Volume 13 | Issue 5 |

 Abstract

This paper introduces TextGuardianship, a Python and AI-based plagiarism detection tool that aims to provide a sound and effective means of detecting potential plagiarism. The system utilizes cutting-edge Natural Language Processing (NLP) methods and machine learning models, such as TF-IDF vectorization, cosine similarity, and semantic analysis, to scan and compare text data with high precision. Main aspects of TextGuardianship are the provision for both direct text uploading and PDF upload, allowing easy content scanning in its original format. The platform also boosts user comprehension through comprehensive reports identifying matched sources and offering similarity scores.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Plagiarism Detection, Natural Language Processing, Text Similarity, Python, TF-IDF, AI in Education

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The revolutionary impact of Question Answering (Q&A) systemsin contemporary e-commerce. These AI solutions deliver immediate and precise answers to customer inquiries, transforming the online buying process. By eliminating repetitive customer communication, Q&A systems lower operational expenses, enhance customer happiness, and liberate human agents to handle complex issues. The analysis identifies the critical impact of Q&A systems as fundamental elements of an effective e-commerce strategy, enabling businesses to succeed in a global digital market. These technology-based tools offer fast, accurate, and personalized responses to customer queries, revolutionizing the experience of online shopping. It basically uses the machine learning and natural language processing technology which it further uses to Utilization of Q&A systems assists in creating a good brand image, giving e-commerce companies an image of being customer-centric and technology- savvy. As customers want instant access to quality information, Q&A systems become crucial for online retailing companies.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Q & A, E- commerce, customer support, Information accessibility, Information Retrieval

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Creative Commons Attribution 4.0 and The Open Definition

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Alzheimer's disease (AD), the leading cause of dementia, remains a major public health challenge. Despite extensive research, its complex genetic, molecular, and environmental underpinnings are not fully understood. This review highlights the role of bioinformatics in integrating and analyzing multi-dimensional datasets from genomics, transcriptomics, proteomics, and neuroimaging to advance AD research. We outline core AD pathologies, including amyloid-beta plaques, tau tangles, and neuroinflammation, which drive neuronal dysfunction and cognitive decline. High-throughput genomic techniques, such as genome-wide association studies and next-generation sequencing, have identified key risk factors beyond the known. Transcriptomic methods, including bulk and single-cell RNA (Ribo Nucleic Acid) sequencing, reveal gene expression dynamics and cell-specific vulnerabilities. Integrated bioinformatics approaches, such as network-based analyses, aid in biomarker discovery for early diagnosis and targeted therapy. Emerging technologies like spatial transcriptomics and multi-omics signal a promising future, reshaping AD research and advancing precision medicine.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Alzheimer's Disease, Bioinformatics, Genomics, Transcriptomics, Biomarker Discovery, Precision Medicine

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The chemical industry is a vital contributor to global economic development; however, its processes often result in significant environmental challenges. Chemical production is responsible for pollution across air, water, and land while contributing to climate change and ecosystem disturbances. This paper examines the environmental impact of chemical manufacturing, explores emerging sustainable technologies, and presents strategies for reducing ecological damage through green chemistry principles and regulatory interventions.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

: Green chemistry, pollution, industrial waste management, chemical sustainability, environmental conservation.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Abstract-- This study analyses investor preferences towards collective fund investments at Cholamandalam Investment and Finance Company Limited ( Chola). With a sample size of 217 repliers, the exploration aims to understand investor mindfulness, geste , and threat perception related to collective finances. Traditional investment opinions are told by multiple factors similar as fiscal pretensions, perceived safety, request knowledge, and digital availability. The study adopts a quantitative exploration approach using structured questionnaires and statistical tools like SPSS for data analysis. crucial ways include descriptive statistics and chance analysis to identify trends and preferences. The findings indicate that the maturity of investors are relatively apprehensive of collective finances, with a preference for open- concluded schemes, daily investments, and expert fiscal advice. Digital platforms significantly impact decision- timber, while safety, diversification, and ease of access remain top precedences. The study also reveals that utmost investors are moderate threat- takers and believe collective finances outperform traditional options like fixed deposits in the long run. Grounded on the perceptivity, the study suggests enhancing investor education, perfecting digital investment tools, and strengthening premonitory services to boost participation and confidence. Overall, collective finances are seen as a stable and effective investment vehicle, especially when supported by professional guidance and strategic investor engagement in moment's unpredictable request terrain.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Investor's Preferences, Mutual Funds, Risk Perception, Financial Awareness

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This project focuses on building a crowdfunding platform powered by blockchain technology, tailored specifically to support educational financing. The proposed system ensures secure, transparent, and decentralized deals, barring the need for interposers. Blockchain ensures donor confidence by recording all transactions in a tamper-resistant ledger. Educational institutions or students can initiate fundraising campaigns, and donors may contribute using cryptocurrency. Through the use of smart contracts, funds are disbursed only when specified conditions are met, thereby safeguarding against misuse. The platform promotes financial accessibility and offers a trustworthy, fraud-resistant solution for educational funding.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

: Blockchain Technology, Crowdfunding, Educational Financing, Cryptocurrency, Secure Transactions

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This research paper provides a critical analysis of the best human resource (HR) practices adopted by the retail sector in the Marathwada region of Maharashtra, India, using only secondary data sources. With rapid retail expansion in India's Tier II and Tier III cities, HR management has become a key determinant of success. Drawing from published academic literature, industry reports, government data, and articles, this study explores practices such as recruitment, training, retention, performance management, and employee engagement. The study identifies challenges faced by HR departments and offers region-specific recommendations. The findings suggest a significant gap between ideal HR practices and their implementation in emerging markets like Marathwada.

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 Keywords

HRM, Retail Sector, Best Practices, Marathwada, Employee Engagement, Attrition, HR Challenges, Secondary Research

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Agricultural productivity is significantly affected by pests and diseases, necessitating early detection and monitoring systems. This paper presents the development of a Pest & Disease detection system integrating IoT sensors to enhance real-time monitoring. The system incorporates an Arduino-based setup with soil moisture sensors and DHT11 sensors for temperature and humidity monitoring, providing environmental parameters that influence pest and disease occurrences. A deep learning model is employed to analyze crop images and predict potential infestations using a generalized YOLOv8 model, which achieved an accuracy of 70.7% for pest detection and 50.7% for disease detection. The lower accuracy of the generalized model can be attributed to the high variability in pest and disease characteristics across different crops, leading to challenges in feature generalization and classification. To address this limitation, individual YOLOv8 models were trained for specific crops, resulting in significantly improved detection accuracy, with an average of 84.55% for banana, 78.93% for rice, 71.71% for sorghum, 93.58% for mango, and 89.31% for tomato. The higher accuracy of these crop-specific models demonstrates the effectiveness of specialized learning over a one-size-fits-all approach. The detected issues, along with sensor data, are displayed on a user-friendly interface, allowing farmers to make informed decisions. Additionally, the system provides recommendations for pest control based on environmental conditions and detected diseases. This work contributes to the advancement of smart agriculture by leveraging web-based platforms and IoT for early pest and disease detection.

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 Keywords

Pest detection, disease identification, IoT sensors, Arduino UNO, soil moisture, DHT11, deep learning, smart agriculture.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Background of the Study: Children's Behavioural development is a vital part of their growth, shaping their interactions, emotional regulation, and social relationships. As parents, it's crucial to recognize early signs of behavioural problems in children to ensure their emotional and psychological well-being of Children Objectives: 1.To assess the knowledge on Behavioural problems among under five mothers.2.To find the association between the knowledge on Behavioural problems among Under five mothers with their selected demographic variables .Research Design: Descriptive research design was adopted to the study. Setting: The study was conducted in selected community. Sample: 60 under five mothers who fulfil the inclusion criteria were selected as sample. Methodology: The study design was Quantitative descriptive research design was used for the study. The Convenient Sampling technique was used to select the sample. The data were collected by administering the structured knowledge questionnaire on Behavioural problems questionnaire to assess the level of knowledge among under five Mothers. Results: Among 60 respondents 15(25%) were adequate knowledge; 25(41.5%) were moderate knowledge; 20(33.3%) were inadequate knowledge Conclusion: The study concluded that under five Mothers have adequate knowledge on Behavioural Problems, they can help determine there is undiagnosed behavioural problems driving their action.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Knowledge, Behavioural problems, under five Mothers, Selected Community

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The rapid advancement of artificial intelligence (AI) has revolutionized language learning by introducing personalized and interactive approaches. This study explored the potential of AI powered virtual assistants in enhancing language learning efficiency. By integrating features, such as voice recognition, real-time text generation, grammar correction, and contextual analysis, these systems create an engaging and adaptive learning environment. Unlike traditional methods, AI-powered tools offer immediate feedback, enabling learners to identify and correct mistakes and fostering better retention and practical language usage. The incorporation of natural language processing (NLP) allows virtual assistants to understand and adapt to diverse accents, dialects, and proficiency levels, thus making them accessible to a global audience. Speech-to-text functionality helps bridge the gap between spoken and written communication, providing learners with insight into pronunciation and grammar. Moreover, gamified elements and progress tracking maintain motivation and ensure consistent improvements. Although challenges such as data privacy and algorithmic bias remain, the continuous refinement of these technologies promises to overcome such barriers. This study concludes that AI-powered virtual assistants have the potential to transform language learning by offering an efficient, personalized, and accessible solution for diverse learners, empowering them to achieve linguistic competence in an increasingly connected world.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial Intelligence, language literacy, foreign languages, online literacy, Ukrainian Universities

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Creative Commons Attribution 4.0 and The Open Definition