Food Sustenance Estimation Using Food Image

2020 ◽  
Vol 20 (04) ◽  
pp. 2050034
Author(s):  
G. Wiselin Jiji ◽  
A. Rajesh
Keyword(s):  
Metabolic Diseases ◽  
Heart Diseases ◽  
Depth Map ◽  
Support Vector ◽  
Health Issues ◽  
Query Image ◽  
Food Items ◽  
Balanced Diet ◽  
Life Threatening ◽  
Food Image

The upcoming generation is at high risk of developing many health issues like heart diseases, metabolic diseases and other life-threatening problems with high mortality as a consequence of obesity due to intake of unhealthy food which is totally deviated from a normal balanced diet with appropriate calories, proteins, vitamins and carbohydrates. In this work, the nutrient intake is calculated using food image. Our system provides efficient segmentation algorithms for separating food items from the plate. The given 2D image of food is converted into 3D image by generating its depth map for volume generation and color, texture and shape features are extracted. These features are fed as input into multi-class support vector machine classifier for learning. The learning phase involves training of various mixed and non mixed food items. The testing phase includes query image segmentation and classification for identifying the type of food and then finding calories using the nutrition data table. We have also estimated the ingredient and decay of food items. Our result shows accurate calorie estimation for various kinds of food items.

scholarly journals Support vector machine the most fruitful algorithm for prognosticating heart disorder

2018 ◽  
Vol 7 (2.26) ◽  
pp. 48
Author(s):  
M Murugesan ◽  
R Elankeerthana
Keyword(s):  
Data Mining ◽  
Heart Disease ◽  
Nearest Neighbor ◽  
Heart Diseases ◽  
Support Vector ◽  
Human Society ◽  
K Nearest Neighbor ◽  
Healthcare Organizations ◽  
Life Threatening ◽  
Medical Examiners

One of the wealthiest areas of research is Data mining that is more popular in healthcare organizations. Heart disease is the main outcome of death in the human society over the recent years. Heart disease is serious life threatening diseases that result to death. In order to save a pan-tient’s life, the doctors and medical examiners are being taking many efforts. The consultant of doctor’s determination can make without the advice of specialists because of the software develop by the advancement in computer technology. In most of the papers, Data mining tech-niques used in the existing method in the research are Naive Bayes, Decision tree, J48, K-Nearest Neighbor (K-NN) (or) Lazy IBK algo-rithms to predict heart diseases. In this paper, support vector machines (SVM) technique will produce the most accuracy prediction rate for heart diseases while comparing to all the other techniques used in data mining. 

scholarly journals Multiclass Classification of Cardiac Arrhythmia Using Improved Feature Selection and SVM Invariants

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Anam Mustaqeem ◽  
Syed Muhammad Anwar ◽  
Muahammad Majid
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Multiclass Classification ◽  
Data Repository ◽  
Support Vector ◽  
Kappa Statistics ◽  
Health Issues ◽  
Feature Selection Technique ◽  
Life Threatening ◽  
The University

Arrhythmia is considered a life-threatening disease causing serious health issues in patients, when left untreated. An early diagnosis of arrhythmias would be helpful in saving lives. This study is conducted to classify patients into one of the sixteen subclasses, among which one class represents absence of disease and the other fifteen classes represent electrocardiogram records of various subtypes of arrhythmias. The research is carried out on the dataset taken from the University of California at Irvine Machine Learning Data Repository. The dataset contains a large volume of feature dimensions which are reduced using wrapper based feature selection technique. For multiclass classification, support vector machine (SVM) based approaches including one-against-one (OAO), one-against-all (OAA), and error-correction code (ECC) are employed to detect the presence and absence of arrhythmias. The SVM method results are compared with other standard machine learning classifiers using varying parameters and the performance of the classifiers is evaluated using accuracy, kappa statistics, and root mean square error. The results show that OAO method of SVM outperforms all other classifiers by achieving an accuracy rate of 81.11% when used with 80/20 data split and 92.07% using 90/10 data split option.

scholarly journals Integration of transcriptomic data identifies key hallmark genes in hypertrophic cardiomyopathy

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jing Xu ◽  
Xiangdong Liu ◽  
Qiming Dai
Keyword(s):  
Machine Learning ◽  
Hypertrophic Cardiomyopathy ◽  
Heart Diseases ◽  
Expression Patterns ◽  
Support Vector ◽  
Rna Seq ◽  
Ppi Network ◽  
Learning Methods ◽  
Transcriptomic Data ◽  
Machine Learning Methods

Abstract Background Hypertrophic cardiomyopathy (HCM) represents one of the most common inherited heart diseases. To identify key molecules involved in the development of HCM, gene expression patterns of the heart tissue samples in HCM patients from multiple microarray and RNA-seq platforms were investigated. Methods The significant genes were obtained through the intersection of two gene sets, corresponding to the identified differentially expressed genes (DEGs) within the microarray data and within the RNA-Seq data. Those genes were further ranked using minimum-Redundancy Maximum-Relevance feature selection algorithm. Moreover, the genes were assessed by three different machine learning methods for classification, including support vector machines, random forest and k-Nearest Neighbor. Results Outstanding results were achieved by taking exclusively the top eight genes of the ranking into consideration. Since the eight genes were identified as candidate HCM hallmark genes, the interactions between them and known HCM disease genes were explored through the protein–protein interaction (PPI) network. Most candidate HCM hallmark genes were found to have direct or indirect interactions with known HCM diseases genes in the PPI network, particularly the hub genes JAK2 and GADD45A. Conclusions This study highlights the transcriptomic data integration, in combination with machine learning methods, in providing insight into the key hallmark genes in the genetic etiology of HCM.

scholarly journals Robust hand gesture recognition using multiple shape-oriented visual cues

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Samy Bakheet ◽  
Ayoub Al-Hamadi
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Pose Estimation ◽  
Depth Map ◽  
Hand Gesture Recognition ◽  
Support Vector ◽  
Hand Gesture ◽  
Hand Pose Estimation ◽  
Time Operation ◽  
Hand Pose

AbstractRobust vision-based hand pose estimation is highly sought but still remains a challenging task, due to its inherent difficulty partially caused by self-occlusion among hand fingers. In this paper, an innovative framework for real-time static hand gesture recognition is introduced, based on an optimized shape representation build from multiple shape cues. The framework incorporates a specific module for hand pose estimation based on depth map data, where the hand silhouette is first extracted from the extremely detailed and accurate depth map captured by a time-of-flight (ToF) depth sensor. A hybrid multi-modal descriptor that integrates multiple affine-invariant boundary-based and region-based features is created from the hand silhouette to obtain a reliable and representative description of individual gestures. Finally, an ensemble of one-vs.-all support vector machines (SVMs) is independently trained on each of these learned feature representations to perform gesture classification. When evaluated on a publicly available dataset incorporating a relatively large and diverse collection of egocentric hand gestures, the approach yields encouraging results that agree very favorably with those reported in the literature, while maintaining real-time operation.

INHERITED METABOLIC DISORDERS AND HEART DISEASES

2019 ◽  
Author(s):  
Vjekoslav Krželj ◽  
Ivana Čulo Čagalj
Keyword(s):  
Metabolic Disorders ◽  
Metabolic Diseases ◽  
Technological Development ◽  
Heart Diseases ◽  
Glycogen Storage ◽  
Acid Oxidation ◽  
Lysosomal Storage ◽  
Glycogen Storage Diseases ◽  
Coronary Diseases ◽  
Inherited Metabolic Disorders

Inherited metabolic disorders can cause heart diseases, cardiomyopathy in particular, as well as cardiac arrhythmias, valvular and coronary diseases. More than 40 different inherited metabolic disorders can provoke cardiomyopathy, including lysosomal storage disorders, fatty acid oxidation defects, organic acidemias, amino acidopathies, glycogen storage diseases, congenital disorders of glycosylation as well as peroxisomal and mitochondrial disorders. If identified and diagnosed on time, some of congenital metabolic diseases could be successfully treated. It is important to assume them in cases when heart diseases are etiologically undefined. Rapid technological development has made it easier to establish the diagnosis of these diseases. This article will focus on common inherited metabolic disorders that cause heart diseases, as well as on diseases that might be possible to treat.

The Role of Psilocybin-Assisted Psychotherapy to Support Patients With Cancer: A Critical Scoping Review of the Research

2021 ◽  
pp. 089801012110390
Author(s):  
Rebecca H. Lehto ◽  
Megan Miller ◽  
Jessica Sender
Keyword(s):  
Mental Health ◽  
Health Issues ◽  
Mushroom Species ◽  
Patients With Cancer ◽  
Existential Suffering ◽  
Vulnerable Patients ◽  
Scoping Literature Review ◽  
Life Threatening ◽  
Self Transcendence

Treatments for addressing psychiatric mental health issues in vulnerable patients with cancer are established. Yet, many patients persist with unrelenting psychological difficulties despite intervention. There is growing interest in the role of psilocybin-assisted psychotherapy for managing treatment-resistant mental health challenges in patients with cancer. Psilocybin is a naturally occurring compound derived from certain mushroom species that can induce entheogenic experiences or an altered state of consciousness. Reed's Self-Transcendence Theory provides a holistic lens to examine existential concerns and mental health in individuals who perceive their illness as potentially life threatening, such as those with cancer. This scoping literature review used Arksey and O’Malley's template to evaluate research examining psilocybin-assisted psychotherapy for patients with cancer. Eight articles met inclusion/exclusion criteria (four quantitative, two mixed methods, and two qualitative). Review findings indicated that the majority of patient experiences were positive, centering on themes of death acceptance, reflection, and broadened spirituality. Although psilocybin-assisted psychotherapy is in early stages of clinical testing, it thus shows promise for carefully screened patients with cancer who have persistent existential suffering. It will be critical for investigators to tailor this emerging intervention to select patients and for clinicians to be engaged in assessment of outcomes and efficacy.

scholarly journals Heart disease prediction using machine learning techniques : a survey

2018 ◽  
Vol 7 (2.8) ◽  
pp. 684 ◽  
Author(s):  
V V. Ramalingam ◽  
Ayantan Dandapath ◽  
M Karthik Raja
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Complex Data ◽  
Learning Techniques ◽  
Vector Machines ◽  
Supervised Learning Algorithms ◽  
Life Threatening

Heart related diseases or Cardiovascular Diseases (CVDs) are the main reason for a huge number of death in the world over the last few decades and has emerged as the most life-threatening disease, not only in India but in the whole world. So, there is a need of reliable, accurate and feasible system to diagnose such diseases in time for proper treatment. Machine Learning algorithms and techniques have been applied to various medical datasets to automate the analysis of large and complex data. Many researchers, in recent times, have been using several machine learning techniques to help the health care industry and the professionals in the diagnosis of heart related diseases. This paper presents a survey of various models based on such algorithms and techniques andanalyze their performance. Models based on supervised learning algorithms such as Support Vector Machines (SVM), K-Nearest Neighbour (KNN), NaïveBayes, Decision Trees (DT), Random Forest (RF) and ensemble models are found very popular among the researchers.

scholarly journals Recognition of Symbolic Gestures Using Depth Information

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Hasan Mahmud ◽  
Md. Kamrul Hasan ◽  
Abdullah-Al-Tariq ◽  
Md. Hasanul Kabir ◽  
M. A. Mottalib
Keyword(s):  
Contextual Information ◽  
Depth Map ◽  
Recognition System ◽  
Support Vector ◽  
Svm Classifier ◽  
Depth Information ◽  
Scale Invariant ◽  
Binary Images ◽  
Depth Images ◽  
Symbolic Gestures

Symbolic gestures are the hand postures with some conventionalized meanings. They are static gestures that one can perform in a very complex environment containing variations in rotation and scale without using voice. The gestures may be produced in different illumination conditions or occluding background scenarios. Any hand gesture recognition system should find enough discriminative features, such as hand-finger contextual information. However, in existing approaches, depth information of hand fingers that represents finger shapes is utilized in limited capacity to extract discriminative features of fingers. Nevertheless, if we consider finger bending information (i.e., a finger that overlaps palm), extracted from depth map, and use them as local features, static gestures varying ever so slightly can become distinguishable. Our work here corroborated this idea and we have generated depth silhouettes with variation in contrast to achieve more discriminative keypoints. This approach, in turn, improved the recognition accuracy up to 96.84%. We have applied Scale-Invariant Feature Transform (SIFT) algorithm which takes the generated depth silhouettes as input and produces robust feature descriptors as output. These features (after converting into unified dimensional feature vectors) are fed into a multiclass Support Vector Machine (SVM) classifier to measure the accuracy. We have tested our results with a standard dataset containing 10 symbolic gesture representing 10 numeric symbols (0-9). After that we have verified and compared our results among depth images, binary images, and images consisting of the hand-finger edge information generated from the same dataset. Our results show higher accuracy while applying SIFT features on depth images. Recognizing numeric symbols accurately performed through hand gestures has a huge impact on different Human-Computer Interaction (HCI) applications including augmented reality, virtual reality, and other fields.

scholarly journals Ayurveda Internal Medicine for the Management of Common Metabolic Disorders W.S.R. to Madhumeha and Sthoulya

2019 ◽  
Vol 9 (5-s) ◽  
pp. 167-169
Author(s):  
Dhananjay S. Khot
Keyword(s):  
Internal Medicine ◽  
Metabolic Syndrome ◽  
Life Style ◽  
Metabolic Disorders ◽  
Dietary Habits ◽  
Metabolic Diseases ◽  
Health Issues ◽  
The Metabolic Syndrome ◽  
Sedentary Life Style

The metabolic disorders are major health issues of today’s scenario and incidences of metabolic diseases increases day by day due to the disturbed pattern of life style. Ayurveda texts have described term “Santarpanjanya Vikaras” which resembles diseases of defective tissue metabolism. Ayurveda mentioned that improper dietary habits and sedentary life style affects state of Agni which resulted Ama production and finally leading to the metabolic syndrome. The vitiation of Dosha, diminish state of Dhatu and blockage of channels, etc. also can initiate pathogenesis of metabolic disorders. The Kayachikitsa branch of Ayurveda recommended use of internal medicine for the management of various metabolic disorders. Considering increased health burden of society due to the metabolic syndrome present article explore role of ayurveda internal medicine for the management of metabolic syndrome. Keywords: Ayurveda, metabolic syndrome, Santarpanjanya, Madhumeha and Sthoulya.       

scholarly journals Cardiovascular Disease Prediction using Data Mining Techniques

2017 ◽  
Vol 10 (2) ◽  
pp. 520-528 ◽  
Author(s):  
Mudasir Kirmani
Keyword(s):  
Data Mining ◽  
Cardiovascular Disease ◽  
Cardiovascular Diseases ◽  
High Risk ◽  
Learning Algorithm ◽  
Heart Diseases ◽  
Circulatory System ◽  
World Health ◽  
Support Vector ◽  
Predictive Tool

Cardiovascular disease represents various diseases associated with heart, lymphatic system and circulatory system of human body. World Health Organisation (WHO) has reported that cardiovascular diseases have high mortality rate and high risk to cause various disabilities. Most prevalent causes for cardiovascular diseases are behavioural and food habits like tobacco intake, unhealthy diet and obesity, physical inactivity, ageing and addiction to drugs and alcohol are to name few. Factors such as hypertension, diabetes, hyperlipidemia, Stress and other ailments are at high risk to cardiovascular diseases. There have been different techniques to predict the prevalence of cardiovascular diseases in general and heart disease in particular from time to time by implementing variety of algorithms. Detection and management of cardiovascular diseases can be achieved by using computer based predictive tool in data mining. By implementing data mining based techniques there is scope for better and reliable prediction and diagnosis of heart diseases. In this study we studied various available techniques like decision Tree and its variants, Naive Bayes, Neural Networks, Support Vector Machine, Fuzzy Rules, Genetic Algorithms, and Ant Colony Optimization to name few. The observations illustrated that it is difficult to name a single machine learning algorithm for the diagnosis and prognosis of CVD. The study further contemplates on the behaviour, selection and number of factors required for efficient prediction.

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