scholarly journals Machine Learning Based Color Classification by Means of Visually Evoked Potentials

2021 ◽  
Vol 11 (24) ◽  
pp. 11882
Author(s):  
Carl Böck ◽  
Lea Meier ◽  
Stephan Kalb ◽  
Milan R. Vosko ◽  
Thomas Tschoellitsch ◽  
...  
Keyword(s):  
Machine Learning ◽  
Evoked Potentials ◽  
Predictive Accuracy ◽  
Neurological Diseases ◽  
Occipital Lobe ◽  
Classification Performance ◽  
Visually Evoked Potentials ◽  
Color Classification ◽  
Stimulus Color ◽  
The Impact

Visually evoked potentials (VEPs) are widely used for diagnoses of different neurological diseases. Interestingly, there is limited research about the impact of the stimulus color onto the evoked response. Therefore, in our study we investigated the possibility of automatically classifying the stimulus color. The visual stimuli were selected to be red/black and green/black checkerboard patterns with equal light density. Both of these stimuli were presented in a random manner to nine subjects, while the electroencephalogram was recorded at the occipital lobe. After pre-processing and aligning the evoked potentials, an artificial neural network with one hidden layer was used to investigate the general possibility to automatically classify the stimulus color in three different settings. First, color classification with individually trained models, color classification with a common model, and color classification for each individual volunteer with a model trained on the data of the remaining subjects. With an average accuracy (ACC) of 0.83, the best results were achieved for the individually trained model. Also, the second (mean ACC = 0.76) and third experiments (mean ACC = 0.71) indicated a reasonable predictive accuracy across all subjects. Consequently, machine learning tools are able to appropriately classify stimuli colors based on VEPs. Although further studies are needed to improve the classification performance of our approach, this opens new fields of applications for VEPs.

scholarly journals Machine learning, transcriptome, and genotyping chip analyses provide insights into SNP markers identifying flower color in Platycodon grandiflorus

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Go-Eun Yu ◽  
Younhee Shin ◽  
Sathiyamoorthy Subramaniyam ◽  
Sang-Ho Kang ◽  
Si-Myung Lee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Feature Selection Method ◽  
Flower Color ◽  
Classification Performance ◽  
Snp Markers ◽  
Rna Seq ◽  
Color Classification ◽  
Dna Pcr ◽  
Selection Of

AbstractBellflower is an edible ornamental gardening plant in Asia. For predicting the flower color in bellflower plants, a transcriptome-wide approach based on machine learning, transcriptome, and genotyping chip analyses was used to identify SNP markers. Six machine learning methods were deployed to explore the classification potential of the selected SNPs as features in two datasets, namely training (60 RNA-Seq samples) and validation (480 Fluidigm chip samples). SNP selection was performed in sequential order. Firstly, 96 SNPs were selected from the transcriptome-wide SNPs using the principal compound analysis (PCA). Then, 9 among 96 SNPs were later identified using the Random forest based feature selection method from the Fluidigm chip dataset. Among six machines, the random forest (RF) model produced higher classification performance than the other models. The 9 SNP marker candidates selected for classifying the flower color classification were verified using the genomic DNA PCR with Sanger sequencing. Our results suggest that this methodology could be used for future selection of breeding traits even though the plant accessions are highly heterogeneous.

scholarly journals Parameter Analysis of Multiscale Two-Dimensional Fuzzy and Dispersion Entropy Measures Using Machine Learning Classification

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1303
Author(s):  
Ryan Furlong ◽  
Mirvana Hilal ◽  
Vincent O’Brien ◽  
Anne Humeau-Heurtier
Keyword(s):  
Machine Learning ◽  
Image Classification ◽  
Classification Performance ◽  
Optimal Choice ◽  
Parameter Analysis ◽  
Two Dimensional ◽  
Machine Learning Classification ◽  
Multiple Image ◽  
Average Maximum ◽  
The Impact

Two-dimensional fuzzy entropy, dispersion entropy, and their multiscale extensions (MFuzzyEn2D and MDispEn2D, respectively) have shown promising results for image classifications. However, these results rely on the selection of key parameters that may largely influence the entropy values obtained. Yet, the optimal choice for these parameters has not been studied thoroughly. We propose a study on the impact of these parameters in image classification. For this purpose, the entropy-based algorithms are applied to a variety of images from different datasets, each containing multiple image classes. Several parameter combinations are used to obtain the entropy values. These entropy values are then applied to a range of machine learning classifiers and the algorithm parameters are analyzed based on the classification results. By using specific parameters, we show that both MFuzzyEn2D and MDispEn2D approach state-of-the-art in terms of image classification for multiple image types. They lead to an average maximum accuracy of more than 95% for all the datasets tested. Moreover, MFuzzyEn2D results in a better classification performance than that extracted by MDispEn2D as a majority. Furthermore, the choice of classifier does not have a significant impact on the classification of the extracted features by both entropy algorithms. The results open new perspectives for these entropy-based measures in textural analysis.

scholarly journals A Novel Machine Learning Algorithm Combined With Multivariate Analysis for the Prognosis of Renal Collecting Duct Carcinoma

2022 ◽  
Vol 11 ◽  
Author(s):  
Liwei Wei ◽  
Yongdi Huang ◽  
Zheng Chen ◽  
Jinhua Li ◽  
Guangyi Huang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Predictive Accuracy ◽  
Collecting Duct ◽  
Histological Grade ◽  
Clinical Decisions ◽  
Duct Carcinoma ◽  
Collecting Duct Carcinoma ◽  
Renal Collecting Duct ◽  
The Impact

ObjectivesTo investigate the clinical and non-clinical characteristics that may affect the prognosis of patients with renal collecting duct carcinoma (CDC) and to develop an accurate prognostic model for this disease.MethodsThe characteristics of 215 CDC patients were obtained from the U.S. National Cancer Institute’s surveillance, epidemiology and end results database from 2004 to 2016. Univariate Cox proportional hazard model and Kaplan-Meier analysis were used to compare the impact of different factors on overall survival (OS). 10 variables were included to establish a machine learning (ML) model. Model performance was evaluated by the receiver operating characteristic curves (ROC) and calibration plots for predictive accuracy and decision curve analysis (DCA) were obtained to estimate its clinical benefits.ResultsThe median follow-up and survival time was 16 months during which 164 (76.3%) patients died. 4.2, 32.1, 50.7 and 13.0% of patients were histological grade I, II, III, and IV, respectively. At diagnosis up to 61.9% of patients presented with a pT3 stage or higher tumor, and 36.7% of CDC patients had metastatic disease. 10 most clinical and non-clinical factors including M stage, tumor size, T stage, histological grade, N stage, radiotherapy, chemotherapy, age at diagnosis, surgery and the geographical region where the care delivered was either purchased or referred and these were allocated 95, 82, 78, 72, 49, 38, 36, 35, 28 and 21 points, respectively. The points were calculated by the XGBoost according to their importance. The XGBoost models showed the best predictive performance compared with other algorithms. DCA showed our models could be used to support clinical decisions in 1-3-year OS models.ConclusionsOur ML models had the highest predictive accuracy and net benefits, which may potentially help clinicians to make clinical decisions and follow-up strategies for patients with CDC. Larger studies are needed to better understand this aggressive tumor.

scholarly journals An Evaluation of Machine Learning Classifiers for Prediction of Attacks to Secure Green IoT Infrastructure

2021 ◽  
Vol 9 (5) ◽  
pp. 549-557
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Predictive Accuracy ◽  
Support Vector ◽  
Detection Accuracy ◽  
Detection Technology ◽  
Maximum Benefit ◽  
Iot Devices ◽  
Artificial Neural Network Ann ◽  
The Impact

Internet of things is an emerging technology that allows many devices to be connected in an unparalleled way. Despite having many beneficial applications, IoT technology presents significant emission risks due to the large number of devices used in the applications. Therefore, to gain maximum benefit from IoT, we must step towards green IT. On the other hand, cloud computing has been successfully used to provide limitless computational storage and other resources for a variety of IoT devices across the internet. Unfortunately, security concerns in cloud computing for IoT are still a concern. Motivated by the goal of creating a better atmosphere for IoT and ensuring its resilience to risks and attacks, this report reveals ways to decrease the impact of energyuse by IoT on the environment. Additionally, it addresses research concerns for IoT security and reflects on how to protect green IoT networks through the use of an effective machine learning intrusion detection technology to deter attacks on IoT platforms. To do that, we first evaluated some existing ML classifiers such Artificial Neural Network (ANN), Support Vector Machine (SVM), Gaussian Naïve Bayes (NB), Decision Tree (DT) and Random Forest (RF) with the old KDD’99 datasets. The accuracy was extremelyhigh for all classifiers except Gaussian NB whose accuracy was < 90%.The SVM is the highest at 99.24% accuracy with a loss of 4.68% in the last epoch of training. However, using a more recent dataset (ISCX1DS2012) on these same ML classifiers, we observed some discrepancies, all the classifiers dropped in their predictive accuracy even after altering the hyper-parameters.The ANN was at its lowest accuracy at 85.92% and the SVM which was relatively accurate dropped to 90.02%. NB algorithm produced approximately 67.9% accuracy which made it less accurate for both datasets. Based on these findings, we proceeded to propose an efficient model with enough hidden layers and nodes to increase the detection accuracy and to outperform the existing ML classifiers when evaluated with a more recent dataset

scholarly journals Deep learning based respiratory sound analysis for detection of chronic obstructive pulmonary disease

2021 ◽  
Vol 7 ◽  
pp. e369
Author(s):  
Arpan Srivastava ◽  
Sonakshi Jain ◽  
Ryan Miranda ◽  
Shruti Patil ◽  
Sharnil Pandya ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Respiratory Diseases ◽  
Predictive Accuracy ◽  
Research Work ◽  
Respiratory Sounds ◽  
Chronic Obstructive ◽  
Learning Approach ◽  
X Rays ◽  
The Impact

In recent times, technologies such as machine learning and deep learning have played a vital role in providing assistive solutions to a medical domain’s challenges. They also improve predictive accuracy for early and timely disease detection using medical imaging and audio analysis. Due to the scarcity of trained human resources, medical practitioners are welcoming such technology assistance as it provides a helping hand to them in coping with more patients. Apart from critical health diseases such as cancer and diabetes, the impact of respiratory diseases is also gradually on the rise and is becoming life-threatening for society. The early diagnosis and immediate treatment are crucial in respiratory diseases, and hence the audio of the respiratory sounds is proving very beneficial along with chest X-rays. The presented research work aims to apply Convolutional Neural Network based deep learning methodologies to assist medical experts by providing a detailed and rigorous analysis of the medical respiratory audio data for Chronic Obstructive Pulmonary detection. In the conducted experiments, we have used a Librosa machine learning library features such as MFCC, Mel-Spectrogram, Chroma, Chroma (Constant-Q) and Chroma CENS. The presented system could also interpret the severity of the disease identified, such as mild, moderate, or acute. The investigation results validate the success of the proposed deep learning approach. The system classification accuracy has been enhanced to an ICBHI score of 93%. Furthermore, in the conducted experiments, we have applied K-fold Cross-Validation with ten splits to optimize the performance of the presented deep learning approach.

scholarly journals Predictive models for cochlear implant outcomes: Performance, generalizability, and the impact of cohort size

2021 ◽  
Vol 25 ◽  
pp. 233121652110661
Author(s):  
Elaheh Shafieibavani ◽  
Benjamin Goudey ◽  
Isabell Kiral ◽  
Peter Zhong ◽  
Antonio Jimeno-Yepes ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cochlear Implant ◽  
Sample Size ◽  
Clinical Decision Making ◽  
Linear Models ◽  
Predictive Accuracy ◽  
Predictive Performance ◽  
Absolute Error ◽  
Learning Approaches ◽  
The Impact

While cochlear implants have helped hundreds of thousands of individuals, it remains difficult to predict the extent to which an individual’s hearing will benefit from implantation. Several publications indicate that machine learning may improve predictive accuracy of cochlear implant outcomes compared to classical statistical methods. However, existing studies are limited in terms of model validation and evaluating factors like sample size on predictive performance. We conduct a thorough examination of machine learning approaches to predict word recognition scores (WRS) measured approximately 12 months after implantation in adults with post-lingual hearing loss. This is the largest retrospective study of cochlear implant outcomes to date, evaluating 2,489 cochlear implant recipients from three clinics. We demonstrate that while machine learning models significantly outperform linear models in prediction of WRS, their overall accuracy remains limited (mean absolute error: 17.9-21.8). The models are robust across clinical cohorts, with predictive error increasing by at most 16% when evaluated on a clinic excluded from the training set. We show that predictive improvement is unlikely to be improved by increasing sample size alone, with doubling of sample size estimated to only increasing performance by 3% on the combined dataset. Finally, we demonstrate how the current models could support clinical decision making, highlighting that subsets of individuals can be identified that have a 94% chance of improving WRS by at least 10% points after implantation, which is likely to be clinically meaningful. We discuss several implications of this analysis, focusing on the need to improve and standardize data collection.

scholarly journals The impact of feature selection on one and two-class classification performance for plant microRNAs

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e2135 ◽  
Author(s):  
Waleed Khalifa ◽  
Malik Yousef ◽  
Müşerref Duygu Saçar Demirci ◽  
Jens Allmer
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Feature Selection Method ◽  
Mature Mirnas ◽  
Classification Performance ◽  
Mirna Detection ◽  
To Come ◽  
One Class Classification ◽  
The Impact ◽  
Better Than

MicroRNAs (miRNAs) are short nucleotide sequences that form a typical hairpin structure which is recognized by a complex enzyme machinery. It ultimately leads to the incorporation of 18–24 nt long mature miRNAs into RISC where they act as recognition keys to aid in regulation of target mRNAs. It is involved to determine miRNAs experimentally and, therefore, machine learning is used to complement such endeavors. The success of machine learning mostly depends on proper input data and appropriate features for parameterization of the data. Although, in general, two-class classification (TCC) is used in the field; because negative examples are hard to come by, one-class classification (OCC) has been tried for pre-miRNA detection. Since both positive and negative examples are currently somewhat limited, feature selection can prove to be vital for furthering the field of pre-miRNA detection. In this study, we compare the performance of OCC and TCC using eight feature selection methods and seven different plant species providing positive pre-miRNA examples. Feature selection was very successful for OCC where the best feature selection method achieved an average accuracy of 95.6%, thereby being ∼29% better than the worst method which achieved 66.9% accuracy. While the performance is comparable to TCC, which performs up to 3% better than OCC, TCC is much less affected by feature selection and its largest performance gap is ∼13% which only occurs for two of the feature selection methodologies. We conclude that feature selection is crucially important for OCC and that it can perform on parwith TCC given the proper set of features.

scholarly journals Prediction of tau accumulation in prodromal Alzheimer’s disease using an ensemble machine learning approach

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jaeho Kim ◽  
Yuhyun Park ◽  
Seongbeom Park ◽  
Hyemin Jang ◽  
Hee Jin Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Data ◽  
Cortical Thickness ◽  
Parietal Lobe ◽  
Occipital Lobe ◽  
Gradient Boosting ◽  
Prodromal Ad ◽  
The Impact

AbstractWe developed machine learning (ML) algorithms to predict abnormal tau accumulation among patients with prodromal AD. We recruited 64 patients with prodromal AD using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. Supervised ML approaches based on the random forest (RF) and a gradient boosting machine (GBM) were used. The GBM resulted in an AUC of 0.61 (95% confidence interval [CI] 0.579–0.647) with clinical data (age, sex, years of education) and a higher AUC of 0.817 (95% CI 0.804–0.830) with clinical and neuropsychological data. The highest AUC was 0.86 (95% CI 0.839–0.885) achieved with additional information such as cortical thickness in clinical data and neuropsychological results. Through the analysis of the impact order of the variables in each ML classifier, cortical thickness of the parietal lobe and occipital lobe and neuropsychological tests of memory domain were found to be more important features for each classifier. Our ML algorithms predicting tau burden may provide important information for the recruitment of participants in potential clinical trials of tau targeting therapies.

scholarly journals Psychopathology and Resilience Following Strict COVID-19 Lockdowns in Hubei, China: Examining Person- and Context-Level Predictors for Longitudinal Trajectories

2022 ◽  
Author(s):  
Shuquan Chen ◽  
Kaiwen Bi ◽  
Pei Sun ◽  
George A. Bonanno
Keyword(s):  
Machine Learning ◽  
Stressful Life Events ◽  
Predictive Accuracy ◽  
Supervised Machine Learning ◽  
Longitudinal Trajectories ◽  
Machine Learning Approach ◽  
The Government ◽  
And Coping ◽  
Chronic Anxiety ◽  
The Impact

In Hubei, China, where the COVID-19 epidemic first emerged, the government has enforced strict quarantine and lockdown measures. Longitudinal studies suggest that the impact of adverse events on psychological adjustment is highly heterogenous. To better understand protective and risk factors that predict longitudinal psychopathology and resilience following strict COVID-19 lockdowns, this study used unsupervised machine learning to identify half-year longitudinal trajectories (April, June, August, and October, 2020) of three mental health outcomes (depression, anxiety, and PTSD) among a sample of Hubei residents (N = 326), assessed a broad range of person- and context-level predictors, and applied LASSO logistic regression, a supervised machine learning approach, to select best predictors for trajectory memberships of resilience and chronic psychopathology. Across outcomes, most individuals remained resilient. Models with both person- and context-level predictors showed excellent predictive accuracy, except for models predicting chronic anxiety. The person-level models showed either good or excellent predictive accuracy. The context-level models showed good predictive accuracy for depression trajectories but were only fair in predicting trajectories of anxiety and PTSD. Overall, the most critical person-level predictors were worry, optimism, fear of COVID, and coping flexibility, whereas important context-level predictors included features of stressful life events, community satisfaction, and family support. This study identified clinical patterns of response to COVID-19 lockdowns and used a combination of risk and protective factors to accurately differentiate these patterns. These findings have implications for clinical risk identifications and interventions in the context of potential trauma.

Emotional Reactivity to Visual Content as Revealed by ERP Component Clustering

2015 ◽  
Vol 29 (4) ◽  
pp. 135-146 ◽  
Author(s):  
Miroslaw Wyczesany ◽  
Szczepan J. Grzybowski ◽  
Jan Kaiser
Keyword(s):  
Emotional Reactivity ◽  
Brain Activity ◽  
Affective State ◽  
Occipital Lobe ◽  
Stimulus Onset ◽  
Emotional States ◽  
Neural Basis ◽  
Temporoparietal Junction ◽  
The Right ◽  
The Impact

Abstract. In the study, the neural basis of emotional reactivity was investigated. Reactivity was operationalized as the impact of emotional pictures on the self-reported ongoing affective state. It was used to divide the subjects into high- and low-responders groups. Independent sources of brain activity were identified, localized with the DIPFIT method, and clustered across subjects to analyse the visual evoked potentials to affective pictures. Four of the identified clusters revealed effects of reactivity. The earliest two started about 120 ms from the stimulus onset and were located in the occipital lobe and the right temporoparietal junction. Another two with a latency of 200 ms were found in the orbitofrontal and the right dorsolateral cortices. Additionally, differences in pre-stimulus alpha level over the visual cortex were observed between the groups. The attentional modulation of perceptual processes is proposed as an early source of emotional reactivity, which forms an automatic mechanism of affective control. The role of top-down processes in affective appraisal and, finally, the experience of ongoing emotional states is also discussed.

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