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Tag: Economics

  • Machine Learning Applications for Accounting Disclosure and Fraud Detection (Advances in Finance, Accounting, and Economics)

    Machine Learning Applications for Accounting Disclosure and Fraud Detection (Advances in Finance, Accounting, and Economics)


    Price: $225.00 – $164.03
    (as of Dec 24,2024 22:57:36 UTC – Details)




    Publisher ‏ : ‎ Business Science Reference; 1st edition (October 2, 2020)
    Language ‏ : ‎ English
    Hardcover ‏ : ‎ 270 pages
    ISBN-10 ‏ : ‎ 1799848051
    ISBN-13 ‏ : ‎ 978-1799848059
    Item Weight ‏ : ‎ 1.95 pounds
    Dimensions ‏ : ‎ 8.75 x 1 x 11.25 inches


    Machine learning has revolutionized the field of accounting, providing new and innovative ways to detect fraud and improve disclosure practices. In the book “Machine Learning Applications for Accounting Disclosure and Fraud Detection (Advances in Finance, Accounting, and Economics)”, experts delve into the cutting-edge applications of machine learning in accounting.

    From predicting financial restatements to analyzing textual disclosures, machine learning algorithms can sift through massive amounts of data to uncover patterns and anomalies that may indicate fraudulent activity. By automating the detection process, these algorithms can flag suspicious transactions and help auditors prioritize their efforts more effectively.

    Furthermore, machine learning can also enhance the quality of financial reporting by improving the accuracy and timeliness of disclosure practices. By analyzing historical data and identifying trends, these algorithms can help organizations make more informed decisions and communicate information more transparently to stakeholders.

    Overall, the integration of machine learning in accounting has the potential to revolutionize the way financial information is analyzed and disclosed. With the help of advanced algorithms and predictive analytics, organizations can stay ahead of fraudsters and ensure compliance with regulatory requirements. “Machine Learning Applications for Accounting Disclosure and Fraud Detection” is a must-read for anyone looking to stay at the forefront of these exciting developments in the field of accounting.
    #Machine #Learning #Applications #Accounting #Disclosure #Fraud #Detection #Advances #Finance #Accounting #Economics

  • The Cultural and Political Economy of Recovery: Social Learning in a post-disaster environment (Routledge Advances in Heterodox Economics)

    The Cultural and Political Economy of Recovery: Social Learning in a post-disaster environment (Routledge Advances in Heterodox Economics)


    Price: $68.99 – $51.74
    (as of Dec 24,2024 04:36:45 UTC – Details)




    Publisher ‏ : ‎ Routledge; 1st edition (November 11, 2013)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 240 pages
    ISBN-10 ‏ : ‎ 0415745438
    ISBN-13 ‏ : ‎ 978-0415745437
    Item Weight ‏ : ‎ 12.8 ounces
    Dimensions ‏ : ‎ 6.14 x 0.55 x 9.21 inches


    In the wake of a natural disaster, the process of recovery goes beyond rebuilding physical infrastructure. It also involves addressing the cultural and political economy of the affected community. In the book “The Cultural and Political Economy of Recovery: Social Learning in a post-disaster environment,” authors delve into the complexities of recovery efforts in a heterodox economic framework.

    Drawing on case studies from various disaster-stricken regions, the book explores how social learning plays a crucial role in shaping recovery outcomes. From grassroots initiatives to government policies, the interactions between different stakeholders are examined to understand the dynamics of post-disaster recovery.

    By challenging traditional economic theories and emphasizing the importance of cultural and political factors, this book offers a fresh perspective on how communities can effectively rebuild and thrive after a disaster. It is a must-read for policymakers, researchers, and practitioners interested in understanding the nuances of recovery in a post-disaster environment.
    #Cultural #Political #Economy #Recovery #Social #Learning #postdisaster #environment #Routledge #Advances #Heterodox #Economics, Disaster Recovery

  • Workbook and Study Guide to Economics By Hakes

    Workbook and Study Guide to Economics By Hakes



    Workbook and Study Guide to Economics By Hakes

    Price : 20.32

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    Looking for a comprehensive workbook and study guide to help you ace your economics course? Look no further than “Workbook and Study Guide to Economics” by Hakes. This comprehensive resource is designed to help students understand key economic concepts, improve problem-solving skills, and prepare for exams.

    With easy-to-follow explanations, practice problems, and detailed solutions, this workbook is the perfect companion for students studying economics at any level. Whether you’re struggling with supply and demand or trying to grasp the intricacies of macroeconomic policies, this workbook has you covered.

    Don’t let economics intimidate you any longer. Pick up a copy of “Workbook and Study Guide to Economics” by Hakes and start mastering the principles of economics today!
    #Workbook #Study #Guide #Economics #Hakes, deep learning

  • The Economics of Data Center MTTR: Calculating the Cost of Downtime and Repair Time

    The Economics of Data Center MTTR: Calculating the Cost of Downtime and Repair Time


    Data centers are the backbone of the modern digital economy, providing the infrastructure necessary to support the vast amount of data that is generated and processed every day. However, like any complex system, data centers are prone to downtime and failures, which can have significant economic consequences for businesses that rely on them.

    One key metric that data center operators use to measure the reliability and efficiency of their operations is Mean Time to Repair (MTTR). MTTR is a measure of how long it takes to repair a failed component or system and bring it back online. The lower the MTTR, the faster a data center can recover from downtime and resume normal operations.

    Calculating the cost of downtime and repair time is essential for data center operators to understand the economic impact of failures and prioritize investments in improving reliability and resilience. The cost of downtime can be calculated by multiplying the average revenue generated per hour by the duration of the downtime. For example, if a data center generates $10,000 per hour in revenue and experiences 4 hours of downtime, the cost of downtime would be $40,000.

    In addition to the cost of downtime, data center operators must also consider the cost of repair time. This includes the cost of labor, replacement parts, and any other expenses associated with restoring the failed component or system. By calculating the cost of repair time, data center operators can determine the optimal balance between investing in preventive maintenance and reactive repairs.

    Reducing MTTR can have a significant impact on the economics of data center operations. By investing in proactive maintenance, redundant systems, and automation tools, data center operators can minimize the risk of failures and shorten the time it takes to recover from downtime. This not only reduces the economic impact of failures but also improves the overall reliability and efficiency of the data center.

    In conclusion, the economics of data center MTTR are crucial for businesses to understand the true cost of downtime and repair time. By calculating the cost of failures and investing in measures to reduce MTTR, data center operators can improve the reliability and efficiency of their operations, ultimately leading to better business outcomes and customer satisfaction.

  • The Economics of Data Center HVAC: Cost-Effective Strategies

    The Economics of Data Center HVAC: Cost-Effective Strategies


    Data centers are the backbone of modern businesses, storing and processing vast amounts of data to keep operations running smoothly. However, running a data center comes with a hefty price tag, especially when it comes to cooling the servers to prevent overheating and ensure optimal performance. In fact, HVAC (Heating, Ventilation, and Air Conditioning) systems can account for up to 40% of a data center’s total energy consumption, making it a significant expense for businesses.

    With the rising demand for data storage and processing capabilities, data center operators are constantly looking for cost-effective strategies to optimize their HVAC systems. By implementing efficient cooling solutions, businesses can not only reduce their energy costs but also improve the overall performance and reliability of their data centers.

    One of the key strategies for cost-effective HVAC in data centers is to implement a modular cooling system. Traditional HVAC systems are designed to cool the entire data center, regardless of the actual heat load. This leads to wasted energy and higher operating costs. Modular cooling systems, on the other hand, allow data center operators to cool specific areas or racks that are generating the most heat, resulting in more efficient cooling and lower energy consumption.

    Another cost-effective strategy is to implement free cooling techniques, such as using outside air or water instead of traditional refrigeration systems to cool the servers. By taking advantage of natural cooling methods, businesses can significantly reduce their energy costs and minimize their environmental impact.

    Furthermore, optimizing airflow within the data center can also help reduce cooling costs. By ensuring proper airflow management and eliminating hot spots, businesses can improve the efficiency of their HVAC systems and prevent unnecessary cooling.

    In addition to implementing efficient cooling solutions, businesses can also leverage data analytics and monitoring tools to optimize their HVAC systems. By collecting and analyzing real-time data on temperature, humidity, and airflow, data center operators can identify areas of improvement and make informed decisions to increase efficiency and reduce costs.

    Ultimately, the economics of data center HVAC come down to finding the right balance between cost and performance. By implementing cost-effective strategies, businesses can not only reduce their energy expenses but also enhance the overall reliability and efficiency of their data centers. With the increasing demand for data storage and processing capabilities, optimizing HVAC systems will be crucial for businesses to stay competitive in the digital age.

  • ECONOMICS OF ARTIFICIAL INTELLIGENCE THE (9780226613338)

    ECONOMICS OF ARTIFICIAL INTELLIGENCE THE (9780226613338)



    ECONOMICS OF ARTIFICIAL INTELLIGENCE THE (9780226613338)

    Price : 176.30

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    The Economics of Artificial Intelligence: A Deep Dive into the Future of Technology (9780226613338)

    Artificial intelligence is rapidly transforming industries and changing the way we live and work. But what are the economic implications of this technological revolution?

    In this post, we will explore the economics of artificial intelligence, focusing on the book “The Economics of Artificial Intelligence: An Agenda” by Joshua Gans, Avi Goldfarb, and Ajay Agrawal. This groundbreaking book delves into the economic impact of AI, addressing key questions such as the potential for job displacement, the role of data in AI development, and the implications for productivity and economic growth.

    With insights from leading experts in the field, this book offers a comprehensive analysis of the economic implications of AI and provides valuable insights for policymakers, business leaders, and anyone interested in the future of technology.

    Don’t miss out on this essential read for understanding the economics of artificial intelligence and how it will shape the future of our economy. Get your hands on a copy of “The Economics of Artificial Intelligence: An Agenda” today!
    #ECONOMICS #ARTIFICIAL #INTELLIGENCE

  • Business Data Science: Combining Machine Learning and Economics to Optimize,…

    Business Data Science: Combining Machine Learning and Economics to Optimize,…



    Business Data Science: Combining Machine Learning and Economics to Optimize,…

    Price : 15.00

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    Business Data Science: Combining Machine Learning and Economics to Optimize Decision-Making

    In today’s data-driven business environment, companies are constantly seeking ways to leverage data and analytics to make better decisions and drive growth. One of the most powerful tools in the data scientist’s arsenal is machine learning, a branch of artificial intelligence that uses algorithms to analyze large amounts of data and uncover patterns and insights.

    But while machine learning is incredibly powerful, it becomes even more effective when combined with insights from economics. By incorporating economic principles into their analysis, data scientists can gain a deeper understanding of how markets work and how various factors impact business outcomes.

    For example, by applying economic theories of supply and demand to their machine learning models, data scientists can better predict consumer behavior and optimize pricing strategies. Additionally, by incorporating concepts from game theory, companies can better understand competitive dynamics and make more strategic decisions.

    Ultimately, by combining machine learning with economics, businesses can gain a more holistic understanding of their operations and make more informed decisions. This powerful combination allows companies to optimize their processes, improve customer satisfaction, and drive growth in an increasingly competitive marketplace.

    So, if you want to stay ahead of the curve in today’s data-driven world, consider incorporating both machine learning and economics into your business data science strategy. The results could be transformative for your organization.
    #Business #Data #Science #Combining #Machine #Learning #Economics #Optimize..

  • Machine Learning Techniques in Econometrics: With Python (Richman Computational Economics)

    Machine Learning Techniques in Econometrics: With Python (Richman Computational Economics)


    Price: $29.99
    (as of Dec 18,2024 10:39:33 UTC – Details)




    ASIN ‏ : ‎ B0DLLC23BT
    Publisher ‏ : ‎ Independently published (October 31, 2024)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 194 pages
    ISBN-13 ‏ : ‎ 979-8345068007
    Item Weight ‏ : ‎ 12.5 ounces
    Dimensions ‏ : ‎ 6 x 0.44 x 9 inches


    Machine Learning Techniques in Econometrics: With Python (Richman Computational Economics)

    In today’s rapidly evolving world of data analysis and artificial intelligence, machine learning techniques have become increasingly important in the field of econometrics. By leveraging the power of machine learning algorithms, economists are able to uncover valuable insights from complex datasets and make more accurate predictions about economic trends.

    In this post, we will explore some of the key machine learning techniques used in econometrics, with a focus on how they can be implemented using the Python programming language. The Richman Computational Economics team has developed a comprehensive guide to help economists and data scientists harness the power of machine learning in their research.

    Some of the topics covered in this guide include:

    – Regression analysis: Learn how to use machine learning algorithms such as linear regression and decision trees to model and analyze economic data.
    – Time series forecasting: Discover how techniques like ARIMA and LSTM can be used to predict future economic trends based on historical data.
    – Clustering and classification: Explore how clustering and classification algorithms can be used to segment and categorize economic data for deeper analysis.
    – Dimensionality reduction: Learn how techniques like PCA and t-SNE can help reduce the complexity of large datasets and extract meaningful patterns.

    By mastering these machine learning techniques, economists can gain a deeper understanding of complex economic systems and make more informed decisions. The Richman Computational Economics guide provides step-by-step instructions and code examples to help readers apply these techniques in their own research projects.

    Whether you are a seasoned economist looking to expand your analytical toolkit or a data scientist interested in applying machine learning to economic data, this guide is a valuable resource for advancing your skills and knowledge. Stay ahead of the curve in econometrics with Machine Learning Techniques in Econometrics: With Python from Richman Computational Economics.
    #Machine #Learning #Techniques #Econometrics #Python #Richman #Computational #Economics

  • The Economics of IPTV: How Streaming Services are Changing the Business Model of Television

    The Economics of IPTV: How Streaming Services are Changing the Business Model of Television


    The rise of Internet Protocol Television (IPTV) has revolutionized the way we consume television content. With the advent of streaming services like Netflix, Hulu, Amazon Prime Video, and Disney+, traditional cable and satellite TV providers are facing stiff competition. This shift towards IPTV has not only changed the way we watch TV, but it has also had a significant impact on the economics of the television industry.

    One of the key ways in which IPTV has changed the business model of television is through the subscription-based model. Unlike traditional cable and satellite TV providers, who typically charge customers a monthly fee for a bundle of channels, IPTV services offer a more flexible pricing structure. Customers can choose from a variety of subscription plans, ranging from basic to premium, and pay only for the content they want to watch. This has led to a more personalized viewing experience for consumers, as they can select the services that best suit their preferences and budget.

    Another way in which IPTV has changed the economics of television is through the elimination of advertising as the primary source of revenue. While traditional TV networks rely heavily on advertising to generate income, streaming services like Netflix and Amazon Prime Video rely on subscription fees for their revenue. This shift has allowed IPTV services to focus more on creating high-quality, original content, rather than catering to advertisers’ demands. As a result, viewers are treated to a wider variety of programming, including niche genres and international content that may not have been available on traditional TV.

    Furthermore, the rise of IPTV has also given rise to a new wave of competition in the television industry. With the entry of tech giants like Apple, Google, and Facebook into the streaming market, traditional TV providers are facing increased pressure to innovate and adapt to the changing landscape. This has led to a wave of mergers and acquisitions in the industry, as companies seek to consolidate their resources and compete more effectively in the IPTV space.

    Overall, the economics of IPTV are reshaping the television industry in profound ways. By offering a more personalized viewing experience, eliminating the reliance on advertising, and fostering increased competition, IPTV services are driving a new era of innovation and creativity in television content. As consumers continue to embrace streaming services, the traditional business model of television is likely to evolve even further, leading to a more dynamic and diverse media landscape for viewers around the world.

  • Prediction Machines, Updated and Expanded: The Simple Economics of Artificial Intelligence

    Prediction Machines, Updated and Expanded: The Simple Economics of Artificial Intelligence


    Price: $30.00 – $18.19
    (as of Dec 17,2024 16:08:55 UTC – Details)




    Publisher ‏ : ‎ Harvard Business Review Press; Revised edition (November 15, 2022)
    Language ‏ : ‎ English
    Hardcover ‏ : ‎ 304 pages
    ISBN-10 ‏ : ‎ 1647824672
    ISBN-13 ‏ : ‎ 978-1647824679
    Item Weight ‏ : ‎ 2.31 pounds
    Dimensions ‏ : ‎ 6.25 x 1 x 9.5 inches


    In the highly anticipated update and expansion of the groundbreaking book “Prediction Machines: The Simple Economics of Artificial Intelligence,” authors Ajay Agrawal, Joshua Gans, and Avi Goldfarb delve even deeper into the transformative power of AI in the world of economics.

    This updated edition explores the latest advancements in AI technology and how they are reshaping industries, businesses, and everyday life. From machine learning algorithms to neural networks, the authors provide a comprehensive overview of the key concepts and applications of AI in economics.

    Drawing on real-world examples and case studies, “Prediction Machines, Updated and Expanded” offers valuable insights into how AI is revolutionizing decision-making processes, creating new opportunities for innovation, and challenging traditional economic models.

    Whether you’re a business leader, economist, or simply curious about the impact of AI on our society, this book is a must-read for anyone looking to understand the future of artificial intelligence and its implications for the global economy. Get ready to explore the simple economics of AI in a whole new light with this updated and expanded edition of “Prediction Machines.”
    #Prediction #Machines #Updated #Expanded #Simple #Economics #Artificial #Intelligence

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