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IEEE International Conference on Systems, Man and Cybernetics

作者： Lichao Ding Jing Zhao Kai Lu Zenghao Hao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Knowledge graphs (KGs) provide a structured representation of the real world through entity-relation triples. However, current KGs are often incomplete, typically containing only a small fraction of all possible facts. This involves inferring missing content from existing information, a task known as link prediction. Existing methods in the field of link prediction struggle with controlling the dimensionality of embedding vectors or suffer from overly complex models. In order to tackle these challenges, we introduce a method in this paper, named Low-Dimensional Embedding Method for Link Prediction Combining Self-Attention and Enhanced-TuckER (LESaET). LESaET leverages both self-attention mechanisms and tensor factorization to learn expressive contextual-enhanced representations of KGs. Specifically, LESaET employs the multi-head self-attention mechanism of Transformer as an encoder to capture the mutual information between entities and relationships, and utilizes Enhanced-TuckER as a decoder, ultimately achieving expressive low-dimensional embeddings for link prediction tasks. LESaET demonstrates competitive performance compared to advanced methods on standard datasets.

关键词： Tensors Stacking Knowledge graphs Predictive models Transformers Vectors Decoding Standards Mutual information Periodic structures

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Opinion-Tree-guided Contrastive Learning for Aspect Sentiment Quadruple Prediction

Opinion-Tree-guided Contrastive Learning for Aspect Sentimen...

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International Conference on Computer Supported Cooperative Work in Design

作者： Zhijun Li Zhenyu Yang Yiwen Li Xiaoyang Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

In recent years, aspect sentiment quadruple prediction (ASQP) has become a popular task in the field of aspect-based sentiment analysis. When modeling comment statements, it is often necessary to consider structural information between sentiment labels. Existing approaches categorize these labels by encoding text and dependency tree structures separately and then incorporating their representations, but this approach fails to effectively capture the structural information between sentiment labels. To address this problem, we propose a method for ASQP called Opinion-Tree-guided Contrastive Learning (OTCL), which embeds structural information directly into the text instead of modeling it separately. Specifically, OTCL utilizes the opinion tree structure to guide the construction of positive samples of the input text during training. By associating the input text with its positive samples, the text encoder can learn to generate textual representations associated with the opinion tree structure. We validate the effectiveness of our method on two commonly used datasets.

关键词： Training Sentiment analysis Federated learning Contrastive learning Predictive models Encoding Task analysis

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HGTA: News Recommendation Based on Hierarchical Granular Semantic Embeddings and Threshold Attention

HGTA: News Recommendation Based on Hierarchical Granular Sem...

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International Conference on Computer Supported Cooperative Work in Design

作者： Yiwen Li Zhenyu Yang Zhijun Li Xiaoyang Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Personalized news recommendation helps users find interesting content among much news information. Most existing news recommendation techniques densely interact a user’s historical clicked news with candidate news to capture their reading interest. However, during this process, even if certain historical clicked news is weakly correlated with the candidate news, the model still interacts with it, generating more noise information when constructing user interests. Simultaneously, the news representation learning process using neural networks is a fine-grained to coarse-grained learning process; i.e., as the network deepens, the fine-grained original news information is easily lost. Therefore, to solve the above problems, this paper proposes a news recommendation model based on hierarchical granular semantic embeddings and threshold attention (HGTA). In HGTA, we propose a threshold attention module to filter unimportant historical clicked news by setting an attention score threshold, i.e., interacting only with historical clicked news that is strongly correlated with the candidate news when capturing user interests. Thus, the model attention is focused, and the noisy interaction information is reduced. Furthermore, when training the neural network, to prevent the loss of fine-grained information, we design a hierarchical granular information extraction module to capture both coarse- and fine-grained news features to better enrich the news semantics. Experiments conducted on the large real-world Microsoft News Dataset (MIND) show that our model achieves highly competitive results in terms of popular metrics. Our code is published at the following URL: https://***/liyiweneven/HGTA.

关键词： Uniform resource locators Training Representation learning Federated learning Semantics Neural networks Noise

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JTEA: Implementing Semi-Supervised Entity Alignment Using Joint Teaching Strategies

JTEA: Implementing Semi-Supervised Entity Alignment Using Jo...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Kai Lu Jing Zhao Lichao Ding Zenghao Hao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Faculty of Computer Science and Technology Shandong Engineering Research Center of Big Data Applied Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Entity alignment(EA) aims to identify equivalent entities in different knowledge graphs or data sources and establish correspondences between them. In recent years, embedding-based methods have made significant progress in the field of entity alignment, yet the shortage of adequate training data (i.e., seed entity pairs) continues to be a primary challenge. Traditional semi-supervised methods typically address this issue by generating potential mappings (i.e., pseudo-mappings) for unlabeled entities. However, these methods often suffer from the influence of erroneous potential mappings or overlook the uncertainty of potential mappings. Therefore, we propose a novel semi-supervised entity alignment method called JTEA. This method combines seed entity pairs with potential mappings through end-to-end joint training, providing strong guidance for model training. In constructing potential mappings, we propose a bidirectional matching strategy (BMS), which generates more detailed potential mappings by jointly adjusting decisions in different directions and uses a joint matching confidence score to characterize its uncertainty. Furthermore, we also introduce a method of matching diversity alignment selection, aiming to screen out more reliable entity alignments to further improve the performance and stability of the model. We conducted a large number of experiments on the benchmark dataset to fully validate the effectiveness of the JTEA method.

关键词： Training Uncertainty Soft sensors Noise Training data Knowledge graphs Data models Stability analysis Reliability Thermal stability

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Deep Transfer Learning Driven Oral Cancer Detection and Classification Model

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Computers, Materials & Continua 2022年第11期73卷 3905-3920页

作者： Radwa Marzouk Eatedal Alabdulkreem Sami Dhahbi Mohamed K.Nour Mesfer Al Duhayyim Mahmoud Othman Manar Ahmed Hamza Abdelwahed Motwakel Ishfaq Yaseen Mohammed Rizwanullah Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam Bin Abdulaziz UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

Oral cancer is the most commonly occurring‘head and neck cancers’across the *** of the oral cancer cases are diagnosed at later stages due to absence of awareness among *** earlier identification of disease is essential for improved outcomes,Artificial Intelligence(AI)and Machine Learning(ML)models are used in this *** this background,the current study introduces Artificial Intelligence with Deep Transfer Learning driven Oral Cancer detection and Classification Model(AIDTLOCCM).The primary goal of the proposed AIDTL-OCCM model is to diagnose oral cancer using AI and image processing *** proposed AIDTL-OCCM model involves fuzzy-based contrast enhancement approach to perform data *** by,the densely-connected networks(DenseNet-169)model is employed to produce a useful set of deep ***,Chimp Optimization Algorithm(COA)with Autoencoder(AE)model is applied for oral cancer detection and ***,COA is employed to determine optimal parameters involved in AE model.A wide range of experimental analyses was conducted on benchmark datasets and the results were investigated under several *** extensive experimental analysis outcomes established the enhanced performance of AIDTLOCCM model compared to other approaches with a maximum accuracy of 90.08%.

关键词： Oral cancer detection image classification artificial intelligence machine learning deep learning

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Performance Evaluation of Deep Learning Models for Water Quality Index Prediction: A Comparative Study of LSTM, TCN, ANN, and MLP

arXiv

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arXiv 2024年

作者： Ismail, Muhammad Abbas, Farkhanda Shah, Shahid Munir Aljawarneh, Mahmoud Dhomeja, Lachhman Das Abbas, Fazila Shoaib, Muhammad Alrefaei, Abdulwahed Fahad Albeshr, Mohammed Fahad Department of Computer Science Karakoram International University Gilgit15100 Pakistan School of Computer Science China University of Geosciences Wuhan430074 China Department of Computing Faculty of Engineering Science and Technology Hamdard University Karachi74600 Pakistan Faculty of Information Technology Applied Science Private University Amman11937 Jordan Department of Information Technology Faculty of Engineering and Technology University of Sindh Jamshoro76080 Pakistan Institute of Soil and Environmental Sciences University of Agriculture Faisalabad Faisalabad38000 Pakistan State Key Laboratory of Hydraulic Engineering Simulation and Safety School of Civil Engineering Tianjin University Tianjin300072 China Department of Zoology College of Science King Saud University Riyadh11451 Saudi Arabia

Environmental monitoring and predictive modeling of Water Quality Index (WQI) through the assessment of the water quality is an important area of research. In order to predict WQI this study utilizes four popular Deep Learning models i.e. Artificial Neural Network (ANN), Temporal Convolutional Network (TCN), Long Short-Term Memory (LSTM), and Multi-Layer Perceptron (MLP). Based on the Area Under the Operating Characteristic Curve (AUC), the per- formance of the employed models is evaluated and the results are compared. Comparative analysis showed that TCN achieved the highest AUC score i.e. 0.94 as compared to the other employed models i.e. MLP: 0.94,’LSTM’: 0.77, and’ANN’: 0.93. This comparative study provides insights into the suitability of various Deep Learning models for predicting WQI, contributing to advancements in environmental monitoring and management practices. © 2024, CC BY.

关键词： Environmental monitoring

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Clustering Medical Transcriptions Using K -Means

Clustering Medical Transcriptions Using K -Means

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Intelligent computing, Communication, Networking and Services (ICCNS), International Conference on

作者： Said Salloum Dina Tahat Khalaf Tahat Raghad Alfaisal Ayham Salloum School of Science Engineering and Environment University of Salford Manchester UK Applied sociology department Al Ain University UAE Media & Creative Industries United Arab Emirates University and Yarmouk University Jordan Faculty of Computing and Meta-Technology Universiti Pendidikan Sultan Idris Tanjung Malim Malaysia College of Medicine University of Sharjah Sharjah UAE

ISBN: (数字)9798350354690

ISBN: (纸本)9798350354706

The clustering of medical transcriptions is an essential task for the categorization and summarization of large volumes of medical records. This paper explores the efficacy of k-means clustering, a well-known unsupervised machine learning algorithm, to discern patterns and segregate medical transcriptions into distinct clusters. We processed a dataset comprising various medical reports, systematically cleaning and preparing the text for analysis. By employing a Term Frequency-Inverse Document Frequency (TF-IDF) approach, we converted the textual data into a vectorized format amenable to machine learning methods. Subsequent dimensionality reduction through Principal Component Analysis (PCA) facilitated the visualization and interpretation of the high-dimensional data in two-dimensional space. The k-means algorithm was then applied, revealing five distinct clusters. Each cluster was characterized by examining the prevalence of key terms, uncovering thematic consistencies that may correspond to particular medical procedures or specialties. The resulting clusters demonstrate the algorithm's potential to automatically categorize medical documentation in a way that mirrors clinical relevance, thereby providing a foundation for improved information management systems in healthcare settings.

关键词： Dimensionality reduction Machine learning algorithms Clustering algorithms Data visualization Medical services Documentation Frequency conversion Information management Mirrors Principal component analysis

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EmoNet: Unveiling Affective States Through Convolutional Neural Networks in Textual Emotion Classification

EmoNet: Unveiling Affective States Through Convolutional Neu...

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Intelligent computing, Communication, Networking and Services (ICCNS), International Conference on

作者： Said Salloum Khalaf Tahat Ahmed Mansoori Raghad Alfaisal Dina Tahat School of Science Engineering and Environment University of Salford Manchester UK Media & Creative Industries United Arab Emirates University and Yarmouk University Jordan Media & Creative Industries department United Arab Emirates University UAE Faculty of Computing and Meta-Technology Universiti Pendidikan Tanjung Malim Malaysia Applied sociology department Al Ain University UAE

ISBN: (数字)9798350354690

ISBN: (纸本)9798350354706

In the burgeoning field of natural language processing, emotion classification from textual data has emerged as a critical task with applications ranging from sentiment analysis to mental health assessment. This paper explores the utilization of Convolutional Neural Networks (CNNs), traditionally dominant in image processing, for classifying emotions in text. Our proposed CNN model leverages the inherent hierarchical structure of language to identify and learn emotion-specific features, with an emphasis on capturing contextual n-grams through convolutional filters. The approach is substantiated by a comprehensive dataset, subjected to rigorous preprocessing and vectorization via TF-IDF to convert text into a numerical format suitable for deep learning. The model's architecture is meticulously crafted, incorporating convolutional layers followed by global max pooling and dense layers, culminating in a softmax activation function tailored for multi-class classification. Our findings demonstrate the model's robustness, achieving a notable accuracy of 96.08% on the test set. This high level of precision is further corroborated by the Receiver Operating Characteristic (ROC) analysis, revealing exceptional area under the curve (AUC) values across various emotion categories. The results suggest that CNNs hold significant promise for emotion recognition tasks in textual data, providing an effective framework for future explorations in the domain.

关键词： Sentiment analysis Emotion recognition Filters Image processing Receivers Mental health Robustness Distance measurement Numerical models Convolutional neural networks

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Intelligent Energy-Aware Thermal Exchange Optimization with Deep Learning Model for IoT-Enabled Smart Healthcare

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Journal of Healthcare Engineering 2023年第1期2023卷 3830857页

作者： Ragab, Mahmoud Binyamin, Sami Saeed Information Technology Department Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department Faculty of Science Al-Azhar University Nasr City Cairo11884 Egypt Centre for Artificial Intelligence in Precision Medicines King Abdulaziz University Jeddah21589 Saudi Arabia Computer and Information Technology Department Applied College King Abdulaziz University Jeddah21589 Saudi Arabia

In recent years, Internet of Things (IoT) and advanced sensor technologies have gained considerable interest in linking different medical devices, patients, and healthcare professionals to improve the quality of medical services in a cost-effective manner. The evolution of the smart healthcare sector has considerably enhanced patient safety, accessibility, and operational competence while minimizing the costs incurred in healthcare services. In this background, the current study develops intelligent energy-aware thermal exchange optimization with deep learning (IEA-TEODL) model for IoT-enabled smart healthcare. The aim of the proposed IEA-TOEDL technique is to group the IoT devices into clusters and make decisions in the smart healthcare sector. The proposed IEA-TEODL technique constructs clusters using the energy-aware chaotic thermal exchange optimization-based clustering (EACTEO-C) scheme. In addition, the disease diagnosis model also intends to classify the collected healthcare data as either presence or absence of the disease. To accomplish this, the proposed IEA-TODL technique involves several subprocesses such as preprocessing, K-medoid clustering-based outlier removal, multihead attention bidirectional long short-term memory (MHA-BLSTM), and weighted salp swarm algorithm (WSSA). The utilization of outlier removal and WSSA-based hyperparameter tuning process assist in achieving enhanced classification outcomes. In order to demonstrate the enhanced outcomes of the IEA-TEODL approach, a wide range of simulations was conducted against benchmark datasets. The simulation results inferred the enhanced outcomes of the IEA-TEODL technique over recent techniques under distinct evaluation metrics. © 2023 Mahmoud Ragab and Sami Saeed Binyamin.

关键词： Internet of things

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Prediction of football matches results: Decision forest against neural networks 18

Prediction of football matches results: Decision forest agai...

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18th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2021

作者： Azeman, Azrel Aiman Mustapha, Aida Razali, Nazim Nanthaamomphong, Aziz Abd Wahab, Mohd Helmy Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Johor Malaysia Faculty of Applied Sciences and Technology Universiti Tun Hussein Onn Malaysia Johor Malaysia Andaman Intelligent Tourism and Service Informatics Center College of Computing Prince of Songkla University Phuket Thailand Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Johor Malaysia

ISBN: (纸本)9780738111278

Modelling football match outcomes is a popular prediction strategy based on either statistical approaches, machine learning approaches or rating system approaches. This paper is set to study football match outcomes prediction (win, lose and draw) for English Premier League Season 2005-2006 consisting of 20 football teams which played 380 matches per season in term of machine learning approaches. Two prediction algorithms are proposed, which are the Multiclass Neural Network and the Multiclass Decision Forest. Past research showed that both decision tree and neural network have capabilities to predict football matches outcomes. Although decision forest is a extended version of decision tree, there are few comparative study between neural network and decision tree in football matches outcomes prediction. The comparative experiments were carried using the Azure Machine Learning Studio and the results showed that the Multiclass Decision Forest performed the best as compared to Multiclass Neural Networks. © 2021 IEEE.

关键词： Forecasting

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