Image Fusion Algorithm at Pixel Level Based on Edge Detection

Chen, Jiming; Chen, Liping; Shabaz, Mohammad

doi:10.1155/2021/5760660

Cited by 33 publications

(24 citation statements)

References 27 publications

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“…Tenfold cross-validation is adopted for each experiment, and the average value of the results of the five experiments is calculated as the final result. After calculating the two feature vectors, the normalized Euclidean distance metric method in [ 20 ] is used.…”

Section: Methodsmentioning

confidence: 99%

[Retracted] Recognition of Protein Network for Bioinformatics Knowledge Analysis Using Support Vector Machine

Kaur

Chitre

Wanjale

et al. 2022

BioMed Research International

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Protein is the material foundation of living things, and it directly takes part in and runs the process of living things itself. Predicting protein complexes helps us understand the structure and function of complexes, and it is an important foundation for studying how cells work. Genome-wide protein interaction (PPI) data is growing as high-throughput experiments become more common. The aim of this research is that it provides a dual-tree complex wavelet transform which is used to find out about the structure of proteins. It also identifies the secondary structure of protein network. Many computer-based methods for predicting protein complexes have also been developed in the field. Identifying the secondary structure of a protein is very important when you are studying protein characteristics and properties. This is how the protein sequence is added to the distance matrix. The scope of this research is that it can confidently predict certain protein complexes rapidly, which compensates for shortcomings in biological research. The three-dimensional coordinates of C atom are used to do this. According to the texture information in the distance matrix, the matrix is broken down into four levels by the double-tree complex wavelet transform because it has four levels. The subband energy and standard deviation in different directions are taken, and then, the two-dimensional feature vector is used to show the secondary structure features of the protein in a way that is easy to understand. Then, the KNN and SVM classifiers are used to classify the features that were found. Experiments show that a new feature called a dual-tree complex wavelet can improve the texture granularity and directionality of the traditional feature extraction method, which is called secondary structure.

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Section: Methodsmentioning

confidence: 99%

[Retracted] Recognition of Protein Network for Bioinformatics Knowledge Analysis Using Support Vector Machine

Kaur

Chitre

Wanjale

et al. 2022

BioMed Research International

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“…To reduce the complexity of the model and prevent overfitting, before modeling, this paper, features are selected using the lasso method [ 16 ]. Lasso improves the traditional, linear regression method provides a new perspective on the general linear regression algorithm on the basis of adding the L1 penalty term, the linear regression parameters have sparsity from the resulting model which has good predictability, and the selected features are related to the prediction.…”

Section: Methodsmentioning

confidence: 99%

ML-Based Texture and Wavelet Features Extraction Technique to Predict Gastric Mesothelioma Cancer

Garg

Sinha²,

Yadav

et al. 2022

BioMed Research International

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Microsatellites are small, repetitive sequences found all across the human genome. Microsatellite instability is the phenomenon of variations in the length of microsatellites induced by the insertion or deletion of repeat units in tumor tissue (MSI). MSI-type stomach malignancy has distinct genetic phenotypes and clinic pathological characteristics, and the stability of microsatellites influences whether or not patients with gastric mesothelioma react to immunotherapy. As a result, determining MSI status prior to surgery is critical for developing treatment options for individuals with gastric cancer. Traditional MSI detection approaches need immunological histochemistry and genetic analysis, which adds to the expense and makes it difficult to apply to every patient in clinical practice. In this study, to predict the MSI status of gastric cancer patients, researchers used image feature extraction technology and a machine learning algorithm to evaluate high-resolution histopathology pictures of patients. 279 cases of raw data were obtained from the TCGA database, 442 samples were obtained after preprocessing and upsampling, and 445 quantitative image features, including first-order statistics of impressions, texture features, and wavelet features, were extracted from the histopathological images of each sample. To filter the characteristics and provide a prediction label (risk score) for MSI status of gastric cancer, Lasso regression was utilized. The predictive label’s classification performance was evaluated using a logistic classification model, which was then coupled with the clinical data of each patient to create a customized nomogram for MSI status prediction using multivariate analysis.

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“…The two approaches described above, however, can only identify one node as being inefficient in each cycle. This is how the author of [ 35 ] identifies multiple vest accounts quickly by taking random walks from a normal node and then performing a similar operation on a node, using the same bar to determine whether it is a vest or not. Then, using the discovered vest node and the same principle to identify multiple vest accounts, the author can quickly identify multiple vest accounts.…”

Section: Graph Algorithmmentioning

confidence: 99%

Preserving the Privacy of Healthcare Data over Social Networks Using Machine Learning

Veeramakali

Shobanadevi

Nayak³

et al. 2022

Computational Intelligence and Neuroscience

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A key challenge in clinical recommendation systems is the problem of aberrant patient profiles in social networks. As a result of a person’s abnormal profile, numerous vests might be used to make fake remarks about them, cyber bullying, or cyber-attacks. Many clinical researchers have done extensive study on this topic. The most recent studies on this topic are summarized, and an overarching framework is provided. When it comes to the methods and datasets that make up the data collection, the feature presentation and algorithm selection layers provide an overview of the various types of algorithm selections available. The categorization and evaluation of diseases and disorders has been one of the major advantages of machine learning in medical. Because it was harder to predict, it rendered it more controllable. It might range from difficult-to-find cancers in the early stages to certain other illnesses spread through the bloodstream. In healthcare, we may pick methods in machine learning depending on reliable outcomes. To do so, we must run the findings through each method. The major issue arises during information training and validation. Because the dataset is so large, eliminating mistakes might be difficult. The providers, other characteristics, various algorithms, data labelling techniques, and assessment criteria are all presented and contrasted in depth. Detecting anomalous users in medical social networks, on the other hand, is a work in progress. The result evaluation layer provides an explanation of how to evaluate and mark up the results of the various algorithm selection layers. Finally, it looks forward to more study in this area.

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Image Fusion Algorithm at Pixel Level Based on Edge Detection

Cited by 33 publications

References 27 publications

[Retracted] Recognition of Protein Network for Bioinformatics Knowledge Analysis Using Support Vector Machine

[Retracted] Recognition of Protein Network for Bioinformatics Knowledge Analysis Using Support Vector Machine

ML-Based Texture and Wavelet Features Extraction Technique to Predict Gastric Mesothelioma Cancer

Preserving the Privacy of Healthcare Data over Social Networks Using Machine Learning

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