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

Engineering and Technology

World University root node

IEEE Access

Multi-layout Unstructured Invoice Documents Dataset: A dataset for Template-free Invoice Processing and its Evaluation using AI Approaches

Education is an essential axe in developing a modern, progressive and industrialized society. People must be ingrained with good quality education to be able to match up with this competitive world. In recent epoch, prenatal education by laying pre-birth prognostication of fetus in the uterus of the mother has been provoked. The grasping power of a fetus in mother's uterus is superior compared to a born child, while in the utero, baby is able hear and remember sounds which baby are able to remember even after their birth. It is also manifested that the work the mother does during the 9 months of pregnancy has a direct consequences on the evolution of child's brain. All the three aspects, i.e., health, values and creativity, go in conjunction in developing the child's mind. All the skills set, values and qualities which are acquired during pregnancy from the mother can be used to anticipate which education stream the child would be likely suitable to opt for in future. This prediction can be achieved with the help of different prediction algorithm of machine learning.

Pre-birth Prognostication of Education and Learning of a Fetus While in the Uterus of the Mother Using Machine Learning

Efficient Automated Processing of the Unstructured Documents using Artificial Intelligence: A Systematic Literature Review and Future Directions

Fetal monitoring usually refers to monitoring fetal heart rate (FHR) for detection fetal well-being. This is important activity carried out by doctors during prepartum, intrapartum phase as per health requirement of patient. Fetal monitoring is required to reduce chances of fetal to become hypoxic (when fetal is deprived from sufficient oxygen) that can cause fetal brain injury and even fetal death. Fetal monitoring plays important role in reducing mortality and morbidity rate. The most common noninvasive fetal monitoring device is cardiotocograph (CTG). CTG captures FHR based on Doppler ultrasound principle and uterine contractions (UC) based on pressure transducers. The present study highlights accuracy limitations of CTG and proposes more accurate noninvasive fetal ECG (NIfECG) as data acquisition methods to acquire FHR and electrohysterogram (EHG) to capture UC. CTG interpretation is one of the decision-making parameters used by doctors for early intervention like caesarian section. CTG interpretation often suffers from inter-observer and intra-observer agreement of CTG patterns which are non-reassuring. To overcome this limitation, computerized analysis can be useful. The present study also discusses usage of machine learning to detect fetal compromise. Further, in addition to FHR and UC analysis, we also propose to use ST waveform analysis to improve results. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Lecture Notes in Networks and Systems

Comprehensive Study of Fetal Monitoring Methods for Detection of Fetal Compromise

i-Manager's Journal on Future Engineering and Technology

Study on Cyberbullying and the Challenges in using Hindi in Social Media

International Journal of Scientific Research in Computer Science {\ldots}

Smart Card : A Single Card Solution for Multiple Activities

Diabetic Retinopathy (DR) is a diabetes complication that affects vision. It is caused by damage to the blood vessels of retina. Early and accurate detection of DR is crucial to reduce likelihood of progression to proliferative retinopathy and blindness. This paper proposes a method for classifying the severity of DR using deep learning. Experiments were conducted by blending the members of EfficientNet for classification of the diabetic retinopathy image as no DR, mild, moderate, severe, or proliferative DR. The models have been trained using different datasets and best model achieved a quadratic kappa score of 0.924377 on the APTOS test dataset. The results are promising and warrant further investigation. The presented model has the potential aid in fast diagnosis for better early detection of DR.

2021 International Conference on Emerging Smart Computing and Informatics, ESCI 2021

Diabetic retinopathy classification using a combination of EfficientNets

Twitter analytics is a classic research area especially with the widespread presence of Big Data in various online media such as—social network sites, online portals for shopping, e-commerce, forums, chats, recommendation systems, and online services. Ascertaining the sentiment behind, the various types of tweets by different persons can provide great insights on various aspects including behavioral patterns. Besides highlighting the newest trends in the field, we retrieved real-time twitter data pertaining to three currently popular hashtags in the Indian context and carried out extensive experimentation analysis about the prevailing sentiment of a strata of population. Inclusion of current challenges, future trends and applications of sentiment analysis from Twitter data makes this novel work very useful for fellow researchers. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A Novel Proof of Concept for Twitter Analytics Using Popular Hashtags: Experimentation and Evaluation

REVISTA GEINTEC-GESTAO INOVACAO E TECNOLOGIAS

Execution Evaluation of AODV Protocol Using NS2 Simulator for Emergency Automobile

Objective: Quantum algorithms are stronger and more secure than classical computers because they run on faster, harder ones and require fewer steps. With Quantum computers, the attackers have high computing power, and with a quantum, the algorithm can easily break the cryptographic system. Lattice is a regularly spaced grid of points stretching to infinity. Quantum safe security algorithms are resistant to both attacks caused by quantum computers and attacks caused by classical computers. Lattice-based cryptography is the post-quantum cryptographic standards resistant to the attacks from quantum computers, hence having the advantage of strong security and high efficiency. The paper's main objective is to study Lattice, lattice properties, Lattice – based cryptographic algorithm to design new Lattice-based cryptographic algorithms that are quantum resistant in the future. Methods: In this paper, lattice-based cryptography is discussed right from its seminal work to its efficient cryptographic schemes. Paper discusses Lattice, lattice properties, lattice problem, the algorithmic solution to lattice problem, and lattice-based cryptography. Findings: After studying post-quantum cryptographic algorithms using Lattice, lattice-based post-quantum cryptographic algorithms are resistant to quantum computer attacks. Novelty: The paper discusses Lattice, properties of Lattice in a simple way. Widely used cryptographic algorithms like RSA, Diffie-Hellman Key exchange, Elliptic Curve Cryptography are not resistant to quantum computer attacks. Paper discusses the importance of a post-quantum algorithm using Lattice that is resistant to quantum computer attacks. © 2021 Seventh Sense Research Group®.