PSICO-A is a new educational system, based on the web, for learning psychology. Its computational architecture consists of a front-end and a back-end. The first one contains a design mode, a reflective mode, a game mode and a simulation mode. These modes are connected to the back-end, which is composed of a rule engine, an evaluation module, a communication module, an expert module, a student module and a metacognitive module. The back-end is the heart of the system analysing the performance of pupils. PSICO-A assembles Boolean equations introducing algorithms such as those of Levenshtein, Hamming, Porter and Oliver. The system design used the programming language PHP5 for a clear and fast interface. PSICO-A is an innovative system because it is the first system in psychology designed for assessing the value of computer-based learning games compared with simulations for teaching the subject. Other systems use virtual environments for teaching subjects like mathematics, physics or ecology to children but the role of digital games and simulations in learning psychology is to date an unexplored field. A preliminary analysis of the motivational value of the system has been performed with sample of undergraduate students, verifying its advantages in terms of to encouraging scientific exploration. An internal evaluation of the system, using the game mode, has been conducted.

Biggest internet security threat is the rise of Botnets having modular and flexible structures. The combined power of thousands of remotely controlled computers increases the speed and severity of attacks. In this paper, we provide a comparative analysis of machine-learning based classification of botnet command & control(C&C) traffic for proactive detection of Peer-to-Peer (P2P) botnets. We combine some of selected botnet C&C traffic flow features with that of carefully selected botnet behavioral characteristic features for better classification using machine learning algorithms. Our simulation results show that our method is very effective having very good test accuracy and very little training time. We compare the performances of Decision Tree (C4.5), Bayesian Network and Linear Support Vector Machines using performance metrics like accuracy, sensitivity, positive predictive value(PPV) and F-Measure. We also provide a comparative analysis of our predictive models using AUC (area under ROC curve). Finally, we propose a rule induction algorithm from original C4.5 algorithm of Quinlan. Our proposed algorithm produces better accuracy than the original decision tree classifier.

In this paper, two routing protocols (AODV and DSR) are simulated under regular operation, single and cooperative black hole attack. This work has been performed by simulator to show consequence of black hole attacks in MANET by using various graphs which are used to collect data in term of several metrics. One common method to perform most of researches in the MANET security field is to simulate and analyze the routing protocols in various scenarios. This work has been based on the implementation and experiments in the OPNET modeler version 14.5. Finally the results have been computed and compared to stumble on which protocol is least affected by these attacks.

The emerging WiMAX (Worldwide Interoperability for Microwave Access) technology (IEEE 802.16) can offer low-cost, high speed and long-range communications. The WiMAX supports a point-to-multipoint (PMP) topology and a mesh topology. A WiMAX network is composed of a Base Station (BS) and multiple Subscriber Station (SS). A BS is the mother node and the SS is the child node, though a SS can also be a mother node of a SS if the child node is connected with him to reach to the BS. The BS serves as a gateway connecting to external networks such as the Internet. Number of nodes situated beside a node is called neighbor nodes. In PMP architecture, there is a multi-hop mesh that can be used to gain the high speed wide area network. Again in mesh topology, it increases the wireless coverage and reconfigures ability. In this mode performance depends on a good routing and scheduling protocol. Routing is the way by which a SS will connect with the BS. A good and efficient routing algorithm along with a scheduling algorithm can improve the total network performance significantly. Scheduling algorithm gives the time slot to all SS in a way so that a SS can transmit data or signal in that time slot. There are many research scopes on IEEE 802.16 mesh network especially in routing and scheduling protocol. The purpose of our thesis work is to propose a new routing algorithm to maximize the performance of the network.

The diminishing expenditure of consumer electronic devices such as digital cameras and digital camcorders along with ease of transportation facilitated by the internet, has lead to a phenomenal rise in the quantity of multimedia data. The need to find a desired image from a collection is shared by many professional groups, including journalists, design engineers and art historians. While the requirements of image users, it can be characterize image queries into three levels. The proposed method based on primitive features such as color and shapes. These features are extracted and used as the basis for a similarity check between images. The shape and color features are extracted through Gradient Edge Detection and color histogram the combination of these features is robust. The experiment results show that the proposed image retrieval is more efficient and effective in retrieving the user interested images.

Along with the growth of the computer system and networks, the mysterious and malicious threats and attacks on the computer systems are also increasing exponentially. There is a need of continuous evaluation of the security of a network and enhancement of the network attack detection system, which will be able to detect different attacks along with the characteristics of the attacks. In previous work, the port scan attack is considered as precursors to an attack and the target was to provide the mitigation technique for the particular port scan attack. There have been relatively few empirical studies done for port scan related attacks and those that do exist may no longer reflect the impact of such attacks on the functionalities of the UTM/network device and on the network. To address this lack of knowledge, this experiment is carried out in fully controlled test bed environment wherein a set of varieties of attack can be simulated and impact of attack(s) is analyzed and appropriate mitigation technique is suggested to mitigate the port scan attack. The experiment result indicates that the port scan mitigation implementation on UTM helps reducing the load on the UTM device and reduces network congestion effectively.

This paper studies the use of Modified Proportional-Integral-Derivative Sliding Mode Controller (MPIDSMC) control used to control a flexible manipulator. The control gain in the MPIDSMC controller has been determined in an empirical way so far. It is a considerable time-consuming process because the control performance depends not only on the control gain but also on the other parameters such as the payload, references and PID joint servo gains. Hence, the control gain must be tuned considering the other parameters. In order to find the optimal control gain for the MPIDSMC controller, a fuzzy logic approach is proposed in this paper. The proposed fuzzy logic scheme finds an optimum control gain that minimizes the tip vibration for the end effector of the flexible manipulator. Tuned gain response results are compared to results for other types of gains. The effectiveness of using the fuzzy logic appears in the reduction of the computational time and the ability to tune the gain with different loading condition and input parameters.

Data mining is the use of algorithms to extract the information and patterns derived by the knowledge discovery in databases process. Classification maps data into predefined groups or classes. It is often referred to as supervised learning because the classes are determined before examining the data. The feasibility and the benefits of the proposed approaches are demonstrated by the means of data mining applications like intrusion detection, direct marketing, and signature verification. A variety of techniques have been employed for analysis ranging from traditional statistical methods to data mining approaches. Bagging and boosting are two relatively new but popular methods for producing ensembles. In this work, bagging is evaluated on real and benchmark data sets of intrusion detection, direct marketing, and signature verification in conjunction with as the base learner. The proposed is superior to individual approach for data mining applications in terms of classification accuracy.