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Internet of Things (IoT) deployed healthcare systems to provide ubiquitous services and assistance for the end-users/patients through their handheld devices/user equipment. The processing of sensitive medical information involves the transfer of effective and timely healthcare services without taking rest. In this article, adaptable service compliance (BASC) is implemented for blockchain-reliant purposes to prevent non-dormant healthcare services. The approach proposed is reliable in overcoming dormancy problems when it comes to accessing and providing end-users with health information. This proposed compliance exploits the type of user assistance requirement in the fore-hand. A distributed ledger does not require a central authority to monitor manipulation, and allows a de-centralized dataset to function safely. Based on the requirement, the distributed ledger accessible at ease is selected, and the information is shared with the end-user. In this method, the compliance is valid until the user's requirement is fulfilled by validating the integrity of the transmitted data. For identifying the accessibility of the available ledger, the backpropagation learning method is used to pass the backward pass to correct the weight vector input variables. The output with learning consists of the verification of affordability, and the background verification verifies the data integrity. The truthfulness inability triggers a fresh outlook for information sharing that is visible from the succeeding ledger. This helps to prevent healthcare services failure, postponements in service, and improve the aid's honesty. In addition, improved network access is achieved at various intervals. [ABSTRACT FROM AUTHOR]

Blockchain technology can solve current interoperability challenges in health information systems and provide the technical standard which ensures a secure sharing of electronic health data between individuals, healthcare providers, medical care institutions, and medical experts. In healthcare management, IoMT devices can deliver real-time sensory data from patients to be analyzed and processed. In IoMT devices within a range of product areas, the confidentiality of patients' health data transmission and privacy is still a big problem. In this paper, the Multi-Modal Secure Data Dissemination Framework (MMSDDF) has been proposed based on blockchain in IoMT for secure patient data access and control. To meet the optimized security and privacy requirements of healthcare data management in IoMT devices, the proposed framework is effectively employed. Blockchain's key has been used to a healthcare application network where the patient's health data can create warnings that are significant to authenticated healthcare providers securely. The simulation outcomes show that the suggested MMSDDF method achieves a high accuracy ratio of 95.8%, a prediction ratio of 92.3%, less delay of 0.48 s, latency range of 0.5, and response time of 1.5% to other existing methods. [ABSTRACT FROM AUTHOR]

Lung cancer is a deadly disease if not diagnosed in its early stages. However, early detection of lung cancer is a challenging task due to the shape and size of its nodules. Radiologists use automated tools for more precise opinion. Automated detection of the affected lung nodules is complicated because of the shape similarity among healthy and unhealthy tissues. Over the years, several expert systems have been developed that help radiologists to diagnose lung cancer effectively. In this article, we have proposed a framework to precisely detect lungs cancer to classify the benign and malignant nodules. The proposed framework is tested using the subset of the publicly available dataset, i.e., the Lung Image Database Consortium image collection (LIDC-IDRI). We applied filtering and noise removal in the pre-processing phase. Furthermore, the adaptive thresholding technique (OTSU) and the semantic segmentation are used to accurately detect the unhealthy lung nodules. Overall, 13 nodules features have extracted using principal components analysis algorithm. In addition, four optimal features are selected based on the classification performance. In the classification phase, 9 different classifiers are employed for the experimentation. Empirical analysis shows that the proposed system outperformed other techniques and provides 99.23% accuracy using a logit boost classifier. [ABSTRACT FROM AUTHOR]

This paper presents a gated-recurrent-unit (GRU) based recurrent neural network (RNN) architecture titled as DSWE-GRNN for multi-class review rating classification problem. Our model incorporates domain-specific word embeddings and does not depend on the reviewer's information because we usually don't have many reviews from the same user to measure the leniency of the user towards a specific sentiment. The RNN based architecture captures the hidden contextual information from the domain-specific word embeddings to effectively and efficiently train the model for review rating classification. In this work, we also demonstrate that downsampling technique for data balancing can be very effective for the model's performance. We have evaluated our model over two datasets i.e IMDB dataset and the Hotel Reviews dataset. The results demonstrate that our model's performance (accuracy) is comparable with or even better than the four baseline methods used for sentiment classification in literature. [ABSTRACT FROM AUTHOR]