references.bib

@inproceedings{nag2021ContextualBIDirectionalAttention,
  title = {Contextual {{BI-Directional Attention Flow With Embeddings From Language Models}}: {{A Generative Approach}} to {{Emotion Detection}}},
  booktitle = {{{AIR}} '21: {{Proceedings}} of the 2021 5th {{International Conference}} on {{Advances}} in {{Robotics}}},
  author = {Nag, Prashant Kumar and Priya R, Vishnu},
  year = 2021,
  month = jun,
  series = {{{AIR2021}}},
  pages = {1--6},
  publisher = {ACM},
  address = {New York, NY, USA},
  doi = {10.1145/3478586.3478629},
  urldate = {2022-05-02},
  abstract = {Detection of Emotions from the text is a tedious task. Presently, existing models failed to detect the emotion in absence of the emotional word in the text. The cause phrase selection which gives a deep insight into emotions is considered to be a tough task. The proposed model for detecting emotions is developed through seven layers. Initially, the dataset is represented in the Topical documents using Adversarial Topic Modelling (ATM). Convolutional Neural Network (CNN) maps each phrase in the topical document to Higher-dimensional vectors, followed by the ELMo Model to obtain the fixed word Embeddings vectors. LSTM is responsible for making the interaction between the words in word embeddings and produces the context and query vectors. The bi-directional Attention flow layer determines the most relevant similarity between Context and Query. Finally, Robustly Optimized BERT (RoBERT) architecture is used to detect the Emotion. It is noted that the proposed multi-stage model detects better emotions than all the existing state-of-art models for detecting emotions.},
  isbn = {978-1-4503-8971-6},
  keywords = {,BiDAF,Deep Learning,ELMo,International Conference,MyPublication,ObsCite,PaperPresented,Phrase Mining,RoBERT,Word Embeddings},
  annotation = {1 citations (Crossref) [2023-07-04]},
  file = {C:\Users\prash\My Drive (prashantnag.workmail@gmail.com)\Digital Garden\05 Resources\ZotfilesPHD\My Articles\Nag and Priya R - 2021 - Contextual BI-Directional Attention Flow With Embeddings From Language Models A Generative Approach.pdf}
}

@inproceedings{nag2023EmotionalIntelligenceArtificial,
  title = {Emotional {{Intelligence Through Artificial Intelligence}}: {{NLP}} and {{Deep Learning}} in the {{Analysis}} of {{Healthcare Texts}}},
  shorttitle = {International {{Conference}} on {{Artificial Intelligence}} for {{Innovations}} in {{Healthcare Industries}}},
  booktitle = {2023 {{International Conference}} on {{Artificial Intelligence}} for {{Innovations}} in {{Healthcare Industries}} ({{ICAIIHI}})},
  author = {Nag, Prashant Kumar and Bhagat, Amit and Vishnu Priya, R. and Khare, Deepak Kumar},
  year = 2023,
  month = dec,
  series = {{{ICAIIHI2023}}},
  pages = {1--7},
  publisher = {IEEE Xplore},
  address = {Raipur, India},
  doi = {10.1109/ICAIIHI57871.2023.10489117},
  urldate = {2024-04-19},
  copyright = {https://doi.org/10.15223/policy-029},
  isbn = {979-8-3503-3091-5},
  keywords = {International Conference,MyPublication,PaperPresented},
  file = {C:\Users\prash\My Drive (prashantnag.workmail@gmail.com)\Digital Garden\05 Resources\ZotfilesPHD\My Articles\Nag et al. - 2023 - Emotional Intelligence Through Artificial Intelligence NLP and Deep Learning in the Analysis of Hea.pdf}
}

@misc{nag2024AdvancingPatientCarea,
  title = {Advancing {{Patient Care}} through {{Text Data}}: {{A Systematic Review}} of {{AI}}, {{Emotional Analysis}}, and {{Patient-Centric Applications}} in {{Healthcare}}},
  shorttitle = {Advancing {{Patient Care}} through {{Text Data}}},
  author = {Nag, Prashant Kumar and Bhagat, Amit and Priya, R Vishnu},
  year = 2024,
  month = jul,
  publisher = {TechRxiv},
  doi = {10.36227/techrxiv.172226053.36460987/v1},
  urldate = {2025-11-11},
  archiveprefix = {TechRxiv},
  copyright = {https://creativecommons.org/licenses/by/4.0/},
  keywords = {Preprint},
  file = {C:\Users\prash\My Drive (prashantnag.workmail@gmail.com)\Digital Garden\05 Resources\ZotfilesPHD\My Articles\Nag et al. - 2024 - Advancing Patient Care through Text Data A Systematic Review of AI, Emotional Analysis, and Patient.pdf}
}

@misc{nag2024EmotionalIntelligenceArtificial,
  title = {Emotional {{Intelligence Through Artificial Intelligence}} : {{NLP}} and {{Deep Learning}} in the {{Analysis}} of {{Healthcare Texts}}},
  author = {Nag, Prashant Kumar},
  year = 2024,
  month = mar,
  publisher = {arXiv},
  doi = {10.48550/arXiv.2403.09762},
  urldate = {2024-06-21},
  abstract = {This manuscript presents a methodical examination of the utilization of Artificial Intelligence (AI) in the assessment of emotions in texts related to healthcare, with a particular focus on the incorporation of Natural Language Processing (NLP) and deep learning technologies. We scrutinize numerous research studies that employ AI to augment sentiment analysis, categorize emotions, and forecast patient outcomes based on textual information derived from clinical narratives, patient feedback on medications, and online health discussions. The review demonstrates noteworthy progress in the precision of algorithms used for sentiment classification, the prognostic capabilities of AI models for neurodegenerative diseases, and the creation of AI-powered systems that offer support in clinical decision-making. Remarkably, the utilization of AI applications has exhibited an enhancement in personalized therapy plans by integrating patient sentiment and contributing to the early identification of mental health disorders. There persist challenges, which encompass ensuring the ethical application of AI, safeguarding patient confidentiality, and addressing potential biases in algorithmic procedures. Nevertheless, the potential of AI to revolutionize healthcare practices is unmistakable, offering a future where healthcare is not only more knowledgeable and efficient but also more empathetic and centered around the needs of patients. This investigation underscores the transformative influence of AI on healthcare, delivering a comprehensive comprehension of its role in examining emotional content in healthcare texts and highlighting the trajectory towards a more compassionate approach to patient care. The findings advocate for a harmonious synergy between AI's analytical capabilities and the human aspects of healthcare, guaranteeing that technological advancements are aligned with the emotional well-being of patients.},
  keywords = {MyPublication,Preprint},
  file = {C:\Users\prash\My Drive (prashantnag.workmail@gmail.com)\Digital Garden\05 Resources\ZotfilesPHD\My Articles\Nag - 2024 - Preprint-Emotional Intelligence Through Artificial Intelligence  NLP and Deep Learning in the Analy.pdf}
}

@misc{nag2024ExpandingAIsRole,
  title = {Expanding {{AI}}'s {{Role}} in {{Healthcare Applications}}: {{A Systematic Review}} of {{Emotional}} and {{Cognitive Analysis Techniques}}},
  shorttitle = {Expanding {{AI}}'s {{Role}} in {{Healthcare Applications}}},
  author = {Nag, Prashant Kumar and Bhagat, Amit and Priya, R Vishnu},
  year = 2024,
  month = aug,
  publisher = {TechRxiv},
  doi = {10.36227/techrxiv.172297472.22422829/v1},
  urldate = {2025-11-11},
  archiveprefix = {TechRxiv},
  copyright = {https://creativecommons.org/licenses/by/4.0/},
  keywords = {cv-hide,Preprint},
  file = {C:\Users\prash\My Drive (prashantnag.workmail@gmail.com)\Digital Garden\05 Resources\ZotfilesPHD\My Articles\Nag et al. - 2024 - Expanding AI's Role in Healthcare Applications A Systematic Review of Emotional and Cognitive Analy 3.pdf}
}

@inproceedings{nag2024LeveragingTextData,
  title = {Leveraging {{Text Data}} in {{Healthcare}}: {{A Systematic Review}} of {{AI-Driven Emotional Analysis}} and {{Patient-Centric Innovations}}},
  shorttitle = {International {{Conference}} on {{Healthcare Innovation}} and {{Smart Systems}}},
  booktitle = {International {{Conference}} on {{Healthcare Innovation}} and {{Smart Systems}} ({{ICHISS}} 2024)},
  author = {Nag, Prashant Kumar and Bhagat, Amit and Priya, R Vishnu and Malviya, Sunil and Mishra, Sanjay},
  year = 2024,
  series = {{{ICHISS2024}}},
  publisher = {AIP Conference Proceedings},
  address = {Raipur, India},
  abstract = {This systematic literature review (SLR) investigates how AI, deep learning (DL), and emotional analysis have been implemented/used in healthcare, and how they have influenced patient care and outcomes. AI techniques include methods of AI in diagnosis that have displayed undeniable promise in the areas of correct diagnosis, treatment plans that are individualized, and patient-therapist interaction. The review includes studies from 2014 to 2024, that explore the various sub-fields, including general healthcare, mental health, chronic disorders, and emergency care. It approves the main fields of deep learning techniques for sentiment analysis and emotion detection in healthcare and the high accuracy of the applications and other issues such as data quality, privacy concerns, model explainability, and assimilation into current healthcare systems. The study of emotional recognition is one such example of research that shows it is the application of AI models in emotional assessments done in real-time and detecting mental health conditions. Nevertheless, ethical and privacy issues are what make it difficult, thus, it is determined that proper consideration for data security and the public's confidence in artificial intelligence are required to solve the issues. The study concludes that further research should be conducted in the fields of data quality improvement and AI model explainability development and that AI systems should be interoperable within healthcare infrastructures. Nevertheless, even though there are barriers, AI and emotional analysis gives healthcare the capability of new patient outcome improvements and even more personalized care.},
  langid = {english},
  keywords = {In Press,International Conference,PaperPresented},
  file = {C:\Users\prash\My Drive (prashantnag.workmail@gmail.com)\Digital Garden\05 Resources\ZotfilesPHD\My Articles\Nag et al. - 2024 - Leveraging Text Data in Healthcare A Systematic Review of AI-Driven Emotional Analysis and Patient-.pdf}
}

@misc{nag2024SystematicReviewAI,
  title = {A {{Systematic Review}} of {{AI}}, {{Emotional Analysis}}, and {{Patient-Centric Applications}} in {{Healthcare}}},
  author = {Nag, Prashant Kumar and Bhagat, Amit and Priya, R Vishnu},
  year = 2024,
  month = dec,
  publisher = {TechRxiv},
  doi = {10.36227/techrxiv.172226053.36460987/v2},
  urldate = {2025-11-11},
  archiveprefix = {TechRxiv},
  copyright = {https://creativecommons.org/licenses/by/4.0/},
  keywords = {/unread,Preprint},
  file = {C:\Users\prash\My Drive (prashantnag.workmail@gmail.com)\Digital Garden\05 Resources\ZotfilesPHD\My Articles\Nag et al. - 2024 - A Systematic Review of AI, Emotional Analysis, and Patient-Centric Applications in Healthcare.pdf}
}

@article{nag2025ExpandingAIsRole,
  title = {Expanding {{AI}}'s {{Role}} in {{Healthcare Applications}}: {{A Systematic Review}} of {{Emotional}} and {{Cognitive Analysis Techniques}}},
  shorttitle = {Expanding {{AI}}'s {{Role}} in {{Healthcare Applications}}},
  author = {Nag, Prashant Kumar and Bhagat, Amit and Vishnu Priya, R.},
  year = 2025,
  month = apr,
  journal = {IEEE Access},
  volume = {13},
  pages = {69129--69160},
  issn = {2169-3536},
  doi = {10.1109/ACCESS.2025.3562131},
  urldate = {2025-11-11},
  abstract = {This systematic literature review (SLR) analyzes the various applications of artificial intelligence (AI) in healthcare, with a particular emphasis on the integration of emotive and cognitive analytical frameworks. The primary aim of this investigation is to thoroughly evaluate the influence of AI technology on patient care by analyzing emotional processes and enabling patient-centered solutions. In this research, we investigate the cognitive and emotional approaches to sentiment analysis and other modeling and forecasting methods using AI. Primary sources include patients' reviews, online health exchanges and doctors' narratives. Key aspects of the present state of affairs are advances in the development of machine learning algorithms for emotion recognition, intracellular fusion of cognitive and affective modes of analysis, and the application of artificial intelligence for the enhancement of clinical support systems. Moreover, these technologies have significantly improved individualized clinical approaches, expedited the early identification of mental health problems, and strengthened the rationale for therapeutic treatments. Despite recent advancements, the discipline still faces numerous persistent obstacles. Pressing issues include the ethical implications of using artificial intelligence, the need to protect patient privacy, and the complexity of detecting biases in algorithms. Nevertheless, the impact of AI on healthcare practices is indisputable, indicating a future marked by a more intelligent, efficient, empathetic, and patient-centered healthcare system. This study examines the consequences of artificial intelligence in healthcare by analyzing its importance in emotional and cognitive computing, tracking ongoing developments, and promoting the use of AI in healthcare while considering individual requirements.},
  keywords = {AI in healthcare,Artificial intelligence,cognitive assessment,data security in AI,Deep learning,deep learning applications,Depression,emotion detection,Emotion recognition,Ethics,Market research,Medical services,Mental health,mental health analytics,NLP in medical texts,patient-centered approaches,sentiment evaluation,Social networking (online),Systematic literature review},
  annotation = {note = \textbraceleft indexing=SCIE; quartile=Q1; IF=3.6; JCR=2023\textbraceright},
  file = {C:\Users\prash\My Drive (prashantnag.workmail@gmail.com)\Digital Garden\05 Resources\ZotfilesPHD\My Articles\Nag et al. - 2025 - Expanding AI’s Role in Healthcare Applications A Systematic Review of Emotional and Cognitive Analy.pdf}
}

@article{r.2023TextbasedEmotionRecognition,
  title = {Text-Based Emotion Recognition Using Contextual Phrase Embedding Model},
  author = {R., Vishnu Priya and Nag, Prashant Kumar},
  year = 2023,
  month = sep,
  journal = {Multimedia Tools and Applications},
  volume = {82},
  number = {23},
  pages = {35329--35355},
  issn = {1573-7721},
  doi = {10.1007/s11042-023-14524-9},
  urldate = {2024-02-05},
  abstract = {In this paper, the proposed approach categories the sentences in the dataset into the various topical documents using the TE-LSTM+SC model. As well as, the model generates semantic words related to topics that are fed into the word embedding like Skip-Gram and FrameNet to build the domain-specific lexicon. The topically related sentences in each document are contextually grouped using Skip-Phrase. Each sentence in contextual group is given to Semantic Role Labelling (SRL). SRL indentify the essential predicate-argument structures with the semantic labels like verb (V) tag or ARGM-NEG or ARGM-PRP or ARGM-CAU or structures with the semantic labels like verb (V) tag or ARGM-NEG or ARGM-PRP or ARGM-CAU or ARGM-MNR or ARGM-MOD. The selected predicate-argument structures are aggregated into a linear layer to form a semantic embedding. Simultaneously, the predicate-argument embedding is segmented to sub words by BERT. The sub-words are transformed to word level through a convolutional layer to acquire the contextual word representation. Finally, semantic embedding and word representation are integrated to efficiently find the emotion of the given sentence. The experimental result proved that the proposed approach outperforms all the state-of-art approaches.},
  langid = {english},
  keywords = {BERT,Emotion recognition,Emotional cause,FrameNet,MyPublication,Semantic role labelling,Skip phrase},
  annotation = {note = \textbraceleft indexing=SCIE; quartile=Q2; IF=3.0; JCR=2023\textbraceright},
  file = {C:\Users\prash\My Drive (prashantnag.workmail@gmail.com)\Digital Garden\05 Resources\ZotfilesPHD\My Articles\R. and Nag - 2023 - Text-based emotion recognition using contextual phrase embedding model.pdf}
}