Deep Learning Based Path Weight Prediction for Complex Network Routing

Ali, Basim; Al-Obaidi, Mohannad; Mahmoud, Hassan

doi:10.25673/123580

Proceedings of International Conference on Applied Innovation in IT · 2026/03/31 · Vol. 14 · Issue 1 · pp. 371–379

Deep Learning Based Path Weight Prediction for Complex Network Routing

Basim Jamil Ali, Mohannad Ali Meteab Al-Obaidi, Hassan Hadi Salih and Moamin A Mahmoud

📄 Download PDF DOI: 10.25673/123580

Abstract

A spectrophotometric method for the determination of sulfamethoxazole (SMZ) was developed using an azo coupling reaction combined with Cloud Point Extraction (CPE) in the presence of the nonionic surfactant Triton X-114. The previously developed azo coupling method demonstrated acceptable accuracy but suffered from limited sensitivity and interference from excipients that affected selectivity. In the present investigation, CPE was employed as a preconcentration step to overcome these limitations, allowing extraction of the colored azo product into the surfactant-rich phase. The method was optimized regarding the surfactant type and concentration, equilibrium temperature, and incubation time. The results showed significant improvement in sensitivity, precision, and accuracy, with a detection wavelength of 489 nm, recoveries around 101.8%, and low relative standard deviations. This technique was successfully applied to pharmaceutical preparations of SMZ without requiring additional complex steps, demonstrating superior sensitivity, selectivity, and environmental compatibility compared with the azo method alone. Thus, CPE provides a simple, cost-effective, and green analytical approach for the determination of sulfamethoxazole in both pure and pharmaceutical forms.

Keywords

Sulfamethoxazole Cloud Point Extraction Triton X-114 Spectrophotometry Pharmaceutical Analysis.

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