A deep learning-based approach for the detection of cucumber diseases. by Lars Raufer, Jasper Wiedey, Malte Mueller, Pascal Penava, Ricardo Buettner

“…In numerous countries, they are fundamental to dietary practices, contributing significantly to the nutritional patterns of various populations. Due to unfavorable environmental conditions, they are highly vulnerable to various diseases. …”
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DCFE-YOLO: A novel fabric defect detection method. by Lei Zhou, Bingya Ma, Yanyan Dong, Zhewen Yin, Fan Lu

“…However, the task of fabric defect detection remains highly challenging due to the complex textures and diverse defect patterns. …”
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Masked Convolutions Within Skip Connections for Video Anomaly Detection by Demetris Lappas, Vasileios Argyriou, Dimitrios Makris

“…This innovative mechanism enhances the preservation of spatial and temporal details, addressing the challenge of effectively detecting anomalies in video data. The proposed methodology is evaluated on popular video datasets, showcasing its effectiveness in capturing intricate patterns and contexts. …”
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Cross-Scenario Subdomain Adaptive Displacement Anomaly Detection in Dams by Yu Wang, Guohua Liu

“…This study presents the first application of subdomain adaptation for cross-scenario anomaly detection in dams, addressing distribution shifts across varying operational conditions. …”
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Design and Analysis of an Expert System for the Detection and Recognition of Criminal Faces by Rishi Gupta, Amit Kumar Gupta, Deepak Panwar, Ashish Jain, Partha Chakraborty

“…In this study, they were analyzed and compared with the many methods of face detection and face recognition, such as HAAR cascades, local binary patterns histogram, support vector machines, convolutional neural networks, and ResNet-34. …”
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Scalable Deep Learning Framework for Public Transit Sign Detection by N. Savioli, E. Turkoz, T. Neumann, L. Liu, Y. Wang, Y. Zhang

“…This innovative approach enables our model to adapt to city-specific visual patterns without manual prompts, achieving accurate detection even for small and distant transit signs. …”
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Unsupervised Anomaly Detection for Volcanic Deformation in InSAR Imagery by Robert Popescu, Nantheera Anantrasirichai, Juliet Biggs

“…Our framework can thus be used to identify deformation at volcanoes without needing prior knowledge about the deformation patterns present there.…”
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Image-Based Malware Detection Using Deep CNN Models by hawraa omran musa, Muhanad Tahrir Younis

“…Deep learning (DL) presents a promising strategy for malware detection by utilizing advanced algorithms that are capable of discerning intricate patterns from extensive datasets. …”
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Improved method for a pedestrian detection model based on YOLO by Yanfei LI, Chengyi DONG

“…The proposed method had superior performance in dense agricultural contexts while improving detection capabilities for pedestrian distribution patterns under complex farmland conditions, including variable lighting and mechanical occlusions. …”
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Improving Electrical Fault Detection Using Multiple Classifier Systems by José Oliveira, Dioeliton Passos, Davi Carvalho, José F. V. Melo, Eraylson G. Silva, Paulo S. G. de Mattos Neto

“…Machine Learning-based fault detection approaches in energy systems have gained prominence for their superior performance. …”
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Detection of ictal apnea refines the clinical spectrum of ATRX syndrome by Galal Banat, Friedrich G. Woermann, Rami Abou Jamra, Christian G. Bien, Christian Brandt

“…During inpatient treatment, numerous respiratory pauses were detected. Repeated video EEG recordings revealed seizure patterns with a left frontocentral origin and an occasional spread to the bifrontal region and episodes of apnea without an EEG correlate. …”
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Liquid biopsy of gliomas with detection of extracellular tumor nucleic acids by Tagir I. Rakhmatullin, Mark Jain, Larisa M. Samokhodskaya, Andrey A. Zuev

“…For the purpose of defining the presence of the tumor component, the tests are used to detect the so-called hot-spot mutations and the patterns of epigenetic regulation, found in specific types of tumors. …”
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Multiscale deformed attention networks for white blood cell detection by Xin Zheng, Qiqi Xu, Shiyi Zheng, Luxian Zhao, Deyang Liu, Liangliang Zhang

“…Traditional WBC detection methods are labor-intensive and time-consuming. …”
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Online Meta-Recommendation of CUSUM Hyperparameters for Enhanced Drift Detection by Jessica Fernandes Lopes, Sylvio Barbon Junior, Leonimer Flávio de Melo

“…The variability in patterns necessitates frequent analysis to sustain high performance by acquiring the hyperparameter. …”
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AnomLite: Efficient binary and multiclass video anomaly detection by Anna K. Zvereva, Mariam Kaprielova, Andrey Grabovoy

“…The initial layers of MobileNetV2 are employed for efficient spatial feature extraction, capturing low-to-mid-level features such as edges and textures, while the last hidden state of an LSTM processes temporal dependencies to identify patterns indicative of anomalies. AnomLite is competitive due to its computational efficiency, requiring only 11 million parameters, and its robustness, achieving a ROC AUC of 0.99, Average Precision of 0.99 and F1-Score (Weighted) of 0.92 and outperforming comparable models in anomaly detection tasks. …”
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Hybrid quantum enhanced federated learning for cyber attack detection by G. Subramanian, M. Chinnadurai

“…The novel STAN used in the proposed work captures the spatio-temporal patterns in the network traffic data. The optimization model QIFA utilizes quantum principles to enhance the federated learning procedure. …”
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A Deep Learning Framework for Enhanced Detection of Polymorphic Ransomware by Mazen Gazzan, Bader Alobaywi, Mohammed Almutairi, Frederick T. Sheldon

“…Key innovations include (1) data augmentation using a Bi-Gradual Minimax Generative Adversarial Network (BGM-GAN) to generate synthetic ransomware attack patterns, addressing data insufficiency; (2) Incremental Mutual Information Selection (IMIS) for dynamically selecting relevant features, adapting to evolving ransomware behaviors and reducing computational overhead; and (3) a Deep Belief Network (DBN) detection architecture, trained on the augmented data and optimized with Uncertainty-Aware Dynamic Early Stopping (UA-DES) to prevent overfitting. …”
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DualAD: Dual adversarial network for image anomaly detection⋆ by Yonghao Wan, Aimin Feng

“…During the inference phase, the authors perform anomaly detection simultaneously in the pixel and latent spaces to identify abnormal patterns more comprehensively. …”
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Detection and quantification of Salmonella spp. in poultry slaughterhouses of southern Brazil by Karen Apellanis Borges, Eduarda Boff Martelo, Lilian Andriva dos Santos, Thales Quedi Furian, Isabel C Cisco, Luciane Manto, Luciana R dos Santos

“…Quantification means varied equally among all of the tested sources, and we could not establish any pattern of variation. A large proportion (52.6%) of cloacal swabs was Salmonella-positive. …”
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Innovative Approaches to Traffic Anomaly Detection and Classification Using AI by Borja Pérez, Mario Resino, Teresa Seco, Fernando García, Abdulla Al-Kaff

“…We compare their performance across real-world applications, highlighting patterns such as the superiority of Transformer-based models in temporal context understanding and the growing use of multimodal inputs for robust detection. …”
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