Forests are the precious natural resources which provide us wood, timber tree, human living essentials and it is the place where flora and fauna live. It helps in balancing the eco system .. But the greedy mankind destroys this natural resource legally and illegally there by eco system gets unbalanced. The prime of the way of exploiting these forest resources is cutting the trees. To conserve the illegal logging of forest wood we proposed a "Wireless Sensor Network based remote forest monitoring and conservation of illegal logging of valuable trees. This system is suitable for large-scale forest monitoring from illegal logging. We have used a cluster of sensor nodes to monitor the cutting of trees. Periodically the sensed data will be transmitted to the monitoring centre, where it will be recorded.

In this paper, we present a device that is used to measure the fetal heart rate during the time of pregnancy. The major component used for this detection is Fetal Digital stethoscope sensor which is to be placed on the abdomen of the pregnant and the signals are processed by the micro-controller used and the accurate fetal heart rate is identified and sent as a text message to the respective mobile phone through the usage of GSM module and also by the usage of EMG sensor the uterus contraction also be simulated as the output on the desktop. This system is very flexible and low cost helps the patient to monitor the fetal heart rate in home.

Ovarian cancer is a serious sickness for elderly women. According to data, it is the seventh leading cause of death in women as well as the fifth most frequent disease worldwide. Many researchers classified ovarian cancer using Artificial Neural Networks (ANNs). Doctors consider classification accuracy to be an important aspect of making decisions. Doctors consider improved classification accuracy for providing proper treatment. Early and precise diagnosis lowers mortality rates and saves lives. On basis of ROI (region of interest) segmentation, this research presents a novel annotated ovarian image classification utilizing FaRe-ConvNN (rapid region-based Convolutional neural network). The input photos were divided into three categories: epithelial, germ, and stroma cells. This image is segmented as well as preprocessed. After that, FaRe-ConvNN is used to perform the annotation procedure. For region-based classification, the method compares manually annotated features as well as trained feature in FaRe-ConvNN. This will aid in the analysis of higher accuracy in disease identification, as human annotation has lesser accuracy in previous studies; therefore, this effort will empirically prove that ML classification will provide higher accuracy. Classification is done using a combination of SVC and Gaussian NB classifiers after the region-based training in FaRe-ConvNN. The ensemble technique was employed in feature classification due to better data indexing. To diagnose ovarian cancer, the simulation provides an accurate portion of the input image. FaRe-ConvNN has a precision value of more than 95%, SVC has a precision value of 95.96%, and Gaussian NB has a precision value of 97.7%, with FR-CNN enhancing precision in Gaussian NB. For recall/sensitivity, SVC is 94.31 percent and Gaussian NB is 97.7 percent, while for specificity, SVC is 97.39 percent and Gaussian NB is 98.69 percent using FaRe-ConvNN.

No abstract

Nowadays, healthy and risk free foods play an important role in our day to day life. So it is necessary to monitor the environmental conditions and parameters which are inside the grain (Barley, Brown rice, and Buckwheat) storage containers and taking care of it. In our proposed system a set of sensors which are integrated inside the grain storage containers and our prime objective is to provide an effective, secure, easily accessible storage in unpredictable weather conditions. In the process of grain storage, temperature and humidity are two factors that can affect the quality of grains. The overall structure of the proposed grain storage system consists of two components, one is the host computer located in control room for information processing and prediction of grain situation, the other is the computer terminal in the granary with grain data acquisition. The main purpose of the system is to acquire data from different sensors and transmit this data wireless connectivity through IOT(Internet of Things) to access the status of granary. If the parameters of the grains exceeds the specified threshold level then the exhaust fans are automatically ON to reduce the exceeded parameters namely temperature and humidity. The proposed system has good reliability, maintainability and cost effectiveness.