Gia Nhu Nguyen

Evolutionary framework for coding area selection from cancer data

AbstractCancer data analysis is significant to detect the codes that are responsible for cancer diseases. It is significant to find out the coding regions from diseases infected biological data. The infected data will be helpful to design proper drugs and will be supportable in laboratory assessments. Codes bear specific meaning on various features as well as symptoms of diseases. Coding of biological data is a key area to get exact information on animals to discover the desired medicine. In the current work, four different machine learning approaches such as support vector machine (SVM), principal component analysis (PCA) technique, neural mapping skyline filtering (NMSF) and Fisher’s discriminant analysis (FDA) were applied for data reduction and coding area selection. The experimental analysis established that the SVM outperforms PCA and FDA. However, due to the mapping facility, NMSF outperforms SVM. Thus, the NMSF achieved the preeminent results among the four techniques. Matthews’s correlation coefficient was used to evaluate the accuracy, specificity, sensitivity, F-measures and error rate of the four methods that are used to determine the coding area. Detailed experimental analysis included comparison study among the four classifiers for the deoxyribonucleic acid dataset.

A survey of the state-of-the-arts on neutrosophic sets in biomedical diagnoses

In real world applications, soft computing is an inspirational domain for encoding imprecision and uncertainty. Soft computing procedures integrated with medical applications can support the existing medical systems to allow solutions for unsolvable problems. Fuzzy techniques have extensive solutions for the medical domain applications; however incorporating a new neutrosophic approaches in the medical domain proves its superiority. The current study reported the main neutrosophic sets (NS) definitions along with different medical applications based on NS. In addition, an extensive discussion for the possibility of prolonging the abilities of the fuzzy systems using the neutrosophic systems was included. The preceding studies established that the NS has a significant role in medical images de-noising, clustering, and segmentation. As a future scope, it was suggested that the neutrosophic medical systems can be exploited for neutrosophic scores; continuous truth/indeterminate/falsity versions of conventional score schemes. The integrated methods of the NS in medical domain would lead to tabular or rule-based mapping from input to output variables. The qualitative simulation of the reported studies established that the neutrosophic model based diagnosis is promising aspirants for future research. Furthermore, the current work highlighted the main medical image processes that can be developed using the NS, including de-noising, thresholding, segmentation, clustering and classification. The general algorithms that can be used to include NS in each task were proposed.

Light microscopy image de-noising using optimized LPA-ICI filter

Microscopic images are often corrupted by noise, where Poisson noise is one of the major types that can damage them. The local polynomial approximation (LPA) filter supported by the intersection confidence interval (ICI) rule was considered as an efficient filter for image de-noising. However, this filter depends on several parameters that affect its performance. In order to determine the optimal parameters, the present study employed the classic LPA-ICI (C-LPA-ICI) filter supported by optimization algorithms, namely the genetic algorithm (GA) and the particle swarm optimization (PSO) in the context of light microscopy imaging systems. Nevertheless, inclusion of the optimization algorithms increased the computational time. A novel automatic technique entitled “Standard Optimized LPA-ICI” (SO-LPA-ICI) is proposed. In this context, the average of the optimized ICI parameters was calculated, which obtained from both LPA-ICI-based GA (G-LPA-ICI) and LPA-ICI-based PSO (P-LPA-ICI). Thus, the proposed SO-LPA-ICI is included the optimal ICI parameters without optimization iterations. This procedure is proposed to speed up the optimized filter. A pool of 50 rats’ renal microscopic images is involved to test the proposed approach. A comparative study was conducted to compare the effectiveness of the four methods, namely C-LPA-ICI, G-LPA-ICI, P-LPA-ICI, and the SO-LPA-ICI for de-noising in the presence of Poisson noise. The experimental results established the outstanding performance of the SO-LPA-ICI in terms of the PSNR, MAE, and MSSIM with 28.27, 7.65, and 0.93 values, respectively. In addition, the proposed approach achieved fast de-noising compared to the G-LPA-ICI and the P-LPA-ICI.

Optimizing feature selection in video-based recognition using Max–Min Ant System for the online video contextual advertisement user-oriented system

The online-advertising has been grown to focus on multimedia interactive model with through the Internet. Our Online Video Advertisement User-oriented (OVAU) system combined the machine learning model for face recognition from camera, multimedia streaming protocols, and video meta-data storage technology. face recognition (FR) is an importance phase which can to enhance the performance of our system. Feature Selection (FS) problem for FR is solved by MMAS-FS algorithms based-on PZMI and DWT features. The features set are represented by digraph G(E, V). Each node used to show the features, and the ability to choose a combination of features is presented the edges connecting between two adjacent nodes. The heuristic information extracted from the selected feature vector as ants pheromone. The feature subset optimal is selected by the shortest length features and best presentation of classifier. The best subset used to classify the face recognition used Nearest Neighbor Classifier (NNC). The experiments were analyzed on FS shows that our algorithm can be easily applied without the priori information of features. The execution assessed of our calculation is more effective than previous approaches for Video-based recognition based on FS problem.

Performance Evaluation of Video-Based Face Recognition Approaches for Online Video Contextual Advertisement User-Oriented System

In this research, we propose the online video contextual advertisement user-oriented system. Our system is a combination of video-based face recognition using machine learning models from the camera with multimedia communications and networking streaming architecture using Meta-data structure to video data storage. The real images captured by the camera will be analyzed based on predefined set of conditions to determine the appropriate object classes. Based on the defined object class, the system will access the multimedia advertising contents database and automatically select and play the appropriate contents. We analyse existing face recognition in videos and age estimation from face images approaches. Our experiment was analyzed and evaluated in performance when we integrate analyze age from the face identification in order to select the optimal approach for our system.

A Performance Analysis of OpenStack Open-Source Solution for IaaS Cloud Computing

Cloud computing is a great design of technology ever made which provides services, applications, and resources through a network. Cloud computing gives the opportunity to use a very large amount of resources on demand. There are many cloud infrastructures as a service (IaaS) frameworks that exist for users, developers, and administrators and they have to make a decision about which environment is best suited for them. In this paper, we focus to analyze and evaluate the performance of the open-source OpenStack for IaaS cloud computing, and give the comparison between OpenStack and VMware. We outline some of our initial findings by providing such a testbed on OpenStack. The experimental results showed the advantages of OpenStack solution in cloud computing where infrastructure is provided as a service.

Ant Colony Optimization Based Load Frequency Control of Multi-area Interconnected Thermal Power System with Governor Dead-Band Nonlinearity

The interconnected thermal power system consists of several areas. Various parameters should be provided to reach power systems’ firm operation. The current work proposed an optimization algorithm, namely Ant colony optimization (ACO) to optimize the Proportional-Integral-Derivative (PID) controller for the load frequency control of two-area interconnected non-reheat thermal power system with Governor dead band nonlinearity. The ACO in used to determine optimal controller’s parameters, where an objective function, namely Integral Time Absolute Error is conducted. A comparative study for the ACO performance to the Craziness based Particle swarm optimization (CPSO) is studied to examine the proposed approach performance in the interconnecting thermal power system. The result established the ACO optimized PID controller response superiority of the compared to the CPSO optimized controller.

A New Ant-Based Approach for Optimal Service Selection with E2E QoS Constraints

In this paper, we study the dynamic service composition becomes a decision problem on which component services should be selected under the E2E QoS constraints. This problem is modeled is a nonlinear optimization problem under several constraints. We have two approaches: local selection is an efficient strategy with polynomial time complexity but can not handle global constraints and the traditional global selection approach based on integer linear programming can handle global constraints, but its poor performance renders it inappropriate for practical applications with dynamic requirements. To overcome this disadvantage,, we proposed a novel Min-Max Ant System algorithm with the utilities heuristic information to search the global approximate optimal. The experiment results show that our algorithm is efficiency and feasibility more than the recently proposed related algorithms.

Cloud Computing and Virtualization

Cloud computing and Virtualization are listed as top strategic technology trends. Analysts say the economics of cloud are compelling with expected savings for business applications of 3-5 times. In this certificate, students study virtualization environments and cloud-base virtualization architecture, developing knowledge and proficiency with cloud and virtualization technologies, mechanisms, platforms, and practices. Holders of this cloud computing certificate will demonstrate an understanding of the architectural concepts of cloud computing platform and procedures for deploying, operating and managing applications in the cloud, as well as knowledge in implementing and managing virtualization technology, one of the essential components to developing effective cloud environments.

Admission control algorithm based on the effective bandwidth in vehicle-to-infrastructure communication.

With pervasive of mobile computing and wireless communication, the vehicular ad-hoc network has become a key technology in modern days information exchange. Due to the fast growing number of vehicles and saturation of the transport infrastructure, it is inhabitable to come out with better solution considering the available resources. In this study, thorough traffic analysis has been carried out to suggest a better model. Further, an attempt has been made to propose an admission control algorithm based-on the parameters evolve from the mathematical analysis. Moreover, the performance analysis has been shown to establish the efficacy of the algorithm.