DETECTION AND RECOGNITION OF IRAQI LICENSE PLATES USING CONVOLUTIONAL NEURAL NETWORKS by Mohammed Hayder Abbas, Zeina Mueen Mohammed

“…This approach leverages the strengths of YOLOv8 in handling complex patterns and variations in license plate designs, showcasing significant promise for real-world applications in vehicle identification and law enforcement. …”
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Detecting Dynamic States of Temporal Networks Using Connection Series Tensors by Shun Cao, Hiroki Sayama

“…Many temporal networks exhibit multiple system states, such as weekday and weekend patterns in social contact networks. The detection of such distinct states in temporal network data has recently been studied as it helps reveal underlying dynamical processes. …”
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Concept Drift Detection in Data Stream Mining : A literature review by Supriya Agrahari, Anil Kumar Singh

“…The traditional classifiers are not expected to learn the patterns in a non-stationary distribution of data. …”
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ADDAEIL: Anomaly Detection with Drift-Aware Ensemble-Based Incremental Learning by Danlei Li, Nirmal-Kumar C. Nair, Kevin I-Kai Wang

“…This design enables unsupervised detection and continuous adaptation to evolving data patterns. …”
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An interpretable approach for trustworthy intrusion detection systems against evasion samples by Ngoc Tai Nguyen, Hien Do Hoang, The Duy Phan, Van-Hau Pham

“… In recent years, Deep Neural Networks (DNN) have demonstrated remarkable success in various domains, including Intrusion Detection Systems (IDS). The ability of DNN to learn complex patterns from large datasets has significantly improved IDS performance, leading to more accurate and efficient threat detection. …”
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Time Series Anomaly Detection Using Signal Processing and Deep Learning by Jana Backhus, Aniruddha Rajendra Rao, Chandrasekar Venkatraman, Chetan Gupta

“…By learning a compact latent representation and remapping the filtered time series, the Autoencoder effectively identifies deviations from normal patterns, allowing us to detect anomalies. Our experiments on several benchmark datasets demonstrate that bandpass filtering consistently improves the performance of deep learning methods, including the Functional Neural Network Autoencoder, by refining the input data. …”
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Fault Detection in MV Switchgears Through Unsupervised Learning of Temperature Conditions by Grazia Iadarola, Alessandro Mingotti, Virginia Negri, Susanna Spinsante

“…This approach enables the early detection of potential faults by identifying anomalous temperature patterns, thus supporting predictive maintenance and extending the lifespan of switchgears. …”
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Advanced Methodology for Fraud Detection in Energy Using Machine Learning Algorithms by Silviu Gresoi, Grigore Stamatescu, Ioana Făgărășan

“…By analyzing historical consumption patterns, anomaly detection techniques, and geospatial data, the proposed system enhances fraud detection capabilities across both smart and non-smart grids. …”
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Detection of false position attacks in VANETs through bagging ensemble learning. by Bekan Kitaw Mekonen, Lemi Bane, Negasa Berhanu Fite

“…Using the VeReMi dataset, our RSU-level detection system analyzes sequential BSMs to detect malicious behavior. …”
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Deep Learning in motion analysis for false start detection in speedway racing by Jacek Krakowian, Łukasz Jeleń

“…The proposed approach introduces image processing techniques with 3D Convolutional Neural Networks (CNNs) and Long-Short- Term Memory (LSTM) networks to analyze rider movements during the starting procedure. Unlike manual detection, which often misses fine movements at the start line, our method uses 3D CNNs to monitor racer movements and applies LSTM networks to assess time-based motion patterns that signal false starts. …”
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A Sparse Pooling Adversarial Learning Framework for Anomaly Event Detection by ZHANG, M., HU, H., LI, Z.

“…The test results demonstrate that the proposed method can effectively learn action patterns and accurately detect abnormal events in community scenarios.…”
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Detecting Rug-Pull: Analyzing Smart Contract Backdoor Codes in Ethereum by Kwan Woo Yu, Byung Mun Lee

“…Additionally, existing backdoor code analysis tools are limited in their ability to detect backdoor codes hidden through modifications to existing patterns or suffer from low accuracy because they rely on comparisons with predefined backdoor codes. …”
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Direct detection of 8-oxo-dG using nanopore sequencing by Marc Pagès-Gallego, Daan M. K. van Soest, Nicolle J. M. Besselink, Roy Straver, Janneke P. Keijer, Carlo Vermeulen, Alessio Marcozzi, Markus J. van Roosmalen, Ruben van Boxtel, Boudewijn M. T. Burgering, Tobias B. Dansen, Jeroen de Ridder

“…Our training approach addresses the rarity of 8-oxo-dG relative to guanine, enabling specific detection. Applied to a tissue culture model of oxidative damage, our method reveals uneven genomic 8-oxo-dG distribution, dissimilar context pattern to C>A mutations, and local 5-mC depletion. …”
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Multichannel convolutional transformer for detecting mental disorders using electroancephalogrpahy records by Mamadou Dia, Ghazaleh Khodabandelou, Syed Muhammad Anwar, Alice Othmani

“…Before feeding the model as low-level features, the input is pre-processed using a common spatial pattern filter, a signal space projection filter, and a wavelet denoising filter. …”
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Unsupervised Feature Representation Based on Deep Boltzmann Machine for Seizure Detection by Tengzi Liu, Muhammad Zohaib Hassan Shah, Xucun Yan, Dongping Yang

“…The Electroencephalogram (EEG) pattern of seizure activities is highly individual-dependent and requires experienced specialists to annotate seizure events. …”
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An interpretable approach for trustworthy intrusion detection systems against evasion samples by Ngoc Tai Nguyen, Hien Do Hoang, The Duy Phan, Van-Hau Pham

“… In recent years, Deep Neural Networks (DNN) have demonstrated remarkable success in various domains, including Intrusion Detection Systems (IDS). The ability of DNN to learn complex patterns from large datasets has significantly improved IDS performance, leading to more accurate and efficient threat detection. …”
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Anomaly detection in cropland monitoring using multiple view vision transformer by Xuesong Liu, Yansong Liu, He Sui, Chuan Qin, Yuanxi Che, Zhaobo Guo

“…Such anomalies can range from unpredictable weather patterns in farmlands to unauthorized intrusions. To surmount this, a comprehensive deep learning pipeline is proposed in this study. …”
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Leveraging glycosylation for early detection and therapeutic target discovery in pancreatic cancer by Tomasz Pienkowski, Katarzyna Wawrzak-Pienkowska, Anna Tankiewicz-Kwedlo, Michal Ciborowski, Krzysztof Kurek, Dariusz Pawlak

“…Recent glycoproteomic studies have illuminated the crucial role of glycosylation in PC progression, revealing altered glycosylation patterns that impact cell adhesion, immune evasion, and tumor invasiveness. …”
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An explainable transformer model for Alzheimer’s disease detection using retinal imaging by Saeed Jamshidiha, Alireza Rezaee, Farshid Hajati, Mojtaba Golzan, Raymond Chiong

“…The Retformer model is trained on datasets of different modalities of retinal images from patients with AD and age-matched healthy controls, enabling it to learn complex patterns and relationships between image features and disease diagnosis. …”
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AI-Based Anomaly Detection and Optimization Framework for Blockchain Smart Contracts by Hassen Louati, Ali Louati, Elham Kariri, Abdulla Almekhlafi

“…The framework integrates Neural Architecture Search (NAS) to automatically design optimal Convolutional Neural Network (CNN) architectures tailored to blockchain data, enabling effective anomaly detection. To address the challenge of limited labeled data, transfer learning is employed to adapt pre-trained CNN models to smart contract patterns, improving model generalization and reducing training time. …”
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