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This article highlights the importance of implementing intelligent monitoring devices with the internet of things (IoT) for observing the amount of charges on different appliances in each household. In India, it has been observed that 20% of power is wasted due to commercial appliances where the amount of charge flow is much excess to corresponding appliances. Therefore, to perceive information about the flow of charges, it is necessary to implement an intelligent device, and it is possible to obtain exact information on the flow of charges with the help of wireless sensor networks (WSN). Even most of the researchers have developed an intelligent device for monitoring the amount of charges but delay, energy consumption, and cost of implementation are much higher. It is always necessary to extract precise information at corresponding time periods for reducing the delay in packet transmission of a specific network. To excerpt such real-time data in the network layer, an active procedure should be followed by integrating dissimilar network areas inside a single cluster, and binary coded artificial neural network (BCANN) is introduced to acquire information about hidden layers. To prove the effect of such integration process, several tests have been prepared using online and offline analyses where simulation results prove to be much effective in case of all different scenarios to an extent of 52.4% when compared to existing methods. [ABSTRACT FROM AUTHOR]

Post market examination is to safeguard end users through the close watch of IVDs offered for sale or distribution in the country. Through this action product owner must aware of the problems arises with the IVD in use and analysis the risk associated with the problem reported and take appropriate action to avoid repetition and ensure quality of the IVDs in the market. Rigorous pre-market assessment of IVDs is done by the regulatory authorities; however, there is no enough system and guidance in place for the monitoring of the post-marketing phase of IVDs in most countries including India. This article has been prepared to facilitate and strengthen the reporting of Events attributable to in-vitro diagnostic medical devices in India, harmonized with WHO, National and International best practices. This article suggested the various stakeholders follow this guidance involved in the post market phase of IVDs relates to monitoring of adverse events, reporting of adverse events, reporting timeline, examination of the complaint to determine source, and the subsequent suitable action. This article also aims to create awareness among users about the importance of reporting adverse events relating to the IVDs in India. In order to monitor the quality of the IVDs available in the country and encourage the reporting, this article also urges the central government to make online portal under respective authorities for mandatory reporting, report suspected counterfeit IVDs, for voluntary reporting serious adverse event, product quality problem, product use/medication error, or/failure that is associated with the use of an IVDs. [ABSTRACT FROM AUTHOR]

Tamil is an old Indian language with a large corpus of literature on palm leaves, and other constituents. Palm leaf manuscripts were a versatile medium for narrating medicines, literature, theatre, and other subjects. Because of the necessity for digitalization and transcription, recognizing the cursive characters found in palm leaf manuscripts remains an open problem. In this research, a unique Convolutional Neural Network (CNN) technique is utilized to train the characteristics of the palm leaf characters. By this training, CNN can classify the palm leaf characters significantly on training phase. Initially, a preprocessing technique to remove noise in the input image is done through morphological operations. Text Line Slicing segmentation scheme is used to segment the palm leaf characters. In feature processing, there are some major steps used in this study, which include text line spacing, spacing without obstacle, and spacing with an obstacle. Finally, the extracted cursive characters are given as input to the CNN technique for final classification. The experiments are carried out with collected cursive Tamil palm leaf manuscripts to validate the performance of the proposed CNN with existing deep learning techniques in terms of accuracy, precision, recall, etc. The results proved that the proposed network achieved 94% of accuracy, where existing ResNet achieved 88% of accuracy., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Gayathri Devi S et al.)