Pruned search: A machine learning based meta-heuristic approach for constrained continuous optimization

AbstractIn this chapter, we consider constant-time algorithms for continuous optimization problems. Specifically, we consider quadratic function minimization and tensor decomposition, both of which have numerous applications in machine learning and data mining. The key component in our analysis is graph limit theory, which was originally developed to study graphs analytically.

Download Full-text

Prediction and determining the effective factors on the survival transplanted kidney for five-year in imbalanced data by the meta-heuristic approach and machine learning

Signal and Data Processing ◽

10.29252/jsdp.15.4.85 ◽

2019 ◽

Vol 15 (4) ◽

pp. 85-94

Author(s):

nasibeh emami ◽

zeinab hassani ◽

◽

Keyword(s):

Machine Learning ◽

Imbalanced Data ◽

Heuristic Approach ◽

Effective Factors ◽

Transplanted Kidney

Download Full-text

A Meta-heuristic Approach for Design of Image Processing Based Model for Nitrosamine Identification in Red Meat Image

Recent Patents on Engineering ◽

10.2174/1872212114999200719145022 ◽

2020 ◽

Vol 14 ◽

Author(s):

Monika Arora ◽

Parthasarathi Mangipudi

Keyword(s):

Machine Learning ◽

Computer Vision ◽

Research Work ◽

Red Meat ◽

Heuristic Approach ◽

Partial Least Square ◽

Operating Characteristics ◽

New Findings ◽

Predicted Model ◽

Meat Samples

Background: Nitrosamine is a chemical, commonly used as preservative in red meat whose intake can cause serious carcinogenic effects on human health. Identification of such malignant chemicals in foodstuffs is an ordeal. Objective: The objective of the proposed research work presents a meta-heuristic approach for nitrosamine detection in red meat using computer vision-based non-destructive method. Method: This paper presents an analytical approach for assessing the quality of meat samples upon storage (24, 48, 72 and 96 hours). A novel machine learning-based method involving strategic selection of discriminatory features of segmented images has been proposed. The significant features were determined by finding p-values using Mann-Whitney U test at 95% confidence interval which were classified using partial least square-discriminant analysis (PLS-DA) algorithm. Subsequently, the predicted model was evaluated by bootstrap technique which projects an outline for preservative identification in meat samples. Results: The simulation results of the proposed meta-heuristic computer vision-based model demonstrate improved performance in comparison to the existing methods. Some of the prevailing machine learning-based methods were analyzed and compared from a survey of recent patents with the proposed technique in order to affirm new findings. The performance of PLS-DA model was quantified by receiver operating characteristics (ROC) curve at all classification thresholds. A maximum of 100% sensitivity and 71.21% specificity was obtained from optimum threshold of 0.5964. The concept of bootstrapping was used for evaluating the predicted model. Nitrosamine content in the meat samples was predicted with 0.8375 correlation coefficient and 0.109 bootstrap error. Conclusion: The proposed method comprehends double-cross validation technique which makes it more comprehensive in discriminating between the edibility of foodstuff which can certainly reinstate conventional methods and ameliorate existing computer-vision methods.

Download Full-text

Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Download Full-text