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Review Paper

The Application of Artificial Intelligence in the Field of Mental Health: A Comprehensive Review

The integration of Artificial Intelligence (AI) into mental health care has ushered in a paradigm shift in how emotional well-being is assessed, monitored, and treated. Among the various AI applications, sentiment and emotion analysis has emerged as a vital tool in extracting psychological insights from unstructured data sources such as clinical notes, therapy transcripts, social media interactions, and mobile health applications. Leveraging advanced models like BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer), alongside psycholinguistic tools such as LIWC (Linguistic Inquiry and Word Count) and VADER (Valence Aware Dictionary and sEntiment Reasoner), AI systems are now capable of understanding and interpreting nuanced emotional expressions in text and speech. This review paper presents a comprehensive synthesis of current research and methodologies related to AI-driven sentiment and emotion detection in the context of mental health. We explore classical and deep learning approaches, hybrid models, and multimodal frameworks applied to diverse datasets including clinical conversations, patient self-reports, and public online content. Real-world applications such as AI-powered chatbots, teletherapy platforms, and real-time monitoring tools are examined in detail. In addition, we discuss the ethical implications, including data privacy, algorithmic bias, and interpretability, which are critical for the safe deployment of AI systems in healthcare settings. The paper concludes with a set of recommendations for future research, emphasizing the need for multimodal integration, real-time analytics, and personalized mental health interventions. This work aims to inform researchers, clinicians, and developers about the current landscape and potential of AI in advancing mental health care.

Published by: Revati Sanjay Mahajan

Author: Revati Sanjay Mahajan

Paper ID: V11I3-1141

Paper Status: published

Published: May 12, 2025

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Research Paper

Advancing Ovarian Cancer Research for Enhanced Subtype Classification and Outlier Detection

Ovarian cancer is challenging due to late diagnosis and diverse subtypes. This study uses CNN-based models (MobileNet, DenseNet) for histopathological image classification and applies machine learning (Logistic Regression, Random Forest, XGBoost) for PCOS outlier detection. The system is supported by a Python backend and an intuitive web interface to assist clinicians. This integrated approach improves diagnostic accuracy and contributes to better patient outcomes.

Published by: Sanika Kashid, Adeetti Khamkar, Sheetal Mhatre

Author: Sanika Kashid

Paper ID: V11I3-1142

Paper Status: published

Published: May 12, 2025

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Research Paper

Detection of Insurance Fraud Using Machine Learning: A Data-Driven Approach for Accurate Classification

Insurance fraud exerts a considerable economic strain on both insurers and policyholders, eroding the foundational trust that sustains the insurance industry. This study presents the development of a robust, scalable machine learning framework tailored for the detection of fraudulent claims. To mitigate the prevalent issue of class imbalance in fraud detection, a synthetically balanced dataset was constructed using the Synthetic Minority Oversampling Technique (SMOTE). The methodological approach incorporates a Random Forest classifier, augmented with domain-informed feature engineering—such as income-to- liability metrics and other fiscal indicators—designed to enhance discriminative power. Model performance is rigorously assessed through a suite of evaluation criteria, including accuracy, precision, recall, F1-score, confusion matrix, and both ROC-AUC and precision-recall curves. The experimental outcomes reveal exceptional classification efficacy, with the model achieving near-flawless predictive accuracy on the balanced dataset. When benchmarked against baseline models, the proposed system demonstrates superior detection capability, particularly in minimizing false negatives. This research underscores the effectiveness of ensemble-based algorithms in the fraud analytics domain and sets the stage for practical deployment in operational insurance environments. Prospective advancements may involve the integration of deep neural networks and real-time anomaly detection using streaming architectures to further enhance scalability and responsiveness.

Published by: Shaba Khatoon, Dr. Ankita Srivastava, Dr. Shish Ahmad

Author: Shaba Khatoon

Paper ID: V11I2-1428

Paper Status: published

Published: May 12, 2025

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Research Paper

AI-Generated Art and Intellectual Property: Navigating the Landscape of Ownership, Authorship, and Copyright

This paper evaluates the implications of artificial intelligence (AI) on traditional concepts of authorship, ownership, and copyright under intellectual property (IP) law, drawing comparative insights from the European Union and China. In consideration of the recent court decisions and the January 2025 U.S. Copyright Office report re-emphasizing the “human ‘creator’” requirement, this work studies how different systems of copyright can respond to creativity aided by AI without undermining the protection of human creators. The paper calls for an approach that separates AI developers from users and allocates rights depending on the amount of human contribution involved. In the end, it offers more IP-centric solutions in response to the challenges of new technologies, policies that enable creation while preserving IP principles in the digital age.

Published by: Daphne Ekpe

Author: Daphne Ekpe

Paper ID: V11I2-1450

Paper Status: published

Published: May 12, 2025

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Research Paper

Machine learning assisted Optical-SAR Radar for Target Classification

A new era of Synthetic Aperture Radar (SAR) has begun in recent years. Convolutional neural networks (CNNs) have garnered a lot of interest lately for their ability to analyze SAR data. This paper thoroughly studies the main subfields of SAR data analysis that CNNs have addressed, including segmentation, change detection, object identification, automatic target recognition, land use and land cover classification, and image denoising. Particular attention has been paid to useful methods like transfer learning and data augmentation. To overcome the issues of a high false alarm rate and the challenges of attaining high-performance detection using traditional approaches, a deep learning-based SAR target identification and classification method is proposed for target detection tasks in complex backdrops. An optical based approach is presented in this study for enhancing the security feature. A machine learning-based radar with better performance is suggested in light of the deep learning-based target models' problems with a high parameter count and memory usage. Even though there have been some significant advancements, deep learning research in radar is still mainly being tested in lab settings and is still in its theoretical stage. There are still a number of obstacles and potential restrictions in the application, including issues with dataset adequacy, robustness of the model, and electromagnetic modelling fidelity. Nonetheless, it is undeniable that deep learning technology will significantly advance radar. As a result, it is wise to recognize the field's current difficulties and potential future paths. Furthermore, it is hoped that this review will give readers fresh opportunities to investigate appropriate deep learning-based methods for radar applications. In this study, Long Short-Term Memory (LSTM) and SqueezeNet model are used for enhancing the accuracy of system designed.

Published by: Priyanka Shukla, Priti Singh, Ashutosh Dubey, Kritika Upadhyay, Pranshu Upadhyay

Author: Priyanka Shukla

Paper ID: V11I2-1454

Paper Status: published

Published: May 11, 2025

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Research Paper

High-Frequency Capacitive Wireless Charging Using a 4-Plate Structure

Electric vehicles (EVs) are traditionally charged in a stationary position using wireless power transfer (WPT) systems. However, dynamic charging—where EVs are charged while in motion—offers a more flexible and efficient alternative. Most current dynamic charging systems are based on Inductive Power Transfer (IPT), which, despite its maturity, is limited by high costs and considerable eddy current losses associated with inductive coils. To address these limitations, this study proposes a high-power dynamic charging system utilizing Capacitive Power Transfer (CPT) for electric vehicle applications. The research focuses on the design and implementation of a capacitive-coupled WPT system, particularly emphasizing the significance of mutual capacitance in the coupler design. Mutual capacitance directly influences the power transfer capability and overall efficiency of the system. By exploring various coupler configurations and optimizing the capacitive plate design, the study aims to enhance the practicality and cost-effectiveness of dynamic EV charging through CPT technology.

Published by: Dr. Muthukannan S, Ananya H R, Deekshitha A V, Jeevitha N U, Ruchitha K C

Author: Dr. Muthukannan S

Paper ID: V11I2-1444

Paper Status: published

Published: May 11, 2025

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