Semi-invasive system for detecting and monitoring dementia patients
- Authors: Yamsanwar, Yash , Patankar, Amol , Kulkarni, Siddhivinayak , Stratton, David , Stranieri, Andrew
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 5th IEEE International Conference for Convergence in Technolog, I2CT 2019
- Full Text: false
- Reviewed:
- Description: Dementia is one of the most prevalent conditions faced by the elderly caused by specific brain cell damage. Various effects of dementia include a loss of memory, reduction in problem solving ability, analytical skills, and decision making capability. Few systems have been developed for the early detection of dementia. Existing systems depend largely on hardware e.g. sensors, gateways. Factors like maintainability and sustainability compromise the efficiency of such systems. This paper presents a novel approach towards the early detection of dementia and aims at eliminating some of the challenges posed by these systems. It also provides a comparati ve study of the cognitive abilities of healthy old-age people and those afflicted by dementia. © 2019 IEEE.
- Description: E1
Pixel N-grams for mammographic lesion classification
- Authors: Kulkarni, Pradnya , Stranieri, Andrew , Ugon, Julien , Mittal, Manish , Kulkarni, Siddhivinayak
- Date: 2017
- Type: Text , Conference proceedings
- Relation: 2017 2nd International Conference on Communication Systems, Computing and IT Applications, CSCITA , Mumbai; 7th-8th April, 2017; published in CSCITA 2017 - Proceedings p. 107-111
- Full Text: false
- Reviewed:
- Description: Automated classification algorithms have been applied to breast cancer diagnosis in order to improve the diagnostic accuracy and turnover time. However, classification accuracy, sensitivity and specificity could still be improved further. Moreover, reducing computational cost is another challenge as the number of images to be analyzed is typically large. In this paper, a novel Pixel N-gram approach inspired from character N-grams in the text retrieval context has been applied for mammographic lesion classification. The experiments on real world database demonstrate that the Pixel N-grams outperform the existing histogram as well as Haralick features with respect to classification accuracy as well as sensitivity. Effect of varying N and using various classifiers is also analyzed in this paper. Results show that optimum value of N is equal to 3 and MLP classifier performs better than SVM and KNN classifier using 3-gram features.
Colour image annotation using hybrid intelligent techniques for image retrieval
- Authors: Kulkarni, Siddhivinayak , Kulkarni, Pradnya
- Date: 2012
- Type: Text , Conference proceedings
- Full Text:
- Description: This paper presents a novel technique for colour image annotation based on neural networks and fuzzy logic. Neural network is proposed for classifying the images based on their contents and fuzzy logic is proposed for interpreting the content of an image in terms of natural language. One of the main aspects of this research is to avoid re-training of the neural networks by training the content of the image. Neural network is not trained on database of images; therefore image can be added or deleted from image database without affecting the training. The proposed hybrid technique is tested on real world colour image dataset and promising results are obtained. © 2012 IEEE.
- Description: 2003010700
Hybrid technique for colour image classification and efficient retrieval based on fuzzy logic and neural networks
- Authors: Fernando, Ranisha , Kulkarni, Siddhivinayak
- Date: 2012
- Type: Text , Conference proceedings
- Full Text:
- Description: Developments in the technology and the Internet have led to increase in number of digital images and videos. Thousands of images are added to WWW every day. To retrieve the specific images efficiently from database or from Internet is becoming a challenge now a day. As a result, the necessity of retrieving images has emerged to be important to various professional areas. This paper proposes a novel fuzzy approach to classify the colour images based on their content, to pose a query in terms of natural language and fuse the queries based on neural networks for fast and efficient retrieval. Number of experiments was conducted for classification and retrieval of images on sets of images and promising results were obtained. The results were analysed and compared with other similar image retrieval system. © 2012 IEEE.
MapReduce neural network framework for efficient content based image retrieval from large datasets in the cloud
- Authors: Venkatraman, Sitalakshmi , Kulkarni, Siddhivinayak
- Date: 2012
- Type: Text , Conference proceedings
- Full Text:
- Description: Recently, content based image retrieval (CBIR) has gained active research focus due to wide applications such as crime prevention, medicine, historical research and digital libraries. With digital explosion, image collections in databases in distributed locations over the Internet pose a challenge to retrieve images that are relevant to user queries efficiently and accurately. It becomes increasingly important to develop new CBIR techniques that are effective and scalable for real-time processing of very large image collections. To address this, the paper proposes a novel MapReduce neural network framework for CBIR from large data collection in a cloud environment. We adopt natural language queries that use a fuzzy approach to classify the colour images based on their content and apply Map and Reduce functions that can operate in cloud clusters for arriving at accurate results in real-time. Preliminary experimental results for classifying and retrieving images from large data sets were quite convincing to carry out further experimental evaluations. © 2012 IEEE.
- Description: 2003010699
Risk-based neuro-grid architecture for multimodal biometrics
- Authors: Venkatraman, Sitalakshmi , Kulkarni, Siddhivinayak
- Date: 2010
- Type: Text , Conference proceedings
- Full Text:
- Description: Recent research indicates that multimodal biometrics is the way forward for a highly reliable adoption of biometric identification systems in various applications, such as banks, businesses, governments