scholarly journals Resourceful Investigation of Malnutrition with Machine Learning Using CNN Procedure

2021 ◽  
pp. 12-19
Author(s):  
Prasad Dhore ◽  
Dr. Yogesh Kumar Sharma ◽  
Dr. Prashant Kumbharkar
Keyword(s):  
Machine Learning ◽  
The Body ◽  
Proper Function ◽  
Digital Form ◽  
Color Analysis ◽  
Pixel Intensity ◽  
Interchange Format ◽  
Human Eyes ◽  
Image Format ◽  
Graphic Interchange Format

This System used to refer to any condition in which the body does not obtain enough nutrients for proper function in this system Image processing is a technique to convert an image into digital form and perform some action on it, in order to get an enhanced image or to citation some useful information from it. There are some types of image that is Tagged Image Format, Joint Photographic Group, Graphic Interchange Format, Portable Network Graphics, Row Image File. Computer can easily classify more than 16 million color where the eye capability has limitation while identifying color and also same people face the problem such as colors carelessness. So the execution nail color Analysis through computer is a higher procedure as compared with human eyes. Human eyes have limited resolution, finding deviation in nearby pixel intensity is not possible for human eyes, but computer vision can detect every pixel appropriately. So, in this system we used the Convolutional Neural Networks (CNN) algorithm & carried out the result. this system is very helpful for the patient, to find out the prediction or detection of diseases without doctor.

Predicting the citation and impact factor of terms for scientific publications using machine learning algorithms

2020 ◽  
Author(s):  
Aleksey Klokov ◽  
Evgenii Slobodyuk ◽  
Michael Charnine
Keyword(s):  
Machine Learning ◽  
Semantic Processing ◽  
The Body ◽  
Machine Learning Algorithms ◽  
Scientific Publications ◽  
Text Data ◽  
Semantic Relationships ◽  
Subject Areas ◽  
The Subject ◽  
Scientific Environment

The object of the research when writing the work was the body of text data collected together with the scientific advisor and the algorithms for processing the natural language of analysis. The stream of hypotheses has been tested against computer science scientific publications through a series of simulation experiments described in this dissertation. The subject of the research is algorithms and the results of the algorithms, aimed at predicting promising topics and terms that appear in the course of time in the scientific environment. The result of this work is a set of machine learning models, with the help of which experiments were carried out to identify promising terms and semantic relationships in the text corpus. The resulting models can be used for semantic processing and analysis of other subject areas.

scholarly journals Using Different Combinations of Body-Mounted IMU Sensors to Estimate Speed of Horses—A Machine Learning Approach

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 798
Author(s):  
Hamed Darbandi ◽  
Filipe Serra Bragança ◽  
Berend Jan van der Zwaag ◽  
John Voskamp ◽  
Annik Imogen Gmel ◽  
...  
Keyword(s):  
Machine Learning ◽  
The Body ◽  
Biomechanical Analysis ◽  
Estimation Accuracy ◽  
Speed Estimation ◽  
Motion Patterns ◽  
Positioning Systems ◽  
Machine Learning Approach ◽  
Global Positioning ◽  
Measurement Units

Speed is an essential parameter in biomechanical analysis and general locomotion research. It is possible to estimate the speed using global positioning systems (GPS) or inertial measurement units (IMUs). However, GPS requires a consistent signal connection to satellites, and errors accumulate during IMU signals integration. In an attempt to overcome these issues, we have investigated the possibility of estimating the horse speed by developing machine learning (ML) models using the signals from seven body-mounted IMUs. Since motion patterns extracted from IMU signals are different between breeds and gaits, we trained the models based on data from 40 Icelandic and Franches-Montagnes horses during walk, trot, tölt, pace, and canter. In addition, we studied the estimation accuracy between IMU locations on the body (sacrum, withers, head, and limbs). The models were evaluated per gait and were compared between ML algorithms and IMU location. The model yielded the highest estimation accuracy of speed (RMSE = 0.25 m/s) within equine and most of human speed estimation literature. In conclusion, highly accurate horse speed estimation models, independent of IMU(s) location on-body and gait, were developed using ML.

ROLE OF DEEPANA-PACHANA IN PANCHAKARMA: A REVIEW

2021 ◽  
pp. 1-4
Author(s):  
Kritika Thakur ◽  
Sangeeta H Toshikahne ◽  
Ravi Sahu ◽  
Rinal Patel
Keyword(s):  
Healthy Food ◽  
The Body ◽  
Proper Function ◽  
Root Cause ◽  
All Diseases

Ayurveda explains that our health is dependent not only on what we eat, but also on how we digest. The most vitamin-fortied diet or healthy food is considered unhealthy if our body isn't able to properly digest, absorb, and assimilate the food. Acc. to ayurvedic classics “Sarve Roga Api Mandagni” means all diseases in the body are caused due to mandagni (due to improper functioning of agni). In other words, the proper function of agni is important for strong vyadhikshamatva shakti (immunity) or bala Agni plays an important role in digestion of food. Mandagni leads to Ama formation which is the root cause of Roga. Utpatti. There are so many diseases originating because of Ama which are grouped under Ampradoshaja Vikara. Deepana and Pachana are considered under the heading of Shamanaushadhis. It is the rst line of treatment selected in order to overcome Ampradoshaja Vikaras. Thus, the treatment is mainly targeted towards the Agni thereby correcting the Ama. Deepana and Pachana are aimed in correcting the Agni which further helps in the proper digestion of Sneha and proper mobilization of dosha from shakha to kostha

scholarly journals Channel-independent recreation of artefactual signals in chronically recorded local field potentials using machine learning

2022 ◽  
Vol 9 (1) ◽  
Author(s):  
Marcos Fabietti ◽  
Mufti Mahmud ◽  
Ahmad Lotfi
Keyword(s):  
Machine Learning ◽  
Open Access ◽  
Short Term Memory ◽  
The Body ◽  
Data Sets ◽  
Additional Information ◽  
Machine Learning Model ◽  
Signal Characteristics ◽  
Wide Range ◽  
Memory Network

AbstractAcquisition of neuronal signals involves a wide range of devices with specific electrical properties. Combined with other physiological sources within the body, the signals sensed by the devices are often distorted. Sometimes these distortions are visually identifiable, other times, they overlay with the signal characteristics making them very difficult to detect. To remove these distortions, the recordings are visually inspected and manually processed. However, this manual annotation process is time-consuming and automatic computational methods are needed to identify and remove these artefacts. Most of the existing artefact removal approaches rely on additional information from other recorded channels and fail when global artefacts are present or the affected channels constitute the majority of the recording system. Addressing this issue, this paper reports a novel channel-independent machine learning model to accurately identify and replace the artefactual segments present in the signals. Discarding these artifactual segments by the existing approaches causes discontinuities in the reproduced signals which may introduce errors in subsequent analyses. To avoid this, the proposed method predicts multiple values of the artefactual region using long–short term memory network to recreate the temporal and spectral properties of the recorded signal. The method has been tested on two open-access data sets and incorporated into the open-access SANTIA (SigMate Advanced: a Novel Tool for Identification of Artefacts in Neuronal Signals) toolbox for community use.

scholarly journals System to assist in the Diagnosis of Diabetes using Ontology and Machine Learning

2019 ◽  
Vol 8 (4) ◽  
pp. 9971-9975
Keyword(s):  
Machine Learning ◽  
Public Health Problem ◽  
Medical Data ◽  
The Body ◽  
Machine Learning Method ◽  
Learning Method ◽  
Automatic Learning ◽  
Early Diabetes ◽  
Data Set ◽  
Medical Tests

Diabetes mellitus has become a public health problem in both developed and developing countries. If it is not treated early, diabetes-related complications in many vital organs of the body can become fatal. Its early detection is very important for early treatment that can prevent the disease from progressing to such complications. This article focuses on designing a system to assist in the diagnosis of diabetes disease based on medical ontology and automatic learning. The proposed method uses automatic learning algorithms as a classifier for the diagnosis of diabetes based on a medical data set. The ontology suggests a pre-processing of a coherent, consistent, interoperable and shareable knowledge basis of data and the machine learning method focuses on classification based on symptoms and medical tests. Based on the experimental results, DDAS not only offers better performance in predicting and diagnosing diabetes in individuals, but also has better accuracy in recommending useful treatment to patients.

scholarly journals Discovering Business Processes from Email Logs using fastText and Process Mining

2020 ◽  
Author(s):  
Yaghoub rashnavadi ◽  
Sina Behzadifard ◽  
Reza Farzadnia ◽  
sina zamani
Keyword(s):  
Machine Learning ◽  
Business Processes ◽  
Oil And Gas ◽  
Process Mining ◽  
The Body ◽  
Process Models ◽  
Supervised Machine Learning ◽  
Implicit Information ◽  
Oil And Gas Sector

<p>Communication has never been more accessible than today. With the help of Instant messengers and Email Services, millions of people can transfer information with ease, and this trend has affected organizations as well. There are billions of organizational emails sent or received daily, and their main goal is to facilitate the daily operation of organizations. Behind this vast corpus of human-generated content, there is much implicit information that can be mined and used to improve or optimize the organizations’ operations. Business processes are one of those implicit knowledge areas that can be discovered from Email logs of an Organization, as most of the communications are followed inside Emails. The purpose of this research is to propose an approach to discover the process models in the Email log. In this approach, we combine two tools, supervised machine learning and process mining. With the help of supervised machine learning, fastText classifier, we classify the body text of emails to the activity-related. Then the generated log will be mined with process mining techniques to find process models. We illustrate the approach with a case study company from the oil and gas sector.</p>

scholarly journals Antibody Complementarity Determining Region Design Using High-Capacity Machine Learning

2019 ◽  
Author(s):  
Ge Liu ◽  
Haoyang Zeng ◽  
Jonas Mueller ◽  
Brandon Carter ◽  
Ziheng Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Structural Information ◽  
High Capacity ◽  
Training Data ◽  
Proper Function ◽  
Integrative Approach ◽  
Machine Learning Method ◽  
Learning Method ◽  
Target Specificity

AbstractThe precise targeting of antibodies and other protein therapeutics is required for their proper function and the elimination of deleterious off-target effects. Often the molecular structure of a therapeutic target is unknown and randomized methods are used to design antibodies without a model that relates antibody sequence to desired properties. Here we present a machine learning method that can design human Immunoglobulin G (IgG) antibodies with target affinities that are superior to candidates from phage display panning experiments within a limited design budget. We also demonstrate that machine learning can improve target-specificity by the modular composition of models from different experimental campaigns, enabling a new integrative approach to improving target specificity. Our results suggest a new path for the discovery of therapeutic molecules by demonstrating that predictive and differentiable models of antibody binding can be learned from high-throughput experimental data without the need for target structural data.SignificanceAntibody based therapeutics must meet both affinity and specificity metrics, and existing in vitro methods for meeting these metrics are based upon randomization and empirical testing. We demonstrate that with sufficient target-specific training data machine learning can suggest novel antibody variable domain sequences that are superior to those observed during training. Our machine learning method does not require any target structural information. We further show that data from disparate antibody campaigns can be combined by machine learning to improve antibody specificity.

scholarly journals Detecting Controversial Articles on Citizen Journalism

2018 ◽  
Vol 11 (1) ◽  
pp. 34
Author(s):  
Alfan Farizki Wicaksono ◽  
Sharon Raissa Herdiyana ◽  
Mirna Adriani
Keyword(s):  
Machine Learning ◽  
Structural Information ◽  
Structural Features ◽  
The Body ◽  
Supervised Machine Learning ◽  
Citizen Journalism ◽  
Learning Approach ◽  
Daily News ◽  
Machine Learning Approach ◽  
Controversial Topic

Someone's understanding and stance on a particular controversial topic can be influenced by daily news or articles he consume everyday. Unfortunately, readers usually do not realize that they are reading controversial articles. In this paper, we address the problem of automatically detecting controversial article from citizen journalism media. To solve the problem, we employ a supervised machine learning approach with several hand-crafted features that exploits linguistic information, meta-data of an article, structural information in the commentary section, and sentiment expressed inside the body of an article. The experimental results shows that our proposed method manages to perform the addressed task effectively. The best performance so far is achieved when we use all proposed feature with Logistic Regression as our model (82.89\% in terms of accuracy). Moreover, we found that information from commentary section (structural features) contributes most to the classification task.

scholarly journals A study of real-world micrograph data quality and machine learning model robustness

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Xiaoting Zhong ◽  
Brian Gallagher ◽  
Keenan Eves ◽  
Emily Robertson ◽  
T. Nathan Mundhenk ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Quality ◽  
Real World ◽  
Image Feature ◽  
Molecular Solids ◽  
Pixel Intensity ◽  
Machine Learning Model ◽  
Model Predictions ◽  
Intensity Normalization ◽  
Model Robustness

AbstractMachine-learning (ML) techniques hold the potential of enabling efficient quantitative micrograph analysis, but the robustness of ML models with respect to real-world micrograph quality variations has not been carefully evaluated. We collected thousands of scanning electron microscopy (SEM) micrographs for molecular solid materials, in which image pixel intensities vary due to both the microstructure content and microscope instrument conditions. We then built ML models to predict the ultimate compressive strength (UCS) of consolidated molecular solids, by encoding micrographs with different image feature descriptors and training a random forest regressor, and by training an end-to-end deep-learning (DL) model. Results show that instrument-induced pixel intensity signals can affect ML model predictions in a consistently negative way. As a remedy, we explored intensity normalization techniques. It is seen that intensity normalization helps to improve micrograph data quality and ML model robustness, but microscope-induced intensity variations can be difficult to eliminate.

scholarly journals Identifikasi Kesegaran Ikan Nila menggunakan Teknik Citra Digital

2020 ◽  
Vol 2 (1) ◽  
pp. 6-10
Author(s):  
Juli Elprida Hutagalung ◽  
Mhd Ihsan Pohan ◽  
Yuli Happy Marpaung
Keyword(s):  
Least Squares ◽  
Least Squares Method ◽  
Image Data ◽  
The Body ◽  
Training Data ◽  
Application System ◽  
Fresh Fish ◽  
Average Value ◽  
Color Changes ◽  
Image Format

Fish contain many nutrients that are very beneficial for the body, but often fish are traded in a state of death as well as being alive. To observe the freshness of tilapia is done by the introduction of color changes that appear on digital images using the least squares method. The purpose of this research is to build an image management application system to detect the freshness of tilapia. The data used are 10 samples of tilapia images which are photographed every 1 hour for 15 hours and obtained 150 image data and then processed and analyzed using the least squares method. The first process begins with image processing by cropping at the edge of the eye of the original image and then proceed with resizing to 1000 x 1000 pixels and changing the image format to *. Png. After the image has been processed then the average value is calculated rata grayscale uses the 'rata_rata Gambar' application system and an equation is stored which is stored as training data on the application system. After the image has been processed then the image is input into the system, the image will be converted into grayscale form and displayed at a predetermined place together with the rgb and grayscale histograms and then calculated using the least squares method. The last process we do is matching the test image with the image stored as training data and we conclude whether the image is (very fresh, fresh, fresh enough, not fresh, or very not fresh), the percentage of freshness of the anchor fish, and the length of time the anchor fish dies. This study used 150 samples of fish images from fresh fish that were still very fresh until the fish were not very fresh (rotten).

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