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Feature Selection of Gene Expression Data Using a Modified Artificial Fish Swarm Algorithm With Population Variation

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IEEE ACCESS 2024年 12卷 72688-72706页

作者： Li, Zong-Zheng Wang, Fang-Ling Qin, Feng Yusoff, Yusliza Binti Zain, Azlan Mohd Univ Teknol Malaysia Fac Comp Skudai 81310 Johor Bahru Malaysia

Microarray data is of great significance for cancer identification at the gene level. In the microarray dataset, only a small number of characteristic genomes have significant classification and identification rates for cancer. How to extract a small number of characteristic genes from a large number of microarray data is a classic NP-hard problem. This paper proposes a practical hybrid approach to implement the feature selection of gene expression from the microarray by combining the F-score algorithm and an improved artificial fish swarm algorithm with population variation (FSA-PV). Firstly, the F-score algorithm eliminates a large number of useless and redundant features in the dataset. Then, FSA-PV is discussed to obtain the ability to jump out of the local optimum while retaining the excellent feature of the subset as much as possible, and the adaptive step and visual are used to adjust the search space and to move the range of the algorithm in different environments to improve the local optimization and global optimization abilities. In addition, a naive Bayesian classifier is used to test the classification accuracy of subsets. Eight classical datasets are used to verify the performance of the proposed mechanism in the experiment part. The results reveal that the classification accuracy using the FSA-PV is significant superior to other algorithms in Breast dataset, and the classification accuracy is more than 90% in 8 cases. It further indicates the robustness and feasibility of the FSA-PV in the gene selection process.

关键词： classification algorithms Feature extraction Visualization Support vector machines Clustering algorithms Wrapping Gene expression Fish schools Feature selection gene expression microarray data modified artificial fish swarm algorithm population variation

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Efficient Anomaly Detection Algorithm for Heart Sound Signal

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IEEE ACCESS 2024年 12卷 139225-139236页

作者： Liu, Zhihai Liu, Wen Gu, Zheng Yang, Feng Xinjiang Normal Univ Sch Comp Sci & Technol Urumqi 830000 Peoples R China Xinjiang Inst Engn Artificial Intelligence & Smart Mine Engn Technol Urumqi 830023 Peoples R China Xinjiang Changsen Data Technol Co Ltd Urumqi 830011 Peoples R China Xinjiang Inst Engn Comp Informat Ctr Urumqi 830023 Peoples R China

According to the latest report by the WHO, cardiovascular disease claims approximately 17.9 million lives annually, making it one of the leading causes of mortality. Hence, early screening and detection of cardiovascular diseases are important for their prevention. Heart sound signals contain a wealth of information on cardiac function and health status. Researchers have recently utilized deep learning methods to detect abnormal features in heart sound signals, thereby facilitating disease diagnosis. Currently, existing heart sound datasets suffer from imbalanced data proportions, complex feature types, and low discriminative power between systolic and diastolic murmurs, resulting in the suboptimal performance of deep learning algorithms in detection. Therefore, we propose a heart sound abnormality detection algorithm based on the Swin Transformer architecture. Firstly, we enhance the ability to extract local texture features of heart sound signals by introducing a convolutional embedding module into the positional encoding layer of the backbone network. Second, we augmented the model's capability to extract the frequency-domain features of heart-sound signals by incorporating a discrete convolutional mapping structure. This structure utilizes discrete cosine transformation in conjunction with convolutional projection to acquire feature matrices, thereby improving classification accuracy. Finally, we employed a Focal Loss function to prioritize abnormal heart-sound samples, enhancing the generalization ability of the model and evaluating the proposed algorithm using the PhysionNet/CinC 2016 public dataset. The results demonstrated an Accuracy of 93.4%, a Specificity of 90.4% and a Sensitivity of 95.7%.

关键词： Transformers Feature extraction Convolutional neural networks Hidden Markov models classification algorithms Cardiovascular diseases Heart beat Abnormality detection cardiovascular disease convolution embedding discrete convolution projection heart sound signal

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Enhancing Early Breast Cancer Detection Through Advanced Data Analysis

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IEEE ACCESS 2024年 12卷 161941-161953页

作者： Rahman, Md. Atiqur Hamada, Mohamed Sharmin, Shayla Afroz Rimi, Tanzina Sanjida Talukder, Atia Imran, Nafees Kobra, Khadijatul Ridwan Ahmed, Md Rabbi, Md Hasan Matin, Md. Mafiul Ameer Ali, M. East West Univ Dept Comp Sci & Engn Dhaka 1212 Bangladesh Univ Aizu Sch Comp Sci & Engn Aizu Wakamatsu Aizuwakamatsu 9650006 Japan Daffodil Int Univ Dept Comp Sci & Engn Dhaka 1216 Bangladesh Daffodil Int Univ Dept Business Adm Dhaka Bangladesh Daffodil Int Univ Dept Software Engn Dhaka 1216 Bangladesh CIFOR Melbourne Vic 3000 Australia

In recent years, breast cancer, originating from breast tissue, has become one of the significant global health challenges for women worldwide, with early detection crucial for improved survival rates. Researchers have proposed numerous detection techniques, and recently, machine learning-based methods have gained considerable attention due to their reusability and speed. Despite various models proposed by researchers for breast cancer detection, there is an ongoing need for more accurate models. This study proposes an enhanced machine-learning approach for breast cancer detection using the Wisconsin Breast Cancer (Diagnostic) (WDBC) dataset. We applied several data preprocessing techniques, including hypothesis testing, feature engineering, scaling, and feature selection. We trained 14 classifiers by selecting the 13 most significant features using a gradient boosting regressor with Bonferroni correction. Our proposed eXtreme Gradient Boosting model demonstrated superior performance, achieving 99.12% accuracy, 0.9767 precision, 1.0 recall, 0.9861 specificity, and 0.9882 F1-score. These results surpass those of previous studies, underscoring the model's potential for early and accurate breast cancer diagnosis. Furthermore, evaluations based on training time and Kappa score indicate that our eXtreme Gradient Boosting model is faster and more reliable.

关键词： Breast cancer Accuracy Cancer Training Boosting Machine learning Support vector machines Nearest neighbor methods Feature extraction Decision trees classification algorithms SMOTE ANOVA test ensembling feature engineering Bonferroni correction breast cancer classification machine learning

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Sensing Within Ultra-Short Duration: Extended Subspace algorithms With Insufficient Snapshots

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IEEE SIGNAL PROCESSING LETTERS 2025年 32卷 1490-1494页

作者： Ma, Teng Feng, Yuxuan Xiao, Yue Lei, Xia Poulkov, Vladimir Univ Elect Sci & Technol China Natl Key Lab Wireless Commun Chengdu 611731 Peoples R China Tech Univ Sofia Fac Telecommun Sofia 1000 Bulgaria

In pursuit of real-time sensing within ultra-short duration, conventional sensing algorithms are gradually failing to fulfill the stringent latency demands. Specifically, traditional subspace-based methods such as multiple signal classification (MUSIC) are hindered by their need for an extensive number of snapshots to accumulate the rank of the spatial covariance matrix (SCM), resulting in poor real-time performance. Moreover, advanced techniques like compressed sensing and machine learning are constrained by requirements for high signal sparsity or suffer from limited generality. To handle these challenges, this paper proposes an innovative extension of subspace theory tailored to insufficient-snapshot scenarios, leveraging the concept of spatio-temporal exchangeability. Based on the defined spatio-temporal correlation predicated on the space translation invariance characteristic of uniform linear arrays, we engineer a pseudo SCM that inherently possesses sufficient rank. This methodology not only resolves the rank-deficiency issue but also fully exploits the array aperture and significantly reduces the noise level. Simulation results are presented, substantiating the feasibility and enhanced performance of the proposed algorithms, marking a significant advancement over existing methodologies.

关键词： Estimation Multiple signal classification Sensors Signal processing algorithms Real-time systems Training Matrix converters Eigenvalues and eigenfunctions classification algorithms Translation Angle estimation spatio-temporal exchangeability multiple signal classification (MUSIC) insufficient snapshots

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Resource-Constraint Deep Forest-Based Intrusion Detection Method in Internet of Things for Consumer Electronic

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2024年第2期70卷 4976-4987页

作者： Chen, Xuejiao Wang, Pan Yang, Yahong Liu, Minyao Nanjing Vocat Coll Informat Technol Sch Commun Nanjing 210046 Peoples R China Nanjing Univ Post & Telecommun Sch Modern Posts Nanjing 210003 Peoples R China

Consumer Electronics (CEs) are smart devices using IoT for connectivity. They're susceptible to attacks like DoS, DDoS, and Web attacks, impairing functions and enabling remote hijacking. Attackers can exploit CEs to target other systems, like vehicles. Malicious code can propagate through networks or CEs, causing vehicle failures. Existing ML/DL based IDS have high classification accuracy and robustness in traditional Internet environments, but they are overly complex for performance improvement, which hinders their deployment in edge small computing environments. Furthermore, the comparison experiments of these intrusion detection algorithms with other algorithms are not sufficiently comprehensive to evaluate their performance in small computing environments. Therefore, balancing "detection performance and resource consumption" is a key issue in CE network detection. To address this issue, this paper proposes a hybrid feature selection model based on chi-square test and information gain combined Ig-Chi, which effectively reduces the feature dimensionality and improves the classification accuracy of classifiers for high-dimensional data sets. Additionally, layered intrusion detection is employed to perform intrusion detection on the data after feature selection. The experiments on four public data sets demonstrate that this method surpasses six ML/DL algorithms in terms of accuracy and resource indicators.

关键词： Intrusion detection Random forests Computational modeling classification algorithms Wireless sensor networks Regression analysis Feature extraction consumer electronic Internet of Things deep learning deep forest resource-constraint

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Application of K-Shell Algorithm in Key Point Identification and Cascading Failure in Urban Public Safety Transportation Network

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IEEE ACCESS 2024年 12卷 32152-32163页

作者： Xiao, You Ren, Yuan Wuhan Univ Sci & Technol Sch Law & Econ Wuhan 430065 Peoples R China Wuhan Univ Sci & Technol Inst Adv Studies Humanities & Social Sci Wuhan 430065 Peoples R China

The rapid development of modern urban public transportation and people's demand for efficient transportation have made intelligent management of urban public transportation networks a hot research field. In view of this, a composite complex traffic network node classification algorithm model is established based on the shell decomposition algorithm combined with a cascading failure model. The final research findings denote that the classification refinement level of the comprehensive shell decomposition value is about 50% higher than that of the WK-shell decomposition value. The comprehensive shell decomposition algorithm, which integrates global and local influence factors, can almost achieve one shell decomposition value per node in classification. The number of layers with nodes below 30 in the actual transportation network dataset test accounts for about 98% of the total number under the improved comprehensive shell decomposition algorithm, and roughly 50% under the composite shell decomposition algorithm, indicating that the accuracy of the former algorithm is higher. When the relationship ratio in robustness testing is 1:1:1, the average comprehensive shell decomposition value is at a higher position, which is about 3% higher than the WK-shell decomposition value and about 5% higher than the traditional shell decomposition value. In addition, when the proportions are 5:1:1, 1:5:1, and 1:1:5, the failure points tested under the comprehensive shell decomposition value are still the most, indicating that the comprehensive shell decomposition value considers more factors, is more cautious, and is closer to the actual situation. Through experimental data, the proposed composite complex traffic network node classification algorithm model, which integrates improved shell decomposition algorithm and cascading failure model, has certain practicality and feasibility.

关键词： classification algorithms Analytical models Neural networks Mathematical models Predictive models Fault diagnosis Public transportation Intelligent systems Management Urban areas Traffic control Intelligent transportation systems Pattern analysis intelligent management shell decomposition algorithm cascading failure model classification algorithm

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Parameter Optimization Based Mud Ring Algorithm for Improving the Maternal Health Risk Prediction

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IEEE ACCESS 2024年 12卷 167245-167261页

作者： Desuky, Abeer S. Hussain, Sadiq Cifci, Mehmet Akif El Bakrawy, Lamiaa M. Mzoughi, Olfa Kraiem, Naoufel Al Azhar Univ Fac Sci Cairo 11754 Egypt Higher Inst Comp Sci & Informat Syst Cairo 11835 Egypt Dibrugarh Univ Examinat Branch Dibrugarh 786004 Assam India Bandirma Onyedi Eylul Univ Dept Comp Engn TR-10200 Balikesir Turkiye Tu Wien Inst Comp Technol A-1040 Vienna Austria Klaipedos Valstybine Kolegija Higher Educ Inst Engn & Informat Dept LT-92294 Klaipeda Lithuania Prince Sattam bin Abdulaziz Univ Coll Comp Engn & Sci Dept Comp Sci Al Kharj 11942 Saudi Arabia King Khalid Univ Coll Comp Sci Abha 62521 Saudi Arabia

Maternal health risk prediction is a critical aspect of public health. This paper proposes a new parameter optimization method to improve maternal health risk prediction using the Mud Ring Algorithm (MRA). The proposed method is implemented in two stages to achieve the desired improvement. In the first stage, the MRA is used to optimize the parameter of Support Vector Machine (SVM) classifier. The MRA-optimized SVM (MRA-SVM) is then evaluated on thirteen real-world datasets to compare its performance with other state-of-the-art optimization algorithms. Subsequently, the performance of the MRA-SVM is specifically compared using a maternal health risk dataset. The maternal health risk dataset, being a medical dataset, faces a significant issue of imbalance. To address this problem, the second stage comes in turn, and the crossover oversampling technique is first employed. Furthermore, other classifiers such as Random Forest and K-Nearest Neighbor are also used for improving the prediction of the maternal health risk dataset. Experimental results indicate that our proposed method delivers highly competitive results compared to six well-known optimization algorithms, evaluated based on Accuracy, G-mean, F-Measure, MCC and Kappa metrics. Moreover, the results demonstrate that using a crossover oversampling method as a preprocessing step, along with MRA to optimize the parameters of SVM (MRA-SVM), Random Forest (MRA-RF), and K-Nearest Neighbor (MRA-KNN), increases prediction accuracy of maternal health risk dataset by 11.8%, 9.11%, and 17.08% respectively, compared to using the three classifiers without MRA and crossover oversampling. In sum, utilizing the crossover oversampling method, combined with the MRA to optimize the parameter of Random Forest classifier, leads to higher prediction performance compared to other recently published algorithms.

关键词： Support vector machines Prediction algorithms Medical diagnostic imaging classification algorithms Kernel Accuracy Diseases Random forests Pregnancy Nearest neighbor methods Maternal health risk mud ring algorithm prediction artificial intelligence optimization parameter of classifiers

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classification of Older Adults Undergoing Two Dual-Task Training Protocols Based on Artificial Intelligent Methods

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IEEE ACCESS 2022年 10卷 3066-3073页

作者： Sarai, Rebeca Trombini-Souza, Francis Gomes de Moura, Vitoria Thaysa Caldas, Rafael Buarque, Fernando Univ Pernambuco Polytech Sch Engn BR-50100010 Recife PE Brazil Univ Pernambuco Dept Phys Therapy BR-56318180 Petrolina Brazil Univ Pernambuco Masters & Doctoral Program Rehabil BR-56318180 Petrolina Brazil Univ Pernambuco Masters & Doctoral Program Funct Performance BR-56318180 Petrolina Brazil

Activities of daily living require efficient management between motor and cognitive tasks, known as dual tasks. The ability to properly perform such activities progressively decreases with aging. Hence, there is much effort in developing methods to identify relevant features and attenuate this functional loss. This study aimed at testing the ability of some intelligent algorithms to identify and differentiate functional features in community-dwelling older adults undergoing two 24-weeks dual-task training protocols. Additionally, we intended to provide a clear breakdown of the functional performance of the participants after undergoing two types of dual-task training protocols. We utilized the database from the EQUIDOSO-I clinical trial as input to four different types of intelligent classifiers. The algorithm's performances were analysed considering accuracy, precision, sensitivity, and specificity. Individually, SVM achieved a higher value for sensitivity for one population group, but the remaining metrics continued to report low metrics indicating poor generalization ability of the model. Nonetheless, AdaBoost presented the most consistent results at the end of the intervention period (T2). No between-group difference affected the models' accomplishments, corroborated by the similar performances at the baseline (T1) and the 6-month follow-up (T3). Therefore, future works should focus on establishing distinct protocols to enhance functional aspects of older adults.

关键词： Training Task analysis Protocols Older adults Aging Digital TV classification algorithms Artificial intelligence dual task gait analysis older adults

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A Tensor-Based Markov Chain Model for Heterogeneous Information Network Collective classification

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2022年第9期34卷 4063-4076页

作者： Han, Chao Chen, Jian Tan, Mingkui Ng, Michael K. Wu, Qingyao Guangxi Univ Sch Elect Engn Nanning 530004 Guangxi Peoples R China South China Univ Technol Sch Software Engn Minist Educ Guangzhou 510641 Peoples R China South China Univ Technol Key Lab Big Data & Intelligent Robot Minist Educ Guangzhou 510641 Peoples R China Univ Hong Kong Dept Math Hong Kong Peoples R China

Heterogeneous Information Network (HIN) collecitve classification studies the problem of predicting labels for one type of nodes in a HIN which contains multiple types of nodes multiple types of links among them. Previous studies have revealed that exploiting relative importance of links is quite useful to improve node classification performance as connected nodes tend to have similar labels. Most existing approaches exploit the relative importance of links either by directly counting the number of connections among nodes or by learning the weight of each type of link from labeled data only. However, these approaches either neglect the importance of types of links to the class labels or may lead to overfitting problem. We propose a Tensor-based Markov chain (T-Mark) approach, which is able to automatically and simultaneously predict the labels for unlabeled nodes and give the relative importance of types of links that actually improve the classification accuracy. Specifically, we build two tensor equations by using the HIN and features of nodes from both labeled and unlabeled data. A Markov chain-based model is proposed and it is solved by an iterative process to obtain the stationary distributions. Theoretical analyses of the existence and uniqueness of such probability distributions are given. Extensive experimental results demonstrate that T-Mark is able to achieve superior performance in the comparison and obtain reasonable relative importance of links.

关键词： Tensors Task analysis Probability distribution classification algorithms Arrays Visualization Training data Heterogeneous information network node classification relative importance of links tensor Markov chain iterative algorithm

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Diversity-Driven Multikernel Collaborative Representation Ensemble for Hyperspectral Image classification

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2022年 15卷 2861-2876页

作者： Su, Hongjun Hu, Yuzheng Lu, Hongliang Sun, Weiwei Du, Qian Hohai Univ Sch Earth Sci & Engn Nanjing 211100 Peoples R China Hohai Univ Jiangsu Prov Engn Res Ctr Water Resources & Envir Nanjing 211100 Peoples R China Ningbo Univ Dept Geog & Spatial Informat Tech Ningbo 315201 Peoples R China Mississippi State Univ Dept Elect & Comp Engn Starkville MS 39762 USA

Recently, kernel collaborative representation classification (KCRC) has shown its outstanding performance in dealing with the problem of linear inseparability in hyperspectral remote sensing image classification. Meanwhile, ensemble learning has attracted great attention in improving the performance of a single classifier. Aiming at the limitation of a single classifier, bagging algorithm based on KCRC(KCRC-bagging) is presented in this article. The KCRC-bagging method uses bootstrap to increase the diversity of base classifiers, thus improving the classification accuracy and generalization performance. In order to reduce the scale of ensemble, a diversity-driven multikernel collaborative representation classifier ensemble approach (DIV-KCRC) is proposed. DIV-KCRC verifies the effectiveness of the representation classifier with the pair of diversity measures, and classifiers with high accuracy and diversity are selected to improve the classification performance and efficiency of the ensemble system. Three real hyperspectral data sets were applied to prove the validity of the proposed method. The experimental results demonstrate that both KCRC-bagging and DIV-KCRC can yield better classification performance than their corresponding base classifiers. In particular, DIV-KCRC provides more reliable classification results than KCRC-bagging.

关键词： Hyperspectral imaging Diversity reception Collaboration Kernel Support vector machines Bagging classification algorithms Bagging diversity measure ensemble learning kernel collaborative representation classification (KCRC)

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