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Abstract:

Diabetes imposes a substantial public health burden; according to the International Diabetes Federation, there were about 3.4 million diabetes related deaths worldwide in 2024, and in Iraq, the Federation reports that one in nine adults lives with diabetes in 2024, with 14,683 adult deaths attributable to diabetes and a total diabetes related health expenditure of 2,078 million United States dollars. The dataset analyzed in this study contains 1,000 records collected in 2020 from two Iraqi teaching hospitals and includes multiple clinical and laboratory measurements with three outcome classes, namely Non diabetic, Pre diabetic, and Diabetic, with a low prevalence of the Pre diabetic class and an imbalanced overall class distribution; the data are challenging because they contain many outliers, non homogeneous covariance matrices across classes, exact duplicate rows that were removed before modelling, and linear correlations among certain variables. The study objective was to train and evaluate models that discriminate among the three classes and yield accurate, well calibrated predictions for future cases in similar clinical settings, but the diagnostic properties of the data limited the applicability of classical discriminant functions; therefore two supervised learners were employed: Classification and Regression Trees (CART) and Extreme Gradient Boosting (XGBoost), together with preprocessing that removed exact duplicate rows and excluded VLDL because it is algebraically derived from triglycerides in mmol per liter as VLDL equals triglycerides divided by 2.2, which would introduce redundancy and multicollinearity. On the held-out test set, XGBoost achieved higher Accuracy at 98.18 percent compared with 97.58 percent for CART and higher Balanced Accuracy at 93.84 percent compared with 88.16 percent for CART, indicating that XGBoost provided the strongest overall operating point for this three-class task while CART remains useful when simple and transparent rules are required.

Keywords:

Classification,XGBoost,CART,Class imbalance,Diabetes,Pre diabetic,

References:

I. American Diabetes Association Professional Practice Committee. 2. Diagnosis and Classification of Diabetes: Standards of Care in Diabetes, 2024. Diabetes Care. 2024;47(Suppl 1):S20–S42. 10.2337/dc24-S002.
II. Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and Regression Trees. Monterey, CA: Wadsworth. (Reprinted by Chapman & Hall/CRC, Taylor & Francis, 2017). 10.1201/9781315139470
III. Brier, G. W. (1950). Verification of forecasts expressed in terms of probability. Monthly Weather Review, 78(1), 1–3. 10.1175/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2
IV. Chen T, Guestrin C. XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016:785–794. doi:10.1145/2939672.2939785.
Duncan BB, et al. IDF Diabetes Atlas 11th edition 2025: global prevalence and key metrics. Nephrology Dialysis Transplantation. 2025. 10.1093/ndt/gfaf177.
V. Friedewald, W. T., Levy, R. I., & Fredrickson, D. S. (1972). Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clinical Chemistry, 18(6), 499–502. 10.1093/clinchem/18.6.499
VI. Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29(5), 1189–1232. 10.1214/aos/1013203451
VII. Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874. https://doi.org/10.1016/j.patrec.2005.10.010
VIII. Guo, R., Liu, J., Yang, X., Wang, X., Xu, Z., & Wu, K. (2025). Enhance health evidence quality in classification tasks. Digital Health, 11, 20552076251314097. 10.1177/20552076251314097
IX. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed.). Springer. 10.1007/978-0-387-84858-7
X. International Diabetes Federation (IDF). IDF Diabetes Atlas, 11th edition (global facts and projections). Available at: https://diabetesatlas.org/data-by-location/global/ and Global Factsheet PDF (2025).
XI. Idhom, M., Fauzi, A., Muhaimin, A., & Caesarendra, W. (2025). Evaluation of CART and XGBoost Methods on Customer Loan Risk Prediction Based on Consumer Behavior. TEM Journal, 14(3), 2624–2630. 10.18421/TEM143-64
XII. Nuankaew, P.; Chaising, S.; Temdee, P. (2021). Average Weighted Objective Distance-Based Method for Type 2 Diabetes Prediction. IEEE Access, 9, 137015–137028. 10.1109/ACCESS.2021.3117269
XIII. Rashid, Ahlam (2020), “Diabetes Dataset”, Mendeley Data, V1,
XIV. 10.17632/wj9rwkp9c2.1
XV. Rainio, O., Teuho, J., & Klén, R. (2024). Evaluation metrics and statistical tests for machine learning. Scientific Reports, 14, 6086. 10.1038/s41598-024-56706-x
XVI. Sabariah, M. K.; Hanifa, A.; Sa’adah, S. (2014). Early detection of Type II Diabetes Mellitus with Random Forest and Classification and Regression Tree (CART). In: Proceedings of the 2014 International Conference on Advanced Informatics: Concept, Theory and Application (ICAICTA), 238–242. IEEE. 10.1109/ICAICTA.2014.7005947
XVII. Sahid, M. A.; Babar, M. U. H.; Uddin, M. P. (2024). Predictive modeling of multi-class diabetes mellitus using machine learning and filtering Iraqi diabetes data dynamics. PLOS ONE, 19(5), e0300785. 10.1371/journal.pone.0300785
XVIII. Sokolova, M., & Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Information Processing & Management, 45(4), 427–437. 10.1016/j.ipm.2009.03.002

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Abstract:

In this work, we use the Mann iteration process rather than the conventional Picard operator to extend fixed point findings in G-metric spaces. Mann iteration is known to provide better convergence properties and stability in fixed point approximations, particularly in cases where Picard iteration fails due to weak contractive conditions. We present a new family of?rational-type contractive conditions and prove the existence and uniqueness of fixed points of single-valued mappings in G-complete G metric spaces. Specifically, we improve upon existing theorems in the literature both by generalizing their?statements as well as strengthening their use through an improved iterative scheme.

Keywords:

G-metric space,Fixed point,Mann iteration,Rational contraction,G-convergence,Iterative approximation,

References:

I. Aldwoah, K., Shah, S. K., Hussain, S., Almalahi, M. A., Arko, Y. A. S., & Hleili, M. (2024). Investigating fractal fractional PDEs, electric circuits, and integral inclusions via (?, ?)-rational type contractions. Scientific Reports, 14(23546), 1–15. 10.1038/s41598-024-74046-8
II. Acar, Ö. (2023). Some recent and new fixed point results on orthogonal metric-like space. Constructive Mathematical Analysis, 6(3), 184–197. 10.33205/cma.1360402
III. Alqahtani, B., Alzaid, S. S., Fulga, A., & Roldán López de Hierro, A. F. (2021). Proinov type contractions on dislocated b-metric spaces. Advances in Difference Equations, 2021(164), 1–16. 10.1186/s13662-021-03329-5
IV. Ege, O., Park, C., & Ansari, A. H. (2020). A different approach to complex valued Gb-metric spaces. Advances in Difference Equations, 2020(152), 1–13. 10.1186/s13662-020-02605-0
V. Imdad, M., Alfaqih, W. M., & Khan, I. A. (2018). Weak ?-contractions and some fixed point results with applications to fractal theory. Advances in Difference Equations, 2018(1), 439. 10.1186/s13662-018-1900-8.
VI. Kumar, M., Ege, O., Mor, V., Kumar, P., & De la Sen, M. (2024). Boyd-Wong type contractions in generalized parametric bipolar metric space. Heliyon, 10(1), e23998. 10.1016/j.heliyon.2024.e23998
VII. Yildirim, I., & Khan, S. Hussain. (2022). Convexity in G-metric spaces and approximation of fixed points by Mann iterative process. International Journal of Nonlinear Analysis and Applications, 13(1), 1957–1964. 10.22075/ijnaa.2021.21435.2259.
VIII. Gaba, Y. U. (2017). Fixed point theorems in G-metric spaces. Journal of Mathematical Analysis and Applications, 455(1), 528–537. 10.1016/j.jmaa.2017.05.062
IX. Kanwal, S., Waheed, S., Rahimzai, A. A., & Khan, I. (2024). Existence of common fuzzy fixed points via fuzzy F-contractions in b-metric spaces. Scientific Reports, 14(7807), 1–14. 10.1038/s41598-024-58451-7
X. Karap?nar, E., Chen, C.-M., Alghamdi, M. A., & Fulga, A. (2021). Advances on the fixed point results via simulation function involving rational terms. Advances in Difference Equations, 2021(409), 1–20. 10.1186/s13662-021-03564-w
XI. Okeke, G. A., Francis, D., & de la Sen, M. (2020). Some fixed point theorems for mappings satisfying rational inequality in modular metric spaces with applications. Heliyon, 6(9), e04785. 10.1016/j.heliyon.2020.e04785
XII. Rao, N. S., & Kalyani, K. (2020). Unique fixed point theorems in partially ordered metric spaces. Heliyon, 6(11), e05563. 10.1016/j.heliyon.2020.e05563
XIII. Rao, N. S. (2022). Coupled fixed points of (??, ??, ??)-contractive mappings in partially ordered b-metric spaces. Heliyon, 8(12), e12442. 10.1016/j.heliyon.2022.e12442
XIV. Rao, N. S., Aloqaily, A., & Mlaiki, N. (2024). Results on fixed points in b-metric space by altering distance functions. Heliyon, 10(7), e33962. 10.1016/j.heliyon.2024.e33962
XV. Rasham, T., Shoaib, A., Park, C., Agarwal, R. P., & Aydi, H. (2021). On a pair of fuzzy mappings in modular-like metric spaces with applications. Advances in Difference Equations, 2021(245), 1–17. 10.1186/s13662-021-03398-6
XVI. Rasham, T., Asif, A., Aydi, H., & De La Sen, M. (2021). On pairs of fuzzy dominated mappings and applications. Advances in Difference Equations, 2021(417), 1–22. 10.1186/s13662-021-03569-5
XVII. Mustafa, Z., & Sims, B. (2009). Fixed point theorems for contractive mappings in complete G-metric spaces. Fixed Point Theory and Applications, 2009, Article 917175. 10.1155/2009/917175
XVIII. Rasham, T., Panda, S. K., Basendwah, G. A., & Hussain, A. (2024). Multivalued nonlinear dominated mappings on a closed ball and associated numerical illustrations with applications to nonlinear integral and fractional operators. Heliyon, 10(7), e34078. 10.1016/j.heliyon.2024.e34078.
XIX. Shoaib, M., Abdeljawad, T., Sarwar, M., & Jarad, F. (2019). Fixed point theorems for multi-valued contractions in b-metric spaces with applications to fractional differential and integral equations. IEEE Access, 7, 127373–127383. 10.1109/ACCESS.2019.2938635
XX. Souayah, N., Mlaiki, N., & Mrad, M. (2018). The GM-contraction principle for mappings on an M-metric spaces endowed with a graph and fixed-point theorems. IEEE Access, 6, 25178–25188. 10.1109/ACCESS.2018.2833147
XXI. Turcanu, T., & Postolache, M. (2024). On a new approach of enriched operators. Heliyon, 10(3), e27890. 10.1016/j.heliyon.2024.e27890

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Abstract:

Modern Industrial Control Systems (ICS) and Supervisory Control and Data Acquisition (SCADA) networks face a growing class of logic-layer attacks in which adversaries silently manipulate configuration or project files instead of deploying traditional malware. Existing defences, such as network intrusion de-tection systems and machine-learning-based anomaly detectors, struggle to ob-serve these pre-deployment logic changes and often incur high operational complexity. This paper presents a lightweight, host-based framework that uses YARA, a rule-based pattern-matching engine, to perform static inspection of XML configuration files generated by SCADA engineering tools. The proposed system is implemented on a Windows 10 engineering workstation using Mod-busPal as a Modbus TCP simulator, Python for file monitoring and GUI devel-opment, and YARA CLI/Python bindings for rule execution. Custom YARA rules are crafted to detect unauthorized Modbus function code 5 (Write Single Coil) operations targeting critical coil addresses, modelling malicious logic injections such as covert actuator activations. In a controlled lab environment, using a va-riety of ModbusPal project files, a combination of benign (no infiltration) and tampered project files, as well as our detection framework, achieved less than 200 milliseconds of latency for detecting true positives (and 0 false positives and 0 false negatives) for the defined ruleset and under a negligible resource over-head. These findings indicate that static logic validation at the host-level would fulfil an effective integrity pre-deployment check for PLC logic in addition to current network-based and behaviour-based ICS security mechanisms, without requiring modification of the installed PLC hardware and network protocol.

Keywords:

SCADA Security,Industrial Control Systems (ICS),YARA,Logic,Modbus,TCP,Host-Based Intrusion Detection,Static Analysis,OT Cybersecurity,

References:

I. Adepu, M., and A. Mathur. “SCADAhunt: Framework for Detecting Pro-cess Control Attacks.” International Journal of Critical Infrastructure Protection, vol. 19, 2017. https://www.sciencedirect.com/science/article/pii/S1874548217300279

II. Cheminod, M., L. Durante, and A. Valenzano. “Review of Security Issues in Industrial Networks.” IEEE Transactions on Industrial Informatics, vol. 9, no. 1, 2013, pp. 277–293. 10.1109/TII.2012.2198666

III. Chung, S. P., et al. “Host-Based Detection of ICS Configuration Tamper-ing.” Proceedings of the Annual Computer Security Applications Confer-ence (ACSAC), 2022. 10.1145/3564625.3564629

IV. Claroty Team82. “MITRE ATT&CK for ICS: Detecting Logic Manipula-tion TTPs.” Claroty Research, 2024. https://claroty.com/team82/research

V. Costin, A. “Towards a Framework for ICS Intrusion Detection.” Black Hat USA, 2020. https://www.blackhat.com/us-20/

VI. Dragos. “FrostyGoop: Modbus Malware Targeting Coils.” ICS Threat De-tection Bulletin, 2024. https://www.dragos.com/

VII. Dragos, Inc. “INCONTROLLER (PIPEDREAM): Highly Capable ICS Toolkit.” Threat Intelligence Report, Apr. 2022. https://www.dragos.com/resources/

VIII. Dressler, F., and P. Sommer. “Using Zeek for ICS Protocol Detection.” Proceedings of the 9th USENIX Workshop on Cyber Security Experimen-tation and Test (CSET), 2021. https://www.usenix.org/conference/cset21

IX. ENISA. “Threat Landscape for Industrial Control Systems.” ENISA Threat Report, 2025. https://www.enisa.europa.eu/publications

X. Forescout Research. The State of Modbus Security. Forescout Labs Tech-nical Brief, 2023. https://www.forescout.com/resources/

XI. Ike, H., et al. “SCAPHY: Behavior-Aware ICS Security Using Physical Traces.” Proceedings of the IEEE International Conference on Industrial Cyber-Physical Systems, 2022. https://ieeexplore.ieee.org/

XII. ICS-CERT. “Advisory (ICS-ALERT-14-281-01) — BlackEnergy Mal-ware.” U.S. Department of Homeland Security, 2014. https://www.cisa.gov/news-events/ics-alerts/ics-alert-14-281-01

XIII. ICS-CERT. “Havex Malware Targeting ICS/SCADA Systems.” Industrial Control Systems Cyber Emergency Response Team, 2013. https://www.cisa.gov/ics

XIV. Kravchik, M., and A. Shabtai. “Detecting Cyber Attacks in Industrial Control Systems Using Convolutional Neural Networks.” Proceedings of the ACM Workshop on Cyber-Physical Systems Security & Privacy, 2018. 10.1145/3264888.3264896

XV. Langner, R. “Stuxnet: Dissecting a Cyberwarfare Weapon.” IEEE Securi-ty & Privacy, vol. 9, no. 3, 2011, pp. 49–51. 10.1109/MSP.2011.67

XVI. Mandiant. “FrostyGoop ICS Malware Technical Analysis.” Mandiant Threat Intelligence, 2024. https://www.mandiant.com/resources

XVII. McLaughlin, S., et al. “The Cybersecurity Landscape in Industrial Con-trol Systems.” Proceedings of the IEEE, vol. 104, no. 5, 2016, pp. 1039–1057. 10.1109/JPROC.2015.2512235

XVIII. Modbus Organization. “Modbus Application Protocol Specification V1.1b3.” 2015. http://modbus.org/docs/Modbus_Application_Protocol_V1_1b3.pdf

XIX. NIST. Guide to Industrial Control Systems (ICS) Security. SP 800-82 Re-vision 3, 2025. https://csrc.nist.gov/publications/detail/sp/800-82/rev-3/final

XX. Nguyen, N., T. Ogawa, and M. Saito. “Integrity Verification for PLC Log-ic Files Using Lightweight Hash Trees.” IEEE Transactions on Industrial Informatics, vol. 21, no. 2, 2025, pp. 1204–1213. https://ieeexplore.ieee.org/

XXI. Searle, J., et al. “LogicLocker: Ransomware for Programmable Logic Controllers.” Georgia Tech ICS Security Lab, 2017. https://arxiv.org/

XXII. TXOne Networks. “PIPEDREAM Local Exploit Analysis.” TXOne Threat Research, 2025. https://www.txone.com/blog/

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Abstract:

This study investigates the issues of energy usage in multipath wireless networks utilizing the Multipath Transport Control Protocol (MPTCP) under application-level timing knobs implemented in socket logic, which allows numerous TCP connections via different pathways. Due to route heterogeneity, MPTCP consumes more energy. Currently, many research works have provided several techniques to optimize energy efficiency; however, they focused on individual systems rather than total performance. This work proposed a stochastic multipath scheduling technique that considers the fluctuations in data transmission rate and path capacity. The scheduling mechanism is associated with the optimization problem to achieve the objectives of maximizing throughput, avoiding congestion, and improving stability. An algorithm is developed to solve multipath data transmission issues by utilizing the drift-based constraints. Simulations are performed to generate results for the comparison of three different optimized MPTCP schemes in the application layer with baseline and conventional protocols. The results are showing considerable improvements in throughput and end-to-end latency

Keywords:

Multipath Transmission,Optimization,Energy Efficiency,TCP,

References:

I. Abbas, Ahmed Saleem. “Technical Comparison between MPTCP and TCP in Heterogeneous Networks.” Int. J. Interact. Mob. Technology, Vol. 16, 2022, pp. 163–175. 10.3991/ijim.v16i19.35299.
II. Aljubayri, Mohammad., Tong Peng, and Mohamad Shikh-Bahaei, “Reduce delay of multipath TCP in IoT networks.” Wireless Networks, vol. 27.no. 6, 2021, pp. 4189-4198. 10.1007/s11276-021-02701-3
III. Aljubayri, Mohammed, Tong Peng, and Mohammad Shikh-Bahae. “Reduce delay of multipath TCP in IoT networks.” Wireless Networks, vol. 27, no. 6, 2022, pp. 4189-4198. 10.1007/s11276-021-02701-3.
IV. Arain, Zulfiqar Arain et al. “Stochastic Optimization of Multipath TCP for Energy Minimization and Network Stability over Heterogeneous Wireless Network.” KSII Transactions on Internet & Information Systems, vol. 15, no. 1, 2021, pp. 11-22. 10.3837/tiis.2021.01.012.
V. Arain, Zulfiqar Arain, et al. “Stochastic Optimization of Multipath TCP for Energy Minimization and Network Stability over Heterogeneous Wireless Network.” KSII Transactions on Internet & Information Systems, vol. 15, no. 1, 2021, pp. 195-215. 10.3837/tiis.2021.01.012.
VI. Bhering, F. et al. “Wireless multipath video transmission: when IoT video applications meet networking—a survey.” Multimedia Systems, vol. 28, no. 3, 2022, pp. 831-850. 10.1007/s00530-021-00885-4.
VII. Cisco, “Visual Networking Index: Forecast and Methodology”, CISCO white paper, vol. 202, 2017, p. 17.
VIII. Cui, HuanXi, et al. “Lyapunov Optimization Based Energy Efficient Congestion Control for MPTCP in HetNets,” Proc. of IEEE 18th International Conference on Communication Technology (ICCT), 2018, pp. 440-445, 10.1109/ICCT.2018.8600159.
IX. Cui Yong, et al. “FMTCP: A Fountain Code-based Multipath Transmission Control Protocol,” IEEE/ACM Transactions on Networking, vol. 23, no. 2, 2015, pp. 465-478. 10.1109/TNET.2014.2300140.
X. Dong, Pingping et al. “An Energy-Saving scheduling algorithm for Multipath TCP in wireless networks.” Electronics, vol. 11, no. 3, 2022, p. 490. 10.3390/electronics11030490
XI. Ji, Lejun, et al. “Research on Attack Signal Feature Extraction Method of Multipath TCP Transmission System Based on Wavelet Energy Entropy.” International Conference on Mobile Networks and Management, vol. 474, 2022, pp. 398-412. 10.1007/978-3-031-32443-7_29.
XII. Ji, Xiaolan., et al. “Adaptive QoS-aware multipath congestion control for live streaming.” Computer Networks, vol. 220, 2023, p. 109470. 10.1016/j.comnet.2022.109470.
XIII. Khan, Imran, et al. “Multipath TCP in smartphones equipped with millimeter wave radios.” In Proceedings of the 15th ACM Workshop on Wireless Network Testbeds, Experimental evaluation & Characterization, vol. 1, 2022, pp. 54-60. 10.1145/3477086.34808.
XIV. Lee, Jae Yong, et al. “Coupled CUBIC Congestion Control for MPTCP in Broadband Networks.” Comput. Syst. Sci. Eng., vol. 45, no. 1, 2023, pp. 99–115. 10.32604/csse.2023.030801.
XV. Lim, Yeon-sup, et al. “Design, Implementation, and Evaluation of Energy-aware Multi-path TCP.” Proc. of the 11th ACM Conference on Emerging Networking Experiments and Technologies, 2015, pp. 1-13. 10.1145/2716281.28361.
XVI. Lim, Yeon-sup. “Cost-Efficient Framework for Mobile Video Streaming using Multi-Path TCP.” KSII Transactions on Internet & Information Systems, vol. 16, no. 4, 2022, pp. 234-243. 10.3837/tiis.2022.04.009.
XVII. ?uczak, ?ukasz Piotr, Przemys?aw Ignaciuk, and Micha? Morawsk “Evaluating MPTCP congestion control algorithms: Implications for streaming in open Internet.” Future Internet, vol. 15, no. 10, 2023, p.328. 10.3390/fi15100328.
XVIII. Majeed, Uzma, et al. “An energy-efficient distributed congestion control protocol for wireless multimedia sensor networks.” Electronics, vol. 11, no. 20, 2022, p. 3265. 10.3390/electronics11203265.
XIX. Matheen, M. A., and S. Sundar. “IoT multimedia sensors for energy efficiency and security: A review of QoS aware and methods in wireless multimedia sensor networks.” International Journal of Wireless Information Networks, vol. 29, no. 4, 2022, pp. 407-418. 10.1007/s10776-022-00567-6.
XX. Palash, Mijanur Rahaman, Kang Chen, and Imran Khan. “Bandwidth-Need Driven Energy Efficiency Improvement of MPTCP Users in Wireless Networks,” IEEE Transactions on Green Comm. & Networking, vol. 3, no. 2, 2019, pp. 343-355. 10.1109/TGCN.2019.2897778.
XXI. Prakash, P. Suman, Dwaram Kavitha, and P. Chenna Reddy. “Energy and congestion-aware load balanced multi-path routing for wireless sensor networks in ambient environments.” Computer Communications, vol. 195, 2022, pp. 217-226. 10.1016/j.comcom.2022.08.012.
XXII. Qiao, Wenxuan., et al. “An AI-enhanced multipath TCP scheduler for open radio access networks.” IEEE Transactions on Green Communications and Networking, vol. 8, no. 3, 2024, pp. 910-923. 10.1109/TGCN.2024.3424202.
XXIII. Tran, Duong Dinh, et al. “Formal analysis of post-quantum hybrid key exchange ssh transport layer protocol.” IEEE Access, vol. 12, 2023, pp. 1672-1687. 10.1109/ACCESS.2023.3347914.
XXIV. Ullah, Y. et al. “A survey on AI-enabled mobility and handover management in future wireless networks: key technologies, use cases, and challenges.” Journal of King Saud University Computer and Information Sciences, vol. 37, no. 4, 2025, p. 47. 10.1007/s44443-025-00048-9.
XXV. Wang, Chengke., et al. “Experience: a three-year retrospective of large-scale multipath transport deployment for mobile applications.” Proceedings of the 29th Annual International Conference on Mobile Computing and Networking, 2023, pp. 1-15. 10.1145/3570361.35925.
XXVI. Wu, Jian, Rui Tan, and Ming Wang, “Energy-Efficient Multipath TCP for Quality-Guaranteed Video over Heterogeneous Wireless Networks,” IEEE Transactions on Multimedia, vol. 21, no. 6, 2019, pp. 1593-1608. 10.1109/TMM.2018.2879748.
XXVII. Zhao Jia, Liu, et al. “Multipath congestion control: Measurement, analysis, and optimization from the energy perspective.” IEEE Transactions on Network Science and Engineering, vol. 10, no. 6, 2023, pp. 3295-3307 10.1109/TNSE.2023.3257034.

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Abstract:

The exponential growth of cloud computing has brought both operational efficiency and complex cybersecurity challenges. Traditional intrusion detection systems (IDS) struggle to adapt to dynamic attack patterns and ensure data confidentiality. This research proposes a hybrid Artificial Intelligence–Cryptographic Framework that integrates deep learning and lightweight encryption to achieve adaptive threat detection while maintaining secure communication within cloud environments. Using the CICIDS 2023 and UNSW-NB15 datasets, the model combines a CNN–LSTM network for behavioral anomaly recognition with AES–ECC encryption for data integrity. Experimental results show a detection accuracy of 98.2 %, an F1-score of 97.9 %, and a 50 % reduction in false positives compared with traditional AI models, while maintaining an average encryption latency of 45 ms. Statistical validation using the Wilcoxon signed-rank test confirmed the significance of these improvements (p < 0.05). The study contributes theoretically by bridging information asymmetry, signaling, and fair-value principles into cybersecurity and practically by providing a scalable, efficient, and trust-aware solution for adaptive cloud protection.

Keywords:

Cloud Security,Artificial Intelligence,Cryptography,Hybrid Framework,Intrusion Detection,AES-ECC Encryption,Adaptive Threat Detection,Cybersecurity,Information Asymmetry,Deep Learning.,

References:

I. Ali, S. (2025). Security and privacy in multi-cloud and hybrid systems. Journal of Cloud Security, 12(3), 45–60.
II. Alazab, M., Alazab, M., & Zhang, J. (2023). AI-driven intrusion detection in cloud environments. Computers & Security, 127, 103056.
III. Alazab, M., Alhyari, S., Awajan, A., & Abdalla, A. (2023). Machine learning-based intrusion detection systems in cloud computing. Computers & Security, 125, 103028.

IV. Alshamrani, M., Bahashwan, A., & Alotaibi, B. (2020). Machine learning techniques for cybersecurity threat detection: A comprehensive review. IEEE Access, 8, 221990–222010.

V. Ahmad, N., & Javed, H. (2023). Hybrid AI–blockchain frameworks for reliable cloud security. Journal of Information Security Research, 12(4), 210–225.

VI. Current Study (2025) refers to the authors’ ongoing research and therefore is not externally published.
VII. Deegan, C. (2022). Fair Value Theory and Its Role in Enhancing Corporate Reporting Transparency. Accounting Perspectives, 18(1), 33–49.
VIII. Hassan, M., Noor, M., & Rahim, R. (2024). Integrating AES and LSTM models for adaptive cloud threat mitigation. Computers & Security, 132, 103355.

IX. Kaur, P., & Singh, S. (2021). Deep learning-based intrusion detection framework using CNN–LSTM model. Future Generation Computer Systems, 115, 225–238.

X. Kim, Y., Park, H., & Seo, J. (2024). Cognitive CNN–LSTM-based intrusion detection for virtualized cloud environments. Expert Systems with Applications, 242, 121816.

XI. Rahman, M., Chowdhury, S., & Alam, K. (2022). Blockchain and AI-enabled hybrid systems for secure cloud infrastructures. IEEE Transactions on Cloud Computing, 10(6), 3624–3637.
XII. Rahman, M., Chowdhury, F., & Zhang, T. (2022). Benchmarking hybrid AI models for adaptive threat detection. Cybersecurity (SpringerOpen), 5(3), 18–32.
XIII. Smith, J., & Jones, A. (2022). Modern cloud architecture and threats. International Journal of Cloud Computing, 9(1), 1–20.

XIV. Zhao, L., Chen, Y., & Li, H. (2022). Federated learning architectures for privacy-preserving cloud intrusion detection. Information Sciences, 603, 112–128.

XV. Zhou, W., Li, P., & Wang, X. (2024). Information asymmetry and trust in AI-driven security frameworks. Journal of Information Technology, 39(2), 211–228.

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Abstract:

Effective time management is vital for undergraduate students to succeed in demanding academic environments, yet scalable assessment tools remain limited. This study introduces a hybrid XGBoost-LSTM framework, integrated with a Python Flask-based web application, to evaluate time management competence among 313 undergraduate students at a college in West Bengal, India. A PCA validated 10-question quiz, derived from a 31-item survey, demonstrated high reliability with Cronbach’s Alpha equal to 0.87. The XGBoost model classified students into Poor, Average, or Good categories with an accuracy of 90% and an F1-score of 0.89, while a RandomForestRegressor achieved an RMSE of 0.21, improving 75.65% over the baseline. SHAP-based analysis identified delaying tasks and scheduling as key predictors. A significant gender difference was found (p=0.013), but no residence differences (p=0.43). A simulated LSTM model was implemented as proof-of-concept for future longitudinal analysis, with an RMSE of 0.21. The Flask application provides real-time categorization and feedback, offering a scalable tool for identifying students needing support. Future work includes longitudinal data collection and cloud-based deployment to enhance regional educational insights.

Keywords:

Time Management,XGBoost,LSTM,Explainable AI,Higher Education,SHAP,

References:

I. Alkhanbouli, R., Almadhaani, H. M. A., Alhosani, F., & Simsekler, M. C. E. (2025). The role of explainable artificial intelligence in disease prediction: A systematic literature review and future research directions. BMC Medical Informatics and Decision Making, 25(1), Article 110. 10.1186/s12911-025-02944-6

II. Band, S. S., Yarahmadi, A., Hsu, C. C., Biyari, M., Sookhak, M., Ameri, R. & Liang, H. W. (2023). Application of explainable artificial intelligence in medical health: A systematic review of interpretability methods. Informatics in Medicine Unlocked, 40, Article 101286. 10.1016/j.imu.2023.101286

III. Halde, R. R. (2016). Application of machine learning algorithms for betterment in education system. In 2016 International Conference on Automatic Control and Dynamic Optimization Techniques (ICACDOT) (pp. 1110–1114). IEEE. 10.1109/ICACDOT.2016.7877759
IV. Kumar, K., Kumar, P., Deb, D., Unguresan, M. L., & Muresan, V. (2023). Artificial intelligence and machine learning based intervention in medical infrastructure: A review and future trends. Healthcare, 11(2), Article 207. 10.3390/healthcare11020207

V. Orrù, G., Monaro, M., Conversano, C., Gemignani, A., & Sartori, G. (2020). Machine learning in psychometrics and psychological research. Frontiers in Psychology, 10, Article 2970. 10.3389/fpsyg.2019.02970

VI. Pendyala, V., & Kim, H. (2024). Assessing the reliability of machine learning models applied to the mental health domain using explainable AI. Electronics, 13(6), Article 1025. 10.3390/electronics13061025

VII. Praveenraj, D. D. W., Habelalmateen, M. I., Shrivastava, A., Kaur, A., Valarmathy, A. S., & Patnaik, C. P. (2024). Behavioral time management analysis: Clustering productivity patterns using K-means. In 2024 IEEE International Conference on Communication, Computing and Signal Processing (IICCCS) (pp. 1–6). IEEE. 10.1109/IICCCS61609.2024.10763887
VIII. Rezazadeh, H., Mahani, A. M., & Salajegheh, M. (2022). Insights into the future: Assessing medical students' artificial intelligence readiness—A cross-sectional study at Kerman University of Medical Sciences (2022). Health Science Reports, 8(5), Article e70870. 10.1002/hsr2.70870

IX. Saranya, A., & Subhashini, R. (2023). A systematic review of explainable artificial intelligence models and applications: Recent developments and future trends. Decision Analytics Journal, 7, Article 100230. 10.1016/j.dajour.2023.100230

X. Shahzad, M. F., Xu, S., Lim, W. M., Yang, X., & Khan, Q. R. (2024). Artificial intelligence and social media on academic performance and mental well-being: Student perceptions of positive impact in the age of smart learning. Heliyon, 10(8), Article e29769. 10.1016/j.heliyon.2024.e29769

XI. Thanasekhar, B., Gomathy, N., Kiruthika, A., & Swarnalaxmi, S. (2019). Machine learning based academic stress management system. In 2019 11th International Conference on Advanced Computing (ICoAC) (pp. 147–151). IEEE. 10.1109/ICoAC48765.2019.8935556

XII. Van der Velden, B. H. M., Kuijf, H. J., Gilhuijs, K. G. A., & Viergever, M. A. (2022). Explainable artificial intelligence (XAI) in deep learning-based medical image analysis. Medical Image Analysis, 79, Article 102470. 10.1016/j.media.2022.102470

XIII. Vimbi, V., Shaffi, N., & Mahmud, M. (2024). Interpreting artificial intelligence models: A systematic review on the application of LIME and SHAP in Alzheimer’s disease detection. Brain Informatics, 11(1), Article 10. 10.1186/s40708-024-00222-1

XIV. Von Keyserlingk, L., Yamaguchi-Pedroza, K., Arum, R., & Eccles, J. S. (2022). Stress of university students before and after campus closure in response to COVID-19. Journal of Community Psychology, 50(1), 285–301. 10.1002/jcop.22561

XV. Wijbenga, L., van der Velde, J., Korevaar, E. L., Reijneveld, S. A., Hofstra, J., & de Winter, A. F. (2024). Emotional problems and academic performance: The role of executive functioning skills in undergraduate students. Journal of Further and Higher Education, 48(2), 196–207. 10.1080/0309877X.2023.2300393
XVI. Wolters, C. A., & Brady, A. C. (2021). College students’ time management: A self-regulated learning perspective. Educational Psychology Review, 33(4), 1319–1351. 10.1007/s10648-020-09519-z

XVII. Woodman, R. J., & Mangoni, A. A. (2023). A comprehensive review of machine learning algorithms and their application in geriatric medicine: Present and future. Aging Clinical and Experimental Research, 35(11), 2363–2397. 10.1007/s40520-023-02552-2

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Abstract:

This study investigated the influence of graphene oxide (GO) on enhancing the mechanical characteristics and microstructure of concrete made of slag cement. Concrete samples were made with and without GO, added in varying dosages from 0.01% to 0.1% by weight of cement. The mechanical performance of these specimens was evaluated through compressive, tensile, and flexural strength tests. The durability was checked through acid and sulphate attack tests. To ensure uniform dispersion of GO within the matrix, polycarboxylate ether-based superplasticizer was employed at a measure of 0.25% by weight of cement. Scanning electron microscopy (SEM) was conducted to observe the microstructural development, while energy-dispersive X-ray spectroscopy (EDX) and X-ray Diffraction (XRD) were used to check the composition of the elements of the GO-modified matrix and its contribution to concrete health. The study found that GO addition is beneficial in enhancing compressive, tensile, and flexural strength up to 61, 109, and 39% at 28 days in comparison with conventional concrete. The acid and sulphate resistance of GO-modified concrete was found to be 46% and 30% better than that of control concrete. The effect of GO up to 0.05% on the properties of concrete is found in an increasing trend. SEM analysis confirmed improved dispersion of GO and enhanced interfacial bonding with cement particles. The EDX and XRD analyses validated the macro-level results. These findings highlight the potential of GO as an effective nanomaterial for improving the performance of slag cement-based composites.

Keywords:

Mechanical characteristics,Graphene oxide,Acid resistance,Sulphate resistance,Microstructure,

References:

I. Acharya, P. K., and S. K. Patro. “Acid Resistance, Sulphate Resistance and Strength Properties of Concrete Containing Ferrochrome Ash (FA) and Lime.” Construction and Building Materials, vol. 120, 2016, pp. 241–250, 10.1016/j.conbuildmat.2016.05.099.
II. Bureau of Indian Standards. IS 10262: Concrete Mix Proportioning – Guidelines. 2nd rev., BIS, 2019, New Delhi, India.
III. Bureau of Indian Standards. IS 383: Coarse and Fine Aggregate for Concrete – Specification. 3rd rev., BIS, 2016, New Delhi, India.
IV. Bureau of Indian Standards. IS 455: Portland Slag Cement – Specification. 5th rev., BIS, 2015, New Delhi, India.
V. Bureau of Indian Standards. IS 516 (Part 1/Section 1): Hardened Concrete – Methods of Test: Compressive, Flexural and Split Tensile Strength. BIS, 2021, New Delhi, India.
VI. Bureau of Indian Standards. IS 516 (Part 2/Section 2): Hardened Concrete – Methods of Test: Initial Surface Absorption. BIS, 2020, New Delhi, India.
VII. Bureau of Indian Standards. IS 5816: Method of Test for Splitting Tensile Strength of Concrete. BIS, 1999, reviewed 2018, New Delhi, India.
VIII. Chaipanich, A., et al. “Compressive Strength and Microstructure of Carbon Nanotubes–Fly Ash Cement Composites.” Materials Science and Engineering A, vol. 527, no. 4–5, 2010, pp. 1063–1067.
IX. Chuah, S., et al. “Nano Reinforced Cement and Concrete Composites and New Perspective from Graphene Oxide.” Construction and Building Materials, vol. 73, 2014, pp. 113–124.
X. Cwirzen, A., K. Habermehl-Cwirzen, and V. Penttala. “Surface Decoration of Carbon Nanotubes and Mechanical Properties of Cement/Carbon Nanotube Composites.” Advances in Cement Research, vol. 20, no. 2, 2008, pp. 65–73.
XI. Fu, K., et al. “Defunctionalization of Functionalized Carbon Nanotubes.” Nano Letters, vol. 1, no. 8, 2001, pp. 439–441.
XII. Gaitero, J. J., I. Campillo, and A. Guerrero. “Reduction of the Calcium Leaching Rate of Cement Paste by Addition of Silica Nanoparticles.” Cement and Concrete Research, vol. 38, no. 8–9, 2008, pp. 1112–1118.
XIII. Gholampour, A., et al. “From Graphene Oxide to Reduced Graphene Oxide: Impact on the Physiochemical and Mechanical Properties of Graphene-Cement Composites.” ACS Applied Materials & Interfaces, vol. 9, no. 49, 2017, pp. 43275–43286.
XIV. Kang, D., et al. “Experimental Study on Mechanical Strength of GO–Cement Composites.” Construction and Building Materials, vol. 131, 2017, pp. 303–308.
XV. Keyvani, A. Huge Opportunities for Industry of Nanofibrous Concrete Technology. PhD thesis, Azarbaijan University of Tarbiat Moallem, 2007.
XVI. Konsta-Gdoutos, M. S., Z. S. Metaxa, and S. P. Shah. “Highly Dispersed Carbon Nanotube Reinforced Cement-Based Materials.” Cement and Concrete Research, vol. 40, no. 7, 2010, pp. 1052–1059.
XVII. Li, G. Y., P. M. Wang, and X. Zhao. “Mechanical Behavior and Microstructure of Cement Composites Incorporating Surface-Treated Multi-Walled Carbon Nanotubes.” Carbon, vol. 43, no. 6, 2005, pp. 1239–1245.
XVIII. Li, G. Y., P. M. Wang, and X. Zhao. “Pressure-Sensitive Properties and Microstructure of Carbon Nanotube Reinforced Cement Composites.” Cement and Concrete Composites, vol. 29, no. 5, 2007, pp. 377–382.
XIX. Li, H., et al. “Microstructure of Cement Mortar with Nanoparticles.” Composites Part B: Engineering, vol. 35, no. 2, 2004, pp. 185–189.
XX. Liu, J., et al. “Fracture Toughness Improvement of Multi-Wall Carbon Nanotubes/Graphene Sheets Reinforced Cement Paste.” Construction and Building Materials, vol. 200, 2019, pp. 530–538, 10.1016/j.conbuildmat.2018.12.141.
XXI. Lv, S., et al. “Effect of GO Nanosheets on Shapes of Cement Hydration Crystals and Their Formation Process.” Construction and Building Materials, vol. 64, 2014, pp. 231–239.
XXII. Lv, S., et al. “Effect of Graphene Oxide Nanosheets on Microstructure and Mechanical Properties of Cement Composites.” Construction and Building Materials, vol. 49, 2013, pp. 121–127.
XXIII. Lv, S. H., et al. “Study of Graphene Oxide Reinforced Toughened Cementitious Composites.” Functional Materials, vol. 44, no. 15, 2013, pp. 2227–2231.
XXIV. Lv, S. H., et al. “Toughening Effect and Mechanism of Graphene Oxide Nanoflakes on Cementitious Composites.” Journal of Composite Materials, vol. 31, no. 3, 2014, pp. 644–652.
XXV. Makar, J. “The Effect of SWCNT and Other Nanomaterials on Cement Hydration and Reinforcement.” Nanotechnology in Civil Infrastructure, edited by K. Gopalakrishnan et al., Springer, 2011, pp. 103–130.
XXVI. Nasibulin, A. G., et al. “A Novel Cement-Based Hybrid Material.” New Journal of Physics, vol. 11, 2009, article 023013.
XXVII. Pan, Z., et al. “Mechanical Properties and Microstructure of a Graphene Oxide–Cement Composite.” Cement and Concrete Composites, vol. 58, 2015, pp. 140–147.
XXVIII. Sáez de Ibarra, Y., et al. “Atomic Force Microscopy and Nanoindentation of Cement Pastes with Nanotube Dispersions.” Physica Status Solidi A, vol. 203, no. 6, 2006, pp. 1076–1081.
XXIX. Sanchez, F., and C. Ince. “Microstructure and Macroscopic Properties of Hybrid Carbon Nanofiber/Silica Fume Cement Composites.” Composites Science and Technology, vol. 69, no. 7–8, 2009, pp. 1310–1318.
XXX. Sedaghat, A., et al. “Investigation of Physical Properties of Graphene Cement Composite for Structural Applications.” Open Journal of Composite Materials, vol. 4, no. 1, 2014, pp. 12–21.
XXXI. Shah, S. P., et al. Highly Dispersed Carbon Nanotubes Reinforced Cement-Based Materials. United States Patent Application Publication No. US2009/0229494 A1, 2009.
XXXII. Shang, Y., et al. “Effect of Graphene Oxide on the Rheological Properties of Cement Pastes.” Construction and Building Materials, vol. 96, 2015, pp. 20–28.
XXXIII. Wang, M., et al. “Study on the Three-Dimensional Mechanism of Graphene Oxide Nanosheets Modified Cement.” Construction and Building Materials, vol. 126, 2016, pp. 730–739.
XXXIV. Wang, Z. M., and H. L. Zhang. “Study on the Mechanical Properties of Graphene Oxide Nanosheet Reinforced Cement-Based Composites.” Industrial Buildings, vol. 51, no. 2, 2021, pp. 153–161.

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Abstract:

Multilateration is a popular geometrical algorithm to determine the location of a mobile smartphone in an indoor environment. In this method, the distance of the smartphone from three or more WiFi access sites is calculated based on the strengths of radio signals. Intermittent measurements of radio signals due to the presence of obstacles in the indoor environment affect the overall localization accuracy. The present work addresses this problem and manages the intermittent measurements issue with an innovative Kalman filter-based approach. The linear interpolation method is applied to obtain uninterrupted coordinate information from WiFi RSS measurements. A Kalman filter is designed that uses these interpolated measurements along with its own sensor data to obtain an optimal localization estimate. Less than 2 meters of final position estimation accuracy is attained in Monte-Carlo simulations, which is better than other state-of-the-art techniques in this domain. Additionally, the performance of this intended approach has been found indistinguishable during frequent loss of measurements, in case of which the conventional trilateration approach could not succeed.

Keywords:

Linear Interpolation,Indoor navigation,Wi-Fi Access Points,Intermittent measurement,Kalman filter,

References:

I. Alfakih, Marwan, Mokhtar Keche, Hadjira Be-noudnine, and Abdelkrim Meche. “Improved Gaussian Mixture Modelling for Accurate Wi-Fi Based Indoor Localization Systems.” Physical Communication, vol. 43, 2020, p. 101218
II. Chen, Jian, Gang Ou, Ao Peng, Lingxiang Zheng, and Jianghong Shi. “An INS/WiFi Indoor Localization System Based on the Weighted Least Squares.” Sensors, vol. 18, no. 5, 2018. 10.3390/s18051458.
III. Golenbiewski, Jaren, and Girma Tewolde. “Wi-Fi Based Indoor Positioning and Navigation System (GPS/INS).” Proceedings of the IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), 2020, pp. 1–7.
IV. Hayward, S. J., Katherine van Lopik, Christopher Hinde, and Andrew A. West. “A Survey of Indoor Location Technologies, Techniques and Applications in Industry.” Internet of Things, vol. 20, 2022, p. 100608.
V. Jan, Shau-Shiun, Shuo-Ju Yeh, and Ya-Wen Liu. “Received Signal Strength Database Interpolation by Kriging for a Wi-Fi Indoor Positioning System.” Sensors, vol. 15, no. 9, 2015, pp. 21377–21393. 10.3390/s150921377.
VI. Kausar, H., and S. Chattaraj. “On Some Issues in Kalman Filter Based Trilateration Algorithms for Indoor Localization Problem.” Proceedings of the IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES), vol. 1, 2022, pp. 431–435. 10.1109/SPICES52834.2022.9774037.
VII. Khokhar, Zulqarnain, and Murtaza A. Siddiqi. “Machine Learning Based Indoor Localization Using Wi-Fi and Smartphone.” Journal of Independent Studies and Research – Computing, vol. 18, no. 1, 2021.
VIII. Koweerawong, Chavalit, Komwut Wipusitwarakun, and Kamol Kaemarungsi. “Indoor Localization Improvement via Adaptive RSS Fingerprinting Database.” Proceedings of the International Conference on Information Networking (ICOIN), 2013, pp. 412–416. 10.1109/ICOIN.2013.6496414.
IX. Kuemper, Daniel, Thorben Iggena, Ralf Toenjes, and Elke Pulvermueller. “Valid.IOT: A Framework for Sensor Data Quality Analysis and Interpolation.” Proceedings of the 9th ACM Multimedia Systems Conference, 2018, pp. 294–303.
X. McCool, Danielle, Peter Lugtig, and Barry Schouten. “Maximum Interpolable Gap Length in Missing Smartphone-Based GPS Mobility Data.” Transportation, vol. 51, no. 1, 2024, pp. 297–327.
XI. Navada, Bhagya, and Santhosh Venkata. “Filter Design Using Data Fusion for a Pneumatic Control Valve.” Serbian Journal of Electrical Engineering, vol. 18, 2021, pp. 49–61. 10.2298/SJEE2101049N.
XII. Poulose, Alwin, Odongo Steven Eyobu, and Dong Seog Han. “A Combined PDR and Wi-Fi Trilateration Algorithm for Indoor Localization.” Proceedings of the International Conference on Artificial Intelligence in Information and Communication (ICAIIC), 2019, pp. 072–077. 10.1109/ICAIIC.2019.8669059.
XIII. Redži?, Milan D., Conor Brennan, and Noel E. O’Connor. “SEAMLOC: Seamless Indoor Localization Based on Reduced Number of Calibration Points.” IEEE Transactions on Mobile Computing, vol. 13, no. 6, 2014, pp. 1326–1337. 10.1109/TMC.2013.107.
XIV. Singh, Navneet, Sangho Choe, and Rajiv Punmiya. “Machine Learning Based Indoor Localization Using Wi-Fi RSSI Fingerprints: An Overview.” IEEE Access, vol. 9, 2021, pp. 127150–127174.
XV. Suroso, Dwi, Farid Adiyatma, Panarat Cherntanomwong, and Pitikhate Sooraksa. “Fingerprint Database Enhancement by Applying Interpolation and Regression Techniques for IoT-Based Indoor Localization.” Emerging Science Journal, vol. 4, 2022, pp. 167–189. 10.28991/esj-2021-SP1-012.
XVI. Yang, Junhua. “Indoor Localization System Using Dual-Frequency Bands and Interpolation Algorithm.” IEEE Internet of Things Journal, vol. 7, no. 11, 2020, pp. 11183–11194. 10.1109/JIOT.2020.2996610.
XVII. Zhang, Xing, et al. “WiFi-Based Indoor Localization with Interval Random Analysis and Improved Particle Swarm Optimization.” IEEE Transactions on Mobile Computing, vol. 23, no. 10, 2024, pp. 9120–9134.

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Abstract:

Wireless Sensor Networks (WSNs) are critical to modern IoT applications, yet their deployment is often constrained by limited energy, dynamic topologies, security vulnerabilities, and stringent Quality-of-Service (QoS) requirements. While existing approaches frequently address these challenges in isolation, this paper introduces a holistic routing framework that synergistically integrates an Improved Fuzzy Logic System (IFLS) with Particle Swarm Optimization (PSO) to balance multiple performance metrics in real time. Our hybrid model dynamically tunes routing parameters and fuzzy rules based on network state—including energy levels, congestion, node density, mobility, and security threats—thereby optimizing cluster-head selection, path stability, and trust-aware communication in UAV-assisted WSNs. Extensive simulations demonstrate that the proposed system achieves a 94.2% packet delivery ratio, reduces energy consumption by 48%, and extends network lifetime by 97% compared to contemporary fuzzy-based and trust-aware routing protocols. The work thus offers a scalable, adaptive, and energy-efficient routing solution suitable for large-scale, resource-constrained, and mobility-prone sensor networks. We also provide complete algorithmic specifications and reproducible simulation setups to facilitate validation and further research.

Keywords:

Wireless Sensor Networks (WSN),Fuzzy Logic,Particle Swarm Optimization (PSO),Energy Efficiency,QoS-Aware Routing,UAV Networks,Trust Management.,

References:

I. Alam, M. M., & Moh, S. (2023). Q-learning-based routing inspired by adaptive flocking control for collaborative unmanned aerial vehicle swarms. Vehicular Communications, 40, 100572.
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III. Aldhaibani, J. A., Yahya, A., & Ahmad, R. B. (2014). Optimizing power and mitigating interference in LTE-A cellular networks through optimum relay location. Elektronika ir Elektrotechnika, 20(7), 73-79.
IV. Chan, K. W., Nirmal, U., & Cheaw, W. G. (2018). Progress on drone technology and their applications: A comprehensive review. In AIP Conference Proceedings (Vol. 2030, p. 020308). Ho Chi Minh, Vietnam: AIP Publishing.
V. Colefax, A. P., Butcher, P. A., Pagendam, D. E., & Kelaher, B. P. (2019). Reliability of marine faunal detections in drone-based monitoring. Ocean & Coastal Management, 174, 108–115.
VI. Giusti, E., et al. (2023). A drone-based multisensory payload for maritime pollutants detections. IEEE Aerospace and Electronic Systems Magazine, 38(2), 4–18.
VII. Hosseinzadeh, M., et al. (2023). A novel fuzzy trust-based secure routing scheme in flying ad hoc networks. Vehicular Communications, 44, 100665.
VIII. Kumbhar, F. H., & Shin, S. Y. (2023). Innovating multi-objective optimal message routing for unified high mobility networks. IEEE Transactions on Vehicular Technology, 72(5), 6571–6583.
IX. Li, J., Zhang, G., Jiang, C., & Zhang, W. (2023). A survey of maritime unmanned search system: Theory, applications and future directions. Ocean Engineering, 285, 115359.
X. Li, S., Wang, F., Gaber, J., & Zhou, Y. (2020). An optimal relay number selection algorithm for balancing multiple performance in flying ad hoc networks. IEEE Access, 8, 225884–225901.
XI. Mahapatra, S. N., Singh, B. K., & Kumar, V. (2022). A secure multi-hop relay node selection scheme based data transmission in wireless ad hoc network via blockchain. Multimedia Tools and Applications, 81(13), 18343–18373.
XII. Mezaal, Y. S., & Abdulkareem, S. F. (2017). Affine cipher cryptanalysis using genetic algorithms. JP Journal of Algebra, Number Theory and Applications, 39(5), 785-802.
XIII. Mezaal, Y. S., Khaleel, S. K., Alameri, B. M., Al-Majdi, K., & Al-Hilali, A. A. (2024). Miniaturized microstrip dual-channel diplexer based on modified meander line resonators for wireless and computer communication technologies. Technologies, 12(5), 57.
XIV. Mezaal, Y. S., Khaleel, S. K., Al-Hillal, A. A., Azeez, A. R., Hemza, M. S., & Al-Majdi, M. (2024). State of art on microstrip resonators, filters, diplexers and triplexers. Journal of Mechanics of Continua and Mathematical Sciences, 19, 1-24.
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XXVII. Tucker, J. P., et al. (2021). White shark behaviour altered by stranded whale carcasses: Insights from drones and implications for beach management. Ocean & Coastal Management, 200, 105477.
XXVIII. Tomah, Nahla Ali , Marwa Jaleel Mohsin, Thanaa Hasan Yousif.(2025). Journal of Optics. Wavelength-dependent cytotoxicity of platinum nanoparticles in photodynamic therapy against hepatocellular carcinoma.
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XXXI. Yahya, A., et al. (2016). Lte-a cellular networks. Multi-hop Relay for Coverage, Capacity and Performance Enhancement.
XXXII. Yang, T., Jiang, Z., Dong, J., Feng, H., & Yang, C. (2018). Multi agents to search and rescue based on group intelligent algorithm and edge computing. In Proceedings of IEEE iThings/GreenCom/CPSCom/SmartData (pp. 389–394). Halifax, NS, Canada: IEEE.
XXXIII. Yousif, Thanaa Hasan , Nahla Ali Tomah, Marwa Jaleel Mohsin(2023). Machine learning-based diagnosis of eye-diseases Indonesian Journal of Electrical Engineering and Computer Science. 32 (2), pp. 787~795.
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Abstract:

This study investigates the effect of antioxidant additives on the performance and emission characteristics of a Common Rail Direct Injection (CRDI) diesel engine fuelled with biodiesel blends. Biodiesel derived from Madhuca indica (Mahua) oil was blended with conventional diesel in different proportions (B10, B20, and B30). To enhance oxidative stability and control NOx emissions, two antioxidants, Butylated Hydroxytoluene (BHT) and Tert-Butylhydroquinone (TBHQ), were added at concentrations of 1000 ppm and 1000 ppm. Experiments were conducted on a single-cylinder, four-stroke, water-cooled CRDI engine at a constant speed of 1500 rpm under varying load conditions. The results showed that the addition of antioxidants improved brake thermal efficiency (BTE) and reduced brake-specific fuel consumption (BSFC) compared to untreated biodiesel blends. A notable reduction in NOx and smoke opacity was achieved with TBHQ, while CO and HC emissions exhibited a marginal increase. The optimal performance and emission trade-off was obtained with the B20 + TBHQ (1000 ppm) blend, demonstrating the potential of antioxidant-treated biodiesel as a sustainable and cleaner fuel for CRDI diesel engines.

Keywords:

Biodiesel blends; Energy Efficiency,CRDI diesel engine; Antioxidant additives; Butylated Hydroxytoluene (BHT); Tert-Butylhydroquinone (TBHQ); Performance characteristics; Emission reduction; Oxidative stability; NOx emissions; Brake thermal efficiency (BTE),

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