Faculty Publications
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Publications by NITK Faculty
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Item Embedding linguistic features in word embedding for preposition sense disambiguation in english—Malayalam machine translation context(Springer Verlag service@springer.de, 2019) Premjith, B.; Padannayil, K.P.; Anand Kumar, M.; Jyothi Ratnam, D.Preposition sense disambiguation has huge significance in Natural language processing tasks such as Machine Translation. Transferring the various senses of a simple preposition in source language to a set of senses in target language has high complexity due to these many-to-many relationships, particularly in English-Malayalam machine translation. In order to reduce this complexity in the transfer of senses, in this paper, we used linguistic information such as noun class features and verb class features of the respective noun and verb correlated to the target simple preposition. The effect of these linguistic features for the proper classification of the senses (postposition in Malayalam) is studied with the help of several machine learning algorithms. The study showed that, the classification accuracy is higher when both verb and noun class features are taken into consideration. In linguistics, the major factor that decides the sense of the preposition is the noun in the prepositional phrase. The same trend was observed in the study when the training data contained only noun class features. i.e., noun class features dominates the verb class features. © Springer Nature Switzerland AG 2019.Item Machine Learning Based Data Quality Model for COVID-19 Related Big Data(Springer Science and Business Media Deutschland GmbH, 2022) Kumar, P.V.; Chandrashekar, A.; Chandrasekaran, K.Big Data is being used in various aspects of technology. The quality of the data being used is essential and needs to be accurate, reliable, and free of defects. The difficulty in improving the quality of big data can be overcome by leveraging computing resources and advanced techniques. In this paper, we propose a solution that utilizes a machine learning (ML) model combined with a data quality model to improve the quality of data. An auto encoder neural network that detects the anomalies in the data is used as the Machine Learning model. This is followed by using the data quality model to ensure the data meets appropriate data quality characteristics. The results obtained from our solution show that the quality of data can be improved efficiently and effortlessly which in turn aids researchers to achieve better results. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Machine learning-based detection and classification of lung cancer(Elsevier, 2022) Dodia, S.; Annappa, A.Cancer is termed to be one of the life-threatening diseases in the world. Among various types of cancer, the highest mortality and morbidity rate recorded is from lung cancer. Computer-aided diagnosis (CAD) systems are used to identify lung cancer nodules. The development of reliable automated algorithms is important to provide doctors with a second opinion. A lung cancer diagnosis is performed in two steps: lung cancer nodule detection and classification. In nodule detection, from a given computed tomography (CT) scan, the nodules and nonnodules are identified. Once the nodules and nonnodules are identified, the next step is to classify the detected nodules as cancerous and noncancerous. This work explores various machine learning classifiers for lung cancer classification. A majority voting scheme is used to classify nodules. An in-depth analysis of different machine learning algorithms’ performance is presented in this work. © 2023 Elsevier Inc. All rights reserved.Item Impact analysis of online education development and implementation using machine learning model(Bentham Science Publishers, 2024) Divakarla, U.; Chandrasekaran, K.Online education is becoming increasingly necessary and in high demand as a result of the current circumstances and the enormous expansion in internet users. Various studies have been done in this area to enhance the positive benefits of offering educational courses online. One of the most crucial concerns for learning contexts like schools and universities, especially during current epidemic period, is the prediction and analysis of students' performance since it aids in the development of practical mechanisms that enhance academic achievement and prevent dropout. Most educational institutions now place a high priority on forecasting and analysing student performance. That is necessary to assist at-risk students, ensure their retention, provide top-notch learning tools and opportunities, and enhance the university's ranking and reputation. This project aims to collect information related to online education and use Machine Learning to predict students' performance. © 2024 Bentham Science Publishers. All rights reserved.Item Artificial Intelligence in Damage Detection of Concrete Structures: Techniques, Integration and Future Directions(Springer Science and Business Media Deutschland GmbH, 2025) Barbhuiya, S.; Das, B.B.The chapter thoroughly explores the pivotal role played by Artificial Intelligence (AI) in the identification of damages in concrete structures. It delves into conventional methods, their limitations, and how AI can effectively complement these approaches. The basics of AI, encompassing machine learning and deep learning, are elucidated within the specific context of damage detection. Additionally, the chapter examines data acquisition and pre-processing techniques tailored for AI models. It sheds light on AI-driven damage detection methodologies, such as the utilization of convolutional neural networks for image analysis, vibration analysis, and AI-enhanced non-destructive testing methods, highlighting their precision in identifying structural issues. Moreover, the chapter investigates the integration of AI into structural health monitoring systems, providing in-depth discussions on data fusion and real-time monitoring. Emphasis is placed on the significance of performance assessment and model validation to ensure the reliability of AI algorithms. The chapter also addresses future trends, including the integration of AI with the Internet of Things (IoT), and delves into ethical considerations in the sphere of infrastructure development. In summary, the chapter underscores AI's transformative potential in revolutionizing damage detection and structural health assessment, contributing to the creation of more resilient and sustainable concrete structures. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.Item Raga classification for Carnatic music(Springer Verlag service@springer.de, 2015) Suma, S.M.; Koolagudi, S.G.In this work, an effort has been made to identify raga of given piece of Carnatic music. In the proposed method, direct raga classification without the use of note sequence has been performed using pitch as the primary feature. The primitive features that are extracted from the probability density function (pdf) of the pitch contour are used for classification. A feature vector of 36 dimension is obtained by extracting some parameters from the pdf. Since non-sequential features are extracted from the signal, artificial neural network (ANN) is used as a classifier. The database used for validating the system consists of 162 songs from 12 ragas. The average classification accuracy is found to be 89.5%. © Springer India 2015.Item Distributed-Intrusion Detection System using combination of Ant Colony Optimization (ACO) and support vector machine (SVM)(Institute of Electrical and Electronics Engineers Inc., 2016) Wankhade, A.; Chandrasekaran, K.Intrusion Detection System (IDS) are playing a very substantial role in protecting computer networks. Still conventional IDS finds itself limited when it comes to distribute intrusion detection. An intruder may conceal its origin of attack by moving from node to node in a network. In order to conquer these limitations, alerts are to be exchanged and correlated in distributed intrusion detection system (DIDS) in a cooperative manner. Because of diversity of network behavior and high growth in development of new types of attacks, intrusion detection algorithm based on fast machine learning methods are of great significance to reduce the false alarm rates with high accuracy of detection rate. This work proposes using a DIDS model for data collection across the network and a hybrid method that classifies the network activities collected in the DIDS model as normal and abnormal. This hybrid method is a combination of popular machine learning algorithms Support Vector Machine (SVM) and Ant Colony Optimization (ACO) which is to be used on a model for DIDS. Also it can detect unseen attacks of intrusion with high detection rate with minimal misclassification. Experiments show that usage of hybrid method on the DIDS model is superior to that of SVM alone or ACO alone both in terms of run-Time efficiency and detection rate. © 2016 IEEE.Item Efficient audio segmentation in soccer videos(Institute of Electrical and Electronics Engineers Inc., 2016) Raghuram, M.A.; Chavan, N.R.; Koolagudi, S.G.; Ramteke, P.B.Identifying different audio segments in videos is the first step for many important tasks such as event detection and speech transcription. Approaches using Mel-Frequency Cepstral coefficients (MFCCs) with Gaussian mixture models (GMMs) and hidden Markov models (HMMs) perform reasonably well in stationary conditions but do not scale to a broad range of environmental conditions. This paper focuses on the audio segmentation in broadcast soccer videos into audio classes such as Silence, Speech Only, Speech Over Crowd, Crowd Only and Excited, with an alternative feature set which is simplistic as well as robust to changes in the environment conditions. Support Vector Machines (SVMs), Neural Networks and Random Forest are used for the classification. The accuracy achieved with SVMs, Neural Networks and Random Forest are 83.80%, 86.07%, and 88.35% respectively. The proposed features and Random Forest classifier are found to achieve better accuracy compared to the other classifiers. © 2016 IEEE.Item Image processing approach to diagnose eye diseases(Springer Verlag service@springer.de, 2017) Prashasthi, P.; Shravya, K.S.; Deepak, A.; Mulimani, M.; Shashidhar, K.G.Image processing and machine learning techniques are used for automatic detection of abnormalities in eye. The proposed methodology requires a clear photograph of eye (not necessarily a fundoscopic image) from which the chromatic and spatial property of the sclera and iris is extracted. These features are used in the diagnosis of various diseases considered. The changes in the colour of iris is a symptom for corneal infections and cataract, the spatial distribution of different colours distinguishes diseases like subconjunctival haemorrhage and conjunctivitis, and the spatial arrangement of iris and sclera is an indicator of palsy. We used various classifiers of which adaboost classifier which was found to give a substantially high accuracy i.e., about 95% accuracy when compared to others (k-NN and naive-Bayes). To enumerate the accuracy of the method proposed, we used 150 samples in which 23% were used for testing and 77% were used for training. © Springer International Publishing AG 2017.Item Temporal topic modeling of scholarly publications for future trend forecasting(Springer Verlag service@springer.de, 2017) Bhopale, A.P.; Kamath S․, S.S.The volume of scholarly articles published every year has grown exponentially over the years. With these growths in both core and interdisciplinary areas of research, analyzing interesting research trends can be helpful for new researchers and organizations geared towards collaborative work. Existing approaches used unsupervised learning methods such as clustering to group articles with similar characteristics for topic discovery, with low accuracy. Efficient and fast topic discovery models and future trend forecasters can be helpful in building intelligent applications like recommender systems for scholarly articles. In this paper, a novel approach to automatically discover topics (latent factors) from a large set of text documents using association rule mining on frequent itemsets is proposed. Temporal correlation analysis is used for finding the correlation between a set of topics, for improved prediction. To predict the popularity of a topic in the near future, time series analysis based on a set of topic vectors is performed. For experimental validation of the proposed approach, a dataset composed of 17 years worth of computer science scholarly articles, published through standard IEEE conferences was used, and the proposed approach achieved meaningful results. © Springer International Publishing AG 2017.