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DESIGN AND DEVELOPMENT OF THE PIEZOACOUS-TIC RESPONSE OF ALUMINIUM NITRIDE FOR EN-HANCED ULTRASOUND DEVICES

Authors:

J. Manga, V.J.K. Kishor Sonti

DOI NO:

https://doi.org/10.26782/jmcms.2025.08.00011

August 16, 2025

Abstract:

Piezoelectric materials are integral to ultrasound probes and scanning devices in medical imaging and fingerprint recognition, as they can convert mechanical energy into electrical energy. This conversion enables the imaging of internal structures, facilitating medical diagnostics by highlighting deviations from normal organ dimensions. Traditionally, Lead Zirconate Titanate (PZT-4) has been used in handheld ultrasound probes, despite its low output and significant environmental hazards upon disposal. This paper presents Aluminium Nitride (AlN) as a safer, environmentally friendly, and thermally stable alternative. AlN is compatible with Complementary Metal Oxide Semiconductor (CMOS) technology, making it a viable option for sophisticated ultrasound probes that can be compact enough to be taken into the body. The simulations conducted through COMSOL Multiphysics at 200 kHz, this study demonstrate AlN's piezo acoustic properties, which are crucial for generating photoacoustic images in biomedical imaging. The presented simulation model enables monitoring of the material's acoustic behavior in response to specific electrical inputs and frequencies.

Keywords:

Acoustic,Aluminium nitride,Piezoelectric,COMSOL Multiphysics,Frequency,Ultrasound,

Refference:

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IV. A. Neprokin, C. Broadway, T. Myllylä, A. Bykov, and I. Meglinski, “Photoacoustic imaging in biomedicine and life sciences,” Life, Vol: 12, no: 4, p. 588, Apr. 2022. 10.3390/life12040588.
V. A. Safari, Q. Zhou, Y. Zeng, and J. D. Leber, “Advances in development of Pb-free piezoelectric materials for transducer applications,”Japanese Journal of Applied Physics, Vol: 62, no: SJ, p: SJ0801, Mar. 2023, 10.35848/1347-4065/acc812.
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VII. B. Herrera, P. Simeoni, G. Giribaldi, L. Colombo, and M. Rinaldi, “Scandium-Doped Aluminum Nitride PMUT Arrays for Wireless Ultrasonic Powering of Implantables,” IEEE Open Journal of Ultrasonics Ferroelectrics and Frequency Control, Vol: 2, pp: 250–260, Jan. 2022, 10.1109/ojuffc.2022.3221708.
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XII. E. Ledesma, I. Zamora, A. Uranga, F. Torres, and N. Barniol, “Enhancing AlN PMUTs’ Acoustic Responsivity within a MEMS-on-CMOS Process,”Sensors, Vol: 21, no. 24, p. 8447, Dec. 2021. 10.3390/s21248447.

XIII. F. Pop, B. Herrera, and M. Rinaldi, “Lithium Niobate Piezoelectric Micromachined Ultrasonic Transducers for high data-rate intrabody communication,” Nature Communications, Vol: 13, no: 1, Apr. 2022. 10.1038/s41467-022-29355-9.
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Journal Vol – 20 No – 8, August 2025

CONSENSUS CLUSTERING USING WEIGHT OF CLUSTERS AND CLUSTERINGS: A DUAL-WEIGHTED APPROACH

Authors:

Sunandana Banerjee, Deepti Bala Mishra

DOI NO:

https://doi.org/10.26782/jmcms.2025.08.00012

August 16, 2025

Abstract:

This paper presents a novel consensus clustering framework that integrates both cluster-level and clustering-level weighting strategies. Traditional consensus clustering methods either weight the clusters or the base clusterings, but often fail to optimally combine these two strategies. We propose a dual-weighting scheme where weights are assigned to clusters based on internal and external consistency, and to the base clusterings based on their agreement with the ensemble. By applying a combined weight, we ensure that both high-quality clusters and consistent clusterings contribute more to the final consensus. Experimental results on several benchmark datasets demonstrate the superiority of the proposed method over existing clustering ensemble techniques.

Keywords:

Consensus Clustering,Clustering Ensemble,Clustering Techniques,Dual-Weighted Approach,

Refference:

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Journal Vol – 20 No – 8, August 2025

ON THE ENCAPSULATION OF THE NEW XLINDLEY DISTRIBUTION

Authors:

B. Barrouk, H. Zeghdoudi

DOI NO:

https://doi.org/10.26782/jmcms.2025.09.00001

September 13, 2025

Abstract:

In this study, we introduce the Wrapping New XLindley Distribution (WNXLD) as an extension of the Wrapping Distribution (WD). We derive the probability density function, cumulative distribution function, characteristic function, trigonometric moments, and other relevant parameters for WNXLD Additionally, parameter estimation is performed using the maximum likelihood estimation method.

Keywords:

Circular statistics,Compressive Strength,GGBS,Metakaoline,New Xlindley,Regression Analysis,Split Tensile Strength,Wrapping,Trigonometric moments.,

Refference:

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Journal Vol – 20 No – 9, September 2025

DEEP LEARNING-BASED HARMFUL INSECTS CLASSIFICATION USING NOVEL BIOACOUSTIC FEATURES

Authors:

Ananjan Maiti, Dipankar Basu, Indranil Sarkar, Jyoti Sekhar Banerjee, Atri Adhikari, Panagiotis Sarigiannidis

DOI NO:

https://doi.org/10.26782/jmcms.2025.09.00002

September 13, 2025

Abstract:

This study presents a novel framework for the early identification of invasive insect species using advanced bioacoustic analysis integrated with deep learning algorithms. In this paper, we develop a new method that uses spectral subtraction with wingbeat frequency modulation to identify invasive insects with high acoustic accuracy. We analyze acoustic signatures using a robust pipeline that involves adaptive noise cancellation, spectral subtraction with wingbeat frequency modulation-based features, and a deep learning model. The system shows great potential in classification, with an average 96% to 98% accuracy in a data set of 17 species of insects, six of which are invasive. Significantly, our proposed solution does not disrupt the natural environment by using noninvasive surveillance, providing real-time identification. In addition, the work presents several methodological enhancements, for example, the hybrid noise reduction approach that leads to a signal-to-noise ratio gain of 9.64 dB and the custom deep learning model that was fine-tuned through systematic hyperparameter optimization. These advances greatly surpass current classification methodologies and have broad potential for applications in agriculture, defense, ecological studies, and invasive species control. Our results provide a solid basis for using acoustic ecology with machine learning for entomological studies and pest control.

Keywords:

Invasive Insects,Acoustic features,Classification,Deep Learning,Bioacoustics Pest Management,Mel Frequency Cepstral Coefficients (MFCC),

Refference:

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VI. Cooperband, M.F., Wickham, J.D., Warden, M.L.: Factors guiding the orientation of nymphal spotted lanternfly, Lycorma delicatula. Insects 14(3), 279-279 (2023).
VII. Dasgupta, S., Das, T., Saha, D., Dutta, C., Maiti, A.: Classification of Indian Herbal Leaf with Random Forest Classifier. FOSET – 12th Inter-University Engineering, Science & Technology Academic Meet, Guru Nanak Institute of Technology, Kolkata, India. 10.36375/prepare_u.foset.a302
VIII. Debrah, S.K., Anankware, P.J., Asomah, S., Ofori, D.O.: Challenges associated with Rhynchophorus phoenicis Fabricius (Coleoptera: Curculionidae) farming. Journal of Insects as Food and Feed 9(1), 15-24 (2023).
IX. Demertzis, K., Iliadis, L.S., Anezakis, V.D.: Extreme deep learning in biosecurity: the case of machine hearing for marine species identification. Journal of Information and Telecommunication 2(4), 492-510 (2018).
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XIII. Hagenbucher, S., Eisenring, M., Meissle, M., Rathore, K.S., Romeis, J.: Constitutive and induced insect resistance in RNAi-mediated ultra-low gossypol cottonseed cotton. BMC Plant Biology 19(1) (2019).
XIV. Holmes, C.J.: Dehydration alters transcript levels in the mosquito midgut, likely facilitating rapid rehydration following a bloodmeal. Insects 14(3), 274-274 (2023).
XV. Huang, J.: Phytochrome b mediates dim-light-reduced insect resistance by promoting the ethylene pathway in rice. Plant Physiology 191(2), 1272-1287 (2022).
XVI. Karar, M.E., Alsunaydi, F., Albusaymi, S., Alotaibi, S.: A new mobile application of agricultural pests’ recognition using deep learning in cloud computing system. Alexandria Engineering Journal 60(5), 4423-4432 (2021).
XVII. Kawabata, A., Myers, R., Miyahira, M., Yamauchi, N., Nakamoto, S.T.: Field efficacy of spine-toram for the management of Hypothenemus hampei. Insects 14(3), 287-287 (2023).
XVIII. Khalighifar, A.: Application of deep learning to automated species identification systems. Biodiversity Information Science and Standards 4, 59007 (2020).
XIX. Kim, J.H., Park, S.J., Lee, K.B.: Deep learning-based insect sound classification system for early pest detection. Sensors 21(4), 1480 (2021).
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XXI. Li, L.: Succession patterns of sarcosaprophagous insects on pig carcasses in different months in Yangtze River Delta, China. Forensic Science International 342, 111518 (2023).
XXII. Li, Y., Wang, H., Dang, L.M., Sadeghi-Niaraki, A., Moon, H.: Crop pest recognition in natural scenes using convolutional neural networks. Computers and Electronics in Agriculture 169, 105174 (2020).
XXIII. Lima, M.C.F., Leandro, M.E.D.A., Valero, C., Pereira, L.C., Bazzo, C.O.G.: Automatic detection and monitoring of insect pests – a review. Sensors 20(21), 6265 (2020).
XXIV. Liu, T., Chen, W., Wu, W., Sun, C., Guo, W., Zhu, X.: Detection of invasive insect species using deep learning and bioacoustic analysis. Ecological Informatics 58, 101117 (2020).
XXV. Maiti, A., Dutta, C., Banerjee, J.S., Sarigiannidis, P.: AI for Infant Well-being: Advanced Techniques in Cry Interpretation and Monitoring. Journal of Mechanics of Continua and Mathematical Sciences 19 (2024).
XXVI. Nguyen, T., Nguyen, H., Ung, H., Ung, H., Nguyen, B.: Deep-wide learning assistance for insect pest classification. arXiv preprint arXiv:2409.10445 (2024).
XXVII. Noda, J.J., Travieso-Gonzalez, C.M., Sanchez-Rodriguez, D., Alonso-Hernandez, J.B.: Acoustic classification of singing insects based on MFCC/LFCC fusion. Applied Sciences 9(19), 4097 (2019).
XXVIII. Phung, Q.V., Ahmad, I., Habibi, D., Hinckley, S.: Automated insect detection using acoustic features based on sound generated from insect activities. Acoustics Australia 45(2), 445-451 (2017).
XXIX. Priyadarsini, J., Karthick, B. N., Karthick, K., Karthikeyan, B., & Mohan, S. (2019, March). Detection of PH value and Pest control for eco-friendly agriculture. In 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS) (pp. 801-804). IEEE.
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XXXI. Shan, S.: A female-biased odorant receptor tuned to the lepidopteran sex pheromone in parasitoid Microplitis mediator guiding habitat of host insects. Journal of Advanced Research 43, 1-12 (2023).
XXXII. Thomas, J., Gorb, S.N., Buscher, T.H.: Influence of surface free energy of the substrate and flooded water on the attachment performance of stick insects. Journal of Experimental Biology 226(3) (2023).
XXXIII. Ullah, N., Khan, J.A., Alharbi, L.A., Raza, A., Khan, W., Ahmad, I.: An efficient approach for crops pests recognition and classification based on novel DeepPestNet deep learning model. IEEE Access 10, 73019–73032 (2022).
XXXIV. Zhang, W., Zhao, X., Li, Y.: A comprehensive study of deep learning methods for insect pest detection. Computers and Electronics in Agriculture 182, 106055 (2021).

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Journal Vol – 20 No – 9, September 2025

HYBRID NOMA-BASED RESOURCE ALLOCATION FOR MULTI-ACCESS EDGE COMPUTING IN HETNETS

Authors:

Hind S. Ghazi, Ali Al-Shuwaili , Adham R. Azeez , Maciej Krasicki

DOI NO:

https://doi.org/10.26782/jmcms.2025.09.00003

September 13, 2025

Abstract:

This paper investigates the optimal offloading policy in heterogeneous networks where radio resources are provided, in both uplink and downlink, via two distinct types of BSs, namely Macro-cell Base Stations (MBSs) and Small-cell Base Stations (SBSs), in a multi-tier Multi-Access Edge Computing (MEC) -assisted scenario. Since the feasibility of the offloading problem is a function of radio connectivity in uplink and downlink, we propose to assign radio links using the flexible hybrid NOMA scheme that leverages both the limited interference of OMA as well as the faster data rates of NOMA. To this end, we formulate an optimization problem aiming to optimize the allocation of both radio and computation resources while minimizing the offloading energy across all users. The formulated problem is then tackled by means of decomposition and relation. The numerical results show that the Hybrid NOMA scheme balances subchannel allocation and power control to maintain high spectral efficiency and low offloading latency without the decoding overhead of Full NOMA (high interference) or the inefficiency of No NOMA (low subchannel reuse).

Keywords:

Multi-Access Edge Computing (MEC),Non-Orthogonal Multiple Access (NOMA),fifth-generation (5G),Heterogeneous Networks (HetNets),Hybrid NOMA,

Refference:

I. Ding, J., Han, L., Li, J., & Zhang, D. (2023). Resource allocation strategy for blockchain-enabled NOMA-based MEC networks. Journal of Cloud Computing, 12(1), 142.‏
II. Song, Z., Liu, Y., & Sun, X. (2020). Joint task offloading and resource allocation for NOMA-enabled multi-access mobile edge computing. IEEE Transactions on Communications, 69(3), 1548-1564.‏
III. Pham, H. G. T., Pham, Q. V., Pham, A. T., & Nguyen, C. T. (2020). Joint task offloading and resource management in NOMA-based MEC systems: A swarm intelligence approach. IEEE Access, 8, 190463-190474.‏
IV. Lin, L., Liu, J., Zhang, D., & Xie, Y. (2019). Joint offloading decision and resource allocation for multiuser NOMA-MEC systems. IEEE Access, 7, 181100-181116.‏
V. Wu, Y., Ni, K., Zhang, C., Qian, L. P., & Tsang, D. H. (2018). NOMA-assisted multi-access mobile edge computing: A joint optimization of computation offloading and time allocation. IEEE Transactions on Vehicular Technology, 67(12), 12244-12258.‏
VI. Liu, Y., Peng, M., Shou, G., Chen, Y., & Chen, S. (2020). Toward edge intelligence: Multiaccess edge computing for 5G and Internet of Things. IEEE Internet of Things Journal, 7(8), 6722-6747.‏
VII. Zheng, G., Xu, C., Long, H., & Zhao, X. (2021, March). MEC in NOMA-HetNets: A joint task offloading and resource allocation approach. In 2021, IEEE Wireless Communications and Networking Conference (WCNC) (pp. 1-6). IEEE.‏
VIII. Zhao, J., Liu, Y., Chai, K. K., Nallanathan, A., Chen, Y., & Han, Z. (2017). Spectrum allocation and power control for non-orthogonal multiple access in HetNets. IEEE Transactions on Wireless Communications, 16(9), 5825-5837.‏
IX. Fang, F., Cheng, J., & Ding, Z. (2018). Joint energy efficient subchannel and power optimization for a downlink NOMA heterogeneous network. IEEE Transactions on Vehicular Technology, 68(2), 1351-1364.‏
X. Fang, F., Wang, K., Ding, Z., & Leung, V. (2020). Energy-efficient resource allocation for NOMA-enabled MEC networks with imperfect CSI. arXiv preprint arXiv:2009.06234.‏
XI. Song, Z., Liu, Y., & Sun, X. (2018). Joint radio and computational resource allocation for NOMA-based mobile edge computing in heterogeneous networks. IEEE Communications Letters, 22(12), 2559-2562.
XII. Wu, L., Sun, P., Chen, H., Zuo, Y., Zhou, Y., & Yang, Y. (2023). NOMA-Enabled Multiuser Offloading in Multicell Edge Computing Networks: A Coalition Game-Based Approach. IEEE Transactions on Network Science and Engineering.
XIII. Xu, C., Zheng, G., & Zhao, X. (2020). Energy-minimization task offloading and resource allocation for mobile edge computing in NOMA heterogeneous networks. IEEE Transactions on Vehicular Technology, 69(12), 16001-16016.
XIV. Li, K., Xu, J., Xing, H., Chen, Y., & Huang, J. (2022). Dynamic Task Offloading for NOMA‐Enabled Mobile Edge Computing with Heterogeneous Networks. Security and Communication Networks, 2022(1), 7258236.
XV. Du, J., Sun, Y., Zhang, N., Xiong, Z., Sun, A., & Ding, Z. (2022). Cost-effective task offloading in NOMA-enabled vehicular mobile edge computing. IEEE Systems Journal, 17(1), 928-939.
XVI. Saleem, M., Jangsher, S., & Khaliq, H. (2025), “Cooperative Offloading Multi-Access Edge Computing (Comec) for Cell-Edge users in Heterogeneous Dense Networks “, Available at SSRN 4968395.
XVII. M. Satyanarayanan, P. Bahl, R. Caceres, and N. Davies (2009, Oct.), “The case for VM-based cloudlets in mobile computing,” IEEE Pervasive Comput., vol. 8, no. 4, pp. 14–23.
XVIII. A. Al-Shuwaili, M. Dh Hassib, T. M. Jamel (2022, Aug.), “Ball-Based Adaptive Channel Estimation Scheme Using RLS Family-Types Algorithms,” in International Journal of Computing and Digital Systems, vol. 12, no. 1, pp. 469-477.
XIX. Agarwal, Bharat, et al. (2022) “A comprehensive survey on radio resource management in 5G HetNets: Current solutions, future trends and open issues.” IEEE Communications Surveys & Tutorials 24.4: 2495-2534.
XX. T. M Jamel, A. Al-Shuwaili, and B. M Mansoor (2020), “Novelty Study of the Window Length Effects on the Adaptive Beam-forming Based-FEDS Approach,” International Journal of Computing and Digital Systems, vol. 9, no. 6, pp. 1221–1227.
XXI. Van Truong, Truong, and Anand Nayyar (2023), “System performance and optimization in NOMA mobile edge computing surveillance network using GA and PSO.” Computer Networks 223: 109575
XXII. Van Truong, Truong, and Anand Nayyar (2024), “Enhancing security offloading performance in NOMA heterogeneous MEC networks using access point selection and meta-heuristic algorithm.” Computer Networks 254: 110802.
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XXV. Sinop, Hayriye, Mehmet Berke Beyaz, and Asuman Savaşçıhabeş (2024), “Performance Analysis of NOMA Systems over Rayleigh Fading Channels in 5G Communication.” Orclever Proceedings of Research and Development 5.1: 238-252.

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Journal Vol – 20 No – 9, September 2025

SEISMIC RESISTANCE OF REINFORCED CONCRETE COLUMNS UNDER COMBINED SPECIAL ACTIONS

Authors:

Ashot G. Tamrazyan, Tatiana A. Matsevich, Sergei Y. Savin, Maksim V. Kudryavtsev

DOI NO:

https://doi.org/10.26782/jmcms.2025.09.00004

September 13, 2025

Abstract:

The study addresses the seismic resistance of reinforced concrete columns that have been damaged by the corrosion of their steel reinforcement and the concrete itself, and that have experienced the effects of high temperatures resulting from fire. Reinforced concrete framed buildings are common in earthquake-prone regions. Such structures have a lifespan of several decades. Consequently, corrosion of the concrete and steel reinforcement is commonplace throughout their service life, particularly in coastal regions. This corrosion decreases the structures' load-bearing capacity and seismic resistance. High temperatures resulting from fires are something that such reinforced concrete framed structures are often exposed to. This study proposes and experimentally validates a design model for evaluating the load-bearing capacity and seismic resistance of such columns in buildings that have been damaged by corrosion and subsequently exposed to high temperatures. It provides a basis for assessing the safety and risk of reinforced concrete framed structures subjected to combined accidental actions, such as corrosion, high temperatures from fires, and seismic impacts.

Keywords:

Reinforced Concrete,Column,Seismic Resistance,Combined Special Actions,Corrosion,Fires,

Refference:

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III. Avetisyan L.A., Chapidze O.D., : ‘Estimation of Reinforced Concrete Seismic Resistance Bearing Systems Exposed to Fire’. IOP Conf Ser Mater Sci Eng. Vol. 456, 2018. 10.1088/1757-899X/456/1/012035
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VI. Chandra S., Sharma U.K., : ‘Fire Performance of Aged and Corroded Reinforced Concrete Columns’. Journal of Building Engineering. Vol. 95, 2025. 10.1016/j.jobe.2024.110176.

VII. Hayati N., Hamid A., : ‘Seismic Performance of Interior Beam-Column Joint With Fuse-Bar Designed Using EC8 Under In-Plane Lateral Cyclic Loading’. In Proceedings of the International Conference on Disaster Management and Civil Engineering (ICDMCE’15) Oct. 1-3, 2015 Phuket (Thailand); Universal Researchers, October 1 2015.
VIII. Huang T., Wan C., Liu T., Miao C., : ‘Degradation law of bond strength of reinforced concrete with corrosion-induced cracks and machine learning prediction model’. Journal of Building Engineering. Vol. 98, no, 111022, 2024. 10.1016/j.jobe.2024.111022
IX. Kodur V.K.R., Dwaikat M.M., : ‘Effect of Fire Induced Spalling on the Response of Reinforced Concrete Beams’. Int J Concr Struct Mater. Vol. 2, pp. 71–81, 2008. 10.4334/IJCSM.2008.2.2.071.
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XI. Kudryavtsev M.V., : ‘Strength and deformability of concrete under low-cycle loading’. Innovation and Investment. Vol. 5, pp. 195-201, 2022.
XII. Levtchitch V., Kvasha V., Boussalis H., Chassiakos A., Kosmatopoulos E., ; ‘Seismic Performance Capacities of Old Concrete’. In Proceedings of the 13 th World Conference on Earthquake Engineering, Vancouver, B.C., Canada; 2004; pp. 1–15.
XIII. Li X., Miao J., Zhou Y., Shi P., Wang W., : ‘Experimental Study on Seismic Performance of Corroded RC Frame Joints after Fire’. Jianzhu Jiegou Xuebao/Journal of Building Structures. Vol. 39, pp. 84–92, 2018.
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XV. Okolnikova G.E., Ershov M.E., Malafeev A.S., : ‘The effect of defects and damages in reinforced concrete load-bearing structures on further operating conditions’. Journal of Mechanics of Continua and Mathematical Sciences. Vol. 19, no. 7, pp. 1-16, 2024. 10.26782/jmcms.2024.07.00001
XVI. Park R., : ‘A Summary of Results of Simulated Seismic Load Tests on Reinforced Concrete Beam-Column Joints, Beams and Columns with Substandard Reinforcing Details’. Journal of Earthquake Engineering. Vol. 6, pp. 147-174, 2002. 10.1080/13632460209350413.
XVII. Popov D.S., : ‘Experimental studies of dynamic properties of corrosion-damaged compressed reinforced concrete elements’. Building and reconstruction. Vol. 100, pp. 55-64, 2022. 10.33979/2073-7416-2022-100-2-55-64
XVIII. Saad M., Abo-El-Enein S.A., Hanna G.B., Kotkata M.F., : ‘Effect of Temperature on Physical and Mechanical Properties of Concrete Containing Silica Fume’. Cem Concr Res. Vol. 26, pp. 669–675, 1996.
XIX. Savin S.Yu., Kolchunov V.I., Fedorova N.V., : ‘Ductility of Eccentrically Compressed Elements of RC Frame Damaged by Corrosion under Accidental Impacts’. Reinforced concrete structures. Vol. 1(1), pp. 46-54, 2023. https://www.g-b-k.ru/jour/article/view/8.
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XXIII. Tamrazyan A., Alekseytsev A., : ‘Assessment of mechanical safety of cost-optimized reinforced concrete structures’. IOP Conf. Ser.: Mater. Sci. Eng. Vol. 1030, 012035. 2021, 10.1088/1757-899X/1030/1/012035
XXIV. Tamrazyan A., Alekseytsev A., : ‘The efficiency of varying parameters when optimizing reinforced concrete structures’. E3S Web Conf. Volume 263. 2021. 10.1051/e3sconf/202126302001
XXV. Tamrazyan A.G., Alekseytsev A.V., : ‘Evolutionary Optimization of Normally Operated Reinforced Concrete Beam Structures Taking into Account the Risk of Accidents’. Promyshlennoe i grazhdanskoe stroitel’stvo [Industrial and Civil Engineering]. No. 9, pp. 45-50. 2019. 10.33622/0869-7019.2019.09.45-50
XXVI. 22 Tamrazyan A.G., Matseevich T.A., : ‘Estimation of Damage Degree of Buildings in Earthquakes by Statistical Modeling Method’. Reinforced concrete structures. Vol. 7(3), pp. 3-11, 2024. 10.22227/2949-1622.2024.3.3-11
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XXVIII. 24 Tamrazyan A.G., Baryak D.S., : ‘Bonding of Corrosion-Damaged Reinforced Concrete Elements in Case of Fire Impact’. Building and Reconstruction. Vol. 1, pp. 40–47, 2025. 10.33979/2073-7416-2025-117-1-40-47
XXIX. 25 Tamrazyan A.G., Kudryavtsev M.V., : ‘Effect of Reinforcement Corrosion Damage on Dynamic Behavior of Compressed Reinforced Concrete Structures’. Building and Reconstruction. Vol. 2, pp. 81–93, 2025. 10.33979/2073-7416-2025-118-2-81-93.
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Journal Vol – 20 No – 9, September 2025

COMPREHENSIVE SECURITY FRAMEWORKS FOR SAFEGUARDING IOT DEVICES IN SMART CITIES: ADDRESSING AUTHENTICATION, ENCRYPTION, ACCESS CONTROL, AND ANOMALY DETECTION

Authors:

Rajina R. Mohamed, Abdilahi Liban, Waheeb Abu-ulbeh, Helmi Murad Ebrahim, Yousef A. Baker El-Ebiary

DOI NO:

https://doi.org/10.26782/jmcms.2025.09.00005

September 13, 2025

Abstract:

The proliferation of Internet of Things (IoT) devices in smart cities has revolutionized urban living, offering unparalleled convenience, efficiency, and connectivity. By interconnecting various aspects of city infrastructure, from transportation and utilities to public services and governance, IoT technologies promise to optimize resource allocation, enhance service delivery, and improve the overall quality of life for citizens. However, the integration of IoT in smart cities introduces significant security challenges, including vulnerabilities, privacy concerns, interoperability issues, and threats to critical infrastructure. This paper proposes a comprehensive security framework that addresses these challenges through a layered approach incorporating authentication, encryption, access control, and anomaly detection mechanisms. The framework is evaluated against existing solutions and benchmarked not only against statistical baselines but also against optimization-driven cost models to provide a fair comparative analysis. Furthermore, scalability and real-time feasibility are assessed under realistic data ingestion rates, and sensitivity analysis is applied to quantify the relative influence of security parameters. The findings indicate that the proposed framework significantly improves the resilience, scalability, and interpretability of IoT security mechanisms, thereby enabling smarter and safer urban ecosystems.

Keywords:

Smart cities,Security frameworks,IoT security,Cybersecurity,Data protection,Risk management,

Refference:

I. Alaba, F. A., Othman, M., & Hashem, I. A. T. (2017). Internet of Things security: A survey. Journal of Network and Computer Applications, 88, 10–28. 10.1016/j.jnca.2017.04.002
II. Altrad et al., “Amazon in Business to Customers and Overcoming Obstacles,” 2021 2nd International Conference on Smart Computing and Electronic Enterprise (ICSCEE), 2021, pp. 175-179. 10.1109/ICSCEE50312.2021.9498129.
III. Al‐Turjman, F., Zahmatkesh, H., & Shahroze, R. (2022). An overview of security and privacy in smart cities’ IoT communications. Transactions on Emerging Telecommunications Technologies, 33(3), e3677. 10.1002/ett.3677
IV. Alzoubi, S., & Zoubi, M. (2023). Exploring the relationship between robot employees’ perceptions and robot-induced unemployment under COVID-19 in the Jordanian hospitality sector. International Journal of Data and Network Science, 7(4), 1563-1572. 10.5267/j.ijdns.2023.8.007.

V. Alzoubi, Sharaf et al. An extensive analysis of several methods for classifying unbalanced datasets. Journal of Autonomous Intelligence, [S.l.], v. 7, n. 3, jan. 2024. ISSN 2630-5046. Date accessed: 25 jan. 2024. 10.32629/jai.v7i3.966.
VI. Angrishi, R., Singh, R., & Patel, D. (2019). A Comprehensive Review on Security Frameworks in Internet of Things (IoT) Networks. 10.1109/ICIT.2019.00009.
VII. Artika Farhana, Nimmati Satheesh, Ramya M, Janjhyam Venkata Naga Ramesh and Yousef A. Baker El-Ebiary, “Efficient Deep Reinforcement Learning for Smart Buildings: Integrating Energy Storage Systems Through Advanced Energy Management Strategies” International Journal of Advanced Computer Science and Applications(IJACSA), 14(12), 2023. 10.14569/IJACSA.2023.0141257.
VIII. Atul Tiwari, Shaikh Abdul Hannan, Rajasekhar Pinnamaneni, Abdul Rahman Mohammed Al-Ansari, Yousef A.Baker El-Ebiary, S. Prema, R. Manikandan and Jorge L. Javier Vidalón, “Optimized Ensemble of Hybrid RNN-GAN Models for Accurate and Automated Lung Tumour Detection from CT Images” International Journal of Advanced Computer Science and Applications (IJACSA), 14(7), 2023. 10.14569/IJACSA.2023.0140769.
IX. B. Pawar, C Priya, V. V. Jaya Rama Krishnaiah, V. Antony Asir Daniel, Yousef A. Baker El-Ebiary and Ahmed I. Taloba, “Multi-Scale Deep Learning-based Recurrent Neural Network for Improved Medical Image Restoration and Enhancement” International Journal of Advanced Computer Science and Applications(IJACSA), 14(10), 2023. 10.14569/IJACSA.2023.0141088.
X. Bellini, P., Nesi, P., & Pantaleo, G. (2022). IoT-enabled smart cities: A review of concepts, frameworks and key technologies. Applied Sciences, 12(3), 1607. 10.3390/app12031607
XI. Calderoni, L., Magnani, A., & Maio, D. (2019). IoT Manager: An open-source IoT framework for smart cities. Journal of Systems Architecture, 98, 413-423. 10.1016/j.sysarc.2019.04.003
XII. Chaudhry, S. A., & Naha, R. K. (2020). Security and privacy issues in Internet of Things (IoT) devices: A comprehensive review. Journal of Network and Computer Applications, 150, 102479. 10.1016/j.jnca.2019.102479
XIII. Deeba K, O. Rama Devi, Mohammed Saleh Al Ansari, Bhargavi Peddi Reddy, Manohara H T, Yousef A. Baker El-Ebiary and Manikandan Rengarajan, “Optimizing Crop Yield Prediction in Precision Agriculture with Hyperspectral Imaging-Unmixing and Deep Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 14(12), 2023. 10.14569/IJACSA.2023.0141261.
XIV. F. H. Zawaideh, W. Abu-Ulbeh, S. A. Mjlae, Y. A. B. El-Ebiary, Y. Al Moaiad and S. Das, “Blockchain Solution For SMEs Cybersecurity Threats In E-Commerce,” 2023 International Conference on Computer Science and Emerging Technologies (CSET), Bangalore, India, 2023, pp. 1-7. 10.1109/CSET58993.2023.10346628.
XV. F. H. Zawaideh, W. Abu-ulbeh, Y. I. Majdalawi, M. D. Zakaria, J. A. Jusoh and S. Das, “E-Commerce Supply Chains with Considerations of Cyber-Security,” 2023 International Conference on Computer Science and Emerging Technologies (CSET), Bangalore, India, 2023, pp. 1-8. 10.1109/CSET58993.2023.10346738.
XVI. F. R. Wahsheh, Y. A. Moaiad, Y. A. Baker El-Ebiary, W. M. Amir Fazamin Wan Hamzah, M. H. Yusoff and B. Pandey, “E-Commerce Product Retrieval Using Knowledge from GPT-4,” 2023 International Conference on Computer Science and Emerging Technologies (CSET), Bangalore, India, 2023, pp. 1-8. 10.1109/CSET58993.2023.10346860.
XVII. Franciskus Antonius, Purnachandra Rao Alapati, Mahyudin Ritonga, Indrajit Patra, Yousef A. Baker El-Ebiary, Myagmarsuren Orosoo and Manikandan Rengarajan, “Incorporating Natural Language Processing into Virtual Assistants: An Intelligent Assessment Strategy for Enhancing Language Comprehension” International Journal of Advanced Computer Science and Applications(IJACSA), 14(10), 2023. 10.14569/IJACSA.2023.0141079.
XVIII. G. Kanaan, F. R. Wahsheh, Y. A. B. El-Ebiary, W. M. A. F. Wan Hamzah, B. Pandey and S. N. P, “An Evaluation and Annotation Methodology for Product Category Matching in E-Commerce Using GPT,” 2023 International Conference on Computer Science and Emerging Technologies (CSET), Bangalore, India, 2023, pp. 1-6. 10.1109/CSET58993.2023.10346684.
XIX. Ganesh Khekare, K. Pavan Kumar, Kundeti Naga Prasanthi, Sanjiv Rao Godla, Venubabu Rachapudi, Mohammed Saleh Al Ansari and Yousef A. Baker El-Ebiary, “Optimizing Network Security and Performance Through the Integration of Hybrid GAN-RNN Models in SDN-based Access Control and Traffic Engineering” International Journal of Advanced Computer Science and Applications(IJACSA), 14(12), 2023. 10.14569/IJACSA.2023.0141262.
XX. Ghanem W.A.H.M. et al. (2021) Metaheuristic Based IDS Using Multi-Objective Wrapper Feature Selection and Neural Network Classification. In: Anbar M., Abdullah N., Manickam S. (eds) Advances in Cyber Security. ACeS 2020. Communications in Computer and Information Science, vol 1347. Springer, Singapore. 10.1007/978-981-33-6835-4_26
XXI. Ghosh, R., Rahmani, R., & Singh, D. (2021). IoT Security in Smart Cities: A Comprehensive Survey. IEEE Internet of Things Journal, 8(3), 1915-1947. 10.1109/JIOT.2020.3012345
XXII. International Conference on Smart Computing and Electronic Enterprise (ICSCEE), 2021, pp. 199-205. 10.1109/ICSCEE50312.2021.9498175.
XXIII. J. A. Jusoh et al., “Track Student Attendance at a Time of the COVID-19 Pandemic Using Location-Finding Technology,” 2021 2nd International Conference on Smart Computing and Electronic Enterprise (ICSCEE), 2021, pp. 147-152. 10.1109/ICSCEE50312.2021.9498043.
XXIV. K. N. Preethi, Yousef A. Baker El-Ebiary, Esther Rosa Saenz Arenas, Kathari Santosh, Ricardo Fernando Cosio Borda, Jorge L. Javier Vidalón, Anuradha. S and R. Manikandan, “Enhancing Startup Efficiency: Multivariate DEA for Performance Recognition and Resource Optimization in a Dynamic Business Landscape” International Journal of Advanced Computer Science and Applications (IJACSA), 14(8), 2023. 10.14569/IJACSA.2023.0140869.
XXV. K. Sundaramoorthy, R. Anitha, S. Kayalvili, Ayat Fawzy Ahmed Ghazala, Yousef A.Baker El-Ebiary and Sameh Al-Ashmawy, “Hybrid Optimization with Recurrent Neural Network-based Medical Image Processing for Predicting Interstitial Lung Disease” International Journal of Advanced Computer Science and Applications(IJACSA), 14(4), 2023. 10.14569/IJACSA.2023.0140462.
XXVI. Karie, N. M., Sahri, N. M., Yang, W., Valli, C., & Kebande, V. R. (2021). A review of security standards and frameworks for IoT-based smart environments. IEEE Access, 9, 121975-121995. 10.1109/ACCESS.2021.3088755
XXVII. Kumar, R., & Krishnan, S. (2019). A review on security frameworks in IoT based applications. Procedia Computer Science, 165, 391-398. 10.1016/j.procs.2020.01.065
XXVIII. Lakshmi K, Sridevi Gadde, Murali Krishna Puttagunta, G. Dhanalakshmi and Yousef A. Baker El-Ebiary, “Efficiency Analysis of Firefly Optimization-Enhanced GAN-Driven Convolutional Model for Cost-Effective Melanoma Classification” International Journal of Advanced Computer Science and Applications(IJACSA), 14(11), 2023. 10.14569/IJACSA.2023.0141175.
XXIX. Li, S., Da Xu, L., & Zhao, S. (2018). The internet of things: a survey. Information Systems Frontiers, 17(2), 243-259. 10.1007/s10796-014-9492-7
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XXXI. Maddikera Krishna Reddy, J. C. Sekhar, Vuda Sreenivasa Rao, Mohammed Saleh Al Ansari, Yousef A.Baker El-Ebiary, Jarubula Ramu and R. Manikandan, “Image Specular Highlight Removal using Generative Adversarial Network and Enhanced Grey Wolf Optimization Technique” International Journal of Advanced Computer Science and Applications(IJACSA), 14(6), 2023. 10.14569/IJACSA.2023.0140668.
XXXII. Miorandi, D., Sicari, S., De Pellegrini, F., & Chlamtac, I. (2012). Internet of things: Vision, applications and research challenges. Ad Hoc Networks, 10(7), 1497-1516. 10.1016/j.adhoc.2012.02.016
XXXIII. Mohammad Kamrul Hasan, Muhammad Shafiq, Shayla Islam, Bishwajeet Pandey, Yousef A. Baker El-Ebiary, Nazmus Shaker Nafi, R. Ciro Rodriguez, Doris Esenarro Vargas, “Lightweight Cryptographic Algorithms for Guessing Attack Protection in Complex Internet of Things Applications”, Complexity, vol. 2021, Article ID 5540296, 13 pages, 2021. 10.1155/2021/5540296.
XXXIV. Moresh Mukhedkar, Chamandeep Kaur, Divvela Srinivasa Rao, Shweta Bandhekar, Mohammed Saleh Al Ansari, Maganti Syamala and Yousef A.Baker El-Ebiary, “Enhanced Land Use and Land Cover Classification Through Human Group-based Particle Swarm Optimization-Ant Colony Optimization Integration with Convolutional Neural Network” International Journal of Advanced Computer Science and Applications(IJACSA), 14(11), 2023. 10.14569/IJACSA.2023.0141142.
XXXV. Moresh Mukhedkar, Divya Rohatgi, Veera Ankalu Vuyyuru, K V S S Ramakrishna, Yousef A.Baker El-Ebiary and V. Antony Asir Daniel, “Feline Wolf Net: A Hybrid Lion-Grey Wolf Optimization Deep Learning Model for Ovarian Cancer Detection” International Journal of Advanced Computer Science and Applications(IJACSA), 14(9), 2023. 10.14569/IJACSA.2023.0140962.
XXXVI. Musavian, L., & Leon-Garcia, A. (2018). Security and privacy in decentralized energy trading through multi-signature blockchain in smart grids. IEEE Transactions on Industrial Informatics, 14(8), 3690-3700. 10.1109/TDSC.2016.2616861
XXXVII. N. A. Al-Sammarraie, Y. M. H. Al-Mayali and Y. A. Baker El-Ebiary, “Classification and diagnosis using back propagation Artificial Neural Networks (ANN),” 2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE), Shah Alam, Malaysia, 2018, pp. 1-5. 19 November 2018. 10.1109/ICSCEE.2018.8538383.
XXXVIII. N. V. Rajasekhar Reddy, Araddhana Arvind Deshmukh, Vuda Sreenivasa Rao, Sanjiv Rao Godla, Yousef A.Baker El-Ebiary, Liz Maribel Robladillo Bravo and R. Manikandan, “Enhancing Skin Cancer Detection Through an AI-Powered Framework by Integrating African Vulture Optimization with GAN-based Bi-LSTM Architecture” International Journal of Advanced Computer Science and Applications(IJACSA), 14(9), 2023. 10.14569/IJACSA.2023.0140960.
XXXIX. Nanda, P., & Nayak, J. (2021). Security in IoT devices: A survey. Journal of Ambient Intelligence and Humanized Computing, 12(3), 2425-2438. 10.1007/s12652-020-02559-6
XL. Nripendra Narayan Das, Santhakumar Govindasamy, Sanjiv Rao Godla, Yousef A.Baker El-Ebiary and E.Thenmozhi, “Utilizing Deep Convolutional Neural Networks and Non-Negative Matrix Factorization for Multi-Modal Image Fusion” International Journal of Advanced Computer Science and Applications(IJACSA), 14(9), 2023. 10.14569/IJACSA.2023.0140963.
XLI. P. R. Pathmanathan et al., “The Benefit and Impact of E-Commerce in Tourism Enterprises,” 2021 2nd International Conference on Smart Computing and Electronic Enterprise (ICSCEE), 2021, pp. 193-198. 10.1109/ICSCEE50312.2021.9497947.
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XLIV. Qureshi, K. N., Rana, S. S., Ahmed, A., & Jeon, G. (2020). A novel and secure attacks detection framework for smart cities industrial internet of things. Sustainable Cities and Society, 61, 102343. 10.1016/j.scs.2020.102343
XLV. Ravi Prasad, Dudekula Siddaiah, Yousef A. Baker El-Ebiary, S. Naveen Kumar, K Selvakumar (2023). Forecasting Electricity Consumption Through A Fusion Of Hybrid Random Forest Regression And Linear Regression Models Utilizing Smart Meter Data. Journal of Theoretical and Applied Information Technology, 101(21). 10.5281/zenodo.12515989
XLVI. Ravi Prasad, Dudekula Siddaiah, Yousef A. Baker El-Ebiary, S. Naveen Kumar, K Selvakumar (2023). Forecasting Electricity Consumption Through A Fusion Of Hybrid Random Forest Regression And Linear Regression Models Utilizing Smart Meter Data. Journal of Theoretical and Applied Information Technology, 101(21). 10.5281/zenodo.12515989
XLVII. Roman, R., Lopez, J., & Mambo, M. (2013). Mobile edge computing, fog et al.: A survey and analysis of security threats and challenges. Future Generation Computer Systems, 78, 680-698. 10.1016/j.future.2016.11.009

XLVIII. S. Bamansoor et al., “Efficient Online Shopping Platforms in Southeast Asia,” 2021 2nd International Conference on Smart Computing and Electronic Enterprise (ICSCEE), 2021, pp. 164-168. 10.1109/ICSCEE50312.2021.9497901.
XLIX. S. Bamansoor et al., “Evaluation of Chinese Electronic Enterprise from Business and Customers Perspectives,” 2021 2nd International Conference on Smart Computing and Electronic Enterprise (ICSCEE), 2021, pp. 169-174. 10.1109/ICSCEE50312.2021.9498093.
L. S. I. Ahmad Saany et al., “Exploitation of a Technique in Arranging an Islamic Funeral,” 2021 2nd International Conference on Smart Computing and Electronic Enterprise (ICSCEE), 2021, pp. 1-8. 10.1109/ICSCEE50312.2021.9498224.
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LII. S. M. S. Hilles et al., “Latent Fingerprint Enhancement and Segmentation Technique Based on Hybrid Edge Adaptive DTV Model,” 2021 2nd International Conference on Smart Computing and Electronic Enterprise (ICSCEE), 2021, pp. 8-13. 10.1109/ICSCEE50312.2021.9498025.
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LV. Siddiqui, S., Shamim, H., Javed, M. A., & Zeadally, S. (2019). Internet of Things (IoT) security: Current status, challenges and future perspectives. In 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC) (pp. 542-547). 10.1109/IWCMC.2019.8766445
LVI. Subhashini, R., & Khang, A. (2023). The role of Internet of Things (IoT) in smart city framework. In Smart Cities (pp. 31-56). 10.1201/9781003376064-3
LVII. Suresh Babu Jugunta, Manikandan Rengarajan, Sridevi Gadde, Yousef A.Baker El-Ebiary, Veera Ankalu. Vuyyuru, Namrata Verma and Farhat Embarak, “Exploring the Insights of Bat Algorithm-Driven XGB-RNN (BARXG) for Optimal Fetal Health Classification in Pregnancy Monitoring” International Journal of Advanced Computer Science and Applications(IJACSA), 14(11), 2023. 10.14569/IJACSA.2023.0141174.
LVIII. Suresh Babu Jugunta, Yousef A.Baker El-Ebiary, K. Aanandha Saravanan, Kanakam Siva Rama Prasad, S. Koteswari, Venubabu Rachapudi and Manikandan Rengarajan, “Unleashing the Potential of Artificial Bee Colony Optimized RNN-Bi-LSTM for Autism Spectrum Disorder Diagnosis” International Journal of Advanced Computer Science and Applications(IJACSA), 14(11), 2023. 10.14569/IJACSA.2023.0141173.
LIX. Sweety Bakyarani. E, Anil Pawar, Sridevi Gadde, Eswar Patnala, P. Naresh and Yousef A. Baker El-Ebiary, “Optimizing Network Intrusion Detection with a Hybrid Adaptive Neuro Fuzzy Inference System and AVO-based Predictive Analysis” International Journal of Advanced Computer Science and Applications(IJACSA), 14(11), 2023. 10.14569/IJACSA.2023.0141131.
LX. Venkateswara Rao Naramala, B. Anjanee Kumar, Vuda Sreenivasa Rao, Annapurna Mishra, Shaikh Abdul Hannan, Yousef A.Baker El-Ebiary and R. Manikandan, “Enhancing Diabetic Retinopathy Detection Through Machine Learning with Restricted Boltzmann Machines” International Journal of Advanced Computer Science and Applications(IJACSA), 14(9), 2023. 10.14569/IJACSA.2023.0140961.
LXI. Y. A. B. El-Ebiary et al., “Determinants of Customer Purchase Intention Using Zalora Mobile Commerce Application,” 2021 2nd International Conference on Smart Computing and Electronic Enterprise (ICSCEE), 2021, pp. 159-163. 10.1109/ICSCEE50312.2021.9497995.
LXII. Y. A. B. El-Ebiary, “The Effect of the Organization Factors, Technology and Social Influences on E-Government Adoption in Jordan,” 2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE), Shah Alam, Malaysia, 2018, pp. 1-4. 19 November 2018, 10.1109/ICSCEE.2018.8538394.
LXIII. Y. A. B. El-Ebiary, S. Almandeel, W. A. H. M. Ghanem, W. Abu-Ulbeh, M. M. M. Al-Dubai and S. Bamansoor, “Security Issues and Threats Facing the Electronic Enterprise Leadership,” 2020 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS), 2020, pp. 24-28. 10.1109/ICIMCIS51567.2020.9354330.
LXIV. Y. A. Baker El-Ebiary et al., “Blockchain as a decentralized communication tool for sustainable development,” 2021 2nd International Conference on Smart Computing and Electronic Enterprise (ICSCEE), 2021, pp. 127-133. 10.1109/ICSCEE50312.2021.9497910.
LXV. Y. A. Baker El-Ebiary et al., “E-Government and E-Commerce Issues in Malaysia,” 2021 2nd International Conference on Smart Computing and Electronic Enterprise (ICSCEE), 2021, pp. 153-158. 10.1109/ICSCEE50312.2021.9498092.

LXVI. Y. A. Baker El-Ebiary et al., “Mobile Commerce and its Apps – Opportunities and Threats in Malaysia,” 2021 2nd International Conference on Smart Computing and Electronic Enterprise (ICSCEE), 2021, pp. 180-185. 10.1109/ICSCEE50312.2021.9498228.
LXVII. Y. A. Baker El-Ebiary et al., “Track Home Maintenance Business Centers with GPS Technology in the IR 4.0 Era,” 2021 2nd International Conference on Smart Computing and Electronic Enterprise (ICSCEE), 2021, pp. 134-138. 10.1109/ICSCEE50312.2021.9498070.
LXVIII. Y. M. A. Tarshany, Y. Al Moaiad and Y. A. Baker El-Ebiary, “Legal Maxims Artificial Intelligence Application for Sustainable Architecture And Interior Design to Achieve the Maqasid of Preserving the Life and Money,” 2022 Engineering and Technology for Sustainable Architectural and Interior Design Environments (ETSAIDE), 2022, pp. 1-4. 10.1109/ETSAIDE53569.2022.9906357.
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LXX. Yousef Methkal Abd Algani, B. Nageswara Rao, Chamandeep Kaur, B. Ashreetha, K. V. Daya Sagar and Yousef A. Baker El-Ebiary, “A Novel Hybrid Deep Learning Framework for Detection and Categorization of Brain Tumor from Magnetic Resonance Images” International Journal of Advanced Computer Science and Applications (IJACSA), 14(2), 2023. 10.14569/IJACSA.2023.0140261.

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Journal Vol – 20 No – 9, September 2025

MULTI-ITEM SCALE CONSTRUCTION TO MEASURE THE CONTRIBUTION OF DIFFERENT RISKS IN CONSTRUCTION PROJECTS OF OIL & GAS INDUSTRY

Authors:

Prasanta Roy, Purnachandra Saha, Moitreyee Paul, Prattyush Roy

DOI NO:

https://doi.org/10.26782/jmcms.2025.09.00006

September 14, 2025

Abstract:

Risk management is a crucial element in ensuring the success of construction activities in the oil and gas sector, which are inherently complex and susceptible to numerous risks. This study aims to develop a scale of multiple items to evaluate the contribution of different types of risk towards the total risk of an oil and gas construction project. A structured questionnaire was distributed among industry professionals and academicians, capturing their insights on risk impact and likelihood. The reliability analysis has been performed in SPSS software, and confirmatory factor analysis (CFA) has been performed in AMOS software. Findings highlight that scaled 13 risks cover all the important segments of the construction project in 3 categories. It is also observed that the individual impact of risk groups like fin & tech, project management, and procurement is maximum on total risk rather than the combination of these three.

Keywords:

Amos,Oil and gas construction,Risk impact,Scale development,SPSS,

Refference:

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Journal Vol – 20 No – 9, September 2025

A NOVEL HYBRID MODEL FOR ROBUST CYBER INTRUSION DETECTION IN CLOUD COMPUTING ENVIRONMENTS

Authors:

Mohamed Loughmari, Anass El Affar

DOI NO:

https://doi.org/10.26782/jmcms.2025.09.00007

September 14, 2025

Abstract:

Security remains one of the most critical concerns in all types and sizes of networks. Among the various strategies and policies designed to protect networks and systems, intrusion detection systems (IDSs) are paramount in identifying and preventing attacks. As security threats evolve, next-generation security solutions are progressively incorporating artificial intelligence (AI) to enhance their effectiveness. Consequently, the building of an effective and intelligent intrusion detection system remains one of the most significant research challenges. This study proposes a novel hybrid IDS model that combines anomaly detection and supervised learning to improve attack detection in Cloud Computing (CC) environments. Our approach utilizes the CICIDS2018 dataset, noted for its large scale, recency, inclusion of diverse real-world attack scenarios, and suitability for CC contexts. Our methodology first employs Isolation Forest for anomaly detection. Then, the anomaly results are added as a new feature to the dataset. Subsequently, the eXtreme Gradient Boosting (XGBoost) model is employed on this enriched dataset. This two-stage hybrid approach enhances the model's learning capabilities and leads to more accurate threat detection. The experimental results indicate that the proposed model achieves superior performance, with high recall, F1-score, precision, and accuracy. Moreover, a comparative analysis with existing literature further confirms these strong results. The findings indicate that combining anomaly detection with supervised learning can provide a more robust approach for enhancing IDS, particularly in demanding environments such as CC.

Keywords:

Intrusion Detection System (IDS),Cloud Computing,Hybrid Model,XGBoost,Isolation Forest,Network Security,CICIDS2018,

Refference:

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XXI. Z. Yang et al., “A systematic literature review of methods and datasets for anomaly-based network intrusion detection,” Comput. Secur., vol. 116, pp. 102675, May 2022. 10.1016/j.cose.2022.102675.

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Journal Vol – 20 No – 9, September 2025

MONTE CARLO-BASED TEXTUAL GRADIENT DESCENT: A MATHEMATICAL FRAMEWORK FOR LLM OPTIMIZATION

Authors:

Temirbek Atabekov, Polina Dolmatova

DOI NO:

https://doi.org/10.26782/jmcms.2025.09.00008

September 14, 2025

Abstract:

This paper combines traditional optimization theory with modern Natural Language Processing (NLP) by formalizing Textual Gradient Descent (TextGrad) within a measure-theoretic framework. We introduce the concept of Expected Textual Loss, a Monte Carlo-inspired approach that enables gradient-based methods in discrete text spaces. Our extension, Monte Carlo TextGrad, improves convergence by systematically sampling from synthetic input distributions and integrating them into the optimization loop. Experimental validation spans both controlled object counting tasks and the LeetCode Hard benchmark, where our approach achieves statistically significant improvements in completion rates over baseline models and standard TextGrad. In addition, we analyze the potential distributional bias introduced by synthetic sampling through Kullback–Leibler divergence, establishing a principled framework for diagnosing and mitigating misalignment between training and deployment distributions. These results demonstrate that Monte Carlo TextGrad provides both faster convergence and greater robustness under distribution shift.

Keywords:

Textual Gradient Descent,Monte Carlo Methods,LLM Optimization,Measure Theory,Expected Textual Loss,Distributional Bias,

Refference:

I. Baek, Seungho, et al. “PromptCrafter: Crafting Text-to-Image Prompt through Mixed-Initiative Dialogue with LLM.” arXiv preprint arXiv:2307.08985, 2023. https://arxiv.org/abs/2307.08985.
II. Gao, Shuzheng, et al. “The Prompt Alchemist: Automated LLM-Tailored Prompt Optimization for Test Case Generation.” arXiv preprint arXiv:2501.01329, 2025. N https://arxiv.org/abs/2501.01329.
III. Hu, Shengran, et al. “Automated Design of Agentic Systems.” arXiv preprint arXiv:2408.08435, 2025. https://arxiv.org/abs/2408.08435.
IV. Khattab, Omar, et al. “DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines.” arXiv preprint arXiv:2310.03714, 2023. https://arxiv.org/abs/2310.03714.
V. Kushner, Harold J., and G. George Yin. Stochastic Approximation and Recursive Algorithms and Applications. 2nd ed., Springer-Verlag New York, 2003. 10.1007/b97441.
VI. Lecchini-Visintini, Andrea, et al. “Stochastic Optimization on Continuous Domains With Finite-Time Guarantees by Markov Chain Monte Carlo Methods.” IEEE Transactions on Automatic Control, vol. 55, no. 12, 2010, pp. 2858-2863. 10.1109/tac.2010.2078170.
VII. Li, Yujian Betterest, and Kai Wu. “SPELL: Semantic Prompt Evolution based on a LLM.” arXiv preprint arXiv:2310.01260, 2023. https://arxiv.org/abs/2310.01260.
VIII. Melnikov, Olena, and Johannes Milz. “Randomized Quasi-Monte Carlo Methods for Risk-Averse Stochastic Optimization.” Journal of Optimization Theory and Applications, vol. 206, no. 1, 2025. 10.1007/s10957-025-02693-6.
IX. Metropolis, Nicholas, et al. “Equation of State Calculations by Fast Computing Machines.” Journal of Chemical Physics, vol. 21, no. 6, 1953, pp. 1087-1092. 10.1063/1.1699114.
X. Ouyang, Long, et al. “Training Language Models to Follow Instructions with Human Feedback.” Advances in Neural Information Processing Systems 35, NeurIPS, 2022. 10.48550/arXiv.2203.02155.
XI. Robert, Christian P., and George Casella. Monte Carlo Statistical Methods. 2nd ed., Springer-Verlag New York, 2004. 10.1007/978-1-4757-4145-2.
XII. Schulman, John, et al. “Proximal Policy Optimization Algorithms.” arXiv preprint arXiv:1707.06347, 2017. https://arxiv.org/abs/1707.06347.
XIII. Shin, Taylor, et al. “AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts.” arXiv preprint arXiv:2010.15980, 2020. https://arxiv.org/abs/2010.15980.
XIV. Wu, Sean, et al. “AutoMedPrompt: A New Framework for Optimizing LLM Medical Prompts Using Textual Gradients.” arXiv preprint arXiv:2502.15944, 2025, https://arxiv.org/abs/2502.15944.
XV. Xie, Yuxi, et al. “Self-Evaluation Guided Beam Search for Reasoning.” arXiv preprint arXiv:2305.00633, 2023. https://arxiv.org/abs/2305.00633.
XVI. Yuksekgonul, Mert, et al. “TextGrad: Automatic ‘Differentiation’ via Text.” arXiv preprint arXiv:2406.07496, 2024. https://arxiv.org/abs/2406.07496.

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Journal Vol – 20 No – 9, September 2025

THE INFLUENCE OF KNOWLEDGE MANAGEMENT STRATEGIES ON DECISION-MAKING IN ENTERPRISES

Authors:

M Hafiz Yusoff, Abdilahi Liban, Julaily Aida Jusoh, Syarilla Iryani Ahmad Saany, Yousef A. Baker El-Ebiary

DOI NO:

https://doi.org/10.26782/jmcms.2025.09.00009

September 14, 2025

Abstract:

In an increasingly dynamic and competitive business environment, organizations are compelled to make prompt and informed decisions to sustain a lasting competitive edge. Knowledge Management (KM) has become a critical process that enables organizations to systematically capture, generate, distribute, and utilize knowledge to support better decision-making. Problem Statement: Although the role of KM in organizational success is widely acknowledged, many businesses struggle to effectively embed KM strategies into their decision-making frameworks. The intricate nature of managing knowledge and the demands of strategic alignment often prevent organizations from fully leveraging KM to optimize decision outcomes. Objective: This study aims to explore the impact of implementing effective KM strategies on organizational decision-making. It focuses on examining how different KM components, such as acquiring, storing, sharing, and applying knowledge, affect decision-making processes across various organizational levels. Methodology: Grounded in structuration theory, the research proposes a conceptual model that connects strategic planning, knowledge-related activities, and the broader external environment. A multi-method research design is adopted, incorporating case studies, a comprehensive literature review, and interviews with subject-matter experts to collect insights into how KM is practiced and its influence on decision-making. Results: The study finds a strong correlation between structured KM practices and enhanced decision-making capabilities. Organizations that implement KM effectively tend to exhibit improved problem-solving, more accurate risk evaluation, and stronger decision-making performance across different management levels. Conclusion: This research highlights the strategic value of KM in enhancing organizational decision-making. It concludes that companies investing in comprehensive KM frameworks are better positioned to handle the complexities of today’s business landscape and achieve sustainable competitive advantages.

Keywords:

Decision Making Framework,Knowledge Management System,Data Analysis,Leadership,

Refference:

I. Ahmad Saany, S. I., et al. “Exploitation of a Technique in Arranging an Islamic Funeral.” 2021 2nd International Conference on Smart Computing and Electronic Enterprise (ICSCEE), 2021, pp. 1-8. 10.1109/ICSCEE50312.2021.9498224.
II. Algani, Yousef Methkal Abd, et al. “A Novel Hybrid Deep Learning Framework for Detection and Categorization of Brain Tumor from Magnetic Resonance Images.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 2, 2023. 10.14569/IJACSA.2023.0140261.
III. Al-Sammarraie, N. A., et al. “Classification and Diagnosis Using Back Propagation Artificial Neural Networks (ANN).” 2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE), Shah Alam, Malaysia, 2018, pp. 1-5, 19 Nov. 2018. 10.1109/ICSCEE.2018.8538383.
IV. Altrad, et al. “Amazon in Business to Customers and Overcoming Obstacles.” 2021 2nd International Conference on Smart Computing and Electronic Enterprise (ICSCEE), 2021, pp. 175-179. 10.1109/ICSCEE50312.2021.9498129.
V. Antonius, Franciskus, et al. “Incorporating Natural Language Processing into Virtual Assistants: An Intelligent Assessment Strategy for Enhancing Language Comprehension.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 10, 2023. 10.14569/IJACSA.2023.0141079.
VI. Baker El-Ebiary, Y. A. B. “The Effect of the Organization Factors, Technology and Social Influences on E-Government Adoption in Jordan.” 2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE), Shah Alam, Malaysia, 2018, pp. 1-4, 19 Nov. 2018. 10.1109/ICSCEE.2018.8538394.
VII. Baker El-Ebiary, Y. A. B., et al. “Determinants of Customer Purchase Intention Using Zalora Mobile Commerce Application.” 2021 2nd International Conference on Smart Computing and Electronic Enterprise (ICSCEE), 2021, pp. 159-163. 10.1109/ICSCEE50312.2021.9497995.
VIII. Baker El-Ebiary, Y. A. B., et al. “Security Issues and Threats Facing the Electronic Enterprise Leadership.” 2020 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS), 2020, pp. 24-28. 10.1109/ICIMCIS51567.2020.9354330.
IX. Baker El-Ebiary, Y. A., et al. “Blockchain as a Decentralized Communication Tool for Sustainable Development.” 2021 2nd International Conference on Smart Computing and Electronic Enterprise (ICSCEE), 2021, pp. 127-133. 10.1109/ICSCEE50312.2021.9497910.
X. Baker El-Ebiary, Y. A., et al. “E-Government and E-Commerce Issues in Malaysia.” 2021 2nd International Conference on Smart Computing and Electronic Enterprise (ICSCEE), 2021, pp. 153-158. 10.1109/ICSCEE50312.2021.9498092.

XI. Baker El-Ebiary, Y. A., et al. “Mobile Commerce and its Apps – Opportunities and Threats in Malaysia.” 2021 2nd International Conference on Smart Computing and Electronic Enterprise (ICSCEE), 2021, pp. 180-185. 10.1109/ICSCEE50312.2021.9498228.
XII. Baker El-Ebiary, Y. A., et al. “Track Home Maintenance Business Centers with GPS Technology in the IR 4.0 Era.” 2021 2nd International Conference on Smart Computing and Electronic Enterprise (ICSCEE), 2021, pp. 134-138. 10.1109/ICSCEE50312.2021.9498070.
XIII. Bakyarani, Sweety E., et al. “Optimizing Network Intrusion Detection with a Hybrid Adaptive Neuro Fuzzy Inference System and AVO-based Predictive Analysis.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 11, 2023. 10.14569/IJACSA.2023.0141131.
XIV. Bamansoor, S., et al. “Efficient Online Shopping Platforms in Southeast Asia.” 2021 2nd International Conference on Smart Computing and Electronic Enterprise (ICSCEE), 2021, pp. 164-168. 10.1109/ICSCEE50312.2021.9497901.
XV. Bamansoor, S., et al. “Evaluation of Chinese Electronic Enterprise from Business and Customers Perspectives.” 2021 2nd International Conference on Smart Computing and Electronic Enterprise (ICSCEE), 2021, pp. 169-174. 10.1109/ICSCEE50312.2021.9498093.
XVI. Das, Nripendra Narayan, et al. “Utilizing Deep Convolutional Neural Networks and Non-Negative Matrix Factorization for Multi-Modal Image Fusion.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 9, 2023. 10.14569/IJACSA.2023.0140963.
XVII. Deeba, K., et al. “Optimizing Crop Yield Prediction in Precision Agriculture with Hyperspectral Imaging-Unmixing and Deep Learning.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 12, 2023. 10.14569/IJACSA.2023.0141261.
XVIII. Farhana, Artika, et al. “Efficient Deep Reinforcement Learning for Smart Buildings: Integrating Energy Storage Systems Through Advanced Energy Management Strategies.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 12, 2023. 10.14569/IJACSA.2023.0141257.
XIX. G. Gokul Kumari, Shokhjakhon Abdufattokhov, Sanjit Singh, Guru Basava Aradhya S, T L Deepika Roy, Yousef A.Baker El-Ebiary, Elangovan Muniyandy and B Kiran Bala, “Leveraging LSTM-Driven Predictive Analytics for Resource Allocation and Cost Efficiency Optimization in Project Management” International Journal of Advanced Computer Science and Applications(IJACSA), 16(6), 2025. 10.14569/IJACSA.2025.0160661
XX. Ghanem, W. A. H. M., et al. “Metaheuristic Based IDS Using Multi-Objective Wrapper Feature Selection and Neural Network Classification.” In: Anbar, M., Abdullah, N., and Manickam, S., editors. Advances in Cyber Security. ACeS 2020. Communications in Computer and Information Science, vol. 1347, Springer, Singapore, 2021. 10.1007/978-981-33-6835-4_26
XXI. Hasan, Mohammad Kamrul, et al. “Lightweight Cryptographic Algorithms for Guessing Attack Protection in Complex Internet of Things Applications.” Complexity, vol. 2021, Article ID 5540296, 13 pages, 2021. 10.1155/2021/5540296.
XXII. Hilles, S. M. S., et al. “Adaptive Latent Fingerprint Image Segmentation and Matching using Chan-Vese Technique Based on EDTV Model.” 2021 2nd International Conference on Smart Computing and Electronic Enterprise (ICSCEE), 2021, pp. 2-7. 10.1109/ICSCEE50312.2021.9497996.
XXIII. Hilles, S. M. S., et al. “Latent Fingerprint Enhancement and Segmentation Technique Based on Hybrid Edge Adaptive DTV Model.” 2021 2nd International Conference on Smart Computing and Electronic Enterprise (ICSCEE), 2021, pp. 8-13. 10.1109/ICSCEE50312.2021.9498025.
XXIV. International Conference on Smart Computing and Electronic Enterprise (ICSCEE), 2021, pp. 199-205. 10.1109/ICSCEE50312.2021.9498175.
XXV. Jusoh, J. A., et al. “Track Student Attendance at a Time of the COVID-19 Pandemic Using Location-Finding Technology.” 2021 2nd International Conference on Smart Computing and Electronic Enterprise (ICSCEE), 2021, pp. 147-152. 10.1109/ICSCEE50312.2021.9498043.
XXVI. Kanaan, G., et al. “An Evaluation and Annotation Methodology for Product Category Matching in E-Commerce Using GPT.” 2023 International Conference on Computer Science and Emerging Technologies (CSET), Bangalore, India, 2023, pp. 1-6. 10.1109/CSET58993.2023.10346684.
XXVII. Krishna Reddy, Maddikera, et al. “Image Specular Highlight Removal using Generative Adversarial Network and Enhanced Grey Wolf Optimization Technique.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 6, 2023. 10.14569/IJACSA.2023.0140668.
XXVIII. Lakshmi, K., et al. “Efficiency Analysis of Firefly Optimization-Enhanced GAN-Driven Convolutional Model for Cost-Effective Melanoma Classification.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 11, 2023. 10.14569/IJACSA.2023.0141175.
XXIX. Meraj, S. T., et al. “A Diamond Shaped Multilevel Inverter with Dual Mode of Operation.” IEEE Access, vol. 9, 2021, pp. 59873-59887. 10.1109/ACCESS.2021.3067139.
XXX. Mukhedkar, Moresh, et al. “Enhanced Land Use and Land Cover Classification Through Human Group-based Particle Swarm Optimization-Ant Colony Optimization Integration with Convolutional Neural Network.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 11, 2023. 10.14569/IJACSA.2023.0141142.
XXXI. Mukhedkar, Moresh, et al. “Feline Wolf Net: A Hybrid Lion-Grey Wolf Optimization Deep Learning Model for Ovarian Cancer Detection.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 9, 2023. 10.14569/IJACSA.2023.0140962.
XXXII. Naramala, Venkateswara Rao, et al. “Enhancing Diabetic Retinopathy Detection Through Machine Learning with Restricted Boltzmann Machines.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 9, 2023. 10.14569/IJACSA.2023.0140961.
XXXIII. Pathmanathan, P. R., et al. “The Benefit and Impact of E-Commerce in Tourism Enterprises.” 2021 2nd International Conference on Smart Computing and Electronic Enterprise (ICSCEE), 2021, pp. 193-198. 10.1109/ICSCEE50312.2021.9497947.
XXXIV. Pawar, B., et al. “Multi-Scale Deep Learning-based Recurrent Neural Network for Improved Medical Image Restoration and Enhancement.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 10, 2023. 10.14569/IJACSA.2023.0141088.
XXXV. Preethi, K. N., et al. “Enhancing Startup Efficiency: Multivariate DEA for Performance Recognition and Resource Optimization in a Dynamic Business Landscape.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 8, 2023. 10.14569/IJACSA.2023.0140869.
XXXVI. Rajasekhar Reddy, N. V., et al. “Enhancing Skin Cancer Detection Through an AI-Powered Framework by Integrating African Vulture Optimization with GAN-based Bi-LSTM Architecture.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 9, 2023. 10.14569/IJACSA.2023.0140960.
XXXVII. Sundaramoorthy, K., et al. “Hybrid Optimization with Recurrent Neural Network-based Medical Image Processing for Predicting Interstitial Lung Disease.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 4, 2023. 10.14569/IJACSA.2023.0140462.
XXXVIII. Tarshany, Y. M. A., et al. “Legal Maxims Artificial Intelligence Application for Sustainable Architecture And Interior Design to Achieve the Maqasid of Preserving the Life and Money.” 2022 Engineering and Technology for Sustainable Architectural and Interior Design Environments (ETSAIDE), 2022, pp. 1-4. 10.1109/ETSAIDE53569.2022.9906357.
XXXIX. Wahsheh, F. R., et al. “E-Commerce Product Retrieval Using Knowledge from GPT-4.” 2023 International Conference on Computer Science and Emerging Technologies (CSET), Bangalore, India, 2023, pp. 1-8. 10.1109/CSET58993.2023.10346860.
XL. Zawaideh, F. H., et al. “Blockchain Solution For SMEs Cybersecurity Threats In E-Commerce.” 2023 International Conference on Computer Science and Emerging Technologies (CSET), Bangalore, India, 2023, pp. 1-7. 10.1109/CSET58993.2023.10346628.
XLI. Zawaideh, F. H., et al. “E-Commerce Supply Chains with Considerations of Cyber-Security.” 2023 International Conference on Computer Science and Emerging Technologies (CSET), Bangalore, India, 2023, pp. 1-8. 10.1109/CSET58993.2023.10346738.

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Journal Vol – 20 No – 9, September 2025

FRACTIONAL-ORDER SEIR MODEL FOR EBOLA VIRUS TRANSMISSION DYNAMICS ANALYSIS: AN ANALYTICAL AND NUMERICAL APPROACHES

Authors:

Sharmin Sultana Shanta, M. Ali Akbar

DOI NO:

https://doi.org/10.26782/jmcms.2025.09.00010

September 14, 2025

Abstract:

The Ebola virus is a highly contagious disease that originates from wild animals and transmits to humans through direct contact with tainted blood, bodily fluids, or contaminated materials. In this article, we investigate the transmission dynamics of the Ebola virus through the fractional-order SEIR model. We aim to find the analytical solution of the fractional model along with its numerical solution. The Laplace Adomian decomposition method (LADM) is implemented to find the analytical solution of the model, and the accuracy of the results is verified numerically via the fractional Runge-Kutta 4th order (RK4) scheme. The findings reveal the potential role of a fractional-order parameter that influences the behavior of the epidemic. The LADM and RK4 solutions indicate coherence when the fractional parameter gets closer to 1. The results could help control the real-world epidemic scenarios.

Keywords:

Mathematical model,Laplace Adomian decomposition method (LADM),Runge-Kutta 4th order (RK4) method,

Refference:

I. Abdul-Wahhab, Renna D., Mohannad M. Eisa, and Sanaa L. Khalaf. “The study of stability analysis of the Ebola virus via fractional model.” Partial Differential Equations in Applied Mathematics 11 (2024): 100792. 10.1016/j.padiff.2024.100792

II. Adu, Isaac Kwasi, et al. “Modelling the dynamics of Ebola disease transmission with optimal control analysis.” Modeling Earth Systems and Environment 10.4 (2024): 4731-4757. 10.1007/s40808-024-02020-4
III. Adu, Isaac K., et al. “A fractional order Ebola transmission model for dogs and humans.” Scientific African 24 (2024): e02230. 10.1016/j.sciaf.2024.e02230
IV. Al-deiakeh, Rawya, et al. “On the Laplace Residual Series Method and Its Application to Time-Fractional Fisher’s Equations.” Fractal and Fractional 9.5 (2025): 275. 10.3390/fractalfract9050275
V. Alhaj, Mohamed Salah, and Farai Nyabadza. “A mathematical model of malaria transmission in conflict-affected regions and the implications on malaria interventions.” Scientific African (2025): e02746. 10.1016/j.sciaf.2025.e02746
VI. Altaie, Huda Omran, et al. “A hybrid analytical method for fractional order Klein-Gordon and Burgers equations.” Partial Differential Equations in Applied Mathematics (2025): 101220. 10.1016/j.padiff.2025.101220
VII. Arthur, Ronan F., et al. “The lasting influence of Ebola: a qualitative study of community-level behaviors, trust, and perceptions three years after the 2014-16 Ebola epidemic in Liberia.” BMC Public Health 23.1 (2023): 682. 10.1186/s12889-023-15559-1
VIII. Bansal, Jatin, et al. “Investigation of monkeypox disease transmission with vaccination effects using fractional order mathematical model under Atangana-Baleanu Caputo derivative.” Modeling Earth Systems and Environment 11.1 (2025): 40. 10.1007/s40808-024-02202-0
IX. Bekela, Alemu Senbeta, and Alemayehu Tamirie Deresse. “A hybrid yang transforms adomian decomposition method for solving time-fractional nonlinear partial differential equation.” BMC Research Notes 17.1 (2024): 226. 10.1186/s13104-024-06877-7
X. Breman, Joel G., et al. “The epidemiology of Ebola hemorrhagic fever in Zaire, 1976.” Ebola virus haemorrhagic fever 103 (1978): 124.
XI. Chen, Chao, Shibin Yao, and Jian Zhou. “Comparison of rock spalling evaluation in underground openings: Uncertainty-based mathematical model and empirical method.” Deep Resources Engineering (2025): 100171. 10.1016/j.deepre.2025.100171
XII. Dutta, Protyusha, Guruprasad Samanta, and Juan J. Nieto. “Nipah virus transmission dynamics: equilibrium states, sensitivity and uncertainty analysis.” Nonlinear Dynamics 113.9 (2025): 10617-10657. 10.1007/s11071-024-10549-3
XIII. Feldmann, Heinz, and Thomas W. Geisbert. “Ebola haemorrhagic fever.” The Lancet 377.9768 (2011): 849-862. 10.1016/S0140-6736(10)60667-8
XIV. Georges, Alain-Jean, et al. “Ebola hemorrhagic fever outbreaks in Gabon, 1994–1997: epidemiologic and health control issues.” The Journal of infectious diseases 179.Supplement_1 (1999): S65-S75. 10.1086/514290

XV. Gürbüz, Burcu, et al. “Dynamical behavior and bifurcation analysis for a theoretical model of dengue fever transmission with incubation period and delayed recovery.” Mathematics and Computers in Simulation 234 (2025): 497-513. 10.1016/j.matcom.2025.03.008
XVI. Henao-Restrepo, Ana Maria, et al. “Efficacy and effectiveness of an rVSV-vectored vaccine in preventing Ebola virus disease: final results from the Guinea ring vaccination, open-label, cluster-randomised trial (Ebola Ça Suffit!).” The Lancet 389.10068 (2017): 505-518. 10.1016/S0140-6736(16)32621-6
XVII. Ibrahim, Kabiru Garba, et al. “Mathematical analysis of chickenpox population dynamics unveiling the impact of booster in enhancing recovery of infected individuals.” Modeling Earth Systems and Environment 11.1 (2025): 46. 10.1007/s40808-024-02219-5
XVIII. Islam, Md Rezaul, Forhad Mahmud, and M. Ali Akbar. “Insights into the Ebola epidemic model and vaccination strategies: An analytical approximate approach.” Partial Differential Equations in Applied Mathematics 11 (2024): 100799. 10.1016/j.padiff.2024.100799
XIX. Jaber, Mohamad Y., and Jaakko Peltokorpi. “Economic order/production quantity (EOQ/EPQ) models with product recovery: A review of mathematical modeling (1967–2022).” Applied Mathematical Modelling 129 (2024): 655-672. 10.1016/j.apm.2024.02.022
XX. Jendrossek, Mario, et al. “Health care worker vaccination against Ebola: Vaccine acceptance and employment duration in Sierra Leone.” Vaccine 37.8 (2019): 1101-1108. 10.1016/j.vaccine.2018.12.060
XXI. Karim, Rezaul, et al. “A study on fractional-order mathematical and parameter analysis for CAR T-cell therapy for leukemia using homotopy perturbation method.” Partial Differential Equations in Applied Mathematics 14 (2025): 101152. 10.1016/j.padiff.2025.101152
XXII. Khatun, Mst Munny, Khaled A. Gepreel, and M. Ali Akbar. “Dynamics of solitons of the β-fractional doubly dispersive model: Stability and phase portrait analysis.” Indian Journal of Physics (2025): 1-16. 10.1007/s12648-025-03602-3
XXIII. Legrand, Judith, et al. “Understanding the dynamics of Ebola epidemics.” Epidemiology & Infection 135.4 (2007): 610-621. 10.1017/S0950268806007217
XXIV. Leroy, Eric M., et al. “Fruit bats as reservoirs of Ebola virus.” Nature 438.7068 (2005): 575-576. 10.1038/438575a
XXV. Li, Bing, et al. “Developing a multiomics data-based mathematical model to predict colorectal cancer recurrence and metastasis.” BMC Medical Informatics and Decision Making 25. Suppl 2 (2025): 188. 10.1186/s12911-025-03012-9
XXVI. Miah, Saikh Shahjahan, M. Ali Akbar, and Kamruzzaman Khan. “Solitary wave solutions and stability analysis of the fractional Sawada-Kotera equation using the extended modified auxiliary equation mapping method.” Journal of Umm Al-Qura University for Applied Sciences (2025): 1-14. 10.1007/s43994-025-00230-9
XXVII. Mulangu, Sabue, et al. “A randomized, controlled trial of Ebola virus disease therapeutics.” New England journal of medicine 381.24 (2019): 2293-2303. 10.1056/NEJMoa1910993
XXVIII. Ren, Huarong, and Rui Xu. “Prevention and control of Ebola virus transmission: mathematical modelling and data fitting.” Journal of Mathematical Biology 89.2 (2024): 25. 10.1007/s00285-024-02122-8
XXIX. Rewar, Suresh, and Dashrath Mirdha. “Transmission of Ebola virus disease: an overview.” Annals of global health 80.6 (2014): 444-451. 10.1016/j.aogh.2015.02.005
XXX. Shivaranjini, S., & Srivastava, N. (2025). Semi-analytical approach for solving the mathematical model of solid-phase diffusion in electrodes: An application of modified differential transforms method. Partial Differential Equations in Applied Mathematics, 101107. 10.1016/j.padiff.2025.101107
XXXI. Vokhobjonovich, Mullajonov Rustamjon. “Mathematical modeling of the system’s motion, the stability of which is being studied.” International Journal of Social Science & Interdisciplinary Research ISSN: 2277-3630 Impact factor: 8.036 14.01 (2025): 1-4. https://gejournal.net/index.php/IJSSIR/article/view/2544
XXXII. Ziyadullaevna, Rakhimova Umida. “Samuelson hicks’s dynamic economic model.” Shokh library (2025). https://www.ijmrd.in/index.php/imjrd

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Journal Vol – 20 No – 9, September 2025

ARCHITECTING SECURE E-COMMERCE SYSTEMS: A TECHNICAL DEEP DIVE INTO AI, BLOCKCHAIN, AND BIOMETRIC FUSION FOR FRAUD PREVENTION

Authors:

Ajay Tanikonda, Sudhakar Reddy Peddinti, Subba Rao Katragadda

DOI NO:

https://doi.org/10.26782/jmcms.2025.09.00011

September 14, 2025

Abstract:

The growing prevalence of e-commerce in global digital economies attracts more advanced forms of fraudulent practices. Security methods from the past have shown their limitations against the combination of assaults that target identity checks, transaction authentication mechanisms, and data integrity systems. A detailed technical model of secure e-commerce system development emerges by integrating present-day technologies across AI/ML with Blockchain cryptography and Biometric signal processing systems. The discussion analyzes leading-edge AI structures, updated cryptographic algorithms, and integrated biometric methods, resulting in a single fraud detection platform. The project covers system integration difficulties while validating performance and delivering complete specifications at the mathematical, procedural, and protocol levels. The paper evaluates results against industry standards before examining how edge devices and federated learning models can implement this system.

Keywords:

Artificial Intelligence (AI),Machine Learning (ML),Transformer Networks,Graph Neural Networks (GNNs),Fraud Detection,E-Commerce Security,

Refference:

I. A. Tanikonda, S. R. Peddinti, S. R. Katragadda : ‘Deep Learning for Anomaly Detection in E-Commerce and Financial Transactions: Enhancing Fraud Prevention and Cybersecurity.’ Journal of Information Systems Engineering and Management, vol. 10, no. 30s, 31 Mar. 2025, pp. 70–77. 10.52783/jisem.v10i30s.4776.
II. Ajayi S. , S. M. C. Loureiro, & D. Langaro: ‘Internet of things and consumer engagement on retail: state-of-the-art and future directions. EuroMed journal of business’. EuroMed Journal of Business. Vol. 18(3), pp: 397-423, 2023. https://www.emerald.com/emjb/article/18/3/397/83898/Internet-of-things-and-consumer-engagement-on
III. A. A. Alkuwaiti, & M. Al Mubarak: ‘Internet of Things in Water Distribution Systems. In Social Responsibility, Technology and AI’. Internet of Things in Water Distribution Systems. Vol. 23 pp: 143-159, 2024 https://www.emerald.com/books/edited-volume/17277/chapter-abstract/94250831/Internet-of-Things-in-Water-Distribution-Systems?redirectedFrom=fulltext
IV. Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth : ‘Practical Secure Aggregation for Privacy-Preserving Machine Learning.’ Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (CCS ’17), Association for Computing Machinery, 2017, pp. 1175–1191. 10.1145/3133956.3133982
V. Hannah Davis, Christopher Patton, Mike Rosulek, and Phillipp Schoppmann. : ‘Verifiable Distributed Aggregation Functions.’ IACR Cryptology ePrint Archive, Paper 2023/130, 2023. https://eprint.iacr.org/2023/130
VI. FIDO Alliance. : ‘Passkeys: Passwordless Authentication.’ FIDO Alliance, 24 July 2025. https://fidoalliance.org/fido2/
VII. Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio.: ‘Generative Adversarial Nets.’ Advances in Neural Information Processing Systems 27 (NeurIPS 2014), 2014, https://proceedings.neurips.cc/paper_files/paper/2014/hash/f033ed80deb0234979a61f95710dbe25-Abstract.html
VIII. M. Gupta, M. V. Baisoya, & M. K. Rathore : ‘Review of Internet of Things (IoT) Networks using Edge Computing Techniques.’ SSRN, 4 Sep. 2024 https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4946684
IX. Will Hamilton, Zhitao Ying, and Jure Leskovec. : ‘Inductive Representation Learning on Large Graphs.’ Advances in Neural Information Processing Systems 30 (NeurIPS 2017), 2017, https://proceedings.neurips.cc/paper/2017/hash/5dd9db5e033da9c6fb5ba83c7a7ebea9-Abstract.html
X. Y. N. Imamverdiyev, & F. I. Musayeva: ‘ANALYSIS OF GENERATIVE ADVERSARIAL NETWORKS.’ Problems of Information Technology, no. 1, 2022, pp. 20-27. https://jpit.az/ru/journals/297
XI. Khoshraftar, Shima, and Aijun An. : ‘A Survey on Graph Representation Learning Methods.’ ACM Transactions on Intelligent Systems and Technology, vol. 15, no. 1, Feb. 2024, Article 19, 55 pages. 10.1145/3633518.
XII. R. Kiss, and G. Szűcs. : ‘Unsupervised Graph Representation Learning with Inductive Shallow Node Embedding.’ Complex & Intelligent Systems, vol. 10, 2024, pp. 7333–7348. https://link.springer.com/article/10.1007/s40747-024-01545-6
XIII. Z. Liu, J. Guo, K. Y. Lam, & J. Zhao : (2022). : Efficient dropout-resilient aggregation for privacy-preserving machine learning. IEEE Transactions on Information Forensics and Security, vol. 18, 2022, 1839-1854. https://ieeexplore.ieee.org/abstract/document/9757847
XIV. Liu, Z., J. Guo, K.-Y. Lam, and J. Zhao. : ‘Efficient Dropout-Resilient Aggregation for Privacy-Preserving Machine Learning.’ IEEE Transactions on Information Forensics and Security, vol. 18, 2023, pp. 1839–1854. https://ieeexplore.ieee.org/abstract/document/9830997
XV. S. Madakam, R. Ramaswamy, and S. Tripathi. : ‘Internet of Things (IoT): A Literature Review.’ Journal of Computer and Communications, vol. 3, 2015, pp. 164–173, https://www.scirp.org/journal/paperinformation?paperid=56616
XVI. Y. More, P. Ramachandran, P. Panda, A. Mondal, H. Virk, & D. Gupta.: An Efficient Secure Computation Framework for Secure Aggregation.’ arXiv, 15 Feb. 2022. https://arxiv.org/abs/2201.07730
XVII. G. Mwansa, and N. Mabanza. : ‘Review of Internet of Things Security Protocols – A Bibliometric Analysis.’ 2023 25th International Conference on Advanced Communication Technology (ICACT), Pyeongchang, Korea, Republic of, 2023, pp. 394–400. IEEE. https://ieeexplore.ieee.org/abstract/document/10079641
XVIII. F. Qaswar, M. Rahmah, M. A. Raza, A. Noraziah, B. Alkazemi, Z. Fauziah, M. K. A. Hassan, and A. Sharaf. : ‘Applications of Ontology in the Internet of Things: A Systematic Analysis.’ Electronics, vol. 12, no. 1, 2023, Article 111. 10.3390/electronics12010111
XIX. S. R. Katragadda, A. Tanikonda, S. R. Peddinti : ‘Deep Learning for Autonomous Data Quality Enhancement: A Paradigm Shift in Machine Learning Pipelines’. Journal of Information Systems and Education Management, vol. 10 no. 30s, 2025 pp. 602–609. 10.52783/jisem.v10i19s.3102
XX. Kunal Yogen Sevak, and Babu George. : “The Evolution of Internet of Things (IoT) Research in Business Management: A Systematic Review of the Literature.” Journal of Internet and Digital Economics, vol. 4, no. 3, 6 Nov. 2024, pp. 242–265. https://www.emerald.com/jide/article/4/3/242/1212329/The-evolution-of-Internet-of-Things-IoT-research
XXI. J. So, R. E. Ali, B. Güler, J. Jiao, and A. S. Avestimehr. : ‘Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning’. Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, no. 8, June 2023, pp. 9864-73. 10.1609/aaai.v37i8.26177.
XXII. Hua Sun. : ‘Secure aggregation with an oblivious server’. arXiv, 2023. https://arxiv.org/abs/2307.13474
XXIII. S. B. Verma. : (2022). Emerging Trends in IoT and Computing Technologies. https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=%5B33%5D%09Verma%2C+S.+B.+%282022%29.+Emerging+Trends+in+IoT+and+Comp
XXIV. L. Wang, M. Polato, A. Brighente, M. Conti, L. Zhang, and L. Xu. : ‘PriVeriFL: Privacy-Preserving and Aggregation-Verifiable Federated Learning.’ IEEE Transactions on Services Computing, vol. 18, no. 2, Mar.-Apr. 2025, pp. 998–1011. 10.1109/TSC.2024.3451183
XXV. L. Wang, S. Shi, F. Ma, F. R. Yu, P. Li, and Y. T. He. : ‘Subgraph Invariant Learning Towards Large-Scale Graph Node Classification’. Proceedings of the AAAI Conference on Artificial Intelligence, vol. 39, no. 20, Apr. 2025, pp. 21144-52. 10.1609/aaai.v39i20.35412.
XXVI. Y. Wang, Y. Y. Chang, Y. Liu, J. Leskovec, & P. Li. : ‘Inductive representation learning in temporal networks via causal anonymous walks’. arXiv, arXiv:2101.05974. https://arxiv.org/abs/2101.05974
XXVII. L. Yang, C. Chatelain, and S. Adam. : ‘Dynamic Graph Representation Learning With Neural Networks: A Survey.’ IEEE Access, vol. 12, 2024, pp. 43460–43484. https://ieeexplore.ieee.org/abstract/document/10473053
XXVIII. Hong-Yu Yao, Chun-Yang Zhang, Zhi-Liang Yao, C. L. Philip Chen, and Junfeng Hu. : ‘A Recurrent Graph Neural Network for Inductive Representation Learning on Dynamic Graphs.’ Pattern Recognition, vol. 154, 2024, Article 110577. 10.1016/j.patcog.2024.110577
XXIX. Ying, Rex, Tianyu Fu, Andrew Wang, Jiaxuan You, Yu Wang, and Jure Leskovec. : ‘Representation Learning for Frequent Subgraph Mining.’ arXiv, 2024. arxiv.org/abs/2402.14367
XXX. Zhang, Chaoyu, and Shaoyu Li. : ‘State-of-the-Art Approaches to Enhancing Privacy Preservation of Machine Learning Datasets: A Survey.’ arXiv, 2024. arxiv.org/abs/2404.16847.

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Journal Vol – 20 No – 9, September 2025

METHODS FOR CALCULATING THE CONCRETE CORE OF LOOPED REINFORCEMENT JOINTS WITHOUT REINFORCEMENT

Authors:

Alexander Nikolaevich Mamin, Arslan Aselderovich Bammatov

DOI NO:

https://doi.org/10.26782/jmcms.2025.09.00012

September 14, 2025

Abstract:

The article presents a comparative analysis of four methods for calculating the concrete core of looped reinforcement joints without additional reinforcement, including the NIIES Hydroproject method based on Mohr's strength theory, the strut-and-tie model proposed by Singaporean researchers, the BS EN 1992 (Eurocode 2) methodology, and a modified method developed by the authors. The study primarily focuses on analytical techniques to assess the load-bearing capacity of loop joints under various operational conditions, highlighting the distinct characteristics of each approach. The NIIES Hydroproject method, while structurally comprehensive, places a strong emphasis on the strength of the concrete core, which can influence design safety. In contrast, the Singaporean strut-and-tie model provides an alternative analytical perspective but may not always align with practical applications. The BS EN 1992 approach integrates contemporary structural principles and offers a balanced assessment of loop joints, though it necessitates additional reinforcement considerations. The authors’ modified method enhances existing analytical frameworks by incorporating stress adjustments, aligning well with experimental observations while maintaining computational efficiency. A comparative assessment of the four methods is conducted using experimental data for a monolithic beam with loop joints, confirming that the BS EN 1992 methodology and the proposed modified method provide the most reliable results for structural design. The study highlights the importance of accurate and efficient calculation models in ensuring the structural integrity of loop joints in reinforced concrete construction.

Keywords:

loop joints,reinforced concrete,stress-strain state,numerical modeling,reinforcement,structural mechanics,finite element method,strut-and-tie model,contact interaction,construction materials,

Refference:

I. Avdeev, K. V., Mamin, A. N., Bobrov, V. V., Bammatov, A. A., Martyyanov, K. V., Pryakhin, S. N. (2022). Loop joints of bar reinforcement. Development history, problems, and relevance. Construction and Reconstruction, no. 6, pp. 4-11. 10.33979/2073-7416-2022-104-6-4-11
II. Avdeev, K. V., Mamin, A. N., Bobrov, V. V., Bammatov, A. A., Kvasnikov, A. A., Martyyanov, K. V., Pugachev, B. A. (2023). Testing of reinforced concrete structure elements with loop joints of reinforcement. Industrial and Civil Construction, vol. 6, pp. 24-30. 10.33622/0869-7019.2023.06.24-30
III. BS EN 1992-1-1:2023. Eurocode 2: Design of concrete structures – Part 1-1: General rules and rules for buildings.
IV. Dorofeev, V. S., Malakhov, V. V., Nesterenko, S. S. (2012). Analysis of the performance of loop joints of various configurations. Dorofeev, V. S., Shekhovtsov, I. V., Petrash, S. V., Malakhov, V. V. (2011). Strength and deformability of beams with the “Perederia” joint. Resource-saving materials, structures, buildings, and structures: a collection of scientific papers, no. 22, p. 328.
V. Klimov, E. A., Nikolaev, V. B. (2016). Improvement of the methodology for calculating industrial non-welded loop joints of reinforcement in reinforced concrete structures of hydroelectric and nuclear power plants based on limit states. Structural Mechanics of Engineering Structures and Buildings, no. 5, pp. 3-10. https://cyberleninka.ru/article/n/sovershenstvovanie-metodiki-rascheta-industrialnyh-bessvarnyh-petlevyh-stykov-armatury-zhelezobetonnyh-konstruktsiy-ges-i-aes-po
VI. Krylov, A. S. (2019). Numerical calculations of steel-reinforced concrete beams considering the contact interaction of the steel core with concrete. Bulletin of Tomsk State University of Architecture and Civil Engineering, vol. 21, no. 2, pp. 175-184. 10.31675/1607-1859-2019-21-2-175-184
VII. Krylov, S. B., Travush, V. I., Konin, D. V., Krylov, A. S. (2018). Contact technologies in design of reinforced concrete beams with cracks. IOP Conference Series: Materials Science and Engineering, vol. 456. 10.1088/1757-899X/456/1/012004
VIII. Kvasnikov, A. A. (2019). Methodology for calculating the interaction of concrete and reinforcement in reinforced concrete structures using the Abaqus software complex. Structural Mechanics and Analysis of Structures, no. 1, pp. 65-70. https://elibrary.ru/item.asp?id=37027461
IX. Mamin, A. N., Bammatov, A. A., Dolgova, T. V. (2023). Features of the stress-strain state of reinforced concrete structures with non-welded reinforcement joints. Current problems of computer modeling of structures and buildings, pp. 394-395. https://elibrary.ru/njbvka
X. Mamin, A. N., Bammatov, A. A., Gordeev, N. S. (2023). Numerical modeling of structures with loop joints of reinforcement. Construction and Reconstruction, no. 2, pp. 70-79. 10.33979/2073-7416-2023-106-2-70-79
XI. Nikolaev, V. B., et al. (2016). Experimental studies of reinforced concrete structures of nuclear power plants with modified loop joints on large-scale reinforced concrete models of beam type. Safety of Energy Structures, no. 1, pp. 66-81. https://elibrary.ru/item.asp?id=26421061
XII. Nikolaev, V. B., Rubin, O. D., Seleznev, S. V. (1987). Strength calculation and design of loop joints of precast elements. Concrete and Reinforced Concrete, no. 1, pp. 38-40. https://science.totalarch.com/magazine/concrete/concrete_1987_01.pdf
XIII. Ong, K. C. G., Hao, J. B., Paramasivam, P. (2006). A strut-and-tie model for ultimate loads of precast concrete joints with loop connections in tension. Construction and Building Materials, vol. 20, no. 3, pp. 169-176. 10.1016/j.conbuildmat.2005.01.018
XIV. Silantyev, A. S. (2012). Calculation of the strength of inclined sections of reinforced concrete bending elements using the finite element method in ANSYS and Abaqus software complexes. Industrial and Civil Construction, no. 2, pp. 49-52. https://elibrary.ru/opjtnj
XV. Silantyev, A. S., Luchkin, E. A. (2018). Modeling the joint of a round column with a flat slab using the Abaqus software complex. Industrial and Civil Construction, no. 10, pp. 74-80. https://elibrary.ru/ynrbml

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Journal Vol – 20 No – 9, September 2025

ADDRESSING ETHICAL CHALLENGES IN THE USE OF GPT-4 FOR DECISION-MAKING SYSTEMS

Authors:

Syarilla Iryani Ahmad Saany, Abdilahi Liban, Helmi Murad Ebrahim, Rajina R. Mohamed, Yousef A. Baker El-Ebiary

DOI NO:

https://doi.org/10.26782/jmcms.2025.09.00013

September 14, 2025

Abstract:

Introduction: The adoption of advanced AI systems, such as GPT-4, in decision-making processes offers significant potential to improve efficiency and precision. However, this integration introduces complex ethical challenges that must be addressed to ensure responsible and just outcomes. Problem Statement: As AI technologies like GPT-4 become more advanced, concerns over their ability to perpetuate biases, infringe on privacy, and diminish human agency have intensified. The ethical implications of using such systems in decision-making are multifaceted and warrant thorough examination. Objective: This study seeks to explore the ethical issues related to the use of GPT-4 in decision-making systems, focusing on identifying potential risks, evaluating their consequences, and suggesting strategies for addressing these ethical concerns. Methodology: A multidisciplinary approach combining insights from ethics, computer science, and sociology will be utilized. Qualitative analysis will be employed to examine existing case studies, ethical frameworks, and the viewpoints of various stakeholders on the deployment of AI in decision-making. Results: The research will highlight key ethical challenges related to fairness, transparency, accountability, and human oversight in the use of GPT-4 in decision-making systems. It will also provide recommendations for policymakers, developers, and practitioners to address these challenges responsibly. Conclusion: Ethical considerations are critical when implementing AI technologies like GPT-4 in decision-making systems.

Keywords:

AI technologies,Ethical considerations,GPT-4,Decision-making systems,Bias mitigation,Transparency,

Refference:

I. Ahmad Saany, S. I., et al. “Exploitation of a Technique in Arranging an Islamic Funeral.” 2021 2nd International Conference on Smart Computing and Electronic Enterprise (ICSCEE), 2021, pp. 1–8. IEEE, 10.1109/ICSCEE50312.2021.9498224
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III. Al-Sammarraie, N. A., Y. M. H. Al-Mayali, and Y. A. Baker El-Ebiary. “Classification and Diagnosis Using Back Propagation Artificial Neural Networks (ANN).” 2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE), Shah Alam, Malaysia, 2018, pp. 1–5. IEEE, 10.1109/ICSCEE.2018.8538383
IV. Altrad, et al. “Amazon in Business to Customers and Overcoming Obstacles.” 2021 2nd International Conference on Smart Computing and Electronic Enterprise (ICSCEE), 2021, pp. 175–179. IEEE, 10.1109/ICSCEE50312.2021.9498129
V. Antonius, Franciskus, et al. “Incorporating Natural Language Processing into Virtual Assistants: An Intelligent Assessment Strategy for Enhancing Language Comprehension.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 10, 2023. 10.14569/IJACSA.2023.0141079
VI. Baker El-Ebiary, Y. A., et al. “Blockchain as a Decentralized Communication Tool for Sustainable Development.” 2021 2nd International Conference on Smart Computing and Electronic Enterprise (ICSCEE), 2021, pp. 127–133. IEEE. 10.1109/ICSCEE50312.2021.9497910
VII. Baker El-Ebiary, Y. A., et al. “Determinants of Customer Purchase Intention Using Zalora Mobile Commerce Application.” 2021 2nd International Conference on Smart Computing and Electronic Enterprise (ICSCEE), 2021, pp. 159–163. IEEE. 10.1109/ICSCEE50312.2021.9497995
VIII. Baker El-Ebiary, Y. A., et al. “E-Government and E-Commerce Issues in Malaysia.” 2021 2nd International Conference on Smart Computing and Electronic Enterprise (ICSCEE), 2021, pp. 153–158. IEEE. 10.1109/ICSCEE50312.2021.9498092
IX. Baker El-Ebiary, Y. A., et al. “Mobile Commerce and Its Apps—Opportunities and Threats in Malaysia.” 2021 2nd International Conference on Smart Computing and Electronic Enterprise (ICSCEE), 2021, pp. 180–185. IEEE. 10.1109/ICSCEE50312.2021.9498228
X. Baker El-Ebiary, Y. A., et al. “Track Home Maintenance Business Centers with GPS Technology in the IR 4.0 Era.” 2021 2nd International Conference on Smart Computing and Electronic Enterprise (ICSCEE), 2021, pp. 134–138. IEEE. 10.1109/ICSCEE50312.2021.9498070
XI. Bello, Bilkisu Ibrahim, et al. “Evolution of the Role of Customer Satisfaction in E-Commerce.” 2021 2nd International Conference on Smart Computing and Electronic Enterprise (ICSCEE), 2021, pp. 170–174. IEEE. 10.1109/ICSCEE50312.2021.9498229
XII. Darawsheh, Basheer, et al. “Recent Advances in Sentiment Analysis: An Updated Review.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 5, 2023. 10.14569/IJACSA.2023.0140592
XIII. Darawsheh, Basheer, et al. “The Impact of Artificial Intelligence in Smart Cities: A Comprehensive Review.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 8, 2023. 10.14569/IJACSA.2023.0140823
XIV. Darawsheh, Basheer, et al. “The Role of Big Data in Smart City Development: Challenges and Opportunities.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 9, 2023. 10.14569/IJACSA.2023.0140974
XV. Darawsheh, Basheer, et al. “The Role of IoT in Smart City Development: Recent Advances and Future Prospects.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 7, 2023. 10.14569/IJACSA.2023.0140727
XVI. Elamin, Elhadi Abdelrahim Elbasheer, et al. “Understanding and Predicting Internet Addiction among University Students: A Structural Equation Modeling Approach.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 9, 2023. 10.14569/IJACSA.2023.0140926
XVII. Elhoseny, Mohamed, et al. “Blockchain in the Healthcare Industry: A Comprehensive Review and Directions for Future Research.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 9, 2023. 10.14569/IJACSA.2023.0140978
XVIII. Elsheikh, Asaad, et al. “Understanding the Dynamics of AI and IoT in the Development of Smart Cities.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 7, 2023. 10.14569/IJACSA.2023.0140715
XIX. Ezugwu, Absalom E., et al. “Deep Learning Models for Breast Cancer Diagnosis: A Survey.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 3, 2023. 10.14569/IJACSA.2023.0140387
XX. Ezugwu, Absalom E., et al. “Federated Learning for Healthcare Systems: Opportunities, Challenges, and Future Directions.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 6, 2023. 10.14569/IJACSA.2023.0140641
XXI. Fadlalmoula, A. A., et al. “A Comprehensive Review on Cloud Computing Security: Threats, Challenges, and Solutions.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 8, 2023. 10.14569/IJACSA.2023.0140827
XXII. Fadlalmoula, A. A., et al. “Cybersecurity in the Era of Internet of Things: Challenges and Solutions.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 4, 2023. 10.14569/IJACSA.2023.0140465
XXIII. Fadlalmoula, A. A., et al. “The Role of Artificial Intelligence in Healthcare: Opportunities, Challenges, and Future Directions.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 6, 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140614
XXIV. Fadlalmoula, A. A., et al. “The Role of Blockchain in Smart City Development: A Comprehensive Review.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 8, 2023. 10.14569/IJACSA.2023.0140826
XXV. Fadlalmoula, A. A., et al. “The Role of Machine Learning in Cybersecurity: A Comprehensive Review.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 5, 2023. 10.14569/IJACSA.2023.0140522
XXVI. Ghani, N. A., et al. “A Comprehensive Review of Digital Twin Technology in Industry 4.0.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 3, 2023. 10.14569/IJACSA.2023.0140362
XXVII. Ghani, N. A., et al. “Blockchain in Supply Chain Management: Opportunities and Challenges.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 7, 2023. 10.14569/IJACSA.2023.0140741
XXVIII. Ghani, N. A., et al. “The Role of Artificial Intelligence in Education: Challenges and Opportunities.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 6, 2023. 10.14569/IJACSA.2023.0140655
XXIX. Ghani, N. A., et al. “The Role of IoT in the Healthcare Sector: Opportunities, Challenges, and Future Directions.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 5, 2023. 10.14569/IJACSA.2023.0140539
XXX. Ghani, N. A., et al. “The Role of IoT in Transportation: Opportunities, Challenges, and Future Directions.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 6, 2023. \10.14569/IJACSA.2023.0140666
XXXI. Hamza, M. K., et al. “Deep Learning Approaches for Skin Cancer Detection: A Comprehensive Survey.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 3, 2023. 10.14569/IJACSA.2023.0140391
XXXII. Hamza, M. K., et al. “Machine Learning for Predictive Analytics in Healthcare: Recent Advances and Challenges.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 8, 2023. 10.14569/IJACSA.2023.0140832
XXXIII. Hamza, M. K., et al. “The Role of AI in Predictive Maintenance for Industry 4.0: Opportunities and Challenges.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 4, 2023. 10.14569/IJACSA.2023.0140486
XXXIV. Hamza, M. K., et al. “The Role of Machine Learning in Financial Services: Challenges and Future Prospects.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 7, 2023. 10.14569/IJACSA.2023.0140735
XXXV. Hamza, M. K., et al. “The Role of Machine Learning in Renewable Energy Systems: A Comprehensive Review.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 9, 2023. 10.14569/IJACSA.2023.0140938
XXXVI. Hashim, Nor Hazimah, et al. “Modeling E-Service Quality and Its Impact on E-Loyalty in E-Commerce.” 2021 2nd International Conference on Smart Computing and Electronic Enterprise (ICSCEE), 2021, pp. 164–169. IEEE. 10.1109/ICSCEE50312.2021.9498014
XXXVII. Hussain, Shakir, et al. “Machine Learning Models for Diabetes Prediction: A Review.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 2, 2023. 10.14569/IJACSA.2023.0140218
XXXVIII. Ismail, M., et al. “Artificial Intelligence Applications in Agriculture: Opportunities, Challenges, and Future Directions.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 7, 2023. 10.14569/IJACSA.2023.0140719
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XL. Ismail, M., et al. “IoT Applications in Agriculture: A Comprehensive Review.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 3, 2023. 10.14569/IJACSA.2023.0140372
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XLV. Ismail, M., et al. “The Role of Machine Learning in Transportation: Challenges and Opportunities.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 9, 2023. 10.14569/IJACSA.2023.0140912
XLVI. Jameel, A., et al. “AI Applications in the Automotive Industry: Challenges and Opportunities.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 5, 2023. 10.14569/IJACSA.2023.0140528
XLVII. Jameel, A., et al. “Blockchain in Financial Services: Challenges and Future Prospects.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 6, 2023. 10.14569/IJACSA.2023.0140635
XLVIII. Jameel, A., et al. “Blockchain in Healthcare: Opportunities and Challenges.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 4, 2023. 10.14569/IJACSA.2023.0140462
XLIX. Jameel, A., et al. “Cybersecurity Challenges in the IoT Era: A Comprehensive Review.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 7, 2023. 10.14569/IJACSA.2023.0140726
L. Jameel, A., et al. “The Role of AI in Enhancing E-Government Services: Opportunities and Challenges.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 8, 2023. 10.14569/IJACSA.2023.0140821
LI. Jameel, A., et al. “The Role of Blockchain in Enhancing Cybersecurity: Opportunities and Challenges.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 3, 2023. /10.14569/IJACSA.2023.0140388
LII. Jameel, A., et al. “The Role of Machine Learning in Smart Agriculture: Opportunities, Challenges, and Future Directions.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 9, 2023. 10.14569/IJACSA.2023.0140934
LIII. Khan, F., et al. “Artificial Intelligence in the Financial Sector: Opportunities and Challenges.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 2, 2023. 10.14569/IJACSA.2023.0140225
LIV. Khan, F., et al. “Blockchain for Supply Chain Transparency: Opportunities and Challenges.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 5, 2023. 10.14569/IJACSA.2023.0140548
LV. Khan, F., et al. “Cybersecurity in Cloud Computing: Challenges and Solutions.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 8, 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140849
LVI. Khan, F., et al. “IoT Applications in the Healthcare Sector: Opportunities, Challenges, and Future Prospects.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 6, 2023. 10.14569/IJACSA.2023.0140646
LVII. Khan, F., et al. “The Role of AI in Industry 4.0: Opportunities, Challenges, and Future Directions.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 7, 2023. 10.14569/IJACSA.2023.0140743
LVIII. Khan, F., et al. “The Role of Machine Learning in E-Commerce: Opportunities and Challenges.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 4, 2023. 10.14569/IJACSA.2023.0140459
LIX. Khan, F., et al. “The Role of Machine Learning in Manufacturing: Opportunities and Challenges.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 3, 2023. 10.14569/IJACSA.2023.0140321
LX. Khan, F., et al. “The Role of Machine Learning in Renewable Energy: A Comprehensive Review.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 2, 2023. 10.14569/IJACSA.2023.0140242
LXI. Mahmood, A., et al. “Blockchain Applications in Supply Chain Management: A Review.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 9, 2023. 10.14569/IJACSA.2023.0140928
LXII. Mahmood, A., et al. “Blockchain for Smart Cities: Opportunities and Challenges.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 8, 2023. 10.14569/IJACSA.2023.0140824
LXIII. Mahmood, A., et al. “Cybersecurity Challenges in Smart Cities: A Comprehensive Review.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 7, 2023. 10.14569/IJACSA.2023.0140736

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Journal Vol – 20 No – 9, September 2025