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A large amount of training data is needed to achieve accurate results in processing deep learning models. Although deep learning has played a successful role in sentiment analysis of texts, a new era in natural language processing has begun with the rise of pre-trained language models. Pre-trained models provide access to a large amount of training data. Emotion analysis in Persian text is challenging because, besides being one of the low resource languages, it needs more accurate methods for processing texts. In this study, a dataset that includes the emotional sentences of Persian literary texts was created. We compared two different methods for analyzing the emotion of Persian sentences. The first method uses the FastText and the Bi-LSTM model for emotion classification. In the second approach, we employ the XLM-R based on a deep bidirectional transformer to extract features from text and apply the Catboost algorithm to guide the model to focus on the most relevant class. The proposed model can be used in all languages, especially low-resource languages and imbalanced datasets. According to the experimental results, the proposed model achieved better binary classification results and employing the XLM-R, and Catboost algorithm yielded remarkable improvement in the multi-classification accuracy.

Density-based method has emerged as a worthwhile class for clustering data streams. Recently, a number of density-based algorithms have been developed for clustering data streams. However, existing density-based data stream clustering algorithms are not without problem. There is a dramatic decrease in the quality of clustering when there is a range in density of data. In this paper, a new method, called the MuDi-Stream, is developed. It is an online-offline algorithm with four main components. In the online phase, it keeps summary information about evolving multi-density data stream in the form of core mini-clusters. The offline phase generates the final clusters using an adapted density-based clustering algorithm. The grid-based method is used as an outlier buffer to handle both noises and multi-density data and yet is used to reduce the merging time of clustering. The algorithm is evaluated on various synthetic and real-world datasets using different quality metrics and further, scalability results are compared. The experimental results show that the proposed method in this study improves clustering quality in multi-density environments.

There are numerous methods which are proposed to mediate semantic Web services. The mediations of semantic services include their discovery, composition, execution, and monitoring. These are performed on both atomic and composite services. Newly proposed methods are required to be evaluated on a dataset. Despite the existence of atomic test collections of semantic services, the number of publicly available test collections containing composite services is not comparable. In this paper, we propose an approach to enlarge the number of composite services in a test collection. We generate new composites by calculating subdigraphs of available composite services. We evaluated our approach on a number of composite services and we could exponentially enlarge the size of test collections.

The current paper presents an investigation of the accuracy of soft-computing techniques in precipitation estimation. The monthly precipitation data from 29 synoptic stations in Serbia from 1946 to 2012 are used as a case study. Despite a number of mathematical functions having been proposed for modeling precipitation estimation, the models still have disadvantages such as being very demanding in terms of calculation time. Soft computing can be used as an alternative to the analytical approach, as it offers advantages such as no required knowledge of internal system parameters, compact solutions for multivariable problems, and fast calculation. Because precipitation prediction is a crucial problem, a process which simulates precipitation with two soft-computing techniques was constructed and presented in this paper, namely, adaptive neurofuzzy inference (ANFIS) and support vector regression (SVR). In the current study, polynomial, linear, and radial basis function (RBF) are applied as the kernel function of the SVR to estimate the probability of precipitation. The performance of the proposed optimizers is confirmed with the simulation results. The SVR results are also compared with the ANFIS results. According to the experimental results, enhanced predictive accuracy and capability of generalization can be achieved with the ANFIS approach compared to SVR estimation. The simulation results verify the effectiveness of the proposed optimization strategies.

Energy is fundamental to, and plays a prominent role in the quality of life. Sustainable energy is important for the benefits it yields. Sustainable energy technologies are clean sources of energy that have a much lower environmental impact than conventional energy technologies. Among the different forms of clean energy, solar energy has attracted a lot of attention as it is not only sustainable, but is also renewable. Because the number of meteorological stations where global solar radiation (GSR) is recorded is limited in Iran, the aim was to develop three distinctive models in order to prognosticate GSR in Tehran Province, Iran. Accordingly, the fuzzy linear regression (FLR), polynomial and radial basis function (RBF) were applied as the kernel function of support vector regression (SVR). Input energies from different meteorological data obtained from the only station in the study region were selected as the model inputs while GSR was chosen as the model output. Instead of minimizing the observed training error, SVR_poly and SVR_rbf attempted to minimize the generalization error bounds so as to achieve generalized performance. The experimental results show that it is possible to achieve enhanced predictive accuracy and capability of generalization via the proposed approach. The calculated root mean square error and correlation coefficient disclosed that SVR_rbf performed well in predicting GSR compared with FLR.

The main purpose of this paper is to determine what joints are most strained in the proposed underactuated finger by adaptive neuro-fuzzy methodology. For this, kinetostatic analysis of the finger structure is established with added torsional springs in every single joint. Since the finger’s grasping forces depend on torsional spring stiffness in the joints, it is preferable to determine which joints have the most influence on grasping forces. Hence, the finger joints experiencing the most strain during the grasping process should be determined. It is desirable to select and analyze a subset of joints that are truly relevant or the most influential to finger grasping forces in order to build a finger model with optimal grasping features. This procedure is called variable selection. In this study, variable selection is modeled using the adaptive neuro-fuzzy inference system (ANFIS). Variable selection using the ANFIS network is performed to determine how the springs implemented in the finger joints affect the output grasping forces. This intelligent algorithm is applied using the Matlab environment and the performance is analyzed. The simulation results presented in this paper show the effectiveness of the developed method.

Plant pathologists detect diseases directly with the naked eye. However, such detection usually requires continuous monitoring, which is time consuming and very expensive on large farms. Therefore, seeking rapid, automated, economical, and accurate methods of plant disease detection is very important. In this study, three different apple diseases appearing on leaves, namely Alternaria, apple black spot, and apple leaf miner pest were selected for detection via image processing technique. This paper presents three soft-computing approaches for disease classification, of artificial neural networks (ANNs), and support vector machines (SVMs). Following sampling, the infected leaves were transferred to the laboratory and then leaf images were captured under controlled light. Next, K-means clustering was employed to detect infected regions. The images were then processed and features were extracted. The SVM approach provided better results than the ANNs for disease classification.

The prerequisite for new versatile grippers is the capability to locate and perceive protests in their surroundings. It is realized that automated controllers are profoundly nonlinear frameworks, and a faultless numerical model is hard to get, in this way making it troublesome to control utilizing tried and true procedure. Here, a design of an adaptive compliant gripper is presented. This design of the gripper has embedded sensors as part of its structure. The use of embedded sensors in a robot gripper gives the control system the ability to control input displacement of the gripper and to recognize specific shapes of the grasping objects. Since the conventional control strategy is a very challenging task, soft computing based controllers are considered as potential candidates for such an application. In this study, the polynomial and radial basis function (RBF) are applied as the kernel function of Support Vector Regression (SVR) to estimate and predict optimal inputs displacement of the gripper according to experimental tests and shapes of grasping objects. Instead of minimizing the observed training error, SVR poly and SVR rbf attempt to minimize the generalization error bound so as to achieve generalized performance. The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by the SVR approach compared to other soft computing methodology.

The probabilistic distribution of wind speed is among the more significant wind characteristics in examining wind energy potential and the performance of wind energy conversion systems. When the wind speed probability distribution is known, the wind energy distribution can be easily obtained. Therefore, the probability distribution of wind speed is a very important piece of information required in assessing wind energy potential. For this reason, a large number of studies have been established concerning the use of a variety of probability density functions to describe wind speed frequency distributions. Although the two-parameter Weibull distribution comprises a widely used and accepted method, solving the function is very challenging. In this study, the polynomial and radial basis functions (RBF) are applied as the kernel function of support vector regression (SVR) to estimate two parameters of the Weibull distribution function according to previously established analytical methods. Rather than minimizing the observed training error, SVR_poly and SVR_rbf attempt to minimize the generalization error bound, so as to achieve generalized performance. According to the experimental results, enhanced predictive accuracy and capability of generalization can be achieved using the SVR approach compared to other soft computing methodologies.

The quantitative assessment of image quality is an important consideration in any type of imaging system. The modulation transfer function (MTF) is a graphical description of the sharpness and contrast of an imaging system or of its individual components. The MTF is also known and spatial frequency response. The MTF curve has different meanings according to the corresponding frequency. The MTF of an optical system specifies the contrast transmitted by the system as a function of image size, and is determined by the inherent optical properties of the system. In this study, the polynomial and radial basis function (RBF) are applied as the kernel function of Support Vector Regression (SVR) to estimate and predict estimate MTF value of the actual optical system according to experimental tests. Instead of minimizing the observed training error, SVR_poly and SVR_rbf attempt to minimize the generalization error bound so as to achieve generalized performance. The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by the SVR_rbf approach in compare to SVR_poly soft computing methodology.

This study proposes a new approach for determining the optimum parameters of a protective spur dike to control scour around existing spur dikes. Several parameters of a protective spur dike were studied to determine their optimum values, including the angle of the protective spur dike relative to the flume wall, its length, and its distance from the main spur dikes, flow intensity, and the diameters of the sediment. To build an effective prediction model, the polynomial and radial basis function are applied as the kernel function of support vector regression (SVR) for prediction of protective spur dike parameters for scour controlling around spur dikes and their performance were compared to Adaptive Neuro Fuzzy System (ANFIS), and Adaptive Neural Network (ANN). Instead of minimizing the observed training error, Polynomial-based SVR (SVR_poly) and radial basis function SVR (SVR_rbf) attempt to minimize the generalization error bound so as to achieve generalized performance. The performance of proposed optimizer was confirmed by experimental results. The results showed that an improvement in predictive accuracy and capability of generalization based SVR can serve as a promising alternative for existing prediction models.

Clustering data streams has drawn lots of attention in the last few years due to their ever-growing presence. Data streams put additional challenges on clustering such as limited time and memory and one pass clustering. Furthermore, discovering clusters with arbitrary shapes is very important in data stream applications. Data streams are infinite and evolving over time, and we do not have any knowledge about the number of clusters. In a data stream environment due to various factors, some noise appears occasionally. Density-based method is a remarkable class in clustering data streams, which has the ability to discover arbitrary shape clusters and to detect noise. Furthermore, it does not need the number of clusters in advance. Due to data stream characteristics, the traditional density-based clustering is not applicable. Recently, a lot of density-based clustering algorithms are extended for data streams. The main idea in these algorithms is using density-based methods in the clustering process and at the same time overcoming the constraints, which are put out by data stream’s nature. The purpose of this paper is to shed light on some algorithms in the literature on density-based clustering over data streams. We not only summarize the main density-based clustering algorithms on data streams, discuss their uniqueness and limitations, but also explain how they address the challenges in clustering data streams. Moreover, we investigate the evaluation metrics used in validating cluster quality and measuring algorithms’ performance. It is hoped that this survey will serve as a steppingstone for researchers studying data streams clustering, particularly density-based algorithms.

Since the web services cause time and cost reduction, these mechanisms generate the ability of reusability. Appending semantics to Web Service Description Language (WSDL), we achieve automatic tests for web services. Therefore, automated test is required to test web services is created by adding semantics to Web Service Description Language (WSDL). We verify a review and conduct test selection techniques. This paper reviews and compares different methods of testing such as Pair- Wise Test (PWT), Orthogonal Array Test (OAT) and General Test (GT). Pair-wise testing approach is a software technique that generates variants of test including all combination of input variables. For example, the selection of regression test has a trade-off between restarting time cost and lessening the faults through the implementation of software changes. Plenty of research goals are achieved to acceptable cost for regression test5, e.g. selecting regression test for application, selecting regression test-based on code changes and specification.

A Web service-based system never fulfills a user's goal unless a failure recovery approach exists. It is inevitable that several Web services may either perish or fail before or during transactions. The completion of a composite process relies on the smooth execution of all constituent Web services. A mediator acts as an intermediary between providers and consumers to monitor the execution of these services. If a service fails, the mediator has to recover the whole composite process or else jeopardize achieving the intended goals. The atomic replacement of a perished Web service usually does not apply because the process of locating a matched Web service is unreliable. Even the system cannot depend on the replacement of the dead service with a composite service. In this paper, we propose an automatic renovation plan for failure recovery of composite semantic services based on an approach of subdigraph replacement. A replacement subdigraph is posed in lieu of an original subdigraph, which includes the failed service. The replacement is done in two separate phases, offline and online, to make the recovery faster. The offline phase foresees all possible subdigraphs, pre-calculates them, and ranks several possible replacements. The online phase compensates the unwanted effects and executes the replacement subdigraph in lieu of the original subdigraph. We have evaluated our approach during an experiment and have found that we could recover more than half of the simulated failures. These achievements show a significant improvement compared to current approaches.

The reliability of tourism-related Web services are crucial. Users do not tend to check and use a service again once it is failed. Researches proved that a simple one-to-one replacement of a failed service is not dependable to recover a system from a total failure. In order to increase the probability of recovery of a failure we use a renovation approach to replace a set of services. Broadening the search area among the services in a graph of services enables us to increase the failure recovery probability. The time complexity is also considered and proved to be low at the failure time by transferring the time-consuming calculations to an offline phase prior to the execution time of the service. The approach is evaluated on a set of services including the tourism-related Web services. The probability of recovery substantially increased to more than 54% of the simulated failures.

Data streams are continuously generated over time from Internet of Things (IoT) devices. The faster all of this data is analyzed, its hidden trends and patterns discovered, and new strategies created, the faster action can be taken, creating greater value for organizations. Density-based method is a prominent class in clustering data streams. It has the ability to detect arbitrary shape clusters, to handle outlier, and it does not need the number of clusters in advance. Therefore, density-based clustering algorithm is a proper choice for clustering IoT streams. Recently, several density-based algorithms have been proposed for clustering data streams. However, density-based clustering in limited time is still a challenging issue. In this paper, we propose a density-based clustering algorithm for IoT streams. The method has fast processing time to be applicable in real-time application of IoT devices. Experimental results show that the proposed approach obtains high quality results with low computation time on real and synthetic datasets.

Composite services are examples of volatile processes, which are prone to failures due to several problems that may occur during the executions. The recovery of their failure at execution time must be done efficiently to survive the system from deviation of its quality of service. Replacing a failed service with another similar service is not always reliable. Substituting a subgraph of the directed graph which represents the composite service with another sequence of services shows a major step in increasing the likelihood of the system’s failure recovery. In this paper, we propose the use of expiration times of provided services to lower the time complexity of subdigraph identifications as well as other steps of a recovery approach. We evaluated our work, which shows a significant improvement to the similar approaches.

Clustering real-time stream data is an important and challenging problem. The existing algorithms have not considered the distribution of data inside micro cluster, specifically when data points are non uniformly distributed inside micro cluster. In this situation, a large radius of micro cluster has to be considered which leads to lower quality. In this paper, we present a density-based clustering algorithm, DMM-Stream, for evolving data streams. It is an online-offline algorithm which considers the distribution of data inside micro cluster. In DMM-Stream, we introduce mini-micro cluster for keeping summary information of data points inside micro cluster. In our method, based on the distribution of the dense areas inside the micro cluster at least one representative point, either micro cluster itself or its mini-micro clusters’ centers, are sent to the offline phase. By choosing a proper mini-micro and micro center, we increase cluster quality while maintaining the time complexity. A pruning strategy is also used to filter out the real data from noise by introducing dense and sparse mini-micro and micro cluster. Our performance study over real and synthetic data sets demonstrates effectiveness of our method.

Density-based clustering can detect arbitrary shape clusters, handle outliers and do not need the number of clusters in advance. However, they cannot work properly in multi density environments. The existing multi density clustering algorithms have some problems in order to be applicable for data streams such as the need of whole data to perform clustering, two-pass clustering and high execution time. Data stream arrives continuously and they have to be processed in limited time and memory. Therefore, we need an algorithm to cluster data stream with different densities as well as to overcome the challenges in clustering data streams. In this paper, we introduce a Multi-Density clustering algorithm for data stream called MuDi-Stream. MuDi-Stream is an online-offline clustering algorithm, in which the online phase forms core-mini-clusters using a new proposed core distance and offline phase clusters the core-mini-clusters based on a density-based method. The new core distance called mini core distance is calculated based on the number of neighboring data points around the core. Therefore, the algorithm has different core distances for different clusters that leads to cover multi density environments. A novel pruning strategy is also used to filter out the real data from the noise by mapping the outliers in the grid. The grid structure keeps the neighbors of the data point to determine mini-core distance and remove noise effectively. Our performance study over synthetic data sets demonstrates effectiveness of our method.

Composite Web services are self-evidently failure-prone. A recovery process is required to survive the execution flow. We propose a method to recover a composite service from a failure. The method replaces a subdigraph of a composite service with its semantically similar one. Our work is motivated by a marked trade-off of multiple needs, i.e. to increase the replacement probability in a minimum delay, a high accuracy and yet no compromise in quality of service. To address the issues, we 1) broaden the subdigraph elimination starting point to even well-executed world-altering services, 2) calculate the prerequisites in an offline phase, and 3) rank the candidate replacements. Our preliminary result shows the efficiency of our method.

Web services are building blocks of interoperable systems. Composing Web services makes the processes capable of doing complex tasks. Composite services may fail during their execution which can be diagnosed by a mediator. The mediator adapts the structure so that the failure is recovered. Moreover, future executions should avoid the situation or organize a strategy to repair the structure with a minimum delay. In this paper the failure reasons of a composite service are reviewed. Furthermore, the requirements of a solution for recovery of a system from a failure are investigated., International Journal of Web & Semantic Technology (IJWesT) Vol.3, No.4, October 2012

Web services foster functionality of current web to service oriented architecture. Nascent semantic web is capable of automating activities by annotating shared ontological semantics to documents and services. Although, a zillion web services with diversity have been made since the inception of its diffusion, it is not a panacea for software development and it is still in its infancy. A middle agent (broker) simplifies the interaction of service providers and service requester, especially in the case that an atomic web service cannot fulfill userpsilas need. The broker composes a desired value-added service and orchestrates the execution of bundled sub-processes. It is inevitable that several constitutive web services may fail during the execution and become unavailable. In this paper, we propose replacement of a sequence of semantic web services in lieu of old composition subgraph which includes perished web service(s) that jeopardized the completion of whole process. We try to perform finding foreseeable replacing graphs, and their compatible alternative subgraphs and ranking of them before exploitation of composite web service. Furthermore, we illuminate an approach for resolving functional differences between old and new subgraphs.

A soft methodology study has been applied on tapered plastic multimode sensors. This study basically used tapered plastic multimode fiber [polymethyl methacrylate (PMMA)] optics as a sensor. The tapered PMMA fiber was fabricated using an etching method involving deionized water and acetone to achieve a waist diameter and length of 0.45 and 10 mm, respectively. In addition, a tapered PMMA probe, which was coated by silver film, was fabricated and demonstrated using a calcium hypochlorite (G70) solution. The working mechanism of such a device is based on the observation increment in the transmission of the sensor that is immersed in solutions at high concentrations. As the concentration was varied from 0 to 6 ppm, the output voltage of the sensor increased linearly. The silver film coating increased the sensitivity of the proposed sensor because of the effective cladding refractive index, which increases with the coating and thus allows more light to be transmitted from the tapered fiber. In this study, the polynomial and radial basis function (RBF) were applied as the kernel function of the support vector regression (SVR) to estimate and predict the output voltage response of the sensors with and without silver film according to experimental tests. Instead of minimizing the observed training error, SVR_poly and SVR_rbf were used in an attempt to minimize the generalization error bound so as to achieve generalized performance. An adaptive neuro-fuzzy interference system (ANFIS) approach was also investigated for comparison. The experimental results showed that improvements in the predictive accuracy and capacity for generalization can be achieved by the SVR_poly approach in comparison to the SVR_rbf methodology. The same testing errors were found for the SVR_poly approach and the ANFIS approach.