Hepatitis C is a prevalent disease in the world. Around 3 to 4 million new cases of Hepatitis C are reported every year across the globe. Effective, timely prediction of the disease can help people know about their Stage of Hepatitis C. To identify the Stage of disease, various noninvasive serum biochemical markers and clinical information of the patients have been used. Machine learning techniques have been an effective alternative tool for determining the Stage of this chronic disease of the liver to prevent biopsy side effects. In this study, an Intelligent Hepatitis C Stage Diagnosis System (IHSDS) empowered with machine learning is presented to predict the Stage of Hepatitis C in a human using Artificial Neural Network (ANN). The dataset obtained from the UCI machine learning repository contains 29 features, out of which the 19 most reverent are selected to conduct the study; 70% of the dataset is used for training and 30% for validation purposes. The precision value is compared with the proposed IHSDS with previously presented models. The proposed IHSDS has achieved 98.89% precision during training and 94.44% precision during validation.

No abstract

No abstract

Heart disease, which is also known as cardiovascular disease, includes various conditions that affect the heart and has been considered a major cause of death over the past decades. Accurate and timely detection of heart disease is the single key factor for appropriate investigation, treatment, and prescription of medication. Emerging technologies such as fog, cloud, and mobile computing provide substantial support for the diagnosis and prediction of fatal diseases such as diabetes, cancer, and cardiovascular disease. Cloud computing provides a cost-efficient infrastructure for data processing, storage, and retrieval, with much of the extant research recommending machine learning (ML) algorithms for generating models for sample data. ML is considered best suited to explore hidden patterns, which is ultimately helpful for analysis and prediction. Accordingly, this study combines cloud computing with ML, collecting datasets from different geographical areas and applying fusion techniques to maintain data accuracy and consistency for the ML algorithms. Our recommended model considered three ML techniques: Artificial Neural Network, Decision Tree, and Naïve Bayes. Real-time patient data were extracted using the fuzzy-based model stored in the cloud.

A robust facial recognition system that has soundness and completeness is essential for authorized control access to lawful resources. Due to the availability of modern image manipulation technology, the current facial recognition systems are vulnerable to different biometric attacks. Image morphing attack is one of these attacks. This paper compares and analyzes state-of-the-art morphing attack detection (MAD) methods. The performance of different MAD methods is also compared on a wide range of source image databases. Moreover, it also describes the morph image generation techniques along with the limitations, strengths, and drawbacks of each morphing technique. Results are investigated and compared with in-depth analysis providing insight into the vulnerabilities of existing systems. This paper provides vital information that is essential for building a next generation morph attack detection system.