Detecting gait-related health problems of the elderly using multidimensional dynamic time warping approach with semantic attributes

We present a health-monitoring system based on the multidimensional dynamic time warping approach with semantic attributes for the detection of health problems in the elderly to prolong their autonomous living. The movement of the elderly user is captured with a motion-capture system that consists of body-worn tags, whose coordinates are acquired by sensors located in an apartment. The output time series of the coordinates are modeled with the proposed data-mining approach in order to recognize the specific health problem of an elderly person. This paper is an extension of our previous study, which proposed four data mining approaches to recognition of health problems, falls and activities of elderly from their motion patterns. The most successful of the four approaches is SMDTW (Multidimensional dynamic time-warping approach with semantic attributes), whose version is used and thoroughly analyzed in this paper. SMDTW is the modification of the DTW algorithm to use with the multidimensional time series with semantic attributes. To test the robustness of the SMDTW approach, this study calculates the DTW on the time series of various lengths. The semantic attributes presented here consist of the joint angles that are able to recognize many types of movement, e.g., health problems, falls and activities, in contrast to the more specific approaches with specific medically defined attributes from the literature. The k-nearest-neighbor classifier using SMDTW as a distance measure classifies movement of an elderly person into five different health states: one healthy and four unhealthy. Even though the new approach is more general and can be used to differentiate other types of activities or health problems, it achieves very high classification accuracy of 97.2%, comparable to the more specific approaches.     

Speech emotion recognition using optimized genetic algorithm-extreme learning machine

Multimedia Tools and Applications - Automatic Emotion Speech Recognition (ESR) is considered as an active research field in the Human-Computer Interface (HCI). Typically, the ESR system is...     

Automatic discovery of Web Query Interfaces using machine learning techniques

The amount of information contained in databases available on the Web has grown explosively in the last years. This information, known as the Deep Web, is heterogeneous and dynamically generated by querying these back-end (relational) databases through Web Query Interfaces (WQIs) that are a special type of HTML forms. The problem of accessing to the information of Deep Web is a great challenge because the information existing usually is not indexed by general-purpose search engines. Therefore, it is necessary to create efficient mechanisms to access, extract and integrate information contained in the Deep Web. Since WQIs are the only means to access to the Deep Web, the automatic identification of WQIs plays an important role. It facilitates traditional search engines to increase the coverage and the access to interesting information not available on the indexable Web. The accurate identification of Deep Web data sources are key issues in the information retrieval process. In this paper we propose a new strategy for automatic discovery of WQIs. This novel proposal makes an adequate selection of HTML elements extracted from HTML forms, which are used in a set of heuristic rules that help to identify WQIs. The proposed strategy uses machine learning algorithms for classification of searchable (WQIs) and non-searchable (non-WQI) HTML forms using a prototypes selection algorithm that allows to remove irrelevant or redundant data in the training set. The internal content of Web Query Interfaces was analyzed with the objective of identifying only those HTML elements that are frequently appearing provide relevant information for the WQIs identification. For testing, we use three groups of datasets, two available at the UIUC repository and a new dataset that we created using a generic crawler supported by human experts that includes advanced and simple query interfaces. The experimental results show that the proposed strategy outperforms others previously reported works.     

Object recognition using laser range finder and machine learning techniques

In recent years, computer vision has been widely used on industrial environments, allowing robots to perform important tasks like quality control, inspection and recognition. Vision systems are typically used to determine the position and orientation of objects in the workstation, enabling them to be transported and assembled by a robotic cell (e.g. industrial manipulator). These systems commonly resort to CCD (Charge-Coupled Device) Cameras fixed and located in a particular work area or attached directly to the robotic arm (eye-in-hand vision system). Although it is a valid approach, the performance of these vision systems is directly influenced by the industrial environment lighting. Taking all these into consideration, a new approach is proposed for eye-on-hand systems, where the use of cameras will be replaced by the 2D Laser Range Finder (LRF). The LRF will be attached to a robotic manipulator, which executes a pre-defined path to produce grayscale images of the workstation. With this technique the environment lighting interference is minimized resulting in a more reliable and robust computer vision system. After the grayscale image is created, this work focuses on the recognition and classification of different objects using inherent features (based on the invariant moments of Hu) with the most well-known machine learning models: k-Nearest Neighbor (kNN), Neural Networks (NNs) and Support Vector Machines (SVMs). In order to achieve a good performance for each classification model, a wrapper method is used to select one good subset of features, as well as an assessment model technique called K-fold cross-validation to adjust the parameters of the classifiers. The performance of the models is also compared, achieving performances of 83.5% for kNN, 95.5% for the NN and 98.9% for the SVM (generalized accuracy). These high performances are related with the feature selection algorithm based on the simulated annealing heuristic, and the model assessment (k-fold cross-validation). It makes possible to identify the most important features in the recognition process, as well as the adjustment of the best parameters for the machine learning models, increasing the classification ratio of the work objects present in the robot's environment.     

Online learning method using support vector machine for surface-electromyogram recognition

Research surface electromyogram (s-EMG) signal recognition using neural networks is a method which identifies the relation between s-EMG patterns. However, it is not sufficiently satisfying for the user because s-EMG signals change according to muscle wasting or to changes in the electrode position, etc. A support vector machine (SVM) is one of the most powerful tools for solving classification problems, but it does not have an online learning technique. In this article, we propose an online learning method using SVM with a pairwise coupling technique for s-EMG recognition. We compared its performance with the original SVM and a neural network. Simulation results showed that our proposed method is better than the original SVM. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Automatic malware classification and new malware detection using machine learning

The explosive growth of malware variants poses a major threat to information security. Traditional anti-virus systems based on signatures fail to classify unknown malware into their corresponding families and to detect new kinds of malware programs. Therefore, we propose a machine learning based malware analysis system, which is composed of three modules: data processing, decision making, and new malware detection. The data processing module deals with gray-scale images, Opcode n-gram, and import functions, which are employed to extract the features of the malware. The decision-making module uses the features to classify the malware and to identify suspicious malware. Finally, the detection module uses the shared nearest neighbor (SNN) clustering algorithm to discover new malware families. Our approach is evaluated on more than 20 000 malware instances, which were collected by Kingsoft, ESET NOD32, and Anubis. The results show that our system can effectively classify the unknown malware with a best accuracy of 98.9%, and successfully detects 86.7% of the new malware.     

Engineering problems in machine learning systems

Machine Learning - Fatal accidents are a major issue hindering the wide acceptance of safety-critical systems that employ machine learning and deep learning models, such as automated driving...     

Automatic classification of data-warehouse-data for information lifecycle management using machine learning techniques

The aim of Information Lifecycle Management (ILM) is to govern data throughout its lifecycle as efficiently as possible and effectively from technical points of view. A core aspect is the question, where the data should be stored, since different costs and access times are entailed. For this purpose data have to be classified, which presently is either done manually in an elaborate way, or with recourse to only a few data attributes, in particular access frequency. In the context of Data-Warehouse-Systems this article introduces an automated and therefore speedy and cost-effective data classification for ILM. Machine learning techniques, in particular an artificial neural network (multilayer perceptron), a support vector machine and a decision tree approach are compared on an SAP-based real-world data set from the automotive industry. This data classification considers a large number of data attributes and thus attains similar results akin to human experts. In this comparison of machine learning techniques, besides the accuracy of classification, also the types of misclassification that appear, are included, since this is important in ILM.     

Deep learning for machine health prognostics using Kernel-based feature transformation

Journal of Intelligent Manufacturing - Prognostic health management minimizes system downtime and improves overall equipment effectiveness. Accurate prediction of remaining useful life (RUL) is key...     

Automatic detection of respiratory arrests in OSA patients using PPG and machine learning techniques

Obstructive sleep apnea is a syndrome which is characterized by the decrease in air flow or respiratory arrest depending on upper respiratory tract obstructions recurring during sleep and often observed with the decrease in the oxygen saturation. The aim of this study was to determine the connection between the respiratory arrests and the photoplethysmography (PPG) signal in obstructive sleep apnea patients. Determination of this connection is important for the suggestion of using a new signal in diagnosis of the disease. Thirty-four time-domain features were extracted from the PPG signal in the study. The relation between these features and respiratory arrests was statistically investigated. The Mann–Whitney U test was applied to reveal whether this relation was incidental or statistically significant, and 32 out of 34 features were found statistically significant. After this stage, the features of the PPG signal were classified with k-nearest neighbors classification algorithm, radial basis function neural network, probabilistic neural network, multilayer feedforward neural network (MLFFNN) and ensemble classification method. The output of the classifiers was considered as apnea and control (normal). When the classifier results were compared, the best performance was obtained with MLFFNN. Test accuracy rate is 97.07 % and kappa value is 0.93 for MLFFNN. It has been concluded with the results obtained that respiratory arrests can be recognized through the PPG signal and the PPG signal can be used for the diagnosis of OSA.

     

Face recognition based on extreme learning machine

Extreme learning machine (ELM) is an efficient learning algorithm for generalized single hidden layer feedforward networks (SLFNs), which performs well in both regression and classification applications. It has recently been shown that from the optimization point of view ELM and support vector machine (SVM) are equivalent but ELM has less stringent optimization constraints. Due to the mild optimization constraints ELM can be easy of implementation and usually obtains better generalization performance. In this paper we study the performance of the one-against-all (OAA) and one-against-one (OAO) ELM for classification in multi-label face recognition applications. The performance is verified through four benchmarking face image data sets.     

Responsive human-computer interaction model based on recognition of facial landmarks using machine learning algorithms

Multimedia Tools and Applications - This paper is addressed on the idea of building up a model to control computer systems by utilizing facial landmarks like eyes, nose and head gestures. The face...     

An extreme learning machine approach for speaker recognition

Over the last two decades, automatic speaker recognition has been an interesting and challenging problem to speech researchers. It can be classified into two different categories, speaker identification and speaker verification. In this paper, a new classifier, extreme learning machine, is examined on the text-independent speaker verification task and compared with SVM classifier. Extreme learning machine (ELM) classifiers have been proposed for generalized single hidden layer feedforward networks with a wide variety of hidden nodes. They are extremely fast in learning and perform well on many artificial and real regression and classification applications. The database used to evaluate the ELM and SVM classifiers is ELSDSR corpus, and the Mel-frequency Cepstral Coefficients were extracted and used as the input to the classifiers. Empirical studies have shown that ELM classifiers and its variants could perform better than SVM classifiers on the dataset provided with less training time.     

Distributed fuzzy learning using the MULTISOFT machine

Describes PARGEFREX, a distributed approach to genetic-neuro-fuzzy learning which has been implemented using the MULTISOFT machine, a low-cost form of personal computers built at the University of Messina. The performance of the serial version is hugely enhanced with the simple parallelization scheme described in the paper. Once a learning dataset is fixed, there is a very high super linear speedup in the average time needed to reach a prefixed learning error, i.e., if the number of personal computers increases by n times, the mean learning time becomes less than 1/n times.     

Efficient multi-criteria optimization on noisy machine learning problems

Recent research revealed that model-assisted parameter tuning can improve the quality of supervised machine learning (ML) models. The tuned models were especially found to generalize better and to be more robust compared to other optimization approaches. However, the advantages of the tuning often came along with high computation times, meaning a real burden for employing tuning algorithms. While the training with a reduced number of patterns can be a solution to this, it is often connected with decreasing model accuracies and increasing instabilities and noise. Hence, we propose a novel approach defined by a two criteria optimization task, where both the runtime and the quality of ML models are optimized. Because the budgets for this optimization task are usually very restricted in ML, the surrogate-assisted Efficient Global Optimization (EGO) algorithm is adapted. In order to cope with noisy experiments, we apply two hypervolume indicator based EGO algorithms with smoothing and re-interpolation of the surrogate models. The techniques do not need replicates. We find that these EGO techniques can outperform traditional approaches such as latin hypercube sampling (LHS), as well as EGO variants with replicates.     

Business intelligence using machine learning algorithms

Multimedia Tools and Applications - Business intelligence, as one of the branches of information technology, is increasingly considered by managers in today’s business world. In order to make...     

Observer-invariant histopathology using genetics-based machine learning

Prostate cancer accounts for one-third of noncutaneous cancers diagnosed in US men and is a leading cause of cancer-related death. Advances in Fourier transform infrared spectroscopic imaging now provide very large data sets describing both the structural and local chemical properties of cells within prostate tissue. Uniting spectroscopic imaging data and computer-aided diagnoses (CADx), our long term goal is to provide a new approach to pathology by automating the recognition of cancer in complex tissue. The first step toward the creation of such CADx tools requires mechanisms for automatically learning to classify tissue types—a key step on the diagnosis process. Here we demonstrate that genetics-based machine learning (GBML) can be used to approach such a problem. However, to efficiently analyze this problem there is a need to develop efficient and scalable GBML implementations that are able to process very large data sets. In this paper, we propose and validate an efficient GBML technique——based on an incremental genetics-based rule learner. exploits massive parallelisms via the message passing interface (MPI) and efficient rule-matching using hardware-implemented operations. Results demonstrate that is capable of performing prostate tissue classification efficiently, making a compelling case for using GBML implementations as efficient and powerful tools for biomedical image processing.