Advances to IoT security using a GRU-CNN deep learning model trained on SUCMO algorithm by Amit Sagu, Nasib Singh Gill, Preeti Gulia, Noha Alduaiji, Piyush Kumar Shukla, Mohd Asif Shah

“…The proposed model is evaluated through experiments on two different datasets i.e., UNSW-NB15 and BoT-IoT, and results demonstrates that proposed work outperforms the traditional work as well as state of the art works.…”
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Single Pixel Imaging Based on Multiple Prior Deep Unfolding Network by Quan Zou, Qiurong Yan, Qianling Dai, Ao Wang, Bo Yang, Yi Li, Jinwei Yan

“…To effectively fuse multiple prior information, we propose three different fusion strategies in the deep reconstruction sub-network. …”
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Facial expression recognition using visible and IR by early fusion of deep learning with attention mechanism by Muhammad Tahir Naseem, Chan-Su Lee, Tariq Shahzad, Muhammad Adnan Khan, Adnan M. Abu-Mahfouz, Khmaies Ouahada

“…The motivation of this research is to address the challenges of accurately recognizing emotions despite variations in expressions across emotions and similarities between different expressions. In this work, we propose an early fusion approach that combines features from visible and infrared modalities using publicly accessible VIRI and NVIE databases. …”
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MultiSenseNet: Multi-Modal Deep Learning for Machine Failure Risk Prediction by Mostafijur Rahman, Md Sabbir Hossain, Uland Rozario, Satyabrata Roy, M. F. Mridha, Nilanjan Dey

“…The model performed consistently across different hyperparameter settings, reaching peak accuracy of 94.7% on the classification dataset and 95.6% on the prediction dataset after 40 training epochs. …”
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Application of U-net models in estimating forest canopy closure based on multi-source remote sensing imagery by Lei Chen, TingTing Yang, ZhiQiang Wu, XinLong Li, YanZhen Lin, Yi Lian

“…These models are optimized by reordering the network output layers and enhancing feature fusion between convolutional and pooling operations. By experimenting with different combinations of multi-parameters with the improved U-Net architectures, we estimate CC and validate the results using airborne Light Detection and Ranging (LiDAR) CC data. …”
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Detecting Fake News in Urdu Language Using Machine Learning, Deep Learning, and Large Language Model-Based Approaches by Muhammad Shoaib Farooq, Syed Muhammad Asadullah Gilani, Muhammad Faraz Manzoor, Momina Shaheen

“…The research uses methods that look at the features of documents and classes to detect fake news in Urdu. Different models were tested, including machine learning models like Naïve Bayes and Support Vector Machine (SVM), as well as deep learning models like Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM), which used embedding techniques. …”
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Application of deep learning models in gastric cancer pathology image analysis: a systematic scoping review by Sijun Xia, Yuanze Xia, Ting Liu, Yiming Luo, Patrick Cheong-Iao Pang

“…The applicability to different types and stages of GC is also unclear. Conclusions Future research must build larger, more diverse, and more representative datasets. …”
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LUNA: Loss-Construct Unsupervised Network Adjustment for Low-Dose CT Image Reconstruction by Ritu Gothwal, Shailendra Tiwari, Shivendra Shivani

“…The optimal update of the network depends on the various loss functions. Different loss functions, including perceptual loss, SSIM loss, WL2 loss, WTV loss, and sinogram loss, are used to guide the overall reconstruction. …”
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Transformer-Based Optimization for Text-to-Gloss in Low-Resource Neural Machine Translation by Younes Ouargani, Noussaim El Khattabi

“…The trials involve optimizing a minimal model, and our complex model with different optimizers; The findings from these trials show that both Adaptive Gradient (AdaGrad) and Adaptive Momentum (Adam) offer significantly better performance than Stochastic Gradient Descent (SGD) and Adaptive Delta (AdaDelta) in the minimal model scenario, however, Adam offers significantly better performance in the complex model optimization task. …”
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A review of machine learning and deep learning for Parkinson’s disease detection by Hajar Rabie, Moulay A. Akhloufi

“…Our evaluation included different algorithms such as support vector machines (SVM), random forests (RF), convolutional neural networks (CNN). …”
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Managing Timing Uncertainties in Worst-Case Design of Machine Learning Applications by Robin Hapka, Rolf Ernst

“…., robot-human collaboration using convolutional neural networks, timing must be considered to operate safely. …”
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ABDviaMSIFAT: Abnormal Crowd Behavior Detection Utilizing a Multi-Source Information Fusion Technique by Ali Ahmad Hamid, S. Amirhassan Monadjemi, Bijan Shoushtarian

“…To solve this issue, we suggest a new method that combines data from various sources with different characteristics to enhance the precision of detecting human behavior in crowds. …”
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Overview and Comparison of Deep Neural Networks for Wildlife Recognition Using Infrared Images by Peter Sykora, Patrik Kamencay, Roberta Hlavata, Robert Hudec

“…In contrast, infrared imaging captures the thermal signatures of animals, providing a robust alternative for wildlife detection and identification. We test a Convolutional Neural Network (CNN) model specifically designed to analyze infrared images, leveraging the unique thermal patterns emitted by different animal species. …”
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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

“…It is especially important when evaluating crops at different phases of harvest and postharvest. Crop disease and damage detection is a high-priority activity because some postharvest diseases or damages, such as decay, can destroy crops and produce poisons that are toxic to humans. …”
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Impact of Channel and System Parameters on Performance Evaluation of Frequency Extrapolation Using Machine Learning by Michael Neuman, Daoud Burghal, Andreas F. Molisch

“…We also find that, in particular, KNN results can be quantitatively and qualitatively different from CNN/MLP and AE. These investigations thus provide insights into meaningful parameter choices for the performance evaluation of new ML algorithms for frequency-domain channel extrapolation.…”
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Development of Deep Learning Model for the Recognition of Cracks on Concrete Surfaces by Tien-Thinh Le, Van-Hai Nguyen, Minh Vuong Le

“…It is also confirmed that the developed DL-based model was robust and efficient, as it can take into account different conditions on the concrete surfaces. The CNN model developed in this study was compared with other works in the literature, showing that the CNN model could improve the accuracy of image classification, in comparison with previously published results. …”
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A Health Status Identification Method for Rotating Machinery Based on Multimodal Joint Representation Learning and a Residual Neural Network by Xiangang Cao, Kexin Shi

“…Second, an orthogonal projection combined with a Transformer is used to enhance the target modality, while a modality attention mechanism is introduced to take into consideration the interaction between different modalities, enabling multimodal fusion. Finally, the fused features are input into a residual neural network (ResNet) for health status identification. …”
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Reconstructing Evapotranspiration in British Columbia Since 1850 Using Publicly Available Tree-Ring Plots and Climate Data by Hang Li, John Rex

“…This can be accomplished using satellite imagery since the 1980s, but prior to that, a different approach is required. Using a global ET dataset (1982 to 2010) with 1 km resolution, climate station information from 1850 to 2010, and 54 tree-ring plots from the International Tree-Ring Data Bank (ITRDB) database, ET reconstructions were developed for each vegetated pixel with point-by-point regressions in British Columbia. …”
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Classification of English Words into Grammatical Notations Using Deep Learning Technique by Muhammad Imran, Sajjad Hussain Qureshi, Abrar Hussain Qureshi, Norah Almusharraf

“…The classification of parts of speech into different grammatical notations is the major problem that non-native English learners face. …”
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Channel computation based on multi-scale attention residual network by Wengang Li, Deli Zhou, Qiong Ye

“…The extracted features are then integrated and exploited through a residual convolutional architecture to derive an estimation of the channel matrix. …”
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