Age Prediction using Image Dataset using Machine Learning

Gender is a central feature of our personality still. In our social life it is also an significant element. Artificial intelligence age predictions can be used in many fields, such as smart human-machine interface growth , health, cosmetics, electronic commerce etc. The prediction of people's sex and age from their facial images is an ongoing and active problem of research. The researchers suggested a number of methods to resolve this problem, but the criteria and actual performance are still inadequate. A statistical pattern recognition approach for solving this problem is proposed in this project.Convolutionary Neural Network (ConvNet / CNN), a Deep Learning algorithm, is used as an extractor of features in the proposed solution. CNN takes input images and assigns value to different aspects / objects (learnable weights and biases) of the image and can differentiate between them. ConvNet requires much less preprocessing than other classification algorithms. While the filters are hand-made in primitive methods, ConvNets can learn these filters / features with adequate training.In this research, face images of individuals have been trained with convolutionary neural networks, and age and sex with a high rate of success have been predicted. More than 20,000 images are containing age, gender and ethnicity annotations. The images cover a wide range of poses, facial expression, lighting, occlusion, and resolution.

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An Innovative Deep Learning Algorithm for Drowsiness Detection from EEG Signal

Computation ◽

10.3390/computation7010013 ◽

2019 ◽

Vol 7 (1) ◽

pp. 13 ◽

Cited By ~ 10

Author(s):

Francesco Rundo ◽

Sergio Rinella ◽

Simona Massimino ◽

Marinella Coco ◽

Giorgio Fallica ◽

...

Keyword(s):

Machine Learning ◽

Visual Information ◽

Learning Algorithm ◽

Eeg Signal ◽

Training Set ◽

Robust Algorithms ◽

Physiological Measurement ◽

Deep Learning Algorithm ◽

Wide Range ◽

Electroencephalogram Eeg

The development of detection methodologies for reliable drowsiness tracking is a challenging task requiring both appropriate signal inputs and accurate and robust algorithms of analysis. The aim of this research is to develop an advanced method to detect the drowsiness stage in electroencephalogram (EEG), the most reliable physiological measurement, using the promising Machine Learning methodologies. The methods used in this paper are based on Machine Learning methodologies such as stacked autoencoder with softmax layers. Results obtained from 62 volunteers indicate 100% accuracy in drowsy/wakeful discrimination, proving that this approach can be very promising for use in the next generation of medical devices. This methodology can be extended to other uses in everyday life in which the maintaining of the level of vigilance is critical. Future works aim to perform extended validation of the proposed pipeline with a wide-range training set in which we integrate the photoplethysmogram (PPG) signal and visual information with EEG analysis in order to improve the robustness of the overall approach.

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Deep Neural Networks for Hierarchical Taxonomic Fossil Classification of Carbonate Skeletal Grains

10.5194/egusphere-egu21-16394 ◽

2021 ◽

Author(s):

Sidhant Idgunji ◽

Madison Ho ◽

Jonathan L. Payne ◽

Daniel Lehrmann ◽

Michele Morsilli ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Learning Algorithm ◽

Training Dataset ◽

Single Class ◽

Deep Learning Algorithm ◽

Human Ability ◽

Wide Range ◽

Potential Applications ◽

Animal Phyla

<p>The growing digitization of fossil images has vastly improved and broadened the potential application of big data and machine learning, particularly computer vision, in paleontology. Recent studies show that machine learning is capable of approaching human abilities of classifying images, and with the increase in computational power and visual data, it stands to reason that it can match human ability but at much greater efficiency in the near future. Here we demonstrate this potential of using deep learning to identify skeletal grains at different levels of the Linnaean taxonomic hierarchy. Our approach was two-pronged. First, we built a database of skeletal grain images spanning a wide range of animal phyla and classes and used this database to train the model. We used a Python-based method to automate image recognition and extraction from published sources. Second, we developed a deep learning algorithm that can attach multiple labels to a single image. Conventionally, deep learning is used to predict a single class from an image; here, we adopted a Branch Convolutional Neural Network (B-CNN) technique to classify multiple taxonomic levels for a single skeletal grain image. Using this method, we achieved over 90% accuracy for both the coarse, phylum-level recognition and the fine, class-level recognition across diverse skeletal grains (6 phyla and 15 classes). Furthermore, we found that image augmentation improves the overall accuracy. This tool has potential applications in geology ranging from biostratigraphy to paleo-bathymetry, paleoecology, and microfacies analysis. Further improvement of the algorithm and expansion of the training dataset will continue to narrow the efficiency gap between human expertise and machine learning.</p>

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AUTOMATIC SEGMENTATION AND INDICATORS MEASUREMENT OF THE VOCAL FOLDS AND GLOTTAL IN LARYNGEAL ENDOSCOPY IMAGES USING MASK R-CNN

Biomedical Engineering Applications Basis and Communications ◽

10.4015/s1016237221500277 ◽

2021 ◽

pp. 2150027

Author(s):

Chiun-Li Chin ◽

Chun-Lung Chang ◽

Yu-Chieh Liu ◽

Yong-Long Lin

Keyword(s):

Clinical Practice ◽

Medical Information ◽

Learning Algorithm ◽

Automatic Segmentation ◽

Vocal Folds ◽

Positive Influence ◽

High Rate ◽

Deep Learning Algorithm ◽

Proposed Model ◽

Clinic Practice

In present clinic practice of otolaryngology, otolaryngologists utilized laryngoscopy to diagnose the larynx lesion of patients preliminarily. Nevertheless, it was challenging for otolaryngologists to interpret the detailed information from laryngoscopy videos comprehensively. In this paper, we proposed Mask R-CNN deep learning algorithm to segment the regions of the vocal folds and glottal from laryngoscopy videos, and self-built algorithm to calculate measured indicators including the length and curvature of vocal folds, the angle of glottal, the area of vocal folds and glottal, and the triangle type composed of vocal folds and glottal. Moreover, in order to provide otolaryngologists critical and immediate medical information during diagnosis, we also provided visualized information, which is labeled on the laryngoscopy images to meet all the needs in clinical practice. From the result of this research, the precision of segmentation has reached a high rate of 90.4% on average. It shows that the model not only achieves great performance in segmentation, but also further proved the indicators are accurate enough to be considered in practical diagnosis. In the future, it is possible for the proposed model to be applied in more kinds of laryngoscopy analyses for more comprehensive diagnosis, which would make a positive influence toward the clinical practice of otolaryngology.

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Abstract P386: Reducing Laboratory Testing in Hospitalized Stroke Patients Using Machine Learning

Stroke ◽

10.1161/str.52.suppl_1.p386 ◽

2021 ◽

Vol 52 (Suppl_1) ◽

Author(s):

Yejin Kim ◽

Xiaoqian Jiang ◽

Sophie Samuel ◽

Sean I Savitz

Keyword(s):

Machine Learning ◽

Laboratory Tests ◽

Complete Blood Count ◽

Learning Algorithm ◽

High Rate ◽

Spatial Correlations ◽

Stroke Patients ◽

Deep Learning Algorithm ◽

Machine Learning Approach ◽

Lab Test

Introduction: Despite previous studies indicating a high rate of overuse, in-hospital laboratory blood testing remains common. Repeated laboratory tests often return normal and lead to increased cost and even anemia. We hypothesize this situation is highly significant among hospitalized stroke patients. We used machine learning to reduce the freqeuency of repeated lab testing for stroke patients as a proof of concept. Method: We used stroke patients in the MIMIC III database to develop a novel deep learning algorithm that takes into account the characteristics of all study patients to predict whether repeating a lab test is necessary and aid in decisions of obtaining subsequent laboratory tests. Our model jointly considers temporal and spatial correlations of lab tests to make predictions of future test abnormality. We included 12 tests for electrolytes, renal function, and Complete Blood Count. We defined an error as recommending the reduction of a laboratory study when the testing would have yielded a critical abnormal result which should have been tested (Table 1). Results: We analyzed 3,178 stroke patients (293,433 lab results). Our training cohort was used to develop the algorithm and the testing data were used to generate results. In the testing cohort, we could maintain a 99.8% accuracy with a 52% reduction of all lab tests, which corresponded to a total reduction of 29,803 lab tests (74 false negatives). Different labs can be reduced at different rates. K and Cl were relatively easy to predict a critical abnormality on repeat testing while Ca and Mg displayed boundaries that were harder to predict abnormalties. Conclusions: Reducing lab testing by 52% from this stroke patient cohort maintained accuracy of testing at 99.8%. Our data suggest that multiple or daily laboratory orders are unlikely to change patient management given the high accuracy proposed by the machine learning approach. Table 1: normal range and critical abnormality for the 12 tests of interest

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iMethyl-Deep: N6 Methyladenosine Identification of Yeast Genome with Automatic Feature Extraction Technique by Using Deep Learning Algorithm

Genes ◽

10.3390/genes11050529 ◽

2020 ◽

Vol 11 (5) ◽

pp. 529 ◽

Cited By ~ 8

Author(s):

Omid Mahmoudi ◽

Abdul Wahab ◽

Kil To Chong

Keyword(s):

Learning Algorithm ◽

Feature Representation ◽

Yeast Genome ◽

Single Nucleotide ◽

Automatic Feature Extraction ◽

Deep Learning Algorithm ◽

Wide Range ◽

Benchmark Datasets ◽

Nucleotide Resolution ◽

Single Nucleotide Resolution

One of the most common and well studied post-transcription modifications in RNAs is N6-methyladenosine (m6A) which has been involved with a wide range of biological processes. Over the past decades, N6-methyladenosine produced some positive consequences through the high-throughput laboratory techniques but still, these lab processes are time consuming and costly. Diverse computational methods have been proposed to identify m6A sites accurately. In this paper, we proposed a computational model named iMethyl-deep to identify m6A Saccharomyces Cerevisiae on two benchmark datasets M6A2614 and M6A6540 by using single nucleotide resolution to convert RNA sequence into a high quality feature representation. The iMethyl-deep obtained 89.19% and 87.44% of accuracy on M6A2614 and M6A6540 respectively which show that our proposed method outperforms the state-of-the-art predictors, at least 8.44%, 8.96%, 8.69% and 0.173 on M6A2614 and 15.47%, 28.52%, 25.54 and 0.5 on M6A6540 higher in terms of four metrics Sp, Sn, ACC and MCC respectively. Meanwhile, M6A6540 dataset never used to train a model.

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Footballer Action Tracking and Intervention Using Deep Learning Algorithm

Journal of Healthcare Engineering ◽

10.1155/2021/5518806 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Guanghui Yang ◽

Lijun Wang ◽

Xiaofeng Xu ◽

Jixiang Xia

Keyword(s):

Learning Algorithm ◽

Experimental Testing ◽

Texture Features ◽

World Cup ◽

Channel Network ◽

Dual Channel ◽

Deep Learning Algorithm ◽

Wide Range ◽

Performance Results ◽

Fully Connected

Fédération Internationale de Football Association is the governing body of the football world cup. The international tournament of football requires extensive training of all football players and athletes. In the training process of footballers, players and coaches recognize the training actions completed by footballers. The training actions are compared with standard actions, calculate losses, and scientifically intervene in the training processes. This intervention is important for better results during the training sessions. Coaches must determine and confirm that every action performed by the footballers meets the minimum standards. It is because the actions of individual players are performed quickly; as a result, the coach’s eye may not produce accurate results as human activities are prone to errors. Therefore, this paper designs and develops a footballer’s motion and gesture recognition and intervention algorithm using a convolutional neural network (CNN). In this proposed algorithm, initially, texture features and HSV features of the footballer’s posture image are extracted and then a dual-channel CNN is constructed. Each characteristic is extracted separately, and the output of the dual-channel network is combined. Finally, the obtained results are passed from a fully connected CNN to estimate and construct the posture image of the footballer. This article performs experimental testing and comparative analysis on a wide range of data and also conducts ablation studies. The experimental work shows that the proposed algorithm achieves better performance results.

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