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Sampath Kumar, R.
- Selection of mixed sampling plan with QSS-1(n;CN,CT) plan as attribute plan indexed through MAPD and AQL
Abstract Views :384 |
PDF Views:117
Authors
Affiliations
1 Department of Statistics, Government Arts College, Coimbatore-641 018, Tamil Nadu, IN
2 Department of Statistics, Muthayammal College of Arts & Science, Rasipuram-637408, Tamil Nadu, IN
3 Department of Statistics, PSG College of Arts and Science, Coimbatore - 641 014, Tamil Nadu, IN
1 Department of Statistics, Government Arts College, Coimbatore-641 018, Tamil Nadu, IN
2 Department of Statistics, Muthayammal College of Arts & Science, Rasipuram-637408, Tamil Nadu, IN
3 Department of Statistics, PSG College of Arts and Science, Coimbatore - 641 014, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 5, No 2 (2012), Pagination: 2096-2099Abstract
This paper presents the procedure for the construction and selection of the mixed sampling plan using MAPD as a quality standard with the QSS-1 (n;CN,CT ) plan as attribute plan. The plans indexed through MAPD and AQL are constructed and compared for their efficiency. Tables are constructed for easy selection of the plan.Keywords
QSS-1, MAPD, AQL, Mixed Sampling PlanReferences
- Devaarul S (2003) Certain studies relating to mixed sampling plans and reliability based sampling plans. Ph.D., Dissertation, Bharathiar Univ., Tamil Nadu, India.
- Dodge HF (1967) A new dual system of acceptance sampling. Technical Report No.16 (New Brunswick, NJ, Stats. centre, Rutgers State Univ.).
- Govindaraju K (1991) Procedures and tables of the selection of zero acceptance number quick switching system for compliance testing. Commun. Stats- Simulation & Comput. 20(1), 157-172.
- Mayer PL (1967) A Note on sum of poisson probabilities and an application. Ann. Inst. Stat. Math. 19, 537-542.
- Radhakrishnan R, Sampath Kumar R and Saravanan PG (2009) Construction of dependent mixed sampling plans using single sampling plan as attribute plan Int. J. Stats. & System, 4(1), 67-74.
- Radhakrishnan, R., and Sampath Kumar, R. (2006a). Construction of mixed sampling plan indexed through MAPD and IQL with single sampling plan as attribute plan. Natl. J. Technol. 2(2), 26-29.
- Radhakrishnan R and Sampath Kumar R (2006b) Construction of mixed sampling plans indexed through MAPD and AQL with chain sampling plan as attribute plan. STARS Int. J. 7(1), 14-22.
- Radhakrishnan R and Sampath Kumar R (2007a) Construction of mixed sampling plans indexed hrough MAPD and IQL with double sampling plan as attribute plan. The J. Kerala Stat. Asso. 18, 13- 22.
- Radhakrishnan R and Sampath Kumar R (2007b) Construction of mixed sampling plans indexed through MAPD and AQL with double sampling plan as attribute plan. The Int. J. Stat. System. 2(2), 33-39.
- Radhakrishnan R and Sampath Kumar R (2009) Construction and comparison of mixed sampling plans having ChSP-(0,1) plan as attribute plan. Int. J. Stat. & Manage. System, 4(1-2), 134-149.
- Romboski LD (1969) An Investigation of quick switching acceptance sampling system. Ph.D. Dissertation, New Brunswick, NJ, Rutgers State University.
- Sampath Kumar R (2007) Construction and selection of mixed variables-attributes sampling plan, Ph.D. Disseration. Dept. Stats. Bharathiar Univ., Coimbatore, TN, India.
- Schilling EG (1967) A General method for determining the operating characteristics of Mixed Variables – attributes sampling plans single side specifications, S.D. known. PhD Dissertation – Rutgers- The State University, New Brunswick, NJ.
- Soundarajan V (1975) Maximum allowable percent defective (MAPD) single sampling Inspection by attribute plans. J. Quality Technol. 7(4), 173-177.
- Soundararajan V and Arumainayagam SD (1988) Modifications of quick switching systems with special reference to crossover point of their composite OC curves. Res. Report No.25, Department of Statistics, Bharathiar University, Coimbatore.
- Impact of Information Technology in Various Sectors
Abstract Views :140 |
PDF Views:0
Authors
A. Sreeram
1,
Y. Srinivas
2,
G. Ananda Rao
3,
S. R. Karumuri
4,
J. Vijayasekhar
5,
R. Sampath Kumar
2
Affiliations
1 Hyderabad Business School, GITAM University, Hyderabad, IN
2 Department of Information Technology, GITAM University, Hyderabad, IN
3 Department of Applied Mathematics, GITAM University, Visakhapatnam, IN
4 Department of EIE, Lakireddy Balireddy College of Engineering, Mylavaram, IN
5 Department of Engineering Mathematics, GITAM University, Hyderabad, IN
1 Hyderabad Business School, GITAM University, Hyderabad, IN
2 Department of Information Technology, GITAM University, Hyderabad, IN
3 Department of Applied Mathematics, GITAM University, Visakhapatnam, IN
4 Department of EIE, Lakireddy Balireddy College of Engineering, Mylavaram, IN
5 Department of Engineering Mathematics, GITAM University, Hyderabad, IN
Source
Oriental Journal of Computer Science and Technology, Vol 5, No 2 (2012), Pagination: 289-294Abstract
IT has brought in several technological developments in every field .IT plays a major role in simplifying various organizational processes. IT has accelerated several business organizations all over the world, helped several businesses realize organizational goals and automate processes by following the principles of usability, efficiency, customer related and a clear communication. Most business enterprises rely on the power of information technology for carrying out their daily tasks conveniently and faster. IT makes complex procedures easier, faster and also helps a lot in avoiding redundancy. It lets individuals access necessary data, ensuring the safety of confidential ones. Information Technology has made every individual completely dependent for even the simplest day to day task.Keywords
:Information Technology.- Intelligent Traffic Management for Vehicular Networks Using Machine Learning
Abstract Views :39 |
PDF Views:1
Authors
Affiliations
1 Department of Information Technology, Vidyavaridhi College of Engineering and Technology, IN
2 Department of Information Technology, Shree L R Tiwari College of Engineering, IN
3 Department of Aeronautical Engineering, Er. Perumal Manimekalai College of Engineering, IN
4 Department of Information Technology, Mukesh Patel School of Technology Management and Engineering, IN
1 Department of Information Technology, Vidyavaridhi College of Engineering and Technology, IN
2 Department of Information Technology, Shree L R Tiwari College of Engineering, IN
3 Department of Aeronautical Engineering, Er. Perumal Manimekalai College of Engineering, IN
4 Department of Information Technology, Mukesh Patel School of Technology Management and Engineering, IN
Source
ICTACT Journal on Communication Technology, Vol 14, No 3 (2023), Pagination: 2998-3004Abstract
As urbanization and vehicular density continue to rise, the efficient management of traffic in vehicular networks becomes increasingly critical. This paper presents an innovative approach to intelligent traffic management leveraging Machine Learning (ML) techniques, specifically employing Support Vector Machines (SVM) with Radial Basis Function (RBF) kernels. The integration of SVM with RBF proves to be particularly effective in capturing complex non-linear relationships within the dynamic and unpredictable vehicular environment. Our proposed system aims to enhance traffic flow, reduce congestion, and improve overall transportation efficiency. The SVM-RBF model is trained on diverse datasets encompassing various traffic scenarios, considering factors such as vehicle speed, density, and historical traffic patterns. Through continuous learning, the system adapts to real-time changes, making it robust and responsive to dynamic traffic conditions. The core functionality of the intelligent traffic management system involves predicting traffic patterns and optimizing signal timings at intersections. The SVM-RBF model excels in its ability to classify and predict intricate traffic behavior, allowing for proactive decision-making. This proactive approach facilitates the timely adjustment of traffic signals, rerouting strategies, and adaptive speed limit recommendations. The effectiveness of the proposed system is validated through extensive simulations and real-world experiments, demonstrating significant improvements in traffic flow and reduction in travel times. Furthermore, the system exhibits scalability, making it suitable for deployment in diverse urban environments.Keywords
Intelligent Traffic Management, Vehicular Networks, Machine Learning, SVM, Radial Basis Function.References
- G. Kiruthiga, G.U. Devi and N.V. Kousik, “Analysis of Hybrid Deep Neural Networks with Mobile Agents for Traffic Management in Vehicular Adhoc Networks”, CRC Press, 2020.
- S. Kannan and M. Gheisari, “Ubiquitous Vehicular Ad-Hoc Network Computing using Deep Neural Network with IoT based Bat Agents for Traffic Management”, Electronics, Vol. 10, No. 7, pp. 785-798, 2021.
- M. Shahverdy and M. Sabokrou, “Driver Behavior Detection and Classification using Deep Convolutional Neural Networks”, Expert Systems with Applications, Vol. 149, pp. 113240-113254, 2020.
- K. Bilstrup, E. Uhlemann, E.G. Strom and U. Bilstrup, “Evaluation of the IEEE 802.11p MAC Method for Vehicleto-Vehicle Communication”, Proceedings of International Conference on Vehicular Technology, pp. 1-5, 2008.
- Y.S. Chia, Z.W. Siew, H.T. Yew, S.S. Yang and K.T.K. Teo, “An Evolutionary Algorithm for Channel Assignment Problem in Wireless Mobile Networks”, ICTACT Journal on Communication Technology, Vol. 3, No. 4, pp. 613-618, 2012.
- L. Liu, Y. Wang, J. Zhang and Q. Yang, “A Secure and Efficient Group Key Agreement Scheme for VANET”, Sensors, Vol. 19, No. 3, pp. 482-494, 2019.
- Y. Agarwal, K. Jain and O. Karabasoglu, “Smart Vehicle Monitoring and Assistance using Cloud Computing in Vehicular Ad Hoc Networks”, International Journal of Transportation Science and Technology, Vol. 7, No. 1, pp. 60-73, 2018.
- P. Kumar, R. Merzouki, B. Conrard and V. Coelen, “Multilevel Modeling of the Traffic Dynamic”, IEEE Transactions on Intelligent Transportation Systems, Vol. 15, No. 3, pp. 1066-1082, 2014.
- M.B. Mansour, C. Salama, H.K. Mohamed and S.A. Hammad, “VANET Security and Privacy-An Overview”, International Journal of Network Security and Its Applications, Vol. 10, No. 2, pp. 13-34, 2018.
- P. Vijayakumar, M. Azees, A. Kannan and L.J. Deborah, “Dual Authentication and Key Management Techniques for Secure Data Transmission in Vehicular Ad Hoc Network”, IEEE Transactions on Intelligent Transportation Systems, Vol. 17, No. 4, pp. 1015-1028, 2016.
- A. Sumathi, “Dynamic Handoff Decision based on Current Traffic Level and Neighbor Information in Wireless Data Networks”, Proceedings of International Conference on Advanced Computing, pp. 1-5, 2012.