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

31,458 Article Results

Effectiveness of digital learning on students’ higher order thinking skills

10.11591/ijere.v13i5.29449
Hamidah Mat , Siti Salina Mustakim , Fazilah Razali , Norliza Ghazali , Asnul Dahar Minghat
In the realm of education, learning and instructional activities play a crucial role in cultivating lasting and meaningful comprehension among science students. This research aims to evaluate the effectiveness of the i-Genius module in enhancing students’ performance in science. The i-Genius module’s development adhered to the ADDIE model, and two specific research questions were formulated: i) is there a statistically significant difference in mean scores between the experimental and control groups? and ii) to what extent can i-Genius contribute to students’ conceptual evolution compared to traditional methods? To address these questions, a sequential mixed-method approach involving interviews, pre-tests, and post-tests was implemented in two distinct schools in the Seremban District. The experimental group comprised 35 participants, and the control group also included 35 students with similar characteristics. Student performance, assessed through pre-test and post-test mean scores, revealed that students exposed to i-Genius achieved significantly higher scores than those exposed to traditional methods in the post-test (t(68)=8.37, p<0.05). This study’s implications lie in its practical application within the school context, offering an alternative instructional tool for teaching science and presenting an instructional model to guide teachers in formulating strategies that encourage problem-posing within the science curriculum.
Volume: 13
Issue: 5
Page: 2817-2824
Publish at: 2024-10-01

Cloud based prediction of epileptic seizures using real-time electroencephalograms analysis

10.11591/ijece.v14i5.pp6047-6056
Gousia Thahniyath , Chelluboina Subbarayudu Gangaiah Yadav , Rajagopalan Senkamalavalli , Shanmugam Sathiya Priya , Stalin Aghalya , Kuppireddy Narsimha Reddy , Subbiah Murugan
This study aims to improve the accuracy of epileptic seizure prediction using cloud-based, real-time electroencephalogram analysis. The goal is to build a strong framework that can quickly process electroencephalogram (EEG) data, extract relevant features, and use advanced machine learning algorithms to predict seizures with high accuracy and low latency by taking advantage of cloud platforms' computing power and scalability. The main objective is to provide patients and their caregivers with timely notifications so that they may control epilepsy episodes proactively. The goal of this project is to improve the lives of people with epilepsy by reducing the impact of seizures and improving treatment results via real-time analysis of EEG data. Cloud computing also allows the suggested seizure prediction system to be more accessible and scalable, meaning more people worldwide could benefit from it. This section discusses the results from five separate datasets of patients with epileptic seizures who underwent EEG analysis with the following details as frontopolar (FP1, FP2), frontal (F3, F4), frontotemporal (F7, F8), central (C3, C4), temporal (T3, T4), parieto-temporal (T5, T6), parietal (P3, P4), occipital (O1, O2), time (HH:MM:SS).
Volume: 14
Issue: 5
Page: 6047-6056
Publish at: 2024-10-01

Satellite image encryption using 2D standard map and advanced encryption standard with scrambling

10.11591/ijece.v14i5.pp5153-5171
Omar Benchikh , Youcef Bentoutou , Nasreddine Taleb
In today’s world, the need for higher levels of security in storing and transferring data has become a key concern. It is essential to safeguard data from any potential information leaks to prevent threats that may compromise data confidentiality. Therefore, to protect critical and confidential satellite imagery, this paper proposes a novel encryption method based on the combination of image bands scrambling with chaos and the advanced encryption standard (AES). The proposed approach aims to enhance the security of satellite imagery while maintaining efficiency and robustness against various attacks. It possesses several appealing technical characteristics, notably a high level of security, a large key space, and resilience to single event upsets (SEUs) and transmission errors. To evaluate the performance of the proposed encryption technique, extensive experiments have been conducted by considering factors such as security level, resistance to SEUs, and computational efficiency. Our results demonstrate that the proposed method achieves a high level of security and a large key space, ensuring the confidentiality and integrity of satellite imagery data. Furthermore, the method exhibits resilience against SEUs and transmission errors, and offers efficient processing, making it suitable for real-world applications.
Volume: 14
Issue: 5
Page: 5153-5171
Publish at: 2024-10-01

The interplay of factors affecting online learning experience in higher education

10.11591/ijere.v13i5.28935
Fredy Fredy , Ratna Purwanty , Desy Kumala Sari , Lastika Ary Prihandoko
Education has undergone a profound transformation, transitioning significantly from traditional face-to-face instructional approaches to a predominant reliance on online learning methodologies. This sudden change leaves questions on how to provide an affective and satisfying online leaning for students. As prior studies revealed, many factors affect the success of implementing online learning, specifically for higher education students. As a response, this quantitative study was intended to investigate the interplay of factors affecting online learning experience in higher education namely anxiety, motivation for learning, self-directed learning, online learning attitude, and computer-internet self-efficacy. An exploratory factor analysis (EFA) included 20 items of online survey distributed to undergraduate students (n=329) from several faculties at one Indonesian university to explore this issue. This study used the partial least squares structural equation modeling (PLS-SEM) application to explore the interplay among six constructs. The results showed that all six constructs namely anxiety, motivation for learning, self-directed learning, online learning attitude, computer-internet self-efficacy, and online learning experience were positively associated. It meant that those factors were statistically proven to affect students’ online learning experiences. Educators could use these results as a consideration in implementing online learning more effectively. Further implications of pedagogical practice and further research are discussed.
Volume: 13
Issue: 5
Page: 3090-3099
Publish at: 2024-10-01

STEM-based digital disaster learning model for disaster adaptation ability of elementary school students

10.11591/ijere.v13i5.29616
Arwin Arwin , Ary Kiswanto Kenedi , Yesi Anita , Hamimah Hamimah , Ciptro Handrianto , Melva Zainil
Efforts are required to enhance community resilience to disasters, especially among elementary school students who are highly vulnerable to losses caused by natural disasters. In previous research, a science, technology, engineering, and mathematics (STEM) based digital disaster learning model was developed for elementary school students, so further research is needed to determine its effect on the adaptability of elementary school students. This study aimed to assess the impact of the STEM-based digital disaster learning model on the disaster adaptation abilities of elementary school students. This research is a quasi-experimental. The data collection instrument is the disaster adaptation ability essay test questions. The data analysis process uses the help of the SPSS 26 application. The findings found an average difference in students' disaster adaptation abilities between STEM-based digital learning models and conventional learning models. This finding was also reinforced by the post-test average scores of students who studied using STEM-based digital disaster learning models, which were higher than those with conventional learning models. So overall, the STEM-based digital disaster learning model increases elementary school students' disaster adaptation abilities. The implications of this research can be used as a reference in developing elementary school students' disaster adaptation abilities.
Volume: 13
Issue: 5
Page: 3248-3258
Publish at: 2024-10-01

Students’ learning motivation in English for specific purpose in Chinese universities

10.11591/ijere.v13i5.29421
Xu Guosheng , Zhang Xiaoyu , Qian Baihui
A large number of studies at home and abroad have demonstrated the importance of university students’ English learning motivation, but few studies have focused on a certain field, or students’ major particularly under the context of Chinese private university. This research explores students’ motivation to learn English for specific purposes (ESP), and 123 student participants majoring in mechanical and electronic engineering in Shandong Huayu University of Technology participated in the research. Data are presented qualitatively and quantitatively. Students filled in a quantitative questionnaire about learning motivation, then SPSS27 was used to analyze relevant data. In addition, focus group interview, as a qualitative means, was used in the research, and 12 student participants who were going to attend ESP class showed their opinions in the group interview. The results showed that students’ learning motivation comes more from teachers’ personal charm, harmonious teacher-student relationship and comfortable and warm learning environment, while setting difficult tasks for them is not their main motivation. The research results provide a useful reference for teachers to adopt teaching practices that motivate students to learn in ESP class.
Volume: 13
Issue: 5
Page: 3402-3410
Publish at: 2024-10-01

Improving trigonometric competency with GeoGebra: a quasi-experimental study in a high school

10.11591/ijere.v13i5.28995
Yulieth Carriazo-Regino , Dougglas Hurtado-Carmona , Andrés Bermudez-Quintero
This quasi-experimental study examines the efficacy of GeoGebra in enhancing trigonometric competence among tenth-grade students in Montería, Colombia. Comparing an experimental group that used GeoGebra with a control group receiving traditional teaching, key competencies such as reasoning and argumentation, communication, representation and modeling, and problem posing and solving were evaluated. Pre-intervention results showed that 88.19% of students in the experimental group had insufficient performance in reasoning and argumentation. After the implementation of GeoGebra, this figure decreased to 5.5%. In competencies of communication, representation, and modeling, the insufficient performance reduced from 85.7% to 5.5%, and in problem posing and solving, from 80.3% to 5.7%. These significant improvements demonstrate the positive impact of GeoGebra on the development of mathematical competencies. The study concludes that GeoGebra is an effective tool for strengthening trigonometric competence in high school students, highlighting the importance of integrating digital technologies in mathematics education. The findings suggest the need for more research on the use of technological tools in mathematics education and support investment in technological resources and teacher training.
Volume: 13
Issue: 5
Page: 2876-2889
Publish at: 2024-10-01

The potential of the internet of things for human activity recognition in smart home: overview, challenges, approaches

10.11591/ijeecs.v36.i1.pp302-317
Khadija Essafi , Laila Moussaid
Human activity recognition (HAR) is a technology that infers current user activities by using the available sensory data network. Research on activity recognition is considered extremely important, particularly when it comes to delivering sensitive services such as healthcare services and live tracking assistance and autonomy. For this purpose, many researchers have proposed a knowledge-driven approach or data-driven reasoning for identification techniques. However, there are multiple limitations associated with these approaches and the resulting models are typically not complete enough to capture all types of human activities. Thus, recent works have suggested combining these techniques through a hybrid model. This paper's goal is to give a brief overview of activity recognition implementation approaches by looking at various sensing technologies used to gather data from internet of things (IoT) gadgets, looking at preprocessing and feature extraction approaches, and then comparing methods used to identify human activities in smart homes, and highlighting their strengths and weaknesses across various fields. Numerous pertinent works were located, and their accomplishments were assessed.
Volume: 36
Issue: 1
Page: 302-317
Publish at: 2024-10-01

Towards robust security in WSN: a comprehensive analytical review and future research directions

10.11591/ijeecs.v36.i1.pp318-337
Tamara Zhukabayeva , Lazzat Zholshiyeva , Khu Ven-Tsen , Yerik Mardenov , Aigul Adamova , Nurdaulet Karabayev , Assel Abdildayeva , Dilaram Baumuratova
One of the most important aspects of the effective functioning of wireless sensor network (WSN) is their security. Despite significant progress in WSN security, there are still several unresolved issues. Many review studies have been published on the problems of possible attacks on WSN and their identification. However, due to the lack of their systematic analysis, it is not possible to fully substantiate practical recommendations for the effective application of the proposed solutions in the field of WSN security. In particular, the creation of methods that provide a high degree of security while minimizing computational effort and costs, and the development of effective methods for detecting and preventing attacks on WSN. The purpose of this document is to fill this gap. The article presents the results of the study in the form of a systematic analysis of the literature with a targeted selection of sources to identify the most effective methods for detecting and preventing attacks on WSN. By identifying the security of WSN, which has not yet been addressed in research works, the review aims to reduce its impact. As a result, our extended taxonomy is presented, including attack types, datasets, effective WSN attack detection methods, countermeasures, and intrusion detection systems (IDS).
Volume: 36
Issue: 1
Page: 318-337
Publish at: 2024-10-01

Cattle weight prediction model using convolutional neural network and artificial neural network

10.11591/ijeecs.v36.i1.pp441-449
Yulianingsih Yulianingsih , Sri Nurdiati , Heru Sukoco , Cece Sumantri
The weight of livestock is a crucial metric for evaluating management efficacy, informing policy decisions, and determining the market value of animals. In certain scenarios, conventional methods such as physical weighing and measurement calculations can prove challenging, including the absence of livestock health records or weighing equipment. This research aims to develop a predictive model for estimating the live weight of cattle through visual assessments and metadata, including age and pixel count, utilizing a combination of convolutional neural network (CNN) and artificial neural network (ANN) methodologies. A total of 223 data were obtained from a local farm before augmentation. The model's predictive capability was successfully demonstrated, with its performance quantified by an average mean absolute percentage error (MAPE) of 10% on test data. This study demonstrates that through the combination of CNN and ANN, as well as optimal parameter tuning, efficient prediction of cattle weight can be achieved.
Volume: 36
Issue: 1
Page: 441-449
Publish at: 2024-10-01

Digitalization of educational plays for quality education

10.11591/ijece.v14i5.pp5443-5457
Younes Alaoui Soulimani , Lotfi Elaachak , Mohammed Bouhorma
Repetitive tests on a learning material help schoolchildren to memorize and to learn this material. Psychologists call this phenomenon the testing effect. Skilled teachers use learning plays to embed routine tests in an engaging way. To widespread this practice, we propose a framework to digitize learning plays embedding routine tests into educational videogames. We have identified the smallest set of game design elements required to build an educational videogame out of a learning play. We have used the self-determination theory to group game design elements, and to define a breakdown structure for engagement engineering. This structure helps select the appropriate design elements for an engagement driver. We have applied the framework to digitize a learning play. We have tested the digital play with 238 schoolchildren who considered it as a video game. The video game tested a proposed pattern to create challenges allowing an engaging flow experience. The pattern increased responses (9%) and created time distortion (24%). Delivering rewards following variable schedules reduced errors (49%) and increased time distortion (16%). This research explores how to digitize learning plays into engaging educational video games and how to design engaging video games to remediate missed learning.
Volume: 14
Issue: 5
Page: 5443-5457
Publish at: 2024-10-01

Comparison of proposed electricity billing mechanism for residential clients of Maharashtra

10.11591/ijece.v14i5.pp4815-4826
Archana Talhar Belge , Sanjay Bodkhe , Sujata Alegavi , Arpit Ravankar , Hemant Kasturiwale , Alok Ranjan
The comparison of three modified electricity billing mechanisms (model I, model II, and model III) for low tension (LT-I) residential consumers of Maharashtra, India, is presented in this paper. Models I and II are presented in detail along with the results in the previous version of this paper in the year 2020 and year 2022. In continuation of this work, model III is presented in this paper in the year 2023. The main components of this mechanism are traditional billing, time-of-day billing, and an optional facility to use renewable energy by implementing net metering. The combination of these elements generates three distinct billing mechanisms. These have several advantages, like the profits of the existing mechanism, renewable integration, grid stability, demand management, cost saving, environmental benefits, and customer empowerment. The projected billing mechanism is developed and implemented in MATLAB software, and a real-time application is created. The comparison between these three mechanisms helps in giving the best mechanism with respect to residential consumers. Lastly, the philosophy for the future extension to this work is presented which is based on the concept of overseas billing mechanism i.e., seasonal time of day tariff of Sacramento Municipal Utility District and Arizona Public Service.
Volume: 14
Issue: 5
Page: 4815-4826
Publish at: 2024-10-01

Comparative evaluation of centralized and decentralized solar street lighting systems

10.11591/ijece.v14i5.pp4869-4878
Kenzie Joviancent , Levin Halim , Christian Fredy Naa
Inadequate lighting can hinder outdoor activities such as traffic or pedestrian access. Solar street lighting system is planned to provide sufficient lighting for roads lacking proper illumination. DIALux software uses simulation to determine the lamp power and pole specifications, followed by applying formulas to establish component specifications. In this research, a performance comparison based on voltage drop and power losses will be conducted for solar street lighting systems with decentralized and centralized systems. With a road length of 130 meters and a width of 5 meters, simulations were performed for each variable of lamps (10, 12, 19, and 30 Watt). The calculations show that 4 streetlights are needed, and simulation results indicated that the most suitable lamp power is 12 Watt. The analysis showed that the centralized and decentralized designs do not have voltage drops exceeding the applicable limit. However, the centralized design has higher power losses amounting to 3.68 Watt. Another advantage of the decentralized design is its independence, with each load powered by a separate solar panel, while the centralized design is vulnerable to the overall system. In conclusion, the decentralized design is more suitable for implementation after comparing the centralized and decentralized designs based on the voltage drop and power losses.
Volume: 14
Issue: 5
Page: 4869-4878
Publish at: 2024-10-01

A novel AI-AVO approach for maximum power generation of PMSG

10.11591/ijeecs.v36.i1.pp99-114
Prashant Kumar S. Chinamalli , Mungamuri Sasikala
Permanent magnet synchronous generators (PMSGs) are necessary for producing wind energy that is both highly reliable and reasonably priced. An inventive control technique for the driven interior PMSG (IPMSG) is presented here to maximize wind energy output and decrease losses. This research established an innovative optimization strategy for the highest wind power generation with reduced overall loss in PMSG-based Wind power generation systems. Considering, that the tip speed ratio (TPR), rotor speed 𝜔𝑟 , and quadrature axis current 𝐼𝑞 are optimized in the proposed work in such a way to enhance wind power generation. Further, the direct axis current 𝐼𝑑 is calculated from the optimized rotor speed 𝜔𝑟. The minimization of core loss is considered as the fitness function, which is a function of the direct current axis 𝐼𝑑and quadrature current axis 𝐼𝑞. The optimization is carried out using the explored aquila with African vulture optimization (EA-AVO) technique, which is the conceptual incorporation of prevailing techniques, like the aquila optimization algorithm (AOA) and the AVO algorithm. The performance of the proposed method is validated over the conventional methods, in terms of power output, losses, efficiency, and convergence analysis. According, the findings show that the proposed method attains less overall loss of 149.62 at the starting stage of 50 rotor speed, and it was 36.46% higher than AQO, 36.17% higher than AVOA, 36.59% higher than GOA methods 36.42%, and higher than WHO+PI approaches.
Volume: 36
Issue: 1
Page: 99-114
Publish at: 2024-10-01

A cost-effective, reliable and accurate framework for multiple-target tracking by detection approach using deep neural network

10.11591/ijece.v14i5.pp5681-5690
Divyaprabha Divyaprabha , Guruprsad Seebaiah
Over the years the area of object tracking and detection has emerged and become ubiquitous owing to its potential contribution towards video surveillance applications. Multiple object tracking (MOT) estimates the trajectory of several objects of interest simultaneously over time in a series of video frames. Even though various research proposals have encouraged the use of machine learning techniques in designing multi-object trackers, the existing solutions need to be more practicable for online tracking due to more complicated algorithms, The study, therefore, introduces a cost-effective tracking solution for multiple–target tracking by detection where it incorporates the you only look once version 4 (YOLOv4) and person re-identification network, which are further integrated with the proposed tracking model, which considers both bounding box and appearance features to handle the motion prediction and data association respectively. The novelty of this approach lies in considering appearance features, which not only help predict tracks through allocations problem solving but also handle the cost of computation problems. Here, the system utilizes a pre-trained association metric with which the occlusion challenges are also handled, whereas the target tracking has taken place even in more extended periods of occlusion, making it suitable with the existing efficient tracking algorithms.
Volume: 14
Issue: 5
Page: 5681-5690
Publish at: 2024-10-01
1 2 3
...
2097 2098
Show 433 of 2098

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