ThinkQuest IT Department, Finolex Academy of Management and technology Ratnagiri

Vol—1 Issue –1 April 2017

IT Department e-Magazine

Volume 1

Issue 1

Editorial

Dr. Vinayak A. Bharadi

Mr. Deven D. Ketkar

Publication

April 2017

Contributions by

Mr. Santosh Jadhav

Dr. Vinayak A. Bharadi.

Mr. Deven Ketkar

Mr. Amar Palwankar

ThinkQuest

3

HoDs Message

IT Department of FAMT has started a nice initiative by Publishing e-Magazine. This contains ar-

ticles by Stakeholders of Departments. I wish them a very best of luck.

Mr. Santosh V. Jadhav

HOD-IT, FAMT

Contents

1. Back to Alma Mater .................................................................................................................................. 2

2. Open Source Content Management System-WordPress .......................................................................... 6

3. Probabilistic Neural Network (PNN) ....................................................................................................... 11

4. Big Data Analytics .................................................................................................................................... 16

5. IT Department Key Events and Achievements ........................................................................................ 20

1. Back to Alma Mater

- Prof. Santosh V Jadhav

What does every FAMTian aspire for? Success!

We present to you a success-story from an FAMT alumnus, Dr.

Vinayak A Bharadi who recently joined as an Associate Professor

in the department of Information Technology. Dr. Vinayak

Bharadi obtained his Bachelor’s degree in Electronics

Engineering from FAMT itself in 2002. Further, he pursued his

Masters Degree in Thadomal Sahani Engineering College

Mumbai followed by PhD from NMIMS Mumbai. Topper of his

time in FAMT, after working in various engineering colleges in

Mumbai for a decade, this year he is back to his alma mater

(FAMT Ratnagiri). Excerpts from a discussion with him.

How would you describe yourself?

I am a person with interest in research and development in the professional front and at personal

level attracted to peace and spirituality. I love to be with my family and friends and spend time

with them.

What guiding principles do you adhere to in life?

Work for the pleasure of the God, don’t be attached to the output but give your best when some

work is assigned to you.

Tell us about some significant memories of FAMT that you hold.

I remember the brainwaves and the quiz competition, which was being held, I was winner of this

in my third year of engineering.

I remember the dramas which was part of the Utopia, Mechanical engineering students were

used to perform great drama, and every year we used to wait for that.

Anything freaky, crazy that you had done at FAMT campus?

I had my first bike ride on FAMT campus, I still remember it… It was Yamaha RX 100 of my

friend and I just drove it for first time here in the ground.

Any reminiscences of the then professors in FAMT, something particularly interesting that

you would like to share with us?

We had great professors, they really made things interesting.

I remember Prof. B K Dash, he used to teach use Electromagnetic Theory, the subject was

difficult but he had great command and I scored good marks in the subject.

Another subject was Control Systems, taught by prof. Venketesh, all bunking students used to

come specifically for his lecture.

Prof. Jadhav taught us C Programming, I got interest in programming since then. Me and my

friend Dr. Amol Ambardekar wrote a program to design Transistor Amplifier Circuit.

Do you still keep in touch with the friends you made in FAMT?

Yes, we have a WhatsApp group and eleven of my classmates are in touch, most of others are on

Facebook.

I meet some of my local friends regularly.

What prompted you to choose teaching as a career?

I got my first job as a lecturer in Government Polytechnic Mumbai, when I worked there for a

year, I realized that there is lots of time for self-improvement, continue learning throughout the

life and I can contribute to the society. Hence, I continued to be working as a teacher.

Tell us how you started your career and what paths it has taken.

Initially I used to do PC maintenance work, then I got opportunity to work as a visiting lecturer

in Government Polytechnic Mumbai, I worked there for five months and was later selected as a

Full Time lecturer on contract basis.

The college allowed me to pursue my Higher Studies, I took admission in Aug 2005 to

Thadomal Shahani College of Engineering (TSEC) for my ME EXTC and completed it by Dec

2007.

During my ME final year project I had Dr. H B Kekre as my PG project guide, he was a great

person, a retired professor form IIT, a fatherly figure for me. He imbibed quality of research and

development in me.

Dr. Kekre advised me to join TSEC as a lecturer in IT department on Ad-hoc basis, I followed

his instructions and joined TSEC. I completed my ME there and then got selected in Thakur

College of Engineering and Technology (TCET) as a lecturer in IT on Ad-hoc basis, further I got

Regular Appointment in TCET as Lecturer in IT Department.

Dr. B K Mishra, Principal of TCET was very helpful, under his guidance and support I was

allowed to pursue my PhD. I enrolled with Mukesh Patel School of Technology Management

and Engineering (MPSTME) at NMIMS University for my PhD in August 2008 under the

guidance of Dr. Kekre Sir.

I completed my PhD in “Biometric Authentication Systems” in Dec 2011. TCET gave me post of

Associate Professor in July 2012 and further gave responsibility of Head of Department in Jan

2014. I worked in TCET for 9 years and got opportunity to work in FAMT in Jan 2017.

So, how did you decide about pursuing PhD?

My PG guide Dr. H B Kekre was PhD supervisor (Guide at NMIMS University) during my final

year I used to visit him to show the report of my ME Project. There he used to work with his

PhD Scholars, I wished I could also work with him.

I asked sir, whether he would be guiding me for further research, and he agreed. He also gave me

the PhD research topic and guided me to make a Statement of Purpose.

Later I applied for NMIMS university for PhD Research Scholar, after my presentation and

Interview the selected me for the research work.

What kind of experience did you have at FAMT that made you qualify for the jobs outside

FAMT?

I got my First job because of the expertise in C-Programming. Me and my friends were very

much fond of programming, during our fourth semester we studied C programming, we used to

work beyond college hours on projects outside the curriculum, this made us understand C

language in depth, also faculty members like Prof. Santosh Jadhav guided us well for further part

of programming, he used to teach us Java also which was not in curriculum, but was very helpful

for career.

Such activities to enrich skills of students helped us to qualify for the jobs outside FAMT.

What’s the secret of your success and what principles do you hold as a teacher?

My father and mother had contributed a lot for my success; they have done so many sacrifices just

to make me study. There constant support and encouragement is one of the driving forces for my

success.

Besides that the teachers and friends have given me company and help to achieve my goals.

During my career, I got introduced to Krishna Consciousness, and I am following our Sanatan

Dharma Spiritual Practices. It has helped me to stabilize in my life during challenging time.

I follow the principal mentioned in the Shrimad Bhagvad Gita that, One has to do its own duty

very cautiously and truthfully without being attached to the output. If your efforts are true, you

will get the desired results, a teacher must be like a roll model to his or her students, I got teachers

like that and I wish to be like them.

2. Open Source Content Management System-

WordPress

Amar R Palwankar

Dept. of Info. Tech,Ratnagiri.

Abstract: Most powerful and widely used open source CMS is wordPress .It is powerful because

it has more than 24,000 plugins and ready made components which enables us to create website

in short span of time.It is written in PHP.It is used mostly for writing blogs,posts,online

discussions etc.

Installations of wordPress on Fedora 25

Prerequisite:Internet connection is required to the System where WordPress is to be installed .

Steps:

1- Open fedora 25 terminal and be a root by issuing su command and root password

2- Fire a command dnf install @"Web Server" wordpress php-mysqlnd mariadb-server

It will install Apache Web server,essential PHP components and with MariaDB server.

3- Enable and start the web server and mariaDB server at the boot time itself

systemctl enable httpd.service mariadb.service

systemctl start httpd.service mariadb.service

4- Now create a database

mysqladmin create test -u root -p

5- Now, set up a special privileged user and password for the database.

mysql -D mysql -u root -p

6- GRANT ALL PRIVILEGES ON test.* TO 'amar'@'localhost' IDENTIFIED BY

'simplepassword';

7-setenforce 0 to make SELinux Permissive

8- Now Next, edit the configuration file for the web server to allow connections. The file to

edit is /etc/httpd/conf.d/wordpress.conf. Change the following line:

Require local to Require all granted

9- Now Configure the wordpress ,edit the /etc/wordpress/wp-config.php file. Provide the

database settings needed so WordPress can use the database you provided

define('DB_NAME', 'test');

/** MySQL database username */

define('DB_USER', 'amar');

/** MySQL database password */

define('DB_PASSWORD', 'simplepassword');

/** MySQL hostname */

define('DB_HOST', 'localhost');

10- Now its time to restart the web server

systemctl restart httpd

11- Open a browser and type the following url

http://IP_Address_of_Server/wordpress

It will display following window

Home page is now available at url

172.16.2.59/wordpress/wp-login.php?

Conclusion:

Here we discussed the steps to install powerful open source CMS WordPress on open source

OS Fedora-25.

3. Probabilistic Neural Network (PNN)

-Prof. Deven Ketkar ( IT Dept.,FAMT)

A probabilistic neural network (PNN) is a feed forward neural network, which was derived

from the Bayesian network and a statistical algorithm called Kernel Fisher discriminant analysis.

It was introduced by D.F. Specht in the early 1990s.

PNN is a type of RBF network, which is suitable for classification of patterns. The

architecture has four layers, an input layer, a hidden layer, a pattern layer and an output layer. The

pattern layer constitutes a neural implementation of a Bayes classifier, where the class dependent

Probability Density Functions (PDF) are approximated using a Parzen estimator. Parzen estimator

gives the PDF by minimizing or reducing the expected risk in classifying the training set

incorrectly. Hence, with the use of Parzen estimator, the classification gets closer to the true

underlying class density functions as the number of training samples increases.

The pattern layer is made of a processing element corresponding to each input vector in

the training set. Each output class must consist of equal number of processing elements otherwise

some classes may be inclined falsely which will result in poor classification results. Each

processing element in the pattern layer is trained once. An element is trained in such a way that it

will return a high output value when an input vector matches the training vector. In order to obtain

more generalization or accuracy, a smoothing factor is included while training the network. This

smoothing factor is also called as a spread value. The pattern layer classifies the input vectors

based on competition, where only the highest match to an input vector wins and generates an

output. Hence only one classification category is generated for any given input vector. If there is

no relation between input patterns and the patterns programmed into the pattern layer, then no

output is generated.

If we compare PNN to the feed forward back propagation network, training of PNN is very

much simpler. Basically, probabilistic networks classify on the basis of Bayesian theory, hence it

is necessary to classify the input vectors into one of the two classes in a Bayesian manner.

In a PNN, the operations are organized into a multilayered feed forward network with four

layers. When an input is present, the first layer computes the distance from the input vector to the

training input vectors. This produces a vector where its elements indicate how close the input is to

the training input. The second layer sums the contribution for each class of inputs and produces its

net output as a vector of probabilities. Finally, a complete transfer function on the output of the

second layer picks the maximum of these probabilities, and produces a 1 (positive identification)

for that class and a 0 (negative identification) for non-targeted classes.

If the probability density function of each of the populations is known, then an unknown,

X, belongs to class “i” if 𝑓𝑖(𝑥) > 𝑓𝑗(𝑥), all 𝑗≠ 𝑖. (1)

Input layer

Each neuron in the input layer represents a predictor variable. In categorical variables, N-

1 neurons are used when there are N number of categories. Then the input neurons feed the values

to each of the neurons in the hidden layer.

Pattern layer

This layer contains one neuron for each case in the training data set. It stores the values of

the predictor variables for the case along with the target value. A hidden neuron computes the

Euclidean distance of the test case from the neuron’s center point and then applies the RBF kernel

function using the sigma values.

Summation layer

For PNN networks there is one pattern neuron for each category of the target variable. The

actual target category of each training case is stored with each hidden neuron; the weighted value

coming out of a hidden neuron is fed only to the pattern neuron that corresponds to the hidden

neuron’s category. The pattern neurons add the values for the class they represent.

Output layer

The output layer compares the weighted votes for each target category accumulated in the

pattern layer and uses the largest vote to predict the target category.

PNN Training

PNN is a useful neural network architecture with slightly different in fundamentals from

back propagation. The architecture is feed forward in nature which is similar to back propagation,

but differs in the way that learning occurs. PNN is supervised learning algorithm but includes no

weights in its hidden layer. Each hidden node represents an example vector, with the example

acting as the weights to that hidden node. These are not adjusted at all. PNN consists of an input

layer, which represents the input pattern or feature vector. The input layer is fully interconnected

with the hidden layer, which consists of the example vectors (the training set for the PNN). The

actual example vector serves as the weights as applied to the input layer.

Finally, an output layer represents each of the possible classes for which the input data can be

classified. However, the hidden layer is not fully interconnected to the output layer. The example

nodes for a given class connect only to that class's output node and none other. One other important

element of the PNN is the output layer and the determination of the class for which the input layer

fits. This is done through a winner-takes-all approach. The output class node with the largest

activation represents the winning class. While the class nodes are connected only to the example

hidden nodes for their class, the input feature vector connects to all examples, and hence influences

their activations. Hence, it is the sum of the example vector activations which determines the class

of the input feature vector.

In PNN algorithm, calculation of the class-node activations is a simple process. For each

class node, the example vector activations are summed, which are the sum of the products of the

example vector and the input vector. The hidden node activation, shown in the following equation

is the product of the two vectors (𝐸 is the example vector, and 𝐹 is the input feature vector).

ℎ𝑖 = 𝐸𝑖𝐹 (2)

The class output activations are then defined as:

𝐶𝑗 =∑ 𝑒ℎ𝑖−1/𝛾2𝑁

𝑖=1

𝑁 (3)

where 𝑁 is the total number of example vectors for this class, ℎ𝑖 is the hidden-node activation, and

γ is a smoothing factor. The smoothing factor is chosen by doing experimentation. If the smoothing

factor is too large, details can be lost, but if the smoothing factor is too small, the classifier may

not generalize well. There is no real training that occurs since the example vectors serve as the

weights to the hidden layer of the network. If we have given an unknown input vector, the hidden

node activations are computed and then summed at the output layer. The class node with the largest

activation determines the class to which the output feature vector belongs. As no training required,

classifying an input vector is fast, depending on the number of classes and example vectors that

are present. It is also easy to add new examples to the network by simply add the new hidden node,

and its output is used by the particular class node. This can be done dynamically as new classified

examples are found. The PNN also generalizes very well when noisy data set is present.

Advantages of PNN

PNN is a classifier which maps any input pattern to a number of classifications. PNN is

famous for its fast training process. PNN converges to an optimal classifier as the size of the

training set increases. Training samples can be added or removed without extensive retraining.

Disadvantages of PNN

Even though PNN has many advantages, it has several disadvantages too. PNN has large

memory requirements. PNN requires a representative training set even more than other types of

neural networks.

One of the most important point in case of PNN is that training set should be thoroughly

representative of the actual population for effective classification. If we are going to add or subtract

training samples, it is similar to adding or removing of neurons in pattern layer. If we are going to

increase training set, PNN asymptotically converges to Bayes classifier. [1]

PNN Architecture

I/p Nodes Hidden Nodes Class Nodes Decision Node

PNN architecture [1]

Relevance of PNN

The Probabilistic Neural Network (PNN) is a new invention in Neural Network technology

where drawbacks in traditional techniques like Multilayer Perceptron (Backpropagation) can be

overcome. The main advantage of PNN is that no training is required and hence training time is

saved. Here, features of training dataset are itself considered as training vectors. From studies,

PNN is showing a great results in classification problems.

Students who are making projects in Image processing and neural network can make use

of PNN and can compare their results with traditional techniques.

References

[1] M. Tim Jones, “Artificial Intelligence A Systems Approach”, Infinity Science Press, 2008.

𝑥1

𝑥2

𝑥𝑛

ℎ1

ℎ2

ℎ3

ℎ𝑛

𝑐1

𝑐2

𝑧

4. Big Data Analytics

Dr. Vinayak A Bharadi.

Associate Professor, IT, FAMT.

Big data analytics is the process of examining large and varied data sets -- i.e., big data -- to uncover hidden

patterns, unknown correlations, market trends, customer preferences and other useful information that

can help organizations make more-informed business decisions.

Big data analytics benefits

Driven by specialized analytics systems and software, big data analytics can point the way to various

business benefits, including new revenue opportunities, more effective marketing, better customer

service, improved operational efficiency and competitive advantages over rivals.

Big data analytics applications enable data scientists, predictive modelers, statisticians and other analytics

professionals to analyze growing volumes of structured transaction data, plus other forms of data that are

often left untapped by conventional business intelligence (BI) and analytics programs. That encompasses

a mix of semi-structured and unstructured data -- for example, internet clickstream data, web server logs,

social media content, text from customer emails and survey responses, mobile-phone call-detail records

and machine data captured by sensors connected to the internet of things.

On a broad scale, data analytics technologies and techniques provide a means of analyzing data sets and

drawing conclusions about them to help organizations make informed business decisions. BI queries

answer basic questions about business operations and performance. Big data analytics is a form of

advanced analytics, which involves complex applications with elements such as predictive models,

statistical algorithms and what-if analyses powered by high-performance analytics systems.

Emergence and growth of big data analytics

The term big data was first used to refer to increasing data volumes in the mid-1990s. In 2001, Doug Laney,

then an analyst at consultancy Meta Group Inc., expanded the notion of big data to also include increases

in the variety of data being generated by organizations and the velocity at which that data was being

created and updated. Those three factors -- volume, velocity and variety -- became known as the 3Vs of

big data, a concept Gartner popularized after acquiring Meta Group and hiring Laney in 2005.

Separately, the Hadoop distributed processing framework was launched as an Apache open source project

in 2006, planting the seeds for a clustered platform built on top of commodity hardware and geared to

run big data applications. By 2011, big data analytics began to take a firm hold in organizations and the

public eye, along with Hadoop and various related big data technologies that had sprung up around it.

Initially, as the Hadoop ecosystem took shape and started to mature, big data applications were primarily

the province of large internet and e-commerce companies, such as Yahoo, Google and Facebook, as well

as analytics and marketing services providers. In ensuing years, though, big data analytics has increasingly

been embraced by retailers, financial services firms, insurers, healthcare organizations, manufacturers,

energy companies and other mainstream enterprises.

Big data analytics technologies and tools

Unstructured and semi-structured data types typically don't fit well in traditional data warehouses that

are based on relational databases oriented to structured data sets. Furthermore, data warehouses may

not be able to handle the processing demands posed by sets of big data that need to be updated

frequently -- or even continually, as in the case of real-time data on stock trading, the online activities of

website visitors or the performance of mobile applications.

As a result, many organizations that collect, process and analyze big data turn to Hadoop and its

companion tools, such as YARN, MapReduce, Spark, HBase, Hive, Kafka and Pig, as well as NoSQL

databases. In some cases, Hadoop clusters and NoSQL systems are being used primarily as landing pads

and staging areas for data before it gets loaded into a data warehouse or analytical database for analysis,

usually in a summarized form that is more conducive to relational structures.

More frequently, however, big data analytics users are adopting the concept of a Hadoop data lake that

serves as the primary repository for incoming streams of raw data. In such architectures, data can be

analyzed directly in a Hadoop cluster or run through a processing engine like Spark. As in data

warehousing, sound data management is a crucial first step in the big data analytics process. Data being

stored in the Hadoop Distributed File System must be organized, configured and partitioned properly to

get good performance on both extract, transform and load (ETL) integration jobs and analytical queries.

Once the data is ready, it can be analyzed with the software commonly used in advanced analytics

processes. That includes tools for data mining, which sift through data sets in search of patterns and

relationships; predictive analytics, which build models for forecasting customer behavior and other future

developments; machine learning, which tap algorithms to analyze large data sets; and deep learning, a

more advanced offshoot of machine learning.

Text mining and statistical analysis software can also play a role in the big data analytics process, as can

mainstream BI software and data visualization tools. For both ETL and analytics applications, queries can

be written in batch-mode MapReduce; programming languages, such as R, Python and Scala; and SQL, the

standard language for relational databases that's supported via SQL-on-Hadoop technologies.

Big data analytics uses and challenges

Big data analytics applications often include data from both internal systems and external sources, such

as weather data or demographic data on consumers compiled by third-party information services

providers. In addition, streaming analytics applications are becoming common in big data environments,

as users look to do real-time analytics on data fed into Hadoop systems through Spark's Spark Streaming

module or other open source stream processing engines, such as Flink and Storm.

Early big data systems were mostly deployed on premises, particularly in large organizations that were

collecting, organizing and analyzing massive amounts of data. But cloud platform vendors, such as Amazon

Web Services (AWS) and Microsoft, have made it easier to set up and manage Hadoop clusters in the

cloud, as have Hadoop suppliers such as Cloudera and Hortonworks, which support their distributions of

the big data framework on the AWS and Microsoft Azure clouds. Users can now spin up clusters in the

cloud, run them for as long as needed and then take them offline, with usage-based pricing that doesn't

require ongoing software licenses.

Potential pitfalls that can trip up organizations on big data analytics initiatives include a lack of internal

analytics skills and the high cost of hiring experienced data scientists and data engineers to fill the gaps.

The amount of data that's typically involved, and its variety, can cause data management issues in areas

including data quality, consistency and governance; also, data silos can result from the use of different

platforms and data stores in a big data architecture. In addition, integrating Hadoop, Spark and other big

data tools into a cohesive architecture that meets an organization's big data analytics needs is a

challenging proposition for many IT and analytics teams, which have to identify the right mix of

technologies and then put the pieces together.

References:

http://searchbusinessanalytics.techtarget.com/definition/big-data-analytics

5. IT Department Key Events and Achievements

1. Dr. Vinayak Ashok Bharadi and Prof. Santosh V. jadhav Got NVDIA GPU grant worth INR 1.5 Lac, this will

be used for Biometrics Application Development

2. IT department had Academic Collaboration with NVDIA for three subjects, Soft Computing, Cloud

Computing and Robotics

3. Dr. Vinayak A Bharadi had a collaborative research with Mr. Joel Philip of Universal CoE, Thane. They

received Microsoft Azure Research Grant worth $5000 for Accessing Microsoft Azure Cloud for Online

Signature recognition Project

4. Two teams of It department participated in AICTE smart India Hackathon final round held in April 2017

5. Two teams of It department students were selected and invited for final presentation of Transform

Maharashtra Challenge, on 01st May 2017

6. IT department students and teachers have presented total 16 papers in International conference

ICEMTE 2017 held in Feb 2017 by LRTCOE, Thane.

7. Dr. Vinayak A Bharadi have published Two Papers in IEEE international conference WOCN 2017, held in

Feb 2017 by TCET Mumbai

शब्दकोडे (रत्नागिरी आणि आसपास)

प्रा. संतोष जाधव १

प्रा. शशांक टोळ्ये २

माहिती तंत्रज्ञान विभाग ३ ४

५ ६

७ ८ ९

१०

११ १२

१३ १४ १५ १६

१७ १८

१९ २०

२१

२२

२३

आडवे शब्द१.फिनोलेक्स कॉलेजच ेसंस्थापक अध्यक्ष, ३.रत्नागिरीतील विद्यमान आमदार, ५.रत्नागिरीतील दसुऱ्या बसस्थानकाचे

ठिकाण , ६.रत्नागिरीजिळील मच्छिमारांसािी उपयूक्त एक सािरी स्थानक/बंदर, ७.रत्नागिरीतील______ या भािात

गथबा राजाची समाधी आहे, ९.अख्याययकेनूसार कोकणची भुमी यांनी यनमााण केली., १०.रत्नागिरी च्जल्ह्यातील दर

तीन िर्ाांनी िंिा अितरते ते ठिकाण, ११.रत्नागिरीच ेविद्यमान निराध्यक्ष , १२.रत्नागिरीतील एक प्रससद्ध

हॉच्स्पटल , १३.कोंकण रेल्हिेिरील रत्नागिरीजिळचा सिाात उंच पूल , १६.स्िामी स्िरूपानंदांच ेसमागधस्थान असणारे

िाि, १७.फिनोलेक्स अकॅडमेी जिळ असलेले एक महाविद्यालय , १९.कोंकण रेल्हिेिरील सिळ्यात लांब बोिदा ,

२१.फिनोलेक्स कॉलेजच ेशिेटच ेसंचालक (आडनाि), २३.रत्नागिरीतील मध्यिती ठिकाण,

उभे शब्द१.रत्नागिरीत _______ यांछया नािाने मोिे क्रीडांिण उभारले आहे. , २.फिनोलेक्स कॉलेजछया विद्यमान अध्यक्षा,

४.रत्नागिरी-िणपतीपुळे मािाािरील एक यनसिारम्य समुद्रफकनारा , ७.S N D T विद्यापीिाशी संलग्न असलेल्हया

महाविद्यालय या िािात आहे , ८.पािसछया पुढे समुद्रफकनाऱ्याजिळील एक प्रससद्ध िणेश मंठदर_____ या िािात

आहे, ९.सािरकरांनी रत्नागिरीत बांधलेल्हया मंठदराच ेनांि, १२.रत्नागिरी च्जल्ह्यातील एक तालुका, १४.रत्नागिरीतील

एका प्रससद्ध ससमेंट कंपनीच ेजुने नाि , १५.रत्नागिरीतील______येथे सािरकर यांचा पुतळा आहे, १८.िोव्याकडे

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