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e-ISSN: 2455-6491 | Published by Global Advanced Research Publication House (GARPH)

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Archives of International Journal of Advanced Innovative Technology in Engineering(IJAITE)

Volume 10 Issue 3 May 2025



1. A Robust Ensemble Learning Framework for Early Detection and Classification of Parkinson’s Disease Using Voice Features

AUTHOR NAME : Komal P. Raut, Dr. Vijaya Balpande

ABSTRACT : Parkinsons disease is a progressive neurodegenerative disorder that significantly impacts motor and vocal functions, making early and accurate diagnosis essential for effective treatment. This study presents an ensemble learning framework for the detection and classification of PD using biomedical voice features, offering a non-invasive and data-driven diagnostic approach. The proposed model combines multiple base classifiers—including Logistic Regression, K-Nearest Neighbors, and Decision Tree—through a soft voting mechanism to improve classification performance and reduce prediction variance. Experimental results demonstrate that the ensemble model outperforms individual classifiers, achieving an accuracy of 95%, with balanced precision, recall, and F1-score values of 91.5%. These results highlight the model’s robustness and generalizability, confirming its suitability for reliable Parkinsons disease detection. The findings suggest that ensemble learning, when applied to voice-based biomedical data, can serve as an effective tool in clinical decision support systems for early PD diagnosis.

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2. A Comprehensive Review of Skin Disease Detection and Classification Using Machine Learning and Deep Learning Techniques

AUTHOR NAME : Budhamala Ankush Gedam, Dr. Namrata Khade

ABSTRACT : Skin diseases are among the most common health concerns worldwide, with timely and accurate diagnosis playing a critical role in effective treatment and patient outcomes. Recent advancements in Artificial Intelligence, particularly in Machine Learning and Deep Learning have shown immense promise in the automated detection and classification of skin conditions. This review provides a comprehensive analysis of existing ML and DL techniques used for skin disease diagnosis, covering classical algorithms such as Support Vector Machines, k-Nearest Neighbors, and Random Forests, as well as state-of-the-art Convolutional Neural Networks, including ResNet, VGG, Inception, and DenseNet. We also explore the role of transfer learning, attention mechanisms, and ensemble models in enhancing diagnostic performance. Moreover, we discuss current challenges including data scarcity, generalization, interpretability, and ethical concerns. Real-world applications such as mobile diagnostic apps, telemedicine integration, and deployment in remote healthcare settings are also examined. This review concludes by underscoring the transformative potential of AI in dermatology and calls for stronger interdisciplinary collaboration between clinicians and data scientists to develop robust, ethical, and scalable diagnostic systems.

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3. Study And Analysis of Tangible and Intangible Activities of Core Market Area of Khamgaon

AUTHOR NAME : Prajay Prashant Awate, Pranita Deepak Bokankar, Tushar Paithankar

ABSTRACT : This study aims to trace the material and immaterial activities that influence market dynamics in Khamgaon, a city in Maharashtra Buldhana region. Khamgaon, popularly known as the Silver City, has historically served as a major commercial hub in the Vidarbha area, and the citys market is critical to its social and economic stability. The study examines the physical infrastructure (tangible elements) that define the markets character, such as street networks, vendor placements, built forms, accessibility, and public amenities, as well as intangible elements like social interactions, cultural practices, and traditional trade patterns. The study determines the market regions strengths and weaknesses through a combination of field surveys, stakeholder interviews, and geographic analysis. It emphasizes issues such as traffic, poor infrastructure, the decline of traditions, and a lack of diversity in public areas. The findings demonstrate that maintaining the markets physical and cultural identity necessitates striking a balance between modernism and preservation. Using the findings, the paper presents strategic recommendations for the future growth of Khamgaon market region. These include encouraging pedestrian-friendly neighbourhoods, merging formal and informal economic sectors, upgrading public facilities, and preserving the cultural ethos through architectural and legislative reforms. The goal of the outcome is to assist stakeholders, local government representatives, and urban planners in building a resilient, welcoming, and culturally dynamic marketplace that satisfies current demands.

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4. Heat Transfer Performance in Double Pipe Heat Exchangers: A Comprehensive Review

AUTHOR NAME : Prof. Niraj A. Dakhore, Prof. Ashish V. Kadu, Prof. Ankit A. Jiwarkar

ABSTRACT : This review paper provides a comprehensive analysis of the heat transfer performance of Double Pipe Heat Exchangers, a crucial component in thermal engineering applications. The focus of this review is on the construction, working principles, and enhancement techniques of DPHEs, with a particular emphasis on the factors influencing their thermal performance. Key findings highlight the significant impact of flow arrangement, fluid properties, and geometric modifications on heat transfer efficiency. The enhancements such as twisted tape inserts, hybrid nanofluids, and finned tubes have been shown to substantially improve performance, although they introduce trade-offs in pressure drop and maintenance. The review also identifies current challenges, including material compatibility, fouling, and the need for energy-efficient solutions in industrial settings. Furthermore, future research directions are explored, particularly in the areas of smart monitoring, eco-friendly fluids, and multi-objective optimization. This paper suggested with recommendations for further investigations into innovative materials, manufacturing techniques, and the integration of digital technologies for real-time performance optimization.

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