Document Relevance Filtering by Natural Language Processing and Machine Learning: A Multidisciplinary Case Study of Patents by Raj Bridgelall

“…These models include extreme gradient boosting, random forest, and support vector machines; a deep artificial neural network; and three natural language processing methods: latent Dirichlet allocation, non-negative matrix factorization, and k-means clustering of a manifold-learned reduced feature dimension. …”
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The data dimensionality reduction and bad data detection in the process of smart grid reconstruction through machine learning. by Bo Yu, Zheng Wang, Shangke Liu, Xiaomin Liu, Ruixin Gou

“…The results show that in the IEEE14-bus node and IEEE118-bus node systems, the overall distribution of the state estimated before and after the attack vector injection is consistent with the initial value. …”
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Modeling of digital spatial processing under conditions of troposphere propagation of centimeter radio waves for wireless telecommunication by Ilia W. Peshkov, Dmitry N. Borisov

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Integration of Gaussian process regression and K means clustering for enhanced short term rainfall runoff modeling by Ozgur Kisi, Salim Heddam, Kulwinder Singh Parmar, Andrea Petroselli, Christoph Külls, Mohammad Zounemat-Kermani

“…These results outperform other ML models, such as Long Short-Term Memory, Support Vector Machines, and Random Forest, reported in the literature. …”
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Soft sensor modeling method for Pichia pastoris fermentation process based on substructure domain transfer learning by Bo Wang, Jun Wei, Le Zhang, Hui Jiang, Cheng Jin, Shaowen Huang

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Gaussian Process Regression Total Nitrogen Prediction Based on Data Decomposition Technology and Several Intelligent Algorithms by WANG Yongshun, CUI Dongwen

“…Total nitrogen (TN) is one of the important indicators to reflect the degree of water pollution and measure the eutrophication status of lakes and reservoirs.To improve the accuracy of TN prediction,based on the empirical wavelet transform (EWT) and wavelet packet transform (WPT) decomposition technology,this paper proposes a Gaussian process regression (GPR) prediction model optimized by osprey optimization algorithm (OOA),rime optimization algorithm (ROA),bald eagle search (BES) and black widow optimization algorithm (BWOA) respectively.Firstly,the TN time series is decomposed into several more regular subsequence components by EWT and WPT respectively.Then,the paper briefly introduces the principles of OOA,ROA,BES,and BWOA algorithms and applies OOA,ROA,BES,and BWOA to optimize GPR hyperparameters.Finally,EWT-OOA-GPR,EWT-ROA-GPR,EWT-BES-GPR,EWT-BWOA-GPR,WPT-OOA-GPR,WPT-ROA-GPR,WPT-BES-GPR,WPT-BWOA-GPR models (EWT-OOA-GPR and other eight models for short) are established to predict the components of TN by the optimized super-parameters.The final prediction results are obtained after reconstruction,and WT-OOA-GPR,WT-ROA-GPR,WT-BES-GPR and WT-BWOA-GPR models based on wavelet transform (WT) are built.Eight models,including EWT-OOA-SVM based on support vector machine (SVM),the paper compares the unoptimized EWT-GPR,WPT-GPR models,and the uncomposed OOA-GPR,ROA-GPR,BES-GPR,and BWOA-GPR models.The models were verified by the monitoring TN concentration time series data of Mudihe Reservoir,an important drinking water source in China,from 2008 to 2022.The results are as follows.① The average absolute percentage error of eight models such as EWT-OOA-GPR for TN prediction is between 0.161% and 0.219%,and the coefficient of determination is 0.999 9,which is superior to other comparison models,with higher prediction accuracy and better generalization ability.② EWT takes into account the advantages of WT and EMD.WPT can decompose low-frequency and high-frequency signals at the same time.Both of them can decompose TN time series data into more regular modal components,significantly improving the accuracy of model prediction,and the decomposition effect is better than that of the WT method.③ OOA,ROA,BES,and BWOA can effectively optimize GPR hyperparameters and improve GPR prediction performance.…”
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Adaptive Optimizable Gaussian Process Regression Linear Least Squares Regression Filtering Method for SEM Images by Dominic Chee Yong Ong, Iksan Bukhori, Kok Swee Sim, Kok Beng Gan

“…It is shown that LSR method to perform better than the rest. Then, Support Vector Machines (SVM) and Gaussian Process Regression (GPR) are tested by pairing it with LSR. …”
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Size-exclusion chromatography as a multi-attribute method for process and product characterization of adeno-associated virus by Sri Hari Raju Mulagapati, Arun Parupudi, Tomasz Witkos, Nick Bond, Xiaoyu Chen, Thomas Linke, Guoling Xi, Albert Ethan Schmelzer, Wei Xu

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A mini review on AI-driven thermal treatment of solid waste: Emission control and process optimization by Dongjie Pang, Cristina Moliner, Tao Wang, Jin Sun, Xinyan Zhang, Yingping Pang, Xiqiang Zhao, Zhanlong Song, Ziliang Wang, Yanpeng Mao, Wenlong Wang

“…This review examines the deployment of AI-optimized control algorithms in processes including pyrolysis, incineration, and gasification. …”
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A deep q-networks model for optimising decision-making process in the context of energy transition modelling by Ana Tănăsescu, Cristian Bucur, Jean Vasile Andrei, Bogdan-George Tudorică, Dorel Mihai Paraschiv, Dorel Dușmănescu

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High‑Temperature Image Pre‐Processing Based on ε‐Ga2O3 Photo‐Synapses by Yingtong Lin, Yuhan Zhu, Feng Wang, Yuchen Cai, Zhengqiao Li, Shuhui Li, Xueying Zhan, Jun He, Chao Jiang, Zhenxing Wang

“…Edge‐based neuromorphic computing with data pre‐processing can help alleviate these burdens and enhance overall system efficiency. …”
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A chaos based image encryption algorithm using Rubik’s cube and prime factorization process (CIERPF) by R. Vidhya, M. Brindha

“…From this random value, the initial vectors of Henon map is obtained and this is iterated to obtain the key sequences to be applied over the Rubik’s cube row and column confusion processes. …”
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Fake News Detection System using Machine Learning with Recurrent Neural Networks by Kuditat O. Jimoh, Lawrence O. Omotosho, Ibrahim K. Ogundoyin, Olajide Y. Adebayo

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Microsporidian infection of mosquito larvae changes the host-associated microbiome towards the synthesis of antimicrobial factors by Artur Trzebny, Abigail D. Taylor, Jeremy K. Herren, Johanna K. Björkroth, Sylwia Jedut, Miroslawa Dabert

“…The host-associated microbiome of the mosquito can play a pivotal role in various physiological processes of this host, including its vector competence for pathogens. …”
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Optimized Whole-Slide-Image H&E Stain Normalization: A Step Towards Big Data Integration in Digital Pathology by Jose L. Agraz, Carlos Agraz, Andrew A. Chen, Charles Rice, Robert S. Pozos, Sven Aelterman, Amanda Tan, Angela N. Viaene, MacLean P. Nasrallah, Parth Sharma, Caleb M. Grenko, Tahsin Kurc, Joel Saltz, Michael D. Feldman, Hamed Akbari, Russell T. Shinohara, Spyridon Bakas, Parker Wilson

“…Building on published graphical method, this research demonstrates a mathematical population or data-driven method that optimizes the dependency on the number of reference WSIs and corresponding aggregate sums, thereby increasing SCN process efficiency. This method expedites the analysis of color convergence 50-fold by using stain vector Euclidean distance analysis, slashing the requirement for reference WSIs by more than half. …”
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Design of a Machine Learning-Based Platform for Currency Market Prediction: A Fundamental Design Model by Kevin Gordillo-Orjuela, Paulo Alonso Gaona-García, Carlos Enrique Montenegro-Marín

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VIRTUAL MODELS OF LIGHT BACKSCATTERING IN RING LASERS by A. S. Bessonov, E. A. Petrukhin

“…A mathematical description of the backscattering processes based on the determination of the complex coupling parameters of counterpropagating waves is presented. …”
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Advancing Geotechnical Evaluation of Wellbores: A Robust and Precise Model for Predicting Uniaxial Compressive Strength (UCS) of Rocks in Oil and Gas Wells by Mohammadali Ahmadi

“…The investigation encompasses Linear Regression, ensemble methods (including Random Forest, Gradient Boosting, XGBoost, and LightGBM), support vector machine-based regression (SVM-SVR), and multilayer perceptron artificial neural network (MLP-ANN) models. …”
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A Bag-of-Words Approach for Information Extraction from Electricity Invoices by Javier Sánchez, Giovanny A. Cuervo-Londoño

“…It is typical to rely on machine-learning techniques to automate the process, reduce manual labor, and minimize errors. …”
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New Heuristics Method for Malicious URLs Detection Using Machine Learning by Maher Kassem Hasan

“…We implemented and optimized three models—Logistic Regression, Random Forest, and Support Vector Machines (SVM)—based on the literature available that indicates the effectiveness of these models. …”
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