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28,451 Article Results

A deep learning-based framework for automatic detection of COVID-19 using chest X-ray and CT-scan images

10.11591/ijai.v14.i4.pp3192-3200
Sivanagireddy Kalli , Bukka Narendra Kumar , Saggurthi Jagadeesh , Kushagari Chandramouli Ravi Kumar
COVID-19 has profoundly impacted global public health, underscoring the need for rapid detection methods. Radiography and radiologic imaging, especially chest X-rays, enable swift diagnosis of infected individuals. This study delves into leveraging machine learning to identify COVID-19 from X-ray images. By gathering a dataset of 9,000 chest X-rays and CT scans from public resources, meticulously vetted by board-licensed radiologists to confirm COVID-19 presence, the research sets a robust foundation. However, further validation is essential expanding datasets to encompass enough COVID-19 cases enhances convolutional neural network (CNN) accuracy. Among various machine learning techniques, deep learning excels in identifying distinct patterns on imaging characteristics discernible in chest radiographs of COVID-19 patients. Yet, extensive validation across diverse datasets and clinical trials is crucial to ensure the robustness and generalizability of these models. The conversation extends into complexities, including ethical considerations around patient privacy and integrating intelligent tech into clinical workflows. Collaborating closely with healthcare professionals ensures this technology complements the established diagnostic approach. Despite the potential to detect COVID-19 using chest X-ray imaging findings, thorough research and validation, alongside ethical deliberations, are vital before implementing it in the healthcare field. The results show that the proposed model achieved classification accuracy and F1 score of 96% and 98%, respectively, for the X-ray images.
Volume: 14
Issue: 4
Page: 3192-3200
Publish at: 2025-08-01

Insights from the vision-mission statements of Philippine and other ASEAN universities: a K-means clustering analysis

10.11591/ijai.v14.i4.pp3386-3394
Julius Ceazar G. Tolentino , John Paul P. Miranda
This study analyzed the vision and mission statements (VMS) of 117 Philippine state universities and colleges (SUCs) and compared them with 330 other ASEAN universities to identify thematic trends and institutional priorities. Using web scraping and K-means clustering, the study identified thematic clusters in VMS. Thematic trends through word frequency and collocation analyses provided further insights and a comparative analysis examined differences between Philippine SUCs and other ASEAN universities. Philippine SUCs’ vision statements formed three clusters: global competitiveness, premier recognition, and regional leadership in science and technology. Mission statements clustered into: mandated functions, global innovation, and advancement in the sciences. Philippine SUCs emphasized institutional prestige, workforce development, and sustainability while other ASEAN universities focus more on knowledge creation, student empowerment, and internationalization. Philippine SUCs aligned their VMS with national development and global ranking metrics and prioritizes institutional recognition and economic contributions more than the other ASEAN universities. Future studies should expand to more private institutions and international comparisons to assess broader higher education trends.
Volume: 14
Issue: 4
Page: 3386-3394
Publish at: 2025-08-01

Challenges of recommender systems in finance and banking: a systematic review

10.11591/ijai.v14.i4.pp2559-2567
Lossan Bonde , Abdoul Karim Bichanga
Recommender systems are widely applied in various domains, including e-commerce, marketing, and education. Despite their popularity, recommender systems are not widely used in finance and banking. This paper aims to identify the challenges associated with using recommender systems in finance and banking and recommend directions for future research. Using a systematic literature review (SLR) method, 52 papers were selected and analyzed. A three-step process was used to make the selection. First, a keyword search was made to identify a seed list of sources. A snowball technique with specific inclusion and exclusion criteria was applied to expand the list. Finally, a quick study was made to produce the final list of sources to consider. Through the study of the 52 relevant papers, three main challenges: i) transparency, ethics, and data privacy; ii) handling complex content information and accounting for multiple user behaviors; and iii) explainability of AI models were identified. This study has established the barriers to adopting recommender systems in the finance and banking industry. Specific subjects of concern identified include cold-start problems, personalization, fraud detection, transparency, and data privacy. The study recommends further research leveraging advanced machine learning models and emerging technologies to fill the gap.
Volume: 14
Issue: 4
Page: 2559-2567
Publish at: 2025-08-01

Modified zero-reference deep curve estimation for contrast quality enhancement in face recognition

10.11591/ijai.v14.i4.pp3274-3286
Muhammad Kahfi Aulia , Dyah Aruming Tyas
Face recognition systems remain challenged by variable lighting conditions. While zero-reference deep curve estimation (Zero-DCE) effectively enhances low-light images, it frequently induces overexposure in normal- and high-brightness scenarios. This study introduces modified Zero-DCE combined with three established enhancement techniques: contrast stretching (CS), contrast limited adaptive histogram equalization (CLAHE), and brightness preserving dynamic histogram equalization (BPDHE). Evaluations employed the extended Yale face database B and face recognition technology (FERET) datasets, with 10 representative samples assessed using the blind/referenceless image spatial quality evaluator (BRISQUE) metric. Modified Zero-DCE with BPDHE produced optimal enhancement quality, achieving a mean BRISQUE score of 16.018. On the extended Yale face database B, visual geometry group 16 (VGG16) integrated with modified Zero-DCE and CLAHE attained 83.65% recognition accuracy, representing a 6.08-percentage-point improvement over conventional Zero-DCE. For the 200-subject FERET subset, residual network 50 (ResNet50) with modified Zero-DCE and CLAHE achieved 67.41% accuracy. Notably, standard Zero-DCE with CLAHE demonstrated superior robustness in extremely low-light conditions, highlighting the illumination-dependent performance characteristics of these enhancement approaches.
Volume: 14
Issue: 4
Page: 3274-3286
Publish at: 2025-08-01

A survey of missing data imputation techniques: statistical methods, machine learning models, and GAN-based approaches

10.11591/ijai.v14.i4.pp2876-2888
Rifaa Sadegh , Ahmed Mohameden , Mohamed Lemine Salihi , Mohamedade Farouk Nanne
Efficiently addressing missing data is critical in data analysis across diverse domains. This study evaluates traditional statistical, machine learning, and generative adversarial network (GAN)-based imputation methods, emphasizing their strengths, limitations, and applicability to different data types and missing data mechanisms (missing completely at random (MCAR), missing at random (MAR), missing not at random (MNAR)). GAN-based models, including generative adversarial imputation network (GAIN), view imputation generative adversarial network (VIGAN), and SolarGAN, are highlighted for their adaptability and effectiveness in handling complex datasets, such as images and time series. Despite challenges like computational demands, GANs outperform conventional methods in capturing non-linear dependencies. Future work includes optimizing GAN architectures for broader data types and exploring hybrid models to enhance imputation accuracy and scalability in real-world applications.
Volume: 14
Issue: 4
Page: 2876-2888
Publish at: 2025-08-01

Blockchain as a digital governance tool: A systematic review

10.11591/ijece.v15i4.pp3986-3995
Cesar Patricio-Peralta , Jimmy Ramirez Villacorta , Milton Amache Sánchez , Jacker Paredes Meneses , Jesús Zamora Mondragon , Luis Segura Terrones , Paul Torres Santos , César Veliz Manrique , Walter Patricio Peralta
This systematic review explores the implementation of blockchain technology as a digital governance tool, focusing specifically on the Peruvian context. In the digital transformation era, blockchain has established itself as an innovative solution to manage and authenticate information. This research focuses on optimizing administrative and governmental processes in Peru, a country where document verification is crucial in legal, financial, educational, and medical procedures. The methodology used follows the problem/population, intervention, comparison, outcome, context (PICOC) model. 56 high-impact articles were selected in Scopus, prioritizing those in the areas of engineering, computer science, and business, and published between 2022 and 2025. The objective was to define the scope and structure of the research questions. These questions address the implementation of blockchain and its applications in digital governance to ensure security and reliability in administrative procedures. Through a comprehensive literature review, we seek to provide a comprehensive view of how blockchain could transform the interaction between citizens and the Peruvian government by automating document verification. In addition, successful cases from other countries and similar sectors will be analyzed, evaluating their feasibility and applicability in the Peruvian context. This approach will allow us to identify both the potential benefits and the challenges and implications associated with the integration of blockchain into government processes in Perú.
Volume: 15
Issue: 4
Page: 3986-3995
Publish at: 2025-08-01

Deep transfer learning for classification of ECG signals and lip images in multimodal biometric authentication systems

10.11591/ijai.v14.i4.pp3160-3171
Latha Krishnamoorthy , Ammasandra Sadashivaiah Raju
Authentication plays an essential role in diverse kinds of application that requires security. Several authentication methods have been developed, but biometric authentication has gained huge attention from the research community and industries due to its reliability and robustness. This study investigates multimodal authentication techniques utilizing electrocardiogram (ECG) signals and face lip images. Leveraging transfer learning from pre-trained ResNet and VGG16 models, ECG signals and photos of the lip area of the face are used to extract characteristics. Subsequently, a convolutional neural network (CNN) classifier is employed for classification based on the extracted features. The dataset used in this study comprises ECG signals and face lip images, representing distinct biometric modalities. Through the integration of transfer learning and CNN classification, improving the reliability and precision of multimodal authentication systems is the primary objective of the study. Verification results show that the suggested method is successful in producing trustworthy authentication using multimodal biometric traits. The experimental analysis shows that the proposed deep transfer learning-based model has reported the average accuracy, F1-score, precision, and recall as 0.962, 0.970, 0.965, and 0.966, respectively.
Volume: 14
Issue: 4
Page: 3160-3171
Publish at: 2025-08-01

Integrating time-frequency features with deep learning for lung sound classification

10.11591/ijece.v15i4.pp3737-3747
Su Yuan Chang , Marni Azira Markom , Zhi Sheng Choong , Arni Munira Markom , Latifah Munirah Kamaruddin , Erdy Sulino Mohd Muslim Tan
Deep learning has transformed medical diagnostics, especially in analyzing lung sounds to assess respiratory conditions. Traditional methods like CT scans and X-rays are impractical in resource-limited settings due to radiation exposure and time consumption, while conventional stethoscopes often lead to misdiagnosis due to subjective interpretation and environmental noise. This study evaluates deep learning models for lung sound classification using the International Conference on Biomedical Health Informatics 2017 dataset, comprising 920 annotated samples from 126 subjects. Pre-processing includes down sampling, segmentation, normalization, and audio clipping, with feature extraction techniques like spectrogram and Mel-frequency cepstral coefficients (MFCC). The adopted automatic lung sound diagnosis network (ASLD-Net) model with triple feature input (time domain, spectrogram, and MFCC) achieved the highest accuracy at 97.25%, followed by the dual feature model (spectrogram and MFCC) at 95.65%. Single-input models with spectrogram and MFCC performed well, while the time domain input alone had the lowest accuracy.
Volume: 15
Issue: 4
Page: 3737-3747
Publish at: 2025-08-01

Development and evaluation of a smart home energy management system using internet of things and real-time monitoring

10.11591/ijece.v15i4.pp3977-3985
Mohamed Imran Mohamed Ariff , Nur Anim Abdul Halim , Mohammad Nasir Abdullah , Samsiah Ahmad , Masurah Mohamad , Anis Zafirah Azmi
This project presents the design and implementation of a smart home energy management system using internet of things (IoT) technology to optimize household energy consumption. The system integrates various sensors, including passive infrared (PIR), light dependent resistor (LDR), and DHT11, to collect real-time environmental data, which is processed by a NodeMCU microcontroller. The microcontroller controls home appliances using relays, while the Blynk mobile app and Streamlit web platform provide users with remote monitoring and control capabilities. Despite successfully optimizing energy usage, the system faces limitations such as high sensor sensitivity and potential hazards during high-load power demonstrations. To address these issues, future work proposes integrating additional sensors for improved accuracy and incorporating renewable energy sources for increased sustainability. This project aims to enhance energy efficiency, provide users with greater control over their energy consumption, and contribute to smart home automation by utilizing real-time data, IoT integration, and user-friendly interfaces.
Volume: 15
Issue: 4
Page: 3977-3985
Publish at: 2025-08-01

Synthesizing strategies and innovations in combating land degradation: a global perspective on sustainability and resilience

10.11591/ijai.v14.i4.pp3133-3142
Gangamma Hediyalad , Ashoka Kukkuvada , Govardhan Hegde Kota
This paper presents a comprehensive examination of land degradation, a critical environmental challenge with far-reaching implications for agricultural productivity, ecosystem sustainability, and socio-economic stability worldwide. With the backdrop of escalating human population pressures and the exacerbating impact of climate change. It delves into the causes and consequences of soil erosion, desertification, salinization, and biodiversity loss, highlighting the interplay between natural processes and anthropogenic activities. Through a detailed review of literature spanning various remediation technologies, conservation practices, and policy frameworks, the paper critically assesses the effectiveness of current land management approaches, including the utilization of biosurfactants, remote sensing technologies, and agroforestry systems. Furthermore, it identifies significant research gaps and future directions, emphasizing the need for quantitative assessments, exploration of socio-economic impacts, and evaluation of restoration techniques. By offering evidence-based recommendations for policymakers and practitioners, this paper contributes to the global dialogue on sustainable land management and aims to catalyze action towards halting the advance of land degradation, ensuring food security, and preserving biodiversity for future generations. This work not only advances our understanding of land degradation challenges but also outlines a path forward for research, policy, and practice in the pursuit of environmental sustainability and resilience.
Volume: 14
Issue: 4
Page: 3133-3142
Publish at: 2025-08-01

Unpacking the drivers of artificial intelligence regulation: driving forces and critical controls in artificial intelligence governance

10.11591/ijai.v14.i4.pp2655-2666
Ibrahim Atoum , Salahiddin Altahat
The burgeoning field of artificial intelligence (AI) necessitates a nuanced approach to governance that integrates technological advancement, ethical considerations, and regulatory oversight. As various AI governance frameworks emerge, a fragmented landscape hinders effective implementation. This article examines the driving forces behind AI regulation and the essential control mechanisms that underpin these frameworks. We analyze market-driven, state-driven, and rights-driven regulatory approaches, focusing on their underlying motivations. Furthermore, critical regulatory controls such as data governance, risk management, and human oversight are highlighted to demonstrate their roles in establishing effective governance structures. Additionally, the importance of international cooperation and stakeholder collaboration in addressing the challenges posed by rapid technological change is emphasized. By providing insights into the strengths, weaknesses, and potential synergies of different governance models, this study contributes to the development of equitable and effective AI regulatory frameworks that encourage innovation while safeguarding societal interests. Ultimately, the findings aim to inform policymakers, industry leaders, and civil society organizations in their efforts to foster a future where AI is utilized responsibly and equitably for the betterment of humanity.
Volume: 14
Issue: 4
Page: 2655-2666
Publish at: 2025-08-01

Optimization model of vehicle routing problem with heterogenous time windows

10.11591/ijece.v15i4.pp4043-4057
Herman Mawengkang , Muhammad Romi Syahputra , Sutarman Sutarman , Gerhard Wilhelm Weber
This study proposes a novel optimization framework for the vehicle routing problem with heterogeneous time windows, a critical aspect in logistics and supply chain operations. Unlike conventional vehicle routing problem (VRP) models that assume uniform service schedules and fleet capacities, our approach acknowledges the diverse time constraints and vehicle specifications often encountered in real-world scenarios. By formulating the problem as a mixed integer linear programming model, we incorporate constraints related to time windows, vehicle load capacities, and travel distances. To tackle the NP-hard complexity, we employ a hybrid strategy combining metaheuristic algorithms with exact methods, thus ensuring both solution quality and computational efficiency. Extensive computational experiments, conducted on benchmark datasets and real-world logistics data, confirm the superiority of our model in terms of solution quality, runtime, and adaptability. These findings underscore the model’s practicality for industries facing dynamic routing requirements and tight service windows. Furthermore, the proposed framework equips decision-makers with a robust tool for optimizing route planning, ultimately enhancing service quality, reducing operational costs, and promoting more reliable delivery outcomes.
Volume: 15
Issue: 4
Page: 4043-4057
Publish at: 2025-08-01

Navigating cyber investigations: strategies and tools for forensic data acquisition

10.11591/ijece.v15i4.pp4022-4030
Srinivas Kanakala , Vempaty Prashanthi , K. V. Sharada
The rapid proliferation of cybercrimes has underscored the critical importance of robust data acquisition methodologies in the field of digital forensics. This research publication explores various aspects of forensic data acquisition, focusing on techniques, tools, and best practices employed by forensic investigators to collect and preserve digital evidence effectively. Beginning with an overview of the escalating cyber threat landscape and the consequential need for forensic investigations, the publication delves into the fundamental concepts of data acquisition, emphasizing the significance of ensuring data integrity and admissibility in legal proceedings. It examines the process of acquiring both volatile and non-volatile data from diverse sources, including hard drives, RAM, and other digital storage media. Furthermore, evaluates a range of forensic imaging and validation methods, encompassing tools such as Belkasoft live RAM capturer, AccessData FTK Imager, and ProDiscover, alongside validation techniques using PowerShell utility and commercial forensic software. Through comprehensive analysis and discussion, this study serves as a valuable resource for forensic practitioners, researchers, and legal professionals seeking to enhance their understanding of forensic data acquisition methodologies in the ever-evolving landscape of cybercrime investigation.
Volume: 15
Issue: 4
Page: 4022-4030
Publish at: 2025-08-01

Evaluation of the dynamic performance and practical limitations of a two-wheeled self-balancing robot

10.11591/ijece.v15i4.pp3613-3620
Rupasinghe Arachchige Don Dhanushka Dharmasiri , Malagalage Kithsiri Jayananda
Two-wheeled self-balancing robots (TWSBR) are statically unstable. However, using closed-loop controllers can stabilize. In this work, the proportional-integral-derivative (PID) controller was designed to maintain the TWSBR stability by adding two zeros and a pole at the origin to the loop gain and by determining the parameter K via root-locus analysis. Then using the K value Kp, Ki, and Kd parameters were calculated. By applying an impulse response to the system, it was found that the system is able to reach a dynamic balance in less than 1.2 seconds with minimum steady-state error. The dynamic performance and limitations of the developed system were investigated. The highest disturbance angle that can be applied to the system while keeping the motor input voltage below 12 V, in order to create counterbalancing torque and achieve dynamic balance, is determined to be θ = 0.0524 rad. Additionally, it was found that the TWSBR system managed to retain stability in a significantly large range of sudden payload changes with the same PID controller.
Volume: 15
Issue: 4
Page: 3613-3620
Publish at: 2025-08-01

Exploring the recurrent and sequential security patch data using deep learning approaches

10.11591/ijece.v15i4.pp4160-4171
Falah Muhammad Alam , Devi Fitrianah
The ever-changing nature of vulnerabilities and the intricacy of temporal connections make the classification of security patch data, both sequential and recurrent, a formidable challenge in cybersecurity. The goal of this research is to improve the efficacy and precision of security patch management by optimizing deep learning models to deal with these issues. In order to assess their performance on the PatchDB dataset, four models were used: recurrent neural networks (RNN), long short-term memory (LSTM), gated recurrent unit (GRU), and bidirectional LSTM (Bi-LSTM). Metrics like F1-score, area under the receiver operating characteristic curve (AUC-ROC), recall, accuracy, and precision were used to evaluate performance. When it came to processing sequential data, the GRU model was the most efficient, with the best accuracy (77.39%), recall (65.63%), and AUC-ROC score (0.8127). With a 75.17% accuracy rate and an AUC-ROC score of 0.7752, the RNN model successfully reduced false negatives. With AUC-ROC scores of 0.7792 and 0.8055, respectively, LSTM and Bi-LSTM had better specificity but more false negatives. To improve cybersecurity operations, decrease mitigation time, and automate the classification of security updates, this study presents a methodology. To improve the models' practicality, future efforts will center on increasing datasets and testing them in real-world settings.
Volume: 15
Issue: 4
Page: 4160-4171
Publish at: 2025-08-01
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