Global remapping emerges as the mechanism for renewal of context-dependent behavior in a reinforcement learning model by David Kappel, Sen Cheng

“…However, the link between context-dependent neural codes and context-dependent renewal is not fully understood.MethodsWe use a deep neural network-based reinforcement learning agent to study the learning dynamics that occur during spatial learning and context switching in a simulated ABA extinction and renewal paradigm in a 3D virtual environment.ResultsDespite its simplicity, the network exhibits a number of features typically found in the CA1 and CA3 regions of the hippocampus. …”
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End-to-End Semantic Leaf Segmentation Framework for Plants Disease Classification by Khalil Khan, Rehan Ullah Khan, Waleed Albattah, Ali Mustafa Qamar

“…Our model uses a deep convolutional neural network based on semantic segmentation (SS). The proposed algorithm highlights diseased and healthy parts and allows the classification of ten different diseases affecting a specific plant leaf. …”
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Static Mechanical Properties and Microscopic Analysis of Hybrid Fiber Reinforced Ultra-High Performance Concrete with Coarse Aggregate by Shengbing Liu, Yonglei Zhang

“…Finally, three kinds of strength parameters are predicted based on the back propagation (BP) neural network system. The absolute value of the relative error between the predicted strength and the experimental value is less than 5%, which indicates that the prediction model proposed in this paper can provide a reference for the multiobjective optimization of the mix proportion of hybrid fiber ultra-high performance concrete.…”
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Identification of diabetic retinopathy lesions in fundus images by integrating CNN and vision mamba models. by Zenglei Liu, Ailian Gao, Hui Sheng, Xueling Wang

“…Considering these analyses, this work presents a comprehensive deep learning model that combines convolutional neural network and vision mamba models. This model is designed to accurately identify and classify diabetic retinopathy lesions displayed in fundus images. …”
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Theoretical and computational investigations on estimation of viscosity of ionic liquids for green adsorbent: Effect of temperature and composition by Zhaoxiong Han

“…While MLP may not match the precision of GPR, its neural network architecture proves effective in capturing non-linear relationships within the data. …”
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Investigation of ANN Architecture for Predicting Load-Carrying Capacity of Castellated Steel Beams by Thuy-Anh Nguyen, Hai-Bang Ly, Van Quan Tran

“…This paper aims to propose an artificial neural network (ANN) model with optimal architecture to predict the load-carrying capacity of CSB with a scheme of the simple beam bearing load located at the center of the beam. …”
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Predictive performance of count regression models versus machine learning techniques: A comparative analysis using an automobile insurance claims frequency dataset. by Gadir Alomair

“…The research involved a comparative evaluation of several models, including Poisson, NB, zero-inflated Poisson (ZIP), hurdle Poisson, zero-inflated negative binomial (ZINB), hurdle negative binomial, random forest (RF), support vector machine (SVM), and artificial neural network (ANN) on an insurance dataset. The performance of these models was assessed using mean absolute error. …”
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Early Warning and Management Method of Abnormal Performance of Tourist Scenic Spots Assisted by Image Recognition Technology by Zhaozhen Song, Jing Lu

“…This study uses the convolutional neural network (CNN) method to realize the image recognition technology of the characteristics of the scenic area’s flow of people and tourists’ preferences, and these characteristics will be displayed to the managers in the form of images. …”
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A Three-Dimensional Anisotropic Diffusion Equation-Based Video Recognition Model for Classroom Concentration Evaluation in English Language Teaching by Yanghong Liu, Jintao Liu

“…DK features provide a nonlinear description of the correlation between successive face images and express face image sequences in the temporal dimension; depth features are extracted by a pretrained depth neural network model that can express the complex nonlinear mapping relationships of images and reflect the more abstract implicit information inside face images. …”
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A generic self-learning emotional framework for machines by Alberto Hernández-Marcos, Eduardo Ros

“…Applied in a case study, an artificial neural network trained on unlabeled agent’s experiences successfully learned and identified eight basic emotional patterns that are situationally coherent and reproduce natural emotional dynamics. …”
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Prediction of Dissolved Oxygen Concentration in Sewage Treatment Process Based on Data Recognition Algorithm by Lili Ma, Jiangping Liu

“…On the one hand, it avoids the excessive random or blind selection of the initial weight threshold of the neural network in the initial stage; on the other hand, in the optimization process of the weight threshold, two types of search mechanisms, FWA and COA, are used to give full play to their respective strengths and to continuously conduct information exchange and mutual cooperation between groups and individuals. …”
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Application of an Internet of Things Oriented Network Education Platform in English Language Teaching by Huizhen Li

“…By analyzing the current situation of the Internet of Things education platform, including its development and structure, this paper constructs the evaluation model of the combination of long-term memory neural network model and gray model and realizes the evaluation of the teaching effect of the Internet of Things education platform. …”
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Analyzing urban public sports facilities for recognition and optimization using intelligent image processing by Zhongqian Zhang

“…The model has been verified with other modern methods, including Faster R-CNN and Convolutional Neural Network (CNN). The results indicate that the SE-DEA model with an accuracy of 94.76% in recognizing sports facilities, outperforming advanced comparative models like Faster R-CNN (74.21%) and CNN (60.54%). …”
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Scenario-adaptive wireless fall detection system based on few-shot learning by Yuting ZENG, Suzhi BI, Lili ZHENG, Xiaohui LIN, Hui WANG

“…A scenario robust fall detection system based on few-shot learning (FDFL) in wireless environment was designed.The performance of existing fall detection methods based on Wi-Fi channel state information (CSI) degrades significantly across scenarios, which requires collecting and marking a large number of CSI samples in each application scenario, resulting in high cost for large-scale deployment.Therefore, the method of few-shot learning was introduced, which can maintain the performance of fall detection with high accuracy when the number of annotated samples in unfa-miliar scenes is insufficient.The proposed FDFL was mainly divided into two stages, source domain meta-training and target domain meta-learning.The meta training stage of the source domain consists of two parts: data preprocessing and classification training.In the data preprocessing stage, the collected original CSI amplitude and phase data were denoised and segmented.In the classification training stage, a large number of processed source domain data samples were used to train a CSI feature extractor based on convolutional neural network.In the meta-learning stage of the target domain, the limited labeled data sampled in the target domain was effectively extracted based on the feature extractor trained in the meta-training module, and then a lightweight machine learning classifier was trained to detect the fall behavior under the cross-scene.Through several experiments in different scenarios, FDFL can achieve an average accuracy of 95.52% for the four classification tasks of falling, sitting, walking and sit down with only a small number of samples in the target domain, and maintain robust detection accuracy for changes in test environment, personnel target and equipment location.…”
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Modeling the Liquid-Phase Adsorption of Cephalexin onto Coated Iron Nanoparticles Using Response Surface and Molecular Modeling by Shabnam Ahmadi, Soumya Ghosh, Alhadji Malloum, Charné Bornman, Christian Osagie, Leili Mohammadi, Chinenye Adaobi Igwegbe

“…In addition, the data was used to test and fit an artificial neural network (ANN) model. Molecular-level DFT calculations on the CEX molecule were carried out. …”
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Theory and Numerical Analysis of Extreme Learning Machine and Its Application for Different Degrees of Defect Recognition of Hoisting Wire Rope by Zhike Zhao, Xiaoguang Zhang

“…ELM was proposed based on the single-hidden layer feed-forward neural network (SLFNN). ELM is characterized by the easier parameter selection rules, the faster converge speed, the less human intervention, and so on. …”
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Improved feature reduction framework for sign language recognition using autoencoders and adaptive Grey Wolf Optimization by Rajeev Goel, Sandhya Bansal, Kavita Gupta

“…A handcrafted artificial neural network serves as the classifier within this integrated framework, denoted as AEGWO-Net. …”
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An Artificial Intelligence Mechanism for the Prediction of Signal Strength in Drones to IoT Devices in Smart Cities by Mohamad Reda A. Refaai, Vinjamuri S. N. C. H. Dattu, H. S. Niranjana Murthy, P. Pramod Kumar, B. Kannadasan, Abdi Diriba

“…Depending on many relevant criteria, an artificial neural network (ANN)-centered precise and effective method is provided to forecast the signal strength from such drones. …”
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Temporal link prediction method based on community multi-features fusion and embedded representation by Yuhang ZHU, Lixin JI, Yingle LI, Haitao LI, Shuxin LIU

“…Dynamic networks integrates time attributes on the basis of static networks, and it contains multiple connotations such as the complexity and dynamics of the network structure.It is a better thinking object for studying complex network link prediction problems in the real world.Its high application value has attracted much attention in recent years.However, most of the research objects of traditional methods are still limited to static networks, and there are problems such as insufficient utilization of network time-domain evolution information and high time complexity.Combining sociological theory, a novel temporal link prediction method was proposed based on community multi-feature fusion embedding representation.The core idea of this method was to analyze the dynamic evolution characteristics of the network, learn the embedded representation vector of nodes within the community, and effectively fuse multiple features to measure the generation probability of the connection between nodes.The network was divided into several subgraphs by using community detection with collective influence weights and the Similarity index was proposed based on the collective influence.Then, the biased random walk and the Skip-gram were used to get the embedded vectors for every node and the Similarity index was proposed based on the random walk within the community.Integrating the collective influence, multiple central features of the community, and the representation vector learned within the community, the Similarity index was proposed based on the multi-features fusion.Compared with classical temporal link prediction methods, including moving average methods, embedded representation methods, and graph neural network methods, experimental results on six real data sets show that the proposed methods based on the random walk within the community and the multi-features fusion both achieve better prediction performance under the evaluation criteria of AUC.…”
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Postcare for Repairing Nerve and Tendon Injury Based on Biomimetic Nano-Parallel Material Composite Protein by Junxia Fu, Xiaohong Tan, Yan Tang

“…It can use the BP algorithm in the neural network algorithm to simulate the parallel arrangement of tendon collagen fibers, which is more efficient than other schemes. …”
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