Hierarchical Reinforcement Learning for Viewpoint Planning with Scalable Precision in UAV Inspection by Hua Wu, Hao Li, Junwei Yu, Yanxiong Wu, Xiaojing Bai, Mengyang Pu, Li Sun, Yihuan Li, Juncheng Liu

“…Additionally, a reward function is designed to enhance inspection precision, enabling collaborative optimization of waypoint positions, viewpoint poses, and focal lengths. …”
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Cardiovascular Disease Detection through Innovative Imbalanced Learning and AUC Optimization by Karthikeyan Palanisamy, Krishnaveni Krishnasamy, Praba Venkadasamy

“…In this paper, we introduce a novel imbalanced learning approach named Imbalanced Maximizing-Area Under the Curve (AUC) Proximal Support Vector Machine (ImAUC-PSVM), which harnesses the foundational principles of traditional PSVM for the detection of CVDs. …”
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Comparative Analysis of Deep Learning Models for Intrusion Detection in IoT Networks by Abdullah Waqas, Sultan Daud Khan, Zaib Ullah, Mohib Ullah, Habib Ullah

“…Each model was assessed under balanced and imbalanced dataset configurations and evaluated using three loss functions (cross-entropy, focal loss, and dual focal loss). …”
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A Deep Learning Framework for Chronic Kidney Disease stage classification by Gayathri Hegde M, P Deepa Shenoy, Venugopal KR, Arvind Canchi

“…Chronic Kidney Disease (CKD) has become more prevalent, leading to a gradual decline in kidney function and, ultimately, in renal failure. Timely detection of the CKD stage is essential for enhancing healthcare services and decreasing morbidity and mortality. …”
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Explainable Deep Multilevel Attention Learning for Predicting Protein Carbonylation Sites by Jian Zhang, Jingjing Qian, Pei Wang, Xuan Liu, Fuhao Zhang, Haiting Chai, Quan Zou

“…To tackle this unresolved challenge, selective carbonylation sites (SCANS) is introduced, a novel deep learning‐based framework. SCANS employs a multilevel attention strategy to capture both local (segment‐level) and global (protein‐level) features, utilizes a tailored loss function to penalize cross‐predictions (residue‐level), and applies transfer learning to augment the specificity of the overall network by leveraging knowledge from pretrained model. …”
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Knee Osteoarthritis Detection and Classification Using Autoencoders and Extreme Learning Machines by Jarrar Amjad, Muhammad Zaheer Sajid, Ammar Amjad, Muhammad Fareed Hamid, Ayman Youssef, Muhammad Irfan Sharif

“…The demand for utilizing deep learning models in order to automate and improve the accuracy of KOA image classification has been increasing. …”
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Live Weight Prediction in Norduz Sheep Using Machine Learning Algorithms by Cihan Çakmakçı

“…The objective of this study was to compare predictive performances of four machine learning (ML) models: Support Vector Machines with Radial Basis Function Kernel (SVMR), Classification and Regression Trees (CART), Random Forest (RF) and Model Average Neural Networks (MANN) to predict live weight from morphological measurements of Norduz sheep (n=93). …”
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A New Hybrid Machine Learning Method for Stellar Parameter Inference by Sujay Shankar, Michael A. Gully-Santiago, Caroline V. Morley

“…The advent of machine learning (ML) is revolutionary to numerous scientific disciplines, with a growing number of examples in astronomical spectroscopic inference, as ML is more powerful than traditional techniques. …”
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Improved Butterfly Optimizer-Configured Extreme Learning Machine for Fault Diagnosis by Helong Yu, Kang Yuan, Wenshu Li, Nannan Zhao, Weibin Chen, Changcheng Huang, Huiling Chen, Mingjing Wang

“…The model is mainly based on an improved butterfly optimizer algorithm- (BOA-) optimized kernel extreme learning machine (KELM) model. Firstly, the roller bearing’s vibration signals in the four states that contain normal state, outer race failure, inner race failure, and rolling ball failure are decomposed into several intrinsic mode functions (IMFs) using the complete ensemble empirical mode decomposition based on adaptive noise (CEEMDAN). …”
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Weakly supervised learning through box annotations for pig instance segmentation by Heng Zhou, Jiuqing Dong, Shujie Han, Seyeon Chung, Hassan Ali, Sangcheol Kim

“…In contrast to traditional methods, which depend on expensive mask annotations, our approach adopts a weakly supervised learning paradigm that reduces annotation cost. Specifically, we enhance the loss function of an existing weakly supervised instance segmentation model to better align with the requirements of pig instance segmentation. …”
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Machine Learning Methods for Evaluation of Technical Factors of Spraying in Permanent Plantations by Vjekoslav Tadić, Dorijan Radočaj, Mladen Jurišić

“…The data from the field research were processed using four machine learning models: quantile random forest (QRF), support vector regression with radial basis function kernel (SVR), Bayesian Regularization for Feed-Forward Neural Networks (BRNN), and Ensemble Machine Learning (ENS). …”
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Analysis and prediction of infectious diseases based on spatial visualization and machine learning by Yunyun Cheng, Yanping Bai, Jing Yang, Xiuhui Tan, Ting Xu, Rong Cheng

“…Finally, a multi algorithm fusion learning model based on stacking technology is proposed to address the problem of poor generalization ability of single algorithm models in prediction; Furthermore, radial basis function network (RBF) was used as a two-level meta learner to fuse the above models, and particle swarm optimization (PSO) was used to optimize RBF parameters to reduce generalization error. …”
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Adaptive Control of the Atmospheric Plasma Spray Process for Functionally Graded Thermal Barrier Coatings by Balachandar Guduri, Romesh C. Batra

“…Functionally graded coatings (FGCs) have a material composition continuously varying through the thickness but uniform in the surface parallel to the coated substrate. …”
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Timing specific requirement of microRNA function is essential for embryonic and postnatal hippocampal development. by Qingsong Li, Shan Bian, Janet Hong, Yoko Kawase-Koga, Edwin Zhu, Yongri Zheng, Lizhuang Yang, Tao Sun

“…The adult hippocampus consists of the dentate gyrus (DG) and the CA1, CA2 and CA3 regions and is essential for learning and memory functions. During embryonic development, hippocampal neurons are derived from hippocampal neuroepithelial cells and dentate granular progenitors. …”
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Hybrid machine learning algorithms accurately predict marine ecological communities by Luciana Erika Yaginuma, Luciana Erika Yaginuma, Fabiane Gallucci, Danilo Cândido Vieira, Paula Foltran Gheller, Simone Brito de Jesus, Thais Navajas Corbisier, Gustavo Fonseca

“…Data was analyzed by means of a hybrid machine learning (ML) approach, which combines unsupervised and supervised methods. …”
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Detection of Banana Diseases Based on Landsat-8 Data and Machine Learning by Renata Retkute, Kathleen S. Crew, John E. Thomas, Christopher A. Gilligan

“…We used a pixel-level random forest (RF) model to predict 11 key vegetation indices (VIs) as a function of historical meteorological conditions, specifically daytime and nighttime temperature from MODIS and precipitation from NASA GES DISC. …”
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Pharmacological and pupillary evidence for the noradrenergic contribution to reinforcement learning in Parkinson’s disease by Claire O’Callaghan, Frank H. Hezemans, Naresh Subramaniam, Rong Ye, Kamen A. Tsvetanov, Alexander G. Murley, Negin Holland, Isabella F. Orlando, Ralf Regenthal, Roger A. Barker, Caroline H. Williams-Gray, Luca Passamonti, Trevor W. Robbins, James B. Rowe

“…Parkinson’s disease is often considered as a model of dopamine deficits, including dopamine’s role in reinforcement learning. However, noradrenergic function is also severely impaired by Parkinson’s disease, contributing to cognitive deficits. …”
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A Deep Learning for Optimization and Visualization of Expressway Toll Lane Management by Pattarapon Klaykul, Wilaiporn Lee, Kanabadee Srisomboon, Luepol Pipanmekaporn, Akara Prayote

“…This paper proposes a novel framework combining deep learning and multi-objective optimization to improve toll plaza efficiency. …”
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Do Preschoolers Use Rules to Represent Their Count List? by Jess Sullivan, Joseph Alvarez, Sophie Cramer-Benjamin, Sadie Holcomb, Melissa Nolan, Alex Morabito, David Barner

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Fine Motor Skills, Executive Function, and School Readiness in Preschoolers with Externalizing Behavior Problems by Atefeh Karimi, Bridget Poznanski, Katie C. Hart, Eliza L. Nelson

“…The objective of this study was to examine whether fine motor skills (FMS) and executive function (EF) are unique predictors of school readiness (SR). …”
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