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IAES International Journal of Artificial Intelligence (IJ-AI)
Published by Institute of Advanced Engineering and Science
ISSN : 20894872     EISSN : 22528938     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Engineering
IAES International Journal of Artificial Intelligence (IJ-AI) publishes articles in the field of artificial intelligence (AI). The scope covers all artificial intelligence area and its application in the following topics: neural networks; fuzzy logic; simulated biological evolution algorithms (like genetic algorithm, ant colony optimization, etc); reasoning and evolution; intelligence applications; computer vision and speech understanding; multimedia and cognitive informatics, data mining and machine learning tools, heuristic and AI planning strategies and tools, computational theories of learning; technology and computing (like particle swarm optimization); intelligent system architectures; knowledge representation; bioinformatics; natural language processing; multiagent systems; etc.
Arjuna Subject : -
Articles 24 Documents
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Search results for , issue "Vol 9, No 2: June 2020" : 24 Documents clear
New concept for cryptographic construction design based on noniterative behavior Abdallah Abouchouar; Fouzia Omary; Khadija Achkoun
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 2: June 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (541.415 KB) | DOI: 10.11591/ijai.v9.i2.pp229-235

Abstract

Nowadays, cryptography especially hash functions require to move from classical paradigms to an original concept able to handle security issues and new hardware architecture challenges as in distributed systems. In fact, most of current hash functions apply the same design pattern that was proved vulnerable against security threats; hence the impact of a potential weakness can be costly. Thus, the solution begins with a deep analysis of divers attack strategies; this way can lead to finding a new approach that enables new innovative and reliable candidates as alternative hash functions. So to achieve this goal, in this article we introduce a new construction design that consists of a non-iterative behavior by combining a parallel block processing and a sequential xor addition process, in order to provide a secure design without changing the expected goal of a hash function, at the same time avoid the use of vulnerable structures.
Classification of RRIM clone series using artificial neural network Faridatul Ama Ismail; Nina Korlina Madzhi; Noor Ezan Abdullah; Hadzli Hashim
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 2: June 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (757.78 KB) | DOI: 10.11591/ijai.v9.i2.pp297-303

Abstract

This paper presents comparative investigation on the classification of rubber latex clone series using Artificial Neural Network (ANN) based on optical sensing technique. Rubber Research Institute of Malaysia (RRIM) introduced the rubber breeding program known as RRIM clone series in order to increase the yield of latex production and the rubber wood to meet the requirement for export and import in upstream sector. Due to the large numbers of clones launched with different characteristics and properties, this bring difficulty such as lack of information regarding to the identification on cloning. Therefore, this work developed an optical based sensing system for classification of the selected RRIM 2000 and 3000 clone series based. Near Infrared Sensors was used as sensing element in order to measure the latex from the top surface and photodiode which received the reflected light from the sensor via reflectance index in term of voltage. The raw obtained data was then used as input parameter for ANN tool which supervised by scaled gradient back propagation and the performance was optimized at 25 neurons with 74.4% accuracy. By using ANN the sensitivity, specificity and accuracy for each clones are measured.  RRIM 3001 shows the highest sensitivity, 94.1% while RRIM 2002 shows the highest specificity of 99.1% accuracy, 93.1%. As a result, the system could differentiate RRIM 2002 more compare to other clones.
Nutrient deficiency detection in Maize (Zea mays L.) leaves using image processing Nurbaity Sabri; Nurul Shafekah Kassim; Shafaf Ibrahim; Rosniza Roslan; Nur Nabilah Abu Mangshor; Zaidah Ibrahim
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 2: June 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (658.346 KB) | DOI: 10.11591/ijai.v9.i2.pp304-309

Abstract

Maize is one of the world's leading food supplies. Therefore, the crop's production must continue to reproduce to fulfill the market demand. Maize is an active feeder, therefore, it need to be adequately supplied with nutrients. The healthy plants will be in deep green color to indicate it consist of adequate nutrient. Current practice to identify the nutrient deficiency on maize leaf is throught a laboratory test. It is time consuming and required agriculture knowledge. Therefore, an image processing approach has been done to improve the laboratory test and eliminate a human error in identification process. The purpose of this research is to help agriculturist, farmers and researchers to identify the type of maize nutrient deficiency to determine an action to be taken. This research using image processing techniques to determine the type of nutrient deficiency that occurs on the plant leaf. A combination of Gray-Level Co-Occurrence Matrix (GLCM), hu-histogram and color histogram has been used as a parameter for further classification process. Random forest technique was used as classifiers manage to achive 78.35% of accuracy. It shows random forest is a suitable classifier for nutrient deficiency detection in maize leaves. More machine learning algorithm will be tested to increase current accuracy.
Evaluation of psoriasis skin disease classification using convolutional neural network Rosniza binti Roslan; Iman Najwa Mohd Razly; Nurbaity Sabri; Zaidah Ibrahim
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 2: June 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (406.626 KB) | DOI: 10.11591/ijai.v9.i2.pp349-355

Abstract

Skin disease has lower impact on mortality compared to others but instead it has greater effect on quality of life because it involves symptoms such as pain, stinging and itchiness.  Psoriasis is one of the ordinary skin diseases which are relapsing, chronic and immune-mediated inflammatory disease.  It is estimated about 125 million people worldwide being infected with various types of skin infection.  Challenges arise when patients only predict the skin type disease they had without being accurately and precisely examined.  This is because as human being, they only observe and look at the diseases on the surface of the skin with their naked eye, where there are some limits, for example, human vision lacks of accuracy, reproducibility and quantification in the collection of image information.  As Plaque and Guttate are the most common Psoriasis skin disease happened among people, this paper presents an evaluation of Psoriasis skin disease classification using Convolutional Neural Network.  A total of 187 images which consist of 82 images for Plaque Psoriasis and 105 images for Guttate Psoriasis has been used which are retrieved from Psoriasis Image Library, International Psoriasis Council (IPC) and DermNet NZ.  Convolutional Neural Network (CNN) is applied in extracting features and analysing the classification of Psoriasis skin disease.  This paper showed the promising used of CNN with the accuracy rate of 82.9% and 72.4% for Plaque and Guttate Psoriasis skin disease, respectively.

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