The recent advancements of Internet of Things (IoT) embedded systems, wireless networks, and biosensors those have assisted in the rapid development of implanting wearable sensors are reviewed here. The applications of the internet of medical things (IoMT) that has gained major attention as an ecosystem of connected clinical systems, computing systems, and medical sensors geared towards improving the quality of healthcare services are also reviewed here. The 5G based AI technology can revolute the perception of healthcare and lifestyle. In light of the importance of IoT platforms and 5G networks, the purpose of this proposed research work is to identify threats that could undermine the integrity, privacy, and security of IoMT systems. Also, the novel blockchain‐based approaches that can help in improving the confidentiality of IoMT network. It has been discovered that IoMT is vulnerable to various types of attacks, including denial of service (DoS), malware, and eavesdropping attack. In addition, IoMT is exposed to various vulnerabilities, such as security, privacy, and confidentiality. Despite multiple security threats, there are novel cryptographic techniques, such as access control, identity authentication, and data encryption that can help in improving the security and reliability of IoMT devices.

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The rapid development to accommodate population growth has a detrimental effect on water quality, which is deteriorating. Consequently, water quality prediction has emerged as a topic of great interest during the past decade. Existing water quality prediction approaches lack the desired accuracy. Moreover, the available datasets have missing values, which reduces the performance efficiency of classifiers. This study presents an automatic water quality prediction method that resolves the issue of missing values from the data and obtains a higher water quality prediction accuracy. This study proposes a nine-layer multilayer perceptron (MLP) which is used with a K-nearest neighbor (KNN) imputer to deal with the problem of missing values. Experiments are performed, and performance is compared with seven machine learning algorithms. Performance is further analyzed regarding two scenarios: deleting missing values and the use of a KNN imputer to deal with missing values. Results suggest that the proposed nine-layer MLP model can achieve an accuracy of 0.99 for water quality prediction with the KNN imputer. K-fold cross-validation further corroborates this performance.

Breast cancer is a common cause of female mortality in developing countries. Screening and early diagnosis can play an important role in the prevention and treatment of these cancers. This study proposes an ensemble learning-based voting classifier that combines the logistic regression and stochastic gradient descent classifier with deep convoluted features for the accurate detection of cancerous patients. Deep convoluted features are extracted from the microscopic features and fed to the ensemble voting classifier. This idea provides an optimized framework that accurately classifies malignant and benign tumors with improved accuracy. Results obtained using the voting classifier with convoluted features demonstrate that the highest classification accuracy of 100% is achieved. The proposed approach revealed the accuracy enhancement in comparison with the state-of-the-art approaches.

During the COVID-19 pandemic, the spread of fake news became easy due to the wide use of social media platforms. Considering the problematic consequences of fake news, efforts have been made for the timely detection of fake news using machine learning and deep learning models. Such works focus on model optimization and feature engineering and the extraction part is under-explored area. Therefore, the primary objective of this study is to investigate the impact of features to obtain high performance. For this purpose, this study analyzes the impact of different subset feature selection techniques on the performance of models for fake news detection. Principal component analysis and Chi-square are investigated for feature selection using machine learning and pre-trained deep learning models. Additionally, the influence of different preprocessing steps is also analyzed regarding fake news detection. Results obtained from comprehensive experiments reveal that the extra tree classifier outperforms with a 0.9474 accuracy when trained on the combination of term frequency-inverse document frequency and bag of words features. Models tend to yield poor results if no preprocessing or partial processing is carried out. Convolutional neural network, long short term memory network, residual neural network (ResNet), and InceptionV3 show marginally lower performance than the extra tree classifier. Results reveal that using subset features also helps to achieve robustness for machine learning models.