Artificial Intelligence Advances
EISSN : 2661-3220
Current Publisher: Bilingual Publishing Co. (10.30564)
Total articles ≅ 5
Articles in this journal
Artificial Intelligence Advances, Volume 1; doi:10.30564/aia.v1i1.608
This paper aims at presenting GFLIB, a Genetic Folding MATLAB toolbox for supervised learning problems. In particular, the goal of GFLIB is to build a concise model of supervised learning, a free, open source MATLAB toolbox for performing classification and regression. The GFLIB specifically designed for most of the features traditionally used to evolve in applications of mathematical models. The toolbox suits all kinds of users, from the users who implemented GFLIB as a “black box”, to the advanced researcher who wants to generate and test new functionalities and parameters of GF algorithm. The toolbox and its documentation are freely available for download at https://github.com/mohabedalgani/gflib.git
Artificial Intelligence Advances, Volume 1; doi:10.30564/aia.v1i1.619
A new approach to a circuit implementation design of quantum algorithm gates for quantum massive parallel fast computing realization is presented. The main attention is focused on the development of design method of fast quantum algorithm operators as superposition, entanglement and interference which are in general time-consuming operations due to the number of products that have to be performed. SW & HW support sophisticated toolkit of supercomputing accelerator of quantum algorithm simulation is described. As example, the method for performing Grover’s interference without product operations introduced. The background of developed information technology is the "Quantum / Soft Computing Optimizer" (QSCOptKBTM) software based on soft and quantum computational intelligence toolkit.
Artificial Intelligence Advances, Volume 1; doi:10.30564/aia.v1i1.688
Shopping Search Engine (SSE) implies a unique challenge for validating distinct items available online in market place. For sellers, having a user listing appear number one in search results is crucial. Buyers tend to click on and buy from the listings which appear first. Search engine optimization devotes that goal to influence such challenges. In current shopping search platforms, lots of irrelevant itemsretrieved from their indices; e.g. retrieving accessories of exact items rather than retrieving the itemsitself, regardless the price of item were considered or not. In our proposal, we exploit the drawbacks of current shopping search engines. In another side, users tend to move from shoppers to another searching for appropriate items where the time is crucial for consumers. The main goal of this research is to combine and merge multiple search results retrieved from some popular shopping sellers in a listof relevant items. Experimental results showed that our approach is efficient and robust for retrieving acomplete list of desired items with respect to all users‟ query keywords.
Artificial Intelligence Advances, Volume 1; doi:10.30564/aia.v1i1.569
Autonomous vehicle is a vehicle that can guide itself without human conduction. It is capable of sensing its environment and moving with little or no human input. This kind of vehicle has become a concrete reality and may pave the way for future systems where computers take over the art of driving. Advanced artificial intelligence control systems interpret sensory information to identify appropriate navigation paths, as well as obstacles and relevant road signs. In this paper, we introduce an intelligent road signs classifier to help autonomous vehicles to recognize and understand road signs. The road signs classifier will be based on an artificial intelligence technique. In particular, a deep learning model is used, Convolutional Neural Networks (CNN). CNN is a widely used Deep Learning model to solve pattern recognition problems like image classification and object detection. CNN has been successfully used to solve computer vision problems because of its methodology in processing images which is similar to the human brain decision making. The evaluation of the proposed pipeline is proved using two different datasets. The proposed CNNs achieved high performance in road sign classification with a validation accuracy of 99.8% and a testing accuracy of 99.6%. The proposed method can be easily implemented for real-time application.
Artificial Intelligence Advances, Volume 1; doi:10.30564/aia.v1i1.925
Indoor Scene understanding and indoor objects detection is a complex high-level task for automated systems applied to natural environments. Indeed, such a task requires huge annotated indoor images to train and test intelligent computer vision applications. One of the challenging questions is to adopt and to enhance technologies to assist indoor navigation for visually impaired people (VIP) and thus improve their daily life quality. This paper presents a new labeled indoor object dataset elaborated with a goal of indoor object detection (useful for indoor localization and navigation tasks). This dataset consists of 8000 indoor images containing 16 different indoor landmark objects and classes. The originality of the annotations comes from two new facts taken into account: (1) the spatial relationships between objects present in the scene and (2) actions possible to apply to those objects (relationships between VIP and an object).This collected dataset presents many specifications and strengths as it presents various data under various lighting conditions and complex image background to ensure more robustness when training and testing objects detectors. The proposed dataset, ready for use, provides 16 vital indoor object classes in order to contribute for indoor assistance navigation for VIP.