Journal Vol – 20 No – 6, June 2025

Abstract:

k^th Fibonacci Prime Labeling is defined as labeling the vertices of a graph with distinct Fibonacci numbers starting since the k^th Fibonacci term sustaining the condition that the , where  and  are labels of any adjacent vertices u and v. Graphs formed by consecutively connecting identical base graphs, linearly or in alternating pattern, is called Snake graph. In this paper, we show that some snake graphs admit k^th Fibonacci prime labeling.

Keywords:

Fibonacci prime graph,k^th Fibonacci prime graph,k-prime graph,snake graphs,

Refference:

I. Baby Smitha K. M. and Thirusangu K, “Distance two labeling of quadrilateral snake families,” International Journal of Pure and Applied Mathematical Sciences, vol. 9, no. 2, pp. 283–298, 2016.
https://www.ripublication.com/ijpams16/ijpamsv9n2_19.pdf
II. Bondy J. A. and Murthy U. S. R., Graph Theory and Application, North Holland, New York, (1976).
III. Burton D.M., Elementary Number Theory, Second Edition, Wm. C. Brown Company Publishers, (1980).
IV. Franklin Thamil Selvi M. S., “Harmonious coloring of central graphs of certain snake graphs,” Appl. Math. Sci, vol. 9, no. 1, pp. 569–578, 2015. 10.12988/ams.2015.4121012
V. Jenifer J and Subbulakshmi M, “Fibonacci prime labeling of snake graph,” South East Asian Journal of Mathematics & Mathematical Sciences, vol. 17, no. 1, pp. 51–58, 2021.
https://rsmams.org/download/articles//2_17_0_1035092602_Paper%206%20FIBONACCI%20PRIME%20LABELING%20OF%20SNAKE%20GRAPH.pdf
VI. Joseph A. Gallian, “A dynamic survey of graph labeling,” Electronic Journal of Combinatorics, vol. 6, no. 25, pp. 4–623, 2022.
https://www.combinatorics.org/files/Surveys/ds6/ds6v25-2022.pdf
VII. Periasamy K and Venugopal K, and Lawrence Rozario Raj P, “kth Fibonacci prime labeling of graphs,” International Journal of Mathematics Trends and Technology-IJMTT, vol. 68, no. 5, pp. 61–67, 2022.
10.14445/22315373/IJMTT-V68I5P510
VIII. Periasamy K and Venugopal K, “Cycle related kth Fibonacci prime labeling of graphs,” International Journal of Food and Nutritional Sciences, vol. 11, no. 3, pp. 1363 1371, 2022. https://ijfans.org/issue-content/cycle-related-kth-fibonacci-prime-labeling-of-graphs-1333
IX. Periasamy K and Venugopal K, “Path related kth Fibonacci prime labeling of graphs,” Journal of Pharmaceutical Negative Results, vol. 11, no. 3, pp. 912–918, 2022. 10.47750/pnr.2022.13.S10.103
X. Ponraj R and Narayanan S. S., “Mean cordiality of some snake graphs,” Palestine Journal of Mathematics, vol. 4, no. 2, pp. 439–445, 2015. https://pjm.ppu.edu/sites/default/files/papers/27%20%20Mean%20cordiality%20of%20some%20snake%20graphs.pdf
XI. Raja Rama Gandhi K., “Divisibility properties of Fibonacci numbers,” South Asian Journal of Mathematics, Vol. 1 (3), pp. 140-144, 2011. https://www.academia.edu/5885548/Divisibility_properties_of_Fibonacci_numbers
XII. Sekar C and Chandrakala S, “Fibonacci prime labeling of graphs,” International Journal of Creative Research Thoughts, vol. 6, no. 2, pp. 995–1001, 2018. https://ijcrt.org/papers/IJPUB1802165.pdf
XIII. Sekar C and Chandrakala S, “Prime labeling of chavatal related graphs,” International Journal of Engineering, Science and Mathematics, vol. 7, no. 4, pp. 433–443, 2018. https://www.ijesm.co.in/uploads/68/5512_pdf.pdf
XIV. Thomas Browning, “The Fibonacci Sequence,” University of California, Berkeley, web content, 2018.
https://math.berkeley.edu/~tb65536/Fibonacci_Exposition.pdf
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XVI. Vaidya S and Prajapati U. M., “Some results on prime and k-prime labeling,” Journal of Mathematics Research, vol. 3, no. 1, pp. 66–75, 2011. 10.5539/jmr.v3n1p66

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Abstract:

Since Wi-Fi networks are dependent on the structure of the network and are unable to supply extra facilities, high latency in non-VLAN networks may be made more variable. Because of this, the isolation problem becomes more significant when it comes to adding problems to certain networks that include WLAN components that are acceptable. Rather than being a genuine relationship, virtual local area networks (VLANs) are a coherence that enables aggregation in a comparable transmission region. As a result, bundles are transmitted to ports that are on the same VLAN. In addition to reducing transmission speed, the layout of trademark VLANs increases the efficiency of faraway organizations. Execution of virtual local area networks (VLAN) significantly enhances the security of distant organizations by lowering the number of sites that get copies of data that are sent by switches. A separate virtual local area network (VLAN) is used to store fundamental data. This study compares remote organizations using virtual local area networks (VLAN) to other types of remote companies. Through the use of document transfer during major rush hour congestion and online reading applications, the proposed network is assessed for typical throughput and latency. Via the use of OPNET 14.5 modeler reconstruction, the simulation was carried out, and the findings indicate that the use of VLAN via remote organization resulted in a reduction in traffic and avoided delays in presentation. As a result of the positive correlation that exists between throughput and traffic, virtual local area networks (VLAN) diminish the throughput of an organization. In addition, we investigated the throughput in a remote VLAN network and discovered that the Wi-Fi traffic was 1500 for VLAN and 950 for non-VLAN based on seconds for bits-sec. This would be an improvement to the Wi-Fi model.

Keywords:

Document Transfer,OPNET,Simulation,Transmission Speed,Wi-Fi networks,Virtual Local Area Networks (VLAN),

Refference:

I. Abdulwahid, M. M., Al-Ani, O. A. S., Mosleh, M. F., and Abd-Alhmeed, R. A. “Investigation of Millimeter-Wave Indoor Propagation at Different Frequencies.” 2019 4th Scientific International Conference Najaf (SICN), 2019, pp. 25–30. IEEE.
II. A. Hafed Mahdi and W. Saeed Majeed, “Controlling the Frequency Response in AC/DC Microgrid using an Energy Storage Device”, IJSER, vol. 1, no. 2, pp. 29–39, Dec. 2022.
III. Abdulwahid, M. M., Al-Hakeem, M. S., Mosleh, M. F., and Abd Alhmeed, R. A. “Investigation and Optimization Method for Wireless AP Deployment Based Indoor Network.” IOP Conference Series:
Materials Science and Engineering, vol. 745, no. 1, 2020, p. 012031.
IV. A. Jasim Mohammed, “Impact of Rain Weather Conditions over Hybrid FSO/58GHz Communication Link in Tropical Region ”, IJSER, vol. 3, no. 3, pp. 117–134, Sep. 2024.
V. Ali, A. H., Abbas, A. N., and Hassan, M. H. “Performance Evaluation of IEEE 802.11g WLANs Using OPNET Modeler.” American Journal of Engineering Research (AJER), vol. 2, no. 12, 2013, pp. 9–15.
VI. Alimi, I. A., and Mufutau, A. O. “Enhancement of Network Performance of an Enterprise’s Network with VLAN.” American Journal of Mobile Systems, Applications and Services, vol. 1, no. 2, 2015, pp. 82–93.
VII. AL-Hakeem, M. S., Burhan, I., and Abdulwahid, M. M. “Hybrid Localization Algorithm for Accurate Indoor Estimation Based IoT Services.” 2020.
VIII. AL-Khaffaf, D. A. J. “Improving LAN Performance Based on IEEE 802.1Q VLAN Switching Techniques.” Journal of University of Babylon, vol. 26, no. 1, 2018, pp. 286–297.
IX. Al-Khraishi, T., and Quwaider, M. “Implementation of VLAN via Wireless Networks Using OPNET Modeler.” Computer Science & Information Technology (CS & IT), no. July 2020, 2019, pp. 57–72. doi:10.5121/csit.2019.91805.
X. Alisa, Z. T. “Evaluating the Performance of Wireless Network Using OPNET Modeler.” International Journal of Computer Applications, vol. 62, no. 13, 2013.
XI. Burhan, I. M., Al-Hakeem, M. S., Abdulwahid, M. M., and Mosleh, M. F. “Investigating the Access Point Height for an Indoor IoT Services.” IOP Conference Series: Materials Science and Engineering, vol. 881, no. 1, July 2020, p. 012116. IOP Publishing.
XII. Chokshi, R., and Yu, C. “Study on VLAN in Wireless Networks.” Technical Report, 2007.
XIII. Elechi, Onyekachi O. “Design and Simulation of Wireless Local Area Network for Administrative Office Using OPNET Network Simulator: A Practical Approach.” Information and Knowledge Management, vol. 4, no. 10, 2014, pp. 27–34.
XIV. H. S. Mewara, B., and Mukesh Kumar Saini. “Performance Analysis of Access Point for IEEE802.11g Wireless LAN Using Opnet Simulator.” International Journal of Advanced Engineering Research and Science (IJAERS), vol. 1, no. 1, 2014, pp. 14–18.
XV. Jayakumar, G., and Ganapathy, G. “Performance Comparison of Mobile Ad-Hoc Network Routing Protocol.” International Journal of Computer Science and Network Security (IJCSNS), vol. 7, no. 11, 2007, pp. 77–84.
XVI. Krupanek, B., and Bogacz, R. “OPNET Modeler Simulations of Performance for Multi Nodes Wireless Systems.” International Journal of Metrology and Quality Engineering, vol. 7, no. 1, 2016, p. 105.
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XVIII. Meen, Parneek Kaur. “Performance Metrics of WLAN for Different Applications Using OPNET.” International Journal of P2P Network Trends and Technology (IJPTT), vol. 4, no. 5, 2014.
XIX. Mittal, I., and Anand, A. “WLAN Architecture.” International Journal of Computer Trends and Technology, vol. 8, no. 3, 2014, pp. 148–151.
XX. Mohsen, D. E., Abbas, E. M., and Abdulwahid, M. M. “Performance Evaluation of 32 WDM-FSO Systems with Different Weather Turbulence Under Variance Launch Power Values.” 2024 IEEE 1st International Conference on Communication Engineering and Emerging Technologies (ICoCET), Sept. 2024, pp. 1–4. IEEE.
XXI. Sarah Ali Abdullah. “Simulation of Virtual LANs (VLANs) Using OPNET.” IOSR Journal of Electronics and Communication Engineering (IOSR-JECE), vol. 11, no. 6, ver. II, 2016, pp. 67–80.
XXII. Shareef, O. A., Abdulwahid, M. M., Mosleh, M. F., and Abd-Alhameed, R. “The Optimum Location for Access Point Deployment Based on RSS for Indoor Communication.” 2019.
XXIII. Shrivastava, L., Bhadauria, S. S., and Tomar, G. S. “Performance Evaluation of Routing Protocols in MANET with Different Traffic Loads.” 2011 International Conference on Communication Systems and Network Technologies, IEEE, 2011, pp. 13–16.
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XXIX. Zaal, R. M., Mosleh, M. F., Abbas, E. I., and Abdulwahid, M. M. “Optimal Coverage Area with Lower Number of Access Point.” IMDC-SDSP 2020: Proceedings of the 1st International Multi-Disciplinary Conference Theme: Sustainable Development and Smart Planning, IMDC-SDSP, Sept. 2020, p. 230.
XXX. Zaal, R. M., Mustafa, F. M., Abbas, E. I., Mosleh, M. F., and Abdulwahid, M. M. “Real Measurement of Optimal Access Point Localizations.” IOP Conference Series: Materials Science and Engineering, vol. 881, no. 1, July 2020, p. 012119. IOP Publishing.

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Abstract:

This paper investigates the effectiveness of two control approaches, Photovoltaic (PV)-Battery Energy Storage Systems (BESS) and SAPF, in harmonizing three-phase AC grids, focusing on power quality (PQ) and dq transformation theory. With the rising integration of renewable energy sources (RES), such as PV systems, ensuring PQ becomes critical. The dual approach comparison aims to assess the performance and suitability of these control strategies. The PV-BESS system utilizes batteries to store excess PV-generated energy, offering grid flexibility and improving stability. Conversely, SAPF controls utilize power electronics to address harmonics and reactive power fluctuations, thereby enhancing grid reliability. Through simulation and analysis, this study evaluates the efficacy, cost-effectiveness, and practicality of both approaches in PQ improvement using dq theory. The findings contribute to advancing grid integration techniques, optimizing renewable energy utilization, and ensuring a robust and stable power supply infrastructure, guided by PQ and dq transformation theory principles.

Keywords:

PV,BESS,Shunt Active Power Filters (SAPF),power quality,PQ and DQ theory,

Refference:

I. Albatran, S., Khalaileh, A. R. A., & Allabadi, A. S. (2020). Minimizing total harmonic distortion of a two-level voltage source inverter using optimal third harmonic injection. IEEE Transactions on Power Electronics, 35(3), 3287–3297.
II. Baimel, D., Belikov, J., Guerrero, J. M., & Levron, Y. (2017). Dynamic modeling of networks, microgrids, and renewable sources in the dq0 reference frame: A survey. IEEE Access, 5, 21323–21335.
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V. Chunduri, R., Gracelin, B., Vanaja, D. S., Priyadarshini, S., Khillo, A., & Ganthia, B. P. (2021). Design and control of a solar photovoltaic fed asymmetric multilevel inverter using computational intelligence. Annals of the Romanian Society for Cell Biology, 25(6), 10471-10484.
VI. Dahono, P. A. (2009). New hysteresis current controller for single-phase full-bridge inverters. IET Power Electronics, 2(5), 585–594.
VII. Das, S. R., Ray, P. K., Sahoo, A. K., Ramasubbareddy, S., Babu, T. S., Kumar, N. M., et al. (2021). A comprehensive survey on different control strategies and applications of active power filters for power quality improvement. Energies, 14(15), 4589.
VIII. De La Fuente, D. V., Rodríguez, C. L. T., Garcerá, G., Figures, E., & González, R. O. (2012). Photovoltaic power system with battery backup with grid-connection and islanded operation capabilities. IEEE Transactions on Industrial Electronics, 60(4), 1571–1581.
IX. Ganthia, B. P., Dharmaprakash, R., Choudhary, T., Muni, T. V., Al-Ammar, E. A., Seikh, A. H., … & Diriba, A. (2022). Simulation Model of PV System Function in Stand‐Alone Mode for Grid Blackout Area. International Journal of Photoenergy, 2022(1), 6202802.
X. Ganthia, B. P., Praveen, B. M., Barkunan, S. R., Marthanda, A. V. G. A., Kumar, N. M. G., & Kaliappan, S. Energy Management In Hybrid Pv-Wind-Battery Storage-Based Microgrid Using Monte Carlo Optimization Technique.
XI. Ganthia, B. P., Praveen, B. M., Kabat, S. R., Mohapatra, B. K., Sethi, R., & Buradi, A. Energy Management In Hybrid Pv-Wind-Battery Storage-Based Microgrid Using Droop Control Technique.
XII. Ganthia, B. P., Panda, S., Remamany, K. P., Chaturvedi, A., Begum, A. Y., Mohan, G., … & Ishwarya, S. (2025). Experimental techniques for enhancing PV panel efficiency through temperature reduction using water cooling and colour filters. Electrical Engineering, 1-27.
XIII. Gu, J., Wang, W., Yin, R., Truong, C. V., & Ganthia, B. P. (2021). Complex circuit simulation and nonlinear characteristics analysis of GaN power switching device. Nonlinear Engineering, 10(1), 555-562.
XIV. He, N., Zhu, Y., Zhao, A., & Xu, D. (2019). Zero-voltage-switching sinusoidal pulse width modulation method for three-phase four-wire inverter. IEEE Transactions on Power Electronics, 34(8), 7192–7205.
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XVII. Jayakumar, N., Vighneshwari, B. D., Mohanty, M., Kabat, S. R., Ganthia, B. P., Rani, N. C., & Kannan, L. V. (2021). Cascade H Bridge Multilevel Inverter with Pwm for Lower Thd, Emi & Rfi Reduction. Annals of the Romanian Society for Cell Biology, 25(6), 2972-2977.
XVIII. Jintakosonwit, P., Fujita, H., Akagi, H., & Ogasawara, S. (2013). Implementation and performance of cooperative control of shunt active filters for harmonic damping throughout a power distribution system. IEEE Transactions on Industry Applications, 39(2), 556–564.
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XXI. Kumar, N., Singh, B., & Panigrahi, B. K. (2019). Grid synchronisation framework for partially shaded solar PV-based microgrid using intelligent control strategy. IET Generation, Transmission & Distribution, 13(6), 829–837.
XXII. Lawder, M., Suthar, B., Northrop, P., De, S., Hoff, C., Leitermann, O., et al. (2014). Battery energy storage system (BESS) and battery management system (BMS) for grid-scale applications. Proceedings of the IEEE, 102(6), 1014–1030.
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XXVI. Mishra, S., Ganthia, B. P., Sridharan, A., Rajakumar, P., Padmapriya, D., & Kaliappan, S. (2022). Optimization of load forecasting in smartgrid using artificial neural network based NFTOOL and NNTOOL. In Journal of Physics: Conference Series (Vol. 2161, No. 1, p. 012068). IOP Publishing.
XXVII. Mohanty, M., Nayak, N., Ganthia, B. P., & Behera, M. K. (2023, June). Power Smoothening of Photovoltaic System using Dynamic PSO with ESC under Partial Shading Condition. In 2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT) (pp. 675-680). IEEE.
XXVIII. Pahadasingh, S., Jena, C., Panigrahi, C. K., & Ganthia, B. P. (2022). JAYA algorithm-optimized load frequency control of a four-area interconnected power system tuning using PID controller. Engineering, Technology & Applied Science Research, 12(3), 8646-8651.
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Abstract:

In the banking sector, safeguarding sensitive financial transactions is critical to maintaining customer trust and regulatory compliance. Cybersecurity threats, ranging from data breaches to unauthorized access, necessitate robust protective measures. However, the majority of research places a strong emphasis on vertex dominance in security networks while ignoring the importance of edge defense for overall security, also hypercube and grid structures are not considered. Furthermore, conventional studies have ignored the potential of hypercube and grid graph structures in enhancing security measures. Hence this research proposed a secure vertex-edge domination (SVED) in hypercube and grid graphs, exploring their applications in optimizing cybersecurity measures for secure transaction monitoring. Moreover, develop a Hidden Markov Model (HMM) framework to enhance the detection of anomalous activities within these graph structures. This algorithm efficiently computes the minimum number of security agents required to monitor transaction flows, thus reducing vulnerabilities. This research not only fills a critical gap in existing network security methodologies but also proposes a novel framework for protecting complex networks from evolving cyber threats, thereby advancing the frontier of cybersecurity and mathematical graph theory.

Keywords:

Secure Vertex-Edge Domination,Hypercube graphs,Grid graphs,Graph theory,Cybersecurity threats,Secure Transaction,

Refference:

I. A. Majeed, and I. Rauf, ‘Graph theory: A comprehensive survey about graph theory applications in computer science and social networks,’ Inventions, vol. 5, no. 1, pp. 10, 2020. 10.3390/inventions5010010
II. A. Sahin, and B. Sahin, ‘Total edge–vertex domination,’ RAIRO-Theoretical Informatics and Applications, vol. 54, pp. 1, 2020.
III. A.I. Al-Alawi, and , M.S.A. Al-Bassam, ‘The significance of cybersecurity system in helping managing risk in banking and financial sector,’ Journal of Xidian University, vol. 14, no. 7, pp. 1523-1536, 2020. 10.37896/jxu14.7/174
IV. A.Q. Stanikzai, and M.A. Shah, ‘Evaluation of cyber security threats in banking systems,’ In 2021 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, pp. 1-4, 2021, December. 10.1109/SSCI50451.2021.9659862
V. B. Dumitrescu, A. Băltoiu, and Ş. Budulan, ‘Anomaly detection in graphs of bank transactions for anti money laundering applications,’ IEEE Access, vol. 10, pp. 47699-47714, 2022. 10.1109/ACCESS.2022.3170467
VI. C. Wang, and H. Zhu, ‘Wrongdoing monitor: A graph-based behavioral anomaly detection in cyber security,’ IEEE Transactions on Information Forensics and Security, vol. 17, pp. 2703-2718, 2022. 10.1109/TIFS.2022.3191493
VII. D. Angel, A. Arputhamary, and S. Saffren, ‘Defense Mechanism for the Nodes of 2-D Meshes and n-cubes,’ In 2021 International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), IEEE, pp. 1-4, 2021, February. 10.1109/ICAECT49130.2021.9392622
VIII. D. Angel, G. Uma, and E. Priyadharshini, ‘Application of Graph theory to Defend Hypercubes and Matching Graph of Hypercube Structures Against Cyber Threats,’ In 2023 First International Conference on Advances in Electrical, Electronics and Computational Intelligence (ICAEECI), IEEE, pp. 1-4, 2023, October. 10.1109/ICAEECI58247.2023.10370916
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XVIII. Z. DeVivo, and R.K. Hladky, ‘New Upper Bounds on the Minimal Domination Numbers of High-Dimensional Hypercubes,’ arXiv preprint arXiv:2409.14621, 2024. 10.48550/arXiv.2409.14621

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Abstract:

Epilepsy, a complex neurological disorder, is particularly challenging to diagnose and manage when driven by genetic factors. This study focuses on the analysis of Idiopathic Partial Epilepsy (IPE) and Idiopathic Epilepsy (IE) in both children and women, using a novel approach combining Modified Particle Swarm Optimization (MPSO) with a 9-rule Adaptive Neuro-Fuzzy Inference System (ANFIS). Four feature extraction techniques—Discrete Wavelet Transform (DWT), Shearlet Transform (SLT), Contourlet Transform (CLT), and Stockwell Transform (SWT)—are employed to process electroencephalogram (EEG) signals. The performance of the proposed MPSO-ANFIS model is evaluated and compared with existing methods. Results indicate that the SWT-ANFIS-MPSO method achieves superior classification accuracy for both IE and IPE patients, highlighting its potential to improve epilepsy diagnosis and treatment strategies.

Keywords:

Idiopathic Partial Epilepsy (IPE),Idiopathic Epilepsy (IE),Modified Particle Swarm Optimization (MPSO),ANFIS,

Refference:

I. A. E. Hramov, A. A. Koronovskii, V. A. Makarov, A. N. Pavlov, E. Sitnikova. : ‘Mathematical methods of signal processing in neuroscience’. In: Wavelets in Neuroscience. Springer Series in Synergetics. pp. 1–13, 2021. 10.1007/978-3-662-43850-3_1
II. A. K. Jaiswal, H. Banka. : ‘Local pattern transformation-based feature extraction techniques for classification of epileptic EEG signals’. Biomedical Signal Processing and Control. Vol. 34, pp. 81–92, 2017. 10.1016/j.bspc.2017.01.005
III. A. Subasi. : ‘EEG signal classification using wavelet feature extraction and a mixture of expert model’. Expert Systems with Applications. Vol. 32, pp. 1048–1093, 2007. 10.1016/j.eswa.2006.02.005
IV. D. Rafik, B. Hocine, M. C. A. Cherif. : ‘Application of empirical mode decomposition and artificial neural network for the classification of normal and epileptic EEG signals’. Biocybernetics and Biomedical Engineering. Vol. 36, pp. 285–291, 2016. 10.1016/j.bbe.2015.10.006
V. E. D. Ubeyli. : ‘Eigenvector methods for automated detection of electrocardiographic changes in partial epileptic patients’. IEEE Transactions on Information Technology in Biomedicine. Vol. 13(4), 2009. 10.1109/TITB.2008.920614
VI. G. Chen. : ‘Automatic EEG seizure detection using dual-tree complex wavelet-Fourier features’. Expert Systems with Applications. Vol. 41, pp. 2391–2394, 2014. 10.1016/j.eswa.2013.09.037
VII. G. Ling, R. Daniel, A. S. Jose, P. Alejandro. : ‘Classification of EEG signals using relative wavelet energy and artificial neural networks’. GEC Conference Proceedings. June, pp. 177–183, 2009. 10.1145/1543834.1543860.
VIII. H. Adeli, N. Dadmehr. : ‘Analysis of EEG records in an epileptic patient using wavelet transform’. Journal of Neuroscience Methods. Vol. 123, pp. 69–87, 2003. 10.1016/s0165-0270(02)00340-0
IX. H. Liang, Z. Lin, F. Yin. : ‘Removal of ECG contamination from diaphragmatic EMG by nonlinear filtering’. Nonlinear Analysis: Theory, Methods & Applications. Vol. 63, pp. 745–753, 2005. 10.1016/j.na.2004.09.018
X. M. N. Do, M. Vetterli. : ‘The contourlet transform: An efficient directional multiresolution image representation’. IEEE Transactions on Image Representation. Vol. 14, pp. 2091–2106, 2006. 10.1109/TIP.2005.859376
XI. P. Swami, A. K. Godiyal, J. Santhosh, B. K. Panigrahi, M. Bhatia, S. Anand. : ‘Robust expert system design for automated detection of epileptic seizures using SVM classifier’. Proceedings of the IEEE International Conference on Parallel, Distributed and Grid Computing. pp. 219–222, Dec 2014. 10.1109/PDGC.2014.7030745
XII. P. Swami, T. K. Gandhi, B. K. Panigrahi, M. Tripathi, S. Anand. : ‘A novel robust diagnostic model to detect seizures in electroencephalography’. Expert Systems with Applications. Vol. 56, pp. 116–130, 2016. 10.1016/j.eswa.2016.02.040
XIII. R. B. Pachori, S. Patidar. : ‘Epileptic seizure classification in EEG signals using second-order difference plot of intrinsic mode functions’. Computer Methods and Programs in Biomedicine. Vol. 113, pp. 494–502, 2014. 10.1016/j.cmpb.2013.11.014
XIV. R. Fisher, W. van Emde Boas, W. Blume, C. Elger, P. Genton, P. Lee, J. Engel. : ‘Epileptic seizures and epilepsy: definitions proposed by the ILAE and the IBE’. Epilepsia. Vol. 46, pp. 470–472, 2005. 10.1111/j.0013-9580.2005.66104.x
XV. R. G. Stockwell. : ‘A basis for efficient representation of the S-transform’. Digital Signal Processing. Vol. 17(1), pp. 371–393, 2007. 10.1016/j.dsp.2006.04.006
XVI. S. Chandaka, A. Chatterjee, S. Munshi. : ‘Cross-correlation aided support vector machine classifier for classification of EEG signals’. Expert Systems with Applications. Vol. 36, pp. 1329–1336, 2009. 10.1016/j.eswa.2007.11.017
XVII. S. Patidar, T. Panigrahi. : ‘Detection of epileptic seizure using Kraskov entropy applied on tunable-Q wavelet transform of EEG signals’. Biomedical Signal Processing and Control. Vol. 34, pp. 74–80, 2017. 10.1016/j.bspc.2017.01.001
XVIII. T. Gandhi, B. K. Panigrahi, S. Anand. : ‘A comparative study of wavelet families for EEG signal classification’. Neurocomputing. Vol. 74, pp. 3051–3057, 2011. 10.1016/j.neucom.2011.04.029
XIX. V. Joshi, R. B. Pachori, A. Vijesh. : ‘Classification of ictal and seizure-free EEG signals using fractional linear prediction’. Biomedical Signal Processing and Control. Vol. 9, pp. 1–5, 2014. 10.1016/j.bspc.2013.08.006
XX. W. Q. Lim. : ‘The discrete shearlet transform: A new directional transform and compactly supported shearlet frames’. IEEE Transactions on Image Processing. Vol. 19, pp. 1166–1180, 2010. 10.1109/TIP.2010.2041410

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Abstract:

Over the past thirty years, reliability engineering has significantly evolved beyond its conventional focus on system reliability indices, profit evaluations, and cost-benefit analyses. With the advent of smart manufacturing, the field now integrates sophisticated stochastic modeling, multi-objective optimization, and AI-powered predictive maintenance. This review highlights key developments, including improvements in the reliability of single-unit, dual-unit, and multi-unit industrial systems, applications in various industries, the incorporation of renewable energy, and AI-driven monitoring and analysis. Furthermore, it identifies current research gaps and presents potential avenues for further innovation in reliability assessment.

Keywords:

AI-Based Predictions,Cost-benefit analysis,Industrial Systems,Markov Processes,Reliability Analysis,Semi-Markov Models,

Refference:

I. Aggarwal, A.K., Kumar, S. and Singh, V. (2015). Mathematical modeling and reliability analysis of the serial processes in feeding system of a sugar plant. International Journal of Systems Assurance Engineering and Management. 10.1007/s13198-015-0360-8
II. Al Amri, S.T., Mathew, A.G. and Rizwan, S.M. (2011). Reliability modeling and analysis of a refinery-based centrifugal pump. Caledonian Journal of Engineering, 7(1), 38-42.
III. Al Balushi, N., Al Khairi, W., Rizwan, S.M. and Taj, S.Z. (2024). Estimation of reliability parameters for power transformers. Journal of Mechanics of Continua and Mathematical Sciences, 19(11), 144-156. 10.26782/jmcms.2024.11.00010
IV. Al Balushi, N., Al Rashdi, S., Rizwan, S.M. and Taj, S.Z. (2024). Probabilistic analysis of power transformers in a power distribution company with six types of failures and inspection. International Journal of Engineering Trends and Technology, 72(4), 15-22. 10.14445/22315381/IJETT-V72I4P102
V. Al Balushi, N., Al Rashdi, S., Rizwan, S.M., Patil, G. and Saravanan, A.M. (2022). Development of a novel fouling-resistant membrane for wastewater treatment. International Journal of Membrane Science and Technology, 9(2), 55-60. 10.15379/2410-1869.2022.09.02.04
VI. Al Balushi, N., Rizwan, S.M., Taj, S.Z. and Al Khairi, W. (2024). Reliability analysis of power transformers of a power distribution company. International Journal of System Assurance Engineering and Management, 15, 1735-1742. 10.1007/s13198-023-02042-8
VII. Al Balushi, N.A., Al Rashdi, S., Al Saadi, S. and Rizwan, S.M. (2023). Study the potential of biological growth on dead-end hollow fiber membrane using oilfield effluent. International Journal of Membrane Science and Technology, 10(1), 31-37. 10.15379/2410-1869.2023.10.01.04
VIII. Al Hemyari, Z.A. and Rizwan, S.M. (2007). Reliability analysis of a two-unit system. Proceedings of the IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 1265-1267. 10.1109/IEEM.2007.4419352
IX. Al Oraimi, S.S., Rizwan, S.M. and Sachdeva, K. (2024). Sensitivity and profitability analysis of two-unit ammonia/urea plant. Reliability: Theory & Applications, 19(1), 376-386. 10.24412/1932-2321-2024-177-376-386
X. Al Rahbi, Y. and Rizwan, S.M. (2020). A comparative analysis between the models of a single component with a single repairman & multiple repairmen of an aluminum industry. Proceedings of the International Conference on Computational Performance Evaluation, 2-4 July, Department of Biomedical Engineering, North-Eastern Hill University, Shillong, Meghalaya, India, 132-135. 10.1109/ComPE49325.2020.9200048
XI. Al Rahbi, Y., Rizwan, S. M., Alkali, B., Cowel, A and Taneja, G. (2017). Reliability analysis of a subsystem in aluminium industry plant. Proceedings of the 6th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO), 20-22 September, Amity Institute of Information Technology, Amity University, Noida, India, 203-207. 10.1109/ICRITO.2017.8342424
XII. Al Rahbi, Y., Rizwan, S. M., Alkali, B., Cowel, A. and Taneja, G. (2018). Reliability analysis of rodding anode plant in aluminium industry with multiple repairmen. Advances and Applications in Statistics, 53(5), 569-597. 10.17654/AS053050569
XIII. Al Rahbi, Y., Rizwan, S.M., Alkali, B., Cowel, A. and Taneja, G. (2017). Reliability analysis of rodding anode plant in aluminium industry. International Journal of Applied Engineering Research, 12(16), 5616-5623. https://www.ripublication.com/ijaer17/ijaerv12n16_27.pdf
XIV. Al Rahbi, Y., Rizwan, S.M., Alkali, B., Cowel, A. and Taneja, G. (2018). Maintenance analysis of a butt thimble removal station in an aluminum plant. International Journal of Mechanical Engineering and Technology, 9(4), 695-703. https://iaeme.com/MasterAdmin/Journal_uploads/IJMET/VOLUME_9_ISSUE_4/IJMET_09_04_078.pdf
XV. Al Rahbi, Y., Rizwan, S.M., Alkali, B., Cowel, A. and Taneja, G. (2019). Reliability analysis of multiple units with multiple repairmen of rodding anode plant in aluminium industry. Advances and Applications in Statistics, 54(1), 151-178. 10.17654/AS054010151
XVI. Al Rahbi, Y., Rizwan, S.M., Alkali, B., Cowel, A. and Taneja, G. (2019). Reliability analysis of a rodding anode plant in aluminum industry with multiple units failure and single repairman. International Journal of System Assurance Engineering and Management, 10, 97-109. 10.1007/s13198-019-00771-3
XVII. Al Rahbi, Y., Rizwan, S.M., Alkali, B.M., Cowel, A. and Taneja, G. (2017). Reliability analysis of a subsystem in aluminium industry plant. 2017 6th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO), 200-203. 10.1109/ICRITO.2017.8342461.
XVIII. Anirvinna, C., Kumar, A., Saini, M. and Meena, M. (2021). Investigating the impact of influential factors of online advertisement on youth’s online buying behavior: A predictive model. Journal of Physics: Conference Series, 1714, 012005. 10.1088/1742-6596/1714/1/012005.
XIX. Babiarz, B. (2010). Reliability assessment of heat supply systems in their operational process. RT&A, 1(16), 7-10. https://www.gnedenko.net/Journal/2010/012010/RTA_1_2010-01.pdf
XX. Chaudhary, A., Jaiswal, S. and Sharma, N. (2023). Probabilistic analysis of a two-unit cold standby system with repair and replacement policies. RT&A, 1(72), 56. https://gnedenko.net/Journal/2023/012023/RTA_1_2023-04.pdf
XXI. Chen, A. and Wu, R.S. (2007). Real-time health index and dynamic preventive maintenance policy for equipment under aging Markovian deterioration. International Journal of Production Research, 15(1), 3351–3379. 10.1080/00207540600677617
XXII. Choudhary, R., Maan, V.S., Kumar, A. and Saini, M. (2024). Performance modeling of crystallization system in sugar plant using RAMD approach. RT&A, 4(80), 301-310. https://gnedenko.net/Journal/2024/042024/RTA_4_2024-24.pdf
XXIII. Dabas, N., Rathee, R. and Sheoran, A. (2023). Reliability analysis of parallel system using priority to preventive maintenance over inspection. RT&A, 1(72), 329-335. 10.24412/1932-2321-2023-172-329-339
XXIV. Genis, Y. (2010). Reliability assessment of systems with periodic maintenance under rare failures of its elements. RT&A, 1(16), 47-50. https://www.gnedenko.net/Journal/2010/RTA_1_2010.pdf
XXV. Gupta, N., Kumar, A. and Saini, M. (2021). Reliability and maintainability investigation of generators in steam turbine power plants using RAMD analysis. Journal of Physics: Conference Series, 1714(1), 012009. 10.1088/1742-6596/1714/1/012009.
XXVI. Kimura, F., Matoba, Y. and Mitsui, K. (2007). Designing product reliability based on total product lifecycle modelling. Annals of the CIRP, 56(1), 163-166. 10.1016/j.cirp.2007.05.039
XXVII. Kumar, A., Saini, M. and Malik, S.C. (2014). Stochastic modeling of a concrete mixture plant with preventive maintenance. International Journal of Engineering Science and Technology Review, 13(5), 183-192. 10.25103/jestr.135.24.
XXVIII. Kumar, A., Singh, R., Saini, M. and Dahiya, O. (2020). Reliability, availability, and maintainability analysis to improve the operational performance of soft water treatment and supply plant. Journal of Engineering Science and Technology Review, 13(5), 183-192. 10.25103/jestr.135.24.
XXIX. Lam, Y. (1988). A note on the optimal replacement problem. Advances in Applied Probability, 20(2), 479-482.
XXX. Luo, M. and Wu, S. (2018). A mean-variance optimization approach collectively pricing warranty policies. International Journal of Production Economics, 196, 101-112. 10.1016/j.ijpe.2017.11.013
XXXI. Malik, S., Komal, Yadav, R.K. and Anju. (2024). Stochastic behaviour of an electronic system subject to machine and operator failure. RT&A, 4(80), 353-362. https://gnedenko.net/Journal/2024/042024/RTA_4_2024-28.pdf
XXXII. Mathew, A.G. and Rizwan, S.M. (2012). Maintenance analysis of port PLCs – a case study. Caledonian Journal of Engineering, 8(2), 52-57.
XXXIII. Mathew, A.G., Rizwan, S.M., Majumder, M.C. and Ramachandran, K.P. (2009). MTSF and the availability of a two-unit CC plant. Proceedings of the International Conference on Modeling, Simulation, and Applied Optimization, 20-22 January, American University of Sharjah, UAE, 1-5. ISBN: 978-9948-427-12-4.
XXXIV. Mathew, A.G., Rizwan, S.M., Majumder, M.C. and Ramachandran, K.P. (2010). Reliability modeling and analysis of a two-unit parallel CC plant with different installed capacities. Journal of Manufacturing Engineering, 5(3), 197-204. https://www.smenec.org/index.php/1/article/view/465
XXXV. Mathew, A.G., Rizwan, S.M., Majumder, M.C. and Ramachandran, K.P. (2011). Reliability modeling and analysis of an identical two-unit parallel CC plant system operative with full installed capacity. International Journal of Performability Engineering, 7(2), 179-185. 10.23940/ijpe.11.2.p179.mag
XXXVI. Mathew, A.G., Rizwan, S.M., Majumder, M.C. and Taneja, G. (2009). Optimization of a single-unit CC plant with a scheduled maintenance policy. Proceedings of the International Conference on Recent Advances in Material Processing Technology, 25-27 February, India, 609-613. ISBN: 978-81-904334-1-9.
XXXVII. Mathew, A.G., Rizwan, S.M., Majumder, M.C., Ramachandran, K.P. and Taneja, G. (2009). Profit evaluation of a single-unit CC plant with scheduled maintenance. Caledonian Journal of Engineering, 5(1), 25-33.
XXXVIII. Mathew, A.G., Rizwan, S.M., Majumder, M.C., Ramachandran, K.P. and Taneja, G. (2010). Comparative analysis between the profits of the two models of a CC plant. Proceedings of the International Conference on Modeling, Optimization and Computing, 28-30 October, National Institute of Technology, Durgapur, India. AIP Conference Proceedings, 1298, 226-231. https://doi.org/10.1063/1.3516306
XXXIX. Mohyuddin, M.R., Samra and Rizwan, S.M. (2015). Perturbation unsteady flows of 1-D fluid. Journal of Advances in Civil Engineering, 1(1), 8-11. 10.18831/djcivil.org/12015011002
XL. Mohyuddin, M.R., Samra and Rizwan, S.M. (2015). The unsteady flows of grade-III fluid. i-manager’s Journal on Mathematics, 4(4), 22-27.
XLI. Nair, S.S. and Meyyappan, P. L. (2024). Reliability analysis of benchmark water distribution system. Communications, 26(1), D1-D10. 10.26552/com.C.2024.005
XLII. Nair, V.G. and Manoharan, M. (2018). Reliability analysis of a multi-state system with common cause failures using Markov regenerative process. RT&A, 13(3), 82-84. https://gnedenko.net/Journal/2018/032018/RTA_3_2018-06.pdf
XLIII. Niwas, R. (2018). Reliability analysis of a maintenance scheduling model under failure free warranty policy. RT&A, 13(3), 49-55. https://gnedenko.net/Journal/2018/032018/RTA_3_2018-04.pdf
XLIV. Padmavathi, N., Rizwan, S.M. and Senguttuvan, A. (2015). Comparative analysis between the reliability models portraying two operating conditions of a desalination plant. International Journal of Core Engineering and Management, 1(12), 1-10.
XLV. Padmavathi, N., Rizwan, S.M., Pal, A. and Taneja, G. (2012). Reliability analysis of an evaporator of a desalination plant with online repair and emergency shutdowns. Arya Bhatta Journal of Mathematics and Informatics, 4(1), 1-12.
XLVI. Padmavathi, N., Rizwan, S.M., Pal, A. and Taneja, G. (2013). Comparative analysis of the two models of an evaporator of a desalination plant. Proceedings of the International Conference on Information and Mathematical Science, 24-26 October, Punjab, India, 418-422.
XLVII. Padmavathi, N., Rizwan, S.M., Pal, A. and Taneja, G. (2013). Probabilistic analysis of an evaporator of a desalination plant with priority for repair over maintenance. International Journal of Scientific and Statistical Computing, 4(1), 1-8. https://www.cscjournals.org/manuscript/Journals/IJSSC/Volume4/Issue1/IJSSC-40.pdf
XLVIII. Padmavathi, N., Rizwan, S.M., Pal, A. and Taneja, G. (2014). Probabilistic analysis of a desalination plant with major and minor failures and shutdown during winter season. International Journal of Scientific and Statistical Computing, 5(1), 15-23. https://www.cscjournals.org/manuscript/Journals/IJSSC/Volume5/Issue1/IJSSC-44.pdf
XLIX. Padmavathi, N., Rizwan, S.M., Pal, A. and Taneja, G. (2014). Probabilistic analysis of a seven-unit desalination plant with minor/major failures and priority given to repair over maintenance. Arya Bhatta Journal of Mathematics and Informatics, 6(1), 219-230.
L. Princy, T. (2023). Some useful pathway models for reliability analysis. RT&A, 1(72), 341-348. https://cyberleninka.ru/article/n/some-useful-pathway-models-for-reliability-analysis
LI. Qinglong, L., Xiaodong, W., Song, X., Xiang, X. and Bo, P. (2025). Analysis of distribution network reliability based on distribution automation technology. Energy Informatics, 8, Article 27. 10.1186/s42162-025-00478-9
LII. Radoń, U. and Zabojszcza, P. (2025). The application of structural reliability and sensitivity analysis in engineering practice. Applied Sciences, 15(1), Article 342. 10.3390/app15010342
LIII. Rizwan, S.M. (2006). Reliability modeling strategy of an industrial system. Proceedings of the First International Conference on Availability, Reliability and Security (ARES’06), 20-22 April, Vienna University of Technology, Austria, 625-630. IEEE. 10.1109/ARES.2006.107
LIV. Rizwan, S.M. (2017). Reliability modeling approach for system analysis. Proceedings of the 6th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO), 20-22 September, Amity Institute of Information Technology, Amity University, Noida, India, 127-127.
LV. Rizwan, S.M. and Mathew, A.G. (2015). Performance analysis of port cranes. International Journal of Core Engineering and Management, 2(1), 133-140. https://ijcem.in/wp-content/uploads/2015/05/Performance_Analysis_of_Port_Cranes .pdf
LVI. Rizwan, S.M. and Taj, S.Z. (2021). Modeling and analysis of port PLC. Advances in Dynamical Systems and Applications, 16(2), 423-440. https://www.ripublication.com/adsa21/v16n2p03.pdf
LVII. Rizwan, S.M. and Thanikal, J.V. (2014). Reliability analysis of a wastewater treatment plant with inspection. i-manager’s Journal on Mathematics, 3(2), 21-26. https://doi.org/10.26634/jmat.3.2.3003
LVIII. Rizwan, S.M., Al Nabhani, H., Al Rahbi, Y. and Alagiriswamy, S. (2022). Reliability analysis of a three-unit pumping system. International Journal of Engineering Trends and Technology, 70(6), 24-31. 10.14445/22315381/IJETT-V70I6P203
LIX. Rizwan, S.M., Chauhan, H. and Taneja, G. (2005). Stochastic analysis of systems with accident and inspection. Emirates Journal of Engineering Research, 10(2), 81-87.
LX. Rizwan, S.M., Khurana, V. and Taneja, G. (2007). Modeling and optimization of a single-unit PLCs’ system. International Journal of Modeling and Simulation, 27(4), 361-368. 10.1080/02286203.2007.11442438
LXI. Rizwan, S.M., Khurana, V. and Taneja, G. (2010). Reliability analysis of a hot standby industrial system. International Journal of Modeling and Simulation, 30(3), 315-322. 10.1080/02286203.2010.11442586
LXII. Rizwan, S.M., Mathew, A.G. and Taneja, G. (2009). Reliability analysis of a continuous casting plant. i-manager’s Journal on Future Engineering and Technology, 5(1), 15-21. 10.26634/jfet.5.1.1014
LXIII. Rizwan, S.M., Mathew, A.G., Majumder, M.C. and Ramachandran, K.P. (2008). Reliability and availability of a continuous casting plant. Proceedings of the IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 8-11 December, Singapore, 541-544. IEEE. 10.1109/IEEM.2008.4737927
LXIV. Rizwan, S.M., Mathew, A.G., Majumder, M.C. and Ramchandran, K. P. (2008). Reliability and availability of a continuous casting plant. IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 541-544. 10.1109/IEEM.2008.4738037
LXV. Rizwan, S.M., Padmavathi, N. and Taneja, G. (2015). Performance analysis of a desalination plant as a single unit with mandatory shutdown during winter. Arya Bhatta Journal of Mathematics and Informatics, 7(1), 195-202.
LXVI. Rizwan, S.M., Padmavathi, N., Pal, A. and Taneja, G. (2013). Probabilistic analysis of an evaporator of a desalination plant with inspection. i-manager’s Journal on Mathematics, 2(1), 27-34. https://doi.org/10.26634/jmat.2.1.2161
LXVII. Rizwan, S.M., Padmavathi, N., Pal, A. and Taneja, G. (2013). Reliability analysis of a seven-unit desalination plant with shutdown during winter season and repair/maintenance on FCFS basis. International Journal of Performability Engineering, 9(5), 523-528. 10.23940/ijpe.13.5.p523.mag
LXVIII. Rizwan, S.M., Padmavathi, N., Taneja, G., Mathew, A.G. and Al Balushi, A.M. (2010). Probabilistic analysis of a desalination unit with nine failure categories. World Congress on Engineering 2010: IAENG International Conference of Applied Mathematics, 30 June – 2 July, Imperial College London, UK, 1877-1880.
LXIX. Rizwan, S.M., Sachdeva, K., Al Rashdi, S., Al Balushi, N. and Taj, S. Z. (2023). Reliability and sensitivity analysis of membrane biofilm fuel cell. International Journal of Engineering Trends and Technology, 71(3), 73-80. 10.14445/22315381/IJETT-V71I3P209
LXX. Rizwan, S.M., Sachdeva, K., Alagiriswamy, S. and Al Rahbi, Y. (2023). Performability and sensitivity analysis of the three pumps of a desalination water pumping station. International Journal of Engineering Trends and Technology, 71(1), 283-292. 10.14445/22315381/IJETT-V71I1P225
LXXI. Rizwan, S.M., Tanavade, S., Sachdeva, K. and Taj, S. (2024). Reliability, availability, and sensitivity analysis of a power distribution system. Proceedings of the International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME 2024), 4-6 November, Malé, Maldives. https://ieeexplore.ieee.org/document/10796315
LXXII. Rizwan, S.M., Tanavade, S., Sachdeva, K. and Taj, S.Z. (2025). Reliability, availability, and sensitivity analysis of a power distribution system. Reliability Theory & Applications, 11(4), March.
LXXIII. Rizwan, S.M., Thanikal, J.V. and Torrijos, M. (2014). A general model for reliability analysis of a domestic wastewater treatment plant. International Journal of Condition Monitoring and Diagnostic Engineering Management, 17(3), 3-6.
LXXIV. Rizwan, S.M., Thanikal, J.V., Padmavathi, N. and Yazidi, H. (2015). Reliability & availability analysis of an anaerobic batch reactor treating fruit and vegetable waste. International Journal of Applied Engineering Research, 10(24), 44075-44079. https://www.ripublication.com/ijaer10/ijaerv10n24_27.pdf
LXXV. Saini, M. and Kumar, A. (2014). Profit analysis of solar photovoltaic system with preventive maintenance. International Journal of Modern Mathematical Sciences, 10(3), 247-259.
LXXVI. Saini, M., Goyal, D., Kumar, A. and Sinwar, D. (2021). Investigation of performance measures of power generating unit of sewage treatment plant. Journal of Physics: Conference Series, 1714, 012008. 10.1088/1742-6596/1714/1/012008.
LXXVII. Sani, B., Gatawa, R.I. and Yusuf, I. (2017). Reliability assessment of deteriorating system. RT&A, 3(46), 20-21. https://www.gnedenko.net/Journal/2017/032017/RTA_3_2017-02.pdf
LXXVIII. Saxena, V., Gupta, R. and Singh, B. (2023). Classical and Bayesian stochastic analysis of a two-unit parallel system with working and rest time of repairman. RT&A, 1(72), 43-44. https://cyberleninka.ru/article/n/classical-and-bayesian-stochastic-analysis-of-a-two-unit-parallel-system-with-working-and-rest-time-of-repairman
LXXIX. Setiadi, Y., Tanyimboh, T.T. and Templeman, A.B. (2005). Modelling errors, entropy and the hydraulic reliability of water distribution systems. Advances in Engineering Software, 36(8), 780–788. 10.1016/j.advengsoft.2005.03.028
LXXX. Sha, N. (2021). A copula approach of reliability analysis for hybrid systems. RT&A, 1(61), 231-232. https://www.gnedenko.net/Journal/2021/012021/RTA_1_2021-20.pdf
LXXXI. Sharma, U. and Drishti. (2024). Reliability modeling of a butter churner and continuous butter-making production system. RT&A, 1(77), 122-131. https://gnedenko.net/Journal/2024/012024/RTA_1_2024-10.pdf
LXXXII. Singh, D. and Taneja, G. (2014). Reliability and economic analysis of a power generating system comprising one gas and one steam turbine with random inspection. Journal of Mathematics and Statistics, 10(4), 436-442. 10.3844/jmssp.2014.436.442.
LXXXIII. Singla, S., Mangla, D., Panwar, P. and Taj, S.Z. (2024). Reliability optimization of a degraded system under preventive maintenance using genetic algorithm. Journal of Mechanics of Continua and Mathematical Science, 19(1), 1-14. jmcms.2024.01.0000
LXXXIV. Singla, S., Sonia, & Panwar, P. (2024). Stochastic optimization and reliability analysis of mushroom plant. RT&A, 1(77), 729-740. https://gnedenko.net/Journal/2024/012024/RTA_1_2024-57.pdf
LXXXV. Sridharan, V. and Kalyan, T.V. (2002). Stochastic analysis of a non-identical two-unit parallel system with common cause failure using GERT technique. Information and Management Sciences, 13(1), 49-57.
LXXXVI. Taj, S.Z. and Rizwan, S.M. (2019). Reliability modeling and analysis of complex industrial systems – a review. i-manager’s Journal on Mathematics, 8(2), 43-60. 10.26634/jmat.8.2.16711
LXXXVII. Taj, S.Z. and Rizwan, S.M. (2021). Estimation of reliability indices of a complex industrial system using best-fit distributions for repair/restoration times. International Journal of Advanced Research in Engineering and Technology, 12(2), 10.34218/IJARET.12.2.2020.012
LXXXVIII. Taj, S.Z. and Rizwan, S.M. (2022). Reliability analysis of a 3-unit parallel system with a single maintenance facility. Advanced Mathematical Models and Applications, 7(1), 93-103. https://jomardpublishing.com/UploadFiles/Files/journals/AMMAV1N1/V7N1/Taj_Rizwan.pdf
LXXXIX. Taj, S.Z. and Rizwan, S.M. (2023). Comparative analysis between three reliability models of a two-unit complex industrial system. Journal of Advanced Research in Applied Sciences and Engineering Technology, 30(2), 243-254. 10.37934/araset.30.2.243254
XC. Taj, S.Z. and Rizwan, S.M. (2024). Comparative analysis between two reliability models of a three-unit complex industrial system. Journal of Multidisciplinary Applied Natural Science, 4(1), 158-164. https://doi.org/10.47352/jmans.2774-3047.202
XCI. Taj, S.Z., Rizwan, S.M. and Taneja, G. (2018). Reliability analysis of a wire drawing system with mandatory rest period. International Journal of Mechanical Engineering and Technology, 9(4), 1-10. https://iaeme.com/MasterAdmin/Journal_uploads/IJMET/VOLUME_9_ISSUE_4/IJMET_09_04_001.pdf
XCII. Taj, S.Z., Rizwan, S.M., Alkali, B., Harrison, D. and Taneja, G. (2017). Reliability analysis of a single machine subsystem of a cable plant with six maintenance categories. International Journal of Applied Engineering Research, 12(8), 1752-1757. https://www.ripublication.com/ijaer17/ijaerv12n8_39.pdf
XCIII. Taj, S.Z., Rizwan, S.M., Alkali, B., Harrison, D. and Taneja, G. (2017). Probabilistic modeling and analysis of a cable plant subsystem with priority to repair over preventive maintenance. i-manager’s Journal on Mathematics, 6(3), 12-21. 10.26634/jmat.6.3.13649
XCIV. Taj, S.Z., Rizwan, S.M., Alkali, B., Harrison, D. and Taneja, G. (2017). Reliability modelling and analysis of a single machine subsystem of a cable plant. Proceedings of the 7th International Conference on Modeling, Simulation, and Applied Optimization (ICMSAO), 4-6 April, American University of Sharjah, UAE. 10.1109/ICMSAO.2017.7934917
XCV. Taj, S.Z., Rizwan, S.M., Alkali, B., Harrison, D. and Taneja, G. (2018). Reliability analysis of a 3-unit subsystem of a cable plant. Advances and Applications in Statistics, 52(6), 413-429. 10.17654/AS052060413
XCVI. Taj, S.Z., Rizwan, S.M., Alkali, B., Harrison, D. and Taneja, G. (2018). Performance analysis of a rod breakdown system. International Journal of Engineering and Technology (UAE), 7(3.4), 243-248. 10.14419/ijet.v7i3.4.16782
XCVII. Taj, S.Z., Rizwan, S.M., Alkali, B., Harrison, D. and Taneja, G. (2018). Performance and cost-benefit analysis of a cable plant with storage of surplus yield. International Journal of Mechanical Engineering and Technology, 9(8), 814-826. https://iaeme.com/Home/article_id/IJMET_09_08_088
XCVIII. Taj, S.Z., Rizwan, S.M., Alkali, B., Harrison, D. and Taneja, G. (2018). Profit analysis of a cable manufacturing plant portraying the winter operating strategy. International Journal of Mechanical Engineering and Technology, 9(11), 370-381. https://iaeme.com/Home/article_id/IJMET_09_11_037
XCIX. Taj, S.Z., Rizwan, S.M., Alkali, B., Harrison, D. and Taneja, G. (2020). Three reliability models of a building cable manufacturing plant: a comparative analysis. International Journal of System Assurance Engineering and Management, 11, 239-246. 10.1007/s13198-020-01012-8
C. Tanavade, S., Joy, V.M., Rizwan, S.M. and Al Bulushi, A.H. (2024). AI-based fault diagnosis in power transformer: A deep learning framework using convolutional neural network based on infrared imaging and Wasserstein generative adversarial network tools. Proceedings of the 32nd Telecommunications Forum (TELFOR 2024), Serbia. 10.1109/TELFOR63250.2024.10819097
CI. Taneja, G., Rizwan, S.M. and Kumar, R. (1992). Profit analysis of a system with perfect repair at partial failure and priority for repair to complete failed unit. Journal of Pure and applied Mathematika Sciences, 35(1-2), 131-141.
CII. Tanrioven, M. and Alam, M.S. (2005). Reliability modeling and assessment of grid-connected PEM fuel cell power plants. Journal of Power Sources, 142(1-2), 264–278. 10.1016/j.jpowsour.2004.10.030
CIII. Tanrioven, M. and Alam, M.S. (2006). Reliability modeling and analysis of stand-alone PEM fuel cell power plants. Renewable Energy, 31(6), 915-933. 10.1016/j.renene.2005.05.004
CIV. Teng, X., Pham, H. and Jeske, D. (2006). Reliability modeling of hardware and software interactions, and its applications. IEEE Transactions on Reliability, 55(4), 571-577. 10.1109/TR.2006.884589
CV. Thanikal, J.V., Yazidi, H., Torrijos, M. and Rizwan, S. M. (2015). Biodegradability and biomethane potential of vegetable, fruit and oil fraction in anaerobic co-digestion. International Journal of Current Research, 7(7), 18379-18382. https://www.journalcra.com/sites/default/files/issue-pdf/9704.pdf
CVI. Tuteja, R.K., Rizwan, S.M. and Taneja, G. (2000). Profit analysis of a system with perfect repair at partial or complete failure. Journal of Pure and Applied Mathematika Sciences, 52(1/2), 7-14.
CVII. Tuteja, R.K., Rizwan, S.M. and Taneja, G. (2000). Profit evaluation of a two-unit cold standby system with tiredness and two types of repairmen. Journal of Indian Society of Statistics and Operation Research, 21(1-4), 1-10.
CVIII. Tynyanyka, A. N. (2018). Computation of quantity of spare parts by the failure rate method. Technology and Design in Electronic Equipment, 2, 42-50.
CIX. Usman, N.M. and Yusuf, I. (2021). Introducing probabilistic models for cost analysis of sachet water plant. RT&A, 4(40), 30-32. https://www.gnedenko.net/Journal/2021/012021/RTA_1_2021-02.pdf
CX. Volovoi, V. (2004). Modeling of system reliability with Petri nets using aging tokens. Reliability Engineering & System Safety, 84(2), 149-161. 10.1016/j.ress.2003.10.013
CXI. Walke, S., Bhambare, P., Rizwan, S. and Al Rashdi, S. (2024). Electrode selection and catalyst evaluation in hydrogen production from alkaline water electrolysis: A review. Chemical and Process Engineering: New Frontiers, 153096. https://journals.pan.pl/Content/133408/e81.pdf
CXII. Walke, S., Rashdi, S.A., Rizwan, S.M. and Bhambare, P. (2024). Machine learning-based optimization of electrolysis parameters in green hydrogen production. Proceedings of the 2024 International Conference on System, Computation, Automation and Networking (ICSCAN), Puducherry, India, 1-6. 10.1109/ICSCAN62807.2024.10894256
CXIII. Wiles, T.D. (2006). Reliability of numerical modelling predictions. International Journal of Rock Mechanics & Mining Sciences, 43(3), 454-472. 10.1016/j.ijrmms.2005.08.001
CXIV. Wu, S. (2018). Doubly geometric processes and applications. Journal of the Operational Research Society, 69(1), 66-77. 10.1057/s41274-017-0217-4
CXV. Wu, S. and Wang, G. (2018). The semi-geometric process and some properties. IMA Journal of Management Mathematics, 29(2), 229-245. 10.1093/imaman/dpx002
CXVI. Xie, L., Zhou, J. and Hao, C. (2004). System-level load–strength interference-based reliability modeling of k-out-of-n system. Reliability Engineering & System Safety, 84(3), 311-317. 10.1016/j.ress.2003.12.003
CXVII. Yazdian, S., Shahanaghi, K. and Makui, A. (2016). Joint optimisation of price, warranty, and recovery planning in remanufacturing of used products under linear and non-linear demand, return, and cost functions. International Journal of Systems Science, 47(5), 1155-1175. 10.1080/00207721.2014.915355
CXVIII. Yazidi, H., Thanikal, J.V. and Rizwan, S.M. (2015). Assessment of ultimate biogas potential of co-digested fruits, vegetables and a mixture of fruits, vegetables and oil substrate. International Journal of Core Engineering and Management, 2(8), 9-28.
CXIX. Yusuf, I. (2014). Comparative reliability analysis of five redundant network flow systems. RT&A, 9(35), 51-54. https://www.gnedenko.net/Journal/2014/042014/RTA_4_2014-05.pdf
CXX. Zhang, C., Ramirez-Marquez, J.E. and Wang, J. (2015). Critical infrastructure protection using secrecy – a discrete simultaneous game. European Journal of Operational Research, 242(1), 212-221.

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Abstract:

Because it regularly results in more spending than any other explanation, coronary disease is the leading source of fear and mortality on a global scale. The WHO estimates that 17.9 million people died annually from heart disease in 2016, which accounted for 31% of all deaths. More than 75% of these fatalities occurred in developing and middle-income nations. We create a coronary disease prediction model based on the patient's clinical history to assess whether or not the patient is most likely to develop a coronary illness. We used several artificial intelligence (AI) techniques, such as the critical backslide and KNN, to predict and group patients with cardiovascular sickness. The given coronary illness hypothesis system utilizes clinical reasoning and reduces the cost. We categorize a patient based on 14 medical characteristics or features to determine whether they are likely to develop a heart condition to anticipate this. Three algorithms are used to train these medical features: Random Forest Classifier, KNN, and Logistic Regression.

Keywords:

Predictive Model,Random Forest Classifier,KNN,Logistic Regression,

Refference:

I. Adelstein, E.C., Liu, J., Jain, S., et al. “Clinical outcomes in cardiac resynchronization therapy-defibrillator recipients 80 years of age and older”. Europace. 2016;18(3):420. 10.1093/europace/euv222.
II. Alba, A.C, Braga, J., Gewarges, M., Walter, S.D., Guyatt, G.H., Ross, H.J. “Predictors of mortality in patients with an implantable cardiac defibrillator: a systematic review and meta-analysis.” Can J Cardiol. 2013;29(12):1729–40. 10.1016/j.cjca.2013.09.024.
III. Bernardini, A., Bindini, L., Antonucci E., Berteotti M., Giusti B., Testa S., Palareti G., Poli D., Frasconi, P., Marcucci, R. “Machine learning approach for prediction of outcomes in anticoagulated patients with atrial fibrillation.” Int J Cardiol. 2024;15(407):132088. 10.1016/j.ijcard.2024.132088
IV. Bozkurt, B.; Ahmad, T.; Alexander, K.M.; Bosak, K.; Breathett, K.; Fonarow, G.C.; Heidenreich, P.; Ho, J.E.; Hsich, E.; Ibrahim, N.E.; et al. “Heart failure epidemiology and outcomes statistics: A report of the Heart Failure” Society of America. J. Card. Fail. 2023, 29, 1412–1451. 10.1016/j.cardfail.2023.07.006
V. Behon, A, Merkel, E.D., Schwertner, W.R., et al. “Long-term outcome of cardiac resynchronization therapy patients in the elderly.” Geroscience. 2023;45(4):2289–301. 10.1007/s11357-023-00739-z. Epub 2023 Feb 17.
VI. Cebro-Márquez, M.; Rodríguez-Mañero, M.; Serrano-Cruz, V.; Vilar-Sánchez, M.E.; González-Melchor, L.; García-Seara, J.; Martínez-Sande, J.L.; Aragón- Herrera, A.; Martínez-Monzonís, M.A.; González-Juanatey, J.R.; et al. “Plasma miR-486-5p Expression Is Upregulated in Atrial Fibrillation Patients with Broader Low-Voltage Areas.” Int. J. Mol. Sci. 2023, 24, 15248. 10.3390/ijms242015248
VII. Förster, C.Y.; Künzel, S.R.; Shityakov, S.; Stavrakis, S. Synergistic “Effects of Weight Loss and Catheter Ablation: Can microRNAs Serve as Predictive Biomarkers for the Prevention of Atrial Fibrillation Recurrence?” Int. J. Mol. Sci. 2024, 25, 4689. 10.3390/ijms25094689
VIII. Ghio, S., Freemantle, N., Scelsi, L., et al. Long-term left ventricular reverse remodelling with cardiac resynchronization therapy: results from the CARE-HF trial. Eur J Heart Fail. 2009;11(5):480–8. 10.1093/eurjhf/hfp034. Epub 2009 Mar 14
IX. Lippi, G.; Sanchis-Gomar, F. “Global epidemiology and future trends of heart failure.” AME Med. J. 2020, 5, 15. 10.21037/amj.2020.03.03
X. Lofrumento, F.; Irrera, N.; Licordari, R.; Perfetti, S.; Nasso, E.; Liotta, P.; Isgrò, G.; Garcia-Ruiz, V.; Squadrito, F.; Carerj, S.; et al. “Off-target effects of P2Y12 receptor inhibitors: Focus on early myocardial fibrosis modulation.” Int. J. Mol. Sci. 2023, 24, 17546. 10.3390/ijms242417546
XI. L’Abbate, S.; Nicolini, G.; Marchetti, S.; Forte, G.; Lepore, E.; Unfer, V.; Kusmic, C. “Lithium treatment induces cardiac dysfunction in mice.” Int. J. Mol. Sci. 2023, 24, 15872. 10.3390/ijms242115872
XII. Picchio, V.; Gaetani, R.; Pagano, F.; Derevyanchuk, Y.; Pagliarosi, O.; Floris, E.; Cozzolino, C.; Bernava, G.; Bordin, A.; Rocha, F.; et al. “Early impairment of paracrine and phenotypic features in resident cardiac mesenchymal stromal cells after thoracic radiotherapy.” Int. J. Mol. Sci. 2024, 25, 2873. 10.3390/ijms25052873
XIII. Sunagawa, Y.; Tsukabe, R.; Irokawa, Y.; Funamoto, M.; Suzuki, Y.; Yamada, M.; Shimizu, S.; Katanasaka, Y.; Hamabe-Horiike, T.; Kawase, Y.; et al. Anserine, “A histidine-containing dipeptide, suppresses pressure overload-induced systolic dysfunction by inhibiting histone acetyltransferase activity of p300 in mice.” Int. J. Mol. Sci. 2024, 25, 2344. 10.3390/ijms25042344
XIV. Salvatori, F.; D’Aversa, E.; Serino, M.L.; Singh, A.V.; Secchiero, P.; Zauli, G.; Tisato, V.; “Epigenetic tuning of wall remodelling in the early phase after myocardial infarction: A novel epidrug approach.” Int. J. Mol. Sci. 2023, 24, 13268. 10.3390/ijms241713268
XV. Thorkelsson, A.; Chin, M.T. “Role of the Alpha-B-crystallin protein in cardiomyopathic disease.” Int. J. Mol. Sci. 2024, 25, 2826. 10.3390/ijms25052826
XVI. Zheng, H.; Xu, Y.; Liehn, E.A.; Rusu, M. “Vitamin C as scavenger of reactive oxygen species during healing after myocardial infarction.” Int. J. Mol. Sci. 2024, 25, 3114. 10.3390/ijms25063114
XVII. Zhang,H.; Dhalla, N.S. “The role of pro-inflammatory cytokines in the pathogenesis of cardiovascular disease.” Int. J. Mol. Sci. 2024, 25, 1082. 10.3390/ijms25021082

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Abstract:

Cardio Vascular Diseases (CVDs) are one of the most prevalent causes of death in the world and require an appropriate early detection method that could satisfactorily integrate diversified patient data available in today's healthcare. Traditional diagnosis is often based on single-modality data, either ECG or imaging, which seldom can unfold the complex and multi-faceted nature of cardiovascular conditions. Moreover, these models have incomplete interpretation and optimization issues, which do not suit their application in a clinical setting. On this, we propose a novel framework for the detection of cardiovascular diseases and presiding analysis through multimodal data fusion, optimized neural networks, and explainable AI techniques. Our approach begins with Deep Canonical Correlation Analysis (DCCA), which fuses multiple modalities of data such as ECG time series, medical imaging, electronic health records, and genetic data into a unified latent representation that represents correlated information across these heterogeneous sources. This will not only enhance the prediction accuracy but also retain modality-specific unique aspects, thus going beyond traditional models. We will go one step beyond this by using a Genetic Algorithm in combination with the Neuro-evolution of Augmenting Topologies for optimization not only for neural network architecture and hyperparameters but also for going into the process. This bioinspired methodology makes dynamic adjustments in the complexity of a model, substantially reducing error rates. To ensure interpretability in our predictions, we will finally integrate Shapley Additive explanations (SHAP) into the multimodal fusion network. SHAP values provide a clear, quantitative measure of the contribution of each feature and modality to the model predictions, most significantly corresponding to a priori known clinical risk factors that offer critical insights for healthcare professionals. Impact: we have more than halved error rates by 15%, reached an Area Under the Curve of 0.92, and demonstrated a very strong correlation with expert-annotated risk scores of r = 0.87 using SHAP values. This framework has set a new standard in the CVD prediction area by putting together cutting-edge AI techniques and practical, interpretable healthcare applications.

Keywords:

Cardiovascular Disease,Correlation Analysis,Deep Canonical Genetic Algorithm,Explainable AI,Multimodal Data Fusion,Scenarios,

Refference:

I. Abdellatif, A., et al. “An Effective Heart Disease Detection and Severity Level Classification Model Using Machine Learning and Hyperparameter Optimization Methods.” IEEE Access, vol. 10, 2022, pp. 79974–79985. 10.1109/ACCESS.2022.3191669.
II. Abrar, S., C. K. Loo, and N. Kubota. “A Multi-Agent Approach for Personalized Hypertension Risk Prediction.” IEEE Access, vol. 9, 2021, 10.1109/ACCESS.2021.3074791.
III. Asma-Ull, H., I. D. Yun, and B. L. Yun. “Regression to Classification: Ordinal Prediction of Calcified Vessels Using Customized ResNet50.” IEEE Access, vol. 11, 2023, pp. 48783–48796. 10.1109/ACCESS.2023.3270562.
IV. Chushig-Muzo, D., et al. “Interpretable Data-Driven Approach Based on Feature Selection Methods and GAN-Based Models for Cardiovascular Risk Prediction in Diabetic Patients.” IEEE Access, vol. 12, 2024, pp. 84292–84305. 10.1109/ACCESS.2024.3412789.
V. Das, S., et al. “Analysis of Cardiac Anomalies by Selection and Extraction of Features Using Machine Learning Methods.” Indian Journal of Computer Science and Engineering, vol. 13, no. 6, 2022. 10.21817/indjcse/2022/v13i6/221306116.
VI. Ghorashi, S., et al. “Leveraging Regression Analysis to Predict Overlapping Symptoms of Cardiovascular Diseases.” IEEE Access, vol. 11, 2023, pp. 60254–60266. 10.1109/ACCESS.2023.3286311.
VII. Ghosh, P., et al. “Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms with Relief and LASSO Feature Selection Techniques.” IEEE Access, vol. 9, 2021, pp. 19304–19326. 10.1109/ACCESS.2021.3053759.
VIII. Jha, P. K., et al. “A Fully Analog Autonomous QRS Complex Detection and Low-Complexity Asystole, Extreme Bradycardia, and Tachycardia Classification System.” IEEE Transactions on Instrumentation and Measurement, vol. 71, 2022, Art. no. 4009813, pp. 1–13. 10.1109/TIM.2022.3216392.
IX. Kancharla, P., and S. S. Channappayya. “Completely Blind Quality Assessment of User Generated Video Content.” IEEE Transactions on Image Processing, vol. 31, 2022, pp. 263–274. 10.1109/TIP.2021.3130541.
X. Loizou, C. P., et al. “Association of Intima-Media Texture with Prevalence of Clinical Cardiovascular Disease.” IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 68, no. 9, Sept. 2021, pp. 3017–3026. 10.1109/TUFFC.2021.3081137.
XI. Mahajan, A., et al. “A Hybrid Feature Selection and Ensemble Stacked Learning Models on Multi-Variant CVD Datasets for Effective Classification.” IEEE Access, vol. 12, 2024, pp. 87023–87038. 10.1109/ACCESS.2024.3412077.
XII. Mondal, S., et al. “An Efficient Computational Risk Prediction Model of Heart Diseases Based on Dual-Stage Stacked Machine Learning Approaches.” IEEE Access, vol. 12, 2024, pp. 7255–7270. 10.1109/ACCESS.2024.3350996.

XIII. Nayak, Debasish Swapnesh Kumar, et al. “Enhancing Cardiovascular Disease Prediction Based on AI and IoT Concepts.” International Journal on Recent and Innovation Trends in Computing and Communication, vol. 11, no. 10, 2023. 10.17762/ijritcc.v11i10.8483.
XIV. Omkari, D. Y., and K. Shaik. “An Integrated Two-Layered Voting (TLV) Framework for Coronary Artery Disease Prediction Using Machine Learning Classifiers.” IEEE Access, vol. 12, 2024, pp. 56275–56290. 10.1109/ACCESS.2024.3389707.
XV. Pati, Abhilash, Manoranjan Parhi, and Binod Kumar Pattanayak. “IHDPM: An Integrated Heart Disease Prediction Model for Heart Disease Prediction.” International Journal of Medical Engineering and Informatics, vol. 14, no. 6, 2022, pp. 564–577. 10.1504/IJMEI.2022.126526.
XVI. Sinha, N., et al. “DASMcC: Data Augmented SMOTE Multi-Class Classifier for Prediction of Cardiovascular Diseases Using Time Series Features.” IEEE Access, vol. 11, 2023, pp. 117643–117655. 10.1109/ACCESS.2023.3325705.
XVII. Swain, Satyaprakash, Mihir Narayan Mohanty, and Binod Kumar Pattanayak. “Precision Medicine in Hepatology: Harnessing IoT and Machine Learning for Personalized Liver Disease Stage Prediction.” International Journal of Reconfigurable & Embedded Systems, vol. 13, no. 3, 2023, pp. 724–734. 10.11591/ijres.v13.i3.pp724-734.
XVIII. Tripathy, Jogeswar, R. Dash, and Binod Kumar Pattanayak. “Unleashing the Power of Machine Learning in Cancer Analysis: A Novel Gene Selection and Classifier Ensemble Strategy.” Research on Biomedical Engineering, vol. 40, no. 1, 2024, pp. 125–137. 10.1007/s42600-023-00335-2.
XIX. Tripathy, Jogeswar, et al. “Combination of Reduction Detection Using TOPSIS for Gene Expression Data Analysis.” Big Data and Cognitive Computing, vol. 6, no. 1, 2022, p. 24. 10.3390/bdcc6010024.
XX. Ullah, T., et al. “Machine Learning-Based Cardiovascular Disease Detection Using Optimal Feature Selection.” IEEE Access, vol. 12, 2024, pp. 16431–16446. https://doi.org/10.1109/ACCESS.2024.3359910.

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Abstract:

In recent times, several sectors and businesses have been doing extensive research on the use 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 acquired falling within the range of (20.33-14.09) dBm over distances ranging from (60-180) km. This demonstrates the dependability of the proposed system.

Keywords:

Bit Error Rate (BER),Dense Wavelength Division Multiplexing (DWDM),Optisystem Software,Radio Frequency Over Fiber (RFOF),

Refference:

I. Al-Obaidi, Sami Hassan, S. (2025). Blue Laser Optical NOMA Communication Applied on Control Drone-to-Underwater Vehicle. Al-Iraqia Journal for Scientific Engineering Research, 4(1), 89–98. https://doi.org/10.58564/IJSER.4.1.2025.299 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.‏
II. Ji, W., & Kang, Z. (2013). Design of WDM RoF PON based on OFDM and optical heterodyne. Journal of Optical Communications and Networking, 5(6), 652–657.
III. Jihad, N. J., & Almuhsan, M. A. A. (2023). Enhancement on the performance of radio-over-fiber ROF technology. Journal of Optics, 1–9.
IV. Juven, M. K., Roy, M., & Dristy, F. T. (2018). A study of the effects of digital modulation and length of optical fiber in a Radio over Fiber (RoF) Communication System (Doctoral dissertation, East West University).
V. Kaur, B., & Sharma, N. (2022). Radio over Fiber (RoF) for Future Generation Networks. In Broadband Connectivity in 5G and Beyond: Next Generation Networks (pp. 161–184). Cham: Springer International Publishing.
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Abstract:

With construction project delays being a key factor influencing their financial sinews, issues related to contract law, thus rendering resources incapable, remain a contemporary issue all over the globe. Conventional techniques for predicting delays often do not deliver concrete predictions due to the multiplicity and dynamic character of construction tasks. In the study discussed here, different machine learning (ML) algorithms were investigated to foresee construction delays, and these include Gaussian Naïve Bayes, Adaboost, Logistic Regression, Gradient Boosting (GB), Random Forest (RF), Decision Tree (DT) and Extreme Gradient Boosting (XGBoost). These models were measured for performance using various metrics such as accuracy, precision, recall, and F1 score to assess their validity in real-life situations. The results indicate that the use of ensemble learning techniques such as Random Forest (RF), and XGBoost scores higher than others, thus exhibiting more accuracy and better predictive capacity. These can relate to convoluted relationships in construction data, which makes them suitable for yet another application in project risk management. In contrast, simpler models like Adaboost and Gaussian Naïve Bayes, despite being interpretable, held lesser predictive accuracy and hence were less qualified for construction delay forecasting. The study highlights the potential of ML-driven predictive models in aiding project management timely by enabling timely identification of prospective delays, thereby allowing for proactive decision-making and project control. Hence, hurdles like quality of data, interpretability of models, and integration with real-time project management systems ought to be surmounted for wide-scale adoption in the industry. Future studies ought to develop hybrid ML models that fit in explainable AI techniques and real-time data on construction applications to assure predictive accuracy and usability in practice. The findings show that ensemble-based delay prediction models have the potential to reduce the uncertainties related to projects, control delays, schedule resources efficiently, and ultimately improve the infrastructure project efficiency in costing and timely project completion.

Keywords:

Adaboost,Construction Delay,Decision Tree,Gaussian Naïve Bayes,Gradient Boosting,Logistic Regression,Random Forest,XGBoost,

Refference:

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Abstract:

The objective of this paper was to design an air purifier system to solve the relevant problem of airborne disease and allergen spread in classrooms and retail settings. While reducing the risk of COVID exposure through airborne particles was the main objective of the system, reducing the risk of Influenza, and the concentration of common allergens such as dust and pollen were also central to the design. The implementation involved a portable air purification system that was affordable and a size appropriate for classrooms and small businesses. The design was tested using CAD designs and various component specifications. These specifications were as follows; filtration ability, airflow capacity, floor space used, noise level, the time before UV decay took place, price of both a prototype and final market design, weight, and time before maintenance was required. This design met all main requirements, excluding the noise level, which was found to be 10% higher than required in the engineering specification. However, this was overruled by the need to reduce costs and increase power. Future recommendations and next steps include adding more advanced electrical control components, considering the effectiveness of UVC light specifically against COVID-19, soundproofing the design, and other minor changes to improve the system.

Keywords:

Indoor Air Quality,Engineering Design,Air Filtration,Respiratory Infection Control,Ventilation Strategies,Air Cleaning Technologies,

Refference:

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Abstract:

Hospital-acquired infections (HAIs), or nosocomial infections, compromise patient safety and the provision of care worldwide. With their extensive patient contact, nurses are key to HAIs transmission and prevention. This article employs a mathematical simulation of HAI dynamics for 60 days in a theoretical 1,000-person hospital ward using a modified Susceptible-Infected-Recovered (SIR) model, with and without nurse interventions such as hand hygiene, patient isolation, personal protective equipment (PPE) use, and environmental disinfection. Enhanced advancements, including the incorporation of genomic and epidemiological data, enhance the model's ability to track transmission clusters, particularly in the case of multidrug-resistant organisms (MDROs) such as MRSA (Illingworth et al., 2022). The simulation demonstrates that nurse interventions reduce infection rate by over 70%, retarding peak and lowering total cases (from ~830 to ~240). Findings are congruent with observations comparing interventions such as chlorhexidine bathing (Climo et al., 2016). Through model assumptions, e.g., asymptomatic transmission, this article offers a concrete basis for hospital decision-making, emphasizing evidence-based nursing and interprofessional infection control practices.

Keywords:

Chlorhexidine Bathing,Healthcare Delivery,Hospital-Acquired Infections (HAIs),Infection Control Patient Safety,Multidrug-Resistant Organisms (MDROs),Nurse-led Interventions,Personal Protective Equipment (PPE),Susceptible-Infected-Recovered (SIR),

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XVIII. Stiller, Andreas, et al. “Relationship between Hospital Ward Design and Healthcare-Associated Infection Rates: A Systematic Review and Meta-Analysis.” Antimicrobial Resistance and Infection Control, vol. 5, 2016, article no. 51. Springer Open. 10.1186/s13756-016-0148-1.
XIX. Temime, L., et al. “Impact of Hand Hygiene Compliance on Nosocomial Infection Rates: A Modeling Approach.” Infection Control & Hospital Epidemiology, vol. 30, no. 8, 2009, pp. 751–758. 10.1086/598672.
XX. Toth, D. J. A., et al. “Forecasting Multidrug-Resistant Organism Emergence: Challenges and Opportunities.” Emerging Infectious Diseases, vol. 29, no. 4, 2023, pp. 677–685. 10.3201/eid2904.221552.
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XXII. Yassi, Annalee, et al. “Protecting the Faces of Health Care Workers: Knowledge Gaps and Research Priorities for Effective Protection Against Occupationally-Acquired Respiratory Infectious Diseases.” International Journal of Environmental Research and Public Health, vol. 8, no. 12, 2011, pp. 314–319. 10.3390/ijerph8010314.

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Abstract:

For both classical and quantum elements of the system, parabolic and semi-parabolic nature paths have been examined and analyzed. We use the most powerful semi-analytical techniques, namely the optimal and modified homotopy perturbation approach, to examine the dynamics of the particle motion with stability analysis. It is demonstrated that the particle's motion on a rotating parabolic path is precisely harmonic oscillator motion with mass depending on location. We find the exact analytical expression for the motion's frequency and amplitude. We then discuss the dependencies of amplitude and frequency on specific parameters and compare the accuracy of the analytical solutions to numerical simulations. We explore the effectiveness of analytical methodologies in solving the complex nature of particle motion and their significance to scientific and technical research.

Keywords:

Analytical solution,Harmonic Oscillator,Parabolic,Particle Motion,Semi-parabolic,Series solution,

Refference:

I. Asad, Jihad, et al. “Asymmetric Variation of a Finite Mass Harmonic Like Oscillator.” Results in Physics, vol. 19, 2020, 103335. https://doi.org/10.1016/j.rinp.2020.103335.
II. Cruz, C. Y., J. Negro, and L. M. Nieto. “On Position-Dependent Mass Harmonic Oscillators.” Journal of Physics: Conference Series, vol. 128, 2008, pp. 12053–12065.
III. Da Costa, B. G., and E. P. Borges. “A Position-Dependent Mass Harmonic Oscillator and Deformed Space.” Journal of Mathematical Physics, vol. 59, 2018, 042101. https://doi.org/10.1063/1.5024913.
IV. Dai, T. Q., and Y. F. Cheng. “Bound State Solutions of the Klein-Gordon
Equation with Position-Dependent Mass for the Inversely Linear Potential.” Physica Scripta, vol. 79, no. 1, 2009, 015007. 10.1088/0031 8949/79/01/015007.
V. Dong, S. H., et al. “Exact Solutions of an Exponential Type Position Dependent Mass Problem.” Results in Physics, vol. 34, 2022, pp. 105294–105298. https://doi.org/10.1016/j.rinp.2022.105294.
VI. Dong, Shi-Hai, et al. “Exact Solutions of an Exponential Type Position Dependent Mass Problem.” Results in Physics, vol. 34, 2022, 105294. https://doi.org/10.1016/j.rinp.2022.105294.
VII. Eigoli, A. K., and M. Khodabakhsh. “A Homotopy Analysis Method for Limit Cycle of the Van der Pol Oscillator with Delayed Amplitude Limiting.” Applied Mathematics and Computation, vol. 217, 2011, pp. 9404–9411. http://dx.doi.org/10.1016/j.amc.2011.04.029.
VIII. El-Nabulsi, R. A. “A Generalized Self-Consistent Approach to Study Position-Dependent Mass in Semiconductors Organic Heterostructures and Crystalline Impure Materials.” Physica E: Low-Dimensional Systems and Nanostructures, vol. 134, 2021, 114295. https://doi.org/10.1016/j.physe.2020.114295.
IX. El-Nabulsi, R. A. “A New Approach to Schrödinger Equation with Position Dependent Mass and Its Implications in Quantum Dots and Semiconductors.”Journal of Physics and Chemistry of Solids, vol. 140, 2020, 109384. https://doi.org/10.1016/j.jpcs.2020.109384.
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Parabola Using Lagrangian and High Accuracy Multi-Step Differential
Transformation Method.” Powder Technology, vol. 258, 2014, pp. 94–98.
10.1016/j.powtec.2014.03.064.
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Abstract:

This study conducted a quantitative analysis to evaluate the statistical significance of climatic parameters such as Relative Humidity (RH), K Index and Total Totals (TT) Index. Given Kolkata's susceptibility to various atmospheric extreme events—including discomfort indices, cyclones, thunderstorms, hailstorms and torrential rains—the city was selected as the focus for this analysis. The research aimed to develop accurate predictive models by performing extensive statistical analyses on available upper air data from Kolkata across all three seasons: summer, winter and the monsoon. Python was utilized for statistical computations to derive semi-empirical relationships between RH, geopotential height and pressure. The primary objective was to establish predictive equations that could be validated against real-time data. The models demonstrated a low Mean Squared Error (MSE) of approximately 20.69, indicating their potential as reliable tools for significant statistical assessments.

Keywords:

Relative Humidity,K Index,TT Index,Atmospheric Instability,Computational Programming,Climate Modeling,Error Analysis,Weather Forecasting,

Refference:

I. Chindaphol, S., et al. “A Suitable Thermal Stress Index for the Elderly in Summer Tropical Climates.” Procedia Engineering, vol. 180, 2017, pp. 932-43. 10.1016/j.proeng.2017.04.253.
II. Dasgupta, S., et al. “A Megacity in a Changing Climate: The Case of Kolkata.” Climate Change, vol. 116, 2013, pp. 747-66. 10.1007/s10584-012-0516-3.
III. Fernando, M., et al. “Analyze and Comparison of the Atmospheric Instability Using K-Index, Lifted Index Total Totals Index Convective Availability Potential Energy (CAPE) and Convective Inhibition (CIN) in Development of Thunderstorms in Sri Lanka During Second Inter-Monsoon.” Multi-Hazard Early Warning and Disaster Risks, edited by D. Amaratunga, et al., Springer, 2021. 10.1007/978-3-030-73003-1_41.
IV. Ghosh, I., et al. “Anomaly Temperature in the Genesis of Tropical Cyclone.” Natural Hazards, vol. 114, 2022, pp. 1477-503. https://doi.org/10.1007/s11069-022-05434-4.
V. Lal, A. “Forecasting of Thunderstorms around Delhi and Jodhpur.” Mausam, vol. 40, no. 3, 1989, pp. 39-40. 10.54302/mausam.v40i3.2116.
VI. Tajbakhsh, S., et al. “Instability Indices and Forecasting Thunderstorms: The Case of 30 April 2009.” Natural Hazards and Earth System Sciences, vol. 12, 2012, pp. 403-13. 10.5194/nhess-12-403-2012.
VII. Talapatra, A., et al. “Geospatial Analysis of the Dynamics of Climate in Kolkata Metropolitan Area.” Journal of Physics: Conference Series, vol. 1964, 2021, p. 042038. 10.1088/1742-6596/1964/4/042038.
VIII. “University of Wyoming Data Archive of South-East Asia (Kolkata Region).” University of Wyoming Department of Atmospheric Science, https://weather.uwyo.edu/upperair/sounding_legacy.html. Accessed 15 March 2025.

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