Under commonwealth of the outlier extinguish inspection, exclusively on the impulsive outlier, the outlier extinguish algorithm is a substantial step, which is early performed prior to further computer vision steps thereupon the iterative mean filter (IMF) is inaugurated for fix value impulsive noise (FVIN) and grown into one of the superior achievement outliers extinguish algorithms. This academic article focuses to investigate the correlative achievement of the outlier extinguish algorithm established on IMF, is inaugurated from mean filter (MF) for carrying out the poor achievement of the aforesaid outlier extinguish algorithms (standard median filter (SMF), MF, and adaptive median filter (AMF)), for FVIN at omnipresent scattering of outlier consistency (5-90%). The analytical experiment comprehensively exploits on bountiful figures (F16, Girl, Lena, and Pepper) that are inspected in order to analyze the correlative achievement of an outlier extinguish algorithm established on IMF. In contrast with the aforesaid outlier extinguish algorithms (SMF, MF, and AMF), the outlier extinguish algorithm established on IMF has superior achievement from the experimental results.
在离群值剔除检查的大环境下，专门针对脉冲离群值的离群值剔除算法是一个重要步骤，它在进一步的计算机视觉步骤之前提前执行，然后针对固定值脉冲噪声（FVIN）启动迭代均值滤波器（IMF），并发展成为成就卓越的离群值剔除算法之一。本文重点研究了建立在 IMF 上的离群值消除算法的相关性能，IMF 由均值滤波器（MF）演变而来，用于消除上述离群值消除算法（标准中值滤波器（SMF）、MF 和自适应中值滤波器（AMF））在离群值一致性普遍分散（5%-90%）的情况下对 FVIN 的不良性能。分析实验全面利用了大量的数据（F16、Girl、Lena 和 Pepper），通过对这些数据的检查，分析了在 IMF 上建立的离群值消除算法的相关成就。从实验结果来看，与上述离群值消除算法（SMF、MF 和 AMF）相比，基于 IMF 的离群值消除算法具有更优越的性能。
{"title":"A comprehensive achievement investigation of iterative mean filter for outlier extinguish aspiration on ubiquitous FVIN","authors":"V. Patanavijit, K. Thakulsukanant","doi":"10.11591/eei.v13i2.5951","DOIUrl":"https://doi.org/10.11591/eei.v13i2.5951","url":null,"abstract":"Under commonwealth of the outlier extinguish inspection, exclusively on the impulsive outlier, the outlier extinguish algorithm is a substantial step, which is early performed prior to further computer vision steps thereupon the iterative mean filter (IMF) is inaugurated for fix value impulsive noise (FVIN) and grown into one of the superior achievement outliers extinguish algorithms. This academic article focuses to investigate the correlative achievement of the outlier extinguish algorithm established on IMF, is inaugurated from mean filter (MF) for carrying out the poor achievement of the aforesaid outlier extinguish algorithms (standard median filter (SMF), MF, and adaptive median filter (AMF)), for FVIN at omnipresent scattering of outlier consistency (5-90%). The analytical experiment comprehensively exploits on bountiful figures (F16, Girl, Lena, and Pepper) that are inspected in order to analyze the correlative achievement of an outlier extinguish algorithm established on IMF. In contrast with the aforesaid outlier extinguish algorithms (SMF, MF, and AMF), the outlier extinguish algorithm established on IMF has superior achievement from the experimental results.","PeriodicalId":37619,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"53 29","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140357305","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}
The majority of the fires are activated through environmental reasons although a minority of them are self-activated. To detect fires several safety systems were introduced. There are wired systems, cameras, satellite systems, and bluetooth feasible to provide a complete image of the world but after a long search period. These systems are not perfect since it prevents fire from finding just at the time, the fire initiates. But, recent technological development in wireless sensor networks (WSN) has spread out its fire detection application. A comprehensive survey on several fire management approaches in WSN propose to discuss various fire detection approaches like early fire detection, energy efficient fire detection, mobile agent-based fire detection, unmanned aerial vehicle (UAV)-based fire detection, threshold-based fire detection, machine learning based fire detection and secure fire detection approaches. Moreover, the comprehensive tabular study of the fire management technique is given that will assist in the suitable selection of approaches to be applied for the detection of fire. Furthermore, WSN uses the clustering method to minimize redundant dataandsecure fire detection approaches collect authenticated data related to fire detection. Early fire detection approaches detects the fire early. Machine learning algorithm detects the fire efficiently.
{"title":"A comprehensive survey on several fire management approaches in wireless sensor networks","authors":"Swetha Rajendran, Navaneethan Chenniappan","doi":"10.11591/eei.v13i2.5833","DOIUrl":"https://doi.org/10.11591/eei.v13i2.5833","url":null,"abstract":"The majority of the fires are activated through environmental reasons although a minority of them are self-activated. To detect fires several safety systems were introduced. There are wired systems, cameras, satellite systems, and bluetooth feasible to provide a complete image of the world but after a long search period. These systems are not perfect since it prevents fire from finding just at the time, the fire initiates. But, recent technological development in wireless sensor networks (WSN) has spread out its fire detection application. A comprehensive survey on several fire management approaches in WSN propose to discuss various fire detection approaches like early fire detection, energy efficient fire detection, mobile agent-based fire detection, unmanned aerial vehicle (UAV)-based fire detection, threshold-based fire detection, machine learning based fire detection and secure fire detection approaches. Moreover, the comprehensive tabular study of the fire management technique is given that will assist in the suitable selection of approaches to be applied for the detection of fire. Furthermore, WSN uses the clustering method to minimize redundant dataandsecure fire detection approaches collect authenticated data related to fire detection. Early fire detection approaches detects the fire early. Machine learning algorithm detects the fire efficiently.","PeriodicalId":37619,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"17 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140352705","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}
Aziz Smaala, Zakaria Laboudi, Asma Saighi, A. Moudjari
Playing multimedia documents in ubiquitous systems may require content adaptation based on gathered context information and accumulated historical data. Several approaches have already been proposed, in which adaptation actions are performed to provide adapted documents. Nevertheless, these approaches focus mainly on efficient use of context information without involving historical users data to improve the adaptation process. Thus, this paper allows for consideration of historical users data during the execution of the adaptation process. To do so, the context elements and the adaptation actions are first modeled using the oriented-object approach and then converted into relational and NoSQL databases schemes. Finally, algorithms for storing, retrieving and analysing data are designed. The proposal is validated by implementing scenarios through a real prototype. At a first step, the performances are measured to estimate the cost of data processing. The experiments show that NoSQL databases excel in data storage and ease of implementation, while relational databases perform well in data retrieve. At a second step, the proposal usefulness is highlighted by showing how historical data contribute to adaptation rules personalization using datadriven rule learning mechanisms rather than defining them explicitly. The analysis algorithm could retain personalized adaptation rules with confidence degree greater than 90%. Overall, the results are satisfactory.
{"title":"On the use of historical data in context-aware multimedia documents adaptation processes","authors":"Aziz Smaala, Zakaria Laboudi, Asma Saighi, A. Moudjari","doi":"10.11591/eei.v13i2.5297","DOIUrl":"https://doi.org/10.11591/eei.v13i2.5297","url":null,"abstract":"Playing multimedia documents in ubiquitous systems may require content adaptation based on gathered context information and accumulated historical data. Several approaches have already been proposed, in which adaptation actions are performed to provide adapted documents. Nevertheless, these approaches focus mainly on efficient use of context information without involving historical users data to improve the adaptation process. Thus, this paper allows for consideration of historical users data during the execution of the adaptation process. To do so, the context elements and the adaptation actions are first modeled using the oriented-object approach and then converted into relational and NoSQL databases schemes. Finally, algorithms for storing, retrieving and analysing data are designed. The proposal is validated by implementing scenarios through a real prototype. At a first step, the performances are measured to estimate the cost of data processing. The experiments show that NoSQL databases excel in data storage and ease of implementation, while relational databases perform well in data retrieve. At a second step, the proposal usefulness is highlighted by showing how historical data contribute to adaptation rules personalization using datadriven rule learning mechanisms rather than defining them explicitly. The analysis algorithm could retain personalized adaptation rules with confidence degree greater than 90%. Overall, the results are satisfactory.","PeriodicalId":37619,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"5 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140354951","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}
Mohamad Afendee Mohamed, Yahaya Garba Shawai, Mohammed Amin Almaiah, M. N. Derahman, Abdalwali Lutfi, Khairul Azmi Abu Bakar
Big number operation has always been a bottleneck to computer system as it imposes high demand on computing power. With a limited power available, operations such as exponentiation and multiplication involving large integer belonging to encryption process requires grave scrutiny. One way to address this issue is by replacing an original complex computation into a sequence of small computations that in the end produces the same results. This paper takes an evolutionary approach to survey numerous articles that have contributed to the advancement of integer representation. Numerous representations were proposed, those that come into play concentrated on reducing non-zero digits and limiting non-zero spacing other than allowing subtraction operation. A comparison was made to distinguish the properties of each method from the others. This detailed outlook can be a guide for identifying the correct representation to be chosen for implementation within specific application.
{"title":"Challenges in data representation for efficient execution of encryption operation","authors":"Mohamad Afendee Mohamed, Yahaya Garba Shawai, Mohammed Amin Almaiah, M. N. Derahman, Abdalwali Lutfi, Khairul Azmi Abu Bakar","doi":"10.11591/eei.v13i2.5437","DOIUrl":"https://doi.org/10.11591/eei.v13i2.5437","url":null,"abstract":"Big number operation has always been a bottleneck to computer system as it imposes high demand on computing power. With a limited power available, operations such as exponentiation and multiplication involving large integer belonging to encryption process requires grave scrutiny. One way to address this issue is by replacing an original complex computation into a sequence of small computations that in the end produces the same results. This paper takes an evolutionary approach to survey numerous articles that have contributed to the advancement of integer representation. Numerous representations were proposed, those that come into play concentrated on reducing non-zero digits and limiting non-zero spacing other than allowing subtraction operation. A comparison was made to distinguish the properties of each method from the others. This detailed outlook can be a guide for identifying the correct representation to be chosen for implementation within specific application.","PeriodicalId":37619,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"57 41","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140357538","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}
An arrhythmia is an irregular heartbeat. Many researchers in the AI field have carried out the automatic classification of arrhythmias, and the issue that has been widely discussed is imbalanced data. A popular technique for overcoming this problem is the synthetic minority oversampling technique (SMOTE) technique. In this paper, the author adds some sampling of data obtained from other datasets into the primary dataset. In this case, the main dataset is the Massachusetts Institute of Technology–Beth Israel Hospital (MIT-BIH) arrhythmia database and an additional dataset from the MIT-BIH supraventricular arrhythmia database. The classification process is carried out with one-dimensional convolutional neural network model (1D-CNN) to perform multiclass and subject-class advancement of medical instrumentation (AAMII) classifications. The results obtained from this study are an accuracy of 99.10% for multiclass and 99.25% for subject-class.
{"title":"The deep convolutional networks for the classification of multi-class arrhythmia","authors":"Muhamad Akbar, Siti Nurmaini, R. U. Partan","doi":"10.11591/eei.v13i2.6102","DOIUrl":"https://doi.org/10.11591/eei.v13i2.6102","url":null,"abstract":"An arrhythmia is an irregular heartbeat. Many researchers in the AI field have carried out the automatic classification of arrhythmias, and the issue that has been widely discussed is imbalanced data. A popular technique for overcoming this problem is the synthetic minority oversampling technique (SMOTE) technique. In this paper, the author adds some sampling of data obtained from other datasets into the primary dataset. In this case, the main dataset is the Massachusetts Institute of Technology–Beth Israel Hospital (MIT-BIH) arrhythmia database and an additional dataset from the MIT-BIH supraventricular arrhythmia database. The classification process is carried out with one-dimensional convolutional neural network model (1D-CNN) to perform multiclass and subject-class advancement of medical instrumentation (AAMII) classifications. The results obtained from this study are an accuracy of 99.10% for multiclass and 99.25% for subject-class.","PeriodicalId":37619,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"28 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140353821","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}
The optimal unmanned aerial vehicle (UAV) path planning using bio-inspired algorithms requires high computation and low convergence in a complex 3D environment. To solve this problem, a hybrid A*-FPA algorithm was proposed that combines the A* algorithm with a flower pollination algorithm (FPA). The main idea of this algorithm is to balance the high speed of the A* exploration ability with the FPA exploitation ability to find an optimal 3D UAV path. At first, the algorithm starts by finding the locally optimal path based on a grid map, and the result is a set of path nodes. The algorithm will select three discovered nodes and set the FPA's initial population. Finally, the FPA is applied to obtain the optimal path. The proposed algorithm's performance was compared with the A*, FPA, genetic algorithm (GA), and partical swarm optimization (PSO) algorithms, where the comparison is done based on four factors: the best path, mean path, standard deviation, and worst path length. The simulation results showed that the proposed algorithm outperformed all previously mentioned algorithms in finding the optimal path in all scenarios, significantly improving the best path length and mean path length of 79.3% and 147.8%, respectively.
{"title":"Unmanned aerial vehicle path planning in a 3D environment using a hybrid algorithm","authors":"A. A. Kareem, M. J. Mohamed, B. K. Oleiwi","doi":"10.11591/eei.v13i2.6020","DOIUrl":"https://doi.org/10.11591/eei.v13i2.6020","url":null,"abstract":"The optimal unmanned aerial vehicle (UAV) path planning using bio-inspired algorithms requires high computation and low convergence in a complex 3D environment. To solve this problem, a hybrid A*-FPA algorithm was proposed that combines the A* algorithm with a flower pollination algorithm (FPA). The main idea of this algorithm is to balance the high speed of the A* exploration ability with the FPA exploitation ability to find an optimal 3D UAV path. At first, the algorithm starts by finding the locally optimal path based on a grid map, and the result is a set of path nodes. The algorithm will select three discovered nodes and set the FPA's initial population. Finally, the FPA is applied to obtain the optimal path. The proposed algorithm's performance was compared with the A*, FPA, genetic algorithm (GA), and partical swarm optimization (PSO) algorithms, where the comparison is done based on four factors: the best path, mean path, standard deviation, and worst path length. The simulation results showed that the proposed algorithm outperformed all previously mentioned algorithms in finding the optimal path in all scenarios, significantly improving the best path length and mean path length of 79.3% and 147.8%, respectively.","PeriodicalId":37619,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"2 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140355592","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}
Ibtissame Mansoury, Dounia El Bourakadi, Ali Yahyaouy, J. Boumhidi
Through the much defiance facing energy today, it has become necessary to rely on wind energy as a source of unlimited renewable energies. However, energy planning and regulation require wind capacity forecasting, because oscillations of wind speed drastically affect directly power generation. Therefore, several scenarios must be provided to allow for estimating uncertainties. To deal with this problem, this paper exploits the major advantages of the regularized extreme learning machine algorithm (R-ELM) and thus proposes a model for predicting the wind energy generated for the next hour based on the time series of wind speed. The R-ELM is combined with the genetic algorithm which is designed to optimize the most important hyperparameter which is the number of hidden neurons. Thus, the proposed model aims to forecast the average wind power per hour based on the wind speed of the previous hours. The results obtained showed that the proposed method is much better than those reported in the literature concerning the precision of the prediction and the time convergence.
{"title":"Optimized extreme learning machine using genetic algorithm for short-term wind power prediction","authors":"Ibtissame Mansoury, Dounia El Bourakadi, Ali Yahyaouy, J. Boumhidi","doi":"10.11591/eei.v13i2.6476","DOIUrl":"https://doi.org/10.11591/eei.v13i2.6476","url":null,"abstract":"Through the much defiance facing energy today, it has become necessary to rely on wind energy as a source of unlimited renewable energies. However, energy planning and regulation require wind capacity forecasting, because oscillations of wind speed drastically affect directly power generation. Therefore, several scenarios must be provided to allow for estimating uncertainties. To deal with this problem, this paper exploits the major advantages of the regularized extreme learning machine algorithm (R-ELM) and thus proposes a model for predicting the wind energy generated for the next hour based on the time series of wind speed. The R-ELM is combined with the genetic algorithm which is designed to optimize the most important hyperparameter which is the number of hidden neurons. Thus, the proposed model aims to forecast the average wind power per hour based on the wind speed of the previous hours. The results obtained showed that the proposed method is much better than those reported in the literature concerning the precision of the prediction and the time convergence.","PeriodicalId":37619,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"12 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140353075","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}
The game cross-efficiency method, a commonly utilized approach for ranking decision-making units in tie-breaking scenarios, is based on secondary goals. However, in certain data envelopment analysis ranking problems, the classical game cross-efficiency method may fail to differentiate all decision-making units effectively. To address this limitation, it is prudent to explore the development of a new method that can enhance the ranking performance of the classical game cross-efficiency approach. In this study, we propose a novel Gibbs entropy linear programming model that integrates both optimistic and pessimistic perspectives of the classical game cross-efficiency method for data envelopment analysis ranking problems. To validate the reliability and utility of our proposed method, we present three examples: the six nursing homes problem, numerical example 2, and an application involving twenty Thai provinces with cash crop data. The reliability of the proposed method is assessed using Spearman’s correlation coefficient (rs) on the numerical examples. The results demonstrate that the rs values for both the proposed method and the classical game crossefficiency method, specifically for the six nursing homes problem, numerical example 2, and the application involving twenty Thai provinces, are determined to be rs=0.998, 0.998, and 0.986 respectively.
{"title":"An optimistic-pessimistic game cross-efficiency method based on a Gibbs entropy model for ranking decision making units","authors":"Noppakun Thongmual, Chanchai Laoha, Narong Wichapa","doi":"10.11591/eei.v13i2.5747","DOIUrl":"https://doi.org/10.11591/eei.v13i2.5747","url":null,"abstract":"The game cross-efficiency method, a commonly utilized approach for ranking decision-making units in tie-breaking scenarios, is based on secondary goals. However, in certain data envelopment analysis ranking problems, the classical game cross-efficiency method may fail to differentiate all decision-making units effectively. To address this limitation, it is prudent to explore the development of a new method that can enhance the ranking performance of the classical game cross-efficiency approach. In this study, we propose a novel Gibbs entropy linear programming model that integrates both optimistic and pessimistic perspectives of the classical game cross-efficiency method for data envelopment analysis ranking problems. To validate the reliability and utility of our proposed method, we present three examples: the six nursing homes problem, numerical example 2, and an application involving twenty Thai provinces with cash crop data. The reliability of the proposed method is assessed using Spearman’s correlation coefficient (rs) on the numerical examples. The results demonstrate that the rs values for both the proposed method and the classical game crossefficiency method, specifically for the six nursing homes problem, numerical example 2, and the application involving twenty Thai provinces, are determined to be rs=0.998, 0.998, and 0.986 respectively.","PeriodicalId":37619,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"18 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140352476","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}
Raid Rafi Omar Al-Nima, Musab Tahseen Salahaldeen Al-Kaltakchi, Hasan A. Abdulla
One of the most significant physiological biometrics is the deoxyribonucleic acid (DNA). It can be found in every human cell as in hair, blood, and skin. In this paper, a special DNA deep learning (SDDL) is proposed as a novel machine learning (ML) model to identify persons depending on their DNAs. The proposed model is designed to collect DNA chromosomes of parents for an individual. It is flexible (can be enlarged or reduced) and it can identify one or both parents of a person, based on the provided chromosomes. The SDDL is so fast in training compared to other traditional deep learning models. Two real datasets from Iraq are utilized called: Real Iraqi Dataset for Kurd (RIDK) and Real Iraqi Dataset for Arab (RIDA). The results yield that the suggested SDDL model achieves 100% testing accuracy for each of the employed datasets.
脱氧核糖核酸（DNA）是最重要的生理生物特征之一。它存在于每个人体细胞中，如头发、血液和皮肤。本文提出了一种特殊的 DNA 深度学习（SDDL），作为一种新型的机器学习（ML）模型，根据 DNA 来识别人的身份。所提出的模型旨在收集个人父母的 DNA 染色体。它非常灵活（可放大或缩小），可根据提供的染色体识别一个人的父母一方或双方。与其他传统深度学习模型相比，SDDL 的训练速度非常快。我们使用了两个来自伊拉克的真实数据集：库尔德人真实伊拉克数据集（RIDK）和阿拉伯人真实伊拉克数据集（RIDA）。结果表明，建议的 SDDL 模型在每个数据集上的测试准确率都达到了 100%。
{"title":"Identifying deoxyribonucleic acids of individuals based on their chromosomes by proposing a special deep learning model","authors":"Raid Rafi Omar Al-Nima, Musab Tahseen Salahaldeen Al-Kaltakchi, Hasan A. Abdulla","doi":"10.11591/eei.v13i2.6198","DOIUrl":"https://doi.org/10.11591/eei.v13i2.6198","url":null,"abstract":"One of the most significant physiological biometrics is the deoxyribonucleic acid (DNA). It can be found in every human cell as in hair, blood, and skin. In this paper, a special DNA deep learning (SDDL) is proposed as a novel machine learning (ML) model to identify persons depending on their DNAs. The proposed model is designed to collect DNA chromosomes of parents for an individual. It is flexible (can be enlarged or reduced) and it can identify one or both parents of a person, based on the provided chromosomes. The SDDL is so fast in training compared to other traditional deep learning models. Two real datasets from Iraq are utilized called: Real Iraqi Dataset for Kurd (RIDK) and Real Iraqi Dataset for Arab (RIDA). The results yield that the suggested SDDL model achieves 100% testing accuracy for each of the employed datasets.","PeriodicalId":37619,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"7 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140353303","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}
Devamekalai Nagasundaram, S. Manickam, S. Laghari, Shankar Karuppayah
Semantic interoperability has emerged as a key barrier amidst the major developments and challenges brought about by the rapid expansion of internet of things (IoT) applications. Establishing interoperability is essential for IoT systems to function optimally, especially across diverse organizations. Despite extensive research in achieving semantic interoperability, dynamic interoperability, a vital facet, remains inadequately addressed. This paper addresses this gap by presenting a fog-based conceptual model designed to facilitate dynamic semantic interoperability in IoT. The model incorporates a single-tier fog layer, providing the necessary processing capabilities to achieve this goal. The study conducts a comprehensive literature review on semantic interoperability, emphasizing latency, bandwidth, total cost, and energy consumption. Results demonstrate the proposed double skin façade (DSF) model’s remarkable 88% improvement in service delay over IoT-SIM and Open IoT, attributed to its efficient load-offloading mechanism and optimized fog layer, offering a 50% reduction in service delay, power consumption, and 86% reduction in network usage compared to existing approaches through data redundancy elimination via pre-processing at the fog layer.
{"title":"Proposed fog computing-enabled conceptual model for semantic interoperability in internet of things","authors":"Devamekalai Nagasundaram, S. Manickam, S. Laghari, Shankar Karuppayah","doi":"10.11591/eei.v13i2.5748","DOIUrl":"https://doi.org/10.11591/eei.v13i2.5748","url":null,"abstract":"Semantic interoperability has emerged as a key barrier amidst the major developments and challenges brought about by the rapid expansion of internet of things (IoT) applications. Establishing interoperability is essential for IoT systems to function optimally, especially across diverse organizations. Despite extensive research in achieving semantic interoperability, dynamic interoperability, a vital facet, remains inadequately addressed. This paper addresses this gap by presenting a fog-based conceptual model designed to facilitate dynamic semantic interoperability in IoT. The model incorporates a single-tier fog layer, providing the necessary processing capabilities to achieve this goal. The study conducts a comprehensive literature review on semantic interoperability, emphasizing latency, bandwidth, total cost, and energy consumption. Results demonstrate the proposed double skin façade (DSF) model’s remarkable 88% improvement in service delay over IoT-SIM and Open IoT, attributed to its efficient load-offloading mechanism and optimized fog layer, offering a 50% reduction in service delay, power consumption, and 86% reduction in network usage compared to existing approaches through data redundancy elimination via pre-processing at the fog layer.","PeriodicalId":37619,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"3 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140353391","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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