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Support Vector Machine (SVM) is supervised machine learning technique which has become a popular technique for e-mail classifiers because its performance improves the accuracy of classification. The proposed method combines gain ratio (GR) which is feature selection method with one-class training SVM to increase the efficiency of the detection proc...
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... The one-class classification is, however, encountered in many real-life situations like, for example, detection of outliers [28], novelties [29], faults [34], spam [33], and abnormal behaviors [36]. Some automatic diagnosis [35], document classification [32], and concept learning [30] problems can be also modelled as the one-class classification problem. ...
The paper proposes a taxonomy for categorizing the main features of the supervised learning classification problems and a notation for the identification of the supervised learning classification problem categories. The proposed taxonomy has been based on the review and analysis of the recent literature. It allowed the construction of the landscape of decision problem factors influencing the supervised learning processes. To enable a concise and coherent identification of supervised classification problems we have suggested a notation enabling description and identification of various supervised learning classification problem types and their critical features. The notation consists of 5 fields representing, in a sequence, a structure and properties of decision classes, structural model and properties of attributes, features of the data source, and the performance measure used for constructing and evaluating a classifier. The proposed notation is open and could be extended in the case of need new developments within the machine learning theory.
... detection can be performed through many approaches, such as using machine learning algorithms [2,3] or investigating OS metrics. ...
Android OS is developing very fast, and because of being an open source OS, it is vulnerable to many problems that are manifested to users directly or indirectly. Poor application launch time is one of these problems. In this paper, a set of sixteen experiments is established to distinguish the factors that have the most evident effects on application launch time in Android mobiles. These factors are application, launch and kill, events, and storage. Mann Kendall (MK) test, one way analysis of variance (ANOVA), and Design of Experiment (DOE) are used to prove the influence of factors statistically. As a result of the experiments, the application factor, especially the third party applications level, has the most prominent effects on application launch time, followed by launch and Kill and events, while storage had the least influence.
The internet makes it easier for people to connect to each other and has become a platform to express ideas and share information with the world. The growth of the internet has indirectly led to the development of social networking sites. The reviews posted by people on these sites implies their opinion, and analysis over reviews is required to understand their intent. In this paper, natural language processing technique and machine learning algorithms are applied to classify the text data. The contributions of the proposed approach are three-fold: 1) chi square selector is applied to select the k-best features, 2) support vector machines is executed to classify the reviews (hyperparameters of the SVM classifier are tuned using GridSearch approach), and 3) bagging algorithm is applied with the base classifier over the newly built SVM classifier. The number of base classifiers of the bagging algorithm is varied accordingly. The results of the proposed approach are compared to the similar existing work, and hence, it is found to achieve better results as compared to the existing systems.
Android operating system, since its first start, is growing very fast and takes a large space in smart devices market. It is built and developed on Linux and designed basically for touch screen devices such as, mobiles, tablets, etc. Mobile devices are markedly complicated and feature-rich; therefore they are prone to reliability of software and performance problems. Because of the small resources, smart devices, such as CPU, RAM, suffer from problems. One of these problems is Software Aging (SA). SA is recognized in long running OSs as a shortage in resources, performance retreating, and finally failure. SA is looked at from two sides, namely the poor response time of application which represents the end user side and the shortage in metrics related to device resources, such as RAM and storage. In this paper, a set of eight experiments is conducted to distinguish SA in Android mobiles. These experiments are conducted to find the correlation between Launch Time (LT) with RAM and storage metrics covered in this paper. Statistical methods, such as Mann Kendall test, Sen’s slope, Spearman rank correlation, and Design of Experiment (DOE) are used to prove the correlation statistically. These experiments assist to detect SA, which will be helpful in the rejuvenation strategy of applications.
تم في هذا البحث استعمال آلة المتجه الداعم Support Vector Machine (SVM) لغرض تصنيف مرضى الكلى بنوعيه الأول(الفشل الكلوي) و الثاني(الالتهابات والحصوات) وبالاعتماد على بعض العوامل المؤثرة في تحديد نوعي المرض , إذ تم أخذ عينة عشوائية حجمها (164) مشاهدة لمرضى الكلى من مستشفى الصدر العام في البصرة , بالاستعانة ببرنامج الحزم الجاهزة ((5/7/2019) R.Version.3.6.1) للمعالجة الإحصائية ، وأخيراً خلص البحث إلى مجموعة من الاستنتاجات كان أهمها أن طريقة آلة المتجه الداعم تعطي نسب دقة عالية في التصنيف واعتمادا على نسبة التعرف الصحيح , إذ بلغت الدقة حوالي 97% .
