The Internet of Medical Things (IoMT) is a huge, exciting new phenomenon that is changing the world of technology and innovating various industries, including healthcare. It has specific applications and changes in the medical world based on what can be done for clinical workflow models. The first and most fundamental thing that IoMT does in healthcare is to bring a flood of new data into medical processes. In this study, an efficient Internet of Medical Things based cancer detection model was proposed. In fact, for many, new fitness monitors and watches are one of the best examples on the Internet; these mobile, portable, wearable devices can record real-time heart rate, blood pressure, and eye movement of cancer patients. These details are sent to doctors or anywhere else. The proposed method leads to a kind of big data renaissance in the health service. The proposed model gets more accuracy while comparing with the existing models. This will help the doctors to analyze the patients’ health report and provides better treatment.
The way organisations handle, evaluate and leverage information in any sector has essentially altered with big data. Healthcare is one of the most promising fields where big data can be used to create a change. In this paper, as applied to the healthcare industry, we surveyed the state-of - the-art security challenges in big data, assessed how security issues arise in the case of large healthcare data, and discussed ways to address them. We concentrated primarily on the lately suggested anonymization and encryption techniques, their strengths and constraints, and envisaged future directions for studies.
In the recent past of advancement in computer vision object detection and identification technologies are most valuable approaches in our day life. It is mostly used to find a different kind of objects being and provide a security in many zones. It becomes very difficult for achieving a best object detection or identification with high rate in a various situation and criteria. While working with different entities researcher job is going to very difficult but providing high availability is good omen to develop advisable, flexible environments. Like MODI i.e. “Multiple Object Detection Interface”. The main aim of this paper is to identify the object on user requirement. Detect the information or content based on the type i.e. color, face, shape or eyes. It is helpful to the user to retrieve the objects based on his requirements while expose his/her analysis on images. Majorly, Multi color identification done through with the help of HSV color channels. Shape Identification Hough cycle/rectangle transformation. Finally choose human gestures as eyes and face detection with the help of HAAR like features. Every aspect is available in the market. We are trying to make it as single platform as MODI.
With the rapid growth of digital content, the need for the automatic text summarizer is arising to provide short text from the long text document. Many research works have been presented for extractive text summarization (ETS). This paper mainly focuses on the graph-based ETS approach for multiple Telugu text documents. A modified Text-Rank algorithm is employed with the noun and verb count of each sentence in the text as the initial score of each node. To get the optimal features, a novel feature selection algorithm called improved Flamingo Search Algorithm (IFSA) is proposed in this paper. Though graph-based ETS is an important approach, the generated summaries are redundant. To reduce the redundancy in the generated summary, Maximum marginal relevance (MMR) (is combined with the modified Text-Rank. Different word embedding techniques such as Fast-Text, Word2vec, TF-IDF, and one hot encoding are utilized to experiment with the proposed approach. The performance of the proposed text summarization approach is evaluated with BLEU and ROUGE in terms of F-measure, Precision, and Recall.
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