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Activity recognition systems are widely used in monitoring physical and physiological conditions as well as observing the short/long term behavioral patterns for the purpose of improving the health and wellbeing of the users. The major obstacle in widespread use of these systems is the need for collecting labeled data to train the activity recognition model. While a personalized model outperforms...
In this paper, we ask whether accurate recognition of activity can be obtained by using a network of smart objects. The approach consists in the classification of certain activities of the subjects: walking, standing, sitting and lying down. The study uses a network of commonly connected objects: a smart watch, a smartphone and a remote control and transported by the participants during an uncontrolled...
The recent advances of Brain Computer Interfaces (BCI) systems, can provide effective assistance for real time prognosis systems for patients who suffered from epileptic seizures. This paper presents an EEG classification strategy for short-term epilepsy prognosis, using software for Brain-Computer Interface (BCI) systems. A training scenario is presented, where significant features are extracted...
Diagnosis and staging of liver diseases are essential for the therapeutic efficacy of medication and treatment strategies. Measuring the Collagen Proportional Area (CPA) in liver biopsies recently becomes an effective tool for the assessment of fibrosis in liver tissues. State of the art image processing techniques are employed to analyze biopsy images, providing objective assessment of diseases severity...
In order to satisfy the higher precision of indoor location-based service (ILBS), scholars have explored a great deal of algorithms based on Wi-Fi, ultrasonic, RFID or infrared, but all of which need additional device settings for transmitting and receiving signals before implementing location recognition. This paper proposed an idea that how to conveniently find the optimal feature or composite features...
In this paper, we propose a low-complexity graphic constellation projection (GCP) algorithm for automatic modulation classification (AMC), where the recovered symbols are projected into artificial graphic constellations. Unlike the existing feature- based (FB) algorithms, we convert the AMC problem into an image recognition problem. Subsequently, the deep belief network (DBN) is adopted to learn the...
Control and monitoring of asthma progress is highly important for patient's quality of life and healthcare management. Emerging tools for self-management of various chronic diseases have the potential to support personalized patient guidance. This work presents the design aspects of the myAirCoach decision support system, with focus on the assessment of three machine learning approaches as support...
This paper explores the potential of Machine Learning (ML) and Artificial Intelligence (AI) to lever Internet of Things (IoT) and Big Data in the development of personalised services in Smart Cities. We do this by studying the performance of four well-known ML classification algorithms (Bayes Network (BN), Naïve Bayesian (NB), J48, and Nearest Neighbour (NN)) in correlating the effects of weather...
Nowadays, the challenge of learning from large scale and imbalanced data set have attracted a great deal of attention from both industry and academia, which is also deemed to be an important task for fraud detection in telecommunication, finance, online commerce. In general, it's almost impossible to train a classification model on the complete data set, especially in the era of big data, due to the...
In view of the problem that support vector machine classifier learning new knowledge is poor in real time, a new algorithm of radar emitter identification based on hull vector and Parzen window density estimation is studied. The algorithm takes the Parzen window density estimation to eliminate the outliers, and reduces the training time by using the hull vector of the sample set. The identification...
In recent years, the problem of classification for high dimensional and class-imbalanced data is found in many fields like bioinformatics and so on. High dimensional problem result in bad classification results because of some combinations of features have adverse effect on classification. Class-imbalanced problem means the number of samples of one class is more than another class, which would make...
In the information age, sentiment classification of internet topics is of great significance. This paper proposes a microblog sentiment classification approach with parallel support vector machine (SVM). The proposed method integrates the features of microblog with preprocessing to ensure the data suitable for sentiment classification. After the preprocessing process, Apache Spark parallel SVM is...
The class imbalance problem occurs when instances in one class are more than that in another. It has been reported to severely hinder classification performance of many traditional classification algorithms and many researchers have paid a great deal of attention to this field. Different kinds of methods have been pro-posed to solve the problem these years, such as resampling methods, integrated learning...
With the introduction of Web 2.0, there has been an extreme increase in the popularity of social bookmarking systems and folksonomies. In this paper, our motive is to develop a recommender system that is based on user assigned tags and content present on web pages. Although the tag recommendations in social tagging systems can be very accurate and personalized, there exists an issue of risk to the...
The relevance of this study is stipulated by the necessity of designing algorithms allowing to improve the efficiency of human face detection and emotions recognition on images with complex background. Purpose: Development of algorithms and software system allowing to improve the efficiency of human face detection and in addition facial expression classification on images with complex background,...
A large body of recent research has focused on the development of supervised buried threat detection algorithms for ground penetrating radar (GPR) data. Such algorithms learn to automatically identify landmines in GPR data based on threat data and non-threat data examples. Training data typically consists of small 2-dimensional images that are extracted from a larger image, or volume, of GPR data...
In this paper we address the problem of classifying cited work into important and non-important to the developments presented in a research publication. This task is vital for the algorithmic techniques that detect and follow emerging research topics and to qualitatively measure the impact of publications in increasingly growing scholarly big data. We consider cited work as important to a publication...
In this paper the practical issues of automotive surface identification system development are considering. The novelty of this work is the combining of different training algorithms, neural network structures and methods to increase the classification accuracy and avoid overfitting of real-world data. The obtained results thereby demonstrate that the use of proposed system architecture and statistical...
In this paper, we combine Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP) in order to detect the cranium and its components; namely, the brain, eyes and mouth. Furthermore, Deformable Part Model (DPM) algorithm is paired with the AdaBoost for training and classification. We use a CT/PET database acquired from the National Biomedical Imaging Archive (NBIA) in order to train and...
Chronic kidney failure (chronic kidney disease ‘CKD’) is a serious disease that related to the gradual loss of kidney function. It is considered one of the health threats in the developing and undeveloped countries At early stages, few symptoms can be detected, where the CKD may not become obvious until significant kidney function impaired occur. CKD treatment focuses on reducing the kidney damage...
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