Sriparna Saha

Indian Institute of Technology Patna

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Pushpak Bhattacharyya

Indian Institute of Technology Bombay

Asif Ekbal

Indian Institute of Technology Jodhpur

Krishanu Maity

Indian Institute of Technology Patna

Chanchal Suman

Meenakshi Academy of Higher Education and Research

Raghav Jain

Indian Institute of Technology Patna

Prince Jha

Indian Institute of Technology Patna

Shweta Yadav

University of Illinois Chicago

Snehanshu Saha

Birla Institute of Technology and Science, Pilani - Goa Campus

Apoorva Singh

Indian Institute of Technology Patna

Tulika Saha

University of Liverpool

Cooperative Institutions

Indian Institute of Technology Patna

101

Chinese Academy of Sciences

Australian National University

Queensland University of Technology

University of Sydney

UNSW Sydney

Centre National de la Recherche Scientifique

Nanyang Technological University

Data61

University of Chinese Academy of Sciences

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Assessment of Rheological Behaviour of Water-in-Oil Emulsions Mediated by Glycolipid Biosurfactant Produced by Bacillus megaterium SPSW1001

Applied Biochemistry and Biotechnology (2021)

Varsha Singh Zairah Waris İbrahim M. Banat Sriparna Saha Padmini Padmanabhan

Bacillus megaterium

10.1007/s12010-021-03717-3

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Citations (8)

Toward Multimodal Complaint Severity Detection From Social Media

IEEE Transactions on Computational Social Systems (2024)

Apoorva Singh Prince Jha Souryadip Das Raghav Jain Sriparna Saha

The prevalence of complaints submitted online and the sheer volume of information made available by social media platforms highlight the need for automated complaint analysis tools. In linguistic studies, complaints have been classified according to how much of personal risk the complainant is willing to take. This is crucial information for understanding the motivations of complainants and how people come up with reasonable means of reparation. Few attempts have been made to use the existing multimodal complaint model, which focuses on improving the textual mode with the help of images, to find specific visual features that help identify complaints. Our aim is to find a solution to this problem. In order to detect complaints and the severity level associated with them in a multitask setting, we propose Multimodal framEwork for Complaint and Severity-level detectIon (MECSI), a novel multimodal framework that uses local and global attributes (in both modalities) and relates them to the textual context. To do this, we add severity-level annotation to the newly released CESAMARD dataset, which is a compilation of reviews and images of products listed on the Amazon website. The experimental findings confirmed the superiority of our proposed model over the state-of-the-art model and other strong rival baselines, proving the efficacy of our proposed framework.

Complaint

10.1109/tcss.2024.3393729

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Addressing domain shift in neural machine translation via reinforcement learning

Expert Systems with Applications (2022)

Amit Kumar Ajay Pratap Anil K. Singh Sriparna Saha

BLEU

10.1016/j.eswa.2022.117039

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Citations (8)

Genre Effect Toward Developing a Multi-Modal Movie Recommendation System in Indian Setting

IEEE Transactions on Consumer Electronics (2023)

Prabir Mondal Pulkit Kapoor Siddharth Singh Sriparna Saha Jyoti Prakash Singh

The Recommendation System (RS) does not require any introduction nowadays and its role in various platforms as an information filtering agent is very crucial. The personalized movie RS recommends the appropriate movies to the user after analyzing his preferences. Most of the recent studies in this RS basically consider the rating values of the user-movie pairs to evaluate user choice. However, our literature survey found that there is an unexplored area where the user feedback is in ordinal value and the movies are from the Indian regional language based. This paper has presented a multi-modal RS for the Hindi movie dataset where the user preferences over movies are from three different classes and these are, "Like", "Neutral" and "Dislike/ Not watched". The proposed model has employed the utilities of a multi-head cross-attention mechanism and as an input, it has used the extracted audio-video information from the movie trailers. Here the information on Hindi movies and their users has been taken from the Flickscore Dataset. The study evaluates a classification model's effectiveness in two critical aspects: (i) the introduction of a novel metric called "GenreLike-Score (GL-score)," which aligns user genre preferences with movie genres, and (ii) the exploration of various audio-video embeddings. We have done thorough ablation studies using different combinations of these two factors and finally concluded how the GL-score is effective in predicting proper preference for user-movie pair. Besides that, different ways of keyframe extraction techniques as well as their different embedding generation processes have been evaluated in the ablation studies. Our classification model performs well when GL-score is considered and proper audio-video embeddings are determined for the studies.

10.1109/tce.2023.3324009

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A Multi-view Multiobjective Partitioning Technique for Search Results Clustering

2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (2021)

Diksha Bansal Rahul Grover Sriparna Saha

The internet has been one of the fast-growing technologies allowing users to search for information, communicate and collaborate, social networking, promote business, and much more. There are over 1 billion sites on the world-wide web ¹ . It is important to understand the user's query and return the top results that are majorly related to the query. In Search Result Clustering, results returned by one or more search engines are clustered into meaningful groups.The Search Results Clustering problem is modelled as a multi-view clustering problem in this study. The problem is then solved using an optimization framework. Experiments have been performed considering various combinations of four views including word-mover distance, BERT, TFIDF, and universal sentence encoder. The multi-objective binary differential evolution (MBDE) approach is used to optimise multiple quality measures computed on clusters produced by different views at the same time. We considered a set of possible solutions where each solution represents partitioning information obtained using different views. An agreement index that checks the consistency of partitionings of multiple views is also optimised. To get a single ensembled partitioning after considering various views, consensus partitioning technique and generative modeling are explored. The proposed model can automatically detect the number of clusters for any query. Our approach outperforms by 3.33% F1-score on three widely-used datasets over state-of-the-art techniques.

10.1109/smc52423.2021.9658944

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A Deep Learning Framework for the Detection of Malay Hate Speech

IEEE Access (2023)

Krishanu Maity Shaubhik Bhattacharya Sriparna Saha Manjeevan Seera

Although social media can efficiently disseminate information, they also facilitate the dissemination of online abuse, harassment, and hate speech. In 2019, United Nations Secretary-General introduced the United Nations Strategy and Plan of Action on Hate Speech in response to the alarming global trend of rising hate speech. It is crucial to prevent hate speech because it can have severe negative effects on both individuals and society. While much research has been conducted on detecting online hate speech in English, little research has been conducted in other languages, such as Malay. In this paper, we present the first benchmark dataset HateM for detecting hate speech in Malay, comprised of over 4,892 annotated tweets. We created a two-channel deep learning model, XLCaps, to effectively manage noisy Malay language posts. One channel's input is the XLNet language model followed by the capsule network, while the other channel's input is the FastText embedding with Bi-GRU. Our proposed model surpasses the baseline models in terms of overall accuracy and F1 measurement, which are 80.69% and 80.41%, respectively. This work contributes to the prevention of hate speech in Malay and can serve as a basis for future study in this area. The approach to effectively managing noisy Malay posts can be also applied to other languages. The code and dataset are available at https://github.com/MaityKrishanu/Hate_Malay .

Malay

Benchmark (surveying)

10.1109/access.2023.3298808

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Citations (13)

Deviation-support based fuzzy ensemble of multi-modal deep learning classifiers for breast cancer prognosis prediction

Scientific Reports (2023)

Nikhilanand Arya Sriparna Saha

Breast cancer is the fifth leading cause of death in females worldwide. Early detection and treatment are crucial for improving health outcomes and preventing more serious conditions. Analyzing diverse information from multiple sources without errors, particularly with the growing burden of cancer cases, is a daunting task for humans. In this study, our main objective is to improve the accuracy of breast cancer survival prediction using a novel ensemble approach. It is novel due to the consideration of deviation (closeness between predicted classes and actual classes) and support (sparsity between predicted classes and actual classes) of the predicted class with respect to the actual class, a feature lacking in traditional ensembles. The ensemble uses fuzzy integrals on support and deviation scores from base classifiers to calculate aggregated scores while considering how confident or uncertain each classifier is. The proposed ensemble mechanism has been evaluated on a multi-modal breast cancer dataset of breast tumors collected from participants in the METABRIC trial. The proposed architecture proves its efficiency by achieving the accuracy, sensitivity, F1-score, and balanced accuracy of 82.88%, 58.64%, 62.94%, and 74.75% respectively. The obtained results are superior to the performance of individual classifiers and existing ensemble approaches.

Closeness

Ensemble Learning

10.1038/s41598-023-47543-5

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Toward Symptom Assessment Guided Symptom Investigation and Disease Diagnosis

IEEE Transactions on Artificial Intelligence (2023)

Abhisek Tiwari Rishav Raj Sriparna Saha Pushpak Bhattacharyya Sarbajeet Tiwari

Automatic disease diagnosis has gained immense popularity and demand over the past few years, and it is emerging as an effective diagnostic assistant to doctors. Diagnosis assistants assist clinicians in conducting a thorough symptom investigation and identifying possible diseases. Doctors correctly diagnose patients by observing only a few symptoms in most cases, even though the diagnosed disease has numerous symptoms. Also, some common symptoms, such as fever and headache, usually emerge due to other symptoms, which do not play a major role in identifying suffering diseases. In this work, we investigate the role of symptom importance in disease diagnosis through several feature engineering techniques and propose a novel symptom assessment guided symptom investigation and disease diagnosis (SA-SIDD) assistant using hierarchical reinforcement learning (HRL). The proposed SA-SIDD assistant first collects an adequate set of symptoms/sign information through conversing with users and then diagnoses a disease based on the extracted symptoms. We incorporated a symptom assessment module with the diagnosis framework that evaluates the relevance of current inspected symptom at each turn and reinforces the assistant to investigate distinctive and context-aligned symptoms using an assessment critic. The proposed methodology outperforms the state-of-the-art method, HRL, on two publicly available datasets, which firmly establishes the crucial role of symptom importance in disease diagnosis and the need for the proposed symptom assessment incorporated disease diagnosis framework. Furthermore, we have also conducted a human evaluation, revealing that the diagnosis method greatly enhances end-user satisfaction because of context-aligned relevant and minimal symptom investigation.

Popularity

Relevance

10.1109/tai.2023.3236897

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Citations (3)

Scientific Document Summarization using Citation Context and Multi-objective Optimization

2022 26th International Conference on Pattern Recognition (ICPR) (2021)

Naveen Saini Sushil Kumar Sriparna Saha Pushpak Bhattacharyya

The rate of publishing scientific articles is increasing day by day which has created difficulty for the researchers to learn about the recent advancements in a faster way. Also, relying on the abstract of these published articles is not a good idea as they cover only broad ideas of the article. The summarization of scientific documents (SDS) addresses this challenge. In this paper, we propose a system for SDS having two components: identifying the relevant sentences in the article using citation context; generation of the summary by posing SDS as a binary optimization problem. For the purpose of optimization, a metaheuristic evolutionary algorithm is utilized. In order to improve the quality of summary, various aspects measuring the relevance of sentences are simultaneously optimized using the concept of multi-objective optimization. Inspired by the popularity of graph-based algorithms like LexRank which is popularly used in solving summarization problems of different real-life applications, its impact is studied in fusion with our optimization framework. An ablation study is also performed to identify the most contributing aspects for the summary generation. We investigated the performance of our proposed framework on two datasets related to the computational linguistic domain, CL-SciSumm 2016 and CL-SciSumm 2017, in terms of ROUGE measures. The results obtained illustrate that our framework effectively improves other existing methods. Further, results are validated using the statistical paired t-test.

Relevance

10.1109/icpr48806.2021.9412201

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Citations (2)

An Empirical Study of Importance of Different Sections in Research Articles Towards Ascertaining Their Appropriateness to a Journal

Lecture notes in computer science (2020)

Tirthankar Ghosal Rajeev Verma Asif Ekbal Sriparna Saha Pushpak Bhattacharyya

Empirical Research

10.1007/978-3-030-64452-9_38

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Citations (3)