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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 51 Documents
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Search results for , issue "Vol 12, No 3: September 2023" : 51 Documents clear
Silhouette vanished contour discovery of aerial view images by exploiting pixel divergence Ravi Babu Devareddi; Atluri Srikrishna
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i3.pp1312-1322

Abstract

An image's edge detection is the process of finding and pinpointing sharp discontinuities in an image. Detecting the edges of an image significantly reduces the quantity of data and removes unnecessary information while keeping the fundamental structural aspects of an image. Edge detection is essential when it comes to image categorization in computer vision and object identification. The primary goal of this research is to investigate several strategies for edge detection and shadow of objects in aerial view images. Machine vision, face detection, medical imaging, and object detection are just a few examples of applications where image segmentation has been widely utilized. Image segmentation is categorizing or identifying sub-patterns in given an image. Many algorithms and strategies for picture segmentation have been presented to improve segmentation issues in each application area. Techniques such as threshold-based and region-based picture segmentation were used in this study. An edge detection method such as Sobel, Prewitt and Roberts and the Canny approach is applied to the benchmark image and compared with the proposed octagonal pixel divergence edge detection (OPDED) algorithm. Results show that the proposed approach is more effective than the other methods, with a quality image with edges.
An adaptable sentence segmentation based on Indonesian rules Johannes Petrus; Ermatita Ermatita; Sukemi Sukemi; Erwin Erwin
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i3.pp1491-1499

Abstract

Sentence segmentation that breaks textual data strings into individual sentences is an important phase in natural language processing (NLP). Each word in the string that is added a punctuation mark such as a period, question mark, or exclamation point, becomes the location for splitting the string. Humans can easily see the punctuation and split the string into sentences, but not machines. Basically, the three punctuation marks also perform other functions so that the sentence segmentation process must really be able to detect whether a word marked with punctuation is a sentence boundary or not. This research proposes a sentence segmentation system called segmentasi kalimat bahasa Indonesia (SKBI) or Indonesian language sentence segmentation by applying a set of rules and can be used in Indonesian texts and can be adapted for English. There are 34 rules built with a combination of 27 fairly complete features that contribute to this research. The experimental results for the Indonesian text show that the SKBI is able to achieve an F1-Score of 96.89% and 97.07% for English. Both need to be improved but now better than previous research.
Handwritten Javanese script recognition method based 12-layers deep convolutional neural network and data augmentation Ajib Susanto; Ibnu Utomo Wahyu Mulyono; Christy Atika Sari; Eko Hari Rachmawanto; De Rosal Ignatius Moses Setiadi; Md Kamruzzaman Sarker
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i3.pp1448-1458

Abstract

Although numerous studies have been conducted on handwritten recognition, there is little and non-optimal research on Javanese script recognition due to its limitation to basic characters. Therefore, this research proposes the design of a handwritten Javanese Script recognition method based on twelve layers deep convolutional neural network (DCNN), consisting of four convolutions, two pooling, and five fully connected (FC) layers, with SoftMax classifiers. Five FC layers were proposed in this research to conduct the learning process in stages to achieve better learning outcomes. Due to the limited number of images in the Javanese script dataset, an augmentation process is needed to improve recognition performance. This method obtained 99.65% accuracy using seven types of geometric augmentation and the proposed DCNN model for 120 Javanese script character classes. It consists of 20 basic characters plus 100 others from the compound of basic and vowels characters.
The discrete wavelet transform based iris recognition for eyes with non-cosmetic contact lens Media Anugerah Ayu; I Komang Yogi Trisna Permana
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i3.pp1118-1127

Abstract

Iris recognition has been used as one of the biometric systems for user authentication, identification, and verification for quite some time. The basis of an iris recognition lies on the matching algorithm, which requires similarities of the iris data in the database with the captured one. In addition, nowadays using non-cosmetic or prescribed contact lenses becomes more popular and more preferred choice of many people, which makes the number of contact lens wearers significantly increases. These eyes with contact lenses add more complexity to the iris recognition process, since it can disturb the matching process which then affect the performance of the system. This situation has motivated this study to propose an iris recognition system that works for eyes with lenses. The proposed iris recognition system for eyes wearing non-cosmetic contact lenses was developed using circular hough transform (CHT) in the preprocessing phase for iris localization and discrete wavelet transform (DWT) for feature extraction. Experiments conducted on the proposed system has shown promising results with good accuracy of 0.95 for eyes with no contact lens and 0.8 for eyes with non-cosmetic lenses. The findings also suggested the importance of the iris localization process to the performance of the recognition system.
Multi level trust calculation with improved ant colony optimization for improving quality of service in wireless sensor network Ahmed Jamal Ahmed; Ali Hashim Abbas; Sami AbdulJabbar Rashid
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i3.pp1224-1237

Abstract

Wireless sensor network (WSN) is the most integral parts of current technology which are used for the real time applications. The major drawbacks in currect technologies are threads due to the creation of false trust values and data congestion. Maximum of the concept of WSNs primarily needs security and optimization. So, we are in the desire to develop a new model which is highly secured and localized. In this paper, we introduced a novel approach namely multi level trust calculation with improved ant colony optimization (MLT-IACO). This approach mainly sub-divided into two sections they are multi level trust calculation which is the combination three levels of trust such as direct trust, indirect trust and random repeat trust. Secondly, improved ant colony optimization technique is used to find the optimal path in the network. By transmitting the data in the optimal path, the congestion and delay of the network is reduced which leads to increase the efficiency. The outcome values are comparatively analyzed based the parameters such as packet delivery ratio, network throughput and average latency. While compared with the earlier research our MLT-IACO approach produce high packet delivery ratio and throughput as well as lower latency and routing overhead.
Vehicle detection system based on shape, color, and time-motion Afritha Amelia; Muhammad Zarlis; Suherman Suherman; Syahril Efendi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i3.pp1070-1082

Abstract

Vehicle detection application can assist in-vehicle surveillance functions and have implications for various fields. A vehicle can be identified through the license number attached to its license plate, the color and its shape. Vehicle detection can make use of multimedia sensors so that the design and detection performances can be optimal. Sensor performances are influenced by factors such as the number of multimedia sensors, sensor placement, sensor positioning, and schemes in case of system failure. This study makes use of multimedia sensors with cameras equipped by a phase detection auto focus (PDAF) technology which is like a pair of eyes to see an object. This study analyses 134 vehicles with number detection and various colors to see the effect on the detection and recognition processes. The cars were passed through the camera 10 times at a speed of 10-15 km/hour with various camera distances and positions. Various values and depths of the images were generated. The farther the distance the higher the disparity values. For maximum distance of 50 m, disparity is 6.20×106 and image depth is 16.88×109. Vehicle color influences detection with orange has the best accuracy, but the gray has the largest path error value.
Fabric defect classification using transfer learning and deep learning Aafaf Beljadid; Adil Tannouche; Abdessamad Balouki
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i3.pp1378-1385

Abstract

The internal inspection of fabrics is one of the most important phases of production in order to achieve high quality standard in the textile industry. Therefore, developing efficient automatic internal control mechanism has been an extremely major area of research. In this paper, the famous architecture GoogLeNet was fine-tuned into two configurations for texture defect classification that was trained on a textile texture database (TILDA). The experimental result, for both configurations, achieved a significant overall accuracy score of 97% for motif and a non-motif-based images and 89% for mixed images. In the results obtained, it was observed that the second model, which updates the last six layers, was more successful than the first one; which updates the last two layers.
An automated machine learning model for diagnosing COVID-19 infection Noor Maher; Suhad A. Yousif
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i3.pp1360-1369

Abstract

The coronavirus disease 2019 (COVID-19) epidemic still impacts every facet of life and necessitates a fast and accurate diagnosis. The need for an effective, rapid, and precise way to reduce radiologists' workload in diagnosing suspected cases has emerged. This study used the tree-based pipeline optimization tool (TPOT) and many machine learning (ML) algorithms. TPOT is an open-source genetic programming-based AutoML system that optimizes a set of feature preprocessors and ML models to maximize classification accuracy on a supervised classification problem. A series of trials and comparisons with the results of ML and earlier studies discovered that most of the AutoML beat traditional ML in terms of accuracy. A blood test dataset that has 111 variables and 5644 cases were used. In TPOT, 450 pipelines were used, and the best pipeline selected consisted of radial basis function (RBF) Sampler preprocessing and Gradient boosting classifier as the best algorithm with a 99% accuracy rate.
Image captioning to aid blind and visually impaired outdoor navigation Ruvita Faurina; Anisa Jelita; Arie Vatresia; Indra Agustian
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i3.pp1104-1117

Abstract

Artificial intelligence technology has dramatically improved the quality of services for human needs, one of which is technology to improve the quality of services for the blind and visually impaired, particularly technology that can help them understand visual sights to facilitate navigation in their daily lives. This study developed an image captioning model to aid the blind and visually impaired in outdoor navigation. The image captioning model employs the encoder-decoder method, with the convolutional neural network (CNN) feature extraction and attention layer as encoders and the long short-term memory (LSTM) as decoders. ResNet101 and ResNet152 are used in the encoder to extract image features. The results of the extraction and caption are forwarded to the attention layer and the LSTM network. The attention layer uses the Bahdanau attention mechanism. The accuracy of the model is calculated using the bilingual evaluation understudy score (BLEU), metric for evaluation of translation with explicit ordering (METEOR) and recall-oriented understudy for gisting evaluation-longest common subsequence (ROUGE-L). ResNet101 performed the best on BLEU-4, scoring 91.811% and 94.0337% in the METEOR evaluation. The captioning results show that the model is quite successful in displaying a simple caption that is suitable for each image.
Pothole recognition using convolution neural networks and transfer learning Chayadevi Senigalakuruba; Suraj Pabba
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i3.pp1204-1209

Abstract

Potholes have been and still are a huge problem for every walk of life. There are many deaths and accidents reported daily due to that very problem. For that reason, pothole recognition comes into the picture. To maintain and preserve a road, it is vital to detect potholes. It also helps in the prevention of accidents. Roads play an important part in day-to-day transportation for every person around the world. But the quality of roads decreases drastically due to the way of usage and aging. The existing methods take much time and manpower to repair the damaged areas. The entire process is slowing down just because an expert team is checking whether there is a pothole at the reported location or not. So, if we automate the process of detection of potholes from a set of images reported from a particular location and appropriately alerting the authorities with the amount of damage, the process speeds up exponentially. We must solve the major problem of pothole recognition by using machine learning algorithms. This paper will discuss a convolution neural network-based and a transfer learning-based solution for pothole recognition.

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