Vol. 10 No. 10 October 2012 International Journal of Computer Science and Information Security
Publication October 2012, Volume 10 No. 10 (Download Full Journal) (Archive) (Download 2)
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Copyright © IJCSIS. This is an open access journal distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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1. Paper 29091215: Student Modeling using Case-Based Reasoning in Conventional Learning System (pp. 1-5)
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Indriana Hidayah, Alvi Syahrina, Adhistya Erna Permanasari
Department of Electrical Engineering and Information Technology, Universitas Gadjah Mada Yogyakarta, Indonesia
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Abstract— Conventional face-to-face classrooms are still the main learning system applied in Indonesia. In assisting such conventional learning towards an optimal learning, formative evaluations are needed to monitor the progress of the class. This task can be very hard when the size of the class is large. Hence, this research attempted to create a classroom monitoring system based on student’s data of Department of Electrical Engineering and Information Technology UGM. In order to achieve the goal, a student modeling using Case-Based Reasoning (CBR) was proposed. A generic student model based on jCOLIBRI 2.3 framework was developed. The model represented student’s knowledge of a subject. The result showed that the system was able to store and retrieve student’s data for suggestion of the current situation and formative evaluation for one of the subject in the Department.
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Keywords- case-based reasoning; student modeling; jCOLIBRI
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2. Paper 30091216: Estimation of Effort In Software Cost Analysis For Heterogenous Dataset Using Fuzzy Analogy (pp. 6-10)
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S. Malathi, Research Scholar, Dept of CSE, Sathyabama University, Chennai, Tamilnadu, India
Dr.S.Sridhar, Research Supervisor,Dept of CSE & IT, Sathyabama University, Chennai, Tamilnadu, India
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Abstract— One of the significant objectives of software engineering community is to use effective and useful models for precise calculation of effort in software cost estimation. The existing techniques cannot handle the dataset having categorical variables efficiently including the commonly used analogy method. Also, the project attributes of cost estimation are measured in terms of linguistic values whose imprecision leads to confusion and ambiguity while explaining the process. There are no definite set of models which can efficiently handle the dataset having categorical variables and endure the major hindrances such as imprecision and uncertainty without taking the classical intervals and numeric value approaches. In this paper, a new approach based on fuzzy logic, linguistic quantifiers and analogy based reasoning is proposed to enhance the performance of the effort estimation in software projects dealing with numerical and categorical data. The performance of this proposed method illustrates that there is a realistic validation of the results while using historical heterogeneous dataset. The results were analyzed using the Mean Magnitude Relative Error (MMRE) and indicates that the proposed method can produce more explicable results than the methods which are in vogue
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Keywords- cost estimation; analogy; fuzzy logic; linguistic values; effort estimation; heterogeneous dataset.
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3. Paper 30091225: Intelligent Algorithm for Optimum Solutions Based on the Principles of Bat Sonar (pp. 11-19)
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Dr. Mohammed Ali Tawfeeq
Department of Computer and Software Eng., College of Engineering – Al-Mustansiriya University, Baghdad – Iraq
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Abstract — This paper presents a new intelligent algorithm that can solve the problems of finding the optimum solution in the state space among which the desired solution resides. The algorithm mimics the principles of bat sonar in finding its targets. The algorithm introduces three search approaches. The first search approach considers a single sonar unit (SSU) with a fixed beam length and a single starting point. In this approach, although the results converge toward the optimum fitness, it is not guaranteed to find the global optimum solution especially for complex problems; it is satisfied with finding “acceptably good” solutions to these problems. The second approach considers multisonar units (MSU) working in parallel in the same state space. Each unit has its own starting point and tries to find the optimum solution. In this approach the probability that the algorithm converges toward the optimum solution is significantly increased. It is found that this approach is suitable for complex functions and for problems of wide state space. In the third approach, a single sonar unit with a moment (SSM) is used in order to handle the problem of convergence toward a local optimum rather than a global optimum. The momentum term is added to the length of the transmitted beams. This will give the chance to find the best fitness in a wider range within the state space. The algorithm is also tested for the case in which there is more than one target value within the interval range such as trigonometric or periodic functions. The algorithm shows high performance in solving such problems. In this paper a comparison between the proposed algorithm and genetic algorithm (GA) has been made. It showed that both of the algorithms can catch approximately the optimum solutions for all of the testbed functions except for the function that has a local minimum, in which the proposed algorithm's result is much better than that of the GA algorithm. On the other hand, the comparison showed that the required execution time to obtain the optimum solution using the proposed algorithm is much less than that of the GA algorithm.
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Keywords- Bat sonar; Genetic Algorithm; Particle swarm optimization
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4. Paper 30091229: PSO-Based Optimal Fuzzy Controller Design for Wastewater Treatment Process (pp. 20-29)
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Sawsan MorKos Gharghory, Computers and Systems Department, Electronics Research Institute, Dokki, Cairo, Egypt
Hanan Ahmed Kamal, Electronics and Communication Engineering Department, Cairo University, Giza, Egypt
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Abstract — Fuzzy logic control (FLC) is a useful modeling tool that can handle the uncertainties and nonlinearities of modern control systems. However the main drawbacks of FLC methodologies is challenging for selecting the optimum tuning parameters. The set of parameters that can be altered to modify the controller performance are fuzzy rules and the parameters of membership functions for each input variable. In all cases, the correct choice of membership functions of the fuzzy sets plays an essential role in the performance of FLC. This paper proposes a method for finding the optimum membership function parameters of a fuzzy system using particle swarm optimization (PSO). As the set of nonlinear differential equations of an aerobic unit for wastewater treatment is a multivariable nonlinear problem, the combination of PSO and FLC named PSO-FLC controller is proposed for further improvements of the system response in both the transient and steady state response. To establish its efficiency, the proposed technique was employed to enhance the triangle membership functions of the fuzzy model of a nonlinear sludge activated system; the results show that the optimized membership functions (MFs) offered better performance than a fuzzy model with heuristically described MFs.
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Keywords-component; PSO; FLC controller; Wastewater treatment process;
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5. Paper 30091237: Anomaly Based Hybrid Intrusion Detection System for Identifying Network Traffic (pp. 30-35)
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G.V. Nadiammai, Department of Computer Science, Karpagam University, Coimbatore, TN, India
M. Hemalatha, Head, Department of Computer Science, Karpagam University, Coimbatore, TN, India
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Abstract — Network intrusion detection system attempts to detect attacks at the time of occurring or after they took place. Since it is reliable and produces less alarm rate but it fails to detect unusual or new attacks. In this paper we propose a hybrid IDS by combining the anomaly based detection approaches like Packet Header Anomaly Detector (PHAD), Network Traffic Anomaly Detector (NETAD), Application Layer Anomaly Detection (ALAD) and Learning Rules for Anomaly Detection (LERAD). The hybrid IDS obtained is evaluated using the KDD Cup 99 traffic data and Tcpdump data (Real Time Data). The number of attacks detected by misuse based IDS is compared with the hybrid IDS obtained by combining anomaly and misuse based IDSs and shows that the hybrid IDS with ALAD and LERAD performs well by detecting 149 attacks out of 180 (83%) attacks after training on one week attack free traffic data.
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Keywords - Intrusion detection; Snort, Packet Header Anomaly Detection (PHAD); Network Traffic Anomaly Detector (NETAD); Application Layer Anomaly Detector (ALAD); Learning Rules for Anomaly Detection (LERAD); KDD Cup99 dataset and Real time traffic data.
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