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Tag: explainable AI

  • Explainable Artificial Intelligence for Intelligent Transportation Systems : …

    Explainable Artificial Intelligence for Intelligent Transportation Systems : …



    Explainable Artificial Intelligence for Intelligent Transportation Systems : …

    Price : 206.04

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    Artificial intelligence (AI) is revolutionizing the way we approach transportation systems, making them more efficient, reliable, and safe. One important aspect of AI in transportation is the concept of Explainable Artificial Intelligence (XAI), which aims to make AI systems more transparent and understandable to users and stakeholders.

    In the context of Intelligent Transportation Systems (ITS), XAI plays a crucial role in ensuring that the decisions made by AI algorithms can be easily explained and interpreted by humans. This is particularly important in scenarios where AI systems are used to make critical decisions, such as route planning, traffic management, and autonomous driving.

    XAI in ITS involves techniques that enable users to understand how AI algorithms reach their conclusions, providing insights into the reasoning behind the decisions made. This not only enhances trust in AI systems but also allows users to identify potential biases, errors, or anomalies in the data or algorithms used.

    By making AI systems more explainable, ITS can benefit from improved decision-making, increased safety, and better overall performance. For example, in autonomous vehicles, XAI can help passengers understand why a vehicle made a specific maneuver or avoided a collision, leading to increased acceptance and adoption of autonomous driving technologies.

    Overall, Explainable Artificial Intelligence is a crucial component of Intelligent Transportation Systems, ensuring that AI algorithms are not only intelligent but also transparent and accountable. By incorporating XAI into ITS, we can create safer, more efficient, and more user-friendly transportation systems for the future.
    #Explainable #Artificial #Intelligence #Intelligent #Transportation #Systems

  • Explainable Ai : Interpreting, Explaining and Visualizing Deep Learning, Pape…

    Explainable Ai : Interpreting, Explaining and Visualizing Deep Learning, Pape…



    Explainable Ai : Interpreting, Explaining and Visualizing Deep Learning, Pape…

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    Explainable AI, also known as XAI, is a rapidly evolving field that aims to make deep learning models more transparent and understandable to humans. In a new paper titled “Interpreting, Explaining and Visualizing Deep Learning,” researchers delve into the importance of explainability in AI systems and propose methods for interpreting, explaining, and visualizing the inner workings of deep learning models.

    The paper highlights the growing need for transparency in AI systems, especially as these models are being deployed in critical applications such as healthcare, finance, and autonomous vehicles. Without the ability to understand how a deep learning model arrives at its decisions, it becomes difficult to trust and rely on these systems.

    The researchers outline various techniques for interpreting deep learning models, such as feature visualization, saliency maps, and attribution methods. These methods allow researchers and developers to gain insights into the model’s decision-making process and identify potential biases or errors.

    Furthermore, the paper discusses the importance of explaining AI models to end-users in a human-readable way. By providing explanations for the decisions made by a deep learning model, users can better understand and trust the system’s outputs.

    Overall, the paper emphasizes the need for explainable AI in order to build trust, ensure accountability, and facilitate the adoption of deep learning models in real-world applications. As AI continues to advance, it is essential that researchers and practitioners prioritize the development of interpretable, explainable, and visualizable AI systems.
    #Explainable #Interpreting #Explaining #Visualizing #Deep #Learning #Pape..

  • Mathematical Fuzzy Logic in the Emerging Fields of Engineering, Finance, and Computer Sciences

    Mathematical Fuzzy Logic in the Emerging Fields of Engineering, Finance, and Computer Sciences


    Price: $88.92 – $76.70
    (as of Dec 28,2024 04:21:38 UTC – Details)




    Publisher ‏ : ‎ Mdpi AG (January 12, 2023)
    Language ‏ : ‎ English
    Hardcover ‏ : ‎ 250 pages
    ISBN-10 ‏ : ‎ 3036559361
    ISBN-13 ‏ : ‎ 978-3036559360
    Item Weight ‏ : ‎ 1.63 pounds
    Dimensions ‏ : ‎ 6.69 x 0.81 x 9.61 inches


    Mathematical Fuzzy Logic in the Emerging Fields of Engineering, Finance, and Computer Sciences

    Fuzzy logic, a mathematical approach that deals with uncertainty and imprecision, is gaining significant attention in various emerging fields such as engineering, finance, and computer sciences. Fuzzy logic allows for more flexibility and adaptability in decision-making processes, making it a valuable tool in complex and uncertain environments.

    In engineering, fuzzy logic is being used to improve control systems, decision-making processes, and pattern recognition. By incorporating fuzzy logic into engineering systems, engineers can better handle uncertainties, variations, and incomplete information, ultimately leading to more efficient and reliable systems.

    In finance, fuzzy logic is being utilized for risk management, portfolio optimization, and market analysis. With the ability to handle vague and uncertain data, fuzzy logic can provide more accurate and robust models for predicting market trends and making investment decisions.

    In computer sciences, fuzzy logic is being applied in various areas such as artificial intelligence, machine learning, and data mining. Fuzzy logic allows for more human-like decision-making processes, enabling computers to better understand and interpret ambiguous and uncertain information.

    Overall, the integration of mathematical fuzzy logic in these emerging fields is proving to be a game-changer, offering new solutions and approaches to complex problems. As technology continues to advance, the importance of fuzzy logic in engineering, finance, and computer sciences will only continue to grow, making it a crucial area of study for future professionals in these fields.
    #Mathematical #Fuzzy #Logic #Emerging #Fields #Engineering #Finance #Computer #Sciences

  • Connected and Automated Vehicles: Integrating Engineering and Ethics (Studies in Applied Philosophy, Epistemology and Rational Ethics, 67)

    Connected and Automated Vehicles: Integrating Engineering and Ethics (Studies in Applied Philosophy, Epistemology and Rational Ethics, 67)


    Price: $169.99 – $129.98
    (as of Dec 28,2024 04:02:04 UTC – Details)




    Publisher ‏ : ‎ Springer; 2023rd edition (September 23, 2023)
    Language ‏ : ‎ English
    Hardcover ‏ : ‎ 209 pages
    ISBN-10 ‏ : ‎ 3031399900
    ISBN-13 ‏ : ‎ 978-3031399909
    Item Weight ‏ : ‎ 1 pounds
    Dimensions ‏ : ‎ 6.25 x 0.75 x 9.5 inches


    In today’s rapidly evolving world of transportation, the integration of engineering and ethics is becoming increasingly important, especially with the rise of connected and automated vehicles (CAVs). The book “Connected and Automated Vehicles: Integrating Engineering and Ethics” delves deep into this topic, exploring the ethical implications of CAVs and how engineers can navigate these complex issues.

    Authored by leading experts in the field, this book offers a comprehensive analysis of the ethical challenges posed by CAVs, such as privacy concerns, liability issues, and the impact on society as a whole. By addressing these ethical dilemmas head-on, the authors provide valuable insights for engineers, policymakers, and other stakeholders involved in the development and deployment of CAVs.

    With the potential to revolutionize transportation and improve road safety, CAVs hold great promise for the future. However, it is essential that ethical considerations are carefully considered and integrated into the design and implementation of these technologies. “Connected and Automated Vehicles: Integrating Engineering and Ethics” serves as a vital resource for anyone seeking to understand and navigate the complex intersection of engineering and ethics in the realm of CAVs.
    #Connected #Automated #Vehicles #Integrating #Engineering #Ethics #Studies #Applied #Philosophy #Epistemology #Rational #Ethics

  • Marketing und Innovation in disruptiven Zeiten (German Edition)

    Marketing und Innovation in disruptiven Zeiten (German Edition)


    Price: $99.99
    (as of Dec 28,2024 03:41:30 UTC – Details)




    Publisher ‏ : ‎ Springer Gabler; 1. Aufl. 2023 edition (January 21, 2023)
    Language ‏ : ‎ German
    Hardcover ‏ : ‎ 508 pages
    ISBN-10 ‏ : ‎ 3658385715
    ISBN-13 ‏ : ‎ 978-3658385712
    Item Weight ‏ : ‎ 2.61 pounds
    Dimensions ‏ : ‎ 6.3 x 0.55 x 9.21 inches


    Marketing und Innovation in disruptiven Zeiten

    In einer Welt, die sich ständig verändert und von technologischen Fortschritten geprägt ist, sind Marketing und Innovation entscheidend für den Erfolg eines Unternehmens. Doch gerade in disruptiven Zeiten, in denen etablierte Geschäftsmodelle in Frage gestellt werden und neue Player auf den Markt drängen, ist es besonders wichtig, sich mit diesen Themen auseinanderzusetzen.

    In unserem neuen Buch “Marketing und Innovation in disruptiven Zeiten” geben wir einen umfassenden Überblick über die aktuellen Trends und Herausforderungen im Bereich Marketing und Innovation. Wir zeigen auf, wie Unternehmen mit den rapiden Veränderungen in der digitalen Welt Schritt halten können und welche Strategien und Tools dabei helfen, erfolgreich zu bleiben.

    Egal ob etabliertes Unternehmen oder Start-up, dieses Buch bietet wertvolle Einblicke und Tipps für alle, die sich mit Marketing und Innovation in disruptiven Zeiten beschäftigen. Lassen Sie sich inspirieren und bereiten Sie Ihr Unternehmen auf die Zukunft vor!

    Jetzt erhältlich in deutscher Sprache. Sichern Sie sich Ihr Exemplar noch heute!
    #Marketing #und #Innovation #disruptiven #Zeiten #German #Edition

  • Deep Learning in Medical Image Analysis

    Deep Learning in Medical Image Analysis


    Price: $107.30 – $89.89
    (as of Dec 28,2024 03:19:48 UTC – Details)




    Publisher ‏ : ‎ Mdpi AG (August 26, 2021)
    Language ‏ : ‎ English
    Hardcover ‏ : ‎ 458 pages
    ISBN-10 ‏ : ‎ 3036514694
    ISBN-13 ‏ : ‎ 978-3036514697
    Item Weight ‏ : ‎ 2.6 pounds
    Dimensions ‏ : ‎ 6.69 x 1.38 x 9.61 inches


    Deep learning has revolutionized the field of medical image analysis by allowing for more accurate and efficient interpretation of complex medical images. With the ability to learn from large datasets, deep learning algorithms can identify patterns and abnormalities in medical images that may be missed by the human eye.

    One of the key applications of deep learning in medical image analysis is in the detection and diagnosis of diseases such as cancer, cardiovascular disease, and neurological disorders. By training deep learning models on vast amounts of medical imaging data, researchers and healthcare professionals can develop algorithms that can accurately identify and classify different types of abnormalities in medical images.

    In addition to disease detection and diagnosis, deep learning has also been used in medical image segmentation, which involves delineating and outlining different structures or organs within an image. This can be particularly useful in surgical planning, radiation therapy, and monitoring disease progression.

    Overall, deep learning has the potential to significantly improve the accuracy, efficiency, and speed of medical image analysis, leading to better patient outcomes and more personalized treatment plans. As the field continues to evolve, we can expect to see even more advancements in the application of deep learning in medical imaging, ultimately leading to better healthcare for all.
    #Deep #Learning #Medical #Image #Analysis

  • Statistical Data Modeling and Machine Learning with Applications II

    Statistical Data Modeling and Machine Learning with Applications II


    Price: $98.13 – $82.91
    (as of Dec 28,2024 02:58:12 UTC – Details)




    Publisher ‏ : ‎ Mdpi AG (July 19, 2023)
    Language ‏ : ‎ English
    Hardcover ‏ : ‎ 344 pages
    ISBN-10 ‏ : ‎ 3036582002
    ISBN-13 ‏ : ‎ 978-3036582009
    Item Weight ‏ : ‎ 2.07 pounds
    Dimensions ‏ : ‎ 6.69 x 1.06 x 9.61 inches


    Statistical Data Modeling and Machine Learning with Applications II: A Deep Dive into Advanced Techniques

    In our previous post, we discussed the basics of statistical data modeling and machine learning. Now, let’s take a deeper dive into advanced techniques and real-world applications.

    1. Ensemble Learning: Ensemble learning is a powerful technique where multiple models are combined to improve prediction accuracy. This includes bagging, boosting, and stacking methods. By leveraging the strengths of different models, ensemble learning can significantly enhance performance.

    2. Deep Learning: Deep learning involves neural networks with multiple layers to learn complex patterns in data. This technique has revolutionized fields like image recognition, natural language processing, and speech recognition. With the advent of frameworks like TensorFlow and PyTorch, deep learning has become more accessible to data scientists.

    3. Time Series Analysis: Time series analysis deals with analyzing and forecasting data points collected over time. This is crucial for applications like stock market prediction, weather forecasting, and demand forecasting. Techniques like ARIMA, LSTM, and Prophet are commonly used for time series analysis.

    4. Anomaly Detection: Anomaly detection involves identifying outliers or unusual patterns in data. This is vital for fraud detection, network security, and predictive maintenance. Machine learning algorithms like Isolation Forest, One-Class SVM, and Autoencoders are commonly used for anomaly detection.

    5. Reinforcement Learning: Reinforcement learning is a type of machine learning where an agent learns to make decisions by interacting with an environment. This technique has been successful in applications like game playing, robotics, and autonomous driving. Algorithms like Q-Learning, Deep Q-Networks, and Policy Gradient methods are commonly used for reinforcement learning.

    6. Natural Language Processing: Natural language processing (NLP) involves analyzing and generating human language using machine learning techniques. This is essential for applications like sentiment analysis, language translation, and chatbots. Techniques like word embeddings, recurrent neural networks, and transformer models are commonly used for NLP.

    In conclusion, statistical data modeling and machine learning offer a wide range of techniques and applications. By mastering these advanced techniques, data scientists can tackle complex problems and extract valuable insights from data. Stay tuned for our next post on practical applications of statistical data modeling and machine learning!
    #Statistical #Data #Modeling #Machine #Learning #Applications

  • Case-Based Reasoning Research and Development: 29th International Conference, ICCBR 2021, Salamanca, Spain, September 13–16, 2021, Proceedings (Lecture Notes in Artificial Intelligence)

    Case-Based Reasoning Research and Development: 29th International Conference, ICCBR 2021, Salamanca, Spain, September 13–16, 2021, Proceedings (Lecture Notes in Artificial Intelligence)


    Price: $84.99
    (as of Dec 28,2024 02:36:38 UTC – Details)




    Publisher ‏ : ‎ Springer; 1st ed. 2021 edition (September 10, 2021)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 340 pages
    ISBN-10 ‏ : ‎ 3030869563
    ISBN-13 ‏ : ‎ 978-3030869564
    Item Weight ‏ : ‎ 1.15 pounds
    Dimensions ‏ : ‎ 6.1 x 0.77 x 9.25 inches


    Join us for the 29th International Conference on Case-Based Reasoning Research and Development (ICCBR 2021) taking place in Salamanca, Spain from September 13-16, 2021. This conference will feature a wide range of topics related to case-based reasoning and its applications in various fields.

    The proceedings of ICCBR 2021 will be published in the prestigious Lecture Notes in Artificial Intelligence series, showcasing the latest research and developments in the field. Researchers, practitioners, and students from around the world will gather to present their work, exchange ideas, and collaborate on cutting-edge research in case-based reasoning.

    Don’t miss this opportunity to be a part of the leading conference in case-based reasoning research and development. Stay tuned for updates on the conference program, keynote speakers, workshops, and more. We look forward to welcoming you to Salamanca for ICCBR 2021!
    #CaseBased #Reasoning #Research #Development #29th #International #Conference #ICCBR #Salamanca #Spain #September #Proceedings #Lecture #Notes #Artificial #Intelligence

  • High Accuracy Partially Monotone Ordinal Classification

    High Accuracy Partially Monotone Ordinal Classification


    Price: $64.50
    (as of Dec 28,2024 02:14:51 UTC – Details)




    Publisher ‏ : ‎ Eliva Press (October 19, 2020)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 193 pages
    ISBN-10 ‏ : ‎ 1636480144
    ISBN-13 ‏ : ‎ 978-1636480145
    Item Weight ‏ : ‎ 9.4 ounces
    Dimensions ‏ : ‎ 6 x 0.44 x 9 inches


    Partial monotonicity is a property that can sometimes be exploited to improve the accuracy of ordinal classification models. In this post, we will discuss the concept of high accuracy partially monotone ordinal classification and how it can be achieved.

    Ordinal classification is a type of classification task where the labels have a predefined order, such as low, medium, and high. In partially monotone ordinal classification, some features exhibit a monotonic relationship with the target variable, while others do not. This means that the relationship between certain features and the target variable is not strictly increasing or decreasing.

    To achieve high accuracy in partially monotone ordinal classification, it is important to identify which features exhibit a monotonic relationship with the target variable and utilize this information to improve the model’s performance. One approach is to use monotonic constraints in the model training process, which ensures that the model’s predictions are consistent with the monotonic relationship between certain features and the target variable.

    Another approach is to use feature engineering techniques to create new features that capture the partial monotonicity of the data. For example, you can create interaction terms between features that exhibit a monotonic relationship with the target variable, or transform non-monotonic features to make them more monotonic.

    Overall, high accuracy partially monotone ordinal classification involves leveraging the partial monotonicity of the data to improve the model’s performance. By identifying and exploiting the monotonic relationships between certain features and the target variable, you can achieve better accuracy in ordinal classification tasks.
    #High #Accuracy #Partially #Monotone #Ordinal #Classification

  • Connected and Automated Vehicles: Integrating Engineering and Ethics (Studies in Applied Philosophy, Epistemology and Rational Ethics, 67)

    Connected and Automated Vehicles: Integrating Engineering and Ethics (Studies in Applied Philosophy, Epistemology and Rational Ethics, 67)


    Price: $169.99
    (as of Dec 28,2024 01:53:25 UTC – Details)




    Publisher ‏ : ‎ Springer; 2023rd edition (September 24, 2024)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 209 pages
    ISBN-10 ‏ : ‎ 3031399935
    ISBN-13 ‏ : ‎ 978-3031399930
    Item Weight ‏ : ‎ 10.9 ounces
    Dimensions ‏ : ‎ 6.1 x 0.48 x 9.25 inches


    Connected and Automated Vehicles: Integrating Engineering and Ethics (Studies in Applied Philosophy, Epistemology and Rational Ethics, 67)

    In a world where technology is advancing at a rapid pace, the integration of engineering and ethics has become more important than ever. This is especially true in the realm of connected and automated vehicles, where questions of safety, privacy, and decision-making algorithms are at the forefront of public discourse.

    In this groundbreaking book, leading experts in the fields of engineering and ethics come together to explore the complex ethical issues surrounding connected and automated vehicles. From the design of self-driving cars to the implications of artificial intelligence in transportation, this book delves into the ethical dilemmas that engineers and policymakers must grapple with as we move towards a more automated future.

    With contributions from philosophers, engineers, and policymakers, Connected and Automated Vehicles offers a comprehensive look at the intersection of technology and ethics in the automotive industry. Whether you’re a student, researcher, or industry professional, this book is essential reading for anyone interested in the ethical considerations of connected and automated vehicles.
    #Connected #Automated #Vehicles #Integrating #Engineering #Ethics #Studies #Applied #Philosophy #Epistemology #Rational #Ethics