Remote patient monitoring and data management have gained much popularity in recent years because of their enhanced access to low‐cost healthcare services. A cloud‐based healthcare system provides numerous solutions for collecting patient data and offers on‐demand well‐managed reports to patients and healthcare providers. However, it equally suffers from single‐point failure, security, privacy, and non‐transparency issues with the data, impacting the continuity of the system. To resolve such concerns, this article proposes an artificial intelligence (AI)‐enabled decentralized healthcare framework that accesses and authenticates Internet of Things (IoT) devices and create trust and transparency in patient healthcare records (PHR). The mechanism is based on the AI‐enabled smart contracts and the conceptualization of the public blockchain network. Alongside this, the framework identifies the malicious IoT nodes in the system. The experimental analyses are performed on the real‐time test environment, and significant improvements are suggested in terms of device energy consumption, data request time, throughput, average latency, and transaction fee.

An effective machine learning implementation means that artificial intelligence has tremendous potential to help and automate financial threat assessment for commercial firms and credit agencies. The scope of this study is to build a predictive framework to help the credit bureau by modelling/assessing the credit card delinquency risk. Machine learning enables risk assessment by predicting deception in large imbalanced data by classifying the transaction as normal or fraudster. In case of fraud transaction, an alert can be sent to the related financial organization that can suspend the release of payment for particular transaction. Of all the machine learning models such as RUSBoost, decision tree, logistic regression, multilayer perceptron, K-nearest neighbor, random forest, and support vector machine, the overall predictive performance of customized RUSBoost is the most impressive. The evaluation metrics used in the experimentation are sensitivity, specificity, precision, F scores, and area under receiver operating characteristic and precision recall curves. Datasets used for training and testing of the models have been taken from kaggle.com.

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With an increasing number of Internet of Things (IoT) devices in the digital world, the attack surface for consumer networks has been increasing exponentially. Most of the compromised devices are used as zombies for attacks such as Distributed Denial of Services (DDoS). Consumer networks, unlike most commercial networks, lack the infrastructure such as managed switches and firewalls to easily monitor and block undesired network traffic. To counter such a problem with limited resources, this article proposes a hybrid anomaly detection approach that detects irregularities in the network traffic implicating compromised devices by using only elementary network information like Packet Size, Source, and Destination Ports, Time between subsequent packets, Transmission Control Protocol (TCP) Flags, etc. Essential features can be extracted from the available data, which can further be used to detect zero-day attacks. The paper also provides the taxonomy of various approaches to classify anomalies and description on capturing network packets inside consumer networks.

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