EEG-Based ADHD Classification Using Autoencoder Feature Extraction and ResNet with Double Augmented Attention Mechanism by Jayoti Bansal, Gaurav Gangwar, Mohammad Aljaidi, Ali Alkoradees, Gagandeep Singh

“…With the use of innovative deep learning techniques, this research seeks to improve the diagnosis of ADHD using EEG data. …”
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A Novel Semi-Supervised Learning Method Based on Fast Search and Density Peaks by Fei Gao, Teng Huang, Jinping Sun, Amir Hussain, Erfu Yang, Huiyu Zhou

“…In addition, our algorithm is compared against several semi-supervised deep learning methods with similar structures. Experimental results demonstrate that the proposed algorithm has better stability than these methods.…”
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Design of an integrated model with temporal graph attention and transformer-augmented RNNs for enhanced anomaly detection by Sai Babu Veesam, Aravapalli Rama Satish, Sreenivasulu Tupakula, Yuvaraju Chinnam, Krishna Prakash, Shonak Bansal, Mohammad Rashed Iqbal Faruque

“…We introduce a new framework to address such challenges by incorporating state-of-the-art deep learning models that improve temporal and spatial context modeling. …”
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Abstractive Summarization of Historical Documents: A New Dataset and Novel Method Using a Domain-Specific Pretrained Model by Keerthana Murugaraj, Salima Lamsiyah, Christoph Schommer

“…Experimental results on our constructed dataset demonstrate that our HistBERTSum-Abs method outperforms recent state-of-the-art deep learning-based methods and achieves results comparable to state-of-the-art LLMs in zero-shot settings in terms of ROUGE-1, ROUGE-2, and ROUGE-L F1 scores. …”
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Multi-source meteorological data assessment on daily runoff simulation in the upper reaches of the Hei River, Northwest China by Huazhu Xue, Yaheng Wang, Guotao Dong, Chenchen Zhang, Yaokang Lian, Hui Wu

“…Study region: The upper reaches of the Hei River Basin, northwest China Study focus: To improve the accuracy and physical consistency of runoff simulations, as well as to compare the applicability of meteorological data obtained from multiple sources, this study integrates physical mechanisms with deep learning methods to construct a coupled model, HIMS-LSTM. …”
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An Enhanced Approach Using AGS Network for Skin Cancer Classification by Hwanyoung Lee, Seeun Cho, Jiyoon Song, Hoyoung Kim, Youjin Shin

“…It was evaluated using eight deep learning classifiers—GoogLeNet, DenseNet201, ResNet50, MobileNet V3, EfficientNet B0, ViT, EfficientNet V2, and Swin Transformers—on the HAM10000 dataset. …”
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Artificial Intelligence and Postpartum Hemorrhage by Sam J Mathewlynn, Mohammadreza Soltaninejad, Sally L Collins, Yang Pan

“…Recently, there has been a surge in interest in using artificial intelligence (AI), including machine learning and deep learning, across many areas of health care. This article explores the application of AI in tackling PPH, including predictive modeling and risk stratification. …”
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Modeling and designing enhancers by introducing and harnessing transcription factor binding units by Jiaqi Li, Pengcheng Zhang, Xi Xi, Liyang Liu, Lei Wei, Xiaowo Wang

“…Here we propose the concept of transcription factor binding unit (TFBU) to modularly model enhancers by quantifying the impact of context sequences surrounding TFBSs using deep learning models. Based on this concept, we develop DeepTFBU, a comprehensive toolkit for enhancer design. …”
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Benchmarking protein language models for protein crystallization by Raghvendra Mall, Rahul Kaushik, Zachary A. Martinez, Matt W. Thomson, Filippo Castiglione

“…Several in silico deep learning methods have been developed to overcome the high attrition rate, cost of experiments and extensive trial-and-error settings, for predicting the crystallization propensities of proteins based on their sequences. …”
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When Remote Sensing Meets Foundation Model: A Survey and Beyond by Chunlei Huo, Keming Chen, Shuaihao Zhang, Zeyu Wang, Heyu Yan, Jing Shen, Yuyang Hong, Geqi Qi, Hongmei Fang, Zihan Wang

“…Most deep-learning-based vision tasks rely heavily on crowd-labeled data, and a deep neural network (DNN) is usually impacted by the laborious and time-consuming labeling paradigm. …”
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Enhanced CATBraTS for Brain Tumour Semantic Segmentation by Rim El Badaoui, Ester Bonmati Coll, Alexandra Psarrou, Hykoush A. Asaturyan, Barbara Villarini

“…Automatic brain tumour segmentation tools in computer vision have integrated powerful deep learning architectures to enable accurate tumour boundary delineation. …”
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Medium-term load forecasting with Power Market Survey: GEPCO case study by Sheeza Mariam Nawaz, Ubaid Ahmed, Adil Amin, Syed Afraz Hussain Shah, Anzar Mahmood

“…The time horizon for MTLF ranges from a few weeks to one year and it has applications in energy management and planning. The deep-learning networks (DLNs), proposed in recent years, have a black-box nature, which reduces the interpretability of the results. …”
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OCT-based diagnosis of glaucoma and glaucoma stages using explainable machine learning by Md Mahmudul Hasan, Jack Phu, Henrietta Wang, Arcot Sowmya, Michael Kalloniatis, Erik Meijering

“…However, current automated diagnostic approaches on Glaucoma diagnosis solely rely on black-box deep learning models, lacking explainability and trustworthiness. …”
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A Study of Lithosphere–Ionosphere Seismic Precursors from Detecting Gamma-Ray and Total Electron Content Anomalies Prior to the 2018 M_L6.2 Hualien Earthquake in Eastern... by Ching-Chou Fu, Hau-Kun Jhuang, Yi-Ying Ho, Tsung-Che Tsai, Lou-Chuang Lee, Cheng-Horng Lin, Ching-Ren Lin, Vivek Walia, I-Te Lee

“…Positive and negative TEC anomalies were observed in Taiwan on 20–21 January and 5 February, respectively, and may be considered as ionospheric precursors to the earthquake. Additionally, deep-learning techniques applied to TEC data facilitate the detection of ionospheric precursors associated with the Hualien earthquake, enabling forecasts of an approaching seismic event. …”
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Seasonal Tree Height Dynamic Estimation Using Multi-source Remotely Sensed Data in Shenzhen by Hang Song, Xuemei Zhang, Ting Hu, Jinglei Liu, Bing Xu

“…By integrating these features with machine learning, deep learning, and optimization methods, we dynamically estimated tree heights in Shenzhen during summer and winter from 2018 to 2023 and validated seasonal and regional scalability. …”
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DGFEG: Dynamic Gate Fusion and Edge Graph Perception Network for Remote Sensing Change Detection by Shengning Zhou, Genji Yuan, Zhen Hua, Jinjiang Li

“…Benefiting from continuous innovations in deep learning algorithms, the accuracy of building change detection (BCD) in remote sensing (RS) has significantly improved. …”
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Multimodal heterogeneous graph fusion for automated obstructive sleep apnea-hypopnea syndrome diagnosis by Haoyu Wang, Xihe Qiu, Bin Li, Xiaoyu Tan, Jingjing Huang

“…It demonstrated superior diagnostic performance compared to conventional machine learning models and existing deep learning approaches, effectively integrating domain knowledge with data-driven learning to produce explainable representations and robust generalization capabilities, which can potentially be utilized for clinical use. …”
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A Study on the Differences in Optimized Inputs of Various Data-Driven Methods for Battery Capacity Prediction by Kuo Xin, Fu Jia, Byoungik Choi, Geesoo Lee

“…This paper extracts 11 types of lithium battery-related health features, and experiments are conducted on two traditional machine learning networks and three advanced deep learning networks in three aspects of input differences. …”
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ZooCNN: A Zero-Order Optimized Convolutional Neural Network for Pneumonia Classification Using Chest Radiographs by Saravana Kumar Ganesan, Parthasarathy Velusamy, Santhosh Rajendran, Ranjithkumar Sakthivel, Manikandan Bose, Baskaran Stephen Inbaraj

“…This article presents a Deep Learning model, Zero-Order Optimized Convolutional Neural Network (ZooCNN), a Zero-Order Optimization (Zoo)-based CNN model for classifying CXR images into three classes, Normal Lungs (NL), Bacterial Pneumonia (BP), and Viral Pneumonia (VP); this model utilizes the Adaptive Synthetic Sampling (ADASYN) approach to ensure class balance in the Kaggle CXR Images (Pneumonia) dataset. …”
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Spatio-Temporal Feature Aware Vision Transformers for Real-Time Unmanned Aerial Vehicle Tracking by Hao Zhang, Hengzhou Ye, Xiaoyu Guo, Xu Zhang, Yao Rong, Shuiwang Li

“…Our experimental results on several challenging benchmark datasets demonstrate that NT-Track surpasses existing lightweight and deep learning trackers in terms of precision and success rate. …”
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