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Human health are to be monitored for glucose level, blood pressure level and intimated at the right time for taking the necessary pills or food. Defense forces, Diabetes victim and victim with heart attack must be given alert on their BP level and accordingly they can react for taking the appropriate pill or food. A victim may go low in his /her BP level while sleeping more vulnerable to this kind of situation which requires salient tracking and alerting system. Smart polymers combined with Biomimetic Elastomers forms the Injectable Polymer hydrogels. Moreover, many IoT devices have been discovered. A study throws light on a fact that most of continuous tracking observations, human skin suffers a bone disorder called Osteoporosis which ultimately leads to deformation of bones. But hydration loss must be reducible for a bi tissue to be discovered; thereby bypassing the drawbacks caused due to standalone IoT sensors. Injectable Polymer hydrogels is already used for plants and animals for avoiding damage to them during research experiments. The polymers must be molded to the required consistency for synthesizing wearable sensors eliminating health hazards. The conventional IoT system is substituted with the biotechnological solution to enhance continuous monitoring and alert system for victim and elderly aged people. Injectable Polymer hydrogels an artificial skin protein constitutes the composition of nutrients and proteins available in the form of powder can be used for manufacturing the wearable devices to maintain the conductivity range in such a way that it does not affect the human skin. It is proposed to synthesize the artificial skin gel in a relative state that it produces accurate results with sensor data transfer also. The solution proposed here provides the safe usage of irremovable monitoring and tracking of health status.

Smart IOT-based devices are used to extract the emotions and sentiment metrics expressed by the course members for calculating the utility function value for each e-learning content. This is mainly used for rating the E-learning content available on online sources. ELMs are used to combine the output of classifier models implemented by CNN and RNN. Text, image, and acoustic data with respect to time are extracted by CNN, and the extracted output is processed by both CNN and RNN for sentiment analysis. The hidden layers of CNN are trained with ensemble-based algorithms, namely EN-ELM and progressive EN-ELM. The output of CNN and RNN is fed to the two consecutive ELM further whose output is fed to the SVM classifier. Unlike other CNN algorithms, ensemble algorithms are used in the hidden neurons of CNN to carry the time series information to the RNN with the help of stumps and identity activation function. The accuracy of progressive convolution neural network is compared to the accuracy of CNN with RNN. Arousal in the accuracy level is detected with 93–97%.

Multimodal sentiment analysis framework is tremendously applied in the medical and healthcare sectors. Identifying the depression, differently disabled person speech recognition, Alzheimer, low pressure or heart problems and those kinds of impairments are widely addressed. Video data is processed for mining feature polarity from acoustic, linguistic and visual data upon extraction from the same. The feature data set extracted from the YouTube videos contains comments, likes, views and shares for expressing the polarity of information conveyed through the streaming videos. Static information from a video file is extracted in the form of linguistic representation. Musical data extracted and transformed in the linguistic form is used for polarity classification using ensemble-based random forest algorithm which has encountered with the error rate of 4.76%. Short feature vectors are expressed in the visualizing musical data and trending YouTube videos data set for utilizing the transformed and SF vectors from video and musical data. Accuracy of the ensemble-based learning is obtained as 91.6% which is tougher than any other algorithms to achieve using the same set of machine learning algorithms. Proper filter wrapping of batch data is used for the split ratio of 5 percentage split window. When SVM is used in discrimination with ensemble random forest algorithm, the predicted results contain an error rate of 2.64% which improves the accuracy of the classifier along the soft margin with an accuracy of 96.53% accuracy.

Internet of Everything (IoE) and Internet of Nano Technology (IoNT) are the most prominently distributed knowledge over reviews on current trends. Nanoparticles communicating in the perceiving and propagating end cause AI and NLP usage in a better manner. IoNT is used as the medical smart watch calorimeter and pulse rate instrumentation. A sensor of IoE category is used in the mobile devices to accurately provide the alert on calorimeter results and pulse sensed. Voice-based results are gained with the help of AI and NLP. The fuzzy expert system is used to decide on the perceptions received by IoNT devices. Human intelligence is incorporated to further query on installation of food diet control mobile applications in the registry of the programmable devices. There is a strong interest to use social networking to enhance the communications among different IoT things. A new paradigm, named Social Internet of Things (SIoT), was recently proposed. Health-problem-specific solutions are extracted from the social networking blogs for solutions in the recent era. Solutions referred and improvement are recorded for recommendations as a social network post with the concerned proprietary owner permission and rights.