Green Ground: Construction and Demolition Waste Prediction Using a Deep Learning Algorithm by Wadha N. Alsheddi, Shahad E. Aljayan, Asma Z. Alshehri, Manar F. Alenzi, Norah M. Alnaim, Maryam M. Alshammari, Nouf K. AL-Saleem, Abdulaziz I. Almulhim

“…The waste management and recycling industry in Saudi Arabia is facing ongoing challenges in reducing the negative impact resulting from the recycling process. Different types of waste lack an efficient and accurate method for classification, especially in cases that require the rapid processing of materials. …”
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Transfer learning-enhanced CNN-GRU-attention model for knee joint torque prediction by Hao Xie, Yingpeng Wang, Tingting Liu, Songhua Yan, Jizhou Zeng, Kuan Zhang

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PlantDeepMeth: A Deep Learning Model for Predicting DNA Methylation States in Plants by Zhongwei Guo, Wenyuan Fan, Chengcheng Cai, Kang Zhang, Xilin Hou, Ying Li, Feng Cheng

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The Application of Deep Learning for Lymph Node Segmentation: A Systematic Review by Jingguo Qu, Xinyang Han, Man-Lik Chui, Yao Pu, Simon Takadiyi Gunda, Ziman Chen, Jing Qin, Ann Dorothy King, Winnie Chiu-Wing Chu, Jing Cai, Michael Tin-Cheung Ying

“…This study evaluates the application of deep learning in lymph node segmentation and discusses the methodologies of various deep learning architectures such as convolutional neural networks, encoder-decoder networks, and transformers in analyzing medical imaging data across different modalities. …”
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CNN for Computer Vision tasks by Р. Ковальчук, О. Польшакова

“…Different types of convolutional operations (expanded, partial, strided) and various CNN models (LeNet, AlexNet, VGGNet, GoogLeNet, ResNet) are also examined. …”
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Multi-branch feature learning based speech emotion recognition using SCAR-NET by Keji Mao, Yuxiang Wang, Ligang Ren, Jinhong Zhang, Jiefan Qiu, Guanglin Dai

“…In this paper, we propose SCAR-NET, an improved convolutional neural network, to extract emotional features from speech signals and implement classification. …”
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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

“…By focusing on the estimation of the margin of stability (MoS), a key kinematic stability parameter, a method using a convolutional neural network, was developed to estimate the MoS from IMU acceleration time-series data. …”
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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

“…This paper presents a novel approach for the automatic identification of cutaneous lesions by integrating convolutional neural networks (CNNs) with long short-term memory (LSTM) networks. …”
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Self-Supervised Foundation Model for Template Matching by Anton Hristov, Dimo Dimov, Maria Nisheva-Pavlova

“…As going deeper in the convolutional neural network (CNN) layers, their filters begin to react to more complex structures and their receptive fields increase. …”
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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

“…Abstract This study explores the potential of integrating bender element signals with a convolutional neural network (CNN) to predict the particle size distribution of relatively uniform sand. …”
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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

“…In the first stage, 14 well-known Convolutional Neural Network (CNN) models were evaluated, and the top three models were identified. …”
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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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