Optimizing maize germination forecasts with random forest and data fusion techniques by Lili Wu, Yuqing Xing, Kaiwen Yang, Wenqiang Li, Guangyue Ren, Debang Zhang, Huiping Fan

Get full text

Machine Learning for Anomaly Detection in Blockchain: A Critical Analysis, Empirical Validation, and Future Outlook by Fouzia Jumani, Muhammad Raza

“…Integrating machine learning algorithms with blockchain has become a significant approach to detecting anomalies such as a 51% attack and double spending. …”
Get full text

An end-to-end deep learning solution for automated LiDAR tree detection in the urban environment by Julian R. Rice, G. Andrew Fricker, Jonathan Ventura

“…This work proposes a novel end-to-end deep learning method for the detection of trees in the urban environment from remote sensing data. …”
Get full text

Enhancing Privacy in IoT-Enabled Digital Infrastructure: Evaluating Federated Learning for Intrusion and Fraud Detection by Amogh Deshmukh, Peplluis Esteva de la Rosa, Raul Villamarin Rodriguez, Sandeep Dasari

Get full text

Ai for anomaly detection in glacier movement identifying climate change effect using machine learning by Kaur Sukhmeen, Kumar Sunil

“…Using machine learning algorithms such as Logistic Regression, KNN, Random Forest, SVMs, and an Ensemble Model with XGBoost and LightGBM, the research seeks to improve the accuracy and reliability of anomaly detection. …”
Get full text

Advances in Skeleton-Based Fall Detection in RGB Videos: From Handcrafted to Deep Learning Approaches by Van-Ha Hoang, Jong Weon Lee, Md. Jalil Piran, Chun-Su Park

“…In this paper, we examine the most recent advances in skeleton-based fall detection in RGB videos, from handcrafted feature-based methods to advanced deep learning algorithms. …”
Get full text

Detection of circulating tumor cells by means of machine learning using Smart-Seq2 sequencing by Krzysztof Pastuszak, Michał Sieczczyński, Marta Dzięgielewska, Rafał Wolniak, Agata Drewnowska, Marcel Korpal, Laura Zembrzuska, Anna Supernat, Anna J. Żaczek

“…This work presents machine-learning-based classifiers that differentiate CTCs from peripheral blood mononuclear cells (PBMCs) based on single cell RNA sequencing data. …”
Get full text

A Fast and Cost-Effective Electronic Nose Model for Methanol Detection Using Ensemble Learning by Bilge Han Tozlu

Get full text

A Device for the Rapid Detection of Benzodiazepines and Synthetic Cannabinoids via Fluorescence Spectroscopy and Machine Learning by A. Power, M. Gardner, C. Pudney

“…Current experiments with established supervised-learning algorithms show favourable results in distinguishing Synthetic Cannabinoids. …”
Get full text

Detection of Psychomotor Retardation in Youth Depression: A Machine Learning Approach to Kinematic Analysis of Handwriting by Vladimir Džepina, Nikola Ivančević, Sunčica Rosić, Blažo Nikolić, Dejan Stevanović, Jasna Jančić, Milica M. Janković

“…After recursive feature elimination, classification was achieved through machine learning algorithms: logistic regression, support vector machine, and random forest. …”
Get full text

Real-Time Waste Detection and Classification Using YOLOv12-Based Deep Learning Model by Mosharof Hossain Dipo, Fahmid Al Farid, Md. Sifti Al Mahmud, Muntasir Momtaz, Shakila Rahman, Jia Uddin, Hezerul Abdul Karim

Get full text

Machine Learning Approaches for Fault Detection in Internal Combustion Engines: A Review and Experimental Investigation by A. Srinivaas, N. R. Sakthivel, Binoy B. Nair

“…This paper concludes with a review of the progress in fault identification in ICE components and prospects, highlighted by an experimental investigation using 16 machine learning algorithms with seven feature selection techniques under three load conditions to detect faults in a four-cylinder ICE. …”
Get full text

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

“…Industry 4.0 requires secure networks as the advancements in IoT and AI exacerbate the challenges and vulnerabilities in data security. This research focuses on detecting Bot-IoT activity using the Bot-IoT UNSW Canberra 2018 dataset. …”
Get full text

The detection of alcohol intoxication using electrooculography signals from smart glasses and machine learning techniques by Rafał J. Doniec, Natalia Piaseczna, Konrad Duraj, Szymon Sieciński, Muhammad Tausif Irshad, Ilona Karpiel, Mirella Urzeniczok, Xinyu Huang, Artur Piet, Muhammad Adeel Nisar, Marcin Grzegorzek

“…Their level of alcoholic intoxication was simulated by drunk vision goggles at three different levels of inebriation (0, 1, 2, and 3‰ blood alcohol content). We used machine learning algorithms (decision trees, support vector machines, nearest-neighbor classifiers, boosted trees, bagged trees, subspace discriminant classifier, subspace k nearest-neighbor classifier, and RUSBoosted Trees) to analyze the data. …”
Get full text

Deep learning algorithm for the automatic assessment of axial vertebral rotation in patients with scoliosis using the Nash–Moe method by Jeoung Kun Kim, Ming Xing Wang, Donghwi Park, Min Cheol Chang

Get full text

Fruit Detection Methods Based on Deep Learning in Agricultural Planting: A Systematic Literature Review by Xinyu Gong, Qiufeng Wu

“…Based on a comprehensive analysis of existing research, we classify deep learning-based fruit detection models into four application scenarios: few-shot detection (addressing limited data availability and high annotation costs), complex scene detection (resolving issues arising from object occlusion, overlapping, and variable illumination), small-target detection (improving performance on low-resolution and densely clustered objects), and real-time detection (designing lightweight algorithms for faster inference). …”
Get full text

Classification and Detection of Rumors Related to COVID-19 Using Machine Learning-Based Smart Techniques by Yancheng Yang, Junqiao Zhai, Shah Nazir

Get full text

Review of Surface-Defect Detection Methods for Industrial Products Based on Machine Vision by Quan Wang, Mengnan Wang, Jiadong Sun, Deji Chen, Pei Shi

“…The detection methods are then categorized into three main groups: traditional image processing, machine learning, and deep learning, with their principles, case studies, limitations, and future development directions analyzed. …”
Get full text

Detection of flap malperfusion after microsurgical tissue reconstruction using hyperspectral imaging and machine learning by Marianne Maktabi, Benjamin Huber, Toni Pfeiffer, Torsten Schulz

“…The purpose of this study was to combine machine learning and neural networks with HSI to develop a method for detecting flap malperfusion after microsurgical tissue reconstruction. …”
Get full text

Applying deep learning to teleseismic phase detection and picking: PcP and PKiKP cases by Congcong Yuan, Jie Zhang

“…Recently, deep learning algorithms exhibit a powerful capability of detecting and picking on P- and S-wave phases. …”
Get full text