Automated detection of sea cucumbers in turbid subtidal marine habitats: An explainable approach by Cheryl Chu, Yi-Fei Gu, Adrian Wong, Bayden D. Russell

“…Deposit-feeding sea cucumbers perform important ecosystem functions, enhancing nutrient cycling and maintaining ecosystem health. …”
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COVID-19 Diagnosis Using Spectral and Statistical Analysis of Cough Recordings Based on the Combination of SVD and DWT by Thabit Sultan Mohammed, Awni Ismail Sultan, Khattab M. Ali Alheeti, Karim Mohammed Aljebory, Hasan Ismail Sultan, Muzhir Shaban Al-Ani

“…Audio-based machine learning technologies can be adopted for automatic analysis of collected data. …”
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Feasibility of an interpreter-mediated neuropsychological test battery for trauma-affected refugees by Søren Kit Bothe, T. Rune Nielsen, Linda Nordin, Sabina Palic, Marie Høgh Thøgersen

“…Untreated cognitive impairment is likely to reduce the effectiveness of mental health interventions.Aim: To assess the operational and clinical feasibility of a neuropsychological test battery specifically developed for trauma-affected refugees from Syria.Method: A neuropsychological test battery was developed to assess executive function, mental speed, attention, and memory. The test battery was administered to 27 refugees from Syria recruited after being referred for specialized trauma treatment. …”
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Knowledge Improved Hybrid DNN–KAN Framework for Intrusion Detection in Wireless Sensor Networks by M. Sriraghavendra, Muna Elsadig, Ines Hilali Jaghdam, S. Abdel-Khalek, B. Galeebathullah, Salem Alkhalaf

“…By incorporating domain knowledge into the loss function, the framework reduces false positives and improves generalization, even with limited training data. …”
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Leveraging hybrid model of ConvNextBase and LightGBM for early ASD detection via eye-gaze analysis by Ranjeet Bidwe, Sashikala Mishra, Simi Bajaj, Ketan Kotecha

“…. • A LightGBM model performed classification using 3-fold cross-validation and found the best parameters for bagging_function, feature_fraction, max_depth, Number_of_leaves and learning_rate with values of 0.8, 0.8, −1, 31 and 0.1 respectively, to improve the model's robustness on unseen data.This proposed method is trained and tested on the publicly available Kaggle dataset, and results are benchmarked with other state-of-the-art methods. …”
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Identification and Characterization of the V(D)J Recombination Activating Gene 1 in Long-Term Memory of Context Fear Conditioning by Edgardo Castro-Pérez, Emilio Soto-Soto, Marizabeth Pérez-Carambot, Dawling Dionisio-Santos, Kristian Saied-Santiago, Humberto G. Ortiz-Zuazaga, Sandra Peña de Ortiz

“…Previous studies from our group suggested that factors known to function in DNA recombination/repair machineries, such as DNA ligases, polymerases, and DNA endonucleases, play a role in LTM. …”
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Lung Segmentation with Lightweight Convolutional Attention Residual U-Net by Meftahul Jannat, Shaikh Afnan Birahim, Mohammad Asif Hasan, Tonmoy Roy, Lubna Sultana, Hasan Sarker, Samia Fairuz, Hanaa A. Abdallah

“…The study indicates that the Dice loss function is more effective in achieving better results. …”
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Few-shot Remote Sensing Imagery Recognition with Compositionality Inductive Bias in Hierarchical Representation Space by Shichao Zhou, Zhuowei Wang, Zekai Zhang, Wenzheng Wang, Yingrui Zhao, Yunpu Zhang

“…We introduce a hierarchical context prediction mechanism for compositional representation learning, utilizing a predictive NCE loss function to encourage global remote sensing scenes to accurately predict similar local parts, and thus automatically inferring compositional representations of high-level but discriminative latent concepts. …”
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A multi-graph convolutional network method for Alzheimer’s disease diagnosis based on multi-frequency EEG data with dual-mode connectivity by Qingjie Xu, Qingjie Xu, Libing An, Haiqiang Yang, Haiqiang Yang, Keum-Shik Hong, Keum-Shik Hong

“…This study aims to address these limitations by developing a novel graph-based deep learning model that fully utilizes both functional and structural information from multi-frequency EEG data.MethodsThis paper introduces a Multi-Frequency EEG data-based Multi-Graph Convolutional Network (MF-MGCN) model for AD diagnosis. …”
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Transcranial direct current stimulation of cerebellum alters spiking precision in cerebellar cortex: A modeling study of cellular responses. by Xu Zhang, Roeland Hancock, Sabato Santaniello

“…Assessing the cellular response to tDCS is challenging because of the uneven, highly stratified cytoarchitecture of the cerebellum, within which cellular morphologies, physiological properties, and function vary largely across several types of neurons. …”
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Comparative analysis of RNN versus IIR digital filtering to optimize resilience to dynamic perturbations in pH sensing for vertical farming by Rolando Hinojosa‐Meza, Martín Montes Rivera, Paulino Vacas‐Jacques, Nivia Escalante‐Garcia, José Alonso Dena‐Aguilar, Aldonso Becerra Sanchez, Ernesto Olvera‐Gonzalez

“…In this work, we propose an advanced filtering scheme based on recurrent neural networks (RNNs) and deep learning to enable efficient control strategies for VF applications. …”
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Predicting disinfection by-products (DBPs) in supply water within a real water distribution network using an artificial neural network by F. Khan, M.F.R. Zuthi, M.S. Rahman, M.A. Akbor, M.D. Hossain, M.N.I. Bhuiyan

“…This study develops an artificial neural network (ANN) model using advanced learning algorithm techniques to predict disinfection by-products (DBPs) in supply water. …”
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TemDeep: a self-supervised framework for temporal downscaling of atmospheric fields at arbitrary time resolutions by L. Wang, Q. Li, Q. Li, Q. Lv, Q. Lv, X. Peng, X. Peng, W. You

“…In addition, to avoid generating abnormal values and to guide the model out of local optima, two regularization terms are integrated into the loss function to enforce spatial and temporal continuity, which further improves the performance by 7.6 %.…”
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An Omni-Dimensional Dynamic Convolutional Network for Single-Image Super-Resolution Tasks by Xi Chen, Ziang Wu, Weiping Zhang, Tingting Bi, Chunwei Tian

“…Additionally, we employ an improved residual network structure combined with a refined Charbonnier loss function to alleviate gradient vanishing and exploding to enhance the robustness of model training. …”
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Online Trajectory Regeneration for Multirotors via a Proportional-Derivative Physics-Informed Neural Network by Mana Ghanifar, Amir Ali Nikkhah, Milad Kamzan, Mohammad Teshnehlab, Morteza Tayefi

“…The PD-PINN incorporates proportional-derivative error terms into a physics-informed learning process, where the loss function explicitly embeds the system’s governing equations into the backpropagation algorithm. …”
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GS-DTA: integrating graph and sequence models for predicting drug-target binding affinity by Junwei Luo, Ziguang Zhu, Zhenhan Xu, Chuanle Xiao, Jingjing Wei, Jiquan Shen

“…GATv2-GCN enhances the model’s ability to focus on important nodes by assigning dynamic attention scores, which improves the learning of the graph structure’s intricate patterns. …”
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Shedding light on the DICER1 mutational spectrum of uncertain significance in malignant neoplasms by D. S. Bug, I. S. Moiseev, Yu. B. Porozov, Yu. B. Porozov, N. V. Petukhova

“…In addition to well-known hotspot mutations in RNAase III domains, DICER1 is characterized by a wide spectrum of variants in all the functional domains; most are of uncertain significance and unstated clinical effects. …”
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Quantum Variational Autoencoder Based on Weak Measurements With Fuzzy Filtering of Input Data by Vyacheslav Korolyov, Maksim Ogurtsov, Oleksandr Khodsinskyi

“…Computer simulations have shown that adapting the fuzzy membership function to the type of input data, increasing the number of latent variables, and selecting the learning rate of the neural network can improve the quality of the reconstruction of the input signal.…”
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Token-Mol 1.0: tokenized drug design with large language models by Jike Wang, Rui Qin, Mingyang Wang, Meijing Fang, Yangyang Zhang, Yuchen Zhu, Qun Su, Qiaolin Gou, Chao Shen, Odin Zhang, Zhenxing Wu, Dejun Jiang, Xujun Zhang, Huifeng Zhao, Jingxuan Ge, Zhourui Wu, Yu Kang, Chang-Yu Hsieh, Tingjun Hou

“…Built on a transformer decoder and trained with causal masking, Token-Mol introduces a Gaussian cross-entropy loss function tailored for regression tasks, enabling superior performance across multiple downstream applications. …”
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Detection of hydrophobicity grade of insulators based on AHC-YOLO algorithm by Shaotong Pei, Weiqi Wang, Peng Wu, Chenlong Hu, Haichao Sun, Keyu Li, Mianxiao Wu, Bo Lan

“…The algorithm integrates a high-performance GPU network (HGNetv2), a mixed local channel attention mechanism (MLCA), lightweight convolution (CSPPC), and the Inner-WIoU loss function, significantly reducing the network’s burden and improving the accuracy of recognizing composite insulator sheds and classifying their hydrophobicity levels. …”
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