Hybrid transformer-CNN and LSTM model for lung disease segmentation and classification by Syed Mohammed Shafi, Sathiya Kumar Chinnappan

“…This hybrid approach enhances the model’s ability to distinguish between different types of lung diseases and reduces diagnostic errors compared to existing methods.…”
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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

“…Furthermore, the model is applied to a different-fields categorization and trend analysis task, demonstrating its capability to capture temporal patterns and perform detailed trend analysis of domain-agnostic textual content. …”
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Study of Recent Image Restoration Techniques: A Comprehensive Survey by Nikita Singhal, Anup Kadam, Pravesh Kumar, Hritik Singh, Aaryan Thakur, Pranay

“…It also mentions the challenge of generalization across the type of degradation, say mixed noise or blur, and across different datasets. The proposed survey indicates the limitations of existing approaches, including computational cost and generalization challenges, and offers insights into possible directions for future research. …”
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Transferability Evaluation in Wi-Fi Intrusion Detection Systems Through Machine Learning and Deep Learning Approaches by Saud Yonbawi, Adil Afzal, Muhammad Yasir, Muhammad Rizwan, Natalia Kryvinska

“…A comprehensive evaluation involving Multilayer Perceptron(MLP), and Convolutional Neural Networks (CNN) models has been executed, uncovering that CNN conspicuously outshines the MLP model.…”
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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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Well logging super-resolution based on fractal interpolation enhanced by BiLSTM-AMPSO by Jian Han, Yu Deng, Bing Zheng, Zhimin Cao

“…Experimental results indicate that, compared to well logging super-resolution methods such as bicubic interpolation, convolutional neural networks, and random forests, the proposed method not only effectively preserves the overall contours and detailed information of the well logging curves but also exhibits good stability across different regions and various well logging curves.…”
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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

“…Experimental results show that the AM-MMFF-GooGleNet model achieves an accuracy of 98.6% in the vehicle localization task, 3.2% higher than the original MT-GooGleNet. The accuracy under different lighting conditions and high background noise scenarios reaches 97.3%, 96.8%, and 95.5%, demonstrating strong environmental adaptability. …”
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Manifold and spatiotemporal learning on multispectral unoccupied aerial system imagery for phenotype prediction by Fared Farag, Trevis D. Huggins, Jeremy D. Edwards, Anna M. McClung, Ahmed A. Hashem, Jason L. Causey, Emily S. Bellis

“…Here, we investigate the ability of various machine learning (ML) approaches to improve yield prediction accuracy in new environments from multispectral timeseries imagery acquired on a set of rice (Oryza sativa L.) experiments with different management treatments and varieties. We also trained deep learning models that perform automated feature extraction and compared these against a suite of other approaches. …”
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An enhanced deep learning-based feature extraction framework for moving object detection by Upasana Panigrahi, Prabodh Kumar Sahoo, Manoj Kumar Panda, Aswini Kumar Samantaray, Ganapati Panda

“…The designed Multi-Scale Feature Pooling Framework (MSFP) guarantees the preservation of multi-scale and multi-dimensional features across different scales. The developed decoder architecture consists of stacked transposed convolutional layers tasked with translating features back into the image. …”
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Development of a CNN-based decision support system for lung disease diagnosis using chest radiographs by B. T. Magar, M. A. Rahman, P. K. Saha, M. Ahmad, M. A. Rashid, H. Higa

“…Extensive testing under three different data distribution conditions demonstrated the model’s superior performance, achieving an average accuracy of 95.7%, precision of 95.3%, recall of 95.3%, and an F1-score of 95.3% for multi-class classification. …”
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Attention-Based CNN Fusion Model for Emotion Recognition During Walking Using Discrete Wavelet Transform on EEG and Inertial Signals by Yan Zhao, Ming Guo, Xiangyong Chen, Jianqiang Sun, Jianlong Qiu

“…Walking as a unique biometric tool conveys important information for emotion recognition. Individuals in different emotional states exhibit distinct walking patterns. …”
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Covariance Matrix Reconstruction to Improve DoA Estimation Using Subspace Method in Low SNR Regime by Sunita Khichar, Wiroonsak Santipach, Lunchakorn Wuttisittikulkij

“…The proposed method also shows substantial improvements in performance with varying numbers of snapshots and under different angle separation conditions. These results highlight the enhanced noise resilience and accuracy of our method.…”
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TDNN achitecture with efficient channel attention and improved residual blocks for accurate speaker recognition by Wenzao Li, Sai Yao, Bing Wan, Linsong Xiao, Chengyu Hou, Yanchuan Zhong, Wengang Zhou

“…Facilitating the integration of information from different scales and channels, resulting in more refined feature representations. …”
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FlowMRI-Net: A generalizable self-supervised 4D flow MRI reconstruction network by Luuk Jacobs, Marco Piccirelli, Valery Vishnevskiy, Sebastian Kozerke

“…The generalizability of the framework is evaluated using aortic and cerebrovascular 4D flow MRI acquisitions acquired on systems from two different vendors for various undersampling factors (R = 8, 16, 24) and compared to compressed sensing locally low rank (CS-LLR) reconstructions. …”
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Multi-Stage Audio-Visual Fusion for Dysarthric Speech Recognition With Pre-Trained Models by Chongchong Yu, Xiaosu Su, Zhaopeng Qian

“…This operation is different from the traditional approach solely based on the movement of lip in audio-visual fusion framework. …”
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Development of an optimized deep learning model for predicting slope stability in nano silica stabilized soils by Ishwor Thapa, Sufyan Ghani, Prabhu Paramasivam, Mitiku Adare Tufa

“…The model’s training and verification were used with the data file containing 3,159 cases of inclination with different percentages of NS, and geotechnical parameters. …”
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Development of Risk Activity Detection System for Forklifts Based on Inertial Sensors by Luciano Radrigan, Sebastian E. Godoy

“…Forklifts are mobile heavy machines that are used to transport, lift, or lower objects without the high physical effort of the operator. They work in different types of industries such as logistics, retail, food, mining, and construction, among others. …”
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A multimodal model in the prediction of the delivery mode using data from a digital twin-empowered labor monitoring system by Jieyun Bai, Xue Kang, Weishan Wang, Ziduo Yang, Weiguang Ou, Yuxin Huang, Yaosheng Lu

“…This could be highly beneficial for dynamically fusing data from different sources throughout the maternal and fetal health lifecycle, from pregnancy to delivery.…”
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Comparative Analysis of Deep Learning Models for Intrusion Detection in IoT Networks by Abdullah Waqas, Sultan Daud Khan, Zaib Ullah, Mohib Ullah, Habib Ullah

“…This study addresses the problem of detecting intrusions in IoT environments by evaluating the performance of deep learning (DL) models under different data and algorithmic conditions. We conducted a comparative analysis of three widely used DL models—Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Bidirectional LSTM (biLSTM)—across four benchmark IoT intrusion detection datasets: BoTIoT, CiCIoT, ToNIoT, and WUSTL-IIoT-2021. …”
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Multiscale Graph Transformer Network With Dynamic Superpixel Pyramid for Hyperspectral Image Classification by Tingting Wang, Yao Sun, Yunfeng Hu

“…To address these limitations, we propose a multi-scale graph transformer network (MSGTN), which captures spatial features at different scales through multiscale graph convolutional networks (GCNs) with adaptive graph structures. …”
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