Search results for: Steffy Angural
3 Bioremediation of Sea Food Waste in Solid State Fermentation along with Production of Bioactive Agents
Authors: Rahul Warmoota, Aditya Bhardwaj, Steffy Angural, Monika Rana, Sunena Jassal, Neena Puri, Naveen Gupta
Abstract:
Seafood processing generates large volumes of waste products such as skin, heads, tails, shells, scales, backbones, etc. Pollution due to conventional methods of seafood waste disposal causes negative implications on the environment, aquatic life, and human health. Moreover, these waste products can be used for the production of high-value products which are still untapped due to inappropriate management. Paenibacillus sp. AD is known to act on chitinolytic and proteinaceous waste and was explored for its potential to degrade various types of seafood waste in solid-state fermentation. Effective degradation of seafood waste generated from a variety of sources such as fish scales, crab shells, prawn shells, and a mixture of such wastes was observed. 30 to 40 percent degradation in terms of decrease in the mass was achieved. Along with the degradation, chitinolytic and proteolytic enzymes were produced, which can have various biotechnological applications. Apart from this, value-added products such as chitin oligosaccharides and peptides of various degrees of polymerization were also produced, which can be used for various therapeutic purposes. Results indicated that Paenibacillus sp. AD can be used for the development of a process for the infield degradation of seafood waste.Keywords: chitin, chitin-oligosaccharides, chitinase, protease, biodegradation, crab shells, prawn shells, fish scales
Procedia PDF Downloads 982 Conceptual Design of a Wi-Fi and GPS Based Robotic Library Using an Intelligent System
Authors: M. S. Sreejith, Steffy Joy, Abhishesh Pal, Beom-Sahng Ryuh, V. R. Sanal Kumar
Abstract:
In this paper an attempt has been made for the design of a robotic library using an intelligent system. The robot works on the ARM microprocessor, motor driver circuit with 5 degrees of freedom with Wi-Fi and GPS based communication protocol. The authenticity of the library books is controlled by RFID. The proposed robotic library system is facilitated with embedded system and ARM. In this library issuance system the previous potential readers’ authentic review reports have been taken into consideration for recommending suitable books to the deserving new users and the issuance of books or periodicals is based on the users’ decision. We have conjectured that the Wi-Fi based robotic library management system would allow fast transaction of books issuance and it also produces quality readers.Keywords: GPS bsed based Robotic library, library management system, robotic library, Wi-Fi library
Procedia PDF Downloads 3071 Online Handwritten Character Recognition for South Indian Scripts Using Support Vector Machines
Authors: Steffy Maria Joseph, Abdu Rahiman V, Abdul Hameed K. M.
Abstract:
Online handwritten character recognition is a challenging field in Artificial Intelligence. The classification success rate of current techniques decreases when the dataset involves similarity and complexity in stroke styles, number of strokes and stroke characteristics variations. Malayalam is a complex south indian language spoken by about 35 million people especially in Kerala and Lakshadweep islands. In this paper, we consider the significant feature extraction for the similar stroke styles of Malayalam. This extracted feature set are suitable for the recognition of other handwritten south indian languages like Tamil, Telugu and Kannada. A classification scheme based on support vector machines (SVM) is proposed to improve the accuracy in classification and recognition of online malayalam handwritten characters. SVM Classifiers are the best for real world applications. The contribution of various features towards the accuracy in recognition is analysed. Performance for different kernels of SVM are also studied. A graphical user interface has developed for reading and displaying the character. Different writing styles are taken for each of the 44 alphabets. Various features are extracted and used for classification after the preprocessing of input data samples. Highest recognition accuracy of 97% is obtained experimentally at the best feature combination with polynomial kernel in SVM.Keywords: SVM, matlab, malayalam, South Indian scripts, onlinehandwritten character recognition
Procedia PDF Downloads 575