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Legal and Bioethical View of Educational Sectors and Industrial Areas of 3D Bioprinting
Recent advancements in three-dimensional printing (3D printing) within the medical field, particularly in the realm of 3D bioprinting, have shown tremendous potential in transforming various medical therapies, offering new approaches to treat organ failure and injury. However, amidst this optimism, several significant ethical and legal challenges remain unresolved before the application and transplantation of 3D bioprinted technology and organs in human subjects can become a reality. This chapter focuses on exploring the ethical and legal constraints associated with 3D bioprinting technology from both educational and industrial perspectives, recognizing their crucial roles as cornerstones for future applications. Furthermore, the analysis of 3D bioprinting technology will be conducted through the lens of the fundamental medical ethics principle, Primum non nocere; First, do no harm. Moreover, the pressing need for effective and timely standalone laws to regulate the subject of 3D printing is emphasized. This urgency arises from the grave concerns posed by the future implications of this technology on Indias scientific research and medical practice. The aim of this paper is to provide a comprehensive examination of the ethical and legal challenges posed by 3D bioprinting technology. By considering both educational and industrial perspectives, this research seeks to shed light on the complexities surrounding the application and transplantation of 3D bioprinted organs. Additionally, the analysis through the principle of Primum non nocere will contribute to the understanding of the ethical implications inherent in this innovative technology. Ultimately, this study advocates for the formulation of appropriate regulations and guidelines through the implementation of effective standalone laws, ensuring the responsible development and utilization of 3D printing technology in the realm of scientific research and medical practice in India. 2024 Scrivener Publishing LLC. -
Legal conundrums of space tourism
Private commercial space tourism carrying passengers to outer space is no longer a distant or far-fetched fantasy, rather it is at verge of becoming an affordable reality with exponential development in space technology including development of Reusable Launch Vehicle (RLV), increasing involvement of private companies like Virgin Galactic, SpaceX, Blue Origin etc. into research and funding of space tourism explorations and applications. It is also receiving huge attention from the public. These developments reflect the infinite possibilities and inevitability of space tourism in near future. However, space tourism may also pose many critical legal issues which must be addressed to ensure the consistent and sustainable development of space tourism, and to secure the rights of all stakeholders involved including operators, passengers, launching State etc. The research paper would highlight the crucial legal issues associated with the space tourism. The paper would critically analyze the efficiency of the present international space treaties in dealing with these issues. At the end, the paper would also attempt to provide few suggestions and solutions to these legal conundrums relating to space tourism. 2021 IAA -
LegalMind System and the LLM-based Legal Judgment Query System
LegalMind-GPT represents a notable advancement in legal technology, specifically tailored for the finance sector. This research paper introduces LegalMind-GPT, a system that integrates Large Language Models (LLMs) to develop a Legal Judgment Query System for financial legal contexts. The study focuses on the application of LLMs, particularly LLAMA-2, Claude AI, and FLAN-T5-Base, for interpreting and analysing complex legal documents in finance. The aim is to evaluate the system's effectiveness in providing accurate legal judgments and insights. The comparative analysis of these LLMs shows that LegalMind-GPT, powered by these models, significantly improves the accuracy and efficiency of legal analysis in the finance domain. 2024 IEEE. -
LENN: Laplacian Probability Based Extended Nearest Neighbor Classification Algorithm for Web Page Retrieval
Web page prediction is the area of interest that enables to tackle the problem of dealing with the massive collection of the web pages, mainly, in retrieving the highly relevant web pages. The hectic challenge of the web page prediction methods relied on time-effective and cost-effective management. The problem of dealing with the issue is tackled using the efficient web page retrieval algorithm. The paper proposes a new classifier called, Laplacian probability based Extended Nearest Neighbor (LENN)that is formed through the integration of the Laplacian probability with the Extended Nearest Neighbor (ENN)classifier. The proposed LENN classifier determines the nearest web pages of the query. Accordingly, the web page retrieval is done in three important steps, such as pre-processing, feature indexing and web page retrieval. The key words are stored in the database for performing the feature match such that the highly relevant web page is retrieved based on the maximum value of the score. The experimentation using five benchmarks prove that the proposed method is effective compared with the existing methods of web page retrieval. The maximum precision, recall, and F-measure of the proposed method is found to be 98%, 96.7%, and 97.3%, respectively. 2019 IEEE. -
Lesion detection in women breasts dynamic contrast-enhanced magnetic resonance imaging using deep learning
Breast cancer is one of the most common cancers in women and the second foremost cause of cancer death in women after lung cancer. Recent technological advances in breast cancer treatment offer hope to millions of women in the world. Segmentation of the breasts Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) is one of the necessary tasks in the diagnosis and detection of breast cancer. Currently, a popular deep learning model, U-Net is extensively used in biomedical image segmentation. This article aims to advance the state of the art and conduct a more in-depth analysis with a focus on the use of various U-Net models in lesion detection in womens breast DCE-MRI. In this article, we perform an empirical study of the effectiveness and efficiency of U-Net and its derived deep learning models including ResUNet, Dense UNet, DUNet, Attention U-Net, UNet++, MultiResUNet, RAUNet, Inception U-Net and U-Net GAN for lesion detection in breast DCE-MRI. All the models are applied to the benchmarked 100 Sagittal T2-Weighted fat-suppressed DCE-MRI slices of 20 patients and their performance is compared. Also, a comparative study has been conducted with V-Net, W-Net, and DeepLabV3+. Non-parametric statistical test Wilcoxon Signed Rank Test is used to analyze the significance of the quantitative results. Furthermore, Multi-Criteria Decision Analysis (MCDA) is used to evaluate overall performance focused on accuracy, precision, sensitivity, F 1 -score, specificity, Geometric-Mean, DSC, and false-positive rate. The RAUNet segmentation model achieved a high accuracy of 99.76%, sensitivity of 85.04%, precision of 90.21%, and Dice Similarity Coefficient (DSC) of 85.04% whereas ResNet achieved 99.62% accuracy, 62.26% sensitivity, 99.56% precision, and 72.86% DSC. ResUNet is found to be the most effective model based on MCDA. On the other hand, U-Net GAN takes the least computational time to perform the segmentation task. Both quantitative and qualitative results demonstrate that the ResNet model performs better than other models in segmenting the images and lesion detection, though computational time in achieving the objectives varies. 2023, The Author(s). -
Let there be Light, but not too much: The Need to Legally Address Light Pollution in India
Electricity and artificial lights were synonymous with economic growth and development. Unfortunately, over usage of artificial lights has proven adverse effects. Research shows that excessive light impacts human health and endangers ecological balance, disturbs wildlife, causes decline in insect, moth, reptile pollution and depletes energy resources. Countries around the world have gradually started recognising light pollution as an emerging challenge and have brought in regulations to curb it. However, India is yet to recognise the threat of light pollution. Against this backdrop, the authors have established the need to recognise light pollution as a matter requiring dedicated and concerted focus. This was achieved through the analysis of recent and credible journal articles category with a cite score of over ten. Reliance was also placed on the light pollution map to understand the intensity of the problem, especially in India. The authors next conducted a study of legal regimes governing light pollution and artificial light, in different jurisdictions around the globe. The paper draws upon the best practices from these jurisdictions and suggests that India adopt techno-legal legislation, at the earliest, to combat light pollution. 2023- Kalpana Corporation. -
LET US DREAMS MIGRATION TO A GLOBAL VIRTUAL CONFERENCE: DELIVERING A SOCIAL ENTREPRENEURIAL EVENT
This case study applies Rudolph et al.s social entrepreneurship model to describe the migration of Let Us Dreams (LUD) face-to-face social entrepreneurial conference to a virtual platform during the COVID-19 pandemic. LUD Triennial International Conference focused on community service initiatives in the areas of education, health, and social services for the purpose of impacting local and international communities in a transformative way. Organizers experienced many positive outcomes (e.g., high attendance and participant satisfaction), human capital, and leadership development of its collaborative volunteer planning teams, and the empowerment of local and global communities. The discussion section elaborates on the social entrepreneurship model findings, and other lessons learned, and provides recommendations for others planning to deliver a virtual or hybrid conference in multicultural contexts. 2024 Cognizant, LLC. -
Level of green computing based management practices for digital revolution and new india
The reality is staring us in the form of global warming, climate changes and air-quality degradation. This reality constitutes an increasing zone on the strategic front. These strategic changes need necessarily to be responded through employees of an organization. Against this backdrop, the Green Information Technology and Green HRM have emerged as a sequel to rapid degradation of our planet due to human activities. Therefore, incorporating the environmentally friendly practices through IT practices, recruitment, training and performance management functions constitute important components of Green IT and HRM. Green information technology is the revolutionary initiatives especially for human resources management practices that lead to digital life towards sustainable society. Keeping this practical and emergent context in view, the present study makes an attempt to develop a framework for assessing the level of green HRM practices actually prevailing in Indian organizations. The requisite data were collected from original sources and clarified with existing sources. The results of the study led to the inference that Information Technology and HRM practices of promoting individual performance needs fine-tuning because any green initiative has necessarily to be a collective exercise by all concerned. BEIESP. -
Level of green computing based management practices for digital revolution and New India /
International Journal of engineerig And Advanced Technology, Vol.8, Issue 3, pp.133-136, ISSN No: 2249-8958. -
Level Shifted Phase Disposition PWM Control for Quadra Boost Multi Level Inverter
This article introduces a novel boost switched capacitor Inverter (NBSCI) that significantly advances existing designs. Many recently developed multilevel voltage source inverters stand out for their ability to reduce the number of DC sources while markedly improving voltage levels with fewer switching devices. Building on these advancements, our work proposes an innovative inverter arrangement that, utilizing 1 DC source, eight switches and 3 capacitors, achieves 9-level output voltage waveforms. The increased range of voltage levels facilitates the generation of high-quality sine wave output signals with minimal Total Harmonic Distortion (THD). Also, this work employs Level shifted - Phase Disposition (LS-PD) pulse width modulation techniques to generate gating signals, ensuring the production of superior output waveforms. The article also presents various simulation results conducted using MATLAB-SIMULINK, providing a comprehensive assessment of the proposed configuration's precise effectiveness under diverse modulation index. 2024 IEEE. -
Level Up Your Learning: The Power of Gamification
In a world overflowing with information and distractions, traditional methods of learning often fall short in engaging and motivating individuals to master new skills. The book Level Up Your Learning: The Power of Gamification explores the captivating intersection of gamification and learning, revealing how incorporating game design principles can revolutionize the way we learn. Embark on a journey that uncovers the dynamic fusion of learning and gamification. Discover how this innovative approach can not only enhance your learning experience but also ignite your intrinsic motivation to tackle even the most complex subjects. Step into the shoes of a game designer as you master the art of crafting gamified learning experiences. This book takes you through the process of creating a captivating learning module, from setting clear learning objectives to strategically implementing badges, points, and narratives. Unearth the secrets to fostering healthy competition and collaboration among learners, while catering to diverse learning styles. Backed by studies and real-world outcomes, understand how gamification not only improves knowledge acquisition but also nurtures crucial skills like problem-solving, critical thinking, and decision-making. Whether you're an educator seeking to captivate your students or a lifelong learner aiming to enhance your skills, this book offers the insights and tools you need to unleash the potential of gamified learning. 2024 by Nova Science Publishers, Inc. All rights reserved. -
Levelling Up Organisational Learning Through Gamification: Based on Evidence from Public Sector Organisations in India
The concept of sustainability brought into focus the need for research into how to measure and achieve sustainable growth. The triple bottom line framework and the resource-based view of the firm suggest the need for organisations to look beyond profits and take into consideration the needs and effectiveness of its workforce. Research suggests that an effective workforce can be achieved through constant learning and development. Organisations have also expressed the need for training techniques that are more effective than the traditional methods. Gamification has been proposed as one such technique, and in the current study, the researchers evaluate the effectiveness of gamification in organisational training. For the purpose of the current study, 120 participants were chosen from public sector organisations in India. This is primarily because the technology-enhanced training effectiveness model (TETEM) suggests that the effectiveness of gamification would depend on the culture of the organisation, and prior research has been based in privately owned firms. The findings are in line with the theory of gamified learning and suggest that participants of the gamified module reported higher levels of learning, reaction and learner motivation. Additionally, learner motivation was found to strengthen the impact of gamification on the learning and reaction. The Author(s) 2022. -
Leveraging and Deployment of AI / ML to Simplify Business Operations among Diverse Sectors during Covid-19 Battle
During the evolution of the COVID-19 outbreak, the necessity for companies to re-evaluate and restructure themselves is still not greater. It will make sense for things to change in the business operations. Most companies redesigned current existing ways of running business operations and capacity to make choices to benefit. The present condition sees Artificial Intelligence as a significant facilitator for companies to make their existing situation better (recover from their economic crisis), reconsider (prepare for a long-term change) and reinvent (completely re-engineer) their business model for long-term gain. Automated bots that could identify items and carry out duties that were previously reserved for people would make companies and other infrastructures operational around the clock, through more significant numbers, and at a lower cost. Simulated actual working conditions, including labour forces, would be created by using Artificial intelligence platforms. Businesses would use machine learning and sophisticated business intelligence to use artificial intelligence to explore better market dynamics and provide consumers with "hyper-personalized" goods. Some of the most compelling case studies can have human intelligence and expertise mixed with AI. Many firms should revamp current business processes and capacity to benefit the company in the near future. In this research paper, we have showcased how artificial intelligence would benefit businesses as they adopt with these current developments and during a condition of pandemic without inhibiting their activities. The research is carried in a descriptive way, choosing the diverse sectors in the economy like Banking & Finance, Manufacturing, Education, Retail, Telecommunications, Entertainment and media to make the research more robust and reliable. 2022 American Institute of Physics Inc.. All rights reserved. -
Leveraging Big Data Analytics and Hadoop in Developing India's Healthcare Services
International Journal of Computer Applications, Vol-89 (16), pp. 44-50. ISSN-0975-8887 -
Leveraging Blockchain Technology forInternet ofThings Powered Banking Sector
Banking sector contributes to 70% of Indian Gross Domestic Product (GDP) and for India to meet its economic aspirations, it should enable this vivacious sector to grow at 810 times of its current pace, in the next ten years. This pace of active growth requires a double engine of sophisticated technology and a tech enabled, scalable, and a secured banking system. Implementing BlockchainTechnology (BCT) in the banking sector, provides a realistic solution which when coupled with devices connected by the Internet of Things(IoT), will result in secured, fast-paced, cost effective, and transparent growth of the sector. The prevalence of personalized banking, secured banking, connected banking, and digital banking are use cases, made possible through interface with IoT. This chapter delves into the opportunities in the banking sector to be explored and challenges to be met in the BCT-IoT implementation process. BCT- and IoT-based opportunities such as peer-to-peer lending, Know Your Customer (KYC) updation, Cross-border transfer payments, syndicate lending, fraud reduction are some of the banking operations that are elaborated. To strengthen the banking network, the consensus algorithm of Blockchainnetwork is much required and the use of IoT devices to act as nodes is pertinent. The blend of both in the banking space has to be further reinforced. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Leveraging Deep Autoencoders for Security in Big Data Framework: An Unsupervised Cloud Computing Approach
Abnormalities recognition in bank transaction big data is the number one issue for stability of financial security system. Due to the rate digital transactions are increasing it is vital to have effective ways. Encryption with deep autoencoder model should be explored as it involves trained neural networks that learn such patterns from the complex transaction data. The following paper demonstrates application of anomaly detection using deep autoencoders in the banking big data transactions. It focuses on the theoretical bases, network design, preparedness and the testing measures for deep autoencoders. On the other hand, it solves problems such as high dimensionality and imbalanced dataset. This research paper shows deep autoencoders effectiveness in deep learning and how the network identifies different fraudulent big data transactions, money laundry and unauthorized access. It also encompasses recent developments of cloud environments and future methods using deep autoencoders including the fact that constant search for new possible solutions is a must. The insights delivered contribute to the discourse in financial security community, which incorporates researchers, practitioners, and policymakers involved in anomaly detection in cloud. 2024 IEEE. -
Leveraging Deep Learning in Hate Speech Analysis on Social Platform
The scope and usage of the Internet have surpassed the expected growth and have proven beyond the basic purpose of being used for networking and telecommunications. It serves as the backbone of the web, and one of the predominant domains that uses the Internet is social media. The concept was conceived in the early 1990s and went on to grow as a powerful medium of people networking along with the Internet. Social networking sites (SNS) acquired a predominant element of the Internet owing to their use and services they offer through the Internet. A few of the most used social networking sites include Twitter and Facebook, which are used synonymous to expressions of text. These SNS allow the users to post photos, videos, and other multimedia content along with text and voice messages that are shared among other users. As with any technology or application, these also have the risk of users posting offensive material and textual content. Hate is being spread through messages, which are in the form of text and also through other materials posted. There is no control to check for the message for the hate content as and when it is posted, and by the time it is deleted by admins, it could have already reached millions of users. This chapter proposes a technique for detecting hate texts in reviews from registered users in the Twitter dataset. The proposed work makes use of improved principle component analysis (IPCA) and modified convolution neural network (MCNN) for detecting hate texts. The advantage of natural language processing is used for building an automated system for the analysis of syntax and semantics of the words. The proposed methodology consists of phases like pre-processing, feature extraction, and process to classify the text. The white spaces in the text are removed through normalization in the pre-processing phase, and also remove special characters such as question marks, punctuations, and exclamatory symbols to remove stop words. The features that are pre-processed are then subjected to feature extraction using IPCA. A set of correlated features are made used for identifying more important features in the data set under consideration. Next, the classification is done for identifying the hate text or for any language abuse. MCNN is applied for the classification of the text into HATE and NON-HATE from the text with better accuracy. The experiments prove that the proposed method has a high level of accuracy even for a large dataset. The results show that the proposed method has better performance in terms of precision, recall, and F-measure when compared with other state-of-the-art methods. 2024 Taylor & Francis Group, LLC. -
Leveraging digital yarn dyeing for colour consistency in apparel weaving
When compared to traditional processes, digital yarn dyeing provides substantial benefits in terms of color control, versatility, and environmental impact. However, technological obstacles and constraints exist. The promise of digital dyeing may be realized by carefully selecting technology, optimizing ink consumption, and adopting stringent quality control methods, resulting in improved colour constancy and a more sustainable textile sector. -
Leveraging Employee Data to Optimize Overall Performance: Using Workforce Analytics
Consistent employee performance is necessary for timely achievement and business success. Many key performance indicators influence an employees organizational performance, such as employee satisfaction, employee work environment, relationship with managers and coworkers, work-life balance, and many more. It becomes critical to regularly understand how these factors are connected to employee performance. One such method that is commonly used in companies is workforce analytics. It is a process that uses data-based intelligence for improving and enhancing management decisions in hiring and constructing compensations in alignment with employee performance. This also helps the management make data-based decisions and predictions, which helps in cost reductions and increases the overall profit. This chapter aims to analyze and report the workforce-related data and visualize the performance of 1,470 employees using published IBM human resources (HR) data made available at https://s3-euw1-ap-pe-df-pch-content-public-p.s3.eu-west-1.amazonaws.com/9781003357070/bb25a486-c036-4524-ab00-446f8eda3fd1/content/www.Kaggle.com xmlns:xlink=https://www.w3.org/1999/xlink>Kaggle.com. The chapter considers the following factors - job involvement, job satisfaction, performance rating, relationship satisfaction, environmental satisfaction, employee tenure, work-life balance, and income level - for data analysis and visualization of employee performance. The chapter aims to adopt descriptive, diagnostic, and predictive analysis using various software like Python, the Konstanz Information Miner (KNIME), and Orange. The visualization will be made using Tableau, Power BI, and Google Data Studio. Thus, the chapter gives a comprehensive insight into the meaning and importance of workforce analytics, different technologies used in workforce analytics, workforce analytics trends and tools, challenges of workforce analytics, and the process of implementation of workforce analytics. 2024 selection and editorial matter, Alex Khang, Sita Rani, Rashmi Gujrati, Hayri Uygun, and Shashi Kant Gupta; individual chapters, the contributors. -
Leveraging ensemble learning for enhanced security in credit card transaction fraudulent within smart cities for cybersecurity challenges
In the age of digital transactions, credit cards have emerged as a prevalent form of payment in smart cities. However, the surge in online transactions has heightened the challenge of accurately discerning legitimate from fraudulent activities. This paper addresses this crucial concern by introducing a pioneering system for detecting fraudulent credit card transactions, particularly within highly imbalanced datasets, in the realm of cybersecurity. This paper proposes a hybrid model to effectively manage imbalanced data and enhance the detection of fraudulent transactions. This paper emphasizes the efficacy of the hybrid approach in proficiently identifying and mitigating fraudulent activities within highly imbalanced datasets, thereby contributing to the reduction of financial losses for both merchants and customers in smart cities. As cybersecurity in smart cities evolves, this paper underscores the significance of ensemble learning and cross-validation techniques in optimizing credit card transaction analysis and fortifying the security of digital payment systems. 2024, Taru Publications. All rights reserved.