Morphological Extreme Learning Machines

Background and Objective: Every second, on average, 8 (eight) new malware are created. So, our goal is to propose an antivirus, endowed with artificial intelligence, able of identifying malwares through models based on fast training and high-performance neural networks. Methods: Our NGAV (Next Generation Antivirus) is equipped with an authorial ELM (Extreme Learning Morphological) machine. Our bmELMs (Bitwise-Morphological ELMs) are inspired by the image processing theory of Mathematical Morphology. We claim that bmELMs are able to adapt in any machine learning dataset. Inspired by Mathematical Morphology, our bmELMs are capable of modeling any form present at the decisions boundaries of neural networks. Results: Our bmELMs results are compared with classical ELMs and evaluated through widely used classification metrics. Our antivirus, provided with Bitwise-Morphology, achieves an average accuracy of 97.88%, 93.07%, 93.07% and 91.74% in malware detection of PE (Portable Executable), Java, JavaScript and PHP, respectively. Conclusions: Our NGAV enables high performance, large capacity of parallelism, and simple, low-power architecture with low power consumption. We concluded that our Bitwise-Morphology assists to the main requirements for the proper operation and confection of antivirus in hardware.

The last few years witnessed great and increasing use in the field of medical image analysis. These tools helped the Radiologists and Doctors to consult while making a particular diagnosis. In this study, we used the relationship between statistical measurements, computer vision, and medical images, along with a logistic regression model to extract breast cancer imaging features. These features were used to tell the difference between the shape of a mass (Fibroid vs. Fatty) by looking at the regions of interest (ROI) of the mass. The final fit of the logistic regression model showed that the most important variables that clearly affect breast cancer shape images are Skewness, Kurtosis, Center of mass, and Angle, with an AUCROC of 88% and an Accuracy of almost 89%. We also came to the conclusion that the Fibroid mass is small and less white than the Fatty mass

Worms such as Slammer, Nimda, and Code Red I are anomalies that affect performance of the global Internet Border Gateway Protocol (BGP). BGP anomalies also include Internet Protocol (IP) prefix hijacks, miss-configurations, and electrical failures. Statistical and machine learning techniques have been recently deployed to classify and detect BGP anomalies. In this paper, we introduce new classification features and apply Support Vector Machine (SVM) models and Hidden Markov Models (HMMs) to design anomaly detection mechanisms. We apply these multi classification models to correctly classify test datasets and identify the correct anomaly types. The proposed models are tested with collected BGP traffic traces and are employed to successfully classify and detect various BGP anomalies.

Breast cancer is second cause of death for women which its risk can be minimized by accurate early detection and appropriate treatment. A lot of data mining techniques have been developed to support breast cancer diagnosis. However, the existing works mostly focus on prediction performance with limited attention with medical professional as end user and applicability aspect in real medical setting. In this paper, we designed and developed intelligent breast cancer prediction in Java called Breast Cancer Clinical Decision Support Systems. The systems has intuitive graphical user interface with eleven functionalities based on discussion with domain expert. Extreme learning machine was utilized as main intelligent component for predicting benign and malignant. Results showed that all functionalities were work well and done without significant delay. The accuracy performance outperformed average manual diagnosis from unaided medical professional. These showed that the proposed system is promising to be applied in the real medical setting to support breast cancer early detection.

Breast cancer is second cause of death for women which its risk can be minimized by accurate early detection and appropriate treatment. A lot of data mining techniques have been developed to support breast cancer diagnosis. However, the existing works mostly focus on prediction performance with limited attention with medical professional as end user and applicability aspect in real medical setting. In this paper, we designed and developed intelligent breast cancer prediction in Java called Breast Cancer Clinical Decision Support Systems. The systems has intuitive graphical user interface with eleven functionalities based on discussion with domain expert. Extreme learning machine was utilized as main intelligent component for predicting benign and malignant. Results showed that all functionalities were work well and done without significant delay. The accuracy performance outperformed average manual diagnosis from unaided medical professional. These showed that the proposed system is promising to be applied in the real medical setting to support breast cancer early detection.

Breast cancer is second cause of death for women which its risk can be minimized by accurate early detection and appropriate treatment. A lot of data mining techniques have been developed to support breast cancer diagnosis. However, the existing works mostly focus on prediction performance with limited attention with medical professional as end user and applicability aspect in real medical setting. In this paper, we designed and developed intelligent breast cancer prediction in Java called Breast Cancer Clinical Decision Support Systems. The systems has intuitive graphical user interface with eleven functionalities based on discussion with domain expert. Extreme learning machine was utilized as main intelligent component for predicting benign and malignant. Results showed that all functionalities were work well and done without significant delay. The accuracy performance outperformed average manual diagnosis from unaided medical professional. These showed that the proposed system is promising to be applied in the real medical setting to support breast cancer early detection.

Breast cancer is the most common cancer in women and one of the major causes of death from cancer among female around the world. The early detection and treatment are the major way to healing. The use of mammary thermography in Mastology is increasing as a complementary imaging technique to early detect lesions. Its use as a screening exam to identify breast disorders has been investigated. The aim of this study is to investigate the behavior of different classification methods while grouping the thermographic images into specific types of lesions. Methods: To evaluate our proposal, we built classifiers based on artificial neural networks, decision trees, Bayesian classifiers, and Haralick and Zernike attributes. The image database is composed by thermographic images acquired at the University Hospital of the Federal University of Pernambuco. These images are clinically classified into the classes cyst, malignant and benign. Moments of Zernike and Haralick were used as attributes. Results: Extreme Learning Machines (ELM) and Multilayer Perceptron networks (MLP) proved to be quite efficient classifiers for classification of breast lesions in thermographic images. Using 75% of the database for training, the maximum value obtained for accuracy was 73.38%, with a Kappa index of 0.6007. This result indicated to a sensitivity of 78% and specificity of 88%. The overall efficiency of the system was 83%. Conclusion: ELM showed to be a promising classifier to be used in the differentiation of breast lesions in thermographic images, due to its low computational cost and robustness.

Breast cancer is the most common cancer in women and one of the major causes of death from cancer among female around the world. The early detection and treatment are the major way to healing. The use of mammary thermography in Mastology is increasing as a complementary imaging technique to early detect lesions. Its use as a screening exam to identify breast disorders has been investigated. The aim of this study is to investigate the behavior of different classification methods while grouping the thermographic images into specific types of lesions. Methods: To evaluate our proposal, we built classifiers based on artificial neural networks, decision trees, Bayesian classifiers, and Haralick and Zernike attributes. The image database is composed by thermographic images acquired at the University Hospital of the Federal University of Pernambuco. These images are clinically classified into the classes cyst, malignant and benign. Moments of Zernike and Haralick were used as attributes. Results: Extreme Learning Machines (ELM) and Multilayer Perceptron networks (MLP) proved to be quite efficient classifiers for classification of breast lesions in thermographic images. Using 75% of the database for training, the maximum value obtained for accuracy was 73.38%, with a Kappa index of 0.6007. This result indicated to a sensitivity of 78% and specificity of 88%. The overall efficiency of the system was 83%. Conclusion: ELM showed to be a promising classifier to be used in the differentiation of breast lesions in thermographic images, due to its low computational cost and robustness.

Classification of breast cancer is essential in determining the type of treatment that should be applied. Thus, a Computer Aided Diagnosis (CADx) may assist radiologists in making appropriate decision based on the classification results. In this paper, the classification is divided into two categories; to classify the cancer into benign and malignant (two classes) and to classify the character of the background tissue either fatty, glandular or dense (multi class). The Haralick texture features and Hu Invariants moments were proposed as the features extraction. There are three phases conducted in this study. The first phase is the preprocessing phase. This is followed by the features extraction phase where combination of moment based features with addition of four features was proposed. The final phase is the classification phase by using SVM classifiers. Results obtained shows that the accuracy of the proposed features are 90.5% and 77.5% for two classes and multi class respectively.

According to the World Health Organization, breast
cancer is the most common type of cancer in women. It is also
the second leading cause of death among women around the
world, becoming the most fatal form of cancer. However, to detect
and classify masses is a hard task even for experts. Therefore,
due to medical experience, different diagnoses to an image are
commonly found. The use of a computer assisted diagnosis is
important to avoid misdiagnoses. In this work, we propose Fuzzy
Morphological Extreme Learning Machines, with hidden layer
kernel based on nonlinear morphological operators of erosion
and dilation. The proposed methods were evaluated using 2.796
images from IRMA database, considering fat, fibroid, dense and
extremely dense tissues. Zernike Moments and Haralick texture
features are used as image descriptors. The proposed model
classifies masses as benign, malignant or normal. Results shows
comparison between Extreme Learning Machines using Sigmoid
and Fuzzy Morphological Kernels, evaluated using classification
rate and Kappa index. When using fuzzy morphological kernels,
classification rate and Kappa value increases for most of cases
analyzed.

According to the World Health Organization, breast
cancer is the most common form of cancer in women. It is
the second leading cause of death among women around the
world, becoming the most fatal form of cancer. However, to
detect and classify masses is a hard task even for experts.
Consequently, due to medical experience, different diagnoses to
an image are commonly found. Therefore, the use of a computer
assisted diagnosis is important to avoid misdiagnoses. In this
work, we propose Morphological Extreme Learning Machines,
with hidden layer kernel based on nonlinear morphological
operators of erosion and dilation. The proposed approach is
evaluated using 2.796 images from IRMA database, considering
fat, fbroid, dense and extremely dense tissues. Zernike Moments
and Haralick texture features are used as image descriptors and
the proposed model classifes the masses in benign, malignant or
normal. Results shows comparison between Extreme Learning
Machines using Sigmoid and Morphological Kernel, which are
evaluated through classifcation rate and Kappa index. When
using morphological kernels, the classifcation rate and Kappa
value increases for most of cases analyzed.

Breast cancer is second cause of death for women which its risk can be minimized by accurate early detection and appropriate treatment. A lot of data mining techniques have been developed to support breast cancer diagnosis. However, the existing works mostly focus on prediction performance with limited attention with medical professional as end user and applicability aspect in real medical setting. In this paper, we designed and developed intelligent breast cancer prediction in Java called Breast Cancer Clinical Decision Support Systems. The systems has intuitive graphical user interface with eleven functionalities based on discussion with domain expert. Extreme learning machine was utilized as main intelligent component for predicting benign and malignant. Results showed that all functionalities were work well and done without significant delay. The accuracy performance outperformed average manual diagnosis from unaided medical professional. These showed that the proposed system is promising to be applied in the real medical setting to support breast cancer early detection.

Breast cancer is one of the leading deadly causes for
women. Detection of this disease in the early stages,
especially before spreading to other organs, can
significantly improve survival patients’ rates.
Medical decision support systems with intelligent
classification systems can give second opinion and
help reducing possible error because of
inexperienced experts, fatigued or improper time
limit medical data examination. Backpropagation
Artificial Neural Networks (BP ANN) has been
extensively used in intelligent breast cancer
diagnosis. Nevertheless, the standard gradient-based
learning algorithm has several drawbacks such as
probability to reach local minima, inefficient
training time, and too many setting parameters.
Recent studies proved that Extreme Learning
Machine (ELM), mathematically and
experimentally, could solve BP ANN limitations in
several problems. In this paper, we implemented
best-parameterized ELM for benign and malignant
breast cancer detection. Results showed that
Sigmoid ELM generally gave the best performances
compared to widely used classification methods
such as Decision Tree, BP ANN, and other ELM
models with 96% accuracy. This method
deployment is promising in medical decision
support systems as its intelligent component.