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31,456 Article Results

A max-max parametric demand response scheduling algorithm for optimizing smart home environment

10.11591/ijece.v14i6.pp6478-6485
Poonam Saroha , Gopal Singh
The majority of the power distribution problems are addressed in this study by outlining a scheduling and allocation system that is based on rules. The smart home environment incorporates the suggested model as the intermediate layer. In a smart home, managing overload and power failure is the primary goal of the suggested max-max-based demand response scheduling method. The proposed model is an extension of the demand response measure while considering the load and failure rate analysis. This model is applied in a real-time environment that processes the historical information of power usage in the environment. This model captures the information available by the resources, centralized database information, and the current request parameters. The control and configuration unit are defined to process the load, history, and demand of the users. In this model, effective resource allocation and scheduling are provided. The proposed model is compared against conventional first come-first serve (FCFS), shortest job first (SJF), longest job first (LJF), demand response, and fuzzy-based demand response methods. The comparative evaluation is done on average delay, failure rate, and task-switching parameters. The analysis results obtained against these parameters confirm that the presented max-max-based parametric demand response scheduling and resource allocation method enhanced the reliability and effectiveness of the smart home environment.
Volume: 14
Issue: 6
Page: 6478-6485
Publish at: 2024-12-01

Anchor selection based deep learning two stage fabric defect localization

10.11591/ijai.v13.i4.pp4711-4721
Hattarki Pooja , Shrideva Soma
Localizing and classifying fabric defects is a crucial step in the quality control process used in the production of textiles. Recently, fabric defect classification and detection have made use of deep learning approaches based on anchor selection. But due to in effectiveness in anchor selection, the computational overhead and localization error are higher in these solutions. As a solution to this problem, this work proposes a two-stage improvised anchor selection deep learning technique. In first stage, quaternion fourier transform frequency domain analysis along with super pixel segmentation is done over the fabric image to select probable defect regions. In the second stage deep learning based regression along with super pixel segment comparison is done over the probable defect regions localize and categorize the defect. Due to effectiveness in selection of probable defect regions and categorization of regions, the defect localization accuracy is increased at a comparative low computational overhead in the proposed two stage improvise anchor selection deep learning technique. Testing against the irish longitudinal study on ageing (TILDA) fabric defect detection dataset, the proposed solution is found to provide 1.2% higher fabric defect localization accuracy at a 3% lower computation overhead compared to most recent existing works.
Volume: 13
Issue: 4
Page: 4711-4721
Publish at: 2024-12-01

Principal decision-making in implementing Merdeka Curriculum in elementary schools: a review

10.11591/ijere.v13i6.28940
Dian Marta Wijayanti , Suwito Eko Pramono , Eko Handoyo
This research uses a literature review that examines current articles on principals’ decision-making in implementing the Merdeka Curriculum in elementary schools. The literature review method is assisted by Open Knowledge Maps (OKM) and literature searches using Google Scholar, ERIC, ScienceDirect, and PubMed databases. The findings of this research suggest that principals’ decision-making in curriculum implementation has a significant impact on the quality of education in schools. By considering these aspects, principals can ensure that the curriculum implemented in their schools meets the needs of students and contributes to the achievement of broader educational goals. Principals’ decision-making in implementing the Merdeka Curriculum requires a deep understanding of education, leadership skills, and the ability to collaborate. The implementation of the Merdeka Curriculum is a significant change in Indonesia’s education system that aims to tailor education to local needs better, develop students’ character, and promote competency-based learning. However, successful implementation requires adequate support, training, and resources for schools and educators. This research is limited to a literature review only. So, in the future, it is necessary to carry out field research in order to reveal the principal’s decision-making in implementing the Merdeka Curriculum with various new methods and adapted to current issues.
Volume: 13
Issue: 6
Page: 4365-4373
Publish at: 2024-12-01

Spectral splitting of speech signal using time varying recursive filters for binaural hearing aids

10.11591/ijai.v13.i4.pp4998-5004
Aparna Chilakawad , Pandurangarao N. Kulkarni
Speech perception in noisy environments is reduced in human with sensorineural hearing loss (SNHL) due to masking. Moderate SNHL cannot be cured medically hence masking effects should be reduced to enhance speech perception. To reduce masking, processing delay and hardware complexity the present paper is proposed a scheme to partition the voice signal into two signals which are complementary to each other by using the filter-bank summation method (FBSM) with a set of time-varying recursive band pass filters. Performance of the filter is evaluated with following measures: perceptual evaluation of speech quality (PESQ), mean opinion score (MOS) for speech quality and modified rhyme test (MRT) for speech intelligibility. The test signals used for the evaluation of quality are a syllable and a word and for the evaluation of intelligibility 300 monosyllabic words are used. The results demonstrated an increase in the quality and intelligibility of processed speech in a noisy environment. As a result, there is an enhancement in perception of processed speech in a noisy environment.
Volume: 13
Issue: 4
Page: 4998-5004
Publish at: 2024-12-01

Impact of start-stop systems on motorcycle fuel savings in urban traffic

10.11591/ijece.v14i6.pp6258-6264
Kevin Murga-Garcia , Rodrigo Chacaltana-Silva , Ernesto Paiva-Peredo
The start/stop (S/S) system implemented in motorcycles aims primarily at fuel savings. This study was conducted to assess the effectiveness of this system in conditions of heavy traffic and traffic lights in Lima, using a virtual channel identifier (VCI) and a technical schedule. The detailed analysis covered critical aspects of the S/S system, the description of the route taken, and its segmentation to understand the number of stops and mileage. Speed limits, schedules, and measurement equipment were established, including the MICODUS-ORBD2 device and the VCI-Hero. The study included tests conducted with and without the MICODUS-ORBD2 device, recording times, distances, and fuel consumption. Data were collected with the S/S activated and deactivated, concluding the system achieves a 10.1% fuel saving. This finding provides valuable insights into understanding the system's effectiveness in actual traffic conditions and emphasizes the importance of maintaining key vehicle components to optimize S/S performance.
Volume: 14
Issue: 6
Page: 6258-6264
Publish at: 2024-12-01

Edge internet of things based smart home passwordless authentication

10.11591/ijece.v14i6.pp7186-7197
Maha Helal , Abdullah Aldawsari , Mousa Al-Akhras , Bayan Abu Shawar , Hani Omar
The internet of things (IoT) has transformed the way appliances and devices are connected and especially in the case of smart homes, in which smart devices can communicate through networks to improve everyday activities. However, it might be difficult to provide a high level of security for the data produced by these devices. Current security mechanisms might not always function adequately in all circumstances, especially when the number of devices increases. This research proposes an edge IoT-based smart home authentication scheme that adopts IPv6. For devices that use a smartphone application, it also offers a passwordless user authentication approach through the use of the smartphone ID and biometrics. The proposed authentication scheme was simulated to verify its ease of use and security. Security and cost analysis was also performed by reviewing and comparing the proposed scheme with previous research on IoT authentication systems. This research finds that the proposed authentication scheme is efficient at shielding home IoT networks from possible attacks, as well as maintaining a high level of usability.
Volume: 14
Issue: 6
Page: 7186-7197
Publish at: 2024-12-01

A combined control method of supply harmonic current and source harmonic voltage for series hybrid active power filter

10.11591/ijece.v14i6.pp6057-6065
Chau Minh Thuyen , Hoai Thuong Nguyen , Phan Thi Bich Thao
The series hybrid active power filter (SHAPF) is known as a very effective harmonic filtering model in power systems. Typically, SHAPF is controlled by a control method based either on the harmonic voltage of the load or on the supply harmonic current. However, the above two methods have the disadvantage of requiring the control coefficient much be large enough, which easily causes system instability. Therefore, this paper presents a new control method for SHAPF. It is a combined control method of the supply harmonic current and the source harmonic voltage. The advantage of the proposed method is the ability to reduce the total harmonic distortion of the supply current and voltage applied to the load with a control coefficient that is not too large. A fuzzy-proportional integral controller is designed for the proposed control method to reduce the compensation error in steady state under variable load conditions. Mathematical models and simulation results have demonstrated the effectiveness of the proposed method in reducing the total harmonic distortion of the supply current, voltage applied to the load and minimize the compensation error at steady state.
Volume: 14
Issue: 6
Page: 6057-6065
Publish at: 2024-12-01

Virtual reality technologies for learning English: an example of using Immerse

10.11591/ijere.v13i6.28148
Olga Lobanova , Elena Fedorova , Alena Vobolevich , Polina Minakova , Lyudmila Rybakova
This study aims to examine the effectiveness of using the Immerse virtual reality technology as an additional tool for learning English. The sample consisted of 120 students randomly selected from the lists of third-year students studying English as a foreign language according to their bachelor’s degree curriculum. The overall sample was divided into two groups. Immerse was a virtual reality technology used for training in an experimental group for three months. The results showed that in the experimental group, there was a significant difference between the pre-test and the post-test (0.35<0.05). In the control group, there were no relevant changes. In particular, these data show that the experimental group significantly outperformed the control group in terms of academic performance in English. However, some of the tasks that tested writing skills showed unsatisfactory results. In general, although virtual reality (VR) does not affect students’ writing skills, its use is crucial for their long-term memorization.
Volume: 13
Issue: 6
Page: 4409-4419
Publish at: 2024-12-01

Development of a digital twin of a network of heating systems for smart cities on the example of the city of Almaty

10.11591/ijece.v14i6.pp6656-6674
Gulnur Tyulepberdinova , Murat Kunelbayev , Olga Shiryayeva , Meruyert Sakypbekova , Nurlan Sarsenbayev , Gulmira Bayandina
In this paper, a digital twin of the network of heating systems for smart cities is developed using the example of the city of Almaty. The study used machine learning algorithms to estimate future thermal energy consumption and develop thermodynamic formulas. This work offers a thorough and in-depth analysis of thermal energy consumption. In addition, the paper identifies the relationship between thermal energy consumption and ambient temperature, and wind uncertainty in certain urban areas using machine learning methods to predict thermal energy consumption. Using both training and regression models, this interdependence is revealed. The obtained forecasts provide useful information for studying the structure of heat consumption in Almaty and reducing heat losses by reducing overheating in the zones of heating networks. In addition, the study analyzes high-resolution spatial data collected from 385 homes and 62 heat transfer circuits located throughout the city during the heating season. The study examines the degree of relationship between the ambient temperature and the amount of heat energy used in the areas of Astana. A minor impact of wind speed is also estimated. These discoveries allow us to use machine learning algorithms to find the location of hot spots and inefficient zones with high losses.
Volume: 14
Issue: 6
Page: 6656-6674
Publish at: 2024-12-01

Maternal hair lead and cytokine pro-inflammatory effects in preterm birth

10.11591/ijphs.v13i4.24146
Qonita Nur Qolby , Julian Dewantiningrum , Besari Adi Pramono , Nani Maharani , Eka Djatnika Nugraha , Muflihatul Muniroh
This case-control study analyzed the lead (Pb), interleukin 6 (IL-6), and tumor necrosis factor alpha (TNF-α) levels in pregnant women with preterm birth (PTB) in Central Java, Indonesia. Hair samples from 72 pregnant women were collected non-invasively. The prenatal exposure to Pb was determined with the total reflection X-ray fluorescence (TXRF) method. Serum IL-6 and TNF-α were examined using enzyme-linked immunosorbent assays (ELISA). The Pb concentration in hair was slightly higher in women with PTB than those without PTB; however, this difference was not statistically significant. An elevated hair Pb level was not associated with increased PTB risk (OR 24.69, 95% CI 0.93–653.82, p>0.05). A serum TNF-α level ≥27 pg/ml, a serum IL-6 level ≥9 pg/ml, and the spouse’s smoking frequency were significantly associated with increased PTB risk (TNF-α OR 42.25, 95% CI 5.26–339.61; IL-6 OR 22.33, 95% CI 3.12–158.54; spouse’s smoking frequency OR 1.28, 95% CI 1.09–1.5), while the maternal hemoglobin concentration significantly decreased PTB risk (OR 0.43, 95% CI 0.2–0.927). This study demonstrates that maternal hair Pb concentration has no significant relationship with PTB. Serum TNF-α, IL-6, and the spouse’s smoking frequency potentially increased PTB risk, while the maternal hemoglobin level is a protective factor.
Volume: 13
Issue: 4
Page: 1826-1833
Publish at: 2024-12-01

Parallel numerical simulation of the 2D acoustic wave equation

10.11591/ijece.v14i6.pp6519-6525
Arshyn Altybay , Dauren Darkenbayev , Nurbapa Mekebayev
Mathematical simulation has significantly broadened with the advancement of parallel computing, particularly in its capacity to comprehend physical phenomena across extensive temporal and spatial dimensions. High-performance parallel computing finds extensive application across diverse domains of technology and science, including the realm of acoustics. This research investigates the numerical modeling and parallel processing of the two-dimensional acoustic wave equation in both uniform and non-uniform media. Our approach employs implicit difference schemes, with the cyclic reduction algorithm used to obtain an approximate solution. We then adapt the sequential algorithm for parallel execution on a graphics processing unit (GPU). Ultimately, our findings demonstrate the effectiveness of the parallel approach in yielding favorable results.
Volume: 14
Issue: 6
Page: 6519-6525
Publish at: 2024-12-01

Eligibility rate of applicant’s LinkedIn account: a naïve bayes classification and visualization

10.11591/ijai.v13.i4.pp4334-4343
Khyrina Airin Fariza Abu Samah , Nurul Athirah Ahmad , Anis Amilah Shari , Hana Fakhira Almarzuki , Zuhri Arafah , Lala Septem Riza , Amir Haikal Abdul Halim
In the digital era, social media platforms like LinkedIn have become famous for recruitment, and recruiters widely use them to find potential employees. The recruitment process is crucial in organizations, as it involves selecting qualified applicants from a diverse pool. However, the screening process and manual recruitment process entail significant time, high costs, and potential bias. Consequently, it may cause recruiting unqualified applicants and may affect the organizations. Thus, this study aims to classify and generate a list of potential job applicants by analyzing seven attributes of their LinkedIn accounts: title, location, skills, education, language, certification, and years of experience. Data are collected from LinkedIn profiles and then undergo data pre-processing. The naive Bayes (NB) algorithm is implemented as the classification algorithm and sets the classification as “eligible” or “ineligible”. The NB model achieved an accuracy testing of 89.8%, indicating good performance in classifying potential job applicants. At the same time, we measure the similarity cosine score to set the mean of the eligibility. The classification results are visualized for the suitable applicants in descending rank, allowing users to choose the applicants’ classification status efficiently. For the system usability, we managed to get 90% from the recruitment expert.
Volume: 13
Issue: 4
Page: 4334-4343
Publish at: 2024-12-01

Impact of students’ feedback on their engagement towards online classes during COVID-19 pandemic

10.11591/ijere.v13i6.28375
Ildikó Rudnák , Khadija Aya Hamza , Ayman Alshaabani , Judit Garamvölgyi
With the emerge of COVID-19 pandemic in Hungary, Hungarian universities have decided to opt for online teaching methods even for foreign language courses. This sudden change has required a better understanding of students’ behaviors in classes also to view the importance of their feedback to enhance teaching quality and teachers’ effectiveness. The purpose of our study was to focus on students’ feedback and its impact on their engagement in the context of online classes, by considering the mediating role of teaching effectiveness in that relationship. Structural equation modelling was used to examine our primary data, which has been collected from a distributed online questionnaire dedicated to 222 students enrolled in Hungarian language courses at MATE University. The findings reveal that students’ feedback has a direct, significant, and positive effect on students’ engagement in online classes and teaching effectiveness, which itself plays a mediating role in that relationship.
Volume: 13
Issue: 6
Page: 3775-3786
Publish at: 2024-12-01

The effect of murottal Al-Quran therapy on pain intensity and oxygen saturation in active phase parting women

10.11591/ijphs.v13i4.23856
Yustiari Yustiari , Wahida Wahida , Khalidatul Khair Anwar , Hasmia Naningsi , Melania Asi , Endah Saraswati
Anxiety in women increases with the start of labor and has a negative impact on mothers and newborns. The aim of this study was to determine the effect of listening to murottal Al-Quran on increasing oxygen saturation and reducing pain in mothers in the active phase of labor. This research was quantitative research with a quasi-experimental design and a preexperimental design one-group pretest-posttest. This study was conducted at the Kendari City Hospital. The sample was determined as a total population, considering that the number of mothers during the first active phase was only 30. The data analysis technique in this study went through two calculation stages: the data normality test by Kolmogorov-Smirnov, Paired Sample Statistics, and all calculations using SPSS. The results of the statistical tests using the Wilcoxon test yielded a p-value of 0.000. Statistically, it can be concluded that there are differences in oxygen saturation values before and after the administration of murottal intervention. It was also found that 27 respondents experienced an increase in oxygen saturation after receiving murottal intervention. In conclusion, murottal intervention increased oxygen saturation.
Volume: 13
Issue: 4
Page: 1834-1840
Publish at: 2024-12-01

Psychometric properties of Indonesian physics motivation questionnaire using Rasch model

10.11591/ijere.v13i6.29285
Moh Irma Sukarelawan , Muhammad Firman Ramadhan , Raden Oktova , Jumadi Jumadi , Ariati Dina Puspitasari , Toni Kus Indratno , Zalik Nuryana
The transition to online learning during the pandemic has significantly impacted Indonesian students’ physics learning motivation. Recognizing the need for culturally relevant tools to assess this motivation, this study aims to adapt the physics motivation questionnaire (PMQ) to the Indonesian context. This research, which was conducted on 107 secondary and vocational school students, had a balanced gender distribution and used survey methodology. The PMQ, which was adapted from the biology motivation questionnaire (BMQ) through a rigorous forward-backward translation process, was analyzed for its psychometric properties using the Rasch model. This analysis focuses on the content aspects of construct validity, assessing content relevance, representativeness, and technical quality. The results show that the PMQ, using a 4-point Likert scale, effectively addresses these elements, thus confirming its reliability and applicability in the Indonesian educational environment. This study concludes that the culturally adapted PMQ is a valuable instrument for evaluating Indonesian students’ motivation to study physics, especially in the context of ongoing changes in the educational landscape.
Volume: 13
Issue: 6
Page: 4279-4286
Publish at: 2024-12-01
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