THE LABORATORY-MODELLING COMPLEX FOR RESEARCH of QUALITY INDICATORS Of TELEVISION TYPE OpTiCAL loСation SYSTEM WORK by R. A. Hutsau, A. S. Solonar, S. V. Tsuprik

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TRUE CROSSING SELECTION OF BEARINGS BY REPLACEMENT METHOD IN TRIANGULATION TASK SOLUTION by U. A. Aparovich, A. I. Minau, T. U. Prakofyeva

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Search and detection of remote sensing objects by A. I. Mitsiukhin

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LOSSLESS COMPRESSION OF THE HALFTONE IMAGES BASED ON RUN-LENGTH ENCODING by H. K. Al-Bahadily, E. G. Makeichik, V. Yu. Tsviatkou, V. K. Kanapelka

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Effective Dose Estimation in Computed Tomography by Machine Learning by Matteo Ferrante, Paolo De Marco, Osvaldo Rampado, Laura Gianusso, Daniela Origgi

“…E calculated by DTS was chosen as the target value for prediction. Different machine learning algorithms were selected, optimizing parameters to achieve the best performance for each algorithm. …”
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Elevating Patient Care With Deep Learning: High-Resolution Cervical Auscultation Signals for Swallowing Kinematic Analysis in Nasogastric Tube Patients by Farnaz Khodami, Amanda S. Mahoney, James L. Coyle, Ervin Sejdic

“…By applying established algorithms to an unseen population, we demonstrated the utility of HRCA signals for swallowing assessment in individuals with NG tubes and showcased the generalizability of algorithms developed in our previous studies. …”
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A Novel Approach to Faster Convergence and Improved Accuracy in Deep Learning-Based Electrical Energy Consumption Forecast Models for Large Consumer Groups by A. Jayanth Balaji, Binoy B. Nair, D. S. Harish Ram, Kuruvachan Kalluvelil George

“…Effectiveness of models is validated with eight deep learning algorithms trained for two forecasting horizons. Results demonstrate a 93.74% improvement on an average in convergence compared to conventional mini-batch sizes, achieving one among top three accuracy across various prediction horizons and user categories. …”
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How large is the universe of RNA-like motifs? A clustering analysis of RNA graph motifs using topological descriptors. by Rui Wang, Tamar Schlick

“…Here we introduce a computational topology-based approach with unsupervised machine-learning algorithms to estimate the database size and content of RNA-like graph topologies. …”
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Artificial intelligence and chordoma: A scoping review of the current landscape and future directions by Eddie Guo, Rafael D. Sanguinetti, Lyndon Boone, Jiawen Deng, Husain Shakil, Mehul Gupta

“…Common algorithms used included convolutional neural networks, support vector machines, random forests, and clustering algorithms. …”
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Detecting Changes in Soil Fertility Properties Using Multispectral UAV Images and Machine Learning in Central Peru by Lucia Enriquez, Kevin Ortega, Dennis Ccopi, Claudia Rios, Julio Urquizo, Solanch Patricio, Lidiana Alejandro, Manuel Oliva-Cruz, Elgar Barboza, Samuel Pizarro

“…Machine learning algorithms, including classification and regression trees (CART) and random forest (RF), modeled the soil parameters (N-ppm, P-ppm, K-ppm, OM%, and EC-mS/m). …”
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Adaptive Optimization of Quantized Perturbation Coefficients for Fiber Nonlinearity Compensation by M. Malekiha, D. V. Plant

“…In this paper, we propose and experimentally verify the adoption of a decision-directed least mean square algorithm for optimization and complexity reduction of PB-NLC equalizer. …”
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IOT based wearable sensor system architecture for classifying human activity by V. Mahalakshmi, Pramod Kumar, Manisha Bhende, Ismail Keshta, Swatiben Yashvantbhai Rathod, Janjhyam Venkata Naga Ramesh

“…The proposed system follows a structured workflow: (i) channel state information (CSI) is extracted from ESP32 modules, (ii) data preprocessing is performed using Hampel and Gaussian filters for noise and outlier removal, (iii) dimensionality reduction is achieved through Principal Component Analysis (PCA) and Discrete Wavelet Transform (DWT), and (iv) activity classification is conducted using Dynamic Time Warping (DTW) and the K-Nearest Neighbors (KNN) algorithm. …”
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ABCD microstates: a new word in EEG mathematical analysis by A. A. Ivanov

“…Here, a fairly new approach to the mathematical processing of EEG signals is overviewed that allows not only to diagnose brain functional state, but also to predict the treatment outcomes in critically ill patients in intensive care units. …”
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基于粗糙集与神经网络的齿轮箱故障诊断 by 刘慧玲, 潘宏侠, 王爱玉

“…A method of combining the rough sets and neural network based on the condition attributes discretization and reduct ion algorithm is proposed to fault diagnosis. …”
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Blinded Validation of Breath Biomarkers of Lung Cancer, a Potential Ancillary to Chest CT Screening. by Michael Phillips, Thomas L Bauer, Renee N Cataneo, Cassie Lebauer, Mayur Mundada, Harvey I Pass, Naren Ramakrishna, William N Rom, Eric Vallières

“…The algorithm predicted discriminant function (DF) values in blinded replicate breath VOC samples analyzed independently at two laboratories (A and B). …”
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Discovery of Large Methane Emissions Using a Complementary Method Based on Multispectral and Hyperspectral Data by Xiaoli Cai, Yunfei Bao, Qiaolin Huang, Zhong Li, Zhilong Yan, Bicen Li

“…Employing a matched filtering algorithm based on EnMAP and AHSI, we detect and extract methane plumes within emission hotspots in China and the United States, and estimate the emission flux rates of individual methane point sources using the IME model. …”
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Reduced incidence of maternal health conditions associated with the home-based newborn care intervention package in Rural Gadchiroli, India: a 13 years before – after comparison by Anand A Bang, Abhay T Bang, Rani Bang, Mahesh Deshmukh, Kalindi Soni, Sanjay Baitule

“…Based on these symptoms and signs, a computer algorithm diagnosed 20 different maternal health conditions. …”
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Remote Management of Heart Failure in Patients with Implantable Devices by Luca Santini, Francesco Adamo, Karim Mahfouz, Carlo Colaiaco, Ilaria Finamora, Carmine De Lucia, Nicola Danisi, Stefania Gentile, Claudia Sorrentino, Maria Grazia Romano, Luca Sangiovanni, Alessio Nardini, Fabrizio Ammirati

“…<b>Results</b>: Precise multi-parameter algorithms, available for ICD and CRT-D patients, have been created, which also use artificial intelligence and are able to predict a new heart failure event more than 30 days in advance. …”
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Application of deep learning convolutional neural networks to identify gastric squamous cell carcinoma in mice by Yuke Ren, Yuke Ren, Shuangxing Li, Di Zhang, Yongtian Zhao, Yanwei Yang, Guitao Huo, Xiaobing Zhou, Xingchao Geng, Zhi Lin, Zhe Qu

“…Five different convolutional neural networks (CNNs)-FCN, LR-ASPP, DeepLabv3+, U-Net, and DenseNet were applied to identify GSCC and non-GSCC regions. Tumor prediction images (algorithm results shown as overlays) derived from the slide images were compared, and the performance of the constructed models was evaluated using Precision, Recall, and F1-score.ResultsThe Precision, Recall, and F1-scores of DenseNet, U-Net, and DeepLabv3 + algorithms were all above 90%. …”
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Machine learning study on magnetic structure of rare earth based magnetic materials by Dan Liu, Jiahe Song, Zhixin Liu, Jine Zhang, Weiqiang Chen, Yinong Yin, Jianfeng Xi, Xinqi Zheng, Jiazheng Hao, Tongyun Zhao, Fengxia Hu, Jirong Sun, Baogen Shen

“…In this work, 11 machine learning algorithms were trained to predict the material magnetic structure. …”
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