Volume-9 Issue-6, March 2021 – International Journal of Recent Technology and Engineering (IJRTE)

F5578039621

IJRTE Journal — Mon, 10 Jan 2022 07:22:02 +0000

Dielectric Cover Layer Thickness Effect on Circular Microstrip Antenna Parameters
V. Saidulu
Dr. V. Saidulu, Assistant Professor, Department of ECE, Mahatma Gandhi Institute of Technology, Gandipet, Hyderabad, India.

Manuscript received on March 22, 2021. | Revised Manuscript received on March 30, 2021. | Manuscript published on March 30, 2021. | PP: 257-261 | Volume-9 Issue-6, March 2021. | Retrieval Number: 100.1/ijrte.F5578039621 | DOI: 10.35940/ijrte.F5578.039621
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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: This paper studies the effect of dielectric cover layer thickness on circular microstrip patch antenna parameters such as gain, bandwidth, beam-width, radiation patterns, return loss and VSWR. The proposed antenna is designed with 2.4GHz frequency in S-Band region. This operating frequency useful in ISM band applications. Circular patch antenna is designed with cavity model analysis and simulated using HFSS simulation software (Electromagnetic simulator). The coaxial probe fed is used for antenna design. In this paper the effect of dielectric cover layer on antenna parameters studied experimentally and comparing their performance characteristics. The simulation results shows that the antenna without dielectric cover layer obtained gain is 4.11dB and antenna with dielectric cover the gain is reduced to 2.87dB to 5.88dB based on thickness of the dielectric cover layer. The antenna bandwidth obtained without dielectric cover is 3% and with dielectric cover its bandwidth is reduced from 0.012GHz to 0.052GHz based on thickness of the cover layer effect. Similarly other parameters are investigated and compared. This proposed circular patch antenna is used in wireless and Wi-Fi applications.
Keywords: Dielectric Cover Layer, Bandwidth, Radiation Patterns, Vswr Etc.

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F5346039621

IJRTE Journal — Mon, 10 Jan 2022 07:17:56 +0000

Smart Internet of Things Based Induction Motor Parameter Monitoring and Control System
M. Ambika ME¹, M. Madhunisha²
¹Ms. M.Ambika ME, Department of Computer Science & Engineering, Sri Shakthi Institute of Engineering and Technology, Coimbatore (Tamil Nadu), India.
²Ms. M.Madhunisha, Department of Computer Science & Engineering, Sri Shakthi Institute of Engineering and Technology, Coimbatore (Tamil Nadu), India.
Manuscript received on February 08, 2021. | Revised Manuscript received on February 02, 2021. | Manuscript published on March 30, 2021. | PP: 253-256 | Volume-9 Issue-6, March 2021. | Retrieval Number: 100.1/ijrte.F5346039621 | DOI: 10.35940/ijrte.F5346.039621
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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: Automation is the use of various control systems for operating equipment such as machinery, processes in industries such as boilers and heat treating ovens, switching on telephone networks, steering and stabilization of ships, aircraft and other applications with minimal or reduced human intervention. This research paper presents advanced approaches using wireless monitoring system for induction motor based on Internet of Things (IoT) for safe and economic data communication. n the first approach, state of the art fault detection strategy is exhibited for induction motor. This research paper depends on the assessment of measured voltage, current, earth leakage, rotor status and speed. Progressed embedded strategy and utilized to isolate and survey the disappointment seriousness. In this procedure, distinctive sensors are associated with the motor, and the quantities are extracted by utilizing a microcontroller. The Graphical User Interface (GUI) with cloud server IoT is used to transmit the data from base station to remote station. This arrangement allows the client to interface with the framework. The proposed research paper based induction motor control system is validated through the simulation in Raspberry Pi 3 environment.
Keywords: Induction Machine, Zigbee Protocol, Wireless control and Monitoring System.

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F5396039621

IJRTE Journal — Mon, 10 Jan 2022 07:13:26 +0000

Mine Waste as Resource: Indian Mining Scenario of Coal and Non Coal Mining Sector
Sanniv Shome¹, Shushil Mhaske², K. Pathak³, M S Tiwari⁴
¹S. Shom, Department of Civil Engineering, Indian Institute of Technology, Kharagpur (West Bengal), India.
²Dr. Sushil Mhaske, Department of Civil Engineering, Indian Institute of Technology, Kharagpur (West Bengal), India.
³Dr. Khanindra Pathak, Professor, Department of Mining Engineering, Indian Institute of Technology, Kharagpur (West Bengal), India.
⁴Dr. Manoj Tiwari, Department of Mining Engineering, Indian Institute of Technology, Kharagpur (West Bengal), India.
Manuscript received on March 08, 2020. | Revised Manuscript received on February 16, 2021. | Manuscript published on March 30, 2021. | PP: 250-252 | Volume-9 Issue-6, March 2021. | Retrieval Number: 100.1/ijrte.F5396039621 | DOI: 10.35940/ijrte.F5396.039621
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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: Mother Nature has bestowed India with huge resources of coal, iron ore, bauxite, manganese and limestone. India has one of the lowest per capita availability of land due to population of more than 1.3 billion. The transformation from under developed to developed economy warrants enormous increase in mineral production. This will generate additional huge quantities of waste. The industry is already facing problems related to land acquisition and environmental clearances. Sustainable development of Indian mineral industry requires reprocessing, reuse and recycling of mine waste. To achieve this, economic and innovative mineral processing methods are required which will result in least damage to ecology and environment.
Keywords: Sustainable Development, Reprocessing, Reuse, Recycling.

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F5582039621

IJRTE Journal — Mon, 10 Jan 2022 07:09:11 +0000

Modelling and Analyzing the Employees’ Engagement in Workplace using Machine learning Tools
Mahassine Bekkari¹, Abdellah El Fallahi²
¹Mahassine BEKKARI*, Logistique, Abdelmalek Essaadi University, Tetuan, Morroco.
²Abdellah EL FALLAHI, Logistique, Abdelmalek Essaadi University, Tetuan, Morroco.
Manuscript received on March 23, 2021. | Revised Manuscript received on March 27, 2021. | Manuscript published on March 30, 2021. | PP: 243-249 | Volume-9 Issue-6, March 2021. | Retrieval Number: 100.1/ijrte.F5582039621 | DOI: 10.35940/ijrte.F5582.039621
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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 a new economy where immaterial capital is crucial, companies are increasingly aware of the necessity to efficiently manage human capital by optimizing its engagement in the workplace. The accession of the human capital through its engagement is an efficient leverage that leads to a real improvement of the companies’ performance. Despite the staple attention towards human resource management, and the efforts undertaken to satisfy and motivate the personnel, the issue of engagement still persists. The main objective of this paper is to study and model the relation between eight predictors and a response variable given by the employees’ engagement. We have used different models to figure out the relation between the predictors and the dependent variable after carrying out a survey of several employees from different companies. The techniques used in this paper are linear regression, ordinal logistic regression, Gradient Boosting Machine learning and neural networks. The data used in this study is the results of a questionnaire completed by 60 individuals. The results obtained show that the neural networks perform slightly the rest of models considering the training and validation error of modelling and also highlight the complex relation linking the predictors and the predicted.
Keywords: Human resources management, machine learning; Neural networks, Boosting machine learning, logistic regression.

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F5575039621

IJRTE Journal — Mon, 10 Jan 2022 06:54:32 +0000

A Survey on Some Big Data Applications Tools and Technologies
Nazia Tazeen¹, K.Sandhya Rani²
¹Nazia Tazeen, Department of Computer Science & Engineering, Sri Padmavathi Mahila Viswa Vidyalayam, Tirupati, India.
²K.Sandhya Rani, Department of Computer Science & Engineering , Sri Padmavathi Mahila Viswa Vidyalayam, Tirupati, India.
Manuscript received on March 22, 2021. | Revised Manuscript received on March 26, 2021. | Manuscript published on March 30, 2021. | PP: 239-242 | Volume-9 Issue-6, March 2021. | Retrieval Number: 100.1/ijrte.F5575039621 | DOI: 10.35940/ijrte.F5575.039621
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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: Big Data is a broad area that deals with enormous chunks of data sets. It is a word for enormous data sets having huge volume, more diverse structures of data originating from diverse sources are growing rapidly. Many data being generated because of fast data transmission between devices concerning different sectors like healthcare, science, media, business, entertainment and engineering. Data collection capacity and its storage is big concern. Apache Hadoop software is a store of accessible source programs to store big data and perform analytics and various other operations related to big data. Many organizations base their decisions by extracting knowledge from huge and complex data, because of this prime cause of decision making, Big Data has to be accurately classified and analyzed. In order to overcome the complex challenges encountered by Big Data, various Big Data tools and technologies have developed. Big Data Applications, tools and technologies used to handle it are briefly discussed in this paper.
Keywords: Big Data, Veracity, Hadoop, structured data, unstructured data

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F5572039621

IJRTE Journal — Mon, 10 Jan 2022 06:52:33 +0000

Plant Leaf Disease Detection and Classification using Optimized CNN Model
Prabavathi S¹, Kanmani P²
¹Prabavathi S*, PG Scholar, Department of Computer Science and Engineering, Christ (Deemed to be University), Bangalore, India.
²Kanmani P, Assistant Professor, Department of Computer Science and Engineering, Christ (Deemed to be University), Bangalore, India.
Manuscript received on March 20, 2021. | Revised Manuscript received on March 26, 2021. | Manuscript published on March 30, 2021. | PP: 233-238 | Volume-9 Issue-6, March 2021. | Retrieval Number: 100.1/ijrte.F5572039621 | DOI: 10.35940/ijrte.F5572.039621
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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: Our economy depends on productivity in agriculture. The quantity and quality of the yield is greatly affected by various hazardous diseases. Early-stage detection of plant disease will be very helpful to prevent severe damage. Automatic systems to detect the changes in the plants by monitoring the abnormal symptoms in its growth will be more beneficial for the farmers. This paper presents a system for automatic prediction and classification of plant leaf diseases. The survey on various diseases classification techniques that can be used for plant leaf disease detection are also discussed. The proposed system will define the cropped image of a plant through image processing and feature extraction algorithms. Enhanced CNN model is designed and applied for about 20,600 images are collected as a dataset. Optimization is done to enhance the accuracy in the system prediction and to show the improvement in the true positive samples classification. The proposed system shows the improvement in the accuracy of prediction as 93.18% for three different species with twelve different diseases.
Keywords: Agriculture, Classification, CNN, Image processing, Optimization.

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F5545039621

IJRTE Journal — Mon, 10 Jan 2022 06:47:11 +0000

Towards Optimization of Malware Detection using Extra-Tree and Random Forest Feature Selections on Ensemble Classifiers
Fadare Oluwaseun Gbenga¹, Adetunmbi Adebayo Olusola², Oyinloye Oghenerukevwe Elohor³
¹Fadare Oluwaseun Gbenga, Department of Computer Science, Ekiti State University, Nigeria.
²Adetunmbi Adebayo Olusola, Professor, Department of Computer Science, Federal University of Technology, Akure.
³Oyinloye Oghenerukevwe Elohor, Department of Computer Science, Ekiti State University, Nigeria.
Manuscript received on March 16, 2021. | Revised Manuscript received on March 25, 2021. | Manuscript published on March 30, 2021. | PP: 223-232 | Volume-9 Issue-6, March 2021. | Retrieval Number: 100.1/ijrte.F5545039621 | DOI: 10.35940/ijrte.F5545.039621
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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 proliferation of Malware on computer communication systems posed great security challenges to confidential data stored and other valuable substances across the globe. There have been several attempts in curbing the menace using a signature-based approach and in recent times, machine learning techniques have been extensively explored. This paper proposes a framework combining the exploit of both feature selections based on extra tree and random forest and eight ensemble techniques on five base learners- KNN, Naive Bayes, SVM, Decision Trees, and Logistic Regression. K-Nearest Neighbors returns the highest accuracy of 96.48%, 96.40%, and 87.89% on extra-tree, random forest, and without feature selection (WFS) respectively. Random forest ensemble accuracy on both Feature Selections are the highest with 98.50% and 98.16% on random forest and extra-tree respectively. The Extreme Gradient Boosting Classifier is next on random-forest FS with an accuracy of 98.37% while Voting returns the least detection accuracy of 95.80%. On extra-tree FS, Bagging is next with a detection accuracy of 98.09% while Voting returns the least accuracy of 95.54%. Random Forest has the highest all in seven evaluative measures in both extra tree and random forest feature selection techniques. The study results uncover the tree-based ensemble model is proficient and successful for malware classification.
Keywords: Extra-tree, random forest, K-Nearest Neighbors, Extreme Gradient Boosting Classifier, Random forest ensemble.

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F5523039621

IJRTE Journal — Mon, 10 Jan 2022 06:42:16 +0000

A Comprehensive Review on Protein Isolates from Legumes
S.Reginold Jebitta¹, Durga Devi P. R², Deva Dharshini L³, Theerdham Naga Sai Harika⁴, Vignesh K.⁵
¹S.Reginold Jebitta, Department of Food Technology, Kalasalinagm Academy of Research and Education, Srivilliputtur (Tamil Nadu), India.
²Durga Devi P. R, Department of Food Technology, Kalasalinagm Academy of Research and Education, Srivilliputtur (Tamil Nadu), India.
³Deva Dharshini L, Department of Food Technology, Kalasalinagm Academy of Research and Education, Srivilliputtur (Tamil Nadu), India.
⁴Theerdham Naga Sai Harika, Department of Food Technology, Kalasalinagm Academy of Research and Education, Srivilliputtur (Tamil Nadu), India.
⁵Vignesh K, Department of Food Technology, Kalasalinagm Academy of Research and Education, Srivilliputtur (Tamil Nadu), India.
Manuscript received on March 10, 2020. | Revised Manuscript received on March 22, 2021. | Manuscript published on March 30, 2021. | PP: 215-222 | Volume-9 Issue-6, March 2021. | Retrieval Number: 100.1/ijrte.F5523039621 | DOI: 10.35940/ijrte.F5523.039621
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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: Legumes play an vital function in human body due to dietary fiber, protein, minerals and vitamins and well-balanced essential amino acid. Legume proteins have gained increasing significance because of preferred functional properties, including gelling and emulsifying properties. Legumes contains anti nutritional compounds like Trypsin inhibitor(TIs), Phytic acid(PA), Tannin, Saponin, Lectins, They are not a major concern for most people, but may become a problem during periods of malnutrition, these can be easily removed by dehulled, cooking, thermal process, germination after soaking. Protein isolates are advanced form of protein containing the greater amount of protein with greater digestibility. There are different types of protein isolates like chickpea, whey protein Pea protein, cowpea protein isolates .The extraction methods of protein isolates are Iso electric extraction and alkaline extraction, citric acid extraction. Our aim of this paper is to optimize the protein isolate for diet people and innovate research in this field to produce some protein enriched food formulations.
Keywords: Legumes; Protein Isolates; Extraction Techniques; Protein Isolates.

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F5507039621

IJRTE Journal — Mon, 10 Jan 2022 06:36:07 +0000

Disease Classification and Prediction using Ensemble Machine Learning Classification Algorithm
B.Meena Preethi¹, P.Radha²
¹B.Meena Preethi, Assistant Professor, Department of Software Systems, Sri Krishna Arts and Science College, Coimbatore (Tamil Nadu), India.
²P.Radha, Assistant Professor, PG & Research Department of Computer Science, Government Arts College, Coimbatore (Tamil Nadu), India.
Manuscript received on March 08, 2020. | Revised Manuscript received on March 25, 2021. | Manuscript published on March 30, 2021. | PP: 202-204 | Volume-9 Issue-6, March 2021. | Retrieval Number: 100.1/ijrte.F5507039621 | DOI: 10.35940/ijrte.F5507.039621
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Abstract: In today’s scenario, disease prediction plays an important role in medical field. Early detection of diseases is essential because of the fast food habits and life. In my previous study for predicting diseases using radiology test report , and to classify the disease as positive or negative three classifiers Naïve Bayes (NB), Support Vector Machine (SVM) and Modified Extreme Learning Machine (MELM was used to increase the accuracy of results. To increase the efficiency of predicting the disease and to find which disease pricks the society, ensemble machine learning algorithm is used. The huge data from the healthcare industry were preprocessed., categorized and analyzed to find out and predict which patient to be treated and given priority and which hits the society the most. Ensemble machine learning’s popularity in the medical industry is due to a variety of factors the Classifiers used are K Nearest Neighbors, Nearest Mean Classifier, Mean Feature Voting Classifier, KDtree KNN, Random Forest. To reduce the manual processes in medical field automating these processes has become important. Electronic medical records and significant advances in health care have given an opportunity to make find out which patients need to be given more importance. Several methodologies and techniques were used to preprocess the data in order to meet the study’ requirements. To improve the performance of machine learning algorithms, feature selections were made using Tabu search. When ensemble prediction is combined with the Random Forest algorithm as the combiner, the results are more reliable. The aim of this study is to create a system to classify Medical records whether it is diseased or not and find out which disease rate has increased. This research will help the society to an individual to get treated easily and take preventive measures to avoid diseases.
Keywords: Machine Learning, K Nearest Neighbors, Nearest Mean Classifier, Mean Feature Voting Classifier, K Dtree KNN, Random Forest

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F5500039621

IJRTE Journal — Mon, 10 Jan 2022 06:27:53 +0000

Adjustable PRPG for Low Power Test Patterns
B. Nadimulla¹, S. Aruna Mastani²
¹B. Nadimulla, M.Tech, VLSI System Design, Jawaharlal Nehru Technological University, Anantapuramu (AP), India.
²Dr. S. Aruna Mastani, Department of ECE, Jawaharlal Nehru Technological University, Anantapuramu (AP), India.
Manuscript received on March 06, 2020. | Revised Manuscript received on March 18, 2021. | Manuscript published on March 30, 2021. | PP: 195-201 | Volume-9 Issue-6, March 2021. | Retrieval Number: 100.1/ijrte.F5500039621 | DOI: 10.35940/ijrte.F5500.039621
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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 power consumption is more in the processes of testing, test vector set compression and controlling of toggling plays a crucial role in reducing the power consumption during test mode. In exploring the controlling techniques of toggling, Pre-Selected Toggling (PRESTO) of test patterns is a technique that can control the toggling of a test patterns in a precise manner in Built-in Self -Test (BIST) architectures. In this paper we modify the architecture of existing Full Version PRESTO that can be used to generate test vectors and in addition binary sequences used as scan chins such that the controlling of sequence of test vectors depends on number of 1’s present in the switch code which is user defined thus reducing the testing time with significant fault coverage, and in addition the optimization is also observed in area and power. The area has decreased by 12.2% and power consumption by 15.43%. The Synthesis and implementation of the architectures are done using Artix7 (xc7a100tcsg324-3) FPGA family. The simulation results have been analyzed through Mentor-graphics Questa-sim 10.7C.
Keywords: Pseudo Random Pattern Generator (PRPG), Linear Feedback Shift Register (LFSR), Pre-Selected Toggling (PRESTO).

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