Interpretable prediction model for hand-foot-and-mouth disease incidence based on improved LSTM and XGBoost by Xiao LI, Shuyu HE, Yan PENG, Rongxin YANG, Lu TAO, Tingqi LOU, Wenqi HE

“…LSTM networks are particularly suitable for addressing long-term memory issues in time-series data. …”
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A hybrid adversarial autoencoder-graph network model with dynamic fusion for robust scRNA-seq clustering by Binhua Tang, Yingying Feng, Xinyu Gao

“…Results Here, we present a novel deep clustering method, scCAGN, based on an adversarial autoencoder (AAE) and a cross-attention graph convolutional network (GCN), to address the above challenges in scRNA-seq data analysis. …”
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A New Modelling and Feature Extraction Method Based on Complex Network and Its Application in Machine Fault Diagnosis by Qi Zhang, Tian Tian, Guangrui Wen, Zhifen Zhang

“…Analysis on the experimental data and bearing life cycle data shows that the method proposed in this paper is effective, revealing that the extracted features have effective separability and high accuracy in fault recognition and the degradation detection of the life cycle of rolling bearings combined with neural networks. …”
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VTA projections to M1 are essential for reorganization of layer 2-3 network dynamics underlying motor learning by Amir Ghanayim, Hadas Benisty, Avigail Cohen Rimon, Sivan Schwartz, Sally Dabdoob, Shira Lifshitz, Ronen Talmon, Jackie Schiller

“…Previous studies demonstrated that skill acquisition requires dopaminergic VTA (ventral-tegmental area) signaling in M1, however little is known regarding the effect of these inputs at the neuronal and network levels. Using dexterity task, calcium imaging, chemogenetic inhibiting, and geometric data analysis, we demonstrate VTA-dependent reorganization of M1 layer 2-3 during motor learning. …”
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Landslide hazard early warning method for rock slopes using a hybrid LSTM-SARIMA data-driven model. by Yongxin Dai, Zijian Li, Jingbiao Lu

“…Rock slope landslides are characterized by their sudden onset and significant destructive power, posing a major threat to human life as well as the safety of equipment and infrastructure.Currently, research on landslide early hazard warning has largely focused on individual components, such as monitoring data analysis or studies on influencing mechanisms. …”
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Artificial intelligence analysis in cyber domain: A review by Liguo Zhao, Derong Zhu, Wasswa Shafik, S Mojtaba Matinkhah, Zubair Ahmad, Lule Sharif, Alisa Craig

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Vibration-based gait analysis via instrumented buildings by Ellis Kessler, Vijaya VN Sriram Malladi, Pablo A Tarazaga

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Scale-free fault-tolerant topology control algorithm in wireless sensor network with optimization of path energy consumption by Hao-ran LIU, Tao HAN, Ya-qian LI, Rong-rong YIN

“…For the issue of path energy consumption produced in the process of transmitting data in large-scale sensor network, an optimization model of network path energy consumption based on the mode of multi-hop was established, and then the law of obtaining the value of the node degree that could minimize the energy consumption of network was deduced. …”
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Deep convolutional neural network (DCNN)-based model for pneumonia detection using chest x-ray images by S. I. Ele, U. R. Alo, H. F. Nweke, A. H. Okemiri, E. O. Uche-Nwachi

“…This study uses a dataset comprising 5,863 Chest X-ray images (JPEG) and 2 categories (Pneumonia/Normal) (anterior-posterior) selected from retrospective cohorts of pediatric patients of one to five years old from Guangzhou Women and Children’s Medical Center, Guangzhou, obtained from Kaggle data repositories. Data Preprocessing was conducted to enhance image quality and extract relevant features, followed by implementing a deep convolutional neural networks (DCNNs) model using TensorFlow’s Keras. …”
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Artificial Neural Network Approaches for Predicting the Heat Transfer in a Mini-Channel Heatsink with Alumina/Water Nanofluid by Mohammad Mahdi Tafarroj, Seyed Soheil Mousavi Ajarostaghi, C.J. Ho, Wei-Mon Yan

“…The multi-layer perceptron (MLP) and radial basis function (RBF) neural networks are employed for the modeling. To apply the artificial neural network analysis, 60 data of experimental works are utilized. …”
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A predictive model for electrospun based Polyvinyl alcohol (PVA) nanofibers diameter using an artificial neural network by Kamran-ul-Haq Khan, Imran Ahmad Siddiqui

“…The most suitable network architecture was determined by considering and examining different topologies in artificial neural networks (ANNs) that composed of single and double hidden layers with different numbers of nodes for each layer. …”
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Automated Evaluation of Continuous and Segmented Chip Geometries Based on Image Processing Methods and a Convolutional Neural Network by Hagen Klippel, Samuel Pflaum, Michal Kuffa, Konrad Wegener

“…Data from manual measurements are used for comparison and validation purposes.…”
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Predicting Wastewater Characteristics Using Artificial Neural Network and Machine Learning Methods for Enhanced Operation of Oxidation Ditch by Igor Gulshin, Nikolay Makisha

“…The SMAPE score of 1.052% on test data demonstrates the model’s accuracy and highlights the potential of integrating artificial neural networks (ANN) and machine learning (ML) with mechanistic models for optimizing wastewater treatment processes. …”
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Enhancing knee osteoarthritis diagnosis with DMS: a novel dense multi-scale convolutional neural network approach by Di Zhang, Yuting Dong, Yao Xu, Junhui Qian, Miaoyu Ye, Qiang Yuan, Jian Luo

“…We compared our model with a standard baseline model and validated it using an unseen-data test set. Results The DMS model exhibited exceptional performance in unseen-data tests, achieving 73.00% average accuracy (ACC) and 92.73% area under the curve (AUC), surpassing the baseline model’s (DenseNet) 63.52% ACC and 88.76% AUC. …”
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Prediction of Future State Based on Up-To-Date Information of Green Development Using Algorithm of Deep Neural Network by Liyan Sun, Li Yang, Junqi Zhu

“…In this study, the focus was on the development of green energy and future prediction for the consumption of current energy sources and green energy development using an improved deep learning (DL) algorithm. In addition to the analysis of the current energy consumption used for the natural gas and oil as fuel, deep neural network algorithm is used to train the system as well as to process the data obtained previously, ranging from literature from the year 2003 until the year 2019, for consumption of fuel. …”
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Development of a questionnaire for problematic social networking sites use: Ensuring content validity through Delphi methodology. by Seungju Lim, Ji-Hyuk Park

“…This study involved three rounds of Delphi surveys to collect both open- and closed-ended responses to the PSNSU questionnaire. Data analysis focused on calculating the content validity ratio (CVR), stability, and consensus of each item. …”
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A Techno-Economic Modeling Approach to 6G Network Deployment: Exploring Costs and Use Case Feasibility by Dimitris Kokkinis, Nikolaos Ioannou, Dimitrios Katsianis, Dimitris Varoutas

“…To support the successful realization of 6G promises, we quantified the technical requirements and assessed the economic viability of the proposed solutions using current 5G data and appropriate multipliers. An example application of our analysis shows that the required performance improvements and network densification lead to significantly higher infrastructure costs, with 6G investments estimated to be 200%–840% higher than those of 5G, depending on the use case. …”
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Wide‐Field Bond Quality Evaluation Using Frequency Domain Thermoreflectance with Deep Neural Network Feature Reconstruction by Amun Jarzembski, Siddharth Nair, Wyatt Hodges, Matthew Jordan, Anthony McDonald, Logan Antiporda, Greg W. Pickrell, Timothy Walsh, Fabio Semperlotti, Jason Neely, Luke Yates

“…Utility of noisy higher frequency FDTR phase maps, i.e., near the computationally predicted sensing depth limit, results in an average prediction error of 11%. Taken together, FDTR with neural network‐based analysis demonstrates subsurface bond monitoring at length scales relevant for heterogeneously integrated microelectronics.…”
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Automatic cattle identification system based on color point cloud using hybrid PointNet++ Siamese network by Pyae Phyo Kyaw, Pyke Tin, Masaru Aikawa, Ikuo Kobayashi, Thi Thi Zin

“…The identification framework is built upon a hybrid PointNet ++ Siamese Network trained with a triplet loss function, ensuring the extraction of discriminative features for accurate cattle identification. …”
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Revolutionizing Clear-Sky Humidity Profile Retrieval with Multi-Angle-Aware Networks for Ground-Based Microwave Radiometers by Yinshan Yang, Zhanqing Li, Jianping Guo, Yuying Wang, Hao Wu, Yi Shang, Ye Wang, Langfeng Zhu, Xing Yan

“…Based on the 7-year (2018–2024) in situ measurements from Beijing, Nanjing, and Shanghai, validation results reveal that AngleNet achieves substantial improvements, with an average R2 of 0.71 and a root mean square error (RMSE) of 10.39%, surpassing conventional models such as LGBM (light gradient boosting machine) and RF (random forest) by over 10% in both metrics, and demonstrating a remarkable 41% increase in R2 and a 10% reduction in RMSE compared to the previous BRNN method (batch normalization and robust neural network). …”
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