Relevant bibliographies by topics / J48 algorithm

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'J48 algorithm'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 February 2022

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Journal articles on the topic "J48 algorithm"

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anaN, N. Sarav, and V. Gaya thri. "Performance and Classification Evaluation of J48 Algorithm and Kendall’s Based J48 Algorithm (KNJ48)." International Journal of Computer Trends and Technology 59, no. 2 (May 25, 2018): 73–80. http://dx.doi.org/10.14445/22312803/ijctt-v59p112.

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Gomathi, S., and V. Narayani. "Early prediction of systemic lupus erythematosus using hybrid K-Means J48 decision tree algorithm." International Journal of Engineering & Technology 7, no. 1.3 (December 31, 2017): 28. http://dx.doi.org/10.14419/ijet.v7i1.3.8982.

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The objective of the paper is to propose an enhanced algorithm for the prediction of chronic, autoimmune disease called Systemic Lupus Erythematosus (SLE). The Hybrid K-means J48 Decision Tree algorithm (HKMJDT) has been proposed for the effective and early prediction of the SLE. The reason for combining both the clustering and classification algorithms is to obtain the best accuracy and to predict the disease in the early stage. The performance of algorithms such as Naïve Bayes, decision tree, random forest, J48 and Hoeffding tree has been combined with K-means clustering algorithm and compared in an effort to find the best algorithm for diagnosing SLE disease. The results of the statistical evaluation with the comparative study show that the effectiveness of different classification techniques depends on the nature and intricacy of the dataset used. K-means combined with J48 algorithm shows the best accuracy rate of 82.14% on the pre-processed data. The work-flow has been proposed to show the execution of the algorithm.
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Rahmawati, Eka, and Candra Agustina. "Implementasi Teknik Bagging untuk Peningkatan Kinerja J48 dan Logistic Regression dalam Prediksi Minat Pembelian Online." Jurnal Teknologi Informasi dan Terapan 7, no. 1 (June 9, 2020): 16–19. http://dx.doi.org/10.25047/jtit.v7i1.123.

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Abstract—The rapid growth of online shopping sites makes business in the virtual world very promising. Purchasing intentions is one of the keys to success in an online store. There are several data mining methods for making predictions on online purchase intentions datasets. Data can represent the characteristics or habits of each user who has visited a site whether it ends with a transaction or not. Some popular algorithms with good performance in data mining include J48 and Logistic Regression. However, in data sometimes there is a problem of class imbalance, so the ensemble technique needs to be applied. One technique that can be applied is bagging. This research examines data using bagging techniques to improve the performance of the J48 algorithm and Logistic Regression. The results of improving the performance of data mining algorithms with these techniques have an accuracy value of 89.68% for the J48 algorithm and 88.50% for the Logistic Regression algorithm. This figure shows an increase when compared with initial testing without using ensemble techniques. Increases were also experienced in Recall, F-Measure, and AUC values. Keywords—purchasing intentions; J48; Logistic Regression; Bagging; Abstrak— Pesatnya situs pembelanjaan online menjadikan bisnis di dunia virtual sangat menjanjikan. Minat pembelian menjadi salah satu kunci kesuksesan pada sebuah toko online. Terdapat beberapa metode data mining untuk melakukan prediksi pada dataset minat pembelian online. Data dapat mewakili karakteristik atau kebiasaan dari setiap user yang telah mengunjungi suatu situs baik berakhir dengan melakukan transaksi ataupun tidak. Beberapa algoritma yang populer dengan kinerja yang baik dalam data mining diantaranya J48 dan Logistic Regreession. Namun, dalam sebuah data terkadang terdapat masalah ketidakseimbangan kelas, sehingga perlu diterapkan teknik ensemble. Salah satu teknik yang dapat diterapkan adalah teknik bagging. Penelitian kali ini mengujikan data dengan teknik bagging untuk meningkatkan kinerja algoritma J48 dan Logistic Regression. Hasil dari peningkatan kinerja algoritma data mining dengan teknik tersebut memiliki nilai akurasi 89.68% untuk algoritma J48 dan 88.50% untuk algoritma Logistic Regression. Angka tersebut menunjukan adanya peningkatan jika dibandingkan dengan pengujian awal tanpa menggunakan teknik ensemble. Peningkatan juga dialami pada nilai Recall, F-Measure, dan AUC. Keywords—Minat Pembelian, J48, Logistic Regression, Bagging
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Tarimer, Ilhan, and Buse Cennet Karadag. "Comparison with Classification Algorithms in Data Mining of a Fuel Automation System's Sales Data." I V, no. I (March 30, 2020): 245–54. http://dx.doi.org/10.31703/ger.2020(v-i).20.

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This article deals with Otobil and pumps sales estimates at fuel stations. The fuel station data used in the study consists of 2384 data in total. Depending upon these data, classification procedures were performed on fuel station sales data using classification algorithms. In the study the classification algorithms that J48, Random Forest, KStar, Logistic Regression, IBk and Naive Bayes algorithms are used to compare the sales data estimations by using a software. The results obtained show that the accuracy rates of the J48 algorithm are more successful than others in general. It understands that these sales estimations shall encourage fuel station owners and association bodies to get more gainful.
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Kaur, Gaganjot, and Amit Chhabra. "Improved J48 Classification Algorithm for the Prediction of Diabetes." International Journal of Computer Applications 98, no. 22 (July 18, 2014): 13–17. http://dx.doi.org/10.5120/17314-7433.

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Yoan Maria Vianny and Erwin Budi Setiawan. "Implementation of Rumor Detection on Twitter Using J48 Algorithm." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 4, no. 5 (October 30, 2020): 775–81. http://dx.doi.org/10.29207/resti.v4i5.2059.

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The existence of rumors on Twitter has caused a lot of unrest among Indonesians. Unrecognized validity confuses users for that information. In this study, an Indonesian rumor detection system is built by using J48 Algorithm in collaboration with Term Frequency Inverse Document Frequency (TF-IDF) weighting method. Dataset contains 47.449 tweets that have been manually labeled. This study offers new features, namely the number of emoticons in display name, the number of digits in display name, and the number of digits in username. These three new features are used to maximize information about information sources. The highest accuracy is obtained by 75.76% using 90% training data and 1.000 TF-IDF features in 1-gram to 3-gram combinations.
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J, Shankar Murthy. "Network Software Vulnerability Identifier using J48 decision tree algorithm." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (August 31, 2021): 1889–92. http://dx.doi.org/10.22214/ijraset.2021.37685.

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Abstract: Software vulnerabilities are the primary causes of different security issues in the modern era. When vulnerability is exploited by malicious assaults, it substantially jeopardizes the system's security and may potentially result in catastrophic losses. As a result, automatic classification methods are useful for successfully managing software vulnerabilities, improving system security performance, and lowering the chance of the system being attacked and destroyed. In the software industry and in the field of cyber security, the ever-increasing number of publicly reported security flaws has become a major source of concern. Because software security flaws play such a significant part in cyber security attacks, relevant security experts are conducting an increasing number of vulnerability classification studies, this project can predict the software vulnerability means the software's in the device are authorized or not and who scan the system multiple times, to identify the vulnerability j48 decision tree algorithm was used. Keywords: Malicious assaults, catastrophic losses, Security flaws, Cyber security, Vulnerability Classifications.
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Wang, Shuaifang, Guohun Zhu, Yan Li, Peng Wen, and Bo Song. "Analysis of Epileptic EEG Signals with Simple Random Sampling J48 Algorithm." International Journal of Bioscience, Biochemistry and Bioinformatics 4, no. 2 (2014): 78–81. http://dx.doi.org/10.7763/ijbbb.2014.v4.314.

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Catal, Cagatay, Serkan Tugul, and Basar Akpinar. "Automatic Software Categorization Using Ensemble Methods and Bytecode Analysis." International Journal of Software Engineering and Knowledge Engineering 27, no. 07 (September 2017): 1129–44. http://dx.doi.org/10.1142/s0218194017500425.

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Software repositories consist of thousands of applications and the manual categorization of these applications into domain categories is very expensive and time-consuming. In this study, we investigate the use of an ensemble of classifiers approach to solve the automatic software categorization problem when the source code is not available. Therefore, we used three data sets (package level/class level/method level) that belong to 745 closed-source Java applications from the Sharejar repository. We applied the Vote algorithm, AdaBoost, and Bagging ensemble methods and the base classifiers were Support Vector Machines, Naive Bayes, J48, IBk, and Random Forests. The best performance was achieved when the Vote algorithm was used. The base classifiers of the Vote algorithm were AdaBoost with J48, AdaBoost with Random Forest, and Random Forest algorithms. We showed that the Vote approach with method attributes provides the best performance for automatic software categorization; these results demonstrate that the proposed approach can effectively categorize applications into domain categories in the absence of source code.
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Lima, Nilsa Duarte da Silva, Irenilza de Alencar Nääs, João Gilberto Mendes dos Reis, and Raquel Baracat Tosi Rodrigues da Silva. "Classifying the Level of Energy-Environmental Efficiency Rating of Brazilian Ethanol." Energies 13, no. 8 (April 21, 2020): 2067. http://dx.doi.org/10.3390/en13082067.

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The present study aimed to assess and classify energy-environmental efficiency levels to reduce greenhouse gas emissions in the production, commercialization, and use of biofuels certified by the Brazilian National Biofuel Policy (RenovaBio). The parameters of the level of energy-environmental efficiency were standardized and categorized according to the Energy-Environmental Efficiency Rating (E-EER). The rating scale varied between lower efficiency (D) and high efficiency + (highest efficiency A+). The classification method with the J48 decision tree and naive Bayes algorithms was used to predict the models. The classification of the E-EER scores using a decision tree using the J48 algorithm and Bayesian classifiers using the naive Bayes algorithm produced decision tree models efficient at estimating the efficiency level of Brazilian ethanol producers and importers certified by the RenovaBio. The rules generated by the models can assess the level classes (efficiency scores) according to the scale discretized into high efficiency (Classification A), average efficiency (Classification B), and standard efficiency (Classification C). These results might generate an ethanol energy-environmental efficiency label for the end consumers and resellers of the product, to assist in making a purchase decision concerning its performance. The best classification model was naive Bayes, compared to the J48 decision tree. The classification of the Energy Efficiency Note levels using the naive Bayes algorithm produced a model capable of estimating the efficiency level of Brazilian ethanol to create labels.
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Dissertations / Theses on the topic "J48 algorithm"

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Χαλέλλη, Ειρήνη. "Σχεδίαση και ανάπτυξη ολοκληρωμένου συστήματος δυναμικής ανάλυσης και πρόβλεψης της επίδοσης εκπαιδευόμενων σε συστήματα ανοιχτής και εξ' αποστάσεως εκπαίδευσης." Thesis, 2014. http://hdl.handle.net/10889/8335.

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Η ραγδαία ανάπτυξη και διείσδυση των νέων τεχνολογιών πληροφορίας και επικοινωνίας έχει επιφέρει ριζικές αλλαγές σε όλους τους τομείς της ανθρώπινης δράσης (Castells, 1998). Ιδιαίτερο ενδιαφέρον παρουσιάζει η επιρροή των τεχνολογιών αυτών στον τομέα της εκπαίδευσης. Οι εξελίξεις στον χώρο της τεχνολογίας και επικοινωνίας καθώς και η διάδοση του Internet μετεξέλιξαν αναπόφευκτα την εκπαιδευτική διαδικασία, από το κλασσικό συγκεντρωτικό μοντέλο σε ένα πιο άμεσο και ευέλικτο: η «εξ’ Αποστάσεως Εκπαίδευση» (e-learning) είναι μια εναλλακτική μορφή εκπαίδευσης, που επιδιώκει να καλύψει τους περιορισμούς της παραδοσιακής εκπαίδευσης. Στην παρούσα μεταπτυχιακή διπλωματική εργασία σχεδιάστηκε και υλοποιήθηκε ένα ολοκληρωμένο σύστημα Δυναμικής Ανάλυσης και Πρόβλεψης της επίδοσης των εκπαιδευομένων, για ένα σύστημα εξ΄ αποστάσεως εκπαίδευσης. Η βασική ιδέα εμφορείται από την ανάγκη των ιδρυμάτων εξ΄ αποστάσεως εκπαίδευσης, για την κάλυψη των εκπαιδευτικών αναγκών και την παροχή υψηλής ποιότητας σπουδών. Η εξόρυξη γνώσης για την πρόβλεψη της επίδοσης των εκπαιδευομένων συμβάλλει καθοριστικά στην επίτευξη υψηλής ποιότητας σπουδών. Η ικανότητα και η δυνατότητα πρόβλεψης της απόδοσης των εκπαιδευομένων μπορεί να φανεί χρήσιμη με αρκετούς τρόπους για την διαμόρφωση ενός συστήματος, που θα μπορεί να αποτρέψει την αποτυχία καθώς και την παραίτηση των εκπαιδευομένων. Αξίζει να σημειωθεί ότι στα συστήματα εξ’ αποστάσεως εκπαίδευσης η συχνότητα «εγκατάλειψης» είναι αρκετά υψηλότερη από αυτή στα συμβατικά πανεπιστήμια. Για την πρόβλεψη της επίδοσης των εκπαιδευομένων, η απαιτούμενη πληροφορία βρίσκεται «κρυμμένη» στο εκπαιδευτικό σύνολο δεδομένων (δλδ. βαθμοί γραπτών εργασιών, βαθμοί τελικής εξέτασης, παρουσίες φοιτητών) και είναι εξαγώγιμη με τεχνικές εξόρυξης. Η χρήση μεθόδων εξόρυξης δεδομένων (data mining) στον τομέα της εκπαίδευσης παρουσιάζει αυξανόμενο ερευνητικό ενδιαφέρον. Ο νέος αυτός «αναπτυσσόμενος» τομέας έρευνας, που ονομάζεται «Εκπαιδευτική Εξόρυξη Δεδομένων», ασχολείται με την ανάπτυξη μεθόδων εξόρυξης «γνώσης» από τα εκπαιδευτικά σύνολα δεδομένων. Πράγμα που επιτυγχάνεται με τη χρήση τεχνικών όπως τα δέντρα απόφασης, τα Νευρωνικά Δίκτυα, Naïve Bayes, k-means, κλπ. Η παρούσα εργασία έχει σχεδιαστεί να προσφέρει ένα μοντέλο εξόρυξης δεδομένων χρησιμοποιώντας τη μέθοδο των δέντρων απόφασης, για το σύστημα τριτοβάθμιας εκπαίδευσης στο ανοιχτό πανεπιστήμιο. Η «γνώση» που προκύπτει από τα δεδομένα εξόρυξης θα χρησιμοποιηθεί με στόχο την διευκόλυνση και την ενίσχυση της μάθησης, καθώς επίσης και στη λήψη αποφάσεων. Στην παρούσα εργασία, εξάγουμε «γνώση» που σχετίζεται με τις επιδόσεις των μαθητών στην τελική εξέταση. Επίσης, γίνεται εντοπισμός των ατόμων που εγκαταλείπουν το μάθημα και των μαθητών που χρειάζονται ιδιαίτερη προσοχή και εντέλει δίνει τη δυνατότητα στους καθηγητές να παράσχουν την κατάλληλη παροχή συμβουλών.
The rapid development and intrusion of information technology and communications have caused radical changes in all sectors of human’s activity. (Castells, 1998). Of particular interest is the great technology’s influence on education. Due to the adoption of the new technologies, e-learning has been emerged and developed. As a result, distance learning has transformed and new possibilities have appeared. It is remarkable that distance learning became and considered as a scout of the new era in education and contributed to the quality of education: e-learning is trying to cover the limitations of conventional teaching environment. In the present thesis, an integrated system of dynamic analysis and prediction of the performance of students in distance education has been designed and implemented. The initial idea for designing this system came from the higher distance education institutes’ need to provide quality education to its students and to improve the quality of managerial decisions. One way to achieve highest level of quality in higher distance education e-learning system is by discovering knowledge from educational data to study the main attributes that may affect the students’ performance. The discovered knowledge can be used to offer a helpful and constructive recommendations to the academic planners in higher distance education institutes to enhance their decision making process, to improve students’ academic performance, trim down failure rate and dropout rate, to assist instructors, to improve teaching and many other benefits. Dropout rates in university level distance learning are definitely higher than those inconventional universities, thus limiting dropout is essential in university-level distance learning.
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Μπουφαρδέα, Ευαγγελία. "Σημασιολογική μοντελοποίηση συμπεριφοράς και μηχανισμός πρόβλεψης απόδοσης εκπαιδευομένων σε συστήματα ανοικτής και εξ' αποστάσεως εκπαίδευσης." Thesis, 2011. http://hdl.handle.net/10889/5059.

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Η ραγδαία εξάπλωση του Internet έχει προκαλέσει σημαντικές αλλαγές σε πολλούς κλάδους της οικονομίας και της κοινωνίας παγκόσμια. Με τη ραγδαία ανάπτυξη των Τεχνολογιών της Πληροφορικής και της Τεχνολογίας, μια νέα μορφή εκπαίδευσης εμφανίστηκε, που δεν είναι άλλη από το e-learning (εκπαίδευση από απόσταση), που έφερε την επανάσταση στο εκπαιδευτικό γίγνεσθαι. Επιπρόσθετα ο Παγκόσμιος Ιστός σταδιακά μετεξελίσσεται στο Σημασιολογικό Παγκόσμιο Ιστό (Semantic Web) νέα μοντέλα και πρότυπα (XML, RDF, OWL) αναπτύσσονται για την προώθηση αυτής της διαδικασίας. Η έκφραση, μετάδοση και αναζήτηση πληροφοριών με χρήση αυτών των προτύπων ανοίγει νέους ορίζοντες στη χρήση του Διαδικτύου. Οι οντολογίες κερδίζουν ολοένα έδαφος για την αναπαράσταση γνώσης. Σε μια μεγάλη οντολογία που περιέχει χρήσιμα δεδομένα για ένα σύστημα εξ’ αποστάσεως εκπαίδευσης, αξίζει κάποιος να ερευνήσει την «κρυμμένη γνώση», δηλαδή να ανακαλύψει πιθανές συσχετίσεις ή συνειρμούς, να βρει πρότυπα ή μορφές που επαναλαμβάνονται ή ακραία φαινόμενα. Η παρούσα διπλωματική εργασία αποτελεί μια επίδειξη τεχνολογίας για την έγκυρη και έγκαιρη πρόβλεψη της απόδοσης των φοιτητών σε ένα σύστημα εξ’ αποστάσεως εκπαίδευσης. Η βασική ιδέα προκύπτει από την ανάγκη να σχεδιαστεί μία οντολογία η οποία θα μπορεί να αποθηκεύσει τη γνώση σχετικά με τις ικανότητες φοιτητών (user profile) σε σχέση με ένα συγκεκριμένο εκπαιδευτικό αντικείμενο (ΠΛΗ23 – Τηλεματική, Διαδίκτυο του Ελληνικού Ανοικτού Πανεπιστημίου (ΕΑΠ) )η οποία έχει πολύ συγκεκριμένη ύλη και 4 υποχρεωτικές γραπτές εργασίες ανά έτος). Στη συνέχεια παρουσιάζονται τα αποτελέσματα μελέτης της ανάλυσης των δεδομένων των φοιτητών με τεχνικές εξόρυξης γνώσης. Η εύρεση των κανόνων πραγματοποιήθηκε μέσω του εργαλείου Weka. Το αποτέλεσμα που προέκυψε είναι μία βάση γνώσης βάσει της οποίας γίνεται έγκαιρα και έγκυρα η πρόβλεψη της συμπεριφοράς του φοιτητή, δηλαδή αν θα καταφέρει να ολοκληρώσει επιτυχώς ή μη τη Θεματική Ενότητα που έχει αναλάβει στο ΕΑΠ, ώστε ο διδάσκων να μπορεί από πολύ νωρίς να υποστηρίξει το φοιτητή με επιπλέον υλικό αν απαιτείται.
The rapid spread of Internet has caused significant changes in many sectors of the economy and society worldwide. From those changes could not be left out of education. With the rapid development of information technologies and technology, a new form of education appears, e-learning (distance education), which revolutionized the educational process. Furthermore, while the World Wide Web gradually transforms into Semantic Web, new standards and models (XML, RDF, OWL) are evolving in order to launch this inquiry. The storage, presentation, transmission and search of information according to those standards open up new horizons in the utilization of the Web. Ontologies are increasingly get used for knowledge representation. A large ontology contains useful data for a system of distance education, deserves someone to investigate the "hidden knowledge", i.e. to discover possible associations or to find patterns or forms that are repeated or extreme events. This thesis is a demonstration of technology for accurate and timely prediction of the performance of students in a system of distance education. The basic idea was to design an ontology that can store knowledge about the students’ skills (user profile) in relation to a specific educational purpose (PLI23 - Telematics, Internet of the Hellenic Open University, which has a very specific matter and 4 mandatory projects per year). Then we present the results of a study analyzing student data mining techniques (data mining-classification). The discovery rules took place via the tool Weka. The result is a knowledge base which is the appropriate tool (Interface teacher) may provide that a student needs on a particular topic (in addition to material help from the teacher), etc.
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Book chapters on the topic "J48 algorithm"

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Khruahong, Sanya, and Pirayu Tadkerd. "Analysis of Scholarship Consideration Using J48 Decision Tree Algorithm for Data Mining." In Lecture Notes in Computer Science, 230–38. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60816-3_26.

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Kumar, V., A. K. Verma, and S. Sarangi. "Fault Diagnosis of Single-Stage Bevel Gearbox by Energy Operator and J48 Algorithm." In Lecture Notes in Mechanical Engineering, 231–39. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9199-0_21.

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Gangabissoon, Tanveer, Amaan Nathoo, Rakshay Ramhith, Bhooneshwar Gopee, and Girish Bekaroo. "Improving Effectiveness of Honeypots: Predicting Targeted Destination Port Numbers During Attacks Using J48 Algorithm." In Smart and Sustainable Engineering for Next Generation Applications, 225–34. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18240-3_21.

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Mukherjee, Subrata, Rishubh Kaushal, Vikash Kumar, and Somnath Sarangi. "A Novel Approach of Gearbox Fault Diagnosis by Using Time Synchronous Averaging and J48 Algorithm." In Lecture Notes in Mechanical Engineering, 927–35. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5463-6_82.

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Tien Bui, Dieu, Biswajeet Pradhan, Inge Revhaug, and Chuyen Trung Tran. "A Comparative Assessment Between the Application of Fuzzy Unordered Rules Induction Algorithm and J48 Decision Tree Models in Spatial Prediction of Shallow Landslides at Lang Son City, Vietnam." In Society of Earth Scientists Series, 87–111. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05906-8_6.

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Bhat, Prashant, and Pradnya Malaganve. "Effect of J48 and LMT Algorithms to Classify Movies in the Web—A Comparative Approach." In Innovations in Computer Science and Engineering, 547–53. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4543-0_58.

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Singh, Mandeep, Navjyot Kaur, Amandeep Kaur, and Gaurav Pushkarna. "A Comparative Evaluation of Mining Techniques to Detect Malicious Node in Wireless Sensor Networks." In Sensor Technology, 881–94. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2454-1.ch042.

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Wireless sensor networks have gained attention over the last few years and have significant applications for example remote supervising and target watching. They can communicate with each other though wireless interface and configure a network. Wireless sensor networks are often deployed in an unfriendly location and most of time it works without human management; individual node may possibly be compromised by the adversary due to some constraints. In this manner, the security of a wireless sensor network is critical. This work will focus on evaluation of mining techniques that can be used to find malicious nodes. The detection mechanisms provide the accuracy of the classification using different algorithm to detect the malicious node. Pragmatically the detection accuracy of J48 is 99.17%, Random Forest is 80.83%, NF Tree is 81.67% and BF Tree is 72.33%. J48 have very high detection accuracy as compared with BF Tree, NF Tree Random Forest.
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Franco, Edgar Cossio, Jorge Alberto Delgado Cazarez, and Carlos Alberto Ochoa Ortiz Zezzatti. "Implementation of an Intelligent Model Based on Machine Learning in the Application of Macro-Ergonomic Methods in a Human Resources Process Based on ISO 12207." In Advanced Macroergonomics and Sociotechnical Approaches for Optimal Organizational Performance, 261–85. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7192-6.ch014.

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The objective of this chapter is to implement an intelligent model based on machine learning in the application of macro-ergonomic methods in human resources processes based on the ISO 12207 standard. To achieve the objective, a method of constructing a Java language algorithm is applied to select the best prospect for a given position. Machine learning is done through decision trees and algorithm j48. Among the findings, it is shown that the model is useful in identifying the best profiles for a given position, optimizing the time in the selection process and human resources as well as the reduction of work stress.
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Mishra, Nilamadhab, and Johny Melese Samuel. "Towards Integrating Data Mining With Knowledge-Based System for Diagnosis of Human Eye Diseases." In Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning, 470–85. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-2742-9.ch024.

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The eye is the most important sensory organ of vision function. But some eye diseases can lead to vision loss, so it is important to identify and treat eye disease as early as possible. Eye care professionals can help protect their patients from vision loss or blindness by recognizing common eye diseases and recommending for an eye exam. Eye diseases with early detection, treatment, and appropriate follow-up care, vision loss, and blindness from eye disease can be prevented or delayed. In this study, rule-based eye disease identification and advising the knowledge-based system are projected. The projected system is targeting using hidden knowledge extracted by employing the extraction algorithm of data mining. To identify the best prediction model for the diagnosis of eye disease, four experiments for four classification algorithms were performed. Finally, the researchers decided to use the rules of the J48 pruned classification algorithm for further use in the development of a knowledge base of KBS because it exhibited better performance with a 98.5% evaluation result.
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Latif, Rana Muhammad Amir, Javed Ferzund, Muhammad Farhan, N. Z. Jhanjhi, and Muhammad Umer. "A Case Study of Career Counseling for ICT." In ICT Solutions for Improving Smart Communities in Asia, 162–84. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-7114-9.ch008.

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In the education system, the students may find counselors, but student-to-counselor ratio is higher, which forces us to implement an automated system for the guidance of the students. Career counseling can be useful for students to evaluate their careers and select the best direction for the future. This chapter aims to explore, develop, and implement the effective means of analyzing student career counseling, guidelines, and decision making. The authors have developed a realistic dataset from a different mindset of students. The research started once the student provides the machine input about the individual choices about taking admission for matriculation, intermediate, and or short course. The machine learning algorithms like logistic model tree, naïve Bayes, J48, and random forest are used to predict career options. In evaluated results, they found the best algorithm based on the accuracy of kappa statistics, mean absolute error, and correctly classified or incorrectly classified for career-related problems.
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Conference papers on the topic "J48 algorithm"

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Bhargava, Neeraj, Sakshi Sharma, Renuka Purohit, and Pramod Singh Rathore. "Prediction of recurrence cancer using J48 algorithm." In 2017 2nd International Conference on Communication and Electronics Systems (ICCES). IEEE, 2017. http://dx.doi.org/10.1109/cesys.2017.8321306.

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Pradeep, K. R., and N. C. Naveen. "Predictive analysis of diabetes using J48 algorithm of classification techniques." In 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I). IEEE, 2016. http://dx.doi.org/10.1109/ic3i.2016.7917987.

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Saravanan, N., and V. Gayathri. "Classification of dengue dataset using J48 algorithm and ant colony based AJ48 algorithm." In 2017 International Conference on Inventive Computing and Informatics (ICICI). IEEE, 2017. http://dx.doi.org/10.1109/icici.2017.8365302.

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Maliha, Shanjida Khan, Tajul Islam, Simanta Kumar Ghosh, Helal Ahmed, Md Rafsun Jony Mollick, and Romana Rahman Ema. "Prediction of Cancer Using Logistic Regression, K-Star and J48 algorithm." In 2019 4th International Conference on Electrical Information and Communication Technology (EICT). IEEE, 2019. http://dx.doi.org/10.1109/eict48899.2019.9068790.

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Maliha, Shanjida Khan, Romana Rahman Ema, Simanta Kumar Ghosh, Helal Ahmed, Md Rafsun Jony Mollick, and Tajul Islam. "Cancer Disease Prediction Using Naive Bayes,K-Nearest Neighbor and J48 algorithm." In 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT). IEEE, 2019. http://dx.doi.org/10.1109/icccnt45670.2019.8944686.

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Kurniabudi, Abdul Harris, Albertus Edward Mintaria, Darmawijoyo, Deris Stiawan, Mohd Yazid bin Idris, and Rahmat Budiarto. "Improving the Anomaly Detection by Combining PSO Search Methods and J48 Algorithm." In 2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI). IEEE, 2020. http://dx.doi.org/10.23919/eecsi50503.2020.9251872.

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Gupta, Daya, Rajni Jindal, Vaibhav Verma, Dilpreet Singh Kohli, and Shashi Kant Sharma. "Predicting student’s behavior in education using J48 algorithm analysis tools in WEKA environment." In Annual International Academic Conference on Business Intelligence and Data Warehousing. Global Science and Technology Forum, 2010. http://dx.doi.org/10.5176/978-981-08-6308-1_51.

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Daud, NurrAina, Nor Laila Mohd Noor, Syed Ahmad Aljunid, Nurulhuda Noordin, and Nur Islami Mohd Fahmi Teng. "Predictive Analytics: The Application of J48 Algorithm on Grocery Data to Predict Obesity." In 2018 IEEE Conference on Big Data and Analytics (ICBDA). IEEE, 2018. http://dx.doi.org/10.1109/icbdaa.2018.8629623.

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Adnan, Masrur, Riyanarto Sarno, and Kelly Rossa Sungkono. "Sentiment Analysis of Restaurant Review with Classification Approach in the Decision Tree-J48 Algorithm." In 2019 International Seminar on Application for Technology of Information and Communication (iSemantic). IEEE, 2019. http://dx.doi.org/10.1109/isemantic.2019.8884282.

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Kolahkaj, Maral, and Madjid Khalilian. "A recommender system by using classification based on frequent pattern mining and J48 algorithm." In 2015 2nd International Conference on Knowledge-Based Engineering and Innovation (KBEI). IEEE, 2015. http://dx.doi.org/10.1109/kbei.2015.7436143.

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