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Journal articles on the topic 'Hybrid classifier'

Author: Grafiati

Published: 11 March 2023

Last updated: 26 July 2025

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1

Demidova, Liliya, and Maksim Egin. "Data classification based on the hybrid intellectual technology." ITM Web of Conferences 18 (2018): 04001. http://dx.doi.org/10.1051/itmconf/20181804001.

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In this paper the data classification technique, implying the consistent application of the SVM and Parzen classifiers, has been suggested. The Parser classifier applies to data which can be both correctly and erroneously classified using the SVM classifier, and are located in the experimentally defined subareas near the hyperplane which separates the classes. A herewith, the SVM classifier is used with the default parameters values, and the optimal parameters values of the Parser classifier are determined using the genetic algorithm. The experimental results confirming the effectiveness of th

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YOUNG KOO, JA, and MYUNGHWAN KIM. "An improved hybrid classifier." International Journal of Remote Sensing 7, no. 3 (1986): 471–76. http://dx.doi.org/10.1080/01431168608954702.

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Zhiwen Yu, Le Li, Jiming Liu, and Guoqiang Han. "Hybrid Adaptive Classifier Ensemble." IEEE Transactions on Cybernetics 45, no. 2 (2015): 177–90. http://dx.doi.org/10.1109/tcyb.2014.2322195.

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Sharma, Richa, and Shailendra Narayan Singh. "An Efficient Hybrid Classifier for Prognosing Cardiac Disease." Webology 19, no. 1 (2022): 5028–46. http://dx.doi.org/10.14704/web/v19i1/web19338.

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Machine learning (ML) is a powerful tool which empowers the practitioners for predictions upon any existing or real- time data. Here, the Machine first understands the valuable patterns from the dataset and then uses that information to make predictions on the unknown data. Further, classification is the commonly used machine learning approach (ML-Approach) to make such predictions. The objective of this work aims to design and development of an ensemble classifier for prognosing cardiovascular disease (heart disease). The developed classifier integrates Support Vector Machine (SVM), K–Nearest

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Demidova, Liliya A. "Two-Stage Hybrid Data Classifiers Based on SVM and kNN Algorithms." Symmetry 13, no. 4 (2021): 615. http://dx.doi.org/10.3390/sym13040615.

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The paper considers a solution to the problem of developing two-stage hybrid SVM-kNN classifiers with the aim to increase the data classification quality by refining the classification decisions near the class boundary defined by the SVM classifier. In the first stage, the SVM classifier with default parameters values is developed. Here, the training dataset is designed on the basis of the initial dataset. When developing the SVM classifier, a binary SVM algorithm or one-class SVM algorithm is used. Based on the results of the training of the SVM classifier, two variants of the training datase

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Ramamoorthy, Karthikamani, and Harikumar Rajaguru. "Exploitation of Bio-Inspired Classifiers for Performance Enhancement in Liver Cirrhosis Detection from Ultrasonic Images." Biomimetics 9, no. 6 (2024): 356. http://dx.doi.org/10.3390/biomimetics9060356.

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In the current scenario, liver abnormalities are one of the most serious public health concerns. Cirrhosis of the liver is one of the foremost causes of demise from liver diseases. To accurately predict the status of liver cirrhosis, physicians frequently use automated computer-aided approaches. In this paper, through clustering techniques like fuzzy c-means (FCM), possibilistic fuzzy c-means (PFCM), and possibilistic c means (PCM) and sample entropy features are extracted from normal and cirrhotic liver ultrasonic images. The extracted features are classified as normal and cirrhotic through t

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Kotsiantis, Sotiris. "A hybrid decision tree classifier." Journal of Intelligent & Fuzzy Systems 26, no. 1 (2014): 327–36. http://dx.doi.org/10.3233/ifs-120741.

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Yu, Zhiwen, Hantao Chen, Jiming Liuxs, Jane You, Hareton Leung, and Guoqiang Han. "Hybrid $k$ -Nearest Neighbor Classifier." IEEE Transactions on Cybernetics 46, no. 6 (2016): 1263–75. http://dx.doi.org/10.1109/tcyb.2015.2443857.

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Janane, S. K. 1. Keerthana M. S. 1. Subbulakshmi B. *1. "HYBRID CLASSIFICATION FOR SENTIMENT ANALYSIS OF MOVIE REVIEWS." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 7, no. 4 (2018): 724–28. https://doi.org/10.5281/zenodo.1228816.

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Internet has provided people a platform to express their opinions and thoughts. Sentiment analysis helps to analyse those opinions and categorize them. This research is done on the movie review dataset obtained from the Internet Movie Database (IMDb). The data is classified using some of the popular learning based classifiers like Naive Bayes, Decision Tree and Support Vector Machine (SVM) classifiers and their accuracies are compared. Finally, the three learning based classifiers are combined using the Majority vote ensemble classifier. It is found that the accuracy obtained from the above sa

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Rangel-Díaz-de-la-Vega, Adolfo, Yenny Villuendas-Rey, Cornelio Yáñez-Márquez, Oscar Camacho-Nieto, and Itzamá López-Yáñez. "Impact of Imbalanced Datasets Preprocessing in the Performance of Associative Classifiers." Applied Sciences 10, no. 8 (2020): 2779. http://dx.doi.org/10.3390/app10082779.

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In this paper, an experimental study was carried out to determine the influence of imbalanced datasets preprocessing in the performance of associative classifiers, in order to find the better computational solutions to the problem of credit scoring. To do this, six undersampling algorithms, six oversampling algorithms and four hybrid algorithms were evaluated in 13 imbalanced datasets referring to credit scoring. Then, the performance of four associative classifiers was analyzed. The experiments carried out allowed us to determine which sampling algorithms had the best results, as well as thei

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Anagnostopoulos, Theodoros, and Christos Skourlas. "Ensemble majority voting classifier for speech emotion recognition and prediction." Journal of Systems and Information Technology 16, no. 3 (2014): 222–32. http://dx.doi.org/10.1108/jsit-01-2014-0009.

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Purpose – The purpose of this paper is to understand the emotional state of a human being by capturing the speech utterances that are used during common conversation. Human beings except of thinking creatures are also sentimental and emotional organisms. There are six universal basic emotions plus a neutral emotion: happiness, surprise, fear, sadness, anger, disgust and neutral. Design/methodology/approach – It is proved that, given enough acoustic evidence, the emotional state of a person can be classified by an ensemble majority voting classifier. The proposed ensemble classifier is construc

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Maddala, Jeevan Babu, Bhargav Reddy Modugulla, Sahithi Amulya Pulusu, Sanjay Mannepalli, Praveen prakash Pamidimalla, and Rukhiya Khanam. "Heart Failure Prediction Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (2024): 1901–11. http://dx.doi.org/10.22214/ijraset.2024.59236.

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Abstract: Cardiovascular Disease (CVD) currently stands as the leading cause of death worldwide. Clinical data analytics encounter a significant challenge in accurately predicting cardiac disease. The healthcare industry generates vast volumes of raw data, necessitating its transformation into meaningful insights through machine learning techniques. The objective is to leverage machine learning models to improve the predictability of survival among cardiac patients. This study employs machine learning classifiers: Random Forest, Gradient Boosting classifier, Extra Tree Classifier, XG-Boost, Ad

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Liu, Su Houn, Hsiu Li Liao, Shih Ming Pi, and Chih Chiang Kao. "Patent Classification Using Hybrid Classifier Systems." Advanced Materials Research 187 (February 2011): 458–63. http://dx.doi.org/10.4028/www.scientific.net/amr.187.458.

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Patents are distributed through hundreds of collections, divided up by general area. A hybrid classifier system thus can be a powerful solution to difficult patent classification problems. In this study, we present a system for classifying patent documents on a hybrid approach by combining multiple text classifiers (Naïve Bayes, KNN and Rocchio). Decisions made by various text classifiers can be combined by voting and sampling mechanisms in the system. A prototype system was developed and tested in a real world task. The results have indicated that the accuracy of the hybrid approach is more s

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Saradha, S., and P. Sujatha. "Prediction of gestational diabetes diagnosis using SVM and J48 classifier model." International Journal of Engineering & Technology 7, no. 2.21 (2018): 323. http://dx.doi.org/10.14419/ijet.v7i2.21.12395.

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Knowledge Discovery in Databases (KDD) process is also known as data mining. It is a most powerful tool for medical diagnosis. Due to hormonal changes, diabetes may occur during pregnancy is referred as Gestational diabetes mellitus (GDM). Pregnant Women with GDM are at highest risk of future diabetes, especially type-2 diabetes. This paper focuses on designing an automated system for diagnosing gestational diabetes using hybrid classifiers as well as predicting the highest risk factors of getting Type 2 diabetes after delivery. One of the common predictive data mining tasks is classification.

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Chang, Yangyang, and Fadi Abu-Amara. "An Efficient Hybrid Classifier for Cancer Detection." International Journal of Online and Biomedical Engineering (iJOE) 17, no. 03 (2021): 76. http://dx.doi.org/10.3991/ijoe.v17i03.19683.

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<span>The early detection of cancer in both healthy and high-risk populations offers increased opportunity for treatment and curative intent. In this paper, we propose a hybrid classifier that produces an efficient classification system for cancer detection in cell datasets. The first part of this work investigates the performance of artificial neural networks (ANN) such as Self-Organizing Feature Map (SOM) and Learning Vector Quantization (LVQ), while in the second part, we present our investigation on the performances of Decision Tree (DT) and its pruning model. We also, in the third p

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Alharbi, Abdulmajeed Atiah, and Jeza Allohibi. "A new hybrid classification algorithm for predicting student performance." AIMS Mathematics 9, no. 7 (2024): 18308–23. http://dx.doi.org/10.3934/math.2024893.

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<abstract><p>Education is essential and increasingly crucial for the development of almost all countries worldwide. As educational data has become increasingly available, scholars have shown a growing interest in exploring the correlation between students' academic achievements and other factors that may impact their performance using machine learning algorithms. This research paper introduces a novel hybrid classifier that aims to predict the academic performance of students by using a combination of different single algorithms. The proposed hybrid classifier (PHC) is compared to

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Hartono, Hartono, Erianto Ongko, and Dahlan Abdullah. "Hybrid approach redefinition with cluster-based instance selection in handling class imbalance problem." International Journal of Advances in Intelligent Informatics 7, no. 3 (2021): 345. http://dx.doi.org/10.26555/ijain.v7i3.515.

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Class Imbalance problems often occur in the classification process, the existence of these problems is characterized by the tendency of a class to have instances that are much larger than other classes. This problem certainly causes a tendency towards low accuracy in minority classes with smaller number of instances and also causes important information on minority classes not to be obtained. Various methods have been applied to overcome the problem of the imbalance class. One of them is the Hybrid Approach Redefinition method which is one of the Hybrid Ensembles methods. The tendency to pay a

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Gill, Harmandeep Singh, and Baljit Singh Khehra. "Hybrid classifier model for fruit classification." Multimedia Tools and Applications 80, no. 18 (2021): 27495–530. http://dx.doi.org/10.1007/s11042-021-10772-9.

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Adivarekar, Pranali Ramesh. "Diabetic Retinopathy Detection using Hybrid Classifier." International Journal for Research in Applied Science and Engineering Technology 7, no. 8 (2019): 133–38. http://dx.doi.org/10.22214/ijraset.2019.8017.

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Yang, Kaixiang, Zhiwen Yu, Xin Wen, et al. "Hybrid Classifier Ensemble for Imbalanced Data." IEEE Transactions on Neural Networks and Learning Systems 31, no. 4 (2020): 1387–400. http://dx.doi.org/10.1109/tnnls.2019.2920246.

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Chandramouli, A., Vemula Rajitha Hyma, Pasumarthi Sai Tanmayi, Thanniru Geervani Santoshi, and B. Priyanka. "Diabetes prediction using Hybrid Bagging Classifier." Entertainment Computing 47 (August 2023): 100593. http://dx.doi.org/10.1016/j.entcom.2023.100593.

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Wang, Wenbo, Lu Chen, Ming Tan, Shaojun Wang, and Amit P. Sheth. "Discovering Fine-grained Sentiment in Suicide Notes." Biomedical Informatics Insights 5s1 (January 2012): BII.S8963. http://dx.doi.org/10.4137/bii.s8963.

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This paper presents our solution for the i2b2 sentiment classification challenge. Our hybrid system consists of machine learning and rule-based classifiers. For the machine learning classifier, we investigate a variety of lexical, syntactic and knowledge-based features, and show how much these features contribute to the performance of the classifier through experiments. For the rule-based classifier, we propose an algorithm to automatically extract effective syntactic and lexical patterns from training examples. The experimental results show that the rule-based classifier outperforms the basel

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Ruppert, Georg S., Mathias Schardt, Gerd Balzuweit, and Mushtaq Hussain. "A Hybrid Classifier for Remote Sensing Applications." International Journal of Neural Systems 08, no. 01 (1997): 63–68. http://dx.doi.org/10.1142/s0129065797000094.

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This paper presents a hybrid — unsupervised and supervised — classifier for land use classification of remote sensing images. The entire satellite image is quantized by an unsupervised Neural Gas process and the resulting codebook is labeled by a supervised majority voting process using the ground truth. The performance of the classifier is similar to that of Maximum Likelihood and is only a little worse than Multilayer Perceptrons while training and classifying requires no expert knowledge after collecting the ground truth. The hybrid classifier is much better suited to classifications with c

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Nalluri, MadhuSudana Rao, Kannan K., Manisha M., and Diptendu Sinha Roy. "Hybrid Disease Diagnosis Using Multiobjective Optimization with Evolutionary Parameter Optimization." Journal of Healthcare Engineering 2017 (2017): 1–27. http://dx.doi.org/10.1155/2017/5907264.

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With the widespread adoption of e-Healthcare and telemedicine applications, accurate, intelligent disease diagnosis systems have been profoundly coveted. In recent years, numerous individual machine learning-based classifiers have been proposed and tested, and the fact that a single classifier cannot effectively classify and diagnose all diseases has been almost accorded with. This has seen a number of recent research attempts to arrive at a consensus using ensemble classification techniques. In this paper, a hybrid system is proposed to diagnose ailments using optimizing individual classifier

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Bogelly, Karthik, C. Rahul Rao, Srikanth Uppari, and K. Shilpa. "Hybrid Classification Using Ensemble Model to Predict Cardiovascular Diseases." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 697–705. http://dx.doi.org/10.22214/ijraset.2023.50111.

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Abstract: Machine Learning is a widely used tool in the healthcare industry. Machine Learning algorithms help to predict and detect the presence of cardiovascular diseases. Such information, if predicted ahead of time, can provide important knowledge to doctors who can then diagnose and deal per patient basis. We work on predicting possible heart diseases in people using Machine Learning algorithms. In this project we perform the comparative analysis of classifiers like Naïve Bayes, SVM, Logistic Regression, Decision trees and Random Forest and we propose an ensemble classifier which perform h

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Paul, Jis, and M. Madheswaran. "Hybrid Neuro-Fuzzy Learning Models for Classification of Motion Sickness Levels Using Biosignals." Journal of Medical Imaging and Health Informatics 11, no. 11 (2021): 2790–805. http://dx.doi.org/10.1166/jmihi.2021.3871.

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Motion sickness is all around as long as there is existence of humans and motion. This sickness has been common in numerous people and due to which it has become the focus area of neurological, psychological and physiological researchers. Most common group of this motion sickness pertains to the category of visual sensitivity; also called visual dependence, wherein people become sick due to visual motion. In this research paper, classification of the levels of motion sickness is done by developing classifiers: (1) k-Nearest neighbour (kNN) classifier (2) Fuzzy c-means classifier (3) ELMAN neur

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Kurama, Onesfole. "A Hybrid Classifier Based on the Generalized Heronian Mean Operator and Fuzzy Robust PCA Algorithms." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 32, no. 02 (2024): 165–84. http://dx.doi.org/10.1142/s0218488524500077.

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We present a new classifier that uses a generalized Heronian mean (GHM) operator, and fuzzy robust principal component analysis (FRPCA) algorithms. The similarity classifier was earlier studied with other aggregation operators, including: the ordered weighted averaging (OWA), generalized mean, arithmetic mean among others. Parameters in the GHM operator makes the new classifier suitable for handling a variety of modeling problems involving parameter settings. Motivated by the nature of the GHM operator, we examine which FRPCA algorithm is suitable for use to achieve optimal performance of the

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Kim, Jin-Chul, Min-Hyun Kim, Han-Enul Suh, Muhammad Tahir Naseem, and Chan-Su Lee. "Hybrid Approach for Facial Expression Recognition Using Convolutional Neural Networks and SVM." Applied Sciences 12, no. 11 (2022): 5493. http://dx.doi.org/10.3390/app12115493.

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Facial expression recognition is very useful for effective human–computer interaction, robot interfaces, and emotion-aware smart agent systems. This paper presents a new framework for facial expression recognition by using a hybrid model: a combination of convolutional neural networks (CNNs) and a support vector machine (SVM) classifier using dynamic facial expression data. In order to extract facial motion characteristics, dense facial motion flows and geometry landmark flows of facial expression sequences were used as inputs to the CNN and SVM classifier, respectively. CNN architectures for

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Delimata, Pawel, and Zbigniew Suraj. "Feature Selection Algorithm for Multiple Classifier Systems: A Hybrid Approach." Fundamenta Informaticae 85, no. 1-4 (2008): 97–110. https://doi.org/10.3233/fun-2008-851-408.

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Many problems in pattern classification and knowledge discovery require a selection of a subset of attributes or features to represent the patterns to be classified. The approach presented in this paper is designed mostly for multiple classifier systems with homogeneous (identical) classifiers. Such systems require many different subsets of the data set. The problem of finding the best subsets of a given feature set is of exponential complexity. The main aim of this paper is to present ways to improve RBFS algorithm which is a feature selection algorithm. RBFS algorithm is computationally quit

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Govindarajan, M., and RM Chandrasekaran. "A Hybrid Multilayer Perceptron Neural Network for Direct Marketing." International Journal of Knowledge-Based Organizations 2, no. 3 (2012): 63–73. http://dx.doi.org/10.4018/ijkbo.2012070104.

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Data Mining is the use of algorithms to extract the information and patterns derived by the knowledge discovery in database process. It is often referred to as supervised learning because the classes are determined before examining the data. In many data mining applications that address classification problems, feature and model selection are considered as key tasks. That is, appropriate input features of the classifier must be selected from a given set of possible features and structure parameters of the classifier must be adapted with respect to these features and a given data set. This pape

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Garfield, Sheila, Stefan Wermter, and Siobhan Devlin. "Spoken language classification using hybrid classifier combination." International Journal of Hybrid Intelligent Systems 2, no. 1 (2005): 13–33. http://dx.doi.org/10.3233/his-2005-2102.

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Qiu, Dahong, Ye Wang, and Bin Bi. "Identify Cross-Selling Opportunities via Hybrid Classifier." International Journal of Data Warehousing and Mining 4, no. 2 (2008): 55–62. http://dx.doi.org/10.4018/jdwm.2008040107.

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Kim, Young-Won, and Il-Seok Oh. "Hybrid Genetic Algorithm for Classifier Ensemble Selection." KIPS Transactions:PartB 14B, no. 5 (2007): 369–76. http://dx.doi.org/10.3745/kipstb.2007.14-b.5.369.

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Kumar, Uttam, S. Kumar Raja, Chiranjit Mukhopadhyay, and T. V. Ramachandra. "Hybrid Bayesian Classifier for Improved Classification Accuracy." IEEE Geoscience and Remote Sensing Letters 8, no. 3 (2011): 474–77. http://dx.doi.org/10.1109/lgrs.2010.2087006.

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Kim, Young-Won, and Il-Seok Oh. "Classifier ensemble selection using hybrid genetic algorithms." Pattern Recognition Letters 29, no. 6 (2008): 796–802. http://dx.doi.org/10.1016/j.patrec.2007.12.013.

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Wen, Hui, Hongguang Fan, Weixin Xie, and Jihong Pei. "Hybrid Structure-Adaptive RBF-ELM Network Classifier." IEEE Access 5 (2017): 16539–54. http://dx.doi.org/10.1109/access.2017.2740420.

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Basawaraj Birajadar, Ganesh, Altaf Osman Mulani, Osamah Ibrahim Khalaf, et al. "Epilepsy Identification using Hybrid CoPrO-DCNN Classifier." International Journal of Computing and Digital Systems 15, no. 1 (2024): 783–96. http://dx.doi.org/10.12785/ijcds/160157.

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Karaca, Yunus Emre, and Serpil Aslan. "Auto-Diagnosis of Lung Cancer with the Proposed Feature Fusion-Based Hybrid Deep Model." Review of Computer Engineering Studies 9, no. 3 (2022): 87–93. http://dx.doi.org/10.18280/rces.090301.

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Early detection of lung cancer increases the response rate to treatment. Therefore, the accuracy of diagnostic methods is of great importance. Reading the patient's medical images by radiologists can cause a severe time cost besides subjective result. In this context, Artificial Intelligence (AI) methods create an innovative field to reduce the workforce of radiologists and obtain objective results. AI methods play a vital role in improving the analysis of the dataset, extracting meaningful features, clustering, and classification. In our study, the data set contains healthy images besides CT

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Kumar, N. Komal, R. Lakshmi Tulasi, and Vigneswari D. "An ensemble multi-model technique for predicting chronic kidney disease." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 2 (2019): 1321–26. https://doi.org/10.11591/ijece.v9i2.pp1321-1326.

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Chronic Kidney Disease (CKD) is a type of lifelong kidney disease that leads to the gradual loss of kidney function over time; the main function of the kidney is to filter the wastein the human body. When the kidney malfunctions, the wastes accumulate in our body leading to complete failure. Machine learning algorithms can be used in prediction of the kidney disease at early stages by analyzing the symptoms. The aim of this paper is to propose an ensemble learning technique for predicting Chronic Kidney Disease (CKD). We propose a new hybrid classifier called as ABC4.5, which is ensemble learn

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Shahzad, H. M., Sohail Masood Bhatti, Arfan Jaffar, Sheeraz Akram, Mousa Alhajlah, and Awais Mahmood. "Hybrid Facial Emotion Recognition Using CNN-Based Features." Applied Sciences 13, no. 9 (2023): 5572. http://dx.doi.org/10.3390/app13095572.

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In computer vision, the convolutional neural network (CNN) is a very popular model used for emotion recognition. It has been successfully applied to detect various objects in digital images with remarkable accuracy. In this paper, we extracted learned features from a pre-trained CNN and evaluated different machine learning (ML) algorithms to perform classification. Our research looks at the impact of replacing the standard SoftMax classifier with other ML algorithms by applying them to the FC6, FC7, and FC8 layers of Deep Convolutional Neural Networks (DCNNs). Experiments were conducted on two

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Lee, Sang-Hwa. "Text Classification of Mixed Model Based on Deep Learning." Tehnički glasnik 17, no. 3 (2023): 367–74. http://dx.doi.org/10.31803/tg-20221228180808.

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At present, deep learning has been widely used many fields, but the research on text classification is still relatively few. This paper makes full use of the good learning characteristics of deep learning, proposes a hybrid model based on deep learning, and designs a text classifier based on the hybrid model. This hybrid model uses two common deep learning models, sparse automatic encoder and deep confidence network, to mix. The hybrid model is mainly composed of three parts, the first two layers are constructed by sparse automatic encoder, the middle layer is a three-layer depth Convolutional

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Kiliç, Kenan, Kazım Kiliç, İbrahim Alper Doğru, and Uğur Özcan. "Comparison of Various Feature Extractors and Classifiers in Wood Defect Detection." Drvna industrija 76, no. 2 (2025): 133–48. https://doi.org/10.5552/drvind.2025.0217.

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Detection of defects on wood during quality processes in the wood industry is extremely important both economically and in terms of production and use. In order to minimize the time and cost loss caused by products obtained with defective wood, manufacturers want to detect defects in wood early by applying quality control process. For this purpose, in this study, some experiments are carried out using texture analysis methods and machine learning classifiers to detect defective wood from wood images. The features of wood images in the dataset taken from literature are extracted separately with

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Sampath, A. K., and N. Gomathi. "Probabilistic Model Based Hybrid Classifier for Character Recognition." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 25, no. 04 (2017): 621–47. http://dx.doi.org/10.1142/s0218488517500271.

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Handwritten character recognition is most crucial one indulging in many of the applications like forensic search, searching historical manuscripts, mail sorting, bank check reading, tax form processing, book and handwritten notes transcription etc. The problem occurrence in the recognition is mainly because of the writing style variation, size variation (length and height), orientation angle etc. In this paper a probabilistic model based hybrid classifier is proposed for the character recognition combining the neural network and decision tree classifiers. In addition to the local gradient feat

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Jain, Mukesh C., and Dr Farha Haneef. "Intelligent Techniques for Emotion Detection in Humans and Emotional States in Plants for Creating a Healthy Environment." International Journal of Environmental Sciences 11, no. 6s (2025): 870–82. https://doi.org/10.64252/2gc3dh43.

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Emotion detection from text has gained significant attention in recent years due to its potential applications in various domains such as social media analysis, customer feedback analysis, and sentiment analysis. This research focuses on employing Natural Language Processing (NLP) techniques, including tokenizers and TF-IDF, along with different classifiers such as a hybrid model, LSTM model, and RF (Random Forest) model, for accurate emotion detection. The initial step involves data preprocessing, where tokenizers are utilized to break down the text into individual tokens or words, enabling f

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Prabhakar, Sunil Kumar, Harikumar Rajaguru, and Dong-Ok Won. "Coherent Feature Extraction with Swarm Intelligence Based Hybrid Adaboost Weighted ELM Classification for Snoring Sound Classification." Diagnostics 14, no. 17 (2024): 1857. http://dx.doi.org/10.3390/diagnostics14171857.

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For patients suffering from obstructive sleep apnea and sleep-related breathing disorders, snoring is quite common, and it greatly interferes with the quality of life for them and for the people surrounding them. For diagnosing obstructive sleep apnea, snoring is used as a screening parameter, so the exact detection and classification of snoring sounds are quite important. Therefore, automated and very high precision snoring analysis and classification algorithms are required. In this work, initially the features are extracted from six different domains, such as time domain, frequency domain,

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Varga, Michal, Ján Jadlovský, and Slávka Jadlovská. "Generative Enhancement of 3D Image Classifiers." Applied Sciences 10, no. 21 (2020): 7433. http://dx.doi.org/10.3390/app10217433.

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In this paper, we propose a methodology for generative enhancement of existing 3D image classifiers. This methodology is based on combining the advantages of both non-generative classifiers and generative modeling. Its purpose is to streamline the synthesis of novel deep neural networks by embedding existing compatible classifiers into a generative network architecture. A demonstration of this process and evaluation of its effectiveness is performed using a 3D convolutional classifier and its generative equivalent—a 3D conditional generative adversarial network classifier. The results of the e

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Souidi, Mohamed Abdou, and Noria Taghezout. "Privacy Protection in Enterprise Social Networks Using a Hybrid De-Identification System." International Journal of Information Security and Privacy 15, no. 1 (2021): 138–52. http://dx.doi.org/10.4018/ijisp.2021010107.

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Enterprise social networks (ESN) have been widely used within organizations as a communication infrastructure that allows employees to collaborate with each other and share files and documents. The shared documents may contain a large amount of sensitive information that affect the privacy of persons such as phone numbers, which must be protected against any kind of disclosure or unauthorized access. In this study, authors propose a hybrid de-identification system that extract sensitive information from textual documents shared in ESNs. The system is based on both machine learning and rule-bas

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N, Komal Kumar, R. Lakshmi Tulasi, and Vigneswari D. "An ensemble multi-model technique for predicting chronic kidney disease." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 2 (2019): 1321. http://dx.doi.org/10.11591/ijece.v9i2.pp1321-1326.

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<span lang="EN-US">Chronic Kidney Disease (CKD) is a type of lifelong kidney disease that leads to the gradual loss of kidney function over time; the main function of the kidney is to filter the wastein the human body. When the kidney malfunctions, the wastes accumulate in our body leading to complete failure. Machine learning algorithms can be used in prediction of the kidney disease at early stages by analyzing the symptoms. The aim of this paper is to propose an ensemble learning technique for predicting Chronic Kidney Disease (CKD). We propose a new hybrid classifier called as ABC4.5

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Muqasqas, Saed A., Qasem A. Al Radaideh, and Bilal A. Abul-Huda. "A Hybrid Classification Approach Based on Decision Tree and Naïve Bays Methods." International Journal of Information Retrieval Research 4, no. 4 (2014): 61–72. http://dx.doi.org/10.4018/ijirr.2014100104.

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Data classification as one of the main tasks of data mining has an important role in many fields. Classification techniques differ mainly in the accuracy of their models, which depends on the method adopted during the learning phase. Several researchers attempted to enhance the classification accuracy by combining different classification methods in the same learning process; resulting in a hybrid-based classifier. In this paper, the authors propose and build a hybrid classifier technique based on Naïve Bayes and C4.5 classifiers. The main goal of the proposed model is to reduce the complexity

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Melese, Tamiru, Tesfahun Berhane, Abdu Mohammed, and Assaye Walelgn. "Credit-Risk Prediction Model Using Hybrid Deep—Machine-Learning Based Algorithms." Scientific Programming 2023 (November 6, 2023): 1–13. http://dx.doi.org/10.1155/2023/6675425.

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Credit-risk prediction is one of the challenging tasks in the banking industry. In this study, a hybrid convolutional neural network—support vector machine/random forest/decision tree (CNN—SVM/RF/DT) model has been proposed for efficient credit-risk prediction. We proposed four classifiers to develop the model. A fully connected layer with soft-max trained using an end-to-end process makes up the first classifier and by deleting the final fully connected with soft-max layer, the other three classifiers—a SVM, RF, and DT classifier stacked after the flattening layer. Different parameter values

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