Pub Date : 2024-07-01DOI: 10.11591/ijeecs.v35.i1.pp274-283
S. Girase, Dr Mangesh Bedekar
This paper addresses the video summarization problem. For the given video goal is to find the subset of frames that capture the important events of the input video and produce a small concise summary. We formulate video summarization as a sequence labeling problem, where for a given input video a subset of frames are selected as a summary video. Based on the principle of semantic segmentation, here each pixel within a frame is assigned to one of the labels, where each frame is assigned a binary label indicating whether it will be included in the summary video or not. We propose a SegNet sequence network (SegNetSN) for video summarization and further extend the work by applying various feature fusion techniques to enhance the input. We performed experiments on the benchmark dataset TVSum.
{"title":"Feature fusion-based video summarization using SegNetSN","authors":"S. Girase, Dr Mangesh Bedekar","doi":"10.11591/ijeecs.v35.i1.pp274-283","DOIUrl":"https://doi.org/10.11591/ijeecs.v35.i1.pp274-283","url":null,"abstract":"This paper addresses the video summarization problem. For the given video goal is to find the subset of frames that capture the important events of the input video and produce a small concise summary. We formulate video summarization as a sequence labeling problem, where for a given input video a subset of frames are selected as a summary video. Based on the principle of semantic segmentation, here each pixel within a frame is assigned to one of the labels, where each frame is assigned a binary label indicating whether it will be included in the summary video or not. We propose a SegNet sequence network (SegNetSN) for video summarization and further extend the work by applying various feature fusion techniques to enhance the input. We performed experiments on the benchmark dataset TVSum.","PeriodicalId":13480,"journal":{"name":"Indonesian Journal of Electrical Engineering and Computer Science","volume":"9 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141715636","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}
Pub Date : 2024-07-01DOI: 10.11591/ijeecs.v35.i1.pp593-600
Nidal M. Turab, H. Owida, Jamal I. Al-Nabulsi
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.
随着数字环境的不断发展，网络安全威胁日益频繁和复杂，人们对使用区块链（BC）技术的兴趣与日俱增。区块链技术具有去中心化、完整性和透明度等理想特性。区块链技术的去中心化特性消除了单点故障，降低了关键系统在定向攻击面前的脆弱性。网络威胁的复杂性和快速演变性要求我们更早采取适应性方法。本综述文件研究了从官方网站收集的几篇论文。本文以使用业连技术提高网络安全为重点，主要关键词包括业连技术、供应链管理、工作证明和权益证明。本综述论文旨在通过威胁评估来研究安全组件，该评估比较了 BC 在不同等级和真实攻击环境中的安全性。论文列举了 BC 的独特特点，强调了 BC 在加强网络安全措施方面的潜力。综述论文还指出，在应对与计算机数据和数字系统有关的安全挑战方面缺乏重点，并呼吁对解决问题进行更深入的讨论。
{"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":"84 3","pages":""},"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}
Pub Date : 2024-07-01DOI: 10.11591/ijeecs.v35.i1.pp494-502
Nishu Chowdhury, Jeenat Sultana, Tanim Rahman, Tanjia Chowdhury, F. Khan, Arpita Chakraborty
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.
{"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":"286 9","pages":""},"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}
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.
{"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":"89 3","pages":""},"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}
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.
{"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":"48 7","pages":""},"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}
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.
糖尿病视网膜病变（DR）是一种渐进性眼部疾病，可导致从无DR到严重DR的不可逆视力损伤，因此需要精确识别以尽早治疗。本研究引入了一种创新的深度学习（DL）方法，在检测 DR 阶段方面超越了传统方法。它评估了在平衡数据集上训练 DL 模型的两种情况。第一种方案通过对比度限制自适应直方图均衡化（CLAHE）和生成式对抗网络（GAN）进行图像增强，第二种方案不涉及任何图像增强。在亚太远程眼科协会 2019 年失明检测（APTOS-2019 BD）数据集上进行测试，增强模型（方案 1）的准确率达到 98%，科恩卡帕得分（CKS）达到 99%，非增强模型（方案 2）的准确率达到 95.4%，科恩卡帕得分（CKS）达到 90.5%。CLAHE 与 GAN 的结合（称为 CLANG）显著提高了模型的性能和可推广性。这一进步对于早期 DR 检测和干预至关重要，为防止糖尿病患者出现不可逆转的视力损失提供了一条新途径。
{"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":"15 1","pages":""},"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}
Pub Date : 2024-07-01DOI: 10.11591/ijeecs.v35.i1.pp175-190
Abdel Nasser Soumana Hamadou, Ciira wa Maina, M. M. Soidridine
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.
{"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":"53 3","pages":""},"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}
Pub Date : 2024-07-01DOI: 10.11591/ijeecs.v35.i1.pp52-61
Mohammed Hicham Zaggaf, Adil Mansouri, A. El Magri, A. Watil, R. Lajouad, L. Bahatti
This paper presents a nonlinear observer for a variable-speed wind energy conversion system (WECS) utilizing a permanent magnet synchronous generator (PMSG). The study addresses the design of high-gain sampled-data observers based on the nonlinear WECS model, supported by formal convergence analysis. An essential aspect of this observer design is the incorporation of a time-varying gain, significantly enhancing system performance. Convergence of estimation errors is demonstrated using the input-to-state stability method. Simulation of the proposed observer is conducted using the MATLAB-Simulink tool. The obtained results are presented and analyzed to showcase the overall effectiveness of the proposed system.
{"title":"Sampled-data observer design for sensorless control of wind energy conversion system with PMSG","authors":"Mohammed Hicham Zaggaf, Adil Mansouri, A. El Magri, A. Watil, R. Lajouad, L. Bahatti","doi":"10.11591/ijeecs.v35.i1.pp52-61","DOIUrl":"https://doi.org/10.11591/ijeecs.v35.i1.pp52-61","url":null,"abstract":"This paper presents a nonlinear observer for a variable-speed wind energy conversion system (WECS) utilizing a permanent magnet synchronous generator (PMSG). The study addresses the design of high-gain sampled-data observers based on the nonlinear WECS model, supported by formal convergence analysis. An essential aspect of this observer design is the incorporation of a time-varying gain, significantly enhancing system performance. Convergence of estimation errors is demonstrated using the input-to-state stability method. Simulation of the proposed observer is conducted using the MATLAB-Simulink tool. The obtained results are presented and analyzed to showcase the overall effectiveness of the proposed system.","PeriodicalId":13480,"journal":{"name":"Indonesian Journal of Electrical Engineering and Computer Science","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141689337","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}
Pub Date : 2024-07-01DOI: 10.11591/ijeecs.v35.i1.pp90-101
Suleman Alnatheer, M. A. Ahmed
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.
{"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":"9 9","pages":""},"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}
Pub Date : 2024-07-01DOI: 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
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.
{"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":"13 8","pages":""},"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}
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