Journal Vol – 19 No – 9, September 2024

Abstract:

A combination of science is a way to accommodate various effective phenomena that could influence the entire life cycle. Recently, emphasis has been placed on the electrochemical polymerization mechanism that is involved in numerous technological applications, including sensors and detectors. Accordingly, daily promises were raised to eliminate life disasters and alleviate the challenges such as water floods as a part of weather changes, which could cause severe damage to life health explicitly, infrastructure, economic productivity, and much more. The proposal considers the matter via compromising the water overflow as well as eliminating the disaster that would come in no warning time and tackling the climate emergency flooding with the potential of water reclamation and offers scholarly suggestions by the requirements of the scientific approach. The investigation clarified the electromagnetic absorber beside the electrochemical polymerization through engagement in the flooded water track stations and the calculation result shows that 19.73% could be absorbed when using 300 grams of polymer gel capacity in 240 grams of water. Generally, the paper explores the electromagnetic flood disaster and how to address it to build a more secure forthcoming.

Keywords:

Wavelength,Climate Change,Water Flood,Electromagnetic Chamber,Frequency,

Refference:

I. Chao‐Song Huang. : ‘Global Pc5 Pulsations From the Polar Cap to the Equator: Wave Characteristics, Phase Variations, Disturbance Current System, and Signal Transmission.’ Journal of Geophysical Research: Space Physics. (2021) 126, 7. 10.1029/2020JA029093
II. E. R. Banfe. : “Abstract of Kelvin Water Dropper,” 2020 IEEE Integrated STEM Education Conference (ISEC), pp. 1-1, 2020. 10.1109/ISEC49744.2020.9397857
III. H. H. Kadar, P. A. A. Rafee and S. S. Sameon. : “Internet of Things (IoT) and Water Crisis,” 4th International Conference on Computer and Information Sciences (ICCOINS), pp. 1-6, 2018. 10.1109/ICCOINS.2018.8510561
IV. I. E. Elmutasim and I. I. Mohd. : “Investigate the Electromagnetic Waves to Desalinate Gulf Water and Beyond.” 7th International Conference on Frontiers of Industrial Engineering (ICFIE), pp. 119-122, 2020. 10.1109/ICFIE50845.2020.9266726
V. I. E. Elmutasim and I. I. Mohd. : “Modeling over the Sea Surface within Elevated Duct,” 7th International Conference on Frontiers of Industrial Engineering (ICFIE), pp. 98-103, 2020, 10.1109/ICFIE50845.2020.9266731
VI. L.Abhishek, R. A. Karthick, K. D. Kumar and G. Sivakumar. : “Efficient water treatment using smart materials,” 2014 International Conference on Smart Structures and Systems (ICSSS), pp. 94-99, 2014. 10.1109/ICSSS.2014.7006180
VII. Mehrotra P, Chatterjee B, Sen S. : ‘EM-Wave Biosensors: A Review of RF, Microwave, mm-Wave and Optical Sensing.’ Sensors (Basel). Vol. 19(5):1013. Published 2019 Feb 27. 10.3390/s19051013
VIII. M. E. Borisova, A. M. Kamalov and Y. K. Osina, “Absorption Phenomena in Capacitors Based on PPS Films,” IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), 2019, pp. 84-86, doi: 10.1109/EIConRus.2019.8657265
IX. N. Anusha, B. Bharathi. : ‘Flood detection and flood mapping using multi-temporal synthetic aperture radar and optical data.’ The Egyptian Journal of Remote Sensing and Space Science. Volume 23(2), pp. 207-219, 2020. ISSN1110-9823, 10.1016/j.ejrs.2019.01.001
X. R. Lopatkiewicz, Z. Nadolny and P. Przybylek. : “The influence of water content on thermal conductivity of paper used as transformer windings insulation.” 2012 IEEE 10th International Conference on the Properties and Applications of Dielectric Materials. pp. 1-4, 2012. 10.1109/ICPADM.2012.6318991
XI. Soraj A. Rahem; Mohsin E. Aldokheily; Athraa H. Mekky. : “Evaluation of fabricated IR absorbing films of polymer nanocapsules.” Eurasian Chemical Communications. Volume 4(12) Pages 1228-1240, December 2022. 10.22034/ecc.2022.345613.1487
XII. Xia, Wenjie, et al., : “Discharge characteristics and bactericidal mechanism of Ar plasma jet with ethanol and oxygen gas admixtures.” Plasma Sources Science and Technologyi. Vol. 28.12, 125005, 2019.
XIII. Xu, T.; Zhu, W.; Sun, J. : ‘Structural Modifications of Sodium Polyacrylate-Polyacrylamide to Enhance Its Water Absorption Rate.’ Coatings 2022, 12, 1234. 10.3390/coatings12091234
XIV. X.Wang, F. Wang, Lanzhigao and R. Chen. : “Understanding and Application of Gauss Theorem in Electrostatic Field,” International Conference on Intelligence Science and Information Engineering, pp. 386-388, 2011. 10.1109/ISIE.2011.118
XV. Zhukovsky, Konstantin V., and Hari M. Srivastava. : “Analytical solutions for heat diffusion beyond Fourier law.” Applied Mathematics and Computation. Vol 293, pp.423-437, 2017

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Abstract:

With the advancement of technology, maintaining secrecy is a crucial concern that requires a variety of skills. A scientific method for protecting communication against unauthenticated access is cryptography. In cryptography, there are several encryption techniques for data security. It has been suggested that new nonstandard encryption techniques are needed to shield communication from conventional threats. This work presents a method that uses graphs together with some algebraic features to provide some new encryption techniques for safe message transfer. The transmission of secret communications will be safer because of the suggested encryption techniques.

Keywords:

Cryptography,Decryption,Encryption,Star Graph,

Refference:

I. Baizhu N., Rabiha Q., Shafiqur R., and Ghulam F., : “Some Graph-Based EncryptionSchemes”, Journal of Mathematics, vol. 2021, no. 6, 2021, 10.1155/2021/6614172.
II. Burton D.M. Elementary Number Theory, 6th Edition, New Delhi:Tata McGraw-Hill Publishing Company Limited, 2007.
III. Chandrasekaran V. M., Praba B., Manimaran A. and Kailash G., : “Data transfer using complete bipartite graph.” IOP Conf. Ser.: Mater. Sci. Eng.,vol. 263, no 4, 2017, 10.1088/1757-899X/263/4/042120.
IV. Charles D. X., Lauter K. E., and Goren E. Z., : “Cryptographic Hash Functionsfrom Expander Graphs.” J Cryptol, vol. 22, 2009, 10.1007/s00145-007-9002-x
V. Harary F. Graph theory, Addison-Wesley Publishing Company, Inc., Reading, Mass., 1969.
VI. Hu J., Liang J., and Dong S., : “A bipartite graph propagation approach for mobile advertising fraud detection.” Mobile Information Systems, vol. 2017, pp. 12, 2017.
VII. Priyadarsini P.L.K., : “A Survey on some Applications of Graph Theory in Cryptography”. Journal of Discrete Mathematical Sciences and Cryptography, vol. 18, 2015, 18. 209-217. 10.1080/09720529.2013.878819.
VIII. Rosen K. H., Elementary Number theory and its Applications, 5th edition, USA, AddisonWesley, 2005.
IX. Selim G. A., : “How to encrypt a graph, International Journal of Parallel.” Emergent and Distributed Systems, vol. 35(6) pp. 668–681, 2020, 10.1080/09720529.2013.878819
X. Sharma A. K. and Mittal S. K., : “Cryptography & Network Security Hash Function Applications, Attacks and Advances: A Review.” Third International Conference on Inventive Systems and Control (ICISC), Coimbatore, India, 2019, pp. 177-188. 10.1109/ICISC44355.2019.9036448.
XI. Sinha D. and Sethi A., “Encryption using network and matrices through signed graphs.” International Journal of Computer Applications, vol. 138(4) pp. 6–13, 2016. 10.5120/ijca2016908780

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Abstract:

A minimum resolving set is a resolving set with the lowest cardinality and its cardinality is a dimension of connected graph , represented by . A dominating set  is a set of vertices such that each  of  is either in  or has at least one neighbor in .  The dominance number of  is the lowest cardinality of such a set. The lowest cardinality of the dominant resolving set is called a dominant metric dimension of , represented by . This paper presents an algorithm for finding the domination resolving number of a graph.

Keywords:

Domination Number,Metric Dimension,Resolving Dominating Set,

Refference:

I. A. A. Khalil. : ‘Determination and testing the domination numbers of Helm graph, web graph and Levi graph using MATLAB’. Journal of Education Science. Vol. 24, pp. 103-116, 2011. https://www.iasj.net/iasj/download/2b430f4e0c4f89fd
II. A. Sugumaran, E. Jayachandran. : ‘Domination number of some graphs’. International Journal of Scientific Development and Research. Vol. 3, pp. 386-391, 2018. https://api.semanticscholar.org/CorpusID:213194763
III. B. Mohamed. : ‘A comprehensive survey on the metric dimension problem of graphs and its types’. International Journal of Theoretical and Applied Mathematics. Vol. 9, pp. 1-5, 2023. 10.11648/j.ijtam.20230901.11
IV. B. Mohamed, L. Mohaisen, M. Amin. : ‘Binary equilibrium optimization algorithm for computing connected domination metric dimension problem’. Scientific Programming. Vol. 2022, pp. 1-15, 2022. 10.1155/2022/6076369
V. B. Mohamed, L. Mohaisen, M. Amin. : ‘Computing connected resolvability of graphs using binary enhanced Harris Hawks optimization’. Intelligent Automation & Soft Computing. Vol. 36, pp. 2349-2361, 2023. 10.32604/iasc.2023.032930
VI. B. Mohamed, M. Amin. : ‘A hybrid optimization algorithms for solving metric dimension problem’. Graph-HOC. Vol. 15, pp. 1-10, 2023. https://ssrn.com/abstract=4504670
VII. B. Mohamed, M. Amin. : ‘Domination number and secure resolving sets in cyclic networks’. Applied and Computational Mathematics. Vol. 12, pp. 42-45, 2023. 10.11648/j.acm.20231202.12
VIII. B. Mohamed, M. Amin. : ‘The metric dimension of subdivisions of Lilly graph, tadpole graph and special trees’. Applied and Computational Mathematics. Vol. 12, pp. 9-14, 2023. 10.11648/j.acm.20231201.12
IX. B. Mohamed. : ‘Metric dimension of graphs and its application to robotic navigation’. International Journal of Computer Applications. Vol. 184, pp. 1-3, 2022. 10.5120/ijca2022922090
X. B. N. Kavitha, I. Kelkar. : ‘Split and equitable domination in book graph and stacked book graph’. International Journal of Advanced Research in Computer Science. Vol. 8, pp. 108-112, 2017. 10.26483/ijarcs.v8i6.4475
XI. C. S. Nagabhushana, B. N. Kavitha, H. M. Chudamani. : ‘Split and equitable domination of some special graph’. International Journal of Science Technology & Engineering. Vol. 4, pp. 50-54, 2017.
XII. F. Muhammad, L. Susilowati. : ‘Algorithm and computer program to determine metric dimension of graph’. Journal of Physics. Vol. 1494, 012018, 2020. 10.1088/1742-6596/1494/1/012018
XIII. H. Al-Zoubi, H. Alzaareer, A. Zraiqat, T. Hamadneh, W. Al-Mashaleh. : ‘On ruled surfaces of coordinate finite type’. WSEAS Transactions on Mathematics. Vol. 21, pp. 765–769, 2022. 10.37394/23206.2022.21.87
XIV. H. Iswadi, E. T. Baskoro, A. N. M. Salman, R. Simanjuntak. : ‘The resolving graph of amalgamation of cycles’. Utilitas Mathematica. Vol. 83, pp. 121-132, 2010. https://api.semanticscholar.org/CorpusID:55139163
XV. I. M. Batiha, B. Mohamed. : ‘Binary rat swarm optimizer algorithm for computing independent domination metric dimension problem’. Mathematical Models in Engineering. Vol. 10, pp. 6-13, 2024. 10.21595/mme.2024.24037
XVI. I. M. Batiha, B. Mohamed, I. H. Jebril. : ‘Secure metric dimension of new classes of graphs’. Mathematical Models in Engineering. Vol. 10, pp. 1-6, 2024. 10.21595/mme.2024.24168
XVII. I. M. Batiha, J. Oudetallah, A. Ouannas, A. A. Al-Nana, I. H. Jebril. : ‘Tuning the fractional-order PID-Controller for blood glucose level of diabetic patients’. International Journal of Advances in Soft Computing and its Applications. Vol. 13, pp. 1–10, 2021. https://www.i-csrs.org/Volumes/ijasca/2021.2.1.pdf
XVIII. I. M. Batiha, M. Amin, B. Mohamed, H. I. Jebril. : ‘Connected metric dimension of the class of ladder graphs’. Mathematical Models in Engineering. Vol. 10, pp. 65–74, 2024. 10.21595/mme.2024.23934
XIX. I. M. Batiha, N. Anakira, A. Hashim, B. Mohamed. : ‘A special graph for the connected metric dimension of graphs’. Mathematical Models in Engineering. Vol. 10, pp. 1-8, 2024. 10.21595/mme.2024.24176
XX. I. M. Batiha, S. A. Njadat, R. M. Batyha, A. Zraiqat, A. Dababneh, S. Momani. : ‘Design fractional-order PID controllers for single-joint robot ARM model’. International Journal of Advances in Soft Computing and its Applications. Vol. 14, pp. 97–114, 2022. 10.15849/IJASCA.220720.07
XXI. K. B. Murthy. : ‘The end equitable domination of dragon and some related graphs’. Journal of Computer and Mathematical sciences. Vol. 7, pp. 160-167, 2016.
XXII. L. Susilowati, I. Sa’adah, R. Z. Fauziyyah, A. Erfanian. : ‘The dominant metric dimension of graphs’. Heliyon. Vol. 6, 03633, 2020. 10.1016/j.heliyon.2020.e03633
XXIII. P. Sumathi, A. Rathi, A. Mahalakshmi. : ‘Quotient labeling of corona of ladder graphs’. International Journal of Innovative Research in Applied Sciences and Engineering. Vol. 1, pp. 1-12, 2017. 10.29027/IJIRASE.v1.i3.2017.80-85
XXIV. R. Alfarisi, Dafik, A. Kristiana. : ‘Resolving domination number of graphs’. Discrete Mathematics, Algorithms and Applications. Vol. 11, 1950071, 2019. 10.1142/S179383091950071X
XXV. R. C. Brigham, G. Chartrand, R. D. Dutton, P. Zhang. : ‘Resolving domination in graphs’. Mathematica Bohemica. Vol. 128, pp. 25-36, 2003. 10.21136/MB.2003.133935
XXVI. S. Kurniawati, D. A. R. Wardani, E. R. Albirri. : ‘On resolving domination number of friendship graph and its operation’. Journal of Physics. Vol. 1465, 012019, 2020. 10.1088/1742-6596/1465/1/012019
XXVII. R. P. Adirasari, H. Suprajitno, L. Susilowati. : ‘The dominant metric dimension of corona product graphs’. Baghdad Science Journal. Vol. 18, 0349, 2021. https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/5039
XXVIII. T. Mazidah, Dafik, Slamin, I. H. Agustin, R. Nisviasari. : ‘Resolving independent domination number of some special graphs’. Journal of Physics. Vol. 1832, 012022, 2021. 10.1088/1742-6596/1832/1/012022

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Abstract:

In Major construction projects execution and performance were being negatively impacted by claims and disputes in terms of cost overrun, quality, stakeholders relationships, and productivity. Therefore understanding the significance of underlying the claims is essential. In this study, the primary root causes behind delay claims and disputes in construction projects were identified, examined, and rated. The significance of these factors was assessed using Relative Importance Index (RII) values. In addition, a machine learning model employing the Random Forest Genetic Algorithm (RFGA) was implemented to foresee the related risks and ascertain their levels. In a pilot survey, the data were collected across multiple construction projects at different phases such as scrutiny stage, design and planning stage, bidding stage, operation stage, and maintenance or after-construction stage. From Relative Important Index values from the statistical approach, it emerges that delay claims are generally causes from the owner followed by project-specific activities. Delays in processing bill payments, natural disasters, lack of contract awareness, and delay in final bill payment are the top causes of delay claims which converted to conflicts and disputes in mostly operating stage. The Random Forest Genetic Algorithm model predicted that factors like altering the original design, reluctance to cooperate by contractor, and increase of wages have lower risk whereas factors Poor site conditions, delay in approvals of schedules and change orders, natural calamities, late in running bill payment, repetition of work due to error in original work are at higher risk in terms of conflict and dispute. The model gives an accuracy of 0.89 and 0.87 for training data and testing data. The study will highlight possible research avenues and enhance project management strategies so that the project succeeds its goal.

Keywords:

Relative Important Index,Construction Delay claims,RFGA,Risk prediction,conflict and dispute,

Refference:

I. Al-Mohsin, Mohammed. “Claim analysis of construction projects in Oman.” Int. J. Adv. Sci. Eng. Inf. Technol 2 (2012): 73-78. DOI: 10.18517/ijaseit.2.2.182
II. Apte, Bhagyashree, and Sudhanshu Pathak. “Review of types and causes of construction claims.” International Journal of Research in Civil Engineering, Architecture and Design 4.2 (2016): 43-50. https://www.ijres.org/papers/Volume-10/Issue-4/Ser-9/F10042732.pdf
III. Gündüz, Murat, Yasemin Nielsen, and Mustafa Özdemir. “Quantification of delay factors using the relative importance index method for construction projects in Turkey.” Journal of management in engineering 29.2 (2013): 133-139. 10.1061/(ASCE)ME.1943-5479.0000129
IV. Horta, I. M., et al. “Performance trends in the construction industry worldwide: an overview of the turn of the century.” Journal of productivity analysis 39 (2013): 89-99. DOI 10.1007/s11123-012-0276-0
V. Kometa, Simon T., Paul O. Olomolaiye, and Frank C. Harris. “Attributes of UK construction clients influencing project consultants’ performance.” Construction Management and economics 12.5 (1994): 433-443. 10.1080/01446199400000053
VI. Sahu, Pramodini, D. K. Bera, and P. K. Parhi. “Gradation of the Relative Significance of the Claims Obtained from Construction Industry.” Recent Developments in Sustainable Infrastructure (ICRDSI-2020)—Structure and Construction Management: Conference Proceedings from ICRDSI-2020 Volume 1. Singapore: Springer Nature Singapore, 2022. 10.1007/978-981-16-8433-3_11
VII. Sambasivan, Murali, and Yau Wen Soon. “Causes and effects of delays in Malaysian construction industry.” International Journal of project management 25.5 (2007): 517-526. 10.1016/j.ijproman.2006.11.007
VIII. Tariq, Junaid, and S. Shujaa Safdar Gardezi. “Study the delays and conflicts for construction projects and their mutual relationship: A review.” Ain Shams Engineering Journal 14.1 (2023): 101815. DOI: 10.1016/j.asej.2023.101815. 10.1016/j.asej.2022.101815
IX. Yaseen, Zaher Mundher, et al. “Prediction of risk delay in construction projects using a hybrid artificial intelligence model.” Sustainability 12.4 (2020): 1514. 10.3390/su12041514
X. Zaneldin, Essam K. “Construction claims in United Arab Emirates: Types, causes, and frequency.” International journal of project management 24.5 (2006): 453-459. 10.1016/j.ijproman.2006.02.006
XI. Zhang, YuXiang, et al. “How does experience with delay shape managers’ making-do decision: Random forest approach.” Journal of Management in Engineering 36.4 (2020): 04020030. 10.1061/(ASCE)ME.1943-5479.0000776

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Abstract:

The physical layer is implemented in the present communication era with new multicarrier modulation schemes such as Generalized Frequency Division Multiplexing with Multi-Input and Multi-Output (MIMO-GFDM) antenna systems to achieve good spectral efficiency and diversity order. This paper presents precoded Space-Time-Frequency MIMO-GFDM performance analysis to improve the bit error rate performance without increasing transmission power and bandwidth compared to conventional techniques. The proposed system also enhances the diversity order over frequency selective fading channels. In general, we need to perform channel matrix inversion operations at the receiver or channel precoding matrix operations at the transmitter to detect the symbols of MIMO-GFDM systems. This paper's proposed scheme completes the same task without performing channel matrix inversion. Orthogonal transform techniques such as Haar, Harley, Walsh-Hadamard, and Slant transforms are used as precoders at the transmitter for the proposed scheme. The simulation results are validated on the MATLAB working platform. We have compared the bit error rate of the PSTF-MIMO-GFDM system with Space-Time (ST) and Space Frequency (SF) as baseline schemes and different orthogonal transform precoding techniques.

Keywords:

MIMO,GFDM,ST,SF,PSTF,

Refference:

I. Alves, Bruno M., et al. “Performance of GFDM over Frequency-Selective Channels.” Proceedings of the International Workshop on Telecommunication 2013.
https://inatel.br/docentes/documents/dayan/Publications/61.pdf
II. Abass, Eman S., Hesham M. El-Badawy, and Hadia M. El-Hennawy. “On the Design of Quasi-Orthogonal Space-Time-Frequency Block Code over MIMO OFDM Channel.” 2011 7th International Conference on Wireless Communications, Networking and Mobile Computing. IEEE, 2011. https://ieeexplore.ieee.org/abstract/document/6040106
III. Bolcskei, Helmut, and Arogyaswami J. Paulraj. “Space-Frequency Coded Broadband OFDM Systems.” 2000 IEEE Wireless Communications and Networking Conference. Conference Record (Cat. No. 00TH8540). Vol. 1. IEEE, 2000. https://ieeexplore.ieee.org/abstract/document/904589
IV. Deepthi, Pasupuleti Sai, et al. “Review of 5G Communications over OFDM and GFDM.” ICCCE 2020: Proceedings of the 3rd International Conference on Communications and Cyber Physical Engineering. Springer Singapore, 2021. https://link.springer.com/chapter/10.1007/978-981-15-7961-5_81
V. Debnath, Sourav, Samin Ahmed, and SM Shamsul Alam. “Performance Comparison of OFDM, FBMC, and UFMC for Identifying the Optimal Solution for 5G Communications.” International Journal of Wireless and Microwave Technologies 13.5 (2023): 1-10. https://www.mecs-press.org/ijwmt/ijwmt-v13-n5/IJWMT-V13-N5-1.pdf
VI. Falkowski, Bogdan J., and Shixing Yan. “Matrix Decomposition and Butterfly Diagrams for Mutual Relations between Hadamard-Haar and Arithmetic Spectra.” IEEE Transactions on Circuits and Systems I: Regular Papers 53.5 (2006): 1119-1129. https://ieeexplore.ieee.org/abstract/document/1629250
VII. Fettweis, Gerhard, Marco Krondorf, and Steffen Bittner. “GFDM—Generalized Frequency Division Multiplexing.” VTC Spring 2009—IEEE 69th Vehicular Technology Conference. IEEE, 2009. https://ieeexplore.ieee.org/abstract/document/5073571
VIII. Kumar, R. Anil, and Kodati Satya Prasad. “Comparative Analysis of OFDM, FBMC, UFMC & GFDM for 5G Wireless Communications.” International Journal of Advanced Science and Technology 29.5 (2020): 2097-2108. http://sersc.org/journals/index.php/IJAST/article/view/10903
IX. Kumar, R. Anil, and K. Satya Prasad. “Performance Analysis of GFDM Modulation in Heterogeneous Network for 5G NR.” Wireless Personal Communications 116.3 (2021): 2299-2319. https://link.springer.com/article/10.1007/s11277-020-07791-4
X. Lee, King F., and Douglas B. Williams. “A Space-Time Coded Transmitter Diversity Technique for Frequency Selective Fading Channels.” Proceedings of the 2000 IEEE Sensor Array and Multichannel Signal Processing Workshop. SAM 2000 (Cat. No. 00EX410). IEEE, 2000. https://ieeexplore.ieee.org/abstract/document/877987
XI. Lin, Yuan-Pei, and See-May Phoong. “BER Minimized OFDM Systems with Channel Independent Precoders.” IEEE Transactions on Signal Processing 51.9 (2003): 2369-2380.
https://ieeexplore.ieee.org/abstract/document/1223548
XII. Mahender, Kommabatla, Tipparti Anil Kumar, and K. S. Ramesh. “Simple Transmit Diversity Techniques for Wireless Communications.” Smart Innovations in Communication and Computational Sciences: Proceedings of ICSICCS 2017, Volume 1. Springer Singapore, 2019. https://link.springer.com/chapter/10.1007/978-981-10-8968-8_28
XIII. Matthe, Maximilian, et al. “Widely Linear Estimation for Space-Time-Coded GFDM in Low-Latency Applications.” IEEE Transactions on Communications 63.11 (2015): 4501-4509. https://ieeexplore.ieee.org/abstract/document/7194753
XIV. Matthé, Maximilian, Luciano Leonel Mendes, and Gerhard Fettweis. “Generalized Frequency Division Multiplexing in a Gabor Transform Setting.” IEEE Communications Letters 18.8 (2014): 1379-1382. https://ieeexplore.ieee.org/abstract/document/6853349
XV. Ramakrishnan, Balamurali, et al. “Analysis of FBMC Waveform for 5G Network Based Smart Hospitals.” Applied Sciences 11.19 (2021): 8895. https://www.mdpi.com/2076-3417/11/19/8895
XVI. Rani, P. Naga, and Ch Santhi Rani. “UFMC: The 5G Modulation Technique.” 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). IEEE, 2016. https://ieeexplore.ieee.org/abstract/document/7919714
XVII. Rohling, Hermann, ed. OFDM: Concepts for Future Communication Systems. Springer Science & Business Media, 2011. https://link.springer.com/book/10.1007/978-3-642-17496-4
XVIII. Suto, Kenji, and Tomoaki Ohtsuki. “Performance Evaluation of Space-Time-Frequency Block Codes over Frequency Selective Fading Channels.” Proceedings IEEE 56th Vehicular Technology Conference. Vol. 3. IEEE, 2002. https://ieeexplore.ieee.org/abstract/document/1040459
XIX. Thepade, Sudeep D., and Smita S. Chavan. “Cosine Walsh and Slant Wavelet Transforms for Robust Image Steganography.” 2013 Tenth International Conference on Wireless and Optical Communications Networks (WOCN). IEEE, 2013. https://ieeexplore.ieee.org/abstract/document/6616220
XX. Vijay, et al. “Intertwine Connection‐Based Routing Path Selection for Data Transmission in Mobile Cellular Networks and Wireless Sensor Networks.” Wireless Communications and Mobile Computing 2022.1 (2022): 8398128. https://onlinelibrary.wiley.com/doi/full/10.1155/2022/8398128
XXI. Wu, Jinsong, Honggang Hu, and Murat Uysal. “High-Rate Distributed Space-Time-Frequency Coding for Wireless Cooperative Networks.” IEEE Transactions on Wireless Communications 10.2 (2010): 614-625. https://ieeexplore.ieee.org/abstract/document/5669241
XXII. Yeh, Hen-Geul. “Design Precoded Space-Time-Frequency 4×1 and 4×2 OFDM Architectures in Frequency-Selective Fading Channels.” IEEE Systems Journal 14.1 (2019): 277-287. https://ieeexplore.ieee.org/abstract/document/8744548

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Abstract:

The goal of this study is to give headways in aeronautical article ID that will help with making recognitions that are both more exact and more precise. Specifically, we revamp the meaning of the article recognition anchor enclose request to remember turns for expansion to level and width, and besides, we make it conceivable to have erratic four corner point structures. Furthermore, the consideration of new anchor boxes gives the model additional adaptability to address protests that are focused at a pivot of turn that gives a 45-degree point. By accomplishing these results, we can make an organization that considers negligible tradeoffs about speed and unwavering quality, while likewise giving more exact restrictions. The latest ways to deal with PC vision and article acknowledgment are for the most part dependent on brain organizations and different advances that utilize profound learning. This powerful field of study is utilized in various applications, including military and observation, aeronautical photography, independent driving, and airborne perception. To precisely locate the location of an item, contemporary object identification techniques make use of bounding boxes that are drawn over the object and have a rectangular form (horizontal and vertical). These orthogonal bounding boxes do not consider the posture of the object, which leads to a decrease in the amount of object localization and restricts subsequent tasks such as object comprehension and tracking. We have used the DOTA dataset to present all of the results, demonstrating the value of flexible object boundaries, particularly with rotated and non-rectangular objects. We have also achieved an accuracy of 98.47% for the detection and classification of aerial objects, with forty percent of the data being used for training and the remaining twenty percent being used for testing. There was a minimum of 2.8 seconds of processing time required for the whole program to be executed to categorize all of the aerial items that were parked on the base.

Keywords:

Aerial Imaging,Aeronautical Article ID,CNN,Classification,DOTA,YOLO,

Refference:

I. Ahmad, M., Khan, A. M., Mazzara, M., Distefano, S., Ali, M., & Sarfraz, M. S. (2020). A fast and compact 3-D CNN for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters, 19, 1-5.
II. Audebert, N.; Le Saux, B.; Lefèvre, S. Beyond RGB: Very High Resolution Urban Remote Sensing with Multimodal Deep Networks. ISPRS J. Photogramm. Remote Sens. 2018, 140, 20–32.
III. Audebert, N., Le Saux, B., & Lefèvre, S. (2019). Deep learning for classification of hyperspectral data: A comparative review. IEEE geoscience and remote sensing magazine, 7(2), 159-173.
IV. A. O’Connell, J. Smith, and A. Keane, “Distribution feeder hosting capacity analysis,” in 2017 IEEE PES Innovative Smart Grid Technologies Conference Turkey (ISGT-Turkey), Sept 2017, pp. 1–6.
V. Ben Hamida, A.; Benoit, A.; Lambert, P.; Ben Amar, C. 3-D Deep Learning Approach for Remote Sensing Image Classification. IEEE Trans. Geosci. Remote Sens. 2018, 56, 4420–4434.
VI. B. G. Bai. Yancheng, “Multi-scale Fully Convolutional Network for Face Detection in the Wild,” IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 2078-2087, 2017.
VII. F. Abayaje et al., “A miniaturization of the UWB monopole antenna for wireless baseband transmission,” vol. 8, no. 1, pp. 256-262, 2020.
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Abstract:

Effective customer service and operational excellence are critical components of corporate success, particularly in today's changing business world. This paper examines the crucial role that inventory management plays in controlling and improving customer service and operational efficiency in businesses. Companies that efficiently manage inventory levels may ensure the timely fulfillment of client orders, minimize stockpiles, and increase efficiency. Furthermore, efficient inventory management contributes to boosting operating efficiency, lowering logistical costs, and increasing profitability. This study extensively reviews the literature and case studies to explore the best strategies utilized in inventory management to attain these objectives. It also investigates the influence of inventory management on performance and offers useful insights for companies looking to use inventory management as a strategic strategy to gain a sustained competitive advantage.

Keywords:

Customer Service,Customer Satisfaction,Operational Efficiency,Inventory Management,

Refference:

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VIII. Barlan-Espino, A. G. “Operational Efficiency and Customer Satisfaction of Restaurants: Basis for Business Operation Enhancement”.” Asia Pacific Journal of Multidisciplinary Research, vol. 5, no. 1, 2017, pp. 122–132.
IX. Beheshti, Hooshang M. “A Decision Support System for Improving Performance of Inventory Management in a Supply Chain Network.” International Journal of Productivity and Performance Management, vol. 59, no. 5, 2010, pp. 452–467, doi:10.1108/17410401011052887.
X. Cadavid, D. C. U., and C. C. Zuluaga. “A Framework for Decision Support System in Inventory Management Area”.” Ninth LACCEI Latin American and Caribbean Conf., LACCEI Pp, 2011, pp. 3–5.
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XII. de Leeuw, Sander, and Jeroen P. van den Berg. “Improving Operational Performance by Influencing Shopfloor Behavior via Performance Management Practices.” Journal of Operations Management, vol. 29, no. 3, 2011, pp. 224–235, doi:10.1016/j.jom.2010.12.009.
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Abstract:

The dynamic of the heat transfer analysis constitutes an important factor that has drawn the attention of many researchers. Heat transfer is evaluated by considering the heat transfer coefficient, the surface area, and the temperature difference between the surface and the surrounding fluid. The computation of the temperature difference across various surface areas shows that increased heat transfer enhances the proportion of the heat conduction rate. In most cases, the system becomes unstable because inappropriate structural elements and outside disturbances, like ambient temperature can readily change the yielding temperature. As a result, the heat exchanger's efficiency needs improvement. A numerical simulation analyzing the performance of a shell and tube heat exchanger indicates that an increase in the surface area leads to a corresponding increase in the heat transfer rate. To optimize system performance, mathematical models were employed for the stability analysis of temperature changes. MATLAB simulations computed temperature differences in quantities of heat and area, thereby obtaining valuable insights for improving heat exchanger design and operation.  

Keywords:

Heat Exchanger,Lyapunov Methods,Numerical,Shell and Tube,Temperature,Stability,

Refference:

I. Abduljalil, A. A., Sohif, B. M., Sopian, K., Sulaiman, M. Y., and Abdulrahman, T. M. ‘CFD applications for Latent Heat Thermal Energy Storage: a Review’. Renewable and Sustainable Energy Reviews, (2013): 353-363.
II. Abdulrahman, A. A., Emhemed., Rosbi, B. M., and Dirman, H. ‘Mathematical Modelling of Industrial Heat Exchanger System’. Applied Mechanics and Materials, Trans Tech Publications, Switzerland 229, no. 23 1 (2012): 2122-2124.
III. Babu, C. R., and Gugulothu, S. K. ‘CFD Analysis of Heat Transfer Enhancement by Using Passive Technique in Heat Exchanger’. International Journal Recent Advances Mechanical Engineering 4, (2015): 99–111.
IV. Borja-Jaimes, V, Adam-Medina, M, García-Morales, J, Cruz-Rojas, A, Gil-Velasco, A and Coronel-Escamilla, A. ‘A Novel Fractional Multi-Order High-Gain Observer Design to Estimate Temperature in a Heat Exchange Process’. Axioms (MDPI), (2023): 1-19, 10.3390/axioms12121107
V. Caputo, A. C., Pelagagge, P. M., and Salini, P. ‘Heat Exchanger Design Based on Economic Optimisation’. Application Thermodynamics Engineering, (2008): 1151–1159.
VI. Dolado, P., Lazaro, A., Marin, J. M., & Zalba, B. ‘Characterization of Melting and Solidification in a Real Scale PCM-Air Heat Exchanger: Numerical Model and Experimental Validation’. Energy Conversion Management, (2011): 1890-1907.
VII. Dubovsky, V., Ziskind , G., and Letan, R. ‘Numerical Study of a PCM-Air Heat Exchanger’s Thermal Performance’. Application Thermodynamics Engineering, (2011): 3453-6247.
VIII. Fallahnezhad, N., and Nasif, H. R. ‘Numerical Solution of Transient Freezing Equations of a Laminar Water Flow in a Channel with Constant Wall Temperature in the Absence of Gravity’. Microgravity Science and Technology 32, (2020): 493–505.
IX. Fernandes, E. J., and Krishnamurthy, S. H. ‘Design and Analysis of Shell and Tube Heat Exchanger’. International Journal Simulation Multi-discipline Design Optimization, (2022): 1-15.
X. Guillaume, D. Modeling and Analysis of Dynamics System. Switzerland: Institute for Dynamic Systems and Control (IDSC) ETH Zurich, 2017.
XI. Hewitt, G. F., Shires, G. L., and Bott, T. R. Process Heat Transfer: Principles and Applications. CRC Press, 2020
XII. Idris, A. A., Adeyemi, K., and Lawal, N. ‘Numerical Investigation of Transient Heat Transfer Process in Organic Phase Change Material (OPCM) – Air heat Exchanger’. Uniabuja Journal of Engineering and Technology 1, no. 1 (2020).: 91-114.
XIII. Jamal-Eddine, S., Tarik, Z., Ahmed, A, M., Merzouki, S., Najim, S. ‘Numerical investigations of the impact of a novel tubular configuration on the performance enhancement of heat exchangers’. Journal of Energy Storage 46 (2022): 10381. 10.1016/j.est.2021.103813
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XVI. Khan , K., Shah, I., Gul, W., Khan, T. A., Ali , Y., and Masood, S. A. ‘Numerical and Experimental Analysis of Shell and Tube Heat Exchanger with Round and Hexagonal Tubes’. Energies (MDPI), (2023): 1-14.
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XVIII. Kumar, P. M., & Chandrasekar, M. ‘CFD Analysis on Heat and Flow Characteristics of Double Helically Coiled Tube Heat Exchanger Handling (Multi-walled Carbon Nanotubes) MWCNTs/water Nanofluids’. Heliyon 5 (2019).: 20-30.
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Abstract:

The Present paper deals with a special type of interpolation problem, in which we have prescribed the values of the function at K_i and K_i+1 and the whole interval is divided into n equal sub-intervals of width.. We will derive a spline function of Degree 3 which will be able to interpolate this polynomial function, we name it three point spline (TPS). We have shown here how to change the next control point during further interpolation. We have also discussed the case, of whether this spline can be used for evaluating curvature. 

Keywords:

Interpolation,Spline Function,Control points,Norm,Quadrature & Parameterization,

Refference:

I. Ahlberg J.H., Nilson E. N. Walsh J. L. : ‘Theory of Splines and Their Applications.’ Mathematics in Science and Engineering. Chapter IV. 1967 Academic Press, New York, https://books.google.co.in/books?id=3bZlDAAAQBAJ&lpg=PR5&pg=PA2#v=onepage&q&f=false
II. Burova, I. G. , : “On left integro-differential splines and Cauchy problem.” International Journal Of Mathematical Models and Methods in Applied Sciences. vol. 9, pp. 683-690, 2015.https://www.naun.org/main/NAUN/ijmmas/2015/b582001-015.pdf
III. Burova I.G., Poluyanov S.V., : “On approximations by polynomial an trigonometrical integro-differential splines”, International Journal of Mathematical Models and Methods in Applied Sciences. vol.10, pp.190-199, 2016. https://elibrary.ru/item.asp?id=27154016
IV. Chikwendu C. R., Oduwole H. K.and Okoro S. I., : “An Application of Spline and Piecewise Interpolation to Heat Transfer (Cubic Case).” Journal of Mathematical Theory and Modeling.” Vol.5, No.6, 2015. https://issuu.com/alexanderdecker/docs/an_application_of_spline_and_piecew.
V. Christian G¨otte, Martin Keller, Till Nattermann, Carsten, Haß, Karl-Heinz Glander andTorstein Bertram. : “Spline-Based Motion Planning for Automated Driving.” (2017). IFAC conference paper available online at www.sciencedirect.com
VI. Ogniewski Jens, C1-continuous. : “Low-complex spline using 3 control points, In motion in games.” 2013. https://otik.uk.zcu.cz/bitstream/11025/35603/1/Ogniewski.pdf
VII. Pandey Ambrish Kumar, Ahmad Q S, Singh Kulbhushan. : “Lacunary Interpolation (0, 2; 3) Problem and Some Comparison from Quartic Splines.” American Journal of Applied Mathematics and Statistics. Vol. 1(6),pp. 117-120, 2013. 10.12691/ajams-1-6-2
VIII. P. Ciarlet Schultz M. and Varga, R. : “Numerical method of high-order accuracy for non linear boundary value problems.” Numer Math. Vol. 9 pp. 394-430. 1967. 10.1007/BF02162155
IX. Rashidinia, J. And Golbabaee A., : “Convergence of numerical solution of a fourth order Boundary value problem,” Applied Mathamatics and Computation. Vol. 171. Pp. 1296-1305, 2005. 10.1016/j.amc.2005.01.117
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XI. Singh Kulbhusan, : “A Special Quintic Spline for (0 1 4) Lacunary Interpolation and Cauchy Initial Value Problem.” Journal of Mechanics Of Continua and Mathematical Sciences. Vol. -14(4), pp 533-537, 10.26782/jmcms.2019.08.00044
XII. Singh Kulbhushan, Pandey Ambrish Kumar (2016) “Lacunary Interpolation at odd and Even Nodes”, International J. of Comp. Applications. Vol. (153) 1, 6. 10.5120/ijca2016910026
XIII. Singh K. B., Pandey Ambrish Kumar and Ahmad Qazi Shoeb, (2012 ) “Solution of a Birkhoff Interpolation problem by a special Spline Function”, International J. of Comp. App.Vol.48, 22-27. 10.5120/7376-0174
XIV. W. Bickley, (1968), “Piecewise cubic interpolation and two point boundary value problems”. The Computer journal 11 206-208. 10.1093/comjnl/11.2.206

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Abstract:

In recent times, several sectors and businesses have been doing extensive research on the usage of Dense Wavelength Division Multiplexing (DWDM) and Radio Frequency Over Fiber (RFOF). These two technologies are considered to be the most significant features. Increasing the data rate was a significant challenge that needed to be addressed, and the goal was to successfully implement a fiber optic system that was dependable and had a high number of associated channels. As a consequence of this, a 64-channel DWDM RFOF system that is capable of supporting a larger number of data rates of 2.56 Tbps has been designed and implemented in this study. A significant number of channels that have been sampled will be chosen for inquiry based on the characteristics of Quality Factor (QF) and Bit Error Rate (BER) that have been researched. This study will be carried out with the assistance of Optisystem software. These findings would be investigated at distances ranging from sixty to one hundred eighty kilometers, with the NRZ modulation format being used and a lunched power of zero decibels per meter. Additionally, the purpose of this study would be to explore the three distinct techniques of compensation, namely pre, post, and symmetrical, to quantify the individual performance of each approach on the suggested system. According to the findings, the use of symmetrical-based compensation yielded the most favorable outcomes, with the average QF produced falling within the range of (20.33-14.09) dBm over distances ranging from (60-180) kilometers. This demonstrates the dependability of the proposed system.

Keywords:

Bit Error Rate,Dense Wavelength Division Multiplexing,Fiber Optic System,Frequency Over Fiber,

Refference:

I. Abdulwahid, M. M., Abdullah, H. K., Ateah, W. M., & Ahmed, S. (2023). Implementation of Automated Water based Level Management Model by using SCADA system and PLC.
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III. Abdulwahid, M. M., & Kurnaz, S. (2023, July). Implementation of two polarization DQPSK WDM Is-OWC system with different precoding schemes for long-reach GEO Inter Satellite Link. In International Conference on Green Energy, Computing and Intelligent Technology (GEn-CITy 2023) (Vol. 2023, pp. 134-141). IET.
IV. Abdulwahid, M. M., Kurnaz, S., Türkben, A. K., Hayal, M. R., Elsayed, E. E., & Juraev, D. A. (2024). Inter-satellite optical wireless communication (Is-OWC) trends: a review, challenges and opportunities. Engineering Applications, 3(1), 1-15.
V. Abdulwahid, M. M., & Kurnaz, S. (2024, February). The utilization of different AI methods-based satellite communications: A survey. In AIP Conference Proceedings (Vol. 3051, No. 1). AIP Publishing.
VI. Almetwali, A. S., Bayat, O., Abdulwahid, M. M., & Mohamadwasel, N. B. (2022, November). Design and analysis of 50 channel by 40 Gbps DWDM-RoF system for 5G communication based on fronthaul scenario. In Proceedings of Third Doctoral Symposium on Computational Intelligence: DoSCI 2022 (pp. 109-122). Singapore: Springer Nature Singapore.
VII. Ballato, J., & Dragic, P. D. (2021). Glass: The carrier of light—Part II—A brief look into the future of optical fiber. International Journal of Applied Glass Science, 12(1), 3-24.‏
VIII. Bhattacharjee, R., Dey, P., & Saha, A. (2022). Implementation of an enhanced 32 channel 256Gbps DWDM based Radio over Fiber optical system for constricted channel spacing employing Fiber Bragg Grating. Optik, 168598.‏
IX. F. Abayaje et al., “A miniaturization of the UWB monopole antenna for wireless baseband transmission,” vol. 8, no. 1, pp. 256-262, 2020.
X. H. A. Hussein, Y. S. Mezaal, and B. M. Alameri, “Miniaturized microstrip diplexer based on fr4 substrate for wireless communications,” Elektronika Ir Elektrotechnika, vol. 27, no. 5, pp. 34-40, 2021.
XI. J. Ali and Y. Miz’el, “A new miniature Peano fractal-based bandpass filter design with 2nd harmonic suppression 3rd IEEE International Symposium on Microwave,” Antenna, Propagation and EMC Technologies for Wireless Communications, Beijing, China, 2009.
XII. Jain, Vishal & Bhatia, Richa. (2021). Review on nonlinearity effect in radio over fiber system and its mitigation. Journal of Optical Communications. 10.1515/joc-2021-0044.
XIII. Kumar, G., & Kumar, S. (2020). Effect of different channel spacings for DWDM system using optical amplifiers. National Academy Science Letters, 1-4.‏
XIV. Kumar, G., & Kumar, S. (2021). Effect of different channel spacings for DWDM system using optical amplifiers. National Academy Science Letters, 44(5), 415-418.
XV. Malak A.A.R & Kurnaz S. (July 2021) ” Design and Implementation of high data rate system based DWDM – RoF technique for 5G Front haul Communication”, Aurum Journal of Engineering system and architecture.
XVI. Mezaal, Y. S., H. T. Eyyuboglu, and J. K. Ali, “A novel design of two loosely coupled bandpass filters based on Hilbert-zz resonator with higher harmonic suppression,” in 2013 Third International Conference on Advanced Computing and Communication Technologies (ACCT), 2013.
XVII. Mohsen, D. E., Hammadi, A. M., & Alaskary, A. J. (2021, July). Design and Implementation of 1.28 Tbps DWDM based RoF system with External Modulation and Dispersion Compensation Fiber. In Journal of Physics: Conference Series (Vol. 1963, No. 1, p. 012026). IOP Publishing.‏
XVIII. Mohsen, D. E., Abbas, E. M., & Abdulwahid, M. M. (2022, June). Design and Implementation of DWDM-FSO system for Tbps data rates with different atmospheric Attenuation. In 2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA) (pp. 1-7). IEEE.
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Abstract:

The study conducts exhaustive field surveys in 67 wards in Bhubaneswar city, Odisha. 29 factors under 10 aspects have been considered for the study to assess the water requirement per household per day. SEM and CART modeling have been used to estimate the water requirement. The SEM model predicts that 4 aspects, namely, expenses, governance, possession, and resources are the major aspects that decide the water requirement of a household. Similarly, construction and repair costs, energy consumption, reinforcement practices, awareness, presence of a garden, presence of washing machine, presence of other appliances, water charges, and the type of storage majorly affect the water requirement. CART predicts energy consumption, storage, construction and repair, and washing machines to be important estimators with MAPE < 1% for the prediction of water requirement. The study reveals that with proper governance and proper use of water-intensive appliances, the required quantity of water can be decreased in any household. Secondly, by abiding by certain rules while using washing machines, like using them daily or weekly two times, etc., the inequity of water among households can be reduced.

Keywords:

Access of water,CART,Structural Equation Modeling,Urban Households,Water Inequity,

Refference:

I. Chandapillai, Jacob, K. P. Sudheer, and S. Saseendran. “Design of water distribution network for equitable supply.” Water resources management 26 (2012): 391-406. 10.1007/s11269-011-9923-x
II. Chang, Li-Yen, and Hsiu-Wen Wang. “Analysis of traffic injury severity: An application of non-parametric classification tree techniques.” Accident Analysis & Prevention 38.5 (2006): 1019-1027. 10.1016/j.aap.2006.04.009

III. Cobham, Alex, Lukas Schlögl, and Andy Sumner. “Inequality and the tails: the Palma proposition and ratio.” Global Policy 7.1 (2016): 25-36. 10.1111/1758-5899.12320
IV. Cole, Stroma. “A political ecology of water equity and tourism: A case study from Bali.” Annals of Tourism Research 39.2 (2012): 1221-1241. 10.1016/j.annals.2012.01.003
V. Conceição, Pedro, and James K. Galbraith. “Constructing long and dense time-series of inequality using the Theil index.” Eastern Economic Journal 26.1 (2000): 61-74. https://www.jstor.org/stable/40325968
VI. Flores Baquero, O., A. Jiménez Fdez. de Palencia, and Agustí Pérez Foguet. “Measuring disparities in access to water based on the normative content of the human right.” Social Indicators Research 127.2 (2016): 741-759. 10.1007/s11205-015-0976-8
VII. Ilaya-Ayza, Amilkar E., et al. “Implementation of DMAs in intermittent water supply networks based on equity criteria.” Water 9.11 (2017): 851. 10.3390/w9110851
VIII. Konisky, David M. “Inequities in enforcement? Environmental justice and government performance.” Journal of Policy Analysis and Management: The Journal of the Association for Public Policy Analysis and Management 28.1 (2009): 102-121. 10.1002/pam.20404
IX. Lele, Sharachchandra, et al. “Match, don’t mix: implications of institutional and technical service modalities for water governance outcomes in south Indian small towns.” Water Policy 20.S1 (2018): 12-35. 10.2166/wp.2018.002
X. Mason, Lisa Reyes. “Beyond improved access: Seasonal and multidimensional water security in urban Philippines.” Global Social Welfare 2 (2015): 119-128. 10.1007/s40609-014-0024-7
XI. Moglia, Magnus, et al. “Application of the water needs index: can Tho City, Mekong Delta, Vietnam.” Journal of Hydrology 468 (2012): 203-212. 10.1016/j.jhydrol.2012.08.036
XII. Molden, Olivia C., Anoj Khanal, and Nita Pradhan. “The pain of water: a household perspective of water insecurity and inequity in the Kathmandu Valley.” Water Policy 22.S1 (2020): 130-145. 10.2166/wp.2018.116
XIII. Motahar, S. “A neural network approach to estimate non-Newtonian behavior of nanofluid phase change material containing mesoporous silica particles.” International Journal of Engineering 34.8 (2021): 1974-1981. 10.5829/ije.2021.34.08b.18
XIV. Pande, Anurag, Mohamed Abdel-Aty, and Abhishek Das. “A classification tree based modeling approach for segment related crashes on multilane highways.” Journal of Safety Research 41.5 (2010): 391-397. 10.1016/j.jsr.2010.06.004
XV. Park, Soyoung, and Jinsoo Kim. “Landslide susceptibility mapping based on random forest and boosted regression tree models, and a comparison of their performance.” Applied Sciences 9.5 (2019): 942. 10.3390/app9050942
XVI. Poonia, Anamika, and Milap Punia. “Associates and determinants of drinking water supply: a case study along urbanrural continuum of semi-arid cities in India.” Urban Water Journal 16.10 (2019): 749-755. 10.1080/1573062X.2020.1729387
XVII. Ramesh, Nandini, et al. “RNA-recognition motif in Matrin-3 mediates neurodegeneration through interaction with hnRNPM.” Acta neuropathologica communications 8 (2020): 1-22. 10.1186/s40478-020-01021-5
XVIII. Ramirez, Sarah M., and Randall Stafford. “Equal and universal access?: water at mealtimes, inequalities, and the challenge for schools in poor and rural communities.” Journal of health care for the poor and underserved 24.2 (2013): 885-891. 10.1353/hpu.2013.0078
XIX. Robinson, Peter B. ““All for some”: water inequity in Zambia and Zimbabwe.” Physics and Chemistry of the Earth, Parts A/B/C 27.11-22 (2002): 851-857. 10.1016/S1474-7065(02)00080-3
XX. Sanatan Nayak, Sanatan Nayak. “Distributional inequality and groundwater depletion: an analysis across major states in India.” (2009): 89-107.
XXI. Seyoum, Selamawit, and Jay P. Graham. “Equity in access to water supply and sanitation in Ethiopia: an analysis of EDHS data (2000–2011).” Journal of Water, Sanitation and Hygiene for Development 6.2 (2016): 320-330. 10.2166/washdev.2016.004
XXII. Soares, Luiz Carlos Rangel, et al. “Inequities in access to and use of drinking water services in Latin America and the Caribbean.” Revista Panamericana de Salud Pública 11.5-6 (2002): 386-396. 10.1590/s1020-49892002000500013
XXIII. Srinivasan, Veena, and Seema Kulkarni. “Examining the emerging role of groundwater in water inequity in India.” Hydrosocial Territories and Water Equity. Routledge, 2017. 97-111. 10.1080/02508060.2014.890998
XXIV. Tiwale, Sachin, Maria Rusca, and Margreet Zwarteveen. “The power of pipes: Mapping urban water inequities through the material properties of networked water infrastructures-The case of Lilongwe, Malawi.” Water Alternatives 11.2 (2018): 314-335. http://www.water-alternatives.org/index.php/alldoc/articles/vol11/v11issue2/439-a11-2-6/file
XXV. Tiwale, Sachin. “Materiality matters: Revealing how inequities are conceived and sustained in the networked water infrastructure-The case of Lilongwe, Malawi.” Geoforum 107 (2019): 168-178. 10.1016/j.geoforum.2019.09.005

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Abstract:

It has been observed that married women refrain from utilizing contraceptive methods owing to a variety of reasons, including postpartum-amenorrhea, side-effects-worries, rarely or never having sex, no contraception while breastfeeding, or frustration with a particular method. The current study sought to investigate the sociodemographic disparities and contributing factors related to married women who are young (15–24 years old) and non-young (25–49 years old) and who do not utilize contemporary contraceptive methods. The research utilized cross-sectional data obtained from the fifth round of the National Family Health Survey (NFHS-5) to explore the determinants of contraceptive non-utilization among 175,652 married women in India. Multiple logistic regression analysis was utilized for the examination. The study found that non-young married females (25-49) had a higher prevalence of not using contemporary contraceptives (72%) compared to young married females (45.8%). The majority of non-users were from the central region (24.4%), followed by the east (17.4%), north-east (17.2%), north (15.9%), south (15.2%), and west region (9.9%). The majority of non-users were non-working and had male and female household heads. Most non-users knew about modern contraceptives but were unaware of family planning on radio and TV. Higher odds ratios in the non-use among married females were found significant among central-region females (OR=2.189, CI: 1.815-2.641), East-region (OR=1.324, CI: 1.148-1.527), South-region (OR=1.262, CI: 1.063-1.497), females who don’t know caste (OR=1.898, CI: 1.176-3.062), females having primary (OR=3.466, CI: 2.889-4.157) and secondary education (OR=2.281, CI: 1.890-2.753), females who used since last birth (OR=1.851, CI: 1.658-2.068) and never used (OR=1.632, CI: 1.474-1.806). The study found that economically disadvantaged females, household heads, and those with multiple children are less likely to avoid birth control methods, while marital status, religion, caste, education, and birth order did not have a significant impact on non-usage. The study highlights sociodemographic disparities in contraceptive use, emphasizing the need to address issues like low education, media exposure, and ignorance towards birth-control practices, and recommends immediate actions to reduce non-use among married females.  

Keywords:

Disparities,Modern Contraceptives,Non-use,SDGs,Young,Non-young,

Refference:

I. “Administrative Divisions of India.” Wikipedia, 23 Feb. 2024. Wikipedia, https://en.wikipedia.org/w/index.php?title=Administrative_divisions_of_India&oldid=1209761117#Zones_and_regions.
II. Dwivedi, L. K., et al. Youth in India: An NFHS Based Study. International Institute for Population Sciences (IIPS) and United Nations Population Fund (UNFPA). Mumbai: IIPS., 2020, http://www.iipsindia.ac.in.
III. Ewerling, Fernanda, et al. “Modern Contraceptive Use among Women in Need of Family Planning in India: An Analysis of the Inequalities Related to the Mix of Methods Used.” Reproductive Health, vol. 18, no. 1, Aug. 2021, p. 173. PubMed, 10.1186/s12978-021-01220-w.
IV. Gebeyehu, Natnael Atnafu, et al. “The Intention on Modern Contraceptive Use and Associated Factors among Postpartum Women in Public Health Institutions of Sodo Town, Southern Ethiopia 2019: An Institutional-Based Cross-Sectional Study.” BioMed Research International, vol. 2020, Oct. 2020, p. e9815465. www.hindawi.com, 10.1155/2020/9815465.
V. Ghike, Sunita, et al. “Awareness and Contraception Practices among Women—An Indian Rural Experience.” South Asian Federation of Obstetrics and Gynecology, January-April 2010;2(1):19-21, chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://jsafog.com/doi/JSAFOG/pdf/10.5005/jp-journals-10006-1053.
VI. Ghule, Mohan, et al. “Barriers to Use Contraceptive Methods among Rural Young Married Couples in Maharashtra, India: Qualitative Findings.” Asian Journal of Research in Social Sciences and Humanities, vol. 5, no. 6, 2015, pp. 18–33. PubMed Central, 10.5958/2249-7315.2015.00132.X.
VII. Girase, Rupal D., et al. “Study of Contraceptive Use in Married Women of Reproductive Age Group in Urban Slum Area of Solapur City.” International Journal of Community Medicine and Public Health, vol. 9, no. 4, Apr. 2022, 10.18203/2394-6040.ijcmph20220841.
VIII. Hamdanieh, Maya, et al. “Assessment of Sexual and Reproductive Health Knowledge and Awareness among Single Unmarried Women Living in Lebanon: A Cross-Sectional Study.” Reproductive Health, vol. 18, no. 1, Jan. 2021, p. 24. BioMed Central, 10.1186/s12978-021-01079-x.
IX. Hazarika, Indrajit. “Women’s Reproductive Health in Slum Populations in India: Evidence From NFHS-3.” Journal of Urban Health : Bulletin of the New York Academy of Medicine, vol. 87, no. 2, Mar. 2010, pp. 264–77. PubMed Central, 10.1007/s11524-009-9421-0.
X. Kargaonkar, Shriram N., et al. “Statistical Analysis of Socio-Demographic Inequalities in Contraceptive Knowledge and Its Usage among Young and Non- Young Females of the Reproductive Age Group in Maharashtra.” JJTU Journal of Renewable Energy Exchange, vol. 11, no. 11, 2023, pp. 95–104.
XI. “Status of Sexual and Reproductive Health of Females in India during the Reproductive Age Group: A Review.” JJTU Journal of Renewable Energy Exchange, vol. 11, no. 1, 2023, pp. 16–24.

XII. Kashyap, Gyan Chandra, et al. “A True Face of Indian Married Couples: Effect of Age and Education on Control over Own Sexuality and Sexual Violence.” PLOS ONE, vol. 16, no. 7, July 2021, p. e0254005. PLoS Journals, 10.1371/journal.pone.0254005.
XIII. Kumari, Suman, and Vasu Siotra. “Indian Females in the Twenty-First Century: How They Have Fared? An Analysis Using Geospatial Techniques.” GeoJournal, vol. 88, no. 4, Aug. 2023, pp. 4279–95. Springer Link, 10.1007/s10708-023-10865-y.
XIV. Mahawar, Priyanka, et al. “Contraceptive Knowledge, Attitude and Practices in Mothers of Infant: A Cross-Sectional Study.” National Journal of Community Medicine, vol. 2, no. 01, 01, June 2011, pp. 105–07.
XV. “Marital Status and Women Empowerment in India.” Sociology International Journal, vol. Volume 2, no. Issue 1, Feb. 2018. medcraveonline.com, 10.15406/sij.2018.02.00030.
XVI. Mukherjee, Ananya, et al. “Contraceptive Behavior and Unmet Need among the Tribal Married Women Aged 15-49 Years: A Cross-Sectional Study in a Community Development Block of Paschim Bardhaman District, West Bengal.” Indian Journal of Public Health, vol. 65, no. 2, 2021, pp. 159–65. PubMed, 10.4103/ijph.IJPH_115_21.
XVII. Nidhi, Sharma, et al. “Knowledge and Practice of Family Planning Among Married Women of Reproductive Age Group in Urban Slums of Amritsar City.” International Journal of Health Sciences and Research, vol. Vol.5; ;, Feb. 2015, pp. 42–48.
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XX. Sharma, Himani, and Shri Kant Singh. “Socioeconomic Inequalities in Contraceptive Use among Female Adolescents in South Asian Countries: A Decomposition Analysis.” BMC Women’s Health, vol. 22, May 2022, p. 151. PubMed Central, 10.1186/s12905-022-01736-8.
XXI. Singh, Mayank, et al. “Patterns in Age at First Marriage and Its Determinants in India: A Historical Perspective of Last 30 Years (1992–2021).” SSM – Population Health, vol. 22, Feb. 2023, p. 101363. ResearchGate, 10.1016/j.ssmph.2023.101363.
XXII. Sowmya, et al. “Contraceptives Utilization and Barriers in Karnataka, Southern India: A Survey on Women Residing in Slums.” Clinical Epidemiology and Global Health, vol. 8, no. 4, Dec. 2020, pp. 1077–81. ScienceDirect, 10.1016/j.cegh.2020.03.023.
XXIII. Srivastava, Shobhit, et al. “Socio-Economic Inequalities in Non-Use of Modern Contraceptives among Young and Non-Young Married Women in India.” BMC Public Health, vol. 23, no. 1, May 2023, p. 797. PubMed, 10.1186/s12889-023-15669-w.
XXIV. “Socio-Economic Inequalities in Non-Use of Modern Contraceptives among Young and Non-Young Married Women in India.” BMC Public Health, vol. 23, no. 1, May 2023, p. 797. PubMed, 10.1186/s12889-023-15669-w.
XXV. Teklehaymanot, Huluf Abraha, et al. Intentions on Contraception Use and Its Associated Factors among Postpartum Women in Aksum Town,Tigray Region, Northern Ethiopia: A Community-Based Cross-Sectional Study.
XXVI. Thulaseedharan, Jissa Vinoda. “Contraceptive Use and Preferences of Young Married Women in Kerala, India.” Open Access Journal of Contraception, vol. 9, Jan. 2018, pp. 1–10. PubMed Central, 10.2147/OAJC.S152178.
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Abstract:

An infrared camera is the best tool for identifying the temperatures in various parts of thermal power plants. In this paper, authors identified the probable faults in different parts of the Boiler using temperature. A well-liked and secure technique to use in condition monitoring and preventative maintenance is infrared thermography. This procedure is directly applied to determine whether the machine is operating normally or not and also to identify the heat pattern that indicates inefficiency and flaws within the machine. For the asset manager, it is the best practice. This procedure lowers the danger and increases operational dependability. Checking bearings and belts, and keeping an eye on electrical rooms, panel boards, rotating motors, boiler operations, etc., are all made easier using infrared thermography. This method helps pinpoint the precise site of the equipment or machine malfunction. It can avoid events that can happen due to destruction from heat radiation and save energy, money, time, and money on repairs. If the operations are prepared for the obstacles, they can be managed successfully. This paper presents details of an Expert system for Boilers in Thermal power plants.

Keywords:

Boiler,Catastrophic Failures,Condition Monitoring,Expert system,IR Thermography,

Refference:

I. Abraham, A. Handbook of Measuring System: Design Rule-Based Expert Systems; John Wiley & Sons: New York, NY, USA, 2005.
II. Akash Singh, Vivek Sharma, Siddhant Mittal, GopeshPandey, DeepaMudgal, Pallav Gupta An overview of problems and solutions for components subjected to freside of boilers- International Journal of Industrial Chemistry (2018) 9:1–15 https://doi.org/10.1007/s40090-017-0133-0
III. ArkaSen, Manik Chandra Majumder, SumitMukhopadhyay and Robin Kumar Biswas, (2017) Multi Attribute Decision Making in Selection of the Most Significant Condition Monitoring Methodology for Rotating Machinery. International Journal of Mechanical Engineering and Technology, 8(3), 2017, pp. 254–263.
IV. Bogdan, M.; Błachnio, J.; Kułaszka, A.; Derlatka, M. Assessing the Condition of Gas Turbine Rotor Blades with the Optoelectronic and Thermographic Methods. Metals 2019, 9, 31.
V. Buchanan, B.G.; Smith, R.G. Fundamentals of Expert Systems. Annu. Rev. Comput. Sci. 1988, 3, 23–58.
VI. Chlebus, E.; Krot, K.; Kuliberda, M. Hybrid Artificial Intelligent Systems: Rule-Based Expert System Dedicated for Technological Applications; Springer: Berlin/Heidelberg, Germany, 2011; pp. 373–380.
VII. Development of an expert system for condition monitoring of submarines using ir thermography MojeswaraRaoDuduku, Kavuluri Lakshmi Narayana, KavuluriVenkataRamana and Chintalapati Sridhar Yesaswi Mechanical Engineering, K L University, Vaddeswaram, 522502, India International Journal of Mechanical Engineering and Technology (IJMET) Volume 8, Issue 4, April 2017, pp. 26–33 Article ID: IJMET_08_04_004 Available online at ttp://iaeme.com/Home/issue/IJMET?Volume=8&Issue=4 ISSN Print: 0976-6340 and ISSN Online: 0976-6359
VIII. Finestum: IR Thermography in marine applications Dated:-28 December 2014, http://finestum.blogspot.in/2014/12/finestum-ir-thermography-in-marine.html.
IX. Gordana M. Bakić,Vera M. ŠijačkiŽeravčić Probability of Failure of Thermal Power Plant Boiler Tubing System Due to Corrosion- FME Transactions (2007) 35, 47-54
X. Infrared Thermography in Marine applications, www.hrcak.srce.hr/file/39840
XI. Infrared Thermography in Marine Industry, http://www.irinfo.org/06-01-2004-handlin/
XII. Khalid, S.; Song, J.; Raouf, I.; Kim, H.S. Advances in Fault Detection and Diagnosis for Thermal Power Plants: A Review of Intelligent Techniques. Mathematics 2023, 11, 1767
XIII. Lahiri, B.B.; Bagavathiappan, S.; Jayakumar, T.; Philip, J. Medical applications of infrared thermography: A review. Infrared Phys. Technol. 2012, 55, 221–235.
XIV. Lisowska, A. Thermographic monitoring of the power transformers. Meas. Autom. Monit. 2017, 63, 154–157. Appl. Sci. 2019, 9, 2253 20 of 22
XV. López-Pérez, D.; Antonino-Daviu, J. Application of Infrared Thermography to Failure Detection in Industrial Induction Motors: Case Stories. IEEE Trans. Ind. Appl. 2017, 53, 1901–1908.
XVI. Meola, C.; Boccardi, S.; Carlomagno, G.M. Infrared Thermography in the Evaluation of Aerospace Composite Materials: Infrared Thermography to Composites; Woodhead Publishing: Cambridge, UK, 2016.
XVII. Osornio-Rios, R.A.; Antonino-Daviu, J.A.; Jesus Romero-Troncoso, R. Recent Industrial Applications of Infrared Thermography: A Review. IEEE Trans. Ind. Inform. 2019, 15, 615–625.
XVIII. Ping Yang SuiSheng Liu Fault Diagnosis for Boilers in Thermal Power Plant by Data Mining- 2004 8th tnternational Conference on Control, Automation. Robotics and Vision Kunming, China, 6-9th December 2004
XIX. Swiderski, W. IR Thermography Nondestructive Testing Methods of Composite Materials Used in Aerospace Applications. In Proceedings of the 12th International Conference on Quantitative Infrared Thermography, The e-Journal of Nondestructive Testing, Mahabalipuram, India, 6–10 July 2016.
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XXII. Yuanyuan Li* , Zhenning Zhao and Yipeng Sun Common Problems of 600 MW Grade Thermal Power Plants- IOP Conf. Series: Materials Science and Engineering 394 (2018) 042036 doi:10.1088/1757-899X/394/4/042036

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Abstract:

Machine learning-based crop recommendation models are invaluable tools for enhancing modern AI-based farming, assisting in decisions about the selection of crops to optimize yield performance and growth. This research introduces an intelligent strategy and explainable artificial intelligence (XAI) principles based on the Convolutional Neural Network (CNN) method due to the growing demand for interpretability in modern farming decision-making, Utilizing the "Smart Agricultural Production Optimizing Engine” dataset procured from Kaggle. The proposed CNN model gives remarkable results through a comprehensive examination of soil and environmental boundaries like Nitrogen (N), Phosphorus (P), Potassium (K) levels, temperature, moistness, pH, and precipitation. Our results illustrate that the proposed framework essentially moves forward the precision of trim suggestions, advertising a promising arrangement for modernizing agricultural practices and guaranteeing maintainable crop yields.

Keywords:

Accuracy Rates,Agricultural Parameters,Convolutional Neural Network (CNN),Crop Recommendation Systems,Precision Agriculture,

Refference:

I. Anand, T., Sinha, S., Mandal, M., Chamola, V., & Yu, F. R. (2021). AgriSegNet: Deep aerial semantic segmentation framework for IoT-assisted precision agriculture. IEEE Sensors Journal, 21(16), 17581-17590.
II. Jin, X. B., Yu, X. H., Wang, X. Y., Bai, Y. T., Su, T. L., & Kong, J. L. (2020). Deep learning predictor for sustainable precision agriculture based on Internet of things system. Sustainability, 12(4), 1433.
III. Asish Mitra, Numerical Simulation Of Laminar Convection Flow And Heat Transfer At The Lower Stagnation Point Of A Solid Sphere., J. Mech. Cont.& Math. Sci., Vol.10, No.1, Pp 1469-1480, 2015
IV. Anguraj K, Thiyaneswaran B, Megashree G, Shri JP, Navya S, Jayanthi J. Crop recommendation on analyzing soil using machine learning. Turkish Journal of Computer and Mathematics Education. 2021; 12(6):1784-91.
V. Bhadouria R, et al. (2019) Agriculture in the era of climate change: Consequences and effects. In Climate Change and Agricultural Ecosystems, Elsevier, 1–23.
VI. Barburiceanu, S., Meza, S., Orza, B., Malutan, R., & Terebes, R. (2021). Convolutional neural networks for texture feature extraction. Applications to leaf disease classification in precision agriculture. IEEE Access, 9, 160085-160103.
VII. Bakthavatchalam K, Karthik B, Thiruvengadam V, Muthal S, Jose D, Kotecha K, et al. IoT framework for measurement and precision agriculture: predicting the crop using machine learning algorithms. Technologies. 2022; 10(1).
VIII. Hassan, H. Khan, I. Uddin, A. Sajid, “Optimal Emerging trends of Deep Learning Technique for Detection based on Convolutional Neural Network”, Bulletin of Business and Economics (BBE), Vol.12, No.4, pp. 264-273, 2023
IX. H. Khan, A. Ali, S. Alshmrany, “Energy-Efficient Scheduling Based on Task Migration Policy Using DPM for Homogeneous MPSoCs”, Computers, Materials & Continua, Vol.74, No.1, pp. 965-981, 2023
X. H. Sarwar, H. Khan, I. Uddin, R. Waleed, S. Tariq, “An Efficient E-Commerce Web Platform Based on Deep Integration of MEAN Stack Technologies”, Bulletin of Business and Economics (BBE), Vol. 12, No.4, pp. 447-453, 2023.

XI. Hammad. A , E. Zhao, “Mitigating link insecurities in smart grids via QoS multi-constraint routing“, In 2016 IEEE International Conference on Communications Workshops (ICC)”, pp. 380-386. 2016
XII. H. Khan, I. Uddin, A. Ali, M. Husain, “An Optimal DPM Based Energy-Aware Task Scheduling for Performance Enhancement in Embedded MPSoC” Computers, Materials & Continua, Vol.74, No.1, pp. 2097-2113, 2023
XIII. Hammad, A. A., Ahmed, “Deep Reinforcement Learning for Adaptive Cyber Defense in Network Security”, In Proceedings of the Cognitive Models and Artificial Intelligence Conference, pp. 292-297, 2016
XIV. H. Khan, M. U. Hashmi, Z. Khan, R. Ahmad, “Offline Earliest Deadline first Scheduling based Technique for Optimization of Energy using STORM in Homogeneous Multi-core Systems,” IJCSNS Int. J. Comput. Sci. Netw. Secure, Vol.18, No.12, pp 125-130, 2018
XV. Hossein Shirazi, Bruhadeshwar. B,”Kn0w Thy Doma1n Name”: Unbiased Phishing Detection Using Domain Name Based Features. In Proceedings Of The 23nd Acm On Symposium On Access Control Models And Technologies (Sacmat ’18). Association For Computing Machinery, New York, NY, USA, pp. 69-75, 2018
XVI. Hussain, S., Rajput, U. A., Kazi, Q. A., & Mastoi, S, “Numerical investigation of thermohydraulic performance of triple concentric-tube heat exchanger with longitudinal fins”, J. Mech. Cont. & Math. Sci, Vol. 16, No. 8, pp 61-73, 2021.
XVII. H. Khan, S. Ahmad, N. Saleem, M. U. Hashmi, Q. Bashir, “Scheduling Based Dynamic Power Management Technique for offline Optimization of Energy in Multi Core Processors” Int. J. Sci. Eng. Res, Vol.9, No.12, pp 6-10, 2018
XVIII. H. Khan, K. Janjua, A. Sikandar, M. W. Qazi, Z. Hameed, “An Efficient Scheduling based cloud computing technique using virtual Machine Resource Allocation for efficient resource utilization of Servers” In 2020 International Conference on Engineering and Emerging Technologies (ICEET), IEEE, pp 1-7, 2020
XIX. Hammad, M., Jillani, R. M., Ullah, S., Namoun, A., Tufail, A., Kim, K. H., & Shah, H, “Security framework for network-based manufacturing systems with personalized customization”, An industry 4.0 approach, Sensors, vol. 23. No. 17-55, 2022
XX. H. Khan, Q. Bashir, M. U. Hashmi, “Scheduling based energy optimization technique in multiprocessor embedded systems” In 2018 International Conference on Engineering and Emerging Technologies (ICEET), IEEE, pp 1-8, 2018
XXI. H. Khan, A. Yasmeen, S. Jan, U. Hashmi, “Enhanced Resource Leveling Indynamic Power Management Techniqueof Improvement In Performance For Multi-Core Processors”, Journal Of Mechanics Of Continua And Mathematical Sciences, Vol.6, No.14, pp. 956-972, 2019
XXII. H. Khan, K. Janjua, A. Sikandar, M. W. Qazi, Z. Hameed, “An Efficient Scheduling based cloud computing technique using virtual Machine Resource Allocation for efficient resource utilization of Servers” In 2020 International Conference on Engineering and Emerging Technologies (ICEET), IEEE, pp 1-7, 2020
XXIII. H. Huang, J. Tan And L. Liu, “Countermeasure Techniques For Deceptive Phishing Attack”, International Conference On New Trends In Information And Service Science, Beijing, pp. 636-641, 2009.
XXIV. H. Khan, M. U. Hashmi, Z. Khan, R. Ahmad, “Offline Earliest Deadline first Scheduling based Technique for Optimization of Energy using STORM in Homogeneous Multi-core Systems” IJCSNS Int. J. Comput. Sci. Netw. Secure, Vol.18, No.12, pp 125-130, 2018
XXV. H. Khan, M. U. Hashmi, Z. Khan, R. Ahmad, A. Saleem, “Performance Evaluation for Secure DES-Algorithm Based Authentication & Counter Measures for Internet Mobile Host Protocol” IJCSNS Int. J. Comput. Sci. Netw. Secure, Vol.18, No.12, pp 181-185, 2018
XXVI. M. Y. A. Khan, F. Khan, H. Khan, S. Ahmed, M. Ahmad, “Design and Analysis of Maximum Power Point Tracking (MPPT) Controller for PV System” Journal of Mechanics of Continua and Mathematical Sciences, Vol.14, No.1, pp 276-288, 2019
XXVII. M. Y. A. Khan, “A GSM based Resource Allocation technique to control Autonomous Robotic Glove for Spinal Cord Implant paralysed Patients using Flex Sensors”, Sukkur IBA Journal of Emerging Technologies, Vol.3, No.2, pp 13-23, 2020
XXVIII. M. Y. A. Khan, “A high state of modular transistor on a 105 kW HVPS for X-rays tomography Applications”, Sukkur IBA Journal of Emerging Technologies, Vol.2, No.2, pp 1-6, 2019
XXIX. M. Shah, S. Ahmed, K. Saeed, M. Junaid, H. Khan, “Penetration testing active reconnaissance phase–optimized port scanning with nmap tool” In 2019 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), IEEE, pp 1-6, 2019
XXX. M. Y. A. Khan, M. Ibrahim, M. Ali, H. Khan, E. Mustafa, “Cost-Benefit Based Analytical Study of Automatic Meter Reading (AMR) and Blind Meter Reading (BMR) used by PESCO (WAPDA),” In 2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), IEEE, pp 1-7, 2020
XXXI. M. Y. A. Khan, “Enhancing Energy Efficiency in Temperature Controlled Dynamic Scheduling Technique for Multi-Processing System on Chip” Sukkur IBA Journal of Emerging Technologies, Vol.2, No.2, pp 46-53,2019
XXXII. M. U. Hashmi, S. A. ZeeshanNajam, “Thermal-Aware Real-Time Task Schedulabilty test for Energy and Power System Optimization using Homogeneous Cache Hierarchy of Multi-core Systems” Journal of Mechanics of Continua and Mathematical Sciences, Vol.14, No.4, pp 442-452, 2023
XXXIII. M. Y. A. Khan, U. Khalil, H. Khan, A. Uddin, S. Ahmed, “Power flow control by unified power flow controller” Engineering, Technology & Applied Science Research, Vol.9, No.2, pp 3900-3904, 2019
XXXIV. R. Waleed, A. Ali, S. Tariq, G. Mustafa, H. Sarwar, S. Saif, I. Uddin, “An Efficient Artificial Intelligence (AI) and Internet of Things (IoT’s) Based MEAN Stack Technology Applications” Bulletin of Business and Economics (BBE), Vol.13, No.2, pp 200-206, 2024
XXXV. S. Khan, I. Ullah, M. U. Rahman, H. Khan, A. B. Shah, R. H. Althomali, M. M. Rahman, “Inorganic-polymer composite electrolytes: basics, fabrications, challenges and future perspectives” Reviews in Inorganic Chemistry, Vol.44, No.3, pp 1-29, 2024.
XXXVI. S. Khan, I. Ullah, H. Khan, F. U. Rahman, M. U. Rahman, M. A. Saleem, A. Ullah, “Green synthesis of AgNPs from leaves extract of Salvia Sclarea their characterization, antibacterial activity and catalytic reduction ability” Zeitschrift für Physikalische Chemie, Vol.238, No.5, pp 931-947, 2024

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