Volume-10 Issue-4, November 2021 – International Journal of Recent Technology and Engineering (IJRTE)

D4895119420

IJRTE Journal — Tue, 11 Jan 2022 10:24:21 +0000

Hybrid Sentiment Analyzer for Opinion Mining: Indian Admission Scenario
Priti Jagwani
Dr. Priti Jagwani Assistant Professor Department. of computer science, Aryabhatta College, University of Delhi.

Manuscript received on October 06, 2020. | Revised Manuscript received on October 25, 2020. | Manuscript published on November 30, 2020. | PP: 219-223 | Volume-9 Issue-4, November 2020. | Retrieval Number: 100.1/ijrte. D4895119420 | DOI: 10.35940/ijrte.D4895.119420
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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: Social media has become one of the widely acclaimed tool for sharing information as well as expressing ideas and emotions. The work depicts the dual aspect task of analyzing and comprehending data available on Twitter platform. This is done using NLP techniques. Using Latent Dirichlet Allocation (LDA) topic technique; the major topics discussed in tweets (of data set taken), have been identified. The input for this Latent Dirichlet Allocation is given by NLP technique – Bag of Words. For further processing, identification of the underlying emotions contained in tweets using the techniques of Sentiment Analysis is done. The result of sentiment analysis is in the polar form. As a case study, a scenario of admissions in India for UG and PG has been considered. The whole process has captured the opinions of stake holders taking part in the admission process. Tweeter data of Indian Institute of Technology (IIT) admission has been used to collect the data in order to conduct the experiment. Major topics discussed in tweets and the fundamental emotions contained are obtained as results along with the polarity of the tweets.
Keywords: Sentiment Analysis, Latent Dirichlet Allocation, Opinion Mining.

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D66291110421

IJRTE Journal — Sat, 04 Dec 2021 07:32:49 +0000

New 2-2 Sigma-Delta Modulators for Different Applications
Slim Tahri¹, Nizar Khitouni², Med Slim Bouhlel³
¹Slim Tahri, SETIT, Institute of Biotechnology of Sfax, University of Sfax Tunisia.
²Nizar Khitouni*, SETIT, Institute of Biotechnology of Sfax, University of Sfax Tunisia.
³Med Salim Bouhlel, SETIT Institute of Biotechnology of Sfax, University of Sfax Tunisia.
Manuscript received on November 20, 2021. | Revised Manuscript received on November 29, 2021. | Manuscript published on November 30, 2021. | PP: 269-272 | Volume-10 Issue-4, November 2021. | Retrieval Number: 100.1/ijrte.D66291110421 | DOI: 10.35940/ijrte.D6629.1110421
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Abstract: This work presents the design of a new 2-2 programmable sigma delta modulator architecture, for different applications, this transformation design of the ΣΔ modulator low-pass, band-pass and high-pass or vice versa with loopbacks addition, which improved the linearity of the converter and reduced the quantization noise. In this work, the MASH structure enables the implementation of stable and high-order modulator. This makes low voltage and low power applications ideal. The simulation result for sigma delta modulator for biomedical applications exhibit a signal to noise ratio is 95 dB @ 250Hz bandwidth and a 75dB @ 200KHz ,85dB @1MHz for pass band modulator. The SNR is about 70dB for 5MHz bandwidth and for high pass application. This tool will allow a development contribution and characterize a system optimization set from the start while remaining at a high level of design that is suitable for electronic systems and models VHDL-AMS, RF, Biomedical.
Keywords: Signal processing, Sigma delta, MASH, Low pass, band pass, high pass, Biomedical, EMG, ECG, EEG, RF.

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D66141110421

IJRTE Journal — Fri, 03 Dec 2021 11:11:27 +0000

Failure Prediction of Wind Turbine using Neural Network and Operation Signal
Dong Hwa Kim¹, Young Sung Kim²
¹Dr. Dong Hwa Kim Researcher, Seoultech NDT Research Center, Seoul National University of Science & Technology, South Korea.
²Young Sung Kim, Seoultech NDT Research Center, Seoul National University of Science & Technology, South Korea.
Manuscript received on November 16, 2021. | Revised Manuscript received on November 29, 2021. | Manuscript published on November 30, 2021. | PP: 261-268 | Volume-10 Issue-4, November 2021. | Retrieval Number: 100.1/ijrte.D66141110421 | DOI: 10.35940/ijrte.D6614.1110421
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Abstract: This paper deals with a novel prediction method for wind turbine by using neural network and operating data. As wind turbine transfer wind energy to electrical power energy, its structure has rotation part that capture wind energy, mechanical part, and electrical part that convert from mechanical rotation to electrical energy. Its working environmental situation is so bad like high mountain, sand desert, and offshore to capture good wind situation. Therefore, its control and monitoring should have high reliability for long terms during operation because its maintenance and repairing is very difficult and economically high cost. As wind turbine system is composed of three parts, there are many components that should be monitored to failure. This paper suggests neural network and operation data-based prediction method that can predict components’ failure through data comparison and neural network’s training function with easy expression of ‘Yes’ or ‘No’ for operator.
Keywords: Wind turbine, Monitoring, Neural network, Prediction

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D66221110421

IJRTE Journal — Fri, 03 Dec 2021 11:00:21 +0000

The Effect of Organizational Ethical Culture as A Moderating Variable in Ethical Decision Making on Tax Consultants
Wiwik Tiswiyanti¹, Haryadi², Sri Rahayu³, Enggar Diah Puspa Arum⁴
¹Wiwik Tiswiyanti*, Department of Doctoral Student, Economics Doctorate, Jambi University. Indonesia.
²Prof. Dr. H. Haryadi, Department of Bachelor of Commerce, Jambi University. Indonesia.
³Dr. Hj. Sri Rahayu, Department of Bachelor of Commerce, Jambi University. Indonesia.
⁴Dr. Enggar Diah Puspa Arum, Department of Bachelor of Commerce, Jambi University. Indonesia.
Manuscript received on November 19, 2021. | Revised Manuscript received on November 30, 2021. | Manuscript published on November 30, 2021. | PP: 256-260 | Volume-10 Issue-4, November 2021. | Retrieval Number: 100.1/ijrte.D66221110421 | DOI: 10.35940/ijrte.D6622.1110421
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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 study aims to analyze the effect of experience on ethical decisions and the influence of organizational ethical culture in moderating the relationship between experience and ethical decisions in tax consultants in the Sumatran Region including Lampung, Bangka Belitung, Palembang and Jambi consisting of 139 respondents. The analysis technique uses SEM WarpPLS with the help of the WarpPLS 7.0 application. The findings in this study, indicate that there is no significant effect between experience and ethical decisions and in an indirect relationship between experience and ethical decisions with organizational ethical culture as a moderating variable showing the results, organizational ethical culture can moderate the relationship with a significant influence. The research has met the requirements of the SEM-Warp PLS fit model.
Keywords: Experience, Organizational Ethical Culture and Ethical Decisions

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D66111110421

IJRTE Journal — Sat, 27 Nov 2021 10:15:39 +0000

Rainfall Prediction using Machine Learning and Deep Learning Algorithms
B.Meena Preethi¹, R.Gowtham², S.Aishvarya³, S.Karthick⁴, D.G.Sabareesh⁵
¹B.Meena Preethi, Department of Computer Science, Sri Krishna Arts and Science College, Coimbatore (Tamil Nadu), India.
²R.Gowtham*, PG Scholar, Department of Computer Science, Sri Krishna Arts and Science College, Coimbatore (Tamil Nadu), India.
³S.Aishvarya, PG Scholar, Department of Computer Science, Sri Krishna Arts and Science College, Coimbatore (Tamil Nadu), India.
⁴S.Karthick, PG Scholar, Department of Computer Science, Sri Krishna Arts and Science College, Coimbatore (Tamil Nadu), India.
⁵D.G.Sabareesh, PG Scholar, Department of Computer Science, Sri Krishna Arts and Science College, Coimbatore (Tamil Nadu), India.
Manuscript received on November 16, 2021. | Revised Manuscript received on November 22, 2021. | Manuscript published on November 30, 2021. | PP: 251-254 | Volume-10 Issue-4, November 2021. | Retrieval Number: 100.1/ijrte.D66111110421 | DOI: 10.35940/ijrte.D6611.1110421
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Abstract: The project entitled as “Rainfall Prediction using Machine Learning & Deep Learning Algorithms” is a research project which is developed in Python Language and dataset is stored in Microsoft Excel. This prediction uses various machine learning and deep learning algorithms to find which algorithm predicts with most accurately. Rainfall prediction can be achieved by using binary classification under Data Mining. Predicting the rainfall is very important in several aspects of one’s country and can help from preventing serious natural disasters. For this prediction, Artificial Neural Network using Forward and Backward Propagation, Ada Boost, Gradient Boosting and XGBoost algorithms are used in this model for predicting the rainfall. There are totally five modules used in this project. The Data Analysis Module will analyse the datasets and finding the missing values in the dataset. The Data Pre-processing includes Data Cleaning which is the process of filling the missing values in the dataset. The Feature Transformation Module is used to modify the features of the dataset. The Data Mining Module is used to train the dataset to models using any algorithm for learning the pattern. The Model Evaluation Module is used to measure the performance of the model and finalize the overall best accuracy for the prediction. Dataset used in this prediction is for the country Australia. This main aim of the project is to compare the various boosting algorithms with the neural network and find the best algorithm among them. This prediction can be major advantage to the farmers in order to plant the types of crops according to the needy of water. Overall, we analyse the algorithm which is feasible for qualitatively predicting the rainfall.
Keywords: Machine learning, Deep learning, Adaboost, Gradient Boosting, XGBoost, ANN

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D66311110421

IJRTE Journal — Sat, 27 Nov 2021 10:01:39 +0000

Computation of Compressive Strength of GGBS Mixed Concrete using Machine Learning
Swati¹, Jitendra Khatti², Kamaldeep Singh Grover³
¹Swati, M.Tech. Scholar, Department of Civil Engineering, Rajasthan Technical University, Kota (Rajasthan), India.
²Jitendra Khatti, PhD Fellow, Department of Civil Engineering, Rajasthan Technical University, Kota (Rajasthan), India.
³Kamaldeep Singh Grover, Professor, Department of Civil Engineering, Rajasthan Technical University, Kota (Rajasthan), India.
Manuscript received on November 20, 2021. | Revised Manuscript received on November 26, 2021. | Manuscript published on November 30, 2021. | PP: 241-250 | Volume-10 Issue-4, November 2021. | Retrieval Number: 100.1/ijrte.D66311110421 | DOI: 10.35940/ijrte.D6631.1110421
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Abstract: Concrete is a composite material formed by cement, water, and aggregate. Concrete is an important material for any Civil Engineering project. Several concretes are produced as per the functional requirements using waste materials or by-products. Many researchers reported that these waste materials or by-products enhance the concrete properties, but the laboratory procedures for determining the concrete properties are time-consuming. Therefore, numerous researchers used statistical and artificial intelligence methods for predicting concrete properties. In the present research work, the compressive strength of GGBS mixed concrete is computed using AI technologies, namely Regression Analysis (RA), Support Vector Machine (SVM), Decision Tree (DT), and Artificial Neural Networks (ANNs). The cement content (CC), C/F ratio, w/c ratio, GGBS (in Kg & %), admixture, and age (days) are selected as input parameters to construct the RA, SVM, DT, ANNs models for computing the compressive strength of GGBS mixed concrete. The CS_MLR, Link_ CS_ SVM, 20LF_CS_DT, and GDM_CS_ANN models are identified as the best architectural AI models based on the performance of AI models. The performance of the best architectural AI models is compared to determine the optimum performance model. The correlation coefficient is computed for input and output variables. The compressive strength of GGBS mixed concrete is highly influenced by age (curing days). Comparing the performance of optimum performance AI models and models available in the literature study shows that the optimum performance AI model outperformed the published models.
Keywords: Compressive strength; GGBS, Support Vector Machine, Artificial Neural Networks

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D66271110421

IJRTE Journal — Fri, 26 Nov 2021 06:06:27 +0000

Dynamic Decoupling and Intelligent Optimal PID Controller Tuning of Multivariable Qua-drones
Dong Hwa Kim
Dong Hwa Kim, Prof., Department of Electrical Power and Control Engineering, Hanbat National University, South Korea .

Manuscript received on November 20, 2021. | Revised Manuscript received on November 25. | Manuscript published on November 30, 2021. | PP: 227-240 | Volume-10 Issue-4, November 2021. | Retrieval Number: 100.1/ijrte.D66271110421 | DOI: 10.35940/ijrte.D6627.1110421
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Abstract: This paper deals with dynamic decoupling and its intelligent PID control method of multivariable qua-drone. Up to this time, many sophisticated intelligent algorithms and control methods for drone systems have been mentioned. However, almost many cases have been focusing on single loop control methods and general multivariable systems. Therefore, we cannot guarantee its stability and optimal response by PID control used in multivariable qua-drone. Herein, this paper suggests a novel control method for PID control for multivariable qua-drone. As first step, this paper decouples dynamic of multivariable qua-drone using diagonal method of system matrix and then applies intelligent method PSO and GA to single loop obtained by decoupling method to obtain optimal response.
Keywords: Drone Control, Genetic Algorithm, Pso , Ziegler Nichols, Multivariable , Intelligent

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D66251110421

IJRTE Journal — Thu, 25 Nov 2021 10:26:24 +0000

Prediction of Geotechnical Properties of Soil using Artificial Intelligence Framework
Jitendra Khatti¹, Kamaldeep Singh Grover²
¹Jitendra Khatti*, PhD Fellow, Department of Civil Engineering, Rajasthan Technical University, Kota (Rajasthan) India.
²Dr. Kamaldeep Singh Grover, Professor, Department of Civil Engineering, Rajasthan Technical University, Kota (Rajasthan) India.
Manuscript received on November 16, 2021. | Revised Manuscript received on November 22, 2021. | Manuscript published on November 30, 2021. | PP: 218-227 | Volume-10 Issue-4, November 2021. | Retrieval Number: 100.1/ijrte.D66251110421 | DOI: 10.35940/ijrte.D6625.1110421
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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 present research work is carried out to predict the geotechnical properties (consistency limits, OMC, and MDD) of soil using AI technologies, namely regression analysis (RA), support vector machine (SVM), Gaussian process regression (GPR), artificial neural networks (ANNs), and relevance vector machine (RVM). The models of machine learning (SVM, GPR), hybrid learning (RVM), and deep learning (ANNs) are constructed in MATLAB R2020a with different configurations. The models of RA are built using the Data Analysis Tool of Microsoft Excel 2019. The input parameters of AI models are gravel, sand, silt, and clay content. The correlation coefficient is calculated for pair of soil datasets. The correlation shows that sand, silt, and clay content play a vital role in predicting soil’s liquid limit and plasticity index. The performance of constructed AI models is compared to determine the optimum performance models. The limited datasets of soil are used in this study. Therefore, artificial neural networks and relevance vector machines could not perform well. Based on the performance of AI models, the Gaussian process regression outperformed the RA, SVM, ANNs, and RVM AI technologies. Hence, the GPR AI approach can predict the geotechnical properties of soil by gravel, sand, silt, and clay content. The Monte-Carlo global sensitivity analysis is also performed, and it is observed that the prediction of geotechnical properties of soil is affected by sand and clay content.
Keywords: Consistency limits, Deep Learning, Geotechnical Properties, Hybrid Learning, Machine Learning,

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D66191110421

IJRTE Journal — Thu, 25 Nov 2021 10:20:50 +0000

Reducing Subcarriers Beam-Squinting of Ultra-Wideband Mobile Communication Systems using Phased Array Antennas
K. Rama Devi¹, M. Nani²
¹Dr. K. Rama Devi*, Assistant Professor, Department of Electronics and Communication Engineering, Jawaharlal Nehru Technological University, Kakinada, (Andhra Pradesh). India.
²M. Nani, Student, M. Tech, Jawaharlal Nehru Technological University, Kakinada, (Andhra Pradesh). India.
Manuscript received on November 16, 2021. | Revised Manuscript received on November 22, 2021. | Manuscript published on November 30, 2021. | PP: 213-217 | Volume-10 Issue-4, November 2021. | Retrieval Number: 100.1/ijrte.D66191110421 | DOI: 10.35940/ijrte.D6619.1110421
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Abstract: There has been increasing demand for accessible radio spectrum with the rapid development of mobile wireless devices and applications. For example, a GHz of spectrum is needed for fifth-generation (5G) cellular communication, but the avail- able spectrum below 6 GHz cannot meet such requirements. Fortunately, spectrum at higher frequencies, in particular, millimeter-wave bands, can be utilized through phased-array analog beamforming to provide access to large amounts of spectrum. However, the gain provided by a phased array is frequency dependent in the wideband system, an effect called beam squint. We examine the nature of beam squint and develop convenient models with a uniform linear array. To further simplify the evaluation of the system performance, an approximated closed-form expression for the array gain is derived. Furthermore, to evaluate the performance of the proposed design, rigorous numerical results concerning different system parameters are provided in this paper.
Keywords: Beam-squinting, Analog architecture, Millimeter wave, Wideband, Array gain, Capacity.

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D66121110421

IJRTE Journal — Thu, 25 Nov 2021 10:16:09 +0000

Advanced Watermarking of Digital Images Showing Robust, Semi Fragile and Fragile Behaviour
Shivanand Pujar¹, Kangana W.M², Chitrashree Kurthkoti³, Abhinandan P. Mangaokar⁴, Jagadish S. Jakati⁵
¹Dr. Shivanand Pujar*, Assistant Professor, Department of Computer Science and Engineering, S. G. Balekundri Institute of Technology, Belagavi (Karnataka), India.
²Ms. Kangana W.M, Assistant Professor, Department of Computer Science and Engineering, S. G. Balekundri Institute of Technology, Belagavi (Karnataka), India.
³Ms. Chitrashree Kurtkoti, Assistant Professor, Department of Computer Science and Engineering, S. G. Balekundri Institute of Technology, Belagavi (Karnataka), India.
⁴Abhinandan P. Mangaonkar, Assistant Professor, Department of Computer Science and Engineering, S. G. Balekundri Institute of Technology, Belagavi (Karnataka), India.
⁵Mr. Jagadish S. Jakati, Assistant Professor, Department of Electronics and Communication Engineering, SGBIT, Belagavi, (Karnataka), India.
Manuscript received on November 16, 2021. | Revised Manuscript received on November 22, 2021. | Manuscript published on November 30, 2021. | PP: 196-212 | Volume-10 Issue-4, November 2021. | Retrieval Number: 100.1/ijrte.D66121110421 | DOI: 10.35940/ijrte.D6612.1110421
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Abstract: Digital Image Watermarking plays an important role when it comes to maintaining digital color picture authentication information. The proposed paper consists mainly of a digital watermarking scheme consisting of discrete wavelet transformation and involving the generation of pn sequence number to embed the watermark and also to extract the watermark from the host image. The technique suggested includes both embedding the watermark and removing it from the host file. Both the method of embedding and extraction consists of generating the pn sequence number values using the random numbers. The technique for all three of the RGB signal sources is included. The watermark symbol is located independently within the RGB image’s red, green, and blue channels. The suggested technique further reveals the improved mode of digital watermarking of images through fragile watermarking of images and semi-fragile digital watermarking of images.
Keywords: Digital Watermarking, Discrete Wavelet Transformation (DWT), pn_sequence (pseudo-noise sequence), PSNR(Peak Signal to Noise Ratio), Fragile Digital Image watermarking, NCC(Normalized Cross-Correlation).

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