IAES International Journal of Artificial Intelligence (IJ-AI)
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.
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<![CDATA[Face mask detection and counting using you only look once algorithm with Jetson Nano and NVDIA giga texel shader extreme ]]>
<![CDATA[Hatem Fahd Al-Selwi]]>;
<![CDATA[Nawaid Hassan]]>;
<![CDATA[Hadhrami Bin Ab Ghani]]>;
<![CDATA[Nur Asyiqin binti Amir Hamzah]]>;
<![CDATA[Azlan Bin Abd. Aziz]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v12.i3.pp1169-1177
<![CDATA[Deep learning and machine learning are becoming more extensively adopted artificial intelligence techniques for machine vision problems in everyday life, giving rise to new capabilities in every sector of technology. It has a wide range of applications, ranging from autonomous driving to medical and health monitoring. For image detection, the best reported approach is the you only look once (YOLO) algorithm, which is the faster and more accurate version of the convolutional neural network (CNN) algorithm. In the healthcare domain, YOLO can be applied for checking the face mask wearing of the people, especially in a public area or before entering any closed space such as a building to avoid the spread of the air-borne disease such as COVID-19. The main challenges are the image datasets, which are unstructured and may grow large, affecting the accuracy and speed of the detection. Secondly is the portability of the detection devices, which are generally dependent on the more portable like NVDIA Jetson Nano or from the existing computer/laptop. Using the low-power NVDIA Jetson Nano system as well as NVDIA giga texel shader extreme (GTX), this paper aims to design and implement real-time face mask wearing detection using the pre-trained dataset as well as the real-time data.]]>
<![CDATA[Deep learning based slope erosion detection ]]>
<![CDATA[Caio Henrique Esquina Limão]]>;
<![CDATA[Thabatta Moreira Alves de Araújo]]>;
<![CDATA[Carlos Renato Lisboa Frances]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v12.i3.pp1428-1438
<![CDATA[Being increasingly present at the most diverse structure health monitoring (SHM) scenarios, many high-performance artificial intelligence techniques have been able to solve structural analysis problems. When it comes to image classification solutions, convolutional neural networks (CNNs) deliver the best results. This scenario encourages us to explore machine learning techniques, such as computer vision, and merge it with different technologies to achieve the best performance. This paper proposes a custom CNN architecture trained with slope erosion images that showed satisfactory results with an accuracy of 96.67%, enabling a precise and improved identification of instability indicators. These instabilities, when detected in advance, prevent disasters and enable proper maintenance to be carried out, given that its integrity directly affects structures built around and above it.]]>
<![CDATA[Classification of cardiac disorders based on electrocardiogram data using a decision tree classification approach with the C45 algorithm ]]>
<![CDATA[Sumiati Sumiati]]>;
<![CDATA[Viktor Vekky Ronald Repi]]>;
<![CDATA[Penny Hendriyati]]>;
<![CDATA[Anharudin Anharudin]]>;
<![CDATA[Afrasim Yusta]]>;
<![CDATA[Agung Triayudi]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v12.i3.pp1128-1138
<![CDATA[The limitations of medical personnel, especially heart disease, cause difficulties in diagnosing heart disorders, so diagnosing heart disorders is not easy, it takes the ability and experience of a cardiologist who has the expertise and experience to be able to accurately diagnose heart disorders. Several studies in the field of computing have been carried out in diagnosing cardiac abnormalities in patients. This study was conducted to accurately test the results of the classification of heart disorders using electrocardiogram medical record data with a C.45 decision tree approach. The results showed that the classification of heart defects obtained a mean squared error (MSE) value of 0.24, a root mean squared error (RMSE) value of 0.49, and an accuracy value of 75.33% with the C4.5 algorithm.]]>
<![CDATA[Early prediction of diabetes diagnosis using hybrid classification techniques ]]>
<![CDATA[Lakshmi Srinivasan]]>;
<![CDATA[Reshma Verma]]>;
<![CDATA[Mysore Dakshinamurthy Nandeesh]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v12.i3.pp1139-1148
<![CDATA[Diabetes can be mentioned as one of the most lethal and constant sicknesses that may cause an arise in the glucose levels. Design and development of performance efficient diagnosis tool is important and plays a vigorous role in initial prediction of disease and help medical experts to start with suitable treatment or medication. The insulin produced by pancreases in the subject’s body will be affected leading to several dysfunctionalities to various body organs such as kidney, heart eyes and nervous system with their normal functionalities. Hence, preliminary stage detection with proper care and medication could reduce the risk of these problems. In the area of medicine to discover patient’s data as well as to attain a predictive model or a set of rules, classification techniques have been continuously used. This study helped diagnose diabetes by selecting three important artificial intelligence (AI) techniques namely the optimal decision tree algorithm model, Type-2 fuzzy expert system and adaptive neuro fuzzy inference system which is modified. In the present research work, a hybrid model is proposed in order to improve the classification prediction and accuracy. The Pima Indian diabetes dataset (PIDD) from machine learning repository dataset was used to carry out validation and predication of the model accuracy.]]>
<![CDATA[Energy-efficient deep learning model for fruit freshness detection ]]>
<![CDATA[Febrian Valentino]]>;
<![CDATA[Tjeng Wawan Cenggoro]]>;
<![CDATA[Gregorius Natanael Elwirehardja]]>;
<![CDATA[Bens Pardamean]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v12.i3.pp1386-1395
<![CDATA[Fruits are usually used as complementary foods because they contain good nutrients such as protein and vitamins. In addition to having good content, it turns out that there are potentially harmful microorganisms contained in fruits caused by decay. Currently, many artificial intelligence (AI) techniques have been proposed in research related to fruit freshness. Deep learning is one of its most prominent types in similar studies. As deep learning typically requires a lot of computation power, it usually consumes a lot of electricity. This is an important concern, especially for agribusiness companies that require AI implementations. Based on these problems, we propose to build a convolutional neural network (CNN) model consisting of six layers to detect fruit freshness and save energy. The CNN model we built uses electrical power ranging from 55 to 73 Watts during the training process and 20 to 27 Watts during the testing process. For accuracy, the result is 98.64%. However, compared to previous studies with the MobileNetV2 model, our model only excels in several aspects, such as recall in fresh banana and fresh oranges, recall and F1-score in Rotten Banana.]]>
<![CDATA[Analysis of new differential evolution variants to solve multi-modal problems ]]>
<![CDATA[Ramesh Khaparde, Amit]]>;
<![CDATA[Poondi Sundarasamy, Ramesh]]>;
<![CDATA[Rajendran, Sathiyaraj]]>;
<![CDATA[Ticku, Amrita]]>;
<![CDATA[Palanichamy, Arunkumar]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v12.i3.pp1352-1359
<![CDATA[Differential evolution algorithm (DE) requires diversified population to solve multi- modalproblems. DE supports non-distributed population. DE versions include differential evolution algorithm with best selection method and species evolution (DESBS) and differential evolution algorithm with hierarchical fair competition modal (HFCDE). This article analyzed the efficiency of HFCDE and DESBS to solve the multi-modal s’ problems. HFCDE and DESBS support non-distributed population structured. HFCDE starts with set of feasible solution then it distributed them in the different hierarchy. HFCDE provides the fair competition. DESBS is another semi distributed, differential evolution algorithm.It starts with set of feasible solutions (population). Later it identifies the niches and create the sub-groups within population. Both HFCDE and DESBS have outperformed the other variant of state-of-art variants of DE. Here, the performance of DESBS and the HFCDE are rigorously tested on the multimodal problems. The success of DESBS over HFCDE in multi-modal difficulties managed to overcome the phenomena of elitism to resolve the complex problems, it has been observed that DESBS performs better than HFCDE in complex multi-modal scenarios.]]>
<![CDATA[Memory aware optimized Hadoop MapReduce model in cloud computing environment ]]>
<![CDATA[Vaishali Sontakke]]>;
<![CDATA[Dayananda R. B.]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v12.i3.pp1270-1280
<![CDATA[In the last decade, data analysis has become one of the popular tasks due to enormous growth in data every minute through different applications and instruments. MapReduce is the most popular programming model for data processing. Hadoop constitutes two basic models i.e., Hadoop file system (HDFS) and MapReduce, Hadoop is used for processing a huge amount of data whereas MapReduce is used for data processing. Hadoop MapReduce is one of the best platforms for processing huge data in an efficient manner such as processing web logs data. However, existing model This research work proposes memory aware optimized Hadoop MapReduce (MA-OHMR). MA-OHMR is developed considering memory as the constraint and prioritizes memory allocation and revocation in mapping, shuffling, and reducing, this further enhances the job of mapping and reducing. Optimal memory management and I/O operation are carried out to use the resource inefficiently manner. The model utilizes the global memory management to avoid garbage collection and MA-OHMR is optimized on the makespan front to reduce the same. MA-OHMR is evaluated considering two datasets i.e., simple workload of Wikipedia dataset and complex workload of sensor dataset considering makespan and cost as an evaluation parameter.]]>
<![CDATA[Preprocessing of leaf images using brightness preserving dynamic fuzzy histogram equalization technique ]]>
<![CDATA[Sreya John]]>;
<![CDATA[Arul Leena Rose Peter Joseph]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v12.i3.pp1149-1157
<![CDATA[Agriculture serves as the backbone of many countries. It provides food and other essential materials as per our requirement. Various kinds of diseases are affecting the agricultural crops which in turn reduce the quantity and quality of the agricultural sector. This can also lead to the decrease in food production thereby affecting the economic growth and development. Even though the symptoms and other impacts of the diseases are outwardly visible, manual identification of diseases and rectification is a tedious and time-consuming process. Therefore, detecting the diseases using an automatic computer-based model will be an effective solution. Image processing methods in conjunction with machine learning algorithms provide greater assistance in the field of plant disease detection. In the proposed work, plant leaf images of 10 crops are collected as the dataset. The images after acquisition are preprocessed using brightness preserving dynamic fuzzy histogram equalization (BPDFHE), an advanced version of histogram equalization and Gaussian filtering. The results are calculated and compared using the parameters such as peak signal to noise ratio (PSNR), structural similarity index (SSIM) and mean square error (MSE). This method performs more accurately than the existing preprocessing approaches.]]>
<![CDATA[Store product classification using convolutional neural network ]]>
<![CDATA[I Made Wiryana]]>;
<![CDATA[Suryadi Harmanto]]>;
<![CDATA[Alfharizky Fauzi]]>;
<![CDATA[Imam Bil Qisthi]]>;
<![CDATA[Zalita Nadya Utami]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v12.i3.pp1439-1447
<![CDATA[Stores sells consumer goods, mainly food products and other household products at retail. The products sold in stores vary greatly, in order to be time efficient in the fast-paced era and the current technological era requires artificial intelligence technology. In the artificial intelligence branch, there is a specific or detailed learning process known as deep learning. One of the branches of deep learning is the convolutional neural network (CNN). This research intends to employ a CNN architecture to facilitate and streamline the time and cost of the store’s product sorting process. The test is conducted with 1,050 product images divided into 35 labels and divided into three data, namely 80% data training 10% data validation and 10% data test. The image used is preprocessed with a size of 256×256 pixels. The data was trained with six convolution layers and an epoch of 50 with an execution time of 33 minutes so as to achieve an accuracy of 91.37%.]]>
<![CDATA[An integrated hybrid metaheuristic model for the constrained scheduling problem ]]>
<![CDATA[Bidisha Roy]]>;
<![CDATA[Asim Kumar Sen]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v12.i3.pp1091-1103
<![CDATA[Several problems in the domains of project management (PM) and operations research (OR) can be classified as optimization problems which are classically non-deterministic polynomial-time hard (NP-hard). One such highly important problem is the resource constrained project scheduling problem (RCPSP). The main aim of this problem is to find a schedule of the lowest and optimum makespan to complete a project, which involves resource as well as precedence constraints. But, being classically NP-hard, the RCPSP requires exponential computational resources as the problem complexity increases. Thus, approximate techniques like computational intelligence (CI) based approaches provide better chances of finding near optimal solutions. This paper presents the usage of a hybrid technique using the phases of teaching learning-based optimization (TLBO) metaheuristic integrated with operators like crossover and mutation from the genetic algorithm (GA). An integrated hybrid using TLBO and 2-point crossover is applied in the teacher and learner phases to the discrete RCPSP problem. Further, to diversify the population, and enhance global search, the mutation operator is applied. The proposed model is extensively tested on well-known benchmark test instances and has been compared with other seminal works. The encouraging results make evident the efficiency of the provided solution for the RCPSP problem of varying magnitudes.]]>
