Article
Environmental Sciences
Sharif Joorabian Shooshtari, Jaber Aazami
Summary: Human activities have a significant impact on land use and land cover (LULC) changes in Zanjan province, Iran. This study assessed the historical distribution of LULC changes and predicted future scenarios for 2035 and 2045. LULC time-series analysis was conducted using Landsat images from 1987, 2002, and 2019. A multi-layer perceptron artificial neural network (MLP-ANN) was used to model the relationships between LULC transitions and explanatory variables. Future land demand was calculated using a Markov chain matrix and multi-objective land optimization. The findings of this study provide valuable insights for effective planning in the study area.
ENVIRONMENTAL MONITORING AND ASSESSMENT
(2023)
Article
Construction & Building Technology
Yuefen Gao, Yang Hang, Mengliang Yang
Summary: This paper introduces the Improved CEEMDAN algorithm and Markov chain correction method to predict air conditioning cooling load more accurately. By decomposing affecting parameters and establishing component prediction model, and using parallel computing to enhance operation speed, the improved model shows improved accuracy and is more suitable for practical applications.
JOURNAL OF BUILDING ENGINEERING
(2021)
Article
Engineering, Multidisciplinary
Xun Wang, Jin Liu, Tao Hou, Chao Pan
Summary: The prediction of potential threats is crucial for situation analysis in aerial defense systems. However, traditional methods often overlook the influence of commander's emotions, leading to poor performance in complex situations. This paper proposes a method called PTP-CE that takes into account commander emotion for potential threat prediction in aerial targets. The method utilizes Bi-directional LSTM network and backpropagation neural network optimized by the sparrow search algorithm. Experimental results demonstrate the efficiency of PTP-CE in state prediction and threat prediction for aerial targets, regardless of the commander's emotional effect.
DEFENCE TECHNOLOGY
(2022)
Article
Agronomy
Ruolan Liu, Shujie Yuan, Lin Han
Summary: This paper analyzes and evaluates a prediction model for temperature in a Bailing mushroom greenhouse using a BP neural network and stepwise regression method based on data from automatic weather stations and microclimate observation stations. The results show that the BP neural network method performs better in predicting temperature variations in different seasons.
Article
Engineering, Mechanical
Yonggang Liu, Jie Li, Jun Gao, Zhenzhen Lei, Yuanjian Zhang, Zheng Chen
Summary: Predicting short-term driving conditions is crucial for energy management and fuel economy improvement of plug-in hybrid electric vehicles. A fused model combining stochastic forecasting and machine learning techniques is established, incorporating Markov chain for transition probability calculation and neural network for learning driving information. Optimizing network parameters with genetic algorithm, the proposed fusion algorithm outperforms single models in prediction precision and difference distribution evaluation.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2021)
Article
Computer Science, Artificial Intelligence
Xinyou Lin, Guangji Zhang, Shenshen Wei
Summary: A vehicle velocity prediction model based on DPR and MC is proposed to reduce prediction errors significantly. The results demonstrate high accuracy of the velocity prediction model and its applicability in energy consumption evaluation.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Junhui Wu, Gang Qin, Jun Cheng, Jinde Cao, Huaicheng Yan, Iyad Katib
Summary: This paper proposes an innovative approach to mitigate the effects of deception attacks in Markov jumping systems by developing an adaptive neural network control strategy. The approach effectively approximates the unbounded false signals injected by deception attacks and establishes a connection between the joint Markov chain and controller.
Article
Biochemical Research Methods
Fei Li, Ziqiao Zhang, Jihong Guan, Shuigeng Zhou
Summary: In this article, a new model for drug-target interaction (DTI) prediction, MINN-DTI, is proposed. By combining Interformer with an improved CMPNN, MINN-DTI effectively captures the two-way impact between drugs and targets, resulting in better prediction performance and interpretability.
Article
Engineering, Industrial
Ahmed El-Awady, Kumaraswamy Ponnambalam
Summary: The development of failure analysis techniques for complex engineering systems faces challenges due to interrelations and uncertainty. Bayesian Networks provide a flexible way to represent such systems probabilistically. Proposed methodologies such as SSBNs and MCSSBNs support efficient prediction of failure probabilities for complex networks with multiple uncertain interconnected variables.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2021)
Article
Green & Sustainable Science & Technology
Bhanage Vinayak, Han Soo Lee, Shirishkumar Gedem
Summary: This study used historical data and a neural network model to predict land use and cover changes in Mumbai and its surrounding area by 2050, showing rapid urban growth, decreased agricultural and barren land areas, and increased urban and forest areas.
Article
Computer Science, Interdisciplinary Applications
Samayita Nag Ray, Surajit Chattopadhyay
Summary: The research reports a univariate analysis of surface air temperature and rainfall in North East India, revealing their non-linear characteristics through Markov modeling and unit root tests. ARIMA and neural network models are used to analyze the data, showing differences in the meteorological phenomenon contributions.
EARTH SCIENCE INFORMATICS
(2021)
Article
Biotechnology & Applied Microbiology
Mingjian Jiang, Shuang Wang, Shugang Zhang, Wei Zhou, Yuanyuan Zhang, Zhen Li
Summary: The article introduces a method that constructs protein and molecular graphs based on sequence and SMILES, and uses graph neural networks to extract features and predict binding affinity. The proposed model, WGNN-DTA, demonstrates simplicity and high accuracy.
Article
Biochemistry & Molecular Biology
Shugang Zhang, Mingjian Jiang, Shuang Wang, Xiaofeng Wang, Zhiqiang Wei, Zhen Li
Summary: The study introduced a new graph-based drug-target affinity prediction model named SAG-DTA, which utilized self-attention mechanisms on drug molecular graphs to obtain effective representations. Different self-attention scoring methods were compared, and two pooling architectures were evaluated. Results demonstrated that SAG-DTA outperformed previous methods and exhibited good generalization ability.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2021)
Article
Biochemical Research Methods
Mei Li, Xiangrui Cai, Sihan Xu, Hua Ji
Summary: In this paper, the authors propose a metapath-aggregated heterogeneous graph neural network (MHGNN) for drug-target interaction (DTI) prediction. By modeling high-order relations via metapaths, MHGNN is able to capture complex structures and rich semantics in the biological heterogeneous graph. Experimental results demonstrate that MHGNN outperforms 17 state-of-the-art methods in drug repositioning.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Mei Li, Xiangrui Cai, Sihan Xu, Hua Ji
Summary: This paper proposes a metapath-aggregated heterogeneous graph neural network (MHGNN) for drug-target interaction (DTI) prediction. MHGNN can capture complex structures and rich semantics in the biological heterogeneous graph, and achieves favorable results in DTI prediction.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Computer Science, Artificial Intelligence
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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)