Cross-Visual Style Change Detection for Remote Sensing Images via Representation Consistency Deep Supervised Learning by Jinjiang Wei, Kaimin Sun, Wenzhuo Li, Wangbin Li, Song Gao, Shunxia Miao, Yingjiao Tan, Wei Cui, Yu Duan

“…To address these limitations, we propose Representation Consistency Change Detection (RCCD), a novel deep learning framework that enforces global style and local spatial consistency of features across encoding and decoding stages for robust cross-visual style change detection. …”
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A hybrid learning network with progressive resizing and PCA for diagnosis of cervical cancer on WSI slides by Nitin Kumar Chauhan, Krishna Singh, Amit Kumar, Ashutosh Mishra, Sachin Kumar Gupta, Shubham Mahajan, Seifedine Kadry, Jungeun Kim

“…The accuracy of the suggested framework on SIPaKMeD data is 99.29% for two-class classification and 98.47% for five-class classification. …”
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Dual-Domain deep prior guided sparse-view CT reconstruction with multi-scale fusion attention by Jia Wu, Jinzhao Lin, Xiaoming Jiang, Wei Zheng, Lisha Zhong, Yu Pang, Hongying Meng, Zhangyong Li

“…However, existing methods often neglect projection data constraints and rely heavily on convolutional neural networks, resulting in limited feature extraction capabilities and inadequate adaptability. …”
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Improving drug-induced liver injury prediction using graph neural networks with augmented graph features from molecular optimisation by Taeyeub Lee, Joram M. Posma

“…Scientific Contribution: DILIGeNN is a GNN framework that extracts graph features from 3D optimised molecular structures as is done in target-based drug discovery and molecular docking simulation. …”
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Early breast cancer detection via infrared thermography using a CNN enhanced with particle swarm optimization by Riyadh M. Alzahrani, Mohamed Yacin Sikkandar, S. Sabarunisha Begum, Ahmed Farag Salem Babetat, Maryam Alhashim, Abdulrahman Alduraywish, N. B. Prakash, Eddie Y. K. Ng

“…To overcome these limitations, this study proposes an automated classification framework that employs convolutional neural networks (CNNs) for distinguishing between malignant and benign thermographic breast images. …”
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Attention-based multimodal deep learning for interpretable and generalizable prediction of pathological complete response in breast cancer by Taishi Nishizawa, Takouhie Maldjian, Zhicheng Jiao, Tim Q. Duong

“…Conclusion We present a robust and interpretable deep learning framework for pCR prediction in breast cancer patients undergoing NAC. …”
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BIM Module for Deep Learning-driven parametric IFC reconstruction by O. Roman, O. Roman, M. Bassier, S. De Geyter, H. De Winter, E. M. Farella, F. Remondino

“…A deep learning (DL)-driven BIM Module for parametric IFC reconstruction is designed to accurately reconstruct both primary and secondary building elements within a BIM framework, starting from unstructured point cloud data captured via Terrestrial Laser Scanning (TLS). …”
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Deep learning-based encryption scheme for medical images using DCGAN and virtual planet domain by Manish Kumar, Aneesh Sreevallabh Chivukula, Gunjan Barua

“…The method uses a Deep Learning (DL) framework to generate a decoy image, which forms the basis for generating encryption keys using a timestamp, nonce, and 1-D Exponential Chebyshev map (1-DEC). …”
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Low-light image enhancement method based on retinex theory and dual-tree complex wavelet transform by Yuqian Zhang, Jie Jiang, Zhan Wang, Qi Zhang, Yudi Jiang, Jun Liu, Zeyao Hou

“…Therefore, this paper proposes a novel LIE framework based on Retinex theory and Dual-Tree Complex Wavelet Transform (DTCWT). …”
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Artificial intelligence and chordoma: A scoping review of the current landscape and future directions by Eddie Guo, Rafael D. Sanguinetti, Lyndon Boone, Jiawen Deng, Husain Shakil, Mehul Gupta

“…Common algorithms used included convolutional neural networks, support vector machines, random forests, and clustering algorithms. …”
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CA-STIM: an interpolation model with spatio-temporal evolution characteristics and cross-attention mechanism for 2D island morphology sequences by Peng Zhang, Wenzhou Wu, Shaochen Shi, Fengyu Li, Fenzhen Su

“…To address this issue, we propose a spatio-temporal interpolation model (CA-STIM) that integrates both external environmental dynamics and the intrinsic spatio-temporal evolution characteristics of island morphology using a convolutional neural network-long short-term memory network (CNN-LSTM) framework with a cross-attention mechanism and a weighted binary cross-entropy loss function. …”
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Deep Learning-Based Algorithm for the Classification of Left Ventricle Segments by Hypertrophy Severity by Wafa Baccouch, Bilel Hasnaoui, Narjes Benameur, Abderrazak Jemai, Dhaker Lahidheb, Salam Labidi

“…This study aims to propose an automated framework for the quantification of LVH extent and the classification of myocardial segments according to hypertrophy severity using a deep learning-based algorithm. …”
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The Emerging Role of Artificial Intelligence in Dermatology: A Systematic Review of Its Clinical Applications by Ernesto Martínez-Vargas, Jeaustin Mora-Jiménez, Sebastian Arguedas-Chacón, Josephine Hernández-López, Esteban Zavaleta-Monestel

“…The risk of bias was assessed qualitatively, using a tailored framework based on study design, dataset transparency, and clinical applicability. …”
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Comparison of Machine Learning and Deep Learning Models Performance in predicting wind energy by Saswati Rakshit, Anal Ranjan Sengupta

“…Therefore, this present study offers a robust framework for researchers and practitioners aiming to leverage machine learning and time series forecasting in the realm of renewable energy prediction. …”
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RL-Cervix.Net: A Hybrid Lightweight Model Integrating Reinforcement Learning for Cervical Cell Classification by Shakhnoza Muksimova, Sabina Umirzakova, Jushkin Baltayev, Young-Im Cho

“…<b>Results:</b> The innovative integration of RL into the CNN framework allowed RL-Cervix.Net to achieve an unprecedented classification accuracy of 99.98% in identifying atypical cells indicative of cervical lesions. …”
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PerfCam: Digital Twinning for Production Lines Using 3D Gaussian Splatting and Vision Models by Michel Gokan Khan, Renan Guarese, Fabian Johnson, Xi Vincent Wang, Anders Bergman, Benjamin Edvinsson, Mario Romero, Jeremy Vachier, Jan Kronqvist

“…We introduce PerfCam, an open source Proof-of-Concept (PoC) digital twinning framework that combines camera and sensory data with 3D Gaussian Splatting and computer vision models for digital twinning, object tracking, and Key Performance Indicators (KPIs) extraction in industrial production lines. …”
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A Model for Diagnosing Mild Nutrient Stress in Facility-Grown Tomatoes Throughout the Entire Growth Cycle by Yunpeng Yuan, Guoxiang Sun, Guangyu Chen, Qihua Zhang, Lingwei Liang

“…This study proposes a deep learning framework based on CNN + LSTM, using canopy near-infrared spectroscopy from different growth stages of tomatoes as input, to diagnose mild stress of nitrogen (N), potassium (K), and calcium (Ca) throughout the entire growth cycle of facility-grown tomatoes. …”
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EKNet: Graph Structure Feature Extraction and Registration for Collaborative 3D Reconstruction in Architectural Scenes by Changyu Qian, Hanqiang Deng, Xiangrong Ni, Dong Wang, Bangqi Wei, Hao Chen, Jian Huang

“…To address these challenges, this paper proposes an efficient deep graph matching registration framework that effectively integrates interpretable feature extraction with network training. …”
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Inductive and Transfer Learning‐Based Hybrid Model Techniques for Accurate and Automated Diagnosis of Neurological Diseases by Saroj Kumar Pandey, Yogesh Kumar Rathore, Sunakshi Mehra, Anurag Sinha, Tarun Raj Kumar, Ankit Kumar, Rekh Ram Janghel, Ayodele Lasisi, Quadri Noorulhasan Naveed, Md. Sazid Reza

“…ABSTRACT Purpose This study presents NeuroDL, a novel deep learning‐based diagnostic framework designed for the automated detection of brain tumors and Alzheimer's disease (AD) using magnetic resonance imaging (MRI). …”
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Binding Affinity Prediction for Pancreatic Ductal Adenocarcinoma Using Drug-Target Descriptors and Artificial Intelligence by Pragya, A. Amalin Prince, Jac Fredo Agastinose Ronickom

“…Our study demonstrates the potential of an AI-driven framework as an effective and scalable solution for disease-specific drug-target interaction prediction, with promising implications for drug repurposing in PDAC.…”
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