access deny [1301]
access deny [1026]
Extracting aspect term is essential for aspect level sentiment analysis; Sentiment analysis collects and extracts the opinions expressed in social media and websites' comments and then analyzes them, helping users and stakeholders understand public views on the issues raised better and more quickly. Aspect-level sentiment analysis provides more detailed information, which is very beneficial for use in many various domains. In this paper, the significant contribution is to provide a data preprocessing method and a deep convolutional neural network (CNN) to label each word in opinionated sentences as an aspect or non-aspect word. The proposed method extracts the terms of the aspect that can be used in analyzing the sentiment of the expressed
The pomegranate Punicagranatum fruit pericarp, contain polyphenolic compounds including alpha and beta punicalagins and ellagic acid, which exhibit remarkable antioxidant activities. The aim of this study was to purify and quantify the phenolic components from different varieties of Pomegranate Pericarp Extracts (PPEs) and determine their antioxidant properties. Methanolic and aqueous extracts of four pomegranate cultivars (Shahvar, Siahsorfeh, Torshsabz and Abdorahimkhany, from Shiraz, Iran) were prepared and total phenolic content of PPEs was determined. PPE components were further purified by XAD-16 column chromatography followed by LH-20 gel filtration. The eluted components were subjected to HPLC analysis to differentiate and quantify
Detecting protein complexes from Protein-Protein interaction network (PPI) is the essence of discovering the rules of cellular world. There is a large amount of PPI data available generated from high throughput experimental data. The huge size of data persuades us to use computational methods instead of experimental methods to detect protein complexes. In past years, many researchers presented their algorithms to detect protein complexes. Most of the presented algorithms use current static PPI networks. New researches proved the dynamicity of cellular systems and so the PPI is not static over time. In this paper, we introduce DPCT to detect protein complexes from dynamic PPI networks. In the proposed method, TAP and GO data are used to make
Background: There is still some debate regarding the most proper anesthetic technique in minor hand surgeries. We hypothesized that both WALANT and forearm torniquet Bier block methods provide effective anesthesia in minor hand surgeries without significant difference. Method: 85 patients consented to participate in this study and were randomized into WALANT and single tourniquet forearm bier block groups. In WALANT group, patients received adrenaline-contained lidocaine without tourniquet and in Bier group, lidocaine was administered accordingly after applying a forearm tourniquet . Due to difference in intervention methods, the study was non-blinded. Need for additional analgesia during surgery, visual analogue scale (VAS) for pain in
The successful of the light-based solutions for some NP-complete problems, such as Hamiltonian path problem, have demonstrated the power of light-based computing. The capabilities of the light-based computing such as massive parallelism of light, allow it to solve hard computational problems in polynomial time, while the conventional computers require exponential time. In this study we show how the light-based solution can be applied to break the Data Encryption Standard (DES). Under the assumption of having one given (plain-text, cipher-text) pair, our method recovers the DES key in a efficient time. We describe how to implement XOR gates, circular shifts, P-boxes, and S-boxes of DES in a light-based approach. The proposed solution encrypt
The website fingerprinting attack is one of the most important traffic analysis attacks that is able to identify a visited website in an anonymizing network such as Tor. It is shown that the existing defense methods against website fingerprinting attacks are inappropriate. In addition, they use large bandwidth and time overhead. In this study, we show that the autocorrelation property is the most important success factor of the website fingerprinting attack. We offer a new effective defense model to resolve this security vulnerability of the Tor anonymity network. The proposed defense model prevents information leakage from the passing traffic. In this regard, a novel mechanism is developed to make the traffic analysis a hard task. This mec
Mining cliques of a network is an important problem that has many applications in different fields like social networks, bioinformatics, and web analysis. In most applications, mining fixed sized cliques, known as k-cliques, is enough. However, mining cliques of a large network is very challenging using current solutions, and it takes a considerable time using a commodity machine. Also, very large networks cannot be efficiently loaded into memory of a single machine. To overcome these limitations, we have proposed a solution named KCminer, which is based on state space search and can be totally fitted into the MapReduce framework. Using the MapReduce framework, it is possible to run KCminer on cloud computing platforms and hence, process ve
Background: The aim of this study was to compare the effect of propofol and ketofol (ketamine-propofol mixture) on EA in children undergoing tonsillectomy.Method: In this randomized clinical trial, 87 ASA class I and II patients, aged 3-12 years, who underwent tonsillectomy, were divided into two groups to receive either propofol 100 ?g/kg/min (group p, n= 44) or ketofol: ketamine 25 ?g/kg/min+ propofol 75 ?g/kg/min (group k, n= 43). Incidence and severity of EA was evaluated using the Pediatric Anesthesia Emergence Delirium (PAED) scales on arrival at the recovery room, and 10 and 30 min after that time.Results: There was no statistically significant difference in demographic data between the two groups. In the ketofol group, the need for
Visual stimulus decoding is an increasingly important challenge in neuroscience. The goal is to classify the activity patterns from the human brain; during the sighting of visual objects. One of the crucial problems in the brain decoder is the selecting informative voxels. We propose a meta‐heuristic voxel selection framework for brain decoding. It is composed of four phases: preprocessing of fMRI data; filtering insignificant voxels; postprocessing; and meta‐heuristics selection. The main contribution is benefiting a meta‐heuristics search algorithm to guide a wrapper voxel selection. The main criterion to nominate a voxel is based on its mutual information with the provided stimulus label. The results show impressive accuracy rates
Microarray techniques are widely used in Gene expression analysis. These techniques are based on discovering submatrices of genes that share similar expression patterns across a set of experimental conditions with coherence constraint. Actually, these submatrices are called biclusters and the extraction process is called biclustering. In this paper we present a novel binary particle swarm optimization model for the gene expression biclustering problem. Hence, we apply the binary particle swarm optimization algorithm with a proposed measure, called Discretized Column-based Measure (DCM) as a novel cost function for evaluating biclusters where biological relevance, MSR and the size of the bicluster are considered as evaluation metrics for our
One of the most important decision making problems in many production systems is identification and determination of products and their quantities according to available resources. This problem is called product-mix. However, in the real-world situations, for existing constrained resources, many companies try to provide some products from external resources to achieve more profits. In this paper, an integrated product-mix-outsourcing problem (IPMO) is considered to answer how many products should be produced inside of the system or purchased from external resources. For this purpose, an algorithm based on Theory of Constraints (TOC) and Branch and Bound (B&B) algorithm is proposed. For investigation of the proposed algorithm, a numerical ex
Predicting disease candidate genes from human genome is a crucial part of nowadays biomedical research. According to observations, diseases with the same phenotype have the similar biological characteristics and genes associated with these same diseases tend to share common functional properties. Therefore, by applying machine learning methods, new disease genes are predicted based on previous ones. In recent studies, some semi-supervised learning methods, called Positive-Unlabeled Learning (PU-Learning) are used for predicting disease candidate genes. In this study, a novel method is introduced to predict disease candidate genes through gene expression profiles by learning hidden Markov models. In order to evaluate the proposed method, it
Malware have been tremendously growing in recent years. Most malware use obfuscation techniques for evasion and hiding purposes, but they preserve the functionality and malicious behavior of original code. Although most research work has been mainly focused on program static analysis, some recent contributions have used program behavior analysis to detect malware at run-time. Extracting the behavior of polymorphic malware is one of the major issues that affects the detection result. In this paper, we propose HM 3 alD, a novel program behavior-aware hidden Markov model for polymorphic malware detection. The main idea is to use an effective clustering scheme to partition the program behavior of malware instances and then apply a novel hidden
The heterogeneity of a network causes major challenges for link prediction in heterogeneous complex networks. To deal with this problem, supervised link prediction could be applied to integrate heterogeneous features extracted from different nodes/relations. However, supervised link prediction might be faced with highly imbalanced data issues which results in undesirable false prediction rate. In this paper, we propose a new kernel-based one-class link predictor in heterogeneous complex networks. Assuming a set of available meta-paths, a graph kernel is extracted based on each meta-path. Then, they are combined to form a single kernel function. Afterwards, one class support vector machine (OC-SVM) would be applied on the positi
Tor is one of the most widely used anonymization networks based on onion router that it preserves user’s privacy and secure data flow over the Internet communications. Due to the growing utilization of Tor, Identifying its weaknesses and fixing them is crucial. This study focuses on the website fingerprinting attack and offers a new procedure based on FFT to calculate the similarity distance between two instances and form a distance matrix. By applying the proposed method, we demonstrate that either accuracy grows significantly or the time complexity reduces such that it is applicable in an online manner. In order to evaluate the capability of the proposed method to defeat user privacy, we applied it in an open-world scenario
Disease gene detection is an important stage in the understanding disease processes and treatment. Some candidate disease genes are identified using many machine learning methods Although there are some differences in these methods including feature vector of genes, the method used to selecting reliable negative data (non-disease genes), and the classification method, the lack of negative data is the most significant challenge of them. Recently, candidate disease genes are identified by semi-supervised learning methods based on positive and unlabeled data. These methods are reasonably accurate and achieved more desirable results versus preceding methods. In this article, we propose a novel Positive Unlabeled (PU) learning technique based up