Volume 14 | Issue 2 |

 Abstract

The aims to examine parents awareness, perception, and buying behaviour regarding organic baby care products. The study focuses on understanding the factors influencing purchase decisions, the level of awareness among parents, and the impact of demographic variables such as age, gender, education, and income on their purchasing patterns. The research is based on primary data collected from 100 respondents in Coimbatore City using a structured questionnaire. Statistical tools such as percentage analysis, Chi-square test, and One-Way ANOVA were employed to analyze the data. The findings reveal that the majority of respondents are young female parents below 25 years with diploma-level education and income below INR30,000. Although awareness about organic baby care products is high, the level of detailed knowledge remains moderate. Natural ingredients and environmental friendliness are the major factors influencing purchase decisions, while high price, limited availability, and lack of clear information act as significant barriers. The study also indicates that income significantly influences the type of organic baby care products purchased, and education plays a significant role in shaping parents' reasons for usage and their belief that organic products contain fewer harmful chemicals. The study concludes that while there is strong market potential for organic baby care products in Coimbatore City, improving affordability, accessibility, and product awareness is essential to enhance purchase behaviour and encourage long-term adoption among parents.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Parents Satisfaction, Parents Experience, purchase behavior, product awareness and satisfaction level of parents.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The agricultural productivity is highly dependent on the climate variability, soil conditions and the management of the resources. Application of conventional decision making has led to the wrong choice of crops, poor yield forecasting and high risk of financial losses among the farmers. To solve these dilemmas, the proposed paper introduces a smart Farm Decision Support System (FDSS) that has been modeled based on the machine learning algorithm, the random forest algorithm. The suggested model is a multi-dimensional analytics system that analyzes farm data such as the soil characteristics, rain distributions, temperature fluctuations, seasonal shifts, the use of fertilizers and past records of crop yield. The classification and regression tasks are performed with the help of the Random Forest algorithm to suggest appropriate crops, predict the performance of yield, predict the amount of fertilizer required, and recommend the profit potential. Because of an ensemble learning, Random Forest improves the predictive accuracy, reduces overfitting, and increases the robustness of the model in different environmental conditions. An easy-to-use dashboard system allows the farmer and the agricultural officers to enter the parameters of the fields and get real-time and data-driven recommendations. The effectiveness of experimentation proves the better accuracy of predictions and more reliable decisions, in comparison to traditional approaches. The proposed system helps in precision agriculture by ensuring that resources are utilized in the most efficient way, less risk is taken, and sustainable farming methods are ensured using artificial intelligence-based analytics.

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 Keywords

Precision Agriculture, Farm Decision Support System, Machine Learning, Random Forest Algorithm, Crop Recommendation, Yield Prediction, Fertilizer Optimization, Profit Estimation, Sustainable Agriculture, Data-Driven Farming

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Kamala Markandaya's Nectar in a Sieve occupies a central place in Indian English fiction for its profound depiction of destitution, famine, and the dislocation of rural communities. Told through the voice of Rukmani, a poor village woman, the novel reveals how entrenched social inequalities, environmental volatility, colonial-era economic pressures, and gendered responsibilities shape the fragile realities of survival. This study explores how the text represents poverty and hunger as more than economic hardships; they function as existential forces that shape identity, dignity, interpersonal bonds, and emotional endurance. By situating the story within the historical and socio-economic conditions of mid-twentieth-century India, the paper illustrates how Markandaya foregrounds the subtle, private anguish of ordinary individuals to expose the pervasive nature of systemic oppression. The analysis argues that the novel's lasting influence lies in its ability to humanize deprivation while simultaneously exposing its devastating effects, offering insights into resilience, moral ambiguity, and the quiet forms of resistance that hunger produces. Through literary interpretation, historical context, and critical engagement, this paper provides a detailed examination of how poverty and hunger operate both literally and symbolically in Nectar in a Sieve, highlighting the novel's enduring significance as a powerful commentary on social inequity.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

deprivation, resilience , moral ambiguity , resistance , social inequity.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Bacteriocins are considered a blessing for the food industry. They act as a preservative to inhibit the growth of bacteria in the food items during the production or preservation process. These bacteriocins are produced by the bacteria, which reduces the growth of closely related bacterial strains. Food safety has become a serious issue in the present era for which bacteriocins, an antimicrobial compound, can effectively fight against food-pathogenic bacteria. Different views of the actions of bacteriocins have been elaborated. Bioengineered bacteriocins, or the hurdle approach to bacteriocins, can increase the efficacy of preservation and reduce the resistance effect against bacteriocins. They are mostly produced by LAB (Lactic acid bacteria), which is naturally present in most food. They can also be combined with antimicrobial proteins, agents, and natural phenolic compounds. The combined approach can act as a barrier to developing bacteriocin-resistant strains. It can also be combined with physical treatments like pulse electric fields and high-pressure processing by which foods can be more effectively preserved. This chapter summarizes and focuses on the importance and potential applications of bacteriocins as a food preservative in the food industry.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Bacteriocins, Bio preservative, Lactic acid bacteria, Hurdle technology

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Creative Commons Attribution 4.0 and The Open Definition

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In recent years, social media platforms have emerged as critical sources of data, providing rich and diverse in formation about public opinions, behaviors, and emotions. Sentiment analysis and emotion detection in social media text have gained considerable attention, serving as essential tools for understanding user sentiments and emotional responses. This survey explores the advancements and methodologies employed in sentiment analysis and emotion detection, focusing on various techniques including rule based, machine learning, and deep learning approaches. It highlights the challenges in here not in analyzing informal and context-rich social media language, such as slang, emojis, and multi lingual content. Additionally, the survey discusses the role of pre-processing strategies, feature extraction methods, and the effectiveness of different models like LSTM, BERT, and Transformer based architectures in capturing complex emotional nuances. The paper also examines the applications of these techniques in diverse domains such as brand monitoring, crisis management, and mental health analysis. By providing a comprehensive overview of the existing literature and emerging trends, this survey aims to guide future research directions and improvements in sentiment analysis and emotion detection in the dynamic context of social media text.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

LSTM,CNN,NLP,SENTIMENT ANALYSIS

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Creative Commons Attribution 4.0 and The Open Definition

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Diabetes mellitus is a chronic metabolic disorder that continues to pose a major global health challenge due to its rising prevalence, long-term complications, and significant economic burden. While traditional therapies such as oral hypoglycemic agents and insulin have long served as the foundation of diabetes management, recent advancements in pharmacological innovation, technology-driven monitoring, and predictive analytics have transformed modern care. Emerging drug classes--including SGLT2 inhibitors, GLP-1 receptor agonists, and next generation insulin analogues--offer improved glycemic control along with cardiovascular and renal protection. Complementary technological innovations such as continuous glucose monitoring systems, insulin pumps, and closed-loop artificial pancreas devices enhance precision, reduce glycemic variability, and promote patient-centred care. Additionally, machine learning-based predictive models support early detection, risk stratification, and personalized treatment planning. Despite these improvements, challenges remain regarding cost, accessibility, long-term safety, and technological literacy. This review highlights recent advances in pharmacological, technological, and analytical strategies, discusses their limitations, and outlines future perspectives in achieving personalized, efficient, and equitable diabetes management.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Diabetes Mellitus, Type 2 Diabetes, SGLT2 Inhibitors, GLP-1 Receptor Agonists, Insulin Therapy, Continuous Glucose Monitoring, Insulin Pumps, Artificial Pancreas, Machine Learning, Predictive Analytics, Pharmacological Advances, Diabetes Technology, Precision Medicine, Glycemic Control, Non-Pharmacological Interventions.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

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comprehensive review on performance of lithium-ion batteries under high-altitude, sub-zero environmental conditions

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The Bone Age Prediction System is a web based tool that was developed to improve the management of bone age determination within a clinical setting. Typical measurement techniques utilize a laborious process that calls for manual interpretation of X-ray photographs combined with extensive subjective judgment by experts in order to derive the final evaluation of bone age. Using Firebase for backend functionality, including authentication and cloud based data storage, and using Streamlit as the front end user interface, the proposed tool gives a secure digital method of overcoming the limitations. The proposed system consists of three main modules, Administration, Patient, and User. Using the User Module, authorized healthcare staff can securely log into the database to manage patient information and maintain records of bone age. Using the Patient Module, patients can receive electronically generated PDF reports automatically and also access information regarding patients individual reports. Administering of the User Module is through the Administration Module. The additional support within the tool for visual analytics and data monitoring can assist with healthcare decision support. Firebase provides scalable, cloud based storage and allows data to be synchronized in real time while supporting secure authentication, providing for efficient and dependable operation of the proposed tool. The Bone Age Prediction System demonstrates how modern web based technology has the potential to improve workflow for healthcare data systems, particularly with the potential to implement machine learning based automatic prediction of bone age as well as cloud based remote.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

CNN, Deep Learning, Hand Wrist X-ray, Medical Image Analysis, Bone Age Prediction

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Creative Commons Attribution 4.0 and The Open Definition

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Environmental risk has emerged as a critical factor influencing credit rating assessments globally. As climate change intensifies and sustainability becomes central to financial decision-making, credit rating agencies are increasingly integrating environmental metrics into their evaluations. This paper explores the relationship between environmental risk and credit rating dynamics using secondary data and literature analysis. It highlights how environmental performance, carbon emissions, and climate vulnerability affect creditworthiness across sectors and regions.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Environmental Risk, Credit Rating, ESG, Climate Vulnerability

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The rapid expansion of blockchain technology and decentralized finance (DeFi) has significantly increased the adoption of smart contracts, particularly on platforms such as Ethereum. Smart contracts are autonomous, self-executing programs deployed on blockchain networks to enforce agreements without intermediaries. Despite their advantages in transparency and automation, vulnerabilities in smart contract code can lead to severe financial losses and system compromises. Historical security incidents, including the The DAO exploit, demonstrate the critical importance of proactive security auditing before deployment. The Smart Contract Vulnerability Auditor is an automated analysis framework designed to detect and mitigate security flaws in blockchain-based smart contracts. The system employs static code analysis, dynamic testing, and pattern-based vulnerability detection techniques to identify common threats such as reentrancy attacks, integer overflow and underflow, access control misconfigurations, unchecked external calls, gas inefficiencies, and timestamp dependencies. By integrating with popular development environments and blockchain testing frameworks, the tool enables real-time vulnerability assessment during the development lifecycle The proposed solution generates detailed audit reports that categorize vulnerabilities based on severity levels and provide remediation suggestions aligned with secure coding standards. This approach reduces reliance on time-consuming manual audits, minimizes human error, and enhances overall contract reliability. Additionally, the system supports scalability to audit multiple contracts efficiently and can be extended to support other blockchain platforms. Overall, the Smart Contract Vulnerability Auditor strengthens decentralized application security by providing an efficient, reliable, and automated auditing mechanism for secure smart contract deployment.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Blockchain, Ethereum, DAO, Dumb, Gesture recognition, Deaf, Flex.

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Creative Commons Attribution 4.0 and The Open Definition