Hybrid CNN-Ensemble Framework for Intelligent Optical Fiber Fault Detection and Diagnosis by Salem Titouni, Idris Messaoudene, Yassine Himeur, Omar Alnaseri, Farouk Chetouah, Boualem Hammache, Massinissa Belazzoug, Shadi Atalla, Wathiq Mansoor

“…This paper introduces a novel Hybrid CNN-Ensemble framework, combining convolutional neural networks (CNNs) for deep feature extraction with ensemble learning techniques including XGBoost, Support Vector Machines (SVM), and Random Forest (RF). The framework processes CNN-extracted features from OTDR traces, with predictions aggregated through a meta-classifier. …”
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Key issues of patient safety during anticancer drug treatment in a day hospital and outpatient setting by A. A. Fedenko, A. A. Kolomeytseva, V. O. Artemova

“…The qualifications level of medical personnel involved in the treatment process should allow early monitoring of various adverse events development. …”
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Assessment of the accuracy of heavy aircraft control, taking into account the functioning of the indicator on the windshield and flight control actuators by A. A. Malchenko, P. S. Kostin, Ya. G. Khatuntsev

“…The features of the process of developing the elements of the indicator on the windshield, namely the indicators of the director ring and the velocity vector, their control law when the aircraft moves along an assigned flight path are presented.  …”
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Milling Tool Wear State Recognition by Vibration Signal Using a Stacked Generalization Ensemble Model by Yang Hui, Xuesong Mei, Gedong Jiang, Tao Tao, Changyu Pei, Ziwei Ma

“…However, the characteristics of milling process limit the accuracy and stability of tool condition monitoring (TCM) employing vibration signals. …”
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A Machine Learning Framework for Student Retention Policy Development: A Case Study by Sidika Hoca, Nazife Dimililer

“…While most previous work relies on wide-ranging data from numerous sources such as surveys, enrolment, and learning management systems, making the process complex and time-consuming, the current study uses minimal data that are readily available in any registration system. …”
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To Close is Impossible to Leave it: Where Should be Put a Comma? by E. A. Pleshkevich

“…The analysis of statistics shows that the negative dynamics in regional library construction is caused by the process of reduction of settlements, its natural decline and aging of the population. …”
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Diagnosis and classification of neuromuscular disorders using Bi-LSTM optimized with grey Wolf optimizer for EMG signals by Ruth Keziah Mani, Balakrishnan Nagaraj

“…Existing approaches range from deep learning techniques such as Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM) to conventional machine learning methods like Support Vector Machines (SVM) and Random Forest. Deep learning automates the process, reducing the dependency on manual feature extraction. …”
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Efficient Dynamic Performance Prediction of Railway Bridges Situated on Small-Radius Reverse Curves by Yumin Song, Bin Hu, Xiaoliang Meng

“…After identifying essential design parameters as data features using Fisher scores, we proceed to input these features into a support vector machine (SVM). Through supervised training with dynamic performance labels, this process empowers the SVM model to predict the dynamic performance of the bridge. …”
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Predicting CO2 adsorption in KOH-activated biochar using advanced machine learning techniques by Raouf Hassan, Alireza Baghban

“…We employed a comprehensive suite of machine learning methods, like convolutional neural networks, random forests, artificial neural networks, linear regression, ridge and lasso regressions, elastic net, support vector machines, decision trees, gradient boosting machines, k-nearest neighbors, light gradient boosting machines, extreme gradient boosting, CatBoost, and Gaussian process, to build predictive models. …”
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Hybrid algorithms for geospatial analysis of dam location points in protective tasks for protected areas by S. Rodriguez Vasquez, N. V. Mokrova

“…This paper presents the way of optimizing the process of locating key terrain points by developing a hybrid algorithm for geospatial analysis in QGIS. …”
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Modeling energy consumption indexes of an industrial cement ball mill for sustainable production by Saeed Chehreh Chelgani, Rasoul Fatahi, Ali Pournazari, Hamid Nasiri

“…However, grinding in tumbling mills is a random process, and a maximum of 5% of this energy would be directly devoted to particle size reduction. …”
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Determination of rice (Oryza sativa L.) drought stress levels based on chlorophyll a fluorescence through independent component analysis by Q. XIA, H. TANG, J.L. TAN, S.I. ALLAKHVERDIEV, Y. GUO

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AI-Driven Optimization of Breakwater Design: Predicting Wave Reflection and Structural Dimensions by Mohammed Loukili, Soufiane El Moumni, Kamila Kotrasova

“…Five ML algorithms—Random Forest, Support Vector Regression, Artificial Neural Network, Decision Tree, and Gaussian Process—were applied and evaluated. …”
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Intelligence model-driven multi-stress adaptive reliability enhancement testing technology by Shouqing Huang, Beichen He, Jing Wang, Xiaoyang Li, Rui Kang, Fangyong Li

“…In terms of mathematical models, we propose a Tuna Swarm Optimization–Gaussian Process Regression (TSO-GPR) model, which combines the global search capability of the tuna swarm optimization algorithm and the accurate prediction capability of Gaussian process regression, effectively handling the complex nonlinear relationships between multiple stresses and failure characteristic. …”
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Prediction of Early Diagnosis in Ovarian Cancer Patients Using Machine Learning Approaches with Boruta and Advanced Feature Selection by Tuğçe Öznacar, Tunç Güler

“…This research aims to assess the feature extraction process from various machine learning techniques for better modelling of ovarian cancer and the selection process in ovarian cancer analysis. …”
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Mechanism and Stability Analysis of Deformation Failure of a Slope by Yingfa Lu, Gan Liu, Kai Cui, Jie Zheng

“…The critical stress state zone is in the process of dynamic change with the development of deformation. …”
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Optimized Application of CGA-SVM in Tight Reservoir Horizontal Well Production Prediction by Chao Wang, Ruogu Wang, Yuhan Lin, Jiafei Zhang, Xiaofei Xie, Zidan Zhao, Yunlin Xu

“…Compared with traditional support vector machine, BP neural network, KNN and naive Bayes, the improved support vector machine has a higher prediction accuracy, and the average error is only 2.7%. …”
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The impact of aging on achilles tendon ossification in mice by Hanhua Cai, Yujian Lan, Huan Liu, Qi Hao

“…This finding suggests a potential link between these processes, opening new avenues for research into diagnostic markers and therapeutic targets. …”
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Elucidating the role of KCTD10 in coronary atherosclerosis: Harnessing bioinformatics and machine learning to advance understanding by Xiaomei Hu, Fanqi Liang, Man Zheng, Juying Xie, Shanxi Wang

“…KCTD10, a protein implicated in a variety of biological processes, has garnered significant attention for its role in cardiovascular diseases and metabolic regulation. …”
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Radar Echo Characteristics and Recognition of Warm-sector Torrential Rain in Sichuan Basin by Luo Hui, Xiao Dixiang, Kuang Qiuming, Qing Quan, Kang Lan

“…They are accompanied by long-term merge when moving. Among 28 processes, a large proportion appears in the northwest and has the longest duration. …”
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