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

According to earlier studies, chest x-ray (CXR) pictures might be used as a preventative measure for artificial intelligence (AI) to identify corona pneumonia. Unfortunately, problems with the collections and research approaches from a scientific and medical standpoint have also surfaced, and we also have concerns about the resilience and susceptibility of AI systems. In this work, we solve these concerns by creating our original information through some kind of retroactive preclinical studies to supplement the data gathered from external factors, leading to a more accurate generation of AI-driven coronavirus detection techniques. In order to objectively analyse research strategy, we created design techniques by modifying datasets, optimised five learning algorithms, and added a variety of sensing circumstances to assess the resilience and analytical effectiveness of the methods. In a 3-class identification situation as opposed to a 4-class research design, the study demonstrates superior overall efficiency of 91-96 percent Sn (sensibility), 94-98 percent Sp (precision), and 90-96 percent PPV. With a reliability score of F1-measure, as well as a g-average of 96% in the 3 class instances of identification, InceptionV3has the best overall result. At the same time, InceptionV3exhibited the best results for COVID-19 pneumonia identification with 86 percent Sn, 99 percent Sp, and 91 percent PPV with an AUC of 0.99 in separating pneumonia with regular CXR. It achieved 0.98 AUC and a stutter stepping mean of 0.99 for those other categories for its capability to distinguish between COVID-19 pneumonia and non-COVID-19 pneumonia. © 2022 IEEE.

World University root node

School of Electronics & Communication Engineering

2022 IEEE 6th Conference on Information and Communication Technology, CICT 2022

Artificial Intelligence for the Detection of Covid-19 Pneumonia on Chest X-Ray

Blockchain is incredibly emerging technologies in today's world. Now a days major issue faced by educational system is regarding fake documents, certificates. There are so many cases registered for the duplication of documents and all those cases go unnoticed. In this blockchain based system information stored is immutable it means it cannot be manipulated. Even if some attacker tries to manipulate the information present in the blockchain then hash value of the block gets changed. So, for this attacker has to recalculate the hash for the all the blocks in network, which is next to impossible. Thus, documents storage in blockchain provides high level security for the same. In this paper we have included various existing approaches for document security using blockchain platform and its benefits and limitations are also included so that one can get proper idea to rework or enhance the existing work with more advance technique. Also, we have highlighted problem of document storage and security enhancement issue in blockchain network. © 2022 IEEE.

School of Computer Engineering & Technology

2022 International Conference for Advancement in Technology, ICONAT 2022

Techniques of Securing Educational Document using Blockchain and IPFS based System: A Review

Navigation is how everything moves around globally, and electronics have provided better solutions for appliances to work seamlessly. There are two broad navigation options for finding your location. One is a Global Positioning System (GPS) system, and the other one is by using the Inertial Measurement Unit (IMU). The IMU is nothing but a combination of accelerometers and gyroscopes. This paper puts light on the Inertial Navigation System (INS), which uses GPS and IMU to get navigation data. GPS and INS give us real-time velocity and position data required for navigation. However, GPS has a slow update rate, up to 1-10Hz, while IMU performs far better at gaining navigation data with an update rate up to 1KHz. IMU accumulates errors and drifts over time while GPS has a low update rate. These drawbacks make both systems unreliable when used alone. The proposed work talks more about the use of both sensors, and Raspberry Pi Zero using a sensor fusion technology. Removing drawbacks of both the sensors and the use of the Kalman Filter are discussed in the paper. © 2022 IEEE.

2022 6th International Conference on Computing, Communication, Control and Automation, ICCUBEA 2022

SENSOR FUSION: An Advance Inertial Navigation System using GPS and IMU

One of the most significant and challenging areas of computer vision is object recognition and tracking, which is extensively utilised in many industries including health care monitoring, autonomous driving, anomaly detection, etc. The tracking of moving objects in videos is actively researched over the past two decades due to its practical applications in many fields such as event analysis, human-computer interaction, crowd analysis, video surveillance, behaviour analysis, etc. The effectiveness of object trackers and detectors has significantly increased with the rapid advancement of deep learning (DL) networks and GPU processing capability. New methods have been presented for object recognition and tracking in video as a result of extensive study in this field. This article addressed the several processes of object tracking in video sequences: object detection, object classification, and object tracking, in order to comprehensively comprehend the key advancements in the object detection and tracking pipeline. Additionally, we thoroughly examine the various approaches available for object recognition, categorization, and tracking. © 2022 IEEE.

Proceedings - International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2022

DeepSORT: Real Time &amp; Multi-Object Detection and Tracking with YOLO and TensorFlow

School of Chemical Engineering

School of Civil Engineering

Materials Today: Proceedings

Adsorption isotherm studies of Methylene blue using activated carbon of waste fruit peel as an adsorbent

Health monitoring and technologies related to health monitoring is an appealing area of research. The electrocardiogram (ECG) has constantly been the mainstream estimation plan to evaluate and analyse cardiovascular diseases. Heart health is important for everyone. Heart needs to be monitored regularly and early warning can prevent the permanent heart damage. Also, heart diseases are the leading cause of deaths worldwide. Hence, this work presents a design of a mini wearable ECG system and is interfacing with the Android application. This framework is created to show and analyse the ECG signal obtained from the ECG wearable system. The ECG signals will be shipped off an Android application via Bluetooth device and Wi-Fi. This system will automatically alert the user through notification. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Lecture Notes on Data Engineering and Communications Technologies

ECG Acquisition Analysis on Smartphone Through Bluetooth and Wireless Communication

Visual Pattern Recognition Networks (VPRNs) delivers remarkable performance using Deep Neural Networks (DNN). Various DNN architectures are available such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and its variants. Continuous emergence of novel architectures, availability of big data and enormous computing powers keeps this area flourishing. However, such DNN based VPRN models are plunged with enormous computational complexities, intense memory requirements, and high power consumptions which restricts its use in edgeAI environments. For instance, a typical pre-trained VGG-16 model needs 500MB memory, has over 138 million parameters and involves 15.5 billion Floating Point Operations (FLOPs). Such overheads demand for compression of VPRN models while retaining its performance. In this regard, various compression methods have been reported in literature. This paper presents experimentation on hybrid compression method which combines pruning and quantization to achieve higher compression ratio for nonsequential as well as sequential DNN based VPRNs. © 2022 IEEE.

2022 International Conference on Signal and Information Processing, IConSIP 2022

Model Compression for Non-Sequential and Sequential Visual Pattern Recognition Networks ― A Hybrid Approach

In the era of Big Data Analytics, information dissemination, data integrity, and identifying unique records from large pool of data poses a big challenge for analysts in entity matching and linking scenarios. Data ingestion from multiple sources of same real-world entity exhibits several data quality issues like redundancy, incorrectness, variations, etc. Also, there are data input errors like typographical/spelling mistakes as well as missing fields. In order to achieve entity resolution, uniqueness and eradicate data redundancy and improve the data quality issues, deduplication is the solution. India being a multi-lingual and multi-cultural country with vast demographic variations, there is a need to develop India-centric model for handling deduplication on various Indian structured data held by various authorities. This research proposes a novel approach catering to India-centric demographic variations, region-specific naming conventions, address standardization using a highly customizable and scalable deep learning approach, by customizing DeepMatcher algorithm along with a synthetic data generation tool reckoning Indian variations of names and addresses in a region-specific manner. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Lecture Notes in Networks and Systems

A Novel Approach of Deduplication on Indian Demographic Variation for Large Structured Data

The research of brain tumor classification by hand is a time-consuming operation with the potential for human mistakes. An accurate and automatic brain tumor classification technique is provided in this approach. To segment the brain part first, a thresholding approach is performed, followed by a morphological operation. Because the brain Magnetic Resonance Imaging(MRI) dataset is limited, the Convolutional Neural Network's (CNN) 'VGG19' transfer learning network is used. With little weights and photos of benign, glioma, and meningioma, VGG19 classification layer is substituted by the softmax layer. After the model is created, it is saved and using Flask, a WebApp is created that will help us to detect the brain tumor using Hybrid Multi-Cloud and Machine Learning Operations(MLOps) tools. Identity and Access Management (IAM) is used to keep the data secure. This end-to-end system can be used for object detection applications in hospitals and local medical clinics. © 2022 IEEE.

2022 IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2022

Brain Tumor Detection using MLops and Hybrid Multi-Cloud

Lung cancer, also known as malignant tumours, is one of the deadly diseases that are prevalent worldwide. It is brought on by the lung tissues' hesitant cell growth. Only when the disease is in its early stages can treatment be begun to cure it. The results of blood tests and computed tomography (CT) scanning are used to find this. Emphasis on the tumours area allows for the detection of the aberrant picture. The sample, which is in jpg format, consists of CT scan images. Convolutional neural networks are used to train the developed framework (CNN). The benefit of using CNNs formed with fuzzy correlation maps is that they do not require considerable image thresholding or the scarce supply of specialized training data. The pre - trained models AlexNet CNN algorithm is presented the output of the fuzzy logic. A deep CNN requires a significant amount of data to analyse in order to get an accurate output. To handle the problem of the restricted size of the CNN set of data, a fuzzy logic-based fuzzy association map is proposed.This work analyses fuzzy-based CNN image classification models and proposes a hybrid CNN fuzzy logic.The use of present technology would increase as a result of this early cancer detection strategy, and cancer prevention would advance. © 2022 IEEE.