http://localhost:8080/xmlui/handle/123456789/1710 2026-06-23T06:33:52Z 2026-06-23T06:33:52Z Rane, Archana L. http://localhost:8080/xmlui/handle/123456789/3542 2023-01-02T08:34:37Z 2015-01-01T00:00:00Z

Title: Clinical Decision Support Model for Prevailing Diseases to Improve Human Life Survivability Authors: Rane, Archana L. Abstract: Constantly increasing amount of heterogeneous prevailing disease patient data can redefines medical research and clinical practice for human life survival. Computational intelligent techniques help to translate them into knowledge base that is applicable in health-care. Prediction of such diseases at early stages is biggest challenge for doctors in the country. Previous studies on prevailing diseases focus on individual diseases rather than many with similar symptom. Few of these models have constraints in finding good parameters with high accuracy. The proposed clinical decision support system in this paper models the patient’s diseases state statically from his heterogeneous data to reveal the correct diagnosis by formalizing the hypothesis based on test results and symptoms of the patient before recommending treatments for the prevailing diseases. Its goal is to assist clinician in diagnosing the patient by analyzing his available data and relevant information. The proposed model designed using data mining techniques such as neural network, decision tree, statistical method, Naive Bayes, classifier and clustering pattern analysis for improving human life survivability. Several clinical data-set are used to evaluate and demonstrate the proposed model for early prediction of prevailing disease.

2015-01-01T00:00:00Z Chhajed, Gyankamal J. Shinde, S.A. http://localhost:8080/xmlui/handle/123456789/3439 2022-08-27T08:48:40Z 2010-04-25T00:00:00Z

Title: Efficient Embedding in B&W Picture Images Authors: Chhajed, Gyankamal J.; Shinde, S.A. Abstract: This paper proposes novel method of embedding and extraction of data in Black and White picture images. The main focus of this method is on steganography in Black & white picture image. This method embeds more number of bits in a block as compared to earlier methods, which are limited to one or two bits. This method suggests the searching of the block of suitable size and choosing it for data embedding. The selection of block size is dependent on the encrypted secret message pattern which is encrypted using a secret key shared by sender and receiver and the pattern of block. Here main aim is to utilize the image as much as possible with its own pattern of black and white pixel. In this method very first the encrypted message pattern is taken and it is matched with the block size of 2X2, 3X3 and like wise. The blocks which are giving maximum matching with the bits of the encrypted message is selected for the embedding. To maintain the visual quality of image we will only change at the maximum 2 pixels in the block if size is more than 2X2 and 1 pixel for 2X2 block. To extract the correct data we will use the odd even feature of the block of size 3X3, which will be utilized to keep the embedding information that is size of the message , block size and locations where data is embedded. This information is also encrypted with secret key for security purpose and embedding will start from the end of the image that is right bottom corner and scanning and embedding will be from right to left and bottom to top. This information is used to extract exact amount of data without the need of checking total image. The secret message is extracted after decoding extracted bits from the stego-image with secret key without using original image accurately. This method will prove to be good for embedding increased capacity of data without causing much distortion

2010-04-25T00:00:00Z Hanchat, D.B. Sayyad, Shabina Shinde, S.A. http://localhost:8080/xmlui/handle/123456789/3435 2022-08-27T08:14:51Z 2010-10-23T00:00:00Z

Title: Defect classification as problem classification for Quality control in the software project management by DTL Authors: Hanchat, D.B.; Sayyad, Shabina; Shinde, S.A. Abstract: There are various reasous and causes which lead to failure of software that may come right from it's starting point of requirement analysis up to launching of product in the market .One has to do the root cause analysis of software failure so that these failures should not be reproducible. There are various problems due to which the software may give the bugs, errors, fault and ultimately the failure. Enlisting the problem, analyzing the problem after reporting is must before the fixing of the problem and going into the root cause of the problem. The classification of the problem will definitely help us to sort out the problems and will help to go to the root of problem. Once problems have been reported it can be classified by using any classification method depending upon the properties and their values. We do combine the decision tree learning with the input as the current problems. Decision Tree will be trained with trainee example with similar type of problems, their "properties and values". The DTL will help us to classify the problem and ultimately give us the sorted problems to do analysis of problem [2]. Analysis and classification of the problems will also help in the quality control of the product. We have taken defects classification as an example in this paper.

2010-10-23T00:00:00Z Daware, G. B. Kshirsagar, A.S. Shinde, S. S Khandalkar, A. S. Desale, M. S. http://localhost:8080/xmlui/handle/123456789/2970 2021-09-07T05:51:59Z 2010-06-01T00:00:00Z

Title: Removal of Azodye And Simultaneous Power Generation by Using Microbial Fuel Cell From Waste Water: A Review Authors: Daware, G. B.; Kshirsagar, A.S.; Shinde, S. S; Khandalkar, A. S.; Desale, M. S. Abstract: A microbial fuel cell (MFC) has great potential for treating wastewater containing azodyes for decolourization, and simultaneous production of electricity with the help of microorganisms as biocatalysts. The concept of MFC has been already well established for the production of electricity; however, not much work has been published regarding dye decolourization and simultaneous electricity generation using MFC. Microbial fuel cells (MFCs) represents an emerging technology that focuses on power generation and effluent treatment. This paper reflects the study of different types of MFC, membrane, electrodes, and substrate, for removal of dye and simultaneous power generation from waste water

2010-06-01T00:00:00Z