A. Kavitha

Artificial Intelligence; Agriculture; Deep Learning; Medicine; Machine Learning; Natural Language Processing; Robotics.

Nowadays Artificial intelligence makes our life easy and comfortable that is hard to imagine that to survive our life without AI technology. We all know that Artificial Intelligence (AI) is a precious gift to human being. Recently it is used in robotics, education, agriculture, computer vision, cyber security, face recognition, speech recognition, self-driving cars, medical image processing, biometrics, bioinformatics, satellite control, disease detection, drugs development, network developments, manufacturing, business,healthcare and medicine. In the digital era AI provides the best results in all most all the domains. This article helps to understand the emerging aspects of AI in various fields.

[1] JahanzaibShabbir, TariqueAnwer, “Artificial Intelligence and its Role in Near Future”, Journal of Latex Class Files, vol.14, no.8, 2015.
[2] Pamina.J, BeschiRaja.J, “Survey On Deep LearningAlgorithms”, International Journal of Emerging Technology and Innovative Engineering, vol.5, no.1, pp.38-43, 2019.
[3] Nadimpalli,M, “Artificial Intelligence Risks and Benefits” International Journal of Innovative Research in Science,Engineering and Technology , vol.6, no.6, June 2017.
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[6] DikshaManaware, “Artificial Intelligence: A New way to improve Indian Agriculture”, International Journal of Current Microbiology and Applied Sciences, vol.9, no.3, pp.1095-1102, 2020.
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[10] Jinjiang Wang, Yulin Ma, Laibin Zhang, Robert X.Gao, Dazhong Wu, “Deep Learning for Smart Manufacturing: Methods and Applications”, Journal of Manufacturing Systems, pp.144–156, 2018.

R. Jeevitha

Crime Scene Investigation; Virtual Reality; Virtual Crime Scene Regeneration; Virtual Evidence Storage; Blockchain; Cloud Database.

This paper presents the recreation of crime scenes through Virtual Reality for crime investigation and evidence storage. 3D image capturing and processing techniques are used to render fully-immersive Virtual Reality environment for investigators to work with. Cloud database and Blockchain technology for evidence storage are explained. The different levels of views on such virtual crime scenes and evidence storage are also expounded.

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[7] Park, Jin and Park, Jong (2017) Blockchain Security in Cloud Computing: Use Cases, Challenges, and Solutions, Symmetry 2017, 9, 164; doi:10.3390/sym9080164.
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[13] Zheng, W., Zheng, Z., Chen, X., Chen, R. and Li, P. (2019) NutBaas: A Blockchain-as-a-Service Platform, W. Zheng et al.: NutBaaS: Blockchain-as-a-Service Platform , IEEE Access, Vol. 7, September 17, 2019, pp. 134422- 134433.
[14] Dib, O., Durand, A. and Brousmiche, K. (2018) Consortium Blockchains: Overview, Applications and Challenges, International Journal on Advances in Telecommunications, Vol.11, no.1&2, 2018, pp. 51-64.
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K. Balasree, Dr. K. Dharmarajan

Big Data; Analytics; Machine Learning; SVM; Decision Tree; Naïve Baye’s; Random Forest.

In rapid development of Big Data technology over the recentyears, this paper discussing about the Machine Learning (ML) playing role that is based on methods and algorithms to Big Data Processing and BigData Analytics. In evolutionary fields and computing fields of developments that both are complementing each other. Big Data: Therapid growth of such data solutions needed to be studied and providedtohandlethentogainthe knowledge fromdatasetsand extractingvaluesdue to the data sets are very high in velocity and variety. The Big dataanalytics are involving and indicating the appropriate data storage and computational outline that enhanced by using Scalable Machine Learning Algorithms and BigData Analytics then the analytics to reveal the massive amounts of hidden data’s and secret correlations. This typeof Analytic information useful for organizations and companies to gain deeper knowledge, development and getting advantages over the competition. When using this Analytics we can predict the accurate implementation over the data. This paper presented about the detailed review of state-of-the-art developments and overview of advantages and challenges in Machine Learning Algorithms over bigdata analytics.

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[2] Hossain, Eklas, et al. “Application of big data and machine learning in smart grid, and associated security concerns: A review.” IEEE Access 7 (2019): 13960-13988.
[3] Bhatnagar, Roheet. “Machine Learning and Big Data processing: a technological perspective andreview.” International Conference on Advanced Machine Learning Technologies and Applications. Springer, Cham, 2018.
[4] Gupta, Preeti, Arun Sharma, and Rajni Jindal. “Scalable machine‐learning algorithms for big data analytics: a comprehensive review.” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 6.6 (2016): 194- 214.
[5] Sagiroglu, Seref, and DuyguSinanc. “Big data: A review.” 2013 international conference on collaboration technologies and systems (CTS). IEEE, 2013.
[6] George, Gerard, Martine R. Haas, and Alex Pentland. “Big data and management.” (2014): 321-326.
[7] Ngiam, Kee Yuan, and Wei Khor. “Big data and machine learning algorithms for health-care delivery.” The Lancet Oncology 20.5 (2019): e262-e273.
[8] Qiu, Junfei, et al. “A survey of machine learning for big data processing.” EURASIP Journal on Advances in Signal Processing 2016.1 (2016): 67.
[9] Angra, Sheena, and Sachin Ahuja. “Machine learning and its applications: A review.” 2017 International Conference on Big Data Analytics and Computational Intelligence (ICBDAC). IEEE, 2017.
[10] Al-Jarrah, Omar Y., et al. “Efficient machine learning for big data: A review.” Big Data Research 2.3 (2015): 87-93.
[11] Hossain, Eklas, et al. “Application of big data and machine learning in smart grid, and associated security concerns: A review.” IEEE Access 7 (2019): 13960-13988.
[12] Ma, Chuang, Hao Helen Zhang, and Xiangfeng Wang. “Machine learning for Big Data analytics in plants.” Trends in plant science 19.12 (2014): 798-808.
[13] Miklosik, Andrej, and Nina Evans. “Impact of big data and machine learning on digital transformation in marketing: A literature review.” IEEE Access (2020).
[14] L’heureux, Alexandra, et al. “Machine learning with big data: Challenges and approaches.” IEEE Access 5 (2017): 7776-7797.
[15] Salkuti, Surender Reddy. “A survey of big data and machine learning.” International Journal of Electrical & Computer Engineering (2088-8708) 10 (2020).
[16] Zhou, Lina, et al. “Machine learning on big data: Opportunities and challenges.” Neurocomputing 237 (2017): 350-361.
[17] Divya, K. Sree, PeyakuntaBhargavi, and S. Jyothi. “Machine learning algorithms in big data analytics.” International Journal of Computer Sciences and Engineering 6.1 (2018): 64-70.
[18] Beam, Andrew L., and Isaac S. Kohane. “Big data and machine learning in health care.” Jama 319.13 (2018): 1317-1318.
[19] Ning, Chao, and Fengqi You. “Optimization under uncertainty in the era of big data and deep learning: When machine learning meets mathematical programming.” Computers & Chemical Engineering 125 (2019): 434-448.
[20] Tu, Chunming, et al. “Big data issues in smart grid–A review.” Renewable and Sustainable Energy Reviews 79 (2017): 1099-1107.
[21] Suthaharan, Shan. “Big data classification: Problems and challenges in network intrusion prediction with machine learning.” ACM SIGMETRICS Performance Evaluation Review 41.4 (2014): 70-73.
[22 Zuo, Renguang, and YihuiXiong. “Big data analytics of identifying geochemical anomalies supported by machine learning methods.” Natural Resources Research 27.1 (2018): 5-13.
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Dr. Ramesh Cheripelli, Swathi Ch

Behavioural Detection; Malware Evasion; Shadow Attack; System Call Obfuscation; Electronic Mail; Authentication; Password; Cross Site Password Reuses

Carefully marked Portable Document Formats (PDFs) are utilized in agreements, contracts, bills, proposals, and arrangements to ensure the genuineness and trustworthiness of their material. A normal client would accept that carefully marked PDF records are conclusive and cannot be additionally altered. Be that as it may, different changes like adding comments to a marked PDF or rounding out structure fields are permitted and do not nullify PDF marks. In this paper, we show that this adaptability permits attackers to totally change a record’s substance while keeping the first signature approval status immaculate.

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Rohit Gupta, Inderdeep Singh

Parabolic Fin; Temperature Distribution; Matrix Method

Heat is not lost equally by each element of the fin but is lost mostly near the base of the fin. Thus there would be wastage of the material if a uniform fin is used. Due to this reason fins of varying cross-sections like hyperbolic fins or parabolic fins are constructed. The parabolic fin of varying cross-section is usually analyzed by ordinary calculus approach. The paper analyses parabolic fin of varying cross-section to find the rate of conduction of heat through it via the application of matrix method. The matrix method has been applied successfully in science and engineering problems and also comes out to be very effective tool to find the temperature distribution and rate of conduction of heat through a parabolic fin.

[1] Rohit Gupta, Amit Pal Singh, Dinesh Verma, Flow of Heat through A Plane Wall, And Through A Finite Fin Insulated At the Tip, International Journal of Scientific & Technology Research, Vol. 8, Issue 10, Oct. 2019, pp. 125-128.
[2] Rohit Gupta, Rahul Gupta, Dinesh Verma, Laplace Transform Approach for the Heat Dissipation from an Infinite Fin Surface, Global Journal Of Engineering Science And Researches, 6(2), February 2019, pp. 96-101.
[3] Rohit Gupta, Rahul Gupta, Heat Dissipation From The Finite Fin Surface Losing Heat At The Tip, International Journal of Research and Analytical Reviews, Vol. 5, Issue 3, Sep. 2018, pp. 138-143.
[4] Rohit Gupta, Neeraj Pandita, Dinesh Verma, Conduction of heat through the thin and straight triangular fin, ASIO Journal of Engineering & Technological Perspective Research (ASIO-JETPR), Volume 5, Issue 1, 2020, pp. 01-03.
[5] Gayatree Behura, Banamali Dalai, Analysis of heat transfer for the varying surface fin, international journal of scientific and engineering research, volume 9, issue 4, April-2018.
[6] D.S. Kumar, Heat and mass transfer, (Seventh revised edition), Publisher: S K Kataria and Sons, 2013.
[7] P.K. Nag, Heat and mass transfer. 3rd Edition. Publisher: Tata McGraw-Hill Education Pvt. Ltd., 2011.
[8] J.P. Holman, Heat transfer. 10th Edition. Publisher: Tata McGraw-Hill Education Pvt. Ltd, 2016.
[9] Rohit Gupta, Neeraj Pandita, Rahul Gupta, Heat conducted through a parabolic fin via Means of Elzaki transform, Journal of Engineering Sciences, Vol. 11, Issue 1, Jan. 2020, pp. 533-535.
[10] Rohit Gupta, Rahul Gupta, Matrix Method For Solving The Schrodinger’s Time – Independent Equation To Obtain The Eigen Functions And Eigen Energy Values of A Particle Inside The Infinite Square Well Potential, IOSR Journal of Applied Physics (IOSR-JAP), Volume 10, Issue 5 Ver. I (Sep. – Oct. 2018), PP. 01-05.
[11] Rohit Gupta, Rahul Gupta, “Matrix method approach for the temperature distribution and heat flow along a conducting bar connected between two heat sources”, Journal of Emerging Technologies and Innovative Research, Volume 5 Issue 9, September 2018, PP. 210-214.
[12] Rohit Gupta, Rahul Gupta, “Matrix method for deriving the response of a series Ł- Ϲ- Ɍ network connected to an excitation voltage source of constant potential”, Pramana Research Journal, Volume 8, Issue 10, 2018.
[13] Rohit Gupta, Rahul Gupta, Sonica Rajput “Response of a parallel Ɫ- Ϲ- ℛ network connected to an excitation source providing a constant current by matrix method”, International Journal for Research in Engineering Application & Management (IJREAM), Vol-04, Issue-07, Oct 2018.
[14] Rohit Gupta, Tarun Singhal, Dinesh Verma, Quantum mechanical reflection and transmission coefficients for a particle through a one-dimensional vertical step potential, International Journal of Innovative Technology and Exploring Engineering, Volume-8, Issue-11, September 2019, PP 2882-2886.
[15] Rohit Gupta, Yuvraj Singh Chib, Rahul Gupta, Design of the resistor-capacitor snubber network for a d. c. circuit containing an inductive load, Journal of Emerging Technologies and Innovative Research (JETIR), Volume 5, Issue 11, November 2018, pp. 68-71.
[16] Rohit Gupta, Rahul Gupta, Sonica Rajput, Analysis of Damped Harmonic Oscillator by Matrix Method, International Journal of Research and Analytical Reviews (IJRAR), Volume 5, Issue 4, October 2018, pp. 479-484.
[17] Rohit Gupta, Rahul Gupta, Heat Dissipation From The Finite Fin Surface Losing Heat At The Tip, International Journal of Research and Analytical Reviews, Volume 5, Issue 3, September 2018, pp. 138-143.
[18] Rohit Gupta, Rahul Gupta, Solving the Half-Infinite Potential Well Problem via the Application of Matrix Method, Engineering and Scientific International Journal (ESIJ), Volume 8, Issue 1, January – March 2021, pp. 35-38.
[19] Neeraj Pandita, and Rohit Gupta. Analysis Of Uniform Infinite Fin Via Means Of Rohit Transform, International Journal Of Advance Research And Innovative Ideas In Education, Volume 6, Issue 6, 2020, pp. 1033-1036.
[20] Rohit Gupta, Dinesh Verma, Heat emitted from a uniform rod with ends maintained at unequal temperatures, “ASIO Journal of Chemistry, Physics, Mathematics & Applied Sciences (ASIO-JCPMAS)”, Volume 4, Issue 1, 2020, pp. 01-03.
[21] Rahul Gupta and Rohit Gupta, Laplace Transform method for obtaining the temperature distribution and the heat flow along a uniform conducting rod connected between two thermal reservoirs maintained at different temperatures, Pramana Research Journal, Volume 8, Issue 9, 2018, pp. 47-54.

I. A. Kankara, T. Adagba, A. Yunusa

Cretaceous Geology; Age-determination; Economic Geology; Sokoto Basin; Northwestern Nigeria.

In this present study, the geology and economic aspect of lithological units of Sokoto Basin, sheet 4 South West Sokoto, part of Nigerian Sedimentary Basins were studied. It is bounded by latitudes N 13° 36′ 30″- N 13° 40′ 45″ and longitudes E 05° 38′ 00″- E 05° 40′ 44″to gently undulating plain which was mapped on the scale of 1:25,000. The methodology adopted was mainly primary data which involved reconnaissance survey and actual or full field survey. The economic deposits include clays/ shale, lateritic ironstones, limestone, phosphate and gypsum, which are mined by the local artisanal miners.

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A.D. Oguizu, J.O. Nwagwu

School Age Children; Iodine Status; Abia State; Nigeria.

Background: Iodine deficiency disorders have continued to be a significant health problem in some Nigerian communities despite universal salt iodization.
Objective: This study was designed to assess the iodine status of school age children (6-12 years) in Umuahia South LGA of Abia State, Nigeria.
Methods: A total of 414 school children were studied. Urine samples were obtained from 84 school children, 30 males and 54 females. The background and socio-economic information, food habit and dietary intake of the respondents were determined using validated questionnaires. Urinary iodine concentration analysis, using Sandell-Kolthoff reaction was used to determine the iodine status of the children. Chi-square was used to determine the relationship between urinary iodine status of the children and the socio-economic characteristics of their parents.
Results: More than half (58.7%) of the children were females while 40.5% were males. About a quarter of the children (40.1%) were 9-10 years, 23.4% were 11-12 years while 36.5% were 6-8 years. Most of the respondents (72.2%) were aware of iodized salt; 14.3% heard about iodized salt from friends, 41.8% heard from the media while 13% heard about iodized salt from the market. Majority of the respondents (94.4%) claimed they consume iodized salt while 4.9% said they use salts that were measured in cups which was not iodized salt. The study revealed that 54.3% of the school children had optimal iodine status while 35.7% had mild iodine deficiency which was higher in males (46.7%) than in females (29.6%). About 5.6% of the respondents had grade 1 goiter. There was a significant association (p< 0.05) between urinary iodine status of the school children and educational status of their fathers’, mothers’, parents’ occupation and income level of fathers.
Conclusion: Nutrition education should be aimed at mothers, caregivers, and school children to promote consumption of iodine rich foods.

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Dr. S. Tephillah Vasantham

Artificial Intelligence; Human Resource Management; Innovation; Human Intelligence.

This paper deals with the Role of Artificial Intelligence (AI) in Human Resource Management (HRM). We can see in the present globalized world, the customary methods of how business is directed are being tested. There could be not, at this point just nearby firms as contenders, yet associations need to contend continually on a worldwide level as innovation is making the world more modest. This infers that for an association to keep awake to date and maintain an upper hand and accepting these new mechanical advancements is critical. HRM includes a wide range of viewpoints, like preparing workers, enrollment, representative relations, and the advancement of the association. People fill in as a wellspring of information and ability which each association can and should draw on. Hence, obtaining and holding these kinds of workers through enrollment assume a major part today. Because of the significance Human Resource (HR) has for the association, the enrollment interaction by which all this asset is acquired is the way to progress. The enlistment cycle used to be longer and take a lot of time and suggest a lot of administrative works for the spotters, anyway this has as of now gradually began to change with online enrollment getting normal. This paper deals with the various applications and the advantages of implementing Artificial Intelligence in Human Resource management.

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Mohammad Azadfallah

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One of the interesting features of Multi-Criteria Decision Making/ Multiple Attribute Decision Making (MCDM/ MADM) is that a number of techniques that can be used to solve the same problem. In general, three common categories of decision problems are choice problem, ranking problem, and sorting problem. While, the issue of choice and ranking problems is more emphasized in MCDM/ MADM, but the literature weakly consider sorting problems. Several solutions for the above problem are suggested (i.e., Flow sort, AHP-Sort, ELECTRE Tri, etc.). Theoretically, there is no reason to be limited to these techniques. Hence, in this paper we propose a novel multi-criteria sorting method that is based on Chebyshev’s theorem. More specifically, different from other studies on MCDM sorting problems, which put more emphasis on the extension of new models, this work attempts to present a general framework using the Chebyshev’s inequality, to transform the results of conventional MCDM models from ranking format to sort mode. Finally, the proposed approach is compared with three existed models. Compared results show that the proposed method is efficient and the results are stable.

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Rahul Gupta, Rohit Gupta, Loveneesh Talwar

Gupta Transform; Series RL and RC Networks circuits; Response

The analysis of electric networks circuits is an essential course in engineering. The response of such networks is usually obtained by mathematical approaches such as Laplace Transform, Calculus Approach, Convolution Theorem Approach, Residue Theorem Approach. This paper presents a new integral transform called Gupta Transform for obtaining the complete response of the series RL and RC networks circuits with a steady voltage source. The response obtained will provide electric current or charge flowing through series RL and RC networks circuits with a steady voltage source. In this paper, the response of the series RL and RC networks circuits with steady excitation source is provided as a demonstration of the application of the new integral transform called Gupta Transform.

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[4] Rohit Gupta, Rahul Gupta, Sonica Rajput, Convolution Method for the Complete Response of a Series Ł-Ɍ Network Connected to an Excitation Source of Sinusoidal Potential, International Journal of Research in Electronics And Computer Engineering, Vol. 7, issue 1, January- March 2019, pp. 658-661.
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[6] Rahul Gupta, Rohit Gupta, Dinesh Verma, Application of Novel Integral Transform: Gupta Transform to Mechanical and Electrical Oscillators, ASIO Journal of Chemistry, Physics, Mathematics & Applied Sciences (ASIO-JCPMAS), 2020, 4(1): 04-07.
[7] Rohit Gupta, Yuvraj Singh Chib, Rahul Gupta, Design of the resistor-capacitor snubber network for a d. c. circuit containing an inductive load, Journal of Emerging Technologies and Innovative Research (JETIR), Volume 5, Issue 11, November 2018, pp. 68-71.
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[10] Rohit Gupta and Loveneesh Talwar, Elzaki Transform Means To Design A Protective RC Snubber Circuit, International Journal of Scientific and Technical Advancements, 6(3), 2020: 45-48.
[11] Rahul Gupta and Rohit Gupta, Impulsive Responses of Damped Mechanical and Electrical Oscillators,International Journal ofScientific and Technical Advancements, 2020, 6(3): 41-44.
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[13] Rohit Gupta, Rahul Gupta, Analysis of RLC circuits with exponential excitation sources by a new integral transform: Rohit Transform, ASIO Journal of Engineering & Technological Perspective Research (ASIO-JETPR), 2020, 5(1): 22-24.
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[15] Rahul Gupta, Rohit Gupta, Loveneesh Talwar, Response of Network Circuits Connected To Impulsive Potential Source via New Integral Transform: Gupta Transform, ASIO Journal of Engineering & Technological Perspective Research (ASIO-JETPR), 5(1), 2020:18-20.