Transfer Learning based Image Classification of Diseased Tomato Leaves with Optimal Fine-Tuning combined with Heat Map Visualization by Sivakumar Palanıswamy, Vijayakumar Vaıthyam Rengarajan, Sandhya Devi Ramıah Subburaj

“…It is presumed that the implementation of deep learning algorithms demands a large amount of data to learn complex features automatically and this can pose a challenge for applications with lesser data to achieve generalization. …”
Get full text

RGB and Point Cloud-Based Intelligent Grading of Pepper Plug Seedlings by Fengwei Yuan, Guoning Ma, Qinghao Zeng, Jinghong Liu, Zhang Xiao, Zhenhong Zou, Xiangjiang Wang

“…Next, a deep learning-based object detection algorithm identifies the positions of individual seedlings in the RGB images. …”
Get full text

Monitoring Gypsiferous Soils by Leveraging Advanced Spaceborne Hyperspectral Imagery via Spectral Indices and a Machine Learning Approach by Najmeh Rasooli, Saham Mirzaei, Stefano Pignatti

“…The gypsum content was retrieved by optical data using three approaches: narrowband indices, spectral absorption features, and machine learning (ML) algorithms. …”
Get full text

Machine learning based identification of suicidal ideation using non-suicidal predictors in a university mental health clinic by Muhammed Ballı, Asli Ercan Dogan, Sevin Hun Senol, Hale Yapici Eser

“…The goal was to uncover less obvious risk factors and provide deeper insights into the complex relationships between psychiatric symptoms and suicidal ideation. Data from 924 university students seeking mental health services were analyzed using seven machine learning algorithms. …”
Get full text

Deep Learning-Based Instance Segmentation of Galloping High-Speed Railway Overhead Contact System Conductors in Video Images by Xiaotong Yao, Huayu Yuan, Shanpeng Zhao, Wei Tian, Dongzhao Han, Xiaoping Li, Feng Wang, Sihua Wang

Get full text

A machine learning prediction model for Cardiac Amyloidosis using routine blood tests in patients with left ventricular hypertrophy by Yuling Pan, Qingkun Fan, Yu Liang, Yunfan Liu, Haihang You, Chunzi Liang

“…In this retrospective study, we aimed to leverage machine learning (ML) to create a diagnostic model for CA using data from routine blood tests. …”
Get full text

Digital remote monitoring of people with multiple sclerosis by Michelangelo Dini, Michelangelo Dini, Giancarlo Comi, Letizia Leocani, Letizia Leocani, Letizia Leocani

“…We conducted a PubMed search to collect original studies aimed at evaluating the use of AI and/or big data for digital remote monitoring of pwMS. We focus on tools and techniques applied to data from wearable sensors, smartphones, and other connected devices, as well as AI-based methods for the analysis of big data.ResultsWearable sensors and machine learning algorithms show significant promise in monitoring motor symptoms, such as fall risk and gait disturbances. …”
Get full text

Machine learning modeling and analysis of prognostic hub genes in cervical adenocarcinoma: a multi target therapy for enhancement in immunosurveillance by Madiha Jabeen Abbasi, Rashid Abbasi, ShuPeng Wu, Md Belal Bin Heyat, Ding Xianfeng, Huijie Jia, Aiwen Zheng

Get full text

Potential use of saliva infrared spectra and machine learning for a minimally invasive screening test for congenital syphilis in infants by Deise Cristina Dal’Ongaro, Cicero Cena, Bruno Spolon Marangoni, Daniele A. Soares-Marangoni

“…This study aimed to explore the development of a method for the diagnosis of congenital syphilis using saliva FTIR spectra and machine learning algorithms in infants aged 0 to 12 months. …”
Get full text

HSDT-TabNet: A Dual-Path Deep Learning Model for Severity Grading of Soybean Frogeye Leaf Spot by Xiaoming Li, Yang Zhou, Yongguang Li, Shiqi Wang, Wenxue Bian, Hongmin Sun

“…However, both conventional field monitoring and machine learning algorithms remain challenged in achieving rapid and accurate detection. …”
Get full text

Predictive analysis of clinical features for HPV status in oropharynx squamous cell carcinoma: A machine learning approach with explainability by Emily Diaz Badilla, Ignasi Cos, Claudio Sampieri, Berta Alegre, Isabel Vilaseca, Simone Balocco, Petia Radeva

“…Materials and Methods:: We employed the RADCURE dataset clinical information to train six Machine Learning algorithms, evaluating them via cross-validation for grid search hyper-parameter tuning and feature selection as well as a final performance measurement on a 20% sample test set. …”
Get full text

Combination of Remote Sensing and Artificial Intelligence in Fruit Growing: Progress, Challenges, and Potential Applications by Danielle Elis Garcia Furuya, Édson Luis Bolfe, Taya Cristo Parreiras, Jayme Garcia Arnal Barbedo, Thiago Teixeira Santos, Luciano Gebler

“…The analysis included the use of remote sensing (orbital and proximal) imagery and ML/DL algorithms to map crop areas, detect diseases, and monitor crop development, among other analyses. …”
Get full text

Serum peptide biomarkers by MALDI-TOF MS coupled with machine learning for diagnosis and classification of hepato-pancreato-biliary cancers by Piya Prajumwongs, Attapol Titapun, Vasin Thanasukarn, Apiwat Jareanrat, Natcha Khuntikeo, Krit Rattanarak, Nisana Namwat, Poramate Klanrit, Arporn Wangwiwatsin, Jarin Chindaprasirt, Supinda Koonmee, Prakasit Sa-Ngiamwibool, Nattha Muangritdech, Sawanya Charoenlappanit, Janthima Jaresitthikunchai, Sittiruk Roytrakul, Watcharin Loilome

“…Abstract This study aimed to investigate the potential of peptide mass fingerprints (PMFs) of the serum peptidome using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), in combination with machine learning algorithms—support vector machine (SVM) and random forest (RF)—for the diagnosis and classification of hepato-pancreato-biliary (HPB) cancers. …”
Get full text

Machine learning-based prediction of carotid intima–media thickness progression: a three-year prospective cohort study by An Zhou, Kui Chen, Kui Chen, Yonghui Wei, Qu Ye, Qu Ye, Yuanming Xiao, Rong Shi, Jiangang Wang, Wei-Dong Li

“…We evaluated seven machine learning algorithms: logistic regression, random forest, XGBoost, support vector machine (SVM), elastic net, decision tree, and neural network. …”
Get full text

Toward Automated Air Leak Localization: A Machine Learning-Enhanced Ultrasonic and LiDAR-SLAM Framework for Industrial Environments by Anthony Schenck, Walter Daems, Jan Steckel

“…To this end, we trained a series of classifiers using real-world experiment data, and show that, by using the best performing classifiers in our leak detection algorithm, we are able to drastically reduce the amount of false-positive leaks, without any meaningful impact on the true-positive leak detection performance. …”
Get full text

Using explainable machine learning and eye-tracking for diagnosing autism spectrum and developmental language disorders in social attention tasks by Adoración Antolí, Adoración Antolí, Francisco Javier Rodriguez-Lozano, José Juan Cañas, Julia Vacas, Julia Vacas, Fátima Cuadrado, Fátima Cuadrado, Araceli Sánchez-Raya, Araceli Sánchez-Raya, Carolina Pérez-Dueñas, Carolina Pérez-Dueñas, Juan Carlos Gámez-Granados

“…Two key challenges must be addressed: the difficulty in reliably distinguishing between disorders with overlapping features, and the efficient management of eye-tracking data to yield clinically meaningful outcomes.PurposeThe aim of this study is to apply explainable machine learning (XML) algorithms to eye-tracking data from social attention tasks involving children with ASD, developmental language disorder (DLD), and typical development (TD), in order to assess classification accuracy and identify the variables that best differentiate between groups.MethodsNinety-three children participated in a visual preference task that paired social and non-social stimuli, specifically designed to capture features characteristic of ASD. …”
Get full text

Synergizing remote sensing, support vector machine, and aeromagnetic data for precise lithological and mineral potential mapping: a case study from Egypt by Hatem M. El-Desoky, Ahmed M. Abdel-Rahman, Hamada El-Awny, Wael Fahmy, Reda Abdu Yousef El-Qassas, Kamal Abdelrahman, Hassan Alzahrani, Peter Andráš, Yahia Z. Amer, Ahmed M. Eldosouky

“…Therefore, we effectively mapped the exposed rock units in the Hamash region of the Eastern Desert of Egypt by using Support Vector Machine (SVM) to Sentinel 2 data through executing machine learning algorithms (MLAs). …”
Get full text

Mapping Burned Forest Areas in Western Yunnan, China, Using Multi-Source Optical Imagery Integrated with Simple Non-Iterative Clustering Segmentation and Random Forest Algorithms i... by Yue Chen, Weili Kou, Wenna Miao, Xiong Yin, Jiayue Gao, Weiyu Zhuang

Get full text

Dengue Early Warning System and Outbreak Prediction Tool in Bangladesh Using Interpretable Tree‐Based Machine Learning Model by Md. Siddikur Rahman, Miftahuzzannat Amrin, Md. Abu Bokkor Shiddik

“…Methods A framework for forecasting DF risk was developed by using high‐performance ML algorithms, namely Random Forests, eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), based on sociodemographic, climate, landscape, and dengue surveillance epidemiological data (January 2000 to December 2021). …”
Get full text

Novel genes involved in vascular dysfunction of the middle temporal gyrus in Alzheimer’s disease: transcriptomics combined with machine learning analysis by Meiling Wang, Aojie He, Yubing Kang, Zhaojun Wang, Yahui He, Kahleong Lim, Chengwu Zhang, Li Lu

“…Finally, combining bulk RNA sequencing data and two machine learning algorithms (least absolute shrinkage and selection operator and random forest), four characteristic Alzheimer’s disease feature genes were identified: somatostatin (SST), protein tyrosine phosphatase non-receptor type 3 (PTPN3), glutinase (GL3), and tropomyosin 3 (PTM3). …”
Get full text