EPI-DynFusion: enhancer-promoter interaction prediction model based on sequence features and dynamic fusion mechanisms by Ao Zhang, Jianhua Jia, Mingwei Sun, Xin Wei

“…Furthermore, we incorporate the Convolutional Block Attention Module (CBAM) to enhance the model’s ability to focus on informative regions. …”
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GNODEVAE: a graph-based ODE-VAE enhances clustering for single-cell data by Zeyu Fu, Chunlin Chen, Song Wang, Junping Wang, Shilei Chen

“…Through systematic evaluation across 10 graph convolutional layers, GAT demonstrated optimal performance, achieving average ARI advantages of 0.108 and 0.112 over alternative graph convolutional layers in VGAE and GNODEVAE architectures respectively, along with ASW advantages of 0.047 and 0.098. …”
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A multimodal educational robots driven via dynamic attention by An Jianliang, An Jianliang

“…In addition, the model integrates a VGG19-based convolutional network for image feature extraction and utilizes a dynamic attention mechanism to dynamically focus on relevant parts of multimodal inputs. …”
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Bayesian optimization of biodegradable polymers via machine learning driven features from low-field NMR data by Ryo Fujita, Yoshifumi Amamoto, Jun Kikuchi

“…This might be potentially insightful for the feasibility of a framework to accelerate polymer development through low-field NMR with minimal property data.…”
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Improved Collaborative Representation Classifier Based on l2-Regularized for Human Action Recognition by Shirui Huo, Tianrui Hu, Ce Li

“…In this paper, a unified improved collaborative representation framework is proposed in which the probability that a test sample belongs to the collaborative subspace of all classes can be well defined and calculated. …”
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Advancing precision dentistry: the integration of multi-omics and cutting-edge imaging technologies—a systematic review by Neelam Das

“…A total of 50 studies published between 2015 and 2024 were selected using a PICOS framework. Analytical tools included meta-analysis (Forest and Funnel plots), risk of bias assessment, VOS viewer-based bibliometric mapping, and GRADE evidence grading.ResultsMulti-omics approaches revealed key biomarkers such as TP53, IL-1, and MMPs in early diagnosis. …”
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Enhancing Malware Detection via RGB Assembly Visualization and Hybrid Deep Learning Models by Esra Eroğlu Demirkan, Murat Aydos

“…This research introduces a novel image-based malware classification framework that uses hybrid-model Convolutional Neural Networks to process RGB images generated from assembly code. …”
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Improving Climate Bias and Variability via CNN‐Based State‐Dependent Model‐Error Corrections by William E. Chapman, Judith Berner

“…Abstract We develop an approach to correct biases in the atmospheric component of the Community Earth System Model using convolutional neural networks (CNNs) to create a corrective model parameterization for online bias reduction. …”
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Machine Learning-Driven D-Glucose Prediction Using a Novel Biosensor for Non-Invasive Diabetes Management by Pardis Sadeghi, Shahriar Noroozizadeh, Rania Alshawabkeh, Nian Xiang Sun

“…Advanced models, such as Convolutional Neural Networks and Recurrent Neural Networks, were used to analyze resistance signals, while classical algorithms served as benchmarks. …”
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Comparison of ResNet-50, EfficientNet-B1, and VGG-16 Algorithms for Cataract Eye Image Classification by Ilham Santoso, Ayub Michaelangelo Manurung, Egia Rosi Subhiyakto

“…This study contributes significantly to the selection of robust models for building an automated cataract detection framework.…”
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TACO: Adversarial Camouflage Optimization on Trucks to Fool Object Detectors by Adonisz Dimitriu, Tamás Vilmos Michaletzky, Viktor Remeli

“…We present Truck Adversarial Camouflage Optimization (TACO), a novel framework that generates adversarial camouflage patterns on 3D vehicle models to deceive state-of-the-art object detectors. …”
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DRSegNet: A cutting-edge approach to Diabetic Retinopathy segmentation and classification using parameter-aware Nature-Inspired optimization. by Sundreen Asad Kamal, Youtian Du, Majdi Khalid, Majed Farrash, Sahraoui Dhelim

“…This is why the outcomes of the presented study can be viewed as promising in terms of the further development of the proposed approach for DR diagnosis, as well as in creating a new reference point within the framework of medical image analysis and providing more effective and timely treatments.…”
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An Ultra-Precision Smoothing Polishing Model for Optical Surface Fabrication with Morphology Gradient Awareness by Guohao Liu, Yonghong Deng, Zhibin Li

“…To improve the surface morphology quality of ultra-precision optical components, particularly in the suppression of mid-spatial frequency (MSF) errors, this paper proposes a morphology gradient-aware spatiotemporal coupled smoothing model based on convolutional material removal. By introducing the Laplacian curvature into the surface evolution framework, a curvature-sensitive “peak-priority” mechanism is established to dynamically guide the local dwell time. …”
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YOLOP-MVF: A Multi-Task Autonomous Driving Perception Detection Method Based on Multi Scale Feature Weighted Fusion by Yanqiu Niu, Jing Zhang

“…To address challenges such as large-scale variations, background interference, and occlusions in multi-task autonomous driving perception, this paper proposes YOLOP-MVF, a multi-task detection framework based on multi-scale feature weighting fusion. …”
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Deep learning-driven medical image analysis for computational material science applications by Li Lu, Mingpei Liang

“…Conventional machine learning approaches struggle with data heterogeneity and the need for extensive labeled datasets.MethodsTo overcome these limitations, we propose a deep learning-driven framework that integrates convolutional neural networks (CNNs) with transformer-based architectures for enhanced feature representation. …”
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Query-Based Instance Segmentation with Dual Attention Transformer for Autonomous Vehicles by Aya Taourirte, Li-Hong Juang

“…To address these challenges, we propose an enhanced QueryInst-based instance segmentation framework. First, we replace the traditional CNN backbone with the DaViT Transformer to extract richer, multi-scale features. …”
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Contrastive learning method for leak detection in water distribution networks by Rongsheng Liu, Tarek Zayed, Rui Xiao

“…The out-of-sample validation results indicate that the proposed leak detection model is robust and effective in unexplored pipelines. The proposed framework significantly advances ML-based leak detection research and supports sustainable water management practices.…”
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A Novel Deeply-Learned Image Quality Analysis Algorithm for Clustering by Zhongzhe Chen, Xing Gao

“…Addressing the limitations of existing deep clustering methods, which struggle with variations in image size and quality and are vulnerable to data noise and model deviations, we propose a deeply-learned clustering paradigm in an unsupervised context. This framework utilizes a multi-layer deep architecture, in which the standard fully-linked layers are replaced by the deep convolutional ones in order to intelligently calculate semantic visual representations. …”
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A Novel Deep Learning Model for Human Skeleton Estimation Using FMCW Radar by Parma Hadi Rantelinggi, Xintong Shi, Mondher Bouazizi, Tomoaki Ohtsuki

“…To address this challenge, we propose a novel deep learning framework integrating convolutional neural networks (CNNs), multi-head transformers, and Bi-LSTM networks to enhance spatiotemporal feature representations. …”
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A Comparative Analysis of Deep Learning Models for Prediction of Microsatellite Instability in Colorectal Cancer by Ziynet Pamuk, Hüseyin Erikçi

“…This study proposes a deep learning-based model for predicting microsatellite instability (MSI) in colorectal cancer using hematoxylin and eosin (H&E)-stained histopathological tissue slides. A classification framework was constructed using convolutional neural networks (CNN) and optimized through transfer learning techniques. …”
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