基于小波包分析和改进自适应遗传算法的齿轮故障诊断 by 戚晓利, 潘紫微

“…The gear is one of the most important elements in the trainsmission system.Aiming at the fault diagnosis problem of gear,a new gear fault diagnosis method that based on wavelet packet analysis and improving adaptive genetic algorithm is put forward,and which is on the basis of synthesizing the advantages of wavelet-packet noise reduction,fuzzy logic,high-order BP neural network and improving adaptive genetic algorithm.The experimental results show that this method has advantage over the routine method in classification precision and training total error control.…”
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Personalized prediction model generated with machine learning for kidney function one year after living kidney donation by Rikako Oki, Toshihio Hirai, Kazuhiro Iwadoh, Yu Kijima, Hiroyuki Hashimoto, Yasunori Nishimura, Taro Banno, Kohei Unagami, Kazuya Omoto, Tomokazu Shimizu, Junichi Hoshino, Toshio Takagi, Hideki Ishida, Toshihito Hirai

“…Abstract Living kidney donors typically experience approximately a 30% reduction in kidney function after donation, although the degree of reduction varies among individuals. …”
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Approximation-Aware Training for Efficient Neural Network Inference on MRAM Based CiM Architecture by Hemkant Nehete, Sandeep Soni, Tharun Kumar Reddy Bollu, Balasubramanian Raman, Brajesh Kumar Kaushik

“…Existing compression techniques often neglect approximation errors incurred during training. This work proposes approximation-aware-training, in which group of weights are approximated using a differential approximation function, resulting in a new weight matrix composed of approximation function's coefficients (AFC). …”
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Estimating Maxillary Sinus Volume Using Smartphone Camera by Christoforos Meliadis, Emily Feng, Ezekiel Johnson, Wendy Zhu, Paramesh Gopi, Vivek Mohan, Peter H. Hwang, Jacob Johnson, Bryant Y. Lin

“…<italic>Conclusions:</italic> This method aligns with the growing focus on telemedicine, presenting significant reductions in healthcare costs and radiation exposure from CT scans. …”
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Design and Analysis of Modular Neural Network-Based PI-Controller Ensemble With Karush-Kuhn-Tucker Conditions for Grid-Connected Photovoltaic Systems Under Ground Fault Conditions by Burla Sridhar, Soorya Prakash Shukla, Ritesh Dash, Arpan Dwivedi, G. C. Biswal, Nivedita Singh, Mukesh Kumar Chandrakar, Kalvakurthi Jyotheeswara Reddy, Bhabasis Mohapatra, Binod Kumar Sahu

“…Experimental validation through hardware implementation demonstrates a 45% reduction in steady-state error, a 30% reduction in THD, and improved reactive power compensation and grid synchronization. …”
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Evaluating LiDAR technology for accurate measurement of tree metrics and carbon sequestration by Suradet Tantrairatn, Auraluck Pichitkul, Nutchanan Petcharat, Pawarut Karaked, Atthaphon Ariyarit

“…Carbon credits play a crucial role in mitigating climate change by incentivizing reductions in greenhouse gas emissions and providing a measurable way to balance carbon dioxide output, fostering sustainable environmental practices. …”
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Spiking Neural Networks Optimized by Improved Cuckoo Search Algorithm: A Model for Financial Time Series Forecasting by Panke Qin, Yongjie Ding, Ya Li, Bo Ye, Zhenlun Gao, Yaxing Liu, Zhongqi Cai, Haoran Qi

“…Furthermore, ICS-SNN significantly outperforms mainstream models such as Long Short-Term Memory (LSTM) and Backpropagation (BP) networks, reducing prediction errors by 10.8% (MAE) and 34.9% (MSE), respectively, without compromising computational efficiency. …”
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Monitoring 5G Backhaul: An In-Band Telemetry Approach for Quality of Service by Eurico Dias, Duarte Raposo, Miguel Luis, Pedro Rito, Susana Sargento

“…The results demonstrate that this approach exhibits minimal measurement errors when assessing the throughput, latency, and Packet Error Rate (PER) of mmWave links. …”
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Evaluating quasi-experimental approaches for estimating epidemiological efficacy of non-randomised field trials: applications in Wolbachia interventions for dengue by Jo Yi Chow, Lin Geng, Somya Bansal, Borame Sue Lee Dickens, Lee Ching Ng, Ary Anthony Hoffmann, Jue Tao Lim

“…Results Wolbachia interventions in Singapore, Malaysia, and Brazil were associated to significant decreases in dengue incidence, with reductions ranging from 48.17% to 69.19%. IEs varied with location and duration. …”
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Data-Driven Computational Methods in Fuel Combustion: A Review of Applications by Jacek Lukasz Wilk-Jakubowski, Lukasz Pawlik, Damian Frej, Grzegorz Wilk-Jakubowski

“…ANN-based models achieved high accuracy in predicting NO_x emissions and flame speed, with some studies reporting mean absolute errors below 5%. GA methods demonstrated effectiveness in fuel blend optimization and geometry design, achieving emission reductions of up to 30% in experimental setups. …”
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Script-Based Material and Geometrical Modeling of Steel–Concrete Composite Connections for Comprehensive Analysis Under Varied Configurations by Dániel Gosztola, Péter Grubits, János Szép, Majid Movahedi Rad

“…Spacings of 2d, 3d, and 4d demonstrated overlapping effects, leading to significant performance reductions, as indicated by comparisons of ultimate load and force–displacement responses. …”
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Using de novo assembly to identify structural variation of eight complex immune system gene regions. by Jia-Yuan Zhang, Hannah Roberts, David S C Flores, Antony J Cutler, Andrew C Brown, Justin P Whalley, Olga Mielczarek, David Buck, Helen Lockstone, Barbara Xella, Karen Oliver, Craig Corton, Emma Betteridge, Rachael Bashford-Rogers, Julian C Knight, John A Todd, Gavin Band

“…Validation of our assembly using k-mer based and alignment approaches suggests that it has high accuracy, with estimated base-level error rates below 1 in 10 kb, although we identify a small number of remaining structural errors. …”
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Convergence rates of eigenvalue problems in perforated domains: the case of small volume by Shen Zhongwei, Zhuge Jinping

“…Our approach uses a known reduction to a degenerate elliptic eigenvalue problem for which a quantitative analysis is carried out.…”
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Daily runoff forecasting using novel optimized machine learning methods by Peiman Parisouj, Changhyun Jun, Sayed M. Bateni, Essam Heggy, Shahab S. Band

“…In the Carson River, the GB model achieves the highest forecasting accuracy, which is significantly improved by ARO, resulting in a 24.8 % reduction in root mean square error (RMSE). The MLP model also benefits notably from ARO, with RMSE improvements of 4.8 % and a substantial 48.9 % reduction in mean absolute error (MAE). …”
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Explainable AutoML models for predicting the strength of high-performance concrete using Optuna, SHAP and ensemble learning by Muhammad Salman Khan, Tianbo Peng, Tianbo Peng, Muhammad Adeel Khan, Asad Khan, Mahmood Ahmad, Mahmood Ahmad, Kamran Aziz, Mohanad Muayad Sabri Sabri, N. S. Abd EL-Gawaad

“…For compressive strength, it reduced the Mean Absolute Error (MAE) by 87.69% and the Root Mean Squared Error (RMSE) by 71.93%. …”
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The effect of resizing on the natural appearance of scintigraphic images: an image similarity analysis by Siraj Ghassel, Amir Jabbarpour, Jochen Lang, Eric Moulton, Eric Moulton, Ran Klein, Ran Klein, Ran Klein, Ran Klein

“…For downsampling, both linear interpolation and sliding window summation yielded similar outcomes for a reduction factor of 2. However, for a reduction factor of 4, only sliding window summation resulted in image similarity metrics in agreement with those at the target grid size.ConclusionsThe study underlines the importance of applying appropriate resizing techniques in nuclear medical imaging to produce realistic images at the target grid size.…”
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Transforming Prediction into Decision: Leveraging Transformer-Long Short-Term Memory Networks and Automatic Control for Enhanced Water Treatment Efficiency and Sustainability by Cheng Qiu, Qingchuan Li, Jiang Jing, Ningbo Tan, Jieping Wu, Mingxi Wang, Qianglin Li

“…Experimental validation on NH₃-N datasets from the SBR system reveals that the proposed model significantly outperforms existing advanced methods in terms of root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R²). …”
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Navigating cognitive boundaries: the impact of CognifyNet AI-powered educational analytics on student improvement by Mrim M. Alnfiai, Faiz Abdullah Alotaibi, Mona Mohammed Alnahari, Nouf Abdullah Alsudairy, Asma Ibrahim Alharbi, Saad Alzahrani

“…Evaluated through rigorous 5-fold cross-validation on a comprehensive dataset of 1200 anonymized student records and validated across multiple educational platforms, including UCI Student Performance and Open University Learning Analytics datasets, CognifyNet demonstrates superior performance over conventional approaches, achieving 10.5% reduction in mean squared error and 83% reduction in mean absolute error compared to baseline random forest models, with statistical significance confirmed through paired t-tests (p < 0.01). …”
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Design of an Iterative Method for Time Series Forecasting Using Temporal Attention and Hybrid Deep Learning Architectures by Yuvaraja Boddu, A. Manimaran

“…This configuration adeptly extracts both spatial and temporal features, yielding a 15% reduction in prediction error across various datasets. …”
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ACSAformer: A crime forecasting model based on sparse attention and adaptive graph convolution by Zhenkai Qin, Baozhong Wei, Baozhong Wei, Caifeng Gao, Caifeng Gao, Feng Zhu, Feng Zhu, Weiqi Qin, Weiqi Qin, Qian Zhang

“…Specifically, on the DS1 dataset, the proposed model achieved a 17.6% reduction in Mean Squared Error (MSE) and a 9.2% reduction in Mean Absolute Error (MAE).DiscussionThese findings confirm that ACSAformer not only improves predictive accuracy and robustness but also offers better computational efficiency, showcasing its potential for application in complex spatiotemporal tasks such as crime forecasting.…”
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