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MITIGATION OF SUPRAHARMONICS IN MICROGRIDS USING PARABOLIC CARRIER-BASED PWM-CONTROLLED SHUNT ACTIVE FILTERS

Authors:

Saad T. Y. Alfalahi, Muhamad Bin Mansor, Afaneen A. Abbood

DOI NO:

https://doi.org/10.26782/jmcms.2025.11.00005

November 12, 2025

Abstract:

Microgrids (MGs) are having more difficulty sustaining power quality (PQ) as renewable energy sources (RES) become more widely integrated. The issue of supraharmonics (SH), characterized by high-frequency emissions spanning from 2 kHz to 150 kHz, holds significant importance. The switching processes used in these RES power electronic converters are to blame for these harmonics. Traditional passive filters and capacitor banks are ill-equipped to deal with the dynamic changes in system characteristics that occur at the PCC. Voltage swell, unbalance, and power factor problems result from this. The Shunt Active Filter (SAF) has shown superior efficacy in mitigating harmonic issues in power systems. Nonetheless, its performance is contingent upon the rapidity and precision of its control algorithms. This paper employs the parabolic carrier-based pulse-width modulation (PWM) technique to regulate current in SAF, thereby minimizing SH. This method incorporates using a pair of positive and negative parabolic PWM carriers to control the switching states of the two switches in the converter phase leg, simultaneously constraining the current tracking error within the nonlinear parabolic region. The proposed filter is designed using the MATLAB/Simulink environment and used in a modelled MG with specified ratings. The results of the harmonic analysis showed a distortion contribution in the SH range of merely 0.03%. The minimal increase in THD when extending the analysis up to 150 kHz demonstrates the active filter’s effectiveness in suppressing SH.

Keywords:

Harmonic Mitigation,Microgrid,Parabolic PWM,Power Quality,Shunt Active Filter,Supraharmonics,

References:

I. Abdrabba, S. I., et al. “Analysis of Feasible Solutions for the Improvement of Voltage Profile in Alkufra Network Containing PV-Generation Unit.” Proceedings of the 14th International Renewable Energy Congress (IREC), Sousse, Tunisia, Mar. 2023, pp. 1–6. 10.1109/IREC59750.2023.10389229
II. Abidin, M. N. Zainal. “IEC 61000-3-2 Harmonics Standards Overview”. Schaffner EMC Inc., Edison, NJ, May 2006. https://www.emcfastpass.com/wp-content/uploads/2017/04/Class_definitions.pdf
III. Addala, S., and I. E. S. Naidu. “Mitigation of PQP in Distributed Generation Using CPD’s.” Proceedings of the International Conference on Futuristic Technologies (INCOFT), Karnataka, India, 25–27 Nov. 2022, pp. 1–4. 10.1109/INCOFT55651.2022.10094476
IV. Alfalahi, S. T. Y., et al. “Sizing Passive Filters for Mitigation of Harmonics in a Low Voltage Network Containing Solar PV Units.” Franklin Open, Vol. 10, No. 1, Mar. 2025, p. 100220. 10.1016/j.fraope.2025.100220
V. Alsaeed, I., and M. Shafiullah. “Harmonic Mitigation Using Hybrid Control Method in Energy Storage Integrated Microgrid.” Proceedings of the 4th International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE), 2022. 10.1109/REEPE53907.2022.9731404
VI. Al-Sharif, Y. M., G. M. Sowilam, and T. A. Kawady. “Harmonic Analysis of Large Grid-Connected PV Systems in Distribution Networks: A Saudi Case Study.” International Journal of Photoenergy, Vol. 2022, Article ID 8821192, 14 pages, Nov. 2022. 10.1155/2022/8821192
VII. Azzam-Jai, A., and M. Ouassaid. “Control of a Multifunctional PV-Integrated SAPF with Simpler Neural Harmonic Extraction.” Proceedings of the 9th International Conference on Systems and Control, Caen, France, 24–26 Nov. 2021, pp. 44–48. 10.1109/ICSC50472.2021.9666700
VIII. Barva, A., and S. Joshi. “A Comprehensive Survey on Hybrid Active Power Filter Topologies, Controller, and Application in Microgrid.” Proceedings of the IEEE Region 10 Symposium (TENSYMP), 2022. 10.1109/TENSYMP54529.2022.9864377
IX. Barva, A. V., and S. Joshi. “Comparative Analysis of Passive, Active, and Hybrid Active Filters for Power Quality Improvement in Grid-Connected Photovoltaic System.” Proceedings of the 7th International Conference on Computer Applications in Electrical Engineering – Recent Advances (CERA), 2023. 10.1109/CERA59325.2023.10455311
X. Barik, P. K., et al. “Simulation and Real-Time Implementation of a Combined Control Strategy-Based Shunt Active Power Filter in Microgrid.” Sustainable Computing: Informatics and Systems, Vol. 35, Feb. 2025. 10.1016/j.suscom.2025.101103
XI. Cheng, J. “IEEE Standard 519-2014 Compliances, Updates, Solutions and Case Studies.” Schneider Electric, presented by Dr. John Cheng, CEng, CEM, CEA, CMVP. https://studylib.net/doc/25803589/ieee-std-519-1992
XII. Chishti, F., S. Murshid, and B. Singh. “Frequency Adaptive Multistage Harmonic Oscillator for Renewable-Based Microgrid under Nonideal Grid Conditions.” IEEE Transactions on Industrial Electronics, vol. 68, no. 1, Jan. 2021, pp. 358–367. 10.1109/TIE.2020.2965474
XIII. “EMC Standards: A Practical Guide for EN 61000-3-2 – Limits for Harmonic Current Emissions.” Stafford: EMC Standards, 2010. https://www.emcstandards.co.uk/files/61000-3-2_mains_harmonics.pdf?utm_source=chatgpt.com
XIV. Fahad, A. H., and M. S. Reza. “Single-Phase Shunt Active Power Filter Using Parabolic PWM for Current Control.” Proceedings of the 7th International Conference on Smart Energy Grid Engineering (SEGE), Oshawa, ON, Canada, Aug. 2019, pp. 134–138. 10.1109/SEGE.2019.8859868
XV. Halpin, S. M. “Revisions to IEEE Standard 519-1992.” Proceedings of the IEEE Power Engineering Society General Meeting, Auburn, AL, USA, 2022. 10.1109/TDC.2006.1668665
XVI. International Energy Agency. Renewables 2023: Analysis and forecast to 2028. Paris, France: IEA, 2023. https://www.iea.org/reports/renewables-2023
XVII. Kancharla, P. “Harmonic Elimination by Reference Current Strategy Using Shunt Active Power Filter (SAPF).” AIP Conference Proceedings, Vol. 3007, No. 1, Feb. 2024. 10.1063/5.0194906
XVIII. Khan, A. S., S. Vijay, and S. Doolla. “Nonlinear Load Harmonic Mitigation Strategies in Microgrids: State of the Art.” IEEE Systems Journal, vol. 16, no. 3, 2022, pp. 4243–4255. 10.1109/JSYST.2021.3130612
XIX. Khan, S. A., M. Junaid, and M. Ali. “Design of an Active Power Filter in Cascaded Power Converters for Harmonic Mitigation.” Proceedings of the International Conference on Emerging Power Technologies (ICEPT), 2023. 10.1109/ICEPT58859.2023.10152426
XX. Kumar, A., R. Garg, and P. Mahajan. “Harmonics Mitigation Techniques in Grid Integrated PV-Based Microgrid: A Comparative Analysis.” Proceedings of the First IEEE International Conference on Advances in Electrical, Computing, Communications and Sustainable Technologies (ICAECT), India, 19–20 Feb. 2021, pp. 634–639. 10.1109/ICAECT49130.2021.9392631
XXI. Li, J., Y. He, L. Xu, and J. Liu. “Design of Hybrid Power Filters for Supraharmonics Suppression in Power Electronics Dominated Power Systems.” IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 12, No. 2, Apr. 2024, pp. 2163–2175. 10.1109/JESTPE.2024.3365167
XXII. Matas-Díaz, F. J., et al. “Active Harmonic Filtering with Selective Overcurrent Limitation for Grid-Forming VSCs: Stability Analysis and Experimental Validation.” IEEE Transactions on Industry Applications, Vol. 60, No. 3, May–June 2024, pp. 4762–4775. 10.1109/TIA.2024.3369595
XXIII. Nireekshana, N., et al. “Adaptive Reactive Power Management with Thyristor-Controlled Transformer and Fixed Capacitor.” International Journal of Innovative Science and Research Technology, Vol. 9, No. 9, Sep. 2024, pp. 2890–2891. 10.38124/ijisrt/IJISRT24SEP1656
XXIV. Sant, A. V. “Shunt Active Power Filtering with Reference Current Generation Based on Dual Second Order Generalized Integrator and LMS Algorithm.” Energy Reports, vol. 8, 2022, pp. 886–893. 10.1016/j.egyr.2022.08.099
XXV. Xu, L., J. Qiu, Y. He, Q. Deng, and C. Lei. “Research on Active Filter for Supraharmonics Suppression of Power Grid.” IEEE Student Conference on Electric Machines and Systems (SCEMS), Xi’an, China, 2020, pp. 735–740. 10.1109/SCEMS48876.2020.9352290

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Journal Vol – 20 No – 11, November 2025

RESEARCH OF CHARACTERISTICS OF THE BATTERY USED ON HYBRID VEHICLES

Authors:

Lam Kim Thanh Vo, Xuan Ngoc Nguyen, Hong Phuc Vo, Tien Phuc Dang, Khoi Nguyen Nguyen, Thanh Tam Tran

DOI NO:

https://doi.org/10.26782/jmcms.2025.11.00006

November 12, 2025

Abstract:

The fount of battery in a hybrid automobile plays an important role in furnishing energy to sustain the seamless functionality of the vehicle and mitigate petroleum expenditure. This power source is mostly used as a collection of many cells combined into a single power source for the vehicle. This research concentrates on considering the charging attributes of the battery supplied in a hybrid automobile, encompassing charging flow, the state of charge (SoC), and the charging environment thermal influence on the efficacy of the battery power. Matlab Simulink software is applied to investigate and simulate the characteristic curves of the above factors to propose effective ways to use batteries in hybrid vehicles. The results of the article are used as basic knowledge for researching the battery, and at the same time serve the development of batteries for electric vehicles in the future.

Keywords:

Charging time,Battery,Hybrid,Matlab Simulink,Pin Ni-MH,

References:

I. Buchmann I., Batteries I., in a Portable World: A Handbook on Rechargeable Batteries for Non-Engineers. Cadex Electronics Inc., 2012.
II. Demirci, O., Taskin, S., Schaltz, E., and Acar Demirci, B. “Review of Battery State Estimation Methods for Electric Vehicles—Part I: SOC Estimation.” Journal of Energy Storage, vol. 87, 2024, article 111435. 10.1016/j.est.2024.111435
III. Dimauro, L. “Power Transmission Systems: From Traditional to Magnetic Gearboxes.” 2021. PhD diss., Politecnico di Torino. IRIS Politecnico di Torino. https://iris.polito.it/retrieve/e384c434-246e-d4b2-e053-9f05fe0a1d67/PhDThesis_Dimauro_Luca.pdf
IV. Ferriday, T. B., and Middleton, P. H. “Alkaline Fuel Cell Technology—A Review.” International Journal of Hydrogen Energy, vol. 46, no. 35, 2021, pp. 18489-18510. 10.1016/j.ijhydene.2021.02.203
V. Gifford, P., Adams, J., Corrigan, D., and Venkatesan, S. “Development of Advanced Nickel/Metal Hydride Batteries for Electric and Hybrid Vehicles.” Journal of Power Sources, vol. 80, nos. 1-2, 1999, pp. 157-63. 10.1016/S0378-7753(99)00070-1
VI. Jung-Ho, W. “Applications of Proton Exchange Membrane Fuel Cell Systems.” Renewable and Sustainable Energy Reviews, vol. 11, no. 8, 2007, pp. 1720-38. 10.1016/j.rser.2006.01.005
VII. Khan, M. M. R., Amin, M. K., and Chakraborty, N. “Advances and Prospects of Biodegradable Polymer Nanocomposites for Fuel Cell Applications.” Biodegradable and Biocompatible Polymer Nanocomposites, edited by Deba Kumar Tripathy et al., Elsevier, 2023, pp. 599-637. 10.1016/B978-0-323-91696-7.00018-0
VIII. Linden, D., and Reddy, T. B. Linden’s Handbook of Batteries, Fourth Edition. McGraw-Hill, 2011, AccessEngineering.
IX. Luntz, A. “Beyond Lithium-Ion Batteries.” SLAC National Accelerator Laboratory, vol. 6, no. 2, 2015, pp. 224-313.
X. Martínez-Sánchez, R., Molina-García, A., Mateo-Aroca, A., and Ramallo-González, A. P. “Evaluating a Nickel–Metal Hydride (NiMH) Battery Regeneration Patent Based on a Non-Intrusive and Unsupervised Prototype.” Batteries, vol. 10, no. 11, 2024, article 402. 10.3390/batteries10110402
XI. Madani, S. S., Ziebert, C., and Marzband, M. “Thermal Behavior Modeling of Lithium-Ion Batteries: A Comprehensive Review.” Symmetry, vol. 15, no. 8, 2023, article 1597. 10.3390/sym15081597
XII. Noga, M., and Juda, Z. “The Application of NiMH Batteries in a Light-Duty Electric Vehicle.” Technical Transactions, no. 1, 2019, pp. 197-222. 10.4467/2353737XCT.19.014.10054
XIII. Nguyen, Q. T. “Solid Oxide Fuel Cell Technology—Features and Applications.” Solid State Ionics, vols. 174, nos. 1-4, 2004, pp. 271-77. 10.1016/j.ssi.2004.07.042
XIV. Pierozynski, B. “On the Low Temperature Performance of Nickel-Metal Hydride (NiMH) Batteries.” International Journal of Electrochemical Science, vol. 6, 2011, pp. 860-66.
XV. Quintero Pulido, D. F., Covrig, C.-F., and Bruchhausen, M. “On the Performance of Portable NiMH Batteries of General Use.” Batteries, vol. 11, no. 1, 2025, article 30. 10.3390/batteries11010030
XVI. Sammes, N., Bove, R., and Stahl, K. “Phosphoric Acid Fuel Cells: Fundamentals and Applications.” Current Opinion in Solid State and Materials Science, vol. 8, no. 5, 2004, pp. 372-78. 10.1016/j.cossms.2005.01.001

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Journal Vol – 20 No – 11, November 2025

A LIGHTWEIGHT EDGE-LAYER GROUP KEY AGREEMENT PROTOCOL FOR IOT USING ELLIPTIC CURVE CRYPTOGRAPHY AND SHAMIR’S SECRET SHARING

Authors:

Kavita Agrawal, P.V.G. D Prasad Reddy, Suresh Chittineni

DOI NO:

https://doi.org/10.26782/jmcms.2025.11.00007

November 12, 2025

Abstract:

A Group Key Agreement Protocol enables secure multi-party communication by establishing a common cryptographic key, which is especially critical at the edge layer of IoT networks where devices often operate in decentralized and resource-constrained environments. However, existing protocols face several challenges, including high computational overhead, single points of failure, and a lack of integrity validation during the Distribution of the Group Key. To address these challenges, we propose a lightweight edge-layer protocol that combines Shamir’s Secret Sharing Scheme (SSS) and Elliptic Curve Cryptography (ECC) for secure and efficient group key distribution among IoT edge devices. ECC (Curve25519) is used for secure peer-to-peer sharing, with key sizes that are 12 times smaller and operations that are four times faster than traditional RSA. SSS splits the group key into shares and reconstructs it using a threshold, reducing computation and eliminating the need for full key generation on each device. It also removes single points of failure because no device retains the complete key. ECC enables secure peer-to-peer exchange of encrypted shares using ChaCha20 for efficient confidentiality. ChaCha20 enhances encryption speed, performing nearly three times faster than AES on resource-constrained devices. To ensure shared authenticity and detect tampering, HMAC is applied. This offers a lightweight integrity check suitable for constrained IoT devices. The proposed protocol is quantitatively validated through entropy and key-strength analysis, confirming 128-bit equivalent security and O(n) scalability up to 100 nodes. Communication-cost evaluation demonstrates low bandwidth overhead, while formal verification using BAN Logic and ProVerif under the Dolev–Yao adversarial model establishes confidentiality, authenticity, and forward secrecy with provable resilience against replay, impersonation, and man-in-the-middle attacks.

Keywords:

ChaCha20,Elliptic Curve Cryptography,Group Key Agreement,HMAC Integrity,IoT Security,Lightweight Cryptography,Secure Key Distribution,Shamir’s Secret Sharing,

References:

I. Abdel Hakeem, S. A., & Kim, H., Centralized threshold key generation protocol based on Shamir Secret Sharing and HMAC authentication, Sensors, 22, 331, 2022. 10.3390/s22010331.
II. Ashraf, Z., Sohail, A., & Yousaf, M., Robust and lightweight symmetric key exchange algorithm for next-generation IoE. Internet of Things, 22, 100703, 2023. 10.1016/j.iot.2023.100703.
III. Cui, W., Cheng, R., Wu, K., Su, Y., & Lei, Y. (2021). A certificateless authenticated key agreement scheme for the power IoT, Energies, 14(19), 6317, 2021. 10.3390/en14196317.
IV. Ding, Z., et al., A lightweight and secure communication protocol for the IoT environment, IEEE Transactions on Dependable and Secure Computing, 21(3),2024, 1050–1067. 10.1109/TDSC.2023.3267979.
V. Fang, D., Qian, Y., & Hu, R. Q., A flexible and efficient authentication and secure data transmission scheme for IoT applications, IEEE Internet of Things Journal, 7(4),2020, 3474–3484. 10.1109/JIOT.2020.2970974.
VI. Ghebleh, M., Kanso, A., & Abuhasan, H., Verifiable secret sharing with changeable access structure, Discrete Mathematics, Algorithms and Applications, 2024. 10.1142/S179383092450037X.
VII. Lee, J., Kim, M., Park, K., Noh, S.-K., Bisht, A., Das, A. K., & Park, Y.-H., Blockchain-based data access control and key agreement system in IoT environment, Sensors, 23(11), 5173, 2023. 10.3390/s23115173
VIII. Lemnouar, N., Security limitations of Shamir’s secret sharing, Journal of Discrete Mathematical Sciences and Cryptography,2022, 1–13. 10.1080/09720529.2021.1961902.
IX. Li, B., Zhang, G., Lei, S., Fu, H., & Wang, J., A Lightweight Authentication And Key Agreement Protocol For Iot Based On ECC, In Proceedings of the 2021 International Conference on Advanced Computing and Endogenous Security, 2022, (pp 1–5), Nanjing, China. 10.1109/IEEECONF52377.2022.10013341.
X. Meng, K., Miao, F., Huang, W., & Xiong, Y., Threshold changeable secret sharing with secure secret reconstruction, Information Processing Letters, 157, 105928, 2020. 10.1016/j.ipl.2020.105928.
XI. Muhammad, T., Allaoua Chelloug, S., Alabdulhafith, M., & Abd El-Latif, A. A., Lightweight authentication protocol for connected medical IoT through privacy-preserving access, Egyptian Informatics Journal, 2024. 10.1016/j.eij.2024.100474.
XII. Oudah, M. S., & Maolood, A. T., Lightweight authentication model for IoT environments based on enhanced elliptic curve digital signature and Shamir Secret Share, International Journal of Intelligent Engineering and Systems, 15(5), 2024,81–90. 10.22266/ijies2022.1031.08.
XIII. R. Subrahmanyam, N. R. Rekha, and Y. V. S. Rao, “Authenticated Distributed Group Key Agreement Protocol Using Elliptic Curve Secret Sharing Scheme,” in IEEE Access, vol. 11, pp. 45243-45254, 2023. 10.1109/ACCESS.2023.3274468.
XIV. Sheikh, A. S., Keerthi, A., Dhuli, S., Likhita, G., Jahnavi, B. S. V. N. J., & Atik, F., A novel security system for IoT applications,In Proceedings of the 2021 12th International Conference on Computing, Communication and Networking Technologies (ICCCNT),2021, (pp. 1–5). Kharagpur, India. 10.1109/ICCCNT51525.2021.9579502.
XV. S., K., & Rengarajan, A., Advancing IoT security: A comprehensive survey of lightweight cryptography solutions. International Journal of Advanced Research in Computer and Communication Engineering, 2024. 10.17148/ijarcce.2024.13511.
XVI. Tomar, A., Gupta, N., D. L., Rani, S. P., & Tripathi, S., Blockchain-assisted authenticated key agreement scheme for IoT-based healthcare system, Internet of Things, 23, 100849, 2023. 10.1016/j.iot.2023.100849.
XVII. Vora, P., Upadhyay, R., & Wazid, M., Secure and lightweight key management scheme for resource-constrained IoT devices, Computer Networks, 245, 110853, 2024. 10.1016/j.comnet.2024.110853.
XVIII. Weidner, M., Klepmann, M., Hugenroth, D., & Beresford, A. R., Key agreement for decentralized secure group messaging with strong security guarantees, In Proceedings,2021, (pp. 2024–2045). 10.1145/3460120.3484542.
XIX. Zhang, R., Zhang, L., Choo, K.-K. R., & Chen, T., Dynamic authenticated asymmetric group key agreement with sender non-repudiation and privacy for group-oriented applications, IEEE Transactions on Dependable and Secure Computing, 20(1),2023, 492–505. 10.1109/TDSC.2021.3138445.

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Journal Vol – 20 No – 11, November 2025

DESIGN A NON-COHERENT MULTI-CARRIER CHAOTIC SYSTEM BASED ON GENERALIZED CARRIER AND PERMUTATION INDEXES MODULATION MECHANISM

Authors:

Ban M. Alameri, Aya Jamal Kamal, Lubna Abbas Ali

DOI NO:

https://doi.org/10.26782/jmcms.2025.11.00008

November 12, 2025

Abstract:

This study introduces a novel system called Generalized Joint Permutation-Carrier Index Modulation Based MC-DCSK (GJPC-IM-MCDCSK) aimed at enhancing energy efficiency, spectral efficacy, and data transmission rates of GCI-DCSK technology by generating and utilizing permuted forms of the reference chaotic series with quasi-orthogonal properties to distribute the modulated bits across each operational carrier. Furthermore, rather than employing correlation detection, the receiving end of the proposed system utilizes greedy detection to streamline the process. Evaluating the data rate, spectral efficiency, and complexity of the proposed GJPC-IM-MCDCSK system against GCI-DCSK, CI-DCSK1, and MC-DCSK technologies illustrates the superior performance of the recommended design. To illustrate the proposed design's superiority, the simulated Bit Error Rate (BER) performance is evaluated for the GJPC-IM-MCDCSK scheme and compared with the GCI-DCSK, CI-DCSK1, and MC-DCSK systems under AWGN and multipath Rayleigh fading channels.

Keywords:

Active carrier,permutation,Index,greedy detection,correlation detection,

References:

I. Al Bassam, N., & Al-Jerew, O. (2021). Design and implementation of enhanced permutation index differential chaos shift keying system. Physical Communication, 46, 101312. 10.1016/j.phycom.2021.101312
II. Au, M., Kaddoum, G., Alam, M. S., Başar, E., & Gagnon, F. (2019). Joint code-frequency index modulation for IoT and multi-user communications. IEEE Journal of Selected Topics in Signal Processing, 13, 1223–1236. 10.1109/JSTSP.2019.2933056
III. Bai, C., Ren, H. P., & Grebogi, C. (2019). Experimental phase separation differential chaos shift keying wireless communication based on matched filter. IEEE Access, 7, 25274–25287. 10.1109/ACCESS.2019.2900729
IV. Basar, E., Wen, M., Mesleh, R., Di Renzo, M., Xiao, Y., & Haas, H. (2017). Index modulation techniques for next-generation wireless networks. IEEE Access, 5, 16693–16746. 10.1109/ACCESS.2017.2737528
V. Başar, E., Aygölü, Ü., Panayırcı, E., & Poor, H. V. (2013). Orthogonal frequency division multiplexing with index modulation. IEEE Transactions on Signal Processing, 61, 5536–5549. 10.1109/TSP.2013.2279771
VI. Cai, X., Xu, W., Lau, F. C. M., & Wang, L. (2020). Joint carrier-code index modulation aided M-ary differential chaos shift keying system. IEEE Transactions on Vehicular Technology, 69, 15486–15499. 10.1109/TVT.2020.3041927
VII. Cai, X., Xu, W., Miao, M., & Wang, L. (2020). Design and performance analysis of a new M-ary differential chaos shift keying with index modulation. IEEE Transactions on Wireless Communications, 19, 846–858. 10.1109/TWC.2019.2949315
VIII. Cai, X., Xu, W., Wang, L., & Xu, F. (2019). Design and performance analysis of differential chaos shift keying system with dual-index modulation. IEEE Access, 7, 26867–26880. 10.1109/ACCESS.2019.2901016
IX. Chen, H., Chen, P., Wang, S., Lai, S., & Chen, R. (2022). Reference-modulated PI DCSK: A new efficient chaotic permutation index modulation scheme. IEEE Transactions on Vehicular Technology, 71, 9663–9673. 10.1109/TVT.2022.3181180
X. Cheng, G., Chen, X., Liu, W., & Xiao, W. (2019). GCI DCSK: Generalized carrier index differential chaos shift keying modulation. IEEE Communications Letters, 23, 2012–2016. 10.1109/LCOMM.2019.2933827
XI. Cheng, G., Wang, L., Chen, Q., & Chen, G. (2018). Design and performance analysis of generalised carrier index M ary differential chaos shift keying modulation. IET Communications, 12, 1324–1331. 10.1049/iet-com.2017.0800
XII. Cheng, G., Wang, L., Xu, W., & Chen, G. (2017). Carrier index differential chaos shift keying modulation. IEEE Transactions on Circuits and Systems II: Express Briefs, 64, 907–911. 10.1109/TCSII.2016.2613093
XIII. Dai, W., Yang, H., Song, Y., & Jiang, G. (2018). Two-layer carrier index modulation scheme based on differential chaos shift keying. IEEE Access, 6, 56433–56444. 10.1109/ACCESS.2018.2872748
XIV. Fang, Y., Peng, D., Ma, H., Han, G., & Li, Y. (2024). A neural network-aided detection scheme for index modulation DCSK system. IEEE Transactions on Vehicular Technology, 73, 2109–2121. 10.1109/TVT.2023.3313813
XV. Fang, Y., Zhuo, J., Ma, H., Mumtaz, S., & Li, Y. (2023). Design and analysis of a new index-modulation aided DCSK system with frequency and time resources. IEEE Transactions on Vehicular Technology, 72, 7411–7425. 10.1109/TVT.2023.3238379
XVI. Galias, Z., & Maggio, G. M. (2001). Quadrature chaos shift keying: Theory and performance analysis. IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, 48, 1510–1519. 10.1109/TCSI.2001.972858
XVII. Hasan, F. S. (2021). Design and analysis of grouping subcarrier index modulation for differential chaos shift keying communication system. Physical Communication, 47, 101325. 10.1016/j.phycom.2021.101325
XVIII. Hasan, F. S., & Fahad, H. F. (2020). Design and analysis of an OFDM based orthogonal multilevel code shifted differential chaos shift keying. Indonesian Journal of Electrical Engineering and Computer Science, 20, 1369–1378. 10.11591/ijeecs.v20.i3.pp1369-1378
XIX. Herceg, M., Vranjes, D., Kaddoum, G., & Soujeri, E. (2018). Commutation code index DCSK modulation technique for high data rate communication systems. IEEE Transactions on Circuits and Systems II: Express Briefs, 65, 1954–1958. 10.1109/TCSII.2018.2817930
XX. Herceg, M., Kaddoum, G., Vranjes, D., & Soujeri, E. (2018). Permutation index DCSK modulation technique for secure multiuser high data rate communication systems. IEEE Transactions on Vehicular Technology, 67, 2997–3011. 10.1109/TVT.2017.2774108
XXI. Huang, T., Wang, L., Xu, W., & Lau, F. C. M. (2016). Multilevel code shifted differential chaos shift keying system. IET Communications, 10, 1189–1195. 10.1049/iet-com.2015.1109
XXII. Kaddoum, G., Richardson, F. D., & Gagnon, F. (2013). Design and analysis of a multi carrier differential chaos shift keying communication system. IEEE Transactions on Communications, 61, 3281–3291. 10.1109/TCOMM.2013.071013.130225
XXIII. Kaddoum, G., Soujeri, E. (2016). NR DCSK: A noise reduction differential chaos shift keying system. IEEE Transactions on Circuits and Systems II: Express Briefs, 63, 648–652. 10.1109/TCSII.2016.2532041
XXIV. Kaddoum, G., Soujeri, E., & Nijsure, Y. (2016). Design of a short reference noncoherent chaos based communication systems. IEEE Transactions on Communications, 64, 680–689. 10.1109/TCOMM.2015.2514089
XXV. Kaddoum, G., Tran, H. V., Kong, L., & Atallah, M. (2017). Design of simultaneous wireless information and power transfer scheme for short reference DCSK communication systems. IEEE Transactions on Communications, 65, 431–443. 10.1109/TCOMM.2016.2619707

XXVI. Lau, F. C. M., Cheong, K. Y., & Tse, C. K. (2003). Permutation based DCSK and multiple access DCSK systems. IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, 50, 733–742. 10.1109/TCSI.2003.812616
XXVII. Li, S., Zhao, Y., & Wu, Z. (2015). Design and analysis of an OFDM based differential chaos shift keying communication system. Journal of Communications, 10, 199–205. 10.12720/jcm.10.3.199-205
XXVIII. Liu, S., Chen, P., & Chen, G. (2021). Differential permutation index DCSK modulation for chaotic communication system. IEEE Communications Letters, 25, 2029–2033
XXIX. Luo, R., Yang, H., Meng, C., & Zhang, X. (2022). A novel SR DCSK based ambient backscatter communication system. IEEE Transactions on Circuits and Systems II: Express Briefs, 69, 1707–1711. 10.1109/TCSII.2021.3109020
XXX. Ma, H., Fang, Y., Chen, P., Mumtaz, S., & Li, Y. (2023). A novel differential chaos shift keying scheme with multidimensional index modulation. IEEE Transactions on Wireless Communications. 10.1109/TWC.2022.3192347
XXXI. Nazar, B., & Hasan, F. S. (2023). Joint grouping subcarrier and permutation index modulations based differential chaos shift keying system. Physical Communication, 61, 102213. 10.1016/j.phycom.2023.102213
XXXII. Nazar, B., & Hasan, F. S. (2024). Enhanced two way cooperative DCSK system via grouping subcarrier permutation index modulation. In Evolution in Signal Processing and Telecommunication Networks (pp. 23–35). Springer. 10.1007/978-981-97-0644-0_3
XXXIII. Nazar, B., & Hasan, F. S. (2024). Performance analysis of two way multi users cooperative communication system based on GSPIM DCSK scheme. AEU , International Journal of Electronics and Communications, 178, 155303. 10.1016/j.aeue.2024.155303
XXXIV. Sui, T., Feng, Y., Jiang, Q., Liu, F., & Zhang, T. (2022). Design and analysis of a short reference orthogonal double bit rate differential chaotic shift keying communication scheme. Electronics, 11. 10.3390/electronics11132020
XXXV. Tao, Y., Fang, Y., Ma, H., Mumtaz, S., & Guizani, M. (2022). Multi carrier DCSK with hybrid index modulation: A new perspective on frequency index aided chaotic communication. IEEE Transactions on Communications, 70, 3760–3773. 10.1109/TCOMM.2022.3169214
XXXVI. Xu, W., & Wang, L. (2017, October 20–23). Performance optimization for short reference differential chaos shift keying scheme. In Proceedings of the IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) (pp. 1–6). 10.1109/ICSPCC.2017.8242434
XXXVII. Yang, H., & Jiang, G. P. (2012). High efficiency differential chaos shift keying scheme for chaos based noncoherent communication. IEEE Transactions on Circuits and Systems II: Express Briefs, 59, 312–316. 10.1109/TCSII.2012.2190859
XXXVIII. Yang, H., & Jiang, G. P. (2013). Reference modulated DCSK: A novel chaotic communication scheme. IEEE Transactions on Circuits and Systems II: Express Briefs, 60, 232–236. 10.1109/TCSII.2013.2251949
XXXIX. Yang, H., Xu, S. Y., & Jiang, G. P. (2021). A high data rate solution for differential chaos shift keying based on carrier index modulation. IEEE Transactions on Circuits and Systems II: Express Briefs, 68, 1487–1491. 10.1109/TCSII.2020.3038163
XL. Yao, W., Zhang, X., Wang, J., Li, Z., Li, J., & Song, H. (2023, May). Design and analysis of a noise reduction PI DCSK system for wireless underground power pipe gallery communications. In Proceedings of the 2023 4th Information Communication Technologies Conference (ICTC) (pp. 44–48). 10.1109/ICTC57116.2023.10154786
XLI. Yaseen, R. A., & Hasan, F. S. (2024). Design and analysis of grouping active subcarrier frequency time index modulation for differential chaos shift keying communication system. Journal of Communications Software and Systems, 20, 173–185. 10.24138/jcomss-2023-0182
XLII. Yaqeen S.Mezaal, Halil T. Eyyuboğlu, and Jawad K. Ali. “New microstrip bandpass filter designs based on stepped impedance Hilbert fractal resonators.” IETE Journal of Research 60.3 (2014): 257-264..
XLIII. Y. S. Mezaal, L. N. Yousif, Z. J. Abdulkareem, H. A. Hussein, and S. K. Khaleel, “Review about effects of IoT and nanotechnology techniques in the development of IoNT in wireless systems,” International Journal of Engineering and Technology, vol. 7, no. 4, pp. 3602–3606, 2018. 10.14419/ijet.v7i4.19615..
XLIV. Yahya, A., J. A. Aldhaibaini, R. B. Ahmad, Z. G. Ali, and R. A. Fayadh, “Enhancing link quality in a multi-hop relay in LTE-A employing directional antenna,” in 2013 IEEE International RF and Microwave Conference (RFM), 2013. 10.1109/RFM.2013.6757227
XLV. Yahya, A., J. A. Aldhaibaini, R. B. Ahmad, M. Zain, and A. S. Salman, “PERFORMANCE ANALYSIS OF TWO-WAY MULTI-USER WITH BALANCE TRANSMITTED POWER OF RELAY IN LTE-A CELLULAR NETWORKS,” Journal of Theoretical & Applied Information Technology, vol. 51, no. 2, 2013.
XLVI. Zainab Falih Hamza, et. al. “Estimating systems reliability functions for the generalized exponential distribution with application”, Period. Eng. Nat. Sci., vol. 11, no. 3, pp. 108–114, May 2023, 10.21533/pen.v11.i3.138

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Journal Vol – 20 No – 11, November 2025

INTERVAL-VALUED OPEN SETS VIA MINIMAL AND MAXIMAL IN INTERVAL-VALUED TOPOLOGICAL SPACES

Authors:

Dijitha Selvendhiran, Navaneethakrishnan Malaisamy

DOI NO:

https://doi.org/10.26782/jmcms.2025.11.00009

November 12, 2025

Abstract:

This study aims to develop and investigate several classes of interval-valued sets within the framework of Interval-Valued topology. Interval-Valued sets, originally proposed by Yao and later enriched through various generalizations, provide a more expressive structure for modelling uncertainty than classical or fuzzy sets. In this paper, we introduce and analyse different categories of Interval-Valued sets, particularly focusing on their weak and strong forms, and we explore how these forms influence topological behaviour. Special emphasis is placed on the study of minimal and maximal Interval-Valued open sets, pre-open sets, and semi-open sets, which serve as extremal elements in the lattice of Interval-Valued topologies. We also examine the interrelationships between various kinds of Interval-Valued closed sets and their generalized counterparts, thereby clarifying how such structures are embedded within the hierarchy of Interval-Valued topology. By establishing these connections, we provide a deeper understanding of the internal organisation of generalized closed sets in this setting. To support the theoretical results, illustrative and carefully constructed examples are presented, which highlight both the necessity and the distinctions among the introduced concepts. Overall, this work contributes to the enrichment of Interval-Valued topological theory and lays the foundation for further applications in mathematical modelling and decision-making under uncertainty.

Keywords:

Mini-IVOs,Maxi-IVOs,Mini-IVSOs,Mini-IVPOs,Maxi-IVSOs ,Maxi-IVPOs ,

References:

I. Çoker, D.: ‘An introduction to intuitionistic fuzzy topological spaces’. Fuzzy Sets and Systems. Vol. 88, pp.81–89, 1997.
II. Dijitha, S. and Navaneetharishnan, M.: ‘A study on semi-closed and semi-open sets in Interval-Valued topology’. Indian Journal of Natural Sciences. Vol. 15(88), pp. 89791–89800, 2025.
III. Dijitha, S. and Navaneethakrishnan, M.: ‘On Interval-Valued closed sets and their generalizations in Interval-Valued Topological Spaces’. Indian Journal of Natural Science. Vol. 16(89), pp. 91176-91183, 2025.
IV. Kim, J., Jun, Y. B., Lee, J. G. and Hur, K.: ‘Topological structures based on interval-valued sets’. Annals of Fuzzy Mathematics and Informatics. Vol. 20, pp. 273–295, 2020.
V. Levine, N.: ‘Generalized closed sets in topology’. Rendiconti del Circolo Matematico di Palermo. Vol. 19, pp. 89–96, 1970.
VI. Raouf, A. G., and Yaseen, Y. J.: ‘On generalization closed set and generalized continuity on intuitionistic topological spaces’. Journal of University of Anbar for Pure Science. Vol. 3, pp. 107–117, 2009.
VII. Saleh, S., Al-Mufarrij, J., and Nahi Alrabeeah, A. A.: ‘On continuity and categorical property of interval-valued topological spaces’. International Journal of Nonlinear Analysis and Applications. Vol. 14, pp. 385–392, 2023.
VIII. Sasikala, G., and Navaneethakrishnan, M.: ‘On intuitionistic preopen sets’. International Journal of Pure and Applied Mathematics. Vol. 116, pp. 281–292, 2017.
IX. Seenivasan, V., Campus, P., and Kalaiselvi, I. S.: ‘A new class of minimal and maximal sets via gsg closed set’. International Journal of Mathematical Analysis. Vol. 7, pp. 2595–2609, 2013.
X. Yao, Y.: ‘Interval sets and interval-set algebras’. In 2009 8th IEEE International Conference on Cognitive Informatics. pp. 307–314, 2009.
XI. Yildirim, E. D., Güler, A. Ç., and Özbakir, O. B.: ‘Minimal intuitionistic open and maximal intuitionistic open sets’. Sigma. Vol. 38, pp. 481–490, 2020.
XII. Zadeh, L. A. : ‘The concept of a linguistic variable and its application to approximate reasoning—I’. Information Sciences, Vol. 8, pp. 199–249, 1975.

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Journal Vol – 20 No – 11, November 2025

COLORECTAL CANCER (CRC) DETECTION USING CONVOLUTIONAL NEURAL NETWORK (CNN)

Authors:

Sombit Pramanik, Snehasish Biswas, Soumyadeep Jana, Asish Mitra

DOI NO:

https://doi.org/10.26782/jmcms.2025.11.00010

November 12, 2025

Abstract:

This research focuses on utilizing Convolutional Neural Networks (CNN) to predict the likelihood of colorectal cancer by analyzing medical images of cancerous and non-cancerous tissues. The model demonstrates strong performance with high precision, recall, and accuracy, and employs various techniques such as data augmentation and early stopping to improve generalization and prevent overfitting. The study highlights the potential of machine learning in enhancing diagnostic accuracy and supporting oncologists in making informed treatment decisions.

Keywords:

CNN,Colorectal Cancer,Confusion matrix,Medical Imaging,

References:

I. Alshakree F, Akbas A, Rahebi J. “Human Identification Using Palm Print Images Based on Deep Learning Methods and Gray Wolf Optimization Algorithm.” Signal Image Video Process. 18, 2024, pp. 961–973. 10.1007/s11760-023-02787-6.
II. Al Shalchi N F A, Rahebi J. “Human Retinal Optic Disc Detection with Grasshopper Optimization Algorithm. Multimed.” Tools Appl. 81, 2022, pp. 24937–24955. 10.1007/s11042-022-12838-8.
III. Ananthakrishnan B, Shaik A, Chakrabarti S, Shukla V, Paul D, Kavitha M S. “Smart Diagnosis of Adenocarcinoma Using Convolution Neural Networks and Support Vector Machines.” Sustainability 15, 2023, pp. 1399-1417. 10.3390/su15021399.
IV. Ben Hamida A, Devanne M, Weber J, Truntzer C, Derangère V, Ghiringhelli F, Forestier G, Wemmert C. “Deep Learning for Colon Cancer Histopathological Images Analysis.” Comput. Biol. Med. 36, 2021, 104730. 10.1016/j.compbiomed.2021.104730.
V. Chahal P K, Pandey S A. “Hybrid Weighted Fuzzy Approach for Brain Tumor Segmentation Using MR Images.” Neural Comput. Appl. 35, 2023, pp. 23877–23891. 10.1007/s00521-021-06010-w.
VI. Du Y, Hu L, Wu G, Tang Y, Cai X, Yin L. “Diagnoses in Multiple Types of Cancer Based on Serum Raman SpectroscopyCombined with a Convolutional Neural Network: Gastric Cancer, Colon Cancer, Rectal Cancer, Lung Cancer.” Spectrochim. Acta Part A Mol. Biomol. Spectrosc. 298, 2023, 122743. 10.1016/j.saa.2023.122743.
VII. Gonzalez Y, Shen C, Jung H, Nguyen D, Jiang S B, Albuquerque K, Jia X. “Semi-Automatic Sigmoid Colon Segmentation in CT for Radiation Therapy Treatment Planning via an Iterative 2.5-D Deep Learning Approach.” Med. Image Anal. 68, 2021, 101896. 10.1016/j.media.2020.101896.

VIII. https://github.com/tampapath/lung_colon_image_set.
IX. Joshi R. “Gender Disparities: 5 Year Survival Rates of Elderly Colorectal Cancer Patients with Depression.” Ph.D. Thesis, Walden University, Minneapolis, MN, USA, 2023.
X. Karthikeyan A, Jothilakshmi S, Suthir S. “Colorectal Cancer Detection Based on Convolutional Neural Networks (CNN) and Ranking Algorithm.” Meas. Sens. 31, 2024, 100976. 10.1016/j.measen.2023.100976.
XI. Kour H, Manhas J, Sharma V. “Usage and Implementation of Neuro-Fuzzy Systems for Classification and Prediction in the Diagnosis of Different Types of Medical Disorders: A Decade Review.” Artif. Intell. Rev. 53, 2020, pp. 4651–4706. 10.1007/s10462-020-09804-x.
XII. Masud M, Sikder N, Al Nahid A, Bairagi A K, Alzain M A. “A Machine Learning Approach to Diagnosing Lung and Colon Cancer Using a Deep Learning-based Classification Framework.” Sensors 21, 2021, pp. 748-767. 10.3390/s21030748.
XIII. Nisha J S, Gopi V P, Palanisamy P. “Automated Colorectal Polyp Detection Based on Image Enhancement and Dual-Path CNN Architecture.” Biomed. Signal Process. Control 73, 2022, 103465. 10.1016/j.bspc.2021.103465.
XIV. Nazari E, Aghemiri M, Avan A, Mehrabian A, Tabesh, H. “Machine Learning Approaches for Classification of Colorectal Cancer with and without Feature Selection Method on Microarray Data.” Gene Rep. 25, 2021, 101419. 10.1016/j.genrep.2021.101419.
XV. Nanni L, Fantozzi C, Loreggia A, Lumini A. “Ensembles of Convolutional Neural Networks and Transformers for Polyp Segmentation.” Sensors 23, 2023, 4688. 10.3390/s23104688.
XVI. Siegel R L, Wagle N S, Cercek A, Smith R A, Jemal A. “Colorectal Cancer Statistics, 2023” J. Clin. 73, 2023, 233–254. 10.3322/caac.21772.
XVII. Talukder M A, Islam M M, Uddin M A, Akhter A, Hasan K F, Moni M A. “Machine Learning-Based Lung and Colon Cancer Detection Using Deep Feature Extraction and Ensemble Learning.” Expert Syst. Appl. 205, 2022, pp. 117695. 10.1016/j.eswa.2022.117695.
XVIII. Tulum G, Osman O, Bolat B, Dandin O, Ergin T, Cuce, F. “Colonic Polyp Classification Using Projection Image and Convolutional Neural Network.” Proceedings of the 2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT), Istanbul, Turkey, 24–26 April 2019, pp. 1–4. 10.1109/EBBT.2019.8741701.
XIX. Yaghoubi A, Khazaei M, Avan A, Hasanian S M, Soleimanpour S. “The Bacterial Instrument as a Promising Therapy for Colon Cancer.” Int. J. Colorectal Dis. 35, 2020, pp. 595–606. 10.1007/s00384-020-03535-9.
XX. Yaghoubi E, Yaghoubi E, Khamees A, Vakili A H. “A Systematic Review and Meta-Analysis of Artificial Neural Network, Machine Learning, Deep Learning, and Ensemble Learning Approaches in Field of Geotechnical Engineering.” Neural Comput Appl. 26, 2024, pp. 12655 – 12699. 10.1007/s00521-024-09893-7.
XXI. Yusupov Z, Yaghoubi E, Yaghoubi, E. “Controlling and Tracking the Maximum Active Power Point in a Photovoltaic System Connected to the Grid Using the Fuzzy Neural Controller.” Proceedings of the 2023 14th International Conference on Electrical and Electronics Engineering (ELECO), Bursa, Turkey, 30 November -–02 December 2023, pp. 1–5. 10.1109/ELECO60389.2023.10416016.
XXII. Zhang R, Zheng Y, Mak T W C, Yu R, Wong S H, Lau, J Y W, Poon C C Y. “Automatic Detection and Classification of Colorectal Polyps by Transferring Low-Level CNN Features from Nonmedical Domain.” IEEE J. Biomed. Health Inform. 21, 2016, pp. 41–47. 10.1109/JBHI.2016.2635662.

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Journal Vol – 20 No – 11, November 2025

INTEGRATED ERGONOMIC APPROACH FOR RESIDENTIAL CHAIR DESIGN: A VALIDATION BASED ON MALAYSIAN ANTHROPOMETRY, RULA, AND EMG

Authors:

L. K. M. Brenda, A.M. Kamarul, M. Y. Yuhazri, W. H. W. Mahmood, A. Z. M. Noor, F. Syaifoelida

DOI NO:

https://doi.org/10.26782/jmcms.2025.12.00001

December 10, 2025

Abstract:

This study presents the design and evaluation of an ergonomic chair developed for Malaysian residential users through the integration of anthropometric data, ergonomic assessment, and experimental validation. Anthropometric dimensions from the Malaysian Anthropometric Database were applied to determine seat height, depth, width, and backrest dimensions suitable for local body proportions. Rapid Upper Limb Assessment (RULA) was conducted using digital manikins representing the 5th, 50th, and 95th percentiles to identify postures with minimal musculoskeletal risk. A 3D CAD model was created in SolidWorks, and finite element analysis (FEA) was performed to evaluate structural integrity under a 150 kg load. A full-scale prototype was validated using electromyography (EMG) testing involving 20 participants of varying height, weight, and body mass index (BMI). Root Mean Square (RMS) values of muscle activation were analyzed to assess comfort and fatigue. Results showed a RULA score of 2, strong structural stability, and low EMG activity, indicating minimal muscle strain. The integration of anthropometry, simulation, and EMG validation confirms the chair’s ergonomic suitability and establishes a framework for locally optimized furniture design.

Keywords:

Chair design,Comfort,Ergonomics,EMG,

References:

I. Aberdam, A., B. Pierrat, and F. Lavaste. “Evaluation of the Comfort of a Novel Chair Design Using Electromyography and Subjective Feedback.” Applied Ergonomics, vol. 82, 2020, pp. 102951, 10.1016/j.apergo.2019.102951.
II. Ansari, S., et al. “Design and Development of an Ergonomic Chair for Students in Educational Settings.” Health Scope, vol. 7, no. 4, 2018, pp. e60531. 10.5812/jhealthscope.60531.
III. Atef, B. “Ergonomics Study and Analysis of Workstations in Tunisian Mechanical Manufacturing.” Human Factors and Ergonomics in Manufacturing & Service Industries, vol. 28, 2018, pp. 166-85. 10.1002/hfm.20713.
IV. Benden, M. R. “Ergonomic Design of the Home Environment.” Journal of Occupational and Environmental Medicine, vol. 60, no. 3, 2018, pp. 150-55. 10.1097/JOM.0000000000001227.
V. Bi, Z. M. “Computer Implementation.” Finite Element Analysis Applications: A Practical Guide to the FEM Project of Production Engineering Domains, edited by Z. M. Bi and Donald W. Mueller, Springer, 2018, pp. 227–80.
VI. Cifrek, M., et al. “Surface EMG Based Muscle Fatigue Evaluation in Biomechanics.” Clinical Biomechanics, vol. 24, no. 4, 2009, pp. 327–40. 10.1016/j.clinbiomech.2009.01.009.
VII. Delvaux, F., J. F. Kaux, and J. L. Croisier. “Lower Limb Muscle Injuries: Risk Factors and Preventive Strategies.” Science & Sports, vol. 32, no. 4, 2017, pp. 179–90. 10.1016/j.scispo.2017.02.003.
VIII. Dempsey, P. G., R. W. McGorry, and W. S. Maynard. “A Survey of Tools and Methods Used by Certified Professional Ergonomists.” Applied Ergonomics, vol. 36, 2005, pp. 489–503. 10.1016/j.apergo.2005.01.007.
IX. Ghazali, M. F., et al. “RULA and REBA Assessments in Computer Laboratories.” National Symposium on Advancements in Ergonomics and Safety (ERGOSYM2009), Universiti Malaysia Perlis, 2009, pp. 3-13.
X. Intertek. “The Definitive Guide to North American Furniture Testing.” Intertek Testing Services, NA, Inc., 2020. Intertek. https://www.intertek.com/furniture/.
XI. Khairuzzaman, M., and S. M. Aljunid. “An Ergonomic Evaluation of Office Chairs Using the Rapid Upper Limb Assessment (RULA) Tool.” International Journal of Industrial Ergonomics, vol. 79, 2020, pp. 103020. 10.1016/j.ergon.2020.103020.
XII. Kim, D. H., et al. “Effect of Rapid Upper Limb Assessment (RULA) Analysis on the Design of Dental Practitioner Chairs.” Journal of Physical Therapy Science, vol. 31, no. 4, 2019, pp. 292-95. 10.1589/jpts.31.292.
XIII. Kumar, S., and A. Khatavkar. “Ergonomic design of office chair: A review.” Journal of Ergonomics, vol. 9, no. 1, 2019, pp. 1-8.
XIV. Maruyama, T., N. Kajii, and M. Gotoh. “Electromyographic Evaluation of the Effect of Lumbar Support Shape and Armrest Height on Upper Body Muscle Activity and Perceived Comfort during Office Work.” Industrial Health, vol. 57, no. 3, 2019, pp. 300-10. 10.2486/indhealth.2018-0055.
XV. Mohamad, D., et al. “Development of a Malaysian Anthropometric Database.” World Engineering Congress 2010: Proceedings of the Conference on Manufacturing Technology and Management, Sarawak, Malaysia, 2010, pp. 4-5.
XVI. Murata, S. “Effect of Seat Shape with a Sloping Front Edge on Leg Blood Flow and Discomfort during Low-Intensity Tasks.” Journal of Occupational Health, vol. 61, no. 4, 2019, pp. 345-52. 10.1539/joh.18-0249-OA.
XVII. Pascual, S.A., and S. Naqvi. “An Investigation of Ergonomics Analysis Tools Used in Industry in the Identification of Work-Related Musculoskeletal Disorders.” International Journal of Occupational Safety and Ergonomics; JOSE, vol. 14, no. 2, 2008, pp. 237-45. 10.1080/10803548.2008.11076774.
XVIII. Shanmugam, M., et al. “Ergonomics design of office chair: A review.” IOP Conference Series: Materials Science and Engineering, vol. 1122, no. 1, 2021, pp. 012073.
XIX. Troiano, A., et al. “Assessment of Force and Fatigue in Isometric Contractions of the Upper Trapezius Muscle by Surface EMG Signal and Perceived Exertion Scale.” Gait & Posture, vol. 28, no. 2, 2008, pp. 179–86. 10.1016/j.gaitpost.2007.08.002.
XX. Wang, L. J., et al. “A Comparative Study of EMG Indices in Muscle Fatigue Evaluation Based on Grey Relational Analysis during All-Out Cycling Exercise.” BioMed Research International, 2018, pp. 1-8. 10.1155/2018/9341215.
XXI. Watanabe, M. “Optimal Reclining Angle of the Backrest of Office Chairs Based on Comfort and Muscle Activity.” Journal of Physical Therapy Science, vol. 32, no. 2, 2020, pp. 136-41. 10.1589/jpts.32.136.

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Journal Vol – 20 No – 12, December 2025

OPTIMIZATION OF ENERGY CONSUMPTION IN WIRELESS SENSOR NETWORKS USING ENERGY EFFICIENT SPHERICAL GRID ROUTING PROTOCOL

Authors:

Ch. Rambabu, Srilakshmi Kaza, Syamala Yarlagadda, P.Anil Kumar

DOI NO:

https://doi.org/10.26782/jmcms.2025.12.00002

December 10, 2025

Abstract:

The primary factor influencing the wireless sensor network (WSN) is the energy consumption of the sensor node. One of the key factors influencing WSN energy consumption is the high power consumption and packet delivery ratio needed for WSN processing. The suggested Energy Efficient Spherical Grid Routing (EESGR) protocol reduces the node's energy consumption to meet the requirements. To choose the cluster heads, the WSN is clustered into a collection of nodes using the pillar k-means clustering method defined in the proposed protocol. One optimization algorithm inspired by nature, the ant lion, is used to create cluster heads for assessing energy consumption in WSNs. The behavior concept of the ant lion is utilized for choosing the best nodes for the selection of the cluster head. The multi-tier spherical grid routing proposed in the paper is used to grid the cluster head generated by the ant-lion optimization algorithm to evaluate the total energy consumed for processing the sensor network. The overall performance of this method is evaluated in Network Simulator 2 (NS2). The proposed method improves performance in throughput, end-to-end delay, packet delivery ratio, and energy consumption compared to the existing techniques.

Keywords:

Ant Lion Optimization Algorithm,Multi-Tier Spherical Grid Routing,Network Simulator,Pillar K-means Clustering,Wireless Sensor Networks (WSN),

References:

I. Azharuddin and Jana, “Particle swarm optimization for maximizing lifetime of wireless sensor networks”, Computers and Electrical Engineering, vol. 51, pp. 26-42, 2016.

II. Ch. Rambabu, V.V.K.D.V. Prasad and K. Satya Prasad, “A Visiting Center based Energy Efficient Data Collection Method for WSN”, International Journal of Recent Technology and Engineering (IJRTE), ISSN: 2277-3878, vol. 8, issue 3, pp.5152-5158, September 2019.
III. Darabkh and Zomot, “An improved cluster head selection algorithm for wireless sensor networks”, Proceedings of the 14th International Wireless Communications and Mobile Computing Conference (IWCMC) IEEE, pp. 65-70, 2018.
IV. El sayed, Sabbeh and Riad, “A distributed fault tolerance mechanism for self-maintenance of clusters in wireless sensor networks”, Arabian Journal for Science and Engineering, vol. 43, issue 12, pp.6891-6907, 2018.
V. G. Xie and F. Pan, “Cluster-based routing for the mobile sink in wireless sensor networks with obstacles”, IEEE Access, vol. 4, pp. 2019-2028, 2016.
VI. Huang, Hong, Zhao and Yuan, “An energy-efficient multi-hop routing protocol based on grid clustering for wireless sensor networks”, Cluster Computing, vol. 20, issue 4, pp.3071-3083, 2017.
VII. Jafarali Jassbi and Moridi, “Fault tolerance and energy efficient clustering algorithm in wireless sensor networks: FTEC”, Wireless Personal Communications, vol. 107, issue 1, pp.373-391, 2019.
VIII. Jiang, C.J., Shi, W.R. and Tang, “Energy-balanced unequal clustering protocol for wireless sensor networks”, The Journal of China Universities of Posts and Telecommunications, vol. 17, issue 4, pp.94-99, 2010.
IX. Kaur and Kumar, “Particle swarm optimization-based unequal and fault tolerant clustering protocol for wireless sensor networks”, IEEE Sensors Journal, vol. 18, issue 11, pp.4614-4622, 2018.
X. Lohani and Varma, “Energy efficient data aggregation in mobile agent based wireless sensor network”, Wireless Personal Communications, vol. 89, issue 4, pp.1165-1176, 2016.
XI. R. Ahmad, R. Wazirali, and T. Abu-Ain, “Machine learning for wireless sensor networks security: An overview of challenges and issues,” Sensors, vol. 22, no. 13, p. 4730, Jun. 2022.
XII. S. Kumari and A. K. Tyagi, “Wireless sensor networks: An introduction,” in Digital Twin and Blockchain for Smart Cities. Beverly, MA, USA : Scrivener Publishing, 2024, pp. 495–528.
XIII. Wang, J., Cao, J., Ji, S. and Park, “Energy-efficient cluster-based dynamic routes adjustment approach for wireless sensor networks with mobile sinks”, The Journal of Supercomputing, vol. 73, issue 7, pp.3277-3290,2017.
XIV. Wang, J., Cao, J., Sherratt, R.S. and Park, “An improved ant colony optimization-based approach with mobile sink for wireless sensor networks”, The Journal of Supercomputing, vol. 74, issue 12, pp.6633-6645. 2018.
XV. W. R. Heinzelman, A. Chandrakasan and H. Balakrishnan, “Energy-efficient communication protocol for wireless microsensor networks”, Proceedings of the 33rd Annual Hawaii International Conference on System Sciences, pp. 1-10, 2000.

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Journal Vol – 20 No – 12, December 2025

THEORETICAL AND ALGORITHMIC ANALYSIS OF FAIR DOMINATION AND SUBDIVISION NUMBERS FOR CYCLE AND CIRCULANT GRAPHS

Authors:

G. Navamani, Reena Mercy M. A., A. Josephine Christilda, Dharmaraj Mohankumar

DOI NO:

https://doi.org/10.26782/jmcms.2025.12.00003

December 11, 2025

Abstract:

This study explores a specialized type of domination in graphs known as fair domination. A fair dominating set (FDS) in a graph R is defined as a dominating set in which every non-member vertex is adjacent to an equal number of vertices within the set. The minimum size of such a set is referred to as the fair domination number, denoted γ_fd (R). We further examine how structural modifications, specifically edge subdivisions, affect this parameter. The fair domination subdivision number, denoted 〖Sd〗_(γ_fd)^+ (R) (or 〖Sd〗_(γ_fd)^- (R)), captures the smallest number of edge subdivisions required to increase or decrease the fair domination number, respectively. Our work focuses on computing these values for two graph families: cycles C_n (with n≥3) and Circulant graphs C_n (1,k),k=2,3. Through detailed analysis, we demonstrate how edge subdivisions impact the fairness condition in domination. To systematically explore fair domination in graphs, we adopt an algorithmic approach that facilitates efficient identification of fair dominating sets and computation of related parameters. Algorithmic techniques have been pivotal in graph theory, particularly in the study of domination-related problems. We introduce an efficient algorithm for identifying fair dominating sets and determining the fair domination number in Circulant graphs of the form〖 C〗_n(1,2) and C_n (1,3), offering insights into their underlying combinatorial structure.

Keywords:

Influence-based vertex covering,Uniform vertex influence,k-regular fair domination,Edge-splitting parameter,Subdivision for fair domination,

References:

I. Blažej, Václav, Jan Matyáš Křišťan, and Tomáš Valla: ‘Computing m-eternal domination number of cactus graphs in linear time’. arXiv. arXiv:2301.05155, 2023. https://doi.org/10.48550/arXiv.2301.05155.
II. Boehmer, Niclas, Tomohiro Koana, and Rolf Niedermeier: ‘A refined complexity analysis of fair districting over graphs.’ Autonomous Agents and Multi-Agent Systems. Vol. 37(1), pp: 13, 2023. 10.48550/arXiv.2102.11864.
III. Caro, Yair, Adriana Hansberg, and Michael Henning: ‘Fair domination in graphs’. Discrete Mathematics. Vol. 312(19), pp: 2905-2914, 2012. 10.1016/j.disc.2012.05.006.
IV. Casado, Alejandra, Jesús Sánchez-Oro, and Anna Martínez-Gavara: ‘Heuristics for the weighted total domination problem’. TOP: An Official Journal of the Spanish Society of Statistics and Operations Research. Vol. 33(2), pp: 395–436, 2025 10.1007/s11750-025-00695-1.
V. Dejter, Italo J: ‘Perfect domination in regular grid graphs’. Australasian Journal of Combinatoricsz. Vol. 42, pp: 99–114, 2007. 10.48550/arXiv.0711.4343.
VI. Enriquez, Enrico, et al : ‘Domination in fuzzy directed graphs’. Mathematics. Vol. 9(17), pp: 2143, 2021. 10.3390/math9172143.
VII. Hajian, Majid, and N. Jafari Rad: ‘Trees and unicyclic graphs with large fair domination number’. Util. Math. Vol. 112, 2022.
VIII. Hajian, Majid, and Nader Jafari Rad: ‘Fair domination number in cactus graphs’. Discussiones Mathematicae Graph Theory. Vol. 39(2), pp: 489-503, 2019.
IX. Hansberg, Adriana: ‘Reviewing some results on fair domination in graphs’. Electronic Notes in Discrete Mathematics. Vol. 43, pp: 367-373, 2013. https://doi.org/10.1016/j.endm.2013.07.054.
X. Harary, Frank. Graph theory (on Demand Printing of 02787). CRC Press, 2018. 10.1201/9780429493768.
XI. Hatami, Hamed, and Pooya Hatami: ‘Perfect dominating sets in the Cartesian products of prime cycles’. The Electronic Journal of Combinatorics, Vol. 14(1), pp: N8, 2007. https://doi.org/10.37236/1009.
XII. Haynes, Teresa W., Stephen Hedetniemi, and Peter Slater: ‘Fundamentals of domination in graphs’. CRC press, 2013. 10.1201/9781482246582.
XIII. Henning, Michael A., Arti Pandey, and Vikash Tripathi: ‘Complexity and algorithms for semipaired domination in graphs’. Theory of Computing Systems, Vol. 64(7), pp: 1225-1241, 2020. 10.48550/arXiv.1904.00964.
XIV. Inza, Ernesto Parra, et al: ‘Algorithms for the global domination problem’. Computers & Operations Research. Vol. 173, pp: 106876, 2025. 10.1016/j.cor.2024.106876.
XV. Jafari Rad, Nader, et al: ‘Total domination in cubic Knödel graphs’. Communications in Combinatorics and Optimization. Vol. 6(2), pp: 221-230, 2021. 10.22049/cco.2020.26793.1143.
XVI. Joseph, J. Paulraj, and S. Arumugam: ‘Domination in subdivision graphs’. J. Indian Math. Soc. Vol. 62, pp: 274-282, 1996.
XVII. Kumar, J. Pavan, and P. Venkata Subba Reddy: ‘Algorithmic aspects of some variants of domination in graphs’. Analele Stiint. Ale Univ. Ovidius Constanta Ser. Mat. Vol. 28(3), pp: 153-170, 2020. 10.48550/arXiv.2002.00002.
XVIII. Kumar, J. Pavan, P. Venkata Subba Reddy, and S. Arumugam: ‘Algorithmic complexity of secure connected domination in graphs’. AKCE International Journal of Graphs and Combinatorics. Vol. 17(3), pp: 1010-1013, 2020. 10.48550/arXiv.2002.00002.
XIX. Lin, Ching-Chi, Cheng-Yu Hsieh, and Ta-Yu Mu: ‘A linear-time algorithm for weighted paired-domination on block graphs’. Journal of Combinatorial Optimization. Vol. 44(1), pp: 269-286, 2022. 10.1007/s10878-021-00767-5.
XX. Miranda, Aldwin T., and Rolito G. Eballe : ‘Domination defect for the join and corona of graphs’. Applied Mathematical Sciences. Vol. 15(12), pp: 615-623, 2021. 10.12988/ams.2021.914597.
XXI. Mu, Ta-Yu, and Ching-Chi Lin: ‘Optimal Algorithm for Paired-Domination in Distance-Hereditary Graphs’. arXiv. arXiv:2411.19476, 2024. 10.48550/arXiv.2411.19476.

XXII. Navamani. G and Reena Mercy M A: ‘Fair domination number of some graphs’. Preprint.
XXIII. Novak, Tina, and Janez Žerovnik: ‘A Linear Time Algorithm for Weighted k-Fair Domination Problem in Cactus Graphs’. Operations Research Forum Cham: Springer International Publishing. Vol. 3(3), 2022. 10.1007/s43069-022-00154-8.
XXIV. Pushpam, P. Roushini Leely, and G. Navamani: ‘Eternal m-security in certain classes of graphs’. J. Combin. Math. Combin. Vol. 92, pp: 25-38, 2015.
XXV. Rad, Nader Jafari: ‘Domination in circulant graphs’. Analele Stiintifice Ale Universitatii Ovidius Constanta, Seria Matematica. Vol. 17(1), pp: 169-176, 2019.
XXVI. Swaminathan, V., et al: ‘Outer complete fair domination in graphs’. Discrete Mathematics, Algorithms and Applications. Vol. 14(03), pp: 2150126, 2022. 10.1142/S1793830921501263.

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Journal Vol – 20 No – 12, December 2025

ENHANCING IOT SECURITY USING AN INTEGRATED BAGGED-LSTM AND GRADIENT BOOSTING ENSEMBLE TECHNIQUE

Authors:

Preeti, Rajender Nath

DOI NO:

https://doi.org/10.26782/jmcms.2025.12.00004

December 11, 2025

Abstract:

The Internet of Things (IoT) links billions of devices, boosts innovation, shares information effortlessly, and is reshaping various industries. The most common Distributed Denial of Service (DDoS) attacks target all layers in the IoT architecture. Even though easy to execute, these sorts of attacks may severely harm targeted systems and networks. This Novel hybrid model uses Bagged Long Short-Term Memory (LSTM) and Gradient Boosting (GB) to address large dimensionality, various feature dimensions, low classification accuracy, and high false positive rates in raw traffic data to improve IoT security against DDoS attacks. To reduce input information redundancy, the Boruta-Pearson Feature Selector (BPFS) gathers key features as model inputs. The Bagged-LSTM design minimises variance to detect anomalies, while Gradient Boosting improves prediction accuracy. The CIC-ISD2017 and CIC DDoS2019 datasets were used to test the hybrid model. Experimental results show that the recommended model outperforms current models with an accuracy of 99%. It is impossible to completely protect your server from these threats, but by using the techniques discussed here, these attacks can be prevented, and the server can focus on fulfilling legitimate requests rather than unauthentic ones.

Keywords:

DDoS attacks,Gradient Boosting (GB),IoT security,long short-term memory (LSTM),

References:

I. Ade, J. V. “Ensemble Learning Methods for DDoS Attack Detection in Cloud Environments: A Comprehensive Review.” International Journal of Science and Engineering Applications, vol. 13, no. 5, 2024, pp. 40–45.
II. Ali, T. E., Chong, Y. W., and S. Manickam. “Machine Learning Techniques to Detect a DDoS Attack in SDN: A Systematic Review.” Applied Sciences, vol. 13, no. 5, 2023, p. 3183.
III. Al-kahtani, M. S., Z. Mehmood, T. Sadad, I. Zada, G. Ali, and M. ElAffendi. “Intrusion Detection in the Internet of Things Using Fusion of GRU-LSTM Deep Learning Model.” Intelligent Automation & Soft Computing, vol. 37, no. 2, 2023.
IV. Alkahtani, H., and T. H. Aldhyani. “Botnet Attack Detection by Using CNN‐LSTM Model for Internet of Things Applications.” Security and Communication Networks, vol. 2021, no. 1, 2021, p. 3806459.
V. A. A. “Majority Vote-Based Ensemble Approach for Distributed Denial of Service Attack Detection in Cloud Computing.” Journal of Cyber Security and Mobility, 2022, pp. 265–278.
VI. Bårli, E. M., A. Yazidi, E. H. Viedma, and H. Haugerud. “DoS and DDoS Mitigation Using Variational Autoencoders.” Computer Networks, vol. 199, 2021, p. 108399.
VII. Cheng, J. R., et al. “DDoS Attack Detection via Multi-Scale Convolutional Neural Network.” Computers, Materials & Continua, vol. 62, no. 3, 2020, pp. 1317–1333.
VIII. DDoS Evaluation Dataset (CIC-DDoS2019). University of New Brunswick, Saint John, NB, Canada, 2019.
IX. Goud, K. S., and G. S. Rao. “Towards an Efficient DDoS Attack Detection in SDN: An Approach with CNN-GRU Fusion.” Proceedings of the Fourth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), IEEE, Jan. 2024, pp. 1–10.
X. Issa, A. S. A., and Z. Albayrak. “DDoS Attack Intrusion Detection System Based on Hybridization of CNN and LSTM.” Acta Polytechnica Hungarica, vol. 20, no. 2, 2023, pp. 1–19.
XI. Koay, A., A. Chen, I. Welch, and W. K. G. Seah. “A New Multi-Classifier System Using Entropy-Based Features in DDoS Attack Detection.” Proceedings of the International Conference on Information Networking (ICOIN), Chiang Mai, 2018, pp. 162–167.
XII. Maheshwari, A., B. Mehraj, M. S. Khan, and M. S. Idrisi. “An Optimized Weighted Voting-Based Ensemble Model for DDoS Attack Detection and Mitigation in SDN Environment.” Microprocessors and Microsystems, vol. 89, 2022, p. 104412.
XIII. Mall, R., K. Abhishek, S. Manimurugan, A. Shankar, and A. Kumar. “Stacking Ensemble Approach for DDoS Attack Detection in Software-Defined Cyber–Physical Systems.” Computers and Electrical Engineering, vol. 107, 2023, p. 108635.
XIV. Mittal, M., K. Kumar, and S. Behal. “Deep Learning Approaches for Detecting DDoS Attacks: A Systematic Review.” Soft Computing, vol. 27, no. 18, 2023, pp. 13039–13075.
XV. Muthukumar, S., and A. K. Ashfauk Ahamed. “A Novel Framework of DDoS Attack Detection in Network Using Hybrid Heuristic Deep Learning Approaches with Attention Mechanism.” Journal of High-Speed Networks, Preprint, 2024, pp. 1–27.
XVI. Okey, O. D., S. S. Maidin, P. Adasme, R. Lopes Rosa, M. Saadi, D. Carrillo Melgarejo, and D. Zegarra Rodríguez. “BoostedEnML: Efficient Technique for Detecting Cyberattacks in IoT Systems Using Boosted Ensemble Machine Learning.” Sensors, vol. 22, no. 19, 2022, p. 7409.

XVII. Priyadarshini, I., P. Mohanty, A. Alkhayyat, R. Sharma, and S. Kumar. “SDN and Application Layer DDoS Attacks Detection in IoT Devices by Attention‐Based Bi‐LSTM‐CNN.” Transactions on Emerging Telecommunications Technologies, vol. 34, no. 11, 2023.
XVIII. Singh, C., and A. K. Jain. “A Comprehensive Survey on DDoS Attacks Detection & Mitigation in SDN-IoT Network.” e-Prime – Advances in Electrical Engineering, Electronics and Energy, 2024, p. 100543.
XIX. Subrmanian, M., K. Shanmugavadivel, P. S. Nandhini, and R. Sowmya. “Evaluating the Performance of LSTM and GRU in Detection of Distributed Denial of Service Attacks Using CICDDoS2019 Dataset.” Proceedings of the 7th International Conference on Harmony Search, Soft Computing and Applications: ICHSA 2022, Springer Nature Singapore, Sept. 2022, pp. 395–406.
XX. Tiwari, R. S. “Model Evaluation.” Fundamentals and Methods of Machine and Deep Learning: Algorithms, Tools and Applications, 2022, pp. 33–100.
XXI. Umar, M. A., Z. Chen, K. Shuaib, and Y. Liu. “Effects of Feature Selection and Normalization on Network Intrusion Detection.” Authorea Preprints, 2024.
XXII. Wang, W., Y. Sheng, J. Wang, X. Zeng, X. Ye, Y. Huang, and M. Zhu. “HAST-IDS: Learning Hierarchical Spatial–Temporal Features Using Deep Neural Networks to Improve Intrusion Detection.” IEEE Access, vol. 6, 2018, pp. 1792–1806.
XXIII. Ye, J., X. Cheng, and J. Zhu. “A DDoS Attack Detection Method Based on SVM in Software-Defined Network.” Security and Communication Networks, vol. 4, July 2018, pp. 1–8.
XXIV. Yousuf, O., and R. N. Mir. “DDoS Attack Detection in Internet of Things Using Recurrent Neural Network.” Computers and Electrical Engineering, vol. 101, 2022, p. 108034.
XXV. Yu, P., and C. Li. “DDoS Attack Detection Method Based on Random Forest.” Applied Research in Computers, vol. 34, no. 10, 2017, pp. 3068–3072.
XXVI. Yulianto, A., P. Sukarno, and N. A. Suwastika. “Improving AdaBoost-Based Intrusion Detection System (IDS) Performance on CIC IDS 2017 Dataset.” Journal of Physics: Conference Series, vol. 1192, 2019, art. no. 012018.
XXVII. Zhang, Y., Y. Liu, X. Guo, Z. Liu, X. Zhang, and K. Liang. “A BiLSTM-Based DDoS Attack Detection Method for Edge Computing.” Energies, vol. 15, no. 21, 2022, p. 7882.

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Journal Vol – 20 No – 12, December 2025

SENSITIVITY AND AVAILABILITY ANALYSIS OF A GAS COMPRESSOR

Authors:

S. Z. Taj, Nabila Al Balushi, Yaqoob Al Rahbi, S M Rizwan, Mohamed Al Ismaili

DOI NO:

https://doi.org/10.26782/jmcms.2025.12.00005

December 11, 2025

Abstract:

In this paper, an availability analysis of a gas compressor extensively used in the oil and gas industry is presented. It aims to investigate possible causes of compressor unavailability and to obtain various reliability indices that reflect the overall system's operational capabilities. Results demonstrate the impact of operating conditions and various faults on compressor reliability, with sensitivity analysis revealing how variations in failure and repair rates affect the overall system’s reliability. The analysis utilizes real data from an oil and gas exploration and production company. The findings offer insights for enhancing compressor robustness and suggest future research directions to address the system’s reliability challenges, contributing to more resilient oil and gas infrastructure.

Keywords:

availability,Markov processes,sensitivity analysis,regenerative processes,reliability analysis.,

References:

I. A. G. Mathew, S. M. Rizwan, M. C. Majumder and K. P. Ramachandran, “Reliability modelling and analysis of a two-unit continuous casting plant,” Journal of the Franklin Institute, vol. 348, no. 7, pp. 1488-1505, 2011.
II. G. Taneja, V. Khurana and S. M. Rizwan, “Economic analysis of a reliability model for two programmable logic controller cold standby system with four types of failure,” Pure Applied Mathematika Sciences, vol. 63, no. 1-2, pp. 65-78, 2006.
III. K. B. Misra, Handbook of Performability Engineering, 1st ed. Springer: London, 2008.
IV. K. Sachdeva, G. Taneja and A. Manocha, “Sensitivity and economic analysis of an insured system with extended conditional warranty,” Reliability: Theory & Applications, vol. 17, no. 3(69), pp. 315-327, 2022.
V. N. Padmavathi, S. M. Rizwan, A. Pal and G. Taneja, “Probabilistic analysis of a desalination plant with major and minor failures and shutdown during winter season,” International Journal of Scientific and Statistical Computing, vol. 5, no. 1, pp. 15-23, 2014.
VI. Nabila Al Balushi, S. M. Rizwan, S. Z. Taj and Waleed Al Khairi, “Probabilistic analysis of power transformers in a power distribution company with six types of failures and inspection,” International Journal of Engineering Trends and Technology, vol. 72, no. 4, pp. 15-22, 2024.
VII. S. M. Rizwan, J. V. Thanikal, N. Padmavathi and H. Yazidi, “Reliability and availability analysis of an anaerobic batch reactor treating fruit and vegetable waste,” International Journal of Applied Engineering Research, vol. 10, no. 24, pp. 44075-44079, 2015.
VIII. S. Z. Taj, “Performance and cost benefit analysis of reliability models for a cable plant,” Ph.D. dissertation, Glasgow Caledonian University, Glasgow, Scotland, U. K., 2023.
IX. S. Z. Taj, S. M. Rizwan and K. Sachdeva, “Reliability and sensitivity analysis of a wastewater treatment plant operating with two blowers as a single system,” in Reliability Engineering for Industrial Processes: An Analytics Perspective. Cham: Springer Nature Switzerland, 2024, pp. 19-39.
X. Yaqoob Al Rahbi, S. M. Rizwan, B. M. Alkali, A. Cowell and G. Taneja, “Reliability analysis of a rodding anode plant in aluminum industry with multiple units failure and a single repairman,” International Journal of System Assurance Engineering and Management, vol. 10, pp. 97-109, 2019.

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Journal Vol – 20 No – 12, December 2025

ENHANCING THE FLEXURAL STRENGTH OF HIGH-PERFORMANCE CONCRETE BEAMS USING BASALT FIBER REINFORCED POLYMER

Authors:

Mohammad Hematibahar, Mosarof SK, Dahi S. Vanus, Makhmud Kharun

DOI NO:

https://doi.org/10.26782/jmcms.2025.12.00006

December 11, 2025

Abstract:

This study investigates the enhancement of flexural strength of high-performance concrete (HPC) beams using basalt fiber reinforced polymer (BFRP) embedded internally at different depths. Four types of beam samples were tested: BFRP placed directly on the bottom (CB0), and BFRP placed at 0.75 cm (CB0.75), 1.25 cm (CB1.25), and 2.25 cm (CB2.25) from the bottom. The concrete mixture, which resembled ultra-high-performance concrete, included binder, fine and coarse aggregates, glass powder, microsilica, and a plasticizer. The results showed that BFRP significantly improved the flexural strength and ductility compared to the control samples without BFRP reinforcement. Optimum performance was achieved by placing the BFRP at 1.25 cm from the bottom (CB1.25), which demonstrated an increase in flexural strength by 1088% (653 kN/m2) and displacement by 0.225 mm compared to the control samples, indicating a balanced distribution of strength and stress. Large distances (e.g., CB2.25) reduce the effectiveness, highlighting the importance of BFRP proximity to tension zones.

Keywords:

Basalt Fiber Reinforced Polymer,Flexural Strength,Ductility,High-Performance Concrete,Reinforced Concrete Beam,

References:

I. Adak D, Sarkar M, Mandal S (2017) Structural performance of nano-silica modified fly-ash based geopolymer concrete. Construction and Building Materials, 4: 430–439. 10.1016/j.conbuildmat.2016.12.111
II. Alaa Hasan H, Neaz Sheikh M, Hadi MNS (2019) Maximum axial load carrying capacity of Fibre Reinforced-Polymer (FRP) bar reinforced concrete columns under axial compression. Structures, 19: 227–233. 10.1016/j.istruc.2018.12.012
III. ASTM C109/C109M-20 (2020) Standard test method for compressive strength of hydraulic cement mortars. ASTM International. Available from: https://store.astm.org/c0109_c0109m-20.html.
IV. ASTM D7522/D7522M-09 (2009) Standard test method for pull-off strength for FRP bonded to concrete substrate. ASTM International. Available from: https://store.astm.org/d7522_d7522m-09.html
V. Barros JAO, Ferreira DR (2008) Assessing the efficiency of CFRP discrete confinement systems for concrete cylinders. Journal of Composites for Construction, 12(2): 134–148. 10.1061/(asce)1090-0268(2008)12:2(134)
VI. Berenguer R, Lima N, Pinto L, Monteiro E, Povoas Y, Oliveira R, Lima NBD (2021) Cement-based materials: Pozzolanic activities of mineral additions are compromised by the presence of reactive oxides. Journal of Building Engineering, 41: 102358. 10.1016/j.jobe.2021.102358
VII. Beskopylny AN, Hematibahar M, Momeni K, Stel’makh SA, Shcherban EM (2025) Performance optimization of masonry mortar with marble dust, spent coffee grounds and peanut shell ash. Civil Engineering Journal, 11(3): 963–987. 10.28991/CEJ-2025-011-03-09
VIII. Chen W, Pham TM, Sichembe H, Chen L, Hao H (2018) Experimental study of flexural behaviour of RC beams strengthened by longitudinal and U-shaped basalt FRP sheet. Composites Part B: Engineering, 134: 114–126. 10.1016/j.compositesb.2017.09.053
IX. Esparham A, Vatin NI, Kharun M, Hematibahar M (2023) A study of modern eco-friendly composite (geopolymer) based on blast furnace slag compared to conventional concrete using the life cycle assessment approach. Infrastructures, 8(3): 58. 10.3390/infrastructures8030058
X. Faleschini F, Zanini MA, Hofer L, Toska K, De Domenico D, Pellegrino C (2020) Confinement of reinforced concrete columns with glass fiber reinforced cementitious matrix jackets. Engineering Structures, 218: 110847. 10.1016/j.engstruct.2020.110847
XI. Guo YC, Xiao SH, Luo JW, Ye YY, Zeng JJ (2018) Confined concrete in fiber-reinforced polymer partially wrapped square columns: axial compressive behavior and strain distributions by a particle image velocimetry sensing technique. Sensors, 18: 4118. 10.3390/s18124118
XII. GB/T 17671-2021 (2021) Test method of cement mortar strength (ISO method). National Standard of the People’s Republic of China. Available from: https://www.codeofchina.com/standard/GBT17671-2021.html.
XIII. Hasanzadeh A, Vatin NI, Hematibahar M, Kharun M, Shooshpasha I (2022) Prediction of the mechanical properties of basalt fiber reinforced high-performance concrete using machine learning techniques. Materials, 15(20): 7165. 10.3390/ma15207165
XIV. Hematibahar M, Hasanzadeh A, Kharun M, Beskopylny AN, Stel’makh SA, Shcherban’ EM (2024) The Influence of three-dimensionally printed polymer materials as trusses and shell structures on the mechanical properties and load-bearing capacity of reinforced concrete. Materials, 17(14): 3413. 10.3390/ma17143413
XV. Hematibahar M, Hasanzadeh A, Kharun M, Milani A, Bakhtiyari A, Namba JY, Martins CH (2025) Influence of 3D-printed fiber geometry and content on the mechanical and fracture behavior of cemented sand. Asian Journal of Civil Engineering, 26(7): 3969–3992. 10.1007/s42107-025-01412-w
XVI. Hematibahar M, Fediuk R, Momeni K, Kharun M, Bhowmik A, Romanovski V (2025) Strategic roadmap for 3D‐printed reinforcement using fused deposition modeling: a state‐of‐the art review. Engineering Reports, 7(6): e70232. 10.1002/eng2.70232
XVII. Hematibahar M, Kharun M (2024) Prediction of concrete mixture design and compressive strength through data analysis and machine learning. Journal of Mechanics of Continua and Mathematical Sciences, 19(3): 1–21. 10.26782/jmcms.2024.03.00001
XVIII. Hematibahar M, Milani A, Fediuk R, Amran M, Bakhtiary A, Kharun M, Mousavi MS (2025) Optimization of 3D-printed reinforced concrete beams with four types of reinforced patterns and different distances. Engineering Failure Analysis Journal, 168(4): 109096. 10.1016/j.engfailanal.2024.109096
XIX. Hematibahar M, SK M, Vanus DS, M. Kharun M (2025) Comparative analysis of steel rebar and polyester fiber reinforced geopolymer concrete: mechanical properties and failure mechanisms. Journal of Mechanics of Continua and Mathematical Sciences, 20(10): 26–41. 10.26782/jmcms.2025.10.00003
XX. Hematibahar M, Vatin NI, Alaraza HAA, Khalilavi A, Kharun M (2022) The prediction of compressive strength and compressive stress-strain of basalt fiber reinforced high-performance concrete using classical programming and logistic map algorithm. Materials, 15(19): 6975. 10.3390/ma15196975
XXI. Kharun M, Alaraza HAA, Hematibahar M, Al Daini R, Manoshin A (2022) Experimental study on the effect of chopped basalt fiber on the mechanical properties of high-performance concrete. AIP Conference Proceedings, 2559: 050017. 10.1063/5.0099042
XXII. Kharun M, Koroteev D (2018) Effect of basalt fibres on the parameters of fracture mechanics of MB modifier based high-strength concrete. MATEC Web of Conferences, 251: 02003. 10.1051/matecconf/201825102003
XXIII. Saribiyik A, Abodan B, Balci MT (2021) Experimental study on shear strengthening of RC beams with basalt FRP strips using different wrapping methods. Engineering Science and Technology, an International Journal, 24(1): 192–204. 10.1016/j.jestch.2020.06.003

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Journal Vol – 20 No – 12, December 2025

FUEL CHARACTERIZATION AND COMPATIBILITY ASSESSMENT OF BERGAMOT PEEL OIL–DIESEL BLENDS FOR CI ENGINE APPLICATIONS

Authors:

K. Karthikeyan, M. Thambidurai

DOI NO:

https://doi.org/10.26782/jmcms.2025.12.00007

December 11, 2025

Abstract:

The present study explores the feasibility of utilizing Bergamot Peel Oil (BPO) as a renewable alternative to conventional diesel fuel in a compression ignition engine. Before engine testing, the physicochemical properties of BPO were tested. FTIR analysis confirmed the presence of oxygenated functional groups such as esters and carbonyl compounds, while CHNS analysis revealed a significant oxygen content, supporting improved combustion characteristics. GC-MS analysis identified major fatty acid methyl esters contributing to the high volatility and calorific value of BPO. Experimental investigations were conducted at varying blending ratios (BPO10, BPO20, BPO30, BPO40, and BPO50) without any engine modifications, evaluating performance, combustion, and emission parameters against baseline diesel. Results indicated an improvement in brake thermal efficiency (BTE) by 3–6% for BPO30–BPO40 blends, while brake specific fuel consumption (BSFC) reduced by up to 9% for higher blends, attributed to better energy content and oxygenation. In-cylinder analysis revealed increased peak pressure and rate of pressure rise (RoPR) with BPO addition, with BPO50 recording the highest peak due to superior oxidation and localized high temperatures. Ignition delay showed a slight increase for higher blends due to lower cetane number, though overall combustion duration remained comparable to diesel. On the emissions front, smoke opacity was significantly reduced by 14–17% for BPO50 owing to enhanced soot oxidation. Carbon monoxide (CO) and unburned hydrocarbons (HC) decreased by 8–12% across all blends, while NOx emissions exhibited a 6–10% rise for higher blends due to an increase in in-cylinder temperatures and oxygen availability. The findings suggest that BPO, with its oxygenated nature and favorable volatility, can partially replace diesel fuel without major engine modifications, particularly in blends up to BPO40, ensuring improved efficiency and cleaner combustion.

Keywords:

Bergamot peel oil,Fuel characterization,Diesel,Engine,Performance,

References:

I. Ashok, B., R. Thundil Karuppa Raj, et al. “Lemon Peel Oil – A Novel Renewable Alternative Energy Source for Diesel Engine.” Energy Conversion and Management 139 (2017): 110–121. 10.1016/j.enconman.2017.02.049
II. Chen, Hui et al. “The Effect of a Pine Oil/Diesel Blend on the Particulate Emission Characteristics of a Diesel Engine under a Pre-Injection Strategy with EGR.” Sustainable Energy & Fuels 7.15 (2023): 3644–3653. Web. 17 Aug. 2025. https://pubs.rsc.org/en/content/articlehtml/2023/se/d3se00581j
III. Chivu, Robert Mădălin et al. “Assessment of Engine Performance and Emissions with Eucalyptus Oil and Diesel Blends.” Energies 2024, Vol. 17, Page 3528 17.14 (2024): 3528. Web. 17 Aug. 2025. https://www.mdpi.com/1996-1073/17/14/3528/htm
IV. Doppalapudi, Arun Teja, Abul Kalam Azad, and Mohammad Masud Kamal Khan. “Exergy, Energy, Performance, and Combustion Analysis for Biodiesel NOx Reduction Using New Blends with Alcohol, Nanoparticle, and Essential Oil.” Journal of Cleaner Production 467 (2024): 142968. Web. 17 Aug. 2025. https://www.sciencedirect.com/science/article/pii/S095965262402417X
V. Duraisamy, Ganesh, Murugan Rangasamy, and Abul K. Hossain. “A Study on Flexible Dual-Fuel and Flexi Combustion Mode Engine to Mitigate NO, Soot and Unburned Emissions.” Fuel 322 (2022): 124276. Web. 19 Aug. 2025. https://www.sciencedirect.com/science/article/abs/pii/S0016236122011280
VI. Ellappan, Sivakumar, and Silambarasan Rajendran. “A Comparative Review of Performance and Emission Characteristics of Diesel Engine Using Eucalyptus-Biodiesel Blend.” Fuel 284 (2021): 118925. Web. 17 Aug. 2025. https://www.sciencedirect.com/science/article/abs/pii/S0016236120319219
VII. Gad, M. S. et al. “Combustion Characteristics of a Diesel Engine Running with Mandarin Essential Oil -Diesel Mixtures and Propanol Additive under Different Exhaust Gas Recirculation: Experimental Investigation and Numerical Simulation.” Case Studies in Thermal Engineering 26 (2021): 101100. Web. 17 Aug. 2025.
https://www.sciencedirect.com/science/article/pii/S2214157X2100263X
VIII. Karthickeyan, V. et al. “Simultaneous Reduction of NOx and Smoke Emissions with Low Viscous Biofuel in Low Heat Rejection Engine Using Selective Catalytic Reduction Technique.” Fuel 255 (2019): 115854. Print.
IX. Nanthagopal, K. et al. “Lemon Essential Oil – A Partial Substitute for Petroleum Diesel Fuel in Compression Ignition Engine.” International Journal of Renewable Energy Research 7.2 (2017): 467–475. Print.
X. Nguyen, Van Nhanh et al. “Engine Behavior Analysis on a Conventional Diesel Engine Combustion Mode Powered by Low Viscous Cedarwood Oil/Waste Cooking Oil Biodiesel/Diesel Fuel Mixture – An Experimental Study.” Process Safety and Environmental Protection 184 (2024): 560–578. Web. 17 Aug. 2025.
https://www.sciencedirect.com/science/article/abs/pii/S0957582024001253
XI. Patel, Ashok K., Basant Agrawal, and B. R. Rawal. “Assessment of Diesel Engine Performance and Emission Using Biodiesel Obtained from Eucalyptus Leaves.” European Journal of Sustainable Development Research 7.1 (2023): em0210. Web. 17 Aug. 2025.
https://www.ejosdr.com.https://doi.org/10.29333/ejosdr/12749
XII. Vallinayagam, R., S. Vedharaj, W. M. Yang, P. S. Lee, et al. “Combustion Performance and Emission Characteristics Study of Pine Oil in a Diesel Engine.” Energy 57 (2013): 344–351. Web. 10.1016/j.energy.2013.05.061
XIII. Vallinayagam, R., S. Vedharaj, W. M. Yang, P. S. Lee, et al. “Operation of Neat Pine Oil Biofuel in a Diesel Engine by Providing Ignition Assistance.” Energy Conversion and Management 88 (2014): 1032–1040. Web. 10.1016/j.enconman.2014.09.052
XIV. Y. Alex, et al. “Study of Engine Performance and Emission Characteristics of Diesel Engine Using Cerium Oxide Nanoparticles Blended Orange Peel Oil Methyl Ester.” Energy Nexus 8 (2022): 100150. Web. 17 Aug. 2025. https://www.sciencedirect.com/science/article/pii/S277242712200105X

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Journal Vol – 20 No – 12, December 2025

POSITIVE SOLUTIONS OF THE SYSTEM OF FIRST-ORDER DIFFERENTIAL EQUATIONS BY RUNGE-KUTTA METHOD FOURTH ORDER

Authors:

Ahmed. O. M. Abubaker

DOI NO:

https://doi.org/10.26782/jmcms.2025.12.00008

December 11, 2025

Abstract:

The Runge-Kutta method, and especially its fourth-order variant (RK4), is perhaps the most widely adopted method for solving ordinary differential equations (ODEs) and their systems. This paper deals specifically with the RK4 method to explain a system of first-order differential equations, and the ability of the method to converge and stabilize positive solutions. It is well known that standard RK4 is both accurate and stable, but to particularly maintain positivity of solutions, where the model represents physical quantities that must be non-negative, such as populations or concentrations, often requires extra techniques. This paper discusses theoretically the RK4 method and systems, their execution, the need for retention of positivity, and methodologies for retention of positivity. Several illustrative examples are included to demonstrate the application of the method and the difficulty of maintaining positivity as well.

Keywords:

Runge-Kutta method,fourth-order,systems of first-order differential equations,positive solutions,

References:

I. Amirul. Md. Islam et al. “Accurate Solutions of Initial Value Problems for Ordinary Differential Equations with the Fourth Order Runge Kutta Method.” Journal of Mathematics Research, 7 (2015): 41. 10.5539/jmr.v7n3p41.
II. Bazuaye, F. E. “A new 4th order hybrid Runge-Kutta methods for solving initial value problems (IVPs).” Pure and Applied Mathematics Journal 7.6 (2018): 78-87.
https://www.sciencepublishinggroup.com/article/10.11648/j.pamj.20180706.11
III. Blanes. S. et al. “Positivity-preserving methods for ordinary differential equations.” ESAIM: Mathematical Modelling and Numerical Analysis (2021). 10.1051/m2an/2022042.
IV. Botelho. F. et al. “On the Numerical Solution of First Order Ordinary Differential Equation Systems.” (2020): 498-511. 10.1201/9780429343315-29
V. Candan. T. et al. “Existence Results for Positive Periodic Solutions to First-Order Neutral Differential Equations.” Mediterranean Journal of Mathematics (2024) 10.1007/s00009-024-02635-y
VI. Dhage. B. et al. “Approximating positive solutions of PBVPs of nonlinear first order ordinary quadratic differential equations.” Appl. Math. Lett., 46 (2015): 133-142. 10.1016/j.aml.2015.02.023.
VII. Haiyan Wang et al. “Positive periodic solutions of singular systems of first order ordinary differential equations.” Appl. Math. Comput., 218 (2010): 1605-1610. 10.1016/j.amc.2011.06.038.
VIII. Ibrahim, Salisu. “Solution of First-Order Differential Equation Using Fourth-Order Runge-Kutta Approach and Adams Bashforth Methods.” International Journal on Recent and Innovation Trends in Computing and Communication 11.11 (2023).https://ijritcc.org/index.php/ijritcc/article/view…
IX. Jemal Demsie Abraha et al. “Comparison of Numerical Methods for System of First Order Ordinary Differential Equations.” Pure and Applied Mathematics Journal, 9 (2020): 32. 10.11648/j.pamj.20200902.11.
X. Jingwei Hu et al. “A Second-Order Asymptotic-Preserving and Positivity-Preserving Exponential Runge-Kutta Method for a Class of Stiff Kinetic Equations.” Multiscale Model. Simul., 17 (2018): 1123-1146. 10.1137/18m1226774.
XI. Martin Redmann et al. “Runge-Kutta methods for rough differential equations.” ArXiv, abs/2003.12626 (2020). 10.31390/josa.3.4.06.
XII. Shior. M., et al. “Solution of First Order Ordinary Differential Equations Using Fourth Order Runge-Kutta Method with MATLAB.” International Journal of Mathematics and Statistics Studies 12.1 (2024): 54-63. 10.37745/ijmss.13
XIII. Stephan Nüßlein et al. “Positivity-Preserving Adaptive Runge-Kutta Methods.” ArXiv, abs/2005.06268 (2020):155-179. 10.2140/camcos.2021.16.155.
XIV. Toparkus. H. et al. “First-order systems of linear partial differential equations: normal forms, canonical systems, transform methods.” Annales Universitatis Paedagogicae Cracoviensis. Studia Mathematica, 13 (2014): 109 – 132. 10.2478/aupcsm-2014-0009.
XV. Younis A. Sabawi et al. “A compact Fourth-Order Implicit-Explicit Runge-Kutta Type Method for Solving Diffusive Lotka–Volterra System.” Journal of Physics: Conference Series, 1999 (2021). 10.1088/1742-6596/1999/1/012103.
XVI. Zaileha Md Ali et al. “Lotka-Volterra Model of Wastewater Treatment in Bioreactor System using 4th Order Runge-Kutta Method.” Science Letters (2022). 10.24191/sl.v16i1.15284.

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Journal Vol – 20 No – 12, December 2025

MAXIMIZING PV POWER EFFICIENCY USING SEAGULL OPTIMIZATION TECHNIQUE WITH HIGH-GAIN VOLTAGE-MULTIPLIER QUADRATIC BOOST CONVERTER

Authors:

Omkar Tripathy, Maheswar Prasad Behera, Litu Kumar Samal, Nithya Palanivel, Jeyanthi Sivasubramanian, Bibhu Prasad Ganthia

DOI NO:

https://doi.org/10.26782/jmcms.2025.12.00009

December 11, 2025

Abstract:

Maximum Power Point Tracking (MPPT) techniques is efficient technique implemented high photovoltaic power generation in modern power system. This paper will present a MPPT strategy with a Seagull Optimization Algorithm (SOA)-based strategy and high-gain Voltage-Multiplier Coupled Quadric Boost Converter to implement a high-efficiency power extraction in PV systems. The SOA takes advantage of the hunting nature of seagulls so that the operating point of the PV array can be optimised and that the global maximum power point can be reached within seconds even in dynamic irradiance and temperature conditions. Combining this smart MPPT approach with a high-gain quadratic boost converter can achieve large voltage step-up on low PV input to decrease converter stress and increase energy harvesting. Through simulation, the proposed method proves to have a higher tracking speed, efficiency, and stability relative to existing ones (Perturb and Observe) (P&O) and Incremental Conductance (IncCond). The SOA-based MPPT is able to effectively prevent local maxima under partial shading conditions to generate optimal power extraction. The offered system demonstrates the high increase in the general energy efficiency, and it can be applied to both grid-connected and stand-alone PV applications. This combination of smart optimization and sophisticated converter design offers a potential remedy on the extraction of the best performance of a PV system under real operating conditions.

Keywords:

Photovoltaic Systems,MPPT,Seagull Optimization Technique,High-Efficiency Power Extraction,High-Gain Quadratic Boost Converter ,

References:

I. Al-Samawi, A. A., Atiyah, A. S., & Al-Jrew, A. H. (2025). Power Optimization of Partially Shaded PV System Using Interleaved Boost Converter-Based Fuzzy Logic Method. Eng, 6(8), 201. 10.3390/eng6080201
II. A.S. Valarmathy, M. Prabhakar. (2024). High gain interleaved boost-derived DC-DC converters – A review on structural variations, gain extension mechanisms and applications. e-Prime – Advances in Electrical Engineering, Electronics and Energy, Volume 8, 2024, 100618, ISSN 2772-6711. 10.1016/j.prime.2024.100618
III. B. P. Ganthia, R. Pradhan, S. Das and S. Ganthia, “Analytical study of MPPT based PV system using fuzzy logic controller,” 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), Chennai, India, 2017, pp. 3266-3269. 10.1109/ICECDS.2017.8390063.
IV. B. P. Ganthia, S. Mohanty, P. K. Rana and P. K. Sahu, “Compensation of voltage sag using DVR with PI controller,” 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), Chennai, India, 2016, pp. 2138-2142. 10.1109/ICEEOT.2016.7755068.
V. Chakole, N., Remamany, K. P., Mohan, G., Sasirekha, P., Kumar, N. M. G., Kumar, C. R., … & Ganthia, B. P. (2025). Optimal Energy Management for Hybrid PV-Wind-Battery Microgrids through Markov Decision Processes Technique. International Journal of Smart Grid-ijSmartGrid, 9(3), 127-145.
VI. Chalh, A., chaibi, R., Hammoumi, A.E. et al. (2022). A novel MPPT design based on the seagull optimization algοrithm for phοtovοltaic systems operating under partial shading. Sci Rep 12, 21804. 10.1038/s41598-022-26284-x
VII. Chikezie M. Emeghara, Satish M. Mahajan, Ali Arzani, Two-stage photovoltaic system with a high-gain fifth-order boost converter, e-Prime – Advances in Electrical Engineering, Electronics and Energy, Volume 13, 2025, 101038, ISSN 2772-6711. 10.1016/j.prime.2025.101038
VIII. 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.
IX. G.Veera Sankara Reddy, S. Vijayaraj, Optimizing high voltage gain interleaved boost converters for PV and wind systems using hybrid deep learning with bitterling fish and secretary bird algorithms, Franklin Open, Volume 11, 2025, 100291, ISSN 2773-1863, 10.1016/j.fraope.2025.100291
X. Himani Daulat, Krishna Chauhan & Tarun Varma. (2025). Empowering quadrature mirror filter bank architectures: dynamic-grey wolf optimization approach for elevated filter orders. Engineering Optimization 57:6, pages 1575-1603.
XI. K Rajaram & R Kannan. (2024). Design and implementation of adaptive pufferfish optimization algorithm based efficient MPPT and DC-DC boost converter for agriculture applications under partial shading conditions. Intelligent Decision Technologies 19:2, pages 1074-1090.
XII. Keerthi Sonam Soma, Balamurugan Ramadoss & Karuppiah Natarajan. (2024). Optimized Maximum Power Point Tracking using Giza Pyramid Construction Algorithm for Photovoltaic Systems. Recent Advances in Electrical & Electronic Engineering 17:10, pages 1023-1041.
XIII. Marlin S & Sundarsingh Jebaseelan. (2024). A comprehensive comparative study on intelligence based optimization algorithms used for maximum power tracking in grid-PV systems. Sustainable Computing: Informatics and Systems 41, pages 100946.
XIV. M. Mohanty, N. Nayak, B. P. Ganthia and M. K. Behera, “Power Smoothening of Photovoltaic System using Dynamic PSO with ESC under Partial Shading Condition,” 2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT), Bhubaneswar, India, 2023, pp. 675-680. 10.1109/APSIT58554.2023.10201763.
XV. Ming-Wei Li, Rui-Zhe Xu, Zhong-Yi Yang, Wei-Chiang Hong, Xiao-Gang An & Yi-Hsuan Yeh. (2024). Optimization approach of berth-quay crane-truck allocation by the tide, environment and uncertainty factors based on chaos quantum adaptive seagull optimization algorithm. Applied Soft Computing 152, pages 111197.
XVI. P. K. Sahu, A. Mohanty, B. P. Ganthia and A. K. Panda, “A multiphase interleaved boost converter for grid-connected PV system,” 2016 International Conference on Microelectronics, Computing and Communications (MicroCom), Durgapur, 2016, pp. 1-6. 10.1109/MicroCom.2016.7522539.
XVII. S. J. Rubavathy et al., “Smart Grid Based Multiagent System in Transmission Sector,” 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore, India, 2021, pp. 1-5. 10.1109/ICIRCA51532.2021.9544644.
XVIII. Shih-Cheng Horng & Shieh-Shing Lin. (2022). Incorporate seagull optimization into ordinal optimization for solving the constrained binary simulation optimization problems. The Journal of Supercomputing 79:5, pages 5730-5758.
XIX. Soham Chakraborty, Amritesh Kumar, A Multilevel Based High Gain Switched Inductor Quadratic DC-DC Boost Converter, IFAC-PapersOnLine, Volume 55, Issue 1, 2022, Pages 448-453, ISSN 2405-8963. 10.1016/j.ifacol.2022.04.074
XX. Sourya Kumar Nej, S. Sreejith, Indrojeet Chakraborty, Dual-Output Multistage Switched-Capacitor Quadratic Boost (MSC-QBC) DC-DC Converter for Solar Photovoltaic Application, IFAC-PapersOnLine, Volume 55, Issue 1, 2022, Pages 965-970, ISSN 2405-8963. 10.1016/j.ifacol.2022.04.159
XXI. Vimal Kumar Pathak, Swati Gangwar & Mithilesh K. Dikshit. (2025). A Comprehensive Survey on Seagull Optimization Algorithm and Its Variants. Archives of Computational Methods in Engineering 32:6, pages 3651-3685.
XXII. Yancang Li, Weizhi Li, Qiuyu Yuan, Huawang Shi & Muxuan Han. (2023). Multi-strategy Improved Seagull Optimization Algorithm. International Journal of Computational Intelligence Systems 16:1.

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Journal Vol – 20 No – 12, December 2025