Browsing by Author "Sivaranjani B"

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BLOCK CHAIN WITH IOT
(Our Heritage Journal, 2020-01-23) Sivaranjani B; Kanishka C; Mahesh J
This paper is regarding Block chain, IOT and the block chain with IOT. Internet of Things plays a major role in the present technological world, but to the point of security and privacy IOT lacks in it. To overcome this Block chain concept arrives. The combination of IOT devices and Blockchain introduces the purest source of IOT data. Blockchain enables IOT’s unique identity, accuracy and consistency of data. Blockchain and IOT both tend to be the world changing technology.
SECURING PATIENTS CONFIDENTIAL INFORMATION USING ECG STEGANOGRAPHY
(by CONFYY in association with PPG institute of technology Coimbatore, 2017-10-20) Sivaranjani B; Radha N
The main goal of this research work is to enhance the security of Patient's medical data. During the transmission, the data is concealed with ECG signal. The ECG signal of the human being vary from one person to person. Like other Biometric traits, ECG cannot be imitated and duplicated. Encryption is one of the best techniques that guarantees the security of sensitive information. This technique not only grants the information's security but also authentication. secret sub keeping system security. digital signature, and etc. Therefore, the purpose of adopting encryption techniques is to ensure the information's integrity and confidentiality, that prevents information from tampering, forgery and counterfeiting. The encryption method used in this work will encrypt the secret data into unreadable form by creating the information inaccessible to any hacker having a random method. In this paper, Haar Wavelet Transformation is used to decompose an ECG signal to different frequency sub-bands. RSA algorithm is used to encrypt the patient information with the help of key pairs. Arnold cat map technique is used to scramble the encrypted data for more security of information. And for embedding, Singular Value Decomposition (SVD) is used for effective transformation with high security. The proposed algorithm is evaluated based on MSE, PSNR, AD, NCC, MD, NAE, BER and accuracy. The experimental results prove that the performance obtained using proposed techniques give better results than the existing techniques.
A SURVEY AND ANALYSIS OF DEEP LEARNING TECHNIQUES FOR BIRD SPECIES CLASSIFICATION
(IEEE Xplore, 2023-06-16) Sivaranjani B; Karpagavalli S
The ability to accurately identify the species of a bird in an image is crucial. A bird’s species identification can be accomplished using images and audios. In earlier periods, the audio of birds are utilized to possibly recognize the different species of birds. But, background noise from things like birds, insects, and the wind makes it difficult for this method to produce a reliable result. Comparatively, observer’s finds images are better than audios. Using images, people are better able to discriminate between birds. However, because of the inexperience of most bird watchers and the similarity of bird forms and backgrounds, identifying birds can be difficult. To address this, Deep Learning (DL) models have been implemented to efficiently extract features from photos collected for recognition. DL models for bird species identification provides more accuracy. The recently proposed transfer learning and spatial pyramid pooling efficiently classify bird spicies. Another recently proposed Mask-CNN based method solved few shot classifcation problem effectively. But, both of these method are suffered to distinguish the subcategory of spicies form main categories. In this article, the of bird species identification techniques are studied in brief to encourage further research in this field. First, the review is planned to investigate the DL algorithms for identifying the different bird species types. Next, the merits and demerits of every algorithms are analyzed based on its performance. Finally, potential improvements are emphasized to achieve greater efficiency in identifying the bird species.