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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.
Arjuna Subject : -
Articles 330 Documents
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Deep Neural Network-based Physical Distancing Monitoring System with TensorRT Optimization Edi Kurniawan
International Journal of Advances in Intelligent Informatics Vol 8, No 2 (2022): July 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

During the COVID-19 pandemic, physical distancing (PD) is highly recommended to stop the transmission of the virus. Deep learning-based object detection is employed to detect people in the crowd. Once the objects have been detected, then the distances between objects can be calculated to determine whether those objects violate physical distancing or not. This work presents the physical distancing monitoring system using a deep neural network. The optimization process is based on TensorRT executed on Graphical Processing Unit (GPU) with Computer Unified Device Architecture (CUDA) platform. This research evaluates the inferencing speed of the well-known object detection model You-Only-Look-Once (YOLO) run on two different Artificial Intelligence (AI) machines. The results show that the inferencing speed in terms of Frame-Per-Second (FPS) increases up to 9 times of the non-optimized ones.
A Novel Hybrid Archimedes Optimization Algorithm for Energy-Efficient Hybrid Flow Shop Scheduling Dana Marsetiya Utama; Ayu An Putri Salima; Dian Setiya Widodo
International Journal of Advances in Intelligent Informatics Vol 8, No 2 (2022): July 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The manufacturing sector accounts for a dominant proportion of global energy consumption. This sector has become the center of attention since the concern of the energy crisis rose. One of the strategies proposed to overcome this issue is implementing appropriate scheduling, such as Hybrid Flow Shop Scheduling. This research aimed to develop a Hybrid Archimedes Optimization Algorithm (HAOA) to solve Energy-Efficient Hybrid Flow Shop Scheduling (EEHFSP). In this research, three stages of EEHFSP are considered in a problem that involves sequence-dependent setup time in the second stage. The removal time also is involved in the second stage. The results indicated that the iteration and the population of HAOA did not affect the removal and processing energy consumptions but affected the setup and idle energy consumptions. The algorithm comparison of ten cases showed that the proposed HAOA resulted in an optimum TEC compared to the other algorithms. The manufacturing sector accounts for a dominant proportion of global energy consumption. This sector has become the center of attention since the concern of the energy crisis rose. One of the strategies proposed to overcome this issue is implementing appropriate scheduling, such as Hybrid Flow Shop Scheduling. This research aimed to develop a Hybrid Archimedes Optimization Algorithm (HAOA) to solve Energy-Efficient Hybrid Flow Shop Scheduling (EEHFSP). In this research, three stages of EEHFSP are considered in a problem that involves sequence-dependent setup time in the second stage. The removal time also is involved in the second stage. The results indicated that the iteration and the population of HAOA did not affect the removal and processing energy consumptions but affected the setup and idle energy consumptions. The algorithm comparison of ten cases showed that the proposed HAOA resulted in an optimum TEC compared to the other algorithms.
Sentiment classification from reviews for tourism analytics Nur Aliah Khairina Mohd Haris; Sofianita Mutalib; Ariff Md Ab Malik; Shuzlina Abdul-Rahman; Siti Nur Kamaliah Kamarudin
International Journal of Advances in Intelligent Informatics Vol 9, No 1 (2023): March 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v9i1.1077

Abstract

User-generated content is critical for tourism destination management as it could help them identify their customers' opinions and come up with solutions to upgrade their tourism organizations as it could help them identify customer opinions. There are many reviews on social media and it is difficult for these organizations to analyse the reviews manually. By applying sentiment classification, reviews can be classified into several classes and help ease decision-making. The reviews contain noisy contents, such as typos and emoticons, which could affect the accuracy of the classifiers. This study evaluates the reviews using Support Vector Machine and Random Forest models to identify a suitable classifier. The main phases in this study are data collection, data preparation, data labelling and modelling phases. The reviews are labelled into three sentiments; positive, neutral, and negative. During pre-processing, steps such as removing the missing value, tokenization, case folding, stop words removal, stemming, and applying n-grams are performed. The result of this research is evaluated by looking at the performance of the models based on accuracy where the result with the highest accuracy is chosen as the solution. In this study, data is data from TripAdvisor and Google reviews using web scraping tools. The findings show that the Support Vector Machine model with 5-fold cross-validation the most suitable classifier with an accuracy of 67.97% compared to Naive Bayes with 61.33% accuracy and Random Forest classifier with 63.55% accuracy. In conclusion, the result of this paper could provide important information in tourism besides determining the suitable algorithm to be used for Sentiment Analysis related to the tourism domain.
An automatic lip reading for short sentences using deep learning nets Maha A Rajab; Kadhim M Hashim
International Journal of Advances in Intelligent Informatics Vol 9, No 1 (2023): March 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v9i1.920

Abstract

One study whose importance has significantly grown in recent years is lip-reading, particularly with the widespread of using deep learning techniques. Lip reading is essential for speech recognition in noisy environments or for those with hearing impairments. It refers to recognizing spoken sentences using visual information acquired from lip movements. Also, the lip area, especially for males, suffers from several problems, such as the mouth area containing the mustache and beard, which may cover the lip area. This paper proposes an automatic lip-reading system to recognize and classify short English sentences spoken by speakers using deep learning networks. The input video extracts frames and each frame is passed to the Viola-Jones to detect the face area. Then 68 landmarks of the facial area are determined, and the landmarks from 48 to 68 represent the lip area extracted based on building a binary mask. Then, the contrast is enhanced to improve the quality of the lip image by applying contrast adjustment. Finally, sentences are classified using two deep learning models, the first is AlexNet, and the second is VGG-16 Net. The database consists of 39 participants (32 males and 7 females). Each participant repeats the short sentences five times. The outcomes demonstrate the accuracy rate of AlexNet is 90.00%, whereas the accuracy rate for VGG-16 Net is 82.34%. We concluded that AlexNet performs better for classifying short sentences than VGG-16 Net.
Gender recognition based fingerprints using dynamic horizontal voting ensemble deep learning Olorunsola Stephen Olufunso; Abraham Eseoghene Evwiekpaefe; Martins Ekata Irhebhude
International Journal of Advances in Intelligent Informatics Vol 8, No 3 (2022): November 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v8i3.927

Abstract

Despite tremendous advancements in gender equality, there are still persistent gender disparities, especially in important human activities. Consequently, gender inequality and related concerns are serious problems in our global society. Major players in the global economy have identified the gender identity system as a crucial stepping stone for bridging the enormous gap in gender-based problems. Extensive research conducted by forensic scientists has uncovered a unique pattern in the fingerprint, and these distinguishing characteristics of fingerprints can be utilized to determine the gender of individuals. Numerous research has revealed various fingerprint-based approaches to gender recognition. This research aims to present a novel dynamic horizontal voting ensemble model with a hybrid Convolutional Neural Network and Long Short Term Memory (CNN-LSTM) deep learning algorithm as the base learner to determine human gender attributes based on fingerprint patterns automatically. More than four thousand Live fingerprint images were acquired and subjected to training, testing, and classification using the proposed model. The results of this study indicated over 99% accuracy in predicting a person’s gender. The proposed model also performed better than other state-of-the-art models, such as ResNet-34, VGG-19, ResNet-50, and EfficientNet-B3, when implemented on the SOCOFing public dataset.
Enhanced feature clustering method based on ant colony optimization for feature selection Hassan Almazini; Ku Ruhana Ku-Mahamud; Hussein Fouad Almazini
International Journal of Advances in Intelligent Informatics Vol 9, No 1 (2023): March 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v9i1.987

Abstract

The popular modified graph clustering ant colony optimization (ACO) algorithm (MGCACO) performs feature selection (FS) by grouping highly correlated features. However, the MGCACO has problems in local search, thus limiting the search for optimal feature subset. Hence, an enhanced feature clustering with ant colony optimization (ECACO) algorithm is proposed. The improvement constructs an ACO feature clustering method to obtain clusters of highly correlated features. The ACO feature clustering method utilizes the ability of various mechanisms, such as local and global search to provide highly correlated features. The performance of ECACO was evaluated on six benchmark datasets from the University California Irvine (UCI) repository and two deoxyribonucleic acid microarray datasets, and its performance was compared against that of five benchmark metaheuristic algorithms. The classifiers used are random forest, k-nearest neighbors, decision tree, and support vector machine. Experimental results on the UCI dataset show the superior performance of ECACO compared with other algorithms in all classifiers in terms of classification accuracy. Experiments on the microarray datasets, in general, showed that the ECACO algorithm outperforms other algorithms in terms of average classification accuracy. ECACO can be utilized for FS in classification tasks for high-dimensionality datasets in various application domains such as medical diagnosis, biological classification, and health care systems.
Identifying threat objects using faster region-based convolutional neural networks (faster R-CNN) Reagan Galvez; Elmer Pamisa Dadios
International Journal of Advances in Intelligent Informatics Vol 8, No 3 (2022): November 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v8i3.952

Abstract

Automated detection of threat objects in a security X-ray image is vital to prevent unwanted incidents in busy places like airports, train stations, and malls. The manual method of threat object detection is time-consuming and tedious. Also, the person on duty can overlook the threat objects due to limited time in checking every person’s belongings. As a solution, this paper presents a faster region-based convolutional neural network (Faster R-CNN) object detector to automatically identify threat objects in an X-ray image using the IEDXray dataset. The dataset was composed of scanned X-ray images of improvised explosive device (IED) replicas without the main charge. This paper extensively evaluates the Faster R-CNN architecture in threat object detection to determine which configuration can be used to improve the detection performance. Our findings showed that the proposed method could identify three classes of threat objects in X-ray images. In addition, the mean average precision (mAP) of the threat object detector could be improved by increasing the input image's image resolution but sacrificing the detector's speed. The threat object detector achieved 77.59% mAP and recorded an inference time of 208.96 ms by resizing the input image to 900 × 1536 resolution. Results also showed that increasing the bounding box proposals did not significantly improve the detection performance. The mAP using 150 bounding box proposals only achieved 75.65% mAP, and increasing the bounding box proposal twice reduced the mAP to 72.22%.
Land cover classification based optical satellite images using machine learning algorithms Arisetra Razafinimaro; Aimé Richard Hajalalaina; Hasina Rakotonirainy; Reziky Zafimarina
International Journal of Advances in Intelligent Informatics Vol 8, No 3 (2022): November 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v8i3.803

Abstract

This article aims to apply machine learning algorithms to the supervised classification of optical satellite images. Indeed, the latter is efficient in the study of land use. Despite the performance of machine learning in satellite image processing, this can change but depends on the nature of the satellite images used. Moreover, when we use the satellite, then the reliability of one classifier can be different from the others. In this paper, we examined the performance of DT, SVM, KNN, ANN, and RF. Analysis factors were used to investigate further their importance for Sentinel 2, Landsat 8, Terra Modis, and Spot 5 images. The results show that the KNN showed the most interesting accuracy during the analysis of medium and low-resolution images with spectral bands lower or equal to 4, with a higher accuracy of about 93%. The RF completely dominated the other analysis cases, where the higher accuracy was about 94%. The classification accuracy is more reliable with high-resolution images than with the other resolution categories. However, the processing times of high-resolution images are much higher. Moreover, higher accuracy was often achieved with more expensive processing times. Besides, almost all machine learning algorithms suffered from the Hugs phenomenon during the analyses. So, before the classification with machine learning, some preprocessing is needed.
Hand–object interaction recognition based on visual attention using multiscopic cyber-physical-social system Adnan Rachmat Anom Besari; Azhar Aulia Saputra; Wei Hong Chin; Kurnianingsih Kurnianingsih; Naoyuki Kubota
International Journal of Advances in Intelligent Informatics Vol 9, No 2 (2023): July 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v9i2.901

Abstract

Computer vision-based cyber-physical-social systems (CPSS) are predicted to be the future of independent hand rehabilitation. However, there is a link between hand function and cognition in the elderly that this technology has not adequately supported. To investigate this issue, this paper proposes a multiscopic CPSS framework by developing hand–object interaction (HOI) based on visual attention. First, we use egocentric vision to extract features from hand posture at the microscopic level. With 94.87% testing accuracy, we use three layers of graph neural network (GNN) based on hand skeletal features to categorize 16 grasp postures. Second, we use a mesoscopic active perception ability to validate the HOI with eye tracking in the task-specific reach-to-grasp cycle. With 90.75% testing accuracy, the distance between the fingertips and the center of an object is used as input to a multi-layer gated recurrent unit based on recurrent neural network architecture. Third, we incorporate visual attention into the cognitive ability for classifying multiple objects at the macroscopic level. In two scenarios with four activities, we use GNN with three convolutional layers to categorize some objects. The outcome demonstrates that the system can successfully separate objects based on related activities. Further research and development are expected to support the CPSS application in independent rehabilitation.
Ensemble deep models for covid-19 pandemic classification using chest x-ray images via different fusion techniques Lamiaa Menshawy; Ahmad H Eid; Rehab F Abdel-Kader
International Journal of Advances in Intelligent Informatics Vol 9, No 1 (2023): March 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v9i1.922

Abstract

A pandemic epidemic called the coronavirus (COVID-19) has already afflicted people all across the world. Radiologists can visually detect coronavirus infection using a chest X-ray. This study examines two methods for categorizing COVID-19 patients based on chest x-rays: pure deep learning and traditional machine learning. In the first model, three deep learning classifiers' decisions are combined using two distinct decision fusion strategies (majority voting and Bayes optimal). To enhance classification performance, the second model merges the ideas of decision and feature fusion. Using the fusion procedure, feature vectors from deep learning models generate a feature set. The classification metrics of conventional machine learning classifiers were then optimized using a voting classifier. The first proposed model performs better than the second model when it concerns diagnosing binary and multiclass classification. The first model obtains an AUC of 0.998 for multi-class classification and 0.9755 for binary classification. The second model obtains a binary classification AUC of 0.9563 and a multiclass classification AUC of 0.968. The suggested models perform better than both the standard learners and state-of-the-art and state-of-the-art methods.

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