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[Particle swarm optimization for the optimal layout of wind turbines inside a wind farm ]]>
<![CDATA[Mariam El jaadi]]>;
<![CDATA[Touria Haidi]]>;
<![CDATA[Doha Bouabdallaoui]]>
<![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.pp1260-1269
<![CDATA[The wind turbine’s output power is heavily affected by the arrangement of the wind turbine location. Wind farm planning endeavors to firstly maximize the farm’s output energy. Secondly, it seeks to minimize the effects of the wake phenomenon. This paper attempts to find the best possible location of a wind turbine inside a square farm using the particle swarm optimization (PSO) method whilst focusing on the three salient cases: the steadiness of wind direction and speed, the variability of the flow direction with a steady speed, and the variability of direction for three discrete wind speeds. The proposed algorithm generated results that will be contrasted to previous studies on the same topic with different metaheuristic methods such as a genetic algorithm. When compared to the optimum findings from prior research, the suggested approach has a reduced cost. It is developed by language C through MATLAB environment considering a square with the dimensions 2×2 kilometers.]]>
<![CDATA[Neural network models selection scheme for health mobile app development ]]>
<![CDATA[Yaya Sudarya Triana]]>;
<![CDATA[Mohd Azam Osman]]>;
<![CDATA[Adji Pratomo]]>;
<![CDATA[Muhammad Fermi Pasha]]>;
<![CDATA[Deris Stiawan]]>;
<![CDATA[Rahmat Budiarto]]>
<![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.pp1191-1203
<![CDATA[Mobile healthcare application (mHealth app) assists the frontline health worker in providing necessary health services to the patient. Unfortunately, existing mHealth apps continue to have accuracy issues and limited number of disease detection systems. Thus, an intelligent disease diagnostics system may help medical staff as well as people in poor communities in rural areas. This study proposes a scheme for simultaneously selecting the best neural network models for intelligent disease detection systems on mobile devices. To find the best models for a given dataset, the proposed scheme employs neural network models capable of evolving altered neural network architectures. Eight neural network models are developed simultaneously and then implemented on the Android Studio platform. Mobile health applications use pre-trained neural network models to provide users with disease prediction results. The performance of the mobile application is measured against the existing available datasets. The trained neural network engines perform admirably, detecting 7 out of 8 diseases with high accuracy ranging from 86% to 100% and a low detection accuracy of 63%. The detection times vary from 0.01 to 0.057 seconds. The developed mHealth app may be used by health workers and patients to improve resource-poor community health services and patients' healthcare quality.]]>
<![CDATA[An optimized approach to enhance the network lifetime through integrated data aggregation and data dissemination in wireless sensor network ]]>
<![CDATA[Anitha Chikkanayakanahalli LokeshKumar]]>;
<![CDATA[Ranganathaiah Sumathi]]>
<![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.pp1291-1301
<![CDATA[Wireless sensor network (WSN) is an integral part of internet of things (IoT), it comprises multiple sensor nodes to sense that data for various applications; also, sensor nodes have limited energy. Hence, several researchers to improvise the network lifetime and reduce energy through machine learning approaches like clustering have used data aggregation; considering the WSN architecture and development of novel use cases and dynamic behavior, data aggregation cannot solve the problem of efficiency solely. Hence integrating data aggregation and data dissemination can provide a research scope to achieve optimal efficiency. This research work introduces an integrated-data aggregation and data dissemination (DADD) to develop an efficient WSN-based model for lifetime enhancement. Integrated-DADD follows two sub-mechanisms; the first part of the mechanism introduces an optimal clustering technique to perform the clustering and optimal parameter tuning is formulated and efficient data aggregation takes place. The second part of the integrated-DADD introduces optimal data dissemination through optimal path selection, which helps in finding the suitable path for data dissemination. Integrated-DADD is evaluated considering the parameters like energy consumption, network lifetime in terms of rounds; active node participation, and communication overhead, comparative analysis indicates that integrated-DADD outperforms the existing model.]]>
<![CDATA[Face recognition in identifying genetic diseases: a progress review ]]>
<![CDATA[Salsabila Aurellia]]>;
<![CDATA[Siti Fauziyah Rahman]]>
<![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.pp1019-1025
<![CDATA[Genetic diseases vary widely. Practitioners often face the complexity of determining genetic diseases. In distinguishing one genetic disease from another, it is difficult to do without a thorough test on the patient or also known as genetic testing. However, in some previous studies, genetic diseases have unique physical characteristics in sufferers. This leads to detecting differences in these physical characteristics to assist doctors in diagnosing people with genetic diseases. In recent years, facial recognition research has been quite active. Researchers continue to develop it from various existing methods, algorithms, approaches, and databases where the application is applied in various fields, one of which is medical imagery. Face recognition is one of the options for identifying disease. The condition of a person's face can be said to be a representation of a person's health. Where the accuracy in early detection can be pretty good, so face recognition is also one of the solutions that can be used to identify various genetic diseases in collaboration with artificial intelligence. This article review will focus more on the development of facial recognition in 2-dimensional images, showing that different methods can produce different results and face recognition can also overcome complex genetic disease variations. ]]>
<![CDATA[A review on object detection for autonomous mobile robot ]]>
<![CDATA[Syamimi Abdul-Khalil]]>;
<![CDATA[Shuzlina Abdul-Rahman]]>;
<![CDATA[Sofianita Mutalib]]>;
<![CDATA[Saidatul Izyanie Kamarudin]]>;
<![CDATA[Siti Sakira Kamaruddin]]>
<![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.pp1033-1043
<![CDATA[The advancement of autonomous mobile robots (AMR) is vastly being discovered and applied to several industries. AMR contributes to the development of artificial intelligence (AI), which focuses on the growth of human-interaction systems. However, it is safe to understand that mobile robots work closely in real-time and under changing surroundings. Similarly, some limitations may affect the efficiency of mobile robots. Thus, to improve the system's efficiency and accuracy, mobile robots should adopt the ability to detect incoming obstacles accurately. This paper presents the findings of a brief technology review aimed at identifying the current state of the art and future needs for AMR in object detection. This review paper is presented in the form of a narrative-literature review. Review articles were collected from 2015 until 2022 from journals or conference papers from well-known sources like IEEE Xplore, Science Direct, Scopus, and Web of Science (WOS). The analysis of the articles was discussed in four main topics, AI, object detection, AMR, and deep learning.]]>
<![CDATA[Text detection and recognition through deep learning-based fusion neural network ]]>
<![CDATA[Sunil Kumar Dasari]]>;
<![CDATA[Shilpa Mehta]]>
<![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.pp1396-1406
<![CDATA[Text recognition task involves recognizing the text from the natural image; it possesses various application, which aids information extraction through data mining from street view like images. Scene text recognition involves two stages i.e., text detection and text recognition, in the past several mechanisms has been proposed for accurate identification, these mechanisms are either traditional approach or deep learning-based. All the existing deep-learning methodology fails as this comprises character data and image data, further this research develops an optimal architecture fusion neural network (FNN) for text identification and recognition. FNN comprises several layers of convolutional neural network (CNN) as well as recurrent neural network (RNN). Within FNN architecture convolutional layer is utilized for the feature extraction and recurrent layer is utilized for attaining the feature classification prediction. Further, an optimal training architecture is established for the enhancement of classification accuracy. Here Devanagari MLT-19 dataset is utilized for the evaluation of FNN. Three different parameters are considered during evaluation i.e., script word identification, character recognition rate (CRR) and word recognition rate (WRR). Further comparison with existing models is performed to establish the proposed model efficiency and it shows FNN methodology observes 98.67% of script identification accuracy, 84.65% of WRR and 92.93% of CRR.]]>
<![CDATA[Blockchain and machine learning in the internet of things: a review of smart healthcare ]]>
<![CDATA[Nwadher Suliman Al-Blihed]]>;
<![CDATA[Nouf Fahad Al-Mufadi]]>;
<![CDATA[Nouf Thyab Al-Harbi]]>;
<![CDATA[Ibrahim Ahmed Al-Omari]]>;
<![CDATA[Mohammed Abdullah Al-Hagery]]>
<![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.pp995-1006
<![CDATA[The healthcare sector has benefited from digital transformation and modern technology. As well is expected to rely even more on the internet of things (IoT) technologies in the near future. Due to the availability of portable medical devices, applications, and mobile health services, all of which have contributed to the development of innovative features for the delivery of healthcare services. With the large number of data issued from the IoT and the importance of using data to benefit from contained in diagnosing diseases, medical records, or monitoring. Furthermore, the expansion of emerging technologies such as robots and machine learning (ML) is supported by the ease with exchanged and shared medical information. Moreover, Blockchain technology enables the creation of secure records for storing medical data in a safe and timely manner. The paper reviews various IoT, Blockchain, and ML applications and systems in the smart healthcare sector to discover many challenges, consequently, it will be easy for researchers who have an interest in these fields to find today and future solutions. This, in turn, will help to enhance the technical services depending on the IoT in ML and Blockchain in the smart healthcare field.]]>
<![CDATA[Fault tolerant control of two tanks system using gain-scheduled type-2 fuzzy sliding mode controller ]]>
<![CDATA[Keltoum Loukal]]>;
<![CDATA[Abderrahmen Bouguerra]]>;
<![CDATA[Samir Zeghlache]]>
<![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.pp1158-1168
<![CDATA[To save the robustness of type 2 fuzzy logic control technique and to avoid the high energy consumption that represents the sliding mode control (SMC) technique control technique, without failing the performance of the system, we propose a new fault tolerant control method based on gain-scheduled sliding mode control with interval type 2 fuzzy logic (FTCGST2FSMC) applied to the hydraulic system (two tanks system) with an actuator fault. The proposed control scheme avoids a difficult modeling, due to the chatter effect of the SMC, guarantees the stability studied by Lyapunov with the robustness of the system. The gains of the control with the SMC controller are modified and changed by an adaptation with a technique based on type 2 fuzzy logic, used to improve the gains of the controller when the fault is added, the proposed FTCGST2FSMC controller has been compared with the sliding mode controller. The results obtained confirm the robustness and the performance of this method, in the presence of the actuator fault effect.]]>
<![CDATA[Machine learning models applied in analyzing breast cancer classification accuracy ]]>
<![CDATA[Anuja Bokhare]]>;
<![CDATA[Puja Jha]]>
<![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.pp1370-1377
<![CDATA[There have been many attempts made to classify breast cancer data, since this classification is critical in a wide variety of applications related to the detection of anomalies, failures, and risks. In this study machine learning (ML) models are reviewed and compared. This paper presents the classification of breast cancer data using various ML models. The effectiveness of models comparatively evaluated through result using benchmark of accuracy which was not done earlier. The models considered for the study are k-nearest neighbor (kNN), decision tree classifier, support vector machine (SVM), random forest (RF), SVM kernels, logistic regression, Naïve Bayes. These classifiers were tested, analyzed and compared with each other. The classifier, decision tree, gets the highest accuracy i.e. 97.08% among all these models is termed as the best ML algorithm for the breast cancer data set.]]>
<![CDATA[Emotions and gesture recognition using affective computing assessment with deep learning ]]>
<![CDATA[Herjuna Artanto]]>;
<![CDATA[Fatchul Arifin]]>
<![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.pp1419-1427
<![CDATA[Emotions have an important role in education. Affective development, attitudes, and emotions in learning are measured using affective assessment. This method is the right way to determine the student’s affective development. However, the process did not run optimally because the teacher found it difficult to collect student’s affective data. This paper describes the development of a system that can assist teachers in carrying out affective assessment. The system was developed using a v-model that aligns the verification phase with the validation. The use of the system is carried out during learning activities. The emotion detection system detects through body gestures using PoseNet to generate emotional data for each student. The detection results are then processed and displayed on an information system in the form of a website for affective assessment. The accuracy of emotion detection got validation values of 84.4% and 80.95% after being tested at school. In addition, the acceptance test with the usability aspect of the system by the teacher got a score of 77.56% and a score of 79.85% by the students. Based on several tests carried out, this developed system can assist the process of implementing affective assessment. ]]>
