Volume-9 Issue-4, February 2020 – International Journal of Innovative Technology and Exploring Engineering (IJITEE)

B7435129219

IJITEE Journal — Thu, 24 Mar 2022 04:20:36 +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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Achieving Authentication of Remotely Stored Data
Vidyullata Devmane¹, B.K.Lande², Jyoti Joglekar³, Hemant Kothari⁴
¹Vidyullata Devmane*, PhD Scholar of Computer Engineering Department, PAHERU Udaipur, Rajasthan, India.
²Dr. B.K.Lande, Professor in Electronics Department, Datta Meghe College of Engineering, Airoli, Navi Mumbai, India.
³Dr. Jyoti Joglekar, Department, Professor in Computer Engineering Departement, K.J.Somaiya College of Engineering, Mumbai, India
⁴Dr. Hemant Kothari, Professor at PAHERU, Udaipur, Rajasthan, India.
Manuscript received on December 17, 2019. | Revised Manuscript received on December 23, 2019. | Manuscript published on January 10, 2020. | PP: 882-884 | Volume-9 Issue-3, January 2020. | Retrieval Number: B7435129219/2020©BEIESP | DOI: 10.35940/ijitee.B7435.019320
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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: Data storage over cloud is a demanding service but it is vulnerable for various attacks. It is required to provide strong security to this data. An authenticity of the data on remote storage is necessary but data owners have to trust on cloud storage for their outsourced data. There is a need of some mechanism to check correctness of the data without compromising its confidentiality. We have studied homomorphic algorithms so that can be applied on the cipher text. Aim of the framework designed here is to enable any authentic user or auditor to verify hash value generated by owner with the hash value of encrypted data which is stored at remote storage for assuring authentication of the data. This can be done with strong mathematical cryptographic algorithms those can be used in the public environment and can provide better performance complexity.
Keywords: Cloud Storage Data Authenticity, Homomorphic Algorithms, Hash tree.
Scope of the Article: Authentication, authorization, Accounting

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C8308019320

IJITEE Journal — Wed, 23 Mar 2022 06:51:48 +0000

Identification of Fatal Familial Insomnia Sleep Disorder Using Ga Optimization with Svm Classifier
Sudha Ramya Karri¹, Daisy Rani Alli², Annepu Bhujanga Rao³
¹Sudha Ramya Karri*, Lecturer, Department of EIE, Govt Polytechnic for Women, Srikakulam, AP, India
²Daisy Rani Alli, Assistant Professor, Dept. of IT, Andhra University, Visakhapatnam, AP, India.
³Annepu Bhujanga Rao, Professor, Dept. of IT, Andhra University, Visakhapatnam, AP, India.
Manuscript received on December 16, 2019. | Revised Manuscript received on December 22, 2019. | Manuscript published on January 10, 2020. | PP: 651-657 | Volume-9 Issue-3, January 2020. | Retrieval Number: C8308019320/2020©BEIESP | DOI: 10.35940/ijitee.C8308.019320
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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: Among the genetic disorders in brain Fatal familial insomnia (FFI) is one of the rare disorder. The inability of sleeping is called as FFI which leads to the process of becoming progressively worse mentally and physically. The recordings of brain functioning is known as Electroencephalogram (EEG) and which seeks a vital role in observing and finding the sleep disorders like FFI. The EEG senses the brain functionality. In this paper , genetic optimization based SVM classifier is used to identify the sleep disorder. The proposed technique is used to optimize the features which are obtained from features reduction techniques like PCA, ICA and LDA. For performing the experimental results Physio Net database is obtained. Preprocessing of the data, performing feature reduction, next feature optimization and then classification using Support vector machine is the process for identifying the FFI. The performance measures such as accuracy, sensitivity, specificity, F1-Score, Recall are computed . The comparison of results obtained using different condition are observed and the ranges are determined for normal class, Bruxism class and FFI class.
Keywords: EEG Signals, Insomnia Sleep Disorder, Genetic Algorithm, SVM Classifier.
Scope of the Article: Algorithm Engineering

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C8139019320

IJITEE Journal — Wed, 23 Mar 2022 06:37:34 +0000

Methods of Image Retrieval Based Cloud
Shaimaa H. Shaker¹, Nuha M. Khassaf²
¹Nuha M. Khassaf*, Computer Sciences, Iraqi Commission for Computer & Informatics Institute for Postgraduate Studies, Baghdad, Iraq.
²Asst. Prof. Dr. Shaimaa H. Shaker , University of Technology,Baghdad, Iraq.
Manuscript received on December 19, 2019. | Revised Manuscript received on December 30, 2019. | Manuscript published on January 10, 2020. | PP: 637-644 | Volume-9 Issue-3, January 2020. | Retrieval Number: C8139019320/2020©BEIESP | DOI: 10.35940/ijitee.C8139.019320
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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: Content Based Image Retrieval (CBIR) introduces for the time being the most used system allowing detecting the visual features of images by using processing techniques due to the challenges of recovering photos depending on the text.The essential mechanism of content based image retrieval system is analysis picture information with low-level feature of an image, including colour, texture, determining edges, that has stages which includes the stage of classification of images, then the stage of extracting features and the stage of finding similar images and finally, evaluating the system and according to the technique or techniques used in each stage from stages of content based image retrieval system to increase the accuracy and speed of recovery for images ,together can using advantages cloud computing, which can provide a number of integrated computer services without being restricted to local resources, which include storage space, processing capabilities, expansion, flexibility and other various services through the Internet and deal with cloud through the platform as a user interface such as Microsoft Azure , Google Cloud platform, Amazon Web Services and others. In this paper, an analytical study of many CBIR techniques in terms of their behavior, feature extraction and work on Cloud Computing. All these techniques have their own avails as well as certain limitations, that means, there is not a one technique that suits superior all sorts of user’s needs,but using of advantages of cloud increase the improvement of performance. The focus be on the technique (features of Principle Component Analysis (PCA)) with advantages of Cloud Computing as a helpful way to extraction the features as shown in the results derived from the comparison.
Keywords: Cloud Computing, Content-based Image Retrieval , feature extraction, PCA technique.
Scope of the Article: Cloud Computing

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L32481081219

IJITEE Journal — Mon, 21 Mar 2022 12:31:20 +0000

Optimization of the Extraction of Shea Butter from Shea Nut using Ovat Methodology
Omodara O. J.¹, Emetere M. E.², Ojewumi M. E.³, Obanla O. R.⁴, Bolujo E. O.⁵
¹Omodara O.J.*, Department of Chemical Engineering, Covenant University, Ota, Ogun State, Nigeria.
²Emetere M.E., Department of Physics, Covenant University, Ota, Ogun State, Nigeria. & Department of Mechanical Engineering Science, University of Johannesburg, South Africa.
³Ojewumi M.E., Department of Chemical Engineering, Covenant University, Ota, Ogun State, Nigeria.
⁴Obanla O.R., Department of Chemical Engineering, Covenant University, Ota, Ogun State, Nigeria.
⁵ Bolujo E.O., Department of Petroleum Engineering, Covenant University, Ota, Ogun State, Nigeria.
Manuscript received on January 16, 2020. | Revised Manuscript received on January 22, 2020. | Manuscript published on February 10, 2020. | PP: 403-410 | Volume-9 Issue-4, February 2020. | Retrieval Number: L32481081219/2020©BEIESP | DOI: 10.35940/ijitee.L3248.029420
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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 yield of Shea butter extracted was optimized by varying kneading time, kneading temperature, and kneading speed using conventional method of varying one variable at a time (OVAT). Helical shaped impeller mounted on a variable speed Tecmix TM 1100 kneader was used to knead the Shea paste. Shea paste produced by milling crushed Shea nut was kneaded with 125% (wt) water at temperatures ranging from 10 to 35 OC for periods ranging from 0.5 to 8.7minutes. The impeller speed was varied between 260 and 1050 rpm. OVAT optimization conditions were established to be time of 5.36minutes, temperature of 15.90C, and kneading speed of 894.4rpm. The optimized Shea butter yield for these conditions was 24.04%.
Keywords: Shea Butter, Kneading, Optimization, Yield, Temperature, Time, Speed.
Scope of the Article: Discrete Optimization

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B7431129219

IJITEE Journal — Sat, 19 Mar 2022 05:23:38 +0000

Partial Replacement of Cement in Concrete by Metakaolin and Marble Dust
Shahid Ashraf Shah¹, Ashish Kumar²
¹Shahid Ashraf shah*, Civil Engineering Department, RIMT University, Punjab, India.
²Ashish Kumar, Civil Engineering Department ,RIMT University, Punjab, India.
Manuscript received on January 15, 2020. | Revised Manuscript received on January 22, 2020. | Manuscript published on February 10, 2020. | PP: 3222-3226 | Volume-9 Issue-4, February 2020. | Retrieval Number: B7431129219/2020©BEIESP | DOI: 10.35940/ijitee.B7431.029420
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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: Though various admixtures are available, confined examinations have been done in India on the use of china soil (Metakaolin) and marble dust for the headway of great bond. Metakaolin (MK) and marble buildup is a mineral admixture, whose potential isn’t yet totally attempted. MK is a valuable cementitious material gotten from warmth treatment of trademark stores of kaolin. MK shows high pozzolana reactivity due to their indistinguishable structure and high surface district. On the other hand move of marble powder (MP) from the marble business is one of the natural issues today. The use of marble powder can be used for improvement reason. The substitutions is done to some degree in various degrees and its effect on properties of concrete is thought about. Partial substitution of bond has been done at 3%, 6%, 9%, 12%, 15% with MK(Metakaolin) and 10%(constant) with MP (Marble Powder). Compressive similarly as inflexibility of bond made with MK-MP has been differentiated and customary concrete of assessment M30. Solidness of bond was in like manner separated with Rapid chloride Migration Test (RCMT). Result exhibits that there is an increment of solidarity with the development of MK and MP. The upgraded quality estimation of concrete was practiced for both compressive similarly as split inflexibility at 9%MK and 10%MP. The results close that, the usage of Metakaolin Concrete (MKC) has improved the introduction of bond under various conditions.
Keywords: Metakaolin, Marble Dust, M 30 concrete.
Scope of the Article: Concrete Engineering

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D1876029420

IJITEE Journal — Sat, 19 Mar 2022 05:14:12 +0000

Mitigation of Torsional Vibrations in Drilling Systems using Adaptive Nonlinear Control System
S. Suba¹, M.Dineshkumar D.Eee²
¹S. Suba*, Assistant Professor, Department of Electrical and Electronics Engineering, College, Mahendrapuri, Mallasamudram, Namakkal.
²M. Dinesh Kumar D. Eee., Department of Electrical and Electronics Engineering, Mahendra Engineering College (Autonomous), Mahendrapuri, Mallasamudram, Namakkal.
Manuscript received on January 18, 2020. | Revised Manuscript received on January 22, 2020. | Manuscript published on February 10, 2020. | PP: 3317-3321 | Volume-9 Issue-4, February 2020. | Retrieval Number: D1876029420/2020©BEIESP | DOI: 10.35940/ijitee.D1876.029420
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Abstract: The reason for this work is to plan a robust yield feedback control way to deal with dispense with torque stick-slip vibrations in boring frameworks. Current industry controllers generally neglect to dispose of stick-slip vibrations, particularly when different torque flex modes assume a job in maniacal assault. In terms of build controller production, a real training string system performs a multi-level model work such as torque mechanics. The proposed controller design is artfully distorted at optimizing the stability with respect to the uncertainty of the nonlinear bit-rock interaction. Based on heroes and intentions. Besides, a closed loop strength examination of the nonlinear preparing string model is displayed. This controller structure system offers a few points of interest contrasted with existing controllers. To begin with, just surface estimations are utilized, barring the requirement for entire estimations underneath it. Second, multi-level training-string dynamics are effectively handled in ways to access state-training controllers. Third, stability is explicitly provided with respect to bit-rock contact uncertainty and closed-loop performance specifications include controller design. The results of the study report confirm that stick-slip vibrations are actually eliminated in realistic drilling scenarios using a controller designed to achieve this state-of control control.
Keywords: Stick-slip Vibrations, Bit-rock, Drilling, Torsional, Adaptive Nonlinear System
Scope of the Article: System Integration

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D1853029420

IJITEE Journal — Sat, 19 Mar 2022 05:02:08 +0000

Improving the Efficiency of Milling with using Of the Milling Cutter with Cover (High Speed Milling (HSM))
Khamza Khamroev¹, Urinov Uygun², Lyudmila Dubrovets³, Sukhrob Shadiev⁴, Samandar Sayfulloev⁵
¹Khamza Khamroev*, Senior Lecturer, Department of Technology of Mechanical Engineering, Bukhara Institute of Engineering Technology, Uzbekistan.
²Urinov Uygun, Doctoral student, Department of Technology of Mechanical Engineering, Bukhara Institute of Engineering Technology, Uzbekistan.
³Lyudmila Dubrovets, Senior Lecturer, Department of Technology of Mechanical Engineering, Bukhara Institute of Engineering Technology, Uzbekistan.
⁴Sukhrob Shadiev, Assistant, Department of Technology of Mechanical Engineering, Bukhara Institute of Engineering Technology, Uzbekistan.
⁵Samandar Sayfulloev, Assistant, Department of Technology of Mechanical Engineering, Bukhara Institute of Engineering Technology, Uzbekistan.
Manuscript received on January 12, 2020. | Revised Manuscript received on January 21, 2020. | Manuscript published on February 10, 2020. | PP: 3310-3316 | Volume-9 Issue-4, February 2020. | Retrieval Number: D1853029420/2020©BEIESP | DOI: 10.35940/ijitee.D1853.029420
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Abstract: The method of high-speed milling has many advantages than conventional milling. Very often, high-speed milling is considered simply a way to increase productivity due to a higher cutting speed than commonly used. It is rarely emphasized that quality of the product can be improved by improving accuracy and improving the surface quality of the cutter.
Keywords: Cutting, High-speed Cutting, High-speed Machining, High-speed Milling, Tool Wear.
Scope of the Article: Enterprise software, middleware, and tools

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D1823029420

IJITEE Journal — Sat, 19 Mar 2022 04:43:17 +0000

Developing Ethiopian Yirgacheffe Coffee Grading Model using a Deep Learning Classifier
J. R. Arunkumar¹, Tagele berihun Mengist²
¹Dr.J.R.Arunkumar*, FCSE, Arbaminch University, Ethiopia.
²Mr. Tagele berihun, FCSE, Arbaminch University, Ethiopia.
Manuscript received on January 15, 2020. | Revised Manuscript received on January 21, 2020. | Manuscript published on February 10, 2020. | PP: 3303-3309 | Volume-9 Issue-4, February 2020. | Retrieval Number: D1823029420/2020©BEIESP | DOI: 10.35940/ijitee.D1823.029420
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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: Coffee grading is the main procedure in producing homogenous local commercial fair system of pricing in the market and export. Grading coffee is a difficult task during the inspection, because it requires training and experience of the experts. In order to tackle grading difficulties in coffee producing industries and corporates have been employed and trained experts. Even if, those experts do not work effectively due to tiredness, costly, time consuming, inconsistency, bias and other factors. Digital image processing techniques based on automatically extracted features have been explored to classify Ethiopian coffee to corresponding quality grade labels. Samples of those coffee beans were taken from Yirgacheffe Coffee Farmers’ Cooperative Union. On average, 228 images were taken from each of three grade values or levels (grade 1, grade 2 and grade 3). The total number of images taken was 684 containing 6138 coffee beans. To extract coffee bean features and build a classification model for grading coffee, the state of art deep learning algorithm called convolutional Neural Network was used. Base on the experimental results classification accuracy obtained with testing coffee bean images for grade 1, grade 2 and grade 3 coffee beans was 99.51%, 97.56%, and 98.04%, respectively with the overall classification accuracy of 98.38%. This shows a promising result, even if, images are captured under the challenging condition without laboratory setup, such as illumination, different resolution, shadow and orientation which affects greatly the performance of the classifier and hence they are the future research direction that needs further investigations of noise removal techniques.
Keywords: Coffee Grade, Image Processing, Deep Learning, CNN, Yirgacheffe Coffee
Scope of the Article: Deep Learning

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D1592029420

IJITEE Journal — Sat, 19 Mar 2022 04:35:49 +0000

Performance Improving of ANN with Preprocessing Stage in Human Face Expression Recognition System
Sumalakshmi C. H.¹, P. Vasuki²
¹Sumalakshmi C. H.*, Research Scholar, Sethu Institute of Technology, Kariapatti, Tamilnadu, India.
²Dr. P. Vasuki, Professor, Department of ECE, Sethu Institute of Technology, Kariapatti, Tamilnadu, India.
Manuscript received on January 17, 2020. | Revised Manuscript received on January 29I, 2020. | Manuscript published on February 10, 2020. | PP: 3298-3302 | Volume-9 Issue-4, February 2020. | Retrieval Number: D1592029420/2020©BEIESP | DOI: 10.35940/ijitee.D1592.029420
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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 many face recognition systems, the important part is face detection. The task of detecting face is complex due to its variability present across human faces including color, pose, expression, position, and orientation. So, by using various modeling techniques it is convenient to recognize various facial expressions. The system proposed consists of three phases, the facial expression database, pre-processing and classification. To simulate and assess recognition efficiency based on different variables (network composition, learning patterns and pre-processing), we present both the Japanese Female Facial Expression Database (JAFFE) and the Extended Cohn-Kanade Dataset (CK+). Comparative approaches of data preprocessing include face detection, translation, normalization of global contrast and histogram equalization. Significant results were obtained with 85.52 percent accuracy particularly in comparison with some other pre-processing phases and raw data in single pre-processing phases. The result indicates the ANN classifier representation produces a satisfactory result which reaches more accuracy.
Keywords: Artificial Neural Network, facial Expression, Database, face Detection, Emotion
Scope of the Article: Pattern Recognition

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D1464029420

IJITEE Journal — Sat, 19 Mar 2022 04:28:20 +0000

A Novel Index for Detecting Line Stability Based on the Structural Characteristics of Power Transmission Systems
J. Vanishree¹, V. Ramesh²
¹J. Vanishree*, School of Electrical and Electronics Engineering, Vellore Institute of Technology, Vellore, India.
²V. Ramesh, *, School of Electrical and Electronics Engineering, Vellore Institute of Technology, Vellore, India.
Manuscript received on January 16, 2020. | Revised Manuscript received on January 23, 2020. | Manuscript published on February 10, 2020. | PP: 3292-3297 | Volume-9 Issue-4, February 2020. | Retrieval Number: D1464029420/2020©BEIESP | DOI: 10.35940/ijitee.D1464.029420
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Abstract: In general, there is a large mismatch between power generation and power demand. Hence to ensure the power quality of power transmission systems has become a challenging task. One major issue in power quality is voltage collapse, which can cause system blackout if not detected in time. There are many measures to detect voltage collapse, among which identification of weak buses and weak lines plays a major role. Weak buses are identified using the Structural Characteristics Method (SCM). A novel stability index based on network structural characteristics is proposed for the identification of weak lines that can cause voltage collapse. This Electrical Distance Stability Index (EDSI) is determined on the basis of the electrical distance between various buses with that of weak buses determined by a structural characteristics method. The major significance of SCM and EDSI is the determination of weak buses and weak lines respectively without the need for load flow analysis unlike the existing methods. The proposed index is implemented on IEEE 6, 14 and 30 bus systems. The results thus obtained by the EDSI are identical and effective when compared with the results of existing line stability indices.
Keywords: Structural Characteristics Method (SCM), Eigenvalue Decomposition, Electrical Distance Stability Index (EDSI), Voltage Collapse, Voltage Stability
Scope of the Article: Structural Engineering

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