Detection of left atrial enlargement in dogs using deep learning in thoracic radiographs by Lucas Auto Lopes, Anaemilia das Neves Diniz, Helio Silva Neto

Get full text

Automated concrete exposed reinforcement and corrosion detection and measurement using coloured illumination by Dow Hamish, Darby Andrew, Perry Marcus, Zhang Feng

“…Additionally, the algorithm’s corrosion surface area measurements produced a maximum relative error of negative 8 %. …”
Get full text

Decoding basic emotional states through integration of an fNIRS-based brain-computer interface with supervised learning algorithms. by Ayşenur Eser, Sinem Burcu Erdoğan

“…Classification performances of three machine learning algorithms, namely the k-Nearest Neighbors (kNN), Ensemble (Subspace kNN) and Support Vector Machines (SVM), in two class and three class classification of positive, neutral and negative states were evaluated with ten runs of a tenfold cross-validation procedure through splitting the data into test, train and validation groups at each run. …”
Get full text

Preliminary analysis of acoustic detection of the Red-throated Caracara in northern Costa Rica by Roberto Vargas-Masís, Diego Quesada

“…Advances in automatic acoustic detection have transformed bird ecology, allowing researchers to analyze bird populations using pattern matching algorithms, machine learning, and random forest models. …”
Get full text

Sentiment Modeling of Instagram Users Towards Traditional and Modern Body Scrubs Using the Naive Bayes Algorithm by Mardiana Mardiana, Kusrini Kusrini

“…The results indicate that neutral sentiment dominates, followed by negative and positive sentiments. The Naive Bayes algorithm demonstrated strong performance, particularly in detecting negative and neutral sentiments, but exhibited a lower recall rate for positive sentiments. …”
Get full text

Classification Model for Bot-IoT Attack Detection Using Correlation and Analysis of Variance by Firgiawan Faira, Dandy Pramana Hostiadi

“…This research focuses on detecting Bot-IoT activity using the Bot-IoT UNSW Canberra 2018 dataset. …”
Get full text

An Ensemble Hybrid Framework: A Comparative Analysis of Metaheuristic Algorithms for Ensemble Hybrid CNN Features for Plants Disease Classification by Khaoula Taji, Ali Sohail, Tariq Shahzad, Bilal Shoaib Khan, Muhammad Adnan Khan, Khmaies Ouahada

“…The automatic identification and categorization of illnesses affecting apple and maize plants is the focus of this paper’s investigation into the implementation of cutting-edge technical solutions, notably machine learning algorithms. Deep learning has recently made significant contributions to the automatic detection and classification of plants diseases specifically in fruits and vegetables. …”
Get full text

Leveraging SARs and Advanced Deep Learning Techniques for Oil Spill Detection in UAE by M. Alkhalifa, A. Alowais, A. Manousakis, A. Manousakis

“…Accidental oil spills are known to reflect negative outcomes on the environment and human health as well as marine life and coastal regions’ economy. …”
Get full text

Lightweight anomaly detection model for UAV networks based on memory-enhanced autoencoders by HU Tianzhu, SHEN Yulong, REN Baoquan, HE Ji, LIU Chengliang, LI Hongjun

“…The hierarchical clustering algorithm was used to divide the composite statistical features, and the separated features were input to multiple small memory-enhanced autoencoders in the integrated architecture for independent training, which reduced the computational complexity and solved the problem of false negatives caused by the overfitting of the reconstruction effect of the traditional autoencoder. …”
Get full text

A Deep Learning Segmentation Model for Detection of Active Proliferative Diabetic Retinopathy by Sebastian Dinesen, Marianne G. Schou, Christoffer V. Hedegaard, Yousif Subhi, Thiusius R. Savarimuthu, Tunde Peto, Jakob K. H. Andersen, Jakob Grauslund

“…Abstract Introduction Existing deep learning (DL) algorithms lack the capability to accurately identify patients in immediate need of treatment for proliferative diabetic retinopathy (PDR). …”
Get full text

Automated Detection of Reduced Ejection Fraction Using an ECG-Enabled Digital Stethoscope by Ling Guo, PhD, Gregg S. Pressman, MD, Spencer N. Kieu, BS, Scott B. Marrus, MD, PhD, George Mathew, PhD, John Prince, PhD, Emileigh Lastowski, MS, Rosalie V. McDonough, MD, MSc, Caroline Currie, BA, John N. Maidens, PhD, Hussein Al-Sudani, MD, Evan Friend, BA, Deepak Padmanabhan, MD, Preetham Kumar, MD, Edward Kersh, MD, Subramaniam Venkatraman, PhD, Salima Qamruddin, MD

“…Recently, electrocardiogram-based algorithms have shown promise in detecting ALVSD. Objectives: The authors developed and validated a convolutional neural network (CNN) model using single-lead electrocardiogram and phonocardiogram inputs captured by a digital stethoscope to assess its utility in detecting individuals with actionably low ejection fractions (EF) in a large cohort of patients. …”
Get full text

Integrating Color and Contour Analysis with Deep Learning for Robust Fire and Smoke Detection by Abror Shavkatovich Buriboev, Akmal Abduvaitov, Heung Seok Jeon

“…This study suggests a unique concatenated convolutional neural network (CNN) model that combines deep learning with hybrid preprocessing methods, such as contour-based algorithms and color characteristics analysis, to provide reliable and accurate fire and smoke detection. …”
Get full text

The inconvenient truth of ground truth errors in automotive datasets and DNN-based detection by Pak Hung Chan, Boda Li, Gabriele Baris, Qasim Sadiq, Valentina Donzella

Get full text

Identifying major depressive disorder among US adults living alone using stacked ensemble machine learning algorithms by Zhao Chen, Hao Liu, Yao Zhang, Fei Xing, Jiabao Jiang, Zhou Xiang, Zhou Xiang, Xin Duan, Xin Duan

“…We constructed a SEML model for MDD detection, incorporating three conventional machine learning algorithms as base models and a Neural Network (NN) as the meta-model. …”
Get full text

A Fast Fault Detection Scheme for Power Converters In Distributed Generation Systems by Gustavo M. S. Azevedo, Marcelo C. Cavalcanti, Francisco A. S. Neves, Fabricio Bradaschia

“… In this paper, a fault detection algorithm based on voltage disturbances is proposed and analyzed. …”
Get full text

Real Time Intrusion Detection System Based on Web Log File Analysis by Rawand Raouf Abdalla, Alaa Khalil Jumaa, Ahmad Freidoon Fadhil

“…In this work, various data preprocessing techniques are applied, and key features are extracted, enhancing the system's ability to effectively detect intrusions. The model was constructed using four machine learning algorithms: gradient-boosted trees, decision tree, random forest, and support vector machine. …”
Get full text

Anomaly Detection in Wireless Sensor Networks Using Immune-Based Bioinspired Mechanism by Ramsha Rizwan, Farrukh Aslam Khan, Haider Abbas, Sajjad Hussain Chauhdary

“…In this paper, we propose a bioinspired solution using Negative Selection Algorithm (NSA) of the AIS for anomalies detection in WSNs. …”
Get full text

Detecting memberships in multiplex networks via nonnegative matrix factorization and tensor decomposition by Fengqin Tang, Xiaozong Wang, Xuejing Zhao, Chunning Wang

“…Multiplex networks provide a powerful data structure for capturing diverse relationships among nodes, and the challenge of community detection within these networks has recently attracted considerable attention. …”
Get full text

A photovoltaic anomaly data identification method based on image feature detection by QIU Yutao, ZHANG Lei, ZHOU Kaiyun, YAN Min, SUN Jintong, LONG Huan

“…To address this， this paper introduces an anomaly data identification algorithm based on image feature detection and dual-threshold processing. …”
Get full text

Multimodal ultrasound deep learning to detect fibrosis in early chronic kidney disease by Xiachuan Qin, Xiaoling Liu, Linlin Xia, Qi Luo, Chaoxue Zhang

Get full text