No abstract

Jordan which is located in the heart of the world contains hundreds of historical and archaeological locations that have a supreme potential in enticing visitors. The impact of clime is important on many aspects of life such as the development of tourism and human health, tourists always wanted to choose the most convenient time and place that have appropriate weather circumstances. The goal of this study is to specify the preferable months (time) for tourism in Jordan regions. Neural network has been utilized to analyze several parameters of meteorologist (raining, temperature, speed of wind, moisture, sun radiation) by analyzing and specify tourism climatic index (TCI) and equiponderate it with THI index. The outcomes of this study shows that the finest time of the year to entice tourists is “ April” which is categorized as to be “extraordinary” for visitors. TCI outcomes indicates that conditions are not convenient for tourism from July to August because of high temperature.

Developing a deep learning (DL) model for image classification commonly demands a crucial architecture organization. Planetary expeditions produce a massive quantity of data and images. However, manually analyzing and classifying flight missions image databases with hundreds of thousands of images is ungainly and yield weak accuracy. In this paper, we speculate an essential topic related to the classification of remotely sensed images, in which the process of feature coding and extraction are decisive procedures. Diverse feature extraction techniques are intended to stimulate a discriminative image classifier. Features extraction is the primary engagement in raw data processing with the purpose of data classification; when it comes across the task of analysis of vast and varied data, these kinds of tasks are considered as time-consuming and hard to be treated with. Most of these classifiers are either, in principle, quite intricate or virtually unattainable to calculate for massive datasets. Stimulated by this perception, we put forward a straightforward, efficient classifier based on feature extraction by analyzing the cell of tensors via layered MapReduce framework beside meta-learning LSTM followed by a SoftMax classifier. Experiment results show that the provided model attains a classification accuracy of 96.7%, which makes the provided model quite valid for diverse image databases with varying sizes.

No abstract

Blockchain as a distributed system that confirms security and reliability have started a new era of a solid and consensus system.Blockchains focus on cryptocurrency is encouraging many other processes to follow the same reliable approach of security.Almost all procedures and operations are now invited to be electronically performed in the digital Ethereum network that has been presented. Moreover, this study proposed the use of an Ethereum network on a blockchain platform in the study when it was moved to a blockchain network to confirm transparency.An E-voting system sample has been tested by using an Ethereum network smart contracts inwhich solidity language and wallets were used. In the voting test, the Ethereum blockchain will be able to collect records in which voters can use their Ethereum wallets or android devices to submit their votes in a consensus node.The researchers studied the voting system taking Jordan as a case study.This study recommended the adaptation of e-voting to support transparency and voters trust to reduce corruption and unreliability in the voting processes. Moreover, the use of this system will allow a voter to vote from home in the time of the pandemic.