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G. Magesh

Bio: G. Magesh is an academic researcher from VIT University. The author has contributed to research in topics: Stock exchange & Malaria. The author has an hindex of 1, co-authored 2 publications receiving 2 citations.

Papers
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Journal ArticleDOI
TL;DR: The measurable deviations in various other entities of the erythrocytes in a malaria patients using image processing techniques, has also been discussed.
Abstract: Human plasmodial malaria is a severe infection of the erythrocytes with significant morbidity and mortality. The dimensional changes induced by P.vivax malaria parasites in erythrocytes in human blood are prominent and varies with the degree of parasitaemia. Aggregation of erythrocytes is a common finding in patients infected with malaria. The changes in the shape of erythrocyte and its cytoplasm have been determined by shape descriptors and gray scale variation of the cytoplasm by microscopic imaging and image processing tools. The computerized shape analysis is carried out from the digital images obtained under microscope by shape descriptors based on projected area, perimeter and form factor,as measured by processing of images of erythrocytes in patients undergoing treatment for malaria. The changes induced in the cytoplasm by the malaria parasite are determined by the scanning of erythrocytes images along the horizontal diameter. The levels of aggregation of erythrocytes corresponding to the levels of infection, is measured and compared with normal samples. Growth of malaria parasite within the cytoplasm of the erythrocyte has also been measured. The measurable deviations in various other entities of the erythrocytes in a malaria patients using image processing techniques, has also been discussed.

2 citations

Book ChapterDOI
01 Jan 2019
TL;DR: With the help of machine learning techniques, it is possible to accurately identify the stock market movement with a total value of $69 trillion.
Abstract: There are 60 major stock exchanges around the world with a total value of $69 trillion. Stocks are traded almost daily. Stock data is available on the Internet right from the beginning. Prediction of stock market is an attractive topic for researchers of different fields. Before the advent of machine learning and data science, stock market movement was primarily analyzed using statistical and technical factors. Now with the help of machine learning techniques, it is possible to accurately identify the stock market movement. Various machine learning techniques like support machine vectors, random forests, gradient boosted trees, etc. have been successfully used in the past to predict stock prices.

1 citations


Cited by
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Journal ArticleDOI
TL;DR: Research efforts in quantification of malaria infection include normalization of images, segmentation followed by features extraction and classification, which were reviewed in detail in this paper.
Abstract: Malaria is a life-threatening disease caused by parasite of genus plasmodium, which is transmitted through the bite of infected Anopheles. A rapid and accurate diagnosis of malaria is demanded for proper treatment on time. Mostly, conventional microscopy is followed for diagnosis of malaria in developing countries, where pathologist visually inspects the stained slide under light microscope. However, conventional microscopy has occasionally proved inefficient since it is time consuming and results are difficult to reproduce. Alternate techniques for malaria diagnosis based on computer vision were proposed by several researchers. The aim of this paper is to review, analyze, categorize and address the recent developments in the area of computer aided diagnosis of malaria parasite. Research efforts in quantification of malaria infection include normalization of images, segmentation followed by features extraction and classification, which were reviewed in detail in this paper. At the end of review, the existent challenges as well as possible research perspectives were discussed.

39 citations

Journal ArticleDOI
TL;DR: This paper proposes detection algorithm for the illegal U-turn vehicles which can cause critical accident among violations of road traffic laws and uses the algorithm of pyramid Lucas-Kanade to reduce the high computational complexity.
Abstract: Today, Intelligent Vehicle Detection System seeks to reduce the negative factors, such as accidents over to get the traffic information of existing system. This paper proposes detection algorithm for the illegal U-turn vehicles which can cause critical accident among violations of road traffic laws. We predicted that if calculated optical flow vectors were shown on the illegal U-turn path, they would be cause of the illegal U-turn vehicles. To reduce the high computational complexity, we use the algorithm of pyramid Lucas-Kanade. This algorithm only track the key-points likely corners. Because of the high computational complexity, we detect center lane first through the color information and progressive probabilistic hough transform and apply to the around of center lane. And then we select vectors on illegal U-turn path and calculate reliability to check whether vectors is cause of the illegal U-turn vehicles or not. Finally, In order to evaluate the algorithm, we calculate process time of the type of algorithm and prove that proposed algorithm is efficiently.

6 citations

Proceedings ArticleDOI
01 Aug 2022
TL;DR: In this paper , a Gramian Angular Field (GAF) and densely connected convolutional network (Dense Net)-based air quality prediction technique is presented, which converts one-dimensional time series of air quality data into two-dimensional images, preserving correlation information between the time series data.
Abstract: Data-driven air quality prediction methods mostly use convolutional neural networks, but the problem of gradient disappearance occurs when the number of network layers increases during network training, and the direct use of air quality-related data as network input results in incomplete feature extraction. Model-based and signal-based air quality prediction methods have problems such as difficult modeling and tedious signal analysis; To overcome these issues, a Gramian Angular Field (GAF) and densely connected convolutional network (Dense Net)-based air quality prediction technique is presented. GAF converts one-dimensional time series of air quality data into two-dimensional images, preserving correlation information between the time series data; the two-dimensional images are fed into Dense Net, which performs feature extraction on the two-dimensional images, improving feature information utilization and achieving accurate air quality prediction. Experiments using data from the UCI air quality data set show that the method can accurately predict future air quality with an MSE error of only 0.0236, demonstrating that the method proposed in this paper can better extract multidimensional time-series feature information and achieve higher air quality prediction accuracy. Further, the method proposed in this paper is expected to be further extended to business big data forecasting, such as stock forecasting, default forecasting, quantitative trading, and other regression problems.