Volume 13 | Issue 4 |

 Abstract

Diasporic literature has always been associated with the loss of identity, the quest for self, nostalgia, and uprootedness from culture and tradition. Home away from home has always been difficult, and now in this 21st century with all the technological advances, it still feels the same. With all these developments in science and technology, it is not always easy to come back to our homeland. With all the adjustments made by us away from home, we somewhere try to create our own identity in a new land. On the verge of creating our own identity, we tend to lose ourselves more. Diasporic writers such as Kiran Desai, Rohinton Mistry, V.S. Naipaul, Jhumpa Lahiri, and Chitra Banerjee Divakaruni have spoken about identity loss, and their stories are always about identifying and creating one's own identity. The Mistress of Species (1997) by Chitra Banerjee Divakaruni is the story of Tilo and her transformation into Maya, where she identifies herself. This paper will focus on the journey of Tilo and her experiences with other characters who are all lost in a foreign land. The magical effects of the spices helped the characters deal with their problems. When Tilo stepped out of her store to assist someone, she broke free from the chains tied to her which represents the society and the role of women who are always guided by society. It is true that in today's time we have come far away from these situations, but doesn't it still feel the same when a woman suddenly does something that she was not expected to do? Globalisation has made us all come together and has helped us to reach a post-modern world, but still creating our own identities and discovering ourselves will always be a continuation for all the upcoming generations.

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 Keywords

Indian Diasporic Writing, Self-quest, loss of identity, Nostalgia, Homeland.

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Creative Commons Attribution 4.0 and The Open Definition

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Over the years, youth entrepreneurship has become a key driver of innovation, employment, and economic development in India. Seeing its potential, the Government of India initiated the 'Startup India' program in 2016 to develop a strong ecosystem that fosters entrepreneurial culture among the youth. This paper discusses the role of such government programs in encouraging and maintaining youth-led startups throughout the nation. Based on an in-depth analysis of policy initiatives, financial support schemes, skill development initiatives, and regulatory changes launched under the Startup India initiative, the research assesses the effectiveness of these interventions among young entrepreneurs. It also analyzes the issues confronted by youth in availing benefits and regional variations in startup development. Based on secondary data, case studies, and current trends, the paper showcases the success of Startup India in empowering future generations of entrepreneurs while providing recommendations on how to further increase the inclusivity and sustainability of such efforts.

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 Keywords

Start-up India, youth entrepreneurship, government initiatives, innovation ecosystem, economic development

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Indian Micro, Small, and Medium Enterprises (MSMEs) are vital to the nation's economy, significantly contributing to GDP, exports, and employment. While globalization and digital transformation present opportunities for these enterprises to expand internationally, numerous challenges hinder their progress. This paper explores these obstacles, including lack of digital infrastructure, technological illiteracy, financial constraints, regulatory complexities, logistical inefficiencies, and difficulties in building trust in foreign markets. The paper presents a multi-faceted approach to address these challenges, encompassing policy reforms, capacity building initiatives, technological advancements, and financial support. It also studies the use of blockchain for secure transactions using Escrow Payment System, Artificial Intelligence for personalized customer engagement, and fintech solutions for seamless cross-border payments. By identifying key policy recommendations, technological interventions, and best practices, this study aims to contribute to the sustainable digital globalization of Indian MSMEs, ensuring their competitiveness in the ever-evolving global economy.

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 Keywords

MSME's, Exports, Logistics, Blockchain, Escrow Payment System

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Detection of Chronic Heart Failure (CHF) is essential for effective intervention and improved patient outcomes. Traditional diagnostic methods, including ECGs and echocardiograms, require specialized equipment and expertise, making them less accessible in resource-limited environments. Modern approach is that an automated CHF detection system leveraging machine learning (ML) techniques, specifically the Random Forest classifier, to analyze heart sound recordings (phonocardiograms - PCG) for easy and simple approach for detecting the CHF risk. This helps the patient to take the required precautions and avoid the risk of the CHF. This project reviews the detection of the CHF risk and helps accurately analyze Heartbeat of a person.

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CHF, ECG, PCG, Random Forest, Web Interface

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To detect and predict heart attack, we use the dataset from the UCI repository by the use of machine learning classifiers. Enhancing accuracy, facilitating early detection, and allocating healthcare resources as efficiently as possible are the objectives. Our main goal is to identify most suitable classifier which we can use for diagnostic applications. To predict heart disease, we use several machine learning approaches are used and compare their accuracy to get best result.This study discovered that the RF approach obtained 78% accuracy coupled with 100% sensitivity and specificity utilizing a heart attack dataset that we gathered from Kaggle three-classification based on k-nearest neighbor (KNN), decision tree (DT), and random forests (RF) algorithms. To construct a model training and testing of data evaluated. Accuracy, precision, recall, and F1-score were used to evaluate the models. With an accuracy of 80.33%, the SVM model outperformed the others.

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Creative Commons Attribution 4.0 and The Open Definition

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Concept of Rajdharma in ancient India and its study in the perspective of Indian constitution

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Concept of Rajdharma in ancient India and its study in the perspective of Indian constitution

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Creative Commons Attribution 4.0 and The Open Definition

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Blood group identification plays a pivotal role in critical medical applications such as blood transfusions, organ transplantation, and trauma care. Conventional methods for determining blood type involve invasive procedures, specialized reagents, and trained personnel--factors that often hinder immediate response in emergencies and limit access in remote or under-resourced environments. This project introduces an innovative, non-invasive method for blood group detection using fingerprint image processing. By leveraging the uniqueness of fingerprint biometrics and the capabilities of modern machine learning techniques, particularly Convolutional Neural Networks (CNNs), the system aims to classify blood groups (A+, A-, B+, B-, AB+, AB-, O+, O-) solely based on fingerprint patterns. This project aims to develop an automated blood group detection system using image processing and fingerprint analysis to provide a non-invasive and rapid approach. The proposed system integrates computer vision techniques using algorithms and AI to interpret images encompassing tasks like image classification, often leveraging deep learning models like Convolutional Neural Networks (CNNs) and biometric analysis to determine an individual's blood group.

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 Keywords

Blood Group Detection, Fingerprint Biometrics, Image Processing, Convolutional Neural Network (CNN),Minutiae Extraction

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The Murshidabad District Museum, located in Jiaganj in West Bengal, is an important cultural institution that presents the region's rich historical and artistic heritage, including artifacts from the Mughal and colonial periods. It is strategically located and has precious collections, but it still lags behind in attracting a substantial number of visitors. The key issues are low awareness, competition from other prominent local sites, transportation barriers, ineffective marketing, and lack of appeal to casual tourists. The museum's collections ranging from the 9th to the 13th centuries, provide a deep exploration of the region's cultural evolution. This paper explores these challenges and proposes strategies to enhance the museum's visibility and visitor engagement. Some of the suggestions include outreach in local media, better signage, transportation, online presence, interactive exhibits, and visitor facilities. All these approaches will allow the Murshidabad District Museum to reach more people, receive more recognition, and become an important cultural landmark in the area.

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Murshidabad District Museum, Cultural heritage, Tourism, Visitor experience, Marketing strategies, Public awareness.

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Creative Commons Attribution 4.0 and The Open Definition

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Artificial Intelligence (AI) is increasingly influencing high-stakes decision-making across various domains, particularly within the criminal justice system, where predictive risk assessment models inform decisions regarding parole, sentencing, and bail. The Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) tool has been widely utilized to assess recidivism risk; however, it has also faced scrutiny due to potential racial biases, especially its disproportionate false positive rates (FPR) for African-American individuals compared to Caucasian and Hispanic counterparts. This study conducts a fairness analysis of COMPAS-based predictions using machine learning models, evaluating the impact of different classifiers on racial groups and examining whether mitigation techniques can improve fairness outcomes. Our analysis employs Logistic Regression, Decision Trees, and Random Forest classifiers to evaluate the fairness of risk assessments. To address these biases, we implement fairness-aware adjustments, which progressively reduce disparities within each classifier. After mitigation, the FPR for African-Americans decreases by 11%, and the Disparate Impact Ratio improves significantly, reducing from 0.11 to 0.08 (Random Forest). These reductions indicate that fairness-aware methods can enhance equitable outcomes while maintaining model performance. We advocate for continued fairness interventions, policy regulations, and interdisciplinary efforts to ensure the responsible deployment of AI in real-world decision-making processes for high-impact applications such as criminal justice, where biased decisions can have severe consequences.

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 Keywords

Machine learning, AI Fairness, Bias Mitigation, Demographic Parity Difference, Sal.44889ocial Impact

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Creative Commons Attribution 4.0 and The Open Definition