Shivani Goel

Impact of Cloud Computing on ERP implementations in Higher Education

Penetration of Higher Education in all regions is increasing all over the globe at a very fast pace. With the increase in the number of institutions offering higher education, ERP implementations has become one of the key ingredient to achieve competitiveness in the market. Many researchers have given their inputs specifying the different nature of ERP implementation in Educational institutions then the corporate organizations. Recently Cloud computing has become a buzzword and it is having applications in many domains. Researchers have already started applying cloud computing in ERP implementations of Higher education. This paper gives an insight into the nature of cloud computing impact on ERP implementations and discusses various issues related to this. Paper comes up with guidelines regarding the use of cloud computing technology in the ERP implementations of Higher Technical Education institutions.

A genetic algorithm based distance-aware routing protocol for wireless sensor networks

A new cluster head selection algorithm in wireless sensor networks, coined as GADA-LEACH is proposed to reduce energy dissipation and improve the network lifetime.It makes use of Genetic Algorithm (GA) for optimized CH selection and concept of relay node for Distance Aware (DA) routing.Fitness function incorporates residual energy of nodes, distance between CH and nodes, and distance of BS from all CHs for better CH selection. A Wireless Sensor Network (WSN) is an aggregation of sensor nodes which are remotely deployed in large numbers, operate autonomously in an unattended environment and have limited energy resource. In most of the hierarchical routing protocols, the cluster head (CH) selection is on the basis of random probability equation. There is a scope to reduce the energy dissipation by improving CH selection procedure. The proposed scheme, coined as GADA-LEACH, makes use of evolutionary genetic algorithm for improving CH selection in legacy LEACH routing protocol in sensor networks. The concept of relay node is introduced which acts as an intermediary between CH and base station (BS) to ease the communication between the CH and BS. The simulation results obtained supports that our proposed algorithm is efficient in terms of network lifetime. Display Omitted

Expert system and it's requirement engineering process

Expert systems are basically developed to help in solving complex problems by reasoning about knowledge already known like a human expert does. It does not follow the procedure as followed in the conventional programming by a developer. In this paper basic introduction of expert systems consisting of their composition, basic characteristics and advantages of expert systems are covered. Apart from this, considering the development process of expert systems, its not as easy to develop successful expert systems as it seems. There are certain factors which can lead to failure of expert systems and among them requirement engineering for expert systems is the one. While developing expert systems developers pay least attention to the requirement engineering process. Instead requirement engineering is very crucial to gather all the requirements that are needed for an expert system. If the requirements do not fulfill all of the clients wishes and needs, then in that case expert system is considered fail even though it works perfectly. Therefore, for successful development of expert systems its necessary that emphasize on requirement engineering process of expert systems should be laid down. Here, analysis of expert system attributes, requirement engineering processes in expert system development and the possible techniques that can be applied to expert system development are done. Next, the most appropriate techniques for the expert system development based on the analysis are proposed.

SAES- expert system for advising academic major

Most students in India choose their undergraduate major solely on the basis of persisting trends in the society. Due to the lack of a holistic guidance system, students often end up making choices solely on the basis of the above parameter, which in eventuality, may fail to align with the students actual interest and inherent aptitude towards a particular major. In this paper we propose an expert system-SAES which aims to provide intelligent advice to the student as to which major he/she should opt. SAES acquires knowledge of academic performances as well as explicit and implicit interests of the candidate. Knowledge representation in SAES is done by the use of a combination of case based and rule based reasoning. SAES draws inferences on the basis of acquired knowledge and also takes into account the degree of dilemma faced by the candidate and the time he/she takes to decide the interest areas. SAES then recommends the most suitable majors for each candidate, which are further classified as strong, mild and weak on the basis of calculated relative probabilities of success. At the end, we analyze results of the test conducted on a working prototype of SAES.

Transformation from LEL to UML

Product Lines have emerged as a well-known approach for software reuse. Requirements of product line are organized into features in Feature Oriented Domain Analysis approach. Feature models are widely used to model the information gathered during domain analysis and it is not simply comprehensible to stakeholders. During the early stages of software development the interaction with stakeholders is mainly inconvenient. For this explanation, natural language (Language Extended Lexicon) is still widely used to model requirements information. It is in general understandable by stakeholders thus encouraging their participation but LEL does not provide design level of a system. To obtain design level of a system there is need to transform LEL symbols to UML class diagram as its elements are in abstract form representing blue print of a system. To achieve this transformation we describe in this paper a transformation process to derive a UML class diagram from natural language oriented requirement model, known as Language Extended. General Terms Language Extended Lexicon, UML class diagram, Feature Models

Vulnerability Management for an Enterprise Resource Planning System

Enterprise resource planning (ERP) systems are commonly used in technical educational institutions(TEIs). ERP systems should continue providing services to its users irrespective of the level of failure. There could be many types of failures in the ERP systems. There are different types of measures or characteristics that can be defined for ERP systems to handle the levels of failure. Here in this paper, various types of failure levels are identified along with various characteristics which are concerned with those failures. The relation between all these is summarized. The disruptions causing vulnerabilities in TEIs are identified .A vulnerability management cycle has been suggested along with many commercial and open source vulnerability management tools. The paper also highlights the importance of resiliency in ERP systems in TEIs.

Specifying safety and critical real-time systems in Z

As a case study, An Automated Teller Machine (ATM) is a safety-critical and real time system. According to requirements of ATM system we have different machine state status, and four different operations: Withdrawal, Deposit, Transfer, and Inquiry. A key part of early design phases are specifications, which span from requirement, to functional, to design, specifications. This paper describes the conceptual and formal models of the ATM. The formal model of the ATM is specified by using formal specification language. We have used the proper specification language i.e. Z notation. For writing the Z-schemas and the notations we have used the Z-word tool. Which support almost all Z symbols are on the Z-Palette. This model is finally checked using Z/EVES toolset. Therefore this paper attempts to formalize an event-based automation system using formal specification method.

Data Mining - Techniques, Methods and Algorithms: A Review on Tools and their Validity

Data mining is the process of extracting the useful data, patterns and trends from a large amount of data by using techniques like clustering, classification, association and regression. There are a wide variety of applications in real life. Various tools are available which supports different algorithms. A summary about data mining tools available and the supporting algorithms is the objective of this paper. Comparison between various tools has also been done to enable the users use various tools according to their requirements and applications. Different validation indices for the validation are also summarized.

An improved approach for solving 0/1 Knapsack Problem in polynomial time using Genetic Algorithms

The 0/1 knapsack is a very well known problem and many approaches have been proposed such as dynamic programming and greedy strategy to solve this problem. But 0/1 knapsack problem is an NP-complete problem. Solving it in a polynomial time is a challenge. It is becoming an important problem because there are many real life applications based on this. Genetic Algorithms have been proved to be a good approach in solving these types of problem and with the help of Genetic Algorithms it will no longer remain a NP-complete problem. A number of numerical experiments are performed and the outcome shows how this approach is better than the previous approach of Genetic Algorithm for solving 0/1 Knapsack Problem.

Systematic Review of Clustering High-Dimensional and Large Datasets

Technological advancement has enabled us to store and process huge amount of data in relatively short spans of time. The nature of data is rapidly changing, particularly its dimensionality is more commonly multi- and high-dimensional. There is an immediate need to expand our focus to include analysis of high-dimensional and large datasets. Data analysis is becoming a mammoth task, due to incremental increase in data volume and complexity in terms of heterogony of data. It is due to this dynamic computing environment that the existing techniques either need to be modified or discarded to handle new data in multiple high-dimensions. Data clustering is a tool that is used in many disciplines, including data mining, so that meaningful knowledge can be extracted from seemingly unstructured data. The aim of this article is to understand the problem of clustering and various approaches addressing this problem. This article discusses the process of clustering from both microviews (data treating) and macroviews (overall clustering process). Different distance and similarity measures, which form the cornerstone of effective data clustering, are also identified. Further, an in-depth analysis of different clustering approaches focused on data mining, dealing with large-scale datasets is given. These approaches are comprehensively compared to bring out a clear differentiation among them. This article also surveys the problem of high-dimensional data and the existing approaches, that makes it more relevant. It also explores the latest trends in cluster analysis, and the real-life applications of this concept. This survey is exhaustive as it tries to cover all the aspects of clustering in the field of data mining.