Faculty Publications
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Publications by NITK Faculty
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Item A semantic search engine for answering domain specific user queries(2013) Kamath S․, S.S.; Piraviperumal, D.; Meena, G.; Karkidholi, S.; Kumar, K.With the exponential growth in web content and due to its sheer volume, the answers provided by traditional search engines by query specific keywords to content has resulted in markedly high recall and low precision. In order to alleviate this problem, the notion of incorporating semantics in content and in Search Engines, i.e., a Semantic Search Engine is increasingly crucial. Several Semantic Search Engines (SSEs) have been proposed and deployed till date and each is inherently different from the other. As such, the objective of this paper is to present a discussion on semantically enhanced search engines for intelligent web content discovery. We also present the architecture of a new SSE based on a bottom up approach that focuses on building a semantic base for Web content first and then carry out the process of querying it for attaining high precision and lower recall. © 2013 IEEE.Item Dynamic and temporal user profiling for personalized recommenders using heterogeneous data sources(Institute of Electrical and Electronics Engineers Inc., 2017) S. Krishnan, G.S.; Kamath S․, S.In modern Web applications, the process of user-profiling provides a way to capture user-specific information, which then serves as a source for designing personalized user experiences. Currently, such information about a particular user is available from multiple online sources/services, like social media applications, professional/social networking sites, location based service providers or even from simple Web-pages. The nature of this data being truly heterogeneous, high in volume and also highly dynamic over time, the problem of collecting these data artifacts from disparate sources, to enable complete user-profiling can be challenging. In this paper, we present an approach to dynamically build a structured user profile, that emphasizes the temporal nature to capture dynamic user behavior. The user profile is compiled from multiple, heterogeneous data sources which capture dynamic user actions over time, to capture changing preferences accurately. Natural language processing techniques, machine learning and concepts of the semantic Web were used for capturing relevant user data and implement the proposed '3D User Profile'. Our technique also supports the representation of the generated user profiles as structured data so that other personalized recommendation systems and Semantic Web/Linked Open Data applications can consume them for providing intelligent, personalized services. © 2017 IEEE.Item Semantic web service selection based on service provider's business offerings(2009) D’Mello, D.A.; Ananthanarayana, V.S.Semantic Web service discovery finds a match between the service requirement and service advertisements based on the semantic descriptions. The matchmaking mechanism might find semantically similar Web services having same matching score. In this paper, the authors propose the semantic Web service selection mechanism which distinguishes semantically similar Web services based on the Quality of Service (QoS) and Business Offerings (BO). To realize the semantic Web service discovery and selection (ranking), we propose the semantic broker based Web service architecture which recommends the best match for the requester based on the requested functionality, quality and business offerings. The authors design the semantic broker which facilitates the provider to advertise the service by creating OWL-S service profile consisting information related to functionality, quality and business offerings. After the service advertisement, the broker computes and records matchmaking information to improve the performance (service query time) of discovery and selection process. The broker also reads requirements from the requester and finds the best (profitable) Web service by matching and ranking the advertised services based on the functionality, capability, quality and business offering.Item Dynamic web service composition based on operation flow semantics(2010) D’Mello, D.A.; Ananthanarayana, V.S.Dynamic Web service composition is a process of building a new value added service using available services to satisfy the requester's complex functional need. In this paper we propose the broker based architecture for dynamic Web service composition. The broker plays a major role in effective discovery of Web services for the individual tasks of the complex need. The broker maintains flow knowledge for the composition, which stores the dependency among the Web service operations and their input, output parameters. For the given complex requirements, the broker first generates the abstract composition plan and discovers the possible candidate Web services to each task of the abstract composition plan. The abstract composition plan is further refined based on the Message Exchange Patterns (MEP), Input/Output parameters, QoS of the candidate Web services to produce refined composition plan involving Web service operations with execution flow. The refined composition plan is then transferred to generic service provider to generate executable composition plan based on the requester's input or output requirements and preferences. The proposed effective Web service discovery and composition mechanism is defined based on the concept of functional semantics and flow semantics of Web service operations. © 2010 Springer-Verlag Berlin Heidelberg.Item A bio-inspired, incremental clustering algorithm for semantics-based web service discovery(Inderscience Enterprises Ltd., 2015) Kamath S?, S.; Ananthanarayana, V.S.Web service discovery is a challenging task due to the widespread availability of published services on the web. In this paper, a service crawler-based web service discovery framework is proposed, that employs information retrieval techniques to effectively retrieve available, published service descriptions. Their functional semantics is extracted for similarity computation and tag generation using natural language processing techniques. The framework is inherently dynamic in nature as new service descriptions may be continually added during periodic crawler runs or existing ones may be removed if service is unavailable. To deal with these issues, a dynamic, incremental clustering approach based on bird flocking behaviour is proposed. Experimental results show that semantic analysis and automatic tagging captured the services' functional semantics in a meaningful way. The algorithm effectively handled the dynamic requirements of the proposed framework by eliminating cluster recomputation overhead and achieved a speed-up factor of 61.8% when compared to hierarchical clustering. © 2015 Inderscience Enterprises Ltd.Item Semantic similarity based context-aware web service discovery using NLP techniques(Rinton Press Inc. sales@rintonpress.com, 2016) Kamath S?, S.S.; Ananthanarayana, V.S.Due to the high availability and also the distributed nature of published web services on the Web, efficient discovery and retrieval of relevant services that meet user requirements can be a challenging task. In this paper, we present a semantics based web service retrieval framework that uses natural language processing techniques to extract a service’s functional information. The extracted information is used to compute the similarity between any given service pair, for generating additional metadata for each service and for classifying the services based on their functional similarity. The framework also adds natural language querying capabilities for supporting exact and approximate matching of relevant services to a given user query. We present experimental results that show that the semantic analysis & automatic tagging effectively captured the inherent functional details of a service and also the similarity between different services. Also, a significant improvement in precision and recall was observed during Web service retrieval when compared to simple keyword matching search, using the natural language querying interface provided by the proposed framework. © Rinton Press.Item Semantics-based Web service classification using morphological analysis and ensemble learning techniques(Springer Science and Business Media Deutschland GmbH, 2016) Kamath S?, S.S.; Ananthanarayana, V.S.With the emergence of the Programmable Web paradigm, the World Wide Web is evolving into a Web of Services, where data and services can be effectively reused across applications. Given the wide diversity and scale of published Web services, the problem of service discovery is a big challenge for service-based application development. This is further compounded by the limited availability of intelligent categorization and service management frameworks. In this paper, an approach that extends service similarity analysis by using morphological analysis and machine learning techniques for capturing the functional semantics of real-world Web services for facilitating effective categorization is presented. To capture the functional diversity of the services, different feature vector selection techniques are used to represent a service in vector space, with the aim of finding the optimal set of features. Using these feature vector models, services are classified as per their domain, using ensemble machine learning methods. Experiments were performed to validate the classification accuracy with respect to the various service feature vector models designed, and the results emphasize the effectiveness of the proposed approach. © 2016, Springer International Publishing Switzerland.Item Discovering composable web services using functional semantics and service dependencies based on natural language requests(Springer New York LLC barbara.b.bertram@gsk.com, 2019) Kamath S?, S.; Ananthanarayana, V.S.The processes of service discovery, selection and composition are crucial tasks in web service based application development. Most web service-driven applications are complex and are composed of more than one service, so, it becomes important for application designers to identify the best service to perform the next task in the intended application’s workflow. In this paper, a framework for discovering composable service sets as per user’s complex requirements is proposed. The proposed approach uses natural language processing and semantics based techniques to extract the functional semantics of the service dataset and also to understand user context. In case of simple queries, basic services may be enough to satisfy the user request, however, in case of complex queries, several basic services may have to be identified to serve all the requirements, in the correct sequence. For this, the service dependencies of all the services are used for constructing a service interface graph for finding suitable composable services. Experiments showed that the proposed approach was effective towards finding relevant services for simple & complex queries and achieved an average accuracy rate of 75.09 % in finding correct composable service templates. © 2017, Springer Science+Business Media New York.Item Semantic context driven language descriptions of videos using deep neural network(Springer Science and Business Media Deutschland GmbH, 2022) Naik, D.; Jaidhar, C.D.The massive addition of data to the internet in text, images, and videos made computer vision-based tasks challenging in the big data domain. Recent exploration of video data and progress in visual information captioning has been an arduous task in computer vision. Visual captioning is attributable to integrating visual information with natural language descriptions. This paper proposes an encoder-decoder framework with a 2D-Convolutional Neural Network (CNN) model and layered Long Short Term Memory (LSTM) as the encoder and an LSTM model integrated with an attention mechanism working as the decoder with a hybrid loss function. Visual feature vectors extracted from the video frames using a 2D-CNN model capture spatial features. Specifically, the visual feature vectors are fed into the layered LSTM to capture the temporal information. The attention mechanism enables the decoder to perceive and focus on relevant objects and correlate the visual context and language content for producing semantically correct captions. The visual features and GloVe word embeddings are input into the decoder to generate natural semantic descriptions for the videos. The performance of the proposed framework is evaluated on the video captioning benchmark dataset Microsoft Video Description (MSVD) using various well-known evaluation metrics. The experimental findings indicate that the suggested framework outperforms state-of-the-art techniques. Compared to the state-of-the-art research methods, the proposed model significantly increased all measures, B@1, B@2, B@3, B@4, METEOR, and CIDEr, with the score of 78.4, 64.8, 54.2, and 43.7, 32.3, and 70.7, respectively. The progression in all scores indicates a more excellent grasp of the context of the inputs, which results in more accurate caption prediction. © 2022, The Author(s).Item AAPFC-BUSnet: Hierarchical encoder–decoder based CNN with attention aggregation pyramid feature clustering for breast ultrasound image lesion segmentation(Elsevier Ltd, 2024) Sushma, B.; Pulikala, A.Breast cancer causes a serious menace to women's health and lives, underscoring the urgency of accurate tumor detection. Detecting both cancerous and non-cancerous breast tumors has become increasingly crucial, with ultrasound imaging emerging as a widely adopted modality for this purpose. However, identifying breast lesions in ultrasound images is a challenging task due to various tumor morphologies, geometry, similar color intensity distributions, and fuzzy boundaries, particularly irregularly shaped malignant tumors. This work proposes an encoder–decoder based U-shaped convolutional neural network (CNN) variant with an attention aggregation-based pyramid feature clustering module (AAPFC) to detect breast lesion regions. The network consists of the U-Net variant as a base network and AAPFC to fuse features extracted at the various levels of the base U-Net using a suitable feature fusion technique. Furthermore, the deformable convolution with adaptive self-attention mechanism is introduced to decode the pyramid features parallel to capture the various geometric features at multi-stages. Two public breast lesion ultrasound datasets consisting 263 malignant, 547 benign and 133 normal images are considered to evaluate the performance of the proposed model and state-of-the-art deep CNN-based segmentation models. The proposed model provides 96% accuracy, 68% Mean-IoU, 97% specificity, 82% sensitivity and 0.747 kappa score respectively. The conducted qualitative and quantitative performance analysis experiments show that the proposed model performs better in breast lesion segmentation on ultrasound images. © 2024 Elsevier Ltd