Classification of Garden Chrysanthemum Flowering Period Using Digital Imagery from Unmanned Aerial Vehicle (UAV) by Jiuyuan Zhang, Jingshan Lu, Qimo Qi, Mingxiu Sun, Gangjun Zheng, Qiuyan Zhang, Fadi Chen, Sumei Chen, Fei Zhang, Weimin Fang, Zhiyong Guan

Get full text

Autonomous Waste Classification Using Multi-Agent Systems and Blockchain: A Low-Cost Intelligent Approach by Sergio García González, David Cruz García, Rubén Herrero Pérez, Arturo Álvarez Sanchez, Gabriel Villarrubia González

“…For the computer vision algorithm, three versions of YOLO (YOLOv8, YOLOv11, and YOLOv12) were used and evaluated with respect to their performance in automatic detection and classification of waste. …”
Get full text

Pest classification: Explainable few-shot learning vs. convolutional neural networks vs. transfer learning by Nitiyaa Ragu, Jason Teo

Get full text

The hybrid feature extraction method for classification of adolescence idiopathic scoliosis using Evolving Spiking Neural Network by Nurbaity Sabri, Haza Nuzly Abdull Hamed, Zaidah Ibrahim, Kamalnizat Ibrahim, Mohd Adham Isa, Norizan Mat Diah

Get full text

TFSNet: A Time–Frequency Synergy Network Based on EEG Signals for Autism Spectrum Disorder Classification by Lijuan Shi, Lintao Ma, Jian Zhao, Zhejun Kuang, Sifan Wang, Han Yang, Haiyan Wang, Qiulei Han, Lei Sun

Get full text

Fusion-driven semi-supervised learning-based lung nodules classification with dual-discriminator and dual-generator generative adversarial network by Ahmed Saihood, Wijdan Rashid Abdulhussien, Laith Alzubaid, Mohamed Manoufali, Yuantong Gu

Get full text

Enhancing indoor place classification for mobile robots using RGB-D data and deep learning architectures by van Eden Beatrice, Botha Natasha

“…Place classification is crucial for a robot's ability to make high- level decisions. …”
Get full text

Leveraging machine learning for data-driven building energy rate prediction by Nasim Eslamirad, Mehdi Golamnia, Payam Sajadi, Francesco Pilla

Get full text

Evaluation and Classification of Behavioral Biases According to Thinking Styles, Risky Investment Intention, and Subjective Financial Literacy by Selim Aren, Hatice Nayman Hamamcı

Get full text

Neurovision: A deep learning driven web application for brain tumour detection using weight-aware decision approach by Thota Rishik Sai Santhosh, Sachi Nandan Mohanty, Nihar Ranjan Pradhan, Tauseef Khan, Morched Derbali

“…In the weight-aware decision module, the class-bucket of a probable output class is updated with the weights of deep models when their predictions match the class. …”
Get full text

Predicting mechanical ventilation duration in ICU patients: A data-driven machine learning approach for clinical decision-making by Shivi Mendiratta, Vinay Gandhi Mukkelli, Esha Baidya Kayal, Puneet Khanna, Amit Mehndiratta

“…Two models were developed: (1) A regression model (n = 323) to predict ventilation duration in days, and (2) A classification model (n = 218, non-tracheostomized) to predict short- (≤3 days) vs. long-term (>3 days) ventilation requirements. …”
Get full text

Customer Clustering and Marketing Optimization in Hospitality: A Hybrid Data Mining and Decision-Making Approach from an Emerging Economy by Maryam Deldadehasl, Houra Hajian Karahroodi, Pouya Haddadian Nekah

“…This study introduces a novel Recency, Monetary, and Duration (RMD) model for customer classification in the hospitality industry. …”
Get full text

Transfer deep learning and explainable AI framework for brain tumor and Alzheimer's detection across multiple datasets by Shtwai Alsubai, Stephen Ojo, Thomas I. Nathaniel, Mohamed Ayari, Jamel Baili, Ahmad Almadhor, Abdullah Al Hejaili

“…The proposed method utilizes a hybrid CNN-VGG16 model, which leverages pre-trained features from the VGG16 architecture to enhance classification performance across three distinct MRI datasets: brain tumor classification, Alzheimer's disease detection, and a third dataset of brain tumors. …”
Get full text

Machine learning based clinical decision tool to predict acute kidney injury and survival in therapeutic hypothermia treated neonates by Elif Keles, Syed Yaseen Ali, Pia Wintermark, Pieter Annaert, Floris Groenendaal, Suzan Şahin, Mehmet Yekta Öncel, Didem Armangil, Esin Koc, Malcolm R. Battin, Alistair J. Gunn, Adam Frymoyer, Valerie Chock, Djalila Mekahli, John van den Anker, Anne Smits, Karel Allegaert, Ulas Bagci

Get full text

Machine learning-assisted classification of hip conditions in pediatric cerebral palsy patients using migration percentage measurements by Sema Ertan Birsel, Ekrem Demirci, Ali Seker, Kadriye Yasemin Usta Ayanoğlu, Emir Oncu, Fatih Ciftci

“…This study highlights the potential of SVM models to enhance diagnostic accuracy, reduce variability in evaluations, and support clinical decision-making. …”
Get full text

GeoDisasterAINet: An Explainable Deep Ensemble Framework for Real-Time Urban and Rural Disaster Classification and Resilience by Akella S. Narasimha Raju, Seelam Sreekanth, Ranjith Kumar Gatla, M. Rajababu, Devineni Gireesh Kumar, Aymen Flah, Claude Ziad El-Bayeh, Khaled A. El-Nagdy, Ali Alzaed

“…There are four successive phases in model performance improvement, with increasingly enhancing classification accuracy, namely an integrated CNN (baseline model), an integrated CNN with XGBoost (feature improvement model), an integrated CNN with XGBoost and Multiclass SVM (refinement model for decision boundary), and LIME segmentation for interpretability and model performance evaluation. …”
Get full text

An In-depth Investigation of OBIA Classification with High-Resolution Imagery: Unravelling the Explanations Behind Deep Learning and Machine Learning by E. O. Yilmaz, T. Kavzoglu

Get full text

A Hybrid Deep Transfer Learning-based Approach for COVID-19 Classification in Chest X-ray Images by Khosro Rezaee, Afsoon Badiei, Hossein Ghayoumi Zadeh, Saeed Meshgini

“…Our hybrid model reduces the feature vector size and classifies it in optimize manner to improve the decision-making process. …”
Get full text

Multi-classification Deep Learning Approach for Diagnosing Stroke Type and Severity Using Multimodal Magnetic Resonance Images by Sahar Felehgari, Payam Sariaslani, Sepideh Shamsizadeh, Saba Felehgari, Anahita Rajabi, Hiwa Mohammadi

“…Results: In stroke classification (normal, ischemic, and hemorrhagic), ACL-MobileNetV1 outperformed other models, achieving 98% accuracy, 99% sensitivity, 98% specificity, and 99% AUC. …”
Get full text

A Multimodal Convolutional Neural Network Based Approach for DICOM Files Classification. , 2025(1), e70107. by Mabirizi, Vicent, Wasswa, William, Kawuma, Simon

Get full text