Archive

Abstract:

The programmable paradigm in web technologies is evolving into a web service model where services and information can be reused by distinct users. Diverse information is present over the web and the problem of relevant information discovery based on location is a big challenge for web information retrieval system. Lack of Intelligent classification of information compounded the problem further. This paper presents an approach that extends information similarity analysis using probable clustering procedure and introduces specific results based on the current location of the user using Google location services. To capture the similarity of functional text, feature vector techniques are employed. Dissimilar words are classified as stop words and eliminated from the query string to reduce the complexity of search space. Location sensitive mechanism fetches only relevant information belonging to the current location of a user. Experiments were performed to compare classification accuracy with respect to various models used for feature vector extraction and result in emphasis the effectiveness of Semantic similarity extractor location-based web service model.

Keywords:

Intelligent service classification,Natural Language Processing,Location sensitive searching,

Refference:

I. A. R. Patil, “An Innovative Approach to Classify and Retrieve Text Documents
using Feature Extraction and Hierarchical Clustering based on Ontology,”
International Conference on Computing, Analytics and Security Trends (CAST)
IEEE, pp. 371–376, 2016.
II. A. I. Pratiwi, “On the Feature Selection and Classification Based on
Information Gain for Document Sentiment Analysis,” Applied Computational
Intelligence and Soft Computing, pp.33-37, 2018.
III. A. Cocos, M. Apidianaki, and C. Callison-burch, “Word Sense Filtering
Improves Embedding-Based Lexical Substitution,” In Proceedings of the 1st
Workshop on Sense, Concept and Entity Representations and their
Applications. pp. 110–119, 2017.
IV. C. Xiong and K. Lv, “An Improved K-means Text Clustering Algorithm By
Optimizing Initial Cluster Centers,” 7th International Conference on Cloud
Computing and Big Data (CCBD) IEEE. pp. 272–275, 2016.
V. D. Sumeet and P. Chowriappa, “Feature Selection and Extraction Strategies in
Data Mining,” Data Mining for Bioinformatics, CRC Press, pp. 113–144, 2012.
VI. D. Li, W. Zhang, S. Shen, and Y. Zhang, “SES-LSH : Shuffle-Efficient
Locality Sensitive Hashing for Distributed Similarity Search,” International
Conference on Web Services (ICWS) IEEE, pp. 822-827, 2017.
VII. F. T. Garc, J. Garc, A. Lucila, S. Orozco, F. Dami, and T. Kim, “Locating
Similar Names Through Locality Sensitive Hashing and Graph Theory,”
Multimedia Tools and Applications Springer, vol. 10, no.12, pp.1-14, 2018.
VIII. H. Shen, T. Li, Z. Li, and F. Ching, “Locality Sensitive Hashing Based
Searching Scheme for a Massive Database,” Third International Conference on
Digital Telecommunications (icdt 2008) IEEE, vol. 47, no. 52, IEEE, pp. 0–5,
2008.
IX. H. A. Atabay, “A Clustering Algorithm based on Integration of K-Means and
PSO,” 1st Conference on Swarm Intelligence and Evolutionary Computation
(CSIEC), IEEE. pp. 59–63, 2016.
X. J. K. Mandal, Advanced Computing and Communication Technologies
Springer, vol. 452, pp.494, 2016.
XI. J. Singh Chouhan and A. Gadwal, “Improving Web Search User Query
Relevance using Content based Page-Rank,” IEEE Int. Conf. Comput.
Commun. Control. IC4 , pp. 1-5, 2016.
XII. J. G, “RKE-CP : Response-based Knowledge Extraction from Collaborative
Platform of Text-based Communication,” International Journal of Advanced
Computer Science and Applications (IJACSA), vol. 8, no. 5, pp. 93–98, 2017.
XIII. K. Mishina, “Word Sense Disambiguation of Adjectives using Dependency
Structure and Degree of Association Between Sentences,” International
Conference on Asian Language Processing (IALP),IEEE. pp. 342–345, 2017.
XIV. M. Lapata and F. Keller, “Web-based Models for Natural Language
Processing,” Transactions on Speech and Language Processing (TSLP) ACM,
vol. 2, no. 1, pp. 1–30, 2005.

XV. M. Kaur, “Text Classification using Clustering Techniques and and PCA,”
Fourth International Conference on Parallel, Distributed and Grid Computing
(PDGC), IEEE, pp. 642-646, 2015.
XVI. M. Aydar and S. Ayvaz, “An Improved Method of Locality-Sensitive Hashing
for Scalable Instance Matching,” Knowledge and Information Systems, vol.58,
no.2, pp. 275-294, 2018.
XVII. R. Collobert, J. Weston, and M. Karlen, “Natural Language Processing from
Scratch,” Transactions on Speech and Language Processing ACM, vol. 1, pp.
1–34, 2000.
XVIII. S. Sharma, Sunita, A. Kumar, and V. Rana, “An Optimum Approach for
Preprocessing of Web User Query,” International Journal of Informatics and
Communication Technology (IJ-ICT), vol. 7, no. 1, pp. 8–12, 2018.
XIX. Sunita, and V. Rana,” Removing Ambiguity Problem Based on Clustering in a
Web Search,” First International Conference on Secure Cyber Computing and
Communication (ICSCCC) IEEE, pp. 9-12, 2018.
XX. Z. Jin, Y. Lai, J. Y. Hwang, S. Kim, and A. J. Teoh, “Ranking Based Locality
Sensitive Hashing Enabled Cancelable Biometrics : Index – of – Max Hashing,”
Transactions on Information Forensics and Security IEEE, vol. 60, no.13, pp.
393-407, 2018.
XXI. Z. Lu, Q. Liao, and D. Li, “Locality Sensitive Hashing Based Deepmatching
for Optical Flow Estimation,” International Conference on Acoustics, Speech
and Signal Processing (ICASSP) IEEE, pp. 1472–1476, 2017.

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Abstract:

I. C. Lee and I. Paik, Stock Market Analysis from Twitter and News Based on Streaming Big Data Infrastructure , in Proceedings - 2017 IEEE 8th International Conference on Awareness Science and Technology, iCAST, Taichung, 2017, pp. 312-317. II. J.V.M. Lakshmi, A Framework Model on Big Data Analytics using Machine Learning Techniques for Prediction on Datasets”, Ph.D. dissertation, Dept. Comp. Sci. and App., Sri Chandrasekhar Univ., Enathur, Kanchipuram, 2018. III. M. M. Seif et al, Stock Market Real Time Recommender Model Using Apache Spark Framework, Springer AMLTA 2018, pp. 671–683, 2018,https://doi.org/10.1007/978-3-319-74690-6_66. IV. M. Shastri, S. Roy, M. Mittal , Stock Price Prediction using Artificial Neural Model: An Application of Big Data, EAI Endorsed Transactions on Scalable Information Systems, 2019 ,vol- 6, issue 20.O. B. Sezer , A. M. Ozbayoglu , An Artificial Neural Network-based Stock Trading System Using Technical Analysis and Big Data Framework , ACMSE 2017, Kennesawtate University, GA, U.S.A., April, 2017,DOI -10.1145/3077286.3077294. V. R. T. Llame et al, Big Data Time Series Forecasting Based on Nearest Neighbours Distributed Computing with Spark, Knowledge Based Systems (2018), DOI: 10.1016/j.knosys.2018.07.026 VI. S. Kalra, S. Gupta, J. S. Prasad, Sentiments Based Forecasting for Stock Exchange using Linear Regression, unpublished. VII. O. B. Sezer , A. M. Ozbayoglu, An Artificial Neural Network-based Stock Trading System Using Technical Analysis and Big Data Framework , ACMSE 2017, Kennesaw State University, GA, U.S.A., April, 2017,DOI - 10.1145/3077286.3077294. VIII. V. K. Menon et al, Bulk Price Forecasting Using Spark over NSE Data Set, International Conference on Data Mining and Big Data, DMBD 2016, pp 137-146. IX. https://www.amazon.in/ONGC-Natural-Corporation-Ltd-2019/productreviews/ 9388426983 X. https://www.autocarindia.com/car-reviews/2018-maruti-suzuki-alto-reviewtest- drive-412662 XI. https://www.autocarindia.com/car-reviews/2018-maruti-suzuki-ciaz-15- diesel-review-test-drive-412307 XII. https://www.auto.ndtv.com/maruti-suzuki-cars/baleno/reviews XIII. https://auto.ndtv.com/maruti-suzuki-cars/swift/reviews XIV. https://www.carwale.com/marutisuzuki-cars/baleno/userreviews XV. https://data-flair.training/forums/topic/what-is-worker-node-in-apache-sparkcluster/ XVI. https://www.mouthshut.com/product-reviews/Dabur-Vatika-Hair-Oilreviews- 925004768 XVII. http://site.clairvoyantsoft.com/understanding-resource-allocationconfigurations- spark-application/ XVIII. https://www.snapdeal.com/product/dabur-chyawanprash- 50g/657387760199/reviews?page=3&sortBy=RECENCY XIX. https://www.yahoofinance.com

Keywords:

Supervised learning,Product Reviews,Google Cloud, Big data,Apache Spark,

Refference:

Implementing machine learning models for the stock’s big data emerged as a
component of algorithmic trading systems. This paper proposed a hybrid stock
prediction model based on the collection of qualitative and quantitative data of
particular stocks. In addition to tweets and news data, product reviews of the specific
companies traded under National Stock Exchange are considered to analyze their effect
on the stock movements. Historical Prices will be integrated with sentiment values
generated from tweets, news and product reviews data to construct the amalgam model
using Apache Spark and HDFS for storage of large data. The proposed model has been
implemented in Google Cloud Platform with different cluster configurations. The paper
compares the prediction accuracy based on various types of input data provided to the
model using some popular machine learning algorithms.

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Abstract:

In this paper generated a key that is a nonlinear Bruer generator and enhancement this key generator in order to increment the randomness the key generated and increment the security to the system. And calculate the autocorrelation property for Bruer generator and enhancement Bruer generator. Now by using the enhancement key generated, will encryption a message and decryption the ciphertext to the original message. Also will be designing the interfaces system for the: password for the user, encryption messages and decryption messages, by using program MATLAB (R2017b).

Keywords:

Cryptography,Stream cipher,LFSR,Key generators,Nonlinear Combining Function,Autocorrelation Property,

Refference:

I. Ahmed Amer Alsaadi and Ayad G. Naser Al-Shammari, 2019, “Enhancement
of Non-Linear Generators and Calculate the Randomness test for
Autocorrelation Property”, Iraqi Journal of Science.
II. Ayad G. Naser Al-Shammari and Rusol M. Shaker Alzewary, 2016, “Design
of High Efficiency Non-linear Keys Generator Based on Shift Registers”, Iraqi
Journal of Science.
III. Abdullah Ayad Ghazi and Faez Hassan Ali, 2018, “Design of New Dynamic
Cryptosystem with High Software Protection”, Iraqi Journal of Science.
IV. A. Kuznetsov, V. Potii, A. Poluyanenko, and V. Stelnik, 2019, “Nonlinear
Functions of Complication for Symmetric Stream Ciphers”,
Telecommunications and Radio Engineering.
V. A. Menezes, P. van Oorschot and S. Vanstone, 1997, “Handbook of Applied
Cryptography”, CRC Press, Inc.
VI. Bemdt M. Gammel, Rainer Gottfert and Oliver Kniffler, 2006, “An NLFSRBased
Stream Cipher”, IEEE International Symposium on Circuits and
Systems, Island of Kos, Greece
VII. Fred Piper, 1983, “stream ciphers”, Springer, Berlin, Heidelberg.
VIII. Gutha Jaya Krishna, Vadlamani Ravi, S. Nagesh, 2018, “Key Generation for
Plain Text in Stream Cipher via Bi-Objective Evolutionary Computing”,
ELSEVIER, Applied Soft Computing Journal
IX. I. Gorbenko, A. Kuznetsov, Y. Gorbenko et al, 2019, “Studies on Statistical
Analysis and Performance Evaluation for Some Stream Ciphers”, International
Journal of Computing.
X. Nikos Komninos, 2007, “Morpheus: stream cipher for software and hardware
applications”, Conference 9th IEEE International Symposium on
Communication Theory and Applications, Ambleside, United Kingdom
XI. Olfa Mannai, Rabei Becheikh and Rhouma Rhouma, 2018, “A new Stream
cipher based on Nonlinear dynamic System”, European Signal Processing
Conference (EUSIPCO).
XII. Poluyanenko, Nikolay, 2017, “Development of the search method for nonlinear
shift registers using hardware, implemented on field programmable gate
arrays”, EUREKA: Physics and Engineering.
XIII. XingyuanWang, XiaojuanWang, Jianfeng Zhao and Zhenfeng Zhang, 2011,
“Chaotic encryption algorithm based on alternant of stream cipher and block
cipher”, Springer, Nonlinear Dynamics.

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Abstract:

In this paper, some of the key types used in the encryption system are displayed, and one type of key generator is displayed (Geffe generator). Matlab 2017 also uses some interfaces to illustrate the frequency test on the Encryption keys. Also, interfaces are displayed for encrypting and decrypting a message.

Keywords:

Encryption,Frequency,LFSR,Decryption,

Refference:

I. D. Watanabe, S. Furuya, H. Yoshida, and B. Preneel, “A New Keystream
Generator MUGI”, 2002 , University of Leuven, Belgium.Modular
Addition” , 2006,University of Leuven, Belgium.
II. Fischer, S, 2008 “Analysis of Lightweight Stream Ciphers” M Sc. thesis.
Department of Physics, University of Berne of nationality Suisse.
III. Ibrahim Abdul Rasool Hamoud,Ayad Ghazi Naser, 2019, “Enhancement of
Non-Linear Generators to Calculate the Randomness Test for Frequency
Property in the Stream Cipher Systems”, Iraqi Journal of Science, University
of Baghdad.
IV. John Apostolopoulos, S.J. Wee, 2001, “Secure scalable streaming enabling
transcoding Without decryption ” , Thessaloniki, Greece.
V. Kevin M. Cuomo and Alan V. Oppenheim, 1993, “Circuit implementation of
synchronized Chaos with applications to communications”, Massachusetts
Institute of Technology, Cambridge.
VI. Rusol M. Shaker Alzewary, Ayad G. Naser Al-Shammar, 2016, “Design of
High Efficiency
VII. Sam Higginbottom, 2019, “Image encryption using block based
transformationAlgorithm “, University of Agriculture Technology and
Sciences, India.
VIII. Souradyuti Paul, Bart Preneel, “On the (In) security of Stream Ciphers Based
on Arrays and Non-linear Keys Generator Based on Shift Registers”, Iraqi
Journal of Science, University of Baghdad.
IX. Lawrence C. Washington,2008, “Elliptic Curves Number Theory and
Cryptography,
X. Mattsson, J., 2006 “Stream Cipher Design”, M S thesis Department of
Computer Science, at The School of Engineering Physics, University of
Royal Institute of Technology, Stockholm, Sweden.
XI. Neal Koblitz, “An elliptic curve implementation of the finite field digital
signature Algorithm” , University of Washington.
XII. Thomas Peyrin, 2016, “Fast Software Encryption”,University
ofBerlin,Germany.
XIII. Yassir Nawaz,”Design of Stream Ciphers and Cryptographic Properties of
NonlinearFunctions”,Waterloo, Ontario, Canada, 2007.

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Abstract:

Currently, the prominence of automatic multi document summarization task belongs to the information rapid increasing on the Internet. Automatic document summarization technology is progressing and may offer a solution to the problem of information overload. Automatic text summarization system has the challenge of producing high quality summary. In this paper, the design of generic text summarization model based on sentence extraction has been redirected into more semantic measure reflecting the two significant objectives: content coverage and diversity when generating summaries from multiple documents as an explicit optimization model. The proposed two models have been then coupled and defined as single-objective optimization problem. Also, different integrations of similarity measures have been introduced and applied to the proposed model in addition to the single similarity measure that bases on using Cosine, Dice and 𝐽𝑎𝑐𝑐𝑎𝑟𝑑 similarity measures for measuring text similarity involving integrating double similarity measures and triple similarity measures. The proposed optimization model has been solved using Genetic Algorithm. Moreover, heuristic initialization has been proposed and injected into the adopted evolutionary algorithm to harness its strength. Document sets supplied by Document Understanding Conference 2002 (𝐷𝑈𝐶2002) have been used for the proposed system as an evaluation dataset and as an evaluation metric, Recall-Oriented Understudy for Gisting Evaluation (𝑅𝑂𝑈𝐺𝐸) toolkit has been used for performance evaluation of the proposed method and for performance comparison against other baseline systems. Comparison results for the proposed optimization based model against other baselines verified that the proposed system outperforms other baseline approaches in terms of 𝑅𝑜𝑢𝑔𝑒 − 2 and 𝑅𝑜𝑢𝑔𝑒 − 1 scores wherein it has recorded a score of 0.4542 for 𝑅𝑜𝑢𝑔𝑒 − 1 and 0.1623 for 𝑅𝑜𝑢𝑔𝑒 − 2.

Keywords:

Heuristic Initialization,integrations of similarity measures,Gisting Evaluation (ROUGE),optimization based model,

Refference:

I. Asad Abdi, Norisma Idris, Rasim M. Alguliev, Ramiz M. Aliguliyev. (2015),
Automatic summarization assessment through a combination of semantic and
syntactic information for intelligent educational systems.
II. Asad Abdi, Norisma Idris, Rasim M Alguliev, Ramiz M Aliguliyev. (2015),
Asad Abdi, Norisma Idris, Rasim M Alguliev, Ramiz M Aliguliyev
III. Anna Huang. (2008), Similarity Measures for Text Document Clustering.
IV. Amit Singhal. (2001), Modern Information Retrieval: A Brief Overview
V. Islam, A. and Inkpen, D. 2008. Semantic text similarity using corpus-based
word similarity and string similarity, ACM Transactions on Knowledge
Discovery from Data 2 (2) Article 10, 25 p.
VI. Pang-Ning; Steinbach, Michael; Kumar, Vipin (2005), Introduction to Data
Mining.

VII. RASIM M. ALGULIEV, RAMIZ M. ALIGULIYEV, AND CHINGIZ A.
MEHDIYEV. (2013), AN OPTIMIZATION APPROACH TO
AUTOMATIC GENERIC DOCUMENT SUMMARIZATION.
VIII. Rasim M. Alguliev, Ramiz M. Aliguliyev, Chingiz A. Mehdiyev. (2011), An
Optimization Model and DPSO-EDA for Document Summarization
IX. Radev, D., Jing, H., Stys, M. and Tam, D. 2004. Centroid-based
summarization of multiple documents, Information Processing &
Management 40 (6) 919–938.
X. Rasim M Alguliev, Ramiz M Aliguliyev, Chingiz A Mehdiyev. (2011), An
optimization model and DPSO-EDA for document summarization.
XI. Rasim M Alguliev, Ramiz M Aliguliyev, Makrufa S Hajirahimova, Chingiz
A Mehdiyev. (2011), MCMR: maximum coverage and minimum redundant
text summarization model
XII. Rasim M Alguliev, Ramiz M Aliguliyev, Nijat R Isazade. (2013),
Formulation of document summarization as a 0-1 nonlinear programming
problem
XIII. Rasim M Alguliev, Ramiz M Aliguliyev, Chingiz A Mehdiyev. (2013), An
optimization approach to automatic generic document summarization
XIV. Rasim M Alguliyev, Ramiz M Aliguliyev, Nijat R Isazade. (2015), An
unsupervised approach to generating generic summaries of documents
XV. Rasmita Rautray, Rakesh Chandra Balabantaray. (2017), Cat swarm
optimization based evolutionary framework for multi document
summarization
XVI. Rasim M Alguliyev, Ramiz M Aliguliyev, Nijat R Isazade, Asad Abdi,
Norisma
XVII. Rada Mihalcea, Courtney Corley, Carlo Strapparava. (2006), Corpus-based
and Knowledge-based Measures of Text Semantic Similarity.
XVIII. saleh et. Al. (2015), A genetic based optimization model for extractive multi
dormant text summarization. Iraqi Journal of Science. 2015;56(2B):1489-98.

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Abstract:

Unemployment rate forecasting has become a particularly promising field of research in recent years because it's an important problem in state planning and management. Since the time series data are rarely pure linear or nonlinear obviously, sometimes contain both components jointly. Therefore, this study introduces new hybrid models contain Three commonly used, first is the Stochastic Linear Autoregressive Moving Average with eXogenous variable (ARMAX) model for modeled the relationship between the unemployment rate and exchange rate, second and third are a nonlinear Generalized Autoregressive Conditional Heteroscedasticity (GARCH) and GARCH with eXogenous variable (GARCHX) used When the assumption of homoscedasticity error variance is violated for the purpose of capture the volatility in the residuals of ARMAX model and to enhance the Forecasting ability of ARMAX model by combining it with other nonlinear models. In this case, to have a better forecasting efficiency, we introduce a hybrid methodology of amalgamating the forecasts from a linear time series model (ARMAX) and from a nonlinear time series model (GARCH, GARCHX) with three different distributions (Normal Distribution, Student’s t-distribution and General Error Distribution (GED)), the last two distributions for capturing fat-tailed properties in residuals time series. The hybrid approach specifically (ARMAX-GARCH) and (ARMAXGARCHX) have been used for modeling and forecasting the unemployment rate in the USA. Diverseapproacheshave beenemployed in the parameters vectorestimation. A comparison evaluation was as well been done based on mean absolute error (MAE), mean absolute percentage error (MAPE), as well as Root mean square error (RMSE) between the hybrid and their individual rival model in accordance with forecasting performance. From investigational results, it is perceived that the hybrid model (ARMAX-GARCHX) is more effectualthan other twin hybrid and individual rival models for the data under study. MATLAB, SAS, and EViews software packages have used for the data analysis

Keywords:

ARMAX,GARCH,GARCHX,Normal distribution,Student-t distribution,General Error distribution (GED),Hybrid model,Unemployment rate,Exchange rate,

Refference:

I. Aldemġr, A and Hapoğlu, H.(2015).” Comparison of ARMAX Model
Identification Results Based on Least Squares Method”. IJMTER, Vol. (02),
No.(10), PP(27-35).
II. 2Bollerslev, T. (1987). “A conditional heteroscedastic time series model for
speculative prices and rates of return”. Review of Economics and Statistics, 69,
542-547.
III. 3Bollerslev, Tim.(1986). “Generalized autoregressive conditional
heteroscedasticity,”. Journal of Econometrics, Vol. (31), No (3), pp (307-327).
IV. 4Brock,.W. A., Dechert,.W., Scheinkman,.J., and LeBaron,.B. (1996).”A test
for independence based on the correlation dimension”. Economic Reviews,
Vol(15),No.(3).pp(197–235).
V. 5Engle, R. (1982).”Autoregressive Conditional Heteroscedasticity with
Estimates of the Variance of United Kingdom Inflation”. Econometrica
,Vol.(50),No(4),pp (987-1007).
VI. 6Engle, R. (2001). “GARCH 101: The use of ARCH/GARCH models in
applied econometrics”. Journal of Economic Perspective, Vol. (15), No. (4),
pp.(157-168).
VII. 7Feng, L., & Shi, Y. (2017). “A simulation study on the distributions of
disturbances in the GARCH model”. Cogent Economics and Finance, Vol.(5),
No.(1), pp (1-19).
VIII. 8Franses, P. H., van Dijk, D. J. C., and Opschoor, A. (2014). “Time Series
Models for Business and Economic Forecasting”, 2nd Edition. Cambridge
University Press.
IX. 9Gao,Y. Zhang,.C and Zhang, L . (2012). “Comparison of GARCH Models
based on Different Distributions”. Journal of Computers, VOL. (7), NO. (8), pp
(1967-1973).

X. 10George E. P. Box; Gwilym M. Jenkins; Gregory C. Reinsel; Greta M. Ljung.
(2015). “Time Series Analysis Forecasting and Control”, Fifth Edition, John
Wiley & Sons Inc. Hoboken, New Jerse
XI. 11Gooijer, J. G. D., & Hyndman, R. J. (2006). “25 years of time-series
forecasting. International Journal of Forecasting”. Vol.(22),No.(3), pp (443-73).y.
XII. 12Han, H., and Kristensen, D. (2014). “Asymptotic Theory for the QMLE in
GARCH-X Models With Stationary and Nonstationary Covariates”. Journal of
Business and Economic Statistics , Vol.(32),No.(3), pp(416–429).
XIII. 13Hickey, E., Loomis, D. G., & Mohammadi, H. (2012). “Forecasting hourly
electricity prices using ARMAX–GARCH models: An application to MISO
hubs”. Energy Economics, Vol (34), No(1), pp(307–315).
XIV. 14Lee, J.H.H., (1996), “A Lagrange Multiplier Test for GARCH models”.
Econometric Letters, Vol. (37), pp (256-271).
XV. 15Ljung, L., (1999).”System Identification Theory for user”,2nd ed. Prentice
Hall Upper Saddle River N.J. London UK.
XVI. 16Mitra, D., & Paul, R. K. (2017). Hybrid time-series models for forecasting
agricultural commodity prices. Model Assisted Statistics and Applications,
Vol.(12),No(3), pp(255–264).
XVII. 17Moeeni, H., & Bonakdari, H. (2017). Impact of Normalization and Input on
ARMAX-ANN Model Performance in Suspended Sediment Load Prediction.
Water Resources Management, Vol(32), No(3), pp(845–863).
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Neural Networks and Fuzzy Models”. Springer, New York.
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approach. Econometrica, Vol(59), pp(347-370).
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Twitter Posts Add Information to the Stock Market ARMAX-GARCH Model”.
Higher School of Economics Research Paper No. WP BRP 54/FE.
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“Financial econometrics: From basics to advanced modeling techniques”. John
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of GARCH Model under Misspecified Probability Distributions: A Monte
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Abstract:

The corrosion rate of brass in sodium chloride solution has been studied by means of weight-loss method. Typically the weight loss of the brass in sodium chloride solution in the occurrence of various concentrations of (1%, 2% and 3%) NaCl solution was determined right after 24 hrs immersion. The weight loss experiment was taken out at temperature 25 ˚C . The corrosion regarding the metal was increased with an increase in the concentration of salt. The effect of corrosion on roughness of brass was investigated. Mechanical properties such as hardness by using Vickers method and compressive test were carried out making use of instron 8872 instrument. The results attained showed that the mechanical properties of brass improved for with and with no immersion method exhibited of which increase the corrosion rate of brass, lead to decrease of the strain and stress, and decrease hardness of metal

Keywords:

Brass,Corrosion Rate,Weight-Loss,Roughness,Hardness,Compressive Test,

Refference:

I. A .Heinrich, Al-Kassab, T., Kirchheim, R., 2007. Investigation of
newaspects in the initial stages of decomposition of Cu2at.%Co with the
tomographic atom probe and the field ion microscope”, Surface and
interface analysis 39(2) , pp240-245.
II. A .Badawy, W., S.S.El-Egamy and A.S.El-Azap,1985.the electrochemical
behaviour of leaded brass in neutral Cl-and SO4-media, Corros
.Sci.,37(12).pp1969-1979.
III. A .Ovat1, F. , Asuquo L.O., . Abam, F.I., 2012. The Influence Of
Aluminum And Manganese On Some Mechanicalproperties Of Brass.
Research Journal in Engineering and Applied Sciences 1(4) pp214-218.
IV. Chen, J., , Li, Z. , Zhao, Y., 2009. Corrosion characteristic of Ce Al brass
in comparison with As Al brass. Materials and Design 30(5) , pp1743–
1747 .
V. Freudenberger, J., Kaumann, A., Klauß, H., Marr, T., Nenkov, K.,
Subramanya Sarma, V., Schultz, L., 2010. Studies on recrystallization of
single-phase copper alloys by resistance measurements, Acta Materialia in
print.
VI. Gad – Alah , A.G., Abou- Romio, MM., Badawy, M.W., Rehan, H.H .,
1991.passivity of brass (Cu-Zn/67-33) and breakdown in neutral and
alkaline solutions containing halide ions . J .Appl.
Electrochem.,21(9),pp829-839.

VII. Heinrich, A., Al-Kassab, T., Kirchheim, R., 2007. Investigation of new
aspects in the initial stages of decomposition of Cu2at.%Co with the
tomographic atom probe and the weld ion microscope, Surface and
interface analysis 39(2) ,pp 240-245.
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Abstract:

Using metals in the industry is widely utilized and have the properties which make it possible to expose it to heat, high force and punch, flexion and modelling. The fore most goal of this study is to deliberate the reported studies about the influence of radius and angle of bending on the concentration of stresses in Aluminum sheet. This research is a quantitative research which is made through reviewing other articles and researches which is concerned with the objective of this article and its applications. Studies and researches were made in order to optimize the methodology of the metal formation to make it less power and time consuming with better formation and less errors.

Keywords:

bending activity,stress concentration,optimization methodology of the metal formation,aluminum sheet,

Refference:

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Abstract:

Due to openness of the deployed environment and transmission medium (Internet), Internet of Things (IoT) suffers from various types of security attacks including Denial of service, Sinkhole, Tampering etc. Securing IoT is achieved a greater research interest and this paper proposes a new secure routing strategy for IoT based on trust model. In this model, initially the nodes of the network are formulated as clusters and the IoT nodes which are more prominent in trustworthiness and energy are only chosen as Cluster Heads. Further a trust evaluation mechanism was accomplished for every Cluster Node at Cluster Head to build a secure route for data transmission from source node to destination node. The trust evaluation is a composition of the communication trust, nobility trust and data trust. Simulation experiments are conducted over the proposed approach and the performance is analyzed through the performance metrics such as Packet Delivery Rate, Network Lifetime, and Malicious Detection Rate. The obtained performance metrics shows the outstanding performance of proposed method even in the increased malicious behavior of network.

Keywords:

Internet of Things,Trust Management,Clustering,Communication Trust,Malicious Detection Rate,Network Lifetime,

Refference:

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Abstract:

This paper presents a simple and fast feature extraction technique for classification of four varieties of rice grain. Three colour models (RGB, HSV and HSI) are obtained from the input colour images. Variance and Covariance features are then extracted from each of the three colour models. The classification of rice grains are then carried out using a Back Propagation Neural Network with adaptive thresholding. The computational time for feature extraction and their classification accuracies are also compared with other feature extraction techniques. It is found that the time taken using variance and covariance features extraction technique is relatively less compared to other feature extraction techniques. It is also seen that the proposed feature extraction technique is able to achieve better classification accuracy as compared to other feature extraction techniques discussed in this paper. Results suggest that the proposed technique is able to yield higher classification accuracy than that of other statistical classifiers like K- Nearest Neighbour (K-NN), Naïve Bayes and Support Vector Machine (SVM). The performances of all four classifiers were also tested against standard data sets.

Keywords:

Image,Colour,Features,Variance,Co-variance,Neural Network,

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