69,161 Result(s)
within
Chapter
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Algorithm Analysis and Problem Complexity
Artificial Intelligence
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Chapter and Conference Paper
Deep Learning for Journalism: The Bibliometric Analysis of Deep Learning for News Production in the Artificial Intelligence Era
This research aims to evaluate the articles published from 2018 to 2023. We focused on the deep learning issues that have risen in the last decade. Deep learning is the popular approach in news research, espec...
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Chapter and Conference Paper
Knowledge-Infused Optimization for Parameter Selection in Numerical Simulations
Many engineering applications rely on simulations based on partial differential equations. Different numerical schemes to approximate solutions exist. These schemes typically require setting parameters to appr...
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Chapter and Conference Paper
GraphSAGE-Based Spammer Detection Using Social Attribute Relationship
Spammers have existed since the birth of the Internet. They constantly pollute the social network environment, seriously degrade user experience and pose a threat to user account security. Finding spammers has...
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Chapter and Conference Paper
Beyond Universal Transformer: Block Reusing with Adaptor in Transformer for Automatic Speech Recognition
Recently, Transformer-based models have excelled in end-to-end (E2E) automatic speech recognition (ASR), enabling deployment on smart devices. However, their large parameter requirements pose challenges for AS...
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Chapter
Multimodal Optimization
This chapter will first introduce the definition of multimodal optimization. Next, most representative evolutionary multimodal optimization algorithms, which are also known as niching methods, will be introduc...
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Chapter and Conference Paper
SD-Attack: Targeted Spectral Attacks on Graphs
Graph learning (GL) models have been applied in various predictive tasks on graph data. But, similarly to other machine learning models, GL models are also vulnerable to adversarial attacks. As a powerful atta...
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Chapter and Conference Paper
Are Graph Embeddings the Panacea?
Graph representation learning has emerged as a machine learning go-to technique, outperforming traditional tabular view of data across many domains. Current surveys on graph representation learning predominant...
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Chapter and Conference Paper
False Negative Sample Aware Negative Sampling for Recommendation
Negative sampling plays a key role in implicit feedback collaborative filtering. It draws high-quality negative samples from a large number of uninteracted samples. Existing methods primarily focus on hard neg...
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Chapter and Conference Paper
TripleS: A Subsidy-Supported Storage for Electricity with Self-financing Management System
In this paper, we propose a Subsidy-Supported Storage (also called TripleS) to assist grid management. Q-learning algorithms first determine the origin subsidies, and the proposed self-financing mechanism then...
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Chapter and Conference Paper
Abnormal Vibration Fault Diagnosis of Reducer Based on Bayesian Network
In order to recognize the fault type of reducer abnormal vibration and reduce the cost of inspection and maintenance, an intelligent diagnosis model is developed. In the case of insufficient historical abnorma...
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Chapter and Conference Paper
Text Extraction and Structuring of Standard Maintenance Documents for Metallurgical Continuous Casting Equipments
The large need of standard maintenance documents (SMD) for metallurgical continuous casting equipments brings a lot of repetitive, time-consuming and laborious work. Therefore through artificial intelligence a...
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Chapter and Conference Paper
Optimal Counterfactual Explanations for k-Nearest Neighbors Using Mathematical Optimization and Constraint Programming
Within the topic of explainable AI, counterfactual explanations to classifiers have received significant recent attention. We study counterfactual explanations that try to explain why a data point received an ...
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Chapter
Let’s Wrap Up: The Final Destination
Patanjali Kashyapa*
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Chapter and Conference Paper
A Novel Population Graph Neural Network Based on Functional Connectivity for Mental Disorders Detection
Accurate and rapid clinical confirmation of psychiatric disorders based on imaging, symptom and scale data has long been difficult. Graph neural networks have received increasing attention in recent years due ...
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Chapter and Conference Paper
A Data-Driven Approach for Building a Cardiovascular Disease Risk Prediction System
Cardiovascular disease is a leading cause of mortality worldwide. The disease can develop without showing apparent symptoms at an early stage, making it difficult for domain experts to provide intervention. Us...
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Chapter and Conference Paper
IMF-PSO: A Particle Swarm Optimization Algorithm for Feature Selection in Classification
Feature selection is an important step in classification. Its goal is to find a set of features that can lead to high classification accuracy with a smaller number of features. This paper addresses feature sel...
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Chapter and Conference Paper
DiffFind: Discovering Differential Equations from Time Series
Given one or more time sequences, how can we extract their governing equations? Single and co-evolving time sequences appear in numerous settings, including medicine (neuroscience - EEG signals, cardiology - E...
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Chapter and Conference Paper
Spatial-Temporal Transformer with Error-Restricted Variance Estimation for Time Series Anomaly Detection
Due to the intricate dynamics of multivariate time series in cyber-physical system, unsupervised anomaly detection has always been a research hotspot. Common methods are mainly based on reducing reconstruction...
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Chapter and Conference Paper
Rethinking Personalized Federated Learning with Clustering-Based Dynamic Graph Propagation
Most existing personalized federated learning approaches are based on intricate designs, which often require complex implementation and tuning. In order to address this limitation, we propose a simple yet effe...
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Chapter and Conference Paper
Using Strongly Solved Mini2048 to Analyze Players with N-tuple Networks
2048 is a stochastic single-player game and there have been many studies of computer players for 2048. The authors believe that 2048 and its players can be useful for analyzing, comparing, and characterizing AI t...
