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Breast cancer was the most diagnosed form of cancer in 2020. Early diagnosis of breast cancer results in a significant improvement in long-term survival rates. Current methods require consultation with experts, which is expensive and time-consuming and thus may not be accessible to all. This paper seeks to train and evaluate supervised machine learning models for the accurate and efficient detection of breast cancer. The Wisconsin Breast Cancer Database dataset describes 30 attributes of cell nuclei, including, but not limited to, their radius, texture, and concavity. It contains 569 instances, 212 of which are malignant tumors. The Random Forest algorithm outperforms other algorithms in classifying breast tumors as either malignant or benign and is thus selected as our primary model. It is trained on two different subsets of the dataset having 16 and 8 features, respectively, identified with the help of multiple feature selection methods. The Random Forest models are tested post hyperparameter tuning on a holdout set, and accuracies of 100% and 99.30% respectively. The models are also compared with four other machine learning classification algorithms: Support Vector Machine (SVM), Decision Tree, Multilayer Perceptron, and K-Nearest Neighbors. The results confirm that Random Forest is the superior method for breast cancer diagnosis. [ABSTRACT FROM AUTHOR]

The increasing spread of monkeypox recently requires more study, investigation, and analysis. Not only to understand the different factors that contribute to the spread and transmission of the disease, but also to devise rules by which the disease can be diagnosed more accurately as the clinical presentation of monkeypox is very similar to that of smallpox. This paper presents a simple but effective strategy for diagnosing monkeypox, which is called Accurate Monkeypox Diagnosing Strategy (AMDS). The proposed AMDS consists of two successive phases, which are; (i) Feature Selection Phase (FSP) and (ii) Classification Phase (CP). During the FSP, the most beneficial features for diagnosing monkeypox are selected; afterwards the CP is applied during which the actual diagnosis takes place. The main contribution of AMDS relies on a proposed improvement of the Traditional Gray Wolf Optimization (TGWO), which is identified as Dynamic Recursive Gray Wolf Optimization (DRGW) and is applied in the FSP to select the most influential features. DRGW solves two of the most effective drawbacks of TGWO, which are; (i) inability to share good positions among wolves' pack and (ii) the lack of an accurate mechanism to locate the potential prey. AMDS has been experimentally evaluated compared to other competitors of recent feature selection techniques, in which CP has been employed using different well-known classifiers such as; Support Vector Machines (SVM), Naïve Bayes (NB), K-Nearest Neighbors (KNN), and Deep Neural Network (DNN). Experimental results conclude that AMDS promotes the diagnosing performance in terms of recall, accuracy, error, precision, and F score. This ensures that the proposed AMDS can operate efficiently in the disease diagnosis, which in turn ensures the effectiveness of the proposed DRGW. [ABSTRACT FROM AUTHOR]

Polycystic Ovary syndrome (PCOS) is the most common serious health problem in women worldwide. It affects the women's fertility and may lead to various health problems like type 2 diabetes, cardiovascular diseases, etc. Early diagnosis of PCOS is recommended for proper treatment. In this paper, we propose SmS: SMOTE-Stacked hybrid model, for the early diagnosis of PCOS. In the proposed model, we combine the ensemble technique 'stacking' with oversampling technique 'SMOTE' i.e., Synthetic minority oversampling technique. In the Stacking ensemble technique, training is performed at two levels: base level and meta level. We use six classifiers Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB) and AdaBoost (AdaB) as base leaners at the base level and consider each of these classifiers separately at the meta -level to implement SmS hybrid models: Stack-LR, Stack-SVM, Stack-DT, Stack-RF, Stack-NB and Stack-AdaB. Standardization, SMOTE and Backward Feature Elimination (BFE) are performed before training the classifiers. We evaluate the performance of the individual classifiers and hybrid models on PCOS dataset (Imbalanced) taken form the Kaggle Repository and observe that Stack-AdaB shows the most promising results. [ABSTRACT FROM AUTHOR]

There is a growing interest in developing feature subset selection schemes for high-dimensional datasets by filter, wrapper, embedded, and hybrid manners. In this paper, we propose a new hybrid (filter-wrapper) feature selection approach. At first, in the filter step, we rank input features according to their relevance with the class label. Afterwards, we apply different clustering methods for the classification of the selected features. We perform an inner and outer cluster ranking based on the primary feature ranking in the next step. Then, different search strategies are performed on the best cluster of features in the wrapper phase. Moreover, we add some of them to the feature set based on the classifiers (nearest neighbor, decision tree, support vector machine, and random forests) feedback. Then, the algorithm goes to the next cluster, and this process is continued till all clusters are met. Finally, we compare the results of the proposed method to the state-of-the-art schemes. Comparison results imply the superiority of the proposed method to the counterparts on eight high-dimensional datasets in terms of accuracy and computational complexity. • A new hybrid feature selection framework named the HyCluster is proposed. • Our algorithm significantly improves the time complexity using feature clustering. • It selects relevant and irredundant features and significantly improves accuracy rates. • Comparison results imply the superiority of the proposed method to the counterparts. [ABSTRACT FROM AUTHOR]

The mainstream computing technology is not efficient in managing massive data and detecting network traffic intrusions, often including big data. The intrusions present in sustained network traffic and the massive host log event data cannot be effectively managed by conventional analytical tools, resulting in a huge number of false positives and a longer training time. This paper presents a novel technique to enhance the intrusion detection process by handling the fundamental big data complexities associated with different forms of heterogeneous security data. To achieve the earlier objective, the ensemble Support Vector Machine (SVM) is integrated with the Chaos Game Optimization (CGO) algorithm. The proposed methodology improves the intrusion classification accuracy and also identifies nine different types of attacks present in the UNSW-NB15 dataset. The efficiency of the proposed methodology is evaluated using statistical analysis and different performance metrics such as precision, recall, F1-score, accuracy, ROC curve, and confusion matrix by comparing it with different baseline models. The proposed methodology obtains an accuracy of 96.29% when compared to the chi-SVM (89.12%) and an improvement of 6.47% is noted in the proposed methodology in terms of accuracy when compared with the chi-SVM. The higher classification accuracy shows that the proposed methodology exhibit a fewer number of false positives when handling the security events in big data platforms. [Display omitted] • The feature selection is carried out by performing the proposed ensemble Support Vector Machine (ESVM) algorithm. • The CGO algorithm fine-tunes the parameters of ensemble SVM to hone up the accuracy. • The experimental analyses are conducted on the UNSW-NB15 big data dataset. [ABSTRACT FROM AUTHOR]

• A new model to classify data based on Support vector machines is introduced. • The model deals with outlier detection and feature selection simultaneously. • Strategies for initializing the big M parameters of the model are developed. • An heuristic algorithm based on kernel search to compute the classifier is presented. • The classification performance of the model is tested on real-life datasets. This paper proposes a robust classification model, based on support vector machine (SVM), which simultaneously deals with outliers detection and feature selection. The classifier is built considering the ramp loss margin error and it includes a budget constraint to limit the number of selected features. The search of this classifier is modeled using a mixed-integer formulation with big M parameters. Two different approaches (exact and heuristic) are proposed to solve the model. The heuristic approach is validated by comparing the quality of the solutions provided by this approach with the exact approach. In addition, the classifiers obtained with the heuristic method are tested and compared with existing SVM-based models to demonstrate their efficiency. [ABSTRACT FROM AUTHOR]