Informatics Publishing Limited

N. Sankar Ram , Paul Rodrigues

Abstract

Research into design rationale in the past has focused on argumentation-based design deliberations. These approaches cannot be used to support change impact analysis effectively because the dependency between design elements and decisions are not well represented and cannot be quantified. Without such knowledge, designers and architects cannot easily assess how changing requirements and design decisions may affect the system. We apply Bayesian Network Model (BNM), to capture the probabilistic causal relationships between design elements and decisions. We employ three different BNMbased reasoning methods to analyze the trade-off between the conflicting quality attributes. Markov blanket discovery algorithms can be used for quality assessment BNMs. Additionally, work will be done to determine how known optimization methods such as Tabu search may be applied in the context of the proposed framework. Ultimately, the goal is to create a possibility of automatic execution of steps involved in architectural optimization.

Keywords

Network Model (BNM), Architectural Tradeoff Analysis Method (ATAM) Software Architectural Analysis Method (SAAM), Software Architecture Assessment using Bayesian Networks (SAABNet), Stake Holders Expects(SHE) , and Markov Blanket (MB)

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Manoj Joseph D'Souza ¹, Paul Rodrigues ¹

Affiliations
1 Department of Computer Science and Engineering, DMI College of Engineering, Chennai – 600123, Tamil Nadu, IN

Abstract

Traditional instructor-centered, lecture-based teaching methods in engineering education have been criticized for being too linear, dogmatic, systematic and constraining. This paper proposes 'Extreme Pedagogy', a student-centered teaching-learning conceptual framework to improve quality of engineering education which is built on four core values: students and teachers and their interactions, working knowledge, collaboration with students and responding to change. Extreme Pedagogy derives its philosophy from Extreme Programming, an agile software methodology. Extreme Pedagogy aims at continuous improvement of student learning, keeping students' needs and satisfaction as its focus.

Keywords

Engineering Education, Extreme Pedagogy, Extreme Programming, Teaching and Learning.

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T. S. Shiny Angel ¹, Paul Rodrigues ², John T. Mesiah Dhas ³, T. Prabhu ⁴

Affiliations
1 SRM University, Chennai, IN
2 Vellammal Engineering College, Chennai, IN
3 RVS Padmavathi College of Engineering &Technology, Chennai, IN
4 Dr.MGR University, Chennai, IN

Abstract

E-learning system is a comprehensive software package that supports for teaching the subject, testing, simulation, discussion or other significant aspect. In software system development process, estimation is playing very important role. The success of any software project largely depends on effective estimation of project effort, time and cost. Estimation helps in setting realistic targets for completing the project. The widely used software estimation techniques like LOC, FP, and COCOMO are not effectively supported for estimating E-Learning systems because the structure and components of E-learning system is different and also the current estimation methodologies given generic solution to all kind of software system. The characteristics and specifications of subject oriented software like E-learning systems not fully come under the limit of existing estimators. They provide wrong estimates and lead customer dissatisfaction due to incompleteness, loss and delay. So in this research paper, along with the concepts of FPA, the new size metric is proposed for E-learning system estimation. This Technique considers the document part and application part of E-Learning system very effectively and estimates the size. In addition with size metric new flexibility metric introduced, it is used to calculate flexibility of software.

Keywords

FPA-Function Point Analysis, DTP–Document for Teaching Part, CCP-Common Computational Part, ELSSIZE–E-Learning System Size.

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S. K. Muthusundar ¹, Paul Rodrigues ², D. Thara ³, Trilysa Anthea Titus ⁴

Affiliations
1 Department of CSE (scholar), Bharath University, Chennai, IN
2 Vellamal Engineering College, Chennai, IN
3 Department of IT, Kings Engineering College, Chennai, IN
4 Department of IT, Kings Engineering College, Chennai, IN

Abstract

The need of increasing the potential benefits and reliability of a project by re-usable Test Automation Framework coupled with open source tools that use the concept of Return on Investment (ROI).Which reduces the execution cost and time of testing cycle and maintenance effort thereby resulting in increased productivity by using additional test cases within a given schedule. In this paper we will discuss how the ROI calculation can also be achieved through TAF Selenium tool that is used to reduce the execution time and cost without any framework costs and optimum usage and uses discrete business keywords which are reusable. High ROI can also be achieved on the basis of cost, time and extensible. Using TAF the script development result will help increase the speed of test execution and provides maximum number of resources, increase productivity and ROI; it provides ease of script generation & management, concurrent, remote execution, test data generation and customized reports with screen shots.

Keywords

Test Automation Framework, ROI Calculation, Tool, Reusable, Driven Approach.

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B. S. Charulatha ¹, Paul Rodrigues ², T. Chitralekha ³

Affiliations
1 Department of IT, Jawahar Engineering College, IN
2 Velammal Engineering College, IN
3 Department of CSE, Pondicherry University, IN

Abstract

Fuzzy clustering algorithms are helpful when there exists a dataset with sub groupings of points having indistinct boundaries and overlap between the clusters. This paper gives an overview of different classical fuzzy clustering algorithm. The fuzzy clustering algorithms can be categorized as classical fuzzy clustering and shape based clustering. The paper describes about the general working behavior, the methodologies followed on these approaches and the parameters which affects the performance of classical fuzzy clustering algorithms.

Keywords

Fuzzy Clustering, Classical Fuzzy Clustering Shape Based Clustering.

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K. Sajeer ¹, Paul Rodrigues ²

Affiliations
1 Department of Computer Science, Research and Development Centre, Bharathiar University, Coimbatore - 641 046, Tamil Nadu, IN
2 DMI Engineering College, Palanchur , Chennai - 600123, Tamil Nadu, IN

Abstract

Objective: Retrieval of information using speech recognition and Neural Network. Methods: A novel method is proposed for information retrieval using speech recognition and Neural Network. Automatic Speech Recognition (ASR) is the technological process that allows translation of information spoken by a human being into corresponding text. Marcovian technique is used to update the sampling weight generated from the input speech. The Kalman filtering technique is used to extract the feature vector (s). Neural Network is used to classify the feature vector (s) based on the energy updation. Results: The performance of the proposed method is evaluated in terms of SNR. The system components are speech processing, feature extraction, training and testing by using neural networks and information retrieval. Conclusion: In the proposed method the retrieve process proved >90% success.

Keywords

Information Retrieval, Kalman Filtering, Marcovian Technique, Neural Network, Speech Recognition.

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Shiny Angel ¹, Paul Rodrigues ²

Affiliations
1 SRM University, Kattankulathur, Chennai - 603203, Tamil Nadu, IN
2 DMI College of Engineering, Chennai – 600123, Tamil Nadu, IN

Abstract

Background/Objectives: Software size estimation is the key factor to determine the planning activities of software development process. Size is the base factor to determine effort, duration, schedule, cost and others that affect the development process. E-Learning system is also a software system support for Computer and internet based teaching and learning process. Development of E-Learning system is under crises because of improper estimates that lead incompleteness, loss and delay, it affects customer satisfaction. To overcome the problem, a research made on the sizing techniques used in industry and the inabilities are identified. Methods/Statistical Analysis: Based on the analysis of all industry oriented size estimation techniques, this research introduces a new sizing technique called Learning Object Point method (LOP). Learning Object Points method is quantifying the size and complexity of an E-Learning system in terms of learning objects and functionalities. Sizing is independent of computer languages, development methodology and technology behind the development. LOP can be estimated early stage of software development process. It is prepared based on the user perspective so users of the E-Learning system have a better understanding of what LOP are measuring. Findings: The performance analysis of Learning Object Point’s method was conducted over Function point Analysis by using different projects developed in the industry. Size and duration calculated using FPA produced wrong results. So the project management activities like planning, scheduling and costing produced imprecise outcomes but LOP produced more close to actual results so it supports project management activities effectively. Applications/Improvements: LOP can be used to size E-Learning applications accurately. Sizing is important component in determining productivity. It is easily understood by the non-technical user. This helps communicate sizing information to a user or customer. Conversion to LOC is similar to FP to LOC conversion. It also supports to estimate any kind software application other than E-Learning system also. Estimate development effort and Cost benefit analysis using LOP. To Derive Business Decisions.

Keywords

E-Learning System, Learning Object Points Method, Size Estimation, Software Project Management, Software Sizing

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B. S. Charulatha ¹, Paul Rodrigues ², T. Chitralekha ³

Affiliations
1 JNTUK, Kakinada – 533003, Andhra Pradesh, IN
2 King Khalid University, SA
3 Central University, Kalapet – 605014, Puducherry, IN

Abstract

Objectives: Internet is the repository of information, which contains enormous information about the past, present which can be used to predict future. To know the unknown users are inclined towards searching the internet rather than referencing the library because of ease of availability. This requirement initiates the need to find the content of a web page with in shortest period of time irrespective of the form the page is. So information and content extraction need to be at a basic generic level and easier to implement without depending on any major software. Methods: The study aims on extraction of information from the available data after the data is digitized. The digitized data is converted to pixel- maps which are universal. The pixel map will not face the issues of the form and the format of the web page content. Statistical method is incorporated to extract the attributes of the images so that issues of language hence text-script and format do not pose problems, the extracted features are presented to the Back Propagation algorithm. Findings: The accuracy is presented and how the content extraction within certain bounds could be possible Tested using unstructured word sets chosen from web pages. The method is demonstrated for mono lingual, multi-lingual and transliterated documents so that the applicability is universal. Applications/Improvement: The method is generic, uses pixel-maps of the data which is software and language independent.

Keywords

Back Propagation, Content Extraction, Information, Statistical, Deterministic.

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S. Angeline Julia ¹, Paul Rodrigues ²

Affiliations
1 Computer Science Department, SRM University, SRM Nagar, Potheri, Kattankulathur, Kancheepuram District, Near Potheri Railway Station, Chennai - 603203, Tamil Nadu, IN
2 Computer Science Department, King Khalid University, Abha, SA

Abstract

Objectives: To improve the quality attributes of software architecture using new patterns. Methods/Statistical Analysis: Software architecture analysis methods are developed to reduce risk and to improve software quality. Patterns which have impact on the quality of software systems are used for the development of software. In this paper, novel Creative Innovative patterns are used. Findings: When Creative Innovative patterns are applied in the case study of hospital management system, the quality attributes like bug fixing cost, scalability, availability, maintainability, usability and reliability are improved. Application/Improvement: This paper uses Hospital management system which is enriched by Creative Innovative patterns and explains how these patterns help to improve the quality attributes of the architecture of a Hospital management system.

Keywords

ATAM, Creative Innovative Patterns Pattern, Hospital Management System, Software Architecture Analysis Methods.

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N. Snehalatha ¹, Paul Rodrigues ²

Affiliations
1 School of Computing, SRM University, Chennai – 603203, Tamil Nadu, IN
2 Department of CSE, College of CS, KKU, Abha, SA

Abstract

Objectives: The main objective is to propose a Potential and Proficient Relay Node Selection (PPRS) Mechanism for progressing MANET Performance. Method/Analysis: Although, Routing issues are under constant study for many years now already. The literature shows that the issue of performance lacking in highly dynamic MANETs has not been completely eliminated. Consequently, the main method adapted by PPRS mechanism is the individual next hop selection consecutively along with the estimation of success probability for selecting that node. Findings: The consequent and persistent next hop selection builds up MANET routes while assuring great communication statistics even under highly dynamic conditions. Novelty/Improvement: The performance of the PPRS mechanism is contrasted with existing EDEAR mechanism using simulations in the network simulator.

Keywords

Energy, MANET, Mobility, Relay Node, Throughput.

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Manoj Joseph D'Souza ¹, Paul Rodrigues ¹

Affiliations
1 Department of Computer Science and Engineering, DMI College of Engineering, Chennai - 600123, Tamil Nadu, IN

Abstract

Students of the millennial generation, born in the late twentieth century, have been raised in an environment of technology, entertainment, information and social connections. Unlike their predecessors in the previous generation, they have different educational expectations. The differences these students bring to the classroom are a challenge and also an opportunity for the instructors to provide useful learning experiences to these students. Traditional pedagogy, which is characterized by lecture-styled teaching seems to be inappropriate to respond to the needs and characteristics of the millennials. This paper discusses the unique characteristics of the millennial students, limitations of the traditional pedagogy to serve the millennials and proposes a novel pedagogy called, Extreme Pedagogy as an alternative to engage effectively today’s students in the engineering classroom.

Keywords

Engineering Education, Extreme Pedagogy, Millennials, Traditional Pedagogy.

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Thangaraj Ethilu ¹, Abirami Sathappan ¹, Paul Rodrigues ²

Affiliations
1 Department of Computer Science and Engineering, Annamalai University, Chidambaram, Tamil Nadu, IN
2 Department of Computer Science and Engineering, DMI College of Engineering, Chennai, Tamil Nadu, IN

Abstract

Software Defined Networking (SDN) has increased a high-level attention in recent years, mainly because of its ability to address the cyber security challenges. Machine learning architectures were developed as the SDN system to detect the security threads; however, present techniques are limited with (i) higher computation time during malicious switch detection, (ii) reduced malicious switch detection rate (MSDR). This paper presents modified deep learning architecture based SDN system consist of two stages: (i) training stage, computes the external feature maps from both trusted and malicious network switches connected to the SDN controller, (ii) testing stage, classifying the trust and malicious switches connected with SDN controller. The feature maps are trained and classified with Modified LeNET Convolutional Neural Networks (CNN) architecture. The proposed methodology is simulated via network simulator under environmental constraint conditions. The results shows that the proposed methodology reduced the malicious switch detection computational time about a half as well as it increased the MSDR to about 6% compared to the conventional methodologies.

Keywords

SDN, Switch, Malicious, CNN, Feature Maps.

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Thangaraj Ethilu ¹, Abirami Sathappan ¹, Paul Rodrigues ²

Affiliations
1 Department of Computer Science and Engineering, Annamalai University, Chidambaram, Tamil Nadu, IN
2 Department of Computer Science and Engineering, King Khalid University, Abha, SA

Abstract

In recent times, Software Defined Networking (SDN) has developed widely to provide capable solutions for future internet services. As with the solutions, SDN brings us a hazardous rise in malicious threats. We investigated a sort of Distributed Denial of Services (DDoS) assault known as an internet services attack, which evaluates the influence of both traffic flow and throughput depletions in order to characterize the abnormalities. This sort of attack has a significant impact on the whole SDN. This paper introduces a deep learning method to improve the performance efficiency of the SDN by classifying the network switch into either a trusted switch or a malicious switch device. In this research, an attack detection methodology for Internet services utilizing Software Defined Networking (SDN) is proposed. The SDN controller may evaluate traffic flow, detect anomalies, and restrict both incoming and outgoing traffic as well as source nodes. The SDN considers a Convolutional Neural Network (CNN) based attack detection system that can identify malicious node. Kaggle datasets are used to test and train CNN and the features such as packet duration, packet count, byte count, accuracy for identifying the flow of trusted and malicious switches. According to the results, the CNN-based attack detection system can identify the attack with an accuracy of 89 percent. The comparison evaluation with the already proposed LeNet CNN of the feature classification proves that the flow is the trusted one and with the constant throughput with the help of the deep learning model.

Keywords

Software Defined Networking (SDN), Kaggle Dataset, Convolutional Neural Networks (CNN), Keras, Internet Service Attack, Malicious Switches, Malicious Node, Distributed Denial of Services.

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