Proceedings of the 3rd International Conference on Advanced Technologies for Societal Applications—Volume 1

(1)

Prashant M. Pawar · R. Balasubramaniam · Babruvahan P. Ronge ·

Santosh B. Salunkhe · Anup S. Vibhute · Bhuwaneshwari Melinamath Editors

Techno-Societal 2020

Proceedings of the 3rd International

Conference on Advanced Technologies

for Societal Applications—Volume 1

(2)

(3)

Prashant M. Pawar · R. Balasubramaniam · Babruvahan P. Ronge · Santosh B. Salunkhe · Anup S. Vibhute · Bhuwaneshwari Melinamath

Editors

Proceedings of the 3rd International

Conference on Advanced Technologies for

Societal Applications—Volume 1

(4)

Editors

Prashant M. Pawar

SVERI’s College of Engineering Pandharpur, Maharashtra, India Babruvahan P. Ronge

SVERI’s College of Engineering Pandharpur, Maharashtra, India Anup S. Vibhute

SVERI’s College of Engineering Pandharpur, Maharashtra, India

R. Balasubramaniam Precision Engineering Center Bhabha Atomic Research Centre Mumbai, Maharashtra, India Santosh B. Salunkhe

SVERI’s College of Engineering Pandharpur, Maharashtra, India Bhuwaneshwari Melinamath SVERI’s College of Engineering Pandharpur, Maharashtra, India

ISBN 978-3-030-69920-8 ISBN 978-3-030-69921-5 (eBook) https://doi.org/10.1007/978-3-030-69921-5

This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed.

The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.

The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

(5)

Ternary Pattern Features Using Ann . . . 603 Shamli V. Jagzap, Lalita A. Palange, Seema A. Atole, and Geeta G. Unhale Object Recognition Using Fuzzy Classifier . . . 613 Seema A. Atole, Shamli V. Jagzap, Lalita A. Palange, and Akshay A. Jadhav An Effective Approach for Accuracy of Requirement

Traceability in DevOps . . . 623 Vinayak M. Sale, Somnath Thigale, B. C. Melinamath, and Siraj Shaikh

Clustering of Fruits Image Based on Color and Shape Using

K-Means Algorithm . . . 639 Vidya Maskar, Kanchan Chouhan, Prashant Bhandare, and Minal Pawar

Modern Education Using Augmented Reality . . . 651 Vishal V. Bandgar, Ajinkya A. Bahirat, Gopika A. Fattepurkar,

and Swapnil N. Patil

OSS Features Scope and Challenges . . . 661 M. K. Jadhav and V. V. Khandagale

Text Summarization and Dimensionality Reduction Using

Ranking and Learning Approach . . . 667 Dipti Bartakke, Santosh Kumar, Aparna Junnarkar, and Somnath Thigale

Properties of Extended Binary Hamming [8, 4, 4] Code Using

MATLAB . . . 683 N. S. Darkunde, S. P. Basude, and M. S. Wavare

Identification of Fake News on Social Media: A New Challenge . . . 689 Dhanashree V. Patil, Supriya A. Shegdar, and Sanjivini S. Kadam

A Smart and Secure Helmet for Safe Riding . . . 703 Ramesh Kagalkar and Basavaraj Hunshal

On Some Properties of Extended Binary Golay [24, 12, 8] Code

Using MATLAB . . . 713 N. S. Darkunde, S. P. Basude, and M. S. Wavare

Encoding Using the Binary Schubert Code [43, 7] Using

MATLAB . . . 725 M. S. Wavare, N. S. Darkunde, and S. P. Basude

Data Mining Techniques for Privacy Preservation in Social

Network Sites Using SVM . . . 733 Vishvas Kalunge and S. Deepika

Near Field Communication (NFC) Technology and Its

Application . . . 745 R. D. Kulkarni

(11)

Contents xi

Performance Analysis of Convolution Neural Network

with Different Architecture for Lung Segmentation . . . 753 Swati P. Pawar and S. N. Talbar

A Secure Data Sharing Platform Using Blockchain

and Fine-Grained Access . . . 761 Shamsundar Bhimade, Prashant Bhandare, Amey Bhatlavande,

and Bharati Deokar

Efficient and Interactive Fuzzy Type Ahead Search in XML Data . . . 769 Laxman Dethe, Geeta Khare, Avdhut Bhise, and Somnath Zambare

IOT Based Interactive Motion Detection Security System Using

Raspberry Pi . . . 779 Geeta Khare, Subhash Pingale, Avdhut Bhise, and Sharad Kawale

Commercially Successful Rural and Agricultural Technologies Cognitive Intelligence of Internet of Things in Precision

Agriculture . . . 789 Rahul Keru Patil and Suhas Shivlal Patil

Flooring: A Risk Factor for Fall-Related Injuries in Elderly

People Housing . . . 797 Unesha Fareq Rupanagudi

Analysis of Construction Readiness Parameters for Highway

Projects . . . 805 Harshvardhan R. Godbole and R. C. Charpe

Biogas Generation by Utilizing Agricultural Waste . . . 819 Prathamesh Chaudhari and Shivangi Thakker

Effect of Change in the Resilient Modulus of Bituminous Mix

on the Design of Flexible Perpetual Pavement . . . 829 Saurabh Kulkarni and Mahadeo Ranadive

Analysis of Three-Stage Open Pan Heat Exchanger Working

on Dual Fuel for Jaggery Making . . . 839 Abhijeet N. Kore and Sanjay S. Lakade

The Performance and Emission Analysis of Diesel Engine

with Sunflower Biodiesel . . . 847 Aniruddha Shivram Joshi and S. Ramesh

Deployable Environment or Healthcare Technologies Experimental Analysis of Effect of Bio-lubricant Between

Tribological Systems of Piston Ring Under the Jatropha Oil . . . 863 Mhetre Rahul Sanjay and L. B. Abhang

(12)

xii Contents

Garbage Monitoring and Collection System Using RFID

Technology . . . 873 Amol A. Kadam, AksahyAjadhav, Dhanraj P. Narsale,

Anil M. Kasture, S. M. Karve, and Manoj A. Deshmukh A Survey on Mental Health Monitoring System Via Social

Media Data Using Deep Learning Framework . . . 879 Satyaki Banerjee and Nuzhat F. Shaikh

An Approach to Heart Disease Prediction with Machine

Learning Using Chatbot . . . 889 Chinmay Nanaware, Arnav Deshmukh, Nikhil Chougala, and Jaydeep Patil Automated Early Detection of Diabetic Retinopathy . . . 897 Supriya Shegdar, Ameya Bhatlavande, Dhanashree Patil,

and Sanjivani Kadam

Product Lifecycle of Automobiles . . . 905 V. K. Bupesh Raja, Ajay Shivsharan Reddy,

Suraj Ramesh Dhavanapalli, D. R. Sai Krishna Sanjay, BH. Jashwanth Varma, and Puskaraj D Sonawwanay Analysis of Heavy Metal Pollutants in the Sediments

from Coastal Sites of Al-Hodiedah Governorates, Yemen . . . 911 Majeed Hazzaa Nomaan, Dipak B. Panaskar, and Ranjitsinh S. Pawar

Development of Low Cost PCR Product Detection System

for Screening and Diagnosis of Infectious Diseases . . . 923 Patil Yogesh Navalsing, Suri Vinod Kumar, Suri Aseem Vinod,

Kar Harapriya, and Thakur Mansee Kapil

Determination of Viscous, Coulomb and Particle Damping

Response in SDOF by Forced Oscillation . . . 935 S. T. Bodare, S. D. Katekar, and Chetan Chaudhari

Assessment of Godavari River Water Quality of Nanded City,

Maharashtra, India . . . 947 P. R. Shaikh, Girish Deore, A. D. Pathare, D. V. Pathare, and R. S. Pawar

Groundwater Quality Assessment in an Around Thermal Power

Plants in Central India . . . 963 V. U. Deshmukh, D. B. Panaskar, P. R. Pujari, and R. S. Pawar

Micro, Nano Manufacturing, Fabrication and Related Applications

XML Based Feature Extraction and Process Sequence Selection

in a CAPP System for Micro Parts . . . 973 G. Gogulraj and S. P. Leokumar

(13)

Contents xiii

To Study and Optimize the Effects of Process Parameters on Taper Angle of Stainless Steel by Using Abrasive Water Jet

Machining . . . 983 Meghna K. Gawade and Vijaykumar S. Jatti

Effect of Process Parameters on Response Measures of Cartridge

Brass Material in Photo Chemical Machining . . . 995 Bandu. A. Kamble, Abhay Utpat, Nitin Misal, and B. P. Ronge

Thermal Performance of Two Phase Closed Thermosyphon

with Acetone as Working Fluid . . . 1005 Shrikant V. Pawar and Abhimanyu K. Chandgude

Effect of Slip on Vortex Formation Near Two-Part Cylinder

with Same Sign Zeta Potential in a Plane Microchannel . . . 1013 Souvik Pabi, Sumit Kumar Mehta, and Sukumar Pati

Optimization of WEDM Parameters During Machining of Ni-75

Using AHP-MOORA Method . . . 1023 S. A. Sonawane and S. S. Wangikar

Structural Analysis of Novel Mini Horizontal Axis Wind

Turbine (NMHAWT) . . . 1031 Pramod Magade, S. P. Chavan, Sushilkumar Magade, and Vikram Gaikwad Fabrication of Tree Type Micro-Mixer with Circular Baffles

Using CO2Laser Machining . . . 1039 Sachin R. Gavali, Sandeep S. Wangikar, Avinash K. Parkhe,

and Prashant M. Pawar

(14)

Sensor Image and Data Driven Societal

Technologies

(15)

Office Monitoring and Surveillance System

Vishal Patil and Yogesh Jadhav

Abstract Facial recognition is a biometric software category that mathematically maps the facial features of a person and stores the data as a face-print. Using machine learning algorithms, the software compares a live capture or digital image to the stored face print to verify an individual’s identity and help automate authentication. Facial recognition will increase protection, recognize unauthorized entry and keep a track of visitors. ID passes are yesterday’s technology Our project’s main task is to identify if the person is an employee or a visitor by using a face recognition system where in security guards job is to watch over the process and stepping in only when the system says that the person is not an employee or when they see something suspicious.

Keywords Face recognition

·

^FaceNet

·

^OpenCV

·

Office monitoring

1 Introduction

A facial recognition system is a technology capable of recognizing and validating an individual from a digital picture or a video source. Facial recognition usually functions in conjunction with captured facial features from a given picture using faces in a database. It is also recognized as a biometric technology based on artificial intelligence, which recognizes the person by detecting patterns based on the facial textures and shape of a person’s face. Even though a face recognition system was initially implemented with the help of the computer, a wider implementation is seen on mobile devices as well and also in different technologies such as robotics wherein it is used as access control in the security system and can be different from other biometrics such as fingerprinting or eye iris recognition systems. It has also

V. Patil (

B

⁾

Computer Engineering Department, K. J. Somaiya Institute of Engineering and Information Technology, Mumbai, India

Y. Jadhav

Computer Science and Engineering Department, Amity School of Engineering and Technology, Amity University, Mumbai, India

https://doi.org/10.1007/978-3-030-69921-5_1

3

(16)

4 V. Patil and Y. Jadhav become popular recently as a marketing and commercial device. Many implementations include automated interaction between humans and computers, video analysis, automatic indexing of images and video archives. In today’s application, facial recognition can be approached in different ways. The primary component analysis (PCA), Linear Discriminant Analysis (LDA), and deep learning are examples of such variables. PCA is a form of dimensional reduction sometimes used to reduce dimensions on a fairly wide dataset. You do this by turning variables into smaller sections in large datasets, while still using large datasets details. The PCA approach is very commonly used as the principle is very straight forward and has a computer-efficient algorithm as illustrated in following Fig.1.

FaceNet is one of the implementations of facial recognition using deep learning.

This is a one-shot approach for learning with Euclidean space to measure each face’s resemblance size. FaceNet is a fairly recent approach used with the Deep Convolutional Network system in Google study in 2015. There were two separate systems in the analysis, the network system for Zeiler and Fergus and the newest network system for Inception. CNN in train practices Stochastic Gradient Descent (SGD) implementing back propagation and AdaGrad standards. FaceNet provides up to 99.63% accuracy from datasets that have labeled Faces in the Wild (LFW)and 95.12% accuracy on YouTube Faces Database. The benefit of using the FaceNet approach is that this model requires only limited alignment to cut the face region relatively tightly. FaceNet’s drawback is that the training cost is very costly because it requires a CPU. Office Monitoring and Surveillance System is a face recognition- based system for providing an efficient security solution for commercial buildings and offices. The project does this by performing facial recognition of people entering the premises and checking if the person is an employee of the company or a Visitor.

If the system detects the personas an employee, he will be granted access to the company. If the system does not detect the person as an employee, they will be provided with a welcome screen. The welcome screen will contain options whether the person has reserved a meeting, and with which employee. The guest user will choose the employee they want to meet and their video will be live-streamed to the employee. If the employee recognizes the guest, they will be granted access;

otherwise, the guest will be directed to a nearby consultant. Along with this system also does object detection and fire recognition. The system uses Google FaceNet, OpenCV, SVC model to perform face recognition. The system contains a web-based application that will be used by all the employees to get notified about the presence of a visitor willing to visit them in the office premises. The same web-based application will be used for storing the visitor details and monitoring them. The application is developed using the Laravel framework. The proposed system will help to reduce the physical procedures, time & efforts required for the same and will save the paperwork as well; to provide security for offices; to ensure the safety of employees and also maximize the efficiency of guards.

(17)

Office Monitoring and Surveillance System 5

Fig. 1 System flow

(18)

6 V. Patil and Y. Jadhav

2 Literature Survey

Wei Wu, Chuanchang Liu, Zhiyuan Su have presented the result of the analysis and the methodology of applying FaceNet. Recent studies show that deep learning approaches to this challenge can produce impressive results. To overcome these challenges, this program uses a profound learning approach. This comes with a module to detect the faces and identification of the faces. The MTCNN facial detection module is very simple, accurate and robust enough for variations in lighting and contexts. This module is based on FaceNet, which teaches you to map the facial images directly at the position of the Euclidean space, where the two-pointed space inside the Euclidean space corresponds directly to the similarity of two facial images.

When this Euclidean space is generated, facial images are transferred as facial vectors to the FaceNet embedding system. Feature vectors are then listed in a SVM to help us easily recognize the face images. The experimental findings indicate that the device is highly accurate, low computational complexity, and has a strong implementation benefit for real time identification [1].

Many businesses have widely adopted video monitoring devices to increase protection in their workforce. There are few visible cameras because of their suscep- tibility to eye strain A kind of control method is used in this paper to solve the problem. The network is made up of wireless and video monitoring devices. When security threats are detected, an alert may be activated by the wireless control device and the video stream from the camera to the computer is enabled. Therefore, the system avoids dangers resulting from machine breakdown or user error and thereby increases industrial efficiency [2,3].

Facial recognition is an essential process to identify and separate the facial region from the majority of the photos of every face detection device. There are numerous techniques proposed, ranging from the simple detection of edges to the advancement of techniques like pattern recognition. The review contrasts two facial recognition approaches the characteristics of them and the local binary patterns focused on impact rate recognition and speed detection. The algorithms have been tested on Microsoft Visual C++ 2010 Express with OpenCV software. Test findings show the reliability and trust of the functionality of the local binary template for implementing a facial recognition device in real time [4].

Paper 4 introduces the presentation on Jetson TX2 with FaceNet and MTCNN algorithms of an advanced and multi-camera face recognition surveillance device.

Presents the analysis of the modern, multi-camera facial recognition tracking system FaceNet and MTCNN algorithms on Jetson TX2. The suggested portable system tracks the target or suspect along with the ID/location of the camera and documents its presence through various camera installations in the database. Triplet loss is one of the most significant things we find in this article. The gap between the anchor and the result, which implies a separate identity, is reduced using Triplet loss procedure, if the result is accurate and indicates the same identity; the gap between the anchor and the sample is then maximized. Triplet loss is one of the easiest ways to practice effective 128-dimensional embedding for any image. The representation of the anchor here is

(19)

Office Monitoring and Surveillance System 7 the frame from which the triplet loss was measured. Three pictures are required to quantify the triplet loss: an anchor, positive and negative [4,5].

Study of paper 5 describes a way of monitoring individuals and obtaining canonical frontal pictures that fit the sensor network model in 2D world coordinates. In front photos, monitoring and identity management have a specific desirability because they are essentially invariant with daily appearance changes. This method has been applied and tested on the FaceNet wired camera prototype network. Their key contribution is to demonstrate how to sense the trajectories of moving objects to acquire canonical vision of high quality while maintaining node capacity. The authors present an overview of the method and display the task algorithm on data obtained from the FaceNet camera network [6–11].

The purpose of project implemented in paper 6 is to create a complete hardware and software framework for the surveillance of entry into a secure facility with modern facial recognition technology (FRT) and lightweight, cheap components.

For visualization in the operation, FRT senses and recognizes individuals as they enter and exit the area protected. The project uses FaceNet FRT to boost facial recognition accuracy by means of a deep-learning approach. The FaceNet FRT is implemented on a Raspberry Pi using the Neural Compute device Intel Movidius with input from two portable cameras [7].

Substantial advancement in the field of identity testing and facial detection has been made lately, taking advantage of advances in the convolution neural networks (CNN). The findings on human level have been exceeded by FaceNet’s performance particularly in terms of accuracy in “Labeled Faces in the wild (LFW)” datasets and “Youtube Faces in the wild (YTF).” The triplet loss in the FaceNet has been proven successful for facial verification. However, if a large dataset is used to train the model, the amount of triples available is explosive. In this article, we propose a simple class-by-class triple loss based on intra-class distance metric learning which can substantially reduce the number of potential triplets to learn, but simplifying the classic three-point loss function hasn’t deteriorated the approach. Experimental tests of the most commonly used LFW and YTF benchmarks show that the model with a class-wise simple triplet loss can achieve the latest results. And the simulation of the dispersal of the learned features based on the MNIST data set has shown the effectiveness of the proposed method of better class separation and discriminating against the other state-of-the-art loss functionality [12–14].

3 Proposed System

Our system provides automated surveillance and monitoring for offices. This system will work on employee’s faces for distinguishing a person from a visitor and an employee of the organization using Face Recognition. The project subject matter is a solution to orthodox methods of logging in employees in physical handwritten logbooks, keeping security guards on duty 24×7, relying solely on sensors to fire breakouts, etc. So, to make up for these fairly unreliable methods, we have come

(20)

8 V. Patil and Y. Jadhav up with a plan that this project will increase the quality and efficiency of security in office premises. The main objective of our project will be to recognize an employee to let them in the premises and greet visitors respectfully keeping security as a higher priority. The model is a deep convolutional neural network, which is learned by means of a three-fold loss function, which induces vectors for one identity to become more identical (smaller distance). A significant breakthrough in this research was the emphasis on teaching a computer to establish embedding directly (instead of removing them from an intermediate layer of a computer).

The system will be fed live image frames through a CCTV [15], which will be used to detect faces coming up in the frame. For the detection of faces, we are using OpenCV. Multiple faces can be detected at once. Once the faces are detected, the faces will be cropped to size and by analyzing the eye orientation, the faces will be aligned to a proper axis. Once Face Alignment is achieved, these images will be fed to Google FaceNet. FaceNet will then extract the features from the image received.

Then, these face embedding systems are used as the basis for training classifier systems on standard face recognition benchmark datasets, achieving state-of-the- art performance. These preprocessed images will be used by the Support Vector Classifier to train the model. This training will be done for each new employee that joins the organization. Once the training phase is completed, the preprocessed data received realtime, will be fed to the Support Vector Classifier Inference model which will recognize the face as an employee or not. Changes (recent scars, beard/non-beard, different lighting conditions) to faces are anticipated to expand its classification performance as illustrated in Fig.2.

4 Experimental Results

We require 40–50 facial data of each employee. Above images are used for training the system. Accuracy achieved ranges from 95 to 100% as shown in Fig.3.

5 Conclusion and Future Scope

We have seen various kinds of techniques in this paper that allow us to detect the face and recognize it in various situations such as dark and light and even in various clothing where the face is partially visible. All the techniques such as viola jones used for object detection in OpenCV and haar cascade classifier which is also used by the help of OpenCV. However, we observed that FaceNet provide significantly better results than the previously mentioned techniques. Additional features in this project can be further added after successful implementation, for example, visitors who have been granted access in the office can be monitored for behavioral analysis;

sentiment analysis to check whether the visitor becomes hostile and creates nuisance, etc. Furthermore, additional features in which employees can be monitored to keep a

(21)

Office Monitoring and Surveillance System 9

Fig. 2 Working of face recognition

Fig. 3 Face recognition

(22)

10 V. Patil and Y. Jadhav check on the performance in work can be implemented. In restricted areas within the company, only employees who are allowed access will be granted entry otherwise no.

References

1. Wu W, Liu C, Su Z (2017) Novel real-time face recognition from video streams. In: Conference on computer systems, electronics and control (ICCSEC), Dalian, pp 1149–1152.https://doi.

org/10.1109/ICCSEC.2017.8446960

2. Xiugang G, Yang L, Ruijin Z, Liang Y (2011) Study on monitoring systems with video capture cards and wireless sensors. In: IEEE conference on electronics, communications and control (ICECC), 03 November 2011

3. Saputra DIS, Amin KM (2016) Face detection and tracking using live video acquisition in camera closed circuit television and webcam. In: 1st international conference on information technology, information systems and electrical engineering (ICITISEE), Yogyakarta, pp 154–

157.https://doi.org/10.1109/ICITISEE.2016.7803065

4. Kadir K, Kamaruddin MK, Nasir H, Safie SI, Bakti ZAK (2014) A comparative study between LBP and Haar-like features for face detection using OpenCV. In: 4th international conference on engineering technology and technopreneuship (ICE2T), Kuala Lumpur, pp 335–339.https://

doi.org/10.1109/ICE2T.2014.7006273

5. Jose E, Greeshma M, Haridas MTP, Supriya MH (2019) Face recognition based surveillance system using FaceNet and MTCNN on Jetson TX2. In: 5th international conference on advanced computing & communication systems (ICACCS), Coimbatore, India, pp 608–613.https://doi.

org/10.1109/ICACCS.2019.8728466

6. Heath K, Guibas L (2007) Facenet: tracking people and acquiring canonical face images in a wireless camera sensor network. In: First ACM/IEEE international conference on distributed smart cameras, Vienna, pp 117–124.https://doi.org/10.1109/ICDSC.2007.4357514

7. Boka A, Morris B (2019) Person recognition for access logging. In: IEEE 9th annual computing and communication workshop and conference (CCWC), Las Vegas, NV, USA, pp 0933–0936.

https://doi.org/10.1109/CCWC.2019.8666483

8. Chien Y-T, Huang Y-S, Jeng S-W, Tasi Y-H, Zhao H-X (2003) A real-time security surveillance system for personal authentication. In: IEEE 37th annua international Carnahan conference on security technology. Proceedings, Taipei, Taiwan, pp 190–195.https://doi.org/10.1109/CCST.

2003.1297558

9. Qezavati H, Majidi B, Manzuri MT (2019) Partially covered face detection in presence of headscarf for surveillance applications. In: 4th international conference on pattern recognition and image analysis (IPRIA), Tehran, Iran, pp 195–199.https://doi.org/10.1109/PRIA.2019.

8786004

10. Cao Y, Pranata S, Nishimura H (2011) Local binary pattern features for pedestrian detection at night/dark environment. In: IEEE international conference on image processing, Brussels, pp 2053–2056.https://doi.org/10.1109/ICIP.2011.6115883

11. William I, Ignatius Moses Setiadi DR, Rachmawanto EH, Santoso HA, Sari CA (2019) Face recognition using FaceNet (Survey, Performance Test, and Comparison). In: Fourth international conference on informatics and computing (ICIC), Semarang, Indonesia, pp 1–6.https://

doi.org/10.1109/ICIC47613.2019.8985786

12. Ming Z, Chazalon J, Luqman MM, Visani M, Burie J (2017) Simple triplet loss based on intra/inter-class metric learning for face verification. In: IEEE international conference on computer vision workshops (ICCVW), Venice, pp 1656–1664.https://doi.org/10.1109/

ICCVW.2017.194

13. Geng X, Zhou Z-H, Smith-Miles K (2008) Individual stable space: an approach to face recognition under uncontrolled conditions. IEEE Trans Neural Networks 19(8):1354–1368

(23)

Office Monitoring and Surveillance System 11 14. Ibrahim R, Zin ZM (2011) Study of automated face recognition system for office door access control application. In: IEEE 3rd international conference on communication software and networks, Xi’an, pp 132–136.https://doi.org/10.1109/ICCSN.2011.6014865

15. Celine J, Sheeja Agustin A (2019) Face recognition in CCTV systems. In: International conference on smart systems and inventive technology (ICSSIT), Tirunelveli, India, pp 111–116.

https://doi.org/10.1109/ICSSIT46314.2019.8987961

(24)

Categorizing Documents by Support Vector Machine Trained Using

Self-Organizing Maps Clustering Approach

Vishal Patil, Yogesh Jadhav, and Ajay Sirsat

Abstract This paper mainly emphasis on the use of machine learning algorithms such as self-organizing maps (SOM) and support vector machines (SVM) for classifying text documents. We have to classify documents effectively and accurately to different classes based on their content. We tested classification of self-organizing map on Reuters R-8 data set and compared the results to three other popular machine learning algorithms: k-means clustering, k nearest neighbor searching, and Naive Bayes classifier. Self-organizing map yielded the highest accuracies as an unsupervised method. Furthermore, the accuracy of self-organizing maps was improved when used together with support vector machines.

Keywords Document Categorization

·

Self-organizing maps (SOM)

·

^Support

vector machines (SVM)

1 Introduction

It is very important to store information in a way that is easy for retrieval. This becomes even more important in the case of electronic information. Every day we search for documents on our computer is it at home and at work. With each passing day the amount of information increases. The problem which we need to overcome is manually storing all related information together and searching for that information.

This may take a lot of effort and time. The solution to this problem is to classify and label documents. Once the documents are classified and ordered, we can go

V. Patil (

B

⁾

Computer Engineering Department, K. J. Somaiya Institute of Engineering and Information Technology, Mumbai, India

Y. Jadhav

Computer Science and Engineering Department, Amity School of Engineering and Technology, Amity University, Mumbai, India

A. Sirsat

Computer Engineering Department, St. John College of Engineering and Management, Mumbai, India

https://doi.org/10.1007/978-3-030-69921-5_2

13

(25)

14 V. Patil et al.

through the documents and search for information according to our needs. Consider a scenario where documents are already ordered and. In such a scenario, we can browse through documents effectively. This is where machine learning comes into focus.

Machine learning algorithms can classify documents automatically making it easy to store them. These algorithms can be supervised or unsupervised. Supervised machine learning algorithms involve human interference. Since it may not be the case that class labels are already known for each document, this may give rise to human error. Also, many a times the document has no title or has an ambiguous title, which makes it difficult to understand the category of the document. Thus we have focused on unsupervised machine learning algorithms for the task of document classification.

We have used Kohonen’s maps or self-organizing maps to classify documents. Self- organizing maps (SOMs) [1] is an effective data Visualization method which can be used to reduce the dimensions of data by using self-organizing neural networks [2].

It is a very popular method for clustering [3] and classification of text documents [4, 5]. We compared self-organizing map with one unsupervised [6] and two supervised machine learning algorithms.

Self-organizing maps gave better performance as compared to k nearest neighbor and k-means clustering with accuracies as high as 90–92%. However, the drawback of SOM is Curse of Dimensionality (COD) because of which its efficiency is affected. Because of this, SOM is not able to differentiate between classes that give the same Euclidean distance even though they have different features. Hence, we have implemented a technique which uses SOM and SVM. SOM takes the unlabeled text documents and forms clusters for these documents which form the training data set for the Support Vect4or Machine (SVM) [7].

This method has a two-fold advantage; it reduces the effect of COD while also eliminating human interference needed to label the training documents. Support Vector Machine have been one of the most widely used machine learning algorithm in most of the text classification application given as input for many a times for large datasets manually classifying and labeling training documents in the preprocessing stage leads to an increase in time and cost complexity. Thus, the unsupervised clustering methods are used which eliminate the need of human involvement for grouping together set of documents. The output of these methods is then Training phase of SVM. This hybrid model gives excellent accuracy while also eliminating the effect of COD. Moreover, we can see that the accuracy of text classification increases after using this hybrid approach.

2 Literature Survey

Support vector machine is one of the most widely used supervised machine learning algorithms. It is one of the most commonly used methods and has found use in a variety of applications due to its excellent accuracy [8] (Fig.1).

(26)

Categorizing Documents by Support Vector Machine … 15 Fig. 1 The optimal

separating hyper plane

However, for some of the classification domains, it might not always be possible to label the training data set of Support vector machine. For certain domains, labeling the training data might be difficult when human expertise is unavailable or when it may be time-consuming and costly to involve humans. Hence, in cases where it may not be possible to label training data, unsupervised clustering methods are introduced as they can perform their task without training the dataset. For domains wherein training data is unavailable, we can use unsupervised machine learning methods, such as SOM and k-Means to group documents based on its content.

A SOM is a single layer neural network. The name artificial neural network is because of the fact that they were originally inspired by the way how biological neurons were thought to work. SOM is made up of multiple units lying along a grid of n-dimensions. In the input layer for every unit has the number of weights and input patterns are same. Training a Self Organizing Map when provided with an input pattern requires calculating the distance between that pattern and every other unit in the neural network [9]. A unit that has the least distance to Best Matching Unit (BMU) is then selected and that pattern is then mapped onto that unit. There will be a one to one correspondence between the mapping of patterns that lie nearby in the input space and the units lying nearby in the output space. This will be visible in the visual map generated by SOM [9,10]. Thus, SOM is topology preserving as neighborhoods of a node are kept intact through the mapping process (Fig.2).

3 Proposed System

The data set we have selected is a variant of the Reuters- 21,578 collection. This dataset from the year 1987 includes 21,578 English Reuters news documents. More than one class labels to some the documents Therefore; we have discarded documents

(27)

16 V. Patil et al.

Fig. 2 SOM architecture

with multiple class labels and selected only those documents which have only one class label.

We then selected the 10 largest classes to obtain the collection of 8008 documents, collection of 8008 documents, consisting of 2254 test documents and 5754 training do over the past two decades, many research works focusing on document classification by machine learning algorithms have been carried out. A large number of these papers focus on improving the efficiency of SVM. Many researchers have proposed a hybrid method which combines unsupervised learning algorithms such as SOM with SVM [7]. Wu’s research shows how we can improve the accuracy of SVM [11] analyzing zebra fish gene expression [12] by analyzing data in the training data set by using SOM. SOM filters the training data for Support vector machines thereby reducing the train dataset by keeping only those neurons in every category of SOM that are their BMUs while it eliminates the remaining we then performed conventional text preprocessing on the dataset as shown in Fig.3.

1. The dataset we have used is the Reuters R8 dataset having 5485 training and 2254 test samples respectively.

2. For preprocessing, firstly words we retransformed to their stems and then, stop words were removed.

3. Next, we calculated the frequencies of resultant words and computed document vectors for all documents.

4. Finally, tf-idf weights were calculated for all resulting word stems.

5. For using a self-organizing map in the document classification task we have to label the map nodes with class labels of the training data set.

6. The labelled nodes then represent the document classes. The map can then classify new documents by mapping them.

SOM is primarily used for labeling and organizing the data samples which are not labeled. As mentioned earlier in this paper, the SOM [2] is affected by COD because it measures similarity using Euclidean distance [7]. Reducing the dimensions of original data in their principle components might be of some help. However, this reduction is unsuitable for some datasets, because every feature consists of some information. Using this may result in loss of information if any one of the dimensions

(28)

Categorizing Documents by Support Vector Machine … 17 Fig. 3 Flowchart showing

Preprocessing steps performed on the data set

(29)

18 V. Patil et al.

is removed [7]. The obtained labels are coupled with the related original data samples for creating a machine labeled training set for the SVM which was machine organized.

The SOM begins the step of clustering the documents by deciding which important features are needed for efficient clustering of the data into groups [7]. A process of statistical cluster analysis is usually used to better the feature extraction which is iterative in nature. The dimensions of data are reduced to two dimensions which simplify viewing and interpretation is obtained using the feature maps [7].

Self-organizing maps generate a map which represents the dataset [7]. When different neurons are fired for each data point a greater number of neurons will give larger number of classes. Thus, to prevent this, the neurons are first grouped in small clusters using Euclidean distance. Many neurons together make a cluster [7]. Every cluster is given a class number. Using the Euclidean distance all the data points are grouped together around these clusters. A number of a particular cluster is assigned to data points with the closest proximity to that cluster [7].

The combination of SOM and SVM which results in a method which uses unsupervised learning and has better generalization ability [7]. This is assumed from the properties of the SVM. SVM is known to be excellent classification methods with respect to performance [7]. The generalization attribute of Support Vector Machine because we implement structural risk minimization which selects an optimal separating hyper plane which guarantee classification with very high accuracy (Fig.4).

4 Results and Conclusion

First we compared different algorithms used for clustering based on their accuracy.

The results are as shown in the Table1and the graph in Fig.6. We can see from the Table1that the accuracy of SOM is 92.45% which is very good when compared to methods such as k means and k nearest neighbor whose accuracies are 90.23% and 89.9% respectively. The accuracy of Naive Bayes is 95.24%. As seen in Fig.6the accuracy of the hybrid model of SOM and SVM is 96.44% which is more than the accuracy obtained from Naive Bayes (Fig.5).

5 Conclusion

We tested Self Organizing Maps for classification of text documents and compared it with some of the commonly used text classification methods. Naive Bayes had the best accuracy overall, but in unsupervised algorithms, SOM turned out to have the best accuracy for text classification. Sometimes, the knowledge and expertise to label and organize documents into separate classes might be unavailable and thus unsupervised methods viz. SOM proves to be useful. Self-organizing maps gave over 90% classification accuracy in the Reuters R-8 Data Set making it is effective for the document classification. Also, the accuracy of self-organizing maps was comparable

(30)

Categorizing Documents by Support Vector Machine … 19

Fig. 4 Block diagram implemented hybrid classification algorithm from [7]

Table 1 Comparison of

clustering methods Algorithm Accuracy (%)

k means 90.23

kNN 89.9

Naive Bayes 95.24

SOM 92.45

to some of the supervised machine learning methods that we tested. The training phase of SOM generates an intuitive visual map, which is useful in a variety of applications.

The learning procedure of this method though costly, is advantageous. Theo- retically, the learning phase of SOM is more time consuming as compared to the learning of k nearest neighbor. However, practically it is the exact opposite. The map has significantly less nodes compared to the training dataset. This result to 10 time’s quicker classification when compared to that obtained from k nearest neighbor using

(31)

20 V. Patil et al.

Fig. 5 Clusters formed after using SOM

Fig. 6 Comparison of various machine learning algorithms

the same data set. We don’t have to build a new map whenever we get a new input document in the dataset, we can map it and perform the learning phase afterwards when there is no new input data available. The results presented above show that when SOM is used along with SVM the accuracy obtained is near to that which we get when the data used for training is classified by supervised machine algorithms.

(32)

Categorizing Documents by Support Vector Machine … 21

References

1. Saarikoski J, Laurikkala J, Jrvelin K, Juhola M (2011) Self-organising maps in document classification: a comparison with six machine learning methods. Adaptive and Natural Computing Algorithms, pp 260–269

2. Haykin S (1999) In neural networks: a comprehensive foundation, Second Edition. Prentice Hall, Upper Saddle River

3. Mary Amala Bai V, Manimegalai D (2010) An analysis of document clustering algorithms. In: International conference on communication control and computing technologies, Ramanathapuram, pp 402–406

4. Ko Y, Seo J (2000) Automatic text categorization by unsupervised learning. In: Proceedings of the 18th international conference on computational linguistics, (COLING2000), pp 453459 5. Indu M, Kavitha KV (2009) Review on text summarization evaluation methods. In: International conference on research advances in integrated navigation systems (RAINS), Bangalore, 2016, pp 1–4

6. Rui W, Liu J, Jia Y (2016) Unsupervised feature selection for text classification via word embedding. In: ICBDA

7. Shafiabady N, Lee LH, Rajkumar R, Kallimani VP, Akram NA, Isa D (2016) Using unsupervised clustering approach to train the support vector machine for text classification.

Neurocomputing 211:4–10

8. Brucher H, Knowlmayer G, Mittermayer MA (2002) Document classification methods for organizing explicit knowledge. In: Proceedings of the 3rd European conference on organiza- tional knowledge, learning and capabilities, (ECOKLC02), Institute of Information Systems, University of Bern, Engehaldenstrasse Bern, Switzerland, Athens, Greece, pp 124–126 9. Lobo VJAS (2009) Application of self-organizing maps to the maritime environment. In:

Popovich VV, Claramunt C, Schrenk M, Korolenko KV (eds) Information fusion and geographic information systems. Lecture Notes in Geoinformation and Cartography. Springer, Berlin, Heidelberg

10. Sigogne A, Constant M (2009) Real-time unsupervised classification of web documents. In:

IMCSIT, pp 281–286. IEEE

11. Li TS, Huang CL (2009) Defect spatial pattern recognition using a hybrid SOMSVM approach in semiconductor manufacturing. Expert Syst Appl 36(1):374–385

12. Wu W, Liu X, Xu M, Peng J, Setiono R (2004) A hybrid SOM-SVM method for analyzing zebra fish gene expression. In: Proceedings of the 17th international conference on pattern recognition (ICPR04) vol 2, pp 323–326

(33)

Bandwidth Improvement of Multilayer Microstrip Patch Antenna by Using Capacitive Feed Technique

for Broadband Applications

Anil K. Rathod, Md. M. Bhakar, M. S. Mathpati, S. R. Chougule, and R. G. Sonkamble

Abstract In this research paper, Bandwidth improvement is investigated in E-shaped Micro strip patch antennas by using multilayer and capacitive feed techniques. Capac- itive feeding technique has been used to cancel the inductive impedance of probe by capacitive patch. The Rectangular capacitive patch has been used separately near the radiating E-shaped design model. Performance analysis of E-shaped conventional and suspended capacitive feed Micro Strip Patch Antenna is done to enhance its parameters like Bandwidth, Gain, and Directivity. The proposed E-shaped multilayer capacitive feed Micro strip patch Antenna is shown improved bandwidth of 238 MHz and Gain of 6.071 dB as compared with conventional candidates. The proposed antenna design models have a working center frequency in the range of 2.36 GHz to 2.59 GHz, which can be used for wireless applications. The Return Loss and VSWR have been investigated in an acceptable range. E-field and Current flow of the antenna are within the desired radiating patch. It has observed that the capacitive feed technique E-shaped design model improves the Bandwidth, Gain, and other antenna parameters over the conventional probe feed antenna.

Keywords E-shaped

·

^Bandwidth

·

^Gain

·

Directivity

·

Suspended techniques

·

Capacitive feed technique

A. K. Rathod (

B

⁾^·Md. M. Bhakar

Department of Electronics & Communication Engineering, G.N.D. Engineering College, Bidar, India

M. S. Mathpati

Department of Electronics & Telecommunication, SVERI’s COE, Pandharpur, India S. R. Chougule

Department of Electronics & Telecommunication, KIT’s COE, Kolhapur, India R. G. Sonkamble

Department of Computer Science and Engineering, SGU, Kolhapur, India

https://doi.org/10.1007/978-3-030-69921-5_3

23

(34)

24 A. K. Rathod et al.

1 Introduction

Nowadays, antennas are an essential part of any wireless communication system.

With technological advancements, antennas are playing a very vital role in communication [1, 2]. Micro strip antennas (MSAs) have been reported that they have lightweight, small-volume several advantages and that they can be made conformal to any type of host surface. Besides, PCB Technology can be used the physical manufacturing of Micro strip patch Antenna so that a large amount of production can be done at a small cost [1]. Defense and other commercial applications of Micro Strip Patch Antenna are alternating choices to replace the conventional Antenna.

However, the MSA has been reported that they are suffering from Narrow Band- width. Increasing the Bandwidth of the MSA has become a primary requirement of research in the field. This is reflected in a large number of papers on the subject published in journals and conference proceedings [1]. However, different types of Broadband MSA design models have been investigated in the last few decades. The coaxial or probe feed techniques can be used in many design methodologies. To impedance matching, we have to do the soldering of the Patch and center conductor of the coaxial connector. We can place at any desired location of feed point to match input impedance; this is the advantage of the probe feeding method [1,3]. A new -shaped micro strip antenna is reported, which increased the bandwidth of the antenna by converting rectangular to E-shape and-shaped micro strip antenna [4].

The stacking of two substrates and slotted patches were used to get the wide bandwidth is investigated [5]. Defected Ground Structure (DGS) is a novel technique used to modify the ground plane to improve the gain and bandwidth is reported [6]. Multilayer stretchable conductors (SCs) are demonstrated for the first time as an alternative cost-effective method for implementing conformal and reconfigurable antennas over flexible substrates [7]. Some of Researchers have been designed patch antenna on single, double, and multilayer dielectric substrate introduced by micromachining process [8]. A miniature patch antenna is proposed for satellite applications in (C-band, X-band, and Ku-band). The designed multiband antenna is sensible for satellite applications [9]. A fractal antenna is designed at 2.4 GHz frequency band which is suitable for Wireless Local Area (WLAN) applications and FR-4 dielectric substrate material is used [10]. To improve the bandwidth and gain of Micro Strip patch antenna deferent methodologies investigated and anyone technique can be adopted.

2 Proposed Antenna Design Model

Figure 1shows the proposed E-shaped mulilayer MSA with capacitive feed. The design Model consists of a copper ground plane, radiating Patch, air gap, and FR-4 as a dielectric substrate. The Capacitive probe feed technique has been used for the impedance matching with the radiating patch.

(35)

Bandwidth Improvement of Multilayer Microstrip … 25

Fig. 1 E-shaped suspended MSA with capacitive feed patch

The air gap acts as a dielectric substrate having a dielectric constant is equal to one. Here the proposed model has become Multilayer consisting of a copper ground plane, air gap with a height of 6 mm, a dielectric substrate with a height of 1.6 mm easily available in the market, and an E-shaped radiating patch [11]. The permittivity can be selected near to 1 to obtain a wide bandwidth. If the dielectric substrate has larger thickness then it gives rise to an increase in probe reactance for the excitation of surface waves and coaxial feed, which reduces the efficiency of the antenna so that dielectric height should be selected properly. This design model is used to investigate the antenna parameters like Operating Frequency, Bandwidth, Return Loss, VSWR, Directivity, Total Gain, Radiation pattern, and E-field.

3 Simulation Results of the Proposed Design Model

3.1 Bandwidth and Return Loss of E-shaped Suspended MSA

Figure2 represents the Bandwidth and Return loss of E-shaped Suspended MSA which gives the bandwidth of 238 MHz and Return Loss−12.98. The return loss of Antenna is in acceptable range.

(36)

Fig. 2 Bandwidth and return loss of E-shaped suspended MSA

Fig. 3 VSWR of E-shaped suspended MSA

3.2 VSWR of E-shaped Suspended MSA

Figure3represents the VSWR of E-shaped Suspended MSA. It shows the VSWR 1.5778 must be less than 2 as per the standards. The given antenna can be radiate and fabrication process can be done.

3.3 Directivity of E-shaped Suspended MSA

Figure4represents the Directivity of E-shaped Suspended MSA is 6.071 dB.

(37)

Fig. 4 Directivity of E-shaped suspended MSA

3.4 Gain of E-shaped Suspended MSA

Figure5represents the Gain of E-shaped Suspended MSA is 6.0196 dB, which is better than the conventional Antena.

3.5 Radiation Pattern of E-shaped Suspended MSA

See Fig.6.

3.6 E-field of E-shaped Suspended MSA

Figure7represnts E-field of E-shaped Suspended MSA.

4 Result Analysis Table of E-shaped Suspended MSA

Table1consists of result analysis table for Hexagonal MSA & Suspended Hexagonal MSA with performance Parameters like Operating Frequency in GHz, Bandwidth in

(38)

Fig. 5 Gain of E-shaped suspended MSA

Fig. 6 Radiation pattern of E-shaped suspended MSA

(39)

Fig. 7 E-field of E-shaped suspended MSA

Table 1 Result analysis table

for E-shaped suspended MSA Parameters E-shaped suspended MSA Operating frequency (GHz) 2.361−2.599

Bandwidth (MHz) 238

Return loss(dB) −12.989

VSWR 1.577

Directivity (dB) 6.071

Total gain (dB) 6.019

MHz, Return Loss in dB, VSWR, Directivity in dB and Total Gain in dB as shown in Table1.

The Bandwidth can be defined as a reappearance extends over which VSWR is always under two (which relates to an arrival loss of 9.5 dB or 11% reflected force).

The VSWR or impedance BW of the MSA is characterized as the recurrence extend over which it is coordinated with that of the feed line inside determined cutoff points.

The Bandwidth of proposed E-shaped multilayer MSA is 238 MHz, which is within the specified limits as shown in Fig.2. Return Loss has been observed in an acceptable range. VSWR can be defined in terms of the input reflection coefficient (G) as:

V S W R= 1+ ||

1− || (1)

VSWR of E-shaped multilayer MSA is less than 2, which is shown in Fig.3. As shown in Fig.4for Directivity and Fig.5for Gain of E-shaped suspended MSA.

Figure6shows the radiation pattern for the proposed antenna. E-field of E-shaped multilayer MSA is shown in Fig.7. All the proposed antenna parameters have been observed in the acceptable range.

(40)

5 Conclusion

In this paper, Bandwidth improvement is investigated in E-shaped Micro strip patch antennas by using multilayer and capacitive feed techniques. The design model of E-shaped multilayer MSA is operating at 2.45 GHz center frequency. The proposed E-shaped multilayer Micro strip patch Antenna is shown improved bandwidth of 238 MHz and gains 6.019 dB, as compared with normal Antenna. The return loss of the proposed designed model is −12.989 dB which is less than −10 dB. The Voltage Standing Wave Ratio (VSWR) has been observed that 1.577, which is in the acceptable range. FR-4 dielectric substrate material is used to model design having relative permittivity (A) of 4.4. It has less cost compare to other materials. The proposed E-shaped multilayer Micro strip patch Antenna can be considered a better candidate for Broadband Applications.

Reference

1. Kumar G, Ray KP (2003) Broadband micro strip antennas. Artech House Boston, London 2. Al-Halawani MG, Al-Najjar M, Abdelazeez MK (2016) Microstrip antenna design for UWB

applications. IEEE. 978-1-5090-2886-3/16/$31.00

3. Faouri YS, Awad NM, Abdelazeez MK (2019) Hexagonal patch antenna with triple band rejections. In: Proceedings on 2019 IEEE Jordan International Joint Conference on Electrical Engineering Information Technology JEEIT 2019, pp 446–448

4. Deshmukh AA, Ray KP (2013) Analysis of broadband Psi ()-shaped micro strip antennas.

IEEE Antennas Propag Mag 55(2)

5. Tiwari K, Pujara D (2015) Multilayer slotted microstrip antenna for Wi-Fi application. IEEE.

978-1-4799-7815-1/15/$31.00

6. Kachare AB, Mathpati MS, Rathod AK (2015) Effect of size and location of the DGS on characteristics of rectangular micro strip patch antenna. Int J Comput Appl (0975–8887), Confer- ence on Emerging Applications of Electronics System, Signal Processing and Computing Technologies (NCESC 2015)

7. Liyakath RA, Takshi A, Mumcu G (2013) Multilayer stretchable conductors on polymer substrates for conformal and reconfigurable antennas. IEEE Antennas Wireless Propag Lett 12 8. Saha R, Maity S, Trigunayat N (2015) Enhancement of gain, bandwidth and directivity of a patch antenna by increasing dielectric layers of the substrate through micromachining technique for RFID application. In: 2015 international conference on advances in computer engineering and applications (ICACEA), IMS Engineering College, Ghaziabad, India, IEEE. 978-1-4673- 6911-4/15/$31.00

9. Nageswara Rao L, Santhosh Kumar V (2020) A novel multiband micro strip patch antenna for satellite applications. Int J Adv Sci Technol (IJAST) 29(3s):177–183. ISSN: 2005-4238 10. Ali KM, Nataraj C et al (2020) Bandwidth analysis of microstrip antenna with fractal design

for 2.4 GHZ wireless applications. Int J Adv Sci Technol (IJAST) 29(1):1344–1358. ISSN:

2005–4238

11. Hong S, Kim Y, Won Jung C (2017) Transparent microstrip patch antennas with multilayer and metal-mesh films. IEEE Antennas Wireless Propag Lett 16(c):772–775

(41)

Use of Median Timbre Features

f

Proceedings of the 3rd International Conference on Advanced Technologies for Societal Applications—Volume 1

Prashant M. Pawar · R. Balasubramaniam · Babruvahan P. Ronge ·

Editors

Contents

Sensor Image and Data Driven Societal Technologies

Use of Median Timbre Features for Speaker Identification

Machine Learning Approach in Cooperative Spectrum Sensing

Design and Simulation of Different Structures of Micro Strip

Design of an IoT Based System for Monitoring and Controlling

Smart Trolley with Automatic Master Follower and Billing

Foldscope to Detect the Growth of Microorganisms on Various

The Effective Use of Deep Learning Network with Software

A Proposed Method for Audio Steganography Using Digital

Non-invasive Methodological Techniques to Determine Health

Light Fidelity (Li-Fi): Future 5G Wireless Connectivity

IoT Model for Heart Disease Detection Using Machine Learning

Content-Based Image Retrieval Using Color Histogram and Bit

Effective Usage of Oversampling with SMOTE for Performance

ICT Based Societal Technologies

Human Age Classification and Estimation Based on Positional

Text Summarization and Dimensionality Reduction Using

On Some Properties of Extended Binary Golay [24, 12, 8] Code

Near Field Communication (NFC) Technology and Its

IOT Based Interactive Motion Detection Security System Using

Analysis of Construction Readiness Parameters for Highway

Analysis of Three-Stage Open Pan Heat Exchanger Working

Garbage Monitoring and Collection System Using RFID

Development of Low Cost PCR Product Detection System

Assessment of Godavari River Water Quality of Nanded City,

To Study and Optimize the Effects of Process Parameters on Taper Angle of Stainless Steel by Using Abrasive Water Jet

Effect of Slip on Vortex Formation Near Two-Part Cylinder

Sensor Image and Data Driven Societal

Vishal Patil and Yogesh Jadhav

1 Introduction

2 Literature Survey

3 Proposed System

4 Experimental Results

References

Categorizing Documents by Support Vector Machine Trained Using

Vishal Patil, Yogesh Jadhav, and Ajay Sirsat

1 Introduction

2 Literature Survey

3 Proposed System

4 Results and Conclusion

References

Bandwidth Improvement of Multilayer Microstrip Patch Antenna by Using Capacitive Feed Technique

Anil K. Rathod, Md. M. Bhakar, M. S. Mathpati, S. R. Chougule, and R. G. Sonkamble

1 Introduction

2 Proposed Antenna Design Model

3 Simulation Results of the Proposed Design Model

3.2 VSWR of E-shaped Suspended MSA

3.4 Gain of E-shaped Suspended MSA

5 Conclusion

Use of Median Timbre Features