Journal
KNOWLEDGE-BASED SYSTEMS
Volume 43, Issue -, Pages 112-122Publisher
ELSEVIER
DOI: 10.1016/j.knosys.2013.01.014
Keywords
Forecasting; Hybrid intelligent system; Genetic fuzzy systems; Clustering; Non-parametric tests
Categories
Ask authors/readers for more resources
Forecasting tourism demand is a crucial issue in the tourism industry and is generally seen to be one of the most complex functions of tourism management. With the accurate forecasted trends and patterns that indicate the sizes, directions and characteristics of future international tourist flows, the government and private sectors can have a well-organized tourism strategy and provide a better infrastructure to serve the visitors and develop a suitable marketing strategy to gain benefit from the growing tourism. With the aim of developing accurate forecasting tools in the tourism industry, this study presents a new hybrid intelligent model that is called Modular Genetic-Fuzzy Forecasting System (MGFFS) by a combination of genetic fuzzy expert systems and data preprocessing. MGFFS is developed in three stage architecture. The first stage is data preprocessing. Some statistical tests are used to choose the key lags that are to be considered in the time series model. Then data transformation and K-means clustering have been applied to develop a modular model for reducing the complexity of the whole data space to become something more homogeneous. In the second stage, extraction of the TSK type fuzzy rule-based system for each cluster will be carried out by means of an efficient genetic learning algorithm that uses symbiotic evolution for fitness assignment. In the last stage, the testing data are first clustered and tourism demand forecasting is done by means of each cluster's fuzzy system. Results show that forecasting accuracy of MGFFS is relatively better than other approaches in literature such as Classical Time Series models, Neuro-Fuzzy systems, and neural network, according to MAPE and RMSE evaluations. Powerful non-parametric statistical tests such as Friedman, Bonferroni, Holm and Hochberg are also used for comparing the performance of MGFFS with others. Based on the statistical tests, MGFFS is better than other models in accuracy and can be used as a suitable forecasting tool in tourism demand forecasting problems. (C) 2013 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
Article
Computer Science, Artificial Intelligence
Confidence-based and sample-reweighted test-time adaptation
Hao Yang, Min Wang, Zhengfei Yu, Hang Zhang, Jinshen Jiang, Yun Zhou
Summary: In this paper, a novel method called CSTTA is proposed for test time adaptation (TTA), which utilizes confidence-based optimization and sample reweighting to better utilize sample information. Extensive experiments demonstrate the effectiveness of the proposed method.
KNOWLEDGE-BASED SYSTEMS (2024)
Article
Computer Science, Artificial Intelligence
A novel method for generating a canonical basis for decision implications based on object-induced three-way operators
Jin Liu, Ju-Sheng Mi, Dong-Yun Niu
Summary: This article focuses on a novel method for generating a canonical basis for decision implications based on object-induced operators (OE operators). The logic of decision implication based on OE operators is described, and a method for obtaining the canonical basis for decision implications is given. The completeness, nonredundancy, and optimality of the canonical basis are proven. Additionally, a method for generating true premises based on OE operators is proposed.
KNOWLEDGE-BASED SYSTEMS (2024)
Review
Computer Science, Artificial Intelligence
Efficient utilization of pre-trained models: A review of sentiment analysis via prompt learning
Kun Bu, Yuanchao Liu, Xiaolong Ju
Summary: This paper discusses the importance of sentiment analysis and pre-trained models in natural language processing, and explores the application of prompt learning. The research shows that prompt learning is more suitable for sentiment analysis tasks and can achieve good performance.
KNOWLEDGE-BASED SYSTEMS (2024)
Article
Computer Science, Artificial Intelligence
M-EDEM: A MNN-based Empirical Decomposition Ensemble Method for improved time series forecasting
Xiangjun Cai, Dagang Li
Summary: This paper presents a new decomposition mechanism based on learned decomposition mapping. By using a neural network to learn the relationship between original time series and decomposed results, the repetitive computation overhead during rolling decomposition is relieved. Additionally, extended mapping and partial decomposition methods are proposed to alleviate boundary effects on prediction performance. Comparative studies demonstrate that the proposed method outperforms existing RDEMs in terms of operation speed and prediction accuracy.
KNOWLEDGE-BASED SYSTEMS (2024)
Article
Computer Science, Artificial Intelligence
Privacy-preserving trust management method based on blockchain for cross-domain industrial IoT
Xu Wu, Yang Liu, Jie Tian, Yuanpeng Li
Summary: This paper proposes a blockchain-based privacy-preserving trust management architecture, which adopts federated learning to train task-specific trust models and utilizes differential privacy to protect device privacy. In addition, a game theory-based incentive mechanism and a parallel consensus protocol are proposed to improve the accuracy of trust computing and the efficiency of consensus.
KNOWLEDGE-BASED SYSTEMS (2024)
Article
Computer Science, Artificial Intelligence
MV-ReID: 3D Multi-view Transformation Network for Occluded Person Re-Identification
Zaiyang Yu, Prayag Tiwari, Luyang Hou, Lusi Li, Weijun Li, Limin Jiang, Xin Ning
Summary: This study introduces a 3D view-based approach that effectively handles occlusions and leverages the geometric information of 3D objects. The proposed method achieves state-of-the-art results on occluded ReID tasks and exhibits competitive performance on holistic ReID tasks.
KNOWLEDGE-BASED SYSTEMS (2024)
Article
Computer Science, Artificial Intelligence
City-scale continual neural semantic mapping with three-layer sampling and panoptic representation
Yongliang Shi, Runyi Yang, Zirui Wu, Pengfei Li, Caiyun Liu, Hao Zhao, Guyue Zhou
Summary: Neural implicit representations have gained attention due to their expressive, continuous, and compact properties. However, there is still a lack of research on city-scale continual implicit dense mapping based on sparse LiDAR input. In this study, a city-scale continual neural mapping system with a panoptic representation is developed, incorporating environment-level and instance-level modeling. A tailored three-layer sampling strategy and category-specific prior are proposed to address the challenges of representing geometric information in city-scale space and achieving high fidelity mapping of instances under incomplete observation.
KNOWLEDGE-BASED SYSTEMS (2024)
Article
Computer Science, Artificial Intelligence
MDSSN: An end-to-end deep network on triangle mesh parameterization
Ruihan Hu, Zhi-Ri Tang, Rui Yang, Zhongjie Wang
Summary: Mesh data is crucial for 3D computer vision applications worldwide, but traditional deep learning frameworks have struggled with handling meshes. This paper proposes MDSSN, a simple mesh computation framework that models triangle meshes and represents their shape using face-based and edge-based Riemannian graphs. The framework incorporates end-to-end operators inspired by traditional deep learning frameworks, and includes dedicated modules for addressing challenges in mesh classification and segmentation tasks. Experimental results demonstrate that MDSSN outperforms other state-of-the-art approaches.
KNOWLEDGE-BASED SYSTEMS (2024)
Article
Computer Science, Artificial Intelligence
Semi-supervised learning with missing values imputation
Buliao Huang, Yunhui Zhu, Muhammad Usman, Huanhuan Chen
Summary: This paper proposes a novel semi-supervised conditional normalizing flow (SSCFlow) algorithm that combines unsupervised imputation and supervised classification. By estimating the conditional distribution of incomplete instances, SSCFlow facilitates imputation and classification simultaneously, addressing the issue of separated tasks ignoring data distribution and label information in traditional methods.
KNOWLEDGE-BASED SYSTEMS (2024)
Article
Computer Science, Artificial Intelligence
Emotion-and-knowledge grounded response generation in an open-domain dialogue setting
Deeksha Varshney, Asif Ekbal, Erik Cambria
Summary: This paper focuses on the neural-based interactive dialogue system that aims to engage and retain humans in long-lasting conversations. It proposes a new neural generative model that combines step-wise co-attention, self-attention-based transformer network, and an emotion classifier to control emotion and knowledge transfer during response generation. The results from quantitative, qualitative, and human evaluation show that the proposed models can generate natural and coherent sentences, capturing essential facts with significant improvement over emotional content.
KNOWLEDGE-BASED SYSTEMS (2024)
Article
Computer Science, Artificial Intelligence
MvTS-library: An open library for deep multivariate time series forecasting
Junchen Ye, Weimiao Li, Zhixin Zhang, Tongyu Zhu, Leilei Sun, Bowen Du
Summary: Modeling multivariate time series has long been a topic of interest for scholars in various fields. This paper introduces MvTS, an open library based on Pytorch, which provides a unified framework for implementing and evaluating these models. Extensive experiments on public datasets demonstrate the effectiveness and universality of the models reproduced by MvTS.
KNOWLEDGE-BASED SYSTEMS (2024)
Article
Computer Science, Artificial Intelligence
An adaptive hybrid mutated differential evolution feature selection method for low and high-dimensional medical datasets
Reham R. Mostafa, Ahmed M. Khedr, Zaher Al Aghbari, Imad Afyouni, Ibrahim Kamel, Naveed Ahmed
Summary: Feature selection is crucial in classification procedures, but it faces challenges in high-dimensional datasets. To overcome these challenges, this study proposes an Adaptive Hybrid-Mutated Differential Evolution method that incorporates the mechanics of the Spider Wasp Optimization algorithm and the concept of Enhanced Solution Quality. Experimental results demonstrate the effectiveness of the method in terms of accuracy and convergence speed, and it outperforms contemporary cutting-edge algorithms.
KNOWLEDGE-BASED SYSTEMS (2024)
Article
Computer Science, Artificial Intelligence
TCM Model for improving track sequence classification in real scenarios with Multi-Feature Fusion and Transformer Block
Ti Xiang, Pin Lv, Liguo Sun, Yipu Yang, Jiuwu Hao
Summary: This paper introduces a Track Classification Model (TCM) based on marine radar, which can effectively recognize and classify shipping tracks. By using a feature extraction network with multi-feature fusion and a dataset production method to address missing labels, the classification accuracy is improved, resulting in successful engineering application in real scenarios.
KNOWLEDGE-BASED SYSTEMS (2024)
Article
Computer Science, Artificial Intelligence
Language model as an Annotator: Unsupervised context-aware quality phrase generation
Zhihao Zhang, Yuan Zuo, Chenghua Lin, Junjie Wu
Summary: This paper proposes a novel unsupervised context-aware quality phrase mining framework called LMPhrase, which is built upon large pre-trained language models. The framework mines quality phrases as silver labels using a parameter-free probing technique on the pre-trained language model BERT, and formalizes the phrase tagging task as a sequence generation problem by fine-tuning on the Sequence to-Sequence pre-trained language model BART. The results of extensive experiments show that LMPhrase consistently outperforms existing competitors in two different granularity phrase mining tasks.
KNOWLEDGE-BASED SYSTEMS (2024)
Article
Computer Science, Artificial Intelligence
Stochastic Gradient Descent for matrix completion: Hybrid parallelization on shared- and distributed-memory systems
Kemal Buyukkaya, M. Ozan Karsavuran, Cevdet Aykanat
Summary: The study aims to investigate the hybrid parallelization of the Stochastic Gradient Descent (SGD) algorithm for solving the matrix completion problem on a high-performance computing platform. A hybrid parallel decentralized SGD framework with asynchronous inter-process communication and a novel flexible partitioning scheme is proposed to achieve scalability up to hundreds of processors. Experimental results on real-world benchmark datasets show that the proposed algorithm achieves 6x higher throughput on sparse datasets compared to the state-of-the-art, while achieving comparable throughput on relatively dense datasets.
KNOWLEDGE-BASED SYSTEMS (2024)
Share Paper
