D2D cooperative caching strategy based on graph collaborative filtering model by Ningjiang CHEN, Linming LIAN, Pingjie OU, Xuemei YUAN

“…A D2D cooperative caching strategy based on graph collaborative filtering model was proposed for the problem of difficulty in obtaining sufficient data to predict user preferences in device-to-device (D2D) caching due to the limited signal coverage of base stations.Firstly, a graph collaborative filtering model was constructed, which captured the higher-order connectivity information in the user-content interaction graph through a multilayer graph convolutional neural network, and a multilayer perceptron was used to learn the nonlinear relationship between users and content to predict user preferences.Secondly, in order to minimize the average access delay, considering user preference and cache delay benefit, the cache content placement problem was modeled as a Markov decision process model, and a cooperative cache algorithm based on deep reinforcement learning was designed to solve it.Simulation experiments show that the proposed caching strategy achieves optimal performance compared with existing caching strategies for different content types, user densities, and D2D communication distance parameters.…”
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Support Vector Regression Based on Grid-Search Method for Short-Term Wind Power Forecasting by Hong Zhang, Lixing Chen, Yong Qu, Guo Zhao, Zhenwei Guo

“…In order to investigate the performance of proposed strategy, forecasting results comparison between two different forecasting models, multiscale SVR and multilayer perceptron neural network applied for power forecasts, are presented. …”
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SiameseNet based on multiple instance learning for accurate identification of the histological grade of ICC tumors by Zhizhan Fu, Fazhi Feng, Xingguang He, Tongtong Li, Xiansong Li, Jituome Ziluo, Zixing Huang, Jinlin Ye

“…Timely and accurate identification of ICC histological grade is critical for guiding clinical diagnosis and treatment planning.MethodWe proposed a dual-branch deep neural network (SiameseNet) based on multiple-instance learning and cross-attention mechanisms to address tumor heterogeneity in ICC histological grade prediction. …”
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Multi-Resolution Multimedia QoE Models for IPTV Applications by Prasad Calyam, Prashanth Chandrasekaran, Gregg Trueb, Nathan Howes, Rajiv Ramnath, Delei Yu, Ying Liu, Lixia Xiong, Daoyan Yang

“…In this paper, we develop psycho-acoustic-visual models that can predict user QoE of multimedia applications in real time based on online network status measurements. Our models are neural network based and cater to multi-resolution IPTV applications that include QCIF, QVGA, SD, and HD resolutions encoded using popular audio and video codec combinations. …”
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TaxiInt: Predicting the Taxi Flow at Urban Traffic Hotspots Using Graph Convolutional Networks and the Trajectory Data by Jinmao Zhang, Huanchang Chen, Yiming Fang

“…Different from other density-clustering-based approaches, neural network, or OD information based models, TaxiInt predicted the taxi flow using the trajectory data of taxis. …”
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Using Machine Learning for Aerostructure Surface Damage Digital Reconstruction by Yijia Wu, Hon Ping Tang, Anthony Mannion, Robert Voyle, Ying Xin

“…Then, we use the prediction result of a feedforward neural network (FNN) to simulate the damage structure and the mapping relationship to explore its reconstructive possibility. …”
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Learning Spatial-Temporal Features of Ride-Hailing Services with Fusion Convolutional Networks by Dapeng Zhang, Feng Xiao, Gang Kou, Jian Luo, Fan Yang

“…In this paper, we propose a new deep learning framework, called the locally connected spatial-temporal fully convolutional neural network ( LC-ST-FCN), to learn the spatial-temporal correlations and local statistical differences among regions simultaneously. …”
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On Modelling and Comparative Study of LMS and RLS Algorithms for Synthesis of MSA by Ahmad Kamal Hassan, Adnan Affandi

“…This paper deals with analytical modelling of microstrip patch antenna (MSA) by means of artificial neural network (ANN) using least mean square (LMS) and recursive least square (RLS) algorithms. …”
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Investigation of an object-detection approach for estimating the rock fragmentation in the open-pit conditions by K. Reshetnikov, M. Ronkin, S. Porshnev

“…Based on the research results, YOLOv7x architecture is proposed as a baseline model. The proposed neural network architecture was trained on a dataset selected by the present authors from digital images of blasted open-pit block fragments, which consisted of 220 images. …”
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DDoS attack detection and defense based on hybrid deep learning model in SDN by Chuanhuang LI, Yan WU, Zhengzhe QIAN, Zhengjun SUN, Weiming WANG

“…Software defined network (SDN) is a new kind of network technology,and the security problems are the hot topics in SDN field,such as SDN control channel security,forged service deployment and external distributed denial of service (DDoS) attacks.Aiming at DDoS attack problem of security in SDN,a DDoS attack detection method called DCNN-DSAE based on deep learning hybrid model in SDN was proposed.In this method,when a deep learning model was constructed,the input feature included 21 different types of fields extracted from the data plane and 5 extra self-designed features of distinguishing flow types.The experimental results show that the method has high accuracy,it’s better than the traditional support vector machine (SVM) and deep neural network (DNN) and other machine learning methods.At the same time,the proposed method can also shorten the processing time of classification detection.The detection model is deployed in SDN controller,and the new security policy is sent to the OpenFlow switch to achieve the defense against specific DDoS attack.…”
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Hybrid LSTM-PSO optimization techniques for enhancing wind power bidding efficiency in electricity markets by Viet Anh Truong, Ngoc Sang Dinh, Thanh Long Duong, Ngoc Thien Le, Cong Dinh Truong, Linh Tung Nguyen

“…Past research has predominantly focused on utilizing meta-heuristic algorithms to optimize neural network structures, while the exploration of deep learning in optimization has remained relatively limited. …”
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Lion Algorithm-Optimized Long Short-Term Memory Network for Groundwater Level Forecasting in Udupi District, India by B. S. Supreetha, Narayan Shenoy, Prabhakar Nayak

“…The prediction accuracy of the hybrid LSTM-LA model was better than that of the feedforward neural network (FFNN) and the isolated LSTM models. The hybrid LSTM-LA-based forecasting model is promising for a larger dataset.…”
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State of health estimation of individual batteries through incremental curve analysis under parameter uncertainty by Yue Zhao, Qian Li, Xiaohui Li, Ge Zhang, Hang Shi, Qinghua Li

“…Subsequently, a method is proposed to fuse these HIs using an artificial neural network to achieve precise SOH estimation. The effectiveness of the proposed method is validated through extensive long‐term degradation experiments on Lithium Cobalt Oxide batteries. …”
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ABAC access control policy generation technique based on deep learning by Aodi LIU, Xuehui DU, Na WANG, Rui QIAO

“…To solve the problem of automatic generation of access control policies, an access control policy generation framework based on deep learning was proposed.Access control policy based on attributes could be generated from natural language texts.This technology could significantly reduce the time cost of access control policy generation and provide effective support for the implementation of access control.The policy generation problem was decomposed into two core tasks, identification of access control policy sentence and access control attribute mining.Neural network models such as BiGRU-CNN-Attention and AM-BiLSTM-CRF were designed respectively to realize identification of access control policy sentence and access control attribute mining, so as to generate readable and executable access control policies.Experimental results show that the proposed method has better performance than the benchmark method.In particular, the average F1-score index can reach 0.941 in the identification task of access control policy sentence, which is 4.1% better than the current state-of-the-art method.…”
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Geographical origin discrimination of Chenpi using machine learning and enhanced mid-level data fusion by Xin Kang Li, Li Jun Tang, Ze Ying Li, Dian Qiu, Zhuo Ling Yang, Xiao Yi Zhang, Xiang-Zhi Zhang, Jing Jing Guo, Bao Qiong Li

“…The K-nearest neighbors and artificial neural network models, using modified mid-level data fusion, provide the best performance, misclassified only one sample. …”
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On-line strength assessment of distribution systems with distributed energy resources by Jifeng Liang, Shiyang Rong, Tengkai Yu, Tiecheng Li, Hanzhang Qu, Ye Cao

“…To predict the strength of distribution systems under various conditions, a rectified linear unit (ReLU) neural network is trained and further reformulated as a mixed-integer linear programming (MILP) problem to verify its robustness and input stability. …”
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Nf-Root: A Best-Practice Pipeline for Deep-Learning-Based Analysis of Apoplastic pH in Microscopy Images of Developmental Zones in Plant Root Tissue by Julian Wanner, Luis Kuhn Cuellar, Luiselotte Rausch, Kenneth W. Berendzen, Friederike Wanke, Gisela Gabernet, Klaus Harter, Sven Nahnsen

“…In short, a deep-learning module deploys deterministically trained convolutional neural network models and augments the segmentation predictions with measures of prediction uncertainty and model interpretability, while aiming to facilitate result interpretation and verification by experienced plant biologists. …”
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A Novel Energy Consumption Prediction Model Integrating Real-Time Traffic State Recognition and Velocity Prediction of BEVs by Yue Li, Yu Jiang, Jianhua Guo, Dong Xie

“…In the energy consumption prediction stage, a particle swarm optimization-radial basis function neural network (PSO-RBFNN) model is employed to estimation the energy consumption. …”
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PSR-SQUARES: SQL reverse synthesis system based on program space reducer by Quansheng DOU, Shun ZHANG, Hao PAN, Huixian WANG, Huanling TANG

“…In order to address the issue of rapid growth of program space in SQUARES, which led to low efficiency in program synthesis, a program space reducer based on deep neural network (DNN) was introduced into the SQUARES framework.A given ＆lt;Queried tables, Query result＆gt; pair was represented as a 2D tensor which was used as input for a DNN.And the output of the DNN was the relevance vector of the target SQL statement synthesis rules.Based on the output of the DNN, the last N rules with weak correlation to the target SQL statement were eliminated, thereby shrinking the program search space and improving the system synthesis efficiency.The architecture of DNN, the method of generating training datasets, and the training process of DNN were described in detail.Furthermore, experimental comparisons between PSR-SQUARES and other representative SQL reverse synthesis systems were conducted.The results show that the overall performance of PSR-SQUARES is superior to other synthesis systems to varying degrees, with the average synthesis time reduced from 251 s in SQUARES to 130 s and the target program synthesis success rate increased from 80% to 89%.…”
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Enhancing Sentiment Analysis with a CNN-Stacked LSTM Hybrid Model by Shao Shuaijie

“…The new model mentioned in this research is a hybrid model containing convolutional neural network (CNN), stacked multi-layer long short-term memory (LSTM) and max pooling layers. …”
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