Recently, an Information and Communications Technology (ICT) devices has become more user-friendly, which raised the problem of power dissipation across the globe and computer systems are one among them. This emerging issue of power dissipation has imposed a very significant issue on the system and software design. The concept of ‘green computing’ gaining popularity and is being considered as one of the most promising technology by the designers of Information Technology (IT) industry, which demonstrate the environmentally responsible way to reduce the power consumption and maximize the energy efficiency. In this paper, we have proposed an energy sustainable framework of the power schemes for operating systems to reduce the power consumption by computer systems and presented a Green Power tool (GP tool). This tool is designed using JAVA technology, which requires least configuration to make a decision for reducing the power consumption and proposed Swift mode algorithm, allows users to input the working time of their choice then after the end of time algorithm starts detection of human activity on the computer system. We also compared the Swift mode algorithm with existing power scheme in the operating system that provides up to 66% of the power saving. Finally, we have profiled the proposed framework to analyze the memory and Central Processing Unit (CPU) performance, which demonstrated that there is no memory leakage or CPU degradation problem and framework’s behavior remain constant under various overhead scenarios of the memory as well as CPU. The proposed framework requires 3–7 MB memory space during its execution.

An important issue faced by risk analysts is how to deal with uncertainties associated with accident scenarios. In industry, one often uses single values de-rived from historical data or literature to estimate events probability or their frequency. However, both dynamic environments of systems and the need to consider rare component failures may make unrealistic this kind of data. In this paper, uncertainty encountered in Layers Of Protection Analysis (LOPA) is considered in the framework of possibility theory. Data provided by reliability databases and/or experts judgments are represented by fuzzy quantities (possibilities). The fuzzy outcome frequency is calculated by extended multiplication using α-cuts method. The fuzzy outcome is compared to a scenario risk tolerance criteria and the required reduction is obtained by resolving a possibilistic decision-making problem under necessity constraint. In order to validate the proposed model, a case study concerning the protection layers of an operational heater is carried out.

Some Web sites developers act as spammers and try to mislead the search engines by using illegal Search Engine Optimizations (SEO) tips to increase the rank of their Web documents, to be more visible at the top 10 SERP. This is since gaining more visitors for marketing and commercial goals. This study is a continuation of a series of Arabic Web spam studies conducted by the authors, where this study is dedicated to build the first Arabic content/link Web spam detection system. This Novel system is capable to extract the set of content and link features of Web pages, in order to build the largest Arabic Web spam dataset. The constructed dataset contains three groups with the following three percentages of spam contents: 2%, 30%, and 40%. These three groups with varying percentages of spam contents were collected through the embedded crawler in the proposed system. The automated classification of spam Web pages used based on the features in the benchmark dataset. The proposed system used the rules of Decision Tree; which is considered as the best classifier to detect Arabic content/link Web spam. The proposed system helps to clean the SERP from all URLs referring to Arabic spam Web pages. It produces accuracy of 90.1099% for Arabic content-based, 93.1034% for Arabic link-based, and 89.011% in detecting both Arabic content and link Web spam, based on the collected dataset and conducted analysis.

There are many business process modelling techniques, which allow to capture features of those processes, but graphical, diagrammatic models seems to be used most in companies and organizations. Although the modelling notations are more and more mature and can be used not only to visualise the process idea but also to implement it in the workflow solution and although modern software allows us to gather a lot of data for analysis purposes, there is still not much commercial used business process optimisation methods. 
In this paper the scheduling / optimisation method for automatic task scheduling in business processes models is described. The Petri Net model is used, but it can be easily applied to any other modelling notation, where the process is presented as a set of tasks, i.e. BPMN (Business Process Modelling Notation). 
The method uses Petri Nets’, business processes’ scalability and dynamic programming concept to reduce the necessary computations, by revising only those parts of the model, to which the change was applied.

This paper presents a CLL resonant converter with DSP based Fuzzy Logic Controller (FLC) for solar panel to battery charging system. The mathematical model of the converters has been developed and simulated using MATLAB. The state space model of the converter is developed; it is used to analysis the steady state stability of the system. The aim of the proposed converter is to regulate and control of the output voltage from the solar panel voltage. The performance of the proposed converter is validated through experiments with a 75-Watt solar panel. The effectiveness of the controller is verified for supply change and load disturbance. The converter is implemented on a TMS320F2407 Digital Signal Processor with 75-Watt PV system. Comparison between experimental and simulations show a very good agreement and the reliability of fuzzy controller.

According to technical statistics, current TCP protocols with approximately 80% Internet applications run on perform very well on wired networks. However, due to the effects of long propagation delay, great band- width asymmetry, high sporadic Bit Error Rate (BER) and etc., TCP performance degrades obviously on the satellite communication networks. To avoid the problems, TP-S, a novel transport control protocol, is introduced for satellite IP networks. Firstly, in order to increase the increment speed of Congestion Window (cwnd) at the beginning of data transmission, the traditional Slow Start strategy is replaced by a new strategy, known as Super Start. Secondly, a new packet lost discriminated scheme based on IP packets alternately sending with different priority is used in the protocol to decouple congestion decision from errors. Thirdly, bandwidth asymmetry problem is avoided by adopting Modified NACK (M-NACK) in receiving ends, which is sent periodically. In addition, the sending strategy in routers is also modified along with other’s changes to support the protocol. Finally, the simulation experiments show that the new protocol can not only significantly enhance throughput performance, but also reduce sharply bandwidth used in the reverse path as compared with traditional TCP protocols and those protocols, which are recently proposed for satellite IP networks.

An Artificial Neural Network (ANN) is an abstract representation of the biological nervous system which has the ability to solve many complex problems. The interesting attributes it exhibits makes an ANN capable of “learning”. ANN learning is achieved by training the neural network using a training algorithm. Aside from choosing a training algorithm to train ANNs, the ANN structure can also be optimized by applying certain pruning techniques to reduce network complexity. The Cat Swarm Optimization (CSO) algorithm, a swarm intelligence-based optimization algorithm mimics the behavior of cats, is used as the training algorithm and the Optimal Brain Damage (OBD) method as the pruning algorithm. This study suggests an approach to ANN training through the simultaneous optimization of the connection weights and ANN structure. Experiments performed on benchmark datasets taken from the UCI machine learning repository show that the proposed CSONN-OBD is an effective tool for training neural networks.

A Mobile Ad hoc Network (MANET) is a collection of autonomous self-organized nodes. They use wireless medium for communication, thus two nodes can communicate directly if and only if they are within each other’s transmission radius in a multi-hop fashion. Many conventional routing algorithms have been proposed for MANETs. An emerging area that has recently captured much attention in network routing researches is Swarm Intelligence (SI). Besides conventional approaches, many new researches have proposed the adoption of Swarm Intelligence for MANET routing. Swarm Intelligence (SI) refers to complex behaviors that arise from very simple individual behaviors and interactions, which is often observed in nature, especially among social insects such as ants, bees, fishes etc. Although each individual has little intelligence and simply follows basic rules using local information obtained from the environment. Ants routing resembles basic mechanisms from distributed Swarm Intelligence (SI) in biological systems and turns out to become an interesting solution where routing is a problem. Ants based routing is gaining more popularity because of its adaptive and dynamic nature. A number of Swarm Intelligence (SI) based algorithms were proposed by researchers. In this paper, we study bio-inspired routing protocols for MANETs.

A magic square of 3×3 and its multiples i.e. (9×9) squares and so on, of order N are composed of (n×n) matrix having filled with numbers in such a way that the totals sum along the rows ,columns and main diagonals adds up the same. By using a special geometrical figure developed.

In this paper we scrutinize the influence of fusion on the face recognition performance. In pattern recognition task, benefiting from different uncorrelated observations and performing fusion at feature and/or decision levels improves the overall performance. In features fusion approach, we fuse (concatenate) the feature vectors obtained using different feature extractors for the same image. Classification is then performed using different similarity measures. In decisions fusion approach, the fusion is performed at decisions level, where decisions from different algorithms are fused using majority voting. The proposed method was tested using face images having different facial expressions and conditions obtained from ORL and FRAV2D databases. Simulations results show that the performance of both feature and decision fusion approaches outperforms the single performances of the fused algorithms significantly.