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[An enhanced support vector regression model for agile projects cost estimation ]]>
<![CDATA[Assia Najm]]>;
<![CDATA[Abdelali Zakrani]]>;
<![CDATA[Abdelaziz Marzak]]>
<![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.pp265-275
<![CDATA[The appearance of agile software development techniques (ASDT) since 2001 has encouraged many organizations to move to an agile approach. ASDT presents an opportunity for researchers and professionals, but it has many challenges as well. One of the most critical challenges is agile effort prediction. Hence, many studies have investigated agile software development cost estimation (ASDCE). The objective of this study is twofold: First, to propose an improved model based on support vector regression with radial bias function kernel (SVR-RBF) enhanced by the optimized artificial immune network (Optainet). Second, to perform a detailed comparative analysis of the proposed method compared to other existing optimization techniques in the literature and applied for ASDCE. The experimental evaluation was carried out by assessing the performance of the proposed method using some trusted measures like standardized accuracy (SA), mean absolute error (MAE), prediction at level p (Pred(p)), mean balanced relative error (MBRE), mean inverted balanced relative error (MIBRE), and logarithmic standard deviation (LSD). Throughout a dataset with 21 agile projects using the leave-one-out cross-validation (LOOCV) technique. The results obtained prove that the proposed model enhances the accuracy of the SVR-RBF model, and it outperforms the majority of existing models in the literature.]]>
<![CDATA[An efficient resource utilization technique for scheduling scientific workload in cloud computing environment ]]>
<![CDATA[Nagendra Prasad Sodinapalli]]>;
<![CDATA[Subhash Kulkarni]]>;
<![CDATA[Nawaz Ahmed Sharief]]>;
<![CDATA[Prasanth Venkatareddy]]>
<![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.pp367-378
<![CDATA[Recently, number of data intensive workflow have been generated with growth of internet of things (IoT’s) technologies. Heterogeneous cloud framework has been emphasized by existing methodologies for executing these data-intensive workflows. Efficient resource scheduling plays a very important role provisioning workload execution on Heterogeneous cloud framework. Building tradeoff model in meeting energy constraint and workload task deadline requirement is challenging. Recently, number of multi-objective-based workload scheduling aimed at minimizing power budget and meeting task deadline constraint. However, these models induce significant overhead when demand and number of processing core increases. For addressing research problem here, the workload is modelled by considering each sub-task require dynamic memory, cache, accessible slots, execution time, and I/O access requirement. Thus, for utilizing resource more efficiently better cache resource management is needed. Here efficient resource utilization (ERU) model is presented. The ERU model is designed to utilize cache resource more efficiently and reduce last level cache failure and meeting workload task deadline prerequisite. The ERU model is very efficient when compared with standard resource management methodology in terms of reducing execution time, power consumption, and energy consumption for execution scientific workloads on heterogeneous cloud platform.]]>
<![CDATA[Optimal economic dispatch using particle swarm optimization in Sulselrabar system ]]>
<![CDATA[Marhatang Marhatang]]>;
<![CDATA[Muhammad Ruswandi Djalal]]>
<![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.pp221-228
<![CDATA[In this study, a particle swarm optimization (PSO) is proposed to optimize the cost of generating thermal plants in the South Sulawesi system. The study was con ducted by analyzing several methods using the lagrange and ant colony optimization (ACO). PSO algorithm converges on the 11th iteration algorithm with the lowest generation cost obtained, which is Rp129687962.17/hour. While the ACO algorithm converges on the 34th iteration with a generation cost of Rp131,473,269.39/hour. The results of optimization using PSO produce a total thermal power of 400.75 MW and losses of 26.15 MW. The PSO method is able to reduce the cost of generating the South Sulawesi system by Rp11,118,312.07/hour or 7.9%. While using the ACO method generates a generation cost of Rp131,473,269.39/hour to generate power of 400,812 MW with losses of 26,219 MW. The ACO method is able to reduce the cost of generating the South Sulawesi system by Rp9,333,004.9/hour or 6.62%. PSO algorithm provides the lowest cost calculation of generator compared with conventional methods and ACO smart methods. This is also shown in the calculation process, the PSO method completes calculations faster than the ACO method.]]>
<![CDATA[Artificial speech detection using image-based features and random forest classifier ]]>
<![CDATA[Choon Beng Tan]]>;
<![CDATA[Mohd Hanafi Ahmad Hijazi]]>;
<![CDATA[Frazier Kok]]>;
<![CDATA[Mohd Saberi Mohamad]]>;
<![CDATA[Puteri Nor Ellyza Nohuddin]]>
<![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.pp161-172
<![CDATA[The ASVspoof 2015 Challenge was one of the efforts of the research community in the field of speech processing to foster the development of generalized countermeasures against spoofing attacks. However, most countermeasures submitted to the ASVspoof 2015 Challenge failed to detect the S10 attack effectively, the only attack that was generated using the waveform concatenation approach. Hence, more informative features are needed to detect previously unseen spoofing attacks. This paper presents an approach that uses data transformation techniques to engineer image-based features together with random forest classifier to detect artificial speech. The objectives are two-fold: (i) to extract image-based features from the melfrequency cepstral coefficients representation of the speech signal and (ii) to compare the performance of using the extracted features and Random Forest to determine the authenticity of voices with the existing approaches. An audio-to-image transformation technique was used to engineer new features in classifying genuine and spoof voices. An experiment was conducted to find the appropriate combination of the engineered features and classifier. Experimental results showed that the proposed approach was able to detect speech synthesis and voice conversion attacks effectively, with an equal error rate of 0.10% and accuracy of 99.93%.]]>
<![CDATA[Oil palm unstripped bunch detector using modified faster regional convolutional neural network ]]>
<![CDATA[Wahyu Sapto Aji]]>;
<![CDATA[Kamarul Hawari Bin Ghazali]]>;
<![CDATA[Son Ali Akbar]]>
<![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.pp189-200
<![CDATA[The palm oil processing industry in Malaysia and Indonesia is significant and plays a vital role in the community's welfare. The efficiency of palm oil mills is characterized by the low number of unstripped bunch (USBs), so USB detection is essential in the palm oil production process. So far, USB detection is done manually and is often ignored because it is labor-intensive. We developed a USB detector based on faster regional convolutional neural network with a modified visual geometry group 16 (VGG16) backbone to solve this problem. To see the performance of our proposed USB detector, we compared it to the faster region based convolutional neural networks (R-CNN) USB detector with the VGG16 standard backbone. Based on the validation test, the USB faster R-CNN detector with modified VGG16 can improve the performance of the USB faster R-CNN detection system based on the original VGG 16 backbone. The proposed system can work faster (100% faster) with an mAP value of 0.782 (7.42% more precise) than the USB Detector with the original VGG16. In the training process, the proposed system on the speed parameter has better training parameters, which is 58.9% faster, the total loss is smaller (43.4% smaller), and the proposed system has better best accuracy (98%) than the previous system (93%). Still, it has a smaller overlap bounding box (23.91% less).]]>
<![CDATA[Image fusion by discrete wavelet transform for multimodal biometric recognition ]]>
<![CDATA[Arjun Benagatte Channegowda]]>;
<![CDATA[Hebbakavadi Nanjundaiah Prakash]]>
<![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.pp229-237
<![CDATA[In today’s world, security plays a crucial role in almost all applications. Providing security to a huge population is a more challenging task. Biometric security is the key player in such type of situation. Using a biometric-based security system more secure application can be built because it is tough to steal or forge. The unimodal biometric system uses only one biometric modality where some of the limitations will arise. For example, if we use fingerprints due to oiliness or scratches, the finger recognition rate may reduce. In order to overcome the drawbacks of unimodal biometrics, multimodal biometric systems were introduced. In this paper, new multimodal fusion methods are proposed, where instead of merging features, database images are fused using discrete wavelet transform (DWT) technique. Face and signature images are fused, features are extracted from the fused image, an ensemble classifier is used for classification, and also experiments are conducted for finger vein and signature images.]]>
<![CDATA[Labeling of an intra-class variation object in deep learning classification ]]>
<![CDATA[Putri Alit Widyastuti Santiary]]>;
<![CDATA[I Ketut Swardika]]>;
<![CDATA[Ida Bagus Irawan Purnama]]>;
<![CDATA[I Wayan Raka Ardana]]>;
<![CDATA[I Nyoman Kusuma Wardana]]>;
<![CDATA[Dewa Ayu Indah Cahya Dewi]]>
<![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.pp179-188
<![CDATA[Machine orientation learning had demonstrated that deep learning (DL)-convolutional neural networks (CNNs) were robust image classifiers with significant accuracy. Although to been functional, DL scope classification as tight, well-defined as possible uses a 2-class object, for instance, cats and dogs. The DL classification faced many challenges, e.g., variation factors, the intra-class variation. This nature is presented in every object, its diversity of an object. The label was an exact given name of an intra-class variation object. Unfortunately, not every object had a specific name, in exceptionally high similarity inside the category. This paper explored those problems in flower plants’ taxonomy naming. In supervised learned of DL, image datasets musted labeled with a meaningful word or phrase that humans are familiar with, a taxonomy naming. Labeled with visual feature extraction brought a fully automatic classification. Flower Plumeria L labeling extracted from perspective dimension scale of petal flower which automatically obtained by contour detection, and peaks of blue green red (BGR) histogram channels from bins histogram after object masked. Dataset collected on photography workbench equipped with webcam and ring light. Results showed labels for intra-class variation of Plumeria L in form of dimension-scale and BGR-peaks. The result of this study presented a novelty in building datasets for intra-class variation for the DL classification.]]>
<![CDATA[A deep learning-based multimodal biometric system using score fusion ]]>
<![CDATA[Chahreddine Medjahed]]>;
<![CDATA[Abdellatif Rahmoun]]>;
<![CDATA[Christophe Charrier]]>;
<![CDATA[Freha Mezzoudj]]>
<![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.pp65-80
<![CDATA[Recent trends in artificial intelligence tools-based biometrics have overwhelming attention to security matters. The hybrid approaches are motivated by the fact that they combine mutual strengths and they overcome their limitations. Such approaches are being applied to the fields of biomedical engineering. A biometric system uses behavioural or physiological characteristics to identify an individual. The fusion of two or more of these biometric unique characteristics contributes to improving the security and overcomes the drawbacks of unimodal biometric-based security systems. This work proposes efficent multimodal biometric systems based on matching score concatenation fusion of face, left and right palm prints. Multimodal biometric identification systems using convolutional neural networks (CNN) and k-nearest neighbors (KNN) are proposed and trained to recognize and identify individuals using multi-modal biometrics scores. Some popular biometrics benchmarks such as FEI face dataset and IITD palm print database are used as raw data to train the biometric systems to design a strong and secure verification/identification system. Experiments are performed on noisy datasets to evaluate the performance of the proposed model in extreme scenarios. Computer simulation results show that the CNN and KNN multi-modal biometric system outperforms most of the most popular up to date biometric verification techniques.]]>
<![CDATA[An efficient machine learning-based COVID-19 identification utilizing chest X-ray images ]]>
<![CDATA[Mahmoud Masadeh]]>;
<![CDATA[Ayah Masadeh]]>;
<![CDATA[Omar Alshorman]]>;
<![CDATA[Falak H Khasawneh]]>;
<![CDATA[Mahmoud Ali Masadeh]]>
<![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.pp356-366
<![CDATA[There is no well-known vaccine for coronavirus disease (COVID-19) with 100% efficiency. COVID-19 patients suffer from a lung infection, where lung-related problems can be effectively diagnosed with image techniques. The golden test for COVID-19 diagnosis is the RT-PCR test, which is costly, time-consuming and unavailable for various countries. Thus, machine learning-based tools are a viable solution. Here, we used a labelled chest X-ray of three categories, then performed data cleaning and augmentation to use the data in deep learning-based convolutional neural network (CNN) models. We compared the performance of different models that we gradually built and analyzed their accuracy. For that, we used 2905 chest X-ray scan samples. We were able to develop a model with the best accuracy of 97.44% for identifying COVID-19 using X-ray images. Thus, in this paper, we attested the feasibility of efficiently applying machine learning (ML) based models for medical image classification.]]>
<![CDATA[Bi-directional long short term memory using recurrent neural network for biological entity recognition ]]>
<![CDATA[Rashmi Siddalingappa]]>;
<![CDATA[Kanagaraj Sekar]]>
<![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.pp89-101
<![CDATA[Biomedical named entity recognition (NER) aims at identifying medical entities from unstructured data. A quintessential task in the supervision of biological databases is handling biomedical terms such as cancer type, DeoxyriboNucleic and RiboNucleic Acid, gene and protein name, and others. However, due to the massive size of online medical repositories, data processing becomes a challenge for a gazetteer without proper annotation. The traditional NER systems depend on feature engineering that is tedious and time-consuming. The research study presents a new model for Bio-NER using recurrent neural network. Unlike existing approaches, the proposed method uses bidirectional traversing with GloVe vector modelling performed at character and word levels. Bio-NER is performed in three stages; firstly, the relevant medical entities in electronic medical records from PubMed were extracted using the skip-gram model. Secondly, a vector representation for each word is created through the 1-hot method. Thirdly, the weights of the recurrent neural network (RNN) layers are adjusted using backward propagation. Finally, the long-short-term memory cells store the previously encountered medical entity to tackle context-dependency. The accuracy and F-score are calculated for each medical entity type. The MacroR, MacroP, and MacroF are equal to 0.86, 0.88, and 0.87. The overall accuracy achieved was 94%.]]>
