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31,456 Article Results

Lung cancer patients survival prediction using outlier detection and optimized XGBoost

10.11591/ijai.v14.i3.pp2146-2157
Wirot Yotsawat , Peetiphart Suebpeng , Saroch Purisangkaha , Akarapon Poonsawad , Kanyalag Phodong
This research aims to improve the prediction’s model for survival time of lung cancer patients by using outlier detection, hyper-parameter optimization, and machine learning technique. The research compares the performance of several methods including multilayer perceptron (MLP), decision tree (DT), linear regression (LR), Bagging, XGBoost, and random forest (RF). The dataset used for the experiment is obtained from the surveillance, epidemiology, and end result (SEER) cancer database, which contains diagnoses data from 2004 to 2015. The total number of records used is 196,031 with 22 features. 10-fold cross-validation is used for training and testing sets. The evaluation metrics are root mean square error (RMSE), mean squared error (MSE), R-squared (R2), and mean absolute error (MAE). The results show that the lung cancer patient survival prediction model using the optimized XGBoost (O-XGBoost) model performs the best with an RMSE of 13.74 and outperforms the baseline-XGBoost model as well as other models. This research will be useful for developing a clinical decision support system for the care of lung cancer patients. Physicians can use the developed model to assess the patient’s chance of survival in order to plan more effective treatment.
Volume: 14
Issue: 3
Page: 2146-2157
Publish at: 2025-06-01

LMS bot: enhanced learning management systems for improved student learning experiences using robotic process automation

10.11591/ijai.v14.i3.pp2044-2054
Mamidyala Durga Prasad , Nandini Balusu
In this paper, a workflow for bot is designed using robotic process automation (RPA) that is used to enhance learning management systems (LMS) by providing content from external sources along with educator made course content for better student learning experiences. Many students prefer to watch YouTube videos for learning, even if they have been taught the same content by an educator. YouTube is a dynamic platform where video rankings change based on viewer engagement, relevance, and newly included videos. This variability poses a challenge for educators seeking to include external videos, as the content environment within the LMS platform is unpredictable and can change significantly. The bot addresses the challenge by conducting periodic searches for related courses and topics on YouTube. It retrieves top-ranked videos based on relevance, which are then seamlessly integrated into external links within LMS. The LMS external links option enhances accessibility by offering videos sorted by popularity, ensuring students receive updated and relevant information seamlessly. The bot efficiently retrieves details of 750 videos from YouTube in just 17 seconds, showcasing its exceptional performance. Moreover, its capability to autonomously update LMS external links content weekly represents an added advantage. The bot is designed and tested using UiPath tool.
Volume: 14
Issue: 3
Page: 2044-2054
Publish at: 2025-06-01

Rooftops detection with YOLOv8 from aerial imagery and a brief review on rooftop photovoltaic potential assessment

10.11591/ijai.v14.i3.pp2282-2290
Md. Sabbir Ahmed , Md. Shohel Arman , Nusrat Tasnim , Md Hafizul Imran , Musabbir Smmak , Touhid Bhuiyan
Recent years have seen significant advancements in the switch from fossil fuel-based energy systems to renewable energy. Decentralized solar photovoltaic (PV) is one of the most promising energy sources since there is a lot of rooftop space, it is easy to install, and the cost of the PV panels is low. The determination of rooftop locations for PV installation is crucial for energy planning. With this context, this study aimed to detect the suitable rooftops of different shapes. The dataset of 5,076 building roofs used in this study was gathered by us utilizing a drone. This study identified ten distinct roof shapes accurately, including triangle, square, penta, hexa, hepta, octa, nona, deca, gabled roof, and hipped roof, using the most recent version of you only live once (YOLO), known as YOLOv8. Recent research revealed, YOLOv8 is more accurate than earlier YOLO models which is the reason of utilizing YOLOv8. Accuracy of this work of rooftops detection is 93.6%. Also, the precision, recall, and F1-score confidence curve showed good performances too. Finally, a brief review of the most recent studies on the evaluation of rooftop PV potential was conducted to provide insight into the use of solar energy.
Volume: 14
Issue: 3
Page: 2282-2290
Publish at: 2025-06-01

Optimizing real-time data preprocessing in IoT-based fog computing using machine learning algorithms

10.11591/ijai.v14.i3.pp1900-1909
Nandini Gowda Puttaswamy , Anitha Narasimha Murthy
In the era of the internet of things (IoT), managing the massive influx of data with minimal latency is crucial, particularly within fog computing environments that process data close to its origin. Traditional methods have been inadequate, struggling with the high variability and volume of IoT data, which often leads to processing inefficiencies and poor resource allocation. To address these challenges, this paper introduces a novel machine learning-driven approach named real-time data preprocessing in IoT-based fog computing using machine learning algorithms (IoT-FCML). This method dynamically adapts to the changing characteristics of data and system demands. The implementation of IoT-FCML has led to significant performance enhancements: it reduces latency by approximately 0.26%, increases throughput by up to 0.3%, improves resource efficiency by 0.20%, and decreases data privacy overhead by 0.64%. These improvements are achieved through the integration of smart algorithms that prioritize data privacy and efficient resource use, allowing the IoT-FCML method to surpass traditional preprocessing techniques. Collectively, the enhancements in processing speed, adaptability, and data security represent a substantial advancement in developing more responsive and efficient IoT-based fog computing infrastructures, marking a pivotal progression in the field.
Volume: 14
Issue: 3
Page: 1900-1909
Publish at: 2025-06-01

Enhancing plagiarism detection using data pre-processing and machine learning approach

10.11591/ijai.v14.i3.pp1940-1950
Vrushali Bhuyar , Sachin N. Deshmukh
Modern technology and the internet have enhanced academic information accessibility, but this has led to a rising global concern about plagiarism. Researchers are actively exploring machine learning as a promising solution for detection. This study underscores the importance of robust data preprocessing for optimal machine learning algorithm performance. Using a dataset of 67 research papers, big five factors (OCEAN), and plagiarism rates, the study employed machine learning to detect plagiarism. The training process involved exposing algorithms to an 80% training subset, followed by evaluating their performance on the remaining 20% in the testing phase, assessing generalization capabilities. For the random forest regressor, bagging regressor, gradient boosting regressor, XGB regressor, and AdaBoost regressor, corresponding root mean squared error (RMSE) are 9.48, 10.66, 11.79, 12.53, and 12.79, respectively. This research contributes novel insights to existing literature by introducing a plagiarism detection model that innovatively integrates outlier detection, normalization, missing value imputation, and feature selection. The unique aspect lies in the effective combination of feature selection and missing value imputation, surpassing previous benchmarks and optimizing precision and efficiency. The approach is metaphorically likened to assembling puzzle pieces, highlighting the distinctive methodology employed in enhancing the performance of the plagiarism detection model using data preprocessing.
Volume: 14
Issue: 3
Page: 1940-1950
Publish at: 2025-06-01

Hybrid optimization tuned deep neural network-based wind power generation system for permanent magnet synchronous generator control

10.11591/ijece.v15i3.pp2599-2615
Prashant Kumar S. S Chinamalli , Mungamuri Sasikala
Wind energy, a cost-effective renewable source, has seen substantial growth. permanent magnet synchronous generator (PMSG) equipped wind turbines demonstrate superior performance in variable-speed applications. However, there remains a notable research gap in optimizing the overall system efficiency for such wind energy systems. Therefore, this research presents to develop a deep learning-based optimization technique that improves the efficiency of PMSG-based wind energy systems by minimizing overall system losses and maximizing energy output. Core loss and rotor speed data were fed into a deep neural network for various operating conditions ranging from 50 to 1000 rpm, to determine optimal system parameters. This work introduces a hybrid lyrebird-based coati optimization algorithm (LB-COA) to optimize the deep neural networks (DNN) classifier, combining two advanced optimization techniques to improve model performance. Simulation results validate that the proposed optimization strategy efficiently boosts the system's dynamic performance and overall power efficiency.
Volume: 15
Issue: 3
Page: 2599-2615
Publish at: 2025-06-01

Novel artificial intelligence-based ensemble learning for optimized software quality

10.11591/ijai.v14.i3.pp1820-1828
Sangeetha Govinda , Agnes Nalini Vincent , Merwa Ramesh Babu
Artificial intelligence (AI) contributes towards improving software engineering quality; however, existing AI models are witnessed to deploy learning-based approaches without addressing various complexities associated with datasets. A literature review showcases an unequilbrium between addressing the accuracy and computational burden. Therefore, the proposed manuscript presents a novel AI-based ensemble learning model that is capable of performing an effective prediction of software quality. The presented scheme adopts correlation-based and multicollinearity-based attributes to select essential feature selection. At the same time, the scheme also introduces a hybrid learning approach integrated with a bio-inspired algorithm for constructing the ensemble learning scheme. The quantified outcome of the proposed study showcases 65% minimized defect density, 94% minimized mean time to failure, 62% minimized processing time of the algorithm, and 43% enhanced predictive accuracy.
Volume: 14
Issue: 3
Page: 1820-1828
Publish at: 2025-06-01

Comparative assessment of an improved asymmetrical fuzzy logic control-based maximum power point tracking for photovoltaic systems under partially shaded conditions

10.11591/ijece.v15i3.pp2642-2654
Athirah Batrisyia Kamal Ariffin , Muhammad Iqbal Zakaria , Wan Noraishah Wan Abdul Munim , Muhammad Nizam Kamarudin , Nabil El Fezazi
This paper presents an enhanced asymmetrical fuzzy logic control (AFLC) based maximum power point tracking (MPPT) algorithm designed for photovoltaic (PV) systems under partial shading conditions (PSCs). With the increasing global energy demand and growing environmental concerns, maximizing solar energy efficiency has become more essential than ever. The proposed AFLC-MPPT algorithm tackles the challenges of accurately tracking the global maximum power point (GMPP) in PSCs, where conventional methods frequently underperform. By utilizing asymmetrical membership functions and optimized rule sets, the algorithm significantly improves sensitivity and precision in detecting and responding to variations in shading. Simulations conducted in MATLAB/Simulink compare the performance of the proposed AFLC-based MPPT with the conventional perturb and observe (P&O) method across multiple shading scenarios. The results demonstrate that the AFLC approach outperforms the conventional method in terms of tracking speed, stability, and overall efficiency, particularly in dynamically changing environmental conditions. Furthermore, the AFLC algorithm provides substantial improvements in voltage regulation, reduces settling time, and minimizes steady-state oscillations, contributing to the more efficient and reliable operation of PV systems under partial shading conditions.
Volume: 15
Issue: 3
Page: 2642-2654
Publish at: 2025-06-01

Comparative analysis of active filters, inductor-capacitor and inductor-capacitor-inductor passive filters in reducing harmonics

10.11591/ijece.v15i3.pp2567-2582
Yulianta Siregar , Naomi Azhari , Nur Nabila Mohamed
Control equipment at substations requires a rectifier to convert alternative current (AC)-direct current (DC) electric current to provide DC power for relays, motors for disconnector switches and power breaker switches, and telecommunications equipment. Rectifiers have non-linear load characteristics, which can result in a waveform that is not pure sinusoidal due to the interaction of fundamental frequency sinusoidal waves with other waves known as harmonics. Therefore, to not interfere with the equipment's work, a filter is needed to reduce the harmonics produced by the rectifier. In this research, using MATLAB/Simulink, prevention was carried out using active filters, inductor-capacitor (LC), and inductor-capacitor-inductor (LCL) passive filters (Ta, Tc, and Td designs) separately. After the research was carried out, it was found that the amount of harmonics before installing the filter was 49.61%. Then, after installing the active filter, the harmonics were reduced to 0.29%, the installation of the passive LC filter was reduced to 9.29%, and the installation of the LCL filter (Ta, Tc, and Td) became 1.44%, 0.29%, and 1.44%.
Volume: 15
Issue: 3
Page: 2567-2582
Publish at: 2025-06-01

Effectiveness of artificial intelligence-driven chatbot responses in diabetes knowledge: a readability and reliability assessment

10.11591/ijai.v14.i3.pp2379-2388
Muhammad Thesa Ghozali , Woro Supadmi , Fadya Bella Suci Maharani , Aloina Jean Rassyifa
Patient education is vital in diabetes management, empowering patients with necessary knowledge and skills to manage condition effectively. However, traditional educational methods often face challenges such as limited access to healthcare professionals and variability in information quality. This study aimed to assess the reliability and readability of artificial intelligence (AI)-driven chatbot responses in disseminating diabetes knowledge. Technically, the diabetes knowledge questionnaire (DKQ-24) was administered to evaluate the effectiveness of AI-driven chatbot in disseminating diabetes-related information. Responses were evaluated for reliability and quality applying the modified DISCERN (mDISCERN) scale and global quality scale (GQS), and readability was assessed using the Flesch reading ease (FRE) score, Flesch-Kincaid grade level (FKGL), gunning fog index (GFI), Coleman-Liau index (CLI), and simple measure of gobbledygook (SMOG). The mean mDISCERN score was 31.50±2.89, indicating generally reliable responses. The median GQS score was 4, reflecting the high overall quality. The readability assessment revealed a mean FRE score of 66.30, indicating that the text was fairly easy to read. FKGL mean score was 6.54±3.19, suggesting the text was suitable for readers at a sixth-grade level. In conclusion, AI-driven chatbot provides reliable and high-quality information on the diabetes self-management, but it requires improvements to enhance accessibility.
Volume: 14
Issue: 3
Page: 2379-2388
Publish at: 2025-06-01

Morphological features for multi-model rice grain classification

10.11591/ijece.v15i3.pp3212-3225
Suma D. , Narendra V. G. , Raviraja Holla M. , Darshan Holla M.
In the realm of agriculture and food processing, the automated classification of rice grains holds significant importance. The diverse varieties of rice available demand a systematic approach to categorization. This study tackles this challenge by employing diverse machine learning models, including support vector machine (SVM), random forest (RF), logistic regression (LR), decision tree (DT), Gaussian naive Bayes (GNB), and k-nearest neighbors (K-NN). The dataset, sourced from Kaggle, features five distinct rice types: Arborio, Basmati, Ipsala, Jasmine, and Karacadag. After the images undergo preprocessing, a set of 13 distinct morphological features is extracted. These features ensure a comprehensive representation of rice grains for accurate classification. This study aims to create an intelligent system for efficient and precise rice grain classification, contributing to optimizing agricultural and food industry processes. Among the models, K-NN demonstrated the highest classification accuracy at 97.80%, surpassing random forest (97.51%), DT (97.48%), GNB (96.99%), SVM (96.85%), and LR (96.05%). Our proposed K-NN-based classification model achieves an accuracy of 97.8%, demonstrating competitive performance and outclassing several state-of-the-art methods such as artificial neural network (ANN) and modified visual geometry group16 (VGG16) while maintaining simplicity and computational efficiency. This underscores the effectiveness of K-NN and RF in enhancing the precision of rice variety classification.
Volume: 15
Issue: 3
Page: 3212-3225
Publish at: 2025-06-01

The influence of sentiment analysis in enhancing early warning system model for credit risk mitigation

10.11591/ijai.v14.i3.pp1829-1838
Angel Karentia , Derwin Suhartono
One important source of bank income is interest income from credit activities, another part of which is obtained from fee-based income. Rapid credit growth is directly proportional to an increase in potential credit risk (counterparty default). In addition to comprehensive credit assessment at the initial stage of credit initiation, banks need to monitor the condition of existing debtors. Empirically, difficulties in handling non-performing loans often occur due to delays in detection and preparation of action plans. In this case, losses due to non-performing loans can have implications for the bank's reputation and worsen its financial performance. This research aims to determine the effect of sentiment analysis (external sentiment prediction model [positive, neutral, and negative] with certain keywords) on the level of accuracy of the early warning system (EWS) model in predicting the credit quality of bank debtors in the coming months. This study found that upgrading EWS with sentiment analysis will give better accuracy levels compared to traditional EWS models. In addition, the predictive power of EWS (traditional and upgraded) is inversely proportional to the prediction period, the longer the target prediction time, and the less predictive power of the EWS model.
Volume: 14
Issue: 3
Page: 1829-1838
Publish at: 2025-06-01

Development of watershed algorithm for identification of diabetic retinopathy based on fundus images

10.11591/ijece.v15i3.pp2845-2856
Surmayanti Surmayanti , Sumijan Sumijan , Saiful Bukhori
Diabetic retinopathy (DR) is a serious complication of diabetes that can lead to blindness if not detected early. This research presents a novel method for the identification of DR using fundus images, employing the Watershed Algorithm for accurate image segmentation and the gray level co-occurrence matrix (GLCM) for texture feature extraction. The image processing pipeline involves several stages, including grayscale conversion, noise reduction through Gaussian and median filters, and Otsu's thresholding to isolate key features such as retinal lesions. The watershed algorithm is applied to delineate the boundaries of abnormal regions, while the GLCM method extracts texture features like contrast, correlation, energy, and homogeneity, which are essential for diagnosing retinal abnormalities. The proposed approach demonstrates a high accuracy rate of 92%, successfully identifying abnormalities in 46 out of 50 fundus images. The method shows significant potential for enhancing early detection of DR, providing accurate segmentation and texture analysis, making it a valuable tool for medical professionals in diagnosing retinal diseases.
Volume: 15
Issue: 3
Page: 2845-2856
Publish at: 2025-06-01

Addressing the challenges of harmonizing law and artificial intelligence technology in modern society

10.11591/ijai.v14.i3.pp2471-2478
Lamprini Seremeti , Sofia Anastasiadou , Andreas Masouras , Stylianos Papalexandris
The invasion of artificial intelligence (AI) in all forms of human activity causes the sudden change of social cohesion into a new hybrid reality, where the static rule of law maybe is overthrown by the instant violations of fundamental human rights, including the general rights of personhood, in its image, honor, and privacy, as well as, of general principles of law, including the principle of the “abuse of rights”, the principle of contractual autonomy, principles of tort liability, and general principles of intellectual property law. In that sense, AI disrupts the acquis due to the poor regulatory quality indicators covering unforeseen occurrences. We call this instantiation AI legal “coup d’état”. This paper constitutes a philosophical thesis statement which is in accordance with the global efforts to legally embed AI into societal systems. As part of an ongoing research on AI and law synergy, this paper focuses on proposing a theoretical framework utilizing category theory to align AI functionalities with traditional legal principles.
Volume: 14
Issue: 3
Page: 2471-2478
Publish at: 2025-06-01

Optimizing switching states using a current predictive control algorithm for multilevel cascaded H-bridge converters in solar photovoltaic integration into power grids

10.11591/ijece.v15i3.pp2726-2734
An Thi Hoai Thu Anh , Tran Hung Cuong
Solar power is the best solution for renewable energy sources. Nowadays, solar power plants are invested and developed strongly in many places. Converting direct current (DC) energy from photovoltaic (PV) systems to the alternating current (AC) grid is critical to widely use this power source at high voltage levels. This paper presents an algorithm to optimize the valve-switching process for a cascading H-bridge multilevel converter (CHB) to convert energy from a PV system connected to the grid. This is done by a model predictive control algorithm (MPC) before a valve switching cycle, its process will be carried out in future forecast cycles and applied in the present time. From there, choose the best switching state for a working cycle. This will ensure the best quality of current and voltage with a low total harmonic distortion (THD) index to connect to the power grid. This method's advantages are reducing volume calculation for the controller, Selecting the most suitable valve switching state to achieve low valve switching frequency, reducing losses, and improving conversion efficiency. The implementation results are proven by simulation and evaluation of results on MATLAB-Simulink software.
Volume: 15
Issue: 3
Page: 2726-2734
Publish at: 2025-06-01
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