the proceedings contain 19 papers. the topics discussed include: digital data protection using Feistel network and DNA cryptography;advancing accurate recognition of handwritten Arabic character: an innovative hybrid ...
ISBN:
(纸本)9798331522810
the proceedings contain 19 papers. the topics discussed include: digital data protection using Feistel network and DNA cryptography;advancing accurate recognition of handwritten Arabic character: an innovative hybrid approach;impact of deep learning-based entity recognition on a feature-based entity linking system: a hybrid approach;comparative analysis of four metaheuristic algorithms for estimating parameters in solar photovoltaic models;an opposition-based learning archerfish hunting optimizer for global optimization;multi-head self-attention based Arabic news recommendation system;a deep learning-based approach with overlapped classes aggregation for intrusion detection in Internet of things networks;a comprehensive review of experimental and computational methods for protein structure prediction and classification;optimizing machinelearning for healthcare fraud detection: a framework using hybrid feature selection and hyperparameter tuning;and leveraging pre-trained transformer models and ensemble learning for fake news detection: a comparative analysis.
Streaming data processing has attracted much more attention and become a key research area in the fields of machinelearning and datamining. Since the distribution of real data may evolve (called concept drift) with ...
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ISBN:
(纸本)9789819984343;9789819984350
Streaming data processing has attracted much more attention and become a key research area in the fields of machinelearning and datamining. Since the distribution of real data may evolve (called concept drift) with time due to many unforeseen factors and real data is usually with imbalanced cluster/class distributions during streaming data processing, drifts occurred in distributions with fewer data objects are easily masked by the larger distributions. this paper, therefore, proposes an unsupervised drift detection approach called Multi-Imbalanced Cluster Discriminator (MICD) to address the more challenging imbalance problem of unlabeled data. It first partitions data into compact clusters, and then learns a discriminator for each cluster to detect drift. It turns out that MICD can detect drift occurrence, locate where the drift occurs, and quantify the extent of the drift. MICD is efficient, interpretable, and has easy-to-set parameters. Extensive experiments on synthetic and real datasets illustrate the superiority of MICD.
Many issues arise in the study of hadiththat are trending in the discipline. these issues vary from the digitization of hadithdata to proper case studies regarding the approximate narrator chain of a particular hadi...
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the proceedings contain 246 papers. the topics discussed include: estimation of recall values and accuracy of gender identification for the different age groups based on voice signals;deep learning and datamining tec...
ISBN:
(纸本)9798350346961
the proceedings contain 246 papers. the topics discussed include: estimation of recall values and accuracy of gender identification for the different age groups based on voice signals;deep learning and datamining techniques for cardiovascular disease prediction: a survey;impact of telepresence of hotel websites on behavioral intention of Indian consumers: a select study;improving the quality of monocular depth estimation using ensemble learning;post-processing deblocking technique for reduction of blocking artifacts;cloud base intrusion detection system using convolutional and supervised machinelearning;implementation and comparison of artificial intelligence techniques in software testing;and an overview of bio-inspired and deep learning model for extraction of land use pattern.
the integration of multimodal data has emerged as a game-changing strategy in advancing smart healthcare, allowing for a holistic comprehension of patient health and tailored treatment strategies. this exploration del...
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the integration of multimodal data has emerged as a game-changing strategy in advancing smart healthcare, allowing for a holistic comprehension of patient health and tailored treatment strategies. this exploration delves into the journey from raw data to insightful wisdom, emphasizing the fusion of various data modalities, notably in CT scans or retinal photographs, to drive smart healthcare innovations. Within this review, we comprehensively examine the fusion of diverse medical data modalities, aiming to unlock a deeper understanding of patient health. Our focus spans various fusion methodologies-from feature selection to rule-based systems, machinelearning, deep learning, and natural language processing. Furthermore, we explore the challenges inherent in fusing multimodal data in healthcare settings. the central focus revolves around determiningthe most efficient and accurate approach, crucial for future research endeavors in Ukrainian language audio-to-text conversion systems. the goal is to ascertain the most effective strategy that will serve as the foundation for further advancements in this domain.
this study investigates how patternrecognition in financial markets can enhance trading strategies through a systematic literature review (SLR). the research focuses on the most frequently studied financial markets, ...
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the healthcare fraud detection industry is in a state of continuous growth, yet it faces notable obstacles, especially in addressing data imbalances. Traditional machinelearning (ML) techniques often inadequately tac...
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this research focuses on searching for Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for Systematic Literature Reviews (SLR). After that, the use of Deep learning (DL) and machine Learnin...
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Anomaly detection in sequential data has become increasingly critical across various domains, with increasing demands for predictive detection capabilities. While recent advances in deep learning have led to various s...
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A common goal in cognitive science involves explaining/predicting human performance in experimental settings. this study proposes a single GEMS computational scientific discovery framework that automatically generates...
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ISBN:
(纸本)9798350386738;9798350386721
A common goal in cognitive science involves explaining/predicting human performance in experimental settings. this study proposes a single GEMS computational scientific discovery framework that automatically generates multiple models for verbal learning simulations. GEMS achieves this by combining simple and complex cognitive mechanisms with genetic programming. this approach evolves populations of interpretable cognitive agents, with each agent learning by chunking and incorporating long-term memory (LTM) and short-term memory (STM) stores, as well as attention and perceptual mechanisms. the models simulate two different verbal learning tasks: the first investigates the effect of prior knowledge on the learning rate of stimulus-response (S-R) pairs and the second examines how backward recall is affected by the similarity of the stimuli. the models produced by GEMS are compared to both human data and EPAM - a different verbal learning model that utilises hand-crafted task-specific strategies. the models automatically evolved by GEMS produced good fit to the human data in both studies, improving on EPAM's measures of fit by almost a factor of three on some of the pattern recall conditions. these findings offer further support to the mechanisms proposed by chunking theory (Simon, 1974), connect them to the evolutionary approach, and make further inroads towards a Unified theory of Cognition (Newell, 1990).
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