Volume 12 | Issue 7 |

 Abstract

The World Economic Forum's 2024 Global Risk survey,highlighted concern towards increasing rates of dissemination of false news around the world as a significant concern in the digital age. In 2019, statistics report on fake news in India revealed that 51% of respondents are concerned about the twisting of facts and 32% worried about the impact of the manipulation of facts and the use of parody to spread the fake news.The present study focuses on the reasons for the spread of misleading propaganda in India and proposes mechanisms to combat such falsity using a non-doctrinal research methodology. The paper emphasized that significant challenges faced by individuals, society, economy, and the judiciary due to the widespread distribution of disinformation and misinformation by the intense media. It suggests that strengthening freedom of speech and expression may help to protect interest in society against dissemination of fake news in India.

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 Keywords

Fake News, Social-media, Misleading information, Misreport, False propaganda

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Creative Commons Attribution 4.0 and The Open Definition

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This study analyzes the monthly sunshine hours across five locations in Chhattisgarh, India, to determine if there are significant differences between them. The analysis is conducted using an ANOVA F-test in Microsoft Excel. The findings indicate no statically significant differences in sunshine hours among the locations.

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ANOVA, Excel, Renewable, Sunshine, Sun hours

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Lung cancer remains one of the leading causes of cancer-related deaths worldwide, necessitating the development of advanced diagnostic tools to improve early detection and treatment outcomes. This study introduces "DeepLungCareNet", a novel deep learning-based framework specifically designed for the prediction and classification of lung cancer from medical imaging data. Leveraging the power of convolutional neural networks (CNNs) and advanced image processing techniques, "DeepLungCareNet" aims to enhance the accuracy and reliability of lung cancer diagnosis. In this research, we utilized the IQ-OTH/NCCD Lung Cancer Dataset available from Kaggle, comprising thousands of labeled medical images, including computed tomography (CT) scans and X-rays. The dataset was preprocessed through a series of steps, including normalization, augmentation, and segmentation, to ensure optimal input quality for the neural network. Our model architecture was meticulously designed to capture intricate patterns and anomalies within the imaging data, utilizing multiple convolutional layers, pooling layers, and fully connected layers to extract and learn meaningful features. The performance of "DeepLungCareNet" was evaluated using various metrics, such as accuracy, precision, recall, F1-score, and support. Comparative analysis with existing state-of-the-art models demonstrated that "DeepLungCareNet" outperformed traditional methods, achieving superior results in both sensitivity and specificity. Additionally, we employed explainability techniques, such as Grad-CAM, to visualize and interpret the regions of interest that significantly contributed to the model's predictions, thus providing valuable insights for medical practitioners. Our findings underscore the potential of "DeepLungCareNet" as a robust tool for early lung cancer detection, offering promising implications for clinical practice and patient care. The proposed framework not only enhances diagnostic accuracy but also aids in reducing false positives and false negatives, ultimately contributing to better prognosis and treatment planning. Future work will focus on expanding the dataset, incorporating multi-modal data, and exploring transfer learning to further refine and validate the model's capabilities.

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 Keywords

DeepLungCareNet, lung cancer, machine learning, deep learning, medical imaging, convolutional neural networks (CNN), ResNet50, computed tomography (CT) scans, early cancer detection, Grad-CAM, diagnostic accuracy, privacy in AI, IQ-OTH/NCCD lung cancer dataset, Adam optimizer

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Creative Commons Attribution 4.0 and The Open Definition

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In today's medical landscape, understanding blood thinners is crucial for cardiac and neurological patients to mitigate the risk of life threatening complications like strokes or heart attacks. Knowledge of mechanisms, dosages, and potential side effects empowers patients, fostering better adherence and outcomes while minimizing adverse events. A study aimed to assess an individualized education program on blood thinners among 60 cardiac and neurological patients at a selected hospital. The objectives of the study were to assess the pretest knowledge regarding blood thinners among patients in a selected hospital, to determine the effectiveness of individualized education program on blood thinners among patients in a selected hospital and to determine the association between pretest knowledge score on blood thinners with their demographic variables in a selected hospital. Using a pre-experimental approach with non-probability purposive sampling, researchers evaluated patients' pre-test knowledge and demographic variables through structured questionnaires. And the data were collected and analyzed by using descriptive and inferential statistics. Results indicated varying levels of knowledge: 1.67% had average, 31.67% good, and 66.67% very good knowledge. Significant associations between pre-test knowledge and demographic variables were identified, highlighting the program's effectiveness in enhancing patient understanding of blood thinners.

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blood thinners, effectiveness

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Crystal structure, dioxole, methyl, single crystal X-ray diffraction and phosphinate; T = 295 K; R factor = 0.0523; wR factor = 0.1154; data-to-parameter ratio = 9.08.

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BHARATIYA PARAMPARAYAM VIVAHA SAMSKARANAM TULANATMAKA SAMIKSANAM

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BHARATIYA PARAMPARAYAM VIVAHA SAMSKARANAM TULANATMAKA SAMIKSANAM

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The rising demand for organic products online underscores the necessity for transparency and personalization. This project aims to develop an Android app featuring advanced rating and recommendation capabilities for organic items. Utilizing complex algorithms and machine learning, the app will analyze extensive data from trusted vendors and certification bodies, offering objective product scores based on purity, environmental impact, and ethical manufacturing. The app will provide personalized recommendations through user-friendly interfaces, enabling consumers to make informed choices aligned with their values. Users can rate, discuss, and evaluate products, enhancing the community-driven platform's knowledge and credibility. This innovative approach, integrating advanced analytics, user-centered design, and robust security, aims to revolutionize e-commerce by promoting sustainable purchasing practices and ensuring transparency in the supply chain. The app is expected to boost user engagement, simplify purchasing, and encourage eco-friendly behavior.

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Creative Commons Attribution 4.0 and The Open Definition

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Organic Products, Machine Learning, Personalization, Transparency, Sustainability, Recommendation System, User Engagement, Supply Chain

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Abstract: Introduction: Artificial intelligence (AI) has emerged as a transformative tool in healthcare, particularly in enhancing patient safety measures. By leveraging AI algorithms and data analytics, healthcare systems can proactively detect, prevent, and mitigate clinical-level patient safety concerns. This introduction outlines the significance of AI in revolutionizing patient safety practices, promising improved outcomes and reduced adverse events. Aim and Objectives: To utilize artificial intelligence (AI) to advance patient safety within healthcare systems by implementing innovative solutions and practices.This systematic literature review had the objective to find and evaluate quantitative research that addressed and reported clinical-level patient safety issues by integrating or using AI. Summary: Artificial intelligence revolutionizes patient safety by leveraging advanced algorithms for early detection of adverse events, personalized medication management, and clinical decision support, though challenges such as data privacy and algorithm bias require ongoing attention for effective implementation. Conclusion: According to this systematic review, if utilized appropriately, AI-enabled decision support systems can help to improve patient safety by facilitating better medication management, error detection, and patient stratification. To learn further about how effectively AI can predict safety outcomes in healthcare settings, further work is still required for these systems' strong validation in hypothetical and actual clinical scenarios.

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Artificial intelligence, Patient safety, Clinical-level, Quantitative studies, Systematic literature review

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Creative Commons Attribution 4.0 and The Open Definition

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The fear of persecution, physical harm or death from the warring parties, combined with insecurity owning to conflict forced Sri Lankan Tamil to leave their homes from 1970s to 2000s to India, a closet neighbour. Principally, the civil war between Sri Lankan security forces and the Liberation Tigers of Tamil Eelam (LTTE) impelled the Sri Lankan Tamil to seek refuge in the southern Indian state of Tamil Nadu. From around three lakhs refugees at one point of time, currently more than 52,000 Tamil refugees live in camps spread all over Tamil Nadu. Although Sri Lankan Tamil refugees are entitled to liberal welfare schemes of Tamil Nadu government and work permits, prolonged life in the camps does not lead to resilience and empowerment, and these refugees consider themselves in limbo, belonging neither to Sri Lanka nor to India and unable to get on with their lives. Elder Tamil refugees eager to return - wish to be disassociated with the label of 'refugee', while younger generation feel desire to settle in India or move to abroad as it would improve quality of life for them. On the whole, there are two sustainable options available to Sri Lankan Tamil refugees in India: repatriation or local integration. Resettlement option is the preferred one but the existing divisions and resentment between the two main ethnic communities and perceived anti-Tamil prejudice of Sinhalese leaders are obstacle in repatriation or return to their home. Other options is get citizenship and settle down in India, which some refugees wish to take it up but given the absence of official mechanism will force them to live further in appalling and subhuman condition as refugees. This paper examines issues related to Sri Lankan Tamils refugees in India and the humanitarian dilemma faced by them. The aim is to contribute to the understanding of how Sri Lankan Tamil refugees coping with post-war Sri Lanka and options available before them.

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 Keywords

Refugees, Sri Lanka, Tamil, India, Tamil Nadu

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Creative Commons Attribution 4.0 and The Open Definition