Article
Computer Science, Artificial Intelligence
Jan Kalina, Ales Neoral, Petra Vidnerova
Summary: Metalearning is an important aspect of artificial intelligence, and this paper focuses on recommending a suitable estimator for nonlinear regression modeling. The research proposes four different approaches for automatic method selection for nonlinear regression and conducts computations on a large number of datasets to evaluate the performance. Additionally, the study presents arguments in favor of a recent nonlinear least weighted squares estimator.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Weigang Wang, Juchao Ma, Chendong Xu, Yunwei Zhang, Ya Ding, Shujuan Yu, Yun Zhang, Yuanjian Liu
Summary: The article proposes a novel feature selection model for dimension reduction and an improved version of the lightweight convolutional neural network, newCNN, to enhance the system's classification performance. By combining newCNN with Support Vector Machines (SVM) to build a hybrid classification (HC) model, the problem of overfitting in the training process is solved, and it demonstrates excellent generalization ability and robustness.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Agronomy
Yuanyuan Liu, Tongzhao Wang, Rong Su, Can Hu, Fei Chen, Junhu Cheng
Summary: Customers are increasingly focusing on the sensory and physicochemical properties of food, leading to the use of near infrared hyperspectral technology to evaluate the quality parameters of Korla fragrant pears. By combining IRIV and LS-SVM, models were constructed to accurately assess the a*, firmness, and SSC of the fruit. The study demonstrated that a combination of spectral preprocessing methods, MSC and S-G, was the most effective in evaluating the quality parameters of the pears.
Article
Agricultural Engineering
Julio W. Torres-Tello, Seokbum Ko
Summary: The deep learning model presented in this paper accurately predicts the moisture content of canola and wheat crops based on hyperspectral images captured by drone flights. This model achieved high coefficients of determination and utilized a novel band selection process. The interpretability analysis allowed for studying individual predictions and could lead to more tailored software and hardware for spectral information analysis.
BIOSYSTEMS ENGINEERING
(2021)
Article
Chemistry, Applied
Rani Amsaraj, Sarma Mutturi
Summary: This study successfully demonstrated the simultaneous detection and quantification of four color adulterants in black tea using FT-IR spectral data and chemometric tools.
JOURNAL OF FOOD COMPOSITION AND ANALYSIS
(2024)
Article
Biochemical Research Methods
Yuantong Li, Fei Wang, Mengying Yan, Edward Cantu, Fan Nils Yang, Hengyi Rao, Rui Feng
Summary: In this article, a novel neural network model called peel learning is proposed for gene expression studies, showing improved prediction accuracy compared to traditional regression models and other deep learning methods. The model effectively incorporates the prior relationship among genes and simplifies the overall structure while optimizing weight parameters through a revised backpropagation algorithm, demonstrating advantages in small sample size studies.
Article
Computer Science, Hardware & Architecture
Mokhtar Mohammadi, Tarik A. Rashid, Sarkhel H. Taher Karim, Adil Hussain Mohammed Aldalwie, Quan Thanh Tho, Moazam Bidaki, Amir Masoud Rahmani, Mehdi Hosseinzadeh
Summary: This study presents a comprehensive investigation of SVM-based intrusion detection and feature selection systems, discussing the adaptation of various SVM classifiers in IDS and highlighting the properties and limitations of these schemes.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2021)
Article
Spectroscopy
Dongyan Zhang, Yi Yang, Gao Chen, Xi Tian, Zheli Wang, Shuxiang Fan, Zhenghua Xin
Summary: The study applied Vis/NIR spectroscopy to evaluate the soluble solids content (SSC) of tomatoes and developed a method for predicting SSC effectively. By measuring tomato samples at different maturity stages and using spectral data from different wavelength ranges, the study established the best prediction model through preprocessing and model building steps.
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
(2021)
Article
Multidisciplinary Sciences
Abdullah Al-Saleh
Summary: The Internet of Things field has posed challenges for network architectures, with cybersecurity being the primary goal for intrusion detection systems (IDSs). To improve IDS performance, researchers have focused on efficiently protecting connected data and devices due to the increasing number and types of attacks. This paper presents a novel IDS model that offers accurate detection in less processing time by reducing computational complexity. The model uses the Gini index method to determine security feature impurity and improve selection processes. It also employs a balanced communication-avoiding support vector machine decision tree method for enhanced intrusion detection accuracy. Evaluation using the UNSW-NB 15 dataset shows that the proposed model achieves a high attack detection performance of approximately 98.5%.
SCIENTIFIC REPORTS
(2023)
Article
Agriculture, Multidisciplinary
Xunlan Li, Zhaoxin Wei, Fangfang Peng, Jianfei Liu, Guohui Han
Summary: This study developed an efficient method for detecting chlorophyll contents and distributions in lemon leaves infected with citrus yellow vein clearing virus. Various algorithms and models were utilized to build prediction models, and it was found that chlorophyll content significantly reduced in infected leaves.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2022)
Article
Automation & Control Systems
Mohammadreza Nasiri Boroujeni, Yaser Samimi, Emad Roghanian
Summary: The paper discusses four methods for monitoring nonlinear fuzzy profiles, including data-driven fuzzy rule-based and extended least square support vector machine approaches, as well as modified fuzzy regression models and fuzzy least square methods based on linearizing transformation. A comparison of the methods in detecting process shifts and their performance in out-of-control conditions was conducted to evaluate their effectiveness.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Salah Eddine Bekhouche, Yassine Ruichek, Fadi Dornaika
Summary: Monitoring driver's drowsiness is crucial for road safety. This paper presents a computer vision-based framework for driver drowsiness detection, which detects the driver's face, extracts deep features, applies temporal feature aggregation and feature selection, and uses a binary classifier to determine drowsiness.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Agricultural Engineering
Zhulin Chen, Xuefeng Wang, Shanshan Sun
Summary: With the development of imaging devices and image processing algorithms, a hybrid feature selection approach was developed for total nitrogen content (TNC) estimation in Aquilaria sinensis. The approach combines different feature selection methods and regression algorithms to improve the accuracy of the estimation models.
BIOSYSTEMS ENGINEERING
(2022)
Article
Chemistry, Physical
Xiang Huang, Shengluo Ma, C. Y. Zhao, Hong Wang, Shenghong Ju
Summary: This study proposes a high-throughput screening framework for designing polymer chains with high thermal conductivity using interpretable machine learning and physical feature engineering. By optimizing physical descriptors and assisting machine learning models, the framework achieves higher prediction accuracy compared to traditional methods. The study also analyzes the contributions of individual descriptors and derives an explicit prediction equation for thermal conductivity. Polymer chains with high thermal conductivity are predominantly pi-conjugated structures with strong intra-chain interactions, resulting in enhanced thermal transport.
NPJ COMPUTATIONAL MATERIALS
(2023)
Article
Engineering, Aerospace
Seyyed Reza Ghaffari-Razin, Amir Reza Moradi, Navid Hooshangi
Summary: A new method for spatio-temporal modeling of ionosphere total electron content (TEC) using least squares support vector machine (LS-SVM) is proposed. The method reduces computational complexity, improves convergence speed and accuracy, and shows better performance in seasonal error analysis.
ADVANCES IN SPACE RESEARCH
(2022)
Article
Materials Science, Ceramics
Nicolas Osenciat, David Berardan, Diana Dragoe, Brigitte Leridon, Stephane Hole, Arun K. Meena, Sylvain Franger, Nita Dragoe
JOURNAL OF THE AMERICAN CERAMIC SOCIETY
(2019)
Article
Physics, Multidisciplinary
L. Hamidouche, E. Geron, S. Hole
Article
Physics, Applied
A. Penillard, L. Beccacce, L. Billot, A. de Rossi, S. Combrie, S. Hole, E. Geron
JOURNAL OF APPLIED PHYSICS
(2019)
Article
Engineering, Multidisciplinary
Maria Merlan, Thierry Ditchi, Yacine Oussar, Stephane Hole, Emmanuel Geron, Jerome Lucas
MEASUREMENT SCIENCE AND TECHNOLOGY
(2019)
Article
Engineering, Electrical & Electronic
Nathalie Morette, Thierry Ditchi, Yacine Oussar
ELECTRIC POWER SYSTEMS RESEARCH
(2020)
Article
Engineering, Electrical & Electronic
Quentin Herbette, Emmanuel Geron, Thierry Dichi, Gregoire Pillet, Jerome Lucas
IET MICROWAVES ANTENNAS & PROPAGATION
(2020)
Article
Engineering, Electrical & Electronic
N. Morette, L. C. Castro Heredia, Thierry Ditchi, A. Rodrigo Mor, Y. Oussar
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2020)
Article
Engineering, Electrical & Electronic
Assane Ndour, Stephane Hole, Paul Leblanc, Thierry Paillat
Summary: This study examines the development of intrinsic electric charges at solid/liquid interfaces using the pressure-wave-propagation (PWP) method. The experimental results show that salted water presents larger charge extent than expected, copper generates more charges in still conditions with glycerol, and pressboard has the largest charge extent. In flowing conditions, signal decrease is attributed to physico-chemical time constant.
JOURNAL OF ELECTROSTATICS
(2021)
Article
Chemistry, Analytical
Ibrahim Mohsen, Thierry Ditchi, Stephane Hole, Emmanuel Geron
Article
Materials Science, Characterization & Testing
C. Boue, S. Hole
Summary: This method combines lock-in thermography with laser excitation to estimate the depth of open cracks in metal with curvilinear shapes contactlessly. By measuring heat diffusion disturbances induced by cracks and analyzing the thermal signature extracted from surface temperature images at various scanning speeds, local evaluation of crack depth can be achieved.
JOURNAL OF NONDESTRUCTIVE EVALUATION
(2022)
Article
Physics, Multidisciplinary
Lin Zheng, Stephane Hole
Summary: This paper investigates charge injection in dielectrics under high voltage and finds that space charge distribution measurements can be influenced by the chosen interface conditions. The study shows that different mounting methods of an insulating polymer (polyethylene) in the measurement setup result in different intrinsic dipoles at the interfaces, which generate interface voltages that interact with energy levels and injection laws. The minimal dipoles occur when the polymer is mounted with hot-pressed carbon-loaded polymer electrodes or coupled with silicone oil and carbon-loaded EVA electrodes. In contrast, aluminum coating or direct contact with aluminum in the presence of silicone oil generates large dipoles compared to gel coupling or gold coating. The experiments were conducted using an ultra-sensitive pressure-wave-propagation measurement setup with low electric field for calibration. Aluminum and stainless steel were used in the setup wave-guide to assess their influence.
Article
Acoustics
E. Marechal, E. Geron, S. Hole
Summary: This paper characterizes the mechanical and electrical properties of PVDF and PVDF-TrFE films using the electro-acoustic reflectometry method in the frequency range of 20 MHz to 2 GHz. In addition, nonuniform poling is observed in PVDF-TrFE samples, leading to the generation of even and odd resonance modes.
JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA
(2023)
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.
