Artificial Intelligence Advances

Journal Information
EISSN : 2661-3220
Published by: Bilingual Publishing Co. (10.30564)
Total articles ≅ 29
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Xinhua Wang, Weikang Wu
Artificial Intelligence Advances, Volume 3; https://doi.org/10.30564/aia.v3i2.3878

Abstract:
AI processor, which can run artificial intelligence algorithms, is a state-of-the-art accelerator,in essence, to perform special algorithm in various applications. In particular,these are four AI applications: VR/AR smartphone games, high-performance computing, Advanced Driver Assistance Systems and IoT. Deep learning using convolutional neural networks (CNNs) involves embedding intelligence into applications to perform tasks and has achieved unprecedented accuracy [1]. Usually, the powerful multi-core processors and the on-chip tensor processing accelerator unit are prominent hardware features of deep learning AI processor. After data is collected by sensors, tools such as image processing technique, voice recognition and autonomous drone navigation, are adopted to pre-process and analyze data. In recent years, plenty of technologies associating with deep learning Al processor including cognitive spectrum sensing, computer vision and semantic reasoning become a focus in current research.
Febronie Nambajemariya, Yongshun Wang, Twizerane Jean D’Amour, Kwizera Niyigena Vincent DePaul, Yao Hu
Artificial Intelligence Advances, Volume 3; https://doi.org/10.30564/aia.v3i2.3651

Abstract:
With the deployment of Connected and Automated Vehicles in the coming decades, road transportation will experience a significant upheaval. CAVs (Connected and Autonomous Vehicles) have been a main emphasis of Transportation and the automotive sector, and the future of transportation system analysis is widely anticipated. The examination and future development of CAVs technology has been the subject of numerous researches. However, as three essential kinds of road users, pedestrians, bicyclists, and motorcyclists have experienced little to no handling. We explored the influence of CAVs on non-motorized mobility in this article and seven various issues that CAVs face in the environment.
A.G. Reshetnikov, S.V. Ulyanov
Artificial Intelligence Advances, Volume 3; https://doi.org/10.30564/aia.v3i2.3849

Abstract:
The technology of knowledge base remote design of the smart fuzzy controllers with the application of the "Soft / quantum computing optimizer" toolkit software developed. The possibility of the transmission and communication the knowledge base using remote connection to the control object considered. Transmission and communication of the fuzzy controller’s knowledge bases implemented through the remote connection with the control object in the online mode apply the Bluetooth or WiFi technologies. Remote transmission of knowledge bases allows designing many different built-in intelligent controllers to implement a variety of control strategies under conditions of uncertainty and risk. As examples, two different models of robots described (mobile manipulator and (“cart-pole” system) inverted pendulum). A comparison of the control quality between fuzzy controllers and quantum fuzzy controller in various control modes is presented. The ability to connect and work with a physical model of control object without using than mathematical model demonstrated. The implemented technology of knowledge base design sharing in a swarm of intelligent robots with quantum controllers. It allows to achieve the goal of control and to gain additional knowledge by creating a new quantum hidden information source based on the synergetic effect of combining knowledge. Development and implementation of intelligent robust controller’s prototype for the intelligent quantum control system of mega-science project NICA (at the first stage for the cooling system of superconducted magnets) is discussed. The results of the experiments demonstrate the possibility of the ensured achievement of the control goal of a group of robots using soft / quantum computing technologies in the design of knowledge bases of smart fuzzy controllers in quantum intelligent control systems. The developed software toolkit allows to design and setup complex ill-defined and weakly formalized technical systems on line.
Falah Al-Akashi, Diana Inkpen
Artificial Intelligence Advances, Volume 3; https://doi.org/10.30564/aia.v3i2.3219

Abstract:
What is a real time agent, how does it remedy ongoing daily frustrations for users, and how does it improve the retrieval performance in World Wide Web? These are the main question we focus on this manuscript. In many distributed information retrieval systems, information in agents should be ranked based on a combination of multiple criteria. Linear combination of ranks has been the dominant approach due to its simplicity and effectiveness. Such a combination scheme in distributed infrastructure requires that the ranks in resources or agents are comparable to each other before combined. The main challenge is transforming the raw rank values of different criteria appropriately to make them comparable before any combination. Different ways for ranking agents make this strategy difficult. In this research, we will demonstrate how to rank Web documents based on resource-provided information how to combine several resources raking schemas in one time. The proposed system was implemented specifically in data provided by agents to create a comparable combination for different attributes. The proposed approach was tested on the queries provided by Text Retrieval Conference (TREC). Experimental results showed that our approach is effective and robust compared with offline search platforms.
Sergey Victorovich Ulyanov
Artificial Intelligence Advances, Volume 3; https://doi.org/10.30564/aia.v3i2.3171

Abstract:
The quantum self-organization algorithm model of wise knowledge base design for intelligent fuzzy controllers with required robust level considered. Background of the model is a new model of quantum inference based on quantum genetic algorithm. Quantum genetic algorithm applied on line for the quantum correlation’s type searching between unknown solutions in quantum superposition of imperfect knowledge bases of intelligent controllers designed on soft computing. Disturbance conditions of analytical information-thermodynamic trade-off interrelations between main control quality measures (as new design laws) discussed in Part I. The smart control design with guaranteed achievement of these tradeoff interrelations is main goal for quantum self-organization algorithm of imperfect KB. Sophisticated synergetic quantum information effect in Part I (autonomous robot in unpredicted control situations) and II (swarm robots with imperfect KB exchanging between “master - slaves”) introduced: a new robust smart controller on line designed from responses on unpredicted control situations of any imperfect KB applying quantum hidden information extracted from quantum correlation. Within the toolkit of classical intelligent control, the achievement of the similar synergetic information effect is impossible. Benchmarks of intelligent cognitive robotic control applications considered.
Navid Moshtaghi Yazdani
Artificial Intelligence Advances, Volume 3; https://doi.org/10.30564/aia.v3i1.3074

Abstract:
In the present paper, a method for reliable estimation of defect profile in CK45 steel structures is presented using an eddy current testing based measurement system and post-processing system based on deep learning technique. So a deep learning method is used to determine the defect characteristics in metallic structures by magnetic field C-scan images obtained by an anisotropic magneto-resistive sensor. Having designed and adjusting the deep convolution neural network and applied it to C-scan images obtained from the measurement system, the performance of deep learning method proposed is compared with conventional artificial neural network methods such as multilayer perceptron and radial basis function on a number of metallic specimens with different defects. The results confirm the superiority of the proposed method for characterizing defects compared to other classical training-oriented methods.
Wai Kit Wong, Chi Jie Tan, Thu Soe Min, Eng Kiong Wong
Artificial Intelligence Advances, Volume 3; https://doi.org/10.30564/aia.v3i1.3249

Abstract:
Visually impaired persons have difficulty in business that dealing with banknote. This paper proposed a Malaysian banknotes detection system using image processing technology and fuzzy logic algorithm for the visually impaired. The Malaysian banknote reader will first capture the inserted banknote image, sending it to the cloud server for image processing via Wi-Fi medium. The cloud server is established to receive the banknote image sending from the banknote reader, processing them using perceptual hashing based image searching and fuzzy logic algorithm, then return the detected banknote’s value results back to the banknote reader. The banknote reader will display the results in terms of voice message played on the mini speaker attached on it, to allow visually impaired persons knowing the banknote’s value. This hardware mechanism reduces the size and costs for the banknote reader carried by the visually impaired persons. Experimental results showed that this Malaysian banknotes detection system reached an accuracy beyond 95% by running test on 600 different worn, torn and new Malaysian banknotes. After the banknote image being taken by the banknote reader’s camera, the system able to detect the banknote value in about 480 mili-seconds to 560 mili-seconds for a single sided banknote recognition. The banknotes detection speed was also comparable with human observers reading banknotes, with the response of 1.0908 second per banknote slight difference reading time. The IoT and image processing concepts were successfully blended and it provides an alternative to aid the visually impaired person their daily business transaction activities in a better way.
Mohammed Daffalla Elradi, Mohamed Hashim Mohamed, Mohammed Elradi Ali
Artificial Intelligence Advances, Volume 3; https://doi.org/10.30564/aia.v3i1.3162

Abstract:
Ransomware attacks have been spreading broadly in the last few years, where attackers deny users’ access to their systems and encrypt their files until they pay a ransom, usually in Bitcoin. Of course, that is the worst thing that can happen; especially for organizations having sensitive information. In this paper we proposed a cyber security awareness program intended to provide end-users with a rescue checklist in case of being attacked with a ransomware as well as preventing the attack and ways to recover from it. The program aimed at providing cyber security knowledge to 15 employees in a Sudanese trading and investment company. According to their cyber behaviour before the program, the participants showed a low level cyber security awareness that with 72% they are likely of being attacked by a ransomware from a phishing email, which is well known for spreading ransomware attacks. The results revealed that the cyber security awareness program greatly diminished the probability of being attacked by a ransomware with an average of 28%. This study can be used as a real-life ransomware attack rescue plan.
Qiaoyu Wang, Kai Kang, ZhiHan Zhang, Demou Cao
Artificial Intelligence Advances, Volume 3; https://doi.org/10.30564/aia.v3i1.2790

Abstract:
Predicting the direction of the stock market has always been a huge challenge. Also, the way of forecasting the stock market reduces the risk in the financial market, thus ensuring that brokers can make normal returns. Despite the complexities of the stock market, the challenge has been increasingly addressed by experts in a variety of disciplines, including economics, statistics, and computer science. The introduction of machine learning, in-depth understanding of the prospects of the financial market, thus doing many experiments to predict the future so that the stock price trend has different degrees of success. In this paper, we propose a method to predict stocks from different industries and markets, as well as trend prediction using traditional machine learning algorithms such as linear regression, polynomial regression and learning techniques in time series prediction using two forms of special types of recursive neural networks: long and short time memory (LSTM) and spoken short-term memory.
Artificial Intelligence Advances, Volume 3; https://doi.org/10.30564/aia.v3i1.3178

Abstract:
Remarkable progress in research has shown the efficiency of Knowledge Graphs (KGs) in extracting valuable external knowledge in various domains. A Knowledge Graph (KG) can illustrate high-order relations that connect two objects with one or multiple related attributes. The emerging Graph Neural Networks (GNN) can extract both object characteristics and relations from KGs. This paper presents how Machine Learning (ML) meets the Semantic Web and how KGs are related to Neural Networks and Deep Learning. The paper also highlights important aspects of this area of research, discussing open issues such as the bias hidden in KGs at different levels of graph representation.
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