scholarly journals A Machine Learning Approach to Study Demographic Alterations in Honeybee Colonies Using SDS–PAGE Fingerprinting

Animals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1823
Author(s):  
Riccardo Cabbri ◽  
Enea Ferlizza ◽  
Elisa Bellei ◽  
Giulia Andreani ◽  
Roberta Galuppi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Paradigm Shift ◽  
Learning Algorithm ◽  
Multiple Stressors ◽  
Emergent Properties ◽  
Learning Approach ◽  
Nearest Neighbours ◽  
Sds Page ◽  
Machine Learning Approach ◽  
Negative Factors

Honeybees, as social insects, live in highly organised colonies where tasks reflect the age of individuals. As is widely known, in this context, emergent properties arise from interactions between them. The accelerated maturation of nurses into foragers, stimulated by many negative factors, may disrupt this complex equilibrium. This complexity needs a paradigm shift: from the study of a single stressor to the study of the effects exerted by multiple stressors on colony homeostasis. The aim of this research is, therefore, to study colony population disturbances by discriminating overaged nurses from proper aged nurses and precocious foragers from proper aged foragers using SDS-PAGE patterns of haemolymph proteins and a machine-learning algorithm. The KNN (K Nearest Neighbours) model fitted on the forager dataset showed remarkably good performances (accuracy 0.93, sensitivity 0.88, specificity 1.00) in discriminating precocious foragers from proper aged ones. The main strength of this innovative approach lies in the possibility of it being deployed as a preventive tool. Depopulation is an elusive syndrome in bee pathology and early detection with the method described could shed more light on the phenomenon. In addition, it enables countermeasures to revert this vicious circle.

scholarly journals An Entropy-Based Machine Learning Algorithm for Combining Macroeconomic Forecasts

Entropy ◽  
2019 ◽  
Vol 21 (10) ◽  
pp. 1015 ◽  
Author(s):  
Carles Bretó ◽  
Priscila Espinosa ◽  
Penélope Hernández ◽  
Jose M. Pavía
Keyword(s):  
Machine Learning ◽  
Gross Domestic Product ◽  
Maximum Entropy ◽  
Simulation Study ◽  
Learning Algorithm ◽  
Predictive Ability ◽  
Learning Approach ◽  
Machine Learning Algorithm ◽  
Machine Learning Approach ◽  
Maximum Entropy Inference

This paper applies a Machine Learning approach with the aim of providing a single aggregated prediction from a set of individual predictions. Departing from the well-known maximum-entropy inference methodology, a new factor capturing the distance between the true and the estimated aggregated predictions presents a new problem. Algorithms such as ridge, lasso or elastic net help in finding a new methodology to tackle this issue. We carry out a simulation study to evaluate the performance of such a procedure and apply it in order to forecast and measure predictive ability using a dataset of predictions on Spanish gross domestic product.

Reconstructive derivational analogy: A machine learning approach to automating redesign

1996 ◽  
Vol 10 (2) ◽  
pp. 115-126 ◽  
Author(s):  
B.D. Britt ◽  
T. Glagowski
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Design Tool ◽  
Design Reuse ◽  
Learning Approach ◽  
Machine Learning Algorithm ◽  
Machine Learning Approach ◽  
Design Plan ◽  
Primary Focus ◽  
Different Parts

AbstractThis paper describes current research toward automating the redesign process. In redesign, a working design is altered to meet new problem specifications. This process is complicated by interactions between different parts of the design, and many researchers have addressed these issues. An overview is given of a large design tool under development, the Circuit Designer's Apprentice. This tool integrates various techniques for reengineering existing circuits so that they meet new circuit requirements. The primary focus of the paper is one particular technique being used to reengineer circuits when they cannot be transformed to meet the new problem requirements. In these cases, a design plan is automatically generated for the circuit, and then replayed to solve all or part of the new problem. This technique is based upon the derivational analogy approach to design reuse. Derivational Analogy is a machine learning algorithm in which a design plan is saved at the time of design so that it can be replayed on a new design problem. Because design plans were not saved for the circuits available to the Circuit Designer's Apprentice, an algorithm was developed that automatically reconstructs a design plan for any circuit. This algorithm, Reconstructive Derivational Analogy, is described in detail, including a quantitative analysis of the implementation of this algorithm.

scholarly journals An Analytical Model for Prediction of Heart Disease using Machine Learning Classifiers

2021 ◽  
Author(s):  
Diti Roy ◽  
Md. Ashiq Mahmood ◽  
Tamal Joyti Roy
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Random Forest ◽  
Learning Algorithm ◽  
Modern Technology ◽  
Learning Approach ◽  
Data Sets ◽  
Machine Learning Classifiers ◽  
Machine Learning Approach ◽  
Day By Day

<p>Heart Disease is the most dominating disease which is taking a large number of deaths every year. A report from WHO in 2016 portrayed that every year at least 17 million people die of heart disease. This number is gradually increasing day by day and WHO estimated that this death toll will reach the summit of 75 million by 2030. Despite having modern technology and health care system predicting heart disease is still beyond limitations. As the Machine Learning algorithm is a vital source predicting data from available data sets we have used a machine learning approach to predict heart disease. We have collected data from the UCI repository. In our study, we have used Random Forest, Zero R, Voted Perceptron, K star classifier. We have got the best result through the Random Forest classifier with an accuracy of 97.69.<i><b></b></i></p> <p><b> </b></p>

scholarly journals Sentiment Analysis Using Hybrid Approach

2021 ◽  
Vol 9 (12) ◽  
pp. 282-285
Author(s):  
Ganesh K. Shinde
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Learning Algorithm ◽  
Hybrid Approach ◽  
Online Review ◽  
Second Step ◽  
Learning Approach ◽  
Machine Learning Classifiers ◽  
Machine Learning Approach ◽  
Mining Methods

Abstract: Most important part of information gathering is to focus on how people think. There are so many opinion resources such as online review sites and personal blogs are available. In this paper we focused on the Twitter. Twitter allow user to express his opinion on variety of entities. We performed sentiment analysis on tweets using Text Mining methods such as Lexicon and Machine Learning Approach. We performed Sentiment Analysis in two steps, first by searching the polarity words from the pool of words that are already predefined in lexicon dictionary and in Second step training the machine learning algorithm using polarities given in the first step. Keywords: Sentiment analysis, Social Media, Twitter, Lexicon Dictionary, Machine Learning Classifiers, SVM.

Sentiment Analysis for Iraqis Dialect in Social Media

2018 ◽  
Vol 1 (2) ◽  
pp. 24-32
Author(s):  
Lamiaa Abd Habeeb
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Learning Approach ◽  
Machine Learning Algorithm ◽  
K Nearest Neighbor ◽  
Multinomial Models ◽  
Ensemble Machine Learning ◽  
Machine Learning Approach

In this paper, we designed a system that extract citizens opinion about Iraqis government and Iraqis politicians through analyze their comments from Facebook (social media network). Since the data is random and contains noise, we cleaned the text and builds a stemmer to stem the words as much as possible, cleaning and stemming reduced the number of vocabulary from 28968 to 17083, these reductions caused reduction in memory size from 382858 bytes to 197102 bytes. Generally, there are two approaches to extract users opinion; namely, lexicon-based approach and machine learning approach. In our work, machine learning approach is applied with three machine learning algorithm which are; Naïve base, K-Nearest neighbor and AdaBoost ensemble machine learning algorithm. For Naïve base, we apply two models; Bernoulli and Multinomial models. We found that, Naïve base with Multinomial models give highest accuracy.

New Heat Flux Model for Antarctica with a Machine Learning Approach

2020 ◽  
Author(s):  
Mareen Lösing ◽  
Jörg Ebbing ◽  
Wolfgang Szwillus
Keyword(s):  
Machine Learning ◽  
Heat Flux ◽  
Learning Algorithm ◽  
Learning Approach ◽  
Data Sets ◽  
Tree Model ◽  
Geothermal Heat ◽  
Boosted Regression Tree ◽  
Radiogenic Heat ◽  
Machine Learning Approach

&lt;p&gt;Improving the understanding of geothermal heat flux in Antarctica is crucial for ice-sheet modelling and glacial isostatic adjustment. It affects the ice rheology and can lead to basal melting, thereby promoting ice flow. Direct measurements are sparse and models inferred from e.g. magnetic or seismological data differ immensely. By Bayesian inversion, we evaluated the uncertainties of some of these models and studied the interdependencies of the thermal parameters. In contrast to previous studies, our method allows the parameters to vary laterally, which leads to a heterogeneous West- and a slightly more homogeneous East Antarctica with overall lower surface heat flux. The Curie isotherm depth and radiogenic heat production have the strongest impact on our results but both parameters have a high uncertainty.&lt;/p&gt;&lt;p&gt;To overcome such shortcomings, we adopt a machine learning approach, more specifically a Gradient Boosted Regression Tree model, in order to find an optimal predictor for locations with sparse measurements. However, this approach largely relies on global data sets, which are notoriously unreliable in Antarctica. Therefore, validity and quality of the data sets is reviewed and discussed. Using regional and more detailed data sets of Antarctica&amp;#8217;s Gondwana neighbors might improve the predictions due to their similar tectonic history. The performance of the machine learning algorithm can then be examined by comparing the predictions to the existing measurements. From our study, we expect to get new insights in the geothermal structure of Antarctica, which will help with future studies on the coupling of Solid Earth and Cryosphere.&lt;/p&gt;

scholarly journals A Survey on Machine Learning Approach to Detect Malware

2021 ◽  
Vol 12 (2) ◽  
Author(s):  
Selvarathi C, Et. al.
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Optimal Solution ◽  
The Internet ◽  
Learning Approach ◽  
Machine Learning Algorithm ◽  
A Algorithm ◽  
Internet Users ◽  
Machine Learning Approach

Malware is one of the predominant challenges for the Internet users. In recent times, the injection of malwares into machines by anonymous hackers have been increased. This drives us to an urgent need of a system that detects a malware. Our idea is to build a system that learns with the previously collected data related to malwares and detects a malware in the give file, if it is present. We propose a various machine learning algorithm to detect a malware and indicates the user about the danger. In particular we propose to use a algorithm which give a optimal solution to hardware and software oriented malwares.

scholarly journals An Analytical Model for Prediction of Heart Disease using Machine Learning Classifiers

2021 ◽  
Author(s):  
Diti Roy ◽  
Md. Ashiq Mahmood ◽  
Tamal Joyti Roy
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Random Forest ◽  
Learning Algorithm ◽  
Modern Technology ◽  
Learning Approach ◽  
Data Sets ◽  
Machine Learning Classifiers ◽  
Machine Learning Approach ◽  
Day By Day

<p>Heart Disease is the most dominating disease which is taking a large number of deaths every year. A report from WHO in 2016 portrayed that every year at least 17 million people die of heart disease. This number is gradually increasing day by day and WHO estimated that this death toll will reach the summit of 75 million by 2030. Despite having modern technology and health care system predicting heart disease is still beyond limitations. As the Machine Learning algorithm is a vital source predicting data from available data sets we have used a machine learning approach to predict heart disease. We have collected data from the UCI repository. In our study, we have used Random Forest, Zero R, Voted Perceptron, K star classifier. We have got the best result through the Random Forest classifier with an accuracy of 97.69.<i><b></b></i></p> <p><b> </b></p>

scholarly journals South Africa Crime Visualization, Trends Analysis, and Prediction Using Machine Learning Linear Regression Technique

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Ibidun Christiana Obagbuwa ◽  
Ademola P. Abidoye
Keyword(s):  
Machine Learning ◽  
South Africa ◽  
Linear Regression ◽  
Learning Algorithm ◽  
Research Work ◽  
Data Repository ◽  
Learning Approach ◽  
Crime Data ◽  
Social Violence ◽  
Machine Learning Approach

South Africa has been classified as one of the most homicidal, violent, and dangerous places across the globe. However, the two elements that pushed South Africa high in the crime rank are the rates of social violence and homicide. It was reported by Business Insider that South Africa is among the most top 15 ferocious nations on earth. By 1995, South Africa was rated the second highest in terms of murder. However, the crime rate has reduced for some years and suddenly rose again in recent years. Due to social violence and crime rates in South Africa, foreign investors are no longer interested in continuing or starting a business with the nation, and hence, its economy is declining. South Africa’s government is looking for solutions to the crime issue and to redeem the image of the country in terms of high crime ranking and boost the confidence of the investors. Many traditional approaches to data analysis in crime-related studies have been done in South Africa, but the machine learning approach has not been adequately considered. The police station and many other agencies that deal with crime hold a lot of databases that can be used to predict or analyze criminal happenings across the provinces of South Africa. This research work aimed at offering a solution to the problem by building a model that can predict crime. The machine learning approach shall be used to extract useful information from South Africa's nine provinces' crime data. A crime prediction system that can analyze and predict crime is proposed. To accomplish this, South Africa crime data on 27 crime categories were obtained from the popular data repository “Kaggle.” Diverse data analytics steps were applied to preprocess the datasets, and a machine learning algorithm (linear regression) was used to build a predictive model to analyze data and predict future crime. The appropriate authorities and security agencies in South Africa can have insight into the crime trends and alleviate them to encourage the foreign stakeholders to continue their businesses.

A Machine Learning Approach in Predicting Mortality Following Emergency General Surgery

2021 ◽  
pp. 000313482110385
Author(s):  
Jeff Gao ◽  
Aziz M. Merchant
Keyword(s):  
Machine Learning ◽  
General Surgery ◽  
Predictive Value ◽  
Learning Algorithm ◽  
Learning Approach ◽  
Machine Learning Algorithm ◽  
Emergency General Surgery ◽  
Asa Classification ◽  
Machine Learning Approach ◽  
Sensitivity Specificity

Background There is a significant mortality burden associated with emergency general surgery (EGS) procedures. The objective of this study was to develop and validate the use of a machine learning approach to predict mortality following EGS. Methods The American College of Surgeons National Surgical Quality Improvement Program database was queried for patients who underwent EGS between 2012 and 2017. We developed a machine learning algorithm to predict mortality following EGS and compared its performance with existing risk-prediction models of American Society of Anesthesiologists (ASA) classification, American College of Surgeon Surgical Risk Calculator (ACS-SRC), and the modified frailty index (mFI) using the area under receiver operative curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results The machine learning algorithm had a very high performance for predicting mortality following EGS, and it had superior performance compared to the ASA classification, ACS-SRC, and the mFI, as measured by the AUC, sensitivity, specificity, PPV, and NPV. Discussion Machine learning approaches may be a promising tool to predict outcomes for EGS, aiding clinicians in surgical decision-making and counseling of patients and family, improving clinical outcomes by identifying modifiable risk factors than can be optimized, and decreasing treatment costs through resource allocation.

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