The SETSCI Conference Proceedings is an Open Access publication created for the storage and indexing of proceedings | Diabetes is a chronic disease affecting millions worldwide, necessitating early and accurate diagnosis for effective management. Traditional diagnostic methods, while reliable, often struggle with efficiency and early-stage detection. Deep learning has emerged as a powerful tool in medical diagnostics, offering improved accuracy and predictive capabilities. This paper introduces a novel deep learning model for diabetes diagnosis, leveraging advanced neural network architectures. Trained and evaluated on standard datasets, the model outperforms conventional methods, demonstrating superior diagnostic accuracy and reliability. Our findings underscore the potential of deep learning to enhance diabetes detection and patient outcomes.

Water resource management increasingly requires allocation methods that address rising demand, climate variability, and operational inefficiency. This study proposes an elite-transfer hybrid Genetic Algorithm–Particle Swarm Optimization framework for constrained water allocation under a weighted-sum scalarization model. The model combines four normalized objectives: minimizing water shortage, an operational cost coefficient, and allocation imbalance while maximizing utilization efficiency through an equivalent minimization term. A bidirectional elite-transfer mechanism links GA-based global exploration with PSO-based local refinement. The framework was evaluated using two capacity-constrained surrogate scenarios anchored to hydrometeorological records from Türkiye: Melekbahçe station (E21A033) in the Upper Euphrates Basin and Beşdeğirmen station (E12A003) in the Sakarya Basin. Daily streamflow records supported scenario construction, while precipitation and air-temperature data characterized local conditions. The proposed method was compared with standalone GA and standalone PSO, Differential Evolution, Grey Wolf Optimizer, a scalarized NSGA-II, adapted Kao–Zahara and Garg GA–PSO hybrids, and a no-elite ablation. All methods used the same objective formulation, normalization bounds, constraint-repair procedure, equal objective weights, tuning protocol, paired random seeds, and a budget of 10,000 objective-function evaluations. Performance was assessed through 30 paired runs per scenario. The proposed framework achieved the lowest mean scalar fitness values—0.1670 for Melekbahçe and 0.1752 for Beşdeğirmen—and reached the predefined convergence region after averages of 4587 and 4780 evaluations, respectively. All fitness and convergence improvements remained significant after Holm correction. Paired rank-biserial correlations ranged from 0.957 to 1.000 against the seven general comparators and were 0.824 and 0.781 against the no-elite ablation. The findings support the framework under the tested surrogate scenarios but do not establish Pareto-front dominance or universal superiority. Future work should examine measured operational data, additional basins, dynamic scheduling, alternative weights, and Pareto-based extensions.

The Polyak–Łojasiewicz (PL) inequality offers a useful way to study gradient-based optimization beyond the setting of strong convexity, while still allowing linear convergence under suitable smoothness conditions. This study develops a unified descent-based analysis for fixed-step gradient descent, safeguarded objective-gap step rules, Armijo backtracking, and mini-batch stochastic gradient descent. For the deterministic methods, the analysis provides explicit contraction bounds that show how convergence depends on smoothness, gradient domination, and step-selection parameters. In the stochastic setting, conditional unbiasedness together with a bounded conditional variance leads to a finite-time expectation bound and an upper estimate for the asymptotic residual neighborhood. The analysis is further extended to generalized gradient inequalities. Smooth explicit gradient descent yields linear or polynomial convergence depending on the exponent, whereas superlinear behavior for exponents below one half is established separately for an implicit decrease sequence. This distinction avoids transferring continuous-time behavior directly to explicit discrete gradient iterations. For continuously differentiable squared-error models, uniform spectral bounds on the Jacobian Gram matrix are used to derive regional gradient domination relative to the zero-loss target. A complementary path-length argument gives a sufficient condition for trajectory containment and establishes convergence to zero loss under the stated regional assumptions. Numerical illustrations based on quadratic objectives, power functions, implicit recurrences, stochastic models, and a constant-kernel least-squares example are used to examine the theoretical bounds, accepted Armijo steps, backtracking costs, convergence regimes, and stochastic uncertainty.