Selective transfer learning with adversarial training for stock movement prediction by Yang Li, Hong-Ning Dai, Zibin Zheng

“…Extensive experiments demonstrate the superiority of our STLAT method. It outperforms state-of-the-art stock prediction solutions on ACC evaluation of 3.76%, 4.12%, 4.89% on ACL18, KDD17 and CN50, respectively.…”
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CFD-FEM Analysis for Functionality Prediction of Multi-Gear Pumps by Olga Zharkevich, Olga Reshetnikova, Tatyana Nikonova, Alexandra Berg, Andrey Berg, Darkhan Zhunuspekov, Oxana Nurzhanova

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Coal and gas outburst prediction based on data augmentation and neuroevolution. by Wenbing Shi, Ji Huang, Gaoming Yang, Shuzhi Su, Shexiang Jiang

“…This paper proposes a CGO risk prediction method based on data augmentation and a neuroevolution algorithm, denoted as ANEAT. …”
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Protein homodimers structure prediction based on deep neural network by A. Y. Hadarovich, A. A. Kalinouski, A. V. Tuzikov

“…The use of the neural network in combination with optimization procedure based on gradient descent method allowed to predict structures for protein homodimers. …”
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Prediction of colorectal cancer diagnosis based on circulating plasma proteins by Silvia Surinova, Meena Choi, Sha Tao, Peter J Schüffler, Ching‐Yun Chang, Timothy Clough, Kamil Vysloužil, Marta Khoylou, Josef Srovnal, Yansheng Liu, Mariette Matondo, Ruth Hüttenhain, Hendrik Weisser, Joachim M Buhmann, Marián Hajdúch, Hermann Brenner, Olga Vitek, Ruedi Aebersold

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A new adaptive grey prediction model and its application by Jianming Jiang, Ming Zhang, Zhongyong Huang

“…In this study, a new fractional-order accumulation generation operation and a novel grey action quantity are designed to improve the grey prediction model. The design of the new accumulation generation operation emphasizes new information and stability, enabling the model to produce more robust prediction results. …”
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AN INTELLIGENT POSTOPERATIVE CHRONIC PAIN PREDICTION SYSTEM (I-POCPP) by Elif Kartal, Fatma Önay Koçoğlu, Zeki Özen, İlkim Ecem Emre, Gürcan Güngör, Pervin Sutaş Bozkurt

“…Several machine learning prediction algorithms were used. POCP status of the patients diagnosed by the anesthesiologists and the prediction results of the models were compared to evaluate the performance of the models. …”
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FUZZY LOGISTIC REGRESSION APPLICATION ON PREDICTIONS CORONARY HEART DISEASE by Vera Febriani, Dian Lestari, Sri Mardiyati, Oktavia Lilyasari

“…We obtained from National Cardiovascular Center Harapan Kita, Jakarta. Evaluation with the Mean Degree of Membership method showed that the model built was feasible and good enough to predict coronary heart disease. …”
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Performer-KAN-Based Failure Prediction for IGBT with BO-CEEMDAN by Yue Xiao, Fanrong Wang

“…The results demonstrate that the proposed method offers a practical and effective solution for real-time IGBT health monitoring and fault prediction in industrial applications.…”
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Self-adaptive prediction and prewarning model of mine gas concentration by Dingwen Dong

“…Abstract In order to expand the function of safety monitoring and control system in coalmine, and realize the accurate real-time prediction and reliable prewarning of mine gas concentration, study the self-adaptive prediction and prewarning method for gas concentration based on Empirical Mode Decomposition (EMD) and Gaussian Process Regression (GPR). …”
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Stroke Prediction Using Deep Learning and Transfer Learning Approaches by Dong-Her Shih, Yi-Huei Wu, Ting-Wei Wu, Huei-Ying Chu, Ming-Hung Shih

“…Finally, the classification experiment is carried out through transfer learning to observe whether the evaluation metrics are further improved. According to the experimental results, this study effectively reduced the false negative rate (FNR) and false positive rate (FPR) of stroke prediction and improved the overall accuracy of stroke prediction through the category imbalance treatment and deep learning method.…”
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Research on the Prediction of Pipelines Corrosion Rate Based on GA-LSSVM by CHEN Yong-hong, SU Yong-sheng, HU Ping

“…Corrosion rate is an important characteristic parameter to reflect the corrosion dynamics process of pipeline.In order to accurately evaluate the long-term operation reliability and remaining life of pipeline, the prediction of corrosion rate is particularly important.Least squares support vector machine(LSSVM)is a method based on machine learning, which is often used in classification and prediction research.Since penalty parameters γ and kernel parameters σ2 are two important parameters of LSSVM, the value of these two parameters can only be obtained by experience in calculation, causing a great impact on the calculation results.In this paper, the genetic algorithm(GA)was used to optimize the parameters, the GA-LSSVM prediction model was built and the model was applied to the prediction of pipeline corrosion rate.Compared with the results of other prediction models, the results showed that the accuracy of GA-LSSVM model and prediction results were relatively higher, which could realize the prediction of pipeline corrosion rate.…”
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Predicting Scientific Research Impacts in Biotechnology by Machine Learning Algorithms by Ghasem Azadi Ahmadabadi

“…This study aims to analyze the interrelationships among variables influencing scientific outputs and to identify the most effective machine learning algorithms for predicting their scientific, social, and economic impacts.Methodology: The current research is applied in purpose and descriptive in method, utilizing a scientometric approach. …”
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Multi-Task Learning for mmWave Transceiver Beam Prediction by Muhammad Qurratulain Khan, Abdo Gaber, Mohammad Parvini, Philipp Schulz, Gerhard Fettweis

“…Performance evaluation over 3rd Generation Partnership Project (3GPP) defined performance indicators demonstrates that the proposed method outperforms existing independent task learning (ITL) and single task learning (STL) beam prediction designs. …”
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Estimation of Genetic Parameters and Prediction for Body Weight of Angus Cattle by Xiaofang Feng, Yu Wang, Jie Zhao, Qiufei Jiang, Yafei Chen, Yaling Gu, Penghui Guo, Juanshan Zheng

“…This study aimed to estimate genetic parameters using different models and predict body weight in Angus cattle to enhance the accuracy of genetic evaluation and support optimal breeding and selection programs. …”
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A Novel Hybrid Approach for UT1-UTC Ultra-Short-Term Prediction Utilizing LOD Series and Sum Series of LOD and First-Order-Difference UT1-UTC by Fei Ye, Minsi Ao, Ningbo Li, Rong Zeng, Xiangqiang Zeng

“…The evaluation demonstrated promising results: (1) The mean absolute errors (MAEs) of the proposed method range from 21 to 869 µs in 1–10-day UT1-UTC predictions. (2) Comparative analysis against zero-/first-/second-order-difference LS + AR and zero-/first-order-difference LS + MAR hybrid method reveals a substantial reduction in MAEs by an average of 54/64/44 µs, and 47/20 µs, respectively, with the proposed method. (3) Correspondingly, the proposed method achieves average improvement percentages of 17%/18%/15%, and 13%/3% in 1–10-day UT1-UTC predictions.…”
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Application of Deep Learning Framework for Early Prediction of Diabetic Retinopathy by Fahad Mostafa, Hafiz Khan, Fardous Farhana, Md Ariful Haque Miah

“…Moreover, the proposed clustering approach can find damaged locations in the retina using the developed Isolate Regions of Interest method, which achieves almost a 90% accuracy. These findings are useful for researchers and healthcare practitioners looking to investigate efficient and effective powerful models for predictive analytics to diagnose diabetic retinopathy.…”
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ANN prediction model of final construction cost at an early stage by Khalid S. Al-Gahtani, Abdullah M. Alsugair, Naif M. Alsanabani, Abdulmajeed A. Alabduljabbar, Abdulmohsen S. Almohsen

“…Previous studies developed models to predict final construction cost (FCC) values based on many inputs, which makes them difficult to use. …”
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Application of artificial neural networks in the prediction of sugarcane juice Pol by Anderson P. Coelho, João V. T. Bettiol, Alexandre B. Dalri, João A. Fischer Filho, Rogério T. de Faria, Luiz F. Palaretti

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Pre-Routing Slack Prediction Based on Graph Attention Network by Jinke Li, Jiahui Hu, Yue Wu, Xiaoyan Yang

“…Subsequently, inspired by the Nonlinear Delay Model (NLDM), the node embeddings are propagated through multiple levels by alternately applying net propagation layers and cell propagation layers. Evaluated on 21 real circuits, the framework achieved a 16.62% improvement in average <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> score for slack prediction and a 15.55% reduction in runtime compared to the state-of-the-art (SOTA) method.…”
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