Relevant bibliographies by topics / WEKA- Waikato Environment for Knowledge Analysis

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Academic literature on the topic 'WEKA- Waikato Environment for Knowledge Analysis'

Author: Grafiati
Published: 3 June 2025
Last updated: 13 July 2025

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Journal articles on the topic "WEKA- Waikato Environment for Knowledge Analysis"

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Altayeb, Muneera, and Areen Arabiat. "Enhancing stroke prediction using the waikato environment for knowledge analysis." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (2024): 3010. http://dx.doi.org/10.11591/ijai.v13.i3.pp3010-3017.

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<p>State-of-the-art data mining tools incorporate advanced machine learning (ML) and artificial intelligence (AI) models, and it is widely used in classification, association rules, clustering, prediction, and sequential models. Data mining is important for the process of diagnosing and predicting diseases in the early stages, and this contributes greatly to the development of the health services sector. This study utilized classification to predict the stroke of a sample of the patient dataset that was taken from Kaggle. The classification model was created using the data mining program waikato environment for knowledge analysis (WEKA). This data mining tool helped identify individuals most at risk of stroke based on analysis of features extracted from the patient’s dataset. These features were used in classification processes according to the naive Bayes (NB), random forest (RF), support vector machine (SVM), and multi-layer perceptron (MLP) algorithms. Analysis of the classification results of the previous algorithms showed that the SVM outperformed other algorithms in terms of accuracy (94.4%), sensitivity (100%), and F-measure (97.1%). However, the NB algorithm had the best performance in terms of precision (95.7%).</p>
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Muneera, Altayeb, and Arabiat Areen. "Enhancing stroke prediction using the waikato environment for knowledge analysis." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (2024): 3010–17. https://doi.org/10.11591/ijai.v13.i3.pp3010-3017.

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State-of-the-art data mining tools incorporate advanced machine learning (ML) and artificial intelligence (AI) models, and it is widely used in classification, association rules, clustering, prediction, and sequential models. Data mining is important for the process of diagnosing and predicting diseases in the early stages, and this contributes greatly to the development of the health services sector. This study utilized classification to predict the stroke of a sample of the patient dataset that was taken from Kaggle. The classification model was created using the data mining program waikato environment for knowledge analysis (WEKA). This data mining tool helped identify individuals most at risk of stroke based on analysis of features extracted from the patient’s dataset. These features were used in classification processes according to the naive Bayes (NB), random forest (RF), support vector machine (SVM), and multi-layer perceptron (MLP) algorithms. Analysis of the classification results of the previous algorithms showed that the SVM outperformed other algorithms in terms of accuracy (94.4%), sensitivity (100%), and F-measure (97.1%). However, the NB algorithm had the best performance in terms of precision (95.7%).
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BAKR, DHER I., JASIM Al-KHALIDI, and HAZIM NOMAN ABED. "Estimation of climatological parameters using ANN and WEKA models in Diyala Governorate, Iraq." Journal of Agrometeorology 27, no. 2 (2025): 210–15. https://doi.org/10.54386/jam.v27i2.2877.

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Artificial Neural Networks (ANN) and Waikato Environment for Knowledge Analysis (WEKA) model were used to estimate the climatic parameters viz. minimum temperature (Tmin), maximum temperature (Tmax), relative humidity (RH), wind velocity (WV) using the time series of monthly data for the period of 1980 to 2022. It was found that the estimation of the climate parameters using the two methods (WEKA and ANN) obtained acceptable values of correlation (R2) and error standards (RMSE and MAE) between the observed and estimated values, but they differed in accuracy. The WEKA method obtained better values in the estimation of the Tmin component than ANN while the estimation of the Tmax, RH, WV, the ANN method was better than the WEAK model in the estimation of the parameters.
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Halawa, Sonibe, and Rita Hamdani. "Implementation of the K-Means Clustering Method in Data Grouping Sales In Asia Africa Dentures Dental." Journal Of Computer Networks, Architecture and High Performance Computing 2, no. 2 (2019): 286–91. http://dx.doi.org/10.47709/cnapc.v2i2.431.

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Data mining can be applied to explore the added value of a set of data in the form of knowledge that had been unknown to them manually. There are several techniques used dala mining eyes, one satuteknik data mining is clustering. Clustering can be used for grouping to something. As can group sales data that is most desirable, and others. Examples of companies engaged in the sale is a dental african Asia. Asia Africa Dental is one area of business engaged in the sale of false teeth. Asia Africa Dental these every day to meet the needs of consumers. But Asia Africa Dental lacking in reviewing products sold. What products are needed consumer and data storage is less effective. Thus the need for a system that can support the company in taking decisions quickly and precisely. So in this study, the authors used the application of K-Means Clustering method. To facilitate the author in analyzing the K-Means Clustering The author using the application Weka (Waikato Environment for Knowledge Analysis) .. The result of the calculation Weka (Waikato Environment for Knowledge Analysis) is inserted into the Visual Basic .Net.
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B. Deepthi, K. V. Siva Prasad Reddy, and B. S. Jubedha. "Analysis of Classification Algorithms in Drug Classification Using Weka Data Mining Tool." December 2022 4, no. 4 (2022): 246–60. http://dx.doi.org/10.36548/jtcsst.2022.4.003.

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Classification algorithms have been found to produce better results in terms of performance and accuracy, when used with drug observation dataset. Three machine learning algorithms such as J48, Naive Bayes, and K-Nearest Neighbor are compared using Waikato Environment for Knowledge Analysis (WEKA) in this paper. In addition, these three well-known classification methods are evaluated based on different Quality of Service parameters to find the best fit classifier for the design of the model. The analysis procedure of dataset and the performance indicators are discussed. These results help to draw a conclusion about which of the three algorithms is the best for drug classification.
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Piekoszewski, Jakub. "Fault diagnosis of roller bearings using selected classifiers." AUTOBUSY – Technika, Eksploatacja, Systemy Transportowe 19, no. 12 (2018): 597–601. http://dx.doi.org/10.24136/atest.2018.460.

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Minor roller bearing damage may lead to serious failures of the de-vice. Thus, it is very important to detect such damage as early as possible to prevent further damage. This paper presents a selection of several theoretical tools from the field of artificial intelligence and their application in roller bearings fault classification. The considered tools are: k-nearest neighbour algorithm, decision tree, support vector machine, feed forward neural network (multilayer perceptron), Bayesian network and neural network with radial basis functions. All numerical experiments presented in the paper were performed with the use of real-world dataset and WEKA (Waikato Environment for Knowledge Analysis) software, available at the server of the University of Waikato.
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Dr., Mahmood Ali Mirza, and Ali Mirza Faheem. "Classification Approach for Analysis of Weather Dataset with Different Training Strategies." Recent Trends in Information Technology and its Application 8, no. 2 (2025): 4–13. https://doi.org/10.5281/zenodo.14997848.

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<em>Due to its extensive content, data mining has emerged as one of the newest areas of study. Finding hidden patterns in a database or other information repository is accomplished through data mining. In order to get knowledge from the patterns, these information is required. The primary objective is to find knowledge from the data. In order to ascertain the playing condition based on the present temperature measurements, we employ a data mining technique in this study termed classification. One effective method for grouping the dataset's characteristics into distinct classes is the classification technique. We employ categorization algorithms such as Random Tree, REP Tree, and Decision Tree (J48) in our methodology. The effectiveness of different categorization methods is then contrasted. We employ a set of open source machine learning algorithms called WEKA (Waikato Environment for Knowledge Analysis) as our tool. </em>
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Wallner, Christoph, Jane Hurst, Björn Behr, Mohammad Abu Tareq Rony, Anthony Barabás, and Gill Smith. "Fanconi Anemia: Examining Guidelines for Testing All Patients with Hand Anomalies Using a Machine Learning Approach." Children 9, no. 1 (2022): 85. http://dx.doi.org/10.3390/children9010085.

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Background: This study investigated the questionable necessity of genetic testing for Fanconi anemia in children with hand anomalies. The current UK guidelines suggest that every child with radial ray dysplasia or a thumb anomaly should undergo further cost intensive investigation for Fanconi anemia. In this study we reviewed the numbers of patients and referral patterns, as well as the financial and service provision implications UK guidelines provide. Methods: Over three years, every patient with thumb or radial ray anomaly referred to our service was tested for Fanconi Anemia. CART Analysis and machine learning techniques using Waikato Environment for Knowledge Analysis were applied to evaluate single clinical features predicting Fanconi anemia. Results: Youden Index and Predictive Summary Index (PSI) scores suggested no clinical significance of hand anomalies associated with Fanconi anemia. CART Analysis and attribute evaluation with Waikato Environment for Knowledge Analysis (WEKA) showed no single feature predictive for Fanconi anemia. Furthermore, none of the positive Fanconi anemia patients in this study had an isolated upper limb anomaly without presenting other features of Fanconi anemia. Conclusion: As a conclusion, this study does not support Fanconi anemia testing for isolated hand abnormalities in the absence of other features associated with this blood disease.
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Ramesh, D., Syed Pasha, and G. Roopa. "A Comparative Analysis of Classification Algorithms on Weather Dataset Using Data Mining Tool." Oriental journal of computer science and technology 10, no. 04 (2017): 788–92. http://dx.doi.org/10.13005/ojcst/10.04.13.

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Data mining has become one of the emerging fields in research because of its vast contents. Data mining is used for finding hidden patterns in the database or any other information repository. This information is necessary to generate knowledge from the patterns. The main task is to extract knowledge out of the information. In this paper we use a data mining technique called classification to determine the playing condition based on the current temperature values. Classification technique is a powerful way to classify the attributes of the dataset into different classes. In our approach we use classification algorithms like Decision Tree (J48), REP Tree and Random Tree. Then we compare the efficiencies of these classification algorithms. The tool we use for this approach is WEKA (Waikato Environment for Knowledge Analysis) a collection of open source machine learning algorithms.
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Venkata Ramana Moorthy, P., B. Sarojamma, and S. Venkatramana Reddy. "Development of Machine Learning models using WEKA for Atmospheric Data." Journal of Physics: Conference Series 2312, no. 1 (2022): 012080. http://dx.doi.org/10.1088/1742-6596/2312/1/012080.

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Abstract There are number of Atmospheric variables such as rainfall, temperature, wind speed, humidity, visibility, Wind gust, Precipitation, etc. For learning covariance of machines by principles, practical and probabilistic approaches are made using Gaussian process. In this paper by taking visibility, time with date, temperature as independent or responding variables and wind speed as dependent or response variable, we fit Gaussian process model. K star is an instance based classifier that classifies the data. RBF network is used for data and is similar structure of Gaussian process but it uses clustering method with weight parameters. Additive regression classifies the variables by using Decision Stump. Decision Tree Regression improves the model by removing the decisions of the tree that are not important in classification. We fit different Waikato Environment for Knowledge Analysis (WEKA) models for atmospheric data and which model is the best based on RMSE values.
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Dissertations / Theses on the topic "WEKA- Waikato Environment for Knowledge Analysis"

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Κανάς, Βασίλειος. "Ταξινόμηση καρκινικών όγκων εγκεφάλου με χρήση μεθόδων μηχανικής μάθησης". Thesis, 2010. http://nemertes.lis.upatras.gr/jspui/handle/10889/4579.

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Σκοπός αυτής της διπλωματικής εργασίας είναι να ερευνηθούν μέθοδοι μηχανικής μάθησης για την ταξινόμηση διαφόρων τύπων καρκινικών όγκων εγκεφάλου με χρήση δεδομένων μαγνητικής τομογραφίας. Η διάγνωση του τύπου του καρκίνου είναι σημαντική για τον κατάλληλο σχεδιασμό της θεραπείας. Γενικά η ταξινόμηση καρκινικών όγκων αποτελείται από επιμέρους βήματα, όπως καθορισμός των περιοχών ενδιαφέροντος (ROIs), εξαγωγή χαρακτηριστικών, επιλογή χαρακτηριστικών, ταξινόμηση. Η εργασία αυτή εστιάζει στα δύο τελευταία βήματα ώστε να εξαχθεί μια γενική επισκόπηση της επίδρασης των εκάστοτε μεθόδων όσον αφορά την ταξινόμηση των διαφόρων όγκων. Τα εξαγόμενα χαρακτηριστικά περιλαμβάνουν χαρακτηριστικά φωτεινότητας και περιγράμματος από συμβατικές τεχνικές απεικόνισης μαγνητικής τομογραφίας (Τ2, Τ1 με έγχυση σκιαγραφικού, Flair,Τ1) καθώς και μη συμβατικές τεχνικές (Μαγνητική τομογραφία αιματικής διήθησης ). Για την επιλογή των χαρακτηριστικών χρησιμοποιήθηκαν διάφορες μέθοδοι φιλτραρίσματος, όπως CFSsubset, wrapper, consistency σε συνδυασμό με μεθόδους αναζήτησης, όπως scatter, best first, greedy stepwise, με τη βοήθεια του πακέτου Waikato Environment for Knowledge Analysis (WEKA). Οι μέθοδοι εφαρμόστηκαν σε 101 ασθενείς με καρκινικούς όγκους εγκεφάλου οι οποίοι είχαν διαγνωστεί ως μετάσταση (24), μηνιγγίωμα (4), γλοίωμα βαθμού 2 (22), γλοίωμα βαθμού 3 (17) ή γλοίωμα βαθμού 4 (34) και επαληθεύτηκαν με τη στρατηγική του αχρησιμοποίητου παραδείγματος (Leave One Out-LOO)<br>The objective of this study is to investigate the use of pattern classification methods for distinguishing different types of brain tumors, such as primary gliomas from metastases, and also for grading of gliomas. A computer-assisted classification method combining conventional magnetic resonance imaging (MRI) and perfusion MRI is developed and used for differential diagnosis. The characterization and accurate determination of brain tumor grade and type is very important because it influences and specifies patient's treatment planning. The proposed scheme consists of several steps including ROI definition, feature extraction, feature selection and classification. The extracted features include tumor shape and intensity characteristics. Features subset selection is performed using two filtering methods, correlation-based feature selection method and consistency method, and a wrapper approach in combination with three different search algorithms (best first, greedy stepwise and scatter). These methods are implemented using the assistance of the WEKA software [20]. The highest binary classification accuracy assessed by leave-one-out (LOO) cross-validation on 102 brain tumors, is 94.1% for discrimination of metastases from gliomas, and 91.3% for discrimination of high grade from low grade neoplasms. Multi-class classification is also performed and 76.29% accuracy achieved.
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Book chapters on the topic "WEKA- Waikato Environment for Knowledge Analysis"

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"Waikato Environment for Knowledge Analysis (WEKA) a Perspective Consideration of Multiple Machine Learning Classification Algorithms and Applications." In Applied Software Development with Python & Machine Learning by Wearable & Wireless Systems for Movement Disorder Treatment via Deep Brain Stimulation. WORLD SCIENTIFIC, 2021. http://dx.doi.org/10.1142/9789811235962_0006.

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Gupta Jaiprakash and Patrick Jon. "Automated validation of patient safety clinical incident classification: Macro analysis." In Studies in Health Technology and Informatics. IOS Press, 2013. https://doi.org/10.3233/978-1-61499-266-0-52.

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Patient safety is the buzz word in healthcare. Incident Information Management System (IIMS) is electronic software that stores clinical mishaps narratives in places where patients are treated. It is estimated that in one state alone over one million electronic text documents are available in IIMS. In this paper we investigate the data density available in the fields entered to notify an incident and the validity of the built in classification used by clinician to categories the incidents. Waikato Environment for Knowledge Analysis (WEKA) software was used to test the classes. Four statistical classifier based on J48, Na&amp;iuml;ve Bayes (NB), Na&amp;iuml;ve Bayes Multinominal (NBM) and Support Vector Machine using radial basis function (SVM_RBF) algorithms were used to validate the classes. The data pool was 10,000 clinical incidents drawn from 7 hospitals in one state in Australia. In first part of the study 1000 clinical incidents were selected to determine type and number of fields worth investigating and in the second part another 5448 clinical incidents were randomly selected to validate 13 clinical incident types. Result shows 74.6% of the cells were empty and only 23 fields had content over 70% of the time. The percentage correctly classified classes on four algorithms using categorical dataset ranged from 42 to 49%, using free-text datasets from 65% to 77% and using both datasets from 72% to 79%. Kappa statistic ranged from 0.36 to 0.4. for categorical data, from 0.61 to 0.74. for free-text and from 0.67 to 0.77 for both datasets. Similar increases in performance in the 3 experiments was noted on true positive rate, precision, F-measure and area under curve (AUC) of receiver operating characteristics (ROC) scores. The study demonstrates only 14 of 73 fields in IIMS have data that is usable for machine learning experiments. Irrespective of the type of algorithms used when all datasets are used performance was better. Classifier NBM showed best performance. We think the classifier can be improved further by reclassifying the most confused classes and there is scope to apply text mining tool on patient safety classifications.
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Kalaiselvi, B. "Machine Learning-Driven AI System for Automated Flow Control." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3373-0330-7.ch004.

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This chapter proposes a new concept of Designing an automatic flow controller that is based on AI and using selected machine learning algorithms. The optimum smart controller uses machine learning concept by learning the knowledge often convectional flow controller. The acquired data set is used to train the built model using ML algorithm using a software tool called Weka 3.8.5 is an open-source software suite developed at the University of Waikato in New Zealand. A model with optimum performance is built, the performance criteria analysis like mean square error, root means square error. The Weka software is used to build a model with 60% of dataset and evaluated with 40% of data set. The evaluated result is Root mean square error ranges from 0.96 to 0.0028. The correlation coefficient is equal to 1 and the percentage Relative absolute error is 0.0467% for simple linear regression and 15.869% for Decision table ML algorithms. Hence the built model functions as smart flow controller and the output of this controller will be imitating the conventional PID controller.
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Amutenya, Jacobine Taukondjele, and Gerald (Augusto) Corzo Perez. "Infrastructures for Data in the Context of Flow Forecasting Using Artificial Neural Network Model for Okavango River in Namibia." In Advances in Environmental Engineering and Green Technologies. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-7998-0163-4.ch007.

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A number of evolutions on data collection and sharing have been published. Countries have collected data, but lack of access and complexity to implement these technologies has limitations. HydroServer Lite, a web-based server for sharing water data, helps to address the need of data sharing and storing in a standard format. Namibia Hydrological Services has no common online system for storing and sharing of water data. This study extends the research on HSL features as data system linked to online ANN forecasting model. This is done by implementing a Namibian HSL using real-time connection to the database to operate in real-time tools developed to visualize and fill in missing data. Lastly, a model was build using Waikato Environment for Knowledge Analysis. Results of the best model obtained are coded in hypertext preprocessor with near real-time data to provide continuous forecast. Linking data system for water resource management in a standard format is practical and promising.
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Conference papers on the topic "WEKA- Waikato Environment for Knowledge Analysis"

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Sen, Anupam. "Data Mining and Principal Component Analysis on Coimbra Breast Cancer Dataset." In Intelligent Computing and Technologies Conference. AIJR Publisher, 2021. http://dx.doi.org/10.21467/proceedings.115.5.

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Machine Learning (ML) techniques play an important role in the medical field. Early diagnosis is required to improve the treatment of carcinoma. During this analysis Breast Cancer Coimbra dataset (BCCD) with ten predictors are analyzed to classify carcinoma. In this paper method for feature selection and Machine learning algorithms are applied to the dataset from the UCI repository. WEKA (“Waikato Environment for Knowledge Analysis”) tool is used for machine learning techniques. In this paper Principal Component Analysis (PCA) is used for feature extraction. Different Machine Learning classification algorithms are applied through WEKA such as Glmnet, Gbm, ada Boosting, Adabag Boosting, C50, Cforest, DcSVM, fnn, Ksvm, Node Harvest compares the accuracy and also compare values such as Kappa statistic, Mean Absolute Error (MAE), Root Mean Square Error (RMSE). Here the 10-fold cross validation method is used for training, testing and validation purposes.
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Daw, Swarnali, and Rohini Basak. "Machine Learning Applications Using Waikato Environment for Knowledge Analysis." In 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC). IEEE, 2020. http://dx.doi.org/10.1109/iccmc48092.2020.iccmc-00065.

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Azeem, Abdul, Gaurav Kumar, and Hasmat Malik. "Application of waikato environment for knowledge analysis based artificial neural network models for wind speed forecasting." In 2016 IEEE 7th Power India International Conference (PIICON). IEEE, 2016. http://dx.doi.org/10.1109/poweri.2016.8077352.

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Basha, Md Shaik Amzad, Peerzadah Mohammad Oveis, R. Pavan Kumar Raju, and M. Martha Sucharitha. "Gems of Prediction: From Clarity to Carats - Unveiling Diamond Prices with Machine Learning in Waikato Environment for Knowledge Analysis." In 2024 International Conference on Distributed Computing and Optimization Techniques (ICDCOT). IEEE, 2024. http://dx.doi.org/10.1109/icdcot61034.2024.10515908.

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Ruecker, Johannes, Patrick Peper, Ulrich Bochtler, and Peter Klar. "Optical identification of valuable materials on printed circuit board assemblies based on sensor fusion." In OCM 2017 - 3rd International Conference on Optical Characterization of Materials. KIT Scientific Publishing, 2017. http://dx.doi.org/10.58895/ksp/1000063696-5.

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Increasing waste of electrical and electronic equipment (WEEE) is a major challenge of today’s society. It affects society in several ways and needs to be solved to prevent loss of important materials and to reduce environmental contamination. Tackling this challenge requires affordable and reliable technological solutions, which enable recycling in a cheap and easy manner. One step to be taken is the recycling of printed circuit board assemblies (PCBAs), which are common in many of the high level devices such as computers or mobile phones. PCBAs include a huge amount of different components and thus belong to the most heterogeneous waste a recycler has to handle. The approach described in this paper is directed towards a reduction of the diversity by identification of specific components on the PCBA, which contain specific materials of interest. These components are then available for automated, selective disassembly. In this contribution classification results of a special descriptor developed for printed circuit boards are shown for three different classification algorithms. The descriptor is based on rather discriminative simple geometric and color features. The necessary data is obtained by a pilot setup, which is also described briefly, and processed withWaikato Environment for Knowledge Analysis (WEKA), a tool for processing big data.
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Reports on the topic "WEKA- Waikato Environment for Knowledge Analysis"

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Sottilare, Robert A. Conducting an Analysis of a Qualitative Dataset Using the Waikato Environment for Knowledge Analysis (WEKA). Defense Technical Information Center, 2015. http://dx.doi.org/10.21236/ada613659.

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