A deep learning strategy for accurate identification of purebred and hybrid pigs across SNP chips by Zipeng Zhang, Zhengwen Fang, Yongwang Du, Yilin He, Changsong Qian, Weijian Ye, Ning Zhang, Jianan Zhang, Xiangdong Ding

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Machine learning for high-risk hospitalization prediction in outpatient individuals with diabetes at a tertiary hospital by Carolina Deina, Flavio S. Fogliatto, Mateus Augusto dos Reis, Beatriz D. Schaan

“…Within this group, 82.98% (512 patients) did not require hospitalization, while 17.02% (105 patients) were hospitalized at least once. Multiple machine learning algorithms were tested, and the combination of XGBoost and Instance Hardness Threshold models displayed the best predictive performance. …”
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Machine Learning (ML) Based Repair-Count and Periodic Maintenance Policy for Multipurpose CNC Machinery by Alifin Fakhri Ikhwanul, Winarno, Fasa Nadia, Darajatun Rizki Achmad, Kusnadi, Safariyani Eva

“…This study deals with developing a maintenance policy optimization framework using a machine learning approach for multipurpose CNC machinery in an automotive part manufacturer. …”
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Improving timing resolution of BGO for TOF-PET: a comparative analysis with and without deep learning by Francis Loignon-Houle, Nicolaus Kratochwil, Maxime Toussaint, Carsten Lowis, Gerard Ariño-Estrada, Antonio J. Gonzalez, Etiennette Auffray, Roger Lecomte

“…To address this, a time walk correction (TWC) can be done by using the rise time measured with a second threshold. Deep learning, particularly convolutional neural networks (CNNs), can also enhance CTR by training with digitized waveforms. …”
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Modeling Analysis of the Relationship between Adolescent Aerobic Exercise and Obesity Reduction Based on Deep Learning by Peng Mu

“…In order to explore the modeling analysis of the relationship between adolescent aerobic exercise and obesity reduction, the relationship modeling method of deep learning algorithm is proposed. This study integrates deep learning algorithms and first uses the changes in body shape, weight, BMI index, body fat, body circumference, and other indicators of adolescent obese before and after aerobic exercise as the initial pheromone distribution matrix and introduces random evolution factor and evolutionary drift threshold to establish the objective function of aerobic exercise to reduce adolescent obesity. …”
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Automated mold defects classification in paintings: A comparison of machine learning and rule-based techniques. by Hilman Nordin, Bushroa Abdul Razak, Norrima Mokhtar, Mohd Fadzil Jamaludin, Adeel Mehmood

“…The technique leverages a feature extraction method called Derivative Level Thresholding to pinpoint suspicious regions within an image. …”
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Innovative deep learning classifiers for breast cancer detection through hybrid feature extraction techniques by S. Vijayalakshmi, Binay Kumar Pandey, Digvijay Pandey, Mesfin Esayas Lelisho

“…This study presents a hybrid classification approach for mammogram analysis by combining handcrafted statistical features and deep learning techniques. The methodology involves preprocessing with the Shearlet Transform, segmentation using Improved Otsu thresholding and Canny edge detection, followed by feature extraction through Gray Level Co-occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM), and 1st-order statistical descriptors. …”
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Memristor-Based Neuromorphic System for Unsupervised Online Learning and Network Anomaly Detection on Edge Devices by Md Shahanur Alam, Chris Yakopcic, Raqibul Hasan, Tarek M. Taha

“…Built using memristor-based analog neuromorphic and in-memory computing techniques, the system integrates two unsupervised autoencoder neural networks—one utilizing optimized crossbar weights and the other performing real-time learning to detect novel intrusions. Threshold optimization and anomaly detection are achieved through a fully analog Euclidean Distance (ED) computation circuit, eliminating the need for floating-point processing units. …”
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The Assessment of Infrasound and Low Frequency Noise Impact on the Results of Learning in Primary School – Case Study by Adam ZAGUBIEŃ, Katarzyna WOLNIEWICZ

“…Measured infrasound noise levels are below hearing threshold.…”
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CRITICAL THINKING BASED ON THE INTEGRATION OF BATIK LOCAL WISDOM IN SCIENCE LEARNING: A TEST DEVELOPMENT by Nurul Iqdami Zuniari, Antuni Wiyarsi

“…Analysis of the distribution of threshold values (b) shows that most questions are in the medium category. …”
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Deep learning assessment of metastatic relapse risk from digitized breast cancer histological slides by I. Garberis, V. Gaury, C. Saillard, D. Drubay, K. Elgui, B. Schmauch, A. Jaeger, L. Herpin, J. Linhart, M. Sapateiro, F. Bernigole, A. Kamoun, A. Filiot, O. Tchita, R. Dubois, M. Auffret, L. Guillou, I. Bousaid, M. Azoulay, J. Lemonnier, M. Sefta, S. Everhard, A. Sarrazin, J-F Reboud, F. Brulport, J. Dachary, B. Pistilli, S. Delaloge, P. Courtiol, F. André, V. Aubert, M. Lacroix-Triki

“…Applying a 5% MFS event probability threshold stratifies patients into low- and high-risk groups. …”
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EHO-Q-LGAN: An EHO-Based Q-Learning GAN for the Timely Diagnosis of Diabetic Retinopathy by Maneesha Vadduri, P. Kuppusamy

“…This research introduces an innovative method for identifying and classifying internal retinal components that combine three advanced models: a dynamic segmentation model, an efficient Elephant Herding Optimization (EHO) Model, and an autoencoder model with Q-Learning Generative Adversarial Network (Q-LGAN) process. …”
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PSO-Optimized Deep Learning for Ultra-Precise Corrosion Detection on HDD Read/Write Heads by Chaiwat Punyammaree, Somyot Kaitwanidvilai

“…This paper presents a novel deep learning approach for automated detection and counting of corrosion pits on Hard Disk Drive (HDD) read/write heads using Scanning Electron Microscopy (SEM) images. …”
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Detection of drainage ditches from LiDAR DTM using U-Net and transfer learning by Holger Virro, Alexander Kmoch, William Lidberg, Merle Muru, Wai Tik Chan, Desalew Meseret Moges, Evelyn Uuemaa

“…Deep learning offers a promising alternative but requires extensive labeled data, often unavailable. …”
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From light sensing to adaptive learning: hafnium diselenide reconfigurable memcapacitive devices in neuromorphic computing by Bashayr Alqahtani, Hanrui Li, Abdul Momin Syed, Nazek El-Atab

“…The proposed devices exhibit (1) optoelectronic synaptic features and perform separate stimulus-associated learning, indicating considerable adaptive neuron emulation, (2) dual light-enabled charge-trapping and memcapacitive behavior within the same MOSCap device, whose threshold voltage and capacitance vary based on the light intensity across the visible spectrum, (3) memcapacitor volatility tuning based on the biasing conditions, enabling the transition from volatile light sensing to non-volatile optical data retention. …”
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Multi-Agent Reinforcement Learning for Distributed Flexible Job Shop Scheduling With Random Job Arrival by Yuhang Yan, Wenchao Yi, Zhi Pei, Yong Chen

“…A deep Q-network (DQN) framework that incorporates a linearly decreasing threshold probability was designed to effectively balance exploration and exploitation during the training phase. …”
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Segmentation of Endothelial Cell Boundaries of Rabbit Aortic Images Using a Machine Learning Approach by Saadia Iftikhar, Andrew R. Bond, Asim I. Wagan, Peter D. Weinberg, Anil A. Bharath

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A Fully Integrated Orthodontic Aligner With Force Sensing Ability for Machine Learning‐Assisted Diagnosis by Hao Feng, Wenhao Song, Ruyi Li, Linxin Yang, Xiaoxuan Chen, Jiajun Guo, Xuan Liao, Lei Ni, Zhou Zhu, Junyu Chen, Xibo Pei, Yijun Li, Jian Wang

“…This aligner exhibits excellent sensitivity for occlusal force detection, with a broad detection threshold and continuous pressure monitoring ability at eight distinct sites. …”
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Detection method of slight bruises of apples based on hyperspectral imaging and RELIEF-extreme learning machine by ZHANG Meng, LI Guanghui

“…Then, based on full wavebands and characteristic wavebands, an extreme learning machine (ELM) model was built, as comparison with support vector machine (SVM) and K- mean algorithm. …”
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Anticipating Optical Availability in Hybrid RF/FSO Links Using RF Beacons and Deep Learning by Mostafa Ibrahim, Arsalan Ahmad, Sabit Ekin, Peter LoPresti, Serhat Altunc, Obadiah Kegege, John F. O'Hara

“…We implement a supervised learning model to anticipate FSO attenuation based on the analysis of RF patterns. …”
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