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Dhivya, S.
- IR Spectral Studies of Indole-3-Carboxaldehyde with Copper II Complex
Abstract Views :139 |
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Affiliations
1 Bharath University, Chennai - 600073, Tamil Nadu, IN
1 Bharath University, Chennai - 600073, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 8, No 32 (2015), Pagination:Abstract
The Schiff base was successfully co-ordinated to the metal ions to form the complex. The stability of the metal Schiff base complex has been found to be maximum for five or six membered rings. Structural features were obtained from IR spectral studies. The data shows that the complex has compostion of ML type.Keywords
Copper (II) Nitrate, Indole-3-Carboxaldehyde, IR Spectra, L-Glutamic Acid, ML Complex- IR Spectral Studies of Indole-3-Carboxaldehyde with Nickel-11 Complex
Abstract Views :148 |
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Authors
Affiliations
1 Bharath University, Chennai - 600073, Tamil Nadu, IN
1 Bharath University, Chennai - 600073, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 8, No 32 (2015), Pagination:Abstract
The Schiff base was successfully co-ordinated to the metal ions to form the complex. The stability of the metal Schiff base complex has been found to be maximum for five or six membered rings. Structural features were obtained from IR spectral studies. The data shows that the complex has compostion of ML typeKeywords
Indole-3-Carboxaldehyde, IR Spectra, L-Glutamic Acid, ML Complex, Nickel (II) Nitrate.- Mamogram Image Classification Using Extreme Learning Machine
Abstract Views :192 |
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Authors
Affiliations
1 Department of Electronics and Communication Engineering, M. Kumarasamy College of Engineering, Karur − 639113, Tamil Nadu, IN
1 Department of Electronics and Communication Engineering, M. Kumarasamy College of Engineering, Karur − 639113, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 11, No 17 (2018), Pagination:Abstract
Objective: To make early diagnosis of breast cancer from mammogram image. Methods/Statistical analysis: To avoid the misdiagnosis, we proposed a system to sort the suspicious masses from the mammogram image by using Extreme learning machine algorithm. The ELM based classifier is used to classify the input data as malignant and benign classes with the abnormal class. The effectiveness of the ELM algorithm is superior to the other existing algorithms for mammogram classification problems with its reduced training time and classification accuracy. Findings: We provide an optimistic method for binary class classification of mammograms using extreme learning machine algorithm. Mammography is a technique which is preferred for early diagnosis of breast cancer. On the other hand, in most cases, it is not easy to differentiate benign and malignant tumor without biopsy, hence misdiagnosis is always possible. The machine learning algorithm provides high accuracy than other techniques and also the execution time is very low when compared to normal diagnosis. The existing methods are very slow compared to this proposed technique. The input images are the mammogram image and the segmentation, preprocessing is performed to remove the noises present. Application/Improvements: The main application of the system is the early diagnosis of cancerous cell present and also classifies the normal and abnormal images.Keywords
Breast Cancer, Extreme Learning Machine, Mammography- Multi Key Generation Scheme for Cloud and IoT Devices
Abstract Views :182 |
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Authors
Affiliations
1 Department of Electronics and Communication Engineering, M. Kumarasamy College of Engineering, Karur – 639113, Tamil Nadu, IN
1 Department of Electronics and Communication Engineering, M. Kumarasamy College of Engineering, Karur – 639113, Tamil Nadu, IN