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001484203 019__ $$a1409546356$$a1409682899
001484203 020__ $$a9783031482328$$q(electronic bk.)
001484203 020__ $$a3031482328$$q(electronic bk.)
001484203 020__ $$z9783031482311
001484203 020__ $$z303148231X
001484203 0247_ $$a10.1007/978-3-031-48232-8$$2doi
001484203 035__ $$aSP(OCoLC)1409806775
001484203 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dEBLCP$$dYDX$$dOCLCO
001484203 049__ $$aISEA
001484203 050_4 $$aQA76.9.D3$$bI584 2023
001484203 08204 $$a005.74$$223/eng/20231120
001484203 1112_ $$aIDEAL (Conference)$$n(24th :$$d2023 :$$cÉvora, Portugal)
001484203 24510 $$aIntelligent Data Engineering and Automated Learning -- IDEAL 2023 :$$b24th International Conference, Évora, Portugal, November 22-24, 2023, Proceedings /$$cPaulo Quaresma, David Camacho, Hujun Yin, Teresa Gonçalves, Vicente Julian, Antonio J. Tallón-Ballesteros, editors.
001484203 2463_ $$aIDEAL 2023
001484203 264_1 $$aCham :$$bSpringer,$$c2023.
001484203 300__ $$a1 online resource (xvii, 549 pages) :$$billustrations (some color).
001484203 336__ $$atext$$btxt$$2rdacontent
001484203 337__ $$acomputer$$bc$$2rdamedia
001484203 338__ $$aonline resource$$bcr$$2rdacarrier
001484203 4901_ $$aLecture Notes in Computer Science,$$x1611-3349 ;$$v14404
001484203 500__ $$aIncludes author index.
001484203 504__ $$aReferences -- Plant Disease Detection and Classification Using a Deep Learning-Based Framework -- 1 Introduction -- 2 Material and Method -- 2.1 Dataset -- 2.2 Data Pre-Processing -- 2.3 Proposed Model -- 3 Experiment and Results -- 3.1 System Setup -- 3.2 Training Regime -- 3.3 Evaluation Protocols -- 3.4 Result and Discussions -- 4 Conclusion and Future Scope -- References -- Evaluating Text Classification in the Legal Domain Using BERT Embeddings -- 1 Introduction -- 2 Related Works -- 2.1 Natural Language Processing for Legal Document Analysis and Classification -- 2.2 Embedding Models
001484203 5050_ $$aMain Track: Optimization of Image Acquisition for Earth Observation Satellites via Quantum Computing -- Complexity-driven sampling for Bagging -- A pseudo-label guided hybrid approach for unsupervised domain adaptation⁾́p6 -- Combining of Markov Random Field and Convolutional Neural Networks for Hyper/Multispectral Image Classification -- Plant Disease Detection and Classification using a Deep learning-based framework -- Evaluating Text Classification in the Legal Domain Using BERT Embeddings -- Rapid and Low-Cost Evaluation of Multi-Fidelity Scheduling Algorithms for Hyperparameter Optimization -- The Applicability of Federated Learning to Official Statistics -- Generating Wildfire Heat Maps with Twitter and BERT -- An urban simulator integrated with a genetic algorithm for efficient traffic light coordination -- GPU-Based Acceleration of the Rao Optimization Algorithms: Application to the Solution of Large Systems of Nonlinear Equations -- Direct determination of Operational Value-at-Risk using Descriptive Statistics -- Using Deep Learning models to Predict the Electrical Conductivity of the influent in a Wastewater Treatment Plant. -Unsupervised Defect Detection for Infrastructure Inspection -- Generating Adversarial Examples using LAD -- Emotion extraction from Likert-Scale questionnaires ⁰́b3 an additional dimension to Psychology Instruments -- Recent applications of pre-aggregation functions -- A Probabilistic Approach: Querying Web Resources In The Presence Of Uncertainty -- Domain Adaptation in Transformer models: Question Answering of Dutch Government Policies -- Sustainable On-Street Parking Mapping with Deep Learning and Airborne Imagery -- Hebbian Learning-Guided Random Walks for Enhanced Community Detection in Correlation-Based Brain Networks -- Hebbian Learning-Guided Random Walks for Enhanced Community Detection in Correlation-Based Brain Networks -- Language Models for Automatic Distribution of Review Notes in Movie Production -- Extracting Knowledge from Incompletely Known Models -- Threshold-based Classification to Enhance Confidence in Open Set of Legal Texts -- Comparing ranking learning algorithms for information retrieval systems -- Analyzing the influence of market event correction for forecasting stock prices using Recurrent Neural Networks -- Measuring the relationship between the use of typical Manosphere discourse and the engagement of a user with the pick-up artist community⁾́p6 -- Uniform Design of Experiments for Equality Constraints -- Globular Cluster Detection in M33 Using Multiple Views Representation Learning -- Segmentation of Brachial Plexus Ultrasound Images Based on Modified SegNet Model -- Unsupervised Online Event Ranking for IT Operations⁾́p6 -- A Subgraph Embedded GIN with Attention for Graph Classification -- A Machine Learning Approach to Predict Cyclists⁰́b9 Functional Threshold Power -- Combining Regular Expressions and Supervised Algorithms for Clinical Text Classification -- MODELING THE INK TUNING PROCESS USING MACHINE LEARNING -- Depth and Width Adaption of DNN for Data Stream Classification with Concept Drifts* -- FETCH: A Memory-Efficient Replay Approach for Continual Learning in Image Classification -- Enhanced SVM-SMOTE with Cluster Consistency for Imbalanced Data Classification -- Preliminary Study on Unexploded Ordnance Classification in Underwater Environment Based on the Raw Magnetometry Data. -- Efficient Model For Probabilistic Web resources under uncertainty -- Unlocking the Black Box: Towards Interactive Explainable Automated Machine Learning -- Machine Learning for Time Series Forecasting Using State Space Models -- Causal graph discovery for explainable insights on marine biotoxin shellfish contamination -- Special Session on Federated Learning and (pre) Aggregation in Machine Learning: Adaptative fuzzy measure for edge detection -- Special Session on Intelligent Techniques for Real-world Applications of Renewable Energy and Green Transport: Prediction and Uncertainty Estimation in Power Curves of Wind Turbines Using -SVR -- Glide Ratio Optimization for Wind Turbine Airfoils based on Genetic Algorithms -- Special Session on Data Selection in Machine Learning: Detecting Image Forgery Using Support Vector Machine and Texture Features -- Instance selection techniques for large volumes of data.
001484203 506__ $$aAccess limited to authorized users.
001484203 520__ $$aThis book constitutes the proceedings of the 24th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2023, held in Elvora, Portugal, during November 22⁰́b324, 2023. The 45 full papers and 4 short papers presented in this book were carefully reviewed and selected from 77 submissions. IDEAL 2023 is focusing on big data challenges, machine learning, deep learning, data mining, information retrieval and management, bio-/neuro-informatics, bio-inspired models, agents and hybrid intelligent systems, and real-world applications of intelligence techniques and AI. The papers are organized in the following topical sections: main track; special session on federated learning and (pre) aggregation in machine learning; special session on intelligent techniques for real-world applications of renewable energy and green transport; and special session on data selection in machine learning.
001484203 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed November 20, 2023).
001484203 650_6 $$aBases de données$$xGestion$$vCongrès.
001484203 650_6 $$aExploration de données (Informatique)$$vCongrès.
001484203 650_6 $$aAgents intelligents (Logiciels)$$vCongrès.
001484203 650_0 $$aDatabase management$$vCongresses.$$vCongresses$$0(DLC)sh2008102037
001484203 650_0 $$aData mining$$vCongresses.$$vCongresses$$0(DLC)sh2008102035
001484203 650_0 $$aIntelligent agents (Computer software)$$vCongresses.$$vCongresses$$0(DLC)sh2008104780
001484203 655_0 $$aElectronic books.
001484203 7001_ $$aQuaresma, Paulo,$$eeditor.$$1https://orcid.org/0000-0002-5086-059X
001484203 7001_ $$aCamacho, David,$$eeditor.$$0(orcid)0000-0002-5051-3475$$1https://orcid.org/0000-0002-5051-3475
001484203 7001_ $$aYin, Hujun,$$d1962-$$eeditor.$$1https://orcid.org/0000-0002-9198-5401
001484203 7001_ $$aGoncalves, Teresa,$$eeditor.$$0(orcid)0000-0002-1323-0249$$1https://orcid.org/0000-0002-1323-0249
001484203 7001_ $$aJulian, Vicente,$$eeditor.$$1https://orcid.org/0000-0002-2743-6037
001484203 7001_ $$aTallón-Ballesteros, Antonio J.,$$eeditor.$$1https://orcid.org/0000-0002-9699-1894
001484203 77608 $$iPrint version:$$aQuaresma, Paulo$$tIntelligent Data Engineering and Automated Learning - IDEAL 2023$$dCham : Springer,c2023$$z9783031482311
001484203 830_0 $$aLecture notes in computer science ;$$v14404.$$x1611-3349
001484203 852__ $$bebk
001484203 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-48232-8$$zOnline Access$$91397441.1
001484203 909CO $$ooai:library.usi.edu:1484203$$pGLOBAL_SET
001484203 980__ $$aBIB
001484203 980__ $$aEBOOK
001484203 982__ $$aEbook
001484203 983__ $$aOnline
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