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Abstract:

In this paper, we present an innovative type of contraction within the framework of T-distance spaces that utilize a b-metric. This advancement allows for the derivation of a new collection of fixed-point results. Additionally, we substantiate our conclusions by offering an illustrative example and showcasing a practical application. Ultimately, we have also derived several fixed-point results that are grounded in our primary findings.

Keywords:

Banach,b-metric spaces,Fixed point,Geraghty contractions,Non-linear contraction,

Refference:

I. Abodayeh, K., Bataihah, A., and Shatanawi, W., 2017. “Generalized Ω-Distance Mappings and Some Fixed Point Theorems.” UPB Sci. Bull., Ser. A, 79, pp. 223–232.
II. Abodayeh, K., Shatanawi, W., Bataihah, A., and Ansari, A.H., 2017. “Some Fixed Point and Common Fixed Point Results Through Ω-Distance Under Nonlinear Contractions.” Gazi Univ. J. Sci., 30(1), pp. 293–302.
III. Abu-Irwaq, I., Shatanawi, W., Bataihah, A., and Nuseir, I., 2019. “Fixed Point Results for Nonlinear Contractions with Generalized Ω-Distance Mappings.” UPB Sci. Bull., Ser. A, 81(1), pp. 57–64.
IV. Al-Tawil, A., Hazaymeh, A., and Bataihah, A., 2025. “Fixed Point Results in 𝜔𝑡-Distance Mappings for Geraghty Type Contractions.” Int. J. Neutrosophic Sci., 26(1), pp. 1–14. 10.54216/IJNS.260101.
V. Aydi, H., Karapinar, E., and Postolache, M., 2012. “Tripled Coincidence Point Theorems for Weak -Contractions in Partially Ordered Metric Spaces.” Fixed Point Theory Appl., 2012, pp. 1–12.
VI. Bakhtin, I.A., 1989. “The Contraction Mapping Principle in Quasimetric Spaces.” Funct. Anal., 30, pp. 26–37.
VII. Banach, S., 1922. “Sur Les Opérations Dans Les Ensembles Abstraits Et Leur Application Aux Équations Intégrales.” Fundam. Math., 3(1), pp. 133–181.
VIII. Bataihah, A., 2024. “Some Fixed Point Results with Application to Fractional Differential Equation via New Type of Distance Spaces.” Results Nonlinear Anal., 7(3), pp. 202–208. 10.31838/rna/2024.07.03.015
IX. Bataihah, A., and Hazaymeh, A., 2025. “Neutrosophic Fuzzy Metric Spaces and Fixed Points Results with Integral Contraction Type.” Int. J. Neutrosophic Sci., 25(3), pp. 561–572. 10.54216/IJNS.250344
X. Bataihah, A., and Hazaymeh, A., 2025. “Quasi Contractions and Fixed Point Theorems in the Context of Neutrosophic Fuzzy Metric Spaces.” Eur. J. Pure Appl. Math., 18(1), p. 5785. 10.29020/nybg.ejpam.v18i1.5785.
XI. Bataihah, A., and Qawasmeh, T., 2024. “A New Type of Distance Spaces and Fixed Point Results.” J. Math. Anal., 15(4), pp. 81–90. 10.54379/jma-2024-4-5.
XII. Bataihah, A., Qawasmeh, T., Batiha, I.M., Jebril, I.H., and Abdeljawad, T., 2024. “Gama Distance Mappings with Application to Fractional Boundary Differential Equation.” J. Math. Anal., 15(5), pp. 99–106. 10.54379/jma-2024-5-7
XIII. Bataihah, A., Qawasmeh, T., and Shatnawi, M., 2022. “Discussion on b-Metric Spaces and Related Results in Metric and G-Metric Spaces.” Nonlinear Funct. Anal. Appl., 27(2), pp. 233–247. 10.22771/nfaa.2022.27.02.02
XIV. Bataihah, A., Shatanawi, W., Qawasmeh, T., and Hatamleh, R., 2020. “On H-Simulation Functions and Fixed Point Results in the Setting of ωt-Distance Mappings with Application on Matrix Equations.” Mathematics, 8(5), p. 837. 10.3390/math8050837
XV. Bataihah, A., Shatanawi, W., and Tallafha, A., 2020. “Fixed Point Results with Simulation Functions.” Nonlinear Funct. Anal. Appl., 25(1), pp. 13–23.10.2298/FIL1608343K
XVI. Bataihah, A., Tallafha, A., and Shatanawi, W., 2020. “Fixed Point Results with Ω-Distance by Utilizing Simulation Functions.” Ital. J. Pure Appl. Math., 43, pp. 185–196.
XVII. Czerwik, S., 1993. “Contraction Mappings in b-Metric Spaces.” Acta Math. Inform. Univ. Ostraviensis, 1(1), pp. 5–11.
XVIII. Geraghty, M.A., 1973. “On Contractive Mappings.” Proc. Am. Math. Soc., 40(2), pp. 604–608.
XIX. Hajjat, M., Bataihah, A., and Hazaymeh, A., 2025. “Some Results on Fixed Points in Generalized Metric Spaces via an Auxiliary Function.” Int. J. Neutrosophic Sci., 26(1), pp. 171–180. 10.54216/IJNS.260115
XX. Hazaymeh, A., and Bataihah, A., 2024. “Results on Fixed Points in Neutrosophic Metric Spaces Through the Use of Simulation Functions.” J. Math. Anal., 15(6), pp. 47–57. 10.54379/jma-2024-6-4.
XXI. Hazaymeh, A.A., and Bataihah, A., 2025. “Neutrosophic Fuzzy Metric Spaces and Fixed Points for Contractions of Nonlinear Type.” Neutrosophic Sets Syst., 77, pp. 96–112. 10.5281/zenodo.14113784
XXII. Karapınar, E., and Fulga, A., 2023. “Discussions on Proinov-C_b-Contraction Mapping on b-Metric Space.” J. Funct. Spaces, 2023(1), p. 1411808. 10.1155/2023/1411808
XXIII. Karapınar, E., Romaguera, S., and Tirado, P., 2022. “Characterizations of Quasi-Metric and G-Metric Completeness Involving w-Distances and Fixed Points.” Demonstr. Math., 55(1), pp. 939–951. 10.1515/dema-2022-0177
XXIV. Malkawi, A.R.M., Talafhah, A., and Shatanawi, W., 2021. “Coincidence and Fixed Point Results for Generalized Weak Contraction Mapping on b-Metric Spaces.” Nonlinear Funct. Anal. Appl., 26(1), pp. 177–195. 10.22771/nfaa.2021.26.01.13
XXV. Malkawi, A., Tallafha, A., and Shatanawi, W., 2022. “Coincidence and Fixed Point Results for (Ψ, L)-m-Weak Contraction Mapping on MR-Metric Spaces.” Ital. J. Pure Appl. Math., 47, pp. 751–768.
XXVI. Qawasmeh, T., Shatanawi, W., Bataihah, A., and Tallafha, A., 2019. “Common Fixed Point Results for Rational (α,β)_φ-mω Contractions in Complete Quasi Metric Spaces.” Mathematics, 7(5), p. 392. 10.3390/math7050392
XXVII. Qawasmeh, T., Shatanawi, W., Bataihah, A., and Tallafha, A., 2021. “Fixed Point Results and (α,β )-Triangular Admissibility in the Frame of Complete Extended b-Metric Spaces and Application.” UPB Sci. Bull., Ser. A Appl. Math. Phys., 1, pp. 113–124.
XXVIII. Shatanawi, W., Abodaye, K.K., and Bataihah, A., 2016. “Fixed Point Theorem Through -Distance of Suzuki Type Contraction Condition.” Gazi Univ. J. Sci., 29(1), pp. 129–133.
XXIX. Shatanawi, W., and Bataihah, A., 2021. “Remarks on G-Metric Spaces and Related Fixed Point Theorems.” Thai J. Math., 19(2), pp. 445–455. https://thaijmath2.in.cmu.ac.th/index.php/thaijmath/article/view/1168
XXX. Shatanawi, W., Maniu, G., Bataihah, A., and Ahmad, F.B., 2017. “Common Fixed Points for Mappings of Cyclic Form Satisfying Linear Contractive Conditions with Omega-Distance.” UPB Sci. Bull., Ser. A, 79, pp. 11–20.
XXXI. Shatanawi, W., Qawasmeh, T., Bataihah, A., and Tallafha, A., 2021. “New Contractions and Some Fixed Point Results with Application Based on Extended Quasi b-Metric Space.” UPB Sci. Bull., Ser. A Appl. Math. Phys., 83(2), pp. 39–48.
XXX. Hazaymeh, A. (2025). Time Fuzzy Soft Sets and its application in design-making. International Journal of Neutrosophic Science, 25(3), 37-50.
XXXI. Hazaymeh, A. (2025). Time Factor’s Impact On Fuzzy Soft Expert Sets International Journal of Neutrosophic Science,25 (3), 155-176.
XXXII. Hazaymeh, A. (2024). Time Effective Fuzzy Soft Set and Its Some Applications with and Without a Neutrosophic. International Journal of Neutrosophic Science, (2), 129-29.
XXXIII. Hazaymeh, A. A. M. (2013). Fuzzy Soft Set And Fuzzy Soft Expert Set: Some Generalizations And Hypothetical Applications (Doctoral dissertation, Universiti Sains Islam Malaysia).
XXXIV. Tahat, A. N., Ahmed, J. H., & Hazaymeh, A. (2025). Shape preserving monotonic and convex data interpolation using rational cubic ball functions. International Journal of Neutrosophic Science, 25(3), 489-89.
XXXV Al-Odat, N. A. “Modification in ratio estimator using rank set sampling.” European Journal of Scientific Research 29.2 (2009): 265-268.
XXXVI Al-Odat, N. A., et al. “Moving extreme ranked set sampling for simple linear regression.” Statistica & Applicazioni 2 (2009): 24-37.
XXXVII Odat, Naser. Epanechnikov-pareto Distribution with Application. International Journal of Neutrosophic Science, vol., no., 2025, pp. 147-155. 10.54216/IJNS.250412

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Abstract:

The present paper deals with the problem of obtaining a unique spline function for approximating a polynomial function. We have given values of the polynomial; its first derivatives are at the node points and also the third derivatives are given at the knot points of the unit interval I = [0, 1]. The problem is solved majorly in two parts, the first part shows the unique existence of the interpolatory spline function and the second part deals with the convergence theorem and error bounds. Later we discussed its applications for computer-aided design and image processing also.

Keywords:

Approximation,Computer-aided design,Image processing,lacunary Interpolation,Modulus of continuity,Spline functions,Taylor’s theorem,error bounds,

Refference:

I. A. K. Verma and A. Sharma, Some interpolatory properties of chebycheff polynomials (0, 2) case modified.Pub l Math. Debrencen., Hung., 336-349, 1961. 10.1215/S0012-7094-61-02842-3
II. Ambrish Kumar Pandey, Q S Ahmad, Kulbhushan Singh, “Lacunary Interpolation (0, 2; 3) Problem and Some Comparison from Quartic Splines”, American Journal of Applied Mathematics and Statistics, Vol. 1, No. 6, 117-120, 2013. 10.12691/ajams-1-6-2
III. Arunesh Kumar Mishra, Kulbhushan Singh, Akhilesh Kumar Mishra,“Spline Function Interpolation Techniques for Generating Smooth Curve”, Journal of Mechanics of Continua and Mathematical Sciences, Vol.-19, No.-9, September (2024) pp 93-103, ISSN: 2454-7190. 10.26782/jmcms.2024.09.00009
IV. Arunesh Kumar Mishra, KulbhushanSingh,“Computational spline interpolation algorithm for solving two point boundary value problems”,4th National Conference 4th National Conference On Recent Advancement In Physical Sciences: NCRAPS- 19–20 December 2022 Srinagar, India., Scopus Indexed, 10.1063/5.0201832
V. Burkett, J. and Verma, A.K.; On Birkhoff Interpolation (0;2) case, Aprox. Theory and its Appl. , 11;2, (June-1995). 10.1007/BF02836279
VI. Carl de Boor, A Practical Guide to Splines, Springer; 4, 16, 1978. A practical guide to splines : De Boor, Carl : Free Download, Borrow, and Streaming : Internet Archive
VII. Chawala, M. M., Jain, M.K. and Subramanian, R.; On numerical Integration of a singular two-point boundary value problem Inter. J. Computer. Math. Vol 31, 187-194, (1990). 10.1080/00207169008803801
VIII. Cheney, E. W.; Interpolation to approximation Theory, Mc Graw Hill, New York, (1966). Introduction to approximation theory by Cheney, E. W. | Open Library
IX. Davydov, O.; On almost Interpolation, Journal of Approx. Theory 91(3), 396-418, (1997). 10.1006/jath.1996.3094
X. Faiz Atahar, Kulbhushan Singh, “Almost Heptic Splines for Modified (0,2,4,6)”, Stochastic Modeling and Applications, Vol. 25 No. 2 pp. 255-251 (Jul-Dec., 2021) ISSN: 0972-3641. https://www.mukpublications.com/sma-vol-25-2-2021.php
XI. Faiz Atahar, Kulbhushan Singh, “Obtaining an Almost quintic spline function in complex plane”, Design Engineering, Year 2021. Issue 9, pp 14703-14708 ISSN: 0011-9342. https://www.scopus.com/sourceid/28687
XII. J. Balazs and P. Turan, Notes on interpolation II. Acta Math. Acad. Sci. Hung., Vol. 8, 195-21,1957. 10.1007/BF02025243
XIII. J. H. Ahlberg, E. N. Nilson, J. L. Walsh, “The Theory of Splines and their Applications”, AcademicPress INC. (London), Chapter 1, pp.1-72, (1967). AHLBERG, Nilson and Walsh – The theory of Splines and Their Applications.pdf
XIV. Jhunjhunwala, N. and Prasad, J.; On some regular and singular problems of Birkhoff interpolation, Internet. J. Math. & Math. Sci. 17 No.2, 217-226, (1994). 10.1155/S0161171294000335
XV. K. B. Singh , Ambrish Kumar Pandey, and QaziShoeb Ahmad, “Solution of a Birkhoff Interpolation Problem by a Special Spline Function”, International J. of Comp. App., Vol.48, 22-27, June 2012. 10.5120/7376-0174
XVI. Kulbhushan Singh, Ambrish Kumar Pandey, “Lacunary Interpolation at odd and Even Nodes”, International J. of Comp. Applications. Vol. (153) 1, 6. Nov 2016. http://pubs.sciepub.com/ajams/1/6/2/index.html
XVII. Kulbhushan Singh, Ambrish Kumar Pandey, “Using a Quartic Spline Function for Certain Birkhoff Interpolation Problem, “ International Journal of Computer Applications Vol. 99– No.3, August 2014. 10.5120/17357-7866

XVIII. Kulbhushan Singh, “A Special Quintic Spline for (0, 1, 4) Lacunary Interpolation and Cauchy Initial Value Problem”, Journal of Mechanics of Continua and Mathematical Sciences” ISSN: 2454-7190, Vo;.-14, No.-3. 10.26782/jmcms.2019.08.00044.
XIX. Lorentz, G. G. ; Approximation Theory, Academic Press Inc. New York, (1973). Approximation Theory by G. Lorentz | Open Library
XX. Lorentz, G. G., Jetter, K. Riemen Schneider, S.D.; Birkhoff Interpolation, Addison-Wesley Publishing, (1983).
XXI. Ramanand Mishra, Akhilesh Kumar Mishra, Kulbhushan Singh“Construction of A Spline Function With Mixed Node Values”, Journal of Mechanics of Continua and Mathematical Sciences, Vol.19, No.-01, Jan. 2024, pp 15-26, ISSN: 2454-7190. 10.26782/jmcms.2024.01.00002
XXII. Ramanand Mishra, Akhilesh Kumar Mishra, Kulbhushan Singh, “Solving A Birkhoff Interpolation Problem For (0,1,5) Data”, Turkish Journal of Computer and Mathematics Education, Vol.12 No.12(2021), 4941-4944, e-ISSN 1309-4653. 10.54060/a2zjournals.jase.30
XXIII. Saxena, A., Singh Kulbhushan; Lacunary Interpolation (0; 0,3) and (0; 0,1,4,) Cases, Journal of Indian Mathematical Society, Vadodara, India.Vol.65 No. 1-4, pp.171-180, (1997). www.indianmathsociety.org.in
XXIV. Saxena, A., Singh Kulbhushan; Lacunary Interpolation by Quintic splines, Vol.66 No.1-4, 0-00, Journal of Indian Mathematical Society, Vadodara, India, (1999). www.indianmathsociety.org.in
XXV. Singh Kulbhushan, Ambrish Pandey, “Lacunary Interpolation (0, 2; 3 Problem” South Pacific Journal of Pure and Appl. Maths,Vol.1 No. 1 pp 49-63, Nov. 2012. www.unitech.ac.pg
XXVI. Singh Kulbhushan ; “Interpolation by quartic splines” African Jour. of Math. And Comp. Sci. Vol. 4(10), pp. 329 – 333, 15 September, 2011. 10.5897/AJMCSR.9000072
XXVII. Singh Kulbhushan; “Lacunary Odd Degree Spline of Higher Order” South Pacific Journal of Pure and Appl. Maths. Vol.1 No. 1 p.p 28-33, Nov.2012. www.unitech.ac.pg
XXVIII. Singh Kulbhushan, Mishra A. K., “A Special Case of Modified Lacunary Interpolation Splines”, International J. of Maths. Sci. &Engg. Appls. (IJMSEA), Vol. 6 No. V, pp. 403-415, Sept. 2012. www.ascent-journals.com

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Abstract:

The evaluation of cracking and displacement in reinforced concrete beams is critical for assessing structural performance and stiffness during service. The effectiveness of the digital image correlation (DIC) method was investigated for evaluating cracks and displacements in reinforced concrete beams. The beams had varying reinforcement ratios and concrete covers as key parameters. Specimens were tested under two-point loading, and the results were recorded using traditional measurement instruments. These results were then compared with the DIC results. The findings demonstrate that the DIC method accurately determines midspan deflection, with a minor error of 2.33% compared to traditional measuring instruments. However, the accuracy of crack width measurements varied due to elastic deformation, with errors ranging from 7.41% to 68% compared to those obtained by crack detectors. These results demonstrate the potential of the DIC method as a reliable tool for detailed analysis of cracking and deformation in reinforced concrete structures, though further refinement may be required for crack width assessment.

Keywords:

Correlation,Cracking,Crack propagation,Digital image,Experiment,Reinforced concrete.,

Refference:

I. Aris, A., Messa, R., Yuki, O., and Hibino, Y:’Application of Digital Image Correlation Method in RC and FRC Beams Under Bending Test’. Geomate Journal. vol. 24(101), pp:118–125,203. 10.21660/2023.101.g12275
II. Bažant, Z. P., and Cedolin, L. ‘Fracture Mechanics of Reinforced Concrete’. Journal of the Engineering Mechanics Division. vol. 106(6), pp: 1287–1306,1980. 10.1061/JMCEA3.0002665
III. Bažant, Z. P., and Oh, B. H.:’Spacing of Cracks in Reinforced Concrete’.Journal of Structural Engineering. vol. 109(9), pp. 2066–2085,1983. 10.1061/(ASCE)0733-9445(1983)109:9(2066)
IV. IV. Blaber, J. A., Adair, B., and Antoniou, A.: ‘Ncorr: Open-Source 2D Digital Image Correlation MATLAB Software’. Experimental Mechanics, vol. 55, pp:1105–1122,2015. 10.1007/s11340-015-0009-1
V. Ding, Y. : ‘Investigations into the Relationship Between Deflection and Crack Mouth Opening Displacement of SFRC Beam’.Construction and Building Materials, vol. 25(5),pp:2432–2440,2011. 10.1016/j.conbuildmat.2010.11.055
VI. Fayyad, T. M., and Lees, J. M., ‘Experimental Investigation of Crack Propagation and Crack Branching in Lightly Reinforced Concrete Beams Using Digital Image Correlation’. Engineering Fracture Mechanics, vol. 182, pp: 487–505,2017. 10.1016/j.engfracmech.2017.04.051
VII. Gali, S., and Subramaniam, K. V. L.: ‘Shear Behavior of Slender and Non-Slender Steel Fiber-Reinforced Concrete Beams’. ACI Structural Journal, vol. 116, p. 149,2019.10.14359/51713307
VIII. Gupta, A. K., and Akbar, H. “Cracking in Reinforced Concrete Analysis’. Journal of Structural Engineering. vol. 110(8),pp. 1735–1746,1984. 10.1061/(ASCE)0733-9445(1984)110:8(1735)
IX. Huang, Y., He, X., Wang, Q., and Xiao, J. ‘Deformation Field and Crack Analyses of Concrete Using Digital Image Correlation Method’.Frontiers of Structural and Civil Engineering. vol. 13(5),pp: 1183–1199,2019. 10.1007/s11709-019-0545-3
X. Kammers, A. D., and Daly, S., ‘Small-Scale Patterning Methods for Digital Image Correlation Under Scanning Electron Microscopy’. Measurement Science and Technology.vol. 22(12), p. 125,20111. 10.1088/0957-0233/22/12/125501
XI. Lacidogna, G., Piana, G., Accornero, F., and Carpinteri, A., ‘Multi-Technique Damage Monitoring of Concrete Beams: Acoustic Emission, Digital Image Correlation, Dynamic Identification’. Construction and Building Materials. vol. 242, p. 118114, 2020. 10.1016/j.conbuildmat.2020.118114
XII. Lee, H.-M., and Yoo, K.-S., ‘A Study on the Metabus-Based Equipment Monitoring System’The Korean Innovation Industry Society. vol. 1(3), pp. 99–105,2023.10.60032/JIIT.2023.1.3.99
XIII. McCormick, N., and Lord, J., ‘Digital Image Correlation for Structural Measurements’. Proceedings of the Institution of Civil Engineers – Civil Engineering, vol. 165(4), 2012, pp. 185–190,2012. 10.1680/cien.11.00040
XIV. Oh, B. H., and Kang, Y. J., ‘New Formulas for Maximum Crack Width and Crack Spacing in Reinforced Concrete Flexural Members.’.ACI Structural Journal. vol. 84, pp: 103–112,1987. 10.14359/2787
XV. Poldon, J. J., Hoult, N. A., and Bentz. E. C., ‘Distributed Sensing in Large Reinforced Concrete Shear Test’. ACI Structural Journal. vol. 116( 5), 2019, pp. 235–245,2019.10.14359/51716765
XVI. Seemab, F., Schmidt, M., Bakhteer, A., Classen, M., and Chudoba, R., ‘Automated Detection of Propagating Cracks in RC Beams Without Shear Reinforcement Based on DIC-Controlled Modeling of Damage Localization’.Engineering Structures. vol. 286, pp: 116–118,2023.10.1016/j.engstruct.2023.116118
XVII. Sindu, B. S., and Saptarshi, S., ‘Multi-Scale Abridged Cement Composite with Enhanced Mechanical Properties’. ACI Materials Journal.vol. 117( 4),2020. 10.14359/51724625
XVIII. Tambusay, A., Suryanto, B., and Suprobo, P., ‘Digital Image Correlation for Cement-Based Materials and Structural Concrete Testing’. Civil Engineering Dimension. vol. 22(1), pp: 6–12,2020.10.9744/ced.22.1.6-12
XIX. Toutanji, H., and Deng, Y., ‘Deflection and Crack-Width Prediction of Concrete Beams Reinforced with Glass FRP Rods’.Construction and Building Materials. vol. 17(1),pp. 69–74,2003.10.1016/S0950-0618(02)00094-6
XX. Verbruggen, S., De Sutter, S., Iliopoulos, S., Aggelis, D.G., and Tysmans, T., ‘Experimental Structural Analysis of Hybrid Composite-Concrete Beams by Digital Image Correlation (DIC) and Acoustic Emission (AE)’. Journal of Nondestructive Evaluation. vol. 35(1), p. 2,2015. 10.1007/s10921-015-0321-9

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Abstract:

Machine learning and the Internet of Things (IoT) have affected every step of the leukemia process, from diagnosis to understanding to therapy. Consequently, this study delves into the planning of an innovative system that employs IoT and machine learning techniques to precisely differentiate leukemic cells. Depending on the patient's samples, the system uses different ways to feature selection and cell classification. To pick the most informative collection of features that enables stable and accurate cell categorization into suitable categories, the offered research relies on strong machine-learning approaches for feature selection. Next, a classification model is used to classify cells based on their properties using the attributes that have been chosen. There is evidence that the suggested approach can classify leukemic cells with an identification rate of up to 99%, which is greater than the current methods. As a novel strategy for managing massive volumes of biological and medical samples, the suggested method will be an invaluable tool for doctors treating leukemia patients. The system's ability to process data from various Internet of Things (IoT) sources should aid its ability to learn and adapt to real-world clinical settings. With the results of this study in hand, we may be able to detect leukemia sooner, with greater precision, and maybe use more tailored treatments for each patient, leading to better results while reducing healthcare expenditures.

Keywords:

Diagnosing,Feature Selection,IoT,Machine Learning,Understanding,Leukemic Cells,

Refference:

I. Almadhor, A., Sattar, U., Al Hejaili, A., Ghulam Mohammad, U., Tariq, U., & Ben Chikha, H: ‘An efficient computer vision-based approach for acute lymphoblastic leukemia prediction’. Frontiers in Computational Neuroscience, vol.16,2022. 10.3389/fncom.2022.1083649
II. Baig, R., Rehman, A., Almuhaimeed, A., Alzahrani, A., & Rauf, H. T: ‘Detecting Malignant Leukemia Cells Using Microscopic Blood Smear Images: A Deep Learning Approach’. Applied Sciences. Vol.12(13), PP.6317,2022. 10.3390/app12136317
III. Das, P. K., Diya, V. A., Meher, S., Panda, R., & Abraham, A.: ’A systematic review on recent advancements in deep and machine learning based detection and classification of acute lymphoblastic leukemia’. IEEE Access.Vol.10(22),2022. 10.1109/ACCESS.2022.3196037
IV. de Sant’, Y. F. D., de Oliveira, J. E. M., & Dantas, D. O.: ’Interpretable Lightweight Ensemble Classification of Normal versus Leukemic Cells’. Computers.vol.11(8),2022. 10.3390/computers11080125
V. Elemam, T., & Elshrkawey, M.: ‘A Highly Discriminative Hybrid Feature Selection Algorithm for Cancer Diagnosis’. The Scientific World Journal.vol 1056490.,2022. 10.1155/2022/1056490
VI. Eluri, N. R., Kancharla, G. R., Dara, S., & Dondeti, V.: ’Cancer data classification by quantum-inspired immune clone optimization-based optimal feature selection using gene expression data: deep learning approach’. Data Technologies and Applications. vol.56(2),pp: 247-282,2022. 10.1108/DTA-05-2020-0109
VII. Frasca, M., Francese, R., Risi, M., & Tortora, G:’A Deep Learning and Genetic Algorithm Based Feature Selection Processes on Leukemia Data’.In 26th International Conference Information Visualisation (IV).2022. 10.1109/IV56949.2022.00074
VIII. Iswarya, M:’Detection of Leukemia using Machine Learning’. In 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC). India.pp. 466-470,2022. 10.1109/ICAAIC53929.2022.9792725
IX. Koul, N., & Manvi, S. S:’Feature selection from gene expression data using simulated annealing and partial least squares regression coefficients’. Global Transitions Proceedings.vol. 3(1), pp:251-256,2022. 10.1016/j.gltp.2022.03.001
X. Noshad, A., & Fallahi, S.: A new hybrid framework based on deep neural networks and JAYA optimization algorithm for feature selection using SVM applied to classification of acute lymphoblastic Leukaemia. In Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization. Vol.11,pp:1549-1566,2022. 10.1080/21681163.2022.2157748
XI. Rawat, J., Rawat, S., Kumar, I., & Devgun, J. S: Identification of malignant lymphoblast cell in bone marrow using machine learning. In Proceedings of the International Conference on Computational Intelligence and Sustainable Technologies: ICoCIST 2021. Singapore.pp. 267-278,2022. 10.1007/978-981-16-6893-7_25
XII. Rupapara, V., Rustam, F., Aljedaani, W., Shahzad, H. F., Lee, E., & Ashraf, I.: ’Blood cancer prediction using leukemia microarray gene data and hybrid logistic vector trees model’. Scientific reports, vol.12(1), 2022. 10.1038/s41598-022-04835-6
XIII. Sallam, N. M., Saleh, A. I., Arafat Ali, H., & Abdelsalam, M. M: ‘An Efficient Strategy for Blood Diseases Detection Based on Grey Wolf Optimization as Feature Selection and Machine Learning Techniques’. Applied Sciences. Vol.12(21), 2022. 10.3390/app122110760
XIV. Sallam, N. M., Saleh, A. I., Ali, H. A., & Abdelsalam, M. M: ’An efficient EGWO algorithm as feature selection for B-ALL diagnoses and its subtypes classification using peripheral blood smear images’. Alexandria Engineering Journal. Vol. 68, pp:39-66,2023. 10.1016/j.aej.2023.01.004
XV. Shobana, M., Balasraswathi, V. R., Radhika, R., Oleiwi, A. K., Chaudhury, S., Ladkat, A. S., & Rahmani, A. W: ‘Classification and Detection of Mesothelioma Cancer Using Feature Selection-Enabled Machine Learning Technique’. BioMed Research International.vol.6,pp:1-6,2022. 10.1155/2022/9900668
XVI. Sridhar, K., Yeruva, A. R., Renjith, P. N., Dixit, A., Jamshed, A., & Rastogi, R: ‘Enhanced Machine learning algorithms Lightweight Ensemble Classification of Normal versus Leukemic Cell’. Journal of Pharmaceutical Negative Results. pp:496-505,2022.10.47750/pnr.2022.13.s09.056
XVII. Simsek, E., Badem, H., & Okumus, I. T: Leukemia Sub-Type Classification by Using Machine Learning Techniques on Gene Expression. In Proceedings of Sixth International Congress on Information and Communication Technology: ICICT 2021. London. pp. 629-637,2022. 10.1007/978-981-16-2102-4_56
XVIII. Saleem, S., Amin, J., Sharif, M., Mallah, G. A., Kadry, S., & Gandomi, A. H.: ‘Leukemia segmentation and classification: A comprehensive survey’. Computers in Biology and Medicine. Vol.150,2022. 10.1016/j.compbiomed.2022.106028
XIX. Wajid, B., Rashid, U., Zahid, S., Anwar, F., Awan, F. G., Anwar, A. R., & Wajid, I.: Survival Rate Prediction of Blood Cancer (Leukemia) Patients Using Machine Learning Algorithms. In 2022 International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering (ETECTE).pp. 1-4,2022 10.1109/ETECTE55893.2022.10007402
XX. Zolfaghari, M., & Sajedi, H: ‘A survey on automated detection and classification of acute leukemia and WBCs in microscopic blood cells’. Multimedia Tools and Applications.vol. 81(5),pp: 6723-6753,2022. 10.1007/s11042-022-12108-7

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Abstract:

Cardiovascular diseases (CD) are the common cause of death worldwide over in developed as well as underdeveloped and developing countries. Early detection and continuous supervision can reduce the mortality rate. Cardiovascular disease diagnosis and accurate diagnosis to enable early treatment. Some of these techniques do not easily diagnose heart diseases at early stages hence, getting treatment late poses a big risk. The present work attempts to better predict this disease from the chest pain symptom, and classify it by designing an efficient machine learning system based on a dataset with 303 patient data made available to the public domain. The four machine learning algorithms that were used for the analysis include Logistic Regression, Random Forest, Support Vector Machines, and Neural Networks to determine which of them is most appropriate for predicting heart diseases. Original data was preprocessed by handling missing values, normalizing features, and using feature extraction techniques. Splitting the dataset into 80% training and 20% testing, cross-validation was performed to validate outcomes on all four models. Although the highest accuracy was reached by the model of the Neural Network by 97%, it was revealed to have tendencies of overfitting. The SVM model achieved the highest accuracy of 97%, and was the most stable and interpretable; therefore, it was considered to be the most suitable for clinical use. Base on the study, there is a promise to champion the use of machine learning models for timely diagnosis of heart diseases by medical practitioners to enhance patient success rates and the overworked health facilities’ performance. The next steps will consist in enlarging a database and implementing these models in supporting clinical practice with real-time diagnostic potential. As a result, the doctors can visualize the patient’s real-time sensor data using the application and start live video streaming if instant medication is required. The proposed system is notified at once through GSM technology.

Keywords:

Artificial Intelligence,Data Preprocessing,Machine Learning,Logistic Regression,Neural Networks,Neural Networks,Random Forest,SVM,

Refference:

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II. Al-Hadhrami, Y., & Hussain, F. K. DDoS attacks in IoT networks: a comprehensive systematic literature review. World Wide Web, Vol 24, no 3, pp 971-1001. 2021.
III. Ali, I., Sabir, S., & Ullah, Z. Internet of things security, device authentication and access control: a review. arXiv preprint arXiv: Vol 14, no 2, pp 1901-1920, 2019.
IV. 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
V. 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
VI. 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
VII. 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
VIII. 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
IX. 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
X. 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. Secur, Vol.18, No.12, pp 125-130, 2018
XI. 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
XII. 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.
XIII. 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
XIV. 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
XV. 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

XVI. 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. 10.1109/ICEET1.2018.8338643
XVII. 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.
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. 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.
XX. 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. Secur, Vol.18, No.12, pp 125-130, 2018.

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Abstract:

Celiac disease is an immune system situation that mostly impacts and damages the small intestine as well as the skeletal system. Celiac Disease is prevalent in the modern population. Individuals with Celiac disease are unable to ingest gluten without experiencing negative health consequences. Insufficient awareness often leads to delayed disease identification. Utilizing computer-based prediction could aid in the early identification of Celiac Disease in individuals, increasing their likelihood of maintaining a typical life. The deep learning approach is conducted using hyper-tuning. The hyperparameters of the Mobile Net classifier are optimized using a novel hybrid approach that combines Wheel Plant Optimization and Fruit Fly Optimization. The suggested model is finally assessed and compared with existing approaches regarding Accuracy, Precision, Recall, and Time. The proposed model demonstrated superior performance by attaining an Accuracy of 99.78% compared to the other methods.

Keywords:

Celiac disease,Immune system disorder,Hybrid Optimization,Mobile-Net.,

Refference:

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IV. Bai, C. Julio and Carolina Ciacci. : ‘World Gastroenterology Organisation Global Guidelines: Celiac Disease February 2017.’ Journal of clinical gastroenterology. Vol. 51(9), pp. 755-768, 2017.
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VI. Calado, João, and Mariana Verdelho Machado. : ‘Celiac Disease Revisited.’ GE-Portuguese Journal of Gastroenterology. Vol. 29(2), pp. 111-124, 2022.
VII. DaFonte, M. Tracey, et al. : ‘Zonulin As A Biomarker For The Development Of Celiac Disease.’ Pediatrics. Vol. 153 (1), 2024.
VIII. Dale, K. Ryan, S. Brent Pedersen and R. Aaron Quinlan. : ‘Pybedtools: A Flexible Python Library For Manipulating Genomic Datasets And Annotations.’ Bioinformatics. Vol. 27(24), pp. 3423-3424, 2011.
IX. Dehghani, Mohammad, et al. : ‘Coati Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm For Solving Optimization Problems.’ Knowledge-Based Systems. Vol. 259, pp. 110011, 2023.
X. Dube, R. Shanta, et al. : ‘Long-Term Consequences Of Childhood Sexual Abuse By Gender Of Victim.’ American journal of preventive medicine. Vol. 28(5), pp. 430-438, 2005.
XI. Hsiao, Yu-Jer, et al. : ‘Application Of Artificial Intelligence-Driven Endoscopic Screening And Diagnosis Of Gastric Cancer.’ World Journal of Gastroenterology. Vol. 27(22), pp. 2979, 2021.
XII. Huang, Chaolin, et al. : ‘Clinical Features Of Patients Infected With 2019 Novel Coronavirus In Wuhan, China.’ The lancet. Vol. 395(10223), pp. 497-506, 2020.
XIII. Ibrahim, Abdelhameed, et al. : ‘A Recommendation System for Electric Vehicles Users Based on Restricted Boltzmann Machine and Waterwheel Plant Algorithms.’ IEEE Access. 2023.
XIV. Jain, Mohit, Vijander Singh, and Asha Rani. : ‘A Novel Nature-Inspired Algorithm For Optimization: Squirrel Search Algorithm.’ Swarm and evolutionary computation, Vol. 44, pp. 148-175, 2019.
XV. Kendall, A. Emily, Sourya Shrestha, and W. David Dowdy. : ‘The Epidemiological Importance of Subclinical Tuberculosis. A Critical Reappraisal.’ American journal of respiratory and critical care medicine. Vol. 203(2), pp. 168-174, 2021.
XVI. Khasanov, Mansur, et al. : ‘Rider Optimization Algorithm For Optimal DG Allocation in Radial Distribution Network.’ 2020 2nd International Conference on Smart Power & Internet Energy Systems (SPIES). IEEE. 2020.
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XVIII. Kumar, Battina Srinuvasu, S.G. Santhi and S. Narayana. : ‘Sailfish Optimizer Algorithm (SFO) for Optimized Clustering in Wireless Sensor Network (WSN).’ Journal of Engineering, Design and Technology. Vol. 20(6), pp. 1449-1467, 2022.
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Abstract:

“Aerobic exercise” is a moderately intense physical activity, which improves your cardio-respiratory fitness and your overall health. A pre-experimental“study” (one group pre-test post-test design) was conducted to assess the effectiveness of a video-assisted teaching module on the reduction of weight gain by aerobic exercise ” among (50) “Obese female students” of “Sashi Bhusan Government Women’s College and City Women’s College (10), Berhampur, Ganjam, Odisha. The objectives of the “study” were to assess the “obesity ” of “Female adolescent college students “ before & after VATM, to evaluate the effectiveness of VATM on the reduction of weight gain by aerobic exercise among obese female students, to find out the association between the post-test measurement score of obese female college students with their selected demographic variables. Data were collected by using a structured demographic variable questionnaire and measurement of height, weight, and BMI schedule. The findings of the study were the mean score & standard deviation value of height were (154.36 ± 5.19) in the pre-test whereas the mean score & standard deviation value of height is (154.36 ± 5.19) in the post-test, the mean score & standard deviation value of weight is (56.78 ± 10.37) in the pre-test, whereas the mean score & standard deviation value of weight is (58.24 ± 12.01) in post-test, the mean score & standard deviation value of BMI is (23.90 ± 4.02) in the pre-test whereas the mean score & standard deviation value of BMI is (24.90 ± 5.02) in post-test respectively. The “P” value of “Demographic Variable wise ” ranges from 0.078 to 0.008. The chi-square (χ2) result shows that there was no significant association found between post-test “measurement ” scores when compared to demographic variables. The findings indicate that “VATM” given on “Reduction of weight ” by “Aerobic exercise” was effective.

Keywords:

Aerobic exercise,Female adolescent college students,Obese female students,Reduction of weight,VATM,

Refference:

I. Atlantis, E., E. H. Barnes, and M. A. Singh. “Efficacy of Exercise for Treating Overweight in Children and Adolescents: A Systematic Review.” International Journal of Obesity, vol. 30, no. 7, 2006, pp. 1027–1040, 10.1038/sj.ijo.0803286.
II. Awasare, Vivek G. “Effect of Aerobic Exercises on Physical Fitness and Body Composition of School Boys.” Review of Research, vol. 2, no. 10, 2013, https://oldror.lbp.world/UploadedData/366.pdf.
III. Badwal, Kulroop Kaur, and Ranjit Singh. “Effect of Short-Term Swiss Ball Training on Physical Fitness.” Biology of Exercise, vol. 9, no. 2, 2013, pp. 5–14.
IV. Bastug, Gulsum, G. Ozcan, and K. Ozcan. “The Effects of CrossFit, Pilates, and Zumba Exercise on Body Composition and Body Image of Women.” International Journal of Sport, Exercise & Training Science, vol. 2, no. 1, 2016, pp. 25–34.
V. Cakmakci, O. “The Effect of 8-Week Pilates Exercise on Body Composition in Obese Women.” Collegium Antropologicum, vol. 35, no. 4, 2011, pp. 1045–1050. https://pubmed.ncbi.nlm.nih.gov/22397236/
VI. Carrel, Aaron L., R. R. Clark, S. E. Peterson, B. A. Nemeth, J. Sullivan, and D. B. Allen. “Improvement of Fitness, Body Composition, and Insulin Sensitivity in Overweight Children in a School-Based Exercise Program: A Randomized, Controlled Study.” Archives of Pediatrics & Adolescent Medicine, vol. 159, no. 10, 2005, pp. 963–968, 10.1001/archpedi.159.10.963
VII. Chen, Haitao, Y. Liu, and L. Li. “A Case Study of a Body Weight Control Programme for Elite Chinese Female Gymnasts in Preparation for the 2008 Olympic Games.” Science of Gymnastics Journal, vol. 1, no. 1, 2009, pp. 15–24.
VIII. Colado, Juan C., N. T. Triplett, V. Tella, P. Saucedo, and J. Abellán. “Effects of Aquatic and Dry Land Resistance Training Devices on Body Composition and Physical Capacity in Postmenopausal Women.” Journal of Human Kinetics, vol. 21, 2009, pp. 33–43. 10.2478/v10078-009-0019-5
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XII. Gappmaier, E., W. Lake, A. G. Nelson, and A. G. Fisher. “Aerobic Exercise in Water Versus Walking on Land: Effects on Indices of Fat Reduction and Weight Loss of Obese Women.” Journal of Sports Medicine and Physical Fitness, vol. 46, no. 4, 2006, pp. 564–569.
XIII. Gezer, Engizn, and Evrim Cakmakci. “The Effect of 8 Weeks Step-Aerobic Exercise Program on Body Composition and Sleep Quality of Sedentary Women.” Ovidius University Annals, Series Physical Education and Sport/Science, Movement and Health, vol. 10, no. 2, 2010, pp. 324–329.
XIV. Gurd, Brendon, and Panagiota Klentrou. “Physical and Pubertal Development in Young Male Gymnasts.” Journal of Applied Physiology, vol. 95, no. 3, 2003, pp. 1011–1015. 10.1152/japplphysiol.00483.2003

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Abstract:

Thyroid cancer, a prevalent and potentially life-threatening disease, demands early detection for effective treatment. This study proposes a novel approach for the early detection of thyroid cancer by employing Time Series Analysis (TSA) and Quadratic Discriminant Analysis (QDA). The integration of these techniques aims to enhance diagnostic accuracy and reliability, providing a valuable tool for clinicians to detect the disease in its early stages. In our approach, TSA was used to extract meaningful patterns and trends from temporal data, offering valuable insights into the evolving health status of the thyroid. Subsequently, QDA was applied to build a robust classification model, using the identified time series features to distinguish between cancerous and non-cancerous cases. The application of TSA and QDA yielded promising results, demonstrating high sensitivity and specificity, and outperforming traditional diagnostic methods. Our model achieved an accuracy of 97.72%, precision of 90.91%, sensitivity of 94.01%, and specificity of 98.2%. The incorporation of temporal dynamics through TSA provided a nuanced understanding of the evolving pathology, contributing to the enhanced accuracy of the diagnostic model. In conclusion, this study introduces a novel methodology for early thyroid cancer detection, combining the strengths of TSA and QDA. The results highlight the effectiveness of this integrated approach in improving diagnostic accuracy, particularly in identifying subtle temporal changes indicative of thyroid cancer. The proposed TSA-QDA model shows superior performance in terms of sensitivity, specificity, and classification accuracy for multi-class TIRADS classification.

Keywords:

Diagnostic Methodologies,Quadratic Discriminant Analysis (QDA),Temporal Dynamics,Thyroid Cancer,Time Series Analysis,TIRADS Classification,

Refference:

I. Baek, J. H.:’Thyroid Cancer Screening: How to Maximize Its Benefits and Minimize Its Harms’. Endocrinology and Metabolism, vol.38(1),pp: 75-77,2023. 10.3803/EnM.2023.105
II. Cheong, B., Teh, H. J. H., Ng, G. S. N., & Huang, K. H. K.:’Thyroid cancer presenting as neck pain at a chiropractic clinic’. Cureus. Vol.15(5),2023. 10.7759/cureus.39276
III. Chen, D. W., Lang, B. H., McLeod, D. S., Newbold, K., & Haymart, M. R.: ‘Thyroid cancer’. The Lancet. vol.401(10387), pp:1531-1544,2023. 10.1016/S0140-6736(23)00020-X
IV. Elisei, R., Grande, E., Kreissl, M. C., Leboulleux, S., Puri, T., Fasnacht, N., & Capdevila, J.: ’Current perspectives on the management of patients with advanced RET-driven thyroid cancer in Europe’. Frontiers in Oncology. Vol.13,2023. 10.3389/fonc.2023.1141314.

V. Gokilavani, M., Sriram, Vijayaragavan, S. P., & Nirmalrani, V: ’Chicken Swarm-Based Feature Selection with Optimal Deep Belief Network for Thyroid Cancer Detection and Classification’. In Computational Intelligence for Clinical Diagnosis.pp: 21-35,2023. 10.1007/978-3-031-23683-9_2
VI. Grant, E. G., & Kwon, D. I.:’Is There a Place for Lymphatic Contrast-enhanced US in Thyroid Cancer?’. Radiology. Vol.307(4),2023. 10.1148/radiol.230560.
VII. Habchi, Y., Himeur, Y., Kheddar, H., Boukabou, A., Atalla, S., Chouchane, A.,& Mansoor, W: ‘Ai in thyroid cancer diagnosis: Techniques, trends, and future directions’. Systems. vol.11(10), pp:519-530,2023. 10.3390/systems11100519
VIII. Huang, J., Xu, S. H., Li, Y. Z., Wang, Y., Li, S. T., Su, H. S., & He, Y. J:’ A study on the detection of thyroid cancer in Hashimoto’s thyroiditis using computed tomography imaging radiomics’. Journal of Radiation Research and Applied Sciences. Vol.16(4),2023. 10.1016/j.jrras.2023.100677
IX. Lee, E., Jeong, S. H., Nam, C. M., Jun, J. K., & Park, E. C.:’ Role of breast cancer screening in the overdiagnosis of thyroid cancer: results from a cross-sectional nationwide survey’. BMC Women’s Health.vol. 23(1), pp:64-75,2023. 10.1186/s12905-023-02205-6
X. Luff, M. K., Kim, J., Tseng, C. H., Livhits, M. J., Yeh, M. W., & Wu, J. X. :’Racial/ethnic disparities in thyroid cancer in California, 1999–2017’. The American Journal of Surgery, vol.225(2),pp: 298-303,2023. 10.1016/j.amjsurg.2022.09.041
XI. Moon, S., Lee, E. K., Choi, H., Park, S. K., & Park, Y. J:’Survival comparison of incidentally found versus clinically detected thyroid cancers: an analysis of a nationwide cohort study’. Endocrinology and Metabolism.vol. 38(1), pp:81-92,2023. 10.3803/EnM.2023.1668
XII. Ma, Y., Zhang, Q., Zhang, K., Ren, F., Zhang, J., Kan, C., & Sun, X.: ‘NTRK fusions in thyroid cancer: pathology and clinical aspects’. Critical Reviews in Oncology/Hematology.vol.103957,2023. 10.1016/j.critrevonc.2023.103957.
XIII. Mu, X., Huang, X., Jiang, Z., Li, M., Jia, L., Lv, Z.,& Mao, J.:’ [18F] FAPI-42 PET/CT in differentiated thyroid cancer: diagnostic performance, uptake values, and comparison with 2-[18F] FDG PET/CT’. European Journal of Nuclear Medicine and Molecular Imaging. vol.50(4),pp: 1205-1215,2023. 10.1007/s00259-022-06067-2.
XIV. Pathak, R. K., Mishra, S., & Sharan, P.:’ Bragg reflector one-dimensional multi-layer structure sensor for the detection of thyroid cancer cells’. TELKOMNIKA (Telecommunication Computing Electronics and Control). vol.21(3), 622-629,2023. 10.12928/telkomnika.v21i3.24282

XV. S. Prabu, B. Thiyaneswaran, M. Sujatha, C. Nalini and S. Rajkumar:’Grid search for predicting coronary heart disease by tuning hyper-parameters. Computer Systems Science and Engineering. vol. 43(2), pp: 737–749, 2022. 10.32604/csse.2022.022739
XVI. Tran, M. H., Gomez, O., & Fei, B.:’A video transformer network for thyroid cancer detection on hyperspectral histologic images’. In Medical Imaging 2023: Digital and Computational Pathology .Vol. 12471, pp: 32-41,2023. 10.1117/12.2654851
XVII. Xiao, J., Meng, S., Zhang, M., Li, Y., Yan, L., Li, X., & Luo, Y.:’Optimal method for detecting cervical lymph node metastasis from papillary thyroid cancer’. Endocrine.vol.79(2), pp:342-348,2023. 10.1007/s12020-022-03213-6
XVIII. Xiao, J., Jiang, J., Chen, W., Hong, T., Li, B., He, X., & Liu, W.: ‘Combination of ultrasound and serological tests for detecting occult lateral lymph node metastases in medullary thyroid cancer’. Cancer Medicine.vol.12(10),pp: 11417-11426,2023. 10.1002/cam4.5856.
XIX. Zhang, Y., Lu, Y. Y., Li, W., Zhao, J. H., Zhang, Y., He, H. Y.,& Luo, Y. K.:’Lymphatic contrast-enhanced US to improve the diagnosis of cervical lymph node metastasis from thyroid cancer’. Radiology.vol.307(4),pp: e221265,2023. 10.1148/radiol.221265

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Abstract:

In the modern era of computers, numerous disciplines are witnessing the development of massive data volumes. Statistics are important in healthcare engineering because they provide insights into various diseases and match patient data. These datasets serve two functions: improving illness prediction and examining large data reservoirs to identify previously unknown disease patterns. Leveraging deep learning models, it becomes feasible to detect and forecast the early stages of numerous diseases based on individual health conditions. Nonetheless, the current landscape of illness prediction encounters several challenges such as inadequate large-scale datasets, logistical delays, the imperative for more precise and dependable predictions, and the intricacy of the models themselves. This paper introduces an innovative method for disease prediction utilizing deep learning, particularly focusing on an ensemble-based multi-disease prediction model. Datasets for lung cancer, cervical cancer, chronic renal disease, Parkinson's illness, and HCC survival are sourced from the trustworthy UCI repository for experimentation. A robust stacked deep ensemble model is proposed combining the InceptionResNetV2, EfficientNetV2L, and Xception architectures. This model integrates pre-processing techniques and employs the Tuna Swarm Optimization (TSO) Algorithm for feature selection in executing multi-label disease prediction. The suggested deep learning algorithms' performance is evaluated using criteria such as precision, specificity, sensitivity, accuracy, and error rate. This assessment demonstrates the potential of the recommended method to significantly contribute to the healthcare system by offering consistent and reliable predictions across various types of illnesses, as shown in a comparative analysis against existing models.

Keywords:

Convolutional Deep Learning,Healthcare,Prediction,Stacking Ensemble Learning,Tuna Swarm Optimization,

Refference:

I. Ampavathi, Anusha and VijayaSaradhi, T., : ‘Multi disease-prediction framework using hybrid deep learning: an optimal prediction model.’ Computer Methods in Biomechanics and Biomedical Engineering. Vol. 24(10), pp. 1146-1168, 2021. 10.1080/10255842.2020.1869726
II. Anand, Vatsala, et al., : ‘Weighted average ensemble deep learning model for stratification of brain tumor in MRI images.’ Diagnostics. Vol. 13(7), pp. 1320, 2023. 10.3390/diagnostics13071320
III. Deb, Sagar Deep, et al., : ‘A multi model ensemble based deep convolution neural network structure for detection of COVID19.’ Biomedical signal processing and control. Vol. 71, pp. 103126, 2022. 10.1016/j.bspc.2021.103126
IV. Dubey, A.K., : ‘Optimized hybrid learning for multi disease prediction enabled by lion with butterfly optimization algorithm.’ Sādhanā. Vol. 46(2), pp. 63, 2021. 10.1007/s12046-021-01574-8
V. Dwight L Evans, Dennis S Charney, : ‘A journal of psychiatric neuroscienceand therapeutics “Mood disorders and medical illness: a major public health problem.’ A journal of psychiatric neuroscience therapeutics. Vol. 54(13), pp. 177-180, 2003. 10.1016/S0006-3223(03)00639-5.
VI. Ehab, E., Xianghua, X., : ‘An Ensemble of Deep Learning-Based Multi-Model for ECG Heartbeat Arrhythmia classification.’ IEEE. Vol. 9, 2021. 10.1109/ACCESS.2021.3098986
VII. Essa, Ehab, and XianghuaXie, : ‘An ensemble of deep learning-based multi-model for ECG heartbeats arrhythmia classification.’ IEEE access, Vol. 9, pp. 103452-103464, 2021. 10.1109/ACCESS.2021.3098986
VIII. Hsu, C.H., Chen, X., Lin, W., Jiang, C., Zhang, Y., Hao, Z & Chung, Y.C.,: ‘Effective multiple cancer disease diagnosis frameworks for improved healthcare using machine learning.’ Measurement. Vol. 175, pp. 109145, 2021. 10.1016/j.measurement.2021.109145

IX. https://archive.ics.uci.edu/dataset/174/parkinsons
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XI. https://archive.ics.uci.edu/dataset/383/cervical+cancer+risk+factors
XII. https://archive.ics.uci.edu/dataset/423/hcc+survival
XIII. https://archive.ics.uci.edu/dataset/62/lung+cancer
XIV. Ismail, Walaa, N., FathimathulRajeena, P.P and Mona, Ali, A.S., : ‘A meta-heuristic multi-objective optimization method for alzheimer’s disease detection based on multi-modal data.’ Mathematics. Vol. 11(4), pp. 957, 2023. 10.3390/math11040957
XV. Kevin Zhou, S., Hayit Greenspan, Christos Davatzikos, James Duncan, S., Bram Van Ginnek, : ‘A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends.’ Case Studies With Progress Highlights, and Future Promises. Vol. 109, pp. 5, 2021. 10.1109/JPROC.2021.3054390
XVI. Khamparia, Aditya, et al., : ‘A novel deep learning-based multi-model ensemble method for the prediction of neuromuscular disorders.’ Neural computing and applications. Vol. 32, pp. 11083-11095, 2020. 10.1007/s00521-018-3896-0
XVII. KhazaeeFadafen, Masoud, and KhosroRezaee, : ‘Ensemble-based multi-tissue classification approach of colorectal cancer histology images using a novel hybrid deep learning framework.’ Scientific Reports. Vol. 13(1), pp. 8823, 2023. 10.1038/s41598-023-35431-x
XVIII. Liu, Hong, et al., : ‘Multi-model ensemble learning architecture based on 3D CNN for lung nodule malignancy suspiciousness classification.’ Journal of Digital Imaging. Vol. 33, pp. 1242-1256, 2020. 10.1109/ACCESS.2021.3098986
XIX. Saurabh, A., Arya, K.V., Meena, Y.K., : ‘MultiFusionNet: Multilayer Multimodal Fusion of Deep Neural Networks for Chest X-Ray Image Classification.’ 10.48550/arXiv.2401.00728
XX. Shaveta, D., Kumar, M., Ayyagari, M.R., Kumar, G., : ‘A Survey of Deep Learning and Its Applications: A New Paradigm to Machine Learning.’ Archives of Computational Methods in Engineering. Vol. 27, pp. 1071-1092, 2022. 10.1007/s11831-019-09344-w
XXI. Wenjie Kang, Lan Lin, Baiwen Zhang, XiaoqiShen, ShuicaiWu, : ‘Multi-model and multi-slice ensemble learning architecture based on 2D convolutional neural networks for Alzheimer’s disease diagnosis.’ Computers in Biology and Medicine. Vol. 136, 2021. 104678. 10.1016/j.compbiomed.2021.104678

XXII. Yawen, X., Jun, W., Zongil, L., Xiaodong, Z., : ‘A deep learning-based multi-model ensemble method for cancer prediction.’ Computer methods and programin Biomedicine. Vol. 153, pp. 1-9, 2018. 10.1016/j.cmpb.2017.09.005
XXIII. Yogesh, K.D., ElviraI Smagilova, Gert Aarts, et al., : ‘Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy.’ International Journal of Information Management. Vol. 57, 2021. 10.1016/j.ijinfomgt.2019.08.002
XXIV. Yuyang He, You Zhou, Tao Wen, Shuang Zhang, Fang Huang, XinyuZou, Xiaogang Ma, YueqinZhu, : ‘A review of machine learning in geochemistry and cosmochemistry: Method improvements and applications.’ Applied Geochemistry. Vol. 140, pp. 105273, 2022. 10.1016/j.apgeochem.2022.105273
XXV. YuyanWang, Dujuan Wang, Na Geng, YanzhangWang, Y. Yunqiang, J. Yaochu, : ‘Stacking-based ensemble learning of decision trees for interpretable prostate cancer.’ Applied Soft Computing. Vol. 77, pp. 188-204, 2019. 10.1016/j.asoc.2019.01.015

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Abstract:

The process of selection and evaluation of organizational solutions is considered. Five approaches to the evaluation of organizational solutions used in domestic practice are disclosed, indicating the area of their application. Based on a comparative analysis, the paper provides evidence for the fundamental distinctions between the mathematical and applied methods of selecting optimal solutions. The mathematical process is presented as a search for the best solution to problems and as a process of finding an extremum. The applied process is presented in the form of various indicators and their combinations. The arguments presented suggest that relying on a singular approach and a single metric for evaluating organizational solutions is deemed unfeasible. The paper substantiates the provisions of probability theory and mathematical statistics in the design of transport facilities, organization of construction, and operation of roads. Ensuring the specified reliability of a transport facility requires taking into account probabilistic factors both at the design stage and at the stages of construction and operation of the facility.

Keywords:

Deterministic Calculations,Elasticity Moduli of the Roadbed,Geometric and Strength Characteristics,Road Pavement,Structure Reliability,Structural Layer Thickness,Strength of the Structure,

Refference:

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III. A. Kotelnikova, I. Penkova, A. Krasnov, A. Mottaeva, R. Kazaryan, D. Dinets, and T. Saifuddin, “Service Economy Strategies for Addressing Fluoride Levels in Tea Leaves: Insights from Science and Management” in Fluoride: Quarterly Journal of The International Society for Fluoride Research Inc., Article No. 278 (2024). https://repository.rudn.ru/en/records/article/record/163044/
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VI. A.D. Kirnev, Organization of construction production (Lan Publishing House, St. Petersburg, 2019), p. 528 https://djvu.online/file/V5qnFFvCnIVSw
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XI. D. Zhdanova, A. Budrin, A. Krasnov, G. Silkina, D. Dinets, A. Mottaeva, R. Kazaryan, A. Lisenkova, V. Buniak, and O. Solodchenkova, “Implementing Effective Service Economy Strategies to Reduce Fluoride Uptake in Clover Fodder: Risk Management in Livestock,” in Fluoride: Quarterly Journal of The International Society for Fluoride Research Inc., Article No. 283 (2024). https://repository.rudn.ru/en/records/article/record/163226/
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XXV. R. Kazaryan, E. Bilonda Tregubova, R. Avetisyan, and I. Doroshin, “Modeling of organizational and technological solutions for quality management of the installation of the structural layers of asphalt concrete,” in Revista De La Universidad Del Zulia, 3ª época, Año 14, 39, 313–332 (2023). 10.46925//rdluz.39.17
XXVI. R. Kazaryan, E. Bykowa, J. Volkova, O. Pirogova, S. Barykin, and P. Kuhtin, “The impact of digitalization on the practice of determining economical cadastral valuation,” in Frontiers in Energy Research, Article No. 982976, (2022). 10.3389/fenrg.2022.982976
XXVII. R. Kazaryan, P. Oleynik, I. Doroshin, and E. Bilonda Tregubova, “Aspects of Heuristic Method of Forming and Assessing the Plan of Contractor Works,” in Revista De La Universidad Del Zulia, 3ª época. Año 15, 42, 245–260 (2024). 10.46925//rdluz.42.14
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Abstract:

Excessive digital use among adolescents is increasingly linked to adverse mental health, reduced productivity, and impaired academic performance. Digital detoxification, a strategy involving temporary disengagement from digital devices, has emerged as a promising intervention. This study evaluates the efficacy of an interventional package designed to promote digital detoxification among adolescents in Bhubaneswar, Odisha. Using a pre-experimental, randomized control group design, 500 students were enrolled and divided into intervention and control groups. Significant reductions in digital use were observed in the intervention group post-intervention (mean difference = 39.5, p< 0.05), while no notable changes were recorded in the control group. The intervention group showed a significant reduction in digital use following the intervention, with the post-test mean score decreasing to 36.2 (SD = 28.46), indicating the effectiveness of the intervention (p < 0.05). In contrast, the control group exhibited no significant change in digital use, as the post-test mean remained at 75.6 (SD = 46.67), suggesting that without intervention, digital use levels remained stable (p > 0.05). The findings underscore the potential of structured interventions to foster healthier digital habits, with broader implications for educational policies and adolescent health.

Keywords:

Adolescent health,Balanced digital use for sustainability,Behavioural health interventions,Digital habits and well-being,Technology addiction,

Refference:

I. Allcott, Hunt, et al. “The Welfare Effects of Social Media.” American Economic Review, vol. 110, no. 3, 2020, pp. 629–676. 10.1257/aer.20190658.
II. Anandpara, G., et al. “A Comprehensive Review on Digital Detox: A Newer Health and Wellness Trend in the Current Era.” Cureus, vol. 16, no. 4, 2024, p. e58719. 10.7759/cureus.58719.

III. Balasubramanian, N. “Impact of Smartphone Abstinence: A Digital Detox Study Among College Students in Chennai.” Journal of Management and Science, vol. 14, no. 4, 2024, pp. 25–32. 10.26524/jms.14.35.
IV. Cherry, Kendra. “The Benefits of Doing a Digital Detox.” Verywell Mind, 30 Sept. 2019, https://www.verywellmind.com/why-and-how-to-do-a-digital-detox-4771321.
V. “Digital Detox.” Lexico by Oxford, 28 Nov. 2017, https://en.oxforddictionaries.com/definition/digital_detox.
VI. El-Khoury, J., et al. “Characteristics of Social Media ‘Detoxification’ in University Students.” The Libyan Journal of Medicine, vol. 16, no. 1, 2021, p. 1846861. 10.1080/19932820.2020.1846861.
VII. Fendel, J. C., et al. “Mindfulness Programs for Problematic Usage of the Internet: A Systematic Review and Meta-Analysis.” Journal of Behavioral Addictions, vol. 13, no. 2, 2024, pp. 327–353. 10.1556/2006.2024.00024.
VIII. Gui, M., and M. Büchi. “From Use to Overuse: Digital Inequality in the Age of Communication Abundance.” Social Science Computer Review, 2019, pp. 1–17.
IX. Hager, Nina, et al. “Digital Detox Research: An Analysis of Applied Methods and Implications for Future Studies.” WIRTSCHAFTSINFORMATIK, 2023, p. 5. https://aisel.aisnet.org/wi2023/5/.
X. Hunt, M. G., et al. “No More FOMO: Limiting Social Media Decreases Loneliness and Depression.” Journal of Social and Clinical Psychology, vol. 37, no. 10, 2018, pp. 751–768. 10.1521/jscp.2018.37.10.751.
XI. Karlsen, F. “Affordances in Digital Detox and Productivity Apps.” AoIR Selected Papers of Internet Research, 2021. 10.5210/spir.v2021i0.1195.
XII. Lawler, M., and S. Gillihan. “How to Do a Digital Detox.” Everyday Health, [cited 5 Nov. 2024], https://www.everydayhealth.com/emotional-health/how-to-do-a-digital-detox-without-unplugging-completely/.
XIII. Lenhart, A., et al. “Teens, Social Media and Technology Overview 2015: Smartphones Facilitate Shifts in Communication Landscape for Teens.” Pew Research Center. Science & Tech., 2015. http://www.pewinternet.org/files/2015/04/PI_TeensandTech_Update2015_0409151.pdf.

XIV. Mhone, C. “Effectiveness of Digital Detox Interventions in Mitigating the Negative Effects of Social Media Among Adolescents and Young Adults in Malawi.” International Journal of Psychology, vol. 8, no. 4, 2023, pp. 43–52. 10.47604/ijp.2422.
XV. Mishra, A., et al. “Is JOMO the Answer to FOMO? Collaborative Digital Detox from Social Media for Adolescents.” Studies in Computational Intelligence, 2024, pp. 99–118. Springer Nature Singapore.
XVI. Mohamed, S. M., et al. “Effect of Digital Detox Program on Electronic Screen Syndrome Among Preparatory School Students.” Nursing Open, vol. 10, no. 4, 2023, pp. 2222–2228. 10.1002/nop2.1472.
XVII. Nascimento, M., et al. “Switching Off to Switch On: An Ontological Inquiry into the Many Facets of Digital Well-Being.” Lecture Notes in Computer Science, Springer Nature Switzerland, 2024, pp. 153–162.
XVIII. Nobbe, L. “The Impact of a Digital Detox Intervention on Smartphone Usage and Procrastination: A Longitudinal Study.” 2024.
XIX. Pathak, D. N. K. “Digital Detox in India.” International Journal of Research in Humanities & Soc. Sciences, vol. 4, no. 8, 2016, pp. 60–67.
XX. Phan, P., et al. “Digital Therapeutics in the Clinic.” Bioengineering & Translational Medicine, vol. 8, no. 4, 2023. 10.1002/btm2.10536.
XXI. Pouwels, Jan L., et al. “Social Media Use and Friendship Closeness in Adolescents’ Daily Lives: An Experience Sampling Study.” Developmental Psychology, vol. 57, no. 2, 2021, pp. 309–323. 10.1037/dev0001148.
XXII. Rani, P. L., and G. M. Buvaneswari. “Digital Detoxification Among Late Adolescence—Need of the Hour.” International Journal of Health Sciences (IJHS), 2022, pp. 6560–6572. 10.53730/ijhs.v6ns1.6402.
XXIII. Ramadhan, R. N., et al. “Impacts of Digital Social Media Detox for Mental Health: A Systematic Review and Meta-Analysis.” Narra Journal, vol. 4, no. 2, 2024, p. e786. 10.52225/narra.v4i2.786.
XXIV. Stronge, S., et al. “Social Media Use Is (Weakly) Related to Psychological Distress.” Cyberpsychology, Behavior, and Social Networking, vol. 22, no. 9, 2019, pp. 604–609.
XXV. Tasso, Antonio F., Hisli Sahin, Nur, and San Roman, Gabriel J. “COVID-19 Disruption on College Students: Academic and Socioemotional Implications.” Psychological Trauma, vol. 13, no. 1, Jan. 2021, pp. 9-15. 10.1037/tra0000996.
XXVI. Syvertsen, T., and G. Enli. “Digital Detox: Media Resistance and the Promise of Authenticity.” Convergence: The International Journal of Research into New Media Technologies, vol. 26, nos. 5–6, 2020, pp. 1269–1283. 10.1177/1354856519847325.
XXVII. Ugur, N. G., and T. Koc. “Time for Digital Detox: Misuse of Mobile Technology and Phubbing.” Procedia—Social and Behavioral Sciences, vol. 195, 2015, pp. 1022–1031. 10.1016/j.sbspro.2015.06.491.
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XXX. Zimmer-Gembeck, Melanie J., et al. “A Closer Look at Appearance and Social Media: Measuring Activity, Self-Presentation, and Social Comparison and Their Associations with Emotional Adjustment.” Psychology of Popular Media, vol. 10, no. 1, Jan. 2021, pp. 74-86. 10.1037/ppm0000277.

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Abstract:

This study addresses the Ergonomic challenges faced by individuals working from home due to insufficiently supportive furniture, which contributes to discomfort, poor posture, and health complications. The aim was to develop a cost-effective, adjustable standing desk design that enhances user comfort by increasing height and workspace area while ensuring stability, postural support, and health benefits. A mathematical modeling approach was employed to analyze the stability, strength, and functional performance of the standing desk. The design specifications included achieving a height of at least 45 cm, a workspace area of 4000 cm2 or greater, and sufficient stability to support 50 pounds of weight, all while maintaining a total cost below $250. Several conceptual designs were considered using a decision-making matrix, with the final design developed using SolidWorks software to model key mechanical components. The model was verified using numerical analysis, including torque calculations for stability assessment. The final design achieved a maximum height of 50.64 cm and a workspace area of 7600 cm2, both exceeding the specified requirements. Stability was verified through torque calculations at the points of structural support, demonstrating that the design could counteract applied forces effectively. An assessment of aesthetic appeal, based on a user survey, indicated an 87% satisfaction rate. The total estimated cost $173.4 met the budgetary constraint, leaving room for additional manufacturing expenses. The results demonstrate that the proposed adjustable standing desk meets all functional, non-functional, and cost constraints, offering a viable ergonomic solution for home office settings. Recommendations are provided for improving use convenience and manufacturability in potential future production stages.

Keywords:

Adjustable Standing Desk,Ergonomic Design,Mathematical Modeling,Stability Analysis,Design Optimization,Solid Work Modeling,

Refference:

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V. Chrisman, et al. “Assessing Sitting and Standing in College Students Using Height-Adjustable Desks.” Health Education Journal, vol. 9, no. 6, 2020, pp. 735-744. 10.1177/0017896920901837
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XII. Providence. “Working from Home and Lower Back Pain: Ergonomics and Exercise to the Rescue.” Providence Blog, 6 Nov. 2020, https://blog.providence.org/blog/working-from-home-and-lower-back-pain-ergonomics-and-exercise-to-the-rescue.
XIII. Robertson, Michelle M., and Michael J. O’Neill. “Reducing Musculoskeletal Discomfort: Effects of an Office Ergonomics Workplace and Training Intervention.” International Journal of Occupational Safety and Ergonomics, vol. 9, no. 4, 2003, pp. 491-502. 10.1080/10803548.2003.11076585
XIV. Sai Tarun Reddy, and C. H. Patel. “Optimized Design, Analysis of Affordable Height-Adjustable Table.” JETIR, vol. 8, no. 5, 2021, https://www.jetir.org/papers/JETIR2105109.pdf.
XV. Openshaw, Scott, Erin Taylor, and Allsteel. Ergonomics and Design: A Reference Guide. Allsteel Inc., 2006. https://ehs.oregonstate.edu/sites/ehs.oregonstate.edu/files/pdf/ergo/ergonomicsanddesignreferenceguidewhitepaper.pdf
XVI. Engineering Toolbox. “Wood Panel and Structure Timber Products.” Engineering Toolbox, n.d., https://www.engineeringtoolbox.com/timber-mechanical-properties-d_1789.html.

XVII. Verbeek, J. “The Use of Adjustable Furniture: Evaluation of an Instruction Programme for Office Workers.” Applied Ergonomics, vol. 22, no. 3, 1991, pp. 179-184. 10.1016/0003-6870(91)90157-d
XVIII. Viggiani, et al. “Multifunctional Home Office Desk Design for Small Residential Environments.” Revista de Ciencia y Tecnología, 2024, pp. 35-44. 10.36995/j.recyt.2024.41.005
XIX. Vermont Wood Studios. “Wood Maple: Color, Grain & Other Characteristics.” Vermont Wood Studios, n.d., https://vermontwoodsstudios.com/content/maple-wood.

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Abstract:

Let Υ denote the class of functions χ(ξ) of the form χ(ξ)=ξ+∑_(n=2)^∞▒a_n ξ^n which are analytic in the open unit disc Δ=\{ ξ∈C: |ξ|<1 }. In recent times investigating the properties of several existing and new subclasses of quasi-convex functions have gained importance and attracted researchers working in the theory of univalent functions. Using the Carlson-Shaffer operator, we introduce new subclasses of quasi-convex functions. The coefficient bounds for functions belonging to the defined function classes are our main results. Further, we establish various well-known results as corollaries to our main results.

Keywords:

Analytic function,quasi-convex,close to convex,close to star,Janowski function,coefficient estimates,Carlson-Shaffer operator,

Refference:

I. Altıntas, O., and Kılıc, Ӧ.Ӧ. (2018). Coefficient estimates for a class containing quasi-convex functions, Turkish Journal of Mathematics, 42(5), 2819-2825. 10.3906/mat-1805-90.
II. Carlson, B. C., and Shaffer, D. B. (1984). Starlike and prestarlike hypergeometric functions, SIAM Journal on Mathematical Analysis, 15(4), 737-745. 10.1137/0515057.
III. Friedland, S. and Schiffer, M. (1976). Global results in control theory with applications to univalent functions, Bulletin of the American Mathematical Society, 82(6), 913-915. 10.1090/S0002-9904-1976-14211-5.
IV. Goodman, A. W. (1972). On close-to-convex functions of higher order, Annales Universitatis Scientiarum Budapestinensis de Rolando Eőtvős Nominatae. Sectio Mathematica, 15, 17-30.
V. Janowski, W. (1973). Some extremal problems for certain families of analytic functions, Annales Polonici Mathematici, 28, 297-326. 10.4064/ap-28-3-297-326.
VI. Kaplan, W. (1952). Close-to-convex schlicht functions, Michigan Mathematical Journal, 1, 169-185.
VII. Karthikeyan, K.R., Amarender Reddy, K., and Murugusundaramoorthy, G. (2022). On classes of Janowski functions associated with a conic domain, Italian Journal of Pure and Applied Mathematics, Vol.-47, 684-698.
VIII. Karthikeyan, K. R., Varadharajan, S., and Lakshmi, S. (2024). Some properties of close-to-convex functions associated with a strip domain, Sahand Communications in Mathematical Analysis, 21(2), 105-124. 10.22130/scma.2023.1987052.1233.
IX. Mehrok, B. S., and Singh, G. (2010). A subclass of close-to-convex functions, International Journal of Mathematical Analysis (Ruse), 4, 1319-1327.
X. Mehrok, B. S., Singh, G., and Gupta, D. (2010). A subclass of analytic functions, Global Journal of Mathematical Sciences: Theory and Practical, 2(1), 91-97.
XI. Mehrok, B. S., and Singh, G. (2012). A subclass of close-to-star functions, International Journal Modern Mathematical Sciences, 4(3), 139-145.
XII Mukhamadullina, G. I., Kornev, K. G., and Alimov, M. M. (1999). Hysteretic effects in the problems of artificial freezing, SIAM Journal on Applied Mathematics, 59(2), 387- 410. 10.1137/S0036139996313782.
XIII. Noor, K. I., and Thomas, D. K. (1980). Quasiconvex univalent functions, International Journal of Mathematics and Mathematical Sciences, 3(2), 255-266. 10.1155/S016117128000018X.
XIV. Noor, K. I. (1987). On quasiconvex functions and related topics, International Journal of Mathematics and Mathematical Sciences, 10(2), 241-258. 10.1155/S0161171287000310.
XV. Prokhorov, D. V. (2001). Coefficients of holomorphic functions, Journal of Mathematical Sciences (New York), 106 (6), 3518-3544. 10.1023/A:1011975914158.
XVI. Reade, M. O. (1955). On close-to-convex univalent functions, Michigan Mathematical Journal, 3, 59-62.
XVII. Rensaa, R. J. (2003). Univalent functions and frequency analysis, Rocky Mountain Journal of Mathematics, 33(2), 743-758. 10.1216/rmjm/1181069976.
XVIII. Singh, G. (2013). Certain subclasses of α-close-to-star functions, International Journal Modern Mathematical Sciences, 7(1), 80-89. 10.1007/s10013-013-0032-4.
XIX. Srivastava, H. M., Altıntas, O. and Serenbay, S. K. (2011). Coefficient bounds for certain subclasses of starlike functions of complex order, Applied Mathematics Letters, 24(8), 1359-1363. 10.1016/j.aml.2011.03.010.
XX. Vasil’ev, A. (2001). Univalent functions in the dynamics of viscous flows, Computational Methods and Function Theory, 1(2), 311-337. 10.1007/BF03320993.
XXI Vasil’ev, A. and Markina, I. (2003). On the geometry of Hele-Shaw flows with small surface tension. Interfaces Free Bound. 5 (2), 183-192. 10.4171/IFB/77.
XXII. Xiong, L., and Liu, X. (2012). A general subclass of close-to-convex functions Kragujevac Journal of Mathematics, 36(2), 251-260.

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Abstract:

Within this paper, we create the ideas of slightly Σα-shelter, shortly sΣα - shelter, and slightly Σα-compact, shortly Σα-compact fuzzy sets. In addition, we have established certain theorems concerning our vision in fuzzy topological spaces, fuzzy subspaces, fuzzy continuity mapping, fuzzy open mapping, fuzzy T₂-spaces (Hausdorff spaces), and their different characterizations. Finally, we have discussed our vision about the “good extension” characteristics

Keywords:

fuzzy sets,fuzzy topological spaces,sΣα-shelter,sΣα-compact,

Refference:

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III. Benchalli, S. S., and G. P. Siddapur. “On the level spaces of fuzzy topological spaces.” Bulletin of Mathematical Analysis and Applications 1.2 (2009): 57-65. https://eudml.org/doc/229234
IV. Chadwick, J. J. “A generalised form of compactness in fuzzy topological spaces.” Journal of Mathematical Analysis and Applications 162.1 (1991): 92-110. 10.1016/0022-247X(91)90180-8
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VI. Chen, Yixiang. “On compactness of induced I (L)-fuzzy topological spaces.” Fuzzy Sets and Systems 88.3 (1997): 373-378. 10.1016/S0165-0114(96)00074-7
VII. Dongsheng, Zhao. “The N-compactness in L-fuzzy topological spaces.” Journal of Mathematical Analysis and Applications 128.1 (1987): 64-79. 10.1016/0022-247x(87)90214-9
VIII. EL-Shafei, M. E., I. M. Hanafy, and A. I. Aggour. “RS-Compactness in L-fuzzy topological spaces.” Kyungpook Mathematical Journal 42.2 (2002): 417-428.
IX. Gantner, T. E., R. C. Steinlage, and R. H. Warren. “Compactness in fuzzy topological spaces.” Journal of Mathematical Analysis and Applications 62.3 (1978): 547-562. 10.1016/0022-247X(78)90148-8
X. Georgiou, D. N., and B. K. Papadopoulos. “On fuzzy compactness.” Journal of Mathematical Analysis and Applications 233.1 (1999): 86-101. 10.1006/jmaa.1999.6268
XI. Guojun, Wang. “A new fuzzy compactness defined by fuzzy nets.” Journal of Mathematical Analysis and Applications 94.1 (1983): 1-23. 10.1016/0022-247X(83)90002-1
XII. Hong, Woo Chorl. “RS-compact spaces.” Journal of the Korean Mathematical Society 17.1 (1980): 39-43.
XIII. Katsaras, A. K. “Ordered fuzzy topological spaces.” Journal of Mathematical Analysis and Applications 84.1 (1981): 44-58. 10.1016/0022-247X(81)90150-5
XIV. Kim, J. K. “α-compact fuzzy topological spaces.” Korean Journal of Computational and Applied Mathematics 1 (1994): 79-84. https://doi.org/10.1007/BF02943052
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XVI. Kudri, S. R. T., and M. W. Warner. “RS-compactness in L-fuzzy topological spaces.” Fuzzy Sets and Systems 86.3 (1997): 369-376. 10.1016/S0165-0114(95)00405-X
XVII. Li, Hong-Yan, and Fu-Gui Shi. “Degrees of fuzzy compactness in L-fuzzy topological spaces.” Fuzzy sets and systems 161.7 (2010): 988-1001. 10.1016/j.fss.2009.10.012
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XXXI. Shi, Fu-Gui. “A new definition of fuzzy compactness.” Fuzzy sets and systems 158.13 (2007): 1486-1495. 10.1016/j.fss.2007.02.006
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XXXVIII. Zadeh, L. A. “Fuzzy Sets.” Information and Control 8.3 (1965):338-353. 10.1016/S0019-9958(65)90241-X

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Abstract:

In this paper, the issues of developing a calculation method for the formation of cracks normal to the longitudinal axis of bent reinforced concrete structures of circular cross-section without prestressing the reinforcement based on a nonlinear deformation model using a bilinear diagram of the state of concrete are considered. The prerequisites based on which theoretical dependences are constructed to determine the complex internal forces of a round normal section before the formation of cracks is presented. Based on stereometry, dependencies are presented to determine the forces in the concrete and reinforcement of the compressed zone and the forces in the stretched zone, respectively, for concrete and reinforcement. Since it is difficult to analytically solve a system of equations with negative and positive exponents, it is recommended to carry out numerical iterative processes to determine the desired unknowns: heights of the compressed zone, maximum stresses in concrete and reinforcement of the compressed zone. Numerical studies have made it possible to determine the value of the elastic-plastic moment of the annular section and to identify its dependence on the strength of concrete.

Keywords:

Circular cross-section,moment of crack formation,neutral axis,arc length,sector area,elastic-plastic moment of resistance,

Refference:

I. Bronshteyn Ilya N., and Semendyayev Konstantin A. Spravochnik po matematike. Dlya inzhenerov i uchashchikhsya VTUZov. – Moskva, «Nauka».1986. S.544. https://www.geokniga.org/books/10582
II. Golyshev Aleksander B., and Bachinskiy Vladimir Y., and Polishchuk Vitaliy P., and Kharchenko Aleksander V. Proyektirovaniye zhelezobetonnykh konstruktsiy. Spravochnoye posobiye. – Kiyev, Budivel’nyk, 1990. https://dwg.ru/lib/1280
III. Karpenko Sergei N. Obshchiy metod rascheta zhelezobetonnykh elementov kol’tsevogo secheniya. // Trudy 1-y Vserossiyskoy konferentsii «Beton na rubezhe tret’yego tysyacheletiya», 2001. https://www.elibrary.ru/item.asp?ysclid=m7vkidp113126215346&edn=slhznp)
IV. Krylov Yuriy K., and Polyakov Igor D. Proletnyye stroyeniya iz tsentrofugirovannogo zhelezobetona. Transportnoye stroitel’stvo. – M., №6, 1970.
V. Kudzis Aleksey P. Zhelezobetonnyye konstruktsii kol’tsevogo secheniya. – Vil’nyus, Mintis, 1975.
VI. Lastochkin Vasiliy G., and Petsol’d Timofei M., and Tarasov Vladimir V. Opyt proizvodstva i primeneniya tsentrofugirovannykh konstruktsiy v promyshlennom stroitel’stve. Obzornaya informatsiya. – Minsk, BelNIINTI, 1985.
VII. Morshteyn, Oleg B. Izgotovleniye tsentrofugirovannykh kolonn s konsolyami dlya promzdaniy. Transportnoye stroitel’stvo. – №12, 1978, https://ais.by/article/opyt-primeneniya-centrifugirovannyh-lineynyh-elementov-s-poperechnymi-secheniyami
VIII. Mukhamediyev Tahir A. Uchet neuprugikh svoystv betona pri raschete zhelezobetonnykh konstruktsiy po obrazovaniyu treshchin. – Stroitel’naya mekhanika i raschet sooruzheniy, № 5, 2018. https://www.elibrary.ru/item.asp?id=35606774&ysclid=m7vlj8gx5n354667764)
IX. Parfenov Sergei G., and Okusok Sergei A. Primeneniye deformatsionnykh modeley dlya rascheta treshchinostoykosti zhelezobetonnykh elementov. V sb. «Fundamental’nyye poiskovyye i prikladnyye issledovaniya RAASN po nauchnomu obespecheniyu razvitiya arkhitektury, gradostroitel’stva i stroitel’noy otrasli Rossiyskoy Federatsii». Tom 2. – Moskva, PAO «T8 Izdatel’skiye Tekhnologii, 2017.
X. Pastushkov Gennadiy P. Unifitsirovannyye karkasnyye zdaniya s tsentrofugirovannymi kolonnami. Obzornaya informatsiya. – Minsk, BelNIINTI, 1988, 52. http://unicat.nlb.by/opac/pls/pages.view_doc?off=0&siz=20&qid=75987&format=full&nn=11)
XI. Petsol’d Timofei M., and Pastushkov Gennadiy P.,. Karkasy proizvodstvennykh zdaniy iz unifitsirovannykh prednapryazhennykh tsentrofugirovannykh elementov. Beton i zhelezobeton. – M., №4, 1990. https://www.booksite.ru/beton/1990/1990_4.pdf
XII. Trekin Nikolay N., and Kodysh Emil N., and Mamin Aleksander N., and Trekin Dmitriy N.,. Improving methods of evaluating the crack resistance of concrete structures. Proceedings 2nd International Workshop «Durability and Sustainability of Concrete Structures (DSCS-2018)», Moscow, Russia. – American Concrete Institute, 2018.
XIII. Trekin Nikolay N., and Kodysh Emil N., and Trekin Dmitriy N. Raschet po obrazovaniyu normal’nykh treshchin na osnove deformatsionnoy modeli. – Promyshlennoye i grazhdanskoye stroitel’stvo, №7, 2016. https://www.elibrary.ru/item.asp?edn=whkjxn&ysclid=m7vlv124h1711223019
XIV. Trekin Nikolay N., and Kodysh Emil N., and Trekin Dmitriy N. Sovershenstvovaniye metoda otsenki treshchinostoykosti izgibayemykh zhelezobetonnykh elementov. – Beton i zhelezobeton, №1, 2020. https://www.elibrary.ru/item.asp?id=44294660&ysclid=m7vlta0tvl531195817
XV. Trekin Nikolay N., and Kodysh Emil N., and Andryan Konstantin R. Treshchinostoykost’ zhelezobetonnykh konstruktsiy kruglykh secheniy. – Academia. Arkhitektura i stroitel’stvo (3). 10.22337/2077-9038-2022-3-104-109
XVI. Zalesov Aleksandr S., and Mukhamediyev Tahir A., and Chistyakov Eugeniy A.. Raschet treshchinostoykosti zhelezobetonnykh konstruktsiy po novym normativnym dokumentam. – Beton i zhelezobeton, №5, 2002. https://www.elibrary.ru/item.asp?id=30748751&ysclid=m7vkmw3xp7950354133
XVII. Zalesov Aleksandr S., and Chistyakov Eugeniy A.. Deformatsionnaya raschetnaya model’ zhelezobetonnykh elementov pri deystvii izgibayushchikh momentov i prodol’nykh sil – Beton i zhelezobeton, №5, 1996. https://www.elibrary.ru/item.asp?edn=xmvqdb&ysclid=m7vkwkelug868968971

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