Articles

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

Advanced Search
Filter Icon

Filters article

Years

FAQ Arrow
Single Year
Range Year
0
0

Source Title

FAQ Arrow

Authors

FAQ Arrow

30,411 Article Results

Stock market liquidity: hybrid deep learning approaches for prediction

10.11591/ijai.v14.i5.pp3624-3633
Mariam Ait Al , Said Achchab , Younes Lahrichi
Predicting stock market liquidity especially in emerging or frontier financial markets, such as the Casablanca stock exchange (CSE), presents significant challenges given the relative narrowness and volatility of these markets. In this paper, we conduct a comprehensive study to address the predictions accuracy gaps between five main deep learning models: convolutional neural network (CNN), long short-term memory (LSTM), bidirectional LSTM (BiLSTM), and two hybrid architectures, CNN-LSTM and CNN-BiLSTM. The proposed methodology focused on training and testing these models on historical data from the CSE, with precision on capturing both spatial and temporal market dynamics. The models were fine-tuned using key hyperparameters and validated on 20% of the dataset to ensure reliable results. The evaluation of performance was conducted using error metrics such as mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). The study demonstrates that the hybrid CNN-biLSTM model consistently outperformed all standalone and other hybrid models in predictive accuracy. This underscores the considerable promise of hybrid deep learning architectures for addressing the unique challenges of predicting stock market liquidity in volatile and emerging financial markets.
Volume: 14
Issue: 5
Page: 3624-3633
Publish at: 2025-10-01

Optimized ensemble framework for predicting hydroponic stock and sales using machine learning

10.11591/ijai.v14.i5.pp3879-3886
Viktor Handrianus Pranatawijaya , Ressa Priskila , Putu Bagus Adidyana Anugrah Putra , Nova Noor Kamala Sari , Efrans Christian , Septian Geges , Novera Kristianti
The increasing global demand for food necessitates the adoption of sustainable agricultural practices. Hydroponic farming, while efficient in resource utilization, faces challenges in accurately predicting stock levels and sales due to dynamic, ever-changing factors. This research presents an optimized ensemble framework for forecasting hydroponic stock levels and sales by integrating linear regression (LR), random forest (RF), and XGBoost, further enhanced through an evolutionary algorithm (EA). The proposed framework is evaluated using root mean square error (RMSE) and mean absolute error (MAE), demonstrating significant accuracy improvements over individual models. The ensemble model achieves an RMSE reduction of 43.82% for stock prediction and 55.3% for sales forecasting compared to the best-performing individual model. Additionally, local interpretable model-agnostic explanations (LIME) are employed to offer stakeholders clear insights into decision-making processes, such as identifying "number of harvested crops" and "sales data" as key drivers of prediction outcomes. This framework supports sustainable development goals (SDGs) 9.3, 12.3, and 12.C by promoting resource efficiency, reducing food waste, and improving small-scale farmer market access. Future research will explore real-time data integration for dynamic adaptation and further model enhancements.
Volume: 14
Issue: 5
Page: 3879-3886
Publish at: 2025-10-01

Let’s be a chef! The antecedents of chef’s key competencies for vocational school students

10.11591/ijere.v14i5.26708
Badraningsih Lastariwati , Tuatul Mahfud
Chefs are considered a factor in the success of a culinary tourism business. Therefore, mastering the chef’s key competencies (CKC) through vocational high schools is very important. Many studies have examined the competence of chefs. Still, the mechanism for getting key competency chefs involving industry commitment (IC), social support (SS), vocational teaching quality (TQ), and occupational self-efficacy (OSE) of culinary student chefs has not been discussed clearly. This study investigates the antecedents of the mastery of key chef competencies for vocational school students. This study involved 392 culinary students at seven vocational schools in Yogyakarta, Indonesia. Data was collected by proportional random sampling through a questionnaire. Amos 18 software is used for structural equation modeling (SEM) analysis. The study’s results revealed that the mastery of the chef’s critical competencies for students was directly and significantly influenced by IC, quality of vocational teaching, and OSE of chefs. In addition, chef OSE is a mediator on the influence of IC, SS, and quality of vocational teaching on mastering the chef’s critical competencies for culinary students. This study’s findings discuss in depth some of the implications for vocational education practitioners that are proposed for further improvement.
Volume: 14
Issue: 5
Page: 4006-4018
Publish at: 2025-10-01

Efficiency search: application of nature-inspired algorithms in artificial intelligence forecasting models

10.11591/ijai.v14.i5.pp3528-3541
José Rolando Neira Villar , Miguel Angel Cano Lengua
This study reviews how nature-inspired optimization algorithms (NIOAs) have been applied to artificial intelligence-based demand forecasting, using preferred reporting items for systematic reviews and meta-analyses (PRISMA) and clustering analysis to examine 36 selected articles. The findings reveal that NIOAs, particularly genetic algorithms and swarm intelligence methods, including their hybrids, have been frequently applied to long short-term memory (LSTM) and other backpropagation neural network models (BPNN). A key insight is the differentiated application of NIOAs depending on network depth: In shallow networks, they have been effectively used to optimize trainable parameters, whereas in deep networks, their role has focused primarily on hyperparameter optimization due to the prohibitive dimensionality of trainable weights. In all studies, NIOA-optimized models consistently outperform conventional baselines based on backpropagation. However, persistent challenges such as excessive execution times and slow convergence have led to the development of more efficient hybrid strategies and adaptive mechanisms for automated exploration-exploitation control. By mapping explored and unexplored pathways, summarizing key outcomes and techniques, and identifying promising methodologies, this review offers a practical foundation to guide future experiments and implementations involving NIOA-based optimization strategies in neural network models. As a conceptual contribution, it also proposes an innovative use of multispace optimization to address one of the most critical challenges identified: the optimization of trainable parameters in deep neural networks.
Volume: 14
Issue: 5
Page: 3528-3541
Publish at: 2025-10-01

Object detection for indoor mobile robot: deep learning approaches review

10.11591/ijai.v14.i5.pp3520-3527
Hind Messbah , Mohamed Emharraf , Mohamed Saber
Efficient object detection is crucial for enabling autonomous indoor robot navigation. This paper reviews current methodologies and challenges in the field, with a focus on deep learning-based techniques. Methods like you only look once (YOLO), region-based convolutional neural networks (R-CNN), and Faster R-CNN are explored for their suitability in real-time detection in dynamic indoor environments. Deep learning models are emphasized for their ability to improve detection accuracy and adaptability to varying conditions. Key performance metrics such as accuracy, speed, and scalability across different object types and environmental scenarios are discussed. Additionally, the integration of object detection with navigation systems is examined, highlighting the importance of accurate perception for safe and effective robot movement. This study provides insights into future research directions aimed at advancing the capabilities of indoor robot navigation through enhanced deep learning-based object detection techniques.
Volume: 14
Issue: 5
Page: 3520-3527
Publish at: 2025-10-01

Recommendation system for football player recruitment using k-nearest neighbor

10.11591/ijai.v14.i5.pp3847-3857
Maukar Maukar , Rodiah Rodiah
In modern professional football, achieving a competitive edge depends not only on on-field performance but also on effective off-field strategies, particularly in player recruitment. This study proposes a machine learning-based recommendation system to support talent identification and optimal player placement using statistical performance data. The model analyzes a wide range of features, including shots, expected goals, expected assists, pass types, offensive contributions, and defensive actions across field zones. The dataset undergoes preprocessing steps such as normalization (per 90 minutes) and dimensionality reduction. A key innovation of this research is the use of principal component analysis (PCA) to reduce feature dimensionality, minimizing redundancy while retaining essential information, which improves model efficiency and scalability. The refined data is then processed using the k-nearest neighbors (KNN) algorithm with cosine similarity, allowing the system to identify players with similar performance profiles based on directional similarity in a high-dimensional space. This combination enhances recommendation accuracy by focusing on performance structure rather than raw values. The resulting system provides actionable insights into player suitability and potential, offering clubs a data-driven tool for informed scouting and recruitment decisions. The approach demonstrates the effectiveness of combining PCA and KNN in optimizing football player recommendation systems.
Volume: 14
Issue: 5
Page: 3847-3857
Publish at: 2025-10-01

Design and analysis of reinforcement learning models for automated penetration testing

10.11591/ijai.v14.i5.pp4061-4073
Suresh Jaganathan , Mrithula Kesavan Latha , Krithika Dharanikota
Our paper proposes a framework to automate penetration testing by utilizing reinforcement learning (RL) capabilities. The framework aims to identify and prioritize vulnerable paths within a network by dynamically learning and adapting strategies for vulnerability assessment by acquiring the network data obtained from a comprehensive network scanner. The study evaluates three RL algorithms: deep Q-network (DQN), deep deterministic policy gradient (DDPG), and asynchronous episodic deep deterministic policy gradient (AE-DDPG) in order to compare their effectiveness for this task. DQN uses a learned model of the environment to make decisions and is hence called model-based RL, while DDPG and AE-DDPG learn directly from interactions with the network environment and are called model-free RL. By dynamically adapting its strategies, the framework can identify and focus on the most critical vulnerabilities within the network infrastructure. Our work is to check how well the RL technique picked security vulnerabilities. The identified vulnerable paths are tested using Metasploit, which also confirmed the accuracy of the RL approach's results. The tabulated findings show that RL promises to automate penetration testing tasks.
Volume: 14
Issue: 5
Page: 4061-4073
Publish at: 2025-10-01

Residual edge dense enhanced module network: a deep learning approach with multi-class SVM for lung tumor stage classification

10.11591/ijai.v14.i5.pp4032-4042
Prabakaran Jayaraman , Pandiaraj Selvaraj , Ashwini Elango
Lung cancer segmentation with positron emission tomography (PET) and computed tomography (CT) images plays a critical role to accurately detect lung cancer. Nevertheless, lung tumor segmentation in PET/CT images were extremely difficult due to the movement caused by respiration. Despite this fact, the lung tumor images shown large number of variations mostly in PET images and CT images. As PET-CT images are acquired concurrently the shape and size of lung tumor varies according to modality. To address these issues, we developed a residual edge dense enhanced module network (REDEM-NET) framework for lung tumor stage classification. The proposed REDEM-NET can process PET and CT images as inputs. In addition, the dense residual convolutional network (DRCN) collects both inputs and extracts high-dimensional features concurrently. The extracted features from both imaging modalities were fed into UNet+++ to obtain multi-level decoded features. The extracted decoded features are concurrently supplied to the pixel level learning module (PELM) and edge level learning module (E2LM) which resulting in two outputs for subsequent learning. The outputs were merged to provide a very precise lung tumor segmentation. Furthermore, segmented tumor was fed to multi-class support vector machine (MC-SVM) for lung tumor stage classification. Moreover, it was able to identify three stages and its substages namely primary tumor, region lymph node and distant metastasis.
Volume: 14
Issue: 5
Page: 4032-4042
Publish at: 2025-10-01

Detection of chronic kidney disease based on ensemble approach with optimal feature selection using machine learning

10.11591/ijai.v14.i5.pp4017-4031
Deepika Amol Ajalkar , Jyoti Yogesh Deshmukh , Mayura Vishal Shelke , Shalini Vaibhav Wankhade , Shwetal Kishor Patil
Chronic kidney disease (CKD) poses a significant health risk globally, necessitating early and accurate detection to ensure timely intervention and effective treatment. This study presents an advanced ensemble machine learning (ML) approach combined with optimal feature selection to enhance the detection of CKD. Using five baseline ML classifiers like gradient boosting (GB), random forest (RF), K-nearest neighbors (KNN), support vector machine (SVM), and decision tree (DT), and utilizing grid search for hyperparameter tuning, the proposed ensemble model capitalizes on the strengths of each algorithm. Our approach was tested on a public benchmark CKD dataset from Kaggle. The experimental results demonstrate that the ensemble model consistently outperforms individual classifiers and existing methods, achieving 97.5% accuracy, precision, recall, and an F1-score of 97.4%. This superior performance underscores the ensemble model's potential as a reliable early CKD detection tool. Integrating ML into CKD diagnostics enhances accuracy. It facilitates the development of automated, scalable diagnostic tools, aiding healthcare professionals in making informed decisions and ultimately improving patient outcomes.
Volume: 14
Issue: 5
Page: 4017-4031
Publish at: 2025-10-01

Unveiling critical features for failure prediction in green internet of things applications

10.11591/ijai.v14.i5.pp4308-4318
Ouiam Khattach , Omar Moussaoui , Mohammed Hassine
The rapid growth of the green internet of things (GIoT) in recent years signifies a transformative shift in internet of things (IoT) solution development. This evolution is driven by technological advancements, heightened environmental awareness, and a global imperative to combat climate change. Ensuring the reliability of GIoT applications is crucial for their success. This study identifies critical features for predicting IoT device failures, enabling early detection and intervention. Using datasets from industry, energy, and agriculture sectors, we employ a feature selection strategy to analyze extensive data from diverse GIoT deployments. Our analysis identifies significant features and integrates key insights from existing literature. Our findings support enhanced predictive maintenance strategies, reduced downtime, and improved overall performance of sustainable IoT solutions.
Volume: 14
Issue: 5
Page: 4308-4318
Publish at: 2025-10-01

Comparison of HSV-color and ANN-HSV-color segmentation for detecting soybean adulteration

10.11591/ijai.v14.i5.pp3734-3743
Farid Rahmat Abadi , Rudiati Evi Masithoh , Lilik Sutiarso , Sri Rahayoe
Soybeans are an important food crop, but their quality is often compromised by contamination with other materials, a process known as adulteration. Conventional methods for detecting adulteration are slow; therefore, there is a need for rapid and non-invasive alternatives. This study aimed to assess the capability of hue-saturation-value (HSV) color segmentation and its combination with artificial neural networks (ANN) to identify adulteration in soybean samples. This research employed image processing and machine learning to segment soybeans mixed with adulterants at concentrations of 5%, 10%, 15%, 20%, and 25%. The HSV method successfully distinguished soybeans and other materials, but some challenges were observed in shadow regions and areas with similar colors. The HSV-ANN model with six hidden layers performed well with a calibration accuracy of R² value of 0.97 and root-mean-square error (RMSE) of 2.16%, which provided more detailed segmentation, although it still had some problems in shadow regions and undetected corn embryo parts. The validation results indicated that the HSV model had an R² value of 0.98 and RMSE of 4.48%, while the HSV-ANN model had an R² value of 0.96 and RMSE of 1.3%. Both models were capable of predicting the levels of adulteration, and the HSV-ANN model proved to be more accurate. It is concluded that both methods are efficient; however, there is a need for more work on modeling and sampling to increase the segmentation precision and decrease the biases, especially in the shadow and overlapped color.
Volume: 14
Issue: 5
Page: 3734-3743
Publish at: 2025-10-01

Learning assistance module based on a small language model

10.11591/ijai.v14.i5.pp4202-4210
Marco Antonio Jinete , Robinson Jiménez-Moreno , Anny Astrid Espitia-Cubillos
This paper presents the development of a low-cost learning assistant embedded in an NVIDIA Jetson Xavier board that uses speech and gesture recognition, together with a long language model for offline work. Using the large language model (LLM) Phi-3 Mini (3.8B) model and the Whisper (model base) model for automatic speech recognition, a learning assistant is obtained under a compact and efficient design based on extensive language model architectures that give a general answer set of a topic. Average processing times of 0.108 seconds per character, a speech transcription efficiency of 94.75%, an average accuracy of 9.5/10 and 8.5/10 in the consistency of the responses generated by the learning assistant, a full recognition of the hand raising gesture when done for at least 2 seconds, even without fully extending the fingers, were obtained. The prototype is based on the design of a graphical interface capable of responding to voice commands and generating dynamic interactions in response to the user's gesture detection, representing a significant advance towards the creation of comprehensive and accessible human-machine interface solutions.
Volume: 14
Issue: 5
Page: 4202-4210
Publish at: 2025-10-01

An energy-efficient and secure framework for wireless sensor networks

10.11591/ijai.v14.i5.pp4151-4161
Maruthi Hanumanthappa Chandrappa , Poornima Govindaswamy
In wireless sensor networks (WSNs), achieving energy efficiency, security, and minimizing route change propagation time is essential for maintaining optimal performance. This paper introduces a new approach that combines Bray Jaccard Curtis-based Calinski Harabasz k-means (BJC-CHKMeans) for clustering and Karl Pearson correlation-based egret swarm optimization algorithm (KPC-ESOA) for selecting the best cluster head (CH) and path, along with classifying long short-term memory with gated recurrent units (CLE-GRU) for detecting harmful nodes. The methodology aims to enhance energy usage, improve routing efficiency, and strengthen security by identifying malicious nodes. Additionally, it integrates a secure routing table using elbow de-swinging k-anonymity (EDS-KA) and employs digital signature algorithm-based Zeta Bernoulli Merkle tree (DSA-ZBMT) to ensure secure communication with sink nodes. The WSN-DS dataset was used for training and testing, with rigorous preprocessing, feature extraction, and selection to maintain data integrity. Experimental results revealed that the proposed BJC-CHKMeans and CLE-GRU models outperform traditional methods in power consumption, latency, and accuracy. The system achieved a power consumption of 2.1 mW for clustering and 1.9 mW for classification, while also providing near-perfect accuracy in detecting harmful nodes. These findings demonstrate that the framework significantly enhances the energy efficiency and security of WSNs, making it a highly effective solution for large, dynamic sensor networks.
Volume: 14
Issue: 5
Page: 4151-4161
Publish at: 2025-10-01

An algorithm for controlling the transmission of video streams in a flying ad hoc network

10.11591/ijai.v14.i5.pp4290-4298
Salah M. M. Alghazali , Wisam K. Mahdloom Aljeazna , Murtadha N. Rasol , Konstantin A. Polshchykov , Rodion V. Likhosherstov
This article discussing the enhancement of video surveillance in various territories through the implementation of a flying ad hoc network (FANET). The primary objective of the surveillance is for search and rescue operations. To optimize the quality of FANET video broadcasting, a decision-making algorithm for video stream management is introduced. This algorithm evaluates the likelihood of achieving high-quality video transmission. Depending on the assessed probabilities, the algorithm recommends one of the following actions: initiating a new video stream transmission, reducing the average length of wireless channels, or discontinuing the transmission of low-information video streams. Computational experiments demonstrate a significant improvement in the accuracy of decision-making regarding the management of video stream transmission to FANET when utilizing the proposed algorithm.
Volume: 14
Issue: 5
Page: 4290-4298
Publish at: 2025-10-01

Facial paralysis image analysis for stroke detection using deep ensemble transfer learning and optimization

10.11591/ijai.v14.i5.pp4074-4089
Kiruthiga Subramaniyan , Chinnasamy Anbuananth , Dhilip Kumar Venkatesan
Facial paralysis (FP) weakens facial muscles, leading to asymmetric facial actions and complicating stroke diagnosis. Machine learning (ML) and deep learning (DL) systems have been explored for diagnosing FP, but the effectiveness of these methods is hindered by the limited size and diversity of available datasets. This study proposes a novel deep ensemble transfer learning method for accurate stroke diagnosis using facial paralysis imaging (DETLM-ASDFPI). The method leverages pre-trained models to reduce computation costs on edge devices. The framework includes data acquisition, preparation, and pre-processing, with image rescaling to standardize input dimensions. Feature extraction is performed using a deep capsule network (DCapsNet) to capture complex features. For stroke detection, an ensemble transfer learning model integrates three classifiers: gated recurrent unit (GRU), deep convolutional neural network (DCNN), and stacked sparse auto-encoder (SSAE). The hippopotamus optimization algorithm (HOA) is applied to fine-tune model parameters. The method was validated using two benchmark datasets, Massachusetts eye and ear infirmary (MEEI) and YouTube facial palsy (YFP), achieving an accuracy of 97.06%, outperforming recent approaches. This research demonstrates the effectiveness of the DETLM-ASDFPI method in accurately diagnosing strokes from FP images while addressing challenges related to dataset limitations and computational efficiency.
Volume: 14
Issue: 5
Page: 4074-4089
Publish at: 2025-10-01
1 2 3
...
2027 2028
Show 138 of 2028

Discover Our Library

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

Explore Now
Library 3D Ilustration