Department of Computer Science and Information Systems

Permanent URI for this collectionhttp://localhost:4000/handle/123456789/1928

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Start date: 2020Subject: search.filters.subject.Deep Learning (DL)

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Now showing 1 - 10 of 19
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    Permuted spectral and permuted spectral-spatial cnn models for polsar-multispectral data based land cover classification
    (Taylor & Francis, 2020-12) Phartiyal, Gopal Singh
    It is a challenge to develop methods which can process the polarimetric synthetic aperture radar (PolSAR) and multispectral (MS) data modalities together without losing information from either for remote sensing applications. This paper presents a study which attempts to introduce novel deep learning-based remote sensing data processing frameworks that utilize convolutional neural networks (CNNs) in both spatial and spectral domains to perform land cover (LC) classification with PolSAR-MS data. Also since earth observation remotely sensed data have usually larger spectral depth than normal camera image data, exploiting the spectral information in remote sensing (RS) data is crucial as well. In fact, convolutions in the sub-spectral space are intuitive and alternative to the process of feature selection. Recently, researchers have gained success in exploiting the spectral information of RS data, especially the hyperspectral data with CNNs. In this paper, exploitation of the spectral information in the PolSAR-MS data via a permuted localized spectral convolution along with localized spatial convolution is proposed. Further, the study in this paper also establishes the significance of performing permuted localized spectral convolutions over non-localized or localized spectral convolutions. Two models are proposed, namely a permuted local spectral convolutional network (Perm-LS-CNN) and a permuted local spectral-spatial convolutional network (Perm-LSS-CNN). These models are trained on ground truth class data points measured directly on the terrain. The evaluation of the generalization performance is done using ground truth knowledge on selected well-known regions in the study areas. Comparison with other popular machine learning classifiers shows that the Perm-LSS-CNN model provides better classification results in terms of both accuracy and generalization.
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    Land cover mapping of mixed classes using 2D CNN with multi-frequency SAR data
    (Elsevier, 2024-07) Phartiyal, Gopal Singh
    Synthetic aperture radar (SAR) data obtained at multiple frequencies and polarizations offers valuable complementary information for classifying mixed classes that exhibit similar backscattering response. Although deep learning-based convolutional neural networks (CNNs) effectively extract features from multi-frequency SAR data, the arbitrary ordering of SAR features may hinder optimal convolution of the best feature sub-space for a specific class and underutilize available multi-frequency data. To address this, a novel CNN transforming SAR feature-space from 1-D to 2-D and employing varied dilation-rate convolutions is introduced. This transformation maximizes unique and localized feature combinations, efficiently utilizing the available feature sub-spaces and extracting discriminative features for accurate classifications, addressing the challenge of arbitrary band neighborhoods. Utilizing dual-polarization SAR data from ALOS-2 PALSAR-2 and Sentinel-1 sensors, the proposed CNN achieves an average f-score of 0.97 and a kappa coefficient of 0.97, an improvement of 11 %, 7 % and 3 % in OA compared to the 1-D, 2-D and 3-D CNN classifiers, without feature transformation. The classifier's generalization ability is evaluated using ground truth knowledge of various heterogeneous classes, and the proposed CNN classifier outperforms others in terms of accuracy metrics and generalization ability.
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    An attention-based deep network for plant disease classification
    (2024) Bera, Asish
    Plant disease classification using machine learning in a real agricultural field environment is a difficult task. Often, an automated plant disease diagnosis method might fail to capture and interpret discriminatory information due to small variations among leaf sub-categories. Yet, modern Convolutional Neural Networks (CNNs) have achieved decent success in discriminating various plant diseases using leave images. A few existing methods have applied additional pre-processing modules or sub-networks to tackle this challenge. Sometimes, the feature maps ignore partial information for holistic description by part-mining. A deep CNN that emphasizes integration of partial descriptiveness of leaf regions is proposed in this work. The efficacious attention mechanism is integrated with high-level feature map of a base CNN for enhancing feature representation. The proposed method focuses on important diseased areas in leaves, and employs an attention weighting scheme for utilizing useful neighborhood information. The proposed Attention-based network for Plant Disease Classification (APDC) method has achieved state-of-the-art performances on four public plant datasets containing visual/thermal images. The best top-1 accuracies attained by the proposed APDC are: PlantPathology 97.74%, PaddyCrop 99.62%, PaddyDoctor 99.65%, and PlantVillage 99.97%. These results justify the suitability of proposed method.
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    Deep Extractive Text Summarization
    (Elsevier, 2020) Sharma, Yashvardhan
    With introduction of deep learning techniques their has been an increase in intelligent classification of text in many applications. Advances in automatic text summarization using deep learning technique is prime focus of research now a days. Earlier traditional approaches for extractive text summarization have been heavily dependent on human engineered features. However, it is a laborious and tedious task. In this paper, a data-driven approach has been used to generate extractive summaries using deep learning. Approach proposed uses paraphrasing techniques to classify sentences as a candidate sentence for inclusion in summary or not.
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    Deep Text Summarization using Generative Adversarial Networks in Indian Languagess
    (Elsevier, 2020) Sharma, Yashvardhan
    Abstractive Text Summarization (ATS) is a task of capturing information from different sources and condense it such that, content is represented well and there is no loss of information. It has been an active area of research for quiet sometime now. ATS is more closer to human generated summaries and have the capability of representing and combining multiple information. With advent of deep learning architectures, many tasks relating to natural language processing have achieved persistent and comparable high performances. It has proven advantageous and showed promising results in machine translation, speech recognition, image captioning and many others using sequence to sequence models. Language tools such as Part of Speech taggers, Named Entity Recognizer for Indian languages are not very competitive and hence, language specific techniques do not perform very well for Indian languages. Deep learning techniques are language agnostic and hence can overcome these shortcomings. In this paper, Generative Adversarial Networks(GAN(s)) are assimilated to create gist for longer piece of text in conjunction to paraphrase detection.
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    Detection of Threat Records by Analyzing the Tweets in Urdu Language Exploring Deep Learning Transformer - Based Models
    (CEUR-WS, 2021) Sharma, Yashvardhan
    As humans, we express sadness, anger, happiness, frustration, bullying, etc., in both physical and virtual worlds. In the virtual world, i.e., social media, we use textual ways to express ourselves. Due to the lack of offensive and threatening language detection mechanisms aggressive behavior in social media is not always followed by an immediate consequence. But the impact of these posts on the victim can cause prolonged mental illness and instigate fear for social media platforms. This paper aims to identify threatening posts using deep learning transformer-based models such as Roberta. The Urdu tweet dataset used in this study has been provided by HASOC-2021 which aims to identify Hate speech and offensive remarks without human assistance. We submitted our model in its subtask B of the 4th subtrack(Abusive and Threatening language detection in Urdu), secured 2nd position on the public leaderboard, and obtained Weighted f1 of 0.5346 and ROC AUC of 0. 8199.
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    Comparative Analysis of Various Machine Learning Based Techniques for Predicting the Virality of Tweets
    (IEEE, 2022) Sharma, Yashvardhan
    Social media has become more popular, and people tend to read the news more often from it than traditional media. But all the information that is posted on the social media platform might not go viral. In this paper, we have analyzed the data from one of the social media platforms, Twitter, and established a few reasons for the virality of tweets. Along with it, given the tweet information and user details to the trained model, we could predict whether the tweets go viral or not. For this, we used multiple architectures from classical machine learning like Random Forest, XGBoost and Lightgbm and Convolutions from Deep Learning and got the highest accuracy using the Lightgbm model. The results show that using both text and image data combined provides better results when compared with using only text or images (unimodal data). The data used is from the competition with full user details, tweet information, and tweet text and image.
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    Domain-Specific Chatbot Development Using the Deep Learning-Based RASA Framework
    (Springer, 2022-08) Sharma, Yashvardhan
    Conversational agents are actively gaining popularity in research because of their ability to imitate human responses in almost every domain. As there are many research enhancements in deep learning models, it becomes challenging to incorporate all these enhancements while developing a conversational agent. One of the main advantages of conversational agents is their ability to answer frequently asked queries without any human involvement and automatically generate the conversation’s story flow. In any educational institution, it becomes difficult for the teaching and non-teaching staff to answer all the students’ queries regarding the course, exam, and other information regarding their daily activities in the institute. Using the deep learning framework, we developed a chatbot to answer various questions related to the education domain, such as exam(timetable, venue) and course-related queries(course handout). The questions are answered by querying databases which can be updated via an administrator’s web browser. The system will first create intents for the use cases and entity recognition mechanisms after connecting the deep learning framework to the database using custom actions. We had created a user interface to allow updates to the database for exam timetable and course information via either file upload or a web page.
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    FakeRevealer: A Multimodal Framework for Revealing the Falsity of Online Tweets Using Transformer-Based Architectures
    (Scitepress, 2023) Sharma, Yashvardhan; Chauhan, Gajendra Singh
    As the Internet has evolved, the exposure and widespread adoption of social media concepts have altered the way news is formed and published. With the help of social media, getting news is cheaper, faster, and easier. However, this has also led to an increase in the number of fake news articles, either by manipulating the text or morphing the images. The spread of fake news has become a serious issue all over the world. In one case, at least 20 people were killed just because of false information that was circulated over a social media platform. This makes it clear that social media sites need a system that uses more than one method to spot fake news stories. To solve this problem, we’ve come up with FakeRevealer, a single-configuration fake news detection system that works on transfer learning based techniques. Our multi-modal archutecture understands the textual features using a language transformer model called DistilRoBERTa and image features are extracted using the Vision Transf ormer (ViTs) that is pre-trained on ImageNet 21K. After feature extraction, a cosine similarity measure is used to fuse both the features. The evaluation of our proposed framework is done over publicly available twitter dataset and results shows that it outperforms current state-of-art on twitter dataset with an accuracy of 80.00% which is 2.23%more, that than the current state-of-art on twitter dataset
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    ArabiziVec: A Set of ArabiziWord Embeddings for Informal Arabic Sentiment Analysis
    (Sentic, 2023) Sharma, Yashvardhan
    The current circumstances of the Arab world have provided bloggers and commenters with various subjects to discuss. Therefore, Arabic-generated content in social media is ramping up continuously. An informal written form of spoken Arabic called Arabizi has recently emerged as a commonly used language in the Arabic space, attracting great interest for sentiment analysis tasks. However, only a few sentiment resources exist, and state-of-the-art language models such as BERT and FastText do not consider Arabizi yet. This paper presents the first version of ArabiziVec, a set of pre-trained distributed word representations. ArabiziVec provides six different word embedding models to deal with Arabizi sentiment analysis challenges. The presented work surpasses all of the baseline sets for each experiment, regardless of whether the test set is from a previously published dataset or an extracted one. To the best of our knowledge, this is one of the first few resources that deals with Arabizi content and semantics in the context of sentiment analysis