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An Optimized Machine Learning Approach for Coronary Artery Disease Detection 冠状动脉疾病检测的优化机器学习方法
IF 1 Q3 Computer Science Pub Date : 2023-01-01 DOI: 10.12720/jait.14.1.66-76
S. Savita, Geeta Rani, Apeksha Mittal
Rising number of fatalities caused by Coronary Artery Disease is a major concern for the public as well as the health industry. Furthermore, diagnostic methods like angiography are expensive and unaffordable for those who are not well-off. Also, angiography is bothersome for the patient due to allergic responses, renal damage, and bleeding where the catheter is inserted. The researchers in literature proposed the machine learning-based approaches for the detection of Coronary Artery Disease. But, these techniques have low accuracy. Thus, there is a scope for optimization of these techniques. The objective of this paper is to develop a machine learning system for the early detection of Coronary Artery Disease early. Also, it employs optimization methods viz. Particle Swarm Optimization, and Firefly Algorithm with Principle Component Analysis based feature extraction and decision tree-based classification. The proposed technique reports an accuracy of 95.3%. Thus, the technological solution may be used as an automatic diagnostic aid.
冠状动脉疾病导致的死亡人数不断上升,是公众和健康行业关注的主要问题。此外,像血管造影这样的诊断方法价格昂贵,对于那些不富裕的人来说负担不起。此外,由于过敏反应、肾损害和导管插入处出血,血管造影对患者来说很麻烦。研究人员在文献中提出了基于机器学习的冠状动脉疾病检测方法。但是,这些技术的准确性很低。因此,这些技术有优化的余地。本文的目的是开发一种用于早期检测冠状动脉疾病的机器学习系统。采用粒子群算法、基于主成分分析的特征提取萤火虫算法和基于决策树的分类算法进行优化。该技术的准确率为95.3%。因此,该技术解决方案可以用作自动诊断辅助工具。
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引用次数: 0
Using IoT-Enabled RFID Smart Cards in an Indoor People-Movement Tracking System with Risk Assessment 在具有风险评估的室内人员运动跟踪系统中使用支持物联网的RFID智能卡
IF 1 Q3 Computer Science Pub Date : 2023-01-01 DOI: 10.12720/jait.14.2.168-177
M. Samonte, Darwin Medel, Joshua Millard N. Odicta, M. Z. Santos
As the COVID-19 pandemic ravaged the planet at a standstill, remote employment seemed inescapable. Still, for some businesses that rely on the on-site presence of employees, this was a lethal blow. As time passed, restrictions got looser and allowed people to strike a balance between on-site and remote work. Thus, tracking people's indoor movements for purposes involving activity inference, security, and contact tracing is more crucial than ever before. This research explores the applicability of (Radio Frequency Identification) RFID contactless smart cards in tracking people's movement within an enclosed establishment by building a proof-of-concept prototype that allows the mentioned purposes. Furthermore, the system underwent multiple test phases to verify that the system meets the functional and non-functional requirements listed to ensure the system's operational success. Consequently, the test results prove that: 1) the system is behaving as intended;2) the system is secure from known high-risk vulnerabilities;and 3) the system satisfies user requirements and standards, thus fulfilling the functional and non-functional requirements for a human-tracking movement system. © 2023 by the authors.
随着COVID-19大流行在全球陷入停滞,远程就业似乎不可避免。不过,对于一些依赖员工现场办公的企业来说,这是一个致命的打击。随着时间的推移,限制越来越宽松,人们可以在现场和远程工作之间取得平衡。因此,为了活动推断、安全和接触者追踪等目的,跟踪人们的室内运动比以往任何时候都更加重要。本研究通过建立一个允许上述目的的概念验证原型,探索(射频识别)RFID非接触式智能卡在跟踪人们在封闭设施内的运动中的适用性。此外,系统经历了多个测试阶段,以验证系统满足列出的功能性和非功能性需求,以确保系统的操作成功。因此,测试结果证明:1)系统的行为符合预期;2)系统是安全的,不存在已知的高风险漏洞;3)系统满足用户需求和标准,从而满足人体跟踪运动系统的功能和非功能需求。©2023作者所有。
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引用次数: 0
Firefly with Levy Based Feature Selection with Multilayer Perceptron for Sentiment Analysis 基于Levy特征选择的萤火虫多层感知机情感分析
IF 1 Q3 Computer Science Pub Date : 2023-01-01 DOI: 10.12720/jait.14.2.342-349
D. Elangovan, V. Subedha
—Sentimental Analysis (SA) has recently received a lot of attention in decision-making because it can extract and analyze sentiments from web-based reviews made by customers. In this case, SA has been used as a Sentiment Classification (SC) problem, in which reviews are typically labeled as positive or negative depending upon online reviews. By combining FS (Feature Selection) and categorization, this work proposes an effective SA method for internet reviews. FireFly (FF) and Levy Flights (FFL) algorithms have been used for extracting features of web-based reviews, and also the Multilayer Perceptron (MLP) framework has been used to categorize the emotions. A standard DVD database displayed the efficacy of the FF-MLP model on the testing. The outcome shows that the suggested FF-MLP system accomplishes enhanced performance with maximum sensitivity of 98.97%, specificity of 93.67%, accuracy of 97.97%, F-score of 98.75, and kappa of 93.32%.
-情感分析(SA)最近在决策中受到了很多关注,因为它可以从基于网络的客户评论中提取和分析情感。在这种情况下,SA被用作情感分类(SC)问题,其中评论通常根据在线评论被标记为积极或消极。本文将特征选择(FS)和分类相结合,提出了一种有效的网络评论分类方法。FireFly (FF)和Levy Flights (FFL)算法被用于提取基于网络的评论的特征,多层感知器(MLP)框架也被用于对情绪进行分类。一个标准的DVD数据库显示了FF-MLP模型在测试中的有效性。结果表明,该系统灵敏度为98.97%,特异性为93.67%,准确率为97.97%,F-score为98.75,kappa为93.32%。
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引用次数: 0
An Efficient Model to Predict Network Packets in TVDC Using Machine Learning 基于机器学习的TVDC网络数据包预测模型
IF 1 Q3 Computer Science Pub Date : 2023-01-01 DOI: 10.12720/jait.14.3.523-531
Ashmeet Kaur Duggal, Meenu Dave
—Internet-based computing allows the sharing of on-demand resources. This computing technique includes data processing and storage to globally separated machines, known as Cloud Computing. Confidentiality and integrity of data on the cloud are vital. The key constraints include effective access control, accessibility, and transmission of files, in a dynamic cloud environment, seeking a Trusted Virtual Data Center (TVDC). So, to overcome challenges such as data security and integrity due to exponentially growing data size, this research paper aims to develop a prediction model using the machine learning approach, which identifies the type of incoming packet on the TVDC. Alternatively, in other words, this system predicts whether the incoming packets on the server in the cloud environment are malicious or not, using the machine learning approach. This research explored artificial intelligence verticals in building systems with learned data structures for efficient data access. This research describes the implementation of machine learning algorithms for an efficient model’s prediction of the type of incoming packet on the server. It has achieved 88% accuracy using the Gradient Boosted Tree classifier. Also, in this study, the author compares the results of two algorithms, Decision Tree and Gradient Boosted Tree, and finally selects the most optimal for this prediction.
-基于互联网的计算允许按需资源共享。这种计算技术包括数据处理和存储到全局分离的机器,称为云计算。云数据的保密性和完整性至关重要。关键的约束条件包括有效的访问控制、文件的可访问性和传输,在动态的云环境中,寻求可信的虚拟数据中心(TVDC)。因此,为了克服由于数据规模呈指数级增长而带来的数据安全和完整性等挑战,本研究论文旨在使用机器学习方法开发一种预测模型,该模型可以识别TVDC上传入数据包的类型。或者,换句话说,该系统使用机器学习方法预测云环境中服务器上的传入数据包是否是恶意的。本研究探索了人工智能在构建具有学习数据结构的系统中的垂直方向,以实现有效的数据访问。本研究描述了机器学习算法的实现,用于有效模型预测服务器上传入数据包的类型。使用梯度增强树分类器,它达到了88%的准确率。此外,在本研究中,作者比较了决策树和梯度提升树两种算法的结果,最终选择了最优的预测算法。
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引用次数: 0
Multi-speaker Speech Separation under Reverberation Conditions Using Conv-Tasnet 混响条件下的多说话人语音分离
IF 1 Q3 Computer Science Pub Date : 2023-01-01 DOI: 10.12720/jait.14.4.694-700
Chunxi Wang, Maoshen Jia, Yanyan Zhang, Lu Li
—The goal of speech separation is to separate the target signal from the background interference. With the rapid development of artificial intelligence, speech separation technology combined with deep learning has received more attention as well as a lot of progress. However, in the “cocktail party problem”, it is still a challenge to achieve speech separation under reverberant conditions. In order to solve this problem, a model combining the Weighted Prediction Error (WPE) method and a fully-convolutional time-domain audio separation network (Conv-Tasnet) is proposed in this paper. The model target on separating multi-channel signals after dereverberation without prior knowledge of the second field environment. Subjective and objective evaluation results show that the proposed method outperforms existing methods in the speech separation tasks in reverberant and anechoic environments.
语音分离的目的是将目标信号与背景干扰分离开来。随着人工智能的快速发展,与深度学习相结合的语音分离技术受到了越来越多的关注,也取得了很大的进步。然而,在“鸡尾酒会问题”中,如何在混响条件下实现语音分离仍然是一个挑战。为了解决这一问题,本文提出了一种将加权预测误差(Weighted Prediction Error, WPE)方法与全卷积时域音频分离网络(convt - tasnet)相结合的模型。该模型的目标是在不事先了解第二场环境的情况下,对去噪后的多通道信号进行分离。主观和客观评价结果表明,该方法在混响和消声环境下的语音分离任务中优于现有方法。
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引用次数: 1
Sequential Decision Making for Elevator Control 电梯控制的顺序决策
Q3 Computer Science Pub Date : 2023-01-01 DOI: 10.12720/jait.14.5.1124-1131
Emre Oner Tartan, Cebrail Ciflikli
—In the last decade Reinforcement Learning (RL) has significantly changed the conventional control paradigm in many fields. RL approach is spreading with many applications such as autonomous driving and industry automation. Markov Decision Process (MDP) forms a mathematical idealized basis for RL if the explicit model is available. Dynamic programming allows to find an optimal policy for sequential decision making in a MDP. In this study we consider the elevator control as a sequential decision making problem, describe it as a MDP with finite state space and solve it using dynamic programming. At each decision making time step we aim to take the optimal action to minimize the total of hall call waiting times in the episodic task. We consider a sample 6-floor building and simulate the proposed method in comparison with the conventional Nearest Car Method (NCM).
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引用次数: 0
Fuzzy Based Butterfly Life Cycle Algorithm for Measuring Company's Growth Performance 基于模糊蝴蝶生命周期算法的公司成长性评价
IF 1 Q3 Computer Science Pub Date : 2023-01-01 DOI: 10.12720/jait.14.1.1-6
Gregoryus Imannuel Perdana, M. Devanda, D. N. Utama
The previous study of the Butterfly Life Cycle Algorithm (BLCA) has been technically realized in two stages of BLCA in measuring a company's growth performance. It was based on a combined method of the Balanced Scorecard (BSC) and Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis. This paper aims to continue the BLCA implementation by performing five stages of BLCA and then improve the algorithm by implementing the Fuzzy Logic (FL) conception into BSC. The implementation of the FL method transforms the bias values in four BSC parameters into a precise value to make the model more precise. A complete BLCA algorithm combined with FL is used to accurately assess companies' growth performance. By doing some corrections to the preceding study’s data of contribution value, the simulation result shows the difference in the performance value of 0.0026 with the previous one.
蝴蝶生命周期算法(Butterfly Life Cycle Algorithm, BLCA)在过去的研究中,已经从技术上实现了蝴蝶生命周期算法在衡量公司成长绩效方面的两个阶段。它是基于平衡计分卡(BSC)和优势、劣势、机会和威胁(SWOT)分析相结合的方法。本文旨在通过执行BLCA的五个阶段来继续BLCA的实现,然后通过将模糊逻辑(FL)概念引入平衡计分卡来改进算法。FL方法的实现将四个BSC参数中的偏置值转换为精确值,使模型更加精确。采用完整的BLCA算法结合FL来准确评估公司的成长绩效。通过对前人研究的贡献值数据进行一些修正,仿真结果显示与前人的性能值相差0.0026。
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引用次数: 0
Comparative Analysis of Machine Learning in Predicting the Treatment Status of COVID-19 Patients 机器学习预测COVID-19患者治疗状况的对比分析
IF 1 Q3 Computer Science Pub Date : 2023-01-01 DOI: 10.12720/jait.14.1.56-65
Anthony Anggrawan, Mayadi Mayadi, Christofer Satria, B. K. Triwijoyo, R. Rismayati
COVID-19 has become a global pandemic that causes many deaths, so medical treatment for COVID-19 patients gets special attention, whether hospitalized or self-isolated. However, the problem in medical action is not easy, and the most frequent mistakes are due to inaccuracies in medical decision-making. Meanwhile, machine learning can predict with high accuracy. For that, or that's why this study aims to propose a data mining classification method as a machine learning model to predict the treatment status of COVID-19 patients accurately, whether hospitalized or self-isolated. The data mining method used in this research is the Random Forest (RF) and Support Vector Machine (SVM) algorithm with Confusion Matrix and k-fold Cross Validation testing. The finding indicated that the machine learning model has an accuracy of up to 94% with the RF algorithm and up to 92% with the SVM algorithm in predicting the COVID-19 patient's treatment status. It means that the machine learning model using the RF algorithm has more accurate accuracy than the SVM algorithm in predicting or recommending the treatment status of COVID-19 patients. The implication is that RF machine learning can help/replace the role of medical experts in predicting the patient's care status.
COVID-19已成为导致许多人死亡的全球大流行,因此无论是住院治疗还是自我隔离,对COVID-19患者的治疗都受到特别关注。然而,医疗行动中的问题并不容易,最常见的错误是由于医疗决策的不准确。同时,机器学习可以进行高精度的预测。因此,或者这就是为什么本研究旨在提出一种数据挖掘分类方法作为机器学习模型,以准确预测COVID-19患者的治疗状况,无论是住院还是自我隔离。本研究使用的数据挖掘方法是随机森林(RF)和支持向量机(SVM)算法,结合混淆矩阵和k-fold交叉验证测试。研究结果表明,机器学习模型在预测COVID-19患者治疗状况时,使用RF算法的准确率高达94%,使用SVM算法的准确率高达92%。这意味着使用RF算法的机器学习模型在预测或推荐COVID-19患者治疗状况方面比SVM算法具有更准确的准确性。这意味着射频机器学习可以帮助/取代医学专家在预测患者护理状况方面的作用。
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引用次数: 1
A Generational Cohort Comparison of Icon Selection Accuracy under Varying Conditions of Icon Entropy and Concreteness 不同图标熵和具体条件下的代际队列图标选择准确性比较
IF 1 Q3 Computer Science Pub Date : 2023-01-01 DOI: 10.12720/jait.14.2.250-256
Kleddao Satcharoen, Pikulkaew Tangtisanon
—The objective of this research was to compare icon selection accuracy under varying icon entropy and concreteness conditions between different generational cohorts ( Millennial, Generation X, and Baby Boomers ). These generational cohorts have different levels of experience with technology, with younger generations often being framed as “digital natives” and holding stronger technological experience and competence in comparison to older groups. Generational groups also have variations in physiological factors including visual acuity and reaction time. Despite these differences between user groups, many user interaction systems and processes are designed for a single user, rather than considering differences in user processing between different groups. Therefore, this study compares generational cohorts in their icon selection accuracy under varying icon conditions, to help identify what generational differences can be observed in this task. The study selected a sample of 150 participants ( n = 50 for each generational cohort ). The experiment was a 2  2  3 design ( entropy ( high / low )  abstractness ( abstract / concrete )  time ( 9 / 6 / 3 seconds ) , with each participant completing 60 trials ( five questions per entropy / abstractness pair over three timed runs ). Results showed that there were significant differences in mean accuracy per trial under all of the time conditions and icon entropy and concreteness conditions . Mean differences showed that under most conditions, Millennial and Generation X participants did not have a significant mean difference, but Baby Boomers were significantly slower under almost all conditions . The implication of this finding is that Baby Boomers are more sensitive to icon abstractness and entropy conditions than other age groups tested .
-本研究的目的是比较不同世代(千禧一代、X一代和婴儿潮一代)在不同图标熵和具体条件下的图标选择准确性。这几代人对技术有着不同程度的体验,年轻一代通常被认为是“数字原住民”,与年长的群体相比,他们拥有更强的技术经验和能力。不同年龄段的人在视觉敏锐度和反应时间等生理因素上也存在差异。尽管用户组之间存在这些差异,但许多用户交互系统和流程都是为单个用户设计的,而没有考虑到不同组之间用户处理的差异。因此,本研究比较了代际队列在不同图标条件下的图标选择准确性,以帮助确定在该任务中可以观察到的代际差异。该研究选取了150名参与者作为样本(每代50人)。实验采用223设计(熵(高/低)抽象性(抽象/具体)时间(9 / 6 / 3秒),每个参与者完成60个试验(每个熵/抽象性对在3次时间内完成5个问题)。结果表明,在所有的时间条件下,在图标熵和具体性条件下,每次试验的平均准确率存在显著差异。平均差异显示,在大多数情况下,千禧一代和X一代的参与者没有显著的平均差异,但婴儿潮一代在几乎所有情况下都明显慢了下来。这一发现的含义是,婴儿潮一代对图标抽象性和熵条件比其他年龄组更敏感。
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引用次数: 0
Advanced Real Time Embedded Book Braille System 先进的实时嵌入式图书盲文系统
IF 1 Q3 Computer Science Pub Date : 2023-01-01 DOI: 10.12720/jait.14.3.510-517
Vasile Dan, I. Nascu, S. Folea
—Reading is an activity that leads to acquiring information and developing a person’s knowledge. Therefore, everyone should have equal access to the same sources of information. Unfortunately, blindness is a disease that restricts the affected people from reading books that are not converted into Braille. This paper describes a novel solution for the real-time conversion of any text into Braille. The system will rely on image processing and a camera to gather the raw text data from any book in physical format. Furthermore, e-books and documents in any digital format, Braille Ready Format (BRF), Portable Embosser Files (PEF), TXT, PDF, or PNG, can be provided for Braille conversion. Image enhancement algorithms, neural networks, and Optical Character Recognition (OCR) algorithms are used to extract accurate content. The process is controlled by a Raspberry Pi 4. A refreshable Braille mechanism, based on an Arduino Due microcontroller, is used to display the dots for each character. The algorithms are implemented to work with the mechanical structure design that was created to reduce the cost and give the user a complete reading experience of any book.
阅读是一种获取信息和发展个人知识的活动。因此,每个人都应该有平等的机会获得相同的信息来源。不幸的是,失明是一种疾病,它限制了受影响的人阅读没有转换成盲文的书籍。本文描述了一种将任何文本实时转换为盲文的新颖解决方案。该系统将依靠图像处理和摄像头从任何物理格式的书籍中收集原始文本数据。此外,电子书和文件在任何数字格式,盲文格式(BRF),便携式压花文件(PEF), TXT, PDF,或PNG,可以提供盲文转换。图像增强算法、神经网络和光学字符识别(OCR)算法用于提取准确的内容。该过程由树莓派4控制。一个可刷新的盲文机制,基于Arduino Due微控制器,用于显示每个字符的点。这些算法是为了配合机械结构设计而实现的,旨在降低成本,并为用户提供完整的阅读体验。
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引用次数: 0
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Journal of Advances in Information Technology
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