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Artykuły w czasopismach na temat „Big Data et algorithmes”

Autor: Grafiati

Data publikacji: 1 lutego 2025

Data aktualizacji: 31 lipca 2025

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1

Benavent, Christophe. "Big Data, algorithme et marketing : rendre des comptes." Statistique et société 4, no. 3 (2016): 25–35. https://doi.org/10.3406/staso.2016.1009.

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Cet article s’intéresse à la question de la mise en oeuvre à vaste échelle d’algorithmes utiles au marketing, et s’intégrant dans une logique de plateforme. Prenant en compte des observations répétées d’externalités négatives produites par les algorithmes : ségrégation, biais de sélection, polarisation, hétérogénéisation, mais aussi leurs faiblesses intrinsèques résultant de la dette technique, de dépendances des données, et du contexte adversial dans lequel ils s’exercent, nous aboutissons à la nécessité d’une redevabilité algorithmique et nous nous questionnons sur la manière dont les algori

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Jauréguiberry, Francis. "L’individu hypermoderne face aux big data." Sociologie et sociétés 49, no. 2 (2018): 33–58. http://dx.doi.org/10.7202/1054273ar.

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Les big data, le datamining et le profilage, avec l’ensemble des applications d’aide à l’action individuelle et collective qui en découle, suscitent à juste titre des inquiétudes en ce qui concerne, d’une part la protection de la vie privée dans un environnement capteur des faits et gestes de chacun, d’autre part les formes de gouvernance de plus en plus informées par des algorithmes prédictifs. Sans négliger ces dangers, une position presque inverse sera ici défendue sous forme d’hypothèse : loin d’entraîner le déclin de l’autonomie individuelle, de soi comme personne singulière capable de ré

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Koch, Olivier. "Les données de la guerre. Big Data et algorithmes à usage militaire." Les Enjeux de l'information et de la communication N° 19/2, no. 2 (2018): 113. http://dx.doi.org/10.3917/enic.025.0113.

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Gori, Roland. "La biopolitique à l’ère des algorithmes." Cliniques méditerranéennes 110, no. 2 (2024): 147–65. http://dx.doi.org/10.3917/cm.110.0147.

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Michel Foucault a largement montré comment l’herméneutique du sujet moderne le conduisait à une externalisation des dispositifs de « technique de soi », mais que dirait-il aujourd’hui face à une connaissance du sujet par lui-même qui passe par Facebook, les Big Data et le numérique, bref un sujet à l’ère des selfies et de l’intelligence artificielle ? Où en sommes-nous aujourd’hui de la manière de gouverner les humains et des techniques de formation pour leur apprendre à se gouverner eux-mêmes ? Il y a véritablement un nouage entre les manières de se connaître, de se fabriquer comme sujet éthi

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Besse, Philippe, Céline Castets-Renard, and Aurélien Garivier. "L’IA du Quotidien peut elle être Éthique ?" Statistique et société 6, no. 3 (2018): 9–31. https://doi.org/10.3406/staso.2018.1083.

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Associant données massives (big data) et algorithmes d’apprentissage automatique (machine learning), la puissance des outils de décision automatique suscite autant d’espoir que de craintes. De nombreux textes législatifs européens (RGPD) et français récemment promulgués tentent d’encadrer les usages de ces outils. Laissant de côté les problèmes bien identifiés de confidentialité des données et ceux d’entrave à la concurrence, nous nous focalisons sur les risques de discrimination, les problèmes de transparence et ceux de qualité des décisions algorithmiques. La mise en perspective détaillée de

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Rialle, Vincent. "Du rêve technoscientifique d’hier aux réalités d’aujourd’hui : l’intelligence artificielle pour la santé entre horreur et enchantement." Droit, Santé et Société N° 2, no. 2 (2021): 28–35. https://doi.org/10.3917/dsso.082.0028.

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L’Intelligence Artificielle dans le domaine de la Santé (IAS) connaît aujourd’hui des développements et un questionnement exceptionnels. Cet article les présente en deux volets. Celui, d’abord, des années d’éveil de l’IAS, qui retrace son assise historique, scientifique et idéologique, laquelle repose sur la figure de l’« expert » et la toute-puissance du raisonnement purement logico-déductif. Celui ensuite de la bascule vers l’intelligence artificielle de masse actuelle, qui repose sur une puissance computationnelle phénoménale et omniprésente, et juxtapose d’impressionnants progrès médicaux

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Viglino, Manon. "La présomption d’innocence à l’ère du numérique." Revue de la recherche juridique, no. 2 (January 5, 2021): 1039–63. http://dx.doi.org/10.3917/rjj.190.1039.

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Les nouvelles technologies se développent, les formes d’intelligence artificielle se perfectionnent. Algorithmes, Big Data et justice prédictive sont aujourd’hui au cœur des débats sur l’avenir de la justice. Loin de dénier les progrès permis par ces nouvelles technologies et les bénéfices des infinies possibilités qu’elles offrent, il convient toutefois d’en souligner les dangers. En particulier, à l’heure de leur officialisation et de leur développement, il semble indispensable de veiller au respect des droits et libertés les plus fondamentaux. En particulier, dans le cadre pénal, la présomp

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Berriche, Amira, Dominique Crié, and Michel Calciu. "Une Approche Computationnelle Ancrée : Étude de cas des tweets du challenge #Movember en prévention de santé masculine." Décisions Marketing N° 112, no. 4 (2024): 79–103. http://dx.doi.org/10.3917/dm.112.0079.

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• Objectif L’objectif de cette étude est de présenter l’approche méthodologique computationnelle ancrée qui repose sur une démarche d’interprétation par les chercheurs des thèmes détectés par les algorithmes d’intelligence artificielle (IA) puis de l’appliquer au cas #Movember. • Méthodologie Une classification non supervisée par LDA et une analyse de sentiment ont été réalisées sur 144 906 tweets provenant de différents pays participants (France, Italie, Belgique, Australie, USA, UK, Arabie Saoudite, etc.). • Résultats Les résultats montrent que le processus de l’engagement individuel au mouv

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Nazeer, Mohammed Yaseer, and Mohammad Tarik Nadir. "Data Deluge Dynamics: Tracing the Evolution and Ramifications of Big Data Phenomenon." International Journal of Research and Innovation in Social Science VIII, no. V (2024): 2147–56. http://dx.doi.org/10.47772/ijriss.2024.805157.

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This paper presents a comprehensive review of the evolution, methodologies, challenges, and implications of Big Data in various domains. Big Data has emerged as a critical resource, offering unprecedented opportunities for decision-making, innovation, and societal advancement. The analysis delves into the historical trajectory of Big Data, examining its evolution from the early 2000s to its current status as a cornerstone of contemporary data-driven practices. Drawing on seminal works by Chen et al. (2014), Manyika et al. (2011), and Kitchin (2014), the review highlights the fundamental charac

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Polton, Dominique. "Les données de santé." médecine/sciences 34, no. 5 (2018): 449–55. http://dx.doi.org/10.1051/medsci/20183405018.

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En matière de santé comme dans d’autres secteurs, une masse croissante de données numérisées provenant de diverses sources est disponible et exploitable. C’est l’un des domaines où le potentiel du Big data apparaît très prometteur, avec de multiples innovations au bénéfice des patients et du système (accélération de la recherche et développement, connaissance des maladies, des facteurs de risque, médecine personnalisée, aide au diagnostic et au traitement, rôle accru des patients, pharmacovigilance, etc.), même si des inquiétudes s’expriment aussi vis-à-vis des impacts sociétaux, économiques e

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Bullich, Vincent, and Viviane Clavier. "Production des données, « Production de la société ». Les Big Data et algorithmes au regard des Sciences de l’information et de la communication." Les Enjeux de l'information et de la communication N° 19/2, no. 2 (2018): 5. http://dx.doi.org/10.3917/enic.025.0005.

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Cotton, Anne-Marie. "European Communication Monitor 2016 : 10 ans de recherche pan-européenne sur la communication stratégique." Revue Communication & professionnalisation, no. 4 (January 26, 2017): 181–205. http://dx.doi.org/10.14428/rcompro.vi4.713.

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L’équipe de recherche du European Communication Monitor (ECM) publie les résultats de la dixième édition de leurs questionnements sur les développements et les dynamiques de la communication stratégique dans 43 pays d’Europe. Dans l’étude 2016 l’analyse des « big data », des algorithmes en communication, des pratiques en communication propres au coaching et au conseil, de l’engagement des parties prenantes, des influenceurs actifs sur les réseaux sociaux, et des savoir, savoir-faire et savoir-être des professionnels de la communication. 2710 professionnels de la communication ont participé à l

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Ibrahim, Nadia, Alaa Hassan, and Marwah Nihad. "Big Data Analysis of Web Data Extraction." International Journal of Engineering & Technology 7, no. 4.37 (2018): 168. http://dx.doi.org/10.14419/ijet.v7i4.37.24095.

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In this study, the large data extraction techniques; include detection of patterns and secret relationships between factors numbering and bring in the required information. Rapid analysis of massive data can lead to innovation and concepts of the theoretical value. Compared with results from mining between traditional data sets and the vast amount of large heterogeneous data interdependent it has the ability expand the knowledge and ideas about the target domain. We studied in this research data mining on the Internet. The various networks that are used to extract data onto different locations

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Abdessamad, Sara. "La jurisprudence « as code », vers quelle substantialité jurisprudentielle ?" Pin Code 32, no. 4 (2024): 1–6. https://doi.org/10.3917/pinc.020.0001.

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La justice prédictive figure parmi les concepts clés de la justice de xxi e siècle, une justice qui reflète l’utilisation des nouvelles technologies « intelligentes », pour consacrer le principe de l’efficience dans la sphère judiciaire, notamment devant une atmosphère universelle en mutation, mais aussi en crise. Ainsi, la robotisation de la justice devient un garde-fou pour satisfaire la concurrence des systèmes juridiques, une concurrence qui encourage par ricochet l’esprit pragmatique. Un esprit qui engendrait selon cette approche calculatrice une jurisprudence « mécanique », élaborée via

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Sumrahadi, Abdullah, Musa Maliki, and Harryanto Aryodiguno. "Navigating the Data Stream: The Intersection of Digital Politics and Indonesian Foreign Policy in the Era of Big Data." Journal of Law and Politics 5, no. 1 (2024): 1–16. http://dx.doi.org/10.69648/rywo5712.

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In the contemporary landscape of international relations, the fusion of digital politics and big data analytics has emerged as a pivotal force reshaping diplomatic strategies and national foreign policy. This paper delves into the intricate interplay between digital politics and Indonesian foreign policy within the expansive realm of big data. Indonesia is a dynamic archipelago boasting a burgeoning digital ecosystem that serves as an illuminating case study to unravel the multifaceted dynamics of this intersection. In this era of information overload (Wood et al., 1998), big data analytics re

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Zolynski, Célia. "OS BIG DATA E OS DADOS PESSOAIS ENTRE OS PRINCÍPIOS DA PROTEÇÃO E DA INOVAÇÃO." Law, State and Telecommunications Review 12, no. 1 (2020): 225–45. http://dx.doi.org/10.26512/lstr.v12i1.30007.

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Objective ”“ The article contrasts the problem of Big Data with the possibilities and limits of personal data protection. It is an original contribution to the academic discussion about the regulation of the Internet and the management of algorithms, focusing on Big Data. Methodology/approach/design ”“ The article provides bibliographic research on the opposition between Big Data and personal data protection, focusing on European Union law and French law. From the research is possible to identify regulatory alternatives do Big Data, whether legal-administrative nature or technological na

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Nguyen Thai, B., and A. Olasz. "RASTER DATA PARTITIONING FOR SUPPORTING DISTRIBUTED GIS PROCESSING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3/W3 (August 20, 2015): 543–51. http://dx.doi.org/10.5194/isprsarchives-xl-3-w3-543-2015.

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In the geospatial sector big data concept also has already impact. Several studies facing originally computer science techniques applied in GIS processing of huge amount of geospatial data. In other research studies geospatial data is considered as it were always been big data (Lee and Kang, 2015). Nevertheless, we can prove data acquisition methods have been improved substantially not only the amount, but the resolution of raw data in spectral, spatial and temporal aspects as well. A significant portion of big data is geospatial data, and the size of such data is growing rapidly at least by 2

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Phan, Thanh Huan, and Hoài Bắc Lê. "A Comprehensive Survey of Frequent Itemsets Mining on Transactional Database with Weighted Items." Journal of Research and Development on Information and Communication Technology 2021, no. 1 (2021): 19–28. http://dx.doi.org/10.32913/mic-ict-research.v2021.n1.967.

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In 1993, Agrawal et al. proposed the first algorithm for mining traditional frequent itemset on binarytransactional database with unweighted items - This algorithmis essential in finding hindden relationships among items inyour data. Until 1998, with the development of various typesof transactional database - some researchers have proposed afrequent itemsets mining algorithms on transactional databasewith weighted items (the importance/meaning/value of itemsis different) - It provides more pieces of knowledge thantraditional frequent itemsets mining. In this article, the authors present a surv

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Bagui, Sikha, and Timothy Bennett. "Optimizing random forests: spark implementations of random genetic forests." BOHR International Journal of Engineering 1, no. 1 (2022): 42–51. http://dx.doi.org/10.54646/bije.2022.09.

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The Random Forest (RF) algorithm, originally proposed by Breiman et al. (1), is a widely used machine learning algorithm that gains its merit from its fast learning speed as well as high classification accuracy. However, despiteits widespread use, the different mechanisms at work in Breiman’s RF are not yet fully understood, and there is stillon-going research on several aspects of optimizing the RF algorithm, especially in the big data environment. To optimize the RF algorithm, this work builds new ensembles that optimize the random portions of the RF algorithm using genetic algorithms, yield

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ALAM, MAHBOOB, and MOHD AMJAD. "A precipitation forecasting model using machine learning on big data in clouds environment." MAUSAM 72, no. 4 (2021): 781–90. http://dx.doi.org/10.54302/mausam.v72i4.3546.

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Numerical weather prediction (NWP) has long been a difficult task for meteorologists. Atmospheric dynamics is extremely complicated to model, and chaos theory teaches us that the mathematical equations used to predict the weather are sensitive to initial conditions; that is, slightly perturbed initial conditions could yield very different forecasts. Over the years, meteorologists have developed a number of different mathematical models for atmospheric dynamics, each making slightly different assumptions and simplifications, and hence each yielding different forecasts. It has been noted that ea

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Stylianos, Sideridis, Pelekis Nikos, and Theodoridis Yannis. "On querying and mining semantic-aware mobility timelines." International Journal of Data Science and Analytics 2, no. 1-2 (2016): 29–44. https://doi.org/10.1007/s41060-016-0030-1.

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The explosion of available positioning information associated with the inferred or user-declared semantics of the respective locations has contributed in what is called the Big Data era by posing new challenges to the mobility data management and mining research community. Motivated by a series of challenges posed in Pelekis et al. (SIGKDD Explor 15(1):23–32, 2013), in this paper, we present a unified framework for the management and the analysis of LifeSteps, i.e., data objects about human mobility including both (raw) spatio-temporal trajectories and their semantic counterpart. In particular

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Qu, Quanbo, Baocang Wang, Yuan Ping, and Zhili Zhang. "Improved Cryptanalysis of a Fully Homomorphic Symmetric Encryption Scheme." Security and Communication Networks 2019 (June 2, 2019): 1–6. http://dx.doi.org/10.1155/2019/8319508.

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Homomorphic encryption is widely used in the scenarios of big data and cloud computing for supporting calculations on ciphertexts without leaking plaintexts. Recently, Li et al. designed a symmetric homomorphic encryption scheme for outsourced databases. Wang et al. proposed a successful key-recovery attack on the homomorphic encryption scheme but required the adversary to know some plaintext/ciphertext pairs. In this paper, we propose a new ciphertext-only attack on the symmetric fully homomorphic encryption scheme. Our attack improves the previous Wang et al.’s attack by eliminating the assu

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Duan, Guiduo, Xiaotong Wang, Tianxi Huang, and Jürgen Kurths. "An Improved Group Similarity-Based Association Rule Mining Algorithm in Complex Scenes." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 02 (2019): 2059005. http://dx.doi.org/10.1142/s0218001420590053.

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Association rule (AR) mining in complex scene has attracted extensive attention of researchers in recent years. Typically, many researchers focused on an algorithm itself and ignored a generalization method to improve the performance of AR mining. Tuna et al., presented a general data structure Speeding-Up AR Structure with Inverted Index Compression (SAII) which could be utilized in most of the existing algorithms to improve their performance IEEE Trans. Cybern. 46(12) (2016) 3059–3072. However, we found that this algorithm consumes a lot of time in re-ordering data because a one-to-one compa

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Aziz, Sameen, Saleem Ullah, Bushra Mughal, Faheem Mushtaq, and Sabih Zahra. "Roman Urdu sentiment analysis using Machine Learning with best parameters and comparative study of Machine Learning algorithms." Pakistan Journal of Engineering and Technology 3, no. 2 (2020): 172–77. http://dx.doi.org/10.51846/vol3iss2pp172-177.

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People talks on the social media as they feel good and easy way to express their feelings about topic, post or product on the ecommerce websites. In the Asia mostly the people use the Roman Urdu language script for expressing their opinion about the topic. The Sentiment analysis of the Roman Urdu (Bilal et al. 2016)language processes is a big challenging task for the researchers because of lack of resources and its non-structured and non-standard syntax / script. We have collected the Dataset from Kaggle containing 21000 values with manually annotated and prepare the data for machine learning

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Rahman, Md Atiqur, and Mohamed Hamada. "Lossless Image Compression Techniques: A State-of-the-Art Survey." Symmetry 11, no. 10 (2019): 1274. http://dx.doi.org/10.3390/sym11101274.

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Modern daily life activities result in a huge amount of data, which creates a big challenge for storing and communicating them. As an example, hospitals produce a huge amount of data on a daily basis, which makes a big challenge to store it in a limited storage or to communicate them through the restricted bandwidth over the Internet. Therefore, there is an increasing demand for more research in data compression and communication theory to deal with such challenges. Such research responds to the requirements of data transmission at high speed over networks. In this paper, we focus on deep anal

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Harsh, Pratap singh Rajput* Shikha Lodhi. "AI In Drug Repurposing." International Journal of Pharmaceutical Sciences 3, no. 5 (2025): 2098–103. https://doi.org/10.5281/zenodo.15396590.

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Artificial Intelligence (AI) has emerged as a transformative force in drug repurposing, offering innovative solutions to accelerate the discovery of new therapeutic applications for existing drugs. This approach significantly reduces development time and costs, particularly in addressing unmet medical needs in rare diseases and complex conditions (Álvarez-Machancoses & Fernández-Martínez, 2019; Cortial et al., 2024). AI-driven drug repurposing leverages machine learning algorithms, deep learning techniques, and big data analytics to systematically identify potential dr

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Yang, Junjie. "Technology Focus: Data Analytics (October 2023)." Journal of Petroleum Technology 75, no. 10 (2023): 93–94. http://dx.doi.org/10.2118/1023-0093-jpt.

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Throughout the present decade, the oil and gas industry is experiencing and enduring digital transformation, marked by continuous evolution. In the pursuit of increased efficiency, companies have embraced cutting-edge technologies in artificial intelligence and big data, effectively automating their existing work flows. Having undergone extensive exploration and refinement, the debate between physics-based models and data-driven models has now found a harmonious middle ground. This convergence has paved the way for significant advancements in the realms of transfer learning and machine-learnin

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Çetin, F., and M. O. Kulekci. "AN EXPERIMENTAL ANALYSIS OF SPATIAL INDEXING ALGORITHMS FOR REAL TIME SAFETY CRITICAL MAP APPLICATION." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-4-2021 (June 17, 2021): 41–48. http://dx.doi.org/10.5194/isprs-annals-v-4-2021-41-2021.

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Abstract. This paper presents a study that compares the three space partitioning and spatial indexing techniques, KD Tree, Quad KD Tree, and PR Tree. KD Tree is a data structure proposed by Bentley (Bentley and Friedman, 1979) that aims to cluster objects according to their spatial location. Quad KD Tree is a data structure proposed by Berezcky (Bereczky et al., 2014) that aims to partition objects using heuristic methods. Unlike Bereczky’s partitioning technique, a new partitioning technique is presented based on dividing objects according to space-driven, in the context of this study. PR Tre

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Flores, Eibar, Hannah Hansen, and Simon Clark. "Semantic Technologies to Model Battery Data and Knowledge." ECS Meeting Abstracts MA2023-02, no. 1 (2023): 112. http://dx.doi.org/10.1149/ma2023-021112mtgabs.

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Data-intensive battery research is here to stay. With open data incentives on the rise and simpler software interfaces granting access to powerful machine learning algorithms, researchers are uncovering a wealth of insights from battery data. However, data without context are just unusable digital bits. Battery data must be appropriately described to render it interoperable and reusable for traditional and data-intensive research. This requires overcoming numerous challenges associated to data heterogeneity, inconsistency, standardization, and meaning. Semantic technologies address these chall

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Dr. Geetanjali Vinayak Kale. "A Comprehensive Study of Text Summarization with Advent of Large Language Models." Communications on Applied Nonlinear Analysis 32, no. 9s (2025): 1073–88. https://doi.org/10.52783/cana.v32.4113.

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Introduction: Communication is at the heart of the human race. With the growth of social media and other communication platforms, the globe is now connected at a single click. People communicate and tend to share information through these platforms. A massive amount of data is being generated and being analysed every second. To tackle the problem of analysing Big Data and withdraw insights from it, is a difficult task. Text summarization is the process of concise representation of textual data so as to extract the most important information out of it. Text summarization plays a major role in a

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Lysaght, Tamra, Hannah Yeefen Lim, Vicki Xafis, and Kee Yuan Ngiam. "AI-Assisted Decision-making in Healthcare." Asian Bioethics Review 11, no. 3 (2019): 299–314. http://dx.doi.org/10.1007/s41649-019-00096-0.

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Abstract Artificial intelligence (AI) is set to transform healthcare. Key ethical issues to emerge with this transformation encompass the accountability and transparency of the decisions made by AI-based systems, the potential for group harms arising from algorithmic bias and the professional roles and integrity of clinicians. These concerns must be balanced against the imperatives of generating public benefit with more efficient healthcare systems from the vastly higher and accurate computational power of AI. In weighing up these issues, this paper applies the deliberative balancing approach

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Suwalska, Aleksandra, and Joanna Polanska. "GMM-Based Expanded Feature Space as a Way to Extract Useful Information for Rare Cell Subtypes Identification in Single-Cell Mass Cytometry." International Journal of Molecular Sciences 24, no. 18 (2023): 14033. http://dx.doi.org/10.3390/ijms241814033.

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Cell subtype identification from mass cytometry data presents a persisting challenge, particularly when dealing with millions of cells. Current solutions are consistently under development, however, their accuracy and sensitivity remain limited, particularly in rare cell-type detection due to frequent downsampling. Additionally, they often lack the capability to analyze large data sets. To overcome these limitations, a new method was suggested to define an extended feature space. When combined with the robust clustering algorithm for big data, it results in more efficient cell clustering. Each

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Willerth, Marc. "Technology Focus: Drilling Automation and Innovation (February 2025)." Journal of Petroleum Technology 77, no. 02 (2025): 54–55. https://doi.org/10.2118/0225-0054-jpt.

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Everywhere you look these days, there is talk of how advances in big data, artificial intelligence (AI), and machine learning (ML) will revolutionize virtually every aspect of our lives. The thought of letting computer algorithms pore over mounds of data collected every day and spit out solutions to our biggest problems is certainly appealing. This month, we will look at how researchers in the drilling domain are using this potential to improve well construction. I would be remiss if I did not caution the reader to maintain some healthy perspective when looking at how other areas have been cha

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Giri, Pradip Raj, and Toyanath Upadhayaya. "Digital Ethics and Mindful Circulation: Navigating the Complexities in the Digital Age." Myagdi Guru 5, no. 1 (2022): 1–10. http://dx.doi.org/10.3126/mg.v5i1.70615.

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In the digital age, characterized by unprecedented connectivity and rapid information exchange, the dynamics of writing, communication, and ethical considerations have undergone profound transformations. This paper aims at exploring why the ethical implications of digital communication are important during circulating and disseminating information in the context of digital ethics. The primary texts for this paper are tweets by former President of United States of America, Donald Trump and some other posts on social media. To support the claim, the researchers use a wide range of scholars such

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Dimitriadis, George, Joana P. Neto, and Adam R. Kampff. "t-SNE Visualization of Large-Scale Neural Recordings." Neural Computation 30, no. 7 (2018): 1750–74. http://dx.doi.org/10.1162/neco_a_01097.

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Electrophysiology is entering the era of big data. Multiple probes, each with hundreds to thousands of individual electrodes, are now capable of simultaneously recording from many brain regions. The major challenge confronting these new technologies is transforming the raw data into physiologically meaningful signals, that is, single unit spikes. Sorting the spike events of individual neurons from a spatiotemporally dense sampling of the extracellular electric field is a problem that has attracted much attention (Rey, Pedreira, & Quian Quiroga, 2015 ; Rossant et al., 2016 ) but is still fa

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Amicelle, Anthony. "Policing & big data. La mise en algorithmes d’une politique internationale." Critique internationale N° 92, no. 3 (2021): 23–48. http://dx.doi.org/10.3917/crii.092.0026.

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Karame, Prosper, Faustin Gashakamba, Valentine Dushimiyimana, Ladislas Nshimiyimana, and Pacifique Ndishimye. "Bioscience Data Literacy At The Interface Of The Environment, Human And Wildlife: One Health-centred education, research and practice perspectives in Rwanda." Biodiversity Information Science and Standards 3 (September 17, 2019): e39312. https://doi.org/10.3897/biss.3.39312.

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Advances in information technology have led to the availability of state-of-the-art technologies which in turn have been enabling the generation of unprecedented amounts of complex, structured or unstructured data sets that are sometimes difficult to process using conventional techniques. In particular, handling these large scale data in terms of collection, and aggregation, synthesis and analysis, interpretation, reporting, sharing and archiving processes, and interpreting them into descriptive models and enable effective interpretation requires continued development of robust computational m

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Jiao, Yang, Bruno Goutorbe, Christelle Grauer, Matthieu Cornec, and Jérémie Jakubowicz. "The categorization challenge organized by Cdiscount on datascience.net in 2015: analysis of the released data set and winning contributions." Statistique et Enseignement 8, no. 2 (2017): 125–35. https://doi.org/10.3406/staso.2017.1375.

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En 2015, Cdiscount a mis la communauté au défi de prévoir la catégorie correcte de ses produits à partir de certains de leurs attributs comme le libellé, la description, le prix ou l’image associée. Les candidats ont eu accès à l’intégralité du catalogue de produits actifs en mai 2015, soit environ 15.8 millions d’items répartis dans 5,789 catégories, hormis une petite partie qui a servi d’ensemble de test. La qualité des données est loin d’être homogène et la répartition des catégories est extrêmement déséquilibrée, ce qui complique la tâche de catégorisation. Les cinq algorithmes gagnants, s

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Laroche, Arnaud. "La « vague de fond » du BigData." Statistique et société 2, no. 4 (2014): 13–18. https://doi.org/10.3406/staso.2014.928.

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Ce qui caractérise l’ère du «BigData » , ce n’est pas seulement la taille des fichiers de données, ou la vitesse à laquelle on doit les traiter : c’est surtout la place qu’on leur attribue dans les processus industriels et de services. Désormais, c’est « la donnée » qui pilote des applications et des décisions. Les cas d’usage se multiplient, du micro-ciblage de publicités au pilotage de réseaux d’énergie ou de transport. Des secteurs comme les télécommunications, l’assurance, la banque sont directement concernés ; et l’information économique et sociale publique sera elle aussi touchée. Ce vas

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Beaudouin, Valérie, and Winston Maxwell. "La prédiction du risque en justice pénale aux états-unis : l’affaire propublica-compas." Réseaux N° 240, no. 4 (2023): 71–109. http://dx.doi.org/10.3917/res.240.0071.

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Un article publié par le média Pro Publica en 2016 considère que le logiciel Compas, utilisé aux États-Unis pour la prédiction de la récidive, porte préjudice à la population noire : « It’s biased against blacks ». La publication crée une onde de choc dans l’espace public et alimente les débats sur l’équité des algorithmes et sur le bien-fondé de ces outils de prédiction du risque. Ces débats étaient jusque-là confinés dans des sphères de spécialistes. En partant de l’affaire ProPublica-Compas, nous avons exploré les différents embranchements de la controverse dans l’arène des « data sciences

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Potier, Victor. "Dominique Cardon, À quoi rêvent les algorithmes ? Nos vies à l’heure du big data." Sociologie 9, no. 3 (2018): 339. http://dx.doi.org/10.3917/socio.093.0339.

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Quellet, Céline. "Cardon Dominique, À quoi rêvent les algorithmes ? Nos vies à l’heure des big data." Recherches sociologiques et anthropologiques 47, no. 2 (2016): 154–56. http://dx.doi.org/10.4000/rsa.1779.

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Ampomah, Ernest Kwame, Zhiguang Qin, and Gabriel Nyame. "Evaluation of Tree-Based Ensemble Machine Learning Models in Predicting Stock Price Direction of Movement." Information 11, no. 6 (2020): 332. http://dx.doi.org/10.3390/info11060332.

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Forecasting the direction and trend of stock price is an important task which helps investors to make prudent financial decisions in the stock market. Investment in the stock market has a big risk associated with it. Minimizing prediction error reduces the investment risk. Machine learning (ML) models typically perform better than statistical and econometric models. Also, ensemble ML models have been shown in the literature to be able to produce superior performance than single ML models. In this work, we compare the effectiveness of tree-based ensemble ML models (Random Forest (RF), XGBoost C

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Badhan, Istiaque Ahmed, MD Nurul Hasnain, MD Hafizur Rahman, Irfan Chowdhury, and MD Abu Sayem. "Strategic Deployment of Advance Surveillance Ecosystems: An Analytical Study on Mitigating Unauthorized U.S. Border Entry." Inverge Journal of Social Sciences 3, no. 4 (2024): 82–94. https://doi.org/10.63544/ijss.v3i4.105.

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This research aims at the intricate challenge of securing the U.S. border by investigating the potential of cutting-edge surveillance technologies. We explore a range of innovations, including artificial intelligence, unmanned aerial vehicles (UAVs), sophisticated sensor networks, and sophisticated data integration systems. Through a combination of case studies, technological assessments, and policy analyses, this work aims to understand how these technologies can enhance border security while navigating the complex landscape of ethical and legal considerations. Our research employs a mixed-me

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Monino, Jean-Louis. "Big Data et Open Data." Marché et organisations N° 51, no. 3 (2024): 169–98. http://dx.doi.org/10.3917/maorg.051.0169.

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« Big Data » et « Open Data » constituent des gisements d’informations encore sous-exploités. La question associée à cet article considère quels éléments peuvent éclairer une meilleure intégration de leur potentiel. Par un travail d’analyses conceptuelles, seront montrées leurs aptitudes à transformer « business », gouvernement et société. Le Big Data donne le pouvoir d’analyser et changer le monde. L’ Open Data assure que ce pouvoir soit partagé et favorise des démarches d’innovation ouverte, suivant, d’une part des organisations détenant des données exploitables, d’autre part des entreprises

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Dinh-Xuan, A. T. "«Big Data» et pneumologie." Revue des Maladies Respiratoires Actualités 7, no. 3 (2015): 197–99. http://dx.doi.org/10.1016/s1877-1203(15)30052-5.

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Baudin, Bruno. "Biologie et big data." Revue Francophone des Laboratoires 2019, no. 509 (2019): 1. http://dx.doi.org/10.1016/s1773-035x(19)30042-5.

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Charaudeau, Marie-Odile, Alexis Fritel, Charles Huot, Philippe Martin, and Laurent Prével. "Et demain ? Archivage et big data." La Gazette des archives 240, no. 4 (2015): 373–84. http://dx.doi.org/10.3406/gazar.2015.5319.

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Wassima, LAKHCHINI, WAHABI Rachid, and EL KABBOURI Mounime. "Artificial Intelligence & Machine Learning in Finance: A literature review." International Journal of Accounting, Finance, Auditing, Management and Economics 3, no. 6-1 (2022): 437–55. https://doi.org/10.5281/zenodo.7454232.

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In the 2020s, Artificial Intelligence (AI) has been increasingly becoming a dominant technology, and thanks to new computer technologies, Machine Learning (ML) has also experienced remarkable growth in recent years; however, Artificial Intelligence (AI) needs notable data scientist and engineers’ innovation to evolve. Hence, in this paper, we aim to infer the intellectual development of AI and ML in finance research, adopting a scoping review combined with an embedded review to pursue and scrutinize the services of these concepts. For a technical literature review, we goose-step the five

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Lin, Ai Qin, Min Li Zheng, Chun Guang Fan, and Lin Yang. "Surface Morphology Simulation of High Speed Milled of Face Milling Cutters." Advanced Materials Research 305 (July 2011): 225–29. http://dx.doi.org/10.4028/www.scientific.net/amr.305.225.

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To surface milling cutters for research object, established considering the single spindle partial pendulum milling cutter tooth surfaces of high speed cutting 3d surface morphology simulation model by using graphic matrix transformation principle and vector algorithms. Comparing the simulation and forecast of surface morphology and surface roughness with the actual machining surface morphology and surface roughness by using the workpiece simulation algorithm meshing, we verify the correctness of the simulation model. The simulation analyses the influence regularity of surface morphology and s

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