Neural network fusion with fine-grained adaptation learning for turnover prediction

Article Computer Science, Artificial Intelligence

An improved generative adversarial network with modified loss function for crack detection in electromagnetic nondestructive testing

Lulu Tian et al.

Summary: An improved GAN is proposed for crack detection in electromagnetic NDT, with additional regulation terms introduced to enhance the contrast ratio of the generated image. Experimental results demonstrate the superiority of the improved GAN over the original one in crack detection tasks.

COMPLEX & INTELLIGENT SYSTEMS (2022)

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Article Economics

A novel deep learning model based on convolutional neural networks for employee churn prediction

Ebru Pekel Ozmen et al.

Summary: Employees are crucial resources for a company, and predicting employee churn is important. The ECDT-GRID model outperforms CNN, ECDT, and basic classification algorithms in terms of accuracy.

JOURNAL OF FORECASTING (2022)

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Article Computer Science, Artificial Intelligence

Rainfall and runoff time-series trend analysis using LSTM recurrent neural network and wavelet neural network with satellite-based meteorological data: case study of Nzoia hydrologic basin

Yashon O. Ouma et al.

Summary: This study compares the performance of LSTM and WNN models for spatio-temporal prediction of rainfall and runoff in scarcely gauged hydrologic basins. Both models showed good predictive capabilities with the increase in number of input parameters.

COMPLEX & INTELLIGENT SYSTEMS (2022)

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Article Computer Science, Information Systems

Exploiting Network Fusion for Organizational Turnover Prediction

Mingfei Teng et al.

Summary: This article introduces a Turnover Influence-based Neural Network (TINN) to enhance organizational turnover prediction by exploiting turnover contagion and utilizing the attention mechanism to improve interpretability. The model dynamically models the impact of turnover contagion and validates its effectiveness through extensive experiments in real-world settings. The results also reveal important interpretable findings regarding the impact of different networks on future organizational turnovers.

ACM TRANSACTIONS ON MANAGEMENT INFORMATION SYSTEMS (2021)

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Article Computer Science, Artificial Intelligence

GRU-based capsule network with an improved loss for personnel performance prediction

Xia Xue et al.

Summary: Predicting personnel future performance is crucial for maintaining core competitive advantages in human resource management. This paper proposes a novel method using GRU model and capsule network to predict personnel performance, along with an improved loss function. The experiments on real-world data demonstrate the effectiveness of the proposed approach in selecting high-potential talents and dealing with imbalanced data issues.

APPLIED INTELLIGENCE (2021)

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Article Computer Science, Artificial Intelligence

Semantic-enhanced sequential modeling for personality trait recognition from texts

Xia Xue et al.

Summary: This paper introduces a novel neural network model for recognizing multiple personality traits, avoiding the dependency on feature engineering and achieving significant performance improvements.

APPLIED INTELLIGENCE (2021)

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Article Telecommunications

Performance Analysis of Satellite-Terrestrial Network of Weibull Fading Channel

Tao Teng et al.

Summary: This study investigates signal transmission in a satellite-terrestrial Weibull system, analyzing an upper bound scheme based on optimal multi-branch transmission strategy, and presenting simplified theoretical results to address the high intractability issue. The effectiveness of this proposed method is demonstrated through performance verification of various parameters.

WIRELESS PERSONAL COMMUNICATIONS (2021)

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Article Computer Science, Information Systems

From Big Data to Deep Data to Support People Analytics for Employee Attrition Prediction

Nesrine Ben Yahia et al.

Summary: In the age of data science and big data analytics, people analytics play a crucial role in reducing employee attrition and improving productivity. The research proposes a deep data-driven approach using a mixed method to construct an employee attrition model, achieving higher accuracy levels compared to previous solutions. Additionally, the study highlights the importance of 'business travel' as a leading motivator for employees in retention strategies.

IEEE ACCESS (2021)

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Article Computer Science, Information Systems