Alzheimer’s disease (AD) is an illness affecting the neurological system in people commonly aged 65 years and older. It is one of the leading causes of dementia and, subsequently, the cause of death as it gradually affects and destroys brain cells. In recent years, the detection of AD has been examined in ways to mitigate its impacts while considering early detection through computer-aided diagnosis (CAD) tools. In this study, we developed deep learning models that focus on early detection and classifying each case, non-demented, moderate-demented, mild-demented, and very-mild-demented, accordingly through transfer learning (TL); an AlexNet, ResNet-50, GoogleNet (InceptionV3), and SqueezeNet by utilizing magnetic resonance images (MRI) and the use of image augmentation. The acquired images, a total of 12,800 images and four classifications, had to go through a pre-processing phase to be balanced and fit the criteria of each model. Each of these proposed models split the data into 80% training and 20% testing. AlexNet performed an average accuracy of 98.05%, GoogleNet (InceptionV3) performed an average accuracy of 97.80%, and ResNet-50 had an average performing accuracy of 91.11%. The transfer learning approach assists when there is not adequate data to train a network from the start, which aids in tackling one of the major challenges faced when working with deep learning.

The PID controller and PI controller has transformed the industry since their day of their discovery. The PID and PI controllers have proved their reliability and value in the industry. In some cases, it gets hard to choose which controller will be the most beneficial and efficient for a specific application. Therefore, some simulations and experiments get done to determine which controller fits best. This paper investigates both controllers, PID and PI controllers, performances and robustness. In this study, the PID controller and PI controller performance and robustness were compared to each other after the simulation was performed using Engine Speed Control Model in MATLAB Simulink [1]. Also, this study presents graphs of the input and output responses for both controllers as well as the proposed parameters values. Both PID and PI controllers were self-tuned, and both controllers show a satisfactory results in all aspects although there were some differences between the two controllers in the results.

Phishing email attacks remain a persistent, rapidly evolving cybersecurity threat. Despite advances in malicious email filters and security protocols, challenges such as oversight and false positives persist, leading users to miss critical communications or inadvertently trust deceptive phishing attempts. To address this issue, we introduce an innovative approach that leverages a large language model to detect phishing emails and provide structured, comprehensive, and insightful threat analyses. Unlike traditional methods that lack transparency, our system generates detailed, human-readable summaries highlighting key threat indicators, rationale behind classifications, and actionable recommendations for each email. We evaluated our system quantitatively using a labeled phishing dataset and qualitatively through expert-annotated assessments by cybersecurity professionals. The model achieved robust binary classification performance (accuracy: 95.05%), and domain experts highly rated the clarity and precision of its explanatory summaries. Our findings demonstrate the potential of large language models as intelligent cybersecurity analysts capable of accurate detection and clear, human-friendly explanations, contributing significantly to the development of transparent and trustworthy phishing detection systems.