Articles

Characteristic of black soy miso with crude bromelain and Lactobacillus plantarum

10.11591/ijaas.v13.i4.pp818-824

Bhakti Etza Setiani , Yoyok Budi Pramono , Nurwantoro Nurwantoro , Bambang Dwiloka , Annisa Shafa Putri Arini , Ilma Muliasari Ramadhaningrum , Elisabeth Febriane Lovita Sudjono

This research aims to improve the diversity of functional foods rich in bioactive peptide components and have a unique flavor for black soybean miso through the addition of crude bromelain and Lactobacillus plantarum. This research uses a completely randomized design with 5 treatments and 4 repetitions to obtain a total of 20 experimental units. The treatments are the different concentrations of crude bromelain addition, namely the control treatment or 0% (T0), 3% (T1), 6% (T2), 9% (T3), and 12 % (T4). The parameters observed are water content, water activity, pH value, and lactic acid bacteria (LAB) viability. The result showed that an increase in the crude bromelain concentration may increase the water content and water activity of the black soybean miso, while the pH value decreases. Based on the viability of the LAB showed a wavering result. Miso with the 9% concentration of crude bromelain is the best result among the others.

Volume: 13

Issue: 4

Page: 818-824

Publish at: 2024-12-01

Thermal imaging-based identification of facial features in noisy environment

10.11591/ijict.v13i3.pp333-343

Palak Mahajan , Pawanesh Abrol , Parveen Kumar Lehana

Face identification is amongst the most efficacious and extensive applications in biometrics involving extraction and locating facial features. With identification being monotonous task attributable to reliance on parameters like varied cameras, fluctuating backgrounds, and exposure to the environment in which an individual is present. Thermal imaging is endeavoring to resolve the accuracy issue of apparent imaging, such as lighting and brightness intensity, among all biometric variables. This paper presents a study of thermal imaging and effective methods involved in the feature extraction process for facial features with thermal imaging under the influence of varied noise. A novel face dataset is created TID comprising 27 thermal images and its corresponding visual band image using Fluke 480 Ti Pro camera. The study analyses detection efficiency of six feature extraction techniques in visible and thermal bands in facial features identification. Also, the influence of noise in the thermal band within the region of interest using feature points FIN, FOUT has been estimated. Throughout TID dataset, ORB extraction technique has been able to identify strongest inlier features FIN to a maximum extent with detection around the nose, eyes, and mouth. Further, results indicate feature detection in thermal images being invariant to effect of noise for detecting facial features.

Volume: 13

Issue: 3

Page: 333-343

Publish at: 2024-12-01

Hybrid improved fuzzy C-means and watershed segmentation to classify Alzheimer’s using deep learning

10.11591/ijai.v13.i4.pp4080-4094

Esraa H. Ali , Sawsan Sadek , Zaid F. Makki

Brain damage and deficits in interactions among brain cells are the primary causes of dementia and Alzheimer’s disease (AD). Despite ongoing research, no effective medications have yet been developed for these conditions. Therefore, early detection is crucial for managing the progression of these disorders. In this study, we introduce a novel tool for detecting AD using non invasive medical tests, such as magnetic resonance imaging (MRI). Our method employs fuzzy C-means clustering to identify features that enhance image accuracy. The standard fuzzy C-means algorithm has been augmented with fuzzy components to improve clustering performance. This enhanced approach optimizes segmentation by extracting image information and utilizing a sliding window to calculate center coordinates and establish a stable group matrix. These critical features are subsequently integrated with a two-phase watershed segmentation process. The resulting segmented images are then used to train an optimal convolutional neural network (CNN) for AD classification. Our methodology demonstrated a 98.20% accuracy rate in the detection and classification of segmented MRI brain images, highlighting its efficacy in identifying disease types.

Volume: 13

Issue: 4

Page: 4080-4094

Publish at: 2024-12-01

Hybrid feature selection of microarray prostate cancer diagnostic system

10.11591/ijeecs.v36.i3.pp1884-1894

Nursabillilah Mohd Ali , Ainain Nur Hanafi , Mohd Safirin Karis , Nur Hazahsha Shamsudin , Ezreen Farina Shair , Nor Hidayati Abdul Aziz

DNA microarray prostate cancer diagnosis systems are widely used, and hybrid feature selection methods are applied to select optimal features to address the high dimensionality of the dataset. This work proposes a new hybrid feature selection method, namely the relief-F (RF)-genetic algorithm (GA) with support vector machine (SVM) classification method. The aim is to evaluate the performance of the proposed method in terms of accuracy, computation time, and the number of selected features. The method is implemented using Python in PyCharm and is evaluated on a DNA microarray prostate cancer. The outcome of this work is a performance comparison table for the proposed methods on the dataset. The performance of GA, particle swarm optimization (PSO), and whale optimization algorithm (WOA) is compared in terms of accuracy, computation time, and the number of selected features. Results show that GA has the highest average accuracy (91.17%) compared to PSO (90.52%) and WOA (85.74%). GA outperforms PSO and WOA due to its superior convergence properties and better alignment with complex problems.

Volume: 36

Issue: 3

Page: 1884-1894

Publish at: 2024-12-01

No-reference image quality assessment based on visual explanation images and deep transfer learning

10.11591/ijeecs.v36.i3.pp1521-1531

Basma Ahmed , Osama A. Omer , Vivek Kumar Singh , Amal Rashed , Mohamed Abdel-Nasser

Quantifying image quality in the absence of a reference image continues to be a challenge despite the introduction of numerous no-reference image quality assessments (NR-IQA) in recent years. Unlike most existing NRIQA methods, this paper proposes an efficient NR-IQA method based on deep visual interpretations. Specifically, the main components of the proposed method are: i) generating a pseudo-reference image (PRI) for the input distorted images, ii) employing a pretrained convolutional network to extract feature maps from the distorted image and the corresponding PRI, iii) producing visual explanation images (VEIs) by using the feature maps of the distorted image and the corresponding PRI, iv) measuring the similarity between the two VEIs using an image similarity metric, and v) employing a non-linear mapping function for quality score alignment. In our experiments, we evaluated the efficacy of the proposed method across various forms of distortion using four benchmark datasets (LIVE, SIQAD, CSIQ, and TID2013). The proposed approach demonstrates parity with the latest methods, as evidenced by comparisons with both hand-crafted NR-IQA and deep learning-based approaches.

Volume: 36

Issue: 3

Page: 1521-1531

Publish at: 2024-12-01

Design and development of coastal marine water quality monitoring based on IoT in achieving implementation of SDGs

10.11591/ijeecs.v36.i3.pp1470-1484

Jaja Kustija , Diki Fahrizal , Muhamad Nasir , Deny Setiawan , Irgi Surya

Indonesia, an archipelagic nation with about 70% ocean territory, relies on oceanographic data for efficient marine environment monitoring and natural resource sustainability. Current data collection is limited by tools measuring only single parameters and lengthy data collection times. This study proposes a marine coastal water quality monitoring tool based on the internet of things (IoT), capable of simultaneously measuring temperature, electrical conductivity, pH, and dissolved oxygen. Utilizing an Atmega328 and a battery lasting up to 119 hours, this system offers a cost-effective solution for real-time oceanographic data collection. Employing the ADDIE methodology, the results demonstrate high measurement accuracy compared to traditional methods, with accuracy of 90.5% for temperature, 93.50% for electrical conductivity, 93.67% for pH, and 96.82% for dissolved oxygen. The development of this tool aims to reduce costs and labor in capturing oceanographic data integrated with IoT, facilitate access and monitoring of water data, and make a significant contribution to achieving SDGs targets. The main focus on the goals of addressing climate change and life underwater, especially in the aspects of water resources management and protection of marine ecosystems in Indonesian.

Volume: 36

Issue: 3

Page: 1470-1484

Publish at: 2024-12-01

Evaluation of the time-of-use tariff responsiveness for plug-in electric vehicle home charging in Malaysia

10.11591/ijaas.v13.i4.pp773-780

Nurliyana Baharin , Mohamad Fani Sulaima , Nofri Yenita Dahlan , Hazlie Mokhlis

Plug-in electric vehicles (PEVS) have become increasingly popular as a viable transportation option as owners can charge them at home. This will add much energy to the house if the users charge their PEVs at home. The PEV charging load will lead to extra energy demand on the distribution network, and the users will need to pay more for electricity if they use the current domestic tariff in Malaysia. This research aims to analyze the PEV charging costs using time-of-use (ToU) tariffs with different time segmentations and price elasticity. The effect of four residential load profile patterns has also been investigated in Malaysia as a case study. Four PEV charging scenarios were created, and the charging times were set according to Malaysian driving styles, with charging times starting at 6 PM, 10 PM, and 9 AM. The PEV and electric vehicle supply equipment (EVSE) are set to be homogeneous, and the EV was assumed to have a minimum state-of-charge of 20%. The main contribution of this paper is the selection of the ToU tariff segmentation, where the structure of the smallest time segmentation gave the lowest electricity bill per month compared to the Tenaga Malaysia Berhad (TNB) domestic tariff.

Volume: 13

Issue: 4

Page: 773-780

Publish at: 2024-12-01

Application of K-means clustering and B-value algorithms for analysis of earthquake-dangerous zones in Java Island

10.11591/ijaas.v13.i4.pp911-919

Bentang Anggarajati , Y Yatini , Wiji Raharjo

Java Island is an island with a high earthquake vulnerability. Therefore, earthquake mitigation measures are needed to reduce the impact of earthquakes. Earthquake mitigation is done by knowing the zones with a high risk of earthquakes and high levels of rock stress. The methods used to map earthquake-prone zones are K-means clustering and B-value. The K-means clustering method can provide earthquake clusters based on their characteristics and the B-value can produce rock stress conditions in the area. The results of this study are that the K-means clustering method produces 7 earthquake clusters with 5 classifications of very low, low, medium, high, and very high. In contrast, the B-value process has a high B-value with a value of 1.2-1.5 in West Java and a low B-value with a value of 0.9-1.2 in the central to the eastern part of Java.

Volume: 13

Issue: 4

Page: 911-919

Publish at: 2024-12-01

Development of image extraction using the centerline method in the identification of appendicitis in ultrasonography

10.11591/ijeecs.v36.i3.pp1750-1758

Syafrika Deni Rizki , Yuhandri Yuhandri , Iskandar Fitri

Appendicitis is a disease that refers to inflammation of the appendix caused by obstruction, or blockage, in the lumen of the appendix. We investigated that this disease can be detected early through medical imaging such as ultrasonography (USG). However, the role of ultrasound in these cases is still limited due to the low visualization rate of the visible appendix. Based on this, this research aims to develop an image extraction process using the Centerline method in the process of identifying appendicitis in ultrasound images. The development of the extraction process is presented in the performance of the centerline and boundary extraction (CBE) algorithm which can represent image objects as boundaries that limit and separate one area from other areas. The research dataset used was 2097 ultrasound images sourced from 90 patients at the West Sumatra Lung Hospital. Based on the tests that have been carried out, it has been proven that it can reduce the width of the image object iteratively until the object is represented as a center line or the thinnest representation. The performance of the CBE algorithm in the identification process is sufficient to provide accuracy results of 92%. These results can be a new extraction concept that can provide accuracy in the identification process.

Volume: 36

Issue: 3

Page: 1750-1758

Publish at: 2024-12-01

Systematic review of artificial intelligence with near-infrared in blueberries

10.11591/ijai.v13.i4.pp3761-3771

Liset Cayhualla Amaro , Sebastian Rau Reyes , María Acuña Meléndez , Christian Ovalle

The fruit quality has a direct impact on how the fruit looks and how tasty the fruit is. The correct use of tools to determine fruit quality is essential to offer the best product for the final consumer. This study has used the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology. The study objective was elaborate a systematic literature review (SLR) about research of the application of techniques based on artificial intelligence to analyze indicators obtained by near infrared spectroscopy (NIRS) and chemometrics to determine the quality of fruits, including blueberries. The most frequently addressed indicator is the soluble solids concentration (SSC) which was used in several studies with techniques such as support vector machines (SVM) and convolutional neural networks (CNN). According to the results obtained, it is possible to use these techniques to predict blueberry quality indicators. There was an acceptable performance and high accuracy of these models. However, future research could cover other techniques and help to provide better quality control of products in food industries.

Volume: 13

Issue: 4

Page: 3761-3771

Publish at: 2024-12-01

Unsupervised hindi word sense disambiguation using graph based centrality measures

10.11591/ijai.v13.i4.pp4957-4964

Prajna Jha , Shreya Agarwal , Ali Abbas , Satyendr Singh , Tanveer Jahan Siddiqui

The task of word sense disambiguation (WSD) plays a key role in multiple applications of natural language processing. In this paper, we propose a novel unsupervised method for targeted Hindi WSD task. First, we create a weighted graph where the nodes correspond to various synsets of the target word and the neighboring context words. The edges in the graph represent the semantic relations between these synsets in the Hindi WordNet hierarchy. A path-based similarity measure, namely Leacock-Chodorow similarity measure, is used to assign weights to edges. An unsupervised weighted graph-based centrality algorithm is used to identify the correct sense of a target word in a given context. The performance of the proposed algorithm is measured on 20 ambiguous Hindi nouns using four different graph-based centrality measures. We observed a maximum accuracy of 66.92% using PageRank centrality measure which is significantly better than earlier reported graph-based Hindi WSD algorithmsevaluated on the same dataset.

Volume: 13

Issue: 4

Page: 4957-4964

Publish at: 2024-12-01

Recognizing Indonesian sign language (Bisindo) gesture in complex backgrounds

10.11591/ijeecs.v36.i3.pp1583-1593

Muhammad Alfhi Saputra , Erdefi Rakun

Sign language, particularly Indonesian sign language (Bisindo), is vital for deaf individuals, but learning it is challenging. This study aims to develop an automated Bisindo recognition system suitable for diverse backgrounds. Previous research focused on greenscreen backgrounds and struggled with natural or complex backgrounds. To address this problem, the study proposes using Faster region-based convolutional neural networks (RCNN) and YOLOv5 for hand and face detection, MobileNetV2 for feature extraction, and long short-term memory (LSTM) for classification. The system is also designed to focus on computational efficiency. YOLOv5 model achieves the best result with a sentence accuracy (SAcc) of 49.29% and a word error rate (WER) of 16.42%, with a computational time of 0.0188 seconds, surpassing the baseline model. Additionally, the system achieved a SacreBLEU score of 67.77%, demonstrating its effectiveness in Bisindo recognition across various backgrounds. This research improves accessibility for deaf individuals by advancing automated sign language recognition technology.

Volume: 36

Issue: 3

Page: 1583-1593

Publish at: 2024-12-01

Impacts of electric vehicle charging stations and DGs on RDS for improving voltage stability using honey badger algorithm

10.11591/ijeecs.v36.i3.pp1379-1388

Madhubabu Thiruveedula , K. Asokan , JBV Subrahmanyam

The intelligent computational technique used in this research handles the multi-objective voltage stability optimization (MOVSO) problem in radial distribution systems (RDS). The objectives of the proposed research are to minimize network loss, lower the average voltage deviation index (AVDI), and improve the voltage stability index (VSI) of RDS by taking into account the recently created distributed generators (DGs) and electric vehicle charging stations (EVCSs). To address the MOVSO problem, a novel and innovative honey badger algorithm (HBA) optimization technique is put forth. The two stages of HBA, known as the "digging" and "honey" phases, are responsible for effectively identifying the ideal position and appropriate quantity of EVCSs and DGs. The standard IEEE 33 node test system with different case studies is considered to validate the performance of HBA. The simulation results of improved voltage profile, minimized power loss, AVDI and improved VSI are tabulated. The proposed HBA fine-tunes the ideal position and size of the EVCSs to significantly enhance RDS performance under higher loading circumstances. To demonstrate the efficacy and originality of the suggested HBA, the numerical results are contrasted with those of earlier soft computing techniques.

Volume: 36

Issue: 3

Page: 1379-1388

Publish at: 2024-12-01

Faults-as-address simulation

10.11591/ijra.v13i4.pp452-468

Vladimir Hahanov , Svetlana Chumachenko , Eugenia Litvinova , Ivan Hahanov , Veronika Ponomarova , Hanna Khakhanova , Georgiy Kulak

Fault-as-address-simulation (FAAS) is a simulation mechanism for testing combinations of circuit line faults, represented by the bit addresses of element logical vectors. The XOR relationship between the test set T and the truth table L of the element forms a deductive vector for fault simulation, using truth table addresses or the logic vector bits. Addresses are used in the simulation matrix to mark those n-combinations of input faults detected at the element's output. The columns of the simulation matrix are treated as n-row addresses to generate an element output row via a deductive vector. There is no transport of input faults to the element output, Only the 1-signals written in the non-input row coordinates of the circuit simulation matrix. The simulation matrix is initially filled with 1-signals along the main diagonal. The line faults detected on the test set of circuits are determined by the inverse of lines good values, which have 1-values in the matrix row corresponding to the output circuit element. The deductive vector is obtained by the XOR-relations between the test set and logical vector in three table operations. The advantage of the proposed FAAS mechanism is the predictable complexity of the algorithm and memory consumption for storing data structures when simulating a test set.

Volume: 13

Issue: 4

Page: 452-468

Publish at: 2024-12-01

Potentiometric field-effect transistor pH sensing in a low-power wide-area network

10.11591/ijaas.v13.i4.pp781-790

Akmal Mustaffa Zulhakim , Wan Fazlida Hanim Abdullah , Ahmad Zaki Abu Bakar , Robaiah Mamat , Ili Shairah Abdul Halim , Muhammad Izzat Alif Muslan , Sukreen Hana Herman

This research paper explores the application of extended-gate field-effect transistors (EGFET) as a potentiometric sensing method for pH detection within an internet of things (IoT) system. The pH EGFET sensor is integrated with a long-range (LoRa) microcontroller, enabling data transmission via a low-power, long-range wide-area network (LoRaWAN) IoT framework to a dedicated IoT application server. The framework utilizes a message queuing telemetry transport (MQTT) broker, employing a publish/subscribe message architecture for efficient data transmission. The study focuses on addressing the problem of determining whether EGFET technology can provide precise and dependable measurements in various settings. To achieve this, the data from the IoT framework is compared with data signals from a semiconductor parametric analyzer and a readout interfacing circuit serial data acquisition (DAQ). From the study, EGFET sensors provide a sensitivity of 61.1 mV/pH with a linearity of 0.9968 through the IoT method. Meanwhile, non-IoT methods yield slightly different sensitivities of 53.1 and 50.5 mV/pH with comparable linearity of 0.9984 and 0.9979. Overall, the research demonstrates the versatility of EGFET technology, highlighting its effective use in various sensing instruments, while ensuring reliable data transfer through the LoRaWAN framework.

Volume: 13

Issue: 4

Page: 781-790

Publish at: 2024-12-01

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31,456 Article Results

Characteristic of black soy miso with crude bromelain and Lactobacillus plantarum

Thermal imaging-based identification of facial features in noisy environment

Hybrid improved fuzzy C-means and watershed segmentation to classify Alzheimer’s using deep learning

Hybrid feature selection of microarray prostate cancer diagnostic system

No-reference image quality assessment based on visual explanation images and deep transfer learning

Design and development of coastal marine water quality monitoring based on IoT in achieving implementation of SDGs

Evaluation of the time-of-use tariff responsiveness for plug-in electric vehicle home charging in Malaysia

Application of K-means clustering and B-value algorithms for analysis of earthquake-dangerous zones in Java Island

Development of image extraction using the centerline method in the identification of appendicitis in ultrasonography

Systematic review of artificial intelligence with near-infrared in blueberries

Unsupervised hindi word sense disambiguation using graph based centrality measures

Recognizing Indonesian sign language (Bisindo) gesture in complex backgrounds

Impacts of electric vehicle charging stations and DGs on RDS for improving voltage stability using honey badger algorithm

Faults-as-address simulation

Potentiometric field-effect transistor pH sensing in a low-power wide-area network

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