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Insights from the vision-mission statements of Philippine and other ASEAN universities: a K-means clustering analysis

10.11591/ijai.v14.i4.pp3386-3394
Julius Ceazar G. Tolentino , John Paul P. Miranda
This study analyzed the vision and mission statements (VMS) of 117 Philippine state universities and colleges (SUCs) and compared them with 330 other ASEAN universities to identify thematic trends and institutional priorities. Using web scraping and K-means clustering, the study identified thematic clusters in VMS. Thematic trends through word frequency and collocation analyses provided further insights and a comparative analysis examined differences between Philippine SUCs and other ASEAN universities. Philippine SUCs’ vision statements formed three clusters: global competitiveness, premier recognition, and regional leadership in science and technology. Mission statements clustered into: mandated functions, global innovation, and advancement in the sciences. Philippine SUCs emphasized institutional prestige, workforce development, and sustainability while other ASEAN universities focus more on knowledge creation, student empowerment, and internationalization. Philippine SUCs aligned their VMS with national development and global ranking metrics and prioritizes institutional recognition and economic contributions more than the other ASEAN universities. Future studies should expand to more private institutions and international comparisons to assess broader higher education trends.
Volume: 14
Issue: 4
Page: 3386-3394
Publish at: 2025-08-01

Music genre classification using Inception-ResNet architecture

10.11591/ijai.v14.i4.pp3300-3310
Fauzan Valdera , Ajib Setyo Arifin
Music genres help categorize music but lack strict boundaries, emerging from interactions among public, marketing, history, and culture. With Spotify hosting over 80 million tracks, organizing digital music is challenging due to the sheer volume and diversity. Automating music genre classification aids in managing this vast array and attracting customers. Recently, convolutional neural networks (CNNs) have been used for their ability to extract hierarchical features from images, applicable to music through spectrograms. This study introduces the Inception-ResNet architecture for music genre classification, significantly improving performance with 94.10% accuracy, precision of 94.19%, recall of 94.10%, F1-score of 94.08%, and 149,418 parameters on the GTZAN dataset, showcasing its potential in efficiently managing and categorizing large music databases.
Volume: 14
Issue: 4
Page: 3300-3310
Publish at: 2025-08-01

Myoelectric grip force prediction using deep learning for hand robot

10.11591/ijai.v14.i4.pp3228-3240
Khairul Anam , Dheny Dwi Ardhiansyah , Muchamad Arif Hana Sasono , Arizal Mujibtamala Nanda Imron , Naufal Ainur Rizal , Mochamad Edoward Ramadhan , Aris Zainul Muttaqin , Claudio Castellini , Sumardi Sumardi
Artificial intelligence (AI) has been widely applied in the medical world. One such application is a hand-driven robot based on user intention prediction. The purpose of this research is to control the grip strength of a robot based on the user’s intention by predicting the grip strength of the user using deep learning and electromyographic signals. The grip strength of the target hand is obtained from a handgrip dynamometer paired with electromyographic signals as training data. We evaluated a convolutional neural network (CNN) with two different architectures. The input to CNN was the root mean square (RMS) and mean absolute value (MAV). The grip strength of the hand dynamometer was used as a reference value for a low-level controller for the robotic hand. The experimental results show that CNN succeeded in predicting hand grip strength and controlling grip strength with a root mean square error (RMSE) of 2.35 N using the RMS feature. A comparison with a state-of-the-art regression method also shows that a CNN can better predict the grip strength.
Volume: 14
Issue: 4
Page: 3228-3240
Publish at: 2025-08-01

Exploring bibliometric trends in speech emotion recognition (2020-2024)

10.11591/ijai.v14.i4.pp3421-3434
Yesy Diah Rosita , Muhammad Raafi'u Firmansyah , Annisaa Utami
Speech Emotion Recognition (SER) is crucial in various real-world applications, including healthcare, human-computer interaction, and affective computing. By enabling systems to detect and respond to human emotions through vocal cues, SER enhances user experience, supports mental health monitoring, and improves adaptive technologies. This research presents a bibliometric analysis of SER based on 68 articles from 2020 to early 2024. The findings show a significant increase in publications each year, reflecting the growing interest in SER research. The analysis highlights various approaches in preprocessing, data sources, feature extraction, and emotion classification. India and China emerged as the most active contributors, with external funding, particularly from the NSFC, playing a significant role in the advancement of SER research. SVM remains the most widely used classification model, followed by KNN and CNN. However, several critical challenges persist, including inconsistent data quality, cross-linguistic variability, limited emotional diversity in datasets, and the complexity of real-time implementation. These limitations hinder the generalizability and scalability of SER systems in practical environments. Addressing these gaps is essential to enhance SER performance, especially for multimodal and multilingual applications. This study provides a detailed understanding of SER research trends, offering valuable insights for future advances in speech-based emotion recognition.
Volume: 14
Issue: 4
Page: 3421-3434
Publish at: 2025-08-01

Federated deep learning intrusion detection system on software defined-network based internet of things

10.11591/ijai.v14.i4.pp3109-3120
Heba Dhirar , Ali H. Hamad
The internet of things (IoT) and software-defined networks (SDN) play a significant role in enhancing efficiency and productivity. However, they encounter possible risks. Artificial intelligence (AI) has recently been employed in intrusion detection systems (IDSs), serving as an important instrument for improving security. Nevertheless, the necessity to store data on a centralized server poses a potential threat. Federated learning (FL) addresses this problem by training models locally. In this work, a network intrusion detection system (NIDS) is implemented on multi-controller SDN-based IoT networks. The interplanetary file system (IPFS) FL has been employed to share and train deep learning (DL) models. Several clients participated in the training process using custom generated dataset IoT-SDN by training the model locally and sharing the parameters in an encrypted format, improving the overall effectiveness, safety, and security of the network. The model has successfully identified several types of attacks, including distributed denial of service (DDoS), denial of service (DoS), botnet, brute force, exploitation, malware, probe, web-based, spoofing, recon, and achieving an accuracy of 99.89% and a loss of 0.005.
Volume: 14
Issue: 4
Page: 3109-3120
Publish at: 2025-08-01

Investigation on low-performance tuned-regressor of inhibitory concentration targeting the SARS-CoV-2 polyprotein 1ab

10.11591/ijai.v14.i4.pp3003-3013
Daniel Febrian Sengkey , Angelina Stevany Regina Masengi , Alwin Melkie Sambul , Trina Ekawati Tallei , Sherwin Reinaldo Unsratdianto Sompie
Hyperparameter tuning is a key optimization strategy in machine learning (ML), often used with GridSearchCV to find optimal hyperparameter combinations. This study aimed to predict the half-maximal inhibitory concentration (IC50) of small molecules targeting the SARS-CoV-2 replicase polyprotein 1ab (pp1ab) by optimizing three ML algorithms: histogram gradient boosting regressor (HGBR), light gradient boosting regressor (LGBR), and random forest regressor (RFR). Bioactivity data, including duplicates, were processed using three approaches: untreated, aggregation of quantitative bioactivity, and duplicate removal. Molecular features were encoded using twelve types of molecular fingerprints. To optimize the models, hyperparameter tuning with GridSearchCV was applied across a broad parameter space. The results showed that the performance of the models was inconsistent, despite comprehensive hyperparameter tuning. Further analysis showed that the distribution of Murcko fragments was uneven between the training and testing datasets. Key fragments were underrepresented in the testing phase, leading to a mismatch in model predictions. The study demonstrates that hyperparameter tuning alone may not be sufficient to achieve high predictive performance when the distribution of molecular fragments is unbalanced between training and testing datasets. Ensuring fragment diversity across datasets is crucial for improving model reliability in drug discovery applications.
Volume: 14
Issue: 4
Page: 3003-3013
Publish at: 2025-08-01

Application of self-organizing map for modeling the Aquilaria malaccensis oil using chemical compound

10.11591/ijai.v14.i4.pp2889-2898
Mohammad Arif Fahmi Che Hassan , Zakiah Mohd Yusoff , Nurlaila Ismail , Mohd Nasir Taib
Agarwood oil, known as ‘black gold’ or the ‘wood of God,’ is a globally prized essential oil derived naturally from the Aquilaria tree. Despite its significance, the current non-standardized grading system varies worldwide, relying on subjective assessments. This paper addresses the need for a consistent classification model by presenting an overview of Aquilaria malaccensis oil quality using the self-organizing map (SOM) algorithm. Derived from the Thymelaeaceae family, Aquilaria malaccensis is a primary source of agarwood trees in the Malay Archipelago. Agarwood oil extraction involves traditional methods like solvent extraction and hydro-distillation, yielding a complex mixture of chromone derivatives, oxygenated sesquiterpenes, and sesquiterpene hydrocarbons. This study categorizes agarwood oil into high and low grades based on chemical compounds, utilizing the SOM algorithm with inputs of three specific compounds: β-agarofuran, α-agarofuran, and 10-epi-φ-eudesmol. Findings demonstrate the efficacy of SOM-based quality grading in distinguishing agarwood oil grades, offering a significant contribution to the field. The non-standardized grading system's inefficiency and subjectivity underscore the necessity for a standardized model, making this research crucial for the agarwood industry's advancement.
Volume: 14
Issue: 4
Page: 2889-2898
Publish at: 2025-08-01

Revolutionizing internet of things intrusion detection using machine learning with unidirectional, bidirectional, and packet features

10.11591/ijai.v14.i4.pp3047-3062
Zulhipni Reno Saputra Elsi , Deris Stiawan , Bhakti Yudho Suprapto , M. Agus Syamsul Arifin , Mohd. Yazid Idris , Rahmat Budiarto
Detection of attacks on internet of things (IoT) networks is an important challenge that requires effective and efficient solutions. This study proposes the use of various machine learning (ML) techniques in classifying attacks using unidirectional, bidirectional, and packet features. The proposed methods that implement decision tree (DT), random forest (RF), extreme gradient boosting classifier (XGBC), AdaBoost (AB) and linear discriminant analysis (LDA) work perfectly with all kinds of datasets and includes. It also works very well with data type-based feature selection (DTBFS) and correlation-based feature selection (CBFS). The experiment results show a significant improvement compared to previous studies and reveals that unidirectional and bidirectional features provide higher accuracy compared to packet features. Furthermore, ML models, particularly DT, and RF, have faster computing times compared to more complex deep learning models. This analysis also shows potential overfitting in some models, which requires further validation with different datasets. Based on these findings, we recommend the use of RF and DT for scenarios with unidirectional and bidirectional features, while AB and LDA for packet features. The study concludes that using the right ML techniques along with features that work in both directions can make an intrusion detection system for IoT networks becomes very accurate.
Volume: 14
Issue: 4
Page: 3047-3062
Publish at: 2025-08-01

A novel fuzzy logic based sliding mode control scheme for non-linear systems

10.11591/ijai.v14.i4.pp2676-2688
Abdul Kareem , Varuna Kumara
Sliding mode control (SMC) has been widely used in the control of non-linear systems due to many inherent properties like superposition, multiple isolated equilibrium points, finite escape time, limit cycle, bifurcation. This research proposes super-twisting controller architecture with a varying sliding surface; the sliding surface being adjusted by a simple single input-single output (SISO) fuzzy logic inference system. The proposed super-twisting controller utilizes a varying sliding surface with an online slope update using a SISO fuzzy logic inference system. This rotates sliding surface in the direction of enhancing the dynamic performance of the system without compromising steady state performance and stability. The performance of the proposed controller is compared to that of the basic super-twisting sliding mode (STSM) controller with a fixed sliding surface through simulations for a benchmark non-linear system control system model with parametric uncertainties and disturbances. The simulation results have confirmed that the proposed approach has the improved dynamic performance in terms of faster response than the typical STSM controller with a fixed sliding surface. This improved dynamic performance is achieved without affecting robustness, system stability and level of accuracy in tracking. The proposed control approach is straightforward to implement since the sliding surface slope is regulated by a SISO fuzzy logic inference system. The MATLAB/Simulink is used to display the efficiency of proposed system over conventional system.
Volume: 14
Issue: 4
Page: 2676-2688
Publish at: 2025-08-01

Challenges of recommender systems in finance and banking: a systematic review

10.11591/ijai.v14.i4.pp2559-2567
Lossan Bonde , Abdoul Karim Bichanga
Recommender systems are widely applied in various domains, including e-commerce, marketing, and education. Despite their popularity, recommender systems are not widely used in finance and banking. This paper aims to identify the challenges associated with using recommender systems in finance and banking and recommend directions for future research. Using a systematic literature review (SLR) method, 52 papers were selected and analyzed. A three-step process was used to make the selection. First, a keyword search was made to identify a seed list of sources. A snowball technique with specific inclusion and exclusion criteria was applied to expand the list. Finally, a quick study was made to produce the final list of sources to consider. Through the study of the 52 relevant papers, three main challenges: i) transparency, ethics, and data privacy; ii) handling complex content information and accounting for multiple user behaviors; and iii) explainability of AI models were identified. This study has established the barriers to adopting recommender systems in the finance and banking industry. Specific subjects of concern identified include cold-start problems, personalization, fraud detection, transparency, and data privacy. The study recommends further research leveraging advanced machine learning models and emerging technologies to fill the gap.
Volume: 14
Issue: 4
Page: 2559-2567
Publish at: 2025-08-01

Imagery based plant disease detection using conventional neural networks and transfer learning

10.11591/ijai.v14.i4.pp2701-2712
Ali Mhaned , Salma Mouatassim , Mounia El Haji , Jamal Benhra
Ensuring the sustainability of global food production requires efficient plant disease detection, challenge conventional methods struggle to address promptly. This study explores advanced techniques, including convolutional neural networks (CNNs) and transfer learning models (ResNet and VGG), to improve plant disease identification accuracy. Using a plant disease dataset with 65 classes of healthy and diseased leaves, the research evaluates these models' effectiveness in automating disease recognition. Preprocessing techniques, such as size normalization and data augmentation, are employed to enhance model reliability, and the dataset is divided into training, testing, and validation sets. The CNN model achieved accuracies of 95.45 and 94.52% for 128×128 and 256×256 image sizes, respectively. ResNet50 proved the best performer, reaching 98.38 and 98.63% accuracy, while VGG16 achieved 97.99 and 98.34%. These results highlight ResNet50's superior ability to capture intricate features, making it a robust tool for precision agriculture. This research provides practical solutions for early and accurate disease identification, helping to improve crop management and food security.
Volume: 14
Issue: 4
Page: 2701-2712
Publish at: 2025-08-01

The growth and trends information technology endangered language revitalization research: Insight from a bibliometric study

10.11591/ijece.v15i4.pp3888-3903
Leonardi Paris Hasugian , Syifaul Fuada , Triana Mugia Rahayu , Apridio Edward Katili , Feby Artwodini Muqtadiroh , Nur Aini Rakhmawati
Since United Nations Educational, Scientific and Cultural Organization (UNESCO) declared endangered languages, researchers have revitalized endangered languages in many fields. This study discusses a bibliometric analysis conducted to investigate research on the topic of revitalization of endangered languages in information technology. The study's aim is to assess research topics by identifying authors, institutions, and countries that influence research collaboration. The Scopus dataset (from 2002-2024) was obtained from journal articles (n=62) and conference papers (n=76) and visualized using VOSviewer 1.6.20. The analysis outcomes reveal a fluctuating trend with an increasing pattern. The United States, Canada, and China were identified as the top three countries in terms of publications. Meanwhile, the University of Alberta, Université du Québec à Montréal, University of Auckland, and University of Hawaiʻi at Mānoa are the most prolific institutions on this topic, with two authors from the Université du Québec à Montréal, Sadat and Le, being the most productive. The dominant research is related to computational linguistics. Meanwhile, topics such as phonetic posteriograms, integrated frameworks, and artificial intelligence are some of the potential research areas that can be explored in the future. Its implications for exposing the extent to which the development of endangered language revitalization can be accommodated in the field of information technology.
Volume: 15
Issue: 4
Page: 3888-3903
Publish at: 2025-08-01

Revolutionizing autism diagnosis using hybrid model for autism spectrum disorder phenotyping

10.11591/ijece.v15i4.pp3904-3912
Vijayalaxmi N. Rathod , Rayangouda H. Goudar
The growing prevalence of autism spectrum disorder (ASD) necessitates efficient data-driven screening solutions to complement traditional diagnostic methods, which often suffer from subjectivity and limited scalability. This study introduces a hybrid ensemble model combining logistic regression (LR) and naive Bayes (NB) for ASD classification across four age groups (toddlers, children, adolescents, and adults) using behavioral screening datasets. By integrating statistical learning and probabilistic inference, the proposed model effectively captured behavioral markers, ensuring a higher classification accuracy and improved generalization. The experimental evaluation demonstrated its superior performance, achieving 94.24% accuracy and 99.40% area under the receiver operating characteristic curve (AUROC), surpassing those of individual classifiers and existing approaches. This artificial intelligence (AI)-driven framework offers a scalable, cost-effective, and accessible solution for both clinical and telemedicine-based ASD screening, facilitating early intervention and risk assessment. This study underscores the transformative potential of AI in neurodevelopmental diagnostics, paving the way for more efficient and widely deployable autistic screening technologies.
Volume: 15
Issue: 4
Page: 3904-3912
Publish at: 2025-08-01

Deep transfer learning for classification of ECG signals and lip images in multimodal biometric authentication systems

10.11591/ijai.v14.i4.pp3160-3171
Latha Krishnamoorthy , Ammasandra Sadashivaiah Raju
Authentication plays an essential role in diverse kinds of application that requires security. Several authentication methods have been developed, but biometric authentication has gained huge attention from the research community and industries due to its reliability and robustness. This study investigates multimodal authentication techniques utilizing electrocardiogram (ECG) signals and face lip images. Leveraging transfer learning from pre-trained ResNet and VGG16 models, ECG signals and photos of the lip area of the face are used to extract characteristics. Subsequently, a convolutional neural network (CNN) classifier is employed for classification based on the extracted features. The dataset used in this study comprises ECG signals and face lip images, representing distinct biometric modalities. Through the integration of transfer learning and CNN classification, improving the reliability and precision of multimodal authentication systems is the primary objective of the study. Verification results show that the suggested method is successful in producing trustworthy authentication using multimodal biometric traits. The experimental analysis shows that the proposed deep transfer learning-based model has reported the average accuracy, F1-score, precision, and recall as 0.962, 0.970, 0.965, and 0.966, respectively.
Volume: 14
Issue: 4
Page: 3160-3171
Publish at: 2025-08-01

Unpacking the drivers of artificial intelligence regulation: driving forces and critical controls in artificial intelligence governance

10.11591/ijai.v14.i4.pp2655-2666
Ibrahim Atoum , Salahiddin Altahat
The burgeoning field of artificial intelligence (AI) necessitates a nuanced approach to governance that integrates technological advancement, ethical considerations, and regulatory oversight. As various AI governance frameworks emerge, a fragmented landscape hinders effective implementation. This article examines the driving forces behind AI regulation and the essential control mechanisms that underpin these frameworks. We analyze market-driven, state-driven, and rights-driven regulatory approaches, focusing on their underlying motivations. Furthermore, critical regulatory controls such as data governance, risk management, and human oversight are highlighted to demonstrate their roles in establishing effective governance structures. Additionally, the importance of international cooperation and stakeholder collaboration in addressing the challenges posed by rapid technological change is emphasized. By providing insights into the strengths, weaknesses, and potential synergies of different governance models, this study contributes to the development of equitable and effective AI regulatory frameworks that encourage innovation while safeguarding societal interests. Ultimately, the findings aim to inform policymakers, industry leaders, and civil society organizations in their efforts to foster a future where AI is utilized responsibly and equitably for the betterment of humanity.
Volume: 14
Issue: 4
Page: 2655-2666
Publish at: 2025-08-01
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