The recent growth of search technology has enabled people to find information more easily. However, most people need to refind information on a daily basis. Finding and refinding are different activities and require different types of support. However, current refinding support systems don't consider this point. This has caused several problems: PVR, loss of contextual information, and difference in search experiences. We discuss these problems and their solutions from a cognitive perspective. We propose a process-recollective refinding support system based on this discussion. We demonstrate a novel approach to refinding information on the web and a specific system as an example.

Highly-available and scalable software systems can be a complex and expensive proposition. Traditional scalable software architectures have not only needed to implement complex solutions to ensure high levels of reliability, but have also required an accurate forecast of traffic to provide a high level of customer service. This traditional software architecture is built around a common three-tier web application model that separates the architecture into presentation, business logic and database layers. This architecture has already been designed to scale out by adding additional hosts at these layers and has built-in performance, failover and availability features. Even with all these developments in architectural designs, some software still lacks in scalability, reliability and efficiency. This paper therefore examines the shortfalls of traditional software architectural problems with a view to addressing them using the cloud computing approach.

Feature selection has been keen area of research in classification problem. Most of the researchers mainly concentrate on statistical measures to select the feature subset. These methods do not provide a suitable solution because the search space increases with the feature size. The FS is a very popular area for applications of population-based random techniques. This paper suggests swarm optimization technique, binary particle swarm optimization technique and its variants, to select the optimal feature subset. The main task of the BPSO is the selection of the features used by the SVM in the classification of spambase data set. The results of our experiments show a very strong relation between number of features and accuracy. Comparison of the optimized results and the un-optimized results showed that the BPSO-MS method could significantly reduce the computation cost while improving the classification accuracy.

The software productivity and quality can be improved by the systematic reuse of software. Reuse and reusability are the two important factors in the software development. Reusability is a prime principle in service oriented architecture. For realizing the reuse of service, the adequacy of ability of service reuse should be properly quantified. The quantification of reuse needs a suite of metrics and many metrics are also available. Each research and publication focuses on different qualities of service to propose a set of metrics for service reusability. This paper studies the service reusability to understand the concept and survey the reusability metrics. The purpose of the paper is exploring the available service reusability metrics for further research.

Image classification is one of classical problems of concern in image processing. There are various approaches for solving this problem. The aim of this paper is bring together two areas in which are Artificial Neural Network (ANN) and Support Vector Machine (SVM) applying for image classification. Firstly, we separate the image into many sub-images based on the features of images. Each sub-image is classified into the responsive class by an ANN. Finally, SVM has been compiled all the classify result of ANN. Our proposal classification model has brought together many ANN and one SVM. Let it denote ANN_SVM. ANN_SVM has been applied for Roman numerals recognition application and the precision rate is 86%. The experimental results show the feasibility of our proposal model.

Wireless sensor networks are often deployed for data-gathering or monitoring in a geographical region. This paper explains an important issue to maintain the fidelity of the sensed data while minimizing energy usage in the network. Nature inspired computation like evolutionary computation, swarm intelligence etc., which offers practical advantages to the researcher facing difficult optimization problems. The genetic algorithms are used for efficient connectivity and coverage. Single Objective Genetic Algorithms (SOGA) method is used to yield good results in terms of Coverage, but the objective’s graph had shown Pareto optimal designs with differing Endurance. However it is attractive to offer Pareto optimal designs to a user willing to settle for a poorer Coverage in order to gain in Endurance, so that the sensor network lasts longer. This explains concept of Multiple Objective Genetic Algorithm (MOGA) and its implementation and results which are compared to those of the SOGA. Endurance and Robustness to deployment inaccuracy tend to work in the same direction. A MOGA was conducted with the Coverage and Robustness as objectives. The main objective of this paper is to propose new Strength Perito Evolutionary Algorithm (SPEA) method along with clustering, this will reduce the distances between the sensor nodes that increase the efficiency of the nodes and also increase the connectivity. This will increase lifetime of sensors and connectivity.

This paper has presented our interest in wireless underwater optical communications. Recent interest in ocean exploration has brought about a desire for developing wireless communication techniques in this challenging environment. Due to its high attenuation in water, a radio frequency (RF) carrier is not the optimum choice. Acoustic techniques have made tremendous progress in establishing wireless underwater links, but they are ultimately limited in bandwidth. In traditional communication systems, constructing a link budget is often relatively straight forward. In the case of underwater optical systems the variations in the optical properties of sea water lead to interesting problems when considering the feasibility and reliability of underwater optical links. The main focus of this paper is to construct an underwater link budget which includes the effects of scattering and absorption of realistic sea water. As well as we have developed the underwater optical wireless communication systems to have shorter ranges, that can provide higher bandwidth (up to several hundred Mbit/sec) communications by the assistant of exciting high brightness blue LED sources, and laser diodes suggest that high speed optical links can be viable for short range application.

The Bin Packing Problem (BPP) is one of the most known combinatorial optimization problems. This problem consists to pack a set of items into a minimum number of bins. There are several variants of this problem; the most basic problem is the one-dimensional bin packing problem (1-BPP). In this paper, we present a new approach based on the quantum inspired cuckoo search algorithm to deal with the 1-BPP problem. The contribution consists in defining an appropriate quantum representation based on qubit representation to represent bin packing solutions. The second contribution is proposition of a new hybrid quantum measure operation which uses first fit heuristic to pack no filled objects by the standard measure operation. The obtained results are very encouraging and show the feasibility and effectiveness of the proposed approach.