Article
Environmental Sciences
Zheng Zhang, Ping Tang, Weixiong Zhang, Liang Tang
Summary: This paper introduces a new time series similarity measure method TAOT for SITS clustering, which effectively alleviates the issues of DTW and improves clustering accuracy according to statistical and visual results on real datasets. TAOT serves as a useful tool to explore the potential of valuable SITS data.
Article
Computer Science, Artificial Intelligence
Mourtadha Badiane, Padraig Cunningham
Summary: This article compares various distance measures used in support vector machine setting, finding that Dynamic Time Warping is the most effective distance measure and kNN is the most effective classifier. However, the pairing of the two may not be the best model, as Dynamic Time Warping paired with Gaussian Support Vector Machine proves to be the most accurate time series classifier.
ARTIFICIAL INTELLIGENCE REVIEW
(2022)
Article
Computer Science, Artificial Intelligence
Tian Wang, Zhaoying Liu, Ting Zhang, Syed Fawad Hussain, Muhammad Waqas, Yujian Li
Summary: This study proposes an Adaptive Feature Fusion Network (AFFNet) to enhance the accuracy of time series classification. The network can adaptively fuse multi-scale temporal and distance features of time series for classification, and it consists of multiple modules for feature extraction and fusion. Experimental results demonstrate that AFFNet achieves higher accuracies than existing models on multiple datasets and is more effective.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Software Engineering
Oscar Escudero-Arnanz, Antonio G. Marques, Cristina Soguero-Ruiz, Inmaculada Mora-Jimenez, Gregorio Robles
Summary: dtwParallel is a Python package that calculates the DTW distance between a collection of MTS. It incorporates functionalities from current DTW libraries and introduces new features such as parallelization and computation of similarity values. It can handle data with different types of features and is designed for use in education, research, and industry. The package's source code and documentation can be found at https://github.com/oscarescuderoarnanz/dtwParallel.
Article
Computer Science, Artificial Intelligence
Ahmed Shifaz, Charlotte Pelletier, Francois Petitjean, Geoffrey I. Webb
Summary: This paper presents multivariate versions of seven commonly used elastic similarity and distance measures for time series data analytics. These measures can compensate for misalignments in the time axis of time series data. The paper adapts two existing strategies used in multivariate Dynamic Time Warping to these measures. Demonstrating their utility in multivariate time series classification using the nearest neighbor classifier, the paper shows that each measure achieves the highest accuracy on at least one dataset, supporting the value of developing a suite of multivariate similarity and distance measures. The paper also constructs a nearest neighbor-based ensemble of the measures, which proves to be competitive with other state-of-the-art single-strategy multivariate time series classifiers.
KNOWLEDGE AND INFORMATION SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Agnieszka Jastrzebska, Gonzalo Napoles, Yamisleydi Salgueiro, Koen Vanhoof
Summary: Time series similarity evaluation is a crucial task for time series clustering and classification. Existing methods lack interpretability, hence this paper proposes a concept-based approach. The proposed approach compares time series using global and local models, resulting in satisfactory classification results.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Automation & Control Systems
Yutao Liu, Yong-An Zhang, Ming Zeng, Jie Zhao
Summary: In this research, a novel shape-based time series averaging algorithm called Shape DTW Weighted Averaging (ShapeDWA) is proposed to address the limitations of DTW Barycenter Averaging (DBA). ShapeDWA combines the advantages of DBA and Cubic-spline DTW (CDTW) methods, allowing for averaging in both the amplitude and time domains. ShapeDWA uses a weighted average to attenuate the effects of noise, outliers, and local amplitude differences, resulting in superior performance compared to DBA and SSG in terms of average discrepancy distance and average time distortion.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Engineering, Electrical & Electronic
Jiancheng Sun, Huimin Niu, Zongqing Tu, Zhinan Wu, Si Chen
Summary: This study proposes a classification method for multivariate time series (MTS) based on kernel matrix. By replacing the traditional covariance matrix with a Gaussian kernel matrix and mapping it into the tangent space of Riemannian manifold, the classification is implemented by choosing a classification algorithm. The experimental results show that the classification performance using the Gaussian kernel matrix outperforms the other methods, indicating that an appropriate kernel function is crucial for improving the classification performance.
ELECTRONICS LETTERS
(2022)
Article
Computer Science, Artificial Intelligence
Abdelmadjid Lahreche, Bachir Boucheham
Summary: The problem of similarity measures within the field of time series classification has led to the development of a new parameter-free measure called LE-DTW, which is designed to quickly and accurately assess similarity between long time series. Experimental results show that LE-DTW performs better than DTW for long time series, while also providing competitive results against popular distance based classifiers. In terms of efficiency, LE-DTW is significantly faster than DTW.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Mathematics
Achilleas Anastasiou, Peter Hatzopoulos, Alex Karagrigoriou, George Mavridoglou
Summary: This work focuses on developing new distance measure algorithms for analyzing causal relationships in financial and economic data. The proposed methodology was applied to a case study involving the classification of 19 EU countries based on health resource variables.
Article
Automation & Control Systems
Yejin Kang, Jongsoo Lee
Summary: In this study, a method for small datasets with ambiguous classification boundaries was developed using randomized learning methods. RVFL and deep RVFL showed similar classification accuracy to deep learning but significantly reduced training time by approximately 99%. ROCKET reduced training time by approximately 91% and improved classification performance by approximately 3%. All randomized learning methods have few hyperparameters, reducing the classification model design time.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Hardware & Architecture
Yandong Zheng, Rongxing Lu, Yunguo Guan, Jun Shao, Hui Zhu
Summary: Similarity query over time series data is important in various applications. Existing solutions still have issues in supporting queries with different lengths, and have limitations in query accuracy and efficiency. In this article, we propose a new efficient and privacy-preserving similarity range query scheme using the time warp edit distance (TWED) as the similarity metric. Our scheme leverages a kd-tree and symmetric homomorphic encryption technique to improve query efficiency and protect data privacy.
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Arun Pandey, Hannes De Meulemeester, Bart De Moor, Johan A. K. Suykens
Summary: This paper proposes a kernel principal component analysis model for multi-variate time series forecasting, using the multi-view formulation of Restricted Kernel Machines. The model utilizes eigenvalue decomposition to solve the training problem and applies kernel ridge regression for linear kernel. For non-linear kernels, a pre-image problem needs to be solved. The model is evaluated on standard time series datasets, benchmarked against related models, and its results are discussed.
Article
Computer Science, Artificial Intelligence
Juan Luis Suarez, Salvador Garcia, Francisco Herrera
Summary: Distance metric learning is a branch of machine learning that focuses on learning distances from data to improve the performance of similarity-based algorithms. This tutorial covers theoretical background, foundations, and popular methods of distance metric learning, evaluating their capabilities through exhaustive testing. Results highlighted outstanding algorithms, with discussion on future prospects and challenges.
Article
Computer Science, Information Systems
Atreyee Mondal, Nilanjan Dey, Simon Fong, Amira S. Ashour
Summary: A novel shape-based image clustering approach using time-series analysis was proposed, extracting shapes of objects based on mean structural similarity index and converting them into one-dimensional time-series data for hierarchical divisive clustering. Experimental results demonstrated the superiority of using Pearson correlation measure in clustering performance.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Article
Computer Science, Interdisciplinary Applications
R. Mortazavi, S. Mortazavi, A. Troncoso
Summary: This study introduces different types of regression trees for viscosity property forecasting in polymer solutions, and proposes a wrapper method to select features. Results show that regression trees predict viscosity with high accuracy, outperforming other forecasting methods.
ENGINEERING WITH COMPUTERS
(2022)
Review
Computer Science, Artificial Intelligence
Pedro Lara-Benitez, Manuel Carranza-Garcia, Jose C. Riquelme
Summary: Research demonstrates that long short-term memory (LSTM) and convolutional networks (CNN) are the best options for time series forecasting, with LSTMs yielding the most accurate predictions; CNNs show more stable performance under different parameter configurations and are also more efficient.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Manuel Carranza-Garcia, Pedro Lara-Benitez, Jorge Garcia-Gutierrez, Jose C. Riquelme
Summary: This study presents an enhanced 2D object detector based on Faster R-CNN specifically designed for autonomous vehicles. By improving anchor generation and addressing performance drop in minority classes, the proposed perspective-aware methodology and spatial information enhancement module significantly increased detection accuracy. Utilizing ensemble models led to a further 9.69% mAP improvement in accuracy.
Article
Oncology
Beatriz Pontes, Francisco Nunez, Cristina Rubio, Alberto Moreno, Isabel Nepomuceno, Jesus Moreno, Jon Cacicedo, Juan Manuel Praena-Fernandez, German Antonio Escobar Rodriguez, Carlos Parra, Blas David Delgado Leon, Eleonor Rivin del Campo, Felipe Counago, Jose Riquelme, Jose Luis Lopez Guerra
Summary: A clinical decision support system (CDSS) was designed to predict survival in lung cancer patients by extracting and analyzing information from routine clinical activity. The CDSS showed potential to enhance survival prediction and assist physicians in providing evidence-based management advice tailored to individual patients' prognosis.
REPORTS OF PRACTICAL ONCOLOGY AND RADIOTHERAPY
(2021)
Article
Computer Science, Information Systems
Belen Vega-Marquez, Isabel A. Nepomuceno-Chamorro, Cristina Rubio-Escudero, Jose C. Riquelme
Summary: In this paper, a genetic-based methodology called OCEAn for ordinal classification is proposed, which utilizes a weighted vote system to make final predictions aiming to minimize classification costs. Experimental results demonstrate that this approach outperforms previous algorithms in the literature.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
J. F. Torres, F. Martinez-Alvarez, A. Troncoso
Summary: In this study, a deep neural network (LSTM) was proposed for short-term electricity consumption forecasting, and optimal parameter values were obtained using a coronavirus optimization algorithm. Empirical analysis using Spanish electricity data over a period of 9.5 years demonstrated that the proposed method achieved a prediction error below 1.5%.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Chemistry, Multidisciplinary
Haotian Wen, Jose Maria Luna-Romera, Jose C. Riquelme, Christian Dwyer, Shery L. Y. Chang
Summary: The morphology of nanoparticles plays a crucial role in determining their properties for various applications. Transmission electron microscopy (TEM) is an effective technique for characterizing nanoparticle morphology at atomic resolution. Developing efficient and automated methods for statistically significant particle metrology is essential for advancing precise particle synthesis and property control.
Article
Thermodynamics
D. Hadjout, J. F. Torres, A. Troncoso, A. Sebaa, F. Martinez- Alvarez
Summary: The economic sector's electricity needs are crucial, making electricity forecasting a vital task. This study presents a novel approach based on ensemble learning to predict monthly electricity consumption for Algeria's economic sector. By combining Long Short Term Memory, Gated Recurrent Unit neural networks, and Temporal Convolutional Networks, the proposed ensemble models outperformed both the company's requirements and traditional individual models. Additionally, statistical tests demonstrated the significance of the developed ensemble models.
Article
Computer Science, Artificial Intelligence
Manuel Carranza-Garcia, F. Javier Galan-Sales, Jose Maria Luna-Romera, Jose C. Riquelme
Summary: This paper proposes a novel data fusion architecture for object detection in autonomous driving, using camera and LiDAR data to achieve reliable performance. With deep learning models and sensor data, our approach significantly outperforms previous methods in various weather and lighting conditions.
INTEGRATED COMPUTER-AIDED ENGINEERING
(2022)
Article
Mathematics, Applied
Pedro Lara-Benitez, Manuel Carranza-Garcia, David Gutierrez-Aviles, Jose C. Riquelme
Summary: This study aims to evaluate the performance of different types of deep learning architectures for data streaming classification. The results indicate that convolutional architectures achieve higher accuracy and efficiency but are also most sensitive to concept drifts.
LOGIC JOURNAL OF THE IGPL
(2023)
Article
Chemistry, Multidisciplinary
Laura Madrid-Marquez, Cristina Rubio-Escudero, Beatriz Pontes, Antonio Gonzalez-Perez, Jose C. Riquelme, Maria E. Saez
Summary: This study introduces a new software tool called MOMIC, which provides a complete analysis environment for analyzing and integrating multi-omics data on a single, easy-to-use platform. It offers high editability, reproducibility, and is of great importance for deriving meaningful biological knowledge.
APPLIED SCIENCES-BASEL
(2022)
Article
Automation & Control Systems
Laura Melgar-Garcia, David Gutierrez-Aviles, Cristina Rubio-Escudero, Alicia Troncoso
Summary: Time series data is a sequence of observations on a variable of interest arranged in chronological order. Streaming time series refers to the continuous arrival of high-speed data with a changing data distribution. Real-time forecasting of streaming time series data allows for the consideration of new patterns in the incoming data, unlike batch models. This paper presents an algorithm that detects novelties and anomalies in real-time using nearest-neighbors based forecasting, which has been tested on Spanish electricity demand data and shown to provide accurate real-time predictions with lower errors.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Laura Melgar-Garcia, David Gutierrez-Aviles, Cristina Rubio-Escudero, Alicia Troncoso
Summary: This paper presents a new distributed forecasting algorithm called StreamWNN for streaming time series, which adaptively adjusts to new patterns in real-time. The algorithm creates a forecasting model based on information fusion of tuples in the offline stage, and incrementally updates the model in the online stage using a buffer with streaming data. The model can be updated daily, monthly, quarterly or based on error thresholds, and has been successfully applied to Spanish electricity demand time series with improved accuracy compared to other algorithms.
INFORMATION FUSION
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Tomas Cabello-Lopez, Manuel Canizares-Juan, Manuel Carranza-Garcia, Jorge Garcia-Gutierrez, Jose C. Riquelme
Summary: This study analyzed wind energy generation data from the Spanish power grid and evaluated the improvement in forecasting quality by detecting concept drifts and retraining models. The experimental results showed that the concept drift approach significantly improved the accuracy of forecasting.
HYBRID ARTIFICIAL INTELLIGENT SYSTEMS, HAIS 2022
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Manuel Carranza-Garcia, Pedro Lara-Benitez, Jose Maria Luna-Romera, Jose C. Riquelme
Summary: The importance of feature selection for forecasting solar irradiance time series using spatio-temporal data is studied, and it is found that proper feature selection significantly enhances the forecasting accuracy.
16TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING MODELS IN INDUSTRIAL AND ENVIRONMENTAL APPLICATIONS (SOCO 2021)
(2022)
Article
Computer Science, Artificial Intelligence
Rui Lv, Dingheng Wang, Jiangbin Zheng, Zhao-Xu Yang
Summary: In this paper, the authors investigate tensor decomposition for neural network compression. They analyze the convergence and precision of tensor mapping theory, validate the rationality of tensor mapping and its superiority over traditional tensor approximation based on the Lottery Ticket Hypothesis. They propose an efficient method called 3D-KCPNet to compress 3D convolutional neural networks using the Kronecker canonical polyadic (KCP) tensor decomposition. Experimental results show that 3D-KCPNet achieves higher accuracy compared to the original baseline model and the corresponding tensor approximation model.
Article
Computer Science, Artificial Intelligence
Xiangkun He, Zhongxu Hu, Haohan Yang, Chen Lv
Summary: In this paper, a novel constrained multi-objective reinforcement learning algorithm is proposed for personalized end-to-end robotic control with continuous actions. The approach trains a single model using constraint design and a comprehensive index to achieve optimal policies based on user-specified preferences.
Article
Computer Science, Artificial Intelligence
Zhijian Zhuo, Bilian Chen, Shenbao Yu, Langcai Cao
Summary: In this paper, a novel method called Expansion with Contraction Method for Overlapping Community Detection (ECOCD) is proposed, which utilizes non-negative matrix factorization to obtain disjoint communities and applies expansion and contraction processes to adjust the degree of overlap. ECOCD is applicable to various networks with different properties and achieves high-quality overlapping community detection.
Article
Computer Science, Artificial Intelligence
Yizhe Zhu, Chunhui Zhang, Jialin Gao, Xin Sun, Zihan Rui, Xi Zhou
Summary: In this work, the authors propose a Contrastive Spatio-Temporal Distilling (CSTD) approach to improve the detection of high-compressed deepfake videos. The approach leverages spatial-frequency cues and temporal-contrastive alignment to fully exploit spatiotemporal inconsistency information.
Review
Computer Science, Artificial Intelligence
Laijin Meng, Xinghao Jiang, Tanfeng Sun
Summary: This paper provides a review of coverless steganographic algorithms, including the development process, known contributions, and general issues in image and video algorithms. It also discusses the security of coverless steganography from theoretical analysis to actual investigation for the first time.
Article
Computer Science, Artificial Intelligence
Yajie Bao, Tianwei Xing, Xun Chen
Summary: Visual question answering requires processing multi-modal information and effective reasoning. Neural-symbolic learning is a promising method, but current approaches lack uncertainty handling and can only provide a single answer. To address this, we propose a confidence based neural-symbolic approach that evaluates NN inferences and conducts reasoning based on confidence.
Article
Computer Science, Artificial Intelligence
Anh H. Vo, Bao T. Nguyen
Summary: Interior style classification is an interesting problem with potential applications in both commercial and academic domains. This project proposes a method named ISC-DeIT, which combines data-efficient image transformer architectures and knowledge distillation, to address the interior style classification problem. Experimental results demonstrate a significant improvement in predictive accuracy compared to other state-of-the-art methods.
Article
Computer Science, Artificial Intelligence
Shashank Kotyan, Danilo Vasconcellos Vargas
Summary: This article introduces a novel augmentation technique called Dynamic Scanning Augmentation to improve the accuracy and robustness of Vision Transformer (ViT). The technique leverages dynamic input sequences to adaptively focus on different patches, resulting in significant changes in ViT's attention mechanism. Experimental results demonstrate that Dynamic Scanning Augmentation outperforms ViT in terms of both robustness to adversarial attacks and accuracy against natural images.
Article
Computer Science, Artificial Intelligence
Hiba Alqasir, Damien Muselet, Christophe Ducottet
Summary: The article proposes a solution to improve the learning process of a classification network by providing shape priors, reducing the need for annotated data. The solution is tested on cross-domain digit classification tasks and a video surveillance application.
Article
Computer Science, Artificial Intelligence
Dexiu Ma, Mei Liu, Mingsheng Shang
Summary: This paper proposes a method using neural dynamics solvers to solve infinity-norm optimization problems. Two improved solvers are constructed and their effectiveness and superiority are demonstrated through theoretical analysis and simulation experiments.
Article
Computer Science, Artificial Intelligence
Francesco Gregoretti, Giovanni Pezzulo, Domenico Maisto
Summary: Active Inference is a computational framework that uses probabilistic inference and variational free energy minimization to describe perception, planning, and action. cpp-AIF is a header-only C++ library that provides a powerful tool for implementing Active Inference for Partially Observable Markov Decision Processes through multi-core computing. It is cross-platform and improves performance, memory management, and usability compared to existing software.
Article
Computer Science, Artificial Intelligence
Zelin Ying, Dawei Cheng, Cen Chen, Xiang Li, Peng Zhu, Yifeng Luo, Yuqi Liang
Summary: This paper proposes a novel stock market trends prediction framework called SMART, which includes a self-supervised stock technical data sequence embedding model S3E. By training with multiple self-supervised auxiliary tasks, the model encodes stock technical data sequences into embeddings and uses the learned sequence embeddings for predicting stock market trends. Extensive experiments on China A-Shares market and NASDAQ market prove the high effectiveness of our model in stock market trends prediction, and its effectiveness is further validated in real-world applications in a leading financial service provider in China.
Article
Computer Science, Artificial Intelligence
Hao Li, Hao Jiang, Dongsheng Ye, Qiang Wang, Liang Du, Yuanyuan Zeng, Liu Yuan, Yingxue Wang, C. Chen
Summary: DHGAT1, a dynamic hyperbolic graph attention network, utilizes hyperbolic metric properties to embed dynamic graphs. It employs a spatiotemporal self-attention mechanism and weighted node representations, resulting in excellent performance in link prediction tasks.
Article
Computer Science, Artificial Intelligence
Jiehui Huang, Zhenchao Tang, Xuedong He, Jun Zhou, Defeng Zhou, Calvin Yu-Chian Chen
Summary: This study proposes a progressive learning multi-scale feature blending model for image deraining tasks. The model utilizes detail dilation and texture extraction to improve the restoration of rainy images. Experimental results show that the model achieves near state-of-the-art performance in rain removal tasks and exhibits better rain removal realism.
Article
Computer Science, Artificial Intelligence
Lizhi Liu, Zilin Gao, Yinhe Wang, Yongfu Li
Summary: This paper proposes a novel discrete-time interconnected model for depicting complex dynamical networks. The model consists of nodes and edges subsystems, which consider the dynamic characteristic of both nodes and edges. By designing control strategies and coupling modes, the stabilization and synchronization of the network are achieved. Simulation results demonstrate the effectiveness of the proposed methods.
