A comprehensive model for concrete strength prediction using advanced learning techniques by Sagar Dhengare, Udaykumar Waghe, Ganesh Yenurkar, Anjana Shyamala

“…This study proposes a novel hybrid machine learning framework to predict the power of eco-friendly concrete containing eco-friendly concrete, copper slag and eggshell powder as partial cement replacement. …”
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BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification by Anderson P. Avila Santos, Breno L. S. de Almeida, Robson P. Bonidia, Peter F. Stadler, Polonca Stefanic, Ines Mandic-Mulec, Ulisses Rocha, Danilo S. Sanches, André C.P.L.F. de Carvalho

“…This study presents BioDeepFuse, a hybrid deep learning framework integrating convolutional neural networks (CNN) or bidirectional long short-term memory (BiLSTM) networks with handcrafted features for enhanced accuracy. …”
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LocPro: A deep learning-based prediction of protein subcellular localization for promoting multi-directional pharmaceutical research by Yintao Zhang, Lingyan Zheng, Nanxin You, Wei Hu, Wanghao Jiang, Mingkun Lu, Hangwei Xu, Haibin Dai, Tingting Fu, Ying Zhou

“…Specifically, LocPro is unique in (a) combining protein representations from the pre-trained large language model (LLM) ESM2 and the expert-driven tool PROFEAT, (b) implementing a hybrid deep neural network architecture that integrates convolutional neural network (CNN), fully connected (FC) layer, and bidirectional long short-term memory (BiLSTM) blocks, and (c) developing a multi-label framework for predicting protein subcellular localization at multiple granularity levels. …”
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Integrated pixel-level crack detection and quantification using an ensemble of advanced U-Net architectures by Rakshitha R, Srinath S, N Vinay Kumar, Rashmi S, Poornima B V

“…This study introduces a hybrid framework that combines convolutional and transformer-based architectures, leveraging their strengths to achieve reliable crack segmentation and pixel-level quantification. …”
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MFEM-CIN: A Lightweight Architecture Combining CNN and Transformer for the Classification of Pre-Cancerous Lesions of the Cervix by Peng Chen, Fobao Liu, Jun Zhang, Bing Wang

“…The core of the framework is the MFEM-CIN hybrid model, which combines Convolutional Neural Networks (CNN) and Transformer to aggregate the correlation between local and global features. …”
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Severity Classification of Parkinson’s Disease via Synthesis of Energy Skeleton Images from Videos Produced in Uncontrolled Environments by Nejib Ben Hadj-Alouane, Arav Dhoot, Monia Turki-Hadj Alouane, Vinod Pangracious

“…This paper proposes a novel framework for the diagnosis and severity classification of PD using video data captured in uncontrolled environments. …”
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Anatomy-Informed Multimodal Learning for Myocardial Infarction Prediction by Ivan-Daniel Sievering, Ortal Senouf, Thabo Mahendiran, David Nanchen, Stephane Fournier, Olivier Muller, Pascal Frossard, Emmanuel Abbe, Dorina Thanou

“…In this work, we propose a novel anatomy-informed multimodal deep learning framework to predict future MI from clinical data and Invasive Coronary Angiography (ICA) images. …”
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YUNet_LLMClaimReport: An Enhanced Automobile Insurance Fraud Detection and Automated Claim Report Generation Using Large Language Models by P. Anand Kumar, S. Sountharrajan

“…In this research, YUNet_LLMClaimReport, a new framework is proposed that combines YOLOv11, U-Net, and a fine-tuned GPT-3.5-turbo large language model to automatically generate claim reports based on the detections and segmentation. …”
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WRRT-DETR: Weather-Robust RT-DETR for Drone-View Object Detection in Adverse Weather by Bei Liu, Jiangliang Jin, Yihong Zhang, Chen Sun

“…To address the challenges of scale variation and visual degradation introduced by harsh weather, we propose WRRT-DETR, a weather-robust object detection framework optimized for small objects. Within this framework, we design a gated single-head global–local attention backbone block (GLCE) to fuse local convolutional features with global attention, enhancing small object distinguishability. …”
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Constraint-aware wind power forecasting with an optimized hybrid machine learning model by Md. Omer Faruque, Md. Alamgir Hossain, S.M. Mahfuz Alam, Muhammad Khalid

“…In response, this paper introduces a novel constraint aware forecasting framework formed by a convolutional neural network (CNN) integrated with a double layer of gated recurrent unit (GRU) and fully connected layers. …”
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Integrating UAV-Based Multispectral Data and Transfer Learning for Soil Moisture Prediction in the Black Soil Region of Northeast China by Tong Zhou, Shoutian Ma, Tianyu Liu, Shuihong Yao, Shenglin Li, Yang Gao

“…This framework involves pre-training a model on a source domain and fine-tuning it with a limited set of target domain samples to achieve high-precision SM inversion. …”
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Utilizing deep learning for intelligent monitoring and early warning of slope disasters in public space design by Wang Ting, Ying Wang

“…The integration of neural network-based prediction with adaptive spatial planning enhances both the precision and timeliness of disaster warnings.DiscussionThis framework offers a transformative, safe, and functional approach to slope disaster management in dynamic public spaces. …”
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Graph Neural Network With Hessian-Based Locally Linear Embedding for Cancer Metastasis Analysis in Lymph Nodes Using DeepLab Segmentation by Senthil Jayapal, R. Annamalai

“…The proposed framework integrates DeepLab, a highly advanced convolutional neural network for image segmentation, to segment the lymph node regions from high-resolution MRI and PET scans. …”
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Resolution-Aware Deep Learning with Feature Space Optimization for Reliable Identity Verification in Electronic Know Your Customer Processes by Mahasak Ketcham, Pongsarun Boonyopakorn, Thittaporn Ganokratanaa

“…To address these challenges, this study proposes a novel and robust identity verification framework that integrates super-resolution preprocessing, a convolutional neural network (CNN), and Monte Carlo dropout-based Bayesian uncertainty estimation for enhanced facial recognition in electronic know your customer (e-KYC) processes. …”
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MedAlmighty: enhancing disease diagnosis with large vision model distillation by Yajing Ren, Zheng Gu, Wen Liu

“…In this approach, we utilize DINOv2—a pre-trained large vision model—as a frozen teacher, and a lightweight convolutional neural network (CNN) as the trainable student. …”
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Urban Functional Zone Mapping by Integrating Multi-Source Data and Spatial Relationship Characteristics by Daoyou Zhu, Xu Dang, Wenjia Shi, Yixiang Chen, Wenmei Li

“…This framework leverages the OpenStreetMap (OSM) road network to partition the study area into functional units, employs a graph model to represent urban functional nodes and their intricate spatial topological relationships, and harnesses the capabilities of Graph Convolutional Network (GCN) to fuse these multi-dimensional features through end-to-end learning for accurate urban function discrimination. …”
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Temporal Dynamics in Short Text Classification: Enhancing Semantic Understanding Through Time-Aware Model by Khaled Abdalgader, Atheer A. Matroud, Ghaleb Al-Doboni

“…These results underscore the potential of the proposed framework to provide deeper insights into the evolving nature of language and its impact on short-text classification. …”
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Educational Psychology Analysis Method for Extracting Students’ Facial Information Based on Image Big Data by Maoyue Zhang

“…The image data type and input and output format based on MapReduce framework are designed, and the image data is optimized by means of serialized files. …”
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Intelligent data-driven system for mold manufacturing using reinforcement learning and knowledge graph personalized optimization for customized production by Chengcai He, Jiaxing Deng, Jingchun Wu, Beicheng Qin, Jinxiang Chen, Yan Li, Qiangsheng Huang

“…The experimental results reveal two key findings: (1) Within the enhanced learning knowledge graph framework, the algorithm—optimized using a graph convolutional network—achieves consistently higher qualification rates across test samples. …”
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MSDCA: A Multi-Scale Dual-Branch Network with Enhanced Cross-Attention for Hyperspectral Image Classification by Ning Jiang, Shengling Geng, Yuhui Zheng, Le Sun

“…To address these limitations, we propose a dual-branch deep learning framework named MSDCA, which performs spatial–spectral joint modeling under limited supervision. …”
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