Crop yield prediction using machine learning: An extensive and systematic literature review by Sarowar Morshed Shawon, Falguny Barua Ema, Asura Khanom Mahi, Fahima Lokman Niha, H.T. Zubair

“…Also, the most applied machine learning algorithms are Linear Regression (LR), Random Forest (RF), and Gradient Boosting Trees (GBT) whereas the most applied deep learning algorithms are Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM). …”
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Bilinear Sequence Regression: A Model for Learning from Long Sequences of High-Dimensional Tokens by Vittorio Erba, Emanuele Troiani, Luca Biggio, Antoine Maillard, Lenka Zdeborová

“…We quantify the improvement that optimal learning brings with respect to vectorizing the sequence of tokens and learning via simple linear regression. …”
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Pathophysiology of the Heart and Clinical Cardiac Anesthesiology by Yu. V. Nikiforov, L. A. Krichevsky

“…The main tasks presented to its investigators were to improve electric cardiac defibrillation and cardioversion, to elaborate the optimal infusiotransfusion therapy tactics during cardiac surgery, to optimize intraoperative myocardial protection, to maintain lung functions under extracorporeal circulation, to search for the most effective and safe schemes of general and epidural anesthesia in cardiosurgical patients, and to substantiate algorithms for their postoperative activation. …”
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A Novel Local Binary Patterns-Based Approach and Proposed CNN Model to Diagnose Breast Cancer by Analyzing Histopathology Images by Mehmet Gul

“…The histopathology images improved with the QS-LBP method were then analyzed with the most commonly used Random Forest and Optimized Forest algorithms among machine learning algorithms. …”
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Predicting Pathological Complete Response Following Neoadjuvant Therapy in Patients With Breast Cancer: Development of Machine Learning–Based Prediction Models in a Retrospective S... by Chun-Chi Lai, Cheng-Yu Chen, Tzu-Hao Chang

“…The application of logistic regression with recursive feature elimination with cross-validation was found to demonstrate the optimal performance among the various algorithms that were evaluated in this study. …”
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Trajectory Planning for Robotic Manipulators in Automated Palletizing: A Comprehensive Review by Samuel Romero, Jorge Valero, Andrea Valentina García, Carlos F. Rodríguez, Ana Maria Montes, Cesar Marín, Ruben Bolaños, David Álvarez-Martínez

“…By reviewing and synthesizing the most recent research, this article sheds light on the state-of-the-art manipulator planning algorithms for pick-and-place tasks in palletizing applications.…”
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Machine learning discovery of the dielectric properties of strontium-containing condensed matter by Dongyang Huang, Jiaxing Fu, Chenghao Yu

“…In this work, machine learning models were successfully developed to capture the relationship between composition and dielectric properties of strontium-containing dielectrics using different algorithms, with hyperparameter optimization performed via grid search. …”
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A Two-Step Approach for Airborne Delay Minimization Using Pretactical Conflict Resolution in Free-Route Airspace by Ramazan Kursat Cecen, Cem Cetek

“…According to this comparison, both metaheuristics algorithms produce near optimal solutions in a reasonably short time. …”
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Validation of Sea Surface Temperature From GK-2A Geostationary Satellite and Error Reduction Considering Impact of Satellite Zenith Angle by Kyung-Ae Park, Hye-Jin Woo, Stephane Saux Picart, Anne O'Carroll, Eunha Sohn, HuiTae Joo, Joon-Soo Lee, Joon-Yong Yang

“…By proposing optimal algorithms for SST retrievals from geostationary satellites, this study is anticipated to improve the monitoring of SSTs in regions covered by GK-2A. …”
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Intelligent deep learning for human activity recognition in individuals with disabilities using sensor based IoT and edge cloud continuum by Mohammed Maray

“…Finally, the deep belief network (DBN) model is employed for HAR. To exhibit the improved performance of the existing IDLTHAR-PDST model, a comprehensive simulation study is accomplished. …”
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A novel method for soil organic carbon prediction using integrated ‘ground-air-space’ multimodal remote sensing data by Yilin Bao, Xiangtian Meng, Huanjun Liu, Mengyuan Xu, Mingchang Wang

“…The SOC prediction experiments conducted in Youyi, the largest state farm in China, demonstrate that Model (iii) achieves the highest accuracy with the GNN model. This model improves coefficient of determination (R2) and ratio of performance to interquartile distance (RPIQ) by 0.09 and 0.28, respectively, and reduces root mean square error (RMSE) by 0.52 g kg−1 compared to Model (ii). …”
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Predictive model for determining the indications for automated 3D ultrasound for screening patients at low risk of developing breast tumors by A. E. Garanina, A. V. Kholin

“…The developed algorithms will help optimize screening and referral for additional examinations, which is of practical importance for improving diagnostics and optimizing healthcare resources.…”
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A Full-Life-Cycle Modeling Framework for Cropland Abandonment Detection Based on Dense Time Series of Landsat-Derived Vegetation and Soil Fractions by Qiangqiang Sun, Zhijun You, Ping Zhang, Hao Wu, Zhonghai Yu, Lu Wang

“…Compared to the traditional yearly land cover-based approach (with an overall accuracy of 77.39%), this algorithm can overcome the propagation of classification errors (with product accuracy from 74.47% to 85.11%), especially in terms of improving the ability to capture changes at finer spatial scales. …”
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Examining the Impact of Topography and Vegetation on Existing Forest Canopy Height Products from ICESat-2 ATLAS/GEDI Data by Yisa Li, Dengsheng Lu, Yagang Lu, Guiying Li

“…Introducing accurate digital elevation model (DEM) data can improve FCH retrieval from spaceborne LiDAR to some extent. …”
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Obstacle avoidance and formation control of multiple unmanned vehicles in complex environments based on artificial potential field method by Yilin MEI, Likun CUI, Xueyan HU, Guangqi HU, Hao WANG

“…Specifically, the success rate of obstacle avoidance in dynamic environments increased by 35% compared to traditional algorithms and by 10% compared to improved algorithms. …”
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Research Progress and Prospect of Green Infrastructure with Public Health Promotion Function by Tongyu LI, Junyi LIXU, Binxia XUE, Yan (USA) SONG

“…Such a framework could significantly enhance urban planning and policy-making by optimizing GI interventions to provide the most significant public health benefits. …”
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ON THE SIMULATION OF MODES ОF ELECTRIC POWER SYSTEMS WITH FACTS by E. D. Halilov

“…It is necessary to reduce the power loss, improve the reliability and quality of power supply and increase the power transmission. …”
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MRSNet: Multi-Resolution Scale Feature Fusion-Based Universal Density Counting Network by Yi Zhang, Wei Song, Mingyue Shao, Xiangchun Liu

“…Thus, preserving features that carry the most relevant information at each scale is crucial for improving counting precision. …”
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The Use of Machine Learning for Analyzing Real-World Data in Disease Prediction and Management: Systematic Review by Norah Hamad Alhumaidi, Doni Dermawan, Hanin Farhana Kamaruzaman, Nasser Alotaiq

“…A substantial portion of studies (n=38, 67%) focused on improving clinical decision-making, patient stratification, and treatment optimization. …”
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Countermeasuring Anti-Ship Missiles for Surface Naval Platforms: A Machine Learning Approach With Explainable Artificial Intelligence by Murat Ertop, Ali Oter, Ali Kara

“…The effective deployment of existing countermeasures and the determination of the most optimal platform maneuvers have also become crucial. …”
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