LungDxNet: AI-Powered Low-Dose CT Analysis for Early Lung Cancer Detection by Jyoti Parashar, Rituraj Jain, Mahesh K. Singh, Ashwani Kumar, Premananda Sahu, Kamal Upreti

“…This study introduces LungDxNet, a deep learning-based framework that integrates transfer learning to enhance diagnostic accuracy and efficiency. …”
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Research on insider threat detection based on personalized federated learning and behavior log analysis by Xiaoyun Ye, Faqin Luo, Huangrongbin Cui, Jinlong Wang, Xiaoyun Xiong, Wencui Zhang, Jiawei Yu, Wenhao Zhao

“…As a distributed machine learning framework, FL enables collaborative model building and analysis while safeguarding data privacy. …”
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An Autism Spectrum Disorder Identification Method Based on 3D-CNN and Segmented Temporal Decision Network by Zhiling Liu, Ye Chen, Xinrui Dong, Jing Liu

“…This study aims to improve the ability to capture spatiotemporal dynamics of brain activity by proposing an advanced framework. (2) Methods: This study proposes an ASD recognition method that combines 3D Convolutional Neural Networks (3D-CNNs) and segmented temporal decision networks. …”
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Lightweight Insulator and Defect Detection Method Based on Improved YOLOv8 by Yanxing Liu, Xudong Li, Ruyu Qiao, Yu Chen, Xueliang Han, Agyemang Paul, Zhefu Wu

“…Secondly, to address the issue of the network model’s complexity hindering its application on UAV equipment, depthwise separable convolution is incorporated for lightweight enhancement within the YOLOv8 algorithm framework. …”
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Thermal canopy segmentation in tomato plants: A novel approach with integration of YOLOv8-C and FastSAM by Hemamalini P, Chandraprakash MK, Laxman RH, Rathinakumari C, Senthil Kumaran G, Suneetha K

“…Advancing this methodology, we introduce a masked region of interest (ROI) temperature extraction technique, enabling targeted temperature extraction of segmented plant regions. This integrated framework, combining YOLOv8-C's detection capabilities with FastSAM's segmentation, is collectively called TCSegNet (Thermal Canopy Segmentation Network). …”
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LEAD-YOLO: A Lightweight and Accurate Network for Small Object Detection in Autonomous Driving by Yunchuan Yang, Shubin Yang, Qiqing Chan

“…To address this dilemma, this paper proposes LEAD-YOLO (Lightweight Efficient Autonomous Driving YOLO), an enhanced network architecture based on YOLOv11n that achieves superior small object detection while maintaining computational efficiency. The proposed framework incorporates three innovative components: First, the Backbone integrates a lightweight Convolutional Gated Transformer (CGF) module, which employs normalized gating mechanisms with residual connections, and a Dilated Feature Fusion (DFF) structure that enables progressive multi-scale context modeling through dilated convolutions. …”
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An enhanced BERT model with improved local feature extraction and long-range dependency capture in promoter prediction for hearing loss by Jing Sun, Yangfan Huang, Jiale Fu, Li Teng, Xiao Liu, Xiaohua Luo

“…Overall, DNABERT-CBL provides an effective framework for accurate promoter prediction, offers valuable insights into gene regulatory mechanisms, and supports the development of gene therapies for hearing loss and related diseases.…”
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Automated Measurement of Air Bubbles Dispersion in Ice Cream Using Machine Learning Methods by Igor A. Korolev

“…The automatic markup program employed the Python programming language, the Keras machine learning library, and the TensorFlow framework. The models were trained using the NVIDIA GTX video accelerator. …”
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Lightweight Brain Tumor Segmentation Through Wavelet-Guided Iterative Axial Factorization Attention by Yueyang Zhong, Shuyi Wang, Yuqing Miao, Tao Zhang, Haoliang Li

“…Methods: This paper presents a lightweight deep learning framework that uses adaptive discrete wavelet decomposition and iterative axial attention to improve 3D brain tumor segmentation. …”
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FR-CapsNet: Enhancing Low-Resolution Image Classification via Frequency Routed Capsules by Hasindu Dewasurendra, Kunmin Yeo, Nhan Thi Cao, Taejoon Kim

“…Furthermore, our method utilizes a global routing framework that considerably reduces computational demands, enabling FR-CapsNet to scale effectively to larger and more diverse datasets. …”
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Enhancing paddy leaf disease diagnosis -a hybrid CNN model using simulated thermal imaging by Jagamohan Padhi, Kunal Mishra, Ashoka Kumar Ratha, Santi Kumari Behera, Prabira Kumar Sethy, Aziz Nanthaamornphong

“…This research underscores the value of integrating thermal imaging with Deep Learning for advancing crop disease management and offers a framework for addressing other crop pathologies.…”
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Joint feature representation optimization and anti-occlusion for robust multi-vessel tracking in inland waterways by Shenjie Zou, Jin Liu, Xiliang Zhang, Zhongdai Wu, Jing Liu, Bing Han

“…To address the issues above, this paper proposes a novel Vessel Status Augmented Track (VSATrack) framework for multi-vessel detection and tracking. …”
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Construction and value of a vestibular function calibration test recognition model based on dual-stream ViT and ConvNeXt architecture by Xu LUO, Peixia WU, Weiming HAO, Yinhong QU, Han CHEN

“…MethodsA vestibular function calibration test recognition model with cross-modal feature fusion was constructed by integrating vision transformer (ViT) and a modified ConvNeXt convolutional network. The model utilized trajectory pictures and spatial distribution maps as inputs, employed a multi-task learning framework to classify calibration data, and to directly evaluate undershoot waveform. …”
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Uncertainty-aware deep learning in healthcare: A scoping review. by Tyler J Loftus, Benjamin Shickel, Matthew M Ruppert, Jeremy A Balch, Tezcan Ozrazgat-Baslanti, Patrick J Tighe, Philip A Efron, William R Hogan, Parisa Rashidi, Gilbert R Upchurch, Azra Bihorac

“…All imaging model architectures used convolutional neural networks or a variation thereof. …”
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Resource Optimization for Grid-Connected Smart Green Townhouses Using Deep Hybrid Machine Learning by Seyed Morteza Moghimi, Thomas Aaron Gulliver, Ilamparithi Thirumarai Chelvan, Hossen Teimoorinia

“…This creates an effective framework for managing energy consumption. The accuracy, efficiency, emissions, and cost are metrics used to evaluate CSGT performance. …”
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The analysis of acquisition system for electronic traffic signal in smart cities based on the internet of things by Yue Ma, Chenglong Wang, Tianlei Fu, Ziting Meng

“…Second, an improved Multi-Task Convolutional Neural Network (MT-CNN) model, called Attention-Mechanism Multi-Modal Feature Fusion GooGleNet (AM-MMFF-GooGleNet), is proposed. …”
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Beyond the Backbone: A Quantitative Review of Deep-Learning Architectures for Tropical Cyclone Track Forecasting by He Huang, Difei Deng, Liang Hu, Yawen Chen, Nan Sun

“…We categorize all DL-based TC tracking models according to the architecture, including recurrent neural networks (RNNs), convolutional neural networks (CNNs), Transformers, graph neural networks (GNNs), generative models, and Fourier-based operators. …”
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Iterative segmentation and classification for enhanced crop disease diagnosis using optimized hybrid U-Nets model by Malathi Chilakalapudi, Sheela Jayachandran

“…Our research proposes a new framework instigated and developed to improve crop disease detection and classification by multifaceted analysis. …”
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scSMD: a deep learning method for accurate clustering of single cells based on auto-encoder by Xiaoxu Cui, Renkai Wu, Yinghao Liu, Peizhan Chen, Qing Chang, Pengchen Liang, Changyu He

“…By integrating innovative computational techniques, the SMD model provides a powerful framework for unraveling cellular complexities, enhancing our understanding of biological processes, and elucidating disease mechanisms. …”
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MEViT: Generalization of Deepfake Detection With Meta-Learning EfficientNet Vision Transformer by Van-Nhan Tran, Hoanh-Su Le, Piljoo Choi, Suk-Hwan Lee, Ki-Ryong Kwon

“…In this study, we propose a model called MEViT, which integrates the EfficientNet Vision Transformer with a meta-learning framework to enhance generalization in deepfake detection. …”
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