scholarly journals A Cooking Recipe Multi-Label Classification Approach for Food Restriction Identification

2020 ◽  
Author(s):  
Larissa Britto ◽  
Luciano Pacífico ◽  
Emilia Oliveira ◽  
Teresa Ludermir
Keyword(s):  
Feature Selection ◽  
Experimental Evaluation ◽  
Food Restriction ◽  
Recommendation System ◽  
Classification Approach ◽  
Computational Costs ◽  
The Past ◽  
Food Restrictions ◽  
Best Fit

Recipe sharing websites have become very popular in the past years, allowing individuals to use such systems in an attempt to find a desired recipe. But sometimes finding recipes which best fit the user's wishes, while still satisfying his food restrictions, may become a very time consuming and difficult task. In this work, we propose a recipe multi-label classification approach as part of a recipe recommendation system for people with food restrictions, in an attempt to automatically identify whether an input recipe or list of ingredients fits into one or more food restrictions, satisfying both user's expectations and needs. The experimental evaluation includes two approaches for feature selection, as a manner to reduce the computational costs for the proposed system.

scholarly journals Automated Feature Selection and Classification for High-Dimensional Biomedical Data

2021 ◽  
Author(s):  
Tammo P.A. Beishuizen ◽  
Joaquin Vanschoren ◽  
Peter A.J. Hilbers ◽  
Dragan Bošnački
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Automated System ◽  
Complex Data ◽  
Biomedical Data ◽  
Selection Methods ◽  
Model Predictions ◽  
Automated Machine Learning ◽  
Feature Selection Techniques ◽  
Best Fit

Abstract Background: Automated machine learning aims to automate the building of accurate predictive models, including the creation of complex data preprocessing pipelines. Although successful in many fields, they struggle to produce good results on biomedical datasets, especially given the high dimensionality of the data. Result: In this paper, we explore the automation of feature selection in these scenarios. We analyze which feature selection techniques are ideally included in an automated system, determine how to efficiently find the ones that best fit a given dataset, integrate this into an existing AutoML tool (TPOT), and evaluate it on four very different yet representative types of biomedical data: microarray, mass spectrometry, clinical and survey datasets. We focus on feature selection rather than latent feature generation since we often want to explain the model predictions in terms of the intrinsic features of the data. Conclusion: Our experiments show that for none of these datasets we need more than 200 features to accurately explain the output. Additional features did not increase the quality significantly. We also find that the automated machine learning results are significantly improved after adding additional feature selection methods and prior knowledge on how to select and tune them.

scholarly journals A review of recommendation system research based on bipartite graph

2021 ◽  
Vol 336 ◽  
pp. 05010
Author(s):  
Ziteng Wu ◽  
Chengyun Song ◽  
Yunqing Chen ◽  
Lingxuan Li
Keyword(s):  
Neural Network ◽  
Bipartite Graph ◽  
Recommendation System ◽  
Research Priorities ◽  
Research Progress ◽  
Full Paper ◽  
Data Sparseness ◽  
The Past ◽  
Interaction History ◽  
Network Processing

The interaction history between users and items is usually stored and displayed in the form of bipartite graphs. Neural network recommendation based on the user-item bipartite graph has a significant effect on alleviating the long-standing data sparseness and cold start of the recommendation system. The whole paper is based on the bipartite graph. An review of the recommendation system of graphs summarizes the three characteristics of graph neural network processing bipartite graph data in the recommendation field: interchangeability, Multi-hop transportability, and strong interpretability. The biggest contribution of the full paper is that it summarizes the general framework of graph neural network processing bipartite graph recommendation from the models with the best recommendation effect in the past three years: embedding layer, propagation update layer, and prediction layer. Although there are subtle differences between different models, they are all this framework can be applied, and different models can be regarded as variants of this general model, that is, other models are fine-tuned on the basis of this framework. At the end of the paper, the latest research progress is introduced, and the main challenges and research priorities that will be faced in the future are pointed out.

scholarly journals Blockchain as a CA: A Provably Secure Signcryption Scheme Leveraging Blockchains

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Tzung-Her Chen ◽  
Ting-Le Zhu ◽  
Fuh-Gwo Jeng ◽  
Chien-Lung Wang
Keyword(s):  
Security Analysis ◽  
Third Party ◽  
Maintenance Cost ◽  
Communication Overhead ◽  
Operational Cost ◽  
Computational Costs ◽  
The Past ◽  
Certificate Chain ◽  
Signcryption Scheme ◽  
Provably Secure

Although encryption and signatures have been two fundamental technologies for cryptosystems, they still receive considerable attention in academia due to the focus on reducing computational costs and communication overhead. In the past decade, applying certificateless signcryption schemes to solve the higher cost of maintaining the certificate chain issued by a certificate authority (CA) has been studied. With the recent increase in the interest in blockchains, signcryption is being revisited as a new possibility. The concepts of a blockchain as a CA and a transaction as a certificate proposed in this paper aim to use a blockchain without CAs or a trusted third party (TTP). The proposed provably secure signcryption scheme implements a designated recipient beforehand such that a sender can cryptographically facilitate the interoperation on the blockchain information with the designated recipient. Thus, the proposed scheme benefits from the following advantages: (1) it removes the high maintenance cost from involving CAs or a TTP, (2) it seamlessly integrates with blockchains, and (3) it provides confidential transactions. This paper also presents the theoretical security analysis and assesses the performance via the simulation results. Upon evaluating the operational cost in real currency based on Ethereum, the experimental results demonstrate that the proposed scheme only requires a small cost as a fee.

scholarly journals Survey on Feature Transformation Techniques for Data Streams

2020 ◽  
Author(s):  
Maroua Bahri ◽  
Albert Bifet ◽  
Silviu Maniu ◽  
Heitor Murilo Gomes
Keyword(s):  
Machine Learning ◽  
Data Streams ◽  
Large Data ◽  
High Dimensional ◽  
Feature Transformation ◽  
Transformation Techniques ◽  
Computational Costs ◽  
The Past ◽  
Fundamental Challenge ◽  
Mining Algorithms

Mining high-dimensional data streams poses a fundamental challenge to machine learning as the presence of high numbers of attributes can remarkably degrade any mining task's performance. In the past several years, dimension reduction (DR) approaches have been successfully applied for different purposes (e.g., visualization). Due to their high-computational costs and numerous passes over large data, these approaches pose a hindrance when processing infinite data streams that are potentially high-dimensional. The latter increases the resource-usage of algorithms that could suffer from the curse of dimensionality. To cope with these issues, some techniques for incremental DR have been proposed. In this paper, we provide a survey on reduction approaches designed to handle data streams and highlight the key benefits of using these approaches for stream mining algorithms.

Intelligent Feature Selection Methods: A Survey

2021 ◽  
Vol 39 (1B) ◽  
pp. 175-183
Author(s):  
Noor Jameel ◽  
Hasanen S. Abdullah
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
Optimization Methods ◽  
Ant Colony ◽  
Modern Times ◽  
The Past ◽  
Paper Survey ◽  
Intelligent Methods

Consider feature selection is the main in intelligent algorithms and machine learning to select the subset of data to help acquire the optimal solution. Feature selection used an extract the relevance of the data and discarding the irrelevance of the data with increment fast to select it and to reduce the dimensional of dataset. In the past, it used traditional methods, but these methods are slow of fast and accuracy. In modern times, however, it uses the intelligent methods, Genetic algorithm and swarm optimization methods Ant colony, Bees colony, Cuckoo search, Particle optimization, fish algorithm, cat algorithm, Genetic algorithm ...etc. In feature selection because to increment fast, high accuracy and easy to use of user. In this paper survey it used the Some the swarm intelligent method: Ant colony, Bees colony, Cuckoo search, Particle optimization and Genetic algorithm (GA). It done take  the related work for each algorithms the swarm intelligent the ideas, dataset and accuracy of the results after that was done to compare the result in the table among the algorithms and learning the better algorithm is discuses in the discussion and why it is better. Finally, it learning who is the advantage and disadvantage for each algorithms of swarm intelligent in feature selection.

scholarly journals Mobile-based Recommendation System for the Tour Package Using the Hybrid Method

2018 ◽  
Vol 12 (8) ◽  
pp. 64
Author(s):  
I Made Agus Wirawan ◽  
I Wayan Bayu Diarsa
Keyword(s):  
Hybrid Method ◽  
Recommendation System ◽  
Naive Bayes ◽  
Bayes Theorem ◽  
Test System ◽  
Naïve Bayes ◽  
Total System ◽  
The Past ◽  
Accuracy Test ◽  
Package Recommendations

<p class="0abstractCxSpFirst">Although it has been a lot of research recommendation of the tourist attraction, there has been no research that discusses the recommendations of tour packages from a collection of travel in the past. Therefore, in this study it is important to conduct a related study 1) The development of a mobile recommendation system using the Hybrid Method. 2) Test system accuracy in providing tour package recommendations.</p><p class="0abstractCxSpLast">The study is using CBR stages in providing travel package recommendations from a collection of travel in the past. There are 4 stages of the process: Retrieve, Reuse, Revise, and Retain. In this study the main focus on the retrieve stage using the method hybrid method. The hybrid method of the mobile recommendation system is the combination of the Naive Bayes method, Bayes Theorem, and Dempster Shafer. Where Naive Bayes is used for calculating the probability of continuous criteria such as age and frequency of visits. The Bayes theorem is used for calculating the probability such as country, gender, and visiting purpose. To determine the mass value of the combination of evidence using the Dempster Shafer method. Based on system accuracy test, stated that the total system accuracy in giving recommendation is 95% consisting of 2 kinds of accuracy is 46% full accuracy and 49% of half accuracy. While the error rate of the system in providing tour package of 5%.</p>

Factors Affecting Employee Attrition and Predictive Modelling Using IBM HR Data

2019 ◽  
Vol 16 (8) ◽  
pp. 3379-3383
Author(s):  
Emad Afaq Khan ◽  
Sumaira Muhammad Hayat Khan
Keyword(s):  
Logistic Regression ◽  
Feature Selection ◽  
Confusion Matrix ◽  
Predictive Modelling ◽  
Problem Statement ◽  
Chi Square ◽  
Factors Affecting ◽  
The Past ◽  
Class 1 ◽  
Employee Attrition

Attrition can be defined as the gradual reduction of a member or person in an organization due to retirement, resignation, or death. The loss can be defined as the number of employees leaving the organization, including voluntary and involuntary resignations. This study is about identifying the factors that affect the attrition and establishing a predictive model for employee attrition. The study first focuses on the problem statement and a breakdown on what attrition does to the organization. Followed by a detailed conceptual breakdown on attrition which is then discussed in the light of predictive modeling with the past supporting researches. The research involves data preprocessing with chi square versus logistic regression for feature selection, machine learning models and their comparison using the confusion matrix, precision, recall and f1-scores. The best results obtained was the logistic regression model with feature selection and the accuracy of the model is 86% with a 98% recall for the class 1 of attrition. The researcher wants to change the view on how attrition problem is tackled. Rather than knowing who to retain, the organization should know who to hire. This research sets a ground rule and tries to change the perspective on tackling the attrition problem.

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