Mathematics

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15 pages, 1724 KiB

Open AccessArticle

A Decentralized Federated Learning Based on Node Selection and Knowledge Distillation

by Zhongchang Zhou, Fenggang Sun, Xiangyu Chen, Dongxu Zhang, Tianzhen Han and Peng Lan

Mathematics 2023, 11(14), 3162; https://doi.org/10.3390/math11143162 - 18 Jul 2023

Cited by 1 | Viewed by 2322

Federated learning has become increasingly important for modern machine learning, especially for data privacy sensitive scenarios. Existing federated learning mainly adopts a central server-based network topology, however, the training process of which is susceptible to the central node. To address this problem, this [...] Read more.

Federated learning has become increasingly important for modern machine learning, especially for data privacy sensitive scenarios. Existing federated learning mainly adopts a central server-based network topology, however, the training process of which is susceptible to the central node. To address this problem, this article proposed a decentralized federated learning method based on node selection and knowledge distillation. Specifically, the central node in this method is variable, and it is selected by the indicator interaction between nodes. Meanwhile, the knowledge distillation mechanism is added to make the student model as close as possible to the teacher’s network and ensure the model’s accuracy. The experiments were conducted on the public MNIST, CIFAR-10, and FEMNIST datasets for both the Independent Identically Distribution (IID) setting and the non-IID setting. Numerical results show that the proposed method can achieve an improved accuracy as compared to the centralized federated learning method, and the computing time is reduced greatly with less accuracy loss as compared to the blockchain decentralized federated learning. Therefore, the proposed method guarantees the model effect while meeting the individual model requirements of each node and reducing the running time. Full article

(This article belongs to the Special Issue Artificial Intelligence and Internet of Things for Intelligent Systems)

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17 pages, 4357 KiB

Open AccessArticle

Blockchain-Enabled M2M Communications for UAV-Assisted Data Transmission

by Abdulaziz Aldaej, Tariq Ahamed Ahanger and Imdad Ullah

Mathematics 2023, 11(10), 2262; https://doi.org/10.3390/math11102262 - 11 May 2023

Viewed by 1624

Abstract

Internet of Things (IoT) technology has uncovered a wide range of possibilities in several industrial sectors where smart devices are capable of exchanging real-time data. Machine-to-machine (M2M) data exchange provides a new method for connecting and exchanging data among machine-oriented communication entities (MOCE). [...] Read more.

Internet of Things (IoT) technology has uncovered a wide range of possibilities in several industrial sectors where smart devices are capable of exchanging real-time data. Machine-to-machine (M2M) data exchange provides a new method for connecting and exchanging data among machine-oriented communication entities (MOCE). Conspicuously, network services will be severely affected if the underneath IoT infrastructure is disrupted. Moreover, it is difficult for MOCEs to re-establish connectivity automatically. Conspicuously, in the current paper, an analysis is performed regarding potential technologies including unmanned aerial vehicles, blockchain, and mobile edge computing (MEC) that can enable the secure establishment of M2M communications networks that have been compromised to maintain the secure transmissible data. Furthermore, a Markov decision process-based joint optimization approach is proposed for blockchain systems that aims to elevate computational power and performance. Additionally, the dueling deep Q-network (DDQ) is incorporated to address the dynamic and complex optimization issue so that UAV selection is ensured to maximize performance. The results of experimental simulation with several statistical attributes suggest that the proposed framework can increase throughput optimally in comparison to state-of-the-art techniques. Additionally, a performance measure of reliability and stability depicts significant enhancement for the proposed framework. Full article

(This article belongs to the Special Issue Artificial Intelligence and Internet of Things for Intelligent Systems)

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13 pages, 1099 KiB

Open AccessArticle

Deep Reinforcement Learning Heterogeneous Channels for Poisson Multiple Access

by Xu Zhang, Pingping Chen, Genjian Yu and Shaohao Wang

Mathematics 2023, 11(4), 992; https://doi.org/10.3390/math11040992 - 15 Feb 2023

Cited by 3 | Viewed by 1488

Abstract

This paper proposes a medium access control (MAC) protocol based on deep reinforcement learning (DRL), i.e., multi-channel transmit deep-reinforcement learning multi-channel access (MCT-DLMA) in heterogeneous wireless networks (HetNets). The work concerns practical unsaturated channel traffic that arrives following a Poisson distribution instead of [...] Read more.

This paper proposes a medium access control (MAC) protocol based on deep reinforcement learning (DRL), i.e., multi-channel transmit deep-reinforcement learning multi-channel access (MCT-DLMA) in heterogeneous wireless networks (HetNets). The work concerns practical unsaturated channel traffic that arrives following a Poisson distribution instead of saturated traffic that arrives before.By learning the access mode from historical information, MCT-DLMA can well fill the spectrum holes in the communication of existing users. In particular, it enables the cognitive user to multi-channel transmit at a time, e.g., via the multi-carrier technology. Thus, the spectrum resource can be fully utilized, and the sum throughput of the HetNet is maximized. Simulation results show that the proposed algorithm provides a much higher throughput than the conventional schemes, i.e., the whittle index policy and the DLMA algorithms for both the saturated and unsaturated traffic, respectively. In addition, it also achieves a near-optimal result in dynamic environments with changing primary users, which proves the enhanced robustness to time-varying communications. Full article

(This article belongs to the Special Issue Artificial Intelligence and Internet of Things for Intelligent Systems)

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23 pages, 37159 KiB

Open AccessArticle

A Seq2Seq Model Improved by Transcendental Learning and Imaged Sequence Samples for Porosity Prediction

by Lijian Zhou, Lijun Wang, Zhiang Zhao, Yuwei Liu and Xiwu Liu

Mathematics 2023, 11(1), 39; https://doi.org/10.3390/math11010039 - 22 Dec 2022

Cited by 1 | Viewed by 1330

Abstract

Since the accurate prediction of porosity is one of the critical factors for estimating oil and gas reservoirs, a novel porosity prediction method based on Imaged Sequence Samples (ISS) and a Sequence to Sequence (Seq2Seq) model fused by Transcendental Learning (TL) is proposed [...] Read more.

Since the accurate prediction of porosity is one of the critical factors for estimating oil and gas reservoirs, a novel porosity prediction method based on Imaged Sequence Samples (ISS) and a Sequence to Sequence (Seq2Seq) model fused by Transcendental Learning (TL) is proposed using well-logging data. Firstly, to investigate the correlation between logging features and porosity, the original logging features are normalized and selected by computing their correlation with porosity to obtain the point samples. Secondly, to better represent the depositional relations with depths, an ISS set is established by slidingly grouping sample points across depth, and the selected logging features are in a row. Therefore, spatial relations among the features are established along the vertical and horizontal directions. Thirdly, since the Seq2Seq model can better extract the spatio-temporal information of the input data than the Bidirectional Gate Recurrent Unit (BGRU), the Seq2Seq model is introduced for the first time to address the logging data and predict porosity. The experimental results show that it can achieve superior prediction results than state-of-the-art. However, the cumulative bias is likely to appear when using the Seq2Seq model. Motivated by teacher forcing, the idea of TL is proposed to be incorporated into the decoding process of Seq2Seq, named the TL-Seq2Seq model. The self-well and inter-well experimental results show that the proposed approach can significantly improve the accuracy of porosity prediction. Full article

(This article belongs to the Special Issue Artificial Intelligence and Internet of Things for Intelligent Systems)

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20 pages, 4100 KiB

Open AccessArticle

An Exploration of Architectural Design Factors with a Consideration of Natural Aspects Based on Web Crawling and Text Mining

by Dongmiao Zhao, Yufeng Liu, Boyi Pei, Xingtian Wang, Sheng Miao and Weijun Gao

Mathematics 2022, 10(23), 4407; https://doi.org/10.3390/math10234407 - 22 Nov 2022

Cited by 1 | Viewed by 1912

Abstract

Architectural construction is responsible for the consumption of large amounts of resources, so the optimization of architectural design and evaluation is significant for sustainable global development. Most architectural assessments focus on energy conservation, novel materials and eco-friendly strategies, but without agreed indicators and [...] Read more.

Architectural construction is responsible for the consumption of large amounts of resources, so the optimization of architectural design and evaluation is significant for sustainable global development. Most architectural assessments focus on energy conservation, novel materials and eco-friendly strategies, but without agreed indicators and criteria. Since the consideration of natural aspects is somewhat fuzzy and vague, this study utilized data mining technology to explore the major factors related to relationships between buildings and nature. By employing the popular technique of web crawling, this study collected 38,320 architectural descriptions from the “Archdaily”, including descriptions of 11 types of buildings, four of which were taken as typical research representatives. The 100 most frequent words were used to create a word cloud. Using Python script, all of the text was refined and processed with the word2vec model, thereby allowing to conduct Agglomerative Hierarchical Clustering (AHC). The frequency of words related to natural aspects were analyzed within 15 architectural design elements. Different building types in different areas have obvious similarities in terms of design elements, so it is feasible to adopt the same evaluation factors for the building evaluation systems of different regions. This paper mainly focuses on improving the accuracy and validity of assessment by providing basic evaluation indicators that could enhance connections between design and evaluation progress, stimulating the improvement of building environmental performance. Full article

(This article belongs to the Special Issue Artificial Intelligence and Internet of Things for Intelligent Systems)

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15 pages, 3124 KiB

Open AccessArticle

Prediction of Natural Rubber Customs Declaration Price Based on Wavelet Decomposition and GA-BP Neural Network Group

by Hongjie Yi, Ke Zhang, Kun Ma, Lijian Zhou and Futong Tang

Mathematics 2022, 10(22), 4264; https://doi.org/10.3390/math10224264 - 15 Nov 2022

Cited by 4 | Viewed by 1261

Abstract

Natural rubber is mainly dependent on import in China, its domestic market price is influenced by the Natural Rubber Customs Declaration Price (NRCDP). Considering the fluctuating properties of the NRCDP, a method of the NRCDP based on Wavelet and the optimized Back Propagation [...] Read more.

Natural rubber is mainly dependent on import in China, its domestic market price is influenced by the Natural Rubber Customs Declaration Price (NRCDP). Considering the fluctuating properties of the NRCDP, a method of the NRCDP based on Wavelet and the optimized Back Propagation (BP) neural network Group using a Genetic Algorithm (W-GA-BPG) is proposed. First, an NRCDP dataset is established based on the original Customs Declaration Price (CDP) dataset collected by Qingdao Customs, in which the commodity types are selected consistently according to the sampling intervals, and the features are deleted if they are less affected by the fluctuation of NRCDP. Secondly, the selected features in NRCDP are decomposed using wavelet transform to obtain a group of feature sequences with different scales. Then, a Group of BP neural networks (BPG) optimized by Genetic Algorithm (GA) is used to predict multiple decomposition sub-sequences, respectively. Finally, the predicted values are obtained through wavelet reconstruction. Combined with the NRCDP dataset, the W-GA-BPG model is established by comparing and analyzing experiments by evaluating the Mean Square Error (MSE) and determination coefficient of the prediction results. The MSE and determination coefficient predicted using the proposed model are 0.0043 and 0.9302, respectively, which is the best prediction effect. Full article

(This article belongs to the Special Issue Artificial Intelligence and Internet of Things for Intelligent Systems)

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17 pages, 16578 KiB

Open AccessArticle

OCPHN: Outfit Compatibility Prediction with Hypergraph Networks

by Zhuo Li, Jian Li, Tongtong Wang, Xiaolin Gong, Yinwei Wei and Peng Luo

Mathematics 2022, 10(20), 3913; https://doi.org/10.3390/math10203913 - 21 Oct 2022

Cited by 1 | Viewed by 1334

Abstract

With the rapid development of the online shopping, the pursuit of outfit compatibility has become a basic requirement for an increasing number of customers. The existing work on outfit compatibility prediction largely focuses on modeling pairwise item compatibility without considering modeling the whole [...] Read more.

With the rapid development of the online shopping, the pursuit of outfit compatibility has become a basic requirement for an increasing number of customers. The existing work on outfit compatibility prediction largely focuses on modeling pairwise item compatibility without considering modeling the whole outfit directly. To address the problem, in this paper, we propose a novel hypergraph-based compatibility modeling scheme named OCPHN, which is able to better model complex relationships among outfits. In OCPHN, we represent the outfit as a hypergraph, where each hypernode represents a category and each hyperedge represents the interactions between multiple categories (i.e., they appear in the same outfit). To better predict outfit compatibility, the hypergraph is transformed into a simple graph, and the message propagation mechanism in the graph convolution network is used to aggregate the neighbours’ information on the node and update the node representations. Furthermore, with learned node representations, an attention mechanism is introduced to compute the outfit compatibility score. Using a benchmark dataset, the experimental results show that the proposed method is an improvement over the strangest baselines in terms of accuracy by about 3% and 1% in the fill-in-the-blank and compatibility prediction tasks, respectively. Full article

(This article belongs to the Special Issue Artificial Intelligence and Internet of Things for Intelligent Systems)

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15 pages, 5674 KiB

Open AccessArticle

Multi-Channel EEG Emotion Recognition Based on Parallel Transformer and 3D-Convolutional Neural Network

by Jie Sun, Xuan Wang, Kun Zhao, Siyuan Hao and Tianyu Wang

Mathematics 2022, 10(17), 3131; https://doi.org/10.3390/math10173131 - 01 Sep 2022

Cited by 14 | Viewed by 2971

Abstract

Due to its covert and real-time properties, electroencephalography (EEG) has long been the medium of choice for emotion identification research. Currently, EEG-based emotion recognition focuses on exploiting temporal, spatial, and spatiotemporal EEG data for emotion recognition. Due to the lack of consideration of [...] Read more.

Due to its covert and real-time properties, electroencephalography (EEG) has long been the medium of choice for emotion identification research. Currently, EEG-based emotion recognition focuses on exploiting temporal, spatial, and spatiotemporal EEG data for emotion recognition. Due to the lack of consideration of both spatial and temporal aspects of EEG data, the accuracy of EEG emotion detection algorithms employing solely spatial or temporal variables is low. In addition, approaches that use spatiotemporal properties of EEG for emotion recognition take temporal and spatial characteristics of EEG into account; however, these methods extract temporal and spatial information directly from EEG data. Since there is no reconstruction of the EEG data format, the temporal and spatial properties of the EEG data cannot be extracted efficiently. To address the aforementioned issues, this research proposes a multi-channel EEG emotion identification model based on the parallel transformer and three-dimensional convolutional neural networks (3D-CNN). First, parallel channel EEG data and position reconstruction EEG sequence data are created separately. The temporal and spatial characteristics of EEG are then retrieved using transformer and 3D-CNN models. Finally, the features of the two parallel modules are combined to form the final features for emotion recognition. On the DEAP, Dreamer, and SEED databases, the technique achieved greater accuracy in emotion recognition than other methods. It demonstrates the efficiency of the strategy described in this paper. Full article

(This article belongs to the Special Issue Artificial Intelligence and Internet of Things for Intelligent Systems)

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