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Knowledge Discovery of Hospital Medical Technology Based on Partial Ordered Structure Diagrams 基于部分有序结构图的医院医疗技术知识发现
Pub Date : 2023-03-24 DOI: 10.4018/ijssci.320499
Dingju Zhu, Jianbin Tan, Guangbo Luo, Haoxiang Gu, Zhanhao Ye, Renfeng Deng, Keyi He, Kai-Leung Yung, Andrew W. H. Ip
So far, no research has used the partial order algorithm for the mining of hospital medical technology. This paper proposed a novel knowledge discovery method of hospital medical technology based on partial ordered structure diagrams, constructed attribute partial ordered structure diagram and object partial ordered structure diagram for the formal context constructed by hospital set and medical technology set, and finally analyzed them using the knowledge discovery method. The experiments show that the partial ordered structure diagram can effectively visualize the structural relationships between hospital sets and medical technology sets, and the distribution characteristics of medical technology sets in hospital sets and the rules of medical technology sets owned by hospital sets can be obtained based on the node, branch, and group structure relationships of the partial ordered structure diagram.
到目前为止,还没有研究将偏序算法用于医院医疗技术的挖掘。提出了一种基于偏序结构图的医院医疗技术知识发现方法,对医院集和医疗技术集构造的形式上下文分别构造属性偏序结构图和对象偏序结构图,并利用知识发现方法对其进行分析。实验表明,偏序结构图能够有效地可视化医院集与医疗技术集之间的结构关系,基于偏序结构图的节点、分支和群结构关系,可以得到医院集中医疗技术集的分布特征以及医院集所拥有的医疗技术集的规则。
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引用次数: 0
Artificial Intelligence Techniques to improve cognitive traits of Down Syndrome Individuals: An Analysis 人工智能技术改善唐氏综合症个体的认知特征:分析
Pub Date : 2023-02-24 DOI: 10.4018/ijssci.318677
Irfan M. Leghari, Syed Asif Ali
Individuals with cognitive impairment survive mental challenges; they hardly perform daily life assignments. The individuals with down syndrome face mild to severe cognitive challenges that affect daily life activities and learning. A goal is to reduce the social and economic burden of their family and to make their lives productive. Achieving these goals requires improvement in limited mental challenge. Most of the work has been done on facial expression, prediction of inhibitory capacity, and prediction of mental deficiency. The review highlights the usefulness of machine learning-techniques, including convolution neural network and artificial neural network, applied to address mental challenge. Based on the gaps of existing AI techniques, the authors provide a recommendation for the identification of mental challenges using a survey-based Software approach, which is focused on analyzing and improving mental challenges from severe-moderate to moderate-mild; and to enhance the academics, social collaboration, and employment capability to the Down syndrome individuals.
有认知障碍的人在智力挑战中幸存下来;他们几乎不执行日常生活任务。唐氏综合症患者面临轻度到严重的认知挑战,影响日常生活活动和学习。一个目标是减轻其家庭的社会和经济负担,使其生活富有成效。实现这些目标需要在有限的智力挑战上有所提高。大部分的工作都是在面部表情、预测抑制能力和预测智力缺陷方面进行的。这篇综述强调了机器学习技术的有用性,包括卷积神经网络和人工神经网络,应用于解决心理挑战。基于现有人工智能技术的差距,作者提出了使用基于调查的软件方法识别心理挑战的建议,该方法侧重于分析和改进从重度-中度到中度-轻度的心理挑战;提高唐氏综合症患者的学业、社会协作和就业能力。
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引用次数: 1
TA-WHI: Text Analysis of Web-Based Health Information 基于网络的健康信息文本分析
Pub Date : 2023-01-27 DOI: 10.4018/ijssci.316972
P. Bagla, Kuldeep Kumar
The healthcare data available on social media has exploded in recent years. The cures and treatments suggested by non-medical experts can lead to more damage than expected. Assuring the credibility of the information conveyed is an enormous challenge. This study aims to categorize the credibility of online health information into multiple classes. This paper proposes a model named Text Analysis of Web-based Health Information (TA-WHI), based on an algorithm designed for this. It categorizes health-related social media feeds into five categories: sufficient, fabricated, meaningful, advertisement, and misleading. The authors have created their own labeled dataset for this model. For data cleaning, they have designed a dictionary having nouns, adverbs, adjectives, negative words, positive words, and medical terms named MeDF. Using polarity and conditional procedure, the data is ranked and classified into multiple classes. The authors evaluate the performance of the model using deep-learning classifiers such as CNN, LSTM, and CatBoost. The suggested model has attained an accuracy of 98% with CatBoost.
近年来,社交媒体上的医疗保健数据呈爆炸式增长。非医学专家建议的治疗方法可能会导致比预期更大的损害。确保所传达信息的可信性是一项巨大的挑战。本研究旨在将网路健康资讯的可信度分为多个类别。本文在此基础上提出了基于web的健康信息文本分析模型(TA-WHI)。它将与健康相关的社交媒体信息分为五类:充分的、捏造的、有意义的、广告的和误导的。作者为这个模型创建了他们自己的标记数据集。为了进行数据清理,他们设计了一个名为MeDF的字典,其中包含名词、副词、形容词、否定词、肯定词和医学术语。使用极性和条件过程,对数据进行排序并分类为多个类。作者使用CNN、LSTM和CatBoost等深度学习分类器评估模型的性能。使用CatBoost,该模型的准确率达到98%。
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引用次数: 0
A Privacy-Preserving Authentic Healthcare Monitoring System Using Blockchain 使用区块链的保护隐私的真实医疗监控系统
Pub Date : 2022-01-01 DOI: 10.4018/ijssci.310942
A. Raj, S. Prakash
Integrating the internet of things (IoT) and healthcare monitoring systems is one of the most dynamic innovations in the research area. Since the tremendous number of IoT devices in smart healthcare systems is increasing exponentially, privacy and security issues related to the patient's data are significant concerns. The authors propose an access control for a healthcare monitoring system using blockchain-based smart contracts. They created four smart contract forms for user registration, authentication, access control including misbehavior detection and access revocation. The sensor automatically measures the patient's health data and filters the data before determining whether to write the data into the blockchain or not. The sensor detects abnormal data and alerts doctors and hospitals for immediate treatment. The efficiency of the proposed framework is verified by performance evaluation based on the Ethereum test environment. The proposed system outperforms existing approaches by reducing deployment and execution latency and average response latency in the real-time smart healthcare system.
物联网(IoT)和医疗监控系统的集成是该研究领域最具活力的创新之一。由于智能医疗系统中的物联网设备数量呈指数级增长,因此与患者数据相关的隐私和安全问题备受关注。作者提出了一种使用基于区块链的智能合约的医疗监控系统的访问控制。他们创建了四种智能合约形式,用于用户注册、身份验证、访问控制(包括错误行为检测和访问撤销)。传感器自动测量患者的健康数据,并在确定是否将数据写入区块链之前对数据进行过滤。传感器检测到异常数据,并提醒医生和医院立即进行治疗。基于以太坊测试环境的性能评估验证了该框架的有效性。该系统通过减少实时智能医疗保健系统中的部署和执行延迟以及平均响应延迟,优于现有方法。
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引用次数: 2
Sustainable Stock Market Prediction Framework Using Machine Learning Models 使用机器学习模型的可持续股票市场预测框架
Pub Date : 2022-01-01 DOI: 10.4018/ijssci.313593
F. García-Peñalvo, Tamanna Maan, Sunil K. Singh, Sudhakar Kumar, Varsha Arya, Kwok Tai Chui, Gaurav Pratap Singh
Prediction of stock prices is a challenging task owing to its volatile and constantly fluctuating nature. Stock price prediction has sparked the interest of various investors, data analysists, and researchers because of high returns on their investments. A sustainable framework for stock price prediction is proposed to quantify the factors affecting the stock price and impact of technology on the ever-changing business world. The proposed framework also helps to understand how technology can be used to predict the future price of stocks by using some historical dataset to produce desirable results using machine learning algorithms. The aim of this research paper is to learn about stock price prediction by using different machine learning algorithms and comparing their performance. The results reveal that Fb-prophet should be preferred for more precise prediction among different ML algorithms.
由于股票价格的波动性和不断波动的性质,预测股票价格是一项具有挑战性的任务。股票价格预测已经引起了各种投资者、数据分析师和研究人员的兴趣,因为他们的投资回报很高。提出了一个可持续的股票价格预测框架,以量化影响股票价格的因素和技术对不断变化的商业世界的影响。提出的框架还有助于理解如何使用技术来预测股票的未来价格,通过使用一些历史数据集来使用机器学习算法产生理想的结果。本研究论文的目的是通过使用不同的机器学习算法并比较它们的性能来学习股票价格预测。结果表明,在不同的机器学习算法中,Fb-prophet应该是更精确的预测首选。
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引用次数: 3
N-Gram-Codon and Recurrent Neural Network (RNN) to Update Pfizer-BioNTech mRNA Vaccine n - gram密码子和递归神经网络(RNN)更新辉瑞- biontech mRNA疫苗
Pub Date : 2022-01-01 DOI: 10.4018/ijssci.305838
Hadj Ahmed Bouarara
In the fight against SARS-CoV-2, Pfizer BioNTech based on synthetic messenger RNA (mRNA) proved to be quicker and more effective even with a small dose of micrograms per injection. Unfortunately, such a vaccine requires very low temperatures to prevent degradation of mRNA. In this paper, we have developed three new models of recurrent neural network (1- simple LSTM 2-BDLSTM 3-BERT) using n-gram-codon technique for the codification of mRNA. The primary aim is to analyse the mRNA sequence and predict the stability/reactivity rates at various codon positions. The results of the predictions will be presented in the form of recommendations to support laboratories in updating Pfizer's BioNTech vaccine. The obtained results were validated by the Stanford OpenVaccine dataset and the evaluation measures recall, precision, f1-score, accuracy and loss.
在与新冠病毒的斗争中,以合成信使RNA (mRNA)为基础的辉瑞生物技术公司(Pfizer BioNTech)在每次注射少量微克的情况下也能更快、更有效地对抗新冠病毒。不幸的是,这种疫苗需要非常低的温度来防止mRNA的降解。在本文中,我们利用n-gram密码子技术开发了三种新的递归神经网络模型(1-简单LSTM - 2-BDLSTM - 3-BERT),用于mRNA的编码。主要目的是分析mRNA序列并预测不同密码子位置的稳定性/反应率。预测结果将以建议的形式提出,以支持实验室更新辉瑞的BioNTech疫苗。获得的结果通过Stanford OpenVaccine数据集和评估指标recall、precision、f1-score、accuracy和loss进行验证。
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引用次数: 3
Detection of Distributed Denial of Service (DDoS) Attacks Using Computational Intelligence and Majority Vote-Based Ensemble Approach 基于计算智能和多数票集成方法的分布式拒绝服务攻击检测
Pub Date : 2022-01-01 DOI: 10.4018/ijssci.309707
Anupama Mishra, B. Joshi, Varsha Arya, A. Gupta, Kwok Tai Chui
The term “distributed denial of service” (DDoS) refers to one of the most common types of attacks. Sending a huge volume of data packets to the server machine is the target of a DDoS attack. This results in the majority of the consumption of network bandwidth and server, which ultimately leads to an issue with denial of service. In this paper, a majority vote-based ensemble of classifiers is utilized in the Sever technique, which results in improved accuracy and reduced computational overhead, when detecting attacks. For the experiment, the authors have used the CICDDOS2019 dataset. According to the findings of the experiment, a high level of accuracy of 99.98% was attained. In this paper, the classifiers use random forest, decision tree, and naïve bayes for majority voting classifiers, and from the results and performance, it can be seen that majority vote classifiers performed better.
术语“分布式拒绝服务”(DDoS)指的是一种最常见的攻击类型。向服务器机器发送大量数据包是DDoS攻击的目标。这将导致大部分网络带宽和服务器的消耗,最终导致拒绝服务的问题。在本文中,在Sever技术中使用了基于多数投票的分类器集成,在检测攻击时提高了准确性并减少了计算开销。在实验中,作者使用了CICDDOS2019数据集。实验结果表明,该方法的准确率达到了99.98%。本文的分类器使用随机森林、决策树和naïve贝叶斯作为多数投票分类器,从结果和性能可以看出,多数投票分类器的表现更好。
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引用次数: 2
Analysis of Protein Structure for Drug Repurposing Using Computational Intelligence and ML Algorithm 基于计算智能和ML算法的药物再利用蛋白质结构分析
Pub Date : 2022-01-01 DOI: 10.4018/ijssci.312562
Deepak Srivastava, Kwok Tai Chui, Varsha Arya, F. G. Peñalvo, Pramod Kumar, Ashutosh Kumar Singh
Proteins are fundamental compounds in biological processes during the analysis of drug target indication for drug repurposing. The identification of relevant features is a necessary step in determining protein structure. A classification technique is used to identify the most important features in a dataset, which is why feature selection is so important. For protein structure prediction, recent research has developed a wide range of new methods to improve accuracy. The authors use principal component analysis (PCA) with correlation-matrix-based feature selection to analyse breast cancer data. In this paper, they discussed a therapeutic agent that is used to reduce the dataset by reduction-based algorithm and after that applied reduced dataset labelled as Standard Gold Dataset on machine learning model to analyze drug target indication. They get the higher accuracy of 92.8%, 93.9%, and 95.3%, each of the three datasets with 200, 500, and 1000 features with SVM with RBF kernel function. Also they found the best result, 97.8%, with the same classifier.
蛋白质是药物靶向适应症分析中生物过程中的基本化合物。鉴定相关特征是确定蛋白质结构的必要步骤。分类技术用于识别数据集中最重要的特征,这就是为什么特征选择如此重要的原因。对于蛋白质结构的预测,最近的研究开发了许多新的方法来提高准确性。作者使用主成分分析(PCA)与相关矩阵为基础的特征选择来分析乳腺癌数据。在本文中,他们讨论了一种治疗剂,该治疗剂通过基于约简的算法对数据集进行约简,然后将标记为标准金数据集的约简数据集应用于机器学习模型上以分析药物靶标指征。使用RBF核函数支持向量机对200、500和1000个特征的三个数据集分别获得了92.8%、93.9%和95.3%的较高准确率。他们还发现,使用相同的分类器,结果最好,为97.8%。
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引用次数: 8
To Study the Impact of Social Network Analysis on Social Media Marketing Using Graph Theory 运用图论研究社交网络分析对社交媒体营销的影响
Pub Date : 2022-01-01 DOI: 10.4018/ijssci.304437
Rupsha Kar
Marketing requires an understanding of relationships and current research has progressed much beyond the simple dyadic relationships to look at how social media networks influence the behavior of customers. Social media's power is fascinating as a seemingly inconsequential figure emerges from the ruins and attracts tens of thousands, if not millions, of followers and thus providing an average individual a huge platform to interact with the rest of the world. Academics have used Network theory and formal network analysis approaches to harvest the large pool of social media influencers available on the internet. The goal of this paper is to use various graph theory algorithms to portray the impact of social network analysis on internet marketing, with a primary focus on social media influencers, and to illustrate a variety of network measurements ideas that may be employed in social media management research that takes into account the enormous social media communication network.
营销需要对关系的理解,目前的研究已经远远超出了简单的二元关系,而是着眼于社交媒体网络如何影响客户的行为。社交媒体的力量是迷人的,一个看似无关紧要的人物从废墟中出现,吸引了数万甚至数百万的粉丝,从而为普通人提供了一个与世界其他地方互动的巨大平台。学者们使用网络理论和正式的网络分析方法来收集互联网上可用的大量社交媒体影响者。本文的目标是使用各种图论算法来描绘社交网络分析对网络营销的影响,主要关注社交媒体影响者,并说明各种网络测量思想,这些思想可能用于考虑到庞大的社交媒体传播网络的社交媒体管理研究。
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引用次数: 3
Computationally Simple and Efficient Method for Solving Real-Life Mixed Intuitionistic Fuzzy 3D Assignment Problems 求解现实生活中混合直觉模糊三维分配问题的计算简单高效方法
Pub Date : 2022-01-01 DOI: 10.4018/ijssci.291715
P. Senthil Kumar
This article addresses the 3-dimensional mixed intuitionistic fuzzy assignment problems (3D-MIFAPs). In this article, firstly, the author formulates an assignment problem (AP) and assumes the parameters are in uncertainty with hesitation. Secondly, based on the nature of the parameter the author defines various types of solid assignment problem (SAP) in uncertain environment. Thirdly, to solve 3D-MIFAP the PSK method for finding an optimal solution of fully intuitionistic fuzzy assignment problem (FIFAP) is extended by the author. Fourthly, the author presents the proofs of the proposed theorems and corollary. Fifthly, the proposed approach is illustrated with three numerical examples and the optimal objective value of 3D-MIFAP is obtained in the form of intuitionistic fuzzy number and the solution is checked with MATLAB and their coding are also given by the author. Sixthly, the author presents the comparison results and their graphical representation, merits and demerits of the proposed and existing methods and finally the author presents conclusion and future research directions.
本文讨论了三维混合直觉模糊分配问题(3D-MIFAPs)。本文首先提出了一个赋值问题(AP),并假设参数处于不确定的犹豫状态。其次,根据参数的性质,定义了不确定环境下各种类型的实体分配问题。第三,为了求解3D-MIFAP问题,作者推广了寻找全直觉模糊分配问题(FIFAP)最优解的PSK方法。第四,给出了所提定理和推论的证明。第五,通过三个数值算例对所提出的方法进行了说明,以直观模糊数的形式得到了3D-MIFAP的最优目标值,并用MATLAB进行了验证,并给出了其编码。第六,给出了本文提出的方法与现有方法的比较结果和图形化表示,并给出了结论和未来的研究方向。
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引用次数: 14
期刊
Int. J. Softw. Sci. Comput. Intell.
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