Sharif Digital Repository

The prediction of rock sawability is very important in the cost estimation and the best planning of the plants. Rock sawability depends on the machine characteristics and rock mechanical properties. In this study, a new classification was developed with the respect to rock mechanical properties such as Uniaxial Compressive Strength, Brazilian tensile strength, Schmidt hammer value and Los Angeles abrasion loss. Using this system the carbonate rock sawability index (CRSi) of several types of carbonate rock was evaluated and classifi ed into fi ve categories and then a new model was developed with the respect to CRSi and machining characteristics by using the statistical analyses for... 

Feature coding has received great attention in recent years as a building block of many image processing algorithms. In particular, the importance of the locality assumption in coding approaches has been studied in many previous works. We review this assumption and claim that using the similarity of data points to a more global set of anchor points does not necessarily weaken the coding method, as long as the underlying structure of the anchor points is considered. We propose to capture the underlying structure by assuming a random walker over the anchor points. We also show that our method is a fast approximation to the diffusion map kernel. Experiments on various data sets show that with a... 

In this study, a minimum distance classification and forward feature selection technique are joined to determine the relationship between weather conditions and the increase of the risk of type A acute aortic dissection (AAD) events in Berlin. The results demonstrate that changes in the amount of cloudiness and air temperature are the most representative weather predictors among the studied parameters. A discrimination surface was developed for the prediction of AAD events 6 h ahead, and it is found that, under a specific amount of cloudiness and air temperature, the risk of AAD events in Berlin increases about 20 %  

Data classification in large scale systems, such as peer-to-peer networks, can be very communication-expensive and impractical due to the huge amount of available data and lack of central control. Frequent data updates pose even more difficulties when applying existing classification techniques in peer-to-peer networks. We propose a distributed, scalable and robust classification algorithm based on k-nearest neighbor estimation. Our algorithm is asynchronous, considers data updates and imposes low communication overhead. The proposed method uses a content based overlay structure to organize data and moderate the number of query messages propagated in the network. Simulation results show that... 

In the present work, the abilities of five different variable selection methods including recursive partial least squares (rPLS), variable importance in projection (VIP), selectivity ratio (SR), significance multivariate correlation (sMC), and PLS loading weights were evaluated on the supervised classification of gas chromatographic fingerprints of saffron using PLS-discriminant analysis (PLS-DA). In this regard, eighty-three saffron samples analyzed by gas chromatography-flam ionization detector (GC-FID), were used as a case study. The GC-FID chromatograms of saffron samples were baseline corrected and aligned using asymmetric least squares (AsLS) and correlation optimized warping (COW)... 

Cardiovascular diseases have been known as one of the main reasons of mortality all around the world. Nevertheless, this disease is preventable if it can be diagnosed in an early stage. Therefore, it is crucial to develop Clinical Decision Support Systems (CDSSs) that are able to help physicians diagnose the disease and its related risks. This study focuses on cardiovascular disease diagnosis in an Iranian community by developing a CDSS, based on Support Vector Machine (SVM) combined with Binary Particle Swarm Optimisation (BPSO). We used SVM as the classifier and benefited enormously from optimisation capabilities of BPSO in model development as well as feature selection. Finally,... 

In this study, a minimum distance classification and forward feature selection technique are joined to determine the relationship between weather conditions and the increase of the risk of type A acute aortic dissection (AAD) events in Berlin. The results demonstrate that changes in the amount of cloudiness and air temperature are the most representative weather predictors among the studied parameters. A discrimination surface was developed for the prediction of AAD events 6 h ahead, and it is found that, under a specific amount of cloudiness and air temperature, the risk of AAD events in Berlin increases about 20 %. © 2015, ISB  

Cellular learning automata (CLA) is a distributed computational model which was introduced in the last decade. This model combines the computational power of the cellular automata with the learning power of the learning automata. Cellular learning automata is composed from a lattice of cells working together to accomplish their computational task; in which each cell is equipped with some learning automata. Wide range of applications utilizes CLA such as image processing, wireless networks, evolutionary computation and cellular networks. However, the only input to this model is a reinforcement signal and so it cannot receive another input such as the state of the environment. In this paper,... 

Male infertility is mostly related to semen and spermatozoa, and any diagnosis or treatment requires the investigation of the motility patterns of spermatozoa. The movements of spermatozoa are fast and involve collision and occlusion with each other. In order to extract the motility patterns of spermatozoa, multi-target tracking (MTT) of spermatozoa is necessary. One of the most important steps of MTT is data association, in which the newly arrived observations are used to update the previous tracks. Dynamic Bayesian network (DBN) is a powerful tool for modeling and solving various types of problems such as tracking and classification. There can also be a hybrid-DBN (HDBN), in which both... 

We have entered the era of networked communications where concepts such as big data and social networks are emerging. The explosion and profusion of available data in a broad range of application domains cause data streams to become an inevitable part of the most real-world applications. In the classification of data streams, there are four major challenges: infinite length, concept drift, recurring and evolving concepts. This paper proposes a novel method to address the mentioned challenges with a focus on the last one. Unlike the existing methods for detection of evolving concepts, we cast joint classification and detection of evolving concepts into optimizing an objective function by... 

Pore facies analysis plays an important role in the classification of reservoir rocks and reservoir simulation studies. The current study proposes a two-step approach for pore facies characterization in the carbonate reservoirs with an example from the Kangan and Dalan formations in the South Pars gas field. In the first step, pore facieswere determined based on Mercury Injection Capillary Pressure (MICP) data in corporation with the Hierarchical Clustering Analysis (HCA) method. Each pore facies represents a specific type of pore geometry indicating the interaction between the primary rock fabric and its diagenetic overprints. In the next step, polynomial meta-models were established based... 

Background and Objectives: Melanoma is a dangerous kind of skin disease with a high death rate, and its prevalence has increased rapidly in recent years. Diagnosis of melanoma in a primary phase can be helpful for its cure. Due to costs for dermatology, we need an automatic system to diagnose melanoma through lesion images. Methods: Here, we propose a sparse representation based method for segmentation and classification of lesion images. The main idea of our framework is based on a kernel sparse representation, which produces discriminative sparse codes to represent features in a high-dimensional feature space. Our novel formulation for discriminative kernel sparse coding jointly learns a... 

We have entered the era of networked communications where concepts such as big data and social networks are emerging. The explosion and profusion of available data in a broad range of application domains cause data streams to become an inevitable part of the most real-world applications. In the classification of data streams, there are four major challenges: infinite length, concept drift, recurring and evolving concepts. This paper proposes a novel method to address the mentioned challenges with a focus on the last one. Unlike the existing methods for detection of evolving concepts, we cast joint classification and detection of evolving concepts into optimizing an objective function by... 

Cardiovascular Disease (CVD) is considered as one of the principal causes of death in the world. Over recent years, this field of study has attracted researchers' attention to investigate heart sounds' patterns for disease diagnostics. In this study, an approach is proposed for normal/abnormal heart sound classification on the Physionet challenge 2016 dataset. For the first time, a fixed length feature vector; called i-vector; is extracted from each heart sound using Mel Frequency Cepstral Coefficient (MFCC) features. Afterwards, Principal Component Analysis (PCA) transform and Variational Autoencoder (VAE) are applied on the i-vector to achieve dimension reduction. Eventually, the reduced... 

So far, many methods have been proposed to classify items based on ABC analysis, but the results of these methods have had relatively low compliance with the principles of ABC. More precisely, collective value and sometimes the number of items belonging to each category in the methods provided do not meet the basic requirements of ABC called Pareto’s principle. In this study, a number of hybrid methodologies including Shannon’s entropy, TOPSIS (the technique for order preference by similarity to ideal solution) and goal programming are respectively used for determining the weight of criteria which are effective in the inventory items classification, calculations of each item value and its... 

Twitter is among the most used microblogging and online social networking services. In Twitter, a name, phrase, or topic that is mentioned at a greater rate than others is called a «trending topic» or simply «trend». Twitter trends has shown their powerful ability in many public events, elections and market changes. Nevertheless, there has been very few works focusing on understanding the dynamics of these trending topics. In this article, we thoroughly examined the Twitter's trending topics of 2018. To this end, we accessed Twitter's trends API for the full year of 2018, and devised six criteria to evaluate our dataset. These six criteria are: lexical analysis, time to reach, trend...