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Exploring patient-patient interactions graphs by network analysis

10.11591/ijai.v14.i3.pp1752-1762
Zaher Salah , Esraa Abu Elsoud , Kamal Salah
Understanding how patient demographics and shared experiences impact interactions is essential for strengthening pa/tient support networks and optimizing health outcomes as personalized healthcare becomes more and more important. To this end, this study explores the patient-patient interactions (PPIs) graph as a network and applies selected network analysis approaches to examine the PPIs network of accutane drug. Two main research questions are addressed by gaining deeper insight at the hidden patterns of reactivity and connectivity among interchanging nodes. There was a negative response to the first research question, which asked if patients react to others that have similar gender and/or age profiles in a consistent way. Patients tended to interact with people of different genders and ages, indicating a high degree of heterogeneity in the network. Negative responses were likewise given to the second research question, which asked if communities inside the network could identify patients based on gender or age profile. Network analysis approaches for community detection failed to distinguish between groups with similar demographic characteristics. Rather, groups seemed to emerge based on other factors, like similarity in patient opinions. The results imply that gender and age do not have a major influence on community membership. Future research will concentrate on applying more sophisticated graph mining techniques to expand these approaches to cover more and larger PPIs networks.
Volume: 14
Issue: 3
Page: 1752-1762
Publish at: 2025-06-01

Application of the adaptive neuro-fuzzy inference system for prediction of the electrical energy production in Jakarta

10.11591/ijai.v14.i3.pp1790-1798
Yoga Tri Nugraha , Catra Indra Cahyadi , Rizkha Rida , Margie Subahagia Ningsih , Dewi Sholeha , Indra Roza
Jakarta, as a rapidly growing urban area, faces challenges in balancing energy demand with supply while addressing environmental concerns associated with traditional energy sources. Electrical energy production prediction in urban environments like Jakarta is crucial for effective energy management, ensuring stable supply, and promoting sustainable development. The prediction of electrical energy production in Jakarta is critical for ensuring stable and sustainable energy supply. This research proposed the application of the adaptive neuro-fuzzy inference system (ANFIS) as a predictive tool specifically tailored for Jakarta's energy production prediction context. The research methodology used in this study is the ANFIS. Five levels make up the architecture of the ANFIS model: output, normalization, defuzzification, rule evaluation, and fuzzification. The fuzzification layer converts input variables into linguistic terms using membership functions, while the rule evaluation layer calculates the activation strength of each rule based on the input values. The predicted results of Jakarta electrical energy production from 2023 to 2028 are 65,288 GWh and there is an annual increase of 5.25%. The error contained in ANFIS is with a root mean square error (RMSE) value of 0.0001058% and a mean absolute percentage error (MAPE) value of 0.00875%.
Volume: 14
Issue: 3
Page: 1790-1798
Publish at: 2025-06-01

Enhancing cyberbullying detection with advanced text preprocessing and machine learning

10.11591/ijece.v15i3.pp3139-3148
Rakesh Bapu Dhumale , Ajay Kumar Dass , Amit Umbrajkaar , Pradeep Mane
The use of social media and the internet has been increasing dramatically in recent years. Cyber-bullying is the term used to describe the misuse of social media by some people who make threatening comments. This has a devastating influence on people's lives, especially those of children and teenagers, and can lead to feelings of depression and suicidal thoughts. The methodology proposed in this paper includes four steps for identifying cyberbullying: preprocessing, feature extraction, classification, and evaluation. The first step is to create a labeled, varied dataset. Word2Vec and term frequency-inverse document frequency are used in feature extraction to transform text into high-dimensional vectors. Word2Vec creates word embeddings using the skip-gram and continuous bag-of-words models, while term frequency-inverse document frequency assesses the text's term relevancy. Support vector machine classifiers are used in the model, and their effectiveness is compared to that of other techniques like logistic regression and naïve Bayes. The classifiers support vector machine, naïve Bayes, and logistic regression were assessed. The maximum accuracy was 95% for the support vector classifier with skip-gram and 93% for continuous bag-of-words. For sentiment categories, F1-scores, recall, and precision were computed. The average precision and recall were 0.77 and 0.79, respectively.
Volume: 15
Issue: 3
Page: 3139-3148
Publish at: 2025-06-01

Name privacy on named data networking: a survey and future research

10.11591/ijece.v15i3.pp3039-3053
Mohammad Shahrul Mohd Shah , Yu-Beng Leau , Mohammed Anbar , Liang Zhao
Information-centric networking (ICN) has gained significant interest in recent years, attracting both academic and industry, it represents a paradigm shift and moving away from the host-based IP networks that dominate today landscape. As ICN technology matures and advances towards real-world deployment, the importance of addressing security and privacy concerns has grown exponentially. The ICN paradigm is deliberately designed to encompass numerous security and privacy features, including but not limited to provenance and privacy. These features, which are often lacking in the host-centric paradigm, inherently form a core aspect of ICN. Nevertheless, due to its relatively recent emergence, the ICN paradigm also presents a range of unresolved privacy challenges. This paper offers a comprehensive survey of the existing literature on privacy primarily focuses on major domains name privacy. We delve into the fundamental principles of existing research and evaluate the limitations of proposed methodologies. In name privacy, we also explore strategies to preserve name privacy. We have identified future research directions and highlighted ongoing challenges in the pursuit of enhancing ICN privacy.
Volume: 15
Issue: 3
Page: 3039-3053
Publish at: 2025-06-01

An optimized transfer learning-based approach for Crocidolomia pavonana larvae classification

10.11591/ijai.v14.i3.pp2270-2281
Risnawati Risnawati , Rodiah Rodiah , Sarifuddin Madenda , Diana Tri Susetianingtias
The increasing demand for mustard greens has driven farmers to continuously improve mustard greens cultivation. One of the challenges in mustard greens cultivation is the presence of insect pests. A significant pest in mustard greens is Crocidolomia pavonana (C. pavonana). C. pavonana damages plants by feeding on various parts, especially the leaves. The initial step in controlling them is insect pest monitoring. Monitoring aims to establish the control threshold. C. pavonana larvae have four instar stages: instar 1, 2, 3, and 4. Identification of the instar larval stages utilizes deep convolutional neural network (CNN) to classify C. Pavonana larvae on mustard greens using ResNet50V2 and DenseNet169 architectures optimized to enhance classification accuracy. The classification evaluation results show that both DenseNet169 and ResNet50V2 models achieve high accuracy, with DenseNet169 reaching the highest accuracy at 97.1%, while ResNet50V2 achieves an accuracy of 94.2%. The lower loss values on the test data compared to the validation data indicate that the deep learning models have successfully captured the patterns in C. pavonana images for classification. This classification process is expected to be one of the activities in monitoring the instar larvae to improve the accuracy of insecticide spraying and enhance mustard greens production.
Volume: 14
Issue: 3
Page: 2270-2281
Publish at: 2025-06-01

Object detection in printed circuit board quality control: comparing algorithms faster region-based convolutional neural networks and YOLOv8

10.11591/ijece.v15i3.pp2796-2808
Jaja Kustija , Diki Fahrizal , Muhamad Nasir , Andi Adriansyah , Muhammad Husni Muttaqin
Along with the development of electronic technology, the integration of numerous components on printed circuit board (PCB) boards has resulted in increasingly complex and intricate layouts. Small defects in traces can lead to failures in electronic functions, making the inspection of PCB surface layouts a critical process in quality control. Given the limitations of manual inspection, which struggles to detect such defects due to their size and complexity, there is a growing need for a PCB inspection system that utilizes automated optical inspection (AOI) based on deep learning detection. This research develops and compares two deep learning algorithms, faster region-based convolutional neural networks (R-CNN) and YOLOv8, to identify the most effective algorithm for detecting defects on PCB layouts. The findings of this study indicate that the YOLOv8 algorithm outperforms faster R-CNN, with the YOLOv8x variant emerging as the best model for defect detection. The YOLOv8x model achieved performance scores of 0.962 (mAP@50), 0.503 (mAP@50:95), 0.953 (Precision), 0.945 (Recall), and 0.949 (F1-score). These results provide a strong foundation for further research into the application of AOI for PCB defect detection and other quality control processes in manufacturing, using optimized deep learning models.
Volume: 15
Issue: 3
Page: 2796-2808
Publish at: 2025-06-01

Depression and post traumatic stress disorder analysis with multi-modal data

10.11591/ijai.v14.i3.pp2358-2366
Rajalakshmi Sivanaiah , Angel Deborah Suseelan , Krupa Elizabeth Thannickal , Sanmati Pirabahar
With an increasing global population and more people living to the age when major depressive disorder (MDD) or post traumatic stress disorder (PTSD) commonly occurs, the number of those who suffer from such disorders is rising. Studies have also shown a high likelihood of comorbidity between these 2 disorders. This comorbidity can worsen symptoms, increase the risk of chronicity, and complicate treatment, significantly impacting patients’ emotional wellbeing and social and occupational functioning. There is a need to enable faster and reliable diagnosis methods, while taking into account the subjectivity of individuals and the role of behavioural cues. The proposed approach analyses the combination of audio, video and text input features (multi-modal data) of the subject to determine the severity class of MDD and PTSD. The DistilBERT transformer is used for learning and building a model with the textual modality and random forest classifiers for the audio and video modalities. An ensemble of these 3 models from 3 modalities performs better in the final classification of MDD and PTSD when compared to individual models. This work also covers a comparison of the models with different splits on the dataset. This ensembled system shows an improved accuracy of 2% to 7% for the MDD and PTSD multi class classification over the models tested on individual modalities.
Volume: 14
Issue: 3
Page: 2358-2366
Publish at: 2025-06-01

Arabic offensive text classification using emojis: Including emoji data in Arabic natural language processing

10.11591/ijece.v15i3.pp3332-3345
Amal Albalawi , Wael M. S. Yafooz
In the digital social media ecosystem, controlling offensive language requires advanced algorithmic tools. This study examines the influence of including emojis translation in the text preprocessing stage of the classification of offensive Arabic text. A novel dataset of 10,000 Arabic tweets was developed, with rigorous annotations to classify content as offensive or non-offensive. The dataset was meticulously annotated and validated using Cohen's kappa (CK) and Krippendorff's Alpha (α) to ensure consistency and accuracy. Several experiments evaluated the dataset with the most common text classification models: seven machine learning (ML) classifiers and three deep learning (DL) models. Two experimental sets were conducted: one with emoji translation in preprocessing to enrich text input and another without emoji translation to directly assess the impact of emojis on classification accuracy. The findings indicate that emojis significantly affect text classification models, with advanced DL models showing higher sensitivity to contextual nuances conveyed by emojis compared to traditional ML classifiers. This research highlights the dual role of emojis, which are often linked to positive emotions and offensive contexts, adding complexity to digital communication. It contributes to the development of more accurate and context-sensitive natural language processing (NLP) tools.
Volume: 15
Issue: 3
Page: 3332-3345
Publish at: 2025-06-01

Analysis of geothermal technology development in the Colombian energy transition to 2050 using system dynamics

10.11591/ijece.v15i3.pp2523-2533
Diego Alberto Carreño , Isaac Dyner , Enrique Ángel Sanint , Andrés Julián Aristizábal
This research analyzes the current and future prospects of geothermal energy in Colombia using a system dynamics model. The study focuses on evaluating geothermal potential linked to hydrothermal systems, surface manifestations like geysers, and areas near volcanoes. The model, projecting up to 2050, offers a comprehensive assessment of national geothermal potential, considering technical, economic, regulatory, and social factors that influence its integration into the energy matrix. Key findings highlight the need for adjustments to the existing regulatory framework, which currently lacks sufficient incentives for geothermal project development. Additionally, the study underscores the importance of implementing stronger government policies and incentives to promote this renewable energy source. Proper social and environmental management, with active involvement of local communities, is also identified as crucial for project success. The system dynamics approach effectively models the complex interrelationships between variables shaping the future of geothermal energy in Colombia. The developed model serves as a novel tool for technological foresight in this strategic field, identifying obstacles and opportunities to unlock Colombia's significant geothermal potential and providing a systemic perspective on this critical issue for the national energy transition.
Volume: 15
Issue: 3
Page: 2523-2533
Publish at: 2025-06-01

Enhancing training performance for small models using data-centric approaches

10.11591/ijece.v15i3.pp2951-2964
Reda A. El-Khoribi , Eid Emary , Amr Essam Hassan
In this work, we propose a new system to improve the performance of classification models by applying data-centric principles. The system optimizes datasets by removing poor-quality samples and generating high-quality synthetic data. We tested the system on various classification models and datasets, measuring its performance with accuracy, precision, recall, and F1-score. The results showed significant improvements in classification performance, highlighting the effectiveness of this data-centric approach. While the scalability to large-scale datasets is still an open question, it offers great potential for future research. This approach could be valuable in critical areas like healthcare, finance, and autonomous systems, where high-quality data is crucial. Future work could explore advanced data augmentation, adapting the system for different data types like text and time-series, and extending it to semi-supervised and unsupervised learning. Our findings emphasize the importance of data quality in achieving better model performance, often overlooked in favor of model architecture. By advancing data-centric artificial intelligence (AI), this work offers a practical framework for researchers and practitioners to optimize datasets and improve machine learning systems.
Volume: 15
Issue: 3
Page: 2951-2964
Publish at: 2025-06-01

Machine learning-based hybrid emotions recognition model using electroencephalogram signals

10.11591/ijece.v15i3.pp3180-3190
Tarun Kumar , Rajendra Kumar , Ram Chandra Singh
This paper uses Hindi video clips to propose an electroencephalogram (EEG) signal-based hybrid system for emotion identification. EEG signals cannot be altered, unlike other forms of expressiveness-like voice and facial emotion. The suggested approach uses a self-created dataset under the control environments. Accuracy is the main objective of the proposed model. This study used a self-created constructed using an 8-channel unicorn black hybrid EEG machine on 30 participants while they viewed Hindi movie video clips mimicking emotions: happy, fearful, sad, and neutral. The proposed model used a two-hybrid classifier support vector machine (SVM) and k-nearest neighbor (KNN), implemented using MATLAB R2017a. In the proposed implementation, the four emotion classification categories (happy, sad, fear, and neutral) observed an average accuracy of 60.832%. The results of the presented study were compared with two recent systems. It was found that the proposed system observed better accuracy for the category of NHP five classes and the category of HP Five Classes.
Volume: 15
Issue: 3
Page: 3180-3190
Publish at: 2025-06-01

Challenges of load balancing algorithms in cloud computing utilizing data mining tools

10.11591/ijece.v15i3.pp3449-3457
Anouar Ben Halima , Hafssa Benaboud
In the cloud computing environment, load balancing plays an important role in the efficient operation of cloud computing, where a multitude of resources serve diverse workloads and fluctuating demands. In the rapidly evolving cloud computing, efficient resource management, and optimization are critical for maximizing performance, scalability, and cost-effectiveness. Load balancing algorithms aim to distribute workloads across cloud resources to ensure optimal utilization and maintain high availability of services. This paper presents a comparative study of load balancing algorithms in cloud computing using data mining tools. It underscores the complexity of selecting algorithms for effective load balancing in scenarios with diverse criteria, emphasizing its critical importance for future research and practical implementations. The experimental results are presented, evaluating the performance of different load balancing algorithms using data-mining tools. The outcomes highlight the substantial difficulties when building a model with unacceptable errors to cover users’ needs while selecting the desired load balancing method.
Volume: 15
Issue: 3
Page: 3449-3457
Publish at: 2025-06-01

Evaluating the feasibility of a photovoltaic-wind-diesel-battery hybrid microgrid for sustainable off-grid electrification in Dakhla, Morocco

10.11591/ijece.v15i3.pp2655-2668
Sara Fennane , Houda Kacimi , Hamza Mabchour , Fatehi ALtalqi , Adil Echchelh
Hybrid renewable energy systems (HRES) present a promising solution for improving energy reliability and reducing costs in remote, off-grid areas. This study explores the feasibility of implementing an HRES in Dakhla, Morocco, where conventional electrical infrastructure is lacking. By integrating photovoltaic (PV) panels, wind turbines, diesel generators, and battery storage, this study aims to optimize energy resource management while balancing technical performance and economic viability. Using real- world data on energy consumption, climatic conditions, and installation constraints, advanced simulation tools such as HOMER were employed to evaluate both technical and economic parameters. The objective was to minimize the cost of energy (COE) while ensuring reliability, availability, and a high renewable fraction. The results, compared with optimization algorithms like genetic algorithm (GA), particle swarm optimization (PSO), and simulated annealing (SA), revealed the PV-wind-diesel-battery configuration as the most cost-effective solution. This configuration resulted in a net present cost (NPC) of $829,380, a COE of $0.160/kWh, and minimal CO2 emissions of 54.9 kg/year. The findings highlight the viability of this hybrid microgrid as a sustainable off-grid electrification solution and emphasize the role of renewable energy in addressing global energy challenges.
Volume: 15
Issue: 3
Page: 2655-2668
Publish at: 2025-06-01

Routing mechanism ensuring congestion free communication in wireless sensor networks enabled by internet of things for applications in smart healthcare

10.11591/ijece.v15i3.pp2874-2887
Kasi Venkata kiran , T. Srinivasa Rao
Recently, the architecture of internet of things (IoT) has been applied towards gathering physical, biological, and dynamic signs of the patients within consumer-oriented electronic-health or health services. In these healthcare systems, various therapeutic sensors are placed on patients to monitor vital signs. However, the process of collecting data in IoT-enabled wireless sensor networks (WSNs) often faces congestion issues, resulting in packet loss, reduced reliability, and decreased throughput. To tackle this challenge, this proposed paper recommends a distributed congestion control algorithm tailored specifically representing IoT-enabled WSNs used in healthcare contexts. The suggested approach improves congestion by employing a priority-based data routing strategy and introduces the precedence queue- based scheduling method to improve reliability. Then the effectiveness of this congestion control process is analyzed statistically, and its performance is verified across extensive simulations and real-life experiments. This solution shows potential for applications like early warning systems for identifying peculiar heart rates, blood pressure, electromyography (EMG), and electrocardiogram (ECG) in hospital or home care settings, thus advancing the current diagnosis capabilities.
Volume: 15
Issue: 3
Page: 2874-2887
Publish at: 2025-06-01

Fault tolerant design for 8-bit Dadda multiplier for neural network applications

10.11591/ijece.v15i3.pp2697-2705
Raji Chandrasekharan , Sarappadi Narasimha Prasad
As digital electronic systems continue to shrink in size, they face increased susceptibility to transient errors, especially in critical applications like neural networks, which are not inherently error-resilient. Multipliers, fundamental components of neural networks, must be both fault tolerant and efficient. However, traditional fault free designs consume excessive power and require substantial silicon real estate. Among existing multiplier architectures, the Dadda multiplier stands out for its speed and efficiency, but it lacks fault tolerance needed for robust neural network applications. Therefore, there is need to design a power efficient and fault free Dadda multiplier that can address these challenges without significantly increasing power consumption or hardware complexity. In this paper a solution involving a fault tolerant Dadda multiplier optimized for neural network applications is proposed. Because of its speed and efficiency when compared to other multipliers Dadda multiplier is used as the base architecture which is designed using carry select adder (CSA) in conjunction with binary to excess one converter to reduce power and complexity. To enhance fault tolerance, self-repairing full adder is used to implement the CSA. This allows the system to detect and correct errors, ensuring robust operation in the presence of transient faults. This combination achieves a power efficient, fault tolerant multiplier with a power consumption of 52.3 mW, reflecting a 3% reduction in power compared to existing designs.
Volume: 15
Issue: 3
Page: 2697-2705
Publish at: 2025-06-01
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