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28,428 Article Results

Deep learning-based stacking ensemble for malaria parasite classification in blood smear images

10.11591/ijeecs.v40.i1.pp508-517
Komal Kumar Napa , Kalyan Kumar Angati , Senthil Murugan Janakiraman , Balamurugan Amoor Gopikrishnan , Bindu Kolappa Pillai Vijayammal , Vattikuti Charan Sri Manikanta Sai
Malaria remains a significant global health challenge, necessitating accurate and efficient diagnostic tools. Deep learning models have emerged as promising solutions for automated malaria detection using microscopic blood smear images. This study evaluates the performance of various convolutional neural network (CNN) architectures, including VGG16, ResNet50, MobileNetV2, and EfficientNet, in classifying infected and uninfected cells. Individual model performances were assessed based on accuracy, precision, recall, and F1-score, with EfficientNet achieving the highest standalone accuracy of 88.0%. To enhance classification performance, a stacking ensemble approach was implemented, using a logistic regression meta-classifier to integrate outputs from multiple models for improved decision-making. The stacking model outperformed individual networks, achieving an accuracy of 89.4%, with precision, recall, and F1- scores surpassing those of standalone models. Challenges in malaria parasite classification—such as high inter-class similarity, variations in staining quality, and class imbalance were addressed through data augmentation and model tuning. These findings highlight the potential of ensemble learning in medical image analysis, paving the way for more accurate and scalable malaria detection systems.
Volume: 40
Issue: 1
Page: 508-517
Publish at: 2025-10-01

Impact of criticality analysis on the operational availability of Scooptrams LH203 in the Huarochirí Mining Industry

10.11591/ijeecs.v40.i1.pp118-126
José Castro Puma , Jorge Augusto Sánchez Ayte , Margarita Murillo Manrique , Jacinto Joaquín Vertiz Osores , Richard Flores Caceres
This study addresses the decline in the operational availability of low-profile loading equipment used in underground mining, a challenge primarily attributed to shortcomings in the implementation of preventive maintenance strategies. The main objective is to propose a preventive maintenance model based on criticality analysis aimed at improving the availability and operational efficiency of such equipment. Adopting a quantitative approach with a non-experimental, cross-sectional design, the research applies a descriptive method to assess the impact of the maintenance plan on equipment availability, using operational and maintenance data collection and analysis. The results reveal a significant increase in equipment availability from 79.20% to 92.57% following the implementation of the model. This highlights the relevance of maintenance strategies grounded in criticality analysis and real-time monitoring technologies. The findings underscore the success of the proposed model in enhancing both availability and operational efficiency, and demonstrate its potential for replication in other mining sectors to promote safer and more efficient operations.
Volume: 40
Issue: 1
Page: 118-126
Publish at: 2025-10-01

Phishing URL prediction – two-phase model using logistic regression and finite state automata

10.11591/ijeecs.v40.i1.pp356-365
Nisha T N , Dhanya Pramod
The human factor in security is more important when they become the carriers of attacks on enterprises. Phishing attacks can be classified as insider attacks when the employees unintentionally participate in the attack propagation. Since complete user training is a myth, enterprises must implement detection tools for phishing attacks on their network perimeters. This research discusses a two-phase model for phishing URL detection, in which the first phase identifies the properties of URLs that detect phishing and their relative weight using logistic regression. The second phase checks the probability of a new URL being categorized as phishing using the knowledge achieved during the first phase using the dynamically created Finite state machines. The model defines a malicious score (MS), which can be used to check any URL in real-time to identify whether it is phishing or not. The model described in this work has been experimented with different benchmarking datasets to verify the performance. The model provided a decent result in classifying a URL as phishing or naive. The malicious score (MS) defined by this model can be used to evaluate any URL and can be used as a filtering mechanism for end-point phishing URL detection. The key contribution is towards developing a two-phase model which evaluates the URL with the help of self-crafted features without reliance on a feature set. This accommodates the model's hyper-competitive phishing URL detection area in cyber security.
Volume: 40
Issue: 1
Page: 356-365
Publish at: 2025-10-01

Machine identification codes of color laser printers: revisiting privacy and security

10.11591/ijeecs.v40.i1.pp137-145
Shreya Arora , Rajendra Kumar Sarin , Pooja Puri
Forging legal documents has been easier and faster with the advancement of technology. Printer identification has become a critical field for tracing criminals and validating the authenticity of documents. The current study uses a non-destructive method to detect and identify covert embedded hidden information (machine identification codes (MIC)). Samples were collected from popular brands, including Xerox and HP color laser printers, to attain this aim. Their printouts were then scanned at 600 dpi using a Konica Minolta scanner. Scanned images were subjected to graphic editors for linear and non-linear adjustments. Following this, yellow-toner dots were observed as a base pattern. Grayscale imaging with a computational approach to analyze the yellow dot patterns was utilized for intensity-focused analysis, with edge detection algorithms applied using Python to enhance and highlight the converted patterns in printed documents. The printouts from Xerox printers exhibited repeating patterns. However, no such detailed information was observed in prints from HP printers, even when analyzed using binary code for deductions. A notable variation was detected in the yellow tracking dots among both brands, which can be instrumental in identifying the origin of printouts and scanned images for forensic investigations. This methodology provides conclusive and dependable accuracy.
Volume: 40
Issue: 1
Page: 137-145
Publish at: 2025-10-01

A multi-path routing protocol for IoT-based sensor networks

10.11591/ijeecs.v40.i1.pp225-235
Udaya Suriya Rajkumar Dhamodharan , Krishna Prasad Karani , Saranya Pichandi , Kavitha Palani , Sathiyaraj Rajendran
Internet of things (IoT) based sensors are to link a big number of low-cost and power-integrated devices in a reliable manner. Numerous military and adventurous applications are regulated by communication among IoT sensors. The multi-path routing protocol (MRP) approach presented in this research to enhance secure routing in IoT sensors is significant. This technique makes use of data transfer routing and the relationships between network components. It finds the most efficient route between the nodes that minimizes communication overhead and is both reliable and economical in terms of shortest duration. The particle swarm optimization (PSO) technique is used to find the shortest path that is most cost-effective. To reach the target node, end-to-end data transmission must transit via intermediary nodes, which are provided by the routing path node history. The optimal path is chosen by MRP from PSO, and it traces the path to identify the intermediate nodes. In the unlikely event of a crisis, MRP offers the most affordable backup route for data transfer. When compared to earlier techniques, the outcomes of these current approaches enhance network efficiency, balance energy consumption among nodes, and routing attacks.
Volume: 40
Issue: 1
Page: 225-235
Publish at: 2025-10-01

Distributed formation control with obstacle and collision avoidance for humanoid robot

10.11591/ijeecs.v40.i1.pp108-117
Faisal Wahab , Bambang Riyanto Trilaksnono
Formation control has become a popular research topic in recent years. A common challenge in formation control is ensuring that robots can avoid obstacles and maintain a safe distance from one another to prevent collisions while forming a formation. In this research, a distributed formation control approach for a multi-robot system (MRS) with obstacle and collision avoidance is presented. The distributed formation control architecture is based on a consensus algorithm and consists of four layers: consensus tracking, consensus-based formation control, behavior, and physical robot layers. The system was implemented and evaluated through both simulations and experiments. Humanoid robots were used as the platform for these implementations. The result of the simulations and experiments show that the distributed formation control system successfully guided the robots into desired formation while also avoiding obstacles and preventing collisions with other robots.
Volume: 40
Issue: 1
Page: 108-117
Publish at: 2025-10-01

Gender identification from tribal speech using several learning techniques

10.11591/ijeecs.v40.i1.pp316-326
Subrat Kumar Nayak , Kumar Surjeet Chaudhury , Nirmal Keshari Swain , Yugandhar Manchala , Ajit Kumar Nayak , Smitaprava Mishra , Nrusingha Tripathy
Language processing and linguistics researchers are interested in gender identification through audio, as human voices have many distinctive features. Although several gender identification algorithms have been developed, the accuracy and efficiency of the system can still be improved. Despite extensive studies on the topic in various languages, there aren’t many studies on gender identification in the KUI language. Using a variety of machine learning (ML) and deep learning (DL) classifiers, including decision tree (DT), multilayer perceptron (MLP), gradient boosting (GB), linear discriminant analysis (LDA), recurrent neural networks (RNN), long short-term memory (LSTM), gated recurrent units (GRU), and transformer, the goal of this study is to assess the accuracy of gender identification among diverse KUI language speakers. To verify the effectiveness of the suggested model, several prediction evaluation metrics were calculated, such as the area under the receiver operating characteristic curve (AUC), F1-score, precision, accuracy, and recall. While the findings are compared to other learning models, the gradient-boosting strategy yielded better results with an accuracy rate of 97.0%.
Volume: 40
Issue: 1
Page: 316-326
Publish at: 2025-10-01

Design of high-efficiency microinverter for a photovoltaic system with low harmonic distortion

10.11591/ijeecs.v40.i1.pp67-77
Walter Naranjo Lourido , Jhon Manuel Sanchez Fierro , Diana Paola Monroy Cadena , Javier Eduardo Martínez Baquero
This article presents the design of a modular pure sine wave microinverter with a high-efficiency maximum power point tracking (MPPT) regulator for photovoltaic (PV) systems. The design starts with a DC/DC buck-boost chopper regulator, simulated using the perturb and observe (P&O) algorithm. Next, a high-frequency DC/AC conversion stage is implemented using a toroidal transformer to achieve various voltage levels and isolated power sources. Finally, a 27-level multilevel inverter is designed to produce a pure sine wave with minimal total harmonic distortion (THD). Simulation results indicate that the microinverter achieves a total efficiency of 90% and produces a pure wave output with 3% harmonic distortion. Compared to commercial solutions, the proposed design enhances efficiency while integrating key components. Additionally, the system maintains a cost-effectiveness and directly proportional to its energy efficiency, making it a viable and cost-effective solution for PV energy conversion.
Volume: 40
Issue: 1
Page: 67-77
Publish at: 2025-10-01

User acceptance model questionnaire generator for information system applications

10.11591/ijeecs.v40.i1.pp297-306
Theda Flare G. Quilala , Rogel Ladia Quilala
To foresee users’ behavior, the technology acceptance model (TAM) and theories applied in different fields were evaluated to understand factors influencing IT adoption. This study analyzed TAMs used in various IT fields where information systems are being adopted and created an application to generate a user acceptance questionnaire for user acceptance studies. These are based on key variables targeted by acceptance models that have already proven effective. In developing the software, questions relating to each attribute are compiled into one database, tagging each attribute to the model so users can select and view which question corresponds to that attribute. Sample questions can now be generated and exported to a word processor. The user acceptance questionnaire generator was successfully developed based on variables and attributes from famous TAMs. The software also passed the test results conducted for every functional requirement. Considering the abovementioned observations, reluctance to embrace and utilize information systems may be minimized by doing a user acceptance study utilizing the questionnaires exported from the generator. For improvement, other researchers may integrate more acceptance models and other variables not covered and create a web app version for broader reach.
Volume: 40
Issue: 1
Page: 297-306
Publish at: 2025-10-01

A feeling classification model in a blood draw situation using power spectrum density and a random forest algorithm

10.11591/ijeecs.v40.i1.pp346-355
Rawinan Praditsangthong , Ekapong Nopawong
Feelings and expressions such as pain, anxiety, and excitement can occur while getting blood drawn. These are the physical symptoms that can occur in some patients. A medical provider cannot know pain or anxiety symptoms, which could cause harm to the patients throughout the procedure. However, electroencephalography (EEG) changes, such as Delta, Theta, Alpha, Beta, and Gamma, are essential to identify the patient’s feelings. These can assist in decreasing danger during the procedures. Therefore, this research aims to investigate the patterns in the power spectrum density (PSD) form to classify two feeling states during blood drawing: normal and anxious feelings. This research focused on alpha, beta, and gamma of the PSD. Thus, a method was designed based on the changing values of alpha, beta, and gamma. Each PSD of three waves was derived at 56 minutes. The pattern from this dataset was applied to classify feeling expressions using a random forest (RF) algorithm. This algorithm was used to create a feeling classification model (FCM). The accuracy of the FCM in classifying feeling differences between normal and anxious feelings was 100%. Thus, this proves that the FCM is highly efficient.
Volume: 40
Issue: 1
Page: 346-355
Publish at: 2025-10-01

Diagnosis of ecosystem misconceptions for high school students in Jakarta

10.11591/ijere.v14i5.28126
Eka Putri Azrai , Muhammad Japar , Robinson Situmorang
Misconception is a condition of different concepts that are owned by scientific concepts. Misconceptions impact learning processes and outcomes, so teachers need to make reductions. The first step to reduce misconceptions is to find the data on students’ misconceptions. This study aims to diagnose high school students’ misconceptions about ecosystems. The survey method research used a three-level multiple-choice test to diagnose ecosystem misconceptions. The research sample were 200 high school students from five high schools in Jakarta, Indonesia. The sample from each school were 40 students. The results showed that students’ understanding of concepts was spread over six levels: understanding concepts, false positive misconceptions, false negative misconceptions, misconceptions, guessing or understanding concepts but lacking confidence, and not understanding concepts with a misconception percentage of 21.41%. Based on the analysis of the ecosystem sub-concept, the highest misconception occurred in the energy flow sub-concept (25.39%) and the second highest in the biogeochemical cycle sub-concept (20.41%). Teachers can use the findings as a basis for designing effective learning to reduce misconceptions so that optimal learning processes and results can be achieved.
Volume: 14
Issue: 5
Page: 3790-3800
Publish at: 2025-10-01

Job performance of human resource management graduates from the employers’ and graduates’ perspectives

10.11591/ijere.v14i5.31959
Dahlee Sadang-Pascua , Jennifer Montenegro-Villanueva
Graduates’ job performance reflects their academic orientation in pursuit of their degrees. Thus, academic institutions should prepare students to be competitive, match the needs of the industry, and become worthy of employment after graduation. This research determines the job performance of human resource management (HRM) graduates in terms of their job competencies, career skills, and team performance from the perception of the graduates and their employers. A quantitative research method with statistical tools such as frequency, percentage, weighted mean, and Mann-Whitney U Test was used. Findings revealed a significant difference in the respondents’ perception, specifically in conveying ideas, use of IT, values, quality work, communication skills, human relations, technical, research, leadership skills, and team performance. The result also shows that graduates perceived themselves as excellent performers, which is in contrast to their employers’ perceptions of them as good performers only regarding their job competencies, career skills, and team performance. The differences in perceptions of the performance of the graduates depicts a mismatch between the academe and the industry requirements that result in a recommendation of thorough review and revision of the HRM curriculum, the teaching methodology, and the strategy of the academic institutions to meet the needs of the industry.
Volume: 14
Issue: 5
Page: 3756-3764
Publish at: 2025-10-01

School innovation climate as a driver of teachers’ innovative work behavior: the mediating role of self-efficacy

10.11591/ijere.v14i5.32757
Safiek Mokhlis , Abdul Hakim Abdullah
Teachers’ innovative work behavior (IWB) is widely recognized as a driving force behind educational improvement in the complex and demanding conditions of the 21st century. Among a wide range of factors that could affect IWB, innovation climate (IC) has emerged as a crucial determinant. However, research exploring the mechanism that mediate the link between IC and IWB is still limited. Drawing upon social cognitive theory (SCT), the present study proposes that teachers’ self-efficacy (SE) acts as a mediator in the relationship between IC and IWB. The study involved 376 teachers at 12 public schools in Kuala Terengganu, Malaysia, who were determined based on a stratified random sampling technique. Analysis of data was implemented through the use of structural equation modeling (SEM) with AMOS software to test causal relationships. Results confirmed that schools’ IC was positively correlated with IWB and that this relationship was partially mediated by teachers’ SE. These results align with SCT, which emphasizes the interaction between individual behavior, environment (IC), and personal factors (SE). To cultivate a culture of innovation and improve educational outcomes, school leaders should actively foster an IC that enhances teachers’ SE, thereby promoting their IWB.
Volume: 14
Issue: 5
Page: 3735-3743
Publish at: 2025-10-01

Empowering professional learning communities: the role of middle leadership and teacher participation in decision-making

10.11591/ijere.v14i5.34392
Sock Beei Yeap , Nurul Jawahir Md Ali
Professional learning communities (PLCs) are essential for fostering collaboration and continuous school improvement. However, their implementation faces significant challenges, including passive teacher attitudes, limited understanding of PLCs, increased workloads, and ineffective execution by school communities. Addressing these challenges requires a supportive organizational structure that enhances PLCs effectiveness. This study aims to examine the effect of middle leadership (MLT) on PLCs. Specifically, this study also examines the mediating effect of teacher participation in decision-making (TM) on the relationship between MLT and PLCs. Data were collected from 284 secondary school teachers in Penang and analyzed using partial least squares structural equation modeling (PLS-SEM). The findings revealed that MLT had a significant effect on PLCs, and TM mediates the relationship between MLT and PLCs. This study provides valuable insights for policymakers and school leaders on fostering a collaborative school culture through effective MLT practices, ultimately strengthening PLCs implementation in Malaysian secondary schools. Furthermore, these findings underscore the significance of empowering teachers participation in decision-making to enhance PLCs effectiveness.
Volume: 14
Issue: 5
Page: 3461-3468
Publish at: 2025-10-01

Efficient fall detection using lightweight network to enhance smart internet of things

10.11591/ijece.v15i5.pp5031-5044
Pinrolinvic D. K. Manembu , Jane Ivonne Litouw , Feisy Diane Kambey , Abdul Haris Junus Ontowirjo , Vecky Canisius Poekoel , Muhamad Dwisnanto Putro
Fall detection automatically recognizes human falls, mainly to monitor and prevent severe injury and potential fatalities. It can be developed by applying deep learning methods to recognize human subjects during fall incidents and implemented in the internet of things (IoT) to monitor patient and elderly individuals’ activity. The development of object detection presents you only look once v8 (YOLOv8) as an influential network, but its efficiency needs to be improved. A modified YOLOv8 architecture is proposed to introduce a novel lightweight network version called YOLOv8-Hypernano (YOLOv8h) that recognizes fall events. The backbone incorporates a combined spatial and channel attention module, which enhances focus on human subjects by concentrating on movement patterns to detect falls more accurately. This work also offers a consecutive selective enhancement (CSE) module to improve efficiency and effectiveness in feature extraction while reducing computational costs. The neck structure is modified by adding a lightweight bottleneck network. The proposed network reconstructs feature maps in depth, paying more attention to accurate human movement patterns and enhancing efficiency and effectiveness in feature extraction. Experimental results of YOLOv8h with the light bottleneck and consecutive selective enhancement modules show giga floating-point operations per seconds (GFLOPS) of 5.6 and 1,194,440 parameters. The model performance is calculated in mean average precision, achieving 0.603 and 0.732 on the Le2i and Fallen datasets, respectively. These results demonstrate that the optimized network improves accuracy performance while maintaining lightweight computing requirements that can run smoothly on IoT devices, achieving comparable speed and efficiency suitable for operation on low-cost computing devices.
Volume: 15
Issue: 5
Page: 5031-5044
Publish at: 2025-10-01
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