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International Journal of Advances in Intelligent Informatics
Published by Universitas Ahmad Dahlan
ISSN : 24426571     EISSN : 25483161     DOI : 10.26555
Core Subject : Science,
Computer Science & IT
International journal of advances in intelligent informatics (IJAIN) e-ISSN: 2442-6571 is a peer reviewed open-access journal published three times a year in English-language, provides scientists and engineers throughout the world for the exchange and dissemination of theoretical and practice-oriented papers dealing with advances in intelligent informatics. All the papers are refereed by two international reviewers, accepted papers will be available on line (free access), and no publication fee for authors.
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Articles 11 Documents
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Search results for , issue "Vol 10, No 2 (2024): May 2024" : 11 Documents clear
Type-2 Fuzzy ANP and TOPSIS methods based on trapezoid Fuzzy number with a new metric Kustiyahningsih, Yeni; Rahmanita, Eza; Khotimah, Bain Khusnul; Purnama, Jaka
International Journal of Advances in Intelligent Informatics Vol 10, No 2 (2024): May 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v10i2.1285

Abstract

Modeling and linguistic representation in the form Interval Type-2 Fuzzy have better accuracy than Type-1 Fuzzy. The type-2 fuzzy set involves more uncertainty than the type-1 fuzzy set. The degree of fuzzy membership is used to explain uncertainty and ambiguity in the real world. This study presents the type-2 Fuzzy Analytic Network Process (ANP) method to determine the weight of each attribute based on the level of interest and the extension method of type-2 Fuzzy TOPSIS to handle problems based on the value of the fuzzy type-2 attribute. Decision-making is based on the assessment of several experts called Multi-Criteria Group Decision Making (MCGDM), using type-2 Fuzzy geometric mean aggregation function. The membership function in this research is type-2 fuzzy based on the trapezoid. The contribution is a hybrid method Type-2 Fuzzy TOPSIS with Fuzzy Type-2 ANP group-based with new metric intervals on fuzzy type-2 for decision making. The results are a hybrid type-2 FANP and FTOPSIS decision-making model to support the best decision-making. Based on a comparison of the accuracy of trapezoid model 1, model 2, and model 3, the best accuracy result is model 3, which is 84%. The research benefits by presenting a hybrid Type-2 Fuzzy TOPSIS and ANP method that improves decision-making accuracy and better handling uncertainty and ambiguity than Type-1 Fuzzy systems.
A novel multi-step prediction model for process monitoring Lee, Yi Shan; Ooi, Sai Kit; Chen, Junghui
International Journal of Advances in Intelligent Informatics Vol 10, No 2 (2024): May 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v10i2.1528

Abstract

In the competitive market, process monitoring can ensure the quality of products, but strong nonlinearities, slow dynamics, and uncertainties characterize the complexities of the large-scale chemical plant. When the fault occurs, it will not influence the process instantaneously but will react after a few time points. After all the products affected by the faults are inspected, it is too late to fix the process. Conventional approaches neither do nor care about early detection before any disturbance significantly affects the process. To estimate disturbances propagated through the process, a multi-step prediction model is essential. The purpose of early process monitoring is to detect any problem with the currently running process as early as possible. In this paper, a multi-step prediction system is proposed. The system is a dynamic model that can capture the dynamic relationship of past process input variables and future process output variables. It provides a lower dimension and a lower noise-contaminated space for data analysis. Particularly, the past input and output process data can be mapped from the observation space into the latent space to acquire their intrinsic properties. The latent variables preserve the dynamic information for future multi-step prediction so that early warning can be achieved. An industrial example of the PVC dying process is presented to show the multistep predictive ability of the proposed method.
A novel convolutional feature-based method for predicting limited mobility eye gaze direction Khaleel, Amal Hameed; Abbas, Thekra H; Ibrahim, Abdul-Wahab Sami
International Journal of Advances in Intelligent Informatics Vol 10, No 2 (2024): May 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v10i2.1370

Abstract

Eye gaze direction is a critical issue since several applications in computer vision technology rely on determining gaze direction, where individuals move their eyes to limited mobility locations for sensory information. Deep neural networks are considered one of the most essential and accurate image classification methods. Several methods of classification to determine the direction of the gaze employ convolutional neural network models, which are VGG, ResNet, Alex Net, etc. This research presents a new method of identifying human eye images and classifying eye gaze directions (left, right, up, down, straight) in addition to eye-closing discrimination. The proposed method (Di-eyeNET) stands out from the developed method (Split-HSV) for enhancing image lighting. It also reduces implementation time by utilizing only two blocks and employing dropout layers after each block to achieve fast response times and high accuracy. It focused on the characteristics of the human eye images, as it is small, so it cannot be greatly enlarged, and the eye's iris is in the middle of the image, so the edges are not important. The proposed method achieves excellent results compared to previous methods, classifying the five directions of eye gaze instead of the four directions. Both the global dataset and the built local dataset were utilized. Compared to previous methods, the suggested method's results demonstrate high accuracy (99%), minimal loss, and the lowest training time. The research benefits include an efficient method for classifying eye gaze directions, with faster implementation and improved image lighting.
Abnormal behavior recognition using SRU with attention mechanism Tay, Nian Chi; Connie, Tee; Ong, Thian Song; Teoh, Andrew Beng Jin; Teh, Pin Shen
International Journal of Advances in Intelligent Informatics Vol 10, No 2 (2024): May 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v10i2.1385

Abstract

In response to the critical need for enhanced public safety measures, this study introduces an advanced intelligent surveillance system designed to autonomously detect abnormal behaviors within public spaces. Leveraging the computational efficiency and accuracy of a Simple Recurrent Unit (SRU) integrated with an attention mechanism, this research delivers a novel approach towards understanding and interpreting human interactions in real-time video footage. Distinctively, the model specializes in identifying two primary categories of abnormal behavior: aggressive two-person interactions such as physical confrontations and collective crowd dynamics, characterized by sudden dispersal patterns indicative of distress or danger. The incorporation of Attention mechanism precisely targets critical elements of behavior, thereby enhancing the model's focus and interpretative clarity. Empirical validation across five benchmark datasets reveals that our model not only outperforms traditional Long Short-Term Memory (LSTM) frameworks in terms of speed by a factor of 1.5 but also demonstrates superior accuracy in abnormal behavior recognition. These findings not only underscore the model's potential in preempting potential safety threats but also mark a significant advancement in the application of deep learning technologies for public security infrastructures. This research contributes to the broader discourse on public safety, offering actionable insights and robust technological solutions to enhance surveillance efficacy and response mechanisms in critical public domains.
AI-Driven Analysis: Optimizing Tertiary Education Policy through Machine Learning Insights Sy, Christian Y; Maceda, Lany L; Abisado, Mideth B
International Journal of Advances in Intelligent Informatics Vol 10, No 2 (2024): May 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v10i2.1525

Abstract

Tertiary education is pivotal in equipping individuals with the necessary knowledge and skills for success, prompting global initiatives for equitable access to quality higher education. The Philippines' Universal Access to Quality Tertiary Education (UAQTE) Act exemplifies this commitment by providing free tertiary education to eligible Filipino students. This study evaluates the UAQTE program's implementation through the perspectives of student beneficiaries, employing a combined approach of qualitative analysis and machine learning techniques. The study utilizes supervised and unsupervised machine learning to analyze student responses, specifically multiclass text classification using BERT and topic modeling with BERTopic. The results reveal insights into students' experiences and perceptions of the UAQTE program. While BERT demonstrates effectiveness in certain categories, challenges such as overfitting and balancing sequence length versus model performance are identified. BERTopic highlights the importance of capturing two-word combinations for enhancing topic coherence. Key themes identified through both approaches include "Educational Opportunity," "Program Implementation," "Financial Support," and "Appreciation and Gratitude," emphasizing their significance within the UAQTE program. Alignment between machine learning analyses and domain experts' insights underscores the relevance and effectiveness of the methodologies employed. Recommendations for optimizing the UAQTE program include refining focus areas, strengthening support systems, incorporating two-word combinations in analysis, and fostering continuous monitoring and interdisciplinary collaboration. By leveraging insights from qualitative and machine learning analyses, administrators can make informed decisions to enhance program effectiveness and comprehensively address students' diverse needs.
Fastener and rail surface defects detection with deep learning techniques Yilmazer, Merve; Karakose, Mehmet
International Journal of Advances in Intelligent Informatics Vol 10, No 2 (2024): May 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v10i2.1237

Abstract

The railways, which are frequently used by countries for both passenger and freight transportation, should be checked periodically. Controls made with classical methods are slow and there are often overlooked faults.  This work suggests a novel deep learning-based technique for identifying fastener and railway track surface defects. Within the scope of the proposed method, firstly,  The railroad track was observed using an autonomous drone. Shaky images in the recorded video were removed with a video stabilization algorithm. Frames were created and labeled from the video and rail and fastener regions were detected using the Faster R-CNN deep neural network. Fault detection was performed through ResNet101v2-based classification using different datasets for  identifying surface detects in rails and different datasets for detection of fasteners. The proposed method was experimentally shown to have a 98% accuracy rate for detecting rail surface flaws and a 95% accuracy rate for detecting fastener flaws. An user interface was developed to display the identified faulty images on computers, tablets and mobile phones via a mobile application. The system, which was previously proposed in a different study, was retrained by going through the video stabilization step, thus improving the fault detection rate, and the method was also included in the user interface module.  This study contributes to the processing of ever-increasing video data with deep learning-based methods. It is also anticipated that it will benefit researchers working in the field of railway non-contact fault detection.
CMT-CNN: colposcopic multimodal temporal hybrid deep learning model to detect cervical intraepithelial neoplasia Mukku, Lalasa; Thomas, Jyothi
International Journal of Advances in Intelligent Informatics Vol 10, No 2 (2024): May 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v10i2.1527

Abstract

Cervical cancer poses a significant threat to women's health in developing countries, necessitating effective early detection methods. In this study, we introduce the Colposcopic Multimodal Temporal Convolution Neural Network (CMT-CNN), a novel model designed for classifying cervical intraepithelial neoplasia by leveraging sequential colposcope images and integrating extracted features with clinical data. Our approach incorporates Mask R-CNN for precise cervix region segmentation and deploys the EfficientNet B7 architecture to extract features from saline, iodine, and acetic acid images. The fusion of clinical data at the decision level, coupled with Atrous Spatial Pyramid Pooling-based classification, yields remarkable results: an accuracy of 92.31%, precision of 90.19%, recall of 89.63%, and an F-1 score of 90.72. This achievement not only establishes the superiority of the CMT-CNN model over baselines but also paves the way for future research endeavours aiming to harness heterogeneous data types in the development of deep learning models for cervical cancer screening. The implications of this work are profound, offering a potent tool for early cervical cancer detection that combines multimodal data and clinical insights, potentially saving countless lives.
Detecting signal transtition in dynamic sign language using R-GB LSTM method Ridwang, Ridwang; Adriani, Adriani; rahmania, Rahmania; Sahrim, Mus’ab; Syahyadi, Asep Indra; Setiaji, Haris
International Journal of Advances in Intelligent Informatics Vol 10, No 2 (2024): May 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v10i2.1445

Abstract

Sign Language Recognition (SLR) helps deaf people communicate with normal people. However, SLR still has difficulty detecting dynamic movements of connected sign language, which reduces the accuracy of detection. This results from a sentence's usage of transitional gestures between words. Several researchers have tried to solve the problem of transition gestures in dynamic sign language, but none have been able to produce an accurate solution. The R-GB LSTM method detects transition gestures within a sentence based on labelled words and transition gestures stored in a model. If a gesture to be processed during training matches a transition gesture stored in the pre-training process and its probability value is greater than 0.5, it is categorized as a transition gesture. Subsequently, the detected gestures are eliminated according to the gesture's time value (t). To evaluate the effectiveness of the proposed method, we conducted an experiment using 20 words in Indonesian Sign Language (SIBI). Twenty representative words were selected for modelling using our R-GB LSTM technique. The results are promising, with an average accuracy of 80% for gesture sentences and an even more impressive accuracy rate of 88.57% for gesture words. We used a confusion matrix to calculate accuracy, specificity, and sensitivity. This study marks a significant leap forward in developing sustainable sign language recognition systems with improved accuracy and practicality. This advancement holds great promise for enhancing communication and accessibility for deaf and hard-of-hearing communities.
Weather classification using meta-based random forest fusion of transfer learning models Gdeeb, Rasha Talib
International Journal of Advances in Intelligent Informatics Vol 10, No 2 (2024): May 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v10i2.1264

Abstract

Weather classification into multiple categories is an essential task for many applications, including farming, military, transport, airlines, navigation, agriculture, etc. A few pieces of research give attention to this field and the current state-of-art methods have limitations, including low accuracy and limited weather conditions. In this study, a new weather classification meta-based fusion of the transfer deep learning model is introduced. The study takes into account all possible weather conditions and utilizes the fusion technique to improve the performance. First, the weather images are pre-processed and a data augmentation process is performed. These images are fed into five transfer deep learning models (XceptionNet, VGG16, ResNet50V2, InceptionV3, and DenseNet201). Then, the meta-based random forest fusion, the meta-based bagging fusion, and the score-level fusion are applied. Finally, all individual and fusion models are evaluated. Experiments were conducted on the WEAPD dataset which includes 11 categories. Results prove that the best performance is related to the meta-based ransom forest fusion method with 96% accuracy. The current study is also compared with the current state-of-art methods, and the comparison proves the robustness and high performance of the current study especially the fact that the current study achieves the best performance on the WEAPD dataset compared to studies worked on the same dataset. The current study proves that meta-based RF fusion is a promising methodology to address the weather classification problem. This outcome can be used by future study to improve the weather classification fusion and ensemble methodologies.
Academic expert finding using BERT pre-trained language model Mannix, Ilma Alpha; Yulianti, Evi
International Journal of Advances in Intelligent Informatics Vol 10, No 2 (2024): May 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v10i2.1497

Abstract

Academic expert finding has numerous advantages, such as: finding paper-reviewers, research collaboration, enhancing knowledge transfer, etc. Especially, for research collaboration, researchers tend to seek collaborators who share similar backgrounds or with the same native languages. Despite its importance, academic expert findings remain relatively unexplored within the context of Indonesian language. Recent studies have primarily relied on static word embedding techniques such as Word2Vec to match documents with relevant expertise areas. However, Word2Vec is unable to capture the varying meanings of words in different contexts. To address this research gap, this study employs Bidirectional Encoder Representations from Transformers (BERT), a state-of-the-art contextual embedding model. This paper aims to examine the effectiveness of BERT on the task of academic expert finding. The proposed model in this research consists of three variations of BERT, namely IndoBERT (Indonesian BERT), mBERT (Multilingual BERT), and SciBERT (Scientific BERT), which will be compared to a static embedding model using Word2Vec. Two approaches were employed to rank experts using the BERT variations: feature-based and fine-tuning. We found that the IndoBERT model outperforms the baseline by 6–9% when utilizing the feature-based approach and shows an improvement of 10–18% with the fine-tuning approach. Our results proved that the fine-tuning approach performs better than the feature-based approach, with an improvement of 1–5%.  It concludes by using IndoBERT, this research has shown an improved effectiveness in the academic expert finding within the context of Indonesian language.

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