Comparison of Different Classification Techniques Using WEKA for Breast Cancer

Intrusion detection is one of the major research problems in network security. It is the process of monitoring and analyzing the events occurring in a computer system in order to detect different security violations. The aim of this paper is to classify activities of a system into two major categories: normal and abnormal activities. In this paper we present the comparison of different classification techniques to detect and classify intrusions into normal and abnormal behaviours using WEKA tool. WEKA is open source software which consists of a collection of machine learning algorithms for Data mining tasks. The algorithms or methods tested are Naive Bayes , j48, OneR, PART and RBF Network Algorithm. The experiments and assessments of the proposed method were performed with NSL-KDD intrusion detection dataset. With a total data of 2747 rows and 42 columns will be used to test and compare performance and accuracy among the classification methods that are used.

Nowadays, for many industries, innovation revolves around two technological improvements, Artificial Intelligence (AI) and machine learning (ML). ML, a subset of AI, is the science of designing and applying algorithms that can learn and work on any activity from past experiences. Of all the innovations in the field of ML models, the most significant ones have turned out to be in medicine and healthcare, since it has assisted doctors in the treatment of different types of diseases. Among them, early detection of breast cancer using ML algorithms has piqued the interest of researchers in this area. Hence, in this work, 20 ML classifiers are discussed and implemented in Wisconsin’s Breast Cancer dataset to classify breast cancer as malignant or benign. Out of 20, 9 algorithms are coded using Python in Colab notebooks and the remaining are executed using the Waikato Environment for Knowledge Analysis (WEKA) software. Among all, the stochastic gradient descent algorithm was found to yield the highest accuracy of 98%. The algorithms that gave the best results have been considered in the development of a novel ensemble model and the same was implemented in both WEKA and Python. The performance of the ensemble model in both platforms is compared based on metrics like accuracy, precision, recall, and sensitivity and investigated in detail. From this experimental comparative study, it was found that the ensemble model developed using Python has yielded an accuracy of 98.5% and that developed in the WEKA has yielded 97% accuracy.

Identification of cybercriminals has been always a challenge for law enforcement agencies, they utilize different techniques and methods to tackle this issue. An effective predictor not only helps law enforcement agencies to chase the criminals but also is beneficial for cyber security experts to profile cyber attackers and their method of attacks and plan broad strategies for preventing future cyber threats. In this research we aim to investigate the effect of classification techniques on prediction of cyber attackers in past and possible future cyber-attacks. Our investigation is based on Open Source Intelligence and historical data about cyber-attacks. To train our proposed predictors, we use different classification algorithms and by comparing their accuracy in prediction of cyber attackers we will nominate the most accurate and reliable model. Finally to evaluate the predictor we apply a test set to discover to what extent a predictor can help law enforcement agencies in their investigations to chase cyber criminals.

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.

This paper proposes a new distributed architecture for supervised classification of large volumes of earth observation data on a cloud computing environment. The architecture supports distributed execution, network communication, and fault tolerance in a transparent way to the user. The architecture is composed of three abstraction layers, which support the definition and implementation of applications by researchers from different scientific investigation fields. The implementation of architecture is also discussed. A software prototype (available online), which runs machine learning routines implemented on the cloud using the Waikato Environment for Knowledge Analysis (WEKA), a popular free software licensed under the GNU General Public License, is used for validation. Performance issues are addressed through an experimental analysis in which two supervised classifiers available in WEKA were used: random forest and support vector machines. This paper further describes how to include other classification methods in the available software prototype.

Electrocardiogram analysis using a combination of statistical, geometric, and nonlinear heart rate variability features

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.

Through the transition of the energy matrix, which has resulted in a more significant share of renewable energy sources, there is a massive integration of Inverter-Based Resources (IBRs) in Electrical Power Systems (EPS). Therefore, in recent years, several researchers have studied the challenges of IBRs for conventional protection systems associated with EPS due to the particular fault contributions of this type of generation. In this context, this work addresses the complexity attributed to IBRs for the fault (short circuits) classification task in transmission lines connected to a wind farm. The study explores the potential of intelligent methods using machine learning algorithms based on decision trees and association rules through the WEKA (Waikato Environment for Knowledge Analysis) software. The results demonstrated the effectiveness of intelligent methods for classifying faults, with success rates exceeding 99%. However, it was found that the signal noise level significantly impacts the accuracy of the evaluated methods. Furthermore, some intelligent techniques have demonstrated the advantage of visibility into their decision-making, returning trees or rules with low complexity after training, which would facilitate the interpretation of this process by end users and its practical implementation.

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

ABSTRACTMobile devices have evolved and experienced an immense popularity over the last few years. This growth however has exposed mobile devices to an increasing number of security threats. Despite the variety of peripheral protection mechanisms described in the literature, authentication and access control cannot provide integral protection against intrusions. Thus, a need for more intelligent and sophisticated security controls such as intrusion detection systems (IDSs) is necessary. Whilst much work has been devoted to mobile device IDSs, research on anomaly‐based or behaviour‐based IDS for such devices has been limited leaving several problems unsolved. Motivated by this fact, in this paper, we focus on anomaly‐based IDS for modern mobile devices. A dataset consisting of iPhone users data logs has been created, and various classification and validation methods have been evaluated to assess their effectiveness in detecting misuses. Specifically, the experimental procedure includes and cross‐evaluates four machine learning algorithms (i.e. Bayesian networks, radial basis function,K‐nearest neighbours and random Forest), which classify the behaviour of the end‐user in terms of telephone calls, SMS and Web browsing history. In order to detect illegitimate use of service by a potential malware or a thief, the experimental procedure examines the aforementioned services independently as well as in combination in a multimodal fashion. The results are very promising showing the ability of at least one classifier to detect intrusions with a high true positive rate of 99.8%. Copyright © 2011 John Wiley & Sons, Ltd.

In the field of financial monitoring, it is necessary to promptly obtain objective assessments of economic entities (in particular, credit institutions) for effective decision-making. Automation of the process of identifying unscrupulous credit institutions based on machine learning methods will allow regulatory authorities to quickly identify and suppress illegal activities. The aim of the research is to substantiate the possibilities of using machine learning methods and algorithms for the automatic identification of unscrupulous credit institutions. It is required to select a mathematical toolkit for analyzing data on credit institutions, which allows tracking the involvement of a bank in money laundering processes. The paper provides a comparative analysis of the results of processing data on the activities of credit institutions using classification methods — logistic regression, decision trees. The author applies support vector machine and neural network methods, Bayesian networks (Two-Class Bayes Point Machine), and anomaly search — an algorithm of a One-Class Support Vector Machine and a PCA-Based Anomaly Detection algorithm. The study presents the results of solving the problem of classifying credit institutions in terms of possible involvement in money laundering processes, the results of analyzing data on the activities of credit institutions by methods of detecting anomalies. A comparative analysis of the results obtained using various modern algorithms for the classification and search for anomalies is carried out. The author concluded that the PCA-Based Anomaly Detection algorithm showed more accurate results compared to the One-Class Support Vector Machine algorithm. Of the considered classification algorithms, the most accurate results were shown by the Two-Class Boosted Decision Tree (AdaBoost) algorithm. The research results can be used by the Bank of Russia and Rosfinmonitoring to automate the identification of unscrupulous credit institutions

Cardiovascular disease is a major global health problem affecting people around the globe. Although modern medicine has contributed in providing significant data to help mitigate cardiovascular disease, however, there is still needs to provide an ideal solution that will help in detecting early cardiovascular diseases. As a result, existing researchers have proposed several hybrid cardiovascular disease detection techniques using both machine learning and deep learning approaches. However, most solution requires improvement especially in term of accuracy detection. In this paper, a tri-hybrid Naïve Bayes classification algorithm for accuracy prediction of early cardiovascular disease detection is developed. In the developed algorithm, the strength of the conventional Naïve Bayes classification algorithm is improved with that of decision tree approach to achieve better accuracy. To further enhance its accuracy, Neural Network (NN) procedure is later incorporated into the improved Naïve Bayes classification algorithm. Implementation of the developed tri-hybrid algorithm is carried out on Waikato Environment for Knowledge Analysis (WEKA) and evaluated using dataset from the kaggle website that contain instances of 1025 and 14 attributes. Experimental results based on confusion matrix for performance evaluation indicates the developed tri-hybrid Naïve Bayes algorithm has achieved high classification accuracy and recall rates of 98.54 and 99% respectively when compared with that of the benchmarked schemes. The performance of the proposed solution deployed can help cardiologist make better prediction in the diagnosis of the heart disease. Although several machine learning algorithms as seen in the literatures are trained on historical data, these algorithms may not be able to accurately predict the risk of heart disease in patients who are exposed to new or emerging risk factors. Further research is to experiments a wider range of algorithms with a build in model that can detect early cardiovascular diseases as well improve accuracy.

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.

The backscatter signal analysis, as the landmine material could vary, has to be as much advanced as possible. One major problem with the conventional methods is that they are not able to detect new plastic landmines. In the recent research, the classification techniques and neural networks (NNs) were exploited for detection. In NNs-based method, a network is trained based on the feature extracted from the data, which leads to landmine detection. Other conventional classification methods, attempts to classify the objects sharing common characteristics. In this letter, an algorithm is introduced based on classification, data reduction and neural networks. Indeed, this algorithm employs neural network and classification method, simultaneously. The simple methods using either neural network or classification separately usually suffer from high rate of risk. In this letter, a novel classifier is proposed such that the data is classified based on similarity. It will be shown that the similarity between signals in a class is more than 90%, which proves the method's efficiency. Moreover, the scattering parameter, having magnitude and phase parts, is used to create an algorithm with parallel process.

Greenhouses used for agricultural production have been expanding around the world because it significantly increases crop yield. Meanwhile, it brings a series of environmental problems that should be considered in agricultural planning and management. The advent of the Google Earth Engine (GEE) cloud platform makes remote sensing image processing more convenient and efficient. It has been widely applied in multiple disciplines, but few studies have investigated the detection of greenhouses. In this research, four different classification methods were applied for comparing their performance in monitoring greenhouses in the Guanzhong Plain, Shaanxi, China using GEE: the Minimum Distance Classifier (MDC), the Support Vector Machine with three kernel functions (linear, SVM-L, polynomial, SVM-P, and radial basis function variations, SVM-R), the Classification and Regression Trees (CART), and the Random Forest (RF). Our results illustrate that these classification techniques’ overall accuracy is >84%. The most accurate classification results were obtained by the SVM-R classifier, with an overall accuracy of 94%, followed by the RF and CART classifier, while the MDC performed worst among these four classifiers. These results would be useful for greenhouse extraction in long time series and large-scale areas, which provides solid information for decision-makers and practitioners for agriculture planning and management.