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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 31 Documents
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Search results for , issue "Vol 10, No 4: December 2021" : 31 Documents clear
Automated vision based defect detection using gray level co-occurrence matrix for beverage manufacturing industry Norhashimah Mohd Saad; A. R. Abdullah; W. H. W. Hasan; N. N. S. A. Rahman; N. H. Ali; I. N. Abdullah
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 4: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i4.pp818-829

Abstract

Defect inspection emerged as an important role for product quality monitoring process since it is a requirement of International Organization for Standardization (ISO) 9001. The used of manual inspection is impractical because of time consuming, human error, tiredness, repetitive and low productivity. Small and medium enterprises (SMEs) are industries that having problems in maintaining the quality of their products due to small capital provided. Therefore, automatic inspection is a promising approach to maintain product quality as well as to resolve the existing problems related to delay outputs and cost burden. This article presents a computerized analysis to detect color concentration defects that occur in beverage production based on texture information provided by gray level co-occurrence matrix (GLCM). Based on the texture information, GLCM cross-section is computed to extract the parameters for features of color concentration. The distance value between two colors is then computed using co-occurrence histogram. The defect results either pass or reject is determined using Euclidean distance and rule-based classification. The experimental results show 100% accuracy which makes the proposed technique can implimented for beverage manufacturing inspection process.
Deep learning techniques for physical abuse detection Srividya M. S; Anala M R; Chetan Tayal
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 4: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i4.pp971-981

Abstract

Physical abuse has become a societal problem. Mostly children, women and old age people are vulnerable to it especially in cases of domestic violence or workplace aggression. Reporting it is in itself a challenge especially if there is a pre-existing relationship between the abuser and victim. In this paper we propose a deep learning technique for human action recognition and human pose identification to tackle physical abuse by detecting it in real time. 3D convolution neural network (CNN) architecture is built using 3D convolution feature extractors which extract both temporal and spatial data in the video. With multiple convolution layer and subsampling layer, the input video has been converted into feature vector. Human pose estimation is done using the detection of key points on the body. Using these points and tracking them from one frame to another gives spatial-temporal features to feed into neural network (NN). We present metrics to measure the accuracies of such systems where real time reporting and fault tolerance capabilities are of utmost importance. Weighted metrics shows accuracy of about 89.42% with precision of about 85.82% and thus shows the effectiveness of the system.
Effect of filter sizes on image classification in CNN: a case study on CFIR10 and Fashion-MNIST datasets Owais Mujtaba Khanday; Samad Dadvandipour; Mohd Aaqib Lone
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 4: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i4.pp872-878

Abstract

Convolution neural networks (CNN or ConvNet), a deep neural network class inspired by biological processes, are immensely used for image classification or visual imagery. These networks need various parameters or attributes like number of filters, filter size, number of input channels, padding stride and dilation, for doing the required task. In this paper, we focused on the hyperparameter, i.e., filter size. Filter sizes come in various sizes like 3×3, 5×5, and 7×7. We varied the filter sizes and recorded their effects on the models' accuracy. The models' architecture is kept intact and only the filter sizes are varied. This gives a better understanding of the effect of filter sizes on image classification. CIFAR10 and FashionMNIST datasets are used for this study. Experimental results showed the accuracy is inversely proportional to the filter size. The accuracy using 3×3 filters on CIFAR10 and Fashion-MNIST is 73.04% and 93.68%, respectively.
MedicPlant: A mobile application for the recognition of medicinal plants from the Republic of Mauritius using deep learning in real-time Sameerchand Pudaruth; Mohamad Fawzi Mahomoodally; Noushreen Kissoon; Fadil Chady
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 4: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i4.pp938-947

Abstract

To facilitate the recognition and classification of medicinal plants that are commonly used by Mauritians, a mobile application which can recognise seventy different medicinal plants has been developed. A convolutional neural network (CNN) based on the TensorFlow framework has been used to create the classification model. The system has a recognition accuracy of more than 90%. Once the plant is recognised, a number of useful information is displayed to the user. Such information includes the common name of the plant, its English name and also its scientific name. The plant is also classified as either exotic or endemic followed by its medicinal applications and a short description. Contrary to similar systems, the application does not require an internet connection to work. Also, there are no pre-processing steps, and the images can be taken in broad daylight. Furthermore, any part of the plant can be photographed. It is a fast and non-intrusive method to identify medicinal plants. This mobile application will help the Mauritian population to increase their familiarity of medicinal plants, help taxonomists to experiment with new ways of identifying plant species, and will also contribute to the protection of endangered plant species.
Biogeography in optimization algorithms: a closer look Padarabinda Palai; Debani Prasad Mishra; Surender Reddy Salkuti
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 4: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i4.pp982-989

Abstract

Biogeography can be broken down into bio and geography, which would imply the geography, i.e., the dispersion of biological organisms. The entire field of biology inspired algorithm is inclined towards providing the most optimal solution for a given problem set. Computer science experts want to always learn from the surroundings. Nature is sporadic and spontaneous and the erratic nature of a habitat is the very differentiating factor between a real world and an ideal world problem. Things change and that nothing remains constant. The diversification of a certain habitat is bound to change through external influences, some for the better, and some for the worse. This paper tries to mimic the natural influences in a habitat in a python environment and try to come up with a minimal objective value after iterating through the given metaheuristic algorithm.
Android based application for visually impaired using deep learning approach Haslinah Mohd Nasir; Noor Mohd Ariff Brahin; Mai Mariam Mohamed Aminuddin; Mohd Syafiq Mispan; Mohd Faizal Zulkifli
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 4: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i4.pp879-888

Abstract

People with visually impaired had difficulties in doing activities related to environment, social and technology. Furthermore, they are having issues with independent and safe in their daily routine. This research propose deep learning based visual object recognition model to help the visually impaired people in their daily basis using the android application platform. This research is mainly focused on the recognition of the money, cloth and other basic things to make their life easier. The convolution neural network (CNN) based visual recognition model by TensorFlow object application programming interface (API) that used single shot detector (SSD) with a pre-trained model from Mobile V2 is developed at Google dataset. Visually impaired persons capture the image and will be compared with the preloaded image dataset for dataset recognition. The verbal message with the name of the image will let the blind used know the captured image. The object recognition achieved high accuracy and can be used without using internet connection. The visually impaired specifically are largely benefited by this research.
Distributed parking management architecture based on multi-agent systems Nihal El Khalidi; Faouzia Benabbou; Nawal Sael
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 4: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i4.pp801-809

Abstract

With the increase of the number of vehicles on the road, several traffic congestion problems arise in the big city, and this has a negative impact on the economy, environment and citizens. The time spent looking for a parking space and the traffic generated contributes to mobility and traffic management problems. Hence the need for smart parking management to help drivers to find vacant spaces in a car park in a shorter time. Today, researchers are considering scenarios in which a large amount of services can be offered and used by drivers and authorities to improve the management of the city's car parks and standards of quality of life. Based on literature on smart parking management system (SPMS), we have established the most important services needed such as reservation, orientation, synchronization, and security. The dynamic distributed and open aspect of the problem led us to adopt a multi-agent modeling to ensure continuous evolution and flexibility of the management system. In this conceptual paper, we propose to structure those services on a multi-agent system (MAS) that covers the whole functions of a distributed SPMS. Each service is provided as an autonomous agent, able to communicate and collaborate with the others to propose optimized parking space to customers.
Brain stroke computed tomography images analysis using image processing: A Review Nur Hasanah Ali; Abdul Rahim Abdullah; Norhashimah Mohd Saad; Ahmad Sobri Muda; Tole Sutikno; Mohd Hatta Jopri
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 4: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i4.pp1048-1059

Abstract

Stroke is the second-leading cause of death globally; therefore, it needs immediate treatment to prevent the brain from damage. Neuroimaging technique for stroke detection such as computed tomography (CT) has been widely used for emergency setting that can provide precise information on an obvious difference between white and gray matter. CT is the comprehensively utilized medical imaging technology for bone, soft tissue, and blood vessels imaging. A fully automatic segmentation became a significant contribution to help neuroradiologists achieve fast and accurate interpretation based on the region of interest (ROI). This review paper aims to identify, critically appraise, and summarize the evidence of the relevant studies needed by researchers. Systematic literature review (SLR) is the most efficient way to obtain reliable and valid conclusions as well as to reduce mistakes. Throughout the entire review process, it has been observed that the segmentation techniques such as fuzzy C-mean, thresholding, region growing, k-means, and watershed segmentation techniques were regularly used by researchers to segment CT scan images. This review is also impactful in identifying the best automated segmentation technique to evaluate brain stroke and is expected to contribute new information in the area of stroke research.
Assessing naive Bayes and support vector machine performance in sentiment classification on a big data platform Redouane Karsi; Mounia Zaim; Jamila El Alami
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 4: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i4.pp990-996

Abstract

Nowadays, mining user reviews becomes a very useful mean for decision making in several areas. Traditionally, machine learning algorithms have been widely and effectively used to analyze user’s opinions on a limited volume of data. In the case of massive data, powerful hardware resources (CPU, memory, and storage) are essential for dealing with the whole data processing phases including, collection, pre-processing, and learning in an optimal time. Several big data technologies have emerged to efficiently process massive data, like Apache Spark, which is a distributed framework for data processing that provides libraries implementing several machine learning algorithms. In order to evaluate the performance of Apache Spark's machine learning library (MLlib) on a large volume of data, classification accuracies and processing time of two machine learning algorithms implemented in spark: naive Bayes and support vector machine (SVM) are compared to the performance achieved by the standard implementation of these two algorithms on large different size datasets built from movie reviews. The results of our experiment show that the performance of classifiers running under spark is higher than traditional ones and reaches F-measure greater than 84%. At the same time, we found that under spark framework, the learning time is relatively low.
Convolutional neural network-based face recognition using non-subsampled shearlet transform and histogram of local feature descriptors Yallamandaiah S; N. Purnachand
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 4: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i4.pp1079-1090

Abstract

Face recognition has been using in a variety of applications like preventing retail crime, unlocking phones, smart advertising, finding missing persons, and protecting law enforcement. However, the ability of face recognition techniques reduces substantially because of changes in pose, illumination, and expressions of the individual. In this paper, a novel face recognition approach based on a non-subsampled shearlet transform (NSST), histogram-based local feature descriptors, and a convolutional neural network (CNN) is proposed. Initially, the Viola-Jones algorithm is used for face detection and then the extracted face region is preprocessed by image resizing operation. Then, NSST decomposes the input image into a low and high-frequency component image. The local feature descriptors such as local phase quantization (LPQ), pyramid of histogram of oriented gradients (PHOG), and the proposed CNN are used for extracting features from the low-frequency component of the NSST decomposition. The extracted features are fused to generate the feature vector and classified using support vector machine (SVM). The efficiency of the suggested method is tested on face databases like Olivetti Research Laboratory (ORL), Yale, and Japanese female facial expression(JAFFE). The experimental outcomes reveal that the suggested face recognition method outperforms some of the state-of-the-art recognition approaches.

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