International Journal of Intelligence Science

Journal Information
ISSN / EISSN : 2163-0283 / 2163-0356
Current Publisher: Scientific Research Publishing, Inc. (10.4236)
Total articles ≅ 106
Archived in
SHERPA/ROMEO
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Latest articles in this journal

Ahmed Laarfi, Veton Kepuska
International Journal of Intelligence Science, Volume 10, pp 83-91; doi:10.4236/ijis.2020.104006

Abstract:
The paper’s purpose is to design and program the four operation-calculators that receives voice instructions and runs them as either a voice or text phase. The Calculator simulates the work of the Compiler. The paper is a practical example programmed to support that it is possible to construct a verbal Compiler.
Lygouras Eleftherios
International Journal of Intelligence Science, Volume 10, pp 41-64; doi:10.4236/ijis.2020.103004

Abstract:
In this paper, a study and evaluation of the combination of GPS/GNSS techniques and advanced image processing algorithms for distressed human detection, positioning and tracking, from a fully autonomous Unmanned Aerial Vehicle (UAV)-based rescue support system, are presented. In particular, the issue of human detection both on terrestrial and marine environment under several illumination and background conditions, as the human silhouette in water differs significantly from a terrestrial one, is addressed. A robust approach, including an adaptive distressed human detection algorithm running every N input image frames combined with a much faster tracking algorithm, is proposed. Real time or near-real-time distressed human detection rates achieved, using a single, low cost day/night NIR camera mounted onboard a fully autonomous UAV for Search and Rescue (SAR) operations. Moreover, the generation of our own dataset, for the image processing algorithms training is also presented. Details about both hardware and software configuration as well as the assessment of the proposed approach performance are fully discussed. Last, a comparison of the proposed approach to other human detection methods used in the literature is presented.
Wei Yanting, Feng Quanxi, Yuan Sainan, Yanting Wei, Quanxi Feng, Sainan Yuan
International Journal of Intelligence Science, Volume 10, pp 22-40; doi:10.4236/ijis.2020.102003

Abstract:
Aimed at improving the insufficient search ability of constraint differential evolution with single constraint handling technique when solving complex optimization problem, this paper proposes a constraint differential evolution algorithm based on ensemble of constraint handling techniques and multi-population framework, called ECMPDE. First, handling three improved variants of differential evolution algorithms are dynamically matched with two constraint handling techniques through the constraint allocation mechanism. Each combination includes three variants with corresponding constraint handling technique and these combinations are in the set. Second, the population is divided into three smaller subpopulations and one larger reward subpopulation. Then a combination with three constraint algorithms is randomly selected from the set, and the three constraint algorithms are run in three sub-populations respectively. According to the improvement of fitness value, the optimal constraint algorithm is selected to run on the reward sub-population, which can share information and close cooperation among populations. In order to verify the effectiveness of the proposed algorithm, 12 standard constraint optimization problems and 10 engineering constraint optimization problems are tested. The experimental results show that ECMPDE is an effective algorithm for solving constraint optimization problems.
Tingwei Liu, Zheng Zheng
International Journal of Intelligence Science, Volume 10, pp 9-21; doi:10.4236/ijis.2020.102002

Abstract:
Artificial intelligence (AI) has been used to develop and advance numerous fields and industries, including finance, healthcare, education, transportation and more. However, in the business negotiation field, such as bargain, the AI has not yet exerted its power. In order to explore the application of AI into business negotiation, we have built an intelligent robot that can help customers that lack negotiation skills when bargaining in their shopping sceneries. This bot can make decision by itself via price prediction function implemented by machine learning algorithms and the tool of decision tree. As a result, our bot has got a positive performance during a used car trade. Although the algorithm of the project is relatively simple, its main contribution is to show the potential application of AI in the business negotiation. We believe that it can provide ideas and directions for the future development of business negotiation robot.
Wissam Abdallah, Nassib Abdallah, Jean-Marie Marion, Mohamad Oueidat, Pierre Chauvet
International Journal of Intelligence Science, Volume 10, pp 65-81; doi:10.4236/ijis.2020.103005

Abstract:
Developing a reliable weather forecasting model is a complicated task, as it requires heavy IT resources as well as heavy investments beyond the financial capabilities of most countries. In Lebanon, the prediction model used by the civil aviation weather service at Rafic Hariri International Airport in Beirut (BRHIA) is the ARPEGE model, (0.5) developed by the weather service in France. Unfortunately, forecasts provided by ARPEGE have been erroneous and biased by several factors such as the chaotic character of the physical modeling equations of some atmospheric phenomena (advection, convection, etc.) and the nature of the Lebanese topography. In this paper, we proposed the time series method ARIMA (Auto Regressive Integrated Moving Average) to forecast the minimum daily temperature and compared its result with ARPEGE. As a result, ARIMA method shows better mean accuracy (91%) over the numerical model ARPEGE (68%), for the prediction of five days in January 2017. Moreover, back to five months ago, in order to validate the accuracy of the proposed model, a simulation has been applied on the first five days of August 2016. Results have shown that the time series ARIMA method has offered better mean accuracy (98%) over the numerical model ARPEGE (89%) for the prediction of five days of August 2016. This paper discusses a multiprocessing approach applied to ARIMA in order to enhance the efficiency of ARIMA in terms of complexity and resources.
Simiao Zhao, Xuanyue Mao, Hanghong Lin, Hao Yin, Peixuan Xu
International Journal of Intelligence Science, Volume 10, pp 1-8; doi:10.4236/ijis.2020.101001

Arpita Kabiraj, Prasun Kumar Nayak, Swapan Raha
International Journal of Intelligence Science, Volume 9, pp 44-58; doi:10.4236/ijis.2019.91003

Maria Del Carmen Cabrera-Hernandez, Marco Antonio Aceves-Fernandez, Juan Manuel Ramos-Arreguin, Jose Emilio Vargas-Soto, Efren Gorrostieta-Hurtado
International Journal of Intelligence Science, Volume 9, pp 67-91; doi:10.4236/ijis.2019.93005

Rongheng Li, Yunxia Zhou
International Journal of Intelligence Science, Volume 9, pp 59-65; doi:10.4236/ijis.2019.92004

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