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Soft Computing Models for Network Intrusion Detection Systems  [PDF]
Ajith Abraham,Ravi Jain
Computer Science , 2004,
Abstract: Security of computers and the networks that connect them is increasingly becoming of great significance. Computer security is defined as the protection of computing systems against threats to confidentiality, integrity, and availability. There are two types of intruders: external intruders, who are unauthorized users of the machines they attack, and internal intruders, who have permission to access the system with some restrictions. This chapter presents a soft computing approach to detect intrusions in a network. Among the several soft computing paradigms, we investigated fuzzy rule-based classifiers, decision trees, support vector machines, linear genetic programming and an ensemble method to model fast and efficient intrusion detection systems. Empirical results clearly show that soft computing approach could play a major role for intrusion detection.
Charming the real world : Python meets the Arduino and physical computing  [cached]
The Python Papers Monograph , 2009,
Abstract: Learn how to use your knowledge of Python to control objects in the Real World (TM) with the Arduino open-source electronics prototyping platform and physical computing.
Soft Computing Optimizer For Intelligent Control Systems Design: The Structure And Applications
Sergey A. Panfilov,Ludmila V. Litvintseva,Ilya S. Ulyanov,Kazuki Takahashi
Journal of Systemics, Cybernetics and Informatics , 2003,
Abstract: Soft Computing Optimizer (SCO) as a new software tool for design of robust intelligent control systems is described. It is based on the hybrid methodology of soft computing and stochastic simulation. It uses as an input the measured or simulated data about the modeled system. SCO is used to design an optimal fuzzy inference system, which approximates a random behavior of control object with the certain accuracy. The task of the fuzzy inference system construction is reduced to the subtasks such as forming of the linguistic variables for each input and output variable, creation of rule data base, optimization of rule data base and refinement of the parameters of the membership functions. Each task by the corresponding genetic algorithm (with an appropriate fitness function) is solved. The result of SCO application is the design of Knowledge Base of a Fuzzy Controller, which contains the value information about developed fuzzy inference system. Such value information can be downloaded into the actual fuzzy controller to perform online fuzzy control. Simulations results of robust fuzzy control of nonlinear dynamic systems and experimental results of application on automotive semi-active suspension control are demonstrated.
Mining the Workload of Real Grid Computing Systems  [PDF]
Marco Guazzone
Computer Science , 2014,
Abstract: Since the mid 1990s, grid computing systems have emerged as an analogy for making computing power as pervasive an easily accessible as an electric power grid. Since then, grid computing systems have been shown to be able to provide very large amounts of storage and computing power to mainly support the scientific and engineering research on a wide geographic scale. Understanding the workload characteristics incoming to such systems is a milestone for the design and the tuning of effective resource management strategies. This is accomplished through the workload characterization, where workload characteristics are analyzed and a possibly realistic model for those is obtained. In this paper, we study the workload of some real grid systems by using a data mining approach to build a workload model for job interarrival time and runtime, and a Bayesian approach to capture user correlations and usage patterns. The final model is then validated against the workload coming from a real grid system.
Analysis of Hybrid Soft and Hard Computing Techniques for Forex Monitoring Systems  [PDF]
Ajith Abraham
Computer Science , 2004,
Abstract: In a universe with a single currency, there would be no foreign exchange market, no foreign exchange rates, and no foreign exchange. Over the past twenty-five years, the way the market has performed those tasks has changed enormously. The need for intelligent monitoring systems has become a necessity to keep track of the complex forex market. The vast currency market is a foreign concept to the average individual. However, once it is broken down into simple terms, the average individual can begin to understand the foreign exchange market and use it as a financial instrument for future investing. In this paper, we attempt to compare the performance of hybrid soft computing and hard computing techniques to predict the average monthly forex rates one month ahead. The soft computing models considered are a neural network trained by the scaled conjugate gradient algorithm and a neuro-fuzzy model implementing a Takagi-Sugeno fuzzy inference system. We also considered Multivariate Adaptive Regression Splines (MARS), Classification and Regression Trees (CART) and a hybrid CART-MARS technique. We considered the exchange rates of Australian dollar with respect to US dollar, Singapore dollar, New Zealand dollar, Japanese yen and United Kingdom pounds. The models were trained using 70% of the data and remaining was used for testing and validation purposes. It is observed that the proposed hybrid models could predict the forex rates more accurately than all the techniques when applied individually. Empirical results also reveal that the hybrid hard computing approach also improved some of our previous work using a neuro-fuzzy approach.
Soft Computing Techniques for Process Control Applications
Rahul Malhotra,Narinder Singh,Yaduvir Singh
International Journal on Soft Computing , 2011,
Abstract: Technological innovations in soft computing techniques have brought automation capabilities to new levelsof applications. Process control is an important application of any industry for controlling the complexsystem parameters, which can greatly benefit from such advancements. Conventional control theory isbased on mathematical models that describe the dynamic behaviour of process control systems. Due to lackin comprehensibility, conventional controllers are often inferior to the intelligent controllers. Softcomputing techniques provide an ability to make decisions and learning from the reliable data or expert’sexperience. Moreover, soft computing techniques can cope up with a variety of environmental and stabilityrelated uncertainties. This paper explores the different areas of soft computing techniques viz. Fuzzy logic,genetic algorithms and hybridization of two and abridged the results of different process control casestudies. It is inferred from the results that the soft computing controllers provide better control on errorsthan conventional controllers. Further, hybrid fuzzy genetic algorithm controllers have successfullyoptimized the errors than standalone soft computing and conventional techniques.
Soft Schemes for Earthquake-Geotechnical Dilemmas  [PDF]
Silvia García
International Journal of Geophysics , 2013, DOI: 10.1155/2013/986202
Abstract: Models of real systems are of fundamental importance in virtually all disciplines because they can be useful for gaining a better understanding of the organism. Models make it possible to predict or simulate a system’s behavior; in earthquake geotechnical engineering, they are required for the design of new constructions and for the analysis of those that exist. Since the quality of the model typically determines an upper bound on the quality of the final problem solution, modeling is often the bottleneck in the development of the whole system. As a consequence, a strong demand for advanced modeling and identification schemes arises. During the past years, soft computing techniques have been used for developing unconventional procedures to study earthquake geotechnical problems. Considering the strengths and weaknesses of the algorithms, in this work a criterion to leverage the best features to develop efficient hybrid models is presented. Via the development of schemes for integrating data-driven and theoretical procedures, the soft computing tools are presented as reliable earthquake geotechnical models. This assertion is buttressed using a broad history of seismic events and monitored responses in complicated soils systems. Combining the versatility of fuzzy logic to represent qualitative knowledge, the data-driven efficiency of neural networks to provide fine-tuned adjustments via local search, and the ability of genetic algorithms to perform efficient coarse-granule global search, the earthquake geotechnical problems are observed, analyzed, and solved under a holistic approach. 1. Introduction There are significant challenges for the future development and application of earthquake-geotechnical engineering that requires innovative approaches within a multidisciplinary framework. Very useful and up-to-date information on the occurrence frequency and impact of earthquake disasters is being assessed and analyzed by a number of organizations around the world. The earthquake-geotechnical engineering is an important bridge between geology, geomorphology, seismology, and civil engineering and serves as the environment where integrated and multidisciplinary approaches can be developed. In such applications, regarding specialized geotechnical engineering merely as a subset of civil engineering will lead to incomplete understanding of problems and the development of inadequate or incomplete solutions. Narrow perspectives can also suffocate progress and innovation. Links between the geosciences, seismology, mathematics, computing, and geotechnical engineering
Soft Computing approaches on the Bandwidth Problem  [PDF]
Gabriela Czibula,Gloria Cerasela Crisan,Camelia-M. Pintea,Istvan-Gergely Czibula
Computer Science , 2012,
Abstract: The Matrix Bandwidth Minimization Problem (MBMP) seeks for a simultaneous reordering of the rows and the columns of a square matrix such that the nonzero entries are collected within a band of small width close to the main diagonal. The MBMP is a NP-complete problem, with applications in many scientific domains, linear systems, artificial intelligence, and real-life situations in industry, logistics, information recovery. The complex problems are hard to solve, that is why any attempt to improve their solutions is beneficent. Genetic algorithms and ant-based systems are Soft Computing methods used in this paper in order to solve some MBMP instances. Our approach is based on a learning agent-based model involving a local search procedure. The algorithm is compared with the classical Cuthill-McKee algorithm, and with a hybrid genetic algorithm, using several instances from Matrix Market collection. Computational experiments confirm a good performance of the proposed algorithms for the considered set of MBMP instances. On Soft Computing basis, we also propose a new theoretical Reinforcement Learning model for solving the MBMP problem.
Hard Data on Soft Errors: A Large-Scale Assessment of Real-World Error Rates in GPGPU  [PDF]
Imran S. Haque,Vijay S. Pande
Computer Science , 2009,
Abstract: Graphics processing units (GPUs) are gaining widespread use in computational chemistry and other scientific simulation contexts because of their huge performance advantages relative to conventional CPUs. However, the reliability of GPUs in error-intolerant applications is largely unproven. In particular, a lack of error checking and correcting (ECC) capability in the memory subsystems of graphics cards has been cited as a hindrance to the acceptance of GPUs as high-performance coprocessors, but the impact of this design has not been previously quantified. In this article we present MemtestG80, our software for assessing memory error rates on NVIDIA G80 and GT200-architecture-based graphics cards. Furthermore, we present the results of a large-scale assessment of GPU error rate, conducted by running MemtestG80 on over 20,000 hosts on the Folding@home distributed computing network. Our control experiments on consumer-grade and dedicated-GPGPU hardware in a controlled environment found no errors. However, our survey over cards on Folding@home finds that, in their installed environments, two-thirds of tested GPUs exhibit a detectable, pattern-sensitive rate of memory soft errors. We demonstrate that these errors persist after controlling for overclocking and environmental proxies for temperature, but depend strongly on board architecture.
Optimization of Operating Systems towards Green Computing  [cached]
Appasami Govindasamy,Suresh Joseph K
International Journal of Combinatorial Optimization Problems and Informatics , 2011,
Abstract: Green Computing is one of the emerging computing technology in the field of computer science engineering and technology to provide Green Information Technology (Green IT). It is mainly used to protect environment, optimize energy consumption and keeps green environment. Green computing also refers to environmentally sustainable computing. In recent years, companies in the computer industry have come to realize that going green is in their best interest, both in terms of public relations and reduced costs. Information and communication technology (ICT) has now become an important department for the success of any organization. Making IT “Green” can not only save money but help save our world by making it a better place through reducing and/or eliminating wasteful practices. In this paper we focus on green computing by optimizing operating systems and scheduling of hardware resources. The objectives of the green computing are human power, electrical energy, time and cost reduction with out polluting the environment while developing the software. Operating System (OS) Optimization is very important for Green computing, because it is bridge for both hardware components and Application Soft wares. The important Steps for green computing user and energy efficient usage are also discussed in this paper.
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