Quantum Annealing support various functionalities like rapidity in processing as well as instantaneous flexibility towards customization of learning resources for the students and proper optimization of the learning trail. …”
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The Problem of Subjectivity in Algorithmic Creativity Organisation by Tetiana Sovhyra

“…Interpretation of meaningful content is a critical problem in relevant practices. The usage of algorithms in creating art products challenges traditional methods of artistic cultural creation, as the artist’s meaning and intentions are not always clear. …”
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Architecture and implementation of ulrb algorithm in R by Francisco Pascoal, Rodrigo Costa, Luís Torgo, Catarina Magalhães, Paula Branco

“…This is inappropriate because such thresholds are arbitrary and lack biological meaning. To solve this problem, we have proposed the utilization of unsupervised machine learning, through the ulrb (“Unsupervised Learning Definition of the Microbial Rare Biosphere”) algorithm, implemented as an R package (v0.1.8). …”
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Detect anomalous quartic gauge couplings at muon colliders with quantum kernel k-means by Shuai Zhang, Ke-Xin Chen, Ji-Chong Yang

“…As a machine learning algorithm, kernel k-means has been demonstrated to be useful for searching NP signals. …”
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Pemodelan K-Means Pada Penentuan Predikat Kelulusan Mahasiswa STMIK Palangka Raya by Lili Rusdiana, Sam'ani Sam'ani

“…K-Means algorithm is used through three stages: initialization , the first iteration and the second iteration. …”
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Method to create and optimize original electric power communication network based on K-means by Xin JIN, Liang YANG, Cheng-ming JIN, Guo-hua SU, Lei SUN

“…The major contribution was using the K-means to create nodes instead of traditional Waxman means in order to get a suitable and strong original electric power communication network.After the nodes were created,a vulnerability analysis was given on the grid of the network topology and check that if it has a network islanding,at last the weak nodes would be reinforced.A simulation about this algorithm shows that this algorithm which combines K-means and vulner-ability can create a electric power communication networks with better resistance of risks.…”
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Hardware-Based Calculation of Mean Square Error for Automatic Target Recognition in SAR Images by Lucas Urbanski, Gustavo Farhat Araujo, Renato Machado, Roberto D'Amore

“…A reference algorithm in Python was used to determine whether the mathematical operations performed by the architecture are correct, comparing the results between different processing phases, such as the determination of the equivalence of coordinates between the target and the models (i.e., coordinates “matching”), partial calculations of the mean squared error, and the final result of the operations. …”
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Secure cloud computing: leveraging GNN and leader K-means for intrusion detection optimization by Raman Dugyala, Premkumar Chithaluru, M. Ramchander, Sunil Kumar, Arvind Yadav, N. Sudhakar Yadav, Diaa Salama Abd Elminaam, Deema Mohammed Alsekait

“…Key contributions of this work include the integration of the Leader K-means algorithm for effective data clustering, improving the IDS’s ability to differentiate between normal and malicious activities. …”
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Combination of Transformed-means Clustering and Neural Networks for Short-Term Solar Radiation Forecasting by M. Ghayekhloo, M. B. Menhaj

“…The proposed Transformed-Means clustering method is based on inverse data transformation and K-means algorithm that presents moreaccurate clustering results when compared to the K-Means algorithm; its improved version and also otherpopular clustering algorithms. …”
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