A novel multi-scale and fine-grained network for large choroidal vessels segmentation in OCT by Wei Huang, Qifeng Yan, Lei Mou, Yitian Zhao, Wei Chen, Wei Chen, Wei Chen

“…To address the problem of low contrast and blurred boundaries of choroidal vessels in OCT images, we developed a large kernel and multi-scale attention module, which can improve the features of the target area through multi-scale convolution kernels, channel mixing and feature refinement. …”
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Research on rock strength prediction model based on machine learning algorithm by Xiang Ding, Mengyun Dong, Wanqing Shen

“…By selecting different features, the optimal feature combination for predicting rock compressive strength was obtained, and the optimal parameters for different models were obtained through the Sparrow Search Algorithm (SSA). …”
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Machine Learning Models for Tracking Blood Loss and Resuscitation in a Hemorrhagic Shock Swine Injury Model by Jose M. Gonzalez, Ryan Ortiz, Lawrence Holland, Austin Ruiz, Evan Ross, Eric J. Snider

“…Hemorrhage leading to life-threatening shock is a common and critical problem in both civilian and military medicine. Due to complex physiological compensatory mechanisms, traditional vital signs may fail to detect patients’ impending hemorrhagic shock in a timely manner when life-saving interventions are still viable. …”
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DepthRL: a weakly supervised approach for monocular depth estimation using deep reinforcement learning by Han Chen, Yongxiong Wang, Jiayi Zhang, Jiapeng Zhang, Zhiqun Pan, Shuwen Jia, Shuai Huang

“…In this network, we introduce the Multiscale Cross Attention Feature Enhancement Module (MCFEM), which enhances the upward transfer of low-order features, allowing high-level semantics to be complemented by low-level local details. …”
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Preparation of future teachers for project activities of schoolchildren by N. Klushina, M. Salpagarova

“…The article presents an analysis of theoretical studies, accumulated pedagogical experience on the problems of project activities of students in general education institutions and the preparation of future teachers for project activities of schoolchildren. …”
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Optimal Consumption, Leisure, and Investment with Partial Borrowing Constraints over a Finite Horizon by Geonwoo Kim, Junkee Jeon

“…We study an optimal consumption, leisure, and investment problem over a finite horizon in a continuous-time financial market with partial borrowing constraints. …”
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L'analyse log-linéaire de tableaux de contingence et le modèle logit II : interaction et rapport de chances à propos d'un problème d'évaluation contingente by Estelle Kah

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Machine Learning in Baseball Analytics: Sabermetrics and Beyond by Wenbing Zhao, Vyaghri Seetharamayya Akella, Shunkun Yang, Xiong Luo

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A multistrategy improved hunger games search algorithm by Qiu Yihui, Zhang Xinqiang, Li Ruoyu, Li Dongyi, Xia Feihan

“…Moreover, a binary variant, BMHGS_V3, using sigmoid transformation, attains an average classification accuracy of 92.3% on ten UCI datasets for feature selection tasks. The proposed MHGS algorithm provides a novel and effective framework for solving complex optimization problems, demonstrating significant theoretical and practical value in the field of computational intelligence.…”
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GrotUNet: a novel leaf segmentation method by Hongfei Deng, Hongfei Deng, Bin Wen, Bin Wen, Cheng Gu, Yingjie Fan

“…The former two make full use of the design ideas of GoogLeNet parallel branching and Resnet residual connectivity, while the latter further mines the fine-grained semantic information distributed in the feature space on the feature map after extraction by the WGRblock module to make the feature expression richer. …”
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Oil well productivity capacity prediction based on support vector machine optimized by improved whale algorithm by Kuiqian Ma, Chunxin Wu, Yige Huang, Pengfei Mu, Peng Shi

“…Aiming at these problems, a well productivity prediction method based on machine learning algorithm was proposed. …”
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Toward Intelligent Financial Advisors for Identifying Potential Clients: A Multitask Perspective by Qixiang Shao, Runlong Yu, Hongke Zhao, Chunli Liu, Mengyi Zhang, Hongmei Song, Qi Liu

“…Then, we propose a Multitask Feature Extraction Model (MFEM), which can leverage useful information contained in these related tasks and learn them jointly, thereby solving the two problems simultaneously. …”
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Evolving Hybrid Deep Neural Network Models for End-to-End Inventory Ordering Decisions by Thais de Castro Moraes, Jiancheng Qin, Xue-Ming Yuan, Ek Peng Chew

“…However, current approaches predominantly rely on feed-forward networks, which may have difficulty capturing temporal correlations in time series data and identifying relevant features, resulting in less accurate predictions. <i>Methods:</i> Addressing this gap, we introduce novel E2E deep learning frameworks that combine Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) for resolving single-period inventory ordering decisions, also termed the Newsvendor Problem (NVP). …”
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Research onconvolutional neural network for reservoir parameter prediction by You-xiang DUAN, Gen-tian LI, Qi-feng SUN

“…As the branch of artificial intelligence,artificial neural network solved many difficult practical problems in pattern recognition and classification prediction field successfully.However,they cannot learn the feature from networks.In recent years,deep learning becomes more and more advanced,but the research on the field of geological reservoir pa-rameter prediction is still rare.A method to predict reservoir parameters by convolutional neural network was presented,which can not only predict reservoir parameters accurately,but also get features of the geological reservoir.The study es-tablished the convolutional neural network model.Results show that the convolutional neural network can be used for reservoir parameter prediction,and get high prediction precision.Moreover,convolutional features from convolutional neural network provided important support for geological modeling and logging interpretation.…”
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Overview of detection techniques for malicious social bots by Rong LIU, Bo CHEN, Ling YU, Ya-shang LIU, Si-yuan CHEN

“…The attackers use social bots to steal people’s privacy,propagate fraud messages and influent public opinions,which has brought a great threat for personal privacy security,social public security and even the security of the nation.The attackers are also introducing new techniques to carry out anti-detection.The detection of malicious social bots has become one of the most important problems in the research of online social network security and it is also a difficult problem.Firstly,development and application of social bots was reviewed and then a formulation description for the problem of detecting malicious social bots was made.Besides,main challenges in the detection of malicious social bots were analyzed.As for how to choose features for the detection,the development of choosing features that from static user features to dynamic propagation features and to relationship and evolution features were classified.As for choosing which method,approaches from the previous research based on features,machine learning,graph and crowd sourcing were summarized.Also,the limitation of these methods in detection accuracy,computation cost and so on was dissected.At last,a framework based on parallelizing machine learning methods to detect malicious social bots was proposed.…”
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Overview of detection techniques for malicious social bots by Rong LIU, Bo CHEN, Ling YU, Ya-shang LIU, Si-yuan CHEN

“…The attackers use social bots to steal people’s privacy,propagate fraud messages and influent public opinions,which has brought a great threat for personal privacy security,social public security and even the security of the nation.The attackers are also introducing new techniques to carry out anti-detection.The detection of malicious social bots has become one of the most important problems in the research of online social network security and it is also a difficult problem.Firstly,development and application of social bots was reviewed and then a formulation description for the problem of detecting malicious social bots was made.Besides,main challenges in the detection of malicious social bots were analyzed.As for how to choose features for the detection,the development of choosing features that from static user features to dynamic propagation features and to relationship and evolution features were classified.As for choosing which method,approaches from the previous research based on features,machine learning,graph and crowd sourcing were summarized.Also,the limitation of these methods in detection accuracy,computation cost and so on was dissected.At last,a framework based on parallelizing machine learning methods to detect malicious social bots was proposed.…”
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Analyzing and Classifying Risks Arising During Innovation Project Implementation in Aircraft Motor-Building by T. B. Timofeeva, E. A. Ozdoeva

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Fast Visual Tracking With Robustifying Kernelized Correlation Filters by Qianbo Liu, Guoqing Hu, Md Mojahidul Islam

“…Occlusion and fast motion problems can be effectively solved by the expansion of the search area. …”
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Self‐Supervised Pre‐Training and Few‐Shot Finetuning for Gas‐Bearing Prediction by Long Han, Xinming Wu, Renjie Chen, Yunhua Shi, Zhanxuan Hu, Huijing Fang

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Small target detection in UAV view based on improved YOLOv8 algorithm by Xiaoli Zhang, Guocai Zuo

“…Firstly, a bi-directional feature pyramid network (BiFPN) is introduced to enhance the fusion capability of the features. …”
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