The growth of abnormal tissue is also called neoplasm which can be differentiated from the surrounding tissues by its structure. This tumour will affect the immune system which is a major leading cause of death around 13% worldwide. Blood diseases such as leukemia replaces the normal blood cells in the bone marrow and the blood. Effective modern drugs can be deployed for the blood diseases such as Chronic Lymphatic Leukemia(CLL). Data mining technique is used to categorize the blood test uniqueness(Hematology) and blood swelling to predict the disease in a early stage. Due to the increase in blood tumour diseases Support Vector Machine(SVM) is proposed for the classification of tumour and hematological data. Fish Swarm algorithm is found more efficient in optimizing the data with high accuracy.
The paper “Design and Implementation of a Smart Home Energy Management System Using IoT and Machine Learning” proposes a system that aims to optimize energy consumption in a smart home environment. The system uses Internet of Things (IoT) devices to collect real-time data on energy usage and machine learning algorithms to predict future consumption patterns. This paper proposes the use of deep neural networks (DNNs) for the design and implementation of a smart home energy management system using IoT and machine learning techniques. The authors demonstrate the effectiveness of the system through experimental results, showing significant energy savings compared to traditional methods. The DNN is built using Keras or Tensor Flow and is trained on extracted features from energy consumption data collected using IoT sensors. The system is implemented with a real-time monitoring system and a user interface for remote access. The proposed system has the potential to save energy and reduce energy costs for households while providing real-time feedback to the user.
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