Unveiling innovation imperatives in agriculture: A data-driven framework for identifying latent needs and regional priorities by Andrea Bonfiglio

“…This methodology is applied to data collected by the Italian Farm Accountancy Data Network, which includes over 64,000 observations from two consecutive three-year periods (2016-2018 and 2019-2021). …”
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Research of service-differentiated admission control based on Markov decision processes in heterogeneous wireless networks by DENG Qiang1, CHEN Shan-zhi2, HU Bo1, SHI Yan1

“…Service-differentiated admission control was studied in heterogeneous wireless networks.Capacity regions of voice and data services in CDMA cellular network and WLAN were derived.A theoretical model of admission control with differentiated services was proposed on basis of Markov decision processes theory,in which the admission actions were specified for each traffic service and system state transition probabilities were formulated.Furthermore,a fuzzy logic admission utility evaluation mechanism was presented based on the analysis of relationship of QoS requirements and network state,then the optimal admission control policy that maximizes overall utility with new and handoff call blocking probability constraints was formulated.The simulation results reveal that network dynamics can be captured by the proposed utility evaluation mechanism.And the average utility earned in the optimal admission control policy was significantly larger than two other schemes in which service differentiation and mobility are not considered,in addition,the new and handoff call blocking probability can be strictly constrained.…”
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Optimization of Table Tennis Swing Action Supported by the Temporal Convolutional Network Algorithm in Deep Learning by Shaoxuan Sun, Hongyu Zheng, Zhixin Lin

“…To enhance the navigation accuracy and interpretability of Unmanned Aerial Vehicles (UAVs) in sports analysis, this study proposes an improved model based on the Temporal Convolutional Network (TCN) algorithm, integrated with Explainable Artificial Intelligence. …”
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International trade market forecasting and decision-making system: multimodal data fusion under meta-learning by Yiming Bai, Muhammad Asif

“…Traditional market analysis tools primarily rely on unidimensional data, such as historical trading records and price trends. …”
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Advanced computational methods for news classification: A study in neural networks and CNN integrated with GPT by Fahim Sufi

“…This research offers substantial practical contributions, providing detailed insights into news source contributions, effective anomaly detection, and predictive trend analysis using neural networks. The theoretical contributions are profound, demonstrating the mathematical integration of GPT with CNNs and recurrent neural networks. …”
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Evaluation of EIGRP IPv6 and RIPng Effectiveness on IPv6 Networks with EVE-NG Emulator by Cahyani Pebriyanti, Ichwan Nul Ichsan

“…The method used is Design Science Research Methodology (DSRM), which includes literature review, network simulation design, data collection, and analysis. …”
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Straightness control method of hydraulic support group pushing system based on neural network compensation by Yunfei WANG, Jiyun ZHAO, He ZHANG, Hao WANG, Yang ZHANG

“…Secondly, a high-order sliding mode state observer is designed to estimate other system states using the measurable position information, while a radial-based neural network-based disturbance observer is designed to estimate and compensate the unknown disturbance forces of the system in real time with using the estimated system state information as the learning data. …”
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A Deep Learning-Based Echo Extrapolation Method by Fusing Radar Mosaic and RMAPS-NOW Data by Shanhao Wang, Zhiqun Hu, Fuzeng Wang, Ruiting Liu, Lirong Wang, Jiexin Chen

“…These variables are encoded jointly with high-resolution (0.5 dB) radar mosaic data to form multiple radar cells as input. A multi-channel radar echo extrapolation network architecture (MR-DCGAN) is then designed based on the DCGAN framework; (3) Since radar echo decay becomes more prominent over longer extrapolation horizons, this study departs from previous approaches that use a single model to extrapolate 120 min. …”
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High-quality control of receiver functions using a capsule neural networkKey points by Mona H. Hegazi, Ahmad M. Faried, Omar M. Saad

“…The proposed capsule neural network achieved an average precision of 80% on the test set. …”
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An Efficient Method for Generating a Super-Sized and Heterogeneous Pore-Throat Network Model of Rock by Chunlei Yu, Wenbin Chen, Junjian Li, Shuoliang Wang

“…At present, there is no algorithm that can generate a micro pore-throat network model at a macro reservoir scale. This study examines algorithms for super-sized pore-throat network reconstruction using actual core sample data. …”
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Simulation of Natural Gas Pipeline Networks Based on Roughness Optimization Algorithm and Global Mesh Refinement by Yi Yang

“…The proposed method was verified by three industrial pipeline network examples. It is found that the average relative errors between the simulated and the measured data of the three cases are reduced by 3.87%, 5.06%, and 6.0%, respectively. …”
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Predicting Transient Anomalous Transport in Two‐Dimensional Discrete Fracture Networks With Dead‐End Fractures by HongGuang Sun, Dawei Lei, Yong Zhang, Jiazhong Qian, Xiangnan Yu

“…Abstract Pollutant transport in discrete fracture networks (DFNs) exhibits complex dynamics that challenge reliable model predictions, even with detailed fracture data. …”
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Deep convolutional neural network for quantification of tortuosity factor of solid oxide fuel cell anode by Masashi KISHIMOTO, Yodai MATSUI, Hiroshi IWAI

“…A deep convolutional neural network model (DCNN) is developed to quantify the tortuosity factor of porous electrodes of solid oxide fuel cells (SOFCs). …”
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Multi-station water level forecasting using advanced graph convolutional networks with adversarial learning by Xinhai Han, Xiaohui Li, Jingsong Yang, Jiuke Wang, Guoqi Han, Jun Ding, Hui Shen, Jun Yan, Dake Chen

“…This spatial dependency analysis enables rapid deployment in different coastal settings. …”
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Research on the prediction of blasting fragmentation in open-pit coal mines based on KPCA-BAS-BP by Shuang Liu, Enxiang Qu, Chun LV, Xueyuan Zhang

“…Traditional empirical formulas and a single neural network model cannot meet the requirements of modern blasting safety. …”
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Improved Multi-Grained Cascade Forest Model for Transformer Fault Diagnosis by Yiyi Zhang, Yuxuan Wang, Jiefeng Liu, Heng Zhang, Xianhao Fan, Dongdong Zhang

“…Dissolved gas analysis (DGA) is an effective online fault diagnosis technique for large oil-immersed transformers. …”
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Enhanced Deep Learning for Robust Stress Classification in Sows from Facial Images by Syed U. Yunas, Ajmal Shahbaz, Emma M. Baxter, Mark F. Hansen, Melvyn L. Smith, Lyndon N. Smith

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PREDICTION INDONESIA COMPOSITE INDEX USING INTEGRATION DECOMPOSITION- NEURAL NETWORK ENSEMBLE DURING VUCA ERA by Imelda Saluza, Ensiwi Munarsih, Faradillah Faradillah, Leriza Desitama Anggraini

“…The results are presented empirically to show the model's effectiveness in reducing prediction errors. First, the actual data is converted into three components; second, with the Neural Network Ensemble (NNE) approach where the initial step of decomposition results is trained using artificial NN with architecture, training data, and topology to produce individual networks; The output is selected using Principal Component Analysis (PCA) and becomes input to the ensemble model, then combined using a simple average and weighted average. …”
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Research on safety risk assessment model of construction engineering based on attention mechanism and graph neural network by Lanfei He, Ran Chen, Jia Hu, Zhenxi Huang, Li Zhou, Hong Zhang

“…The comprehensive evaluation of multi-dimensional and multi-level risks of construction projects is realized by constructing an evaluation model that combines attention mechanism and graph neural network. In terms of data analysis, this paper uses the historical data of several actual construction projects as training and test samples, covering many key risk areas such as construction period, quality, and capital. …”
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An effective scheduling in data centres for efficient CPU usage and service level agreement fulfilment using machine learning by Rohit Daid, Yogesh Kumar, Yu-Chen Hu, Wu-Lin Chen

“…The proposed research utilises the neural network and linear regression analysis to perform the classification and compares the performance for the efficient CPU usage.…”
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