Recently, in December 2019 the Coronavirus disease surprisingly influenced the lives of millions of people in the world with its swift spread. To support medical experts/doctors with the overpowering challenge of prediction of total cases in India, a machine-learning algorithm was developed. In this research article, the author describes the possibility of predicting the COVID-19 total, active cases, death and cured cases in India up to 25th June 2020 by applying linear regression and support vector machine. It is extremely tricky to manage the occurrence of corona virus since it is expanding exponentially day to day and is difficult to handle with a limited number of doctors and beds to treat the infected individuals with limited time. Hence, it is essential to develop a machine learning based computerized predicting model. The development effort in this article is based on publicly available data that is downloaded from KAGGLE to estimate the spread of the disease within a short period. We have calculated the RMSE, R2, MAE of LR and SVR models and concluded that the RMSE of linear regression is less than the SVR. Therefore, the LR will help doctors to forecast for the next few days.
–In this paper, we emphasis on the method by which a sick livestock can be diagnosed of the probable infections and predict the type of disease. Proposed an approach to distinguish whether an MRI picture of a brain contains a possible tumor of livestock. Designed a computer-aided detection approach to detect a brain tumor in its early stage by using deep neural network using Keras and Tensor flow. The main problem faced by a farmer/livestock owner is that ofthe geographical distances of the sick animal from the healthcenter or the doctors who can treat and suggest the possible cure. By leveraging the modern technology in application and developments in Machine Learning and IOT technology the above-mentioned problem can be addressed as of the optimal approach for the farmer. The Detection of tumor is first predicted by Convolution Neural Network based Deep neural network using Keras and Tensorflow, followed is by which the MRI image is pre-processed to isolate the noise and any artefacts. The results are carried out by proposed method which can communicate directly to the cattle farmer using IOT. However the resultant of the computer aided process will automate the detection of diseas by which the farmers can directly know whether the cattle got effected with tumor or not. Time complexity can be significantly reduced with the proposed method. Eventually, computer aided system will assist the radiologist and the doctor in concluding of any illness on the livestock.
In the cement factories, a rotary kiln is a pyro-processing device that is used to raise the temperature of the materials in a continuous process. Temperature monitoring is an essential process in the rotary kiln to yield high quality clinker and it has been implemented using various image processing techniques. In this paper we are measuring and controlling the temperature of rotational kiln in cement industry to get proper clinker ouput. Burning zone flame images are captured using CCD(Charge Coupled Device) camera and are processed using image processing with PID(Proportion Integration and Derivative) controller and which are programmed on raspberry pi card with the help of python language, also the captured images and attributes are transferred to authorized mobile/pc through Raspberry PI by selecting the IP address of mobile or PC. All the attributes received in the mobile in the form of web page the according to the object following data temperature controlled and object is ceaselessly followed to get the proper clinker output. Picture handling calculation with Open cv, as indicated by the calculation the edge estimation of the camera is settled. The frame value of the camera is set. Conversion from RGB color space to HSV color space is achieved and the reference color threshold value is determined. The range esteem estimated by the camera is contrasted and the reference esteem. In this study temp of rotational kiln is measured effectively using PID controller, this controller continuously control the temperature of revolving kiln by varying the i/p images of burning zone at finally fix one flame which is giving 1400degc.
The power generated from the solar PV panels by photovoltaic effect is varying on the particular day. To extract the peak power from the solar panels maximum power point tracking (MPPT) techniques are developed. The demerits of conventional MPPT techniques are slow tracking of the peak point and inaccurate setting of the peak operating voltage point VMPP. The paper proposes the various MPPT techniques and the particle swarm optimization (PSO) MPPT technique. The proposed PSO-MPPT technique improves the productivityand performance of the system. The comparison of various MPPT algorithms based on the performance characteristics are discussed in the paper. The proposed particle swarm optimization MPPT algorithm is efficient, simple and accurate which increases the panel power, by controlling the duty cycle of the switching pulse in the DC converter section under varying weather conditions. The proposed particle swarm optimization MPPT technique is simulated in MATLAB/SIMULINK.
Consideration of public health problem issues, one of the most common diseases in public is cancer. Most of the women population is suffering from breast cancer which is the most well known appearance of cancer in metropolitan cities of India and abroad. There many number of imaging modalities to diagnose cancerous cells. Among those, mammography is alone an imaging modality which diagnoses the breast cancer at an early stage. Furthermore, this modality involves X-rays which are more harmful to human health and make the patient inconvenience. Through the mammogram, doctors can analyze, estimate and evaluate the cancer stage so that doctors can give better and correct treatment to the patients. With this mortality and death rates can also be diminished up to some extent. In this paper, the author proposed an intelligent system to identify and find out the severity of breast cancer. By using a thermal based sensor which is of negative Temperature Coefficient (NTC) available with C-MET Thrissur which replaces Mammography. The stage at which the cancer is progressing is classified with the help of Intelligent System Algorithms which works on the temperature data obtained from the thermal device. The data is pre-processed and applied to multilayered backpropogation neural network model. The neural network classifies the preprocessed images into normal, benign and cancer. The output of the network is presented to the doctors through graphs and displays.
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