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Research and implementation of the medical text analysis algorithm for predicting mortality 研究和实施预测死亡率的医学文本分析算法
Q2 Mathematics Pub Date : 2024-06-01 DOI: 10.11591/ijeecs.v34.i3.pp1965-1977
Zhenisgul Rakhmetullina, Saule Belginova, Alibekkyzy Karlygash, Aigerim Ismukhamedova, Shynar Tezekpaeva
Mortality prediction has a role to play in the development of a descriptive measure of the quality of care that provides a fair and equitable means of comparing and evaluating hospitals. This article describes a study of a medical text analysis algorithm for mortality prediction that used big data in the form of unstructured medical notes. The article describes the concept of using text mining technology for medical systems, a method for preprocessing medical data to predict patient mortality, an algorithm for predicting patient deaths based on the logistic regression classifier and presents a software module for implementing the proposed algorithm.
死亡率预测在制定医疗质量的描述性衡量标准方面可以发挥作用,它为比较和评估医院提供了一种公平公正的方法。本文介绍了一项针对死亡率预测的医学文本分析算法的研究,该算法使用了非结构化医疗笔记形式的大数据。文章介绍了在医疗系统中使用文本挖掘技术的概念、预处理医疗数据以预测患者死亡率的方法、基于逻辑回归分类器预测患者死亡的算法,并介绍了实现所提算法的软件模块。
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引用次数: 0
Enhancing EEG-based brain-computer interface systems through efficient machine learning classification techniques 通过高效的机器学习分类技术改进基于脑电图的脑机接口系统
Q2 Mathematics Pub Date : 2024-06-01 DOI: 10.11591/ijeecs.v34.i3.pp2045-2054
Ferdi Ahmed Yassine, Ghazli Abdelkader
Advances in the fields of neuroscience and computer science have greatly enhanced the human brain’s ability to communicate and interact with the surrounding environment. In addition, recent steps in machine learning (ML) have increased the use of electroencephalography (EEG)-based BCIs for artificial intelligence (AI) applications. The prevailing challenge in recording EEG sensor data is that the captured signals are mixed with noise, which makes their effective use difficult. Therefore, strengthening the classification stage becomes extremely important and plays a major role in addressing this problem. In this study, we chose five most widely used classification models that obtained the best results in this field and tested them on two open-source databases. We also focused on improving the hyperparameters of each algorithm to obtain best results. Our results indicate excellent results on the first dataset and acceptable for most models on the second, while RF showed superior performance on both with an accuracy of 100% on the first dataset and 86.47% on the second. This was achieved with the lowest training costs, and better performance compared to previous works we evaluated that used the same databases. These results provide valuable insights and advance the development of brain-computer interface (BCI) technology and design.
神经科学和计算机科学领域的进步极大地增强了人脑与周围环境进行交流和互动的能力。此外,最近在机器学习(ML)方面取得的进展也增加了基于脑电图(EEG)的 BCI 在人工智能(AI)应用中的使用。记录脑电图传感器数据时普遍面临的挑战是,捕捉到的信号中混杂着噪声,这使得其难以有效利用。因此,加强分类阶段变得极为重要,并在解决这一问题方面发挥着重要作用。在本研究中,我们选择了在这一领域获得最佳结果的五个最广泛使用的分类模型,并在两个开源数据库中对它们进行了测试。我们还重点改进了每种算法的超参数,以获得最佳结果。我们的结果表明,第一个数据集的结果非常好,第二个数据集的结果大多数模型都可以接受,而 RF 在这两个数据集上都表现出色,第一个数据集的准确率为 100%,第二个数据集的准确率为 86.47%。这是以最低的训练成本实现的,与我们评估过的使用相同数据库的前几部作品相比,性能更好。这些结果提供了宝贵的见解,推动了脑机接口(BCI)技术和设计的发展。
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引用次数: 0
Enhancing Hadoop distributed storage efficiency using multi-agent systems 利用多代理系统提高 Hadoop 分布式存储效率
Q2 Mathematics Pub Date : 2024-06-01 DOI: 10.11591/ijeecs.v34.i3.pp1814-1822
Rabie Mahdaoui, Manar Sais, J. Abouchabaka, N. Rafalia
Distributed storage systems play a pivotal role in modern data-intensive applications, with Hadoop distributed file system (HDFS) being a prominent example. However, optimizing the efficiency of such systems remains a complex challenge. This research paper presents a novel approach to enhance the efficiency of distributed storage by leveraging multi-agent systems (MAS). Our research is centered on enhancing the efficiency of the HDFS by incorporating intelligent agents that can dynamically assign storage tasks to nodes based on their performance characteristics. Utilizing a decentralized decision-making framework, the suggested approach based on MAS considers the real-time performance of nodes and allocates storage tasks adaptively. This strategy aims to alleviate performance bottlenecks and minimize data transfer latency. Through extensive experimental evaluation, we demonstrate the effectiveness of our approach in improving HDFS performance in terms of data storage, retrieval, and overall system efficiency. The results reveal significant reductions in job execution times and enhanced resource utilization, there by offering a promising avenue for enhancing the efficiency of distributed storage systems.
分布式存储系统在现代数据密集型应用中发挥着举足轻重的作用,Hadoop 分布式文件系统(HDFS)就是一个突出的例子。然而,优化此类系统的效率仍然是一项复杂的挑战。本研究论文提出了一种利用多代理系统(MAS)提高分布式存储效率的新方法。我们的研究重点是通过纳入智能代理来提高 HDFS 的效率,这些智能代理可以根据节点的性能特征将存储任务动态分配给节点。基于 MAS 的建议方法利用分散决策框架,考虑节点的实时性能,并自适应地分配存储任务。这一策略旨在缓解性能瓶颈,最大限度地减少数据传输延迟。通过广泛的实验评估,我们证明了我们的方法在提高 HDFS 的数据存储、检索和整体系统效率方面的有效性。结果表明,作业执行时间大幅缩短,资源利用率得到提高,从而为提高分布式存储系统的效率提供了一条大有可为的途径。
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引用次数: 0
Encrypted image processing using compression and reversible data hiding 利用压缩和可逆数据隐藏技术进行加密图像处理
Q2 Mathematics Pub Date : 2024-06-01 DOI: 10.11591/ijeecs.v34.i3.pp1593-1602
Yasmina Zine, M. Boumehed, N. H. Said
Reversible data hiding within encrypted images reversible data hiding in encrypted images (RDH-EI) is a highly effective technique for image processing in the field of encryption. This paper, propose a RDH-EI technique, which utilizes bit-plane compression and various image scanning directions to generate vacant space for data embedding, referred to as vacating room. Initially, the prediction error of the pre-processed image is computed. Subsequently, each bit-plane image is converted into a bit-stream by following the pixel scan order employed before compression. The compressed image is then encrypted employing a stream cipher. Through the process of substitution, the secret data and additional information are incorporated into the acquired image without any knowledge of the original content or the encrypted key. Finally, the generated image is transmitted or archived. The experiments provide evidence that the proposed method surpasses the most advanced methods currently available.
加密图像中的可逆数据隐藏 加密图像中的可逆数据隐藏(RDH-EI)是加密领域中一种高效的图像处理技术。本文提出了一种 RDH-EI 技术,该技术利用位平面压缩和不同的图像扫描方向来产生用于数据嵌入的空位,即空位。首先,计算预处理图像的预测误差。随后,按照压缩前采用的像素扫描顺序,将每个位平面图像转换为位流。然后使用流密码对压缩后的图像进行加密。通过置换过程,秘密数据和附加信息被纳入获取的图像中,而原始内容或加密密钥却一无所知。最后,生成的图像被传输或存档。实验证明,所提出的方法超越了目前最先进的方法。
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引用次数: 0
Game-based augmented reality learning of Sarawak history in enhancing cultural heritage preservation 基于游戏的增强现实技术学习沙捞越历史,加强文化遗产保护
Q2 Mathematics Pub Date : 2024-06-01 DOI: 10.11591/ijeecs.v34.i3.pp1718-1729
Clive Lai Yi Cheng, Goh Eg Su, J. Ahmad, Tole Sutikno
The augmented reality (AR) technology had been proliferating for years. However, the implementation of AR technology still has room to be explored, especially in the form of cultural heritage preservation. The aim of this study is to enhance AR technology in game-based cultural heritage and history preservation in Sarawak, as well as supplement the gamified experiences in learning James Brooke’s history. Three research objectives are proposed: to design an AR game prototype for the history of James Brooke; to develop an AR game prototype with a collaborative learning element; and to evaluate an AR game prototype for enhancing cultural heritage preservation. This study proposes a game-based prototype that contains AR markers to assign each with different game features. Furthermore, collaborative learning theory is enhanced through AR experiences with multiplayer support. The game-based prototype is evaluated by a group of participants through prototype measuring and testing. The participants feel mediocre about the challenge and knowledge factors of the prototype. Overall, this study highlights the enhancement of cultural heritage preservation through AR game-based experiences intensively learned from James Brooke’s history in Sarawak. These implementations have an apparent promising contribution to make in protecting the available cultural heritage in Sarawak and extensively to the country’s cultural heritage preservation.
多年来,增强现实(AR)技术一直在蓬勃发展。然而,AR 技术的应用仍有探索空间,尤其是在文化遗产保护方面。本研究的目的是加强 AR 技术在沙捞越州以游戏为基础的文化遗产和历史保护中的应用,并补充学习詹姆斯-布鲁克历史的游戏化体验。本研究提出了三个研究目标:为詹姆斯-布鲁克的历史设计一个 AR 游戏原型;开发一个具有协作学习元素的 AR 游戏原型;以及评估一个用于加强文化遗产保护的 AR 游戏原型。本研究提出了一个基于游戏的原型,其中包含 AR 标记,每个标记都具有不同的游戏功能。此外,还通过支持多人游戏的 AR 体验增强了协作学习理论。一组参与者通过原型测量和测试对基于游戏的原型进行了评估。参与者对原型的挑战性和知识因素感觉一般。总之,本研究强调了通过从沙捞越詹姆斯-布鲁克的历史中深入学习的基于 AR 的游戏体验来加强文化遗产保护。这些实施方案在保护沙捞越现有文化遗产和广泛促进国家文化遗产保护方面做出了明显的贡献。
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引用次数: 0
Microstrip antenna system for communication capabilities applications 用于通信能力应用的微带天线系统
Q2 Mathematics Pub Date : 2024-06-01 DOI: 10.11591/ijeecs.v34.i3.pp1643-1653
Fredelino A. Galleto Jr., Aaron Don M. Africa, Alyssa Joie F. Tablada, John Ernesto G. Amadora Jr., Ira Third L. Burgos, Alliyah Mae K. Borebor, Rocelle Andrea S. Belandres, Rafael Dominic L. Montaño
In this comparative study, seven different microstrip antenna shapes, including rectangular, elliptical, triangular, inset fed, H-notch, and E-notch, were observed and analyzed, focusing on their suitability for global positioning system (GPS) application in microsatellites. To enable meaningful comparison, the study utilized the optimal resonant frequency in GPS applications, which is 1.57542 GHz. All the antenna designs have been generated using MATLAB’s Antenna Toolbox and are 100% efficient under ideal conditions with zero polarization loss, which is assumed in the link budget analysis. The results show that each antenna shape has been found to offer distinct advantages and limitations. Along with this, the circular and elliptical patch antenna presented a well-balanced performance, which is suitable for GPS applications. However, the elliptical shape falls behind the circular shape, which was determined to be the most optimal choice for GPS application, providing excellent isotropic antenna gain, return loss, voltage standing wave ratio (VSWR), and strong link budget analysis results.
在这项比较研究中,观察和分析了七种不同形状的微带天线,包括矩形、椭圆形、三角形、嵌入式馈电、H 型缺口和 E 型缺口,重点是它们在微型卫星中应用全球定位系统(GPS)的适用性。为了进行有意义的比较,研究采用了 GPS 应用中的最佳谐振频率,即 1.57542 千兆赫。所有天线设计都是使用 MATLAB 的天线工具箱生成的,在链路预算分析中假定的零极化损耗的理想条件下,效率均为 100%。结果表明,每种天线形状都具有明显的优势和局限性。其中,圆形和椭圆形贴片天线性能均衡,适合 GPS 应用。然而,椭圆形天线的性能落后于圆形天线,后者被认为是 GPS 应用的最佳选择,能提供出色的各向同性天线增益、回波损耗、电压驻波比(VSWR)和较强的链路预算分析结果。
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引用次数: 0
Improved vigenere using affine functions surrounded by two genetic crossovers for image encryption 使用仿射函数和两个遗传交叉包围的改进 vigenere,用于图像加密
Q2 Mathematics Pub Date : 2024-06-01 DOI: 10.11591/ijeecs.v34.i3.pp1787-1799
Hamid El Bourakkadi, Abdelhakim Chemlal, Hassan Tabti, Mourad Kattass, A. Jarjar, A. Benazzi
This paper presents an improved method for encrypting color images, surpassing the effectiveness of genetic crossover and substitution operations. The technique incorporates dynamic random functions to enhance the integrity of the resulting vector, increasing temporal complexity to thwart potential attacks. The improvement involves integrating genetic crossover and utilizing two extensive pseudorandom replacement tables derived from established chaotic maps in cryptography. Following the controlled vectorization of the original image, our approach initiates with a first genetic crossover inspired by deoxyribonucleic acid (DNA) behavior at the pixel level. This genetic crossover is succeeded by a confusion-diffusion lap, reinforcing the connection between encrypted pixels and their neighboring counterparts. The confusion-diffusion process employs dynamic pseudorandom affine functions at the pixel level. Then a second genetic crossover operator is applied. Simulations conducted on a diverse set of images with varying sizes and formats showcase the robustness of our method against statistical, brute-force, and differential attacks.
本文提出了一种改进的彩色图像加密方法,其效果超过了基因交叉和替换操作。该技术结合了动态随机函数,以提高生成向量的完整性,增加时间复杂性,从而挫败潜在的攻击。这项改进包括整合基因交叉和利用两个广泛的伪随机替换表,这两个表源自密码学中已确立的混沌图。在对原始图像进行受控矢量化之后,我们的方法从像素级的脱氧核糖核酸(DNA)行为中汲取灵感,进行第一次基因交叉。随后是混淆扩散,加强加密像素与其相邻像素之间的联系。混淆扩散过程采用像素级动态伪随机仿射函数。然后应用第二个遗传交叉算子。在一组不同大小和格式的图像上进行的仿真表明,我们的方法对统计攻击、暴力攻击和差分攻击具有很强的抵御能力。
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引用次数: 0
Blockchain-based e-voting system in a university 大学中基于区块链的电子投票系统
Q2 Mathematics Pub Date : 2024-06-01 DOI: 10.11591/ijeecs.v34.i3.pp1915-1923
Adil Marouan, Morad Badrani, Nabil Kannouf, Abderrahim Zannou, Abdelaziz Chetouani
The blockchain-based electronic voting (e-voting) system, offers universities a safe, easy-to-use platform that enhances accuracy and integrity. Despite that, it is challenging to integrate the blockchain-based e-voting system with current platforms and private data. Managing latency is another requirement during the blockchain transactions (votes/elections). In this work, we suggested a novel system that uses smart contracts on the consortium blockchain to address these constraints. The voters and electors in a university can vote and elect respecting the rules established in smart contracts. The miners validate transactions using proof of work (PoW) and proof of stake (PoS). Data integrity and voter validity are ensured via the SHA-256 hash algorithm and the ECDSA signature. The implementation results demonstrate that the suggested method works better than the state-of-the-art. exceeds the state-of-the-art in terms of gas cost and execution time.
基于区块链的电子投票(e-voting)系统为大学提供了一个安全、易用的平台,提高了准确性和完整性。尽管如此,将基于区块链的电子投票系统与当前平台和私人数据整合起来仍具有挑战性。管理延迟是区块链交易(投票/选举)期间的另一项要求。在这项工作中,我们提出了一个新颖的系统,在联盟区块链上使用智能合约来解决这些限制。大学中的投票人和选举人可以根据智能合约制定的规则进行投票和选举。矿工使用工作证明(PoW)和权益证明(PoS)验证交易。通过 SHA-256 哈希算法和 ECDSA 签名确保数据完整性和选民有效性。实施结果表明,建议的方法比最先进的方法更有效。
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引用次数: 0
Revolutionization of augmented reality in tourism via deep learning 通过深度学习实现增强现实技术在旅游业中的革命性应用
Q2 Mathematics Pub Date : 2024-06-01 DOI: 10.11591/ijeecs.v34.i3.pp2055-2064
Yasmin Chuupa Essa, Saumya Chaturvedi, Shiraz Khurana
Tourism has become an integral part of social and economic development across the globe. It does not only serve as a recreational activity but also as a source of revenue for the nation. The paper systematically explores the potential enhancements in the tourist experience through cutting-edge technology. Employing deep learning methods, the study specifically concentrates on refining augmented reality encounters for visitors. The proposed approach utilizes deep learning algorithms to optimize and tailor tourists’ augmented reality experiences, addressing current sectoral challenges like customization and engagement shortcomings. The methodology’s selection is predicated on it is capability to elevate user experience, accurately identify objects, offer visual guided tours, integrate historical context, and ultimately propel augmented reality adoption in tourism. Notably, the investigation culminates in a noteworthy average accuracy of 99% when incorporating deep learning to enhance augmented reality in tourism.
旅游业已成为全球社会和经济发展不可或缺的一部分。它不仅是一种娱乐活动,也是国家的收入来源。本文系统地探讨了通过尖端技术提升游客体验的潜力。该研究采用深度学习方法,专门集中于完善游客的增强现实体验。所提出的方法利用深度学习算法来优化和定制游客的增强现实体验,以解决当前行业面临的挑战,如定制和参与方面的不足。之所以选择这种方法,是因为它能够提升用户体验、准确识别物体、提供可视化导游、整合历史背景,并最终推动增强现实技术在旅游业中的应用。值得注意的是,在结合深度学习增强增强现实技术在旅游业中的应用时,调查的平均准确率达到了 99%。
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引用次数: 0
Plant pathology identification using local-global feature level based on transformer 利用基于变压器的局部-全局特征水平识别植物病理学
Q2 Mathematics Pub Date : 2024-06-01 DOI: 10.11591/ijeecs.v34.i3.pp1582-1592
Manh-Hung Ha, Duc-Chinh Nguyen, Manh-Tuan Do, Dinh-Thai Kim, X. Le, Ngoc-Thanh Pham
Deep learning plays a crucial role in addressing the challenge of plant disease identification in the field of agriculture. Detecting diseases in plants requires extensive effort, along with a comprehensive understanding of various plant diseases and increased processing time. Balancing both speed and accuracy in predicting leaf diseases in plants can significantly improve crop production and reduce environmental damage. In this paper, we examined deseases on popular plants in agriculture. We proposed a novel model to predict crop pathology on a feature space of global-local based on transformer aggregation. Paticular, we use refined feature of different layer to correlate semantics from high-level feature and low-level feature. Besides, to capture the extended temporal scale across the entire image, we employ a transformer to discern long-range dependencies among frames. Subsequently, the enhanced features incorporating these dependencies are inputted into a classifier for preliminary crop pathology prediction. The plant village dataset and VietNam strawberry disease (VNStr) dataset were utilized for training and disease classification in the experiments. Extensive experiments show that the proposed method outperforms by 99.18% and 94.05% accuracy in plant village and VNStr, respectivly. The model after being judged was applied on Android devices and therefore is easy to use.
深度学习在应对农业领域植物病害识别的挑战方面发挥着至关重要的作用。检测植物病害需要付出大量努力,同时还需要全面了解各种植物病害并增加处理时间。在预测植物叶片病害时兼顾速度和准确性,可以显著提高作物产量,减少对环境的破坏。本文研究了农业中常见植物的病害。我们提出了一种基于变换器聚合的全局-局部特征空间预测作物病害的新型模型。我们利用不同层的细化特征来关联高层特征和低层特征的语义。此外,为了捕捉整个图像的扩展时间尺度,我们使用变换器来辨别帧与帧之间的长距离依赖关系。随后,将包含这些依赖关系的增强特征输入分类器,进行初步的作物病理预测。实验中使用了植物村数据集和越南草莓病害(VNStr)数据集进行训练和病害分类。大量实验表明,所提出的方法在植物村和 VNStr 的准确率分别为 99.18% 和 94.05%。经过判断后的模型可应用于安卓设备,因此易于使用。
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引用次数: 0
期刊
Indonesian Journal of Electrical Engineering and Computer Science
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