Volume 13 | Issue 5 |

 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

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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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 Keywords

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

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Creative Commons Attribution 4.0 and The Open Definition

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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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 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

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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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Knowledge, Behavioural problems, under five Mothers, Selected Community

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Creative Commons Attribution 4.0 and The Open Definition

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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

 Abstract

The integration of Artificial Intelligence (AI) in cybersecurity has transformed the digital defense landscape, fortifying systems against evolving threats and cyberattacks. This revolutionary approach combines machine learning, natural language processing, and automation to address the dynamic and complex nature of modern cyber threats. AI-powered systems offer unparalleled capabilities in proactive threat detection, real-time response, and predictive analysis, empowering organizations to identify and mitigate risks before they escalate. By analyzing vast volumes of data, AI detects anomalies, recognizes malicious patterns, and responds to threats faster and more accurately than traditional methods. Behavioral analytics further enhance security by monitoring user activity to detect deviations indicative of insider threats or unauthorized access. Automated response mechanisms reduce the time required for incident mitigation, improving overall system resilience. Additionally, AI strengthens endpoint security by integrating intelligent monitoring and adaptive measures to counter sophisticated attacks. However, the application of AI in cybersecurity is not without challenges, including algorithmic biases, adversarial attacks, and the need for human oversight to address ethical concerns. Despite these challenges, AI remains a critical enabler in strengthening digital defenses, fostering a robust security posture, and ensuring the reliability of digital infrastructures in an increasingly interconnected world. This paper explores the transformative role of AI in cybersecurity, discussing its advancements, applications, and the challenges that accompany its adoption, ultimately highlighting its potential to redefine digital armor in the face of escalating cyber threats.

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 Keywords

AI in cybersecurity, machine learning, threat detection, behavioral analytics, incident response, endpoint security, automation, digital defense, adversarial attacks, cybersecurity challenges.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

On February 28, 1966, the Mizo National Front (MNF) revolted against India's government, aiming to establish an independent nation-state for the Mizo people. In its initial response to the government's actions to quell the uprising in 1966, the Indian government undertook several counter-insurgencies including deployment of an air strike against Mizoram residents and MNF volunteers. National media coverage of the air attack and the Mizo people's experiences during this difficult period is minimal. Movies about this insurrection may be seen in Mizo regional cinema, although they seldom reach national audiences. Film, as a visual medium, is an effective way to transmit information. Several studies have demonstrated that films have the power to affect people and shift viewers' perspectives. With the help of qualitative textual analysis, this paper study how the insurgency issue has been portrayed in three selected Mizo language films V battalion (2006), Rise of the Heroes (2023) and Ambush (2023). The study examined the impact of insurgency and counter-insurgency activities on people in Mizoram. The findings indicated that citizens are constantly fearful of both militants and security forces, demonstrating how difficult it is for civilians to live a regular life amid the insurgency. The three films under examination present MNF volunteers as Indian army liberators and heroes. The Indian army's members are portrayed as being ungrateful, callous, and degrading, and they frequently hurt civilians. To seize power, they frequently merge settlements, set fire to villages, and subject locals to torture in exchange for information.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

insurgency, portrayal, regional film, Mizo films, representation study

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Creative Commons Attribution 4.0 and The Open Definition

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The comprehensive analysis of a E-Commerce Order Processing and performance, leveraging the robust visualization and analytical capabilities of Power BI and utilizing Microsoft Excel as the primary data source. The objective of this analysis is to provide actionable insights into key operational areas, including inventory management, order fulfilment, sales trends, customer behavior, and overall supply chain efficiency. By transforming raw Excel data into interactive dashboards and insightful visualizations, which can empower stakeholders with a clear understanding of current performance, identify areas for improvement, and facilitate data-driven decision- making to optimize retail operations and enhance profitability The methodology employed in this report involves a multi-stage process. Initially, relevant sales and supply chain data, encompassing transaction details, product information, inventory levels, shipping records, and customer demographics, were extracted and consolidated from various Excel work books

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Creative Commons Attribution 4.0 and The Open Definition

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Power BI , Microsoft Excel

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Creative Commons Attribution 4.0 and The Open Definition

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Abstract--Cloud computing transformed data storage and retrieval with scalable, cost-efficient, and flexible approaches. Data leakage is one of the most critical security threats that exposes confidential information to unauthorized users and cyber attacks. This paper introduces a full security framework based on AES-256 encryption, watermarking, and fake object insertion to safeguard cloud data. AES-256 encryption ensures data confidentiality, watermarking ensures traceability with identifiers, and insertion of simulated objects ensures a decoy to detect unauthorized access attempts. The system architecture, workflow, and algorithms suggested are compared to determine its utility to prevent data breaches. A comparative study of existing approaches is also carried out to study their feasibility and limitations. The results establish that encryption, watermarking, and decoy-based detection greatly enhance cloud security with insider threat prevention and accountability. This research is significant through the introduction of a robust and viable solution to data leakage prevention, thus supporting further development in cloud security solutions.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Cloud Computing, Data Leakage Detection, AES-256, Watermarking, Fake Object Insertion

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Creative Commons Attribution 4.0 and The Open Definition