Treatment plans often involve multiple neurodevelopmental condition marked by difficulties in social sessions with different therapists, and the absence of a standardized interaction, communication impediments, and repetitive behaviors. This fragmented approach continue to pose significant challenges due to the variability in can impede effective communication and coordination among symptomatology and the necessity for multidisciplinary care healthcare providers, adversely affecting the quality of care. This paper investigates the potential of Artificial Furthermore, parents and caregivers may find it challenging to access Intelligence (AI) to augment the capabilities of healthcare and manage the extensive records necessary for consistent treatment, professionals and caregivers in managing ASD. We have developed further complicating the overall management of ASD. a sophisticated algorithm designed to analyze facial and bodily Artificial Intelligence (AI) presents a promising solution to the expressions during daily activities of both autistic and non-autistic complexities involved in diagnosing and treating Autism Spectrum children, leading to the development of a powerful deep learningbased Disorder (ASD) [6]. AI-powered tools have the potential to autism detection system. Our study demonstrated that AI standardize the diagnostic process by analyzing extensive datasets to models, specifically the Xception and ResNet50V2 architectures, uncover patterns and correlations that might be overlooked by human achieved high accuracy in diagnosing Autism Spectrum Disorder evaluators.

The growing number of cyber-attacks against Industrial Control Systems (ICS) in recent years has elevated security concerns due to the potential catastrophic impact. Considering the complex nature of ICS, detecting a cyber-attack in them is extremely challenging and requires advanced methods that can harness multiple data modalities. This research utilizes network and sensor modality data from ICS processed with a deep multi-modal cyber-attack detection model for ICS. Results using the Secure Water Treatment (SWaT) system show that the proposed model can outperform existing single modality models and recent works in the literature by achieving 0.99 precision, 0.98 recall, and 0.98 f-measure, which shows the effectiveness of using both modalities in a combined model for detecting cyber-attacks.

Financial sector and especially the insurance industry collect vast volumes of text on a daily basis and through multiple channels (their agents, customer care centers, emails, social networks, and web in general). The information collected includes policies, expert and health reports, claims and complaints, results of surveys, and relevant social media posts. It is difficult to effectively extract label, classify, and interpret the essential information from such varied and unstructured material. Therefore, the Insurance Industry is among the ones that can benefit from applying technologies for the intelligent analysis of free text through Natural Language Processing (NLP). In this paper, CRL+, a novel text classification model combining Contrastive Representation Learning (CRL) and Active Learning is proposed to handle the challenge of using semi-supervised learning for text classification. In this method, supervised (CRL) is used to train a RoBERTa transformer model to encode the textual data into a contrastive representation space and then classify using a classification layer. This (CRL)-based transformer model is used as the base model in the proposed Active Learning mechanism to classify all the data in an iterative manner. The proposed model is evaluated using unstructured obituary data with objective to determine the cause of the death from the data. This model is compared with the CRL model and an Active Learning model with the RoBERTa base model. The experiment shows that the proposed method can outperform both methods for this specific task.