Pub Date : 1900-01-01DOI: 10.32604/JQC.2021.016315
Mingdao Lu, Peng Wei, Mingshu He, Yinglei Teng
With the increasing of civil aviation business, flight delay has become a key problem in civil aviation field in recent years, which has brought a considerable economic impact to airlines and related industries. The delay prediction of specific flights is very important for airlines’ plan, airport resource allocation, insurance company strategy and personal arrangement. The influence factors of flight delay have high complexity and non-linear relationship. The different situations of various regions and airports, and even the deviation of airport or airline arrangement all have certain influence on flight delay, which makes the prediction more difficult. In view of the limitations of the existing delay prediction models, this paper proposes a flight delay prediction model with more generalization ability and corresponding machine learning classification algorithm. This model fully exploits temporal and spatial characteristics of higher dimensions, such as the influence of preceding flights, the situation of departure and landing airports, and the overall situation of flights on the same route. In the process of machine learning, the model is trained with historical data and tested with the latest actual data. The test result shows that the model and this machine learning algorithm can provide an effective method for the prediction of flight delay.
{"title":"Flight Delay Prediction Using Gradient Boosting Machine Learning Classifiers","authors":"Mingdao Lu, Peng Wei, Mingshu He, Yinglei Teng","doi":"10.32604/JQC.2021.016315","DOIUrl":"https://doi.org/10.32604/JQC.2021.016315","url":null,"abstract":"With the increasing of civil aviation business, flight delay has become a key problem in civil aviation field in recent years, which has brought a considerable economic impact to airlines and related industries. The delay prediction of specific flights is very important for airlines’ plan, airport resource allocation, insurance company strategy and personal arrangement. The influence factors of flight delay have high complexity and non-linear relationship. The different situations of various regions and airports, and even the deviation of airport or airline arrangement all have certain influence on flight delay, which makes the prediction more difficult. In view of the limitations of the existing delay prediction models, this paper proposes a flight delay prediction model with more generalization ability and corresponding machine learning classification algorithm. This model fully exploits temporal and spatial characteristics of higher dimensions, such as the influence of preceding flights, the situation of departure and landing airports, and the overall situation of flights on the same route. In the process of machine learning, the model is trained with historical data and tested with the latest actual data. The test result shows that the model and this machine learning algorithm can provide an effective method for the prediction of flight delay.","PeriodicalId":284655,"journal":{"name":"Journal of Quantum Computing","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121530414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.32604/jqc.2022.039913
Goma Tshivetta Christian Fersein Jorvialom, Lord Amoah
{"title":"Reversible Data Hiding with Contrast Enhancement Using Bi-histogram Shifting and Image Adjustment for Color Images","authors":"Goma Tshivetta Christian Fersein Jorvialom, Lord Amoah","doi":"10.32604/jqc.2022.039913","DOIUrl":"https://doi.org/10.32604/jqc.2022.039913","url":null,"abstract":"","PeriodicalId":284655,"journal":{"name":"Journal of Quantum Computing","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133514663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.32604/JQC.2020.015688
Saptarshi Sahoo, Amit Kumar Mandal, P. Samanta, Indranil Basu, Pratik Roy
Quantum Computing and Quantum Information Science seem very promising and developing rapidly since its inception in early 1980s by Paul Benioff with the proposal of quantum mechanical model of the Turing machine and later By Richard Feynman and Yuri Manin for the proposal of a quantum computers for simulating various problems that classical computer could not. Quantum computers have a computational advantage for some problems, over classical computers and most applications are trying to use an efficient combination of classical and quantum computers like Shor’s factoring algorithm. Other areas that are expected to be benefitted from quantum computing are Machine Learning and deep learning, molecular biology, genomics and cancer research, space exploration, atomic and nuclear research and macro-economic forecasting. This paper represents a brief overview of the state of art of quantum computing and quantum information science with discussions of various theoretical and experimental aspects adopted by the researchers.
{"title":"A critical overview on Quantum Computing","authors":"Saptarshi Sahoo, Amit Kumar Mandal, P. Samanta, Indranil Basu, Pratik Roy","doi":"10.32604/JQC.2020.015688","DOIUrl":"https://doi.org/10.32604/JQC.2020.015688","url":null,"abstract":"Quantum Computing and Quantum Information Science seem very promising and developing rapidly since its inception in early 1980s by Paul Benioff with the proposal of quantum mechanical model of the Turing machine and later By Richard Feynman and Yuri Manin for the proposal of a quantum computers for simulating various problems that classical computer could not. Quantum computers have a computational advantage for some problems, over classical computers and most applications are trying to use an efficient combination of classical and quantum computers like Shor’s factoring algorithm. Other areas that are expected to be benefitted from quantum computing are Machine Learning and deep learning, molecular biology, genomics and cancer research, space exploration, atomic and nuclear research and macro-economic forecasting. This paper represents a brief overview of the state of art of quantum computing and quantum information science with discussions of various theoretical and experimental aspects adopted by the researchers.","PeriodicalId":284655,"journal":{"name":"Journal of Quantum Computing","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131397544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.32604/JQC.2020.015855
Tao Chen, Zhiguo Qu, Yi Chen
To solve the problem of hiding quantum information in simplified subsystems, Modi et al. [1] introduced the concept of quantum masking. Quantum masking is the encoding of quantum information by composite quantum states in such a way that the quantum information is hidden to the subsystem and spreads to the correlation of the composite systems. The concept of quantum masking was developed along with a new quantum impossibility theorem, the quantum no-masking theorem. The question of whether a quantum state can be masked has been studied by many people from the perspective of the types of quantum states, the number of masking participants, and error correction codes. Others have studied the relationships between maskable quantum states, the deterministic and probabilistic masking of quantum states, and the problem of probabilistic masking. Quantum masking techniques have been shown to outperform previous strategies in quantum bit commitment, quantum multi-party secret sharing, and so on.
{"title":"The Development and Application of Quantum Masking","authors":"Tao Chen, Zhiguo Qu, Yi Chen","doi":"10.32604/JQC.2020.015855","DOIUrl":"https://doi.org/10.32604/JQC.2020.015855","url":null,"abstract":"To solve the problem of hiding quantum information in simplified subsystems, Modi et al. [1] introduced the concept of quantum masking. Quantum masking is the encoding of quantum information by composite quantum states in such a way that the quantum information is hidden to the subsystem and spreads to the correlation of the composite systems. The concept of quantum masking was developed along with a new quantum impossibility theorem, the quantum no-masking theorem. The question of whether a quantum state can be masked has been studied by many people from the perspective of the types of quantum states, the number of masking participants, and error correction codes. Others have studied the relationships between maskable quantum states, the deterministic and probabilistic masking of quantum states, and the problem of probabilistic masking. Quantum masking techniques have been shown to outperform previous strategies in quantum bit commitment, quantum multi-party secret sharing, and so on.","PeriodicalId":284655,"journal":{"name":"Journal of Quantum Computing","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131846190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Guorong Cui, Hao Li, Yachuan Zhang, Rongjing Bu, Yan Kang, Jinyuan Li, Yang Hu
The traditional K-means clustering algorithm is difficult to determine the cluster number, which is sensitive to the initialization of the clustering center and easy to fall into local optimum. This paper proposes a clustering algorithm based on self-organizing mapping network and weight particle swarm optimization SOM&WPSO (Self-Organization Map and Weight Particle Swarm Optimization). Firstly, the algorithm takes the competitive learning mechanism of a self-organizing mapping network to divide the data samples into coarse clusters and obtain the clustering center. Then, the obtained clustering center is used as the initialization parameter of the weight particle swarm optimization algorithm. The particle position of the WPSO algorithm is determined by the traditional clustering center is improved to the sample weight, and the cluster center is the “food” of the particle group. Each particle moves toward the nearest cluster center. Each iteration optimizes the particle position and velocity and uses K-means and K-medoids recalculates cluster centers and cluster partitions until the end of the algorithm convergence iteration. After a lot of experimental analysis on the commonly used UCI data set, this paper not only solves the shortcomings of K-means clustering algorithm, the problem of dependence of the initial clustering center, and improves the accuracy of clustering, but also avoids falling into the local optimum. The algorithm has good global convergence.
传统的K-means聚类算法难以确定聚类数，对聚类中心初始化敏感，容易陷入局部最优。提出了一种基于自组织映射网络和权粒子群优化的聚类算法SOM&WPSO (Self-Organization Map and weight particle swarm optimization)。首先，该算法利用自组织映射网络的竞争学习机制，将数据样本划分为粗聚类并获得聚类中心;然后，将得到的聚类中心作为权重粒子群优化算法的初始化参数。WPSO算法通过将传统聚类中心改进为样本权值来确定粒子的位置，聚类中心是粒子群的“食物”。每个粒子都向最近的星团中心移动。每次迭代对粒子位置和速度进行优化，并使用K-means和K-medoids重新计算聚类中心和聚类分区，直到算法收敛迭代结束。通过对常用的UCI数据集进行大量的实验分析，本文不仅解决了K-means聚类算法的缺点，即初始聚类中心的依赖性问题，提高了聚类的精度，而且避免了陷入局部最优。该算法具有良好的全局收敛性。
{"title":"Weighted Particle Swarm Clustering Algorithm for Self-Organizing Maps","authors":"Guorong Cui, Hao Li, Yachuan Zhang, Rongjing Bu, Yan Kang, Jinyuan Li, Yang Hu","doi":"10.32604/jqc.2020.09717","DOIUrl":"https://doi.org/10.32604/jqc.2020.09717","url":null,"abstract":"The traditional K-means clustering algorithm is difficult to determine the cluster number, which is sensitive to the initialization of the clustering center and easy to fall into local optimum. This paper proposes a clustering algorithm based on self-organizing mapping network and weight particle swarm optimization SOM&WPSO (Self-Organization Map and Weight Particle Swarm Optimization). Firstly, the algorithm takes the competitive learning mechanism of a self-organizing mapping network to divide the data samples into coarse clusters and obtain the clustering center. Then, the obtained clustering center is used as the initialization parameter of the weight particle swarm optimization algorithm. The particle position of the WPSO algorithm is determined by the traditional clustering center is improved to the sample weight, and the cluster center is the “food” of the particle group. Each particle moves toward the nearest cluster center. Each iteration optimizes the particle position and velocity and uses K-means and K-medoids recalculates cluster centers and cluster partitions until the end of the algorithm convergence iteration. After a lot of experimental analysis on the commonly used UCI data set, this paper not only solves the shortcomings of K-means clustering algorithm, the problem of dependence of the initial clustering center, and improves the accuracy of clustering, but also avoids falling into the local optimum. The algorithm has good global convergence.","PeriodicalId":284655,"journal":{"name":"Journal of Quantum Computing","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120943326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.32604/jqc.2021.018114
Saasha Joshi, Deepti Gupta
{"title":"Grover’s Algorithm in a 4-Qubit Search Space","authors":"Saasha Joshi, Deepti Gupta","doi":"10.32604/jqc.2021.018114","DOIUrl":"https://doi.org/10.32604/jqc.2021.018114","url":null,"abstract":"","PeriodicalId":284655,"journal":{"name":"Journal of Quantum Computing","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124267073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Study on Quantum Finance Algorithm: Quantum Monte Carlo Algorithm based on European Option Pricing","authors":"Jianzhi Hu, Shao-yi Wu, Yezhou Yang, Qin-Sheng Zhu, Xiao-Yu Li, Shan Yang","doi":"10.32604/jqc.2022.027683","DOIUrl":"https://doi.org/10.32604/jqc.2022.027683","url":null,"abstract":"","PeriodicalId":284655,"journal":{"name":"Journal of Quantum Computing","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134580957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.32604/jqc.2022.026658
Samuel Kofi Akpatsa, Hang Lei, Xiaoyu Li, Victor-Hillary Kofi Setornyo Obeng, Ezekiel Mensah Martey, Prince Clement Addo, Duncan Dodzi Fiawoo
: The ability of pre-trained BERT model to achieve outstanding performances on many Natural Language Processing (NLP) tasks has attracted the attention of researchers in recent times. However, the huge computational and memory requirements have hampered its widespread deployment on devices with limited resources. The concept of knowledge distillation has shown to produce smaller and faster distilled models with less trainable parameters and intended for resource-constrained environments. The distilled models can be fine-tuned with great performance on a wider range of tasks, such as sentiment classification. This paper evaluates the performance of DistilBERT model and other pre-canned text classifiers on a Covid-19 online news binary classification dataset. The analysis shows that despite having fewer trainable parameters than the BERT-based model, the DistilBERT model achieved an accuracy of 0.94 on the validation set after only two training epochs. The paper also highlights the usefulness of the ktrain library in facilitating the building, training, and application of state-of-the-art Machine Learning and Deep Learning models.
{"title":"Online News Sentiment Classification Using DistilBERT","authors":"Samuel Kofi Akpatsa, Hang Lei, Xiaoyu Li, Victor-Hillary Kofi Setornyo Obeng, Ezekiel Mensah Martey, Prince Clement Addo, Duncan Dodzi Fiawoo","doi":"10.32604/jqc.2022.026658","DOIUrl":"https://doi.org/10.32604/jqc.2022.026658","url":null,"abstract":": The ability of pre-trained BERT model to achieve outstanding performances on many Natural Language Processing (NLP) tasks has attracted the attention of researchers in recent times. However, the huge computational and memory requirements have hampered its widespread deployment on devices with limited resources. The concept of knowledge distillation has shown to produce smaller and faster distilled models with less trainable parameters and intended for resource-constrained environments. The distilled models can be fine-tuned with great performance on a wider range of tasks, such as sentiment classification. This paper evaluates the performance of DistilBERT model and other pre-canned text classifiers on a Covid-19 online news binary classification dataset. The analysis shows that despite having fewer trainable parameters than the BERT-based model, the DistilBERT model achieved an accuracy of 0.94 on the validation set after only two training epochs. The paper also highlights the usefulness of the ktrain library in facilitating the building, training, and application of state-of-the-art Machine Learning and Deep Learning models.","PeriodicalId":284655,"journal":{"name":"Journal of Quantum Computing","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121042182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：