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IAES International Journal of Artificial Intelligence (IJ-AI)
Published by Institute of Advanced Engineering and Science
ISSN : 20894872     EISSN : 22528938     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Engineering
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.
Arjuna Subject : -
Articles 1,808 Documents
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Evaluating the machine learning models based on natural language processing tasks Meeradevi, Meeradevi; B. J., Sowmya; B. N., Swetha
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1954-1968

Abstract

In the realm of natural language processing (NLP), a diverse array of language models has emerged, catering to a wide spectrum of tasks, ranging from speaker recognition and auto-correction to sentiment analysis and stock prediction. The significance of language models in enabling the execution of these NLP tasks cannot be overstated. This study proposes an approach to enhance accuracy by leveraging a hybrid language model, combining the strengths of long short-term memory (LSTM) and gated recurrent unit (GRU). LSTM excels in preserving long-term dependencies in data, while GRU's simpler gating mechanism expedites the training process. The research endeavors to evaluate four variations of this hybrid model: LSTM, GRU, bidirectional long short-term memory (Bi-LSTM), and a combination of LSTM with GRU. These models are subjected to rigorous testing on two distinct datasets: one focused on IBM stock price prediction, and the other on Jigsaw toxic comment classification (sentiment analysis). This work represents a significant stride towards democratizing NLP capabilities, ensuring that even in resource-constrained settings, NLP models can exhibit improved performance. The anticipated implications of these findings span a wide spectrum of real-world applications and hold the potential to stimulate further research in the field of NLP. 
A study on the impact of artificial intelligence on talent sourcing Hemachandran, Varun Chand; Kumar, Kurakula Arun; Sikanda, Syarul Azlina; Sabharwal, Seema; Kumar, Sivaprakasam Arun
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp1-8

Abstract

Talent sourcing is one of the most effective mechanisms to engage with the talent pool and convert a candidate into an applicant. Today, machine learning has emerged as a trend to assist employers in addressing recruitment challeng-es with the help of tools such as neuro-linguistic programming (NLP) and automated assessments. 80% of the executives strongly believe deep learning makes candidate screening highly efficient. Including current start-ups globally, only 15% use artificial intelligence (AI) and are expected to increase by 31%. The study focused on the impact of AI in recruitment process. There are a few metrics, such as application completion rate, number of candidates per filled position, cost per hire, and so on. Here we would like to analyze the impact of using AI in various phases of hiring in the organization.
A hybrid hue saturation lightness, gray level co-occurrence matrix, and k-nearest neighbour for palm-sugar classification Jumarlis, Mila; Mulyadi, Ida; Mirfan, Mirfan; Imawati, Irmawati; Mardiah, Mardiah; Faisal, Muhammmad; Anisa, Hairin
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp2934-2945

Abstract

In recent years, there has been an increasing demand for high-quality raw materials driven by consumers and the food industry. This study aims to build a model to predict the type of palm sugar using a hybrid method of hue-saturation-lightness (HSL), gray level co-occurrence matrix (GLCM), and K-nearest neighbor (KNN). The price of palm sugar is determined based on the type and ingredients used. However, due to the lack of public knowledge in distinguishing the types of palm sugar, there is the potential for price manipulation that can harm the community. The accuracy rate of 97.6% of the palm sugar type prediction results shows that the model that was built has worked very well. The results have practical implications, such as developing automated systems to classify palm species in specific industries to benefit economics and operational efficiency. Future research directions may explore the integration of advanced machine-learning techniques and real-time image processing for further improving classification performance and scalability in industrial applications.
Ubiquitous-cloud-inspired deterministic and stochastic service provider models with mixed-integer-programming Sumarlin, Sumarlin; Zarlis, Muhammad; Suherman, Suherman; Efendi, Syahril
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1304-1311

Abstract

The ubiquitous computing system is a paradigm shift from personal computing to physical integration. This study focuses on the deterministic and stochastic service provider model to provide sub-services to computing nodes to minimize rejection values. This deterministic service provider model aims to reduce the cost of sending data from one place to another by considering the processing capacity at each node and the demand for each sub-service. At the same time, stochastic service provider aims to optimize service provision in a stochastic environment where parameters such as demand and capacity may change randomly. The novelties of this research are the deterministic and stochastic service provider models and algorithms with mixed integer programming (MIP). The test results show that the solution found meets all the constraints and the smallest objective function value. Stochastic modeling minimizes denial of service problems during wireless sensor network (WSN) distribution. The model resented is the ability of wireless sensors to establish connections between distributed computing nodes. Stochastic modeling minimizes denial of service problems during WSN distribution.
The prediction of thermal sensation in building using support vector machine and extreme gradient boosting Effendy, Nazrul; Abiyu Fadhilah, Muhammad Zhafran; Kraton, Danang Wahyu; Abrar, Haidar Alghazian
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp2963-2970

Abstract

The building has great potential for energy savings as one of the locations that humans often occupy. In addition to energy efficiency, humans must consider environmental sustainability and the comfort of the building's occupants. Conditioning of indoor air quality, including those related to thermal comfort, continues to be pursued to be more economical, one of which is to utilize the prediction of occupants' thermal sensations. The prediction results can be utilized to adjust room air conditions more economically. This paper proposes using extreme gradient boosting (XGBoost) and support vector machine (SVM) to predict the thermal sensation in the building. The built environment parameters are preprocessed, and the thermal sensation is predicted by intelligent systems. The ten variables that most influence the level of accuracy of this thermal sensation prediction system are thermal preference vote, indoor operative temperature, Griffith's neutral temperature, indoor globe temperature, mean radiant temperature, Indoor air temperature, predicted mean vote, and outdoor mean temperature. SVM with four features, XGBoost and XGBoost with hyperparameter tuning, achieve an accuracy of 99.45%, 97.81%, and 98.08%, respectively. Regarding computational complexity, training an SVM system with the same number of features requires a shorter time than XGBoost training. The same thing also happened with the test of the SVM system, which required a shorter time compared to the time for the examination of the XGBoost system.
Combining convolutional neural networks and spatial-channel “squeeze and excitation” block for multiple-label image classification Borvornvitchotikarn, Thuvanan; Yooyativong, Thongchai
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp368-374

Abstract

In emergency rooms and intensive care units, catheters and tubes are used to keep critically ill patients alive. Appropriate catheter or tube insertion is crucial to avoiding serious complications. Such issues can be rectified if they are identified early. Chest X-rays are commonly used to assess catheter placement. Convolutional neural networks (CNN) have recently been found to enhance multi-label classification tasks on chest X-rays images. Furthermore, attention modules have shown the effect of enhancing spatial encoding on network feature maps. This research analyzed the experiments of each CNN model with different attention blocks. Resnet200D with batch normalization and spatial-channel squeeze and excitation block (BN+scSE) is the best architecture for multiple-label image classification on a chest X-rays dataset from National Institutes of Health Clinical Center (NIH) with multiple catheters and tubes. Then came EfficientNetB5 with BN+scSE and Inception_v3 with spatial squeeze and channel excitation block, respectively.
An enhanced domain ontology model of database course in computing curricula Rahayu, Nur W.; Ferdiana, Ridi; Kusumawardani, Sri S.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1339-1347

Abstract

The ACM/IEEE Computing Curricula 2020 includes the study of relational databases in four of its six disciplines. However, a domain ontology model of multidisciplinary database course does not exist. Therefore, the current study aims to build a domain ontology model for the multidisciplinary database course. The research process comprises three phases: a review of database course contents based on the ACM/IEEE Computing Curricula 2020, a literature review of relevant domain ontology models, and a design research phase using the NeOn methodology framework. The ontology building involves the ontology reuse and reengineering of existing models, along with the construction of some classes from a non-ontological resource. The approach to ontology reuse and reengineering demonstrates ontology reusability. The final domain ontology model is then evaluated using two ontology syntactic metrics: Relationship Richness and Information Richness. These metrics reflect the diversity of relationships and the breadth of knowledge in the model, respectively. In conclusion, the current research contributes to the Computing Curricula by providing an ontology model for a multidisciplinary database course. The model, developed through ontology reuse and reengineering and the integration of non-ontological resources, exhibits more diverse relationships and represents a broader range of knowledge.
Enhanced you only look once approach for automatic phytoplankton identification Wisnu Ardhi, Ovide Decroly; Retnaningsih Soeprobowati, Tri; Adi, Kusworo; Prakasa, Esa; Rachman, Arief
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3426-3436

Abstract

Conventionally, identifying phytoplankton species is challenging due to human taxonomical knowledge limitations. Advanced technology can overcome this problem. A novel model that accurately enhances phytoplankton detection and identification classification by combining asymmetric convolution and vision transformers (ACVIT) within the YOLOv8m framework is promoted with ACVIT-YOLO. The performance of this model surpasses the original YOLOv8m model, exhibiting a notable 2.4% enhancement in precision, 5.5% improvement in recall, and 1.1% gain in mAP 50 score. The enhanced effectiveness of ACVIT-YOLO compared to the YOLOv8m model, further demonstrated by the decreased giga floating-point operations (GFLOP), decreased parameter count, and compact dimensions, significantly improves the automation of phytoplankton species identification. This suggests that the ACVIT-YOLO model could produce a better prediction system for identifying phytoplankton with similar accuracy to the original YOLOv8m model but with lower computational power and resource usage.
Object detection of the bornean orangutan nests using drone and YOLOv5 Teguh, Rony; Dwijaya Maleh, I Made; Sagit Sahay, Abertun; Porkab Pratama, Muhamad; Simon, Okta
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1640-1649

Abstract

Object detection methods when applied to ecology and conservation can help to identify and monitor endangered species and their habitats. Using drones for this purpose has become increasingly popular due to their ability to cover large areas quickly and efficiently. In this study, we aim to implement object detection using YOLOv5 to detect orangutan nests in forests. To conduct our experiment, we collect drone imagery under different conditions. We propose to use the original YOLOv5 to implement our model. The detection and monitoring of orangutan nests can help conservationists to identify critical habitats, monitor population, and design effective conservation strategies. Additionally, the use of drones can reduce the need for on-the-ground surveys, which can be time-consuming, expensive, and logistically challenging. In our study proposes a model for detecting orangutan nests in forests using a drone and the YOLOv5. Our model predicted 1,970 training images and 414 labeled orangutan nests, with a precision of 0.973, recall 0.949, accuracy mean average precision (mAP)_0.5 is 0.969, and mAP_0.5:0.95 is 0.630. The model finished 217 epochs in 58 hours and had a high object detection accuracy. The model has a 99.9% accuracy in detecting the number of orangutan nests.
Hyperspectral image classification with spectral-spatial feature integration and ensemble learning N, Bhavatarini; Prashant, Jyothi Aracot
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp2591-2602

Abstract

Hyperspectral imaging (HSI) has emerged as a robust remote sensing and medical imaging tool. However, HSI classification remains a challenging problem due to the high-dimensional data and the need for efficient feature selection and enhancement techniques. The proposed work addresses the problem of spatial feature extraction in spectral-spatial HSI classification tasks. This paper introduces an innovative model addressing the intricacies of spatial feature extraction in spectral-spatial HSI classification tasks, employing a fusion of spectral and spatial features through an adaptive kernel-based Gaussian filtering mechanism to elevate the quality of HSI data and augment classification performance. The classification is executed using three distinct classifiers, whose decisions are harmoniously integrated within an ensemble learning framework to optimize outcomes. The effectiveness of the proposed system is meticulously evaluated across three diverse datasets, Indian Pine, Pavia, and Salinas. This study also compares the model's efficiency against the existing similar work presented in the literature. The results show that the proposed work outperforms existing methods with constantly showing 99% accuracy and kappa score for each dataset, demonstrating its potential applications in various domains such as remote sensing and medical imaging.

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