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[Design and analysis of a multi-agent e-learning system using prometheus design tool ]]>
<![CDATA[Kennedy E. Ehimwenma]]>;
<![CDATA[Sujatha Krishnamoorthy]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 1: March 2021 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v10.i1.pp9-23
<![CDATA[Agent unified modeling languages (AUML) are agent-oriented approaches that supports the specification, design, visualization and documentation of an agent-based system. This paper presents the use of prometheus AUML approach for the modeling of a Pre-assessment System of five interactive agents. The Pre-assessment System, as previously reported, is a multi-agent-based e-learning system that is developed to support the assessment of prior learning skills in students so as to classify their skills and make recommendation for their learning. This paper discusses the detailed design approach of the system in a step-by-step manner; and domain knowledge abstraction and organization in the system. In addition, the analysis of the data collated and models of prediction for future pre-assessment results are also presented.]]>
<![CDATA[A secured automated bimodal biometric electronic voting system ]]>
<![CDATA[Kennedy Okokpujie]]>;
<![CDATA[John Abubakar]]>;
<![CDATA[Samuel John]]>;
<![CDATA[Etinosa Noma-Osaghae]]>;
<![CDATA[Charles Ndujiuba]]>;
<![CDATA[Imhade Princess Okokpujie]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 1: March 2021 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v10.i1.pp1-8
<![CDATA[Insecurity, rigging and violence continue to mar electoral processes in developing nations. It has been difficult to enforce security and transparency in the voting process. This paper proposes a secure and automated bimodal voting system. The system uses three security layers, namely, a unique ID code, a token passcode that expires every five minutes and biometrics (iris and fingerprint). A scanner captures the fingerprint and iris of eligible voters. The fingerprint and iris images stored along with the corresponding particulars in a database. The software implemented is a .net managed code in C#. The result of this system shows the system is transparent, fast and fraud-free. The proposed method had a failure to enroll (FTE) and a failure to capture (FTC) of zero.]]>
<![CDATA[Deployment of internet of things-based cloudlet-cloud for surveillance operations ]]>
<![CDATA[Edje E. Abel]]>;
<![CDATA[Abd Latiff Muhammad Shafie]]>;
<![CDATA[Weng Howe Chan]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 1: March 2021 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v10.i1.pp24-34
<![CDATA[This research proposes the design of internet of things (IoT) camera/toxic gas sensors for the surveillance of a nation’s borders. Also, a wearable radio frequency identification (RFID) tag with built-in body-temperature/heartbeat sensors, for monitoring the health status and locations of military personnel while on border patrol duty or in battlefield combats. Mobile micro-controllers are deployed to gather sensed data retrieved from the sensors/RFID tags and transmitted to a cloudlet situated at the command control center, located 200 meters away from the sensor devices. Consequently, sensed data are dispatch to the cloud data center when there is a need for offline data mining or analysis. The distinguishing feature of our proposed system from previous researches is that the health status and locations of troops (soldiers) are monitored while they are in border patrol duty or in battlefield combats. Also, the introduction of cloudlet services closer to the IoT sensor devices for collection of sensed data. This way, the sensed data or information gathered at the cloudlet will aid timely information retrieval that will speed up intelligence gathering for strategic military operations, especially in critical situations. This is an innovative attempt to apply IoT-enabled cloudlet-based cloud computing to support military operations.]]>
<![CDATA[Expert role in image classification using CNN for hard to identify object: distinguishing batik and its imitation ]]>
<![CDATA[Zohanto Widyantoko]]>;
<![CDATA[Titik Purwati Widowati]]>;
<![CDATA[Isnaini Isnaini]]>;
<![CDATA[Paras Trapsiladi]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 1: March 2021 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v10.i1.pp93-100
<![CDATA[In this research we try to solve the recognition problem in differentiating between batik and its imitation. Batik is an Indonesian heritage of process in making traditional textile product that is now endangered by the existence of imitation products. We try to compare two popular CNN model to classify batik products into five classes. The classes are tulis, cap, print warna, print malam, cabut warna. Tulis and cap are genuine batik, and the other three are an imitation. We realize that this problem is go beyond the recognition of fine grained image problem, it is a hard to identify image problem because even the batik experts is having a hard time identifying batik and its imitation if only based on its picture. The two CNN models, inceptionV3 and mobilenetV2 were trained on three types of image. One type is a freely taken image, the other two were taken based on the experts suggestion. The accuracy score shows that the model trained with the suggestion based picture perform better than the one trained with the random picture.]]>
<![CDATA[Effect of data-augmentation on fine-tuned CNN model performance ]]>
<![CDATA[Ramaprasad Poojary]]>;
<![CDATA[Roma Raina]]>;
<![CDATA[Amit Kumar Mondal]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 1: March 2021 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v10.i1.pp84-92
<![CDATA[During the last few years, deep learning achieved remarkable results in the field of machine learning when used for computer vision tasks. Among many of its architectures, deep neural network-based architecture known as convolutional neural networks are recently used widely for image detection and classification. Although it is a great tool for computer vision tasks, it demands a large amount of training data to yield high performance. In this paper, the data augmentation method is proposed to overcome the challenges faced due to a lack of insufficient training data. To analyze the effect of data augmentation, the proposed method uses two convolutional neural network architectures. To minimize the training time without compromising accuracy, models are built by fine-tuning pre-trained networks VGG16 and ResNet50. To evaluate the performance of the models, loss functions and accuracies are used. Proposed models are constructed using Keras deep learning framework and models are trained on a custom dataset created from Kaggle CAT vs DOG database. Experimental results showed that both the models achieved better test accuracy when data augmentation is employed, and model constructed using ResNet50 outperformed VGG16 based model with a test accuracy of 90% with data augmentation & 82% without data augmentation.]]>
<![CDATA[Linear discriminant analysis and support vector machines for classifying breast cancer ]]>
<![CDATA[Zuherman Rustam]]>;
<![CDATA[Yasirly Amalia]]>;
<![CDATA[Sri Hartini]]>;
<![CDATA[Glori Stephani Saragih]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 1: March 2021 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v10.i1.pp253-256
<![CDATA[Breast cancer is an abnormal cell growth in the breast that keeps changed uncontrolled and it forms a tumor. The tumor can be benign or malignant. Benign could not be dangerous to health and cancerous, but malignant could be has a probability dangerous to health and be cancerous. A specialist doctor will diagnose the patient and give treatment based on the diagnosis which is benign or malignant. Machine learning offer times efficiency to determine a cancer cell. The machine will learn the pattern based on the information from the dataset. Support vector machines and linear discriminant analysis are common methods that can be used in the classification of cancer. In this study, both of linear discriminant analysis and support vector machines are compared by looking from accuracy, sensitivity, specificity, and F1-score. We will know which methods are better in classifying breast cancer dataset. The result shows that the support vector machine has better performance than the linear discriminant analysis. It can be seen from the accuracy is 98.77%.]]>
<![CDATA[Breast cancer prediction model with decision tree and adaptive boosting ]]>
<![CDATA[Tsehay Admassu Assegie]]>;
<![CDATA[R. Lakshmi Tulasi]]>;
<![CDATA[N. Komal Kumar]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 1: March 2021 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v10.i1.pp184-190
<![CDATA[In this study, breast cancer prediction model is proposed with decision tree and adaptive boosting (Adboost). Furthermore, an extensive experimental evaluation of the predictive performance of the proposed model is conducted. The study is conducted on breast cancer dataset collected form the kaggle data repository. The dataset consists of 569 observations of which the 212 or 37.25% are benign or breast cancer negative and 62.74% are malignant or breast cancer positive. The class distribution shows that, the dataset is highly imbalanced and a learning algorithm such as decision tree is biased to the benign observation and results in poor performance on predicting the malignant observation. To improve the performance of the decision tree on the malignant observation, boosting algorithm namely, the adaptive boosting is employed. Finally, the predictive performance of the decision tree and adaptive boosting is analyzed. The analysis on predictive performance of the model on the kaggle breast cancer data repository shows that, adaptive boosting has 92.53% accuracy and the accuracy of decision tree is 88.80%, Overall, the adaboost algorithm performed better than decision tree.]]>
<![CDATA[Prediction of the effects of environmental factors towards COVID-19 outbreak using AI-based models ]]>
<![CDATA[Khalid Mahmoud]]>;
<![CDATA[Hatice Bebiş]]>;
<![CDATA[A. G. Usman]]>;
<![CDATA[A. N. Salihu]]>;
<![CDATA[M. S. Gaya]]>;
<![CDATA[Umar Farouk Dalhat]]>;
<![CDATA[R. A. Abdulkadir]]>;
<![CDATA[M. B. Jibril]]>;
<![CDATA[S. I. Abba]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 1: March 2021 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v10.i1.pp35-42
<![CDATA[The need for elucidating the effects of environmental factors in the determination of the novel corona virus (COVID-19) is very vital. This study is a methodological study to compare three different test models (1. Artificial neural networks (ANN), 2. Adaptive neuro fuzzy inference system (ANFIS), 3. A linear classical model (MLR)) used to determine the relationship between COVID-19 spread and environmental factors (temperature, humidity and wind). These data were obtained from the studies (Pirouz, Haghshenas, Haghshenas, & Piro, 2020) with confirmed COVID-19 patients in Wuhan, China, using temperature, humidity and wind as the independent variables. The measured and the predicted results were checked based on three different performance indices; Root mean square error (RMSE), determination coefficient (R2) and correlation coefficient (R). The results showed that ANFIS and ANN are more promising over the classical MLR models having an average R-values of 0.90 in both calibration and verification stages. The findings indicated that ANFIS outperformed MLR and ANN. In addition, their performance skills boosted up to 25% and 9% respectively based on the determination coefficient for the prediction of confirmed COVID-19 cases in Wuhan city of China. Overall, the results depict the reliability and ability of AI-based models (ANFIS and ANN) for the simulation of COVID-19 using the effects of various environmental variables. ]]>
<![CDATA[Generalized swarm intelligence algorithms with domain-specific heuristics ]]>
<![CDATA[P. Matrenin]]>;
<![CDATA[V. Myasnichenko]]>;
<![CDATA[N. Sdobnyakov]]>;
<![CDATA[D. Sokolov]]>;
<![CDATA[S. Fidanova]]>;
<![CDATA[L. Kirilov]]>;
<![CDATA[R. Mikhov]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 1: March 2021 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v10.i1.pp157-165
<![CDATA[In recent years, hybrid approaches on population-based algorithms are more often applied in industrial settings. In this paper, we present the approach of a combination of universal, problem-free Swarm Intelligence (SI) algorithms with simple deterministic domain-specific heuristic algorithms. The approach focuses on improving efficiency by sharing the advantages of domain-specific heuristic and swarm algorithms. A heuristic algorithm helps take into account the specifics of the problem and effectively translate the positions of agents (particle, ant, bee) into the problem's solution. And a Swarm algorithm provides an increase in the adaptability and efficiency of the approach due to stochastic and self-organized properties. We demonstrate this approach on two non-trivial optimization tasks: scheduling problem and finding the minimum distance between 3D isomers.]]>
<![CDATA[Implementation of decision tree algorithm on FPGA devices ]]>
<![CDATA[Kritika Malhotra]]>;
<![CDATA[Amit Prakash Singh]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 1: March 2021 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v10.i1.pp131-138
<![CDATA[Machine learning techniques are rapidly emerging in large number of fields from robotics to computer vision to finance and biology. One important step of machine learning is classification which is the process of finding out to which category a new encountered observation belongs based on predefined categories. There are various existing solutions to classification and one of them is decision tree classification (DTC) which can achieve high accuracy while handling the large datasets. But DTC is computationally intensive algorithm and as the size of the dataset increases its running time also increases which could be from some hours to days even. But thanks to field programmable gate arrays (FPGA) which could be used for large datasets to achieve high performance implementation with low energy consumption. Along with FPGA’s, python is used for accelerating the application development and python is leveraged by using python productivity for zynq (PYNQ), a python development environment for application development. This paper provides the literature review of an implementation of DTC for FPGA devices along with future work that can be done.]]>
