Psychoinformatics : the behavioral analytics
- Authors: Nimje, Sparsh , Katade, Jayesh , Dunbray, Nachiket , Mavale, Shreyas , Kulkarni, Siddhivinayak , Firmin, Sally
- Date: 2022
- Type: Text , Conference paper
- Relation: 3rd International Conference on Communication, Computing and Electronics Systems, ICCCES 2021, Coimbatore, India, 28-29 October 2021, Proceedings of Third International Conference on Communication, Computing and Electronics Systems Vol. 844, p. 547-562
- Full Text: false
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- Description: Human behavior is very complex and cannot be explained using traditional mathematical models. Intermediate forms, such as those obtained from personality data, can be used to predict behavioral aspects of a person, creating the hypothesis that arbitrating psychological models can be drawn directly from recordings of behavior. In recent years, smartphone addiction has increased to a great extent. Since the excessive use of smartphones has negatively affected our daily life, many applications to reduce dependence on smartphones have been developed around the world. Personal attributes or personality types can be extracted from data obtained directly from smart phones without the interaction of participants who may have social or health interventions. Many people who excessively use their smartphones have an uncontrollable urge to use the Internet. Internet addiction refers to uncontrolled use of the Internet which causes hindrance in our daily life. Due to its negative impact on the education and lives of people, it is necessary to detect tendencies of people toward addictive behavior and provide them with preventative support and treatment. Similarly, the development of social media has seen rapid growth in its usage. People often find themselves overusing utilities such as virtual communication, texting, and sharing information which have also caused various behavioral problems. This study provides a summary of the various methods and studies done on these behavioral problems and to analyze different techniques, and machine learning models are used to predict addictive personality types. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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
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- 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
Framework for Integration of Medical Image and Text-Based Report Retrieval to Support Radiological Diagnosis
- Authors: Kulkarni, Siddhivinayak , Savyanavar, Amit , Kulkarni, Pradnya , Stranieri, Andrew , Ghorpade, Vijay
- Date: 2017
- Type: Text , Book chapter
- Relation: Biomedical Signal and Image Processing in Patient Care p. 86-122
- Full Text: false
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- Description: In healthcare systems, medical devices help physicians and specialists in diagnosis, prognosis, and therapeutics. As research shows, validation of medical devices is significantly optimized by accurate signal processing. Biomedical Signal and Image Processing in Patient Care is a pivotal reference source for progressive research on the latest development of applications and tools for healthcare systems. Featuring extensive coverage on a broad range of topics and perspectives such as telemedicine, human machine interfaces, and multimodal data fusion, this publication is ideally designed for academicians, researchers, students, and practitioners seeking current scholarly research on real-life technological inventions. In healthcare systems, medical devices help physicians and specialists in diagnosis, prognosis, and therapeutics. As research shows, validation of medical devices is significantly optimized by accurate signal processing. Biomedical Signal and Image Processing in Patient Care is a pivotal reference source for progressive research on the latest development of applications and tools for healthcare systems. Featuring extensive coverage on a broad range of topics and perspectives such as telemedicine, human machine interfaces, and multimodal data fusion, this publication is ideally designed for academicians, researchers, students, and practitioners seeking current scholarly research on real-life technological inventions.
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
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- 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.
A new reliability analysis method based on the conjugate gradient direction
- Authors: Ezzati, Ghasem , Mammadov, Musa , Kulkarni, Siddhivinayak
- Date: 2015
- Type: Text , Journal article
- Relation: Structural and Multidisciplinary Optimization Vol. 51, no. 1 (2015), p. 89-98
- Full Text: false
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- Description: Reliability-based design optimization (RBDO) is an important area in structural optimization. A principal step of the RBDO process is to solve a reliability analysis problem. This problem has been considered in inner loop of double-loop RBDO approaches. Although many algorithms have been developed for solving this problem, there are still some challenges. Existing algorithms do not have good convergence rates and often diverge. There is a need to develop more efficient and stable algorithms that can be used for evaluating all performance functions sufficiently. In this paper, a new method, called “Conjugate Gradient Analysis (CGA) Method”, is proposed to apply in the reliability analysis problems. This method is based on the conjugate gradient method. Some mathematical problems are provided in order to demonstrate the advantages of the proposed method compared with the existing methods. © 2014, Springer-Verlag Berlin Heidelberg.
Constructing an inter-post similarity measure to differentiate the psychological stages in offensive chats
- Authors: Miah, Md Waliur Rahman , Yearwood, John , Kulkarni, Siddhivinayak
- Date: 2015
- Type: Text , Journal article
- Relation: Journal of the Association for Information Science and Technology Vol. 66, no. 5 (2015), p. 1065-1081
- Full Text: false
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- Description: Offensive Internet chats, particularly the child-exploiting type, tend to follow a documented psychological behavioral pattern. Researchers have identified some important stages in this pattern. The psychological stages broadly include befriending, information exchange, grooming, and approach. Similarities among the posts of a chat play an important role in differentiating as well as in identifying these stages. In this article a novel similarity measure is constructed which gives high Inter-post-similarity among the chat-posts within a particular behavioral stage and low inter-post-similarity across different behavioral stages. A psychological stage corpus-based dictionary is constructed from mining the terms associated with each stage. The dictionary works as a background knowledge-base to support the similarity measure. To find the inter-post similarity a modified sentence similarity measure is used. The proposed measure gives improved recognition of inter-stage and intra-stage similarity among the chat posts compared with other types of similarity measures. The pairwise inter-post similarity is used for clustering chat-posts into the psychological stages. Results of experiments demonstrate that the new clustering method gives better results than some current clustering methods.
Visual character N-grams for classification and retrieval of radiological images
- Authors: Kulkarni, Pradnya , Stranieri, Andrew , Kulkarni, Siddhivinayak , Ugon, Julien , Mittal, Manish
- Date: 2014
- Type: Text , Journal article
- Relation: International Journal of Multimedia & Its Applications Vol. 6, no. 2 (April 2014), p. 35-49
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- Description: Diagnostic radiology struggles to maintain high interpretation accuracy. Retrieval of past similar cases would help the inexperienced radiologist in the interpretation process. Character n-gram model has been effective in text retrieval context in languages such as Chinese where there are no clear word boundaries. We propose the use of visual character n-gram model for representation of image for classification and retrieval purposes. Regions of interests in mammographic images are represented with the character n-gram features. These features are then used as input to back-propagation neural network for classification of regions into normal and abnormal categories. Experiments on miniMIAS database show that character n-gram features are useful in classifying the regions into normal and abnormal categories. Promising classification accuracies are observed (83.33%) for fatty background tissue warranting further investigation. We argue that Classifying regions of interests would reduce the number of comparisons necessary for finding similar images from the database and hence would reduce the time required for retrieval of past similar cases.
Investigating the social implications of biometrics and the need for global biometric uniformity
- Authors: Leicester, Phillip , Kulkarni, Siddhivinayak
- Date: 2013
- Type: Text , Journal article
- Relation: International Journal for Infonomics Vol. 6, no. 3/4 (2013), p. 731-735
- Full Text: false
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- Description: This research paper looks at the social implications of biometrics pertaining to the ethics of privacy and the ownership of individual biometric data, including how these issues can be resolved through the establishment of a Biometric Commission by introducing global standardised biometric uniformity and the guidelines that will ensure their technological foundations. Much of the distrust that engulfs society is due to the past performances and policy implementations that governments have initiated surrounding biometric technology and its miss use beyond the realms of individual identification for security purposes. There needs to be total transparency from governments and organisations that use biometric technology for security identification in how every individuals biometric data will be used, stored and the ethical standards provided in eliminating many of the implications that every society has towards on how their biometric data will be used.
Challenges of Challenges of Deploying RFID Technology for Reducing Medical Identity Theft
- Authors: Leicester, Phillip , Kulkarni, Siddhivinayak
- Date: 2012
- Type: Text , Journal article
- Relation: International Journal of Infonomics Vol. 5, no. 3/4 (September/December 2012 2012), p. 597-602
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- Description: The healthcare industry is the biggest user of RFID technology due to its mobility in delivering data, tracking and surveillance of every individual, pathology tests, medications and the management of patient data. Because this technology is so vital within the healthcare/hospital environment this paper investigates and analyses the challenges as well as the issues facing RFID technology in implementing and providing security in guarding against the occurrences of medical identity theft. This form of identity theft is life threating as it adds medical data to a patients file who didn’t receive treatment for whatever conditions the imposter obtained as a result of their criminal activity. In preventing medical identity theft requires specific proposals from a policy, social and technological perspective.
- Description: C1
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
Emergence phenomenon and fuzzy logic in meaningful image segmentation and retrieval
- Authors: Deb, Sagarmay , Kulkarni, Siddhivinayak
- Date: 2012
- Type: Text , Book chapter
- Relation: Machine Learning Algorithms for Problem Solving in Computational Applications: Intelligent Techniques p. 167-178
- Full Text: false
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- Description: Content-based image retrieval is a difficult area of research in multimedia systems. The research has proven extremely difficult because of the inherent problems in proper automated analysis and feature extraction of the image to facilitate proper classification of various objects. An image may contain more than one object, and to segment the image in line with object features to extract meaningful objects and then classify it in high-level like table, chair, car and so on has become a challenge to the researchers in the field. The latter part of the problem, the gap between low-level features like colour, shape, texture, spatial relationships, and high-level definitions of the images is called the semantic gap. Until this problem is solved in an effective way, the efficient processing and retrieval of information from images will be difficult to achieve. In this chapter, the authors explore the possibilities of how emergence phenomena and fuzzy logic can help solve these problems of image segmentation and semantic gap.
Fingerprint feature extraction and classification by learning the characteristics of fingerprint patterns
- Authors: Kulkarni, Siddhivinayak
- Date: 2012
- Type: Text , Journal article
- Relation: Neural Network World Vol. 21, no. 3 (2012), p. 219-226
- Full Text: false
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- Description: This paper presents a two stage novel technique for fingerprint feature extraction and classification. Fingerprint images are considered as texture patterns and Multi Layer Perceptron (MLP) is proposed as a feature extractor. The same fingerprint patterns are applied as input and output of MLP. The characteristics output is taken from single hidden layer as the properties of the fingerprints. These features are applied as an input to the classifier to classify the-features into five broad classes. The preliminary experiments were conducted on small benchmark database and the found results were promising. The results were analyzed and compared with other similar existing techniques. © ICS AS CR 2011.
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.
Machine learning algorithms for problem solving in computational applications: Intelligent techniques
- Authors: Kulkarni, Siddhivinayak
- Date: 2012
- Type: Text , Book
- Relation: Machine Learning Algorithms for Problem Solving in Computational Applications: Intelligent Techniques
- Full Text: false
- Reviewed:
- Description: Machine learning is an emerging area of computer science that deals with the design and development of new algorithms based on various types of data. Machine Learning Algorithms for Problem Solving in Computational Applications: Intelligent Techniques addresses the complex realm of machine learning and its applications for solving various real-world problems in a variety of disciplines, such as manufacturing, business, information retrieval, and security. This premier reference source is essential for professors, researchers, and students in artificial intelligence as well as computer science and engineering.
Machine learning approach for content based image retrieval
- Authors: Kulkarni, Siddhivinayak
- Date: 2012
- Type: Text , Book chapter
- Relation: Machine Learning Algorithms for Problem Solving in Computational Applications: Intelligent Techniques p. 1-11
- Full Text: false
- Reviewed:
- Description: Developments in technology and the Internet have led to an increase in number of digital images and videos. Thousands of images are added to WWW every day. Content based Image Retrieval (CBIR) system typically consists of a query example image, given by the user as an input, from which low-level image features are extracted. These low level image features are used to find images in the database which are most similar to the query image and ranked according their similarity. This chapter evaluates various CBIR techniques based on fuzzy logic and neural networks and 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. A number of experiments were conducted for classification, and retrieval of images on sets of images and promising results were obtained.
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
Preface
- Authors: Kulkarni, Siddhivinayak
- Date: 2012
- Type: Text , Book chapter
- Relation: Machine Learning Algorithms for Problem Solving in Computational Applications: Intelligent Techniques p. xvi-xx
- Full Text: false
- Reviewed:
- Description: Machine learning is an emerging area of computer science that deals with the design and development of new algorithms based on various types of data. Machine Learning Algorithms for Problem Solving in Computational Applications: Intelligent Techniques addresses the complex realm of machine learning and its applications for solving various real-world problems in a variety of disciplines, such as manufacturing, business, information retrieval, and security. This premier reference source is essential for professors, researchers, and students in artificial intelligence as well as computer science and engineering.
Texture feature extraction and classification by combining statistical and neural based technique for efficient CBIR
- Authors: Kulkarni, Siddhivinayak , Kulkarni, Pradnya
- Date: 2012
- Type: Text , Conference paper
- Relation: 2012 Int. Conf. on MulGraB 2012, the 2012 Int. Conf. on BSBT 2012, and the 1st Int. Conf. on Intelligent Urban Computing, IUrC 2012, Held as Part of the Future Generation Information Technology Conference, FGIT 2012 Vol. 353 CCIS, p. 106-113
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- Description: This paper presents a technique based on statistical and neural feature extractor, classifier and retrieval for real world texture images. The paper is presented into two stages, texture image pre-processing includes downloading images, normalizing into specific rows and columns, forming non-overlapping windows and extracting statistical features. Co-occrance based statistical technique is used for extracting four prominent texture features from an image. Stage two includes, feeding of these parameters to Multi-Layer Perceptron (MLP) as input and output. Hidden layer output was treated as characteristics of the patterns and fed to classifier to classify into six different classes. Graphical user interface was designed to pose a query of texture pattern and retrieval results are shown. © 2012 Springer-Verlag.
- Description: 2003010656
Decision support based needs assessment for cancer patients
- Authors: Stranieri, Andrew , Kulkarni, Siddhivinayak , Macfadyen, Alyx , Love, Anthony , Vaughan, Stephen
- Date: 2011
- Type: Text , Conference paper
- Relation: Australasian workshop on health informatics and knowledge management (HIKM)
- Full Text: false
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- Description: Regular assessment of wellness or quality of life for patients throughout a cancer journey is important so as to identify aspects of life that could lead to distress and impede recovery or acceptance. The emerging trends in assessment are to deploy validated, quality of life instruments on touchscreen computers in medical waiting rooms. However, these add to workload of health care professionals and can be impersonal for patients to use. In this article, an alternate approach is presented that involves a decision support system with natural dialogue that elicits the patient's specific context in a far finer grained manner than is possible with questionnaire based instruments. The system includes a model of heuristics that health care professionals in a locality use to make inferences regarding a patient's quality of life and avenues for referral.
- Description: E1
Detection of child exploiting chatsfrom a mixed chat dataset as a text classification task
- Authors: Yearwood, John , Miah, Md Waliur Rahman , Kulkarni, Siddhivinayak
- Date: 2011
- Type: Text , Conference paper
- Relation: Proceedings of Australasian Language Technology Association Workshop
- Full Text: false
- Reviewed:
- Description: There is a rapidly growing body of work in the use of Embodied Conversational Agents (ECA) to convey complex contextual relationships through verbal and non-verbal communication, in domains ranging from military C2 to e-learning. In these applications the subject matter expert in often naive to the technical requirements of ECAs. ENGAGE (the Extensible Natural Gesture Animation Generation Engine) is desgined to automatically generate appropriate and 'realistic' animation for ECAs based on the content provided to them. It employs syntactic analysis of the surface text and uses predefined behaviours for the ECA. We discuss the design of this system, its current applications and plans for its future development.