In this paper, a Computer Aided Diagnosis (CAD) system is proposed to provide a comprehensive analytic method for extracting the most significant features of Alzheimer's disease (AD). It consists of three stages: feature selection, feature extraction, and classification. This proposal selects the features that have different intensity level at all images and discarding the features that have the same intensity level to reach the fewer subset of features that have the most impact distinctive of AD. Then reduces the features by proposing a new feature extraction algorithm that minimizes intra separately distance of AD features. Finally, a Linear Support Vector Machine (SVM) classifier was used to perform binary classifications among AD patients. The data set that used for testing the proposed model consists of 120 cross-sectional Structural MRI images from the Open Access Series of Imaging Studies (OASIS) database. Experiments have been conducted on Open Access Series of Imaging Studies (OASIS) database. The results show that the highest classification performance is obtained using the proposed model, and this is very promising compared to Principle Component Analysis (PCA) and Linear Discriminate Analysis (LDA).
Cloud computing is a modern technology for dealing with large-scale data. The Cloud has been used to process the selection and placement of replications on a large scale. Most previous studies concerning replication used mathematical models, and few studies focused on artificial intelligence (AI). The Artificial Bee Colony (ABC) is a member of the family of swarm intelligence based algorithms. It simulates bee direction to the final route and has been proven to be effective for optimization. In this paper, we present the different costs and shortest route sides in the Cloud with regard to replication and its placement between data centers (DCs) through Multi-Objective Optimization (MOO) and evaluate the cost distance by using the knapsack problem. ABC has been used to solve shortest route and lower cost problems to identify the best selection for replication placement, according to the distance or shortest routes and lower costs that the knapsack approach has used to solve these problems. Multi-objective optimization with the artificial bee colony (MOABC) algorithm can be used to achieve highest efficiency and lowest costs in the proposed system. MOABC can find an optimal solution for the best placement of data replicas according to the minimum distance and the number of data transmissions, affording low cost with the knapsack approach and availability of data replication.Low cost and fast access are characteristics that guide the shortest route in the CloudSim implementation as well. The experimental results show that the proposed MOABC is more efficient and effective for the best placement of replications than compared algorithms. INDEX TERMS Cloud computing, multi-objective optimization, artificial bee colony, replication, cloudsim, and knapsack problem.
The different discrete transform techniques such as discrete cosine transform (DCT), discrete sine transform (DST), discrete wavelet transform (DWT), and mel-scale frequency cepstral coefficients (MFCCs) are powerful feature extraction techniques. This article presents a proposed computer-aided diagnosis (CAD) system for extracting the most effective and significant features of Alzheimer's disease (AD) using these different discrete transform techniques and MFCC techniques. Linear support vector machine has been used as a classifier in this article. Experimental results conclude that the proposed CAD system using MFCC technique for AD recognition has a great improvement for the system performance with small number of significant extracted features, as compared with the CAD system based on DCT, DST, DWT, and the hybrid combination methods of the different transform techniques.
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