A great diversity comes in the field of medical sciences because of computing capabilities and improvements in techniques, especially in the identification of human heart diseases. Nowadays, it is one of the world’s most dangerous human heart diseases and has very serious effects the human life. Accurate and timely identification of human heart disease can be very helpful in preventing heart failure in its early stages and will improve the patient’s survival. Manual approaches for the identification of heart disease are biased and prone to interexaminer variability. In this regard, machine learning algorithms are efficient and reliable sources to detect and categorize persons suffering from heart disease and those who are healthy. According to the recommended study, we identified and predicted human heart disease using a variety of machine learning algorithms and used the heart disease dataset to evaluate its performance using different metrics for evaluation, such as sensitivity, specificity, F-measure, and classification accuracy. For this purpose, we used nine classifiers of machine learning to the final dataset before and after the hyperparameter tuning of the machine learning classifiers, such as AB, LR, ET, MNB, CART, SVM, LDA, RF, and XGB. Furthermore, we check their accuracy on the standard heart disease dataset by performing certain preprocessing, standardization of dataset, and hyperparameter tuning. Additionally, to train and validate the machine learning algorithms, we deployed the standard K-fold cross-validation technique. Finally, the experimental result indicated that the accuracy of the prediction classifiers with hyperparameter tuning improved and achieved notable results with data standardization and the hyperparameter tuning of the machine learning classifiers.

Android is one of the leading operating systems for smartphones in terms of market share and usage. Unfortunately, it is also an appealing target for attackers to compromise its security through malicious applications. To tackle this issue, domain experts and researchers are trying different techniques to stop such attacks. All the attempts of securing the Android platform are somewhat successful. However, existing detection techniques have severe shortcomings, including the cumbersome process of feature engineering. Designing representative features require expert domain knowledge. There is a need for minimizing human experts intervention by circumventing handcrafted feature engineering. Deep learning could be exploited by extracting deep features automatically. Previous work has shown that operational codes (opcodes) of executables provide key information to be used with deep learning models for the detection process of malicious applications. The only challenge is to feed opcodes information to deep learning models. Existing techniques use one-hot encoding to tackle the challenge. However, the one-hot encoding scheme has severe limitations. In this paper, we introduce (1) a novel technique for opcodes embedding, which we name Op2Vec, and (2) based on the learned Op2Vec, we have developed a dataset for end-to-end detection of Android malware. Introducing the end-to-end Android malware detection technique avoids expert-intensive handcrafted feature extraction and ensures automation. Some of the recent deep learning-based techniques showed significantly improved results when tested with the proposed approach and achieved an average detection accuracy of 97.47, precision of 0.976, and F1 score of 0.979.

Agile methodology has been noted as the mostly used method for software development over the past few years. This methodology guarantees the rapid delivery of software products with lower cost and greater customer contentment. The adoption of agile methods was initially applied in the software development industry for single, small project teams following the principles of Agile Manifesto. The intense need of the time is to identify from management perspective the motivators that can be focused on to adopt agile methodologies in large-scale projects. Therefore, the main purpose of the study is to develop from management perspective a priority-based taxonomy of motivators that helps in scaling agile methodologies in large-scale projects. The taxonomy is developed in two steps. In the first step, data are collected from a survey and open literature. In the second step, the analytical hierarchy process (AHP) is implemented on the collected data that produced the proposed priority-based taxonomy of the motivators. With the help of AHP model, the 21 motivators of successful project management are categorized into four main categories, i.e., “organization management,” “team,” “customer and technology,” and “process.” Furthermore, the AHP analysis is used to rank the motivators and their respective categories in order of their importance. The findings of this study are further reviewed and validated by the agile experts in other organizations. The proposed priority-based taxonomy of motivators can be used as a handbook that gives guidelines on scalability of agile methodologies to large projects, which helps in successful management of large-scale projects.