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31,456 Article Results

A new intensity-modulated radiation therapy with deep learning heart rate prediction framework for smart health monitoring

10.11591/ijeecs.v37.i1.pp300-314
Saravanan Madderi Sivalingam , Syed Thisin
This research paper monitors the patient’s health using sensor data, cloud, and big data Hadoop tools and used to predict heart attack and related results were discussed in detail. The integration of big data, and wearable sensors in pervasive computing has significantly enhanced healthcare services. This proposal focuses on developing an advanced healthcare monitoring system tailored for tracking the activities of elderly individuals. The wearable sensors are placed on humans at a right angle, left arm, right arm, and chest to collect the data. The large data are split into smaller segments using the map and reduce process of big data Hadoop tools. The intensity-modulated radiation therapy (IMRT) approach is used for the mapping phase and deep convolutional neural network (DCNN), deep belief network (DBN), and long short-term memory (LSTM) and proposed deep learning heart rate prediction (DLHRP) algorithms are used for the combiner/reduce phase. The reduction process combines similar segments of data to predict identical classes to predict the severity of human conditions. The proposed IMRT-DLHRP system has improved performance of 96.34% accuracy compared with 84.25%, 89.47%, and 91.58% compared to DCNN, DBN, and LSTM respectively, therefore proposed framework has significant improvement over existing approaches.
Volume: 37
Issue: 1
Page: 300-314
Publish at: 2025-01-01

Enhancing fake profile detection through supervised and hybrid machine learning: a comparative analysis

10.11591/ijeecs.v37.i1.pp257-268
Ismail Bensassi , Oussama Ndama , Mohamed Kouissi , El Mokhtar En-Naimi
In modern times, social networks have become ubiquitous platforms facilitating widespread information dissemination, resulting in significant daily data generation. This increase in data production encompasses a wide range of user-generated content, which in turn promotes the proliferation of fraudulent users creating fake profiles and engaging in deceptive activities. This article aims to address this challenge by employing machine learning algorithms to accurately identify fake profiles. The research involves a thorough analysis of various user behaviors, engagement metrics, and content attributes within social platforms. The primary goal is to develop robust models capable of effectively detecting deceptive profiles by meticulously examining user activities and content characteristics. The study explores the application of robust methodologies such as K-means and K-medoids clustering, alongside supervised machine learning classifiers including K-nearest neighbors (KNN), support vector machine (SVM), Bernoulli Naïve Bayes (NB), logistic regression, and linear support vector classification (SVC), specifically tailored for the detection of fake profiles.
Volume: 37
Issue: 1
Page: 257-268
Publish at: 2025-01-01

Improving the MSMEs data quality assurance comprehensive framework with deep learning technique

10.11591/ijeecs.v37.i1.pp613-626
Mujiono Sadikin , Purwanto S. Katidjan , Arif Rifai Dwiyanto , Nurfiyah Nurfiyah , Ajif Yunizar Pratama Yusuf , Adi Trisnojuwono
In the year of 2022 the ministry of cooperatives and small and medium enterprises (SMEs) executed a complete data collection program for the cooperatives and micro small and medium enterprises (MSMEs) profile. As the complexity of the process and the uniqueness of the data characteristics, plenty of risks must be mitigated. The most challenging risk is the possibility of reduced data quality. This study is performed to validate the proposed comprehensive framework to ensure the quality data of cooperatives and MSME. The proposed framework aims to prevent, detect, repair, and recover dirty data to achieve the required data quality minimum standard. We investigated many techniques namely rule-based, selection-based, and deep learning-based. By applying the framework, 6,850,000 missing values are found and corrected, whereas the number of instant data containing attribute values that do not follow the domain constraints or integrity rule is 4,082,630. The first deep learning task applied in the framework is MSME activity image description (image captioning) generated by the convolutional neural network-recurrent neural network (CNN-RNN) model. By using 1000 MSME images as data training, the model’s performance is quite good, achieving the average BLEU score of Culinary 0,3149, Fashion 0,4868, and creative products 0,5086. So far, the proposed framework can contribute to supporting MSME one data as the Indonesian government program.
Volume: 37
Issue: 1
Page: 613-626
Publish at: 2025-01-01

A hybrid firefly algorithm for the sales representative planning problem

10.11591/ijeecs.v37.i1.pp406-415
Mourad Bouatouche , Khaled Belkadi
In the rapidly increasing pharmaceutical sector, sales representatives are employed by pharmaceutical manufacturers and distributors to inform and educate physicians. To convince providers to prescribe the medications to their patients, these representatives rely on their product expertise and people’s abilities to close deals. Instead of making direct sales, pharmaceutical sales representatives help medical professionals get the medications, treatments, and information they need to give their patients the best care possible. Furthermore, they inform the public about novel and occasionally life-saving treatments and share interesting medical developments. This study presents a hybrid methodology that integrates the benefits of local search and the firefly algorithm (FA) to determine the optimal planning for a sales representative. The objective is to maximize the rewards while adhering to certain constraints. The objective is to maximize the rewards while adhering to certain limits. Utilizing local search, the hybrid algorithm enhances firefly’s global search behaviour and produces the best possible sales presentation planning. The experimental findings demonstrate the superior performance of the suggested algorithm compared to the FA and other literature methods in the sense of enhancing the convergence rate and preventing local minima. Furthermore, it can enhance the best-known solution for most benchmark instances.
Volume: 37
Issue: 1
Page: 406-415
Publish at: 2025-01-01

A predictive model for postpartum depression: ensemble learning strategies in machine learning

10.11591/ijeecs.v37.i1.pp443-451
Winda Ayu Fazraningtyas , Bahbibi Rahmatullah , Husni Naparin , Mohammad Basit , Nor Asiah Razak
Postpartum depression (PPD) presents a significant mental health challenge for mothers following childbirth. While the precise cause of this condition remains unknown, preventive measures and treatments are available. This study aims to employ ensemble learning techniques, utilizing C4.5 decision tree (DT), gradient boosting tree (GBT), and extreme gradient boosting (XGBoost), to predict the occurrences of PPD in the Banjarmasin, South Kalimantan, Indonesia. The predictive model developed encompasses a dataset comprising 317 records gathered from postpartum mothers in hospitals, community health services, and midwifery clinics (referred to as Model 1). Furthermore, resampling techniques (Model 2) were employed to address class imbalance. Additionally, feature selection including forward selection and backward elimination (Model 3) were implemented to enhance model performance. The findings reveal that XGBoost, combined with resampling methods, achieved the highest accuracy rate at 87.57%. Feature selection identified five crucial factors associated with PPD incidence: marital status, number of living children, history of depression, fear of delivery, and family relationships. The utilization of ensemble learning strategies for PPD prediction yields reliable outcomes that can be applied within clinical settings. Exploring alternative ensemble learning strategies such as random forest and adaptive boosting could further optimize model performance and warrant consideration in future research endeavours.
Volume: 37
Issue: 1
Page: 443-451
Publish at: 2025-01-01

Land scene classification using diversity promoting metric learning-convolutional neural network

10.11591/ijeecs.v37.i1.pp269-278
Kampa Ratna Babu , Kampa Kanthi Kumar , Akula Suneetha
The land scene classification by remote sensing images predicts semantic class of image blocks by removing visual primitives in remote sensing images. However, there is a problem of within-class diversity and between-class similarity that degrades a performance of scene classification. In this research, the diversity promoting metric learning–convolutional neural network (DPML-CNN) method is proposed for classifying land scene images. The metric learning with convolutional neural network (CNN) maps the same scene image class closer and the different class scenes as far as possible which makes the method much discrimination. The diversity promoting in metric learning is used to reduce the overlapping of the same scene class by uncorrelation of every parameter and provides unique information for those parameters. The UC Merced, AID, and NWPU RESISC45 datasets are utilized in this research for evaluating the proposed DPML-CNN method with evaluation metrics like accuracy and kappa coefficient. The DPML-CNN method reached highest accuracy of 99.27% and 99.84% for 50% and 80% training ratios on the UC Merced dataset when compared to other existing methods like multi-level semantic feature clustering attention (MLFC-Net) and global context spatial attention (GCSA-Net).
Volume: 37
Issue: 1
Page: 269-278
Publish at: 2025-01-01

Design and testing of a nutrient solution control system for soilless culture using mathematical models

10.11591/ijeecs.v37.i1.pp101-110
Sirinya Chaila , Chaiyong Soemphol
The optimization of nutrient management is crucial for successful soilless plant cultivation, where precise control of fertilizer application significantly impacts plant growth. This research addresses the challenge of developing an effective nutrient control system tailored for soilless cultivation by focusing on regulating electrical conductivity (EC) levels in nutrient solutions. The proposed system utilizes mathematical models and linear regression techniques to manage the nutrient solution mixing ratio. To ensure accuracy, sensors were calibrated, achieving a 99.59% accuracy rate for pH measurement and 95.25% for EC measurement. Experimental validation of the system demonstrated that, with a target EC range of 1.5-2.3 mS/cm, a 10 L solution volume yielded a maximum error rate of 1.75% and an average error of 0.95%. In contrast, a 50 L solution volume showed a slight increase in maximum error rate to 2.89% and an average error of 2.08%. These results highlight the system’s capability to precisely adjust EC levels using a defined linear regression model for AB liquid fertilizer ratios. In conclusion, the developed system effectively controls nutrient levels, demonstrating its potential for enhancing nutrient management in hydroponic farming applications.
Volume: 37
Issue: 1
Page: 101-110
Publish at: 2025-01-01

Facial emotion recognition based on upper features and transfer learning

10.11591/ijeecs.v37.i1.pp530-539
Ennaji Fatima Zohra , El Kabtane Hamada
Facial expression recognition (FER) in the upper face focuses on the analysis and recognition of emotions based on features extracted from the upper region of the face. This region typically affects the eyes, eyebrows, forehead, and sometimes the upper cheeks. Since these areas are often less affected by face masks or other facial coverings, FER algorithms can concentrate on capturing and interpreting the relevant facial cues, such as eye movements, eyebrow positions, and forehead wrinkles, to accurately recognize and classify different emotions. By focusing on the upper face, FER systems can mitigate the impact of occlusions caused by masks and still provide meaningful insights into the emotional states of individuals. In this work, a FER approach focusing on the upper region is proposed. Several experiments have been made using the CK+ dataset in addition to a comparison between the emotion recognition scores using the upper and the entire face in order to determine whether this area can reflect the real expressed emotion. The results of our approach are promising compared to previous studies with an accuracy up to 96%.
Volume: 37
Issue: 1
Page: 530-539
Publish at: 2025-01-01

Integrating ELECTRA and BERT models in transformer-based mental healthcare chatbot

10.11591/ijeecs.v37.i1.pp315-324
Junta Zeniarja , Cinantya Paramita , Egia Rosi Subhiyakto , Sindhu Rakasiwi , Guruh Fajar Shidik , Pulung Nurtantio Andono , Anamarija Jurcev Savicevic
Over the last decade, the surge in mental health disorders has necessitated innovative support methods, notably artificial intelligent (AI) chatbots. These chatbots provide prompt, tailored conversations, becoming crucial in mental health support. This article delves into the use of sophisticated models like convolutional neural network (CNN), long-short term memory (LSTM), efficiently learning an encoder that classifies token replacements accurately (ELECTRA), and bidirectional encoder representation of transformers (BERT) in developing effective mental health chatbots. Despite their importance for emotional assistance, these chatbots struggle with precise and relevant responses to complex mental health issues. BERT, while strong in contextual understanding, lacks in response generation. Conversely, ELECTRA shows promise in text creation but is not fully exploited in mental health contexts. The article investigates merging ELECTRA and BERT to improve chatbot efficiency in mental health situations. By leveraging an extensive mental health dialogue dataset, this integration substantially enhanced chatbot precision, surpassing 99% accuracy in mental health responses. This development is a significant stride in advancing AI chatbot interactions and their contribution to mental health support.
Volume: 37
Issue: 1
Page: 315-324
Publish at: 2025-01-01

A comparative study of pre-trained models for image feature extraction in weather image classification using orange data mining

10.11591/ijeecs.v37.i1.pp241-249
Pafan Doungpaisan , Peerapol Khunarsa
This paper presents a detailed comparative analysis of pre-trained models for feature extraction in the domain of weather image classification. Utilizing the orange data mining toolkit, we investigated the effectiveness of six prominent pre-trained models-InceptionV3, SqueezeNet, VGG-16, VGG-19, painter, and DeepLoc-in accurately classifying weather phenomena images. Among these models, InceptionV3, in conjunction with neural networks, emerged as the most effective, achieving a classification accuracy (CA) of 96.1%. Painter and SqueezeNet also showed strong performance, with accuracies of 95.1% and 86.7%, respectively, although they were surpassed by InceptionV3. VGG-16 and VGG-19 provided moderate accuracy, while DeepLoc underperformed significantly with a maximum accuracy of 56%. Neural networks consistently outperformed other classifiers across all models. This study highlights the critical importance of selecting appropriate pre-trained models to enhance the accuracy and reliability of weather image classification systems.
Volume: 37
Issue: 1
Page: 241-249
Publish at: 2025-01-01

MODIS-NDVI and wheat yield patterns and predictions in Taounate, Morocco

10.11591/ijeecs.v37.i1.pp648-659
Sara Bouskour , Mohamed Hicham Zaggaf , Lhoussain Bahatti , Soumaya Zayrit
This study is devoted to the use of varied analytical methods to elucidate the complex relationship between normalized difference vegetation index (NDVI) and wheat production in Taounate, Morocco based on MODIS Satellite data. Linear regression (LR), with a coefficient of determination (R²) of 0.93, provided a solid basis, while the decision tree (DT) showed significant performance with an R² of 0.81. Support vector regression (SVR) performed well with an R² of 0.96, highlighting its ability to capture the non-linear nuances of the data. Given the complexity inherent in the observed relationships, characterized by non-linear variations, we opted for a combined approach. K-means, closely linked to SVR, was integrated for its ability to identify homogeneous subgroups in the data (R2 up to 0.98). This combination made it possible to circumvent the limits of strictly linear methods, thus reinforcing the robustness of our analysis. These results underline the capacity of the chosen methodology to decode the interactions between NDVI and wheat production in the complex context of Taounate. By providing clear and nuanced perspectives, this study helps inform agricultural decisions and build resilience to climate challenges in the region.
Volume: 37
Issue: 1
Page: 648-659
Publish at: 2025-01-01

Lung cancer detection using hybrid integration of autoencoder feature extraction and ML techniques

10.11591/ijeecs.v37.i1.pp416-424
Annemneedi Lakshmanarao , Nirmal Gopal , Nagagopiraju Vullam , Mandapati Sridhar , Modalavalasa Krishna Kanth , Uma Maheswari Rayudu
Lung cancer posed a significant global health challenge, necessitating innovative approaches for early detection and accurate diagnosis. In this paper, CT scan images for lung cancer with three classes namely benign, malignant, and normal are collected from Kaggle. We initially applied conventional machine learning (ML) algorithms including support vector machine (SVM), random forests (RF), decision trees (DT), logistic regression (LR), naive bayes (NB), and k-nearest neighbor for lung cancer detection. The results with these conventional algorithms are recorded. Later, we proposed a novel hybrid model that integrated diverse machine learning algorithms to further enhance accuracy. Our approach combined the power of autoencoders for feature extraction. Using Autoencoder technique, features from images are extracted and a new feature vector is created. Later, the same conventional ML classifiers applied and achieved enhanced performance. The hybrid model demonstrated remarkable performance in identifying lung cancer cases when compared to individual classifiers. Through extensive experimentation, we showcased the efficacy of our integrated framework, achieving high accuracy, precision, recall and F1-score metrics across multiple classifiers. This hybrid approach represented a significant advancement in lung cancer detection, offering a versatile and robust solution for early diagnosis and personalized treatment strategies in clinical settings.
Volume: 37
Issue: 1
Page: 416-424
Publish at: 2025-01-01

Particle swarm optimization for beamforming design in a cognitive radio

10.11591/ijeecs.v37.i1.pp154-163
Mossaab Atzemourt , Younes Chihab , Omar Bencharef , Zakaria Hachkar
Beamforming is essential for improving transmission in wireless sensor networks (WSNs), particularly in cognitive radio networks (CRNs) with several secondary users (SU) equipped with transmitting antennas. Optimizing beamforming while minimizing interference with primary users (PU) is of great interest. This study proposes an improved particle swarm optimization (PSO) algorithm to enhance beamforming performance. This approach aims to maximize the power of the beam directed to the SU receiver while controlling interference in the PU protection region. The results show that this algorithm constantly improves beam focus and signal-to-noise ratio to effectively optimize beamforming. Firstly, beam focusing becomes narrower as the number of antenna elements increases, generating optimal transmission conditions. Secondly, the algorithm achieves a considerable improvement in signal-to-noise ratio as the number of antenna elements increases. Furthermore, optimization performance improves as the number of antenna elements increases, as shown by the best fitness values. The simulations also illustrate the performance of the proposed method.
Volume: 37
Issue: 1
Page: 154-163
Publish at: 2025-01-01

ADALINE-based synchronous detection for enhanced shunt APF performance

10.11591/ijeecs.v37.i1.pp35-47
Abdesslam Ryad Mebarek , Leila Merabet , Chouaib Rahli , Salah Saad
Power quality issues caused by current harmonics from nonlinear and unbalanced loads are a growing concern. This paper presents a novel control strategy for four-wire shunt active power filters (SAPF) that surpasses existing conventional methods in mitigating harmonics and power factor correction. The strategy employs an improved synchronous detection method (SDM) enhanced by an adaptive linear neural network (ADALINE) trained using the least mean square (LMS) algorithm. This approach accurately estimates harmonic frequencies, enabling the SAPF to generate precise compensation currents. The effectiveness of the proposed method is validated through MATLAB-Simulink simulations under balanced supply conditions, encompassing diverse load scenarios. These simulation results are compared with those obtained using instantaneous power theory (IPT). They demonstrate the ability of the proposed method to achieve excellent harmonic identification and elimination, to comply with IEEE 519 harmonic limits, to ensure sinusoidal and balanced line currents, and to compensate for reactive power and neutral current. Furthermore, its simple architecture and noise robustness make it a promising solution for enhancing power quality.
Volume: 37
Issue: 1
Page: 35-47
Publish at: 2025-01-01

Bit-rate aware effective inter-layer motion prediction using multi-loop encoding structure

10.11591/ijeecs.v37.i1.pp569-579
Sandeep Gowdra Siddaramappa , Gowdra Shivanandappa Mamatha
Recently, there has been a notable increase in the use of video content on the internet, leading for the creation of improved codecs like versatile-video-coding (VVC) and high-efficiency video-coding (HEVC). It is important to note that these video coding techniques continue to demonstrate quality degradation and the presence of noise throughout the decoded frames. A number of deep-learning (DL) algorithm-based network structures have been developed by experts to tackle this problem; nevertheless, because many of these solutions use in-loop filtration, extra bits must be sent among the encoding and decoding layers. Moreover, because they used fewer reference frames, they were unable to extract significant features by taking advantage from the temporal connection between frames. Hence, this paper introduces inter-layer motion prediction aware multi-loop video coding (ILMPA-MLVC) techniques. The ILMPA-MLVC first designs an multi-loop adaptive encoder (MLAE) architecture to enhance inter-layer motion prediction and optimization process; second, this work designs multi-loop probabilistic-bitrate aware compression (MLPBAC) model to attain improved bitrate efficiency with minimal overhead; the training of ILMPA-MLVC is done through novel distortion loss function using UVG dataset; the result shows the proposed ILMPA-MLVC attain improved peak-singal-to-noise-ratio (PSNR) and structural similarity (SSIM) performance in comparison with existing video coding techniques.
Volume: 37
Issue: 1
Page: 569-579
Publish at: 2025-01-01
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