Volume-7 Issue-11, August 2018 – International Journal of Innovative Technology and Exploring Engineering (IJITEE)

K25170871118

IJITEE Journal — Fri, 16 Dec 2022 11:07:06 +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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A Jaya Algorithm Trained Neural Network for Stock Market Prediction
Puspanjali Mohapatra¹, Reeva Mishra², Tapas Kumar Patra³
¹Puspanjali Mohapatra, Department of Computer Science and Engineering, International Institute of Information Technology (IIIT), Bhubaneswar (Odisha), India.
²Reeva Mishra, Department of Computer Science and Engineering, International Institute of Information Technology (IIIT), Bhubaneswar (Odisha), India.
³Tapas Kumar Patra, Department of Electronics and Instrumentation Engineering, College of Engineering and Technology (CET), Bhubaneswar (Odisha), India.
Manuscript received on 15 August 2018 | Revised Manuscript received on 27 August 2018 | Manuscript published on 30 November 2018 | PP: 9-13 | Volume-7 Issue-11, August 2018 | Retrieval Number: K25170871118/18©BEIESP
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© 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 demonstrates how the two types of FLANN models (Functional link artificial neural network models) i.e. Chebyshev-FLANN (CFLANN) and Trigonometric-FLANN (TFLANN) are trained using Jaya algorithm to predict the Stock Market Indices. The intention of the current paper is putting forward a contrast between popular training algorithms such as Back Propagation (BP) and Jaya algorithm. The BP and Jaya algorithm trained FLANN models are examined for predicting stock indices for a day and a week ahead. The stock indices BSE500, DJIA and NASDAQ with few technical indicators are taken as inputs in this experimental time series data. The study confirms the superiority of Jaya algorithm trained FLANN models to the traditional BP trained FLANN models. The Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE) are used for performance evaluation. The simulation study is done using python3 in Anaconda environment.
Keyword: Stock Market Prediction, BP, Jaya algorithm, MAPE, MSE
Scope of the Article: Marketing and Social Sciences

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K25160871118

IJITEE Journal — Fri, 16 Dec 2022 11:02:17 +0000

Classification of Telephone Subscriber Errors Based on Text Messages in Vietnamese Language
Phan Thi Ha¹, Phuong Nguyen²
¹Dr. Phan Thi Ha, Department of Information Technology, Posts and Telecommunications Institute of Technology, Hanoi, Vietnam.
²Dr. Phuong Nguyen, Department of Information Technology, Posts and Telecommunications Institute of Technology, Hanoi, Vietnam.
Manuscript received on 15 August 2018 | Revised Manuscript received on 27 August 2018 | Manuscript published on 30 November 2018 | PP: 5-8 | Volume-7 Issue-11, August 2018 | Retrieval Number: K25160871118/18©BEIESP
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© 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 article describes a method for automatically classifying telecommunications subscriber errors based on text messages, using a machine learning method Support Vector Machine (SVM). The SVM method trains and tests on a set of data obtained from the text messages in Vietnamese of the actual line workers sending to the service operation centers. The results show that the proposed classification method using the SVM gives high accuracy and can be applied in practice.
Keyword: Text Classification, Natural Language Processing, Learning Support Vector Machine.
Scope of the Article: Classification

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K25150871118

IJITEE Journal — Fri, 16 Dec 2022 10:56:57 +0000

An Efficient Approach to Reduce Text Dimension for Precise Text Classification for Big Data
Akshada Bhanushali¹, Pravin Rahate²
¹Akshada Bhanushali, PG Student, Department of Computer Engineering, Datta Meghe College of Engineering, Navi Mumbai (Maharashtra), India.
²Pravin Rahate, Assistant Professor, Department of Computer Engineering, Datta Meghe College of Engineering, Navi Mumbai (Maharashtra), India.
Manuscript received on 15 August 2018 | Revised Manuscript received on 27 August 2018 | Manuscript published on 30 November 2018 | PP: 1-4 | Volume-7 Issue-11, August 2018 | Retrieval Number: K25150871118/18©BEIESP
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© 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 today’s society, few famous news websites such as Google and sina server gives information for users. But recently with the continuous development of information technology, the quantity of disorder data is increasing in volume. Text classification and organization has become a task. The traditional manual classification of news text not only consumes a lot of human and financial resources, but classification is also not achieved quickly. This paper makes a research about the news text classification. A news text classification model is proposed based on Latent Dirichlet Allocation (LDA) and Domain Word Filtering. The model reduces the features dimension of the news text effectively and gets good classification results. This model uses topic model to reduce text dimension and get good features as the dimension of the news texts is too high.
Keyword: Topic Model, LDA, Domain Word Filtering, News Website, Text Classification
Scope of the Article: Classification

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