Category Archives: Uncategorized

Authors: Rahul Jaiswar, Abhishek Kumar Sharma, Meena Rai

Abstract: The present study focuses on the morphological identification of dominant liverwort genera in the Kumaon hills of Uttarakhand, India, with particular emphasis on sporophyte characters for accu-rate taxonomic resolution. Field investigations were conducted in the Jageshwar region of Almora district and its adjoining areas up to Jageshwar Dham, a moist temperate zone characterized by mixed broad-leaved forests, shaded rock surfaces, and anthropogenically influenced temple com-plexes. These varied habitats form a mosaic of microenvironments favourable for the establish-ment of thalloid liverworts. During the survey, members of the families Aytoniaceae, Marchantiaceae, and Targioniaceae were recorded across soil, rock, and wall substrates. The liv-erwort flora documented comprised five species belonging to the genera Plagiochasma, Targio-nia, and Marchantia. Species of Plagiochasma and Targionia formed extensive patches on ex-posed to semi-shaded soil and rocky slopes, whereas Marchantia species were predominant in persistently moist, partially shaded habitats. These distributional patterns indicate clear ecological preferences among the dominant taxa within the study area. Overall, the investigation highlights the rich representation of complex thalloid liverworts in the Jageshwar landscape and underscores the significance of habitat heterogeneity in shaping bryophytic diversity in the mid-altitude Ku-maon Himalaya.

DOI: http://doi.org/10.5281/zenodo.17879162

Published by: vikaspatanker

Authors: Jai Gupta, Shreya Upadhyaya, Dr. H S Guruprasad

Abstract: This paper introduces a smart shoe that helps people move around inside buildings using gentle vibrations. Instead of relying on GPS or online maps, it tracks steps and direction with the phone’s built-in motion sensors. The method used is called pedestrian dead reckoning, which figures out position based on movement patterns. A matching app made with Flutter holds custom digital floor plans for different places indoors. Users can plan paths or get guided directions straight from their phone. Commands are sent wirelessly to small computers in each shoe using Wi-Fi signals. These tiny controllers then turn on one of two vibrating pads per foot – indicating turns or when they’ve reached the spot. The setup offers a complete, standalone way to navigate – ideal for indoor demos, restricted areas, or studies helping people with vision loss. Tests show it guides users step by step with precision while giving steady touch-based alerts on the go, proving that wearable navigation using only PDR can work reliably, no outside systems needed.

 

Published by: vikaspatanker

Authors: Aqsa Khalid

Abstract: Sustainability has emerged as a central driver of strategic transformation within the beauty and lifestyle segment of the fast-moving consumer goods (FMCG) industry. The expression “Green is the New White” captures the sector’s movement away from conventional, resource-intensive production practices toward environmentally responsible, ethically governed, and transparently communicated business models. Drawing on secondary data from international sustainability frameworks, peer-reviewed studies, market intelligence reports, and corporate disclosures, this research employs thematic analysis to identify three dominant patterns: Sustainable Product and Packaging Innovation, the Growth of Green Consumerism, and Regulatory–Reputational Pressures. The findings demonstrate that sustainability now underpins brand reputation, competitive advantage, and long-term sectoral resilience. The study concludes that beauty and lifestyle FMCG companies must embed environmental stewardship throughout the value chain to remain relevant in an evolving global marketplace.

Published by: vikaspatanker

Authors: Suraj Kumar, Mr. Vaibhav Singh Sekhawat

Abstract: The automation of anatomical and pathological region identification in clinical imaging has become a cornerstone of modern diagnostics. This review presents a systematic exploration of machine learning paradigms—from classical statistical models to cutting-edge foundation architectures—and their role in transforming segmentation accuracy, speed, and generalizability. We dissect foundational techniques such as kernel-based classifiers, ensemble tree models, and probabilistic graphical frameworks, contrasting them with deep learning systems including convolutional, recurrent, and transformer-based networks. Performance metrics from 2022–2025 benchmarks are synthesized across MRI, CT, ultrasound, and pathology datasets. We address persistent barriers—annotation scarcity, class imbalance, domain shift, and computational overhead—and evaluate mitigation strategies like transfer learning, synthetic data generation, and prompt-driven inference. A dedicated section introduces 2020–2025 breakthroughs: vision transformers, large-scale pre-trained models (e.g., MedSAM), diffusion-based synthesis, and hybrid neuro-symbolic systems. The convergence of these innovations signals a paradigm shift toward universal, data-efficient, and clinically deployable segmentation.

Published by: vikaspatanker

Authors: Unnati Raikwal, Abhishek Kumar, Subrata Sahana

Abstract: The increasing reliance on ATS in the hiring process demands that job seekers prepare ATS-compatible resumes. Unfortunately, most applicants lack technical insight into how to format their resumes in accord with ATS automated filtering requirements. To address this challenge, we propose an AI- driven resume builder endowed with real-time chat and smart enhancement capabilities. The system provides integrations with Gemini AI for real-time suggestions, ImageKit for background removal and image optimization, and MongoDB for structured storage of resume data. Users can start with templates, upload pre-existing files, edit the content of their resumes, and enhance phrasing with AI-powered augmentation. [7] This solution saves time while avoiding common ATS-compatibility problems in the creation of professional resumes. Experimental results showed improved keyword alignment, structural consistency, and clarity of content compared to traditional resume builders, which will, in turn, enhance the chances of success in job applications.

DOI: https://doi.org/10.5281/zenodo.17864311

Published by: vikaspatanker

Authors: Judy Ann O. Gagate, Dolly Ann A. Lupido, Professor Jayson D. Magalona

Abstract: This study examined the implementation of online learning and its effect on students’ learning at Eastern Samar State University (ESSU), Borongan Campus. Using a descriptive-correlational research design, the study investigated how online learning platforms, communication mechanisms, digital resources, and instructional strategies influenced students’ academic performance, comprehension, motivation, engagement, and overall learning satisfaction. A total of 150 undergraduate students participated by answering a validated researcher-made questionnaire administered through Google Forms. Findings revealed that students generally perceived online learning positively, noting that learning platforms were accessible, instructors provided clear guidance, and learning materials were sufficient. Results also showed that online learning contributed to improved digital literacy, independent learning skills, and time management. However, students reported challenges such as intermittent internet connectivity, device limitations, and reduced interaction with instructors. Statistical analysis confirmed a significant relationship between online learning implementation and students’ learning outcomes. The study concludes that while online learning is effective and beneficial, its success depends greatly on the quality of instructional delivery, technological access, and continuous institutional support. Recommendations were formulated to further enhance online learning implementation in ESSU

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Authors: Saroj Singh

Abstract: The integration of Artificial Intelligence (AI) into teaching methodology is reshaping traditional classroom strategies, opening new pathways for innovation, personalization, and efficiency in education. AI technologies such as adaptive learning platforms, intelligent tutoring systems, automated assessment tools, and data-driven analytics are gradually transforming how teachers design, deliver, and evaluate learning experiences. Unlike conventional methods that often rely on uniform approaches, AI introduces the capacity to customize learning content according to individual student needs, learning pace, and preferred styles, thereby fostering inclusivity and enhancing engagement. Teachers are increasingly able to shift their roles from knowledge transmitters to facilitators and mentors, using AI-generated insights to guide interventions, provide targeted support, and cultivate higher-order thinking skills. The transformative impact of AI in classroom strategies is visible across multiple dimensions. Firstly, AI supports differentiated instruction by offering personalized pathways that address the strengths and weaknesses of diverse learners. Secondly, real-time feedback and automated grading save valuable instructional time, enabling teachers to focus more on interactive, student-centered activities. Thirdly, predictive analytics help identify at-risk students early, empowering educators to implement timely interventions. Additionally, AI-driven immersive tools, including virtual reality and natural language processing applications, enrich learning environments and make complex concepts more accessible. However, the integration of AI into teaching also raises critical challenges such as data privacy, ethical considerations, teacher preparedness, and equitable access to digital resources. This article explores how AI is redefining teaching methodologies by aligning technological innovation with pedagogical goals. It emphasizes the dual role of AI as both a supportive assistant for teachers and a personalized guide for students. The discussion highlights examples of AI applications in curriculum delivery, assessment, and classroom management, while also acknowledging limitations and areas for future research. By transforming classroom strategies, AI not only enhances the effectiveness of teaching but also repositions education as a dynamic, learner-centered process. The study concludes that while AI cannot replace the human element in teaching, it can significantly complement and enrich the educational experience when thoughtfully integrated into pedagogy.

DOI: http://doi.org/10.5281/zenodo.17814824

Published by: vikaspatanker

Authors: Ritesh Kumar

Abstract: The integration of Artificial Intelligence (AI) in education has transformed traditional instructional methods by enabling real-time data-driven personalization of learning. This qualitative case study investigates the implementation of an AI-powered personalized learning platform within a blended learning environment at a private secondary school in Bengaluru, India. The study aims to explore how AI supports personalized learning in practice, the experiences of students and teachers using the system, and the broader implications for pedagogy, curriculum, and educational equity. Blended learning—combining face-to-face instruction with digital platforms—has gained traction in recent years, especially with the rise of hybrid learning post-COVID-19. Within this context, AI promises a transformative potential to analyze individual learning patterns and provide customized pathways for student progress. However, the successful integration of AI tools into everyday teaching remains a challenge, particularly in diverse educational contexts. This study adopts a qualitative case study design to provide in-depth insight into how AI can both support and complicate the goals of personalized learning. Data were collected through semi-structured interviews with six secondary school students, three teachers, and one administrator; classroom observations during AI-facilitated sessions; and analysis of related documents such as lesson plans and platform analytics. Thematic analysis was used to code and interpret qualitative data, focusing on key themes such as learner engagement, teacher adaptation, infrastructural readiness, and ethical concerns around data use. Findings indicate that AI facilitated adaptive learning, increased learner autonomy, and allowed for differentiated instruction that better met the needs of both high-achieving and struggling students. Teachers reported a shift in their roles—from content deliverers to learning facilitators—which many found empowering but also challenging due to limited professional development. While students appreciated the gamified and interactive nature of the platform, some experienced anxiety when faced with continuous feedback or algorithm-driven performance tracking. Several barriers to effective implementation were identified, including inconsistent access to digital devices, unreliable internet connectivity, and concerns over student data privacy. Furthermore, the importance of aligning AI outputs with curriculum objectives and local pedagogical practices was emphasized. Ethical considerations, particularly the opaque nature of algorithmic decisions and the lack of digital literacy among students, emerged as critical areas needing attention. This study concludes that while AI can significantly enhance personalized learning within blended environments, it is not a one-size-fits-all solution. The findings offer valuable implications for educators, policymakers, and ed-tech developers committed to responsible and inclusive use of AI in education.

DOI: http://doi.org/10.5281/zenodo.17814800

Published by: vikaspatanker

Authors: Priyadharshini P, M.Gayathri

Abstract: This research paper presents a comprehensive implementation and evaluation of the Single Image Denoising Diffusion Model (SinDDM) for sophisticated image editing and synthesis tasks using only a single training image. Unlike conventional diffusion-based generative models that rely on extensive datasets, SinDDM employs an innovative multi-scale training strategy to learn hierarchical priors from a single input image. The model supports a wide range of image manipulation tasks, including artistic style transfer, semantic image harmonization, region-of-interest (ROI) guided editing, and CLIP-based text-guided content generation. Experimental results demonstrate that SinDDM consistently produces coherent, high-quality, and semantically aligned outputs without requiring extensive training data or pre-trained encoders, making it particularly suitable for personalized applications and data-efficient computational scenarios. This paper provides detailed architectural insights, implementation methodologies, comparative analysis, and potential applications of the proposed framework

DOI: http://doi.org/10.5281/zenodo.17862616

Published by: vikaspatanker

Authors: Mohammed Khan, Jyoti Gupta

Abstract: This study conducts a geospatial analysis of Channapatna’s urban fabric, focusing on the spatial distribution and interrelationship of green spaces, water bodies (blue infrastructure), and transportation networks. Leveraging Google Maps and other mapping tools, the paper identifies the placement and accessibility of parks, urban lakes, river systems, and transit corridors within the town. Findings reveal a landscape shaped by both ecological assets—such as Shettahalli and Kudlur lakes—and robust connectivity via road and rail, highlighting critical roles in urban quality, economic activity, and environmental sustainability. This research presents a comprehensive geospatial analysis of Channapatna’s green spaces, water bodies, and transportation infrastructure, using Google Maps and other spatial mapping tools to generate a nuanced urban profile. The study systematically maps the distribution and accessibility of public parks, open areas, lakes, and rivers, assessing their impact on land use, environmental quality, and urban well-being. Through NDVI and Air Quality Index analysis, the research highlights disparities in green space allocation, emphasizing their role in city resilience, ecological health, and recreation. The examination of Channapatna’s blue infrastructure uncovers significant deterioration: key water bodies like Shettahalli and Kudlur Lakes, once lifelines for agriculture and community use, now face acute pollution and encroachment. Extensive sewage inflow, lack of Underground Drainage (UGD) systems, encroachment, and unregulated dumping threaten water quality, agricultural productivity, and public health. The study reviews recent policy interventions and ongoing planning efforts—including proposals for a dedicated Sewage Treatment Plant (STP) and expansion of UGD—framing these within the broader context of sustainable urban management.

DOI: http://doi.org/10.5281/zenodo.17876819

Published by: vikaspatanker