Exploring Machine Learning Models for Vault Safety in ICL Implantation: A Comparative Analysis of Regression and Classification Models by Qing Zhang, Qi Li, Zhilong Yu, Ruibo Yang, Emmanuel Eric Pazo, Yue Huang, Hui Liu, Chen Zhang, Salissou Moutari, Shaozhen Zhao

“…In the task of predicting vault > 750 µm vs. ≤ 750 µm, random forest emerged as the most effective classifier, achieving accuracy of 86 ± 9% and an AUC of 0.88. …”
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IHML: Incremental Heuristic Meta-Learner by Onur Karadeli, Kıymet Kaya, Şule Gündüz Öğüdücü

“…The results show that the proposed model achieves more accuracy (in average % 10 and at most % 71 improvements) compared to the baseline machine learning models in the literature.…”
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Performance Statistics of Autoregressive Short and Ultrashort Signal Detectors by V. M. Kutuzov, V. P. Ipatov, S. S. Sokolov

“…Parametric spectral estimation methods provide an improved level of frequency resolution compared to matched signal processing conventionally used in radar technology. …”
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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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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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Developing advanced datadriven framework to predict the bearing capacity of piles on rock by Kennedy C. Onyelowe, Shadi Hanandeh, Viroon Kamchoom, Ahmed M. Ebid, Fabián Danilo Reyes Silva, José Luis Allauca Palta, José Luis Llamuca Llamuca, Siva Avudaiappan

“…The developed framework provides engineers and practitioners with a powerful tool for improving pile design accuracy, reducing uncertainties, and optimizing construction practices. …”
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Machine learning allows robust classification of lung neoplasm tissue using an electronic biopsy through minimally-invasive electrical impedance spectroscopy by Georgina Company-Se, Virginia Pajares, Albert Rafecas-Codern, Pere J. Riu, Javier Rosell-Ferrer, Ramon Bragós, Lexa Nescolarde

“…This study aims to use classification algorithms with minimally invasive electrical impedance spectroscopy to improve neoplastic lung tissue identification during biopsies. …”
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Recent technical advancements and clinical applications of MR-guided radiotherapy in lung cancer treatment by Chi Ma, Xiao Wang, Ke Nie, Zhenyu Xiong, Keying Xu, Ning Yue, Yin Zhang

“…Additionally, MRgRT could potentially allow multileaf collimator (MLC) tracking to further improve the treatment efficiency. Recent technological innovations, including AI-powered auto-contouring algorithms, deep-learning (DL) based prediction models, and adaptive treatment strategies, further optimize MRgRT by improving workflow efficiency and reducing treatment time. …”
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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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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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Energy Efficient Heat Exchange Network for the Oil Vacuum Distillation Facility by Ved V.E., Ilchenko M.V., Myronov A.N.

“…The cost curves tools carry out the search of an optimal minimum temperature difference value. …”
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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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Impact of occupancy behavior on building energy efficiency: What’s next in detection and monitoring technologies? by Wenjie Song, John Calautit

“…Finally, economic feasibility considerations such as installation costs and associated energy savings achieved through occupancy-driven heating, ventilation and air-conditioning (HVAC ) optimization are crucial for large-scale implementation. …”
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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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RSWD-YOLO: A Walnut Detection Method Based on UAV Remote Sensing Images by Yansong Wang, Xuanxi Yang, Haoyu Wang, Huihua Wang, Zaiqing Chen, Lijun Yun

“…Furthermore, to optimize the detection performance under hardware resource constraints, we apply knowledge distillation to RSWD-YOLO, thereby further improving the detection accuracy. …”
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Harnessing AI forward and backward chaining with telemetry data for enhanced diagnostics and prognostics of smart devices by Muhammad Shoaib Farooq, Rizwan Pervez Mir, Atif Alvi, Kilian Tutusaus, Eduardo Garcia Villena, Fadwa Alrowais, Hanen Karamti, Imran Ashraf

“…Accurate early diagnosis and prediction of potential failures have the capability to greatly improve maintenance efficiency, minimize downtime, and optimize cost.…”
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The Role of Artificial Intelligence (AI) in the Future of Forestry Sector Logistics by Leonel J. R. Nunes

“…Results: The results demonstrated that optimization algorithms reduced transportation costs and carbon emissions. …”
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Energy-Efficient Trajectory Planning With Joint Device Selection and Power Splitting for mmWaves-Enabled UAV-NOMA Networks by Ahmad Gendia, Osamu Muta, Sherief Hashima, Kohei Hatano

“…Dynamic selection of suitable hovering spots within the target zone where the battery-constrained UAV needs to be positioned as well as calibrated NOMA power control with proper device pairing are critical for optimized performance. We propose cost-subsidized multiarmed bandit (CS-MAB) and double deep Q-network (DDQN)-based solutions to jointly address the problems of dynamic UAV path design, device pairing, and power splitting for downlink data transmission in NOMA-based systems. …”
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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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