Autism spectrum disorder (ASD) poses significant challenges in early detection, necessitating innovative approaches for accurate identification. In this study, we propose a novel method utilizing machine learning models trained on a diverse dataset comprising facial images and behavioral ADOS scores. Employing cutting-edge convolutional neural network (CNN) architectures such as MobileNetV2, ResNet50, and InceptionV3, alongside a bespoke CNN model tailored for ASD detection, we explore the efficacy of our approach. Additionally, we introduce a multimodal concatenation model that integrates image features with behavioral scores to enhance predictive performance. Our results showcase promising outcomes, with the multimodal concatenation model achieving a remarkable accuracy of 97.05%. Furthermore, our models demonstrate competitive precision, recall, F1 score, and area under the ROC curve (AUC), underscoring their potential to facilitate early ASD diagnosis. These findings signify the significance of leveraging multimodal data fusion techniques to augment ASD detection accuracy, thereby contributing to advancements in early intervention strategies.

Water resources are crucial natural assets that all living organisms rely upon. Water is essential for consumption, industrial processes, and farming activities. In recent years, human activities and natural disasters have contributed to water pollution. It is vital to manage water quality to achieve sustainable development. This article examines the water quality index and the use of machine learning and deep learning algorithms in managing various water resources. Machine learning models play a role in different systems for water treatment and management such as predicting water quality and developing technologies to address water-related challenges. Additionally, it suggests methods to reduce water pollution and enhance the quality of various water resources including lakes, rivers, groundwater. It also evaluates different models that provide insights into natural disasters (for instance, GIS offers data on water levels in different areas, which can help prevent floods and droughts in real-time scenarios). Issues related to water resource management is investigated using machine learning algorithms, deep learning algorithms, and hybrid algorithms, with metrics such as RMSE (Root Mean Square Error), MAE (Mean Absolute Error), MAPE (Mean Absolute Percent Error), R² (Coefficient of Determination), MSE (Mean Square Error), and RAE (Relative Absolute Error). Many studies focus on hybrid algorithms for analyzing water resource management. In this work, we have proved that water quality analysis achieved better results in ML and DL models. Over the next decade, the integration of machine learning tools is anticipated to accelerate the development of sustainable water resource management strategies.