INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY, 2024
The health insurance industry faces complex and dynamic IT environments that demand efficiency, s... more The health insurance industry faces complex and dynamic IT environments that demand efficiency, security, and scalability. Infrastructure automation plays a crucial role in enabling health insurance providers to streamline IT operations, maintain compliance, and quickly respond to evolving business needs. This research paper explores the role of Ansible and YAML in automating IT infrastructure builds in the health insurance industry, with a focus on provisioning, configuration management, and orchestration. We discuss the challenges and benefits of automation in a highly regulated sector and provide a detailed examination of how Ansible and YAML can be leveraged to build, maintain, and scale IT environments efficiently while ensuring compliance and security.
The health insurance industry is a growing business, and the IT infrastructure needed to accommodate the increasing needs of the market demands is highly scalable. Automation is an important part of this sector since it enables organizations to automate their processes, eliminate manual errors, and get more efficient work. The best infrastructure automation tool is Ansible – an open-source configuration management and deployment tool which applies the readability and adaptability of YAML to IT environment creation and management.
In this study, we’ll discuss the use cases and pros and cons of Ansible and YAML for infrastructure automation in health insurance by reviewing the key features, implementation methods, and case studies to illustrate how the technology has been used to great effect.
International Journal of Leading Research Publication, 2024
dimensional, complex data. Unlike supervised learning, which relies on labeled data for model tra... more dimensional, complex data. Unlike supervised learning, which relies on labeled data for model training, unsupervised learning methods aim to identify hidden patterns and structures in unlabeled data. Recent developments in unsupervised learning methods are examined in this paper, with an emphasis on dimensionality reduction, anomaly detection, and clustering. These methods are essential for effective data analysis because high-dimensional data, sometimes known as the "curse of dimensionality," poses serious difficulties for conventional machine learning algorithms. We go over the development of these techniques, their uses, difficulties, and the most recent advancements in the field of high-dimensional data handling. In the age of high-dimensional data, unsupervised learning techniques have grown in importance. This study explores the developments in dimensionality reduction, anomaly detection, and clustering techniques for intricate, high-dimensional datasets. It examines the unique challenges posed by such data and the innovative approaches that have emerged to tackle them.
Science Data and Learning Machine ,Intelligence Artificial of Journal, 2023
In artificial intelligence (AI) and machine learning (ML), transfer learning is a well-known tech... more In artificial intelligence (AI) and machine learning (ML), transfer learning is a well-known technique that enables models to generalize knowledge from one domain to another with little data. Its ability to overcome the difficulties of limited labeled data, particularly in complex tasks where obtaining large amounts of labeled data is costly or impractical, has drawn a lot of attention. This essay examines the idea of transfer learning, its uses, and different methods that make it easier to move knowledge from one field to another. We discuss the advantages and disadvantages of several important approaches, including few-shot learning, domain adaptation, and fine-tuning. The study also addresses the issues that still need to be resolved in the field, such as reducing domain disparities and creating transfer learning algorithms that are more effective. Lastly, we examine transfer learning's prospects and how it might affect AI developments in different sectors.
INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY, 2024
A promising paradigm that lessens the need for sizable labeled datasets for machine learning mode... more A promising paradigm that lessens the need for sizable labeled datasets for machine learning model training is self-supervised learning (SSL). SSL models are able to learn data representations through pretext tasks by utilizing unlabeled data. These representations can then be refined for tasks that come after. The development of self-supervised learning, its underlying techniques, and its potential to address the difficulties associated with obtaining labeled data are all examined in this paper. We go over the main self-supervised methods, their uses, and how they might improve the generalization and scalability of machine learning models. We also look at the difficulties in implementing SSL in various domains and potential avenues for future research. This study investigates how self-supervised learning strategies can result in notable gains across a range of machine learning tasks, especially when there is a shortage of labeled data. [1] [2]
INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY, 2022
An agent learns the best behaviors through trial and error in Reinforcement Learning (RL), a pote... more An agent learns the best behaviors through trial and error in Reinforcement Learning (RL), a potent paradigm for decision-making problems. The majority of RL algorithms have historically operated in a static environment, with fixed reward functions and underlying system dynamics. On the other hand, environments in real-world applications are frequently dynamic and subject to sudden changes. The difficulties of using reinforcement learning (RL) in dynamic environments are covered in this paper, along with new developments in RL algorithms that can adjust to these shifting circumstances. The potential of these techniques in a variety of industries, including robotics, finance, healthcare, and autonomous driving, is highlighted as we examine important approaches like continual learning, meta-RL, and the incorporation of uncertainty. However, the majority of Reinforcement Learning algorithms currently in use are mainly made for environments that are static or change slowly, which limits their use in real-world situations where uncertainty and constant change are commonplace. The opportunities and difficulties of creating Reinforcement Learning algorithms that can adjust to dynamically changing environments are examined in this research paper. We go over a number of promising strategies, such as the use of non-parametric methods like Gaussian Processes, the integration of physics-informed models, and hierarchical and modular Reinforcement Learning architectures. By making these developments, we hope to open the door for a fresh breed of reinforcement learning systems that can prosper in the face of constant uncertainty and change.
International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences, 2025
At the nexus of artificial intelligence (AI) and quantum computing lies the emerging field of qua... more At the nexus of artificial intelligence (AI) and quantum computing lies the emerging field of quantum machine learning (QML). By speeding up the computation of intricate algorithms, quantum computers have the potential to transform a number of fields, including machine learning, by outperforming classical computers by an exponential amount in specific tasks. This essay examines the fundamental ideas of quantum computing, how it applies to machine learning, and the potential advantages and difficulties of QML. We examine several quantum algorithms, including quantum versions of support vector machines, clustering, and neural networks, that can improve machine learning models. We also go over QML's drawbacks, present research directions, and potential future developments, providing insights into how quantum technologies might transform AI in the ensuing decades. With the potential to outperform traditional supercomputers in resolving important issues in a variety of fields, including machine learning, quantum computing has become a ground-breaking technology. This study investigates the fascinating nexus between artificial intelligence and quantum computing, looking at how quantum machine learning might revolutionize classification, pattern recognition, and data processing.
International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences, 2019
The foundations of NAS (Neural Architecture Search) are covered in this paper, along with the tra... more The foundations of NAS (Neural Architecture Search) are covered in this paper, along with the trade-offs associated with automating model design and how they affect deep learning performance. It offers a fair assessment of the advantages and disadvantages of different search strategies and considers potential future developments in this area. An inventive and promising technique for automating deep neural network (DNN) design is NAS. The limitations of human-designed architectures may be addressed by NAS, which has the potential to find extremely effective and performant models by using algorithms to explore and optimize model architectures. The fundamental workings of NAS, the trade-offs associated with its application, and its effect on deep learning performance are all examined in this paper. We examine the various NAS approaches, including gradient-based techniques, evolutionary algorithms, and reinforcement learning (RL). The study also looks at the scalability problems, computational costs, and how NAS advances state-of-the-art models in a variety of fields, such as reinforcement learning, natural language processing, and image classification. Finally, we go over the present difficulties, possible future paths, and real-world uses of NAS.
International Journal of Scientific Research in Engineering and Management, 2021
Models that can process human language in a variety of applications have been developed as a resu... more Models that can process human language in a variety of applications have been developed as a result of the quick development of natural language processing (NLP). Scaling NLP technologies to support multiple languages with minimal resources is still a major challenge, even though many models work well in high-resource languages. By developing models that can comprehend and produce text in multiple languages, especially those with little linguistic information, multilingual natural language processing (NLP) seeks to overcome this difficulty. This study examines the methods used in multilingual natural language processing (NLP), such as data augmentation, transfer learning, and multilingual pre-trained models. It also talks about the innovations and trade-offs involved in developing models that can effectively handle multiple languages with little effort. Many low-resource languages have been underserved by the quick advances in natural language processing, which have mostly benefited high-resource languages. The methods for creating multilingual NLP models that can efficiently handle several languages with little resource usage are examined in this paper. We discuss unsupervised morphology-based approaches to expand vocabularies, the importance of community involvement in low-resource language technology, and the limitations of current multilingual models. With the creation of strong language models capable of handling a variety of tasks, the field of natural language processing has advanced significantly in recent years. But not all languages have benefited equally from the advancements, with high-resource languages like English receiving disproportionate attention. [9] As a result, there are huge differences in the performance and accessibility of natural language processing (NLP) systems for the languages spoken around the world, many of which are regarded as low-resource. Researchers have looked into a number of methods for developing multilingual natural language processing (NLP) models that can comprehend and produce text in multiple languages with little effort in order to rectify this imbalance. Using unsupervised morphology-based techniques to increase the vocabulary of low-resource languages is one promising strategy.
International Journal of Scientific Research in Engineering and Management, 2024
"Learning to learn," or meta-learning, has become a potent strategy for enhancing the effectivene... more "Learning to learn," or meta-learning, has become a potent strategy for enhancing the effectiveness and versatility of machine learning models. Meta-learning algorithms aim to learn general strategies and principles that can be applied to a range of learning problems, in contrast to traditional machine learning approaches that concentrate on solving a particular problem. One of the main benefits of meta-learning is its capacity to use prior knowledge and experience to speed up learning in new tasks. Meta-learning seeks to improve models' generalization across tasks by allowing algorithms to learn from both data and prior learning experiences, which lessens the need for intensive task-specific training. The idea of meta-learning, its main algorithms, and how it can increase learning efficiency across tasks are all examined in this paper. We look at the different meta-learning frameworks, including model-based, metric-based, and optimization-based methods, and assess how well they work in various contexts. Lastly, we go over the practical uses and difficulties of meta-learning, emphasizing its potential in domains like robotics, reinforcement learning, and few-shot learning.
INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY, 2020
An important turning point in the development of artificial intelligence (AI) has been reached wi... more An important turning point in the development of artificial intelligence (AI) has been reached with the introduction of large language models (LLMs), such as BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pretrained Transformer). Numerous applications in fields like language translation, content production, and customer service are made possible by these models' ability to produce text that is both coherent and contextually relevant. However, there are significant ethical concerns brought up by the growing use of LLMs, such as those pertaining to bias, privacy, accountability, and misuse potential. This study examines the ethical ramifications, deployment risks, and how LLMs will influence AI applications in the future. It talks about ways to reduce these risks and make sure LLMs are created and applied appropriately. Large language models have garnered a lot of interest and been used in many downstream applications due to their impressive performance on a variety of tasks. These potent models do, however, come with some risks, including the possibility of private data leaks, the creation of offensive or dangerous content, and the development of superintelligent systems without sufficient security. The ethical ramifications of large language models are examined in this paper, with particular attention paid to the risks involved and how models such as GPT and BERT will influence AI applications in the future.
International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences, 2023
By using machine learning techniques, Natural Language Processing (NLP) has made tremendous progr... more By using machine learning techniques, Natural Language Processing (NLP) has made tremendous progress in comprehending and producing human language. Nevertheless, despite recent developments, there are still obstacles in the way of allowing machines to use structured knowledge meaningfully and reason contextually. By offering structured, semantic knowledge that can support NLP models, Knowledge Graphs (KGs), which depict relationships between entities in a graph structure, present a promising answer to these problems. This study examines how Knowledge Graphs (KGs) can improve reasoning, context comprehension, and information retrieval in natural language processing (NLP) systems. Additionally, we look at existing methods for integrating KGs with NLP models, such as graph-based neural networks, and emphasize how they affect different NLP tasks like text summarization, named entity recognition, and question answering. The difficulties and potential paths for integrating Knowledge Graphs and NLP to enhance performance in practical applications are covered in the paper's conclusion. A new method for organizing and utilizing structured data is knowledge graphs, which offer a means of illustrating the connections between important ideas, entities, and facts. Knowledge graphs can improve natural language processing (NLP) systems' capacity to reason about text, comprehend context, and produce more precise and pertinent results. In order to improve named entity recognition, text classification, and question answering, among other NLP tasks, this paper investigates the integration of knowledge graphs with NLP.
INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY, 2024
The health insurance industry faces complex and dynamic IT environments that demand efficiency, s... more The health insurance industry faces complex and dynamic IT environments that demand efficiency, security, and scalability. Infrastructure automation plays a crucial role in enabling health insurance providers to streamline IT operations, maintain compliance, and quickly respond to evolving business needs. This research paper explores the role of Ansible and YAML in automating IT infrastructure builds in the health insurance industry, with a focus on provisioning, configuration management, and orchestration. We discuss the challenges and benefits of automation in a highly regulated sector and provide a detailed examination of how Ansible and YAML can be leveraged to build, maintain, and scale IT environments efficiently while ensuring compliance and security.
The health insurance industry is a growing business, and the IT infrastructure needed to accommodate the increasing needs of the market demands is highly scalable. Automation is an important part of this sector since it enables organizations to automate their processes, eliminate manual errors, and get more efficient work. The best infrastructure automation tool is Ansible – an open-source configuration management and deployment tool which applies the readability and adaptability of YAML to IT environment creation and management.
In this study, we’ll discuss the use cases and pros and cons of Ansible and YAML for infrastructure automation in health insurance by reviewing the key features, implementation methods, and case studies to illustrate how the technology has been used to great effect.
International Journal Of Core Engineering & Management, 2022
Blockchain technology's decentralized, transparent, and secure systems have made it a disruptive ... more Blockchain technology's decentralized, transparent, and secure systems have made it a disruptive force in a number of industries. Scalability is still a major barrier to the widespread use of blockchain, though, especially in systems with large transaction volumes. Conventional consensus techniques, like Proof of Work (PoW) and Proof of Stake (PoS), frequently suffer from latency, network congestion, and transaction throughput issues. There is a chance to solve these scalability concerns with artificial intelligence (AI). Blockchain performance can be greatly enhanced by AI through resource management, transaction validation, congestion prediction, and consensus algorithm optimization. However, there are trade-offs when incorporating AI into blockchain systems, including security risks, privacy issues, and computational overhead. In order to improve blockchain scalability, this paper investigates AIdriven solutions, analyzes related trade-offs, and offers insights into how AI can be successfully incorporated into blockchain networks to get around scalability issues. Blockchain technology combined with artificial intelligence has the potential to revolutionize a number of sectors and uses. This study looks at how AI affects blockchain scalability, emphasizing the trade-offs and potential solutions. Although blockchain technology has been heralded as a game-changing invention that makes safe and decentralized record-keeping possible, its scalability has proven to be an ongoing problem. Conversely, artificial intelligence can increase productivity and open up new avenues for revenue generation and cost reduction.
International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences, 2023
The goal of the developing field of explainable artificial intelligence (XAI) is to make complex ... more The goal of the developing field of explainable artificial intelligence (XAI) is to make complex AI models, especially deep learning (DL) models, which are frequently criticized for being "black boxes" more interpretable. Understanding how deep learning models make decisions is becoming crucial for accountability, fairness, and trust as deep learning is used more and more in various industries. This paper offers a thorough analysis of the strategies and tactics used to improve the interpretability of deep learning models, including hybrid approaches, post-hoc explanations, and model-specific strategies. We examine the trade-offs between interpretability, accuracy, and computational complexity and draw attention to the difficulties in applying XAI in high-stakes domains like autonomous systems, healthcare, and finance. The study concludes by outlining the practical applications of XAI, such as how it affects ethical AI implementation, regulatory compliance, and decision-making.
ESP Journal of Engineering & Technology Advancements, 2023
With its decentralized structure and unchangeable record-keeping system, blockchain technology ha... more With its decentralized structure and unchangeable record-keeping system, blockchain technology has gained widespread acceptance in a number of industries, including supply chain management, healthcare, and finance. However, there are issues with scalability, security, and efficiency with its conventional implementation. One way to automate transactions and processes on the blockchain is through smart contracts, which are self-executing agreements with the terms of the contract directly written into lines of code. When combined with smart contracts, artificial intelligence (AI) can unleash a new range of capabilities, such as predictive analytics, adaptive contract execution, and autonomous decision-making. The potential, design, implementation, and effects on blockchain networks of AI-driven smart contracts are the main topics of this paper. It explores the advantages, difficulties, and uses of this integration in addition to the direction that AI-enhanced smart contract systems will take in the future.
International Journal of Scientific Research in Engineering and Management, 2023
The integration of Artificial Intelligence (AI) and Blockchain technology offers transformative p... more The integration of Artificial Intelligence (AI) and Blockchain technology offers transformative potential for the healthcare industry. Blockchain provides a secure, immutable, and transparent infrastructure for managing healthcare data, while AI enhances decision-making, predictive analytics, and personalized treatment. This research paper explores the synergies between AI and Blockchain in healthcare systems, focusing on how these technologies can improve patient care, streamline operations, enhance data security, and address key challenges like interoperability and trust. Through case studies and a detailed examination of their applications, this paper highlights the potential benefits and challenges of implementing AI in Blockchain-enabled healthcare systems, with a vision toward a more secure, efficient, and patient-centric healthcare ecosystem.
International Journal of Scientific Research in Engineering and Management, 2021
As the digital landscape evolves and cyber threats become increasingly sophisticated, traditional... more As the digital landscape evolves and cyber threats become increasingly sophisticated, traditional security systems struggle to keep up with the volume, variety, and velocity of attacks. Artificial Intelligence (AI) has emerged as a powerful tool for enhancing cybersecurity by enabling the automated detection, analysis, and mitigation of threats in real-time. By leveraging machine learning (ML) algorithms, natural language processing (NLP), and anomaly detection, AI can process vast amounts of data, identify patterns, and respond to potential threats faster and more accurately than conventional methods. This paper explores the role of AI in modern cybersecurity, focusing on its applications in threat detection and mitigation. It examines how AI systems, such as intrusion detection systems (IDS), security information and event management (SIEM) platforms, and endpoint protection tools, are being used to combat cyber threats. The paper also discusses the challenges associated with implementing AI in cybersecurity, including false positives, adversarial attacks, and the need for continuous training, and offers insights into future trends in AI-driven threat mitigation.
JOURNAL FOR ARTIFICIAL INTELLIGENCE, MACHINE LEARNING AND DATA SCIENCE, 2025
Although artificial intelligence (AI) and machine learning (ML) have revolutionized a number of i... more Although artificial intelligence (AI) and machine learning (ML) have revolutionized a number of industries, their application in vital fields like healthcare, finance, and criminal justice has sparked questions about robustness, bias, and fairness. The significance of addressing these issues in AI and ML systems is examined in this paper. We look at how bias occurs, different ways to make things more equitable, and ways to make machine learning algorithms more resilient. The study suggests frameworks for incorporating robustness and fairness into AI systems while guaranteeing social impact and ethical considerations. We offer a comprehensive overview of the opportunities and difficulties in developing AI systems that are both reliable and equitable, as well as recommendations for future research directions. As machine learning algorithms are used more and more in different fields, it is more important than ever to address issues with bias, fairness, and robustness. The current state of research in this field is thoroughly reviewed in this paper, which also examines the various definitions and approaches to fairness, the methods for enhancing the robustness of these algorithms, and the types and sources of biases that can occur in machine learning models. We go over the connections between these three crucial areas and point out the difficulties and possible solutions in developing AI systems that are more moral and reliable. We present a taxonomy of bias types, fairness definitions, and robustness techniques based on a synthesis of recent literature. We also go over the trade-offs and practical uses of these methods. The goal of this paper is to be a useful tool for practitioners and researchers who are trying to create machine learning models that are impartial, reliable, and equitable.
INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY, 2021
Due to developments in artificial intelligence (AI), particularly deep learning, information retr... more Due to developments in artificial intelligence (AI), particularly deep learning, information retrieval (IR) has undergone significant change in recent years. Search engines and chatbots now comprehend user queries and provide precise, pertinent, and contextual responses thanks to deep learning algorithms. This paper discusses artificial intelligence (AI) and modern information retrieval systems, with a focus on how deep learning models can be applied to search engine and chatbot query interpretation. It examines the challenges and advancements in the field, such as semantic search and natural language processing (NLP), and how these technologies can improve user experience. I also discuss pertinent applications and future directions in AI-based information retrieval. Recent years have seen a dramatic change in the field of information retrieval, primarily due to developments in deep learning and its use in natural language processing. These developments have greatly benefited search engines and chatbots, two well-known examples of information retrieval systems, which now understand queries better and provide more pertinent and contextual responses.
Artificial intelligence (AI) systems have advanced significantly and are now used in important fi... more Artificial intelligence (AI) systems have advanced significantly and are now used in important fields like finance, healthcare, and autonomous driving. Their extensive use has, however, exposed a serious flaw: their vulnerability to hostile attacks. These attacks use tiny, well-planned changes to input data to make AI models behave badly or predict things incorrectly, frequently undetected by humans. The nature of adversarial attacks on AI systems, how they are created, their ramifications, and the different defense strategies that have been put forth to protect AI models are all examined in this paper. Our goal is to improve knowledge and resilience against adversarial threats in practical applications by offering a summary of the main adversarial attack methods and defenses.
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Papers by Gaurav Kashyap
The health insurance industry is a growing business, and the IT infrastructure needed to accommodate the increasing needs of the market demands is highly scalable. Automation is an important part of this sector since it enables organizations to automate their processes, eliminate manual errors, and get more efficient work. The best infrastructure automation tool is Ansible – an open-source configuration management and deployment tool which applies the readability and adaptability of YAML to IT environment creation and management.
In this study, we’ll discuss the use cases and pros and cons of Ansible and YAML for infrastructure automation in health insurance by reviewing the key features, implementation methods, and case studies to illustrate how the technology has been used to great effect.
However, the majority of Reinforcement Learning algorithms currently in use are mainly made for environments that are static or change slowly, which limits their use in real-world situations where uncertainty and constant change are commonplace.
The opportunities and difficulties of creating Reinforcement Learning algorithms that can adjust to dynamically changing environments are examined in this research paper. We go over a number of promising strategies, such as the use of non-parametric methods like Gaussian Processes, the integration of physics-informed models, and hierarchical and modular Reinforcement Learning architectures. By making these developments, we hope to open the door for a fresh breed of reinforcement learning systems that can prosper in the face of constant uncertainty and change.
With the potential to outperform traditional supercomputers in resolving important issues in a variety of fields, including machine learning, quantum computing has become a ground-breaking technology. This study investigates the fascinating nexus between artificial intelligence and quantum computing, looking at how quantum machine learning might revolutionize classification, pattern recognition, and data processing.
Many low-resource languages have been underserved by the quick advances in natural language processing, which have mostly benefited high-resource languages. The methods for creating multilingual NLP models that can efficiently handle several languages with little resource usage are examined in this paper. We discuss unsupervised morphology-based approaches to expand vocabularies, the importance of community involvement in low-resource language technology, and the limitations of current multilingual models.
With the creation of strong language models capable of handling a variety of tasks, the field of natural language processing has advanced significantly in recent years. But not all languages have benefited equally from the advancements, with high-resource languages like English receiving disproportionate attention. [9] As a result, there are huge differences in the performance and accessibility of natural language processing (NLP) systems for the languages spoken around the world, many of which are regarded as low-resource.
Researchers have looked into a number of methods for developing multilingual natural language processing (NLP) models that can comprehend and produce text in multiple languages with little effort in order to rectify this imbalance. Using unsupervised morphology-based techniques to increase the vocabulary of low-resource languages is one promising strategy.
Large language models have garnered a lot of interest and been used in many downstream applications due to their impressive performance on a variety of tasks. These potent models do, however, come with some risks, including the possibility of private data leaks, the creation of offensive or dangerous content, and the development of superintelligent systems without sufficient security. The ethical ramifications of large language models are examined in this paper, with particular attention paid to the risks involved and how models such as GPT and BERT will influence AI applications in the future.
A new method for organizing and utilizing structured data is knowledge graphs, which offer a means of illustrating the connections between important ideas, entities, and facts. Knowledge graphs can improve natural language processing (NLP) systems' capacity to reason about text, comprehend context, and produce more precise and pertinent results. In order to improve named entity recognition, text classification, and question answering, among other NLP tasks, this paper investigates the integration of knowledge graphs with NLP.
The health insurance industry is a growing business, and the IT infrastructure needed to accommodate the increasing needs of the market demands is highly scalable. Automation is an important part of this sector since it enables organizations to automate their processes, eliminate manual errors, and get more efficient work. The best infrastructure automation tool is Ansible – an open-source configuration management and deployment tool which applies the readability and adaptability of YAML to IT environment creation and management.
In this study, we’ll discuss the use cases and pros and cons of Ansible and YAML for infrastructure automation in health insurance by reviewing the key features, implementation methods, and case studies to illustrate how the technology has been used to great effect.
As machine learning algorithms are used more and more in different fields, it is more important than ever to address issues with bias, fairness, and robustness. The current state of research in this field is thoroughly reviewed in this paper, which also examines the various definitions and approaches to fairness, the methods for enhancing the robustness of these algorithms, and the types and sources of biases that can occur in machine learning models. We go over the connections between these three crucial areas and point out the difficulties and possible solutions in developing AI systems that are more moral and reliable.
We present a taxonomy of bias types, fairness definitions, and robustness techniques based on a synthesis of recent literature. We also go over the trade-offs and practical uses of these methods. The goal of this paper is to be a useful tool for practitioners and researchers who are trying to create machine learning models that are impartial, reliable, and equitable.
Recent years have seen a dramatic change in the field of information retrieval, primarily due to developments in deep learning and its use in natural language processing. These developments have greatly benefited search engines and chatbots, two well-known examples of information retrieval systems, which now understand queries better and provide more pertinent and contextual responses.