Efficient Argan Tree Deforestation Detection Using Sentinel-2 Time Series and Machine Learning by Younes Karmoude, Soufiane Idbraim, Souad Saidi, Antoine Masse, Manuel Arbelo

“…This study monitors changes in an argan forest near Agadir, Morocco, from 2017 to 2023 using Sentinel-2 satellite imagery and advanced image processing algorithms. Various machine learning models were evaluated for argan tree detection, with LightGBM achieving the highest accuracy when trained on a dataset integrating spectral bands, temporal features, and vegetation indices information. …”
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Anomaly Detection of Wind Turbines Based on Deep Small-World Neural Network by Yaguang LI, Meng LI

“…Since accurately labeled data are usually difficult to obtain in real industries, this paper proposed a novel deep small-world neural network (DSWNN) on the basis of unsupervised learning to detect the early failure of wind turbines. …”
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An enhanced CNN-Bi-transformer based framework for detection of neurological illnesses through neurocardiac data fusion by Kavita Rawat, Trapti Sharma

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Photograph-based machine learning approach for automated detection and differentiation of aerial blight disease in soybean crops by Mukta Nainwal, Anurag Satpathi, Saqib Shamsi, Ali Salem, Ajeet Singh Nain, Dinesh Kumar Vishwakarma, Ahmed Elbeltagi, Salah El-Hendawy, Mohamed A. Mattar

“…By integrating smartphone imageries into sophisticated algorithms, our automated system offers a scalable solution for disease detection. …”
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A deep contrastive learning-based image retrieval system for automatic detection of infectious cattle diseases by Veerayuth Kittichai, Morakot Kaewthamasorn, Apinya Arnuphaprasert, Rangsan Jomtarak, Kaung Myat Naing, Teerawat Tongloy, Santhad Chuwongin, Siridech Boonsang

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False data injection attack dataset for classification, identification, and detection for IIoT in Industry 5.0Zenodo by A K M Ahasan Habib, Mohammad Kamrul Hasan, Rosilah Hassan, Shayla Islam, Huda Saleh Abbas

“…The approach we employ makes it easier to create and assess effective machine learning (ML) and deep learning (DL) algorithms for efficient FDI attack detections. …”
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Dual-Channel CNN-Based Framework for Automated Rebar Detection in GPR Data of Concrete Bridge Decks by Sepehr Pashoutani, Mohammadsajjad Roudsari, Jinying Zhu

“…This paper presents two automated rebar detection algorithms based on Convolutional Neural Network (CNN) machine learning techniques. …”
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AISLEX: Approximate individual sample learning entropy with JAX by Ondrej Budik, Milan Novak, Florian Sobieczky, Ivo Bukovsky

“…We present AISLEX, an online anomaly detection module based on the Learning Entropy algorithm, a novel machine learning-based information measure that quantifies the learning effort of neural networks. …”
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Oversampling and undersampling for intrusion detection system in the supervisory control and data acquisition IEC 60870‐5‐104 by M. Agus Syamsul Arifin, Deris Stiawan, Bhakti Yudho Suprapto, Susanto Susanto, Tasmi Salim, Mohd Yazid Idris, Rahmat Budiarto

“…Gradient boosting, decision tree, and random forest algorithms are used as classifiers for the intrusion detection system models. …”
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Schizophrenia Detection and Classification: A Systematic Review of the Last Decade by Arghyasree Saha, Seungmin Park, Zong Woo Geem, Pawan Kumar Singh

“…Background/Objectives: Artificial Intelligence (AI) in healthcare employs advanced algorithms to analyze complex and large-scale datasets, mimicking aspects of human cognition. …”
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Novel Approaches for the Early Detection of Glaucoma Using Artificial Intelligence by Marco Zeppieri, Lorenzo Gardini, Carola Culiersi, Luigi Fontana, Mutali Musa, Fabiana D’Esposito, Pier Luigi Surico, Caterina Gagliano, Francesco Saverio Sorrentino

“…Through the fast and accurate analysis of massive amounts of imaging data, artificial intelligence (AI), in particular machine learning (ML) and deep learning (DL), has emerged as a promising method to improve the early detection and management of glaucoma. …”
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Artificial Intelligence in Wind Turbine Fault Detection and Diagnosis: Advances and Perspectives by Nejad Alagha, Anis Salwa Mohd Khairuddin, Zineddine N. Haitaamar, Obada Al-Khatib, Jeevan Kanesan

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A network intrusion detection method designed for few-shot scenarios by Weichen HU, Congyuan XU, Yong ZHAN, Guanghui CHEN, Siqing LIU, Zhiqiang WANG, Xiaolin WANG

“…Existing intrusion detection techniques often require numerous malicious samples for model training.However, in real-world scenarios, only a small number of intrusion traffic samples can be obtained, which belong to few-shot scenarios.To address this challenge, a network intrusion detection method designed for few-shot scenarios was proposed.The method comprised two main parts: a packet sampling module and a meta-learning module.The packet sampling module was used for filtering, segmenting, and recombining raw network data, while the meta-learning module was used for feature extraction and result classification.Experimental results based on three few-shot datasets constructed from real network traffic data sources show that the method exhibits good applicability and fast convergence and effectively reduces the occurrence of outliers.In the case of 10 training samples, the maximum achievable detection rate is 99.29%, while the accuracy rate can reach a maximum of 97.93%.These findings demonstrate a noticeable improvement of 0.12% and 0.37% respectively, in comparison to existing algorithms.…”
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Unbiased High-Precision Cloud Detection for Advanced Himawari Imager Using Automatic Machine Learning by Bochun Liu, Jinming Ge, Qingyu Mu, Chi Zhang, Xiaoyu Hu, Jiajing Du, Yanyan Wu, Bo Wang, Xiang Li, Jianping Huang, Qiang Fu

“…However, existing machine learning-based cloud detection algorithms have limitations, particularly in data preprocessing and feature engineering, and few are designed to cover the entire geostationary satellite observation region. …”
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A Tunnel Lining Line Identification Algorithm Based on Supervised Heatmap by Heng SONG, Yisheng ZHANG, Tianbao GENG, Dongjie WANG

“…Conclusions The results showed that the grid classification task, outer-point supervision, and anti-noise disturbance proposed in this study can effectively enhance the effectiveness of tunnel lining line identification and mitigate the challenge of detecting dense keypoints. The proposed algorithm can provide technical support for interpreting ground-penetrating radar non-destructive testing data in engineering construction.…”
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Design of the DETECT project: automated cardiac arrest detection and activation of the emergency medical chain integrated into a wristband by Roos Edgar, Kambiz Ebrahimkheil, Niels T.B. Scholte, Catharina E. Jansen, Rypko J. Beukema, Marc A. Brouwer, Eelko Ronner, Aysun Cetinyurek-Yavuz, Marit van Barreveld, Marcel G.W. Dijkgraaf, Peter C. Stas, Eric Boersma, Niels van Royen, Judith L. Bonnes

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A novel machine learning model for perimeter intrusion detection using intrusion image dataset. by Shahneela Pitafi, Toni Anwar, I Dewa Made Widia, Zubair Sharif, Boonsit Yimwadsana

“…When handling high-dimensional data, the existing Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm faces efficiency issues due to its complexity and varying densities. …”
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Surface Defect Detection for Small Samples of Particleboard Based on Improved Proximal Policy Optimization by Haifei Xia, Haiyan Zhou, Mingao Zhang, Qingyi Zhang, Chenlong Fan, Yutu Yang, Shuang Xi, Ying Liu

“…Deep reinforcement learning-based detection methods have been shown to exhibit strong generalization ability and sample utilization efficiency when the number of samples is limited. …”
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Intrusion detection system based on machine learning using least square support vector machine by Pratik Waghmode, Manideep Kanumuri, Hosam El-Ocla, Tanner Boyle

“…Traditional intrusion detection techniques are no longer accurate and effective enough to handle the demands of the big data age. …”
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Hyperspectral Imaging Combined with Deep Learning for the Early Detection of Strawberry Leaf Gray Mold Disease by Yunmeng Ou, Jingyi Yan, Zhiyan Liang, Baohua Zhang

“…Overall, the fused feature-based model can reduce the dimensionality of the classification data and effectively improve the predicting accuracy and precision of the classification algorithm.…”
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