Detection of Defects on Metal Surfaces Based on Deep Learning by Onur Cem Han, Uğurhan Kutbay

“…We propose a new method to improve defect detection rates, reduce labor losses, decrease data sizes, and improve energy efficiency. …”
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Artificial Intelligence for Smoking Detection: A Review of Machine Learning and Deep Learning Approaches by Mohammed Al-Hayali, Fawziya Ramo

“…Recent advances in deep learning, machine learning, Artificial Intelligence (AI), big data analytics, and computer vision have greatly enhanced smoking detection. …”
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Uncertainty Quantification in Data Fusion Classifier for Ship-Wake Detection by Maice Costa, Daniel Sobien, Ria Garg, Winnie Cheung, Justin Krometis, Justin A. Kauffman

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Epileptic Seizure Detection in EEG Signals Using Machine Learning and Deep Learning Techniques by Hepseeba Kode, Khaled Elleithy, Laiali Almazaydeh

“…This research presents a novel approach to detecting epileptic seizures leveraging the strengths of Machine Learning (ML) and Deep Learning (DL) algorithms in EEG signals. …”
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When Two are Better Than One: Synthesizing Heavily Unbalanced Data by Francisco Ferreira, Nuno Lourenco, Bruno Cabral, Joao Paulo Fernandes

“…Fraud Detection systems are examples of such systems where Machine Learning algorithms leverage information to classify financial transactions as legitimate or illicit. …”
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A comparative study of machine learning algorithms for fall detection in technology-based healthcare system: Analyzing SVM, KNN, decision tree, random forest, LSTM, and CNN by Afuan Lasmedi, Isnanto R. Rizal

“…The superiority of CNN and LSTM in detecting more complex fall patterns aligns with previous studies emphasizing the capabilities of deep learning models in sensor data classification. …”
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A systematic review of machine learning and deep learning approaches in plant species detection by Deepti Barhate, Sunil Pathak, Bhupesh Kumar Singh, Amit Jain, Ashutosh Kumar Dubey

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An Empirical Comparison of Machine Learning and Deep Learning Models for Automated Fake News Detection by Yexin Tian, Shuo Xu, Yuchen Cao, Zhongyan Wang, Zijing Wei

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Detection of child depression using machine learning methods. by Umme Marzia Haque, Enamul Kabir, Rasheda Khanam

“…The Boruta algorithm has been utilized in association with a Random Forest (RF) classifier to extract the most important features for depression detection among the high correlated variables with target variable. …”
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The Detection of Past and Future Land Use and Land Cover Change in Ugam Chatkal National Park, Uzbekistan, Using CA-Markov and Random Forest Machine Learning Algorithms by Bokhir Alikhanov, Bakhtiyor Pulatov, Luqmon Samiev

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Enhancing Fracture Detection in Remote Settings: Evaluating the Efficacy of FIXUS AI Deep Learning Algorithms in Identifying Fifth Metatarsal Fractures Using Mixed-Quality X-rays by Atta Taseh MD, Alireza Gholipour PhD, Mani Eftekhari, Alireza Ebrahimi MD, Alexandra F. Flaherty MD, MS, Alexandra F. Flaherty MD, MS, Sumner Jones, Varun Nukala, Gregory R. Waryasz MD, Daniel Guss MD, MBA, John Y. Kwon MD, Christopher W. DiGiovanni MD, Lorena Bejarano-Pineda MD, Soheil Ashkani-Esfahani MD

“…Conclusion: This study highlights the crucial role of image quality in developing deep learning models for detecting fifth metatarsal fractures. …”
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Detecting Unbalanced Network Traffic Intrusions With Deep Learning by S. Pavithra, K. Venkata Vikas

“…To overcome these challenges, this project proposes a novel hybrid Intrusion Detection System using machine learning algorithms, which includes XGBoost, Long Short-Term Memory (LSTM), Mini-VGGNet, and AlexNet, which is used to handle the unbalanced network traffic data. …”
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Advancing invasive species monitoring: A free tool for detecting invasive cane toads using continental-scale data by Franco Ka Wah Leung, Lin Schwarzkopf, Slade Allen-Ankins

“…Invasive species pose a significant threat to global biodiversity and ecosystem health, necessitating effective monitoring tools for early detection and management. Here, we present the development and assessment of a user-friendly and transferable monitoring tool for the invasive cane toad (Rhinella marina) using passive acoustic monitoring (PAM) and machine learning algorithms. …”
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Artificial intelligence-driven ensemble deep learning models for smart monitoring of indoor activities in IoT environment for people with disabilities by Munya A. Arasi, Hussah Nasser AlEisa, Amani A. Alneil, Radwa Marzouk

“…Conventional machine learning and deep learning approaches effectively detect human activity. …”
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Multi-strategy enhanced marine predator algorithm: performance investigation and application in intrusion detection by Zhongmin Wang, Yujun Zhang, Jun Yu, YuanYuan Gao, Guangwei Zhao, Essam H. Houssein, Rui Zhong

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Machine Learning in Microwave Medical Imaging and Lesion Detection by Wenyi Shao

“…This paper reviews ML algorithms, data acquisition, training techniques, and applications that have emerged in recent years. …”
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ADDAEIL: Anomaly Detection with Drift-Aware Ensemble-Based Incremental Learning by Danlei Li, Nirmal-Kumar C. Nair, Kevin I-Kai Wang

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Investigate the Use of Deep Learning in IoT Attack Detection by Mohamed Saddek Ghozlane, Adlen Kerboua, Smaine Mazouzi, Lakhdar Laimeche

“…In this field, deep learning algorithms have provided encouraging results in the discovery and classification of intrusions in IoT devices. …”
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Detecting Textual Propaganda Using Machine Learning Techniques by Akib Mohi Ud Din Khanday, Qamar Rayees Khan, Syed Tanzeel Rabani

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Performance Comparison of Random Forest and Decision Tree Algorithms for Anomaly Detection in Networks by Rafiq Fajar Ramadhan, Wahid Miftahul Ashari

“…From the study result, it can be conclude that the Decision Tree algorithm performs better in detecting anomalies in binary data with an accuracy of 99,71%. …”
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