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Advances in Information Sciences and Applications

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 37223

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Guest Editor
Department of Computer Science and Engineering, Chung-Ang University, 84 Heukseok-ro, Heukseok-dong, Dongjak-gu, Seoul 06974, Republic of Korea
Interests: Artificial Intelligence; machine learning; neural architecture design; feature engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid advancement of artificial intelligence, information science proves its importance again as a core technique for realizing effective, efficient, and robust intelligent systems. Although the information techniques frequently used for the analytical purpose, such as optimizing artificial neural networks' generalization performance, also impacts all the processing pipeline for transforming data to information such as data preparation, preprocessing, modeling, analysis, interpretation, and evaluation. As a result, it plays an essential role in diverse fields relating information sciences such as intelligent systems, genetic algorithms and modeling, expert and decision support systems, bioinformatics, self-adaptation systems, self-organizational systems, data engineering, data fusion, perceptions and pattern recognition, and text processing. This Special Issue aims to solicit and publish papers that provide a clear view of state-of-the-art research activities in information sciences and diverse backgrounds in engineering, mathematics, statistics, computer science, biology, cognitive science, neurobiology, behavioral sciences, and biochemistry. We, therefore, encourage submissions in, but not limited to the following areas:

  • Data Preparation, Preprocessing, and Transformation: Data cleansing, data normalization, data quantization, data fusion, missing value treatment, feature creation, feature selection, feature extraction, imbalance data treatment;
  • Search, Optimization, and Planning: Metaheuristic algorithms and modeling, hybrid search algorithms, combinatorial optimization, adaptive and supervisory control, self-adaptation and self-organizational systems, mobile robot path planning;
  • Modeling, Learning, and Analysis: Supervised learning, unsupervised learning, reinforcement learning, metric learning, transfer learning, federated learning, neural architecture search and design, network compression and quantization, symbolic and statistical learning, ensemble system, error optimization;
  • The measure of Information, Dependency, and Uncertainty: Kullback-Leibler divergence, Renyi entropy, cross-entropy, Tsallis entropy, differential entropy, mutual information, interaction information, normalized mutual information, symmetric uncertainty.
  • Applications: Manufacturing, automation robots, mobile robots, virtual reality, image processing systems, computer vision systems, genomics and bioinformatics, language engine design, human–computer interface, text abstraction, text summarization, finance and economics modeling.

Prof. Dr. Jaesung Lee
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

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Keywords

  • Information Science
  • Data Preprocessing
  • Search and Optimization
  • Machine Learning
  • Deep Learning
  • Reinforcement Learning
  • Statistical Modeling
  • Interpretation
  • Evaluation
  • Information-based Application
  • Measure of Information

Related Special Issue

Published Papers (16 papers)

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Research

16 pages, 2072 KiB  
Article
Adversarial Decision-Making for Moving Target Defense: A Multi-Agent Markov Game and Reinforcement Learning Approach
by Qian Yao, Yongjie Wang, Xinli Xiong, Peng Wang and Yang Li
Entropy 2023, 25(4), 605; https://doi.org/10.3390/e25040605 - 02 Apr 2023
Cited by 3 | Viewed by 1847
Abstract
Reinforcement learning has shown a great ability and has defeated human beings in the field of real-time strategy games. In recent years, reinforcement learning has been used in cyberspace to carry out automated and intelligent attacks. Traditional defense methods are not enough to [...] Read more.
Reinforcement learning has shown a great ability and has defeated human beings in the field of real-time strategy games. In recent years, reinforcement learning has been used in cyberspace to carry out automated and intelligent attacks. Traditional defense methods are not enough to deal with this problem, so it is necessary to design defense agents to counter intelligent attacks. The interaction between the attack agent and the defense agent can be modeled as a multi-agent Markov game. In this paper, an adversarial decision-making approach that combines the Bayesian Strong Stackelberg and the WoLF algorithms was proposed to obtain the equilibrium point of multi-agent Markov games. With this method, the defense agent can obtain the adversarial decision-making strategy as well as continuously adjust the strategy in cyberspace. As verified in experiments, the defense agent should attach importance to short-term rewards in the process of a real-time game between the attack agent and the defense agent. The proposed approach can obtain the largest rewards for defense agent compared with the classic Nash-Q and URS-Q algorithms. In addition, the proposed approach adjusts the action selection probability dynamically, so that the decision entropy of optimal action gradually decreases. Full article
(This article belongs to the Special Issue Advances in Information Sciences and Applications)
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22 pages, 4890 KiB  
Article
A Hybrid Genetic Algorithm Based on Imitation Learning for the Airport Gate Assignment Problem
by Cong Ding, Jun Bi and Yongxing Wang
Entropy 2023, 25(4), 565; https://doi.org/10.3390/e25040565 - 25 Mar 2023
Viewed by 1251
Abstract
Airport gates are the main places for aircraft to receive ground services. With the increased number of flights, limited gate resources near to the terminal make the gate assignment work more complex. Traditional solution methods based on mathematical programming models and iterative algorithms [...] Read more.
Airport gates are the main places for aircraft to receive ground services. With the increased number of flights, limited gate resources near to the terminal make the gate assignment work more complex. Traditional solution methods based on mathematical programming models and iterative algorithms are usually used to solve these static situations, lacking learning and real-time decision-making abilities. In this paper, a two-stage hybrid algorithm based on imitation learning and genetic algorithm (IL-GA) is proposed to solve the gate assignment problem. First of all, the problem is defined from a mathematical model to a Markov decision process (MDP), with the goal of maximizing the number of flights assigned to contact gates and the total gate preferences. In the first stage of the algorithm, a deep policy network is created to obtain the gate selection probability of each flight. This policy network is trained by imitating and learning the assignment trajectory data of human experts, and this process is offline. In the second stage of the algorithm, the policy network is used to generate a good initial population for the genetic algorithm to calculate the optimal solution for an online instance. The experimental results show that the genetic algorithm combined with imitation learning can greatly shorten the iterations and improve the population convergence speed. The flight rate allocated to the contact gates is 14.9% higher than the manual allocation result and 4% higher than the traditional genetic algorithm. Learning the expert assignment data also makes the allocation scheme more consistent with the preference of the airport, which is helpful for the practical application of the algorithm. Full article
(This article belongs to the Special Issue Advances in Information Sciences and Applications)
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21 pages, 337 KiB  
Article
Analyzing the Effect of Imputation on Classification Performance under MCAR and MAR Missing Mechanisms
by Philip Buczak, Jian-Jia Chen and Markus Pauly
Entropy 2023, 25(3), 521; https://doi.org/10.3390/e25030521 - 17 Mar 2023
Cited by 1 | Viewed by 1198
Abstract
Many datasets in statistical analyses contain missing values. As omitting observations containing missing entries may lead to information loss or greatly reduce the sample size, imputation is usually preferable. However, imputation can also introduce bias and impact the quality and validity of subsequent [...] Read more.
Many datasets in statistical analyses contain missing values. As omitting observations containing missing entries may lead to information loss or greatly reduce the sample size, imputation is usually preferable. However, imputation can also introduce bias and impact the quality and validity of subsequent analysis. Focusing on binary classification problems, we analyzed how missing value imputation under MCAR as well as MAR missingness with different missing patterns affects the predictive performance of subsequent classification. To this end, we compared imputation methods such as several MICE variants, missForest, Hot Deck as well as mean imputation with regard to the classification performance achieved with commonly used classifiers such as Random Forest, Extreme Gradient Boosting, Support Vector Machine and regularized logistic regression. Our simulation results showed that Random Forest based imputation (i.e., MICE Random Forest and missForest) performed particularly well in most scenarios studied. In addition to these two methods, simple mean imputation also proved to be useful, especially when many features (covariates) contained missing values. Full article
(This article belongs to the Special Issue Advances in Information Sciences and Applications)
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17 pages, 598 KiB  
Article
A New Reliability Coefficient Using Betting Commitment Evidence Distance in Dempster–Shafer Evidence Theory for Uncertain Information Fusion
by Yongchuan Tang, Shuaihong Wu, Ying Zhou, Yubo Huang and Deyun Zhou
Entropy 2023, 25(3), 462; https://doi.org/10.3390/e25030462 - 06 Mar 2023
Cited by 2 | Viewed by 1476
Abstract
Dempster–Shafer evidence theory is widely used to deal with uncertain information by evidence modeling and evidence reasoning. However, if there is a high contradiction between different pieces of evidence, the Dempster combination rule may give a fusion result that violates the intuitive result. [...] Read more.
Dempster–Shafer evidence theory is widely used to deal with uncertain information by evidence modeling and evidence reasoning. However, if there is a high contradiction between different pieces of evidence, the Dempster combination rule may give a fusion result that violates the intuitive result. Many methods have been proposed to solve conflict evidence fusion, and it is still an open issue. This paper proposes a new reliability coefficient using betting commitment evidence distance in Dempster–Shafer evidence theory for conflict and uncertain information fusion. The single belief function for belief assignment in the initial frame of discernment is defined. After evidence preprocessing with the proposed reliability coefficient and single belief function, the evidence fusion result can be calculated with the Dempster combination rule. To evaluate the effectiveness of the proposed uncertainty measure, a new method of uncertain information fusion based on the new evidence reliability coefficient is proposed. The experimental results on UCI machine learning data sets show the availability and effectiveness of the new reliability coefficient for uncertain information processing. Full article
(This article belongs to the Special Issue Advances in Information Sciences and Applications)
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16 pages, 688 KiB  
Article
An Efficient Quantum Secret Sharing Scheme Based on Restricted Threshold Access Structure
by Lei Li and Zhi Li
Entropy 2023, 25(2), 265; https://doi.org/10.3390/e25020265 - 31 Jan 2023
Cited by 2 | Viewed by 1521
Abstract
Quantum secret sharing is an important branch of quantum cryptography, and secure multi-party quantum key distribution protocols can be constructed using quantum secret sharing. In this paper, we construct a quantum secret sharing scheme built on a constrained (t, n ) [...] Read more.
Quantum secret sharing is an important branch of quantum cryptography, and secure multi-party quantum key distribution protocols can be constructed using quantum secret sharing. In this paper, we construct a quantum secret sharing scheme built on a constrained (t, n ) threshold access structure, where n is the number of participants and t is the threshold number of participants and the distributor. Participants from two different sets perform the corresponding phase shift operations on two particles in the GHZ state passed to them, and then t1 participants with the distributor can recover the key, where the participant recovering the key measures the particles received by himself and finally obtains the key through the collaboration of the distributors. Security analysis shows that this protocol can be resistant to direct measurement attacks, interception retransmission attacks, and entanglement measurement attacks. This protocol is more secure, flexible, and efficient compared with similar existing protocols, which can save more quantum resources. Full article
(This article belongs to the Special Issue Advances in Information Sciences and Applications)
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18 pages, 589 KiB  
Article
Multi-Objective Multi-Instance Learning: A New Approach to Machine Learning for eSports
by Kokten Ulas Birant and Derya Birant
Entropy 2023, 25(1), 28; https://doi.org/10.3390/e25010028 - 23 Dec 2022
Viewed by 1969
Abstract
The aim of this study is to develop a new approach to be able to correctly predict the outcome of electronic sports (eSports) matches using machine learning methods. Previous research has emphasized player-centric prediction and has used standard (single-instance) classification techniques. However, a [...] Read more.
The aim of this study is to develop a new approach to be able to correctly predict the outcome of electronic sports (eSports) matches using machine learning methods. Previous research has emphasized player-centric prediction and has used standard (single-instance) classification techniques. However, a team-centric classification is required since team cooperation is essential in completing game missions and achieving final success. To bridge this gap, in this study, we propose a new approach, called Multi-Objective Multi-Instance Learning (MOMIL). It is the first study that applies the multi-instance learning technique to make win predictions in eSports. The proposed approach jointly considers the objectives of the players in a team to capture relationships between players during the classification. In this study, entropy was used as a measure to determine the impurity (uncertainty) of the training dataset when building decision trees for classification. The experiments that were carried out on a publicly available eSports dataset show that the proposed multi-objective multi-instance classification approach outperforms the standard classification approach in terms of accuracy. Unlike the previous studies, we built the models on season-based data. Our approach is up to 95% accurate for win prediction in eSports. Our method achieved higher performance than the state-of-the-art methods tested on the same dataset. Full article
(This article belongs to the Special Issue Advances in Information Sciences and Applications)
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20 pages, 2602 KiB  
Article
Path Planning Research of a UAV Base Station Searching for Disaster Victims’ Location Information Based on Deep Reinforcement Learning
by Jinduo Zhao, Zhigao Gan, Jiakai Liang, Chao Wang, Keqiang Yue, Wenjun Li, Yilin Li and Ruixue Li
Entropy 2022, 24(12), 1767; https://doi.org/10.3390/e24121767 - 02 Dec 2022
Cited by 5 | Viewed by 1619
Abstract
Aiming at the path planning problem of unmanned aerial vehicle (UAV) base stations when performing search tasks, this paper proposes a Double DQN-state splitting Q network (DDQN-SSQN) algorithm that combines state splitting and optimal state to complete the optimal path planning of UAV [...] Read more.
Aiming at the path planning problem of unmanned aerial vehicle (UAV) base stations when performing search tasks, this paper proposes a Double DQN-state splitting Q network (DDQN-SSQN) algorithm that combines state splitting and optimal state to complete the optimal path planning of UAV based on the Deep Reinforcement Learning DDQN algorithm. The method stores multidimensional state information in categories and uses targeted training to obtain optimal path information. The method also references the received signal strength indicator (RSSI) to influence the reward received by the agent, and in this way reduces the decision difficulty of the UAV. In order to simulate the scenarios of UAVs in real work, this paper uses the Open AI Gym simulation platform to construct a mission system model. The simulation results show that the proposed scheme can plan the optimal path faster than other traditional algorithmic schemes and has a greater advantage in the stability and convergence speed of the algorithm. Full article
(This article belongs to the Special Issue Advances in Information Sciences and Applications)
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15 pages, 1951 KiB  
Article
Retrieval and Ranking of Combining Ontology and Content Attributes for Scientific Document
by Xinyu Jiang, Bingjie Tian and Xuedong Tian
Entropy 2022, 24(6), 810; https://doi.org/10.3390/e24060810 - 10 Jun 2022
Cited by 3 | Viewed by 1516
Abstract
Traditional mathematical search models retrieve scientific documents only by mathematical expressions and their contexts and do not consider the ontological attributes of scientific documents, which result in gaps between the queries and the retrieval results. To solve this problem, a retrieval and ranking [...] Read more.
Traditional mathematical search models retrieve scientific documents only by mathematical expressions and their contexts and do not consider the ontological attributes of scientific documents, which result in gaps between the queries and the retrieval results. To solve this problem, a retrieval and ranking model is constructed that synthesizes the information of mathematical expressions with related texts, and the ontology attributes of scientific documents are extracted to further sort the retrieval results. First, the hesitant fuzzy set of mathematical expressions is constructed by using the characteristics of the hesitant fuzzy set to address the multi-attribute problem of mathematical expression matching; then, the similarity of the mathematical expression context sentence is calculated by using the BiLSTM two-way coding feature, and the retrieval result is obtained by synthesizing the similarity between the mathematical expression and the sentence; finally, considering the ontological attributes of scientific documents, the retrieval results are ranked to obtain the final search results. The MAP_10 value of the mathematical expression retrieval results on the Ntcir-Mathir-Wikipedia-Corpus dataset is 0.815, and the average value of the NDCG@10 of the scientific document ranking results is 0.9; these results prove the effectiveness of the scientific document retrieval and ranking method. Full article
(This article belongs to the Special Issue Advances in Information Sciences and Applications)
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21 pages, 1125 KiB  
Article
Traceable Scheme of Public Key Encryption with Equality Test
by Huijun Zhu, Qingji Xue, Tianfeng Li and Dong Xie
Entropy 2022, 24(3), 309; https://doi.org/10.3390/e24030309 - 22 Feb 2022
Cited by 2 | Viewed by 1588
Abstract
Public key encryption supporting equality test (PKEwET) schemes, because of their special function, have good applications in many fields, such as in cloud computing services, blockchain, and the Internet of Things. The original PKEwET has no authorization function. Subsequently, many PKEwET schemes have [...] Read more.
Public key encryption supporting equality test (PKEwET) schemes, because of their special function, have good applications in many fields, such as in cloud computing services, blockchain, and the Internet of Things. The original PKEwET has no authorization function. Subsequently, many PKEwET schemes have been proposed with the ability to perform authorization against various application scenarios. However, these schemes are incapable of traceability to the ciphertexts. In this paper, the ability of tracing to the ciphertexts is introduced into a PKEwET scheme. For the ciphertexts, the presented scheme supports not only the equality test, but also has the function of traceability. Meanwhile, the security of the proposed scheme is revealed by a game between an adversary and a simulator, and it achieves a desirable level of security. Depending on the attacker’s privileges, it can resist OW-CCA security against an adversary with a trapdoor, and can resist IND-CCA security against an adversary without a trapdoor. Finally, the performance of the presented scheme is discussed. Full article
(This article belongs to the Special Issue Advances in Information Sciences and Applications)
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15 pages, 3797 KiB  
Article
Missing Value Imputation Method for Multiclass Matrix Data Based on Closed Itemset
by Mayu Tada, Natsumi Suzuki and Yoshifumi Okada
Entropy 2022, 24(2), 286; https://doi.org/10.3390/e24020286 - 16 Feb 2022
Cited by 3 | Viewed by 2413
Abstract
Handling missing values in matrix data is an important step in data analysis. To date, many methods to estimate missing values based on data pattern similarity have been proposed. Most previously proposed methods perform missing value imputation based on data trends over the [...] Read more.
Handling missing values in matrix data is an important step in data analysis. To date, many methods to estimate missing values based on data pattern similarity have been proposed. Most previously proposed methods perform missing value imputation based on data trends over the entire feature space. However, individual missing values are likely to show similarity to data patterns in local feature space. In addition, most existing methods focus on single class data, while multiclass analysis is frequently required in various fields. Missing value imputation for multiclass data must consider the characteristics of each class. In this paper, we propose two methods based on closed itemsets, CIimpute and ICIimpute, to achieve missing value imputation using local feature space for multiclass matrix data. CIimpute estimates missing values using closed itemsets extracted from each class. ICIimpute is an improved method of CIimpute in which an attribute reduction process is introduced. Experimental results demonstrate that attribute reduction considerably reduces computational time and improves imputation accuracy. Furthermore, it is shown that, compared to existing methods, ICIimpute provides superior imputation accuracy but requires more computational time. Full article
(This article belongs to the Special Issue Advances in Information Sciences and Applications)
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15 pages, 16297 KiB  
Article
A Novel Adaptive Feature Fusion Strategy for Image Retrieval
by Xiaojun Lu, Libo Zhang, Lei Niu, Qing Chen and Jianping Wang
Entropy 2021, 23(12), 1670; https://doi.org/10.3390/e23121670 - 12 Dec 2021
Cited by 4 | Viewed by 2294
Abstract
In the era of big data, it is challenging to efficiently retrieve the required images from the vast amount of data. Therefore, a content-based image retrieval system is an important research direction to address this problem. Furthermore, a multi-feature-based image retrieval system can [...] Read more.
In the era of big data, it is challenging to efficiently retrieve the required images from the vast amount of data. Therefore, a content-based image retrieval system is an important research direction to address this problem. Furthermore, a multi-feature-based image retrieval system can compensate for the shortage of a single feature to a certain extent, which is essential for improving retrieval system performance. Feature selection and feature fusion strategies are critical in the study of multi-feature fusion image retrieval. This paper proposes a multi-feature fusion image retrieval strategy with adaptive features based on information entropy theory. Firstly, we extract the image features, construct the distance function to calculate the similarity using the information entropy proposed in this paper, and obtain the initial retrieval results. Then, we obtain the precision of single feature retrieval based on the correlation feedback as the retrieval trust and use the retrieval trust to select the effective features automatically. After that, we initialize the weights of selected features using the average weights, construct the probability transfer matrix, and use the PageRank algorithm to update the initialized feature weights to obtain the final weights. Finally, we calculate the comprehensive similarity based on the final weights and output the detection results. This has two advantages: (1) the proposed strategy uses multiple features for image retrieval, which has better performance and more substantial generalization than the retrieval strategy based on a single feature; (2) compared with the fixed-feature retrieval strategy, our method selects the best features for fusion in each query, which takes full advantages of each feature. The experimental results show that our proposed method outperforms other methods. In the datasets of Corel1k, UC Merced Land-Use, and RSSCN7, the top10 retrieval precision is 99.55%, 88.02%, and 88.28%, respectively. In the Holidays dataset, the mean average precision (mAP) was 92.46%. Full article
(This article belongs to the Special Issue Advances in Information Sciences and Applications)
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30 pages, 19214 KiB  
Article
An Improved Moth-Flame Optimization Algorithm with Adaptation Mechanism to Solve Numerical and Mechanical Engineering Problems
by Mohammad H. Nadimi-Shahraki, Ali Fatahi, Hoda Zamani, Seyedali Mirjalili and Laith Abualigah
Entropy 2021, 23(12), 1637; https://doi.org/10.3390/e23121637 - 06 Dec 2021
Cited by 52 | Viewed by 4029
Abstract
Moth-flame optimization (MFO) algorithm inspired by the transverse orientation of moths toward the light source is an effective approach to solve global optimization problems. However, the MFO algorithm suffers from issues such as premature convergence, low population diversity, local optima entrapment, and imbalance [...] Read more.
Moth-flame optimization (MFO) algorithm inspired by the transverse orientation of moths toward the light source is an effective approach to solve global optimization problems. However, the MFO algorithm suffers from issues such as premature convergence, low population diversity, local optima entrapment, and imbalance between exploration and exploitation. In this study, therefore, an improved moth-flame optimization (I-MFO) algorithm is proposed to cope with canonical MFO’s issues by locating trapped moths in local optimum via defining memory for each moth. The trapped moths tend to escape from the local optima by taking advantage of the adapted wandering around search (AWAS) strategy. The efficiency of the proposed I-MFO is evaluated by CEC 2018 benchmark functions and compared against other well-known metaheuristic algorithms. Moreover, the obtained results are statistically analyzed by the Friedman test on 30, 50, and 100 dimensions. Finally, the ability of the I-MFO algorithm to find the best optimal solutions for mechanical engineering problems is evaluated with three problems from the latest test-suite CEC 2020. The experimental and statistical results demonstrate that the proposed I-MFO is significantly superior to the contender algorithms and it successfully upgrades the shortcomings of the canonical MFO. Full article
(This article belongs to the Special Issue Advances in Information Sciences and Applications)
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14 pages, 1261 KiB  
Article
Personal Interest Attention Graph Neural Networks for Session-Based Recommendation
by Xiangde Zhang, Yuan Zhou, Jianping Wang and Xiaojun Lu
Entropy 2021, 23(11), 1500; https://doi.org/10.3390/e23111500 - 12 Nov 2021
Cited by 11 | Viewed by 2657
Abstract
Session-based recommendations aim to predict a user’s next click based on the user’s current and historical sessions, which can be applied to shopping websites and APPs. Existing session-based recommendation methods cannot accurately capture the complex transitions between items. In addition, some approaches compress [...] Read more.
Session-based recommendations aim to predict a user’s next click based on the user’s current and historical sessions, which can be applied to shopping websites and APPs. Existing session-based recommendation methods cannot accurately capture the complex transitions between items. In addition, some approaches compress sessions into a fixed representation vector without taking into account the user’s interest preferences at the current moment, thus limiting the accuracy of recommendations. Considering the diversity of items and users’ interests, a personalized interest attention graph neural network (PIA-GNN) is proposed for session-based recommendation. This approach utilizes personalized graph convolutional networks (PGNN) to capture complex transitions between items, invoking an interest-aware mechanism to activate users’ interest in different items adaptively. In addition, a self-attention layer is used to capture long-term dependencies between items when capturing users’ long-term preferences. In this paper, the cross-entropy loss is used as the objective function to train our model. We conduct rich experiments on two real datasets, and the results show that PIA-GNN outperforms existing personalized session-aware recommendation methods. Full article
(This article belongs to the Special Issue Advances in Information Sciences and Applications)
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45 pages, 9570 KiB  
Article
Selection of the Optimal Number of Topics for LDA Topic Model—Taking Patent Policy Analysis as an Example
by Jingxian Gan and Yong Qi
Entropy 2021, 23(10), 1301; https://doi.org/10.3390/e23101301 - 03 Oct 2021
Cited by 32 | Viewed by 4376
Abstract
This study constructs a comprehensive index to effectively judge the optimal number of topics in the LDA topic model. Based on the requirements for selecting the number of topics, a comprehensive judgment index of perplexity, isolation, stability, and coincidence is constructed to select [...] Read more.
This study constructs a comprehensive index to effectively judge the optimal number of topics in the LDA topic model. Based on the requirements for selecting the number of topics, a comprehensive judgment index of perplexity, isolation, stability, and coincidence is constructed to select the number of topics. This method provides four advantages to selecting the optimal number of topics: (1) good predictive ability, (2) high isolation between topics, (3) no duplicate topics, and (4) repeatability. First, we use three general datasets to compare our proposed method with existing methods, and the results show that the optimal topic number selection method has better selection results. Then, we collected the patent policies of various provinces and cities in China (excluding Hong Kong, Macao, and Taiwan) as datasets. By using the optimal topic number selection method proposed in this study, we can classify patent policies well. Full article
(This article belongs to the Special Issue Advances in Information Sciences and Applications)
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19 pages, 1528 KiB  
Article
Robust Aggregation Operators for Intuitionistic Fuzzy Hypersoft Set with Their Application to Solve MCDM Problem
by Rana Muhammad Zulqarnain, Imran Siddique, Rifaqat Ali, Dragan Pamucar, Dragan Marinkovic and Darko Bozanic
Entropy 2021, 23(6), 688; https://doi.org/10.3390/e23060688 - 29 May 2021
Cited by 40 | Viewed by 2623
Abstract
In this paper, we investigate the multi-criteria decision-making complications under intuitionistic fuzzy hypersoft set (IFHSS) information. The IFHSS is a proper extension of the intuitionistic fuzzy soft set (IFSS) which discusses the parametrization of multi-sub attributes of considered parameters, and accommodates more hesitation [...] Read more.
In this paper, we investigate the multi-criteria decision-making complications under intuitionistic fuzzy hypersoft set (IFHSS) information. The IFHSS is a proper extension of the intuitionistic fuzzy soft set (IFSS) which discusses the parametrization of multi-sub attributes of considered parameters, and accommodates more hesitation comparative to IFSS utilizing the multi sub-attributes of the considered parameters. The main objective of this research is to introduce operational laws for intuitionistic fuzzy hypersoft numbers (IFHSNs). Additionally, based on developed operational laws two aggregation operators (AOs), i.e., intuitionistic fuzzy hypersoft weighted average (IFHSWA) and intuitionistic fuzzy hypersoft weighted geometric (IFHSWG), operators have been presented with their fundamental properties. Furthermore, a decision-making approach has been established utilizing our developed aggregation operators (AOs). Through the established approach, a technique for solving decision-making (DM) complications is proposed to select sustainable suppliers in sustainable supply chain management (SSCM). Moreover, a numerical description is presented to ensure the validity and usability of the proposed technique in the DM process. The practicality, effectivity, and flexibility of the current approach are demonstrated through comparative analysis with the assistance of some prevailing studies. Full article
(This article belongs to the Special Issue Advances in Information Sciences and Applications)
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17 pages, 2676 KiB  
Article
Multi-Scale Aggregation Graph Neural Networks Based on Feature Similarity for Semi-Supervised Learning
by Xun Zhang, Lanyan Yang, Bin Zhang, Ying Liu, Dong Jiang, Xiaohai Qin and Mengmeng Hao
Entropy 2021, 23(4), 403; https://doi.org/10.3390/e23040403 - 28 Mar 2021
Cited by 3 | Viewed by 2337
Abstract
The problem of extracting meaningful data through graph analysis spans a range of different fields, such as social networks, knowledge graphs, citation networks, the World Wide Web, and so on. As increasingly structured data become available, the importance of being able to effectively [...] Read more.
The problem of extracting meaningful data through graph analysis spans a range of different fields, such as social networks, knowledge graphs, citation networks, the World Wide Web, and so on. As increasingly structured data become available, the importance of being able to effectively mine and learn from such data continues to grow. In this paper, we propose the multi-scale aggregation graph neural network based on feature similarity (MAGN), a novel graph neural network defined in the vertex domain. Our model provides a simple and general semi-supervised learning method for graph-structured data, in which only a very small part of the data is labeled as the training set. We first construct a similarity matrix by calculating the similarity of original features between all adjacent node pairs, and then generate a set of feature extractors utilizing the similarity matrix to perform multi-scale feature propagation on graphs. The output of multi-scale feature propagation is finally aggregated by using the mean-pooling operation. Our method aims to improve the model representation ability via multi-scale neighborhood aggregation based on feature similarity. Extensive experimental evaluation on various open benchmarks shows the competitive performance of our method compared to a variety of popular architectures. Full article
(This article belongs to the Special Issue Advances in Information Sciences and Applications)
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