scholarly journals Lifting the Performance of a Heuristic for the Time-Dependent Travelling Salesman Problem through Machine Learning

Algorithms ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 340
Author(s):  
Gianpaolo Ghiani ◽  
Tommaso Adamo ◽  
Pierpaolo Greco ◽  
Emanuela Guerriero
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Travelling Salesman Problem ◽  
Computing Time ◽  
Time Dependent ◽  
Machine Learning Techniques ◽  
Travelling Salesman ◽  
Distribution Management ◽  
Learning Techniques ◽  
Average Improvement

In recent years, there have been several attempts to use machine learning techniques to improve the performance of exact and approximate optimization algorithms. Along this line of research, the present paper shows how supervised and unsupervised techniques can be used to improve the quality of the solutions generated by a heuristic for the Time-Dependent Travelling Salesman Problem with no increased computing time. This can be useful in a real-time setting where a speed update (or the arrival of a new customer request) may lead to the reoptimization of the planned route. The main contribution of this work is to show how to reuse the information gained in those settings in which instances with similar features have to be solved over and over again, as it is customary in distribution management. We use a method based on the nearest neighbor procedure (supervised learning) and the K-means algorithm with the Euclidean distance (unsupervised learning). In order to show the effectiveness of this approach, the computational experiments have been carried out for the dataset generated based on the real travel time functions of two European cities: Paris and London. The overall average improvement of our heuristic over the classical nearest neighbor procedure is about 5% for London, and about 4% for Paris.

scholarly journals Classification Framework for Healthy Hairs and Alopecia Areata: A Machine Learning (ML) Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Choudhary Sobhan Shakeel ◽  
Saad Jawaid Khan ◽  
Beenish Chaudhry ◽  
Syeda Fatima Aijaz ◽  
Umer Hassan
Keyword(s):  
Machine Learning ◽  
Alopecia Areata ◽  
Nearest Neighbor ◽  
Autoimmune Disorder ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Classification Framework ◽  
Learning Techniques ◽  
Machine Learning Model ◽  
Image Set

Alopecia areata is defined as an autoimmune disorder that results in hair loss. The latest worldwide statistics have exhibited that alopecia areata has a prevalence of 1 in 1000 and has an incidence of 2%. Machine learning techniques have demonstrated potential in different areas of dermatology and may play a significant role in classifying alopecia areata for better prediction and diagnosis. We propose a framework pertaining to the classification of healthy hairs and alopecia areata. We used 200 images of healthy hairs from the Figaro1k dataset and 68 hair images of alopecia areata from the Dermnet dataset to undergo image preprocessing including enhancement and segmentation. This was followed by feature extraction including texture, shape, and color. Two classification techniques, i.e., support vector machine (SVM) and k -nearest neighbor (KNN), are then applied to train a machine learning model with 70% of the images. The remaining image set was used for the testing phase. With a 10-fold cross-validation, the reported accuracies of SVM and KNN are 91.4% and 88.9%, respectively. Paired sample T -test showed significant differences between the two accuracies with a p < 0.001 . SVM generated higher accuracy (91.4%) as compared to KNN (88.9%). The findings of our study demonstrate potential for better prediction in the field of dermatology.

scholarly journals A Comparative Study on University Admission Predictions Using Machine Learning Techniques

2021 ◽  
pp. 537-548
Author(s):  
Prince Golden ◽  
Kasturi Mojesh ◽  
Lakshmi Madhavi Devarapalli ◽  
Pabbidi Naga Suba Reddy ◽  
Srigiri Rajesh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Education Systems ◽  
University Admissions ◽  
The Troubles ◽  
Learning Techniques ◽  
Cloud Servers

In this era of Cloud Computing and Machine Learning where every kind of work is getting automated through machine learning techniques running off of cloud servers to complete them more efficiently and quickly, what needs to be addressed is how we are changing our education systems and minimizing the troubles related to our education systems with all the advancements in technology. One of the the prominent issues in front of students has always been their graduate admissions and the colleges they should apply to. It has always been difficult to decide as to which university or college should they apply according to their marks obtained during their undergrad as not only it’s a tedious and time consuming thing to apply for number of universities at a single time but also expensive. Thus many machine learning solutions have emerged in the recent years to tackle this problem and provide various predictions, estimations and consultancies so that students can easily make their decisions about applying to the universities with higher chances of admission. In this paper, we review the machine learning techniques which are prevalent and provide accurate predictions regarding university admissions. We compare different regression models and machine learning methodologies such as, Random Forest, Linear Regression, Stacked Ensemble Learning, Support Vector Regression, Decision Trees, KNN(K-Nearest Neighbor) etc, used by other authors in their works and try to reach on a conclusion as to which technique will provide better accuracy.

scholarly journals The Role of Medication Data to Enhance the Prediction of Alzheimer’s Progression Using Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Shaker El-Sappagh ◽  
Tamer Abuhmed ◽  
Bader Alouffi ◽  
Radhya Sahal ◽  
Naglaa Abdelhade ◽  
...  
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Multimodal Data ◽  
Learning Techniques ◽  
Early Progression ◽  
Neuroimaging Data

Early detection of Alzheimer’s disease (AD) progression is crucial for proper disease management. Most studies concentrate on neuroimaging data analysis of baseline visits only. They ignore the fact that AD is a chronic disease and patient’s data are naturally longitudinal. In addition, there are no studies that examine the effect of dementia medicines on the behavior of the disease. In this paper, we propose a machine learning-based architecture for early progression detection of AD based on multimodal data of AD drugs and cognitive scores data. We compare the performance of five popular machine learning techniques including support vector machine, random forest, logistic regression, decision tree, and K-nearest neighbor to predict AD progression after 2.5 years. Extensive experiments are performed using an ADNI dataset of 1036 subjects. The cross-validation performance of most algorithms has been improved by fusing the drugs and cognitive scores data. The results indicate the important role of patient’s taken drugs on the progression of AD disease.

Machine Learning Methods as a Test Bed for EEG Analysis in BCI Paradigms

2020 ◽  
pp. 1577-1597
Author(s):  
Kusuma Mohanchandra ◽  
Snehanshu Saha
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Machine Learning Techniques ◽  
Analytical Models ◽  
Support Vector ◽  
Test Bed ◽  
K Nearest Neighbor ◽  
Eeg Signals ◽  
Broad Perspective ◽  
Learning Techniques

Machine learning techniques, is a crucial tool to build analytical models in EEG data analysis. These models are an excellent choice for analyzing the high variability in EEG signals. The advancement in EEG-based Brain-Computer Interfaces (BCI) demands advanced processing tools and algorithms for exploration of EEG signals. In the context of the EEG-based BCI for speech communication, few classification and clustering techniques is presented in this book chapter. A broad perspective of the techniques and implementation of the weighted k-Nearest Neighbor (k-NN), Support vector machine (SVM), Decision Tree (DT) and Random Forest (RF) is explained and their usage in EEG signal analysis is mentioned. We suggest that these machine learning techniques provides not only potentially valuable control mechanism for BCI but also a deeper understanding of neuropathological mechanisms underlying the brain in ways that are not possible by conventional linear analysis.

On the Use of Artificial Intelligence Techniques in Crop Monitoring and Disease Identification

2021 ◽  
pp. 166-186
Author(s):  
Muzaffer Kanaan ◽  
Rüştü Akay ◽  
Canset Koçer Baykara
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Nearest Neighbor ◽  
Crop Yields ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Disease Identification ◽  
Use Of Technology ◽  
Learning Techniques

The use of technology for the purpose of improving crop yields, quality and quantity of the harvest, as well as maintaining the quality of the crop against adverse environmental elements (such as rodent or insect infestation, as well as microbial disease agents) is becoming more critical for farming practice worldwide. One of the technology areas that is proving to be most promising in this area is artificial intelligence, or more specifically, machine learning techniques. This chapter aims to give the reader an overview of how machine learning techniques can help solve the problem of monitoring crop quality and disease identification. The fundamental principles are illustrated through two different case studies, one involving the use of artificial neural networks for harvested grain condition monitoring and the other concerning crop disease identification using support vector machines and k-nearest neighbor algorithm.

An Analytical Study on Machine Learning Techniques

2021 ◽  
pp. 137-157
Author(s):  
Law Kumar Singh ◽  
Pooja ◽  
Hitendra Garg ◽  
Munish Khanna ◽  
Robin Singh Bhadoria
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Ethical Issues ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Behavior Prediction ◽  
K Nearest Neighbor ◽  
Learning Techniques ◽  
Learning Machine ◽  
And Behavior

The last few months have produced a remarkable expansion in research and deep study in the field of machine learning. Machine learning is a technique in which the set of the methods are used by the computers to make prediction, improve prediction and behavior prediction based on dataset. The learning techniques can be classified as supervised and unsupervised learning. The focus is on supervised machine learning that covers all the predictions problem for which we had the dataset in which the outcome is already known. Some of the algorithm like naive bayes, linear regression, SVM, k-nearest neighbor, especially neural network have gain growth in this area. The classifiers of machine learning are completely unconstrained with the assumptions of statistical and for that they are adapted by complex data. The authors have demonstrated the application of machine learning techniques and its ethical issues.

Machine Learning Methods as a Test Bed for EEG Analysis in BCI Paradigms

2017 ◽  
pp. 186-206
Author(s):  
Kusuma Mohanchandra ◽  
Snehanshu Saha
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Machine Learning Techniques ◽  
Analytical Models ◽  
Support Vector ◽  
Test Bed ◽  
K Nearest Neighbor ◽  
Eeg Signals ◽  
Broad Perspective ◽  
Learning Techniques

Machine learning techniques, is a crucial tool to build analytical models in EEG data analysis. These models are an excellent choice for analyzing the high variability in EEG signals. The advancement in EEG-based Brain-Computer Interfaces (BCI) demands advanced processing tools and algorithms for exploration of EEG signals. In the context of the EEG-based BCI for speech communication, few classification and clustering techniques is presented in this book chapter. A broad perspective of the techniques and implementation of the weighted k-Nearest Neighbor (k-NN), Support vector machine (SVM), Decision Tree (DT) and Random Forest (RF) is explained and their usage in EEG signal analysis is mentioned. We suggest that these machine learning techniques provides not only potentially valuable control mechanism for BCI but also a deeper understanding of neuropathological mechanisms underlying the brain in ways that are not possible by conventional linear analysis.

Human Computer Interface Using Electroencephalography for Driver Behavior Classification

2019 ◽  
Author(s):  
Vamsi K. Manchala ◽  
Alvaro V. Clara ◽  
Susheelkumar C. Subramanian ◽  
Sangram Redkar ◽  
Thomas Sugar
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Driver Behavior ◽  
Driving Behavior ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Affective States ◽  
K Nearest Neighbor ◽  
Learning Techniques ◽  
Artificial Neural Network Ann

Abstract It is important to know and be able to classify the drivers’ behavior as good, bad, keen or aggressive, which would aid in driver assist systems to avoid vehicle crashes. This research attempts to develop, test, and compare the performance of machine learning methods for classifying human driving behavior. It also proposes to correlate driver affective states with the driving behavior. The major contributions of this work are to classify the driver behavior using Electroencephalograph (EEG) while driving simulated vehicle and compare them with the behavior classified using vehicle parameters and affective states. The study involved both classical machine learning techniques such as k-Nearest Neighbor (KNN), Support Vector Machine (SVM), Artificial Neural Network (ANN) and latest “unsupervised” Hybrid Deep Learning techniques, and compared the accuracy of classification across subjects, various driving scenarios and affective states.

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