Medical outcomes must be tracked in order to enhance quality initiatives, healthcare management, and mass education. Thoracic surgery data have been acquired for those who underwent major lung surgery for primary lung cancer, a field in which there has been little research and few reliable recommendations have been made for lung cancer patients. Early detection of lung cancer benefits therapy choices and increases the odds of a patient surviving a lung cancer infection. Using a Hybrid Genetic and Support Vector Machine (GA-SVM) methodology, this study proposes a method for identifying lung cancer patients. To estimate postoperative life expectancy, ensemble machine-learning techniques were applied. The article also presents a strategy for estimating a patient’s life expectancy following thoracic surgery after the detection of cancer. To perform the prediction, hybrid machine-learning methods were applied. In ensemble machine-learning algorithms, attribute ranking and selection are critical components of robust health outcome prediction. To enhance the efficacy of algorithms in health data analysis, we propose three attribute ranking and selection procedures. Compared to other machine-learning techniques, GA-SVM achieves an accuracy of 85% and a higher F1 score of 0.92. The proposed algorithm was compared with two recent state-of-the-art techniques and its performance level was ranked superior to those of its counterparts.
A huge number of applications available for Android-based smartphone devices have emerged over the past years. Due to which a huge number of malicious applications has been growing explosively. Many approaches have been proposed to ensure the security and quality of application in the markets. Usually, Machine Learning approaches are utilized in the classification process of malicious application detection. Calculating accurate results of characterizing applications behaviors, or other features, has a direct effect on the results with Machine Learning calculations. Android applications emerge so quickly. The behavior of current applications has gotten progressively malicious. The extraction of malware-infected features from applications is thus become a difficult task. According to our knowledge, a ton of features have been extricated in existing work however no survey has overviewed the features built for identifying malicious applications efficiently. In this paper, we will in general give an extensive review of such sort of work that identifies feature applications by describing various practices of uses with various kinds of features. In this survey we have discussed the following dimensions: extraction and selection of feature methods if any, methods of detection and evaluation performed. In light of our review, we notice the issues of investigating malware-affected features from applications, give the scientific categorization and demonstrate the future headings.
Micro-expression (ME) is one of the key psychological stress reactions. It is a modest, spontaneous facial mechanism. ME has significant applicability in a variety of psychologically-related sectors because to its precision and unpredictability with regard to psychological manifestations. Nevertheless, the current Micro-expression recognition (MER) algorithms have poor accuracy and a limited quantity of ME data, and this study issue has not been thoroughly investigated. Therefore, we present an approach for deep learning based on a Spatio-temporal capsule network (STCP-Net). STCP-Net has four components: a jitter reduction module, a differential feature extraction module, an STCP module, and a fully linked layer. The first two modules are aimed to extract diversifying differential features more precisely and to limit the influence of head jitter. The STCP module is used to extract Spatio-temporal features layer by layer, taking the temporal and geographical connection between features into account. This research runs sufficient trials using the Leave One Subject Out (LOSO) methodology for cross-validation using the CASMEII dataset. The conclusion and analysis demonstrate that the algorithm is innovative and efficient.
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