Manuscripts

Recent Papers

Research Paper

Application of Optical Communication in FMCW Radar

Frequency-modulated continuous waves (FMCW) radars are long-range, frequency-modulated electromagnetic sensors that can perceive their environment in three dimensions. Recent introductions of RADARs with frequencies ranging from 60 GHz to 300 GHz have expanded their possible applications due to their improved precision in angle, range, and velocity. FMCW RADARs have a better resolution and are more accurate than narrowband and ultra-wideband (UWB) RADARs. They offer several important benefits, such as long-range perception, resistance to rain and lightning, and more, and they are less costly than cameras and LiDARs. Even yet, their outputs are less dense and noisy than those of other RADAR technologies, and their ability to measure target velocities requires the employment of specifically created algorithms. Recently, radar sensors have become more and more common in a variety of industries, such as automotive, defense, and surveillance. This is because radar sensors can withstand a wide range of conditions, such as extreme heat, bright light, and bad weather. The simulation results were performed using Optisystem 22.0 and MATLAB (R2024b). The results demonstrate that 40 mW of power is effectively utilized for target identification, with the best technique for moving targets being direct detection.

Published by: Priyanka Shukla, Priti Singh

Author: Priyanka Shukla

Paper ID: V11I3-1151

Paper Status: published

Published: May 12, 2025

Full Details
Research Paper

Context Management in Generative AI

Context management is a fundamental challenge in generative AI, directly influencing the coherence, relevance, and quality of AI-generated outputs. This paper explores the concept of context in generative AI, focusing on the difficulties models face in maintaining long-term, dynamic, and global context across interactions. Key challenges include context loss in long-term dialogues, balancing between immediate and overarching context, handling context switching in multi-turn conversations, and addressing ambiguity or incomplete context. Additionally, we examine the impact of contextual drift, scalability issues, and resource constraints. By understanding these challenges, we highlight the importance of developing more sophisticated context management techniques to improve AI's ability to generate consistent, relevant, and user-centered outputs. Finally, we discuss the implications of context management for various applications, including conversational AI, content generation, and personalized recommendations.

Published by: Rushikesh Joshi, Omkar Jainak, Naveena Bhat, Khushal Patil, Dr. Swapnaja Ubale

Author: Rushikesh Joshi

Paper ID: V11I3-1137

Paper Status: published

Published: May 12, 2025

Full Details
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

Full Details
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

Full Details
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

Full Details
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

Full Details
Request a Call
If someone in your research area is available then we will connect you both or our counsellor will get in touch with you.

    [honeypot honeypot-378]

    X
    Journal's Support Form
    For any query, please fill up the short form below. Try to explain your query in detail so that our counsellor can guide you. All fields are mandatory.

      X
       Enquiry Form
      Contact Board Member

        Member Name

        [honeypot honeypot-527]

        X
        Contact Editorial Board

          X

            [honeypot honeypot-310]

            X