Dual intent view contrastive learning for knowledge aware recommender systems by Jianhua Guo, Zhixiang Yin, Shuyang Feng, Donglin Yao, Shaopeng Liu

“…DIVCL employs a dual-view representation learning approach using Graph Neural Networks (GNNs), consisting of two distinct views: a local view based on the user-item interaction graph and a global view based on the user-item-entity knowledge graph. …”
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Automated strabismus detection and classification using deep learning analysis of facial images by Mahsa Yarkheir, Motahhareh Sadeghi, Hamed Azarnoush, Mohammad Reza Akbari, Elias Khalili Pour

“…The proposed methodology leverages Convolutional Neural Networks (CNNs) to achieve high accuracy in both binary (strabismus vs. normal) and multi-class (eight-class deviation angle for esotropia and exotropia) classification tasks. …”
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Gait Perception via Actual and Estimated Pneumatic Physical Reservoir Output by Junyi Shen, Tetsuro Miyazaki, Swaninda Ghosh, Toshihiro Kawase, Kenji Kawashima

“…Traditional approaches to gait perception—such as estimating joint angles and walking conditions—often rely on electronic sensors and neural networks, which can compromise wearability and impose high computational demands. …”
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Ulnar variance detection from radiographic images using deep learning by Sahar Nooh, Abdelrahim Koura, Mohammed Kayed

“…Advanced Convolutional Neural Networks are exploited instead of traditional manual methods. …”
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Feature Fusion Model Using Transfer Learning and Bidirectional Attention Mechanism for Plant Pipeline Leak Detection by Yujin Han, Yourak Choi, Jonghyuk Lee, Ji-Hoon Bae

“…In Stage 1, one-dimensional convolutional neural networks (1D CNNs) are pre-trained to extract root mean square (RMS) and frequency-domain features from acoustic signals. …”
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Attribute Selection Impact on Linear and Nonlinear Regression Models for Crop Yield Prediction by Alberto Gonzalez-Sanchez, Juan Frausto-Solis, Waldo Ojeda-Bustamante

“…Multiple linear regression, stepwise linear regression, M5′ regression trees, and artificial neural networks (ANN) were ranked. The models were built using real data of eight crops sowed in an irrigation module of Mexico. …”
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A comprehensive evaluation of ai techniques for air quality index prediction: RNNs and transformers by Pablo Andrés Buestán Andrade, Pedro Esteban Carrión Zamora, Anthony Eduardo Chamba Lara, Juan Pablo Pazmiño Piedra

“… This study evaluates the effectiveness of Recurrent Neural Networks (RNNs) and Transformer-based models in predicting the Air Quality Index (AQI). …”
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Machine learning models for predicting the bearing capacity of shallow foundations: A Comparative study and sensitivity analysis by Hamid Mohammadnezhad, Seyedmohammad Eslami

“…In this study, classic machine learning regression methods such as KNN, SVM and Decision Tree based models alongside the utilization of Artificial Neural Networks (ANN) regression are examined and compelling results are demonstrated. …”
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Facial masks and soft‐biometrics: Leveraging face recognition CNNs for age and gender prediction on mobile ocular images by Fernando Alonso‐Fernandez, Kevin Hernandez‐Diaz, Silvia Ramis, Francisco J. Perales, Josef Bigun

“…However, state‐of‐the‐art solutions in related tasks such as identity or expression recognition employ large Convolutional Neural Networks, whose use in mobile devices is infeasible due to hardware limitations and size restrictions of downloadable applications. …”
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Vegetation Classification in a Mountain–Plain Transition Zone in the Sichuan Basin, China by Wenqian Bai, Zhengwei He, Yan Tan, Guy M. Robinson, Tingyu Zhang, Xueman Wang, Li He, Linlong Li, Shuang Wu

“…By combining feature sets and applying feature elimination algorithms, the classification performance of one-dimensional convolutional neural networks (1D-CNNs), Random Forest (RF), and Multilayer Perceptron (MLP) was evaluated to determine the optimal feature combinations and methods. …”
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Enhanced Adaptive Neural-Fuzzy Inference System for Dynamic Time Series Prediction Using Self-Feedback and Hybrid Training by Andrew Topper, Honglei Yao

“…Predicting time series, especially those originating from chaotic and nonlinear dynamic systems, is a critical research area with broad applications across various fields. Neural networks and fuzzy systems have emerged as leading methods for forecasting chaotic time series. …”
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Humatch - fast, gene-specific joint humanisation of antibody heavy and light chains by Lewis Chinery, Jeliazko R. Jeliazkov, Charlotte M. Deane

“…Humatch consists of three lightweight Convolutional Neural Networks (CNNs) trained to identify human heavy V-genes, light V-genes, and well-paired antibody sequences with near-perfect accuracy. …”
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A Hybrid Prognostic Approach for Remaining Useful Life Prediction of Lithium-Ion Batteries by Wen-An Yang, Maohua Xiao, Wei Zhou, Yu Guo, Wenhe Liao

“…Empirical comparisons show that the proposed hybrid prognostic approach using the selective kernel ensemble-based RVM learning algorithm performs better than the hybrid prognostic approaches using the popular learning algorithms of feedforward artificial neural networks (ANNs) like the conventional backpropagation (BP) algorithm and support vector machines (SVMs). …”
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Learning Power Systems Waveform Incipient Patterns Through Few-Shot Meta-Learning by Lixian Shi, Qiushi Cui, Yang Weng, Yigong Zhang, Shilong Chen, Jian Li, Wenyuan Li

“…Finally, an IF detection model based on convolutional neural networks (CNNs) is obtained through the fine-tuning process. …”
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Advanced Air Quality Forecasting Using an Enhanced Temporal Attention-Driven Graph Convolutional Long Short-Term Memory Model With Seasonal-Trend Decomposition by Yuvaraja Boddu, A. Manimaran, B. Arunkumar, M. Sucharitha, J. Suresh Babu

“…Future research will focus on enhancing the model’s adaptability through multi-scale spatial attention mechanisms and exploring hybrid architectures with Graph Neural Networks (GNNs) to further refine its applicability in real-time air quality forecasting scenarios. …”
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Effects of Subthalamic Nucleus Deep Brain Stimulation on Facial Emotion Recognition in Parkinson’s Disease: A Critical Literature Review by S. Kalampokini, E. Lyros, P. Lochner, K. Fassbender, M. M. Unger

“…FER worsening after STN DBS can be attributed to the functional role of the STN in limbic circuits and the interference of STN stimulation with neural networks involved in FER, including the connections of the STN with the limbic part of the basal ganglia and pre- and frontal areas. …”
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PhotoD with LSST: Stellar Photometric Distances Out to the Edge of the Galaxy by Lovro Palaversa, Željko Ivezić, Neven Caplar, Karlo Mrakovčić, Bob Abel, Oleksandra Razim, Filip Matković, Connor Yablonski, Toni Šarić, Tomislav Jurkić, Sandro Campos, Melissa DeLucchi, Derek Jones, Konstantin Malanchev, Alex I. Malz, Sean McGuire, Mario Jurić

“…Mrakovčić et al. how to utilize neural networks to accelerate this performance by up to an order of magnitude. …”
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Comparative use of different AI methods for the prediction of concrete compressive strength by Mouhamadou Amar

“…The simulations used artificial neural networks or deep learning, generalized linear, decision tree, random forest, support vector machine, and gradient-boosted tree models to predict the compressive strength of 8 concrete mix designs containing different SCMs. …”
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Classification Performance Comparison of BERT and IndoBERT on SelfReport of COVID-19 Status on Social Media by Irwan Budiman, Mohammad Reza Faisal, Astina Faridhah, Andi Farmadi, Muhammad Itqan Mazdadi, Triando Hamonangan Saragih, Friska Abadi

“…Many deep learning-based algorithms, such as Convolutional Neural Networks (CNN) or Long Short-Term Memory (LSTM), have been used for text classification. …”
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Design optimization of the podded propulsor considering pod housing drag and propulsion motor performance by Yuhang Zhou, Chengjiang Liu, Yao Yan, Quan Chen, Wenliang Ke

“…The Latin hypercube sampling method generated samples of the motor and pod housing for simulation, and radial basis function neural networks were used to create approximate models for the motor performance and pod housing drag. …”
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