H-ConvLSTM to Estimate Reference Evapotranspiration From Air Temperature and Relative Humidity by Abdul Haris, M. Marimin, Sri Wahjuni, Budi Indra Setiawan

“…These three algorithms have been extensively evaluated and validated due to their ability to predict and estimate ETo data using temperature (T), relative humidity (RH), and solar radiation (Rs) as variables. This paper presents a comparison of the results of the three algorithms using only two variables, namely temperature and relative humidity, without the inclusion of solar radiation variables. …”
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Analyzing spatiotemporal variation in suspended particulate matter in lakes using remote sensing by WEI Junyan, ZHAO Yiming, HAO Yanling, JIA Xiaoxue, MA Xinyan

“…The most influential variable was the B4·B5, followed by B4, B4+B5, and B5·(B4+B5). ③ From 2017 to 2023, annual average SPM concentrations ranged from 8.43 to 11.68 mg/L, showing a slight downward trend. …”
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Towards the Development of the Clinical Decision Support System for the Identification of Respiration Diseases via Lung Sound Classification Using 1D-CNN by Syed Waqad Ali, Muhammad Munaf Rashid, Muhammad Uzair Yousuf, Sarmad Shams, Muhammad Asif, Muhammad Rehan, Ikram Din Ujjan

“…Respiratory disorders are commonly regarded as complex disorders to diagnose due to their multi-factorial nature, encompassing the interplay between hereditary variables, comorbidities, environmental exposures, and therapies, among other contributing factors. …”
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Feature Fusion to Improve YOLOv8 for Segmenting and Classifying Aerial Images of Tree Crowns by Ziyi Sun, Bing Xue, Mengjie Zhang, Jan Schindler

“…Instance segmentation techniques based on convolutional neural networks (CNNs) is a vital tool for accurately identifying and segmenting individual tree crowns, which plays an essential role in environmental monitoring and forest management. …”
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Deep Fuzzy Credibility Surfaces for Integrating External Databases in the Estimation of Operational Value at Risk by Alejandro Peña, Lina M. Sepúlveda-Cano, Juan David Gonzalez-Ruiz, Nini Johana Marín-Rodríguez, Sergio Botero-Botero

“…The stability provided by the DFCS model could be evidenced through the structure exhibited by the aggregate loss distributions (ALDs), which are obtained as a result of the convolution process between frequency and severity random variables for each database and which are expected to achieve similar structures to the probability distributions suggested by Basel II agreements (lean, long tail, positive skewness) against the OR modeling. …”
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Enhancing Slip, Trip, and Fall Prevention: Real-World Near-Fall Detection with Advanced Machine Learning Technique by Moritz Schneider, Kevin Seeser-Reich, Armin Fiedler, Udo Frese

“…This study systematically tests several machine-learning architectures for near-fall detection using the Prev-Fall dataset, which consists of high-resolution inertial measurement unit (IMU) data from 110 workers. Convolutional neural networks (CNNs), residual networks (ResNets), convolutional long short-term memory networks (convLSTMs), and InceptionTime models were trained and evaluated over a range of temporal window lengths using a neural architecture search. …”
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Landslide Susceptibility Prediction Based on a CNN–LSTM–SAM–Attention Hybrid Model by Honggang Wu, Jiabi Niu, Yongqiang Li, Yinsheng Wang, Daohong Qiu

“…In this study, we propose a Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Spatial Attention Mechanism (SAM) hybrid deep learning model designed for spatial landslide susceptibility prediction. …”
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SWRD–YOLO: A Lightweight Instance Segmentation Model for Estimating Rice Lodging Degree in UAV Remote Sensing Images with Real-Time Edge Deployment by Chunyou Guo, Feng Tan

“…However, Unmanned Aerial Vehicle (UAV)-based lodging detection faces challenges such as complex backgrounds, variable lighting, and irregular lodging patterns. …”
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Enhanced Localisation and Handwritten Digit Recognition Using ConvCARU by Sio-Kei Im, Ka-Hou Chan

“…Predicting the motion of handwritten digits in video sequences is challenging due to complex spatiotemporal dependencies, variable writing styles, and the need to preserve fine-grained visual details—all of which are essential for real-time handwriting recognition and digital learning applications. …”
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A pyramid Gaussian pooling based CNN and transformer hybrid network for smoke segmentation by Guiqian Wang, Feiniu Yuan, Hongdi Li, Zhijun Fang

“…Abstract Visual smoke semantic segmentation is a challenging task due to semi‐transparency, variable shapes, and complex textures of smoke. To improve segmentation performance, a convolutional neural network and transformer hybrid network are proposed based on pyramid Gaussian pooling (PGP) for smoke segmentation. …”
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An attack detection method based on deep learning for internet of things by Yihan Yu, Yu Fu, Taotao Liu, Kun Wang, Yishuai An

“…However, current attack detection methods struggle to identify complex and variable attack methods, resulting in a high false positive rate. …”
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Advancements and Challenges in Character Recognition: A Comparative Analysis of CNN and Deep Learning Approaches by Yang Ximin

“…This paper provides a comprehensive review of character recognition technologies, focusing on the application of Convolutional Neural Networks (CNN) and deep learning methodologies. …”
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Towards real-world monitoring scenarios: An improved point prediction method for crowd counting based on contrastive learning. by Rundong Cao, Jiazhong Yu, Ziwei Liu, Qinghua Liang

“…In open environments, complex and variable backgrounds and dense multi-scale targets are two key challenges for crowd counting. …”
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Intelligent model for forecasting fluctuations in the gold price by Mahdieh Tavassoli, Mahnaz Rabeei, Kiamars Fathi Hafshejani

“…Purpose: The present study aims to identify the most important variables affecting the fluctuations of gold prices. …”
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FORMAL REPRESENTATION OF THE PIXEL-BY-PIXEL CLASSIFICATION PROCESS USING A MODIFIED WANG-MENDEL NEURAL NETWORK by Oleksii Kolomiitsev, Volodymyr Pustovarov

“…The following methods and models are used: methods and models of fuzzy set theory (fuzzy Wang-Mendel neural network, interval fuzzy sets of the second type), methods and models of deep learning methodology (convolutional neural network for image segmentation (auto coder) U-net). …”
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Lightweight Deep Learning Model for Fire Classification in Tunnels by Shakhnoza Muksimova, Sabina Umirzakova, Jushkin Baltayev, Young-Im Cho

“…This model integrates MobileNetV3 for spatial feature extraction, Temporal Convolutional Networks (TCNs) for temporal sequence analysis, and advanced attention mechanisms, including Convolutional Block Attention Modules (CBAMs) and Squeeze-and-Excitation (SE) blocks, to prioritize critical features such as flames and smoke patterns while suppressing irrelevant noise. …”
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Optimized CNN-Bi-LSTM–Based BCI System for Imagined Speech Recognition Using FOA-DWT by Meenakshi Bisla, Radhey Shyam Anand

“…Neural correlates of speech imagery EEG signals are variable and weak as compared to the vocal state; hence, it is challenging to interpret them using machine learning (ML)–based classifiers. …”
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Prediction of the packaging chemical migration into food and water by cutting-edge machine learning techniques by Behzad Vaferi, Mohsen Dehbashi, Reza Yousefzadeh, Ali Hosin Alibak

“…This research uses five renowned AI-based techniques (namely, long short-term memory, gradient boosting regressor, multi-layer perceptron, Random Forest, and convolutional neural networks) to anticipate chemical migration from packaging materials to the food/water structure, considering variables such as temperature, chemical characteristics, and packaging/food types. …”
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Machine learning-based state of charge estimation: A comparison between CatBoost model and C-BLSTM-AE model by Abderrahim Zilali, Mehdi Adda, Khaled Ziane, Maxime Berger

“…The C-BLSTM-AE model achieves a low Mean Absolute Error (MAE) of 0.52 % under fixed ambient temperature conditions and maintains a MAE of 1.03 % for variable ambient temperatures. The CatBoost model achieves a MAE of 0.69 % with fixed temperature settings and a MAE of 1.09 % under variable temperature conditions.…”
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Multimodal anomaly detection in complex environments using video and audio fusion by Yuanyuan Wang, Yijie Zhao, Yanhua Huo, Yiping Lu

“…The algorithm combines the innovative methods of spatio-temporal feature extraction and noise suppression, and aims to improve the processing performance, especially in complex environments, by introducing an improved Variable Auto Encoder (VAE) structure. The model named Spatio-Temporal Anomaly Detection Network (STADNet) captures the spatio-temporal features of video images through multi-scale Three-Dimensional (3D) convolution module and spatio-temporal attention mechanism. …”
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