Khursheed Aurangzeb

Generative adversarial networks (GANs) have fundamentally transformed the deep learning field since they enable the creation of synthetic data that closely matches real world data. This paper presents a comprehensive and up‐to‐date review of GANs variants and their revolutionary impact in several domains, and major developments in the new applications. We demonstrate the GANs architecture, versatility of GANs variants such as CycleGAN, bidirectional generative adversarial networks (BiGAN), quantum‐enhanced GAN (Q‐GAN) and StyleGAN. In this work, different GANs variants are analysed and combined with the proposed improvements that enhance stability and performance. Despite their outstanding performance, GANs suffer from instability and mode collapse during training. This paper examines various strategies and improvements to enhance GANs stability and performance, including hybrid architectures that integrate GANs with other deep learning models and practical utility in domain‐specific expert systems. Moreover, in the context of deepfakes, it considers the ethical and legal reflections of GANs generated content. We also contribute to a comprehensive perspective on current applications and future prospects of GANs, underscoring their versatility and promise in areas such as image synthesis, medical imaging, anomaly detection, code synthesis and urban simulation.

Edge intelligence for consumer electronics and cooperative systems must preserve confidentiality and privacy while meeting tight latency and energy budgets. Existing approaches lack a unified control plane that jointly adapts cryptography and differential privacy to time-varying risk and resources. This study aims to design a field-driven framework that couples security posture, key evolution, and privacy budgeting on heterogeneous edge graphs including vehicular, UAV, and roadside nodes. This study introduce MISEI, which fuses HyphaKey and SporeDP under a dual-field diffusion controller. HyphaKey performs multi-path anastomosis mixing of PUF-seeded “spore” secrets with XOR/KDF pipelines and rotation rates r^t_i governed by a defense-hormone field H^t_i ; path selection solves a utility–risk objective on conductivities C^t_ij . SporeDP updates local privacy budgets ε^t_i via anisotropic, field-weighted Laplacians that depend on H^t_i and a nutrient field N^t_i , with global caps enforced by fairness weights w^t_i and budget B^t. A policy layer maps field states to encryption mode, tag length, rotation cadence, and DP calibration, subject to latency/energy constraints. Lightweight inference executes on-device with INT8 quantization, while bandwidth/offloading follow field-aware allocations. In simulation, MISEI achieved mean key-compromise probability 4.9×10⁻⁷, ≥3-path resistance, median forward-secrecy window 0.86 s, average latency 30.7 ms with 95^th percentile 86.4 ms, and total effective privacy loss 27.9 with utility 92.0–93.4% at Σ ε≤24.

As a widely popular health food, Nori faces an increasingly urgent demand for automated production and quality control. Traditional manual inspections suffer from a strong subjectivity, low efficiency, and environmental interferences, making it difficult to implement a continuous and quick-paced production. This paper proposes a multi-channel conveyor-based nori image sensing system that integrates sensing, computing, and control technologies to achieve efficient image capture. An optimized optical layout is constructed, through a hardware-software co-design, a sealed imaging environment. A lightweight contour recognition method is proposed to overcome the contour recognition redundancy. To enhance the batch demarcation, a batch tracking mechanism is established based on the state transition. Finally, on account of the image noise complexity, singular value decomposition and fast fourier transform are fused to denoise collaboratively. An improved genetic algorithm is introduced to optimize the denoising parameters, forming a collaborative denoising method. Experimental results confirm that the system outperforms traditional methods in contour recognition accuracy, batch tracking stability, and image denoising effectiveness, while demonstrating high processing frame rates suitable for high-speed production lines. This work provides a reliable and efficient capture solution for nori production, showcasing a practical application of integrated sensing-computing-control in consumer electronics automation.