Research

17 pages, 2063 KiB

Open AccessArticle

Business Purchase Prediction Based on XAI and LSTM Neural Networks

by Bratislav Predić, Milica Ćirić and Leonid Stoimenov

Electronics 2023, 12(21), 4510; https://doi.org/10.3390/electronics12214510 - 02 Nov 2023

Viewed by 1297

The black-box nature of neural networks is an obstacle to the adoption of systems based on them, mainly due to a lack of understanding and trust by end users. Providing explanations of the model’s predictions should increase trust in the system and make [...] Read more.

The black-box nature of neural networks is an obstacle to the adoption of systems based on them, mainly due to a lack of understanding and trust by end users. Providing explanations of the model’s predictions should increase trust in the system and make peculiar decisions easier to examine. In this paper, an architecture of a machine learning time series prediction system for business purchase prediction based on neural networks and enhanced with Explainable artificial intelligence (XAI) techniques is proposed. The architecture is implemented on an example of a system for predicting the following purchases for time series using Long short-term memory (LSTM) neural networks and Shapley additive explanations (SHAP) values. The developed system was evaluated with three different LSTM neural networks for predicting the next purchase day, with the most complex network producing the best results across all metrics. Explanations generated by the XAI module are provided with the prediction results to the user to allow him to understand the system’s decisions. Another benefit of the XAI module is the possibility to experiment with different prediction models and compare input feature effects. Full article

(This article belongs to the Special Issue Recent Advances in Data Science and Information Technology)

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15 pages, 2410 KiB

Open AccessArticle

Design and Implementation of EinStein Würfelt Nicht Program Monte_Alpha

by Chih-Hung Chen, Sin-Yi Chiu and Shun-Shii Lin

Electronics 2023, 12(13), 2936; https://doi.org/10.3390/electronics12132936 - 04 Jul 2023

Cited by 1 | Viewed by 850

Abstract

The game of EinStein würfelt nicht involves an element of uncertainty due to die rolling, which poses a big challenge in the development of computer game programs. However, the intriguing nature of probabilistic elements has made this game popular in computer game competitions. [...] Read more.

The game of EinStein würfelt nicht involves an element of uncertainty due to die rolling, which poses a big challenge in the development of computer game programs. However, the intriguing nature of probabilistic elements has made this game popular in computer game competitions. This study aimed to develop a high-strength EinStein würfelt nicht program that utilizes an efficient bitboard representation for the game board as well as pre-established probability distribution tables and extensively uses bitwise operations to improve the efficiency of game tree expansion. Additionally, this study attempted to replace random simulation with an evaluation function to enhance the accuracy of the Upper Confidence bounds applied to Trees algorithm. Through this design, we improved the strength of our program, and we hope that this program will be able to achieve additional excellent results in future computer game tournaments. Full article

(This article belongs to the Special Issue Recent Advances in Data Science and Information Technology)

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16 pages, 2083 KiB

Open AccessArticle

Load Disaggregation Based on a Bidirectional Dilated Residual Network with Multihead Attention

by Yifei Shu, Jieying Kang, Mei Zhou, Qi Yang, Lai Zeng and Xiaomei Yang

Electronics 2023, 12(12), 2736; https://doi.org/10.3390/electronics12122736 - 19 Jun 2023

Cited by 1 | Viewed by 1091

Abstract

Load disaggregation determines appliance-level energy consumption unintrusively from aggregated consumption measured by a single meter. Deep neural networks have been proven to have great potential in load disaggregation. In this article, a temporal convolution network, mainly consisting of residual blocks with bidirectional dilated [...] Read more.

Load disaggregation determines appliance-level energy consumption unintrusively from aggregated consumption measured by a single meter. Deep neural networks have been proven to have great potential in load disaggregation. In this article, a temporal convolution network, mainly consisting of residual blocks with bidirectional dilated convolution, the GeLu activation function, and multihead attention, is proposed to improve the prediction accuracy of individual appliances. Bidirectional dilated convolution is applied to enlarge the receptive field and effectively extract load features from historical and future information. Meanwhile, GeLU is introduced into the residual structure to overcome the “dead state” issue of traditional ReLU. Furthermore, multihead attention aims to improve the prediction accuracy by giving different weights according to the importance of different-level load features. The proposed model is validated using the REDD and UK-DALE datasets. Among six existing neural networks, the experimental results demonstrate that the proposed algorithm achieves the least average errors when disaggregating four appliances in terms of mean absolute error (MAE) and signal aggregate error (SAE), respectively, reduced by

22.33 %

and

60.58 %

compared with the model with the second-best performance on the REDD dataset. Additionally, the proposed algorithm shows superior results in identifying the on/off state in four appliances from the UK-DALE dataset. Full article

(This article belongs to the Special Issue Recent Advances in Data Science and Information Technology)

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14 pages, 2965 KiB

Open AccessArticle

Single and Multiple Separate LSTM Neural Networks for Multiple Output Feature Purchase Prediction

by Milica Ćirić, Bratislav Predić, Dragan Stojanović and Ivan Ćirić

Electronics 2023, 12(12), 2616; https://doi.org/10.3390/electronics12122616 - 10 Jun 2023

Cited by 4 | Viewed by 1827

Abstract

Data concerning product sales are a popular topic in time series forecasting due to their multidimensionality and wide presence in many businesses. This paper describes the research in predicting the timing and product category of the next purchase based on historical customer transaction [...] Read more.

Data concerning product sales are a popular topic in time series forecasting due to their multidimensionality and wide presence in many businesses. This paper describes the research in predicting the timing and product category of the next purchase based on historical customer transaction data. Given that the dataset was acquired from a vendor of medical drugs and devices, the generic product identifier (GPI) classification system was incorporated in assigning product categories. The models built are based on recurrent neural networks (RNN) and long short-term memory (LSTM) neural networks with different input and output features, and training datasets. Experiments with various datasets were conducted and optimal network structures and types for predicting both product category and next purchase day were identified. The key contribution of this research is the process of data transformation from its original purchase transaction format into a time series of input features for next purchase prediction. With this approach, it is possible to implement a dedicated personalized marketing system for a vendor. Full article

(This article belongs to the Special Issue Recent Advances in Data Science and Information Technology)

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18 pages, 10697 KiB

Open AccessArticle

A Hybrid Forecast Model of EEMD-CNN-ILSTM for Crude Oil Futures Price

by Jingyang Wang, Tianhu Zhang, Tong Lu and Zhihong Xue

Electronics 2023, 12(11), 2521; https://doi.org/10.3390/electronics12112521 - 02 Jun 2023

Cited by 1 | Viewed by 1097

Abstract

Crude oil has dual attributes of finance and energy. Its price fluctuation significantly impacts global economic development and financial market stability. Therefore, it is necessary to predict crude oil futures prices. In this paper, a hybrid forecast model of EEMD-CNN-ILSTM for crude oil [...] Read more.

Crude oil has dual attributes of finance and energy. Its price fluctuation significantly impacts global economic development and financial market stability. Therefore, it is necessary to predict crude oil futures prices. In this paper, a hybrid forecast model of EEMD-CNN-ILSTM for crude oil futures price is proposed, which is based on Ensemble Empirical Mode Decomposition (EEMD), Convolutional Neural Network (CNN), and Improved Long Short-Term Memory (ILSTM). ILSTM improves the output gate of Long Short-Term Memory (LSTM) and adds important hidden state information based on the original output. In addition, ILSTM adds the learning of cell state at the previous time in the forget gate and input gate, which makes the model learn more fully from historical data. EEMD decomposes time series data into a residual sequence and multiple Intrinsic Mode Functions (IMF). Then, the IMF components are reconstructed into three sub-sequences of high-frequency, middle-frequency, and low-frequency, which are convenient for CNN to extract the input data’s features effectively. The forecast accuracy of ILSTM is improved efficiently by learning historical data. This paper uses the daily crude oil futures data of the Shanghai Energy Exchange in China as the experimental data set. The EEMD-CNN-ILSTM is compared with seven prediction models: Support Vector Regression (SVR), Multi-Layer Perceptron (MLP), LSTM, ILSTM, CNN-LSTM, CNN-ILSTM, and EEMD-CNN-LSTM. The results of the experiment show the model is more effective and accurate. Full article

(This article belongs to the Special Issue Recent Advances in Data Science and Information Technology)

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21 pages, 5404 KiB

Open AccessArticle

Performance Improvement of Speech Emotion Recognition Systems by Combining 1D CNN and LSTM with Data Augmentation

by Shing-Tai Pan and Han-Jui Wu

Electronics 2023, 12(11), 2436; https://doi.org/10.3390/electronics12112436 - 27 May 2023

Cited by 1 | Viewed by 1786

Abstract

In recent years, the increasing popularity of smart mobile devices has made the interaction between devices and users, particularly through voice interaction, more crucial. By enabling smart devices to better understand users’ emotional states through voice data, it becomes possible to provide more [...] Read more.

In recent years, the increasing popularity of smart mobile devices has made the interaction between devices and users, particularly through voice interaction, more crucial. By enabling smart devices to better understand users’ emotional states through voice data, it becomes possible to provide more personalized services. This paper proposes a novel machine learning model for speech emotion recognition called CLDNN, which combines convolutional neural networks (CNN), long short-term memory neural networks (LSTM), and deep neural networks (DNN). To design a system that closely resembles the human auditory system in recognizing audio signals, this article uses the Mel-frequency cepstral coefficients (MFCCs) of audio data as the input of the machine learning model. First, the MFCCs of the voice signal are extracted as the input of the model. Local feature learning blocks (LFLBs) composed of one-dimensional CNNs are employed to calculate the feature values of the data. As audio signals are time-series data, the resulting feature values from LFLBs are then fed into the LSTM layer to enhance learning on the time-series level. Finally, fully connected layers are used for classification and prediction. The experimental evaluation of the proposed model utilizes three databases: RAVDESS, EMO-DB, and IEMOCAP. The results demonstrate that the LSTM model effectively models the features extracted from the 1D CNN due to the time-series characteristics of speech signals. Additionally, the data augmentation method applied in this paper proves beneficial in improving the recognition accuracy and stability of the systems for different databases. Furthermore, according to the experimental results, the proposed system achieves superior recognition rates compared to related research in speech emotion recognition. Full article

(This article belongs to the Special Issue Recent Advances in Data Science and Information Technology)

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13 pages, 21780 KiB

Open AccessArticle

A Dual Long Short-Term Memory Model in Forecasting the Number of COVID-19 Infections

by Jung-Pin Lai and Ping-Feng Pai

Electronics 2023, 12(3), 759; https://doi.org/10.3390/electronics12030759 - 02 Feb 2023

Viewed by 1100

Abstract

Since the outbreak of the Coronavirus Disease 2019 (COVID-19), the spread of the epidemic has been a major international public health issue. Hence, various forecasting models have been used to predict the infectious spread of the disease. In general, forecasting problems often involve [...] Read more.

Since the outbreak of the Coronavirus Disease 2019 (COVID-19), the spread of the epidemic has been a major international public health issue. Hence, various forecasting models have been used to predict the infectious spread of the disease. In general, forecasting problems often involve prediction accuracy decreasing as the horizon increases. Thus, to extend the forecasting horizon without decreasing performance or prediction, this study developed a Dual Long Short-Term Memory (LSTM) with Genetic Algorithms (DULSTMGA) model. The model employed predicted values generated by LSTM models in short-forecasting horizons as inputs for the long-term prediction of LSTM in a rolling manner. Genetic algorithms were applied to determine the parameters of LSTM models, allowing long-term forecasting accuracy to increase as long as short-term forecasting was accurate. In addition, the compartment model was utilized to simulate the state of COVID-19 and generate numbers of infectious cases. Infectious cases in three countries were employed to examine the feasibility and performance of the proposed DULSTMGA model. Numerical results indicated that the DULSTMGA model could obtain satisfactory forecasting accuracy and was superior to many previous studies in terms of the mean absolute percentage error. Therefore, the designed DULSTMGA model is a feasible and promising alternative for forecasting the number of infectious COVID-19 cases. Full article

(This article belongs to the Special Issue Recent Advances in Data Science and Information Technology)

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13 pages, 2362 KiB

Open AccessArticle

Alleviating Class-Imbalance Data of Semiconductor Equipment Anomaly Detection Study

by Da Hoon Seol, Jeong Eun Choi, Chan Young Kim and Sang Jeen Hong

Electronics 2023, 12(3), 585; https://doi.org/10.3390/electronics12030585 - 24 Jan 2023

Cited by 1 | Viewed by 1457

Abstract

Plasma-based semiconductor processing is highly sensitive, thus even minor changes in the procedure can have serious consequences. The monitoring and classification of these equipment anomalies can be performed using fault detection and classification (FDC). However, class imbalance in semiconductor process data poses a [...] Read more.

Plasma-based semiconductor processing is highly sensitive, thus even minor changes in the procedure can have serious consequences. The monitoring and classification of these equipment anomalies can be performed using fault detection and classification (FDC). However, class imbalance in semiconductor process data poses a significant obstacle to the introduction of FDC into semiconductor equipment. Overfitting can occur in machine learning due to the diversity and imbalance of datasets for normal and abnormal. In this study, we suggest a suitable preprocessing method to address the issue of class imbalance in semiconductor process data. We compare existing oversampling models to reduce class imbalance, and then we suggest an appropriate sampling strategy. In order to improve the FC performance of plasma-based semiconductor process data, it was confirmed that the SMOTE-based model using an undersampling technique such as Tomek link is effective. SMOTE-TOMEK, which removes multiple classes and makes the boundary clear, is suitable for FDC to classify minute changes in plasma-based semiconductor equipment data. Full article

(This article belongs to the Special Issue Recent Advances in Data Science and Information Technology)

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24 pages, 8371 KiB

Open AccessArticle

Adaptive Multilevel Coloring and Significant Texture Selecting for Automatic Deep Learning Image Transfer

by Hsien-Chu Wu, Yu-Chi Liu, Yen-Yu Chen and Yu-Yen Weng

Electronics 2022, 11(22), 3750; https://doi.org/10.3390/electronics11223750 - 15 Nov 2022

Viewed by 1296

Abstract

This paper proposes an image style transfer technique based on target image color and style, which improves the limitations of previous studies that only consider inter-image color transfer and use only deep learning for style transfer. First, an adaptive multilevel cut is made [...] Read more.

This paper proposes an image style transfer technique based on target image color and style, which improves the limitations of previous studies that only consider inter-image color transfer and use only deep learning for style transfer. First, an adaptive multilevel cut is made based on the luminance distribution of the two image pixels, and then a color transfer is applied to each region. Next, deep learning is used to select effective features for the target image, and the convolutional layer determines the extent of effective features by using the structural similarity index (SSIM) and black blocks. Selecting a convolutional layer with more effective features can reduce the limitations of the deep learning style transfer that requires artificial control parameters. The proposed method improves image quality by automatically simulating the color and style of the target image and controlling the parameters without human intervention. By evaluating the similarity between the result image and the target image, the proposed method can reduce the gap of variance by more than two times, and the result image can display a balance between the color and style of the target image. Full article

(This article belongs to the Special Issue Recent Advances in Data Science and Information Technology)

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