Contactless Diagnosis Method and Unsupervised Learning for Panel-Level Photovoltaic Plant Operation and Maintenance by Yifan Wang, Jianrong Xu, Fei Xu, Long Deng, Chensheng Jin, Wenzhong Bao, Yufeng Xie

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Automated Tomato Leaf Disease Detection Using Image Processing: An SVM-Based Approach with GLCM and SIFT Features by Rashid Khan, Nasir Ud Din, Asim Zaman, Bingding Huang

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Identification of Perceptual Phonetic Training Gains in a Second Language Through Deep Learning by Georgios P. Georgiou

“…Results: The results demonstrated good model performance across a range of metrics, confirming that learners’ gains in phonetic training could be effectively detected by the algorithm. Conclusions: This research underscores the potential of deep learning techniques to track improvements in phonetic training, offering a promising and practical approach for evaluating language learning outcomes and paving the way for more personalized, adaptive language learning solutions. …”
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Toward lightweight intrusion detection systems using the optimal and efficient feature pairs of the Bot-IoT 2018 dataset by Erman Özer, Murat İskefiyeli, Jahongir Azimjonov

“…Next, 10 full-feature-based intrusion detection systems were developed by training the 10 machine learning algorithms with the 12 full features. …”
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Diabetic Retinopathy Detection Using DL-Based Feature Extraction and a Hybrid Attention-Based Stacking Ensemble by Sanjana Rajeshwar, Shreya Thaplyal, Anbarasi M., Siva Shanmugam G.

“…This paper addresses this challenge by utilizing advanced deep learning (DL) algorithms with established image processing techniques to enhance accuracy and efficiency in detection. …”
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Identification of line status changes using phasor measurements through deep learning networks by N. E. Gotman, G. P. Shumilova

“…Deep Learning arises as a computational learning technique in which high level abstractions are hierarchically modelled from raw data. …”
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Optimizing Tumor Detection in Brain MRI with One-Class SVM and Convolutional Neural Network-Based Feature Extraction by Azeddine Mjahad, Alfredo Rosado-Muñoz

“…Additionally, the pure CNN model—trained directly for classification without OCSVM—outperformed hybrid methods with an accuracy of 97.83%, highlighting the effectiveness of deep convolutional networks in directly learning discriminative features from MRI data. This approach enables reliable detection of brain tumor anomalies without requiring labeled pathological data, offering a promising solution for clinical contexts where abnormal samples are scarce. …”
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Frontier machine learning techniques for melanoma skin cancer identification and categorization: An in-Depth review by Viomesh Singh, Kavita A. Sultanpure, Harshwardhan Patil

“…In contrast to the conventional biopsy method, which is both laborious and costly, machine learning algorithms offer a viable alternative for early detection, reducing the burden on specialists while concurrently augmenting the diagnostic accuracy of skin lesions. …”
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Real time counting method for coal mine drill pipes based on deep learning by Fukai ZHANG, Yiran SUN, Xufeng WU, Aijun LI, Peiyang LI, Dengke WANG, Guan YUAN, Shan ZHAO, Haiyan ZHANG

“…Experiments on the CMDPC dataset show that the improved Drill-YOLOv8 model performs well in mAP@0.5 The mAP @ [0.5:0.95] index has increased by 3.0% and 2.7% respectively, effectively solving the problem of false detection and missed detection of drilling head and drill pipe targets under strong light, water vapor, and occlusion environments, and the detection speed has reached 86 frames per second; At the same time, the weighted average error rate of the counting inference algorithm Pipe Count is 2%, showing good robustness against multi scene data, and the processing speed reaches 40 frames per second, meeting real-time counting requirements.…”
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Digital security risk identification and model construction of smart city based on deep learning by Zhilei Zhao

“…Abstract In view of the network security risks caused by the integration of the Industrial Internet of Things (IIoT) in the construction of smart cities, this research proposes a digital security identification model (DL-DSIM) based on deep learning, which aims to improve the data transmission efficiency and system security in the smart city environment. …”
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The robustness of popular multiclass machine learning models against poisoning attacks: Lessons and insights by Majdi Maabreh, Arwa Maabreh, Basheer Qolomany, Ala Al-Fuqaha

“…Data set poisoning is a severe problem that may lead to the corruption of machine learning models. …”
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Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks by Matteo Marouf, Pierre Machart, Vikas Bansal, Christoph Kilian, Daniel S. Magruder, Christian F. Krebs, Stefan Bonn

“…Augmenting sparse cell populations with cscGAN generated cells improves downstream analyses such as the detection of marker genes, the robustness and reliability of classifiers, the assessment of novel analysis algorithms, and might reduce the number of animal experiments and costs in consequence. cscGAN outperforms existing methods for single-cell RNA-seq data generation in quality and hold great promise for the realistic generation and augmentation of other biomedical data types.…”
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Vase-Life Monitoring System for Cut Flowers Using Deep Learning and Multiple Cameras by Ji Yeong Ham, Yong-Tae Kim, Suong Tuyet Thi Ha, Byung-Chun In

“…The VMS integrates camera imaging with the YOLOv8 (You Only Look Once version 8) deep learning algorithm to continuously monitor major physiological parameters including flower opening, fresh weight, water uptake, and gray mold disease incidence. …”
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Sustainable Parking Space Management Using Machine Learning and Swarm Theory—The SPARK System by Artur Janowski, Mustafa Hüsrevoğlu, Malgorzata Renigier-Bilozor

“…The integration of the YOLOv9 (You Only Look Once) segmentation algorithm with Artificial Bee Colony (ABC) optimization, combined with the use of crowdsourced data and deep learning for image analysis, significantly enhances the SPARK system’s operational efficiency. …”
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ForestSemantic: a dataset for semantic learning of forest from close-range sensing by Xinlian Liang, Hanwen Qi, Xuejie Deng, Jianchang Chen, Shangshu Cai, Qingjun Zhang, Yunsheng Wang, Antero Kukko, Juha Hyyppä

“…Semantic annotations of 3D forest scenes are fundamental for DL algorithm developments. Its necessity has become more urgent as DL is data-driven and requires large amount of training and verification data. …”
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Detection of Malicious Office Open Documents (OOXML) Using Large Language Models: A Static Analysis Approach by Jonas Heß , Kalman Graffi

“…As a supplementary tool to contemporary antivirus software, it is currently able to assist in the analysis of malicious Microsoft Office documents by identifying and summarising potentially malicious indicators with a foundation in evidence, which may prove to be more effective with advancing technology and soon to surpass tailored machine learning algorithms, even without the utilisation of signatures and detection rules. …”
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DCA-YOLOv8: A Novel Framework Combined with AICI Loss Function for Coronary Artery Stenosis Detection by Hualin Duan, Sanli Yi, Yanyou Ren

“…In order to improve the accuracy and efficiency of detection, a new deep-learning technique based on a coronary artery stenosis detection framework (DCA-YOLOv8) is proposed in this paper. …”
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Robust Drone Video Analysis for Occluded Urban Traffic Monitoring Based on Deep Learning by Carlos Gellida-Coutino, Reyes Rios-Cabrera, Alan Maldonado-Ramirez, Anand Sanchez-Orta

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High-Knee-Flexion Posture Recognition Using Multi-Dimensional Dynamic Time Warping on Inertial Sensor Data by Annemarie F. Laudanski, Arne Küderle, Felix Kluge, Bjoern M. Eskofier, Stacey M. Acker

“…Relating continuously collected inertial data to the activities or postures performed by the sensor wearer requires pattern recognition or machine-learning-based algorithms, accounting for the temporal and scale variability present in human movements. …”
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Biochemical Oxygen Demand Prediction Based on Three-Dimensional Fluorescence Spectroscopy and Machine Learning by Xu Zhang, Yihao Zhang, Xuanyi Yang, Zhiyun Wang, Xianhua Liu

“…The aim of this study was to propose a facile method for predicting biochemical oxygen demand by fluorescence signals using three-dimensional fluorescence spectroscopy and parallel factor analysis in combination with a machine learning algorithm. The water samples were incubated for five days using the national standard method, during which the dissolved oxygen contents and three-dimensional fluorescence spectroscopy data were measured at eight-hour intervals. …”
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