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Morphological Accuracy Data Clustering: A Novel Algorithm for Enhanced Cluster Analysis 形态精度数据聚类:增强聚类分析的新算法
IF 2.9 Q2 Engineering Pub Date : 2024-05-22 DOI: 10.1155/2024/3795126
Abdel Fatah Azzam, A. Maghrabi, Eman El-Naqeeb, Mohammed Aldawood, H. ElGhawalby
In today’s data-driven world, we are constantly exposed to a vast amount of information. This information is stored in various information systems and is used for analysis and management purposes. One important approach to handle these data is through the process of clustering or categorization. Clustering algorithms are powerful tools used in data analysis and machine learning to group similar data points together based on their inherent characteristics. These algorithms aim to identify patterns and structures within a dataset, allowing for the discovery of hidden relationships and insights. By partitioning data into distinct clusters, clustering algorithms enable efficient data exploration, classification, and anomaly detection. In this study, we propose a novel centroid-based clustering algorithm, namely, the morphological accuracy clustering algorithm (MAC algorithm). The proposed algorithm uses a morphological accuracy measure to define the centroid of the cluster. The empirical results demonstrate that the proposed algorithm achieves a stable clustering outcome in fewer iterations compared to several existing centroid-based clustering algorithms. Additionally, the clusters generated by these existing algorithms are highly susceptible to the initial centroid selection made by the user.
在数据驱动的当今世界,我们不断接触到大量信息。这些信息存储在各种信息系统中,用于分析和管理。处理这些数据的一个重要方法就是进行聚类或分类。聚类算法是数据分析和机器学习中使用的强大工具,可根据相似数据点的固有特征将其归类。这些算法旨在识别数据集中的模式和结构,从而发现隐藏的关系和见解。通过将数据划分为不同的群组,聚类算法可以实现高效的数据探索、分类和异常检测。在本研究中,我们提出了一种新颖的基于中心点的聚类算法,即形态精度聚类算法(MAC 算法)。该算法使用形态准确度来定义聚类的中心点。实证结果表明,与现有的几种基于中心点的聚类算法相比,所提出的算法能以较少的迭代次数获得稳定的聚类结果。此外,这些现有算法生成的聚类极易受用户初始中心点选择的影响。
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引用次数: 0
Indonesian Lip-Reading Detection and Recognition Based on Lip Shape Using Face Mesh and Long-Term Recurrent Convolutional Network 利用人脸网格和长期递归卷积网络,基于唇形检测和识别印尼语读唇语
IF 2.9 Q2 Engineering Pub Date : 2024-04-18 DOI: 10.1155/2024/6479124
Aripin, Abas Setiawan
Communication through speech can be hindered by environmental noise, prompting the need for alternative methods such as lip reading, which bypasses auditory challenges. However, the accurate interpretation of lip movements is impeded by the uniqueness of individual lip shapes, necessitating detailed analysis. In addition, the development of an Indonesian dataset addresses the lack of diversity in existing datasets, predominantly in English, fostering more inclusive research. This study proposes an enhanced lip-reading system trained using the long-term recurrent convolutional network (LRCN) considering eight different types of lip shapes. MediaPipe Face Mesh precisely detects lip landmarks, enabling the LRCN model to recognize Indonesian utterances. Experimental results demonstrate the effectiveness of the approach, with the LRCN model with three convolutional layers (LRCN-3Conv) achieving 95.42% accuracy for word test data and 95.63% for phrases, outperforming the convolutional long short-term memory (Conv-LSTM) method. The proposed approach outperforms Conv-LSTM in terms of accuracy. Furthermore, the evaluation of the original MIRACL-VC1 dataset also produced a best accuracy of 90.67% on LRCN-3Conv compared to previous studies in the word-labeled class. The success is attributed to MediaPipe Face Mesh detection, which facilitates the accurate detection of the lip region. Leveraging advanced deep learning techniques and precise landmark detection, these findings promise improved communication accessibility for individuals facing auditory challenges.
通过语音进行交流可能会受到环境噪声的阻碍,因此需要采用读唇术等替代方法,绕过听觉障碍。然而,由于每个人嘴唇形状的独特性,准确解读嘴唇动作受到阻碍,因此必须进行详细分析。此外,印尼语数据集的开发解决了现有数据集(主要是英语数据集)缺乏多样性的问题,促进了更具包容性的研究。本研究提出了一种使用长期递归卷积网络(LRCN)训练的增强型唇读系统,考虑了八种不同类型的唇形。MediaPipe Face Mesh 可精确检测唇部地标,使 LRCN 模型能够识别印尼语。实验结果证明了该方法的有效性,具有三个卷积层的 LRCN 模型(LRCN-3Conv)在单词测试数据中的准确率达到 95.42%,在短语测试数据中的准确率达到 95.63%,优于卷积长短期记忆法(Conv-LSTM)。就准确率而言,所提出的方法优于 Conv-LSTM。此外,在原始 MIRACL-VC1 数据集的评估中,LRCN-3Conv 的准确率也达到了 90.67%,超过了之前的单词标签类研究。这一成功归功于 MediaPipe 脸部网格检测,它有助于准确检测嘴唇区域。利用先进的深度学习技术和精确的地标检测,这些研究结果有望改善面临听觉挑战的个人的交流无障碍性。
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引用次数: 0
Emotion Modeling in Speech Signals: Discrete Wavelet Transform and Machine Learning Tools for Emotion Recognition System 语音信号中的情感建模:用于情绪识别系统的离散小波变换和机器学习工具
IF 2.9 Q2 Engineering Pub Date : 2024-04-02 DOI: 10.1155/2024/7184018
K. Daqrouq, A. Balamesh, O. Alrusaini, A. Alkhateeb, A. S. Balamash
Speech emotion recognition (SER) is a challenging task due to the complex and subtle nature of emotions. This study proposes a novel approach for emotion modeling using speech signals by combining discrete wavelet transform (DWT) with linear prediction coding (LPC). The performance of various classifiers, including support vector machine (SVM), K-Nearest Neighbors (KNN), Efficient Logistic Regression, Naive Bayes, Ensemble, and Neural Network, was evaluated for emotion classification using the EMO-DB dataset. Evaluation metrics such as area under the curve (AUC), average prediction accuracy, and cross-validation techniques were employed. The results indicate that KNN and SVM classifiers exhibited high accuracy in distinguishing sadness from other emotions. Ensemble methods and Neural Networks also demonstrated strong performance in sadness classification. While Efficient Logistic Regression and Naive Bayes classifiers showed competitive performance, they were slightly less accurate compared to other classifiers. Furthermore, the proposed feature extraction method yielded the highest average accuracy, and its combination with formants or wavelet entropy further improved classification accuracy. On the other hand, Efficient Logistic Regression exhibited the lowest accuracies among the classifiers. The uniqueness of this study was that it investigated a combined feature extraction method and integrated them to compare with various forms of combinations. However, the purposes of the investigation include improved performance of the classifiers, high effectiveness of the system, and the potential for emotion classification tasks. These findings can guide the selection of appropriate classifiers and feature extraction methods in future research and real-world applications. Further investigations can focus on refining classifiers and exploring additional feature extraction techniques to enhance emotion classification accuracy.
由于情绪的复杂性和微妙性,语音情绪识别(SER)是一项具有挑战性的任务。本研究结合离散小波变换(DWT)和线性预测编码(LPC),提出了一种利用语音信号进行情感建模的新方法。使用 EMO-DB 数据集评估了支持向量机 (SVM)、K-近邻 (KNN)、高效逻辑回归、Naive Bayes、Ensemble 和神经网络等各种分类器在情绪分类中的性能。评估指标包括曲线下面积(AUC)、平均预测准确率和交叉验证技术。结果表明,KNN 和 SVM 分类器在区分悲伤与其他情绪方面表现出较高的准确性。集合方法和神经网络在悲伤情绪分类方面也表现出色。虽然高效逻辑回归和 Naive Bayes 分类器表现出了很强的竞争力,但与其他分类器相比,它们的准确率略低。此外,所提出的特征提取方法获得了最高的平均准确率,其与形声或小波熵的结合进一步提高了分类准确率。另一方面,在所有分类器中,高效逻辑回归的准确率最低。这项研究的独特之处在于,它研究了一种组合特征提取方法,并将其与各种形式的组合进行了整合比较。不过,调查的目的包括提高分类器的性能、系统的高效性以及情感分类任务的潜力。这些发现可以指导今后的研究和实际应用中选择合适的分类器和特征提取方法。进一步的研究可以侧重于改进分类器和探索其他特征提取技术,以提高情感分类的准确性。
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引用次数: 0
A Novel Deep Learning-Based Data Analysis Model for Solar Photovoltaic Power Generation and Electrical Consumption Forecasting in the Smart Power Grid 基于深度学习的新型数据分析模型,用于智能电网中的太阳能光伏发电和用电预测
IF 2.9 Q2 Engineering Pub Date : 2024-04-02 DOI: 10.1155/2024/9257508
C. Mbey, Felix Ghislain Yem Souhe, Vinny Junior Foba Kakeu, A. Boum
With the installation of solar panels around the world and the permanent fluctuation of climatic factors, it is, therefore, important to provide the necessary energy in the electrical network in order to satisfy the electrical demand at all times for smart grid applications. This study first presents a comprehensive and comparative review of existing deep learning methods used for smart grid applications such as solar photovoltaic (PV) generation forecasting and power consumption forecasting. In this work, electrical consumption forecasting is long term and will consider smart meter data and socioeconomic and demographic data. Photovoltaic power generation forecasting is short term by considering climatic data such as solar irradiance, temperature, and humidity. Moreover, we have proposed a novel hybrid deep learning method based on multilayer perceptron (MLP), long short-term memory (LSTM), and genetic algorithm (GA). We then simulated all the deep learning methods on a climate and electricity consumption dataset for the city of Douala. Electrical consumption data are collected from smart meters installed at consumers in Douala. Climate data are collected at the climate management center in the city of Douala. The results obtained show the outperformance of the proposed optimized method based on deep learning in the both electrical consumption and PV power generation forecasting and its superiority compared to basic methods of deep learning such as support vector machine (SVM), MLP, recurrent neural network (RNN), and random forest algorithm (RFA).
随着太阳能电池板在世界各地的安装以及气候因素的长期波动,因此,在智能电网应用中为电网提供必要的能源以随时满足电力需求非常重要。本研究首先对用于太阳能光伏发电预测和电力消耗预测等智能电网应用的现有深度学习方法进行了全面的比较综述。在这项工作中,电力消耗预测是长期性的,将考虑智能电表数据以及社会经济和人口数据。光伏发电预测是短期预测,将考虑太阳辐照度、温度和湿度等气候数据。此外,我们还提出了一种基于多层感知器(MLP)、长短期记忆(LSTM)和遗传算法(GA)的新型混合深度学习方法。然后,我们在杜阿拉市的气候和电力消耗数据集上模拟了所有深度学习方法。用电数据来自安装在杜阿拉用户处的智能电表。气候数据由杜阿拉市气候管理中心收集。得出的结果表明,基于深度学习的优化方法在用电量和光伏发电量预测方面均表现出色,与支持向量机(SVM)、MLP、递归神经网络(RNN)和随机森林算法(RFA)等深度学习基本方法相比更具优势。
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引用次数: 0
A Hybrid Expert System for Estimation of the Manufacturability of a Notional Design 估算名义设计可制造性的混合专家系统
IF 2.9 Q2 Engineering Pub Date : 2024-03-26 DOI: 10.1155/2024/4985090
Alexander Sommers, Shahram Rahimi, Tonya G. McCall, Emily Wall, Althea Henslee, Larry Dalton, Paul D. Babin, Nathan Watson, Gehendra Sharma, Milan D. Parmar
The more “manufacturable” a product is, the “easier” it is to manufacture. For two different product designs targeting the same role, one may be more manufacturable than the other. Evaluating manufacturability requires experts in the processes of manufacturing, “manufacturing process engineers” (MPEs). Human experts are expensive to train and employ, while a well-designed expert system (ES) could be quicker, more reliable, and provide higher performance and superior accuracy. In this work, a group of MPEs (“Team A”) externalized a portion of their expertise into a rule-based expert system in cooperation with a group of ES knowledge engineers and developers. We produced a large ES with 113 total rules and 94 variables. The ES comprises a crisp ES which constructs a Fuzzy ES, thus producing a two-stage ES. Team A then used the ES and a derivation of it (the “MAKE A”) to conduct assessments of the manufacturability of several “notional” designs, providing a sanity check of the rule-base. A provisional assessment used a first draft of the rule-base, and MAKE A, and was of notional wing designs. The primary assessment, using an updated rule-base and MAKE A, was of notional rotor blade designs. We describe the process by which this ES was made and the assessments that were conducted and conclude with insights gained from constructing the ES. These insights can be summarized as follows: build a bridge between expert and user, move from general features to specific features, do not make the user do a lot of work, and only ask the user for objective observations. We add the product of our work to the growing library of tools and methodologies at the disposal of the U.S. Army Engineer Research and Development Center (ERDC). The primary findings of the present work are (1) an ES that satisfied the experts, according to their expressed performance expectations, and (2) the insights gained on how such a system might best be constructed.
产品的 "可制造性 "越强,制造起来就越 "容易"。对于针对同一角色的两种不同产品设计,其中一种可能比另一种更具可制造性。评估可制造性需要制造工艺方面的专家,即 "制造工艺工程师"(MPE)。人类专家的培训和聘用成本高昂,而精心设计的专家系统(ES)可以更快、更可靠,并提供更高的性能和卓越的准确性。在这项工作中,一组 MPE("团队 A")与一组 ES 知识工程师和开发人员合作,将他们的部分专业知识外化为基于规则的专家系统。我们制作了一个大型 ES,共有 113 条规则和 94 个变量。该 ES 包括一个简明 ES,它构建了一个模糊 ES,从而产生了一个两阶段 ES。然后,团队 A 使用该 ES 及其衍生物("MAKE A")对几种 "名义 "设计的可制造性进行评估,对规则库进行合理性检查。临时评估使用了规则库和 MAKE A 的初稿,针对的是假想机翼设计。主要评估使用了更新后的规则库和 MAKE A,对名义转子叶片设计进行了评估。我们介绍了该 ES 的制作过程和所进行的评估,并总结了从构建 ES 中获得的启示。这些见解可归纳如下:在专家和用户之间架起一座桥梁,从一般特征转向具体特征,不要让用户做大量的工作,只要求用户提供客观的观察结果。美国陆军工程研究与发展中心(ERDC)的工具和方法库正在不断扩大,我们将把我们的工作成果加入其中。这项工作的主要成果是:(1)根据专家们表达的性能预期,开发出了令他们满意的 ES;(2)在如何以最佳方式构建此类系统方面获得了深刻见解。
{"title":"A Hybrid Expert System for Estimation of the Manufacturability of a Notional Design","authors":"Alexander Sommers, Shahram Rahimi, Tonya G. McCall, Emily Wall, Althea Henslee, Larry Dalton, Paul D. Babin, Nathan Watson, Gehendra Sharma, Milan D. Parmar","doi":"10.1155/2024/4985090","DOIUrl":"https://doi.org/10.1155/2024/4985090","url":null,"abstract":"The more “manufacturable” a product is, the “easier” it is to manufacture. For two different product designs targeting the same role, one may be more manufacturable than the other. Evaluating manufacturability requires experts in the processes of manufacturing, “manufacturing process engineers” (MPEs). Human experts are expensive to train and employ, while a well-designed expert system (ES) could be quicker, more reliable, and provide higher performance and superior accuracy. In this work, a group of MPEs (“Team A”) externalized a portion of their expertise into a rule-based expert system in cooperation with a group of ES knowledge engineers and developers. We produced a large ES with 113 total rules and 94 variables. The ES comprises a crisp ES which constructs a Fuzzy ES, thus producing a two-stage ES. Team A then used the ES and a derivation of it (the “MAKE A”) to conduct assessments of the manufacturability of several “notional” designs, providing a sanity check of the rule-base. A provisional assessment used a first draft of the rule-base, and MAKE A, and was of notional wing designs. The primary assessment, using an updated rule-base and MAKE A, was of notional rotor blade designs. We describe the process by which this ES was made and the assessments that were conducted and conclude with insights gained from constructing the ES. These insights can be summarized as follows: build a bridge between expert and user, move from general features to specific features, do not make the user do a lot of work, and only ask the user for objective observations. We add the product of our work to the growing library of tools and methodologies at the disposal of the U.S. Army Engineer Research and Development Center (ERDC). The primary findings of the present work are (1) an ES that satisfied the experts, according to their expressed performance expectations, and (2) the insights gained on how such a system might best be constructed.","PeriodicalId":44894,"journal":{"name":"Applied Computational Intelligence and Soft Computing","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140380174","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
Semisupervised Learning-Based Word-Sense Disambiguation Using Word Embedding for Afaan Oromoo Language 基于半监督学习的词义消歧法(使用单词嵌入)用于阿法安奥罗莫语
IF 2.9 Q2 Engineering Pub Date : 2024-03-14 DOI: 10.1155/2024/4429069
Tabor Wegi Geleta, Jara Muda Haro
Natural language is a type of language that human beings use to communicate with each other. However, it is very difficult to communicate with a machine-understandable language. Finding context meaning is challenging the activity of automatically identifying machine translation, indexing engines, and predicting neighbor words in natural language. Many researchers around the world investigated word-sense disambiguation in different languages, including the Afaan Oromo language, to solve this challenge. Nevertheless, the amount of effort for Afaan Oromo is very little in terms of finding context meaning and predicting neighbor words to solve the word ambiguity problem. Since the Afaan Oromo language is one of the languages developed in Ethiopia, it needs the latest technology to enhance communication and overcome ambiguity challenges. So far, this work aims to design and develop a vector space model for the Afaan Oromo language that can provide the application of word-sense disambiguation to increase the performance of information retrieval. In this work, the study has used the Afaan Oromo word embedding method to disambiguate a contextual meaning of words by applying the semisupervised technique. To conduct the study, 456,300 Afaan Oromo words were taken from different sources and preprocessed for experimentation by the Natural Language Toolkit and Anaconda tool. The K-means machine learning algorithm was used to cluster similar word vocabulary. Experimental results show that using word embedding for the proposed language’s corpus improves the performance of the system by a total accuracy of 98.89% and outperforms the existing similar systems.
自然语言是人类用来相互交流的一种语言。然而,使用机器可理解的语言进行交流却非常困难。在自然语言中,寻找上下文含义是对自动识别机器翻译、索引引擎和预测邻近词等活动的挑战。世界各地的许多研究人员都在研究不同语言的词义消歧,包括阿法安奥罗莫语,以解决这一难题。然而,阿法安奥罗莫语在寻找上下文含义和预测邻近词以解决词义模糊问题方面所做的努力非常少。由于阿法安奥罗莫语是埃塞俄比亚开发的语言之一,它需要最新的技术来加强交流和克服歧义难题。到目前为止,这项工作旨在为阿法安奥罗莫语设计和开发一个向量空间模型,该模型可提供词义消歧应用,以提高信息检索的性能。在这项工作中,研究使用了阿法安奥罗莫语词嵌入方法,通过应用半监督技术来消歧单词的上下文含义。为了进行这项研究,我们从不同来源获取了 456,300 个阿法安奥罗莫语单词,并通过自然语言工具包和 Anaconda 工具进行了预处理。使用 K-means 机器学习算法对相似词汇进行聚类。实验结果表明,在拟议的语言语料库中使用单词嵌入提高了系统的性能,总准确率达到 98.89%,优于现有的类似系统。
{"title":"Semisupervised Learning-Based Word-Sense Disambiguation Using Word Embedding for Afaan Oromoo Language","authors":"Tabor Wegi Geleta, Jara Muda Haro","doi":"10.1155/2024/4429069","DOIUrl":"https://doi.org/10.1155/2024/4429069","url":null,"abstract":"Natural language is a type of language that human beings use to communicate with each other. However, it is very difficult to communicate with a machine-understandable language. Finding context meaning is challenging the activity of automatically identifying machine translation, indexing engines, and predicting neighbor words in natural language. Many researchers around the world investigated word-sense disambiguation in different languages, including the Afaan Oromo language, to solve this challenge. Nevertheless, the amount of effort for Afaan Oromo is very little in terms of finding context meaning and predicting neighbor words to solve the word ambiguity problem. Since the Afaan Oromo language is one of the languages developed in Ethiopia, it needs the latest technology to enhance communication and overcome ambiguity challenges. So far, this work aims to design and develop a vector space model for the Afaan Oromo language that can provide the application of word-sense disambiguation to increase the performance of information retrieval. In this work, the study has used the Afaan Oromo word embedding method to disambiguate a contextual meaning of words by applying the semisupervised technique. To conduct the study, 456,300 Afaan Oromo words were taken from different sources and preprocessed for experimentation by the Natural Language Toolkit and Anaconda tool. The K-means machine learning algorithm was used to cluster similar word vocabulary. Experimental results show that using word embedding for the proposed language’s corpus improves the performance of the system by a total accuracy of 98.89% and outperforms the existing similar systems.","PeriodicalId":44894,"journal":{"name":"Applied Computational Intelligence and Soft Computing","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140241593","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
The Characteristics of Circular Fermatean Fuzzy Sets and Multicriteria Decision-Making Based on the Fermatean Fuzzy t-Norm and t-Conorm 圆形费马忒模糊集的特征与基于费马忒模糊 t-Norm 和 t-Conorm 的多标准决策制定
IF 2.9 Q2 Engineering Pub Date : 2024-02-10 DOI: 10.1155/2024/6974363
R. A., I. V., K. S., A. H, Arifmohammed K. M.
When diverse decision makers are involved in the decision-making process, taking average of decision values might not reflect an accurate point of view. To overcome such a scenario, the circular Fermatean fuzzy (CFF) set, an advancement of the Fermatean fuzzy (FF) set, and the interval-valued Fermatean fuzzy set (IVFFS) are introduced in this current study. The proposed CFF set is a circle with a centre as association value (AV) and nonassociation value (NAV) with a radius at most equal to 2. It is built in such a way that it covers all the decision makers’ opinion value through a circle. Due to its geometric structure, the CFF set resolves ambiguity and risk more accurately and effectively than FF and IVFF. FF t-norm and t-conorm are used to investigate the properties of CFF sets, subsequent to which the algebraic operations between them are defined. A couple of CFF distance measures between CFF numbers are introduced and used in the selection of an electric autorickshaw along with the CFF weighted averaging and geometric aggregation operators. The overview and comparison analysis of the generated reports exemplifies the viability and compatibility of the CFF set strategy for selecting the best choices.
当不同的决策者参与决策过程时,取决策值的平均值可能无法反映准确的观点。为了克服这种情况,本研究引入了圆形费马泰模糊集(CFF),它是费马泰模糊集(FF)和区间值费马泰模糊集(IVFFS)的一种进步。拟议的 CFF 集是一个以关联值(AV)和非关联值(NAV)为圆心、半径最多等于 2 的圆。由于其几何结构,CFF 集比 FF 和 IVFF 能更准确、更有效地解决模糊性和风险问题。FF t-norm 和 t-conorm 用于研究 CFF 集的特性,随后定义了它们之间的代数运算。介绍了 CFF 数字之间的几个 CFF 距离度量,并将其与 CFF 加权平均和几何聚合算子一起用于电动人力车的选择。对生成的报告进行概述和比较分析,证明了 CFF 集策略在选择最佳方案方面的可行性和兼容性。
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引用次数: 0
The Characteristics of Circular Fermatean Fuzzy Sets and Multicriteria Decision-Making Based on the Fermatean Fuzzy t-Norm and t-Conorm 圆形费马忒模糊集的特征与基于费马忒模糊 t-Norm 和 t-Conorm 的多标准决策制定
IF 2.9 Q2 Engineering Pub Date : 2024-02-10 DOI: 10.1155/2024/6974363
R. A., I. V., K. S., A. H, Arifmohammed K. M.
When diverse decision makers are involved in the decision-making process, taking average of decision values might not reflect an accurate point of view. To overcome such a scenario, the circular Fermatean fuzzy (CFF) set, an advancement of the Fermatean fuzzy (FF) set, and the interval-valued Fermatean fuzzy set (IVFFS) are introduced in this current study. The proposed CFF set is a circle with a centre as association value (AV) and nonassociation value (NAV) with a radius at most equal to 2. It is built in such a way that it covers all the decision makers’ opinion value through a circle. Due to its geometric structure, the CFF set resolves ambiguity and risk more accurately and effectively than FF and IVFF. FF t-norm and t-conorm are used to investigate the properties of CFF sets, subsequent to which the algebraic operations between them are defined. A couple of CFF distance measures between CFF numbers are introduced and used in the selection of an electric autorickshaw along with the CFF weighted averaging and geometric aggregation operators. The overview and comparison analysis of the generated reports exemplifies the viability and compatibility of the CFF set strategy for selecting the best choices.
当不同的决策者参与决策过程时,取决策值的平均值可能无法反映准确的观点。为了克服这种情况,本研究引入了圆形费马泰模糊集(CFF),它是费马泰模糊集(FF)和区间值费马泰模糊集(IVFFS)的一种进步。拟议的 CFF 集是一个以关联值(AV)和非关联值(NAV)为圆心、半径最多等于 2 的圆。由于其几何结构,CFF 集比 FF 和 IVFF 能更准确、更有效地解决模糊性和风险问题。FF t-norm 和 t-conorm 用于研究 CFF 集的特性,随后定义了它们之间的代数运算。介绍了 CFF 数字之间的几个 CFF 距离度量,并将其与 CFF 加权平均和几何聚合算子一起用于电动人力车的选择。对生成的报告进行概述和比较分析,证明了 CFF 集策略在选择最佳方案方面的可行性和兼容性。
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引用次数: 0
YOLO-UNet Architecture for Detecting and Segmenting the Localized MRI Brain Tumor Image 用于检测和分割局部磁共振成像脑肿瘤图像的 YOLO-UNet 架构
IF 2.9 Q2 Engineering Pub Date : 2024-02-08 DOI: 10.1155/2024/3819801
Nur Iriawan, A. A. Pravitasari, Ulfa S. Nuraini, Nur I. Nirmalasari, Taufik Azmi, Muhammad Nasrudin, Adam F. Fandisyah, K. Fithriasari, S. W. Purnami, Irhamah, Widiana Ferriastuti
Brain tumor detection and segmentation are the main issues in biomedical engineering research fields, and it is always challenging due to its heterogeneous shape and location in MRI. The quality of the MR images also plays an important role in providing a clear sight of the shape and boundary of the tumor. The clear shape and boundary of the tumor will increase the probability of safe medical surgery. Analysis of this different scope of image types requires refined computerized quantification and visualization tools. This paper employed deep learning to detect and segment brain tumor MRI images by combining the convolutional neural network (CNN) and fully convolutional network (FCN) methodology in serial. The fundamental finding is to detect and localize the tumor area with YOLO-CNN and segment it with the FCN-UNet architecture. This analysis provided automatic detection and segmentation as well as the location of the tumor. The segmentation using the UNet is run under four scenarios, and the best one is chosen by the minimum loss and maximum accuracy value. In this research, we used 277 images for training, 69 images for validation, and 14 images for testing. The validation is carried out by comparing the segmentation results with the medical ground truth to provide the correct classification ratio (CCR). This study succeeded in the detection of brain tumors and provided a clear area of the brain tumor with a high CCR of about 97%.
脑肿瘤的检测和分割是生物医学工程研究领域的主要问题,由于脑肿瘤在磁共振成像中的形状和位置各不相同,这一直是个难题。磁共振图像的质量对于清晰显示肿瘤的形状和边界也起着重要作用。清晰的肿瘤形状和边界将提高医疗手术的安全性。分析这些不同范围的图像类型需要精细的计算机量化和可视化工具。本文采用深度学习方法,结合卷积神经网络(CNN)和全卷积网络(FCN)方法,对脑肿瘤核磁共振图像进行检测和分割。其基本发现是利用 YOLO-CNN 检测和定位肿瘤区域,并利用 FCN-UNet 架构分割肿瘤区域。这项分析提供了自动检测和分割以及肿瘤位置。使用 UNet 进行的分割在四种情况下运行,并根据最小损失和最大准确度值选出最佳方案。在这项研究中,我们使用了 277 幅图像进行训练,69 幅图像进行验证,14 幅图像进行测试。验证是通过比较分割结果和医学基本真实值来提供正确分类率(CCR)。这项研究成功地检测出了脑肿瘤,并提供了清晰的脑肿瘤区域,CCR 高达约 97%。
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引用次数: 0
YOLO-UNet Architecture for Detecting and Segmenting the Localized MRI Brain Tumor Image 用于检测和分割局部磁共振成像脑肿瘤图像的 YOLO-UNet 架构
IF 2.9 Q2 Engineering Pub Date : 2024-02-08 DOI: 10.1155/2024/3819801
Nur Iriawan, A. A. Pravitasari, Ulfa S. Nuraini, Nur I. Nirmalasari, Taufik Azmi, Muhammad Nasrudin, Adam F. Fandisyah, K. Fithriasari, S. W. Purnami, Irhamah, Widiana Ferriastuti
Brain tumor detection and segmentation are the main issues in biomedical engineering research fields, and it is always challenging due to its heterogeneous shape and location in MRI. The quality of the MR images also plays an important role in providing a clear sight of the shape and boundary of the tumor. The clear shape and boundary of the tumor will increase the probability of safe medical surgery. Analysis of this different scope of image types requires refined computerized quantification and visualization tools. This paper employed deep learning to detect and segment brain tumor MRI images by combining the convolutional neural network (CNN) and fully convolutional network (FCN) methodology in serial. The fundamental finding is to detect and localize the tumor area with YOLO-CNN and segment it with the FCN-UNet architecture. This analysis provided automatic detection and segmentation as well as the location of the tumor. The segmentation using the UNet is run under four scenarios, and the best one is chosen by the minimum loss and maximum accuracy value. In this research, we used 277 images for training, 69 images for validation, and 14 images for testing. The validation is carried out by comparing the segmentation results with the medical ground truth to provide the correct classification ratio (CCR). This study succeeded in the detection of brain tumors and provided a clear area of the brain tumor with a high CCR of about 97%.
脑肿瘤的检测和分割是生物医学工程研究领域的主要问题,由于脑肿瘤在磁共振成像中的形状和位置各不相同,这一直是个难题。磁共振图像的质量对于清晰显示肿瘤的形状和边界也起着重要作用。清晰的肿瘤形状和边界将提高医疗手术的安全性。分析这些不同范围的图像类型需要精细的计算机量化和可视化工具。本文采用深度学习方法,结合卷积神经网络(CNN)和全卷积网络(FCN)方法,对脑肿瘤核磁共振图像进行检测和分割。其基本发现是利用 YOLO-CNN 检测和定位肿瘤区域,并利用 FCN-UNet 架构分割肿瘤区域。这项分析提供了自动检测和分割以及肿瘤位置。使用 UNet 进行的分割在四种情况下运行,并根据最小损失和最大准确度值选出最佳方案。在这项研究中,我们使用了 277 幅图像进行训练,69 幅图像进行验证,14 幅图像进行测试。验证是通过比较分割结果和医学基本真实值来提供正确分类率(CCR)。这项研究成功地检测出了脑肿瘤,并提供了清晰的脑肿瘤区域,CCR 高达约 97%。
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Applied Computational Intelligence and Soft Computing
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