High-throughput behavioral screening in Caenorhabditis elegans using machine learning for drug repurposing by Antonio García-Garví, Antonio-José Sánchez-Salmerón

“…However, these methods present certain limitations in detecting subtle and non-linear patterns. …”
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Filament Type Recognition for Additive Manufacturing Using a Spectroscopy Sensor and Machine Learning by Gorkem Anil Al, Uriel Martinez-Hernandez

“…This study presents a novel approach for filament recognition in fused filament fabrication (FFF) processes using a multi-spectral spectroscopy sensor module combined with machine learning techniques. …”
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RISKS REDUCING THROUGH INTELLIGENT HEADLIGHT MANAGEMENT: OPTIMIZING Q-LEARNING FOR ELECTRIC VEHICLES by Pitchaya Jamjuntr, Chanchai Techawatcharapaikul, Pannee Suanpang

“…Evaluation of the performance of the adaptive Q-learning system is presented in this study in terms of safety metrics such as visibility distance and energy efficiency indicators such as power consumption through comprehensive simulations across various turning scenarios. …”
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SynthSecureNet: An Improved Deep Learning Architecture with Application to Intelligent Violence Detection by Ntandoyenkosi Zungu, Peter Olukanmi, Pitshou Bokoro

“…We present a new deep learning architecture, named SynthSecureNet, which hybridizes two popular architectures: MobileNetV2 and ResNetV2. …”
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Optimal ACL Policy Placement in Hybrid SDN Networks: A Reinforcement Learning Approach by Wajid Ullah Khan, Nadir Shah, Gabriel-Miro Muntean, Haleem Farman, Moustafa M. Nasralla, Shan Ullah, Muhammad Shabir

“…This coexistence, however, presents significant challenges in network management and control, particularly in the efficient implementation of Access Control List (ACL) policies. …”
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Omega 3 and Spirulina maxima on learning and spatial memory in Rattus norvegicus var. Albinus by Juan Luis Rodríguez Vega, Richard Fredy García Ishimine, Jorge Luis Campos Reyna, Davis Alberto Mejías Pinedo, José Elías Cabrejo-Paredes, César Salvador Sánchez Marín, César Wilson Arellano Sánchez

“…Results: It was evidenced that all the experimental groups that were administered spirulina and omega-3 presented an improvement in the learning time or acquisition phase compared to the control group. …”
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Improving automated scoring of prosody in oral reading fluency using deep learning algorithm by Kuo Wang, Xin Qiao, George Sammit, Eric C. Larson, Joseph Nese, Akihito Kamata

“…Automated assessing prosody of oral reading fluency presents challenges due to the inherent difficulty of quantifying prosody. …”
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Pointing stabilization of a 1 Hz high-power laser via machine learning by Alessio Amodio, Dan Wang, Curtis Berger, Hai-En Tsai, Samuel K. Barber, Jeroen van Tilborg, Alexander Picksley, Zachary Eisentraut, Neel Rajeshbhai Vora, Mahek Logantha, Qing Ji, Russell Wilcox, Qiang Du, Anthony Gonsalves

“…Achieving this stability is especially challenging for the low-repetition-rate lasers in current LPAs. We present a machine learning method that predicts and corrects laser pointing instabilities in real-time using a high-frequency pilot beam. …”
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CLASEG: advanced multiclassification and segmentation for differential diagnosis of oral lesions using deep learning by Afnan Al-Ali, Ali Hamdi, Mohamed Elshrif, Keivin Isufaj, Khaled Shaban, Peter Chauvin, Sreenath Madathil, Ammar Daer, Faleh Tamimi, Raidan Ba-Hattab

“…Abstract Oral cancer has a high mortality rate primarily due to delayed diagnoses, highlighting the need for early detection of oral lesions. This study presents a novel deep learning framework for multi-class classification-based segmentation, enabling accurate differential diagnosis of 14 common oral lesions—benign, pre-malignant, and malignant—across various mouth locations using photographic images. …”
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Anomaly detection using machine learning and adopted digital twin concepts in radio environments by Mohamed Hussien Moharam, Omar Hany, Ahmed Hany, Amenah Mahmoud, Mariam Mohamed, Sohila Saeed

“…This study integrates machine learning with anomaly detection frameworks to enhance wireless network security. …”
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A multi-modal dental dataset for semi-supervised deep learning image segmentation by Yaqi Wang, Fan Ye, Yifei Chen, Chengkai Wang, Chengyu Wu, Feng Xu, Zhean Ma, Yi Liu, Yifan Zhang, Mingguo Cao, Xiaodiao Chen

“…Therefore, this paper presents a multimodal dataset for Semi-supervised Tooth Segmentation (STS-Tooth) in dental PXI and CBCT, named STS-2D-Tooth and STS-3D-Tooth. …”
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Transformer-Based Deep Learning for Mesoscale Eddy Detection in Sea Surface Temperature Maps by Chen Ji, Wenyang Xu, Xiangtian Zheng, Yasmeen Ahmed, Saad Ahmed Jamal, Fakhar Imam, Mohammed Saleh Ali Muthanna, Maha Ibrahim, Sajid Ullah, Dmitry E. Kucher

“…These eddies’ precise identification and categorization can improve climate modeling, ocean circulation research, and environmental surveillance. This study presents an innovative methodology for mesoscale eddy detection utilizing Transformer-based deep learning models, namely, Swin Transformer U-Net and SegFormer, to categorize ocean eddies from sea surface temperature (SST) maps sourced from the copernicus marine environment monitoring service. …”
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Machine Learning-Based Classification of Sulfide Mineral Spectral Emission in High Temperature Processes by Carlos Toro, Walter Díaz, Gonzalo Reyes, Miguel Peña, Nicolás Caselli, Carla Taramasco, Pablo Ormeño-Arriagada, Eduardo Balladares

“…Accurate classification of sulfide minerals during combustion is essential for optimizing pyrometallurgical processes such as flash smelting, where efficient combustion impacts resource utilization, energy efficiency, and emission control. This study presents a deep learning-based approach for classifying visible and near-infrared (VIS-NIR) emission spectra from the combustion of high-grade sulfide minerals. …”
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MultiSenseNet: Multi-Modal Deep Learning for Machine Failure Risk Prediction by Mostafijur Rahman, Md Sabbir Hossain, Uland Rozario, Satyabrata Roy, M. F. Mridha, Nilanjan Dey

“…It also excelled in precision, recall, F1-score, and AUC metrics compared to traditional machine learning models and recent deep learning architectures. …”
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Deep Learning Forecasting and Statistical Modeling for Q/V-Band LEO Satellite Channels by Bassel Al Homssi, Chiu C. Chan, Ke Wang, Wayne Rowe, Ben Allen, Ben Moores, Laszlo Csurgai-Horvath, Fernando Perez Fontan, Sithamparanathan Kandeepan, Akram Al-Hourani

“…Hence, radio channel forecasting is crucial for operators to adjust and maintain the link’s quality. This paper presents a practical approach for Q/V-band modeling for low Earth orbit satellite channels based on tools from machine learning and statistical modeling. …”
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Inverse Design of Plasmonic Nanostructures Using Machine Learning for Optimized Prediction of Physical Parameters by Luana S. P. Maia, Darlan A. Barroso, Aêdo B. Silveira, Waleska F. Oliveira, André Galembeck, Carlos Alexandre R. Fernandes, Dayse G. C. Bandeira, Benoit Cluzel, Auzuir R. Alexandria, Glendo F. Guimarães

“…In this case, Random Forest presented the best performance, with a lower risk of overfitting. …”
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Deep Learning-Enhanced Diagnosis of Sow Pregnancy Through Low-Frequency Ultrasound Imaging by Tae-kyeong Kim, Yo-Han Choi, Jun-Seon Hong, Hyun-Ju Park, Yong-Min Kim, Jo-Eun Kim, Ji-Hwan Lee, Soo-Jin Sa, Yong-Dae Jeong, Jin-Soo Kim, Hyun-chong Cho

“…This study introduces an innovative approach for sow pregnancy diagnosis using deep learning techniques to analyze low-frequency ultrasound images. …”
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Frontier machine learning techniques for melanoma skin cancer identification and categorization: An in-Depth review by Viomesh Singh, Kavita A. Sultanpure, Harshwardhan Patil

“…Machine learning is like the Sherlock Holmes of artificial intelligence. …”
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Optimizing Deep Learning Models for Fire Detection, Classification, and Segmentation Using Satellite Images by Abdallah Waleed Ali, Sefer Kurnaz

“…The findings underscore the significant potential of optimized machine learning approaches in predicting extreme events, such as wildfires, and improving fire management strategies. …”
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Machine-learning prediction models for any blood component transfusion in hospitalized dengue patients by Md. Shahid Ansari, Dinesh Jain, Sandeep Budhiraja

“…This study therefore investigated the risk factors, performance and effectiveness of eight different machine-learning algorithms to predict blood component transfusion requirements in confirmed dengue cases admitted to hospital. …”
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