Earticle

To design an adaptive intelligent tutoring system which can manage both different disciplinary domains and a guide for the learner is difficult. The specialization of the analysis treatments is responsible for the loss of reusability in other disciplinary domains. The analysis is didactic and thus strongly connected to the domain concerned. It results that an intelligent tutoring system is consequently, specialized in a type of taught knowledge and not easily transposable to other domains. To propose a model transposable to different domains of learning, the former has to take into account this diversity and to situate the learning activity. In this paper, we will show how to produce a guide model parameterized by the learning domain. Our objective was to develop an adaptive intelligent tutoring system based on item response theory and metrics, adapted for letting the learners work in several disciplinary fields in the University of Annaba. In this context, our constraint is threefold: to represent knowledge relative to several disciplinary domains, to propose interactive activities to the learners and finally, to be able to support student guidance in her/his course by proposing her/him relevant support activities when he meets difficulties.

In this paper, a real-time Human-Computer Interaction (HCI) based on the hand data glove and K-NN classifier for gesture recognition is proposed. HCI is moving more and more natural and intuitive way to be used. One of the important parts of our body is our hand which is most frequently used for the Interaction in Digital Environment and thus complexity and flexibility of motion of hands are the research topics. To recognize these hand gestures more accurately and successfully data glove is used. Here, gloves are used to capture current position of the hand and the angles between the joints and then these features are used to classify the gestures using K-NN classifier. The gestures classified are categorized as clicking, rotating, dragging, pointing and ideal position. Recognizing these gestures relevant actions are taken, such as air writing and 3D sketching by tracking the path helpful in virtual augmented reality (VAR). The results show that glove used for interaction is better than normal static keyboard and mouse as the interaction process is more accurate and natural in dynamic environment with no distance limitations. Also it enhances the user’s interaction and immersion feeling.

The feature selection for classification is a very active research field in data mining and optimization. Its combinatorial nature requires the development of specific techniques (such as filters, wrappers, genetic algorithms, simulated annealing, and so on) or hybrid approaches combining several optimization methods. In this context, the support vector machine recursive feature elimination (SVM-RFE), is distinguished as one of the most effective methods. However, the RFE-SVM algorithm is a greedy method that only hopes to find the best possible combination for classification. To overcome this limitation, we propose an alternative approach with the aim to combine the RFE-SVM algorithm with local search operators based on operational research and artificial intelligence. To assess the contributions of our approach, we conducted a series of experiments on datasets from UCI Machine Learning Repository. The experimental results which we obtained are very promising and show the contribution of the local search on the classification process. The main conclusion is that the reuse of features previously removed during the RFE-SVM process improves the quality of the final classifier.

Quality of Service (QoS) provision is of fundamental concern in high performance data networks. The system’s load and capacity evaluation are dependent on prescribed QoS parameters. The Service Level Agreements among telecom operators focus on their provision. In ubiquitous networks, different technologies with varying architectures and design merge together to enable the make before break concept. In such scenarios, QoS parameters are very critical. The paper presents a QoS enhancement framework for ubiquitous networks aimed at evaluating and routing the received network traffic on basis of intelligently evaluated parameters, enabling the network to perform efficiently.

The paper aims to study numerically on some special cases of mathematical model of An Ecological Ammensalism with multiple constraints. The mathematical model comprises of Ammensal-enemy species pair with cover for Ammensal, alternative resources for enemy and migration for both the species. The model is formed by a couple of first order non linear ordinary differential equations.Some relations among the carrying capacity of enemy species, inhibition coefficient of Ammensal species and the dominance reversal time are investigated.

In recent years, radio frequency identification(RFID) technology has moved from obscurity into mainstream applications that speed up in identifying objects(e.g. manufactured goods, healthcare services, etc.) which affect our daily lives. However, there are several issues arises such as RFID tag collision problem which occurs due to the usage characteristics of radio frequency communication. Therefore, to answer these issues, we proposed the tag identification method which uses a multi-reader to minimize tag collision and to reduce the tag recognition time. The proposed method analyze the tag recognition time by applying the adaptive group separation(AGS) algorithm. Based on our experimental results, the proposed method shows the improvement rate of over 40% increased on processing time.

The tests were carried out with the spark timing adjusted to the maximum brake torque timing in various equivalence ratios and engine speeds for gasoline and natural gas operations. In this work, the lower heating value of gasoline is about 13.6% higher than that of natural gas. Based on the experimental results, the natural gas operation causes an increase of about 6.2% brake special fuel consumption, 22% water temperature difference between outlet and inlet engine, 3% exhaust valve seat temperature, 2.3% brake thermal efficiency and a decrease of around 20.1% maximum brake torque, 6.8% exhaust gas temperature and 19% lubricating oil temperature when compared to gasoline operation. The results also revealed that, over the entire range of engine speed and equivalence ratios, the exhaust gas temperature and the lubricating oil temperature for gasoline operation is higher than that of natural gas operation while the exhaust valve seat temperature for natural gas operation is higher.

Task scheduling is essential for the suitable operation of multiprocessor systems. The task scheduling is prime significance of multiprocessor parallel systems. In this paper, an efficient method based on genetic algorithms is developed to solve the multiprocessor scheduling problem. The paper also aims to provide a comparative study of incorporating heuristics such as ‘Earliest Deadline First (EDF)’ and ‘Shortest Computation Time First (SCTF)’ separately with genetic algorithms. We exhibit efficiency of Node duplication Genetic Algorithm (NGA) based technique by comparing against some of the existing deterministic scheduling techniques for minimizing inter processor traffic communication. A comparative study of the results obtained from simulations shows that genetic algorithm can be used to schedule tasks to meet deadlines, in turn to obtain high processor utilization. Performance analysis of NGA is compared with GA, FCFS and List Scheduler. Experimental results show that the NGA is betters than the others. Performance of NGA, GA, List Scheduling, and FCFS is almost 64.1%, 55.55%, 52.08, and 41.033% respectively.

Javanese puppet that is also called wayang is one of traditional culture from Java beside traditional music and traditional dance. There are 8 types of wayang in Indonesia such as wayang beber, wayang purwa, wayang madya, etc. In this case, we focus on wayang purwa especially for wayang kulit. There are 223 pieces of wayang kulit for one set. The art of wayang kulit is not only from the real form, but also from the shadow of the puppet. It can be more artistic depending on the motive of small holes on the wayang kulit form. This paper presented a new shadow modeling approach from the real puppet form. We called Color Selective method, which uses image processing especially in color processing concept to get the shadow of the wayang kulit. The method will separate the wayang kulit image and it’s background which it takes 10% of image height and width from four corners of the image. This paper gives a result that color selective method as a new shadow modeling can be applied to get approach of wayang kulit’s shadow model, where wayang kulit image is as the input. As the result, color selective method about 90% same as the input image.

Presently the vendors of ERP software are facing threats as well as opportunities by new technological challenges. In particular, business intelligence and cloud computing are, besides others, important trends in the ERP market. It is crucial for the ERP vendors to gain strong positions in both fields to remain competitive. Since the software market is often influenced by network effect we analyzed four big ERP vendors, namely Microsoft, Oracle, Sage, and SAP with respect to these effects on business intelligence and cloud computing. We enriched our analysis by IBM which is not an ERP vendor but is a leading IT company in both fields: business intelligence as well as cloud computing. The four ERP vendors have notably different histories. While SAP started as an ERP vendor, Oracl began as a vendor of data base systems. Microsoft’s most important products are still the operating system Windows and its office suite. Sage, like SAP, is an ERP vendor but primarily addresses SME while SAP’s main strength is still in the field of EPR systems for large co-operations.

This paper is presented to study the effect of temperature using 5 % biodiesel blended on diesel engine performance. The one-dimensional numerical analysis of GT-Power software is used to simulate the commercial four cylinder diesel engine. The standard fuel data base of GT-Power does not contain any biodiesel. Therefore, the authors have measured typical physical properties of the fuel before it can be installed into the GT-Power data base. The diesel engine is simulated to study the characteristic of engine performance when the engine is operating with fuel blend as an alternative fuel. The simulations are conducted at full load condition where the temperature are varies from 300K to 500K. The simulation results show that the brake power and brake torque reduced maximum of 1.39 % and 1.13 % respectively for the engine operating with fuel blend at different temperatures. It was shown that the insignificant different is due to the small gap between energy content values. The decrease in the lower heating value caused an increase in the brake specific fuel consumption thus reduces the brake thermal efficiency of engine performance at full load.

Design a nonlinear controller for second order nonlinear uncertain dynamical systems (e.g., Internal Combustion Engine) is one of the most important challenging works. This paper focuses on the design of a robust backstepping adaptive feedback linearization controller (FLC) for internal combustion (IC) engine in presence of uncertainties. In order to provide high performance nonlinear methodology, feedback linearization controller is selected. Pure feedback linearization controller can be used to control of partly unknown nonlinear dynamic parameters of IC engine. In order to solve the uncertain nonlinear dynamic parameters, implement easily and avoid mathematical model base controller, Mamdani’s performance/error-based fuzzy logic methodology with two inputs and one output and 49 rules is applied to pure feedback linearization controller. The results demonstrate that the error-based fuzzy feedback linearization controller is a model-free controllers which works well in certain and partly uncertain system. Pure feedback linearization controller and error-based feedback linearization like controller with have difficulty in handling unstructured model uncertainties. To solve this problem applied backstepping-based tuning method to error-based fuzzy feedback linearization controller for adjusting the feedback linearization controller gain ( ). This controller has acceptable performance in presence of uncertainty (e.g., overshoot=1%, rise time=0.48 second, steady state error = 1.3e-9 and RMS error=1.8e-11).