As the leading cause of dementia worldwide, Alzheimer's disease afflicts millions, with progressively impaired abilities to carry out daily activities or communicate and even recognize faces. Although the cause behind lupus is not fully understood, it probably reflects lifestyle choices and environmental factors as well as genetic propensity. The largest obstacles in the diagnosis of these diseases are their often subtle early manifestations and absence of sensitive detection paradigms. Deep-learning algorithms first came to the forefront of medical imaging just a few years ago and were celebrated as sophisticated diagnostic aids, able to spot subtle signs in scans usually hidden from human eyes. We are benefitting from the use of these state-of-the-art algorithms to improve Alzheimer's detection, with one of the largest MRI datasets available today (more than 86,000 images) being used to train our model. In view of this vast data set, it was appreciably combined one to be accurate-centric diagnostic tool. The performance of our novel deep learning model is strong and provided state-of-the-art validation accuracy (99.63%), surpassing existing models These figures highlight the great promise of our model as a verifiable method for detecting early-stage Alzheimer's disease - a significant concern in controlling and managing disease progression. Our research truly is a major step forward in the field of Alzheimer's disease diagnosis by employing cutting-edge deep learning techniques. Early diagnosis allows for better treatment and lower disease burden that can prevent morbidity, mortality and even change many patient outcomes. This is a considerable improvement toward diagnosing Alzheimer's disease with the help of artificial intelligence and presents an expectation for more exact and timely finding.

Large language models (LLMs) now generate fluent natural language and source code, creating challenges for authorship attribution, academic integrity, and software supply-chain security. Most existing detectors for AI-generated content are evaluated separately on natural language or source code, often under matched train–test conditions that can overestimate real-world reliability. We present a paired-prompt benchmark for human-versus-machine detection across English text, Python code, and mixed text–code documents. The benchmark includes $\mathrm{22,141}$ instances from HC3, CodeSearchNet, MBPP, and HumanEval across training, validation, and test partitions, plus Mix-Eval, a mixed-content set of 997 Jupyter-notebook-style samples. We evaluate RoBERTa-large for text, GraphCodeBERT and CodeBERT-base for code, a unified RoBERTa-base detector trained on both modalities, and zero-shot baselines. Fine-tuned detectors achieve near-perfect in-distribution performance, with AUROC $1.0000\pm 0.0000$ and accuracy above $99.5\%$. Across five instruction-tuned generator families of varying size (3.8B–7B) and architecture, with the human and problem distributions held fixed, cross-generator transfer causes negligible degradation (AUROC spread $0.0002$; drops of at most $0.0003$). In contrast, domain shift is the main failure mode: on MBPP+HumanEval, GraphCodeBERT drops to $0.85\pm 0.02$ AUROC and CodeBERT-base to $0.67\pm 0.02$. On Mix-Eval, the unified detector outperforms a routed text–code pipeline by 21 AUROC points ($0.96$ vs. $0.75$), largely because of router failures on mixed inputs. Training-time augmentation improves low-false-positive performance, while legacy supervised detectors show systematic class inversion on modern LLM outputs. These results show that reliable deployment requires cross-domain evaluation, mixed-content testing, and calibration beyond in-distribution accuracy.