Diagnosis of Alternaria disease and leafminer pest on tomato leaves using image processing techniques.

BACKGROUND: Diseases such as Alternaria and pests such as leafminer threaten tomato as one of the most widely used agricultural products. These pests and diseases first damage the leaves of tomatoes, then the flowers, and finally the fruit. Therefore, the damage to the tomato tree must be controlled in its early stages. It is difficult for farmers to distinguish Alternaria disease from leafminer pest at the early and middle stages of their outbreak on tomato leaves. In the present study, 272 tomato leaf images were prepared from the farm of the Vali-e-Asr University of Rafsanjan, including 100 healthy leaves and 172 infected leaves with both Alternaria and leafminer at the initial stages. The image processing technique, texture, neural networks and adaptive network-based fuzzy inference system (ANFIS) classifiers were used to diagnose Alternaria disease and leafminer pest on this dataset. RESULTS: The results showed that the ANFIS classifier achieved an accuracy of 84.71% when performing an equal error rate, 87.78% in the area under the curve, and 85.23% in 3.26 s on the central processing unit for the segmentation of Alternaria disease and leafminer pest in RGB color space. Also, the accuracy of 90% and 98% were obtained for segmentation and classification on the PlantVillage dataset in YCBCR color space. CONCLUSION: The present study suggests a high classification accuracy for an intelligent selection of pixel values to train the ANFIS classifier. This classifier has high accuracy and speed, low sensitivity to the light intensity of images, and practical application in diagnosing various diseases and pests on numerous datasets. © 2022 Society of Chemical Industry.

Integration of multi-objective PSO based feature selection and node centrality for medical datasets.

In the past decades, the rapid growth of computer and database technologies has led to the rapid growth of large-scale medical datasets. On the other, medical applications with high dimensional datasets that require high speed and accuracy are rapidly increasing. One of the dimensionality reduction approaches is feature selection that can increase the accuracy of the disease diagnosis and reduce its computational complexity. In this paper, a novel PSO-based multi objective feature selection method is proposed. The proposed method consists of three main phases. In the first phase, the original features are showed as a graph representation model. In the next phase, feature centralities for all nodes in the graph are calculated, and finally, in the third phase, an improved PSO-based search process is utilized to final feature selection. The results on five medical datasets indicate that the proposed method improves previous related methods in terms of efficiency and effectiveness.

Health-aware food recommendation system with dual attention in heterogeneous graphs.

Recommender systems (RS) have been increasingly applied to food and health. However, challenges still remain, including the effective incorporation of heterogeneous information and the discovery of meaningful relationships among entities in the context of food and health recommendations. To address these challenges, we propose a novel framework, the Health-aware Food Recommendation System with Dual Attention in Heterogeneous Graphs (HFRS-DA), for unsupervised representation learning on heterogeneous graph-structured data. HFRS-DA utilizes an attention technique to reconstruct node features and edges and employs a dual hierarchical attention mechanism for enhanced unsupervised learning of attributed graph representations. HFRS-DA addresses the challenge of effectively leveraging the heterogeneous information in the graph and discovering meaningful semantic relationships between entities. The framework analyses recipe components and their neighbours in the heterogeneous graph and can discover popular and healthy recipes, thereby promoting healthy eating habits. We compare HFRS-DA using the Allrecipes dataset and find that it outperforms all the related methods from the literature. Our study demonstrates that HFRS-DA enhances the unsupervised learning of attributed graph representations, which is important in scenarios where labelled data is scarce or unavailable. HFRS-DA can generate node embeddings for unused data effectively, enabling both inductive and transductive learning.

Early and High-Accuracy Diagnosis of Parkinson's Disease: Outcomes of a New Model.

Parkinson's disease (PD) is one of the significant common neurological disorders of the current age that causes uncontrollable movements like shaking, stiffness, and difficulty. The early clinical diagnosis of this disease is essential for preventing the progression of PD. Hence, an innovative method is proposed here based on combining the crow search algorithm and decision tree (CSADT) for the early PD diagnosis. This approach is used on four crucial Parkinson's datasets, including meander, spiral, voice, and speech-Sakar. Using the presented method, PD is effectively diagnosed by evaluating each dataset's critical features and extracting the primary practical outcomes. The used algorithm was compared with other machine learning algorithms of k-nearest neighbor (KNN), support vector machine (SVM), naive Baye (NB), multilayer perceptron (MLP), decision tree (DT), random tree, logistic regression, support vector machine of radial base functions (SVM of RBFs), and combined classifier in terms of accuracy, recall, and combination measure F1. The analytical results emphasize the used algorithm's superiority over the other selected ones. The proposed model yields nearly 100% accuracy through various trials on the datasets. Notably, a high detection speed achieved the lowest detection time of 2.6 seconds. The main novelty of this paper is attributed to the accuracy of the presented PD diagnosis method, which is much higher than its counterparts.

Impact of Centrality Measures on the Common Neighbors in Link Prediction for Multiplex Networks.

Complex networks are representations of real-world systems that can be better modeled as multiplex networks, where the same nodes develop multi-type connections. One of the important concerns about these networks is link prediction, which has many applications in social networks and recommender systems. In this article, similarity-based methods such as common neighbors (CNs) are the mainstream. However, in the CN method, the contribution of each CN in the likelihood of new connections is equally taken into account. In this work, we propose a new link prediction method namely Weighted Common Neighbors (WCN), which is based on CNs and various types of Centrality measures (including degree, k-core, closeness, betweenness, Eigenvector, and PageRank) to predict the formation of new links in multiplex networks. So, in this model, each CN has a different impact on the node connection likelihood. Moreover, we investigate the impact of interlayer information on improving the performance of link prediction in the target layer. Using Area under the ROC Curve and precision as evaluation metrics, we perform a comprehensive experimental evaluation of our proposed method on seven real multiplex networks. The results validate the improved performance of our proposed method compared with existing methods, and we show that the performance of proposed methods is significantly improved while using interlayer information in multiplex networks.

Graph-based relevancy-redundancy gene selection method for cancer diagnosis.

Nowadays, microarray data processing is one of the most important applications in molecular biology for cancer diagnosis. A major task in microarray data processing is gene selection, which aims to find a subset of genes with the least inner similarity and most relevant to the target class. Removing unnecessary, redundant, or noisy data reduces the data dimensionality. This research advocates a graph theoretic-based gene selection method for cancer diagnosis. Both unsupervised and supervised modes use well-known and successful social network approaches such as the maximum weighted clique criterion and edge centrality to rank genes. The suggested technique has two goals: (i) to maximize the relevancy of the chosen genes with the target class and (ii) to reduce their inner redundancy. A maximum weighted clique is chosen in a repetitive way in each iteration of this procedure. The appropriate genes are then chosen from among the existing features in this maximum clique using edge centrality and gene relevance. In the experiment, several datasets consisting of Colon, Leukemia, SRBCT, Prostate Tumor, and Lung Cancer, with different properties, are used to demonstrate the efficacy of the developed model. Our performance is compared to that of renowned filter-based gene selection approaches for cancer diagnosis whose results demonstrate a clear superiority.

Gene selection for microarray data classification via multi-objective graph theoretic-based method.

In recent decades, the improvement of computer technology has increased the growth of high-dimensional microarray data. Thus, data mining methods for DNA microarray data classification usually involve samples consisting of thousands of genes. One of the efficient strategies to solve this problem is gene selection, which improves the accuracy of microarray data classification and also decreases computational complexity. In this paper, a novel social network analysis-based gene selection approach is proposed. The proposed method has two main objectives of the relevance maximization and redundancy minimization of the selected genes. In this method, on each iteration, a maximum community is selected repetitively. Then among the existing genes in this community, the appropriate genes are selected by using the node centrality-based criterion. The reported results indicate that the developed gene selection algorithm while increasing the classification accuracy of microarray data, will also decrease the time complexity.

Decoding clinical biomarker space of COVID-19: Exploring matrix factorization-based feature selection methods.

One of the most critical challenges in managing complex diseases like COVID-19 is to establish an intelligent triage system that can optimize the clinical decision-making at the time of a global pandemic. The clinical presentation and patients' characteristics are usually utilized to identify those patients who need more critical care. However, the clinical evidence shows an unmet need to determine more accurate and optimal clinical biomarkers to triage patients under a condition like the COVID-19 crisis. Here we have presented a machine learning approach to find a group of clinical indicators from the blood tests of a set of COVID-19 patients that are predictive of poor prognosis and morbidity. Our approach consists of two interconnected schemes: Feature Selection and Prognosis Classification. The former is based on different Matrix Factorization (MF)-based methods, and the latter is performed using Random Forest algorithm. Our model reveals that Arterial Blood Gas (ABG) O2 Saturation and C-Reactive Protein (CRP) are the most important clinical biomarkers determining the poor prognosis in these patients. Our approach paves the path of building quantitative and optimized clinical management systems for COVID-19 and similar diseases.

A novel link prediction algorithm for protein-protein interaction networks by attributed graph embedding.

The prediction of interactions in protein networks is very critical in various biological processes. In recent years, scientists have focused on computational approaches to predict the interactions of proteins. In protein-protein interaction (PPI) networks, each protein is accompanied by various features, including amino acid sequence, subcellular location, and protein domains. Embedding-based methods have been widely applied for many network analysis tasks, such as link prediction. The Deepwalk algorithm is one of the most popular graph embedding methods that capture the network structure using pure random walking. Here in this paper, we treat the protein-protein interaction prediction problem as a link prediction in attributed networks, and we use an attributed embedding approach to predict the interactions between proteins in the PPI network. In particular, the present paper seeks to present a modified version of Deepwalk based on feature selection for solving link prediction in the protein-protein interaction, which will benefit both network structure and protein features. More specifically the feature selection step consists of two distinct parts. First, a set of relevant features are selected from the original feature set, such that the dimensionality of features is reduced. Second, in the selected set of features, each feature is assigned with a weight based on its significance and therefore the contribution of each feature is distinguished from others. In this method, the new random walk model for link prediction will be introduced by integrating network structure and protein features, based on the assumption that two nodes on the network will be linked since they are nearby in the network. In order to justify the proposal, the authors carry out many experiments on protein-protein interaction networks for comparison with the state-of-the-art network embedding methods. The experimental results from the graphs indicate that our proposed approach is more capable compared to other link prediction approaches and increases the accuracy of prediction.

Spectral clustering on protein-protein interaction networks via constructing affinity matrix using attributed graph embedding.

The identification of protein complexes in protein-protein interaction networks is the most fundamental and essential problem for revealing the underlying mechanism of biological processes. However, most existing protein complexes identification methods only consider a network's topology structures, and in doing so, these methods miss the advantage of using nodes' feature information. In protein-protein interaction, both topological structure and node features are essential ingredients for protein complexes. The spectral clustering method utilizes the eigenvalues of the affinity matrix of the data to map to a low-dimensional space. It has attracted much attention in recent years as one of the most efficient algorithms in the subcategory of dimensionality reduction. In this paper, a new version of spectral clustering, named text-associated DeepWalk-Spectral Clustering (TADW-SC), is proposed for attributed networks in which the identified protein complexes have structural cohesiveness and attribute homogeneity. Since the performance of spectral clustering heavily depends on the effectiveness of the affinity matrix, our proposed method will use the text-associated DeepWalk (TADW) to calculate the embedding vectors of proteins. In the following, the affinity matrix will be computed by utilizing the cosine similarity between the two low dimensional vectors, which will be considerable to improve the accuracy of the affinity matrix. Experimental results show that our method performs unexpectedly well in comparison to existing state-of-the-art methods in both real protein network datasets and synthetic networks.

Decoding Clinical Biomarker Space of COVID-19: Exploring Matrix Factorization-based Feature Selection Methods.

One of the most critical challenges in managing complex diseases like COVID-19 is to establish an intelligent triage system that can optimize the clinical decision-making at the time of a global pandemic. The clinical presentation and patients’ characteristics are usually utilized to identify those patients who need more critical care. However, the clinical evidence shows an unmet need to determine more accurate and optimal clinical biomarkers to triage patients under a condition like the COVID-19 crisis. Here we have presented a machine learning approach to find a group of clinical indicators from the blood tests of a set of COVID-19 patients that are predictive of poor prognosis and morbidity. Our approach consists of two interconnected schemes: Feature Selection and Prognosis Classification. The former is based on different Matrix Factorization (MF)-based methods, and the latter is performed using Random Forest algorithm. Our model reveals that Arterial Blood Gas (ABG) O 2 Saturation and C-Reactive Protein (CRP) are the most important clinical biomarkers determining the poor prognosis in these patients. Our approach paves the path of building quantitative and optimized clinical management systems for COVID-19 and similar diseases.