Indonesian Journal of Electrical Engineering and Computer Science最新文献_第3页

Feature fusion-based video summarization using SegNetSN 使用 SegNetSN 进行基于特征融合的视频摘要分析

Indonesian Journal of Electrical Engineering and Computer Science Pub Date : 2024-07-01 DOI: 10.11591/ijeecs.v35.i1.pp274-283

S. Girase, Dr Mangesh Bedekar

引用次数: 0

Harnessing the power of blockchain to strengthen cybersecurity measures: a review 利用区块链的力量加强网络安全措施：综述

Indonesian Journal of Electrical Engineering and Computer Science Pub Date : 2024-07-01 DOI: 10.11591/ijeecs.v35.i1.pp593-600

Nidal M. Turab, H. Owida, Jamal I. Al-Nabulsi

{"title":"Harnessing the power of blockchain to strengthen cybersecurity measures: a review","authors":"Nidal M. Turab, H. Owida, Jamal I. Al-Nabulsi","doi":"10.11591/ijeecs.v35.i1.pp593-600","DOIUrl":"https://doi.org/10.11591/ijeecs.v35.i1.pp593-600","url":null,"abstract":"As the digital environment continues to evolve with the increasing frequency and complexity of cybersecurity threats, there is growing interest in using blockchain (BC) technology. BC is a technology with desirable properties such as decentralization, integrity, and transparency. The decentralized nature of BC eliminates single points of failure, reducing the vulnerability of critical systems to targeted attacks. The complex and rapidly evolving nature of cyber threats requires an earlier and adaptive approach. This review paper examined several papers collected from official websites. Focusing on using BC technology to improve cybersecurity, the main keywords of the review paper were BC technology, supply chain management, proof of work, and proof of stake. This review paper aims to investigate the security components through a threat assessment that compares the security of BC in different classes and real attack environments. It highlights the potential of BC to strengthen cybersecurity measures, citing unique features. The review paper also points out that there is a lack of focus on addressing security challenges related to computer data and digital systems and calling for a deeper discussion on problem-solving.","PeriodicalId":13480,"journal":{"name":"Indonesian Journal of Electrical Engineering and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141696115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Potato leaf disease detection through ensemble average deep learning model and classifying the disease severity 通过集合平均深度学习模型检测马铃薯叶片病害并对病害严重程度进行分类

Indonesian Journal of Electrical Engineering and Computer Science Pub Date : 2024-07-01 DOI: 10.11591/ijeecs.v35.i1.pp494-502

Nishu Chowdhury, Jeenat Sultana, Tanim Rahman, Tanjia Chowdhury, F. Khan, Arpita Chakraborty

{"title":"Potato leaf disease detection through ensemble average deep learning model and classifying the disease severity","authors":"Nishu Chowdhury, Jeenat Sultana, Tanim Rahman, Tanjia Chowdhury, F. Khan, Arpita Chakraborty","doi":"10.11591/ijeecs.v35.i1.pp494-502","DOIUrl":"https://doi.org/10.11591/ijeecs.v35.i1.pp494-502","url":null,"abstract":"The varying crop species, symptoms of crop diseases, and environmental conditions make early detection of potato leaf disease difficult. Potato leaf diseases are difficult to identify in their early stages because of these reasons. An ensemble model is developed using the ResNet50V2 and DenseNet201 transfer learning algorithms in this study for identifying potato leaf diseases. For this work, 5,702 images were collected from the potato leaf disease dataset and the Plant Village Potato dataset. The datasets include valid, test, and train subdirectories, and the images are taken on 5 epochs. By including three more dense layers in each model and then ensemble that model, the performance of leaf classification may also be improved. Accurately and appropriately, the suggested ensemble averaging model identifies potato leaf phases. So, the accuracy of the suggested ensemble model is achieved with perfect precision. On the second level, the severity of the disorder is assessed using the K mean clustering algorithm. To determine the disease's severity, this system examines each pixel in the early and late blight images. It will be classified as severe if more than 50% of the pixels are damaged.","PeriodicalId":13480,"journal":{"name":"Indonesian Journal of Electrical Engineering and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141692338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Accurate detection of melanoma skin cancer using fuzzy based SegNet model and normalized stacked LSTM network 使用基于模糊的 SegNet 模型和归一化堆叠 LSTM 网络准确检测黑色素瘤皮肤癌

Indonesian Journal of Electrical Engineering and Computer Science Pub Date : 2024-07-01 DOI: 10.11591/ijeecs.v35.i1.pp323-334

Woothukadu Thirumaran Chembian, K. Sankar, Seerangan Koteeswaran, Kandasamy Thinakaran, Periyannan Raman

{"title":"Accurate detection of melanoma skin cancer using fuzzy based SegNet model and normalized stacked LSTM network","authors":"Woothukadu Thirumaran Chembian, K. Sankar, Seerangan Koteeswaran, Kandasamy Thinakaran, Periyannan Raman","doi":"10.11591/ijeecs.v35.i1.pp323-334","DOIUrl":"https://doi.org/10.11591/ijeecs.v35.i1.pp323-334","url":null,"abstract":"Early detection of melanoma skin cancer (MSC) is critical in order to prevent deaths from fatal skin cancer. Even though the modern research methods are effective in identifying and detecting skin cancer, it is a challenging task due to a higher level of color similarity between melanoma non-affected areas and affected areas, and a lower contrast between the skin portions and melanoma moles. For highlighting the aforementioned problems, an efficient automated system is proposed for an early diagnosis of MSC. Firstly, dermoscopic images are collected from two benchmark datasets namely, international skin imaging collaboration (ISIC)-2017 and PH2. Next, skin lesions are segmented from dermoscopic images by implementing a fuzzy based SegNet model which is a combination of both deep fuzzy clustering algorithm and the SegNet model. Then, hybrid feature extraction (ResNet-50 model and local tri-directional pattern (LTriDP) descriptor) is performed to capture the features from segmented skin lesions. These features are given into the normalized stacked long short-term memory (LSTM) network to categorize the classes of skin lesions. The empirical evaluation reveals that the proposed normalized stacked LSTM network achieves 98.98% and 98.97% of accuracy respectively on the ISIC2017 and PH2 datasets, and these outcomes are more impressive than those of the conventional detection models.","PeriodicalId":13480,"journal":{"name":"Indonesian Journal of Electrical Engineering and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141713402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Smart solar maintenance: IoT-enabled automated cleaning for enhanced photovoltaic efficiency 智能太阳能维护：物联网自动清洁提高光伏效率

Indonesian Journal of Electrical Engineering and Computer Science Pub Date : 2024-07-01 DOI: 10.11591/ijeecs.v35.i1.pp14-19

Puviarasi Ramalingam, Jayashree Kathirvel, Arul Doss Adaikalam, D. Somasundaram, Pushpa Sreenivasan

{"title":"Smart solar maintenance: IoT-enabled automated cleaning for enhanced photovoltaic efficiency","authors":"Puviarasi Ramalingam, Jayashree Kathirvel, Arul Doss Adaikalam, D. Somasundaram, Pushpa Sreenivasan","doi":"10.11591/ijeecs.v35.i1.pp14-19","DOIUrl":"https://doi.org/10.11591/ijeecs.v35.i1.pp14-19","url":null,"abstract":"This innovative project aims to increase the effectiveness and user experience of solar panel systems by introducing a state-of-the-art dust and speck removal system. Leveraging cutting-edge technology, the system demonstrates a remarkable 32% increase in power output compared to dirty solar panels. The approach is characterized by its reliance on the universe as the system controller, reducing the need for manual intervention and minimizing the workforce required for panel cleaning. The proposed timed system utilizes water and wipers, facilitated by internet of things (IoT) technology, microcontrollers, and sensor modules for efficient and automated operation. An Android application provides user control and notifications about ongoing processes. The system’s adaptability for various settings is emphasized, offering a portable solution. The smart IoT based automatic solar panel cleaning ensures reliable performance, underscoring the project’s commitment to improve scalability, cost-efficiency, performance, integrity, and consistency.","PeriodicalId":13480,"journal":{"name":"Indonesian Journal of Electrical Engineering and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141715064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhanced diabetic retinopathy detection and classification using fundus images with ResNet50 and CLAHE-GAN 利用 ResNet50 和 CLAHE-GAN 增强眼底图像的糖尿病视网膜病变检测和分类能力

Indonesian Journal of Electrical Engineering and Computer Science Pub Date : 2024-07-01 DOI: 10.11591/ijeecs.v35.i1.pp366-377

Sowmyashree Bhoopal, Mahesh Rao, Chethan Hasigala Krishnappa

{"title":"Enhanced diabetic retinopathy detection and classification using fundus images with ResNet50 and CLAHE-GAN","authors":"Sowmyashree Bhoopal, Mahesh Rao, Chethan Hasigala Krishnappa","doi":"10.11591/ijeecs.v35.i1.pp366-377","DOIUrl":"https://doi.org/10.11591/ijeecs.v35.i1.pp366-377","url":null,"abstract":"Diabetic retinopathy (DR), a progressive eye disorder, can lead to irreversible vision impairment ranging from no DR to severe DR, necessitating precise identification for early treatment. This study introduces an innovative deep learning (DL) approach, surpassing traditional methods in detecting DR stages. It evaluated two scenarios for training DL models on balanced datasets. The first employed image enhancement via contrast limited adaptive histogram equalization (CLAHE) and a generative adversarial network (GAN), while the second did not involve any image enhancement. Tested on the Asia pacific tele-ophthalmology society 2019 blindness detection (APTOS-2019 BD) dataset, the enhanced model (scenario 1) reached 98% accuracy and a 99% Cohen kappa score (CKS), with the non-enhanced model (scenario 2) achieving 95.4% accuracy and a 90.5% CKS. The combination of CLAHE and GAN, termed CLANG, significantly boosted the model's performance and generalizability. This advancement is pivotal for early DR detection and intervention, offering a new pathway to prevent irreversible vision loss in diabetic patients.","PeriodicalId":13480,"journal":{"name":"Indonesian Journal of Electrical Engineering and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141706022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hybrid RIS-assisted interference mitigation for heterogeneous networks 异构网络的混合 RIS 辅助干扰缓解技术

Indonesian Journal of Electrical Engineering and Computer Science Pub Date : 2024-07-01 DOI: 10.11591/ijeecs.v35.i1.pp175-190

Abdel Nasser Soumana Hamadou, Ciira wa Maina, M. M. Soidridine

{"title":"Hybrid RIS-assisted interference mitigation for heterogeneous networks","authors":"Abdel Nasser Soumana Hamadou, Ciira wa Maina, M. M. Soidridine","doi":"10.11591/ijeecs.v35.i1.pp175-190","DOIUrl":"https://doi.org/10.11591/ijeecs.v35.i1.pp175-190","url":null,"abstract":"Reconfigurable intelligent surfaces (RIS) have evolved as a low-cost and energy- efficient option to increase wireless communication capacity. In this research, we suggest using hybrid RIS (H-RIS) to reduce interference in heterogeneous networks (HetNet). In contrast to traditional passive RIS, a hybrid RIS is suggested, which is fitted with a few active elements to not only reflect but also amplify incident signals for a significant performance increase. By jointly optimising the passive and active coefficients of the H-RIS, we aim to maximise the rate of the small cell user (SUE). We presented an effective alternating optimisation (AO)-based phase shift matrix coefficients (AO-PMC) technique to tackle this problem by iteratively optimising these variables because the optimisation problem is not convex. The simulation results demonstrate that, in comparison to the passive RIS-assisted HetNet scheme and the scheme without RIS, the suggested scheme, with just 8% of active elements, can enable HetNet to gain superior spectral efficiency (SE) and energy efficiency (EE). The outcomes also demonstrate that, in the majority of the cases taken into account, H-RIS can outperform the active RIS-assisted HetNet scheme.","PeriodicalId":13480,"journal":{"name":"Indonesian Journal of Electrical Engineering and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141709000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sampled-data observer design for sensorless control of wind energy conversion system with PMSG 无传感器控制 PMSG 风能转换系统的采样数据观测器设计

Indonesian Journal of Electrical Engineering and Computer Science Pub Date : 2024-07-01 DOI: 10.11591/ijeecs.v35.i1.pp52-61

Mohammed Hicham Zaggaf, Adil Mansouri, A. El Magri, A. Watil, R. Lajouad, L. Bahatti

引用次数: 0

ADKNN fostered BIST with Namib Beetle optimization algorithm espoused BISR for SoC-based devices ADKNN 利用 Namib Beetle 优化算法促进 BIST，支持基于 SoC 设备的 BISR

Indonesian Journal of Electrical Engineering and Computer Science Pub Date : 2024-07-01 DOI: 10.11591/ijeecs.v35.i1.pp90-101

Suleman Alnatheer, M. A. Ahmed

{"title":"ADKNN fostered BIST with Namib Beetle optimization algorithm espoused BISR for SoC-based devices","authors":"Suleman Alnatheer, M. A. Ahmed","doi":"10.11591/ijeecs.v35.i1.pp90-101","DOIUrl":"https://doi.org/10.11591/ijeecs.v35.i1.pp90-101","url":null,"abstract":"Redundancy analysis is a widely used method in fault-tolerant memory systems, and it is essential for large-size memories. In current security operations centers (SoCs), memory occupies most of the chip space. To correct these memories using a conventional external equipment test approach is more difficult. To overcome this issue, memory creators utilize redundancy mechanism for substituting the columns and rows along with a spare one to increase output of the memories. In this study, a built-in-self-test (BIST) to test memories and built-in-self-repair (BISR) mechanism to repair the faulty cells for any recent SoC devices is proposed. The BIST, based on adaptive activation functions with a deep Kronecker neural network (ADKNN), not only detects the defect but also determines the kind of defect. The BISR block uses the Namib Beetle optimization algorithm (NBOA) to fix the mistakes in the memory under test (MUT). The study attempts to determine how the characteristics of SoC-based devices change in the real world and then contributes to the suggested controller blocks. Performance metrics such as slice register, region, delay, maximum operating frequency, power consumption, minimum clock period, and access time evaluate performance. Comparing the proposed ADKNN-NBOA-BIST-BISR scheme to existing BIST, BISR, and BISD-based methods reveals its significant performance.","PeriodicalId":13480,"journal":{"name":"Indonesian Journal of Electrical Engineering and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141694151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Use of explainable AI to interpret the results of NLP models for sentimental analysis 使用可解释的人工智能解释情感分析 NLP 模型的结果

Indonesian Journal of Electrical Engineering and Computer Science Pub Date : 2024-07-01 DOI: 10.11591/ijeecs.v35.i1.pp511-519

V. Bidve, Pathan Mohd. Shafi, Pakiriswamy Sarasu, A. Pavate, Ashfaq Shaikh, Santosh Borde, Veer Bhadra Pratap Singh, Rahul Raut

{"title":"Use of explainable AI to interpret the results of NLP models for sentimental analysis","authors":"V. Bidve, Pathan Mohd. Shafi, Pakiriswamy Sarasu, A. Pavate, Ashfaq Shaikh, Santosh Borde, Veer Bhadra Pratap Singh, Rahul Raut","doi":"10.11591/ijeecs.v35.i1.pp511-519","DOIUrl":"https://doi.org/10.11591/ijeecs.v35.i1.pp511-519","url":null,"abstract":"The use of artificial intelligence (AI) systems is significantly increased in the past few years. AI system is expected to provide accurate predictions and it is also crucial that the decisions made by the AI systems are humanly interpretable i.e. anyone must be able to understand and comprehend the results produced by the AI system. AI systems are being implemented even for simple decision support and are easily accessible to the common man on the tip of their fingers. The increase in usage of AI has come with its own limitation, i.e. its interpretability. This work contributes towards the use of explainability methods such as local interpretable model-agnostic explanations (LIME) to interpret the results of various black box models. The conclusion is that, the bidirectional long short-term memory (LSTM) model is superior for sentiment analysis. The operations of a random forest classifier, a black box model, using explainable artificial intelligence (XAI) techniques like LIME is used in this work. The features used by the random forest model for classification are not entirely correct. The use of LIME made this possible. The proposed model can be used to enhance performance, which raises the trustworthiness and legitimacy of AI systems.","PeriodicalId":13480,"journal":{"name":"Indonesian Journal of Electrical Engineering and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141708869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0