Volume-9 Issue-5, March 2020 – International Journal of Innovative Technology and Exploring Engineering (IJITEE)

E2528039520

IJITEE Journal — Mon, 14 Mar 2022 04:04:24 +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. Challenges of Industry and their Solutions, Communications #Advanced Manufacturing Technologies #Artificial Intelligence #Autonomous Robots #Augmented Reality #Big Data Analytics and Business Intelligence #Cyber Physical Systems (CPS) #Digital Clone or Simulation #Industrial Internet of Things (IIoT) #Manufacturing IoT #Plant Cyber Security #Smart Solutions – Wearable Sensors and Smart Glasses #System Integration #Small Batch Manufacturing #Visual Analytics #Virtual Reality #3D Printing # Internet of Things (IoT) #Internet of Things (IoT) & IoE & Edge Computing #Distributed Mobile Applications Utilizing IoT #Security, Privacy and Trust in IoT & IoE #Standards for IoT Applications #Ubiquitous Computing #Block Chain-enabled IoT Device and Data Security and Privacy #Application of WSN in IoT #Cloud Resources Utilization in IoT #Wireless Access Technologies for IoT #Mobile Applications and Services for IoT #Machine/ Deep Learning with IoT & IoE #Smart Sensors and Internet of Things for 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Modeling Method for Leveraging Data Quality in Healthcare Big Data
Madhu H. K.¹, D. Ramesh²
¹Madhu H. K.*, Assistant Professor, Department of Master of Computer Applications, Bangalore Institute of Technology, Bengaluru, India.
²Dr. D. Ramesh, Professor and HOD, Sri Siddaratha Academy of Higher Eduction, Tumkur, India.
Manuscript received on February 12, 2020. | Revised Manuscript received on February 22, 2020. | Manuscript published on March 10, 2020. | PP: 1373-1379 | Volume-9 Issue-5, March 2020. | Retrieval Number: E2528039520/2020©BEIESP | DOI: 10.35940/ijitee.E2528.039520
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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: An accurate diagnosis of the healthcare-based Big Data will always demand a significant level of quality in its input data itself, which is a serious level of concern in the area of healthcare analytics. Review of existing approaches shows that there has been various learning-based approaches being used for disease diagnosis which often ignores various issues viz. data aggregation, presence of error prone data, accuracy etc. Therefore, this paper presents a novel framework which offers cost effective modeling of the aggregation process of healthcare-big data followed by facilitating solution towards identifying and rectifying all the positions within a database system where there are presence of an error. The proposed system offer a mechanism where the error-prone data has been identified and substituted with data of better quality in order to offer better analytical outcomes. The study offers a strong baseline in order to leverage the data quality in healthcare big data.
Keywords: Analytics, Big Data, Data Aggregation, Error, Healthcare, Medical Data.
Scope of the Article: Big data analytics application systems

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E2470039520

IJITEE Journal — Mon, 14 Mar 2022 03:54:01 +0000

Texture Characterization and Classification to Detect Brain Tumor
N. Kavitha¹, Sai Sundara Amulya Ganti², K. Sudha Rani³, K. Mani Kumari⁴, B. Bhagya Sree⁵
¹N. Kavitha*, B.Tech, Department of ECE, Guru Nanak Engineering College, JNTUH, Hyderabad, Telangana, India.
²Sai Sundara Amulya Ganti, B.Tech, Department of EIE, VNR VJIET, Telangana, India.
³Dr. K Sudha Rani, Ph.D., Department of Biomedical Instrumentation, JNTU Kakinada, Andhra Pradesh, India.
⁴K. Mani Kumari, Pursing Ph.D., Osmania University, Hyderabad, Telangana, India.
⁵B. Bhagyasree, M. Tech, Department of Digital Systems, Osmania University, Hyderabad, Telangana, India.
Manuscript received on February 12, 2020. | Revised Manuscript received on February 22, 2020. | Manuscript published on March 10, 2020. | PP: 1370-1372 | Volume-9 Issue-5, March 2020. | Retrieval Number: E2470039520/2020©BEIESP | DOI: 10.35940/ijitee.E2470.039520
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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 the field of medical sciences, brain tumor detection has immense significance. Extraction of peculiar tumor portion along with certain features is possible with the use of methods that come under image processing. In the recent years techniques like segmentation and morphological have been undertaken to detect the set of unusual cells that grow in the brain which might be malignant or benign. This paper deals with characterization of texture to obtain Haralick features, with texture being the principle attribute of an image and finds lot of application in image processing. This involves the use of SVM classifier in the algorithm to classify texture in order to detect brain tumor. It has been tested for 70 images and statistical parameters have been calculated and the obtained accuracy is 97.1%, precision is 98.4% and sensitivity is 98%.
Keywords: GLCM, Haralick features, SVM Classifier, Texture Defect Detection
Scope of the Article: Classification

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E2894039520

IJITEE Journal — Mon, 14 Mar 2022 03:46:51 +0000

Design and Implementation of Strong Authentication Model to Improve Performance of Cloud Computing in Hybrid Cloud Environment
Anil Gupta¹, Durgesh Kumar Mishra²
¹Anil Gupta*, CSE Department, Mewar University, Chittorgarh (RJ), India.
²Durgesh Kumar Mishra, CSE Department, Sri Aurobindo Institute of Technology (SAIT), Indore, MP India.
Manuscript received on February 12, 2020. | Revised Manuscript received on February 22, 2020. | Manuscript published on March 10, 2020. | PP: 1323-1328 | Volume-9 Issue-5, March 2020. | Retrieval Number: E2894039520/2020©BEIESP | DOI: 10.35940/ijitee.E2894.039520
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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: Cloud computing became a part of everyday life. We allow us to support the device with accessibility across platforms regardless of the location. It has become vulnerable to several security threats because of public environment and internet participation. At this point the attack by hackers can break down the security and leak the information. Data is recognized in the cloud computing as an important corporate asset which must be safeguarded from unwanted internal or external threats. Data encryption provides data protection on sensitive data but also raises computation and memory overhead during large data processing on cloud.Cloud computing also requires low overhead operation to keep the computation as soon as possible. To authenticate the client we apply modified Kerberos protocol.
Keywords: Cloud, Cloud Computing, Data Protection, Kerberos protocol.
Scope of the Article: Cloud Computing

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E2156039520

IJITEE Journal — Mon, 14 Mar 2022 03:38:57 +0000

Modification of Рolymer Wastes and Obtaining Com Comрosites Based on Them Annotation
Shixaliyev Kerem
Shixaliyev Kerem, Department of Organic Substances and technology of macromolecular Compounds Chemical-Technological Faculty Azerbaijan State Oil and Industry University, Baku, Azerbaijan Baku, AZADLIQ Avenue.

Manuscript received on February 12, 2020. | Revised Manuscript received on February 22, 2020. | Manuscript published on March 10, 2020. | PP: 1072-1076 | Volume-9 Issue-5, March 2020. | Retrieval Number: E2156039520/2020©BEIESP | DOI: 10.35940/ijitee.E2156.039520
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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: The sources of scientific literature on the research have been investigated, various approaches to the processing and modification of polymers have been analyzed and the main direction of the study have been identified. The changes in the properties of thin layers of industrially produced low-density polyethylene (LDPE) grades 10803-20 and 16603-011 operating under different climatic conditions have been studied. It has been determined that changes with different intensities occur in the structure and in this connection in the physical and mechanical properties of LDPE that was in operation at different time intervals and in different climatic conditions of the Absheron Peninsula and the Aran region: melt flow index of polymer alloy, tensile strength at break, relative extension and values of other indicators are deteriorating. In polymer samples is formed 24,2 – 28,6% of insoluble phase and as a result of oxidation up to 12,3 – 16,8% carbonyl, hydroxyl and other functional groups.
Keywords: Polymer, Polyethylene, Recycling, Modification, Mineral Filler, Physical and Mechanical Properties, Waste, Ecology, Melting Temperature, Crumb Rubber, Polymer-Bitumen, Binder.
Scope of the Article: Mechanical Design

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E2418039520

IJITEE Journal — Mon, 14 Mar 2022 03:29:38 +0000

Efficient Horizontal Scaling of Databases using Data Sharding Technique
Ragul R¹, Arokia Paul Rajan R²
¹Ragul R, MSc Student, Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India.
²Dr Arokia Paul Rajan *, Associate Professor, Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India.
Manuscript received on February 12, 2020. | Revised Manuscript received on February 22, 2020. | Manuscript published on March 10, 2020. | PP: 590-593 | Volume-9 Issue-5, March 2020. | Retrieval Number: E2418039520/2020©BEIESP | DOI: 10.35940/ijitee.E2418.039520
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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: As the new era of computing technology begins with the start of the 21st century, the data produced by each person are increasing enormously day-by-day. Companies started to track one’s personal details from applications like media sharing, social networks, eCommerce, etc. since they have started to generate a lot of data traffic. The Database, where these heavy loads of generated data have been stored, had to scale out to handle the network traffic. Cloud computing arrival carves a solution for this trafficking problem by making data sharding a suitable option for scaling the data. This paper discusses about the importance of database sharding and the distribution of the database over the different server and also how sharding is helpful to scale the incoming amount of enormous data to control the data trafficking issues in the cloud database. Also compares the difference in the performance of database with and without sharding techniques using SQL server instance.
Keywords: Distributed Database, Scalability, Database Sharding, Horizontal Sharding
Scope of the Article: Software Engineering Techniques and Production Perspectives

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E2697039520

IJITEE Journal — Thu, 24 Feb 2022 06:32:32 +0000

Process Mining Based Software for Automated Business Process Discovery
Dharmendra Patel¹, Pranav Vyas²
¹Dr.Dharmendra Patel*, Smt. Chandaben Mohanbhai Patel Institute of Computer Applications, Charotar University of Sciecne and Technology, Changa, Ta Petlad, Dist Anand, Gujarat, India.
²Dr.Pranav Vyas, Smt. Chandaben Mohanbhai Patel Institute of Computer Applications, Charotar University of Sciecne and Technology, Changa, Ta Petlad, Dist Anand, Gujarat, India.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 22, 2020. | Manuscript published on March 10, 2020. | PP: 2413-2416 | Volume-9 Issue-5, March 2020. | Retrieval Number: E2697039520/2020©BEIESP | DOI: 10.35940/ijitee.E2697.039520
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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: Automated Business Process Discovery is a rising field that depends vigorously on computer software. Software do the automatic analysis of the several documents such as audits and event logs and generate useful, novel, hidden and fascinating information from that. The information produce from the software recognize the process model as well as investigates varieties and gives clients a vastly improved picture of what a particular business process resembles, and how changes would influence the business in general. This paper presents the common framework activities of process mining in the context to all well known software. The paper also describes the open source process mining software with their operational characteristics. Finally, paper represents the role of process mining software for various famous industries.
Keywords: Process Mining, Event Log, Process Discovery, Framework Activities
Scope of the Article: Quality assurance process, standards, and systems

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E2671039520

IJITEE Journal — Thu, 24 Feb 2022 06:24:15 +0000

An Effective Model to Alert a Drowsy Driver using Eye Closure Rate
Pavan Kumar Tadiparthi¹, Venkata Suma Priya Kankipati², Jessie Alekhya Kandimalla³, Priyanka Gottimukkala⁴, Pradeep Kumar Bheemavarapu⁵
¹Pavan Kumar Tadiparthi, Associate Professor in the department of information Technology, MVGR College of Engineering, Vizianagaram, Andhra Pradesh, India.
²Venkata Suma Priya Kankipati, under graduate in Information Technology, MVGR College of Engineering, Vizianagaram, Andhra Pradesh, India.
³Jessie Alekhya Kandimalla, Under Graduate in Information Technology, MVGR College of Engineering, Vizianagaram, Andhra Pradesh, India
⁴Priyanka Gottimukkala, Under Graduate in Information Technology, MVGR College of Engineering, Vizianagaram, Andhra Pradesh, India.
⁵Pradeep Kumar Bheemavarapu, Under Graduate in Information Technology, MVGR College of Engineering, Vizianagaram, Andhra Pradesh, India
Manuscript received on February 10, 2020. | Revised Manuscript received on February 20, 2020. | Manuscript published on March 10, 2020. | PP: 2409-2412 | Volume-9 Issue-5, March 2020. | Retrieval Number: E2671039520/2020©BEIESP | DOI: 10.35940/ijitee.E2671.039520
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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: Transportation plays a major role in today’s world. To move from one place to another place (long distances) which cannot be covered by walk, we use vehicles which consumes less time to reach destination. According to statistics, by 2050 the urban population will increase by 68% which leads to an increase in transportation that causes pollution and increase in the rate of road accidents. There are many methods and prevention measures to control pollution. The road accidents are caused due to distracted driving, high speed, drowsy driving and disobeying traffic rules. Among these, drowsy driving has been a cause for 20% of road accidents which is because of fatigue driving. In this article, a model is proposed based on image processing technique which is segmentation and a deep convolutional neural network architecture to improve the performance of the model when compared to the existing models. The proposed model works with better performance in different lighting conditions.
Keywords: Image Segmentation, Drowsiness, Convolutional Neural Network, Haar feature selection, driver fatigue.
Scope of the Article: Artificial Intelligent Methods, Models, Techniques

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E2631039520

IJITEE Journal — Thu, 24 Feb 2022 06:14:51 +0000

Predictive Model for Reservoir Level of Peruvannamuzhi Dam in India
Shyju. S¹, Lini Mathew²
¹Shyju. S*, Scholar, Department of Electrical Engineering, National Institute of Technical Teachers Training and Research (NITTTR), Chandigarh under Punjab University, Punjab, India.
²Dr. Lini Mathew, Professor, Department of Electrical Engineering, National Institute of Technical Teachers Training and Research (NITTTR), Chandigarh, Punjab, India.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 23, 2020. | Manuscript published on March 10, 2020. | PP: 2402-2408 | Volume-9 Issue-5, March 2020. | Retrieval Number: E2631039520/2020©BEIESP | DOI: 10.35940/ijitee.E2631.039520
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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: Peruvannamuzhi Dam is built on the Kuttiady River at Kozhikode district, Kerala, India. The main purpose of the dam is to store water safely for irrigation and to control flood at the downstream area. Hydroelectric power plant is not associated with this Dam. At present, the basic dam operation parameters like reservoir level, rain data, and outflow rate are measured manually. There is no provision for inflow measurement. Most of the state reservoirs are almost full when heavy rainfall occurs during monsoon. The opening and closing of the gates of the dam depend only on the current water level of the reservoir. So the dam operators are forced to open suddenly all the shutters of the dam when water reaches Full Reservoir Level and this is a compensatory procedure. The sudden release of water simultaneously from different reservoirs caused flood in Kerala. This paper presents Internet of Things based automatic monitoring of different parameters for dam management and predict reservoir level after a particular period, say 2 hours. The above specified dam parameters is measured with different sensors like ultrasonic sensor and rain sensor. NodeMCU upload the measured data to cloud. Thing speak IoT platform provide these data to user. Thing speak provide MATLAB link and which analyze and predict reservoir level. These data and predictions are easily available to dam authorities, dam researchers, farmers and public through their mobile phone or Personal Computer. With the help of this prediction, the operator can open the shutter in an anticipatory manner. This predictive model can be used for flood control.
Keywords: Internet of Things, Dam automation, Thingspeak, Ultrasonic sensor, NodeMCU, Prediction.
Scope of the Article: Probabilistic Models and Methods

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E2533039520

IJITEE Journal — Thu, 24 Feb 2022 06:08:32 +0000

Genetic Algorithms for Localization in WSN
Gondhi Navabharat Reddy¹, Sruthi Setlam², D Kiran Kumar³, A Navya, Natasha Saude⁴
¹Gondhi Navabharat Reddy*, Assistant Professor, Department of ECE, Vignan Institute of Science and Technology, Bhuvanagiri, Telangana, India.
²A Navya, Assistant Professor, Department of ECE, Vignan Institute of Science and Technology, Bhuvanagiri, Telangana, India.
³D Kiran Kumar, Assistant Professor, Department of ECE, Vignan Institute of Science and Technology, Bhuvanagiri, Telangana, India.
⁴Sruthi Setlam, Assistant Professor, Department of ECE, Vignan Institute of Science and Technology, Bhuvanagiri, Telangana, India.
⁵Natasha Saude, Assistant Professor, Department of ECE, Vignan Institute of Science and Technology, Bhuvanagiri, Telangana, India.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 26, 2020. | Manuscript published on March 10, 2020. | PP: 2307-2401 | Volume-9 Issue-5, March 2020. | Retrieval Number: E2533039520/2020©BEIESP | DOI: 10.35940/ijitee.E2533.039520
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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 wireless sensor networks, localization is a way to track the exact location of sensor nodes. Occasionally node localization may not be accurate due to the absence or limitation of anchor nodes. To reduce the mean localization error, soft computing techniques such as BAT and bacterial foraging driven bat algorithm (BDBA) are utilized in literature. For better localization with reduced error, in this paper, firefly driven bat algorithm (FDBA) is proposed, which combines the heuristic of firefly and BAT algorithms. Our proposed FDBA algorithm provides better localization in terms of error of 60% and 40 % less error as compared to BAT and BDBA algorithm, respectively.
Keywords: Localization, BAT Algorithm, BDBA Driven Bat Algorithm, Firefly Driven Bat Algorithm, mean Localization Error, Wireless Sensor Network.
Scope of the Article: Energy Harvesting and Transfer for Wireless Sensor Networks

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E2438039520

IJITEE Journal — Thu, 24 Feb 2022 05:57:26 +0000

Problem Faced by Precast Industry to Implement in Residential Construction
Vaishali Rama Turai¹, Ajit R. Patil²
¹Vaishali Rama Turai*, Civil Engineering department, JJT University, City,Jaipur, Country, India.
²Dr. Ajit Rajdhar Patil, Civil Engineering department, G H Raisoni College of Engineering and Research, City-Pune, Country-India.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 27, 2020. | Manuscript published on March 10, 2020. | PP: 2394-2396 | Volume-9 Issue-5, March 2020. | Retrieval Number: E2438039520/2020©BEIESP | DOI: 10.35940/ijitee.E2438.039520
Open Access | Ethics and Policies | Cite | Mendeley
© 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: The Precast industry is booming industry now a day, but then also the implementation ratio of precast member in residential construction work is not up to the mark. As we all know that precast having numerous advantages over the cast in situ construction method, for example it saves the total time of construction which indirectly reduces the cost of construction but still we are lagging behind in implementation of precast in it. In this research we have listed out some problem which can be cause of less implementation of precast in residential construction buildings. As discussed in paper, there are so many factors are affected on Implementation of Precast in Residential Construction Sector For example: Technical Issues and General Issues. In Technical Issues Joint stability problem during Erection, Standard size of precast element, Leakage Issues, Design change related problem, Requirement of Standard Rate per Panel, End user Profit, Additional Taxes, General Issues are: Transportation of Precast Element, Loading and Unloading problems, Transportation to sight, Storage Area, Skilled Labour Research has done and data is collected through Questionery survey, Field survey, and research survey.
Keywords: Precast Industry is Booming Industry, Numerous Advantages, Lagging Behind in Implementation of Precast, less Implementation.
Scope of the Article: Challenges of Industry and their Solutions, Communications.

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