The proceedings contain 273 papers. The topics discussed include: transfer learning based neural machine translation of English-Khasi on low-resource settings;integration of renewable energy sources with power managem...
The proceedings contain 273 papers. The topics discussed include: transfer learning based neural machine translation of English-Khasi on low-resource settings;integration of renewable energy sources with power management strategy for effective bidirectional vehicle to grid power transfer;classification of breast thermal images into healthy/cancer group using pre-trained deep learning schemes;detection of network attacks using machinelearning and deep learning models;a hybrid classifier-based ontology driven image tag recommendation framework for social image tagging;sarcasm detection using bidirectional encoder representations from transformers and graph convolutional networks;a factor based multiple imputation approach to handle class imbalance;smart facial emotion recognition with gender and age factor estimation;and a hybrid data-driven framework for spam detection in online social network.
The proceedings contain 276 papers. The topics discussed include: transfer learning based neural machine translation of English-Khasi on low-resource settings;integration of renewable energy sources with power managem...
The proceedings contain 276 papers. The topics discussed include: transfer learning based neural machine translation of English-Khasi on low-resource settings;integration of renewable energy sources with power management strategy for effective bidirectional vehicle to grid power transfer;classification of breast thermal images into healthy/cancer group using pre-trained deep learning schemes;offline HWR accuracy enhancement with image enhancement and deep learning techniques;detection of network attacks using machinelearning and deep learning models;a hybrid classifier-based ontology driven image tag recommendation framework for social image tagging;sarcasm detection using bidirectional encoder representations from transformers and graph convolutional networks;a factor based multiple imputation approach to handle class imbalance;smart facial emotion recognition with gender and age factor estimation;and a hybrid data-driven framework for spam detection in online social network.
The proceedings contain 35 papers. The topics discussed include: modelling of tsunami wave generated by volcanic eruption using impulsive disturbance for 2022, Tonga Island tsunami;enumerating the k-fold configuration...
ISBN:
(纸本)9798331539696
The proceedings contain 35 papers. The topics discussed include: modelling of tsunami wave generated by volcanic eruption using impulsive disturbance for 2022, Tonga Island tsunami;enumerating the k-fold configurations in multi-class classification problems;computational analysis of Quran text using machinelearning and large language models;questionable fairness in federated learning;predicting the length of hospital stay using machinelearning: an explainable artificial intelligence approach;the bagged tree ensemble model for predicting steel industry energy consumption profile;the role of business intelligence in transforming HR practices: a focus on employee retention and performance management;and forecasting new mobile site location suitability using machinelearning.
The proceedings contain 40 papers. The special focus in this conference is on database and Expert Systems Applications. The topics include: Synthetic data in Automatic Number Plate Recognition;An Untold Tale of&#...
ISBN:
(纸本)9783031143427
The proceedings contain 40 papers. The special focus in this conference is on database and Expert Systems Applications. The topics include: Synthetic data in Automatic Number Plate Recognition;An Untold Tale of Scientific Collaboration: SCCH and AC 2 T;on the Creation and Maintenance of a Documentation Generator in an Applied Research Context;towards the Digitalization of Additive Manufacturing;Twenty Years of Successful Translational Research: A Case Study of Three COMET Centers;data Integration, Management, and Quality: From Basic Research to Industrial Application;building a YouTube Channel for Science Communication;introduction of Visual Regression Testing in Collaboration Between Industry and Academia;vibration Analysis for Rotatory Elements Wear Detection in Paper Mill machine;applying Time-Inhomogeneous Markov Chains to Math Performance Rating;introducing data Science Techniques into a Company Producing Electrical Appliances;a Technology Transfer Portal to Promote Industry-Academia Collaboration in South-Tyrol;fast and Automatic Object Registration for Human-Robot Collaboration in Industrial Manufacturing;sending Spies as Insurance Against Bitcoin Pool Mining Block Withholding Attacks;risks in DeFi-Lending Protocols - An Exploratory Categorization and Analysis of Interest Rate Differences;battling the Bullwhip Effect with Cryptography;Reporting of Cross-Border Transactions for Tax Purposes via DLT;securing File System Integrity and Version History Via Directory Merkle Trees and Blockchains;taxation of Blockchain Staking Rewards: Propositions Based on a Comparative Legal Analysis;comparison Framework for Blockchain Interoperability Implementations;A Comparative Analysis of Anomaly Detection Methods for Predictive Maintenance in SME;towards Strategies for Secure data Transfer of IoT Devices with Limited Resources;application of Validation Obligations to Security Concerns;mode Switching for Secure Edge Devices;a Lifecycle Framework for Semantic Web machine Learn
The proceedings contain 35 papers. The special focus in this conference is on Mining Humanistic data. The topics include: Digitally Assisted Planning and Monitoring of Supportive Recommendations in Canc...
ISBN:
(纸本)9783031083402
The proceedings contain 35 papers. The special focus in this conference is on Mining Humanistic data. The topics include: Digitally Assisted Planning and Monitoring of Supportive Recommendations in Cancer Patients;CAIPI in Practice: Towards Explainable Interactive Medical Image Classification;a Deep Q Network-Based Multi-connectivity Algorithm for Heterogeneous 4G/5G Cellular Systems;simulating Blockchain Consensus Protocols in Julia: Proof of Work vs Proof of Stake;Maximum Likelihood Estimators on MCMC Sampling Algorithms for Decision Making;employing Natural Language Processing Techniques for Online Job Vacancies Classification;Probabilistic Quantile Multi-step Forecasting of Energy Market Prices: A UK Case Study;proactive Buildings: A Prescriptive Maintenance Approach;performance Meta-analysis for Big-data Univariate Auto-Imputation in the Building Sector;non-intrusive Diagnostics for Legacy Heat-Pump Performance Degradation;a 5G-Based Architecture for Localization Accuracy;anomaly Detection in Small-Scale Industrial and Household Appliances;an Innovative Software Platform for Efficient Energy, Environmental and Cost Planning in Buildings Retrofitting;deep learning-Based Segmentation of the Atherosclerotic Carotid Plaque in Ultrasonic Images;An Intelligent Grammar-Based Platform for RNA H-type Pseudoknot Prediction;An Automated 2D U-Net Segmentation Method for the Identification of Cancer Brain Metastases Using MRI Images;The Use of Robotics in Critical Use Cases: The 5G-ERA Project Solution;fundamental Features of the Smart5Grid Platform Towards Realizing 5G Implementation;experimentation Scenarios for machinelearning-Based Resource Management;efficient data Management and Interoperability Middleware in Business-Oriented Smart Port Use Cases;5G for the Support of Smart Power Grids: Millisecond Level Precise Distributed Generation Monitoring and Real-Time Wide Area Monitoring;monitoring Neurological Disorder Patients via Deep learning Based Facial Expressions An
This article proposes a method for monitoring the mental health status of college students based on machinelearning models. By integrating multidimensional data such as psychological assessment questionnaires and dai...
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Improving the current level of skill in seasonal climate prediction is urgent for achieving sustainable socioeconomic development, and this is especially true in China where meteorological disasters are experienced fr...
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Improving the current level of skill in seasonal climate prediction is urgent for achieving sustainable socioeconomic development, and this is especially true in China where meteorological disasters are experienced frequently. In this study, based upon big climate data and traditional statistical prediction experiences, a merged machinelearning model(Y-model) was developed to address this, as well as to further explore unknown potential predictors. In Y-model, empirical orthogonal function analysis was firstly applied to reduce the data dimensionality of the target predictand(temperature and precipitation in the four seasons over China). Image recognition techniques were used to automatically identify possible predictors from the big climate data. These predictors, associated with significant circulation anomalies, were recombined into a large ensemble according to different threshold settings for five factors determining the statistical forecast skill. Facebook Prophet was chosen to conduct the independent hindcasts for each season's climate at a lead time of two months. During 2011–2022, the seasonal climate in China was skillfully predicted by Y-model, with an averaged pattern correlation coefficient skill of 0.60 for temperature and 0.24 for precipitation, outperforming CFSv2. Potential predictor analysis for recent extreme events suggested that prior signals from the Indian Ocean and the stratosphere were important for determining the super Mei-yu in 2020, while the prior sea surface temperature over the western Pacific and the soil temperature over West Asia may have contributed to the extreme high temperatures in 2022. Our study provides new insights for seasonal climate prediction in China.
machinelearning models often excel in controlled environments but may struggle with noisy, incomplete, or shifted real-world data. Ensuring that these models maintain high performance despite these imperfections is c...
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ISBN:
(纸本)9783031777370;9783031777387
machinelearning models often excel in controlled environments but may struggle with noisy, incomplete, or shifted real-world data. Ensuring that these models maintain high performance despite these imperfections is crucial for practical applications, such as medical diagnosis or autonomous driving. This paper introduces a novel framework to systematically analyse the robustness of machinelearning models against noisy data. We propose two empirical methods: (1) Noise Tolerance Estimation, which calculates the noise level a model can withstand without significant degradation in performance, and (2) Robustness Ranking, which ranks machinelearning models by their robustness at specific noise levels. Utilizing Cohen's kappa statistic, we measure the consistency between a model's predictions on original and perturbed datasets. Our methods are demonstrated using various datasets and machinelearning techniques, identifying models that maintain reliability under noisy conditions.
Aiming at the problems of low classification accuracy and long classification time in traditional uncertain big data stream classification algorithm, a fast classification algorithm for uncertain big data stream based...
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The proceedings contain 28 papers. The topics discussed include: identification of the onset of dementia of older adults in the age of internet of things;applying Internet of things and machine-learning for personaliz...
ISBN:
(纸本)9781728104041
The proceedings contain 28 papers. The topics discussed include: identification of the onset of dementia of older adults in the age of internet of things;applying Internet of things and machine-learning for personalized healthcare: issues and challenges;identification of illegal forum activities inside the dark net;kernel logistic regression: a robust weighting for imbalanced classes with noisy labels;video-based measurement of physiological parameters using peak-to-valley method for minimization of initial dead zone;domain knowledge driven FRBR and cataloguing for the future libraries;a review of strengths and weaknesses of spatiotemporal data analysis techniques;and using electronic health records and machinelearning to make medical-related predictions from non-medical data.
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