SiCRNN: A Siamese Approach for Sleep Apnea Identification via Tracheal Microphone Signals by Davide Lillini, Carlo Aironi, Lucia Migliorelli, Leonardo Gabrielli, Stefano Squartini

“…Multiple experimental runs were carried out to determine the optimal network configuration and the most suitable type and frequency range for the input data. …”
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Artificial intelligence in pancreatic intraductal papillary mucinous neoplasm imaging: A systematic review. by Muhammad Ibtsaam Qadir, Jackson A Baril, Michele T Yip-Schneider, Duane Schonlau, Thi Thanh Thoa Tran, C Max Schmidt, Fiona R Kolbinger

“…Methodologically, convolutional neural network (CNN)-based algorithms were most commonly used. …”
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Determining the Level of Threat in Maritime Navigation Based on the Detection of Small Floating Objects with Deep Neural Networks by Mirosław Łącki

“…Several neural network structures were compared to find the most efficient solution, taking into account the speed and efficiency of network training and its performance during testing. …”
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Short-Term Target Maneuvering Trajectory Prediction Using DTW–CNN–LSTM by Haifeng Guo, Jinyi Yang, Xianyong Jing, Peng Zhang

“…This approach allows us to identify and select the most analogous historical data, which we then utilize as our training dataset. …”
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Review of image classification based on deep learning by Fu SU, Qin LV, Renze LUO

“…In recent years,deep learning performed superior in the field of computer vision to traditional machine learning technology.Indeed,image classification issue drew great attention as a prominent research topic.For traditional image classification method,huge volume of image data was of difficulty to process and the requirements for the operation accuracy and speed of image classification could not be met.However,deep learning-based image classification method broke through the bottleneck and became the mainstream method to finish these classification tasks.The research significance and current development status of image classification was introduced in detail.Also,besides the structure,advantages and limitations of the convolutional neural networks,the most important deep learning methods,such as auto-encoders,deep belief networks and deep Boltzmann machines image classification were concretely analyzed.Furthermore,the differences and performance on common datasets of these methods were compared and analyzed.In the end,the shortcomings of deep learning methods in the field of image classification and the possible future research directions were discussed.…”
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A survey: Breast Cancer Classification by Using Machine Learning Techniques by Ruaa Hassan Mohammed Ameen, Nasseer Moyasser Basheer, Ahmed Khazal Younis

“…The Naïve Bayes, the K-nearest neighbors (KNN), the Support Vector Machine (SVM), the Random Forest, the Logistic Regression, Multilayer Perceptron (MLP), fuzzy classifier, and Convolutional Neural Network (CNN) classifiers, are the most widely used technologies in this field. …”
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Mathematical Analysis and Performance Evaluation of the GELU Activation Function in Deep Learning by Minhyeok Lee

“…Selecting the most suitable activation function is a critical factor in the effectiveness of deep learning models, as it influences their learning capacity, stability, and computational efficiency. …”
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Stock Price Prediction in the Financial Market Using Machine Learning Models by Diogo M. Teixeira, Ramiro S. Barbosa

“…Fundamental concepts of technical analysis are explored, such as exponential and simple averages, and various global indices are analyzed to be used as inputs for machine learning models, including Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and XGBoost. The results show that while each model possesses distinct characteristics, selecting the most efficient approach heavily depends on the specific data and forecasting objectives. …”
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Can Artificial Intelligence Technology Help Achieving Good Governance: A Public Policy Evaluation Method Based on Artificial Neural Network by Zhinan Xu, Zijun Liu, Hang Luo

“…By leveraging empirical data and a deep learning model based on convolutional neural networks (CNN), the model achieves a high accuracy of 93.40%, surpassing most comparable models. …”
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DoS and DDoS Attack Detection in IoT Infrastructure using Xception Model with Explainability by Nelly Elsayed, Zag ElSayed, Ahmed Abdelgawad

“… The denial of service (DoS) and distributed denial of service (DDoS) attacks are considered the most frequent attacks targeting the Internet of Things (IoT) network infrastructure globally. …”
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Deep Learning for Urban Tree Canopy Coverage Analysis: A Comparison and Case Study by Grayson R. Morgan, Danny Zlotnick, Luke North, Cade Smith, Lane Stevenson

“…Several methods have been used to obtain these data, but remote sensing image classification is one of the fastest and most reliable over large areas. However, most studies have tested only one or two classification methods to accomplish this while using costly satellite imagery or LiDAR data. …”
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SiNC: Saliency-injected neural codes for representation and efficient retrieval of medical radiographs. by Jamil Ahmad, Muhammad Sajjad, Irfan Mehmood, Sung Wook Baik

“…Neuronal activation features termed as neural codes from different CNN layers are comprehensively studied to identify most appropriate features for representing radiographs. …”
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Machine learning opportunities to predict obstetric haemorrhages by Yu. S. Boldina, A. A. Ivshin

“…Machine learning is based on computer algorithms, the most common among them in medicine are the decision tree (DT), naive Bayes classifier (NBC), random forest (RF), support vector machine (SVM), artificial neural network (ANNs), deep neural network (DNN) or deep learning (DL) and convolutional neural network (CNN). …”
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Machine Learning-Based Approaches for Breast Density Estimation from Mammograms: A Comprehensive Review by Khaldoon Alhusari, Salam Dhou

“…Machine learning methods can be further broken down into two categories: traditional machine learning and deep learning approaches. The most commonly utilized models are support vector machines (SVMs) and convolutional neural networks (CNNs), with classification accuracies ranging from 76.70% to 98.75%. …”
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Internet of Things and Deep Learning for Citizen Security: A Systematic Literature Review on Violence and Crime by Chrisbel Simisterra-Batallas, Pablo Pico-Valencia, Jaime Sayago-Heredia, Xavier Quiñónez-Ku

“…A total of 45 studies published between 2010 and 2024 were selected, revealing that most research, primarily from India and China, focuses on cybersecurity in IoT networks (76%), while fewer studies address the surveillance of physical violence and crime-related events (17%). …”
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Harnessing the power of AI: Advanced deep learning models optimization for accurate SARS-CoV-2 forecasting. by Muhammad Usman Tariq, Shuhaida Binti Ismail, Muhammad Babar, Ashir Ahmad

“…Our research aims to determine the most reliable and accurate model for forecasting SARS-CoV-2 cases in the region. …”
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Advancements in Herpes Zoster Diagnosis, Treatment, and Management: Systematic Review of Artificial Intelligence Applications by Dasheng Wu, Na Liu, Rui Ma, Peilong Wu

“…Medical images (9/26, 34.6%) and electronic medical records (7/26, 26.9%) were the most commonly used data types. Classification tasks (85.2%) dominated AI applications, with neural networks, particularly multilayer perceptron and convolutional neural networks being the most frequently used algorithms. …”
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An Efficient Encoding Spectral Information in Hyperspectral Images for Transfer Learning of Mask R-CNN for Instance Segmentation of Tomato Sepals by Zeljana Grbovic, Marko Panic, Vladan Filipovic, Sanja Brdar, Hendrik de Villiers, Manon Mensink, Aneesh Chauhan

“…The most vulnerable parts of tomatoes are the tips of the sepals, which are the primary entry points for fungal spores. …”
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Efficient Robot Localization Through Deep Learning-Based Natural Fiduciary Pattern Recognition by Ramón Alberto Mena-Almonte, Ekaitz Zulueta, Ismael Etxeberria-Agiriano, Unai Fernandez-Gamiz

“…These images are processed by a convolutional neural network (CNN), designed to detect the most distinctive features of the environment. …”
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Identify suitable artificial groundwater recharge zones using hybrid deep learning models by Navaz Khalillollahi, Mohsen Isari, Hamed Faroqi, Kaywan Othman Ahmed, Kamran Nobakht Vakili, Miklas Scholz, Saad Sh. Sammeng

“…This study evaluated four deep learning models for delineating groundwater recharge zones: Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), and hybrid deep learning Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU). …”
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