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Journal articles on the topic 'Features original'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Kulkarni, Anushka, Prachi Kedar, Aishwarya Pupala, and Priyanka Shingane. "Original vs Counterfeit Indian Currency Detection." ITM Web of Conferences 32 (2020): 03047. http://dx.doi.org/10.1051/itmconf/20203203047.

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Currency is used to carry out not only business but also for various other transactions to get access to various services and commodities. There are a total of 7 denominations for the Indian currency each with unique features to distinguish them from each other and with various and distinct security features to prevent them from fraudulent copying. However,with the evolution of technology, there is also an increase in the ways in which fake forms of these currencies are created. These fake or counterfeit notes have various ill-effect on society. The proposed system will be used to check the ge

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Tursunovich, Rustamov Ilkhom, Uralova Nigora Abduvaliebna, and Alimova Dilnoza Khamid Qizi. "Pragmatics in genre features of original texts." ACADEMICIA: AN INTERNATIONAL MULTIDISCIPLINARY RESEARCH JOURNAL 11, no. 2 (2021): 1151–54. http://dx.doi.org/10.5958/2249-7137.2021.00476.6.

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Sekisov, G., and V. Litvintsev. "ALLUVIAL MINERAL UNIFORMITY: ORIGINAL COMPOSITION AND FEATURES." Transbaikal State University Journal 24, no. 7 (2018): 41–50. http://dx.doi.org/10.21209/2227-9245-2018-24-7-41-50.

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Caseau, Yves. "Some original features of the LAURE language." ACM SIGPLAN OOPS Messenger 4, no. 2 (1993): 199–200. http://dx.doi.org/10.1145/157710.157753.

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Meloy, T. P. "Distribution of original surface features during comminution." Powder Technology 41, no. 2 (1985): 197–202. http://dx.doi.org/10.1016/0032-5910(85)87038-8.

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І.Г., Яловега, Сидоров М.В. та Гончаров Д.О. "МЕТОДОЛОГІЧНІ ОСНОВИ ДОСЛІДЖЕННЯ ЕЛАСТИЧНОСТІ ПОПИТУ ТА ПРОПОЗИЦІЇ". ЗБІРНИК НАУКОВИХ ПРАЦЬ ХАРКІВСЬКОГО НАЦІОНАЛЬНОГО ПЕДАГОГІЧНОГО УНІВЕРСИТЕТУ ІМЕНІ Г.С. СКОВОРОДИ "ЕКОНОМІКА", № 15 (18 липня 2015): 51–60. https://doi.org/10.5281/zenodo.20695.

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In the article the methodological features of the study of the concept of elasticity functions as an important direction of application of differential calculus in economics. Highlight features original concept, the central concept of differential calculus, which is important in the economy and is the foundation of basic economic concept of elasticity. The problem of matching process teaching of mathematical analysis for students of our university requirements and the present state of scientific development. On the basis of educational materials on mathematical analysis the failure of illustra

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Pawening, Ratri Enggar, Tio Darmawan, Rizqa Raaiqa Bintana, Agus Zainal Arifin, and Darlis Herumurti. "FEATURE SELECTION METHODS BASED ON MUTUAL INFORMATION FOR CLASSIFYING HETEROGENEOUS FEATURES." Jurnal Ilmu Komputer dan Informasi 9, no. 2 (2016): 106. http://dx.doi.org/10.21609/jiki.v9i2.384.

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Datasets with heterogeneous features can affect feature selection results that are not appropriate because it is difficult to evaluate heterogeneous features concurrently. Feature transformation (FT) is another way to handle heterogeneous features subset selection. The results of transformation from non-numerical into numerical features may produce redundancy to the original numerical features. In this paper, we propose a method to select feature subset based on mutual information (MI) for classifying heterogeneous features. We use unsupervised feature transformation (UFT) methods and joint mu

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Nasipuri, Bikram, and Vibhash Jha. "Role of rock structure in drainage development in the Rangit river basin, Sikkim-Darjeeling Himalaya." National Geographical Journal of India 67, no. 4 (2021): 356–70. http://dx.doi.org/10.48008/ngji.1783.

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The geology and tectonics of an area play a very important role in the development of geomorphic features and drainage of a region. The Rangit River Basin is a part of Sikkim-Darjeeling Himalaya that contains very significant geological and tectonic characteristics. The basin has mainly three rock groups that are Central Crystalline Complex, the Daling group and the Gondwana group. The spectacular feature of this basin is Rangit Window, where the underlying Gondwana rock group is exposed from overlaying Daling group. Rangit basin exhibits both folded and fault structures which are indeed uniqu

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Vaičenonienė, Jurgita, and Jolanta Kovalevskaitė. "Lexical and Morphological Features of Translational Lithuanian." Sustainable Multilingualism 14, no. 1 (2019): 208–35. http://dx.doi.org/10.2478/sm-2019-0010.

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Summary In Lithuanian public and academic discourse, discussions about the influence of English have received considerable attention. Much has been written on the English borrowings in Lithuanian or various translation strategies applied at word, phrase or syntactic levels of language, whereas there have been only few attempts to investigate how Lithuanian translated from English differs from original language. This is why we found it interesting to investigate lexical an morphological features of translated Lithuanian applying the methods of corpus liguistics. For research purposes, we used a

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Perrin, C., L. Prestimonaco, G. Servelle, R. Tilhac, M. Maury, and P. Cabrol. "Aragonite-Calcite Speleothems: Identifying Original and Diagenetic Features." Journal of Sedimentary Research 84, no. 4 (2014): 245–69. http://dx.doi.org/10.2110/jsr.2014.17.

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황선주. "The original features of the Dunhuang edition Wenxindiaolong." CHINESE LITERATURE 55, no. ll (2008): 19–54. http://dx.doi.org/10.21192/scll.55..200805.002.

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Ando, Michiyo, and Yuji Hakoda. "Asymmetric Effects on Recognition of Animate Objects by Children." Psychological Reports 86, no. 3 (2000): 995–99. http://dx.doi.org/10.2466/pr0.2000.86.3.995.

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The present study examined the effects of type of change in a picture (addition or deletion), and the effects of presentation time on children's recognition of animate objects (butterflies and cats). Five- or 6-yr.-old children viewed original pictures in a learning phase for 6 or 10 sec., and then they viewed in a test phase originals and altered pictures in which features were added to or deleted from original pictures. They were required to answer whether test stimuli had been seen before. Analysis showed that, although children discovered added features more frequently than deleted feature

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Jiang, Peng, and Xiaodong Cai. "A Symmetric Dual-Drive Text Matching Model Based on Dynamically Gated Sparse Attention Feature Distillation with a Faithful Semantic Preservation Strategy." Symmetry 17, no. 5 (2025): 772. https://doi.org/10.3390/sym17050772.

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A new text matching model based on dynamic gated sparse attention feature distillation with a faithful semantic preservation strategy is proposed to address the fact that text matching models are susceptible to interference from weakly relevant information and that they find it difficult to obtain key features that are faithful to the original semantics, resulting in a decrease in accuracy. Compared to the traditional attention mechanism, with its high computational complexity and difficulty in discarding weakly relevant features, this study designs a new dynamic gated sparse attention feature

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Zhang, Dehuan, Wei Cao, Jingchun Zhou, Yan-Tsung Peng, Weishi Zhang, and Zifan Lin. "Two-Branch Underwater Image Enhancement and Original Resolution Information Optimization Strategy in Ocean Observation." Journal of Marine Science and Engineering 11, no. 7 (2023): 1285. http://dx.doi.org/10.3390/jmse11071285.

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In complex marine environments, underwater images often suffer from color distortion, blur, and poor visibility. Existing underwater image enhancement methods predominantly rely on the U-net structure, which assigns the same weight to different resolution information. However, this approach lacks the ability to extract sufficient detailed information, resulting in problems such as blurred details and color distortion. We propose a two-branch underwater image enhancement method with an optimized original resolution information strategy to address this limitation. Our method comprises a feature

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L, Manaljav, and Otgontuul T. "Linguistic Features in the Chinese Original of Lao Qida." Mongolian Journal of Foreign Languages and Culture 24, no. 1 (2020): 1–4. http://dx.doi.org/10.22353/mjflc2020114.

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Li, Shu Fang, Qin Jia, and Hong Liang. "Research of Red Tide Algae Images Feature Selection Method Based on ReliefF and SBS." Applied Mechanics and Materials 507 (January 2014): 806–9. http://dx.doi.org/10.4028/www.scientific.net/amm.507.806.

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In order to Red Tide algae present real-time automatic classification method of high accuracy rate, this paper proposes using ReliefF-SBS for feature selection. Namely feature analysis about Red Tide algae image original data set. And on this basis, feature selection to remove the irrelevant features and redundant features from the original feature set feature, to get the optimal feature subset, and reduce their impact on the classification accuracy. Meanwhile compare the classification results before and after SVM and KNN two kinds feature selection classifiers.

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Sulaiman, Dawlat Mustafa, Adnan Mohsin Abdulazeez, and Habibollah Haron. "Double stages of feature extarction-based GFPMI for colored finger vein identification." Indonesian Journal of Electrical Engineering and Computer Science 18, no. 2 (2020): 927. http://dx.doi.org/10.11591/ijeecs.v18.i2.pp927-937.

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Today, finger vein recognition has a lot of attention as a promising approach of biometric identification framework and still does not meet the challenges of the researchers on this filed. To solve this problem, we propose s double stage of feature extraction schemes based localized finger fine image detection. We propose Globalized Features Pattern Map Indication (GFPMI) to extract the globalized finger vein line features basede on using two generated vein image datasets: original gray level color, globalized finger vein line feature, original localized gray level image, and the colored local

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Dawlat, Mustafa Sulaiman, Mohsin Abdulazeez Adnan, and Haron Habibollah. "Double stages of feature extarction-based GFPMI for colored finger vein identification." Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) 18, no. 2 (2020): 927–37. https://doi.org/10.11591/ijeecs.v18.i2.pp927-937.

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Today, finger vein recognition has a lot of attention as a promising approach of biometric identification framework and still does not meet the challenges of the researchers on this filed. To solve this problem, we propose s double stage of feature extraction schemes based localized finger fine image detection. We propose Globalized Features Pattern Map Indication (GFPMI) to extract the globalized finger vein line features basede on using two generated vein image datasets: original gray level color, globalized finger vein line feature, original localized gray level image, and the colored local

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Zhang, Zhikang, Zhongjie Zhu, Yongqiang Bai, Yiwen Jin, and Ming Wang. "Multi-Scale Feature Fusion Point Cloud Object Detection Based on Original Point Cloud and Projection." Electronics 13, no. 11 (2024): 2213. http://dx.doi.org/10.3390/electronics13112213.

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Existing point cloud object detection algorithms struggle to effectively capture spatial features across different scales, often resulting in inadequate responses to changes in object size and limited feature extraction capabilities, thereby affecting detection accuracy. To solve this problem, we present a point cloud object detection method based on multi-scale feature fusion of the original point cloud and projection, which aims to improve the multi-scale performance and completeness of feature extraction in point cloud object detection. First, we designed a 3D feature extraction module base

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Han, Dongying, Kai Liang, and Peiming Shi. "Intelligent fault diagnosis of rotating machinery based on deep learning with feature selection." Journal of Low Frequency Noise, Vibration and Active Control 39, no. 4 (2019): 939–53. http://dx.doi.org/10.1177/1461348419849279.

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In the absence of a priori knowledge, manual feature selection is too blind to find the sensitive features which can effectively classify the different fault features. And it is difficult to obtain a large number of typical fault samples in practice to train the intelligent classifier. A novel intelligent fault diagnosis method based on feature selection and deep learning is proposed for rotating machine mechanical in the paper. In this method, the deep neural network is not only used for feature extraction but also for fault diagnosis. First, the deep neural network 1 is used to extract featu

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SKREBETS, Volodymyr, and Iryna SHAVRINA. "FEATURES AND MAIN TYPOLOGIES OF THE ORIGINAL RELIGIOUS BELIEFS AND CULTS." Філософія та політологія в контексті сучасної культури 16, no. 1 (2024): 42–49. http://dx.doi.org/10.15421/352417.

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The article focuses on the research in the field of religious studies and the history of religion, specifically, the original religious cults and beliefs. The original beliefs are the basis of any modern religion. It is important to understand the history of formation of the ideas from the original communities’ times to the first states and up to current times. They allow us to better understand the religious beliefs and practices of our ancestors, as well as to find out how they influenced the formation of culture and society. Important aspects of the original religious beliefs, such as animi

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Shi, Haikuo, Jing Hu, Xiaolong Zhang, Shuting Jin, and Xin Xu. "Prediction of drug-target interactions based on substructure subsequences and cross-public attention mechanism." PLOS One 20, no. 5 (2025): e0324146. https://doi.org/10.1371/journal.pone.0324146.

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Drug-target interactions (DTIs) play a critical role in drug discovery and repurposing. Deep learning-based methods for predicting drug-target interactions are more efficient than wet-lab experiments. The extraction of original and substructural features from drugs and proteins plays a key role in enhancing the accuracy of DTI predictions, while the integration of multi-feature information and effective representation of interaction data also impact the precision of DTI forecasts. Consequently, we propose a drug-target interaction prediction model, SSCPA-DTI, based on substructural subsequence

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Liu, Xiaojian, Qian Lei, and Kehong Liu. "A Graph-Based Feature Generation Approach in Android Malware Detection with Machine Learning Techniques." Mathematical Problems in Engineering 2020 (May 27, 2020): 1–15. http://dx.doi.org/10.1155/2020/3842094.

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An explosive spread of Android malware causes a serious concern for Android application security. One of the solutions to detecting malicious payloads sneaking in an application is to treat the detection as a binary classification problem, which can be effectively tackled with traditional machine learning techniques. The key factors in detecting Android malware with machine learning techniques are feature selection and generation. Most of the existing approaches select and generate features without fully examining the structures of programs, and thus the important semantic information associat

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Xi, Ke, and Cheng Cai. "Feature selected based on PCA and optimized LMC." MATEC Web of Conferences 336 (2021): 06034. http://dx.doi.org/10.1051/matecconf/202133606034.

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In this article, we propose an optimization algorithm for the original LMC [1] (Large Margin Classifier). We use PCA [2] (Principal Component Analysis) to reduce the dimensionality of the images, and then put the data after dimensionality reduction into the optimized LMC for the feature selection [3]. We will get several features with the greatest distinction. We use these features to classify images. Finally, the experiment shows that the accuracy of the optimized LMC under the same dimensions is higher than that of the original LMC, and in many cases, the accuracy of the optimized LMC after

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Kaur, Bineet, and Garima Joshi. "Lower Order Krawtchouk Moment-Based Feature-Set for Hand Gesture Recognition." Advances in Human-Computer Interaction 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/6727806.

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The capability of lower order Krawtchouk moment-based shape features has been analyzed. The behaviour of 1D and 2D Krawtchouk polynomials at lower orders is observed by varying Region of Interest (ROI). The paper measures the effectiveness of shape recognition capability of 2D Krawtchouk features at lower orders on the basis of Jochen-Triesch’s database and hand gesture database of 10 Indian Sign Language (ISL) alphabets. Comparison of original and reduced feature-set is also done. Experimental results demonstrate that the reduced feature dimensionality gives competent accuracy as compared to

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Dong, Fei, Xiao Yu, Enjie Ding, Shoupeng Wu, Chunyang Fan, and Yanqiu Huang. "Rolling Bearing Fault Diagnosis Using Modified Neighborhood Preserving Embedding and Maximal Overlap Discrete Wavelet Packet Transform with Sensitive Features Selection." Shock and Vibration 2018 (2018): 1–29. http://dx.doi.org/10.1155/2018/5063527.

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In order to enhance the performance of bearing fault diagnosis and classification, features extraction and features dimensionality reduction have become more important. The original statistical feature set was calculated from single branch reconstruction vibration signals obtained by using maximal overlap discrete wavelet packet transform (MODWPT). In order to reduce redundancy information of original statistical feature set, features selection by adjusted rand index and sum of within-class mean deviations (FSASD) was proposed to select fault sensitive features. Furthermore, a modified feature

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Orlov, S. D., and L. P. Necheporenko. "Creating of original materials of spring oat with new features." Scientific Papers of the Institute of Bioenergy Crops and Sugar Beet, no. 24 (December 24, 2016): 60–66. http://dx.doi.org/10.47414/np.24.2016.216894.

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Bastacky, Sheldon I., Kirk J. Wojno, Patrick C. Walsh, Marne J. Carmichael, and Jonathan I. Epstein. "Original Articles: Prostate Cancer: Pathological Features of Hereditary Prostate Cancer." Journal of Urology 153, no. 3S (1995): 987–92. http://dx.doi.org/10.1016/s0022-5347(01)67619-5.

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Abdujalilova, Feruza Sh. "FEATURES OF CONFUSING PHRASES IN OUR LANGUAGE." CURRENT RESEARCH JOURNAL OF PHILOLOGICAL SCIENCES 03, no. 02 (2022): 16–19. http://dx.doi.org/10.37547/philological-crjps-03-02-04.

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This article discusses confusing phrases and their features in our language. Phraseological confusion is analyzed in its original and figurative sense. We have tried to find and analyze examples of these phraseologies from works of art.

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Mweshi, George. "Feature Selection using Genetic Programming." Zambia ICT Journal 3, no. 2 (2019): 11–18. http://dx.doi.org/10.33260/zictjournal.v3i2.62.

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Extracting useful and novel information from the large amount of collected data has become a necessity for corporations wishing to maintain a competitive advantage. One of the biggest issues in handling these significantly large datasets is the curse of dimensionality. As the dimension of the data increases, the performance of the data mining algorithms employed to mine the data deteriorates. This deterioration is mainly caused by the large search space created as a result of having irrelevant, noisy and redundant features in the data. Feature selection is one of the various techniques that ca

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Qi, Jiajin, Xu Gao, and Nantian Huang. "Mechanical Fault Diagnosis of a High Voltage Circuit Breaker Based on High-Efficiency Time-Domain Feature Extraction with Entropy Features." Entropy 22, no. 4 (2020): 478. http://dx.doi.org/10.3390/e22040478.

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The fault samples of high voltage circuit breakers are few, the vibration signals are complex, the existing research methods cannot extract the effective information in the features, and it is easy to overfit, slow training, and other problems. To improve the efficiency of feature extraction of a circuit breaker vibration signal and the accuracy of circuit breaker state recognition, a Light Gradient Boosting Machine (LightGBM) method based on time-domain feature extraction with multi-type entropy features for mechanical fault diagnosis of the high voltage circuit breaker is proposed. First, th

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Jia, Bin-Bin, and Min-Ling Zhang. "Multi-dimensional Classification via Selective Feature Augmentation." Machine Intelligence Research 19, no. 1 (2022): 38–51. http://dx.doi.org/10.1007/s11633-022-1316-5.

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AbstractIn multi-dimensional classification (MDC), the semantics of objects are characterized by multiple class spaces from different dimensions. Most MDC approaches try to explicitly model the dependencies among class spaces in output space. In contrast, the recently proposed feature augmentation strategy, which aims at manipulating feature space, has also been shown to be an effective solution for MDC. However, existing feature augmentation approaches only focus on designing holistic augmented features to be appended with the original features, while better generalization performance could b

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Jo, Insik, Sangbum Lee, and Sejong Oh. "Improved Measures of Redundancy and Relevance for mRMR Feature Selection." Computers 8, no. 2 (2019): 42. http://dx.doi.org/10.3390/computers8020042.

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Many biological or medical data have numerous features. Feature selection is one of the data preprocessing steps that can remove the noise from data as well as save the computing time when the dataset has several hundred thousand or more features. Another goal of feature selection is improving the classification accuracy in machine learning tasks. Minimum Redundancy Maximum Relevance (mRMR) is a well-known feature selection algorithm that selects features by calculating redundancy between features and relevance between features and class vector. mRMR adopts mutual information theory to measure

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Liang, Lei, and Zhisheng Gao. "SharDif: Sharing and Differential Learning for Image Fusion." Entropy 26, no. 1 (2024): 57. http://dx.doi.org/10.3390/e26010057.

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Image fusion is the generation of an informative image that contains complementary information from the original sensor images, such as texture details and attentional targets. Existing methods have designed a variety of feature extraction algorithms and fusion strategies to achieve image fusion. However, these methods ignore the extraction of common features in the original multi-source images. The point of view proposed in this paper is that image fusion is to retain, as much as possible, the useful shared features and complementary differential features of the original multi-source images.

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Yang, Xiwang, Xiaoyan Xu, Yarong Wang, et al. "The Fault Diagnosis of a Plunger Pump Based on the SMOTE + Tomek Link and Dual-Channel Feature Fusion." Applied Sciences 14, no. 11 (2024): 4785. http://dx.doi.org/10.3390/app14114785.

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Mechanical condition monitoring data in real engineering are often severely unbalanced, which can lead to a decrease in the stability and accuracy of intelligent diagnosis methods. In this paper, a fault diagnosis method based on the SMOTE + Tomek Link and dual-channel feature fusion is proposed to improve the performance of the sample imbalance fault diagnosis method, taking the piston pump of a turnout rutting machine as the research object. Combining the data undersampling method and the oversampling method to redistribute the collected normal data and fault data makes the diagnostic model

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SLAMU, WUSHOUR, JUMING CAO, and XINHUI YAO. "SHARP FEATURES EXTRACTION FROM POINT CLOUDS." International Journal of Image and Graphics 12, no. 04 (2012): 1250029. http://dx.doi.org/10.1142/s0219467812500295.

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As sharp feature manipulation plays an important role in point clouds processing, a novel mean curvature flow-based framework for sharp feature extraction from point clouds is presented in this paper. That is, for input point clouds, a general purpose mean curvature flow-based point clouds smoothing operator is applied on them, thereby, obtaining a smoothing version of the original point clouds. The sharp feature points are labeled as points whose displacements between original point clouds and their smoothing version get local extreme. Implementation of our method on both synthesized and real

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Huang, Wenchao, Bo Wei, and Chang Wei. "Feature optimization combined with UPerNet-Twins model for eucalyptus extraction from Sentinel-2A image." Journal of Physics: Conference Series 2724, no. 1 (2024): 012023. http://dx.doi.org/10.1088/1742-6596/2724/1/012023.

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Abstract To improve the extraction accuracy of eucalyptus from Sentinel-2A image, two key factors of feature construction and extraction model are considered. The original band spectrum, custom vegetation index, red edge spectral index, and texture features are obtained from the image. The Relief F-PSO-SVM model is used to screen out the best feature subset. A UPerNet-Twins combination model is used to realize the high-precision extraction of eucalyptus for the study area. The experiments show that the original spectrum plays a significant role in the extraction of eucalyptus. In addition, the

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Zhang, W., C. Yue, C. Cui, and L. Meng. "A MAPPING METHOD OF INTEGRATING MULTI-SCALE RIVER THEMATIC MAPS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 12, 2017): 573–77. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-573-2017.

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Small-scale maps are generally used in spatial analysis for fast calculation, but part of important features are missing due to its generalization level, which makes the analysis results less accurate. Therefore, it is necessary to improve feature completeness of smallscale maps. The goal of this paper is to put forward a mapping method of integrating the existing multi-scale river thematic maps. In order to achieve this goal, this paper proposed an algorithm for multi-scale line features matching by calculating the distance from node to polyline and an integrating algorithm by simplifying, sh

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Yang, Tao, Rui Jiang, HongLi Deng, and XiaoMei Tang. "A network traffic identification method based on AutoEncoder - a feature selection algorithm." Journal of Physics: Conference Series 2593, no. 1 (2023): 012007. http://dx.doi.org/10.1088/1742-6596/2593/1/012007.

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Abstract Traffic identification methods consider a large number of traffic features, resulting in low identification efficiency. To address the efficiency problem of traffic recognition, this paper proposes an efficient network traffic recognition method, AutoEncoder-based traffic recognition (AE-NTI). The method first preprocesses the original dataset and converts it into a two-dimensional grayscale image. Then, feature selection is performed by an improved feature selection algorithm based on AutoEncoder. The algorithm consists of a feature scorer, which globally scores all features, and a f

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Zhang, Larry, Anthony Ngo, Jason A. Thomas, et al. "Neuropsychological test validation of speech markers of cognitive impairment in the Framingham Cognitive Aging Cohort." Exploration of Medicine 2, no. 3 (2021): 232–52. http://dx.doi.org/10.37349/emed.2021.00044.

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Aim: Although clinicians primarily diagnose dementia based on a combination of metrics such as medical history and formal neuropsychological tests, recent work using linguistic analysis of narrative speech to identify dementia has shown promising results. We aim to build upon research by Thomas JA & Burkardt HA et al. (J Alzheimers Dis. 2020;76:905–2) and Alhanai et al. (arXiv:1710.07551v1. 2020) on the Framingham Heart Study (FHS) Cognitive Aging Cohort by 1) demonstrating the predictive capability of linguistic analysis in differentiating cognitively normal from cognitively impaired part

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Baskar, S. S., and Dr L. Arockiam. "A Novel LAS-Relief Feature Selection Algorithm for Enhancing Classification Accuracy in Data mining." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 11, no. 8 (2013): 2921–27. http://dx.doi.org/10.24297/ijct.v11i8.7047.

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Feature selection is an important task in data mining and machine learning domain. The main objective of feature selection is to find a relevant feature that predicts the knowledge better than the original set of features. This can be achieved by removing irrelevant or redundant features from original data sets. Feature selection involves a significant task of selecting relevant features from the feature space for data mining and pattern recognition. In this paper, the new approach has been introduced on feature selection on Relief based on Median Variance model. The new approach is named as L

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Tsoulos, Ioannis G., Alexandros T. Tzallas, and Dimitrios Tsalikakis. "Prediction of COVID-19 Cases Using Constructed Features by Grammatical Evolution." Symmetry 14, no. 10 (2022): 2149. http://dx.doi.org/10.3390/sym14102149.

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A widely used method that constructs features with the incorporation of so-called grammatical evolution is proposed here to predict the COVID-19 cases as well as the mortality rate. The method creates new artificial features from the original ones using a genetic algorithm and is guided by BNF grammar. After the artificial features are generated, the original data set is modified based on these features, an artificial neural network is applied to the modified data, and the results are reported. From the comparative experiments done, it is clear that feature construction has an advantage over o

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Liang, Xiao Long, Wei Hua Li, and Xin Lin. "Design for Fast Adaboost with Feature Selection." Advanced Materials Research 816-817 (September 2013): 566–69. http://dx.doi.org/10.4028/www.scientific.net/amr.816-817.566.

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Since the original Adaboost algorithm is very time-consuming in training, we have designed an improved Adaboost, which adds a process of feature selection to the original Adaboost algorithm. After each round of training, we retain the features whose error rate to classify the samples are relatively low and remove the features with high error rate. In this way the time for the next training is reduced, and the whole algorithm is accelerated.

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Yang, Jianwei, Guosheng Cheng, and Ming Li. "Extraction of Affine Invariant Features Using Fractal." Advances in Mathematical Physics 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/950289.

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An approach based on fractal is presented for extracting affine invariant features. Central projection transformation is employed to reduce the dimensionality of the original input pattern, and general contour (GC) of the pattern is derived. Affine invariant features cannot be extracted from GC directly due to shearing. To address this problem, a group of curves (which are called shift curves) are constructed from the obtained GC. Fractal dimensions of these curves can readily be computed and constitute a new feature vector for the original pattern. The derived feature vector is used in questi

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Saberi-Movahed, Farid, Mahdi Eftekhari, and Mohammad Mohtashami. "Supervised feature selection by constituting a basis for the original space of features and matrix factorization." International Journal of Machine Learning and Cybernetics 11, no. 7 (2019): 1405–21. http://dx.doi.org/10.1007/s13042-019-01046-w.

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Zhao, X. M., Q. H. Hu, Y. G. Lei, and M. J. Zuo. "Vibration-based fault diagnosis of slurry pump impellers using neighbourhood rough set models." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 224, no. 4 (2010): 995–1006. http://dx.doi.org/10.1243/09544062jmes1777.

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Rough set models have been widely used as a method for feature selection in fault diagnosis. A neighbourhood rough set model can deal with both nominal and numerical features, but selecting the neighbourhood size for its application may be a challenge. In this article, the authors illustrate that using a common neighbourhood size for all features may overestimate or underestimate a feature's dependency degree. The neighbourhood rough set model is then modified by setting different neighbourhood sizes for different features. The modified model is applied to the fault diagnosis of slurry pump im

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Averbeck, Bruno B., and Lizabeth M. Romanski. "Principal and Independent Components of Macaque Vocalizations: Constructing Stimuli to Probe High-Level Sensory Processing." Journal of Neurophysiology 91, no. 6 (2004): 2897–909. http://dx.doi.org/10.1152/jn.01103.2003.

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Neurons in high-level sensory cortical areas respond to complex features in sensory stimuli. Feature elimination is a useful technique for studying these responses. In this approach, a complex stimulus, which evokes a neuronal response, is simplified, and if the cell responds to the reduced stimulus, it is considered selective for the remaining features. We have developed a feature-elimination technique that uses either the principal or the independent components of a stimulus to define a subset of features, to which a neuron might be sensitive. The original stimulus can be filtered using thes

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Hamdi, Nezha, Khalid Auhmani, and Moha M’Rabet Hassani. "A New Approach Based on Quantum Clustering and Wavelet Transform for Breast Cancer Classification: Comparative Study." International Journal of Electrical and Computer Engineering (IJECE) 5, no. 5 (2015): 1027. http://dx.doi.org/10.11591/ijece.v5i5.pp1027-1034.

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Feature selection involves identifying a subset of the most useful features that produce the same results as the original set of features. In this paper, we present a new approach for improving classification accuracy. This approach is based on quantum clustering for feature subset selection and wavelet transform for features extraction. The feature selection is performed in three steps. First the mammographic image undergoes a wavelet transform then some features are extracted. In the second step the original feature space is partitioned in clusters in order to group similar features. This op

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Cui, Ziyang, Yi Wang, Xiaodong Chen, and Huaiyu Cai. "A Joint LiDAR and Camera Calibration Algorithm Based on an Original 3D Calibration Plate." Sensors 25, no. 15 (2025): 4558. https://doi.org/10.3390/s25154558.

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An accurate extrinsic calibration between LiDAR and cameras is essential for effective sensor fusion, directly impacting the perception capabilities of autonomous driving systems. Although prior calibration approaches using planar and point features have yielded some success, they suffer from inherent limitations. Specifically, methods that rely on fitting planar contours using depth-discontinuous points are prone to systematic errors, which hinder the precise extraction of the 3D positions of feature points. This, in turn, compromises the accuracy and robustness of the calibration. To overcom

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Liang, Yi, Turdi Tohti, and Askar Hamdulla. "False Information Detection via Multimodal Feature Fusion and Multi-Classifier Hybrid Prediction." Algorithms 15, no. 4 (2022): 119. http://dx.doi.org/10.3390/a15040119.

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In the existing false information detection methods, the quality of the extracted single-modality features is low, the information between different modalities cannot be fully fused, and the original information will be lost when the information of different modalities is fused. This paper proposes a false information detection via multimodal feature fusion and multi-classifier hybrid prediction. In this method, first, bidirectional encoder representations for transformers are used to extract the text features, and S win-transformer is used to extract the picture features, and then, the traine

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