Qualification: 
M.Tech, B-Tech
anjalit@am.amrita.edu

Anjali T. serves as Assistant Professor in the Department of Computer Science and Engineering, Amritapuri Campus. She has completed Masters in Technology. She has 6 years and 8 months of academic experience.

Publications

Publication Type: Conference Paper

Year of Publication Title

2019

C. Dev, Kumar, K., Palathil, A., Anjali T., and Panicker, V., “Machine Learning Based Approach for Detection of Lung Cancer in DICOM CT Image”, in Ambient Communications and Computer Systems, Singapore, 2019.[Abstract]


Lung cancer is one of the leading causes of cancer among all other types of cancer. Thus, an early and effective identification of lung cancer can increase the survival rate among patients. This method presents a computer-aided classification method in computerized tomography images of lungs. In the proposed system, MATLAB has been used for implementing all the procedures. The various stages involved include image acquisition, image preprocessing, segmentation, feature extraction and support vector machine (SVM) classification. First, the DICOM format lung CT image is passed as input which undergoes preprocessing. Then, a threshold value is calculated and image is segmented into left lung and right lung. After that 33 features of each segmented lung are taken and passed as input to the SVM. Finally, the image is classified as cancerous or non-cancerous based on the training data. This method aims to give more satisfactory results when compared to other existing systems.

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2018

Anjali T., Dr. N. Rakesh, and Akshay, K. M. P., “A Novel based Decision tree for Content Based Image Retrieval: An Optimal Classification Approach”, in 7th International Conference on Communication and Signal Processing, Adhiparasakthi Engineering College Melmaruvathur,Chennai, India, 2018.[Abstract]


In this 21 st century, the development of multimedia technology is at its peak. Content based image retrieval (CBIR) is an Inventive methodology which can retrieve images based on consequently inferred highlights, for example, color, texture, features and surface of an object. The application of this technology spread's from medical science to national security including a vast variety of fields. In this system the main challenge is image indexing and similarity ranking for the proper retrieval. A solution to this problem is introducing a Firefly optimization combined Decision Tree (FF-DT) classifier to reduce the computational complexity in the classification stage of feature extraction. Precision rate and recall rate are the evaluation matrices used to evaluate the efficiency of this work. The result obtained from the experiment proves that the proposed approach is more efficient in CBIR system than the existing methods.

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2018

Dr. N. Rakesh, Anjali T., and B. Uma Maheswari, “Implementing MIMO-OFDM for the Improvement of the System Performance in WPAN”, in International conference on Computer Networks and Inventive Communication Technologies (ICCNCT - 2018), Hotel Arcadia, Coimbatore, 2018.[Abstract]


Developing cost-effective solutions to deliver broadband connection to undeserved or under-connected communities using fixed wireless access technology can be achieved by canopy networks. The former can be implemented using MIMO-OFDM technique. The implementations of MIMO-OFDM in systems have shown promising results in terms of system performance improvement. This paper presents high-speed FFT algorithms for wireless personal area network (WPAN) applications which uses high data rate. In WPAN, the FFT/IFFT block plays the major role. To eliminate the huge burden of the computational requirement of the FFT/IFFT process, the divide and conquer algorithm is employed here. This method not only improves the efficiency but is also easy to implement. In this work, the concept of Discrete Fourier Transform is discussed and the same is implemented using MATLAB for the MIMO-OFDM process.

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