Prediction of seepage flow through earthfill dams using machine learning models by Issam Rehamnia, Ahmed Mohammed Sami Al-Janabi, Saad Sh. Sammen, Binh Thai Pham, Indra Prakash

“…In this study, three machine learning models, namely, the Multilayer Perceptron Neural Networks (MLPNN), the Generalized Regression Neural Networks (GRNN) and the Radial Basis Function Neural Networks (RBFNN) were used for predicting seepage flow through an earthfill dam. …”
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Comparative Study of Person Re-Identification Techniques Based on Deep Learning Models by Mossaab Idrissi Alami, Abderrahmane Ez-zahout, Fouzia Omary

“…This study explores deep metric learning models, specifically Siamese and Triplet networks, to improve Re-ID performance. …”
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CYCLONE: recycle contrastive learning for integrating single-cell gene expression data by Han Ji, Xinwei He, Hongwei Li

“…Abstract Background Combining single-cell transcriptome sequencing results from several batches reduces batch effect, which improves our understanding of cellular identity and function. Results This paper introduces CYCLONE, a new method for integrating single-cell gene expression data using a recycle contrastive learning network. …”
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Self-Supervised Spatiotemporal Representation Learning for Skeleton-Based Human Action Recognition by Jinhyeok Park, Seoung Bum Kim

“…To further enhance the learning process, the loss function is redefined using a cross-correlation matrix, introducing a non-contrastive SSL approach. …”
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The Implementation of Eclectic Methods in Arabic Learning Based on All in One System Approach by Zulfa Amalia Wahidah, Umi Baroroh, Abdur Rasheed-Mahmoud Mukadam

“…Each method has different function and characteristic. One method is not enough to achieve four language skills in Arabic language learning.  …”
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Brain Tumor Identification and Classification of MRI Images Using Deep Learning Techniques by Zheshu Jia, Deyun Chen

“…The segmenting function is distinguished by a high level of uniformity between anatomy and the neighboring brain tissue. …”
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Distributed Deep Reinforcement Learning Via Split Computing For Connected Autonomous Vehicles by Rauch Robert, Gazda Juraj

“…While this approach has been explored in computer vision, it remains largely unexplored for reinforcement learning scenarios. We introduce a novel autoencoder trained directly through Deep Q-Network (DQN) rewards, wherein we optimize autoencoder layers using the DQN reward function while maintaining all other layers frozen. …”
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The neuroscientific basis of flow: Learning progress guides task engagement and cognitive control by Hairong Lu, Dimitri Van der Linden, Arnold B. Bakker

“…Results showed that task engagement, indicated by feelings of flow and low distractibility, is a function of learning progress. Electroencephalography data further revealed that learning progress is associated with enhanced proactive preparation (e.g., reduced pre-stimulus contingent negativity variance and parietal alpha desynchronization) and improved feedback processing (e.g., increased P3b amplitude and parietal alpha desynchronization). …”
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Comparison of various machine learning regression models based on Human age prediction by Dr.Manaf K Hussein

“…In this study, five widely used machine learning  regression models (Linear support vector regression (L-SVR), radial basis function support vector regression (RBF-SVR), relevance vector regression (RVR), Elastic Net and Gaussian process regression (GPR)) were trained and evaluated to predict brain age using volumes of brain regions data. …”
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Formal Verification of Spatio-Temporal Rules Guided Safe Reinforcement Learning for CPS by YIN Chan, ZHU Yi, WANG Jinyong, CHEN Xiaoying, HAO Guosheng

“…However, when facing an unknown environment and dealing with complex tasks, deep reinforcement learning based on black boxes cannot guarantee the security of the system and the interpretability of reward function settings. …”
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Data-driven protease engineering by DNA-recording and epistasis-aware machine learning by Lukas Huber, Tim Kucera, Simon Höllerer, Karsten Borgwardt, Sven Panke, Markus Jeschek

“…Abstract Protein engineering has recently seen tremendous transformation due to machine learning (ML) tools that predict structure from sequence at unprecedented precision. …”
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Dynamic Tuning and Multi-Task Learning-Based Model for Multimodal Sentiment Analysis by Yi Liang, Turdi Tohti, Wenpeng Hu, Bo Kong, Dongfang Han, Tianwei Yan, Askar Hamdulla

“…However, previous studies have focused on optimizing model architecture, neglecting the impact of objective function settings on model performance. Given this, this study introduces a new framework, DMMSA, which utilizes the intrinsic correlation of sentiment signals and enhances the model’s understanding of complex sentiments. …”
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Assisted Diagnosis of Alzheimer’s Disease Based on Deep Learning and Multimodal Feature Fusion by Yu Wang, Xi Liu, Chongchong Yu

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Development of a Neuroevolution Machine Learning Potential of Al-Cu-Li Alloys by Fei Chen, Han Wang, Yanan Jiang, Lihua Zhan, Youliang Yang

“…The results obtained from this potential function are compared with those from Density Functional Theory (DFT) calculations, with training errors of 2.1 meV/atom for energy, 47.4 meV/Å for force, and 14.8 meV/atom for virial, demonstrating high training accuracy. …”
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Fault Diagnosis of Rolling Bearing Based on Modified Deep Metric Learning Method by Zengbing Xu, Xiaojuan Li, Hui Lin, Zhigang Wang, Tao Peng

“…In order to solve the misclassification caused by the traditional deep metric learning based on distance metric function, a similarity criterion based on Yu norm is introduced into the traditional deep metric learning. …”
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Enhancing Mathematical Education Through Mobile Learning: A Problem-Based Approach by Javier Martínez-Gómez, Juan Francisco Nicolalde

“…The present study examines the intermediary function of a mobile education application, conceived under the problem-based learning approach, in the field of mathematics. …”
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Fish Disease Detection Using Image Based Machine Learning Technique in Aquaculture by Md Shoaib Ahmed, Tanjim Taharat Aurpa, Md. Abul Kalam Azad

“…In the second portion, we extract the involved features to classify the diseases with the help of the Support Vector Machine (SVM) algorithm of machine learning with a kernel function. The processed images of the first portion have passed through this (SVM) model. …”
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An Enhanced Comprehensive Learning Particle Swarm Optimizer with the Elite-Based Dominance Scheme by Chengcheng Chen, Xianchang Wang, Helong Yu, Nannan Zhao, Mingjing Wang, Huiling Chen

“…When it comes to the particle swarm optimization (PSO), the comprehensive learning PSO (CLPSO) is a well-established evolutionary algorithm that introduces a comprehensive learning strategy (CLS), which effectively boosts the efficacy of the PSO. …”
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Machine learning analysis of sex and menopausal differences in the gut microbiome in the HELIUS study by Esther M. C. Vriend, Henrike Galenkamp, Hilde Herrema, Max Nieuwdorp, Bert-Jan H. van den Born, Barbara J. H. Verhaar

“…Since some of sex differences in gut microbiome composition and function could not be explained by covariates, we recommend sex stratification in future microbiome studies.…”
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Semi-Supervised Learned Autoencoder for Classification of Events in Distributed Fibre Acoustic Sensors by Artem Kozmin, Oleg Kalashev, Alexey Chernenko, Alexey Redyuk

“…The integrated loss function incorporates elements from both the autoencoder and the classifier, guiding the autoencoder to extract features relevant for accurate event classification. …”
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