In this paper a novel approach for making a statistical comparison of meta-heuristic stochastic optimization algorithms over multiple single-objective problems is introduced, where a new ranking scheme is proposed to obtain data for multiple problems. The main contribution of this approach is that the ranking scheme is based on the whole distribution, instead of using only one statistic to describe the distribution, such as average or median. Averages are sensitive to outliers (i.e., the poor runs of the stochastic optimization algorithms) and consequently medians are sometimes used. However, using the common approach with either averages or medians, the results can be affected by the ranking scheme that is used by some standard statistical tests. This happens when the differences between the averages or medians are in some ϵ-neighborhood and the algorithms obtain different ranks though they should be ranked equally given the small differences that exist between them. The experimental results obtained on Black-Box Benchmarking 2015, show that our approach gives more robust results compared to the common approach in cases when the results are affected by outliers or by a misleading ranking scheme.
COBISS.SI-ID: 30670119
Automatic food image recognition systems are alleviating the process of food-intake estimation and dietary assessment. However, due to the nature of food images, their recognition is a particularly challenging task, which is why traditional approaches in the field have achieved a low classification accuracy. Deep neural networks have outperformed such solutions, and we present a novel approach to the problem of food and drink image detection and recognition that uses a newly-defined deep convolutional neural network architecture, called NutriNet. This architecture was tuned on a recognition dataset containing 225,953 512 × 512 pixel images of 520 different food and drink items from a broad spectrum of food groups, on which we achieved a classification accuracy of 86.72%, along with an accuracy of 94.47% on a detection dataset containing 130,517 images. We also performed a real-world test on a dataset of self-acquired images, combined with images from Parkinson’s disease patients, all taken using a smartphone camera, achieving a top-five accuracy of 55%, which is an encouraging result for real-world images. Additionally, we tested NutriNet on the University of Milano-Bicocca 2016 (UNIMIB2016) food image dataset, on which we improved upon the provided baseline recognition result. An online training component was implemented to continually fine-tune the food and drink recognition model on new images. The model is being used in practice as part of a mobile app for the dietary assessment of Parkinson’s disease patients.
COBISS.SI-ID: 30597671
A humid atmosphere with water drops is among the most problematic environments for pressure sensors. In general the sensors are protected against harmful effects of media with conformal coatings which, however, in some way affect the characteristics of the sensor. LTCC technology enables manufacturing of the sensing structures that may operate in harsh environments without additional protection. Independently of the implemented protection we should be aware of the changes in the sensor response to the occasional contact with water drops, which result from the changed thermal conditions in the sensing structure. Our experiments demonstrated the influence of the protective coating on the response of the sensor exposed to a humid environment or immersed in the water as well as the effects of the changes in the response due to the water drops applied on the sensor without protection. Appropriate measures taken on the basis of forecasting sensor operation in condensing environments are a part of proactive system maintenance.
COBISS.SI-ID: 30846247
Evidence-based dietary information represented as unstructured text is a crucial information that needs to be accessed in order to help dietitians follow the new knowledge arrives daily with newly published scientific reports. Different named-entity recognition (NER) methods have been introduced previously to extract useful information from the biomedical literature. They are focused on, for example extracting gene mentions, proteins mentions, relationships between genes and proteins, chemical concepts and relationships between drugs and diseases. In this paper, we present a novel NER method, called drNER, for knowledge extraction of evidence-based dietary information. To the best of our knowledge this is the first attempt at extracting dietary concepts. DrNER is a rule-based NER that consists of two phases. The first one involves the detection and determination of the entities mention, and the second one involves the selection and extraction of the entities. We evaluate the method by using text corpora from heterogeneous sources, including text from several scientifically validated web sites and text from scientific publications. Evaluation of the method showed that drNER gives good results and can be used for knowledge extraction of evidence-based dietary recommendations.
COBISS.SI-ID: 30594343
Simultaneous broadcasting of multiple messages from the same source vertex in synchronous networks is considered under restrictions that each vertex receives at most one message in a unit time step, every received message can be sent out only in the next time step, no message is sent to already informed vertices. The number of outgoing messages is unrestricted, messages have unit length, and we assume full-duplex mode. In previous works we developed a concept of level-disjoint partitions to study simultaneous broadcasting under this model. In this work, we consider the optimal number of level-disjoint partitions. We also provide a necessary condition in terms of eccentricity and girth on existence of k v-rooted level-disjoint partitions of optimal height. In particular, we provide a structural characterization of graphs admitting two level-disjoint partitions with the same root.
COBISS.SI-ID: 31040807