Sharif Digital Repository

The biophysical and ultrasonographic properties of the skin change in papulosquamous diseases. Aims: To identify biophysical and ultrasonographic properties for the differentiation of five main groups of papulosquamous skin diseases. Methods: Fifteen biophysical and ultrasonographic parameters were measured by multiprobe adapter system and high-frequency ultrasonography in active lesions and normal control skin in patients with chronic eczema, psoriasis, lichen planus, pityriasis rosea and parapsoriasis/mycosis fungoides. Using histological diagnosis as a gold standard, a decision tree analysis was performed based on the mean percentage changes of these parameters [(lesion-control/control)... 

Driving behavior monitoring by smartphone sensors is one of the most investigated approaches to ameliorate road safety. Various methods are adopted in the literature; however, to the best of our knowledge, their robustness to the prediction of new unseen data from different drivers and road conditions is not explored. In this paper, a two-phase Machine Learning (ML) method with taking advantage of high-pass, low-pass, and wavelet filters is developed to detect driving brakes and turns. In the first phase, accelerometer and gyroscope filtered time series are fed into Random Forest and Artificial Neural Network classifiers, and the suspicious intervals are extracted by a high recall. Following... 

Accidents involving falls and falling objects (group I) are highly frequent accidents in the construction industry. While being hit by a vehicle, electric shock, collapse in the excavation and fire or explosion accidents (group II) are much less frequent, they make up a considerable proportion of severe accidents. In this study, multiple-correspondence analysis, decision tree, ensembles of decision tree and association rules methods are employed to analyse a database of construction accidents throughout Iran between 2007 and 2011. The findings indicate that in group I, there is a significant correspondence among these variables: time of accident, place of accident, body part affected, final... 

Pronoun resolution is one of the challenges of natural language processing (NLP). The proposed solutions range from heuristic rule-based to machine learning data driven approaches. In this article, we follow a previous machine learning approach on Persian pronoun anaphora resolution. The primary goal of this paper is to improve the results, mainly by extracting more balanced data through using heuristic rules in instance sampling, and utilizing more relevant features in classification. Using PCAC2008 dataset, we consider noun phrase structure as a way to extract more suitable training data. Incorporated features include syntactic and semantic information. Finally, we train and test different... 

This paper proposes the use of a new binary decision tree, which we call a soft decision tree, to improve generalization performance compared to the conventional ‘hard’ decision tree method that is used to cluster context-dependent model parameters in statistical parametric speech synthesis. We apply the method to improve the modeling of fundamental frequency, which is an important factor in synthesizing natural-sounding high-quality speech. Conventionally, hard decision tree-clustered hidden Markov models (HMMs) are used, in which each model parameter is assigned to a single leaf node. However, this ‘divide-and-conquer’ approach leads to data sparsity, with the consequence that it suffers... 

Software repositories such as GitHub host a large number of software entities. Developers collaboratively discuss, implement, use, and share these entities. Proper documentation plays an important role in successful software management and maintenance. Users exploit Issue Tracking Systems, a facility of software repositories, to keep track of issue reports, to manage the workload and processes, and finally, to document the highlight of their team’s effort. An issue report is a rich source of collaboratively-curated software knowledge, and can contain a reported problem, a request for new features, or merely a question about the software product. As the number of these issues increases, it... 

The real-time availability of the Internet has engaged millions of users around the world. The usage of regional languages is being preferred for effective and ease of communication that is causing multilingual data on social networks and news channels. People share ideas, opinions, and events that are happening globally i.e., sports, inflation, protest, explosion, and sexual assault, etc. in regional (local) languages on social media. Extraction and classification of events from multilingual data have become bottlenecks because of resource lacking. In this research paper, we presented the event classification task for the Urdu language text existing on social media and the news channels by... 

We propose a novel hybrid learning approach to gain situation awareness in smart environments by introducing a new situation identifier that combines an expert system and a machine learning approach. Traditionally, expert systems and machine learning approaches have been widely used independently to detect ongoing situations as the main functionality in smart environments in various domains. Expert systems lack the functionality to adapt the system to each user and are expensive to design based on each setting. On the other hand, machine learning approaches fail in the challenge of cold start and making explainable decisions. Using both of these approaches enables the system to use user's... 

Cost overrun risks are declared to be dynamic and interdependent. Ignoring the relationship between cost overrun risks during the risk assessment process is one of the primary reasons construction projects go over budget. Conversely, recent studies have failed to account for potential interrelationships between risk factors in their machine learning (ML) models. Additionally, the presented ML models are not interpretable. Thus, this study contributes to the entire ML process using a Bayesian network (BN) classifier model by considering the possible interactions between predictors, which are cost overrun risks, to predict cost overrun and assess cost overrun risks. Furthermore, this study... 

Bitcoin as the current leader in cryptocurrencies is a new asset class receiving significant attention in the financial and investment community and presents an interesting time series prediction problem. In this paper, some forecasting models based on classical like ARIMA and machine learning approaches including Kriging, Artificial Neural Network (ANN), Bayesian method, Support Vector Machine (SVM) and Random Forest (RF) are proposed and analyzed for modelling and forecasting the Bitcoin price. While some of the proposed models are univariate, the other models are multivariate and as a result, the maximum, minimum and the opening daily price of Bitcoin are also used in these models. The... 

There are several approaches to emotions in AI, most of which are inspired by human emotional states and their arousal mechanisms. These approaches usually use high-level models of human emotions that are too complex to be directly applicable in simple artificial systems. It seems that a new approach to emotions, based on their functional role in information processing in mind, can help us to construct models of emotions that are both valid and simple. In this paper, we will try to present a model of emotions based on their role in controlling the attention. We will evaluate the performance of the model and show how it can be affected by some structural and environmental factors. © 2008 The... 

Multilayer perceptron (MLP) neural networks have been used extensively for estimation/regression of parameters. Moreover, recent studies have shown that learning algorithms of MLP which are based on Gaussian function are more accurate. In this paper, the mass attenuation coefficient (MAC) of gamma radiation for light-weight materials (e.g. O-8), mid-weight materials (e.g. Al-13), and heavy-weight materials (e.g. Pb-82) is modelled using Gaussian function based regularization of MLP (i.e. Bayesian regularization (BR)) and by a modular estimator. The results are compared with the Reference results. To show better performance of the utilized algorithm, the results of the different supervised... 

We present our work on end-to-end training of acoustic models using the lattice-free maximum mutual information (LF-MMI) objective function in the context of hidden Markov models. By end-to-end training, we mean flat-start training of a single DNN in one stage without using any previously trained models, forced alignments, or building state-tying decision trees. We use full biphones to enable context-dependent modeling without trees, and show that our end-to-end LF-MMI approach can achieve comparable results to regular LF-MMI on well-known large vocabulary tasks. We also compare with other end-to-end methods such as CTC in character-based and lexicon-free settings and show 5 to 25 percent... 

This study presents a passive model to detect islanding conditions of synchronous distributed generation resources in a distribution network or a microgrid. The proposed approach uses the classification and regression tree algorithm for distinguishing between islanding and non-islanding situations. It utilises the rate of change of frequency (ROCOF) and harmonic content of the equivalent reactance seen at the location of distributed generation as input features for decision tree construction. Indeed the thresholds of the proposed input features are extracted by the decision tree algorithm. The output if-then rules of the decision tree algorithm are then utilised to make a new relay logic for... 

Reviewing the various Partial Discharges (PD data mining researches which have been reported so far, this study compares the performance of different feature spaces and different classifiers employed for PD classification in insulation condition monitoring of power transformers. In this process, first a knowledge basis is developed through construction of 4 different types of PD models in the high voltage laboratory. Background noise is considered as one class in this knowledge basis. The high frequency time domain current signals of high voltage equipment are captured over one power frequency cycle. The single PD activities within this captured signal are extracted by application of a... 

3D scene understanding is one of the most important problems in the field of computer vision. Although, in the past decades, considerable attention has been devoted on the 2D scene understanding problem, now with the development of the depth sensors (like Microsoft Kinect), the 3D scene understanding has become a very challenging task. Traditionally, the scene understanding problem was considered as the semantic labeling of each image pixel. Semantic labeling of RGB-D images has not attained a comparable success, as the RGB semantic labeling, due to the lack of a challenging dataset. With the introduction of an RGB-D dataset, called NYU-V2, it became possible to propose a novel method to... 

An adaptive wide-area damping controller (WADC) scheme based on gain scheduling (GS) technique and decision tree (DT) approach is presented. For the predefined operating scenarios, several robust controllers are designed, so that each controller performs well within a certain range of load/generation variation and contingencies. Considering the best acting controller in each operating condition as the target of classification, several DTs are trained using stage-wise additive modeling by multiclass exponential loss function (SAMME). The weighted combination of DTs is referred to as additive model DT (AMDT) that is used to interpolate the controller coefficients from phasor measurement unit...