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

Engineering and Technology

Software Development Lifecycle (SDLC) is a systematic process used to achieve high quality software that meets customer requirements. During SDLC requirements, engineering plays an important role. Prioritisation helps to focus on the most important requirements in terms of importance, cost, penalty, time and risk. Stakeholders (users, developers) of the software product identify requirements. The two major activities of requirement engineering process are requirements classification and requirements prioritisation. Sometimes requirement mentioned by stakeholder can be of both types, i.e. functional and non-functional. So it is challenging to classify requirements separately in two different categories. There are many fundamental prioritisation techniques available to prioritise software requirements. In this paper, we have compared existing requirements prioritisation techniques based on ease of use, speed, scalability and accuracy. Our literature study suggests that the appropriate requirements prioritisation technique has to be selected that can help software developer to minimise the risk, penalty. In automating various tasks of software engineering, machine learning (ML) has shown useful positive impact. This paper discusses the various algorithms used to classify and prioritise the software requirements. The results in terms of performance, scalability and accuracy from different studies are contradictory in nature due to variations in research methodologies and the type of dataset used. Based on the literature survey conducted, we propose a new architecture that will use both types of datasets, i.e. Software Requirement Specifications (SRS) and user text reviews to create a generalised model. Our proposed architecture will attempt to extract features which can be used to train the model using ML algorithms. The ML algorithms for classifying and prioritising software requirements will be developed and assessed based on performance, scalability and accuracy. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

School of Computer Engineering & Technology

Lecture Notes in Networks and Systems

Software Requirements Classification and Prioritisation Using Machine Learning

In recent years, overcrowded unlicensed spectrum is devastating spectral efficiency of communications in regional and rural broadband wireless networks. Cognitive radio allows opportunistic use of a licensed spectrum without interfering with primary users (PU) which overcome the scarcity problem of the available spectrum. The occurrence of carrier frequency offset (CFO) degrades the performance of the orthogonal frequency division multiplexing (OFDM). OFDM fulfills the requirements of cognitive radio, and hence OFDM is an appropriate choice for cognitive radio. When OFDM is used for cognitive radio applications, sensitivity to frequency offset remains an issue. This paper surveys various techniques present to estimate carrier frequency offset for OFDM cognitive radio. It covers required parameters to estimate, i.e., training symbols, estimation range, and complexity. This paper also presents simulation results that show extended estimation range of the frequency offset at good performance in the presence of narrowband interference. This method uses correlation among L identical parts of the training symbol at the receiver side to estimate the frequency offset. The estimation range is achieved up to ± L/2. © 2021, Springer Nature Singapore Pte Ltd.

School of Electronics & Communication Engineering

Lecture Notes in Electrical Engineering

An Improved Carrier Frequency Offset Estimation Under Narrowband Interference in OFDM Cognitive Radio

Currently, various high lift devices are being employed in the general aviation industry. The performance of these high lift devices depends on various parameters, among which the geometrical or positional parameters are one of the most important as their influence is very prominent on the aerodynamics of the wing. Hence, attention is to be given to the optimization of these devices to increase their efficiency and performance. This article focuses on the optimization of high lift devices such as leading edge slats and trailing edge flaps. The optimization is achieved with four defined configurations for each high lift device and then compared their results with each other to find the best configuration; here the best performance refers to maximization of section coefficient of lift. A two-dimensional CFD (computational Fluid Dynamics) analysis performed on best three element airfoil using SolidWorks Flow Simulation tool with variation in aerodynamic parameters as lift and pitching moment. The obtained results have an accuracy of 1.09% for maximum coefficient of lift and 0.4% for landing condition respectively. Thus the optimized NACA2412 three-element airfoil gives good aerodynamic performance. © 2021 Elsevier Ltd. All rights reserved.

World University root node

School of Mechanical Engineering

Materials Today: Proceedings

Aerodynamic effects of leading edge (LE) slats and slotted trailing edge (TE) flaps on NACA-2412 airfoil in prospect of optimization

Artificial Intelligence and Machine Learning Methods for Renewable Energy

In 5G cellular communications, Non-Orthogonal Multiple Access (NOMA) is the advanced radio access technique. Compared to other Orthogonal Multiple Access (OMA) schemes, NOMA is more efficient and supports a large number of users. However, as the number of users increases, OMA based schemes may not meet the emerging requirements such as very high spectral efficiency, very low latency, and massive device connectivity. NOMA allows access to several users in the same resource block, such as a time slot, subcarrier, or spreading code. This work mainly emphases on the power domain NOMA with Multiple Input Multiple Output (MIMO), which combines many users in the power domain and uses channel gain difference between multiple users. Various user signals can also spread over the channel at the transmitter side. Due to the availability of multi-users, the SIC on the receiver side cannot be able to perform the sequential decoding of the users. Whale Rank Successive Interference Cancellation (WRSIC) overcome the limitation in the existing SIC by ranking the user for successive cancellation of interference in the system. A WRSIC based Multi-User Detection (MUD) algorithm is used on the receiver side to identify the signal required. Thus, the novel WRSIC-MIMO NOMA method maximizes the channel capacity and eliminate the interference. Simulation results show the performance of the proposed scheme with different parameters and the comparison with the other schemes. © 2021 Taylor's University. All rights reserved.

Journal of Engineering Science and Technology

Noma multiuser detection with mimo for maximizing the system capacity and eliminate interference

The process of big data handling refers to the efficient management of storage and processing of a very large volume of data. The data in a structured and unstructured format require a specific approach for overall handling. The classifiers analyzed in this paper are correlative na{\"{i}}ve Bayes classifier (CNB), Cuckoo Grey wolf CNB (CGCNB), Fuzzy CNB (FCNB) and Holoentropy CNB (HCNB). These classifiers are based on the Bayesian principle and work accordingly. The CNB is developed by extending the standard na{\"{i}}ve Bayes classifier with applied correlation among the attributes to become a dependent hypothesis. The cuckoo search and grey wolf optimization algorithms are integrated with the CNB classifier and significant performance improvement is achieved. The resulting classifier is called a cuckoo grey wolf correlative na{\"{i}}ve Bayes classifier (CGCNB). Also, the performance of the FCNB and HCNB classifiers are analyzed with CNB and CGCNB by considering accuracy, sensitivity, specificity, memory and execution time.