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Relevant bibliographies by topics / FEATURE SELECTION TECHNIQUE

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Academic literature on the topic 'FEATURE SELECTION TECHNIQUE'

Author: Grafiati

Published: 11 September 2023

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Journal articles on the topic "FEATURE SELECTION TECHNIQUE"

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Sharaff, Aakanksha, Naresh Kumar Nagwani, and Kunal Swami. "Impact of Feature Selection Technique on Email Classification." International Journal of Knowledge Engineering-IACSIT 1, no. 1 (2015): 59–63. http://dx.doi.org/10.7763/ijke.2015.v1.10.

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Salama, Mostafa A., and Ghada Hassan. "A Novel Feature Selection Measure Partnership-Gain." International Journal of Online and Biomedical Engineering (iJOE) 15, no. 04 (2019): 4. http://dx.doi.org/10.3991/ijoe.v15i04.9831.

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Multivariate feature selection techniques search for the optimal features subset to reduce the dimensionality and hence the complexity of a classification task. Statistical feature selection techniques measure the mutual correlation between features well as the correlation of each feature to the tar- get feature. However, adding a feature to a feature subset could deteriorate the classification accuracy even though this feature positively correlates to the target class. Although most of existing feature ranking/selection techniques consider the interdependency between features, the nature of i

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Sikri, Alisha, N. P. Singh, and Surjeet Dalal. "Analysis of Rank Aggregation Techniques for Rank Based on the Feature Selection Technique." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 3s (2023): 95–108. http://dx.doi.org/10.17762/ijritcc.v11i3s.6160.

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In order to improve classification accuracy and lower future computation and data collecting costs, feature selection is the process of choosing the most crucial features from a group of attributes and removing the less crucial or redundant ones. To narrow down the features that need to be analyzed, a variety of feature selection procedures have been detailed in published publications. Chi-Square (CS), IG, Relief, GR, Symmetrical Uncertainty (SU), and MI are six alternative feature selection methods used in this study. The provided dataset is aggregated using four rank aggregation strategies:

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Goswami, Saptarsi, Amit Kumar Das, Amlan Chakrabarti, and Basabi Chakraborty. "A feature cluster taxonomy based feature selection technique." Expert Systems with Applications 79 (August 2017): 76–89. http://dx.doi.org/10.1016/j.eswa.2017.01.044.

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Jain, Rahi, and Wei Xu. "HDSI: High dimensional selection with interactions algorithm on feature selection and testing." PLOS ONE 16, no. 2 (2021): e0246159. http://dx.doi.org/10.1371/journal.pone.0246159.

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Feature selection on high dimensional data along with the interaction effects is a critical challenge for classical statistical learning techniques. Existing feature selection algorithms such as random LASSO leverages LASSO capability to handle high dimensional data. However, the technique has two main limitations, namely the inability to consider interaction terms and the lack of a statistical test for determining the significance of selected features. This study proposes a High Dimensional Selection with Interactions (HDSI) algorithm, a new feature selection method, which can handle high-dim

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Ramineni, Vyshnavi, and Goo-Rak Kwon. "Diagnosis of Alzheimer’s Disease using Wrapper Feature Selection Method." Korean Institute of Smart Media 12, no. 3 (2023): 30–37. http://dx.doi.org/10.30693/smj.2023.12.3.30.

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Alzheimer’s disease (AD) symptoms are being treated by early diagnosis, where we can only slow the symptoms and research is still undergoing. In consideration, using T1-weighted images several classification models are proposed in Machine learning to identify AD. In this paper, we consider the improvised feature selection, to reduce the complexity by using wrapping techniques and Restricted Boltzmann Machine (RBM). This present work used the subcortical and cortical features of 278 subjects from the ADNI dataset to identify AD and sMRI. Multi-class classification is used for the experiment i.e

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Zabidi, A., W. Mansor, and Khuan Y. Lee. "Optimal Feature Selection Technique for Mel Frequency Cepstral Coefficient Feature Extraction in Classifying Infant Cry with Asphyxia." Indonesian Journal of Electrical Engineering and Computer Science 6, no. 3 (2017): 646. http://dx.doi.org/10.11591/ijeecs.v6.i3.pp646-655.

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<p>Mel Frequency Cepstral Coefficient is an efficient feature representation method for extracting human-audible audio signals. However, its representation of features is large and redundant. Therefore, feature selection is required to select the optimal subset of Mel Frequency Cepstral Coefficient features. The performance of two types of feature selection techniques; Orthogonal Least Squares and F-ratio for selecting Mel Frequency Cepstral Coefficient features of infant cry with asphyxia was examined. OLS selects the feature subset based on their contribution to the reduction of error,

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Miftahushudur, Tajul, Chaeriah Bin Ali Wael, and Teguh Praludi. "Infinite Latent Feature Selection Technique for Hyperspectral Image Classification." Jurnal Elektronika dan Telekomunikasi 19, no. 1 (2019): 32. http://dx.doi.org/10.14203/jet.v19.32-37.

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The classification process is one of the most crucial processes in hyperspectral imaging. One of the limitations in classification process using machine learning technique is its complexities, where hyperspectral image format has a thousand band that can be used as a feature for learning purpose. This paper presents a comparison between two feature selection technique based on probability approach that not only can tackle the problem, but also improve accuracy. Infinite Latent Feature Selection (ILFS) and Relief Techniques are implemented in a hyperspectral image to select the most important f

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Saifan, Ahmad A., and Lina Abu-wardih. "Software Defect Prediction Based on Feature Subset Selection and Ensemble Classification." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 14, no. 2 (2020): 213–28. http://dx.doi.org/10.37936/ecti-cit.2020142.224489.

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Two primary issues have emerged in the machine learning and data mining community: how to deal with imbalanced data and how to choose appropriate features. These are of particular concern in the software engineering domain, and more specifically the field of software defect prediction. This research highlights a procedure which includes a feature selection technique to single out relevant attributes, and an ensemble technique to handle the class-imbalance issue. In order to determine the advantages of feature selection and ensemble methods we look at two potential scenarios: (1) Ensemble model

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Ali, Tariq, Asif Nawaz, and Hafiza Ayesha Sadia. "Genetic Algorithm Based Feature Selection Technique for Electroencephalography Data." Applied Computer Systems 24, no. 2 (2019): 119–27. http://dx.doi.org/10.2478/acss-2019-0015.

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Abstract High dimensionality is a well-known problem that has a huge number of highlights in the data, yet none is helpful for a particular data mining task undertaking, for example, classification and grouping. Therefore, selection of features is used frequently to reduce the data set dimensionality. Feature selection is a multi-target errand, which diminishes dataset dimensionality, decreases the running time, and furthermore enhances the expected precision. In the study, our goal is to diminish the quantity of features of electroencephalography data for eye state classification and achieve

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Dissertations / Theses on the topic "FEATURE SELECTION TECHNIQUE"

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Tan, Feng. "Improving Feature Selection Techniques for Machine Learning." Digital Archive @ GSU, 2007. http://digitalarchive.gsu.edu/cs_diss/27.

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As a commonly used technique in data preprocessing for machine learning, feature selection identifies important features and removes irrelevant, redundant or noise features to reduce the dimensionality of feature space. It improves efficiency, accuracy and comprehensibility of the models built by learning algorithms. Feature selection techniques have been widely employed in a variety of applications, such as genomic analysis, information retrieval, and text categorization. Researchers have introduced many feature selection algorithms with different selection criteria. However, it has been disc

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Loscalzo, Steven. "Group based techniques for stable feature selection." Diss., Online access via UMI:, 2009.

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Vege, Sri Harsha. "Ensemble of Feature Selection Techniques for High Dimensional Data." TopSCHOLAR®, 2012. http://digitalcommons.wku.edu/theses/1164.

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Data mining involves the use of data analysis tools to discover previously unknown, valid patterns and relationships from large amounts of data stored in databases, data warehouses, or other information repositories. Feature selection is an important preprocessing step of data mining that helps increase the predictive performance of a model. The main aim of feature selection is to choose a subset of features with high predictive information and eliminate irrelevant features with little or no predictive information. Using a single feature selection technique may generate local optima. In this t

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Gustafsson, Robin. "Ordering Classifier Chains using filter model feature selection techniques." Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-14817.

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Context: Multi-label classification concerns classification with multi-dimensional output. The Classifier Chain breaks the multi-label problem into multiple binary classification problems, chaining the classifiers to exploit dependencies between labels. Consequently, its performance is influenced by the chain's order. Approaches to finding advantageous chain orders have been proposed, though they are typically costly. Objectives: This study explored the use of filter model feature selection techniques to order Classifier Chains. It examined how feature selection techniques can be adapted to ev

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Zhang, Fu. "Intelligent feature selection for neural regression : techniques and applications." Thesis, University of Warwick, 2012. http://wrap.warwick.ac.uk/49639/.

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Feature Selection (FS) and regression are two important technique categories in Data Mining (DM). In general, DM refers to the analysis of observational datasets to extract useful information and to summarise the data so that it can be more understandable and be used more efficiently in terms of storage and processing. FS is the technique of selecting a subset of features that are relevant to the development of learning models. Regression is the process of modelling and identifying the possible relationships between groups of features (variables). Comparing with the conventional techniques, In

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Muteba, Ben Ilunga. "Data Science techniques for predicting plant genes involved in secondary metabolites production." University of the Western Cape, 2018. http://hdl.handle.net/11394/7039.

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Masters of Science<br>Plant genome analysis is currently experiencing a boost due to reduced costs associated with the development of next generation sequencing technologies. Knowledge on genetic background can be applied to guide targeted plant selection and breeding, and to facilitate natural product discovery and biological engineering. In medicinal plants, secondary metabolites are of particular interest because they often represent the main active ingredients associated with health-promoting qualities. Plant polyphenols are a highly diverse family of aromatic secondary metabolites th

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Strand, Lars Helge. "Feature selection in Medline using text and data mining techniques." Thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2005. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9249.

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<p>In this thesis we propose a new method for searching for gene products gene products and give annotations associating genes with Gene Ontology codes. Many solutions already exists, using different techniques, however few are capable of addressing the whole GO hierarchy. We propose a method for exploring this hierarchy by dividing it into subtrees, trying to find terms that are characteristics for the subtrees involved. Using a feature selection based on chi-square analysis and naive Bayes classification to find the correct GO nodes.</p>

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Ni, Weizeng. "A Review and Comparative Study on Univariate Feature Selection Techniques." University of Cincinnati / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1353156184.

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Dang, Vinh Q. "Evolutionary approaches for feature selection in biological data." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2014. https://ro.ecu.edu.au/theses/1276.

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Data mining techniques have been used widely in many areas such as business, science, engineering and medicine. The techniques allow a vast amount of data to be explored in order to extract useful information from the data. One of the foci in the health area is finding interesting biomarkers from biomedical data. Mass throughput data generated from microarrays and mass spectrometry from biological samples are high dimensional and is small in sample size. Examples include DNA microarray datasets with up to 500,000 genes and mass spectrometry data with 300,000 m/z values. While the availability

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Miller, Corey Alexander. "Intelligent Feature Selection Techniques for Pattern Classification of Time-Domain Signals." W&M ScholarWorks, 2013. https://scholarworks.wm.edu/etd/1539623620.

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Time-domain signals form the basis of analysis for a variety of applications, including those involving variable conditions or physical changes that result in degraded signal quality. Typical approaches to signal analysis fail under these conditions, as these types of changes often lie outside the scope of the domain's basic analytic theory and are too complex for modeling. Sophisticated signal processing techniques are required as a result. In this work, we develop a robust signal analysis technique that is suitable for a wide variety of time-domain signal analysis applications. Statistical p

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Books on the topic "FEATURE SELECTION TECHNIQUE"

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K, Kokula Krishna Hari, and K. Saravanan, eds. Exploratory Analysis of Feature Selection Techniques in Medical Image Processing. Association of Scientists, Developers and Faculties, 2016.

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Raza, Muhammad Summair, and Usman Qamar. Understanding and Using Rough Set Based Feature Selection: Concepts, Techniques and Applications. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4965-1.

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Raza, Muhammad Summair, and Usman Qamar. Understanding and Using Rough Set Based Feature Selection: Concepts, Techniques and Applications. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9166-9.

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Raza, Muhammad Summair, and Usman Qamar. Understanding and Using Rough Set Based Feature Selection: Concepts, Techniques and Applications. Springer, 2017.

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Raza, Muhammad Summair, and Usman Qamar. Understanding and Using Rough Set Based Feature Selection: Concepts, Techniques and Applications. Springer, 2019.

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Raza, Muhammad Summair, and Usman Qamar. Understanding and Using Rough Set Based Feature Selection: Concepts, Techniques and Applications. Springer Singapore Pte. Limited, 2020.

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Raza, Muhammad Summair, and Usman Qamar. Understanding and Using Rough Set Based Feature Selection: Concepts, Techniques and Applications. Springer Singapore Pte. Limited, 2018.

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Grant, Stuart A., and David B. Auyong. Basic Principles of Ultrasound Guided Nerve Block. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780190231804.003.0001.

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This chapter provides a clinical description of ultrasound physics tailored to provide the practitioner a solid background for optimal imaging and needle guidance technique during regional anesthesia. Important ultrasound characteristics are covered, including optimization of ultrasound images, transducer selection, and features found on most point-of-care systems. In-plane and out-of-plane needle guidance techniques and a three-step process for visualizing in-plane needle insertions are presented. Next, common artifacts and errors including attenuation, dropout, and intraneural injection are

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Thrumurthy, Sri G., Tania S. De Silva, Zia M. Moinuddin, and Stuart Enoch. EMQs for the MRCS Part A. Oxford University Press, 2013. http://dx.doi.org/10.1093/oso/9780199645640.001.0001.

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Specifically designed to help candidates revise for the MRCS exam, this book features 250 extended matching questions divided into 96 themes, covering the whole syllabus. Containing everything candidates need to pass the MRCS Part A EMQ section of the exam, the book focuses intensively on topics relating to principles of surgery-in-general, including peri-operative care, post-operative management and critical care, surgical technique and technology, management and legal issues in surgery, clinical microbiology, emergency medicine and trauma management, and principles of surgical oncology. The

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Thrumurthy, Sri G., Tania Samantha De Silva, Zia Moinuddin, and Stuart Enoch. SBA MCQs for the MRCS Part A. Oxford University Press, 2012. http://dx.doi.org/10.1093/oso/9780199645633.001.0001.

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Specifically designed to help candidates revise for the MRCS exam, this book features 350 Single Best Answer multiple choice questions, covering the whole syllabus. Containing everything candidates need to pass the MRCS Part A SBA section of the exam, it focuses intensively on the application of basic sciences (applied surgical anatomy, physiology, and pathology) to the management of surgical patients. The high level of detail included within the questions and their explanations allows effective self-assessment of knowledge and quick identification of key areas requiring further attention. Var

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Book chapters on the topic "FEATURE SELECTION TECHNIQUE"

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Singh, Upendra, and Sudhakar Tripathi. "Protein Classification Using Hybrid Feature Selection Technique." In Communications in Computer and Information Science. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-3433-6_97.

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Naveen, Nekuri, and Mandala Sookshma. "Adaptive Feature Selection and Classification Using Optimization Technique." In Frontiers in Intelligent Computing: Theory and Applications. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9186-7_17.

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Guru, D. S., Mostafa Ali, and Mahamad Suhil. "A Novel Feature Selection Technique for Text Classification." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1498-8_63.

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Nagaraj, Naik, B. M. Vikranth, and N. Yogesh. "Recursive Feature Elimination Technique for Technical Indicators Selection." In Communications in Computer and Information Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08277-1_12.

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Zheng, Hai-Tao, and Haiyang Zhang. "Online Streaming Feature Selection Using Sampling Technique and Correlations Between Features." In Web Technologies and Applications. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45817-5_4.

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Christy, A., and G. Meera Gandhi. "Feature Selection and Clustering of Documents Using Random Feature Set Generation Technique." In Advances in Data Science and Management. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0978-0_6.

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Lee, Kee-Cheol. "A Technique of Dynamic Feature Selection Using the Feature Group Mutual Information." In Methodologies for Knowledge Discovery and Data Mining. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48912-6_19.

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LeKhac, NhienAn, Bo Wu, ChongCheng Chen, and M.-Tahar Kechadi. "Feature Selection Parallel Technique for Remotely Sensed Imagery Classification." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39643-4_45.

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Seeja, K. R. "A Novel Feature Selection Technique for SAGE Data Classification." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39678-6_9.

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Alharbi, Abdullah Semran, Yuefeng Li, and Yue Xu. "Integrating LDA with Clustering Technique for Relevance Feature Selection." In AI 2017: Advances in Artificial Intelligence. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63004-5_22.

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Conference papers on the topic "FEATURE SELECTION TECHNIQUE"

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Battisti, Felipe de Melo, and Tiago Buarque Assunção de Carvalho. "Threshold Feature Selection PCA." In Symposium on Knowledge Discovery, Mining and Learning. Sociedade Brasileira de Computação - SBC, 2022. http://dx.doi.org/10.5753/kdmile.2022.227718.

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Classification algorithms encounter learning difficulties when data has non-discriminant features. Dimensionality reduction techniques such as PCA are commonly applied. However, PCA has the disadvantage of being an unsupervised method, ignoring relevant class information on data. Therefore, this paper proposes the Threshold Feature Selector (TFS), a new supervised dimensionality reduction method that employs class thresholds to select more relevant features. We also present the Threshold PCA (TPCA), a combination of our supervised technique with standard PCA. During experiments, TFS achieved h

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Bibi, K. Fathima, and M. Nazreen Banu. "Feature subset selection based on Filter technique." In 2015 International Conference on Computing and Communications Technologies (ICCCT). IEEE, 2015. http://dx.doi.org/10.1109/iccct2.2015.7292710.

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Wiratsin, In-On, and Lalita Narupiyakul. "Feature Selection Technique for Autism Spectrum Disorder." In CCEAI 2021: 5th International Conference on Control Engineering and Artificial Intelligence. ACM, 2021. http://dx.doi.org/10.1145/3448218.3448241.

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Tayal, Devendra K., Neha Srivastava, and Neha. "Feature Selection using Enhanced Nature Optimization Technique." In 2023 International Conference on Advances in Intelligent Computing and Applications (AICAPS). IEEE, 2023. http://dx.doi.org/10.1109/aicaps57044.2023.10074104.

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S, Abdul Razak M., Nirmala C. R, Chetan B. B, Mohammed Rafi, and Sreenivasa B. R. "Online feature Selection using Pearson Correlation Technique." In 2022 IEEE 7th International Conference on Recent Advances and Innovations in Engineering (ICRAIE). IEEE, 2022. http://dx.doi.org/10.1109/icraie56454.2022.10054267.

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López Jaimes, Antonio, Carlos A. Coello Coello, and Debrup Chakraborty. "Objective reduction using a feature selection technique." In the 10th annual conference. ACM Press, 2008. http://dx.doi.org/10.1145/1389095.1389228.

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Wang, Yong, Adam J. Brzezinski, Xianli Qiao, and Jun Ni. "Heuristic Feature Selection for Shaving Tool Wear Classification." In ASME 2016 11th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/msec2016-8547.

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In this paper, we develop and apply feature extraction and selection techniques to classify tool wear in the shaving process. Because shaving tool condition monitoring is not well-studied, we extract both traditional and novel features from accelerometer signals collected from the shaving machine. We then apply a heuristic feature selection technique to identify key features and classify the tool condition. Run-to-life data from a shop-floor application is used to validate the proposed technique.

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Meng Wang, Shudong Sun, Ganggang Niu, Yuanzhi Tu, and Shihui Guo. "A feature selection technique based on equivalent relation." In 2011 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC). IEEE, 2011. http://dx.doi.org/10.1109/aimsec.2011.6010707.

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Liogiene, Tatjana, and Gintautas Tamulevicius. "SFS feature selection technique for multistage emotion recognition." In 2015 IEEE 3rd Workshop on Advances in Information, Electronic and Electrical Engineering (AIEEE). IEEE, 2015. http://dx.doi.org/10.1109/aieee.2015.7367299.

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Mary, I. Thusnavis Bella, A. Vasuki, and M. A. P. Manimekalai. "An optimized feature selection CBIR technique using ANN." In 2017 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT). IEEE, 2017. http://dx.doi.org/10.1109/iceeccot.2017.8284550.

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Reports on the topic "FEATURE SELECTION TECHNIQUE"

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Zhao, George, Grang Mei, Bulent Ayhan, Chiman Kwan, and Venu Varma. DTRS57-04-C-10053 Wave Electromagnetic Acoustic Transducer for ILI of Pipelines. Pipeline Research Council International, Inc. (PRCI), 2005. http://dx.doi.org/10.55274/r0012049.

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In this project, Intelligent Automation, Incorporated (IAI) and Oak Ridge National Lab (ORNL) propose a novel and integrated approach to inspect the mechanical dents and metal loss in pipelines. It combines the state-of-the-art SH wave Electromagnetic Acoustic Transducer (EMAT) technique, through detailed numerical modeling, data collection instrumentation, and advanced signal processing and pattern classifications, to detect and characterize mechanical defects in the underground pipeline transportation infrastructures. The technique has four components: (1) thorough guided wave modal analysis

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Searcy, Stephen W., and Kalman Peleg. Adaptive Sorting of Fresh Produce. United States Department of Agriculture, 1993. http://dx.doi.org/10.32747/1993.7568747.bard.

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This project includes two main parts: Development of a “Selective Wavelength Imaging Sensor” and an “Adaptive Classifiery System” for adaptive imaging and sorting of agricultural products respectively. Three different technologies were investigated for building a selectable wavelength imaging sensor: diffraction gratings, tunable filters and linear variable filters. Each technology was analyzed and evaluated as the basis for implementing the adaptive sensor. Acousto optic tunable filters were found to be most suitable for the selective wavelength imaging sensor. Consequently, a selectable wave

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Tayeb, Shahab. Taming the Data in the Internet of Vehicles. Mineta Transportation Institute, 2022. http://dx.doi.org/10.31979/mti.2022.2014.

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As an emerging field, the Internet of Vehicles (IoV) has a myriad of security vulnerabilities that must be addressed to protect system integrity. To stay ahead of novel attacks, cybersecurity professionals are developing new software and systems using machine learning techniques. Neural network architectures improve such systems, including Intrusion Detection System (IDSs), by implementing anomaly detection, which differentiates benign data packets from malicious ones. For an IDS to best predict anomalies, the model is trained on data that is typically pre-processed through normalization and f

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Robert Nourgaliev, Nam Dinh, and Robert Youngblood. Development, Selection, Implementation and Testing of Architectural Features and Solution Techniques for Next Generation of System Simulation Codes to Support the Safety Case if the LWR Life Extension. Office of Scientific and Technical Information (OSTI), 2010. http://dx.doi.org/10.2172/1004227.

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Lylo, Taras. Російсько-українська війна в інтерпретаціях іранського видання «The Tehran Times»: основні ідеологеми та маніпулятивні прийоми. Ivan Franko National University of Lviv, 2023. http://dx.doi.org/10.30970/vjo.2023.52-53.11730.

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The article analyzes the main ideologemes in the Iranian English-language newspaper The Tehran Times about the Russian-Ukrainian war. Particular attention is paid to such ideologemes as “NATO-created Ukraine war”, “Western racism”, “an average European is a victim of the US policy”. The author claims that the newspaper is a repeater of anti-Ukrainian ideologemes by the Russian propaganda, including such as “coup d’état in Ukraine”, “denazification”, “special military operation”, “conflict in Ukraine”, “genocide in Donbas”, but retranslates them in a specific way: the journalists of The Tehran

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Riccardella, Scott. PR-335-143705-R01 Study on Reliability of In-ditch NDE for SCC Anomalies. Pipeline Research Council International, Inc. (PRCI), 2018. http://dx.doi.org/10.55274/r0011529.

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Abstract:

Pipeline operators are increasingly finding pipeline degradation in the form of crack-like defects associated with stress corrosion cracking and are challenged in selecting and employing nondestructive examination techniques that can reliably determine the maximum depth and axial depth profile of these anomalies. This information is essential in determining whether the line is fit for service and for how long; which areas require repair; what repair methods may be deemed acceptable; and whether in-line inspection was successful in detecting and prioritizing anomalies. This work further investi

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