DeepATsers: a deep learning framework for one-pot SERS biosensor to detect SARS-CoV-2 virus by Ankhbayar Nyamdavaa, Kiran Kaladharan, Erdene-Ochir Ganbold, Seungdo Jeong, Seonuck Paek, Yansen Su, Fan-Gang Tseng, Tseren-Onolt Ishdorj

“…The deep learning model deployed optimal hyperparameters and outperformed in most measurements comparing supervised machine learning methods such as RF, GBM, SVM, and KNN, both with and without augmented spectral datasets. …”
Get full text

Convolutional transform learning based fusion framework for scale invariant long term target detection and tracking in unmanned aerial vehicles by Fatma S. Alrayes, Nazir Ahmad, Asma Alshuhail, Menwa Alshammeri, Ali Alqazzaz, Hassan Alkhiri, Jehad Saad Alqurni, Yahia Said

“…Moreover, the graph convolutional neural network (GCN) technique is employed for long-term target detection and tracking models. Finally, the hybrid nonlinear whale optimization algorithm with sine cosine (SCWOA) is implemented for the optimum choice of the hyperparameters involved in the GCN technique. …”
Get full text

Detection of Substation Pollution in District Heating and Cooling Systems: A Comprehensive Comparative Analysis of Machine Learning and Artificial Neural Network Models by Emrah ASLAN, Yıldırım ÖZÜPAK

“…The machine learning algorithms and the Convolutional Neural Network model are trained to perform fault detection at different contamination levels. In order to improve the performance of the machine learning models, hyperparameter tuning was performed by Grid Search Optimization method. …”
Get full text

An Application of Deep Learning Models for the Detection of Cocoa Pods at Different Ripening Stages: An Approach with Faster R-CNN and Mask R-CNN by Juan Felipe Restrepo-Arias, María José Montoya-Castaño, María Fernanda Moreno-De La Espriella, John W. Branch-Bedoya

“…Faster R-CNN achieved a mean average precision (mAP) of 64.15%, while Mask R-CNN reached 60.81%, with the highest per-class precision in mature pods (C4) but weaker detection in early stages (C1). To improve model robustness, the dataset was subsequently augmented and balanced, followed by targeted hyperparameter optimization for both architectures. …”
Get full text

Training a convolutional neural network for exoplanet classification with transit photometry data by Juliana Wang

“…This paper applies a convolutional neural network (CNN) to the Kepler dataset, which consists of time-series light curve data from the Kepler Space Telescope, used for detecting exoplanets through transit events. The final CNN architecture, with hyperparameters set as (300, 200, 200, 100, 100), was identified as the best-performing model after evaluating multiple configurations. …”
Get full text

A synergistic approach for enhanced eye blink detection using wavelet analysis, autoencoding and Crow-Search optimized k-NN algorithm by M. Chandralekha, N. Priyadharshini Jayadurga, Thomas M. Chen, Mithileysh Sathiyanarayanan, Kasif Saleem, Mehmet A. Orgun

“…Abstract This research endeavor introduces a state-of-the-art, assimilated approach for eye blink detection from Electroencephalography signals. It combines the prominent strategies of wavelet analysis, autoencoding, and a Crow-Search-optimized k-Nearest Neighbors to enhance the performance of eye blink detection from EEG signals. …”
Get full text

Deep learning with ensemble-based hybrid AI model for bipolar and unipolar depression detection using demographic and behavioral based on time-series data by Naga Raju Kanchapogu, Sachi Nandan Mohanty

“…Machine learning (ML) and deep learning (DL) offer automated approaches to detect depression using behavioral and demographic data.Methods This study proposes a hybrid AI framework combining structured demographic features with synthetic actigraph time-series data. …”
Get full text

Methodological Validation of Machine Learning Models for Non-Technical Loss Detection in Electric Power Systems: A Case Study in an Ecuadorian Electricity Distributor by Carlos Arias-Marín, Antonio Barragán-Escandón, Marco Toledo-Orozco, Xavier Serrano-Guerrero

“…Detecting fraudulent behaviors in electricity consumption is a significant challenge for electric utility companies due to the lack of information and the complexity of both constructing patterns and distinguishing between regular and fraudulent consumers. …”
Get full text

An intelligent framework for skin cancer detection and classification using fusion of Squeeze-Excitation-DenseNet with Metaheuristic-driven ensemble deep learning models by J. D. Dorathi Jayaseeli, J Briskilal, C. Fancy, V. Vaitheeshwaran, R. S. M. Lakshmi Patibandla, Khasim Syed, Anil Kumar Swain

“…This manuscript designs and develops a Detection of Skin Cancer Using an Ensemble Deep Learning Model and Gray Wolf Optimization (DSC-EDLMGWO) method. …”
Get full text

OperonSEQer: A set of machine-learning algorithms with threshold voting for detection of operon pairs using short-read RNA-sequencing data. by Raga Krishnakumar, Anne M Ruffing

“…We show that our approach detects operon pairs that are missed by current methods by comparing our predictions to publicly available long-read sequencing data. …”
Get full text

Toward Reliable Post-Disaster Assessment: Advancing Building Damage Detection Using You Only Look Once Convolutional Neural Network and Satellite Imagery by César Luis Moreno González, Germán A. Montoya, Carlos Lozano Garzón

Get full text

Deep learning-based approach to third molar impaction analysis with clinical classifications by Yunus Balel, Kaan Sağtaş

“…Abstract This study developed a deep learning model for the automated detection and classification of impacted third molars using the Pell and Gregory Classification, Winter’s Classification, and Pederson Difficulty Index. …”
Get full text

Utilization of Deep Learning YOLO V9 for Identification and Classification of Toraja Buffalo Breeds by Abdul Rachman Manga', Herawati Herawati, Purnawansyah Purnawansyah

“…This study aims to develop and evaluate a buffalo breed detection system that supports the cultural practices of the Toraja community, particularly in the context of the Rambu Solo’ ceremony. …”
Get full text

A Convolutional Neural Network Model for Classifying Resting Tremor Amplitude in Parkinson’s Disease by Augusto Ielo, Serena Dattola, Lilla Bonanno, Paolo De Pasquale, Alberto Cacciola, Angelo Quartarone, Maria Cristina De Cola

Get full text

Multi-Temporal Remote Sensing Satellite Data Analysis for the 2023 Devastating Flood in Derna, Northern Libya by Roman Shults, Ashraf Farahat, Muhammad Usman, Md Masudur Rahman

“…The various sets of hyperparameters for classification were considered. The high-resolution GEOEYE-1 images were used for precise change detection using image differencing (pixel-to-pixel comparison and geographic object-based image analysis (GEOBIA) for extracting building), whereas Sentinel-2 data were employed for the classification and further change detection by classified images. …”
Get full text

Identification of Spambots and Fake Followers on Social Network via Interpretable AI-Based Machine Learning by Danish Javed, Noor Zaman Zaman, Navid Ali Khan, Sayan Kumar Ray, Arafat Al-Dhaqm, Victor R. Kebande

“…To this end, we propose an interpretable machine learning (ML) framework, leveraging multiple ML algorithms with hyperparameters optimized through cross-validation, to enhance the detection process. …”
Get full text

Lightweight ECG signal classification via linear law-based feature extraction by Péter Pósfay, Marcell T Kurbucz, Péter Kovács, Antal Jakovác

Get full text

A Portable Real-Time Electronic Nose for Evaluating Seafood Freshness Using Machine Learning by Muhammad Rafi Mahfuz Setyagraha, Hurul Aini Nurqamaradillah, Laksamana Mikhail Hermawan, Nyoman Raflly Pratama, Ledya Novamizanti, Dedy Rahman Wijaya

“…This study presents an electronic nose (e-nose) system designed to assess seafood freshness using gas sensors and machine learning (ML) algorithms. The system detects volatile organic compounds (VOCs) released during spoilage and employs hyperparameter-optimized ML models for both classification (fresh vs. not fresh) and regression (shelf-life prediction). …”
Get full text

Detecting the Distribution of Callery Pear (<i>Pyrus calleryana</i>) in an Urban U.S. Landscape Using High Spatial Resolution Satellite Imagery and Machine Learning by Justin Krohn, Hong He, Timothy C. Matisziw, Lauren S. Pile Knapp, Jacob S. Fraser, Michael Sunde

“…Using Planetscope imagery, we trained a random forest model to detect Callery pear (<i>Pyrus calleryana</i>) throughout a diverse urban landscape in Columbia, Missouri. …”
Get full text

A Method for Predicting Coal-Mine Methane Outburst Volumes and Detecting Anomalies Based on a Fusion Model of Second-Order Decomposition and ETO-TSMixer by Qiangyu Zheng, Cunmiao Li, Bo Yang, Zhenguo Yan, Zhixin Qin

“…Furthermore, we propose an anomaly detection framework based on STL decomposition and dual lonely forests. …”
Get full text