scholarly journals Foodwiser: Be Wise with What You Eat

2021 ◽  
Vol 9 (12) ◽  
pp. 1398-1402
Author(s):  
Aniket Tatte
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Food Intake ◽  
Food Habits ◽  
Work From Home ◽  
Stated Problem ◽  
Day By Day

Abstract: With the rise of online food ordering websites, maintaining a proper diet and staying healthy has become an important part of a person's lifestyle. But with the rising work from home trend, maintaining a proper diet and being aligned with the fitness goals is becoming tougher day by day. Also fora person having abnormal food habits, it becomes really tough to maintain a repository of the food intake, manage nutrients and carbs intake, etc. Thus with an aim to solve the above stated problem, we present the application Foodwiser: Be wise with whatyou eat. Keywords: [Deep Learning, CNN, Machine Learning ]

scholarly journals On the Methods for Detecting Brain Tumor from MRI images

2020 ◽  
Vol 9 (9s) ◽  
pp. 37-44
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Brain Tumor ◽  
Medical Imaging ◽  
Tumor Detection ◽  
Gray Level ◽  
Wide Spread ◽  
Emerging Trends ◽  
Day By Day ◽  
High Diversity

Brain tumor detection from MRI images is a challenging process due to high diversity in the tumor pixels of different peoples. Automatic detection has got wide spread acclaim because the manual detection by experts is time consuming and prone to error in judgment. Due to its high mortality rate, detection of tumor automatically is a new emerging technique in bio medical imaging. Here we present a review of few methods from simple thresholding to advanced deep learning methods for segmentation of tumor from MRI data. The segmentation of tumor methods is classified to image segmentation using gray level processing, machine learning and deep learning. The results of various methods are compared to find the best methods available. As medical imaging methods have improving day by day this review will help to understand emerging trends in brain tumor detection.

Sentiment Analysis by using deep learning and Machine learning Techniques: A Review

2021 ◽  
Vol 10 (2) ◽  
pp. 754-761
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Online Social Networks ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Growing Up ◽  
Specific Subject ◽  
Learning Techniques ◽  
Day By Day

More and more individuals are now using online social networks and resources throughout this day and age to not only interact and to communicate but also for sharing their views, experiences, ideas, impression about anything. The analysis of sentiments is the identification and categorization of these views to evaluate public opinions on a specific subject, question, product, etc. Day by day, the relevance of sentiment analysis is growing up. Machine learning is an area or field of computer science where, without being specifically programmed, computers can learn. Deep learning is the part of machine learning and deals with the algorithm, which is most widely used as Neural network, neural belief, etc., in which neuronal implementations are considered. For sentiment analysis, it compares their performance and accuracy so then it can be inferred that deep learning techniques in most of the cases provide better results. The gap in the precision of these two approaches, however, is not as important enough in certain situations, and so it is best to apply and use the machine learning approaches and methods because these are simpler in terms of Implementation

scholarly journals A Survey on Explainability: Why Should We Believe the Accuracy of A Model?

2020 ◽  
Author(s):  
Prarthana Dutta ◽  
Naresh Babu Muppalaneni ◽  
Ripon Patgiri
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
New Technologies ◽  
Research Community ◽  
Learning Technologies ◽  
The World ◽  
Current Scenario ◽  
Day By Day ◽  
Modern Technologies

The world has been evolving with new technologies and advances day-by-day. With the advent of various learning technologies in every field, the research community is able to provide solution in every aspect of life with the applications of Artificial Intelligence, Machine Learning, Deep Learning, Computer Vision, etc. However, with such high achievements, it is found to lag behind the ability to provide explanation against its prediction. The current situation is such that these modern technologies are able to predict and decide upon various cases more accurately and speedily than a human, but failed to provide an answer when the question of why to trust its prediction is put forward. In order to attain a deeper understanding into this rising trend, we explore a very recent and talked-about novel contribution which provides rich insight on a prediction being made -- ``Explainability.'' The main premise of this survey is to provide an overview for researches explored in the domain and obtain an idea of the current scenario along with the advancements published to-date in this field. This survey is intended to provide a comprehensive background of the broad spectrum of Explainability.

scholarly journals Employing Deep Learning and Time Series Analysis to Tackle the Accuracy and Robustness of the Forecasting Problem

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Haseeb Tariq ◽  
Muhammad Kashif Hanif ◽  
Muhammad Umer Sarwar ◽  
Sabeen Bari ◽  
Muhammad Shahzad Sarfraz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Crime Prevention ◽  
Future Trend ◽  
Violent Crimes ◽  
Statistical Tool ◽  
Forecasting Models ◽  
The World ◽  
Day By Day

Crime is a bone of contention that can create a societal disturbance. Crime forecasting using time series is an efficient statistical tool for predicting rates of crime in many countries around the world. Crime data can be useful to determine the efficacy of crime prevention steps and the safety of cities and societies. However, it is a difficult task to predict the crime accurately because the number of crimes is increasing day by day. The objective of this study is to apply time series to predict the crime rate to facilitate practical crime prevention solutions. Machine learning can play an important role to better understand and analyze the future trend of violations. Different time-series forecasting models have been used to predict the crime. These forecasting models are trained to predict future violent crimes. The proposed approach outperforms other forecasting techniques for daily and monthly forecast.

scholarly journals Prediction of Stock Value using Pattern Matching Algorithm based on Deep Learning

2019 ◽  
Vol 8 (2S6) ◽  
pp. 31-35 ◽  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Future Trends ◽  
Matching Algorithm ◽  
Stock Value ◽  
Learning Machine ◽  
The Relationship ◽  
Stock Values ◽  
Day By Day

In this paper we began with finding ways to predict stock value flows of stock using deep learning. The purpose of this paper is to analyze the patterns in stock value and to analyze the relationship from stock values by deep running to predict what patterns will happen next stock value. In this paper we made the data by dividing the stock value information of the time series for a certain period of time and the pattern of stock value by analyzing these data. It is configured the model to be used for deep learning and learned the patterned time series information using the created model. And then it is predicted the next pattern of stock value. This paper focused machine learning. It is used of a time-series stock value information to predict the rise and fall of stock value. This paper is about how to analyze and how to predict. On the other hand, we can expect trend of stock value with high probability by analyzing pattern of current chart and anticipating pattern to follow. This is about what the deep-learning machine will analyze and predict for what. If we analysis the patterns used in this paper more clearly and concisely, and if more learning is carried out, we will be able to make clearer predictions with no noise for future trends. As interest in stock forecasts and machine learning develops fast, performance is expected to improve day by day.

Dietary intake of tryptophan tied emotion-related impulsivity in humans

2019 ◽  
pp. 1-8 ◽  
Author(s):  
Florian Javelle ◽  
Descartes Li ◽  
Philipp Zimmer ◽  
Sheri L. Johnson
Keyword(s):  
Amino Acids ◽  
Amino Acid ◽  
Food Intake ◽  
Essential Amino Acid ◽  
Food Habits ◽  
Psychological Disorder ◽  
Serotonin Synthesis ◽  
Amino Acid Tryptophan ◽  
Pass Through ◽  
Three Factor

Abstract. Emotion-related impulsivity, defined as the tendency to say or do things that one later regret during periods of heightened emotion, has been tied to a broad range of psychopathologies. Previous work has suggested that emotion-related impulsivity is tied to an impaired function of the serotonergic system. Central serotonin synthesis relies on the intake of the essential amino acid, tryptophan and its ability to pass through the blood brain barrier. Objective: The aim of this study was to determine the association between emotion-related impulsivity and tryptophan intake. Methods: Undergraduate participants (N = 25, 16 women, 9 men) completed a self-rated measure of impulsivity (Three Factor Impulsivity Index, TFI) and daily logs of their food intake and exercise. These data were coded using the software NutriNote to evaluate intakes of tryptophan, large neutral amino acids, vitamins B6/B12, and exercise. Results: Correlational analyses indicated that higher tryptophan intake was associated with significantly lower scores on two out of three subscales of the TFI, Pervasive Influence of Feelings scores r =  –.502, p < . 010, and (lack-of) Follow-Through scores, r =  –.407, p < . 050. Conclusion: Findings provide further evidence that emotion-related impulsivity is correlated to serotonergic indices, even when considering only food habits. It also suggests the need for more research on whether tryptophan supplements might be beneficial for impulsive persons suffering from a psychological disorder.

Deep Learning Based High-Resolution Remote Sensing Image classification

2017 ◽  
Vol 7 (10) ◽  
pp. 22
Author(s):  
Sumit Kaur
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Image Classification ◽  
Language Processing ◽  
Object Perception ◽  
Remote Sensing Image ◽  
Research Area ◽  
Remote Sensing Image Classification ◽  
Unsupervised Algorithms

Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification. 

The Role of Physical Exercise and Diet in management of Medoroga (STHAULYA) Reference Novel Coronavirus Covid 19

2021 ◽  
Vol 11 (SPL1) ◽  
pp. 1585-1592
Author(s):  
Roshna Sukhdeoji Bhutada
Keyword(s):  
Vitamin D ◽  
Vitamin C ◽  
Physical Movement ◽  
Work From Home ◽  
Causative Factors ◽  
Exercise Routine ◽  
Novel Coronavirus ◽  
Day By Day ◽  
General Wellbeing

Nowadays, due to Covid-19 pandemic circumstance, numerous individuals are Staying from home. Understudy is additionally concern with the online class from home, because of which all physical movement of all individual has been stopped. Medoroga is one of the dominating metabolic problems and driving reason for mortality. Numerous patients with Covid infection 2019 (COVID-19) have identified with the metabolic disorder during the lockdown. The general wellbeing proposes (Work from Home, requests, gyms, terminations of garden and wellness focuses) to forestall Covid-19 spread can possibly decrease day by day physical movement. Ideas of Agni, Prakriti, strategy for victualing ought to be given equivalent consideration while choosing ones dietary and exercise routine to turn away/control Medoroga (STHAULYA). Organizing of diet is generally important to support insusceptibility. According to numerous investigates to give valuable pabulum which contains Zinc, Vitamin C, Vitamin D and invulnerability. It is practically equivalent to Medoroga referenced in Ayurveda compositions. Strick likeness outwardly inspected in both customary arrangement of medication and Ayurveda while portraying its causative components, outcomes and preventive part of exercise and diet in its administration. Striking is outwardly analyzed in both Traditional arrangements of medication and Ayurveda depicting its causative factors, and preventive capacity of movement and diet in its pandemic Covid-19.

Data science in economics: comprehensive review of advanced machine learning and deep learning methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Ferdinand Filip ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
State Of The Art ◽  
Science Methods ◽  
Learning Models ◽  
Diverse Range ◽  
Hybrid Machine ◽  
Economics Research

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

Deep Learning for text in limted data settings

2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Text Classification ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Text Data ◽  
Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

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