The Discriminants of Long and Short Duration Failures in Fulfillment Sortation Equipment: A Machine Learning Approach by Abed Mutemi, Fernando Bacao

“…It has also resulted in a better understanding of the flaws that cause long-term failures, which is now being used to build more sophisticated failure prediction and risk-mitigation systems for fulfillment equipment.…”
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Deployment and Operation of Battery Swapping Stations for Electric Two-Wheelers Based on Machine Learning by Yu Feng, Xiaochun Lu

“…Then, on a 3000 m grid scale, a prediction model of BSS quantity with random forest, support vector regression, and gradient-boosting decision tree algorithm was built. …”
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MiR-124 Radiosensitizes human colorectal cancer cells by targeting PRRX1. by Yuqin Zhang, Lin Zheng, Jing Huang, Fei Gao, Xiaoshan Lin, Lian He, Dan Li, Zhijun Li, Yi Ding, Longhua Chen

“…We identified PRRX1, a new EMT inducer and stemness regulator as a novel direct target of miR-124 by using target prediction algorithms and luciferase assay. PRRX1 knockdown could sensitize CRC cells to IR similar to the effects caused by miR-124. …”
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Clustering and Vectorizing Acoustic Emission Events of Large Infrastructures’ Normal Operation by Theocharis Tsenis, Vassilios Kappatos

“…Latest machine learning outlier detection algorithms, like One-Class Support Vector Machine, Variational Auto-Encoder, and others, were incorporated, producing a vector of confidence on upcoming out-of-the-normal gate operation and failure prediction, achieving detection contrast enhancement on out-of-the-normal operation points up to 400%.…”
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Leveraging Feature Fusion of Image Features and Laser Reflectance for Automated Fish Freshness Classification by Caner Balım, Nevzat Olgun, Mücahit Çalışan

“…This work presents an original contribution by demonstrating, for the first time, the effectiveness of combining low-cost laser sensing and deep visual features for freshness prediction, with potential for real-time mobile deployment.…”
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The siRNA Non-seed Region and Its Target Sequences Are Auxiliary Determinants of Off-Target Effects. by Piotr J Kamola, Yuko Nakano, Tomoko Takahashi, Paul A Wilson, Kumiko Ui-Tei

“…These observations may prove useful as parameters for off-target prediction algorithms and improve siRNA 'specificity' design rules.…”
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A Comparison of the Impacts of Radiosonde and AMSU Radiance Observations in GSI Based 3DEnsVar and 3DVar Data Assimilation Systems for NCEP GFS by Govindan Kutty, Xuguang Wang

“…The impact of observations can be dependent on many factors in a data assimilation (DA) system including data quality control, preprocessing, skill of the model, and the DA algorithm. The present study focuses on comparing the impacts of observations assimilated by two different DA algorithms. …”
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Accounting Management and Optimizing Production Based on Distributed Semantic Recognition by Ruina Guo, Shu Wang, Guangsen Wei

“…Additionally, a semantic recognition module is introduced to process the voice signals and generate prediction results. By leveraging extensive accounting management and production data for learning and analysis, the algorithm automatically uncovers patterns and laws within the data, extracting valuable information. …”
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Expanding kinetoplastid genome annotation through protein structure comparison. by Juan Manuel Trinidad-Barnech, José Sotelo-Silveira, Darío Fernández Do Porto, Pablo Smircich

“…Recently, significant advancements have been made in in-silico protein structure prediction and algorithms for rapid and precise large-scale protein structure comparisons. …”
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Blind HDR image quality assessment based on aggregating perception and inference features by Donghui Wan, Xiuhua Jiang, Qiangguo Yu

“…These gradient similarity maps and deep feature maps are subsequently aggregated for quality prediction using support vector regression (SVR). Experimental results demonstrate that the proposed method achieves outstanding performance, outperforming other state-of-the-art HDR IQA metrics.…”
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Risk Measurement of TAVR Surgical Complications Based on Unbalanced Multilabel Classification Approaches by Yue Zhang, Yuantao Xie

“…The model used in this paper bridges the gap between medical risk prediction and insurance actuarial science, provides a practical data modelling foundation and algorithmic support for the future development of post-operative complication insurance products that precisely align with clinical risk.…”
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Water Level Detection and Flood Early Warning System Using Image Processing by Muhammad Akmal Ilmi, I Komang Somawirata, Michael Ardita

“…The YOLO (You Only Look Once) algorithm is utilized for real-time object detection, such as water levels, aiming to enhance prediction accuracy. …”
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Research on Air Temperature Inversion Method Based on Land Surface Temperature of Different Land Surface Cover by Rui Fang, Xiaofang Shan, Qinli Deng

“…Due to the characteristics of the algorithms, it is recommended to prioritize random forest for prediction when the sample range covers the observed data range and to use BP neural networks when it does not. …”
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Machine Learning Applied to Improve Prevention of, Response to, and Understanding of Violence Against Women by Mariana Carolyn Cruz-Mendoza, Roberto Angel Melendez-Armenta, Juana Canul-Reich, Julio Muñoz-Benítez

“…The methodology integrates Random Forest (RF) and Gradient Boosting Classifier (GBC) algorithms to classify IPV cases by leveraging historical data for predictive analysis. …”
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Artificial intelligence in prostate cancer by Wei Li, Ruoyu Hu, Quan Zhang, Zhangsheng Yu, Longxin Deng, Xinhao Zhu, Yujia Xia, Zijian Song, Alessia Cimadamore, Fei Chen, Antonio Lopez-Beltran, Rodolfo Montironi, Liang Cheng, Rui Chen, Yuanyuan Ji

“…The advancement of artificial intelligence (AI), particularly the utilization of deep learning (DL) algorithms, has brought about substantial progress in assisting the diagnosis, treatment, and prognosis prediction of PCa. …”
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Utilising AI technique to identify depression risk among doctoral students by Changhong Teng, Chunmei Yang, Qiushi Liu

“…Based on the data from the 2019 Nature Global Doctoral Student Survey, we first screened 13 highly relevant features from a total of 37 features potentially related to the risk of depression among doctoral students by Random Forest algorithm. Subsequently, we trained the optimal prediction model to predict the doctoral students with depression risk using a Multilayer Perceptron (MLP), achieving an accuracy of 89.09% on the test set. …”
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Shield tunneling efficiency and stability enhancement based on interpretable machine learning and multi-objective optimization by Wenli Liu, Yang Chen, Tianxiang Liu, Wen Liu, Jue Li, Yangyang Chen

“…These metrics collectively reflect the model’s excellent performance in prediction accuracy, ability to explain data variability, and control of prediction bias. …”
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Artificial intelligence‐driven insights: Precision tracking of power plant carbon emissions using satellite data by Zeqi Zhang, Di Leng, Yingjie Li, Xuanang Gui, Yuheng Cheng, Junhua Zhao, Zhengwen Zhang, Amer M. Y. M. Ghias

“…This study uses a new sophisticated pipeline that combines tropospheric monitoring instrument satellite data, power plant attributes, and advanced artificial intelligence algorithms to build a predictive carbon emission model. …”
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Vibration, current, torque, RPM dataset for multiple fault conditions in industrial-scale electric motors under randomized speed and load variationsMendeley DataMendeley DataMendel... by Wonho Jung, Junho Kim, Kangmin Jang, Sung-Hyun Yun, Daeguen Lim, Minje Jin, Yong-Hwa Park

“…Unlike existing public datasets that often assume constant speed or isolated fault types, this dataset uniquely incorporates multi-fault, multi-severity conditions under randomized speed/load variations, filling critical gaps in real-world applicability for robust fault diagnosis algorithms. The dataset enables robust evaluation of machine learning models and signal processing algorithms for fault detection, condition monitoring, and predictive maintenance in rotating machinery. …”
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StrideHD: A Binary Hyperdimensional Computing System Utilizing Window Striding for Image Classification by Dehua Liang, Jun Shiomi, Noriyuki Miura, Hiromitsu Awano

“…Our evaluation also shows that compared with two popular HD algorithms, the singlepass StrideHD model achieves a 27.6<inline-formula> <tex-math notation="LaTeX">$\times$ </tex-math></inline-formula> and 8.2<inline-formula> <tex-math notation="LaTeX">$\times$ </tex-math></inline-formula> reduction in inference memory cost without hurting the classification accuracy, while the iterative mode further provides 8.7<inline-formula> <tex-math notation="LaTeX">$\times$ </tex-math></inline-formula> memory efficiency. …”
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