Reversible Data Hiding in Absolute Moment Block Truncation Codes via Arithmetical and Logical Differential Coding by Ching-Chun Chang, Yijie Lin, Jui-Chuan Liu, Chin-Chen Chang

“…An indicator is recorded to specify which two values are involved in the embedding process. The embedded secret data can be correctly extracted using a corresponding two-stage extraction process based on the indicator. …”
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A data augmentation approach to enhance breast cancer detection using generative adversarial and artificial neural networks by Alawee Wissam H., Al-Haddad Luttfi A., Basem Ali, Al-Haddad Abdullah A.

“…Prior to analysis, the images were subjected to a sophisticated data augmentation process that leveraged data denoising, contrast enhancement, and the application of a generative adversarial network (GAN). …”
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Bidirectional f-Divergence-Based Deep Generative Method for Imputing Missing Values in Time-Series Data by Wen-Shan Liu, Tong Si, Aldas Kriauciunas, Marcus Snell, Haijun Gong

“…Although various methods have been proposed in recent years, many struggle with limitations and reduced accuracy, particularly when the missing rate is high. In this work, we present a novel f-divergence-based bidirectional generative adversarial imputation network, tf-BiGAIN, designed to address these challenges in time-series data imputation. …”
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Collaborative AI Dysarthric Speech Recognition System With Data Augmentation Using Generative Adversarial Neural Network by Yibo He, Kah Phooi Seng, Li-Minn Ang

“…Experiments conducted on the UASpeech corpus demonstrate a significant reduction in the word error rate (WER) compared to the baseline approach. Specifically, the three-stage data enhancement process achieves a reduction in the WER for the fine-tuned Wav2Vec2-XLSR and Whisper-Tiny models by 9.81% and 6.56%, respectively, with an average WER of 13.58% for the best performing system. …”
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Future Outlook of Highway Operations with Implementation of Innovative Technologies Like AV, CV, IoT and Big Data by Muhammad Azmat, Sebastian Kummer, Lara Trigueiro Moura, Federico Di Gennaro, Rene Moser

“…This is increasing the number of vehicles on roads at an alarming rate and existing infrastructure and conventional methods of traffic management are becoming inefficient both on highways and in urban areas. …”
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Sports data analysis of track and field athletes based on IID-OPK and NonIID-OPK algorithms by Xiaofeng Chen

“…The traditional K-means clustering algorithm randomly selects clustering center points, which can easily lead to unstable clustering results and be affected by outliers, making it unable to handle complex sports data of track and field athletes. Therefore, the research introduces the Independent and Identically Distributed (IID) and Non-Independent and Identically Distributed (NonIID) ideas based on the K-means algorithm to optimize the data analysis process. …”
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Antecedent Effect Models as an Exploratory Tool to Link Climate Drivers to Herbaceous Perennial Population Dynamics Data by Aldo Compagnoni, Dylan Childs, Tiffany M. Knight, Roberto Salguero‐Gómez

“…We hypothesize that the effect of yearly climate is too complex, weak, and confounded by other factors to be easily predicted using monthly precipitation and temperature data. On the other hand, in our case study, antecedent effect models show biologically sensible correlations between two precipitation anomalies and multiple vital rates. …”
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Evaluation of dental students’ attitude to elderly patients based on demographic data: a cross-sectional study by Büşra Karabulut Gençer, Ayşe Aslı Şenol, Bengü Doğu Kaya, Pınar Yılmaz Atalı, Bilge Tarçın

“…The aim of this study was to evaluate the attitudes of dental students towards elderly patients during their educational progress, which is a crucial period in the formation of dentists’ approaches to patients and treatment, and to consider the influence of demographic data in this process. Methods In this cross-sectional study, a two-part questionnaire was administered to 3rd-, 4th-, and 5th-year students at Marmara University, Faculty of Dentistry. …”
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Augmenting insights into heat transfer performance of direct-contact evaporator: An interpretable data-driven hybrid strategy by Yinzhen Tan, Bo Yu, Jianxin Pan, Wuliang Yin, Min Wang, Hua Wang, Kai Yang, Qingtai Xiao

“…This research proposed an interpretable data-driven hybrid strategy for evaluating heat transfer enhancement performance in the low-temperature waste heat recovery systems. …”
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Machine learning-based voice assistant: optimizing the efficiency of speech conversion for people with speech disorders by M.H. Antor, N.V. Chudinovskikh, M.V. Bachurin, A.A. Shurpikov, N.A. Khlebnikov, B.A. Bredikhin

“…We implemented data preprocessing methods, including filtering, down-sampling, and splitting, to prevent overfitting and decrease processing power as well as time. …”
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Spatio-temporal graph neural networks for power prediction in offshore wind farms using SCADA data by S. Daenens, S. Daenens, S. Daenens, T. Verstraeten, T. Verstraeten, T. Verstraeten, P.-J. Daems, P.-J. Daems, A. Nowé, J. Helsen, J. Helsen, J. Helsen

“…A temporal component is introduced by feeding a time series of input features into the graph, processed through a long short-term memory (LSTM) network before being passed to the GNN. …”
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Komparasi Data Mining Naive Bayes dan Neural Network memprediksi Masa Studi Mahasiswa S1 by Azahari Azahari, Yulindawati Yulindawati, Dewi Rosita, Syamsuddin Mallala

“…From the testing data, an accuracy rate only 57.63%. The other side, prediction accuracy of the neural network is 72.58%, so this alternative method is the best chosen. …”
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Applying mortality data to improve emergency care planning for haemodialysis patients: a quality improvement project by Lisa Tang, Usha Appalsawmy

“…Conclusion: Reviewing mortality data provides valuable insights into the care received by our patients and helps identify key themes for improvement within our patient population. …”
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Reversible Adversarial Examples with Minimalist Evolution for Recognition Control in Computer Vision by Shilong Yang, Lu Leng, Ching-Chun Chang, Chin-Chen Chang

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An Efficient Multisensor Hybrid Data Fusion Approach Based on Artificial Neural Networks and Particle Swarm Optimization Algorithms by Luc Eyembe Ihonock, Jean-François Dikoundou Essiben, Benjamin Salomon Diboma, Joe Suk Yong

“…Data fusion plays a crucial part in performance evaluation processes in multisensor systems; thus, it is important to use an effective technique to cut down on errors. …”
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Machine learning for epithelial ovarian cancer platinum resistance recurrence identification using routine clinical data by Li-Rong Yang, Mei Yang, Liu-Lin Chen, Yong-Lin Shen, Yuan He, Zong-Ting Meng, Wan-Qi Wang, Feng Li, Zhi-Jin Liu, Lin-Hui Li, Yu-Feng Wang, Xin-Lei Luo

“…Conversely, the LR model based on lasso regression yielded commendable results as well; it achieved an AUC of 0.738, a sensitivity of 0.541, a specificity of 0.836, with a cut-off value established at 0.154 and an accuracy rate of 79.6%. Finally, we visualized both models through nomograms to illustrate the significance of each variable involved in their development.ConclusionsWe have successfully developed predictive models for platinum-resistant recurrence of epithelial ovarian cancer, utilizing routine clinical and laboratory data. …”
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POISSON REGRESSION MODELING GENERALIZED IN MATERNAL MORTALITY CASES IN ACEH TAMIANG REGENCY by Riska Novita Sari, Ulya Nabilla, Riezky Purnama Sari

“…Maternal Mortality Rate (MMR) is the number of maternal deaths due to the process of pregnancy, childbirth, and postpartum which is used as an indicator of women's health degrees. …”
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Test and Analysis of the Influence of Polish Grinding Technology on Wear Performance of Gear Running Stage by Guangxiang Zhang, Yushuang Li, Xu Zhang

“…The results show that the polishing grinding technology can effectively improve the wear performance of the gear in the running process，reduce the wear rate of the gear，shorten the running cycle of the gear，and make the wear process of the gear in the running process more moderate. …”
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Comparison of Nonlinear Models for Describing the Growth Curve of Pekin Ducks by Navid Ghavi Hossein‐Zadeh

“…The performance of the models was systematically evaluated, and the behaviour of the models was scrutinized throughout the process of fitting nonlinear regression curves. The overall goodness‐of‐fit for each model to a variety of data profiles was determined through the application of the adjusted coefficient of determination, root mean square error, Akaike's information criterion and Bayesian information criterion. …”
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The Influence of Profitability, Firm Size, and Company Growth on Firm Value by Fidela Malva Kaulika, Imronudin Imronudin

“…Ethical considerations, including data collection and analysis processes, were strictly followed to ensure the integrity of the study. …”
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