A deep transfer learning based convolution neural network framework for air temperature classification using human clothing images by Maqsood Ahmed, Xiang Zhang, Yonglin Shen, Nafees Ali, Aymen Flah, Mohammad Kanan, Mohammad Alsharef, Sherif S. M. Ghoneim

“…In this paper, we propose a transfer learning CNN framework for classifying air temperature levels from human clothing images. …”
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Classification of white blood cells (leucocytes) from blood smear imagery using machine and deep learning models: A global scoping review. by Rabia Asghar, Sanjay Kumar, Arslan Shaukat, Paul Hynds

“…While WBC classification was originally rooted in conventional ML, there has been a notable shift toward the use of DL, and particularly convolutional neural networks (CNN), with 54.4% of identified studies (n = 74) including the use of CNNs, and particularly in concurrence with larger datasets and bespoke features e.g., parallel data pre-processing, feature selection, and extraction. …”
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AirStrum: A virtual guitar using real-time hand gesture recognition and strumming technique by Beulah ARUL, Shashank PANDA, Tushar NAIR

“…Subsequently, a model based on a Convolutional Neural Network (CNN) is trained and validated using the employed dataset to adeptly recognize and classify guitar chords. …”
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Archaeological Site Detection: Latest Results from a Deep Learning Based Europe Wide Hillfort Search by Jürgen Landauer, Simon Maddison, Giacomo Fontana, Axel G. Posluschny

“…The methodology utilized the Atlas of Hillforts of Britain and Ireland to train a CNN on LiDAR datasets and tested the model’s transferability to Germany and Italy. …”
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RETRACTED ARTICLE: Sign language recognition using the fusion of image and hand landmarks through multi-headed convolutional neural network by Refat Khan Pathan, Munmun Biswas, Suraiya Yasmin, Mayeen Uddin Khandaker, Mohammad Salman, Ahmed A. F. Youssef

“…A multi-headed convolutional neural network (CNN) model has been proposed and tested with 30% of the dataset to train these two layers. …”
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Advanced Defect Detection on Curved Aeronautical Surfaces Through Infrared Imaging and Deep Learning by Leith Bounenni, Mohamed Arbane, Clemente Ibarra-Castanedo, Yacine Yaddaden, Sreedhar Unnikrishnakurup, Andrew Ngo Chun Yong, Xavier Maldague

“…We achieve a more comprehensive and precise assessment of defects by integrating deep learning with infrared imaging based on the U-net model for segmentation and the CNN model for classification. The proposed model was rigorously tested on both a simulation dataset and an experimental dataset, demonstrating its robustness and effectiveness in accurately identifying and assessing defects on aerospace surfaces. …”
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Automated Defect Detection in Solar Cell Images Using Deep Learning Algorithms by Montaser Abdelsattar, Ahmed Abdelmoety, Mohamed A. Ismeil, Ahmed Emad-Eldeen

“…The research paper investigates how well 24 distinct convolutional neural network (CNN) architectures— Residual network (ResNet), densely connected convolutional networks (DenseNet), visual geometry group (VGG), Inception, mobile network (MobileNet), Xception, SqueezeNet, and AlexNet—classify solar cells into defected and non-defective categories. …”
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Spatial transcriptome reveals histology-correlated immune signature learnt by deep learning attention mechanism on H&E-stained images for ovarian cancer prognosis by Chun Wai Ng, Kwong-Kwok Wong, Barrett C. Lawson, Sammy Ferri-Borgogno, Samuel C. Mok

“…Methods In this study, 773 WSIs of H&E-stained tumor sections from 335 patients with treatment naïve high-grade serous ovarian cancer who were included in The Cancer Genome Atlas (TCGA) Pan-Cancer study were used to train, and validate, and to test a ResNet101 CNN model modified with attention mechanism. WSIs from patients in an independent cohort were used to further evaluate the model. …”
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A composite improved attention convolutional network for motor imagery EEG classification by Wenzhe Liao, Zipeng Miao, Shuaibo Liang, Linyan Zhang, Chen Li

“…CIACNet utilizes a dual-branch convolutional neural network (CNN) to extract rich temporal features, an improved convolutional block attention module (CBAM) to enhance feature extraction, temporal convolutional network (TCN) to capture advanced temporal features, and multi-level feature concatenation for more comprehensive feature representation.ResultsThe CIACNet model performs well on both the BCI IV-2a and BCI IV-2b datasets, achieving accuracies of 85.15 and 90.05%, respectively, with a kappa score of 0.80 on both datasets. …”
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A Novel Two-Stage Deep Learning Model for Network Intrusion Detection: LSTM-AE by Vanlalruata Hnamte, Hong Nhung-Nguyen, Jamal Hussain, Yong Hwa-Kim

“…The deep neural network (DNN) and convolutional neural network (CNN) are examined in this article as types of deep learning models for developing a flexible and effective IDS capable of detecting and comparing them with the proposed model in detecting cyber-attacks. …”
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Classifying IoT Botnet Attacks With Kolmogorov-Arnold Networks: A Comparative Analysis of Architectural Variations by Phuc Hao do, Tran Duc Le, Truong Duy Dinh, van Dai Pham

“…We conducted a comparative analysis of five KAN architectures, including Original-KAN, Fast-KAN, Jacobi-KAN, Deep-KAN, and Chebyshev-KAN, against models like Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and Gated Recurrent Units (GRU). …”
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Multi-label classification with deep learning techniques applied to the B-Scan images of GPR by El Karakhi, Soukayna, Reineix, Alain, Guiffaut, Christophe

“…Three deep learning models: VGG-16, ResNet-50 and adapted CNN were used as pre-trained models for transfer learning. …”
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Machine Learning-Based Detection of Anomalies, Intrusions, and Threats in Industrial Control Systems by Denis Benka, Dusan Horvath, Lukas Spendla, Gabriel Gaspar, Maximilian Stremy

“…The results demonstrate that the 1D CNN model achieved the highest accuracy (0.92) and F1 score (0.91) with minimal processing time, making it ideal for real-time intrusion detection. …”
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Innovative segmentation technique for aerial power lines via amplitude stretching transform by Pengfei Xu, Nor Anis Asma Sulaiman, Yafei Ding, Jiangwei Zhao

“…The proposed algorithm is compared with the main power line segmentation algorithms, such as Region Convolutional Neural Networks(R-CNN) and Phase Stretch Transform(PST). The average values of evaluation indicators PPA, MMPA and MMIoU of the image segmentation results of the proposed algorithm reach 0.96, 0.96 and 0.95 respectively, and the average time lag of detection is less than 0.2s, indicating that the accuracy and real-time performance of the segmentation results of the proposed algorithm are significantly better than those of the above algorithms.…”
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Deep learning classification of MGMT status of glioblastomas using multiparametric MRI with a novel domain knowledge augmented mask fusion approach by İlker Özgür Koska, Çağan Koska

“…Integrating the information in different MRI sequences and leveraging the high entropic capacity of deep neural networks, we built a 3D ROI-based custom CNN classifier for the automatic prediction of MGMT methylation status of glioblastoma in multi-parametric MRI. …”
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Exploring the Effectiveness of Machine Learning and Deep Learning Techniques for EEG Signal Classification in Neurological Disorders by Souhaila Khalfallah, William Puech, Mehdi Tlija, Kais Bouallegue

“…Moreover, deep learning models, including Convolutional Neural Networks (CNN) and ChronoNet, demonstrated accuracy rates ranging from 92.5% to 100%. …”
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RETRACTED ARTICLE: An intelligent dynamic cyber physical system threat detection system for ensuring secured communication in 6G autonomous vehicle networks by Shanthalakshmi M, Ponmagal R S

“…So we present a novel approach to mitigating these security risks by leveraging pre-trained Convolutional Neural Network (CNN) models for dynamic cyber-attack detection within the cyber-physical systems (CPS) framework of AVs. …”
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Deep Learning and Recurrence Information Analysis for the Automatic Detection of Obstructive Sleep Apnea by Daniele Padovano, Arturo Martinez-Rodrigo, José M. Pastor, José J. Rieta, Raul Alcaraz

“…Moreover, these results obtained by the proposed CNN-based recurrence analysis of HRV also outperformed traditional time–frequency models, which have yielded values of accuracy lower than 65%. …”
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Integrated Spatiotemporal Hybrid Solar PV Generation Forecast Between Countries on Different Continents Using Transfer Learning Method by Bowoo Kim, Kaouther Belkilani, Gerd Heilscher, Marc-Oliver Otto, Jeung-Soo Huh, Dongjun Suh

“…The proposed CL-Transformer model outperformed established machine learning models such as LSTM, CNN-LSTM, and Transformer, consistently demonstrating superior predictive capabilities. …”
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A Hybrid Transformer Architecture for Multiclass Mental Illness Prediction Using Social Media Text by Adnan Karamat, Muhammad Imran, Muhammad Usman Yaseen, Rasool Bukhsh, Sheraz Aslam, Nouman Ashraf

“…In this study, we propose a hybrid transformer architecture, comprising MentalBERT and MelBERT pretrained language models, cascaded with CNN models to generate and concatenate deep features. …”
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