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Recently, more neuroscience researches focus on the role of dendritic structure during the neural computation. Inspired by the specified topologies of numerous dendritic trees, we proposed a single neural model with a particular dendritic structure. The dendrites are composed of several branches, and these branches correspond to three distributions in coordinate, which are used to classify the training...
In this study, an ultrasensitive biological boron nitride-based nanotube (Bio-BNNT) sensor is modeled and investigated by means of neural approach. The type of configuration studied is a cantilevered BNNT resonator sensor with an attached mass at the tip. The idea behind our resonator sensor is based on the determination of the natural BNNT frequency shift induced by added biological mass. A multilayer...
System identification is the process of developing a mathematical model of a system using input and output knowledge of system. Identification of nonlinear system is well known problem due to its unpredictability and complexity. The nonlinear system for identification is Inverted Pendulum in this work which is well known benchmark system in control system theory due to it's highly nonlinear and unstable...
In this work we provide details on a new and effective approach able to generate Gaussian Mixture Models (GMMs) for the classification of aggregated time series. More specifically, our procedure can be applied to time series that are aggregated together by adding their features. The procedure takes advantage of the additive property of the Gaussians that complies with the additive property of the...
Ocular biometrics in the visible spectrum has emerged as an area of significant research activity. In this paper, we propose two convolution-based models for verifying a pair of periocular images containing the iris, and compare the two approaches amongst each other as well as with a baseline model. In the first approach, we perform deep learning in an unsupervised manner using a stacked convolutional...
In the area of Non-Intrusive Load Monitoring (NILM), many approaches need a supervised procedure of appliance modelling, in order to provide the informations about the appliances to the disaggregation algorithm and to obtain the disaggregated consumptions related to each one of them. In many approaches, the appliance modelling relies on the consumption footprint, which is a typical working cycle of...
We live in the era of big data with dataset sizes growing steadily over the past decades. In addition, obtaining expert labels for all the instances is time-consuming and in many cases may not even be possible. This necessitates the development of advanced semi-supervised models that can learn from both labeled and unlabeled data points and also scale at worst linearly with the number of examples...
This paper presents a structural organization of declarative memories using a new model of spiking neurons. Using this model we propose a self-organizing mechanism to build episodic and semantic memories on the cognitive level. Neurons in this approach represent symbolic concepts that are stored and associated with each other based on the observed events in the environment. We demonstrate that the...
Directional Change (DC) is a technique to summarize price movements in a financial market. According to the DC concept, data is sampled only when the magnitude of price change is significant according to the investor. Unlike with time series, DC samples data at irregular time intervals. In this paper, we propose a contrarian trading strategy that is based on the DC concept. We examine the profitability...
Keeping track of the multiple passwords, PINs, memorable dates and other authentication details needed to gainremote access to accounts is one of modern life's less appealingchallenges. The employment of a voice-based verification as abiometric technology for both children and adults could be agood replacement to the old fashioned memory dependentprocedure. Using voice for authentication could be...
Every year football clubs trade players in order to build competitive rosters able to compete for success, increase the number of their supporters and amplify sponsors and media attention. In the complex system described by the football transfer market can we identify the strategies pursued by successful teams? Where do they search for new talents? Does it pay to constantly change the club roster?...
For many years, neural networks have gained gigantic interest and their popularity is likely to continue because of the success stories of deep learning. Nonetheless, their applications are mostly limited to static and not temporal patterns. In this paper, we apply time warping invariant Echo State Networks (ESNs) to time-series classification tasks using datasets from various studies in the UCR archive...
Data Science is an emerging field of science, which requires a multi-disciplinary approach and should be built with a strong link to emerging Big Data and data driven technologies, and consequently needs re-thinking and re-design of both traditional educational models and existing courses. The education and training of Data Scientists currently lacks a commonly accepted, harmonized instructional model...
Ultrasound as a well-established imaging modality is widely used in imaging lymph nodes for clinical diagnosis and disease analysis. Quantitative analysis of lymph node features, morphology, and relations can provide valuable information for diagnosis and immune system studies. For such analysis, it is necessary to first accurately segment the lymph node areas in ultrasound images. In this paper,...
The integration of neural networks into agent based models can provide a better understanding of dynamic agent responses when modelling complex systems. Additionally, due to the nature of agent based models and the networks that exist in them, individual neural networks can be trained in a supervised learning environment and assigned to individual agents. The advantage of using this approach is that...
Classification of temporal data sequences is a fundamental branch of machine learning with a broad range of real world applications. Since the dimensionality of temporal data is significantly larger than static data, and its modeling and interpreting is more complicated, performing classification and clustering on temporal data is more complex as well. Hidden Markov models (HMMs) are well-known statistical...
The aim of this paper is to analyze available maturity models in the context of assessment of the maturity of IT systems that support communication processes in HCM. The paper presents theoretical issues connected with the evolution of information systems in context of support Human Capital Management (HCM) in a modern organization. Selected problems connected with assessment of maturity were presented,...
The electrical energy consumption associated with sanitary water heating makes up a large part of the total load associated with residential energy consumption, and therefore load models thereof could find use in various Energy Management (EM) applications. This paper presents the results of an investigation to model the electrical load associated with the combined sanitary hot water heating systems...
This paper discuss on developing a system called Entreportfolio as an effort to help entrepreneurs create industry networks quickly and easily via online system. The target users are mainly entrepreneurs including startup, SME, agencies and researcher. By collecting entrepreneurs profile in a system and connects entrepreneurial agencies, SMEs and researcher under one stop center, this system is linked...
Ecological Momentary Assessment (EMA) techniques gain more ground in studies and data collection among different disciplines. Decision tree algorithms and their ensemble variants are widely used for classifying this type of data, since they are easy to use and provide satisfactory results. However, most of these algorithms do not take into account the multiple levels (per-subject, per-day, etc.) in...
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