scholarly journals Correction to: Machine learning applied to stock index performance enhancement

2021 ◽  
Author(s):  
Tien-Yu Hsu
Keyword(s):  
Machine Learning ◽  
Performance Enhancement ◽  
Stock Index

scholarly journals A Simple Ensemble Learning Knowledge Distillation

2020 ◽  
Author(s):  
Himel Das Gupta ◽  
Kun Zhang ◽  
Victor S. Sheng
Keyword(s):  
Machine Learning ◽  
Ensemble Learning ◽  
Performance Enhancement ◽  
Portable Devices ◽  
Computational Power ◽  
Small Machine ◽  
Learning Tasks ◽  
Machine Learning Applications ◽  
Knowledge Distillation ◽  
Small Models

Deep neural network (DNN) has shown significant improvement in learning and generalizing different machine learning tasks over the years. But it comes with an expense of heavy computational power and memory requirements. We can see that machine learning applications are even running in portable devices like mobiles and embedded systems nowadays, which generally have limited resources regarding computational power and memory and thus can only run small machine learning models. However, smaller networks usually do not perform very well. In this paper, we have implemented a simple ensemble learning based knowledge distillation network to improve the accuracy of such small models. Our experimental results prove that the performance enhancement of smaller models can be achieved through distilling knowledge from a combination of small models rather than using a cumbersome model for the knowledge transfer. Besides, the ensemble knowledge distillation network is simpler, time-efficient, and easy to implement.

Performance enhancement of FSO communication system using machine learning for 5G/6G and IoT applications

Optik ◽  
2021 ◽  
pp. 168430
Author(s):  
Lepuri Jathin Sravan Kumar ◽  
Prabu Krishnan ◽  
Biradher Shreya ◽  
Sudhakar M.S.
Keyword(s):  
Machine Learning ◽  
Communication System ◽  
Performance Enhancement ◽  
Iot Applications

Performance Enhancement of Optical Comb Based Microwave Photonic Filter by Machine Learning Technique

2020 ◽  
Vol 38 (19) ◽  
pp. 5302-5310 ◽  
Author(s):  
Run-Kai Shiu ◽  
You-Wei Chen ◽  
Peng-Chun Peng ◽  
Justin Chiu ◽  
Qi Zhou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Performance Enhancement ◽  
Machine Learning Technique ◽  
Microwave Photonic ◽  
Learning Technique

scholarly journals Prediction of Stock Index of Tata Steel using Hybrid Machine Learning based Optimization Techniques

2019 ◽  
Vol 8 (2) ◽  
pp. 3186-3193
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Prediction Models ◽  
Market Price ◽  
Optimization Techniques ◽  
Stock Index ◽  
Kernel Principal Component Analysis ◽  
Support Vector ◽  
Stock Price Prediction ◽  
Hybrid Machine

The trend of stock price prediction has always been in the focal point of analytical activity in financial domain for both the researchers and investors. Prediction with accuracy is very essential for improved investment decisions that imbibe minimum risk factors. Due to this, majority of investors depend upon that intelligent trading system which generates better forecasting results. As forecasting stock market price with high accuracy is quite a challenging task for the analysts, machine learning has been adopted as one of the popular techniques to predict future trends. Even if there are many recognized analytical time series analysis that are categorized either under soft computing or under conventional statistical techniques like fuzzy logic, artificial neural networks and genetic algorithms, researchers have been looking for more appropriate techniques which can exhibit improved results. In this paper, we developed different hybrid machine learning based prediction models and compared their efficiency. Dimension reduction techniques such as orthogonal forward selection (OFS) and kernel principal component analysis (KPCA) are used separately with support vector regression (SVR) and teaching learning based optimization (TLBO) to predict the stock price of Tata Steel. The performance of both the proposed approach is evaluated with 4143days daily transactional data of Tata steels stocks price, which was collected from Bombay Stock Exchange (BSE). We compared the results of both OFS-SVR-TLBO and KPCA-SVR-TLBO hybrid models and concludes that by incorporating KPCA is more practicable and performs better results than OFS

scholarly journals Classification of malignant tumours in breast ultrasound using unsupervised machine learning approaches

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Wei-Chung Shia ◽  
Li-Sheng Lin ◽  
Dar-Ren Chen
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Performance Enhancement ◽  
Feature Selection Method ◽  
Classification Performance ◽  
Significant Feature ◽  
Learning Approaches ◽  
Malignant Tumours ◽  
Local Weight ◽  
Predictive Values

AbstractTraditional computer-aided diagnosis (CAD) processes include feature extraction, selection, and classification. Effective feature extraction in CAD is important in improving the classification’s performance. We introduce a machine-learning method and have designed an analysis procedure of benign and malignant breast tumour classification in ultrasound (US) images without a need for a priori tumour region-selection processing, thereby decreasing clinical diagnosis efforts while maintaining high classification performance. Our dataset constituted 677 US images (benign: 312, malignant: 365). Regarding two-dimensional US images, the oriented gradient descriptors’ histogram pyramid was extracted and utilised to obtain feature vectors. The correlation-based feature selection method was used to evaluate and select significant feature sets for further classification. Sequential minimal optimisation—combining local weight learning—was utilised for classification and performance enhancement. The image dataset’s classification performance showed an 81.64% sensitivity and 87.76% specificity for malignant images (area under the curve = 0.847). The positive and negative predictive values were 84.1 and 85.8%, respectively. Here, a new workflow, utilising machine learning to recognise malignant US images was proposed. Comparison of physician diagnoses and the automatic classifications made using machine learning yielded similar outcomes. This indicates the potential applicability of machine learning in clinical diagnoses.

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