Archive

Abstract:

Since it works well, concrete is a critical building element. Researchers seek to develop their properties more to make them more economical. Different waste materials and fibers in concrete are checked for this reason. The research aims at analyzing and evaluating the mechanical performance of the compressive, splitting tensile and bending strength of concrete with the addition of lathe as steel fiber refurbishment into the matrix of cement. Different mixes of 0 percent, 0.5 percent, 1 percent, 1.5 percent, 2.5 and 3 percent waste fiber are produced. Results demonstrated that the slump value of mixes decreases, as fiber reinforcement, the higher the waste, the lower the workability. Adding the lathe waste to concrete increases the structural properties of concrete, such as compressive, tensile and bend strength. The application of 1.5% of lathe waste raises compressive intensity up to 26.52%, of 13.70% and 16.12%, respectively, for 7, 14 and 28 cure days. With the introduction of 1.5% of the waste lathe, tensile intensity rises to 20.84% for 28 days. Also bending strength was improved by increasing lathe waste steel fibers.

Keywords:

:lathe waste steel fiber,Fiber reinforcement, workability test,Mechanical strength,Scanning Electron microscopy,

Refference:

I. Bazgir, A., 2016. The behavior of steel fibre reinforced concrete material and its effect on the impact resistance of slabs (Doctoral dissertation, City University London).

II. Boulekbache, B., Hamrat, M., Chemrouk, M., and Amziane, S., 2010. Flowability of fibre-reinforced concrete and its effect on the mechanical properties of the material. Construction and Building Materials, 24(9), pp.1664-1671.

III. De Lacalle, L.N.L., Lamikiz, A., de Larrinoa, J.F., and Azkona, I., 2011. Advanced cutting tools. In Machining of hard materials (pp. 33-86). Springer London.

IV. Hollaway, L.C., 2010. A review of the present and future utilization of FRP composites in the civil infrastructure concerning their important in-service properties. Construction and building materials, 24(12), pp.2419-2445.

V. Hansen, T.C., 1986. Recycled aggregates and recycled aggregate concrete second state-of-the-art report developments 1945–1985. Materials and Structures, 19(3), pp.201-246.

VI. Johnston, C.D., 1985, April. Properties of steel fibre reinforced mortar and concrete. In Proceedings of International Symposium on Fibrous Concrete (pp. 29-47).

VII. Knapton, J., 2003. Ground bearing concrete slabs: specification, design, construction, and behavior. Thomas Telford.

VIII. Kosmatka, S.H., Kerkhoff, B. and Panarese, W.C., 2002. Design and control of concrete mixtures (Vol. 5420, pp. 60077-1083). Skokie, IL: Portland cement Association.

IX. Kumar, P.K., and Kumar, M., 2017. Experimental Investigations on Cement Concrete by Using Different Steel Waste as a Fibre to Strengthen the M30 Concrete. Imperial Journal of Interdisciplinary Research, 3(6).

X. Li, V.C., 2002. Large volume, high‐performance applications of fibers in civil engineering. Journal of Applied Polymer Science, 83(3), pp.660-686.

XI. Morgan, D.R., and Mowat, D.N., 1984. A comparative evaluation of plain, mesh, and steel fiber reinforced concrete. Special Publication, 81, pp.307-324.

XII. Masood Fawwad ,Asad-ur-Rehman Khan, Behaviour of Full Scale Reinforced Concrete Beams Strengthened with Textile Reinforced Mortar (TRM), J. Mech. Cont.& Math. Sci.Vol.-14, No.-3, May-June , pp 65-82 .

XIII. Pacheco, F., and Labrincha, J.A., 2013. The future of construction materials research and the seventh UN Millennium Development Goal: A few insights. Construction and building materials, 40, pp.729-737.

XIV. Qureshi, Z.N., Raina, Y.M., and Rufaie, S.M.A., 2016. Strength Characteristics Analysis of Concrete Reinforced With Lathe Machine Scrap. International Journal of engineering research and general science, 4(4), pp.210-217.

XV. Ramadoss, P., and Nagamani, K., 2008. A new strength model for the high-performance fiber-reinforced concrete. Computers and Concrete, 5(1), pp.21-36.

XVI. Sen, T., and Reddy, H.J., 2011, April. Finite element simulation of retrofitting of RCC beam using fibre composite (natural fibre). In 2011 3rd International Conference on Electronics Computer Technology (Vol. 6, pp. 29-33).

XVII. Shin, H.O., Yoon, Y.S., Lee, S.H., Cook, W.D., and Mitchell, D., 2014. Effect of steel fibers on the performance of ultrahigh-strength concrete columns. Journal of materials in civil engineering, 27(4), p.04014142.

XVIII. Shrivastavaa, P., and Joshi, Y., 2014. Reuse of Lathe Waste Steel Scrap in Concrete Pavements. International Journal of Engineering Research and Applications, 4(12), pp.45-54.

XIX. Soroushian, P., and Bayasi, Z., 1991. Fiber type effects on the performance of steel fiber reinforced concrete. Materials Journal, 88(2), pp.129-134.

XX. Sarath Chandra Kumar B., SadasivanKaruppusamy, K. Ramesh, Correlation between Compressive Strength and Split Tensile Strength of GGBS and MK Based Geopolymer Concrete using Regression Analysis, J. Mech. Cont.& Math. Sci.,Vol.-14, No.-1, January-February (2019), pp 21-36,

XXI. Tang, S.W., Yao, Y., Andrade, C., and Li, Z.J., 2015. Recent durability studies on the concrete structure. Cement and Concrete Research, 78, pp.143-154.

XXII. Zollo, R.F., 1997. Fiber-reinforced concrete: an overview after 30 years of development. Cement and Concrete Composites, 19(2), pp.107-122.

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Abstract:

Millimeter-wave is one of the keyenabling technologies in state-of-the-art mobile communication known as 5G to cope with the ever-increasing traffic demand mobile users, low latency requirements for mission-critical situations, and massive machine-type communication. 5G channel modeling has been a complex problem due to the utilization of unlicensed mmWave bands as they are extremely sensitive towards their surrounding environment because of their small wavelengths. This work comprises the analysis of several mmWave bands (28, 38, 60, and 73 GHz) in the NLOS scenario of the UMi environment considered in Single Input Single Output (SISO) system using an open-source simulator named NYUSIM. NYUSIM uses a Time cluster (TC) - spatial lobe approach to cluster any measured or Ray traced data. The simulator supports carrier frequency up to 100GHz while an RF bandwidth of 0 to 800MHz. It supports UMi, UMa, and RMa environments for both LOS and NLOS scenarios while different antenna characteristics can also be tuned to get the desired analysis. The results are produced in 3D characteristics graphs, text, and MATLAB based (mat) extension.

Keywords:

5G mmWave,Time Cluster,Spatial Channel Modelling,NYUSIM ,

Refference:

I 3GPP, “Specification #: 21.915,” 11 June 2017. [Online]. Available: https://portal.3gpp.org/desktopmodules/Specifications/SpecificationDetails.aspx?specificationId=3389. [Accessed 28 Feb 2019].
II A. Maltsev, A. Pudeyev, A. Lomayev, and I. Bolotin, “Channel modeling in the next generation mmWave Wi-Fi: IEEE 802.11ay standard,” in 22nd European Wireless Conference, Oulu, Finland, 2016.
III A. Fricke et al., “TG3d Channel Modelling Document (CMD),” March 2016. [Online]. Available: https://mentor.ieee.org/802.15/dcn/14/15-14-0310-19-003d-channel-modeling-document.docx. [Accessed 9 March 2019].
IV Cisco Inc., “VNI Global Fixed and Mobile Internet Traffic Forecasts,” CISCO, Feb 2019.
V EURECOM , “OpenAirInterface – 5G software alliance for democratising wireless innovation,” EURECOM , [Online]. Available: https://www.openairinterface.org/. [Accessed 11 Feb 2019].
VI G. R. Maccartney, T. S. Rappaport, S. Sun and S. Deng, “Indoor Office Wideband Millimeter-Wave Propagation Measurements and Channel Models at 28 and 73 GHz for Ultra-Dense 5G Wireless Networks,” IEEE Access, vol. 3, pp. 2388-2424, 2015.
VII “IEEE Standard for High Data Rate Wireless Multi-Media Networks–Amendment 1: High-Rate Close Proximity Point-to-Point Communications,” IEEE Std 802.15.3e-2017 (Amendment to IEEE Std 802.15.3-2016), pp. 1-178, 7, June 2017.
VIII International Telecommunication Union (ITU), “Guidelines for evaluation of radio interface technologies for IMT-2020,” Nov 2017. [Online]. Available: https://www.itu.int/pub/R-REP-M.2412-2017. [Accessed 3 March 2019].
IX J. Medbo et al., “Channel modeling for the fifth-generation mobile communications,” in The 8th European Conference on Antennas and Propagation (EuCAP 2014), The Hague, 2014.
X J. Hasch, E. Topak, R. Schnabel, T. Zwick, R. Weigel, and C. Waldschmidt, “Millimeter-Wave Technology for Automotive Radar Sensors in the 77 GHz Frequency Band,” IEEE Transactions on Microwave Theory and Techniques, vol. 60, no. 3, pp. 845-860, March 2012.
XI J. I. Smith, “A computer-generated multipath fading simulation for mobile radio,” IEEE Transactions on Vehicular Technology, vol. 24, no. 3, p. 39–40, Aug 1975.
XII L. Liu et al., “The COST 2100 MIMO channel model,” IEEE Wireless Communications, vol. 19, no. 6, pp. 92-99, December 2012.
XIII “Maxmize Your Digital Performance and Gain a Competitive Edge | Riverbed,” Riverbed Inc., [Online]. Available: https://www.riverbed.com/sg/index.html. [Accessed 12 March 2019].
XIV M. K. Samimi and T. S. Rappaport, “Local Multipath Model Parameters for Generating 5G Millimeter-Wave 3GPP-like Channel Impulse Response,” in 10th European Conference on Antennas and Propagation (EuCAP), April 2016.
XV M. K. Samimi and T. S. Rappaport, “3-D Millimeter-Wave Statistical Channel Model for 5G Wireless System Design,” IEEE Transactions on Microwave Theory and Techniques, vol. 64, no. 7, pp. 2207-2225, July 2016.
XVI mmMAGIC, “mm-Wave based Mobile Radio Access Network for 5G Integrated Communications,” European Commission’s 5G PPP, [Online]. Available: https://5g-mmmagic.eu/. [Accessed 11 March 2019].
XVII NSNAM, “ns-3 | a discrete-event network simulator for internet systems,” NSNAM, 30 June 2008. [Online]. Available: https://www.nsnam.org/. [Accessed 2 Feb 2019].
XVIII OpenSim Ltd., “OMNeT++ Discrete Event Simulator,” OpenSim Ltd., [Online]. Available: https://omnetpp.org/. [Accessed 16 Feb 2019].
XIX “PyLayers: Propagation and Localization Simulator,” University of Rennes 1, [Online]. Available: http://pylayers.github.io/pylayers/. [Accessed 9 Feb 2019].
XX R. Hasan, M. M. Mowla, M. A. Rashid, M. K. Hosain and I. Ahmad, “A Statistical Analysis of Channel Modeling for 5G mmWave Communications,” in International Conference on Electrical, Computer and Communication Engineering (ECCE), Cox’sBazar, Bangladesh, 2019.
XXI R. J. Weiler et al., “Quasi-deterministic millimeter-wave channel models in MiWEBA,” EURASIP Journal on Wireless Communications and Networking, vol. 1, no. 84, 2016.
XXII R. H. Clarke, “A statistical theory of mobile-radio reception,” The Bell System Technical Journal, vol. 47, no. 6, p. 957–1000, July 1968.
XXIII REMCOM, “Wireless EM Propagation Software – Wireless InSite – remcom.com,” REMCOM, [Online]. Available: https://www.remcom.com/wireless-insite-em-propagation-software/. [Accessed 14 Feb 2019].

XXIV S. M. Shamim, M. S. Hossain, G. M. K. Ta-seen, M. B. A. Miah and M. S. Uddin, “Performance Analysis of Omni-Directional and Directional Power Delay Profile for Millimeter-Wave 5G Cellular Networks in LOS Environment,” in International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE), Gazipur, Bangladesh, 2018.
XXV S. Jaeckel, L. Raschkowski, K. Börner, and L. Thiele, “QuaDRiGa: A 3-D multi-cell channel model with time evolution for enabling virtual field trials,” IEEE Transactions on Antennas and Propagation, vol. 62, no. 6, p. 3242–3256, June 2014.
XXVI S. Sun et al., “Investigation of Prediction Accuracy, Sensitivity, and Parameter Stability of Large-Scale Propagation Path Loss Models for 5G Wireless Communications,” IEEE Transactions on Vehicular Technology, vol. 65, no. 5, pp. 2843-2860, May 2016.
XXVII Siradel, “S_5GChannel – 5G channel simulation platform – Siradel,” SIRADEL , [Online]. Available: https://www.siradel.com/software/connectivity/s_5gchannel/. [Accessed 7 Feb 2019].
XXVIII S. Sun, G. R. MacCartney and T. S. Rappaport, “A novel millimeter-wave channel simulator and applications for 5G wireless communications,” in IEEE International Conference on Communications (ICC), Paris, 2017.
XXIX Subba Rao D., Dr. N.S. Murti Sarma, A Secure and Efficient Scheduling Mechanism for Emergency Data Transmission in IOT, J. Mech. Cont.& Math. Sci.,Vol.-14, No.-1, January-February (2019), pp 432-443.
XXX N. Malathi, B. Srinivas, K. Sainath, J. Hemanth Kumar, SOC IP Interfaces¬¬¬-A Hybrid Approach-Implementation using Open Core Protocol, J. Mech. Cont.& Math. Sci.,Vol.-14, No.-4, July-August (2019), pp 481-491
XXXI T. S. Rappaport, S. Y. Seidel and K. Takamizawa, “Statistical channel impulse response models for factory and open plan building radio communication system design,” IEEE Transactions on Communications, vol. 39, no. 5, p. 794–807, May 1991.
XXXII T. Nitsche, C. Cordeiro, A. B. Flores, E. W. Knightly, E. Perahia, and J. C. Widmer, “IEEE 802.11ad: directional 60 GHz communication for multi-Gigabit-per-second Wi-Fi [Invited Paper],” IEEE Communications Magazine, vol. 52, no. 12, pp. 132-141, December 2014.
XXXIII T. S. Rappaport, G. R. MacCartney, M. K. Samimi and S. Sun, “Wideband Millimeter-Wave Propagation Measurements and Channel Models for Future Wireless Communication System Design,” IEEE Transactions on Communications, vol. 63, no. 9, pp. 3029-3056, Sept 2015.

XXXIV T. S. Rappaport, Y. Xing, G. R. MacCartney, A. F. Molisch, E. Mellios, and J. Zhang, “Overview of Millimeter Wave Communications for Fifth-Generation (5G) Wireless Networks—With a Focus on Propagation Models,” IEEE Transactions on Antennas and Propagation, vol. 65, no. 12, pp. 6213-6230, Dec. 2017.
XXXV T. S. Rappaport et al., “Millimeter Wave Mobile Communications for 5G Cellular: It Will Work!,” IEEE Access, vol. 1, pp. 335-349, 2013.
XXXVI T. S. Rappaport, S. Sun and M. Shafi, “Investigation and Comparison of 3GPP and NYUSIM Channel Models for 5G Wireless Communications,” in IEEE 86th Vehicular Technology Conference (VTC-Fall), Toronto, 2017.
XXXVII Vienna 5G Simulators, “Vienna 5G Simulators nt.tuwien.ac.at,” Vienna Cellular Communications Simulators (VCCS), [Online]. Available: https://www.nt.tuwien.ac.at/research/mobile-communications/vccs/vienna-5g-simulators/. [Accessed 5 Feb 2019].
XXXVIII V. Fung, T. S. Rappaport, and B. Thoma, “Bit error simulation for pi /4 DQPSK mobile radio communications using two-ray and measurement-based impulse response models,” IEEE Journal on Selected Areas in Communications, vol. 11, no. 3, pp. 393-405, April 1993.
XXXIX White Paper, “5G Channel Model for bands up to100 GHz,” 21 Sep 2016. [Online]. Available: http://www.5gworkshops.com/5GCM.html. [Accessed 9 March 2019].
XL Y. Q. J.and T. S. Rappaport, “Cellular broadband millimeter-wave propagation and angle of arrival for adaptive beam steering systems (invited paper),” in IEEE Radio and Wireless Symposium, Santa Clara, CA, 2012.
XLI Y. Yu, Y. Liu, W. Lu and H. Zhu, “Propagation model and channel simulator under indoor stair environment for machine-to-machine applications,” in Asia-Pacific Microwave Conference (APMC), Nanjing, Dec 2015.

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Abstract:

Concrete is a composite material made up of cement, aggregates, water, and sometimes suitable admixture. Concrete production requires a huge amount of natural materials. These natural materials excessive usage causing depletion of natural resources and also posing risk to the environment due to climatic change. Now a day’s climate change and environmental pressures are global issues worldwide. At the same time, different industries are generating a huge amount of waste products which goes to dumping sites causing land pollution. This is a key time to substitute natural materials with these waste materials of different industries. In the present study, cement is partially replaced by fumed silica and fine aggregates are partially replaced by the synergy of waste marble dust and glass powder. Mechanical properties and microscopic analysis of samples were done to get a better understanding of replacement effects. From mechanical strength test results, it was concluded that controlled concrete samples show the highest strength. 

Keywords:

Green concrete,Fume Silica,Waste Marble Dust,Waste Glass Powder,Strength,

Refference:

I ASTM C150 “Standard Specifications for Portland Cement.
II Aliabdoet.al, 2014. “Re-use of marble waste dust in the production of cement and concrete”. Constr. Build. Mater. 50, 28-41.
III Asel b. Zubaid et.al, “Study the effect of recycled glass on the mechanical properties of Green Concrete”. Intl. Conference on Tech. & Materials for Renewable Energy, Env. & Sustainability, Beirut Lebanon, April 2017.
IV Azmatullah,Adil, Afridi,Atif Afridi, Inayatullah Khan, USE OF SUGARCANE BAGASSE ASH AS A PARTIAL REPLACEMENT OF CEMENT IN CONCRETE, J.Mech.Cont.& Math. Sci., Vol.-14, No.2, March-April (2019) pp 72-86.
V Jowhar Hayat, Saqib Shah, Faisal Hayat Khan, Mehr E Munir, Study on Utilization of Different Lightweight Materials Used in the Manufacturingof Lightweight Concrete Bricks/Blocks, J.Mech.Cont.& Math. Sci., Vol.-14, No.2, March-April (2019), pp 58-71
VI P. Shekar et.al, “ Green Concrete for Better Sustainable Environment” International Research Journal of Engineering and Technology Volume 4, Issue 03, March 2017.
VII Tanveer Hussain et.al, “Strength Properties of Concrete by Using Micro Silica and Nano Silica” International Journal ofResearch Engineering in Technology Vol. 3(10), Pp. 103-108.
VIII Verma et.al, “Effect of micro silica on the strength of concrete withordinary Portland cement” Research Journal of Engineering Science, Sept 2012.

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Abstract:

Mobile Ad-hoc Networks (MANETs) is a decentralized, self-configure autonomous network having no fixed infrastructure. It consists of a mobile node that can move freely. In MANETs, there is no centralized control and authority system. Routing protocols enable the discovery of routes among different nodes and facilitate communication within the networks and minimized overhead and network consumption. For this purpose, different routing protocols can be used. These protocols can be hybrid, proactive, and reactive. The Optimized Link State Routing (OLSR) is a proactive routing protocol that is widely used in MANETs.  This research paper presents the performance of the OLSR protocol for two different mobility models which are the Random Waypoint Mobility Model (RWMM) and the Random Based Mobility Model (RBMM). In this paper, we have evaluated the performance of OLSR protocols for Constant Bit Ratio (CBR), Packet Delivery Ratio (PDR), Packet Drop Ratio (PDR), End-to-End Delay (EED), Data Packet Delivered (DPD), Routing Overhead Normalization (RON) and Average Throughput Ratio (ATR) based on RWMM and RBMM mobility models.

Keywords:

OLSR,RBMM,RWMM,MANET,

Refference:

I. Adam, N., M. Ismail, and J. Abdullah. Effect of node density on performances of three MANET routing protocols. in 2010 International Conference on Electronic Devices, Systems, and Applications. 2010. IEEE.
II. Aujla, G.S. and S.S. Kang, Comparative Analysis of AODV, DSR, GRP, OLSR, and TORA by varying Number of Nodes with FTP and HTTP Applications over MANETs. International Journal of Computer Applications, 2013. 65(2).
III. Ariyakhajorn, J., P. Wannawilai, and C. Sathitwiriyawong. A comparative study of random waypoint and gauss-markov mobility models in the performance evaluation of manet. in 2006 International Symposium on Communications and Information Technologies. 2006. IEEE.
IV. Ahmad, N., and S.Z. Hussain, Performance analysis of adaptive routing protocol based on different mobility models with varying network size. 2013.
V. Azwar, H., M. Batool, and U. Farooq, Performance analysis of AODV, DSR, OLSR, and DSDV Routing Protocols using NS2 Simulator. International Journal of Technology and Research, 2017. 5(3): p. 56-59.
VI. Bai, F., & Helmy, A. (2004). A survey of mobility models. Wireless Adhoc Networks. University of Southern California, USA, 206, 147.

VII. Bakalis, P., et al. Performance evaluation of constant bit rate and variable bit rate traffic models on Vehicular Ad hoc network using a dynamic source routing protocol. in the 3rd IEEE International Conference on Adaptive Science and Technology (ICAST 2011). 2011. IEEE.
VIII. Clausen, T., & Jacquet, P. (2003). Optimized Link State Routing Protocol (OLSR) RFC 3626. Network Working Group. Internet Engineering Task Force (IETF).
IX. Dumic, E., et al., Transmission of 3D Video Content, in 3D Visual Content Creation, Coding, and Delivery. 2019, Springer. p. 195-221.
X. Khatkar, A., and Y. Singh. Performance evaluation of hybrid routing protocols in mobile ad hoc networks. in 2012 Second International Conference on Advanced Computing & Communication Technologies. 2012. IEEE.
XI. Kumawat, V. and B.S. Jangra, Performance Analysis of different Routing Protocol for WSN. International Journal of Computer Applications, 2017. 975: p. 8887.
XII. L. Breslau, D. Estrin, K. Fall, S. Floyd, J. Heidemann, A. Helmy, P. Huang, S. McCanne, K. Varadhan, Y. Xu, and H. Yu, Advances in network simulation, in IEEE Computer, vol. 33, no. 5, May 2000, pp. 59—67
XIII. Maan, F., and N. Mazhar. MANET routing protocols vs mobility models: A performance evaluation. in 2011 Third International Conference on Ubiquitous and Future Networks (ICUFN). 2011. IEEE.
XIV. Mahajan, S. and V. Chopra, Performance Evaluation of MANET routing protocols with scalability using QoS metrics of VoIP Applications. Department of Computer Science Engineering, DAVIET Jalandhar.(Februray 2013), 2013.
XV. Mohammed ZohdyAbdulhady ,Loay E. George, “Characterization of Individual Mobility and Society Using CDR Data”, J. Mech. Cont.& Math. SciVol.-14, No.-5, September – October (2019) , pp 6-15
XVI. Naeem Abid,Shahryar Shafique,Sheeraz Ahmad, Nadeem Safwan, Sabir Awan, Fahim Khan, “Techno-economic planning with different topologies of Fiber to the Home access networks with Gigabit Passive Optical Network technologies”, J. Mech. Cont.& Math. SciVol.-14, No.-4, July-August (2019), pp 595-612
XVII. Nunes, B.A.A., et al., A machine learning framework for TCP round-trip time estimation. EURASIP Journal on Wireless Communications and Networking, 2014. 2014(1): p. 47.
XVIII. Pandey, K., S.K. Raina, and R.S. Rao. Performance analysis of routing protocols for vehicular adhoc networks using NS2/SUMO. in the 2015 IEEE International Advance Computing Conference (IACC). 2015. IEEE.

XIX. Radwan, A.A., T.M. Mahmoud, and E.H. Hussein. AntNet-RSLR: a proposed ant routing protocol for MANETs. in 2011 Saudi International Electronics, Communications and Photonics Conference (SIECPC). 2011. IEEE.
XX. Sajjad, M., S, Khalid., T, Hussain., A, A, Waseem., I. Khalil., I. Ali., N. Gul., Impact of Jelly Fish Attackonthe Performance of DSR Routing Protocol in MANETs.
XXI. Sarkar, S.K., T.G. Basavaraju, and C. Puttamadappa, Ad hoc mobile wireless networks: principles, protocols, and applications. 2016: CRC Press.
XXII. Soni, S.J., and J.S. Shah. Evaluating Performance of OLSR Routing Protocol for Multimedia Traffic in MANET Using NS2. in 2015 Fifth International Conference on Communication Systems and Network Technologies. 2015. IEEE.
XXIII. Shelja, S., and K. Suresh. Performance improvement of OLSR protocol by modifying the Routing Table construction mechanism. in 2014 International Conference on Reliability Optimization and Information Technology (ICROIT). 2014. IEEE.
XXIV. Sharma, C., Literature survey of AODV and DSR reactive routing protocols. International Journal of Computer Applications, 2015. 975: p. 8887.
XXV. Sheikh, S., R. Wolhuter, and G. Van Rooyen. A comparative analysis of MANET routing protocols for low-cost rural telemetry Wireless Mesh Networks. in 2015 International Conference on Emerging Trends in Networks and Computer Communications (ETNCC). 2015. IEEE.
XXVI. Wang, J., et al., HOPNET: A hybrid ant colony optimization routing algorithm for mobile ad hoc network. Ad Hoc Networks, 2009. 7(4): p. 690-705

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Abstract:

In this paper, an open-source simulator named MYUSIM is utilized to find the impact of the Human Blockage loss and Outdoor to Indoor (O2I) loss on the best candidate of 5G mmWave (38 GHz) in the NLOS UMi environment which has been proven the authors in their previous study. For accurate channel modeling, the human blockage and O2I losses play a vital role as in real life situations these losses occur. The previous study includes an ideal condition in which these losses were not considered. NYUSIM uses a four-state Markov process to determine human blockage and two modes for O2I losses which include “High loss mode” for highly lossy materials like concrete walls and infrared reflecting glasses and “Low loss mode” for low loss materials like standard glasses and woods etc. These works are proof to the statement that there is a significant impact of the human and O2I losses on 5G mmWave bands which includes a smaller number of spatial lobes formed, lesser power is received, the pathloss is increased, etc. Therefore, these losses must be considered for modeling the next-generation mobile communication system i.e 5G.

Keywords:

5G,mmWaves,Human Blockage Loss,Outdoor to Indoor Loss,NYUSIM,Mobile Communication,

Refference:

I. Aalto University, AT&T, BUPT, CMCC, Ericsson, Huawei, Intel, KT Corporation, Nokia, NTT DOCOMO, New York University, Qualcomm, Samsung, University of Bristol, and the University of Southern, “White paper on “5G Channel Model for bands up to100 GHz”,” 21 Oct 2016. [Online]. Available: http://www.5gworkshops.com/5GCM.html. [Accessed 29 8 2020].
II. G. R. MacCartney et al., “Rapid fading due to human blockage in pedestrian crowds at 5G millimeter-wave frequencies,” in IEEE Global Communications Conference, 2017.
III. G. R. MacCartney, Jr. and T. S. Rappaport, “A flexible millimeter-wave channel sounder with absolute timing,” IEEE Journal on Selected Areas in Communications, vol. 35, no. 6, p. 1402–1418, Jun 2017.
IV. G. R. MacCartney, Jr. and T. S. Rappaport, “Study on 3GPP rural macrocell path loss models for millimeter-wave wireless communications,” in IEEE International Conference on Communications (ICC), 2017.
V. G. R. MacCartney and T. S. Rappaport, “Millimeter-wave base station diversity for 5G coordinated multipoint (CoMP) applications,” in IEEE Transactions on Wireless Communications, May 2019.
VI. J. I. Smith, “A computer-generated multipath fading simulation for mobile radio,” IEEE Transactions on Vehicular Technology, vol. 24, no. 3, p. 39–40, Aug 1975.
VII. J. Lota, S. Sun, T. S. Rappaport and A. Demostheno, “5G ULA With Beamforming and Spatial Multiplexing at 28, 37, 64 and 71 GHz for Outdoor Urban Communication: A Two-Level Approach,” IEEE Transactions on Vehicular Technology, vol. 66, no. 11, pp. 9972-9985, Nov 2017.
VIII. J. G. Andrews et al., “Modeling and analyzing millimeter wave cellular systems,” IEEE Trans. on Comm., vol. 65, no. 1, p. 403–430, Jan 2017.
IX. K. Haneda et al., “5G 3GPP-Like channel models for outdoor urban microcellular and macrocellular environments,” in IEEE 83rd Vehicular Technology Conference (VTC Spring), May 2016.
X. K. Haneda et al., “Indoor 5G 3GPP-like channel models for office and shopping mall environments,” in IEEE International Conference, May 2016.
XI. K. Zeman, P. Masek, M. Stusek, J. Hosek, and P. Sil, “Accuracy comparison of propagation models for mmWave communication in NS-3,” in 9th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), Munich, 2017.
XII. M. S. Jamal and S. Baseer, Analysis of Channel Modelling for 5G mmWave Communication [Unpublished Master’s thesis], Peshawar: University of Engineering & Technology, 2020
XIII. M. K. Samimi and T. S. Rappaport, “3-D Millimeter-Wave Statistical Channel Model for 5G Wireless System Design,” IEEE Transactions on Microwave Theory and Techniques, vol. 64, no. 7, pp. 2207-2225, July 2016.
XIV. Malathi N., B. Srinivas, K. Sainath, J. Hemanth Kumar, “SOC IP Interfaces¬¬¬-A Hybrid Approach-Implementation using Open Core Protocol”, J. Mech. Cont.& Math. Sci.,Vol.-14, No.-4, July-August (2019), pp 481-491
XV. R. H. Clarke, “A statistical theory of mobile-radio reception,” The Bell System Technical Journal, vol. 47, no. 6, p. 957–1000, July 1968.
XVI. R. W. Heath and D. J. Love, “Multimode antenna selection for spatial multiplexing systems with linear receivers,” IEEE Transactions on Signal Processing, vol. 53, no. 8, pp. 3042-3056, Aug. 2005.
XVII. S. Jain, “Mobile VNI Forecast 2017-2022: 5G emerges and is here to stay!!,” CISCO Inc., 26 2 2019. [Online]. Available: https://blogs.cisco.com/sp/mobile-vni-forecast-2017-2022-5g-emerges. [Accessed 9 9 2019].
XVIII. S. Jaeckel, L. Raschkowski, K. Börner, and L. Thiele, “QuaDRiGa: A 3-D multi-cell channel model with time evolution for enabling virtual field trials,” IEEE Transactions on Antennas and Propagation, vol. 62, no. 6, p. 3242–3256, June 2014.
XIX. S. Sun, G. R. MacCartney and T. S. Rappaport, “A novel millimeter-wave channel simulator and applications for 5G wireless communications,” in IEEE International Conference on Communications (ICC), Paris, 2017.
XX. S. Sun et al., “Investigation of Prediction Accuracy, Sensitivity, and Parameter Stability of Large-Scale Propagation Path Loss Models for 5G Wireless Communications,” IEEE Transactions on Vehicular Technology, vol. 65, no. 5, pp. 2843-2860, May 2016.
XXI. Subba Rao D., Dr. N.S. Murti Sarma, “A Secure and Efficient Scheduling Mechanism for Emergency Data Transmission in IOT”, J. Mech. Cont.& Math. Sci.,Vol.-14, No.-1, January-February (2019), pp 432-443.
XXII. T. S. Rappaport, G. R. MacCartney, M. K. Samimi, and S. Sun, “Wideband Millimeter-Wave Propagation Measurements and Channel Models for Future Wireless Communication System Design,” IEEE Transactions on Communications, vol. 63, no. 9, pp. 3029-3056, Sept 2015.
XXIII. T. S. Rappaport, S. Sun and M. Shafi, “Investigation and Comparison of 3GPP and NYUSIM Channel Models for 5G Wireless Communications,” in IEEE 86th Vehicular Technology Conference (VTC-Fall), Toronto, 2017.
XXIV. T. S. Rappaport, Y. Qiao, J. I. Tamir, J. N. Murdock, and E. Ben-Dor, “Cellular broadband millimeter-wave propagation and angle of arrival for adaptive beam steering systems (invited paper),” in IEEE Radio and Wireless Symposium, Santa Clara, CA, 2012
XXV. T. Bai and R. W. Heath, “Coverage analysis for millimeter wave cellular networks with blockage effects,” in IEEE Global Conference on Signal and Information Processing, 2013
XXVI. T. S. Rappaport, S. Y. Seidel and K. Takamizawa, “Statistical channel impulse response models for factory and open plan building radio communication system design,” IEEE Transactions on Communications, vol. 39, no. 5, p. 794–807, May 1991.
XXVII. T. S. Rappaport et al., “Millimeter Wave Mobile Communications for 5G Cellular: It Will Work!” IEEE Access, vol. 1, pp. 335-349, 2013.
XXVIII. Y. Xing, O. Kanhere, S. Ju, and T. S. Rappaport, “Indoor wireless channel properties at millimeter-wave and sub-Terahertz frequencies: Reflection, scattering, and path loss,” in Proc. 2019 Global Communications Conferences, Dec. 2019.
XXIX. Y. Yu, Y. Liu, W. Lu and H. Zhu, “Propagation model and channel simulator under indoor stair environment for machine-to-machine applications,” in Asia-Pacific Microwave Conference (APMC), Nanjing, Dec 2015.

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Abstract:

Microstructure studies in concrete are unique techniques for understanding the morphological features of concrete. In this research work, four mixture of concrete has been prepared by replacement of normal cement with geopolymer in 0 %, 50%, 80% and 100% of different ratio with recycled aggregates. Both class of fly ash F and C has been used with an alkaline activator (NaOH and Na₂SiO₃).In each mixture, the alkaline liquid, sodium hydroxide (Noah), and sodium silicate were dependent on the amount of fly ash, while the ratio of NaOH to Na₂SiO₃ is maintained 2.5 for all concrete. After costing twelve cylinders 150mm x 300 mm and twelve 152.4 mm x 152.4 mm x 609.6 mm concrete beams were cured for 28 days ata normal temperature of 27°C water. The physical and chemical properties have beeninvestigated in this research. The SEM and XRF analysis of all samples has been compared with the controlled sample. Which all samples have been compared with a controlled sample, to identify the changing of compressive and flexural strength in each sample.

Keywords:

Microstructure,Geopolymer,Normal cement,SEM,XRF,

Refference:

I. A.S, Adithya & Palanisamy, Magudeaswaran. (2017). SEM Analysis of Sustainable High-Performance Concrete. 6. 10.15680/IJIRSET.2017.0606016.

II. Chopra, Divya & Siddique, Rafat & , Kunal. (2015). Strength, permeability, and microstructure of self-compacting concrete containing rice husk ash. Biosystems Engineering. 130. 72-80. 10.1016/j.biosystemseng.2014.12.005.

III. Chan, W.W.J & Wu, C.M.L. (2000). The durability of concrete with high cement replacement. Cement and Concrete Research. 30. 865-879. 10.1016/S0008-8846(00)00253-2.

IV. Jowhar Hayat, Saqib Shah, Faisal Hayat Khan, Mehr E Munir, Study on Utilization of Different Lightweight Materials Used in the Manufacturingof Lightweight Concrete Bricks/Blocks, J. Mech. Cont.& Math. Sci.,Vol.-14, No.2, March-April (2019), pp 58-71

V. Li, Hui & Xiao, Hui-gang & Yuan, Jie & Ou, Jinping. (2004). The microstructure of Cement Mortar with Nano-Particles. Composites Part B: Engineering. 35. 185-189. 10.1016/S1359-8368(03)00052-0.

VI. Meyer, C. (2009). The Greening of the Concrete Industry. Cement & Concrete Composites – CEMENT CONCRETE COMPOSITES. 31. 601-605. 10.1016/j.cemconcomp.2008.12.010.

VII. P. Duxson, J. L. Provis, G. C. Lukey, and J. S. J. Van Deventer, Cement and Concrete Research,37 (2007) 1590-1597

VIII. Patankar, Subhash &Jamkar, Sanjay & Ghugal, Yuwaraj. (2013). Effect of Water-to-Geopolymer Binder Ratio on the Production of Fly ash Based Geopolymer Concrete. Journal. 2. 10.13140/2.1.4792.1284.

IX. Shi XS, Collins FG, Zhao XL, Wang QY. Mechanical properties and microstructure analysis of fly ash geopolymeric recycled concrete. J Hazard Mater. 2012; 237-238:20-29. doi:10.1016/j.jhazmat.2012.07.070.

X. Singh, Malkit& Siddique, Rafat. (2014). Compressive strength, drying shrinkage, and chemical resistance of concrete incorporating coal bottom ash as a partial or total replacement of sand. Construction and Building Materials. 68. 39–48. 10.1016/j.conbuildmat.2014.06.034.

XI. Sudhakar M., HeeralalMudavath, G. Kalyan KumaR, MECHANICAL STRENGTH AND STIFFNESS BEHAVIOUR OF CLASS F-POND ASH, J. Mech. Cont.& Math. Sci.,Vol.-14, No.-6, November – December (2019), pp 264-282

XII. Vaitkevičius, Vitoldas & Šerelis, Evaldas & Hilbig, Harald. (2014). The effect of glass powder on the microstructure of ultra-high performance concrete. Construction and Building Materials. 68. 102–109. 10.1016/j.conbuildmat.2014.05.101.

XIII. Van Gemert, Dionys. “Synergies between Polymers and Cement Concrete Providing Opportunities for Sustainable Construction.” Advanced Materials Research, vol. 687, Trans Tech Publications, Ltd., Apr. 2013, pp. 12–20. Crossref, doi:10.4028/www.scientific.net/amr.687.12.

XIV. https://doi.org/10.1016/j.conbuildmat.2013.12.051

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Abstract:

In this paper, we study the computational strategy for the implementation of orthogonal moments to two-dimensional images. Automatic and accurate classification of Magnetic Resonance Images is of importance for the interpretation and analysis of these images and for this purpose different techniques have been proposed.  In this paper, we present Legendre Polynomial and two different classification-based methods for the classification of normal and abnormal MRI Images. In the first step, we apply Legendre polynomial to extract features from MRI images. In the second stage, two classifiers have been used which are employed to classify these images as normal and abnormal images. The proposed method was tested on tests with 75 images in which 15 images belong to the normal category images and the remaining 60 are abnormal images. The result derived from the confusion matrix test yielded a classification accuracy of 100.0% for these images.

Keywords:

Legendre Polynomials,Shifted Legendre Polynomials,Classification,MRI Images,Image Processing,

Refference:

Ban N Dhannoon and Loay E George, Color image compression using polynomial and quadtree coding techniques, International Journal of Scientific & Engineering Research 4 (2013), no. 11.
II. EA El-Dahshan, Abdel-Badeeh M Salem, and Tamer H Younis, A hybrid technique for automatic mri brain images classification, Studia Univ. Babes-Bolyai, Informatica 54 (2009), no. 1, 55–67.
III. Exact legendre moment computation for gray level images, Pattern Recognition 40 (2007), no. 12, 3597–3605.
IV. Florin Gorunescu, Data mining techniques in computer-aided diagnosis: Non-invasive cancer detection, Pwaset 25 (2007), 427–430.
V. Harris Drucker, Christopher JC Burges, Linda Kaufman, Alex J Smola, and Vladimir Vapnik, Support vector regression machines, Advances in neural information processing systems, 1997, pp. 155–161.
VI. Hashem Kalbkhani, Mahrokh G Shayesteh, and Behrooz Zali-Vargahan, Robust algorithm for brain magnetic resonance image (mri) classification based on garch variances series, Biomedical Signal Processing and Control 8 (2013), no. 6, 909–919.
VII. Irshad Khalil, Adnan Khalil, Sami Ur Rehman, Hammad Khalil, Rahmat Ali Khan, and Fakhre Alam, Classification of ecg signals using legendre moments, International Journal of Bioinformatics and Biomedical Engineering 1 (2015), no. 3, 284–291.
VIII. Khalid M Hosny, Efficient computation of legendre moments for gray level images, International Journal of Image and Graphics 7 (2007), no. 04, 735–747.
IX. Kemal Polat, Bayram Akdemir, and Salih Gu¨ne¸s, Computer-aided diagnosis of ecg data on the least square support vector machine, Digital Signal Processing 18 (2008), no. 1, 25–32.
X. K. Laxmi Narayanamma, R. V. Krishnaiah, P. Sammulal, An Efficient
Statistical Feature Selection Based Classification, J. Mech. Cont.& Math.
Sci.,Vol.-14, No.-4, JulyAugust (2019) , pp 27-40
XI. Michael Reed Teague, Image analysis via the general theory of moments, JOSA 70 (1980), no. 8, 920–930.
XII. Madhubanti Maitra and Amitava Chatterjee, Hybrid multiresolution slantlet transform and fuzzy c-means clustering approach for normal-pathological brain mr image segregation, Medical engineering & physics 30 (2008), no. 5, 615–623.
XIII. M. K. Kundum S. Das, M. Chowdhury, An mr brain images classifier via principal component analysis and kernel support vector machine, Progress in Electromagnetics Research 137 (2013), 1–17
XIV. Sandeep Chaplot, LM Patnaik, and NR Jagannathan, Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network, Biomedical Signal Processing and Control 1 (2006), no. 1, 86–92.
XV. Xingxing Zhou, Shuihua Wang, Wei Xu, Genlin Ji, Preetha Phillips, Ping Sun, and Yudong Zhang, Detection of the pathological brain in MRI scanning based on wavelet-entropy and naive Bayes classifier, International Conference on Bioinformatics and Biomedical Engineering, Springer, 2015, pp. 201–209.
XVI. Yudong Zhang, Zhengchao Dong, Lenan Wu, and Shuihua Wang, A hybrid method for MRI brain image classification, Expert Systems with Applications 38 (2011), no. 8, 10049–10053.
XVII.Vasanthselvakumar R, Balasubramanian M, Palanivel S, “Detection and
Classification of Kidney Disorders using Deep Learning Method”,
J. Mech.Cont.& Math. Sci.,Vol.-14,No.2, March-April (2019), pp 258-270.

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Abstract:

Most of the countries around the world are under locked down due the pandemic. Every country has imposed a strict travel restrictions and has stopped all types of visas and tourist activities. This created a major impact on aviation sector and the tourist sector. Even the people not effected from Covid-19 and in real emergence are not able travel from one place to another. Some countries have laid down quarantine rules, which will be a major hindrance to emergency travelers and for tourists. All passengers traveling are tested for COVID-19 using RT-PCR, which can take between 48 to 72 hours to produce the result.  But in some cases people who are tested negative even after 3 or 4 RT-PCR tests shows a typical pneumonia in the CT Scan or in a chest X-ray. If the aviation sector relies only on the RT-PCR test, many patients may be missed. In order to reduce the risk to some extent and prevent a high-risk patient from traveling, the passenger can be asked to upload his / her chest X-ray prior to travel. Using an X-ray of the chest, we can predict the possibility of Covid-19 cases before the patients are physically examined. This technique cannot replace the RT-PCR test, but can be a stand-by tool to help detect Covid-19 prior to the RT-PCR test. It would also help to identify patients who are highly prone for the infection. In this paper, we developed a CNN from scratch to identify a patient infected with COVID from a chest X-ray image. The model was trained with the chest X-ray of normal and COVID patients. Later the model was tested on two datasets, one publicly available in GitHub, and the other dataset was compiled from the Italian Society of Medical and Interventional Radiology website using web scrapping. The model produced an accuracy of 96.48 percent with the training dataset. To further improve accuracy, we used the same dataset on a pre-trained network (VGG16) and achieved an accuracy of around 99 per cent.

Keywords:

Covid-19,Chest X-ray image,CNN,VGG16,Transfer learning,

Refference:

I. Ali Narin, Ceren Kaya, and Ziynet Pamuk, “Automatic detection of coronavirus disease (COVID-19) using x-ray images and deep convolutional neural networks”,arXiv preprint arXiv:2003.10849, 2020.
II. Asif Iqbal Khan, JunaidLatief Shah, and Mudasir Bhat “Coronet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images”,arXiv preprint arXiv:2004.04931, 2020.
III. Lin Li, Lixin Qin, Zeguo Xu, Youbing Yin, Xin Wang, Bin Kong, Junjie Bai, Yi Lu, Zhenghan Fang, Qi Song, et al,“Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest ct. Radiology”,RSNA ,page 200-205, 2020.https://doi.org/10.1148/radiol.2020201178
IV. LindaWang and AlexanderWong “COVID-Net: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest radiography images” arXiv preprint arXiv:2003.09871, 2020.
V. Min Zhou, Yong Chen, Dexiang Wang, Yanping Xu, Weiwu Yao, Jingwen Huang, XiaoyanJin, Zilai Pan, Jingwen Tan, LanWang, et al,“Improved deep learning model for differentiating novel coronavirus pneumonia and influenza pneumonia”, medRxiv, 30 March 2020. DOI: 10.1101/2020.03.24.20043117
VI. Ophir Gozes, MaayanFrid-Adar, Hayit Greenspan, Patrick D. Browning, Huangqi Zhang, Wenbin Ji, Adam Bernheim, and Eliot Siegel, “Rapid AI development cycle for the coronavirus (COVID-19) pandemic: Initial results for automated detection & patient monitoring using deep learning CT image analysis”,arXiv preprint arXiv:2003.05037, 2020.
VII. Rezaul Karim, Till DAűhmen, Dietrich Rebholz-Schuhmann, Stefan Decker, Michael Cochez, Oya
VIII. Beyan, “DeepCOVIDExplainer: Explainable COVID-19 Predictions Based on Chest X-ray Images”,eess.IV, April 2020.
IX. Tulin Ozturk, Muhammed Talo, Eylul Azra Yildirim, Ulas Baran Baloglu, Ozal Yildirim, and U Rajendra Acharya, “Automated detection of covid-19 cases using deep neural networks with x-ray images”, Computers in Biology and Medicine 121, pp. 103792, 2020.
X. World Health Organization. Coronavirus disease 2019 (COVID-19) situation report, June 2020.
XI. Wei-jie Guan, Zheng-yi Ni, Yu Hu,Wen-hua Liang, Chun-quanOu, Jian-xing He, Lei Liu, Hong Shan, Chun-liang Lei, David SC Hui, et al , “Clinical characteristics of coronavirus disease 2019 in china”, The New England Journal of Medicine, med 2020;382, pp.1708-20, 2020

Vasanthselvakumar R, Balasubramanian M, Palanivel S, Detection and Classification of Kidney Disorders using Deep Learning Method”, J.Mech.Cont.& Math.Sci.Vol.-14, No.2, March-April (2019), pp 258-270.

SozanSulaimanMaghdid,Tarik Ahmed Rashid, Sheeraz Ahmed, Khalid Zaman, M.Khalid Rabbani, “Analysis and Prediction of Heart Attacks Based on Design of Intelligent Systems”, J.Mech.Cont.& Math.Sci.Vol.-14, No.-4, July-August (2019), pp 628-645

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Abstract:

According to facts from ministry of girls and children development, as many as 20 kids on an average go missing in the national capital each day. And handiest 30 percent of the kids are reunited with their families, the respite remain entrenched. Of the 20 children, the handiest one or two kids goes missing on their personal, ultimate kids are abducted.  Kidnapped kids are pushed into toddler labor, flesh trade, domestic provider or begging racket. Despite CCTV cameras, preserving an eye fixed at the roads and a hi-tech police force guarding the residents, predominant cities in India has grown to be a hub of kidnappers each as a transit and destination point. The alarming records embody abortive to induce police officers into urgency. To get rid of kid abduction, we proposed to built a virtual agent - Tracking and monitoring elementary school kids (TMESK system) meant to design a at ease gadget that continuously tracks and monitor kids and alert the parents, school management if anything went wrong. In proposing model Linear Support Vector Machine used to train TMESK system using GPS trajectory data and smart IOT wearable gadgets alert when kids exits from a safe zone or enters to unexpected location change, TMESK sends an alert message to their mother and father, caretakers and nearest police station to make sure the safety of the child.

Keywords:

Internet of Things (IOT),Global Positioning system (GPS),Deep Neural Network (DNN),

Refference:

I. A. Jatti, M. Kannan, R. M. Alisha, P. Vijayalakshmi and S. Sinha, “Design and development of an IOT based wearable device for the safety and security of women and girl children,” 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), Bangalore, 2016, pp. 1108-1112.
II. Al-Lawati, Anwaar & Al-Jahdhami, Shaikha & Al-Belushi, Asma & Al-Adawi, Dalal & Awadalla, Medhat & Al-Abri, Dawood. (2015). RFID-based system for school children transportation safety enhancement. 10.1109/IEEEGCC.2015.7060047.
III. FINKELHOR, DAVID, GERALD T. HOTALING, and ANDERA J. SEDLAK. “The Abduction of Children by Strangers and Nonfamily Members: Estimating the Incidence Using Multiple Methods.” Journal of Interpersonal Violence 7, no. 2 (June 1992): 226–43.
IV. I. Torre, F. Koceva, O. R. Sanchez and G. Adorni, “A framework for personal data protection in the IoT,” 2016 11th International Conference for Internet Technology and Secured Transactions (ICITST), Barcelona, 2016, pp. 384-391.
V. J Manasa, SN Kumar. ” Distinguishing Stress Based on Social Interactions in Social Content Area ” .International Journal of Pure and Applied Mathematics, 2018
VI. Kumar, S. Naresh, P. Pramod Kumar, C. H. Sandeep, and S. Shwetha. “Opportunities for applying deep learning networks to tumour classification.” Indian Journal of Public Health Research & Development 9, no. 11 (2018): 742-747.
VII. Mori, Y.; Kojima, H.; Kohno, E.; Inoue, S.; Ohta, T.; Kakuda, Y.; Ito, A, “A Self-Configurable New Generation Children Tracking System Based on Mobile Ad Hoc Networks Consisting of Android Mobile Terminals,” Autonomous Decentralized Systems (ISADS), 2011 10th International Symposium on , vol., no., pp.339,342, 23-27 March 2011.
VIII. M. Egele, C. Kruegel, E. Kirda, and G. Vigna. Pios: Detecting privacy leaks in ios applications. In NDSS, 2011
IX. Naik, K. Seena and E. Sampathkumar S. R. Sudarshan. “Smart Healthcare Monitoring System Using Raspberry Pi On IoT Platform. ” (2019).
X. N. Z. Hamid, A. L. Asnawi, W. H. W. Morshidi, A. A. Ruslan, N. A. Jundi and H. A. M. Ramli, “Design of a Wireless Device for Monitoring Human Critical Condition at Industrial Workplace,” 2018 7th International Conference on Computer and Communication Engineering (ICCCE), Kuala Lumpur, 2018, pp. 252-257.
XI. P. P. Harlikar and P. C. Bhaskar, “Development of Real Time Life Secure and Tracking System for Swimmers,” 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC), Erode, India, 2019, pp. 850-854.
XII. Q. Liao, “Study of SVM-based Intelligent Dispatcher for Parallel Machines Scheduling with Sequence-dependent Setup Times,” 2018 6th International Conference on Mechanical, Automotive and Materials Engineering (CMAME), Hong Kong, 2018, pp. 46-50.
XIII. Spilman, Sarah. (2006). Child Abduction, Parents’ Distress, and Social Support. Violence and victims. 21. 149-65. 10.1891/vivi.21.2.149.
XIV. Sripada, Naresh Kumar et al. “Support Vector Machines to Identify Information towards Fixed-Dimensional Vector Space.” International Journal of Innovative Technology and Exploring Engineering (IJITEE). (2019).
XV. S. Hwang, J. Jeong and Y. Kang, “SVM-RBM based Predictive Maintenance Scheme for IoT-enabled Smart Factory,” 2018 Thirteenth International Conference on Digital Information Management (ICDIM), Berlin, Germany, 2018, pp. 162-167.
XVI. Statista, “Forecasted value of the global wearable devices market from 2012 to 2018 (in billion U.S. dollars),” http://www.statista.com/statistics/259372/wearabledevice-market-value/.
XVII. Support Vector Machines – Hands-On Machine Learning with Scikit-Learn and TensorFlow,https://www.oreilly.com/library/view/hands-on-machine-learning/9781491962282/ch05.html
XVIII. Saranya, J.; Selvakumar, J., “Implementation of children tracking system on android mobile terminals,” Communications and Signal Processing (ICCSP), 2013 International Conference on , vol., no., pp.961,965, 3-5 April 2013.
XIX. S Venkatesulu, K Seena Naik, J Kiran Naik, E Sudarshan , ” Design And Analysis Of Folded Printed Quadrifilar Helical Antenna For Gps Application ,” ICT for Competitive Strategies: Proceedings of 4th International Conference on Information and Communication Technology for Competitive Strategies (ICTCS 2019), December 13th-14th, 2019, Udaipur, India
XX. S. Naresh Kumar et al., 2019. “A STUDY ON DEEP Q-LEARNING AND SINGLE STREAM Q-NETWORK ARCHITECTURE”. International Journal of Advanced Science and Technology 28 (20), 586 -592.
XXI. T. Yan, Y. Lu, and N. Zhang, “Privacy disclosure from wearable devices,” in Proceedings of the the 2015 Workshop, pp. 13–18, ACM, Hangzhou, China, June 2015.
XXII. Toumi, H., “Four-year-old girl left alone in school bus dies”. Available at: http://gulfnews.com/news/gulf/qatar/four-year-old-girl-left-alone-in-school-bus-dies-1.628394 [Accessed: 11 Aug. 2014

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