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31,456 Article Results

Enhancement of YOLOv5 for automatic weed detection through backbone optimization

10.11591/ijai.v14.i1.pp658-666
Mohammed Habib , Salma Sekhra , Adil Tannouche , Youssef Ounejjar
In the context of our research project, which involves developing a robotic system capable of eliminating weeds using deep learning technics, the selection of powerful object detection model is essential. Object detectors typically consist of three components: backbone, neck, and prediction head. In this study, we propose an enhancement to the you only look once version 5 (YOLOv5) network by using the most popular convolutional neural networks (CNN) networks (such as DarkNet and MobileNet) as backbones. The objective of this study is to identify the best backbone that can improve YOLOv5 's performance while preserving its other layers (neck and head). In terms of detecting and ultra-localizing pea crops. Additionally, we compared their results with those of the most commonly used object detectors. Our findings indicate that the fastest models among the networks studied were MobileNet, YOLO-tiny, and YOLOv5, with speeds ranging from 5 to 14 milliseconds per image. Among these models, MobileNetv1 demonstrated the highest accuracy, achieving average precision (AP) score of 89.3% for intersection over union (IoU) threshold of 0.5. However, the accuracy of this model decreased when we increased the threshold, suggesting that it does not provide perfect crop delineation. On the other hand, while YOLOv5 had a lower AP score than MobileNetv1 at an IoU threshold of 0.5, it exhibited greater stability when faced with variations in this threshold.
Volume: 14
Issue: 1
Page: 658-666
Publish at: 2025-02-01

The crucial role of artificial intelligence in addressing climate change

10.11591/ijai.v14.i1.pp1-11
Laberiano Andrade-Arenas , Domingo Hernández Celis , Cesar Yactayo-Arias
Addressing climate change is one of the fundamental priorities at a global level, given its significant impact on both the environment and society. This systematic literature review explores the role of artificial intelligence (AI) in addressing climate change. It identified applications, contributions to predicting extreme events, techniques used, ethical challenges, and associated biases. The rapid systematic literature review (RSL) was conducted using databases such as Scopus, Dimensions, directory of open access journals (DOAJ), and IEEE Xplore. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement was used to ensure the completeness and transparency of the analysis. 40 articles were selected that were published between 2018 and 2023 and addressed AI in climate change. The findings show that AI is being used to predict and mitigate extreme climate events, estimate the greenhouse effect, and predict temperatures. In addition, innovative techniques such as hybrid machine learning models, convolutional neural networks, artificial neural networks, support vector machines, and logistic regression. In conclusion, AI offers a promising approach to addressing climate change, with transformative potential in predicting and mitigating its effects. However, continuous ethical considerations are required to guarantee its conscientious and efficient utilization.
Volume: 14
Issue: 1
Page: 1-11
Publish at: 2025-02-01

Psychometric properties of emotional intelligent scale: the application for university students in Indonesia

10.11591/ijere.v14i1.28599
Amin Akbar , Zulakbal Abd Karim , Jaffry Zakaria , Suryanef Suryanef , Muh Khairul Wajedi Imami , Utami Syahdiah , Hilwa Alfiani Fitri
The current research aims to assess the psychometric properties of the emotional intelligence scale among university students in Indonesia. This research used a survey design. The current research participants were 288 university students in Palembang, West Nusa Tenggara, Kupang, Yogyakarta, and Jakarta, Indonesia. Two procedures were used to analyze the data: exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). The results showed that EFA with comprehensive techniques that compromise parallel analysis yielded a 5-factor solution: empathy with five items, handling relationships with five items, motivation for oneself with six items, self-awareness with seven items, and managing emotion with eight items. The five factors solution was confirmed through CFA with the value: Chi square=2.631, Tucker-Lewis’s index (TLI)=.804, comparative fit index (CFI)=.823, root mean square error of approximation (RMSEA)=.075, and standardized root mean residual (SRMR)=.743. The scale validation and reliability were tested through average variance extracted (AVE) with the value ranged from 0.271 to 0.594, construct reliability (CR) with the value ranged from 0.706 to 0.879, and Cronbach’s alpha with the value ranged from 0.706 to 0.879. Therefore, based on psychometric analysis, the scale is valid and reliable to be used in measuring emotional intelligence among university students in Indonesia.
Volume: 14
Issue: 1
Page: 157-170
Publish at: 2025-02-01

Performance analysis of a neuromodel for breast histopathology decision support system

10.11591/ijai.v14.i1.pp102-108
Adedayo Olukayode Ojo , Adetoro Mayowa Sola , Florence Omolara Ojo , Moses Onibonoje Oluwafemi
Breast cancer detection and diagnosis are crucial in reducing mortality rates among women globally. This research article explores an artificial intelligence technique for early breast cancer detection, aiding doctors in making informed decisions for improved patient management. The study employs histopathological analysis of breast tissue microscopically to detect abnormalities, with the aim of categorizing normal tissue, benign lesions, in situ carcinoma, and invasive carcinoma. The proposed technique utilizes an artificial neural network trained using the resilient backpropagation algorithm (RP_ANN). The study further compares the observed performance with those of three other algorithms, including gradient descent algorithm (GDA_ANN), Levenberg-Marquardt algorithm (LM_ANN), and layer sensitivity-based (LSB_ANN) algorithm based on various evaluation metrics. RP_ANN and LSB_ANN demonstrated superior performance, with high validation and training variance accounted for (VAF) and low root mean squared error (RMSE). The results underscore the potential of deep learning-based algorithms for improving breast cancer detection, promising better patient outcomes and enhanced diagnostic accuracy.
Volume: 14
Issue: 1
Page: 102-108
Publish at: 2025-02-01

Exploring teacher perceptions, commitment, and beliefs in STEM education: a systematic literature review analysis

10.11591/ijere.v14i1.30608
Sariati Talib , Bity Salwana Alias , Mohd Effendi Ewan Mohd Matore
In recent times, science, technology, engineering and mathematics (STEM) education has gained interest in preparing students to face the challenges of the modern world. STEM fields are important for nurturing and shaping a wide range of skills in society. Therefore, to ensure students acquire the necessary skills and knowledge, STEM education has become a priority in education systems around the world, with teachers playing an important role in ensuring the success of STEM education. Therefore, teachers’ perceptions, commitments and beliefs need to be explored to gain understanding, needs and challenges for teachers to implement STEM education. This study analyzed past research findings through the systematic literature review (SLR) method with the findings of 28 articles. The findings of this past study reveal the challenges, and opportunities faced by educators through their perceptions, commitments, and beliefs in STEM education for the actions of policymakers and school leaders.
Volume: 14
Issue: 1
Page: 442-451
Publish at: 2025-02-01

GeneXPress card: development and evaluation of educational card game for DNA transcription subtopics in genetics

10.11591/ijere.v14i1.30741
Nurfarizah Amani Norizan , Adibah Abu Bakar , Syazwan Saidin
Understanding deoxyribonucleic acid (DNA) transcription poses a substantial challenge in studying genetics, often due to lack of understanding and traditional teaching methods that fail to engage students. This study introduces GeneXPress, an innovative card game, aimed at enhancing the learning experience of DNA transcription for higher education biology students. Using the ADDIE model, this preliminary study assessed the usability of the GeneXPress card game through a survey of 169 biology education students at Universiti Pendidikan Sultan Idris, Malaysia. A quantitative approach was employed to analyze the survey data, evaluating the game’s usability using descriptive statistics, including mean scores and standard deviations. The findings reveal high usability and positive views of GeneXPress, covering various aspects such as goals, design, components and organization, playability and usefulness, with average scores of above 3.79. The positive perception of GeneXPress among students highlights its ability to engage and motivate learners, making the study of complex genetic concepts more accessible and enjoyable. In conclusion, it underscores the vital role of innovative teaching aids in enhancing student engagement and understanding, paving the way for future explorations into educational tools that cater to the evolving needs of learners and educators alike.
Volume: 14
Issue: 1
Page: 482-491
Publish at: 2025-02-01

Optimizing seismic sequence clustering with rapid cube-based spatiotemporal approach

10.11591/ijai.v14.i1.pp737-748
Silviya Hasana , Wina Permana Sari , Rojali Rojali , Devi Fitrianah
Due to their extensive volume and range of features, seismic data is regarded as highly complex data. Earthquakes that typically composed of foreshocks, mainshocks, and aftershocks, exhibit a unique sensitivity to temporal dimension, a characteristic that differs them from other natural hazards. Foreshocks and aftershocks that emanate from a similar epicenter, often display temporal patterns that contribute significantly to determining a sequence. This study introduces a density cube-based approach to cluster spatiotemporal seismic data. It addresses spatial irregularities observed in earthquake clusters and incorporates temporal aspects, acknowledging that seismic events originating from a similar epicenter could occur in separate time frames. We achieved the highest Silhouette score of 0.935 in daily-based clustering and 0.782 in weekly-based clustering. Notably, our analysis reveals a trend where weekly clustering lambda λ tend to be lower (λ=0.01) than in daily clustering (λ=0.1, λ=0.5), thus emphasizing the significance of temporal granularity where daily clustering requires higher λ to capture rapid fluctuations, while weekly clustering benefits from lower λ to cover broader trends. These findings enhance the understanding of the nuanced interplay of temporal dynamics in seismic sequence analysis.
Volume: 14
Issue: 1
Page: 737-748
Publish at: 2025-02-01

Overcoming imbalanced rice seed germination classification: enhancing accuracy for effective seedling identification

10.11591/ijai.v14.i1.pp62-72
Muhlasah Novitasari Mara , Sidiq Syamsul Hidayat , Farika Tono Putri , Dwi Rahmawati , Sri Wahyuni , Muhamad Cahyo Ardi Prabowo , Noer Ni'mat Syamsu Kabir , Ragil Tri Indra
This study aimed to automatically classify rice seedling germination on day seven using image analysis. The categories included normal, abnormal, and dead seeds. Due to the rarity of abnormal seedlings, capturing their images resulted in imbalanced data. To address this, abnormal categories were combined into a single class. We compared logistic regression, random forest, and deep learning models (VGG19, VGG16, Alex Net) for classification. Surprisingly, logistic regression achieved the highest accuracy (93.89%) and F1-scores (0.96 normal, 0.81 abnormal) despite the imbalanced data and complex task. The effectiveness of logistic regression for rice seedling classification with imbalanced data has been demonstrated in this novel research. Historically, deep learning models dominate image recognition, but our findings suggest simpler models can excel in specific scenarios, especially with limited data availability. This highlights the importance of selecting models based on data characteristics. The urgency for this research stems from the need for efficient and accurate rice seedling evaluation. Improved classification can enhance agricultural practices and optimize resource allocation.
Volume: 14
Issue: 1
Page: 62-72
Publish at: 2025-02-01

Energy analysis of active photovoltaic cooling system using water flow

10.11591/ijece.v15i1.pp1-14
Ant. Ardath Kristi , Erwin Susanto , Agus Risdiyanto , Agus Junaedi , Rudi Darussalam , Noviadi Arief Rachman , Ahmad Fudholi
An active water-cooling system is one of several technologies that has been proven to be able to reduce heat losses and increase electrical energy in photovoltaic (PV) module. This research discusses a comparative experimental study of three pump activation controls in cooling of PV module with the aim of evaluating specifically the PV output power, net energy gain, water flow rate, and module temperature reduction. The three pump activation controls being compared are continuously active during the test, active based on setpoint temperature, and active by controlling the pump voltage using pulse width modulation (PWM) control in adjusting water flow rate smoothly. The results show that controlling the pump voltage using PWM in the PV cooling process produces energy of 437.95 Wh, slightly lower than the others and the average module cooling temperature is 35.24 °C, higher of 1-3 °C than the others. Nevertheless, PWM control of cooling pump has resulted the percentage of net energy gain of 9.94%, greater than other controls, and with an average flow rate of 2.17 L/min, more efficient than the others. Thus, this control is quite effective as it can produce higher net PV energy yield and lower water consumption.
Volume: 15
Issue: 1
Page: 1-14
Publish at: 2025-02-01

Cognitive restructuring as a panacea for maladaptive behaviors among primary school children

10.11591/ijere.v14i1.29393
Nonso Ngozika Bisong , Blessing Agbo Ntamu , Veronica Akwenabuaye Undelikwo
This study investigated the efficacy of cognitive restructuring as a panacea for maladaptive behaviors among primary school children in Calabar Municipal, Cross River State, Nigeria. The study was a quasi-experimental study using a pre-test-post-test experimental design. Data were collected from pupils in primary four. Data was collected over twelve weeks. The mean and standard deviation of the pre-and post-test scores were calculated and analysis of covariance (ANCOVA) was used to determine the differences in the two scores. There were 39 students recruited for the study. The study found that maladaptive behaviors such as aggression and disobedience were pervasive among primary school pupils in Calabar. Notably, the findings indicated that cognitive restructuring had a statistically significant impact on reducing aggressive behavior, though its influence on disobedient behavior was not as pronounced. The paper concluded that cognitive restructuring is an effective intervention strategy for addressing maladaptive behaviors among primary school pupils. The intervention achieved its intended goal of improving children’s behavior and provided a framework for more sustainable behavior management strategies.
Volume: 14
Issue: 1
Page: 398-405
Publish at: 2025-02-01

Deep learning architectures for location and identification in storage systems

10.11591/ijai.v14.i1.pp592-601
Anny Astrid Espitia Cubillos , Robinson Jimenez Moreno , Esperanza Rodríguez Carmona
This document exposes the application of two deep learning models based on ResNet-18 architectures, intended for the location and identification of products in storage areas. One model obeys a tree structure and the other a structure under an ouroboron cycle. The performance of both models is evaluated using the metrics of training time, processing time and level of learning precision, which allows recommendations to be made regarding which one should be used for order preparation purposes, based on multilevel feature extraction. The total training time of the first model is 34.65 minutes and the second 40.43 minutes. The analysis of results allowed the detection parameters to be adjusted, finally with the refined models, through confusion matrices, precision results greater than 90% and processing times are obtained, which for model 1 is 6.8565 seconds and for model 2 is 4.884 seconds. For practical purposes, training times are not relevant, as are the precision and processing times for selecting the most convenient model according to the end user's objectives.
Volume: 14
Issue: 1
Page: 592-601
Publish at: 2025-02-01

Integrating ethnoscience in inquiry-creative learning: a new breakthrough in enhancing critical thinking

10.11591/ijere.v14i1.29259
Ahmad Harjono , Ni Nyoman Sri Putu Verawati , Wahyudi Wahyudi , Syifa'ul Gummah , Saiful Prayogi
Incorporating knowledge of culture and local wisdom values (ethnosciences) into scientific investigation is a new breakthrough in the current study. The integration of ethnosciences and scientific investigation is a valuable pathway that serves not only as a tool for acquiring new knowledge but also for developing critical thinking (CT) skills essential in solving real-world problems. This research aims to enhance CT skills among prospective science teachers (PSTs) through a teaching program that integrates ethnosciences with inquiry-creative learning. This particular investigation employs a mixed method approach, combining both quantitative and qualitative methods. The quantitative study utilized a randomized pretest-posttest control group design, with traditional expository teaching employed as the comparison group. Qualitative methods were specifically used in this study through semi-structured interviews with lecturer, particularly regarding the PSTs’ engagement in the implementation of learning program. CT skill tests and interview forms were used as data collection instruments. The study findings reveal that integrating ethnosciences with inquiry-creative learning has a significant effect on the development of CT skills compared to traditional instruction. The findings highlight the effectiveness of this approach in improving CT skills among PSTs and contribute to future research in the field of science education.
Volume: 14
Issue: 1
Page: 636-647
Publish at: 2025-02-01

Timed concurrent system modeling and verification of home care plan

10.11591/ijece.v15i1.pp870-882
Acep Taryana , Dieky Adzkiya , Muhammad Syifa'ul Mufid , Imam Mukhlash
A home care plan (HCP) can be integrated with an electronic medical records (EMR) system, serving as an example of a real-time system with concurrent processes. To ensure effective operation, HCPs must be free of software bugs. In this paper, we explore the modeling and verification of HCPs from the perspective of scheduling data operationalization. Specifically, we investigate how patients can obtain home services while preventing scheduling conflicts in the context of limited resources. Our goal is to develop and verify robust models for this purpose. We employ formalism to construct and validate the model, following these steps: i) develop requirements and specifications; ii) create a model with concurrent processes using timed automata; and iii) verify the model using UPPAAL tools. Our study focuses on HCP implementation at a regional general hospital in Banyumas District, Central Java, Indonesia. The results include models and specifications based on timed automata and timed computation tree logic (TCTL). We successfully verified a concurrent model that utilizes synchronized counter variables and a sender-receiver approach to analyze collision constraints arising from the synchronization of patient and resource plans.
Volume: 15
Issue: 1
Page: 870-882
Publish at: 2025-02-01

Sailfish-cat algorithm-enhanced generative adversarial network for attack detection in internet of things-Fog network authentication

10.11591/ijece.v15i1.pp1109-1122
Pallavi Kanthamangala Niranjan , Ravikumar Venkatesh
The internet of things (IoT) has emerged as a prominent and influential concept within the realm of computing. Various attack detection methods are devised for detecting attacks in IoT-Fog environment. Despite all these efforts, attack detection still remained as a challenging task due to factors such as low latency, resource constraints of IoT devices, scalability issues, and distribution complexities. All these challenges are addressed in this paper by designing an efficient attack detection technique named as sailfish- cat optimization-based generative adversarial network (SaCO-based GAN) tailored for the IoT-Fog framework. This proposed approach introduces the SaCO-based GAN for IoT-Fog attack detection utilizing deep learning and feature-based classification, validated through experiments showing superior performance metrics. Notably, the SaCO optimization technique is utilized to train the GAN. Experimental results demonstrate the efficacy of the SaCO-based GAN with a maximum recall of 92.15%, a maximum precision of 91.21%, and a maximum F-Measure of 92.16%, outperforming existing techniques in IoT-Fog attack detection. The paper recommends enhancing scalability, implementing real-time detection strategies, rigorously testing robustness against diverse attack scenarios, and integrating with existing IoT security frameworks for practical deployment.
Volume: 15
Issue: 1
Page: 1109-1122
Publish at: 2025-02-01

Personality types persistency, occupational consistency, and occupational satisfaction of graduates

10.11591/ijere.v14i1.31471
Ann Gathigia Waruita , Ciriaka Muriithi Gitonga , Edwin Benson Atitwa
Personality type affects career path and can determine an individual’s job satisfaction or dissatisfaction after graduation. In Kenya, high graduate unemployment has forced many to seek jobs unrelated to their qualifications or personalities. The purpose of the current study was to examine persistency of Holland’s personality types, mediating effect of consistency of career choices, and the degree of occupational satisfaction, informed by Holland’s theory. Longitudinal cohort research design was adopted, to access participants involved in a study conducted in 2012, from which 76 participants were accessed and provided required data. Data was collected using Holland’s self-directed search 4th edition questionnaire and an interview schedule. Spearman’s rank correlation was used to determine correlation between persistency of personality types and occupational satisfaction. Logistic regression was used to check the mediating effects of consistency on the relationship between personality types and occupational satisfaction. Results of the study indicate that there was a positive significant relationship between persistency of personality types and occupational satisfaction at p<0.05; mediating effect of consistency on the relationship between personality types and occupational satisfaction was statistically significant at (β=0.254, p<0.05). This study highlights the importance of persistency of personality types and consistency of career choices in ensuring occupational satisfaction.
Volume: 14
Issue: 1
Page: 319-331
Publish at: 2025-02-01
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