Deep Learning-Based Safety Helmet Detection in Engineering Management Based on Convolutional Neural Networks by Yange Li, Han Wei, Zheng Han, Jianling Huang, Weidong Wang

“…The experiment results demonstrate that the presented deep learning-based model using the SSD-MobileNet algorithm is capable of detecting the unsafe operation of failure of wearing a helmet at the construction site, with satisfactory accuracy and efficiency.…”
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A Comparison of YOLOv8 Series Performance in Student Facial Expressions Detection on Online Learning by Dewi Tresnawati, Shopi Nurhidayanti, Nina Lestari

“…However, research on facial expression detection in online learning environments is still limited, especially in the use of the YOLOv8 algorithm. …”
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Federated Learning With Sailfish-Optimized Ensemble Models for Anomaly Detection in IoT Edge Computing Environment by Aravam Babu, A. Bagubali

“…FL enables decentralized training on IoT devices, reducing the risk of data breaches. However, anomaly detection performance is often hindered by suboptimal parameter selection. …”
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Malware Detection and Classification in Android Application Using Simhash-Based Feature Extraction and Machine Learning by Wafaa Al-Kahla, Eyad Taqieddin, Ahmed S. Shatnawi, Rami Al-Ouran

“…These vectors are then used to train different Machine Learning algorithms for detecting and classifying malware. …”
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An optimized domain-specific shrimp detection architecture integrating conditional GAN and weighted ensemble learning by L. Ravi Kumar, Ravi Kumar Tata, T. R. Mahesh, Endris Mohammed Ali

“…To address this, our research introduces the synthetic data generation for “enhanced shrimp detection using integrated augmentation (ESDIA)” approach to detect shrimps. …”
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A Comparative Evaluation of Machine Learning-Based Intrusion Detection Systems for Securing Cloud Environments by Mohammad Shadi Alhakeem, Khawla Bin Ajlan

“…In this context, implementing robust protection measures, such as Intrusion Detection Systems (IDS), is essential to mitigate potential threats and safeguard sensitive data. …”
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Detection of Transformer Faults: AI-Supported Machine Learning Application in Sweep Frequency Response Analysis by Hakan Çuhadaroğlu, Yılmaz Uyaroğlu

“…Six different machine learning algorithms were applied to detect these conditions. …”
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Using Natural Language Processing and Machine Learning to Detect Online Radicalisation in the Maldivian Language, Dhivehi by Hussain Ibrahim, Ahmed Ibrahim, Michael N. Johnstone

“…The contributions of this research to the existing body of knowledge include datasets in the form of labelled radical/non-radical text, sentiment corpus of radical words and primary interview data of self-deradicalised individuals and a technique for detection of radicalisation text in Dhivehi for the first time using Machine Learning. …”
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Deep learning to classify the ripeness of coffee fruit in the mechanized harvesting process by Marco Antonio Zanella, Mikel Barrio-Conde, Jaime Gomez-Gil, Javier Manuel Aguiar-Perez, María Ángeles Pérez-Juárez, Fabio Moreira da Silva

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Exploiting heart rate variability for driver drowsiness detection using wearable sensors and machine learning by Zakwan AlArnaout, Chamseddine Zaki, Yehia Kotb, Mouhammad AlAkkoumi, Nour Mostafa

“…The proposed approach is evaluated using real-driving data and supervised machine learning techniques. …”
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A Simplified Gaussian Approach for Asphalt Crack Detection based on Deep Learning and RGB images by W. Darwish, W. Darwish, W. Ahmed, W. Ahmed

“…We propose a workflow to detect cracks using YOLOv9 deep learning algorithm combined with statistical analysis through principal component (PCA) and Gaussian distribution. …”
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Intrusion detection method for IoT in heterogeneous environment by LIU Jing, MU Zelin, LAI Yingxu

“…In order to address the issue of inadequate training efficiency and subpar model performance encountered by Internet of things (IoT) devices when dealing with resource constraints and non-independent and identically distributed (Non-IID) data, a novel personalized pruning federated learning frame work for IoT intrusion detection was put forth. …”
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Privacy-preserving detection and classification of diabetic retinopathy using federated learning with FedDEO optimization by Dasari Bhulakshmi, Dharmendra Singh Rajput

“…FL enables collaborative learning across multiple decentralized devices while maintaining data privacy. …”
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Applied Machine Learning to Anomaly Detection in Enterprise Purchase Processes: A Hybrid Approach Using Clustering and Isolation Forest by Antonio Herreros-Martínez, Rafael Magdalena-Benedicto, Joan Vila-Francés, Antonio José Serrano-López, Sonia Pérez-Díaz, José Javier Martínez-Herráiz

“…The primary objective is to enhance the effectiveness of companies’ control efforts and improve the efficiency of anomaly detection tasks. The approach begins with a comprehensive exploratory data analysis, followed by the application of unsupervised machine learning techniques to identify anomalies. …”
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Improved Cd Detection in Rice Grain Using LIBS with Husk-Based XGBoost Transfer Learning by Weiping Xie, Jiang Xu, Lin Huang, Yuan Xu, Qi Wan, Yangfan Chen, Mingyin Yao

“…These samples were processed into rice husk, brown rice, and polished rice groups, and the reference Cd content was measured by ICP-MS. The XGBoost algorithm, known for its excellent performance in handling high-dimensional data and nonlinear relationships, was applied to construct both the XGBoost base model and the XGBoost-based transfer learning model to predict Cd content in brown rice and polished rice. …”
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Machine learning-based model for acute asthma exacerbation detection using routine blood parameters by Youpeng Chen, Junquan Sun, Yabang Chen, Enzhong Li, Jiancai Lu, Huanhua Tang, Yifei Xie, Jiana Zhang, Lesi Peng, Haojie Wu, Zhangkai J. Cheng, Baoqing Sun

“…Significant variables were identified through logistic regression, and 12 machine learning algorithms were used to construct diagnostic models, which were evaluated using Receiver Operating Characteristic (ROC) analysis, calibration, and Decision Curve Analysis (DCA). …”
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STIED: a deep learning model for the spatiotemporal detection of focal interictal epileptiform discharges with MEG by Raquel Fernández-Martín, Alfonso Gijón, Odile Feys, Elodie Juvené, Alec Aeby, Charline Urbain, Xavier De Tiège, Vincent Wens

“…Clinical MEG analysis in epileptic patients traditionally relies on the visual identification of IEDs, which is time consuming and partially subjective. Automatic, data-driven detection methods exist but show limited performance. …”
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A Novel Two-Stage Deep Learning Model for Network Intrusion Detection: LSTM-AE by Vanlalruata Hnamte, Hong Nhung-Nguyen, Jamal Hussain, Yong Hwa-Kim

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Unifying heterogeneous hyperspectral databases for in vivo human brain cancer classification: Towards robust algorithm development by Alberto Martín-Pérez, Beatriz Martinez-Vega, Manuel Villa, Raquel Leon, Alejandro Martinez de Ternero, Himar Fabelo, Samuel Ortega, Eduardo Quevedo, Gustavo M. Callico, Eduardo Juarez, César Sanz

“…Hyperspectral imaging, combined with machine and deep learning algorithms, has shown promise for detecting brain cancer in vivo. …”
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A Multi-Strategy Active Learning Framework for Enhanced Peripheral Blood Cell Image Detection by Yuheng Feng, Jiangtao He, Linjin Wang, Wuchen Yang, Sihan Deng, Lanlin Li, Xinwei Li

“…To address these challenges, we propose a novel composite multi-strategy active learning framework using YOLOv5 for enhanced peripheral blood cell detection. …”
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