A New Hybrid Breast Cancer Diagnosis Model Using Deep Learning Model and ReliefF

Breast cancer is a dangerous type of cancer usually found in women and is a significant research topic in medical science. In patients who are diagnosed and not treated early, cancer spreads to other organs, making treatment difficult. In breast cancer diagnosis, the accuracy of the pathological diagnosis is of great importance to shorten the decision-making process, minimize unnoticed cancer cells and obtain a faster diagnosis. However, the similarity of images in histopathological breast cancer image analysis is a sensitive and difficult process that requires high competence for field experts. In recent years, researchers have been seeking solutions to this process using machine learning and deep learning methods, which have contributed to significant developments in medical diagnosis and image analysis. In this study, a hybrid DCNN + ReliefF is proposed for the classification of breast cancer histopathological images, utilizing the activation properties of pre-trained deep convolutional neural network (DCNN) models, and the dimension-reduction-based ReliefF feature selective algorithm. The model is based on a fine-tuned transfer-learning technique for fully connected layers. In addition, the models were compared to the k-nearest neighbor (kNN), naive Bayes (NB), and support vector machine (SVM) machine learning approaches. The performance of each feature extractor and classifier combination was analyzed using the sensitivity, precision, F1-Score, and ROC curves. The proposed hybrid model was trained separately at different magnifications using the BreakHis dataset. The results show that the model is an efficient classification model with up to 97.8% (AUC) accuracy. © 2022 Lavoisier. All rights reserved

Ensemble residual network-based gender and activity recognition method with signals

Nowadays, deep learning is one of the popular research areas of the computer sciences, and many deep networks have been proposed to solve artificial intelligence and machine learning problems. Residual networks (ResNet) for instance ResNet18, ResNet50 and ResNet101 are widely used deep network in the literature. In this paper, a novel ResNet-based signal recognition method is presented. In this study, ResNet18, ResNet50 and ResNet101 are utilized as feature extractor and each network extracts 1000 features. The extracted features are concatenated, and 3000 features are obtained. In the feature selection phase, 1000 most discriminative features are selected using ReliefF, and these selected features are used as input for the third-degree polynomial (cubic) activation-based support vector machine. The proposed method achieved 99.96% and 99.61% classification accuracy rates for gender and activity recognitions, respectively. These results clearly demonstrate that the proposed pre-trained ensemble ResNet-based method achieved high success rate for sensors signals. © 2020, Springer Science+Business Media, LLC, part of Springer Nature

EEG-based driving fatigue detection using multilevel feature extraction and iterative hybrid feature selection

Brain activities can be evaluated by using Electroencephalogram (EEG) signals. One of the primary reasons for traffic accidents is driver fatigue, which can be identified by using EEG signals. This work aims to achieve a highly accurate and straightforward process to detect driving fatigue by using EEG signals. Two main problems, which are feature generation and feature selection, are defined to achieve this aim. This work solves these problems by using two different approaches. Deep networks are efficient feature generators and extract features in low, medium, and high levels. These features can be generated by using multileveled or multilayered feature extraction. Therefore, we proposed a multileveled feature generator that uses a one-dimensional binary pattern (BP) and statistical features together, and levels are created using a one-dimensional discrete wavelet transform (1D-DWT). A five-level fused feature extractor is presented by using BP, statistical features of 1D-DWT together. Moreover, a 2-layered feature selection method is proposed using ReliefF and iterative neighborhood component analysis (RFINCA) to solve the feature selection problem. The goals of the RFINCA are to choose the optimal number of features automatically and use the effectiveness of ReliefF and neighborhood component analysis (NCA) together. A driving fatigue EEG dataset was used as a testbed to denote the effectiveness of eighteen conventional classifiers. According to the experimental results, a highly accurate EEG classification approach is presented. The proposed method also reached 100.0% classification accuracy by using a k-nearest neighborhood classifier.</p

Robust Algorithms for Estimating Vehicle Movement from Motion Sensors Within Smartphones

Building sustainable traffic control solutions for urban streets (e.g., eco-friendly signal control) and highways requires effective and reliable sensing capabilities for monitoring traffic flow conditions so that both the temporal and spatial extents of congestion are observed. This would enable optimal control strategies to be implemented for maximizing efficiency and for minimizing the environmental impacts of traffic. Various types of traffic detection systems, such as inductive loops, radar, and cameras have been used for these purposes. However, these systems are limited, both in scope and in time. Using GPS as an alternative method is not always viable because of problems such as urban canyons, battery depletion, and precision errors. In this research, a novel approach has been taken, in which smartphone low energy sensors (such as the accelerometer) are exploited. The ubiquitous use of smartphones in everyday life, coupled with the fact that they can collect, store, compute, and transmit data, makes them a feasible and inexpensive alternative to the mainstream methods. Machine learning techniques have been used to develop models that are able to classify vehicle movement and to detect the stop and start points during a trip. Classifiers such as logistic regression, discriminant analysis, classification trees, support vector machines, neural networks, and Hidden Markov models have been tested. Hidden Markov models substantially outperformed all the other methods. The feature quality plays a key role in the success of a model. It was found that, the features which exploited the variance of the data were the most effective. In order to assist in quantifying the performance of the machine learning models, a performance metric called Change Point Detection Performance Metric (CPDPM) was developed. CPDPM proved to be very useful in model evaluation in which the goal was to find the change points in time series data with high accuracy and precision. The integration of accelerometer data, even in the motion direction, yielded an estimated speed with a steady slope, because of factors such as phone sensor bias, vibration, gravity, and other white noise. A calibration method was developed that makes use of the predicted stop and start points and the slope of integrated accelerometer data, which achieves great accuracy in estimating speed. The developed models can serve as the basis for many applications. One such field is fuel consumption and CO2 emission estimation, in which speed is the main input. Transportation mode detection can be improved by integrating speed information. By integrating Vehicle (Phone) to Infrastructure systems (V2I), the model outputs, such as the stop and start instances, average speed along a corridor, and queue length at an intersection, can provide useful information for traffic engineers, planners, and decision makers

New scalable machine learning methods: beyond classification and regression

Programa Oficial de Doutoramento en Computación . 5009V01[Abstract] The recent surge in data available has spawned a new and promising age of machine learning. Success cases of machine learning are arriving at an increasing rate as some algorithms are able to leverage immense amounts of data to produce great complicated predictions. Still, many algorithms in the toolbox of the machine learning practitioner have been render useless in this new scenario due to the complications associated with large-scale learning. Handling large datasets entails logistical problems, limits the computational and spatial complexity of the used algorithms, favours methods with few or no hyperparameters to be con gured and exhibits speci c characteristics that complicate learning. This thesis is centered on the scalability of machine learning algorithms, that is, their capacity to maintain their e ectivity as the scale of the data grows, and how it can be improved. We focus on problems for which the existing solutions struggle when the scale grows. Therefore, we skip classi cation and regression problems and focus on feature selection, anomaly detection, graph construction and explainable machine learning. We analyze four di erent strategies to obtain scalable algorithms. First, we explore distributed computation, which is used in all of the presented algorithms. Besides this technique, we also examine the use of approximate models to speed up computations, the design of new models that take advantage of a characteristic of the input data to simplify training and the enhancement of simple models to enable them to manage large-scale learning. We have implemented four new algorithms and six versions of existing ones that tackle the mentioned problems and for each one we report experimental results that show both their validity in comparison with competing methods and their capacity to scale to large datasets. All the presented algorithms have been made available for download and are being published in journals to enable practitioners and researchers to use them.[Resumen] El reciente aumento de la cantidad de datos disponibles ha dado lugar a una nueva y prometedora era del aprendizaje máquina. Los éxitos en este campo se están sucediendo a un ritmo cada vez mayor gracias a la capacidad de algunos algoritmos de aprovechar inmensas cantidades de datos para producir predicciones difíciles y muy certeras. Sin embargo, muchos de los algoritmos hasta ahora disponibles para los científicos de datos han perdido su efectividad en este nuevo escenario debido a las complicaciones asociadas al aprendizaje a gran escala. Trabajar con grandes conjuntos de datos conlleva problemas logísticos, limita la complejidad computacional y espacial de los algoritmos utilizados, favorece los métodos con pocos o ningún hiperparámetro a configurar y muestra complicaciones específicas que dificultan el aprendizaje. Esta tesis se centra en la escalabilidad de los algoritmos de aprendizaje máquina, es decir, en su capacidad de mantener su efectividad a medida que la escala del conjunto de datos aumenta. Ponemos el foco en problemas cuyas soluciones actuales tienen problemas al aumentar la escala. Por tanto, obviando la clasificación y la regresión, nos centramos en la selección de características, detección de anomalías, construcción de grafos y en el aprendizaje máquina explicable. Analizamos cuatro estrategias diferentes para obtener algoritmos escalables. En primer lugar, exploramos la computación distribuida, que es utilizada en todos los algoritmos presentados. Además de esta técnica, también examinamos el uso de modelos aproximados para acelerar los cálculos, el dise~no de modelos que aprovechan una particularidad de los datos de entrada para simplificar el entrenamiento y la potenciación de modelos simples para adecuarlos al aprendizaje a gran escala. Hemos implementado cuatro nuevos algoritmos y seis versiones de algoritmos existentes que tratan los problemas mencionados y para cada uno de ellos detallamos resultados experimentales que muestran tanto su validez en comparación con los métodos previamente disponibles como su capacidad para escalar a grandes conjuntos de datos. Todos los algoritmos presentados han sido puestos a disposición del lector para su descarga y se han difundido mediante publicaciones en revistas científicas para facilitar que tanto investigadores como científicos de datos puedan conocerlos y utilizarlos.[Resumo] O recente aumento na cantidade de datos dispo~nibles deu lugar a unha nova e prometedora era no aprendizaxe máquina. Os éxitos neste eido estanse a suceder a un ritmo cada vez maior gracias a capacidade dalgúns algoritmos de aproveitar inmensas cantidades de datos para producir prediccións difíciles e moi acertadas. Non obstante, moitos dos algoritmos ata agora dispo~nibles para os científicos de datos perderon a súa efectividade neste novo escenario por mor das complicacións asociadas ao aprendizaxe a grande escala. Traballar con grandes conxuntos de datos leva consigo problemas loxísticos, limita a complexidade computacional e espacial dos algoritmos empregados, favorece os métodos con poucos ou ningún hiperparámetro a configurar e ten complicacións específicas que dificultan o aprendizaxe. Esta tese céntrase na escalabilidade dos algoritmos de aprendizaxe máquina, é dicir, na súa capacidade de manter a súa efectividade a medida que a escala do conxunto de datos aumenta. Tratamos problemas para os que as solucións dispoñibles teñen problemas cando crece a escala. Polo tanto, deixando no canto a clasificación e a regresión, centrámonos na selección de características, detección de anomalías, construcción de grafos e no aprendizaxe máquina explicable. Analizamos catro estratexias diferentes para obter algoritmos escalables. En primeiro lugar, exploramos a computación distribuída, que empregamos en tódolos algoritmos presentados. Ademáis desta técnica, tamén examinamos o uso de modelos aproximados para acelerar os cálculos, o deseño de modelos que aproveitan unha particularidade dos datos de entrada para simplificar o adestramento e a potenciación de modelos sinxelos para axeitalos ao aprendizaxe a gran escala. Implementamos catro novos algoritmos e seis versións de algoritmos existentes que tratan os problemas mencionados e para cada un deles expoñemos resultados experimentais que mostran tanto a súa validez en comparación cos métodos previamente dispoñibles como a súa capacidade para escalar a grandes conxuntos de datos. Tódolos algoritmos presentados foron postos a disposición do lector para a súa descarga e difundíronse mediante publicacións en revistas científicas para facilitar que tanto investigadores como científicos de datos poidan coñecelos e empregalos

Exploiting Feature Selection in Human Activity Recognition: Methodological Insights and Empirical Results Using Mobile Sensor Data

Human Activity Recognition (HAR) using mobile sensor data has gained increasing attention over the last few years, with a fast-growing number of reported applications. The central role of machine learning in this field has been discussed by a vast amount of research works, with several strategies proposed for processing raw data, extracting suitable features, and inducing predictive models capable of recognizing multiple types of daily activities. Since many HAR systems are implemented in resource-constrained mobile devices, the efficiency of the induced models is a crucial aspect to consider. This paper highlights the importance of exploiting dimensionality reduction techniques that can simplify the model and increase efficiency by identifying and retaining only the most informative and predictive features for activity recognition. More in detail, a large experimental study is presented that encompasses different feature selection algorithms as well as multiple HAR benchmarks containing mobile sensor data. Such a comparative evaluation relies on a methodological framework that is meant to assess not only the extent to which each selection method is effective in identifying the most predictive features but also the overall stability of the selection process, i.e., its robustness to changes in the input data. Although often neglected, in fact, the stability of the selected feature sets is important for a wider exploitability of the induced models. Our experimental results give an interesting insight into which selection algorithms may be most suited in the HAR domain, complementing and significantly extending the studies currently available in this field

Classification of De novo post-operative and persistent atrial fibrillation using multi-channel ECG recordings

Atrial fibrillation (AF) is the most sustained arrhythmia in the heart and also the most common complication developed after cardiac surgery. Due to its progressive nature, timely detection of AF is important. Currently, physicians use a surface electrocardiogram (ECG) for AF diagnosis. However, when the patient develops AF, its various development stages are not distinguishable for cardiologists based on visual inspection of the surface ECG signals. Therefore, severity detection of AF could start from differentiating between short-lasting AF and long-lasting AF. Here, de novo post-operative AF (POAF) is a good model for short-lasting AF while long-lasting AF can be represented by persistent AF. Therefore, we address in this paper a binary severity detection of AF for two specific types of AF. We focus on the differentiation of these two types as de novo POAF is the first time that a patient develops AF. Hence, comparing its development to a more severe stage of AF (e.g., persistent AF) could be beneficial in unveiling the electrical changes in the atrium. To the best of our knowledge, this is the first paper that aims to differentiate these different AF stages. We propose a method that consists of three sets of discriminative features based on fundamentally different aspects of the multi-channel ECG data, namely based on the analysis of RR intervals, a greyscale image representation of the vectorcardiogram, and the frequency domain representation of the ECG. Due to the nature of AF, these features are able to capture both morphological and rhythmic changes in the ECGs. Our classification system consists of a random forest classifier, after a feature selection stage using the ReliefF method. The detection efficiency is tested on 151 patients using 5-fold cross-validation. We achieved 89.07% accuracy in the classification of de novo POAF and persistent AF. The results show that the features are discriminative to reveal the severity of AF. Moreover, inspection of the most important features sheds light on the different characteristics of de novo post-operative and persistent AF.</p

Applying Machine Learning Algorithms for the Analysis of Biological Sequences and Medical Records

The modern sequencing technology revolutionizes the genomic research and triggers explosive growth of DNA, RNA, and protein sequences. How to infer the structure and function from biological sequences is a fundamentally important task in genomics and proteomics fields. With the development of statistical and machine learning methods, an integrated and user-friendly tool containing the state-of-the-art data mining methods are needed. Here, we propose SeqFea-Learn, a comprehensive Python pipeline that integrating multiple steps: feature extraction, dimensionality reduction, feature selection, predicting model constructions based on machine learning and deep learning approaches to analyze sequences. We used enhancers, RNA N6- methyladenosine sites and protein-protein interactions datasets to evaluate the validation of the tool. The results show that the tool can effectively perform biological sequence analysis and classification tasks. Applying machine learning algorithms for Electronic medical record (EMR) data analysis is also included in this dissertation. Chronic kidney disease (CKD) is prevalent across the world and well defined by an estimated glomerular filtration rate (eGFR). The progression of kidney disease can be predicted if future eGFR can be accurately estimated using predictive analytics. Thus, I present a prediction model of eGFR that was built using Random Forest regression. The dataset includes demographic, clinical and laboratory information from a regional primary health care clinic. The final model included eGFR, age, gender, body mass index (BMI), obesity, hypertension, and diabetes, which achieved a mean coefficient of determination of 0.95. The estimated eGFRs were used to classify patients into CKD stages with high macro-averaged and micro-averaged metrics

Development of a Response Planner Using the UCT Algorithm for Cyber Defense

A need for a quick response to cyber attacks is a prevalent problem for computer network operators today. There is a small window to respond to a cyber attack when it occurs to prevent significant damage to a computer network. Automated response planners offer one solution to resolve this issue. This work presents Network Defense Planner System (NDPS), a planner dependent on the effectiveness of the detection of the cyber attack. This research first explores making classification of network attacks faster for real-time detection, the basic function Intrusion Detection System (IDS) provides. After identifying the type of attack, learning the rewards to use in the NDPS is the second important area of this research. For NDPS to assemble the optimal plan, learning the rewards for resulting network states is critical and often depends on the preferences of the network operator. Using neural networks, the second area of this research demonstrates that capturing the preferences through samples is feasible. After training the neural network, a model can be created to obtain reward estimates. The research performed in these two areas complement the final portion of the research which is assembling the optimal plan through using the Upper Bounds on Confidence for Trees (UCT) algorithm. NDPS is implemented using the UCT algorithm which allows for quick plan formulation by searching through predicted network states based on available network actions. UCT can effectively create a plan quickly and is guaranteed to provide the optimal plan, according to rewards used, if enough time is allotted. NDPS is tested against eight random attack scenarios. For each attack scenario, the plan is polled at specific time intervals to test how quickly the optimal plan can be formulated. Results demonstrate the feasibility of NDPS to be used in real world scenarios since the optimal plans for each attack type can be formulated in real-time allowing for a rapid system response