Online Learning to Cache and Recommend in the Next Generation Cellular Networks by Krishnendu S. Tharakan, B. N. Bharath, Vimal Bhatia

“…An efficient caching can be achieved by predicting the popularity of the files accurately. It is well known that the popularity of a file can be nudged by using recommendation, and hence it can be estimated accurately leading to an efficient caching strategy. …”
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BiLSTM-Based Parallel CNN Models With Attention and Ensemble Mechanism for Twitter Sentiment Analysis by Anas W. Abulfaraj

“…Our methodology incorporates four classifiers to produce text class predictions. Among them, five algorithms are selected for evaluation: Ridge Classifier (RC), Linear Discriminant Analysis (LDA), Extra Trees (ET), and Light Gradient Boosting Machine (LightGBM). …”
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Approach to Enhancing Panoramic Segmentation in Indoor Construction Sites Based on a Perspective Image Segmentation Foundation Model by Juho Han, Sebeen Yoon, Mingyun Kang, Taehoon Kim

“…The proposed method iteratively executes SAM with adjusted input parameters to extract objects of varying sizes and subsequently applies filtering algorithms to retain valid objects. Then, label assignment and merging processes are performed based on the predictions from the target model to improve segmentation accuracy. …”
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Remote Sensing Techniques for Assessing Snow Avalanche Formation Factors and Building Hazard Monitoring Systems by Natalya Denissova, Serik Nurakynov, Olga Petrova, Daniker Chepashev, Gulzhan Daumova, Alena Yelisseyeva

“…As climate change accelerates shifts in snowfall and temperature patterns, it is increasingly important to improve our ability to monitor and predict avalanches. This review explores the use of remote sensing technologies in understanding key geomorphological, geobotanical, and meteorological factors that contribute to avalanche formation. …”
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The intelligent fault identification method based on multi-source information fusion and deep learning by Dashu Guo, Xiaoshuang Yang, Peng Peng, Lei Zhu, Handong He

“…Second, the importance of each influencing factor is predicted using 4 machine learning methods. Finally, fault identification is carried out on the fault identification map, which is fused with multi-source feature information, using the Convolutional Neural Network Model. …”
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Photovoltaic Power Estimation for Energy Management Systems Addressing NMOT Removal with Simplified Thermal Models by Juan G. Marroquín-Pimentel, Manuel Madrigal-Martínez, Juan C. Olivares-Galvan, Alma L. Núñez-González

“…The proposed estimation algorithm converts specific Numerical Weather Prediction data and solar module specifications into photovoltaic power output, which can be used in energy management applications to provide economic and ecological benefits. …”
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Modeling the Effect of Greenways’ Multilevel Visual Characteristics on Thermal Perception in Summer Based on Bayesian Network and Computer Vision by Yongrong Zheng, Siren Lan, Jiayi Zhao, Yuhan Liu, Songjun He, Chang Liu

“…Using the hill climbing algorithm in R to construct a Bayesian network, model validation results indicated prediction accuracies of 0.799 for TS and 0.838 for TP. …”
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Intelligent error-proof logic multi-factor cooperative active optimization based on knowledge base and generative adversarial network by Jinming Liu, Sheng Yang, Dunlin Zhu, Tianyun Luo, Jinglong He, Shengyuan Li

“…In order to solve the reduced prediction accuracy caused by insufficient training samples, data imbalance and improper feature selection of the intelligent error prevention system in the power grid, an intelligent errorproof logic multi-factor cooperative active optimization based on a knowledge base and generative adversarial network was designed. …”
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A neural network design for black-box identification of converter impedance models in arbitrary operating conditions by CHEN Bing, ZHAO Chongbin, JIANG Qirong, WANG Xu, WANG Fangming

“…In the model verification phase, the network is fed with set operating conditions, achieving highly accurate identification of stable operating conditions and offline prediction.…”
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Flexural strengthening of corroded steel beams with CFRP by using the end anchorage: Experimental, numerical, and machine learning methods by Amin Shabani Ammari, Younes Nouri, Habib Ghasemi Jouneghani, Seyed Amin Hosseini, Arash Rayegani, Mehrdad Ebrahimi, Pooria Heydari

“…The results indicated that corrosion in the upper flange gave the most severe strength reduction up to 39.7 %, although this was effectively mitigated by CFRP reinforcement. The ML prediction showed that the CatBoost algorithm had the highest accuracy, with an R2 score of 0.954. …”
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AI-Driven Optimization of Multilayer Thin-Film Structures for Advanced Optical Applications by Emad Alkhazraji, Sani Mukhtar, Haitham Khaled, Jaime Viegas

“…The FCNN model is trained to predict reflectance spectra with high accuracy over a wide design space defined by the thicknesses of individual layers. …”
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Development of a prognostic model for chemotherapy response and identification of TNFAIP2 as a target in colorectal cancer by Cheng Zhou, Chuan-Hai Xu, Min Xu, Xin-Kun Huang, Bin Zhu

“…Abstract Oxaliplatin, a key chemotherapeutic agent, often induces resistance in colorectal cancer (CRC) treatment, highlighting the urgent need for reliable biomarkers to predict treatment efficacy. In this study, we aimed to identify key genes associated with oxaliplatin resistance in CRC and to evaluate their potential as prognostic biomarkers. …”
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Advancements in Underwater Optical Wireless Communication: Channel Modeling, PAPR Reduction, and Simulations With OFDM by Liwei Yang, Zeyang Bi, Xue Liang, Lihao Zhao, Jiade Zhang, Jingyi Peng

“…This work systematically evaluated and validated several optical attenuation models, leading to the development of an approach that significantly improves the accuracy of optical signal behavior prediction in underwater environments. The simulations using the Monte Carlo algorithm revealed critical insights into signal propagation, enabling more precise modeling of UOWC channels under varying conditions. …”
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Deep Learning-Based Atmospheric Visibility Detection by Yawei Qu, Yuxin Fang, Shengxuan Ji, Cheng Yuan, Hao Wu, Shengbo Zhu, Haoran Qin, Fan Que

“…This paper systematically reviews the applications of various deep learning models—Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and Transformer networks—in visibility estimation, prediction, and enhancement. Each model’s characteristics and application methods are discussed, highlighting the efficiency of CNNs in spatial feature extraction, RNNs in temporal tracking, GANs in image restoration, and Transformers in capturing long-range dependencies. …”
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Digital mapping of soil organic carbon stocks in the forest lands of Dominican Republic by Efraín Duarte, Erick Zagal, Juan A. Barrera, Francis Dube, Fabio Casco, Alexander J. Hernández

“…Model A reported the lowest prediction error and uncertainty with an R2 of 0.83, an RMSE of 35.02 Mg C ha−1. …”
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Statistical Analysis of High-Resolution Coherent Monopulse Radar Sea Clutter by Hong Zhu, Qingping Wang, Ning Tai, Jingjian Huang, Naichang Yuan

“…A statistical analysis that properly characterizes sea clutter processes is indispensable both for optimum detection algorithm design and for performance prediction problems in maritime surveillance applications. …”
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Energy-efficient outdoor air flow control in ventilation systems by A. B. Sulin, A. A. Nikitin, T. V. Ryabova, S. S. Muraveinikov, I. N. Sankina

“…The expected microclimate parameters predicted assessment in real time opens up the possibility of using such elements and algorithms for controlling the ventilation and air conditioning system, which provide the required air quality with minimal energy consumption. …”
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Noise Processing Method of MEMS Tilt Sensor Using Improved Kalman Filter Based on Quantum Particle Swarm Optimization by Yutong Ge, Weizheng Ren, Kaile Yu, Yiran Zhang, Yuxiao Li

“…The parameters of Kalman filter are adaptively optimized by QPSO, which addresses the issues of local optima and premature convergence during the optimization and correction process of prediction system. Then Kalman filter is carried out with the optimal parameters to achieve the effect of denoising. …”
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Lavender hydrosol analysis using UV spectroscopy data and partial least squares regression by Sára Preiner, Bálint Levente Tarcsay, Dóra Pethő, Norbert Miskolczi

“…After optimization the established PLS model showed an R2 score above 0.95 for the prediction of hydrosol composition changes during cross-validation. …”
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Population-Level Cell Trajectory Inference Based on Gaussian Distributions by Xiang Chen, Yibing Ma, Yongle Shi, Yuhan Fu, Mengdi Nan, Qing Ren, Jie Gao

“…The simulation studies indicate that CPvGTI excels in pseudo-time prediction and structural reconstruction compared to existing methods. …”
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