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Abstract:

Relying on the fact that expenses on managing employee turnover costs a lot to the organizations, understanding on the contributors of high turnover becomes crucial. The present paper is focussed on this fact and progresses with an objective to explore the relevant factors influencing employee turnover and put forth their ranking based on their strength of influence. The study successfully concluded four reliable factors – personal, job influencers, environment & working conditions and benefits & welfare measures, as factors influencing employee turnover in the industries selected as sample. The responses of the respondents from manufacturing, mining and services sectors from North east India, were analysed for its reliability and data reduction using SPSS package software. The study further applied Grey Relational analysis method for prioritizing the explored factors for meaningful conclusions.Based on the analysis, the study concludes that statements belonging to employee benefits and welfare measures factor were ranked above all as major influencers for employee turnover in the sample organization represented in the study. The study suggests a roadmap to determine which factors guide towards higher employee turnover and turnover in an organization. They should concentrate on the items for better improvement plans facilitating retention in future.

Keywords:

Employee Turnover,Employee Attrition,Manufacturing,Services,Employee retention,India,

Refference:

I. Abbasi, S. and Hollman, K. (2000), “Turnover: the real bottomline”, Public
Personnel Management, 29 (3), 333-342.
II. Adhikari, A. (2009). Factors affecting employee attrition: a multiple
regression approach. IUP Journal of Management Research, 8(5), 38.
III. Arthur, J. B. (1994). Effects of human resource systems on manufacturing
performance and turnover. Academy of Management Journal, 37, 670-687
IV. Beriha, G. S., Patnaik, B., Mahapatra, S. S., &Sreekumar. (2011).
Occupational health and safety management using grey relational analysis: an
Indian perspective. International Journal of Indian Culture and Business
Management, 4(3), 298-324.
V. Deng, J. (1982). System and Control Letter. Control Problems of Grey
System, 1(5), 288-94.

VI. Dobhal& Nigam, 2018. Employee Attrition and Employee Satisfaction: A
Study of HR, Performance Appraisal & Training Practices in Defence PSUs
in India, IOSR Journal of Business and Management (IOSR-JBM), 20(2), 01-
27
VII. Field, A. (2009). Discovering statistics using SPSS. Sage publications.
VIII. Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., &Tatham, R. (2010).
Multivariate data analysis. Pearson.
IX. Herman, R.E. (1999), “Hold on to the people you need”, HR Focus Special
Report on Recruitment and Retention, June, Supplement 11.
X. Ho, J. S. Y., Downe, A. G., &Loke, S. P. (2010). Employee attrition in the
Malaysian service industry: Push and pull factors. IUP Journal of
Organizational Behavior, 9.
XI. Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1),
31-36.
XII. Kim, H. and Stoner, M. (2008), “Burnout and turnover intention among
social workers: effects of role stress, job autonomy and social support”,
Administration in Social Work, 32(3), 5-25.
XIII. Latha, K. L. (2013). A study on employee attrition and retention in
manufacturing industries. BVIMSR’s Journal of Management Research
(BJMR), 5(1), 1-23.
XIV. Liu, S., & Lin, Y. (2006). Grey information: theory and practical
applications. Springer Science & Business Media.
XV. Liu, S., Forrest, J., & Yang, Y. (2012). A brief introduction to grey systems
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XVI. MacCallum, R. C., Widaman, K. F., Zhang, S., & Hong, S. (1999). Sample
size in factor analysis. Psychological methods, 4(1), 84.
XVII. Magner, N., Welker, R. and Johnson, G. (1996), “The interactive effects of
participation and outcome favorability in performance appraisal on turnover
intentions and evaluations of supervisors”, Journal of Occupational &
Organizational Psychology, 69, 135-143.
XVIII. Moran, J., Granada, E., Míguez, J. L., &Porteiro, J. (2006). Use of grey
relational analysis to assess and optimize small biomass boilers. Fuel
Processing Technology, 87(2), 123-127.
XIX. Nulty, D. D. (2008). The adequacy of response rates to online and paper
surveys: what can be done? Assessment & evaluation in higher
education, 33(3), 301-314.
XX. Nunnally, J. C., & Bernstein, I. H. (1994). Psychological theory. New York,
NY: MacGraw-Hill.
XXI. Sahney, S. (2011). Delighting customers of management education in India: a
student perspective, part I. The TQM Journal, 23(6), 644-658.
XXII. Sahu, A. and Gupta, M. (1999), “An Empirical Analysis of Employee
Turnover in a Software Organization”, Indian Journal of Industrial Relations,
35(1), 55-73.
XXIII. Saini, P., & Subramanian, V. (2014). Employee attrition in selected
industries: ITES, Banking, Insurance and Telecommunication in Delhi &
NCR.

XXIV. Saleem M and Affandi H (2014), HR Practices and Employees Retention, an
empirical analysis of Pharmaceutical sector of Pakistan, IOSR Journal of
Business and Management, 16(6).
XXV. Sekaran, U., &Bougie, R. (2016). Research methods for business: A skill
building approach. John Wiley & Sons.
XXVI. Udechukwu, I.I. and Mujtaba, B.G. (2007), “Determining the probability that
an employee will stay or leave the organization: a mathematical and
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XXVII. Vinit Singh Chauhan, Druvesh Patel (2013). „Employee Turnover: A
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675-687.

 

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Abstract:

The Indian management education sector is experiencing a highly competitive and complex environment today. Following which, the Universities and other higher educational institutions have realised the importance of being distinct from their competitors. One of the major pathway to do so is maintaining high standards in educational service quality which will foster developing positive bonding with the students. The present study is carried out with an objective to explore the dimensions influencing the service quality in management education particularly in public university system and to prioritize the dimensions from the perspective of management students. The study engaged exploratory factor analysis and independent RIDIT analysis methodology to analyse the survey responses of 211 management students of public universities. The analysis yielded seven perceived service quality dimensions,namely physical factors, leisure factors, academic factors, industry collaborations, responsiveness, learning outcome and personality development as perceived by the students from EFA. The individual items of these dimensions were then prioritised using RIDIT analysis for further interpretations and business insights. This study may benefit the university decision makers in business studies to formulate policies and strategies to assure superior students satisfaction which can later benefit the university by showing positive behavioural intentions.

Keywords:

Perceived service quality,management education,RIDIT analysis,student satisfaction,higher education,

Refference:

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for Measuring ServiceQuality in Indian Higher Education Sector.
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Psychometrika, 55(4), 603-616.
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comparative study of selected state owned and private universities. Indian
Journal of Marketing, 45(4), 32-42.
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business management education in India: A study in topsismodeling. Asia
Pacific Journal of Marketing and Logistics, 27(2), 208-225.
VI. Clemes, M. D., Cohen, D. A., & Wang, Y. (2013). Understanding Chinese
university students’ experiences: an empirical analysis. Asia Pacific Journal
of Marketing and Logistics, 25(3), 391-427.
VII. Crosby, P.B. (1979). Quality Is Free. McGraw-Hill, New York, NY.
VIII. DeShieldsJr, O. W., Kara, A., &Kaynak, E. (2005). Determinants of business
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two-factor theory. International journal of educational management, 19(2),
128-139.
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L.(2010). Multivariate data analysis. Pearson.
XIV. Jain, R., Sahney, S., &Sinha, G. (2013). Developing a scale to measure
students’ perception of service quality in the Indian context. The TQM
Journal, 25(3), 276-294.
XV. Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1),
31-36.
XVI. Kaushal, S. K. (2013). The importance of apparel product attributes for
teenaged buyers. NMIMS Management Review, 23, 45-64.
XVII. Kondasani, R. K. R. (2016). Managing Customer Perceived Service Quality
in Private Healthcare Sector in India (Doctoral dissertation).
XVIII. Kumar, R. V., & Bhattacharyya, S. (2017). Modeling consumer opinion using
RIDIT and grey relational analysis. In,Handbook of research on intelligent
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education: an exploratory study of customer impressions of service quality.
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size in factor analysis. Psychological methods, 4(1), 84.
XXI. Mahajan, R., Agrawal, R., Sharma, V., &Nangia, V. (2014). Factors affecting
quality of management education in India: An interpretive structural
modelling approach. International Journal of Educational Management,
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33(3), 301-314.
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Application of RIDIT method to likert scale surveys (Doctoral Dissertation).
Retrieved from http://ethesis.nitrkl.ac.in/1518/1/PDF.pdf

XXXV. Punia, B. K., &Kundu, S. C. (2005). Management Education in India:
Towards Quality Standards and Global Competitiveness. Deep and Deep
Publications.
XXXVI. Sadhukhan, S., Banerjee, U. K., &Maitra, B. (2015). Commuters’ perception
towards transfer facility attributes in and around metro stations: experience in
Kolkata. Journal of Urban Planning and Development, 141(4), 04014038.
XXXVII. Sahney, S. (2011). Delighting customers of management education in India: a
student perspective, part I. The TQM Journal, 23(6), 644-658.
XXXVIII. Sahney, S. (2011). Delighting customers of management education in India: a
student perspective, part II. The TQM Journal, 23(5), 531-548.
XXXIX. Schall, M. (2003). Best practices in the assessment of hotel-guest attitudes.
Cornell Hotel and Restaurant Administration Quarterly, 44, 51-65.
XL. Sekaran, U., &Bougie, R. (2016). Research methods for business: A skill
building approach. John Wiley & Sons.
XLI. Sohail, S. and Shaikh, N.M. (2004). Quest for excellence in business
education: a study of student impression of service quality. The International
Journal of Educational Management, 18(1), 58-65.
XLII. Sultan, P., & Wong, H. (2010). Performance-based service quality model: an
empirical study on Japanese universities. Quality Assurance in Education,
18(2), 126-143.
XLIII. Temtime, Z. T., &Mmereki, R. N. (2011). Challenges faced by graduate
business education in Southern Africa: perceptions of MBA participants.
Quality assurance in education, 19(2), 110-129.
XLIV. Uwawunkonye, E. G., &Anaene, O. I. C. (2013). A comparative study
between ridit and modified ridit analysis. American Journal of Theoretical
and Applied Statistics, 2(6), 248-254.
XLV. Verma, S., & Prasad, R. K. (2013). Measuring the Satisfaction Gap in
Management Education: A Road Map for Achieving Excellence. Journal of
Business and Management, 1(3), 96-108.
XLVI. Yusof, A. R. M., Hassan, Z. F., Rahman, S. A., &Ghouri, A. M. (2012).
Educational service quality at public higher educational institutions: A
proposed framework and importance of the sub-dimensions.

 

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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:

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using Feature Extraction and Hierarchical Clustering based on Ontology,”
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Improves Embedding-Based Lexical Substitution,” In Proceedings of the 1st
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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.
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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
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applications”, Conference 9th IEEE International Symposium on
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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:

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No.(10), PP(27-35).
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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).
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Models for Business and Economic Forecasting”, 2nd Edition. Cambridge
University Press.
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based on Different Distributions”. Journal of Computers, VOL. (7), NO. (8), pp
(1967-1973).

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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.
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behaviour of leaded brass in neutral Cl-and SO4-media, Corros
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Aluminum And Manganese On Some Mechanicalproperties Of Brass.
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in comparison with As Al brass. Materials and Design 30(5) , pp1743–
1747 .
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Subramanya Sarma, V., Schultz, L., 2010. Studies on recrystallization of
single-phase copper alloys by resistance measurements, Acta Materialia in
print.
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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.

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aspects in the initial stages of decomposition of Cu2at.%Co with the
tomographic atom probe and the weld ion microscope, Surface and
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Corrosion Reduction for Brass Alloy by Using Different Nano-Coated
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Sciences, 14(3),pp. 30-46.
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196.

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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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of sheet metal forming: a review. The international journal of
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response surface method, numerical simulation and design of
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V. & GORJI, A. 2009. The study of spring-back of CK67 steel
sheet in V-die and U-die bending processes. Materials &
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IV. BERNEDER, J., PRILLHOFER, R., ENSER, J., RANK, G. &
GROHMANN, T. Characterization of AMAG AL6-CHA sheet
material for Chassis application in the automotive industry.
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Microstructural control of aluminum sheet used in automotive
applications. Materials Characterization, 35, 23-39.
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element analysis of spring-back of V-bending sheet metal
forming processes. Journal of materials processing technology,
148, 15-24.

VII. COURT, S., GATENBY, K. & LLOYD, D. 2001. Factors
affecting the strength and formability of alloys based on Al–3
wt.% Mg. Materials Science and Engineering: A, 319, 443-447.
VIII. DE CODES, R. N., HOPPERSTAD, O., ENGLER, O.,
LADEMO, O.-G., EMBURY, J. & BENALLAL, A. 2011.
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homogenization on particles in the Al–Mg–Mn alloy AA 5454–
Experiment and simulation. Journal of Alloys and Compounds,
560, 111-122.
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element analysis of springback in bending of aluminium sheets.
Materials & design, 23, 223-229.
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Analytical modeling and numerical simulation for three-roll
bending forming of sheet metal. The International Journal of
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modeling of top roller position for three-roller cylindrical
bending of plates and its experimental verification. Journal of
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Springback determination of sheet metals in an air bending
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