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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
 808   809   810   811   812 
Comparative analysis of machine learning models for breast cancer prediction and diagnosis: a dual-dataset approach Muhammad Zeerak Awan; Muhammad Shoaib Arif; Mirza Zain Ul Abideen; Kamaleldin Abodayeh
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i3.pp2032-2044

Abstract

Breast cancer is ranked as a significant cause of mortality among females globally. Its complex nature poses principal challenges for physicians and researchers for rapid diagnosis and prognosis. Hence, machine learning algorithms are employed to forecast and identify diseases. This study discusses the comparative analysis of seven machine learning models, e.g., logistic regression (LR), support vector machine (SVM), k-nearest neighbor classifier (KNN), decision tree classifier (DT), random forest classifier (RF), Naïve Bayes (NB), and artificial neural network (ANN) to predict breast cancer using Wisconsin breast cancer and breast cancer datasets. In the Wisconsin breast cancer dataset, KNN depicted 99% accuracy, followed by RF (98%), SVM (96%), NB (96%), LR (96%), ANN (93%), and DT (92%). On the contrary, in the breast cancer (BC) dataset, the highest accuracy was achieved by LR at 83%, and the lowest was achieved by DT (65%), which depicted that the numeric dataset WBC has better accuracy than the breast cancer dataset.
Syntactic analysis of complex sentences containing Arabic psychological verbs Asmaa Amzali; Asmaa Kourtin; Mohammed Mourchid; Abdelaziz Mouloudi; Samir Mbarki
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp312-321

Abstract

Complex Arabic sentences, especially those containing Arabic psychological verbs, follow a common underlying structure characterized by two essential components: the predicate and the subject. In addition, there are two optional elements: the head and the complement. These sentences, rooted in basic noun phrases (NPs), can be expanded within the predicate, subject, or complement, resulting in compound structures. This study aims to develop a syntactic analyzer for parsing complex sentences containing Arabic psychological verbs. To achieve this, we will use the dictionary generated from the lexicon-grammar table of Arabic psychological verbs, which contains all lexical, syntactic, semantic, and transformational information related to these verbs. Then, we will extend an existing analyzer to recognize and label all grammatical structures within complex sentences containing Arabic psychological verbs. Finally, we will evaluate the efficiency of this analyzer through tests on different texts and corpora.
The effect of contact pressure on photoplethysmography Xin Gong; Chengbo Yu; Yiguo Xuan
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1589-1603

Abstract

In the bioinformation photoplethysmography (PPG) measurement, the precision and repeatability could be impacted by the contact pressure between the optical sensor and the measurement site. Taking the finger’s geometrical, mechanical, and optical characteristics into consideration, finite element models and Monte Carlo (MC) simulation methods were used to quantitatively analyze the effects of the deformation of different finger layers under contact pressure on its optical parameters and PPG signals during fingertip spectroscopic detection. Firstly, a 3D axisymmetric finger model was established, pelican optimization was used to find the parameter lamp that caused the simulation to best match the finger pressing behavior, modeled the deformation of each layer, and quantified the changes in their absorption and scattering coefficient. Then, before and after pressure application, photon propagation in the reflectance and transmittance modalities within the diastolic and systolic finger tissues at 660 nm and 940 nm were studied by MC simulation. The result shows that contact pressure significantly altered the thickness of the dermis and subcutaneous tissues, a decrease in tissue thickness caused an increase in optical coefficient, which resulted in a reduction in normalized pulsatile reflectance and a boost in transmittance, and the change was dependent on wavelength.
Big data vehicle density management in vehicular ad-hoc network Mouad Tantaoui; Mehdi Moukhafi; Idriss Chana
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 1: January 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i1.pp314-323

Abstract

Smart city project is today a domain of interest to community research which play well-known role in road traffic management. Data exchange became complicated in terms of capacity in the intelligent transport system (ITS), and without the raise of big data, the treatment is very difficult to manage. vehicular ad-hoc network (VANETs) faces many challenges mainly the voluminous data generated by different actors of VANET environment. We propose a real time anomalies detection system in an instantaneous way with parallel data treatment. The system method intends to compute precisely vehicle density at each section on each road, which help to handle the traffic and forward to vehicles information about the road and the best safe path to reach their destination. Also, we build anomalies prediction system based on machine learning framework, it is a good solution for avoiding traffic congestion and limiting the risk of accidents. The simulation results demonstrate that the proposed system method reduces congestion greatly by taking into account the load balancing and therefore avoids saturation and reduces accidents. It should also be noted that the results obtained show that the system is characterized by low latency and high accuracy.
Machine learning approaches for predicting postpartum hemorrhage: a comprehensive systematic literature review Dewi Pusparani Sinambela; Bahbibi Rahmatullah; Noor Hidayah Che Lah; Ahmad Wiraputra Selamat
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i3.pp2087-2095

Abstract

Postpartum hemorrhage (PPH) represents a significant threat to maternal health, particularly in developing countries, where it remains a leading cause of maternal mortality. Unfortunately, only 60% of pregnant women at high risk for PPH are identified, leaving 40% undetected until they experience PPH. To address this critical issue and ensure timely intervention, leveraging rapidly advancing technology with machine learning (ML) methodologies for maternal health prediction is imperative. This review synthesizes findings from 43 selected research articles, highlighting the predominant ML techniques employed in PPH prediction. Among these, logistic regression (LR), extreme gradient boosting (XGB), random forest (RF), and decision tree (DT) emerge as the most frequently utilized methods. By harnessing the power of ML, we aim to foster technological advancements in the healthcare sector, with a particular focus on maternal health and ultimately contribute to the reduction of maternal mortality rates worldwide.
Modeling and adaptive neuro-fuzzy inference system control of quarter electric vehicle Rachida Baz; Khalid El Majdoub; Fouad Giri; Ossama Ammari
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp745-755

Abstract

Electric vehicles (EVs) have gained importance in recent years, prompting the development of several control systems to improve their efficiency and performance. In this work, a quarter electric vehicle (QEV) was controlled using a conventional proportional integral derivative (PID) and fuzzy controller to examine and compare with the response of the adaptive neuro-fuzzy inference system (ANFIS) controller. The response of the ANFIS controller was evaluated using MATLAB/Simulink according to different parameters and compared with those of other controllers. In addition, the simulation was based on different driving conditions such as the acceleration and deceleration modes and the type of road: wet and dry. The simulations were carried out on a longitudinal electric vehicle model based on a brushless DC motor, including the Pacejka tire model. The results showed that the ANFIS controller outperformed the PID and fuzzy logic controllers, providing superior dynamic responsiveness and stability when the ANFIS controller smoothly followed the input speed and the longitudinal slip value reached 3%.
Optimizing high availability multi-controller placement in SDN/NFV 5G networks: a survey Samer Mohammed Rasool; Yassine Boujelben; Faouzi Zarai
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i3.pp1800-1813

Abstract

In meeting the diverse and occasionally conflicting quality of service (QoS) requirements associated with modern communication networks, 5G technology has emerged as a pivotal player. In its architecture, 5G has adopted network function virtualization (NFV) and cloud-based approaches, aiming to simplify network and service deployment, operational processes, and management. The convergence of software defined networking (SDN) and NFV offers an effective solution, enabling scalable and high-performance 5G networks. However, this integration poses critical challenges, with the placement of SDN controllers being a central concern due to its significant impact on network performance, covering aspects such as latency, costs, and energy efficiency. This challenge is known as the controller placement problem (CPP). The central theme of this paper revolves around the intricate relationship between 5G core networks, virtualization technology, and the pressing concern of SDN controller placement, underscoring its significance in the modern networking landscape. We provide a survey of recent methodologies aimed at solving the CPP within the realm of SDN, with a particular focus on resiliency and high availability.
A review on learning analytics in mobile learning and assessment Teik Heng Sun; Muhammad Modi Lakulu; Noor Anida Zaria Mohd Noor
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1924-1941

Abstract

Employers are facing difficulties in selecting the most suitable candidates for employment and the transition from education to work is challenging for young graduates. Therefore, it is important to have indicators that could show the suitability of a potential candidate for his/her chosen job. A person who possesses knowledge but lacks confidence may struggle to perform assigned tasks, while an overly confident person with limited knowledge is likely to make errors in their job. Although there is existing research on learning analytics related to assessments, the research on learning analytics specifically focused on the confidence-knowledge relationship based on assessment data is still lacking. This article aims to examine the application of analytics in providing insights based on assessment data that can be utilized by potential employers. To achieve this, a systematic review was carried out, analyzing a total of 141 articles. The findings contribute to a better understanding of the use of assessment analytics in identifying the knowledge-confidence quadrants of students.
Development of a payload for monitoring biological samples in microgravity and hypergravity conditions Canto-Vivanco, Elber E.; Ramos-Cosi, Sebastian; Romero-Alva, Victor N.; Vargas-Cuentas, Natalia I.; Roman-Gonzalez, Avid
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp78-89

Abstract

This research aims to address the need for monitoring the behavior of organic and inorganic materials in hypergravity conditions. To fulfill this objective, a container with specific features was designed. The container has a box with a lid, measuring 10×10×10 cm, conforming to the 1U volume of the CubeSat standard. It includes four cylindrical spaces to accommodate the sample wells. The container was 3D printed using polylactic acid (PLA) wire. For the electronic components, four ESP32-CAM modules were utilized, with two programmed to capture and upload photos to the cloud, and the other two programmed to capture and store photos on a micro SD memory card. Additionally, four light emitting diodes (LEDs) were incorporated to illuminate the well spaces. The total weight of the container is 450 grams, and it has a maximum wireless upload distance of 10 meters to the cloud. The storage capacity of the SD memory card determines the number of images that can be saved.
Convolutional neural network-based techniques and error level analysis for image tamper detection Vijaya Shetty Sadanand; Shruthi Shetty Janardhana; Sowmya Purushothaman; Sarojadevi Hande; Ramya Prakash
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 2: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i2.pp1100-1107

Abstract

Photographs are the foremost powerful and trustworthy media of expression. At present, digital pictures not only serve forged information but also disseminate deceptive information. Users and experts with various objectives edit digital photographs. Images are frequently used as proof of reality or fact, therefore fake news or any publication that makes use of photos that have been altered in any way has a larger chance of deceiving readers. There is a need for a high-resolution image analysis model that processes individual pixels in images and a substantial amount of diverse image data, to detect image falsification. Convolutional neural network (CNN) with error level analysis (ELA) adopted in this research is found to be an ideal deep learning concept for detecting image manipulation. The model exhibited a validation accuracy of 99.6%, 99.7%, and 99.4% for CASIA V1.0, CASIA V2.0 and MICC datasets respectively. The accuracy for handmade tampered images was found to be 99.2%.

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