Article
Biotechnology & Applied Microbiology
Wei Wang, Yu Zhang, Dong Liu, HongJun Zhang, XianFang Wang, Yun Zhou
Summary: This study focuses on the identification of binding sites between DNA-binding proteins and drugs. By analyzing residue interaction network features and sequence features, a predictor for protein-drug binding sites was built. The study found that residue interaction network features can effectively describe DNA-binding proteins, and binding sites with high betweenness and high closeness values are more likely to interact with drugs.
FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
M. Tanveer, A. Tiwari, R. Choudhary, M. A. Ganaie
Summary: This study proposes a novel large scale pinball twin support vector machine (LPTWSVM) to address the limitations of the twin support vector machines (TWSVMs), using a unique pinball loss function and improving model performance by eliminating matrix inversion calculation and minimizing structural risk.
Article
Biochemical Research Methods
Shima Shafiee, Abdolhossein Fathi, Ghazaleh Taherzadeh
Summary: Peptide-binding proteins play important roles in various applications. SPPPred is a novel ensemble machine learning-based approach that can predict protein-peptide binding residues with consistent and comparable performance.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Biology
Xue Wang, Yaqun Zhang, Bin Yu, Adil Salhi, Ruixin Chen, Lin Wang, Zengfeng Liu
Summary: The PPISP-XGBoost method proposed in the study uses XGBoost to predict PPI sites by extracting and optimizing features, achieving higher accuracy compared to existing methods on multiple datasets. The results demonstrate the effectiveness of PPISP-XGBoost in enhancing the prediction of PPI sites.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
Article
Computer Science, Artificial Intelligence
Chun-Na Li, Yuan-Hai Shao, Huajun Wang, Yu-Ting Zhao, Naihua Xiu, Nai-Yang Deng
Summary: This paper investigates the general forms and characteristics of nonparallel support vector machines (NSVMs) and categorizes them into two types. It reveals the advantages and defects of different types and points out the inconsistency problems. Based on this observation, a novel max-min distance-based NSVM is proposed with desired consistency. The proposed NSVM has the consistency of training and test and the consistency of metric, and it assigns each sample an ascertained loss.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Xiaoming Wang, Shitong Wang, Zengxi Huang, Yajun Du
Summary: This paper introduces a novel method called sparse support vector machine guided by radius-margin bound (RMB-SSVM) to efficiently condense the basis vectors in support vector machines. By selecting basis vectors and learning corresponding coefficients with a criterion related to SVM's generalization ability, the RMB-SSVM model can yield better performance.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Sambhav Jain, Reshma Rastogi
Summary: This paper proposes Parametric non-parallel support vector machines for binary pattern classification. The model brings noise resilience and sparsity by intelligently redesigning the Support vector machine optimization. The experimental results validate its scalability for large scale problems.
Article
Computer Science, Information Systems
Sebastian Maldonado, Julio Lopez, Carla Vairetti
Summary: The predictive performance of classification methods relies heavily on the nature of the environment and dataset shift issue. A novel Fuzzy Support Vector Machine strategy is proposed in this paper to improve performance by redefining the loss function and applying aggregation operators to deal with dataset shift. Our methods outperform traditional classifiers in terms of out-of-time prediction using simulated and real-world dataset for credit scoring.
INFORMATION SCIENCES
(2021)
Article
Biochemical Research Methods
Cangzhi Jia, Meng Zhang, Cunshuo Fan, Fuyi Li, Jiangning Song
Summary: Formator is a novel predictor developed for identifying lysine formylation sites, achieving high accuracy through ensemble learning strategy and feature extraction methods. Empirical studies demonstrate its superior performance compared to existing prediction tools, indicating great potential for identifying novel lysine formylation sites.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2021)
Article
Agronomy
Junliang Fan, Jing Zheng, Lifeng Wu, Fucang Zhang
Summary: Accurate estimation of plant transpiration (T) is crucial for agricultural production, and this study investigated the use of machine learning models to estimate daily T of summer maize. Incorporating soil water content and leaf area index variables improved model performance, with the deep neural network (DNN) model slightly outperforming others.
AGRICULTURAL WATER MANAGEMENT
(2021)
Review
Computer Science, Information Systems
Arijit Chakraborty, Sajal Mitra, Debashis De, Anindya Jyoti Pal, Ferial Ghaemi, Ali Ahmadian, Massimiliano Ferrara
Summary: Protein-Protein Interaction (PPI) is a crucial network in biology that requires fast, accurate, and critical analysis, with Support Vector Machine (SVM) being an effective tool for solving complex classification problems.
Article
Biochemical Research Methods
Yumeng Yan, Sheng-You Huang
Summary: DeepHomo, a deep learning model for predicting inter-protein residue-residue contacts, integrates various information sources and achieves high precision, outperforming existing methods on both experimentally determined structures and simulated targets.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Biochemistry & Molecular Biology
Kuan-Hsi Chen, Yuh-Jyh Hu
Summary: Protein-protein interactions are crucial for biological functions, and predicting the residue pairs responsible for these interactions is important for understanding diseases and designing drugs. Computational approaches like RRI-Meta, which integrates different classifiers and considers multiple feature types, have shown superior performance compared to current prediction tools. Conducting experiments using the same data from previous literature, RRI-Meta demonstrated its effectiveness and capability for distinguishing between interacting and noninteracting residues.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2021)
Article
Genetics & Heredity
Huseyin Avni Tac, Mustafa Koroglu, Ugur Sezerman
Summary: A machine learning method was developed to predict new A-to-I editing sites in RNA by training on information derived from neighboring sequences of experimentally verified A-to-I editing sites. The method showed high predictive performance and outperformed other classifiers in accuracy.
FUNCTIONAL & INTEGRATIVE GENOMICS
(2021)
Article
Computer Science, Artificial Intelligence
Wangyong Lv, Tingting Li, Huali Ren, Shijing Zeng, Jiao Zhou
Summary: The IDH-MSVM algorithm adjusts the distance between hyperplanes and classical margins to handle multiclassification problems more flexibly. Experimental results on UCI standard data sets show that this method achieves better classification accuracy for multiclass data compared to other algorithms.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Automation & Control Systems
Haojing Shen, Sihong Chen, Ran Wang, Xizhao Wang
Summary: This article proposes a framework that combines cost-sensitive classification and adversarial learning to protect special classes from attacks. A new defense model is built based on the Min-Max property and random distribution analysis, which performs better when facing attacks.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Sihong Chen, Haojing Shen, Ran Wang, Xizhao Wang
Summary: This paper presents a method using a multi-exit network to enhance adversarial robustness, demonstrating its effectiveness in reducing adversarial perturbations and preventing overfitting. Compared to traditional PGD adversarial training, this approach can improve model robustness in less time.
Review
Computer Science, Artificial Intelligence
Farhad Pourpanah, Ran Wang, Chee Peng Lim, Xi-Zhao Wang, Danial Yazdani
Summary: The Artificial Fish Swarm Algorithm (AFSA) is a Swarm Intelligence (SI) methodology inspired by the ecological behaviors of fish schooling. It has been widely used for solving real-world optimization problems due to its flexibility, fast convergence, and insensitivity to initial parameter settings. This paper provides a concise review of continuous AFSA and its improvements, hybrid models, and applications. It also discusses parameter modifications, procedure, and sub-functions of AFSA, along with the reasons for enhancements and comparison results with other methods. Furthermore, hybrid, multi-objective, and dynamic AFSA models for continuous optimization problems are analyzed, and future research directions for advancing AFSA-based models are highlighted.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Computer Science, Artificial Intelligence
Jingchao Cao, Wenhui Wu, Ran Wang, Sam Kwong
Summary: In this study, a CNN-based algorithm for no-reference image quality assessment (NR-IQA) is proposed based on object detection and self-correction measurement. Experimental results demonstrate that the proposed method achieves state-of-the-art performance and exhibits strong generalization ability.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Hong Zhu, Xizhao Wang, Ran Wang
Summary: Monotonic classification is a widespread task in real-life applications. Existing algorithms are sensitive to noise data, while the proposed FMKNN method constructs monotonic classifiers by taking advantage of fuzzy dominance relation, reducing the disturbance caused by noise.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Wenhui Wu, Yujie Chen, Ran Wang, Le Ou-Yang
Summary: This paper proposes a semi-supervised self-representative kernel concept factorization (S3RKCF) method that integrates adaptive kernel learning and low-dimensional data representation learning into a unified model. An adaptive local geometric structure is acquired in the KCF-induced self-representation space to facilitate data representation learning. Limited supervisory information is imposed as constraints to enhance the discriminability of data representation. The proposed S3RKCF outperforms state-of-the-art methods in clustering and classification tasks according to experimental results.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Yuxiang Yang, Xing Tian, Wing W. Y. Ng, Ran Wang, Ying Gao, Sam Kwong
Summary: This paper proposes an occluded face retrieval method using a generator, discriminator, and deep hashing retrieval network. It reconstructs occluded face images using a face inpainting model and generates compact similarity-preserving hashing codes for better retrieval performance.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2023)
Article
Biochemical Research Methods
Bo Li, Ke Jin, Le Ou-Yang, Hong Yan, Xiao-Fei Zhang
Summary: The single-cell RNA sequencing (scRNA-seq) technique is used to analyze gene expression patterns in complex tissues at single-cell resolution, but dropout events can hinder downstream analyses. We developed a new imputation method, scTSSR2, which combines matrix decomposition with two-side sparse self-representation to effectively impute dropout events in scRNA-seq data. Comparative experiments show that scTSSR2 outperforms existing imputation methods in terms of computational speed and memory usage. We also provide a user-friendly R package, scTSSR2, for denoising scRNA-seq data and improving data quality.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Computer Science, Artificial Intelligence
Shuyue Chen, Ran Wang, Jian Lu
Summary: Multi-label Active Learning (MLAL) is an effective method that improves the performance of multi-label classifiers with less annotation effort. This paper proposes a deep reinforcement learning (DRL) model to explore a general evaluation method for MLAL and addresses label correlation and data imbalanced problems using a self-attention mechanism and a reward function. Experimental results show that the DRL-based MLAL method achieves comparable results to other methods reported in the literature.
Article
Computer Science, Artificial Intelligence
Lin Xiao, Yongjun He, Yaonan Wang, Jianhua Dai, Ran Wang, Wensheng Tang
Summary: In this work, a new segmented VPZNN (SVPZNN) is proposed to handle the dynamic quadratic minimization issue (DQMI). Compared to previous ZNN models, SVPZNN achieves shorter convergence time and better robustness.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Ali Raza Shahid, Mehmood Nawaz, Xinqi Fan, Hong Yan
Summary: This article proposes a view-adaptive mechanism that transforms the skeleton view into a more consistent virtual perspective, reducing the influence of view variations.
IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Ran Wang, Shuyue Chen, Yu Yu
Summary: Version space is a crucial concept in supervised learning, but its application in multi-label active learning has not been explored. This paper extends the version space theory from single-label scenario to multi-label scenario, establishes a spatial structure for the multi-label version space, and proposes a simplified representation and a new multi-label active learning algorithm. The algorithm is further enhanced by addressing the issue of class imbalance in multi-label data. Experimental comparisons demonstrate the feasibility and effectiveness of the proposed methods.
PATTERN RECOGNITION
(2023)
Article
Computer Science, Artificial Intelligence
Farhad Pourpanah, Moloud Abdar, Yuxuan Luo, Xinlei Zhou, Ran Wang, Chee Peng Lim, Xi-Zhao Wang, Q. M. Jonathan Wu
Summary: Generalized zero-shot learning (GZSL) trains a model to classify data samples when some output classes are unknown. Semantic information of seen and unseen classes is used to bridge the gap between them. This review paper provides an overview, discusses categorization and representative methods, benchmark datasets, applications, and research gaps of GZSL.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Yuheng Jia, Sirui Tao, Ran Wang, Yongheng Wang
Summary: In this article, a simple and effective CA matrix self-enhancement framework is proposed to improve clustering performance by extracting high-confidence information and propagating it to the CA matrix.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Biochemistry & Molecular Biology
Debby D. Wang, Moon-Tong Chan
Summary: Predicting the binding affinity of protein-ligand complexes is crucial for structure-based drug design. In this study, the authors introduce intermolecular contact profiles (IMCPs) as descriptors for machine-learning-based binding affinity prediction. IMCPs show better accuracy and interpretability compared to other similar descriptors, providing concise structural information for protein-ligand complexes.
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
(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.