Interpreting the CTCF-mediated sequence grammar of genome folding with AkitaV2. by Paulina N Smaruj, Fahad Kamulegeya, David R Kelley, Geoffrey Fudenberg

“…In sum, we present a framework for using neural network models to probe the sequences instructing genome folding and provide a number of predictions to guide future experimental inquiries.…”
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InGSA: integrating generalized self-attention in CNN for Alzheimer's disease classification by Faisal Binzagr, Anas W. Abulfaraj

“…To this end, the developed framework consists of a new contrast enhancement approach, named haze-reduced local-global (HRLG). …”
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Deep Learning-Based Navigation System for Automatic Landing Approach of Fixed-Wing UAVs in GNSS-Denied Environments by Ying-Xi Lin, Ying-Chih Lai

“…Based on a deep learning model framework, this study conducts experiments within the simulation environment, verifying system stability under various assumed conditions, thereby avoiding the risks associated with real-world testing. …”
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Machine learning-enabled multiscale modeling platform for damage sensing digital twin in piezoelectric composite structures by Somnath Ghosh, Saikat Dan, Preetam Tarafder

“…The PUCCDM model-simulated macroscopic electromechanical and damage fields, in conjunction with RAMPs, provide a comprehensive time-dependent dataset for a convolutional long-short-term memory (ConvLSTM) network to learn microstructure-dependent electrical and damage field correlations. …”
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A hybrid deep learning and differential evolution approach for accurate fake news detection by Shailendra Pratap Singh, Naween Kumar, Gyanendra Kumar, Ahamed Shafeeq B.M.

“…This study presents a novel hybrid approach combining deep learning techniques with Differential Evolution (DE) optimization to enhance the accuracy and scalability of fake news detection systems. The proposed framework integrates Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and attention mechanisms for robust feature extraction and classification. …”
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Multi-Function Working Mode Recognition Based on Multi-Feature Joint Learning by Lei Liu, Minghua Wu, Dongyang Cheng, Wei Wang

“…To address these challenges, this paper proposes a joint learning framework based on a hybrid model combining convolutional neural networks (CNNs) and Transformers for MFR working mode recognition. …”
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A one-stage anchor-free keypoints detection model for fast electric vehicle charging port detection and pose extraction by Feifei Hou, Qiwen Meng, Xinyu Fan, Yijun Wang

“…To address these issues, this study introduces FasterEVPoints, a state-of-the-art convolutional neural network (CNN) model integrating partial convolution (PConv) with FasterNet. …”
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A Combined Windowing and Deep Learning Model for the Classification of Brain Disorders Based on Electroencephalogram Signals by Dina Abooelzahab, Nawal Zaher, Abdel Hamid Soliman, Claude Chibelushi

“…Data selection employs a windowing technique, while the feature extraction and classification stages use a deep learning framework combining a convolutional neural network (CNN) and a Long Short-Term Memory (LSTM) network. …”
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Study on incentive mechanism of reward and punishment on work efficiency of PCB welder based on recurrence quantification analysis and electroencephalogram signals by Zhang Qian, Mingyue Guo, Fuwang Wang

“…This study not only confirms the feasibility of EEG in incentive evaluation but also addresses the insufficient sensitivity of traditional cognitive load monitoring by integrating RQA features and a dynamic classification framework, providing a quantifiable neuroscientific basis for optimizing enterprise incentive mechanisms.…”
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Mapping of soil sampling sites using terrain and hydrological attributes by Tan-Hanh Pham, Kristopher Osterloh, Kim-Doang Nguyen

“…This study introduces a deep learning-based tool that automates soil sampling site selection using spectral images. The proposed framework consists of two key components: an extractor and a predictor. …”
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AGU2-Net: Multi-Scale U²-Net Enhanced by Attention Gate Mechanism for Image Tampering Localization by Kefei Wu, Lin Li, Qingyan Li

“…Although deep learning algorithms based on convolutional neural networks (CNNs) have made notable progress in image forgery detection, they still face certain limitations in effectively detecting and localizing tampered areas due to the subtle nature of existing manipulation traces. …”
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Flexible integration of spatial and expression information for precise spot embedding via ZINB-based graph-enhanced autoencoder by Jiacheng Leng, Jiating Yu, Ling-Yun Wu, Hongyang Chen

“…To address these issues, we introduce Spot2vector, a computational framework that leverages a graph-enhanced autoencoder integrating zero-inflated negative binomial distribution modeling, combining both graph convolutional networks and graph attention networks to extract the latent embeddings of spots. …”
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Scalable 3D reconstruction for X-ray single particle imaging with online machine learning by Jay Shenoy, Axel Levy, Kartik Ayyer, Frédéric Poitevin, Gordon Wetzstein

“…Here, we introduce X-RAI (X-Ray single particle imaging with Amortized Inference), an online reconstruction framework that estimates the structure of 3D macromolecules from large X-ray single particle datasets. …”
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Towards Explainable Graph Embeddings for Gait Assessment Using Per-Cluster Dimensional Weighting by Chris Lochhead, Robert B. Fisher

“…The latent graph embeddings produced by this framework led to a novel semi-supervised weighting function which quantifies and ranks the most important joint features, which are used to provide a description for each pathology. …”
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Bridging the Gap Between Computational Efficiency and Segmentation Fidelity in Object-Based Image Analysis by Fernanda Pereira Leite Aguiar, Irenilza de Alencar Nääs, Marcelo Tsuguio Okano

“…This study presents a scalable and efficient framework designed to advance machine learning applications in complex image analysis tasks by incorporating methodologies for image quantization and automated segmentation.…”
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Neural Image Compression and Explanation by Xiang Li, Shihao Ji

“…In this article, we propose a novel end-to-end Neural Image Compression and Explanation (NICE) framework that learns to (1) explain the predictions of convolutional neural networks (CNNs), and (2) subsequently compress the input images for efficient storage or transmission. …”
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Physics-Informed Decoupled Calibration for Fourier Ptychographic Microscopy by Mingdi Liu, Junzhao Liang, Yanxiong Wu, Zicong Luo, Rui Xie, Jiaxiong Luo, Lisong Yan

“…To address this, we propose a physically decoupled correction framework integrating convolutional neural network (CNN), simulated annealing (SA) algorithms, and GPU parallel acceleration. …”
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Hybrid CNN–BiLSTM–DNN Approach for Detecting Cybersecurity Threats in IoT Networks by Bright Agbor Agbor, Bliss Utibe-Abasi Stephen, Philip Asuquo, Uduak Onofiok Luke, Victor Anaga

“…The model’s performance is evaluated using the IoT-23 and Edge-IIoTset datasets, which encompass over ten distinct attack types. The proposed framework achieves a remarkable 99% accuracy on both datasets, outperforming existing state-of-the-art IoT cybersecurity solutions. …”
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Data augmentation-assisted deep learning of hand-drawn partially colored sketches for visual search. by Jamil Ahmad, Khan Muhammad, Sung Wook Baik

“…To cope with these issues, we propose to fine-tune a deep convolutional neural network (CNN) using augmented dataset to extract features from partially colored hand-drawn sketches for query specification in a sketch-based image retrieval framework. …”
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Enhancing elderly care services through integrated sentiment analysis and knowledge reasoning: A deep learning approach by Yongguan Ai, Shiwei Chu, Juan Wang, Nianfang Xu

“…This study proposes a pioneering integrated care model for elderly care service robots that integrates sentiment analysis and knowledge reasoning through a deep learning framework. The primary objective of this research is to address the limitations of current elderly care robots in providing emotionally intelligent and personalized care. …”
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