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Leveraging transparency and privacy through blockchain technology
Blockchain is a conveyed record innovation that can be utilized to keep exchanges in a safe and straightforward way. This makes it a promising innovation for various applications, for example inventory network the executives, monetary administrations, and medical services. One of the vital advantages of blockchain is its capacity to guarantee information consistency. This is on the grounds that all information on the blockchain is put away in a disseminated way, and every hub in the organization has a duplicate of the record. This makes it truly challenging for any one party to mess with the information. One more key advantage of blockchain is its straightforwardness. All exchanges on the blockchain are public, and anybody can see them. This can assist with building trust and straightforwardness among partners. Blockchain can likewise present difficulties regarding information security. This is on the grounds that all information on the blockchain is put away in a public record. This implies that anybody with admittance to the blockchain can see the information, including delicate data, for example individual recognizable proof numbers (PII). There are various ways of tending to the difficulties of information protection in blockchain. One methodology is to utilize encryption to safeguard delicate information. Another 2024, IGI Global. All rights reserved. -
Leveraging the Synergy of Edge Computing and IoT in Supply Chain Management
This article investigates the possibilities of integrating edge computing and IoT in supply chain management, as well as the adoption of disruptive technologies such as blockchain integration, digital twins, robotics, and autonomous systems. Operational efficiency can be considerably enhanced by establishing a linked and intelligent supply chain ecosystem. The benefits of this technology include increased openness, efficiency, and resilience in supply chain processes. Among the benefits include real-time product tracking, environmental sustainability, enhanced production, and cost savings. The use of blockchain technology in a three-tiered Supply Chain Network (SCN) shows promise in terms of boosting supply chain transparency and security. The SCOR model is also discussed as a comprehensive framework for optimising supply chain processes. However, concerns such as data privacy, security, and employment displacement must be solved before firms can fully reap the benefits of new technologies. Overall, embracing these innovations has the potential to revolutionise supply chain management and create trust among stakeholders. 2023 IEEE. -
Leveraging Robotic Process Automation (RPA) in Business Operations and its Future Perspective
Robotic Process Automation (RPA) is used to automate the business process operations including its capabilities to mimic the routine tasks, which requires less human intervention. RPA has seen crucial take-up practically throughout the last few years because of its capacity to reduce expenses and quickly associate heritage applications. Fundamentally RPA would perform automated tasks much like as an individual to accomplish objectives productively and adequately. This article analyses the features in current business conditions to comprehend the movement of RPA and automated interaction has carried to substitute the businesses with automated tasks. RPA is an innovative technology which utilizes software programming to execute enormous capacity assignments that are routine and time-consuming in the business cycle. RPA streamlines by playing out those undertakings proficiently as it reduces cost and saves assets of an association as programming works till the finishing of the assignment. This study aligns with the descriptive approach and leveraging Robotic Process Automation into business operations. This article also addresses the different players in the RPA Technological segment. This study also discussed and suggested selecting RPA Vendors in a future perspective. 2023 American Institute of Physics Inc.. All rights reserved. -
Leveraging Model Distillation as a Defense Against Adversarial Attacks Based on Deep Learning
Adversarial attacks on deep learning models threaten machine learning system security and reliability. The above attacks use modest data alterations to produce erroneous model results while being undetected by humans. This work suggests model distillation to prevent adversarial perturbations. The student model is taught to emulate the teacher model in model distillation. This is done using teacher model soft outputs. Our idea is that this strategy organically strengthens the student model against adversarial assaults by keeping the teacher model's essential knowledge and generalization capabilities while reducing weaknesses. Distilled models are more resilient to adversarial assaults than non-distilled models, according to experiments. These models also perform similarly on undamaged, uncorrupted data. The results show that model distillation may be a powerful defense against machine learning adversaries. This method protects model resilience and performance. 2023 IEEE. -
Leveraging Machine Learning: Advanced Algorithms for Soil Data Analysis and Feature Extraction in Arid and Semi-arid Regions with Expert Systems
India is culturally diverse nation at large. There are two words of symphony one is tradition and second one is inherited agriculture. India has long historical advantage having conventional agricultural practices and the scope for it to dive into day to day life as agriculturist. Happiness shrinks as people grow into modern world current trend of agriculture faces a monument challenge and needs immediate address to survive. Now withstanding with this phrase of human life on earth its necessary to give more importance to soil rather than the existence. Soil health is more paramount in this equation, as it directly influences crop growth and yield. Traditionally, analysing a few key soil properties has been the cornerstone of soil treatment practices. However, this approach often overlooks the complex interplay between various soil characteristics. To overcome the above hurdle present research incorporates the method of multivariate data analysis with selective advanced algorithms in machine learning to find suitability to predict best fit algorithm in real time data sets in arid and semi-arid zones of kolar district in Karnataka. The purpose is to draw the attention of stake holders to leveraging the new technology to deploying them into effective assessment in building expert system to incorporate in regular use on handy devices. This penetrates the results by two extremely good classifications algorithms Decision Tree and Gradient Boosting emerged as winner with 99% accuracy. In contrast, Passive Aggressive and Linear SVC produced below average of 36% accuracy. The ensemble algorithms of SMOTE on Random Forest and Stochastic Decent Gradient produced the acceptable accuracy of 83%. This input helped dynamically to build ready to use expert systems for farmers. The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024. -
Leveraging machine learning models for intelligent hazard management
[No abstract available] -
Leveraging history to invoke nationalism: from the annals of history to social engineering of present and future in Hindi cinema
Nationalism calls for ones loyalty and affiliation towards their chosen nation. Various versions of nationalism emphasise that one must prioritise said nation above themselves and their personal ethics, hence, allowing the nation to overpower the nationalists individuality. In this article, we use Critical Discourse Analysis to deconstruct the narratives of nationalism as portrayed in two popular films, viz. The Kashmir Files and Uri: The Surgical Strike, which are based on real historical eventsthe exodus of Kashmiri Hindus and the surgical strike by the Indian Army in retaliation to the Uri attack. Both films use narrative strategies to frame key historical events into certain ideological contexts, and hence they serve the populist purpose of swaying viewers opinion in favour of the dominant socio-political class. 2024 Informa UK Limited, trading as Taylor & Francis Group. -
Leveraging FinTech for the Advancement of Circular Economy
During the past six decades, there has been a lot of emphasis on increasing production and fulfilling the demands of the fast-growing population. As a result, there has been unprecedented utilization and depletion of natural resources and harm to the environment. It was rightly realized by government and policymakers that there is an indispensable need to align economic development with the environment. In other words, the world needs to pursue environmentally friendly economic development. In order to achieve sustainable development, the thought leaders devised a new approach called circular economy. The circular economy focuses on reusing and recycling materials to reduce the consumption of natural resources and minimize waste creation. In recent years, financial technology commonly known as FinTech has become a significant part of commercial activities across many industries. FinTech has benefited organizations and users in terms of cost and time saving with a high degree of reliability. This article outlines the ways in which FinTech supports the cause of a circular economy. It also explores the impediments in this path. 2024 Scrivener Publishing LLC. -
Leveraging Financial Data to Optimize Automation: An Industry 4.0 approach
Industry 4.0 is a transformative approach that leverages advanced technologies to enhance business efficiency and productivity. Automation is a crucial aspect of next-generation industry, and leveraging financial data is essential to optimizing the automation process. This chapter discusses the role of financial data in optimizing automation processes using an I-4.0 approach. Financial data is derived from various sources and can be collected through different methods, such as automated data collection, manual entry, or using sensors and Internet of Things (IoT) devices. The integration of these sources can pose challenges for businesses. The chapter outlines techniques for automation optimization, such as machine learning, predictive analytics, and business process reengineering. Optimizing automation using financial data offers various benefits for businesses, including cost savings, improved quality, and increased profitability. However, there are challenges that businesses face in leveraging financial data, including the integration of various data sources and formats and the need for skilled personnel to analyze and interpret the data. The successful implementation of automation and optimization of processes can lead to sustainable growth and enhanced operations, making it crucial for businesses to remain competitive in the I-4.0 era. By leveraging financial data to optimize automation processes, businesses can maximize their potential and drive growth. Overall, this chapter highlights the significance of financial data in automation optimization and provides insights into the benefits and challenges that businesses must consider when leveraging financial data for optimization. 2024 selection and editorial matter, Nidhi Sindhwani, Rohit Anand, A. Shaji George and Digvijay Pandey; individual chapters, the contributors. -
Leveraging Ensemble Methods for Accurate Prediction of Customer Spending Scores in Retail
This study primarily aims to estimate consumer spending trends in a retail context. The goal is to identify the best model for predicting Purchasing Scores, which indicate customer loyalty and potential income, using demographic and financial data. The dataset included information about customers' age, gender, and annual income, and the objective was to analyze their Spending Scores. Several regression models were tested, including Linear Regression, Random Forest, Gradient Boosting, K-Nearest Neighbors (KNN), and Lasso Regression. To improve the models, we engineered features like Age Squared, Income per Age, and Spending Score per Income. Each model was trained and tested using 3fold cross-validation. We evaluated their performance with Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R2) metrics. The results showed significant differences in model performance. The Random Forest model stood out, with the lowest Mean Absolute Error (MAE) of 0.33, Root Mean Square Error (RMSE) of 0.52, and the highest R-squared (R22) score of 0.9997. Gradient Boosting also performed well, achieving a Mean Absolute Error (MAE) of 1.77, Root Mean Square Error (RMSE) of 2.41, and an Rsquared (R2) score of 0.9930. While Linear Regression showed moderate accuracy, KNN and Lasso Regression had higher errors and lower R2 values, indicating less reliable predictions. The findings suggest that ensemble methods, particularly Random Forest, excel at predicting customer Spending Scores. The high accuracy and reliability of this model point to its potential for customer segmentation and targeted marketing strategies, ultimately enhancing customer relationship management and boosting business value. Further refinement and exploration of additional features could further improve these prediction capabilities. 2024 IEEE. -
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. -
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 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 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 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 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 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 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. -
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. -
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.