Optimal multispectral sensor configurations through machine learning for cognitive agriculture

2021 ◽  
Vol 69 (4) ◽  
pp. 336-344
Author(s):  
Florian Becker ◽  
Andreas Backhaus ◽  
Felix Johrden ◽  
Merle Flitter
Keyword(s):  
Machine Learning ◽  
Cost Effective ◽  
Viable Alternative ◽  
Health State ◽  
Sensor Systems ◽  
Work Processes ◽  
Non Invasive ◽  
Classification Tasks ◽  
Farming Industry ◽  
Hyperspectral Sensor

Abstract Hyperspectral sensor systems play a key role in the automation of work processes in the farming industry. Non-invasive measurements of plants allow for an assessment of the vitality and health state and can also be used to classify weeds or infected parts of a plant. However, one major downside of hyperspectral cameras is that they are not very cost-effective. In this paper, we show, that for specific tasks, multispectral systems with only a fraction of the wavelength bands and costs of a hyperspectral system can lead to promising results for regression and classification tasks. We conclude that for the ongoing automation efforts in the context of cognitive agriculture reduced multispectral systems are a viable alternative.

scholarly journals Rapid identification of wood species using XRF and neural network machine learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Aaron N. Shugar ◽  
B. Lee Drake ◽  
Greg Kelley
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Wood Species ◽  
Cost Effective ◽  
Rapid Identification ◽  
Invasive Technique ◽  
X Ray ◽  
Non Invasive ◽  
Cost Effective Alternative

AbstractAn innovative approach for the rapid identification of wood species is presented. By combining X-ray fluorescence spectrometry with convolutional neural network machine learning, 48 different wood specimens were clearly differentiated and identified with a 99% accuracy. Wood species identification is imperative to assess illegally logged and transported lumber. Alternative options for identification can be time consuming and require some level of sampling. This non-invasive technique offers a viable, cost-effective alternative to rapidly and accurately identify timber in efforts to support environmental protection laws and regulations.

scholarly journals Non-invasive health prediction from visually observable features

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 918
Author(s):  
Fan Yi Khong ◽  
Tee Connie ◽  
Michael Kah Ong Goh ◽  
Li Pei Wong ◽  
Pin Shen Teh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cost Effective ◽  
Healthcare Services ◽  
Skin Surface ◽  
Health Condition ◽  
Machine Learning Techniques ◽  
Disease Spread ◽  
Computational Time ◽  
Learning Approaches ◽  
Non Invasive

Background: The unprecedented development of Artificial Intelligence has revolutionised the healthcare industry. In the next generation of healthcare systems, self-diagnosis will be pivotal to personalised healthcare services. During the COVID-19 pandemic, new screening and diagnostic approaches like mobile health are well-positioned to reduce disease spread and overcome geographical barriers. This paper presents a non-invasive screening approach to predict the health of a person from visually observable features using machine learning techniques. Images like face and skin surface of the patients are acquired using camera or mobile devices and analysed to derive clinical reasoning and prediction of the person’s health. Methods: In specific, a two-level classification approach is presented. The proposed hierarchical model chooses a class by training a binary classifier at the node of the hierarchy. Prediction is then made using a set of class-specific reduced feature set. Results: Testing accuracies of 86.87% and 76.84% are reported for the first and second-level classification. Empirical results demonstrate that the proposed approach yields favourable prediction results while greatly reduces the computational time. Conclusions: The study suggests that it is possible to predict the health condition of a person based on his/her face appearance using cost-effective machine learning approaches.

scholarly journals Age and life expectancy clocks based on machine learning analysis of mouse frailty

2019 ◽  
Author(s):  
Michael B Schultz ◽  
Alice E Kane ◽  
Sarah J Mitchell ◽  
Michael R MacArthur ◽  
Elisa Warner ◽  
...  
Keyword(s):  
Machine Learning ◽  
Life Expectancy ◽  
Strong Predictor ◽  
Cost Effective ◽  
Chronological Age ◽  
Random Forest Regression ◽  
Non Invasive ◽  
Longevity Genes ◽  
Clinical Models ◽  
Learning Analysis

ABSTRACTThe identification of genes and interventions that slow or reverse aging is hampered by the lack of non-invasive metrics that can predict life expectancy of pre-clinical models. Frailty Indices (FIs) in mice are composite measures of health that are cost-effective and non-invasive, but whether they can accurately predict health and lifespan is not known. Here, mouse FIs were scored longitudinally until death and machine learning was employed to develop two clocks. A random forest regression was trained on FI components for chronological age to generate the FRIGHT (Frailty Inferred Geriatric Health Timeline) clock, a strong predictor of chronological age. A second model was trained on remaining lifespan to generate the AFRAID (Analysis of Frailty and Death) clock, which accurately predicts life expectancy and the efficacy of a lifespan-extending intervention up to a year in advance. Adoption of these clocks should accelerate the identification of novel longevity genes and aging interventions.

scholarly journals Age and life expectancy clocks based on machine learning analysis of mouse frailty

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Michael B. Schultz ◽  
Alice E. Kane ◽  
Sarah J. Mitchell ◽  
Michael R. MacArthur ◽  
Elisa Warner ◽  
...  
Keyword(s):  
Machine Learning ◽  
Life Expectancy ◽  
Strong Predictor ◽  
Cost Effective ◽  
Chronological Age ◽  
Random Forest Regression ◽  
Non Invasive ◽  
Longevity Genes ◽  
Clinical Models ◽  
Learning Analysis

Abstract The identification of genes and interventions that slow or reverse aging is hampered by the lack of non-invasive metrics that can predict the life expectancy of pre-clinical models. Frailty Indices (FIs) in mice are composite measures of health that are cost-effective and non-invasive, but whether they can accurately predict health and lifespan is not known. Here, mouse FIs are scored longitudinally until death and machine learning is employed to develop two clocks. A random forest regression is trained on FI components for chronological age to generate the FRIGHT (Frailty Inferred Geriatric Health Timeline) clock, a strong predictor of chronological age. A second model is trained on remaining lifespan to generate the AFRAID (Analysis of Frailty and Death) clock, which accurately predicts life expectancy and the efficacy of a lifespan-extending intervention up to a year in advance. Adoption of these clocks should accelerate the identification of longevity genes and aging interventions.

Evaluation of Diagnostic Performance of Machine Learning Algorithms to Classify the Fetal Heart Rate Baseline from Cardiotocograph

2022 ◽  
Vol 9 (3) ◽  
pp. 0-0
Keyword(s):  
Machine Learning ◽  
Fetal Heart ◽  
Learning Algorithms ◽  
Cost Effective ◽  
Machine Learning Algorithms ◽  
Invasive Technique ◽  
Visual Estimation ◽  
Altman Plot ◽  
Non Invasive

Cardiotocography (CTG) is the widely used cost-effective, non-invasive technique to monitor the fetal heart and mother’s uterine contraction pressure to assess the wellbeing of the fetus. The most important parameters of fetal heart is the baseline upon which the other parameters viz. acceleration, deceleration and variability depend. Accurate classification of the baseline into either normal, bradycardia or tachycardia is thus important to assess the fetal-health. Since visual estimation has its limitations, the authors use various Machine Learning Algorithms to classify the baseline. 110 CTG traces from CTU-UHB dataset, were divided into three subsets using stratified sampling to ensure that the sample is the accurate depiction of the population. The results were analyzed using various statistical methods and compared with the visual estimation by three obstetricians. FURIA provided greatest accuracy of 98.11%. From the analysis of Bland-Altman Plot FURIA was also found to have best agreement with physicians’ estimation.

A novel use of contrast-enhanced ultrasound in uterine artery embolization

2020 ◽  
Vol 4 ◽  
pp. 8
Author(s):  
Jemianne Bautista Jia ◽  
Eric Mastrolonardo ◽  
Mateen Soleman ◽  
Ilya Lekht
Keyword(s):  
Uterine Artery Embolization ◽  
Uterine Artery ◽  
Imaging Modality ◽  
Cost Effective ◽  
Contrast Enhanced Ultrasound ◽  
Artery Embolization ◽  
Non Invasive ◽  
Contrast Enhanced

Contrast-enhanced ultrasound (CEUS) is a cost-effective, quick, and non-invasive imaging modality that has yet to be incorporated in uterine artery embolization (UAE). We present two cases that demonstrate the utility of CEUS in UAE for the identification of uterine-ovarian collaterals which otherwise can result in ineffective fibroid treatment and non-target embolization.

Application of Machine Learning Approaches for the Design and Study of Anticancer Drugs

2019 ◽  
Vol 20 (5) ◽  
pp. 488-500 ◽  
Author(s):  
Yan Hu ◽  
Yi Lu ◽  
Shuo Wang ◽  
Mengying Zhang ◽  
Xiaosheng Qu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Design ◽  
Anticancer Drugs ◽  
Nearest Neighbor ◽  
Cost Effective ◽  
Support Vector ◽  
Learning Approaches ◽  
K Nearest Neighbor ◽  
Activity Prediction ◽  
Linear Discriminant

Background: Globally the number of cancer patients and deaths are continuing to increase yearly, and cancer has, therefore, become one of the world&#039;s highest causes of morbidity and mortality. In recent years, the study of anticancer drugs has become one of the most popular medical topics. </P><P> Objective: In this review, in order to study the application of machine learning in predicting anticancer drugs activity, some machine learning approaches such as Linear Discriminant Analysis (LDA), Principal components analysis (PCA), Support Vector Machine (SVM), Random forest (RF), k-Nearest Neighbor (kNN), and Naïve Bayes (NB) were selected, and the examples of their applications in anticancer drugs design are listed. </P><P> Results: Machine learning contributes a lot to anticancer drugs design and helps researchers by saving time and is cost effective. However, it can only be an assisting tool for drug design. </P><P> Conclusion: This paper introduces the application of machine learning approaches in anticancer drug design. Many examples of success in identification and prediction in the area of anticancer drugs activity prediction are discussed, and the anticancer drugs research is still in active progress. Moreover, the merits of some web servers related to anticancer drugs are mentioned.

Review of Various Tasks Performed in the Preprocessing Phase of a Diabetic Retinopathy Diagnosis System

2020 ◽  
Vol 16 (4) ◽  
pp. 397-426
Author(s):  
Muhammad Nadeem Ashraf ◽  
Muhammad Hussain ◽  
Zulfiqar Habib
Keyword(s):  
Diabetic Retinopathy ◽  
Blood Vessels ◽  
Early Stage ◽  
Spherical Shape ◽  
Cost Effective ◽  
Lesion Detection ◽  
Diabetic Patients ◽  
Cad System ◽  
Non Invasive ◽  
Optic Discs

Diabetic Retinopathy (DR) is a major cause of blindness in diabetic patients. The increasing population of diabetic patients and difficulty to diagnose it at an early stage are limiting the screening capabilities of manual diagnosis by ophthalmologists. Color fundus images are widely used to detect DR lesions due to their comfortable, cost-effective and non-invasive acquisition procedure. Computer Aided Diagnosis (CAD) of DR based on these images can assist ophthalmologists and help in saving many sight years of diabetic patients. In a CAD system, preprocessing is a crucial phase, which significantly affects its performance. Commonly used preprocessing operations are the enhancement of poor contrast, balancing the illumination imbalance due to the spherical shape of a retina, noise reduction, image resizing to support multi-resolution, color normalization, extraction of a field of view (FOV), etc. Also, the presence of blood vessels and optic discs makes the lesion detection more challenging because these two artifacts exhibit specific attributes, which are similar to those of DR lesions. Preprocessing operations can be broadly divided into three categories: 1) fixing the native defects, 2) segmentation of blood vessels, and 3) localization and segmentation of optic discs. This paper presents a review of the state-of-the-art preprocessing techniques related to three categories of operations, highlighting their significant aspects and limitations. The survey is concluded with the most effective preprocessing methods, which have been shown to improve the accuracy and efficiency of the CAD systems.

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