Articles

Access the latest knowledge in applied science, electrical engineering, computer science and information technology, education, and health.

Advanced Search
Filter Icon

Filters article

Years

FAQ Arrow
Single Year
Range Year
0
0

Source Title

FAQ Arrow

Authors

FAQ Arrow

27,404 Article Results

Design and development of an automated spirulina (Arthrospira platensis) algae cultivator

10.11591/ijeecs.v39.i1.pp139-147
Miguel Q. Mariñas II , Mark Joseph B. Enojas , Daryll C. Balolong , Charissa Zandra B. Correa , Lemmuel Keith C. Roldan , Mark Lester Teves , Christian Mari Dela Cruz
The cultivation of algae has gotten more attention from algae enthusiasts who have seen the benefits of algae in many uses. To maximize productivity, the parameters for growth of this algae must be controlled, such as pH, turbidity, light intensity, and the mixture solution for optimal growth. In this paper, an automated spirulina algae cultivator is designed and developed in a small-scale pond to replace the existing manual process. The system developed is composed of compact and modular cultivation unit, ph sensor, water level sensor, turbidity sensor, light intensity sensor, and motor actuators for mixing solutions. Each parameter was controlled individually in an on-off control system. A simple nutrient addition program (SNAP) solution is also used for better growth productivity by maximizing its nutrient contents. The pH is maintained at 9 to 12 for a healthy biomass output. Daily weight measurement was conducted using an analytical balance to monitor its growth. Using the developed prototype recorded a 33% higher rate of productivity over the manual process. This setup can potentially be used as a model for mass production of spirulina algae.
Volume: 39
Issue: 1
Page: 139-147
Publish at: 2025-07-01

Effects of hyperparameter tuning on random forest regressor in the beef quality prediction model

10.11591/csit.v6i2.p159-166
Ridwan Raafi'udin , Yohanes Aris Purwanto , Imas Sukaesih Sitanggang , Dewi Apri Astuti
Prediction models for beef meat quality are necessary because production and consumption were significant and increasing yearly. This study aims to create a prediction model for beef freshness quality using the random forest regressor (RFR) algorithm and to improve the accuracy of the predictions using hyperparameter tuning. The use of near-infrared spectroscopy (NIRS) in predicting beef quality is an easy, cheap, and fast technique. This study used six meat quality parameters as prediction target variables for the test. The R² metric was used to evaluate the prediction results and compare the performance of the RFR with default parameters versus the RFR with hyperparameter tuning (RandomSearchCV). Using default parameters, the R-squared (R²) values for color (L*), drip loss (%), pH, storage time (hour), total plate colony (TPC in cfu/g), and water moisture (%) were 0.789, 0.839, 0.734, 0.909, 0.845, and 0.544, respectively. After applying hyperparameter tuning, these R² scores increased to 0.885, 0.931, 0.843, 0.957, 0.903, and 0.739, indicating an overall improvement in the model’s performance. The average performance increase for prediction results for all beef quality parameters is 0.0997 or 14% higher than the default parameters.
Volume: 6
Issue: 2
Page: 159-166
Publish at: 2025-07-01

Bibliometric analysis and short survey in CT scan image segmentation: identifying ischemic stroke lesion areas

10.11591/csit.v6i2.p91-101
Wahabou K. Taba Chabi , Sèmèvo Arnaud R. M. Ahouandjinou , Manhougbé Probus A. F. Kiki , Adoté François-Xavier Ametepe
Ischemic stroke remains one of the leading causes of mortality and long-term disability worldwide. Accurate segmentation of brain lesions plays a crucial role in ensuring reliable diagnosis and effective treatment planning, both of which are essential for improving clinical outcomes. This paper presents a bibliometric analysis and a concise review of medical image segmentation techniques applied to ischemic stroke lesions, with a focus on tomographic imaging data. A total of 2,014 publications from the Scopus database (2013–2023) were analyzed. Sixty key studies were selected for in-depth examination: 59.9% were journal articles, 29.9% were conference proceedings, and 4.7% were conference reviews. The year 2023 marked the highest volume of publications, representing 17% of the total. The most active countries in this area of research are China, the United States, and India. "Image segmentation" emerged as the most frequently used keyword. The top-performing studies predominantly used pre-trained deep learning models such as U-Net, ResNet, and various convolutional neural networks (CNNs), achieving high accuracy. Overall, the findings show that image segmentation has been widely adopted in stroke research for early detection of clinical signs and post-stroke evaluation, delivering promising outcomes. This study provides an up-to-date synthesis of impactful research, highlighting global trends and recent advancements in ischemic stroke medical image segmentation.
Volume: 6
Issue: 2
Page: 91-101
Publish at: 2025-07-01

HepatoScan: Ensemble classification learning models for liver cancer disease detection

10.11591/csit.v6i2.p167-175
Tella Sumallika , Raavi Satya Prasad
Liver cancer is a dangerous disease that poses significant risks to human health. The complexity of early detection of liver cancer increases due to the unpredictable growth of cancer cells. This paper introduces HepatoScan, an ensemble classification to detect and diagnose liver cancer tumors from liver cancer datasets. The proposed HepatoScan is the integrated approach that classifies the three types of liver cancers: hepatocellular carcinoma, cholangiocarcinoma, and angiosarcoma. In the initial stage, liver cancer starts in the liver, while the second stage spreads from the liver to other parts of the body. Deep learning is an emerging domain that develops advanced learning models to detect and diagnose liver cancers in the early stages. We train the pre-trained model InceptionV3 on liver cancer datasets to identify advanced patterns associated with cancer tumors or cells. For accurate segmentation and classification of liver lesions in computed tomography (CT) scans, the ensemble multi-class classification (EMCC) combines U-Net and mask region-based convolutional network (R-CNN). In this context, researchers use the CT scan images from Kaggle to analyze the liver cancer tumors for experimental analysis. Finally, quantitative results show that the proposed approach obtained an improved disease detection rate with mean squared error (MSE)-11.34 and peak signal-to-noise ratio (PSNR)-10.34, which is high compared with existing models such as fuzzy C-means (FCM) and kernel fuzzy C-means (KFCM). The classification results obtained based on detection rate with accuracy-0.97%, specificity-0.99%, recall-0.99%, and F1S-0.97% are very high compared with other existing models.
Volume: 6
Issue: 2
Page: 167-175
Publish at: 2025-07-01

A Custom RISC-V Based Architecture for Real-Time ECG Feature Extraction

10.11591/ijres.v14.i2.pp%p
Vinayak Vikram Shinde , Sheetal Umesh Bhandari , Deepti Snehal Khurge , Satyashil Dasharath Nagarale , Ujwal Ramesh Shirode
The need for small, energy-efficient biomedical signal processing systems has grown along with the popularity of wearable health tracking systems. In order to facilitate real-time processing in wearable health-monitoring devices, this paper presents Register Transfer Level (RTL) design and simulation of a custom architecture for real-time Electrocardiogram (ECG) feature extraction. The architecture includes a Reduced Instruction Set Computer-V (RISC-V) based control core and dedicated hardware blocks such as Arithmetic and Logical Unit (ALU), Memory, Control Unit for processing ECG signals to extract ECG features such as R-peaks and heart rate. The design flow includes RTL design, synthesis, and validation. It uses ECG datasets and EDA tools. The purpose of this paper is to come up with a small, power-efficient solution for wearable tech to process biomedical data.
Volume: 14
Issue: 2
Page:
Publish at: 2025-06-10

Bridging education and CSR: the role of corporate foundations in Malaysia’s human capital development

10.11591/ijere.v14i3.27925
Naziatul Aziah Mohd Radzi , Khai Ern Lee , Sharina Abdul Halim , Chamhuri Siwar , Mukhriz Izraf Azman Aziz
The outlined target in sustainable development goals (SDG) relating to educational aspects indicates that education plays an important role and should become the primary concern of various parties. Hence, this study focused on the implications of corporate social responsibility (CSR) in educational aspects towards the development of human capital from the viewpoints of selected corporate foundations in Malaysia. This study was conducted through a questionnaire survey in which data were analyzed using IBM SPSS Statistics 20 software. The target population for this study were individuals involved with CSR initiatives conducted by four selected corporate foundations which are the implementers and students who received assistance from corporate foundations. The analysis shows that educational activities in CSR initiatives undertaken by corporate foundations have helped in the development of human capital. Both implementers and recipients give priority to scholarship sponsorships followed by school assistance, organizing workshops and seminars as valuable activities for individuals. The CSR initiatives in educational aspects have highlighted the role of corporate foundations as agents that can help individuals to achieve their dreams of pursuing tertiary education. The involvement of corporate foundations in education has created value for companies and certainly for society, in which corporate foundations have established relationships with stakeholders, as explained in the stakeholder theory.
Volume: 14
Issue: 3
Page: 1717-1730
Publish at: 2025-06-01

Evaluating the effectiveness of intervention on professional and pedagogical skills among prospective physics teachers

10.11591/ijere.v14i3.31864
Dian Artha Kusumaningtyas , Moh. Irma Sukarelawan , Muhammad Syahriandi Adhantoro , Wahyu Nanda Eka Saputra
This study evaluates the effectiveness of a targeted intervention designed to enhance the professional and pedagogical skills of prospective physics teachers, addressing a key gap in teacher education. The research involved an experimental group that received the intervention and a control group that did not. The research subjects in the experimental and control groups were 120 each. To rigorously assess the impact, Whitney and Wilcoxon’s statistical tests were employed to compare pretest and posttest outcomes. Additionally, Wright map analysis was used to visualizes kill development. The results revealed a significant improvement in the professional and pedagogical skills of the experimental group compared to the control group, as indicated by Mann-Whitney test (U=1274.500, p<0.05 and U=421.500, p<0.05). The Wright map analysis further demonstrated that the experimental group experienced more consistent and substantial gains in pedagogical skills. This study contributes to the field by demonstrating the effectiveness of interventions in improving the skills of prospective physics teachers, offering educational policy recommendations, and filling important gaps in the literature. Moreover, it emphasizes the critical role of ongoing evaluation in the continuous development of teacher training programs. By addressing these areas, this research provides valuable insights that can inform the design and implementation of more effective teacher training strategies.
Volume: 14
Issue: 3
Page: 2290-2303
Publish at: 2025-06-01

Technological leadership in industry 4.0 education: influence of digital transformation and ICT adoption

10.11591/ijere.v14i3.30804
Asma Khaleel Abdallah , Ivan Trifonov , Vadim Samusenkov
The objective of this article is a systematic investigation into the effectiveness of information and communication technologies (ICT) usage within the framework of the educational model “industry 4.0”, focusing on the influence of digital transformation on technological leadership in educational institutions. The problem is insufficient technical equipment, uneven distribution of resources, and insufficient support for teachers. The solution lies in systematic innovative training and support for teachers, creating incentives to increase their motivation. The study employs an experimental research design, utilizing survey methods. The subjects of the research include six directors, six teachers, and 120 students from educational institutions in the United Arab Emirates (UAE) and the Russian Federation. According to the survey results, teachers have a positive attitude toward using ICT. A majority of teachers believe that the use of ICT has a positive impact on students’ academic achievements. Responses to open-ended questions indicate a lack or uneven distribution of technical equipment, emphasizing the need for training and support for teachers. One teacher suggesting the “introduction of incentives and rewards” raises the issue of creating a reward system for teachers, which could affect their motivation. Regarding students’ academic performance, the results show that students in educational institutions with active ICT integration demonstrate better results.
Volume: 14
Issue: 3
Page: 2358-2368
Publish at: 2025-06-01

Information and communication technology-based learning practices and teacher professional development

10.11591/ijere.v14i3.31910
Suardi Suardi , Faridah Faridah , Sultan Sultan , Herman Herman
The rapid development of information technology has implications for its massive use in the field of education. Expectations for teachers to integrate technology into their learning practices and professional development are increasing. The teacher’s ability to integrate technology in these two activities is influenced by various factors. However, previous research has not focused on uncovering how gender, experience, certification status, and social media can contribute to information and communication technology (ICT)-based learning practices and professional competency development for teachers. Based on this gap, this research was designed to investigate the contribution of gender, experience, certification status, and social media access to teachers’ ICT-based learning practices and professional competency development. The current research was designed as a cross-sectional survey. A total of 1,756 elementary school teachers in South Sulawesi, Indonesia, were involved as research samples through online questionnaire data collection. The research results showed that there were differences in teachers’ learning practices and professional development intentions based on work experience and intensity of social media access. However, no differences were found in gender variables and certification status. Thus, these two variables become key elements in integrating ICT in learning in the future. These findings will be beneficial for teacher training institutions and policy makers.
Volume: 14
Issue: 3
Page: 1633-1642
Publish at: 2025-06-01

Evaluating the influence of climate change knowledge on intention towards pro-environmental behavior

10.11591/ijere.v14i3.32173
Mee Yeang Chan , Lilia Halim , Nurfaradilla Mohamad Nasri
Pro-environmental behavior is essential for mitigating climate change, with climate change knowledge often considered a prerequisite for fostering intentions toward such behaviors. However, the specific types of climate change knowledge that most effectively promote pro-environmental behavior remain unclear, indicating a need for further investigation. This survey was conducted among 308 randomly selected Form Two students (average age 14) to respond to a questionnaire consisting of 24 items. The study aimed to explore the relationship between various types of climate change knowledge and students’ intention to engage in pro-environmental behavior. Using SPSS version 25.0 software, both descriptive and inferential analyses (correlation and multiple regression) were conducted. Findings revealed that students had the highest level of knowledge regarding mitigation actions, followed by knowledge of the causes and impacts of climate change respectively. All three types of knowledge were significantly and positively correlated with the intention to engage in pro-environmental behavior. Regression analysis showed that students’ knowledge on mitigation actions influence the most to the intention compared to the other types of climate change knowledge. The study recommends enhancing students’ understanding of the causes of climate change, which could, in turn, improve their knowledge of impacts and better guide their mitigation actions, ultimately fostering higher levels of pro-environmental behavior.
Volume: 14
Issue: 3
Page: 1567-1576
Publish at: 2025-06-01

The impact of meme integration on university students’ active learning

10.11591/ijere.v14i3.31589
Beatriz María Sastre-Hernández , María Peana Chivite-Cebolla , Miguel Ángel Echarte Fernández , Álvaro Mendo Estrella , Javier Jorge-Vázquez , Sergio Luis Náñez Alonso
This study investigates the application of memes as a didactic tool in university-level social sciences education to address the learning needs of generation Z students. In the present study, the problem of the reduction in the academic performance of students is presented to us. With this research we have sought to contrast if the meme tool can help to give an answer to this problem. The methodology was implemented in business administration, economics, and law courses. Students were tasked with designing memes related to course content. A total of 110 memes were submitted by students, and 45 participants completed an evaluation questionnaire. Correlations and a linear regression model were used mainly for data analysis. Regarding the analysis of the results obtained in specific business subjects, where 68 students were evaluated, it should be noted that the meme variable is the second most significant variable in the final grade obtained. This data seems to indicate that, if the students have been able to synthesize part of the contents in memes, this has helped them in a better assimilation of the subject, and to pass it successfully. We certainly know that young people spend a lot of their time on platforms, and the language of memes is familiar to them. These findings suggest that memes can be an effective and engaging educational tool, offering valuable benefits in the digital age for both students and educators.
Volume: 14
Issue: 3
Page: 2167-2182
Publish at: 2025-06-01

Impact of entrepreneurial education policies on reducing bullying among university students with anatomical and physiological disabilities: review

10.11591/ijere.v14i3.31967
Eman Rababah , Esra Hamdan , Raed Halalsheh , Bayan Rababah
This study examines the impact of entrepreneurial education (EE) policies on reducing bullying (Tanamor) among university students with anatomical and physiological disabilities and special needs. Using a descriptive approach grounded in theoretical literature, the study identifies positive outcomes, such as enhanced self-confidence and peer respect among students with disabilities. It highlights the role of EE in creating inclusive environments that mitigate bullying. The review underscores the necessity for further research, including longitudinal studies to understand the long-term impact of these educational strategies. The findings advocate for integrating EE into university policies to support the well-being and academic success of students with disabilities.
Volume: 14
Issue: 3
Page: 1824-1833
Publish at: 2025-06-01

Enhanced time series forecasting using hybrid ARIMA and machine learning models

10.11591/ijeecs.v38.i3.pp1970-1979
Vignesh Arumugam , Vijayalakshmi Natarajan
Accurate energy demand forecasting is essential for optimizing resource management and planning within the energy sector. Traditional time series models, such as ARIMA and SARIMA, have long been employed for this purpose. However, these methods often face limitations in handling nonstationary data, complexity in model tuning, and susceptibility to overfitting. To address these challenges, this study proposes a hybrid approach that integrates traditional statistical models with advanced computational methods. By combining the strengths of both approaches, the proposed models aim to enhance predictive accuracy, improve computational efficiency, and maintain robustness across varied energy datasets. Experimental results demonstrate that these hybrid models consistently outperform standalone traditional methods, providing more reliable and precise forecasts. These findings underscore the potential of hybrid methodologies in advancing energy demand forecasting and supporting more effective decision-making in energy management.
Volume: 38
Issue: 3
Page: 1970-1979
Publish at: 2025-06-01

Chaotic crow search enhanced CRNN: a next-gen approach for IoT botnet attack detection

10.11591/ijeecs.v38.i3.pp1745-1754
Veena Antony , Nainan Thangarasu
Internet of things (IoT) botnet attack detection is crucial for reducing and identifying hostile threats in networks. To create efficient threat detection systems, deep learning (DL) and machine learning (ML) are currently being used in many sectors, mostly in information security. The botnet attack categorization problem is difficult as data dimensionality increases. By combining convolutional and recurrent neural layers, our work effectively addressed the vanishing and expanding gradient difficulties, improving the ability to capture spatial and temporal connections. The problem of weight decaying and class imbalance affects the accuracy rate of the existing DL models. In convolutional neural network (CNN), fully connected layer optimizes the hyperparameters by utilizing its comprehension of the chaotic crow search method. The chaotic mapping maintains equilibrium between the global and local search spaces. The crow's strategy for hiding food is the main source of inspiration for optimizing the learning rate, weight, and bias components involved in the prediction process. When compared to other existing algorithms, the UNSW-NB15 dataset's results for IoT botnet attack detection in the presence of a high degree of class imbalance demonstrated the effectiveness of the proposed convolutional recurrent neural network (CRNN) boosted with chaotic crow searching algorithm, which produced the highest detection rate with the lowest false alarm rate.
Volume: 38
Issue: 3
Page: 1745-1754
Publish at: 2025-06-01

Enhancing business analytics predictions with hybrid metaheuristic models: a multi-attribute optimization approach

10.11591/ijeecs.v38.i3.pp1830-1839
Rahmad B. Y. Syah , Marischa Elveny , Mahyuddin K. M. Nasution
This approach aims to optimize business analytical predictions through multiattribute optimization using a hybrid metaheuristic model based on the modified particle swarm optimization (MPSO) and gravitational search optimization (GSO) algorithms. This research uses a variety of data, such as revenue, expenses, and customer behavior, to improve predictive modeling and achieve superior results. MPSO, an interparticle collaborative mechanism, efficiently explores the search space, whereas GSO models’ gravitational interactions between particles to solve optimization problems. The integration of these two algorithms can improve the performance of business analytical predictions by increasing model precision and accuracy, as well as speeding up the optimization process. Model validation test results, precision 95.60%, recall 96.35%, accuracy 96.69%, and F1 score 96.11%. This research contributes to the development of more sophisticated and effective business analysis techniques to face the challenges of an increasingly complex business world.
Volume: 38
Issue: 3
Page: 1830-1839
Publish at: 2025-06-01
1 2 3
...
1826 1827
Show 8 of 1827

Discover Our Library

Embark on a journey through our expansive collection of articles and let curiosity lead your path to innovation.

Explore Now
Library 3D Ilustration