DualGCN-GE: integration of spatiotemporal representations from whole-blood expression data with dual-view graph convolution network to identify Parkinson’s disease subtypes by Wei Zhang, Zeqi Xu, Ruochen Yu, Mingfeng Jiang, Qi Dai

“…This DualGCN-GE method has proposed dual-view graph convolution network(GCN) to integrate temporal and topological features underlying whole-blood expression data, thus detecting PD-PACE subtypes. …”
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AG-MS3D-CNN multiscale attention guided 3D convolutional neural network for robust brain tumor segmentation across MRI protocols by Umesh Kumar Lilhore, R. Sunder, Sarita Simaiya, Majed Alsafyani, M. D. Monish Khan, Roobaea Alroobaea, Hamed Alsufyani, Abdullah M. Baqasah

“…Recently, deep learning models, particularly Convolutional Neural Networks (CNNs), have shown great promise in automating this process. …”
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PIC2O-Sim: A physics-inspired causality-aware dynamic convolutional neural operator for ultra-fast photonic device time-domain simulation by Pingchuan Ma, Haoyu Yang, Zhengqi Gao, Duane S. Boning, Jiaqi Gu

“…In this work, we thoroughly investigate the synergy between neural operator designs and the physical property of Maxwell equations and introduce a physics-inspired AI-based FDTD prediction framework PIC2O-Sim. PIC2O-Sim features a causality-aware dynamic convolutional neural operator as its backbone model that honors the space–time causality constraints via careful receptive field configuration and explicitly captures the permittivity-dependent light propagation behavior via an efficient dynamic convolution operator. …”
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MOD3NN: A Framework for Automatic Signal Modulation Detection Using 3D CNN by Vishal Perekadan, Chaity Banerjee, Tathagata Mukherjee, Eduardo Pasiliao, Hovannes Kulhandjian, Michel Kulhandjian

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An Efficient and Low-Complexity Transformer-Based Deep Learning Framework for High-Dynamic-Range Image Reconstruction by Josue Lopez-Cabrejos, Thuanne Paixão, Ana Beatriz Alvarez, Diodomiro Baldomero Luque

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Hybrid deep learning framework for robust time-series classification: Integrating inception modules with residual networks by Duong Thi Kim Chi, Nguyen Thi Mai Trang, Tran Ba Minh Son, Nguyen Ngoc Thao, Thanh Q. Nguyen

“…In this study, we propose InceptionResNet, a hybrid deep learning framework that integrates the residual learning mechanism of ResNet into the InceptionTime architecture. …”
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A Hybrid CNN–BiLSTM Framework Optimized with Bayesian Search for Robust Android Malware Detection by Ibrahim Mutambik

“…These findings confirm the framework’s high efficiency, adaptability, and practical applicability, making it a compelling solution for Android malware detection in today’s evolving threat landscape.…”
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Study on Novel Surface Defect Detection Methods for Aeroengine Turbine Blades Based on the LFD-YOLO Framework by Wei Deng, Guixiong Liu, Jun Meng

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Enhancing Lidar-Based 3D Classification Through an Improved Deep Learning Framework With Residual Connections by Shih-Lin Lin, Jun-Yi Wu

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FROM PIXELS TO DIAGNOSIS: A DEEP LEARNING FRAMEWORK FOR HISTOPATHOLOGICAL IMAGE ANALYSIS IN CANINE TESTICULAR PATHOLOGY

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An ensemble deep learning framework for multi-class LncRNA subcellular localization with innovative encoding strategy by Wenxing Hu, Yan Yue, Ruomei Yan, Lixin Guan, Mengshan Li

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Recent Advancement in Postharvest Loss Mitigation and Quality Management of Fruits and Vegetables Using Machine Learning Frameworks by Abha Singh, Gayatri Vaidya, Vishal Jagota, Daniel Amoako Darko, Ravindra Kumar Agarwal, Sandip Debnath, Erich Potrich

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Advanced dynamic ensemble framework with explainability driven insights for precision brain tumor classification across datasets by Retinderdeep Singh, Sheifali Gupta, Ashraf Osman Ibrahim, Lubna A. Gabralla, Salil Bharany, Ateeq Ur Rehman, Seada Hussen

“…The proposed system integrates fine-tuned Convolutional Neural Network (CNN), ResNet-50 and EfficientNet-B5 to create a dynamic ensemble framework that addresses existing challenges. …”
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An Open-Source Deep Learning Framework for Scalable Urban Heat Island Detection Using Geospatial Data by M. Akintola, G. Neziri

“…The framework leverages a U-Net convolutional architecture with attention mechanisms to predict land surface temperature (LST) and delineate UHI hotspots. …”
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YOLOv8-BaitScan: A Lightweight and Robust Framework for Accurate Bait Detection and Counting in Aquaculture by Jian Li, Zehao Zhang, Yanan Wei, Tan Wang

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Enhanced prediction of ionic liquid toxicity using a meta-ensemble learning framework with data augmentation by Safa Sadaghiyanfam, Hiqmet Kamberaj, Yalcin Isler

“…The framework uses Recursive Feature Elimination for feature selection and GridSearchCV for tuning the best hyperparameters. …”
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A deep learning based framework for enhanced reference evapotranspiration estimation: evaluating accuracy and forecasting strategies by Suman Saurabh Sarkar, Jatin Bedi, Sushma Jain

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A Novel Multi-Objective Fuzzy Deep Learning Framework for Predictive Maintenance in Industrial Internet of Things by Jiangang Feng, Jicheng Kan

“…As industrial systems advance within the framework of Industry 4.0, predictive maintenance (PdM) has become essential to minimize downtime and improve operational efficiency in industrial Internet of Things (IIoT) environments. …”
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A Deep Learning-Based Diagnostic Framework for Shaft Earthing Brush Faults in Large Turbine Generators by Katudi Oupa Mailula, Akshay Kumar Saha

“…A key innovation lies in the use of FFT-derived spectrograms from both voltage and current waveforms as dual-channel inputs to the CNN, enabling automatic feature extraction of time–frequency patterns associated with different SEB fault types. The proposed framework combines advanced signal processing and convolutional neural networks (CNNs) to automatically recognize fault-related patterns in shaft grounding current and voltage signals. …”
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A hybrid framework for classifying and detecting aircraft cabin anomalous sounds under heavy background noise by Jiaan Chen, Tian Gan, Chenyi Zhao, Huayong Zhao, Ci Song, Xiaole Wang, Zhenyu Huang

“…Anomalous sounds produced in an aircraft cabin are early warnings of potential safety hazards, yet they are hard to label and detect when masked by heavy background noise. A two-stage hybrid framework is introduced to tackle both challenges. …”
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