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Journal articles on the topic 'J48 Classifier'

Author: Grafiati

Published: 5 June 2025

Last updated: 15 July 2025

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1

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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Taha Chicho, Bahzad, Adnan Mohsin Abdulazeez, Diyar Qader Zeebaree, and Dilovan Assad Zebari. "Machine Learning Classifiers Based Classification For IRIS Recognition." Qubahan Academic Journal 1, no. 2 (2021): 106–18. http://dx.doi.org/10.48161/qaj.v1n2a48.

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Classification is the most widely applied machine learning problem today, with implementations in face recognition, flower classification, clustering, and other fields. The goal of this paper is to organize and identify a set of data objects. The study employs K-nearest neighbors, decision tree (j48), and random forest algorithms, and then compares their performance using the IRIS dataset. The results of the comparison analysis showed that the K-nearest neighbors outperformed the other classifiers. Also, the random forest classifier worked better than the decision tree (j48). Finally, the best

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Sun, Pei Pei, Quan Yin Zhu, Lei Zhou, and Yong Jun Zhang. "Comparative Analysis of Text Categorizer on Science and Technology Intelligence." Applied Mechanics and Materials 530-531 (February 2014): 502–5. http://dx.doi.org/10.4028/www.scientific.net/amm.530-531.502.

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In order to more effectively classify the science and technology intelligence text, the idea that classifying science and technology intelligence text categorization based on different classifiers is proposed. The experiment is done with two thousand Chinese texts based on three different classifiers in this paper. Among these classifiers, the rate of correctly classified instances with NaiveBayes Classifier is 96.95 percent and J48 Classifiers is 97.59. The highest of three classifiers is SMO Classifier and its correct rate is 98.65 percent. According to the analysis of experimental results,

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Bohani, Farah Aqilah, Farah Syazwani Mohamed Rashid, Yuzi Mahmud, and Sitti Rachmawati Yahya. "ANALYZING THE IMPACT OF FEATURE SELECTION USING INFORMATION GAIN FOR AIRLINES' CUSTOMER SATISFACTION." MALAYSIAN JOURNAL OF COMPUTING (MJOC) 9, no. 1 (2024): 1673–89. http://dx.doi.org/10.24191/mjoc.v9i1.24163.

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Feature selection has become a focus of research in many fields that deal with machine learning and data mining because it makes classifiers cost-effective, faster, and more accurate. In this paper, the impact of feature selection using filter methods such as Information Gain is shown. The impact of feature selection has been analyzed based on the accuracy of two classifiers: J48 and Naïve Bayes. The Airline Customer Satisfaction datasets have been used for comparing with and without applying Information Gain. As a result, J48 achieved 0.33% and 0.29% improvements in accuracy after applying In

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Solanki, Yogendra Singh, Prasun Chakrabarti, Michal Jasinski, et al. "A Hybrid Supervised Machine Learning Classifier System for Breast Cancer Prognosis Using Feature Selection and Data Imbalance Handling Approaches." Electronics 10, no. 6 (2021): 699. http://dx.doi.org/10.3390/electronics10060699.

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Nowadays, breast cancer is the most frequent cancer among women. Early detection is a critical issue that can be effectively achieved by machine learning (ML) techniques. Thus in this article, the methods to improve the accuracy of ML classification models for the prognosis of breast cancer are investigated. Wrapper-based feature selection approach along with nature-inspired algorithms such as Particle Swarm Optimization, Genetic Search, and Greedy Stepwise has been used to identify the important features. On these selected features popular machine learning classifiers Support Vector Machine,

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Camargo, Flávio F., Edson E. Sano, Cláudia M. Almeida, José C. Mura, and Tati Almeida. "A Comparative Assessment of Machine-Learning Techniques for Land Use and Land Cover Classification of the Brazilian Tropical Savanna Using ALOS-2/PALSAR-2 Polarimetric Images." Remote Sensing 11, no. 13 (2019): 1600. http://dx.doi.org/10.3390/rs11131600.

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This study proposes a workflow for land use and land cover (LULC) classification of Advanced Land Observing Satellite-2 (ALOS-2) Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) images of the Brazilian tropical savanna (Cerrado) biome. The following LULC classes were considered: forestlands; shrublands; grasslands; reforestations; croplands; pasturelands; bare soils/straws; urban areas; and water reservoirs. The proposed approach combines polarimetric attributes, image segmentation, and machine-learning procedures. A set of 125 attributes was generated using polarimetric ALOS-2/P

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Asim Shahid, Muhammad, Muhammad Mansoor Alam, and Mazliham Mohd Su’ud. "A fact based analysis of decision trees for improving reliability in cloud computing." PLOS ONE 19, no. 12 (2024): e0311089. https://doi.org/10.1371/journal.pone.0311089.

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The popularity of cloud computing (CC) has increased significantly in recent years due to its cost-effectiveness and simplified resource allocation. Owing to the exponential rise of cloud computing in the past decade, many corporations and businesses have moved to the cloud to ensure accessibility, scalability, and transparency. The proposed research involves comparing the accuracy and fault prediction of five machine learning algorithms: AdaBoostM1, Bagging, Decision Tree (J48), Deep Learning (Dl4jMLP), and Naive Bayes Tree (NB Tree). The results from secondary data analysis indicate that the

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Waruwu, Jurisman, Wira Hadinata, Siska Febriyani, and Rini Wijayanti. "Data Mining Technique in Detecting and Predicting Cyber In Marketplace Sector." Jurnal Informatika 1, no. 1 (2022): 8–11. http://dx.doi.org/10.57094/ji.v1i1.350.

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Marketplace is one business solution that can be profitable, because it is not bound by time and place. However, marketplace can be misused by irresponsible parties, and can harm others. Then a pattern is needed to predict cybercrime in order to prevent it. To get a pattern, we can use data mining. This paper presents a general idea about the model of Data Mining techniques and diverse cybercrimes in market place applications. This paper implements data mining techniques like K-Means, Influenced Association Classifier and J48 Prediction tree for investigating the cybercrime data sets. K-means

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Aljammal, Ashraf H., Salah Taamneh, Ahmad Qawasmeh, and Hani Bani Salameh. "Machine Learning Based Phishing Attacks Detection Using Multiple Datasets." International Journal of Interactive Mobile Technologies (iJIM) 17, no. 05 (2023): 71–83. http://dx.doi.org/10.3991/ijim.v17i05.37575.

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Nowadays, individuals and organizations are increasingly targeted by phishing attacks, so an accurate phishing detection system is required. Therefore, many phishing detection techniques have been proposed as well as phishing datasets have been collected. In this paper, three datasets have been used to train and test machine learning classifiers. The datasets have been archived by Phish-Tank and UCI Machine Learning Repository. Furthermore, Information Gain algorithm have been used for features reduction and selection purpose. In addition, six machine learning classifiers have been evaluated,

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Mihǎescu, Marian Cristian, Paul Ştefan Popescu, and Dumitru Dan Burdescu. "J48 list ranker based on advanced classifier decision tree induction." International Journal of Computational Intelligence Studies 4, no. 3/4 (2015): 313. http://dx.doi.org/10.1504/ijcistudies.2015.072879.

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Alothman, Zainab, Mouhammd Alkasassbeh, and Sherenaz Al-Haj Baddar. "An efficient approach to detect IoT botnet attacks using machine learning." Journal of High Speed Networks 26, no. 3 (2020): 241–54. http://dx.doi.org/10.3233/jhs-200641.

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The numerous security loopholes in the design and implementation of many IoT devices have rendered them an easy target for botnet attacks. Several approaches to implement behavioral IoT botnet attacks detection have been explored, including machine learning. The main goal of previous studies was to achieve the highest possible accuracy in distinguishing normal from malicious IoT traffic, with minimal regard to the identification of the particular type of attack that is being launched. In this study, we present a machine learning based approach for detecting IoT botnet attacks that not only hel

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Saputra, Christian Eka, Derwin Suhartono, and Rini Wongso. "Question Categorization using Lexical Feature in Opini.id." ComTech: Computer, Mathematics and Engineering Applications 8, no. 4 (2017): 229. http://dx.doi.org/10.21512/comtech.v8i4.4026.

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This research aimed to categorize questions posted in Opini.id. N-gram and Bag of Concept (BOC) were used as the lexical features. Those were combined with Naïve Bayes, Support Vector Machine (SVM), and J48 Tree as the classification method. The experiments were done by using data from online media portal to categorize questions posted by user. Based on the experiments, the best accuracy is 96,5%. It is obtained by using the combination of Bigram Trigram Keyword (BTK) features with J48 Tree as classifier. Meanwhile, the combination of Unigram Bigram (UB) and Unigram Bigram Keyword (UBK) with a

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Jaya, M. Izham, and Mohd Faizal Ab Razak. "Dynamic Ransomware Detection for Windows Platform Using Machine Learning Classifiers." JOIV : International Journal on Informatics Visualization 6, no. 2-2 (2022): 469. http://dx.doi.org/10.30630/joiv.6.2-2.1093.

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In this world of growing technological advancements, ransomware attacks are also on the rise. This threat often affects the finance of individuals, organizations, and financial sectors. In order to effectively detect and block these ransomware threats, the dynamic analysis strategy was proposed and carried out as the approach of this research. This paper aims to detect ransomware attacks with dynamic analysis and classify the attacks using various machine learning classifiers namely: Random Forest, Naïve Bayes, J48, Decision Table and Hoeffding Tree. The TON IoT Datasets from the University of

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BABY AKULA, R.S.PARMAR, M. P. RAJ, and K. INDUDHAR REDDY. "Prediction for rice yield using data mining approach in Ranga Reddy district of Telangana, India." Journal of Agrometeorology 23, no. 2 (2021): 242–48. http://dx.doi.org/10.54386/jam.v23i2.75.

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 In order to explore the possibility of crop estimation, data mining approach being multidisciplinary was followed. The district of Ranga Reddy, Telangana State, India has been chosen for the study and its year wise average yield data of rice and daily weather over a period of 31 years i.e. from 1988-2019 (30th to 47th Standard Meteorological Weeks). Data mining tool WEKA (V3.8.1). Min- Max Normalization technique followed by Feature Selection algorithm, ‘cfsSubsetEval’ was also adopted to improve quality and accuracy of data mining algorithms. Thus, after cleaning and sorting of data, f

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Gaikwad, D. P., and S. V. Chaitanya. "Grading Method of Ensemble and Genetic Algorithm for Intrusion Detection System." SAMRIDDHI : A Journal of Physical Sciences, Engineering and Technology 14, Spl-2 issu (2022): 262–70. http://dx.doi.org/10.18090/samriddhi.v14spli02.11.

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Intrusion Detection System is very important tool for network security. However, Intrusion Detection System suffers from the problem of handling large volume of data and produces high false positive rate. In this paper, a novel Grading method of ensemble has proposed to overcome limitation of intrusion detection system. Partial decision tree (PART), RIpple DOwn Rule (RIDOR) learner and J48 decision tree have used as base classifiers of Grading classifier. Optimzed Genetic Search algorithm have used for selection of features.These three base classifiers have graded using RandomForest decision t

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Banubakode, Abhijit, and Mohammed Gadhia. "Query Optimization in Object Oriented Database Using Cursor with Special Reference to Parallel Processing." SAMRIDDHI : A Journal of Physical Sciences, Engineering and Technology 14, Spl-2 issu (2022): 301–6. http://dx.doi.org/10.18090/samriddhi.v14spli02.18.

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Intrusion Detection System is very important tool for network security. However, Intrusion Detection System suffers from the problem of handling large volume of data and produces high false positive rate. In this paper, a novel Grading method of ensemble has proposed to overcome limitation of intrusion detection system. Partial decision tree (PART), RIpple DOwn Rule (RIDOR) learner and J48 decision tree have used as base classifiers of Grading classifier. Optimzed Genetic Search algorithm have used for selection of features.These three base classifiers have graded using RandomForest decision t

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Chayangkoon, Narongsak, and Anongnart Srivihok. "Text classification model for methamphetamine-related tweets in Southeast Asia using dual data preprocessing techniques." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 4 (2021): 3617. http://dx.doi.org/10.11591/ijece.v11i4.pp3617-3628.

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<span>Methamphetamine addiction is a prominent problem in Southeast Asia. Drug addicts often discuss illegal activities on popular social networking services. These individuals spread messages on social media as a means of both buying and selling drugs online. This paper proposes a model, the “text classification model of methamphetamine tweets in Southeast Asia” (TMTA), to identify whether a tweet from Southeast Asia is related to methamphetamine abuse. The research addresses the weakness of bag of words (BoW) by introducing BoW and Word2Vec feature selection (BWF) techniques. A domain-

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Narongsak, Chayangkoon, and Srivihok Anongnart. "Text classification model for methamphetamine-related tweets in Southeast Asia using dual data preprocessing techniques." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 4 (2021): 3617–28. https://doi.org/10.11591/ijece.v11i4.pp3617-3628.

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Methamphetamine addiction is a prominent problem in Southeast Asia. Drug addicts often discuss illegal activities on popular social networking services. These individuals spread messages on social media as a means of both buying and selling drugs online. This paper proposes a model, the “text classification model of methamphetamine tweets in Southeast Asia” (TMTA), to identify whether a tweet from Southeast Asia is related to methamphetamine abuse. The research addresses the weakness of bag of words (BoW) by introducing BoW and Word2Vec feature selection (BWF) techniques. A domain-

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Ropelewska, Ewa. "The Application of Computer Image Analysis Based on Textural Features for the Identification of Barley Kernels Infected with Fungi of the Genus Fusarium." Agricultural Engineering 22, no. 3 (2018): 49–56. http://dx.doi.org/10.1515/agriceng-2018-0026.

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AbstractThe aim of this study was to develop discrimination models based on textural features for the identification of barley kernels infected with fungi of the genus Fusarium and healthy kernels. Infected barley kernels with altered shape and discoloration and healthy barley kernels were scanned. Textures were computed using MaZda software. The kernels were classified as infected and healthy with the use of the WEKA application. In the case of RGB, Lab and XYZ color models, the classification accuracies based on 10 selected textures with the highest discriminative power ranged from 95 to 100

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Ghanem, Souhila, Raphaël Couturier, and Pablo Gregori. "An Accurate and Easy to Interpret Binary Classifier Based on Association Rules Using Implication Intensity and Majority Vote." Mathematics 9, no. 12 (2021): 1315. http://dx.doi.org/10.3390/math9121315.

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In supervised learning, classifiers range from simpler, more interpretable and generally less accurate ones (e.g., CART, C4.5, J48) to more complex, less interpretable and more accurate ones (e.g., neural networks, SVM). In this tradeoff between interpretability and accuracy, we propose a new classifier based on association rules, that is to say, both easy to interpret and leading to relevant accuracy. To illustrate this proposal, its performance is compared to other widely used methods on six open access datasets.

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Garg, Sharvan Kumar, Deepak Kumar Sinha, and Nidhi Bhatia. "Performance of Hoeffding Tree and C4.5 Algorithms to Envisage an Occurrence of Hepatitis–A Liver Disease." Journal of Computational and Theoretical Nanoscience 17, no. 6 (2020): 2423–29. http://dx.doi.org/10.1166/jctn.2020.8911.

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Premature forecasting of hepatitis is extremely imperative to save an individual years and take appropriate steps to control the ailment. Decision Tree algorithms have been effectively useful in a variety of fields particularly in medicinal discipline. This manuscript investigates the premature forecasting of hepatitis by means of a variety of decision tree algorithms. In this manuscript, we build up a Hepatitis prediction model that can aid medical experts in envisaging Hepatitis condition supported on the medicinal data of patients. At the outset, we have chosen 19 imperative medicinal attri

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Trivedi, Shrawan Kumar, and Shubhamoy Dey. "Analysing user sentiment of Indian movie reviews." Electronic Library 36, no. 4 (2018): 590–606. http://dx.doi.org/10.1108/el-08-2017-0182.

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Purpose To be sustainable and competitive in the current business environment, it is useful to understand users’ sentiment towards products and services. This critical task can be achieved via natural language processing and machine learning classifiers. This paper aims to propose a novel probabilistic committee selection classifier (PCC) to analyse and classify the sentiment polarities of movie reviews. Design/methodology/approach An Indian movie review corpus is assembled for this study. Another publicly available movie review polarity corpus is also involved with regard to validating the re

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Al-Smadi, Mohammad, Mahmoud Al-Ayyoub, Huda Al-Sarhan, and Yaser Jararweh. "An Aspect-Based Sentiment Analysis Approach to Evaluating Arabic News Affect on Readers." JUCS - Journal of Universal Computer Science 22, no. (5) (2016): 630–49. https://doi.org/10.3217/jucs-022-05-0630.

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Great challenges arise due to the rapid growth of online data. The widespread use of online social networks (OSN) have enabled the generation of massive amounts of raw data where users post their own material. One interesting example of user generated data is their political views and opinions. The ability to crawl OSN and automatically analyze their political content is of undeniable importance. However, this requires automated methods for posts' tone analysis, sentiment analysis, and emotional affect. The purpose of this paper is to evaluate Arabic news posts affect on readers using a novel

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R, Sasi Regha, and Uma Rani R. "A fuzzy based enhancement on prism and J48 classifier prediction of student performance." International Journal of Advanced Technology and Engineering Exploration 5, no. 42 (2018): 89–95. http://dx.doi.org/10.19101/ijatee.2018.542014.

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Somehalli Kapanigowda, Nithin, Hemanth Krishna, Shamanth Vasanth, and Ananthapadmanabha Thammaiah. "Internal combustion engine gearbox bearing fault prediction using J48 and random forest classifier." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 4 (2023): 4467. http://dx.doi.org/10.11591/ijece.v13i4.pp4467-4476.

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<span lang="EN-US">Defective bearings in four-stroke engines can compromise performance and efficiency. Early detection of bearing difficulties in 4-stroke engines is critical. Four-stroke gasoline engines that vibrate or make noise can be used to diagnose issues. Using time, frequency, and time-frequency domain approaches, the vibrational features of healthy and diseased tissues are examined. Problems are only detectable by vibration or sound. The fault is identified through statistical analysis of seismic and audio data using frequency and time-frequency analysis. Vibration must be min

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Nithin, Somehalli Kapanigowda, Krishna Hemanth, Vasanth Shamanth, and Thammaiah Ananthapadmanabha. "Internal combustion engine gearbox bearing fault prediction using J48 and random forest classifier." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 4 (2023): 4467–76. https://doi.org/10.11591/ijece.v13i4.pp4467-4476.

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Defective bearings in four-stroke engines can compromise performance and efficiency. Early detection of bearing difficulties in 4-stroke engines is critical. Four-stroke gasoline engines that vibrate or make noise can be used to diagnose issues. Using time, frequency, and time-frequency domain approaches, the vibrational features of healthy and diseased tissues are examined. Problems are only detectable by vibration or sound. The fault is identified through statistical analysis of seismic and audio data using frequency and time-frequency analysis. Vibration must be minimized prior to examinati

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Ivanova, Krassimira, Iliya Mitov, and Peter L. Stanchev. "Applying Associative Classifier PGN for Digitised Cultural Heritage Resource Discovery." Digital Presentation and Preservation of Cultural and Scientific Heritage 1 (September 30, 2011): 117–26. http://dx.doi.org/10.55630/dipp.2011.1.13.

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Resource discovery is one of the key services in digitised cultural heritage collections. It requires intelligent mining in heterogeneous digital content as well as capabilities in large scale performance; this explains the recent advances in classification methods. Associative classifiers are convenient data mining tools used in the field of cultural heritage, by applying their possibilities to taking into account the specific combinations of the attribute values. Usually, the associative classifiers prioritize the support over the confidence. The proposed classifier PGN questions this common

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Jalali, Maryam, Navid Reza Ghasemi, Samane Nematolahi, and Najaf Zare. "Survivability Prediction of Breast Cancer Patients Using Three Data Mining Methods: A Comparative Study." Epidemiology and Health System Journal 11, no. 1 (2024): 7–12. http://dx.doi.org/10.34172/ehsj.26085.

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Background and aims: Breast cancer (BC) is the leading cause of mortality among women. Early diagnosis is crucial for effective treatment. This study applied suitable data mining methods that provide rules and present influential prognostic factors on the survival time of BC patients. Methods: The dataset consisted of 1574 women diagnosed between January 2002 and December 2012 at the Cancer Registry Center of Nemazi hospital in Fars Province, Iran. Patients were classified based on prognostic factors using three popular data mining methods, including decision tree (J48), Naïve Bayes (NB), and

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Naseem, Rashid, Zain Shaukat, Muhammad Irfan, et al. "Empirical Assessment of Machine Learning Techniques for Software Requirements Risk Prediction." Electronics 10, no. 2 (2021): 168. http://dx.doi.org/10.3390/electronics10020168.

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Software risk prediction is the most sensitive and crucial activity of Software Development Life Cycle (SDLC). It may lead to the success or failure of a project. The risk should be predicted earlier to make a software project successful. A model is proposed for the prediction of software requirement risks using requirement risk dataset and machine learning techniques. In addition, a comparison is made between multiple classifiers that are K-Nearest Neighbour (KNN), Average One Dependency Estimator (A1DE), Naïve Bayes (NB), Composite Hypercube on Iterated Random Projection (CHIRP), Decision Ta

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Madar, Inamul Hasan. "Identification of marker genes in Alzheimer's disease using a machine-learning model." Bioinformation 17, no. 2 (2021): 363–68. http://dx.doi.org/10.6026/97320630017363.

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Alzheimer's Disease (AD) is one of the most common causes of dementia, mostly affecting the elderly population. Currently, there is no proper diagnostic tool or method available for the detection of AD. The present study used two distinct data sets of AD genes, which could be potential biomarkers in the diagnosis. The differentially expressed genes (DEGs) curated from both datasets were used for machine learning classification, tissue expression annotation and co-expression analysis. Further, CNPY3, GPR84, HIST1H2AB, HIST1H2AE, IFNAR1, LMO3, MYO18A, N4BP2L1, PML, SLC4A4, ST8SIA4, TLE1 and N4BP

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Ms., Reena Ostwal, and Anil Pimpalapure Dr. "A Study of Network Intrusion Detection using Machine Learning." International Journal of Engineering Research and Science 10, no. 6 (2024): 14–22. https://doi.org/10.5281/zenodo.15203765.

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<strong>Abstract</strong><strong>—</strong> Network security engineers work to keep services available all the time by handling intruder attacks. Intrusion Detection System (IDS) is one of the obtainable mechanisms that is used to sense and classify any abnormal actions. Therefore, the IDS must be always up to date with the latest intruder attacks signatures to preserve confidentiality, integrity, and availability of the services. The speed of the IDS is a very important issue as well learning the new attacks. This research work illustrates how the Knowledge Discovery and Data Mining (or

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Evans, Huw Prosser, Athanasios Anastasiou, Adrian Edwards, et al. "Automated classification of primary care patient safety incident report content and severity using supervised machine learning (ML) approaches." Health Informatics Journal 26, no. 4 (2019): 3123–39. http://dx.doi.org/10.1177/1460458219833102.

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Learning from patient safety incident reports is a vital part of improving healthcare. However, the volume of reports and their largely free-text nature poses a major analytic challenge. The objective of this study was to test the capability of autonomous classifying of free text within patient safety incident reports to determine incident type and the severity of harm outcome. Primary care patient safety incident reports (n=31333) previously expert-categorised by clinicians (training data) were processed using J48, SVM and Naïve Bayes. The SVM classifier was the highest scoring classifier for

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Chandran, Bhuvaneswari. "An Image based Diagnostic System for Lung Disease Classification." Journal of Communications Technology, Electronics and Computer Science 3 (December 29, 2015): 6. http://dx.doi.org/10.22385/jctecs.v3i0.6.

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Model-based detection and classification of nodules are two major steps in CAD systems design and evaluation. A common health problem, lung diseases are the most prevailing medical conditions throughout the world. In this paper, Lung diseases are automatically classified as Emphysema, Bronchitis, Pleural effusion and normal lung.The lung CT images are taken as input, preprocessing is applied, feature extraction is done by various methods such as Gabor filter extracts the texture features, walsh hadamard transform extracts the pixel co-efficient values, and a fusion method is proposed in this w

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Mahtabi, Ghorban, Barkha Chaplot, Hazi Mohammad Azamathulla, and Mahesh Pal. "Classification of Hydraulic Jump in Rough Beds." Water 12, no. 8 (2020): 2249. http://dx.doi.org/10.3390/w12082249.

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This paper presents a classification using a decision tree algorithm of hydraulic jump over rough beds based on the approach Froude number, Fr1. Specifically, 581 datasets, from literature, were analyzed. Of these, 280 datasets were for natural rough beds and 301 were for artificial rough beds. The said dataset was divided into four classes based on the energy losses. To compare the performance of the decision tree classifier (J48), a multi-layer neural network (NN) was used. The results suggest an improved performance in terms of classification accuracy by the J48 algorithm in comparison to t

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Al-Shargabi, Bassam, Fekry Olayah, and Waseem AL Romimah. "An Experimental Study for the Effect of Stop Words Elimination for Arabic Text Classification Algorithms." International Journal of Information Technology and Web Engineering 6, no. 2 (2011): 68–75. http://dx.doi.org/10.4018/jitwe.2011040106.

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In this paper, an experimental study was conducted on three techniques for Arabic text classification. These techniques are Support Vector Machine (SVM) with Sequential Minimal Optimization (SMO), Naïve Bayesian (NB), and J48. The paper assesses the accuracy for each classifier and determines which classifier is more accurate for Arabic text classification based on stop words elimination. The accuracy for each classifier is measured by Percentage split method (holdout), and K-fold cross validation methods, along with the time needed to classify Arabic text. The results show that the SMO classi

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Gaber, Ahmed, Hassan A. Youness, Alaa Hamdy, Hammam M. Abdelaal, and Ammar M. Hassan. "Automatic Classification of Fatty Liver Disease Based on Supervised Learning and Genetic Algorithm." Applied Sciences 12, no. 1 (2022): 521. http://dx.doi.org/10.3390/app12010521.

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Fatty liver disease is considered a critical illness that should be diagnosed and detected at an early stage. In advanced stages, liver cancer or cirrhosis arise, and to identify this disease, radiologists commonly use ultrasound images. However, because of their low quality, radiologists found it challenging to recognize this disease using ultrasonic images. To avoid this problem, a Computer-Aided Diagnosis technique is developed in the current study, using Machine Learning Algorithms and a voting-based classifier to categorize liver tissues as being fatty or normal, based on extracting ultra

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Joshuva, A., and V. Sugumaran. "A Comparative Study for Condition Monitoring on Wind Turbine Blade using Vibration Signals through Statistical Features: a Lazy Learning Approach." International Journal of Engineering & Technology 7, no. 4.10 (2018): 190. http://dx.doi.org/10.14419/ijet.v7i4.10.20833.

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This study is to identify whether the wind turbine blades are in good or faulty conditions. If faulty, then the objective to find which fault condition are the blades subjected to. The problem identification is carried out by machine learning approach using vibration signals through statistical features. In this study, a three bladed wind turbine was chosen and faults like blade cracks, hub-blade loose connection, blade bend, pitch angle twist and blade erosion were considered. Here, the study is carried out in three phases namely, feature extraction, feature selection and feature classificati

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Rahman, Abdur, Nutan Farah, Lamia Alam, and Tauseef Ibne. "An Analytical Comparison on Filter Feature Extraction Method in Data Mining using J48 Classifier." International Journal of Computer Applications 124, no. 13 (2015): 1–8. http://dx.doi.org/10.5120/ijca2015905706.

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Priya, M., and M. Rajeshwari. "A Data Mining Approach for Intrusion Detection in a Computer Network." Asian Journal of Computer Science and Technology 8, S1 (2019): 94–97. http://dx.doi.org/10.51983/ajcst-2019.8.s1.1942.

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As activities being done on the internet keep expanding every day due to the fact that we are in the era of the information age, securing sensitive and crucial data on computer networks against malicious attacks tends to be a challenging issue. Designing effective Intrusion Detection Systems (IDSs) with maximized accuracy and low rate of false alarms is an imperative need in the world of cyber-attacks. This work was designed to employ an ensemble data mining technique for improving IDSs by carrying out some experiments using the KDD 99 intrusion dataset. Dataset was fragmented into five, repre

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Fouad, Maha, Dr Mahmoud M. Abd ellatif, Prof Mohamed Hagag, and Dr Ahmed Akl. "Prediction Of Long Term Living Donor Kidney Graft Outcome: Comparison Between Different Machine Learning Methods." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 14, no. 2 (2014): 5419–31. http://dx.doi.org/10.24297/ijct.v14i2.2066.

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Predicting the outcome of a graft transplant with high level of accuracy is a challenging task In medical fields and Data Mining has a great role to answer the challenge. The goal of this study is to compare the performances and features of data mining technique namely Decision Tree , Rule Based Classifiers with Compare to Logistic Regression as a standard statistical data mining method to predict the outcome of kidney transplants over a 5-year horizon. The dataset was compiled from the Urology and Nephrology Center (UNC), Mansoura, Egypt. classifiers were developed using the Weka machine lear

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Kavitha, B., P. Parthiban, M. Goel, et al. "Assessment and Recurrence of Kidney Stones Through Optimized Machine Learning Tree Classifiers Using Dietary Water Quality Parameters and Patient’s History." Advanced Science, Engineering and Medicine 12, no. 10 (2020): 1219–23. http://dx.doi.org/10.1166/asem.2020.2681.

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Kidney stone disease is a result of combination of food items consuming, drinking water quality and genetic heritability, which has been observed to be more prone (both occurrence and recurrence) to certain geographic regimes as Thanjavur suburbs of Tamil Nadu in southern India. The research carried out involves collection of medical information of Kidneystone patients of the study area and survey of their dietary habits including drinking water quality (through laboratory study), selection of suitable classifier to model the Kidney stone recurrence with the most contributing of 22 parameters

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N. Shah Zainudin, M., Md Nasir Sulaiman, Norwati Musapha, Thinagaran Perumal, and Raihani Mohamed. "Solving Classification Problem Using Ensemble Binarization Classifier." International Journal of Engineering & Technology 7, no. 4.31 (2018): 280–84. http://dx.doi.org/10.14419/ijet.v7i4.31.23381.

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Binarization strategy is broadly applied in solving various multi-class classification problems. However, the classifier model learning complexity tends to increase when expanding the number of problems into several replicas. One-Versus-All (OVA) is one of the strategies which transforming the ordinal multi-class classification problems into a series of two-class classification problems. The final output from each classifier model is combined in order to produce the final prediction. This binarization strategy has been proven as superior performance in accuracy than ordinal multi-class classif

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Fauzy, Che Yayah, Imran Ghauth Khairil, and Ting Choo-Yee. "Parallel classification and optimization of telco trouble ticket dataset." TELKOMNIKA (Telecommunication, Computing, Electronics and Control) 19, no. 3 (2021): 872–85. https://doi.org/10.12928/telkomnika.v19i3.18159.

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In the big data age, extracting applicable information using traditional machine learning methodology is very challenging. This problem emerges from the restricted design of existing traditional machine learning algorithms, which do not entirely support large datasets and distributed processing. The large volume of data nowadays demands an efficient method of building machinelearning classifiers to classify big data. New research is proposed to solve problems by converting traditional machine learning classification into a parallel capable. Apache Spark is recommended as the primary data proce

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Parvez, Md Hasnat, Most Moriom Khatun, Sayed Mohsin Reza, Md Mahfujur Rahman, and Md Fazlul Karim Patwary. "Prediction of Potential Future IT Personnel in Bangladesh using Machine Learning Classifier." Global Disclosure of Economics and Business 6, no. 1 (2017): 7–18. http://dx.doi.org/10.18034/gdeb.v6i1.112.

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Bangladesh is one of the most promising developing countries in IT sector, where people from several disciplines and experiences are involved in this sector. However, no direct analysis in this sector is published yet, which covers the proper guideline for predicting future IT personnel. Hence this is not a simple solution, training data from real IT sector are needed and trained several classifiers for detecting perfect results. Machine learning algorithms can be used for predicting future potential IT personnel. In this paper, four different classifiers named as Naive Bayes, J48, Bagging and

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Achmad, Arwan, and Sagita Denny. "Determining Basis Test Paths Using Genetic Algorithm and J48." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 5 (2018): 3333–40. https://doi.org/10.11591/ijece.v8i5.pp3333-3340.

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Basis test paths is a method that uses a graph contains nodes as a representation of codes and the lines as a sequence of code execution steps. Determination of basis test paths can be generated using a Genetic Algorithm, but the drawback was the number of iterations affect the possibility of visibility of the appropriate basis path. When the iteration is less, there is a possibility the paths do not appear all. Conversely, if the iteration is too much, all the paths have appeared in the middle of iteration. This research aims to optimize the performance of Genetic Algorithms for the generatio

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Aljammal, Ashraf H., Ibrahim Al-Oqily, Mamoon Obiedat, Ahmad Qawasmeh, Salah Taamneh, and Fadi I. Wedyan. "Anomaly intrusion detection using machine learning- IG-R based on NSL-KDD dataset." Bulletin of Electrical Engineering and Informatics 13, no. 6 (2024): 4466–74. http://dx.doi.org/10.11591/eei.v13i6.7308.

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Cybersecurity is challenging for security guards because of the rising quantity, variety, and frequency of attacks and malicious activities in cyberspace. Intrusion attacks are among the most common types of cyberspace attacks. Therefore, an intrusion detection system (IDS) is in high demand to accurately detect and mitigate their impact. In this paper, an anomaly IDS using machine learning and information gain-rank (IG-R) is proposed to improve the detection accuracy of intrusions. The network security lab-knowledge discovery dataset (NSL-KDD) is used to train and test the proposed IDS. Initi

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Kalfountzou, Elpida, Lefkothea Papada, Christos Tourkolias, Sevastianos Mirasgedis, Dimitris Kaliampakos, and Dimitris Damigos. "A Comparative Analysis of Machine Learning Algorithms in Energy Poverty Prediction." Energies 18, no. 5 (2025): 1133. https://doi.org/10.3390/en18051133.

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Given the limited potential of conventional statistical models, machine learning (ML) techniques in the field of energy poverty have attracted growing interest, especially during the last five years. The present paper adds new insights to the existing literature by exploring the capacity of ML algorithms to successfully predict energy poverty, as defined by different indicators, for the case of the “Urban Region of Athens” in Greece. More specifically, five energy poverty indicators were predicted on the basis of socio-economic/technical variables through training different machine learning cl

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Olofintuyi, S. S., and T. O. Omotehinwa. "Performance Evaluation of Supervised Ensemble Cyber Situation Perception Models for Computer Network." advances in multidisciplinary & scientific research journal publication 12, no. 1 (2021): 1–14. http://dx.doi.org/10.22624/aims/cisdi/2021/v12n1p1.

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The trend at which cyber threats are gaining access to companies, industries and other sectors of the economy is becoming alarming, and this is posting a serious challenge to network administrators, governments and other business owners. A formidable intrusion detection system is needed to outplay the activities of the cyberattacks. An ensemble system is believed to perform better than a single classifier. With this fact, five different Machine Learning (ML) ensemble algorithms are suggested at the perception phase of Situation Awareness (SA) model for threat detection and the algorithms inclu

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Pandian, A., Stephen Wahi, Yash Tokas, K. Manikandan, and V. V.Ramalingams. "Analysis of Writer Styles in Punjabi." International Journal of Engineering & Technology 7, no. 4.19 (2018): 407–11. http://dx.doi.org/10.14419/ijet.v7i4.19.23174.

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Author Identification alludes to the issue of distinguishing the creator of a mysterious content. From the machine learning perspective, this is a solitary mark content arrangement assignment. This errand is done on the supposition that the creator of an obscure content can be separated by looking at a couple of lexical highlights extricated from that obscure content with those of writings having known writers. In this paper, Authorship Identification process is connected on Punjabi verse dataset comprising of Punjabi ballads composed by 5 unique writers. Different highlights extensively order

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Kumar, Sunil, Saroj Ratnoo, and Jyoti Vashishtha. "HYPER HEURISTIC EVOLUTIONARY APPROACH FOR CONSTRUCTING DECISION TREE CLASSIFIERS." Journal of Information and Communication Technology 20, Number 2 (2021): 249–76. http://dx.doi.org/10.32890/jict2021.20.2.5.

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Decision tree models have earned a special status in predictive modeling since these are considered comprehensible for human analysis and insight. Classification and Regression Tree (CART) algorithm is one of the renowned decision tree induction algorithms to address the classification as well as regression problems. Finding optimal values for the hyper parameters of a decision tree construction algorithm is a challenging issue. While making an effective decision tree classifier with high accuracy and comprehensibility, we need to address the question of setting optimal values for its hyper pa

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