neernational t banking intconferance paper hariharan23900.pdf

Proceedings of the 2nd International Multidisciplinary
Conference on Information Science, Management
Research and Social Sciences
(ICISMRSS – 2021)
19th
March 2021
Editor
Dr. Aneeshkumar A. S.
Head of the Department,
Dept. of Information System Management,
Alpha Arts and Science College, Chennai, India
978-93-5457-137-4

Proceedings of the 2nd
International Multidisciplinary Conference on
Information Science, Management Research and Social Sciences
(ICISMRSS – 2021)
© Alpha Arts and Science College, Chennai, March 2021. All rights reserved.
No part of the material protected by this Copyright notice may be reproduced or utilized in
any form or by any means, electronic or mechanical including photocopying, recording or by
any information storage and retrieval system, without prior written permission from the
copyright owner. Statement and options in this proceedings or those of the contributors and
the publisher assume no responsibility for them.
Alpha Arts and Science College
Post Box No. 30, Thundalam,
Porur, Chennai, India – 600 116
Website: http://www.conference17.wixsite.com/alpha
Mail-id: ismrss@alphagroup.edu

MESSAGE FROM CHAIRPERSON
With immense pleasure, I congratulate the Departments of Information Systems
Management, Commerce and Business Administration of Alpha Arts and Science College for
organizing 2nd
International Multidisciplinary Conference on Information Science, Management
Research and Social Sciences on the Friday, 19th
March 2021.
The Alpha Group of Educational Institutions has institutionalised ‘Seek, Share and Serve’
as its motto for a continued search for knowledge and information, and share the received
wisdom with the community to selflessly serve mankind. We have diligently developed our
academic philosophy by laying great emphasis on the pursuit of knowledge and its application of
skills to help in its dissemination. Our underlying philosophy is to create new benchmarks by
incorporating new initiatives in the field of education through modern and innovative teaching
learning methodologies.
The Quality research and effective presentation of the research work is the need of the
hour. I’m greatly delighted that this International Conference will draw in the participation of a
large number of eminent personalities, academicians, research scholars and industrialists who
willing be sharing their experiences and research results of Multi-disciplinary subjects which
would definitely have a meaningful impact on the participants.
My heartiest appreciations to the Principal, Convenor, Organizing Secretaries and Faculty
Members for their sincere efforts to make this event a success and for editing the proceedings to
promote the spirit of research development in Alpha.
Dr. (Mrs.) Grace George
Chairperson
Alpha Group of Institutions

MESSAGE FROM THE VICE-CHAIRPERSON
I am glad to know that the Departments of Information Systems Management, Commerce
and Business Administration of Alpha Arts and Science College are organizing 2nd
International
Multidisciplinary Conference on Information Science, Management Research and Social
Sciences on 19th
March 2021.
In today’s world where change is constant and fast- paced, there is a need to be pliable to
the unremitting innovative and improvised models and trends in education research and that is
what Alpha endeavours to do. It provides an encouraging environment to generate an equilibrium
in the academic, emotional, spiritual, creative and physical development of everyone who enters
into its portals.
Information Technology and Management is a critical domain in modern organizations
that enhance the everyday business process. It helps to face challenges by developing and
deploying new technological solutions, such as content or document management systems, data
warehousing or portal applications. Data analysis provides a clinical approach to research
conclusion with the help of proper statistical treatment. At Alpha, we nurture and mould our
learners to acquire knowledge and the application of these skills for creative and critical
interpretation with an ethical and societal consciousness.
I am sure this conference will provide every participant with a better insight into world-
class research in the field of Multidisciplinary Applications.
I extend my heartiest congratulations to the Principal, Convenor, Secretary and the
organizing team for the effort put in by them to enhance the importance and applications of
Research in this era and I also wish their conference a grand success.
Mrs. Suja George
Vice- Chairperson
Alpha Group of Institutions

International
Registration ●Approval ● Accreditation ● Association
Crown University Int'l Chartered Inc (CUICI)
International Centre for Curriculum & Research Development
Santa Cruz Campus, Argentina, South America
(URL: https://www.crownintl.education/)
Santa Cruz, Argentina ● California, USA ● Bong, Liberia ● Cotonou, Benin ● Worldwide Online Operations
UNESCO Laureate Prof. Sir Bashiru Aremu
Vice Chancellor, (CUICI) & President, (ICCRD)
From the Desk of Chief Mentor,
IMCISMRSS-2021
This is an important juncture of all of us regarding this International Conference. Today (19-
03-2021) on this auspicious day I would like to congratulate and thanks Dr. Aneeshkumar A,
the Convener and whole organizing committee and team of AASC of this International
Science, organized by the Alpha Arts and Science College (NAAC Accredited, TUV Certified &
The University of Madras affiliated), Chennai, India. We are thankful to the Alpha Arts and
Science College for their proposal to conduct this event in association with the International
Centre for Curriculum & Research Development (ICCRD) under the leadership of Dr. P.K.
Paul, Executive Secretary, ICCRD. It is creditable to note that as ICCRD is under the CUICI,
Argentina (and worldwide on-campus and online operations) is actively engaged for further
promotion of research and development activities. Therefore all the participants including
paper presenters and whole knowledge community will get benefit from the same.
With Thanks to everyone,
UNESCO Laureate Prof. Sir Bashiru Aremu
Vice Chancellor, (CUICI) & President, (ICCRD)
19-03-2021, Santa Cruz, Argentina, South America

2nd
International Multidisciplinary Conference
on Information Science, Management Research,
and Social Sciences
Organized by the Alpha Arts and Science College, Chennai, India
In association with ICCRD, Crown University Intl Charted (CUICI), Delaware, USA, & Santacruz,
Argentina
MESSAGE
An innovative application of scientific inventions to solve varieties of
problems of human beings in society leads to the development of
engineering and technology. It is believed that continuous research and
innovations in engineering and technology and managing them
systematically can only solve basic (human need based) problems,
comfortable (human wants based) problems, and desires (human dream
based) problems of society. The systematic management of 21st
century
Engineering and Technologies includes identifying the potential
breakthrough technologies and developing them systematically to realize the dreams to take
the civilization to its supersaturated level with abilities such as ubiquitous, omnipotent, and
immortal superhuman beings. In this regard, the International Multidisciplinary Conference
on Information Science, Management Research and Social Science is being organized Today
(19-03-2021)by the Alpha Arts and Science College (NAAC Accredited, TUV Certified &
The University of Madras affiliated), Chennai, India under the Convener-ship of Dr.
Aneeshkumar A, in association with the International Centre for Curriculum & Research
Development (ICCRD) under the leadership of Dr. P.K. Paul, Executive Secretary, ICCRD to
help young and experienced researchers to exchange new ideas, concepts, and methods in
Science, Technology, Management, and Social Science. I wish all the best for all participants
and online paper presenters of the conference, which helps further research and contributions
in the field and future endeavour in society.
With Thanks to everyone,
Dr. P. S. Aithal
M.Sc.(Physics), M.Sc.(E-Business), M.I.T(I.T.), M.Tech.(I.T.), Ph.D.(Physics), Ph.D.(Business Management),
PostDoc.@PRL, Ahmedabad, PostDoc.@CREOL, UCF, USA.
Vice Chancellor
Srinivas University
Mangalore, Karnataka, India

Message from Keynote Speaker
It gives me great pleasure to extend my warm greetings to the 2nd
International
Sciences (ICISMRSS – 2021) hosted by the Alpha Arts and Science College.
In this Covid -19 situation, the entire world is affected. But I am happy to hear that the
Research and knowledge sharing is going to make through this Conference. The theme and
objective of the conference is to facilitate an International forum for worldwide researchers,
Academicians, Scientists and Industrialists to put forward their insights and research
contributions.
Dr. Sharif M. Abu Karsh
Associate Professor in Finance,
Faculty of Administrative and Financial Sciences,
Arab American University, Palestine.

Professor
Department of Marketing, Faculty of Business Studies, Jagannath University
9-10, Chittaranjan Avenue, Dhaka-1100, Bangladesh. Website: www.jnu.ac.bd
E-mail: mjarif2004@yahoo.com, mjarif2006@gmail.com, arif@mkt.jnu.ac.bd
Member, Boardof Trustees
Crown University Int'l Chartered Inc. (CUICI)
Santa CruzCampus, Argentina, South America
Website: www.crownintl.education
Santa Cruz, Argentina ● California, USA ● Bong, Liberia ● Cotonou, Benin ● Worldwide
Online Operations
Message from International Advisory Board Member and Keynote Speaker
IMCISMRSS-2021
It is an immense pleasure for me that Alpha Arts and Science College (NAAC Accredited, TUV Certified
& The University of Madras affiliated), Chennai, India is going to organize the 2nd
International
Multidisciplinary Conference on Information Science, Management Research and Social Sciences
(IMCISMESS)-2021 on 19 March 2021 in association with the International Centre for Curriculum &
Research Development (ICCRD) of Crown University Int’l Chartered Inc. (CUICI), Santa Cruz,
Argentina and worldwide partner on-campus and online operations, I would like to congratulate and thank
Dr. Aneeshkumar, A.S., the Convener and whole organizing committee and other associated team
members of this international conference. I would also like to thank Dr. P. K. Paul, Executive Secretary,
ICCRD, CUICI and Executive Director (MCIS) & Assistant Professor (IST), Raiganj University, India
for his dynamic leadership to organize such an auspicious event as a program chair. I am thankful to the
Alpha Arts and Science College and ICCRD, CUICI for taking the initiative of gathering international
research scholars, academicians and professionals under this platform of acquiring and dissemination of
knowledge through research and development activities. I hope all the valuable participants including
paper presents, discussants, session chairs and the whole research-based knowledge community will be
benefitted from the conference.
I wish the success of the international conference.
Professor Md. Zahir Uddin Arif
Professor, Department of Marketing, Faculty of Business Studies,
Jagannath University, Dhaka, Bangladesh
Member, Board of Trustees, Crown University Int'l Chartered Inc. (CUICI)
President, Bangladesh Renaissance Foundation (BRF) &
Advisor, Bangladesh Tourism Explorers Association (BTEA)
19 March 2021, Friday

International
Registration ●Approval ● Accreditation ● Association
Crown University Int'l Chartered Inc (CUICI)
International Centre for Curriculum & Research Development
Santa Cruz Campus, Argentina, South America
(URL: https://www.crownintl.education/)
Santa Cruz, Argentina ● California, USA ● Bong, Liberia ● Cotonou, Benin ● Worldwide Online Operations
From the Desk of Program Chair, IMCISMRSS-2021
It is gives me an immense pleasure to act as Program Chair for this International Conference-IMCISMRSS-2021.
It is an important auspicious day for me since this International Multidisciplinary Conference on Information
Science, Management Research and Social Science (IMCISMRSS-21) is multidisciplinary one. I am thankful
to the organizing body i.e. Alpha Arts and Science College which is a NAAC Accredited, TUV Certified & The
University of Madras affiliated institute located at Chennai, India for their interest to hold an important event
with the International Centre for Curriculum & Research Development (ICCRD). With my position of Executive
Secretary of ICCRD, I rendering my sincere thanks and appreciation to the whole organizers. I believe this
conference is going to be a perfect one instead of online mode for the input of new ideas, knowledge, sharing with
the cooperation of Core Organizing Team, Chief Guest, Speakers, Session Chairs, Paper Presenters. I offer my love
and wish to remarkable 240 Paper presenters to make this Conference as a true and biggest knowledge event in
this field.
Best wishes, love and blessings to all who are associated with IMCISMRSS-2021 once
again!
Dr. P.K. Paul, PhD, PDFC, FIARA, FBSS
Executive Secretary, ICCRD
(For its worldwide campus & online operations)
&
Program Chair, IMCISMRSS-2021
19-03-2021

MESSAGE FROM PRINCIPAL
Alpha Group of Institutions is hallmark of pioneering educational excellence. The 50
year old legacy proves time and again that the learners seeking successful career choose
Alpha. Alpha Arts and Science College has always been a torch bearer for the flagship
events promoting the spirit of Alpha.
It is very pleasing to note that, Department of Information Systems Management,
Commerce and Business Administration in Association with ICCRD, Crown University
International Chartered Incorporated, USA and Argentina jointly organizing 2nd
International
Science (Virtual Conference) on 19th
March 2021.
Globalisation, privatisation and digitalisation today have dramatically reshaped the
education system in India and have created tremendous opportunities for internationalisation,
especially transnational or cross-border education. Various educational institutions have
partnered with foreign institutions to provide best form of education to the students. In the
present world very interesting and creative ideas feed the global market. It is interesting to
see that innovation and development have become technology centric. But it is also
necessary that the business models, branding, design and the entire course of ideation should
be ethical. In that case the quality of research is the only parameter for healthy growth and
hence very vital.
The Conference aims to bring different ideologies under one roof and provide
opportunities to exchange ideas face to face, to establish research relations and to find global
partners for future collaboration.
The conferences and educational forums should uphold the quality of learning and
encourage research. I congratulate the Faculty, Staff and Students of the Department of
Information Systems Management, Commerce and Business Administration for planning and
organizing virtual conference. I am sure that the theme of the conference will provide
meaningful platform to researchers and all the participants to exchange their experiences and
ideas. The guidance given by eminent researchers will be an intellectual treat to the
delegates.
I wish the International conference a Grand Success!!!
Dr. Sripriya Arunachalam
Principal
Alpha Arts and Science College

Preface
The Departments of Information Systems Management, Commerce and Business
Administration, Alpha Arts and Science College, Porur, Chennai delighted to organize 2nd
International Multidisciplinary Conference on Information Science, Management Research
and Social Sciences (ICISMRSS – 2021) in association with ICCRD, Crown University Intl
Charted (CUICI), Delaware, USA, & Santacruz, Argentina on 19th
March 2021 and feel pleasure
to presenting you the Proceedings of the Conference. It is going to be the flagship publication
regarding various dimensions of research.
It is an ideal opportunity to present research experiences, latest developments,
innovations in different fields of research and multidisciplinary approaches. The objective of
this Multidisciplinary platform is to provide a common forum for a constructive dialogue on
theoretical concepts and practical ideas. It brings together Academicians, Research Scholars,
Scientists, Industrial Professionals and Students and share their ideas, research results and to
explore collaborations.
The information presented herein should help to open up new avenues for research
and provide growers to with new ideas to help them improve their technical as well as
research efficiency. I am very happy to say that we have received overwhelming number of
contributions and enthusiastic support for this Conference.
I take both pleasure and honour to extend my gratitude to the distinguished Chief
Guest, Keynote Speakers and Session Chairs for accepting my invitation and providing
wonderful talk over here.
I express our thanks to all those who succour us in various ways in making the
International Multidisciplinary Conference a splendid success. I express our gratitude to Dr.
Grace George, Chair-Person, Alpha Group of Institutions and Mrs. Suja George, Vice
Chairperson, Chair Person, Alpha Group of Institutions for providing support and facilitation.
I am grateful to Dr. Sripriya Arunachalam, Principal, Alpha Arts and Science College for the
constant support and guidance. I am pleased to thank and express sincere appreciation to
Organizing Secretaries & Heads of Commerce and Business Administration, and my
Colleagues who work as Organizing Committee. This is also the time to thank Student
volunteers for their support.
I would thank the Paper presenters for their willingness to share their latest research
and ideas. Without their efforts this Conference would not be possible. I expect your
participation in future events and express thanks for carrying this International Conference to
the Academic heights.
Dr. Aneeshkumar A.S.
Conference Convener

Contents
S. No. Title Page No.
1. Calculation of Gamma-ray Mass Absorption Coefficients
for some Geological Compounds for Energy of 10–150 keV 1-3
Dr. Samir A. Hamouda, Essam al-Talhi, Dr. Mohamed B. Abdelmalik
2. A Comparative Syntactic Typological Study of
Iranian Languages: A case of Persian and Kurdish 4-9
Dr. Ali Ashrafi
3. The Influence of Board Independent on Firm Performance of
Sri Lankan Listed Companies 10-14
Dr. M. C. A. Nazar
4. Financial Liquidity, Asset Management and Financial Performance
in Indonesia Listed Companies 15-22
Dr. Mohammad Arief
5. Response of a Permanent Magnet Moving Coil Instrument via
the Application of Rohit Transform 23- 26
Dr. Rohit Gupta, Dr. Rahul Gupta, Dr. Anamika
6. A Comparative Study of Automobile Industry With specific
Reference to Honda & Bajaj Two-wheelers at Patan, Gujarat 27-29
Dr. Chanduji, P. Thakor
7. Raspberry-pi Based Face Identification Attendance System 30-33
Prof. P.N. Khairnar, Mr. Suryawanshi Tejas, Ms. Savale Harshada,
Kekan Pooja, Dhatrak Swapnil
8. A Study on Impact of Social Media among Select Arts and Science
College Students in Coimbatore City 34-37
Dr. V. Mathan Kumar, Mrs. R. Naveena, Dr. R. Velmurugan
9. Soft Computing 38-45
Khadri S S
10. Dynamic Restriction's Flexible and Safe Sharing in
Cloud Computing System 46-57
Dr. Elavarasan Ganesan, Dr. S. Veni, Dr. K. Arumugam
11. Cyber Threats and Measures on E-Banking an Overview 58-63
Amith Kumar Reddy, Dr. Megharaja.B

12. A Machine Learning approach for Phishing Website Detection 64-68
M. Rama Bai, CH. Ranjith Kumar, J. SreeDevi, M. NagaSujini
13. Arduino Based Solar Tracking System for
Energy Improvement of PV Solar Panel 69-72
Smita Dinker, Dr. Anil Kumar, Dr. Akhilendra Yadav
14. Stages of Supply Chain Management 73-75
S. Tephillah Vasantham
15. A Study on the Complications of Love Marriage and Arrange Marriage 76-79
Dr. Aneesh Kumar A. S., R. Gomathy, S. Balaji, M. Shanthini
16. Role of Social Media in Promoting Women Safety
using Sentimental Analysis 80-83
S. Nandhini
17. Priority Based Dynamic Resource Allocation and
Scheduling for WiMAX Networks 84-91
Dr. A. Saranya
18. An Impression of Opinion Mining 92-95
G. Meenakshi, A. Kaleemullah, Dr. P. Rizwan Ahmed
19. A Case Study on How Technology Enabled Home Delivery
Is Helping Indian Restaurant Industry Survive Covid 19 Crisis 96-107
Dr Tahir Sufi
20. Child Tracking System 108- 111
Ms. J. S. Anita Lily, Mohamed Nagoor Meeran M
21. Impact of Corruption on Business Ethics in India: An Overview 112- 115
Dr. Manjit Kour, Dr. Rajinder Kaur
22. Impact of Digital Media Students during In Covid-19 Period 116-120
Dr. AneeshKumar A.S., Mohammed Sadik.A, Naveen Kumar.V , Praiscey. S
23. Predicting Hospital Admissions from the Emergency Department
using Machine Learning 121-125
N. Rajaveerasekar
24. Novel Multi-Keyword Search On Encrypted Data in the Cloud 126-130
Mr. Prince Solomon Raj T S

25. 5G Wireless Communication Systems Facing Prospects and Challenges 131 – 138
R. Shanthiprabha
26. Stock Prediction using Machine Learning 139-142
Dr. J. Dhilipan, D. B. Shanmgam, Imran Quraishi
27. E-Voting Application Using Identity Based Secure Gateway 143-147
T. Insamul Hug
28. MOSA: A Multi-objective Optimization in Edge Server Offloading
using Scheduling Algorithms 148-154
P. Anusha, R. V. Siva Balan
29. Impact of Modern Technology in Education: Current Scenario 155-158
Vidhya. P
30. Some Aspects of Picture Fuzzy Lattices 159-161
Dr. P. Bharathi
31. Early Diabetic Retinopathy Detection using
Deep Learning Algorithms 162-166
Bhonde Swati B, Walunj Yashashree A, Unde Komal B,
Vaishnav Rutuja K, Sonawane Chhaya N.
32. GUI Based Disease Prediction and Comparative Analysis of the
Supervised Machine Learning Algorithms 167-170
S. Rupesh kumar
33. Portrayal of Indian Society in ArvindAdiga’s “The White Tiger” 171-173
S. Senthilkumar, Dr. S. HenryKishore
34. An Independent Domination of a Zero Divisor Graph 174-177
S. Kalaiselvi, Dr. J. Golden Ebenezer Jebamani, Dr. P. Namasivayam
35. Semantic Based Search over Encrypted Data and
Collaborative Access Control 178-182
J. Jagan
36. E-Purchasing Trends for the Time of Covid-19 Pandemic 183-185
P. Nivetha
37. An Analytical Study of Effect of Mass Media in Promotion of
Games in Jammu & Kashmir 186-189
Dr. Rajwinder Pal Singh, Dr.Manjit Kaur

38. Improving Short Text Classification with Semantic Expansion
using Word Cluster 190-192
S. Akash Vaidya
39. Emerging Aspects of Ai for Smart Life 193-196
A. Kavitha
40. Mining Technique Issues in Internet Community 197-200
A. Mary Theresa
41. A Developer Centered Bug Prediction Model 201-205
S. Nivedika
42. Stress and Depression of Life for Human 206-210
R. Shanmugapriya
43. A Growth and Performance of Defence Manufacturing Product
under Make In India Phenomenon 211-217
Dr. G. Venugopal
44. Android Based E-Bus Pass Registration and Renewal System
using Cloud Computing System 218-221
M. Dwash
45. Amazon Review Classification and Sentimental Analysis on
Mobile Products with R 222-226
N. Vijay Kumar
46. Effectiveness of Students Studies in Online Classes
during the Pandemic Period 227-229
S. Rubika
47. Closed –Loop System for Network Automation 230-232
C. Vasuki
48. An Effecting Xquery Keyword Using Xml Query Processing 233-236
M. Papitha
49. Practice of Innovation for Business Sustainability 237-238
N. Pradeep Kumar
50. Effect of Performance on Meaningful Work 239-242
Dr. A. Thilaha Dharmarajan, S. Girija

51. A Study on Recompense of Data Mining Classification Techniques 243-246
B. Sathyavani, S. Joseph Gabriel, Dr. P. Rizwan Ahmed
52. Performance of Intrusion Detection System using Data Mining 247-249
P. Saraswathi, Dr. M. Mohammed Ismail, Dr. P. Rizwan Ahmed
53. A Study on Employee Retention Strategy of Selected
Engineering Companies in Coimbatore 250-253
Nimisha. P L, Udisha. O, Dr. J. Sudarvel, Dr. R. Velmurugan
54. Customer Attitude towards Mobile Services especially to
BSNL Services in Tirunelveli Town 254-260
X. Stella Mary, Dr. K. Viswa Sarojini Devi
55. Enhanced Work Sharing Algorithm for Efficient Resource
Utilization using Nearby Mobile Devices 261-264
K. Pavithra
56. Online Attendance Management System 265-267
S. Vinitha
57. A Study on Cash Management of NBFC in India with
Special Reference to Indiabulls Housing Finance Limited 268-277
Zahid Ajaz Bhat, Dr. A. Elankumaran, Ramesh Vankadoth
58. Embedded Extended Visual Cryptography Scheme 278-283
Dr. B. Chithra
59. Reorganization of Organizational Commitment
using Leadership Styles 284-290
Dr. Aasim Mir
60. A Study on Data Mining Based Botnet Detection in
IoT Environments 291-295
H. J. Felcia Bel
61. Day of the Week Effect with Reference to BSE Sensex Index 296-299
Dr. J. Sudarvel, Dr. R. Velmurugan, P. Sathiyabama, Dr. S. Jegadeeswari
62. Effective Management Style: Modern Workplace 300-301
S. G. Sujitha, Dr. D. ShanthiRevathi
63. Efficient Waste Management System using IoT 302-306
S. Loganayagi, Dr. C. Jeyabharathi

64. Employability Skills of Students from Management Studies
in Rural Colleges Telangana 307-313
M. Kathyaine, Dr. S. Jayakani
65. A Study on Customer Satisfaction towards E-Banking
(A Study With Reference to Salem City) 314-318
Dr. S. Gnanalakshmi, P.Selvi
66. Impact of Primary Agricultural Co-operative Societies
Service Quality on Customer Satisfaction 319-322
Dr. V.Gopinathan, Dr. R.Velmurugan, Dr. J. Sudarvel, Dr. P. Easwaran
67. The Impact of Human Resource Planning: In Organizational Performance 323-324
M. Keshavani, Dr. D. ShanthiRevathi
68. Unveiling the Dark Side of Australian History through Poetry 325-327
Dr. Mariya Aslam
69. E-Payment for Toll-Gate Processing Using QR Technology 328-332
Vishnu Prasath. R, Karthik. K, Samuel. B, Pavithra. S, Ashok. P
70. Esafe for Document Protection 333-338
B Khadeej Ul Umerah, M Hima Bindu, Dr. B Abdul Raheem
71. An Enhanced Trust based Ad-hoc On-demand Distance Vector
Routing Protocol for WSN 339-348
Dr. Vignesh Ramamoorthy H, Priyadharshini N
72. Opportunities of Small Scale Entrepreneur in Solar Market 349-352
P. Easwaran, Dr. V. Krishnaveni, Dr. J. Sudarvel, Dr. R. Velmurugan
73. Data Mining, Knowledge Discovery and its Applications 353-356
K. Kavitha, A. Zakiuddin Ahmed, Dr. P. Rizwan Ahmed
74. Synthesis of Metallic Nanoparticles for Biomedical Applications 357-362
S. Priya, B. Sandiyaa
75. Smart Gloves used for Blind Visually Impaired
using Wearable Technology 363-367
S. Karthik, Dr. N. Satish
76. Factors Governing the Performance of QR Code – User Preview 368-373
Dr. R. Seranmadevi, Dr. A. Senthil Kumar

77. Novel Most Frequent Pattern Mining Approach using
Distributed Computing Environment 374-375
M. Srimathi, J. Aamir Azeez, Dr. P. Rizwan Ahmed
78. A Study on the Factors for Gender Inequality among
Employees in Cantonment, Trichy District 376-380
B. Netali Nambigai
79. A Study on Customer Satisfaction towards Net Banking with
Special Reference to General Banking Customers in Dindigul District 381-384
N. Hariharan, Dr. M. Karuppanasamy
80. Study of RC Beam-Column Joint Design for Pure Torsion 385 - 391
Nilesh Kisan Khairnar, Dr. Ravikant Pareek, Dr. Sachin B. Kandekar

Proceedings of the
2nd
International Multidisciplinary Conference on Information Science, Management Research and Social Science
19th
March 2021
1
ISBN: 978-93-5457-137-4
Calculation of Gamma-ray Mass Absorption Coefficients
for some Geological Compounds for Energy of 10–150 keV
Samir A. Hamouda*1
, Essam al-Talhi#2
, Mohamed B. Abdelmalik#3
1
Associate Professor,Department of Physics, University of Benghazi, Libya
2
Ph.D. Scholar, Department of Physics, University of Benghazi, Libya
3
Associate Professor,Department of Earth Sciences, University of Benghazi, Libya
Abstract— Information about gamma-ray mass absorption coefficients in different geological compounds with
different percentage to their rations is vital for accurate gamma-ray spectroscopy analysis. In this paper, gamma-
ray absorption coefficients for energy (10-150 keV) were calculated for Corundum (AL2O3), Quartz (SiO2),
Calcite (CaCO3), Hematite (Fe2O3), and Magnetite (Fe3O4) compounds with their % mixtures.
Keywords — Mass Absorption Coefficients; Gamma-Ray Spectroscopy; Geology; Mixtures.
1. Introduction
The most important description of the amount of
radiation penetration into the material is the Linear
Attenuation Coefficient, which is a quantity that
depends on the energy of the incident photon and the
atomic number of the material. This quantity
represents a fraction of the energy lost from the
incident photon for every 1cm penetrated through the
material. The unit of linear absorption coefficient µ is
cm-1
. According to Lambert - Beer’s law [1-6], when
a narrow photon beam of single-energy hʋ0 and a flux
density N0 (the number of photons per unit area and
time) falls on a homogeneous medium and penetrates
it a distance (x), the flux density of the transmitted
photon beam from this medium is N and is given as:
(1)
This law shows that the transmitted photons
decrease exponentially with the thickness of the
absorbent material. It can be stated that, when photon
energies are greater than electron binding energies, µ
is directly proportional to the physical density of the
absorbent material [5, 7]. Therefore, equation (1) can
be rewritten as follows:
(2)
Where (µ/ρ) is the mass absorption coefficient
(cm2
/g). This quantity (µ/ρ) is related to the
probability (or cross-section) that the beam will
interact with matter and contribute in some way. Each
interaction somehow contributes to the loss of a
number of photons from the incident photon ray even
though the scattering of the photon does not lead to its
absorption from the photon beam. This probability
gives a measure of the mass absorption of the photons
that pass through the medium and since there are
three basic interactions that occur when the photon
interacts with the material, and these interactions are
the phenomenon of photoelectric absorption (ph),
Compton scattering (C) and Rayleigh scattering (R),
the mass absorption coefficient It can be written as
follows:
R
C
ph )
(
)
(
)
(
)
(











(3)
2. Mass Absorption Coefficient of Mixtures
When the absorbent material is a homogeneous
mixture of different types of elements (chemical
compound) or a mixture, the mass attenuation
coefficient is given as [6].
(4)
Where ᴡj represents the weight of the component
j, µj represents the element's linear attenuation
coefficient for element j and ρj is the density of
element j.

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3. Calculation of the Mass Absorption
Coefficient of some Geological
Components
The mass absorption coefficients for Corundum
(AL2O3), Quartz (SiO2), Calcite (CaCO3), Hematite
(Fe2O3), and Magnetite (Fe3O4) compounds with
their % mixtures were calculated as a function of
incident photon energies (10 – 150 keV). Table1
shows the mass absorption coefficient of a mixture in
terms of the energy of the incident photon.
Figure1 shows the calculated mass absorption
coefficients for AL2O3, SiO2 and Fe3O4 as a function
of incident photon energies. It can be seen from
figure1, that Haematite (density 5.3 gm./cm3
) has
higher mass absorption coefficients than Corundum
(density 4.02 gm./cm3
) and Quartz (density 2.65
gm./cm3
) due to differences in density.
Table1: The calculated mass absorption
coefficients for different compounds as a function
of incident photon energies
Table2 shows the calculations of the mass
absorption coefficients of two mixtures in fixed
proportions in terms of the energy of the incident
photon.
Figure2 shows the mass absorption coefficients of
two mixtures with 50% proportions in the photon
energy range (10 – 150 keV). As shown in figure2
that the two curves for the mass absorption
coefficients of two mixtures (CaCO3 50 %; AL2O3
50 %) and (SiO2 50 %; AL2O3 50 %) are very close
to each other due to the fact that (CaCO3 and SiO2)
have almost the same density values.
Fig.1: shows mass absorption coefficients for AL2O3 ,
SiO2 and Fe3O4 in the energy range (10 – 150 keV).
Table2: Calculations of the mass absorption coefficients
of two mixtures in terms of the energy of the incident
photon
Magneti
te
Fe3O4
Hematit
e
Fe2O3
Calcite
CaCO3
Quartz
SiO2
Corundu
m
AL2O3
Energ
y
(keV)
Total mass absorption coefficient (cm2
/g)
407.93
272.38
61.25
25.98
39.53
10
131.77
87.97
19.11
7.80
11.89
15
60.30
40.26
8.28
3.39
05.20
20
19.28
12.88
2.65
1.17
01.82
30
8.66
5.79
1.26
0.63
00.99
40
4.72
3.16
0.77
0.43
00.70
50
2.97
1.99
0.54
0.34
00.56
60
1.53
1.03
0.36
0.26
00.44
80
1.00
0.67
0.28
0.23
00.38
100
0.57
0.39
0.21
0.19
00.32
150
SiO2 20 %
CaCO3 60 %
AL2O3 20 %
Energy
( keV )
Mass absorption coefficients of three
mixtures (cm2/g)
49.85748552
10
15.41015454
15
6.689505311
20
2.192178901
30
1.085724707
40
0.689576363
50
0.510042313
60
0.358827563
80
0.296490347
100
0.234766092
150

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Fig.2: The mass absorption coefficient of two
mixtures in the photon energy range (10 – 150 keV)
Table3 shows the mass absorption coefficient of
three compounds with different ratios in terms of the
energy of the incident photon. Figure3 shows the
projection of the calculated mass absorption
coefficients of three compounds (SiO2 20 %, CaCO3
60 % and AL2O3 20 %) as a function of incident
photon energies. This curve shows higher values of
mass absorption coefficients for the mixture which
made up of light and relatively higher atomic
numbers.
Table3: Calculated mass absorption coefficient of three
compounds with different ratios as a function of
incident photon energies
Fig.3: shows the curve of mass absorption coefficient
of three compounds with different proportions as a
function of incident photon energies (10-150 keV).
5. Conclusion
In x-ray and gamma- ray spectroscopy analysis, it
is always required appropriate correction for data
matrix of geological materials. However, there are
many corrections to be taken into account. Such
corrections depend on the type of analysis required.
One such important correction is mass absorption
coefficients. Since the mass absorption coefficients
for materials cannot be measured experimentally due
to the fact that there are many different scattering
processes involved. Therefore, seeking a theoretical
method for calculation of mass absorption
coefficients of materials appears to be the only
method that is available. This paper deals with
estimation of mass absorption coefficients for some
geological compounds and their mixtures for incident
photon energies (10 -150 keV).
Reference
[1] W. Heitler, (1955), “Quantum Theory of Radiation”,
Sec. 26, Oxford
[2] R. D. Evans, (1955), “The atomic nucleus”, McGraw
Hill Book Company, Inc., New York.
[3] C. M. Davisson and R. D. Evans, (1952), “Gamma-ray
absorption coefficient‟. Rev. Mod. Phys, V24,79-103.
[4] Harvey Hall, (1936), “The theory of photoelectric
absorption for x-rays and Gamma-rays”, Reviews of
modern physics, Vol 8, pp358-377.
[5] J. Hubbel , (1982), “Photon mass Attenuation and
Energy-Absorption Coefficients from 1keV to 20keV”,
Int. J. appl. radiat. isot, .vol 33 ,1269-1290.
SiO2 50 %
AL2O3 50 %
CaCO3 50 %
AL2O3 50 %
Energy
( keV )
Mass absorption coefficients (cm2/g)
32.75
50.39
10
9.85
15.50
15
4.29
6.74
20
1.49
2.23
30
0.81
1.13
40
0.56
0.73
50
0.45
0.55
60
0.35
0.40
80
0.30
0.33
100
0.25
0.27
150

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A Comparative Syntactic Typological Study of
Iranian Languages: A case of Persian and Kurdish
Ali Ashrafi
Researcher, English Department, University of Applied Science and Technology, Tehran, Iran
Abstract— Iranian dialects are subgroups of Indo-Iranian dialects and are arranges dependent on their course of
events and locale. All Iranian dialects of the center and current occasions have some normal highlights. The plain
word request is generally action word last, and the time framework depends on two action word lines, present and
past, no matter what. While the current stem proceeds with the Old Iranian present that was acquired
straightforwardly from Indo-European, the previous stem depends on a participatory type of the action word
finishing off with - ta. The Iranian dialects are probably going to be spoken by in excess of 80 million individuals
in a wide zone from Turkey with Zaza as the westernmost to China with Sarikoli as the western that the
easternmost Iranian language and primarily cover the entirety of Iran, Afghanistan and Tajikistan. The
advancement of the Iranian dialects can be analyzed in three significant chronicled periods: Old Iranian (up to the
fourth/third century BC), Central Iranian (from the fourth/third century BC to the eighth/ninth century AD) and
new Iranian language (since 900 AD). Two of the antiquated Iranian dialects are known and archived, Avestan
and Old Persian. The Central Iranian dialects (approx. 300 BC - AD 950) are significantly more various. They are
partitioned into two principle gatherings, western and eastern. Current Iranian dialects fall into two enormous
"Eastern" and "Western" gatherings, with "Northern" and "Southern" sub-gatherings, separately. The
qualification between the eastern and western Iranian dialects lies in the fundamental geological dispersion of the
principle speakers of these dialects in the east or west of Lut deserts in Iran.
Keywords — Iranian Dialects; Kurdish Dialects; Syntactic Typological; Indo-Iranian Dialects; Combinatorial
Language Frameworks.
1. Introduction
The dialects expressed in the present Iran, (for
example, Baluchi, Zaza, Persian, Kurdish and Gorani)
are viewed as western Iranian dialects, and the
dialects verbally expressed toward the east in
Tajikistan, Afghanistan and western China, (for
example, Yaghnobi, Shughni and Wakhi) are Eastern
dialects. From the start, this topographical
differentiation is persuading and simple, however it is
likewise deceptive on the grounds that such
customary terms don't generally compare to the
current genuine geological circumstance of the
dialects. For instance, the Tajik and Darian lingos of
Persian are western, albeit geologically in the east.
Ossetic, then again, which has a place with the
northeastern gathering, is spoken in the Caucasus,
which speaks to the northwest of the present Iranian-
talking region. Every one of these Eastern and
Western dialects has its own sub-bunches that
partition it into Northern and Southern classes. Every
one of these classes has some exceptional highlights.
The northwestern Iranian dialects are various, yet the
main ones can be named as follows: Kurdish (Turkey,
Iran, Iraq, Syria and a few pieces of the Caucasus);
Taleshi (Iran, Azerbaijan); Baluchi (Iran, Pakistan,
Afghanistan, Turkmenistan and the Persian Gulf);
Gilaki (Iran); Mazandarani (Iran); Zaza (Turkey);
Gurani (Iran, Iraq); Bashkardi (Iran); Parachi
(Afghanistan); Ormuri (Afghanistan, Pakistan);
Semnani and related dialect (Iran); Tat lingos with an
attention on Tabriz, Zanjan, Qazvin and Saveh (Iran);
Vafsi and Ashtiyani (Iran); Central Iranian tongues
with center around Kashan, Esfahan, Yazd, Kerman
and Dashte-Kavir (Iran). Every one of these dialects,
Persian is without a doubt the most generally utilized
language. The official language of Iran created as
ahead of schedule as the ninth century and is a
continuation of Middle Persian. Notwithstanding, it is
currently surprisingly simpler regarding formal
language structure. The Kurdish dialects are
vernacular continuums spoken by Kurds in Kurdistan
Regions of Iran, Iraq and Turkey. The three Kurdish

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dialects are Kurmanji (North Kurdish), Sorani
(Middle Kurdish) and South Kurdish (Palewani). The
Kurdish language is the third biggest Iranian language
after Persian and Pashtu and has various lingos.
Kurdish is installed in a complex multilingual setting,
and the effect of language contact stays a focal issue
in Kurdish etymology. This investigation will look at
the word request, word structure, and word
punctuation and word decision of Persian and Kurdish
dialects, which have a place with the Indo-Iranian
language gathering.
2. Literature Review
Iranian dialects in the part of Indo-Iranian dialects
are one of the under explored territories. With
progresses in innovation and correspondence,
neighborhood dialects are being failed to remember.
Directing investigations like this will help keep up
and separate the contrasts between language families.
The syntactic examples of various dialects are
regularly contrasted and the point of remaking a
typical tribal framework or changes in a single
framework after some time. In any case, this is just
conceivable in the event that we realize that the
dialects are connected. This is genealogically related
back to a typical precursor. This sort of syntactic
examination is normally alluded to as verifiable
language structure, diachronic grammar or authentic
near punctuation. Likewise, the subject of this
investigation is the examination of related dialects,
where likenesses are deciphered as acquired from a
typical progenitor. This investigation will explicitly
respond to the accompanying exploration questions:
1. What are the similitudes between Kurdish
language and Persian Languages thinking about
combinatorial frameworks of these dialects?
2. What are the contrasts between Kurdish language
and Persian Languages thinking about
combinatorial frameworks of these dialects?
Table 1. The advancement of the Iranian dialects
Old Iranian Up to the fourth/third century
BC
Central Iranian Up to the fourth/third century
BC
New Irania Since 900 AD
Table 2. The three Kurdish dialects
Kurmanji North Kurdish
Sorani Middle Kurdish
South Kurdish Palewani
Table 3. The northwestern Iranian dialects
Kurdish Turkey
Iran
Iraq
Syria
Caucasus
Taleshi Iran
Azerbaijan
Baluchi Iran
Pakistan
Afghanistan
Turkmenistan
Gilaki Iran
Zaza Turkey
Gurani Iran
Iraq
Bashkardi Iran
The point of this examination is to discover and
order the syntactic typological variety of Persian and
Kurdish dialects. The outcomes syntactic typological
contemplates are frequently completed with various
purposes. Haspelmath (2014) specifies a portion of
the results and advantages of such investigations:
• Facilitating unknown dialect learning.
• Detecting areal examples.
• Finding invariant examples among the variety (for
example syntactic universals, or general syntactic
standards)
• Explaining why dialects are how they are (i.e.,
clarifying the universals)
• Explaining how language obtaining is conceivable
in spite of the destitution of the improvement
Despite the fact that these destinations are free,
they rely upon the objective of discovering invariant
examples (or universals). Besides, this is the most
conspicuous part of relative grammar. Consequently,

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near sentence structure is additionally here and there
called syntactic universals research.
3. Methodology
The specialist in this examination will use a
relative plan. In near investigations the analysts
attempt to discover the similitudes and contrasts
between the factors of the examination, which in the
current task are syntactic typological variety of
Persian and Kurdish. The near assessment of the
similitudes and contrasts between the combinatorial
language frameworks is called similar grammar or
syntactic typology. Considering the language
frameworks of Persian and Kurdish this examination
will be research the likenesses and contrasts between
the two dialects.
4. Discussions
4.1 The Semantic Typology and Language
There has been various examinations on semantic
typology and language variety in Iran. The typology
of Iranian dialects was inspected by Anonby,
TaheriArdali and Hayes (2019). They delineated the
goals of the Atlas of Languages of Iran (ALI)
research program. The map book design, the
examination approach, and the primer outcomes were
created. Explicit fascinating themes were the structure
and substance of the poll on language information;
managing restricting viewpoints on the status of
"dialects" and "tongues" through an adaptable,
multidimensional arrangement organization; and the
function of progressing correlations between language
dispersion appraisals and hard dialect information.
Etymological typology with an Iranian point of view
was the subject of an investigation by Dabir-
Moghaddam (2012). He accepted that the Iranian
dialects spoken in Iran have an entrancing typological
characteristic. They all advantage extraordinarily
from the arrangement as a typological boundary. In
this article, he examined thoughts from Comrie (1978)
in which he recommended the five potential language
types dependent on case markers and verbal
arrangements, and referenced the status of a few
Kurdish assortments and a lingo of Talyshi and
Davani comparable to the understanding . He
demonstrated that type (d), ie the three-section
framework in Comries phrasing, where S, An and P
each have their own name, and type (e) for which he
didn't recommend a name, however a sort wherein An
and P are distinguished indistinguishably, are
exceptionally profitable and stable frameworks in the
Iranian dialects referenced. Shahiditabar and
Pourghasemian (2016) contemplated the multilingual
setting of Iran and provided details regarding
conceivable language changes and contended against
Rezvani and Asadpour (2008).
The structure utilized in this examination was the
"change in clear time" presented by Lindguist (2009).
As to initial segment of the examination, the
outcomes indicated that the 1979 Islamic Revolution
in Iran entered a few words in the dialects inspected
in this investigation, generally Arabic or Islamic
phrasing. The lexical changes have additionally arisen
in Kurdish, as certain words have been obtained from
Turkish and Persian. The other aftereffect of the
examination was that both Turkish and Persian had
traded a couple of words on account of their contact.
Arabic as the language of Islam influenced both
Persian and Turkish just as Kurdish. As to the second
piece of the examination, the Turkish structure had
not changed, and what Rezvani and Asadpour
(Rezvani and Asadpour, 2008) announced about
Turkish and Persian was generally problematic. The
above examinations just had a typological perspective
on Iranian dialects. Notwithstanding, utilizing a
similar plan, the current investigation will direct a
syntactic typological investigation of Persian and
Kurdish that is exceptional to the analyst.
4.2 Syntactic Comparison of two Languages
Kurdish and Persian are both Indo-European
dialects. The two dialects were additionally affected
by the Avestan language and were comparable in
numerous articulation structures until the Middle
Ages. The etymological likenesses between the two
dialects are exceptionally wide, and Kurdish, because
of its closeness to old and unique roots, can be a
hotspot for restoring and recreating deserted
structures in Persian. Furthermore, there are two
different ways to reestablish words and use word-
building examples to assist the Kurdish language with
enhancing the Persian language. The Kurdish name
generally alludes to the language of the individuals

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who live in the rugged land situated in the west of the
Iranian level. Today, this land incorporates the eastern
and southern pieces of Turkey, northeastern Syria,
northern Iraq and the western districts of Iran, which
is aggregately called Kurdistan, and the language of
its occupants, in spite of its various divisions, is all
Kurdish. In spite of the fact that this language has
numerous and differed sources, it in the long run
appeared as a firm and brought together language
called Kurdish. General etymologists concur that the
Kurdish language, in a way that can be known as a
rational assortment as a free language, has been the
methods for trading thoughts of a gathering of
individuals living in Mesopotamia since the main
thousand years BC. They are known as the Kurdish
country.
Today, the political outskirts of the Kurdish land
are partitioned into four separate parts under the
standard of four banners and three unique dialects,
and each part is definitely impacted by the language
of the decision nation. In Iraqi and Syrian-
overwhelmed Kurdistan, the recurrence of Arabic
words is without a doubt higher, and in parts under
Turkish and Iranian principle, Turkish and Persian,
individually. Be that as it may, the Kurdish language
holds its tendency and over the long haul opposes the
assault of unfamiliar words by making words and
alluding to the first Kurdish words. Then again,
Avesta as a strict book of Iranians and Avestan
language as a language that contains the majority of
the writings of the old time of Iran have profound and
solid binds with Persian and besides with Kurdish.
Kurdish, through Akkadian, Aramaic and Syriac
dialects, has additionally settled powerless binds with
the old Arabic language, and among these dialects,
Turkish is the most unknown dialect to Kurdish. Then
again, during innovation, another gathering of
unfamiliar words has entered the Kurdish language
with current advancements, the quantity of which is
expanding step by step, and it is amazing that the
cycle of word development about these unfamiliar
words is both moderate and insufficient. Kurdish
dialects are partitioned into six classifications, which
are:
Indo-European order
 Indo-Iranian
 Iranian
 Western Iran
 Northwestern Iranian
Kurdish dialects:
 Kurmanji
 Kelhuri
 Zaza-Goran (Horami)
 Lori
 Sorani
 Laki
It should be added that every one of these tongues
has its own lingos and sub-dialect, underneath which
we will quickly allude to some of them in every class
and know its topography.
Kurmanji Dialect: Incorporates dialect, for
example, Jaziri, Behdinani (Badinani), Shamzinani,
Butani, and so forth This geology is all the Kurdish
locales of Turkey (northern Kurdistan), part of the
northern districts of Kurdistan (southern Kurdistan),
northern pieces of Iranian Kurdistan ( East Kurdistan),
the Kurdish areas of Armenia, Syrian Kurdistan
(West Kurdistan), the Kurds of Khorasan and
numerous different locales and nations of the previous
Soviet Union where the Kurds are available.
Kelhuri Dialect: Incorporates tongues: Grossi,
Keliai, Fili, Zanganeh, Gorani, Qasri, and so on, This
topography in Iranian Kurdistan (East Kurdistan)
incorporates the regions of Kermanshah, Kurdistan,
Ilam and part of Hamedan and in the domain of
Kurdistan (southern Kurdistan) in urban communities
Because: Khaneqin, Mendali, Zarbatieh, Badreh,
Jasan, Jalula and different regions are associated with
it.
Zaza-Goran Dialect (Horami): It comprises of
two sections, Zazaki and Horami, which are
exceptionally far separated geologically, however
phonetically near one another, and this closeness
incorporates language, jargon, way to express words,
and so forth In spite of the fact that they are not
related as far as ancestral association and geology.
Horami topographical territory remembers zones for
Kermanshah and Kurdistan territories in Iranian
Kurdistan and parts of Sulaimaniyah area in Iraqi
Kurdistan. Zazaki topographical area is likewise in

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Dersim and parts of Al-Azigh, Arzanjan and
Diyarbakir.
Lori Dialect: Incorporates dialect, for example,
Khormavehi, Chegni, Bakhtiari, Mamasani, and so on
This topography is in Iranian Kurdistan (East
Kurdistan), Lorestan, Kohkiluyeh and Boyer-Ahmad,
Chaharmahal and Bakhtiari, portions of Isfahan, Fars,
Khuzestan, Markazi, Hamedan. Furthermore,
incorporates Ilam.
Sorani Dialect: Incorporates dialect: Makri,
Babani, Sorani, Ardalani and Jafi. This topography
within the sight of its talking clans, ie in Iraqi
Kurdistan (southern Kurdistan) in the territories of
Erbil, Sulaymaniyah, Kirkuk, and encompassing
urban areas, and in Iranian Kurdistan (eastern
Kurdistan) in the regions of Mokrian (West
Azerbaijan) and Kurdistan to the focal point of
Sanandaj.
Laki Dialect: Incorporates tongues, for example,
Osmanvand, Jalalvand, Chaghlundi, Kakavandi .Its
topography in Iranian Kurdistan (East Kurdistan),
Harsin in Kermanshah region and encompassing
territories and towns, Noorabad, Aleshtar, Kuhdasht
and Delfan in Lorestan territory, a few regions
Includes Ilam region. It should be noticed that in
certain regions, for example, Kuhdasht, Lak and Lor
populaces live respectively in this geology.
Fig. 4: Indo-European order
Table 5. Kurdish dialect in different regions
Kurmanji Jaziri, Behdinani (Badinani), Shamzinani,
Butani
Turkey (northern Kurdistan), of Iranian
Kurdistan ( East Kurdistan), the Kurdish areas
of Armenia, Syrian Kurdistan (West
Kurdistan), the Kurds of Khorasan
Kelhuri Grossi, Keliai, Fili, Zanganeh, Gorani, Qasri
Iranian Kurdistan (East Kurdistan),
Kermanshah, Kurdistan, Ilam and part of
Hamedan
Zaza-
Goran
(Horami)
Zazaki and Horami
Kermanshah and KIranian Kurdistan and parts
of Sulaimaniyah area in Iraqi Kurdistan,
Dersim and parts of Al-Azigh, Arzanjan and
Diyarbakira
Lori Khormavehi, Chegni, Bakhtiari, Mamasani
Iranian Kurdistan (East Kurdistan), Lorestan,
Kohkiluyeh and Boyer-Ahmad, Chaharmahal
and Bakhtiari, portions of Isfahan, Fars,
Khuzestan, Markazi, Hamedan
Sorani Makri, Babani, Sorani, Ardalani and Jafi
Iraqi Kurdistan (southern Kurdistan),
Sulaymaniyah, Kirkuk, Iranian Kurdistan
(eastern Kurdistan)Mokrian (West
Azerbaijan), Sanandaj (Iran)
Laki Osmanvand, Jalalvand, Chaghlundi,
Kakavandi
Iranian Kurdistan (East Kurdistan),
Kermanshah, Noorabad, Aleshtar, Lorestan,
Ilam
4.3 Kurdish Words
The primary piece of Kurdish words is from
antiquated Iranian roots. After the Arab intrusion,
regardless of the spread of Islam and as indicated by
it, Arabic culture and writing in an enormous piece of
Kurdistan, as in different pieces of the Middle East,
for different reasons, including rugged and
disconnected climate of Kurdish speakers, the
Kurdish language had the option to recuperate from
the surge of neighboring dialects, particularly in
the field of Composing. Kurdish has the least level
of Arabic words contrasted with other Middle Eastern
dialects that have been affected by Arabic, and
obviously the vast majority of the words included are
strict words.

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4.4 Vernacular
Table 6. 8 Vowels of Kurdish
Kurdish a e ê c î o u û
Persian A
(in Persian
‫)|آ‬
E
(in
Persian ‫)ا‬
EE
(in
Persian‫ا‬ )
Noequivalent
in Persian
AI
(in Persian
‫)ای‬
O
(in Persian
‫)أ‬
Noequivalent
in Persian
OU
(In Persian ‫)او‬
Table 7. 23 Consonants of Kurdish
Kurdish Persian
B B (in Persian‫ب‬ )
C GE (In persian ‫)ج‬
Ç CH (in Persian ‫)چ‬
D De (in Persian ‫)د‬
F Fe (in Persian ‫)ف‬
G Ge (in Persian ‫)گ‬
H He (In Perisna ‫)ه‬
J Zhe (in Persian ‫)ژ‬
K Ke(in Persian ‫)ک‬
L Le (in Persian ‫)ل‬
M ME(in Persian ‫)م‬
N Ne(in Persian ‫)ن‬
P PE(in Persian ‫)پ‬
Q GHE(in Persian (‫ق‬
R RE(in Persian‫ر‬ )
S SE(In persian ‫)س‬
Ş SHE(in persian ‫)ش‬
T TE(in Persian ‫)ت‬
V Noequivalent in Persian
W VE(in Persian ‫)و‬
X KHE(in Persian ‫)خ‬
Y YE(in Perisan‫ی‬ )
Z ZE(in Persian‫ز‬ )
5. Conclusion
According to these clarifications about the
relationship of the Kurdish language with the other
three dialects, it should be said that the most
comparability and closeness among Kurdish and
Persian can be found. As referenced, the material
Iranian language (a language that emerged from the
combination of the crude material language with the
outsider clans that joined the Medes) structures the
Kurdish body and structure today, and this language
has an Indo-European nature that is the mother of all
antiquated Iranian dialects. Therefore, we see the
most similitude among Kurdish and Persian, Kurdish
and Persian are the two subsets of Indo-Iranian
dialects and have numerous lexical likenesses because
of the presence of a typical progenitor. Obviously, the
closeness among Kurdish and Persian until the
Middle Ages was likewise huge, and what we see as
clear separation and contrasts (particularly in the field
of articulation) is identified with the new period and
the impact of the Arabic language on Persian. Be that
as it may, because of the presence of pre-Aryan words
and roots in Kurdish and the artifact of human
progress in the area, Kurdish can never be considered
as a lingo of Persian.
References
[1] Bernard Comrie (June 28, 1990),"The World's Major
Languages Reprint Edition",Publisher : Oxford
University Press, ISBN-10 : 0195065115, ISBN-13 :
978-0195065114, Language: English
[2] Haig & Öpengin, 2014, "Introduction to Special Issue :
Kurdish - A critical research overview, Kurdish
Studies, 4, (1), 25-50
[3] Alice C. Harris & Lyle Campbell (1998),"Historical
syntax in cross-linguistic perspective", (Cambridge
Studies in Linguistics 74). Cambridge: Cambridge
University Press, 1998. Pp. xvii+488
[4] Anonby, E., Taheri-Ardali, M., & Hayes, A. (2019).
The Atlas of the Languages of Iran (ALI): A Research
Overview. Journal Iranian Studies, 52, 1-2
[5] Comrie, B. (1978). Ergativity. In W. Lehmann (ed.),
Syntactic Typology. Sussex: The Harvester Press
[6] Comrie, B. (2009). The World's Major Languages.
Routledge: New York
[8] Dabir-Moghaddam, M. (2012). Linguistic Typology:
An Iranian Perspective. J. of Universal Language, 13-1
[9] Haig, G., & Öpengin, E. (2014). Introduction to
Special Issue Kurdish: A critical research overview,
Kurdish StudiesVolume, 2(2), pp. 99 – 122
[10]Haspelmath, M. (2014). Comparative syntax: The
Routledge Handbook of Syntax. Abingdon: Routledge
[11]Lindquist, H. (2009). Corpus Linguistics and the
Description of English. Edinburgh. Edinburgh
[12]Rezani, P. &Asadpour, H. (2008). Language Contact
and Language Change: Evolution or Putrefy (A Cross-
Cultural view of Multilingualism). 6th International
METU Postgraduate Conference in Linguistics and
Language Teaching

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The Influence of Board Independent on Firm Performance of
Sri Lankan Listed Companies
M. C. A. Nazar
Senior Lecturer, Department of Accountancy and Finance, South Eastern University of Sri Lanka,
University Park, 32360, Oluvil, Sri Lanka
Abstract — The study investigates the effect of board independent on firm performance of Sri Lankan listed
companies. This study employs a cross section analysis of 120 firms as sample of listed firms in Colombo Stock
Exchange (CSE) for the financial year ending2019 and multivariate analyses are used to test the proposed
hypothesis. The board independent variable being the independent variable and firm performance (ROA) variable
being the dependent variable are confirmed under regression model. The results of the study display that board
independence is negatively connected with ROA. Further, Board size and CEO duality are significantly
negatively associated with ROA. Moreover, a control variable of firm size is significantly positively connected
with ROA and leverage is negatively related with ROA though not significant. The findings of the study indicate
mixed results which are in consistent with empirical evidence of developed nation.
Keywords — Board Independent; Board of Directors; Firm Performance; ROA.
1. Introduction
Corporate boards are the principal and leading
inside corporate governance devices and play an
important part in the monitoring management and
bring into line the interest of shareholders with
management (Brennan, 2006). Good corporate
governance demonstrates itself in the efficiency of the
directorate and the management of the company. The
board of directors is accountable for mitigating self-
interest activities as well as dropping losses caused by
sub-optimal decisions by executives (Fama & Jensen,
1983; Jensen & Meckling, 1976). The board
efficiency remains hard to understand and define, as
there is major argument about the roles and tasks that
would be allocated to the board. The board efficiency
dimensions include searching the environment for
threats and opportunities offer direction and feedback
to the CEO; and, draw out a network of contacts and
sources of knowledge to strengthen firm performance
(Leung et al., 2014). This is a broader definition than
the traditional board effectiveness in monitoring and
controlling managers.
The recently revised code of best practice on
corporate governance 2013 was issued by the
Institute of Chartered Accountants of Sri Lanka
and Securities and Exchange Commission. The
code recommends that the board to have a balance
of executive and Non-executive directors such that
no individual or small group of individuals can
control the board’s decision making process. In
addition, commends that the responsibility of both
the chairman and the CEO should not hold by the
same person to ensure the balance of power and
authority. This paper is structured as follows. In
Section 2, we reviewed the theoretical and empirical
literature and hypothesis development. In Section 3,
the data and methodology are presented. In Section 4,
the results are discussed and Section 5 concluded.
2. Literature Review and Hypothesis
Development
The board of directors might contain executive
and non-executive memberships. The non-executive
directors play a dynamic part in monitoring the
movements of the CEO and executive directors to
confirm that the interest of shareholders are healthy
be concerned for and to add to the diversity of skills
and knowledge of the directors (Weir & Laing, 2001).
Consistent with this argument, Liu et al. (2015) found
a positive association between non-executives and
firm performance measured by return on asset (ROA)
and return on equity (ROE) in Pakistan.

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Existing work in the analytical agency
tradition (Stiles and Taylor, 2001) suggests that a
higher proportion of outside directors should be
associated with stronger financial performance.
Furthermore, Zubaidah et al. (2009) found that
independent non-executive directors contribute
significantly in the long term performance of the
company. In addition, Dehaenc et al. (2001)
concluded that there was a significant positive
relationship between the independent directors’
percentage in a firm and return on equity (ROE)
among Belgium companies. O’ Connell and
Creamer (2010) found that there was a positive and
significant association between the percentage of
non- executive directors on the board and firm
performance. Furthermore, Dahya and McConnell
(2005) found the same results in the UK firms. The
appointment of financial outside directors to a
public corporation is associated with positive
abnormal return among medium size companies
(Lee et al,1999). On the other hand, Agrawal and
Knoeber (1996) found a significant negative
relationship between outside board members and
firm performance. This result is also supported by
Bhagat and Black (1999) with the findings of firms
having more outside directors performs poorer than
other firms.
However, some other studies like those of
Kajola (2008) and Peng (2004) found insignificant
association between the ratio of outside directors to
the whole board and its effect on firm performance.
Empirically, research on non-executive director in
relation to firm performance is inconsistent.
Therefore, the hypothesis can be formulated as
follows:
H1: There is positive relationship between the
percentage of independent non-executive director
and firm performance (ROA).
3. Data Analysis and Research Method
3.1 Data and Sample
The data use in the form of secondary data.
The data and information for this study collected
from the Colombo Stock Exchange (CSE)
websites, annual reports, journals and CSE
publications. The total listed companies in the CSE
contained 293 companies in 2019 have been
categories under 20 different sectors. The sample
consists of 120 non-financial public listed
companies in Sri Lanka whose annual reports are
available in 2019.
3.2 Research Model
A cross - sectional ordinary least square
regression model used test the developed
hypotheses for this study. The regression model
utilized to test the relationship between the board
independence and ROA are as follows:
Return on Assets (ROA) =
α + β1 Board Independent + β2Board size + β3 CEO
Duality + β4Firm size + β5 Leverage + ei
3.3 Variables and Descriptions
Return on Asset = Net Income / Total Assets
Board Independent = No. of outside directors /
Total No. of directors
Board Size = No. of directors on the board
CEO Duality = It is equal to one if the post is hold
by same person as the CEO and
board Chair, or is zero otherwise
Firm size = Natural log of total assets reported on
annual report
Leverage = Total debt / Total equity
4. Data Analysis and Discussions
Descriptive statistics is necessary to obtain
sample characteristics. Table 1 provides
descriptive statistics for the variable of board
characteristics used in the study.
4.1 Descriptive Analysis

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ROA Board
Independen
ce
Board Size CEO
Duality
Firm Size Leverage
Mean 0.062 0.715 7.940 0.138 9.665 0.367
Median 0.057 0.714 8.000 0.000 9.694 0.140
Maximum 0.544 1.000 12.000 1.000 11.202 7.371
Minimum -0.793 0.250 3.000 0.000 8.056 0.000
Std. Dev. 0.120 0.204 1.966 0.346 0.618 0.777
Skewness -2.260 -0.245 0.271 2.100 -0.118 6.678
Kurtosis 26.678 2.246 2.493 5.410 2.931 58.418
Jarque-Bera 2808.536 3.906 2.657 113.333 0.292 15706.160
Probability 0.000 0.142 0.265 0.000 0.864 0.000
Sum 7.165 82.897 921.000 16.000 1121.136 42.593
Sum Sq.
Dev.
1.650 4.777 444.578 13.793 43.877 69.514
Observations 120 120 120 120 120 120
Descriptive statistics of the variables are
presented in Table 1.The descriptive statistics include
the mean, median, standard deviation, minimum and
maximum values. The average firm performance
(ROA) is 6.2%. On average 71% of overall board
members are independent directors, a range of 25% to
100% of total directors. The number of directors on
Sri Lankan board is between 3 and 12 with an
average board size in the selected firms is about
8 persons. This result is reliable with the study by
Fooladi (2012) and Zubaidah et al (2009). In addition,
of all the firms studied, 87% of them adapt the 2 tier
board structure implying that about 13% of the firms
have their CEOs and Board chairman positions
combined in one personality. This suggests that way
for agency problems originating from conflict of
interest are minimized.
4.2 Correlation Analysis
Table2: Correlation results
ROA
Board
Independe
nce
Board
Size
CEO
Duality
Firm
Size
Leverag
e
ROA 1
Board
Independence
-0.141 1
Board Size -0.116 -0.177** 1
CEO Duality -0.168* -0.105 -0.039 1
Firm Size 0.229*** -0.277*** 0.307*** -0.009 1
Leverage -0.102 0.084 0.040 -0.024 0.184** 1
* Correlation is significant at the 0.1 level (2-tailed)** Correlation is significant at the 0.05 level (2-
tailed), *** Correlation is significant at the 0.01 level (2-tailed)
Table 1: Descriptive Statistics

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Table 2 shows the correlation results among
the variables. Board independence is negatively
correlated with ROA. And also, board size is
negatively correlated with ROA. In addition,
CEO duality is significantly negatively
correlated with ROA at the
10% level. Furthermore, Firm size is significantly
positively correlated with ROA. But, leverage is
negatively linked with ROA.
4.3 Regression Analysis
Table 3: Regression results
Dependent Variable: ROA
Included observations: 120
Variable Coefficient Std. Error t-
Statistic
Prob.
C -0.252609 0.169308 -1.492013 0.1386
BIND -0.045822 0.048667 -0.941527 0.3485
BOARDSIZ -0.010536 0.005038 -2.091384 0.0388
CEODUAL -0.058673 0.027098 -2.165181 0.0326
FIRMSIZE 0.044606 0.016883 2.642047 0.0095
LEVERAGE -0.016773 0.012387 -1.354105 0.1785
R-squared 0.348373
Adjusted R-
squared
0.306138
Table 3 shows the regression between board
characteristics and ROA. The relationship between
the number of the independent directors from the
board and firm performance (ROA) is not statistically
significant; the board composition has a negative
relationship with ROA though insignificant. This is
consistent with the findings of Agrawal and
Knoeber (1996) and Bhagat and black (1999).
Therefore, Hypothesis 1 which stated that there is
positive relationship between the percentage of
independent non-executive director and firm
performance is rejected and null hypotheses
accepted. Further, the size of the board of directors
and CEO duality are significantly negatively
associated with ROA. These findings are supported
with Yermack (1996) and Fosberg
and Nelson (1999). In accordance with the available
literature, the study includes control variables in the
regression analysis. Table 3 indicates that firm size is
significantly positively interrelated with ROA.
Leverage is negatively influence on firm performance
(ROA).
5. Conclusion and Limitations
This study explores whether the board
independent influence on firm performance (ROA)
for 120 firms as sample of listed companies in
Colombo Stock Exchange. This research used the
ROA as proxy measurer for firm performance. The
results of the study provide that board independent
does not influence significantly and positively on
ROA. In contrast it is negatively affect the ROA.
Although it is widely believed that, outside directors
will promote shareholders wealth due to their legally
vested responsibility and although board independent
is recommended in many international corporate
codes of best practices, even in Sri Lanka. The reason
may be behind this result, it is contended that insider
directors are the most effective directors because they
have more information about the firm than outsider
directors and thus outside directors must rely on them
to make decisions. Further both board size and CEO
duality are significantly negatively associated with

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ROA. In the case of control variables, firm size is
significantly positively related with ROA and
leverage is negatively associated with ROA. The
results of the study are mostly consistent with the
previous studies and it shows the importance of board
characteristics should be highlighted in order to
improve the firm performance.
The major limitations of this study are as follows:
First, the study based on the cross sectional study
which is concern about one year period may not
provide more generalized result. Second, this study
used only ROA as a performance measure can
include ROE, ROCE and Tobin Q in order to get
generalized results. It is highly recommended that
future research should be analyzed more than one
year because the effect of independent.
References
[1] Agrawal, A., & Knoeber, C. R. (1996).Firm
performance and mechanisms to control agency
problems between managers and shareholders .Journal
of Financial and Quantitative Analysis, 31(3), 377-
397.
[2] Akbar, M.,Hussain, S., Ahmad, T., & Hassan, S.
(2019) Corporate governance and firm performance in
Pakistan: Dynamic Panel Estimation. Abasyn Journal
of Social Sciences, 12 (2).213-230.
[3] Bhagat, S., & Black, B. (1999). The uncertain
relationship between board composition and firm
performance. The Business Lawyer, 921-963.
[4] Brennan, N. (2006). Boards of directors and firm
performance: is there an expectations gap? Corporate
Governance: An International Review, 14(6), 577-593.
[5] Dahya, J., &Mc Connell, J.J. (2005), Outside directors
and board decision, Journal of corporate Finance,
11(1-2), 37-60.
[6] Dehaene, A., De Vuyst, V., &Ooghe, H. (2001).
Corporate performance and board structure in Belgian
companies. Long Range Planning, 34(3), 383-398.
[7] Fama, E. F., & Jensen, M. C. (1983).Separation of
ownership and control. The Journal of Law &
Economics, 26(2), 301-325.
[8] Fooladi, M. (2012).Board characteristics and firm
performance, Journal of Modern Accounting and
Auditing, 8(5), 688-694.
[9] Fosberg, R.H., & Nelson, M.R., (1999), Leadership
structure and firm performance, International Review
of Financial Analysis, 8(1), 83-96.
[10]Jensen, M.C., & Meckling, W.H.(1976),Theory of the
firm: managerial behavior, agency cost and ownership
structure, Journal of Financial Economics, 3, 305-360.
[11]Lee, Y.S., Rosenteins, S., & Wyatt, J.G. (1999).The
value of financial outside directors on corporate
boards, International Review of Economics and
Finance, 8(4), 421-431.
[12]Leung, S., Richardson, G., & Jaggi, B. (2014).
Corporate board and board committee independence,
firms performance, and family ownership
concentration: An analysis based on Hong Kong firms,
10(1), 16-31.
[13]Liu, Y., Miletkov, M. K., Wei, Z., & Yang, T.
(2015).Board independence and firm performance in
China.Journal of corporate Finance, 30, 223-244.
[14]O’Connell, V., & Cramer, N. (2010).The relationship
between firm performance and board characteristics in
Ireland. European Management Journal, 28(5), 387-
399.
[15]Stiles, P. & Taylor, B. (2001) Boards at work: how
directors view their roles and responsibilities. Oxford
University Press, Oxford.
[16]Weir, L. & Laing.D. (2001). Governance structures,
director independence and corporate performance in
the U.K. European Business Review, 13(2), 86-94,
2001.
[17]Yermack, D. (1996). Higher market valuation of
companies with a small board of directors. Journal of
Financial Economics, 40(2), 185-211.
[18]Zubaidah, Z.A., Nurmala, M.K., & Kamaruzaman, J.
(2009), Board structure and corporate performance in
Malaysia, International Journal of Economics and
Finance, Vol. 1(1), pp. 150-164.

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Financial Liquidity, Asset Management and Financial Performance
in Indonesia Listed Companies
Mohammad Arief
Faculty of Economics and Business, Department of Management, University of Trunojoyo Madura, Indonesia
Abstract — This study aims to examine the effect of liquidity, asset management, cash turnover and capital
structure on financial performance in manufacturing companies listed on the Indonesia Stock Exchange. The
research was conducted with a quantitative research approach. This type of research is descriptive research. The
population in the study as many as 159 companies and the number of samples of 85 manufacturing companies are
listed on the Stock Exchange Indonesia. The variables related to this research are liquidity (current ratio), asset
management (total asset turnover), cash turnover, capital structure (debt to equity ratio) and Return on Asset. The
research method used is the classical assumption test method and multiple linear regression analysis. The results
showed that partially liquidity (current ratio) had a significant effect on Return on Asset, Asset Management
(total asset turnover) had a significant effect on Return on Asset, and Cash Turnover (debt to equity ratio) had no
significant effect. Return on Asset and Capital Structure have no significant effect on Return on Asset (ROA).
Simultaneously Liquidity (current ratio), Asset Management (total asset turnover), Turnover Cash (cash turnover)
and Capital Structure (debt to equity ratio) affect significantly to the Financial Performance in Indonesia Listed
Companies.
Keywords — Liquidity; Management of Asset; Cash Turnover; Capital Structure; Financial Performance.
1. Introduction
The economic development of every company in
the current era of globalization is very dynamic.
Wherein each company has a main purpose
especially, obtaining profit. And to maintain this
profit, an examination of its financial health is
required. The way to see financial health is seen from
its financial ratios. Where in practice there are five
financial ratios, namely the Liquidity Ratio, Solvency
Ratio, Activity Ratio, Market Value Ratio, and
Profitability Race.
So from the five financial ratios, the researcher
uses the liquidity ratio to measure the company's
ability to overcome short-term debt, namely the
analysis used is the Current Ratio and cash turnover.
The solvency ratio is used to determine the extent to
which the company's assets are financed with debt,
which is measured using the Debt to Equity Ratio and
the activity ratio used is TATO (Total Assets
Turnover). So that researchers can use the four ratios
to measure how much it affects financial
performance, namely ROA (Return On Assets),
especially in manufacturing companies listed on the
Indonesia Stock Exchange.
Table 1. Research Phenomena
Issuers Code Year Current Assets Total Assets Sales Total Equity Income After
Tax
AKPI
2016 870.146.141.000 2.615.909.190.000 2.047.218.639.000 1.120.035.169.000 52.393.857.000
2017 1.003.030.428.000 2.745.325.853.000 2.064.857.643.000 1.126.612.491.000 13.333.970.000
2018 1.233.718.090.000 3.070.410.492.000 2.387.420.036.000 1.233.853.753.000 64.226.271.000
TALF
2016 325.494.162.443 881.673.021.959 569.419.992.907 751.895.484.850 30.137.707.324
2017 351.255.636.607 921.240.988.517 646.087.885.410 766.164.851.785 21.465.856.784
2018 405.988.723.901 984.597.771.989 741.055.147.778 808.378.986.806 43.976.734.000
Source: Indonesia Stock Exchange Data

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Based on table 1 above shows 2 examples of
manufacturing companies, the phenomenon that
occurs in the company PT. Argha Karya Prima Ind.
(AKPI) Tbk from 2017 to 2018 experienced an
increase in current assets of IDR.
230,687,662,000.Meanwhile, net profit after tax has
increased from IDR. 13,333,970,000 in 2017 to IDR.
64,226,271,000 in 2018. This company also
experienced an increase in sales from 2016 to 2017
amounting to IDR. 17,639,004,000, while the net
profit after tax has decreased from IDR.
52,393,857,000 in 2016 to IDR. 13.33397 billion in
the year 2017.In the company PT. Tunas Alfin
(TALF) Tbk from 2016 to 2017 experienced an
increase in total assets of IDR. 39,567,966,558.
Meanwhile, net profit after tax decreased from IDR.
30,137,707,324 in 2016 and IDR. 21,465,856,784 in
2017. This company also experienced an increase in
total equity in 2017 to 2018 amounting to IDR.
42,214,155,023, while the net profit after tax has
increased from IDR. 21,465,856,784 in 2017 to IDR.
43,976,734,000 in 2018.
2. Literature Review
2.1 Liquidity
According to (Fahmi, 2017), the liquidity ratio is
the potential for a company to meet its short-term
obligations in a timely manner. According to (Hery,
2015), argues that the ratio to measure liquidity is the
current ratio. Current ratio is used to measure a
company's potential to meet its short-term obligations
(debt) which are due soon by using total current
assets. The indicator formula used for looking for the
current ratio is as follows:
𝐶𝑢𝑟𝑟𝑒𝑛𝑡 𝑅𝑎𝑡𝑖𝑜 =
𝐶𝑢𝑟𝑟𝑒𝑛𝑡 𝐴𝑠𝑠𝑒𝑡𝑠
𝐶𝑢𝑟𝑟𝑒𝑛𝑡 𝐿𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑖𝑒𝑠
2.2 Asset Management
Asset management is the structured process of
cost-effective level, operating, maintaining,
upgrading, and disposing of assets. This term is most
often used in the world of finance to describe objects
and companies that manage investments on behalf of
others.
According to (Brigham & Houston, 2010), found
the ratio to measure asset management is the total
assets turnover ratio. A total asset turnover ratio is a
ratio used to measure the turnover of all company
assets and is calculated by dividing sales by total
assets. The indicator formula used to find the total
asset turnover ratio is as follows:
𝑇𝑜𝑡𝑎𝑙𝐴𝑠𝑠𝑒𝑡𝑠𝑇𝑢𝑟𝑛𝑜𝑣𝑒𝑟 =
𝑆𝑎𝑙𝑒𝑠
𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠
2.3 Cash Turnover
According to (Nuriyani & Zannati, 2017), cash
turnover is a period starting at the time of turning
cash where cash was invested in working capital
liquidity highest level in order to achieve a high sales
volume as well. According to (Nurafika & Almadany,
2018), the formula whichused to find the cash
turnover ratio is as follows:
Cash Turnover =
𝑆𝑎𝑙𝑒𝑠
𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝐶𝑎𝑠ℎ
𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝐶𝑎𝑠ℎ =
𝐶𝑎𝑠ℎ 𝐵𝑒𝑔𝑖𝑛𝑛𝑖𝑛𝑔 + 𝐶𝑎𝑠ℎ 𝐸𝑛𝑑
2
2.4 Capital Structure
According to (Fahmi, 2017), Capital Structure is
a description of the form of the company's financial
scale, namely the capital it owns sourced from long-
term debt (long-term liabilities) and own capital
(shareholders ' equity) is a financial source for a
company.
According to (Hery, 2015), argues that the ratio
to measure capital structure is the Debt to Equity
Ratio. Debt to Equity Ratio is a ratio used to measure
the proportion of debt to equity. The indicator
formula used to find the Debt to Equity Ratio is as
follows:
𝐷𝑒𝑏𝑡𝑡𝑜𝐸𝑞𝑢𝑖𝑡𝑦𝑅𝑎𝑡𝑖𝑜 =
𝑇𝑜𝑡𝑎𝑙 𝐷𝑒𝑏𝑡
𝑇𝑜𝑡𝑎𝑙 𝐶𝑎𝑝𝑖𝑡𝑎𝑙
2.5 Financial Performance
According to (Fahmi, 2017), financial
performance is a description and research conducted
to see to what extent a company has implemented it

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by using proper and precise financial implementation
rules.
According to (Hery, 2017), argues that the ratio
for measuring financial performance is Return on
Assets. Return on Assets is a ratio that shows the
results (return) on the use of company assets in
creating net income. According to (Nurafika &
Almadany, 2018), the formula of the indicators used
to men looking for returnon assets are as follows:
𝑅𝑒𝑡𝑢𝑟𝑛 𝑂𝑛 𝐴𝑠𝑠𝑒𝑡
=
𝑁𝑒𝑡 𝑃𝑟𝑜𝑓𝑖𝑡 𝐴𝑓𝑡𝑒𝑟 𝑇𝑎𝑥
𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠
𝑋 100%
3. Research Framework
3.1 The Effect of Liquidity on Financial Performance
The ratio used in liquidity is Current Ratio.
According to (Subramanyam & Wild, 2014), the
current ratio demonstrated its ability to me filled the
current liabilities. If the increasingly high number of
current assets to current liabilities, the greater the
confidence that obligation well will be paid, then the
dynamically affect financial performance.
3.2 The Effect of Asset Management on Financial
Performance
The ratio used in asset management is the ratio of
total asset turnover. According to (Hery, 2017), total
assets turnover show how the effectiveness of the
company's total assets in generating sales so it is able
to improve the performance of the financial fine.
3.3 Effect of Cash Turnover on Financial Performance
According to (Widiasmoro, 2017), cash turnover
shows the ability of cash to generate income, so it can
affect the company's overall financial performance.
3.4 The Effect of Capital Structure on Financial
Performance
The ratio used in the capital structure is the Debt
to Equity Ratio. According to (Lestari & Goddess,
2016), the value of debt- to -equity ratio, which is low
indicates a company's Return on Asset (ROA) are
good because the higher the company's ability to pay
its obligations.
Research Framework - To find out the
relationship between the independent variable (x) and
the dependent variable (y) used in this study, it can be
seen in Figure 1 below:
Fig.1: Research framework
3.5 Research Hypothesis
Based on the conceptual framework of the
hypothesis above, the hypothesis proposed in this
study is as follows:
H1: Liquidity (CR) has a significant effect on Return
on Asset (ROA) in manufacturing companies
listed on the Indonesia Stock Exchange.
H2: Asset Management (TATO) has a significant
effect on Return on Asset (ROA) in
manufacturing companies listed on the Indonesia
Stock Exchange.
H3: Cash Turnover has a significant effect on Return
listed on the Indonesia Stock Exchange.
H4: Capital Structure (DER) has a significant effect
on Return on Asset (ROA) in manufacturing
companies listed on the Indonesia Stock
Exchange.
H5: Liquidity (CR), Asset Management (TATO),
Cash Turnover and Capital Structure (DER)
simultaneously have a significant effect on
Return on Asset (ROA) in manufacturing
Exchange.
4. Research Methods
4.1 Research Approach
In this research approach, researchers used
quantitative research methods. According to

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(Sugiyono, 2011), quantitative research can be
defined as a research method based on the philosophy
of positivism, used for researching specific
populations or samples, sampling techniques are
generally carried out randomly, data collection using
research instruments, quantitative / statistical data
analysis with the aim of testing predetermined
hypotheses. The philosophy of positivism views that
phenomenon as concrete, observable and measurable.
Variable dependent in this study is liquidity proxies
with Current Ratio, Asset Management proxied by
Total Assets Turnover Ratio, Cash Turnover, and
Capital Structure which is proxied by the Debt to
Equity Ratio.
4.2 Population and Sample
According to (Sugiyono, 2011), population is a
generalization area consisting of objects / subjects
that have certain qualities and characteristics that are
determined by researchers to be studied and then
draw conclusions. The population in this study are
manufacturing companies listed on the Indonesia
Stock Exchange, amounting to 159 companies in
2016-2018
Table 2. Research Sample
No. Criteria Number
1. Manufacturing company listed on the Indonesia Stock Exchange 159
2. Manufacturing companies that do not publish their financial
reports regularly during the 2016-2018 period
42
3. Manufacturing companies that incur losses in the period
2016-2018
43
Number of Research Samples 85
Total Sample (company 85 x 3 years) 255
Source: www.idx.co.id (data processed)
4.3 Classic Assumption Test
Normality Test - The normality test aims to
determine whether in the regression model, the
confounding variable or residual. The normality test
is used to test whether the data is normally distributed
or not.
Table 3. Normality Test
4.4 Statistical Analysis
Based on the results of the Kolmogorov Smirnov
Test in table 3.1 above, it shows that the variables CR
(X 1), TATO (X 2), CT (X 3), DER (X 4) andROA
(Y) can be said to benormally distributed. This can be
seen from the results of the Kolmogorov Smirnov test
showing the Asymp value. Sig.(2-tailed) 0.903> 0.05.
Fig. 2: Normality Test – Histogram
Based on the graphic display histogram in Figure
2 above shows the curve line in the middle and not
dominant to the right or to the left so that it can be
said that the residuals generated distributed
throughout the normal way.

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Fig.3: Normality Test -Probability Plot
Based on graphic display normality probability
plot in the figure 3 can be seen that the points follow
the diagonal line so that it can be concluded that the
data is normally distributed.
Multicollinearity Test - The multicollinearity test
aims to test whether the regression model finds a
correlation between the independent variables.
Multicollinearity testing is done by looking at the VIF
between the independent variables.
Based on table below shows that the tolerance
value for the CR variable is 0.542> 0.10, the tolerance
value for the TATO variable is 0.944> 0.10, the
tolerance value for the CT variable was 0.769> 0.10,
and the tolerance value for the DER variable was
0.609 > 0.10. And the VIF value of the CR variable is
1.845< 10.00, the VIF value of the TATO variable is
1.059<10.00, the VIF value of the CT variable 1.301
<10.00, and the VIF value of the DER variable 1.641
<10.00. This can indicate that the tolerance value >
from 0.10 and the VIF value <than 10.00, then from
this result there is no multicollinearity and it can be
concluded that there is no correlation between the
independent variables.
Model
Unstandardized
Coefficients
Stand
ardize
d
Coeffi
cients
t Sig.
95,0%
Confidence
Interval for B Correlations
CollinearitySt
atistics
B
Std.
Error Beta
Lower
Bound
Upper
Bound
Zero-
order Partial Part
Tolera
nce VIF
1 (Constant) .027 .053 .512 .609 -.077 .131
SQRT_CR .078 .020 .344 3.797 .000 .037 .118 .387 .276 .254 .542 1.845
SQRT_TATO .110 .028 .271 3.941 .000 .055 .165 .269 .285 .263 .944 1.059
SQRT_CT -.003 .003 -.057 -.747 .456 -.010 .004 -.165 -.056 -.050 .769 1.301
SQRT_DER -.005 .026 -.016 -.189 .850 -.056 .046 -.228 -.014 -.013 .609 1.641
4.5 Heteroscedasticity Test
The regression test can be said to be good if there
is no heteroscedasticity problem, in this study the
researcher took the method by using a chart diagram
(scatter plot). With the premise of: 1). If there is a
specific pattern such as dots that form a particular
pattern of regular (wavy, widened, then narrowed), it
can be classified heteroscedasticity. 2). If there is no
clear pattern, as well as the points spread above and
below then it does not happen the problem of
heteroscedasticity.
Based on the display in the figure 4 above, it can
be seen that the dots are spread randomly no upward
or downward. Thus it can be concluded that there is
no heteroscedasticity problem. Fig. 4: Heteroscedasticity Test Scatterplot
Table 4. Multicollinearity Test

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Autocorrelation Test - Test autocorrelation aims
to test whether the linear regression model was no
correlation between errors intruder in period t with
bullies errors in the periods t-1 (previous). In this
research test used is the Model Summary, where if d
<dl or d> 4-dl then there is autocorrelation, then if du
< d < 4-du there is no autocorrelation and if dl <d <
du or 4-du <d <4- dl then there is no conclusion.
From the statistical results in table 5 above, it can
be seen that dl <d < du, namely 1.7109 <1.773
<1.8017, it can be concluded that there is no
autocorrelation.
4.6 Multiple Linear Regression Analysis Test
Multiple linear regression analysis is used to find
the relationship or influence between the independent
variables (Current Ratio, Total Asset Turnover, Cash
Turnover, and Debt to Equity Ratio) on the dependent
variable (Return on Asset). According to (Sanusi,
2011), multiple linear regression is basically an
extension of simple linear regression, namely
increasing the number independent variables
previously only became two or more independent
variables. In this study, there are four independent
variables (independent) and one dependent variable
(dependent). Thus, multiple linear regressions can be
formulated by:
𝑌 = 𝑎 + 𝑏1𝑋1 + 𝑏2𝑋2 + 𝑏3𝑋3 + 𝑏4𝑋4 + 𝑒
5. Results and Discussion
5.1 Partial t-test
The statistical test basically shows how far the
influence of one explanatory / independent variable
individually in explaining the variation of the
dependent variable. The null hypothesis (H0) to be
tested is whether a parameter (bi) is equal to zero.
Table 5. Partial test result
Model
Unstandardized
Coefficients
Standa
rdized
Coeffi
cients
t Sig.
B
Std.
Error Beta
1 (Constant) .027 .053 .512 .609
SQRT_CR .078 .020 .344 3.797 .000
SQRT_TATO .110 .028 .271 3.941 .000
SQRT_CT -.003 .003 -.057 -.747 .456
SQRT_DER -.005 .026 -.016 -.189 .850
From table 5 above shows 0.000<0.05, these
results indicate that CR is significant to ROA. Other
results can also be seen from the t count of 3,797>
from the t-table, namely amounting to 1,974. t table is
obtained by the formula (α / 2; nk-1), namely 0.025;
174 and the results of the tm table show 1.974. This
indicates accepting the stated alternative hypothesis
that an independent variable individually affects the
independent variable.
TATO test can also be seen from 0,000 <from
0.05, this shows that TATO is significant for ROA.
Meanwhile t arithmetic amounted to 3.941 > of t table
1.974 suggesting accept the alternative hypothesis
TATO. Furthermore, testing of CT can also be seen
from 0.456 > from 0.05, this shows that CT is not
significant for ROA. Meanwhile t arithmetic of -
0.747 < of t table 1.974 showed reject this alternative
hypothesis CT, and for testing the DER shows 0.850>
0.05, this result shows that DER is not significant to
ROA. And also this shows that rejecting the
alternative hypothesis can be seen that t count is -
0.189 <from t table 1.974.
5.2 F-Test
The F-test is used to show whether all the
independent variables included in the model have a
joint influence on the dependent variable.
Table 6. F-Test Result
From the results of statistical testing in table 6, it
can be seen that the significant value is 0.000 and less
than 0.05, and the result of F count shows a value of
12.291 which is greater than the F table of 2.42. F
table in the can of the results of the calculation of
F(k;nk)=F(4;176) and after being seen from the F
table it shows that the result is 2.42. From the results
of the statistical test, the independent variables CR

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(X1), TATO (X2), CT (X3) and DER (X4) affect the
dependent variable ROA (Y).
The Effect of Current Ratio to the Return On
Asset - From the partial test results, it can be seen
that the results of statistical testing through SPSS
show that the TATO test is seen from the Sig value in
the Coefficients table, namely 0.000 <0.05, these
results indicate that CR is significant on ROA. Other
results can also be seen from the t count of 3,797 >
from the t table which is equal to 1.974. The t table is
obtained by the formula (α / 2 ; nk-1), namely 0.025;
174 and the t table results show 1.974. This indicates
accepting the alternative hypothesis which states that
an independent variable individually affects the
independent variable.
The Effect of Total Asset Turnover Of Return On
Assets - From the partial test results, it can be seen
show that the TATO test is seen from the Sig value in
the Coefficients table is 0.000 <from 0.05, this
indicates that TATO is significant towards ROA.
While the t arithmetic amounted to 3.941> from t
table 1.974, this indicates accepting the alternative
hypothesis of TATO.
The Effect of Cash Turnover Of Return On Assets
- From the partial test results, it can be seen that the
results of statistical testing through SPSS show that
the CT test is seen from the Sig value in the
Coefficients table, namely 0.456> Of 0.05, regard this
indicates that the CT is not significant to ROA.
Meanwhile t count se large -0.747<t table 1.974
showed reject this alternative hypothesis CT.
The Effect of Debt to Equity Ratio Of Return On
Assets - From the partial test results, it can be seen
show that the DER test shows the Sig value on the
Coefficients table, namely 0.850> 0.05, this result
shows that DER is not significant to ROA. And also it
shows the men megrim alternative hypothesis can be
seen that t arithmetic of -0.189 <of t table 1.974.
6. Conclusion
From the results of this study the following
conclusions were obtained:
 Liquidity (Current Ratio) partially positive and
significant impact on Return on Asset (ROA) in
manufacturing companies that listed on the
Indonesia Stock Exchange, so the first hypothesis
(H1) in this study is accepted.
 Asset Management (Total Assets Turnover)
partially has a positive and significant effect on
Return on Asset (ROA) in manufacturing
Exchange, so that the second hypothesis (H2) in
this study is accepted.
 Turnover Cash (Cash Turnover) partially and no
significant negative effect on the Return on Asset
(ROA) in manufacturing companies listed in
Indonesia Stock Exchange, making it the third
hypothesis (H3) in this study ditola k.
 Capital Structure (Debt to Equity Ratio) partially
has a negative and insignificant effect on Return
listed on the Indonesia Stock Exchange, so the
fourth hypothesis (H4) in this study is rejected.
 Current Ratio, Total Asset Turnover, Cash
Turnover and Debt to Equity Ratio simultaneously
significant effect on Return on Asset (ROA) in
manufacturing companies listed in Indonesia Stock
Exchange, so hypothesis fifth (H5) in this study
received.
6.1 Suggestion
The suggestions put forward by the researcher
regarding the research results are:
 For manufacturing companies, management
should pay attention to Current Ratio and Total
Assets Turn over because these variables can
affect Return on Asset (ROA).
 For further research, it is advisable to download
handicraft study variables net income or size of the
company because the coefficient of determination
showed 78.1% of the variation of the dependent
variable (ROA) might be explained by variables
outside the research.
References
[1] Burksaitiene, D., Draugele, L. (2018), Capital structure
impact on liquidity management. International Journal
of Economics, Business and Management Research,
2(1), 110-127.

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