the proceedings contain 49 papers. the special focus in this conference is on intelligentdataengineering and automatedlearning. the topics include: GPU-Based Acceleration of the Rao Optimization Algorithm...
ISBN:
(纸本)9783031482311
the proceedings contain 49 papers. the special focus in this conference is on intelligentdataengineering and automatedlearning. the topics include: 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: – 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;complexity-Driven Sampling for Bagging;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;extracting Automatically a Domain Ontology from the "Book of Properties" of the Archbishop’s Table of Braga;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 withthe Pick-Up Artist Community;uniform Design of Experiments for Equality Constraints;a Pseudo-Label Guided Hybrid Approach for Unsupervised Domain Adaptation;globular Cluster Detection in M33 Using Multiple Views Representation learning;segmentation of Brachial Plexus
this study addresses battery failure in motorized wheel chairs, which are essential for the mobility of individuals with disabilities. the main objective was to concept a comprehensive dataset comprising six attribute...
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ISBN:
(纸本)9783031777370;9783031777387
this study addresses battery failure in motorized wheel chairs, which are essential for the mobility of individuals with disabilities. the main objective was to concept a comprehensive dataset comprising six attributes that directly impact battery life, consisting of 498 instances. Using the Random Forest algorithm, we demonstrate the ability to accurately predict battery failures. the results highlight the necessity for proactive measures to prevent battery degradation and extend its lifespan.
SMS Spam Detection has increasingly garnered attention due to the widespread use of mobile devices. Currently, most SMS spam detection model training methods rely on centralized data collection, which poses numerous p...
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ISBN:
(纸本)9783031777301;9783031777318
SMS Spam Detection has increasingly garnered attention due to the widespread use of mobile devices. Currently, most SMS spam detection model training methods rely on centralized data collection, which poses numerous privacy threats and creates security vulnerabilities that expose sensitive information. this study aims to propose a training method that does not require data sharing between parties, based on a federated learning system. In this paper, we experiment with FedAvg, FedAvgM, and FedAdam algorithms using a fine-tuned PhoBERT model tailored for the SMS spam classification task. the results show that the FedAvg algorithm achieves high performance with an accuracy of 99.38% in the IID setting, while the FedAdam algorithm proves more effective in the Non-IID setting, yielding a model with an accuracy of up to 98.5%. this study demonstrates that models like PhoBERT trained with FL algorithms can achieve classification capabilities comparable to centralized data training methods, highlighting the significant potential of FL for natural language processing models without the need for centralized data collection.
Instance selection (IS) serves as a vital preprocessing step, particularly in addressing the complexities associated with high-dimensional problems. Its primary goal is the reduction of data instances, a process that ...
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LoockMe is an artificial intelligence-powered location scouting platform that combines deep learning image analysis, cutting-edge machine learning natural language processing (NLP) for automated content annotation, an...
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ISBN:
(数字)9783031342042
ISBN:
(纸本)9783031342035;9783031342042
LoockMe is an artificial intelligence-powered location scouting platform that combines deep learning image analysis, cutting-edge machine learning natural language processing (NLP) for automated content annotation, and intelligent search. the platform's objective is to label input images of local landscapes, and/or any other assets that regional film offices want to expose to those interested in identifying potential locations for the film production industry. the deep learning-based image analysis achieved high classification performance with an AUCscore of 99.4%. Moreover, the state-of-the-art machine learning NLP module enhances the platform's capabilities by analyzing text descriptions of the locations and thus allowing for automated annotation, while the intelligent search engine combines image analysis with NLP to extract relevant context from available data. the proposed artificial intelligence platform has the potential to substantially assist asset publishers and revolutionize the location scouting process for the film production industry in Greece.
Nowadays, Machine learning models are widely used in many fields and employed to solve problems from different sectors. However, we often face issues when running these models in case the training data is insufficient...
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Computational Ethology provides automated and precise measurement of animal behavior. Artificial Intelligence (AI) techniques have also introduced the enhanced capabilities to interpret experimental data in order to e...
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ISBN:
(数字)9783031342042
ISBN:
(纸本)9783031342035;9783031342042
Computational Ethology provides automated and precise measurement of animal behavior. Artificial Intelligence (AI) techniques have also introduced the enhanced capabilities to interpret experimental data in order to extract accurate ethograms allowing the comparison of animal models with high discriminative power. In this short review we introduce the most recent software tools that employ AI tools for this endeavor, including the popular deep learning approaches.
Noisy binary search (NBS) aims to find the closest element to a target value within a sorted array through erroneous queries. In an ideal NBS environment where the error rate remains constant, and the costs of all que...
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ISBN:
(纸本)9798350334586
Noisy binary search (NBS) aims to find the closest element to a target value within a sorted array through erroneous queries. In an ideal NBS environment where the error rate remains constant, and the costs of all queries are the same, the maximum likelihood estimation (MLE) procedure has been proven to be the optimal decision strategy. However, in some non-ideal NBS problems, boththe error rates and the costs are dependent on the queries, and in some cases, finding the optimal decision strategies can be intractable. We propose to use deep reinforcement learning to approximate the optimal decision strategy in the NBS problem, in which an intelligent agent is used to interact withthe NBS environment. A dueling double deep Q-network guides the agent to take action at each step, either to generate a query or to stop the search and predict the target value. An optimized policy will be derived by training the network in the NBS environment until convergence. By evaluating our proposed algorithm on a non-ideal NBS environment, visual field test, we show that the performance of our proposed algorithm surpasses baseline visual field testing algorithms by a large margin.
State-space models (SSMs) are becoming mainstream for time series analysis because their flexibility and increased explainability, as they model observations separately from unobserved dynamics. Critically, using SSMs...
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automated machine learning (AutoML) has transformed the process of selecting optimal machine learning (ML) models by autonomously searching for the most appropriate ones and fine-tuning associated hyperparameters. thi...
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