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Attribute grouping-based naive Bayesian classifier

Science China(Information Sciences) 2025年第3期68卷 125-149页

作者： Yulin HE Guiliang OU Philippe FOURNIER-VIGER Joshua Zhexue HUANG Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) College of Computer Science & Software Engineering Shenzhen University

The naive Bayesian classifier(NBC) is a supervised machine learning algorithm having a simple model structure and good theoretical interpretability. However, the generalization performance of NBC is limited to a large extent by the assumption of attribute independence. To address this issue, this paper proposes a novel attribute grouping-based NBC(AG-NBC), which is a variant of the classical NBC trained with different attribute groups. AG-NBC first applies a novel effective objective function to automatically identify optimal dependent attribute groups(DAGs). Condition attributes in the same DAG are strongly dependent on the class attribute, whereas attributes in different DAGs are independent of one another. Then,for each DAG, a random vector functional link network with a SoftMax layer is trained to output posterior probabilities in the form of joint probability density estimation. The NBC is trained using the grouping attributes that correspond to the original condition attributes. Extensive experiments were conducted to validate the rationality, feasibility, and effectiveness of AG-NBC. Our findings showed that the attribute groups chosen for NBC can accurately represent attribute dependencies and reduce overlaps between different posterior probability densities. In addition, the comparative results with NBC, flexible NBC(FNBC), tree augmented Bayes network(TAN), gain ratio-based attribute weighted naive Bayes(GRAWNB), averaged one-dependence estimators(AODE), weighted AODE(WAODE), independent component analysis-based NBC(ICA-NBC), hidden naive Bayesian(HNB) classifier, and correlation-based feature weighting filter for naive Bayes(CFW) show that AG-NBC obtains statistically better testing accuracies, higher area under the receiver operating characteristic curves(AUCs), and fewer probability mean square errors(PMSEs) than other Bayesian classifiers. The experimental results demonstrate that AG-NBC is a valid and efficient approach for alleviating the attribute i

关键词： naive Bayesian classifier attribute independence assumption attribute grouping dependent attribute group posterior probability class-conditional probability

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Research on a Deep Learning Method for Speech Recognition

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2024年第9期51卷 1272-1280页

作者： Xiao, Jia Xiaolin, Sun Artificial Intelligence and Software Engineering Nanyang Normal University Nanyang473061 China

Deep convolutional neural network (CNN) has been widely used in speech recognition technology. The model based on deep CNN can effectively improve the quality of human-computer interaction. However, the existing CNN with fixed convolutional kernel size has a disadvantage on extracting data features. It is hard to effectively identify whether the extracted features sufficient or not. As a result, a self-tuning convolutional kernel (STCK) algorithm is proposed to solve the mentioned problem. Firstly, the computational process of STCK algorithm is derived. Then the calculation formula of the convolutional kernel size is obtained. Meanwhile, Bark-spectrum is introduced to extract the spectrogram of speech signal, which is used as the CNN input to adapt to the human hearing. In addition, the data enhancement strategies are proposed, namely frame channel shielding and Bark-band channel shielding. The presented strategies can further improve the generalization ability of the recognition model. The experimental results show that, compared with another two models (the CNN model without STCK algorithm and the CNN model without the data enhancement strategy), the training loss of the proposed method is minimum. And the recognition error rates for the test samples are reduced by 3.9% and 1%, respectively. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Convolutional neural networks

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ICU-Net: A U-shaped Low-Dose CT Image Denoising Network Based on Codec Structure

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第6期49卷 147-158页

作者： Shangguan, Tonghang Yang, Cai Zhang, Yingqi School of Artificial Intelligence and Software Engineering Nanyang Normal University Henan Nanyang473061 China

Aims to address the issue of low-dose CT images (LDCT) introducing a considerable amount of noise due to radiation reduction, which subsequently results in a reduction in image quality and an impact on the validity of medical evaluations, a codec-based denoising model ICU-Net for LDCT images is proposed. The model utilizes an improved ConvNext block (ICB) for feature learning to extract feature data at different scales. Channel and spatial hybrid attention mechanisms (ECA) are introduced to suppress noise and artifacts. Furthermore, a blended loss function is implemented to counteract image over smoothing, which results in a denoised image that is more closely aligned with a normal-dose CT(NDCT) image. Experimental results show that the ICU-Net effectively suppresses the noise and artifacts in LDCT images. In comparison with the current denoising methods, the algorithm performs well and retains more texture details. The algorithm achieved PSNR, SSIM and RMSE values of 25.1285, 0.7217, and 43.0125 respectively, achieving the best results among the models compared. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Computerized tomography

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CM-OOA：An Energy-Efficient Clustering Algorithm for Wireless Sensor Networks Using Chaotic Mapping and Osprey Optimization

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第12期49卷 173-190页

作者： Jia, Songhao Shao, Wenqian Yang, Cai Jia, Shuya Yuan, Yaohui Chen, Huiyuan Zhang, Haiyu School of Artificial Intelligence and Software Engineering Nanyang Normal University Henan Nanyang473061 China

A wireless sensor network (WSN) represents a promising approach for establishing self-organizing wireless networks comprising a substantial number of wireless sensors, with the objective of facilitating communication in regions where the existing communication infrastructure has been severely disrupted. In order to address the issue of excessive energy consumption by cluster heads and central nodes in emergency communication networks of wireless sensor networks, this paper proposes an emergency communication algorithm for wireless sensor networks based on chaos mapping and osprey optimization. Firstly, an optimization algorithm based on chaos theory is used to select the virtual position of the initial population of the Osprey optimization algorithm. This is achieved by simulating the randomness and unpredictability of chaotic systems. Secondly, the osprey optimization algorithm and the improved fitness function are used to select the optimal cluster head combination. In the selection process, six factors, such as the energy level of network nodes, the distance between cluster heads, the distance between cluster heads and base stations, the distance between cluster heads and ordinary nodes, the variance of the distance between cluster heads and base stations and the variance of the distance between cluster heads, are comprehensively considered. Finally, the heuristic function of FA-star algorithm is used to select the next hop node to transmit the message. The results of the simulation demonstrate that the residual energy of the CM-OOA algorithm is 14% higher than that of the CGWOA algorithm following the transmission of 1000 data rounds. This figure is 54% higher than that observed for the PSO-C algorithm. The findings demonstrate that the CM-OOA algorithm effectively extends the network lifetime and preserves a favorable load balance in diverse network settings. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Base stations

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Research on Cluster Routing Protocol Based on Genetic Algorithm and Astar Algorithm

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Informatica (Slovenia) 2024年第14期48卷 219-232页

作者： Shao, Wenqian Jia, Songhao Yang, Cai Shangguan, Tonghang School of Artificial Intelligence and Software Engineering Nanyang Normal University Henan Nanyang473061 China

In routing protocols for wireless sensor networks, the energy depletion of cluster head nodes and centre nodes is too fast, resulting in a short network life cycle. Aiming at the problem of excessive energy depletion of cluster head nodes and centre nodes in clustering routing protocols for wireless sensor networks, a clustering routing protocol based on genetic algorithm and Astar algorithm (GADM-A*) is proposed. Firstly, in the process of selecting cluster head nodes, the optimal cluster head combination is selected by optimising the genetic algorithm and improving the goodness-of-fit function. This is achieved by considering five factors: the energy level of nodes, the number of times a node has become a cluster head, the distance between the cluster heads, the distance between the cluster head and ordinary nodes, and the distance between the cluster head and the base station. Secondly, the Astar algorithm, which is a heuristic function of node energy and transmission distance, is employed to select the optimal path for inter-cluster routing. Finally, the sensor network transmits the information. The GADM-A* algorithm extends the network lifetime by balancing the overall energy consumption of the network. Simulation results show that the proposed protocol has 53% network energy remaining after 1000 rounds of data transmission, which is an average of 34.3% improvement in remaining energy over existing algorithms. The node mortality rate is 12%, which is 45% lower than the average mortality rate of the remaining algorithms. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Genetic algorithms

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Block Incremental Dense Tucker Decomposition with Application to Spatial and Temporal Analysis of Air Quality Data

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Computer Modeling in engineering & Sciences 2024年第4期139卷 319-336页

作者： SangSeok Lee HaeWon Moon Lee Sael Department of Artificial Intelligence Ajou UniversitySuwon-si16499Korea Department of Software and Computer Engineering Ajou UniversitySuwon-si16499Korea

How can we efficiently store and mine dynamically generated dense tensors for modeling the behavior of multidimensional dynamic data?Much of the multidimensional dynamic data in the real world is generated in the form of time-growing *** example,air quality tensor data consists of multiple sensory values gathered from wide locations for a long *** data,accumulated over time,is redundant and consumes a lot ofmemory in its raw *** need a way to efficiently store dynamically generated tensor data that increase over time and to model their behavior on demand between arbitrary time *** this end,we propose a Block IncrementalDense Tucker Decomposition(BID-Tucker)method for efficient storage and on-demand modeling ofmultidimensional spatiotemporal *** that tensors come in unit blocks where only the time domain changes,our proposed BID-Tucker first slices the blocks into matrices and decomposes them via singular value decomposition(SVD).The SVDs of the time×space sliced matrices are stored instead of the raw tensor blocks to save *** modeling from data is required at particular time blocks,the SVDs of corresponding time blocks are retrieved and incremented to be used for Tucker *** factor matrices and core tensor of the decomposed results can then be used for further data *** compared our proposed BID-Tucker with D-Tucker,which our method extends,and vanilla Tucker *** show that our BID-Tucker is faster than both D-Tucker and vanilla Tucker decomposition and uses less memory for storage with a comparable reconstruction *** applied our proposed BID-Tucker to model the spatial and temporal trends of air quality data collected in South Korea from 2018 to *** were able to model the spatial and temporal air quality *** were also able to verify unusual events,such as chronic ozone alerts and large fire events.

关键词： Dynamic decomposition tucker tensor tensor factorization spatiotemporal data tensor analysis air quality

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Integrating Ontology-Based Approaches with Deep Learning Models for Fine-Grained Sentiment Analysis

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Computers, Materials & Continua 2024年第10期81卷 1855-1877页

作者： Longgang Zhao Seok-Won Lee The Knowledge-Intensive Software Engineering(NiSE)Research Group Department of Artificial IntelligenceAjou UniversitySuwon City16499Republic of Korea Department of Software and Computer Engineering Ajou UniversitySuwon City16499Republic of Korea

Although sentiment analysis is pivotal to understanding user preferences,existing models face significant challenges in handling context-dependent sentiments,sarcasm,and nuanced *** study addresses these challenges by integrating ontology-based methods with deep learning models,thereby enhancing sentiment analysis accuracy in complex domains such as film reviews and restaurant *** framework comprises explicit topic recognition,followed by implicit topic identification to mitigate topic interference in subsequent sentiment *** the context of sentiment analysis,we develop an expanded sentiment lexicon based on domainspecific corpora by leveraging techniques such as word-frequency analysis and word ***,we introduce a sentiment recognition method based on both ontology-derived sentiment features and sentiment *** evaluate the performance of our system using a dataset of 10,500 restaurant reviews,focusing on sentiment classification *** incorporation of specialized lexicons and ontology structures enables the framework to discern subtle sentiment variations and context-specific expressions,thereby improving the overall sentiment-analysis *** results demonstrate that the integration of ontology-based methods and deep learning models significantly improves sentiment analysis accuracy.

关键词： Deep learning ontology fine-grained sentiment analysis online reviews

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Coverage Holes Recovery in Hybrid Sensor Networks Based on Key Perceptual Intersection 22

Coverage Holes Recovery in Hybrid Sensor Networks Based on K...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Li, He Sun, Shixian Dong, Chuang Fu, Zhufeng Zhao, Cong Qi, Qinglei University of Nanyang Normal School of Artificial Intelligence and Software Engineering Nanyang China

ISBN: (纸本)9798331509712

Wireless sensor networks are widely used in various applications such as military surveillance, wildlife monitoring, industrial process monitoring and so on. Detecting and recovering from coverage holes is a major issue in wireless sensor networks. The gradual energy depletion of sensor nodes with limited battery energy leads to dysfunction of some of the nodes, thus creating holes in the monitored area. In this paper, propose a method to detect and recover coverage holes in wireless sensor networks. This method first divides the network into equally sized units, and then selects a representative node for each unit, called an agent. Then, the percentage of each cell covered by nodes can be accurately calculated and holes can be detected. Finally, the holes are recovered using the average of the key perceptual intersections as the initial value of the global optimal point of the particle swarm optimization algorithm. Simulation indicates show that the algorithm reduces the network energy consumption, reduces the distance travelled by the mobile nodes and increases the percentage of network holes recovery as compared to other algorithms. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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BMLP:behavior-aware MLP for heterogeneous sequential recommendation

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Frontiers of Computer Science 2024年第3期18卷 235-237页

作者： Weixin LI Yuhao WU Yang LIU Weike PAN Zhong MING College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) Shenzhen 518123China

1 Introduction Recommender systems can effectively alleviate the problem of information ***,traditional recommendation methods cannot capture users’dynamic *** recommendation methods model user sequences to obtain more accurate and dynamic user ***,deep learning-based sequential recommendation methods have achieved great *** is proposed to capture the sequential information[1,2].Attention-based methods[3]use attention mechanisms to learn relationships between ***-based methods[4−6]transform sequences into graph structures to capture relationships of ***,they have the following two limitations.

关键词： sequences behavior sequential

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INTRODUCING MLOPS TO FACILITATE REPRODUCIBLE MODEL DEVELOPMENT ON OMICS DATA 13th

INTRODUCING MLOPS TO FACILITATE REPRODUCIBLE MODEL DEVELOPME...

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13th International Conference on Simulation and Modelling in the Food and Bio-Industry, FOODSIM 2024

作者： Ruggeri, Dario Vidács, László Applied Artificial Intelligence Research Group Department of Software Engineering University of Szeged Hungary

ISBN: (纸本)9789492859297

Foodomics, bridging food science with omics technologies, confronts challenges of data complexity and the critical need for reproducibility in its interdisciplinary exploration. An analysis of the recent literature indicates a sparse application of neural networks (NN) and machine learning (ML), compounded by a significant shortfall in research transparency, where code and data sharing remain scarce. This scenario restricts the advancement and validation of scientific discoveries within Foodomics. This paper presents a discussion on the potential of Machine Learning Operations (MLOps), particularly focusing on the ClearML framework, to enhance reproducibility and streamline research workflows. While MLOps c annot d irectly r emedy t he issue of data unavailability, it offers r obust s olutions t o ensure the reproducibility of studies that do share their code. We elucidate how features of ClearML, including data management, experiment tracking, and automated HPO, can be leveraged to foster improved reproducibility, efficient an alysis, an d th e ac celeration of discovery in Foodomics research. By detailing the capabilities of MLOps frameworks, we highlight their value in addressing some of the pressing challenges faced by the Foodomics community, advocating for a shift towards more open, transparent, and reproducible research practices. © 2024, EUROSIS-ETI. All rights reserved.

关键词： Experiment Management Foodomics MLOps Neural Networks Reproducibility

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