Multiple Sources Of Information Research Articles

The Influence of Interpersonal Communication Participants on Child Nutrition Promotion in Kilifi County, Kenya

Purpose: This study analyzed the influence of Interpersonal Communication participants in the promotion of child nutrition in Kilifi County, Kenya.
 Materials and Methods: The study which was guided by the Social Penetration Theory and employed the mixed methods research design. A sample of 10 Health Professionals drawn from Public Healthcare facilities and 247 residents with children aged 5 years and below was drawn from Ganze Constituency in Kilifi County. Data was collected through questionnaires and interviews. Quantitative data was analyzed using descriptive and inferential statistics while thematic analysis was used to analyze qualitative data. Data was presented in tabular form.
 Findings: The study found that multiple sources of child nutrition information existed and influenced the nutrition of a child in different degrees. Such sources included Health Professionals, Family members, Male care givers, opinion leaders and peers. Among these sources Health professionals were identified as the most preferred source of child nutrition information. Additionally, findings indicated that Interpersonal Communication participants largely contributed to the promotion of complementary feeding and an understanding of the effects of malnutrition.
 Implications to Theory, Practice and Policy: This study recommends that mechanisms should be put in place to monitor and evaluate messages emanating from informal sources of child nutrition information in order to negate any false narratives that can counter strides made in the promotion of child nutrition emanating from such sources. This study also recommends that Health Professionals should invest in developing a positive relationship with patients so as to increase the depth and breadth of child nutrition information they can provide.

An English Translation Error Correction System Based on Multi-Feature Fusion

Multi-feature fusion is an advanced computational technique that combines multiple sources of information or characteristics to improve the performance of a system or model. In various fields such as computer vision, natural language processing, and machine learning, this approach integrates diverse features like visual cues, textual data, and contextual information to enhance accuracy, robustness, and efficiency. Multi-feature fusion-based English translation represents a cutting-edge approach in natural language processing, where diverse linguistic features are amalgamated to refine the accuracy and quality of translation outcomes. By integrating various elements such as syntax, semantics, context, and linguistic patterns, this method significantly enhances the translation process, ensuring more nuanced and contextually precise results. This paper introduces an innovative English Translation Error Correction System (ETECS) founded on Multi-Feature Fusion, supplemented by a bi-gram Statistical Classification Neural Network (n-gramCNN). In response to the growing demand for accurate and efficient translation error detection and correction, this research endeavors to enhance the quality of English translations through a comprehensive and advanced methodology. Using a dataset of 1,000 English text samples with corresponding translations, ETECS demonstrates an impressive error detection accuracy of 95%. Upon identifying errors, the system achieves a correction accuracy of 92%, effectively rectifying grammatical, syntactical, and semantic inconsistencies. Moreover, ETECS outperforms baseline error correction systems by a significant margin, showcasing its superiority in handling complex translation errors. The bi-gram Statistical Classification Neural Network (n-gramCNN) contributes to this success by achieving an F1 score of 0.94, highlighting its effectiveness in classifying and correcting translation errors.

Ecological baselines in the Eastern Mediterranean Sea shifted long before the availability of observational time series.

Native biodiversity loss and invasions by nonindigenous species (NIS) have massively altered ecosystems worldwide, but trajectories of taxonomic and functional reorganization remain poorly understood due to the scarcity of long-term data. Where ecological time series are available, their temporal coverage is often shorter than the history of anthropogenic changes, posing the risk of drawing misleading conclusions on systems' current states and future development. Focusing on the Eastern Mediterranean Sea, a region affected by massive biological invasions and the largest climate change-driven collapse of native marine biodiversity ever documented, we followed the taxonomic and functional evolution of an emerging "novel ecosystem", using a unique dataset on shelled mollusks sampled in 2005-2022 on the Israeli shelf. To quantify the alteration of observed assemblages relative to historical times, we also analyzed decades- to centuries-old ecological baselines reconstructed from radiometrically dated death assemblages, time-averaged accumulations of shells on the seafloor that constitute natural archives of past community states. Against expectations, we found no major loss of native biodiversity in the past two decades, suggesting that its collapse had occurred even earlier than 2005. Instead, assemblage taxonomic and functional richness increased, reflecting the diversification of NIS whose trait structure was, and has remained, different from the native one. The comparison with the death assemblage, however, revealed that modern assemblages are taxonomically and functionally much impoverished compared to historical communities. This implies that NIS did not compensate for the functional loss of native taxa, and that even the most complete observational dataset available for the region represents a shifted baseline that does not reflect the actual magnitude of anthropogenic changes. While highlighting the great value of observational time series, our results call for the integration of multiple information sources on past ecosystem states to better understand patterns of biodiversity loss in the Anthropocene.

Earthquake recommendations in Europe: Types and diversity

Disaster literature agrees that knowledge alone is insufficient to promote the adoption of preparedness by populations. Nevertheless, this is a prerequisite for initiating behaviour. Governments and Civil Protection authorities are among the multiple sources of information on preparedness and appropriate disaster behaviours. If these sources are trusted, members of the public will accept the information they transmit. However, few studies have addressed the content of risk communication messages from European authorities. We analysed and discussed the frequency and diversity of household earthquake preparedness and response measures recommended by European Civil Protection authorities in countries exposed to seismic risk, using 84 official website documents and texts from 25 countries. Authorities highly recommended behaviours to apply during an earthquake, focusing on physical protection and conforming to the literature. However, official recommendations weakly recommended planning measures, a well-established preparedness component in the literature. Furthermore, authorities failed to reinforce skill acquisition, insurance acquisition, structural mitigation, and other mitigation measures. Nevertheless, non-structural mitigation was highly advised and was more frequent than stockpiling recommendations. The results also pointed to concerns about the behaviour of residents during and after an earthquake, misinformation, and to the diversity of measures in each country, disagreeing with previous research. Local differences and factors related to the process of selecting recommendations may explain the results. This study contributes to the literature on earthquake recommendations, risk communication, household preparedness and response, and practical applications of preparedness and response literature.

Increasing Trust in Climate Vulnerability Projections

AbstractProjecting future trends is emerging as a key focus of community‐based climate vulnerability assessments. In these mostly qualitative studies, understanding of current vulnerability processes is used as a basis for identifying who and what are vulnerable to future changes, where, and why, and characterizing the key drivers of vulnerability and how they might change. Few, if any, of these studies engage with approaches for validating findings, reflecting the difficulties of validating mostly qualitative projections of highly uncertain futures, absence of directly measurable vulnerability outcomes, and lack of data on vulnerability drivers. Given the challenges of projecting future trends, this absence undermines trust in such work and limits opportunities to learn. This paper illustrates, with examples, how validation can be incorporated into the study design of community‐based climate vulnerability assessments through: (a) examination of retrospective projections to assess projection skill, (b) evaluation of projections made for future time periods which have since passed for consistency with what actually happened, (c) comparison of projections with empirical research that attempts to understand and constrain the effects of climate change using observed effects of weather variation, and (d) incorporation of demonstrated “best practices” into projection development, such as acknowledgment of areas of uncertainty, integration of diverse viewpoints, and utilization of multiple sources of information.

MHIF-MSEA: a novel model of miRNA set enrichment analysis based on multi-source heterogeneous information fusion.

Introduction: MicroRNAs (miRNAs) are a class of non-coding RNA molecules that play a crucial role in the regulation of diverse biological processes across various organisms. Despite not encoding proteins, miRNAs have been found to have significant implications in the onset and progression of complex human diseases. Methods: Conventional methods for miRNA functional enrichment analysis have certain limitations, and we proposed a novel method called MiRNA Set Enrichment Analysis based on Multi-source Heterogeneous Information Fusion (MHIF-MSEA). Three miRNA similarity networks (miRSN-DA, miRSN-GOA, and miRSN-PPI) were constructed in MHIF-MSEA. These networks were built based on miRNA-disease association, gene ontology (GO) annotation of target genes, and protein-protein interaction of target genes, respectively. These miRNA similarity networks were fused into a single similarity network with the averaging method. This fused network served as the input for the random walk with restart algorithm, which expanded the original miRNA list. Finally, MHIF-MSEA performed enrichment analysis on the expanded list. Results and Discussion: To determine the optimal network fusion approach, three case studies were introduced: colon cancer, breast cancer, and hepatocellular carcinoma. The experimental results revealed that the miRNA-miRNA association network constructed using miRSN-DA and miRSN-GOA exhibited superior performance as the input network. Furthermore, the MHIF-MSEA model performed enrichment analysis on differentially expressed miRNAs in breast cancer and hepatocellular carcinoma. The achieved p-values were 2.17e(-75) and 1.50e(-77), and the hit rates improved by 39.01% and 44.68% compared to traditional enrichment analysis methods, respectively. These results confirm that the MHIF-MSEA method enhances the identification of enriched miRNA sets by leveraging multiple sources of heterogeneous information, leading to improved insights into the functional implications of miRNAs in complex diseases.

Data Fusion of RGB and Depth Data with Image Enhancement.

Since 3D sensors became popular, imaged depth data are easier to obtain in the consumer sector. In applications such as defect localization on industrial objects or mass/volume estimation, precise depth data is important and, thus, benefits from the usage of multiple information sources. However, a combination of RGB images and depth images can not only improve our understanding of objects, capacitating one to gain more information about objects but also enhance data quality. Combining different camera systems using data fusion can enable higher quality data since disadvantages can be compensated. Data fusion itself consists of data preparation and data registration. A challenge in data fusion is the different resolutions of sensors. Therefore, up- and downsampling algorithms are needed. This paper compares multiple up- and downsampling methods, such as different direct interpolation methods, joint bilateral upsampling (JBU), and Markov random fields (MRFs), in terms of their potential to create RGB-D images and improve the quality of depth information. In contrast to the literature in which imaging systems are adjusted to acquire the data of the same section simultaneously, the laboratory setup in this study was based on conveyor-based optical sorting processes, and therefore, the data were acquired at different time periods and different spatial locations. Data assignment and data cropping were necessary. In order to evaluate the results, root mean square error (RMSE), signal-to-noise ratio (SNR), correlation (CORR), universal quality index (UQI), and the contour offset are monitored. With JBU outperforming the other upsampling methods, achieving a meanRMSE = 25.22, mean SNR = 32.80, mean CORR = 0.99, and mean UQI = 0.97.

A PiRNA-disease association model incorporating sequence multi-source information with graph convolutional networks

There is growing evidence that PIWI-interacting RNA (piRNA) is widely involved in the proliferation, invasion, and metastasis of malignant tumors, playing an important regulatory role in numerous human physiological and pathological processes. Disease-associated piRNAs are expected to be biomarkers and novel therapeutic targets for early diagnosis and prognosis of malignant tumors. However, most previous computational models did not fully focus on the rich representation ability of multiple sources of information in piRNA sequences, which affected their performance in predicting piRNA-disease associations (PDAs). In this work, we propose a model, iSG-PDA, which combines the multi-source information of piRNA sequences with graph convolutional neural networks to predict potential PDAs. More specifically, we first fuse multi-source information including piRNA sequences and disease semantics to enhance the expressiveness of data, then deeply mine the advanced hidden features of PDA using graph convolutional networks, and finally exploit random forest to accurately determine the associations between piRNAs and diseases. In the golden standard dataset, the proposed model realized a prediction accuracy of 91.96% at the AUC of 0.9184. In ablation experiments and comparisons with other different models, iSG-PDA exhibits strong competitiveness. Moreover, the results of the case study indicate that 17 of the top 20 PDAs in the proposed model predictive score were confirmed. These preliminary results reveal that iSG-PDA is an effective computational method for predicting PDAs and can provide reliable disease candidate piRNAs for biological experiments.

A hybrid data fusion approach with twin CNN architecture for enhancing image source identification in IoT environment

AbstractWith the proliferation of digital devices in internet of things (IoT) environment featuring advanced visual capabilities, the task of Image Source Identification (ISI) has become increasingly vital for legal purposes, ensuring the verification of image authenticity and integrity, as well as identifying the device responsible for capturing the original scene. Over the past few decades, researchers have employed both traditional and machine‐learning methods to classify image sources. In the current landscape, data‐driven approaches leveraging deep learning models have emerged as powerful tools for achieving higher accuracy and precision in source prediction. The primary focus of this research is to address the complexities arising from diverse image sources and variable quality in IoT‐generated multimedia data. To achieve this, a Hybrid Data Fusion Approach is introduced, leveraging multiple sources of information to bolster the accuracy and robustness of ISI. This fusion methodology integrates diverse data streams from IoT devices, including metadata, sensor information, and contextual data, amalgamating them into a comprehensive data set for analysis. This study introduces an innovative approach to ISI through the implementation of a Twin Convolutional Neural Network Architecture (TCA) aimed at enhancing the efficacy of source classification. In TCA, the first CNN architecture, referred to as DnCNN, is employed to eliminate noise from the original data set, generating 256 × 256 patches for both training and testing. Subsequently, the second CNN architecture is employed to classify images based on features extracted from various convolutional layers using a 3 × 3 filter, thereby enhancing prediction efficiency. The proposed model demonstrates exceptional accuracy in effectively classifying image sources, showcasing its potential as a robust solution in the realm of ISI.

Knowledge and Participation of Physical Education Teachers in the Official Sports Events

21st century teachers are expected to develop 21st century competencies in students in more diverse classrooms, while increasing the study of teaching and learning. Teachers must update their teaching techniques, adopt creative teaching methods, and mobilize multiple sources of information to achieve this goal. In this paper, 150 teachers from Guangdong University of Science and Technology (GUT), Guangdong Foreign Studies University (GUFS) and Guangdong Institute of Science and Technology (GDPST) were evaluated by using literature, expert interviews, questionnaire and mathematical statistics to evaluate their knowledge in physical education teaching, including their physical education teaching experience, contact with students and their knowledge The ability to integrate teaching technology. This will further determine the level of teachers' participation in official sporting events, including their willingness, athletic skills and the impact of participation. The main conclusions of the study are as follows:1. The sample used in the study was primarily composed of teachers that were aged 25-29 years old and were mostly female. Their teaching experience ranged from 10 years and above, making the sample have a large population of well-experienced educators. Lastly, during sports events, these teachers would mostly have the role of being officials when participating. 2.For teaching knowledge, the respondents showed to have agreeable positive responses in all fields of experience, engagement, and technology. Although, the knowledge on technology scored the highest, indicating that the respondents had more knowledge in using this when participating in sports events. 3.The assessments showed no significant relationship in the teachers’ knowledge in teaching sports when compared to the different demographic factors assessed. 4.For teachers’ participation in sports events, there was a fairly agreeable positive assessment for the overall analysis. Although when looking at the indicators individually, only the impacts from participation scored positively, making this a strong indicator in the teachers’ participation. On the other hand, willingness and skills scored poorly, indicating the need to improve these two areas.5. The assessments showed no significant relationship in the teachers’ participation in sports when compared to the different demographic factors assessed. 6.Lastly, a positive correlation was found only between the teachers’ engagement and the teachers’ impacts from participation, confirming that these two indicators are linked and affect one another in the study.

Research on the Construction of English Translation Model for Speech Recognition Based on Multiple Information Sources and Lexical Assistance

Research on the Construction of English Translation Model for Speech Recognition Based on Multiple Information Sources and Lexical Assistance

Edge-cloud computing oriented large-scale online music education mechanism driven by neural networks

With the advent of the big data era, edge cloud computing has developed rapidly. In this era of popular digital music, various technologies have brought great convenience to online music education. But vast databases of digital music prevent educators from making specific-purpose choices. Music recommendation will be a potential development direction for online music education. In this paper, we propose a deep learning model based on multi-source information fusion for music recommendation under the scenario of edge-cloud computing. First, we use the music latent factor vector obtained by the Weighted Matrix Factorization (WMF) algorithm as the ground truth. Second, we build a neural network model to fuse multiple sources of music information, including music spectrum extracted from extra music information to predict the latent spatial features of music. Finally, we predict the user’s preference for music through the inner product of the user vector and the music vector for recommendation. Experimental results on public datasets and real music data collected by edge devices demonstrate the effectiveness of the proposed method in music recommendation.

Identify risk factors affecting participation of Turkish women in mammography screening for breast cancer prevention

PurposeCancer screening is a public health intervention aiming to reduce cancer-caused deaths. This study aims to determine the factors affecting the mammography screening time among women aged 40–69.MethodsThe micro dataset obtained from the Türkiye Health Survey conducted by the Turkish Statistical Institute (TurkStat) in 2019 and 2022 was used in the present study. Stereotype logistic regression was used to determine the variables affecting mammography screening and period for breast cancer prevention in women in Türkiye.ResultsGiven the results achieved from the analysis, it was found that factors such as age, marital status, general health condition, comorbidity, receiving psychosocial support, high blood lipid levels, and performing breast self-examinations affected women’s adherence to cancer screening programs.ConclusionSince adherence to mammography increases with age, it is recommended to pay importance to education for women approaching the age of mammography screening. Educated individuals are expected to have access to multiple sources of information as to cancer and to access this information more easily. In order to gain more insight into the recommended preventive measures and outcomes related to cancer, it is suggested to review policies, which will increase the educational level of women, and provide privileges in the field of education.

Fault Diagnosis of Wind Turbine Blades Based on Image Fusion and ResNet

In the diagnosis of wind turbine blade faults, the information provided by a single sensor is limited. To address this issue and take advantage of complementary features among multiple fault information sources, while enhancing fault diagnosis accuracy, a method for diagnosing wind turbine blade faults is proposed. This method combines Image Fusion Convolutional Neural Network (IFCNN) with the ResNet network. Firstly, the time-frequency representation of vibration data is obtained using wavelet transform. The time-frequency representation and blade fault images are input into the IFCNN to obtain fused images containing two categories of fault features. Next, the ResNet convolutional neural network is employed to automatically extract non-linear features from the fused images, establishing a classification model for blade fault images. Experimental results demonstrate that, with limited training data, the classification accuracy of this method can reach 86.7%, outperforming fault diagnosis models trained with single fault information. This approach offers a new perspective and method for the fusion of multiple fault information in the field of wind turbine blade fault diagnosis

Sleep restriction alters the integration of multiple information sources in probabilistic decision-making.

The detrimental effects of sleep loss on overall decision-making have been well described. Due to the complex nature of decisions, there remains a need for studies to identify specific mechanisms of decision-making vulnerable to sleep loss. Bayesian perspectives of decision-making posit judgement formation during decision-making occurs via a process of integrating knowledge gleaned from past experiences (priors) with new information from current observations (likelihoods). We investigated the effects of sleep loss on the ability to integrate multiple sources of information during decision-making by reporting results from two experiments: the first implementing both sleep restriction (SR) and total sleep deprivation (TSD) protocols, and the second implementing an SR protocol. In both experiments, participants were administered the Bayes Decisions Task on which optimal performance requires the integration of Bayesian prior and likelihood information. Participants in Experiment 1 showed reduced reliance on both information sources after SR, while no significant change was observed after TSD. Participants in Experiment 2 showed reduced reliance on likelihood after SR, especially during morning testing sessions. No accuracy-related impairments resulting from SR and TSD were observed in both experiments. Our findings show SR affects decision-making through altering the way individuals integrate available sources of information. Additionally, the ability to integrate information during SR may be influenced by time of day. Broadly, our findings carry implications for working professionals who are required to make high-stakes decisions on the job, yet consistently receive insufficient sleep due to work schedule demands.

Lq-norm multiple kernel fusion regression for self-cleansing sediment transport

Experimental and modeling studies have been conducted to develop an approach for self-cleansing rigid boundary open channel design such as drainage and sewer systems. Self-cleansing experiments in the literature are mostly performed on circular channel cross-section, while a few studies considered self-cleansing sediment transport in small rectangular channels. Experiments in this study were carried out in a rectangular channel with a length of 12.5 m, a width of 0.6 m, a depth of 0.7 m and having an automatic control system for regulating channel slope, discharge and sediment rate. Behind utilizing collected experimental data in this study, existing data in the literature for rectangular channels are used to develop self-cleansing models applicable for channel design. Through the modeling procedure, this study recommends Lq-norm multiple kernel fusion regression (LMKFR) techniques for self-cleansing sediment transport. The LMKFR is a regression technique based on the regularized kernel regression method which benefits from the combination of multiple information sources to improve the performance using the Lq-norm multiple kernel learning framework. The results obtained by LMKFR are compared to support vector regression benchmark and existing conventional regression self-cleansing sediment transport models in the literature for rectangular channels. The superiority of LMKFR is illustrated in an accurate modeling as compared with its alternatives in terms of various statistical error measurement criteria. The encouraging results of LMKFR can be linked to utilization of several kernels which are fused effectively using an Lq-norm prior that captures the intrinsic sparsity of the problem at hand. Promising performance of LMKFR technique in this study suggests it as an effective technique to be examined in similar environmental, hydrological and hydraulic problems.

Designing a Digital Health Solution: A Platform for Automated Surveillance of Fungal Infection.

Surveillance of invasive fungal infection (IFI) requires laborious review of multiple sources of clinical information, while applying complex criteria to effectively identify relevant infections. These processes can be automated using artificial intelligence (AI) methodologies, including applying natural language processing (NLP) to clinical reports. However, developing a practically useful automated IFI surveillance tool requires consideration of the implementation context. We employed the Design Thinking Framework (DTF) to focus on the needs of end users of the tool to ensure sustained user engagement and enable its prospective validation. DTF allowed iterative generation of ideas and refinement of the final digital health solution. We believe this approach is key to increasing the likelihood that the solution will be implemented in clinical practice.

Rapid Online Corrections for Proprioceptive and Visual Perturbations Recruit Similar Circuits in Primary Motor Cortex.

An important aspect of motor function is our ability to rapidly generate goal-directed corrections for disturbances to the limb or behavioral goal. The primary motor cortex (M1) is a key region involved in processing feedback for rapid motor corrections, yet we know little about how M1 circuits are recruited by different sources of sensory feedback to make rapid corrections. We trained two male monkeys (Macaca mulatta) to make goal-directed reaches and on random trials introduced different sensory errors by either jumping the visual location of the goal (goal jump), jumping the visual location of the hand (cursor jump), or applying a mechanical load to displace the hand (proprioceptive feedback). Sensory perturbations evoked a broad response in M1 with ∼73% of neurons (n = 257) responding to at least one of the sensory perturbations. Feedback responses were also similar as response ranges between the goal and cursor jumps were highly correlated (range of r = [0.91, 0.97]) as were the response ranges between the mechanical loads and the visual perturbations (range of r = [0.68, 0.86]). Lastly, we identified the neural subspace each perturbation response resided in and found a strong overlap between the two visual perturbations (range of overlap index, 0.73-0.89) and between the mechanical loads and visual perturbations (range of overlap index, 0.36-0.47) indicating each perturbation evoked similar structure of activity at the population level. Collectively, our results indicate rapid responses to errors from different sensory sources target similar overlapping circuits in M1.

Comparison of effect estimates between preprints and peer-reviewed journal articles of COVID-19 trials

BackgroundPreprints are increasingly used to disseminate research results, providing multiple sources of information for the same study. We assessed the consistency in effect estimates between preprint and subsequent journal article of COVID-19 randomized controlled trials.MethodsThe study utilized data from the COVID-NMA living systematic review of pharmacological treatments for COVID-19 (covid-nma.com) up to July 20, 2022. We identified randomized controlled trials (RCTs) evaluating pharmacological treatments vs. standard of care/placebo for patients with COVID-19 that were originally posted as preprints and subsequently published as journal articles. Trials that did not report the same analysis in both documents were excluded. Data were extracted independently by pairs of researchers with consensus to resolve disagreements. Effect estimates extracted from the first preprint were compared to effect estimates from the journal article.ResultsThe search identified 135 RCTs originally posted as a preprint and subsequently published as a journal article. We excluded 26 RCTs that did not meet the eligibility criteria, of which 13 RCTs reported an interim analysis in the preprint and a final analysis in the journal article. Overall, 109 preprint–article RCTs were included in the analysis. The median (interquartile range) delay between preprint and journal article was 121 (73–187) days, the median sample size was 150 (71–464) participants, 76% of RCTs had been prospectively registered, 60% received industry or mixed funding, 72% were multicentric trials. The overall risk of bias was rated as ‘some concern’ for 80% of RCTs. We found that 81 preprint–article pairs of RCTs were consistent for all outcomes reported. There were nine RCTs with at least one outcome with a discrepancy in the number of participants with outcome events or the number of participants analyzed, which yielded a minor change in the estimate of the effect. Furthermore, six RCTs had at least one outcome missing in the journal article and 14 RCTs had at least one outcome added in the journal article compared to the preprint. There was a change in the direction of effect in one RCT. No changes in statistical significance or conclusions were found.ConclusionsEffect estimates were generally consistent between COVID-19 preprints and subsequent journal articles. The main results and interpretation did not change in any trial. Nevertheless, some outcomes were added and deleted in some journal articles.

A novel ensemble model of multi-class credit assessment based on multi-source fusion theory

With the development of artificial intelligence technology, the assessment method based on machine learning, especially the ensemble learning method, has attracted more and more attention in the field of credit assessment. However, most of the ensemble assessment models are complex in structure and costly in time for parameter tuning, few of them break through the limitations of lightweight, universal and efficient. This paper present a new ensemble model for personal credit assessment. First, considering the conflicts and differences among multiple sources of information, a new method is proposed to correct the category prior information by using the difference measure. Then, the revised prior information is fused with the current sample information with the help of Bayesian data fusion theory. The model can integrate the advantages of multiple benchmark classifiers to reduce the interference of uncertain information. To verify the effectiveness of the proposed model, several typical ensemble classification models are selected and empirically studied using real customer credit data from a commercial bank in China, and the results show that among various assessment criteria: the proposed model not only effectively improves the multi-class classification performance, but also outperforms other advanced multi-class classification credit assessment models in terms of parameter tuning and generalizability. This paper supports commercial banks and other financial institutions examination and approval work.