Category Archives: Uncategorized

Authors: Vaishnavi Tayade, guidance of Prof. Sudhir Dhomane and , Prof. Anand Pande

Abstract: Performing arts and cultural institutions are essential components of every society as they preserve traditions, encourage creativity, and strengthen social identity. Architecture plays a crucial role in shaping these cultural experiences by creating spaces that inspire artistic expression and meaningful interaction. Beyond providing functional infrastructure, architectural design influences how performers perform, how audiences perceive performances, and how communities engage with cultural activities. This research paper explores the relationship between architecture and performing arts, emphasizing the impact of spatial planning, acoustics, lighting, circulation, material selection, and cultural symbolism on human experience. The study examines how architectural spaces contribute to emotional engagement, creativity, cultural continuity, and community participation. The paper draws upon historical examples ranging from ancient Greek theatres and traditional Indian temple complexes to contemporary cultural institutions and performing arts centers. It investigates how architecture has continuously evolved to accommodate changing artistic practices while preserving cultural heritage. Special emphasis is placed on user-centered design, where the needs of performers, audiences, artists, and visitors are carefully considered. The research also discusses climate-responsive architecture, flexible performance spaces, sustainable design strategies, and the integration of public spaces that encourage social interaction. Several national and international case studies have been analyzed to identify successful architectural approaches that enhance performing arts and cultural experiences. These include iconic cultural landmarks, multi-functional performance venues, and community-oriented cultural centers. The study concludes that architecture serves not only as a physical setting for performances but also as an active medium that enriches artistic expression, strengthens cultural identity, and creates memorable experiences. By integrating functionality, sustainability, technology, and cultural values, architects can design environments that inspire creativity and preserve cultural heritage for future generations.

DOI: https://doi.org/10.5281/zenodo.21256703

Published by: vikaspatanker

Authors: Published by Vaishali Darda under , Dr Sudhir Dhomane and , Dilip Jade

Abstract: This paper investigates how globalisation reshapes architecture and environment in two contrasting settings: the corporate curtain-wall office towers of Gurugram, India, and the tourism-driven villa economy of Bali, Indonesia. The central research question is how the wholesale transplantation of foreign building typologies and the selective extraction of vernacular aesthetics generate measurable thermodynamic, hydrological, and cultural costs when detached from climatic and cosmological context, producing what geographers term architectural placelessness. The study combines a comparative literature review of vernacular and post-liberalisation architecture with a quantitative synthesis of published energy performance, groundwater, and land use data, modeled and triangulated against real anchor datasets (Delhi-NCR peak power demand and Bali land conversion statistics) where direct metered series are unavailable. Findings show that unoptimized glass curtain-wall towers in Gurugram's composite climate consume roughly fourteen times the cooling energy of traditional masonry havelis, with modeled cooling energy use intensity rising by an estimated 38.6% between 2015 and 2025, compounding an already acute groundwater deficit now exceeding twice the sustainable recharge threshold. In Bali, aesthetic extraction without the retention of Tri Hita Karana cosmology and Subak irrigation governance has driven sustained conversion of sawah rice terraces and skewed water allocation toward tourism. The paper concludes that architectural globalisation is not environmentally or culturally neutral: it substitutes climatic responsiveness and cultural meaning with mechanical and infrastructural dependency. It recommends regionally calibrated performance codes, hybrid typologies that reintroduce thermal mass and passive strategies, and governance models that protect indigenous land and water institutions.

DOI: https://doi.org/10.5281/zenodo.21256263

Published by: vikaspatanker

Authors: Sampada Wasudeo Dhore has published a paper in guidance of, Dr. Sudhir V. Dhomane and , Prof. Anand Pande

Abstract: Agricultural Interpretation Centres serve to educate the public about farming and ecology through immersive experiences. However, their success depends heavily on architectural design that engages visitors. This study examines how architecture can be used as a medium of experiential learning in such centres. A literature review and case study analysis identify strategies linking spatial design to interactive knowledge. Key findings show that features like clear wayfinding, sensory-rich exhibits, and integration with the landscape can significantly enhance visitor engagement. For example, balanced natural light and natural materials improve comfort and focus, while tactile displays and environmental sounds strengthen understanding. The aim is to propose architectural strategies-covering site planning, flexible layout, accessibility, and sustainability-that make agricultural knowledge tangible. In conclusion, the study finds that architecture is not just a container for exhibits but an active teacher: thoughtful design can transform Agriculture Centres into compelling, multi-sensory learning environments.

DOI: https://doi.org/10.5281/zenodo.21256149

Published by: vikaspatanker

Authors: Kiran Paul Kanikaram

Abstract: Software testing is among the most time-consuming and challenging stages of the development lifecycle to automate. Effective testing requires contextual awareness, including an understanding of code semantics and the identification of edge cases. The advancement of Large Language Models (LLMs) has enhanced the feasibility of automated testing by enabling unit, end-to-end, and exploratory testing as well as supporting a more agentic Quality Assurance (QA) process. The following paper will discuss the current use of large language models in software testing, with an emphasis on test case creation, bug detection, and self-healing automated systems based on natural language prompts. Although LLMs are providing novel ways to improve testing efficiency, there are certain obstacles that require special attention. They include incorrect output due to hallucinations, incomplete test coverage, and decreased reliability as the software grows. To better understand the obstacles, the example of a checkout module taken from the existing literature is discussed. The results show that while LLM-based testing methods can achieve useful test coverage, they do not necessarily outperform search-based methods. The analysis concludes that the value of LLMs in Quality Assurance is maximized through a human-in-the-loop approach, supported by a five-layer governance framework. As a result, the role of the QA professional is evolving toward that of a test-quality engineer and AI supervisor, requiring an expanded skillset.

DOI: https://doi.org/10.5281/zenodo.21217705

Published by: vikaspatanker

Authors: Research Scholar Mr. Narender Kumar, Associate Professor Dr. Pramod Kumar

Abstract: The primary objective of this research is to develop and evaluate an effective machine learning-based framework for the early detection of ransomware attacks. The study investigates a range of machine learning techniques, including supervised classification, anomaly detection, and clustering methods, to distinguish ransomware activities from legitimate system behavior. It focuses on extracting and analyzing critical behavioral features such as file access patterns, process execution characteristics, and network communication activities to train predictive models capable of achieving high detection accuracy while minimizing false positive rates.

DOI: https://doi.org/10.5281/zenodo.21217459

Published by: vikaspatanker

Authors: Zohaib Ali

Abstract: Image denoising is a foundational stage of the image signal processing (ISP) pipeline: its output quality bounds every downstream task, including demosaicing, compression, retrieval, and recognition. State-of-the-art deep denoisers (DnCNN, FFDNet, CBDNet) achieve strong quality but are typically designed and evaluated for offline, GPU-server settings, leaving embedded and real-time deployment as a secondary concern addressed only through post-hoc compression. This paper designs a denoiser to be lightweight from the outset rather than compressed after the fact. We propose a 3-layer residual convolutional network (5.9K parameters) with an auxiliary noise-level input channel (FFDNet-style conditioning), trained across a range of noise levels rather than a single fixed level. In a controlled study, we first show that a comparable single-noise-trained variant generalizes poorly outside its training noise level (PSNR drops from 28.6 dB at sigma=25 to below classical-filter performance at sigma=50). We then show that noise-level conditioning directly closes this gap: the conditioned model matches or exceeds Gaussian blur and median filtering across sigma in {10, 25, 50} without retraining, using three orders of magnitude fewer parameters than DnCNN. A channel-width ablation (C=16, 24, 32) further shows that quality does not increase monotonically with capacity under a fixed training budget, underscoring that training schedule — not just architecture size — is central to the lightweight-denoising design space. All results are produced by directly executing the accompanying code (no benchmark numbers are copied from other papers); we report exact scope, data, and hardware limitations in Section 6 alongside a concrete roadmap to full-scale benchmark evaluation (BSD68, Set12, SIDD).

DOI: https://doi.org/10.5281/zenodo.21233013

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Authors: Bandaru Udayasree

Abstract: The rapid growth of e-commerce startups has created significant opportunities for innovation and economic development; however, a large proportion of these ventures fail due to inadequate financial planning and uncertain profitability. Accurate estimation of start-up capital requirements and early prediction of business profitability are therefore essential for entrepreneurs, investors, and financial institutions. This research presents a machine learning-based framework for estimating start-up capital and predicting the profitability of e-commerce startups using historical business and financial data. The proposed system analyzes critical parameters such as funding amount, investment history, operational expenses, revenue projections, market trends, and business characteristics to identify patterns associated with successful and profitable ventures. Multiple machine learning algorithms, including Decision Tree, Random Forest, Gradient Boosting, Logistic Regression, and Multi-Layer Perceptron (MLP), are trained and evaluated to determine the most effective prediction model. Data preprocessing techniques such as feature selection, handling missing values, and normalization are applied to improve model performance and reliability. Experimental results demonstrate that the proposed framework achieves high prediction accuracy, enabling data-driven decision-making for startup planning and investment evaluation. The developed system provides an intelligent decision support tool that assists entrepreneurs in estimating initial capital requirements, assessing business profitability, minimizing financial risk, and improving the likelihood of long-term business success in the competitive e-commerce ecosystem.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue3.503

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Authors: Dr. Lakshmi Kunhikrishnan, Dr.J.Elanchezhian, Dr.K.Karnavel, Mrs.P.Aruna, Dr.V.V.Rajasegharan, Mr. Dhianeshwar, J

Abstract: A proper irrigation system is considered as the back bone of agriculture. To provide a channelized irrigation system various irrigation setups and techniques are targeted to meet different yields and for different purposes. Cheyyur has two major types of crops cultivated in two different seasons with two different irrigation setups. The irrigation setups need to be changed whenever the crops are changed. These difficulties not only cause wastage of water but also decreases the overall yield of the crops. To improve the improvement facilities a hybridized setup was modelled fabricated and implements in and around the parts of Cheyyur district to improve their irrigation abilities. The proposed project was accepted by the department of science and technology. The hybridized model was designed and fabricated. Optimization of the flow of the suction pressure was conducted and simulation has been carried out using Genetic Algorithm and performance was analysed using Fminconalgorithm in MATLAB to hybridized and integrate the two different irrigation system. Modelling studies were carried out to improve the suction and the control flow rate of 986 litres per hour to 70 Litres per hours to the agricultural field. This applied optimization technique proves to improve the yield of the crops.

DOI: http://doi.org/10.5281/zenodo.21188506

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Authors: Dr.Pavithra Lakshminarasimhan

Abstract: Early infancy is a critical period for brain development, where social interaction plays a foundational role in shaping communication, emotional regulation, and cognitive growth. With increasing shifts toward nuclear family systems and digital engagement, concerns have emerged regarding reduced caregiver-infant interaction. This paper explores the relationship between early social deprivation and developmental outcomes, particularly behaviours resembling autism. While autism is a neurodevelopmental condition with strong genetic underpinnings, this paper emphasises that environmental factors may influence developmental expression without causing autism, often leading to delays or autism-like presentations.

DOI: https://doi.org/10.5281/zenodo.21192947

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Authors: Sarah Thompson, Robert Evans, Christopher Walker, Emma Robinson, Chaitanya Srinivas, Sai Nishil

Abstract: The rapid advancement of Artificial Intelligence (AI) is transforming enterprise customer relationship management (CRM) platforms by enabling intelligent automation, predictive analytics, and data-driven decision-making. Salesforce, as a leading cloud-based CRM platform, provides a robust ecosystem for integrating AI technologies that enhance business processes, customer engagement, and operational efficiency. This paper explores the integration of Artificial Intelligence into Salesforce ecosystems to support intelligent business automation across sales, marketing, customer service, and enterprise operations. It examines the role of AI-powered capabilities such as machine learning, natural language processing, predictive modeling, intelligent recommendations, and automated workflow management in optimizing organizational performance. The study also discusses architectural considerations, integration frameworks, data management strategies, security requirements, and governance mechanisms necessary for successful AI adoption within Salesforce environments. Furthermore, the paper analyzes the benefits, challenges, and implementation best practices associated with AI-driven automation initiatives, highlighting their impact on productivity, customer experience, and digital transformation objectives. The findings demonstrate that the strategic integration of AI technologies within Salesforce ecosystems enables organizations to achieve scalable automation, enhanced decision intelligence, improved customer-centric operations, and sustainable competitive advantage in an increasingly digital business landscape.

DOI: http://doi.org/10.5281/zenodo.21187848

Published by: vikaspatanker