درخت حوزه‌های تخصصی

Environmental pollution and rapid depletion are among the chief concerns about fossil fuels such as oil, gas, and coal. Renewable energy sources do not suffer from such limitations and are considered the best choice to replace fossil fuels. The present study develops a mathematical model for optimal allocation of regional renewable energy to meet a country-wide demand and its other essential aspects. The ultimate purpose is to minimize the total cost by planning, including power plant construction and maintenance costs and transmission costs. Minimum-cost flow equations are embedded in the model to determine how regions can supply energy to other regions or rely on them to fulfill annual demand. In order to verify the applicability of the model, it is applied to a real-world case study of Iran to determine the optimal renewable energy generation-transmission decisions for the next decade. Results indicate that the hydroelectric and solar power plants should generate the majority of the generated renewable electricity within the country, according to the optimal solution. Moreover, regarding the significant population growth and waste generation in the country’s large cities, biomass power plants can have the opportunity to satisfy a remarkable portion of electricity demand.

Developed an integrated intellectual computerized system of ecological-economic monitoring, modeling, and managing the life cycle of the products of technogenic enterprises of transport engineering, which is presented in the form of a 3-equation structure, functioning in conditions of instability. The proposed paradigm system life cycle management applicable to any other control system of large and complex systems, such as techno-genic type. This new paradigm of complex systems and process management, including technical systems. The system is based on the following findings: concepts, principles, a set of non-linear models, decision-making methods, and the environmental and economic governance, integral criteria. As an example, in this paper offer solution to the problem of estimating the cost of the product life cycle of railway transport.

K nearest neighbor algorithm is one of the most frequently used techniques in data mining for its integrity and performance. Though the KNN algorithm is highly effective in many cases, it has some essential deficiencies, which affects the classification accuracy of the algorithm. First, the effectiveness of the algorithm is affected by redundant and irrelevant features. Furthermore, this algorithm does not consider the differences between samples, which led the algorithm to have inaccurate predictions. In this paper, we proposed a novel scheme for improving the accuracy of the KNN classification algorithm based on the new weighting technique and stepwise feature selection. First, we used a stepwise feature selection method to eliminate irrelevant features and select highly correlated features with the class category. Then a new weighting method was proposed to give authority value to each sample in train dataset based on neighbor categories and Euclidean distances. This weighting approach gives a higher preference to samples that have neighbors with close Euclidean distance while they are in the same category, which can effectively increase the classification accuracy of the algorithm. We evaluated the accuracy rate of the proposed method and analyzed it with the traditional KNN algorithm and some similar works with the use of five real-world UCI datasets. The experiment results determined that the proposed scheme (denoted by WAD-KNN) performed better than the traditional KNN algorithm and considered approaches with the improvement of approximately 10% accuracy.

Internet users all over the world are increasing day by day and showing great interest for online shopping. The main reason for the high usage of the internet is the affordable price of mobile gadgets and low internet tariff plans. Consumer behavior is influenced by various factors such as culture, social class, reference groups relationship, family, income level and income independency, age, gender, etc. The purpose of this study was to find out the differences in buying behaviors among the university students. The study was carried out using google survey with a sample size of 236 students randomly selected from the university of India and Saudi Arabia. The study shows that University students of both countries have more online shopping experience because they use the internet more frequently and they have larger internet usage. Nowadays Students are more computer professionals and those who use the internet for their study work and assignments work are more active in online shopping. As per student’s opinion, they said few things remember in our mind when coming to payment options credit card is the safest option or using online services like pay pal and google wallet services also a good way but finally, cash on delivery is the best way of shopping online. The result of this study would contribute marketers who want to penetrate the market in India and in Kingdom of Saudi Arab, who are already present in the market and desire to take care of the loyalty and satisfaction of their customers.

The evolution of today's application technologies requires a certain level of robustness, reliability and ease of integration. We choose the Fields Programmable Gate Array (FPGA) hardware description language to implement the facial recognition algorithm based on "Eigen faces" using Principal Component Analysis. In this paper, we first present an overview of the PCA used for facial recognition, then use a VHSIC Hardware Description Language (VHDL) simulation and design platform, which is the ISE. We describe the operation of each block and implement, thereafter, the computation of the global centered images. This corresponds to the first step of the PCA algorithm to assess its performance. The comparison of the results of this implementation with that of MATLAB confirmed the operability and effectiveness of this method for centralizing images. We also implemented the last part of this algorithm which is the computation of the Manhattan distance. The tests have given very satisfactory results.

The great development witnessed by our current age has led to the emergence of many diverse modern technologies, one of these advanced technologies is Hologram technology and Augmented reality technology, These two technologies are somewhat similar to somewhat, as it can be said that they perform almost the same purpose, and at the same time, Hologram technology differs from augmented reality in several aspects, as the way in which the three-dimensional images are created and the properties of that images itself. This paper aims to compare and study the similarities and differences between Hologram technology and Augmented reality technology. It is a standard comparison as the comparison takes place according to a number of different aspects of both technologies. Comparing the characteristics of the two technologies showed that there is no one of them excels over the other, but according to different systems and situations, it is maybe better and more appropriate to use one of them than using the other one.

The Web has become the most important information source for most of us. Unfortunately, there is no guarantee for the correctness of information on the Web. Moreover, different websites often provide conflicting information on a subject. Several truth discovery methods have been proposed for various scenarios, and they have been successfully applied in diverse application domains. In this paper, we have attempted to answer the question whether the truth is relevant. We conducted an experimental study in which we analyzed and compared the results of two different truth discovery methods: Relevance-based sources ranking and Majority vote. We have found that the truth is not always held by the most relevant sources on the web. Sometimes the truth is given by the majority vote of the crowd. In addition, we have proposed a method of presenting the results of truth discovery with gradual degrees of belief. A method that allows to configure and target the desired level of trust.

The special issue aims to cover the latest research topics in designing and deploying visual information analytics and management techniques using deep learning. It is intended to serve as a platform to researchers who want to present research in deep learning. The special issue focuses explicitly on deep learning and its application in visual computing and signal processing. It emphasizes on the extent to which Deep Learning can help specialists in understanding and analyzing complex images and signals. The field of Visual Information Analytics and Management is considered in its broadest sense and covers both digital and analog aspects. This involves development of techniques for image analysis, understanding and restoration. Deep learning techniques are effective for visual analytics. Deep learning is a fast growing area and is gaining impetus for application in various fields. Therefore, in this special issue, the objective is to publish articles related to deep learning in various problems of visual information analytics and management.

In order to achieve the sensing, communication and processing tasks of Wireless Sensor Networks, an energy-efficient routing protocol is required to manage the dissipated energy of the network and to minimalize the traffic and the overhead during the data transmission stages. Clustering is the most common technique to balance energy consumption amongst all sensor nodes throughout the network. In this paper, a multi-objective bio-inspired algorithm based on the Firefly and the Shuffled frog-leaping algorithms is presented as a clustering-based routing protocol for Wireless Sensor Networks. The multi-objective fitness function of the proposed algorithm has been performed on different criteria such as residual energy of nodes, inter-cluster distances, cluster head distances to the sink and overlaps of clusters, to select the proper cluster heads at each round. The parameters of the proposed approach in the clustering phase can be adaptively tuned to achieve the best performance based on the network requirements. Simulation outcomes have displayed average lifetime improvements of up to 33.95%, 32.62%, 12.1%, 13.85% compared with LEACH, ERA, SIF and FSFLA respectively, in different network scenarios.