Semi-automated Software Requirements Categorisation using Machine Learning Algorithms

Requirement engineering is a mandatory phase of the Software development life cycle (SDLC) that includes defining and documenting system requirements in the Software Requirements Specification (SRS). As the complexity increases, it becomes difficult to categorise the requirements into functional and non-functional requirements. Presently, the dearth of automated techniques necessitates reliance on labour-intensive and time-consuming manual methods for this purpose. This research endeavours to address this gap by investigating and contrasting two prominent feature extraction techniques and their efficacy in automating the classification of requirements. Natural language processing methods are used in the text pre-processing phase, followed by the Term Frequency – Inverse Document Frequency (TF-IDF) and Word2Vec for feature extraction for further understanding. These features are used as input to the Machine Learning algorithms. This study compares existing machine learning algorithms and discusses their correctness in categorising the software requirements. In our study, we have assessed the algorithms Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), Neural Network (NN), K-Nearest Neighbour (KNN) and Support Vector Machine (SVM) on the precision and accuracy parameters. The results obtained in this study showed that the TF-IDF feature selection algorithm performed better in categorising requirements than the Word2Vec algorithm, with an accuracy of 91.20% for the Support Vector Machine (SVM) and Random Forest algorithm as compared to 87.36% for the SVM algorithm. A 3.84% difference is seen between the two when applied to the publicly available PURE dataset. We believe these results will aid developers in building products that aid in requirement engineering

Secure Fog Computing System using Emoticon Technique

Fog computing is a distributed computing infrastructure in whichsome application services are provided at the edge of the network in smart devices( IoT devices) and some applications are handled in cloud. Fog computing operates on network ends instead of working entirely from a centralized cloud. It facilitates the operation of storage, compute, analysis and other services between edge devices mostly IoT devices and cloud computing data centres. The main objective of Fog computing is to process the data close to the edge devices .The problem that occurs in Fog is confidentiality and security of data . To overcome this problem, we have proposed the Dual-Encryption to data using Emoticon Technique which is combination of Cryptography and Steganography. In this method, first data is encrypted and then encrypted data is hidden with the cover text like emoticons. So Dual-Encryption enhances data security and reliability. Even if the covered text is accessed by unauthorized person, only encrypted data of original data can be viewed not the actual data

BUSINESS DIARY - An Interactive and Intelligent Platform for SME’s

There are currently many online trading platforms in the Internet. However, they have various drawbacks and are not welcome by sellers who just want a simple and yet intelligent and user-friendly platform for reaching buyers. Traditional methods are costly and not suitable for small and medium enterprises. This project focuses on creating such a platform that allows sellers to reach buyers efficiently. With advent of several successful e-commerce web portals like amazon.com, ebay.com, flipkart.com etc., world is witnessing an explosion of service providers as well as service consumers. The approach is to attain beneficial flow for end users who are looking for services which match their custom requirements rather that best (and hence more costly) service.This idea is focused on the development of web application to facilitate such a need with an aim to providing an intelligent user-interface to both the sellers and the buyers

Designing Combo Recharge Plans for Telecom Subscribers Using Itemset Mining Technique

Now a days Machine Learning has become an integral part of human research. People are tending to select more automatic system rather than going with the manual handling. Data mining has the huge effect on business analysis as all business relies on their behaviour of customers. Mining the behaviour of customers can help the very existence of the company. This paper has proposed the way to satisfy customers in telecommunication market by knowing the customer’s recharge pattern. It can enhance their will to use the same service provider. By mining the recharge pattern of individual customer, this system will help telecom service providers to prepare combo plans, which will indeed be less than the individual recharges. For mining such kind of data, we are using FP Growth algorithm, it allows frequent item set discovery without candidate item set generation. FP Growth is two step approach, first it builds a compact data structure called the FP-tree and then Extracts frequent item sets directly from the FP-tree

Data Hiding using Emoticons

In 21st century Digital communication has become a very essential part of day to day life of every human. It has continuously evolved over the year. It has made communication easier. People can communicate anywhere and anytime large amount of data is transmitted every day, every second. Here data security comes into picture. Each and every individual want their data to be secure and doesn’t want it to be used in an unauthorized way. There are various techniques to provide data security such as cryptography and steganography. Cryptography is the science of encompassing the principles and methods of transforming a plain text message into one that is unintelligible and then, that message back to its original form. Steganography is the art and science of hiding information by embedding messages into other messages. Steganography means “Covered Writing” in Greek. Both the techniques have their own limitations; to overcome those limitations, here in this paper, we are combining both the techniques to enhance data security