Waikato Environment For Knowledge Analysis Research Articles

Benchmarking Datasets from Malaria Cytotoxic T-cell Epitopes Using Machine Learning Approach.

Background:Epitope prediction remains a major challenge in malaria due to the unique parasite biology, in addition to rapidly evolving parasite sequence variation in Plasmodium species. Although several models for epitope prediction exist, they are not useful in Plasmodium specific epitope development. Hence, it was proposed to use machine learning based methods to develop a peptide sequence based epitope predictor specific for malaria.Methods:Model datasets were developed and performance was tested using various machine learning algorithms. Machine learning classifiers were trained on epitope data using sequence features and comparison of amino acid physicochemical properties was done to yield a valid prediction model.Results:The findings from the analysis reveal that the model developed using selected classifiers after preprocessing by Waikato Environment for Knowledge Analysis (WEKA) performed better than other methods. The datasets for benchmarks of performance are deposited in the repository https://github.com/githubramaadiga/epitope_dataset .Conclusion:The study is the first in-silico study on benchmarking Plasmodium cytotoxic T cell epitope datasets using machine learning approach. The peptide based predictors have been used for the first time to classify cytotoxic T cell epitopes in malaria. Algorithms has been evaluated using real datasets from malaria to obtain the model.

Comparative Evaluation of Academic Performance in Waikato Environment for Knowledge Analysis Using Multiple Classification Algorithms

Comparative Evaluation of Academic Performance in Waikato Environment for Knowledge Analysis Using Multiple Classification Algorithms

Optimised Surface Condition Classification of Flexible Road Pavement Using AutoWEKA Model

Abstract The development of pavement management tools using intelligent algorithms requires a robust form of data mining – data classification for efficient and reliable analysis. The aim of this study is to investigate and optimally classify the surface condition of flexible road pavement along 60 km length of the Zaria – Kaduna Federal Highway in Northern Nigeria for maintenance decision. The study used data mining technique for the classification of pavement surface condition into good, satisfactory, fair, poor, very poor, serious or failed. A field survey was carried out to examine the surface area and length of various surface defects such as cracks, potholes, rutting and edge failure within Chainages measuring 200 meters apart, which was used to compute the Pavement Condition Index (PCI) values and section classification in accordance with procedures stated in ASTM D6433. The AutoWEKA model of Waikato Environment for Knowledge Analysis (WEKA) software was used to optimally classify the surface condition of the highway. Results indicated that 79.67% of the 300 total instances considered by the model were correctly classified while 20.33% of the instances were incorrectly classified. The optimum surface condition classification showed that worse pavement surface conditions of the sampled site were ‘Poor’, ‘Very Poor’ and ‘Failed’ at 77 (32.22%), 51 (21.34%) and 54 (22.59%) instances respectively of the correctly classified 239 instances out of the 300 total instances sampled. Based on its present condition, 76.15% of the road segment was bad. The rehabilitation or reconstruction of the Zaria – Kaduna Federal Highway was therefore recommended for improved condition and optimum performance.

Effective Estimation of Hourly Global Solar Radiation Using Machine Learning Algorithms

The precise estimation of solar radiation is of great importance in solar energy applications with respect to installation and capacity. In estimate modelling on selected target locations, various computer-based and experimental methods and techniques are employed. In the present study, the Multilayer Feed-Forward Neural Network (MFFNN), K -Nearest Neighbors ( K -NN), a Library for Support Vector Machines (LibSVM), and M5 rules algorithms, which are among the Machine Learning (ML) algorithms, were used to estimate the hourly average solar radiation of two geographic locations on the same latitude. The input variables that had the most impact on solar radiation were identified and grouped as a result of 29 different applications that were developed by using 6 different feature selection methods with Waikato Environment for Knowledge Analysis (WEKA) software. Estimation models were developed by using the selected data groups and all input variables for each target location. The results show that the estimations developed with the feature selection method were more successful for target locations, and the radiation potentials were similar. The performance of the estimation models was evaluated by comparing each model with different statistical indicators and with previous studies. According to the RMSE, MAE, R 2 , and SMAPE statistical scales, the results of the most successful estimation models that were developed with MFFNN were 0.0508-0.0536, 0.0341-0.0352, 0.9488-0.9656, and 7.77%-7.79%, respectively.

New Interfaces and Approaches to Machine Learning When Classifying Gestures within Music.

Interactive music uses wearable sensors (i.e., gestural interfaces—GIs) and biometric datasets to reinvent traditional human–computer interaction and enhance music composition. In recent years, machine learning (ML) has been important for the artform. This is because ML helps process complex biometric datasets from GIs when predicting musical actions (termed performance gestures). ML allows musicians to create novel interactions with digital media. Wekinator is a popular ML software amongst artists, allowing users to train models through demonstration. It is built on the Waikato Environment for Knowledge Analysis (WEKA) framework, which is used to build supervised predictive models. Previous research has used biometric data from GIs to train specific ML models. However, previous research does not inform optimum ML model choice, within music, or compare model performance. Wekinator offers several ML models. Thus, we used Wekinator and the Myo armband GI and study three performance gestures for piano practice to solve this problem. Using these, we trained all models in Wekinator and investigated their accuracy, how gesture representation affects model accuracy and if optimisation can arise. Results show that neural networks are the strongest continuous classifiers, mapping behaviour differs amongst continuous models, optimisation can occur and gesture representation disparately affects model mapping behaviour; impacting music practice.

311 A brief overview, comparison and practical applications of machine learning models

Abstract This is a hands-on workshop offered as a pre-conference training opportunity for researchers interested in applying machine learning techniques to animal science datasets with the purpose of classifying, clustering, performing linear and non-linear regressions or selecting a subset of features relevant to further studies. The objective of this workshop is to provide the audience with a way to formulate a problem such that it will be solvable by machine learning techniques and apply an exploratory analysis of various machine learning on different datasets. The workshop is structured in a hands-on format and includes a brief overview of basic notions about machine learning, a description of relevant models and evaluation metrics followed by a practical session. The practical session requires each attendee to bring their own laptop and have already installed the Waikato Environment for Knowledge Analysis (Weka) workbench for machine learning available from https://www.cs.waikato.ac.nz/ml/weka/ and all freely available machine learning models. The Weka installation of freely available machine learning models can be achieved by using the Weka Package Manager available from the Tools menu in the main application. Detailed information will be provided 2 weeks before the beginning of the workshop (week of July 5, 2020) at the following URL:http://animalbiosciences.uoguelph.ca/~dtulpan/conferences/asas2020_mlworkshop/

Investigating surface condition classification of flexible road pavement using data mining techniques

ABSTRACT The lack of sufficient, accurate and time series dataset on road pavement surface condition in most developing countries over time has posed a serious challenge to highway engineers in terms of developing reliable pavement management system. This study examined the trend of flexible road pavement behaviour and surface condition classification from a historic dataset using intelligent algorithms such as; Random Forest (RF), Decision Tree (DT) and Naïve Bayes (NB) theory implemented in the Waikato Environment for Knowledge Analysis (WEKA) software. Some randomly selected highway links were considered for the investigation. The surface condition attributes considered included the percentage of fatigue cracks, average rut depth and drainage condition in a relatively comprehensive database obtained from the Federal Ministry of Power, Works and Housing Nigeria. Results of the analysis revealed the performance trend and classification of road pavement surface conditions based on surface distresses. The analysis indicated that, the RF and DT algorithms yielded accurate classification against the NB algorithm which could not handle instances of missing data efficiently, hence incurred classification errors. The use of experience-based models that could handle challenges of missing data in a dataset was recommended for the development of road pavement management system using such database.

Flood Detection and Susceptibility Mapping Using Sentinel-1 Time Series, Alternating Decision Trees, and Bag-ADTree Models

Flooding is one of the most damaging natural hazards globally. During the past three years, floods have claimed hundreds of lives and millions of dollars of damage in Iran. In this study, we detected flood locations and mapped areas susceptible to floods using time series satellite data analysis as well as a new model of bagging ensemble-based alternating decision trees, namely, bag-ADTree. We used Sentinel-1 data for flood detection and time series analysis. We employed twelve conditioning parameters of elevation, normalized difference’s vegetation index, slope, topographic wetness index, aspect, curvature, stream power index, lithology, drainage density, proximities to river, soil type, and rainfall for mapping areas susceptible to floods. ADTree and bag-ADTree models were used for flood susceptibility mapping. We used software of Sentinel application platform, Waikato Environment for Knowledge Analysis, ArcGIS, and Statistical Package for the Social Sciences for preprocessing, processing, and postprocessing of the data. We extracted 199 locations as flooded areas, which were tested using a global positioning system to ensure that flooded areas were detected correctly. Root mean square error, accuracy, and the area under the ROC curve were used to validate the models. Findings showed that root mean square error was 0.31 and 0.3 for ADTree and bag-ADTree techniques, respectively. More findings illustrated that accuracy was obtained as 86.61 for bag-ADTree model, while it was 85.44 for ADTree method. Based on AUC, success and prediction rates were 0.736 and 0.786 for bag-ADTree algorithm, in order, while these proportions were 0.714 and 0.784 for ADTree. This study can be a good source of information for crisis management in the study area.

Kinetic Analysis of Label-Free Microscale Collagen Gel Contraction Using Machine Learning-Aided Image Analysis.

Pulmonary fibrosis is a deadly lung disease, wherein normal lung tissue is progressively replaced with fibrotic scar tissue. An aspect of this process can be recreated in vitro by embedding fibroblasts into a collagen matrix and providing a fibrotic stimulus. This work expands upon a previously described method to print microscale cell-laden collagen gels and combines it with live cell imaging and automated image analysis to enable high-throughput analysis of the kinetics of cell-mediated contraction of this collagen matrix. The image analysis method utilizes a plugin for FIJI, built around Waikato Environment for Knowledge Analysis (WEKA) Segmentation. After cross-validation of this automated image analysis with manual shape tracing, the assay was applied to primary human lung fibroblasts including cells isolated from idiopathic pulmonary fibrosis patients. In the absence of any exogenous stimuli, the analysis showed significantly faster and more extensive contraction of the diseased cells compared to the healthy ones. Upon stimulation with transforming growth factor beta 1 (TGF-β1), fibroblasts from the healthy donor showed significantly more contraction throughout the observation period while differences in the response of diseased cells was subtle and could only be detected during a smaller window of time. Finally, dose-response curves for the inhibition of collagen gel contraction were determined for 3 small molecules including the only 2 FDA-approved drugs for idiopathic pulmonary fibrosis.

Estimating the Heating Values of Napier Grass (Pennisetum purpureum Schumach) and Wild Sugarcane (Sacharrum spontaneum L.) Biomass using Proximate Composition

The study aimed to determine the heating values (HV) and proximate composition of the four varieties of Napier Grass (Pennisetum purpureum Schumach) and native wild sugarcane (Sacharrum spontaneum L.) to generate a mathematical model for predicting HV of Napier Grass varieties and wild sugarcane biomass. The four varieties of Napier Grass namely King, Florida, Dwarf, and Princess Caroline as well as the wild sugarcane were used in this study. The proximate composition such as moisture, ash, organic matter (OM), carbon (C), nitrogen (N), C/N ratio, and heating value (HV) were determined to generate mathematical models. Only ash, nitrogen, organic matter (OM) and carbon (C) have statistically significant r values toward the heating value. Positive correlation was obtained in OM and C whereas negative correlations were obtained in ash and N. Linear regression using Waikato Environment for Knowledge Analysis (WEKA) machine learning generated mathematical models for group variables for predicting the heating value (HV). The organic matter (OM) content showed the most accurate model among the group variables used with adjusted R2 value of 0.900, Pearson’s r value of 0.915 and root mean square error (RMSE) value of 481.64 MJ/kg. Among the proximate composition parameters tested, OM is the most accurate predictor of the HV of the Napier Grass varieties and wild sugarcane biomass.

An analysis of non-cultivable bacteria using WEKA.

The study of metagenomics from high throughput sequencing data processed through Waikato Environment for Knowledge Analysis (WEKA) is gaining momentum in recent years. Therefore, we report an analysis of metagenome data generated using T-RFLP followed by using the SMO (Sequential minimal optimization) algorithm in WEKA to identify the total amount of cultured and uncultured microorganism present in the sample collected from multiple sources.

How does the Government of Indonesia empower SMEs? An analysis of the social cognition found in newspapers

PurposeThis study aims to acknowledge that most Indonesian small and medium enterprises (SMEs) experience slow growth. It highlighted that this sluggishness is because of some falsification of Indonesia’s ecological psychology. It focuses on investigating the situated cognition that probably supports this falsification, such as affordance, a community of practice, embodiment and the legitimacy of peripheral participation situated cognition and social intelligence theories.Design/methodology/approachThis study obtained data from published newspapers between October 2016 and February 2019. The authors used the Waikato Environment for Knowledge Analysis and the J48 C.45 algorithm. The authors analyzed the data using the emergence of news probability for both the Government of Indonesia (GoI) and Indonesian society and the situated cognition concerning the improvement of the SMEs. The authors inferred ecological psychology from these published newspapers in Indonesia that the engaged actions were still suppressed, in comparison with being and doing.FindingsThis study contributes to the innovation and leadership policies of the SMEs’ managerial systems and the GoI. After this study identified the backward-looking practices, which the GoI and the people of Indonesia held, this study recommended some policies to help create a forward-looking orientation. The second one is also a policy for the GoI, which needs to reduce the discrepancy between the signified and the signifier, as recommended by the structuralist theory. The last one is suggested by the social learning theory; policies are needed that relate to developing the SMEs’ beliefs, attitudes and behavior. It means that the GoI should prepare the required social contexts, which are in motoric production and reinforcement. Explicitly, the authors argue that the GoI facilitates SMEs by emphasizing the internal learning process.Research limitations/implicationsThe authors present some possibilities for the limitations of this research. The authors took into account that this study assumes the SMEs are all the same, without industrial clustering. It considers that the need for social learning and social cognition by the unclustered industries is equal. Second, the authors acknowledge that Indonesia is an emerging country, and its economic structure has three levels of contributors; the companies listed on the Indonesian Stock Exchange, then the SMEs and the lowest level is the underground economy. Third, the authors did not distinguish the levels of success for the empowerment programs that are conducted by either the GoI or the local governments. This study recognizes that the authors did not measure success levels. It means that the authors only focused on the knowledge content.Practical implicationsFrom these pieces of evidence, this study constructed its strategies. The authors offer three kinds of policies. The first is the submission of special allocation funds from which the GoI and local governments develop their budgets for the SMEs’ social learning and social cognition. The second is the development of social learning and social cognition’s curricula for both the SMEs’ owners and executive officers. The third is the need for a national knowledge repository for all the Indonesian SMEs. This repository is used for the dissemination of knowledge.Originality/valueThis study raises argumental novelties with some of the critical reasoning. First, the authors argue that the sluggishness of the Indonesian SMEs is because of some fallacies in their social cognition. This social cognition is derived from the cultural knowledge that the GoI and people of Indonesia disclosed in the newspapers. This study shows the falsifications from the three main perspectives of the structuration, structuralist and social learning theories. Second, this study can elaborate on the causal factor for the sluggishness of Indonesia’s SMEs, which can be explained by philosophical science, especially its fallacies (Hundleby, 2010; Magnus and Callender, 2004). The authors expand the causal factors for each gap in every theory, which determined the SMEs’ sluggishness through the identification of inconsistencies in each dimension of their structuration, structuralism and social learning. This study focused on the fallacy of philosophical science that explains the misconceptions about the SMEs’ improvement because of faulty reasoning, which causes the wrong moves to be made in the future (Dorr, 2017; Pielke, 1999).

Web-based Application for Classification Using Naïve Bayes and K-means Clustering (Case Study: Tic-tac-toe Game)

A database can consist of numerical and non-numerical attributes. However, several data processing algorithms, such as K-means clustering, can be used only in a dataset with numerical attributes. Data generalization by using Naïve Bayes and K-means clustering methods is usually employed WEKA (Waikato environment for knowledge analysis) application. Although the strength of WEKA lies in increasingly complete and sophisticated algorithms, the success of data mining still lies in the knowledge factor of the human implementer. The task of collecting high-quality data and knowledge of modeling and the use of appropriate algorithms is needed to guarantee the accuracy of the expected formulations. In this paper, we propose a simple web-based application that can be used like WEKA. The methodology used in this study includes several stages. The first stage is the preparation of data, which is the tic-tac-toe game dataset that is converted to CSV (comma-separated values) format. The next stage is the process of modifying data from non-numeric to numeric, specifically for clustering with the K-means algorithm. Afterward, the calculation of the distance between data is conducted and followed by data clustering. The final stage is the summary of these processes and results. From the experimental results, it was found that clustering can be done on categorical attributes that are transformed first into the numerical form using web-based applications.

A neural network forecasting of relativistic electron flux at geostationary orbit: solar activity phase dependence

Geostationary-satellite anomalies are mainly caused by extreme space environment such as flux enhancements of relativistic electrons. A prediction of the relativistic electrons at geostationary orbit (GEO) is crucial to alert of the flux enhancements before it reaches a harmful condition. This work investigates contribution of solar activities at various phases in the daily electron flux (E > 2 MeV) at GEO measured by GOES satellites during the solar cycles 23-24. An alternative method based on the solar activity and feed forward neural network (NN) scheme with back-propagation learning in Waikato Environment for Knowledge Analysis is adopted to predict the electron fluxes in GEO by sorting the solar activity into maximum, descending, and ascending phases. As the model inputs, the historical daily sum K p and the > 2 MeV electron fluxes are used. Results indicate that the prediction capability of the NN model is dependent on the solar activity in relation to the sum K p. Crossing the test over the similar solar phases gives a better prediction result than crossing over different phases. The NN model based on the lagged sum K p and log-flux input are more suitable for the forecasting during the descending and ascending phases, while the lagged log-flux input is more suitable during the maximum phase.

Application of Machine Learning Algorithms to Predict Diabetes in Pregnant Women

With the growing numbers of diabetic patients across North America, the United States of America has recorded a higher percentage of people diagnosed with diabetes. The number constitutes 11 percent of the total population in the USA. These numbers spread across various races, ethnicity, gender, and income bracket. In the United States of America, diabetes is common among adults with a family income lower than the federal poverty level. Numerous scholars have developed predicting and classifying models to analyze diabetic data to infer the result generated by their models to support clinical decisions. In this paper, predictive analytics using dynamic machine learning algorithms will detect patterns and relationships from pregnancy data. Instant diagnosis and prediction of diabetes will give patients more time for preventive care and appropriate treatment. The physician used the precision and accuracy of the model to advise patients medically on risk factors and the better way to manage diabetes. The predictive model built in Waikato Environment for Knowledge Analysis (WEKA) was used to analyze pregnancy over 2500 patient data using Naïve Bayes and Decision Tree algorithms. The data served as input data to train the model using these attributes (Pregnancy week, Glucose, Skin Thickness, Blood Pressure, BMI, Age, Insulin, Diabetes Pedigree Score, and Outcome). An incremental data pre-processing method was adopted to remove noisy data. The data was calibrated into 70 percent for training and 30 percent for testing. Decision Tree accuracy and the precision rate is at an average of 80 percent, while Naïve Bayes underperforms because of its inability to learn and identify patterns within the datasets. Keywords: Pregnancy; Algorithms; CRISP-DM; Patient; WEKA

Implementasi Algoritma C4.5 Mengetahui Penyebab Perceraian Dalam Pernikahan (Studi Kasus: Pengadilan Agama Medan Kelas I-A)

Divorce is one act that is hated by Allah. But it is permissible if a husband and wife cannot live together again, when both parties have fulfilled a deadlock in reconciling it will end with a decision that is divorced. Data mining is an automatic analysis of large or complex amounts of data with the aim of determining important patterns or trends. Data mining processing to classify the causes of marriage divorce using C4.5 algorithm by collecting data and classifying data using tree structure representation where each node represents the value of the attribute, the branch represents the value of the attribute and the leaf represents nodes that can search history data for classifying the causes of divorce based on previous traits, so that rules are found that are interconnected with each other. The tools used are Weka (The Waikato Environment for Knowledge Analysis) which is an open source application, Weka has 4 menu tools that can be used, including explorer, experimenter, Knowledgeflow and simple CLI. It has the advantage of many data mining and machine learning algorithms, the results of which are used to predict a set of data

A machine learning-based approach to enhancing social media marketing

Social media (SM) represent beneficial channels for marketers, business promoters and consumers. To acquire continuous revenues and more active customers, key business players should understand the behaviour and purchase preferences of buyers. To predict the buying decisions of purchasers, data about purchase intentions and desires have to be extracted with the help of data mining techniques. The purpose of this paper is to examine social media data analytics using machine learning tools; this new approach for developing a social media marketing strategy employs the Waikato Environment for Knowledge Analysis (WEKA). WEKA is compared with other algorithms of interest and found to outperform its peers, especially with regard to parameters such as precision, recall, and F-measure, indicating that WEKA performs better than other approaches.

Prediction of Mortality Rate of COVID-19 Patients using Machine Learning Techniques in Nigeria

Corona virus disease pandemic have highly destructive effects around the world and this virus has affected both developed and developing nations. In this paper, predictive model for the mortality rate of patients infected with corona virus in Nigeria using data mining techniques is developed. Oral interview was conducted with virologist at health institution (The Federal medical centre, Owo, Ondo state, Nigeria) to ask for some basic factors that causes mortality in infected corona virus patients. Online survey was done based on these ten basic factors and three hundred and two responses were collected and preprocessed. A ten fold cross validation technique was used to partition the datasets into training and testing data in which predictive models were developed using data mining algorithms (Multilayer Perception, Naïve Bayes, Decision Tree and Decision Rule) . Waikato Environment for Knowledge Analysis (WEKA) was used to simulate the models and the result shows that the four models developed have the capability to forecast mortality rate of corona virus adequately. Conclusively, multilayer perception has the highest level of performance with 85% accuracy. Multilayer Perception model is effective, reliable and is recommended to forecast the rate of mortality of patients infected with corona virus. Moreover, this prediction is important because the death of any patients is emotional and physically challenging to the morning families

Prediction of Preeclampsia and Intrauterine Growth Restriction: Development of Machine Learning Models on a Prospective Cohort.

BackgroundPreeclampsia and intrauterine growth restriction are placental dysfunction–related disorders (PDDs) that require a referral decision be made within a certain time period. An appropriate prediction model should be developed for these diseases. However, previous models did not demonstrate robust performances and/or they were developed from datasets with highly imbalanced classes.ObjectiveIn this study, we developed a predictive model of PDDs by machine learning that uses features at 24-37 weeks’ gestation, including maternal characteristics, uterine artery (UtA) Doppler measures, soluble fms-like tyrosine kinase receptor-1 (sFlt-1), and placental growth factor (PlGF).MethodsA public dataset was taken from a prospective cohort study that included pregnant women with PDDs (66/95, 69%) and a control group (29/95, 31%). Preliminary selection of features was based on a statistical analysis using SAS 9.4 (SAS Institute). We used Weka (Waikato Environment for Knowledge Analysis) 3.8.3 (The University of Waikato, Hamilton, NZ) to automatically select the best model using its optimization algorithm. We also manually selected the best of 23 white-box models. Models, including those from recent studies, were also compared by interval estimation of evaluation metrics. We used the Matthew correlation coefficient (MCC) as the main metric. It is not overoptimistic to evaluate the performance of a prediction model developed from a dataset with a class imbalance. Repeated 10-fold cross-validation was applied.ResultsThe classification via regression model was chosen as the best model. Our model had a robust MCC (.93, 95% CI .87-1.00, vs .64, 95% CI .57-.71) and specificity (100%, 95% CI 100-100, vs 90%, 95% CI 90-90) compared to each metric of the best models from recent studies. The sensitivity of this model was not inferior (95%, 95% CI 91-100, vs 100%, 95% CI 92-100). The area under the receiver operating characteristic curve was also competitive (0.970, 95% CI 0.966-0.974, vs 0.987, 95% CI 0.980-0.994). Features in the best model were maternal weight, BMI, pulsatility index of the UtA, sFlt-1, and PlGF. The most important feature was the sFlt-1/PlGF ratio. This model used an M5P algorithm consisting of a decision tree and four linear models with different thresholds. Our study was also better than the best ones among recent studies in terms of the class balance and the size of the case class (66/95, 69%, vs 27/239, 11.3%).ConclusionsOur model had a robust predictive performance. It was also developed to deal with the problem of a class imbalance. In the context of clinical management, this model may improve maternal mortality and neonatal morbidity and reduce health care costs.

Compression versus traditional machine learning classifiers to detect code-switching in varieties and dialects: Arabic as a case study

AbstractThe occurrence of code-switching in online communication, when a writer switches among multiple languages, presents a challenge for natural language processing tools, since they are designed for texts written in a single language. To answer the challenge, this paper presents detailed research on ways to detect code-switching in Arabic text automatically. We compare the prediction by partial matching (PPM) compression-based classifier, implemented in Tawa, and a traditional machine learning classifier sequential minimal optimization (SMO), implemented in Waikato Environment for Knowledge Analysis, working specifically on Arabic text taken from Facebook. Three experiments were conducted in order to: (1) detect code-switching among the Egyptian dialect and English; (2) detect code-switching among the Egyptian dialect, the Saudi dialect, and English; and (3) detect code-switching among the Egyptian dialect, the Saudi dialect, Modern Standard Arabic (MSA), and English. Our experiments showed that PPM achieved a higher accuracy rate than SMO with 99.8% versus 97.5% in the first experiment and 97.8% versus 80.7% in the second. In the third experiment, PPM achieved a lower accuracy rate than SMO with 53.2% versus 60.2%. Code-switching between Egyptian Arabic and English text is easiest to detect because Arabic and English are generally written in different character sets. It is more difficult to distinguish between Arabic dialects and MSA as these use the same character set, and most users of Arabic, especially Saudis and Egyptians, frequently mix MSA with their dialects. We also note that the MSA corpus used for training the MSA model may not represent MSA Facebook text well, being built from news websites. This paper also describes in detail the new Arabic corpora created for this research and our experiments.