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Journal articles on the topic 'Feature based approach'

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Published: 3 June 2025

Last updated: 31 July 2025

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1

Yeol Lee, Jae, and Kwangsoo Kim. "A feature-based approach to extracting machining features." Computer-Aided Design 30, no. 13 (1998): 1019–35. http://dx.doi.org/10.1016/s0010-4485(98)00055-4.

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Zhi-Rong Zhong, Zhi-Rong Zhong, Hong-Fu Zuo Zhi-Rong Zhong, Shi-Ying Lei Hong-Fu Zuo, Jia-Chen Guo Shi-Ying Lei, and Heng Jiang Jia-Chen Guo. "Semi-supervised Learning Based EEG Detection Approach for Rehabilitation Engineering." 電腦學刊 33, no. 3 (2022): 099–111. http://dx.doi.org/10.53106/199115992022063303008.

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<p>A semi-supervised learning based EEG signal detection method was studied in this paper. The feature engineering system of this paper was established, which contains novel AutoEncoders mapping features. The optimal channel combination for all subjects was determined to improve recognition accuracy by ReliefF algorithm and recursive feature elimination. What&rsquo;s more, the semi-supervised learning method based on pseudo-labelling was introduced to the character recognition method, in which the training samples were dynamically reorganized and updated, so that the proposed method

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Jing, Zhang, sheng Kang Bao, Jiang Bo, and Zhang Di. "A novel sketch-based 3D model retrieval approach based on skeleton." International Journal of Informatics and Communication Technology (IJ-ICT) 8, no. 1 (2019): 1–12. https://doi.org/10.11591/ijict.v8i1.pp1-12.

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Since the skeleton represents the topology structure of the query sketch and 2D views of 3D model, this paper proposes a novel sketch-based 3D model retrieval algorithm which utilizes skeleton characteristics as the features to describe the object shape. Firstly, we propose advanced skeleton strength map (ASSM) algorithm to create the skeleton which computes the skeleton strength map by isotropic diffusion on the gradient vector field, selects critical points from the skeleton strength map and connects them by Kruskal's algorithm. Then, we propose histogram feature comparison algorithm whi

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GUAN, SHENG-UEI, YINAN QI, and CHUNYU BAO. "AN INCREMENTAL APPROACH TO MSE-BASED FEATURE SELECTION." International Journal of Computational Intelligence and Applications 06, no. 04 (2006): 451–71. http://dx.doi.org/10.1142/s1469026806002064.

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Feature selection plays an important role in classification systems. Using classifier error rate as the evaluation function, feature selection is integrated with incremental training. A neural network classifier is implemented with an incremental training approach to detect and discard irrelevant features. By learning attributes one after another, our classifier can find directly the attributes that make no contribution to classification. These attributes are marked and considered for removal. Incorporated with a minimum squared error (MSE) based feature ranking scheme, four batch removal meth

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Pachet, François, and Pierre Roy. "Analytical Features: A Knowledge-Based Approach to Audio Feature Generation." EURASIP Journal on Audio, Speech, and Music Processing 2009 (2009): 1–23. http://dx.doi.org/10.1155/2009/153017.

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Chaudhari, Kajalben Mohanbhai Mrs. Hetal Bhaidasna. "A Survey On Image Mosaicing Using Feature Based Approach." INTERNATIONAL JOURNAL OF ENGINEERING DEVELOPMENT AND RESEARCH Volume 5, Issue 1 | March 2017 (2017): Page Number(s) — 565–568. https://doi.org/10.5281/zenodo.583719.

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Image mosaicing is consider as an active research area in computer vision and computer graphics. Image stitching techniques can be categorized into two approaches: Direct technique and Feature based techniques. Direct techniques compare all the pixel intensities of the images with each other, and Feature based techniques used to determine a relationship between images through distinct features extracted from the processed images.

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Shapiro, Larry S., and J. Michael Brady. "Feature-based correspondence: an eigenvector approach." Image and Vision Computing 10, no. 5 (1992): 283–88. http://dx.doi.org/10.1016/0262-8856(92)90043-3.

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Qiao, Li Hong, and Jian Feng Wu. "An Approach to Region-Based Feature Recognition for Structural Parts." Advanced Materials Research 482-484 (February 2012): 2114–17. http://dx.doi.org/10.4028/www.scientific.net/amr.482-484.2114.

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Feature-based numerical control programming can enhance the process planning efficiency for complex structural parts in aeronautic industry. Feature recognition is often being a useful tool to the domain. In order to handle the variety and uncertainty of the feature interpretation of feature recognition of structural parts, a region-based feature recognition approach is proposed. On the basis of the characteristics of the structural parts, the approach employs the fact that topology surfaces of structural parts have directions, and utilizes region as the foundation of feature recognition. By r

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Thepade, Sudeep, Rik Das, and Saurav Ghosh. "Decision fusion-based approach for content-based image classification." International Journal of Intelligent Computing and Cybernetics 10, no. 3 (2017): 310–31. http://dx.doi.org/10.1108/ijicc-07-2016-0025.

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Purpose Current practices in data classification and retrieval have experienced a surge in the use of multimedia content. Identification of desired information from the huge image databases has been facing increased complexities for designing an efficient feature extraction process. Conventional approaches of image classification with text-based image annotation have faced assorted limitations due to erroneous interpretation of vocabulary and huge time consumption involved due to manual annotation. Content-based image recognition has emerged as an alternative to combat the aforesaid limitation

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Nardone, Davide, Angelo Ciaramella, and Antonino Staiano. "A Sparse-Modeling Based Approach for Class Specific Feature Selection." PeerJ Computer Science 5 (November 18, 2019): e237. http://dx.doi.org/10.7717/peerj-cs.237.

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In this work, we propose a novel Feature Selection framework called Sparse-Modeling Based Approach for Class Specific Feature Selection (SMBA-CSFS), that simultaneously exploits the idea of Sparse Modeling and Class-Specific Feature Selection. Feature selection plays a key role in several fields (e.g., computational biology), making it possible to treat models with fewer variables which, in turn, are easier to explain, by providing valuable insights on the importance of their role, and likely speeding up the experimental validation. Unfortunately, also corroborated by the no free lunch theorem

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Buqing Cao, Buqing Cao, Weishi Zhong Buqing Cao, Xiang Xie Weishi Zhong, Lulu Zhang Xiang Xie, and Yueying Qing Lulu Zhang. "A Multi-modal Feature Fusion-based Approach for Mobile Application Classification and Recommendation." 網際網路技術學刊 23, no. 6 (2022): 1417–27. http://dx.doi.org/10.53106/160792642022112306023.

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<p>With the rapid growth of the number and type of mobile applications, it becomes challenging to accurately classify and recommend mobile applications according to users&rsquo; individual requirements. The existing mobile application classification and recommendation methods, for one thing, do not take into account the correlation between large-scale data and model. For another, they also do not fully exploit the multi-modal, fine-grained interaction features with high-order and low-order in mobile application. To tackle this problem, we propose a mobile application classification a

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Vijaya, Aparna, Pritam Dash, and V. Neelanarayanan. "Migration of Enterprise Software Applications to Multiple Clouds: A Feature Based Approach." Lecture Notes on Software Engineering 3, no. 2 (2015): 101–6. http://dx.doi.org/10.7763/lnse.2015.v3.174.

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Hwang, Sun Hee, and Donna Lardiere. "Plural-marking in L2 Korean: A feature-based approach." Second Language Research 29, no. 1 (2013): 57–86. http://dx.doi.org/10.1177/0267658312461496.

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This study examined the second language (L2) acquisition of the Korean plural marker -tul by native speakers of English. Seventy-seven learners at four Korean proficiency levels along with 31 native Korean-speaking controls completed five tasks designed to probe for knowledge of particular features and restrictions associated with so-called intrinsic and extrinsic plural-marking in Korean. The results suggest that knowledge of both types of plural developed with increasing proficiency. However, the features associated with the intrinsic plural, which is more similar to the English plural in te

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Yeh, Chia-Cheng, Yang-Lang Chang, Mohammad Alkhaleefah, et al. "YOLOv3-Based Matching Approach for Roof Region Detection from Drone Images." Remote Sensing 13, no. 1 (2021): 127. http://dx.doi.org/10.3390/rs13010127.

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Due to the large data volume, the UAV image stitching and matching suffers from high computational cost. The traditional feature extraction algorithms—such as Scale-Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF), and Oriented FAST Rotated BRIEF (ORB)—require heavy computation to extract and describe features in high-resolution UAV images. To overcome this issue, You Only Look Once version 3 (YOLOv3) combined with the traditional feature point matching algorithms is utilized to extract descriptive features from the drone dataset of residential areas for roof detection. Un

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Tarek, Md Hasan, Suravi Akhter, Sumon Ahmed, Md Shariful Islam, Mohammad Shoyaib, and Zerina Begum. "A Clustering based Feature Selection Approach using Maximum Spanning Tree." Dhaka University Journal of Applied Science and Engineering 7, no. 2 (2023): 47–55. http://dx.doi.org/10.3329/dujase.v7i2.65094.

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Mutual information (MI) based feature selection methods are getting popular as its ability to capture the nonlinear and linear relationship among random variables and thus it performs better in different fields of machine learning. Traditional MI based feature selection algorithms use different techniques to find out the joint performance of features and select the relevant features among them. However, to do this, in many cases, they might incorporate redundant features. To solve these issues, we propose a feature selection method, namely Clustering based Feature Selection (CbFS), to cluster

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OSTROSI, E., and M. FERNEY. "Feature modeling using a grammar representation approach." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 19, no. 4 (2005): 245–59. http://dx.doi.org/10.1017/s0890060405050171.

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In intelligent computer-aided design the concept of intelligence is related to that of integration. Using feature-based computer-aided design models is thought to make a complete integration. This paper presents a feature recognition approach based on the use of a feature grammar. Given the complexity of feature recognition in interactions, the basic idea of the approach is to find the latent and logical structure of features in interaction. The approach includes five main phases. The first phase, called regioning, identifies the potential zones for the birth of features. The second phase, cal

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Atteia, Ghada, Rana Alnashwan, and Malak Hassan. "Hybrid Feature-Learning-Based PSO-PCA Feature Engineering Approach for Blood Cancer Classification." Diagnostics 13, no. 16 (2023): 2672. http://dx.doi.org/10.3390/diagnostics13162672.

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Acute lymphoblastic leukemia (ALL) is a lethal blood cancer that is characterized by an abnormal increased number of immature lymphocytes in the blood or bone marrow. For effective treatment of ALL, early assessment of the disease is essential. Manual examination of stained blood smear images is current practice for initially screening ALL. This practice is time-consuming and error-prone. In order to effectively diagnose ALL, numerous deep-learning-based computer vision systems have been developed for detecting ALL in blood peripheral images (BPIs). Such systems extract a huge number of image

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Ke, Jiankang, Faxing Lu, Yifei Liu, and Bing Fu. "A ResNet1D-AttLSTM-Based Approach for Ship Trajectory Classification." Applied Sciences 15, no. 7 (2025): 3489. https://doi.org/10.3390/app15073489.

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To improve the feature extraction method for ship trajectories and enhance trajectory classification performance, this paper proposes a ship trajectory classification model that combines a one-dimensional residual network (ResNet1D) and an attention-based Long short-term memory network (AttLSTM). The model aims to address the limitations of traditional methods in extracting feature patterns jointly represented by non-adjacent local regions in ship trajectories, optimized through the introduction of a self-attention mechanism. Specifically, the model first utilizes the ResNet1D module to progre

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Kangra, Kirti, and Jaswinder Singh. "A genetic algorithm-based feature selection approach for diabetes prediction." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 2 (2024): 1489. http://dx.doi.org/10.11591/ijai.v13.i2.pp1489-1498.

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<p>Genetic algorithms have emerged as a powerful optimization technique for feature selection due to their ability to search through a vast feature space efficiently. This study discusses the importance of feature selection for prediction in healthcare and prominently focuses on diabetes mellitus. Feature selection is essential for improving the performance of prediction models, by finding significant features and removing unnecessary among them. The study aims to identify the most informative subset of features. Diabetes is a chronic metabolic disorder that poses significant health chal

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Kirti, Kangra, and Singh Jaswinder. "A genetic algorithm-based feature selection approach for diabetes prediction." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 2 (2024): 1489–98. https://doi.org/10.11591/ijai.v13.i2.pp1489-1498.

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Genetic algorithms have emerged as a powerful optimization technique for feature selection due to their ability to search through a vast feature space efficiently. This study discusses the importance of feature selection for prediction in healthcare and prominently focuses on diabetes mellitus. Feature selection is essential for improving the performance of prediction models, by finding significant features and removing unnecessary among them. The study aims to identify the most informative subset of features. Diabetes is a chronic metabolic disorder that poses significant health challenges wo

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Keshkeh, Kinan, Aman Jantan, and Kamal Alieyan. "A MACHINE LEARNING CLASSIFICATION APPROACH TO DETECT TLS-BASED MALWARE USING ENTROPY-BASED FLOW SET FEATURES." Journal of Information and Communication Technology 21, No.3 (2022): 279–313. http://dx.doi.org/10.32890/jict2022.21.3.1.

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Transport Layer Security (TLS) based malware is one of the most hazardous malware types, as it relies on encryption to conceal connections. Due to the complexity of TLS traffic decryption, several anomaly-based detection studies have been conducted to detect TLS-based malware using different features and machine learning (ML) algorithms. However, most of these studies utilized flow features with no feature transformation or relied on inefficient flow feature transformations like frequency-based periodicity analysis and outliers percentage. This paper introduces TLSMalDetect, a TLS-based malwar

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Jothi G., Hannah Inbarani H., Ahmad Taher Azar, Khaled M. Fouad, and Sahar Fawzy Sabbeh. "Modified Dominance-Based Soft Set Approach for Feature Selection." International Journal of Sociotechnology and Knowledge Development 14, no. 1 (2022): 1–20. http://dx.doi.org/10.4018/ijskd.289036.

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Big data analysis applications in the field of medical image processing have recently increased rapidly. Feature reduction plays a significant role in eliminating irrelevant features and creating a successful research model for Big Data applications. Fuzzy clustering is used for the segment of the nucleus. Various features, including shape, texture, and color-based features, have been used to address the segmented nucleus. The Modified Dominance Soft Set Feature Selection Algorithm (MDSSA) is intended in this paper to determine the most important features for the classification of leukaemia im

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Doo-Won Lee. "A Feature-based Approach for Addressee Honorification." Jungang Journal of English Language and Literature 56, no. 2 (2014): 205–29. http://dx.doi.org/10.18853/jjell.2014.56.2.010.

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24

Abusham, Eimad E. A., Andrew T. B. Jin, Wong E. Kiong, and G. Debashis. "Face Recognition Based on Nonlinear Feature Approach." American Journal of Applied Sciences 5, no. 5 (2008): 574–80. http://dx.doi.org/10.3844/ajassp.2008.574.580.

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Xiao, Xinyi, and Hanbin Xiao. "Autonomous Robotic Feature-Based Freeform Fabrication Approach." Materials 15, no. 1 (2021): 247. http://dx.doi.org/10.3390/ma15010247.

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Robotic additive manufacturing (AM) has gained much attention for its continuous material deposition capability with continuously changeable building orientations, reducing support structure volume and post-processing complexity. However, the current robotic additive process heavily relies on manual geometric reasoning that identifies additive features, related building orientations, tool approach direction, trajectory generation, and sequencing all features in a non-collision manner. In addition, multi-directional material accumulation cannot ensure the nozzle always stays above the building

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JIN, Wen-biao, Xu-song WANG, and Zhi-feng ZHANG. "Approach to feature-based image mesh generation." Journal of Computer Applications 30, no. 9 (2010): 2427–30. http://dx.doi.org/10.3724/sp.j.1087.2010.02427.

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Sungshim Hong. "English Partitive Constructions: A Feature-based Approach." English Language and Linguistics 24, no. 2 (2018): 87–109. http://dx.doi.org/10.17960/ell.2018.24.2.005.

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Mammone, R. J., Xiaoyu Zhang, and R. P. Ramachandran. "Robust speaker recognition: a feature-based approach." IEEE Signal Processing Magazine 13, no. 5 (1996): 58. http://dx.doi.org/10.1109/79.536825.

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Sanfilippo, Emilio M. "Feature-based product modelling: an ontological approach." International Journal of Computer Integrated Manufacturing 31, no. 11 (2018): 1097–110. http://dx.doi.org/10.1080/0951192x.2018.1497814.

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Lu, Tongwei, Ling Peng, and Yanduo Zhang. "Edge feature based approach for object recognition." Pattern Recognition and Image Analysis 26, no. 2 (2016): 350–53. http://dx.doi.org/10.1134/s1054661816020243.

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Motavalli, S., S. H. Cheraghi, and Rafie Shamsaasef. "Feature-based modeling; An object oriented approach." Computers & Industrial Engineering 33, no. 1-2 (1997): 349–52. http://dx.doi.org/10.1016/s0360-8352(97)00109-5.

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Fu, Q., P. Harnois, L. Logrippo, and J. Sincennes. "Feature interaction detection: a LOTOS-based approach." Computer Networks 32, no. 4 (2000): 433–48. http://dx.doi.org/10.1016/s1389-1286(00)00009-8.

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33

Zamanian, M. K., S. J. Fenves, C. R. Thewalt, and S. Finger. "A feature-based approach to structural design." Engineering with Computers 7, no. 1 (1991): 1–9. http://dx.doi.org/10.1007/bf01208341.

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Han, Deqiang, Chongzhao Han, and Yi Yang. "Modified center-based feature line classification approach." Frontiers of Electrical and Electronic Engineering in China 5, no. 2 (2010): 173–78. http://dx.doi.org/10.1007/s11460-010-0004-3.

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MEDLAND, A. J., and G. MULLINEUX. "A constraint approach to feature-based design." International Journal of Computer Integrated Manufacturing 6, no. 1-2 (1993): 34–38. http://dx.doi.org/10.1080/09511929308944553.

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Zhang, Jing, Bao Sheng Kang, Bo Jiang, and Di Zhang. "A novel sketch-based 3D model retrieval approach based on skeleton." International Journal of Informatics and Communication Technology (IJ-ICT) 8, no. 1 (2019): 1. http://dx.doi.org/10.11591/ijict.v8i1.pp1-12.

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<span>Since the skeleton represents the topology structure of the query sketch and 2D views of 3D model, this paper proposes a novel sketch-based 3D model retrieval algorithm which utilizes skeleton characteristics as the features to describe the object shape. Firstly, we propose advanced skeleton strength map (ASSM) algorithm to create the skeleton which computes the skeleton strength map by isotropic diffusion on the gradient vector field, selects critical points from the skeleton strength map and connects them by Kruskal's algorithm. Then, we propose histogram feature comparison algor

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37

Sánchez, David, Montserrat Batet, David Isern, and Aida Valls. "Ontology-based semantic similarity: A new feature-based approach." Expert Systems with Applications 39, no. 9 (2012): 7718–28. http://dx.doi.org/10.1016/j.eswa.2012.01.082.

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Hijazi, Samah, and Vinh Truong Hoang. "A Constrained Feature Selection Approach Based on Feature Clustering and Hypothesis Margin Maximization." Computational Intelligence and Neuroscience 2021 (July 26, 2021): 1–18. http://dx.doi.org/10.1155/2021/5554873.

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In this paper, we propose a semisupervised feature selection approach that is based on feature clustering and hypothesis margin maximization. The aim is to improve the classification accuracy by choosing the right feature subset and to allow building more interpretable models. Our approach handles the two core aspects of feature selection, i.e., relevance and redundancy, and is divided into three steps. First, the similarity weights between features are represented by a sparse graph where each feature can be reconstructed from the sparse linear combination of the others. Second, features are t

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Singh, Bharat. "Multi-Feature Segmentation and Cluster based Approach for Product Feature Categorization." International Journal of Information Technology and Computer Science 8, no. 3 (2016): 33–42. http://dx.doi.org/10.5815/ijitcs.2016.03.04.

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Chen, Jianting, Shuhan Yuan, Dongdong Lv, and Yang Xiang. "A novel self-learning feature selection approach based on feature attributions." Expert Systems with Applications 183 (November 2021): 115219. http://dx.doi.org/10.1016/j.eswa.2021.115219.

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Zhang, Shenggang, Shujuan Jiang, and Yue Yan. "A Software Defect Prediction Approach Based on Hybrid Feature Dimensionality Reduction." Scientific Programming 2023 (July 20, 2023): 1–14. http://dx.doi.org/10.1155/2023/5585130.

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Software defect prediction (SDP) is designed to assist software testing, which can reasonably allocate test resources to reduce costs and improve development efficiency. In order to improve the prediction performance, researchers have designed many defect-related features for SDP. However, feature redundancy (FR) and irrelevance caused by the increasing dimensions of data will greatly degrade the performance of defect prediction. In order to solve the problems, researchers have proposed various data dimensionality reduction methods. These methods can be simply divided into two categories of me

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Chamundeswari, G., G. P. S. Varma, and C. Satyanarayana. "Line Segment-Based Clustering Approach With Self-Organizing Maps." Journal of Information Technology Research 14, no. 4 (2021): 33–44. http://dx.doi.org/10.4018/jitr.2021100103.

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Clustering techniques are used widely in computer vision and pattern recognition. The clustering techniques are found to be efficient with the feature vector of the input image. So, the present paper uses an approach for evaluating the feature vector by using Hough transformation. With the Hough transformation, the present paper mapped the points to line segment. The line features are considered as the feature vector and are given to the neural network for performing clustering. The present paper uses self-organizing map (SOM) neural network for performing the clustering process. The proposed

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Fu, Z., and A. de Pennington. "Geometric Reasoning Based on Graph Grammar Parsing." Journal of Mechanical Design 116, no. 3 (1994): 763–69. http://dx.doi.org/10.1115/1.2919448.

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It has been recognized that future intelligent design support environments need to reason about the geometry of products and to evaluate product functionality and performance against given constraints. A first step towards this goal is to provide a more robust information model which directly relates to design functionality or manufacturing characteristics, on which reasoning can be carried out. This has motivated research on feature-based modelling and reasoning. In this paper, an approach is presented to geometric reasoning based on graph grammar parsing. Our approach is presented to geometr

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Rahamat, Basha S., Rani J. Keziya, and Yadav J. J. C. Prasad. "A Novel Summarization-based Approach for Feature Reduction Enhancing Text Classification Accuracy." Engineering, Technology & Applied Science Research 9, no. 6 (2019): 5001–5. https://doi.org/10.5281/zenodo.3566535.

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Automatic summarization is the process of shortening one (in single document summarization) or multiple documents (in multi-document summarization). In this paper, a new feature selection method for the nearest neighbor classifier by summarizing the original training documents based on sentence importance measure is proposed. Our approach for single document summarization uses two measures for sentence similarity: the frequency of the terms in one sentence and the similarity of that sentence to other sentences. All sentences were ranked accordingly and the sentences with top ranks (with a thre

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Mohanty, Figlu, Suvendu Rup, Bodhisattva Dash, Banshidhar Majhi, and M. N. S. Swamy. "Mammogram classification using contourlet features with forest optimization-based feature selection approach." Multimedia Tools and Applications 78, no. 10 (2018): 12805–34. http://dx.doi.org/10.1007/s11042-018-5804-0.

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Mashhour, Emad Mohamed, Enas M. F. El Houby, Khaled Tawfik Wassif, and Akram I. Salah. "Feature Selection Approach based on Firefly Algorithm and Chi-square." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 4 (2018): 2338. http://dx.doi.org/10.11591/ijece.v8i4.pp2338-2350.

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Dimensionality problem is a well-known challenging issue for most classifiers in which datasets have unbalanced number of samples and features. Features may contain unreliable data which may lead the classification process to produce undesirable results. Feature selection approach is considered a solution for this kind of problems. In this paperan enhanced firefly algorithm is proposed to serve as a feature selection solution for reducing dimensionality and picking the most informative features to be used in classification. The main purpose of the proposedmodel is to improve the classification

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Kang, Nan Nan, Xiao Fang Wang, and Rong Rong Zhang. "Image Classification Based on Color Topic Model." Applied Mechanics and Materials 556-562 (May 2014): 4770–73. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.4770.

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This paper addresses semantic image classification with topic model, which focusing on discovering a hidden semantic to solve the semantic gap between low-level visual feature and high-level feature. In our approach, Latent Dirichlet Allocation (LDA) model successfully reflect the high level features and the RGB SIFT features which integrating the Scale-invariant feature transform (SIFT) features with color features on the assumption that pictures generated by mixture of latent semantic which we called topics. The proposed approach has a sufficient theoretical basis and the experimental evalua

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Emad, Mohamed Mashhour, M. F. El Houby Enas, Tawfik Wassif Khaled, and I. Salah Akram. "Feature Selection Approach based on Firefly Algorithm and Chi-square." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 4 (2018): 2338–50. https://doi.org/10.11591/ijece.v8i4.pp2338-2350.

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Dimensionality problem is a well-known challenging issue for most classifiers in which datasets have unbalanced number of samples and features. Features may contain unreliable data which may lead the classification process to produce undesirable results. Feature selection approach is considered a solution for this kind of problems. In this paperan enhanced firefly algorithm is proposed to serve as a feature selection solution for reducing dimensionality and picking the most informative features to be used in classification. The main purpose of the proposed model is to improve the classificatio

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Ding, Chao, Nurbol Luktarhan, Bei Lu, and Wenhui Zhang. "A Hybrid Analysis-Based Approach to Android Malware Family Classification." Entropy 23, no. 8 (2021): 1009. http://dx.doi.org/10.3390/e23081009.

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With the popularity of Android, malware detection and family classification have also become a research focus. Many excellent methods have been proposed by previous authors, but static and dynamic analyses inevitably require complex processes. A hybrid analysis method for detecting Android malware and classifying malware families is presented in this paper, and is partially optimized for multiple-feature data. For static analysis, we use permissions and intent as static features and use three feature selection methods to form a subset of three candidate features. Compared with various models,

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Marin, Ivana, Saša Mladenović, Sven Gotovac, and Goran Zaharija. "Deep-Feature-Based Approach to Marine Debris Classification." Applied Sciences 11, no. 12 (2021): 5644. http://dx.doi.org/10.3390/app11125644.

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The global community has recognized an increasing amount of pollutants entering oceans and other water bodies as a severe environmental, economic, and social issue. In addition to prevention, one of the key measures in addressing marine pollution is the cleanup of debris already present in marine environments. Deployment of machine learning (ML) and deep learning (DL) techniques can automate marine waste removal, making the cleanup process more efficient. This study examines the performance of six well-known deep convolutional neural networks (CNNs), namely VGG19, InceptionV3, ResNet50, Incept

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