Article
Computer Science, Hardware & Architecture
Huru Hasanova, Muhammad Tufail, Ui-Jun Baek, Jee-Tae Park, Myung-Sup Kim
Summary: In this article, a machine learning based Sine Cosine Weighted K-Nearest Neighbour (SCA_WKNN) algorithm is proposed for heart disease prediction, which learns from data stored in blockchain. The proposed algorithm achieves higher accuracy compared to other algorithms. Blockchain-based storage also achieves higher throughput.
COMPUTERS & ELECTRICAL ENGINEERING
(2022)
Article
Automation & Control Systems
J. A. Romero-del-Castillo, Manuel Mendoza-Hurtado, Domingo Ortiz-Boyer, Nicolas Garcia-Pedrajas
Summary: Multi-label learning is an important field in machine learning research, and the multi-label k-nearest neighbor method is one of the most successful algorithms. However, allocating the appropriate value of k is a challenge in difficult classification tasks, as different regions may require different k values. We propose a simple yet powerful method to set local k values, obtaining the optimal value by optimizing the local effect of different k values near each prototype.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Maximiliano Cubillos, Sanne Wohlk, Jesper N. Wulff
Summary: This study proposes a bi-objective algorithm based on the k-nearest neighbors method for imputing missing values in data with continuous variables and multilevel structures. Results from simulation studies show that the proposed method outperforms benchmark methods in cases with high intraclass correlation, reducing estimation bias and coefficient of determination.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Automation & Control Systems
Zekang Bian, Chi Man Vong, Pak Kin Wong, Shitong Wang
Summary: This study proposes a novel classification method based on FKNN called A-FKNN that learns the optimal k value for each testing sample, and a faster version called FA-FKNN is designed. Experimental results show that both A-FKNN and FA-FKNN outperform other methods in terms of classification accuracy, with FA-FKNN having a shorter running time.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Danny Hartanto Djarum, Zainal Ahmad, Jie Zhang
Summary: RBOSR is a new approach that improves the performance and efficiency of the PM2.5 stacked model. It significantly reduces training time and outperforms the original model.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Yongda Cai, Joshua Zhexue Huang, Jianfei Yin
Summary: This paper proposes a new method called adaptive k-nearest neighbors similarity graph (AKNNG) for constructing a better graph structure. By assigning different k values to different data points and automatically adjusting the k value based on the similarity graph, the AKNNG method improves clustering accuracies and reduces construction time.
Article
Computer Science, Artificial Intelligence
Zhibin Pan, Yiwei Pan, Yidi Wang, Wei Wang
Summary: The LMKNN classifier has better performance and robustness compared to the KNN classifier, but the unreliable nearest neighbor selection rule and single local mean vector strategy severely impact its classification performance.
Article
Computer Science, Information Systems
Shanshan Liu, Pedro Reviriego, Jose Alberto Hernandez, Fabrizio Lombardi
Summary: This paper explores how to provide protection and error tolerance for classifiers by exploiting the algorithmic properties, applied to the k Nearest Neighbors classifier, and proposes a time-based modular redundancy scheme to reduce the number of re-computations needed effectively.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING
(2021)
Article
Computer Science, Hardware & Architecture
Payam Bahrani, Behrouz Minaei-Bidgoli, Hamid Parvin, Mitra Mirzarezaee, Ahmad Keshavarz
Summary: Despite advancements in recommender systems, there is still room for improvement. This study developed an improved method using weighted averaging and a Gaussian mixture model, which showed more accurate results and faster execution time compared to traditional methods.
JOURNAL OF SUPERCOMPUTING
(2023)
Article
Computer Science, Information Systems
Jiawei Yang, Yu Chen, Sylwan Rahardja
Summary: Traditional outlier detectors have neglected the group-level factor in calculating outlier scores for objects in data, resulting in the inability to capture collective outliers. To address this issue, a framework called neighborhood representative (NR) is proposed, enabling existing outlier detectors to efficiently detect outliers, including collective outliers, while maintaining computational integrity. By selecting representative objects, scoring them, and applying the score to collective objects, NR achieves this without altering existing detectors. NR is compatible with existing detectors and improves performance on eleven real-world datasets by an average of 8% (0.72 to 0.78 AUC) relative to twelve state-of-the-art outlier detectors. The implementation of NR can be found at www.OutlierNet.com for reproducibility. (c) 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
INFORMATION SCIENCES
(2023)
Article
Statistics & Probability
Emre Demirkaya, Yingying Fan, Lan Gao, Jinchi Lv, Patrick Vossler, Jingbo Wang
Summary: This work introduces a novel two-scale DNN method by linearly combining two DNN estimators with different subsampling scales to reduce bias and achieve the optimal nonparametric convergence rate under the fourth-order smoothness condition.
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
(2022)
Article
Computer Science, Artificial Intelligence
Iurii Konovalenko, Andre Ludwig
Summary: Real-time temperature monitoring is crucial in cold pharmaceutical supply chains to prevent product quality deterioration from extreme temperature exposure. A new hybrid k-NN algorithm, based on principles of local similarity and neighborhood homogeneity, outperforms traditional k-NN in accuracy and precision for temperature alarms in pharmaceutical supply chains.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Junnan Li, Qingsheng Zhu, Quanwang Wu, Zhu Fan
Summary: Class imbalance is a significant factor leading to performance deterioration in classifiers. Techniques such as SMOTE and its extension, NaNSMOTE, have been successful in addressing this issue and have been proven effective on real data sets.
INFORMATION SCIENCES
(2021)
Article
Chemistry, Physical
Abdulrahman A. Almehizia, Ahmed M. Naglah, Hamad M. Alkahtani, Umme Hani, Mohammed Ghazwani
Summary: This research comprehensively investigates the solubility characteristics of five distinct drugs under varying pressure and temperature conditions using a machine learning technique. The study finds that the Polynomial Regression model optimized with the Harmony Search algorithm performs the best in predicting drug solubility.
JOURNAL OF MOLECULAR LIQUIDS
(2023)
Article
Mathematics, Applied
S. Anvari, M. Abdollahi Azgomi, M. R. Ebrahimi Dishabi, M. Maheri
Summary: K-Nearest Neighbors (KNN) is a classification algorithm that uses supervised machine learning and a voting system. The performance of KNN depends on factors like class distribution, scalability, and equal values for all training samples. Variations of KNN, such as fuzzy KNN, weighted KNN, and KNN with variable neighbors, have been proposed to improve its accuracy. This paper introduces a weighted KNN based on the Whale Optimization Algorithm, which assigns weights to training samples using an optimized weight matrix. Experimental results show that the proposed algorithm outperforms both weighted KNN based on Genetic Algorithm (GA) and classic KNN.
IRANIAN JOURNAL OF FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Shubiao Wu, Ali Asghar Heidari, Siyang Zhang, Fangjun Kuang, Huiling Chen
Summary: This paper proposes a Gaussian barebone mutation enhanced SMA (GBSMA) to improve the shortcomings of the original SMA algorithm. The GBSMA algorithm introduces a Gaussian function for faster convergence and expands the search space, and also uses a differential evolution update strategy for better global search performance. Experimental results show that GBSMA outperforms the original SMA and other similar algorithms in terms of convergence speed and solution accuracy.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Engineering, Biomedical
Shuhui Hao, Changcheng Huang, Ali Asghar Heidari, Zhangze Xu, Huiling Chen, Maha M. Althobaiti, Romany F. Mansour, Xiaowei Chen
Summary: This study proposes an improved water cycle algorithm and applies it to multi-threshold image segmentation to achieve higher-quality segmentation of lupus nephritis images.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Engineering, Multidisciplinary
Jie Xing, Hanli Zhao, Huiling Chen, Ruoxi Deng, Lei Xiao
Summary: In this work, an improved Whale Optimization Algorithm (QGBWOA) is proposed to address the problems of falling into local optimum and slow convergence. Quasi-opposition-based learning and Gaussian barebone mechanism are introduced to enhance the searching ability and diversity of WOA. Experimental results on benchmark datasets demonstrate the significantly improved convergence accuracy and speed of QGBWOA. Furthermore, applications in feature selection and multi-threshold image segmentation validate its capability in solving complex real-world problems.
JOURNAL OF BIONIC ENGINEERING
(2023)
Article
Engineering, Multidisciplinary
Yan Han, Weibin Chen, Ali Asghar Heidari, Huiling Chen
Summary: Coronavirus Disease 2019 (COVID-19) is a severe global epidemic, and it is crucial to quickly and accurately identify COVID-19 for controlling the spread of the virus. This paper proposes an improved multi-verse optimizer algorithm called RDMVO for COVID-19 lesion segmentation in Chest X-ray images. Experimental results show that RDMVO is highly competitive compared to other meta-heuristic algorithms in benchmark functions and image segmentation experiments.
JOURNAL OF BIONIC ENGINEERING
(2023)
Article
Engineering, Multidisciplinary
Chao Lin, Pengjun Wang, Ali Asghar Heidari, Xuehua Zhao, Huiling Chen
Summary: This paper proposes an improved algorithm named RCSSSA based on SSA, which enhances the convergence accuracy and speed by adding real-time update mechanism, communication strategy, and selective replacement strategy. Experimental results demonstrate that RCSSSA can converge faster and achieve better optimization results compared to traditional swarm intelligence and other improved algorithms.
JOURNAL OF BIONIC ENGINEERING
(2023)
Article
Mathematical & Computational Biology
Ruyi Dong, Junjie Du, Yanan Liu, Ali Asghar Heidari, Huiling Chen
Summary: To address the issues of poor robustness and adaptability in traditional control methods, the deep deterministic policy gradient (DDPG) algorithm is improved by incorporating a hybrid function with multiple rewards. The experience replay mechanism of DDPG is also enhanced through a combination of priority sampling and uniform sampling, resulting in accelerated convergence. Experimental results in a simulation environment demonstrate that the improved DDPG algorithm achieves accurate control of robot arm motion, with a higher success rate of 91.27% compared to the original DDPG algorithm, thereby exhibiting improved environmental adaptability.
FRONTIERS IN NEUROINFORMATICS
(2023)
Article
Mathematical & Computational Biology
Mingjing Wang, Long Chen, Ali Asghar Heidari, Huiling Chen
Summary: Harris Hawks optimization (HHO) is a versatile swarm optimization approach that addresses a wide range of optimization problems. However, it suffers from inadequate exploitation and slow convergence rates in certain numerical optimization scenarios. In this study, the fireworks algorithm's explosion search mechanism is integrated into HHO, resulting in a fireworks explosion-based HHO framework (FWHHO) that successfully overcomes these limitations. Experimental results demonstrate that FWHHO outperforms state-of-the-art algorithms and significantly improves upon existing HHO and fireworks algorithms. Additionally, FWHHO is successfully applied to the diagnosis of COVID-19 using biochemical indices, with statistical evidence indicating its potential as a computer-aided approach for early warning and therapy/diagnosis of COVID-19.
FRONTIERS IN NEUROINFORMATICS
(2023)
Article
Thermodynamics
Xuemeng Weng, Ping Xuan, Ali Asghar Heidari, Zhennao Cai, Huiling Chen, Romany F. Mansour, Mahmoud Ragab
Summary: This research proposes an improved sine cosine algorithm based on crossover mechanism and pattern search algorithm to optimize multiple objectives in a power system with various FACTS devices. By using sine and cosine theory and evolutionary strategy to continuously sample and update the solutions, the proposed method improves the quality of the solutions. The algorithm has been experimentally validated to be effective in optimizing the optimal power flow of the entire power system and FACTS equipment.
Article
Computer Science, Artificial Intelligence
Mingjing Wang, Xiaoping Li, Long Chen, Huiling Chen
Summary: A multi-objective evolutionary algorithm integrating decomposition and harris hawks learning (MOEA/D-HHL) is proposed for medical machine learning, which guarantees good variety and systematic solutions. The algorithm's performance is evaluated using benchmarks and is then applied to medical cancer gene expression data sets for feature selection, classification accuracy, and correlation measures. Experimental results show that MOEA/D-HHL outperforms current methods on clinically relevant data for lupus nephritis and pulmonary hypertension.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Automation & Control Systems
Yuli Feng, Chunmei Zhang, Huiling Chen, Ran Li
Summary: This paper investigates the issue of finite-time synchronization and topology identification of delayed multi-group models with stochastic perturbation and multiple dispersal. Based on the graph-theoretic approach, a novel finite-time topology identification scheme is proposed via drive-response synchronization. Several useful finite-time topology identification criteria are presented. The topology identification of delayed multi-group models with stochastic perturbation and multiple dispersal is acquired in finite time under the desired feedback controller. Finally, a numerical example is given to validate the effectiveness of the result developed.
ASIAN JOURNAL OF CONTROL
(2023)
Article
Engineering, Biomedical
Yan Han, Weibin Chen, Ali Asghar Heidari, Huiling Chen, Xin Zhang
Summary: This paper proposes the CBQMVO algorithm, which extends the original MVO algorithm by introducing three strategies to address the issues of slow convergence speed and falling into local optimum. Experimental results demonstrate that CBQMVO performs well on some unimodal and complex competition functions, and achieves better segmentation effect in breast cancer pathologic image segmentation compared to other metaheuristic algorithms.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Engineering, Biomedical
Jiaochen Chen, Zhennao Cai, Ali Asghar Heidari, Huiling Chen, Qiuxiang He, Jose Escorcia-Gutierrez, Romany F. Mansour
Summary: The scholarly world has shown great interest in medical image segmentation due to its complex nature and important role in medical diagnosis and treatment systems. Multi-threshold image segmentation (MTIS) is a popular technique for this purpose, known for its simplicity and straightforwardness. This paper introduces an improved Differential Evolution (DE) algorithm called AGDE, based on MTIS, which was used to evaluate its high performance at IEEE CEC 2017. Experimental results showed that the proposed image segmentation method outperformed its competitors, making it a promising approach for medical image segmentation.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Engineering, Multidisciplinary
Hanjie Ma, Lei Xiao, Zhongyi Hu, Ali Asghar Heidari, Myriam Hadjouni, Hela Elmannai, Huiling Chen
Summary: Feature selection is a data pre-processing method used in bioinformatics, finance, and medicine to reduce dataset dimensionality. Traditional approaches struggle with high-dimensional information. We propose an enhanced Whale Optimization Algorithm (SCLWOA) that incorporates sine chaos and comprehensive learning strategies to improve feature selection and algorithm performance.
JOURNAL OF BIONIC ENGINEERING
(2023)
Article
Engineering, Multidisciplinary
Ruyi Dong, Lixun Sun, Long Ma, Ali Asghar Heidari, Xinsen Zhou, Huiling Chen
Summary: In this research, Boosting kernel search optimizer (BKSO) is introduced to solve the combined economic emission dispatch (CEED) problem. BKSO performs better than the standard KSO in terms of exploitation ability, robustness, and convergence rate. Experimental results show that BKSO outperforms other optimization algorithms in statistical results, convergence curves, fuel costs, and pollution emissions.
JOURNAL OF BIONIC ENGINEERING
(2023)
Article
Automation & Control Systems
Chunmei Zhang, Huiling Chen, Qin Xu, Yuli Feng, Ran Li
Summary: This article discusses a class of stochastic hybrid delayed coupled systems with multiple weights, and derives several conditions for asymptotic synchronization and topology identification of the systems based on Kirchhoff's Matrix-Tree Theorem and Lyapunov stability theory.
NONLINEAR ANALYSIS-HYBRID SYSTEMS
(2024)
Article
Biology
Seyyed Bahram Borgheai, Alyssa Hillary Zisk, John McLinden, James Mcintyre, Reza Sadjadi, Yalda Shahriari
Summary: This study proposed a novel personalized scheme using fNIRS and EEG as the main tools to predict and compensate for the variability in BCI systems, especially for individuals with severe motor deficits. By establishing predictive models, it was found that there were significant associations between the predicted performances and the actual performances.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Hongliang Guo, Hanbo Liu, Ahong Zhu, Mingyang Li, Helong Yu, Yun Zhu, Xiaoxiao Chen, Yujia Xu, Lianxing Gao, Qiongying Zhang, Yangping Shentu
Summary: In this paper, a BDSMA-based image segmentation method is proposed, which improves the limitations of the original algorithm by combining SMA with DE and introducing a cooperative mixing model. The experimental results demonstrate the superiority of this method in terms of convergence speed and precision compared to other methods, and its successful application to brain tumor medical images.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jingfei Hu, Linwei Qiu, Hua Wang, Jicong Zhang
Summary: This study proposes a novel semi-supervised point consistency network (SPC-Net) for retinal artery/vein (A/V) classification, addressing the challenges of specific tubular structures and limited well-labeled data in CNN-based approaches. The SPC-Net combines an AVC module and an MPC module, and introduces point set representations and consistency regularization to improve the accuracy of A/V classification.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Omair Ali, Muhammad Saif-ur-Rehman, Tobias Glasmachers, Ioannis Iossifidis, Christian Klaes
Summary: This study introduces a novel hybrid model called ConTraNet, which combines the strengths of CNN and Transformer neural networks, and achieves significant improvement in classification performance with limited training data.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Juan Antonio Valera-Calero, Dario Lopez-Zanoni, Sandra Sanchez-Jorge, Cesar Fernandez-de-las-Penas, Marcos Jose Navarro-Santana, Sofia Olivia Calvo-Moreno, Gustavo Plaza-Manzano
Summary: This study developed an easy-to-use application for assessing the diagnostic accuracy of digital pain drawings (PDs) compared to the classic paper-and-pencil method. The results demonstrated that digital PDs have higher reliability and accuracy compared to paper-and-pencil PDs, and there were no significant differences in assessing pain extent between the two methods. The PAIN EXTENT app showed good convergent validity.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Biao Qu, Jialue Zhang, Taishan Kang, Jianzhong Lin, Meijin Lin, Huajun She, Qingxia Wu, Meiyun Wang, Gaofeng Zheng
Summary: This study proposes a deep unrolled neural network, pFISTA-DR, for radial MRI image reconstruction, which successfully preserves image details using a preprocessing module, learnable convolution filters, and adaptive threshold.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Alireza Rafiei, Milad Ghiasi Rad, Andrea Sikora, Rishikesan Kamaleswaran
Summary: This study aimed to improve machine learning model prediction of fluid overload by integrating synthetic data, which could be translated to other clinical outcomes.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jinlian Ma, Dexing Kong, Fa Wu, Lingyun Bao, Jing Yuan, Yusheng Liu
Summary: In this study, a new method based on MDenseNet is proposed for automatic segmentation of nodular lesions from ultrasound images. Experimental results demonstrate that the proposed method can accurately extract multiple nodules from thyroid and breast ultrasound images, with good accuracy and reproducibility, and it shows great potential in other clinical segmentation tasks.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jiabao Sheng, SaiKit Lam, Jiang Zhang, Yuanpeng Zhang, Jing Cai
Summary: Omics fusion is an important preprocessing approach in medical image processing that assists in various studies. This study aims to develop a fusion methodology for predicting distant metastasis in nasopharyngeal carcinoma by mitigating the disparities in omics data and utilizing a label-softening technique and a multi-kernel-based neural network.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Zhenxiang Xiao, Liang He, Boyu Zhao, Mingxin Jiang, Wei Mao, Yuzhong Chen, Tuo Zhang, Xintao Hu, Tianming Liu, Xi Jiang
Summary: This study systematically investigates the functional connectivity characteristics between gyri and sulci in the human brain under naturalistic stimulus, and identifies unique features in these connections. This research provides novel insights into the functional brain mechanism under naturalistic stimulus and lays a solid foundation for accurately mapping the brain anatomy-function relationship.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Qianqian Wang, Mingyu Zhang, Aohan Li, Xiaojun Yao, Yingqing Chen
Summary: The development of PARP-1 inhibitors is crucial for the treatment of various cancers. This study investigates the structural regulation of PARP-1 by different allosteric inhibitors, revealing the basis of allosteric inhibition and providing guidance for the discovery of more innovative PARP-1 inhibitors.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Qing Xu, Wenting Duan
Summary: In this paper, a dual attention supervised module, named DualAttNet, is proposed for multi-label lesion detection in chest radiographs. By efficiently fusing global and local lesion classification information, the module is able to recognize targets with different sizes. Experimental results show that DualAttNet outperforms baselines in terms of mAP and AP50 with different detection architectures.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Kaja Gutowska, Piotr Formanowicz
Summary: The primary aim of this research is to propose algorithms for identifying significant reactions and subprocesses within biological system models constructed using classical Petri nets. These solutions enable two analysis methods: importance analysis for identifying critical individual reactions to the model's functionality and occurrence analysis for finding essential subprocesses. The utility of these methods has been demonstrated through analyses of an example model related to the DNA damage response mechanism. It should be noted that these proposed analyses can be applied to any biological phenomenon represented using the Petri net formalism. The presented analysis methods extend classical Petri net-based analyses, enhancing our comprehension of the investigated biological phenomena and aiding in the identification of potential molecular targets for drugs.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Hansle Gwon, Imjin Ahn, Yunha Kim, Hee Jun Kang, Hyeram Seo, Heejung Choi, Ha Na Cho, Minkyoung Kim, Jiye Han, Gaeun Kee, Seohyun Park, Kye Hwa Lee, Tae Joon Jun, Young-Hak Kim
Summary: Electronic medical records have potential in advancing healthcare technologies, but privacy issues hinder their full utilization. Deep learning-based generative models can mitigate this problem by creating synthetic data similar to real patient data. However, the risk of data leakage due to malicious attacks poses a challenge to traditional generative models. To address this, we propose a method that employs local differential privacy (LDP) to protect the model from attacks and preserve the privacy of training data, while generating medical data with reasonable performance.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Siwei Tao, Zonghan Tian, Ling Bai, Yueshu Xu, Cuifang Kuang, Xu Liu
Summary: This study proposes a transfer learning-based method to address the phase retrieval problem in grating-based X-ray phase contrast imaging. By generating a training dataset and using deep learning techniques, this method improves image quality and can be applied to X-ray 2D and 3D imaging.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
