English Teaching Achievement Prediction by Big Data Analysis under Deep Intervention by Junfang Guo

“…First, data analysis methods and decision tree algorithms are analyzed. Second, in data mining technology, the C4.5 decision tree method is used to construct an English score prediction model. …”
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An Incremental Learning Ensemble Strategy for Industrial Process Soft Sensors by Huixin Tian, Minwei Shuai, Kun Li, Xiao Peng

“…The ability to handle large amounts of data incrementally and efficiently is indispensable for modern machine learning (ML) algorithms. According to the characteristics of industrial production process, we address an ILES (incremental learning ensemble strategy) that incorporates incremental learning to extract information efficiently from constantly incoming data. …”
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Considerations in the search for epistasis by Marleen Balvert, Johnathan Cooper-Knock, Julian Stamp, Ross P. Byrne, Soufiane Mourragui, Juami van Gils, Stefania Benonisdottir, Johannes Schlüter, Kevin Kenna, Sanne Abeln, Alfredo Iacoangeli, Joséphine T. Daub, Brian L. Browning, Gizem Taş, Jiajing Hu, Yan Wang, Elham Alhathli, Calum Harvey, Luna Pianesi, Sara C. Schulte, Jorge González-Domínguez, Erik Garrisson, Lorentz workshop on epistasis, Michael P. Snyder, Alexander Schönhuth, Letitia M. F. Sng, Natalie A. Twine

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A COMPARATIVE STUDY OF HEADWAY-BASED AND TRANSPORT SYSTEM-BASED ASSIGNMENTS OF PUBLIC TRANSPORT IN VISUM: THE CITY OF KRYVYI RIH CASE by Volodymyr SISTUK

“…This paper introduces a thorough comparison of algorithms involved in public transport assignment processes, using the transport model of the city of Kryvyi Rih in Ukraine. …”
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Analysis and synthesis of images of environmentally oriented innovative technologies of construction production by V. I. Telichenko, A. A. Lapidus, M. Yu. Slesarev

“…The review of images of breakthrough technologies of construction production showed the possibility of integrating live and artificial intelligence with appropriate terminological and methodological elaboration of databases and information logistics algorithms for automated generation of new technological inventions. …”
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ON THE SIMULATION OF MODES ОF ELECTRIC POWER SYSTEMS WITH FACTS by E. D. Halilov

“…In regard with the introduction of FACTS devices in power systems, there is an arising need to develop appropriate algorithms and implement them in the form of software for analyzing and controlling the established modes of power systems. …”
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Decoding Subjective Understanding: Using Biometric Signals to Classify Phases of Understanding by Milan Lazic, Earl Woodruff, Jenny Jun

“…AU patterns associated with each phase were then identified through the application of six supervised machine learning algorithms. Distinct AU patterns were found for all five phases, with gradient boosting machine and random forest models achieving the highest predictive accuracy. …”
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Mapping urban green structures using object-based analysis of satellite imagery: A review by Shivesh Kishore Karan, Bjørn Tobias Borchsenius, Misganu Debella-Gilo, Jonathan Rizzi

“…Future directions for UGS mapping include the integration of deep learning algorithms, advancements in satellite data technologies, and the development of standardized classification frameworks. …”
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Machine Learning Model-Based Applications for Food Management in Alzheimer’s Using Regression Analysis Approach by Sajadul Hassan Kumhar, Prabhakara Rao Kapula, Harveen Kaur, Radeep R. Krishna, Mudasir M Kirmani, Vijay Anant Athavale, Mohd Wazih Ahmad

“…Scientists are trying to find a solution using some machine learning (ML) techniques. The ML algorithms used for this purpose are neural networks, support vector machines, regression and many more. …”
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Impact and optimization of vehicle charging scheduling on regional clean energy power supply network management by Penghui Xu, Xiaobo Wang, Zhichao Li

“…The comprehensive model integrates electric vehicle charging stations, distributed photovoltaic power generation systems, wind farms, and battery energy storage devices and enables the charging process to be accurately controlled with real-time monitoring and intelligent algorithms. In particular, the demand forecasting model based on machine learning effectively solves the dilemma of matching the charging load with a clean energy supply. …”
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More Benefits of Adding Sparse Random Links to Wireless Networks: Yet Another Case for Hybrid Networks by Gunes Ercal

“…Further, we theoretically demonstrate that as long as k is polylogarithmic in the network size, G 1 has rapidly mixing random walks with high probability, which also exponentially improves upon the mixing time of the purely wireless random geometric graph, which yields direct improvement to the performance of distributed gossip algorithms as well as normalized edge connectivity. …”
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Neural Radiance Fields for High-Fidelity Soft Tissue Reconstruction in Endoscopy by Jinhua Liu, Yongsheng Shi, Dongjin Huang, Jiantao Qu

“…The experimental results show that our method can effectively focus on the soft tissue regions in the image, achieving higher fidelity in detail and geometric structural integrity in reconstruction compared to state-of-the-art algorithms. Feedback from the user study indicates high participant scores for our method.…”
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Parallel convolutional neural network and empirical mode decomposition for high accuracy in motor imagery EEG signal classification. by Jaipriya D, Sriharipriya K C

“…Traditional EEG pattern recognition algorithms typically involve two key steps: feature extraction and feature classification, both crucial for accurate operation. …”
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A comprehensive analysis of deep learning and transfer learning techniques for skin cancer classification by Manishi Shakya, Ravindra Patel, Sunil Joshi

“…This research investigates three approaches for classifying skin cancer images. (1) Utilizing three fine-tuned pre-trained networks (VGG19, ResNet18, and MobileNet_V2) as classifiers. (2) Employing three pre-trained networks (ResNet-18, VGG19, and MobileNet v2) as feature extractors in conjunction with four machine learning classifiers (SVM, DT, Naïve Bayes, and KNN). (3) Utilizing a combination of the aforementioned pre-trained networks as feature extractors in conjunction with same machine learning classifiers. All these algorithms are trained using segmented images which are achieved by using the active contour approach. …”
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Hybrid Decision-Making Frameworks under Complex Spherical Fuzzy N-Soft Sets by Muhammad Akram, Maria Shabir, Ahmad N. Al-Kenani, José Carlos R. Alcantud

“…We then produce three algorithms for multiattribute decision-making that take advantage of these elements. …”
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Critical considerations and computational tools in plant genome editing by Dipnarayan Saha, Alok Kumar Panda, Subhojit Datta

“…Computational tools and algorithms play a crucial role in construct design and gRNA selection to minimize off-target effects and also to optimize their delivery techniques. …”
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MetaboLabPy—An Open-Source Software Package for Metabolomics NMR Data Processing and Metabolic Tracer Data Analysis by Christian Ludwig

“…This includes a choice of algorithms for automated phase correction, segmental alignment, spectral scaling, variance stabilisation, export to various software platforms, and analysis of metabolic tracing data. …”
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Research and application of mining AI video edge computing technology by ZHANG Liya, HAO Bonan, MA Zheng, YANG Zhifang

“…A lightweight software development kit (SDK) framework based on the register machine was proposed to decouple operators, improving the parallel computing ability of the algorithms and reducing the storage requirements of the SDK. …”
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Lithium Battery Life Prediction for Electric Vehicles Using Enhanced TCN and SVN Quantile Regression by Xinyue Li, Jiangwei Chu

“…The need for lithium battery life forecast algorithms has increased due to the global acceptance of electric vehicles. …”
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Impact of Climate Change and Air Pollution Forecasting Using Machine Learning Techniques in Bishkek by Erkin Isaev, Boobek Ajikeev, Urmatbek Shamyrkanov, Kenjebek-uulu Kalnur, Karimov Maisalbek, Roy C. Sidle

“…We evaluate the performance of several machine learning algorithms applied to air quality and meteorology datasets and compare prediction accuracies of Bishkek air quality given its significant public importance. …”
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