Machine learning (ML) is a powerful tool that delivers insights hidden in Internet of Things (IoT) data. These hybrid technologies work smartly to improve the decision-making process in different areas such as education, security, business, and the healthcare industry. ML empowers the IoT to demystify hidden patterns in bulk data for optimal prediction and recommendation systems. Healthcare has embraced IoT and ML so that automated machines make medical records, predict disease diagnoses, and, most importantly, conduct real-time monitoring of patients. Individual ML algorithms perform differently on different datasets. Due to the predictive results varying, this might impact the overall results. The variation in prediction results looms large in the clinical decision-making process. Therefore, it is essential to understand the different ML algorithms used to handle IoT data in the healthcare sector. This article highlights well-known ML algorithms for classification and prediction and demonstrates how they have been used in the healthcare sector. The aim of this paper is to present a comprehensive overview of existing ML approaches and their application in IoT medical data. In a thorough analysis, we observe that different ML prediction algorithms have various shortcomings. Depending on the type of IoT dataset, we need to choose an optimal method to predict critical healthcare data. The paper also provides some examples of IoT and machine learning to predict future healthcare system trends.

The rapid increase in the number of consumers and a dynamic change like cloud computing causes different challenges for the service providers to fulfil the service level agreement (SLA). When the SLA violation occurs, the SaaS providers not only to pay the agreed penalty in terms of money, but the trusted relationship may affect that impact on lose of current or potential customers. Therefore, the interacting parties need a proactive SLA management system that alerts beforehand the service provider when it detects any expected violation and provides an optimal decision system to regulate and scale available resources to avoid service violation. Most of the existing studies focused on predicting SLA violation to avoid SLA violation, however, they have ignored considering the process of rescheduling and renegotiation while dealing with service violation that plays a vital role to prevent service violation. In this paper, we propose a fuzzy rescheduling/negotiation (re-SchdNeg) decision support system that offers the best alternative solution for SaaS providers to apply rescheduling techniques to allocate potential violated jobs on available resources. When the system finds the available services are insufficient to handle the possible violated job, the system will start a renegotiation session.

This study presents a multi-agent framework for Arabic scam analysis, with roles for classification, behavioural interpretation and educational guidance. A total of 3000 messages were translated and enriched from English-language collections associated with the Short Message Service (SMS) Spam Collection, Enron and SmishTank. The experiments examine whether dataset-specific multilingual Text-to-Text Transfer Transformer (mT5) training improves classification and whether cultural annotations and an additional cultural training objective improve performance. The training, validation and test partitions contained 2400, 300 and 300 messages. Across ten random seeds, dataset-specific classifiers achieved 88.17% mean test accuracy and 88.07% macro-averaged F1 score (macro-F1); the 95% confidence interval for mean accuracy was 87.36–88.97%. Accuracy exceeded combined-data mT5 by 1.90 percentage points (Holm-adjusted $p=0.0250$). Mean accuracies were 98.50%, 92.90% and 73.10% for SMS, email and SmishTank-derived messages. In exploratory cultural comparisons, supplying annotations increased encoder–decoder accuracy by 1.80 points, while adding the cultural objective increased it by 1.23 points; neither difference was statistically significant after adjustment. The 150-message agent assessment yielded 84.7% mean integrated threat accuracy. The results support dataset-specific classification within the evaluated collections and show limited benefits from the tested cultural formulation. Independent Arabic data and user studies are priorities for extending the framework.