Volume 12 | Issue 6 |

 Abstract

The birth of a daughter in an Indian household triggers a complex interplay of emotions and cultural norms. This paper explores the nuanced dynamics of the mother-daughter relationship in Indian literature, focusing on Manju Kapur's Difficult Daughters and Arundhati Roy's The God of Small Things. Through an analysis of the protagonists Virmati and Ammu's experiences, it aims to address a research gap concerning personal development, identity formation, and familial dynamics. Utilizing Showalter's feminist theory and contemporary research on mother-daughter bonds, the paper investigates how maternal influence shapes protagonists' choices and self-perception. It scrutinizes the impact of societal expectations and patriarchal norms on familial dynamics, particularly in romantic decisions. Moreover, it examines instances of rebellion against traditional gender roles and their repercussions within the family. By delving into familial interactions and external relationships, the study unveils the broader societal implications of protagonists' experiences. It enriches the discourse on familial influence, romantic decisions, and gender dynamics in Indian society, offering a nuanced understanding of the intricate relationships shaping individual identity and societal norms.

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 Keywords

mothers, daughters, feminism, gender dynamics

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Sarso Fasal Ki Lagat v Aay Ka Anya Nakdi Faslo Ki Lagat Aay Ke Sath Tulnatmak Adhyan

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Sarso Fasal Ki Lagat v Aay Ka Anya Nakdi Faslo Ki Lagat Aay Ke Sath Tulnatmak Adhyan

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Children's safety and well-being are vital considerations in any society, and law enforcement organisations play a critical role in protecting children from crime on a nationwide scale. This paper provides an overview of a comprehensive study that investigates the diverse function of police in numerous elements of child protection. The study investigates the many functions of the police force in dealing with crimes against children, such as prevention, intervention, and response. It dives into the legislative and policy frameworks that regulate police child protection actions, emphasising the need of coordination with other stakeholders such as social agencies, schools etc. Furthermore, the study examines the problems that law enforcement organisations confront in their efforts to safeguard children from crime, such as resource constraints, training requirements, and the developing nature of threats in the digital era. The paper also emphasises effective tactics and best practises by police for increasing child protection efforts and improving outcomes.

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Child Crime, Police, Role of Law Enforcement, Best Strategies.

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Chest X-ray (CXR) pictures are essential for the diagnosing various respiratory diseases, but accurate and efficient disease classification remains challenging. This project proposes a novel approach for disease classification from CXR pictures by employing CNN's or Convolutional Neural Networks with sophisticated algorithms including Efficient Net, Resent, Dense Net, CNN, and VGG. By utilizing these algorithms, our system seeks to enhance the efficiency and accuracy of disease classification, enabling timely diagnosis and treatment planning. Through comprehensive testing and evaluation, we prove that our method is efficient in accurately classifying chest diseases, paving the way for enhanced healthcare diagnostics and patient care.

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Efficient Disease Classification from Chest X-Rays Using CNN with DenseNet-201

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The contributions of control systems and information technology are important in the functioning of today's world. Further, these systems have brought changes in communication modalities that have been no less profound. Combining industrial safety concepts with the powerful communication features of Internet, feasible network solutions can be designed using either wired or wireless networks. In these networks, Web based remote Safety is classified into different categories based on the research directions such as major research, Safety the process/ operations with the web enabled platform, process Safety with control features over the web platform and Safety, control as well as reporting and fault diagnosis features. The web enabled Safety and control platform have many advantages for the engineering community as well for the business community such as Safety the process and operational performance of the entire system from a remote location, providing expert support for performance optimization and minimizing the downtime between failures on the system control level or plant level. Recent research attempts that have been made in the web-based platforms for applications at different levels are in the areas of educational laboratory experimental studies, industrial applications etc., where the operations are required to be continuously monitored, controlled and fault diagnosis to be done away from plant locations. However, detailed analysis of various web-based remote Safety and control systems shows that most of the difficulties exist in architecture, data transfer, web-based graphical user interface, multiple user access, security and safety.

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Safety, World Wide Web, Supervisory Control and Data Acquisition.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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The employment nature in the insurance sector is extremely challenging due to acute flexibility through resource management. Since, mostly for all employees, the sources of job reward remain limited to the salary only expectation to gain incentives and create a threatening atmosphere of competition and loss of job security. This situation creates job stress and brings decline in performance and lack of commitment towards job. As the insurance sector is dynamic in nature, economic fluctuations can create uncertainty. The main object of this study is to investigate the relationship between job stress and organizational commitment among insurance sector employees in palakkad district. The study aims to provide insights into the challenges faced by insurance sector employees. A sample of 93 employees was selected by stratified sampling method. The findings of the study reveal that there is significant negative relationship between job stress and organizational commitment among insurance sector employees.

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resource management, insurance sector, customer expectation, job stress, organisational commitment

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

???? ?????? ???? ?? ??? ??? ?????? ?? ?????? ???? ?? ??????? ?? ????? ?? ????? ??? ??????? ??????? ??? ??????? ???? ??, ???? ????? ?? ????? ??? ?????? ?? ?????? ??? ???? ?????? ?? ?? ???????? ?? ?? ???? ???? ??, ???? ?? ???? ?????????? ? ??????? ??? ?? ??????? ?????? ???????? ???????? ?? ???? ??? ??? ????? ????? ???? ?? ????? ??? ??? ???? ?????????? ??? ???? ????? ?? ? (???????? ????? ?????? ????? 2022), ?? ??? ???? ???? ??????? ?????????? ?? ??????? ?? ?????? ???? ??, ?? ?????? ??? ????? ??? ?? ???? ?????????? ?? ???????-????? ???????? (2018-2022) ???? ??? ??? ??? ??? ???????? ???? ?????????? ??? ?? ??? ???? 10 ??? ?? ??? 6 ??? ?? ???? ???? ???? ?? ??? ?????????? ?? ????? ??? ??? ?? ?????????? ??? ???? ??????? ??? ???????? ???? ?????????? ???? ???? ??, ????? ???????? ??? ???? ????? ?? ???????? ?????? ????? ??? ???? ??????? ??? ???

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Creative Commons Attribution 4.0 and The Open Definition

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Cardiovascular diseases (CVDs) remain a leading cause of mortality globally, necessitating effective risk prediction models for early identification and intervention. In recent years, artificial intelligence (AI) has emerged as a promising tool for predicting cardiovascular events, offering the potential to enhance risk stratification and clinical decision-making. This review provides a comprehensive analysis of current trends and future directions in AI-based models for predicting cardiovascular events. We survey the literature to examine the various AI techniques, including machine learning (ML) algorithms, deep learning models, and ensemble methods, employed for cardiovascular risk prediction. Additionally, we discuss the key features, strengths, and limitations of these models, highlighting their potential clinical applications and challenges. Furthermore, we explore emerging trends such as multimodal data fusion, interpretability, and personalized medicine, and their implications for advancing cardiovascular risk prediction. By synthesizing existing research findings and identifying areas for future exploration, this review aims to provide insights for researchers, clinicians, and policymakers involved in cardiovascular disease management and prevention.

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Cardiovascular diseases, artificial intelligence (AI), machine learning (ML), risk prediction

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The rapid advancements in medical imaging and computational techniques have significantly improved brain tumor diagnosis and treatment. OpenCV, an open-source computer vision library, has emerged as a pivotal tool in enhancing brain tumor image segmentation. This review explores the recent developments in utilizing OpenCV for brain tumor segmentation, highlighting the integration of traditional image processing methods and modern machine learning algorithms. Key techniques such as thresholding, edge detection, and morphological operations are discussed in the context of their implementation via OpenCV. Additionally, the paper addresses the challenges encountered, including the variability in tumor appearance, the need for high computational resources, and the necessity for large annotated datasets. The review concludes by identifying future directions, emphasizing the potential of OpenCV in combination with deep learning frameworks to achieve more accurate and efficient brain tumor segmentation.

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 Keywords

medical imaging, brain tumor, OpenCV, image segmentation, deep learning.

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Creative Commons Attribution 4.0 and The Open Definition