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).

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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:

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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,

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APPLICATION PARTIAL LEAST SQUARE STRUCTURAL EQUATION TO DEVELOP A JOB SEARCH SUCCESS MEASUREMENT MODEL

Authors:

Vy Dang Bich Huynh, Quyen Le Hoang Thuy To Nguyen, Phuc Van Nguyen, Phong Thanh Nguyen

DOI NO:

https://doi.org/10.26782/jmcms.2018.12.00005

Abstract:

The positive impact of social capital on job search success has been supported in the literature, however the research community has not reached a consensus because social capital is not always good, especially in terms of bonding. This paper explores the role of bonding social capital on several dimensions of job search success. The partial least square structural equation model was used with input data from 400 undergraduates, obtained from a field survey in Ho Chi Minh City, Vietnam. The results confirm the positive role of bonding social capital on acquired job quality, job search cost, and job search convenience. Keywords: education, job search success, partial least square structural equation model (PLS-SEM), social capital

Keywords:

,,,

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CONSTRUCTION HEALTH AND SAFETY CONDITIONS AND CLIMATE IN PAKISTAN

Authors:

Muhammad Hasnain, Adeed khan, Saqib Shah, Muhammad Majid Naeem, Marvan Raza

DOI NO:

https://doi.org/10.26782/jmcms.2018.12.00006

Abstract:

Developed economies have realized construction health and safety issue and have improved the working site condition by continuously emphasizing on the issue. Sadly, the case is different in developing countries particularly in the Indian subcontinent where the injury and death rate is high due to poor health and safety conditions. The paper examines the current health and safety practices, legislations and the management of Health and safety of Pakistan, a country in the Indian subcontinent. The data reviewed is organized around developing countries and the culture affecting health and safety in these countries is discussed. Moreover, the secondary data focuses on health and safety management system, behavioral aspects of the stakeholders, general health conditions of workers associated to the construction industry and the construction industry of Pakistan is also discussed. For the achievement of objectives, both, qualitative and quantitative methodologies are adopted (i-e questionnaire survey and interviews). The questionnaire and the interviews mainly focus on the contractors, workers, designers and the clients. The findings from these methods indicates that majority of the respondents have a poor degree of health and safety awareness. It also reveals that there are general health problems faced by the workers, people are hesitant to record and report the accident at site and showed the key behavioral aspects affecting the health and safety.

Keywords:

OSHA,CDM,HSE,MSD,SME,PPE,

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Complexity Based Approach for Architecture Evaluation

Authors:

Maushumi Lahon, Uzzal Sharma

DOI NO:

https://doi.org/10.26782/jmcms.2018.12.00007

Abstract:

Architecture Evaluation is a means to reduce risk and save cost. It holds the key to success of the system being developed. Various evaluation methods exist which have specific objectives and basis and all contribute to enhance product quality. In this paper a Complexity UML Based Architecture Evaluation (CUBAE) approach is proposed to evaluate the architecture of a system built using CBSD approach. . The proposed approach estimates the complexity of the architecture from the UML representation of different views of the architecture. Earlier works on complexity measures of UML representations found in literature are used along with proposed measures for complexity calculation. This complexity measure may be used to assess and compare architecture representing the same system along with other measures like modifiability and different quality attributes used for evaluating the architecture.

Keywords:

CBSD,Architecture evaluation,UML,Complexity,Metrics,

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Prediction of Heating and Cooling Load to improve Energy Efficiency of Buildings Using Machine Learning Techniques

Authors:

Srihari J, Santhi B

DOI NO:

https://doi.org/10.26782/jmcms.2018.12.00008

Abstract:

Global warming has been a severe threat to humanityand greenhouse gases emitted from power plants is one of the major causes of global warming. In this paper, we use machine learning to incorporate energy efficiency techniques to buildings by predicting the Heating and Cooling Load using eight input features.Heating load is the amount of heat per unit time that a building needs to maintain the temperature at an established level whereas Cooling load is the amount of heat per unit time that must be removed. Heating, cooling, and ventilation systems are used to handle heating and cooling load. We train four regression (linear regression, Lasso, Ridge, and Elastic-Net) and three gradient boosting models (GBM, XGBoost, and LightGBM) and test them to compare their performance using 768 rows of data of residential buildings. We observe that the gradient boosting models perform significantly better than the standard regression models for both Heating Load and Cooling Load. XGBoost achieves the highest R-squared score of 0.99 for Heating Load and 0.99 for Cooling Load. From the results of this study, we conclude that machine learning techniques can predict Heating Load and Cooling Load with high accuracy. The obtained Heating load and cooling load values can be used to install efficient heating, cooling and ventilation systems and thus reduce both energy consumption and money.

Keywords:

Energy efficiency,Heating Load,Cooling Load,Machine Learning,

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A REVIEW OF PERVIOUS CONCRETE PAVEMENT & TEST ON GEO TEXTILE

Authors:

Adil Afridi, Atif Afridi, Farhan Zafar

DOI NO:

https://doi.org/10.26782/jmcms.2018.12.00009

Abstract:

Pervious concrete pavement could be a distinctive and effective thanks to capture storm water and permit it to course into the bottom therefore recharging groundwater, reducing storm water runoff, and meeting U.S. Environmental Protection Agency (EPA) storm water laws. this technique has been counseled by independent agency and geotechnical engineers as a Best Management Practices (BMPs) for the management of storm water runoff. This pavement technology creates additional economical land use by eliminating the necessity for retention ponds, swales, and alternative storm water management devices. receptive surface treatments retain the water sub-surface because it bit by bit infiltrates into the soil; holding the storm water in multiple air voids or cells conjointly aiding in water quality through degradation of hydrocarbons into greenhouse emission and water, and retentive metals within the structure keeps them from the groundwater table Despite the employment of receptive systems for nearly thirty years within the USA, not tons of analysis has been performed on the long run absorption of contaminants within the concrete microstructure. many studies showcase the removal potency of those pavements within the 1st few years of service, stating it's shown higher than seventy five p.c potency in removal of contaminants, this investigation targeted on varied receptive concrete treatments decisive optimum strength, voids, infiltration and voids. in addition geochemical work on trace metal sorption, major component adverse effects and water quality edges was performed on existing tons on MTSU field.

Keywords:

concrete pavemen, water runoff,optimum strength,

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