VOLUME 2, ISSUE 9, SEPTEMBER 2025

Abstract: Online education has transformed access to learning but often presents a key challenge: learner isolation. This paper examines the critical role of peer interaction in transitioning students from isolation to meaningful collaboration in virtual learning environments. By reviewing literature and analysing student feedback, the study identifies the benefits of peer engagement for academic performance, emotional well-being, and course completion. It also explores challenges such as uneven participation and technological barriers. Findings suggest that intentional design of peer-based activities significantly enhances student engagement and reduces dropout rates. The study concludes with actionable recommendations for institutions and educators to foster collaboration through structured peer interaction in online courses.

Abstract: The Hilbert Filter (HF) is designed to shift the phase of the input signal by 90 degrees.
The Hilbert Filter is used in many fields, most notably in communication engineering, especially in single side band SSB modulators and demodulators.
In these applications, two components of the audio signal or message signal are generated: the first component is ether the direct in-phase or sinusoidal component, while the second component is shifted by 90 degrees relative to the first. These is achieved using a digital FIR–Type Hilbert Filter.
In this paper, we propose the design and implementation mechanism for FIR digital HF based on the use of Cyclone II EP2C20F484C7 FPGA from ALTERA, placed on education and development board DE-1. The designed filter has the following parameters: -Clock frequency: FCLK=50 MHz.
-Sampling frequency: fsam= 50 MHz.
-Frequency range of the Hilbert filter (HF): (0.05…0.95) x fsam/2 = (1.25 MHz,….,23.75 MHz).
-Type of input signal is: sinusoidal of frequency: finp1=1.25 MHz, finp2=2.5 KHz, finp3=3.75 MHz, finp4=4 MHz. -The ROM capacity for the stored input signal samples is 8192X9 bits, and their values are positive within the range from (0 to 511).
-Frequency range: (0.001 Hz…25 MHz).
-Frequency Resolution: (0.001 Hz).
-Signal amplitude (5V).
Digital designs using FPGA allow the system to be modified and developed to obtain better results through reprogramming according to the user's desire.

Keywords: Hilbert filter, FIR, DDFS, FPGA.

Abstract: As digital platforms continue to shape consumer behaviour, the suitability of ordering food online has led to a surge in user-generated reviews. Interpreting user perspectives has become essential, and sentiment analysis offers a structured method to uncover emotions conveyed through textual data. A more granular technique, Aspect-Based Sentiment Analysis (ABSA), identifies sentiment specific to particular aspects of a service or product. This study introduces a lightweight, rule-based ABSA framework tailored to food delivery reviews. The model incorporates methods like dependency parsing and negation recognition to enhance sentiment detection. From a collection of 2,345 reviews, a subset of 40 was manually annotated to assess performance. The system achieved strong metrics in terms of precision, recall, and F1-score, representing its fitness for use in domains with limited annotated data or computational resources.

Keywords: Aspect-Based Sentiment Analysis, Dependency Parsing, Opinion Mining, Sentiment Polarity

Abstract: This research paper presents the development of an advanced Internet of Things (IOT)-based Intravenous (IV) Infusion Monitoring System, incorporating a Moving Average Filter (MAF) to optimize sensor data accuracy and reliability. The system is designed to facilitate real-time monitoring of essential physiological parameters, including IV fluid level, body temperature, and heart rate, thereby enhancing the scope and efficiency of patient care, particularly in remote or resource-limited settings. By integrating data filtering techniques, specifically the MAF algorithm, the system effectively suppresses random noise and transient fluctuations that typically degrade sensor performance, ensuring more stable and accurate readings. The system are simulated in Proteus, The proposed solution showcases significant potential for remote patient monitoring, smart hospital environments, and home-based healthcare systems, offering a cost-effective, scalable, and technically robust alternative to conventional infusion monitoring methods. The integration of wireless connectivity and real-time data processing further aligns the system with modern telemedicine and healthcare IOT frameworks.

Keywords: IOT, Vital sign Monitoring, Moving Average Filter, Arduino, Proteus, Data Filtering

Abstract: One of the main causes of liver-related morbidity in the world today is Fatty Liver Disease (FLD), which includes both Alcoholic Fatty Liver Disease (AFLD) and Non-Alcoholic Fatty Liver Disease (NAFLD). There are effective intervention and prevention of serious liver disorders, such as cirrhosis and liver cancer, which depend on the early and accurate detection of Fatty Liver Disease (FLD). Liver biopsies and other traditional diagnostic techniques are intrusive and frequently unfeasible for widespread screening. To enhance the prediction accuracy of detecting FLD through clinical, biochemical and imaging data, this study explores the application of Machine Learning(ML) techniques. The research investigates into advanced methodologies for processing medical imaging data, encompassing diverse data pre-processing strategies, feature selection approaches and model training techniques which include ensemble methods such as Random Forest, Gradient Boosting and XGBoost and deep learning techniques. Further regularization and resampling approaches are used to address the issues of over fitting and class imbalance. Higher accuracy, precision, recall, and F1 scores compared to conventional techniques show how the combination of strong feature engineering, hyper parameter tweaking, and sophisticated ML models greatly improves diagnostic performance. The present paper establishes the groundwork for further research in predictive health analytics and demonstrates the promise of ML-driven methods in clinical settings for non-invasive, precise and scalable FLD identification.

Keywords: Machine Learning, Supervised Learning Algorithms, Image Mining Techniques, Fatty Liver Disease, Accuracy.

Abstract: This study investigates the factors influencing employability readiness among final-year postgraduate management students in Tamil Nadu. A sample of 420 students from Alagappa and Manonmaniam Sundaranar Universities was surveyed using a structured questionnaire, with strong reliability and validity established through pilot testing and factor analysis. The results show that personal attributes, institutional support, and policy awareness significantly shape students’ perceptions of skill development, which in turn strongly determine their adoption of employability strategies. These strategies emerged as the most powerful contributors to employability readiness, highlighting the active role of student engagement beyond academic learning. However, challenges including financial constraints, digital limitations, and language barriers reduced participation, particularly among rural and low-income students. Gender differences favoured female students, while no significant differences were observed between universities. Overall, the findings emphasize the mediating role of perceptions and the systemic nature of employability barriers in Tamil Nadu’s higher education context.

Keywords: Employability readiness, Skill development, Student perceptions, Institutional support, Policy integration, Higher education in Tamil Nadu

Abstract: The Indian insurance sector is experiencing significant transformation driven by regulatory reforms, technological advancements, and evolving consumer preferences. Despite market liberalization and increased competition from private insurers, Life Insurance Corporation of India (LIC) continues to hold a dominant market share of over 61% in the life insurance segment (IRDAI Annual Report, 2024). This study examines LIC's performance in Hassan district, Karnataka, focusing on four key dimensions: insurance awareness and policy ownership patterns, customer satisfaction levels, competitive market dynamics, and digital service adoption. The research employed a quantitative approach using primary data collected through structured questionnaires from 100 randomly selected respondents across Hassan district, building upon the methodology for urban, semi urban and rural insurance market studies. Data was analyzed using descriptive statistics and correlation analysis to evaluate LIC's market position amidst growing competition from private insurers. The findings reveal that while LIC maintains strong brand trust and market presence, there are significant opportunities for improvement in digital service delivery and product customization. Customer awareness levels showed notable variations. The study contributes to the existing literature on regional insurance market dynamics by highlighting specific challenges in policy servicing and claim settlement processes, while identifying opportunities for enhancing customer engagement through technology integration. These insights provide actionable recommendations for LIC to strengthen its market position and improve service delivery in regional markets, contributing to the broader understanding of insurance sector dynamics in tire III districts like Hassan in India.

Keywords: Life Insurance Corporation (LIC), Insurance Awareness, Market Competition, Customer Satisfaction, Digital Transformation.

Abstract: A distributed collection of related and connected systems with additional resources made available on a pay- per-use basis is known as cloud computing. It has more to do with other computing technologies, such as autonomous computing, grid, and utility computing, among others. Because of its virtualization technology, the Cloud differs from other platforms, particularly the Grid. Cloud service providers (CSPs) like Google, Amazon, Salesforce.som, Microsoft, and others store and manage client data in their data centres. Cloud computing is entirely internet-dependent technology. CSP is in charge of keeping an eye on and maintaining the data that is outsourced. Data leaks, insecure interfaces, resource sharing, data availability, and insider attacks are just a few of the security risks and problems that can arise from having little control over the data. Security, availability, performance, lack of standards, higher usage costs, and difficulty integrating with on-premise IT resources are the first problems. Because they don't know where their data will be stored, when it will be available to them, or how it will be processed by others, many customers don't fully rely on the cloud environment. For these reasons, a large number of researchers focus on the cloud's "security." This study proposes a technique of confidentiality called Magic Square Obfuscation (MSO) to enhance data security and add complexity to the obfuscated text.

Keywords: cloud, data security, CSP, confidentiality, obfuscation.