MIT-World Peace University (MIT-WPU) is a top educational institute located in Pune, India. It is recognized within&nbsp;UGC under Government of Maharashtra Act No. XXXV 2017.&nbsp;

It is part of the MIT group of institutions (established in 1983) which covers 10+ campuses across India and has more than 50,000+ students enrolled across various disciplines at a given point of time. The institute houses 15 schools ranging from Engineering &amp; Technology, Design, Pharmacy, Commerce, Governance among others. It offers undergraduate, postgraduate and PhD programs among different disciplines.&nbsp;

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MIT World Peace University

School of Computer Engineering & Technology

Requirement Engineering (RE) plays an integral role throughout the process of software development. Requirement identification and prioritisation are the foremost phases of the RE process. Latest RE research work uses Machine Learning (ML) algorithms to tackle RE problems such as identifying requirements and assigning priorities to requirements, which have given better results than that of traditional natural language processing methods. An adequate understanding of these ML methods, however, is still lacking. The aim of this study is to understand which of the ML algorithms is likely to classify and prioritise the requirements efficiently and how they can be evaluated. It is observed that the current approaches are having constraints of scalability and complexity. Different methods used for the text preprocessing of requirements from SRS and user reviews are also proposed. 6 different ML algorithms and 6 different prioritisation algorithms, which are most common methods, are found. The most popular performance parameters used are accuracy, precision and recall. The limitations of these ML approaches are irrespective of dependency of requirements, priorities are assigned to requirements, the results with respect to scalability and speed is inferior. © 2021 IEEE.

Engineering and Technology

Proceedings of the Confluence 2021: 11th International Conference on Cloud Computing, Data Science and Engineering

Classification and prioritisation of software requirements using machine learning - A systematic review

Smart devices are exciting and upcoming advancements in the field of Internet of things (IOT). It is an application of Raspberry Pi, from furniture in the residential and commercial sector to everyday devices like watches and headphones, every object is becoming smarter and hence, making human lives much easier and efficient. Users can actively interact with these devices using voice commands. These smart devices can also assist a user in their personal daily activities as well as in medical use. Currently, various variations of the smart mirror based on software and hardware compatibility as well as based on applications and uses are available in the market. The goal of this paper is to understand the working, various applications, and purposes of the smart mirror according to the varying features in different versions. This paper critically analyses the different versions of the smart mirror and its different features and applications. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

World University root node

Smart Innovation, Systems and Technologies

A Study Based on Advancements in Smart Mirror Technology

Nowadays, Project-based learning is an integral part of many professional courses in Engineering. Projectbased learning is a student-centric approach in which students learn about a course by working in groups to solve an open-ended real world problem. In our opinion, it takes students near to the real world at a very early stage. For students, it acts as a tool to learn concepts and principles, to adapt to the evolving ICT industrial requirements and challenges. The objective here is to convey our systematic teaching and learning method, based on Project- based learning, to effectively learn the principles and practices of Software Engineering. Also, it prepares students how to apply these theoretical concepts to solve the real world problems by working in a team. For more than 10 years we are conducting this activity very rigorously for Third Year Computer Engineering students. This activity incorporates applying theory of Software Engineering to develop experiential learning by developing a mini-project using Database and Web Technology. Our 7- point method for development of mini-project has continuous Assessment through critical reviews and presentations by students. We conducted survey for all batches and took their feedback to understand the role of Project Based Learning in their Professional Career. Feedback of these students helps us to update our methodology and/or course contents. Applying Database technology and getting acquainted with development of user interface is bit easy for students but applying the Software Development Life Cycle [3] learnt in Software Engineering is quite challenging. There are many skills they learn and acquire while developing mini-project like questions to be asked during field work, design strategy and analysis through modelling, teamwork[4], time management, communication skills and many more which takes them a step closer and works as a basic building block for their growth as a software professional. Our survey questionnaire gives insight of all and acts as a measuring tool. This paper addresses our 7-point method, design, our process of evaluation and challenges faced by the students and faculty during this process with the help of representative projects. © 2021, Rajarambapu Institute Of Technology. All rights reserved.

Journal of Engineering Education Transformations

Project based learning methodology: An effective way of learning software engineering through database design and web technology project

Advances in Machine Learning and Computational Intelligence

NFC-Based Smart Insulin Pump, Integrated with Stabilizing Technology for Hand Tremor and Parkinson's Victims

International Journal for Research in Applied Science and Engineering Technology

A Survey Paper on Automated Customer following Shopping Cart and Billing System

International Journal of Pervasive Computing and Communications

Seek-and-destroy algorithm for optimal resource allocation and security analysis in software-defined vehicular networks

Data

Multi-Layout Invoice Document Dataset (MIDD): A Dataset for Named Entity Recognition

In today's world, we find a wide variety of search options and we may have difficulty selecting what we really need. The recommendation System plays an important part in dealing with these problems. A recommender system is a framework that is a filtering system that filters the data with various algorithms and recommends the user with the most relevant data. Recommendation systems are productive customization mechanisms, often up-to-date and recommendations based on current consumer preferences. These systems have shown to be extremely helpful in different areas of e-commerce, education, movies, music, books, films, scientific papers and various products. This paper reviews many approaches of recommendation techniques with their upsides and downsides and diverse performance measures. We have reviewed various articles, analyzed their technique and approach, major features of the algorithm utilized and potential areas for improvement in that research work.

Product Recommendation System: A Systematic Literature Review

From2018 to 2024, the global hair extension market is projected to grow by 8%, reaching more than 5 billion. Hair care was the second-largest segment in the global beauty industry in 2017, accounting for 18% of total sales, after skincare. A big driver of this market is the need to enhance one's physical appearance which most of the times are affected by any hair loss diseases like cancer, alopecia, pregnancy, thyroid, and so on. Endorsing wigs and hair extensions is becoming increasingly common on social media. Specialty shops, hair salons and beauty stores, and hair clinics account for a large portion of the sale of hair wigs and extensions.Our System provides a unique analytical view for solving problems of customers by helping them choose amongst various brands of hair extensions. It does this by using the video's content to perform sentiment analysis rather than just focusing on public comments on the videos. It also performs statistical analysis and provides an overview about the best channels and influencers. To give a better decision base to customers, the paper focuses on providing content extraction of various YouTube videos covering fashion influencers' reviews as well as common people's reviews and for the investors, insights of market trends to judge its potential. © 2021 IEEE.

Proceedings - 2021 1st IEEE International Conference on Artificial Intelligence and Machine Vision, AIMV 2021

YouTube Content Analysis for Hair Extension's Business

Covid-19 crossed all international, national, regional boundaries so declared as Pandemic across the globe. Since one and half year every country is struggling to deal with this disease either by using preventive measures like lockdown, sanitization or with cure measure like treatment, isolation etc. Most important preventive measure adopted by every country now is Vaccination. But due to confusion, uncertainty and lack of awareness people have different opinion and sentiment about the vaccination drive. There is a drastic requirement to understand the People's sentiments on the vaccine, which may vary for different regions. Especially when these sentiments became vocal on social media, it may create adverse impact on the Vaccination Drive. This paper discusses predictive analytics and visualization methods for analyzing and predicting the summarized sentiments using social media channel: twitter. This approach is useful to provide support the vaccination drive by spreading awareness about the vaccine in the region with extreme negative sentiments. Corona Vaccine related tweets are analyzed and classified using various classification techniques as Bernoulli Naïve Bayes, Linear SVC and Random Forest. Visualization techniques are discussed to showcase summarized sentiments region-wise. This approach is effective and useful for support for vaccination drive for Covid-19 spread prevention. © 2021 IEEE.