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Hybrid deep neural network model for aspect and opinion extraction with multi-head attention-driven sentiment analysis

10.11591/ijict.v15i2.pp769-777
Abhinandan Shirahatti , Ramesh Medar , Vijay Rajpurohit , Sanjeev Kaulgud , Mrutyunjaya Mathad Shivamurthaiah
Finding and extracting significant features from review sentences is known as aspect triplet extraction, and it provides succinct information on the elements that users have addressed. This method makes sentiment analysis and opinion mining easier, which helps to provide an adequate understanding of user opinions in reviews. This research presents a novel approach to achieve aspect-sentiment triplet-extraction (ASTE) using a deep neural network and transformer-based multi-head attention model. The proposed hybrid model adopts a pipeline methodology, concurrently extracting opinions and aspects while performing sentiment classification. The study addresses the intricate challenge of identifying triplets that capture nuanced relationships between terms and sentences, employing a deep neural network for joint extraction of aspects and opinions using a sequential tagging method. Sentiment classification is seamlessly integrated into the pipeline, treating sentiment recognition as a classification task, and aspect and opinion extraction as text-extraction challenges. Evaluations was out experimentally on the SemEval 2016 restaurant dataset demonstrate the effectiveness of the model, despite issues with unequal distribution of data.
Volume: 15
Issue: 2
Page: 769-777
Publish at: 2026-06-01

Machine learning centered energy optimization in mobile edge computing: a review

10.11591/ijict.v15i2.pp465-476
Chandapiwa Mokgethi , Tshiamo Sigwele , Kabo Clifford Bhende , Aone Maenge , Selvaraj Rajalakshmi
Current literature reviews on machine learning-based approaches for mobile edge computing (MEC) energy optimization often lack in-depth gap analysis and fail to identify trends or offer actionable insights. Most focus narrowly on comparing MEC frameworks without critically evaluating or benchmarking prior research. This review contributes by addressings these gaps via analysis of existing reviews and related studies, with a focus on ML models, research objectives, evaluation metrics, datasets, tools, and gap identification. The review method follows a systematic literature review (SLR) using the PRISMA framework for transparency and reproducibility. Key findings reveal persistent challenges in energy consumption, computational overhead, cost, and poor performance in accuracy, QoS, latency, scalability, and carbon footprint. Deep reinforcement learning (DRL) emerges as the most commonly used model (55%), while TensorFlow (35%) is the most adopted tool, valued for its flexibility and robust community support. The AudioSet dataset is frequently used (28%) due to its compatibility. However, methodology limitations include dependency on study quality and exclusion of grey literature, context sensitivity. The review concludes by recommending advanced solutions such as serverless computing, liquid cooling, containerization, software-defined power, quantum computing, and blockchain to drive future MEC energy optimization.
Volume: 15
Issue: 2
Page: 465-476
Publish at: 2026-06-01

Business intelligence and its impact on organizational decision-making: a systematic review

10.11591/ijict.v%vi%i.pp%p
Cesar Patricio-Peralta , Hernan Peña Carnero , Jesús Mondragon , Adan Eugenio Contreras Angeles , Marina Vargas Vega , Walter Patricio Peralta , Marco Mayor Ravines , Juan Mayor Gamero , Cesar Paccha Rufasto
This research examines in detail how business intelligence (BI) supports and guides organizations in decision-making for their plans. The paper warns that the BI tool must be adapted to users' real needs. It's super crucial to keep all the important info in one spot. This optimizes resources and boosts the system's capabilities. The study used a set approach to tackle its main question. This included much searching through big science lists. Scopus and Web of Science were on the list. The search term was a particular word used to pinpoint documents. The review looked at studies from 2019 to 2025. Initially, we found 77 papers. Rules were then applied to include or exclude papers. These descartes criteria take into account the kind of paper, the language used, and how relevant it is to the subject. In the end, 24 papers went through the peer review process. These were reviewed following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. The findings indicate that the application of BI considerably improves the group’s ability to attain superior goals. Some research showed a 93% boost in productivity. Profits went up by 65%, too. These results come only from articles written in English, Spanish, and Portuguese. They mainly focus on explaining the functioning in wealthier nations. The results really show off the main perks of BI. It facilitates informed decision-making more easily for all organisations.
Volume: 15
Issue: 2
Page: 741-749
Publish at: 2026-06-01

Utilizing the machine learning-driven techniques used to ECG dataset for predicting coronary heart disease

10.11591/ijict.v15i2.pp719-728
Mohd Osama , Rajesh Kumar , Chandrakant Kumar Singh
The worldwide cause of mortality is cardiovascular heart disease. The automatic prediction of heart disease can be made to possible for accurate detection in initial stage. In recent year, the artificial intelligence approaches giving promising outcomes in predicting various types of cardiovascular conditions. The main focous of this work is to implementation of various machine learning techniques used to predict cardiovascular heart disease (CHD) using electrocardiogram (ECG) datasets. ECG provide the electrical Signal from the heart that identify the presence of disease or not. The preprocessing method are used for improving the quality of ECG signals and extract the features from ECG of patients. There are several well-established machine learning techniques, including support vector machine (SVM) and K-nearest neighbour (KNN)., logistic regression and decision tree classifier used for prediction of the disease. So, our finding of this paper will provide the new understanding regarding CHD prediction using different machine learning techniques. The Decision Tree-based machine learning model demonstrated excellent performance, achieving 98% accuracy, 96% precision, 100% recall, and an F1-score of 97%, which is better than rest of other comparative machine learning models. Finaly expermental results shows that decision tree approach providing better outcome amongs all the algorithms with respect to all above mensioned parameter.
Volume: 15
Issue: 2
Page: 719-728
Publish at: 2026-06-01

Manufacturing mycelium moulds under controlled conditions using IoT

10.11591/ijict.v15i2.pp880-890
Subbulakshmi V. , Jeevaa Katiravan , Parvathy M. , Sridevi S.
In the process of making plastics, potentially dangerous substances like colourants or stabilisers are added. One example is phthalates, which are used to make PVC. The ecology is significantly impacted by the way plastic products are disposed of as well. The majority of plastics can take a long time to biodegrade lengthy time to break down if disposed of in a landfill. The issue of plastic trash is getting worse. Plastic is incredibly valuable due to its cheap availability and low cost of production; however, its recyclability has been oversold. Mycelium mould is a fantastic substitute for plastic. Mycelium is more efficient in terms of biodegradability and sustainability compared to plastic. The properties of Mycelium include heat insulation, fire resistance, water resistant, acoustic insulation, low weight, vegan meat, beauty products, and mainly bio-degradable. All these features make mycelium our only last chance to win the war against the plastic with greater potential than the other alternatives for plastics available in the market currently. Here, we have shown how mycelium can be grown in the most efficient way ever without any contamination and faster growth cycle. The primary goal is to lower the cost of mycelium mould, lessen mycelium spoiling, and accelerate its growth cycle by offering an ideal growing.
Volume: 15
Issue: 2
Page: 880-890
Publish at: 2026-06-01

A new modified characteristic equation for optimal coordination of directional overcurrent relays

10.11591/ijict.v15i2.pp789-796
Neelakanteshwar Rao Battu , Surender Reddy Salkuti
The integration of distributed generation (DG) into power systems is increasing to meet the requirements of the utility system. Renewable energy sources are given priority due to their clean energy and high consistency advantages. Integration of DG into the system makes the bi-directional flow of current. Directional type overcurrent relays are usually used for protection of lines associated with bidirectional power flows. The installation of DGs, (especially, inverter-based) invites challenges to the existing protection schemes. A new modified characteristic equation-based approach is proposed in this paper to obtain the faster operational time of relays. The relay coordination scheme proposed in this paper is applied to an 8-bus test system integrated with the solar-based photovoltaic integrated distributed generator (PVIDG). The comparative analysis between the conventional and proposed approaches is done.
Volume: 15
Issue: 2
Page: 789-796
Publish at: 2026-06-01

Enhancing support vector machine performance using particle swarm optimization for sentiment analysis

10.11591/ijict.v15i2.pp523-534
Christofer Satria , Anthony Anggrawan , Peter Wijaya Sugijanto , Husain Husain , I Nyoman Yoga Sumadewa , Victoria Cynthia Rebecca
Recently, social media has established itself as a leading platform in various sectors. Meanwhile, text extraction and sentiment analysis classification have attracted significant attention in research. Regrettably, traditional sentiment analysis often falls short of accurately capturing sentiment nuances. At the same time, machine learning has enabled more effective sentiment analysis, data mining, and classification, as well as the development of models that incorporate artificial intelligence. Therefore, the purpose of this study is to optimize sentiment analysis of public opinion in social media regarding Grand Prix motorcycle racing (MotoGP) and World Superbike (WSBK) events using machine learning and an optimized machine learning method. This study applies the support vector machine (SVM) machine learning method and enhances its performance through optimization by integrating it with the particle swarm optimization (PSO) algorithm. This study found that the SVM method achieved 80.15% accuracy, 75.63% recall, and 76.89% F1-score. In contrast, the SVM method combined with PSO achieves accuracies of 81.82%, 79.9%, and 79.62% for recall, precision, and F1-score, respectively, in classifying the sentiment of sporting events. The implications suggest that applying Hybrid SVM with PSO significantly enhances classification accuracy in sentiment analysis.
Volume: 15
Issue: 2
Page: 523-534
Publish at: 2026-06-01

A review of sensemaking design elements: towards an affordances typology

10.11591/ijict.v15i2.pp488-496
Fadzlin Ahmadon , Murni Mahmud , Muna Azuddin
This study explores the intersection of interaction design and sensemaking within digital systems, aiming to identify and categorize key affordances that enhance user sensemaking. Starting with a focused literature review, key design elements such as tagging and annotation are identified, important for effective sensemaking in interaction design. Drawing on Maier's construct of affordances, the behaviours of these design elements are analyzed to derive specific affordances integral to enhancing user experience. The primary objective is to develop a generalized affordance typology that supports sensemaking across various digital systems. This typology organizes the derived affordances into broad themes such as effortless discovery, expressive freedom, collaborative engagement, cognitive support, insight enhancement, and user empowerment. This typology serves as a tool for interaction designers, facilitating the application of these themes in various design scenarios to create more intuitive and effective digital environment for sensemaking.
Volume: 15
Issue: 2
Page: 488-496
Publish at: 2026-06-01

An integrated EHS-based risk assessment framework for hazardous substance storage: regulatory gap analysis in Thailand

10.11591/ijaas.v15.i2.pp555-572
Hengheng Jarunongkran , Therdpong Daengsi
This study examines hazardous substance storage risk assessment within the environmental, health, and safety (EHS) framework, focusing on Thailand’s regulatory context. Using a systematic review and comparative regulatory analysis, the research evaluates existing risk assessment methodologies and identifies structural and implementation gaps in current practices. The findings reveal deficiencies in the integration of toxicological data with legal requirements, fragmented regulatory enforcement, limited standardization, and insufficient practical tools for workplace application. A comparative assessment of international frameworks, including European Union Registration, Evaluation, Authorization, and Restriction of Chemicals (EU REACH), United States Occupational Safety and Health Administration (US OSHA), and Japan’s Chemical Substances Control Law (CSCL), highlights best practices in proactive risk management and transparency. Based on these insights, the study proposes an integrated EHS-based risk assessment framework that aligns Department of Industrial Works (DIW) regulations with international standards such as ISO 14001, ISO 45001, and globally harmonized system (GHS). The proposed model supports proactive risk identification, regulatory coherence, and improved stakeholder awareness, contributing to enhanced chemical safety governance and ASEAN harmonization.
Volume: 15
Issue: 2
Page: 555-572
Publish at: 2026-06-01

Transforming electric vehicle charging through solar integration and high-frequency magnetic induction for seamless wireless power transfer

10.11591/ijece.v16i3.pp1118-1131
Selvan Chinnaiyan , Prabhakar Manickam , Madhu Chandra G. , Aarthi V. , Narendra Babu C. R.
The rapid adoption of electric vehicles (EVs) is constrained by limited charging infrastructure, prolonged charging duration, grid dependency, and inefficiencies in conventional wireless charging systems. To address these challenges, this paper proposes a solar-integrated high-frequency inductive wireless charging framework that enables efficient, contactless, and partially dynamic EV charging. The proposed system combines a photovoltaic (PV) energy harvesting subsystem with maximum power point tracking (MPPT), a high-frequency resonant inductive coupling mechanism using a series–series (SS) topology, and an intelligent solar inductive synergy optimization algorithm (SISOA) for adaptive power and energy storage management. The theoretical foundation of the system is based on Faraday’s law of electromagnetic induction and resonant magnetic coupling to enhance mutual inductance and power transfer efficiency. Simulation studies conducted in MATLAB/Simulink demonstrate that the proposed approach achieves a mutual inductance of 82.5, an output voltage of 500 V, and an output power of 4,800 W, while reducing overall power losses to 21.18% and improving system efficiency to 94.5%. The results further reveal that vehicle speed and the number of receiver coils significantly influence charging effectiveness and state-of-charge performance.
Volume: 16
Issue: 3
Page: 1118-1131
Publish at: 2026-06-01

Diabetic retinopathy detection using SWIN transformer

10.11591/ijict.v15i2.pp750-758
Sheetal J. Nagar , Nikhil Gondaliya
Diabetic retinopathy (DR) is a diabetes related eye disorder that damages the retina. DR is among the most specific complications of diabetes. A vital challenge for automated detection systems in medical image diagnosis is to minimize the false negative rate for patients’ timely treatment. This paper presents a novel strategy employing the shifted window (SWIN) Transformer for efficiently modeling local and global visual information to address this challenge. We have proposed our work to maximize the true positive ratio and minimize the false negative ratio for the automated process of diagnosing the level of DR, so that patients with positive signs of DR can be predicted most accurately and can save vision. The results suggest that SWIN Transformer architecture, along with the contrast-limited adaptive histogram equalization (CLAHE) technique, provides a robust option for developing a reliable DR detection system. The results indicate that the proposed approach achieves 96% weighted recall across all the levels of DR detection and 97.45% validation accuracy for the eyePACS DR detection dataset, as well as 99% weighted recall across all the levels of DR detection, along with 99.26% validation accuracy for APTOS 2019 Blindness Detection dataset. Thus, this study aimed to develop a DR detection system focused on minimizing false negatives using the SWIN transformer.
Volume: 15
Issue: 2
Page: 750-758
Publish at: 2026-06-01

Improving warehouse efficiency of sparepart storage through comparison of dedicated and class-based storage methods

10.11591/ijaas.v15.i2.pp470-478
Prayoga Prima Hermawan , Qurtubi Qurtubi
Warehouses play a crucial role in supporting the efficiency of supply chain operations, especially in material management and goods movement. However, in a company engaged in the paper printing and recycling industry, inefficiency in searching and retrieving goods in the spare parts warehouse is a major challenge, with a sub-optimal layout hampering smooth operations. This research offers a new perspective by evaluating the effectiveness of two storage methods, namely class-based storage and dedicated storage, in the context of spare parts warehouses that have different layout characteristics. This study aims to compare the two methods in improving material-handling efficiency and optimizing warehouse layout. The results show that the class-based storage method is more effective in reducing travel distance and material-handling time than dedicated storage. In addition, this method can improve operational efficiency and support the smooth production process through the arrangement of goods based on picking frequency. The theoretical implications of this research contribute to the development of warehousing management literature, while practically, the results of this study can serve as a guide for industries in implementing more efficient warehouse layout strategies.
Volume: 15
Issue: 2
Page: 470-478
Publish at: 2026-06-01

Estimating road transport emissions in Thailand using data-driven modeling approaches

10.11591/ijaas.v15.i2.pp501-517
Phachaya Chaiwchan , Kunyanuth Kularbphettong
Traffic emissions remain a pivotal factor in Bangkok’s air quality issues, particularly as the city expands and vehicle types become more varied and traditional emission-inventory tools, such as Computer Programme to Calculate Emissions from Road Transport (COPERT) and laboratory driving cycles, are useful but often fall short when applied to congested real-world conditions. This work responds to that gap by combining laboratory measurements, regulatory emission factors, and vehicle-registration information to estimate CO2 emissions at the vehicle level. Data from 1,846,111 registered vehicles (January 2023–January 2025) were consolidated from Environmental Protection Agency (EPA) fuel-economy tests, COPERT/Euro standards, and the Thai DLT registry. After harmonizing attributes across all sources, additional features were created and screened using recursive feature elimination (RFE). Three models were trained and evaluated using coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). CatBoost provided the most reliable predictions (RMSE =23.4 g/km; MAE =14.6 g/km; R2 =0.921), followed by deep neural network (DNN) (R2 =0.887) and support vector regression (SVR) (R2 =0.846). RFE consistently identified fuel consumption measures, emission factors, and tailpipe CO2 as the most influential variables and the results provide a clearer picture of how vehicle technologies contribute to Bangkok’s CO2 burden and support policy measures such as emission taxes, incentives for low-emission vehicles, and monitoring of high-emission groups.
Volume: 15
Issue: 2
Page: 501-517
Publish at: 2026-06-01

Increasing the efficiency of deep learning performance using adaptive filters

10.11591/ijaas.v15.i2.pp775-789
Suad Khairi Mohammed , Sabah A. Gitaffa , Reem I. Dawai
Deep learning algorithms have become one of the most important innovative technologies that have entered almost all areas of life. These technologies perform complex operations and deal with huge data sets. One of the benefits of deep learning is the inherent flexibility in developing approximate estimates for vast and diverse data sets. Data scientists can develop approximate estimates of almost anything using deep learning and neural networks. The main challenges in deep learning include the problem of data quality and quantity while ensuring large, diverse, and high-quality datasets. It also suffers from the problem of providing computational resources due to the high demand for powerful devices, such as processing and memory units. Additionally, it suffers from the problem of interpretability of the case due to difficulty in understanding and explaining typical decisions. This study proposes an innovative method to reduce these problems in the working mechanisms of deep learning algorithms by merging their layers and hybridizing them using adaptive digital filters. These filters help provide devices for efficient resources and memory units, in addition to the capabilities of analyzing and interpreting various states of processing unit availability. In this study, models of hybrid deep learning techniques with adaptive digital filters were designed and implemented, obtaining good results in reducing training error rates, improving the efficiency of outputs, and reducing the computational effort to high levels.
Volume: 15
Issue: 2
Page: 775-789
Publish at: 2026-06-01

Multi-level redundancy with internet of things battery supply for fault mitigation in grid-tied photovoltaic systems

10.11591/ijaas.v15.i2.pp622-633
Habib Satria , Muhammad Fadlan Siregar , Indri Dayana , Dadan Ramdan , Muhammad Irwanto , Syafii Syafii
The development of grid-tied photovoltaic (PV) systems in tropical regions remains a strategic focus for achieving sustainable clean energy. However, energy conversion efficiency is often hampered by fluctuations in panel surface temperature and electrical faults. To ensure long-term system reliability, this study implements a multi-level redundancy architecture integrated with dynamic internet of things (IoT) monitoring for fault mitigation in grid-tied PV systems. The system employs a machine learning (ML) method using the k-nearest neighbors (KNN) algorithm for thermal classification, achieving an accuracy of 84% in identifying normal (25 °C to 35 °C) and overheating conditions. Furthermore, an electrical redundancy layer is designed with an automatic tripping mechanism that activates when the current exceeds a 1.30 A threshold, demonstrating a rapid response latency of 150 ms. To ensure monitoring resilience, the system is supported by a dedicated 18650 Li-ion battery backup. The implementation results confirm that this multi-level protection framework effectively monitors real time energy usage, prevents critical component damage, and enhances the overall safety of household-scale PV installations. This research provides a scalable and intelligent solution for fault mitigation, supporting the broader adoption of renewable energy in tropical environments.
Volume: 15
Issue: 2
Page: 622-633
Publish at: 2026-06-01
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