Deep Learning-Based Prediction of Physical Stability considering Class Imbalance for Amorphous Solid Dispersions by Hanbyul Lee, Junghyun Kim, Suyeon Kim, Jimin Yoo, Guang J. Choi, Young-Seob Jeong

“…After the preprocessing, it performs the learning process using a carefully designed neural network of simple but effective structure. Experimental results show that the proposed model has faster training convergence speed and better test performance compared to the existing DNN model. …”
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Association of individual-based morphological brain network alterations with cognitive impairment in type 2 diabetes mellitus by Die Shen, Xuan Huang, Ziyu Diao, Jiahe Wang, Kun Wang, Weiye Lu, Shijun Qiu, Shijun Qiu

“…These findings offer new insights into the neural network mechanisms underlying T2DM-associated brain damage and cognitive impairment.…”
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Comparative study on deep and machine learning approaches for predicting wind pressures on tall buildings by Mosbeh R. Kaloop, Abidhan Bardhan, Pijush Samui, Jong Wan Hu, Mohamed Elsharawy

“…Two deep learning methods viz deep belief network (DBN) and deep neural network (DNN), and five machine learning methods namely feedforward neural network, extreme learning machine, weighted extreme learning machine, random forest, and gradient boosting machine were evaluated, and compared in predicting the design wind pressures on tall buildings. …”
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Global-Local Self-Attention-Based Long Short-Term Memory with Optimization Algorithm for Speaker Identification by Pravin Marotrao Ghate, Bhagvat D. Jadhav, Shriram Sadashiv Kulkarni, Pravin Balaso Chopade, Prabhakar N. Kota

“…Kaedah GLSA-LSTM dengan EN-GWO mencapai ketepatan 99.36% pada dataset TIMIT dan ketepatan 93.45% pada dataset VoxCeleb 1, berbanding dengan SincNet dan Generative Adversarial Network (SincGAN) serta Hybrid Neural Network – Support Vector Machine (NN-SVM). …”
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Utilizing MFCCs and TEO-MFCCs to Classify Stress in Females Using SSNNA by Nur Aishah Zainal, Ani Liza Asnawi, Siti Noorjannah Ibrahim, Nor Fadhillah Mohamed Azmin, Norharyati Harum, Nora Mat Zin

“…With the assistance of the Stress Speech Neural Network Architecture (SSNNA), an improved accuracy of 93.9% was achieved. …”
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Dynamics of specialization in neural modules under resource constraints by Gabriel Béna, Dan F. M. Goodman

“…Using a simple, toy artificial neural network setup that allows for precise control, we find that structural modularity does not in general guarantee functional specialization (across multiple measures of specialization). …”
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Enhancing stock index prediction: A hybrid LSTM-PSO model for improved forecasting accuracy. by Xiaohua Zeng, Changzhou Liang, Qian Yang, Fei Wang, Jieping Cai

“…The long short-term memory neural network (LSTM) widely employed in stock price prediction due to its ability to address long-term dependence and transmission of historical time signals in time series data. …”
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Research on Management Model Based on Deep Learning by Yuting Zhao

“…Proper management models lead to strategies and decisions help to success organization. Deep neural network (DNN) is proposed to make good prediction for organization for increasing the cost and reduce risk in companies and institutions. …”
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Monitoring Model of Concrete Dam Deformation Based on Gray Wolf Optimization Algorithm by CHEN Shuyun, ZHOU Wenzhong, GU Yanchang, WANG Hong

“…Dam deformation is one of the most reliable indicators that can reflect the actual condition of concrete dam.Modeling and analysis of dam deformation monitoring data is an effective way to reflect the operation behavior of concrete dam and seek for the internal law of deformation.In addition,the mathematical model can be applied to predict the deformation.In order to improve the accuracy of modeling and deformation prediction,this paper proposes a deformation monitoring model of concrete dam based on gray wolf optimization algorithm,applies the model to the engineering examples and compares the results with that from the statistical model established by the conventional stepwise regression method and the BP-neural network.The results show that the monitoring model based on the gray wolf optimization algorithm has higher accuracy and better prediction effect.Therefore,the gray wolf algorithm is superior in establishing deformation model,which provides a new reference for deformation analysis and prediction of concrete dam.…”
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Optimal Exponential Synchronization of Chaotic Systems with Multiple Time Delays via Fuzzy Control by Feng-Hsiag Hsiao

“…This study presents an effective approach to realize the optimal exponential synchronization of multiple time-delay chaotic (MTDC) systems. First, a neural network (NN) model is employed to approximate the MTDC system. …”
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Simplified-Boost Reinforced Model-Based Complex Wind Signal Forecasting by Qiushuang Lin, Chunxiang Li

“…In this paper, an adaptive residual convolutional neural network (CNN) is developed, aiming at achieving not only high precision but also high adaptivity for various wind signals with varying complexity. …”
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Social network bursty topic discovery based on RNN and topic model by Lei SHI, Junping DU, Meiyu LIANG

“…The data is noisy and diverse,with a large number of meaningless topics in social network.The traditional method of bursty topic discovery cannot solve the sparseness problem in social network,and require complicated post-processing.In order to tackle this problem,a bursty topic discovery method based on recurrent neural network and topic model was proposed.Firstly,the weight prior based on RNN and IDF were constructed to learn the relationship between words.At the same time,the word pairs were constructed to solve the sparseness problem.Secondly,the “spike and slab” prior was introduced to decouple the sparsity and smoothness of the bursty topic distribution.Finally,the burstiness of words were leveraged to model the bursty topic and the common topic,and automatically discover the bursty topics.To evaluate the effectiveness of proposed method,the various experiments were conducted.Both qualitative and quantitative evaluations demonstrate that the proposed RTM-SBTD method outperforms favorably against several state-of-the-art methods.…”
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A Hybrid Deep Learning Model for Trash Classification Based on Deep Trasnsfer Learning by Zhen Yuan, Jinfeng Liu

“…With the development of deep learning, it is possible to use the deep convolutional neural network for trash classification. In order to classify the trash of the TrashNet dataset, which consists of six classes of garbage images, this paper proposes a hybrid deep learning model based on deep transfer learning, which includes upper and lower streams. …”
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Application of Big Data Analysis in Cost Control of Marine Fishery Breeding by Yunsong Gu

“…We establish the cost analysis model of marine fishery, use the big data correlation analysis method to conduct distributed mining on the cost characteristics of marine fishery, deeply grasp the relevance of data characteristics, use the information fusion method to build the constraint parameter analysis model of aquaculture cost operation, limit the amount of calculation, and use the adaptive neural network weighted training method to adaptively optimize the cost control to avoid falling into local optimization. …”
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Multi-channel QoT prediction method in wide-area optical backbone network based on ensemble learning by Xiaochuan SUN, Zhigang LI, Minghui ZHANG, Guan GUI

“…Due to the fact that in dynamic wide-area optical backbone network the accuracies of the existing prediction methods were insufficient,a novel prediction method on quality of transmission (QoT) of optical channel was proposed based on ensemble learning theory.Firstly,under the framework of stacked ensemble learning,a base-learner including five multilayer perceptron (MLP) model was built,which could achieve homomorphic ensemble learning of sample data through parallel combination.Subsequently,the new training set fused from the predicted results of the preceding base-learner was used to training the meta-learner composed of a single MLP.The simulation results show that compared with the used deep neural network,the proposed method can obtain a more excellent nonlinear approximation in the scenarios of the single-channel and multi-channels,and the prediction accuracies have the improvements of 1.93% and 3.82% respectively.…”
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Establishing a Novel Algorithm for Highly Responsive Storage Space Allocation Based on NAR and Improved NSGA-III by Peijian Wu, Yulu Chen

“…First, we developed a model based on the NAR neural network to predict order requests. Subsequently, we used the improved NSGA-III based on good point set theory to construct a multiobjective optimization model to minimize resource loss, maximize efficiency in goods selection, and maximize goods accumulation. …”
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The Underwater Projectile Launching Process Prediction Based on Reduced Order Method by Ke Deng, Yi Jiang, Huan-Xing Liu

“…The prediction of the initial stage of the launching velocity (which is the most important part of the launching process) is realized by establishing the neural network between the working condition parameters and the POD basis coefficients. …”
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Cryptocurrency Financial Risk Analysis Based on Deep Machine Learning by Si Chen

“…The electronic economy is very dangerous and must be approached with great caution, so as to avoid or minimize the risks that occur in such cases. Deep neural network (DNN) algorithm was improved to predict the Bitcoin price and then achieve the main goal of reducing financial risks to proceed with electronic business, and good estimation was achieved by using informative data such as transactions and currency return. …”
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A Personalized Travel Route Recommendation Model Using Deep Learning in Scenic Spots Intelligent Service Robots by Qili Tang

“…This paper proposes a personalized tourist interest demand recommendation model based on deep neural network. Firstly, the basic information data and comment text data of tourism service items are obtained by crawling the relevant website data. …”
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Reference frame list optimization algorithm in video coding by quality enhancement of the nearest picture by Junyan HUO, Ruipeng QIU, Yanzhuo MA, Fuzheng YANG

“…Interframe prediction is a key module in video coding, which uses the samples in the reference frames to predict those in the current picture, thus helps to represent the complex video by transmitting a small amount of the prediction residual.In lossy video coding, the qualities of reference frames are affected by the quantization distortion, which lead to poor prediction accuracy and performance degradation.Targeted at the low latency video services, a reference frame list optimization algorithm was proposed, which enhanced the quality of the nearest reference frame by a deep learning-based convolutional neural network, and integrated the enhanced reference frame into the reference frame list to improve the accuracy of interframe prediction.Compared with H.265/HEVC reference software HM16.22, the proposed algorithm provides BD-rate savings of 9.06%, 14.92% and 13.19% for Y, Cb and Cr components, respectively.…”
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