A Novel Approach for Missing Value Replacement in MLP-RMSProp Based Classification Model

2019 ◽  
Vol 7 (11) ◽  
pp. 6-19
Author(s):  
G. Suresh ◽  
S. Saraswathi
Diagnostics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 121
Author(s):  
Roberta Risoluti ◽  
Giuseppina Gullifa ◽  
Vittorio Fineschi ◽  
Paola Frati ◽  
Stefano Materazzi

Chronothanatology has always been a challenge in forensic sciences. Therefore, the importance of a multidisciplinary approach for the characterization of matrices (organs, tissues, or fluids) that respond linearly to the postmortem interval (PMI) is emerging increasingly. The vitreous humor is particularly suitable for studies aimed at assessing time-related modifications because it is topographically isolated and well-protected. In this work, a novel approach based on thermogravimetry and chemometrics was used to estimate the time since death in the vitreous humor and to collect a databank of samples derived from postmortem examinations after medico–legal evaluation. In this study, contaminated and uncontaminated specimens with tissue fragments were included in order to develop a classification model to predict time of death based on partial least squares discriminant analysis (PLS-DA) that was as robust as possible. Results demonstrate the possibility to correctly predict the PMI even in contaminated samples, with an accuracy not lower than 70%. In addition, the correlation coefficient of the measured versus predicted outcomes was found to be 0.9978, confirming the ability of the model to extend its feasibility even to such situations involving contaminated vitreous humor.


Symmetry ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 757
Author(s):  
Yongke Pan ◽  
Kewen Xia ◽  
Li Wang ◽  
Ziping He

The dataset distribution of actual logging is asymmetric, as most logging data are unlabeled. With the traditional classification model, it is hard to predict the oil and gas reservoir accurately. Therefore, a novel approach to the oil layer recognition model using the improved whale swarm algorithm (WOA) and semi-supervised support vector machine (S3VM) is proposed in this paper. At first, in order to overcome the shortcomings of the Whale Optimization Algorithm applied in the parameter-optimization of the S3VM model, such as falling into a local optimization and low convergence precision, an improved WOA was proposed according to the adaptive cloud strategy and the catfish effect. Then, the improved WOA was used to optimize the kernel parameters of S3VM for oil layer recognition. In this paper, the improved WOA is used to test 15 benchmark functions of CEC2005 compared with five other algorithms. The IWOA–S3VM model is used to classify the five kinds of UCI datasets compared with the other two algorithms. Finally, the IWOA–S3VM model is used for oil layer recognition. The result shows that (1) the improved WOA has better convergence speed and optimization ability than the other five algorithms, and (2) the IWOA–S3VM model has better recognition precision when the dataset contains a labeled and unlabeled dataset in oil layer recognition.


2020 ◽  
Author(s):  
Tae-jun Choi ◽  
Honggu Lee

AbstractDefense responses are a highly conserved behavioral response set across species. Defense responses motivate organisms to detect and react to threats and potential danger as a precursor to anxiety. Accurate measurement of temporal defense responses is important for understanding clinical anxiety and mood disorders, such as post-traumatic stress disorder, obsessive compulsive disorder, and generalized anxiety disorder. Within these conditions, anxiety is defined as a state of prolonged defense response elicitation to a threat that is ambiguous or unspecific. In this study, we aimed to develop a data-driven approach to capture temporal defense response elicitation through a multi-modality data analysis of physiological signals, including electroencephalogram (EEG), electrocardiogram (ECG), and eye-tracking information. A fear conditioning paradigm was adopted to develop a defense response classification model. From a classification model based on 42 feature sets, a higher order crossing feature set-based model was chosen for further analysis with cross-validation loss of 0.0462 (SEM: 0.0077). To validate our model, we compared predicted defense response occurrence ratios from a comprehensive situation that generates defense responses by watching movie clips with fear awareness and threat existence predictability, which have been reported to correlate with defense response elicitation in previous studies. We observed that defense response occurrence ratios are correlated with threat existence predictability, but not with fear awareness. These results are similar to those of previous studies using comprehensive situations. Our study provides insight into measurement of temporal defense responses via a novel approach, which can improve understanding of anxiety and related clinical disorders for neurobiological and clinical researchers.


2017 ◽  
Vol 2 ◽  
pp. 24-33 ◽  
Author(s):  
Musbah Zaid Enweiji ◽  
Taras Lehinevych ◽  
Аndrey Glybovets

Cross language classification is an important task in multilingual learning, where documents in different languages often share the same set of categories. The main goal is to reduce the labeling cost of training classification model for each individual language. The novel approach by using Convolutional Neural Networks for multilingual language classification is proposed in this article. It learns representation of knowledge gained from languages. Moreover, current method works for new individual language, which was not used in training. The results of empirical study on large dataset of 21 languages demonstrate robustness and competitiveness of the presented approach.


2018 ◽  
Vol 11 (4) ◽  
pp. 86 ◽  
Author(s):  
Lei Xu ◽  
Takuji Kinkyo ◽  
Shigeyuki Hamori

We propose a novel approach that combines random forests and the wavelet transform to model the prediction of currency crises. Our classification model of random forests, built using both standard predictors and wavelet predictors, and obtained from the wavelet transform, achieves a demonstrably high level of predictive accuracy. We also use variable importance measures to find that wavelet predictors are key predictors of crises. In particular, we find that real exchange rate appreciation and overvaluation, which are measured over a horizon of 16–32 months, are the most important.


2019 ◽  
Vol 9 (15) ◽  
pp. 3135 ◽  
Author(s):  
Mrinmoy Sarkar ◽  
Dhiman Chowdhury ◽  
Celia Shahnaz ◽  
Shaikh Anowarul Fattah

Electrical network frequency (ENF) is a signature of a power distribution grid. It represents the deviation from the nominal frequency (50 or 60 Hz) of a power system network. The variations in ENF sequences within a grid are subject to load fluctuations within that particular grid. These ENF variations are inherently located in a multimedia signal, which is recorded close to the grid or directly from the mains power line. Thus, the specific location of a recording can be identified by analyzing the ENF sequences of the multimedia signal in absence of the concurrent power signal. In this article, a novel approach to location-stamp authentication based on ENF sequences of digital recordings is presented. ENF patterns are extracted from a number of power and audio signals recorded in different grid locations across the world. The extracted ENF signals are decomposed into low outliers and high outliers frequency segments and potential feature vectors are determined for these ENF segments by statistical and signal processing analysis. Then, a multi-class support vector machine (SVM) classification model is developed to verify the location-stamp information of the recordings. The performance evaluations corroborate the efficacy of the proposed framework.


2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Huamin Zhao ◽  
Defang Xu ◽  
Olarewaju Lawal ◽  
Shujuan Zhang

How to quickly and accurately judge the maturity of muskmelon is very important to consumers and muskmelon sorting staff. This paper presents a novel approach to solve the difficulty of muskmelon maturity stage classification in greenhouse and other complex environments. The color characteristics of muskmelon were used as the main feature of maturity discrimination. A modified 29-layer ResNet was applied with the proposed two-way data augmentation methods for the maturity stages of muskmelon classification using indoor and outdoor datasets to create a robust classification model that can generalize better. The results showed that code data augmentation which is the first way caused more performance degradation than input image augmentation—the second way. This established the effectiveness of the code data augmentation compared to image augmentation. Nevertheless, the two-way data augmentations including the combination of outdoor and indoor datasets to create a classification model revealed an excellent performance of F1 score ∼99%, and hence the model is applicable to computer-based platform for quick muskmelon stages of maturity classification.


2019 ◽  
Vol 9 (10) ◽  
pp. 2149 ◽  
Author(s):  
Yoon-Young Choi ◽  
Heeseung Shon ◽  
Young-Ji Byon ◽  
Dong-Kyu Kim ◽  
Seungmo Kang

Missing value imputation approaches have been widely used to support and maintain the quality of traffic data. Although the spatiotemporal dependency-based approaches can improve the imputation performance for large and continuous missing patterns, additionally considering traffic states can lead to more reliable results. In order to improve the imputation performances further, a section-based approach is also needed. This study proposes a novel approach for identifying traffic-states of different spots of road sections that comprise, namely, a section-based traffic state (SBTS), and determining their spatiotemporal dependencies customized for each SBTS, for missing value imputations. A principal component analysis (PCA) was employed, and angles obtained from the first principal component were used to identify the SBTSs. The pre-processing was combined with a support vector machine for developing the imputation model. It was found that the segmentation of the SBTS using the angles and considering the spatiotemporal dependency for each state by the proposed approach outperformed other existing models.


Knowledge discovery is also known as Data mining in databases, in recent years that technique plays a major role in research area. Data mining in healthcare domain has noteworthy usage in real world. The mining method can enable the healthcare field for the enhancement of institutionalization of its administrations and become quicker with best in class technologies. Innovation utilization isn't restricted to basic leadership in undertakings, yet spread to different social statuses in all fields. In this paper a novel approach for the detection of brain tumor is proposed. The novel approach uses the classification technique of K-nearest neighbor (KNN) and for ignoring the error of the dataset image SOM (self-organizing map) algorithm has been used. Discrete wavelet transform (DWT) is used for transforming input image data set, in which RGB color of input data image has been converted into gray scale. Then it has been classified using KNN after that the error avoiding algorithm has been carried out. This will help to differentiate tumor cells and the normal cells. The presence of tumor in brain image is detected using parametric analysis by simulation.


2012 ◽  
Vol 38 ◽  
pp. 1067-1071 ◽  
Author(s):  
Rajashree Senapti ◽  
Kailash Shaw ◽  
Sashikala Mishra ◽  
Debahuti Mishra

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