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| ida intelligent data analysis 2019: Advances in Intelligent Data Analysis XIX Pedro Henriques Abreu, Pedro Pereira Rodrigues, Alberto Fernández, João Gama, 2021-04-12 This book constitutes the proceedings of the 19th International Symposium on Intelligent Data Analysis, IDA 2021, which was planned to take place in Porto, Portugal. Due to the COVID-19 pandemic the conference was held online during April 26-28, 2021. The 35 papers included in this book were carefully reviewed and selected from 113 submissions. The papers were organized in topical sections named: modeling with neural networks; modeling with statistical learning; modeling language and graphs; and modeling special data formats. |
| ida intelligent data analysis 2019: Advances in Intelligent Data Analysis XVIII Michael R. Berthold, Ad Feelders, Georg Krempl, 2020-04-22 This open access book constitutes the proceedings of the 18th International Conference on Intelligent Data Analysis, IDA 2020, held in Konstanz, Germany, in April 2020. The 45 full papers presented in this volume were carefully reviewed and selected from 114 submissions. Advancing Intelligent Data Analysis requires novel, potentially game-changing ideas. IDA’s mission is to promote ideas over performance: a solid motivation can be as convincing as exhaustive empirical evaluation. |
| ida intelligent data analysis 2019: Intelligent Data Analysis Deepak Gupta, Siddhartha Bhattacharyya, Ashish Khanna, Kalpna Sagar, 2020-07-13 This book focuses on methods and tools for intelligent data analysis, aimed at narrowing the increasing gap between data gathering and data comprehension, and emphasis will also be given to solving of problems which result from automated data collection, such as analysis of computer-based patient records, data warehousing tools, intelligent alarming, effective and efficient monitoring, and so on. This book aims to describe the different approaches of Intelligent Data Analysis from a practical point of view: solving common life problems with data analysis tools. |
| ida intelligent data analysis 2019: Advances in Intelligent Data Analysis XXII Ioanna Miliou, Nico Piatkowski, Panagiotis Papapetrou, 2024-04-15 The two volume set LNCS 14641 and 14642 constitutes the proceedings of the 22nd International Symposium on Intelligent Data Analysis, IDA 2024, which was held in Stockholm, Sweden, during April 24-26, 2024. The 40 full and 3 short papers included in the proceedings were carefully reviewed and selected from 94 submissions. IDA is an international symposium presenting advances in the intelligent analysis of data. Distinguishing characteristics of IDA are its focus on novel, inspiring ideas, its focus on research, and its relatively small scale. |
| ida intelligent data analysis 2019: Advances in Intelligent Data Analysis XX Tassadit Bouadi, Elisa Fromont, Eyke Hüllermeier, 2022-04-06 This book constitutes the proceedings of the 20th International Symposium on Intelligent Data Analysis, IDA 2022, which was held in Rennes, France, during April 20-22, 2022. The 31 papers included in this book were carefully reviewed and selected from 73 submissions. They deal with high quality, novel research in intelligent data analysis. |
| ida intelligent data analysis 2019: Advances in Intelligent Data Analysis XXI Bruno Crémilleux, Sibylle Hess, Siegfried Nijssen, 2023-03-31 This book constitutes the proceedings of the 21st International Symposium on Intelligent Data Analysis, IDA 2022, which was held in Louvain-la-Neuve, Belgium, during April 12-14, 2023. The 38 papers included in this book were carefully reviewed and selected from 91 submissions. IDA is an international symposium presenting advances in the intelligent analysis of data. Distinguishing characteristics of IDA are its focus on novel, inspiring ideas, its focus on research, and its relatively small scale. |
| ida intelligent data analysis 2019: Advances in Intelligent Data Analysis XXIII Georg Krempl, Kai Puolamäki, Ioanna Miliou, 2025-05-01 This volume constitutes the proceedings of the 23rd International Symposium on Intelligent Data Analysis, IDA 2025, which was held in Konstanz, Germany, during May 7–9, 2025. The 35 full papers included in the proceedings were carefully reviewed and selected from 91 submissions. They were organized in topical sections as follows: Applications of data science, foundations of data science; natural language processing; temporal and streaming data; and explainable and interpretable data science. |
| ida intelligent data analysis 2019: Recent Advances in Material, Manufacturing, and Machine Learning Rajiv Gupta, Devendra Deshmukh, Awanikumar P. Patil, Naveen Kumar Shrivastava, Jayant Giri, R.B. Chadge, 2023-05-26 The role of manufacturing in a country’s economy and societal development has long been established through their wealth generating capabilities. To enhance and widen our knowledge of materials and to increase innovation and responsiveness to ever-increasing international needs, more in-depth studies of functionally graded materials/tailor-made materials, recent advancements in manufacturing processes and new design philosophies are needed at present. The objective of this volume is to bring together experts from academic institutions, industries and research organizations and professional engineers for sharing of knowledge, expertise and experience in the emerging trends related to design, advanced materials processing and characterization, and advanced manufacturing processes. |
| ida intelligent data analysis 2019: Intelligent Computing and Applications Subhransu Sekhar Dash, Swagatam Das, Bijaya Ketan Panigrahi, 2020-09-29 This book presents the peer-reviewed proceedings of the 5th International Conference on Intelligent Computing and Applications (ICICA 2019), held in Ghaziabad, India, on December 6–8, 2019. The contributions reflect the latest research on advanced computational methodologies such as neural networks, fuzzy systems, evolutionary algorithms, hybrid intelligent systems, uncertain reasoning techniques, and other machine learning methods and their applications to decision-making and problem-solving in mobile and wireless communication networks. |
| ida intelligent data analysis 2019: Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning Rani, Geeta, Tiwari, Pradeep Kumar, 2020-10-16 By applying data analytics techniques and machine learning algorithms to predict disease, medical practitioners can more accurately diagnose and treat patients. However, researchers face problems in identifying suitable algorithms for pre-processing, transformations, and the integration of clinical data in a single module, as well as seeking different ways to build and evaluate models. The Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning is a pivotal reference source that explores the application of algorithms to making disease predictions through the identification of symptoms and information retrieval from images such as MRIs, ECGs, EEGs, etc. Highlighting a wide range of topics including clinical decision support systems, biomedical image analysis, and prediction models, this book is ideally designed for clinicians, physicians, programmers, computer engineers, IT specialists, data analysts, hospital administrators, researchers, academicians, and graduate and post-graduate students. |
| ida intelligent data analysis 2019: Discovering Drift Phenomena in Evolving Landscapes Marco Piangerelli, Bardh Prenkaj, Ylenia Rotalinti, Ananya Joshi, Giovanni Stilo, 2025-02-24 This book constitutes the post-conference proceedings of the First International Workshop on Discovering Drift Phenomena in Evolving Landscapes, DELTA 2024, held in Barcelona, Spain, on August 26, 2024. The 9 full papers presented together with 1 short paper were carefully reviewed and selected from 17 submissions. The papers are grouped into three topical sections, namely: adaptive and robust learning in dynamic environments; challenges and solutions in drift detection and anomaly explanation; and innovative approaches to concept drift detection and landscape shifts. |
| ida intelligent data analysis 2019: Advanced Intelligent Computing Technology and Applications De-Shuang Huang, Prashan Premaratne, Baohua Jin, Boyang Qu, Kang-Hyun Jo, Abir Hussain, 2023-07-30 This three-volume set of LNCS 14086, LNCS 14087 and LNCS 14088 constitutes - in conjunction with the double-volume set LNAI 14089-14090- the refereed proceedings of the 19th International Conference on Intelligent Computing, ICIC 2023, held in Zhengzhou, China, in August 2023. The 337 full papers of the three proceedings volumes were carefully reviewed and selected from 828 submissions. This year, the conference concentrated mainly on the theories and methodologies as well as the emerging applications of intelligent computing. Its aim was to unify the picture of contemporary intelligent computing techniques as an integral concept that highlights the trends in advanced computational intelligence and bridges theoretical research with applications. Therefore, the theme for this conference was Advanced Intelligent Computing Technology and Applications. Papers that focused on this theme were solicited, addressing theories, methodologies, and applications in science and technology. |
| ida intelligent data analysis 2019: Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance Rana, Dipti P., Mehta, Rupa G., 2021-06-04 Over the last two decades, researchers are looking at imbalanced data learning as a prominent research area. Many critical real-world application areas like finance, health, network, news, online advertisement, social network media, and weather have imbalanced data, which emphasizes the research necessity for real-time implications of precise fraud/defaulter detection, rare disease/reaction prediction, network intrusion detection, fake news detection, fraud advertisement detection, cyber bullying identification, disaster events prediction, and more. Machine learning algorithms are based on the heuristic of equally-distributed balanced data and provide the biased result towards the majority data class, which is not acceptable considering imbalanced data is omnipresent in real-life scenarios and is forcing us to learn from imbalanced data for foolproof application design. Imbalanced data is multifaceted and demands a new perception using the novelty at sampling approach of data preprocessing, an active learning approach, and a cost perceptive approach to resolve data imbalance. Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance offers new aspects for imbalanced data learning by providing the advancements of the traditional methods, with respect to big data, through case studies and research from experts in academia, engineering, and industry. The chapters provide theoretical frameworks and the latest empirical research findings that help to improve the understanding of the impact of imbalanced data and its resolving techniques based on data preprocessing, active learning, and cost perceptive approaches. This book is ideal for data scientists, data analysts, engineers, practitioners, researchers, academicians, and students looking for more information on imbalanced data characteristics and solutions using varied approaches. |
| ida intelligent data analysis 2019: The Confluence of Cryptography, Blockchain and Artificial Intelligence Ankita Sharma, Nayancy, Rajat Verma, 2025-05-26 With blockchain underpinning cryptocurrencies and improving IoT security, this book uncovers the evolution of blockchain (1.0 to 4.0) and its applications. It also introduces AI, discussing its development, paradigms, and industry-wide impact. The book explores the integration of cryptography, blockchain, and artificial intelligence (AI) in areas such as big data, bioinformatics, IoT, 5G, and Industry 4.0. It highlights how these technologies drive the digital revolution, enabling multi-agent systems, autonomous models, and enhanced security. |
| ida intelligent data analysis 2019: International Conference on Artificial Intelligence for Smart Community Rosdiazli Ibrahim, K. Porkumaran, Ramani Kannan, Nursyarizal Mohd Nor, S. Prabakar, 2022-11-13 This conference proceeding gather a selection of peer-reviewed papers presented at the 1st International Conference on Artificial Intelligence for Smart Community (AISC 2020), held as a virtual conference on 17–18 December 2020, with the theme Re-imagining Artificial Intelligence (AI) for Smart Community to apply computational intelligence for biomedical instruments, automation & control, and smart community to develop suitable solution for various real-world application. The conference virtually brought together researchers, scientists, engineers, industrial professionals, and students presenting important results in the related field of healthcare technology, soft computing technologies, IoT, evolutionary computations, automation and control, smart manufacturing and smart cities. Researchers and scientist working in the allied domain of Artificial Intelligence and others will find the book useful as it will contain some latest computational intelligence methodologies and applications. |
| ida intelligent data analysis 2019: Applied Computational Intelligence, Informatics and Big Data Gwanggil Jeon, Xiangjie Kong, 2025-05-22 This book LNICST 616 constitutes the proceedings of the First International Conference on Applied Computational Intelligence, Informatics and Big Data, ACIIBD 2024, held in Guangzhou, China, during July 26–28, 2024. The 3 full papers and 15 shot papers were carefully reviewed and selected from 56 submissions. This Proceedings cover topics on Internet of Things, Information Communication Technology, Edge Computing, Mobile Computing, Neural Network, Intelligent Control System, Real-Time Information System, Intelligent Perception and many other cutting-edge fields and disciplines. |
| ida intelligent data analysis 2019: Artificial Intelligence and Mental Health Care Jorge Piano Simoes, Peter ten Klooster, Jannis Kraiss, Patrick K. A. Neff, Uli Niemann, 2024-08-09 New developments in machine learning (ML) and artificial intelligence (AI) hold great promise to revolutionize mental health care. In this context, ML and AI have been deployed for several different goals, including 1) the early detection of mental disorders, 2) the optimization of personalized treatments based on the individual characteristics of patients, 3) the better characterization of disorders detrimental to mental well-being and quality of life, as well as a better description of projected trajectories over time, and 4) the development of new treatments for mental health care. Despite their great potential to transform mental health care and occasional breakthroughs, ML and AI have not yet fully achieved these goals. This research topic aims to bridge the gap between the potential uses of ML and AI and their practical application in standard mental health care. More specifically, we welcome original research submissions applying ML and AI to promote public health by reducing the burden of chronic disorders with detrimental effects on well-being (e.g., psychopathological distress), and improving quality of life. We also welcome submissions applying ML and AI in heterogeneous datasets (e.g., subjective scales and questionnaires, biomarkers, (neuro)psychological assessments, etc.) from Big Data sources (e.g., large datasets of clinical populations, electronic health records from nationally representative cohorts, and/or biobanks, studies using experiencing sampling methods, etc.) to gain mechanistic insight on how different chronic conditions associated with psychopathological distress can affect patient well-being and quality of life. Finally, we also welcome opinion papers and reviews on how to develop AI applications in mental health care responsibly, while integrating biopsychosocial aspects of patients to promote better mental health care. |
| ida intelligent data analysis 2019: Machine Learning and Knowledge Discovery in Databases Massih-Reza Amini, Stéphane Canu, Asja Fischer, Tias Guns, Petra Kralj Novak, Grigorios Tsoumakas, 2023-03-16 The multi-volume set LNAI 13713 until 13718 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2022, which took place in Grenoble, France, in September 2022. The 236 full papers presented in these proceedings were carefully reviewed and selected from a total of 1060 submissions. In addition, the proceedings include 17 Demo Track contributions. The volumes are organized in topical sections as follows: Part I: Clustering and dimensionality reduction; anomaly detection; interpretability and explainability; ranking and recommender systems; transfer and multitask learning; Part II: Networks and graphs; knowledge graphs; social network analysis; graph neural networks; natural language processing and text mining; conversational systems; Part III: Deep learning; robust and adversarial machine learning; generative models; computer vision; meta-learning, neural architecture search; Part IV: Reinforcement learning; multi-agent reinforcement learning; bandits and online learning; active and semi-supervised learning; private and federated learning; . Part V: Supervised learning; probabilistic inference; optimal transport; optimization; quantum, hardware; sustainability; Part VI: Time series; financial machine learning; applications; applications: transportation; demo track. |
| ida intelligent data analysis 2019: Integration Challenges for Analytics, Business Intelligence, and Data Mining Azevedo, Ana, Santos, Manuel Filipe, 2020-12-11 As technology continues to advance, it is critical for businesses to implement systems that can support the transformation of data into information that is crucial for the success of the company. Without the integration of data (both structured and unstructured) mining in business intelligence systems, invaluable knowledge is lost. However, there are currently many different models and approaches that must be explored to determine the best method of integration. Integration Challenges for Analytics, Business Intelligence, and Data Mining is a relevant academic book that provides empirical research findings on increasing the understanding of using data mining in the context of business intelligence and analytics systems. Covering topics that include big data, artificial intelligence, and decision making, this book is an ideal reference source for professionals working in the areas of data mining, business intelligence, and analytics; data scientists; IT specialists; managers; researchers; academicians; practitioners; and graduate students. |
| ida intelligent data analysis 2019: Intelligent Internet of Everything for Automated and Sustainable Farming Ray, Biplob, Hassan, Jahan, Huang, Hailong, Islam, Nahina, Shahadat, Zaglul, 2025-05-01 With the convergence of technology in agriculture, intelligent Internet of Everything (IoE) creates efficient sustainable farming practices. The integration of Internet of Things (loT), robotics, and data analytics optimize the technology used for efficient farming practices and improved environmental conditions. By leveraging the power of IoE, this approach enhances productivity and crop quality and addresses critical challenges such as climate change, labor shortages, and food security, laying the groundwork for a resilient and tech-driven agricultural future. Intelligent Internet of Everything for Automated and Sustainable Farming explores IoE in smart farming applications. It examines the advancements of drone technologies and AI in agriculture sustainability, using real world issues as examples on how to expertly use IoE in smart sustainable agriculture. This book covers topics such as agriculture technology, smart farming, and autonomous weeding, and is a useful resource business owners, engineers, agriculturalists, farmers, academicians, scientists, and researchers. |
| ida intelligent data analysis 2019: Data Governance, DevSecOps, and Advancements in Modern Software Elbaghazaoui, Bahaa Eddine, Amnai, Mohamed, Gherabi, Noreddine, 2025-04-24 In today’s digital landscape, data governance, DevSecOps, and advancements in modern software development have become critical in secure and efficient technology ecosystems. As organizations rely on large amounts of data and sophisticated software systems to drive innovation and business success, the need for improved frameworks to manage, protect, and optimize this data increases. Data governance ensures data is accurate, secure, and compliant with regulations, while DevSecOps, an integrated approach to development, security, and operations, empowers teams to build, test, and utilize software with security embedded through its lifecycle. Along with the latest advancements in modern software technologies, these concepts form the foundation for building resilient, secure, and scalable applications. The intersection of these practices shapes the future of how software is developed, deployed, and governed, and further research may provide both opportunities and challenges for connection. Data Governance, DevSecOps, and Advancements in Modern Software explores the integration of key technologies and methodologies that define the modern digital landscape, with a focus on DataOps, DevSecOps, data governance, and software architecture. It provides a comprehensive guide to managing data workflows and enhancing operational efficiency while embedding security at every stage of the development lifecycle. This book covers topics such as data science, artificial intelligence, and resilient systems, and is a useful resource for data scientists, engineers, software developers, business owners, researchers, and academicians. |
| ida intelligent data analysis 2019: Semantic Intelligent Computing and Applications Mangesh M. Ghonge, Pradeep Nijalingappa, Renjith V. Ravi, Shilpa Laddha, Pallavi Vijay Chavan, 2023-12-18 Artificial intelligence advancements, machine intelligence innovations, and semantic web developments together make up semantic intelligence technologies. The edited book integrates artifi cial intelligence, machine learning, IoT, blockchain, and natural language processing with semantic web technologies. This book also aims to offer real-life solutions to the pressing issues currently being faced by semantic web technologies. |
| ida intelligent data analysis 2019: Metaheuristic and Machine Learning Optimization Strategies for Complex Systems R., Thanigaivelan, M., Suchithra, S., Kaliappan, Mothilal, T., 2024-07-17 In contemporary engineering domains, optimization and decision-making issues are crucial. Given the vast amounts of available data, processing times and memory usage can be substantial. Developing and implementing novel heuristic algorithms is time-consuming, yet even minor improvements in solutions can significantly reduce computational costs. In such scenarios, the creation of heuristics and metaheuristic algorithms has proven advantageous. The convergence of machine learning and metaheuristic algorithms offers a promising approach to address these challenges. Metaheuristic and Machine Learning Optimization Strategies for Complex Systems covers all areas of comprehensive information about hyper-heuristic models, hybrid meta-heuristic models, nature-inspired computing models, and meta-heuristic models. The key contribution of this book is the construction of a hyper-heuristic approach for any general problem domain from a meta-heuristic algorithm. Covering topics such as cloud computing, internet of things, and performance evaluation, this book is an essential resource for researchers, postgraduate students, educators, data scientists, machine learning engineers, software developers and engineers, policy makers, and more. |
| ida intelligent data analysis 2019: Intelligent Analytics With Advanced Multi-Industry Applications Sun, Zhaohao, 2021-01-08 Many fundamental technological and managerial issues surrounding the development and implementation of intelligent analytics within multi-industry applications remain unsolved. There are still questions surrounding the foundation of intelligent analytics, the elements, the big characteristics, and the effects on business, management, technology, and society. Research is devoted to answering these questions and understanding how intelligent analytics can improve healthcare, mobile commerce, web services, cloud services, blockchain, 5G development, digital transformation, and more. Intelligent Analytics With Advanced Multi-Industry Applications is a critical reference source that explores cutting-edge theories, technologies, and methodologies of intelligent analytics with multi-industry applications and emphasizes the integration of artificial intelligence, business intelligence, big data, and analytics from a perspective of computing, service, and management. This book also provides real-world applications of the proposed concept of intelligent analytics to e-SMACS (electronic, social, mobile, analytics, cloud, and service) commerce and services, healthcare, the internet of things, the sharing economy, cloud computing, blockchain, and Industry 4.0. This book is ideal for scientists, engineers, educators, university students, service and management professionals, policymakers, decision makers, practitioners, stakeholders, researchers, and others who have an interest in how intelligent analytics are being implemented and utilized in diverse industries. |
| ida intelligent data analysis 2019: Soft Computing and Signal Processing Jiacun Wang, G. Ram Mohana Reddy, V. Kamakshi Prasad, V. Sivakumar Reddy, 2019-01-16 The book presents selected research papers on current developments in the field of soft computing and signal processing from the International Conference on Soft Computing and Signal Processing (ICSCSP 2018). It includes papers on current topics such as soft sets, rough sets, fuzzy logic, neural networks, genetic algorithms and machine learning, discussing various aspects of these topics, like technological, product implementation, contemporary research as well as application issues. |
| ida intelligent data analysis 2019: Simulating Science Ramón Alvarado, 2023-08-23 This book provides a philosophical framework to understand computer simulations as scientific instruments. This is in sharp contrast to existing philosophical approaches on the subject, which have historically understood computer simulations as either formal abstractions or as broadly construed empirical practices. In order to make its case, the volume contains a thorough examination of conventional philosophical approaches as well as their respective limitations. Yet, also, unlike other accounts of computer simulations from the perspective of the philosophy of science, this book incorporates insights from the philosophy of technology and the history of science. Hence, the book offers philosophers of science, technologists and other researchers interested in the topic, a thorough overview of the philosophical issues regarding the design, development and deployment of computer simulations in science and science-based policy making. |
| ida intelligent data analysis 2019: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications Ingela Nyström, Yanio Hernández Heredia, Vladimir Milián Núñez, 2019-10-25 This book constitutes the refereed conference proceedings of the 24rd Iberoamerican Congress on Pattern Recognition, CIARP 2019, held in Havana, Cuba, in October 2019. The 70 papers presented were carefully reviewed and selected from 128 submissions. The papers are organized in topical sections named: Data Mining: Natural Language Processing and Text Mining; Image Analysis and Retrieval; Machine Learning and Neural Networks; Mathematical Theory of Pattern Recognition; Pattern Recognition and Applications; Signals Analysis and Processing; Speech Recognition; Video Analysis. |
| ida intelligent data analysis 2019: Research Anthology on Bioinformatics, Genomics, and Computational Biology Management Association, Information Resources, 2024-03-19 In the evolving environment of bioinformatics, genomics, and computational biology, academic scholars are facing a challenging challenge – keeping informed about the latest research trends and findings. With unprecedented advancements in sequencing technologies, computational algorithms, and machine learning, these fields have become indispensable tools for drug discovery, disease research, genome sequencing, and more. As scholars strive to decode the language of DNA, predict protein structures, and navigate the complexities of biological data analysis, the need for a comprehensive and up-to-date resource becomes paramount. The Research Anthology on Bioinformatics, Genomics, and Computational Biology is a collection of a carefully curated selection of chapters that serves as the solution to the pressing challenge of keeping pace with the dynamic advancements in these critical disciplines. This anthology is designed to address the informational gap by providing scholars with a consolidated and authoritative source that sheds light on critical issues, innovative theories, and transformative developments in the field. It acts as a single reference point, offering insights into conceptual, methodological, technical, and managerial issues while also providing a glimpse into emerging trends and future opportunities. |
| ida intelligent data analysis 2019: Hardware-Aware Probabilistic Machine Learning Models Laura Isabel Galindez Olascoaga, Wannes Meert, Marian Verhelst, 2021-05-19 This book proposes probabilistic machine learning models that represent the hardware properties of the device hosting them. These models can be used to evaluate the impact that a specific device configuration may have on resource consumption and performance of the machine learning task, with the overarching goal of balancing the two optimally. The book first motivates extreme-edge computing in the context of the Internet of Things (IoT) paradigm. Then, it briefly reviews the steps involved in the execution of a machine learning task and identifies the implications associated with implementing this type of workload in resource-constrained devices. The core of this book focuses on augmenting and exploiting the properties of Bayesian Networks and Probabilistic Circuits in order to endow them with hardware-awareness. The proposed models can encode the properties of various device sub-systems that are typically not considered by other resource-aware strategies, bringing about resource-saving opportunities that traditional approaches fail to uncover. The performance of the proposed models and strategies is empirically evaluated for several use cases. All of the considered examples show the potential of attaining significant resource-saving opportunities with minimal accuracy losses at application time. Overall, this book constitutes a novel approach to hardware-algorithm co-optimization that further bridges the fields of Machine Learning and Electrical Engineering. |
| ida intelligent data analysis 2019: ECAI 2020 G. De Giacomo, A. Catala, B. Dilkina, 2020-09-11 This book presents the proceedings of the 24th European Conference on Artificial Intelligence (ECAI 2020), held in Santiago de Compostela, Spain, from 29 August to 8 September 2020. The conference was postponed from June, and much of it conducted online due to the COVID-19 restrictions. The conference is one of the principal occasions for researchers and practitioners of AI to meet and discuss the latest trends and challenges in all fields of AI and to demonstrate innovative applications and uses of advanced AI technology. The book also includes the proceedings of the 10th Conference on Prestigious Applications of Artificial Intelligence (PAIS 2020) held at the same time. A record number of more than 1,700 submissions was received for ECAI 2020, of which 1,443 were reviewed. Of these, 361 full-papers and 36 highlight papers were accepted (an acceptance rate of 25% for full-papers and 45% for highlight papers). The book is divided into three sections: ECAI full papers; ECAI highlight papers; and PAIS papers. The topics of these papers cover all aspects of AI, including Agent-based and Multi-agent Systems; Computational Intelligence; Constraints and Satisfiability; Games and Virtual Environments; Heuristic Search; Human Aspects in AI; Information Retrieval and Filtering; Knowledge Representation and Reasoning; Machine Learning; Multidisciplinary Topics and Applications; Natural Language Processing; Planning and Scheduling; Robotics; Safe, Explainable, and Trustworthy AI; Semantic Technologies; Uncertainty in AI; and Vision. The book will be of interest to all those whose work involves the use of AI technology. |
| ida intelligent data analysis 2019: Big Data and Networks Technologies Yousef Farhaoui, 2019-07-17 This book reviews the state of the art in big data analysis and networks technologies. It addresses a range of issues that pertain to: signal processing, probability models, machine learning, data mining, databases, data engineering, pattern recognition, visualization, predictive analytics, data warehousing, data compression, computer programming, smart cities, networks technologies, etc. Data is becoming an increasingly decisive resource in modern societies, economies, and governmental organizations. In turn, data science inspires novel techniques and theories drawn from mathematics, statistics, information theory, computer science, and the social sciences. All papers presented here are the product of extensive field research involving applications and techniques related to data analysis in general, and to big data and networks technologies in particular. Given its scope, the book will appeal to advanced undergraduate and graduate students, postdoctoral researchers, lecturers and industrial researchers, as well general readers interested in big data analysis and networks technologies. |
| ida intelligent data analysis 2019: Recent Trends and Future Direction for Data Analytics Kumari, Aparna, 2024-05-14 In an increasingly data-centric world, scholars and practitioners grapple with the complexities of harnessing data analytics effectively across various industries. The challenge lies in navigating the rapid evolution of methodologies, identifying emerging trends, and understanding the nuanced applications of data analytics in real-world scenarios. This gap between theory and practice inhibits academic progress. It hampers industry innovation, leaving stakeholders needing help to leverage data to its full potential. Recent Trends and Future Direction for Data Analytics presents a compelling solution. By delving into real-world case studies spanning supply chain management, marketing, healthcare, and finance, this book bridges the gap between theory and practice, offering invaluable insights into the practical applications of data analytics. A systematic exploration of fundamental concepts, advanced techniques, and specialized topics equips scholars, researchers, and industry professionals with the knowledge and tools needed to navigate the complexities of data analytics with confidence. |
| ida intelligent data analysis 2019: Artificial Intelligence, Machine Learning, and Optimization Tools for Smart Cities Panos M. Pardalos, Stamatina Th. Rassia, Arsenios Tsokas, 2022-01-09 This volume offers a wealth of interdisciplinary approaches to artificial intelligence, machine learning and optimization tools, which contribute to the optimization of urban features towards forming smart, sustainable, and livable future cities. Special features include: New research on the design of city elements and smart systems with respect to new technologies and scientific thinking Discussions on the theoretical background that lead to smart cities for the future New technologies and principles of research that can promote ideas of artificial intelligence and machine learning in optimized urban environments The book engages students and researchers in the subjects of artificial intelligence, machine learning, and optimization tools in smart sustainable cities as eminent international experts contribute their research results and thinking in its chapters. Overall, its audience can benefit from a variety of disciplines including, architecture, engineering, physics, mathematics, computer science, and related fields. |
| ida intelligent data analysis 2019: Human-Computer Interaction Masaaki Kurosu, Ayako Hashizume, 2024-05-31 This five-volume set LNCS 14684-14688 constitutes the refereed proceedings of the Human Computer Interaction thematic area of the 26 International Conference on Human-Computer Interaction, HCII 2024, held in Washington, DC, USA, during June 29 – July 4, 2024. The total of 1271 papers and 309 posters included in the HCII 2024 proceedings was carefully reviewed and selected from 5108 submissions. The VAMR 2024 proceedings were organized in the following topical sections: Part I: HCI Theory and Design and Evaluation Methods and Tools; Emotions in HCI. Part II: Human-Robot Interaction; Child-Computer Interaction. Part III: HCI for Mental Health and Psychological Wellbeing; HCI in Healthcare. Part IV: HCI, Environment and Sustainability; Design and User Experience Evaluation Case Studies. Part V: Multimodality and Natural User Interfaces; HCI, AI, Creativity, Art and Culture. |
| ida intelligent data analysis 2019: Statistical Machine Learning for Human Behaviour Analysis Thomas Moeslund, Sergio Escalera, Gholamreza Anbarjafari, Kamal Nasrollahi, Jun Wan, 2020-06-17 This Special Issue focused on novel vision-based approaches, mainly related to computer vision and machine learning, for the automatic analysis of human behaviour. We solicited submissions on the following topics: information theory-based pattern classification, biometric recognition, multimodal human analysis, low resolution human activity analysis, face analysis, abnormal behaviour analysis, unsupervised human analysis scenarios, 3D/4D human pose and shape estimation, human analysis in virtual/augmented reality, affective computing, social signal processing, personality computing, activity recognition, human tracking in the wild, and application of information-theoretic concepts for human behaviour analysis. In the end, 15 papers were accepted for this special issue. These papers, that are reviewed in this editorial, analyse human behaviour from the aforementioned perspectives, defining in most of the cases the state of the art in their corresponding field. |
| ida intelligent data analysis 2019: Algorithms in Advanced Artificial Intelligence R. N. V. Jagan Mohan, B. H. V. S. Rama Krishnam Raju, V. Chandra Sekhar, T. V. K. P. Prasad, 2025-05-23 Algorithms in Advanced Artificial Intelligence is a collection of papers on emerging issues, challenges, and new methods in Artificial Intelligence, Machine Learning, Deep Learning, Cloud Computing, Federated Learning, Internet of Things, and Blockchain technology. It addresses the growing attention to advanced technologies due to their ability to provide “paranormal solutions” to problems associated with classical Artificial Intelligence frameworks. AI is used in various subfields, including learning, perception, and financial decisions. It uses four strategies: Thinking Humanly, Thinking Rationally, Acting Humanly, and Acting Rationally. The authors address various issues in ICT, including Artificial Intelligence, Machine Learning, Deep Learning, Data Science, Big Data Analytics, Vision, Internet of Things, Security and Privacy aspects in AI, and Blockchain and Digital Twin Integrated Applications in AI. |
| ida intelligent data analysis 2019: Intelligent Optimization Modelling in Energy Forecasting Wei-Chiang Hong, 2020-04-01 Accurate energy forecasting is important to facilitate the decision-making process in order to achieve higher efficiency and reliability in power system operation and security, economic energy use, contingency scheduling, the planning and maintenance of energy supply systems, and so on. In recent decades, many energy forecasting models have been continuously proposed to improve forecasting accuracy, including traditional statistical models (e.g., ARIMA, SARIMA, ARMAX, multi-variate regression, exponential smoothing models, Kalman filtering, Bayesian estimation models, etc.) and artificial intelligence models (e.g., artificial neural networks (ANNs), knowledge-based expert systems, evolutionary computation models, support vector regression, etc.). Recently, due to the great development of optimization modeling methods (e.g., quadratic programming method, differential empirical mode method, evolutionary algorithms, meta-heuristic algorithms, etc.) and intelligent computing mechanisms (e.g., quantum computing, chaotic mapping, cloud mapping, seasonal mechanism, etc.), many novel hybrid models or models combined with the above-mentioned intelligent-optimization-based models have also been proposed to achieve satisfactory forecasting accuracy levels. It is important to explore the tendency and development of intelligent-optimization-based modeling methodologies and to enrich their practical performances, particularly for marine renewable energy forecasting. |
| ida intelligent data analysis 2019: Bioinformatics in Microbiota Xing Chen, Hongsheng Liu, Qi Zhao, 2020-06-22 |
| ida intelligent data analysis 2019: Advanced Multimedia and Ubiquitous Engineering James J. Park, Laurence T. Yang, Young-Sik Jeong, Fei Hao, 2019-08-21 This book presents the combined proceedings of the 13th International Conference on Multimedia and Ubiquitous Engineering (MUE 2019) and the 14th International Conference on Future Information Technology (Future Tech 2019), both held in Xi'an, China, April 24 - 26, 2019. The aim of these two meetings was to promote discussion and interaction among academics, researchers and professionals in the field of ubiquitous computing technologies. These proceedings reflect the state of the art in the development of computational methods, involving theory, algorithms, numerical simulation, error and uncertainty analysis and novel applications of new processing techniques in engineering, science, and other disciplines related to ubiquitous computing. |
| ida intelligent data analysis 2019: Machine Learning and Generative AI in Smart Healthcare Purushotham, Swarnalatha, Prabu, S., 2024-08-28 The healthcare landscape is constantly evolving, and one of the most significant concerns that healthcare professionals deal with is understanding how to use biomedical intelligence to improve patient outcomes. With the increasing complexity of healthcare computing systems, including technologies like deep learning and the Internet of Things, it can be challenging to navigate these advancements. Machine Learning and Generative AI in Smart Healthcare is a practical tool for healthcare professionals, researchers, and policymakers who are seeking to implement biomedical intelligence solutions. It provides a clear roadmap for using prescriptive and predictive analytics in machine learning to enhance healthcare outcomes. Going beyond the basics, it delves into healthcare computing and networking complexities. By delving into topics such as data mining, disease prediction, and AI applications, deep learning approaches, decision support systems, and optimization techniques, this book equips readers with the practical knowledge they need to optimize healthcare delivery and management. |
IDA Pro 9 SP1 安装和插件配置 - 吾爱破解 - 52pojie.cn
Feb 16, 2025 · 运行ida-pro_90sp1_x64win.exe安装ida; 修改IdaPro9Beta-Keygen-iRabbit.py文件的部分内容,复制到ida根目录; python运行keygen,自动修补; 修改patched文件后缀,替换ida.dll …
IDA Pro 8.3 绿色版（2024.2.26更新） - 吾爱破解 - 52pojie.cn
IDA Pro 8.3 绿色版是@Hmily 、@微笑一刀 和@云在天 基于泄露的IDA Pro 8.3 Windows版本制作，解压后运行“IDA_Pro_8.3_绿化工具”即可一键绿化，绿色版主要三大功能：一、禁止不必要 …
IDA 9.1 & IDA 8.5 算法分析 - 吾爱破解 - 52pojie.cn
Mar 22, 2025 · 看到分享了 8.5 安装包，之前的 kg 失效了，才发现替换成了 9.x 的注册模式。做了简单分析，整理如下：调试版本为 9.0（240905），新注册机制都一样，ida.dll 中导 ...
IDA Pro 9.0.241217 SP1 - 吾爱破解 - 52pojie.cn
Jan 14, 2025 · * ida.dll.patched * ida32.dll.patched * idapro.hexlic 这三个文件就是破解好的文件. 最后, 我们需要备份原版的 ida.dll 和 ida32.dll 文件, 并将 ida.dll.patched 和 ida32.dll.patched …
[调试逆向] IDA 7.0pro 使用（基础篇） - 吾爱破解
Mar 27, 2020 · 关于ida的使用 还有很多高端的技巧，例如远程动态调试，打补丁，ida-python脚本，修复栈平衡等 逆向时长一年半的菜鸡 ，主要方向是ctf逆向,pwn 也做一些c++ win32开发 和 …
ida pro mcp 强大的 IDA MCP 插件，AI 助力逆向分析 - 吾爱破解
Apr 10, 2025 · 用 IDA Pro MCP + AI 打造智能逆向工作流。真是强大啊，AI 改变世界。 MCP 现在太火了，紧跟潮流，坛友发了GhidraMCP贴，我来发 IDA MCP 贴。 ida-pro-mcp 可用功 …
IDA Plugin - 『逆向资源区』 - 吾爱破解 - 52pojie.cn
5 days ago · Karta IDA插件识别给定代码中的开源代码库. arryboom • 2021-11-6 02:24. arryboom 2021-11-6 02:24: 74968: 娜美 2023-9-20 10:57 IDA计算偏移值IDAPython插件 - [阅读权限 10] …
IDA v8.4.240215 Free & Demo & sdk_tools - 吾爱破解 - 52pojie.cn
Feb 18, 2024 · 免责声明： 吾爱破解所发布的一切破解补丁、注册机和注册信息及软件的解密分析文章仅限用于学习和研究目的；不得将上述内容用于商业或者非法用途，否则，一切后果请用 …
IDA&Frida 学习 - 吾爱破解 - 52pojie.cn
Mar 16, 2023 · IDA View_Hooks类，用于处理在IDA视图中双击和单击事件; 插件类，实现插件的初始化、运行和退出; 前置知识. frida ida就不说了，主要说一下 其他的知识. …
IDA 9.0 安装Findcrypt插件踩坑分享 - 吾爱破解 - 52pojie.cn
Dec 18, 2024 · 我这里ida使用的python是系统的主Python版本，版本为3.10.5，pip也是对应版本的。按照以往版本的ida这样操作之后打开ida即可使用findcrypt，然而此时遇到了报 …
IDA Pro 9 SP1 安装和插件配置 - 吾爱破解 - 52pojie.cn
Feb 16, 2025 · 运行ida-pro_90sp1_x64win.exe安装ida; 修改IdaPro9Beta-Keygen-iRabbit.py文件的部分内容,复制到ida根目录; python运行keygen,自动修补; 修改patched文件后缀,替换ida.dll …
IDA Pro 8.3 绿色版（2024.2.26更新） - 吾爱破解 - 52pojie.cn
IDA Pro 8.3 绿色版是@Hmily 、@微笑一刀 和@云在天 基于泄露的IDA Pro 8.3 Windows版本制作，解压后运行“IDA_Pro_8.3_绿化工具”即可一键绿化，绿色版主要三大功能：一、禁止不必要 …
IDA 9.1 & IDA 8.5 算法分析 - 吾爱破解 - 52pojie.cn
Mar 22, 2025 · 看到分享了 8.5 安装包，之前的 kg 失效了，才发现替换成了 9.x 的注册模式。做了简单分析，整理如下：调试版本为 9.0（240905），新注册机制都一样，ida.dll 中导 ...
IDA Pro 9.0.241217 SP1 - 吾爱破解 - 52pojie.cn
Jan 14, 2025 · * ida.dll.patched * ida32.dll.patched * idapro.hexlic 这三个文件就是破解好的文件. 最后, 我们需要备份原版的 ida.dll 和 ida32.dll 文件, 并将 ida.dll.patched 和 ida32.dll.patched …
[调试逆向] IDA 7.0pro 使用（基础篇） - 吾爱破解
Mar 27, 2020 · 关于ida的使用 还有很多高端的技巧，例如远程动态调试，打补丁，ida-python脚本，修复栈平衡等 逆向时长一年半的菜鸡 ，主要方向是ctf逆向,pwn 也做一些c++ win32开发 和 …
ida pro mcp 强大的 IDA MCP 插件，AI 助力逆向分析 - 吾爱破解
Apr 10, 2025 · 用 IDA Pro MCP + AI 打造智能逆向工作流。真是强大啊，AI 改变世界。 MCP 现在太火了，紧跟潮流，坛友发了GhidraMCP贴，我来发 IDA MCP 贴。 ida-pro-mcp 可用功 …
IDA Plugin - 『逆向资源区』 - 吾爱破解 - 52pojie.cn
5 days ago · Karta IDA插件识别给定代码中的开源代码库. arryboom • 2021-11-6 02:24. arryboom 2021-11-6 02:24: 74968: 娜美 2023-9-20 10:57 IDA计算偏移值IDAPython插件 - [阅读权限 10] …
IDA v8.4.240215 Free & Demo & sdk_tools - 吾爱破解 - 52pojie.cn
Feb 18, 2024 · 免责声明： 吾爱破解所发布的一切破解补丁、注册机和注册信息及软件的解密分析文章仅限用于学习和研究目的；不得将上述内容用于商业或者非法用途，否则，一切后果请用 …
IDA&Frida 学习 - 吾爱破解 - 52pojie.cn
Mar 16, 2023 · IDA View_Hooks类，用于处理在IDA视图中双击和单击事件; 插件类，实现插件的初始化、运行和退出; 前置知识. frida ida就不说了，主要说一下 其他的知识. …
IDA 9.0 安装Findcrypt插件踩坑分享 - 吾爱破解 - 52pojie.cn
Dec 18, 2024 · 我这里ida使用的python是系统的主Python版本，版本为3.10.5，pip也是对应版本的。按照以往版本的ida这样操作之后打开ida即可使用findcrypt，然而此时遇到了报 …
