Big data et traçabilité numérique : Les sciences sociales face à la quantification massive des individus / / Pierre-Michel Menger, Simon Paye

• Record Nr.: UNINA9910227357903321
• Autore: Beuscart Jean-Samuel
• Pubbl/distr/stampa: Paris, : Collège de France, 2017
• ISBN: 2-7226-0467-1
• Descrizione fisica: 1 online resource (218 p.)
• Altri autori (Persone): BoullierDominique; CochoyFranck; DagiralÉric; DenisJérôme; GoëtaSamuel; HarcourtBernard E; MengerPierre-Michel; ParasieSylvain; PayeSimon; PontilleDavid; TiffonGuillaume; TornyDidier; VayreJean-Sébastien
• Soggetti: Social Sciences, Interdisciplinary; Sociology; Big data; données massives; science des données; sciences sociales; sociologie; Internet; sociologie du travail; Big Data; sociology; Web; data sciences; social sciences
• Lingua di pubblicazione: Francese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Sommario/riassunto: Les traces numériques de l’activité des individus, des entreprises, des administrations, des réseaux sociaux sont devenues un gisement considérable. Comment ces données sont-elles prélevées, stockées, valorisées, et vendues ? Et que penser des algorithmes qui convertissent en outil de contrôle et de persuasion l’information sur les comportements, les actes de travail et les échanges ? Les big data sont-elles à notre service ou font-elles de nous les rouages consentants du capitalisme informationnel et relationnel ? Les sciences sociales enquêtent sur les enjeux sociaux, éthiques, politiques et économiques de ces transformations. Mais elles sont elles aussi de plus en plus consommatrices de données numériques de masse. Cet ouvrage collectif explore l’expansion de la traçabilité numérique dans ces deux dimensions, marchande et scientifique. L’ouvrage est dirigé par Pierre-Michel Menger, professeur au Collège de France et titulaire de la chaire « Sociologie du travail créateur », et par Simon Paye, maître de conférences à l’université de Lorraine, sociologue du travail et des groupes professionnels.

The 4th Joint International Conference on Deep Learning, Big Data and Blockchain (DBB 2023) / / edited by Muhammad Younas, Irfan Awan, Salima Benbernou, Dana Petcu

• Record Nr.: UNINA9910743692703321
• Autore: Younas Muhammad
• Pubbl/distr/stampa: Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023
• ISBN: 9783031423178; 3031423178
• Edizione: [1st ed. 2023.]
• Descrizione fisica: 1 online resource (148 pages)
• Collana: Lecture Notes in Networks and Systems, , 2367-3389 ; ; 768
• Altri autori (Persone): AwanIrfan; BenbernouSalima; PetcuDana
• Disciplina: 006.3
• Soggetti: Computational intelligence; Engineering - Data processing; Computational Intelligence; Data Engineering
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Intro -- Preface -- Organization -- Contents -- Block Chain Systems -- Distributed Ledger Technology for Collective Environmental Action -- 1 Introduction -- 2 Literature Background -- 3 Design Science Research Methodology -- 4 DLT Prototype Construction and Evaluation -- 4.1 Prototype Design Components -- 4.2 Prototype Functional Logic Components -- 4.3 Prototype Evaluation -- 5 Discussion of Empirical Findings -- 6 Conclusions -- References -- Moving Towards Blockchain-Based Methods for Revitalizing Healthcare Domain -- 1 Introduction -- 2 Blockchain Technology Fundamentals -- 2.1 Key Concepts -- 2.2 Blockchain Taxonomy -- 3 Blockchain Technology in Service of Healthcare -- 4 Related Works -- 4.1 Research Methodology -- 4.2 Our Research Foresight Regarding Healthcare Challenges -- 4.3 Blockchain Adoption in Healthcare Domain -- 5 Discussion -- 6 Our Forthcoming Proposition -- 7 Conclusion -- References -- Design of a Tokenized Blockchain Architecture for Tracking Trade in the Global Defense Market -- 1 Introduction -- 2 ; Related Work -- 3 Value of Blockchain for Trades in Defense Market -- 4 Design and Implementation of a NFT Based Decentralized Architecture -- 4.1 System Design -- 4.2 Implementation and Testing -- 5 Conclusion -- References -- Requirements for Interoperable Blockchain Systems: A Systematic Literature Review -- 1 Introduction -- 1.1 Research Problem -- 1.2 Key Contributions -- 2 Blockchain Interoperability Overview -- 2.1 Related Studies -- 3 Methodology -- 4 Results and Discussion -- 4.1 Technical and semantic interoperability requirements. -- 4.2 Organizational Interoperability Requirements -- 4.3 Legal Interoperability Requirements -- 5 Conclusion -- References -- Deep Learning and Healthcare Applications -- PENN: Phase Estimation Neural Network on Gene Expression Data -- 1 Introduction -- 2 Related Work -- 3 Method.; 3.1 Objective Function of PENN -- 4 Results -- 4.1 Dataset -- 4.2 Experiments -- 4.3 Implementation -- 5 Conclusion -- References -- MRIAD: A Pre-clinical Prevalence Study on Alzheimer's Disease Prediction Through Machine Learning Classifiers -- 1 Introduction -- 2 Related Work -- 3 Research Methodology -- 3.1 Development and Testing Approach -- 3.2 Data Source -- 3.3 Data Preprocessing -- 3.4 Feature Selection -- 4 Results and Discussion -- 5 Conclusions -- References -- Exploring the Link Between Brain Waves and Sleep Patterns with Deep Learning Manifold Alignment -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Dataset -- 3.2 Deep Learning Manifold Alignment Method -- 4 Experimental Results -- 5 Conclusion and Future Work -- References -- Machine Learning and Commercial Systems -- YOLOv5 for Automatic License Plate Recognition in Smart Cities -- 1 Introduction -- 2 Applications of ALPR -- 2.1 Use Cases of ALPR -- 2.2 Object Detection with Deep Learning Techniques -- 3 Related Work -- 4 Experimentation and Results -- 4.1 Methodology -- 4.2 Results -- 5 Conclusion -- References -- An Investigation into Predicting Flight Fares in India Using Machine Learning Models -- 1 Introduction -- 2 Literature Review -- 2.1 Empirical Approach to Determine Changes of Airfares and Customer Behavior When Purchasing Flight Tickets -- 2.2 Statistical Approaches for Determining Changes in the Airfare -- 2.3 Supervised Machine Learning for Determining the Changes in the Airfares -- 3 Research Methodology -- 4 Design Specifications -- 5 Evaluation Results and Discussion -- 5.1 Ensemble Model Analysis -- 5.2 Basic Machine Learning Model Results -- 6 Conclusion and Future Work -- References -- Securing Internet of Things (IoT) Devices Through Distributed Ledger Technologies (DLTs) and World Wide Web Consortium (W3C) Standards -- 1 Introduction.; 2 Overview of IoT and DLTs -- 2.1 Overview of IoT -- 2.2 Overview of DLTs -- 3 DLT-Based Applications and Services for IoT -- 4 Proposed Architecture -- 5 Conclusion and Future Work -- References -- Analysis and Forecast of Energy Demand in Senegal with a SARIMA Model and an LSTM Neural Network -- 1 Introduction -- 2 Analysis of Woyofal Customers Database -- 3 Building a Forecasting Model of Electricity Demand -- 3.1 Forecasting Electricity Demand with a SARIMA Model -- 3.2 Forecasting Electricity Demand with an LSTM Neural Network -- 4 Deploying the Forecasting Model in a Web Application -- 5 Conclusion -- References -- Author Index.
• Sommario/riassunto: This book constitutes refereed articles which present research work on new and emerging topics such as distributed ledger technology, blockchains and architectures, smart cities, machine learning and deep learning techniques and application areas such as flight pricing, energy demand and healthcare. The intended readership of the book include researchers, developers and practitioners in the areas of deep learning, ; big data and blockchains technologies and their applications.