Importance Analysis of Vegetation Change Factors in East Africa Based on Machine Learning by Zhang Xiumei, Ma Bo, Zhang Yijie

“…Precipitation was the most important climatic factor affecting vegetation changes. …”
Get full text

On the usage of artificial intelligence in leprosy care: A systematic literature review. by Hilson Gomes Vilar de Andrade, Elisson da Silva Rocha, Kayo H de Carvalho Monteiro, Cleber Matos de Morais, Danielle Christine Moura Dos Santos, Dimas Cassimiro Nascimento, Raphael A Dourado, Theo Lynn, Patricia Takako Endo

“…It can result in physical disabilities and functional loss and is particularly prevalent amongst the most vulnerable populations in tropical and subtropical regions worldwide. …”
Get full text

A Model for Diagnosing Mild Nutrient Stress in Facility-Grown Tomatoes Throughout the Entire Growth Cycle by Yunpeng Yuan, Guoxiang Sun, Guangyu Chen, Qihua Zhang, Lingwei Liang

“…The study compares the diagnostic performance of Random Forest (RF), Support Vector Machine (SVM), Partial Least Squares (PLS), Convolutional Neural Networks (CNNs), and CNN + Long Short-Term Memory (LSTM) models for detecting mild nutrient stress in facility-grown tomatoes. …”
Get full text

Development of metastasis and survival prediction model of luminal and non-luminal breast cancer with weakly supervised learning based on pathomics by Hui Liu, Linlin Ying, Xing Song, Xueping Xiang, Shumei Wei

“…Objective Breast cancer stands as the most prevalent form of cancer among women globally. …”
Get full text

Wearable Regionally Trained AI-Enabled Bruxism-Detection System by Anusha Ishtiaq, Jahanzeb Gul, Zia Mohy Ud Din, Azhar Imran, Khalil El Hindi

“…The other eight classifiers have provided accuracies in descending order such as Convolutional Neural Network, Long Short-Term Memory, k-Nearest Neighbors, and Decision Tree 0.98; Logistic Regression 0.96; Support Vector Machine 0.97, and Naïve Bayes 0.89, respectively. …”
Get full text

Binding Affinity Prediction for Pancreatic Ductal Adenocarcinoma Using Drug-Target Descriptors and Artificial Intelligence by Pragya, A. Amalin Prince, Jac Fredo Agastinose Ronickom

“…Pancreatic ductal adenocarcinoma (PDAC) is the most common and aggressive form of pancreatic cancer, accounting for 90% of all pancreatic malignancies. …”
Get full text

SkinIncept: an ensemble transfer learning-based approach for multiclass skin disease classification using InceptionV3 and InceptionResNetV2 by Md. Hasan Imam Bijoy, Md. Mahbubur Rahman, Abdus Sattar, Aminul Haque, Mohammad Shamsul Arefin, Pranab Kumar Dhar, Tetsuya Shimamura

“…This study addresses this critical issue by developing a robust and accurate system for classifying Bangladesh’s ten most common skin diseases using convolutional neural networks (CNNs)-based transfer learning models. …”
Get full text

Impact of lens autofluorescence and opacification on retinal imaging by Frank G Holz, Maximilian Pfau, Monika Fleckenstein, Raffael Liegl, Geena C Rennen, Marc Vaisband, Jan Hasenauer

“…A regression model for predicting image quality was developed using a convolutional neural network (CNN). Correlation analysis was conducted to assess the association of lens scores, with retinal image quality derived from human or CNN annotations.Results Retinal image quality was generally high across all imaging modalities (IR (8.25±1.99) >GAF >BAF (6.6±3.13)). …”
Get full text

Enhanced Disc Herniation Classification Using Grey Wolf Optimization Based on Hybrid Feature Extraction and Deep Learning Methods by Yasemin Sarı, Nesrin Aydın Atasoy

“…The proposed approach begins with feature extraction using ResNet50, a deep convolutional neural network known for its robust feature representation capabilities. …”
Get full text

Forecasting Indoor Air Quality in Mexico City Using Deep Learning Architectures by Jorge Altamirano-Astorga, J. Octavio Gutierrez-Garcia, Edgar Roman-Rangel

“…The deep learning architectures explored were multilayer perceptrons, long short-term memory neural networks, 1-dimension convolutional neural networks, and hybrid architectures, from which LSTM rose as the best-performing architecture. …”
Get full text

Machine-learning-based reconstruction of long-term global terrestrial water storage anomalies from observed, satellite and land-surface model data by N. Mandal, P. Das, K. Chanda, K. Chanda

“…Climate indices, like the Oceanic Niño Index and Dipole Mode Index, are selected as optimal predictors for a large number of grid cells globally, along with TWSAs from LSM outputs. The most effective machine learning (ML) algorithms among convolutional neural network (CNN), support vector regression (SVR), extra trees regressor (ETR) and stacking ensemble regression (SER) models are evaluated at each grid cell to achieve optimal reproducibility. …”
Get full text

ANN-SVM-IP: An Innovative Method for Rapidly and Efficiently Detecting and Classifying of External Defects of Apple Fruits by Nashaat M. Hussain Hassan, Mohamed M. Hassan Mahmoud, Mohamed A. Ismeil, M. Mourad Mabrook, A. A. Donkol, A. M. Mabrouk

“…The first phase attempts to detect exterior defects in apples by applying two proposed convolution kernels that were capable of identifying damaged sections of apples. …”
Get full text

SIG-ShapeFormer: A Multi-Scale Spatiotemporal Feature Fusion Network for Satellite Cloud Image Classification by Xuan Liu, Zhenyu Lu, Bingjian Lu, Zhuang Li, Zhongfeng Chen, Yongjie Ma

“…The temporal evolution of cloud systems plays a crucial role in accurate classification, particularly under the coexistence of multiple weather systems. However, most existing models—such as those based on convolutional neural networks (CNNs), Transformer architectures, and their variants like Swin Transformer—primarily focus on spatial modeling of static images and do not explicitly incorporate temporal information, thereby limiting their ability to effectively integrate spatiotemporal features. …”
Get full text

An interpretable wheat yield estimation model using an attention mechanism-based deep learning framework with multiple remotely sensed variables by Mingqi Li, Pengxin Wang, Kevin Tansey, Yue Zhang, Fengwei Guo, Junming Liu, Hongmei Li

“…The attention weights indicated that the most significant variable influencing wheat yield was FPAR, followed by LAI and VTCI. …”
Get full text

Hybrid Reinforcement Learning-Based Collision Avoidance Algorithm for Autonomous Vehicle Clusters by Chubing Guo, Jianshe Wu, Panzheng Luo, Zhigang Wang, Kai Zhang, Ziyi Yang, Zengfa Dou, Kan Song

“…The experimental results show that the proposed algorithm in this study showed a high success rate of collision avoidance and a low average reaction time under most collision avoidance times.…”
Get full text

Energy-Efficient on-Board Radio Resource Management for Satellite Communications via Neuromorphic Computing by Flor Ortiz, Nicolas Skatchkovsky, Eva Lagunas, Wallace A. Martins, Geoffrey Eappen, Saed Daoud, Osvaldo Simeone, Bipin Rajendran, Symeon Chatzinotas

“…To benchmark the performance of the proposed model, we implement conventional Convolutional Neural Networks (CNN) on a Xilinx Versal VCK5000, and provide a detailed comparison of accuracy, precision, recall, and energy efficiency for different traffic demands. …”
Get full text

Development of Smart Models to Accurately Predict Dynamic Viscosity of CO2-Saturated Polyethylene Glycol by Ayat Hussein Adhab, Morug Salih Mahdi, Bhavesh Kanabar, Anupam Yadav, Ranganathaswamy M K, Rishabh Thakur, Parveen Kumar, Braj Krishna, Aseel Salah Mansoor, Usama Kadem Radi, Nasr Saadoun Abd, Samim Sherzod

“…Ultimately, multilayer perceptron artificial neural network model is found to be the most accurate method for predicting CO2-saturated PEG viscosity.…”
Get full text

FTIR-Based Microplastic Classification: A Comprehensive Study on Normalization and ML Techniques by Octavio Villegas-Camacho, Iván Francisco-Valencia, Roberto Alejo-Eleuterio, Everardo Efrén Granda-Gutiérrez, Sonia Martínez-Gallegos, Daniel Villanueva-Vásquez

“…The study assessed the performance of ML algorithms, such as k-nearest neighbors (k-NN), support vector machines (SVM), naive Bayes (NB), random forest (RF), and artificial neural networks architectures (including convolutional neural networks (CNNs) and multilayer perceptrons (MLPs)). …”
Get full text

A forestry investigation: Exploring factors behind improved tree species classification using bark images by Gokul Kottilapurath Surendran, Deekshitha, Martin Lukac, Martin Lukac, Jozef Vybostok, Martin Mokros

“…Additionally, pre-processing techniques such as scaling can enhance accuracy to a certain extent. Convolutional Neural Networks (CNNs) consistently deliver the highest accuracy, even with diverse datasets, but fine-tuning these algorithms poses significant challenges for interdisciplinary researchers. …”
Get full text

Wearable Artificial Intelligence for Sleep Disorders: Scoping Review by Sarah Aziz, Amal A M Ali, Hania Aslam, Alaa A Abd-alrazaq, Rawan AlSaad, Mohannad Alajlani, Reham Ahmad, Laila Khalil, Arfan Ahmed, Javaid Sheikh

“…Respiratory data were used by 25 of 46 (54%) studies as the primary data for model development, followed by heart rate (22/46, 48%) and body movement (17/46, 37%). The most popular algorithm was the convolutional neural network, adopted by 17 of 46 (37%) studies, followed by random forest (14/46, 30%) and support vector machines (12/46, 26%). …”
Get full text