An enhanced substation equipment detection method based on distributed federated learning by Zhuyun Li, Qiutong Qin, Yingyi Yang, Xiaoming Mai, Yuya Ieiri, Osamu Yoshie

“…This study addresses two key challenges: detecting diverse equipment under scale variations, occlusions, and real-time constraints, and ensuring data privacy given geographically dispersed, sensitive substation data. …”
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An Enhanced Deep Learning Model for Effective Crop Pest and Disease Detection by Yongqi Yuan, Jinhua Sun, Qian Zhang

“…Traditional machine learning methods struggle with plant pest and disease image recognition, particularly when dealing with small sample sizes, indistinct features, and numerous categories. …”
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Deep Learning-Based Postural Asymmetry Detection Through Pressure Mat by Iker Azurmendi, Manuel Gonzalez, Gustavo García, Ekaitz Zulueta, Elena Martín

“…In this paper we will discuss the application of deep learning algorithms in the analysis of pressure data for the detection of postural asymmetries in 139 patients aged 3 to 20 years. …”
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Insurance claims estimation and fraud detection with optimized deep learning techniques by P. Anand Kumar, S. Sountharrajan

“…Unlike traditional statistical methods, which often struggle with the intricate nature of insurance claims data, deep learning models performs well in handling diverse variables and factors influencing claim outcomes. …”
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Cardiovascular Disease Detection through Innovative Imbalanced Learning and AUC Optimization by Karthikeyan Palanisamy, Krishnaveni Krishnasamy, Praba Venkadasamy

“…In this paper, we introduce a novel imbalanced learning approach named Imbalanced Maximizing-Area Under the Curve (AUC) Proximal Support Vector Machine (ImAUC-PSVM), which harnesses the foundational principles of traditional PSVM for the detection of CVDs. …”
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Balanced Multi-Class Network Intrusion Detection Using Machine Learning by Faraz Ahmad Khan, Asghar Ali Shah, Nizal Alshammry, Saifullah Saif, Wasim Khan, Muhammad Osama Malik, Zahid Ullah

“…An anomaly-based NIDS has attracted researchers to develop a system to detect malign traffic in a network using Machine Learning (ML) models. …”
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Unsupervised Learning With Hybrid Models for Detecting Electricity Theft in Smart Grids by Ali Jaber Almalki

“…Data on power use is examined by unsupervised algorithms to find abnormalities, which are then further examined by the Random Forest classifier for increased precision. …”
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Deep Transfer Learning Approach in Smartwatch-Based Fall Detection Systems by Alessandro Leone, Andrea Manni, Gabriele Rescio, Pietro Siciliano, Andrea Caroppo

“…This study introduces a fall detection system utilizing an affordable consumer smartwatch and smartphone with edge computing capabilities for implementing AI algorithms. …”
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Impact of Machine Learning on Intrusion Detection Systems for the Protection of Critical Infrastructure by Avinash Kumar, Jairo A. Gutierrez

“…This paper investigates the efficacy of various machine learning algorithms for anomaly detection within critical infrastructure, using the Secure Water Treatment (SWaT) dataset, a comprehensive collection of time-series data from a water treatment testbed, to experiment upon and analyze the findings. …”
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Federated Learning-Based Credit Card Fraud Detection: A Comparative Analysis of Advanced Machine Learning Models by Zheng Han

“…Challenges for credit card fraud monitoring include highly imbalanced datasets and the need for advanced models to detect fraud patterns. This paper introduced federated learning and discussed a few federated learning algorithms applied to the problem—these methods include Federated Graph Attention Network with Dilated Convolution Neural Network (FedGAT-DCNN), FedAvg with Convolutional Neural Network (CNN), and Federated Averaging with Distance-based Weighted Aggregation (FedAvg-DWA) with Random Forest (RF). …”
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Detection of Attacks in Network Traffic with the Autoencoder-Based Unsupervised Learning Method by Yalçın Özkan

“…This study focuses on detecting abnormal traffic on networks through deep learning algorithms, and a deep autoencoder model architecture that can be used to detect attacks is recommended. …”
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Fault Detection in Photovoltaic Systems Using a Machine Learning Approach by Jossias Zwirtes, Fausto Bastos Libano, Luis Alvaro de Lima Silva, and Edison Pignaton de Freitas

“…This study presents three main contributions: the implementation and comparison of multiple machine learning models for fault detection, an investigation into the feasibility of identifying these faults using only electrical and environmental data, and an analysis of model performance in a photovoltaic system different from the one used for training. …”
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Deep Learning in Defect Detection of Wind Turbine Blades: A Review by Katleho Masita, Ali N. Hasan, Thokozani Shongwe, Hasan Abu Hilal

“…Insights are provided into challenges such as class imbalance, limited labeled datasets, and environmental noise, alongside emerging solutions like semi-supervised learning and data augmentation. The paper concludes by emphasizing the transformative potential of deep learning in achieving automated, accurate, and efficient defect detection in wind turbine blades. …”
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Integrating Sentiment Analysis With Machine Learning for Cyberbullying Detection on Social Media by Maram Fahaad Almufareh, Noor Zaman Jhanjhi, Mamoona Humayun, Ghadah Naif Alwakid, Danish Javed, Saleh Naif Almuayqil

“…This paper presents a unique framework that integrates sentiment analysis with machine learning algorithms to enhance the detection of cyberbullying on social media. …”
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Comparative Analysis of Deep Learning Models for Intrusion Detection in IoT Networks by Abdullah Waqas, Sultan Daud Khan, Zaib Ullah, Mohib Ullah, Habib Ullah

“…This study addresses the problem of detecting intrusions in IoT environments by evaluating the performance of deep learning (DL) models under different data and algorithmic conditions. …”
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Real-Time Fire Object Detection System Using Machine Learning by Akuthota Venkata Bhargavi., Syed Khadar Basha., Ramineni Dhanush., Guduru Vikram.

“…This paper introduces a fire object detection system that employs machine learning algorithms to enhance early detection of fire breakout and response to the same. …”
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Image Based Detection of Coating Wear on Cutting Tools with Machine Learning by Jan Wolf, Nithin Kumar Bandaru, Martin Dienwiebel, Hans-Christian Möhring

“…Therefore, this research provides an efficient data processing and classification framework for detecting coating wear on cutting tools.…”
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ENHANCING CYBERSECURITY THROUGH MALWARE DETECTION BASED ON MACHINE LEARNING TECHNIQUE by Amer Mohamed Shhatha, Omar Ibrahim Alsaif

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RF-Based UAV Detection and Identification Enhanced by Machine Learning Approach by Yash Vasant Ahirrao, Rana Pratap Yadav, Sunil Kumar

“…Machine learning algorithms are trained to make decisions based on these extracted features. …”
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Radiomics-based machine learning for automated detection of Pneumothorax in CT scans. by Hanieh Alimiri Dehbaghi, Karim Khoshgard, Hamid Sharini, Samira Jafari Khairabadi, Farhad Naleini

“…The used machine learning algorithms are Gradient Tree Boosting (GBM), eXtreme Gradient Boosting (XGBoost), and Light GBM. …”
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