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Ethnobotanical study of traditional herbal plants used by local people of Seshachalam Biosphere Reserve in Eastern Ghats, India

100%

Reddy A. M. , Babu M. V. S. , Rao R. R.

Herba Polonica

2019

tom 65

nr 1

Introduction: Ethnobotany is the study of medicinal plants used by local people, with particular importance of old-styled tribal beliefs and information. Ethnobotanical studies focus on ethnic knowledge of Adivasi people and development of data bases on ethnic knowledge but also focuses on preservation and regeneration of traditional beliefs and maintenance of traditional knowledge. Objective: The aim of present study is to highlight the traditional actions of herbal plants used by inborn Yanadi community of Seshachalam Biosphere Reserve, Eastern Ghats of Andhra Pradesh, India. Methods: The ethnobotanical field survey was conducted according to the methods adopted by some authors. In-depth interviews, interactions were conducted with tribal physicians of Yanadi, Nakkala and Irula as well as other tribes practicing and experiencing the use of plant-based medicine. A normal inquiry form was used to gather the appropriate data on herbal plants and their usage of inborn people’s lifestyle. Extensive consultations among local people and detailed documentation of the usage of plants were carried out in 2014–2017. The aged outmoded opinions and imposts of indigenous people conceded on by word of opening were documented. Results: A total of 266 medicinally used plant species belonging to 216 genera and 88 families were recognized with help of inborn herbal healers. The study also chronicled the mode of herbal arrangements, mode of the use of herbal plants in various disorders. The study exposed that native people of Seshachalam Biosphere Reserve have good medicinal information and also have preserved plant-based medicinal system of their ascendants used all their diseases. Most of medicinal plants are used in the treatment of indigestion, snake bite and skin diseases. The authors feel that this type of study certainly helps identify ethnic leads for drug development in future. Conclusions: The ethnobotanical investigation of Seshalam Biosphere area has revealed that the tribes possess good knowledge on plant-based medicine but as they are towards in advanced exposure to transformation, their information on traditional uses of plants is slowly getting eroded. The authors plead for intensive crosscultural studies involving all ethnic tribes in the country for prioritizing or short listing of ethnic leads for various disorders for ultimately developing global level drugs for human welfare and economy development.

Bayesian HCS-based multi-SVNN: A classification approach for brain tumor segmentation and classification using Bayesian fuzzy clustering

100%

Raju A. R. , Suresh P. , Rao R. R.

Biocybernetics and Biomedical Engineering

2018

tom Vol. 38, no. 3

646--660

Brain tumor segmentation and classification is the interesting area for differentiating the tumerous and the non-tumerous cells in the brain and to classify the tumerous cells for identifying its level. The conventional methods lack the automatic classification and they consumed huge time and are ineffective in decision-making. To overcome the challenges faced by the conventional methods, this paper proposes the automatic method of classification using the Harmony-Crow Search (HCS) Optimization algorithm to train the multi-SVNN classifier. The brain tumor segmentation is performed using the Bayesian fuzzy clustering approach, whereas the tumor classification is done using the proposed HCS Optimization algorithm-based multi-SVNN classifier. The proposed method of classification determines the level of the brain tumor using the features of the segments generated based on Bayesian fuzzy clustering. The robust features are obtained using the information theoretic measures, scattering transform, and wavelet transform. The experimentation performed using the BRATS database conveys proves the effectiveness of the proposed method and the proposed HCS-based tumor segmentation and classification achieves the classification accuracy of 0.93 and outperforms the existing segmentation methods.