Volume-11, Issue-3

May-June, 2025

Research Paper

1. 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 MhatreResearch Area: Medical Imaging, Artificial Intelligence, Healthcare Technology

Organisation: Usha Mittal Institute of Technology, Mumbai, MaharashtraKeywords: Ovarian Cancer, Subtype Classification, PCOS, CNN, MobileNet, DenseNet, Outlier Detection, XGBoost, Machine Learning.

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Review Paper

2. 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 MahajanResearch Area: Artificial Intelligence In Healthcare

Organisation: Tilak Maharashtra Vidyapeeth, PuneKeywords: Artificial Intelligence, Mental Health, Sentiment Analysis, Emotion Detection, NLP, BERT, GPT, LIWC, VADER, Machine Learning, Deep Learning

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Research Paper

3. 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 UbaleResearch Area: Software Engineering, AI

Organisation: Marathwada Mitra Mandal's College of Engineering, PuneKeywords: Context Management, Generative AI, Artificial Intelligence (AI), Contextual Drift, Long-Term Context, Global Context, Local Context, Context Switching, Ambiguous Context, Incomplete Context, Scalability, Resource Constraints, Attention Mechanisms, Memory Networks, Neural Turing Machines (NTM), Contextual Embeddings, BERT, RoBERTa, Dynamic Context Retrieval, Recency Bias, Forgetting Mechanisms, Reinforcement Learning (RL), Multi-Agent Learning, Fine-Tuning, Transfer Learning, Privacy, Data Security, Transparency, Explainability, Bias, Fairness, Accountability, Misuse, Human-AI Interaction, User Autonomy, Ethical Considerations

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Research Paper

4. 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 SinghResearch Area: Computer Science & Electronics

Organisation: Rama University, Kanpur, Uttar PradeshKeywords: Radar, Sensors, Surveillance, Target Detection

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Research Paper

5. Instagramming Architecture: The Social Media Revolution in Architectural Photography

This research examines the profound impact of Instagram on architectural photography in the 21st century. Once a professional and editorial endeavor, architectural photography has been transformed by social media into a participatory and highly aestheticized activity. Instagram's algorithm-driven visuals, hashtag culture, and global reach have changed how architecture is captured, consumed, and even designed. This paper explores the visual aesthetics promoted by Instagram, the algorithmic pressures on photographers and designers, and the ethical and cultural consequences of a platform-dominated gaze. Through extensive case studies—ranging from Ricardo Bofill’s Muralla Roja and Thomas Heatherwick’s Vessel to India’s Rani ki Vav and Studio Mumbai’s handcrafted works—the study explores both the creative opportunities and serious challenges introduced by this digital revolution. It argues that while Instagram has broadened the audience for architecture, it has also commodified space and design into fleeting visual content, often at the cost of cultural depth and spatial integrity.

Published by: Sourav M SResearch Area: Architecture

Organisation: PES University, Bengaluru, KarnatakaKeywords: Architectural Photography, Instagram, Technology

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Research Paper

6. Finger-Print Based Vehicle Starter

The Fingerprint-Based Vehicle Starter system enhances vehicle security by using biometric authentication to control engine access. It replaces traditional keys with a fingerprint sensor, allowing only authorized users to start the vehicle. When a registered fingerprint is detected, the system activates the ignition through a microcontroller. If the fingerprint is unrecognized, the engine remains locked. This method prevents unauthorized access and reduces the risk of theft. The system is reliable, user-friendly, and cost-effective, making it suitable for modern vehicles. It demonstrates the practical use of biometrics in improving automotive safety and access control.

Published by: Gagan D D, Abhilash S G, Kruthika A N, Jnaneshwari G S, Rammurthy DResearch Area: Electronics

Organisation: Rajeev Institute of Technology, Aduvalli, KarnatakaKeywords: Biometric Authentication, Fingerprint Recognition, Vehicle Security, Engine Start System, Microcontroller, Access Control, Anti-Theft System, Fingerprint Sensor, Automotive Safety, Keyless Ignition.

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Research Paper

7. Design and Development of HEV

Hybrid Electric Vehicles (HEVs) represent a transformative advancement in automotive technology aimed at reducing fuel consumption and minimizing environmental impact. The study conducts a comprehensive analysis of various HEV architectures—including series, parallel, and seriesparallel configurations—to identify the most suitable system for optimal performance and effective energy management. Critical components such as electric motors, battery packs, regenerative braking systems, and power electronics are carefully selected and integrated to achieve an optimal balance between efficiency, performance, and cost. Additionally, special emphasis is placed on wheel alignment optimization to improve vehicle stability and reduce rolling resistance. The resulting prototype exhibits a significant improvement in both fuel economy and emission reduction compared to conventional vehicles, underscoring the potential of hybrid technologies in advancing sustainable transportation.

Published by: Vrushali Shankar Rupnawar, Vishwajeet Vikas Gholap, Giram Dhananjay Ram, Rohan Barikrao Rupnawar, Rohan Manohar Shelak, Dnyaneshwar Sukhadev shinde, Adarsh Siddheshwar Jankar, Rutuja Sanjay Dethe, Gaurav Mahadev Deokate, Vrushali Navnath WaghmareResearch Area: Enginnering

Organisation: SKN Sinhgad College of Engineering Korti-PandharpurKeywords: Hybrid Electric Vehicle (HEV), Electric Hybrid System, Internal Combustion Engine (ICE), Electric Propulsion Battery-Powered Vehicle Fuel Efficiency, Emission Reduction, Regenerative Braking, Energy Recovery Systems, Urban Mobility Solutions, Vehicle Powertrain Design

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Research Paper

8. Next Step: Find the Next Step in your Career

Choosing the right academic specialisation is a pivotal decision in a student's educational journey and has a profound impact on their future career. However, many students struggle with this choice due to a lack of clarity about their interests, strengths, and the job market relevance of different specialisations. The "Next Step" project aims to bridge this gap by offering a data-driven, survey-based guidance system that helps students identify the most suitable specialization based on their interests and aptitudes. The system utilizes a structured questionnaire designed to assess key personal and cognitive traits, such as analytical thinking, creativity, and technical enthusiasm. Based on the responses, the system suggests the most relevant specialisation, such as Artificial Intelligence, Data Science, or Cybersecurity, and subsequently provides a curated list of corresponding job roles. The solution is implemented as a web application, offering students a seamless and interactive experience while also allowing administrators to manage job role data dynamically. This approach not only improves self-awareness among students but also aligns their academic direction with industry demand, thus reducing the skills gap. The "Next Step" platform exemplifies how interest-based guidance can be transformed into an effective educational tool through the integration of survey methodologies, web technologies, and dynamic data mapping. It lays a scalable foundation for future career guidance systems that are personalized, adaptive, and aligned with real-world opportunities.

Published by: Harshal Patil, Divyansh Dubey, Harsh Singh Parihar, Aditya Upadhye, Shahin MakubhaiResearch Area: Computer Science & Engineering

Organisation: MIT ADT University, Pune, MaharashtraKeywords: Specialization Selection, Career Guidance, Survey-Based Recommendation System, Job Role Mapping, Student Career Planning, Data-Driven Counseling, Career Path Prediction, Educational Decision Support, Skill-Based Role Matching, Academic Specialization Recommendation

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Online paper publication is ongoing for the current issue and authors can submit their paper for this issue until Ongoing Submissions.