{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,24]],"date-time":"2024-09-24T04:08:02Z","timestamp":1727150882420},"reference-count":78,"publisher":"European Alliance for Innovation n.o.","issue":"38","license":[{"start":{"date-parts":[[2022,5,3]],"date-time":"2022-05-03T00:00:00Z","timestamp":1651536000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["EAI Endorsed Trans Energy Web"],"abstract":"Many novel technologies of property energy and cell, solar power, batteries, and high-efficient combustion are widely investigated to conserve energy and reduce emissions. Transmission lines (TLs) play a serious role in transmitting generated electricity to different distribution units in facility engineering. The transmission lines function as a link between shoppers and a Power Station. Faults usually occur within the transmission when positioned in an open field. Quick identification and sick line faults square measures required for the conventional operation of the plant. A way like distinct moving ridge rework (DWT) and (EMD) is used to locate and identify faults to resolve this disruption. DWT is used to break down fault transients, as a result of which the info can be collected at the same time in each time and frequency domain. EMD decomposes the TLs voltage into Intrinsic Mode operation (IMFs). Four varieties of fault signals are square measurements produced by the grid-connected facility. Line faults square measure induced MATLAB\/Simulink mistreatment.<\/jats:p>","DOI":"10.4108\/ew.v9i38.733","type":"journal-article","created":{"date-parts":[[2022,5,3]],"date-time":"2022-05-03T08:48:36Z","timestamp":1651567716000},"page":"e7","source":"Crossref","is-referenced-by-count":1,"title":["Smart Technology Based Empirical Mode Decomposition (EMD) Approach for Autonomous Transmission Line Fault Detection Protection"],"prefix":"10.4108","volume":"9","author":[{"given":"Nasser Ali Hasson","family":"Al-Zubaydi","sequence":"first","affiliation":[]}],"member":"2587","published-online":{"date-parts":[[2022,5,3]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Ahmed R. AdlyMahmoud A. ElsaddA novel wavelet packet transform-based fault identification procedures in HV transmission line based on current signals International Journal of Applied Power Engineering, Vol.8, No.1, April 2019.","key":"2014","DOI":"10.11591\/ijape.v8.i1.pp11-21"},{"doi-asserted-by":"crossref","unstructured":"Ahmed R. Adlya, Shady H. E. Abdel Aleemb, Mostafa A. Algabalawyc, F. Juradod, Ziad M. Alie,\u201d A novel protection scheme for multi-terminal transmission lines based on wavelet transform Electric Power Systems Research\u201d 183 (2020) 106286.","key":"2015","DOI":"10.1016\/j.epsr.2020.106286"},{"doi-asserted-by":"crossref","unstructured":"Q. Jiang, X. Li, B. Wang, and H. Wang, \u201cPMU-Based Fault Location Using Voltage Measurements in Large Transmission Networks,\u201d IEEE Trans. Power Del., vol. 27, no. 3, pp. 1644\u20131652, 2012.","key":"2016","DOI":"10.1109\/TPWRD.2012.2199525"},{"doi-asserted-by":"crossref","unstructured":"Sunil Singh D. N. Vishwakarma \u201cIntelligent Techniques for Fault Diagnosis in Transmission lines -An Overview2015\u201d International Conference on Recent Developments in Control, Automation and Power Engineering (RDCAPE)","key":"2017","DOI":"10.1109\/RDCAPE.2015.7281410"},{"doi-asserted-by":"crossref","unstructured":"M. Singh, B. K. Panigrahi and R. P. Maheshwari, \"Transmission line fault detection and classification,\" 2011 International Conference on Emerging Trends in Electrical and Computer Technology, 2011, pp. 15-22.","key":"2018","DOI":"10.1109\/ICETECT.2011.5760084"},{"unstructured":"B.Ravindranath Reddy, M. Vijaya Kumar, M.Suryakalavathi, Ch. Prasanth Babu \u201cFault detection, classification and location on transmission lines using wavelet transform \u201c2009 Annual Report Conference on Electrical Insulation and Dielectric Phenomena.","key":"2019"},{"doi-asserted-by":"crossref","unstructured":"Mohammad Amin Jarrahi, Haidar Samet and Ali Sahebi \u201cAn EMD Based Fault Type Identification Scheme in Transmission Line \u201c2016 24th Iranian Conference on Electrical Engineering (ICEE).","key":"2020","DOI":"10.1109\/IranianCEE.2016.7585558"},{"unstructured":"M. Gowrishankar, 1 2P. Nagaveni and 3P. Balakrishnan \u201cTransmission Line Fault Detection and Classification Using Discrete Wavelet Transform and Artificial Neural Network \u201cMiddle-East Journal of Scientific Research 24 (4): 1112-1121, 2016.","key":"2021"},{"doi-asserted-by":"crossref","unstructured":"Bilal Masood, Umar Saleem, Nadeem Anjum \u201cFaults Detection and Diagnosis of Transmission Lines using wavelet Transformed based Technique \u201c2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT).","key":"2022","DOI":"10.1109\/AEECT.2017.8257776"},{"doi-asserted-by":"crossref","unstructured":"M. J. Reddy and D. K. Mohanta, \u201cA wavelet-fuzzy combined approach for classification and location of transmission line faults,\u201d Electrical Power and Energy Systems, Elsevier, vol. 29, pp. 669\u2013678,2007.","key":"2023","DOI":"10.1016\/j.ijepes.2007.05.001"},{"doi-asserted-by":"crossref","unstructured":"P. S. Bhowmik, P. Purkait, and K. Bhattacharya, \u201cElectrical Power and Energy Systems A novel wavelet transform aided neural network-based transmission line fault analysis method,\u201d Electrical Power and Energy Systems, Elsevier, vol. 31, pp. 213\u2013219, 2009.","key":"2024","DOI":"10.1016\/j.ijepes.2009.01.005"},{"doi-asserted-by":"crossref","unstructured":"S. Ekici, \u201cEnergy and entropy-based feature extraction for locating fault on transmission lines by using neural network and wavelet packet decomposition,\u201d Expert Systems with Applications, Elsevier, vol. 34, pp. 2937\u20132944, 2008.","key":"2025","DOI":"10.1016\/j.eswa.2007.05.011"},{"doi-asserted-by":"crossref","unstructured":"E. Koley, K. Verma, and S. Ghosh, \u201cAn improved fault detection classification and location scheme based on wavelet transform and artificial neural network for six phase transmission line using single end data only,\u201d Springer plus, vol. 4, no. 1, p. 551, 2015.","key":"2026","DOI":"10.1186\/s40064-015-1342-7"},{"unstructured":"Balvinder Singh Om Prakash Mahela Tanuj Manglani \u201cDetection and Classification of Transmission Line Faults Using Empirical Mode Decomposition and Rule Based Decision Tree-Based Algorithm\u201d 978-1-5386-7339-3\/18\/$31.00 \u00a92018 IEEE.","key":"2027"},{"doi-asserted-by":"crossref","unstructured":"M. Jamil, S. K. Sharma, and R. Singh, \u201cFault detection and classification in an electrical power transmission system using artificial neural network,\u201d Springer plus, vol. 4, no. 1, p. 334, 2015.","key":"2028","DOI":"10.1186\/s40064-015-1080-x"},{"doi-asserted-by":"crossref","unstructured":"U. B. Parikh, B. Das, and R. Maheshwari, \u201cFault classification technique for series compensated transmission line using support vector machine,\u201d Int. J. Electr. Power Energy Syst. Elsevier, vol. 32, no. 6, pp. 629\u2013636, 2010.","key":"2029","DOI":"10.1016\/j.ijepes.2009.11.020"},{"doi-asserted-by":"crossref","unstructured":"S. Ekici, \u201cSupport Vector Machines for classification and locating faults on transmission lines,\u201d Applied Soft Computing, Elsevier, vol.12, pp. 1650\u20131658, 2012.","key":"2030","DOI":"10.1016\/j.asoc.2012.02.011"},{"doi-asserted-by":"crossref","unstructured":"Mahanty, R.N. and P.B. Dutta Gupta, \u201cA fuzzy logic-based fault classification approach using current samples only, Electric Power Systems Research, 2007 77: 501-507.","key":"2031","DOI":"10.1016\/j.epsr.2006.04.009"},{"doi-asserted-by":"crossref","unstructured":"Das, B. and J.V. Reddy, \u201cFuzzy-logic based classification scheme for digital distance protection, IEEE Trans. Power Del,2008 20(2): 609-616.","key":"2032","DOI":"10.1109\/TPWRD.2004.834294"},{"unstructured":"Ben Hessine, M., H. Jouini and S. Chebbi, \u201cFault detection and classification approaches using artificial neural networks, Mediterranean Electrotechnical Conference (MELECON), Beirut,2016 pp: 515-519.","key":"2033"},{"doi-asserted-by":"crossref","unstructured":"Seethalakshmi, K., S.N. Singh, and S.C. Srivastava, \u201cA classification approach using support vector machines to prevent distance relay maloperation under power swing and voltage instability,\u201d IEEE 2014 Trans. Power Del., 27(3): 1124-1133.","key":"2034","DOI":"10.1109\/TPWRD.2011.2174808"},{"doi-asserted-by":"crossref","unstructured":"Jafarian, P. and M. Sanaye-Pasand, \u201cHigh- Frequency Transients-Based Protection of Multiterminal Transmission Lines Using the SVM Technique\u201d IEEE 2013 Trans.","key":"2035","DOI":"10.1109\/TPWRD.2012.2215925"},{"doi-asserted-by":"crossref","unstructured":"B. J. Mampilly and S. V. S, \"Transmission Lines Fault Detection using Empirical Mode Decomposition in a Grid-Connected Power System,\" 2020 International Conference on Power Electronics and Renewable Energy Applications (PEREA), 2020, pp. 1-6.","key":"2036","DOI":"10.1109\/PEREA51218.2020.9339814"},{"doi-asserted-by":"crossref","unstructured":"Youssef OAS \u201cCombined fuzzy-logic wavelet-based fault classification technique for power system relaying\u201d IEEE Trans Power Delivery 2004;19(2):582\u20139.","key":"2037","DOI":"10.1109\/TPWRD.2004.826386"},{"doi-asserted-by":"crossref","unstructured":"Youssef OAS. \u201cAn optimized fault classification technique based on support-vector-machines \u201cIEEE\/PES Power Syst Conf Expos 2009:1\u20138.","key":"2038","DOI":"10.1109\/PSCE.2009.4839949"},{"doi-asserted-by":"crossref","unstructured":"Sevakula RK, Verma NK. \u201cWavelet transforms for fault detection using SVM in power systems\u201d IEEE Int Conf Power Electron Drives Energy Syst, Bengaluru, India; December 2012.","key":"2039","DOI":"10.1109\/PEDES.2012.6484324"},{"doi-asserted-by":"crossref","unstructured":"Livani H, Evrenosoglu CY. \u201cA fault classification method in power systems using DWT and SVM classifier\u201d IEEE\/PES Trans Distrib Conf Expo 2012:1\u20135.","key":"2040","DOI":"10.1109\/TDC.2012.6281686"},{"doi-asserted-by":"crossref","unstructured":"Shukla S, Mishra S, Singh B.\u201d Empirical-mode decomposition with Hilbert transform for power-quality assessment. IEEE Trans Power Delivery 2009;24:2159\u201365","key":"2041","DOI":"10.1109\/TPWRD.2009.2028792"},{"doi-asserted-by":"crossref","unstructured":"Manjula M, Sarma AVRS, Mishra S.\u201d Empirical mode decomposition based probabilistic neural network for faults classification. Int Conf Power Energy Syst 2011:1\u20135.","key":"2042","DOI":"10.1109\/ICPES.2011.6156670"},{"doi-asserted-by":"crossref","unstructured":"Manjula M, Sarma AVRS, Mishra S. Detection and classification of voltage sag causes based on empirical mode decomposition. \u201cAnnual IEEE India Conf. 2011:1\u20135.","key":"2043","DOI":"10.1109\/INDCON.2011.6139581"},{"doi-asserted-by":"crossref","unstructured":"Martin, F., Aguado, J.A, \u201cWavelet-based ANN approach for Transmission line protection,\u201d IEEE transaction on Power Delivery 18(4), 1572\u20131574 (2003).","key":"2044","DOI":"10.1109\/TPWRD.2003.817523"},{"doi-asserted-by":"crossref","unstructured":"Martin, F. and J.A. Aguado. Wavelet-based ANN approach for transmission line protection, IEEE Transactions on Power Delivery, 18: 1572-1574, 2003.","key":"2045","DOI":"10.1109\/TPWRD.2003.817523"},{"doi-asserted-by":"crossref","unstructured":"D. Das, N. Singh, and A. Sinha, \u2018A Comparison of Fourier Transform and Wavelet Transform Methods for Detection and Classification of Faults on Transmission Lines,\u2019 2006 IEEE Power India Conference, 2006.","key":"2046","DOI":"10.1109\/POWERI.2006.1632580"},{"doi-asserted-by":"crossref","unstructured":"Sunil Singh, D. N. Vishwakarma, Amit Kumar & Shashank \u201cTo A novel methodology for fault detection, classification and location in transmission system based on DWT & ANFIS Journal of Information and Optimization Sciences Oct 16, 2017.","key":"2047","DOI":"10.1080\/02522667.2017.1372129"},{"doi-asserted-by":"crossref","unstructured":"B. Prabhu Kavin, S. Ganapathy,\u201d A New Digital Signature Algorithm for Ensuring the Data Integrity in Cloud using Elliptic Curves,\u201d The International Arab Journal of Information Technology, vol. 18, no. 2, pp. 180-190, 2021.","key":"2048","DOI":"10.34028\/iajit\/18\/2\/6"},{"doi-asserted-by":"crossref","unstructured":"A.K. Gupta, Y. K. Chauhan, and T Maity, \u201cExperimental investigations and comparison of various MPPT techniques for photovoltaic system,\u201d S\u0101dhan\u0101, Vol. 43, no. 8, pp.1-15, 2018.","key":"2049","DOI":"10.1007\/s12046-018-0815-0"},{"doi-asserted-by":"crossref","unstructured":"Nageswara Rao A, Vijaya Priya P, Kowsalya M, Gnanadass R. Wide-area monitoring for energy system: a review. International Journal of Ambient Energy. 2019 Jul 4;40(5):537-53.","key":"2050","DOI":"10.1080\/01430750.2017.1399458"},{"doi-asserted-by":"crossref","unstructured":"Jain, A., & Kumar, A. Desmogging of still smoggy images using a novel channel prior. Journal of Ambient Intelligence and Humanized Computing, 12(1), 1161-1177, 2021.","key":"2051","DOI":"10.1007\/s12652-020-02161-1"},{"doi-asserted-by":"crossref","unstructured":"Ghai, D., Gianey, H. K., Jain, A., & Uppal, R. S. Quantum and dual-tree complex wavelet transform-based image watermarking. International Journal of Modern Physics B, 34(04), 2050009, 2020.","key":"2052","DOI":"10.1142\/S0217979220500095"},{"doi-asserted-by":"crossref","unstructured":"V. Mohan, H. Chhabra, A. Rani, and V. Singh, \u201cRobust self-tuning fractional order PID controller dedicated to a non-linear dynamic system,\u201d Journal of Intelligent & Fuzzy Systems, vol. 34, no. 3, pp. 1467-1478, 2018.","key":"2053","DOI":"10.3233\/JIFS-169442"},{"doi-asserted-by":"crossref","unstructured":"A.K. Gupta, \u201cSun Irradiance Trappers for Solar PV Module to Operate on Maximum Power: An Experimental Study,\u201d Turkish Journal of Computer and Mathematics Education, Vol. 12, no.5, pp.1112-1121, 2021.","key":"2054","DOI":"10.17762\/turcomat.v12i5.1759"},{"doi-asserted-by":"crossref","unstructured":"Rao AN, Vijayapriya P. A robust neural network model for monitoring online voltage stability. International Journal of Computers and Applications. 2019 Sep 17:1-10.","key":"2055","DOI":"10.1080\/1206212X.2019.1666224"},{"doi-asserted-by":"crossref","unstructured":"H. Chhabra, V. Mohan, A. Rani, and V. Singh, \u201cMulti objective PSO tuned fractional order PID control of robotic manipulator,\u201d in the international symposium on intelligent systems technologies and applications, 2016, pp. 567-572: Springer.","key":"2056","DOI":"10.1007\/978-3-319-47952-1_45"},{"doi-asserted-by":"crossref","unstructured":"A.K. Gupta, Y.K Chauhan, and T Maity, \u201cA new gamma scaling maximum power point tracking method for solar photovoltaic panel Feeding energy storage system,\u201d IETE Journal of Research, vol.67, no.1, pp.1-21, 2018.","key":"2057","DOI":"10.1080\/03772063.2018.1530617"},{"doi-asserted-by":"crossref","unstructured":"P. Rajesh, C. Naveen, Anantha Krishan Venkatesan, and Francis H. Shajin, \u201cAn optimization technique for battery energy storage with wind turbine generator integration in unbalanced radial distribution network\u201d, Journal of Energy Storage, Vo. 43, pp 1-12, 2021.","key":"2058","DOI":"10.1016\/j.est.2021.103160"},{"doi-asserted-by":"crossref","unstructured":"F. Arslan, B. Singh, D. K. Sharma, R. Regin, R. Steffi, and S. Suman Rajest, \u201cOptimization Technique Approach to Resolve Food Sustainability Problems,\u201d 2021 International Conference on Computational Intelligence and Knowledge Economy, 2021, pp. 25-30.","key":"2059","DOI":"10.1109\/ICCIKE51210.2021.9410735"},{"doi-asserted-by":"crossref","unstructured":"Jain, A., Dwivedi, R. K., Alshazly, H., Kumar, A., Bourouis, S., & Kaur, M. Design and Simulation of Ring Network-on-Chip for Different Configured Nodes Computers, Materials, & Continua; Henderson Vol. 71, Iss. 2, (2022): 4085-4100.","key":"2060","DOI":"10.32604\/cmc.2022.023017"},{"doi-asserted-by":"crossref","unstructured":"Kumar, A., & Jain, A. Image smog restoration using oblique gradient profile prior and energy minimization. Frontiers of Computer Science, 15(6), 1-7, 2021.","key":"2061","DOI":"10.1007\/s11704-020-9305-8"},{"doi-asserted-by":"crossref","unstructured":"Anantha Krishnan. V and N. Senthil Kumar, \u201cReal-Time Simulation Analysis of LM Algorithm-Based NN For The Control of VSC In Grid Connected PV-Diesel Microgrid Using OP4500 RT-Lab Simulator\u201d, International Journal of Power and Energy Systems, Acta Press, Vol. 42, No. 10, pp. 1-10, 2022.","key":"2062","DOI":"10.2316\/J.2022.203-0419"},{"unstructured":"Gupta, N., Vaisla, K. S., Jain, A., Kumar, A., & Kumar, R. Performance Analysis of AODV Routing for Wireless Sensor Network in FPGA Hardware. Computer Systems Science and Engineering, 39(2), 1-12, 2021.","key":"2063"},{"doi-asserted-by":"crossref","unstructured":"Gupta, N., Jain, A., Vaisla, K. S., Kumar, A., & Kumar, R. Performance analysis of DSDV and OLSR wireless sensor network routing protocols using FPGA hardware and machine learning. Multimedia Tools and Applications, 80(14), 22301-22319, 2021.","key":"2064","DOI":"10.1007\/s11042-021-10820-4"},{"unstructured":"Agrawal, N., Jain, A., & Agarwal, A. Simulation of Network on Chip for 3D Router Architecture. International Journal of Recent Technology and Engineering, 8, 58-62, 2019.","key":"2065"},{"doi-asserted-by":"crossref","unstructured":"Sharma, S. K., Jain, A., Gupta, K., Prasad, D., & Singh, V. An internal schematic view and simulation of major diagonal mesh network-on-chip. Journal of Computational and Theoretical Nanoscience, 16(10), 4412-4417, 2019.","key":"2066","DOI":"10.1166\/jctn.2019.8534"},{"doi-asserted-by":"crossref","unstructured":"Misra, N. R., Kumar, S., & Jain, A. A Review on E-waste: Fostering the Need for Green Electronics. In 2021 International Conference on Computing, Communication, and Intelligent Systems, (pp.1032-1036). IEEE, 2021.","key":"2067","DOI":"10.1109\/ICCCIS51004.2021.9397191"},{"doi-asserted-by":"crossref","unstructured":"Kumar, S., Jain, A., Kumar Agarwal, A., Rani, S., & Ghimire, A. Object-Based Image Retrieval Using the U-Net-Based Neural Network. Computational Intelligence and Neuroscience, 2021.","key":"2068","DOI":"10.1155\/2021\/4395646"},{"doi-asserted-by":"crossref","unstructured":"G. A. Ogunmola, B. Singh, D. K. Sharma, R. Regin, S. S. Rajest and N. Singh, \u201cInvolvement of Distance Measure in Assessing and Resolving Efficiency Environmental Obstacles,\u201d 2021 International Conference on Computational Intelligence and Knowledge Economy, 2021, pp. 13-18.","key":"2069","DOI":"10.1109\/ICCIKE51210.2021.9410765"},{"doi-asserted-by":"crossref","unstructured":"D. Kumar, D.Mehrotra, and R. Bansal, \u201cMetaheuristic Policies for Discovery Task Programming Matters in Cloud Computing.\u201d Proceedings of the 4th International Conference on Computing Communication and Automation (ICCCA) 2018, pp. 1-5, 2018.","key":"2070","DOI":"10.1109\/CCAA.2018.8777579"},{"doi-asserted-by":"crossref","unstructured":"Jain, A., Gahlot, A. K., Dwivedi, R., Kumar, A., & Sharma, S. K. Fat Tree NoC Design and Synthesis. In Intelligent Communication, Control and Devices (pp. 1749-1756). Springer, Singapore, 2018.","key":"2071","DOI":"10.1007\/978-981-10-5903-2_180"},{"doi-asserted-by":"crossref","unstructured":"Jain, A., Dwivedi, R., Kumar, A., & Sharma, S. Scalable design and synthesis of 3D mesh network on chip. In Proceeding of International Conference on Intelligent Communication, Control and Devices (pp. 661-666). Springer, Singapore, 2017.","key":"2072","DOI":"10.1007\/978-981-10-1708-7_75"},{"doi-asserted-by":"crossref","unstructured":"D. K. Sharma, B. Singh, M. Raja, R. Regin, and S. S. Rajest, \u201cAn Efficient Python Approach for Simulation of Poisson Distribution,\u201d 2021 7th International Conference on Advanced Computing and Communication Systems, 2021, pp. 2011-2014.","key":"2073","DOI":"10.1109\/ICACCS51430.2021.9441895"},{"doi-asserted-by":"crossref","unstructured":"D. Kumar, S. Kumar, and R. Bansal. \u201cMulti-objective multi-join query optimisation using modified grey wolf optimisation.\u201d International Journal of Advanced Intelligence Paradigms, vol.17, no.1-2, pp. 67-79, 2020.","key":"2074","DOI":"10.1504\/IJAIP.2020.108760"},{"doi-asserted-by":"crossref","unstructured":"D. K. Sharma, B. Singh, E. Herman, R. Regine, S. S. Rajest and V. P. Mishra, \u201cMaximum Information Measure Policies in Reinforcement Learning with Deep Energy-Based Model,\u201d 2021 International Conference on Computational Intelligence and Knowledge Economy, 2021, pp. 19-24.","key":"2075","DOI":"10.1109\/ICCIKE51210.2021.9410756"},{"doi-asserted-by":"crossref","unstructured":"D. Kumar, S. Kumar, R. Bansal and P.Singla. \u201cA Survey to Nature Inspired Soft Computing.\u201d International Journal of Information System Modeling and Design, vol. 8, no. 2, pp.112-133, 2017.","key":"2076","DOI":"10.4018\/IJISMD.2017040107"},{"doi-asserted-by":"crossref","unstructured":"Nageswa Rao AR, Vijaya P, Kowsalya M. Voltage stability indices for stability assessment: a review. International Journal of Ambient Energy. 2021 May 19;42(7):829-45.","key":"2077","DOI":"10.1080\/01430750.2018.1525585"},{"doi-asserted-by":"crossref","unstructured":"A.K. Gupta, T. Maity, H. Anandakumar, and Y.K Chauhan, \u201cAn electromagnetic strategy to improve the performance of PV panel under partial shading,\u201d Computers & Electrical Engineering, Vol. 90, pp.106896. 2021.","key":"2078","DOI":"10.1016\/j.compeleceng.2020.106896"},{"doi-asserted-by":"crossref","unstructured":"A.K. Gupta, Y.K Chauhan, and T Maity and R Nanda, \u201cStudy of Solar PV Panel Under Partial Vacuum Conditions: A Step Towards Performance Improvement,\u201d IETE Journal of Research, pp.1-8, 2020.","key":"2079","DOI":"10.1080\/03772063.2020.1749145"},{"doi-asserted-by":"crossref","unstructured":"Rao AN, Vijayapriya P, Kowsalya M, Rajest SS. Computer Tools for Energy Systems. International Conference on Communication, Computing and Electronics Systems 2020, pp. 475-484. Springer, Singapore.","key":"2080","DOI":"10.1007\/978-981-15-2612-1_46"},{"doi-asserted-by":"crossref","unstructured":"D. Chauhan, A. Kumar, P. Bedi, V. A. Athavale, D. Veeraiah, and B. R. Pratap, \u201cAn effective face recognition system based on Cloud based IoT with a deep learning model,\u201d Microprocessors and Microsystems, vol. 81, p. 103726, Mar. 2021.","key":"2081","DOI":"10.1016\/j.micpro.2020.103726"},{"doi-asserted-by":"crossref","unstructured":"V. A. Athavale, A. Bansal, S. Nalajala, and S. Aurelia, \u201cIntegration of blockchain and IoT for data storage and management,\u201d Materials Today: Proceedings, Oct. 2020, doi: 10.1016\/j.matpr.2020.09.643.","key":"2082","DOI":"10.1016\/j.matpr.2020.09.643"},{"doi-asserted-by":"crossref","unstructured":"S. C. Gupta, D. Kumar, and V. Athavale, \u201cA Review on Human Action Recognition Approaches,\u201d 2021 10th IEEE International Conference on Communication Systems and Network Technologies, Jun. 2021, doi: 10.1109\/csnt51715.2021.9509646.","key":"2083","DOI":"10.1109\/CSNT51715.2021.9509646"},{"doi-asserted-by":"crossref","unstructured":"D. Kumar, D.Mehrotra, and R. Bansal. \u201cQuery Optimization in Crowd-Sourcing Using Multi-Objective Ant Lion Optimizer.\u201d International Journal of Information Technology and Web Engineering, vol. 14, no. 4, pp. 50-63, 2019.","key":"2084","DOI":"10.4018\/IJITWE.2019100103"},{"doi-asserted-by":"crossref","unstructured":"S. Nagpal, V. A. Athavale, A. K. Saini, and R. Sharma, \u201cIndian Health Care System is Ready to Fight Against COVID-19 A Machine Learning Tool for Forecast the Number of Beds,\u201d 2020 Sixth International Conference on Parallel, Distributed and Grid Computing, Nov. 2020, doi: 10.1109\/pdgc50313.2020.9315825.","key":"2085","DOI":"10.1109\/PDGC50313.2020.9315825"},{"unstructured":"P. Sharma, V. Athavale, and A. Sinha, \u201cDevelopment of delay controller system modelin MANET,\u201d 2019. Accessed: Mar 19, 2022. [Online]. Available: https:\/\/www.ijitee.org\/wpcontent\/uploads\/papers\/v8i5\/E2883038519.pdf.","key":"2086"},{"doi-asserted-by":"crossref","unstructured":"V. A. Athavale, \u201cDigital Twin - A Key Technology driver in Industry 4.0,\u201d Engineering Technology Open Access Journal, vol. 4, no. 1, Aug. 2021.","key":"2087","DOI":"10.19080\/ETOAJ.2021.04.555628"},{"doi-asserted-by":"crossref","unstructured":"Aakanksha Singhal and D.K. Sharma, \u201cNew Generalized \u2018Useful\u2019 Entropies using Weighted Quasi-Linear Mean for Efficient Networking,\u201d Mobile Networks and Applications, https:\/\/doi.org\/10.1007\/s11036-021-01858, pp. 1\u201311, 2022.","key":"2088","DOI":"10.1007\/s11036-021-01858-7"},{"doi-asserted-by":"crossref","unstructured":"Kumar, S., Jain, A., Shukla, A. P., Singh, S., Raja, R., Rani, S., ... & Masud, M. A Comparative Analysis of Machine Learning Algorithms for Detection of Organic and Nonorganic Cotton Diseases. Mathematical Problems in Engineering, 2021.","key":"2089","DOI":"10.1155\/2021\/1790171"},{"unstructured":"Agarwal, A. K., & Jain, A. Synthesis of 2D and 3D NoC mesh router architecture in HDL environment. Journal of Advanced Research in Dynamical and Control Systems, 11(4), 2573-2581, 2019.","key":"2090"},{"doi-asserted-by":"crossref","unstructured":"Jain, A., Kumar, A., & Sharma, S. (2015). Comparative Design and Analysis of Mesh, Torus and Ring NoC. Procedia Computer Science, 48, 330-337, 2015.","key":"2091","DOI":"10.1016\/j.procs.2015.04.190"}],"container-title":["EAI Endorsed Transactions on Energy Web"],"original-title":[],"link":[{"URL":"https:\/\/publications.eai.eu\/index.php\/ew\/article\/download\/733\/590","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/publications.eai.eu\/index.php\/ew\/article\/download\/733\/590","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,21]],"date-time":"2024-09-21T16:37:49Z","timestamp":1726936669000},"score":1,"resource":{"primary":{"URL":"https:\/\/publications.eai.eu\/index.php\/ew\/article\/view\/733"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,3]]},"references-count":78,"journal-issue":{"issue":"38","published-online":{"date-parts":[[2022,5,3]]}},"URL":"https:\/\/doi.org\/10.4108\/ew.v9i38.733","relation":{},"ISSN":["2032-944X"],"issn-type":[{"type":"electronic","value":"2032-944X"}],"subject":[],"published":{"date-parts":[[2022,5,3]]}}}