Transformer-Based Detection and Clinical Evaluation System for Torsional Nystagmus by Ju-Hyuck Han, Yong-Suk Kim, Jong Bin Lee, Hantai Kim, Jong-Yeup Kim, Yongseok Cho

“…This model employs a self-supervised learning framework comprising two main components: a Decoder module, which learns rotational transformations from image data, and a Finder module, which subsequently estimates the torsion angle. …”
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FPA-based weighted average ensemble of deep learning models for classification of lung cancer using CT scan images by Liang Zhou, Achin Jain, Arun Kumar Dubey, Sunil K. Singh, Neha Gupta, Arvind Panwar, Sudhakar Kumar, Turki A. Althaqafi, Varsha Arya, Wadee Alhalabi, Brij B. Gupta

“…This study proposes a novel lung cancer detection framework using a Flower Pollination Algorithm (FPA)-based weighted ensemble of three high-performing pretrained Convolutional Neural Networks (CNNs): VGG16, ResNet101V2, and InceptionV3. …”
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Method to Use Transport Microsimulation Models to Create Synthetic Distributed Acoustic Sensing Datasets by Ignacio Robles-Urquijo, Juan Benavente, Javier Blanco García, Pelayo Diego Gonzalez, Alayn Loayssa, Mikel Sagues, Luis Rodriguez-Cobo, Adolfo Cobo

“…We demonstrate this by training several U-Net convolutional neural networks to enhance spatial resolution (reducing it to half the original gauge length), filtering traffic signals by vehicle direction, and simulating the effects of alternative cable layouts. …”
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A Systematic Review of Reimagining Fashion and Textiles Sustainability with AI: A Circular Economy Approach by Hiqmat Nisa, Rebecca Van Amber, Julia English, Saniyat Islam, Georgia McCorkill, Azadeh Alavi

“…This systematic review explores the applications of AI in evaluating clothing quality and condition within the framework of a circular economy, with a focus on supporting second-hand clothing resale, charitable donations by NGOs, and sustainable recycling practices. …”
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Hybrid AI and semiconductor approaches for power quality improvement by Ravikumar Chinthaginjala, Asadi Srinivasulu, Anupam Agrawal, Tae Hoon Kim, Sivarama Prasad Tera, Shafiq Ahmad

“…The research addresses key power quality challenges - including voltage sags, swells, harmonics, and transient disturbances - through a data-driven framework that combines traditional control techniques with adaptive learning models. …”
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Machine Learning-Based Analysis of Travel Mode Preferences: Neural and Boosting Model Comparison Using Stated Preference Data from Thailand’s Emerging High-Speed Rail Network by Chinnakrit Banyong, Natthaporn Hantanong, Supanida Nanthawong, Chamroeun Se, Panuwat Wisutwattanasak, Thanapong Champahom, Vatanavongs Ratanavaraha, Sajjakaj Jomnonkwao

“…It conducts a comparative assessment of predictive capabilities between the conventional Multinomial Logit (MNL) framework and advanced data-driven methodologies, including gradient boosting algorithms (Extreme Gradient Boosting, Light Gradient Boosting Machine, Categorical Boosting) and neural network architectures (Deep Neural Network, Convolutional Neural Network). …”
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Defect R-CNN: A Novel High-Precision Method for CT Image Defect Detection by Zirou Jiang, Jintao Fu, Tianchen Zeng, Renjie Liu, Peng Cong, Jichen Miao, Yuewen Sun

“…To address these issues, we propose Defect R-CNN, a novel detection framework designed to capture the structural characteristics of defects in CT images. …”
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Lightweight CNN model for automatic detection and depth estimation of subsurface voids using GPR B-scan data by Abdelaziz Mojahid, Driss EL Ouai, Khalid EL Amraoui, Khalil EL-Hami, Hamou Aitbenamer, Jochem Verrelst, Pier Matteo Barone

“…Consequently, this study proposes a Convolutional Neural Network (CNN)-based framework for the automated detection and depth estimation of subsurface cavities from Ground Penetrating Radar (GPR) B-scan images. …”
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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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