cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Imam Much Ibnu Subroto
Contact Email
imam@unissula.ac.id
Phone
-
Journal Mail Official
ijai@iaesjournal.com
Editorial Address
-
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
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 1,722 Documents
 138   139   140   141   142 
Hypovigilance detection based on analysis and binary classification of brain signals El Hadiri, Abdeljalil; Bahatti, Lhoussain; El Magri, Abdelmounime; Lajouad, Rachid
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp984-991

Abstract

Road safety has now become a priority for drivers and citizens alike, given its considerable impact on the economy and human life, which is reflected in the increase in the number of accidents worldwide. This increase is linked to a number of factors, drowsiness being one of the main causes that can lead to tragic consequences. Various systems have been developed to monitor the state of alertness. The main idea adopted in this paper is based on the integration of a biosensor to acquire the cerebral signal, then the processing and analysis of the characteristics required to detect the two states of the driver using intelligent machine learning algorithms. Two models were chosen to carry out this binary classification: The K-nearest neighbour (KNN) and logistic regression (LR) classifiers. The experimental simulation results show that the first model outperforms the second in terms of accuracy, with a percentage of 97.83% for k=3. This could lead to the development of a new safety machine brain system based on classification to control vehicle speed deceleration or activate self-driving mode in the event of hypovigilance.
A machine learning-based approach for detecting communication failures in internet of things networks Kumari Vemuri, Ratna; Kumar Chinta Kunta, Job Prasanth; Madduru, Pavan; Senthilraja, Perumal; Ravi Raju, Yallapragada; Kodali, Yamini
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp2026-2034

Abstract

In industrial systems, the exchange of massive content, such as high-quality video and large sensing data, among industrial internet of things devices (IIoTDs) is essential, often under strict deadlines. Utilizing millimeter-wave (mmWave) frequencies at 28 and 60 GHz can meet the requirements of industrial internet of things (IIoT) by offering high data rates. However, in the mmWave band, the use of directional antennas is imperative due to the short wavelength, rendering directional links susceptible to adverse effects like deafness problems, where a communicating node fails to receive signals from other transmitting nodes. To mitigate the deafness problem, this paper proposes a machine learning-based communication failure identification scheme for reliable device-to-device (D2D) communication in the mmWave band. The proposed scheme determines the type of network failure (deafness/interference) based on the IIoTD's state parameters. Furthermore, we introduce machine learning based directional medium access control (ML-DMAC) to enhance throughput and minimize the duration of deafness in D2D communication. Performance evaluations demonstrate that the proposed ML-DMAC outperforms existing schemes, achieving approximately 31% higher aggregate throughput and an 88% reduction in deafness duration.
Flame analysis and combustion estimation using large language and vision assistant and reinforcement learning Martınez, Fredy; Rendón, Angélica; Penagos, Cristian
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp1853-1862

Abstract

In this study, we present an advanced approach for flame analysis and combustion quality estimation in carbonization furnaces utilizing large language and vision assistant (LLaVA) and reinforcement learning from human feedback (RLHF). The traditional methods of estimating combustion quality in carbonization processes rely heavily on visual inspection and manual control, which can be subjective and imprecise. Our proposed methodology leverages multimodal AI techniques to enhance the accuracy and reliability of flame similarity measures. By integrating LLaVA’s high-resolution image processing capabilities with RLHF, we create a robust system that iteratively improves its predictive accuracy through human feedback. The system analyzes real-time video frames of the flame, employing sophisticated similarity metrics and reinforcement learning algorithms to optimize combustion parameters dynamically. Experimental results demonstrate significant improvements in estimating oxygen levels and overall combustion quality compared to conventional methods. This approach not only automates and refines the combustion monitoring process but also provides a scalable solution for various industrial applications. The findings underscore the potential of AI-driven techniques in advancing the precision and efficiency of combustion systems.
Comparative analysis of machine learning models for fake news detection in social media Eddine Elbaghazaoui, Bahaa; Amnai, Mohamed; Fakhri, Youssef; Choukri, Ali; Gherabi, Noreddine
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp1951-1959

Abstract

The rapid rise of information sharing on social media has amplified the spread of fake news, making its detection increasingly critical. As fake news continues to proliferate, the need for efficient detection mechanisms has become more urgent to protect users from misinformation and disinformation. This paper presents a comparative analysis of multiple machine learning models for detecting text based fake news on social media platforms. Using models such as gradient boosting, XGBoost, and linear support vector classifier (SVC) on the Infor mation Security and Object Technology (ISOT) fake news dataset, the study demonstrates that gradient boosting achieves the highest accuracy of 99.61%, while XGBoost provides a strong balance with 99.59% accuracy and a signifi cantly lower execution time, making it more suitable for real-time applications. These results offer valuable insights into the trade-offs between accuracy and computational efficiency, contributing to the development of more practical de tection systems and future research in the field.
How ambidextrous entrepreneurial leaders react to the artificial intelligence boom Indravathy, Kandasamy; Abd Rahim, Noorlizawati
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp1708-1718

Abstract

Artificial intelligence (AI) now plays a central role in enhancing business competitiveness by transforming systems, frameworks, and managerial strategies. This study employs a systematic literature review (SLR) approach, utilizing the 'Consensus AI' search platform to explore the characteristics and roles of ambidextrous entrepreneurial leaders in the AI era. Consensus AI is an AI-powered search engine that automates the processes of reviews, literature searches, screening, and data extraction. It also utilizes 'research question searches' within SLRs to avoid the challenges of ambiguity and irrelevant information associated with 'keyword searches,' delivering more directly relevant results and finding featured snippets that answer specific questions. A research gap exists concerning how ambidextrous leadership adapts to the AI boom, highlighting leadership dynamics in the digital age. The findings emphasize the critical role of ambidextrous entrepreneurial leadership (AEL) in guiding organizations through the AI boom, enabling them to leverage AI for innovation, agility, and competitiveness. Organizations that effectively implement AEL by integrating AI technologies can position themselves for long-term success. Key insights show the importance of AEL approaches, and future research may explore challenges that arise for ambidextrous entrepreneurial leaders in the era of AI, such as ethical considerations and organizational culture.
Heterogeneous semantic graph embedding assisted edge-sensitive learning for cross-domain recommendation Ramchandra Patil, Pravin; Basavaraju, Pramod Halebidu
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp1839-1852

Abstract

In the digital age, recommendation systems navigate vast alternatives. Content-based, collaborative filtering, deep-driven, and cross-domain recommendation (CDR) have been studied significantly but face cold-start and data sparsity. Though CDR methods outperform others, they struggle to optimize user-item matrices. Recent graph-based CDR methods improve efficiency by leveraging additional user-item interactions; however, optimizing graph features remains an open research area. Moreover, current techniques do not consider the impact of noise items (unrelated) on recommendation accuracy. To address this gap, this paper develops a heterogeneous semantic graph-embedding (HSGE) edge-pruning model that leverages user ratings and item metadata in the source and target domains to recommend items to target domain users. To achieve it, at first Word2Vec method is applied to explicit and implicit details, followed by Node2Vec-driven graph embedding matrix generation. Our HSGE method obtains user-user, user-item, and item-item connections to achieve more semantic features. To improve accuracy, our model prunes edges that drop source domain items and allied edges unrelated to the target domain users. Subsequently, the retained HSGE matrices from both domains are processed for element-wise attention. A multi-layer perceptron with cosine similarity processed combined features matrices to generate top-N recommendations with superior hit-rate (HR) and normalized discounted cumulative gain (NDCG).
Intravenous drug administration application for pediatric patients via augmented reality Puangsuwan, Kritsada; Kajornkasirat, Siriwan; Wongpanich, Jaruphat; Kaewsuk, Chulalak; Puangsuwan, Simaporn
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp2412-2422

Abstract

This research presents the development of the intravenous drug administration application for pediatric patients using augmented reality (AR) technology, with a primary focus on aiding nursing students in administering medications accurately to reduce the risk of errors. The system architecture encompasses two core components: the creation of medication preparation videos and detailed drug information, and the design of a mobile application featuring medication list display, drug dosage calculation, user satisfaction assessment, and intravenous drug information addition. The system classifies users into administrators and nursing students, allowing administrators to manage user information in the member database. the application seamlessly integrates Visual Studio Code, flutter, dart programming language, firebase database, and AR.js Studio for QR code-linked videos. Operating in four main parts, namely users, mobile application, member database, and results display, the IDA application enables users to log in, access detailed drug information, calculate dosages, and view AR-based medication preparation videos. Tested with 111 nursing students, the system demonstrated functionality, completeness, and accuracy. The Likert scale-based evaluation revealed high satisfaction levels in content, design, functionality, and benefits received, affirming the intravenous drug administration application's effectiveness in pediatric intravenous drug management through AR, offering an innovative solution for nursing education and error reduction.
MedicalBERT: enhancing biomedical natural language processing using pretrained BERT-based model Reddy, K. Sahit; Ragavenderan, N.; K., Vasanth; N. Naik, Ganesh; H, Vishalakshi Prabhu; G. S., Nagaraja
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp2367-2378

Abstract

Recent advances in natural language processing (NLP) have been driven by pretrained language models like BERT, RoBERTa, T5, and GPT. These models excel at understanding complex texts, but biomedical literature, with its domain-specific terminology, poses challenges that models like Word2Vec and bidirectional long short-term memory (Bi-LSTM) can't fully address. GPT and T5, despite capturing context, fall short in tasks needing bidirectional understanding, unlike BERT. Addressing this, we proposed MedicalBERT, a pretrained BERT model trained on a large biomedical dataset and equipped with domain-specific vocabulary that enhances the comprehension of biomedical terminology. MedicalBERT model is further optimized and fine-tuned to address diverse tasks, including named entity recognition, relation extraction, question answering, sentence similarity, and document classification. Performance metrics such as the F1-score, accuracy, and Pearson correlation are employed to showcase the efficiency of our model in comparison to other BERT-based models such as BioBERT, SciBERT, and ClinicalBERT. MedicalBERT outperforms these models on most of the benchmarks, and surpasses the general-purpose BERT model by 5.67% on average across all the tasks evaluated respectively. This work also underscores the potential of leveraging pretrained BERT models for medical NLP tasks, demonstrating the effectiveness of transfer learning techniques in capturing domain-specific information.
A new wrapper feature selection approach for binary ransomware detection Chaieb, Omar; Nabil, Kannouf; Benabdellah, Mohammed
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp2104-2112

Abstract

Concerns about ransomware attacks have heightened in recent years for both individuals and organizations. Detecting these malicious attacks poses considerable challenges for cybersecurity professionals, particularly due to their ever-evolving nature. Although behavior-based detection methods show promise in recognizing new ransomware variants, they face significant hurdles, especially in managing the massive volumes of data generated from real-time malware behavior monitoring, leading to high dimensionality. This paper introduces a new feature selection approach specifically for binary ransomware detection. Our method emphasizes assessing the impact of feature categories on the effectiveness and speed of detection algorithms. It involves two stages: the first stage selects the most relevant groups (categories) of features, while the second ranks and identifies the important features within those categories. Experimental results indicate that our approach surpasses similar studies regarding accuracy and ability to minimize the original features set. Moreover, both computation speed and accuracy are notably enhanced when using the selected subset compared to the original features.
Effective task allocation in fog computing environments using fractional selectivity model Kannughatta Ranganna, Prasanna Kumar; Gaddadevara Matt, Siddesh; Babu Jayachandra, Ananda; Kumara Mahadevachar, Vasantha
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp2444-2458

Abstract

In recent scenario, fog computing is a new technology deployed between cloud computing systems and internet of things (IoT) devices to filter out important information from a massive amount of collected IoT data. Cloud computing offers several advantages, but also has the disadvantages of high latency and network congestion, when processing a vast amount of data collected from various devices and sources. For overcoming these problems in fog computing environments, an efficient model is proposed in this article for precise load balancing (LB). The proposed fractional selectivity model significantly handles LB in fog computing by reducing network bandwidth consumption, latency, task-waiting time, and also enhances the quality of experience. The proposed model allocates the required resources by eliminating sleepy, unreferenced, and long-time inactive services. The fractional selectivity model’s performance is investigated on three application scenarios, namely virtual reality (VR) game, electroencephalogram (EEG) healthcare, and toy game. The efficiency of the introduced model is analyzed on the basis of makespan, average resource utilization (ARU), load balancing level (LBL), total cost, delay, and energy consumption. Specifically, in comparison to the traditional task allocation models, the proposed model reduces almost 5 to 15% of the total cost and makespan time.

Page 140 of 173 | Total Record : 1722

 138   139   140   141   142 

Filter by Year

2012 2025


Reset
Filter By Issues
All Issue Vol 14, No 6: December 2025 Vol 14, No 5: October 2025 Vol 14, No 4: August 2025 Vol 14, No 3: June 2025 Vol 14, No 2: April 2025 Vol 14, No 1: February 2025 Vol 13, No 4: December 2024 Vol 13, No 3: September 2024 Vol 13, No 2: June 2024 Vol 13, No 1: March 2024 Vol 12, No 4: December 2023 Vol 12, No 3: September 2023 Vol 12, No 2: June 2023 Vol 12, No 1: March 2023 Vol 11, No 4: December 2022 Vol 11, No 3: September 2022 Vol 11, No 2: June 2022 Vol 11, No 1: March 2022 Vol 10, No 4: December 2021 Vol 10, No 3: September 2021 Vol 10, No 2: June 2021 Vol 10, No 1: March 2021 Vol 9, No 4: December 2020 Vol 9, No 3: September 2020 Vol 9, No 2: June 2020 Vol 9, No 1: March 2020 Vol 8, No 4: December 2019 Vol 8, No 3: September 2019 Vol 8, No 2: June 2019 Vol 8, No 1: March 2019 Vol 7, No 4: December 2018 Vol 7, No 3: September 2018 Vol 7, No 2: June 2018 Vol 7, No 1: March 2018 Vol 6, No 4: December 2017 Vol 6, No 3: September 2017 Vol 6, No 2: June 2017 Vol 6, No 1: March 2017 Vol 5, No 4: December 2016 Vol 5, No 3: September 2016 Vol 5, No 2: June 2016 Vol 5, No 1: March 2016 Vol 4, No 4: December 2015 Vol 4, No 3: September 2015 Vol 4, No 2: June 2015 Vol 4, No 1: March 2015 Vol 3, No 4: December 2014 Vol 3, No 3: September 2014 Vol 3, No 2: June 2014 Vol 3, No 1: March 2014 Vol 2, No 4: December 2013 Vol 2, No 3: September 2013 Vol 2, No 2: June 2013 Vol 2, No 1: March 2013 Vol 1, No 4: December 2012 Vol 1, No 3: September 2012 Vol 1, No 2: June 2012 Vol 1, No 1: March 2012 More Issue