Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item NN based ontology mapping(Springer Verlag service@springer.de, 2013) Manjula Shenoy, K.; Shet, K.C.; Dinesh Acharya, U.The Semantic Web presents new opportunities for enabling modelling, sharing and reasoning with knowledge available on the web. These are made possible through the formal representation of the knowledge domain with ontologies. Ontology is also seen as a key factor for enabling interoperability across heterogeneous systems. Ontology mapping is required for combining distributed and heterogeneous ontologies. This paper introduces you to the problem of heterogeneity, and need for ontologies and mapping. Also gives an overview of some such ontology mapping systems together with a new system using neural network. The system designed takes OWL files as input and determines all kinds of matching such as 1:1,1:n,n:1,n:n between the entities. © 2013 Springer-Verlag.Item Automated stock price prediction and trading framework for Nifty intraday trading(2013) Bhat, A.A.; Kamath S․, S.S.Research on automated systems for Stock price prediction has gained much momentum in recent years owing to its potential to yield profits. In this paper, we present an automatic trading system for Nifty for deciding the buying and selling calls for intra-day trading that combines various methods to improve the quality and precision of the prediction. Historical data has been used to implement the various technical indicators and also to train the Neural Network that predicts movement for intra-day Nifty. Further, Sentiment Analysis techniques are applied to popular blog articles written by domain experts and to user comments to find sentiment orientation, so that analysis can be further improved and better prediction accuracy can be achieved. The system makes a prediction for every trading day with these methods to forecast if next day will be a positive day or negative. Further, buy and sell calls for intra-day trading are also decided by the system thus achieving full automation in stock trading. © 2013 IEEE.Item When and where?: Behavior dominant location forecasting with micro-blog streams(IEEE Computer Society, 2018) Gautam, B.; Annappa, B.; Singh, A.; Agrawal, A.The proliferation of smartphones and wearable devices has increased the availability of large amounts of geospatial streams to provide significant automated discovery of knowledge in pervasive environments, but most prominent information related to altering interests have not yet adequately capitalized. In this paper, we provide a novel algorithm to exploit the dynamic fluctuations in user's point-of-interest while forecasting the future place of visit with fine granularity. Our proposed algorithm is based on the dynamic formation of collective personality communities using different languages, opinions, geographical and temporal distributions for finding out optimized equivalent content. We performed extensive empirical experiments involving, real-time streams derived from 0.6 million stream tuples of micro-blog comprising 1945 social person fusion with graph algorithm and feed-forward neural network model as a predictive classification model. Lastly, The framework achieves 62.10% mean average precision on 1,20,000 embeddings on unlabeled users and surprisingly 85.92% increment on the state-of-the-art approach. © 2018 IEEE.Item Anomaly-Based NIDS Using Artificial Neural Networks Optimised with Cuckoo Search Optimizer(Springer Verlag service@springer.de, 2019) Rithesh, K.Anomaly detection in network traffic is one of the major concerns for the researches and the network administrators. Presence of anomalies in network traffic could indicate a possible intrusion on the network, increasing the need for a fast and reliable network intrusion detection system (NIDS). A novel method of using an artificial neural network (ANN) optimised with Cuckoo Search Optimizer (CSO) is developed in this research paper to act as network monitoring and anomaly detection system. Two subsets of the KDD Cup 99 dataset have been considered to train and test our model, one of 2000 instances and the other of 10,000 instances, along with the complete dataset of 61,593 instances and I have compared the result with the BCS-GA algorithm and the fuzzy K-means clustering algorithm optimised with PSO in terms of precision, recall and f1-score, and the training time for the model with the selected database instances. © 2019, Springer Nature Singapore Pte Ltd.Item Employing Differentiable Neural Computers for Image Captioning and Neural Machine Translation(Elsevier B.V., 2020) Sharma, R.; Kumar, A.; Meena, D.; Pushp, S.In the history of artificial neural networks, LSTMs have proved to be a high-performance architecture at sequential data learning. Although LSTMs are remarkable in learning sequential data but are limited in their ability to learn long-term dependencies and representation of certain data structures because of the lack of external memory. In this paper, we tackled two main tasks, one is language translation and other is image captioning. We approached the problem of language translation by leveraging the capabilities of the recently developed DNC architectures. Here we modified the DNC architecture by including dual neural controllers instead of one and an external memory module. Inside our controller, we employed a neural network with memory-augmentation which differs from the original differentiable neural computer, we implemented a dual controller's system in which one controller is for encoding the query sequence whereas another controller is for decoding the translated sequences. During the encoding cycle, new inputs are read and the memory is updated accordingly. In the decoding cycle, the memory is protected from any writing from the decoding controller. Thus, the decoder phase generates a translated sequence at a time step. Therefore, the proposed dual controller neural network with memory-augmentation is then trained and tested on the Europarl dataset. For the image captioning task, our architecture is inspired by an end-to-end image captioning model where CNN's output is passed to RNN as input only once and the RNN generates words depending on the input. We trained our DNC captioning model on 2015 MSCOCO dataset. In the end, we compared and shows the superiority of our architecture as compared to conventionally used LSTM and NTM architectures. © 2020 The Authors. Published by Elsevier B.V.Item NBA MVP Prediction and Historical Analysis Using Cross-Era Comparison Approaches(Institute of Electrical and Electronics Engineers Inc., 2024) Godbole, I.; Murali, S.S.; Sowmya Kamath, S.In order to understand the crucial player statistics that decide the Most Valuable Player (MVP) Trophy, this research study dives into a substantial 32-year dataset of the National Basketball Association (NBA). We build a predictive framework trained on historical player statistics and MVP voting results using a sophisticated ensemble of machine learning models, including Support Vector Machines (SVM), ElasticNet, AdaBoost, Random Forest and Back-propagation Neural Network (BP). We determine the key elements influencing this renowned award by evaluating connections between player stats and MVP picks. Our research provides insights into the MVP selection process by utilising the models' ability to capture complex patterns and nonlinear interactions, providing stakeholders with a reliable tool for assessing player performances.This work advances the discourse surrounding the NBA MVP Trophy and enriches our comprehension of player value assessment. Also, the prediction models are used to conduct various historical analysis experiments, by finding an objective method to compare performances of players from different eras. © 2024 IEEE.Item Improving Structural Safety with Machine Learning: Shear Strength Prediction in Interior Beam-Column Joints(Institute of Electrical and Electronics Engineers Inc., 2024) Sidvilasini, S.; Palanisamy, T.Determining the shear capacity of joints between columns and beams within a structure is crucial to guarantee its safety and stability. It directly impacts buildings' structural integrity, cost-effectiveness, and resilience, making it a critical aspect of structural engineering and construction. Estimating shear properties in beam-column joints is done via machine learning due to its ability to capture complex relationships, adapt to diverse data, and automatically identify relevant features, potentially offering improved accuracy and insights compared to traditional methods. This paper includes creating a machine-learning regression model for predicting joint shear strength in interior beam-column joints. It involves the analysis of a comprehensive dataset comprising 445 data points with 17 variables sourced from 100 research papers. The primary objective is to craft a machine-learning regression model capable of accurately forecasting joint shear strength. To achieve this goal, a multitude of methodologies have been explored, including the application of 2 machine learning regression techniques and two codes of practice (Step-wise Linear Regression, Medium neural networks, and EN 1998-1:2004, NZS 3101:1-2006). Of the two methods, step-wise linear regression gave the best results in predicting the shear capacity of interior column-beam connections. © 2024 IEEE.Item Improving Machine Learning Models with Hybrid Metaheuristic Algorithm for Software Defect Prediction(Springer Science and Business Media Deutschland GmbH, 2025) Das, M.; Prasad, N.; Mohan, B.R.Software defect prediction has always been an area of interest in the field of software engineering. As the prediction of software defects plays a vital role, researchers are focusing more on metaheuristic algorithms to develop better prediction models. In this paper, we focused on the parameter tuning of the machine learning (ML) models using hybrid metaheuristic algorithms. Here, we have used three metaheuristic algorithms, namely sparrow search, wolf pack, and artificial bee colony optimization algorithm (ABC), to optimize the hyperparameters of the ML model. We have developed a hybrid version of these algorithms for better performance. The sparrow search algorithm (SSA) has high search accuracy and slow convergence speed with the advantages of good stability and strong robustness. The Wolf Pack Algorithm (WPA) has a robust global optimization ability, fast convergence speed, and various optimization strategies. The artificial bee colony (ABC) optimization algorithm has the advantage of not being influenced by the initial parameters, thus enabling search in a wider search space. Considering the strongest features of the aforementioned algorithms, two new hybrid algorithms have been developed, namely sparrow search algorithm-wolf pack algorithm (SSA-WPA) and sparrow search algorithm-artificial bee colony (SSA-ABC). These two algorithms are combined with the artificial neural network and XGBOOST model for better accuracy. To achieve the correctness of the proposed method, it is being verified by five defective NASA datasets and compared with the base methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.