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;

&nbsp;

MIT World Peace University

School of Electronics & Communication Engineering

Engineering and Technology

World University root node

School of Computer Engineering & Technology

Advances in Communication and Computational Technology

Evaluation of SC-FDMA Physical Link Using USRP

Computed Tomography (CT) images of the patient's cranium are widely used in the diagnosis of hemorrhage. They are reviewed by highly skilled professionals to find the existence, location and type of hemorrhage. This process is complex, labor-intensive and requires lot of time. The massive progress in machine learning techniques and availability of high computational power has made it possible to detect such hemorrhages automatically. The paper provides survey of recent deep learning approaches for automatic detection of such acute intracranial hemorrhage and its subtypes. Further the experimental results using windowing technique inspired by practical radiologist's approach and relevant research papers is provided to verify its efficacy as a pre-processing step before feeding DICOM images to the network. Transfer learning approach is used to detect hemorrhage types. The open source Kaggle platform is utilized for computational resources and for carrying out the experimental study. Some key evaluation metrics and loss functions addressing the data imbalance underlying with the problem statement are also presented. The paper concludes with addressing key insights into open issues in the proposed domain. © 2021 IEEE.

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

A survey on deep learning for intracranial hemorrhage detection

International Journal of Engineering Applied Science and Technology

Applications of Computer Vision in Agriculture

International journal for research in applied science and engineering technology

Indoor Air Quality Monitoring System

International Journal of Management, Technology And Engineering

AI Coach for Badminton

Agriculture is a very important factor for human beings to make economy. 45% of world’s population depends on agriculture for their livelihood. Fruit production and harvesting is too essential. It has a great demand in national as well as international markets. But fruits harvesting is still done with the traditional methods which requires huge man-power, too much of cost and time consuming. Therefore, the use of innovative agriculture based technologies and systems are very needful for the agriculture field. So in this paper, combination of all the very recent fruit detection and fruit segmentation techniques using deep-learning (DL) approach have been considered. Comparative study of all the fruit detection and fruit segmentation techniques has been done. Based on review, conclusions have been made to give directions to future research challenges along with the future scope.

Second International Conference on Image Processing and Capsule Networks. ICIPCN 2021

A Review on Fruit Detection and Segmentation Techniques in Agricultural Field

This research work discusses the design and simulation of essential blocks of Field Programmable Gate Arrays (FPGA), such as N-channel metal-oxide-semiconductor (NMOS) Pass Transistor, Static Random-Access Memory (SRAM), Input/Output block (IOB), and Slice in LTspice. The blocks are referred from the Spartan-3 FPGA family and are designed in 180 nm technology. The blocks designed here can be configured to the desired functionality by placement and routing of the individual blocks in Verilog to routing (VTR). © 2021 IEEE.

2021 4th International Conference on Recent Developments in Control, Automation and Power Engineering, RDCAPE 2021

Design of FPGA Building Blocks Using LTspice®

The aim of image captioning research is to caption and annotate an image with a sentence that explains the image. It's not tough for humans but it is for machines, to make sense out of what is actually there but not seen. Facebook and Google, for example, use image recognition to monitor where you are, what you do, and other activities. In Proposed work, natural language processing and Deep Learning applied through Object Detection and Text Generation is used to generate description of an image automatically. This architecture includes a dense attention model with a CNN Encoder and RNN Decoder, an average BLEU score of 51.77 was observed. © 2021 IEEE.

2021 International Conference on Computational Performance Evaluation, ComPE 2021

Image Captioning using Deep Learning

Lately, human activity recognition has pulled in an expanding measure of consideration from research and industry networks. Human activity recognition is the consistently sprouting exploration zone as it finds magnificent utilizations in surveillance, healthcare, and many real-life problems. This paper presents a technique to automatically recognize human activity from a video sequence. An integrated features approach using Histogram of Gradient (HOG) local feature descriptor and Principal Component Analysis (PCA) as a global feature is proposed in this paper. Optimized Support Vector Machine(SVM), Artificial Neural Network (ANN) used as a classifier. The proposed model is trained and tested on Benchmark KTH dataset, results obtained are comparable with existing methods. The proposed technique achieved the activity recognition accuracy of 99.21 percent. The experimental results confirms that the embedded feature approach and optimization techniques for classifier improves the performance of human activity recognition © 2021 IEEE.

An improved model for human activity recognition by integrated feature approach and optimized SVM

Voice Emotion Recognition (VER) is a dynamic and has implications on a wide range of research areas. Use of a computer for voice emotion recognition is a way to study the voice signal of a speaker, as well as is a process that is altered by inner emotions. Human Machine Interface (HMI) is very vital and opted to implement this effectively and an innovative way. To develop new recognition methods, this research paper evaluates the basic emotions of human. Accurate detection of emotional states can be further used as a machine learning database for interdisciplinary experiments. The proposed system is an algorithmic method that first extracts the audio signal from the microphone, preprocesses it, and then evaluates the parameters based on various characteristics. The model is trained through the Mel Frequency Cepstral Coefficient (MFCC) and PRAAT (Speech Analysis in Phonetics) coefficients. By creating a feature map using these, Convolutional Neural Networks (CNN) effectively learn and classify the attributes of perceived signals of basic emotions such as sadness, surprise, happiness, anger, fear, neutral and disgust. The proposed method provides good recognition rate. © 2021 IEEE.