Articles

Novel five-patch compact microstrip Yagi-alike antenna for Ka-band applications

10.11591/ijeecs.v37.i2.pp878-887

Raj Kumar Singh , Kumari Mamta , Navin Kumar Sinha , Vinay Kumar Choudhary

This paper discusses the process of designing and fabricating a novel compact microstrip patch Yagi-like antenna having five-patch radiating element at operating frequency 31 GHz with a bandwidth of 1 GHz. The developed design aims to optimize the antenna performance. The overall dimension of the antenna being 17× 14 × 0.8 mm3, based on RT Duroid 5880 substrate having dielectric loss tangent of 0.0009 and relative permittivity 2.2. The effectiveness of the performance of proposed design was evaluated using the electromagnetic solver Ansoft high-frequency structure simulator (HFSS) and validated by the laboratory measurements on the antenna prototype. The measured results are consistent with the simulation prediction. The designed antenna achieved directional radiation and the performances with voltage standing wave ratio (VSWR) < 1.32, return loss -17 dB and gain of 6 dBi. The measured results are compared with those existing in literature. The proposed antenna design has proven very effective in terms of the intended design and parameters which make it suitable for satellite application and wireless communication.

Volume: 37

Issue: 2

Page: 878-887

Publish at: 2025-02-01

A novel secure and energy aware LOADng routing protocol for IoT: an application to smart agriculture

10.11591/ijeecs.v37.i2.pp1005-1013

Touhami Sana , Belghachi Mohamed

In the burgeoning domain of the internet of things (IoT), efficient and secure communication protocols are crucial for the seamless operation of diverse applications. This paper proposes a novel routing protocol, termed secure and energy aware LOADng (SEA-LOADng), tailored for IoT deployments in the context of smart agriculture. The protocol is designed to address the unique challenges posed by agricultural environments, including limited energy resources and the need for robust security measures. The proposed protocol leverages LOADng, a lightweight and efficient routing protocol suitable for low-power and lossy networks characteristic of IoT deployments. Through innovative energy-aware mechanisms, it optimizes the power usage of IoT devices, thus prolonging their operational lifespan and reducing maintenance overhead. Moreover, stringent security measures are integrated into the protocol to safeguard sensitive data transmitted within the IoT network. To assess the efficacy of the proposed protocol, comprehensive simulations are carried out using realistic smart agriculture scenarios. The results demonstrate significant improvements in energy efficiency compared to LOADng protocol, while maintaining robust security against hello flood attack.

Volume: 37

Issue: 2

Page: 1005-1013

Publish at: 2025-02-01

Analysis of LLC resonant converter performance with PIDD2 controller for electric vehicle application

10.11591/ijeecs.v37.i2.pp749-757

Sathya K. , K. P. Guruswamy

The key uses of the latest developments is electric vehicles (EV’s). As a result, several researchers were drawn to EV’s control to propose appropriate controllers and predicted that control engineers face a challenge when it comes to regulating the LLC resonant converter output voltage. In this regard, the study proposes a PID Type modified controller for regulation of voltage across output in LLC resonant converter. The design and control procedure of this modified proportional integral derivative double derivative (PIDD2) is explained along with EDF modeling in LLC resonant converter. This work proposes to use two controllers to drive the voltage output of a resonant converter LLC to constantly track the desired value. Proportional integral derivative controller (PID) is the first, while the PIDD2 method is the foundation of the second. Every controller has undergone simulation testing and the results are compared based on how the evaluated controllers respond dynamically in accordance with settling time, rising time and overshoot.

Volume: 37

Issue: 2

Page: 749-757

Publish at: 2025-02-01

Multivariate analysis: geography, demographics, and Texas’ post-COVID education

10.11591/ijere.v14i1.30002

Shifang Tang , Zhuoying Wang , Lei Zhang , David D. Jimenez

This study examines the impact of geographic locale on educational outcomes in Texas, focusing on the post-COVID-19 educational landscape. The study evaluates the impact of geographical location on the educational outcomes of eighth-grade students by analyzing STAAR test scores as indicators of academic achievement while adjusting for previous academic results and demographic factors. A sample of 1,145 public school districts across Texas was analyzed, encompassing city, suburban, town, and rural settings. The findings indicate that while geographic locale has a discernible impact on academic achievement, this effect is moderate and intertwined with demographic factors. The research found that rural students unexpectedly outperformed their urban counterpart’s post-pandemic, controlling for their pre-pandemic performance. However, the persistent lower performance in urban districts emphasizes the need to reevaluate educational dynamics. The integration of demographic variables reveals that while they mitigate the influence of geography, they emerge as strong influencers of academic performance on their own. This underscores the need for policies that address the complex interplay between geographic, demographic, and socio-economic factors to narrow educational disparities. The study suggests that targeted interventions are necessary to address the specific needs of different locales, considering the nuanced effects of the pandemic on educational equity.

Volume: 14

Issue: 1

Page: 332-340

Publish at: 2025-02-01

IT risks associated with information theft in the financial system - a systematic review

10.11591/ijeecs.v37.i2.pp1339-1351

Frank Cabanillas-Allca , Sebastian Chaquila-Muñoz , Orlando Iparraguirre-Villanueva

This research paper systematically reviews the financial system’s computer security risks associated with information theft. The objective is to explore the security risks and their implications concerning information theft in the economic system. Three research questions were formulated to identify these risks, their nature, and potential consequences to achieve this objective. Fifty-five articles obtained from reliable databases linked to both study variables were analyzed using the PRISMA methodology. To ensure the validity and reliability of the information, various filters were applied, such as year, keywords, and elimination of duplicate articles. In addition, an exhaustive reading of the content of each article was carried out, organizing all the information through a systematization matrix. After a thorough review of the research articles, mostly written in English and representing 34.55% of the total in 2023, risks associated with the financial sector were identified, including malware, ransomware, phishing, distributed denial of service (DDoS), hybrid XSS, eavesdropping, and social engineering. Geographically, India leads with 14.55% of the articles, followed by South Korea and the United States, with 12.72% each, while the other countries have lower percentages. In conclusion, these risks coincide with previous research and the consequences they generate, highlighting the importance of this type of study for the basis of scientific research.

Volume: 37

Issue: 2

Page: 1339-1351

Publish at: 2025-02-01

Identification of the satisfaction of university students through sentiment analysis: a systematic review

10.11591/ijere.v14i1.26640

Omar Freddy Chamorro-Atalaya , Florcita Aldana-Trejo , Nestor Alvarado-Bravo , Constantino Nieves-Barreto , Santiago Aguilar-Loyaga , Carlos Gamarra-Bustillos , Almintor Torres-Quiroz , Alípio Riveros-Cuéllar , Manuel Pérez-Samanamud , Luciano Pérez-Guevara

In these times it is necessary to use tools based on artificial intelligence (AI) that contribute to improving the quality of university education for the benefit of students. This article aims to define the state of the question on the application of sentiment analysis in the identification of student satisfaction, based on the systematic review of scientific publications. The research is of an exploratory level and of a mixed approach. The data collection method was based on the preferred reporting items for systematic reviews and meta-analyses (PRISMA) declaration, managing to focus the review based on 27 publications, downloaded from Scopus, ERIC, and Google Scholar. From the systematic review, the following conclusions were reached: the fields of application to a greater extent are found in the academic field and university well-being. Likewise, regarding the contributions achieved, these focused to a greater extent on aspects of the teaching activity, valuing their performance and contributing to their feedback for the redesign of didactic strategies. Finally, in terms of limitations, they focused mainly on the low student participation regarding the use of sentiment analysis to identify student satisfaction; this is due to the lack of regulations or regulations for it is application in the university environment.

Volume: 14

Issue: 1

Page: 37-49

Publish at: 2025-02-01

Detect and envision of pandemic disease exposure using CNN

10.11591/ijeecs.v37.i2.pp948-958

Ch. Rupa , Kanakam Siva Rama Prasad , Aremanda Lakshmi Rajeswari , Elika Sambasiva Rao , Mohammad Sirajuddin

COVID-19 has emerged as a pandemic, affecting millions globally with its high transmission rate, especially in colder climates. The virus's multiple mutations have made it progressively harder to detect and manage. Despite widespread awareness of preventive measures such as masks and sanitizers, early detection remains critical. Traditional methods like blood tests are time-consuming, and existing studies utilizing fuzzy K-means clustering, principal component analysis (PCA), stochastic discriminant analysis (SDA), decision trees (DT), and support vector machines (SVM) have faced limitations, including small datasets, insufficient accuracy, inadequate medical data, weak methodologies, and failure to consider primary symptoms. This work proposes a deep learning (DL) convolutional neural network (CNN) architecture utilizing CT scan images of the lungs for the rapid and accurate identification of COVID-19 infections. The approach leverages the Visual Geometry Group 16 (VGG16) model to extract significant features, such as size and color differences, from computed tomography (CT) scan images, facilitating a swift and precise diagnosis. The VGG16 model, implemented using the Keras library on top of TensorFlow, processes the preprocessed images through neural network layers to classify the images as COVID-19 positive or negative. The proposed model demonstrates a high accuracy rate of 94.12%, indicating that this method is both efficient and reliable for detecting COVID-19, offering a significant improvement over conventional diagnostic techniques and existing studies.

Volume: 37

Issue: 2

Page: 948-958

Publish at: 2025-02-01

The influence of perceive teacher support on high school students’ learning engagement in China

10.11591/ijere.v14i1.29898

Liu Yang , Lim Hooi Lian

This study explores the influence of perceived teacher support on learning engagement and the mediating role played by psychological capital of senior high school students. There were 572 Chinese senior high school students from Inner Mongolia Autonomous Region in China completed a ranger of self-report questionnaires. The results of this study showed that perceived teacher support was significantly and positively related to learning engagement of senior high school students, and positively correlated with their psychological capital which consists of four dimensions: hope, self-efficacy, optimism, and resilience. The psychological capital was significantly and positively associated with learning engagement among senior high school students and mediated the relationships between perceived teacher support and learning engagement.

Volume: 14

Issue: 1

Page: 575-582

Publish at: 2025-02-01

Compressor performance prediction: gradient boosting regression model and sensitivity analysis

10.11591/ijeecs.v37.i2.pp1201-1208

Kuo-Chien Liao , Hom-Yu Wu , Hung-Ta Wen , Jui-Tang Sung , Muhamad Hidayat , Will Wei-Juen Wang

This study introduces the use of gradient boosting regression (GBR) models to estimate the compressor performance of aero-engines. The model exhibits a mean absolute error (MAE) of 0.078, showcasing superior performance compared to previous studies. Through sensitivity analysis, optimal values for three key parameters were determined: 280 estimators, a max depth of 9, and a learning rate of 0.085. Furthermore, a comparison with a prior study revealed an impressive MAE value lower than 0.002, highlighting the GBR model’s success in accurately predicting compressor performance. This demonstrates the model’s effectiveness and predictive accuracy, making it a valuable tool for aero-engine compressor performance estimation.

Volume: 37

Issue: 2

Page: 1201-1208

Publish at: 2025-02-01

Navigating the smart contract threat landscape: a systematic review

10.11591/ijeecs.v37.i2.pp1209-1224

Unyime Ufok Ibekwe , Uche M. Mbanaso , Nwojo Agwu Nnanna , Umar Adam Ibrahim

Smart contracts have emerged as a transformative technology within the blockchain ecosystem, facilitating the automated and trustless execution of agreements. Their adoption spans diverse sectors such as education, agriculture, healthcare, government, real estate, transportation, supply chain, and global initiatives like Central Bank Digital Currencies (CBDCs). However, the security of smart contracts has become a significant concern, as vulnerabilities in their design and implementation can lead to severe consequences such as financial losses and system failures. This systematic review consolidates findings from 78 selected research articles, identifying key vulnerabilities affecting smart contracts and categorizing them into a taxonomy encompassing code-level, environment-dependent, and user-related vulnerabilities. It also examines the threats that exploit these vulnerabilities and the most effective detection techniques. The domain-based classification presented in this review aims to assist researchers, software engineers, and developers in identifying and mitigating significant security flaws related to the design, implementation, and deployment of smart contracts. A comprehensive understanding of these issues is essential for enhancing the security and reliability of the blockchain ecosystem, ultimately fostering the development of more secure and robust decentralized applications for end users.

Volume: 37

Issue: 2

Page: 1209-1224

Publish at: 2025-02-01

Implementation of augmented reality as a revolutionary approach in computer stores

10.11591/ijeecs.v37.i2.pp913-927

Dedi Gunawan , Aisyah Mutia Dawis , Ismail Setiawan , Ita Permatahati , Rahmad Ardhani , Sigit Setiyanto

The adoption of augmented reality (AR) spans various fields, from education to business. Currently, many businesses utilize AR to boost customer engagement and enhance product understanding. This research focuses on developing and examining an AR application on a Solo computer store’s website to improve customer engagement. Results indicate that AR significantly enhances the shopping experience, deepens product comprehension, and increases website interactions. Features like 3D product visualization and detailed information enable customers to make more informed purchasing decisions. A questionnaire with 25 respondents revealed a high acceptance rate of the AR application, averaging 92%. Additionally, AR was shown to increase customer engagement, potentially boosting sales by up to 35%, reducing operating costs by 20%, and enhancing productivity by 15%. The study also found differing preferences across age groups: older respondents (40-70 years) favored traditional website features without AR and were less comfortable with markerless technology, whereas younger consumers (18-39 years) were more attracted to AR-enhanced websites. These insights offer valuable guidance for the Solo computer store to craft innovative marketing strategies tailored to the diverse preferences and needs of their customers.

Volume: 37

Issue: 2

Page: 913-927

Publish at: 2025-02-01

Optimizing dynamic response and stability of pressure-controlled swash plate type axial piston pump

10.11591/ijeecs.v37.i2.pp771-780

Vivek Verma , Sachin Kumar , Apurva Anand

The main objective of the paper is to explore the role of lead-lag compensators in improving the performance of control systems for variable delivery hydraulic axial piston pumps (VAPP). These compensators offer a range of benefits, including stability enhancement, transient response optimization, frequency response modification, disturbance rejection, and robustness improvement. A mathematical model of the hydro-mechanical system is developed, and the transfer function for the dynamic system is established. The simulation of the model with lead-lag compensator significantly enhanced the phase margin to 55.70 and gain margin to 12.3 dB ensuring robust control for pressure-controlled VAPP, whereas the uncompensated system is marginally stable. The compensated system exhibits better transient and steady-state response. The optimized lead-lag compensated system achieves a maximum percentage overshoot of 12.1% and a settling time of 1.95 sec. This is a substantial improvement compared to the uncompensated system with a maximum % overshoot of 20.5% and a settling time of 2.39 sec. The improved response tends to induce greater damping (ζ) in the compensated system from 0.015 to 0.108 and increases leakage coefficient (K) from 3.38×10-12 m3/Pa.s to 24.34×10-12 m3/Pa.s. Optimized lag-lead compensator ensures stability, responsiveness adapting effectively to dynamic operating conditions of VAPP for aerospace application.

Volume: 37

Issue: 2

Page: 771-780

Publish at: 2025-02-01

Enhancing malware detection capabilities using deep learning with advanced hyperparameter tuning

10.11591/ijeecs.v37.i2.pp985-994

Walid El Mouhtadi , Yassine Maleh , Soufyane Mounir

As the threat landscape evolves with sophisticated malware and advanced persistent threats (APTs), the need for effective detection solutions increases. Traditional methods, such as signature-based and heuristic analysis, struggle to keep up with rapidly changing malicious activities. While machine learning offers a promising approach, it often falls short due to the manual extraction and selection of features, leading to time-consuming and error-prone processes. This research introduces a novel malware detection solution leveraging deep learning and focusing on portable executable (PE) file analysis to address these weaknesses. By customizing the hyperparameters of artificial neural networks (ANN), convolutional neural networks (CNN), and recurrent neural networks (RNN), the proposed approach enhances detection capabilities. The primary objective is to overcome the limitations of traditional and machine learning methods by tailoring these deep learning algorithms. The methodology includes a comparative study to demonstrate the advantages of the customized approach over conventional methods. Key findings reveal the proposed solution’s superior performance, accuracy, and adaptability in combating evolving cyber threats. This research contributes to the development of robust and adaptive malware detection solutions.

Volume: 37

Issue: 2

Page: 985-994

Publish at: 2025-02-01

High body temperature detection solution through touchless machine for health monitoring

10.11591/ijai.v14.i1.pp166-171

Siddharth Swami , Lalit Mohan Joshi , Mohammed Ismail Iqbal , Meera Sharma , Amar Jeet Rawat , Sameer Dev Sharma , Rajesh Singh

The demand for reliable health monitoring systems has surged in today's health-conscious society. Body temperature monitoring is crucial for preserving health and preventing infectious disease outbreaks. In this study an Arduino uno hardware board with a touchless temperature sensor is proposed to detect elevated body temperature, indicating fever and early signs of illness. The system prioritizes real-time health surveillance, accessibility, and usability, blending seamlessly with normal life. Arduino's versatility allows the system to function covertly, uphold privacy and autonomy, and foster wellbeing. The goal is to highlight the system's ability to function covertly, uphold privacy and autonomy, and foster wellbeing. This technology exemplifies the synergy between personal wellness and contemporary technologies, offering a useful and adaptable fever detection solution for various contexts, including homes and public areas.

Volume: 14

Issue: 1

Page: 166-171

Publish at: 2025-02-01

Human motion classification by micro-doppler radar using intelligent algorithms

10.11591/ijece.v15i1.pp455-466

Andres Felipe Arias Ballen , Edith Paola Estupiñan Cuesta , Juan Carlos Martinez Quintero

This article introduces a technique for detecting four human movements using micro-doppler radar and intelligent algorithms. Micro-doppler radar exhibits the capability to detect and measure object movements with intricate detail, even capturing complex or non-rigid motions, while accurately identifying direction, velocity, and motion patterns. The application of intelligent algorithms enhances detection efficiency and reduces false alarms by discerning subtle movement patterns, thereby facilitating more accurate detection and a deeper understanding of observed object dynamics. A continuous wave radar setup was implemented utilizing a spectrum analyzer and radio frequency (RF) generator capturing signals in a spectrogram centered at 2,395 MHz. Six models were assessed for image classification: VGG-16, VGG-19, MobileNet, MobileNet V2, Xception, and Inception V3. A dataset comprising 500 images depicting four movements-running, walking, arm raising, and jumping-was curated. Our findings reveal that the most optimal architecture in terms of training time, accuracy, and loss is VGG-16, achieving an accuracy of 96%. Furthermore, precision values of 96%, 100%, and 98% were obtained for the movements of walking, running, and arm raising, respectively. Notably, VGG-16 exhibited a training loss of 4.191E-04, attributed to the utilization of the Adam optimizer with a learning rate of 0.001 over 15 epochs and a batch size of 32.

Volume: 15

Issue: 1

Page: 455-466

Publish at: 2025-02-01

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31,456 Article Results

Novel five-patch compact microstrip Yagi-alike antenna for Ka-band applications

A novel secure and energy aware LOADng routing protocol for IoT: an application to smart agriculture

Analysis of LLC resonant converter performance with PIDD2 controller for electric vehicle application

Multivariate analysis: geography, demographics, and Texas’ post-COVID education

IT risks associated with information theft in the financial system - a systematic review

Identification of the satisfaction of university students through sentiment analysis: a systematic review

Detect and envision of pandemic disease exposure using CNN

The influence of perceive teacher support on high school students’ learning engagement in China

Compressor performance prediction: gradient boosting regression model and sensitivity analysis

Navigating the smart contract threat landscape: a systematic review

Implementation of augmented reality as a revolutionary approach in computer stores

Optimizing dynamic response and stability of pressure-controlled swash plate type axial piston pump

Enhancing malware detection capabilities using deep learning with advanced hyperparameter tuning

High body temperature detection solution through touchless machine for health monitoring

Human motion classification by micro-doppler radar using intelligent algorithms

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