Artificial intelligence (AI) and machine learning (ML) in healthcare are approaches to make people’s lives easier by anticipating and diagnosing diseases more swiftly than most medical experts. There is a direct link between the insurer and the policyholder when the distance between an insurance business and the consumer is reduced to zero with the use of technology, especially digital health insurance. In comparison with traditional insurance, AI and machine learning have altered the way insurers create health insurance policies and helped consumers receive services faster. Insurance businesses use ML to provide clients with accurate, quick, and efficient health insurance coverage. This research trained and evaluated an artificial intelligence network-based regression-based model to predict health insurance premiums. The authors predicted the health insurance cost incurred by individuals on the basis of their features. On the basis of various parameters, such as age, gender, body mass index, number of children, smoking habits, and geolocation, an artificial neural network model was trained and evaluated. The experimental results displayed an accuracy of 92.72%, and the authors analyzed the model’s performance using key performance metrics.

With growing awareness and concerns regards to Cloud Computing and Information Security, there is growing awareness and usage of Security Algorithms into data systems and processes. This paper presents a brief overview and comparison of Cryptographic algorithms, with an emphasis on Symmetric algorithms which should be used for Cloud based applications and services that require data and link encryption. In this paper we review Symmetric and Asymmetric algorithms with emphasis on Symmetric Algorithms for security consideration on which one should be used for Cloud based applications and services that require data and link encryption

There are serious security issues with the quick growth of IoT devices, which are increasingly essential to Industry 4.0. These gadgets frequently function in challenging environments with little energy and processing power, leaving them open to cyberattacks and making it more difficult to implement intrusion detection systems (IDS) that work. In order to address this issue, this study presents a unique feature selection algorithm based on basic statistical methods and a lightweight intrusion detection system. This methodology improves performance and cuts training time by 27–63% for a variety of classifiers. By utilizing the most discriminative features, the suggested methods lower the computational overhead and improve the detection accuracy. The IDS achieved over 99.9% accuracy, precision, recall, and F1-Score on the dataset IoTID20, with consistent performance on the NSLKDD dataset.