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

Fetal heart rate (FHR) monitoring is done for accessing fetal wellbeing during antepartum and intrapartum phases. Although noninvasive fetal electrocardiogram (NIfECG) is a potential data acquisition method for FHR, extraction of fetal electrocardiogram (ECG) from the abdominal ECG (aECG) is one of the major challenging research areas. This chapter proposed and assessed a method suitable for single channel based on principal component analysis (PCA) for extracting fetal ECG. Maternal R peaks and fetal R peaks were detected using Pan Tomkins algorithm (PTA) and improved Pan Tomkins algorithm (IPTA), respectively. Performance of fetal QRS detection is assessed using two open-access databases available online. The method shows satisfactory performance when compared with similar methods and makes it suitable for using a single channel system.

School of Computer Engineering & Technology

Designing User Interfaces With a Data Science Approach

Fetal ECG Extraction: Principal Component Analysis Method for Extraction of Fetal ECG

A new wave of digitization is reshaping the construction industry, but it also brings a wealth of new prospects. Building materials manufacturing, planning, design, and construction are all in a supply chain projected to be boosted by artificial intelligence. Along with this, facility management is also expected to benefit from this technology. Choosing the optimal project management solution, particularly for major building projects, is sometimes complicated and fraught with uncertainty. As per the original references, the success and efficiency of project management rely on using three pillars called “Management involvement, ”create and streaming systems,” and “using the tools. Tools and procedures are directly in the knowledge areas of executives and engineers. On the other hand, the other two pillars rely on the outputs of the tools and procedures indirectly. As a result, tools and methods play a critical part in project management’s effective implementation. However, artificial intelligence includes collecting data, induction, and experiment analysis to comprehend human intelligence modeling and non-numerical methods to solve complicated issues. Hence it can be used as a tool in four major fields: Integration Management, Project Procurement Management, Project Risk Management, and Project Communications Management; they can be used in numerous applications.

School of Civil Engineering

Turkish Journal of Computer and Mathematics Education

Enhancing the Supply Chain Management in Construction using Artificial Intelligence

World University root node

School of Polymer Engineering

International Journal of Membrane Science and Technology

Acid-Modified ZnO Nanoparticles Embedded Polysulfone Membranes for Separation of Copper from Industrial Wastewater

2022 IEEE 7th International conference for Convergence in Technology, I2CT 2022

Real-Time Sign Language Recognition using Deep Learning Techniques

School of Mechanical Engineering

International Journal of Heat and Technology

Effect of Rectangular Fins on Heat Transfer Characteristics of Domestic Cookware

School of Electronics & Communication Engineering

Smart Innovation, Systems and Technologies

Breast Cancer Prediction Using Machine Learning Techniques

School of Polytechnic and Skill Development

Cybernetics and Systems

Deep E-Learning RecommendNet: An Acute E-Learning Recommendation System with Meta-Heuristic-Based Hybrid Deep Learning Architecture

Individual feature selection algorithms, used for processing high-dimensional multi-source heterogeneous data may lead to weak predictions. The traditional single method process may not ensure the selection of relevant features. The selections of features are susceptible to the changes in input data, and thus fail to perform consistently. These challenges can be overcome by having a robust feature selection algorithm that generates a subset of original features and evaluates the candidate set to check for its relevance. Also, it determines the feasibility of the selected subset of features. The fundamental tasks of selecting feature subset minimize the complexity of the model and help to facilitate the further processing of the model. The limitations of using single feature selection technique can be reduced by combining multiple techniques to generate the effective features. There is a need to design efficient approaches and technique for estimating the feature relevance. This ensemble approach will help to include diversity at input data level, as well as the computational technique. The proposed method---Ensemble Bootstrap Genetic Algorithm (EnBGA)---generates the effective feature subset for the multi-source heterogeneous data. Various univariate and multivariate base selectors are combined together to ensure the robustness and stability of the algorithm. In this pandemic of COVID-19, it's observed that patients already diagnosed with diseases such as diabetes had an increased mortality rate. The proposed method performs feature analysis for such data, where the Genetic Algorithm searches the feature subset and extracts the most relevant features.

Effective Feature Selection Using Ensemble Techniques and Genetic Algorithm

Lecture Notes in Electrical Engineering

Predictive Analytics for Real-Time Agriculture Supply Chain Management: A Novel Pilot Study

Multimedia Tools and Applications

Adaptive recurrent nonnegative matrix factorization with phase compensation for Single-Channel speech enhancement