Article
Computer Science, Artificial Intelligence
Yinan Guo, Zirui Zhang, Fengzhen Tang
Summary: Feature selection is important in machine learning to reduce complexity and simplify interpretation. A novel non-linear method proposed in this paper uses kernelized multi-class support vector machines and fast recursive feature elimination to select features that work well for all classes, resulting in lower computational time complexity.
PATTERN RECOGNITION
(2021)
Article
Computer Science, Hardware & Architecture
Yosef Masoudi-Sobhanzadeh, Shabnam Emami-Moghaddam
Summary: This study proposes a machine learning-based method to predict botnets, addressing the limitations of existing methods in real-time application, functionality, and consideration of attack types. The results show that the proposed method accurately classifies data streams into relevant groups and achieves a trade-off between feature selection and prediction model performance.
Article
Biochemical Research Methods
Nguyen Quoc Khanh Le, Wanru Li, Yanshuang Cao
Summary: This study created a new pipeline to predict protein crystallization propensity using protein sequence. The pipeline includes feature selection, dimensionality reduction, and algorithm training, and achieved higher accuracy rates on three different datasets, providing a new solution for the challenge of multistage protein crystallization in computational biology.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Computer Science, Artificial Intelligence
Javier Alcaraz, Martine Labbe, Mercedes Landete
Summary: This paper introduces a Support Vector Machine with feature selection and proposes a bi-objective evolutionary algorithm to approximate the Pareto optimal frontier. Extensive computational experiments are conducted to compare the results obtained by different methods, and the properties of points in the Pareto frontier are discussed.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Wojciech Dudzik, Jakub Nalepa, Michal Kawulok
Summary: This paper addresses the optimization problem of SVMs for binary classification of difficult datasets, introducing an evolutionary technique and a co-evolutionary scheme. Experimental results show that the proposed algorithm outperforms popular supervised learners and other techniques for optimizing SVMs.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Environmental Sciences
Christopher A. A. Ramezan
Summary: Feature selection is important in remote sensing analysis to improve classification accuracy and reduce computational complexity. However, the generalizability and transferability of feature selection results depend on different classification models and datasets. While feature selection results can provide insights for analysis, they may not always provide comparable accuracies when applied to other classification models or similar remotely sensed datasets. Therefore, feature selection should be individually conducted for each training set to determine the optimal feature set for the classification model.
Article
Biotechnology & Applied Microbiology
Hisham Abdeltawab, Fahmi Khalifa, Yaser ElNakieb, Ahmed Elnakib, Fatma Taher, Norah Saleh Alghamdi, Harpal Singh Sandhu, Ayman El-Baz
Summary: This paper proposes a machine learning-based system to predict the required level of respiratory support in COVID-19 patients. The system utilizes a two-stage classification approach to predict different levels of respiratory support. The research uses a dataset collected from tertiary care hospitals at the University of Louisville Medical Center and demonstrates the use of feature selection and dimensionality reduction methods.
BIOENGINEERING-BASEL
(2022)
Article
Computer Science, Interdisciplinary Applications
Raymond Chiong, Zongwen Fan, Zhongyi Hu, Fabian Chiong
Summary: This study introduces an improved support vector machine method for predicting body fat percentage. The method incorporates bias error control and feature selection to enhance prediction accuracy, outperforming other prediction models in experimental results.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2021)
Article
Biochemical Research Methods
Chun Qiu, Sai Li, Shenghui Yang, Lin Wang, Aihui Zeng, Xufeng Zhang
Summary: The study identified key genes related to the mechanisms of glioblastoma occurrence and established a classification model using the CFS method. The accuracy of the model was 76.25% and 70.3% in different tests. PPP2R2B and CYBB may serve as potential biomarkers for the diagnosis of glioblastomas.
CURRENT BIOINFORMATICS
(2021)
Article
Management
In Gyu Lee, Sang Won Yoon, Daehan Won
Summary: This article introduces a cost-effective 1-norm support vector machine with group feature selection to address the cost uncertainty of features. It also proposes an efficient algorithm for solving the models. Experimental results show that the model achieves competitive outcomes in terms of prediction accuracy and economic performance.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Management
Asuncion Jimenez-Cordero, Juan Miguel Morales, Salvador Pineda
Summary: Feature selection has become a challenging issue in machine learning, particularly in classification problems. Support Vector Machine is a widely used technique in classification tasks, with various methodologies proposed for selecting the most relevant features in SVM. The authors introduce an embedded feature selection method based on a min-max optimization problem to balance model complexity and classification accuracy, showcasing efficiency and usefulness in benchmark datasets.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)
Article
Chemistry, Multidisciplinary
Md Al Mehedi Hasan, Jungpil Shin, Md Maniruzzaman
Summary: This study proposes an efficient approach for writer identification based on online handwritten Kanji characters, utilizing a support vector machine classifier. Experimental results show that this method achieves 99.0% accuracy for text-independent identification and 99.6% accuracy for text-dependent writer identification.
APPLIED SCIENCES-BASEL
(2022)
Article
Mathematics
Behzad Pirouz, Behrouz Pirouz
Summary: This article discusses the design of linear Support Vector Machine (SVM) classification techniques as multi-objective optimization problems. The authors focus on applying sparse optimization to feature selection for multi-objective optimization linear SVM. They emphasize the advantages of considering linear SVM classification techniques as multi-objective optimization problems.
Article
Biology
Song Yang, Lejing Lou, Wangjia Wang, Jie Li, Xiao Jin, Shijia Wang, Jihao Cai, Fangjun Kuang, Lei Liu, Myriam Hadjouni, Hela Elmannai, Chang Cai
Summary: This paper proposes a new algorithm called SCACO, which combines slime mould foraging behavior and collaborative hunting to improve the convergence accuracy and solution quality of ACOR. It also optimizes the ability of ACO to jump out of local optima using an adaptive collaborative hunting strategy. The performance of SCACO is compared with nine basic algorithms and nine variants, demonstrating its effectiveness in classification prediction for the diagnosis of tuberculous pleural effusion.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Computer Science, Artificial Intelligence
Mohammed Alshutbi, Zhiyong Li, Moath Alrifaey, Masoud Ahmadipour, Muhammad Murtadha Othman
Summary: The decisions of experts and the evaluation of patient data play crucial roles in breast cancer analysis. Machine learning techniques can aid in quickly examining and diagnosing medical data, reducing potential errors caused by inexperienced decision-makers. This study proposes an intelligent cancer classification method that selects a feature subset and optimizes the parameters of the SVM classifier using the Jaya algorithm. The method is applied to accurately characterize a breast cancer dataset and compared with other classifiers, demonstrating its effectiveness.
NEURAL COMPUTING & APPLICATIONS
(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.