Twitter sentiment analysis is an automated process of analyzing the text data which determining the opinion or feeling of public tweets from the various elds. For example, in marketing eld, political eld huge number of tweets is posting with hash tags every moment via internet from one user to another user. This sentiment analysis is a challenging task for the researchers mainly to correct interpretation of context in which certain tweet words are di cult to evaluate what truly is negative and positive statement from the huge corpus of tweet data. This problem violates the integrity of the system and the user reliability can be signi cantly reduced. In this paper, we identify the each tweet word and we are assigning a meaning into it. The feature work is combined with tweet words, word2vec, stop words and integrated into the deep learning techniques of Convolution neural network model and Long short Term Memory, these algorithms can identify the pattern of stop word counts with its own strategy. Those two models are well trained and applied for IMDB dataset which contains 50,000 movie reviews. With huge amount of twitter data is processed for predicting the sentimental tweets for classi cation. With the proposed methodology, the samples are experimentally collected from the real-time environment can be discriminated well and the e cacy of the system is improved. The result of Deep Learning algorithms aims to rate the review tweets and also able to identify movie review with testing accuracy as 87.74% and 88.02%.

The Internet of Things (IoT) is a promising technology which interconnects the available resources to offer reliable and effective smart objects. The smart objects act as a definitive building block in the development of interdisciplinary cyberphysical systems and smart ubiquitous frameworks. The IoT revolution is improving the potential of healthcare infrastructures for providing quality care to patients and assisted living. IoT is renovating the traditional healthcare system with promising technical, economic and social forecasts. The current researches in the IoT have opened more possibilities in the field of medicine that aims to improve the quality of healthcare with minimum cost. This survey paper explores the advances in Human Healthcare Internet of Things (H 2 IoT) and analyses the present-day networks, architectures, topologies, platforms, services and applications in healthcare. This paper also surveys the challenges in H 2 IoT design, privacy, security, threats and attack classification.

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In recent days, the Internet of Things (IoT) used to connect many devices and communicate with each other, which created a greater impact on animal healthcare systems. IoT devices are in the form of wearable's that have been used to track the activities of humans. Now, the wearable devices are used in monitoring the activities of the animals. Internet of Things in Animal Healthcare (IoTAH) uses the biosensors and software for monitoring and maintaining the animal health records. These kinds of technologies make a precise health status and sickness projection which are most effective in humans but it can be applied to animals with few changes. Some of those recent technologies acquired the importance of their use in animal healthcare and development. The integration of available medical sensors creates a connected digital platform that empowers the connectivity with pets and livestock with improved efficiency. This article describes the scope of biosensors, computing, communicating, and wearable technologies available for animals. The main intention of this article is to review the recent advancements in the field of animal healthcare which includes domestic, farm, and wild animals. This article reviews the smart technologies available for various categories of animals. The outcomes of this survey are expected to improve the future research and development of animal welfare systems.

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