Articles

Access the latest knowledge in applied science, electrical engineering, computer science and information technology, education, and health.

Advanced Search
Filter Icon

Filters article

Years

FAQ Arrow
Single Year
Range Year
0
0

Source Title

FAQ Arrow

Authors

FAQ Arrow

28,188 Article Results

Study of design thinking and software engineering integration in education and training

10.11591/ijeecs.v39.i2.pp1384-1398
Muhammad Ihsan Zul , Suhaila Mohd. Yasin , Dadang Syarif Sihabudin Sahid
Integrating design thinking (DT) with software engineering (SE) is widely applied in industry, serving as a reference for SE in education and training. The industry has various integration models, but researchers and educators mainly adapt them for education. A clear understanding of DT-SE integration models is essential to figuring out their implementation. This study examines existing DT-SE integration models, challenges, and integration methods using Kitchenham’s framework in education and training. The paper was collected from ScienceDirect, IEEEXplore, Scopus, ACM, SpringerLink, and Google Scholar, yielding 593 initial publications, with 43 selected for in-depth analysis. Findings indicate that the d.school model is the most widely adopted DT model. Key challenges include team dynamics, process management, complexity, and cultural factors. DT is integrated into requirements engineering (RE) due to its user-centered nature, though only two studies explicitly describe DT-SE integration models, both applied early in SE processes. These findings suggest educational practices align with industry trends in model adoption and integration focus. Educators and practitioners can use these insights to design or adapt integration models suitable for education and training by shaping curricula that emphasize user-centered design, collaboration, and the extension of DT practices beyond RE-strengthening its impact for education and training.
Volume: 39
Issue: 2
Page: 1384-1398
Publish at: 2025-08-01

Designing an automated matching model to enhance recruitment process

10.11591/ijeecs.v39.i2.pp1081-1091
Sahar Idwan , Ebaa Fayyoumi , Haneen Hijazi , Izzeddin Matar
Detecting qualified candidates for a vacant position is a difficult task, especially when there are numerous applicants. This delays team development in finding the appropriate individual at the right moment. Adopting a well-structured selection process will create opportunities for new aspects and ideas. In this paper, the matching job applicant (MJA) model is developed to assist all parties, the employers and the employees simultaneously by providing a fair, transparent unbiased solution constructed by using a mathematical machine. This provides a clear justification in the decision-making process in addition to advising the applicants with the most suitable positions that fits their qualifications.
Volume: 39
Issue: 2
Page: 1081-1091
Publish at: 2025-08-01

Enhancing acoustic environment classification for hearingimpaired individuals using hybrid CNN and RFE

10.11591/ijeecs.v39.i2.pp906-913
Sunilkumar M. Hattaraki , Shankarayya G. Kambalimath
Individuals who are deaf or hard of hearing experience considerable difficulties in distinguishing sounds in various acoustic environments, which affects their communication ability and overall quality of life. Existing auditory assistive technologies currently face challenges with real-time classification and adaptation to changing noise conditions, underscoring the need for more reliable and accurate classification models. This research bridges the existing gap by creating a hybrid classification framework that integrates convolutional neural networks (CNN) and random forest ensemble (RFE) to enhance the accuracy of environmental sound classification. The study utilizes Mel-frequency cepstral coefficients (MFCCs) for feature extraction and principal component analysis (PCA) for dimensionality reduction, thus facilitating the efficient processing of real-world audio data. The proposed methodology improves classification accuracy across various environmental conditions. Experimental evaluations demonstrate superior performance, achieving a training accuracy of 94.93% and a testing accuracy of 93.41%, thereby exceeding conventional machine learning methods. By overcoming limitations in existing models, this research contributes to the development of adaptive hearing assistance systems with enhanced noise classification capabilities. The results have significant implications for the development of smart hearing aids, real-time noise classification, and auditory scene analysis. Ultimately, this research enhances assistive hearing technologies, promoting greater accessibility, communication, and inclusion for hearing-impaired individuals, thus contributing positively to society.
Volume: 39
Issue: 2
Page: 906-913
Publish at: 2025-08-01

An implementation of GAN analysis for criminal face identification system

10.11591/ijeecs.v39.i2.pp963-972
Ayesha Sarosh , Govindu Komali , Vishnu Vardhan Battu , Laxmaiah Kocharla , Eswaree Devi Kopparavuri , Ooruchintala Obulesu , Praveen Mande , Amanulla Mohammad
In recent times, the criminal activities are growing at an exponential rate. For the prevention of crime, one of the main issues that are before the police are accurate identification of criminals and on the other hand the availability of police officers are not adequate. The most tedious task is tracking the suspect once a crime was committed. Over the years, several technical solutions have been presented to detect the criminals however most of them were not effective. One of the most significant characteristics for the identification of a person is face. Even identical twins have their own unique faces. Face identification is a challenging topic in computer vision because the human face is a dynamic entity with a high degree of visual variation. In this area, identification accuracy and speed are significant challenges. Hence to solve these issues, an implementation of generative adversarial network (GAN) analysis for criminal face identification system is presented. GAN is used for the identification of criminals. Recall, precision, accuracy, and F1-score are used to assess the performance of the presented technique. Compared to previous models, this model will achieve better performance for criminal face detection.
Volume: 39
Issue: 2
Page: 963-972
Publish at: 2025-08-01

Comparison of robust machine learning algorithms on outliers and imbalanced spam data

10.11591/ijeecs.v39.i2.pp1130-1144
Dodo Zaenal Abidin , Jasmir Jasmir , Errisya Rasywir , Agus Siswanto
Effective spam detection is essential for data security, user experience, and organizational trust. However, outliers and class imbalance can impact machine learning models for spam classification. Previous studies focused on feature selection and ensemble learning but have not explicitly examined their combined effects. This study evaluates the performance of random forest (RF), gradient boosting (GB), and extreme gradient boosting (XGBoost) under four experimental scenarios: (i) without synthetic minority over-sampling technique (SMOTE) and outliers, (ii) without SMOTE but with outliers, (iii) with SMOTE and without outliers, and (iv) with SMOTE and with outliers. Results show that XGBoost achieves the highest accuracy (96%), an area under the curve-receiver operating characteristic (AUCROC) of 0.9928, and the fastest computation time (0.6184 seconds) under the SMOTE and outlier-free scenario. Additionally, RF attained an AUCROC of 0.9920, while GB achieved 0.9876 but required more processing time. These findings emphasize the need to address class imbalance and outliers in spam detection models. This study contributes to developing more robust spam filtering techniques and provides a benchmark for future improvements. By systematically evaluating these factors, it lays a foundation for designing more effective spam detection frameworks adaptable to real-world imbalanced and noisy data conditions.
Volume: 39
Issue: 2
Page: 1130-1144
Publish at: 2025-08-01

Handling missing values and clustering industrial liquid waste using K-medoids

10.11591/ijeecs.v39.i2.pp1411-1420
Ratih Hafsarah Maharrani , Prih Diantono Abda'u , Ganjar Ndaru Ikhtiagung , Nur Wahyu Rahadi , Zaenurrohman Zaenurrohman
The textile industry is a significant contributor to environmental pollution due to its wastewater, which contains hazardous substances such as dyes, heavy metals, and chemicals that can severely harm aquatic ecosystems. Effective management of this wastewater is crucial to mitigate its environmental impact. This study focuses on classifying industrial liquid waste data using the K-medoids clustering method, chosen for its robustness to noise and outliers compared to K-means. To address challenges in wastewater data processing, such as missing values and varying data scales, two approaches are compared: replacing missing values with zero and K-nearest neighbors (KNN) imputation, alongside Z-score normalization for data uniformity. The clustering quality is evaluated using the Davies-Bouldin index (DBI) for cluster variations of k=2, 3, 4, and 5. The results show that the best clustering quality is achieved at k=2, with the smallest DBI values obtained using KNN imputation (0.139) and zero replacement (0.149). The superior performance of KNN imputation highlights its effectiveness in handling missing data. These findings provide valuable insights into the characteristics of textile industry wastewater pollution, offering a robust framework for effective wastewater management. The study concludes with practical recommendations for policymakers and industry stakeholders to adopt advanced data-driven approaches for sustainable wastewater treatment strategies.
Volume: 39
Issue: 2
Page: 1411-1420
Publish at: 2025-08-01

A novel multimodal model for detecting Vietnamese toxic news using PhoBERT and Swin Transformer V2

10.11591/ijeecs.v39.i2.pp1350-1359
Ngoc An Le , Xuan Dau Hoang , Xuan Hanh Vu , Thi Thu Trang Ninh
News articles with fake, toxic or reactionary content are currently posted and spreaded very strongly due to the popularity of the Internet and especially the explosion of social networks and online services in cyberspace. Toxic news, especially reactionary news aimed at Vietnam, such as online articles spreading false information, slandering leaders, inciting destruction of the great national unity bloc, have a great impact on social life because they can spread quickly and have many forms of expression, such as news in the forms of text, images, videos, or a combination of text and images. Due to the seriousness of articles posting fake, toxic or reactionary news in cyberspace, there have been a number of studies in Vietnam and abroad for detection and prevention. However, most of the proposals focus on handling fake and toxic news posted using the English language. Furthermore, due to a large number of online news are posted in the form of images, or text embedded in images and videos, it is very difficult to process these news, leading to a relatively low detection rate. This paper proposes a multimodal model based on the combination of PhoBERT and Swin Transformer V2 for detecting fake and toxic news in both forms of text and images. Comprehensive experiments conducted on a dataset of 8,000 text and image news articles demonstrate that the proposed multimodal model surpasses both individual models and previous approaches, achieving 95% accuracy and 95% F1-score.
Volume: 39
Issue: 2
Page: 1350-1359
Publish at: 2025-08-01

Advanced deep attention neural inference network for enhanced arrhythmia detection and accurate classification

10.11591/ijeecs.v39.i2.pp1164-1175
H. Sumitha , M. Devanathan
Arrhythmias are irregular heartbeats that can lead to severe health risks, including sudden cardiac death, necessitating accurate and timely detection for effective treatment. Traditional diagnostic methods such as stress tests, resting electrocardiograms (ECGs), and 24-hour Holter monitors are limited by their monitoring capacity and often result in delayed diagnoses, compromising patient safety. To address these challenges, this paper introduces the deep attention neural inference network (DANIN) methodology. DANIN integrates one-dimensional ECG signals with two-dimensional spectral images using multi-modal feature fusion, capturing comprehensive cardiac information in both temporal and frequency domains. The methodology employs advanced deep attention network-based models for superior feature extraction, recognizing intricate patterns and long-range dependencies within the data. Additionally, the inclusion of an inference model system enhances interpretability and usability, making the model highly suitable. Further, DANIN is evaluated considering the MIT-BIH dataset, and extensive comparative analysis with state-of-the-art techniques demonstrates that DANIN significantly improves accuracy, precision, recall, and F1-score, highlighting its potential to revolutionize arrhythmia detection and improve patient outcomes.
Volume: 39
Issue: 2
Page: 1164-1175
Publish at: 2025-08-01

Digital and academic libraries through cloud computing

10.11591/ijeecs.v39.i2.pp896-905
Karthika Sivanandham , Dominic John , Sivankalai Sivankalai
In an era characterized by the dominance of digital information, libraries have undergone significant transformations, evolving from traditional brickand-mortar institutions to dynamic hubs of digital knowledge. The emergence of digital libraries, which give users access to vast collections of digital resources, has facilitated this evolution. However, effective management of digital resources poses numerous challenges, including issues related to storage, preservation, and accessibility. In response, cloud computing has developed as a powerful solution for addressing these challenges and revolutionizing how libraries operate. Cloud computing reduces the need for expensive infrastructure expenditures and increases flexibility and scalability by allowing libraries to store, manage, and access digital resources remotely over the internet. This paper examines the intersection of digital libraries and cloud computing, examining the role of cloud computing in modern libraries and its implications for the future of information management. By analyzing current trends, case studies, and best practices, this paper provides insights into the benefits and challenges of adopting cloud computing in the context of academic libraries.
Volume: 39
Issue: 2
Page: 896-905
Publish at: 2025-08-01

Machine learning framework and tools in precision farming

10.11591/ijeecs.v39.i2.pp1063-1071
Patil Sagar Baburao , R. B. Kulkarni , Suchita S. Patil
Farming using machine learning (ML) techniques has a role to play in the current globalization scenario due to the advantages it offers for costeffective harvesting of the crop. The areas such as crop disease detection, soil nutrient detection, fertilizer analysis and optimization, weather and irrigation schedule prediction, are investigated utilizing a range of deep learning and ML techniques, such as K-nearest neighbors (KNNs), convolutional neural networks (CNNs), and support vector machines (SVMs). The article concentrates on preparing the recommendation system for the farmer to take a quick and timely decision for crop disease, use of optimal fertilizer for crop growth, and water requirement prediction to overcome water wastage. A massive amount of data, including image data from publicly accessible sources, such as PlantVillage, Kaggle is used to train the model. Sensor data is fed into the ML model for the nutrients analysis and water requirement analysis. An Android application is developed, which can be used from any handheld device by the farmers to take advantage of the proposed recommendation system. The result shows the promising future with better accuracy than previously available models in the same area. Parameters including recall, accuracy, precision, and F1-score are considered to gauge performance.
Volume: 39
Issue: 2
Page: 1063-1071
Publish at: 2025-08-01

The development of contextual chat interactions with retrieval-augmented generation system for facilitating learning hadith

10.11591/ijeecs.v39.i2.pp987-995
Rio Nurtantyana , Yudi Priyadi , Eko Darwiyanto
This study explores the development and implementation of a retrieval-augmented generation (RAG) system using the large language model (LLM) to enhance the learning of hadith through a chat interface for high school students. This study addresses challenges in optimizing RAG configurations and problems associated with traditional educational methods that lack interactivity. In addition, the RAG system was designed to replace real teacher interactions, offering a chat feature that provides contextual answers to real-life scenarios related to Hadith. Various configurations were tested, with a focus on the Matn component, achieving a high accuracy score with a mean of .754 and demonstrating efficiency in context relevance with a mean of .797. Results indicated significant accessibility using our RAG system for learning hadith via WhatsApp’s chat interface. Hence, this study highlights the potential of RAG systems in transforming educational environments and offers insights into the development of technology for interactive Hadith learning solutions.
Volume: 39
Issue: 2
Page: 987-995
Publish at: 2025-08-01

Deep learning-based multi-tier sensitivity analysis network for document sensitivity classification

10.11591/ijeecs.v39.i2.pp1249-1260
Sadiya Ansari , Shameem Akther
In the digital age, the exponential growth of data necessitates robust and efficient systems for document classification to maintain data security and compliance. Text classification plays a crucial role in identifying sensitive information by automatically categorizing documents based on their content. Using advanced machine learning and deep learning models, it analyzes text to detect keywords, patterns, and contextual cues that indicate the presence of sensitive data. This paper presents a novel framework, the multi-tier sensitivity analysis network (MTSAN), designed to accurately classify documents into public, private, and confidential categories. The proposed system integrates several advanced components, including the multi-tier sensitivity encoding network (MTSEN). MTSAN leverages a combination of convolutional networks and graph convolutional networks (GCNs) to capture both local and global contextual information. The dual-scope graph convolution block (DSGCB) is introduced to address both global dependencies and local dynamics, employing a novel fusion mechanism to merge global and local features effectively. Additionally, the cross-tier information fusion block (CTIFB) facilitates the seamless integration of multi-level features, further refining the classification process. The results demonstrate that the proposed MTSAN model outperforms traditional machine learning approaches and contemporary deep learning models such as bidirectional encoder representations from transformers (BERT), achieving superior accuracy and F1 scores in classifying sensitive information.
Volume: 39
Issue: 2
Page: 1249-1260
Publish at: 2025-08-01

Performance analysis and comparison of machine learning algorithms for predicting heart disease

10.11591/ijai.v14.i4.pp2849-2863
Neha Bhadu , Jaswinder Singh
Heart disease (HD) is a serious medical condition that has an enormous effect on people's quality of life. Early as well as accurate identification is crucial for preventing and treating HD. Traditional methods of diagnosis may not always be reliable. Non-intrusive methods like machine learning (ML) are proficient in distinguishing between patients with HD and those in good health. The prime objective of this study is to find a robust ML technique that can accurately detect the presence of HD. For this purpose, several ML algorithms were chosen based on the relevant literature studied. For this investigation, two different heart datasets the Cleveland and Statlog datasets were downloaded from Kaggle. The analysis was carried out utilizing the Waikato environment for knowledge analysis (WEKA) 3.9.6 software. To assess how well various algorithms predicted HD, the study employed a variety of performance evaluation metrics and error rates. The findings showed that for both the datasets radio frequency is a better option for predicting HD with an accuracy and receiver operating characteristic (ROC) values of 94% and 0.984 for the Cleveland dataset and 90% and 0.975 for the Statlog dataset. This work may aid researchers in creating early HD detection models and assist medical practitioners in identifying HD.
Volume: 14
Issue: 4
Page: 2849-2863
Publish at: 2025-08-01

Investigation on low-performance tuned-regressor of inhibitory concentration targeting the SARS-CoV-2 polyprotein 1ab

10.11591/ijai.v14.i4.pp3003-3013
Daniel Febrian Sengkey , Angelina Stevany Regina Masengi , Alwin Melkie Sambul , Trina Ekawati Tallei , Sherwin Reinaldo Unsratdianto Sompie
Hyperparameter tuning is a key optimization strategy in machine learning (ML), often used with GridSearchCV to find optimal hyperparameter combinations. This study aimed to predict the half-maximal inhibitory concentration (IC50) of small molecules targeting the SARS-CoV-2 replicase polyprotein 1ab (pp1ab) by optimizing three ML algorithms: histogram gradient boosting regressor (HGBR), light gradient boosting regressor (LGBR), and random forest regressor (RFR). Bioactivity data, including duplicates, were processed using three approaches: untreated, aggregation of quantitative bioactivity, and duplicate removal. Molecular features were encoded using twelve types of molecular fingerprints. To optimize the models, hyperparameter tuning with GridSearchCV was applied across a broad parameter space. The results showed that the performance of the models was inconsistent, despite comprehensive hyperparameter tuning. Further analysis showed that the distribution of Murcko fragments was uneven between the training and testing datasets. Key fragments were underrepresented in the testing phase, leading to a mismatch in model predictions. The study demonstrates that hyperparameter tuning alone may not be sufficient to achieve high predictive performance when the distribution of molecular fragments is unbalanced between training and testing datasets. Ensuring fragment diversity across datasets is crucial for improving model reliability in drug discovery applications.
Volume: 14
Issue: 4
Page: 3003-3013
Publish at: 2025-08-01

Image analysis and machine learning techniques for accurate detection of common mango diseases in warm climates

10.11591/ijai.v14.i4.pp2935-2944
Md Abdullah Al Rahib , Naznin Sultana , Nirjhor Saha , Raju Mia , Monisha Sarkar , Abdus Sattar
Mangoes are valuable crops grown in warm climates, but they often suffer from diseases that harm both the trees and the fruits. This paper proposes a new way to use machine learning to detect these diseases early in mango plants. We focused on common issues like mango fruit diseases, leaf diseases, powdery mildew, anthracnose/blossom blight, and dieback, which are particularly problematic in places like Bangladesh. Our method starts by improving the quality of images of mango plants and then extracting important features from these images. We use a technique called k-means clustering to divide the images into meaningful parts for analysis. After extracting ten key features, we tested various ways to classify the diseases. The random forest algorithm stood out, accurately identifying diseases with a 97.44% success rate. This research is crucial for Bangladesh, where mango farming is essential for the economy. By spotting diseases early, we can improve mango production, quality, and the livelihoods of farmers. This automated system offers a practical way to manage mango diseases in regions with similar climates.
Volume: 14
Issue: 4
Page: 2935-2944
Publish at: 2025-08-01
1 2 3
...
1879 1880
Show 26 of 1880

Discover Our Library

Embark on a journey through our expansive collection of articles and let curiosity lead your path to innovation.

Explore Now
Library 3D Ilustration