A New Cloud Computing Architecture for the Classification of Remote Sensing Data

The performances of regular support vector machines and random forests are experimentally compared for hyperspectral imaging land cover classification. Special characteristics of hyperspectral imaging dataset present diverse processing problems to be resolved under robust mathematical formalisms such as image classification. As a result, pixel purity index algorithm is used to obtain endmember spectral responses from Indiana pine hyperspectral image dataset. The generalized reduced gradient optimization algorithm is thereafter executed on the research data to estimate fractional abundances in the hyperspectral image and thereby obtain the numeric values for land cover classification. The Waikato environment for knowledge analysis (WEKA) data mining framework is selected as a tool to carry out the classification process by using support vector machines and random forests classifiers. Results show that performance of support vector machines is comparable to that of random forests. This study makes a positive contribution to the problem of land cover classification by exploring generalized reduced gradient method, support vector machines, and random forests to improve producer accuracy and overall classification accuracy. The performance comparison of these classifiers is valuable for a decision maker to consider tradeoffs in method accuracy versus method complexity.

This paper presents a comparative evaluation of classification algorithms using Waikato Environment for Knowledge Analysis (WEKA) software. The main goal of the paper is to conduct a comprehensive comparison and determine which predictive modelling technique is best for the problem of classifying breast cancer recurrence. The dataset for this study consists of 286 instances (201 instances belong to recurrence class and 85 instances belong to non-recurrence class) and 10 attributes. Comparison analysis is conducted for Naïve Bayes, J48, K*, Random Forest, Multilayer Perceptron (MLP) and Support Vector Machine (SVM) models using different parameters. The performance of the developed models is calculated using the following evaluation metrics: accuracy, precision, sensitivity, specificity, mean absolute error, ROC curves and AUC values. Contribution of the attributes to the classification models is assessed by measuring information gain. Results show that J48 model and the SVM algorithm give the highest accuracy, which is 75.5% and 79.6%, respectively. Implementation of SVM algorithm also shows the highest sensitivity of 99%, while the highest precision is obtained by MLP algorithm which is 79%. In addition, SVM algorithm possesses the lowest mean absolute error. Furthermore, by measuring information gain, it is revealed that a degree of malignant tumour contributes more than other attributes to recurrence of breast cancer.

The diagnosis of incipient fault is important for power transformer condition monitoring. Incipient faults are monitored by conventional and artificial intelligence based models. Key gases, percentage value of gases, and ratio of the Doernenburg, Roger, IEC methods are input variables to artificial intelligence models, which affects the accuracy of incipient fault diagnosis, so selection of the most influencing relevant input variable is an important research area. With this main objective, Waikato Environment for Knowledge Analysis software is applied to 360 simulated samples having different operating lives to find the most influencing input parameters for incipient fault diagnosis in the gene expression programming model. The Waikato Environment for Knowledge Analysis identifies%C2H2,%C2H4,%CH4, C2H6/C2H2, C2H2/C2H4, CH4/H2, C2H4/C2H6, and C2H2/CH4 as the most relevant input variables in incipient fault diagnosis, and it is used for fault diagnosis using different artificial intelligence methods, i.e., artificial neural networks, fuzzy logic, support vector machines, and gene expression programming. The compared results shows that gene expression programming gives better results than the artificial neural network, fuzzy logic, and support vector machine with accuracy variation from 98.15 to 100%, proving the gene expression programming method can be used in transformer fault diagnosis research.

The look for methodologies that can make inferences from externally supplied data develop broad hypotheses that are subsequently used to create forecasts concerning future events is known as supervised machine learning (SML). This study examine machine learning (ML) classification strategies, compares supervised learning algorithms, and determines foremost efficient classification algorithm based on the data set, number of instances, and variables (features). ML with the Waikato Environment for Knowledge Analysis (WEKA) tool, 7 different machine learning algorithms were considered: Decision Table, Random Forest (RF), Naive Bayes (NB), support vector machine (SVM), neural networks (Perceptron), JRip, and Decision Tree (J48). Time it takes to make a design and be concise (accuracy) are factors on the one end, and the kappa statistic and Mean Absolute Error (MAE) are factors on the other. For supervised predictive machine learning to work, machine learning algorithms must be accurate, and error-free.KeywordsMachine learningMean absolute errorWEKADeep learning

Breast cancer is the most common type of cancer in Egyptian women. According to the International Agency for Research on Cancer, the causes of breast cancer are not yet fully known but some of the main risk factors have been identified and taken into consideration in researches performed on patients with high risk of breast cancer. In this paper various classification techniques are used to classify whether breast cancer is recurrent or non-recurrent for a number of patients. Classification techniques used are K-Nearest Neighbor (KNN), Decision Trees (DT), Naive Bayes (NB), Support Vector Machines (SVM) and ensemble techniques Bagging, Voting and Random Forest (RF). The dataset is taken from the University of California Irvine (UCI) machine learning repository and experiments are conducted with Waikato Environment for Knowledge Analysis (WEKA) data mining tool. The research conducted goes through two phases, in the first phase the Random Forest classifier produced the best results with (84.3%) accuracy and the second phase, voting ensemble classifier produced the best results of 89.9% accuracy. The system model show an improvement in the overall accuracy compared to other researches done on the same dataset.

The definition of the data mining can be told as to extract information or knowledge from large volumes of data. Statistical and machine learning techniques are used for the determination of the models to be used for data mining predictions. Today, data mining is used in many different areas such as science and engineering, health, commerce, shopping, banking and finance, education and internet. This study make use of WEKA (Waikato Environment for Knowledge Analysis) to compare the different classification techniques on energy efficiency datasets. In this study 10 different Data Mining methods namely Bagging, Decorate, Rotation Forest, J48, NNge, K-Star, Naive Bayes, Dagging, Bayes Net and JRip classification methods were applied on energy efficiency dataset that were taken from UCI Machine Learning Repository. When comparing the performances of algorithms it’s been found that Rotation Forest has highest accuracy whereas Dagging had the worst accuracy.

The main challenges of predictive analytics revolve around the handling of datasets, especially the disproportionate distribution of instances among classes in addition to classifier-suitability issues. This unequal spread causes imbalance learning and severely obstructs prediction accuracy. In this paper, the performances of six classifiers and the effect of data balancing (DB) and formation approaches for predicting pregnancy outcome (PO) were investigated. Synthetic minority oversampling technique (SMOTE), resampling with and without replacement, were adopted for data imbalance treatment. Six classifiers including random forest (RF) were evaluated on each resampled dataset with four test modes using Waikato Environment for Knowledge Analysis and R programming libraries. The results of analysis of variance performed separately using F-measure and root mean squared error showed that mean performance of classifiers across the datasets varied significantly (F=117.9; p=0.00) at 95% confidence interval, while turkey multi-comparison test revealed RF(mean=0.78) and SMOTE (mean=0.73) as having significantly different means. The RF model on SMOTE produced each PO class accuracy ≥0.89, area under the curve ≥ 0.96 and coverage of 97.8% and was adjudged the best classifier-DB method pair. However, there was no significant difference (F=0.07, 0.01; p=1.000) in the mean performances of classifiers across test data modes respectively. It reveals that train/test data modes insignificantly affect classification accuracy, although there are noticeable variations in computational cost. The methodology significantly enhance the predictive accuracy of minority classes and confirms the importance of data-imbalance treatment, and the suitability of RF for PO classification.

The development of data-mining applications such as classification and clustering has shown the need for machine learning algorithms to be applied to large scale data. In this paper we present the comparison of different classification techniques using Waikato Environment for Knowledge Analysis or in short, WEKA. WEKA is an open source software which consists of a collection of machine learning algorithms for data mining tasks. The aim of this paper is to investigate the performance of different classification or clustering methods for a set of large data. The algorithm or methods tested are Bayes Network, Radial Basis Function, Pruned Tree, Single Conjunctive Rule Learner and Nearest Neighbors Algorithm. A fundamental review on the selected technique is presented for introduction purposes. The data breast cancer data with a total data of 6291 and a dimension of 699 rows and 9 columns will be used to test and justify the differences between the classification methods or algorithms. Subsequently, the classification technique that has the potential to significantly improve the common or conventional methods will be suggested for use in large scale data, bioinformatics or other general applications.KeywordsMachine LearningData MiningWEKAClassificationBioinformatics

Subcontractors contribute to almost 90% of the overall construction work hence, assessing the performance of their work from commencement till the completion stage is essential. This paper focuses on identifying different factors affecting the subcontracted work performance and developing a predictive model using classification - based algorithm to find the proficient subcontractors.
 Data collected from the building construction projects was analyzed and utilized. Expert validation method was carried out to validate the factors that were obtained from literature review and a survey was conducted to assess the subcontractor’s performance level. Different classification algorithms such as Naïve Bayes, Logistic, Multilayer Perceptron, Sequential Minimal Optimization (SMO), KStar, J48 and Random Forest were applied to the collected data. Waikato Environment for Knowledge Analysis (WEKA), an opensource machine learning tool was used to compare the performance of several algorithms. Statics were generated and compared using k-folds cross validation (k=10) method.
 Among the seven algorithms/classifiers, Random Forest had the highest accuracy in schedule performance model and Multilayer Perceptron in quality performance model

Most oral injuries are diagnosed by histopathological analysis of a biopsy, which is an invasive procedure and does not give immediate results. On the other hand, Raman spectroscopy is a real time and minimally invasive analytical tool with potential for the diagnosis of diseases. The potential for diagnostics can be improved by data post-processing. Hence, this study aims to evaluate the performance of preprocessing steps and multivariate analysis methods for the classification of normal tissues and pathological oral lesion spectra. A total of 80 spectra acquired from normal and abnormal tissues using optical fiber Raman-based spectroscopy (OFRS) were subjected to PCA preprocessing in the z-scored data set, and the KNN (K-nearest neighbors), J48 (unpruned C4.5 decision tree), RBF (radial basis function), RF (random forest), and MLP (multilayer perceptron) classifiers at WEKA software (Waikato environment for knowledge analysis), after area normalization or maximum intensity normalization. Our results suggest the best classification was achieved by using maximum intensity normalization followed by MLP. Based on these results, software for automated analysis can be generated and validated using larger data sets. This would aid quick comprehension of spectroscopic data and easy diagnosis by medical practitioners in clinical settings.

In acute ischaemic stroke, identifying brain tissue at high risk of infarction is important for clinical decision-making. This tissue may be identified with suitable classification methods from magnetic resonance imaging data. The aim of the present study was to assess and compare the performance of five popular classification methods (adaptive boosting, logistic regression, artificial neural networks, random forest and support vector machine) in identifying tissue at high risk of infarction on human voxel-based brain imaging data. The classification methods were used with eight MRI parameters, including diffusion-weighted imaging and perfusion-weighted imaging obtained in 55 patients. The five criteria used to assess the performance of the methods were the area under the receiver operating curve (AUCroc ), the area under the precision-recall curve (AUCpr ), sensitivity, specificity and the Dice coefficient. The methods performed equally in terms of sensitivity and specificity, while the results of AUCroc and the Dice coefficient were significantly better for adaptive boosting, logistic regression, artificial neural networks and random forest. However, there was no statistically significant difference between the performances of these five classification methods regarding AUCpr , which was the main comparison metric. Machine learning methods can provide valuable prognostic information using multimodal imaging data in acute ischaemic stroke, which in turn can assist in developing personalized treatment decision for clinicians after a thorough validation of methods with an independent data set.

The management and monitoring of land use in geothermal fields are crucial for the sustainable utilization of water resources, as well as for striking a balance between the production of renewable energy and the preservation of the environment. This study primarily compared Support Vector Machine (SVM) and Random Forest (RF) machine learning methods, using satellite imagery from Landsat 8 and Sentinel 2 between 2021 and 2023, to monitor land use in the Patuha geothermal area. The objective is to improve sustainable water management practices by accurately categorizing different land cover types. This comparative analysis assessed the efficacy of these techniques in upholding water sustainability in geothermal regions. This study examined the application of SVM and RF machine learning techniques, with particular emphasis on parameter refinement and model assessment, to enhance land use classification accuracy. By employing Kernlab and e1071 for algorithm comparison, the research sought to produce a precise Land Use Model Map, which underscores the significance of advanced analytical techniques in environmental management. This approach was of utmost importance in improving land use monitoring and reinforcing sustainable practices. The comparative evaluation of SVM and RF methods for land use classification demonstrates the superiority of RF in terms of accuracy, stability, and precision, particularly in intricate urban settings, hence establishing it as the preferred model for tasks demanding high reliability. The application of SVM and RF for monitoring land use in geothermal areas is in alignment with Sustainable Development Goals (SDGs) 6 and 15, as it fosters sustainable water management and the conservation of ecosystems. Doi: 10.28991/HEF-2024-05-02-06 Full Text: PDF

Forecasting Stock Index Movement: A Comparison of Support Vector Machines and Random Forest

Background/Objectives: Recent studies emphasized on using ensemble models over single ones to solve credit scoring problems. The objective of this study is to build a heterogeneous ensemble classifier model with an improved classification accuracy. Methods: This study focuses on developing a heterogeneous ensemble classifier using Logistic Regression, K-nearest neighbor, Decision tree, Random Forest, Naïve Base and Support vector machine as base classifiers and Random Forest, Logistic Regression and Support vector machine as meta-classifiers. The proposed model is built using these six base classifiers for ensemble aggregation. A feature selection algorithm based on the random forest technique is used for selecting the best features. A stacking and voting method are used for building ensemble model. Findings: The ensemble classifier gives superior predictive performance than single classifiers SVM, DT, RF, NB, KNN and LR with an accuracy of 91.56% for Australian dataset and 84.35% for German dataset. Novelty: The proposed model uses stacking and majority voting method for ensemble classification. Initially, stacking is applied to the base classifiers. This is done in two levels. First the training dataset is split into 10 folds for cross validation. The output of each classifier is taken, and the dataset is updated with the meta-features. In the second level, three meta-classifiers (MC), namely LR, SVM and RF are used. Majority voting is applied to the output of these meta-classifiers for the prediction. Keywords: Credit scoring; ensemble model; SVM; DT; RF; NB; KNN; LR

In recent years, there have been rapid improvements in both remote sensing methods and satellite image availability that have the potential to massively improve burn severity assessments of the Alaskan boreal forest. In this study, we utilized recent pre- and post-fire Sentinel-2 satellite imagery of the 2019 Nugget Creek and Shovel Creek burn scars located in Interior Alaska to both assess burn severity across the burn scars and test the effectiveness of several remote sensing methods for generating accurate map products: Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), and Random Forest (RF) and Support Vector Machine (SVM) supervised classification. We used 52 Composite Burn Index (CBI) plots from the Shovel Creek burn scar and 28 from the Nugget Creek burn scar for training classifiers and product validation. For the Shovel Creek burn scar, the RF and SVM machine learning (ML) classification methods outperformed the traditional spectral indices that use linear regression to separate burn severity classes (RF and SVM accuracy, 83.33%, versus NBR accuracy, 73.08%). However, for the Nugget Creek burn scar, the NDVI product (accuracy: 96%) outperformed the other indices and ML classifiers. In this study, we demonstrated that when sufficient ground truth data is available, the ML classifiers can be very effective for reliable mapping of burn severity in the Alaskan boreal forest. Since the performance of ML classifiers are dependent on the quantity of ground truth data, when sufficient ground truth data is available, the ML classification methods would be better at assessing burn severity, whereas with limited ground truth data the traditional spectral indices would be better suited. We also looked at the relationship between burn severity, fuel type, and topography (aspect and slope) and found that the relationship is site-dependent.