Volume 13 | Issue 5 |

 Abstract

Science education is at a vital part as it grapples with the demands of a knowledge-grounded, globalized society. This study investigates the pedagogical, institutional, and policy-related challenges impeding effective science teaching at the secondary level in India. Employing a mixed-methods approach across diverse school settings, the study analyzes teacher practices, student perceptions, and systemic constraints. Results indicate a predominant reliance on didactic teaching methods, limited integration of inquiry-based approaches, and minimal contextualization of scientific content. Female and rural learners face disproportionate barriers to meaningful engagement. The paper advocates a restructured science education model grounded in constructivist pedagogy, culturally responsive curriculum, and robust teacher development. Implications for policy and practice are discussed, aiming to make science education more inclusive, engaging, and socially relevant.

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 Keywords

science education, constructivism, inquiry-based learning, inclusive pedagogy, STEM, education reform, secondary education

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

BalanceSheet Pro is a robust financial management system that streamlines accounting processes and enhances overall financial tracking and reporting. It integrates features like transaction recording, balance sheet generation, income statement preparation, and financial analysis into a single, user-friendly platform. Designed for scalability and efficiency, BalanceSheet Pro bridges the gap between technology and financial management. In the dynamic realm of web development, our BalanceSheet Pro website serves as a gateway to efficient and accurate financial management. This system encapsulates a seamless process for recording, tracking, and analyzing financial data, facilitating better decision-making for businesses of all sizes.

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 Keywords

Automated Accounting Financial Reporting Real-time Financial Reporting Mobile Financial Management Social Login Integration Error Handling and Validation Data Security and Privacy Skill Profiling (if related to user role matching or recommendations) Data Visualization Real-time Updates

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Abstract- The tribes in India have diverse cultures and populations and live primarily on land and forests. The tribes are close to nature on a socio-cultural, and physical level has greatly influenced how they are conceptualized in addition to having their unique languages, customs, and beliefs, which they live in a small community. Whereas, there are up to 62 different tribal communities with 75 Particular Vulnerable Tribal Groups (PVTGs) spread across the different parts of India. There are some indigenous ethnic groups within the tribal society one of the most intriguing facets of India's tribal history is the sociocultural life of the Kharias. The Kharia tribe is generally classified into three types, such as Dudh, Dhelki, and Hill, Sabar, or Erenga Kharia, although some authors and scholars have divided them up into numerous sections and named them according to their regional places. The more interesting is that the Hill Kharias practices hunting, and gathering and they depend mostly upon forest and land resources. Hill Kharia is also known as Pahadi or Sabar Kharia. Whereas, the other sections of the Kharia Dudh and Dhelki are advanced and have taken to plough cultivation and are better off than the Hill-Kharia. The Paper will articulate socio-cultural practices among the Kharia Tribe in different parts of India, such as Jharkhand, Chattisgarh, Odisha, West Bengal etc. The paper mainly focused on: -

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 Keywords

Keywords: - Kharia Tribe, Kharias Nomenclature, Origin and Identity, Religion, Marriage, Prohibition/ Purification, Totem and Mahadoklo.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The quick development of real-time voice applications, including virtual assistants, live transcription, automated call centers, and interactive voice response systems, has fueled the need for precise, low-latency Automatic Speech Recognition (ASR) systems that can efficiently operate under resource-limited situations. Designing streaming ASR systems that provide high transcription accuracy while keeping computational overhead and memory utilization in check has become essential for building better user experiences and facilitating deployment on cloud platforms and edge devices. This work introduces a new streaming ASR system based on memory-friendly deep learning models optimized for real-time transcription. The main focus is to reach a compromise between reducing inference latency and maintaining transcription accuracy, allowing the system to be responsive despite processing continuous speech input. The method focuses on the design and incorporation of lean neural network models, including streaming-efficient versions of recurrent neural networks (RNNs), convolutional neural networks (CNNs), and transformers, optimized to fit into minimal memory while remaining effective for streams

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 Keywords

Streaming Automatic Speech Recognition with Memory-Efficient Models, Predictive Analysis, Machine Learning, Data Science, Speech Processing, Demand Forecasting, Inventory Management, Python, Real-Time Monitoring, Real-Time Transcription, Automatic Speech Recognition (ASR), Pruning and Compression, Deep Learning, Speech-to-Text Systems

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Breast cancer is the second most common and deadly disease among women and is a serious global health concern. Despite advances in medical technology, breast cancer rates are still rising. It is expected that by 2040, there will be more than three million new cases every year. Targeted medicines have greatly changed the way breast cancer is treated, allowing for personalized approaches that reduce side effects and improve patient outcomes. This review provides a detailed look at the latest developments in targeted breast cancer treatments. It examines different types of breast cancer, such as hormone receptor-positive (HR+), HER2-positive (HER2+), and triple-negative breast cancer (TNBC), and recognizes the variety of the disease. It offers treatment plans tailored to each type's specific characteristics. The review also explores how genetic mutations like BRCA1/2 and PIK3CA and molecular profiling help in choosing the right therapy. It discusses targeted medicines like monoclonal antibodies and small molecule inhibitors, explaining how they work and supporting their effectiveness with clinical trial data. The study also looks at potential new drugs and addresses ongoing challenges, such as resistance to treatment, opportunities for combining therapies, and new methods for classifying patients. This study aims to provide healthcare professionals, researchers, and patients with a comprehensive overview of the latest advancements in targeted breast cancer therapy. The goal is to develop customized, effective treatments to strengthen the fight against this challenging disease.

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 Keywords

Therapy, Hormone Receptor-Positive (Hr+), Her2-Positive (Her2+), Triple-Negative Breast Cancer

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Cancer remains one of the leading causes of mortality worldwide, with lung and colon cancer being among the most prevalent types. Early and accurate detection plays a crucial role in improving patient survival rates. In this study, we propose a deep learning-based approach for the automated detection of lung and colon cancer using microscopic histopathological images. The proposed model leverages convolutional neural networks (CNNs) and transformer-based architectures to extract high-level features from digitized tissue samples. The dataset used for training and validation comprises publicly available histopathological images, preprocessed and augmented to enhance model generalization. Experimental results demonstrate that our method achieves high classification accuracy, outperforming traditional machine learning approaches. The findings suggest that deep learning can serve as a reliable tool to assist pathologists in cancer diagnosis, potentially reducing diagnostic time and improving early detection rates.

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 Keywords

Deep Learning, Cancer Detection, Lung Cancer, Colon Cancer, Histopathological Images, Convolutional Neural Networks (CNN), Medical Imaging, Machine Learning, Artificial Intelligence, Digital Pathology, Biomedical Image Analysis.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The study's objective was to assess the impact of nutrition education among adolescent girls. Totally 50 subjects, were conveniently selected from the hostel, in Chennai. A study was conducted among the subjects. A pre-tested self-structured questionnaire was used for pre and post-test, and nutrition education was given using innovative games and educating them about the importance of Healthy food choices So the study has been initiated. A standard test survey questionnaire is applied before participation begins and reapplied after the end of the program. The study has highlighted that intervention in the form of nutrition education can significantly improve the knowledge about the importance of healthy food It has been concluded that there is a 35.5 % significant increase in knowledge at post-assessment. It is important to have this kind of nutrition education to adolescent girls

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 Keywords

Key words: nutrition education, helathy food choices

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This project presents an innovative AI application designed to enhance the job application process by leveraging large language models (LLMs) to analyze user resumes. The application provides comprehensive feedback, including a summarization of skills, identification of strengths and weaknesses, tailored suggestions for improvement, and recommended job titles based on the user's qualifications. Additionally, the tool incorporates web scraping capabilities using Selenium to extract relevant job details from LinkedIn, ensuring that candidates have access to current opportunities. By streamlining the resume evaluation process, the application significantly reduces preparation time by 30%, allowing candidates to tailor their resumes more effectively and increase their chances of securing interviews. The development utilizes various libraries, including NumPy, Pandas, Streamlit, and LangChain, facilitating an intuitive user interface and efficient data handling. Overall, this application aims to empower job seekers with actionable insights and enhance their employment prospects in a competitive market.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Large Language Models (LLMs) , Natural Language Processing (NLP) , LangChain , Resume Parsing , Web Scraping , Selenium , Streamlit , Python , NumPy , Pandas , Data Analysis , Automation , Skill Summarization , Resume Evaluation , Job Matching , Career Guidance , Employment Optimization , Job Role Recommendations , Time Efficiency , Candidate Empowerment , Enhanced Job Applications

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Creative Commons Attribution 4.0 and The Open Definition

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This project explores the role of Artificial Intelligence (AI) in enhancing personalized digital marketing campaigns for Surya Information Solutions Pvt. Ltd. The study examines how AI technologies, such as machine learning algorithms, data analytics, and natural language processing, can be leveraged to create tailored marketing strategies that effectively meet the needs and preferences of individual customers. By analysing consumer behaviour, AI can help in predicting trends, segmenting target audiences, and optimizing content delivery across digital platforms. The research aims to evaluate the impact of AI-driven personalization on customer engagement, conversion rates, and overall business performance, providing insights into the potential benefits and challenges for Surya Information Solutions Pvt. Ltd. in adopting AI for their marketing initiatives.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial Intelligence (AI), Personalized Digital Marketing,Conversion Rates,Marketing Strategy.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The detection and maintenance of underground water channels, particularly for water flow monitoring and pipeline repairs, are critical for efficient urban water management. This project proposes an Arduino UNO based robot equipped with Doppler radar to detect water flow within underground water pipes. The robot's movement is controlled remotely using a Bluetooth interface, and the detected data is transmitted to a mobile app in real-time. In addition to water detection, a buzzer is activated to alert the operator when significant water flow is detected. The robot's mobility is achieved through DC motors, allowing for precise control of its movement via a mobile app. This system aims to provide an efficient, real-time solution for underground water pipe inspection and repair, minimizing disruption and improving water management operations.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Arduino UNO, Motor Driver, Water channel inspection, Doppler Radar.

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Creative Commons Attribution 4.0 and The Open Definition