Methodology for detecting anomalies in cyber attack assessment data using Random Forest and Gradient Boosting in machine learning by A. S. Kechedzhiev, O. L. Tsvetkova, A. I. Dubrovina

“…The research aims to detect anomalies in data using machine learning models, in particular random forest and gradient boosting, to analyze network activity and detect cyberattacks. …”
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Towards automated and real-time multi-object detection of anguilliform fishes from sonar data using YOLOv8 deep learning algorithm by Tao Huang, Xiaoqin Zang, Grigoriy Kondyukov, Zhangshuan Hou, Guanze Peng, Joachim Pander, Josef Knott, Juergen Geist, Meklit Berihun Melesse, Paul Jacobson, Zhiqun Daniel Deng

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Anomaly Detection Using Machine Learning in Hydrochemical Data From Hot Springs: Implications for Earthquake Prediction by Ruijie Zhu, Fengtian Yang, Xiaocheng Zhou, Jiao Tian, Yongxian Zhang, Miao He, Jingchao Li, Jinyuan Dong, Ying Li

“…Abstract This study explores the potential of machine learning algorithms for earthquake prediction, utilizing fluid chemical anomaly data from hot springs. …”
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Comparative analysis of machine learning algorithms for money laundering detection by Sunday Adeola Ajagbe, Simphiwe Majola, Pragasen Mudali

“…This research examined contemporary machine learning (ML) algorithms, including XGBoost, K-Nearest Neighbors, Random Forest, Isolation Forest, and Support Vector Machines, to analyze transaction data for anomalies indicative of fraudulent behavior. …”
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Deep learning algorithms for detecting fractured instruments in root canals by Ekin Deniz Çatmabacak, İrem Çetinkaya

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Robotic arm target detection algorithm combined with deep learning by ZHANG Lei, ZHANG Wang, YUAN Yuan

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Synergizing advanced algorithm of explainable artificial intelligence with hybrid model for enhanced brain tumor detection in healthcare by Kamini Lamba, Shalli Rani, Mohammad Shabaz

“…As understanding reasoning behind their predictions is still a great challenge for the healthcare professionals and raised a great concern about their trustworthiness, interpretability and transparency in clinical settings. Thus, an advanced algorithm of explainable artificial intelligence (XAI) has been synergized with hybrid model comprising of DenseNet201 network for extracting the most important features based on the input Magnetic resonance imaging (MRI) data following supervised algorithm, support vector machine (SVM) to distinguish distinct types of brain scans. …”
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Road Event Detection and Classification Algorithm Using Vibration and Acceleration Data by Abiel Aguilar-González, Alejandro Medina Santiago

“…In this work, we propose a Random Forest-based event classification algorithm designed to handle the unique patterns of vibration and acceleration data in road event detection for an urban traffic scenario. …”
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Pathways to chronic disease detection and prediction: Mapping the potential of machine learning to the pathophysiological processes while navigating ethical challenges by Ebenezer Afrifa‐Yamoah, Eric Adua, Emmanuel Peprah‐Yamoah, Enoch O. Anto, Victor Opoku‐Yamoah, Emmanuel Acheampong, Michael J. Macartney, Rashid Hashmi

“…Machine learning (ML) techniques offer considerable promise in unlocking new pathways for data‐driven chronic disease risk assessment and prognosis. …”
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Novel metrics and LSH algorithms for unsupervised, real-time anomaly detection in multi-aspect data streams by Samira Khodabandehlou, Alireza Hashemi Golpayegani

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Deep learning detects entire multiple-size lunar craters driven by elevation data and topographic knowledge by Liyang Xiong, Yanxiang Wang, Haoyu Cao, Yingchao Ren, Sijin Li, Yang Chen, Guoan Tang

“…Therefore, in this study, we propose a deep learning Crater Detection Algorithms (CDA), called Lunar Topographic Knowledge Attention U-Net (LTKAU-Net) that integrates a Digital Elevation Model (DEM) and topographic knowledge. …”
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A Combined Approach Of Adasyn And Tomeklink For Anomaly Network Intrusion Detection System Using Some Selected Machine Learning Algorithms by Nasiru Ige Salihu, Muhammed Nazeer Musa, Awujola J. Olalekan

“…Securing computer networks against malicious attacks requires an efficient Network Intrusion Detection System (IDS). While machine learning techniques are commonly used for anomaly-based intrusion detection, data imbalance challenges conventional algorithms, leading to biased predictions and reduced accuracy. …”
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Deep Learning Multi-Modal Melanoma Detection: Algorithm Development and Validation by Nithika Vivek, Karthik Ramesh

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Advanced Methodology for Fraud Detection in Energy Using Machine Learning Algorithms by Silviu Gresoi, Grigore Stamatescu, Ioana Făgărășan

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Sara Detection on Social Media Using Deep Learning Algorithm Development by M. Khairul Anam, Lucky Lhaura Van FC, Hamdani Hamdani, Rahmaddeni Rahmaddeni, Junadhi Junadhi, Muhammad Bambang Firdaus, Irwanda Syahputra, Yuda Irawan

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Contrastive Learning Algorithm for Low-Resource Cryptographic Attack Event Detection by Peng Luo, Rangjia Cai, Yuanbo Guo

“…Thus, we propose a method CLAD: Contrastive Learning Algorithm for Detecting Low-resource Cryptographic Attack Event. …”
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Research on the Application of Deep Learning Algorithm in the Damage Detection of Steel Structures by Qingyun Ge, Caimei Li, Fulian Yang

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Using Machine Learning Algorithms in Intrusion Detection Systems: A Review by Mazin S. Mohammed, Hasanien Ali Talib

“…Future research directions include advanced ML algorithms for IoT data, integration of anomaly detection, exploration of federated learning, and combining ML with other cybersecurity techniques. …”
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A deep learning-based algorithm for the detection of personal protective equipment. by Bo Tong, Guan Li, Xiangli Bu, Yang Wang, Xingchen Yu

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Deep Learning-Based Data-Assisted Channel Estimation and Detection by Hamidreza Hashempoor, Wan Choi

“…Alongside, we develop the Correctness Classifier, a classification algorithm adept at distinguishing correctly detected data by leveraging the denoised received signal. …”
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