728 Result(s)
within
Fu** Lin
Computer Science
Data Mining and Knowledge Discovery
Information Storage and Retrieval
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Article
DQN-PACG: load regulation method based on DQN and multivariate prediction model
Demand response plays a pivotal role in modern smart grid systems, aiding in balancing energy consumption. However, the increasing energy demands of contemporary society have placed a significant burden on pow...
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Article
Open AccessRandomnet: clustering time series using untrained deep neural networks
Neural networks are widely used in machine learning and data mining. Typically, these networks need to be trained, implying the adjustment of weights (parameters) within the network based on the input data. In...
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Article
Open AccessEstablishment of an automatic diagnosis system for corneal endothelium diseases using artificial intelligence
To use artificial intelligence to establish an automatic diagnosis system for corneal endothelium diseases (CEDs).
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Article
Enhancing trust and privacy in distributed networks: a comprehensive survey on blockchain-based federated learning
While centralized servers pose a risk of being a single point of failure, decentralized approaches like blockchain offer a compelling solution by implementing a consensus mechanism among multiple entities. Mer...
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Article
An approach for fuzzy group decision making and consensus measure with hesitant judgments of experts
In some actual decision-making problems, experts may be hesitant to judge the performances of alternatives, which leads to experts providing decision matrices with incomplete information. However, most existin...
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Article
Open AccessThe differences in gastric cancer epidemiological data between SEER and GBD: a joinpoint and age-period-cohort analysis
The burden of gastric cancer (GC) should be further clarified worldwide, and helped us to understand the current situation of GC.
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Article
Fairmod: making predictions fair in multiple protected attributes
Predictive models such as decision trees and neural networks may produce predictions with unfairness. Some algorithms have been proposed to mitigate unfair predictions when protected attributes are considered ...
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Article
Evidence-based adaptive oversampling algorithm for imbalanced classification
Classification task is complicated by several facts including skewed class proportion and unclear decision regions due to noise, class overlap, small disjunct, caused by large within-class variation. These iss...
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Article
A black-box model for predicting difficulty of word puzzle games: a case study of Wordle
The popular word-filling game Wordle has gained widespread attention since its release in 2022. Much attention has been paid to find the optimal strategy. However, this article proposes a black-box prediction ...
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Article
Incremental image retrieval method based on feature perception and deep hashing
How to propose an image retrieval algorithm with adaptable model and wide range of applications for large-scale datasets has become a critical technical problem in current image retrieval. This paper proposed ...
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Chapter and Conference Paper
OR-SPESC: Design of an Advanced Smart Contract Language for Data Ownership
Owing to the open and sharing characteristics, blockchain can be applied for data ownership management in data circulation. The smart contract, as a kernel technique of blockchain, is a program code that can a...
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Chapter and Conference Paper
Towards Automated End-to-End Health Misinformation Free Search with a Large Language Model
In the information age, health misinformation remains a notable challenge to public welfare. Integral to addressing this issue is the development of search systems adept at identifying and filtering out mislea...
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Article
Mining parallel sentences from internet with multi-view knowledge distillation for low-resource language pairs
The neural machine translation (NMT), which relies on a large training data (bilingual parallel sentences, for NMT) to obtain the state-of-the-art performance, is similar with deep learning. In order to constr...
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Chapter and Conference Paper
Federated Conversational Recommender Systems
Conversational Recommender Systems (CRSs) have become increasingly popular as a powerful tool for providing personalized recommendation experiences. By directly engaging with users in a conversational manner ...
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Chapter and Conference Paper
Countering Mainstream Bias via End-to-End Adaptive Local Learning
Collaborative filtering (CF) based recommendations suffer from mainstream bias – where mainstream users are favored over niche users, leading to poor recommendation quality for many long-tail users. In this pa...
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Chapter and Conference Paper
Handling Concept Drift in Non-stationary Bandit Through Predicting Future Rewards
We present a study on the non-stationary stochastic multi-armed bandit (MAB) problem, which is relevant for addressing real-world challenges related to sequential decision-making. Our work involves a thorough ...
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Chapter and Conference Paper
Large Language Models are Zero-Shot Rankers for Recommender Systems
Recently, large language models (LLMs) (e.g., GPT-4) have demonstrated impressive general-purpose task-solving abilities, including the potential to approach recommendation tasks. Along this line of research, thi...
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Article
Visual feature segmentation with reinforcement learning for continuous sign language recognition
Continuous sign language recognition (CSLR) involves inputting a video that contains unbroken signs and outputting a prediction of the sign gloss sequence. Our research found that the visual features extracted...
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Article
Open AccessAutomated segmentation of choroidal neovascularization on optical coherence tomography angiography images of neovascular age-related macular degeneration patients based on deep learning
Optical coherence tomography angiography (OCTA) has been a frequently used diagnostic method in neovascular age-related macular degeneration (nAMD) because it is non-invasive and provides a comprehensive view ...
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Article
User view dynamic graph-driven sequential recommendation
In most recommendation scenarios, user information is difficult to obtain due to user privacy and data protection issues. Some graph-based methods can learn the user’s feature information through the structura...
