scholarly journals Risk-Aware Machine Learning Classifier for Skin Lesion Diagnosis

2019 ◽  
Vol 8 (8) ◽  
pp. 1241 ◽  
Author(s):  
Aryan Mobiny ◽  
Aditi Singh ◽  
Hien Van Nguyen
Keyword(s):  
Machine Learning ◽  
Skin Lesion ◽  
Learning Algorithm ◽  
Learning System ◽  
Clinical Settings ◽  
Learning Technology ◽  
Learning Classifier ◽  
Common Skin ◽  
Intraepithelial Carcinoma ◽  
Physician Team

Knowing when a machine learning system is not confident about its prediction is crucial in medical domains where safety is critical. Ideally, a machine learning algorithm should make a prediction only when it is highly certain about its competency, and refer the case to physicians otherwise. In this paper, we investigate how Bayesian deep learning can improve the performance of the machine–physician team in the skin lesion classification task. We used the publicly available HAM10000 dataset, which includes samples from seven common skin lesion categories: Melanoma (MEL), Melanocytic Nevi (NV), Basal Cell Carcinoma (BCC), Actinic Keratoses and Intraepithelial Carcinoma (AKIEC), Benign Keratosis (BKL), Dermatofibroma (DF), and Vascular (VASC) lesions. Our experimental results show that Bayesian deep networks can boost the diagnostic performance of the standard DenseNet-169 model from 81.35% to 83.59% without incurring additional parameters or heavy computation. More importantly, a hybrid physician–machine workflow reaches a classification accuracy of 90 % while only referring 35 % of the cases to physicians. The findings are expected to generalize to other medical diagnosis applications. We believe that the availability of risk-aware machine learning methods will enable a wider adoption of machine learning technology in clinical settings.

Application of a Rough Set-Based Inductive Learning System

1993 ◽  
Vol 18 (2-4) ◽  
pp. 209-220
Author(s):  
Michael Hadjimichael ◽  
Anita Wasilewska
Keyword(s):  
Machine Learning ◽  
Rough Set ◽  
Presidential Election ◽  
Predictive Accuracy ◽  
Learning Algorithm ◽  
Inductive Learning ◽  
Real Data ◽  
Semantic Content ◽  
Learning System ◽  
Voter Preferences

We present here an application of Rough Set formalism to Machine Learning. The resulting Inductive Learning algorithm is described, and its application to a set of real data is examined. The data consists of a survey of voter preferences taken during the 1988 presidential election in the U.S.A. Results include an analysis of the predictive accuracy of the generated rules, and an analysis of the semantic content of the rules.

scholarly journals Segmentation of Affected Crops using Deep Learning

2019 ◽  
Vol 8 (5S3) ◽  
pp. 232-234
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Monitoring System ◽  
Learning Algorithm ◽  
Target Area ◽  
Learning Technology ◽  
Machine Learning Algorithm ◽  
Normal Human ◽  
Human Monitoring ◽  
Disease Attack

Deep Learning technology can accurately predict the presence of diseases and pests in the agricultural farms. Upon this Machine learning algorithm, we can even predict accurately the chance of any disease and pest attacks in future For spraying the correct amount of fertilizer/pesticide to elimate host, the normal human monitoring system unable to predict accurately the total amount and ardent of pest and disease attack in farm. At the specified target area the artificial percepton tells the value accurately and give corrective measure and amount of fertilizers/ pesticides to be sprayed.

Early prediction of chronic disease using an efficient machine learning algorithm through adaptive probabilistic divergence based feature selection approach

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sandeepkumar Hegde ◽  
Monica R. Mundada
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Chronic Disease ◽  
Learning Algorithm ◽  
Predictive Ability ◽  
Experimental Result ◽  
Data Set ◽  
Content Type ◽  
Learning Classifier ◽  
Feature Selection Approach

Purpose According to the World Health Organization, by 2025, the contribution of chronic disease is expected to rise by 73% compared to all deaths and it is considered as global burden of disease with a rate of 60%. These diseases persist for a longer duration of time, which are almost incurable and can only be controlled. Cardiovascular disease, chronic kidney disease (CKD) and diabetes mellitus are considered as three major chronic diseases that will increase the risk among the adults, as they get older. CKD is considered a major disease among all these chronic diseases, which will increase the risk among the adults as they get older. Overall 10% of the population of the world is affected by CKD and it is likely to double in the year 2030. The paper aims to propose novel feature selection approach in combination with the machine-learning algorithm which can early predict the chronic disease with utmost accuracy. Hence, a novel feature selection adaptive probabilistic divergence-based feature selection (APDFS) algorithm is proposed in combination with the hyper-parameterized logistic regression model (HLRM) for the early prediction of chronic disease. Design/methodology/approach A novel feature selection APDFS algorithm is proposed which explicitly handles the feature associated with the class label by relevance and redundancy analysis. The algorithm applies the statistical divergence-based information theory to identify the relationship between the distant features of the chronic disease data set. The data set required to experiment is obtained from several medical labs and hospitals in India. The HLRM is used as a machine-learning classifier. The predictive ability of the framework is compared with the various algorithm and also with the various chronic disease data set. The experimental result illustrates that the proposed framework is efficient and achieved competitive results compared to the existing work in most of the cases. Findings The performance of the proposed framework is validated by using the metric such as recall, precision, F1 measure and ROC. The predictive performance of the proposed framework is analyzed by passing the data set belongs to various chronic disease such as CKD, diabetes and heart disease. The diagnostic ability of the proposed approach is demonstrated by comparing its result with existing algorithms. The experimental figures illustrated that the proposed framework performed exceptionally well in prior prediction of CKD disease with an accuracy of 91.6. Originality/value The capability of the machine learning algorithms depends on feature selection (FS) algorithms in identifying the relevant traits from the data set, which impact the predictive result. It is considered as a process of choosing the relevant features from the data set by removing redundant and irrelevant features. Although there are many approaches that have been already proposed toward this objective, they are computationally complex because of the strategy of following a one-step scheme in selecting the features. In this paper, a novel feature selection APDFS algorithm is proposed which explicitly handles the feature associated with the class label by relevance and redundancy analysis. The proposed algorithm handles the process of feature selection in two separate indices. Hence, the computational complexity of the algorithm is reduced to O(nk+1). The algorithm applies the statistical divergence-based information theory to identify the relationship between the distant features of the chronic disease data set. The data set required to experiment is obtained from several medical labs and hospitals of karkala taluk ,India. The HLRM is used as a machine learning classifier. The predictive ability of the framework is compared with the various algorithm and also with the various chronic disease data set. The experimental result illustrates that the proposed framework is efficient and achieved competitive results are compared to the existing work in most of the cases.

scholarly journals Implementation of modified SARSA learning technique in EMCAP

2017 ◽  
Vol 7 (1.5) ◽  
pp. 274
Author(s):  
D. Ganesha ◽  
Vijayakumar Maragal Venkatamuni
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Process ◽  
Learning Algorithm ◽  
Research Work ◽  
Learning System ◽  
State Action ◽  
Learning Technique ◽  
Markov Decision ◽  
Experiment Analysis

This research work presents analysis of Modified Sarsa learning algorithm. Modified Sarsa algorithm.  State-Action-Reward-State-Action (SARSA) is an technique for learning a Markov decision process (MDP) strategy, used in for reinforcement learning int the field of artificial intelligence (AI) and machine learning (ML). The Modified SARSA Algorithm makes better actions to get better rewards.  Experiment are conducted to evaluate the performace for each agent individually. For result comparison among different agent, the same statistics were collected. This work considered varied kind of agents in different level of architecture for experiment analysis. The Fungus world testbed has been considered for experiment which is has been implemented using SwI-Prolog 5.4.6. The fixed obstructs tend to be more versatile, to make a location that is specific to Fungus world testbed environment. The various parameters are introduced in an environment to test a agent’s performance. This modified   SARSA learning algorithm can   be more suitable in EMCAP architecture.  The experiments are conducted the modified   SARSA Learning system gets   more rewards compare to existing  SARSA algorithm.

Evaluation of maker space index system based on machine learning and intelligent interactive system

2020 ◽  
Vol 39 (4) ◽  
pp. 5941-5952
Author(s):  
Yang Chunhe
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Index System ◽  
Evaluation System ◽  
Learning System ◽  
Interactive System ◽  
Learning Technology ◽  
Integrated Learning System ◽  
Performance Evaluation System ◽  
Innovation Space

Machine learning technology is the core of artificial intelligence and the basis of computer intelligence. In recent years, machine learning technology has integrated and developed different learning methods, and the research of integrated learning system with more flexible and efficient form is also emerging. In this paper, the authors analyze the maker space index system based on machine learning and intelligent interactive system. As a comprehensive innovation and entrepreneurship platform, mass innovation space has the characteristics of both existing entrepreneurship service system and knowledge innovation driven. Through the index score calculation, the related evaluation system is constructed, the final score of social support system is 61.4.Multi-factor performance evaluation system based on machine learning and artificial intelligence,this paper reveals the development and change law of maker space, and provides theoretical basis for the future operation and decision-making of maker space.

scholarly journals Digital conversations about depression among Hispanics and non-Hispanics in the US: A big‐data, machine learning analysis

2021 ◽  
Author(s):  
Ruby Castilla-Puentes ◽  
Anjali Dagar ◽  
Dinorah Villanueva ◽  
Laura Jimenez-Parrado ◽  
Liliana. Gil Valleta ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Open Source ◽  
Source Analysis ◽  
Clinical Settings ◽  
Learning Technology ◽  
The Us ◽  
Negative Tone ◽  
Online Conversation ◽  
Learning Analysis

Abstract Background Digital conversations can offer unique information into the attitudes of Hispanics with depression outside of formal clinical settings and help generate useful information for medical treatment planning. Our study aimed to explore the big data from open-source digital conversations among Hispanics with regard to depression, specifically attitudes toward depression comparing Hispanics and non-Hispanics using machine learning technology. Methods Advanced machine‐learning empowered methodology was used to mine and structure open‐source digital conversations of self‐identifying Hispanics and non-Hispanics who endorsed suffering from depression and engaged in conversation about their tone, topics, and attitude towards depression. The search was limited to 12 months originating from US internet protocol (IP) addresses. Results A total of 441, 000 unique conversations about depression, including 43,000 (9.8%) for Hispanics, were posted. Source analysis revealed that 48% of conversations originated from topical sites compared to 16% on social media. Several critical differences were noted between Hispanics and non-Hispanics. In a higher percentage of Hispanics, their conversations portray “negative tone” due to depression (66% vs 39% non-Hispanics), show a resigned/hopeless attitude (44% vs. 30%) and were about ‘living with’ depression (44% vs. 25%). There were important differences in the author's determined sentiments behind the conversations among Hispanics and non-Hispanics. Conclusion In this first of its kind big data analysis of nearly a half-million digital conversations about depression using machine learning we found that Hispanics engage in an online conversation about negative, resigned, and hopeless attitude towards depression more often than non-Hispanic.

scholarly journals Analysis of Earthquake Forecasting in India Using Supervised Machine Learning Classifiers

2021 ◽  
Vol 13 (2) ◽  
pp. 971
Author(s):  
Papiya Debnath ◽  
Pankaj Chittora ◽  
Tulika Chakrabarti ◽  
Prasun Chakrabarti ◽  
Zbigniew Leonowicz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Vital Role ◽  
Earthquake Forecasting ◽  
Supervised Machine Learning ◽  
Learning Technology ◽  
Learning Classifier ◽  
Moderate Earthquake ◽  
Supervised Machine Learning Classifiers ◽  
Simple Logistic ◽  
Classifier Algorithms

Earthquakes are one of the most overwhelming types of natural hazards. As a result, successfully handling the situation they create is crucial. Due to earthquakes, many lives can be lost, alongside devastating impacts to the economy. The ability to forecast earthquakes is one of the biggest issues in geoscience. Machine learning technology can play a vital role in the field of geoscience for forecasting earthquakes. We aim to develop a method for forecasting the magnitude range of earthquakes using machine learning classifier algorithms. Three different ranges have been categorized: fatal earthquake; moderate earthquake; and mild earthquake. In order to distinguish between these classifications, seven different machine learning classifier algorithms have been used for building the model. To train the model, six different datasets of India and regions nearby to India have been used. The Bayes Net, Random Tree, Simple Logistic, Random Forest, Logistic Model Tree (LMT), ZeroR and Logistic Regression algorithms have been applied to each dataset. All of the models have been developed using the Weka tool and the results have been noted. It was observed that Simple Logistic and LMT classifiers performed well in each case.

scholarly journals Dynamic Nearest Neighbor: An Improved Machine Learning Classifier and Its Application in Finances

2021 ◽  
Vol 11 (19) ◽  
pp. 8884
Author(s):  
Oscar Camacho-Urriolagoitia ◽  
Itzamá López-Yáñez ◽  
Yenny Villuendas-Rey ◽  
Oscar Camacho-Nieto ◽  
Cornelio Yáñez-Márquez
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Support Vector ◽  
Human Beings ◽  
Classifier Ensembles ◽  
Learning Classifier ◽  
Learning Techniques ◽  
Supervised Classifiers ◽  
Real World Datasets

The presence of machine learning, data mining and related disciplines is increasingly evident in everyday environments. The support for the applications of learning techniques in topics related to economic risk assessment, among other financial topics of interest, is relevant for us as human beings. The content of this paper consists of a proposal of a new supervised learning algorithm and its application in real world datasets related to finance, called D1-NN (Dynamic 1-Nearest Neighbor). The D1-NN performance is competitive against the main state of the art algorithms in solving finance-related problems. The effectiveness of the new D1-NN classifier was compared against five supervised classifiers of the most important approaches (Bayes, nearest neighbors, support vector machines, classifier ensembles, and neural networks), with superior results overall.

scholarly journals A College Student Behavior Analysis and Management Method Based on Machine Learning Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xiaoying Shen ◽  
Chao Yuan
Keyword(s):  
Machine Learning ◽  
College Students ◽  
Behavior Analysis ◽  
School Safety ◽  
Student Behavior ◽  
Learning Algorithm ◽  
Learning Ability ◽  
Learning Technology ◽  
Digital Campus ◽  
Management Policies

A digital campus will generate a large amount of student-related data. How to analyze and apply these data has become the key to improving the management level of students. The analysis of student behavior data can not only assist schools in early warning of dangerous events and strengthen school safety but also can use real data to describe student behavior, thereby providing quantitative data support for scholarship and grant evaluation. This paper takes a university student as the research object, collects various data in the digital campus platform, and uses an adaptive K -means algorithm in the machine learning algorithm to cluster the data. Analyze the behavior of college students from the clustering results, so as to provide a basis for the education management and learning ability improvement of college students. Specifically, the student’s study, life, and consumption data are selected as the data to describe the student’s behavior at school. This data is input into the adaptive K -means algorithm to obtain different types of student consumption habits, living habits, and learning habits. Through the analysis results, it can be found that the problem of the group of students with low financial ability, the problem of too long online time for students, and the number of books borrowed are too low. According to the characteristics of these problems, teachers and schools are provided with targeted management suggestions. The analysis of student behavior based on machine learning technology provides a reference for the formulation of students’ school management policies and provides teachers with information on students’ personality characteristics, which is conducive to improving teachers’ teaching effects. In short, the management of the results of student behavior analysis can provide a basis for the school to formulate reasonable management policies, thereby promoting precision management and scientific decision-making.

scholarly journals Customer satisfaction prediction with Michigan-style learning classifier system

2019 ◽  
Vol 1 (11) ◽  
Author(s):  
Keivan Borna ◽  
Shokoofeh Hoseini ◽  
Mohammad Ali Mehdi Aghaei
Keyword(s):  
Machine Learning ◽  
Customer Satisfaction ◽  
Adaptive System ◽  
Learning System ◽  
Classification Algorithms ◽  
Learning Classifier System ◽  
Classifier System ◽  
Learning Classifier ◽  
Genetic Base ◽  
Base Machine

Abstract Many different classification algorithms can be use in order to analyze, classify and predict data. Learning classifier system (LCS) which is known as a genetic base machine learning system, combines the machine learning with evolutionary computing and other heuristics to produce an adaptive system that learns to solve a particular problem. This paper uses the Michigan style LCS, in the context of bank customer satisfaction to classify customers into two different groups: unsatisfied/satisfied customers. Three different Rule Compaction strategies are used to compare the rule population’s accuracy and micro/macro population size. The result specifies features that mostly influence prediction.

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