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Accepted Papers
Effects Of Green Production On Effective Exchange Rate In Iran: PARTICULAR Swarm Optimization Algorithm

Saeed Iranmanesh, PHD student, Department of Economics, Faculty of Management and Economics, Shahid Bahonar University of Kerman, Kerman, Iran

ABSTRACT

In this paper macroeconomic data related to the Islamic Republic of Iran have been analyzed and analyzed. These data have been obtained from the World Bank. Raw data related to the Islamic Republic of Iran for the period (1993-2017). The purpose of this paper is to obtain effective exchange rate data in the Islamic Republic of Iran that is affected by green production. For this purpose, a series of indicators is needed to estimate the effective exchange rate data analysis model. The models are indicators of the degree of openness of the economy, the size of the government, and green production as variables that process data. The raw data used in this article are: effective exchange rate, export, import, government expenditure, production. GDP, foreign direct investment, carbon dioxide (CO2) emissions and inflation have been used. Effective exchange rates have the potential to be used in future foreign exchange market research. In addition, effective exchange rates that are influenced by green production can have environmental implications for a countrys foreign exchange market.

KEYWORDS

Effective Exchange Rate, Green Production, Particle Swarm Optimization Algorithm, Optimization.


The Future of Online Learning using Artificial Intelligence

Yew Kee Wong, School of Information Engineering, HuangHuai University, Henan, China

ABSTRACT

Online learning is the emerging technique in education and learning during the COVID-19 pandemic period. Traditional learning is a complex process as learning patterns, approach, skills and performance varies from person to person. Adaptive online learning focuses on understanding the learner’s performance, skills and adapts to it. The use of advanced technology also provides a means to analyse the behavioural learning pattern. As it provides the detailed skill mapping and performance which enables the learner to understand the areas needs to be improved. The information can also be used by assessors to improve the teaching approach. Advanced online learning system using artificial intelligence is an emerging concept in the coming years. In this new concept, the classes are not taken face-to-face in a classroom but through an electronic medium as a substitute. These virtual learning approach are gaining importance every day and very soon they are going to be an integral part of our world. Taking up these virtual learning through an electronic medium is termed as online learning. We proposed two new models which are powered by artificial intelligence (AI) tools. A number of examples of using these new models are presented.

KEYWORDS

Analysis Algorithm, Artificial Intelligence, Hybrid Integrated Model, Online Learning, Progressive Response Learning.


On the K-Nearest Neighbor Rule for Classification of Curves under Spatial Dependence

Ahmad Younso, Department of mathematical statistics, Faculty of sciences, Damascus University/Syria

ABSTRACT

The classical k-nearest neighbor (k-NN) rule of classification for spatially dependent functional data (curves) will be considered. The (strong consistency ) consistency of the classifier will be established based on training data drawn from (β-) α-mixing random field. The results of this paper extend some previous results to the spatial case.

KEYWORDS

Bayes rule; training data; k-NN rule; random field, smoothing parameter; consistency.


Performance Evaluation Tools in Wireless Sensor Networks

Souhila Silmi1,2, Zouina Doukha1, Samira Moussaoui1, 1Department of Computer Science, USTHB University, RIMAA Lab., B P N°32 El Alia, 16000 Bab Ezzouar, Algiers, Algeria, 2Department of Computer Science,Higher Normal School Elbachir El-ibrahimi-Kouba,B P N°92 16308 Vieux-Kouba, Algiers, Algeria

ABSTRACT

The growing need to integrate new technologies into Wireless Sensor Networks (WSNs) has led to the increasing requirement for development and implementation. From the current systems, it can be seen that several research have been made since several years ago, but the WSN deployment still a challenging task. Yet, this is done by real deployment withtestbeds or bysimulation tools when real deployment could be costly and time-consuming. Each kind of deployment has advantages and limits. With this in mind, the purpose of this paper is to review the implementation and evaluationprocess in WSNs. So, we describe relevant testbeds and simulation tools, and their features. Lastly, we conduct an experimentationstudy using these testbeds and simulations to highlight their pro and cons. To display how we obtained our results, we implement a localization protocol as a use case. The aim of this work is to give more clarity to future-work for better implementation in order to improve reliability, accuracy and time consumed. Hence, researchers will be able to choice the appropriate tools in the process of development and deployment for each new proposal in this fields.

KEYWORDS

Wireless Sensor Network, Testbeds, Experimentation platform, Simulation Tools.


The use of Big Data in Machine Learning Algorithm

Yew Kee Wong, School of Information Engineering, HuangHuai University, Henan, China

ABSTRACT

In the information era, enormous amounts of data have become available on hand to decision makers. Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be studied and provided in order to handle and extract value and knowledge from these datasets. Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. Such minimal human intervention can be provided using big data analytics, which is the application of advanced analytics techniques on big data. This paper aims to analyse some of the different machine learning algorithms and methods which can be applied to big data analysis, as well as the opportunities provided by the application of big data analytics in various decision making domains.

KEYWORDS

Artificial Intelligence, Big Data Analysis, Machine Learning.


An Improved Framework for C-v2X Systems with Data Integration and Identity-Based Authentication

Rui Huang, Lianyungang Jierui Electronics Co., Ltd., city: Lianyungang, zip code: 222061

ABSTRACT

Current trends of autonomous driving apply the hybrid of on-vehicle and roadside smart devices to perform collaborative data sensing and computing, so as to achieve a comprehensive and stable decision making. The integrated system is usually named as C-V2X. However, several challenges have significantly hindered the development and adoption of such systems. For example, the difficulty of accessing multiple data protocols of multiple devices at the bottom layer, and the centralized deployment of computing arithmetic power. Therefore, this work proposes a novel framework for the design of C-V2X systems. First, a highly aggregated architecture is designed with fully integration on multiple traffic data resources. Then a multi-level information fusion model is designed based on multi-sensors in vehicle-road coordination. The model can fit different detection environments, detection mechanisms, and time frames. Finally, a lightweight and efficient identity-based authentication method is given. The method can realize bidirectional authentication between end devices and edge gateways.

KEYWORDS

Network Protocols, Wireless Network, Mobile Network, Virus, Worms &Trojon.


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