Sumita Mishra

Precision Sugarcane Monitoring Using SVM Classifier

Abstract India is an agriculture based economy and sugarcane is one of the major crops produced in northern India. Productivity of sugarcane decreases due to inappropriate soil conditions and infections caused by various types of diseases; timely and accurate disease diagnosis, plays an important role towards optimizing crop yield. This paper presents a system model for monitoring of sugarcane crop, the proposed model continuously monitors parameters (temperature, humidity and moisture) responsible for healthy growth of the crop in addition KNN clustering along with SVM classifier is utilized for infection identification if any through images obtained at regular intervals. The data has been transmitted through a wireless network from the site to the control unit. Model achieves an accuracy of 96%. on a sample of 200 images; the model was tested at Lolai, near Malhaur, Gomti Nagar Extension.

Comprehensive Visible Light Communication System For Healthcare

Health care monitoring is becoming essential with the increase in high risk diseases among the people of all ages. Various wireless sensors are present which continuously monitor body parameters such as pulse rate, ECG etc and record them. Radio frequency communication is the largely accepted channel for the realization of patient health monitoring technology which presents enormous potential for future healthcare application. Visible Light Communication is an emerging technology that can be utilized for similar applications with an edge overradio-frequency, which suffers from multiple issues for example interference with medical instruments and in some cases cause hazards to the health of patient. Visible Light Communication uses LED as a transmitter and it can become a better alternate for the wireless data transmission in various healthcare systems. This paper presents in brief Visible light communication (VLC) for indoor medical applications.

Automated Detection of Acute Leukemia Using K-mean Clustering Algorithm

Leukemia is a hematologic cancer which develops in blood tissue and triggers rapid production of immature and abnormal shaped white blood cells. Based on statistics it is found that the leukemia is one of the leading causes of death in men and women alike. Microscopic examination of blood sample or bone marrow smear is the most effective technique for diagnosis of leukemia. Pathologists analyze microscopic samples to make diagnostic assessments on the basis of characteristic cell features. Recently, computerized methods for cancer detection have been explored towards minimizing human intervention and providing accurate clinical information. This paper presents an algorithm for automated image based acute leukemia detection systems. The method implemented uses basic enhancement, morphology, filtering and segmenting technique to extract region of interest using k-means clustering algorithm. The proposed algorithm achieved an accuracy of 92.8% and is tested with Nearest Neighbor (KNN) and Naive Bayes Classifier on the data-set of 60 samples.