Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Efficient location selection for computations of expensive Log-Gabor features using directional enhancement: For robust localization of lane markings in cluttered scenes(Institute of Electrical and Electronics Engineers Inc., 2016) Ghimire, P.; Kadagad, S.Vision-based estimation tasks, such as lane marking localization, can be more robust to noise and false signals when utilizing pattern recognition and machine learning techniques as opposed to only low level computer vision operations. Computationally expensive features like Gabor filter responses can be very robust to changes to illumination and other noise. However, machine learning techniques can also be prohibitively slow for time critical applications if such computationally expensive features are calculated for all pixel locations in an input scene. We describe a method to pick the most likely locations for which to compute robust features in order to identify locations of lane markings in highly cluttered scenes. Locations for which features are computed are selected using a novel iterative directional enhancement and thresholding on the perspective image. This drastically reduces the number of locations for which expensive features have to be computed, thus improving latency while retaining precision of the machine learning method. Our method is thus a cascaded classifier scheme that uses low level computer vision operations followed by pattern recognition techniques. We evaluate the performance of our system by checking the overlap of estimates of left and right lane boundaries and lane midline with corresponding annotations. © 2016 IEEE.Item Robust Dialect Identification System using Spectro-Temporal Gabor Features(Institute of Electrical and Electronics Engineers Inc., 2018) Chittaragi, N.B.; Mothukuri, S.P.; Hegde, P.; Koolagudi, S.G.Automatic identification of dialects of a language is gaining popularity in the field of automatic speech recognition (ASR) systems. The present work proposes an automatic dialect identification (ADI) system using 2D Gabor and spectral features. A comprehensive study of the five dialects of a Dravidian Kannada language has been taken up. Gabor filters representing spectro-temporal modulations attempt in emulation of the human auditory system concerning signal processing strategies. Hence, they are able to well perceive human voices in tern recognize dialectal variations effectively. Also, spectral features Mel frequency cepstral coefficients (MFCC) are derived. A single classifier based support vector machine (SVM) and ensemble based extreme random forest (ERF) classification methods are employed for recognition. The effectiveness of the Gabor features for ADI system is demonstrated with proposed Kannada dialect dataset along with a standard intonation variation in English (IViE) dataset for British English dialects. The Gabor features have shown better performance over MFCC features with both datasets. Better recognition performance of 88.75% and 99.16% is achieved with Kannada and IViE dialect datasets respectively. Proposed Gabor features have demonstrated better performances even under noisy conditions. © 2018 IEEE.Item Stock price movements classification using machine and deep learning techniques-the case study of indian stock market(Springer Verlag service@springer.de, 2019) Naik, N.; Mohan, B.R.Stock price movements forecasting is an important topic for traders and stock analyst. Timely prediction in stock yields can get more profits and returns. The predicting stock price movement on a daily basis is a difficult task due to more ups and down in the financial market. Therefore, there is a need for a more powerful predictive model to predict the stock prices. Most of the existing work is based on machine learning techniques and considered very few technical indicators to predict the stock prices. In this paper, we have extracted 33 technical indicators based on daily stock price such as open, high, low and close price. This paper addresses the two problems, first is the technical indicator feature selection and identification of the relevant technical indicators by using Boruta feature selection technique. The second is an accurate prediction model for stock price movements. To predict stock price movements we have proposed machine learning techniques and deep learning based model. The performance of the deep learning model is better than the machine learning techniques. The experimental results are significant improves the classification accuracy rate by 5% to 6%. National Stock Exchange, India (NSE) stocks are considered for the experiment. © Springer Nature Switzerland AG 2019.Item Spectral Feature Based Kannada Dialect Classification from Stop Consonants(Springer, 2019) Chittaragi, N.B.; Hegde, P.; Mothukuri, S.K.P.; Koolagudi, G.K.This study focuses on the investigation of the significance of stop consonants in view of the classification of Kannada dialects. Majority of the studies proposed have shown the existence of evidential differences in the pronunciation of vowels across dialects. However, consonant based studies on dialect processing are found to be comparatively lesser. In this work, eight stop consonants are used for characterization of five Kannada dialects. Acoustic characteristics such as cepstral coefficients, formant frequencies, spectral flux, and rolloff features are explored from spectral analysis of stops. The consonant dataset is derived from standard Kannada dialect dataset consisting of 2417 consonants obtained from 16 native speakers from each dialect. Support vector machine (SVM) and decision tree-based extreme gradient boosting (XGB) ensemble classification methods are employed for automatic recognition of Kannada dialects. The research findings show that the stops existing for shorter duration also convey dialectal linguistic cues. Combination of spectral properties has contributed to the identification of distinct dialect-specific information across Kannada dialects. © 2019, Springer Nature Switzerland AG.Item Mineral identification using unsupervised classification from hyperspectral data(Springer, 2020) Gupta, P.; Venkatesan, M.Hyperspectral imagery is one of the research areas in the field of remote sensing. Hyperspectral sensors record reflectance of object or material or region across the electromagnetic spectrum. Mineral identification is an urban application in the field of remote sensing of Hyperspectral data. Challenges with the hyperspectral data include high dimensionality and size of the hyperspectral data. Principle component analysis (PCA) is used to reduce the dimension of data by band selection approach. Unsupervised classification technique is one of the hot research topics. Due to the unavailability of ground truth data, unsupervised algorithm is used to classify the minerals present in the remotely sensed hyperspectral data. K-means is unsupervised clustering algorithm used to classify the mineral and then further SVM is used to check the classification accuracy. K-means is applied to end member data only. SVM used k-means result as a labelled data and classify another set of dataset. © Springer Nature Singapore Pte Ltd 2020.Item Benchmarking semantic, centroid, and graph-based approaches for multi-document summarization(Springer Science and Business Media Deutschland GmbH info@springer-sbm.com, 2021) Agrawal, A.; George, R.A.; Ravi, S.S.; Kamath S․, S.Multi-document summarization (MDS) is a pre-programmed process to excerpt data from various documents regarding similar topics. We aim to employ three techniques for generating summaries from various document collections on the same topic. The first approach is to calculate the importance score for each sentence using features including TF-IDF matrix as well as semantic and syntax similarity. We build our algorithm to sort the sentences by importance and add it to the summary. In the second approach, we use the k-means clustering algorithm for generating the summary. The third approach makes use of the Page Ranking algorithm wherein edges of the graph are formed between sentences that are syntactically similar but are not semantically similar. All these techniques have been used to generate 100–200 word summaries for the DUC 2004 dataset. We use ROUGE scores to evaluate the system-generated summaries with respect to the manually generated summaries. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021.Item Classification of Medicinal Plants Using Machine Learning(Springer Science and Business Media Deutschland GmbH, 2022) Meshram, R.S.; Patil, N.Nowadays, peoples are not having information about the surrounding plants and their medicinal values. If some person wants to know about the medicinal plants, they have to contact the person who is having deep knowledge about the medicinal plants and its uses. In order to solve this problem we can use the current technology to give a tool which will help the common people to know more about the medicinal plants. For doing this we can use many machine learning techniques for classifying the medicinal plants with more accuracy. Different kind of medicinal plant species are available on the planet earth but classification of the Particular medicinal plant is very difficult without knowing about the plants first. The information about the medicinal plants is collected by the scientists and urban people. Generally this kind of knowledge is passed through generation to generation and sometimes there might be some changes in the information and its contents. So according to the current situation we can use the machine learning technology to make the tool which will be helpful to solve the medicinal plant classification problem. Machine learning model can easily classify the medicinal plants after the feature extraction and applying the model. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Comparative Assessment of Different Machine Learning Models to Estimate Daily Soil Moisture(Springer Science and Business Media Deutschland GmbH, 2023) Nagashree, G.E.; Nema, M.K.Soil moisture is vital as it is the primary governing factor of agriculture production and natural vegetation growth. It plays an essential role in understanding the hydrological cycle and its effect on weather and climate, and its precise prediction helps to manage the water resources optimally. Prediction of soil moisture is dependent on surface meteorological variables and soil attributes. Existing soil moisture models/prediction methods are inaccurate, and developing an optimum mathematical model for it is difficult. This study evaluates the performance of four machine learning models (deep neural network (DNN) regression, support vector machine (SVM), multiple layer perceptron (MLP), and multi-linear regression (MLR) to estimate the soil moisture conditions. The models were tested for soil moisture at two depths (25 and 50 cm depth) using the meteorological data of two stations located in a Lesser Himalayan catchment. The model outputs were compared with the observed data, and intercomparison was also made. The model performance was evaluated based on MAPE, RMSE, Nash–Sutcliffe efficiency coefficient (EN–S), and R2. The study results indicated that the DNN model outperforms the other prediction models with the highest efficacy for both stations. Therefore, the DNN model can be endorsed to estimate soil moisture when primary meteorological data are available, and it can be promising for water-efficient agriculture applications and draught management. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Critical Review on Heart Disease Prediction: A Machine Learning Approach(Institute of Electrical and Electronics Engineers Inc., 2023) Mahapatro, S.R.; Mahapatra, R.K.; Shet, N.S.V.; Prusty, S.B.; Satapathi, G.S.; Manjukiran, B.; Reddy, G.; Chandana, O.; Divya, N.; DImri, P.The heart is the second-most significant organ in the human body after the brain, which is the most significant organ. All of the body's organs are nourished and the blood is circulated. In the medical field, it might be difficult to anticipate the development of heart diseases. Data analytics is crucial for developing predictions based on new information, and it helps hospitals predict diseases. Every year, cardiovascular diseases account for more than 31 % of all fatalities globally. Different Machine learning algorithms are in this paper to predict heart disease. It presents a general overview of the previous work and offers insight into the current algorithm. © 2023 IEEE.Item IIMH: Intention Identification in Multimodal Human Utterances(Association for Computing Machinery, 2023) Keerthan Kumar, T.G.; Dhakate, H.; Koolagudi, S.G.Intention identification is a challenging problem in the field of natural language processing, speech processing, and computer vision. People often use contradictory or ambiguous words in different contexts, which can sometimes be very confusing to identify the intention behind an utterance. Intention identification has many practical applications in the fields of natural language processing, sentiment analysis, social media analysis, robotics, and human-computer interaction, where valuable insights into user behavior can be achieved by identifying intention. In this work, we propose a model to determine whether an utterance made by a person is intentional or not intentional. To achieve this, we collected a multimodal dataset containing text, video, and speech from various TV shows, movies, and YouTube videos and labeled them with their corresponding intention. Feature extraction is done at both utterance and word levels to get useful information from all three modalities. We trained the baseline model using SVM to set a benchmark performance. We designed an architecture to detect the contradiction between positive spoken words with negative facial expressions or speech to identify an utterance as non-intentional. Along with the architecture, we used different approaches for classification and got the best results with the Support vector machine (SVM) classifier using RBF kernel, with an accuracy of 78.83% and proven to be better compared to the baseline approach. © 2023 ACM.