scholarly journals DiversityScanner: Robotic discovery of small invertebrates with machine learning methods

2021 ◽  
Author(s):  
Lorenz Wuehrl ◽  
Christian Pylatiuk ◽  
Matthias Giersch ◽  
Florian Lapp ◽  
Thomas von Rintelen ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Dna Barcoding ◽  
Molecular Techniques ◽  
The Body ◽  
Body Volume ◽  
Mixed Sample ◽  
Invertebrate Diversity ◽  
Invertebrate Biodiversity ◽  
Large Numbers

Invertebrate biodiversity remains poorly explored although it comprises much of the terrestrial animal biomass, more than 90% of the species-level diversity, supplies many ecosystem services. Increasing anthropogenic threads also require regular monitoring of invertebrate communities. The main obstacle is specimen- and species-rich samples consisting of thousands of small specimens. Traditional sorting techniques require manual handling based on morphology and are too slow and labor intensive. Molecular techniques based on metabarcoding struggle with obtaining reliable abundance information. We here present a fully automated sorting robot for small specimens that are detected in the mixed sample using a convolutional neural network. Each specimen is then moved from the mixed sample to a well of a 96-well microplate in preparation for DNA barcoding. Prior to movement, the specimen is being photographed and assigned to 14 particularly common "classes" of insects in Malaise trap samples. The average assignment precision for the classes is 91.4 % (75-100 %) based on a preliminary neural network that is expected to improve further as more images are used for training. In order to obtain biomass information, the specimen images are also used to measure the specimen length and estimate the body volume. We outline how the "DiversityScanner" robot can be a key component for tackling and monitoring invertebrate diversity by generating large numbers of images that become training sets for species-, genus-, or family level convolutional neural networks, once the imaged specimens are classified with DNA barcodes. The robot also allows for taxon-specific subsampling of large invertebrate samples. We conclude that the combination of automation, machine learning, and DNA barcoding has the potential to tackle invertebrate diversity at an unprecedented scale.

scholarly journals A Machine Learning Technique to Analyze Depressive Disorders

2021 ◽  
Author(s):  
Dixita Mali ◽  
Kritika Kumawat ◽  
Gaurav Kumawat ◽  
Prasun Chakrabarti ◽  
Sandeep Poddar ◽  
...  
Keyword(s):  
Neural Network ◽  
Mental Health ◽  
Machine Learning ◽  
Depressive Disorders ◽  
Random Trees ◽  
Machine Learning Technique ◽  
Machine Learning Classifiers ◽  
Large Numbers ◽  
Learning Technique ◽  
The Impact

Abstract Depression is an ordinary mental health care problem and the usual cause of disability worldwide. The main purpose of this research was to determine that how depression affects the life of an individual. It is a leading cause of morbidity and death. Over the last 50–60 years, large numbers of studies published various aspects including the impact of depression. The main purpose of this research is to determine whether the person is suffering from depression or not. The dataset of Depression has been taken from the Kaggle website. Guided Machine Learning classifiers have helped in the highest accuracy of a dataset. Classifiers like XGBoost Tree, Random Trees, Neural Network, SVM, Random Forest, C5.0, and Bay Net. From the result, it is evident that the C5.0 classifier is giving the highest accuracy with 83.94 % and for each classifier, the result is derived based without pre-processing.

scholarly journals Developing a Humanoid Robot Platform

2018 ◽  
Vol 8 (03) ◽  
Author(s):  
Nuralem Abizov ◽  
Jia Yuan Huang ◽  
Fei Gao
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Humanoid Robot ◽  
The Body ◽  
Machine Learning Algorithms ◽  
Current Position ◽  
The Neural Network ◽  
Fully Connected ◽  
And Robotics ◽  
Robot Platform

This paper is focused on developing a platform that helps researchers to create verify and implement their machine learning algorithms to a humanoid robot in real environment. The presented platform is durable, easy to fix, upgrade, fast to assemble and cheap. Also, using this platform we present an approach that solves a humanoid balancing problem, which uses only fully connected neural network as a basic idea for real time balancing. The method consists of 3 main conditions: 1) using different types of sensors detect the current position of the body and generate the input information for the neural network, 2) using fully connected neural network produce the correct output, 3) using servomotors make movements that will change the current position to the new one. During field test the humanoid robot can balance on the moving platform that tilts up to 10 degrees to any direction. Finally, we have shown that using our platform we can do research and compare different neural networks in similar conditions which can be important for the researchers to do analyses in machine learning and robotics

scholarly journals THYROID DETECTION USING MACHINE LEARNING

2021 ◽  
Vol 5 (9) ◽  
Author(s):  
Chandan R ◽  
Chetan Vasan ◽  
Chethan MS ◽  
Devikarani H S
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Blood Test ◽  
The Body ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Disease Prediction ◽  
Thyroid Disorders ◽  
Nearest Neighbours ◽  
Web App

The Thyroid gland is a vascular gland and one of the most important organs of a human body. This gland secretes two hormones which help in controlling the metabolism of the body. The two types of Thyroid disorders are Hyperthyroidism and Hypothyroidism. When this disorder occurs in the body, they release certain type of hormones into the body which imbalances the body’s metabolism. Thyroid related Blood test is used to detect this disease but it is often blurred and noise will be present. Data cleansing methods were used to make the data primitive enough for the analytics to show the risk of patients getting this disease. Machine Learning plays a very deciding role in the disease prediction. Machine Learning algorithms, SVM - support vector machine, decision tree, logistic regression, KNN - K-nearest neighbours, ANNArtificial Neural Network are used to predict the patient’s risk of getting thyroid disease. Web app is created to get data from users to predict the type of disease.

Wheelchair Control Based on Facial Gesture Recognition

2019 ◽  
Vol 12 (2) ◽  
pp. 104-122 ◽  
Author(s):  
J. Emmanuel Vázquez ◽  
Manuel Martin-Ortiz ◽  
Ivan Olmos-Pineda ◽  
Arturo Olvera-Lopez
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
User Interface ◽  
Gesture Recognition ◽  
The Body ◽  
Experimental Results ◽  
Natural User Interface ◽  
The Face

In this article, an approach for controlling a wheelchair using gestures from the user's face is presented, particularly some commands for the basic control operations required for driving a wheelchair are recognized. In order to recognize the face gestures an Artificial Neural Network which is trained since it is one of the most successful classifiers in Pattern Recognition. In particular, the authors' proposed method is useful for controlling a wheelchair when the user has restricted (or zero) mobility in some parts of the body such as: legs, arms or hands. According to their experimental results, the proposed approach provides a successful tool for controlling a wheelchair through a Natural User Interface based on machine learning.

scholarly journals An Overview Studying of Deep Learning

2020 ◽  
pp. 394-398
Author(s):  
Mya Sandar Kyin ◽  
Zaw Lin Oo ◽  
Khin Mar Cho
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Computational Models ◽  
Unstructured Data ◽  
Levels Of Abstraction ◽  
Large Numbers ◽  
Multiple Levels ◽  
Multiple Processing

Deep learning is a subfield of machine learning however both drop under the broad category of artificial intelligence. Deep learning is what powers the most human-like artificial intelligence that consents computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. Deep learning is making major advances in solving problems hence categorized in wider section of artificial intelligence. The main advantage of Deep Learning is to create an artificial neural network that can learn and make intelligent decisions on its own and to process large numbers of features makes deep learning very powerful when dealing with unstructured data.

scholarly journals An Assessment of Gpu and Cpu based Convolutional Neural Network for Classification of White Blood Cells

2020 ◽  
Vol 9 (7) ◽  
pp. 554-559
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Blood Cell ◽  
Blood Cells ◽  
White Blood Cells ◽  
Image Data ◽  
The Body ◽  
Machine Learning Techniques ◽  
Vast Number

White blood cell (Leukocytes) is made up of bone marrow located in the blood and lymph tissue. They are portion of the human body’s immune system, thereby helping the body system to fight against infection and other related diseases. The number of leukocytes in the blood is usually part of a complete blood cell (CBC) test, which may be used to check for conditions such as infection, inflammation, allergies, and leukemia. Automation of variance count of leukocytes offers valuable information to medical pathologist to diagnose and treat of many blood based diseases. Early characterization and classification of blood sample is a major lacuna in the medical field, giving rise to lots of challenges for pathologist to adequately predict blood based disease. Several successful efforts have been made to address the aforementioned challenges with the use of machine learning generally and Convolution Neural Network in particular. However the processor configuration which can result in real time, and accurate classification of the high dimensional pattern is imminent, and a vast number of researchers are not explicit on the system configuration used to obtain the result in their report, which is the crux of this research. In this research,12,500 augment images of blood cells was obtained from the Kaggle Repository online. The leukocytes are contained in the blood smear image and categorized into five major types of their types: Neutrophil, Eosinophil, Basophil, Lymphocyte and Monocyte. The color, geometric and texture features are used by the pathologists to differentiate the leukocytes. The Simulation was done using python programing language and python libraries including Keras, pandas, sklearn, numpy, scipy and matplot for potting of graphs of results. The simulation was done on both CPU and GPU processor to compare the performance of the processors on CNNs based classification of the data. While CPU has faster clock speed GPU has more cores. Hence the evaluation metrics used which are precision, specificity, sensitivity, training accuracy and validation accuracy revealed that GPU processor outperforms CPU in terms of the stated metrics of comparison. Therefore a high configuration processor (GPU), which handles graphics better is recommended for processing image data that involves the use of machine learning techniques

scholarly journals Revisiting the role of synaptic plasticity and network dynamics for fast learning in spiking neural networks

2021 ◽  
Author(s):  
Anand Subramoney ◽  
Guillaume Bellec ◽  
Franz Scherr ◽  
Robert Legenstein ◽  
Wolfgang Maass
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Synaptic Plasticity ◽  
Learning Process ◽  
The Body ◽  
Network State ◽  
Fast Learning ◽  
Morris Water Maze Task ◽  
The Brain

AbstractSpike-based neural network models have so far not been able to reproduce the capability of the brain to learn from very few, often even from just a single example. We show that this deficiency of models disappears if one allows synaptic weights to store priors and other information that optimize the learning process, while using the network state to quickly absorb information from new examples. For that, it suffices to include biologically realistic neurons with spike frequency adaptation in the neural network model, and to optimize the learning process through meta-learning. We demonstrate this on a variety of tasks, including fast learning and deletion of attractors, adaptation of motor control to changes in the body, and solving the Morris water maze task – a paradigm for fast learning of navigation to a new goal.Significance StatementIt has often been conjectured that STDP or other rules for synaptic plasticity can only explain some of the learning capabilities of brains. In particular, learning a new task from few trials is likely to engage additional mechanisms. Results from machine learning show that artificial neural networks can learn from few trials by storing information about them in their network state, rather than encoding them in synaptic weights. But these machine learning methods require neural networks with biologically unrealistic LSTM (Long Short Term Memory) units. We show that biologically quite realistic models for neural networks of the brain can exhibit similar capabilities. In particular, these networks are able to store priors that enable learning from very few examples.

Analysis of the Anterior Secretory Cells of Onchidohs Muricata (Nudibranchia)

1981 ◽  
Vol 39 ◽  
pp. 614-615
Author(s):  
Roy Skidmore
Keyword(s):  
Endoplasmic Reticulum ◽  
Secretory Granules ◽  
Golgi Body ◽  
The Body ◽  
Secretory Cells ◽  
Secretory Vesicles ◽  
High Magnification ◽  
Large Numbers ◽  
Membrane Bound ◽  
Sole Of The Foot

The long-necked secretory cells in Onchidoris muricata are distributed in the anterior sole of the foot. These cells are interspersed among ciliated columnar and conical cells as well as short-necked secretory gland cells. The long-necked cells contribute a significant amount of mucoid materials to the slime on which the nudibranch travels. The body of these cells is found in the subepidermal tissues. A long process extends across the basal lamina and in between cells of the epidermis to the surface of the foot. The secretory granules travel along the process and their contents are expelled by exocytosis at the foot surface.The contents of the cell body include the nucleus, some endoplasmic reticulum, and an extensive Golgi body with large numbers of secretory vesicles (Fig. 1). The secretory vesicles are membrane bound and contain a fibrillar matrix. At high magnification the similarity of the contents in the Golgi saccules and the secretory vesicles becomes apparent (Fig. 2).

Deadlift Recognition and Application based on Multiple Modalities using Recurrent Neural Network

2020 ◽  
Vol 2020 (17) ◽  
pp. 2-1-2-6
Author(s):  
Shih-Wei Sun ◽  
Ting-Chen Mou ◽  
Pao-Chi Chang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Network Structure ◽  
Inertial Sensors ◽  
Body Movement ◽  
The Body ◽  
Body Parts ◽  
Multimodal Sensing ◽  
Multiple Devices ◽  
Neural Network Structure

To improve the workout efficiency and to provide the body movement suggestions to users in a “smart gym” environment, we propose to use a depth camera for capturing a user’s body parts and mount multiple inertial sensors on the body parts of a user to generate deadlift behavior models generated by a recurrent neural network structure. The contribution of this paper is trifold: 1) The multimodal sensing signals obtained from multiple devices are fused for generating the deadlift behavior classifiers, 2) the recurrent neural network structure can analyze the information from the synchronized skeletal and inertial sensing data, and 3) a Vaplab dataset is generated for evaluating the deadlift behaviors recognizing capability in the proposed method.

Adsorption Isotherm Predictions for Multiple Molecules in MOFs Using the Same Deep Learning Model

2019 ◽  
Author(s):  
Ryther Anderson ◽  
Achay Biong ◽  
Diego Gómez-Gualdrón
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Molecular Simulation ◽  
Large Scale ◽  
Learning Model ◽  
Operating Conditions ◽  
Small Subset ◽  
Screening Methods ◽  
Large Set ◽  
Metal Organic

<div>Tailoring the structure and chemistry of metal-organic frameworks (MOFs) enables the manipulation of their adsorption properties to suit specific energy and environmental applications. As there are millions of possible MOFs (with tens of thousands already synthesized), molecular simulation, such as grand canonical Monte Carlo (GCMC), has frequently been used to rapidly evaluate the adsorption performance of a large set of MOFs. This allows subsequent experiments to focus only on a small subset of the most promising MOFs. In many instances, however, even molecular simulation becomes prohibitively time consuming, underscoring the need for alternative screening methods, such as machine learning, to precede molecular simulation efforts. In this study, as a proof of concept, we trained a neural network as the first example of a machine learning model capable of predicting full adsorption isotherms of different molecules not included in the training of the model. To achieve this, we trained our neural network only on alchemical species, represented only by their geometry and force field parameters, and used this neural network to predict the loadings of real adsorbates. We focused on predicting room temperature adsorption of small (one- and two-atom) molecules relevant to chemical separations. Namely, argon, krypton, xenon, methane, ethane, and nitrogen. However, we also observed surprisingly promising predictions for more complex molecules, whose properties are outside the range spanned by the alchemical adsorbates. Prediction accuracies suitable for large-scale screening were achieved using simple MOF (e.g. geometric properties and chemical moieties), and adsorbate (e.g. forcefield parameters and geometry) descriptors. Our results illustrate a new philosophy of training that opens the path towards development of machine learning models that can predict the adsorption loading of any new adsorbate at any new operating conditions in any new MOF.</div>

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