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

Engineering and Technology

Resting-state fMRI (rsfMRI) was firstly characterized by Biswal et al [1] in the year 1995 and since then it is used for studying patients with various neurosurgical, neurologic, and other brain-related disorders. Various statistical methods are used to analyze the resting-state functional magnetic resonance imaging connectivity. This paper attempts to explore more on the learning of resting-state functional magnetic resonance imaging connectivity. This study will help to understand the usage of rsfMRI to evaluate functional connectivity in the human brain and its usage for surgical decision-making for drug-resistant epilepsy. This paper explains the significance of such connectivity analysis, the methods available for it, and the shortcomings associated with the data.

School of Electronics & Communication Engineering

School of Computer Engineering & Technology

World University root node

2021 2nd International Conference for Emerging Technology, INCET 2021

Resting-State Functional Connectivity Analysis for diagnosis of Epilepsy patients

In this paper, artificial neural network model developed for the prediction of PM2.5 particulate matter is discussed. Along with artificial neural network, multiple linear regression model is developed for the prediction. Both the techniques are compared for the prediction of PM2.5. The data for the training and testing is collected from OpenAq platform. The result shows that observed and predicted values are in close agreement. Artificial neural network is proved to be better than multiple regression model for the proposed application. Proposed method can also be used for data imputation technique for pollutant dataset. © 2021 IEEE

School of Electrical Engineering

2021 International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)

Artificial Neural Network for the Prediction of Particulate Matter (PM 2.5)

Hand gesture is a natural interaction method, and hand gesture recognition is familiar in human–computer interaction. Yet, the variations, as well as the complexity of hand gestures such as self-structural characteristics, views, and illuminations, made hand gesture recognition as a challenging task. Nowadays, the human–computer interaction area enhancement leads to putting interest in the dynamic hand gesture segmentation based on the gesture recognition system. Apart from the lengthy clinical success, dynamic hand gesture segmentation through webcam vision seems challenging due to the light effects, partial occlusion, and complicated environment. Hence, to segment the entire hand gesture region and enhance the segmentation accuracy, this paper develops an improved segmentation and deep learning-based strategy for dynamic hand gesture recognition. The data is gathered from the ISL benchmark dataset that consists of both static as well as dynamic images. The initial process of the proposed model is the pre-processing, which is being performed by grey scale conversion and histogram equalization. Further, the segmentation of gestures is done by the novel Adaptive Hough Transform (AHT), where the theta angle is tuned. Once the segmentation of gestures is done, the optimized Deep Convolutional Neural Network (Deep CNN) is used for gesture recognition. The learning rate, epoch count, and hidden neurons are tuned by the same heuristic concept. As the main contribution, the segmentation and classification are enhanced by the hybridization of Electric Fish Optimization (EFO), and Whale Optimization Algorithm (WOA) called Electric Fish-based Whale Optimization Algorithm (E-WOA). The training of optimized Deep CNN is handled by Dynamic Time Warping (DTW) for avoiding redundant frames, thus enhancing the performance of dynamic hand gestures. Quantitative measurement is accomplished for evaluating hand gesture segmentation and recognition, which portrays the superior behaviour of the proposed model.

Multimedia Tools & Applications

Adaptive hough transform with optimized deep learning followed by dynamic time warping for hand gesture recognition

Bone Age Assessment (BAA) is a frequent clinical procedure used to evaluate endocrinology, genetics, and development issues in children. BAA is commonly used because it is simple, with minimum exposure to radiation and multiple ossification centers for the maturity evaluation. The two techniques by which bone age is estimated are the 'Greulich Pyle (GP) method and the Tanner Whitehouse (TW) method.' Whichever method is used, each technique faces the limitation of intra- and inter-operator variability. Therefore these methods need to be automated. We have suggested an existing segmentation approach to segment each bone or 'region of interest (ROI)' from the hand wrist x-ray using u-net architecture. The proposed approach has been trained on 48 images and tested on 24 X-ray hand images to produce an average Dice coefficient of 0.88 ± 0.095. Thus, the suggested technique can offer an approach to segment x-ray images with annotated regions ROIs accurately. © 2021 IEEE.

International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021

Segmentation Using CNN for BAA using Modified TW3 method

International Research Journal of Engineering and Technology

Survey paper on Traffic Management System using IOT for Emergency Vehicles

Recent statistics show that malware attacks have been increased by over 97% in the past two years. Among these, a large portion is due to Ransomware, a subset of malware. Ransomware codes are easily available as Ransomware as-a-service (RaaS). Because of it, there is a significant threat to the world, as this is a malware which generates high revenues and is creating a viable criminal business model. Because of this the systems of private companies, individuals, or public service providers are at stake and can suffer a severe disruption and financial loss. There are two methods for the detection and analysis to be done for the detection of ransomware. One is the Static detection approach and the other is the Dynamic detection approach. We have done the detection using the Dynamic approach. This paper focuses on detection of ransomware and benign applications using machine learning algorithms for dynamic detection of ransomware. Our experimentation results show that high accuracy is obtained using the KNN algorithm.

International Advanced Computing Conference

Detection of Ransomware on Windows System Using Machine Learning Technique: Experimental Results

Nowadays, the authenticity of the RFID tags cannot be assured in the supply chain since these can be easily duplicated in the public space. We propose a novel Product Ownership Management System (POMS) of products for anti-counterfeits that can be used in the post supply chain by using the QR code. With the projected POMS, a consumer can reject the buying of counterfeits by scanning a QR code, if the seller does not have their proprietorship. This paper gives an application of the system that will help to overcome the problems related with the presently functioning supply chain management system and runs the mechanism to show the ownership of the products.