CT-based radiomics deep learning signatures for non-invasive prediction of metastatic potential in pheochromocytoma and paraganglioma: a multicohort study by Yongjie Zhou, Yuan Zhan, Jinhong Zhao, Linhua Zhong, Fei Zou, Xuechao Zhu, Qiao Zeng, Jiayu Nan, Lianggeng Gong, Yongming Tan, Lan Liu

“…Results The support vector machine radiomics and 2D ResNet-50 models demonstrated good performance. …”
Get full text

Research on the Rapid Detection of Formaldehyde Emission From Wood-Based Panels Based on the AMSHKELM by Yinuo Wang, Huanqi Zheng, Hua Wang, Yucheng Zhou

“…The multi-strategy improved black-winged kite algorithm then optimizes key parameters of the successive variational mode decomposition (SVMD) and hybrid kernel extreme learning machine (HKELM). …”
Get full text

Controlling Product Properties in Forming Processes Using Reinforcement Learning—An Application to V-Die Bending by Ciarán-Victor Veitenheimer, Dirk Alexander Molitor, Viktor Arne, Peter Groche

“…For this reason, numerous scientists have addressed this issue by developing control approaches like self-optimizing machine tools or the control of product properties. …”
Get full text

Classification of Leafy Diseases of Brassica Rapa (Chinese Cabbage) using Selected Parameters in an Uncontrolled Environment via Image Processing by Jonalyn G. Ebron, Giane Roldan B. Apuada, Dante Lean R. Parra, Jan Peter Fran T. Virtucio

“…Systematic experimentation determined optimal environmental parameters for image capture, including distance, angle, time, and brightness. …”
Get full text

Unraveling the power of radiomics: prediction and exploration of lymph node metastasis in stage T1/2 esophageal squamous cell carcinoma by Yu Zhang, Long Liu, Mengyu Han, Linrui Li, Qibing Wu, Xin Wang

“…We retrospectively analyzed 374 surgically treated ESCC patients from two centers, employing six machine-learning algorithms to derive an optimal radiomics score. …”
Get full text

Detection and Tracking of Environmental Sensing System for Construction Machinery Autonomous Operation Application by Junyi Chen, Qipeng Cai, Xinhai Hu, Qihuai Chen, Tianliang Lin, Haoling Ren

“…The superiority of the optimized detection model is verified through real-time target detection tests at different speeds and under different states. …”
Get full text

Artificial Intelligence for Unstructured Data Processing by Yohanes Bowo Widodo, Febrianti Widyahastuti, Mohammad Narji, Sondang Sibuea

“…By using deep learning models and advanced algorithms, AI can identify patterns and relationships in complex data, thereby providing deeper insights for better decision making. …”
Get full text

Convolutional network learning of self-consistent electron density via grid-projected atomic fingerprints by Ryong-Gyu Lee, Yong-Hoon Kim

“…The effectiveness of DeepSCF is demonstrated using a complex carbon nanotube-based DNA sequencer model. This work evidences that the nearsightedness in electronic structure can be optimally represented via the spatial locality in CNNs, offering insight into the success of various machine learning-based atomistic materials simulations.…”
Get full text

The Comparison of Activation Functions in Feature Extraction Layer using Sharpen Filter by Oktavia Citra Resmi Rachmawati, Ali Ridho Barakbah, Tita Karlita

“…These findings contribute to optimizing CNN architectures, offering a valuable reference for future work in image processing and other machine-learning applications that rely on feature extraction layers. …”
Get full text

Optimising the Selection of Input Variables to Increase the Predicting Accuracy of Shear Strength for Deep Beams by Mohammed Majeed Hameed, Faidhalrahman Khaleel, Mohamed Khalid AlOmar, Siti Fatin Mohd Razali, Mohammed Abdulhakim AlSaadi

“…One of the major obstacles in building an accurate prediction model is optimising the input variables. Therefore, developing an efficient algorithm to select the optimal input parameters that have the highest information content to represent the target and minimise redundant data is very important. …”
Get full text

Combined Prediction of Dust Concentration in Opencast Mine Based on RF-GA-LSSVM by Shuangshuang Xiao, Jin Liu, Yajie Ma, Yonggui Zhang

“…Initially, the random forest (RF) algorithm is employed to identify key features from the meteorological and dust concentration data collected on site, ultimately selecting five indicators—temperature, humidity, stripping amount, wind direction, and wind speed—as the input variables for the prediction model. Next, the data are split into a training set and a test set at a 7:3 ratio, and the genetic algorithm (GA) is applied to optimize the least squares support vector machine (LSSVM) model for predicting dust concentration in opencast mines. …”
Get full text

Explaining the high skill of reservoir computing methods in El Niño prediction by F. Guardamagna, F. Guardamagna, C. Wieners, C. Wieners, H. A. Dijkstra, H. A. Dijkstra

“…Using a conditional nonlinear optimal perturbation (CNOP) approach, we compare the initial error propagation in a deterministic Zebiak–Cane (ZC) ENSO model and that in an RC trained on synthetic observations derived from a stochastic ZC model. …”
Get full text

Propiedades estructurales de ejes huecos y sólidos con una grieta plana//Structural proprieties of hollow and whole axles, with a flat crack by Jon‐Ariza De‐Miguel, Sergiy S. Gutyrya, Yuri M. Jomyak

Get full text

Exploiting historical agronomic data to develop genomic prediction strategies for early clonal selection in the Louisiana sugarcane variety development program by Dipendra Shahi, James Todd, Kenneth Gravois, Anna Hale, Brayden Blanchard, Collins Kimbeng, Michael Pontif, Niranjan Baisakh

“…When both NS and TRS, which can be available as early as stage 2, were considered in a multi‐trait selection model, the PA for SY in stage 5 could increase up to 0.66 compared to 0.30 with a single‐trait model. …”
Get full text

Chinese Clinical Named Entity Recognition With Segmentation Synonym Sentence Synthesis Mechanism: Algorithm Development and Validation by Jian Tang, Zikun Huang, Hongzhen Xu, Hao Zhang, Hailing Huang, Minqiong Tang, Pengsheng Luo, Dong Qin

“…In recent years, with the continuous development of machine learning, deep learning models have replaced traditional machine learning and template-based methods, becoming widely applied in the CNER field. …”
Get full text

摆线齿准双曲面齿轮模拟加工系统的界面设计及相关检验 by 李权才, 王星星, 付丽, 乔雪涛

“…The interface design principle and constitution of simulation machining system of epicycloidal hypoid gears is briefly introduced.The interface content of simulation machining system of epicycloidal hypoid gear is described in detail,including interface booting,data input,gear parameters design,feasibility test,strength verification,result output,help and prompt.The solid three-dimensional model of cutter tool and gear blank can be established by the simulation machining system of epicycloidal hypoid gear,it can be used to check and analyze the cutter interference,tooth surface scratches and gear ridge grooves.A theoretical basis for optimization design and manufacture of epicycloidal hypoid gears is provided.…”
Get full text

DriftShield: Autonomous Fraud Detection via Actor-Critic Reinforcement Learning With Dynamic Feature Reweighting by Jialei Cao, Wenxia Zheng, Yao Ge, Jiyuan Wang

“…Traditional rule-based methods and static machine learning models require frequent manual updates, failing to autonomously adapt to emerging fraud strategies. …”
Get full text

An overview of ahead geological detection technologies in tunnels by Dingchao Chen, Xiangyu Wang, Jianbiao Bai, Yuan Chu, Xian Wang, Jiaxin Zhao, Menglong Li

“…Furthermore, this paper explores the emerging role of intelligent technologies, including artificial intelligence (AI) and machine learning (ML), in enhancing real-time data analysis, predictive modeling, and decision-making processes in tunnel geological detection. …”
Get full text

AI-Driven Belt Failure Prediction and Prescriptive Maintenance with Motor Current Signature Analysis by João Paulo Costa, José Torres Farinha, Mateus Mendes, Jorge O. Estima

“…The methodology involves the collection and pre-processing of raw spectral data from industrial assets, followed by the training and optimization of predictive models. The effectiveness of the approach is demonstrated through extensive testing against real-world data, showcasing its ability to accurately forecast belt failures and enable proactive maintenance strategies. …”
Get full text

A review of the critical conditions required for effective hole cleaning while horizontal drilling by Amir Shokry Youssef, Ahmed Abdulhamid Mahmoud, Salaheldin Elkatatny, Talal Al Shafloot

“…It discusses different methodologies, including empirical correlations, experimental studies, machine learning models, and modeling techniques, used to assess hole cleaning efficiency. …”
Get full text