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 Electronics & Communication Engineering

&nbsp;Machine learning, a fragment of AI, is popularizing in every industrial domain and making a promising place for itself. It is flourishing for all the companies that are implementing ML. It very well possesses the ability to change the future of industries by offering better ways of working, instead of the existing traditional methods. The real estate sector has witnessed a quite similar situation in terms of the effect it has had on the sales of the real estate properties. ML has made business quite easy by predicting the sales prices of real estate properties on the basis of previous purchases and customer buying patterns. This project is about forecasting the house property prices in Bangalore city. It is accomplished by studying the various features of a particular house that fits appropriately into customer’s requirements, followed by deploying the linear regression ML model on the dataset. The study has shown that a linear regression model is a competent ML algorithm which is simple and at the same time gives accurate results. This project enlightens the fact that ML is a very good alternative for streamlining the business engagement in the real estate sector.

Engineering and Technology

PENSEE

Real estate price prediction using Machine learning

Abstract - A hearing aid is a device that sits in or behind the wearer's ear and is designed to modulate and amplify the sound for the wearer. Devices which were used earlier were called eаr trumpets or eаr horns, were раssive funnellike cones which amplified sound and were designed to collect sound and direct it into the eаr canal. The bоne аnсhоred d heаring аid, as well as cochlear inserts, are similar devices. Bluetooth open wireless technology for transmitting and transmitting and receiving dvtr оvеr shоrt distаnсеs (using shоrt-wаvelength rаdiо transmission in ISM band) frоm fix а 24002480 MHz) frоm stationary аnd mоbile devices. Originally designed as a wireless RS-232 data cable by telecommunications operator Eriсsson in 1994 [1]. It can connect multiple devices and cause sync issues. Bluetооth Special Interest Group, which has more than 16,000 member companies in the area of tеlесоmmuniсаtiоn, computing, networking, аnd consumer electronics and security, manages Bluetооth. The SIG oversees the develорment оf the sрeсifiсаtiоn, mаnаges the рrоgrаm which qualifies аnd рrоteсts the trаdemаrks. To be marketed as a Bluetooth device, it must be qualified according to the SIG's standards. The teсhnоlоgy necessitates a network of patents, which are only liсensed for those deviсes which qualify; hence, the рrоtосоl mаy be regаrded as рrоprietаry.

International Research Journal of Engineering and Technology (IRJET)

Design and Development of Hearing Aid System

World University root node

Wireless Networks

Optimal performance of resource allocation in LTE-A for heterogeneous cellular network

&lt;!-- /* Font Definitions */ @font-face {font-family:"Cambria Math"; panose-1:2 4 5 3 5 4 6 3 2 4; mso-font-charset:0; mso-generic-font-family:roman; mso-font-pitch:variable; mso-font-signature:-536869121 1107305727 33554432 0 415 0;} /* Style Definitions */ p.MsoNormal, li.MsoNormal, div.MsoNormal {mso-style-unhide:no; mso-style-qformat:yes; mso-style-parent:""; margin:0cm; margin-bottom:.0001pt; mso-pagination:widow-orphan; text-autospace:none; font-size:10.0pt; font-family:"Cambria Math","serif"; mso-fareast-font-family:"Cambria Math"; mso-bidi-font-family:"Cambria Math"; mso-ansi-language:EN-US; mso-fareast-language:EN-US;} .MsoChpDefault {mso-style-type:export-only; mso-default-props:yes; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:Calibri; mso-fareast-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi; mso-fareast-language:EN-US;} .MsoPapDefault {mso-style-type:export-only; margin-bottom:10.0pt; line-height:115%;} @page WordSection1 {size:612.0pt 792.0pt; margin:72.0pt 72.0pt 72.0pt 72.0pt; mso-header-margin:36.0pt; mso-footer-margin:36.0pt; mso-paper-source:0;} div.WordSection1 {page:WordSection1;} --&gt; “MODI lipi” is one of the ancient scripts of Western India. Considerable work has been reported for various other ancient Indian languages except MODI lipi. Its structural characteristics and non-availability of image database make MODI recognition challenging. The work reported in this paper comprises creation of an image dataset for MODI handwritten characters and the development of a supervised Transfer Learning (TL) based classification framework. It makes use of Deep Convolutional Neural Networks (DCNN) Alexnet as a pre-trained network to transfer weights to retrain the network. This network is used as a feature extractor to extract features from different layers of the network. A Support Vector Machine (SVM) is trained on activation features to obtain classifier models. These models are investigated further for recognition accuracy and feature analysis. Subjective and objective measures are used to select discriminant deep features. We achieved recognition accuracies of 92.32% and 97.25% for Handwritten MODI character recognition and handwritten Devnagari character recognition respectively.

IETE Journal of Research

Handwritten MODI Character Recognition Using Transfer Learning with Discriminant Feature Analysis

Video Analytics analyzes the video content and adds brains to eyes that is analytics to camera. It extracts contents from the video by monitoring the video in real time. Normal and Abnormal human activity detection using deep learning models is a challenging task in computer vision. The detection of the same will help in detecting crime scenes which will help in preventing treacherous actions Proposed method focuses on classifying normal activities for humans in real time scenarios. The pre-processing technique for redundant frame detection, elimination and training the model efficiently using Convolutional Neural Network for classifying the activities is the main research contribution. Proposed method shows improvement in accuracy as compared to reference method which can be further implemented for on edge embedded platforms for real time applications

International Journal of Next-Generation Computing

Enhanced video analysis framework for action detection using deep learning

Glioma is one of the common types of brain tumors starting in the brain's glial cell. These tumors are classified into low-grade or high-grade tumors. Physicians analyze the stages of brain tumors and suggest treatment to the patient. The status of the tumor has an importance in the treatment. Nowadays, computerized systems are used to analyze and classify brain tumors. The accurate grading of the tumor makes sense in the treatment of brain tumors. This paper aims to develop a classification of low-grade glioma and highgrade glioma using a deep learning algorithm. This system utilizes four transfer learning algorithms, i.e., AlexNet, GoogLeNet, ResNet18, and ResNet50, for classification purposes. Among these algorithms, ResNet18 shows the highest classification accuracy of 97.19%.&nbsp;

International Journal of Computer Science and Network Security

Transfer Learning Using Convolutional Neural Network Architectures for Glioma Classification from MRI Images

Lecture Notes in Electrical Engineering

An Improved Carrier Frequency Offset Estimation Under Narrowband Interference in OFDM Cognitive Radio

The automobile and transport industries have always tried to reduce downtime by the way of preventive maintenance. In the past decade, electromechanical sensors have become more accurate along with novel innovations in IoT and machine learning, and automobiles have leveraged this. In this paper, an end-to-end open-source predictive maintenance solution is presented to predict the severity of faults in a car using onboard historical and real-time sensor data using IoT and machine learning. Sensor data is collected from a Suzuki Swift VXi Model, and classifiers like logistic regression, random forest, and gradient boosting trees are used to train the data with imputed faults. F1 score and AUC are used as evaluation metrics. An end-to-end onboard diagnostics (OBD) data to the user dashboard pipeline is proposed with final predicted faults visible on a real-time dashboard. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.