Computer Science and Engineering

This research is the result of curiosity. The question behind it was: what is the relationship between the order of a derivative and the power of the derivative? Through a series of empirical and heuristic tests, it was
possible to discover that a relationship exists, and the task of this study was to figure out how true it could be and how it could be proven.

In the world we are accustomed to seeing a light bulb either on or off, two unique states that cannot overlap. What if a light bulb could remain both on and off at the same time? In this study, how to apply this theory to computers will be explored.

The evolving landscape of the U.S. healthcare system necessitates a comprehensive understanding of the various factors influencing Medicare enrollment trends and consumer costs, particularly in relation to broker commissions. This paper conducts a detailed datadriven analysis of the impact that Medicare broker commissions have on enrollment patterns and associated consumer expenditures. Utilizing extensive datasets derived from Medicare

The healthcare industry is on the precipice of a transformative evolution, particularly evident in the realm of claims processing, which has historically been characterized by complexity, inefficiency, and a significant propensity for human error. The integration of automation and artificial intelligence (AI) into claims processing systems represents a paradigm shift that promises to enhance operational efficiency while simultaneously addressing long-standing challenges within the sector. This article delves into the multifaceted role of automation and AI in revolutionizing claims processing, elucidating how these technologies can streamline operations, mitigate human error, and facilitate real-time processing of claims.

In the evolving landscape of healthcare financing, Health Reimbursement Arrangements (HRAs) and Health Savings Accounts (HSAs) have emerged as pivotal mechanisms intended to enhance cost management for both consumers and employers. Despite their potential, the underutilization of these accounts presents a significant challenge, which, if addressed, could yield substantial improvements in healthcare expenditure and insurance efficiency. This research employs a comprehensive data-driven approach to analyze the multifaceted effects of underutilized HRAs and HSAs on overall healthcare spending and the efficiency of insurance plans.

This article provides an overview of the most important function that data analytics plays in the revolutionising of banking and financial services. The purpose of this study is to investigate the ways in which contemporary data platforms enable a comprehensive 360-degree view of customers, optimise loan processes through predictive analytics, boost corporate operations through automation, and provide personalised customer experiences among other benefits. By utilising advanced analytics, financial institutions can streamline their operations, enhance their decisionmaking processes, adhere to regulatory requirements, and create stronger relationships with their customers. Furthermore, this highlights the revolutionary influence that datadriven strategies have in terms of driving operational efficiency and strategic growth within the financial sector.

Robot offer a method that is both structured and portable. The data and the ETL process can also be visualised and analysed with the assistance of data visualisation tools like as Tableau, Power BI, and Qlik Sense. To supply this company with information that is helpful, this study makes use of the nine-step process that was created to implement an Online Analysis Processing (OLAP) database and a data warehouse. Small and medium-sized businesses will find it simpler to analyse data because of the data being performed into dashboards. Additionally, a great deal of helpful information can be shared in a short amount of time and in an effective manner.

The processing of many types of information is required for the various business decisions that are made at different levels. When seen from this perspective, the utilisation of appropriate tools will contribute to the process of making good business decisions. These kinds of decisions are intended to be supported in an efficient manner by the framework of the business intelligence system that has been presented. To find out how effective business intelligence (BI) is for companies, this study set out to identify the determinants. Findings from the study point to the need for a business intelligence (BI) efficacy model to enhance advisory services. Making sure decision-makers get the right information at the right time in the right format is the whole point of this paradigm. This study used a quantitative research approach and purposive sampling to recruit research participants from a telecoms company. Business intelligence (BI) departments are directly related to an organization's capacity for making sound decisions. The parts of the business intelligence effectiveness model propose focusing on certain areas to improve the organization's information flow, provide easier access to information, make better decisions, and increase productivity.

Food is essential for human survival. Some of the most crucial factors to think about are minimising food waste, optimising the supply chain, and enhancing food logistics, delivery, and safety. The achievement of these goals is greatly aided by the application of AI and ML. Proliferation of ever-more-powerful and pervasive computer networks has enabled modern logistical and industrial systems. Within these networks, there is a continual influx of fresh data from many sources, generated by sensors, equipment, systems, intelligent devices, and humans. Thanks to computers' ever-improving capabilities, we can now analyse Big Data more quickly, thoroughly, and in-depth than ever before. These developments have rejuvenated and ushered in a new era called Industry 4.0 or the Smart Factory, which has increased the importance of artificial information technology (AI). Machine learning and artificial intelligence are discussed in this article within the context of the food industry and business. Important uses of this technique include supply chain optimisation, crop selection, logistics, food distribution, and processing plant predicting maintenance.

The optimization of pharmaceutical products is the focus of recent advances in digital medicine methods. The pharmaceutical sector could benefit greatly from artificial intelligence (AI) in several ways, including accelerated product development, improved product quality, and more efficient therapy. This article provides a comprehensive analysis of the current state of artificial intelligence (AI) in the pharmaceutical product lifecycle. A search was conducted in PubMed and IEEE Xplore for all articles published between January and March of 2022. After screening for relevant outcomes, publication genres, and data sufficiency, 73 papers (1.2%) were kept out of 6131. To conduct the systematic review and meta-analysis, we followed the guidelines laid out by the PRISMA statement. By the very nature of their implementation, all AI systems fall into several overlapping classes. Clinical trials and pre-clinical tests accounted for 34% of the 177 initiatives that utilized AI. New small molecule design systems come in at 33%, putting them in second position. Novel drug target discovery is the third most common area for AI implementation. This feature is available in almost a quarter of the systems. Surprisingly, 102 systems (or 57% of the total) focus only on a single domain. When it comes to functionality and coverage of the lifespan, none of the systems are up to the task. When DR, AMD, and Nevus are detected all at once, our comprehensive AI method achieves a high diagnosis accuracy. Higher sensitivities with little effect on specificity are made possible with the incorporation of pathology-specific algorithms. In addition, it lessens the possibility of overlooking by accident findings.

In the era of big data, the efficient management and analysis of data have become paramount for businesses seeking to gain competitive advantages. Traditional data warehousing and ETL (Extract, Transform, Load) processes are increasingly challenged by the volume, velocity, and variety of data. To address these challenges, the integration of cloud-native Robotic Process Automation (RPA) and Artificial Intelligence (AI) presents a promising approach to architecting intelligent data pipelines. This research explores the design and implementation of such intelligent pipelines, emphasizing how they leverage cloud-native RPA and AI technologies to automate data warehousing processes and advance analytics capabilities. The study begins by analyzing the core components and architectural considerations for building intelligent data pipelines. Central to this architecture is the application of cloudnative RPA, which automates repetitive and time-consuming tasks within the ETL framework. RPA's ability to interact with disparate data sources and perform routine data handling tasks without manual intervention streamlines the ETL process, reduces operational costs, and minimizes human error. Additionally, RPA's scalability in cloud environments enables organizations to handle large-scale data operations efficiently. Complementing RPA, AI technologies play a critical role in enhancing data quality and enabling advanced analytics. AI-driven tools, such as machine learning algorithms and natural language processing models, are employed to transform raw data into actionable insights. These AI technologies support advanced data cleaning, anomaly detection, and pattern recognition, thereby improving the accuracy and reliability of the data warehouse. Real-time analytics capabilities are also significantly enhanced through AI, facilitating prompt and informed decision-making in dynamic business environments.

In the contemporary business landscape, the integration of Artificial Intelligence (AI) and Machine Learning (ML) into data-driven strategies has emerged as a pivotal factor for organizational success and competitive advantage. This paper delineates a comprehensive framework for leveraging AI and ML to enhance business analytics, improve decision-making processes, and foster organizational growth. The framework proposed herein serves as a strategic guide for businesses seeking to harness the transformative potential of these technologies. AI and ML technologies have revolutionized the domain of business analytics by providing sophisticated tools for data processing, pattern recognition, and predictive modeling. The application of AI algorithms facilitates the extraction of actionable insights from vast and complex datasets, enabling organizations to make informed decisions with unprecedented accuracy. Machine learning models, with their capacity for adaptive learning and iterative refinement, offer dynamic analytical capabilities that are crucial for navigating the rapidly evolving business environment.

In the era of big data, the optimization of machine learning models within cloud-based data warehousing systems has emerged as a critical domain of research and application. This paper presents an in-depth analysis of advanced data science techniques aimed at enhancing the performance and scalability of machine learning models in such environments. Cloud-based data warehousing systems offer substantial advantages, including scalability, flexibility, and the ability to handle vast amounts of data, yet they also introduce unique challenges related to model optimization. Model selection, hyperparameter tuning, and deployment strategies are pivotal aspects of optimizing machine learning models in these contexts. The paper begins by exploring model selection techniques tailored for cloud-based systems, emphasizing the need for models that not only perform well in theory but also scale efficiently with large datasets and distributed computing resources. The selection process involves evaluating various algorithms and architectures, including ensemble methods, deep learning models, and emerging techniques such as transformer-based architectures, considering their suitability for the specific requirements of cloud environments. Hyperparameter tuning represents another critical area of focus. The paper delves into advanced methods for hyperparameter optimization, including grid search, random search, and more sophisticated approaches such as Bayesian optimization and genetic algorithms.

Healthcare systems have evolved rapidly; driven by the need for efficient and accurate claims processing in order to reduce fraud, errors, and increase operational efficiency. "The existing traditional manual methods are errorprone, time-consuming and costlier in terms of administration. This chapter provides insight into how Machine Learning (ML) and Automation are fundamentally changing the way that healthcare claims are being managed in a revolutionary manner. We explore essential ML techniques in the form of predictive analytics, anomaly detection, natural language processing (NLP), and robotic process automation (RPA), that further enhance claims validation, fraud detection, and adjudication. This chapter explores various case studies and tangible implementations which have reported better claim accuracy, shorter processing times and increased fraud deterrent potential. Moreover, we explore challenges including data privacy, ethical implications, and regulatory compliance in AI-based automation. Further more, we discuss challenges such as data privacy, ethical considerations, and regulatory compliance in AIdriven automation. This will not only lead to a flowless operation and quicker settlement of the claim but also reduce the operational costs and, in return, improve the patient satisfaction in quality of care.

The integration of Artificial Intelligence (AI) and Data Analytics in healthcare has emerged as a transformative force in improving the efficiency, accuracy, and accessibility of medical services. This research paper examines how AI-driven models and data analytics techniques are being harnessed to provide smarter healthcare solutions. Through the application of machine learning, predictive analytics, and data mining, healthcare providers can now analyze vast amounts of patient data, offering more accurate diagnostics, personalized treatment plans, and enhanced clinical decision-making. In particular, AI algorithms such as neural networks and deep learning are utilized for early disease detection, improving patient outcomes by predicting medical events before they occur. Results from case studies and clinical trials indicate that AI and data analytics have successfully reduced diagnostic errors, enhanced treatment efficiency, and facilitated faster decision-making, leading to improved patient satisfaction and cost-effective care. However, challenges remain, including data privacy concerns, the need for large and diverse datasets, and the requirement for further validation in real-world healthcare settings. This paper concludes by discussing the future potential of AI and data analytics to revolutionize healthcare systems globally, emphasizing the importance of interdisciplinary collaboration, ethical considerations, and continuous innovation.

Healthcare claims processing is a vital but complicated part of the medical industry, plagued by inefficiencies and manual errors, and drawn-out approval cycles. The use of Automation, powered by Artificial Intelligence (AI), Machine Learning (ML), and Robotic Process Automation (RPA) is revolutionizing eh claims management paradigm through smarter, accurate and faster processes. This article focuses on using automation to ease healthcare claims automation, and reduce manual processing, potential for submitting fraudulent claims and adhering with industry standards. We focus on the core technologies that will facilitate this shift Natural Language Processing (NLP) for data extraction, predictive analytics for fraud detection and blockchain for secure transactions. Additionally, we assess the influence of automation on operational expenses, claims processing times, and patient satisfaction. This paper uses case studies and data-driven findings to demonstrate how automation not only significantly increases the efficiency of claims processing but can also help streamline regulatory compliance processes. We also identify some key challenges that must be addressed in order to realize this potential, including the risk of data privacy breaches, and integration difficulties that could hinder the effective implementation of this new technology. The results indicate that implementing automated healthcare claims processing is an essential evolution in redesigning the healthcare system.

Cloud computing has emerged as a transformative force in the healthcare industry, revolutionizing how organizations deliver patient care, manage data, and optimize operations. This comprehensive article examines the evolution from traditional infrastructure to cloud-based solutions, exploring fundamental service models, including Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). The article investigates various deployment models-public, private, and hybrid clouds-and their specific applications in healthcare settings. It further analyzes the transformative impact on patient care, operational efficiency, and

This comprehensive article explores the transformative role of Customer Relationship Management (CRM) systems in modern healthcare delivery. The article examines how CRM platforms revolutionize patient care through enhanced engagement, streamlined operations, and data-driven decision-making capabilities. It investigates the significant impact of healthcare CRM on personalized care delivery, patient experience, and operational efficiency while addressing the technical and organizational implementation challenges. The article demonstrates how CRM systems facilitate better care coordination, improve patient outcomes, and contribute to financial sustainability in healthcare organizations. Special attention is given to the unique features of healthcare CRM systems that differentiate them from traditional CRM platforms, highlighting their crucial role in advancing patient-centered care models and supporting healthcare providers in delivering more effective, efficient, personalized medical services.

The integration of Artificial Intelligence in healthcare represents a transformative advancement in modern medicine, particularly in diagnostic applications and personalized treatment approaches. This comprehensive article examines the current state and future prospects of AI in healthcare, focusing on medical diagnosis, data analytics, and precision medicine. The article encompasses various applications, from imaging diagnostics and pattern recognition to personalized treatment planning, highlighting significant improvements in diagnostic accuracy, workflow efficiency, and patient outcomes.