Fowlkes-Mallows Index Research Articles

Beta Distribution Weighted Fuzzy C-Ordered-Means Clustering

The fuzzy C-ordered-means clustering (FCOM) is a fuzzy clustering algorithm that enhances robustness and clustering accuracy through the ordered mechanism based on fuzzy C-means (FCM). However, despite these improvements, the FCOM algorithm’s effectiveness remains unsatisfactory due to the significant time cost incurred by its ordered operation. To address this problem, an investigation was conducted on the ordered weighted model of the FCOM algorithm leading to proposed enhancements by introducing the beta distribution weighted fuzzy C-ordered-means clustering (BDFCOM). The BDFCOM algorithm utilises the properties of the Beta distribution to weight sample features, thus not only circumventing the time cost problem of the traditional ordered mechanism but also reducing the influence of noise. Experiments were conducted on six UCI datasets to validate the effectiveness of the BDFCOM, comparing its performance against seven other clustering algorithms using six evaluation indices. The results show that compared to the average of the other seven algorithms, BDFCOM improves about 15 percent on F1-score, 11 percent on Rand Index, 13 percent on Adjusted Rand Index, 3 percent on Fowlkes-Mallows Index and 16 percent on Jaccard Index. For the other two ordered mechanism FCM algorithms, the time consumption was also reduced by 90.15 percent on average. The proposed algorithm, which designs a new way of feature weighting for ordered mechanisms, advances the field of ordered mechanisms.And, this paper provides a new method in the application field where there is a lot of noise in the dataset.

A robust deep learning system for screening of obstructive sleep apnea using T-F spectrum of ECG signals

Obstructive sleep apnea (OSA) is a non-communicable sleep-related medical condition marked by repeated disruptions in breathing during sleep. It may induce various cardiovascular and neurocognitive complications. Electrocardiography (ECG) is a useful method for detecting numerous health-related disorders. ECG signals provide a less complex and non-invasive solution for the screening of OSA. Automated and accurate detection of OSA may enhance diagnostic performance and reduce the clinician’s workload. Traditional machine learning methods typically involve several labor-intensive manual procedures, including signal decomposition, feature evaluation, selection, and categorization. This article presents the time-frequency (T-F) spectrum classification of de-noised ECG data for the automatic screening of OSA patients using deep convolutional neural networks (DCNNs). At first, a filter-fusion algorithm is used to eliminate the artifacts from the raw ECG data. Stock-well transform (S-T) is employed to change filtered time-domain ECG into T-F spectrums. To discriminate between apnea and normal ECG signals, the obtained T-F spectrums are categorized using benchmark Alex-Net and Squeeze-Net, along with a less complex DCNN. The superiority of the presented system is measured by computing the sensitivity, specificity, accuracy, negative predicted value, precision, F1-score, and Fowlkes-Mallows index. The results of comparing all three utilized DCNNs reveal that the proposed DCNN requires fewer learning parameters and provides higher accuracy. An average accuracy of 95.31% is yielded using the proposed system. The presented deep learning system is lightweight and faster than Alex-Net and Squeeze-Net as it utilizes fewer learnable parameters, making it simple and reliable.

FuzzyBandit An Autonomous Personalized Model Based on Contextual Multi Arm Bandits Using Explainable AI

In the era of artificial cognizance, context-aware decision-making problems have attracted significant attention. Contextual bandit addresses these problems by solving the exploration versus exploitation dilemma faced to provide customized solutions as per the user’s liking. However, a high level of accountability is required, and there is a need to understand the underlying mechanism of the black box nature of the contextual bandit algorithms proposed in the literature. To overcome these shortcomings, an explainable AI (XAI) based FuzzyBandit model is proposed, which maximizes the cumulative reward by optimizing the decision at each trial based on the rewards received in previous observations and, at the same time, generates explanations for the decision made. The proposed model uses an adaptive neuro-fuzzy inference system (ANFIS) to address the vague nature of arm selection in contextual bandits and uses a feedback mechanism to adjust its parameters based on the relevance and diversity of the features to maximize reward generation. The FuzzyBandit model has also been empirically compared with the existing seven most popular art of literature models on four benchmark datasets over nine criteria, namely recall, specificity, precision, prevalence, F1 score, Matthews Correlation Coefficient (MCC), Fowlkes–Mallows index (FM), Critical Success Index (CSI) and accuracy.

Deciphering spatial domains from spatially resolved transcriptomics with Siamese graph autoencoder.

Cell clustering is a pivotal aspect of spatial transcriptomics (ST) data analysis as it forms the foundation for subsequent data mining. Recent advances in spatial domain identification have leveraged graph neural network (GNN) approaches in conjunction with spatial transcriptomics data. However, such GNN-based methods suffer from representation collapse, wherein all spatial spots are projected onto a singular representation. Consequently, the discriminative capability of individual representation feature is limited, leading to suboptimal clustering performance. To address this issue, we proposed SGAE, a novel framework for spatial domain identification, incorporating the power of the Siamese graph autoencoder. SGAE mitigates the information correlation at both sample and feature levels, thus improving the representation discrimination. We adapted this framework to ST analysis by constructing a graph based on both gene expression and spatial information. SGAE outperformed alternative methods by its effectiveness in capturing spatial patterns and generating high-quality clusters, as evaluated by the Adjusted Rand Index, Normalized Mutual Information, and Fowlkes-Mallows Index. Moreover, the clustering results derived from SGAE can be further utilized in the identification of 3-dimensional (3D) Drosophila embryonic structure with enhanced accuracy. Benchmarking results from various ST datasets generated by diverse platforms demonstrate compelling evidence for the effectiveness of SGAE against other ST clustering methods. Specifically, SGAE exhibits potential for extension and application on multislice 3D reconstruction and tissue structure investigation. The source code and a collection of spatial clustering results can be accessed at https://github.com/STOmics/SGAE/.

NonPC: Non-parametric clustering algorithm with adaptive noise detecting

Graph-based clustering performs efficiently for identifying clusters in local and nonlinear data Patterns. The existing methods face the problem of parameter selection, such as the setting of k of the k-nearest neighbor graph and the threshold in noise detection. In this paper, a non-parametric clustering algorithm (NonPC) is proposed to tackle those inherent limitations and improve clustering performance. The weighted natural neighbor graph (wNaNG) is developed to represent the given data without any prior knowledge. What is more, the proposed NonPC method adaptively detects noise data in an unsupervised way based on some attributes extracted from wNaNG. The algorithm works without preliminary parameter settings while automatically identifying clusters with unbalanced densities, arbitrary shapes, and noises. To assess the advantages of the NonPC algorithm, extensive experiments have been conducted compared with some classic and recent clustering methods. The results demonstrate that the proposed NonPC algorithm significantly outperforms the state-of-the-art and well-known algorithms in Adjusted Rand index, Normalized Mutual Information, and Fowlkes-Mallows index aspects.

Automatic Breast Mass Lesion Detection in Mammogram Image

Mammography imaging is one of the most successful techniques for breast cancer screening and detecting breast lesions. Detection of the Region of Interest (ROI) (where the possible abnormalities could be present) is the backbone for the success of any Computer-Aided Detection or Diagnosis (CADx) system. In this paper, to assist the CADx system, one computational model is proposed to detect breast mass lesions from mammogram images. At the beginning of the process, pectoral muscles from the mammograms are removed as a pre-processing step. Then by applying an automatic thresholding scheme with the required image processing techniques, different regions of breast tissues are ranked to detect the possible suspected region to refine the further segmentation task. One seeded region growing approach is proposed with an automatic seed selection criterion to detect the suspected region to segment the ROI. The proposed model has very less user intervention as maximum of the parameters are computed automatically. To evaluate the performance of the proposed model, it is compared with four different methods with six different evaluation metrics viz., Jaccard & Dice co-efficient, relative error, segmentation accuracy, error and Fowlkes–Mallows index (FMI). On the proposed model, 57 mammogram images are tested, consisting of four different cases that are collected from the publicly available benchmark database. The qualitative and quantitative analyses are performed to evaluate the proposed model. The best dice co-efficient, Jaccard co-efficient, accuracy, error and FMI values observed are 0.9506, 0.9471, 95.62%, 4.38% and 0.932, respectively. The superiority of the model over six state-of-the-art compared methods is well evident from the experimental results.

A statistical comparison between Matthews correlation coefficient (MCC), prevalence threshold, and Fowlkes–Mallows index

Even if assessing binary classifications is a common task in scientific research, no consensus on a single statistic summarizing the confusion matrix has been reached so far. In recent studies, we demonstrated the advantages of the Matthews correlation coefficient (MCC) over other popular rates such as cross-entropy error, F1 score, accuracy, balanced accuracy, bookmaker informedness, diagnostic odds ratio, Brier score, and Cohen’s kappa. In this study, we compared the MCC to other two statistics: prevalence threshold (PT), frequently used in obstetrics and gynecology, and Fowlkes–Mallows index, a metric employed in fuzzy logic and drug discovery. Through the investigation of the mutual relations among three metrics and the study of some relevant use cases, we show that, when positive data elements and negative data elements have the same importance, the Matthews correlation coefficient can be more informative than its two competitors, even this time.

Application of Principal Component Analysis to advancing digital phenotyping of plant disease in the context of limited memory for training data storage

Despite its widespread employment as a highly efficient dimensionality reduction technique, limited research has been carried out on the advantage of Principal Component Analysis (PCA)–based compression/reconstruction of image data to machine learning-based image classification performance and storage space optimization. To address this limitation, we designed a study in which we compared the performances of two Convolutional Neural Network-Random Forest Algorithm (CNN-RF) guava leaf image classification models developed using training data from a number of original guava leaf images contained in a predefined amount of storage space (on the one hand), and a number of PCA compressed/reconstructed guava leaf images contained in the same amount of storage space (on the other hand), on the basis of four criteria – Accuracy, F1-Score, Phi Coefficient and the Fowlkes–Mallows index. Our approach achieved a 1:100 image compression ratio (99.00% image compression) which was comparatively much better than previous results achieved using other algorithms like arithmetic coding (1:1.50), wavelet transform (90.00% image compression), and a combination of three transform-based techniques – Discrete Fourier (DFT), Discrete Wavelet (DWT) and Discrete Cosine (DCT) (1:22.50). From a subjective visual quality perspective, the PCA compressed/reconstructed guava leaf images presented almost no loss of image detail. Finally, the CNN-RF model developed using PCA compressed/reconstructed guava leaf images outperformed the CNN-RF model developed using original guava leaf images by 0.10% accuracy increase, 0.10 F1-Score increase, 0.18 Phi Coefficient increase and 0.09 Fowlkes–Mallows increase.

Towards lowering computational power in IoT systems: Clustering algorithm for high-dimensional data stream using entropy window reduction

In a world of connectivity empowered by the advancement of the Internet of Things (IoT), an infinite number of data streams have emerged. Thus, data stream clustering is crucial for extracting hidden knowledge and data mining. Various data stream clustering methods have lately been introduced. Yet, the majority of such algorithms are affected by the curse of high dimensionality. Lately, a fully online buffer-based clustering algorithm for handling evolving data streams (BOCEDS) was developed. Similarly to other existing density-based clustering methods, BOCEDS is not capable of handling high-dimensional data and has high computational power and high memory utilization. This paper introduces an Entropy Window Reduction (EWR) algorithm, which is an improved version of the BOCEDS technique. EWR is a fully online clustering technique for handling high-dimensional data streams using feature ranking and sorting. This process is accomplished by calculating the entropy of specific features with respect to the time window. The findings of the experiments are compared to the outcomes of BOCEDS, CEDAS, and MuDi-Stream algorithms. The outcomes indicate that the EWR algorithm outperformed the baseline clustering algorithms. The results are demonstrated using the KDDCup’99 dataset in terms of quality and complexity evaluation on the average of F-Measures, Jaccard Index, Fowlkes–Mallows index, Purity, and Rand Index as well as the memory usage and computational power with 88%, 66%, 81%, 100%, and 66%, respectively. The results also show low memory usage and computing power in comparison with the baseline algorithms.

Determining the Quality of a Dataset in Clustering Terms

The purpose of the theoretical considerations and research conducted was to indicate the instruments with which the quality of a dataset can be verified for the segmentation of observations occurring in the dataset. The paper proposes a novel way to deal with mixed datasets containing categorical and continuous attributes in a customer segmentation task. The categorical variables were embedded using an innovative unsupervised model based on an autoencoder. The customers were then divided into groups using different clustering algorithms, based on similarity matrices. In addition to the classic k-means method and the more modern DBSCAN, three graph algorithms were used: the Louvain algorithm, the greedy algorithm and the label propagation algorithm. The research was conducted on two datasets: one containing on retail customers and the other containing wholesale customers. The Calinski–Harabasz index, Davies–Bouldins index, NMI index, Fowlkes–Mallows index and silhouette score were used to assess the quality of the clustering. It was noted that the modularity parameter for graph methods was a good indicator of whether a given set could be meaningfully divided into groups.

Clustering heterogeneous categorical data using enhanced mini batch K-means with entropy distance measure

<span lang="EN-US">Clustering methods in data mining aim to group a set of patterns based on their similarity. In a data survey, heterogeneous information is established with various types of data scales like nominal, ordinal, binary, and Likert scales. A lack of treatment of heterogeneous data and information leads to loss of information and scanty decision-making. Although many similarity measures have been established, solutions for heterogeneous data in clustering are still lacking. The recent entropy distance measure seems to provide good results for the heterogeneous categorical data. However, it requires many experiments and evaluations. This article presents a proposed framework for heterogeneous categorical data solution using a mini batch k-means with entropy measure (MBKEM) which is to investigate the effectiveness of similarity measure in clustering method using heterogeneous categorical data. Secondary data from a public survey was used. The findings demonstrate the proposed framework has improved the clustering’s quality. MBKEM outperformed other clustering algorithms with the accuracy at 0.88, v-measure (VM) at 0.82, adjusted rand index (ARI) at 0.87, and Fowlkes-Mallow’s index (FMI) at 0.94. It is observed that the average minimum elapsed time-varying for cluster generation, </span><em><span lang="EN-US">k</span></em><span lang="EN-US"> at 0.26 s. In the future, the proposed solution would be beneficial for improving the quality of clustering for heterogeneous categorical data problems in many domains.</span>

PSTCNN: Explainable COVID-19 diagnosis using PSO-guided self-tuning CNN.

Since 2019, the coronavirus disease-19 (COVID-19) has been spreading rapidly worldwide, posing an unignorable threat to the global economy and human health. It is a disease caused by severe acute respiratory syndrome coronavirus 2, a single-stranded RNA virus of the genus Betacoronavirus. This virus is highly infectious and relies on its angiotensin-converting enzyme 2-receptor to enter cells. With the increase in the number of confirmed COVID-19 diagnoses, the difficulty of diagnosis due to the lack of global healthcare resources becomes increasingly apparent. Deep learning-based computer-aided diagnosis models with high generalisability can effectively alleviate this pressure. Hyperparameter tuning is essential in training such models and significantly impacts their final performance and training speed. However, traditional hyperparameter tuning methods are usually time-consuming and unstable. To solve this issue, we introduce Particle Swarm Optimisation to build a PSO-guided Self-Tuning Convolution Neural Network (PSTCNN), allowing the model to tune hyperparameters automatically. Therefore, the proposed approach can reduce human involvement. Also, the optimisation algorithm can select the combination of hyperparameters in a targeted manner, thus stably achieving a solution closer to the global optimum. Experimentally, the PSTCNN can obtain quite excellent results, with a sensitivity of 93.65%±1.86%, a specificity of 94.32%±2.07%, a precision of 94.30%±2.04%, an accuracy of 93.99%±1.78%, an F1-score of 93.97%±1.78%, Matthews Correlation Coefficient of 87.99%±3.56%, and Fowlkes-Mallows Index of 93.97%±1.78%. Our experiments demonstrate that compared to traditional methods, hyperparameter tuning of the model using an optimisation algorithm is faster and more effective.

PSTCNN: Explainable COVID-19 diagnosis using PSO-guided self-tuning CNN

Since 2019, the coronavirus disease-19 (COVID-19) has been spreading rapidly worldwide, posing an unignorable threat to the global economy and human health. It is a disease caused by severe acute respiratory syndrome coronavirus 2, a single-stranded RNA virus of the genus Betacoronavirus. This virus is highly infectious and relies on its angiotensin-converting enzyme 2-receptor to enter cells. With the increase in the number of confirmed COVID-19 diagnoses, the difficulty of diagnosis due to the lack of global healthcare resources becomes increasingly apparent. Deep learning-based computer-aided diagnosis models with high generalisability can effectively alleviate this pressure. Hyperparameter tuning is essential in training such models and significantly impacts their final performance and training speed. However, traditional hyperparameter tuning methods are usually time-consuming and unstable. To solve this issue, we introduce Particle Swarm Optimisation to build a PSO-guided Self-Tuning Convolution Neural Network (PSTCNN), allowing the model to tune hyperparameters automatically. Therefore, the proposed approach can reduce human involvement. Also, the optimisation algorithm can select the combination of hyperparameters in a targeted manner, thus stably achieving a solution closer to the global optimum. Experimentally, the PSTCNN can obtain quite excellent results, with a sensitivity of 93.65% ± 1.86%, a specificity of 94.32% ± 2.07%, a precision of 94.30% ± 2.04%, an accuracy of 93.99% ± 1.78%, an F1-score of 93.97% ± 1.78%, Matthews Correlation Coefficient of 87.99% ± 3.56%, and Fowlkes-Mallows Index of 93.97% ± 1.78%. Our experiments demonstrate that compared to traditional methods, hyperparameter tuning of the model using an optimisation algorithm is faster and more effective.

A Novel Neighborhood Granular Meanshift Clustering Algorithm

The most popular algorithms used in unsupervised learning are clustering algorithms. Clustering algorithms are used to group samples into a number of classes or clusters based on the distances of the given sample features. Therefore, how to define the distance between samples is important for the clustering algorithm. Traditional clustering algorithms are generally based on the Mahalanobis distance and Minkowski distance, which have difficulty dealing with set-based data and uncertain nonlinear data. To solve this problem, we propose the granular vectors relative distance and granular vectors absolute distance based on the neighborhood granule operation. Further, the neighborhood granular meanshift clustering algorithm is also proposed. Finally, the effectiveness of neighborhood granular meanshift clustering is proved from two aspects of internal metrics (Accuracy and Fowlkes–Mallows Index) and external metric (Silhouette Coeffificient) on multiple datasets from UC Irvine Machine Learning Repository (UCI). We find that the granular meanshift clustering algorithm has a better clustering effect than the traditional clustering algorithms, such as Kmeans, Gaussian Mixture and so on.

Rapid phylogenetic analysis using open reading frame content patterns acquired by Oxford nanopore sequencing.

Phylogenetic analysis based on core genome single nucleotide polymorphisms (cgSNPs) using whole-genome sequencing (WGS) is increasingly used in epidemiological investigations of bacteria. The approach, however, is both resource intensive and time-consuming. Oxford Nanopore Technologies (ONT) sequencing is capable of real-time data analysis but the high error rate hampers its application in cgSNP-based phylogenetic analysis. Here, we developed a cgSNP-independent phylogenetic analysis method using ONT read assemblies by focusing on open reading frame (ORF) content patterns. WGS data of 66 Enterobacter hormaechei strains acquired by both ONT and Illumina sequencing and 162 strains obtained from NCBI database were converted to binary sequences based on the presence or absence of ORFs using BLASTn. Phylogenetic trees calculated from binary sequences (ORF trees) were compared with cgSNP trees derived from Illumina sequences. Clusters of closely related strains in the cgSNP trees formed comparable clusters in the ORF trees built with binary sequences, and the tree topologies between them were similar based on Fowlkes-Mallows index. The ORF-based phylogenetic analysis using ONT sequencing may be useful in epidemiological investigations and offer advantages over the cgSNP-based approach. Conversion of assembled WGS data to binary sequences based on the presence or absence of ORFs circumvents read error concerns with ONT sequencing. Since ONT sequencing generates data in real time and does not require major investment, this ORF-based phylogenetic analysis method has the potential to enable phylogenetic and epidemiological analysis at the point of care.

A review and performance evaluation of clustering frameworks for single-cell Hi-C data.

The three-dimensional genome structure plays a key role in cellular function and gene regulation. Single-cell Hi-C (high-resolution chromosome conformation capture) technology can capture genome structure information at the cell level, which provides the opportunity to study how genome structure varies among different cell types. Recently, a few methods are well designed for single-cell Hi-C clustering. In this manuscript, we perform an in-depth benchmark study of available single-cell Hi-C data clustering methods to implement an evaluation system for multiple clustering frameworks based on both human and mouse datasets. We compare eight methods in terms of visualization and clustering performance. Performance is evaluated using four benchmark metrics including adjusted rand index, normalized mutual information, homogeneity and Fowlkes-Mallows index. Furthermore, we also evaluate the eight methods for the task of separating cells at different stages of the cell cycle based on single-cell Hi-C data.

Comparing Multiple Locus Variable-Number Tandem Repeat Analyses with Whole-Genome Sequencing as Typing Method for Salmonella Enteritidis Surveillance in The Netherlands, January 2019 to March 2020.

ABSTRACTIn the Netherlands, whole-genome sequencing (WGS) was implemented as routine typing tool for Salmonella Enteritidis isolates in 2019. Multiple locus variable-number tandem repeat analyses (MLVA) was performed in parallel. The objective was to determine the concordance of MLVA and WGS as typing methods for S. Enteritidis isolates. We included S. Enteritidis isolates from patients that were subtyped using MLVA and WGS-based core-genome Multilocus Sequence Typing (cgMLST) as part of the national laboratory surveillance of Salmonella during January 2019 to March 2020. The concordance of clustering based on MLVA and cgMLST, with a distance of ≤5 alleles, was assessed using the Fowlkes-Mallows (FM) index, and their discriminatory power using Simpson’s diversity index. Of 439 isolates in total, 404 (92%) were typed as 32 clusters based on MLVA, with a median size of 4 isolates (range:2 to 141 isolates). Based on cgMLST, 313 (71%) isolates were typed as 48 clusters, with a median size of 3 isolates (range:2 to 39 isolates). The FM index was 0.34 on a scale from 0 to 1, where a higher value indicates greater similarity between the typing methods. The Simpson’s diversity index of MLVA and cgMLST was 0.860 and 0.974, respectively. The median cgMLST distance between isolates with the same MLVA type was 27 alleles (interquartile range [IQR]:17 to 34 alleles), and 2 alleles within cgMLST clusters (IQR:1-5 alleles). This study shows the higher discriminatory power of WGS over MLVA and a poor concordance between both typing methods regarding clustering of S. Enteritidis isolates.IMPORTANCE Salmonella is the most frequently reported agent causing foodborne outbreaks and the second most common zoonoses in the European Union. The incidence of the most dominant serotype Enteritidis has increased in recent years. To differentiate between Salmonella isolates, traditional typing methods such as pulsed-field gel electrophoresis (PFGE) and multiple locus variable-number tandem repeat analyses (MLVA) are increasingly replaced with whole-genome sequencing (WGS). This study compared MLVA and WGS-based core-genome Multilocus Sequence Typing (cgMLST) as typing tools for S. Enteritidis isolates that were collected as part of the national Salmonella surveillance in the Netherlands. We found a higher discriminatory power of WGS-based cgMLST over MLVA, as well as a poor concordance between both typing methods regarding clustering of S. Enteritidis isolates. This is especially relevant for cluster delineation in outbreak investigations and confirmation of the outbreak source in trace-back investigations.

Artificial intelligence based liver portal tract region identification and quantification with transplant biopsy whole-slide images

Liver fibrosis staging is clinically important for liver disease progression prediction. As the portal tract fibrotic quantity and size in a liver biopsy correlate with the fibrosis stage, an accurate analysis of portal tract regions is clinically critical. Manual annotations of portal tract regions, however, are time-consuming and subject to large inter- and intra-observer variability. To address such a challenge, we develop a Multiple Up-sampling and Spatial Attention guided UNet model (MUSA-UNet) to segment liver portal tract regions in whole-slide images of liver tissue slides. To enhance the segmentation performance, we propose to use depth-wise separable convolution, the spatial attention mechanism, the residual connection, and multiple up-sampling paths in the developed model. This study includes 53 histopathology whole slide images from patients who received liver transplantation. In total, 6,012 patches derived from 30 images are used for our deep learning model training and validation. The remaining 23 whole slide images are utilized for the model testing. The average liver portal tract segmentation performance of the developed MUSA-UNet is 0.94 (Precision), 0.85 (Recall), 0.89 (F1 Score), 0.89 (Accuracy), 0.80 (Jaccard Index), and 0.91 (Fowlkes–Mallows Index), respectively. The clinical Scheuer fibrosis stage presents a strong correlation with the resulting average portal tract fibrotic area (R = 0.681, p<0.001) and portal tract percentage (R = 0.335, p = 0.02) computed from the MUSA-UNet segmentation results. In conclusion, our developed deep learning model MUSA-UNet can accurately segment portal tract regions from whole-slide images of liver tissue biopsies, presenting its promising potential to assist liver disease diagnosis in a computational manner.

Classification of cardiac diseases from ECG signals through bio inspired classifiers with Adam and R-Adam approaches for hyperparameters updation

This study emphasizes the classification of different cardiac diseases through bio-inspired classifiers with and without hyperparameters selection. Principal Component Analysis (PCA), Linearity Preserving Projection (LPP), Kernel-Linear Discriminant Analysis (K-LDA), and Variational Bayesian Matrix Factorization (VBMF) are used for the dimensionality reduction of ECG Signals. After the dimensionality reduction, the ECG features were classified using several classifiers such as Particle Swarm Optimization (PSO), Fish Swarm Optimization (FSO), Whale Optimization Algorithm (WOA), Grey Wolf Optimization (GWO), Support Vector Machine with (RBF (kernel)), K Nearest Neighbor (KNN) and Naïve Bayesian Classifier (NBC). The average accuracy for the classifiers without hyperparameters selection is 64.99%. Further, the classifiers (PSO, FSO, WOA, and GWO) performance is enhanced through the selection of hyperparameters by the Adam and Randomized Adam (R-Adam) approaches. The average accuracy for the classifiers with Adam and R-Adam is at 72.32% and 85.63%. In this research, the MIT-BIH datasets of Atrial Fibrillation (AF), Premature Ventricular Contraction (PVC), ST Change (ST), Supra Ventricular Tachycardia (SVT), Ventricular Tachycardia (VT) and Normal Sinus Rhythm (NSR) are utilized from the physionet archived database. The Overall Accuracy (OA), Mean Square Error (MSE), F1-Score (F1), Fowlkes Mallows Index (FM), Mathew Correlation Coefficient (MCC) and Error Rate (ER) of the different classifiers with and without Adam and R-Adam methods are evaluated. The result demonstrates that the GWO-R-Adam classifier with the LPP dimensionality reduction technique with 98.90% overall accuracy in classifying {ST}-{NSR} classes outperforms all other classifiers in terms of classification performance measures.

Rough Set Based Classification and Feature Selection Using Improved Harmony Search for Peptide Analysis and Prediction of Anti-HIV-1 Activities

AIDS, which is caused by the most widespread HIV-1 virus, attacks the immune system of the human body, and despite the incredible endeavors for finding proficient medication strategies, the continuing spread of AIDS and claiming subsequent infections has not yet been decreased. Consequently, the discovery of innovative medicinal methodologies is highly in demand. Some available therapies, based on peptides, proclaim the treatment for several deadly diseases such as AIDS and cancer. Since many experimental types of research are restricted by the analysis period and expenses, computational methods overcome the issues effectually. In computational technique, the peptide residues with anti-HIV-1 activity are predicted by classification method, and the learning process of the classification is improved with significant features. Rough set-based algorithms are capable of dealing with the gaps and imperfections present in real-time data. In this work, feature selection using Rough Set Improved Harmony Search Quick Reduct and Rough Set Improved Harmony Search Relative Reduct with Rough Set Classification framework is implemented to classify Anti-HIV-1 peptides. The primary objective of the proposed methodology is to predict the peptides with an anti-HIV-1 activity using effective feature selection and classification algorithms incorporated in the proposed framework. The results of the proposed algorithms are comparatively studied with existing rough set feature selection algorithms and benchmark classifiers, and the reliability of the algorithms implemented in the proposed framework is measured by validity measures, such as Precision, Recall, F-measure, Kulczynski Index, and Fowlkes–Mallows Index. The final results show that the proposed framework analyzed and classified the peptides with a high predictive accuracy of 96%. In this study, we have investigated the ability of a rough set-based framework with sequence-based numeric features to classify anti-HIV-1 peptides, and the experimentation results show that the proposed framework discloses the most satisfactory solutions, where it rapidly congregates in the problem space and finds the best reduct, which improves the prediction accuracy of the given dataset.