An Improved Quantum-Behaved Particle Swarm Optimization Algorithm Combined with Reinforcement Learning for AUV Path Planning by HanBin Zhang, XianPeng Shi

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Deep learning-based semantic segmentation for rice yield estimation by analyzing the dynamic change of panicle coverage by Hyeok-Jin Bak, Eun-Ji Kim, Ji-Hyeon Lee, Sungyul Chang, Dongwon Kwon, Woo-Jin Im, Woon-Ha Hwang, Jae-Ki Chang, Nam-Jin Chung, Wan-Gyu Sang

“…Time-series panicle coverage data, extracted from the segmented images, were fitted to a piecewise function to model their growth and decline dynamics. …”
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Targeted urinary metabolomics combined with machine learning to identify biomarkers related to central carbon metabolism for IBD by Miao-Lin Lei, Guan-Wei Bi, Xiao-Lin Yin, Xiao-Lin Yin, Yue Wang, Yue Wang, Yue Wang, Zi-Ru Sun, Zi-Ru Sun, Zi-Ru Sun, Xin-rui Guo, Hui-peng Zhang, Xiao-han Zhao, Feng Li, Feng Li, Yan-Bo Yu

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Leveraging Artificial Intelligence and Machine Learning for Characterizing Protein Corona, Nanobiological Interactions, and Advancing Drug Discovery by Turkan Kopac

“…Future developments may include advanced deep-learning models and multimodal data integration to enhance protein function prediction. …”
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Analysis Method of Agricultural Total Factor Productivity Based on Stochastic Block Model (SBM) and Machine Learning by Yanzi Li, Cai Chen, Fuqiang Liu, Jian Wang

“…As a result, this paper proposes an analytical approach for agricultural total factor productivity based on the stochastic block model (SBM), which combines the benefits of statistics and machine learning. It uses the SBM direction distance function and the Luenberger productivity index to measure the agricultural efficiency, total factor productivity, and their components. …”
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Three-Dimensional Extended Target Tracking and Shape Learning Based on Double Fourier Series and Expectation Maximization by Hongge Mao, Xiaojun Yang

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Probabilistic Fatigue Assessment Based on Bayesian Learning for Wind-Excited Long-Span Bridges Installed with WASHMS by Zhi-Wei Chen, Xiao-Ming Wang

“…Bayesian learning approach is then applied to determine the probabilistic fatigue damage model. …”
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Development of Crisis and Emergency Risk Communication (CERC) activities and an Evaluation of their Impact on Learning: Geoscience students’ perceptions by Servel Miller, Derek France, Katharine Welsh

“…This requires a clear plan and set of principles that enables the stakeholders to function effectively in a crisis. Students hoping to work in the emergency and natural hazard management field need to develop these skills. …”
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MODERNIZING MOUNTAIN EDUCATION: ESTABLISHING A TECHNOLOGY-BASED ISLAMIC BOARDING SCHOOL TO ENHANCE LEARNING QUALITY by Agung Wijaksono, Albadri Albadri

“…Beyond education, these institutions also function as centers for social and economic development, nurturing the character and skills of students. …”
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A Case of “Subtle” Missed Complex Elbow Dislocation - Learning from Mistakes Made on Plain Radiography by Asad Khan, Yasir Salam Siddiqui, Mohd. Baqar Abbas, Mohd. Arshad Rahman, Pranjal Agrawal, Nikhil Sharma

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Automatic speech analysis combined with machine learning reliably predicts the motor state in people with Parkinson’s disease by Tabea Thies, Elisa Mallick, Johannes Tröger, Ebru Baykara, Doris Mücke, Michael T. Barbe

“…Abstract It is still under debate whether levodopa treatment improves speech functions in Parkinson’s disease (PD). Therefore, speech functions of people with PD were compared in medication-OFF condition (withdrawal of PD medication for at least 12 h) and medication-ON condition (after receiving 200 mg of soluble levodopa). …”
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FCMI-YOLO: An efficient deep learning-based algorithm for real-time fire detection on edge devices. by Junjie Lu, Yuchen Zheng, Liwei Guan, Bing Lin, Wenzao Shi, Junyan Zhang, Yunping Wu

“…Finally, the Inner-DIoU loss function is proposed to optimize bounding box regression. …”
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Methodological proposal for the incorporation of the clinical simulation cases to the system of teaching-learning methods in the Pediatric’s rotacional internship by Luis Alberto Corona Martínez, Mercedes Fonseca Hernández, Raúl López Férnandez, Nicolás Ramón Cruz Pérez

“…In the proposal is defined how participate the different components of the educational process in the stay, in their reorganization in function of the incorporation of the simulation of cases; as well as the incorporation of some necessary methodological recommendations .The proposal has been applied during the year 2006-2007 and had a very good acceptance for the students. …”
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A deep learning short-term traffic flow prediction method considering spatial-temporal association by Yang ZHANG, Yue HU, Dongrong XIN

“…The short-term traffic flow prediction is too dependent on the time correlation characteristics, which due to the problems that the correlation factors of the spatial correlation characteristics are too complicated and difficult to quantify.In response to this defect, a deep learning short-term traffic flow prediction method considering spatial-temporal association was proposed.Firstly, by constructing a spatial association measurement function that simultaneously considers distance, flow similarity, and speed similarity, the spatial correlation between the target road segment and the surrounding associated road segments was quantified and predicted.Then, a convolutional neural network model with long short-term memory neurons embedded was constructed.The long short-term memory neurons were used to extract the temporal correlation characteristics between the data, and the spatial correlation metric and the convolution transmission of traffic data were used to extract the spatial correlation characteristics between the data, so as to realize the traffic flow prediction considering the spatial-temporal association.The experimental results show that the proposed method can adapt to short-term forecasting under different traffic flow characteristics such as weekdays and weekends, and the prediction accuracy is better than that of the classical methods.In weekdays and weekends, the forecast bias are 10.45% and 12.35% respectively.…”
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Mutual-Energy Inner Product Optimization Method for Constructing Feature Coordinates and Image Classification in Machine Learning by Yuanxiu Wang

“…As a key task in machine learning, data classification is essential to find a suitable coordinate system to represent the data features of different classes of samples. …”
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Vehicle recognition in acoustic sensor networks using multiple kernel sparse representation over learned dictionaries by Rui Wang, Wenming Cao, Zhihai He

“…In the proposed multiple kernel sparse representation–based classification method, acoustic features of vehicles are extracted and mapped into a high-dimensional feature space using a kernel function, which combines multiple kernels to obtain linearly separable samples. …”
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Learning a cross-scale cross-view decoupled denoising network by mining Omni-channel information by Song Qian, Yan Xue, Youbao Chang

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Harnessing the synergy of statistics and deep learning for BCI competition 4 dataset 4: a novel approach by Gauttam Jangir, Nisheeth Joshi, Gaurav Purohit

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Machine learning to predict killer whale (Orcinus orca) behaviors using partially labeled vocalization data by Sophia Sandholm

“…Despite that, with a careful combination of recent machine learning techniques, including a ResNet-34 convolutional neural network and a custom loss function designed for partially labeled learning, a 96.1% general behavior label classification accuracy on previously unheard segments is achieved. …”
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Robust machine learning based Intrusion detection system using simple statistical techniques in feature selection by Sunil Kaushik, Akashdeep Bhardwaj, Ahmad Almogren, Salil bharany, Ayman Altameem, Ateeq Ur Rehman, Seada Hussen, Habib Hamam

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