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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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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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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Real-Time Multi-Task Deep Learning Model for Polyp Detection, Characterization, and Size Estimation by Phanukorn Sunthornwetchapong, Kasichon Hombubpha, Kasenee Tiankanon, Satimai Aniwan, Pasit Jakkrawankul, Natawut Nupairoj, Peerapon Vateekul, Rungsun Rerknimitr

“…In this work, we present a modified convolutional neural network (CNN) based deep learning (DL) model to perform these tasks in real-time, utilizing existing object detection models: YOLOv5 and YOLOv8. …”
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SP-YOLO: A Real-Time and Efficient Multi-Scale Model for Pest Detection in Sugar Beet Fields by Ke Tang, Yurong Qian, Hualong Dong, Yuning Huang, Yi Lu, Palidan Tuerxun, Qin Li

“…The model integrates a CNN and transformer (CAT) into the backbone network to capture global features. …”
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WED-YOLO: A Detection Model for Safflower Under Complex Unstructured Environment by Zhenguo Zhang, Yunze Wang, Peng Xu, Ruimeng Shi, Zhenyu Xing, Junye Li

“…Compared with Faster R-CNN, YOLOv5, YOLOv7, and YOLOv10, the WED-YOLO achieved the highest <i>mAP</i> value. …”
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Artificial Intelligence in Pediatric Epilepsy Detection: Balancing Effectiveness With Ethical Considerations for Welfare by Marina Ramzy Mourid, Hamza Irfan, Malik Olatunde Oduoye

“…Deep learning models, such as CNN‐LSTM, have also enhanced seizure detection from video by capturing subtle movement and expression cues. …”
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Peningkatan Performa Pengenalan Wajah pada Gambar Low-Resolution Menggunakan Metode Super-Resolution by Muhammad Imaduddin Abdur Rohim, Auliati Nisa, Muhammad Nurkhoiri Hindratno, Radhiyatul Fajri, Gembong Satrio Wibowanto, Nova Hadi Lestriandoko, Pesigrihastamadya Normakristagaluh

“…Penelitian ini menunjukkan bahwa metode SR dari kategori General Basic CNN-based FSR dapat digunakan untuk meningkatkan kinerja face recognition pada gambar LR, seperti pada KTP-el. …”
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Visual impairment prevention by early detection of diabetic retinopathy based on stacked auto-encoder by Shagufta Almas, Fazli Wahid, Sikandar Ali, Ahmed Alkhyyat, Kamran Ullah, Jawad Khan, Youngmoon Lee

“…The classification is performed across one healthy (normal) stage and four DR stages: mild, moderate, severe, and proliferative. Unlike traditional CNN approaches, our method offers improved reliability by reducing time complexity, minimizing errors, and enhancing noise reduction. …”
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Estimating the Severity of Oral Lesions Via Analysis of Cone Beam Computed Tomography Reports: A Proposed Deep Learning Model by Sare Mahdavifar, Seyed Mostafa Fakhrahmad, Elham Ansarifard

“…After preprocessing the data, a deep learning model, referred to as CNN-LSTM, was developed, which aims to detect the degree of severity of the problem based on analysis of the radiologist's report. …”
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Dual-hybrid intrusion detection system to detect False Data Injection in smart grids. by Saad Hammood Mohammed, Mandeep S Jit Singh, Abdulmajeed Al-Jumaily, Mohammad Tariqul Islam, Md Shabiul Islam, Abdulmajeed M Alenezi, Mohamed S Soliman

“…Additionally, the IDS employs a hybrid deep learning classifier that integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to capture the smart grid data's spatial and temporal features. …”
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Analysis of tensile properties in tempered martensite steels with different cementite particle size distributions by Kengo Sawai, Keiya Sugiura, Toshio Ogawa, Ta-Te Chen, Fei Sun, Yoshitaka Adachi

“…We succeeded in developing image-based regression models with high accuracy using a convolutional neural network (CNN). Moreover, gradient-weighted class activation mapping (Grad-CAM) suggested that fine cementite particles and coarse and spheroidal cementite particles are the dominant factors for tensile strength and total elongation, respectively.…”
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Surveying Nearshore Bathymetry Using Multispectral and Hyperspectral Satellite Imagery and Machine Learning by David Hartmann, Mathieu Gravey, Timothy David Price, Wiebe Nijland, Steven Michael de Jong

“…The U-Net, trained on 49 Sentinel-2 images, and the 2D-3D CNN, trained on PRISMA imagery, had a Mean Absolute Error (MAE) of approximately 1 m for depths up to 20 m and were superior to band ratio models by ~40%. …”
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An In-Depth Study of Personalized Anesthesia Management Models in Gastrointestinal Endoscopy Based on Multimodal Deep Learning by Hanqi Shi, Hongyu Wang, Xibing Ding, Zheng Dang

“…Compared with LSTM networks integrated with convolutional neural networks (CNN) and support vector machines (SVM), the LSTM model combined with GMO and sparse matrix classifiers, along with personalized physiological data, achieved a recall rate of 83% and an F1-score of 0.711 in drug usage prediction. …”
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