Review on Image Representation Compression and Retrieval Approaches

Image representation and compression is one of the important fields of computer vision that contribute to the reduction of size of an image and other types of application areas such as image restoration, retrieval, etc. Image representation is important with respect to storage of image information, and it further extends to the compression, which may be lossy or lossless. Image compression can be applied to various applications which mainly include medical imaging, traffic monitoring, military, multimedia transmission, smart cell devices, and almost in all the domains that require less transmission and storage cost, specifically image retrieval processing. This chapter presents the various image representation compression and retrieval approaches. The retrieval approaches on personal computer and smart cell devices are discussed. Finally, the key issues are identified for image representation compression and retrieval on the basis of performance evaluation parameters like encoding time, decoding time, compression ratio, precision, recall, and elapsed time.

A Clustering Approach Based on Cooperative Agents to Improve Decision Support in ERP

Multi-agent systems (MAS) are a powerful technology for the design and implementation of autonomous intelligent systems that can handle distributed problem solving in a complex environment. This technology has played an important role in the development of data mining systems in the last decade, the purpose of which is to promote the extraction of information and knowledge from a large database and to make these systems more scalable. In this chapter, the authors present a clustering system based on cooperative agents through a centralized and common ERP database to improve decision support in ERP systems. To achieve this, they use multi-agent system paradigm to distribute the complexity of k-means algorithm in several autonomous entities called agents, whose goal is to group records or observations on similar objects classes. This will help business decision makers to make good decisions and provide a very good response time by the use of the multi-agent system. To implement the proposed architecture, it is more convenient to use the JADE platform while providing a complete set of services and have agents comply with the specifications FIPA.

Possible Approaches for Character Recognition With Existing Methodologies and State-of-the-Art Techniques

The world started to talk about optical character recognition (OCR) around 1870. Then over another 25 years OCR systems were designed for industrial applications. And now the OCR software is easily available online for free, through products like Acrobat reader, WebOCR, etc. But still the research is on. Do we need to switch direction or introduce new hypothesis are some of the key questions? The purpose of this chapter is to answer the above questions and propose new methods for character recognition.

Web-Based Decision Support System for Solving Multiple-Objective Decision-Making Problems

A web-based Decision Support System WebOptim for solving multiple objective optimization problems is presented. The system WebOptim is designed in a modular principle, extensively using XML as communication standard and web services. Its basic characteristics are: user-independent, multisolver-admissibility, method-independent, heterogeneity, web-accessibility. Core system module is an original generalized interactive scalarizing method. It incorporates a number of thirteen interactive methods. Most of the known scalarizing approaches (reference point approach, reference direction approach, classification approach etc.) are realized in this method. The Decision Maker (DM) can choose the most suitable for him/her form for setting his/her preferences: objective weights, aspiration levels, aspiration directions, aspiration intervals. This information could be changed interactively by the DM during the solution process. Depending on the DM's preferences form the suitable scalarizing method is chosen automatically. The chapter begins with an overview of Decision Support Systems (DSS). Examples of DSSs and their applications are discussed.

Approach of Modelization and Management on Software Components

The complexity of information systems leads to poor data management and therefore bad decisions. The advantage of using component-based software engineering is to divide an information system into subsystem blocks with less complexity. In fact, a component is intended to provide specific services as management of the combination and communication between the units of the system. This chapter addresses this problem by developing information systems, proposing a component identification approach and the management pattern for data management. In this work, three developed views are taken into account, which include modelization and the design to achieve the purpose of defining and building the components and how they can be assembled. A component is intended to provide specific services, as a combination and communication management between the system units, and the manager component is the important and complementary paradigm pattern and added value for the development of software systems.

Agile Supplier Assessment Using Generalized Interval-Valued Trapezoidal Fuzzy Numbers

The supplier assessment has always been a key area to be developed for necessary advancement and evaluation by the researchers. In this chapter, the authors react upon the agile characteristics and furnish an agile supplier assessment decision support system to be utilized by the managers of the distinguish firm for assessing the agile characteristics of the partner supplier firm. The authors presented a modeling based on generalized interval-valued trapezoidal fuzzy numbers (GIVTFNs) to assess the status of partner supplier concerning agile characteristics. A multi-criterion decision-making (MCDM) appraisement module is built by the authors and a decision support system is developed by them to judge the scale of agile characteristics in the supply chain (SC) network of the supplier firm. A second-level hierarchy appraisement module is discussed by the authors to illustrate the procedural implementation of the proposed work. Here, the authors proposed a decision support model, which can grab the subjective views of the decision makers. The authors utilized GIVTFNs to clutch the uncertainty and vagueness of the measures and their metrics (sub-measures) of the discussed agile platform. In this study, a fuzzy performance important index model is presented to recognize the weak and strong agile measures and metrics of the supplier firm. The major intention of the authors in this study is to deliver a knowledge-based technical model for the sake of determining the quality of agile strategies by the supplier firm in their SC network.

Bat Algorithm With Generalized Fly for Combinatorial Production Optimization Problems

A set of metaheuristics has proved its efficiency in solving rapidly NP-hard problems. Several combinatorial and continuous optimization areas drew profit from these powerful alternative techniques. This chapter intends to describe a discrete version of bat algorithm (BA) combined to generalized walk evolutionary (GEWA), also called bat algorithm with generalized fly or walk (BAG) in order to solve discrete industrial optimization. The first case of study is the well-known hybrid flow shop scheduling. The second one concerns the operating theatre that represents a critical manufacturing system, as the products delivered are patients. The last problem is the redundancy optimization (ROP) for series-parallel multi-state power system (MSS). Its resolution involves the selection of components with an appropriate level of redundancy to maximize system reliability with constrained cost. A universal moment generating function (UMGF) is used to estimate reliabilities. The modified bat algorithm on specific benchmarks was compared with the original one, and other results taken from the literature of each case study.

A Multicriteria Spatiotemporal System for Influenza Epidemic Surveillance

Influenza has been a growing concern for the public health decision makers/policy makers. Indeed, they are in need of a real geo-making tool for monitoring and surveillance. The chapter aims to introduce a novel spatiotemporal decision system based on multicriteria ranking method, information geographic system (GIS), and SEIRSW system for public health. The later was designed, implemented, and validated in previous research for influenza risk assessment. The authors highlight the use of PROMETHEE II ranking method of multi-criteria decision analysis in GIS that incorporates various factors to monitor and identify potential high-risk areas of seasonal influenza and disease mapping. Factors related to the risk of seasonal influenza are obtained from simulation system and constitute the input values of PROMETHEE II ranking method for the 26 communes of the city of Oran, Algeria. The proposed system has demonstrated analytical capabilities in targeting high-risk spots and influenza surveillance monitoring system and it can help public health policy makers prioritize in their response goals and evaluate control strategies.

Simultaneous Modelling-to-Generate-Alternatives Procedure Employing the Firefly Algorithm

“Real-world” decision-making applications generally contain multifaceted performance requirements riddled with incongruent performance specifications. This is because decision making typically involves complex problems that are riddled with incompatible performance objectives and contain competing design requirements which are very difficult—if not impossible—to capture and quantify at the time that the supporting decision models are actually constructed. There are invariably unmodelled elements, not apparent during model construction, which can greatly impact the acceptability of the model's solutions. Consequently, it is preferable to generate several distinct alternatives that provide multiple disparate perspectives to the problem. These alternatives should possess near-optimal objective measures with respect to all known objective(s), but be maximally different from each other in terms of their decision variables. This maximally different solution creation approach is referred to as modelling-to-generate-alternatives (MGA). This chapter provides an efficient optimization algorithm that simultaneously generates multiple, maximally different alternatives by employing the metaheuristic firefly algorithm. The efficacy of this mathematical programming approach is demonstrated on a commonly tested engineering optimization benchmark problem.

Wireless Sensor Networks

Wireless sensor networks (WSNs) can be classified among the blazing domains of research in the recent era. WSNs have enormous day-to-day life real-time applications due their low priced, self-computing, powerful, autonomous small sensor nodes which have a small storage capacity, restricted non-removable non-rechargeable battery, and a restricted computational capacity. The applicability of WSNs are in almost all domains, like observing environmental conditions, human healthcare tracking systems, position tracking and monitoring, industry automation, process tracking and controlling, tracking and monitoring objects, mammal, human being, and control, and many more. This chapter briefly explores the basic concepts, components, network architecture, design issues, challenges, routing protocols, application domains, implemented applications, etc. in the field of WSNs. It also focuses on the performance evaluation parameters to check, analyze, diagnose, examine, and determine the performance of WSNs. At the end, the chapter concludes with the scope of research in the field of wireless sensor networks.