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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 66 Documents
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Search results for , issue "Vol 35, No 2: August 2024" : 66 Documents clear
Effectiveness of VGG19 in deep learning for brain tumor detection Arlis, Syafri; Putra, Muhammad Reza; Yanto, Musli
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp1210-1218

Abstract

Image processing in the diagnosis of disease is one of the jobs that is currently developing in the world of health. Diagnosis is carried out by utilizing the role of image processing to provide a level of accuracy in diagnosis results and provide efficiency to medical personnel. This research aims to develop a brain tumor object detection process using a deep learning (DL) approach to magnetic resonance images (MRI) images. This development was carried out to optimize the brain tumor diagnosis process by playing the role of the image extraction process. This research dataset was sourced from the M. Djamil Padang Provincial General Hospital with a total of 3370 MRI images. The results of this work report show that DL performance is capable of carrying out the detection process automatically with an accuracy level of 97,83%. The results of the development of the extraction process can work effectively in ensuring brain tumor objects are precise and accurate. Overall, this research can make a major contribution to maximizing the diagnosis process and assisting medical personnel in the early treatment of brain tumor patients.
Automated Alzheimer’s disease detection and classification based on optimized deep learning models using MRI Saini, Rashmi; Singh, Suraj; Semwal, Prabhakar
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp1333-1342

Abstract

Alzheimer’s disease (AD) is a devastating neurologic condition characterized by brain atrophy and neuronal loss, posing a significant global health challenge. Early detection is paramount to impede its progression. This study aims to construct an optimized deep learning (DL) framework for early AD detection and classification using magnetic resonance images (MRI) scans. The classification task involves distinguishing between four AD stages: mild demented (MD), very mild demented (VmD), moderate demented (MoD), and non-demented (ND). To achieve effective classification, three DL models (VGG16, InceptionV3, and ResNet50) are implemented and fine-tuned. A systematic evaluation is conducted to optimize hyper-parameters, with extensive experimentation. The results demonstrate superior classification performance of the customized DL models compared to state-of-the-art methods. Specifically, visual geometry group 16 (VGG16) achieves the highest accuracy of 95.85%, followed by ResNet50 with 89.38%, while InceptionV3 yields the lowest accuracy of 87.23%. This study highlights the critical role of selecting appropriate DL models and customizing them for accurate AD detection and classification across various stages, offering significant insights for advancing clinical diagnosis and treatment strategies.
Personal identification system based on multidimensional electroencephalographic signals Abdel-Gahffar, Eman A.; Salama, May A
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp1053-1060

Abstract

Personal authentication using electroencephalographic (EEG) signals, is one of the important applications in brain computer interface (BCI). In this work we investigate the use of EEG signals as a biometric trait. Multidimensional EEG signals were represented as symmetric positive-definite (SPD) matrices on a Riemannian manifold. Two experiments are performed in the first; we use minimum distance to Riemannian mean (MDRM) as a classifier. In the second; SPD matrices are vectorized, and the generated vectors are used to train various machine learning (ML) classifiers. MDRM classifier achieved a correct recognition rate (CRR) of 96.92% , while ML classifiers achieved CRR from 95.39% to 99.45%.
Hybrid model for sentiment analysis combination of PSO, genetic algorithm and voting classification Srivastava, Garima; Singh, Vaishali; Kumar, Sachin
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp1151-1161

Abstract

As social network services like Weibo and Twitter have grown in popularity, natural language processing (NLP) has seen a great deal of interest in sentiment analysis of social media messages and Information mining. Social media users, whose numbers are always increasing, have the ability to exchange information on their platforms. The study of sentiment, domains and themes are closely related. Manually collecting enough labelled data from the vast array of subjects covered by large-scale social media to train sentiment classifiers across several domains would be extremely difficult. The literature review conducted concludes that models already proposed in the previous researches are not able to achieve good accuracy. This work suggests a unique model that combines of genetic algorithm and particle swarm optimization to effectively extract the features and then the voting technique is applied for the classification. Model proposed is compared with 4 ensemble datasets achieving a consistent accuracy of more than 90% for three different diversified database owing to natural selection of sequences by GA and at the same time achieves a fast convergence with PSO, the model may be employed for highly accurate recommenders demanding precision and accuracy.
Tailoring therapies: a frontier approach to pancreatic cancer with AI-driven multiomics profiling Jawahar, Janiel; Rajendran, Paramasivan Selvi
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp1253-1262

Abstract

Pancreatic cancer is often diagnosed at an advanced stage when treatment options are limited. Being one of the deadliest cancers that mandates longer medication and treatment phases, there is an inevitable need to have the knowledge of drug response of anti-pancreatic cancer drugs before it is recommended for a patient. AI-driven drug response prediction has proven potential to personalize treatment strategies, improve therapeutic outcomes, and reduce adverse effects and treatment costs for cancer patients. In this research work, we have accounted for the use of different drug descriptors and their core structures known as scaffolds along with three cell line features, chromatin profiling, reverse phase protein array, and metabolomics data to build a feature engineered dataset for drug response prediction tested on various computational learning models. The 53 unique drugs against 18 unique pancreatic cancer cell lines were taken as the raw dataset. The initial dataset having a large dimension was feature selected using an ensemble method derived from five different techniques. The dataset was evaluated on various computational methods and an accuracy of 89% was achieved using the TabNet architecture. Furthermore, the common scaffolds that were persistently found among the drugs that possess high IC50-valued drug clusters were also recorded.
Assembling and testing optoelectronic system to record and process signals from fiber-optic sensors Kalizhanova, Aliya; Kozbakova, Ainur; Wojcik, Waldemar; Kunelbayev, Murat; Amirgaliyev, Beibut; Aitkulov, Zhalau
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp812-822

Abstract

The given research presents assembling and testing optoelectronic system to record and process signals from fiber-optic sensors. The main optoelectronic systems to record and process the signals from fiber-optic sensors are light source controller and optical power detector. There was assembled controller diagram, which apart from light source includes current source for its adequate operation, as well as the systems necessary for stabilizing its working point. The scheme was modelled for specifying nominal and maximum operation criteria. Construction has been designed in the way, that light source controller includes structures of the current regulation and stabilization super luminescent diode (SLED) and temperature stabilization. Apart from that, there was assembled the microsystem of optical power detector additionally to the light detector, which includes the microsystems of intensification and filtration of the signal measured, processing analog data into digital form, microcontroller, used for preliminary data analysis. Data of optoelectronic systems diagram to record and process the signals from fiber-optic sensors has high response speed, low noise level and sufficient progress.
Ransomware attack awareness: analyzing college student awareness for effective defense Syamsuar, Dedy; Pakdeetrakulwong, Udsanee; Jacob, Deden Witarsyah; Chandra, Felixius Arelta
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp1122-1130

Abstract

There are growing concerns about security as the usage of computers in academic settings continues to increase. This research aims to investigate the level of awareness among university students regarding security threats associated with ransomware. This study examines students' behaviour and preventive motivation for ransomware attacks, along with the measures taken to mitigate these security threats. The study model combines the theory of planned behaviour (TPB) and preventive motivation theory (PMT) with additional threat awareness (TA) variables. The research findings indicate a high level of awareness regarding the dangers. TA has a positive influence on other factors, as indicated by the significant t-values (perceived severity (PS)=4.479, perceived vulnerability (PV)=3.251, response efficacy (RE)=14.344, and self-efficacy (SE)=8.034). This research also demonstrates that subjective norm (SN) and affective responses (AR) have a key impact on behavioural intention (BI). Moreover, two of the preventive motivation factors, PS and PV, significantly contribute to BI, while the other two (RE and SE) did not show a significant contribution to BI.
Touch-free tissue dispensing device Mohd Zaki, Nurul Shahira; Nik Dzulkefli, Nik Nur Shaadah; Abdullah, Rina; Ismail, Syila Izawana; Omar, Suziana
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp795-803

Abstract

In this paper, an innovative solution for the everyday issue of tissue dispensing is presented. With billions of tissues distributed daily, the current dispensers often face challenges, as revealed in studies of frequently damaged units. The primary objective was to enhance this fundamental item, aiming to simplify users’ lives. The key innovation lies in granting users control over the tissue dispenser’s rolling mechanism. Introducing the Arduino UNO microcontroller-powered smart tissue dispenser. Operated by a stepper motor, the dispenser reacts to the user’s needs. Activation occurs when the infrared sensor detects hands, prompting the motor to release the appropriate amount of tissue. It’s like witnessing magic, yet it’s simply the ingenuity of technology at play. The software for the Arduino UNO, serving as the project’s controller, is compiled, and uploaded using the Arduino IDE. The performance of this automatic tissue dispenser indicates success in addressing common issues and facilitating effortless tissue retrieval.
Defence against adversarial attacks on IoT detection systems using deep belief network Sharipuddin, Sharipuddin; Winanto, Eko Arip
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp1073-1081

Abstract

An Adversarial attack is a technique used to deceive machine learning models to make incorrect predictions by providing slightly modified inputs from the original. Intrusion detection system (IDS) is a crucial tool in computer network security for the detection of adversarial attacks. Deep learning is a trending method in both research and industry, and this study proposes the use of a deep belief network (DBN). DBN can recognize data with small differences, but is also vulnerable to adversarial attacks. Therefore, this research suggests an internet of things-intrusion detection system (IoT-IDS) architecture using a DBN that can counter adversarial attacks. The chosen adversarial attack for this study is the fast gradient sign method (FGSM) used to evaluate the IoT IDS using the DBN model. Testing was conducted in two scenarios: first, the model was trained without adversarial attacks; second, the model was trained with adversarial attacks. The test results indicate that the DBN model struggles to detect FGSM attacks, achieving an accuracy of only 46% when it is not trained with adversarial attacks. However, after training with the FGSM dataset, the DBN model successfully detected adversarial attacks with an accuracy of 97%.
Lifetime (Bx) improvement of PV inverter using Si-SiC H-IGBT/Diode: a reliability approach Ramavath, Muneeshwar; Puvvula Venkata, Rama Krishna
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp704-710

Abstract

Technological advancements have made it possible to harness the power of renewable energy sources. The efficiency of power electronic devices has increased to almost 98%. In order to reduce the risks of failure and maintain the operation of photovoltaic (PV)-based energy converters, reliable devices are needed. Due to the increasing number of wide-bandgap silicon in electronic converters, the need for more efficient and reliable devices has become more prevalent. However, the cost of these devices is a major issue. Hence, in this work extensive analysis of hybrid silicon (Si)-IGBT and silicon carbide (SiC) antiparallel Diode (H-IGBT/Diode) based PV inverter is proposed to improve the lifetime (Bx). A reliability oriented lifetime assessment is performed on a test case of single stage three kilowatt photovoltaic inverter with 600 V/30 A hybrid switch. Long term mission profile for one year is considered for evaluation at B. V. Raju Institute of Technology (BVRIT), Telangana, India. Finally, B10 lifetime is calculated, comparison analysis is presented between conventional Si-IGBT and proposed Si-SiC H-IGBT/Diode. The results of the study revealed that the H-IGBT exhibited a significant increase in PV inverter reliability.

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