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Irrig Sci 11:189–195. https:\u002F\u002Fdoi.org\u002F10.1007\u002FBF00189457\nJaiswal S, Ballal MS (2020) Fuzzy inference based irrigation controller for agricultural demand side management. Comput Electron Agr 175:105537. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2020.105537\nEl-Naggar AG, Hedley CB, Horne D, Roudier P, Clothier BE (2020) Soil sensing technology improves application of irrigation water. Agr Water Manage 228(20):105901. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agwat.2019.105901\nCoates RW, Delwiche MJ, Broad A, Holler M (2013) Wireless sensor network with irrigation valve control. Comput Electron Agr 96(96):13–22. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2013.04.013\nNavarro-Hellín H, Torres-Sánchez R, Soto-Valles F, Albaladejo-Pérez C, Domingo-Miguel RA (2015) wireless sensors architecture for efficient irrigation water management. Agr Water Manage 151:64–74. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agwat.2014.10.022\nNam WH, Kim T, Hong EM, Choi JY, Kim JT (2017) A Wireless Sensor Network (WSN) application for irrigation facilities management based on Information and Communication Technologies (ICTs). Comput Electron Agr 143:185–192. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2017.10.007\nAqeel UR, Abbasi AZ, Islam N, Ahmed Z (2014) A review of wireless sensors and networks’ applications in agriculture. Comput Stand Inter 36(2):263–270. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.csi.2011.03.004\nHuang W, Yang F (2020) Design of intelligent watering system of flower based on zigbee and WiFi. IOP Conference Series: Mater Sci Eng 768:042010. https:\u002F\u002Fdoi.org\u002F10.1088\u002F1757-899X\u002F768\u002F4\u002F042010\nKumar P, Motia S, Reddy SRN (2023) Integrating wireless sensing and decision support technologies for real-time farmland monitoring and support for effective decision making. Int J Inf Tecnol 15:1081–1099. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs41870-018-0218-9\nNabi F, Jamwal S, Padmanbh K (2022) Wireless sensor network in precision farming for forecasting and monitoring of apple disease: a survey. Int J Inf Tecnol 14:769–780. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs41870-020-00418-8\nRajput A, Kumaravelu V (2019) Scalable and sustainable wireless sensor networks for agricultural application of Inter of things using fuzzy c-means algorithm. Sustain Comput: Inform Syst 22(7):62–74. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.suscom.2019.02.003\nRajput A, Kumaravelu V (2020) Fuzzy logic–based distributed clustering protocol to improve energy efficiency and stability of wireless smart sensor networks for farmland monitoring systems. Int J Commun Syst 33:e4239. https:\u002F\u002Fdoi.org\u002F10.1002\u002Fdac.4239\nNikolidakis S, Kandris D, Vergados D, Christos D (2015) Energy efficient automated control of irrigation in agriculture by using wireless sensor networks. Comput Electron Agr 113:154–163. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2015.02.004\nTiglao N, Alipio M, Balanay J, Saldivar E, Tiston J (2020) Agrinex: A low-cost wireless mesh-based smart irrigation system. Measurement 161:107874. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.measurement.2020.107874\nErcan AM, Najmul M (2022) Wireless communication protocols in smart agriculture: A review on applications, challenges and future trends. Ad Hoc Networks 136:102982. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.adhoc.2022.102982\nQiu M, Ming Z, Li J (2013) Informer homed routing fault tolerance mechanism for wireless sensor networks. J Syst Architect 59:260–270. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.sysarc.2012.12.003\nAgarkhed J, Dattatraya PY, Patil S (2021) Multi-QoS constraint multipath routing in cluster-based wireless sensor network. Int J Inf Tecnol 13:865–876. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs41870-020-00461-5\nDeepakraj D, Raja K (2021) Markov-chain based optimization algorithm for efficient routing in wireless sensor networks. Int J Inf Tecnol 13:897–904. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs41870-021-00622-0\nSharma N, Singh K, Singh BM (2020) A load based transmission control protocol for wireless sensor networks. Int J Inf Tecnol 12:577–583. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs41870-018-0127-y\nKurumbanshi S, Rathkanthiwar S (2018) Increasing the lifespan of wireless adhoc network using probabilistic approaches: a survey. Int J Inf Tecnol 10:537–542. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs41870-018-0177-1\nHuang HJ, Zhang JB, Zhang X, Yi BS, Fan QL, Li F (2017) EMGR: Energy-efficient multicast geographic routing in wireless sensor networks. Comput Netw 129:51–63. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.comnet.2017.08.011\nXie J, Gao P, Wang W, Lu H, Xu X, Hu G (2018) Design of wireless sensor network bidirectional nodes for intelligent monitoring system of micro-irrigation in Litchi Orchards. IFAC-Papers On Line 51(17):449–454. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.ifacol.2018.08.176\nOliveira L, Rodrigues J, Kozlov SA, Rabêlo R, Furtado V (2019) Performance assessment of long-range and Sigfox protocols with mobility support. Int J Commun Syst 32:e3956. https:\u002F\u002Fdoi.org\u002F10.1002\u002Fdac.3956\nFernando M, Thales T, Ana E, Luís H (2020) Experimental vs. simulation analysis of LoRa for vehicular communications. Comput Commun 160:299–310. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.comcom.2020.06.006\nFlorita NJB, Senatin ANM, Zabala AMA, Tan W (2020) Opportunistic Lora-based gateways for delay-tolerant sensor data collection in urban settings. Comput Commun 154:410–432. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.comcom.2020.02.066\nSciullo L, Trotta A, Di-Felice M (2020) Design and performance evaluation of a LoRa-based mobile emergency management system (LOCATE). Ad Hoc Net 96(1):101993.1-101993.17. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.adhoc.2019.101993\nNóbrega L, Gonçalves P, Pedreiras P, Pereira J (2019) An IoT-based solution for intelligent farming. Sensors 19:603. https:\u002F\u002Fdoi.org\u002F10.3390\u002Fs19030603\nZhang XY, Lou XK, Zhang LX, Shan YC (2020) Irrigation remote control system based on LoRa intelligence. J Phys 1635:012067. https:\u002F\u002Fdoi.org\u002F10.1088\u002F1742-6596\u002F1635\u002F1\u002F012067\nFroiz-Míguez I, Lopez-Iturri P, Fraga-Lamas P, Celaya-Echarri M, Blanco-Novoa Ó, Azpilicueta L, Falcone F, Fernández-Caramés TM (2020) Design, implementation, and empirical validation of an IoT smart irrigation system for fog computing applications based on LoRa and LoRaWAN sensor nodes. Sensors 20:6865. https:\u002F\u002Fdoi.org\u002F10.3390\u002Fs20236865\nSharma DK et al (2021) Gauss-sigmoid based clustering routing protocol for wireless sensor networks. Int J Inf Tecnol 13:2569–2577. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs41870-019-00391-x\nLiang R, Zhao L, Wang P (2020) Performance evaluations of LoRa wireless communication in building environments. Sensors 20(14):3828. https:\u002F\u002Fdoi.org\u002F10.3390\u002Fs20143828\nNicoleta C, Paula H (2020) Forest Fire Detection System using LoRa Technology. Int J Adv Comput Sci Appl 11:5. https:\u002F\u002Fdoi.org\u002F10.14569\u002FIJACSA.2020.0110503\nSwain M, Hashmi MF, Singh R, Hashmi AW (2021) A cost-effective LoRa-based customized device for agriculture field monitoring and precision farming on IoT platform. Int J Commun Syst 34:e4632. https:\u002F\u002Fdoi.org\u002F10.1002\u002Fdac.4632\nLorite IJ, Santos C, García-Vila M, Carmona MA, Fereres E (2013) Assessing irrigation scheme water use and farmers’ performance using wireless telemetry systems. Comput Electron Agric 98:193–204. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2013.08.007\nZapata N, Salvador R, Cavero J (2013) Field test of an automatic controller for solid-set sprinkler irrigation. Irrig Sci 31:1237–1249. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00271-012-0397-2\nZheng L, Li M, Wu C, Ye H, Ji R, Deng X, Che Y, Fu C, Guo W (2011) Development of a smart mobile farming service system. Math Comput Model 54(3–4):1194–1203. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.mcm.2010.11.053\nSebastian S, Petros S (2020) Wireless technologies for agricultural monitoring using internet of things device with energy harvesting capabilities. Comput Elect Agric 172:105338. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2020.105338",{"EN":176},"An automatic drip irrigation system (ADIS) based on the LoRa protocol was developed and then evaluated in a corn field. The system is composed of an irrigation cloud platform, a base station, wireless nodes, soil moisture sensors and solenoid valves. The system was installed in a corn field and tested for the maximum communication distance during the sowing stage without a canopy. In addition, the wireless node power consumption and communication stability of the system on different corn growth stages were also tested. The data collected from the cornfield showed that maximum stable communication distance between the base station and the nodes was up to 1300 m at sowing stage without canopy obstructions. Although the average packet loss rate was about 10% at a distance of 1000 m when the maximum of crop’s leaf area index was reached at corn mature stage, the automatic drip system still could run successfully by means of three retransmissions in signal management. Besides, with a polling period of longer than 10 min, the lifetime of valve control node which was powered by a 6000 mA·h lithium battery could cover whole growth period of corn. 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citation_author=S Sadeghi, N Bagheri, MA Abdelraheem; citation_volume=52; citation_publication_date=2017; citation_pages=34-48; citation_doi=10.1016\u002Fj.micpro.2017.05.007; citation_id=CR12\ncitation_journal_title=Int J Adv Comput Sci Appl; citation_title=SIT: a lightweight encryption algorithm for secure internet of things; citation_author=M Usman, IA Ahmed, M Imran, S Khan, U Ali; citation_publication_date=2017; citation_doi=10.14569\u002FIJACSA.2017.080151; citation_id=CR13\ncitation_title=High throughput novel architecture of SIT cipher for IoT application, nanoelectronics circuits and communication systems; citation_publication_date=2021; citation_id=CR14; citation_author=Z Mishra; citation_author=S Mishra; citation_author=B Acharya; citation_publisher=Springer Singapore\ncitation_journal_title=Microprocess Microsyst; citation_title=SFN: a new lightweight block cipher; citation_author=L Li, B Liu, Y Zhou, Y Zou; citation_volume=60; citation_publication_date=2018; citation_pages=138-150; citation_doi=10.1016\u002Fj.micpro.2018.04.009; citation_id=CR15\nSehrawat D, Gill NS, Devi M (2019) Comparative analysis of lightweight block ciphers in IoT-enabled smart environment. 2019 6th International Conference on Signal Processing and Integrated Networks (SPIN), (pp. 915--920)\ncitation_journal_title=J Ambient Intell Humaniz Comput; citation_title=LRBC: a lightweight block cipher design for resource constrained IoT devices; citation_author=A Biswas, A Majumdar, S Nath, A Dutta, K Baishnab; citation_publication_date=2020; citation_doi=10.1007\u002Fs12652-020-01694-9; citation_id=CR17\ncitation_journal_title=Int J Comput Digital Syst; citation_title=A hybrid lightweight cipher algorithm; citation_author=SQ Al-Rahman, A Sagheer, O Dawood; citation_publication_date=2021; citation_doi=10.12785\u002Fijcds\u002F110138; citation_id=CR18\ncitation_journal_title=KSII Trans Internet Inform Syst (TIIS); citation_title=LCB: light cipher block an ultrafast lightweight block cipher for resource constrained IOT security applications; citation_author=S Roy, S Roy, A Biswas, KL Baishnab; citation_volume=15; citation_issue=11; citation_publication_date=2021; citation_pages=4122-4144; citation_id=CR19\ncitation_journal_title=J Theor Appl Inform Technol; citation_title=DNA cryptographic approaches: state of art, opportunities, and cutting edge perspectives; citation_author=MA Alhija, N Turab, A Abuthawabeh, H Abuowida, J Al Nabulsi; citation_volume=100; citation_issue=18; citation_publication_date=2022; citation_pages=5346-5358; citation_id=CR20\ncitation_title=New trends in cryptography: quantum, blockchain, lightweight, chaotic, and DNA cryptography; citation_inbook_title=New frontiers in cryptography; citation_publication_date=2020; citation_pages=65-87; citation_id=CR21; citation_author=KS Mohamed; citation_publisher=Springer International Publishing\nContiki-NG, the OS for Next Generation IoT Devices. (n.d.). Retrieved from \n                https:\u002F\u002Fwww.contiki-ng.org\u002F\n                \n               Access date 02 Jan 2023 \n                https:\u002F\u002Fwww.contiki-ng.org\u002F\n                \n              \ncitation_journal_title=IEEE Sens J; citation_title=RPL-based routing protocols in IoT applications: a review; citation_author=H Kharrufa, HA Al-Kashoash, AH Kemp; citation_volume=19; citation_issue=15; citation_publication_date=2019; citation_pages=5952-5967; citation_doi=10.1109\u002FJSEN.2019.2910881; citation_id=CR23\nLazarevska M, Farahbakhsh R, Shakya NM, Crespi N (2018) Mobility supported energy efficient routing protocol for IoT based healthcare applications, 2018 IEEE Conference on Standards for Communications and Networking (CSCN). pp. 1–5\nRana M, Mamun Q, Islam R (2020) Current lightweight cryptography protocols in smart city IoT networks: a survey. arXiv preprint \n                arXiv:2010.00852\n                \n              \ncitation_journal_title=Int J Electr Comput Eng; citation_title=Best S-box amongst differently sized S-boxes based on the avalanche effect in the advance encryption standard algorithm; citation_author=HM Taher, SQ Al-Rahman, SA Shawkat; citation_volume=12; citation_issue=6; citation_publication_date=2022; citation_pages=2088-8708; citation_id=CR26\ncitation_journal_title=IEEE Trans Comput; citation_title=Lightweight ciphers and their side-channel resilience; citation_author=A Heuser, S Picek, S Guilley, N Mentens; citation_volume=69; citation_issue=10; citation_publication_date=2017; citation_pages=1434-1448; citation_doi=10.1109\u002FTC.2017.2757921; citation_id=CR27",{"EN":268},"The purpose of this study is to secure data in internet of medical things (IoMT) environment while saving energy to improve objects lifetime. 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In: GLOBECOM 2017–2017 IEEE global communications conference, Singapore, 2017, pp 1–6",{"doi":537},"10.1109\u002FGLOCOM.2017.8255027",{"id":20,"text":539,"url":20,"identifiers":540},"Damodaran N, Haruni E, Kokhkharova M et al (2020) Device free human activity and fall recognition using WiFi channel state information (CSI). In: CCF trans. pervasive comp. interact., pp 1–17",{"doi":541},"10.1007\u002Fs42486-020-00027-1",{"id":20,"text":543,"url":20,"identifiers":544},"Ma Y, Zhou G, Wang S (2019) WiFi sensing with channel state information: a survey. ACM Comput Surv 52:3 (Article 46, 36 pp)",{"doi":545},"10.1145\u002F3310194",{"id":20,"text":547,"url":20,"identifiers":548},"Xia Z, Zhang Y (2016) Dual-carrier noncontact vital sign detection with a noise suppression scheme based on phase-locked loop. IEEE Trans Microw Theory Tech 64(11):4003–4011",{"doi":549},"10.1109\u002FTMTT.2016.2608772",{"id":20,"text":551,"url":20,"identifiers":552},"Zhao Y, Ashe J, Yu T (2016) Respiration monitoring using a wireless network with space and frequency diversities. In: 2016 IEEE int. conf. on consumer electronics (ICCE), pp 474–477",{"doi":553},"10.1109\u002FICCE.2016.7430696",{"id":20,"text":555,"url":20,"identifiers":556},"Zhao H, Hong H, Sun L, Li Y, Li C, Zhu X (2017) Noncontact physiological dynamics detection using low-power digital-IF Doppler radar. In: IEEE trans. on instrumentation and measurement, pp 1780–1788",{"doi":557},"10.1109\u002FTIM.2017.2669699",{"id":20,"text":559,"url":20,"identifiers":560},"Yang Z, Pathak PH, Zeng Y, Liran X, Mohapatra P (2017) Vital sign and sleep monitoring using millimeter wave. ACM Trans Sen Netw 13:1–14",{"doi":561},"10.1145\u002F3051124",{"id":20,"text":563,"url":20,"identifiers":564},"Hostettler R, Kaltiokallio O, Yiğitler H, Sarkka S, Jäntti R (2017) RSS-based respiratory rate monitoring using periodic gaussian processes and kalman filtering. In: 2017 25th European signal processing conf. (EUSIPCO), pp 256–260",{"doi":565},"10.23919\u002FEUSIPCO.2017.8081208",{"id":20,"text":567,"url":20,"identifiers":568},"Hillyard P, Luong A, Abrar AS, Patwari N, Sundar K, Farney R, Burch J, Porucznik C, Pollard SH (2018) Experience: cross-technology radio respiratory monitoring performance study, In Proceedings of the 24th annual int. conf. on mobile computing and networking (MobiCom ’18), New York, pp 487–496",{"doi":569},"10.1145\u002F3241539.3241560",{"id":20,"text":571,"url":20,"identifiers":572},"Wang X, Yang C, Mao S (2017) Phasebeat: exploiting csi phase data for vital sign monitoring with commodity WiFi devices. In 2017 IEEE 37th int. conf. on distributed computing syst. (ICDCS), pp 1230–1239",{"doi":573},"10.1109\u002FICDCS.2017.206",{"id":20,"text":575,"url":20,"identifiers":576},"Gu Y, Zhang X, Liu Z, Ren F (2019) WiFi-based real-time breathing and heart rate monitoring during sleep. In: 2019 IEEE global communications conference (GLOBECOM), Waikoloa, pp 1–6",{"doi":577},"10.1109\u002FGLOBECOM38437.2019.9014297",{"id":20,"text":579,"url":20,"identifiers":580},"Liu J, Chen Y, Wang Y, Chen X, Cheng J, Yang J (2018) Monitoring vital signs and postures during sleep using WiFi signals. IEEE Internet Things J 5:2071–2084",{"doi":581},"10.1109\u002FJIOT.2018.2822818",{"id":20,"text":583,"url":20,"identifiers":584},"Yu B, Wang Y, Niu K, Zeng Y, Gu T, Wang L, Guan C, Zhang D (2021) WiFi-sleep: sleep stage monitoring using commodity wi-fi devices. IEEE Internet Things J 8:13900–13913",{"doi":585},"10.1109\u002FJIOT.2021.3068798",{"id":20,"text":587,"url":20,"identifiers":588},"Xu Z, Guo A, Chen L (2020) Respiratory rate estimation of standing and sitting people using wifi signals. In: IEEE MTT-S international wireless symposium (IWS), Shanghai, 2020. https:\u002F\u002Fdoi.org\u002F10.1109\u002FIWS49314.2020.9360137",{"doi":589},"10.1109\u002FIWS49314.2020.9360137",{"id":20,"text":591,"url":20,"identifiers":592},"Forbes G, Massie S, Craw S (2020) WiFi-based human activity recognition using raspberry pi. In: 2020 IEEE 32nd international conference on tools with artificial intelligence (ICTAI), Baltimore. https:\u002F\u002Fdoi.org\u002F10.1109\u002FICTAI50040.2020.00115",{"doi":593},"10.1109\u002FICTAI50040.2020.00115",{"id":20,"text":595,"url":20,"identifiers":596},"Wang J, Solomon A, Patwari N (2022) Chapter 10 - Received power-based vital signs monitoring, Contactless Vital Signs Monitoring, pp 205–230 https:\u002F\u002Fdoi.org\u002F10.1016\u002FB978-0-12-822281-2.00019-6",{"doi":597},"10.1016\u002FB978-0-12-822281-2.00019-6",{"id":20,"text":599,"url":20,"identifiers":600},"Zhang D, Zeng Y, Zhang F, Xiong J (2022) Chapter 11 - WiFi CSI-based vital signs monitoring, Contactless Vital Signs Monitoring, pp 231–255 https:\u002F\u002Fdoi.org\u002F10.1016\u002FB978-0-12-822281-2.00020-2",{"doi":601},"10.1016\u002FB978-0-12-822281-2.00020-2",{"id":20,"text":603,"url":20,"identifiers":604},"Chen C et al (2018) TR-BREATH: time-reversal breathing rate estimation and detection. IEEE Trans Biomed Eng 65:489–501",{"doi":605},"10.1109\u002FTBME.2017.2699422",{"id":20,"text":607,"url":20,"identifiers":608},"Fekr AR, Janidarmian M, Radecka K, Zilic Z (2014) A medical cloud-based platform for respiration rate measurement and hierarchical classification of breath disorders. Sensors 14:11204–11224",{"doi":609},"10.3390\u002Fs140611204",{"id":20,"text":611,"url":20,"identifiers":612},"Bagave P, Linssen J, Teeuw W, Brinke JK, Meratnia N (2019) Channel state information (CSI) analysis for predictive maintenance using convolutional neural network (CNN). In: DATA’19: proceedings of the 2nd workshop on data acquisition to analysis, pp 51–56",{"doi":613},"10.1145\u002F3359427.3361917",{"id":20,"text":615,"url":20,"identifiers":616},"Ni A, Azarang A, Kehtarnavaz N (2021) A review of deep learning-based contactless heart rate measurement methods. Sensors 21:3719",{"doi":617},"10.3390\u002Fs21113719",{"id":20,"text":619,"url":20,"identifiers":620},"Deng D, Li X, Zhao M, Rabie KM, Kharel R (2020) Deep learning-based secure MIMO communications with imperfect CSI for heterogeneous networks. Sensors 20(6):1730",{"doi":621},"10.3390\u002Fs20061730",{"id":20,"text":623,"url":20,"identifiers":624},"Wang X, Yang C, Mao S (2017) PhaseBeat: exploiting CSI phase data for vital sign monitoring with commodity WiFi devices. In: 2017 IEEE 37th international conference on distributed computing systems (ICDCS), Atlanta, pp 1230–1239",{"doi":573},{"id":20,"text":626,"url":20,"identifiers":627},"Gao Q, Wang J, Ma X, Feng X, Wang H (2017) CSI-based device-free wireless localization and activity recognition using radio image features. IEEE Trans Veh Technol 66(11):10346–10356",{"doi":628},"10.1109\u002FTVT.2017.2737553",{"id":20,"text":630,"url":20,"identifiers":631},"Chen Z, Zhang L, Jiang C, Cao Z, Cui W (2019) WiFi CSI based passive human activity recognition using attention based BLSTM. IEEE Trans Mobile Comput 18(11):2714–2724",{"doi":632},"10.1109\u002FTMC.2018.2878233",{"id":20,"text":634,"url":20,"identifiers":635},"Xiao C, Han D, Ma Y, Qin Z (2019) CsiGAN: robust channel state information-based activity recognition with GANs. IEEE Internet Things J 6(6):10191–10204",{"doi":636},"10.1109\u002FJIOT.2019.2936580",{"id":20,"text":638,"url":20,"identifiers":639},"Zhang D, Hu Y, Chen Y, Zeng B (2019) BreathTrack: tracking indoor human breath status via commodity WiFi. IEEE Internet Things J 6(2):3899–3911",{"doi":640},"10.1109\u002FJIOT.2019.2893330",{"id":20,"text":642,"url":20,"identifiers":643},"Wang F, Zhang F, Wu C, Wang B, Liu KJ (2020) Respiration tracking for people counting and recognition. IEEE Internet Things J 7(6):5233–5245",{"doi":644},"10.1109\u002FJIOT.2020.2977254",{"id":20,"text":646,"url":20,"identifiers":647},"Liu J, Liu H, Chen Y, Wang Y, Wang C (2020) Wireless sensing for human activity: a survey. IEEE Commun Surv Tutor 22(3):1629–1645",{"doi":648},"10.1109\u002FCOMST.2019.2934489",{"id":20,"text":650,"url":20,"identifiers":651},"Cheng X, Huang B, Zong J (2021) Device-free human activity recognition based on GMM-HMM using channel state information. IEEE Access 9:76592–76601",{"doi":652},"10.1109\u002FACCESS.2021.3082627",{"id":20,"text":654,"url":20,"identifiers":655},"Chen S, Yang W, Xu Y, Geng Y, Xin B, Huang L (2022) AFall: Wi-Fi-based device-free fall detection system using spatial angle of arrival. IEEE Trans Mobile Comput. https:\u002F\u002Fdoi.org\u002F10.1109\u002FTMC.2022.3157666",{"doi":656},"10.1109\u002FTMC.2022.3157666",{"id":20,"text":658,"url":20,"identifiers":659},"Muaaz M, Chelli A, Gerdes MW, Pätzold M (2022) Wi-Sense: a passive human activity recognition system using Wi-Fi and convolutional neural network and its integration in health information systems. Ann Telecommun 77:163–175",{"doi":660},"10.1007\u002Fs12243-021-00865-9",{"id":20,"text":662,"url":20,"identifiers":663},"Tomasi B, Decurninge A, Guillaud M (2016) SNOPS: short non-orthogonal pilot sequences for downlink channel state estimation in FDD massive MIMO, Washington, DC. https:\u002F\u002Fdoi.org\u002F10.1109\u002FGLOCOMW.2016.7849046",{"doi":664},"10.1109\u002FGLOCOMW.2016.7849046",{"id":20,"text":666,"url":20,"identifiers":667},"Wang H, Zhang D, Ma J, Wang Y, Wang Y, Wu D, Gu T, Xie B (2016) Human respiration detection with commodity wifi devices: Do user location and body orientation matter? In: Proceedings of the 2016 ACM Int. Joint Conf. on Pervasive and Ubiquitous Computing (UbiComp ’16), New York, pp 25–36",{"doi":668},"10.1145\u002F2971648.2971744",{"id":20,"text":670,"url":20,"identifiers":671},"Xie Y, Li Z, Li M (2019) Precise power delay profiling with commodity Wi-Fi. IEEE Trans Mobile Comput 18:1342–1355",{"doi":672},"10.1109\u002FTMC.2018.2860991",{"id":674,"createTime":675,"updateTime":676,"relativeEntities":677,"slug":678,"properties":679,"entityType":181,"verifyStatus":182,"verifyTime":676,"verifyNote":183,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":688,"fullTextUrl":20,"authors":689,"publicationType":229,"publisherRelationship":720,"citationCount":20,"citationInfo":20,"publishDate":746,"publishYear":258,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":20,"openAccess":20,"references":20,"isForceReanalyzing":259},"0a8fbcca-986c-4c1b-8359-101d44a183f1","2024-01-17T18:43:59.128+00:00","2024-12-05T23:56:35.232+00:00",[],"Hybrid-optimized-using-grey-wolf-flower-pollination-for-wireless-sensor-network-routing",{"references":680,"abstract":682,"title":684,"doi":686},{"VOID":681},"Xu Y (2021) Simulation of optimal selection algorithm for wireless sensor cluster head node Bayesian statistical network. J Intell Fuzzy Syst. https:\u002F\u002Fdoi.org\u002F10.3233\u002FJIFS-219093\nYadav A, Kumar S, Vijendra S (2018) Network life time analysis of WSNs using particle swarm optimization. Procedia Comput Sci 132:805–815\nSun Y, Dong W, Chen Y (2017) An improved routing algorithm based on ant colony optimization in wireless sensor networks. IEEE Commun Lett 21(6):1317–1320\nMartinaa M, Santhi B, Raghunathan A (2020) An energy-efficient and novel populated cluster aware routing protocol (PCRP) for wireless sensor networks (WSN). J Intell Fuzzy Syst 39(6):8529–8542\nXu C, Xiong Z, Zhao G, Yu S (2019) An energy-efficient region source routing protocol for lifetime maximization in WSN. IEEE Access 7:135277–135289\nSharma R, Singh U (2021) Fuzzy based energy efficient clustering for designing WSN-based smart parking systems. Int J Inf Technol 13:2381–2387\nTunca C, Isik S, Donmez MY, Ersoy C (2015) Ring routing: an energy-efficient routing protocol for wireless sensor networks with a mobile sink. IEEE Trans Mob Comput 14(9):1947–1960\nMansourkiaie F, Ahmed MH (2016) Optimal and near-optimal cooperative routing and power allocation for collision minimization in wireless sensor networks. IEEE Sens J 16(5):1398–1411\nLiu H-H, Jia-Jang Su, Chou C-F (2017) On energy-efficient straight-line routing protocol for wireless sensor networks. IEEE Syst J 11(4):2374–2382\nSirdeshpande N, Udupi V (2017) Fractional lion optimization for cluster head-based routing protocol in wireless sensor network. J Franklin Inst 354(11):4457–4480\nChang Y, Tang H, Li B, Yuan X (2017) Distributed joint optimization routing algorithm based on the analytic hierarchy process for wireless sensor networks. IEEE Commun Lett 21(12):2718–2721\nSharma D, Bhondekar AP (2018) Traffic and energy aware routing for heterogeneous wireless sensor networks. IEEE Commun Lett 22(8):1608–1611\nHuarui Wu, Zhu H, Miao Y (2018) An energy efficient cluster-head rotation and relay node selection scheme for farmland heterogeneous wireless sensor networks. Wireless Pers Commun 101:1639–1655\nGambhir A, Payal A, Arya R (2018) Performance analysis of artificial bee colony optimization-based clustering protocol in various scenarios of WSN. Procedia Computer Science 132:183–188\nLi X, Keegan B, Mtenzi F, Weise T, Tan M (2019) Energy-efficient load balancing ant based routing algorithm for wireless sensor networks. IEEE Access 7:113182–113196\nSun Z, Wei M, Gang Qu (2019) Secure routing protocol based on multi-objective ant-colony-optimization for wireless sensor networks. Appl Soft Comput 77:366–375\nRambabu B, Venugopal Reddy A, Janakiraman S (2019) Hybrid artificial bee colony and monarchy butterfly optimization algorithm (HABC-MBOA)-based cluster head selection for WSNs. J King Saud Univ Comput Inf Sci 34(5):1895–1905\nBhardwaj R, Kumar D (2019) Multi-objective fractional particle lion algorithm for the energy aware routing in the WSN. Pervasive Mob Comput 58:1–16\nXiuwu Yu, Qin L, Renrong X (2019) Uneven clustering routing algorithm based on glowworm swarm optimization. Ad Hoc Netw 93:1–8\nAgarkhed J, Kadrolli V, Patil S (2020) Fuzzy based multi-level multi-constraint multi-path reliable routing in wireless sensor network. Int J Inf Technol 12:1133–1146\nHan Y, Li G, Xu R, Su J, Li J, Wen G (2020) Clustering the wireless sensor networks: a meta-heuristic approach. IEEE Access 8:214551–214564\nMalisetti NR, Pamula VK (2020) Performance of Quasi oppositional butterfly optimization algorithm for cluster head selection in WSNs. Procedia Comput Sci 171:1953–1960\nWang M, Wang S, Zhang B (2020) APTEEN routing protocol optimization in wireless sensor networks based on combination of genetic algorithms and fruit fly optimization algorithm. Ad Hoc Netw 102:1–7\nAl Mazaideh M, Levendovszky J (2021) Multi-hop routing algorithm for WSNs based on compressive sensing and multiple objective genetic algorithm. J Commun Netw 23(2):138–147\nDevika G, Ramesh D, Karegowda AG (2021) Energy optimized hybrid PSO and wolf search based LEACH. Int J Inf Technol 13:21–732\nGajala QS, Kumar SS (2021) Novel hybridized crow whale optimization and QoS based bipartite coverage routing for secure data transmission in wireless sensor networks. J Intell Fuzzy Syst 41(1):2085–2099\nGulganwa P, Jain S (2022) EES-WCA: energy efficient and secure weighted clustering for WSN using machine learning approach. Int J Inf Technol 14:135–144\nQuoc DN, Liu N, Guo D (2022) A hybrid fault-tolerant routing based on Gaussian network for wireless sensor network. J Commun Netw 24(1):37–46\nYao Y-D, Li X, Cui Y-P, Deng L, Wang C (2022) Game theory and coverage optimization based Multihop routing protocol for network lifetime in wireless sensor networks. IEEE Sens J 22(13):13739–13752\nGuo J, Gao H, Liu Z, Huang F, Zhang J, Li X, Ma J (2023) ICRA: an intelligent clustering routing approach for UAV ad hoc networks. IEEE Trans Intell Transp Syst 24(2):2447–2460",{"EN":683},"Establishing energy efficient communication with minimum delay in wireless sensor networks (WSN) is a challenging task. Routing protocols are evolved to meet the WSN communication challenges. Optimal path selection for data transmission enhances the network lifetime and quality of services. Various optimization models have evolved so far for WSN routing. However, due to high convergence time and cost, there is a gap towards developing a better model to attain efficient routing. A hybrid optimization model using grey wolf optimization and flower pollination optimization algorithm is presented in this research work to select optimal route for data transmission. In the proposed network structure, the cluster heads are selected using Type 2 fuzzy logic and optimal paths are selected using a hybrid optimization algorithm considering the energy, delay, lifetime and delay parameters. The performance of proposed hybrid optimization is validated through simulation analysis and compared with existing optimization algorithms. The proposed routing model exhibits better performances attaining of minimum delay, energy consumption, and maximum throughput.",{"EN":685},"Hybrid optimized using grey wolf-flower pollination for wireless sensor network routing",{"VOID":687},"10.1007\u002Fs41870-023-01240-8","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs41870-023-01240-8",[690,705],{"id":691,"sortIndex":21,"researcher":20,"roles":692,"affiliations":693,"properties":702},"978701bb-7548-48c3-8d38-7daff92d6af6",[190],[694],{"id":20,"sortIndex":21,"affiliation":695,"properties":20},{"id":696,"createTime":697,"updateTime":697,"relativeEntities":698,"slug":20,"properties":699,"entityType":89,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"7638fa89-8bea-418e-8584-fe9127f718fb","2024-02-13T19:09:42.028+00:00",[],{"title":700},{"VI":701},"Department of Electronics and Communication Engineering, Annamalai University, Chidambaram, India",{"title":703},{"VI":704},"R. 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IEEE Trans Dependable Secur Comput 12(6):615–625",{"doi":978},"10.1109\u002FTDSC.2014.2382601",{"id":20,"text":980,"url":20,"identifiers":981},"Lv X, Mu Y, Li H (2014) Non-interactive key establishment for bundle security protocol of space DTNs. IEEE Trans Inf Forensics Secur 9(1):5–13",{"doi":982},"10.1109\u002FTIFS.2013.2289993",{"id":20,"text":984,"url":20,"identifiers":985},"Lv X, Li H (2014) Error-and loss-tolerant bundle fragment authentication for space DTNs. Front Comput Sci 8(6):1012–1023",{"doi":986},"10.1007\u002Fs11704-014-3365-6",{"id":20,"text":988,"url":20,"identifiers":989},"Puri P, Singh MP (2013) A survey paper on routing in delay-tolerant networks. In: Proceedings international conference on information systems and computer networks (ISCON), IEEE, pp 215–220",{"doi":990},"10.1109\u002FICISCON.2013.6524206",{"id":20,"text":992,"url":20,"identifiers":993},"Saha S, Nandi S, Verma R, Sengupta S, Singh K, Sinha V, Das SK (2016) Design of efficient lightweight strategies to combat DoS attack in delay tolerant network routing. Wirel Netw. doi:\n                        10.1007\u002Fs11276-016-1320-1",{"doi":994},"10.1007\u002Fs11276-016-1320-1",{"id":20,"text":996,"url":20,"identifiers":997},"Guo H, Wang X, Cheng H, Huang M (2016) A routing defense mechanism using evolutionary game theory for delay tolerant networks. Appl Soft Comput 38:469–476",{"doi":998},"10.1016\u002Fj.asoc.2015.10.019",{"id":20,"text":1000,"url":20,"identifiers":1001},"Fatimah A, Johari R (2016) Part: performance analysis of routing techniques in delay tolerant network. In: Proceedings of the international conference on internet of things and cloud computing, ACM, p 76",{"doi":1002},"10.1145\u002F2896387.2900328",{"id":20,"text":1004,"url":20,"identifiers":1005},"Guo Y, Schildt S, Pogel T, Wolf L (2013) Detecting malicious behavior in a vehicular DTN for public transportation. In: Global information infrastructure symposium, IEEE, pp 1–8",{"doi":1006},"10.1109\u002FGIIS.2013.6684378",{"id":20,"text":1008,"url":20,"identifiers":1009},"Diep PT, Yeo CK (2015) Detecting flooding attack in delay tolerant networks by piggybacking encounter records. In: 2nd international conference on information science and security (ICISS), IEEE, pp 1–4",{"doi":1010},"10.1109\u002FICISSEC.2015.7370995",{"id":20,"text":1012,"url":20,"identifiers":1013},"Chen H, Lou W, Wang Z, Wang Q (2015) A secure credit-based incentive mechanism for message forwarding in noncooperative DTNs. In: Proceedings IEEE transactions on vehicular technology, vol. 9545, pp 1–1",{},{"id":20,"text":1015,"url":20,"identifiers":1016},"Zhou J, Dong X, Cao Z, Vasilakos AV (2015) Secure and privacy preserving protocol for cloud-based vehicular DTNs. IEEE Trans Inf Forensics Secur 10(6):1299–1314",{"doi":1017},"10.1109\u002FTIFS.2015.2407326",{"id":20,"text":1019,"url":20,"identifiers":1020},"Ansa G, Criuckshank H, Sun Z, Al-Siyabi M (2011) A DOS-resilient design for delay tolerant networks. In: Proceedings 7th international wireless communications and mobile computing conference (IWCMC), IEEE, pp 424–429",{"doi":1021},"10.1109\u002FIWCMC.2011.5982571",{"id":20,"text":1023,"url":20,"identifiers":1024},"Li Q, Gao W, Zhu S, Cao G (2013) To lie or to comply: defending against flood attacks in disruption tolerant networks. IEEE Trans Dependable Secur Comput 10(3):168–182",{"doi":1025},"10.1109\u002FTDSC.2012.84",{"id":20,"text":1027,"url":20,"identifiers":1028},"Chuah M, Yang P, Han J (2007) A ferry-based intrusion detection scheme for sparsely connected ad hoc networks. In: Proceedings of the 4th annual international conference on mobile and ubiquitous systems: computing, networking and services, MobiQuitous",{"doi":1029},"10.1109\u002FMOBIQ.2007.4451068",{"id":20,"text":1031,"url":20,"identifiers":1032},"Ren Y, Chuah MC, Yang J, Chen Y (2010) Muton: detecting malicious nodes in disruption-tolerant networks. In: Wireless communications and networking conference (WCNC), IEEE, pp. 1–6",{"doi":1033},"10.1109\u002FWCNC.2010.5506574",{"id":20,"text":1035,"url":20,"identifiers":1036},"Bucur D, Iacca G, Gaudesi M, Squillero G, Tonda A (2016) Optimizing groups of colluding strong attackers in mobile urban communication networks with evolutionary algorithms. Appl Soft Comput J 40:416–426",{"doi":1037},"10.1016\u002Fj.asoc.2015.11.024",{"id":20,"text":1039,"url":20,"identifiers":1040},"Lee FC, Goh W, Yeo CK (2010) A queuing mechanism to alleviate flooding attacks in probabilistic delay tolerant networks. In: Proceedings 6th advanced international conference on telecommunications (AICT), IEEE, pp 329–334",{"doi":1041},"10.1109\u002FAICT.2010.78",{"id":20,"text":1043,"url":20,"identifiers":1044},"Spyropoulos T, Psounis K, Raghavendra CS (2005) Spray and wait: an efficient routing scheme for intermittently connected mobile networks. 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Proc Natl Acad Sci USA 37:205\nRehman HU, Azam N, Yao J, Benso A (2017) A three-way approach for protein function classification. PLoS ONE 12(2):0171702\nKabli F, Hamou RM, Amine A (2017) New classification system for protein sequences. In 2017 First International Conference on Embedded and Distributed Systems (EDiS), IEEE. Oran, Algeria, pp. 1–6\nBankapur, Sanjay, and Nagamma Patil (2018) Protein Secondary Structural Class Prediction Using Effective Feature Modeling and Machine Learning Techniques. In 2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE). IEEE pp.18–21\nLima, Emerson Correia, Fábio Lima Custódio, Gregório Kappaun Rocha, and Laurent E. Dardenne (2018) Estimating Protein Structure Prediction Models Quality Using Convolutional Neural Networks. In 2018 International Joint Conference on Neural Networks (IJCNN), IEEE pp. 1–6\nFang, Chao, Yi Shang, and Dong Xu. (2017) A New Deep Neighbor Residual Network for Protein Secondary Structure Prediction. In 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE pp. 66–71\nIqbal MJ, Faye I, Said AM, Samir BB (2014) Data mining of protein sequences with amino acid position-based feature encoding technique. In: Herawan T, Deris MM, Abawajy J (eds) Proceedings of the First International Conference on Advanced Data and Information Engineering. Springer, Singapore\nAnfinsen C (1972) The formation and stabilization of protein structure. Biochem J 128:737\nDictionary (2019) Amino. https:\u002F\u002Fwww.dictionary.com\u002F. Accessed 25 March 2019\nAmino acid, [Online]. Available: https:\u002F\u002Fen.wikipedia.org\u002F. Accessed 22 May 2015\nRobles V, Larrañaga P, Peña JM, Menasalvas E, Pérez MS, Herves V, Wasilewska A (2004) Bayesian network multi-classifiers for protein secondary structure prediction. Artif Intell Med 31:117\nBreiman L (2001) Random forests. Mach Learn 45(1):5–32\nProtein data bank. Availabe https:\u002F\u002Fwww.kaggle.com\u002Fshahir\u002Fprotein-data-set#pdb_data_seq.csv\nHochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780\nHawkins J, Boden M (2005) The Applicability of recurrent neural networks for biological sequence analysis. IEEE\u002FACM Trans Comput Biol Bioinform 2(3):243–253\nJain G, Sharma M, Agarwal B (2019) Optimizing semantic LSTM for spam detection. Int J Inf Technol 11:239–250\nChhachhiya D, Sharma A, Gupta M (2019) Designing optimal architecture of recurrent neural network (LSTM) with particle swarm optimization technique specifically for educational dataset. Int J Inf Technol 11(1):159–163",{"EN":1112},"Proteins class and function prediction is one of the most significant task in computational bioinformatics. The information about the protein functions and class plays a vital role in understanding biological cells and has a great impact on human life in factors such as personalized medicine. The technical advancement in the areas of biological aspects and understanding of biological processes results in features and characteristics of important Proteins. Prediction of amino acid sequence involves prediction of amino sequence folding and its structures from the primary sequence obtained. In this work, Machine learning prediction algorithms have applied for protein class prediction. This method takes consideration of macromolecules of biological significances. Later the solution focuses on the understanding of different protein family, subsequently classify the protein family type sequence. This is achieved through machine learning algorithms Naive Bayes (NB) and Random forest (RF) algorithms with count vectorized feature and LSTM. These algorithms are used to classify the protein family on its protein sequence. Finally, result shows that LSTM predicts the protein class more accurately than the RF, and NB algorithm. LSTM achieves an accuracy of 96% whereas RF & NB with an accuracy of 91% and 86%.",{"EN":1114},"Protein class prediction based on Count Vectorizer and long short term memory",{"VOID":1116},"10.1007\u002Fs41870-020-00528-3","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs41870-020-00528-3",[1119,1134,1149,1161],{"id":1120,"sortIndex":382,"researcher":20,"roles":1121,"affiliations":1122,"properties":1131},"7ee2a86a-d409-4b38-b10a-357ad4ba7e98",[190],[1123],{"id":20,"sortIndex":21,"affiliation":1124,"properties":20},{"id":1125,"createTime":1126,"updateTime":1126,"relativeEntities":1127,"slug":20,"properties":1128,"entityType":89,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"54c02f85-9f7e-4cc4-b5fc-1ca5360e0231","2024-01-01T18:44:05.513+00:00",[],{"title":1129},{"VI":1130},"School of Computing and Information Technology, REVA University, Bengalore, India",{"title":1132},{"VI":1133},"Sunilkumar S. Manvi",{"id":1135,"sortIndex":206,"researcher":20,"roles":1136,"affiliations":1137,"properties":1146},"1d082c0c-91e3-4ae4-b6d7-8e875311b44b",[190],[1138],{"id":20,"sortIndex":21,"affiliation":1139,"properties":20},{"id":1140,"createTime":1141,"updateTime":1141,"relativeEntities":1142,"slug":20,"properties":1143,"entityType":89,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"24edff4d-ed2e-4ab9-856e-eab98f2fcbe5","2024-01-01T18:44:05.476+00:00",[],{"title":1144},{"VI":1145},"Department of Information Science and Engineering, Ramaiah Institute of Technology, Bengalore, India",{"title":1147},{"VI":1148},"Mithun Raj",{"id":1150,"sortIndex":21,"researcher":20,"roles":1151,"affiliations":1152,"properties":1158},"a4d79c0a-3ac5-4aaf-9dea-0517001c5b97",[190],[1153],{"id":20,"sortIndex":21,"affiliation":1154,"properties":20},{"id":1140,"createTime":1141,"updateTime":1141,"relativeEntities":1155,"slug":20,"properties":1156,"entityType":89,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":1157},{"VI":1145},{"title":1159},{"VI":1160},"S. R. Mani Sekhar",{"id":1162,"sortIndex":188,"researcher":20,"roles":1163,"affiliations":1164,"properties":1170},"379bf592-fa8c-42e7-8899-9ff2f8598ad7",[190],[1165],{"id":20,"sortIndex":21,"affiliation":1166,"properties":20},{"id":1140,"createTime":1141,"updateTime":1141,"relativeEntities":1167,"slug":20,"properties":1168,"entityType":89,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":1169},{"VI":1145},{"title":1171},{"VI":1172},"G. M. Siddesh",{"url":1117,"publisher":1174,"properties":1195},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1175,"slug":10,"properties":1176,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":1180,"manageAffiliations":1181,"indexDatabases":1182,"url":119,"thumbnailPath":20,"statistic":1190,"gsStatistic":20,"type":161,"analyzePriority":20},[],{"issn":1177,"eissn":1178,"title":1179},{"VOID":13},{"VOID":15},{"EN":17},[],[],[1183],{"id":95,"indexDatabase":1184,"url":108,"indexYears":109,"academicFieldIds":1189,"indexDatabaseRanking":118},{"id":97,"createTime":98,"updateTime":99,"relativeEntities":1185,"label":1186,"description":1187,"key":105,"publicationTags":1188,"standard":20},[],{"EN":102,"VI":102},{"EN":102,"VI":104},[107],[111,112,113,114,115,116,117],{"impactFactor":21,"impactFactorByYear":1191,"i10Index":128,"i10IndexLast5Year":129,"totalPublication":130,"totalPublicationByYear":1192,"totalCitation":140,"totalCitationByYear":1193,"totalCitationPerPublication":150,"totalCitationPerPublicationByYear":1194,"hindexLast5Year":160,"hindex":160},{"2018":122,"2019":123,"2020":124,"2021":125,"2022":126,"2023":127},{"2017":132,"2018":133,"2019":134,"2020":135,"2021":136,"2022":137,"2023":138,"2024":139},{"2017":142,"2018":143,"2019":144,"2020":145,"2021":146,"2022":147,"2023":148,"2024":149},{"2017":152,"2018":153,"2019":154,"2020":155,"2021":156,"2022":157,"2023":158,"2024":159},{"volume":1196,"pages":1198},{"VOID":1197},"13",{"VOID":1199},"341-348","2020-10-11",2020,{"id":1203,"createTime":1204,"updateTime":1205,"relativeEntities":1206,"slug":1207,"properties":1208,"entityType":181,"verifyStatus":182,"verifyTime":1205,"verifyNote":183,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":1217,"fullTextUrl":20,"authors":1218,"publicationType":229,"publisherRelationship":1263,"citationCount":20,"citationInfo":20,"publishDate":1288,"publishYear":1289,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":20,"openAccess":20,"references":20,"isForceReanalyzing":259},"94af9a7a-5802-4bcd-a91b-72e2c52788b9","2024-02-11T08:26:18.123+00:00","2025-02-06T23:49:26.693+00:00",[],"Mutual-character-dialogue-generation-with-semi-supervised-multitask-learners-and-awareness",{"references":1209,"abstract":1211,"title":1213,"doi":1215},{"VOID":1210},"Schatzmann J, Weilhammer K, Stuttle M, Young S (2006) A survey of statistical user simulation techniques for reinforcement-learning of dialogue management strategies. Knowl Eng Rev 21(2):97–126. https:\u002F\u002Fdoi.org\u002F10.1017\u002FS0269888906000944\nShang L, Lu Z, Li H (2015) Neural responding machine for short-text conversation. arXiv preprint arXiv:1503.02364. https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.1503.02364\nHu B, Lu Z, Li H, Chen Q (2014) Convolutional neural network architectures for matching natural language sentences. Adv Neural Inf Process Syst 27:1–9\nSong Y, Yan R, Li X, Zhao D, Zhang M (2016) Two are better than one: an ensemble of retrieval-and generation-based dialog systems. arXiv preprint arXiv:1610.07149. https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.1610.07149\nRitter A, Cherry C, Dolan B (2011) Data-driven response generation in social media. In: Empirical Methods in Natural Language Processing (EMNLP)\nVinyals O, Le Q (2015) A neural conversational model. arXiv preprint arXiv:1506.05869\nSerban I, Sordoni A, Bengio Y, Courville A, Pineau J (2016) Building end-to-end dialogue systems using generative hierarchical neural network models. In: Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 30, No. 1). https:\u002F\u002Fdoi.org\u002F10.1609\u002Faaai.v30i1.9883\nSong H, Zhang WN, Cui Y, Wang D, Liu T (2019) Exploiting persona information for diverse generation of conversational responses. arXiv preprint arXiv:1905.12188. https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.1905.12188\nZhang S, Dinan E, Urbanek J, Szlam A, Kiela D, Weston J (2018) Personalizing dialogue agents: I have a dog, do you have pets too?. arXiv preprint arXiv:1801.07243. https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.1801.07243\nLi J, Monroe W, Ritter A, Galley M, Gao J, Jurafsky D (2016) Deep reinforcement learning for dialogue generation. arXiv preprint arXiv:1606.01541. https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.1606.01541\nLi J, Galley M, Brockett C, Spithourakis GP, Gao J, Dolan B (2016) A persona-based neural conversation model. arXiv preprint arXiv:1603.06155. https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.1603.06155\nMazaré PE, Humeau S, Raison M, Bordes A (2018) Training millions of personalized dialogue agents. arXiv preprint arXiv:1809.01984. https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.1809.01984\nWolf T, Sanh V, Chaumond J, Delangue C (2019) Transfertransfo: a transfer learning approach for neural network based conversational agents. arXiv preprint arXiv:1901.08149. https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.1901.08149\nHasson U, Ghazanfar AA, Galantucci B, Garrod S, Keysers C (2012) Brain-to-brain coupling: a mechanism for creating and sharing a social world. Trends Cogn Sci 16(2):114–121\nLiu Q, Chen Y, Chen B, Lou JG, Chen Z, Zhou B, Zhang D (2020) You impress me: dialogue generation via mutual persona perception. arXiv preprint arXiv:2004.05388. https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2004.05388\nRadford A, Wu J, Child R, Luan D, Amodei D, Sutskever I (2019) Language models are unsupervised multitask learners. OpenAI blog 1(8):9\nJi B (2023) Based on text augmentation personalized dialog generation with persona-sparse data. In: 2023 4th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT), Nanjing, China, pp 717–720.https:\u002F\u002Fdoi.org\u002F10.1109\u002FAINIT59027.2023.10212566\nPandey S, Sharma S, Wazir S (2022) Mental healthcare chatbot based on natural language processing and deep learning approaches: ted the therapist. Int J Inf Technol 14(7): 3757–3766. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs41870-022-00999-6\nBajaj D, Goel A, Gupta SC, Batra H (2022) MUCE: a multilingual use case model extractor using GPT-3. Int J Inf Technol 14(3):1543–1554\nAli I, Yadav D (2021) Question reformulation based question answering environment model. Int J Inf Technol 13(1):59–67. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs41870-019-00332-8\nRajan RP, Jose DV (2023) Text summarization using residual-based temporal attention convolutional neural network. Int J Inf Technol. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs41870-023-01581-4\nShafi N, Chachoo MA (2023) Query intent recognition by integrating latent dirichlet allocation in conditional random field. Int J Inf Technol 15(1):183–191. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs41870-022-01108-3\nVajrobol V, Aggarwal N, Shukla U, Saxena GJ, Singh S, Pundir A (2023) Explainable cross-lingual depression identification based on multi-head attention networks in Thai context. Int J Inf Technol. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs41870-023-01512-3\nYouness F, Madkour MA, Elshenawy A (2023) Dialog generation for Arabic chatbot. Int J Inf Technol. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs41870-023-01519-w\nNarynov S, Zhumanov Z, Gumar A, Khassanova M and Omarov B (2021) Chatbots and conversational agents in mental health: a literature review. In: 2021 21st International Conference on Control, Automation and Systems (ICCAS), Jeju, Korea, Republic of, pp 353–358. https:\u002F\u002Fdoi.org\u002F10.23919\u002FICCAS52745.2021.9649855\nKhalaf AA, Hashim AHA, Olowolayemo A & Funke R (2021) Artificial intelligent applications for mental health support: a review paper. Engineering Professional Ethics and Education 2021 (ICEPEE'21), 22\nGoel R, Vashisht S, Dhanda A and Susan S (2021) An empathetic conversational agent with attentional mechanism. In: 2021 International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, India, pp 1–4. https:\u002F\u002Fdoi.org\u002F10.1109\u002FICCCI50826.2021.9402337\nBahdanau D, Chorowski J, Serdyuk D, Brakel P & Bengio Y (2016) End-to-end attention-based large vocabulary speech recognition. In: 2016 IEEE international conference on acoustics, speech and signal processing (ICASSP) (pp 4945–4949). IEEE\nXu S, Song H, Wu R and Shi J (2023) A natural language understanding model based on encoding fusion for power marketing indicator answering. In: 2023 2nd Asia Conference on Electrical, Power and Computer Engineering (EPCE), Xiamen, China, pp 13–17. https:\u002F\u002Fdoi.org\u002F10.1109\u002FEPCE58798.2023.00011\nMaree M, Al-Qasem R & Tantour B (2023) Transforming legal text interactions: leveraging natural language processing and large language models for legal support in Palestinian cooperatives. Int J Inf Technol 16:551–558 (2024). https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs41870-023-01584-1\nSingh SK, Kumar S and Mehra PS (2023) Chat GPT & Google Bard AI: a review. In: 2023 International Conference on IoT, Communication and Automation Technology (ICICAT), Gorakhpur, India, pp 1–6. https:\u002F\u002Fdoi.org\u002F10.1109\u002FICICAT57735.2023.10263706\nSakulwichitsintu S (2023) ParichartBOT: a chatbot for automatic answering for postgraduate students of an open university. Int J Inf Technol 15(3):1387–1397\nVaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. Adv Neural Inf Process Syst 30\nKhalaf AA, Hashim AHA, Olowolayemo A & Funke R (2023) Generative interactive psychotherapy expert (GIPE) bot. IJCSNS International Journal of Computer Science and Network Security, Vol. 23 No. 4\nDevlin J, Chang MW, Lee K, Toutanova K (2018) Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805. https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.1810.04805\nSerban I, Sordoni A, Lowe R, Charlin L, Pineau J, Courville A, Bengio Y (2017) A hierarchical latent variable encoder-decoder model for generating dialogues. In: Proceedings of the AAAI Conference on Artificial Intelligence\nPapineni K, Roukos S, Ward T, Zhu WJ (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics\nLi J, Galley M, Brockett C, Gao J, Dolan B (2015) A diversity-promoting objective function for neural conversation models. arXiv preprint arXiv:1510.03055. https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.1510.03055\nAli I, Yadav D (2021) Question reformulation based question answering environment model. Int J Inf Technol 13:59–67\nPaszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L, Desmaison A (2019) Pytorch: an imperative style, high-performance deep learning library. Adv Neural Inf Process Syst 32. 8026–8037 arXiv e-prints https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.1912.01703\nGu JC, Ling ZH, Zhu X, Liu Q (2019) Dually interactive matching network for personalized response selection in retrieval-based chatbots. arXiv preprint arXiv:1908.05859. https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.1908.05859\nBahdanau D, Cho K, Bengio Y (2014) Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473. https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.1409.0473\nKingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980",{"EN":1212},"Consistent efforts have been ongoing to improve the friendliness and reliability of informal dialogue systems. However, most research focuses solely on mimicking human-like answers. Therefore, the interlocutors’ awareness features of the dialogue system are left unexplored. Meanwhile, cognitive science research reveals that awareness is a crucial indicator of an effective, high-quality informal conversation. This research aims to boost the quality of the conversational generation system by factoring in awareness of the interlocutors in the design and training of the dialogue system model. The Generative Pre-Trained Transformer-2 (GPT-2) model was implemented into the Persona Perception (P2) Bot to achieve the objectives of this study. This was to precisely develop model's understanding, P2 Bot was implemented using a transmitter–receiver-based structure. The P2 Bot leverages mutual persona awareness to improve the quality of customized dialogue generation. GPT-2 is a 1.5B parameter transformer model that produces state-of-the-art accuracy in a zero-shot setting on seven of the eight evaluated language modeling datasets. The observations of the proposed model on a sizable open-source dataset, PERSONA-CHAT, proved successful, with improvement above the state-of-the-art baselines in both automatic measures and human assessments. The model has achieved 82.2% accuracy on Hits@1 performance metrics in the original data and 68.8% on the revised data. On the human evaluation, the model scored an average of 2.66, pointing out that the responses provided were coherent and informative. A dialogue generation model with character and awareness which can communicate like an informative human expert was introduced. This study presents the submerging of GPT-2 model on a mutual persona perception dialogue generating model.",{"EN":1214},"Mutual character dialogue generation with semi-supervised multitask learners and awareness",{"VOID":1216},"10.1007\u002Fs41870-023-01720-x","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs41870-023-01720-x",[1219,1236,1251],{"id":1220,"sortIndex":206,"researcher":20,"roles":1221,"affiliations":1222,"properties":1233},"58b26a1e-fa72-42b9-ab08-0f1eaf84df35",[190],[1223],{"id":20,"sortIndex":21,"affiliation":1224,"properties":20},{"id":1225,"createTime":1226,"updateTime":1227,"relativeEntities":1228,"slug":1229,"properties":1230,"entityType":89,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"03645faf-ac2a-41fc-a65b-960dc86e1135","2024-02-11T08:26:18.171+00:00","2025-06-11T16:44:03.216+00:00",[],"Department-of-Computer-Science-Faculty-of-Information-and-Communication-Technology-International-Islamic-University-Malaysia-IIUM-Kuala-Lumpur-Malaysia",{"title":1231},{"VI":1232},"Department of Computer Science, Faculty of Information and Communication Technology, International Islamic University Malaysia (IIUM), Kuala Lumpur, Malaysia",{"title":1234},{"VI":1235},"Akeem Olowolayemo",{"id":1237,"sortIndex":21,"researcher":20,"roles":1238,"affiliations":1239,"properties":1248},"0fcc95ee-a443-4e43-b14b-459865277a4b",[190],[1240],{"id":20,"sortIndex":21,"affiliation":1241,"properties":20},{"id":1242,"createTime":1243,"updateTime":1243,"relativeEntities":1244,"slug":20,"properties":1245,"entityType":89,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"11ac268d-4d9c-4c26-99ca-783a44ffe902","2024-02-11T08:26:18.143+00:00",[],{"title":1246},{"VI":1247},"Department of Electrical and Computer Engineering, Faculty of Engineering, International Islamic University Malaysia (IIUM), Kuala Lumpur, Malaysia",{"title":1249},{"VI":1250},"Ayesheh Ahrari Khalaf",{"id":1252,"sortIndex":188,"researcher":20,"roles":1253,"affiliations":1254,"properties":1260},"f3641218-44d5-4932-a443-8e9b4a66937f",[190],[1255],{"id":20,"sortIndex":21,"affiliation":1256,"properties":20},{"id":1242,"createTime":1243,"updateTime":1243,"relativeEntities":1257,"slug":20,"properties":1258,"entityType":89,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":1259},{"VI":1247},{"title":1261},{"VI":1262},"Aisha Hassan Abdalla 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P, Sood SP, Bajaj R, Kumar Y (2021) Air quality monitoring for Smart eHealth system using firefly optimization and support vector machine. Int J Inf Technol 13(5):1847–1859\nWorld Health Organization. Annual report (2021): WHO Asia-Pacific Centre for Environment and Health in the Western Pacific Region. No. WPR\u002F2022\u002FDPM\u002F001. WHO Regional Office for the Western Pacific, 2022\nBosch P (2002) The European Environment Agency focuses on EU-policy in its approach to sustainable development indicators. Stat J U N Econ Comm Eur 19(1–2):5–18\nSahoo AK, Jena RK (2022) Loss minimisation of induction motor-driven electric vehicle using teamwork optimisation. Int J Ambient Energy 43(1):8123–8134\nDe Klerk ML, Saha AK (2021) A comprehensive review of advanced traction motor control techniques suitable for electric vehicle applications. IEEE Access\nEl Ouanjli N, Derouich A, El Ghzizal A, Motahhir S, Chebabhi A, El Mourabit Y, Taoussi M (2019) Modern improvement techniques of direct torque control for induction motor drives-a review. Protect Control Modern Power Syst 4(1):1–12\nSavarapu S, Qutubuddin M, Narri Y (2022) Modified brain emotional controller- based ripple minimization for SVM-DTC of sensorless induction motor drive. IEEE Access 10:40872–40887\nTarusan SAA, Jidin A, Jamil MLM (2022) The optimization of torque ripple reduction by using DTC-multilevel inverter. ISA Trans 121:365–379\nZhang Z, Wei H, Zhang W, Jiang J (2021) Ripple attenuation for induction motor finite control set model predictive torque control using novel fuzzy adaptive techniques. Processes 9(4):710\nMaity P, Vijayakumari A (2020) Fuzzy-enabled direct torque control for low torque ripple in induction motors for EV applications. In International Conference on emerging trends and advances in electrical engineering and renewable energy (pp 371–383). Springer, Singapore\nTatte Y (2021) Torque ripple minimization with modified comparator in DTC based three-level five-phase inverter fed five-phase induction motor. IET Power Electron 14(9):1713–1723\nBen Salem F, Feki M (2019) An improved DTC Induction motor for electric vehicle propulsion: an intention to provide a comfortable ride. Towards green logistics, solving transport problems, pp 185–201\nSubramaniam SK, Rayappan JX, Sukumar B (2022) Fuzzy-based estimation of reference flux, reference torque and sector rotation for performance improvement of DTC-IM drive. Automatika 63(3):440–453\nKumar DK, Das GTR (2020) Adaptive fuzzy controller based self-regulated reference stator flux estimator of direct torque control for three level inverter fed IPMSM. Int J Intell Eng Syst 13(2):11–19\nKumar SS, Xavier RJ, Balamurugan S (2018) Development of ANFIS-based reference flux estimator and FGS-tuned speed controller for DTC of induction motor. Automatika 59(1):11–23\nKorkmaz F, Cakir MF, Korkmaz Y, Topaloglu I (2012) Fuzzy-based stator flux optimizer design for direct torque control. Int J Instrum Control Syst (IJICS) 2(4):1212.0160\nArias A, Romeral L, Aldabas E, Jayne M (2005) Stator flux optimised Direct Torque Control system for induction motors. Elect Power Syst Res 73(3):257–265\nGanjewar SP, Pahariya Y (2022) Modified MRAS approach for sensorless speed control of induction motor for reliability improvement. Int J Inf Technol 14(3):1595–1602\nPatel PD, Pandya SN (2022) Comparative analysis of torque ripple for direct torque control based induction motor drive with different strategies. Aust J Elect Electron Eng 19(4):1–19\nDepenbrock M (1988) Direct self-control (DSC) of inverter-fed induction machine. IEEE Trans Power Electron 3:420–429\nKhan H, Khatoon S, Gaur P (2021) Comparison of various controller design for the speed control of DC motors used in two wheeled mobile robots. Int J Inf Technol 13(2):713–720\nKhan H, Khatoon S, Gaur P, Khan SA (2022) Speed control comparison of wheeled mobile robot by ANFIS, Fuzzy and PID controllers. Int J Inf Technol 14(4):1893–1899\nSahoo AK, Jena RK (2022) Improved DTC strategy with fuzzy logic controller for induction motor driven electric vehicle. AIMS Electron Elect Eng 6(3):296–316",{"EN":1300},"This paper presents a novel reference flux selection technique for reducing torque ripple in an induction motor-based direct torque control (DTC) strategy for electric vehicle (EV) applications. Due to its fast torque response and simplicity, DTC is more popular for EVs. However, a hysteresis controller and limited voltage vectors in DTC result in a variable switching frequency, which creates torque and flux ripples. These ripples can be mitigated by choosing an appropriate reference flux according to the driving scenario instead of a fixed reference. The torque ripple as a function of flux and its optimal value for which the ripple will be the minimum are established numerically here. The effectiveness of the proposed approach over conventional DTC and fuzzy DTC is verified using a 50-hp induction motor (IM) drive in simulation using MATLAB in different operating conditions. As performance indicators, torque and flux ripple, current harmonics, and integral square error are evaluated.",{"EN":1302},"Reduction of torque ripple in induction motor-driven electric vehicle using optimized stator flux",{"VOID":1304},"10.1007\u002Fs41870-023-01172-3","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs41870-023-01172-3",[1307,1322],{"id":1308,"sortIndex":188,"researcher":20,"roles":1309,"affiliations":1310,"properties":1319},"8a91c9df-f4f1-4340-ac78-f6410d14cde8",[190],[1311],{"id":20,"sortIndex":21,"affiliation":1312,"properties":20},{"id":1313,"createTime":1314,"updateTime":1314,"relativeEntities":1315,"slug":20,"properties":1316,"entityType":89,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"f753c36f-829d-4e2d-a362-a892013f2a8a","2024-01-25T01:33:25.219+00:00",[],{"title":1317},{"VI":1318},"Department of Electrical Engineering, CAPGS, BPUT, Rourkela, India",{"title":1320},{"VI":1321},"Ranjan Kumar Jena",{"id":1323,"sortIndex":21,"researcher":20,"roles":1324,"affiliations":1325,"properties":1334},"dcd5dc75-6302-4586-8f7d-4d073915f8f6",[190],[1326],{"id":20,"sortIndex":21,"affiliation":1327,"properties":20},{"id":1328,"createTime":1329,"updateTime":1329,"relativeEntities":1330,"slug":20,"properties":1331,"entityType":89,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"e93bb661-9953-4388-8437-6c70de976e69","2024-01-25T01:33:25.154+00:00",[],{"title":1332},{"VI":1333},"Department of Electrical Engineering, OUTR, BHUBANESWAR, India",{"title":1335},{"VI":1336},"Anjan Kumar Sahoo",{"url":1305,"publisher":1338,"properties":1359},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1339,"slug":10,"properties":1340,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":1344,"manageAffiliations":1345,"indexDatabases":1346,"url":119,"thumbnailPath":20,"statistic":1354,"gsStatistic":20,"type":161,"analyzePriority":20},[],{"issn":1341,"eissn":1342,"title":1343},{"VOID":13},{"VOID":15},{"EN":17},[],[],[1347],{"id":95,"indexDatabase":1348,"url":108,"indexYears":109,"academicFieldIds":1353,"indexDatabaseRanking":118},{"id":97,"createTime":98,"updateTime":99,"relativeEntities":1349,"label":1350,"description":1351,"key":105,"publicationTags":1352,"standard":20},[],{"EN":102,"VI":102},{"EN":102,"VI":104},[107],[111,112,113,114,115,116,117],{"impactFactor":21,"impactFactorByYear":1355,"i10Index":128,"i10IndexLast5Year":129,"totalPublication":130,"totalPublicationByYear":1356,"totalCitation":140,"totalCitationByYear":1357,"totalCitationPerPublication":150,"totalCitationPerPublicationByYear":1358,"hindexLast5Year":160,"hindex":160},{"2018":122,"2019":123,"2020":124,"2021":125,"2022":126,"2023":127},{"2017":132,"2018":133,"2019":134,"2020":135,"2021":136,"2022":137,"2023":138,"2024":139},{"2017":142,"2018":143,"2019":144,"2020":145,"2021":146,"2022":147,"2023":148,"2024":149},{"2017":152,"2018":153,"2019":154,"2020":155,"2021":156,"2022":157,"2023":158,"2024":159},{"volume":1360,"pages":1361},{"VOID":254},{"VOID":1362},"1333-1346","2023-02-22"]