A Hybrid Model for Financial Portfolio Optimization Based on LS-SVM and a Clustering Algorithm

2019 ◽  
pp. 173-186
Author(s):  
Ivana P. Marković ◽  
Jelena Z. Stanković ◽  
Miloš B. Stojanović ◽  
Jovica M. Stanković
Keyword(s):  
Portfolio Optimization ◽  
Hybrid Model ◽  
Clustering Algorithm ◽  
Financial Portfolio

Financial Portfolio Optimization

2021 ◽  
pp. 231-240
Author(s):  
Mansi Gupta ◽  
Sonali Semwal ◽  
Shivani Bali
Keyword(s):  
Portfolio Optimization ◽  
Financial Portfolio

scholarly journals Hybrid Models for Adaptive Allocation of Electricity for Households

2019 ◽  
Vol 9 (2S) ◽  
pp. 369-376
Keyword(s):  
Time Series ◽  
Hybrid Model ◽  
Clustering Algorithm ◽  
Time Series Data ◽  
Active Power ◽  
Sensor Data ◽  
Series Data ◽  
Adaptive Allocation ◽  
Forecasting Methods ◽  
Better Than

In this paper, we analyze, model, predict and cluster Global Active Power, i.e., a time series data obtained at one minute intervals from electricity sensors of a household. We analyze changes in seasonality and trends to model the data. We then compare various forecasting methods such as SARIMA and LSTM to forecast sensor data for the household and combine them to achieve a hybrid model that captures nonlinear variations better than either SARIMA or LSTM used in isolation. Finally, we cluster slices of time series data effectively using a novel clustering algorithm that is a combination of density-based and centroid-based approaches, to discover relevant subtle clusters from sensor data. Our experiments have yielded meaningful insights from the data at both a micro, day-to-day granularity, as well as a macro, weekly to monthly granularity.

scholarly journals Categorizing Diseases from Leaf Images Using a Hybrid Learning Model

Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2073
Author(s):  
Devi N. ◽  
Leela Rani P. ◽  
Guru Gokul AR. ◽  
Raju Kannadasan ◽  
Mohammed H. Alsharif ◽  
...  
Keyword(s):  
Hybrid Model ◽  
Clustering Algorithm ◽  
Hybrid Learning ◽  
Critical Issue ◽  
Learning Model ◽  
Rapid Identification ◽  
Plant Diseases ◽  
Support Vector ◽  
Learning Machine ◽  
Occurrence Matrix

Plant diseases pose a severe threat to crop yield. This necessitates the rapid identification of diseases affecting various crops using modern technologies. Many researchers have developed solutions to the problem of identifying plant diseases, but it is still considered a critical issue due to the lack of infrastructure in many parts of the world. This paper focuses on detecting and classifying diseases present in the leaf images by adopting a hybrid learning model. The proposed hybrid model uses k-means clustering for detecting the disease area from the leaf and a Convolutional Neural Network (CNN) for classifying the type of disease based on comparison between sampled and testing images. The images of leaves under consideration may be symmetrical or asymmetrical in shape. In the proposed methodology, the images of various leaves from diseased plants were first pre-processed to filter out the noise present to get an enhanced image. This improved image enabled detection of minute disease-affected regions. The infected areas were then segmented using k-means clustering algorithm that locates only the infected (diseased) areas by masking the leaves’ green (healthy) regions. The grey level co-occurrence matrix (GLCM) methodology was used to fetch the necessary features from the affected portions. Since the number of fetched features was insufficient, more synthesized features were included, which were then given as input to CNN for training. Finally, the proposed hybrid model was trained and tested using the leaf disease dataset available in the UCI machine learning repository to examine the characteristics between trained and tested images. The hybrid model proposed in this paper can detect and classify different types of diseases affecting different plants with a mean classification accuracy of 92.6%. To illustrate the efficiency of the proposed hybrid model, a comparison was made against the following classification approaches viz., support vector machine, extreme learning machine-based classification, and CNN. The proposed hybrid model was found to be more effective than the other three.

scholarly journals Financial Portfolio Optimization using Monte Carlo and Operation Research

2020 ◽  
Vol 175 (34) ◽  
pp. 43-46
Author(s):  
Noureen M. Noaman ◽  
Mohamed A. El-dosuky ◽  
Abdelrahman Karawia
Keyword(s):  
Monte Carlo ◽  
Portfolio Optimization ◽  
Operation Research ◽  
Financial Portfolio

Multi-attribute decision making applied to financial portfolio optimization problem

2020 ◽  
Vol 158 ◽  
pp. 113527
Author(s):  
Gustavo H.M. Mendonça ◽  
Fernando G.D.C. Ferreira ◽  
Rodrigo T.N. Cardoso ◽  
Flávio V.C. Martins
Keyword(s):  
Decision Making ◽  
Portfolio Optimization ◽  
Optimization Problem ◽  
Multi Attribute Decision Making ◽  
Financial Portfolio ◽  
Portfolio Optimization Problem
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