Infrared imaging a new non-invasive machine learning technology for animal husbandry

With economic growth, automobiles have become an irreplaceable means of transportation and travel. Tires are important parts of automobiles, and their wear causes a large number of traffic accidents. Therefore, predicting tire life has become one of the key factors determining vehicle safety. This paper presents a tire life prediction method based on image processing and machine learning. We first build an original image database as the initial sample. Since there are usually only a few sample image libraries in engineering practice, we propose a new image feature extraction and expression method that shows excellent performance for a small sample database. We extract the texture features of the tire image by using the gray-gradient co-occurrence matrix (GGCM) and the Gauss-Markov random field (GMRF), and classify the extracted features by using the K-nearest neighbor (KNN) classifier. We then conduct experiments and predict the wear life of automobile tires. The experimental results are estimated by using the mean average precision (MAP) and confusion matrix as evaluation criteria. Finally, we verify the effectiveness and accuracy of the proposed method for predicting tire life. The obtained results are expected to be used for real-time prediction of tire life, thereby reducing tire-related traffic accidents.

Download Full-text

Non-invasive cuff-less blood pressure estimation using a hybrid deep learning model

Optical and Quantum Electronics ◽

10.1007/s11082-020-02667-0 ◽

2021 ◽

Vol 53 (2) ◽

Author(s):

Sen Yang ◽

Yaping Zhang ◽

Siu-Yeung Cho ◽

Ricardo Correia ◽

Stephen P. Morgan

Keyword(s):

Machine Learning ◽

Blood Pressure ◽

Deep Learning ◽

Absolute Error ◽

Learning Model ◽

Physiological Measurement ◽

British Hypertension Society ◽

Non Invasive ◽

Blood Pressure Estimation ◽

Deep Learning Model

AbstractConventional blood pressure (BP) measurement methods have different drawbacks such as being invasive, cuff-based or requiring manual operations. There is significant interest in the development of non-invasive, cuff-less and continual BP measurement based on physiological measurement. However, in these methods, extracting features from signals is challenging in the presence of noise or signal distortion. When using machine learning, errors in feature extraction result in errors in BP estimation, therefore, this study explores the use of raw signals as a direct input to a deep learning model. To enable comparison with the traditional machine learning models which use features from the photoplethysmogram and electrocardiogram, a hybrid deep learning model that utilises both raw signals and physical characteristics (age, height, weight and gender) is developed. This hybrid model performs best in terms of both diastolic BP (DBP) and systolic BP (SBP) with the mean absolute error being 3.23 ± 4.75 mmHg and 4.43 ± 6.09 mmHg respectively. DBP and SBP meet the Grade A and Grade B performance requirements of the British Hypertension Society respectively.

Download Full-text

A NOVEL EXTENSIVE EX-VIVO OCT DATABASE FROM MURINE MODELS OF COLORECTAL CANCER

British Journal of Surgery ◽

10.1093/bjs/znab160.030 ◽

2021 ◽

Vol 108 (Supplement_3) ◽

Author(s):

J Bote ◽

J F Ortega-Morán ◽

C L Saratxaga ◽

B Pagador ◽

A Picón ◽

...

Keyword(s):

Colorectal Cancer ◽

Machine Learning ◽

Structural Information ◽

Ex Vivo ◽

Ground Truth ◽

Colon Polyps ◽

Learning Methods ◽

Non Invasive ◽

Machine Learning Methods ◽

In Situ Methods

Abstract INTRODUCTION New non-invasive technologies for improving early diagnosis of colorectal cancer (CRC) are demanded by clinicians. Optical Coherence Tomography (OCT) provides sub-surface structural information and offers diagnosis capabilities of colon polyps, further improved by machine learning methods. Databases of OCT images are necessary to facilitate algorithms development and testing. MATERIALS AND METHODS A database has been acquired from rat colonic samples with a Thorlabs OCT system with 930nm centre wavelength that provides 1.2KHz A-scan rate, 7μm axial resolution in air, 4μm lateral resolution, 1.7mm imaging depth in air, 6mm x 6mm FOV, and 107dB sensitivity. The colon from anaesthetised animals has been excised and samples have been extracted and preserved for ex-vivo analysis with the OCT equipment. RESULTS This database consists of OCT 3D volumes (C-scans) and 2D images (B-scans) of murine samples from: 1) healthy tissue, for ground-truth comparison (18 samples; 66 C-scans; 17,478 B-scans); 2) hyperplastic polyps, obtained from an induced colorectal hyperplastic murine model (47 samples; 153 C-scans; 42,450 B-scans); 3) neoplastic polyps (adenomatous and adenocarcinomatous), obtained from clinically validated Pirc F344/NTac-Apcam1137 rat model (232 samples; 564 C-scans; 158,557 B-scans); and 4) unknown tissue (polyp adjacent, presumably healthy) (98 samples; 157 C-scans; 42,070 B-scans). CONCLUSIONS A novel extensive ex-vivo OCT database of murine CRC model has been obtained and will be openly published for the research community. It can be used for classification/segmentation machine learning methods, for correlation between OCT features and histopathological structures, and for developing new non-invasive in-situ methods of diagnosis of colorectal cancer.

Download Full-text

Digital twins, artificial intelligence, and machine learning technology to identify a real personalized motion axis of the tibiotalar joint for robotics in total ankle arthroplasty

International Orthopaedics ◽

10.1007/s00264-021-05175-2 ◽

2021 ◽

Author(s):

Philippe Hernigou ◽

Romain Olejnik ◽

Adonis Safar ◽

Sagi Martinov ◽

Jacques Hernigou ◽

...

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Total Ankle Arthroplasty ◽

Learning Technology ◽

Tibiotalar Joint ◽

Ankle Arthroplasty ◽

Digital Twins

Download Full-text

Implementation Framework for a Blockchain-Based Federated Learning Model for Classification Problems

Symmetry ◽

10.3390/sym13071116 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1116

Author(s):

Zeba Mahmood ◽

Vacius Jusas

Keyword(s):

Machine Learning ◽

Learning Model ◽

Global Perspective ◽

Learning Technology ◽

Classification Problems ◽

Zero Knowledge ◽

Privacy And Security ◽

Implementation Framework ◽

The Past

This paper introduces a blockchain-based federated learning (FL) framework with incentives for participating nodes to enhance the accuracy of classification problems. Machine learning technology has been rapidly developed and changed from a global perspective for the past few years. The FL framework is based on the Ethereum blockchain and creates an autonomous ecosystem, where nodes compete to improve the accuracy of classification problems. With privacy being one of the biggest concerns, FL makes use of the blockchain-based approach to ensure privacy and security. Another important technology that underlies the FL framework is zero-knowledge proofs (ZKPs), which ensure that data uploaded to the network are accurate and private. Basically, ZKPs allow nodes to compete fairly by only submitting accurate models to the parameter server and get rewarded for that. We have conducted an analysis and found that ZKPs can help improve the accuracy of models submitted to the parameter server and facilitate the honest participation of all nodes in FL.

Download Full-text

Research on the expression of new visual intelligence system based on machine learning technology

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189835 ◽

2021 ◽

pp. 1-9

Author(s):

Zuoshan Li

Keyword(s):

Machine Learning ◽

Intelligent System ◽

Science And Technology ◽

Learning Technology ◽

3D Animation ◽

Motion Data ◽

Intelligence System ◽

New Science ◽

Continuous Progress ◽

Internet Of Things Technology

With the continuous progress of society, the level of science and technology of the country has made a leap forward development, the research energy of various industries on new science and technology continues to deepen, greatly promoting the promotion of science and technology. At the same time, with the increase in social pressure, more and more people pursue spiritual relaxation, and appropriate leisure and entertainment activities have gradually become a part of people’s life. Film plays an irreplaceable role in leisure and entertainment. Mainly from the background of the development of the film industry towards intelligent direction, and then use machine learning technology to study the application of film animation production and film virtual assets analysis and investigation. Based on the Internet of things technology, we also vigorously develop the ways and methods of visual expression of movies, and at the same time introduce new expression modes to promote the expression effect of the intelligent system. Finally, by comparing various algorithms in machine learning technology, the results of intelligent expression of random number forest algorithm in machine learning technology are more accurate. The system is also applied to 3D animation production to observe the measurement error of 3D motion data and facial expression data.

Download Full-text

Machine Learning and Radiomics Applications in Esophageal Cancers Using Non-Invasive Imaging Methods—A Critical Review of Literature

Cancers ◽

10.3390/cancers13102469 ◽

2021 ◽

Vol 13 (10) ◽

pp. 2469

Author(s):

Chen-Yi Xie ◽

Chun-Lap Pang ◽

Benjamin Chan ◽

Emily Yuen-Yuen Wong ◽

Qi Dou ◽

...

Keyword(s):

Machine Learning ◽

Quantitative Imaging ◽

Risk Groups ◽

Review Of Literature ◽

Primary Tumors ◽

Non Invasive ◽

Significant Difference ◽

Recent Developments ◽

Health Significance ◽

Imaging Markers

Esophageal cancer (EC) is of public health significance as one of the leading causes of cancer death worldwide. Accurate staging, treatment planning and prognostication in EC patients are of vital importance. Recent advances in machine learning (ML) techniques demonstrate their potential to provide novel quantitative imaging markers in medical imaging. Radiomics approaches that could quantify medical images into high-dimensional data have been shown to improve the imaging-based classification system in characterizing the heterogeneity of primary tumors and lymph nodes in EC patients. In this review, we aim to provide a comprehensive summary of the evidence of the most recent developments in ML application in imaging pertinent to EC patient care. According to the published results, ML models evaluating treatment response and lymph node metastasis achieve reliable predictions, ranging from acceptable to outstanding in their validation groups. Patients stratified by ML models in different risk groups have a significant or borderline significant difference in survival outcomes. Prospective large multi-center studies are suggested to improve the generalizability of ML techniques with standardized imaging protocols and harmonization between different centers.

Download Full-text

Analytical Approach for the Study of Teotihuacan Mural Paintings from the Techinantitla Complex

Minerals ◽

10.3390/min11050508 ◽

2021 ◽

Vol 11 (5) ◽

pp. 508

Author(s):

José Luis Ruvalcaba-Sil ◽

Luis Barba ◽

Edgar Casanova-González ◽

Alejandro Mitrani ◽

Margarita Muñoz ◽

...

Keyword(s):

Fiber Optics ◽

Infrared Imaging ◽

Blue Pigment ◽

Large Set ◽

Non Invasive ◽

Mural Paintings ◽

Interdisciplinary Research Project ◽

Manufacturing Techniques ◽

Materials Used ◽

The 1960S

Techinantitla building complex, in the Amanalco neighborhood of the ancient city of Teotihuacan, is famous for the iconography and quality of the mural paintings found in this site. A significant part of this heritage has been lost due to looting. In recent years, an interdisciplinary research project was developed to study the limited patrimony that was left. As part of this study, we first employed geophysical techniques to reconstruct the architectural pattern of the compound’s remaining walls, where other paintings may still be found. Then, we applied a non-invasive methodology to characterize a large set of fragments recovered in the 1980s and to gain information on their pigments and manufacturing techniques. This methodology included False Color Infrared Imaging, X-ray Fluorescence and Fiber-Optics Reflectance Spectroscopy, and led to the identification of hematite, calcite, malachite, azurite and an unidentified blue pigment. The results were compared with a previous study performed on a set of Techinantitla mural paintings looted in the 1960s. A broader comparison with contemporary mural paintings from other Teotihuacan complexes shows good agreement in the materials used. These results may suggest a standardization in the making of Teotihuacan mural painting during the Xolapan period (350 to 550 AD).

Download Full-text