U-Net-based VGG19 model for improved facial expression recognition by Xiaohu ZHAO, Jingyi ZHANG, Mingzhi JIAO, Lixun XIE, Lanfei WANG, Weiqing SUN, Di ZHANG

“…This design ensures the seamless integration of features from different layers, which is crucial for accurate facial expression recognition, as it maximizes the information yielded from each layer. …”
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Utilizing Inertial Measurement Units for Detecting Dynamic Stability Variations in a Multi-Condition Gait Experiment by Yasuhirio Akiyama, Kyogo Kazumura, Shogo Okamoto, Yoji Yamada

“…However, the correlation between MoS and the Lyapunov exponent or MSTS index was weak, suggesting that these indices may capture different aspects of gait stability.…”
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Enhanced melanoma and non-melanoma skin cancer classification using a hybrid LSTM-CNN model by Sara M. M. Abohashish, Hanan H. Amin, E. I. Elsedimy

“…In the proposed approach, the image of each skin lesion is divided into a sequence of tags of a particular size, which is then treated by the LSTM network to capture temporal dependence and relevant relationships between different spatial regions. This patching sequence allows the modeling system to analyze the local pattern in the image. …”
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Self-Supervised Foundation Model for Template Matching by Anton Hristov, Dimo Dimov, Maria Nisheva-Pavlova

“…Currently available methods fail when insufficient training data are available or big variations in the textures, different modalities, and weak visual features exist in the images, leading to limited applications on real-world tasks. …”
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Snoring Sound Recognition Using Multi-Channel Spectrograms by Ziqiang YE, Jianxin PENG, Xiaowen ZHANG, Lijuan SONG

“…This paper proposes a snoring sound detection algorithm using a multi-channel spectrogram and convolutional neural network (CNN). The sleep recordings from 30 subjects at the hospital were collected, and four different feature maps were extracted from them as model input, including spectrogram, Mel-spectrogram, continuous wavelet transform (CWT), and multi-channel spectrogram composed of the three single-channel maps. …”
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Visualizing Plant Disease Distribution and Evaluating Model Performance for Deep Learning Classification with YOLOv8 by Abdul Ghafar, Caikou Chen, Syed Atif Ali Shah, Zia Ur Rehman, Gul Rahman

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Trajectory Prediction and Intention Recognition Based on CNN-GRU by Jinghao Du, Dongdong Lu, Fei Li, Ke Liu, Xiaolan Qiu

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Machine learning-based pattern recognition of Bender element signals for predicting sand particle-size by Yong-Hoon Byun, Juik Son, Jungmin Yun, Hyunwook Choo, Jongmuk Won

“…A one-dimensional CNN analyzed time-series signals from bender elements across four sand types with particle sizes ranging from 0.5 to approximately 7 mm, under vertical stresses of 10, 50, and 150 kPa in three different cutoff frequencies (10, 50, and 100 kHz). …”
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A U-Shaped Architecture Based on Hybrid CNN and Mamba for Medical Image Segmentation by Xiaoxuan Ma, Yingao Du, Dong Sui

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Deep learning based model predictive control of active filter inverter as interface for photovoltaic generation by Amin Rasoulian, Hadi Saghafi, Mohammadali Abbasian, Majid Delshad

“…The numerical results based on different load types show an efficient performance of the proposed system and verify the superiority of the proposed method in comparison with the conventional MPC and several state‐of‐the‐arts shallow and deep based MPC for the PVs in terms of the total harmonic distortion (THD) and frequency switching.…”
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CNN-Based Phase Matching for the OAM Mode Selection in Turbulence Heterodyne Coherent Mitigation Links by Chunyong Yang, Kaige Shan, Jun Chen, Jin Hou, Shaoping Chen

“…It is worth mentioning that intelligent phase matching (IPM) of the OAM beams based on the convolutional neural network (CNN) is the remarkable feature. …”
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A data-driven approach for predicting remaining intra-surgical time and enhancing operating room efficiency by Saleem Ramadan, Mohammad Abu-Shams, Sameer Al-Dahidi, Ibrahim Odeh, Najat Almasarwah

“…The dataset comprises labeled laparoscopic cholecystectomy videos (time labels for different phases) used to train and evaluate the CNN. …”
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Triple-Stream Deep Feature Selection with Metaheuristic Optimization and Machine Learning for Multi-Stage Hypertensive Retinopathy Diagnosis by Süleyman Burçin Şüyun, Mustafa Yurdakul, Şakir Taşdemir, Serkan Biliş

“…The proposed method provides superior accuracy in the diagnosis of HR at different severity levels compared to single-model CNN approaches. …”
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A Study of Deep Learning Models for Audio Classification of Infant Crying in a Baby Monitoring System by Denisa Maria Herlea, Bogdan Iancu, Eugen-Richard Ardelean

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Comparison between observer-based and AI-based reading of CBCT datasets: An interrater-reliability study by Dirk Schulze, Lutz Häußermann, Julian Ripper, Thomas Sottong

“…Objective: To assess the performance of human observers and convolutional neural networks (CNNs) in detecting periodontal lesions in cone beam computed tomography (CBCT), a total of 38 datasets were examined. …”
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An enhanced deep learning model for accurate classification of ovarian cancer from histopathological images by Anik Kumar Saha, Muntezar Rabbani, Anika Saba Ibte Sum, M. F. Mridha, Md Mohsin Kabir

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Deep Learning-Based Performance Prediction of Electric Submersible Pumps Under Viscous and Gas–Liquid Flow Conditions by Haiwen Zhu, Hong Yu, Qiang Sun, Qiuchen Wang, Haorong Jing, Rakhymzhan Abdikadyrov

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Bringing Intelligence to SAR Missions: A Comprehensive Dataset and Evaluation of YOLO for Human Detection in TIR Images by Mostafa Rizk, Israa Bayad

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End-Edge Collaborative Lightweight Secure Federated Learning for Anomaly Detection of Wireless Industrial Control Systems by Chi Xu, Xinyi Du, Lin Li, Xinchun Li, Haibin Yu

“…Experimental results demonstrate that the proposed strategy achieves 99% accuracy on different datasets, where at least 89.6% wireless communication cost is reduced and tampering/injecting attacks are defended.…”
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Selective Intensity Ensemble Classifier (SIEC): A Triple-Threshold Strategy for Microscopic Malaria Cell Image Classification by Abdulaziz Anorboev, Sarvinoz Anorboeva, Javokhir Musaev, Esanbay Usmanov, Dosam Hwang, Yeong-Seok Seo, Jeongkyu Hong

“…This involves training three separate convolutional neural network models on the same images processed with different pixel-intensity thresholds: original, pixels above 100, and pixels above 200. …”
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