IR thermography & NN models for damaged component thickness detection by Chunming Ai, Haichuan Lin, Pingping Sun

“…The numerical simulation data were divided into a training set and a prediction set in an 8:2 ratio. …”
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Impact of structural setting on hydrocarbon trapping mechanism of onshore Niger Delta basin, Nigeria, using seismic attribute analysis by Amarachukwu Alphonsus Ibe, Oyewole Temitope Ezekiel

“…These were achieved through the integration of 3D seismic and well log data that was acquired from an Onshore Niger Delta basin, Nigeria. …”
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Hard-coded backdoor detection method based on semantic conflict by Anxiang HU, Da XIAO, Shichen GUO, Shengli LIU

“…The current router security issues focus on the mining and utilization of memory-type vulnerabilities, but there is low interest in detecting backdoors.Hard-coded backdoor is one of the most common backdoors, which is simple and convenient to set up and can be implemented with only a small amount of code.However, it is difficult to be discovered and often causes serious safety hazard and economic loss.The triggering process of hard-coded backdoor is inseparable from string comparison functions.Therefore, the detection of hard-coded backdoors relies on string comparison functions, which are mainly divided into static analysis method and symbolic execution method.The former has a high degree of automation, but has a high false positive rate and poor detection results.The latter has a high accuracy rate, but cannot automate large-scale detection of firmware, and faces the problem of path explosion or even unable to constrain solution.Aiming at the above problems, a hard-coded backdoor detection algorithm based on string text semantic conflict (Stect) was proposed since static analysis and the think of stain analysis.Stect started from the commonly used string comparison functions, combined with the characteristics of MIPS and ARM architectures, and extracted a set of paths with the same start and end nodes using function call relationships, control flow graphs, and branching selection dependent strings.If the strings in the successfully verified set of paths have semantic conflict, it means that there is a hard-coded backdoor in the router firmware.In order to evaluate the detection effect of Stect, 1 074 collected device images were tested and compared with other backdoor detection methods.Experimental results show that Stect has a better detection effect compared with existing backdoor detection methods including Costin and Stringer: 8 hard-coded backdoor images detected from image data set, and the recall rate reached 88.89%.…”
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Deception detection based on micro-expression and feature selection methods by Shusen Yuan, Zilong Shao, Zhongjun Ma, Ting Cao, Hongbo Xing, Yong Liu, Yewen Cao

“…Feature importance analysis indicated that micro-expression (ME) information had a significant impact on the deception detection task. The proposed framework was evaluated on two publicly available data sets, achieving accuracies of 98.07% and 98.23% on the real-life and MU3D data sets, respectively, thus demonstrating its superiority over prior approaches in the literature.…”
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Machine learning detection of heteroresistance in Escherichia coliResearch in context by Andrei Guliaev, Karin Hjort, Michele Rossi, Sofia Jonsson, Hervé Nicoloff, Lionel Guy, Dan I. Andersson

“…Then we trained several machine learning models on 80% of this data set aiming to detect HR isolates. We compared performance of the best ML models on the remaining 20% of the data set with a baseline model based solely on the presence of β-lactamase genes. …”
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Explainable correlation-based anomaly detection for Industrial Control Systems by Ermiyas Birihanu, Imre Lendák

“…This study proposes an explainable correlation-based anomaly detection method for ICS. The optimal window size of the data is determined using Long Short-Term Memory Networks—Autoencoder (LSTM-AE) and the correlation parameter set is extracted using the Pearson correlation. …”
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Detection of the Interdependence of Economic Development and Environmental Performance at the Industry Level by Nina Ilysheva, Elena Karanina, Elena Baldesku, Ulugbek Zakirov

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Assessing the quality of tuberculosis evaluation for children with prolonged cough presenting to routine community health care settings in rural Uganda. by Carina Marquez, J Lucian Davis, Achilles Katamba, Priscilla Haguma, Emmanuel Ochom, Irene Ayakaka, Gabriel Chamie, Grant Dorsey, Moses R Kamya, Edwin Charlebois, Diane V Havlir, Adithya Cattamanchi

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Code vulnerability detection method based on graph neural network by Hao CHEN, Ping YI

“…The schemes of using neural networks for vulnerability detection are mostly based on traditional natural language processing ideas, processing the code as array samples and ignoring the structural features in the code, which may omit possible vulnerabilities.A code vulnerability detection method based on graph neural network was proposed, which realized function-level code vulnerability detection through the control flow graph feature of the intermediate language.Firstly, the source code was compiled into an intermediate representation, and then the control flow graph containing structural information was extracted.At the same time, the word vector embedding algorithm was used to initialize the vector of basic block to extract the code semantic information.Then both of above were spliced to generate the graph structure sample data.The multilayer graph neural network model was trained and tested on graph structure data features.The open source vulnerability sample data set was used to generate test data to evaluate the method proposed.The results show that the method effectively improves the vulnerability detection ability.…”
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Hyperspectral Wavelength Selection and Integration for Bruise Detection of Korla Pears by Yiming Fang, Fan Yang, Zhu Zhou, Lujun Lin, Xiaoqin Li

“…Most modern research used the feature wavelength set selected by a single selection method which is generally unable to handle the wide variability of the hyperspectral data. …”
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Application of machine learning methods for automated detection of network intrusions by M. V. Babicheva, I. A. Tretyakov

“…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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Machine vision-based automatic fruit quality detection and grading by Amna, Muhammad Waqar AKRAM, Guiqiang LI, Muhammad Zuhaib AKRAM, Muhammad FAHEEM, Muhammad Mubashar OMAR, Muhammad Ghulman HASSAN

“…Different image processing algorithms including pre-processing, thresholding, morphological and bitwise operations combined with a deep leaning algorithm, i.e., convolutional neural network (CNN), were applied to fruit images for the detection of defective fruit. The data set used for training CNN model consisted of fruit images collected from a publicly-available data set and captured fruit images: 1799 and 1017 for mangoes and tomatoes, respectively. …”
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An Intelligent IoT and ML-Based Water Leakage Detection System by Mohammed Rezwanul Islam, Sami Azam, Bharanidharan Shanmugam, Deepika Mathur

“…At first, an experiment was set up to capture real-life audio data of leak and non-leak signals. …”
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Advances in the Detection and Identification of Bacterial Biofilms Through NIR Spectroscopy by Cristina Allende-Prieto, Lucía Fernández, Pablo Rodríguez-Gonzálvez, Pilar García, Ana Rodríguez, Carmen Recondo, Beatriz Martínez

“…The samples were organized into two groups, a control set and a test set, for the purpose of performing a comparative analysis. …”
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Locomotive Maintenance Management System for Crack Detection Standard Operating by 王铁城

“…In order to ensure safe production needs of railway locomotive maintenance system, we develop a set of software of locomotive maintenance management system for crack detection standard operating. …”
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Hyperspectral Anomaly Detection: Comparative Evaluation in Scenes with Diverse Complexity by Dirk Borghys, Ingebjørg Kåsen, Véronique Achard, Christiaan Perneel

“…Anomaly detection (AD) in hyperspectral data has received a lot of attention for various applications. …”
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Time anomaly detection in the duration of civil trials in Italian justice by Antonio Esposito, Beniamino Di Martino, Rosario Ammendolia, Pietro Lupi, Massimo Orlando, Wei Liang

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“Schmallenberg” virus: Analysis of the Epidemiological Data and Assessment of Impact by European Food Safety Authority

“…EFSA coordinated the collation of SBV epidemiological data during 2011–2012 in order to obtain comparable data for Europe. …”
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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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