Journal Vol – 15 No -9, September 2020

Abstract:

From an environmental point of view, emission from the engine exhaust system is a serious problem. Alternative fuels are encouraged for this search. Hydroxy gas (HHO) is Considered to be one of the secondary sustainable energy to meet the strict emission standards and maintain the greenhouse effect. Therefore, this paper experiment is carried out adding a secondary fuel hydrogen gas with diesel fuel in the CI engine. HHO is one of the best Choices that pertains to the fuel's complete combustion and thus also helps to reduce harmful gas emissions. The experiment is carried out on the 4-stroke, Single cylinder engine, using HHO for a diesel engine. At the engine inlet manifold, the HHO gas is supplied by the HHO kit. The HHO gas mixes with fuel, and enhances the process of combustion. The experimental investigation was performed for different HHO gas pressures, and the efficiency was evaluated and compared to pure diesel. The results show that  HHO Performance at inlet pressure 3 kg/cm²,Mechanical efficiency is increased by 5%,Brake thermal efficiency is increased by 7%,Specific fuel consumption is decreased by 0.0262 Kg/KWH, Volumetric efficiency is increased by 5.3%  compared to pure diesel.

Keywords:

Hydrogen,Alternate Fuel,Electrolysis,Electrodes,Fuel Consumption,Emission,

Refference:

I. Ali can yilmazet al effect of hydroxy (hho) gas addition on performance and exhaust emissions in compression ignition engines, international journal of hydrogen energy. (2010).
II. Akter Rabeya, Md. Hasanuzzaman and 3 & 4Akio Miyara, “Similarity solution of heat and mass transfer of a thin liquid film over moving saturated porous medium in presence of thermal radiation”, J. Mech. Cont. & Math. Sci., Vol.-13, No.-3, July-August (2018), pp 26-41.
III. A Mitra, “Computational Modeling of Boundary-Layer Flow of a Nanofluid Past a Nonlinearly Stretching Sheet”, J. Mech. Cont. & Math. Sci., Vol.-13, No.-1, March – April (2018) Pages, pp 101-114
IV. Birtas.a.,. et. al., (2011). The effect of hrg gas addition on diesel engine combustion characteristics and exhaust emissions. International journal of hydrogen energy, 36(18), 12007-12014.
V. Daniel M. Madyira and Wayne G. Harding “Effect of HHO on Four Stroke Petrol Engine Performance”, 9 th South African Conference on Computational and Applied Mechanics Somerset West. (2014),
VI. Ghulam abbas gohar and hassan raza., comparative analysis of performance characteristics of CI engine with and without hho gas (2017) advances in automobile engineering, doi: 10.4172/2167-7670.1000172.
VII. Miyamoto, t., hasegawa, h., mikami, m., kojima, n., kabashima, h., & urata,y. (2011). Effect of hydrogen addition to intake gas on combustion and exhaust emission characteristics of a diesel engine. International journal of hydrogen energy, 36 (20), 13138-13149.
VIII. M. Loknath et.al. “performance analysis of HHO gas addition on single cylinder four stroke S.I engine”. Journal of Xidian University, https://doi.org/10.37896/jxu14.6/063 2020, (14).
IX. M. Loknath et. al. “Thermodynamic Analysis of Solar Organic Rankine Cycle by using Working Fluid for Low Temperature Application”. International Journal of Recent Technology and Engineering, 2019, (8)
X. Mustafi Nirendra N, Ruhul AM, et. al., (2013) An Investigationon the production of brown gas (HHO) as an alternative automotive fuel by water electrolysis. Paper 14:239-244.
XI. Prithivirajan. k et. al. the performance of ic engine with (diesel-hydrogen) dual fuel (2015) international journal of advances in mechanical and civil engineering, issn: 2394-2827.
XII. Ramanjaneyulu B et al. “Performance Analysis on 4-S Si Engine Fueled With HHO Gasand LPG Enriched Gasoline”. International Journal of Engineering Research & Technology (IJERT), 2 (2013),
XIII. Ramanjaneyulu B. et. al. “International journal of ambient energy” DOI:10.1080/01430750.2020.1745885 2020, (14).
XIV. S. Bari and M. Mohammad Esmaeil, “Effect of H2/O2 addition in increasing the thermal Efficiency of a diesel engine,” Elsevier-Fuel, 2010,
XV. Shashikant Jadhav (2014), “Investigating the Effect of Oxy-Hydrogen (HHO) gas and Gasoline Blend Addition on the Performance of Constant Speed Internal Combustion Engines”, International Engineering Research Journal (IERJ), Special Issue Page 26-31, ISSN 2395-1621.
XVI. Wang j, et. al. (2009) effect of partially premixed and hydrogen addition on natural gas direct-injection lean combustion. Int j hydrogen energy 34: 9239-9247.

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Abstract:

GI-FI or gigabit remote alludes to a faraway correspondence at a facts tempo of multiple billion bits (gigabit) each second. GI-FI will assist with pushing remote correspondences to quicker drive. For a long time links administered the world. Optical strands assumed a predominant job for its higher piece rates and quicker transmission. In any case, the establishment of links made a more prominent trouble and along these lines drove remote access. The first of this is Bluetooth which can cover 9-10mts. Wi-Fi tailed it having an inclusion zone of 91mts.Almost as clearly, presentation of Wi-Fi remote structures has established a progressive solution for "closing mile" trouble. GI-FI is a remote innovation, which guarantees rapid short range information moves with velocities of up to 5 Gbps inside a scope of 10 meters. The GI-FI works of the 60GHz recurrence band. This recurrence band is as of now for the most part unused. It is fabricated utilizing (CMOS) innovation. This remote innovation named as GI-FI. The advantages and highlights of this new innovation can be useful for use being developed by the up and coming age of gadgets and spots. Right now, the examinationis performed between GI-FI and some of existing advances with fast enormous records moves inside seconds it is relied upon that GI-FI to be the favored remote innovation utilized in the home and office of the future.

Keywords:

GI-F,CMOS,Bluetooth,Wi-Fi,

Refference:

I. Aditya Hegde1, Ashwini Hegde2 1, 2 Electronics and Instrumentation, BMS College” Review of GI-Fi Technology, ” International Journal for Research in Applied Science & Engineering Technology (IJRASET) ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor:6.887Volume 5 Issue X, October 2017- Available at www.ijraset.com
II . Bura Vijay Kumar, YerrollaChanti, NagenderYamsani, Srinivas Aluvala, Bandi Bhaskar “Design a Cost Optimum for 5g Mobile Cellular Network Footing on NFV and SDN”, International Journal of Recent Technology and Engineering (IJRTE), ISSN: 2277-3878, Volume-8, Issue-2S3, July 2019.
III. GI-FI technology “Wikipedia”
IV. Harshavardhan, A., and K. Shruthi. “AN EFFECTIVE IMPLEMENTATIONOFFAULTY NODE DETECTION IN MOBILE WIRELESS NETWORK.” International Journal of Advanced Research in Computer Science, vol. 8, no. 8, Oct. 2017, pp. 705–08. ijarcs.info, doi:10.26483/ijarcs.v8i8.4877.
V. J.Santhan Kumar Reddy “gi-fi technology” Gokula Krishna College of Engineering.
VI . Jyoti Tewari, Swati Arya College of Engineering,Teerthankar Mahaveer University Moradabad, Uttarpradesh, India.” Evolution of Gi-Fi Technology over other Technologies “ IJCSN International Journal of Computer Science and Network, Volume 2, Issue 3, June 2013.ISSN (Online) : 2277-5420 www.ijcsn.org
VII. Jeny K JFifth Semester BSc Computer Science Vimala College, Thrissu” Gi-fi Technology” International Journal of Engineering Research & Technology (IJERT)ISSN: 2278-0181Published by, www.ijert.orgNSDMCC – 2015 Conference Proceedings Special Issue – 2016.
VIII. Marzieh yazdanipour, Mina Yazdanipour, Afsaneh Yazdanipour, Amin Mehdipour,” Evaluation of Gi-Fi Technology for Short-Range, High-Rate Wireless Communication” UACEE International Journal of Advances in Computer Networks and its Security- Volume 2: Issue 3 [ISSN 2250 – 3757].
IX. NagendarYamsani, Bura Vijay Kumar, Srinivas Aluvala, Mahesh Dandugudum, G. Sunil Reddy, “An Improved Load Balancing in MANET Using on-Demand Multipath Routing Protocol”, International Journal of Engineering &Technology, 7 (1.8) (2018) pp.222-225.
X. Naeem Abid,Shahryar Shafique,Sheeraz Ahmad, Nadeem Safwan, Sabir Awan, Fahim Khan6, “Techno-economic planning with different topologies of Fiber to the Home access networks with Gigabit Passive Optical Network technologies”, J. Mech. Cont.& Math. Sci.,Vol.-14, No.-4, July-August (2019), pp 595-612.
XI. P.Kumara Swamy, Dr.C.V.Guru Rao, Dr.V.Janaki, “Functioning of secure key authentication scheme in” in International Journal of Pure and Applied Mathemat, Volume 118, Issue 14, Page No(s) 27 – 32, MAR. 2018, [ISSN(Print):1314-3395]
XII. Srinivas Aluvala, K. Raja Sekar,, Deepika Vodnala, “A Novel Technique for Node Authentication in Mobile Ad-hoc Networks” in Elsevier – Perspectives in Science, Volume 8, Issue 1, Page No(s) 680 -682, SEP. 2016, [ISSN(Print):2213-0209]

XIII. YerrollaChanti, Kothanda Raman, K. Seenanaik, Dandugudum Mahesh, B.Bhaskar” An Enhanced on Bidirectional LI-FI Attocell Access Point Slicing and Virtualization using Das2 Conspire” International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8, Issue-2S3, July 2019
XIV. YerrollaChanti, Dr. K. Seena Naik, Rajesh Mothe, NagendarYamsani, Swathi Balija,“A modified Elliptic Curve Cryptography Technique for Securing Wireless Sensor Networks” International Journal of Engineering &Technology”, 2018.
XV. YerrollaChanti, Bandi Bhaskar, NagendarYamsani, “LI-FITECHNOLOGY UTILIZED IN LEVERAGED TO POWER IN AVIATION SYSTEM ENTERTAINMENT THROUGH WIRELESS COMMUNICATION”, J. Mech. Cont.& Math. Sci., Vol.-15, No.-6, June (2020) pp 405-412.

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Abstract:

On the roads the vehicles have been increasing due to the increase in population and controlling of visitors is one of the hard tasks for the people who control the traffic. The regular traffic congestion at important junctions becomes more problems for the emergency vehicles and must wait until the green signal. These results theincrease in pollutant levels and wastage of time, pollution levels may increase to a huge scale. Previously the traffic manages strategies used like magnetic loop detectors, induction loop detectors are buried on the street aspect offer the confined traffic records, and necessitate separate monitoring systems for site visitors counting and for traffic surveillance. Here the assignment proposes to put in force an artificial density traffic control machine the usage of photograph processing and Raspberry pi.

Keywords:

Microcontroller,Raspberry-pi, RFID,

Refference:

I Arabelli, R.R. &Rajababu, D. 2019, “Transformer optimal protection using internet of things”, International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 11, pp. 2169-2172.

II Arabelli, R.R. &Revuri, K. 2019, “Fingerprint and Raspberri Pi based vehicle authentication and secured tracking system”, International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 5, pp. 1051-1054.

III Bura Vijay Kumar, Yerrolla Chanti, D. Kothandaraman, A. Harshavardhan, Sangameshwar Kanugula,”INTERNET OF THINGS MIDDLEWARE ARCHITECTURE FOR COMMUNICATION”, Journal for the Study of Research, Dec 2019, Volume XI, Issue XII, pp.46-67.

IV Bura Vijay Kumar, Yerrolla Chanti, Nagender Yamsani, Srinivas Aluvala, Bandi Bhaskar,” Design a Cost Optimum for 5g Mobile Cellular Network Footing on NFV and SDN”, International Journal of Recent Technology and Engineering (IJRTE), Volume-8, Issue-2S3, July 2019, pp.1121-1129.

V D. Kothandaraman and C. Chellappan, (2016), “Direction Detecting System of Indoor Smartphone Users Using BLE in IoT”, Circuits and Systems, vol. 7, no.8, pp.1492-1503.

VI D. Kothandaraman, M. Sheshikala, K. Seena Naik, Y. Chanti, B. Vijay kumar, “Design of an Optimized Multicast Routing Algorithm for the Internet of Things”, International Journal of Recent Technology and Engineering (IJRTE), Volume-8 Issue-2, July 2019, pp. 4048-4053.

VII Harshavardhan, A., and K. Shruthi. “AN EFFECTIVE IMPLEMENTATION OF FAULTY NODE DETECTION IN MOBILE WIRELESS NETWORK.” International Journal of Advanced Research in Computer Science, vol. 8, no. 8, Oct. 2017, pp. 705–08.

VIII K. Seena Naik and E. Sudarshan “Smart Healthcare Monitoring System using Raspberry Pion IoT Platform”, ARPN Journal of Engineering and Applied Sciences ©2006-2019 Asian Research Publishing Network (ARPN). All rights reserved. VOL. 14, NO. 4, FEBRUARY2019. ISSN 1819-6608.

IX Mittapelli Nikitha, Rajeshwar Rao Arabella,―Smart Monitoring and Controlling of Home Appliances using Internet of Things‖, International Journal of Recent Technology and Engineering (IJRTE),November 2019, Volume-8 Issue-4, pp. 9570-9573.

X M. Sheshikala, D. Rajeswara Rao and R. Vijaya Prakash, Computation Analysis for Finding Co–Location Patterns using Map–Reduce Framework, Indian Journal of Science and Technology, Vol 10(8), DOI: 10.17485/ijst/2017/v10i8/106709, February 2017.

XI S.Lokesh, T.Prahlad Reddy, “An Adaptive Traffic Control System Using Raspberry PI”, INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY, June 2014, pp.831-835.

XII Srivastava Alok, Rajeev Ratan, “Flag Com: Energy Efficient Secure Routing Protocol”, J. Mech. Cont.& Math. Sci., Vol.-14, No.-1, January-February (2019), pp 489-501.

XIII Subba Rao D., N.S. Murti Sarma, “A Secure and Efficient Scheduling Mechanism for Emergency Data Transmission in IOT”. Vol.-14, No.-1, January-February (2019), pp 432-443

XIV Yerrolla Chanti1, Bandi Bhaskar2, Nagendar Yamsani3 “Li-Fi Technology Utilized In Leveraged To Power In Aviation System Entertainment Through Wireless Communication “Journal Of Mechanics Of Continua And Mathematical Sciences, Vol.-15, No.-6, June (2020) pp 411-418.

XV YerrollaChanti, Kothanda Raman, K. Seenanaik, Dandugudum Mahesh, B.Bhaskar” An Enhanced On Bidirectional LI-FI Attocell Access Point Slicing And Virtualization Using Das2 Conspire” International Journal Of Recent Technology And Engineering (IJRTE) ISSN: 2277-3878, Volume8, Issue-2S3, July 2019.

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Abstract:

Online social forums are a great place to express one’s opinions on others' work. But due to the threat of harassment and abuse online, many people stop expressing themselves and give up on seeking different opinions. This leads to the complete shutdown of the user comments section in many communities. Hence, there is a need to identify an efficient way to detect the level of toxicity in the comments posted online, which will be helpful to the content moderators who monitor the data obtained from the comments section on online forums. In this paper, we train various machine learning and deep learning models like NB-SVM, LSTM, BERT on the toxic comments dataset and analyze which approach is efficient for the task of classification of toxic comments.

Keywords:

Classification,NB-SVM,BiLSTMs,BERT,Comments,ContentModeration,

Refference:

I. Amir Moradibaad1, RaminJalilian Mashhoud2, Use Dimensionality Reduction and SVM Methods to Increase the Penetration Rate of Computer Network, J. Mech. Cont.& Math. Sci., Vol.-14, No.-4, July-August (2019), pp 8-26.

II. Arif Ullah, Umeriqbal, Ijaz Ali Shoukat,Abdul Rauf, O Y Usman,Sheeraz Ahmed, Zeeshan Najam, An Energy-Efficient Task Scheduling using BAT Algorithm for Cloud Computing, Vol.-14, No.-4, J. Mech. Cont.& Math. Sci.,July-August (2019), pp 613-627.
III. Classification of Abusive Comments in Social Media using Deep Learning, Published in 2019 Proceedings of the Third International Conference on Computing Methodologies and Communication (ICCMC 2019) IEEE Xplore
IV. Is preprocessing of text worth your time for toxic comment classification, Int’l Conf. Artificial Intelligence | ICAI’18 |
V. Julian Risch and Ralf KrestelHasso: Toxic Comment Detection in Online Discussions, Plattner Institute, University of Potsdam.
VI. Jacob Devlin,Ming-Wei Chang,Kenton Lee,Kristina Toutanova, Google AI Language: BERT: Pre-training of Deep Bidirectional Transformers forLanguage Understanding
VII. Sidawang and Christofer D.manning: Baselines and Bigrams: Simple, Good Sentiment and Topic Classification, Department of Computer Science Stanford University

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Abstract:

The study is an attempt to present the selected respondent of the banana cultivators in the Guntur district. More specifically, it analyses on the low price fixed on banana bunches, storage facilities to banana bunches, lack of information about the demand and supply of banana bunches, middlemen commitment for offering prices, awareness of insurance services for the banana cultivators, and other related problems faced by the banana cultivators.

Keywords:

Banana bunch – Demand and supply,Fixation of prices, storage facilities,Middlemen commitment for offering prices,Awareness of insurance services,

Refference:

I A Chavan, S P Kalyankar, S V Wakle (Jan 2001), A study of marketing of banana in the Parbhani market of Maharashtra state.

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III C Arputhraj, K S Nair (Jan 1988) Economics of banana in Kerala,

IV D D S Deshmukh (Jan 2013), Constraints in Banana marketing and scope of improvement: A case study for the Jalgaon region.

V Diana, A., Rogers, K., Pauline, B., Winnie, A., Lucy, A. & Margaret, B., 2007. Indigenous Knowledge in Agriculture: A Case Study of the Challenges in Sharing Knowledge of Past Generations in a Globalized Context in Uganda.

VI Flett, R., Alpass, F., Humphries, S., Massey, C., Morriss, S. & Long, N., 2004. The Technology Acceptance Model and Use of Technology in New Zealand Dairy Farming. Agricultural Systems, 80, pp. 199-211. doi:10.1016/j.agsy.2003.08.002.

VII Hymavathi, C.H., Koneru, K.(2019). Investor’s perception towards the Indian commodity market: An empirical analysis concerningthe Amaravathi region of Andhra Pradesh. International Journal of Innovative Technology and Exploring Engineering. 8(7), pp. 1708-1714.

VIII Hymavathi, C.H., Koneru, K.(2018). Investors’ awareness towards commodities market concerning GUNTUR city, Andhra Pradesh. International Journal of Engineering and Technology(UAE). 7(2), pp. 1104-1106.

IX Hymavathi, C., Koneru, K. (2019). Role of perceived risk in mutual funds selection behavior: An analysis among the selected mutual fund investors. International Journal of Engineering and Advanced Technology. 8(4), pp. 1913-1920.

X KishanVarma, M.S., Koneru, K., Yedukondalu, D.(2019). Affect of worksite wellness interventions towards occupational stress. International Journal of Recent Technology and Engineering. 8(1), pp. 2874-2879.

XI K C Chennarayadu (Jan 1990), Land use efficiency of banana-An application of frontier production Function.

XII K S Biradar,D V Kasar. (Jan 1984) A Study of the relative efficiency of co-operatives vis-a-vis other Agencies in the marketing of Jalgaon banana in the Delhi market.

XIII Kamal MS, Ali MA, Alam.MF., Cost and return analysis of banana cultivation under institutional loan in Bogra, Bangladesh, Feb 15.

XIV Kathirvel N. 2008. Banana Production in Karur District. Kisan World 35:13-14.

XV Manukonda et al. (2019). What Motivates Students To Attend Guest Lectures?. The International Journal of Learning in Higher Education. Volume 26, Issue 1. 23-34.

XVI M. WaliUllah,RizwanaKawser, M. Alhaz Uddin, A Direct Analytical Method for Finding an Optimal Solution for Transportation Problems, J. Mech. Cont. & Math. Sci., Vol.-9, No.-2, January (2015), pp 1311-1320.

XVII Neelima, J., Koneru, K.(2019). Assessing the role of organizational culture in determining employee performance – empirical evidence from the Indian pharmaceutical sector. International Journal of Innovative Technology and Exploring Engineering. 8(7), pp. 1701-1707.

XVIII N Aijan (Jan 1986) Regulated markets in Tamil Nadu: A Malady remedy analysis. Agricultural Situation in India,

XIX P K Ray. (Jan 2007) Banana Production and Research in Bihar: Present Status and Future trusts, Banana: Technology Advances.

XX P Maurya (Jan 1996) Profitability of banana plantation in the Hajipur district in Bihar. Bihar.

XXI R Mehta (Jan 2000), Analysis of seasonality in prices of Agricultural Commodities Agricultural Situation in India

XXII Sivakoti Reddy, M. (2019). Impact of RSERVQUAL on customer satisfaction: A comparative analysis between traditional and multi-channel retailing. International Journal of Recent Technology and Engineering. 8(1), pp. 2917-2920.

XXIII Sivakoti Reddy, M., Venkateswarlu, N.(2019). Customer relationship management practices and their impact over customer purchase decisions: A study on the selected private sector banks housing finance schemes. International Journal of Innovative Technology and Exploring Engineering. 8(7), pp. 1720-1728.

XXIV Sivakoti Reddy, M., Murali Krishna, S.M.(2019). The influential role of retail service quality in food and grocery retailing: A comparative study between traditional and multi-channel retailing. International Journal of Management and Business Research. 9(2), pp. 68-73.

XXV Sivakoti Reddy, M., Naga Bhaskar, M., Nagabhushan, A. (2016). The saga of silicon plate: An empirical analysis of the impact of socio-economic factors of farmers on the inception of solar plants. International Journal of Control Theory and Applications. 9(29), pp. 257-266.

XXVI Suhasini, T., Koneru, K. (2019). Employee engagement through HRD practices on employee satisfaction and employee loyalty: Empirical evidence from the Indian IT industry.
International Journal of Engineering and Advanced Technology. 8(4), pp. 1788-1794.

XXVII Suhasini, T. Koneru, K. (2018). A study on employee engagement driving factors and their impact over employee satisfaction – An empirical evidence from Indian it industry. International Journal of Mechanical Engineering and Technology. 9(4), pp. 725-732.
XXVIII. V. Sravani Chari1, M. Sivakoti Reddy2, Sustainable Consumption: A Study
on Factors Affecting Green Consumer Behavior, J. Mech. Cont.& Math.
Sci., Vol.-14, No.-5, September-October (2019) pp 850-861

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Abstract:

Speech emotion recognition (SER) research field extends back to 1996, but still one main obstacle still exists, which is achieving real-time SER systems. The once-imaginary relationship between humans and robots is rapidly approaching reality. Robots already play major roles, particularly in manufacturing, but until recently, they did only what they were programmed to do. However, with the development of artificial intelligence (AI) approaches, SER researchers are seeking to move robotics to a higher level, giving them the ability to predict human actions and recognize facial expressions and allowing them to interact with humans in more natural and clever ways. Humans are complicated; understanding only what they say is insufficient for all situations. One complication is that humans express identical emotions in multiple ways. For robots to act more like humans, understand them, and follow their orders in more intelligent ways, they need to understand emotions to make appropriate decisions. Thus, to reach the ideal SER state, a more up-to-date survey that considers how SER research has evolved over the past decade is needed. In this survey, our main goal is to explain the different research approaches followed in the SER field particularly Path 6, which represents a new technique in the SER field. To clarify the techniques for readers, details of the SER systems and their different approaches will be elaborated.

Keywords:

feature extraction ,feature selection and classification,real-time system,robotics,SER,

Refference:

I. A. Álvarez, B. Sierra, A. Arruti, J.-M. López-Gil, and N. Garay-Vitoria, “Classifier subset selection for the stacked generalization method applied to emotion recognition in speech,” Sensors, vol. 16, no. 1, pp. 21, Jan. 2016, doi: 10.3390/s16010021.
II. A. Bhavan, P. Chauhan, and R. R. Shah, “Bagged support vector machines for emotion recognition from speech,” Knowl. Based Syst., vol. 184, pp. 104886, Mar. 2019, doi: 10.1016/j.knosys.2019.104886.
III. A. H. Ton-That and N. T. Cao, “Speech emotion recognition using a fuzzy approach,” J. Intell. Fuzzy Syst., vol. 36, no. 2, pp. 1587–1597, Jul. 2019, doi: 10.3233/JIFS-18594.
IV. A. Huang and P. Bao, “Human vocal sentiment analysis, arXiv preprint arXiv:1905.08632,” 2019.
V. A. Jalili, S. Sahami, C.-Y. Chi, and R. Amirfattahi, “Speech emotion recognition using cyclostationary spectral analysis,” in 2018 IEEE 28th Int. Workshop Mach. Learn. Signal Process. (MLSP), Aalborg, Denmark, Feb. 2018, pp. 1–6.
VI. A. Milton, S. T. Selvi, and Language, “Class-specific multiple classifiers scheme to recognize emotions from speech signals,” Comput. Speech, vol. 28, no. 3, pp. 727–742, Apr. 2014, doi: 10.1016/j.csl.2013.08.004.
VII. A. S. Popova, A. G. Rassadin, and A. A. Ponomarenko, “Emotion recognition in sound,” in Int. Conf. Neuroinformatics, Moscow, Feb. 2017, pp. 117–124.
VIII. Burkhardt F., Paeschke A., Rolfes M., Sendlmeier W., “Database of German Emotional Speech Proceedings Interspeech,” Weiss, BA J Lisbon jornal, Portugal, Sept. pp. 4-8, 2005.
IX. C. Huang, W. Gong, W. Fu, and D. Feng, “A research of speech emotion recognition based on deep belief network and SVM,” Math. Problems Eng., vol. 2014, no. 1, pp. 1–4, Aug. 2014, doi: 10.1155/2014/749604.
X. C. S. Ooi, K. P. Seng, L.-M. Ang, and L. W. Chew, “A new approach of audio emotion recognition,” Expert Syst. Appl., vol. 41, no. 13, pp. 5858–5869, Sept. 2014, doi: 10.1016/j.eswa.2014.03.026.
XI. F. Dellaert, T. Polzin, and A. Waibel, “Recognizing emotion in speech,” in Proc. 4th Int. Conf. Spoken Language Process. ICSLP’96, Philadelphia, PA, Oct. 1996, pp. 1970–1973.
XII. G. Deshmukh, A. Gaonkar, G. Golwalkar, and S. Kulkarni, “Speech based emotion recognition using machine learning,” in 2019 3rd Int. Conf. Comput. Methodologies Commun. (ICCMC), Erode, Jun. 2019, pp. 812–817.
XIII. G. Trigeorgis et al., “Adieu features? end-to-end speech emotion recognition using a deep convolutional recurrent network,” in 2016 IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP), Piscataway, NJ, Apr. 2016, pp. 5200–5204.
XIV. H. Holmström and V. Zars, “Effect of Feature Extraction when Classifying Emotions in Speech-an Applied Study,” UMEA university, Faculty of Science and Technology, Department of Computing Science, pp. 1-30, 2018.
XV. H. Kaya and A. A. Karpov, “Efficient and effective strategies for cross-corpus acoustic emotion recognition,” Neurocomputing, vol. 275, pp. 1028–1034, Sept. 2018, doi: 10.1016/j.neucom.2017.09.049.
XVI. J. G. Rázuri, D. Sundgren, R. Rahmani, A. Moran, I. Bonet, and A. Larsson, “Speech emotion recognition in emotional feedbackfor human-robot interaction,” Int. J. Advanced Res. Artificial Intell., vol. 4, no. 2, pp. 20–27, Jul. 2015, doi: 10.14569/IJARAI.2015.040204.
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XXVI. L. Kerkeni, Y. Serrestou, M. Mbarki, K. Raoof, M. A. Mahjoub, and C. Cleder, “Automatic speech emotion recognition using machine learning,” in Social Media and Machine Learning: IntechOpen, 2019.
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XXXIV. Nithya Roopa S., Prabhakaran M and Betty.P, “Speech Emotion Recognition using Deep Learning,” International Journal of Recent Technology and Engineering (IJRTE), Vol.7, no. 4S, Nov. 2018.
XXXV. N. Salankar and A. Mishra, “Statistical feature selection approach for classification of emotions from speech,” Mar. 2020, doi: 10.2139/ssrn.3527262.
XXXVI. P. Ekman and W. V. Friesen, Pictures of Facial Affect. Palo Alto, CA: Consulting Psychologists Press, 1976.
XXXVII. P. Kalapatapu, S. Goli, P. Arthum, and A. Malapati, “A study on feature selection and classification techniques of indian music,” Procedia Comput. Sci., vol. 98, pp. 125–131, May 2016, doi: 10.1016/j.procs.2016.09.020.
XXXVIII. R. Afdhal, R. Ejbali, and M. Zaied, “Primary emotions and recognition of their intensities,” Comput. J., pp. bxz162, 2020, doi: 10.1093/comjnl/bxz162.
XXXIX. S. Chebbi and S. B. Jebara, “On the use of pitch-based features for fear emotion detection from speech,” in 2018 4th Int. Conf. Advanced Technol. Signal Image Process. (ATSIP), Sousse, Tunisia, Mar. 2018, pp. 1–6.
XL. S. Jagtap, “Speech based emotion recognition using various features and SVM classifier,” Int. J. Res. Appl. Sci. Eng. Technol. (IJRASET), vol. 7, no. 3, Nov. 2019, doi: 10.22214/ijraset.2019.3018.
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Abstract:

In the current era of a highly trained business environment in banking, organizations encounter transpiring challenges in form of optimization and acquisition of human resources. Being valuable and scarce capabilities, human resourcesareconsideredasasourceoftenablevyingmastery.Thesuccessofabanking organization depends upon several factors; however, one of the most crucial factors that influence the organization's performance is its employee. The HRM practices like Training, Team Work, Performance Appraisal, and Compensation has an imperative impact on the banks. Human resources play an integral role in achieving aninnovative and high-quality service/ product. The present study through the SWOT evaluation attempts to examine and analyze the impact of all these factors and the role of training anddevelopmentofprivatesectorbankingemployeesinIndia.Alsotoassessthepresent statusoftheemployeeeffectivenessindischargingtherolesandresponsibilitiesintune with the objectives of the bank. The effectiveness of the various facets of training i.e. employee’s attitude towards the application of practice; training inputs; quality of training programs and training inputs to the actualjob.

Keywords:

Human Resource Management Practices ,HRM,SWOT,training programs,Training,Performance Appraisal,Team Work,Employee Participation,

Refference:

I. Ashok Kumar Katta,P. SubbaRao,S. Venkata Ramana, HRD – Banks in the ICT Era a Focus on Private sector Banks, J. Mech. Cont. & Math. Sci., Vol – 14, No -5, October 2019, pp 614-624.
II. Bandaru Srinivasa Rao,Nagendra Kumar Turaga, A Comparative Study between New and Loyal Customer Complaint Behaviour in Context of Service Recovery Failures of Indian Banking Sector, J. Mech. Cont. & Math. Sci, Vol – 14 No -5, October 2019, PP 943-957
III. Cooper, B., Wang, J., Bartram, T., & Cooke, F. L. (2019). Well‐being‐oriented human resourcemanagementpracticesandemployeeperformanceintheChinesebankingsector:The role of social climate and resilience. Human ResourceManagement.
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VI. Goswami, R., Pandey, M., & Vashisht, A., (2017). Training and Development Practices in Public and Private sector banks: A Comparative Study. IJARIIE-ISSN(O)-2395-4396,3(3).
VII. Goyal, K. A., & Joshi, V. (2012). Indian banking industry: Challenges and opportunities. International Journal of Business Research and Management, 3(1),18-28.
VIII. Hameed, D. S. S., Rajinikanth, J., & Mohanraj, P. (2014). A Conceptual Study on Training and Development Programs of Bank Employees. International Journal of Advanced Research in Computer Science and Management Studies,2(5).
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Abstract:

In recent years the biometric system lacks in security due to fraudulent access. Old systems relayed on Multi-Spectral Imaging (MSI) for security which is found to be ineffective. The advanced technology in the biometric system to improve security is Image Quality Assessments (IAQ). In the previous system, the Multi-Spectral Imaging (MSI) was implemented in which the usual digital protection mechanisms and enhanced security systems are not effective. A novel software based biometric detection system is proposed here to detect the fraudulent biometric access attempts. It is used to enhance the security of biometric recognition systems. In this system from the original image, 30 image quality features are extracted, the same acquired for authentication purposes. Among various biometric recognition, finger recognition, iris recognition and face recognition are presented by using image quality assessment technique.

Keywords:

Biometric,Finger Print,Multiplexer,Image Quality Assessment (IAQ),Multi Spectral Imaging (MSI) ,

Refference:

I. A. M. Saad Emam Saad, “A Systematical Review Study to Investigate the Use of Biometric Security Techniques in Automatic Teller Machines: Insight from the Past 15 Years,” 2019 1st International Informatics and Software Engineering Conference (UBMYK), Ankara, Turkey, pp. 1-4, 2019, doi: 10.1109/UBMYK48245.2019.8965494.
II. Amirhosein Dastgiri, Pouria Jafarinamin, Sami Kamarbaste3, Mahdi Gholizade, “Face Recognition using Machine Learning Algorithms”, J. Mech. Cont.& Math. Sci., Vol.-14, No.-3, May-June (2019) pp 216-233.
III. B. Biggio, Z. Akhtar, G. Fumera, G. L. Marcialis, and F. Roli. Security evaluation of biometric authentication systems under real spoofing attacks. IET Biometrics, 1(1):11-24, 2012.
IV. Faisel Ghazi Mohammed, Waleed khaled Eesee, “Human Gait Recognition using Neural Network Multi-Layer Perceptron”, J. Mech. Cont.& Math. Sci., Vol.-14, No.-3, May-June (2019) pp 234-244
V. J. Galbally, S. Marcel and J. Fierrez, “Image Quality Assessment for Fake Biometric Detection: Application to Iris, Fingerprint, and Face Recognition,” in IEEE Transactions on Image Processing, vol. 23, no. 2, pp. 710-724, Feb. 2014, doi: 10.1109/TIP.2013.2292332.
VI. Jude Hemanth & Valentina Emilia Balas, ed.. Biologically Rationalized Computing Techniques For Image Processing Applications. Springer. p. 116. 2018. ISBN 9783319613161.
VII. Lee, Dongjae & Choi, Kaekyoung & Kim, Jaihie.. A Robust Fingerprint Matching Algorithm Using Local Alignment. 3. 803-806. 10.1109/ICPR.2002.1048141. 2002.
VIII. N. Rajeswaran, T.Samraj Lawrence, R.P.Ramkumar, N. Thangadurai “An Efficient Technique to Remove Gaussian Noise and Improve the Quality of Magnetic Resonance Image” International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-8 Issue-10, August 2019.
IX. S. Prabhakar, S. Pankanti and A. K. Jain, “Biometric recognition: Security and privacy concerns”, IEEE Security Privacy, vol. 1, no. 2, pp. 33-42, Mar./Apr. 2003.

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Abstract:

Various heuristic, as well as also meta-heuristic protocols, were related to acquiring the most excellent possibilities. Today period is much attracted alongside the provisioning of self-management, self-learnable, self-healable, as well as likewise self-configurable smart systems. To secure self-manageable Smart Cloud, many Expert systems and additionally Machine Learning (AI-ML) approaches as well as also algorithms are brought back. In this assessment, new style in the treatment of AI-ML approaches, they utilized regions, the main reason, their perks as well as additionally demerits are highlighted. These tactics are more grouped as instance-based machine learning strategies as well as encouragement, learning procedures based upon their ability to learn. This paper provides the details about ML platform architecture and cloud-based MLframework.

Keywords:

Machine Learning,AI,cloud computing,

Refference:

I. B. Werther – Pre-industrial age of big data, June 2012, http://www.platfora.com/pre-industrial- age-of-big-data/

II. D. Pop, G. Iuhasz – Survey of Machine Learning Tools and Libraries, Institute e-Austria Timi¸soara Technical Report, 2011

III. J Manasa, SN Kumar .”Distinguishing Stress Based on Social Interactions in Social Content Area”.International Journal of Pure and Applied Mathematics, 2018

IV. Komuravelly Sudheer Kumar et al, “A Narrative Improvement Techniques Used with The Expert Systems.” (2019)

V. Kumar, P. Pramod, C. H. Sandeep, and S. Naresh Kumar. “An overview of the factors affecting handovers and effectively highlights of handover techniques for next generation wireless networks.” Indian Journal of Public Health Research & Development, no. 11 (2018): 722-725.

VI. Kumar, S. Naresh, P. Pramod Kumar, C. H. Sandeep, and S. Shwetha. “Opportunities for applying deep learning networks to tumor classification.” Indian Journal of Public Health Research & Development, no. 11 (2018): 742-747.

VII. L. Tierney, A. J. Rossini, Na Li – Snow: A parallel computing framework for the R System, Int J Par- allel Prog (2009) 37:78–90, DOI 10.1007/s10766-008-0077-2

VIII. Pasha, S.N., Ramesh, D., Kodhandaraman, D. &Salauddin, M.D. 2019, “A research to enhance the old manuscript resolution using deep learning mechanism”, International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 6 Special Issue 4, pp. 1597-1599.
IX. Riaz Muhammad, Samad Baseer, “Authentication and Privacy Challenges for Internet of Things Smart Home Environment”, J. Mech. Cont. & Math. Sci., Vol.-14, No.-1, January-February (2019), pp 258-275

X. Sheshikala, M et al, “Natural Language Processing and Machine Learning Classifier used for Detecting the Author of the Sentence ”. International Journal of Recent Technology and Engineering (IJRTE) (2019).

XI. Sripada, Naresh Kumar et al. “Support Vector Machines to Identify Information towards Fixed-Dimensional Vector Space.” International Journal of Innovative Technology and Exploring Engineering (IJITEE),(2019).

XII. S. Naresh Kumar et al. “A Study on Deep Qlearning and Single Stream Q-Network Architecture”,International Journal of Advanced Science and Technology,2019.

XIII. Sheshikala, M., Kothandaraman, D., Vijaya Prakash, R. & Roopa, G. 2019, “Natural language processing and machine learning classifier used for detecting the author of the sentence”, International Journal of Recent Technology and Engineering, vol. 8, no. 3, pp. 936-939.

XIV. S. R. Upadhyaya – Parallel approaches to ma- chine learning—A comprehensive survey, Journal of Parallel and Distributed Computing, Volume 73, Issue 3, March 2013, Pages 284–292.
XV. Sandeep CH. , S. Naresh Kumar2, P. Pramod Kumar3, “SIGNIFICANT ROLE OF SECURITY IN IOT DEVELOPMENT AND IOT ARCHITECTURE”, J. Mech. Cont. & Math. Sci., Vol.-15, No.-6, June (2020) pp 168-178

XVI. Y. Yu, M. Isard, D. Fetterly, M. Budiu, U. Erlings- son, P. Kumar Gunda, J. Currey – DryadLINQ: A System for General-Purpose Distributed Data- Parallel Computing Using a High-Level Language, In OSDI, 2008

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Abstract:

In this paper we prove the existence of solution of coupled system of quadratic hybrid functional integral equations. Our main result is based on the standard tools of fixed point theory. The Existence and locally attractivity is proved in R₊

Keywords:

Quadratic Hybrid Functional Integral Equations,Banach Algebras,R-L Fractional Derivative,Hybrid FPT,Existence result,

Refference:

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VI. Ahmad B., Sivasundaram S: “Existence and uniqueness results for nonlinear boundary value problems of fractional differential equations with separated boundary conditions”, Commun. Appl. Anal. 13, 121-228 (2009).
VII. Ahmad B, Sivasundaram, S: “On four-point nonlocal boundary value problems of nonlinear Integro-differential equations of fractional order”, Appl. Math. Comput. 217, 480-487 (2010).
VIII. Ahmad B., Ntouyas SK., Alsaedi A., “Existence result for a system of coupled hybrid fractional differential equations”, Sci. World J. 2013, Article ID 426438(2013).
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XII. B.D.Karande, “Fractional Order Functional Integro-Differential Equation in Banach Algebras”, Malaysian Journal of Mathematical Sciences, Volume 8(S), (2014), 1-16.
XIII. B.D.Karande, “Global attractively of solutions for a nonlinear functional integral equation of fractional order in Banach Space”,AIP Conf. Proc. “10th international Conference on Mathematical Problems in Engineering, Aerospace and Sciences”1637 (2014), 469-478.
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