Journal Vol – 13 No -4, October 2018

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).

XII.M.Kavitha devi, P.Venkatesh, ̳An ImprovedCollaborative Recommender System„2009 First International Conference on Networks &Communications-© 2009 IEEE DOI 10.1109/NetCoM.2009.69(2009).

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/

View Download

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

View Download

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.

View Download