Predicting Morphological Changes Along a Macrotidal Coastline Using a Two‐Stage Machine Learning Model by Pavitra Kumar, Nicoletta Leonardi

“…LSTM model achieved a testing regression of 0.92 for one‐step‐ahead (6 months) predictions of change in sediment volume time series, while sequence‐to‐sequence model achieved the testing regression of 0.96 for three‐time‐ahead (1.5 years) predictions and 0.88 for ten‐time‐step‐ahead (5 years) prediction.…”
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A lightweight multi-deep learning framework for accurate diabetic retinopathy detection and multi-level severity identification by Amad Zafar, Kwang Su Kim, Muhammad Umair Ali, Jong Hyuk Byun, Jong Hyuk Byun, Seong-Han Kim

“…The next step involves the use of transfer learning for further subclassification of DR severity (i.e., mild, moderate, severe DR, and proliferative DR). …”
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USING CORPORATE MECHANISMS TO IMPROVE ACADEMIC ENGLISH LANGUAGE PROFICIENCY AMONG POSTGRADUATE STUDENTS: IMPACT ON THE LEARNING PROCESS by Ольга Володимирівна Чайковська

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Using Graph Neural Networks in Reinforcement Learning With Application to Monte Carlo Simulations in Power System Reliability Analysis by Oystein Rognes Solheim, Boye Annfelt Hoverstad, Magnus Korpas

“…This paper presents a novel method for power system reliability studies that combines graph neural networks with reinforcement learning. Monte Carlo methods are the backbone of probabilistic power system reliability analyses. …”
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Enhancing the students’ perception of machine learning methods-based drug formulation using R_programming educational protocols by Rania M. Hathout, Shaimaa S. Ibrahim

“…Abstract Background Recently, the need for artificial intelligence (AI) and machine learning (ML) methods in drug development and research is gaining high concern and more grounds. …”
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Construction of a Real-Time Detection for Floating Plastics in a Stream Using Video Cameras and Deep Learning by Hankyu Lee, Seohyun Byeon, Jin Hwi Kim, Jae-Ki Shin, Yongeun Park

“…The results showed that the trained YOLOv8 model achieved an overall F1-score of 0.982 in the validation step and 0.980 in the testing step. Detection performance yielded mAP scores of 0.992 (IoU = 0.5) and 0.714 (IoU = 0.5:0.05:0.95). …”
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Harnessing machine learning approach for hardness optimization of Al-Si alloy composites reinforced with coconut shell ash by M Poornesh, Shreeranga Bhat, Mithun Kanchan

“…The article offers a novel contribution to materials science and sustainable engineering through a novel and structured step-by-step AutoML approach.…”
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Effectiveness of a Four-Stage Death Education Model Based on Constructivist Learning Theory for Trainee Nursing Students by Yang Y, Li Y, Fu J, Guo D, Xue J

“…In the evaluation of the content of the course, the teaching methods of the course, and the teaching results, most of the trainee nurses gave a better evaluation.Conclusion: The four-step death education model based on constructivist learning theory significantly improved the death coping skills and attitudes of trainee nursing students. …”
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Mahalanobis distance–based kernel supervised machine learning in spectral dimensionality reduction for hyperspectral imaging remote sensing by Jing Liu, Yulong Qiao

“…Spectral dimensionality reduction is a crucial step for hyperspectral image classification in practical applications. …”
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Tuning the ICON-A 2.6.4 climate model with machine-learning-based emulators and history matching by P. Bonnet, L. Pastori, M. Schwabe, M. Giorgetta, F. Iglesias-Suarez, V. Eyring, V. Eyring

“…This is traditionally a computationally expensive step as it requires a large number of climate model simulations. …”
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Improving diagnosis of primary aldosteronism through education: a modified Delphi study to identify key learning points by Jocelyn Widjaja, Jun Yang, Julia Harrison

“…Objective: To determine expert consensus on key information about PA that should ideally be taught to medical students as a step toward improving the detection of this common, underdiagnosed, and often easily treated condition. …”
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A deep learning framework for virtual continuous glucose monitoring and glucose prediction based on life-log data by Min Hyuk Lim, Hyocheol Chae, Jeongwon Yoon, Insik Shin

“…Abstract While continuous glucose monitoring (CGM) has revolutionized metabolic health management, widespread adoption remains limited by cost constraints and usage burden, often resulting in interrupted monitoring periods. We propose a deep learning framework for glucose level inference that operates independently of prior glucose measurements, utilizing comprehensive life-log data. …”
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A novel voice in head actor critic reinforcement learning with human feedback framework for enhanced robot navigation by Alabhya Sharma, Ananthakrishnan Balasundaram, Ayesha Shaik, Chockalingam Aravind Vaithilingam

“…Our system strategically combines GPT and Gemini powered LLMs as Actor and Critic components within a reinforcement learning (RL) loop for continuous learning and adaptation. …”
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A Deep Learning Approach to Automated Treatment Classification in Tuna Processing: Enhancing Quality Control in Indonesian Fisheries by Johan Marcus Tupan, Fredrik Rieuwpassa, Beni Setha, Wilma Latuny, Samuel Goesniady

“…This study provides a significant step forward in automated fish processing assessment technology, offering a promising solution to longstanding challenges in the marine processing industry.…”
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Autonomous Drone-Based Pollination Systems for Enhancing Crop Yield in Orchards Using IoT and Machine Learning Optimization by Khan Roohee, Lalnunthari Banti

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High precision water quality retrieval in Dianchi Lake using Gaofen 5 data and machine learning methods by Yuewen Feng, Jun Zhang, Sanjie Guo, Yunbai Zhang, Zhongwei Zhang

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Pit Collapse Risk Fusion Early-Warning Method Based on Machine Learning and Improved Cloud Dempster–Shafer by Jiajia Zeng, Bo Wu, Cong Liu

“…The main contributions include (1) presenting a new input to the fusion model by optimizing the machine learning model through a multi-step rolling method, and then using the basic probability assignment values obtained from the cloud model as input to the fusion model and (2) developing an improved methodology to address the paradoxical results of the fusion of traditional Dempster–Shafer evidence theory when there is a high level of conflict in multi-source risk prediction data. …”
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An automated Machine Learning based approach for a reproducible and efficient evaluation of industrial Charpy V-notch specimens by Adrian Herges, Björn-Ivo Bachmann, Sebastian Scholl, Frank Mücklich

“…Our approach involves a multi-step preprocessing routine that incorporates color thresholding and connected component analysis to first detect the various Charpy V-notch specimen bundles according to their sample material affiliation and testing temperature. …”
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A machine learning based authentication and intrusion detection scheme for IoT users anonymity preservation in fog environment. by Khondokar Oliullah, Md Whaiduzzaman, Md Julkar Nayeen Mahi, Tony Jan, Alistair Barros

“…In this paper, we incorporate a two-step authentication process, starting with anonymous authentication using a secret ID with Elliptic Curve Cryptography (ECC), followed by an intrusion detection algorithm for users flagged as suspicious activity. …”
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Enhanced metal ion adsorption using ZnO-MXene nanocomposites with machine learning-based performance prediction by Abhishek Kagalkar, Swapnil Dharaskar, Nitin Chaudhari, Vinay Vakharia, Rama Rao Karri

“…Abstract The efficacy of ZnO-MXene nanocomposites as extremely effective adsorbents for the removal of metal ions from wastewater is investigated in this work. The two-step chemical method used to create composites showed how temperature affected their shape. …”
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