Introduction: Parkinson’s disease (PD) is a neurodegenerative illness that impairs normal human movement. The primary cause of PD is the deficiency of dopamine in the human brain. PD also leads to several other challenges, including insomnia, eating disturbances, excessive sleepiness, fluctuations in blood pressure, sexual dysfunction, and other issues.Methods: The suggested system is an extremely promising technological strategy that may help medical professionals provide accurate and unbiased disease diagnoses. This is accomplished by utilizing significant and unique traits taken from spiral drawings connected to Parkinson’s disease. While PD cannot be cured, early administration of drugs may significantly improve the condition of a patient with PD. An expeditious and accurate clinical classification of PD ensures that efficacious therapeutic interventions can commence promptly, potentially impeding the advancement of the disease and enhancing the quality of life for both patients and their caregivers. Transfer learning models have been applied to diagnose PD by analyzing important and distinctive characteristics extracted from hand-drawn spirals. The studies were carried out in conjunction with a comparison analysis employing 102 spiral drawings. This work enhances current research by analyzing the effectiveness of transfer learning models, including VGG19, InceptionV3, ResNet50v2, and DenseNet169, for identifying PD using hand-drawn spirals.Results: Transfer machine learning models demonstrate highly encouraging outcomes in providing a precise and reliable classification of PD. Actual results demonstrate that the InceptionV3 model achieved a high accuracy of 89% when learning from spiral drawing images and had a superior receiver operating characteristic (ROC) curve value of 95%.Discussion: The comparison results suggest that PD identification using these models is currently at the forefront of PD research. The dataset will be enlarged, transfer learning strategies will be investigated, and the system’s integration into a comprehensive Parkinson’s monitoring and evaluation platform will be looked into as future research areas. The results of this study could lead to a better quality of life for Parkinson’s sufferers, individualized treatment, and an early classification.

This paper explores the integration of generative artificial intelligence (AI) into adaptive learning systems to create customized learning support aids for students with disabilities, as traditional educational aids commonly fail to meet the diversity in the needs of these learners, and generative AI has shown innovative solutions that can offer real-time adaptation to content with personalized learning experiences. This paper introduces ALGA-Ed, a novel adaptive learning system utilizing generative AI that includes (i) a user profile module that captures cognitive, sensory, and behavioral profiles; (ii) generative AI models that create personalized multimodal content in real-time; (iii) an adaptive feedback mechanism using reinforcement learning to adjust content delivery based on real-time engagement metrics dynamically; and (iv) a real-time monitoring system that tracks progress and adapts learning pathways accordingly. The framework leverages heterogeneous datasets, including real and synthetic data, to effectively address diverse disability profiles. Pilot studies demonstrate the effectiveness of the framework in improving participation, retention, and learning outcomes for students with disabilities. This study enhances adaptive learning by encouraging inclusion via AI-driven tailoring and providing a basis for further advancements in AI-powered education catered for children with impairments. The source code for this research will be publicly available at https://github.com/aasimwadood/ALGA-Ed.

This study provides an in-depth exploration of the use of multimodal techniques in developing adaptive learning systems designed for students with special needs, using various neural network models: a Baseline Neural Network, Convolutional Neural Network, Attention Model, LSTM, GRU, and Transformer models. Adaptive learning systems play a major role in the customization of the educational process in such a way as to ensure the specific cognitive, behavioural and emotional needs of students with special educational needs are catered for. The models were trained using a sizeable amount of multimodal data that recorded factors such as academic achievement, behavioral interactions, and environmental factors. From our observations, it can be noted that transformer models and attention mechanisms allowed for higher performance in predicting and fulfilling the needs of students, and were more effective than simpler neural architectures. These findings suggest that more advanced and intelligent neural networks can improve how students with different learning abilities consume content and consequently provide better educational answers and solutions to the market. To identify the important features affecting student performance, various Explainable AI (XAI) techniques were utilized, including SHAP (Shapley Additive Explanations) values, logistic regression-based feature importance analysis, LIME (Local Interpretable Model-Agnostic Explanations), partial dependency plots, and whose global surrogate model was a Decision Tree Regressor. These XAI instruments allowed the model’s decision to be explained and visualized so that teachers could understand the main beneficiaries of improved educational results. Such XAI methods enable transparency in how the models make decisions, thus allowing educators to understand the reasons for certain recommended learning paths.

Introduction: Autism Spectrum Disorder (ASD) significantly impacts society by highlighting the need for inclusive education, healthcare, and employment systems that support neurodiversity. This challenges societal norms and promotes greater awareness, understanding, and acceptance, encouraging communities to become more inclusive and supportive of individuals with diverse abilities. Methods: The main novelty is to identify and explore the key factors affecting ASD in Saudi Arabia and Egypt, aiming to improve early diagnosis through Explainable Artificial Intelligence (XAI) techniques, specifically SHapley Additive exPlanations (SHAP), LIME (Local Interpretable Model-agnostic Explanations), and Permutation Feature Importance (PFI). The research primarily employed decision tree (DT) and K-Nearest Neighbors (KNN) models combined with explainable AI (XAI), leading to significant improvements in diagnostic accuracy. The system was applied to a real dataset collected from various locations across Saudi Arabia, which is publicly available on Kaggle. Additionally, another ASD dataset from the Data Science Bank repository, sourced from participants in North Cairo Governorate, Egypt, was used for testing the system. Before analysis, the data was validated by removing outliers, filling missing data, and confirming the relevance of selected features. The study aims to enhance early diagnosis through XAI methods, including SHAP, LIME, and PFI. Results: The results show that KNN, when combined with XAI, achieved a high accuracy of 97% on the Saudi Arabia dataset and 92% on the Egypt dataset. Discussion: This approach has proven to be effective in developing more accurate and straightforward AI models for ASD diagnosis. It demonstrates that integrating advanced AI techniques with practical clinical applications can significantly improve the healthcare system in Saudi Arabia, leading to earlier ASD detection, better-informed treatment plans, and ultimately, an improved quality of life for those affected.