Volume-11 Issue-9, August 2022 – International Journal of Innovative Technology and Exploring Engineering (IJITEE)

J10610881019

IJITEE Journal — Fri, 14 Oct 2022 07:15:52 +0000

The International Journal of Innovative Technology and Exploring Engineering (IJITEE) has ISSN: 2278-3075 (online), an open-access, peer-reviewed, periodical monthly international journal. This journal is published by Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) in January, February, March, April, May, June, July, August, September, October, November and December.The journal aims to publish high–quality peer–reviewed original articles in the area of Engineering and Technology that covers Computer Science and Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering and all interdisciplinary streams of Engineering and Technology. #Smart Computing and Information Processing #Signal and Speech Processing #Image Processing and Pattern Recognition #WSN#Artificial Intelligence and machine learning #Data mining and warehousing #Data Analytics #Deep learning #Bioinformatics #High Performance computing #Advanced Computer Networking #Cloud Computing #IoT #Parallel Computing on GPU #Human Computer Interactions #Recent Trends in Microelectronics and VLSI Design #Process & Device Technologies #Low-Power Design #Nanometer-Scale Integrated Circuits #Application specific ICs (ASICs) #FPGAs #Nanotechnology #Nano Electronics and Quantum Computing #3. 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Experimental Analysis for Semantic Based Large Scale Service Composition using Deep Learning
Jackulin Sam Gini. A¹, A. Chandrasekar²
¹Jackulin Sam Gini.A, Research Scholar, Sathyabama Institute of Science and Technology, Chennai-600 119, India
²A. Chandrasekar, Professor& Head, Department of Computer Science Engineering, St. Joseph’s College of Engineering, Old Mamallapuram Road, Chennai-600 119, India
Manuscript received on 01 August 2019 | Revised Manuscript received on 05 August 2019 | Manuscript published on 30 August 2019 | PP: 4280-4283 | Volume-8 Issue-10, August 2019 | Retrieval Number: J10610881019/19©BEIESP | DOI: 10.35940/ijitee.J1061.0881019
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: In Service Oriented Architecture (SOA) web services plays important role. Web services are web application components that can be published, found, and used on the Web. Also machine-to-machine communication over a network can be achieved through web services. Cloud computing and distributed computing brings lot of web services into WWW. Web service composition is the process of combing two or more web services to together to satisfy the user requirements. Tremendous increase in the number of services and the complexity in user requirement specification make web service composition as challenging task. The automated service composition is a technique in which Web Service Composition can be done automatically with minimal or no human intervention. In this paper we propose a approach of web service composition methods for large scale environment by considering the QoS Parameters. We have used stacked autoencoders to learn features of web services. Recurrent Neural Network (RNN) leverages uses the learned features to predict the new composition. Experiment results show the efficiency and scalability. Use of deep learning algorithm in web service composition, leads to high success rate and less computational cost.
Keywords: Web Services, Web Service Composition, Semantic Web, Deep Learning.
Scope of the Article: Deep Learning

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G92210811922

IJITEE Journal — Sat, 20 Aug 2022 09:26:14 +0000

Exploring the Potential of Natural Compounds Against Pro-Inflammatory Cytokine Proteins Involved in Worsening COVID-19 and Against COVID-19 Proteins: An in Silico Approach
Aayushka P. Nashte¹, Sneha T. Patnawar², Guruprasad N. Kulkarni³, Pallavi S. Patil⁴
¹Aayushka Nashte, Student, Department of Biotechnology Engineering, Kolhapur Institute of Technology’s (Autonomous), College of Engineering Kolhapur (Maharashtra), India.
²Sneha T. Patnawar, Assistant Professor, Department of Biotechnology Engineering, Kolhapur Institute of Technology’s (Autonomous), College of Engineering Kolhapur (Maharashtra), India.
³Guruprasad Kulkarni, Student, Department of Biotechnology Engineering, Kolhapur Institute of Technology’s (Autonomous), College of Engineering Kolhapur (Maharashtra), India.
⁴Dr. Pallavi S. Patil, Associate Professor & Head, Department of Biotechnology Engineering, Kolhapur Institute of Technology’s (Autonomous), College of Engineering Kolhapur (Maharashtra), India.
Manuscript received on 20 July 2022 | Revised Manuscript received on 13 August 2022 | Manuscript Accepted on 15 August 2022 | Manuscript published on 30 August 2022 | PP: 74-98 | Volume-11 Issue-9, August 2022. | Retrieval Number: 100.1/ijitee.G92210811922 | DOI: 10.35940/ijitee.G9221.0811922
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a causative agent for global pandemic disease nCOVID’19, has directed the attention of the scientific community towards the development of effective vaccines and drugs. Attempts are being made for repurposing existing drugs known for their antiviral activities, and test the traditional herbal medicines, natural compounds, nutraceuticals known for their health benefiting and immune-boosting activity against SARSCoV-2. Multidimensional approach is employed to find effective drugs against SARS-CoV2. In this study, 108 natural compounds were examined in inhibiting human protein targets which are responsible for worsening the condition of COVID-19 through a virtual screening approach. This study has screened the natural compounds 3-Hydroxydecanedioic acid, Cucurbitacin S, Diosgenin which may exhibit inhibitory activity against IL-1 receptor,IL-6 receptor respectively. It has also revealed that Dihydro-beta-ergocryptine and cucurbitacin S may exhibit inhibitory activity against TNF-alpha receptor. Further it concluded that exfoliazone shows better inhibitory activity against P38-MAPK and NF-κB pathway inhibitory proteins. These natural compounds could lead the way for future drug discovery, design and development. This will help researchers to scout new drugs in drug discovery(in-vitro and in-vivo studies).
Keywords: Docking Studies, Insilico Analysis, Covid-19, Cytokine Storm, Natural Compounds.
Scope of the Article: Bio-Science and Bio-Technology

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G92480811922

IJITEE Journal — Sat, 20 Aug 2022 09:16:12 +0000

Effect of Carbon Nanotubes Volume Fraction on the Deflections and Stresses of Laminated Hybrid Composite Plates using First-Order Shear Deformation Theory
D. Dhanunjaya Raju¹, V. V. Subba Rao²
¹D. Dhanunjaya Raju, Ph.D Research Scholar, Department of Mechanical Engineering, University College of Engineering Kakinada, Jawaharlal Nehru Technological University Kakinada, Kakinada (A.P), India.
²Dr. V. V. Subba Rao, Professor, Department of Mechanical Engineering, University College of Engineering Kakinada, Jawaharlal Nehru Technological University Kakinada, Kakinada (A.P), India.
Manuscript received on 01 August 2022 | Revised Manuscript received on 16 August 2022 | Manuscript Accepted on 15 August 2022 | Manuscript published on 30 August 2022 | PP: 67-73 | Volume-11 Issue-9, August 2022. | Retrieval Number: 100.1/ijitee.G92480811922 | DOI: 10.35940/ijitee.G9248.0811922
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Carbon Nanotubes reinforced composite materials are widely used in aerospace engineering due to high strength-to-weight ratios, which can be tailored per requirements. The behaviour of the composite structures depends on various factors like, Lamination scheme, Ply orientation and loading conditions. In the present work, Static deflection and stresses analysis of a Carbon Nano Tube reinforced laminated hybrid composite plate extends the First Order Shear Deformation Theory with variable ply angles and volume fractions of CNT. A micromechanics model based on the Mori-Tanaka method is used to calculate the properties of CNT reinforced laminate. In this analysis, hybrid composite plates mainly consist of Graphite/epoxy, Kevlar/epoxy and CNT/ polystyrene sub-laminates. The lamination scheme and CNT volume fraction play a vital role in the non-dimensional deflections and stresses. The main intention of this work is to enhance the suitability of CNT reinforced Hybrid composite plates under static loading for structural applications.
Keywords: Carbon Nanotubes, CNT Reinforced Laminates, Hybrid Composites, Static Deflection, Stress Analysis.
Scope of the Article: Hybrid Composites

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G92450811922

IJITEE Journal — Sat, 06 Aug 2022 12:31:02 +0000

Enhancing AES with Key Dependent S-Box and Transpose MDS Matrix
S Fazila¹, B Reddaiah², S Sai Ramya³, B J Job Karuna Sagar⁴, C Swetha⁵
¹S Fazila, Department of Computer Science and Technology, Yogi Vemana University, Kadapa, India.
²B Reddaiah, Department of Computer Science and Technology, Yogi Vemana University, India.
³S Sai Ramya, Department of Computer Science and Technology, Yogi Vemana University, Kadapa, India.
⁴B J Karuna Sagar, Department of Computer Science and Technology, Yogi Vemana University, kadapa, India.
⁵C Swetha, Department of Computer Science and Technology, Yogi Vemana University, Kadapa, India.
Manuscript received on 30 July 2022 | Revised Manuscript received on 06 August 2022 | Manuscript Accepted on 15 August 2022 | Manuscript published on 30 August 2022. | PP: 61-66 | Volume-11 Issue-9, August 2022. | Retrieval Number: 100.1/ijitee.G92450811922 | DOI: 10.35940/ijitee.G9245.0811922
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Privacy is an important feature in transmission of data. Due to continuous flow of data over network, there may be a chance of attacks on sensitive data, either passive or active. To provide security, Cryptography plays a vital role by scrambling data into unreadable form. Several techniques have been developed to provide security for digital data transmission. AES is one of the efficient encryption engaged for the past decade. There is every need to improve its security mechanism periodically. In this paper we implement Transpose mix column and S-BOX Rotation to enhance efficiency. This proposed model provides more Avalanche Effect than traditional AES. Moreover, Diffusion and Confusion are crucial in Cryptography. The more the diffusion rate the more secure the data is and is shown in this proposed model. However, S-Box rotation provides more confusion. AES is most efficiently used in Wi-Fi, particularly in WPA2-PSK(AES), so enhancing security mechanism is needed.
Keywords: AES, Transpose Mix Column, S-Box Rotation, Symmetric Encryption.
Scope of the Article: Encryption Methods and Tools

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G92440811922

IJITEE Journal — Sat, 06 Aug 2022 10:02:41 +0000

Analysis of the Performance of Various Load Balancing Algorithms for SDN in Data Center Networks
Sujayanth K Vishwakarma¹, Pavithra H²
¹Sujayanth K Vishwakarma, Department of Computer Science and Engineering, RV College of Engineering Bengaluru (Karnataka), India.
²Pavithra H, Department of Computer Science and Engineering, RV College of Engineering Bengaluru (Karnataka), India.
Manuscript received on 30 July 2022 | Revised Manuscript received on 04 August 2022 | Manuscript Accepted on 15 August 2022 | Manuscript published on 30 August 2022. | PP: 55-60 | Volume-11 Issue-9, August 2022. | Retrieval Number: 100.1/ijitee.G92440811922 | DOI: 10.35940/ijitee.G9244.0811922
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: In order to fulfil the demands of modern IT, it is necessary to address the many limitations of current networking infrastructures. Software Defined Network (SDN) is the new networking technique to get around these constraints. Existing Network Infrastructures use static switches, which results in a substandard utilization of the network resources, which is one of the main problems. The main issues with the present networking trend are the slow response and delays. In this research different load-balancing algorithms for Software Defined Network based Data Center network is implemented to overcome mentioned issues. The POX controller is used in the implementation and the Mininet emulator is used to simulate the network functionality. The load balancing algorithms program is written in the Python language, which is also used for creation of the network topology. Finally, Siege HTTP load testing tool is used to test network performance. The testing is focused on Quality of Service pa- rameters transaction rate, average response time and throughput. The results show that the Weighted algorithm outperforms all other considered algorithms.
Keywords: Load Balancing Algorithms, Software Defined Net-Working, Data Center Network, POX Controller
Scope of the Article: Data Analytics Modelling and Algorithms

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G92470811922

IJITEE Journal — Sat, 06 Aug 2022 09:56:30 +0000

Volatility Clustering of Select Sectoral Indices in the BSE Stock Market
D. Vijayalakshmi¹, G. C. Thanya²
¹Dr. D. Vijayalakshmi, Department of Commerce, PSGR Krishnammal College for Women, Coimbatore (Tamil Nadu), India.
²Thanya. G.C, Department of Commerce, PSGR Krishnammal College for Women, Coimbatore (Tamil Nadu), India.
Manuscript received on 30 July 2022 | Revised Manuscript received on 03 August 2022 | Manuscript Accepted on 15 August 2022 | Manuscript published on 30 August 2022. | PP: 47-54 | Volume-11 Issue-9, August 2022. | Retrieval Number: 100.1/ijitee.G92470811922 | DOI: 10.35940/ijitee.G9247.0811922
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Volatility is a standard measure of financial vulnerability and it plays a vital role in analyzing the risk of the securities market. It is traditionally measured using the standard deviation, which indicates how the price of a stock is clustered around the mean or moving average. The intent of the study is to analyse the volatility clustering of six select sectoral indices such as S&P BSE AUTO (Automobile), S&P BSE BANKEX (Bank) , S&P BSE FMCG (Fast Moving Consumer Goods), S&P BSE IT (Information Technology), S&P BSE METAL ( Metals), and S&P BSE OIL & GAS (Oil & Gas Industries). A sample of 2726 days of observations for 11 years period from 03.01.2011 to 31.12.2021 has been taken for the study. The econometric model namely ARCH and GARCH have been applied to analyse the data. The result of the study reveals the presence of volatility clustering in the select six sectoral indices. Metal Sector has shown the higher phase of volatility.
Keywords: Volatility Clustering, Sectoral Indices, ARCH Model, GARCH Model
Scope of the Article: Clustering

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G92430811922

IJITEE Journal — Sat, 06 Aug 2022 09:50:50 +0000

GLCM and LSTM Recurrent Neural Networks Integrated with Machine Learning Techniques to Identify Plant Disease
Nithyananda B Devadiga¹, Akshatha K N²
¹Nithyananda B Devadiga, Department of Computer Science, R N Shetty PU College, Kundapura (Karnataka), India.
²Akshatha K N, Department of Botany, RN Shetty PU College, Kundapura (Karnataka), India.
Manuscript received on 28 July 2022 | Revised Manuscript received on 01 August 2022 | Manuscript Accepted on 15 August 2022 | Manuscript published on 30 August 2022. | PP: 44-46 | Volume-11 Issue-9, August 2022. | Retrieval Number: 100.1/ijitee.G92430811922 | DOI: 10.35940/ijitee.G9243.0811922
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Plant diseases are very impactful towards the overall effectiveness and quality management of the agricultural sector. In recent years, deep learning methods have been used as a way to identify these diseases, based on neural networks. The study presents GLCM and LSTM Recurrent Neural Networks Integrated with Machine Learning towards the identification of plant diseases. It has been found that the process is very accurate and can assess diverse plants disease characteristics dataset as well.
Keywords: GLCM and LSTM System, Deep Learning, RNN, Plant Disease Identification
Scope of the Article: Deep Learning

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G91900811922

IJITEE Journal — Sat, 30 Jul 2022 11:59:38 +0000

A Review on Quality Assurance in Mobile Ad Hoc Network and Applications
Rohit Katiyar¹, Mohit Gangwar², Vineet Kumar Singh³
¹Rohit Katiyar, Lecturer, Department of Information Technology, Government Polytechnic, Aurai, Bhadohi India.
²Dr. Mohit Gangwar, Professor & Head, Department of Information Technology, IET, Dr. RMLA University, Ayodhya (UP) India.
³Vineet Kumar Singh, Assistant Professor, Department of Information Technology, IET, Dr. RMLA University, Ayodhya (UP) India.
Manuscript received on 05 July 2022 | Revised Manuscript received on 30 July 2022 | Manuscript Accepted on 15 August 2022 | Manuscript published on 30 August 2022 | PP: 40-43 | Volume-11 Issue-9, August 2022 | Retrieval Number: 100.1/ijitee.G91900811922 | DOI: 10.35940/ijitee.G9219.0811922
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Mobile Ad Hoc Network (MANET) routing is highly dynamic network & challenged by power and bandwidth constrained as well as frequent topology changes to which it must adapt to and converge quickly. As we know that it is not possible to give a continuous significant amount of power to mobile devices of MANET so that it could be active for long time. As MANET nodes are powered by batteries with limited lifetime. One of the most existing challenge for MANET is in this Energy constrained to increase the time between recharging. It is possible if and only if energy consumption in communication is less without increasing interruption or packet loss.
Keywords: Mobile, Ad Hoc Network, MANET, Dynamic, Energy Consumption, Packet Loss, Frequent Topology
Scope of the Article: Mobile Adhoc Network

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G92130811922

IJITEE Journal — Fri, 22 Jul 2022 11:13:35 +0000

Sustain our Variable Population Through Differential Evolution Algorithm and Agriculture Innovation
A. Ranjeeth¹, M. Sujitha²
¹A. Ranjeeth, Assistant Professor, Department of Computer Science and Engineering, IFET College of Engineering Villupuram (Tamil Nadu), India.
²M. Sujitha, UG Scholar, Department of Computer Science and Engineering, IFET College of Engineering Villupuram (Tamil Nadu), India.
Manuscript received on 15 July 2022 | Revised Manuscript received on 21 July 2022 | Manuscript Accepted on 15 August 2022 | Manuscript published on 30 August 2022 | PP: 36-39 | Volume-11 Issue-9, August 2022 | Retrieval Number: 100.1/ijitee.G92130811922 | DOI: 10.35940/ijitee.G9213.0811922
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Agriculture is a subdivision that provides considerably to the business-related incident of our country. As the world’s population grows, Agricultural production has overtaken all other industries as its most significant source of growth. Because the need for food was lower in the 1970’s due to a smaller population compared to today’s population statistics, that think that the world’s public will extend to 9.9 billion by 2050, humanity must plan for agricultural production to feed these exponentially growing numbers. Therefore, we need huge agricultural lands to produce the grains and crops necessary for the current population. But much of the formerly arable land has been abandoned during periods of drought, and due to poor planning and poor water management during the dry season, saving arable land from drought is inevitable. Making a structured plan for water management can be one solution to mitigating large amounts of farmland disappearing. Only 12% of the land can be used for agriculture, but we need to feed 9.9 billion people 70% more food by 2050. That’s enough to feed 10 billion people (we’re at 7.6 billion right now). Despite this excess, hunger persisted. So we have come up with the Differential Evolution Algorithm, which predicts the future population’s food needs based on past and present agricultural data. This helps to solve hunger in the near future and also helps limit drought on land. This study aims to highlight the importance of agricultural information systems for agricultural development, identify the strengths and weaknesses of current systems, and provide recommendations for improving their performance. We will return to the results of previous studies on this issue. Finally, general conclusions about farm information systems are highlighted, suggesting implications for better farm information systems.
Keywords: Agriculture, Differential Evolution Algorithm, population, predict
Scope of the Article: Sustainable Structures

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G91890811922

IJITEE Journal — Fri, 22 Jul 2022 11:01:25 +0000

Implementation of an Educational Chatbot using Rasa Framework
Supreetha H V¹, Sandhya S²
¹Supreetha H V, Department of Computer Network Engineering at RV College of Engineering (RVCE) Bengaluru (Karnataka), India.
²Dr. Sandhya S, Assistant Professor, Department of Computer Science and Engineering, RV College of Engineering (RVCE) Bengaluru (Karnataka), India.
Manuscript received on 04 July 2022 | Revised Manuscript received on 17 July 2022 | Manuscript Accepted on 15 August 2022 | Manuscript published on 30 August 2022 | PP: 29-35 | Volume-11 Issue-9, August 2022 | Retrieval Number: 100.1/ijitee.G91890811922 | DOI: 10.35940/ijitee.G9189.0811922
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Abstract: The growth in Artificial Intelligence (AI), Big-data,and Internet-of-Things (IOT) technologies has increased chabot’s application in many areas. Some of the applications of chatbot can be seen in areas such as social media, e-commerce, healthcare, stock market, education, banking sector etc. Most of the high-end chatbots are deployed inside e-commerce, banking and health websites. There is a need to deploy the chatbots in educational website to improve interactivity of the educational platforms. The main target users of this website is rural students. In rural areas, probability of students dropping school after some age is common because, there won’t be proper monitoring of students and also sometimes facilities will be less. With e- learning, anyone can learn everything with limited cost. The key insight of developing this e-learning website is to provide a chatbot which can motivate rural students towards education. Thus a single platform where users can learn different courses, take quizzes, and chat with the bot is developed. It also provides an additional facility of tracking the scores of the quizzes and giving personalized recommendation systems to improve the scores. The chatbot will also help users to find details aboutfaculties and help users to set an appointment with distant faculties in online mode for doubts clarification. Flask micro- framework is used for developing the website. Firebase is used to store the data. RASA framework is used in developing the chatbot. Finally a content based filtering is used to givepersonalized recommendation systems.
Keywords: Chatbots, Content based learning, Firebase, Natural Language Processing, Recommendation Systems, RASA.
Scope of the Article: Natural Language Processing

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