Category Archives: Uncategorized

Authors: Bhuvanesh G, Arun Das, Shivanand Chinnappanavar, Ravikumar M

Abstract: This study aimed to assess suitable lands for agricultural purposes in the Gundlupet taluk of Chamarajanagar district. Leveraging the widely used Analytic Hierarchy Process (AHP) integrated with Geographic Information System (GIS), this research conducted a thorough land use suitability analysis. Key parameters including geomorphological and geological features, relief, slope, drainage density, rainfall, soil texture, and land use and land cover were considered in the analysis. Weights were assigned to these parameters based on their significance and importance, resulting in the generation of an agricultural land suitability map divided into three categories. Upon excluding forested and reservoir areas from the reclassified suitability map, the study estimated that 19.59% of the study area (266 sq. km) is highly suitable for agricultural production, 67.6% (918 sq. km) is moderately suitable, and 12.81% (174 sq. km) is unsuitable for agricultural production in this region. This framework facilitates the early zoning of agricultural land for protection, ensuring sustainable land use development in the future.

DOI: http://doi.org/10.5281/zenodo.20783644

Published by: vikaspatanker

Authors: Lohitha Lakshmi K, Hema Sri S, Shaik Reshma, Hima Sai Nandhan P, Manoj Kumar Reddy S D V

Abstract: Data plays a key role in analysis and machine learning, but working with real-world datasets is often challenging because they usually contain missing values, duplicate entries, inconsistencies, and noise that can affect the accuracy of results. Data cleaning is therefore an essential step, yet it can be time-consuming and often requires programming knowledge, making it less convenient for many users. In this work, we present DataForge, a data preprocessing system designed to make the cleaning process simpler and more accessible. The platform allows users to upload datasets and perform cleaning operations without writing code, using a mix of statistical methods and simple intelligent techniques to handle issues such as missing data, outliers, and duplicate records. Overall, DataForge focuses on reducing the effort required for data preparation while still helping users work with more reliable datasets. This approach also helps users get a clearer idea of their data without going into too much technical detail.

Published by: vikaspatanker

Authors: Ms. Snehal Kadbhane, Mr. Ritesh Khandagale, Dr. Vijaykumar Kale, Dr. Mahesh Thakare, Vaibhav Narwade

Abstract: Background: The near-universal reliance on high-sucrose vehicles in paracetamol oral syrups creates an increasingly untenable clinical tension for vulnerable patient populations—diabetic individuals experiencing glycemic excursions, children at heightened risk of dental caries, and obese or metabolically compromised patients. With global diabetes prevalence now exceeding 537 million adults and dental caries ranking as the world's most prevalent non-communicable condition, the pharmacoeconomic and public health argument for sugar-free alternatives has become irrefutable. Methods: Five trial formulations (F1–F5) of a sugar-free paracetamol oral syrup at 120 mg/5 mL were developed using a Quality by Design (QbD) framework. Sorbitol (20–30% w/v), hydroxypropyl methylcellulose K4M (0.25–0.75% w/v), and sucralose (30–70 mg/100 mL) were systematically varied while all other excipients were held constant. Formulations were evaluated for organoleptic acceptability, pH, viscosity, drug content, density, surface tension, sedimentation ratio, and antimicrobial preservative effectiveness per USP <51> Category 2. The optimized formulation (F3) underwent 90-day accelerated stability testing per ICH Q1A(R2) at 40°C ± 2°C/75% ± 5% RH and was benchmarked against a commercially marketed sugar-free reference product. Results: F3, containing sorbitol 25% w/v, HPMC K4M 0.50% w/v, and sucralose 50 mg/100 mL, emerged as the optimized formulation. It exhibited a pH of 5.82 ± 0.02, viscosity of 92 ± 2.5 cps, drug content of 99.4 ± 0.5% of label claim, and a palatability score of 4.5/5.0—superior to both lower-concentration variants and the marketed comparator (4.2/5.0). Accelerated stability studies confirmed drug content above 98.6% and p-aminophenol below 0.08% at day 90, well within pharmacopoeial limits. All five challenge organisms met USP <51> Category 2 acceptance criteria. Conclusion: The optimized sugar-free paracetamol syrup demonstrates pharmacopoeial compliance, chemical and microbiological stability supportive of a 24-month shelf life, and patient acceptability equivalent or superior to a marketed reference. The formulation strategy—combining a polyol bulk sweetener with a high-intensity non-caloric sweetener and a cellulose-ether viscosity modifier—provides a scientifically validated, clinically advantageous platform for analgesic-antipyretic therapy in patient populations for whom conventional sucrose-based preparations are contraindicated or undesirable.

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Authors: Om Sawant, Gunjan Shahade, Atul Sanap, Shailesh Pawar, Madhuri Shinde

Abstract: The widespread adoption of Internet of Things (IoT) systems has resulted in a large volume of time-sensitive data that requires fast and efficient processing. Although cloud platforms provide extensive computational capabilities, the physical separation between data-producing devices and remote cloud infrastructures frequently introduces noticeable delays, jitter, and bandwidth inefficiencies. Fog computing addresses these shortcomings by relocating processing tasks toward the network’s periphery; however, the decentralized and heterogeneous composition of fog resources complicates the design of effective scheduling strategies. Recent progress in Artificial Intelligence (AI), especially in the field of Deep Reinforcement Learning (DRL), have enabled adaptive and context-aware scheduling solutions capable of responding to dynamic changes in fog–cloud systems. This study presents an in-depth examination of AI-oriented scheduling mechanisms for fog computing, with emphasis on system design principles, algorithmic trends, and comparative performance outcomes. Conventional scheduling heuristics, machine-learning-based methods, and contemporary DRL approaches—including multi-agent and multi-objective frameworks—are critically analyzed. The review also identifies persistent challenges related to scalability, mobility, resource constraints, and security-aware decision-making. Overall, the findings demonstrate that AI-driven scheduling enhances responsiveness, load distribution, and resource utilization in emerging fog-supported IoT environments.

DOI: http://doi.org/10.5281/zenodo.20769734

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Authors: Kiran P. Borase, Sachin K. Dahake

Abstract: This research focuses on the design and optimization of a motorcycle swing arm using a bio-inspired honeycomb structure aimed at achieving significant weight reduction while enhancing stiffness and durability. A conventional swing arm was modelled using Solid Works and compared with an optimized honeycomb-reinforced structure through Finite Element Analysis (FEA) in ANSYS. The inclusion of honeycomb geometry demonstrates improved structural efficiency, reduced stress concentration, lower deformation, and an expected weight reduction of 15–20%. The study establishes the feasibility of integrating nature-inspired geometrical patterns into mechanical components to achieve superior performance in lightweight engineering applications.

DOI: http://doi.org/10.5281/zenodo.20766895

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Authors: Ashutosh Kale, Swapnil Yeole, Onkar Sonawane, Jayesh Wani, Vedant Rajput

Abstract: This analysis paper examines ResiPlan AI, an intelligent web-based system designed to automate residential floor plan generation using artificial intelligence and machine learning techniques. The system addresses significant barriers in traditional architectural design—such as high cost, complexity, and reliance on expert knowledge—by enabling non-expert users to generate optimized 2D and 3D layouts through simple inputs like plot size, room count, and architectural style. By integrating Stable Diffusion 1.5 with ControlNet, ResiPlan AI ensures structural adherence while maintaining creative flexibility. This paper critically evaluates the system’s architecture, technical approach, limitations, and future potential, positioning it within the broader context of generative AI in architectural design.

DOI: http://doi.org/10.5281/zenodo.20766823

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Authors: Kishor Ugale, Tushar Pandhi

Abstract: Traditional Voting plays a very important role in modern republic systems. Electronic voting (e-Voting) refers to any method of casting or recording votes through electronic technologies. Voting machines consist of the whole combination of mechanical, electromechanical, or electronic components-along with the necessary software, firmware, and documentation-used for programming, controlling, and supporting the voting process. The e-Voting system discussed here uses biological validation, notably fingerprint identification, to verify voter identity. In this method, fingerprint matching is employed to confirm the user’s identity. This proposed work bears by differentiating sample fingerprint patterns to show whether the fingerprints real from the match individual. The primary objective of this system is to simplify and improve the regulation of the voting mechanism. The proposed solution is designed to encourage full participation by enabling every eligible voter to take part in elections. This is achieved through an Android application that permits human being to cast their balloting digitally. Implementing online voting across both Android and web-based platforms increases the reliability and effectiveness of the election process. The system aims to offer a convenient, user-friendly, and secure method for recording and counting votes. Online voting can reduce operational costs, boost voter turnout, and facilitate better communication in the middle of voters and candidates. The core target of the implemented system is to provide a voting mechanism that authorizes singles to submit secure and confidential ballots over a network, addressing the restrictions of traditional voting methods, which are often time-consuming and vulnerable to security issues.

DOI: http://doi.org/10.5281/zenodo.20766695

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Authors: Khushboo Lokhande, Samruddhi Mahajan, Sayali Pawar, Janhavi Wankhede, Vijay More

Abstract: Vaani2Mudra is an online assistive communication platform that converts spoken or written language into gestures representing Indian Sign Language (ISL). The platform utilizes a compact speech recognition model to process voice input and employs natural language processing methods to restructure spoken content into a format compatible with ISL. Through a rule-based linguistic framework, the system eliminates redundant grammatical elements and standardizes text for gesture mapping. For multilingual functionality, Marathi language input is first converted to English before further processing. The output is presented through a series of pre-established ISL gesture visuals shown on a web-based interface. This system prioritizes ease of use, instantaneous processing, and user accessibility, positioning it as an effective tool for learning environments and assistive communication applications.

DOI: http://doi.org/10.5281/zenodo.20766405

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Authors: Nupur Jadhav, Atharva Bhusnale, Pritesh Gupta, Sakshi Jadhav, Vaishali Hiray

Abstract: Magnetic Resonance Imaging (MRI) is very impor-tant in the detection of neurological defects because it possesses high resolution that enables good visualization of soft-tissue structure. However, diagnostic clarity is often hindered by low-resolution scans due to the short time of acquisition, motion artifacts and hardware constraints. Recent advances in deep learning, such as Enhanced Super-Resolution Generative Ad-versarial Networks (Real-ESRGAN), have demonstrated strong capabilities of perceptual-driven image enhancement.This paper discusses Real-ESRGAN-based MRI super-resolution strategies, their architectural advantages and clinical potential benefits, in preserving fine anatomical and pathological details much better than CNN-based and conventional interpolation methods. We also present a conceptual AI-enabled deployment framework, where Real-ESRGAN is handled by a clinician support chatbot for application in web-based interaction, tele-radiology accessibility and diagnostic help. Clinical validation including metrics such as PSNR, SSIM,LPIPS and sFRC is investigated. The study emphasizes the need for interpretable, regulation-ready models to bridge AI-driven MRI enhancement with real-world diagnostic workflows.

DOI: http://doi.org/10.5281/zenodo.20766158

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Authors: Samruddhi Maheshkumar Aher, Harshali Rajendra Bagul, Diksha Ravindra Nirbhavane, Ashwini Nandu Pawar, Puneet Eknath Patel

Abstract: E-commerce platforms generate millions of product listings, often causing information overload and generic, non-personalized suggestions. Traditional recommendation systems operate as black boxes, resulting in limited user trust due to the lack of transparency. This paper proposes an AI-driven Explainable Product Recommendation System integrating Large. Language Models (LLaMA-2), FAISS semantic search, and SHAP-based interpretability. The system processes natural language queries, interprets intent, retrieves relevant products across multiple platforms, and generates human-readable explanations. Experimental evaluation demonstrates improved accuracy, transparency, and user satisfaction compared to traditional recommendation approaches.

DOI: http://doi.org/10.5281/zenodo.20766113

Published by: vikaspatanker