Development and application of a model for the automatic evaluation and classification of onions (Allium cepa L.) using a Deep Neural Network (DNN) by Piotr Rybacki, Przemysław Przygodziński, Przemysław Łukasz Kowalczewski, Zuzanna Sawinska, Ireneusz Kowalik, Andrzej Osuch, Ewa Osuch

“… Evaluating onions for size, shape, damage, colour and discolouration is the first and most important step in classifying them for raw material quality, processing and the horticultural and agri-food sectors. …”
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Insights into gait performance in Parkinson's disease via latent features of deep graph neural networks by Jiecheng Wu, Jiecheng Wu, Ning Su, Xinjin Li, Xinjin Li, Chao Yao, Jipeng Zhang, Xucheng Zhang, Wei Sun

“…Fortunately, advancements in computer science have provided serial ways to calculate gait-related parameters, offering a more accurate alternative to the complex and often imprecise assessments traditionally relied upon by trained professionals. However, most of the current methods depend on data preprocessing and feature engineering, often require domain knowledge and laborious human involvement, and require additional manual adjustments when dealing with new tasks.MethodsTo reduce the model's reliance on data preprocessing, feature engineering, and traversal rules, we employed the Spatial-Temporal Graph Convolutional Networks (ST-GCN) model. …”
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Clinical validation of an artificial intelligence algorithm for classifying tuberculosis and pulmonary findings in chest radiographs by Thiago Fellipe Ortiz de Camargo, Thiago Fellipe Ortiz de Camargo, Guilherme Alberto Sousa Ribeiro, Guilherme Alberto Sousa Ribeiro, Maria Carolina Bueno da Silva, Luan Oliveira da Silva, Pedro Paulo Teixeira e Silva Torres, Denise do Socorro da Silva Rodrigues, Mayler Olombrada Nunes de Santos, William Salibe Filho, Marcela Emer Egypto Rosa, Magdala de Araujo Novaes, Thiago Augusto Massarutto, Osvaldo Landi Junior, Elaine Yanata, Marcio Rodrigues da Cunha Reis, Gilberto Szarf, Pedro Vieira Santana Netto, Joselisa Peres Queiroz de Paiva

“…Notably, both Groups reported minimal influence of the algorithm on their decisions in most cases.…”
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Preprocessing Method for Performance Enhancement in CNN-Based STEMI Detection From 12-Lead ECG by Yeonghyeon Park, Il Dong Yun, Si-Hyuck Kang

“…We mostly focus on enhancing the detecting performance using a preprocessing technique. …”
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Fault diagnosis method for rigid guides in vertical shaft hoisting systems by WANG Jianfeng, JIN Yuanzhi, ZHANG Yong, WANG Yongzhen, HE Jiacong

“…To improve the accuracy of rigid guide fault identification, Residual Attention One-Dimensional Convolutional Neural Network (RA1DCNN) was proposed. …”
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A dual-phase deep learning framework for advanced phishing detection using the novel OptSHQCNN approach by Srikanth Meda, Vangipuram Sesha Srinivas, Killi Chandra Bhushana Rao, Repudi Ramesh, Narasimha Rao Yamarthi

“…Background Phishing attacks are now regarded as one of the most prevalent cyberattacks that often compromise the security of different communication and internet networks. …”
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TSD-Net: A Traffic Sign Detection Network Addressing Insufficient Perception Resolution and Complex Background by Chengcheng Ma, Chang Liu, Litao Deng, Pengfei Xu

“…By incorporating the C3k2 module and dynamic convolution into the network, the framework achieves enhanced feature extraction flexibility while maintaining high computational efficiency. …”
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Choice of machine learning models for predicting the development of psychological disorders in people with hypothireosis and hyperthireosis by Нурал Гулієв

“…For the experiment, all features were converted into quantitative ones to calculate convolution values. The evaluation criteria are given in the paper. …”
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Signal Processing for Transient Flow Rate Determination: An Analytical Soft Sensor Using Two Pressure Signals by Faras Brumand-Poor, Tim Kotte, Enrico Gaspare Pasquini, Katharina Schmitz

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Maize yield estimation in Northeast China’s black soil region using a deep learning model with attention mechanism and remote sensing by Xingke Li, Yunfeng Lyu, Bingxue Zhu, Lushi Liu, Kaishan Song

“…This framework integrates a one-dimensional convolutional neural network (1D-CNN), bidirectional gated recurrent units (BiGRU), and an attention mechanism to effectively characterize and weight key segments of input data. …”
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The classification of EMG signals using machine learning for the construction of a silent speech interface. by Chandrashekhar, V.

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An efficient graph attention framework enhances bladder cancer prediction by Taghreed S. Ibrahim, M. S. Saraya, Ahmed I. Saleh, Asmaa H. Rabie

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Benchmarking the NXP i.MX8M+ neural processing unit: smart parking case study by Edgar Chaves-González, Luis G. León-Vega

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A comprehensive survey on RGB-D-based human action recognition: algorithms, datasets, and popular applications by Yumin Zhang, Yanyong Wang

“…Abstract Due to the rapid advances in computer vision and deep learning, human action recognition has become one of the most important representative tasks for video understanding. …”
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A study on Chinese language Cross-Modal pedestrian image information retrieval by Yan Xie, Jian Qu

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Explainable AI for Alzheimer Detection: A Review of Current Methods and Applications by Fatima Hasan Saif, Mohamed Nasser Al-Andoli, Wan Mohd Yaakob Wan Bejuri

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The Comparison of Activation Functions in Feature Extraction Layer using Sharpen Filter by Oktavia Citra Resmi Rachmawati, Ali Ridho Barakbah, Tita Karlita

“…This study investigates the impact of five widely used activation functions—ReLU, SELU, ELU, sigmoid, and tanh—on convolutional neural network (CNN) performance when combined with sharpening filters for feature extraction. …”
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A Proposed Method for Deep Learning-Based Automatic Tracking with Minimal Training Data for Sports Biomechanics Research by Daichi Yamashita, Minoru Matsumoto, Takeo Matsubayashi

“…These annotated frames are subsequently used to train a deep learning model that leverages a pre-trained VGG16 network as its backbone and incorporates an additional convolutional head. Feature maps extracted from three intermediate layers of VGG16 are processed by the head network to generate a probability map, highlighting the most likely locations of the key points. …”
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A Method of Simplified Synthetic Objects Creation for Detection of Underwater Objects from Remote Sensing Data Using YOLO Networks by Daniel Klukowski, Jacek Lubczonek, Pawel Adamski

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A Global Deep Learning Perspective on Australia-Wide Monthly Precipitation Prediction by Luyi Shen, Guoqi Qian, Antoinette Tordesillas

“…Driven by this motivation, we present a specific spatiotemporal deep learning model that well integrates matrix factorization and temporal convolutional networks, along with essential year-month covariates and key climatic drivers, to analyze and forecast monthly precipitation in Australia. …”
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