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Approximately 19 million people die each year from cardiovascular and chronic respiratory diseases. As a result of the recent Covid-19 epidemic, blood pressure, cholesterol, and blood sugar levels have risen. Not only do healthcare institutions benefit from studying physiological vital signs, but individuals also benefit from being alerted to health problems in a timely manner. This study uses machine learning to categorize and predict cardiovascular and chronic respiratory diseases. By predicting a patient's health status, caregivers and medical professionals can be alerted when needed. We predicted vital signs for 180 seconds using real-world vital sign data. A person's life can be saved if caregivers react quickly and anticipate emergencies. The tree-based pipeline optimization method (TPOT) is used instead of manually adjusting machine learning classifiers. This paper focuses on optimizing classification accuracy by combining feature pre-processors and machine learning models with TPOT genetic programming making use of linear and Prophet models to predict important indicators. The TPOT tuning parameter combines predicted values with classical classification models such as Naïve Bayes, Support Vector Machines, and Random Forests. As a result of this study, we show the importance of categorizing and increasing the accuracy of predictions. The proposed model achieves its adaptive behavior by conceptually incorporating different machine learning classifiers. We compare the proposed model with several state-of-the-art algorithms using a large amount of training data. Test results at the University of Queensland using 32 patient's data showed that the proposed model outperformed existing algorithms, improving the classification of cardiovascular disease from 0.58 to 0.71 and chronic respiratory disease from 0.49 to 0.70, respectively, while minimizing the mean percent error in vital signs. Our results suggest that the Facebook Prophet prediction model in conjunction with the TPOT classification model can correctly diagnose a patient's health status based on abnormal vital signs and enables patients to receive prompt medical attention. • Propose an in-depth analysis of the sensors dataset used in the designed model. • Develop the model for chronic respiratory and cardiovascular disease. • Results showed that the designed model outperforms the SOTA approaches. [ABSTRACT FROM AUTHOR]

Massive data parallelism can be achieved by using general-purpose graphics processing units (GPGPU) with the help of the OpenCL framework. When smaller data with higher GPU memory is executed, it results in a low resource utilization ratio and energy inefficiencies. Up until now, there is no existing model to share GPU for further execution. In addition, if the kernel pair requires the same computation resource, then kernel merging also results in a significant increase in execution time. Therefore, optimal device selection, as well as kernel merging, can significantly speed up the execution performance for a batch of jobs. This paper proposes a kernel merging method that leads to high GPU occupancy. As a result, it reduces execution time and increases GPU utilization. Additionally, a machine learning (ML)-based GPU sharing mechanism is presented to select pairs of kernels in OpenCL frameworks. The model first selects suitable architecture for the jobs and then merges GPU kernels for better resource utilization. From all the GPU candidates, the optimal pair of the kernel concerning data size is selected. The experimental results show that the developed model can achieve 0.91 F1-measure for device selection and 0.98 for the scheduling scheme of kernel merging. • A ML-based kernel merging classification model to predict the suitability of OpenCL kernels. • Extraction and selection of significant features to forecast the speedup of the merged kernels. • A better result is obtained by using the 15 mainstream benchmark applications. [ABSTRACT FROM AUTHOR]

Ransomware continues to pose a significant threat to cybersecurity by extorting money from users by locking their devices and personal data. The attackers force the payment of a ransom in order to restore access to personal files. Because of the structural similarity, detection of ransomware and benign applications becomes vulnerable to evasion attacks. Ensemble learning can provide countermeasures, while attackers can use the same technique to improve the effectiveness of their respective attacks. This motivates us to investigate whether the distinct ensemble method can achieve better performance when combined with the voting-based method. This research proposes a hybrid approach that examines permissions, text, and network-based features both statically and dynamically by monitoring memory usage, system call logs, and CPU usage. Ensemble machine learning analyzers on static and dynamic features extracted from Android malware applications (ransomware and non-ransomware) are then trained in the designed model. Our experimental results show that the proposed ensemble classification and detection technique can classify unknown static and dynamic ransomware behavior to mitigate adversarial evasion attacks. [Display omitted] • The designed model extracts and analyzes static network-based features. • Two ML-based ensemble models are proposed for static and dynamic feature sets. • The model is for Android ransomware adversarial evasion attacks. [ABSTRACT FROM AUTHOR]

Mining patterns with high utilization (or called high-utility itemset mining, HUIM) is considered a major issue in recent decades especially in the market (e.g., supermarket) engineering since it reveals the profitable information/products for decision-making. Many existing works focused on mining high-utility itemsets from databases that revealed a very large amount of patterns. This process cannot make the precise decision in a short time, e.g., real-time and online decision-making system, since it is not a trivial task to find the appropriate and useful information from a huge amount of the discovered knowledge in a limited time. Mining closed patterns with high utilization (or called closed high-utility pattern mining) is an alternative way to reveal fewer and concise patterns with high utilization in market engineering. However, many past works considered the complete mining progress of all HUIs and they do not consider the correlation between transactions thus when the transactions are not highly relevant, the trained model could not be perfectly used for the predication, which shows inappropriate results in machine learning tasks. In this paper, we consider the clustering models that can divide the transactions into the proper groups based on their correlation, which can be used to make a better accuracy model for prediction. To reduce the mining progress of the CHUIs, a compact GA model is also used in the mining progress, thus in the large-scale situation, a sufficient number of satisfied CHUIs can be discovered in a limited time. Based on the designed framework, the designed model can, not only mine the good enough CHUIs in the limited time for the large-scale environment but also the adopted clustering concept can make a better predictive model in the machine learning process, thus higher accuracy results can be obtained. Experimental results are then evaluated to compare the state-of-the-art CHUI-Miner and CLS-Miner with the developed DcGA model, and the designed DcGA achieves the best runtime performance and satisfied mining results. • A clustering model is utilized to group the transactions with high correlation. • A compact GA is utilized to find the satisfied patterns in a limit time. • Better performance is obtained compared to the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Recently, numerous Machine Learning (ML) algorithms have been applied in many areas of cybersecurity. However, most of these systems can only be seen as a black box to users. To improve our understanding of such systems, adversarial machine learning approaches can be used. The main features are detected by analyzing the extent of such changes, which helps in identifying the main reasons for misclassification. In this paper, the presented approach has obtained satisfactory results that accurately explains the reasons for misclassifications. Some features of the presented method can be applied to any classifier with defined gradients without the need for modifications. The proposed model can be extended to perform more diagnoses and it can be used for a deeper analysis of systems, obtaining more than 95% accuracy classification on the used datasets in the experiments. [Display omitted] • Explains misclassifications by data-driven AI models using an adversarial approach. • Compute the minimum number of changes to input features required. • Increased average classification accuracy by 2.5% post modification. • Designed a black-box attack to test the correctness and trustworthiness. • Used explanation maps to examine the effectiveness of attacks. [ABSTRACT FROM AUTHOR]