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31,456 Article Results

Breast cancer prediction using genetic algorithm and sand cat swarm optimization algorithm

10.11591/ijeecs.v37.i2.pp849-858
Velu Sangeetha , Maniyambadi Vaneeta , Arjuna Mamatha , Muniya Shoba , Sugatur Ramamurthy Deepa , Velusamy Sujatha , Shanmugam Sujatha
Breast cancer is the second leading type of cancer, which is mainly found in women and which increases the death rate among women. Early detection and diagnosis of breast cancer can reduce its occurrence and the death rate. Unfortunately, even if cancer treatment is initiated quickly after diagnosis, cancer may relapse because cancer cells may continue to exist in the body, which is also a major problem faced by women who fear facing the same treatment twice. So, detecting cancer at its early stage and predicting the recurrence of it is a major issue in the medical field that needs to be solved. Machine learning (ML) algorithms such as support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), and voting classifier (VC) are used for breast cancer prediction. Due to high-dimension data, the predicted results using Machine learning algorithms will increase the errors and decrease the accuracy. So, bioinspired algorithms such as the genetic algorithm (GA) and sand cat swarm optimization (SCSO) are used to reduce the data dimension. Convolutional neural network (CNN) is used for feature extraction from the image dataset. CNN algorithms are used for feature selection, which selects the important features for classification and prediction by applying 10 cross-validation methods. The proposed model using bioinspired optimization algorithms outcomes will yield high accuracy and the best solution.
Volume: 37
Issue: 2
Page: 849-858
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

Educational impact and ethical considerations in using Chatbots in Academia

10.11591/ijeecs.v37.i2.pp1150-1167
Dina M. Ibrahim , Njood K. Al-harbi , Amal A. Al-Shargabi
Chatbots are getting better every day due to the advancements in their capabilities in today’s technological age. This study aims to assess the efficacy of ChatGPT-4 and Gemini in producing scientific articles. Two types of prompts are given: direct questions and complete scenarios. Subsequently, we evaluate the educational and ethical aspects of the produced material by employing statistical analysis. We verify the credibility of references, detect any instances of plagiarism, and ensure the precision of the articles generated by the chatbot. In addition, we utilize topic modeling to assess the extent to which the content of the articles corresponds to the specified topic. According to the findings, Gemini outperformed ChatGPT-4, specifically in scenario questions, where it achieved an accuracy rate of 85%, while ChatGPT-4 only achieved 35% accuracy.
Volume: 37
Issue: 2
Page: 1150-1167
Publish at: 2025-02-01

Efficient deep learning approach for enhancing plant leaf disease classification

10.11591/ijeecs.v37.i2.pp1112-1120
Meroua Belmir , Wafa Difallah , Abdelkader Ghazli
The widespread occurrence of plant diseases is a major factor in the reduction of agricultural output, affecting both crop quality and quantity. These diseases typically begin on the leaves, influenced by alterations in plant structure and growing techniques, and can eventually spread over the entire plant. This results in a notable decrease in crop variety and yield. Successfully managing these diseases depends on accurately classifying and detecting leaf infections early, which is essential for controlling their spread and ensuring healthy plant growth. To address these challenges, this paper introduces an efficient approach for detecting plant leaf diseases. A concatenation of pre-trained convolutional neural networks (CNN) for enhanced plant leaf disease using transfer learning technique is implemented, with a specific focus on accurate early detection, utilizing the comprehensive new plant diseases dataset. The combined residual network-50 (ResNet-50) with densely connected convolutional network-121 (DenseNet-121) architecture aims to provide an efficient and reliable solution to these critical agricultural concerns. Various evaluation metrics were utilized to evaluate the robustness of the proposed hybrid model. The proposed ResNet-50 with the DenseNet-121 hybrid model achieved a rate of accuracy of 99.66%.
Volume: 37
Issue: 2
Page: 1112-1120
Publish at: 2025-02-01

A review and bibliometric analysis of traceability system development in the agricultural and food sector in Indonesia

10.11591/ijeecs.v37.i2.pp1064-1076
Yusnan Hasani Siregar , Pradeka Brilyan Purwandoko , Wiwiek Harsonowati , Muhammad Achirul Nanda , Rudy Tjahjohutomo , Diana Atma Budiman , Laila Rahmawati , Novita Dwi Susanti
Several technologies and methods for traceability systems in the agriculture and food sectors have developed rapidly in recent decades. There has been an increase in traceability system research in many developing countries, including Indonesia. Our review collects data from the Scopus database to study the development and dynamics of research on traceability systems and to identify emerging technological trends in the field. This paper uses bibliometric analysis by VOSviewer to find out studies regarding traceability. Our findings reveal traceability system research in Indonesia encompasses 1,264 documents within the Scopus database from 1998 to 2022. The number of studies on traceability systems has increased significantly after 2016. Most scholarly articles on traceability technology are disseminated as conference proceedings. These traceability systems have been established and are widely adopted to ensure the quality and safety of agricultural and food products, monitor species diversity, and oversee environmental parameters. The objective of the user influences the development of the traceability system. Technologies such as deoxyribonucleic acid (DNA) barcoding, unmanned aerial vehicles (UAVs), satellites, wireless sensor networks (WSNs), blockchain, product tagging, spectroscopy, and smart packaging rapidly advance to enhance traceability capabilities.
Volume: 37
Issue: 2
Page: 1064-1076
Publish at: 2025-02-01

Recognizing geographical locations using a GAN-based text-to-image approach

10.11591/ijeecs.v37.i2.pp1168-1182
Dina M. Ibrahim , Amal A. Al-Shargabi
Generating photo-realistic images that align with the text descriptions is the goal of the text-to-image generation (T2I) model. They can assist in visualizing the descriptions thanks to advancements in machine learning algorithms. Using text as a source, generative adversarial networks (GANs) can generate a series of pictures that serve as descriptions. Recent GANs have allowed oldest T2I models to achieve remarkable gains. However, they have some limitations. The main target of this study is to address these limitations to enhance the text-to-image generation models to enhance location services. To produce high-quality photos utilizing a multi-step approach, we build an attentional generating network called AttnGAN. The fine-grained image-text matching loss needed to train the AttnGAN’s generator is computed using our multimodal similarity model. With an inception score of 4.81 on the PatternNet dataset, our AttnGAN model achieves an impressive R-precision value of 70.61 percent. Because the PatternNet dataset comprises photographs, we’ve added verbal descriptions to each one to make it a text-based dataset instead. Many experiments have shown that AttnGAN’s proposed attention procedures, which are critical for text-to-image production in complex circumstances, are effective.
Volume: 37
Issue: 2
Page: 1168-1182
Publish at: 2025-02-01

Performance analysis of 10 machine learning models in lung cancer prediction

10.11591/ijeecs.v37.i2.pp1352-1364
Joselyn Zapata-Paulini , Michael Cabanillas-Carbonell
Lung cancer is one of the diseases with the highest incidence and mortality in the world. Machine learning (ML) models can play an important role in the early detection of this disease. This study aims to identify the ML algorithm that has the best performance in predicting lung cancer. The algorithms that were contrasted were logistic regression (LR), decision tree (DT), k-nearest neighbors (KNN), gaussian Naive Bayes (GNB), multinomial Naive Bayes (MNB), support vector classifier (SVC), random forest (RF), extreme gradient boosting (XGBoost), multilayer perceptron (MLP) and gradient boosting (GB). The dataset used was provided by Kaggle, with a total of 309 records and 16 attributes. The study was developed in several phases, such as the description of the ML models and the analysis of the dataset. In addition, the contrast of the models was performed under the metrics of specificity, sensitivity, F1 count, accuracy, and precision. The results showed that the SVC, RF, MLP, and GB models obtained the best performance metrics, achieving 98% accuracy, 98% precision, and 98% sensitivity.
Volume: 37
Issue: 2
Page: 1352-1364
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

Bias in artificial intelligence: smart solutions for detection, mitigation, and ethical strategies in real-world applications

10.11591/ijai.v14.i1.pp32-43
Agariadne Dwinggo Samala , Soha Rawas
Artificial intelligence (AI) technologies have revolutionized numerous sectors, enhancing efficiency, innovation, and convenience. However, AI's rise has highlighted a critical concern: bias within AI algorithms. This study uses a systematic literature review and analysis of real-world case studies to explore the forms, underlying causes, and methods for detecting and mitigating bias in AI. We identify key sources of bias, such as skewed training data and societal influences, and analyze their impact on marginalized communities. Our findings reveal that algorithmic transparency and fairnessaware learning are among the most effective strategies for reducing bias. Additionally, we address the challenges of regulatory frameworks and ethical considerations, advocating for robust accountability mechanisms and ethical development practices. By highlighting future research directions and encouraging collective efforts toward fairness and equity, this study underscores the importance of addressing bias in AI algorithms and upholding ethical standards in AI technologies.
Volume: 14
Issue: 1
Page: 32-43
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

Advancing chronic pain relief cloud-based remote management with machine learning in healthcare

10.11591/ijeecs.v37.i2.pp1042-1052
Nagarajan Mohankumar , Sandeep Reddy Narani , Soundararajan Asha , Selvam Arivazhagan , Subramanian Rajanarayanan , Kuppan Padmanaban , Subbiah Murugan
Healthcare providers face a significant challenge in the treatment of chronic pain, requiring creative responses to enhance patient outcomes and streamline healthcare delivery. It suggests using cloud-based remote management with machine learning (ML) to alleviate chronic pain. Wearable device data, electronic health record (EHR) data, and patient-reported outcomes are all inputs into the suggested system’s data analysis pipeline, which combines support vector machines (SVM) with recurrent neural networks (RNN). SVM’s powerful classification skills make it possible to classify patients’ risks and predict how they will react to therapy. RNNs are very good at processing sequential data, which means they may identify trends in patient symptoms and drug adherence over time. By integrating these algorithms, healthcare professionals may create individualized treatment programs that consider each patient’s preferences and specific requirements. Early intervention and proactive treatment of pain symptoms are made possible by the system’s ability to monitor patients in real-time remotely. The system is further improved by using predictive analytics to identify patients who could benefit from extra support services and to forecast when they will have acute pain episodes. The proposed approach can change the game regarding managing chronic pain. It provides data-driven, individualized treatment that improves patient outcomes while cutting healthcare expenses.
Volume: 37
Issue: 2
Page: 1042-1052
Publish at: 2025-02-01

A joint learning classification for intent detection and slot filling with domain-adapted embeddings

10.11591/ijeecs.v37.i2.pp1306-1316
Yusuf Idris Muhammad , Naomie Salim , Anazida Zainal
For dialogue systems to function effectively, accurate natural language understanding is vital, relying on precise intent recognition and slot filling to ensure smooth and meaningful interactions. Previous studies have primarily focused on addressing each subtask individually. However, it has been discovered that these subtasks are interconnected and achieving better results requires solving them together. One drawback of the joint learning model is its inability to apply learned patterns to unseen data, which stems from a lack of large, annotated data. Recent approaches have shown that using pretrained embeddings for effective text representation can help address the issue of generalization. However, pretrained embeddings are merely trained on corpus that typically consist of commonly discussed matters, which might not necessarily contain domain specific vocabularies for the task at hand. To address this issue, the paper presents a joint model for intent detection and slot filling, harnessing pretrained embeddings and domain specific embeddings using canonical correlation analysis to enhance the model performance. The proposed model consists of convolutional neural network along with bidirectional long short-term memory (BiLSTM) for efficient joint learning classification. The results of the experiment show that the proposed model performs better than the baseline models.
Volume: 37
Issue: 2
Page: 1306-1316
Publish at: 2025-02-01

Enhancing data cleaning process on accounting data for fraud detection

10.11591/ijeecs.v37.i2.pp1014-1022
Mohamad Affendi Abdul Malek , Kamarularifin Abd Jalil
Data cleaning is a crucial step in fraud detection as it involves identifying and correcting any inaccuracies or inconsistencies in the data. This can help to ensure that the data being used for fraud detection is reliable and accurate, which in turn can improve the effectiveness of fraud detection algorithms. Due to the overwhelming amount of data, data cleaning specific for fraud detection is a very important activity for the auditor to find the appropriate information. Therefore, a new accounting data cleaning for fraud detection is needed. In this paper, an enhancement of the process of fraud detection by accounting auditors through the implementation of accounting data cleaning technique is proposed. The proposed technique was embedded in a prototype system called accounting data cleaning for fraud detection (ADCFD). Through experiment, the performance of the proposed technique through ADCF is compared with those obtained from the IDEA system, using the same dataset. The results show that the proposed enhanced technique through ADCFD system performed better than the IDEA system.
Volume: 37
Issue: 2
Page: 1014-1022
Publish at: 2025-02-01

The social impact of artificial intelligence chatbots on college students

10.11591/ijere.v14i1.29469
Azman Hakimi , Reeda Li Meng Yue , Mariam Sufiah Muhsin , Maisarah Abu Bakar , Crendy Tan Yen Teng , Kususanto Ditto Prihadi
This study aims to investigate the impact of the freely accessible artificial intelligence chatbots (AICB) that might disrupt the teaching and learning pattern in higher education. While some education stakeholders developed strong opposition towards the AICB usage, condemning it as academic dishonesty, there are others believe the AICB might even improve the students’ learning. A total of 160 urban college students were purposively selected and requested to respond to the scales of ChatGPT acceptance and trust, academic self-efficacy, and university mattering to test the hypothesis that the acceptance and trust towards AICB should improve academic self-efficacy and general mattering among the students. The results indicated that academic self-efficacy partially mediates the contribution of AICB on the societal mattering. In other words, the findings suggest that students who trust and accept AICB usage would likely to believe that they can perform academically better and therefore they feel they are more meaningful to the society. Limitations and suggestions for future research are discussed.
Volume: 14
Issue: 1
Page: 10-16
Publish at: 2025-02-01

SRCNN-based image transmission for autonomous vehicles in limited network areas

10.11591/ijeecs.v37.i2.pp903-912
Anindya Afina Carmelya , Arief Suryadi Satyawan , Galura Muhammad Suranegara , Mokhamamad Mirza Etnisa Haqiqi , Helfy Susilawati , Nizar Alam Hamdani , Puput Dani Prasetyo Adi
High-quality images are crucial for navigation, obstacle detection, and environmental understanding, but transmitting high-resolution images over constrained networks presents significant challenges. This study introduces an image transmission system using super-resolution convolutional neural networks (SRCNN) to enhance image quality without increasing bandwidth requirements by transmitting low-resolution images and upscaling them with SRCNN. The first phase of the research involved data collection, in which information was acquired directly from an appropriate locus to produce training, validation, and testing datasets. The second, three SRCNN models (915, 935, and 955) were trained using such a training dataset. The last was an evaluation, in which model 915 showed quick learning and stable performance with initial high loss, while model 935 had rapid convergence but potential overfitting. Model 955 achieved high initial performance. Three SRCNN model configurations were tailored to the specific needs of autonomous electric vehicles operating in limited areas, such as the locus. Input image resolution ranged from 128×128 pixels to 256×256 pixels, while output resolution varied from 256×256 pixels to 512×512 pixels. These resolutions can be acceptable for efficient image transmission over IEEE 802.11ac, but on the long range (LoRa) network, it still produces some delay.
Volume: 37
Issue: 2
Page: 903-912
Publish at: 2025-02-01
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