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Research Paper

Organizational Hierarchy: An in-Depth Literature Review

This paper combines the views of 50+ research articles on organisational hierarchy. Upon a thorough analysis of the literature, six subthemes emerged, namely Age differences, Impact on women, Abuse of power, Conflict management, Digital workplaces and Covid-19 . These were analysed in detail to see the effects they had on organisational hierarchy. Abuse of power and conflict within and across hierarchies was understood through a multitude of research papers concerning the two. We also looked at how age and gender roles and stereotypes have an influence on how the individuals are treated and the amount of advancement opportunities that they get due to them belonging to a certain category. Lastly, to keep up with the current times, we also viewed how hierarchies fluctuate and modify due to the existence of digital workplaces and how they have had drastic changes in the organisation since they started using digital workplaces in the Covid-19 era.

Published by: Shaarvi Dashora

Author: Shaarvi Dashora

Paper ID: V10I4-1206

Paper Status: published

Published: August 29, 2024

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Review Paper

The Role of Artificial Intelligence in Enhancing Intraoperative Decision-Making

Artificial Intelligence (AI) is transforming surgical practice, particularly in intraoperative decision-making. This study explores the current applications of AI in providing real-time decision support during surgery, focusing on its impact on reducing complication rates, operation times, and improving patient recovery. By integrating AI tools with surgical practice, the potential to enhance precision and patient outcome is significant. However, challenges such as algorithmic bias and ethical implications of AI in surgery require careful consideration.

Published by: Shayaan Khan

Author: Shayaan Khan

Paper ID: V10I4-1218

Paper Status: published

Published: August 26, 2024

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Research Paper

Waste Sorting and Recycling with AI: Implementing Faster R-CNN for Object Detection

The increasing volume of waste generated worldwide presents significant environmental challenges, necessitating efficient and automated waste management solutions. This paper explores the application of artificial intelligence (AI) in waste sorting and recycling, focusing on the implementation of the Faster R-CNN (Region-based Convolutional Neural Network) model for object detection. Faster R-CNN is renowned for its accuracy and speed in detecting and classifying objects within images, making it an ideal candidate for real-time waste segregation tasks.Our research involves training the Faster R-CNN model on a diverse dataset comprising various waste categories, including plastics, metals, paper, and organic waste. The model's performance is evaluated based on metrics such as precision, recall, and mean Average Precision (mAP). The experimental results demonstrate the model's high accuracy in distinguishing between different types of waste materials, thereby facilitating effective sorting processes.

Published by: P RAVI KIRAN, MIDHUN CHAKKARAVARTHY

Author: P RAVI KIRAN

Paper ID: V10I4-1201

Paper Status: published

Published: August 26, 2024

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Research Paper

CNN-Based Moving Object Detection in Low-Light and Adverse Weather Conditions

The detection of moving objects in video sequences is a critical task for numerous applications, including autonomous driving, surveillance, and robotics. However, this task becomes significantly more challenging under low-light and adverse weather conditions, where traditional detection methods often fail. This paper presents a novel approach leveraging Convolutional Neural Networks (CNNs) for robust moving object detection in such challenging environments.Our proposed method incorporates advanced CNN architectures specifically designed to handle the complexities introduced by low-light and adverse weather conditions. We integrate a multi-stage preprocessing pipeline that enhances image quality and visibility before feeding the frames into the detection network. Additionally, we employ a temporal convolutional network to effectively utilize temporal information, improving detection accuracy and stability over consecutive frames.Extensive experiments are conducted on benchmark datasets and newly curated video sequences captured under various low-light and adverse weather conditions. The results demonstrate that our approach significantly outperforms state-of-the-art methods in terms of detection accuracy and robustness. Our CNN-based solution not only excels in detecting moving objects but also maintains high performance in real-time applications, proving its practicality and efficiency.This research highlights the potential of advanced CNN techniques in overcoming the limitations posed by challenging environmental conditions, paving the way for more reliable and resilient object detection systems in real-world scenarios.

Published by: KODETI HARITHA RANI, Midhun Chakkaravarthy

Author: KODETI HARITHA RANI

Paper ID: V10I4-1200

Paper Status: published

Published: August 26, 2024

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Research Paper

Economically Weaker Section Allocations- Impact, Misuse, and Implementation Framework

This paper talks about the economically weaker section of the society. It includes information about different topics; The Impact of Affordable general housing for the urban poor, Misuse of Affordable Housing Schmes and its direct consequences, Source of Finance, Economic parameters, Sanitation and Location, Critical issues in the affordable housing sector, Infrastructure Parameters, Services, Regulations, International evidence. This paper examines the depth of different major problems about the economically weaker section and the other parameters that affect this.

Published by: Ridhima Kapur

Author: Ridhima Kapur

Paper ID: V10I4-1197

Paper Status: published

Published: August 24, 2024

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Others

Advances in Dental Implant Technology

This abstract delves into the rapidly evolving field of implant dentistry, highlighting the continuous advancements in dental technology that are revolutionising oral health restoration and enhancing smiles. The exploration of implant innovations spans a range of cutting-edge technologies, including digital imaging, computer-aided design and manufacturing (CAD/CAM), guided surgery techniques, immediate load implants, biocompatible materials, and augmented reality. These innovations collectively contribute to a more precise, efficient, and patient-centered approach, ushering in a new era in dental care. As advancements in implant technology continue to expand the frontiers of dental care, the future holds numerous promising developments. The fusion of artistic and scientific principles in implantology aims not only to replace missing teeth but to do so with a high degree of sophistication, respecting the unique oral anatomy of each patient. This abstract encourages an examination of the dynamic relationship between implant innovations and dental technology, presenting a future where new developments not only restore smiles but also transform the nature of personalised, precise, and patient-focused oral health care.

Published by: Dr. Rishika Dogra

Author: Dr. Rishika Dogra

Paper ID: V10I4-1205

Paper Status: published

Published: August 21, 2024

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