CNN-ViT Fusion with Adaptive Attention Gate for Brain Tumor MRI Classification: A Hybrid Deep Learning Model

Early detection and classifying brain tumors using Magnetic Resonance Imaging (MRI) images is highly important but difficult to extract in medical images. Convolutional Neural Networks (CNNs) are good at capturing both local texture and spatial information whereas Vision Transformers (ViTs) are good at capturing long-range global dependencies. We propose a new hybrid architecture that combines a SqueezeNet-style CNN branch with a MobileViT-style global transformer branch, through an Adaptive Attention Gate mechanism, in this paper. The gate learns dynamically per-sample, per-feature weights to weight the contribution of each branch, allowing context-sensitive merging of local and global representations. The proposed model has a test accuracy of 97.60, a precision of 97.30, a recall of 97.50, an F1-score of 97.40, and a macro-average area under the curve (AUC) of 0.9946 with a trained and evaluated on the Brain Tumor MRI Dataset (Kaggle). These scores are higher than single CNN and ViT baselines, and current competitive fusion methods, showing that dynamic feature weighting is an effective way to classify medical images.

Predictive Modeling of Natural Medicinal Compounds for Alzheimer Disease Using Cheminformatics

The most common cause of dementia is Alzheimer disease, a progressive neurodegenerative disorder affecting older adults that gradually impairs memory, cognition, and behavior. It is characterized by the accumulation of abnormal proteins in the brain, including amyloid-beta plaques and neurofibrillary tangles of tau protein, which disrupt neuronal communication and lead to neuronal death. Early manifestations typically include mild memory impairment and reduced ability to acquire new information. As the disease progresses, patients experience severe cognitive decline, loss of independence, and significant personality and behavioral changes. Although the exact etiology of Alzheimer disease remains unclear, factors such as age, genetic predisposition, lifestyle, and cardiovascular health contribute to its development. While no definitive cure exists, early diagnosis, pharmacological interventions, and supportive care can slow progression and improve quality of life. This study presents a predictive cheminformatics-based model for identifying natural medicinal compounds with potential therapeutic efficacy against Alzheimer disease. The model functions as a drug screening system utilizing molecular descriptors and machine learning to detect anti-Alzheimer activity. More than 7,000 compounds from ChEBI, SynSysNet, and INDOFINE were preprocessed using Open Babel and analyzed with Dragon descriptors. A Random Forest classifier trained on approved treatments achieved moderate performance, with precision of 0.5970 and recall of 0.6590, identifying 73 candidate compounds. Key descriptors included atomic polarizability, bond multiplicity, and non-hydrogen bond counts.These findings demonstrate the value of cheminformatics in early-stage drug discovery for Alzheimer disease.

Humanoid Robot Pitch Axis Stabilization using Linear Quadratic Regulator with Fuzzy Logic and Capture Point

This paper aims for a controller that can stabilize a position-controlled humanoid robot when standing still or walking on synthetic grass even when subjected to external disturbances. Two types of controllers are designed and implemented: ankle strategy and stepping strategy. The robot's joints consist of position-controlled servos which can be complicated to model analytically due to nonlinearities and non-measurable parameters, hence the dynamic model of the humanoid robot is acquired using a non-recursive least squares system identification. This model is also used to design a Kalman Filter to estimate the system states from noisy inertial measurement unit (IMU) sensor and design a linear quadratic regulator (LQR) controller. To handle the nonlinearities, the LQR controller is extended with fuzzy logic algorithm that changes the LQR gain value based on angle and angular velocity membership functions. The proposed control system can maintain the humanoid robot's stability around the pitch axis when subject to pendulum disturbances or even restraining force from a spring balance.