scholarly journals Toward Emotion-Aware Computing: A Loop Selection Approach Based on Machine Learning for Speculative Multithreading

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 3675-3686 ◽  
Author(s):  
Bin Liu ◽  
Jinrong He ◽  
Yaojun Geng ◽  
Lvwen Huang ◽  
Shuqin Li
Keyword(s):  
Machine Learning ◽  
Speculative Multithreading ◽  
Selection Approach

DJournal

2021 ◽  
pp. 265-283
Author(s):  
Shantanu Kumar Rahut ◽  
Razwan Ahmed Tanvir ◽  
Sharfi Rahman ◽  
Shamim Akhter
Keyword(s):  
Machine Learning ◽  
Scientific Community ◽  
Human Interaction ◽  
Scholarly Publication ◽  
Electronic Submission ◽  
Blockchain Technology ◽  
Selection Approach

The paper reviewing process evaluates the potentiality, quality, novelty, and reliability of an article prior to any scholarly publication. However, a number of recent publications are pointing towards the occurrence of the biasness and mistreatments during the progression of the reviewing process. Therefore, the scientific community is involved to standardize the reviewing protocols by introducing blind and electronic submission, selecting eligible reviewers, and supporting an appropriate checklist to the reviewers. The amplification of reviewing with decentralization and automation can solve the mentioned problems by limiting the possibility of human interaction. This chapter proposes and implements a decentralized and anonymous paper reviewing system (DJournal) using blockchain technology. DJournal eliminates all the trust issues related to the reviewing process but improves reliability, transparency, and streamlining capabilities with up-gradation of the machine learning-based reviewer selection approach.

scholarly journals Identifying Potential miRNA Biomarkers for Gastric Cancer Diagnosis Using Machine Learning Variable Selection Approach

2022 ◽  
Vol 12 ◽  
Author(s):  
Neda Gilani ◽  
Reza Arabi Belaghi ◽  
Younes Aftabi ◽  
Elnaz Faramarzi ◽  
Tuba Edgünlü ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gastric Cancer ◽  
Variable Selection ◽  
Prediction Models ◽  
Strong Relationship ◽  
Training Sample ◽  
Machine Learning Algorithms ◽  
Molecular Events ◽  
Selection Approach ◽  
Ontological Analysis

Aim: This study aimed to accurately identification of potential miRNAs for gastric cancer (GC) diagnosis at the early stages of the disease.Methods: We used GSE106817 data with 2,566 miRNAs to train the machine learning models. We used the Boruta machine learning variable selection approach to identify the strong miRNAs associated with GC in the training sample. We then validated the prediction models in the independent sample GSE113486 data. Finally, an ontological analysis was done on identified miRNAs to eliciting the relevant relationships.Results: Of those 2,874 patients in the training the model, there were 115 (4%) patients with GC. Boruta identified 30 miRNAs as potential biomarkers for GC diagnosis and hsa-miR-1343-3p was at the highest ranking. All of the machine learning algorithms showed that using hsa-miR-1343-3p as a biomarker, GC can be predicted with very high precision (AUC; 100%, sensitivity; 100%, specificity; 100% ROC; 100%, Kappa; 100) using with the cut-off point of 8.2 for hsa-miR-1343-3p. Also, ontological analysis of 30 identified miRNAs approved their strong relationship with cancer associated genes and molecular events.Conclusion: The hsa-miR-1343-3p could be introduced as a valuable target for studies on the GC diagnosis using reliable biomarkers.

scholarly journals An Effective Multi-Label Feature Selection Model Towards Eliminating Noisy Features

2020 ◽  
Vol 10 (22) ◽  
pp. 8093
Author(s):  
Jun Wang ◽  
Yuanyuan Xu ◽  
Hengpeng Xu ◽  
Zhe Sun ◽  
Zhenglu Yang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Learning Performance ◽  
Space Structures ◽  
Learning Tasks ◽  
Feature Spaces ◽  
Selection Approach ◽  
Label Correlations ◽  
Feature Selection Approach ◽  
Low Dimensional

Feature selection has devoted a consistently great amount of effort to dimension reduction for various machine learning tasks. Existing feature selection models focus on selecting the most discriminative features for learning targets. However, this strategy is weak in handling two kinds of features, that is, the irrelevant and redundant ones, which are collectively referred to as noisy features. These features may hamper the construction of optimal low-dimensional subspaces and compromise the learning performance of downstream tasks. In this study, we propose a novel multi-label feature selection approach by embedding label correlations (dubbed ELC) to address these issues. Particularly, we extract label correlations for reliable label space structures and employ them to steer feature selection. In this way, label and feature spaces can be expected to be consistent and noisy features can be effectively eliminated. An extensive experimental evaluation on public benchmarks validated the superiority of ELC.

scholarly journals Regional feature extraction of various fishes based on chemical and microbial variable selection using machine learning

2018 ◽  
Vol 10 (18) ◽  
pp. 2160-2168 ◽  
Author(s):  
Taiga Asakura ◽  
Kenji Sakata ◽  
Yasuhiro Date ◽  
Jun Kikuchi
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Variable Selection ◽  
Fish Species ◽  
Integrated Analysis ◽  
Habitat Features ◽  
Regional Feature ◽  
Selection Approach

We introduce a method for extracting regional and habitat features of various fish species based on chemical and microbial correlations that incorporate integrated analysis and a variable selection approach.

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