Volume-9 Issue-10, August 2020 – International Journal of Innovative Technology and Exploring Engineering (IJITEE)

I8119078919

IJITEE Journal — Mon, 17 Oct 2022 04:29:43 +0000

The International Journal of Innovative Technology and Exploring Engineering (IJITEE) has ISSN: 2278-3075 (online), an open-access, peer-reviewed, periodical monthly international journal. This journal is published by Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) in January, February, March, April, May, June, July, August, September, October, November and December.The journal aims to publish high–quality peer–reviewed original articles in the area of Engineering and Technology that covers Computer Science and Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering and all interdisciplinary streams of Engineering and Technology. #Smart Computing and Information Processing #Signal and Speech Processing #Image Processing and Pattern Recognition #WSN#Artificial Intelligence and machine learning #Data mining and warehousing #Data Analytics #Deep learning #Bioinformatics #High Performance computing #Advanced Computer Networking #Cloud Computing #IoT #Parallel Computing on GPU #Human Computer Interactions #Recent Trends in Microelectronics and VLSI Design #Process & Device Technologies #Low-Power Design #Nanometer-Scale Integrated Circuits #Application specific ICs (ASICs) #FPGAs #Nanotechnology #Nano Electronics and Quantum Computing #3. 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Big Data Architecture for Intelligent Transport Systems Optimization
Mounica.B,¹ Lavanya.K²
¹Mounica .B, School of Computer Science and Engineering(SCOPE) ,VIT University, Vellore, India.
²Dr. K. Lavanya, School of Computer Science and Engineering(SCOPE) , VIT University, Vellore ,India.
Manuscript received on 30 June 2019 | Revised Manuscript received on 05 July 2019 | Manuscript published on 30 July 2019 | PP: 1281-1286| Volume-8 Issue-9, July 2019 | Retrieval Number: I8119078919/19©BEIESP | DOI: 10.35940/ijitee.I8119.078919
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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: Day by day as the volume of data is being generated massively, storing of data and processing of data becomes a ever growing challenge in intelligent transport system (ITS). In intelligent transport system there are different areas to concentrate like smart parking systems, dynamic toll charging, smart traffic management etc. This paper is mainly focused on big data architecture for intelligent transport system for dynamic toll charging, traffic management and traffic analysis related data collection from various sources. The data collected from various sources can be in the form of structured data, semi structured data and unstructured data. Because of verity of data collected, this paper gives an idea about which data model is appropriate depending on data collected for transportation system.
Keywords: Big data architecture, Data model, ETL tool, Hadoop, Intelligent transport system, Spark.
Scope of the Article: Computer Architecture and VLSI

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H6746068819

IJITEE Journal — Sat, 15 Oct 2022 09:13:02 +0000

Structured Assessment of Student Outcomes “a” and “e” in Mathematics and Science Courses for Engineering Students
Seifedine Kadry¹, Rabih Roufayel²
¹Seifedine Kadry, Department of mathematics and computer science, faculty of science, Beirut Arab University, Lebanon.
²Rabih Rougayel, Department of Molecular and Cellular Biology, University of Guelph, 50 Stone Road East, Guelph, On-tario, N1G 2W1, Canada.
Manuscript received on 30 June 2019 | Revised Manuscript received on 05 July 2019 | Manuscript published on 30 July 2019 | PP: 215-218 | Volume-8 Issue-9, July 2019 | Retrieval Number: H6746068819/19©BEIESP | DOI: 10.35940/ijitee.H6746.078919
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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: Skills, performance, and ability attributes are known as student outcomes that are gained during graduation. In order to evaluate quality assurance of these outcomes, a set of specific and targeted curriculum and extra-curriculum activities must be used for precise as-assessment. Meeting the required outcomes, a set of desired courses have to be selected to insure that the desired outcome is highly satisfied. “Highly” covered can be identified using Bloom’s levels, number of covered hours, weight of its assessment and others. Student outcomes are directly linked to course goals that are identified using a set of measurable verbs. In this paper, we will be using two outcomes “a” and “e” to structurally assess both direct and indirect outcomes in mathematics and other scientific courses using a partial differential equation method by defining appropriate measurable course goals. The strategy to assess the outcome is also explained.
Keywords: Accreditation, assessment, students’ outcomes, Bloom’s Taxonomy, computer science.
Scope of the Article: Social Sciences

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J76150891020

IJITEE Journal — Tue, 18 Jan 2022 06:42:49 +0000

Indian Stock Markets Data Analysis and Prediction using Macroeconomics Indictors in Machine Learning
Jaskarn Singh¹, Amit Chhabra²
¹Jaskarn Singh, Student Department of Computer Engineering & Technology, Guru Nanak Dev University, Amritsar (Punjab), India.
²Amit Chhabra, Assistant Professor, Department of Computer Engineering & Technology, Guru Nanak Dev University, Amritsar (Punjab), India.
Manuscript received on July 24, 2020. | Revised Manuscript received on August 05, 2020. | Manuscript published on August 10, 2020. | PP: 484-486 | Volume-9 Issue-10, August 2020 | Retrieval Number: 100.1/ijitee.J76150891020 | DOI: 10.35940/ijitee.J7615.0891020
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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: Machine Learning plays a unique role in the world of stock market when it comes to the trend prediction. Machine learning library MLIB helps in determining the future values of stocks. With the help of this research one can find the ups and downs of stock market by providing a signal for the same and done by analyzing the previous stock data. This study is based on analysis of stock data from 2000 to 2009 which includes top fifty companies of various sectors from all over India. Six stock data indicators known as, Bollinger Band, Relative Strength Index(RSI), Stochastic Oscillator, Williams % R, Moving Average Convergence Divergence (MACD), Rate of Change applied on the nineteen years of stock data then results of these indicators are compiled and finally with the use of machine learning libraries like Numpy, Pandas, Matplotlib, Sklearn a random forest algorithm is applied on the compiled result to predict the stock movement , these libraries which splits the results into two sets training set and testing set which also boost up the result and gives you the better prediction.
Keywords: Random Forest, Stock, Machine Learning.
Scope of the Article: Machine Learning

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J76190891020

IJITEE Journal — Tue, 18 Jan 2022 06:38:19 +0000

Optical Character Reader & Text To Speech Conversion using Correlations & Speech Synthesis
Avinash Rai¹, Shivani Sonker²
¹Dr. Avinash Rai*, Department of Electronics and Communication, University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, (M.P), India.
²Ms. Shivani Sonker, Department of Electronics and Communication, University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, (M.P), India.
Manuscript received on July 22, 2020. | Revised Manuscript received on August 03, 2020. | Manuscript published on August 10, 2020. | PP: 478-483 | Volume-9 Issue-10, August 2020 | Retrieval Number: 100.1/ijitee.J76190891020 | DOI: 10.35940/ijitee.J7619.0891020
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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 modern era of image processing, recognizing content or information from an image is process of electronic conversion into machine encoded text. Advanced systems that are capable of producing high accuracy for multi-font recognition are now becoming commonplace, and with the support of digital consent formatting. Some programs are able to retrieve formats that are very close to the original page including images, columns, and other non-text items. Proposed system is able to recognize text from an image and convert it into editable text along with speech conversion. System uses Correlation model for OCR (Optical Character Recognition) and Speech Synthesis for TTS (Text To Speech) conversion. Correlation is a measurement of the similarities between two similar objects such as the predefined alphabets and recognizing a combination of those alphabets from an image. Speech synthesis is an artificial expression of human speech. The computer program that has been used this feature is called a speech computer as well as speech synthesizer that can be implemented on the basis of software or hardware primitives. The text-to-speech system (TTS) converts a standard language text into a speech; some programs provide figurative language presentations such as typed text in speech. System is capable enough to acquire high level of accuracy with less false recognition. It is required to built an effective text scanner that can recognize text from an image with less error rate. System has been implemented in MATLAB and various pre-processing filters have been applied for better enhancement and extraction. Hand written text can also be recognized with an effective manner.
Keywords: OCR, TTS, Speech Synthesis, Correlation Model, Machine Encoding, Image Processing.
Scope of the Article: Machine Learning

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J75470891020

IJITEE Journal — Tue, 18 Jan 2022 06:33:18 +0000

Effective Location of Shear Wall and Bracings for Multistoried Asymmetrical Building
Khaja Moinuddin¹, B K Kolhapure²
¹Khaja Moinuddin, PG Scholar Department of Civil Engineering, P.D.A College of Engineering, Kalaburagi (Karnataka), India.
²B K Kolhapure, Associate Professor, Department of Civil Engineering, P. D. A College of Engineering, Kalaburagi (Karnataka), India.
Manuscript received on July 25, 2020. | Revised Manuscript received on August 10, 2020. | Manuscript published on August 10, 2020. | PP: 471-477 | Volume-9 Issue-10, August 2020 | Retrieval Number: 100.1/ijitee.J75470891020 | DOI: 10.35940/ijitee.J7547.0891020
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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: Earthquake happens all around the globe and it is a natural calamity and can occur across the world. It affects the structure by producing tough ground signals. To overwhelmed the Earthquake there is establishment of Shear wall and Bracing to increase the crosswise stiffness, ductility of the structure. To plan a building storey drift and crosswise displacement are crucial. The building is analyzed by Linear static and Linear dynamic method by E-tab software. In present paper G+25 multistoried building is analyzed by insertion of Shear wall and bracing at Corners, End and central core of the structure. The responses like Displacement, Storey drift, Time period and Base shear is calculated and equated.
Keywords: E-TAB, Shear Wall, Bracings, Linear static method, Linear dynamic method.
Scope of the Article: Building and Environmental Acoustics

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H6474069820

IJITEE Journal — Tue, 18 Jan 2022 06:28:53 +0000

Joint Hypergraph Learning using feature fusion for Image Retrieval
Aruna Bhaskar Karri¹, Kona Vinay Praveen Kumar²
¹Aruna Bhaskar Karri, Associate Professor, Department of Information Technology, Aditya Engineering College, Surampalem, East Godavari District, (A.P), India.
²Kona Vinay Praveen Kumar*, Department of Computer science and Engineering, Aditya Engineering College, Surampalem, East Godavari District, (A.P), India
Manuscript received on July 14, 2020. | Revised Manuscript received on July 23, 2020. | Manuscript published on August 10, 2020. | PP: 467-470 | Volume-9 Issue-10, August 2020 | Retrieval Number: 100.1/ijitee.H6474069820 | DOI: 10.35940/ijitee.H6474.0891020
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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 picture sharing sites like Flicker become increasingly well known, broad researchers focus on tagbased picture recovery (TBIR). It is one of the essential approaches to discover pictures contributed by social clients. In this exploration field, label data and various visual highlights have been explored. Be that as it may, most existing strategies utilize these visual includes independently or successively. In this paper, we propose a worldwide and neighborhood visual highlights combination way to deal with get familiar with the significance of pictures by hypergraph approach. A hypergraph is built first by using worldwide, neighborhood visual highlights and tag data. At that point, we propose a pseudo-significance input system to get the pseudo positive pictures. At last, with the hypergraph and pseudo importance input, we receive the hypergraph learning calculation to figure the pertinence score of each picture to the inquiry. Trial results illustrate the adequacy of the proposed methodology.
Keywords: Hypergraph, Pseudo-positive, Successively, Flicker.
Scope of the Article: Information Retrieval

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G5821059720

IJITEE Journal — Tue, 18 Jan 2022 06:23:29 +0000

OHKWR: Offline Handwritten Kannada Words Recognition using SVM Classifier with CNN
Ramesh G¹, Sandeep Kumar N², Champa H. N³
¹Ramesh. G*, Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bangalore, India.
²Sandeep Kumar N, Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bangalore, India.
³Champa H. N, Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bangalore, India.
Manuscript received on July 13, 2020. | Revised Manuscript received on July 26, 2020. | Manuscript published on August 10, 2020. | PP: 458-466 | Volume-9 Issue-10, August 2020 | Retrieval Number: 100.1/ijitee.G5821059720 | DOI: 10.35940/ijitee.G5821.0891020
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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 field of handwriting recognition, Robust algorithms for recognition and character segmentation are presented for multilingual Indian archive images of Devanagari and Latin scripts. These report basically suffer from their format organizations, low print and local skews quality and contain intermixed messages (machine-printed and manually written). In order to overcome these drawbacks, a character segmentation algorithm is proposed for kannada handwriting recognition. In this work, in initial steps we are obtained the segmentation paths by using the characters of structural property and also the graph distance theory whereas overlapped and connected character are separated. Finally, we are calculated results by using the SVM classifier. In proposed recognition of character, they are three new geometrical shapes based on new features such as center pixel of character is obtained by first and second feature and third feature is calculation purpose we are used in neighborhood information of text pixels. Benchmarking results represent that proposed algorithms have best work identified with other contemporary methodologies, where best recognition rates and segmentation are obtained.
Keywords: Convolutional Neural Network, Computer Vision, character recognition, Word recognition, SVM classifier.
Scope of the Article: Classification

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J76360891020

IJITEE Journal — Tue, 18 Jan 2022 06:18:40 +0000

Variables Analysis of Tourism Apps Development in Influencing Tourist Travel Experience
Qistina Donna Lee Abdullah¹, Aimuni Athirah Binti Latif²
¹Qistina Donna Lee Abdullah, Faculty of Applied and Creative Arts, University Malaysia Sarawak, Kota Samarahan, Malaysia.
²Aimuni Athirah Binti Latif, Faculty of Applied and Creative Arts, University Malaysia Sarawak, Kota Samarahan, Malaysia.
Manuscript received on July 23, 2020. | Revised Manuscript received on August 03, 2020. | Manuscript published on August 10, 2020. | PP: 453-457 | Volume-9 Issue-10, August 2020 | Retrieval Number: 100.1/ijitee.J76360891020 | DOI: 10.35940/ijitee.J7636.0891020
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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: Dumping of apps that emphasized on how to develop tourism industry and help travel organization to promoting their business. However, every app has own advantages and disadvantages. Producing an apps need a properly research about needs and demands of tourist or traveller itself. The tourism apps have its own influence towards tourists when it’s come to their satisfaction of travelling. Producing of tourism apps can be expansion if the producer increases the requirements, by doing a research of tourist’s needs and demand. Every tourist has their own needs and demands that are influenced by their preference, identity and family background. These factors are developed from many criteria, such as their culture which influence their identity, and their power of buying which influences their taste and preferences. Tourism industry is a wide field, before certain products are produced and certain services are offered, the producers need to analyse their target market. Thus, the success of a tourism App depends on how well and deep the producer has managed to explore and study about the target users. Research that linked between product and user needs on tourism apps are a big deal to explore. Successful of apps depends on how producer of tourism apps study about the users. The finding of paper is variables analysis of tourism apps that can be used to developing new prototype of tourism apps based on tourist needs. This paper will introduce the best variables that have been analysing to be interesting features that can be including in tourism apps.
Keywords: Tourism Sector, Tourism Organization, Apps, Travellers, Development Tourism Apps.
Scope of the Article: Mobile App Design and Development

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J76040891020

IJITEE Journal — Tue, 18 Jan 2022 06:14:38 +0000

Ductility Behaviour of Rectangular Compression Members Retrofitted by Modified Technique of FRP Wrapping
L. Manjunatha¹, B. V. Sachin², H. Sharada Bai³
¹L. Manjunatha*, Department of Civil Engineering, Nitte Meenakshi Institute of Technology, Yelahanka, Bengaluru, India.
²B. V. Sachin, Department of Civil Engineering, SJB Institute of Technology, Kengeri, Bengaluru, India.
³H. Sharada Bai, Department of Civil Engineering, University Visveswaraya College of Engineering, K.R. Circle, Bengaluru, India.
Manuscript received on July 16, 2020. | Revised Manuscript received on July 29, 2020. | Manuscript published on August 10, 2020. | PP: 445-452 | Volume-9 Issue-10, August 2020 | Retrieval Number: 100.1/ijitee.J76040891020 | DOI: 10.35940/ijitee.J7604.0891020
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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 presents an experimental investigation on ductility behaviour of reinforced concrete compression members, rectangular in cross section, modified to elliptical shape in cross section by bonding precast segment covers followed by Carbon Fiber Reinforced Polymer wrapping (CFRP) under concentric and eccentric loading conditions. Eighteen reinforced concrete rectangular compression members of size 100mm×150mm in cross section and 300mm in height were prepared using normal-strength concrete. Reinforcement ratio was kept at minimum, to simulate compression members that need retrofitting. Out of eighteen specimens, nine specimens were converted to elliptical shape in cross section. From nine remaining rectangular specimens, three specimens retained as it is without wrapping FRP and designated as Group1, remaining six specimens were wrapped with one and two layers of CFRP and designated as Group2. Out of nine elliptical specimens, three specimens were retained as it is without wrapping FRP and designated as Group3, remaining six elliptical specimens were wrapped with one and two layers of CFRP and designated as Group4. Specimens were tested upto failure under monotonic axial compression with concentric and eccentric load conditions. From the experimental results, it is observed that rectangular compression members shape modified to ellipse in cross section and then wrapped with CFRP show outstanding increase in the ultimate load carrying capacity which may be due to increased cross sectional area and effective confinement of FRP wrapping. As the number of layers of CFRP increases the ultimate load carrying capacity increases. With increase in eccentricity, the ultimate loads of the compression members were found to be decreased. Elliptical specimens wrapped with one and two layers of CFRP reported exponential increase in deformation ductility under concentric load condition and considerable increase under eccentric load condition compared to rectangular specimens wrapped with CFRP.
Keywords: Compression Members, CFRP Wrapping, Precast Segments, Elliptical Columns.
Scope of the Article: Artificial Intelligent Methods, Models, Techniques

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J76030891020

IJITEE Journal — Tue, 18 Jan 2022 06:04:18 +0000

Classification of Breast Cancer Histopathology Images using Machine Learning Algorithms
Kavya K¹, Savita K Shetty²
¹Kavya K, Department of Software Engineering, M. S. Ramaiah Institute of Technology, Bengaluru, India.
²Savita K. Shetty, Assistant Professor, Department of Computer Science and Engineering M.S. Ramaiah Institute of Technology, Bengaluru, India.
Manuscript received on July 27, 2020. | Revised Manuscript received on August 10, 2020. | Manuscript published on August 10, 2020. | PP: 441-444 | Volume-9 Issue-10, August 2020 | Retrieval Number: 100.1/ijitee.J76030891020 | DOI: 10.35940/ijitee.J7603.0891020
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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: Machine Learning (ML), provides system the capacity to learn instinctively and allows systems to improve themselves with past experience and without being programmed specifically. In the field of Medical Science, ML plays important role. ML is being used to develop new practices in medical science which deals with huge patient data. Breast Cancer is a chronic disease commonly diagnosed in women. According to the survey by WHO, rank of breast cancer is at number one as compared to other cancers in female. BC has two kinds of tumour: Benign Tumour (BT), and Malignant Tumour (MT). BTs are treated as non-cancerous cells. MTs are treated as cancerous cells. The unidentified MTs in time stretch to other organs. Treatment procedure for BT and MT is different. So, it is salient to determine precisely whether a tumour is BT or MT. In this proposed model, Histopathology Images are used as dataset. These Histopathology images are pre-processed using Gaussian Blur and K-means Segmentation. The pre-processed data fed into feature extraction model. ML algorithms such as Support Vector Machine (SVM), Random Forest (RF) and Convolution Neural Network (CNN) are applied to extracted features. Performance of these algorithms is Analysed using accuracy, precision, recall and F1-score. CNN gives the highest accuracy with 87%.
Keywords: Machine Learning (ML), Breast Cancer (BC), Histopathology.
Scope of the Article: Machine Learning

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