Blockchain Scalability Analysis and Improvement of Bitcoin Network through Enhanced Transaction Adjournment Techniques
Data Lake System for Essay-Based Questions: A Scenario for the Computer Science Curriculum
Creating Secure Passwords through Personalized User Inputs
Optimizing B-Cell Epitope Prediction: A Novel Approach using Support Vector Machine Enhanced with Genetic Algorithm
Gesture Language Translator using Morse Code
Efficient Agent Based Priority Scheduling and LoadBalancing Using Fuzzy Logic in Grid Computing
A Survey of Various Task Scheduling Algorithms In Cloud Computing
Integrated Atlas Based Localisation Features in Lungs Images
A Computational Intelligence Technique for Effective Medical Diagnosis Using Decision Tree Algorithm
A Viable Solution to Prevent SQL Injection Attack Using SQL Injection
SOA [Service Oriented Architecture] and service-orientation have laid the foundation for a variety of emergent service technology innovations, while the original building blocks of SOA and service-orientation continue to evolve by embracing fundamental service technologies, concepts and practices. These new technology innovations do not replace service-orientation; they use it as their basis. Service-orientation continues to evolve towards a factory approach, towards industrializing integrated platforms, such as BI, Master Data Management (MDM), mobile front-ends, BPM, adaptive processes, Big Data and Cloud Computing - all of which add architectural layers upon SOA-based infrastructure. All of these technologies can interface via standardized data and functions, published as service contracts, in order to avoid redundancy - that's service-orientation [3]. Advance software modularity focuses on the novel requirements of modularizing and uniting software schemes on desires and architecture plans such as [1,2]. Aspectoriented requirements engineering, Requirements and architecture design techniques for software product line engineering; Requirements engineering for service-oriented systems [12]. This paper basically deals with requirements engineering for service-oriented systems, needs for SOA, SOA practices in the mobile age, industrial SOA toolkit, it's maturity as well as SOA & user interfaces.
In this paper, we introduce a new learning algorithm for neural network. A New Adaptive bacteria foraging optimization based on particle swarm optimization(ABFO_PSO) is used in learning neural network. This paper reviews Feed Forward Neural Network (FFANN), and the drawback of back- propagation learning method. Particle Swarm Optimization (PSO) is also described. Moreover, using the PSO in learning neural network is reviewed. Bacterial Foraging Optimization (BFO) is a novel heuristic algorithm inspired from forging behavior of E. coli. It is predominately used to find solutions for real-world problems, but it has problem with time and convergence behaviour. To introduce ABFO_PSO, that provide solution for BFO problem, we make a hybrid between PSO and BFO. Moreover, BFO and ABFO_PSO are applied for learning neural network. The comparison between the results of ABFO_PSO, BFO and PSO for learning neural network shows the strength of new method.
A major challenge of Dynamic reconfiguration technique is to maintain Quality of Service, which is meant to reduce application disruption during the system transformation. Dynamic reconfiguration [4] technique involves the ability to change the system's functionality or topology while the system is running. This technique involves safe dynamic reconfiguration such as insertion, removal and replacement of components. Dynamic reconfiguration technique looks very much like traditional control system model of 'sense-plan-act'. Wei li [1] discussed the problem for componentbased software systems. In [1] the authors have defined the spectrum of QoS characteristics, the requirements for QoS characteristics are analyzed and solutions proposed to achieve them. They further classified the prior work based on QoS characteristics and then realized by abstract reconfiguration strategies. First-in-first-out order is important to some applications like Railway Reservation Systems which is not discussed in [1]. The Proposed work concentrates on ensuring the order of requests during reconfiguration. The concept can be proved by simulating it on the online applications to detect the QoS violations. The most important conclusion from our investigation is that the classified QoS Characteristics can be fully achieved under some acceptable constraints.
Job outsourcing in grid computing generally face the problem of security threats and doubtful trustworthiness of remote resources. On the other hand, the scheduled processes at the remote host may exploit the given privileges and misuse the services of the remote host. We developed a monitoring system using Hidden Markov Model to detect such anomalous behavior of the processes at the remote host. The main objective of this paper is to build a monitoring system, a predictive model capable of discriminating between normal and abnormal behavior of a process in its run-time. The model is built with the parameters derived from the type of operations performed by the process. The monitoring system flags any observation that has a significant deviation from the observed model. The system is built using Hidden Markov Model (HMM) and the parameters are identified for building the monitoring system includes CPU-Limit, Memory-Limit, File- Limit, File-Size- Limit, Process-Limit etc. An HMM is initially trained with the normal flow of operations of a process. The incoming process operation is rejected when it is not accepted by the trained HMM with sufficiently high probability and also we extract the state sequence followed by the process in its run-time. From this state sequence, we determine the number of violations in resource access than permitted or allowed. At the same time we try to ensure that genuine processes are not rejected. The system is implemented in UNIX environment.
Segmentation of the pulmonary lobes is relevant in clinical practice and particularly challenging for cases with severe diseases or incomplete fissures. In this work, an automated segmentation approach is presented that performs a transformation on Computed Tomography (CT) scans to subdivide the lungs into lobes. Content- based image retrieval has been a major research area with major focus on features extraction, due to its impact on image retrieval performance. When applying this in the medical field, required different feature extraction method that integrate some domain specific knowledge for effective image retrieval. Here a novel method called atlas based segmentation is proposed. Atlas methods usually require the use of image registration in order to align the atlas image or images to a new, unseen image. This method provides complementary information from past cases with confirmed diagnoses, to lung tissue classification and quantification in CT images. The system exploits the location of the pathological lung tissue and allows significant improvement in terms of early retrieval precision when compared to the approach based on global features only.
