Affordable droplet-based flow analyzer with peristaltic micro-pumps for fluorescent ammonium sensing by Mingtao Sun, Yiyu Jiang, Wenshan Liang, Hui Zeng, Huiwen Chen, Min Zhang

“…The analyzer provides a limit of detection of 0.02 μM (3σ) and an RSD of 0.15 % (n=10, 1 μM ammonium). …”
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Optimizing Monkeypox Lesions Detection With a Lightweight Hybrid Model by Mehdhar S. A. M. Al-Gaashani, Reem Ibrahim Alkanhel, Dina S. M. Hassan, Abduljabbar S. Ba Mahel, Ahmed Aziz, Mashael M. Khayyat, Ammar Muthanna

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Automated Feature Extraction in Brain Tumor by Magnetic Resonance Imaging Using Gaussian Mixture Models by Ahmad Chaddad

“…GMM features demonstrated the best performance by the comparative study using principal component analysis (PCA) and wavelet based features. …”
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Identification method for wheel/rail tread defects based on integrated partial convolutional network by CHENG Xiang, HE Jing, ZHANG Changfan, JIA Lin

“…Given the difficulties associated with accurately detecting minor wheelset damages, an enhanced adaptive spatial feature fusion (E-ASFF) detection approach was introduced. …”
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Ultrasound super resolution imaging for accurate uterus tumor detection and malignancy prediction by Ashwini Sawant, Sujata Kulkarni, Milind Sawant

“…Enhanced ultrasound images can reach even higher accuracy, cementing them as plausible alternatives to MRI. A comparative analysis of copious relevant image de-speckling, image enhancement, segmentation, and feature extraction methods are carried out. …”
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Contactless Detection of pH Change in a Liquid Analyte by Dylan Gustafson, Dominic Klyve

“…Second, the change is detected using a Linear Discriminant Analysis model using values from an inverse Fourier transform of the frequency data as its features. …”
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Comparison of Moving Average and Differential Operation for Wheeze Detection in Spectrograms by Meng-Lun Hsueh, Jin-Peng Chen, Bing-Yuh Lu, Huey-Dong Wu, Pei-Yi Liu

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Deep learning-based edge detection for random natural images by Kanija Muntarina, Rafid Mostafiz, Sumaita Binte Shorif, Mohammad Shorif Uddin

“…Edge detection plays a critical role in computer vision, particularly in the analysis of random natural images. …”
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APPLICATION OF CLASSIFICATION BASED ASSOCIATION (CBA) FOR MONKEYPOX DISEASE DETECTION by Qoria Yudi Pratama, Mohammad Isa Irawan

“…CBA integrates the advantages of classification and association analysis, allowing the classification process and a deeper understanding of the strength of the relationship between features in the dataset through the analysis of metrics such as support and confidence. …”
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Detecting and Analysing Possible Outliers in Global Stock Market Returns by Ali A. Shehadeh, Sadam M. Alwadi, Mohammad I. Almaharmeh

“…The main objective of the paper is to provide an extensive analysis of the main characteristics, features and effects of the detected outlier returns. …”
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Detection Of Sentence Modality On French Automatic Speech-to-text Transcriptions by Luisa Orosanu, Denis Jouvet

“…The classifiers based on linguistic features outperform the classifiers based on prosodic features. …”
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BLSTM based night-time wildfire detection from video. by Ahmet K Agirman, Kasim Tasdemir

“…Distinguishing fire from non-fire objects in night videos is problematic if only spatial features are to be used. Those features are highly disrupted under low-lit environments because of several factors, such as the dynamic range limitations of the cameras. …”
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Anomaly Detection for MVB Network Based on Logistic Ensemble Learning by Huizhen WANG, Lide WANG, Yueyi YANG, Ping SHEN

“…Multifunction vehicle bus (MVB) has been widely used in rail transit vehicles, but the poor working environment may result in the performance degradation of MVB network, which even endangers the driving safety in serious cases. Based on the analysis of common faults in MVB network, with extracting the network state features from the MVB physical layer and data link layer, an detection method based on heterogeneous Logistic ensemble learning to detect MVB network anomaly and avoid breakdown maintenance to the maximum extent was proposed. …”
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Intrusion detection model based on fuzzy theory and association rules by Jianwu ZHANG, Jiasen HUANG, Di ZHOU

“…An intrusion detection model based on fuzzy theory and improved Apriori algorithm was proposed.The BV-Apriori algorithm was used to generate the matching rule base,and the problem of excessive boundary in the continuous data partitioning process was solved by fuzzy set technology.The real-time analysis of the relationship between features and the update of the rule base were completed,and the intrusion detection model BVA-IDS (Boolean vector Apriori-intrusion detection system) was built.The results show that the mining efficiency of the BV-Apriori algorithm is significantly improved when compared with the existing Apriori-BR algorithm,in addition,the BVA-IDS model also performs well on intrusion detection indicators with high detection accuracy,and low false positive rate and false negative rate.…”
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Intrusion detection model based on fuzzy theory and association rules by Jianwu ZHANG, Jiasen HUANG, Di ZHOU

“…An intrusion detection model based on fuzzy theory and improved Apriori algorithm was proposed.The BV-Apriori algorithm was used to generate the matching rule base,and the problem of excessive boundary in the continuous data partitioning process was solved by fuzzy set technology.The real-time analysis of the relationship between features and the update of the rule base were completed,and the intrusion detection model BVA-IDS (Boolean vector Apriori-intrusion detection system) was built.The results show that the mining efficiency of the BV-Apriori algorithm is significantly improved when compared with the existing Apriori-BR algorithm,in addition,the BVA-IDS model also performs well on intrusion detection indicators with high detection accuracy,and low false positive rate and false negative rate.…”
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AGILE Gamma-Ray Detection of the Exceptional GRB 221009A by Marco Tavani, Giovanni Piano, Andrea Bulgarelli, Luca Foffano, Alessandro Ursi, Francesco Verrecchia, Carlotta Pittori, Claudio Casentini, Andrea Giuliani, Francesco Longo, Gabriele Panebianco, Ambra Di Piano, Leonardo Baroncelli, Valentina Fioretti, Nicolò Parmiggiani, Andrea Argan, Alessio Trois, Stefano Vercellone, Martina Cardillo, Lucio Angelo Antonelli, Guido Barbiellini, Patrizia Caraveo, Paolo W. Cattaneo, Andrew W. Chen, Enrico Costa, Ettore Del Monte, Guido Di Cocco, Immacolata Donnarumma, Yuri Evangelista, Marco Feroci, Fulvio Gianotti, Claudio Labanti, Francesco Lazzarotto, Paolo Lipari, Fabrizio Lucarelli, Martino Marisaldi, Sandro Mereghetti, Aldo Morselli, Luigi Pacciani, Alberto Pellizzoni, Francesco Perotti, Piergiorgio Picozza, Maura Pilia, Massimo Rapisarda, Andrea Rappoldi, Alda Rubini, Paolo Soffitta, Massimo Trifoglio, Valerio Vittorini, Fabio D’Amico

“…Here we report on the detection by the AGILE satellite in the MeV–GeV energy range of the remarkable long-duration gamma-ray burst GRB 221009A. …”
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An Enhanced Image Segmentation Technique-Based on Motion Detection Algorithm by Zaid Sh. Bakr, Hamzah M. Marhoon, Ammar Alaythawy

“… This paper presents a prototype for an intelligent, self-contained theft detection system designed for small-scale applications. …”
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An Advanced LSTM Framework for Pest Detection and Classification in Agricultural Settings by Bhoi Manjulata, Sharma Priti

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Application of machine learning methods for automated detection of network intrusions by M. V. Babicheva, I. A. Tretyakov

“…To train the models, a sample with signs of normal and abnormal traffic was prepared, and it was thinned and balanced as a result of preliminary statistical analysis. Five machine learning algorithms were selected and tested, both on a training set of features and on a real test set obtained experimentally. …”
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Generalization challenges in video deepfake detection: methods, obstacles, and technological advances by LI Junjie, WANG Jianzong, ZHANG Xulong, QU Xiaoyang

“…Regarding the challenges of cross-dataset detection, the paper analyzes how differences in feature distributions between datasets lead to the performance degradation of traditional deep learning models in cross-domain applications. …”
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