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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An Efficient Group Convolution and Feature Fusion Method for Weed Detection by Chaowen Chen, Ying Zang, Jinkang Jiao, Daoqing Yan, Zhuorong Fan, Zijian Cui, Minghua Zhang

“…The results show that the YEF model outperforms the original YOLOv8 model, Faster R-CNN, RetinaNet, TOOD, RTMDet, and YOLOv5 in detection performance. …”
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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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pACP-HybDeep: predicting anticancer peptides using binary tree growth based transformer and structural feature encoding with deep-hybrid learning by Shahid, Maqsood Hayat, Wajdi Alghamdi, Shahid Akbar, Ali Raza, Rabiah Abdul Kadir, Mahidur R. Sarker

“…The selected feature vector is subsequently trained using a CNN + RNN-based deep learning model. Our proposed pACP-HybDeep model demonstrated a high training accuracy of 95.33%, and an AUC of 0.97. …”
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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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KANDiff: Kolmogorov–Arnold Network and Diffusion Model-Based Network for Hyperspectral and Multispectral Image Fusion by Wei Li, Lu Li, Man Peng, Ran Tao

“…Convolutional Neural Networks (CNNs) effectively extract local features through their convolutional layers and achieve noise suppression via layer-by-layer feature representation. Therefore, the MergeCNN module is further introduced to enhance the fusion effect, resulting in smoother and more accurate outcomes. …”
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Application of Artificial Intelligence in Radiological Image Analysis for Pulmonary Disease Diagnosis: A Review of Current Methods and Challenges by Karolina Zalewa, Joanna Olszak, Wojciech Kapłan, Dominika Orłowska, Lidia Bartoszek, Marta Kaus, Natalia Klepacz

“…Conclusions AI, especially CNN-based DL models, is reshaping radiology by advancing diagnostic capabilities. …”
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Intelligent model for forecasting fluctuations in the gold price by Mahdieh Tavassoli, Mahnaz Rabeei, Kiamars Fathi Hafshejani

“…The study also employed Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Multi-Layer Perceptron (MLP) neural network models in deep learning mode to predict gold price fluctuations. …”
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Urdu Handwritten Characters Data Visualization and Recognition Using Distributed Stochastic Neighborhood Embedding and Deep Network by Mujtaba Husnain, Malik Muhammad Saad Missen, Shahzad Mumtaz, Dost Muhammad Khan, Mickäel Coustaty, Muhammad Muzzamil Luqman, Jean-Marc Ogier, Hizbullah Khattak, Sikandar Ali, Ali Samad

“…We performed three tasks in a disciplined order; namely, (i) we generated a state-of-the-art dataset of both the Urdu handwritten characters and numerals by inviting a number of native Urdu participants from different social and academic groups, since there is no publicly available dataset of such type till date, then (ii) applied classical approaches of dimensionality reduction and data visualization like Principal Component Analysis (PCA), Autoencoders (AE) in comparison with t-Stochastic Neighborhood Embedding (t-SNE), and (iii) used the reduced dimensions obtained through PCA, AE, and t-SNE for recognition of Urdu handwritten characters and numerals using a deep network like Convolution Neural Network (CNN). The accuracy achieved in recognition of Urdu characters and numerals among the approaches for the same task is found to be much better. …”
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Internet of Things Assisted Plant Disease Detection and Crop Management Using Deep Learning for Sustainable Agriculture by Eman A. Al-Shahari, Ghadah Aldehim, Mohammed Aljebreen, Jehad Saad Alqurni, Ahmed S. Salama, Sitelbanat Abdelbagi

“…DL algorithms, specifically convolutional neural networks (CNN), analyze this massive dataset, facilitating timely and accurate recognition of plant diseases. …”
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Real-time detection and monitoring of public littering behavior using deep learning for a sustainable environment by Eaman Alharbi, Ghadah Alsulami, Sarah Aljohani, Waad Alharbi, Somayah Albaradei

“…This dataset was then used to train different models, including LRCN, CNN-RNN, and MoViNets. After extensive testing, MoViNets demonstrated the most promising results. …”
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SACNN‐IDS: A self‐attention convolutional neural network for intrusion detection in industrial internet of things by Mimonah Al Qathrady, Safi Ullah, Mohammed S. Alshehri, Jawad Ahmad, Sultan Almakdi, Samar M. Alqhtani, Muazzam A. Khan, Baraq Ghaleb

“…The proposed architecture has a self‐attention layer to calculate the input attention and convolutional neural network (CNN) layers to process the assigned attention features for prediction. …”
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