Volume 13 | Issue 5 |

 Abstract

Cardiovascular diseases (CVDs) are a major global health concern, ranking among the top causes of death worldwide, and highlight the urgent need for strategies that enable early detection and prompt treatment. One effective approach for proactive cardiac health management is the ongoing monitoring of electrocardiogram (ECG) signals. This paper presents an affordable and portable ECG monitoring system that utilizes the ESP32 microcontroller along with a specialized ECG sensor. The heart's electrical activity is collected using the Arduino IDE, transmitted wirelessly, and displayed in real-time on a standard laptop through a custom HTML-based interface. The system focuses on real-time functionality, cost-effectiveness, and accessibility, making it a promising solution for personal health monitoring and remote healthcare, especially in areas with limited resources. The proposed system's affordability and simplicity aim to broaden access to essential cardiac health diagnostics.

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Creative Commons Attribution 4.0 and The Open Definition

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Wearing the appropriate protective gear is essential for worker safety and operational effectiveness in factories and other industrial settings. Manual checks are frequently labor-intensive and unreliable when used to enforce PPE regulations. Using machine learning and computer vision, this project presents an intelligent, automated system that watches live camera feeds and determines whether employees are wearing the necessary safety equipment, including gloves, helmets, vests, and protective eyewear. The system immediately flags the problem and records the incident for review if someone is discovered to be non-compliant. The solution's automation of this procedure lowers human error, enhances workplace safety, and fortifies compliance with safety rules.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Personal Protective Equipment (PPE), Safety Compliance, Machine Learning (ML), Computer Vision, YOLOV11, Real-time Detection, Workplace Safety, Object Detection, Industrial Monitoring, Automated Safety System

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Creative Commons Attribution 4.0 and The Open Definition

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Small and medium-sized enterprises (SMEs) are crucial to many economies, yet their operational efficiency is frequently hindered by constrained resources and a lack of technical expertise. Introducing the "Small-Medium Business Enterprises Manager App," this paper outlines the creation of a versatile, cross-platform solution designed specifically for SMEs. The app consolidates key business management features--such as invoicing, expense tracking, and financial reporting--into a single, cohesive platform.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Financial Reporting, Digital Transformation, Small and Medium-Sized Enterprises (SMEs), Business Management App, Inventory Tracking

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

ABSTRACT Knowledge about the uses of rubber tree was known to the people of Arunachal Pradesh since many years before. They used to collect its latex at early age from naturally grown Rubber Tree (ficuselastica species is replaced by Heveabrasiliensis. was planted in 1980 for experimental purposes and it came to be used for commercial purposes since 1994. The study area is the main latex production area in the state. The study covered about the constraints for the rubber cultivation in the area. As per survey a report, people of the area is much interested to grow rubb but due to lots of constraints they could not reach at target. To achieve the objectives of the study, many tools have been used. For the reliability, information collected from sources and the field was analyzed by using MS word, MS excel and SPSS 18 p generating appropriate qualitative and quantitative result. The information has been gathered through field investigation and 500 household from rubber cultivators were taken as sample. It is also found that 2,500.00 hectares (approx) of land un Knowledge about the uses of rubber tree was known Pradesh since many years before. They used to collect its latex at early age from ficuselastica) but this Heveabrasiliensis. First rubber was planted in 1980 for experimental purposes and it ed for commercial purposes since 1994. The study area is the main latex production area in the state. The study covered about the constraints for the rubber cultivation in the area. As per survey a report, people of the area is much interested to grow rubber but due to lots of constraints they could not reach at target. To achieve the objectives of the study, many tools have been used. For the reliability, information collected from sources and the field was analyzed by using MS word, MS excel and SPSS 18 package for generating appropriate qualitative and quantitative result. The information has been gathered through field investigation and 500 household from rubber cultivators were taken as sample. It is also found that 2,500.00 hectares (approx) of land under rubber plantation with 580 hectare under latex production.

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Creative Commons Attribution 4.0 and The Open Definition

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Keywords: Natural Natural rubber, rubber, plantation, transformation, Ficuselastica, hevea-brasiliensis, transformation, Ficuselastica, hevea constraints

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Creative Commons Attribution 4.0 and The Open Definition

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In the age of digitalization, Big Data has now emerged as a powerful tool for achieving innovation in customer segmentation and personalization. This paper outlines the manner in which Big Data analytics, together with machine learning models, can greatly enhance business strategies focused on customer engagement. A blend of literature review, primary research, and data-driven analysis using Python highlights the ability of clustering algorithms to identify distinct customer segments. In addition, the paper discusses the challenges of implementing Big Data strategies, such as data integration, privacy issues, and skills shortages. The paper concludes with strategic recommendations for utilizing Big Data to gain deeper customer insights and competitive advantage.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Big Data, Segmentation, Personalization, Machine Learning, K-means Clustering.

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Creative Commons Attribution 4.0 and The Open Definition

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Brand affinity is a crucial concept in marketing that reflects the deep emotional connection between customers and a brand. Unlike brand awareness or brand loyalty, which focus on recognition and repeat purchases, brand affinity measures the strength of a customer's personal identification and attachment to a brand. It signifies a relationship where customers not only prefer a brand but actively advocate for it, integrate it into their lifestyles, and develop long term loyalty. The findings can help develop more effective and emotionally resonant branding strategies that foster deeper customer relationships and enhance brand equity. Customer satisfaction surveys and feedback mechanisms play a crucial role in gathering insights to support these strategies. This shift underscores the growing importance of customer brand affinity an emotional connection that goes beyond mere satisfaction or loyalty. Despite its significance, there is limited empirical research on the specific factors that drive brand affinity and how it influences consumer behaviour. understanding the concepts of branding growth of consumer durable industry importance of brand positioning and impact on brand loyalty. factors influencing the brand preference of consumer durable products: It impact of customer brand affinity on purchasing behaviour and long-term loyalty. The findings demonstrate that emotional connections, trust, and perceived brand value are key factors driving strong brand affinity This research study aimed to recognize the assumption of consumers on the numerous aspects that affecting brand positioning and assess the influence of brand placing on brand name commitment, customer loyalty

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Customer Loyalty, Financial Services, Financial Services, Financial Products, Non-Banking Financial Company (NBFC), Customer Retention, Financial Services, Brand Equity, Emotional Attachment

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Exporters face significant challenges in managing credit risk due to the complexities of international trade, including currency fluctuations, political instability, and varying regulatory environments. This study examines the financial stability of exporters across different regions, emphasizing the role of credit risk management policies. Using primary data collected from exporters, key credit risk mitigation tools such as credit insurance, letters of credit, and government-backed schemes are evaluated for their impact on financial stability. Statistical methods, including descriptive analysis, correlation, and factor analysis, are employed to uncover relationships between financial stability and regional dynamics. The findings reveal that credit insurance significantly enhances financial stability, while letters of credit and government schemes offer moderate support. Regional comparisons show minimal variation in financial stability scores, indicating consistent effectiveness of credit policies across diverse export regions. The study highlights critical insights for policymakers and financial institutions to refine credit risk strategies tailored to exporters' needs, ensuring resilience and growth in global trade markets.

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Creative Commons Attribution 4.0 and The Open Definition

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Credit risk policies - financial stability - exporters.

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Creative Commons Attribution 4.0 and The Open Definition

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This project introduces a cutting-edge Multiple Disease Prediction System that leverages advanced machine learning algorithms to provide accurate and efficient health risk assessments for multiple diseases such as diabetes, heart disease, and Parkinson's disease. Developed using Python and deployed through a Streamlit-based web interface, the system allows users to input personalized health parameters and receive instant, data-driven predictions. By moving beyond traditional single-disease models, this integrated approach offers improved prediction accuracy, comprehensive health evaluation, and greater convenience in preventive healthcare.The system's modular design allows for easy scalability, enabling the addition of more disease models in the future. The frontend offers an intuitive and responsive user experience, while the backend employs pre-trained models stored in serialized .sav files for quick loading and real-time processing. In addition to its predictive capabilities, the application incorporates a pill reminder system, which notifies users of their prescribed medication schedules, thereby promoting medication adherence and enhancing treatment outcomes.This holistic approach not only facilitates early diagnosis and timely intervention but also encourages proactive health monitoring. The lightweight and cross-platform nature of the system ensures compatibility with various devices, making it accessible to a wide user base. This project represents a significant advancement in digital healthcare solutions, aligning with modern demands for remote, AI-powered medical tools and contributing to improved patient care and public health outcomes.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Multiple Disease Prediction, Machine Learning, Streamlit, Python, Healthcare Technology, Predictive Analytics, Digital Health , User Friendly Interface, Real-Time Prediction, Health Management System, AI in Healthcare, Health Risk Assessment, Medical Adherence.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The rapid increase in the generation of solid and hazardous waste due to continuous economic growth, urbanization, and industrialization has become a critical challenge for both national and local governments. In 2006, municipal solid waste generation worldwide was estimated at 2.02 billion tonnes, reflecting a 7% annual increase since 2003. Improper handling, segregation, and disposal of this waste pose significant risks to public health, the environment, and resource recovery. In many countries, especially India, waste segregation is still carried out manually by rag pickers, exposing them to hazardous materials and severe health issues such as infections, cancer, and heavy metal poisoning.To address these issues, this paper proposes the development of an Automated Waste Segregator (AWS)--a cost-effective, user-friendly system designed for household use. The AWS utilizes sensors and microcontrollers to automatically sort waste into three categories: metallic waste, wet waste, and dry waste. A moisture sensor is used to identify wet waste, a metal detector sensor for metallic items, and an IR/plastic detection sensor for dry non-metallic waste like plastic. The system is powered by an Arduino Uno and uses servo motors for mechanical segregation. The segregated waste is deposited into designated bins. When bins are full, notifications can be sent to authorized personnel and uploaded to the cloud, improving waste collection efficiency.This automated system not only minimizes health risks and manual labor but also enhances recycling potential and environmental sustainability. The proposed design aims to make household-level waste segregation accessible, practical, and scalable for broader municipal waste management systems.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Waste Management, Arduino Uno, Metal Detector Sensor, Moisture Sensor, Plastic Detection, IR Sensor, Servo Motor, LCD Display, Solid Waste, Wet Waste, Dry Waste, Hazardous Waste, Sensors, Cloud Notification, Environmental Sustainability, Household Waste Segregation, Public Health Safety, Municipal Solid Waste, Hospital Waste

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Creative Commons Attribution 4.0 and The Open Definition

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This study examined the role of cognitive and emotional strength in enhancing performance among elite boxers, focusing on mental resilience, decision-making, and emotional regulation. Fifty boxers (aged 18-30) were randomly assigned to an experimental group, which underwent a 12-week psychological training program, or a control group. Assessments using the Mental Toughness Questionnaire (MTQ48), Emotional Regulation Scale (ERS), and match performance analysis revealed significant improvements in mental toughness, emotional regulation, and decision-making under pressure in the experimental group (p < 0.01). Gender analysis showed no differences in cognitive outcomes, though females demonstrated higher emotional regulation post-intervention (p < 0.05). The findings support the integration of structured psychological training into boxing programs to enhance both cognitive and emotional skills, with attention to potential gender-specific needs

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Cognitive Strength, Emotional Regulation, Elite Boxing, Mental Toughness, Psychological Training, Gender Differences.

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Creative Commons Attribution 4.0 and The Open Definition