Archive

Abstract:

The important facts that causes the shortfall in the supply of electrical energy in Pakistan is discussed in this research work. The basic causes due to which the decline in supply and a review about the energy potential in Pakistan were analyzed. It is also investigated that how much important is to utilize the renewable energy and how it will be useful to tackle the shortfall. The solution for that problem is given i.e. to construct small hydro- electric power station on the run of river. This paper will be very helpful for minimizing the shortfall of electricity in Pakistan. To tackle the energy crisis different solutions were given, that is further divided into three terms. Short term solution, Medium term solution, and Long term solution .In short term solution ,the line losses will be reduced, and Power generating capacity will be improved. In medium term solution, the renewable energy resources will be installed. And in long term solution, the thermal power fuel, the myth of Thar coal, stand-alone power projects will be replaced and also the national grid will be dismantled to overcome these crisis.

Keywords:

Power generating capacity,Energy Crisis,Supply and demand,Renewable Energy,Energy Sources,

Refference:

I. Five steps to solving Pakistan’s energy crisis–The Express Tribune Blog, By
Adnan Khalid Rasool Published: March 3, 2012
II. Muhammad Zulqarnain Abbasi, M. Aamir Aman, Hamza Umar Afridi, Akhtar
Khan. Electrical Engineering Department, IQRA National University,
Peshawar, Pakistan.“Sag-Tension Analysis of AAAC Overhead Transmission
lines for Hilly Areas” International Journal of Computer Science and
Information Security (IJCSIS), Vol. 16, No. 4, April 2018.
III. National Transmission and despatch company, Power System Statistics,2016-
2017
IV. Pakistan Energy Year Book, (2017)
V. US Department of Energy 2002
VI. World Bank report 2017
VII. WAPDA Annual Report 2016-17, Water and Power Development Authority
Pakistan. Department of energy, office of energy efficiency and Renewable
Energy Geothermal Energy Program.

Similarity Based Feature Weighting for Inter Domain Classification of Text

Authors:

Brindha.G.R, Santhi.B

DOI NO:

https://doi.org/10.26782/jmcms.2018.10.00014

Abstract:

Intra domain supervised classification of online reviews is vastly analysed by current studies. At the same time, the level of performance declines when training is performed with one domain and testing with reviews of a different domain. The main fact behind this reduction is the domain distribution difference and the feature vector difference. Also the semantic of each word in a corpus differs based on its usage in domains. The objective of this study is to propose a new similarity based feature weighting technique for text reviews for enhancing the accuracy of inter domain classification. Different training and testing domains are weighted by proposed probability based statistical techniques for the classification by Support Vector Machine (SVM) and Transductive Support Vector Machine (TSVM). TSVM performs much better for this cross domain classification. The fact behind the performance of TSVM is its Transductive learning even with the small training set. The correlation between source and target domain and its influence on classification accuracy are analysed in detail using the outcome of existing feature weighting and proposed weighting techniques.

Keywords:

Text processing,Feature weighting, Transductive Support Vector Machine,Cross domain classification,

Refference:

I.Andreevskaia, A., & Bergler, S. (2008). When specialists and generalists work together: Overcoming domain dependence in sentiment tagging.Proceedings of ACL-08: HLT, 290-298.

II.Blitzer, J., Dredze, M., & Pereira, F. (2007). Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification. InProceedings of the 45th annual meeting of the association of computational linguistics(pp. 440-447).

III.Bollegala, D., Weir, D., & Carroll, J. (2011, June). Using multiple sources to construct a sentiment sensitive thesaurus for cross-domain sentiment classification. InProceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1(pp. 132-141). Association for Computational Linguistics.

IV.Bollegala, D., Weir, D., & Carroll, J. (2013).Cross-domain sentiment classification using a sentiment sensitive thesaurus.IEEE transactions on knowledge and data engineering,25(8), 1719-1731.

V.Brindha, G. R., Swaminathan, P., & Santhi, B. (2016). Performance analysis of new word weighting procedures for opinion mining.Frontiers of Information Technology & Electronic Engineering,17(11), 1186-1198.

VI.Chenlo, J. M., Hogenboom, A., & Losada, D. E. (2014). Rhetorical structure theory for polarity estimation: An experimental study.Data & Knowledge Engineering,94, 135-147.

VII.Deng, Z. H., Luo, K. H., & Yu, H. L. (2014). A study of supervised term weighting scheme for sentiment analysis.Expert Systems with Applications,41(7), 3506-3513.

VIII.Gao, S., & Li, H. (2011, October). A cross-domain adaptation method for sentiment classification using probabilistic latent analysis. InProceedings of the 20th ACM international conference on Information and knowledge management(pp. 1047-1052). ACM.

IX.He, Y., Lin, C., & Alani, H. (2011, June). Automatically extracting polarity-bearing topics for cross-domain sentiment classification. InProceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1(pp. 123-131). Association for Computational Linguistics.

X.Jiang, J., & Zhai, C. (2007, November). A two-stage approach to domain adaptation for statistical classifiers. InProceedings of the sixteenth ACM conference on Conference on information and knowledge management(pp. 401-410). ACM.

XI.Manning, C.,Raghavan, P.,andSchütze,H.(2008) Introduction to Information Retrieval, Cambridge University Press, ISBN:0521865719

XII.Pan, S. J., Ni, X., Sun, J. T., Yang, Q., & Chen, Z. (2010, April). Cross-domain sentiment classification via spectral feature alignment. InProceedings of the 19th international conference on World wide web(pp. 751-760). ACM.

XIII.Pang, B., Lee, L., & Vaithyanathan, S. (2002, July). Thumbs up?: sentiment classification using machine learning techniques. InProceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10(pp. 79-86). Association for Computational Linguistics.

XIV.Raaijmakers, S., & Kraaij, W. (2010, January). Classifier calibration for multi-domain sentiment classification. InICWSM.

XV.Tan, S., Wu, G., Tang, H., & Cheng, X. (2007, November). A novel scheme for domain-transfer problem in the context of sentiment analysis. InProceedings of the sixteenth ACM conference on Conference on information and knowledge management(pp. 979-982). ACM.

XVI.Van de Camp, M., & Van den Bosch, A. (2012). The socialist network.Decision Support Systems,53(4), 761-769.

XVII.Vapnik, V. (2013).The nature of statistical learning theory. Springer science & business media.

XVIII.Wang, B. K., Huang, Y. F., Yang, W. X., & Li, X. (2012). Short text classification based on strong feature thesaurus.Journal of Zhejiang University SCIENCE C,13(9), 649-659.

XIX.Wei, C. P., Lin, Y. T., & Yang, C. C. (2011). Cross-lingual text categorization: Conquering language boundaries in globalized environments.Information Processing & Management,47(5), 786-804.

XX.Wei, C. P., Yang, C. S., Lee, C. H., Shi, H., & Yang, C. C.(2014). Exploiting poly-lingual documents for improving text categorization effectiveness.Decision Support Systems,57, 64-76.

XXI.Wu, Q., Tan, S., & Cheng, X. (2009, August). Graph ranking for sentiment transfer. InProceedings of the ACL-IJCNLP 2009 Conference Short Papers(pp. 317-320). Association for Computational Linguistics.

Investigation of Self – Dithering Technique on MASH 1-1-1 and Third Order Error – Output Feedback Modulator

Authors:

Sohail Imran Saeed, Khalid Mahmood, Mehr-e-Munir

DOI NO:

https://doi.org/10.26782/jmcms.2018.10.00015

Abstract:

Digital delta sigma modulator (DDSM) is integral part of the divider of PLL based fractional –N frequency synthesizer. The output of DDSM is notorious for spurious tones in its output. Generally, the inherent periodicity of DDSM is considered the main reason for generation of these tones. The recent researched focus on the role of linear feedback shift register (LFSR) based pseudorandom dither which is added with input of DDSM to break its periodicity. Since, an ideal random sequence cannot be realized; the periodic nature of LFSR dither itself is considered a sour to energize these spurious tones appearing at the output of synthesizer. The self-dithering technique is claimed to perform the efficient dithering of the input of DDSM without using LFSR. In this paper we investigate the use of self-dithering technique with MASH 1-1-1 & EOFM mash that is claimed to be as effective as LFSR dither.

Keywords:

Power generating capacity,Energy Crisis,Supply and demand,Renewable Energy,Energy Sources,

Refference:

An Improvised Recommendation System For Mobile Plans Using Similarity Fusion

Authors:

Neetu Singh, V.K Jain

DOI NO:

https://doi.org/10.26782/jmcms.2018.10.00016

Abstract:

Recommendations help humans in making decisions and hence contribute in increase of user satisfaction. For good recommendations; the recommender should be more precise. From past decades, Collaborative Filtering (CF) has been explored by researchers because of its efficiency and effectiveness. The main objective of CF is to find most similar items using various similarity measures. This research paper proposes improvised mobile recommender that significantly increases accuracy for recommended right plans for mobile users using similarity fusion. Experimental results show that the proposed recommender using similarity fusions provide better recommendation quality.

Keywords:

Recommender System, Cellular networks,Similarities,data plans,Similarity fusion,

Refference:

I.Anand, S. S., & Mobasher, B. Intelligent techniques for web personalization. In Proceedings of the 2003 International Conference on Intelligent Techniques for Web Personalization (pp. 1–36). Springer-Verlag(2003).

II.Blondel VD, Decuyper A, Krings G,” A survey of results on mobile phone datasets analysis”. EPJ Data Sci , pp 4-10(2015).

III.C. Porcel, E. Herrera-Viedma,“Dealing with incomplete information in a fuzzy linguistic recommender system to disseminate information in university digital libraries”, Knowledge-Based Systems, vol. 23, No. 1, pp 32–39 (2010).

IV.C. Porcel, J.M. Moreno, E. Herrera-Viedma,” A multi-disciplinar recommender system to advice research resources in university digital libraries”, Expert Systems with Applications, vol. 36 , No. 10, pp 12520–12528 (2009).

V.Deshpande, M., Karypis, G. “Item-Based Top-N Recommendation Algorithms”. ACM Trans. On Information Systems(2004).

VI.Hill, W., Stead, L., Rosenstein, M., and Furnas, G., “Recommending and evaluating choices in a virtual community of use”, Proceedings of the ACM(CHI’95), New York, 1995, pp. 194-201,(1995).

VII.Kantor, P. B., Ricci, F., Rokach, L., & Shapira, B. (2011). Recommender systems handbook. Springer.

VIII.Mahmood, T., & Ricci, F. Improving recommender systems with adaptive conversational strategies. In Proceedings of the 20th ACM Conference on Hypertext and Hypermedia (pp. 73–82) (2009).

IX.Marlin, B. “Collaborative Filtering: A Machine Learning Perspective”. Phd thesis. University of Toronto ( 2004).

X.Meenakshi, Prof. Pravin Nimbalkar,” An Improvised Recommendation System on Top-N, Unrated and Point of Interest Recommendations Regularized with User Trust and Item Ratings”,IJRCCE,Vol. 5, Issue 8,( 2017).

XI.Miritello G, Rubén L, Cebrian M, Moro E,” Limited communication capacity unveils strategies for human interaction”. Sci Rep 3:1950 (2013).

XIII.Neetu Singh, Puneet Kumar & Anil Kumar Dahiya,” RWYW: Recommend What You Want -A Recommender for Mobile Plans”, International Journal of Innovations & Advancement in Computer Science,Vol. 7, No.2 , pp 135-144 ( 2018).

XIV.Neetu Singh, V.K Jain,”A novel Item Recommender for mobile plans”.IJCSIS , Vol. 16 No. 9,(2018).

XV.Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P.,and Riedl, J., “GroupLens: an open architecture for collaborative filtering of Netnews”, Proceedings of the CSCW conference, Chapel Hill, NC, pp.175-186.( 1994).

XVI.Rongfei, J., Maozhong, J., & Chao, L. A new clustering method for collaborative filtering. In International Conference on Networking and Information Technology (pp. 488–492) (2010).

XVII.Rucker, J., and Polanco, M.J., “Personalized navigation for the Web”, Communications of the ACM, March, 40(3), pp. 73-75,(1997).

XVIII.Saaty, T.L., “Fundamentals of decision making andpriority theory with the analytic hierarchy process”,RWS Publications, Pittsburgh, PA, (1994).

XIX.Sarwar, B. M., Karypis, G., Konstan, J., Riedl, J. 2001. “Item-Based Collaborative Filtering Recommendation Algorithms”.WWW (2001).

XX.The mahout website.” http://mahout.apache.org/

Challenges been faced by Mobile Operators in Pakistan for transition from 2G to 3G & 4G Mobile Services

Authors:

Shahid Latif, Mehr-e-Munir

DOI NO:

https://doi.org/10.26782/jmcms.2018.10.00017

Abstract:

Mobile communication has been transformed in Pakistan by issuance of Third Generation (3G) and Fourth Generation (4G) licenses. Introduction of new technologies has changed the mobile users existing lust for more data and at an extremely high transmission rate. The third and fourth generation technology transitions have enormously improved network performance as compared to old and legacy Time Division Multiple Access (TDMA) technology. Especially, the Long Term Evolution wireless network brings all set to convert the existing mobile networks into end-to-end IP networks. In this review paper, it will be considered what challenges have been faced by the mobile companies in Pakistan for migration of mobile wireless networks from existing technology to 3G CDMA (Code Division Multiple Access) and 4G LTE (Orthogonal frequency division multiplex) networks. The main challenges faced by managers for shifting from existing 2G infra-structures to new 3G and 4G infra-structures are network planning and achieving Quality of Service (QoS) parameter’s for this transition.

Keywords:

Second Generation,Third Generation,Fourth Generation,wireless network planning,Long Term Evolution,Internet Protoco,Code Division Multiple Access,Quality of Service,

Refference:

I.Aggarwal, P., Arora, P. & Neha (2013).Migration from 2G to 4G Mobile Technology. Advance in Electronic and Electric Engineering,3, 1251-1264.

II.Balasubramanian, D. (2006). QoS in cellular networks. Tech. Rep.

III.Bhatti, S. I. (2014, April 24). $1.1 billion raised from 3G, 4G auction. DAWN. Retrieved from http://www.dawn.com/news/1101760/11-billion-raised-from-3g-4g-auction.

IV.Dahiya, A. (2016, June 8).Evolution of Mobile Communication from 1(G) to 4G, 5G, 6G, 7G. Retrieved from https://www.linkedin.com/pulse/evolution-mobile-communication-from-1g-4g-5g-6g-7g-pmp-cfps.

V.Gabriel, C. (2012). Managing the new mobile data network. The challenge of deploying mobile broadband systems for profit. Retrieved from http://amdocs.com/Documents/wp-Managing-the-New-Mobile-Network.pdf,

VI.Gupta, P., & Patil, P. (2009). 4G-a new era in wireless telecommunication. Magister Program in S/W Engineering, Malardalen University.

VII.Janjua, S. (n.d.). 3G/4G LAUNCH IN PAKISTAN. Retrieved from http://www.abnamro.com.pk/2015/01/30/3g-4g-launch-in-pakistan/

VIII.Krendzel, A. (2005). Network planning aspects for 3G/4G mobile systems. Tampere University of Technology.

IX.Martin, C. (2012, October 9). What is 4G? A complete guide to 4G. Retrieved from http://www.pcadvisor.co.uk/feature/mobile-phone/what-is-4g-complete-guide-4g-3403880/.

X.Mishra, A. R. (2004). Fundamentals of cellular network planning and optimisation: 2G/2.5 G/3G… evolution to 4G. John Wiley & Sons.

XI.Mustaqim, M., Khan, K., & Usman, M. (2012). LTE-Advanced: requirements and technical challenges for 4G cellular network. Journal of Emerging Trends in Computing and Information Sciences, 3(5), 665-671.

XII.Pakistani Telecom Spectrum Auction. (2016). In Wikipedia. Retrieved December 15, 2016, from https://en.wikipedia.org/wiki/Pakistani_Telecom_Spectrum_Auction.

XIII.Rouse, M. (2009). 3G (third generation of mobile telephony). TechTarget. Retrieved from http://searchtelecom.techtarget.com/definition/3G.

XIV.Sequerah, A. (2014, November 17). Unified Network Planning, Network Optimization and Service Assurance for LTE. Retrieved from http://www.infovista.com/blog/index.php/2014/11/17/unified-network-planning-network-optimization-and-service-assurance-for-lte/

XV.Yusufzai, A. (2016). PTA Awards 4G Spectrum and License to Telenor Pakistan. Propakistani. Retrieved fromhttps://propakistani.pk/2016/07/15/pta-awards-4g-spectrum-and-license-to-telenor-pakistan.

XVI.Pakistan Telecommunication Authority. Annual Report 2016. Retrieved from http://www.pta.gov.pk/index.php?option=com_content&task=view&id=2265&Itemid=740

Accident prevention by detection of Drowsiness using Heart rate and body temperature sensing

Authors:

ParomitaDas, Soumyendu Bhattacharjee, Biswarup Neogi

DOI NO:

https://doi.org/10.26782/jmcms.2018.10.00018

Abstract:

Fatigue or sleep is a crucial factor in traffic accidents especially for long distance journeys. In this article, an innovative module depicts for automatic driver drowsiness detection based on heart rate and skin temperature. This system aims towards detecting and alert the driver to prevent accidents. Bothsensor performance has been utilized and modulated through the Arduino microcontroller and produce output. Achieve better accuracy for detecting sleep, a new method that is the combination of the heart rate sensor, as well as body temperature sensor, is proposed. Also, the proposed system can monitor the heart rate and body temperature continuously for detecting the health status of the driver also. Experimental results show high accuracy in each section which makes this system reliable for driver sleep detection and alarm system.

Keywords:

Driver drowsiness detection,Accident prevention,Heart rate sensor,Body temperature sensor,

Refference:

I.Alshaqaqi, B., Baquhaizel, A. S., Ouis, M. E. A., Boumehed, M., Ouamri, A., &Keche, M. (2013, May). Driver drowsiness detection system. In Systems, Signal Processing and their Applications (WoSSPA), 2013 8th International Workshop on (pp. 151-155). IEEE.

II.Banik, B. C., Ghosh, M., Das, A., Banerjee, D., Paul, S., & Neogi, B. (2017, March). Design of mind-controlled vehicle (MCV) &study of EEG signal for three mental states. In Devices for Integrated Circuit(DevIC), 2017 (pp. 808-812). IEEE.

III. Bhattacharjee, S., Das, Z., Das, A. K., Roy, S., & Neogi, B. (2014, January). An approach towards error less ECG signal equation basedon computational simulation aspect with modelingof cardiovascular disorder diagnosis. In Control, Instrumentation, Energy and Communication (CIEC), 2014 International Conference on (pp. 181-185). IEEE.

IV. Charles, A. C., Janet, C. Z., Joseph, M. R., Martin, C. M. E., & Elliot, D. W. (1980). Timingof REM sleep is coupledto the circadian rhythm of body temperature in man. Sleep, 2(3), 329-346.

V.Chieh, T. C., Mustafa, M. M., Hussain, A., Zahedi, E., &Majlis, B. Y. (2003, August). Driver fatigue detection using steering grip force. In Research and Development, 2003. SCORED 2003. Proceedings. Student Conference on (pp. 45-48). IEEE.

VI. Iampetch, S., Punsawad, Y., &Wongsawat, Y. (2012, December). EEG-based mental fatigue prediction for driving application. In Biomedical Engineering International Conference (BMEiCON), 2012 (pp. 1-5). IEEE.

VII. Lin, C. T., Wu, R. C., Liang, S. F., Chao, W. H., Chen, Y. J., & Jung, T. P. (2005). EEG-based drowsiness estimation for safety driving using independent component analysis. IEEE Transactions on Circuits and Systems I: Regular Papers, 52(12), 2726-2738.

VIII. Tian, Z., & Qin, H. (2005, October). Real-time driver’s eye state detection. In Vehicular Electronics and Safety, 2005. IEEE International Conference on (pp. 285-289). IEEE.

IX. Tsunoda, M., Endo, T., Hashimoto, S., Honma, S., &Honma, K. I. (2001). Effects of light and sleep stages on heart rate variability in humans. Psychiatry and clinical neurosciences, 55(3), 285-286.

X. Vitabile, S., De Paola, A.,&Sorbello, F. (2010, April). Bright pupil detection in an embedded, real-time drowsiness monitoring system. In Advanced Information Networking and Applications (AINA), 2010 24th IEEE International Conference on (pp. 661-668). IEEE.

A New Image Steganography Method using Message Bits Shuffling

Authors:

Prithwish Das, Kushal Chakraborty, Sayak Sinha, Atanu Das

DOI NO:

https://doi.org/10.26782/jmcms.2018.12.00001

Abstract:

Steganography has been considered as a technique of message hiding within another carrier multimedia data. Messages in the form of image (with embedded handwritten or typed texts) are often embedded in several ways within another image in image steganography. DCT based schemes are undertaken in the frequency domain methods in addition to usual plain text message embedding. Most of the message image hiding techniques embeds image bit string without considering any shuffling schemes to deal with the said string before embedding. Present work targeted to incorporate message hiding essentially with shuffled and re-shuffled bit strings in different ways prior to DCT operation. A new method has been proposed with these shuffling schemes to enhance the security level of the encryption. Investigations with the proposed image steganography method show that the new methods performed better than normal image steganography techniques without shuffling schemes. Performance of the proposed method is evaluated using Peak-Signal-to-Noise Ratio (PSNR) and Mean Square Error (MSE). Results show that the shuffling bit steganography method outperformed the common DCT based schemes without shuffling.

Keywords:

Image Steganograph, DCT,Message Bit Shuffling,

Refference:

I. A. ElSayed, A. Elleithy, P. ThungaandZ. Wu,“Highly secure image steganography algorithm using curvelet transform and DCT encryption”, Proc. of Systems, Applications and Technology Conference (LISAT), 2015 IEEE Long Island, pp. 1-6. May, 2015.

II. A. Jawedand A. Das,“Security Enhancement in Audio Steganography by RSA Algorithm”, Int.Journal of Electronics and Communication Technology, Vol.: 6, Issue:1, spl-1, pp. 139-142, Jan 2015.

III.A. K. GulveandM. S. Joshi, “A High Capacity Secured Image Steganography Method with Five Pixel Pair Differencing and LSB Substitution”, Int. J. of Image, Graphics and Signal Processing, Vol.:7, No. 5, pp. 66-74, 2015,DOI: 10.5815/ijigsp.2015.05.08

IV.B. G. BanikandS. K. Bandyopadhyay,“Implementation of image steganography algorithm using scrambled image and quantization coefficient modification in DCT”, Proc. of IEEE Int. Conf. on Research in Computational Intelligence and Communication Networks (ICRCICN), pp. 400-405, 2015

V.B. Mann,“How many times should you shuffle a deck of cards”, Topics in Contemporary Probability and Its Applications, Vol.:15, pp. 1-33, 1995

VI.C. C.Chang, T. S. Chen and L. Z. Chung,“A steganographic method based upon JPEG and quantization table modification”, Information Sciences, Vol.: 141, Issue: 1, pp. 123-138, 2002

VII.E Walia, P Jain and N Navdeep,“An Analysis of LSB & DCT based Steganography”, Global Journal of Computer Science and Technology, 10(1)(Ver 1.0), pp. 4-8, April 2010

VIII.F. Yonggang, “Anovel public key watermarking scheme based on shuffling”, Proc. of IEEE International Conference on Convergence Information Technology-2007, pp. 312-317, 2007

IX.K. Hwang andF. Briggs,Parallel processing and computer architecture, Me Graw Hill 164, 1984

X.K. Peng and B. Feng,“A shuffling scheme with strict and strong security”, Proc of Fourth IEEE International Conference on Emerging Security Information Systems andTechnologies (SECURWARE), 2010

XI.K.S.Shete, M.PatilandJ. S. Chitode, “Least Significant Bit and Discrete Wavelet Transform Algorithm Realization for Image Steganography Employing FPGA”, Int. J. of Image, Graphics and Signal Processing, Vol.: 8, No.6, pp.48-56, 2016.DOI: 10.5815/ijigsp.2016.06.06

XII.L. Guo, J. Ni, W. Su, C. Tang and Y. Q. Shi, “Using statistical image model for JPEG steganography: uniform embedding revisited”,IEEE Transactions on Information Forensics and Security, Vol.: 10, Issue:12, pp. 2669-2680, 2015

XIII.M. Bilal, S. Imtiaz, W. Abdul andS. Ghouzali. “Zero-steganography using DCT and spatial domain”, Proc. of 2013 ACS Int. Conf. on in Computer Systems and Applications (AICCSA), IEEE, pp. 1-7, May, 2013

XIV.M. Zamani, A. A.Manaf, R. B. Ahmad, A. M. Zeki andS. Abdullah,“A Genetic Algorithm-Based Approach for Audio Steganography”, World Academy of Science, Engineering and Technology, 2009

XV.M. Zamani, A.A. Manaf, R. Ahmad, F. Jaryani, H. Taherdoost, S. S. Chaeikar andH.R. Zeidanloo. “A novel approach for genetic audio watermarking”, Journal of Information Assurance and Security,Vol.: 5, pp.102-111, 2010

XVI.P. Das, S. Rayand A. Das, “An Efficient Embedding Technique in Image Steganography Using Lucas Sequence”, International Journal of Image, Graphics & Signal Processing, Vol.: 9, Issue: 9, pp. 51-58, 2017

XVII.S. Chandran, and K. Bhattacharyya, “Performance analysis of LSB, DCT, and DWT for digital watermarking application using steganography”, Proc. of IEEE Int. Conf. on Electrical, Electronics, Signals, Communicationand Optimization (EESCO), 2015

XVIII.S. Hemalatha, U. D. Acharya, A. RenukaandR. K.Priya, “A Secure Color Image Steganography in Transform Domain”, International Journal on Cryptography and Information Security (IJCIS), Vol. 3, Issue: 1, March 2013

XIX.S. Lahiri, P. Paul, S.Banerjee, S.Mitra, A. MukhopadhyayandM. Gangopadhyaya,“Image steganography on coloured images using edge based Data Hiding in DCT domain”, Proc. of 2016 IEEE 7thAnnual Information Technology, Electronics and Mobile Communication Conference (IEMCON), pp. 1-8, October 2016

XX.S. S. Jaber, H. A. Fadhil, A. Khalib, I. ZahereelandR. A. Kadhim, “Survey on Recent Digital Image Steganography Techniques”, Journal of Theoretical & Applied Information Technology, Vol.: 66, Issue:3, pp. 714-728, 2014

XXI.W. B. PennebakerandJ.L. Mitchell,JPEG: Still Image Data Compression Standard, Van Nostrand Reinhold, New York, 1993

Free-Space Optical channel turbulence analysis based on lognormal distribution and stochastic differential equation

Authors:

TayyabaGul Tareen, Shahryar Shafique, Mehr-e-Munir

DOI NO:

https://doi.org/10.26782/jmcms.2018.12.00002

Abstract:

An Optical wave propagating through a free-space optical channel may severely experience the intensity fluctuations that can result in channel gain fluctuations and fading. This paper provide a model that can analyze the influence of inevitable turbulence effect on a free-space channel which is based on the stochastic differential equation to synthesis lognormal distributed samples with a corresponding correlation time. The numerical analysis of theoretical model is presented and compared for performance evaluation. To examine the resemblance between numerical and theoretical analysis, two properties of free-space optical channel is considered including the probability density function and auto-covariance property. The model showed distinctive performance results when modelling typical channel situations.

Keywords:

Auto-covariance,Free-space optica,lognormal distribution,stochasticdifferential equation (SDE),Turbulence effects ,

Refference:

I.A. D. Horchler, “Matlab toolbox for the numerical solution of stochastic differential equations,” https://github.com/horchler/SDETools (2013). Version 1.2.

II.A. Jurado-Navas, J. Maria, M. Castillo-Vazquez,and A. Puerta-Notario, “A computationally efficient numerical simulation for generating atmospheric optical scintillation,” in Numerical Simulations ofPhysical and Engineering Processes (InTech,2011), pp. 157–180.

III.B. Epple, “Simplified channel model for simulation of free-space optical communications,” IEEE/OSA J. Opt. Commun. Netw. 2, 293–304 (2010).

IV.D. Bykhovsky, D. Elmakayes, and S.Arnon, Experimental evaluation of free space links in the presence of turbulence for server backplane,” J. Lightwave Technol. 33, 2777–2783 (2015).

V.H. Zhai, B. Wang, J. Zhang, and A. Dang,“Fractal phase screen generation algorithm for atmospheric turbulence,” Appl. Opt. 54, 4023–4032 (2015).

VI.I. Toselli, O. Korotkova, X. Xiao, and D.G. Voelz, “SLM-based laboratory simulations of tolmogorov and non-Kolmogorov anisotropic turbulence,” Appl. Opt. 54, 4740–4744 (2015).

VII.K.-H. Kim, T. Higashino, K. Tsukamoto,and S. Komaki, “Optical fading analysis considering spectrum of optical scintillation in terrestrial free-space optical channel,” in International Conference on Space Optical Systems and Applications (ICSOS), Santa Monica, California, 2011, pp. 58–66.

VIII.L. C. Andrews and R. L. Phillips, Laser Beam Propagation through Random Media, 2nd ed (SPIE, 2005).

IX.N. Blaunstein, S. Arnon, N. Kopeika, and A. Zilberman, Applied Aspects of Optical Communication and LIDAR (Auerbach, 2009)

X.P. E. Kloeden and E. Platen, Numerical Solution of Stochastic Differential Equations (Springer, 2010).

XI.S. Primak, V. Kontorovitch, and V.Lyandres, tochastic methods and their applications to communications: stochastic differential equations approach (Wiley, 2005).

XII.V. S. Pugachev and I. N. Sinitsyn, Stochastic Systems: Theory and Applications (World Scientific, 2002).

XIII.V. Kontorovich and V. Lyandres, “Stochastic differential equations: anapproach to the generation of continuous non-Gaussian processes,”IEEE Trans. Signal Process. 43, 2372–2385 (1995).

APPLICATION OF SWOT FOR CONSTRUCTION COMPANY QUALITY MANAGEMENT USING BUILDING INFORMATION MODELLING

Authors:

Phong Thanh Nguyen, Thu Anh Nguyen, Quyen Le Hoang Thuy To Nguyen, Vy Dang Bich Huynh

DOI NO:

https://doi.org/10.26782/jmcms.2018.12.00003

Abstract:

Building Information Modelling (BIM) has made considerable progress over the past few decades regarding information technology applied in the construction industry. In developed countries, governmental organizations and private companies had published many valuable and quality academic studies regarding BIM. However, few studies have mentioned the application of SWOT modelling to develop a strategy for applying the BIM 360 Field in construction and engineering companies. This paper presents an overview of the BIM 360 Field application in construction quality management. Suitable strategies could be used to enhance the quality assurance of construction project management.

Keywords:

Refference:

I.Azhar, S., Building information modelling (BIM): Trends, benefits, risks, and challenges for the AEC industry.Leadership and management in engineering, 2011. 11(3): p. 241-252.

II.Barlish, K. and K.Sullivan, How to measure the benefits of BIM—A case study approach.Automation in construction, 2012. 24: p. 149-159.

III.Bryde, D., M. Broquetas, and J.M. Volm, The project benefits of building information modelling (BIM).International journal of project management, 2013. 31(7): p. 971-980.

IV.Chong, H.-Y., J.S. Wong, and X. Wang, An explanatory case study on cloud computing applications in the built environment.Automation in construction, 2014. 44: p. 152-162.

V.Chuang, T.-H., B.-C. Lee, and I.-C. Wu. Applying cloud computing technology to BIM visualization and manipulation. in 28th International Symposium on Automation and Robotics in Construction. 2011.

VI.Cox, S., J. Perdomo, and W. Thabet. Construction field data inspection using pocket PC technology. in International Council for Research and Innovation in Building and Construction, CIB w78 conference. 2002.

VII.Davies, R. and C. Harty, Implementing ‘Site BIM’: a case study of ICT innovation on a large hospital project.Automation in Construction, 2013. 30: p. 15-24.

VIII.Eastman, C., et al., BIM handbook: A guide to building information modelling for owners, managers, designers, engineers and contractors. 2011: John Wiley & Sons.

IX.Fernandes, R.P.L., Advantages and disadvantages of BIM platforms on construction site.2013.X.Gleason, B.E., et al. The Use of Mobile Devices to Create Value in Quality Management Systems. in 50th ASC Annual International Conference Proceedings. 2014.

XI.Jiao, Y., et al., Towards cloud augmented reality for construction application by BIM and SNS integration.Automation in construction, 2013. 33: p. 37-47.

XII.Li, J., et al., A project-based quantification of BIM benefits.International Journal of Advanced Robotic Systems, 2014. 11(8): p. 123.

XIII.Lin, Y.-C. and Y.-C. Su, Developing mobile-and BIM-based integrated visual facility maintenance management system.The Scientific World Journal, 2013. 2013.

XIV.McGuire, B., et al., Bridge information modelling for inspection and evaluation.Journal of Bridge Engineering, 2016. 21(4): p. 04015076.

XV.Matthews, J., et al., Real time progress management: Re-engineering processes for cloud-based BIM in construction.Automation in Construction, 2015. 58: p. 38-47.

XVI.Moran, M.S., Assessing the benefits of a field data management tool.2012.

XVII.Nguyen, P.T., et al., Facilities management in high rise buildings using building information modeling.International Journal of Advanced and Applied Sciences, 2017. 4(2): p. 1-9.

XVIII.Nguyen, P.T., et al., Project success evaluation using TOPSIS algorithm.Journal of Engineering and Applied Sciences,2016. 11(8): p. 1876-1879.

XIX.Nguyen, P.T., et al., Ranking project success criteria in power engineering companies using fuzzy decision-making method.International Journal of Advanced and Applied Sciences, 2018. 5(8): p. 91-94.

XX.Phong, N.T. and N.L.H.T.T. Quyen, Application fuzzy multi-attribute decision analysis method to prioritize project success criteria.AIP Conference Proceedings, 2017. 1903(1): p. 070011.

XXI.Sawhney, A. and J.U. Maheswari, Design coordination using cloud-based smart building element models.International Journal of Computer Information Systems and Industrial Management Applications, 2013. 5: p. 445-453.

XXII.Tsai, Y.-H., S.-H. Hsieh, and S.-C. Kang, A BIM-enabled approach for construction inspection, in Computing in Civil and Building Engineering (2014). 2014. p. 721-728.

XXIII.Wang, J., et al., Integrating BIM and LiDAR for real-time construction quality control.Journal of Intelligent & Robotic Systems, 2015. 79(3-4): p. 417-432.

XXIV.Wong, J., et al., A review of cloud-based BIM technology in the construction sector.Journal of information technology in construction, 2014. 19: p. 281-291.

XXV.Wang, L.-C., Enhancing construction quality inspection and management using RFID technology.Automation in construction, 2008. 17(4): p. 467-479.

Relationship between Organizational Environment and Teacher’s Citizenship Behaviour

Authors:

Muhammad Tahir Khan Farooqi, Dr. Shehzad Ahmed, Dr. AsifIqbal, Sabahat Parveen

DOI NO:

https://doi.org/10.26782/jmcms.2018.12.00004

Abstract:

The aim of the study was to investigate the correlation between organizational environment and teachers’ citizenship behaviour. The research study was quantitative and correlational design was used. Survey technique was used. The population of the study comprises Elementary School Teachers (ESTs) of Mathematics. Multistage random sampling was used to select four districts (Faisalabad, Multan, Sargodha and Jhang). Further, 20 schools (10 males & 10 females) and 4 teachers from each school were randomly selected. The data from selected sample were collected using survey method. SPSS version 24 was used to analyze the data. Pearson r and ANOVA were used. The analysis revealed that there exist significant and positive relationship between organizational environment and teachers’ citizenship behaviour.

Keywords:

Organizational environment,Citizenship behaviour,Multistage random sampling,

Refference:

I.Abusidualghoul, V. J. (2014).Complexity theory & the measure of organizations(Doctoral dissertation, School of Management).

II.Aldrich, H. (2008).Organizations and environments.Stanford University Press. Ali, U., &Waqar, S. (2013). Teachers’ organizational citizenship behavior working under different leadership styles.Pakistan Journal of Psychological Research,28(2), 297.

III.Armstrong, M., & Taylor, S. (2014).Armstrong’s handbook of human resource management practice.Kogan Page Publishers.IV.Baird, L., &Meshoulam, I. (1988). Managing two fits of strategic human resource management.Academy of Management review,13(1), 116-128.

IV.Barbuto, J. E., Brown, L. L., Wilhite, M. S., & Wheeler, D. W. (2001, December). Testing the underlying motives of organizational citizenship behaviors: A field study of agricultural co-op workers.In28th Annual National Agricultural Education Research Conference(pp.539-553).

V.Belogolovsky, E., &Somech, A. (2010). Teachers’ organizational citizenship behavior: Examining the boundary between in-role behavior and extra-role behavior from the perspective of teachers, principals and parents.Teaching and Teacher education,26(4), 914-923.

VI.Bogler, R., &Somech, A. (2004). Influence of teacher empowerment on teachers’ organizational commitment, professional commitment and organizational citizenship behavior in schools.Teaching and teacher education,20(3), 277-289.

VII.Brief, A. P., &Motowidlo, S. J. (1986).Prosocial organizational behaviors.Academy of management Review,11(4), 710-725.

VIII.Bowling, N. A. (2010).Effects of job satisfaction and conscientiousness on extra-role behaviors.Journal of Business and Psychology,25(1), 119-130.

IX.Brown, J. S., &Duguid, P. (1991). Organizational learning and communities-of-practice: Toward a unified view of working, learning, and innovation.Organization science,2(1), 40-57.

X.Callon, M. (1987). Society in the making: the study of technology as a tool for sociological analysis.The social construction of technological systems: New directions in the sociology and history of technology, 83-103.

XI.Cameron, K. (2010).Organizational effectiveness.John Wiley & Sons, Ltd.Ellemers, N., De Gilder, D., &Haslam, S. A. (2004).Motivating individuals and groups at work: A social identity perspective on leadership and group performance.Academy of Management review,29(3), 459-478.

XII.Evans, W. R., & Davis, W. D. (2005). High-performance work systems and organizational performance: The mediating role of internal social structure.Journal of management,31(5), 758-775.

XIII.Farooqi, M. T. K., &Akhtar, M. S. (2014). Development and Validation of Organizational Environment Scale (OES) for University Teachers.Journal of Research & Reflections in Education (JRRE),8(1).

XIVForson, M. C. (2012). Incentives as a Motivational tool.Impact of Motivation on Employees Performance,22.

XV.Gagné, M., &Deci, E. L. (2005). Self-determination theory and work motivation. Journal of Organizational Behavior, 26 (4), 331 –362.

XVI.Holly, P., &Southworth, G. (2005).The developing school.Routledge.Igbaekemen, G. O., &Odivwri, J. E. (2015). Impact of Leadership Style on Organization Performance: A Critical Literature Review.Arabian Journal of Business and Management Review,2015.

XVII.Indris, S., &Primiana, I. (2015). Internal and external environment analysis on the performance of small and medium industries (Smes) in Indonesia.International Journal of Scientific & Technology Research,4(4), 188-198.

XVIII.Jernigan III, I. E., Beggs, J. M., &Kohut, G. F. (2002). Dimensions of work satisfaction as predictors of commitment type.Journal of Managerial Psychology,17(7), 564-579.

XIX.Jones, G. R., & Jones, G. R. (2010).Organizational theory, design, and change.Karambayya, R. (1990). Contexts for organizational citizenship behavior: Do high performing and satisfying units have better citizens.Unpublished Paper, York University, Ontario.

XX.Katz, D., & Kahn, R. L. (1966).The social psychology of organizations. Wiley: New York.Lesser, E. L., &Storck, J. (2001).Communities of practice and organizational performance.IBM systems journal,40(4), 831-841.

XXI.Luthans, F., & Youssef, C. M. (2007).Emerging positive organizational behavior.Journal of management,33(3), 321-349.

XXII.Manzoor, Q. A. (2012). Impact of employees’ motivation on organizational effectiveness.Business management and strategy,3(1), 1.

XXIII.Mintzberg, H. (1991). The effective organization: forces and forms.MIT Sloan Management Review,32(2), 54.

XXIV.Oakland, J. S. (2001).Total organizational excellence: Achieving world-class performance. Routledge.

XXV.Organ, D. W. (1988).Organizational citizenship behavior: The good soldier syndrome. Lexington Books/DC Heath and Com.

XXVI.Podsakoff, P. M., MacKenzie, S. B., Moorman, R. H., & Fetter, R. (1990). Transformational leader behaviors and their effects on followers’ trust in leader, satisfaction, and organizational citizenship behaviors.The leadership quarterly,1(2), 107-142.

XXVII.Podsakoff, N. P., Whiting, S. W., Podsakoff, P. M., &Blume, B. D. (2009). Individual-and organizational-level consequences of organizational citizenship behaviors: A meta-analysis.

XXVIII. Posdakoff, P. M., & Mackenzie, S. B. (1994).Organizational citizenshipbehaviors and sales unit effectiveness.Journal of marketing research, 351-363.

XXIX. Podsakoff, P. M., MacKenzie, S. B., Paine, J. B., &Bachrach, D. G. (2000). Organizational citizenship behaviors: A critical review of the theoretical and empirical literature and suggestions for future research.Journal of management,26(3), 513-563.

XXX.Robbins, S. P. (2001).Organizational behavior, 14/E. Pearson Education India.XXXI. Rosenzweig, P. M., & Singh, J. V. (1991).Organizational environments and the multinational enterprise.Academy of Management review,16(2), 340-361.

XXXII. Schwarz, R. (2002).The skilled facilitator: A comprehensive resource for consultants, facilitators, managers, trainers, and coaches. John Wiley &Sons.

XXXIII. Spisak, B. R., Nicholson, N., & vanVugt, M. (2011). Leadership inorganizations: An evolutionary perspective. InEvolutionary psychology in the business sciences(pp. 165-190).Springer Berlin Heidelberg.

XXXIV. Steiner, G. A. (2010).Strategic planning.Simon and Schuster.Sun, L. Y., Aryee, S., & Law, K. S. (2007). High-performance human resource practices, citizenship behavior, and organizational performance: A relational perspective.Academy of management Journal,50(3), 558-577.

XXXV. Trevino, L. K. (1986). Ethical decision making in organizations: A person situation interactionist model.Academy of management Review,11(3), 601 617.

XXXVI. Trivedi, A. (2013). Personality as a Predictor of Accountability.Available at SSRN 2314585.

XXXVII. Turvey, M. T. (1990). Coordination.American psychologist,45(8), 938.Vinokur-Kaplan, D. (2009). Motivating work performance in humanservices organizations.The handbook of human services management, 209 237.

XXXVIII. Walz, S. M., &Niehoff, B. P. (1996, August).Organizational citizenshipbehaviours and affection on organizational effectiveness.Academy ofManagement Proceedings(Vol. 1996,No. 1, pp. 307-311).Academy ofManagement.

XXXIX. Williams, L. J., & Anderson, S.E. (1991).Job satisfaction and organizational commitment as predictors oforganizational citizenship and in-role behaviors.Journal of management,17(3), 601-617.

XXXX. Xyrichis, A., & Ream, E. (2008). Teamwork: a concept analysis.Journal of advanced nursing,61(2), 232-241.

XXXXI. Yen, H. R., &Niehoff, B. P. (2004). Organizational citizenship behaviors and organizational effectiveness: Examining relationships in Taiwanese banks.Journal of Applied Social Psychology,34(8), 1617-1637.