International Diversified Portfolio Optimization With Artificial Neural Networks

2019 ◽  
pp. 201-223
Author(s):  
Mehmet Fatih Bayramoglu ◽  
Cagatay Basarir
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Machine Learning Techniques ◽  
Portfolio Performance ◽  
Foreign Companies ◽  
Developed Markets ◽  
Learning Techniques ◽  
Artificial Neural ◽  
Diversified Portfolio

Investing in developed markets offers investors the opportunity to diversify internationally by investing in foreign firms. In other words, it provides the possibility of reducing systematic risk. For this reason, investors are very interested in developed markets. However, developed are more efficient than emerging markets, so the risk and return can be low in these markets. For this reason, developed market investors often use machine learning techniques to increase their gains while reducing their risks. In this chapter, artificial neural networks which is one of the machine learning techniques have been tested to improve internationally diversified portfolio performance. Also, the results of ANNs were compared with the performances of traditional portfolios and the benchmark portfolio. The portfolios are derived from the data of 16 foreign companies quoted on NYSE by ANNs, and they are invested for 30 trading days. According to the results, portfolio derived by ANNs gained 10.30% return, while traditional portfolios gained 5.98% return.

scholarly journals HOW MANY PARACHUTISTS WILL BE NECESSARY TO FIND A NEEDLE IN A PASTORAL - WHO IS A LUCKY ONE?

2014 ◽  
pp. 126-134
Author(s):  
Akira Imada
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Intrusion Detection ◽  
Computer Network ◽  
Machine Learning Techniques ◽  
Network Intrusion Detection ◽  
Network Intrusion ◽  
Learning Techniques ◽  
Artificial Neural

This article is a consideration on computer network intrusion detection using artificial neural networks, or whatever else using machine learning techniques. We assume an intrusion to a network is like a needle in a haystack not like a family of iris flower, and we consider how an attack can be detected by an intelligent way, if any.

scholarly journals Survival prediction among heart patients using machine learning techniques

2021 ◽  
Vol 19 (1) ◽  
pp. 134-145
Author(s):  
Abdulwahab Ali Almazroi ◽  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Decision Tree ◽  
Machine Learning Techniques ◽  
World Health ◽  
Support Vector ◽  
Survival Prediction ◽  
Learning Techniques ◽  
Artificial Neural

<abstract><p>Cardiovascular diseases are regarded as the most common reason for worldwide deaths. As per World Health Organization, nearly $ 17.9 $ million people die of heart-related diseases each year. The high shares of cardiovascular-related diseases in total worldwide deaths motivated researchers to focus on ways to reduce the numbers. In this regard, several works focused on the development of machine learning techniques/algorithms for early detection, diagnosis, and subsequent treatment of cardiovascular-related diseases. These works focused on a variety of issues such as finding important features to effectively predict the occurrence of heart-related diseases to calculate the survival probability. This research contributes to the body of literature by selecting a standard well defined, and well-curated dataset as well as a set of standard benchmark algorithms to independently verify their performance based on a set of different performance evaluation metrics. From our experimental evaluation, it was observed that decision tree is the best performing algorithm in comparison to logistic regression, support vector machines, and artificial neural networks. Decision trees achieved $ 14 $% better accuracy than the average performance of the remaining techniques. In contrast to other studies, this research observed that artificial neural networks are not as competitive as the decision tree or support vector machine.</p></abstract>

scholarly journals Machine Learning Classifiers for Modeling Soil Characteristics by Geophysics Investigations: A Comparative Study

2020 ◽  
Vol 10 (17) ◽  
pp. 5734
Author(s):  
Chee Soon Lim ◽  
Edy Tonnizam Mohamad ◽  
Mohammad Reza Motahari ◽  
Danial Jahed Armaghani ◽  
Rosli Saad
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Decision Tree ◽  
Soil Characteristics ◽  
Machine Learning Techniques ◽  
Chi Square ◽  
Interaction Detection ◽  
Testing Accuracy ◽  
Artificial Neural

To design geotechnical structures efficiently, it is important to examine soil’s physical properties. Therefore, classifying soil with respect to geophysical parameters is an advantageous and popular approach. Novel, quick, cost, and time effective machine learning techniques can facilitate this classification. This study employs three kinds of machine learning models, including the Decision Tree, Artificial Neural Networks, and Bayesian Networks. The Decision tree models included the chi-square automatic interaction detection (CHAID), classification and regression trees (CART), quick, unbiased, and efficient statistical tree (QUEST), and C5; the Artificial Neural Networks models included Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF); and BN models included the Tree Augmented Naïve (TAN) and Markov Blanket, which were employed to predict the soil classifications using geophysics investigations and laboratory tests. The performance of each model was assessed through the accuracy, stability and gains. The results showed that while the BAYESIANMARKOV model achieved the highest overall accuracy (100%) in training phase, this model achieved the lowest accuracy (34.21%) in testing phases. Thus, this model had the worst stability. The QUEST had the second highest overall training accuracy (99.12%) and had the highest overall testing accuracy (94.74%). Thus, this model was somewhat stable and had an acceptable overall training and testing accuracy to predict the soil characteristics. The future studies can use the findings of this paper as a benchmark to classify the soil characteristics and select the best machine learning technique to perform this classification.

scholarly journals Artificial Neural Networks and Machine Learning techniques applied to Ground Penetrating Radar: A review

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xisto L. Travassos ◽  
Sérgio L. Avila ◽  
Nathan Ida
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Ground Penetrating Radar ◽  
Data Interpretation ◽  
Machine Learning Techniques ◽  
Computational Techniques ◽  
Data Detection ◽  
Artificial Neural ◽  
Ground Penetrating

Ground Penetrating Radar is a multidisciplinary Nondestructive Evaluation technique that requires knowledge of electromagnetic wave propagation, material properties and antenna theory. Under some circumstances this tool may require auxiliary algorithms to improve the interpretation of the collected data. Detection, location and definition of target’s geometrical and physical properties with a low false alarm rate are the objectives of these signal post-processing methods. Basic approaches are focused in the first two objectives while more robust and complex techniques deal with all objectives at once. This work reviews the use of Artificial Neural Networks and Machine Learning for data interpretation of Ground Penetrating Radar surveys. We show that these computational techniques have progressed GPR forward from locating and testing to imaging and diagnosis approaches.

BUILDING CONSISTENCIES FOR PARTIALLY DEFINED CONSTRAINTS WITH DECISION TREES AND NEURAL NETWORKS

2007 ◽  
Vol 16 (04) ◽  
pp. 683-706 ◽  
Author(s):  
ARNAUD LALLOUET ◽  
ANDREI LEGTCHENKO
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Decision Trees ◽  
Optimization Problems ◽  
Machine Learning Techniques ◽  
Incomplete Knowledge ◽  
Learning Techniques ◽  
Variable Domains ◽  
Constraint Learning

Partially Defined Constraints can be used to model the incomplete knowledge of a concept or a relation. Instead of only computing with the known part of the constraint, we propose to complete its definition by using Machine Learning techniques. Since constraints are actively used during solving for pruning domains, building a classifier for instances is not enough: we need a solver able to reduce variable domains. Our technique is composed of two steps: first we learn a classifier for each constraint projections and then we transform the classifiers into a propagator. The first contribution is a generic meta-technique for classifier improvement showing performances comparable to boosting. The second lies in the ability of using the learned concept in constraint-based decision or optimization problems. We presents results using Decision Trees and Artificial Neural Networks for constraint learning and propagation. It opens a new way of integrating Machine Learning in Decision Support Systems.

scholarly journals Multiclass decomposition and Artificial Neural Networks for intrusion detection and identification in Internet of Things environments

2021 ◽  
Author(s):  
Cristiano Antonio de Souza ◽  
João Vitor Cardoso ◽  
Carlos Becker Westphall
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Internet Of Things ◽  
False Positives ◽  
Machine Learning Techniques ◽  
Detection And Identification ◽  
Learning Techniques ◽  
Artificial Neural ◽  
Stage Analysis ◽  
The Internet Of Things

The Internet of Things (IoT) systems have limited resources, making it difficult to implement some security mechanisms. It is important to detect attacks against these environments and identify their type. However, existing multi-class detection approaches present difficulties related to false positives and detection of less common attacks. Thus, this work proposes an approach with a two-stage analysis architecture based on One-Vs-All (OVA) and Artificial Neural Networks (ANN) to detect and identify intrusions in fog and IoT computing environments. The results of experiments with the Bot-IoT dataset demonstrate that the approach achieved promising results and reduced the number of false positives compared to state-of-the-art approaches and machine learning techniques.

scholarly journals A Machine Learning Approach as a Surrogate for a Finite Element Analysis: Status of Research and Application to One Dimensional Systems

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1654
Author(s):  
Poojitha Vurtur Badarinath ◽  
Maria Chierichetti ◽  
Fatemeh Davoudi Kakhki
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Finite Element Analysis ◽  
Finite Element ◽  
Artificial Neural Networks ◽  
Stress Distribution ◽  
Maintenance Scheduling ◽  
Element Analysis ◽  
One Dimensional ◽  
Artificial Neural

Current maintenance intervals of mechanical systems are scheduled a priori based on the life of the system, resulting in expensive maintenance scheduling, and often undermining the safety of passengers. Going forward, the actual usage of a vehicle will be used to predict stresses in its structure, and therefore, to define a specific maintenance scheduling. Machine learning (ML) algorithms can be used to map a reduced set of data coming from real-time measurements of a structure into a detailed/high-fidelity finite element analysis (FEA) model of the same system. As a result, the FEA-based ML approach will directly estimate the stress distribution over the entire system during operations, thus improving the ability to define ad-hoc, safe, and efficient maintenance procedures. The paper initially presents a review of the current state-of-the-art of ML methods applied to finite elements. A surrogate finite element approach based on ML algorithms is also proposed to estimate the time-varying response of a one-dimensional beam. Several ML regression models, such as decision trees and artificial neural networks, have been developed, and their performance is compared for direct estimation of the stress distribution over a beam structure. The surrogate finite element models based on ML algorithms are able to estimate the response of the beam accurately, with artificial neural networks providing more accurate results.

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