Volume 13 | Issue 7

 Abstract

Authors use humour as a tool in their works to create amusement, laughter, and to break the monotony or boredom. To use this tool the authors either use isolated words or complete sentences. This is done to make the text more enjoyable to read. Humour is one of the most important tools that interests the readers and helps them to comprehend and connect with the text. Since the main purpose is to entertain the readers, the authors would sometimes coin new words. Ruskin Bond is a pioneer of children's literature in India. He is a versatile author because he has written in different genres. Nature has been the important theme in most of his works. The other theme that is found more often in his works is humour. He has used most of the humour elements in his autobiographical essays, written especially for children. This research expounds on Ruskin Bond's usage of different elements of humour taken from select works of his autobiographical essays, which can be compared to some of the humour elements found in the essays of G.K. Chesterton.

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 Keywords

Ruskin Bond, Humour elements, Humour, Autobiographical Essays

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This paper presents a Fake News Detection System designed using supervised machine learning techniques to classify news statements as real or fake. With the growing challenge of misinformation on digital platforms, there is a critical need for automated, efficient, and interpretable solutions to verify the credibility of news content. The proposed system employs a lightweight, text-based approach that converts news statements into numerical feature vectors using Term Frequency-Inverse Document Frequency (TF-IDF) with 1-4 gram representations. These feature vectors are then used to train a Logistic Regression classifier for binary classification. The system is implemented as a modular Python application comprising a training phase, which builds and evaluates the machine learning model on labeled datasets, and a prediction phase, which provides real-time verification through a simple command-line interface. Experimental results demonstrate that the system achieves a good balance between accuracy, computational efficiency, and interpretability. The proposed solution offers a practical tool for mitigating the impact of fake news and can be extended in the future with more advanced machine learning models, larger datasets, and deployment as a web or mobile application.

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Healthcare Chatbot, Disease Prediction, Decision Tree, Support Vector Machine, Symptom Analysis, PDF Report Generation, SQLite, AI in Healthcare, Virtual Triage, Command-Line Interface, Machine Learning, Severity Analysis.

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This paper presents an AI-powered Healthcare Chatbot that integrates rule-based reasoning with machine learning techniques to perform preliminary medical assessments based on user-reported symptoms. Designed to address the challenges of healthcare accessibility, particularly in remote and resource-constrained settings, the system leverages Decision Tree and Support Vector Machine (SVM) classifiers trained on curated medical datasets to predict probable diseases. Through a command-line conversational interface, users interactively describe symptoms and receive predictions, along with tailored recommendations including precautions, medications, and natural remedies. The system incorporates a secondary validation mechanism for enhanced diagnostic reliability and a severity analysis to advise on the urgency of professional consultation. Key features include secure storage of session histories in an SQLite database, auto-generation of detailed PDF diagnosis reports, and complete history management ensuring privacy. Unlike existing commercial health apps, this solution is interpretable, offline-capable, transparent in its predictions, and suitable for academic and educational use. Experimental results demonstrate the chatbot's potential as an intelligent virtual triage tool that empowers users to make informed health decisions while reducing unnecessary hospital visits.

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Healthcare Chatbot, Disease Prediction, Decision Tree, Support Vector Machine, Symptom Analysis, PDF Report Generation, SQLite, AI in Healthcare, Virtual Triage, Command-Line Interface, Machine Learning, Severity Analysis.

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Masanjore Dam is a big freshwater dam located in the dumka district of the Santhal pargana division belonging to Jharkhand State. This dam is built on the Mayurakshi river and serves as a boon for the santhal pargana region of Jharkhand state and some parts of West Bengal state. In this dam, we conducted a study of freshwater mollusks during the three season ( Pre-monsoon, Monsoon, Post-Monsoon) of 2024-2025 . In every season we collected a molluscs from two sites :- Jibanpur village area and Jhajhapara village area. We collected a total of 7 species belonging to 2 classes:- Gastropoda and Bivalvia ,5 families and 6 genera. Among them, Filopaludina bengalensis was found in highest numbers in all the season while Lymnaea accuminata was obtained in the lowest numbers.

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Mollusks, Genera, Season, Freshwater, Masanjore Dam, Santhal Pargana

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The present research, study on effect of social competence and emotional maturity among government and private school students. Social competency is the capacity to interact with others effectively, manage social situations with empathy and understanding, and maintain healthy relationships, whereas emotional maturity is the capacity to understand, regulate, and communicate emotions in a constructive and healthy manner. The sample consist of 120 students (60 boys & 60 girls). Data is collected with the help of Social Competence Scale and Emotional Maturity Scale. t-Test statistical technique is used for analysis of the data. According to the findings, there are notable differences in social competence and emotional maturity between students attending government and private schools, as well as between students who are boys and girls. Future studies on emotional maturity and social competence are crucial, particularly to understand the ways in which these characteristics impact academic performance, interpersonal relationships, and mental health. Analyzing developmental patterns, cultural influences, and intervention strategies can enhance emotional regulation, empathy, and flexibility, which will benefit clinical, educational, and organizational settings.

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Social Competence, Emotional Maturity, Government, Private, Students

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Mpox or monkeypox is zoonotic illness caused due to monkeypox virus (an Ortho poxvirus). Mpox is close relative of Variola(smallpox) virus. MPXV is a double-stranded DNA virus transmitted by droplet exposure via exhaled large droplets, direct contact (Sexual/Infected skin lesions, contagious materials such as towels, bedding) MPXV was first detected in 1958 in an outbreak of a vesicular disease among captive monkeys transported from Africa to Denmark for research purposes. The largest animal reservoirs of the virus have been found in squirrels, giant pouched rats. In Aug.1970 the first case of monkeypox was identified in 9-year-old child in the village of Bukenda in the equatorial region of Zaire (now DRC). Mpox is endemic in both Central Africa and West Africa. The recent outbreaks of MPXV have now occurred in several countries on almost all components. Symptoms of Mpox include Fever, Headache, Myalgia, Characteristics rash that develops into papules which evolve to vesicles, pustules and crusts in the genital, anal or oral regions. Diagnosis can be made through presence of Ortho poxvirus DNA in PCRs from lesion swab or body fluids. Patients with severe manifestations and people at risk of severe disease (e.g. immunosuppressed people) could benefit from antiviral treatment which includes (e.g. tecovirimat, brincidovir, cidofovir) and vaccinia immune globulin intravenous (VIGIV). JYNEEOS(TM)(live replication incompetent vaccinia virus) and ACAM 2000(live, replication competent vaccinia virus) are two vaccines currently available as medical counter measures for Ortho poxvirus such as monkeypox

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Monkeypox, Orthopoxvirus, democratic republic of Congo, Prodromal stage, PCR kit.

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The current study investigates the association of serious games (SGs) preference with cultural heritage, community, educational, and sustainable development (SD). Quantitative and qualitative research methods were used to achieve this objective, and data were collected using the survey technique. An original and historical HTML5 memory serious game was developed to give the audience at the museum a fast, informative look at the dinosaur era in Egypt. The study's sample consisted of 104 students residing in different countries. The reliability of the questionnaire was evaluated via Cronbach Alpha (0.953), showing data authenticity. The results obtained from the survey showed significant associations (P <= 0.05) of students' preference after playing that SG on community and educational development, cultural heritage (CH), and SD via chi-square test. The study concluded that SGs are now associated with community and educational development, SD, and CH. The results also concluded that it should be designed for a wider audience with low violence, positivism, responsible gaming, and educational value. Hopefully, the results of this study will help develop games incorporating users' recommendations for SGs.

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serious games; sustainable development; educational development; cultural heritage; serious games preference.

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The present design is based on the theory of planned behavior and tests the demography variable role in TPB's major variable. The study examined age, education, and income as independent variables and attitude towards chocolate buying intention and buyer behavior as dependent variables. Results showed no significant differences in buyer behavior among education and income groups. However, younger respondents showed higher buyer behavior. No significant differences were found in attitudes towards chocolate among different age and income groups. High-income consumer groups showed significant differences in buying intentions.

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Attitude, buying intention , buyer behavior

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Ayurveda is a life science dedicated to protecting human health. The eyes are vital sensory organs that are susceptible to harmful influences. Ayurvedic methods, known as Kriyakalpa, safeguard eye health, with Tarpana being a straightforward yet effective technique. This method strengthens and heals the eyes from various disorders.

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Sensory organ, Kriyakalpa, Tarpana

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Chronic Kidney Disease (CKD) is a critical health concern affecting millions globally, often progressing silently until it reaches an advanced stage. Early detection and accurate prediction of CKD can significantly reduce the risk of kidney failure, improve patient outcomes, and decrease the financial burden of long-term treatments like dialysis and transplantation. Traditional diagnostic methods are time-consuming, expensive, and sometimes inaccessible in remote areas. In response to these challenges, this project proposes a data-driven approach using machine learning to predict CKD at an early stage based on clinical and physiological parameters. The primary objective of this study is to develop and evaluate various machine learning models for predicting CKD using a publicly available dataset from the UCI Machine Learning Repository. The dataset contains 400 instances with 24 attributes, including age, blood pressure, blood sugar levels, serum creatinine, and more. The data was preprocessed to handle missing values, normalize numerical features, and encode categorical variables. Following preprocessing, multiple supervised learning algorithms were implemented and evaluated, including Logistic Regression, Random Forest, Support Vector Machines (SVM), K Nearest Neighbors (KNN), and Decision Trees. Model evaluation was performed using performance metrics such as accuracy, precision, recall, F1-score, and Area Under the ROC Curve (AUC-ROC). Among all models tested, the Random Forest classifier delivered the highest predictive accuracy and robustness. Cross-validation was used to ensure generalizability and avoid overfitting. Additionally, feature importance analysis was conducted to identify the most influential parameters contributing to CKD prediction. This project demonstrates the practical applicability of machine learning in the healthcare domain, particularly in predictive diagnostics. The outcomes of this study can aid healthcare professionals in early identification of CKD and can serve as a foundation for building intelligent diagnostic support systems. Future enhancements may include deploying the model as a web or mobile application, integrating real-time hospital data, and expanding the dataset for higher precision and clinical relevance.

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Logistic Regression, Support Vector Machines, Random Forest

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Climate is a critical determinant of agricultural productivity, influencing crop growth, livestock health, and overall farm output. In recent years, growing concerns about the impacts of climate change on agriculture have led to a surge in research focused on this issue. Much of the existing literature examines the potential physical effects of climate change, including variations in crop and livestock yields, and explores the resulting economic implications. This study provides a comprehensive review of the impacts of climate change on agricultural systems. It highlights the significance of human adaptation strategies, assesses potential regional disparities in agricultural vulnerability, and examines projected shifts in food production patterns and market prices in response to changing climatic conditions.

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Regional climate effects, Physical effects ,Adaptations,Agriculture, Climate change,

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This review explores the antidiabetic mechanisms of Pyrus pashia, such as the inhibition of key carbohydrate-hydrolysing enzymes (?-amylase and ?-glucosidase), enhancement of insulin secretion, and potent antioxidant and anti-inflammatory effects. In vitro and in vivo studies have demonstrated the plant's ability to lower blood glucose levels, improve glucose tolerance, and protect pancreatic ?-cells from oxidative damage.Pear fruits have been reported to contain abundant bioactive compounds and exhibit antidiabetic activity. In this study, pear (Pyrus pashia) fermentation broth was sequentially extracted by five solvents with increasing polarity (petroleum ether, chloroform, ethyl acetate, n-butanol, and water) to evaluate its antioxidant and hypothermic activities, and then the main compounds of the fraction with the highest activity were assessed, which might be responsible for such activities.Eleven monomeric compounds were seriatim examined for ?-glucosidase inhibitory activity to fish out anti-diabetic components. For the first time, this study delineated pear peel's anti-diabetic prospect of treatment for type 2 diabetes.

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Phyrus pashia , antidiabetic activity, diabetes

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Aquaculture is a vital sector in global food production, yet its success is heavily dependent on optimal water quality management. This research investigates the application of fuzzy modeling to assess and predict key water quality parameters in aquaculture systems. Recognizing the inherent complexities and uncertainties in aquatic environments, fuzzy logic provides a robust framework for integrating diverse parameters such as temperature, dissolved oxygen, pH, ammonia levels, and turbidity. Through the development of a comprehensive fuzzy model, this study aims to analyze the interactions between various water quality factors and their impacts on aquatic species health, growth rates, and overall system productivity. By employing fuzzy inference systems, the research enables better decision-making for aquaculture management, allowing stakeholders to adapt to fluctuating environmental conditions and optimize aquaculture practices. The findings highlight the effectiveness of fuzzy modeling as a tool for improving water quality monitoring and management strategies, ultimately contributing to more sustainable aquaculture operations. This approach not only enhances the resilience of aquaculture systems but also supports the long-term viability of aquatic resources in the face of environmental challenges.

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Aquaculture, Modeling, Fuzzy, Membership function, Analysis, Water quality

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This review article discusses the disruptive possibilities of no-code AI in closing the skills gap related to AI in the Software as a Service (SaaS) environment. These platforms democratize development of AI and enable non technical professionals, engineers, project and line managers, and business analysts to directly work with artificial intelligence tools creating more collaboration and innovation. Its paper discusses the advantages of no-code AI such as the faster development loop, economical, and even more accessible and compares it to other conventional AI development paradigms. The review demonstrates, via case studies, the benefits of the combination of different data sources through the no-code platform to create more efficient AI models and decision making. Their implications to the practitioners and policymakers are outlined, and it is conveyed that education, training, and universal technological policies are necessary. At the conclusion of the paper, recommendations as to the future research are provided, where the emphasis was made on the long-term effect of no-code AI systems, the potential of their cross-industry application, and the creation of the best practice frameworks, in which such systems can be deployed.

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No-code AI, AI skills gap, SaaS ecosystems, machine learning, democratization of AI, data integration, predictive models, software development, AI adoption, cross-functional collaboration, AI empowerment.

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Abutilon crispum, Anti-inflammatory activity, Phytochemical screening, Soxhlet evaporation, Aspirin.

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This article is the description of a machine learning system predicting chemotherapy response in patients on the basis of genomic and clinical data. One stacking ensemble model is constructed, that is, Random Forest and XGBoost classifiers are constructed, and Logistic Regression meta-learner is added to improve the accuracy of the predictions. Training and validation are carried on a synthetic dataset with 1,243 samples with 29 gene expression, and clinical attributes similar to real-life oncology data. A strong preprocessing pipeline -data imputation, encoding and feature scaling has been used in order to ascertain data quality. The proposed ensemble model performs well with precision, recall and F1-score and ROC AUC confirming its accuracy to be 99.50 on the test set. The system will also be deployed through a simple Streamlit web application and clinical inference will be in real-time. The study shows that it is possible to apply machine learning to the domain of personalized oncology decision support systems.

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Chemotherapy response prediction, machine learning, stacking ensemble, Random Forest, XGBoost, logistic regression, biomedical data, Streamlit, clinical decision support, personalized medicine. ______________________________________________________________________________________________________

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Welfare measures can take on both statutory and non-statutory forms. Statutory regulations compel employers to offer specific benefits to their employees beyond their regular wages or salaries. A study conducted on Labor the Public Sector Transport Corporation's Welfare Measures provide insight into the welfare policies implemented by this company. This research scrutinizes the various aspects of labor welfare measures provided to the workforce. It also addresses the laborers' perceptions and satisfaction levels regarding these welfare measures and suggests ways to enhance them within the Tamil Nadu State Transport Corporation. Industries in rural areas are more susceptible to labor-related issues. To mitigate these problems, it's imperative to prioritize employee health, safety, and welfare measures. Management must ensure that the facilities promised to employees are effectively delivered. This analysis aims to prevent potential by encouraging the use of personal protective equipment, ports and docks may reduce accidents and injuries. In rural industries, all levels of management must give paramount importance to welfare, health, and safety. They should engage with workers on-site to address their concerns and observe work processes and equipment. It's crucial to establish clear lines of responsibility from top to bottom in each workplace, ensuring that workers are informed about who is responsible for various health and safety matters. These efforts ultimately enhance workers' quality of life by alleviating their financial burdens. The workplace should offer essential amenities to meet the basic needs of employees. In this current study, we investigate how employee welfare facilities affect a manufacturing organization's productivity. Employees are the backbone of any organization and should benefit from various plans such as pension schemes, children's education support, and insurance. Moreover, the company's policies play a significant role in reducing social issues among employees, like substance abuse. In conclusion, while employees generally find the company's welfare facilities satisfactory, there is room for further improvement. Such improvements can boost efficiency, effectiveness, and productivity, helping the organization achieve its goals.

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Safety, Security, Welfare Measures, Awareness, Disputes, Chi-square Test

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This paper focuses on the goddess circle which got created in the state of Awadh in the 18 th century with its establishment. It brings out the fact that at the time when inequality and discrimination was reigning everywhere, when women were considered as a plaything in the hands of men, and expected to live within the confines of curtains, how the Begums of Awadh remained active in all the fields from politics to culture enjoyed a luxurious life, even overshadowed men like Nawabs, ministers, courtiers and lived not as a puppet but on their own terms independently.

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THE GODDESS CIRCLE: BEGUMS LUXURIOUS LIFESTYLE AND STELLAR CONTRIBUTION IN THE STATE OF AWADH

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Development of entrepreneurship is a modern and complex concept. It is the primary requirement for the development of any country. In the context of a developing country like India, development of entrepreneurship is a new concept. Various important problems of a developing country like unbalanced regional development, centralization of economic power, minimum productivity, unemployment, unprofitable investment, inefficient production, lack of industrial education and training etc. can be resolved only through entrepreneurship development programs. This is the reason why today the government of every country, especially the government of a developed country like India, is giving top priority to entrepreneurship development programs. The Government of India and the State Government are running various schemes and programs for the development of entrepreneurship. They are providing various types of inspiration and facilities and are creating various organizations and institutions for this work.

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Entrepreneurship, Entrepreneurs, Economic Growth, Development, Programme

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Abstract The academic review identifies the contribution of Information and Communication Technologies (ICT) and mass communication media in improving online learning and education, particularly during the post-pandemic period. To rely on international literature sources and recent empirical results, the paper will stress the relevance of blended learning models, the contribution of digital literacy, the importance of the learner engagement aspect, and the issues of infrastructure and pedagogy on online learning systems. It names several of the major pedagogical changes as flipped learning, ICT-mediated collaboration, and practice-centered instruction. The review also highlights the significance of psychological support and motivating actions to maintain the participation in the virtual environment. The study will help guided educators, policymakers, and technologists' best practices and omissions on the contemporary paradigm of digital education.

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Keywords: Online Education, Information and Communication Technology (ICT), Blended Learning, Digital Pedagogy, Learner Engagement.

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Creative Commons Attribution 4.0 and The Open Definition