University course timetabling is one of the important and time consuming issues that each University is involved with it at the beginning of each. This problem is in class of NP-hard problem and is very difficult to solve by classic algorithms. Therefore optimization techniques are used to solve them and produce optimal or near optimal feasible solutions instead of exact solutions. Genetic algorithms, because of multidirectional search property of them, are considered as an efficient approach for solving this type of problems. In this paper three new hybrid genetic algorithms for solving the university course timetabling problem (UCTP) are proposed: FGARI, FGASA and FGATS. In proposed algorithms, fuzzy logic is used to measure violation of soft constraints in fitness function to deal with inherent uncertainly and vagueness involved in real life data. Also, randomized iterative local search, simulated annealing and tabu search are applied, respectively, to improve exploitive search ability and prevent genetic algorithm to be trapped in local optimum. The experimental results indicate that the proposed algorithms are able to produce promising results for the UCTP.

In garment production engineering, sizing system plays an important role for manufacturing of clothing. The standards for defining the size label are a critical issue. Locating the right garment size for a customer depends on the label as an interface. In this research work intend to approach that it could be used for developing sizing systems by data mining techniques applied to Indian anthropometric dataset. We propose a new approach of two-stage data mining procedure for labelling the shirt types exclusively for Indian men. In the first stage , clustering technique applied on the original dataset, to categorise the size labels. Then these clusters are used for supervised learning in the second stage for classification. A sizing system classifies a specific population into homogeneous subgroups based on some key body dimensions. The space with these dimensions gives raise to complexity for finding uniform standards. This enables us to have an interface as a communication tool among manufacturers, retailers and consumers. This sizing system is developed for the men’s age ranges between 25 and 66 years. Main attribute happens to be the chest size as clearly visible in the data set. We have obtained classifications for men’s shirt attributes based on clustering techniques.

In this paper we have applied Gabor filter for fiducial point localization. After obtaining the fiducial points the number of fiducial points are reduced using a distance formula. The distance of each of this fiducial point is then calculated by the distance formula and stored in the database of the system. The same methodology is also applied on the input face which is to be matched with the faces available in the database. Then a fuzzy preference relation matrix is obtained . the largest eigen value of this matrix is then determined by algebraic method or numerical method depending on the order of the matrix. To apply the numerical method which is more easier for large order matrices we have used the C programming of this method . Once the largest eigen value is determined the corresponding priority vector can easily be obtained from which we can easily match the input face with the database.

Among the various means of available resource protection including biometrics, password based system is most simple, user friendly, cost effective and commonly used, but this system having high sensitivity with attacks. Most of the advanced methods for authentication based on password encrypt the contents of password before storing or transmitting in physical domain. But all conventional cryptographic based encryption methods are having its own limitations, generally either in terms of complexity, efficiency or in terms of security. In this paper a simple method is developed that provide more secure and efficient means of authentication, at the same time simple in design for critical systems. Apart from protection, a step toward perfect security has taken by adding the feature of intruder detection along with the protection system. This is possible by merging various security systems with each other i.e password based security with keystroke dynamic, thumb impression with retina scan associated with the users. This new method is centrally based on user behavior and users related security system, which provides the robust security to the critical systems with intruder detection facilities.

Significant work has already been done for complex quadratics. However, the dynamics of rational functions and their properties are equally interesting. In this paper we have generated computer images from a C++ computer program. We have then developed an artificial neural network model using predictive modeling software based on RMS type of error out of two samples of points obtained from the generated images. The imaginary part of sample II was predicted by applied the real parts of sample I and sample II to the artificial neural network. The real part of sample II was more important than the real part of sample I in predicting the imaginary part of sample II. The predicted imaginary part of sample II was then imported to Matlab Signal Processing Tool (SPTool) via Matlab workspace. We have applied a stable band pass filter to the model to eliminate noise from it for its analysis. A modulated signal produced reveals that the methodology used shall be applied to explore properties of computer generated images from the generated wavelet. We have further imported the predicted imaginary part of sample II to autoSIGNAL software for time and frequency range analysis of the continuous wavelet transform.

Scrum does not provide any direction about how to engineer a software product. The project team has to adopt suitable agile process model for the engineering of software. XP process model is mainly focused on engineering practices rather than management practices. The design of XP process makes it suitable for simple and small size projects and not appropriate for medium and large projects. A fine integration of management and engineering practices is desperately required to build quality product to make it valuable for customers. In this research a novel framework hybrid model is proposed to achieve this integration. The proposed hybrid model is actually an express version of Scrum model. It possesses features of engineering practices that are necessary to develop quality software as per customer requirements and company objectives. A case study is conducted to validate the proposal of hybrid model. The results of the case study reveal that proposed model is an improved version of XP and Scrum model.

In dynamic insensitive application specific network, WSN consists of hundred and thousands of sensor nodes densely deployed in the sensor field. Majority of sensor nodes are static having power limitations, low network throughput, message transfer delays and computation power limits that are major obstacles. The limited communication range of WSN nodes, link asymmetry, and the characteristics of the physical environment lead to a major sources of QoS degradation in WSNs. The potential applications of the WSNs typically range from those in defense, military, environmental monitoring, health monitoring and civilian surveillance applications etc. All of these applications being omnipresent in nature necessitate appropriate heavy secure mechanisms to ensure the data security and privacy. On the other hand, the WSN nodes, being extremely resource constrained, it is really challenging to devise the WSNs security protocols. So need to propose dynamic high level cross layer security mechanism for detecting various attacks and the countermeasures taken to avoid the same without comprising any network resources.

Artificial Neural Network is a branch of Artificial intelligence and has been accepted as a new computing technology in computer science fields. This paper reviews the field of Artificial intelligence and focusing on recent applications which uses Artificial Neural Networks (ANN’s) and Artificial Intelligence (AI). It also considers the integration of neural networks with other computing methods Such as fuzzy logic to enhance the interpretation ability of data. Artificial Neural Networks is considers as major soft-computing technology and have been extensively studied and applied during the last two decades. The most general applications where neural networks are most widely used for problem solving are in pattern recognition, data analysis, control and clustering. Artificial Neural Networks have abundant features including high processing speeds and the ability to learn the solution to a problem from a set of examples. The main aim of this paper is to explore the recent applications of Neural Networks and Artificial Intelligence and provides an overview of the field, where the AI & ANN’s are used and discusses the critical role of AI & NN played in different areas.