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.
Information efficiency is gaining more importance in the development as well as application sectors of information technology. Data mining is a computer-assisted process of massive data investigation that extracts meaningful information from the datasets. The mined information is used in decision-making to understand the behavior of each attribute. Therefore, a new classification algorithm is introduced in this paper to improve information management. The classical C4.5 decision tree approach is combined with the Selfish Herd Optimization (SHO) algorithm to tune the gain of given datasets. The optimal weights for the information gain will be updated based on SHO. Further, the dataset is partitioned into two classes based on quadratic entropy calculation and information gain. Decision tree gain optimization is the main aim of our proposed C4.5-SHO method. The robustness of the proposed method is evaluated on various datasets and compared with classifiers, such as ID3 and CART. The accuracy and area under the receiver operating characteristic curve parameters are estimated and compared with existing algorithms like ant colony optimization, particle swarm optimization and cuckoo search.
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.
In recent times, data warehousing achieved tremendous attention in various organizations including universities to analyze important aspects of their academic environment. In this paper, we present an automatic design system to integrate the functionality of both the requirement-driven and data-driven approaches. In addition, it is established on the basis of i* framework and Dimensional Fact Model (DFM) which is used to design the actions of actors, and the relationship existing among the agents in DW (Data Warehouse) environment. Furthermore, the proposed system introduces and illustrates an automatic design technique based on a logical programming to perform the integration of different data sources using UML multidimensional schemas that are reconciled with data sources to improvise the conceptual quality.
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