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[Smart pools of data with ensembles for adaptive learning in dynamic data streams with class imbalance ]]>
<![CDATA[Radhika Vikas Kulkarni]]>;
<![CDATA[S. Revathy]]>;
<![CDATA[Suhas Haribhau Patil]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 1: March 2022 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v11.i1.pp310-318
<![CDATA[Streaming data incorporates dynamicity due to a nonstationary environment where data samples may endure class imbalance and change in data distribution over the period causing concept drifts. In real-life applications learning in dynamic data streams, is vitally important and challenging. A combined solution to adapt to class imbalance and concept drifts in dynamic data streams is rarely addressed by researchers. With this motivation, the current communication presents the online ensemble model smart pools of data with ensembles for class imbalance adaptive learning (SPECIAL) to learn in skewed and drifting data streams. It employs an ageing-based G-mean maximization strategy to adapt to dynamicity in data streams. It employs smart data-pools with the local expertise ensemble to classify samples lying in the same data-pool. The empirical and statistical study on different evaluation metrics exhibits that SPECIAL is more adaptive to class imbalanced dynamic data streams than the state-of-the-art algorithms.]]>
<![CDATA[Inspection based risk management of electric distribution overhead lines ]]>
<![CDATA[Adnan Hasan Tawafan]]>;
<![CDATA[Dhafer Mayoof Alshadood]]>;
<![CDATA[Fatima Kadhem Abd]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 1: March 2022 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v11.i1.pp1-12
<![CDATA[This paper introduces a comprehensive process based on inspection for determination of medium voltage (MV) overhead line condition and it was tried that all factors influencing the outage of distribution network integrated into one index that called condition index. A condition based failure rate model has been proposed and its unknown parameters are calculated based on historical data. Shortest path problem (SPP) model has been proposed for the long term scheduling of maintenance and reinvestment. Objective function includes sum of the reinvestment, maintenance costs, failure costs and energy not supplied (ENS) costs with considering budget and labor constraints. Finally, as a result of this research, optimal combination of various actions such as reinvestment, preventive maintenance (PM) and tree trimming and it’s scheduling has been determined over the ten and five-year horizon. Results confirmed acceptable performance of proposed method because of compliance with actual condition and engineering judgment.]]>
<![CDATA[Privacy preserving human activity recognition framework using an optimized prediction algorithm ]]>
<![CDATA[Kambala Vijaya Kumar]]>;
<![CDATA[Jonnadula Harikiran]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 1: March 2022 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v11.i1.pp254-264
<![CDATA[Human activity recognition, in computer vision research, is the area of growing interest as it has plethora of real-world applications. Inferring actions from one or more persons captured through a live video has its immense utility in the contemporary era. Same time, protecting privacy of humans is to be given paramount importance. Many researchers contributed towards this end leading to privacy preserving action recognition systems. However, having an optimized model that can withstand any adversary models that strives to disclose privacy information. To address this problem, we proposed an algorithm known optimized prediction algorithm for privacy preserving activity recognition (OPA-PPAR) based on deep neural networks. It anonymizes video content to have adaptive privacy model that defeats attacks from adversaries. The privacy model enhances the privacy of humans while permitting highly accurate approach towards action recognition. The algorithm is implemented to realize privacy preserving human activity recognition framework (PPHARF). The visual recognition of human actions is made using an underlying adversarial learning process where the anonymization is optimized to have an adaptive privacy model. A dataset named human metabolome database (HMDB51) is used for empirical study. Our experiments with using Python data science platform reveal that the OPA-PPAR outperforms existing methods.]]>
<![CDATA[Adaptive weight assignment scheme for multi-task learning ]]>
<![CDATA[Aminul Huq]]>;
<![CDATA[Mst. Tasnim Pervin]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 1: March 2022 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v11.i1.pp173-178
<![CDATA[Deep learning based models are used regularly in every applications nowadays. Gen- erally we train a single model on a single task. However, we can train multiple tasks on a single model under multi-task learning (MTL) settings. This provides us many benefits like lesser training time, training a single model for multiple tasks, reducing overfitting, and improving performances. To train a model in multi-task learning settings we need to sum the loss values from different tasks. In vanilla multi-task learning settings we assign equal weights but since not all tasks are of similar difficulty we need to allocate more weight to tasks which are more difficult. Also improper weight assignment reduces the performance of the model. We propose a simple weight assignment scheme in this paper which improves the performance of the model and puts more emphasis on difficult tasks. We tested our methods performance on both image and textual data and also compared performance against two popular weight assignment methods. Empirical results suggest that our proposed method achieves better results compared to other popular methods.]]>
<![CDATA[Green building factor in machine learning based condominium price prediction ]]>
<![CDATA[Suraya Masrom]]>;
<![CDATA[Thuraiya Mohd]]>;
<![CDATA[Abdullah Sani Abd Rahman]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 1: March 2022 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v11.i1.pp291-299
<![CDATA[The negative impact of massive urban development promotes the inclusion of green building aspects in the real estate and property industries. Green building is generally defined as an environmentally friendly building, which rapidly emerged as a national priority in many countries. Acknowledging the benefits of green building, Green Certificate and Green Building Index (GBI) has been used as one of the factors in housing prices valuation. To predict a housing price, a robust approach is crucial, which can be effectively gained from the machine learning technique. As research on green building with machine learning techniques is rarely reported in the literature, this paper presents the fundamental design and the comparison results of three machine learning algorithms namely deep learning (DL), decision tree (DT), and random forest (RF). Besides the performance comparisons, this paper presents the specific weight correlation in each of the machine learning models to describe the importance of the green building to the model. The results indicated that RF has been outperformed others while Green Certificate and GBI have only been slightly important in the DL model.]]>
<![CDATA[A sound event detection based on hybrid convolution neural network and random forest ]]>
<![CDATA[Muhamad Amirul Sadikin Md Afendi]]>;
<![CDATA[Marina Yusoff]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 1: March 2022 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v11.i1.pp121-128
<![CDATA[Sound event detection (SED) assists in the detainment of intruders. In recent decades, several SED methods such as support vector machine (SVM), K-Means clustering, principal component analysis, and convolution neural network (CNN) on urban sound have been developed. Advanced work on SED in a rare sound event is challenging because it has limited exploration, especially for surveillance in a forest environment. This research provides an alternative method that uses informative features of sound event data from a natural forest environment and evaluates the CNN capabilities of the detection performances. A hybrid CNN and random forest (RF) are proposed to utilize a distinctive sound pattern. The feature extraction involves mel log energies. The detection processes include refinement parameters and post-processing threshold determination to reduce false alarms rate. The proposed CNN-RF and custom CNN-RF models have been validated with three types of sound events. The results of the suggested approach have been compared with wellregarded sound event algorithms. The experiment results demonstrate that the CNN-RF assesses the superiority with remarkable improvement in performance, up to a 0.82 F1 score with a minimum false alarms rate at 10%. The performance shows a functional advantage over previous methods.]]>
<![CDATA[Systematic development of real-time driver drowsiness detection system using deep learning ]]>
<![CDATA[Tarig Faisal]]>;
<![CDATA[Isaias Negassi]]>;
<![CDATA[Ghebrehiwet Goitom]]>;
<![CDATA[Mohammed Yassin]]>;
<![CDATA[Anees Bashir]]>;
<![CDATA[Moath Awawdeh]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 1: March 2022 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v11.i1.pp148-160
<![CDATA[Advancements in globalization have significantly seen a rise in road travel. This has also led to increased car accidents and fatalities, which become a global cause of concern. Driver's behavior, including drowsiness, contributes to many of the road deaths. The main objective of this study is to develop a system to diminish mishaps caused by the driver's drowsiness. Recently deep convolutional neural networks have been used in multiple applications, including identifying and anticipate driver drowsiness. However, limited studies investigated the systematic optimization of convolutional neural networks (CNNs) hyperparameters, which could lead to better anticipation of driver drowsiness. To bridge this gap, a holistic approach based on the deep learning method is proposed in this paper to anticipate the drivers' drowsiness and provide an alerting mechanism to prevent drowsiness related accidents. To ensure optimal performance achievement by the system, a database of real-time images preprocessed via Haar cascade's classifiers is used to systematically optimize the CNN model's hyperparameters. Different metrics, including accuracy, precision, recall, F1-score, and confusion matrix, are used to evaluate the performance of the model. The training evaluation results of the optimal model achieved an accuracy of 99.87%, while the testing results accurately classify the drowsy driver with 97.98%.]]>
<![CDATA[AraBERT transformer model for Arabic comments and reviews analysis ]]>
<![CDATA[Hicham EL Moubtahij]]>;
<![CDATA[Hajar Abdelali]]>;
<![CDATA[El Bachir Tazi]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 1: March 2022 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v11.i1.pp379-387
<![CDATA[Arabic language is rich and complex in terms of word morphology compared to other Latin languages. Recently, natural language processing (NLP) field emerges with many researches targeting Arabic language understanding (ALU). In this context, this work presents our developed approach based on the Arabic bidirectional encoder representations from transformers (AraBERT) model where the main required steps are presented in detail. We started by the input text pre-processing, which is, then, segmented using the Farasa segmentation technique. In the next step, the AraBERT model is implemented with the pertinent parameters. The performance of our approach has been evaluated using the ARev dataset which contains more than 40,000 comments-remarks records relate to the tourism sector such as hotel reviews, restaurant reviews and others. Moreover, the obtained results are deeply compared with other relevant states of the art methods, and it shows the competitiveness of our approach that gives important results that can serve as a guide for further improvements in this field.]]>
<![CDATA[Dataset for classification of computer graphic images and photographic images ]]>
<![CDATA[Halaguru Basavarajappa Basanth Kumar]]>;
<![CDATA[Haranahalli Rajanna Chennamma]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 1: March 2022 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v11.i1.pp137-147
<![CDATA[The recent advancements in computer graphics (CG) image rendering techniques have made it easy for the content creators to produce high quality computer graphics similar to photographic images (PG) confounding the most naïve users. Such images used with negative intent, cause serious problems to the society. In such cases, proving the authenticity of an image is a big challenge in digital image forensics due to high photo-realism of CG images. Existing datasets used to assess the performance of classification models are lacking with: (i) larger dataset size, (ii) diversified image contents, and (iii) images generated with the recent digital image rendering techniques. To fill this gap, we created two new datasets, namely, ‘JSSSTU CG and PG image dataset’ and ‘JSSSTU PRCG image dataset’. Further, the complexity of the new datasets and benchmark datasets are evaluated using handcrafted texture feature descriptors such as gray level co-occurrence matrix, local binary pattern and VGG variants (VGG16 and VGG19) which are pre-trained convolutional neural network (CNN) models. Experimental results showed that the CNN-based pre-trained techniques outperformed the conventional support vector machine (SVM)-based classifier in terms of classification accuracy. Proposed datasets have attained a low f-score when compared to existing datasets indicating they are very challenging.]]>
<![CDATA[Design of the use of chatbot as a virtual assistant in banking services in Indonesia ]]>
<![CDATA[Bhakti Prabandyo Wicaksono]]>;
<![CDATA[Amalia Zahra]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 1: March 2022 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v11.i1.pp23-33
<![CDATA[A chatbot is a computer program designed to simulate an interactive communication to user (human) via text, audio, or video. Currently several banks in Indonesia have adopted chat technology in customer service. The application of artificial intelligence in customer service aims to prepare banks for the challenges of industry banking 4.0. In addition, it is also to solve problems currently faced by customer service. Implementing chatbot platform in banking in Indonesia is not just plug and play, although there are quite a lot of chatbot platforms available, including Rasa Platform, Botika Platform, and Kata.ai Platform. However, this study only evaluates two chatbot platforms, namely Rasa and Botika, where the two platforms are considered not yet able to be immediately adopted by banks. This is because the application of banking technology in Indonesia must refer to regulatory regulations, including those related to environmental needs, language, speed, and accuracy to understand the intent of users. Hence, research is needed to decide which chatbot platform can be implemented in the banking industry without violating regulatory regulations. From the results of evaluations conducted using the usability and hedonic motivation system adoption system (HMSAM) methods, it is found that users prefer Botika platform to be implemented in the banking industry.]]>
