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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 9,138 Documents
 848   849   850   851   852 
Machine learning based prediction of production using real time data of a point bottom sealing and cutting machine Mary Diana, Fathima Rani Irudaya; Rajendran, Subha; Muthusamy, Selvadass
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1376-1386

Abstract

The packaging sector utilizes polypropylene based flexible materials for diverse product packaging with customization options in size and design achieved through advanced flexographic printing and point bottom sealing and cutting machines. Accurately estimating production time and quantity is vital for efficient planning and cost estimation, with factors like material dimensions, thickness, and cutting machine speed influencing production output. Understanding the intricate relationship between these parameters is essential for comprehending their impact on production time and quantity. Predicting production quantity before production begins helps in determining machine runtime and associated costs. In large-scale production systems, machine learning (ML) has proven to be a useful tool for resource allocation and predictive scheduling. An attempt has been made in this paper to develop an intelligent model for predicting the yield of a cutting machine using artificial neural network (ANN), support vector regression (SVR), regression tree ensemble (RTE) and gaussian process regression (GPR). The most crucial features for prediction were identified and the hyperparameters of the ML models were optimized to create efficient models for prediction. A comparative analysis of the four models revealed that the GPR model was simple and effective with least training time and prediction error.
A hybrid firefly algorithm for the sales representative planning problem Bouatouche, Mourad; Belkadi, Khaled
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp406-415

Abstract

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.
MODIS-NDVI and wheat yield patterns and predictions in Taounate, Morocco Bouskour, Sara; Zaggaf, Mohamed Hicham; Bahatti, Lhoussain; Zayrit, Soumaya
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp648-659

Abstract

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.
Lung cancer prediction with advanced graph neural networks Moozhippurath, Bineesh; Natarajan, Jayapandian
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1077-1084

Abstract

This research aims to enhance lung cancer prediction using advanced machine learning techniques. The major finding is that integrating graph convolutional networks (GCNs) with graph attention networks (GATs) significantly improves predictive accuracy. The problem addressed is the need for early and accurate detection of lung cancer, leveraging a dataset from Kaggle's "Lung Cancer Prediction Dataset," which includes 309 instances and 16 attributes. The proposed A-GCN with GAT model is meticulously engineered with multiple layers and hidden units, optimized through hyperparameter adjustments, early stopping mechanisms, and Adam optimization techniques. Experimental results demonstrate the model's superior performance, achieving an accuracy of 0.9454, precision of 0.9213, recall of 0.9743, and an F1 score of 0.9482. These findings highlight the model's efficacy in capturing intricate patterns within patient data, facilitating early interventions and personalized treatment plans. This research underscores the potential of graph-based methodologies in medical research, particularly for lung cancer prediction, ultimately aiming to improve patient outcomes and survival rates through proactive healthcare interventions.
Effective autism spectrum disorder sensory and behavior data collection using internet of things Kumar, Vittalraju Chetan; Umesh, Dadadahalli Ramu
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1274-1283

Abstract

Wireless body area networks (WBANs) connected with wearable internet of things (WIoT) offer useful features including sensory information collection, analysis, and transmission for continuous behavior monitoring of autism spectrum disorder (ASD) patients. Due to users’ mobility and time-driven sensed data, data collection becomes very difficult. The current approach employs cluster-based multi-objective path-optimized data collection mechanisms that have experienced hotspot issues leading to loss of energy and coverage problems near the base stations. This work presents the high energy and reliable sensory and behavior data collection (HERSBDC) mechanism to address the research difficulties. To ensure network coverage, the HERSBDC initially provides a new uneven clustering mechanism. Next, multi-objective-based cluster head (CH) selection metrics are proposed. The final step is the creation of a multi-objective routing path to gather vital ASD data more reliably and energy-efficiently. Comparing the proposed HERSBDC algorithm to the low energy adaptive cluster-hierarchy (LEACH)-based, and distributed energy-efficient clustering and routing (DECR) methods, the simulation results demonstrate that the HERSBDC mechanism achieves a much better lifetime by 62.28% and 11.89%, the delivery ratio by 15.04% and 9.51%, with minimal delay by 52.65%, and 9.65%, and routing overhead by 32.05%, and 42.65%, respectively.
Enhanced SMS spam classification using machine learning with optimized hyperparameters Hafidi, Nasreddine; Khoudi, Zakaria; Nachaoui, Mourad; Lyaqini, Soufiane
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp356-364

Abstract

Short message service (SMS) text messages are indispensable, but they face a significant issue with spam. Therefore, there is a need for robust models capable of classifying SMS messages as spam or non-spam. Machine learning offers a promising approach for this classification, based on existing datasets. This study explores a comparison of several techniques, including logistic regression (LR), support vector machines (SVM), gradient boosting (GB), and neural networks (NN). Hyperparameters play a crucial role in the performance of these models, and their optimization is essential for achieving high accuracy. To this end, we employ an evolutionary programming approach for hyperparameter optimization. This approach evaluates the performance of these models before and after hyperparameter optimization, aiming to identify the most effective model for SMS spam classification.
Detection and severity classification of ataxia using gait features and a hybrid model Pushpalatha, Srikantaswamy; Jayaprakash, Vidyarani Hamppayanamaligae; Krishnamurthy, Sunitha
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp560-568

Abstract

Ataxia, a neurological disorder characterized by impaired coordination and unsteady movements, presents significant challenges for accurate diagnosis and classification. traditional machine-learning (ML) and deep-learning (DL) models often struggle to achieve high accuracy in predicting and classifying this complex condition. This study addresses these limitations by introducing a novel hybrid model, XGBoost-multi-layer-perceptron (XGB-MLP), specifically designed to enhance the accuracy of ataxia prediction and classification. The objective of this research is to develop a more reliable and precise diagnostic tool that outperforms existing ML and DL approaches. The methodology involved integrating the strengths of XGBoost, known for its powerful gradient boosting, with the multi-layer perceptron (MLP) neural network, creating a robust hybrid model. The proposed XGB-MLP model was rigorously tested against conventional models like random forest (RF), logistic regression (LR), support vector machine (SVM), MLP, and standalone XGBoost. The findings reveal that the XGB-MLP model achieves outstanding accuracy rates of 98.91% for ataxia prediction and 97.91% for classification, significantly surpassing the performance of the traditional models.
A novel AI model for the extraction and prediction of Alzheimer disease from electronic health record V. Maju, Sonam; Sirya Pushpam, Gnana Prakasi Oliver
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1023-1031

Abstract

Dark data is an emerging concept, with its existence, identification, and utilization being key areas of research. This study examines various aspects and impacts of dark data in the healthcare domain and designs a model to extract essential clinical parameters for Alzheimer's from electronic health records (EHR). The novelty of dark data lies in its significant impact across sectors. In healthcare, even the smallest data points are crucial for diagnosis, prediction, and treatment. Thus, identifying and extracting dark data from medical data corpora enhances decision-making. In this research, a natural language processing (NLP) model is employed to extract clinical information related to Alzheimer's disease, and a machine learning algorithm is used for prediction. Named entity recognition (NER) with SpaCy is utilized to extract clinical departments from doctors' descriptions stored in EHRs. This NER model is trained on custom data containing processed EHR text and associated entity annotations. The extracted clinical departments can then be used for future Alzheimer's diagnosis via support vector machine (SVM) algorithms. Results show improved accuracy with the use of extracted dark data, highlighting its importance in predicting Alzheimer's disease. This research also explores the presence of dark data in various domains and proposes a dark data extraction model for the clinical domain using NLP.
Modified-vehicle detection and localization model for autonomous vehicle traffic system Juyal, Amit; Sharma, Sachin; Bhadula, Shuchi
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1183-1200

Abstract

The modification of vehicles for financial gain is an evolving tendency observed in India. Recognizing and detecting of these modified illicit cars is an important but critical task in autonomous vehicles (AV). It is always possible for a cyclist or pedestrian to traverse obstacles or other fixed objects that appear in front of any moving vehicle. Vehicles that are autonomous or self-driving require a different system to quickly identify both stationary and moving objects. A deep learning model named you only look once version 5 (YOLOv5)-convolutional block attention module (CBAM) is proposed here for the Indian traffic system which is based on YOLOv5m. The proposed algorithm, YOLOv5-CBAM, has three major components. The first module, the backbone module is employed for feature extraction. The second module is to detect static as well as dynamic objects at the same time and the third CBAM module is adopted in the backbone and neck part, which mainly focuses on the more prominent features. Two cross stage partial (CSP) modules were used after every convolutional layer resulting in an additional head to the proposed model. Four head modules equipped with anchor boxes performed the final detection. For the present dataset, the proposed model showed 98.2% mean average precision (mAP), 98.4% precision, and 94.8% recall as compared to the original YOLOv5m.
Archimedes assisted LSTM model for blockchain based privacy preserving IoT with smart cities Somanathan Pillai, Sanjaikanth E Vadakkethil; Vallabhaneni, Rohith; Vaddadi, Srinivas A; Addula, Santosh Reddy; Ananthan, Bhuvanesh
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp488-497

Abstract

Presently, the emergence of internet of things (IoT) has significantly improved the processing, analysis, and management of the substantial volume of big data generated by smart cities. Among the various applications of smart cities, notable ones include location-based services, urban design and transportation management. These applications, however, come with several challenges, including privacy concerns, mining complexities, visualization issues and data security. The integration of blockchain (BC) technology into IoT (BIoT) introduces a novel approach to secure smart cities. This work presents an Archimedes assisted long short-term memory (LSTM) model intrusion detection for BC based privacy preserving (PP) IoT with smart cities. After the stage of pre-processing, the LSTM is utilized for automated feature extraction and classification. At last, the Archimedes optimizer (AO) is utilized to optimize the LSTM’s hyper-parameters. In addition, the BC technology is utilized for securing the data transmission.

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