Classifier ensemble for uncertain data stream classification

Currently available algorithms for data stream classification are all designed to handle precise data, while data with uncertainty or imperfection is quite natural and widely seen in real-life applications. Uncertainty can arise in attribute values as well as in class values. In this paper, we focus on the classification of streaming data that has different degrees of uncertainty within class values. We propose two types of ensemble based algorithms, Static Classifier Ensemble (SCE) and Dynamic Classifier Ensemble (DCE) for mining uncertain data streams. Experiments on both synthetic and real-life data set are made to compare and contrast our proposed algorithms. The experimental results reveal that DCE algorithm outperforms SCE algorithm

A Novel Autonomous Perceptron Model for Pattern Classification Applications

Pattern classification represents a challenging problem in machine learning and data science research domains, especially when there is a limited availability of training samples. In recent years, artificial neural network (ANN) algorithms have demonstrated astonishing performance when compared to traditional generative and discriminative classification algorithms. However, due to the complexity of classical ANN architectures, ANNs are sometimes incapable of providing efficient solutions when addressing complex distribution problems. Motivated by the mathematical definition of a quantum bit (qubit), we propose a novel autonomous perceptron model (APM) that can solve the problem of the architecture complexity of traditional ANNs. APM is a nonlinear classification model that has a simple and fixed architecture inspired by the computational superposition power of the qubit. The proposed perceptron is able to construct the activation operators autonomously after a limited number of iterations. Several experiments using various datasets are conducted, where all the empirical results show the superiority of the proposed model as a classifier in terms of accuracy and computational time when it is compared with baseline classification models

Penerapan Extreme Learning Machine Dan Modifikasi Simulated Annealing Untuk Identifikasi Penyakit Tanaman Jarak Pagar

Tanaman Jarak Pagar merupakan tanaman yang memiliki banyak fungsi dan kegunaan untuk keperluan sehari-hari seperti biodiesel dan alat-alat kecantikan, akan tetapi tanaman ini tidak dapat juga terlepas dari penyakit. Sistem pakar dapat diterapkan dalam melakukan identifikasi sehingga dapat membantu baik petani maupun penyuluh untuk melakukan identifikasi penyakit. Metode yang dapat digunakan salah satunya adalah metode Extreme Learning Machine. Extreme Learning Machine sudah pernah dilakukan dan hasil akurasi yang diberikan masih perlu peningkatan. Optimasi nilai bobot pada Extreme Learning Machine dapat meningkatkan nilai akurasi. Optimasi dilakukan menggunakan Simulated Annealing dan menggunakan pohon keputusan memberikan hasil yang lebih baik dari sebelumnya, dengan rata-rata akurasi terbaik sebesar 90,955% dan akurasi maksimal sebesar 94,74%

A STATIC CODE ANALYSIS AND PATTERN RECOGNITION ALGORITHM-DRIVEN, QUANTITATIVE, MATHEMATICAL MODEL-ORIENTED RISK ASSESSMENT FRAMEWORK OF CLOUD-BASED HEALTH INFORMATION APPLICATIONS

According to a survey, the healthcare industry is one of the least cloud-adopting industries. The low adoption reflects the healthcare industry's ongoing concerns about the security of the cloud. Business applications, according to another survey, are among the most vulnerable components of business information systems. Many risk assessment frameworks available today, particularly for health information applications, require significant customization before they can be used. This study created a new framework to assess cloud risks specifically for their health information applications, utilizing data-driven risk assessment methodologies to avoid surveys, interviews, and meetings for data collection. For the feasibility study, the open-source application codes were chosen from over 190 million GitHub repositories using a decision tree method, while a purposive sampling method was used to choose for a simulated patient information database from the healthcare industry. Using these methods, the researcher discovered security warnings and privacy violation suspects and subsequently converted them into quantitative measures to calculate the risks of the cloud-based health information application and a database. The significance of this study lies in the collection of data directly from applications and databases with a quantitative approach for risk calculation