Balochistan University of Engineering and Technology

The spread of deepfakes poses significant security concerns, demanding reliable detection methods. However, diverse generation techniques and class imbalance in datasets create challenges. We propose CAE-Net, a Convolution- and Attention-based weighted Ensemble network combining spatial and frequency-domain features for effective deepfake detection. The architecture integrates EfficientNet, Data-Efficient Image Transformer (DeiT), and ConvNeXt with wavelet features to learn complementary representations. We evaluated CAE-Net on the diverse IEEE Signal Processing Cup 2025 (DF-Wild Cup) dataset, which has a 5:1 fake-to-real class imbalance. To address this, we introduce a multistage disjoint-subset training strategy, sequentially training the model on non-overlapping subsets of the fake class while retaining knowledge across stages. Our approach achieved 94.46% accuracy and a 97.60% AUC, outperforming conventional class-balancing methods. Visualizations confirm the network focuses on meaningful facial regions, and our ensemble design demonstrates robustness against adversarial attacks, positioning CAE-Net as a dependable and generalized deepfake detection framework.

This study quantifies the impact of projected climate change on the daily streamflow of the Brahmaputra River (Bahadurabad outlet, Bangladesh) using four data-driven models: support vector machine (SVM), random forest (RF), long short-term memory (LSTM), and bidirectional LSTM (Bi-LSTM). Trained on 1981-2008 data with rainfall and temperature as predictors and tested on 2009-2014 (Bi-LSTM and RF outperformed with R-2 approximate to 0.90), the models were forced with bias-corrected projections from 13 CMIP6 GCMs under six composite scenarios (coolest to wettest) for the 2030s, 2050s, and 2080s. Key findings indicate a significant alteration of the hydrograph, characterized by an earlier monsoon rise, higher July-August flow plateaus (similar to 50,000-70,000 m(3)/s by the 2080s under warm/wet scenarios), and a slower recession. Projections also show intensifying extremes, with median monsoon monthly maxima reaching similar to 66,000-90,000 m(3)/s by the 2080s, model-dependent. Crucially, the analysis reveals a trend toward greater seasonal variability, where the wet season becomes wetter while the dry season may become even drier, particularly under the driest and coolest scenarios. Mean annual flow changes by the 2080s range from -1% (coolest) to +50% (wettest). The results unanimously project stronger, longer monsoon flows and amplified peaks, and substantially extended flood risk, particularly under warmer and wetter futures. [GRAPHICS]

Urban land-use allocation represents a complex multi-objective optimization problem critical for sustainable urban development policy. This paper presents novel computational intelligence approaches for optimizing land-use allocation in mixed-use areas, addressing inherent trade-offs between land-use compatibility and economic objectives. We develop multiple optimization algorithms, including custom variants integrating differential evolution with multi-objective genetic algorithms. Key contributions include: (1) CR+DES algorithm leveraging scaled difference vectors for enhanced exploration, (2) systematic constraint relaxation strategy improving solution quality while maintaining feasibility, and (3) statistical validation using Kruskal-Wallis tests with compact letter displays. Applied to a real-world case study with 1290 plots, CR+DES achieves 3.16% improvement in land-use compatibility compared to state-of-the-art methods, while MSBX+MO excels in price optimization with 3.3% improvement. Statistical analysis confirms that algorithms incorporating difference vectors significantly outperform traditional approaches across multiple metrics. The constraint relaxation technique enables broader solution space exploration while maintaining practical constraints. These findings provide urban planners and policymakers with evidence-based computational tools for balancing competing objectives in land-use allocation, supporting more effective urban development policies in rapidly urbanizing regions.

The development of scalable quantum networks requires coherent interfaces capable of converting microwave photons used in superconducting quantum processors into optical photons suitable for long-distance fiber transmission. This review surveys recent progress in microwave-to-optical quantum transduction across optomechanical, electro-optic, and magneto-optic platforms, with emphasis on conversion efficiency, bandwidth, added noise, and operating temperature. In addition to standard metrics, we propose the internal efficiency eta_in and the magnon decay rate kappa_m/2pi as normalized parameters that enable fairer comparison across heterogeneous implementations. Optomechanical systems achieve internal phonon-to-photon efficiencies of 93

Pyrolysis is key to transforming biomass and organic waste into useful energy products. However, standard integer-order models poorly capture the memory and heredity effects that significantly affect the thermal decomposition dynamics of pyrolysis. In this study, we developed a pyrolysis model in the Atangana-Baleanu-Caputo (ABC) sense to more accurately depict the memory-based behavior of biomass decomposition under non-isothermal conditions. The proposed ABC fractional derivative model extends the classical two-stage kinetic model by combining a fractional derivative with a non-local and non-singular kernel, allowing past thermal states to influence the present system dynamics. Theoretical analyses demonstrate the positivity, existence, and uniqueness of solutions as well as Ulam-Hyers (UH) and generalized UH stability. To illustrate the dynamic response of the system, a predictor-corrector method was employed to compute approximate solutions under various fractional orders and heating rates. Numerical studies have shown that smaller fractional orders improve the rate of thermal conversion and maintain controlled temperature levels. These results show that fractional order modeling is a reliable and useful method for studying pyrolysis processes. It could also help with the design and improvement of biomass conversion systems that use less energy.