Machine Learning Creates a Simple Endoscopic Classification System that Improves Dysplasia Detection in Barrett’s Oesophagus amongst Non-expert Endoscopists by Vinay Sehgal, Avi Rosenfeld, David G. Graham, Gideon Lipman, Raf Bisschops, Krish Ragunath, Manuel Rodriguez-Justo, Marco Novelli, Matthew R. Banks, Rehan J. Haidry, Laurence B. Lovat

“…These generate a simple algorithm to accurately predict dysplasia. Once taught to non-experts, the algorithm significantly improves their rate of dysplasia detection. …”
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Power System Operation Stability Assessment Method Based on Deep Convolutional Neural Network by Jinman Luo, Yuqing Li, Qile Wang, Liyuan Liu

“…METHODS: We adopt the hierarchical real-time prediction model to evaluate the stability and instability of the determined stable samples and unstable samples, thereby improving the timeliness and accuracy of transient evaluation. …”
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Small fixed-wing UAV Precision Aerial Drop capability development by Antonić Vuk, Trifković Milenko, Molović Vojimir, Milenković-Babić Miodrag

“…Precision aerial drop software module is presented, focusing on the automated payload drop algorithm with continually calculated impact point prediction (CCIP) and UAV guidance to the continually calculated release point (CCRP). …”
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Forecast-Aided Converter-Based Control for Optimal Microgrid Operation in Industrial Energy Management System (EMS): A Case Study in Vietnam by Yeong-Nam Jeon, Jae-ha Ko

“…The forecasted load data is then used to optimize charge/discharge schedules for energy storage systems (ESS) using a Particle Swarm Optimization (PSO) algorithm. The strategy is validated using real-site data from a Vietnamese industrial complex, where the proposed method demonstrates enhanced load prediction accuracy, cost-effective ESS operation, and multi-microgrid flexibility under weather variability. …”
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Novel machine learning driven design strategy for high strength Zn Alloys optimization with multiple constraints by Chenfeng Pan, Wenwen Lin, Jianxing Zhou, Wei Jian, Ka Chun Chan, Yuk Lun Chan, Lu Ren

“…Furthermore, a ML-based Zn alloy composition design system (ZACDS) was proposed by integrating the Bayesian optimization algorithm. A novel high-strength Zn alloy was successfully designed using ZACDS, demonstrating good agreement between predicted and experimental mechanical properties. …”
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Ultrafast silicon photonic reservoir computing engine delivering over 200 TOPS by Dongliang Wang, Yikun Nie, Gaolei Hu, Hon Ki Tsang, Chaoran Huang

“…Experimental results demonstrate state-of-the-art performance in prediction, emulation, and classification tasks across various machine learning applications. …”
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A Reduced-Order Model for Active Suppression Control of Vehicle Longitudinal Low-Frequency Vibration by Donghao Hao, Changlu Zhao, Ying Huang

“…Establishing a prediction model, with linearity and few dof (degree of freedom), is a key step for the design of a control algorithm based on the modern control theory. …”
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Graph Knowledge Structure for Attentional Knowledge Tracing With Self-Supervised Learning by Zhaohui Liu, Sainan Liu, Weifeng Gu

“…In addition, we incorporate the improved representation algorithm into the loss function to handle data sparsity, thereby improving prediction accuracy. …”
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RIM-MAC:a receiver initiated multi-session MAC protocol for underwater acoustic networks by Chao LI, Yong-jun XU, Bo-yu DIAO, Feng WANG, Qi WANG

“…,neighboring nodes’ propagation delay maps and expected transmission schedules) to launch multiple simultaneous sessions.RIM-MAC increases the chances of concurrent transmissions in the underwater channel and achieve the channel reuse at both the sender and receiver side,improving the overall throughput at least 36% compared to the typical MAC protocols.Moreover,a traffic prediction based adaptive data polling scheme named fair traffic algorithm (FTA) guarantees fairness across multiple contending nodes.Extensive simulation results have confirmed that the RIM-MAC protocol outperforms existing MAC protocols and provides fair medium access in representative long propagation delay scenarios.…”
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Online State of Charge Estimation of Lithium-Ion Cells Using Particle Filter-Based Hybrid Filtering Approach by Ming Zhang, Kai Wang, Yan-ting Zhou

“…However, if the system is a moving EV during rapid acceleration or regenerative braking or when using heating or air conditioning, most of the existing works suffer from poor prediction of state and state estimation error covariance, leading to the problem of accuracy degeneracy of the algorithm. …”
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Network pharmacognosy of Galphimia glauca: Mapping the molecular landscape of a traditional Mexican medicinal plant. by María José Cambero Acosta, Guillermo de Anda-Jáuregui

“…Using network pharmacognosy, we analyzed the interactions of G. glauca's bioactive compounds, Galphimines A-I, with human protein targets. SwissTargetPrediction identified 214 unique protein targets across the galphimines, revealing a core-periphery structure in a bipartite network where 41 targets are shared among all compounds. …”
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Cotton Disease Detection Using TensorFlow Machine Learning Technique by Sandeep Kumar, Rajeev Ratan, J. V. Desai

“…In this paper, the CNN algorithm is used to create the prediction model by leveraging the TensorFlow’s Keras API. …”
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Discover the road sequences of bus lines using bus stop information and historical bus locations by Ying Zhou, Guiyuan Jiang, Guifeng Jiang

“…Discovering the road sequences traveled by the bus lines is of great importance for public transport management, such as bus journey time prediction and multimodal travel route recommendation, as the road sequence provides important information on roadway characteristics such as the number of intersections and traffic signals, road type, and number of lanes. …”
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Uncertainty evaluation of surface profile measurement error based on adaptive sparse grid polynomial chaos expansion by Ke Zhang, Xinya Zheng, Ruiyu Zhang

“…Numerical and experimental validations based on the car door outer surface demonstrate that compared to the ISO standard method, this method achieves a mean prediction error of less than 0.3% and a standard deviation relative error of less than 0.5% with a reduction of 99.6% in the number of sample calculation points. …”
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Optimization of Oil Yield from the Macro Algae Spirogyra by Solvent Extraction Process Using RSM and ANN by S. Aravind, Debabrata Barik, Nagaraj Ashok

“…The coefficient of determination, R2 of 98.92%, and the mean absolute percentage deviation (MAPD) of 0.492% for ANN revealed that the current model created with a network topology of 3 : 11 : 1 with tansig (hyperbolic tangent sigmoid) transfer function in the input layer and purelin (pure linear) transfer function in the output layer trained with trainlm (Levenberg–Marquardt) algorithm found to provide the optimal solution with better accuracy in prediction of the output. …”
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A reversible database watermarking method non-redundancy shifting-based histogram gaps by Yan Li, Junwei Wang, Xiangyang Luo

“…First, an integer data histogram is constructed with the absolute value of the prediction error of the data as a variable. Second, the positional relationship between each column and the gap in the histogram is analyzed to find out all the columns adjacent to the gap. …”
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A Method of Abnormal Behavior Detection for Safety Site Surveillance by Wenjing Wang, Yangyang Zhang, QingE Wu

“…The experimental results show that the accuracy of the abnormal behavior detection method proposed in this paper can better discern the beginning and end of behavior occurrence, abnormal behavior prediction, behavior online detection, and other aspects from the visual image data stream, and the correct detection rate is more than 90%, which reduces the consumption of human resources. …”
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Multiobjective Reliability-Based Design Optimization of Recycled Aggregates Used in Corrosive Environment Based on Response Surface Modelling by Abdol Ghaium Dehvari, Mohsen Khazaei, Mohammad Reza Sohrabi, Mahmoud Miri

“…The effects of some environmental parameters including humidity percentage, temperature, and chloride concentration were also examined on concrete electrical resistance. The prediction models for mechanical and durability parameters of concrete were obtained using the response surface method. …”
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Modeling Spatial Data with Heteroscedasticity Using PLVCSAR Model: A Bayesian Quantile Regression Approach by Rongshang Chen, Zhiyong Chen

“…We apply a Bayesian quantile regression (BQR) of the partially linear varying coefficient spatial autoregressive (PLVCSAR) model for spatial data to improve the prediction of performance. It can be used to capture the response of covariates to linear and nonlinear effects at different quantile points. …”
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Research on intelligent routing with VAE-GAN in the internet of body. by Song Qian, Tianping Zhang, Siping Hu

“…The traditional artificial intelligence routing algorithm cannot deal with the low model prediction accuracy and poor generalization ability caused by large noise and small data volume. …”
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