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Design an Adaptive Hybrid Approach for Genetic Algorithm to Detect Effective Malware Detection in Android Division 基于遗传算法的自适应混合检测方法在Android Division中实现有效的恶意软件检测
Pub Date : 2021-07-23 DOI: 10.36548/JUCCT.2021.2.006
B. Vivekanandam
Data pre-processing is critical for handling classification issues in the field of machine learning and model identification. The processing of big data sets increases the computer processing time and space complexity while decreasing classification model precision. As a result, it is necessary to develop an appropriate method for selecting attributes. This article describes a machine learning technique to solve functional selection by safeguarding the selection and mutation operators of genetic algorithms. During population calculations in the training set, the proposed method is adaptable. Furthermore, for various population sizes, the proposed method gives the best possible probability of resolving function selection difficulties during training process. Furthermore, the proposed work is combined with a better classifier in order to detect the different malware categories. The proposed approach is compared and validated with current techniques by using different datasets. In addition to the test results, this research work utilizes the algorithm for solving a real challenge in Android categorization, and the results show that, the proposed approach is superior. Besides, the proposed algorithm provides a better mean and standard deviation value in the optimization process for leveraging model effectiveness at different datasets.
在机器学习和模型识别领域,数据预处理是处理分类问题的关键。大数据集的处理增加了计算机处理的时间和空间复杂度,降低了分类模型的精度。因此,有必要开发一种合适的方法来选择属性。本文介绍了一种机器学习技术,通过保护遗传算法的选择算子和变异算子来解决功能选择问题。在训练集的总体计算过程中,该方法具有较强的适应性。此外,对于不同的人口规模,该方法给出了解决训练过程中函数选择困难的最佳概率。此外,所提出的工作与更好的分类器相结合,以检测不同的恶意软件类别。通过使用不同的数据集,将所提出的方法与现有技术进行了比较和验证。除了测试结果外,本研究工作还将该算法用于解决Android分类中的一个实际挑战,结果表明,本文提出的方法具有优越性。此外,该算法在优化过程中提供了更好的均值和标准差值,以充分利用模型在不同数据集上的有效性。
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引用次数: 22
Efficient Two Stage Identification for Face mask detection using Multiclass Deep Learning Approach 基于多类深度学习的面罩检测高效两阶段识别
Pub Date : 2021-07-19 DOI: 10.36548/JUCCT.2021.2.004
R. Dhaya
The World Health Organization (WHO) considers the COVID-19 Coronavirus to be a global pandemic. The most effective form of protection is to wear a face mask in public places. Moreover, the COVID-19 pandemic prompted all the countries to set up a lockdown to prevent viral transmission. According to a survey study, the use of facemasks at work decreases the chances of fast transmission. If the facemasks are not used or are worn incorrectly, it contributes to the third and fourth waves of the corona virus spreading throughout the world. This motivates us to conduct an efficient investigation of the face mask identification system and monitor people, who use suitable face mask in public places. Deep learning is the most effective approach for detecting whether or not a person is wearing a face mask in a crowded area. Using a multiclass deep learning technique, this research study proposes an efficient two stage identification (ETSI) for face mask detection. Whereas, the binary classification does not offer information about face mask detection and error. The proposed approach employs CNN's "ReLU" activation function to detect the face mask. Furthermore, in the current pandemic crisis, this research article offers a very efficient and precise approach for identifying COVID-19. Precision has increased as a result of the employment of a multi-class abbreviation in the final output.
世界卫生组织(WHO)认为COVID-19冠状病毒是一种全球大流行。在公共场所戴口罩是最有效的防护措施。此外,COVID-19大流行促使所有国家都建立了封锁,以防止病毒传播。根据一项调查研究,在工作中使用口罩可以减少快速传播的机会。如果不使用口罩或佩戴不当,就会导致冠状病毒在世界各地传播的第三波和第四波。这促使我们对口罩识别系统进行有效的调查,并监测在公共场所使用合适口罩的人。深度学习是在拥挤的地方检测一个人是否戴口罩的最有效方法。本研究利用多类深度学习技术,提出了一种有效的两阶段识别(ETSI)人脸检测方法。然而,二值分类不能提供关于口罩检测和错误的信息。该方法采用CNN的“ReLU”激活函数对口罩进行检测。此外,在当前的大流行危机中,本研究文章提供了一种非常有效和精确的方法来识别COVID-19。由于在最终输出中使用了多类缩写,精度得到了提高。
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引用次数: 14
Comparison of Stock Price Prediction Models using Pre-trained Neural Networks 基于预训练神经网络的股价预测模型比较
Pub Date : 2021-07-19 DOI: 10.36548/JUCCT.2021.2.005
C. Anand
Several intelligent data mining approaches, including neural networks, have been widely employed by academics during the last decade. In today's rapidly evolving economy, stock market data prediction and analysis play a significant role. Several non-linear models like neural network, generalized autoregressive conditional heteroskedasticity (GARCH) and autoregressive conditional heteroscedasticity (ARCH) as well as linear models like Auto-Regressive Integrated Moving Average (ARIMA), Moving Average (MA) and Auto Regressive (AR) may be used for stock forecasting. The deep learning architectures inclusive of Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNN), Multilayer Perceptron (MLP) and Support Vector Machine (SVM) are used in this paper for stock price prediction of an organization by using the previously available stock prices. The National Stock Exchange (NSE) of India dataset is used for training the model with day-wise closing price. Data prediction is performed for a few sample companies selected on a random basis. Based on the comparison results, it is evident that the existing models are outperformed by CNN. The network can also perform stock predictions for other stock markets despite being trained with single market data as a common inner dynamics that has been shared between certain stock markets. When compared to the existing linear models, the neural network model outperforms them in a significant manner, which can be observed from the comparison results.
包括神经网络在内的几种智能数据挖掘方法在过去十年中被学术界广泛采用。在经济快速发展的今天,股票市场数据预测和分析发挥着重要作用。一些非线性模型如神经网络、广义自回归条件异方差(GARCH)和自回归条件异方差(ARCH)以及线性模型如自回归综合移动平均(ARIMA)、移动平均(MA)和自回归(AR)可用于股票预测。本文使用卷积神经网络(CNN)、长短期记忆(LSTM)、循环神经网络(RNN)、多层感知器(MLP)和支持向量机(SVM)等深度学习架构,通过使用先前可用的股票价格对组织进行股票价格预测。使用印度国家证券交易所(NSE)数据集训练具有日收盘价的模型。对随机选择的几个样本公司进行数据预测。从对比结果可以看出,现有模型的性能明显优于CNN。该网络还可以对其他股票市场进行股票预测,尽管它是用单一市场数据作为某些股票市场之间共享的共同内部动态进行训练的。与现有的线性模型相比,神经网络模型的表现明显优于线性模型,这从对比结果中可以看出。
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引用次数: 36
Blockchain Framework for Communication between Vehicle through IoT Devices and Sensors 通过物联网设备和传感器实现车辆之间通信的区块链框架
Pub Date : 2021-07-17 DOI: 10.36548/JUCCT.2021.2.003
J. S. Raj
The advent of autonomous vehicles is indeed a potential field of research in today's situation. Connected Vehicles (CV) have received a lot of attention in the last decade, which has resulted in CV as a Service (CVaaS). With the advent of taxi services, there is a need for or demand for robust, seamless, and secure information transmission between the vehicles connected to a vehicular network. Thus, the concept of vehicular networking is transformed into novel concept of autonomous and connected vehicles. These autonomous vehicles will serve as a better experience by providing instant information from the vehicles via congestion reduction. The significant drawback faced by the invention of autonomous vehicles is the malicious floor of intruders, who tend to mislead the communication between the vehicles resulting in the compromised smart devices. To address these concerns, the best methodology that will protect and secure the control system of the autonomous vehicle in real time is blockchain. This research work proposes a blockchain framework in order to address the security challenges in autonomous vehicles. This research work enhances the security of smart vehicles thereby preventing intruders from accessing the vehicular network. To validate the suggested technique, money security criteria such as changing stored user ratings, probabilistic authentication scenarios, smart device compromise, and bogus user requests were employed. The observed findings have been documented and analysed, revealing an 82% success rate.
在今天的情况下,自动驾驶汽车的出现确实是一个潜在的研究领域。在过去十年中,互联汽车(CV)受到了广泛关注,从而产生了汽车即服务(CVaaS)。随着出租车服务的出现,连接到车辆网络的车辆之间需要可靠、无缝和安全的信息传输。因此,车联网的概念转变为自主和互联汽车的新概念。这些自动驾驶汽车将通过减少拥堵,提供来自车辆的即时信息,从而提供更好的体验。自动驾驶汽车发明面临的一个重大缺点是入侵者的恶意攻击,他们往往会误导车辆之间的通信,导致智能设备受损。为了解决这些问题,实时保护和保护自动驾驶汽车控制系统的最佳方法是区块链。这项研究工作提出了一个区块链框架,以解决自动驾驶汽车的安全挑战。本研究提高了智能车辆的安全性,从而防止入侵者进入车辆网络。为了验证建议的技术,采用了诸如更改存储的用户评级、概率身份验证场景、智能设备泄露和虚假用户请求等货币安全标准。观察到的结果已被记录和分析,显示出82%的成功率。
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引用次数: 3
Machine Learning Algorithms Performance Analysis for VLSI IC Design VLSI集成电路设计中的机器学习算法性能分析
Pub Date : 2021-07-14 DOI: 10.36548/JUCCT.2021.2.002
J. Chen, Kong-Long Lai
The design of an analogue IC layout is a time-consuming and manual process. Despite several studies in the sector, some geometric restrictions have resulted in disadvantages in the process of automated analogue IC layout design. As a result, analogue design has a performance lag when compared to manual design. This prevents the deployment of a large range of automated tools. With the recent technical developments, this challenge is resolved using machine learning techniques. This study investigates performance-driven placement in the VLSI IC design process, as well as analogue IC performance prediction by utilizing various machine learning approaches. Further, several amplifier designs are simulated. From the simulation results, it is evident that, when compared to the manual layout, an improved performance is obtained by using the proposed approach.
模拟IC版图的设计是一个耗时且人工的过程。尽管在该领域进行了一些研究,但一些几何限制导致了自动化模拟IC布局设计过程中的缺点。因此,与手动设计相比,模拟设计具有性能滞后。这阻碍了大量自动化工具的部署。随着最近技术的发展,这一挑战可以通过机器学习技术来解决。本研究探讨了VLSI IC设计过程中性能驱动的放置,以及利用各种机器学习方法进行模拟IC性能预测。此外,还对几种放大器设计进行了仿真。仿真结果表明,与手工布局相比,该方法的性能得到了显著提高。
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引用次数: 0
Energy Management Scheme in Hierarchical Integrated Energy Systems with Coalition 带联盟的分层集成能源系统的能源管理方案
Pub Date : 2021-05-25 DOI: 10.36548/JEEA.2021.1.004
Nayana
Often, coalitions are formed by the hierarchical integrated energy systems (HIESs) and their evolutionary process which is driven by the benefits of stakeholders and consolidate energy consumers and producers. Several literature have failed to analyze the operation of HIES under the impact of multiple coalitions. At the lower level, multiple users, in the middle level, the multiple distributed energy stations (DESs) and at the upper level, one natural gas and one electricity utility company structure is used for analyzing the HIES operation with a trading scheme. The Lagrange function is used for deriving the optimal operation strategy based analytical function for each probable coalition and each market participant comprising of users and the DESs. It is evident from the results that in a single coalition, the profits linked to other DESs will decrease while increasing the profit of one DES with technological enhancements, users show an aversion towards DESs with high generation coefficient while they are attracted to the ones that enable reduction of heat and electricity price. Maintaining their isolation is preferred by high heat and electricity consuming DESs at the same energy price. Other coalitions and their operations are not affected by the change in parameters of one coalition.
通常,联盟是由分层综合能源系统(hess)及其演化过程形成的,该过程是由利益相关者的利益驱动的,并巩固了能源消费者和生产者。一些文献未能分析多重联盟影响下HIES的运作。采用多级用户、多级分布式能源站(DESs)、多级天然气公司和电力公司的结构,分析了HIES在交易方案下的运行情况。利用拉格朗日函数推导出由用户和决策系统组成的每个可能联盟和每个市场参与者的最优运营策略分析函数。从结果可以明显看出,在单一联盟中,与其他分布式发电系统相关的利润将减少,而随着技术的提高,一个分布式发电系统的利润将增加,用户对发电系数高的分布式发电系统表现出厌恶,而能够降低热量和电力价格的分布式发电系统则吸引用户。在相同的能源价格下,高热量和电力消耗的DESs更倾向于保持其隔离。其他联盟及其行动不受一个联盟参数变化的影响。
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引用次数: 1
Comparative Analysis of Signal Processing Techniques for Fault Detection in Three Phase Induction Motor 三相异步电动机故障检测信号处理技术的比较分析
Pub Date : 2021-04-19 DOI: 10.36548/JEI.2021.1.006
Thomas Amanuel, Amanuel Ghirmay, Huruy Ghebremeskel, Robel Ghebrehiwet, Weldekidan Bahlibi
Signal processing is considered as an efficient technique to detect the faults in three-phase induction motors. Detection of different varieties of faults in the rotor of the motor are widely studied at the industrial level. To extend further, this research article presents the analysis on various signal processing techniques for fault detection in three-phase induction motor due to the damages in rotor bar. Usually, Fast Fourier Transform (FFT) and STFT are used to analyze the healthy and faulty motor conditions based on the signal characteristics. The proposed study covers the advantages and limitations of the proposed wavelet transform (WT) and each technique for detecting the broken bar of induction motors. The good frequency information can be collected from FFT techniques for handling multiple faults identification in three-phase induction motor. Despite the hype, the detection accuracy gets reduced during the dynamic condition of the machine because the frequency information on sudden time changes cannot be employed by FFT. The WT method signal analysis is compared with FFT to propose fault detection method for induction motor. The WT method is proving better accuracy when compared to all existing methods for signal information analysis. The proposed research work has simulated the proposed method with MATLAB / SIMULINK and it helps to effectively detect the healthy and faulty conditions of the motor.
信号处理被认为是三相异步电动机故障检测的一种有效方法。电机转子各种故障的检测在工业层面上得到了广泛的研究。在此基础上,本文分析了三相异步电动机转子条损坏故障检测的各种信号处理技术。通常,快速傅里叶变换(FFT)和短时傅里叶变换(STFT)是基于信号特征分析电机健康状态和故障状态的常用方法。本研究涵盖了所提出的小波变换(WT)的优点和局限性,以及用于感应电机断条检测的各种技术。快速傅里叶变换技术可以收集到良好的频率信息,用于三相异步电动机的多故障识别。尽管大肆宣传,但由于FFT无法利用突然时间变化的频率信息,因此在机器动态状态下检测精度降低。将小波变换的信号分析方法与快速傅里叶变换的信号分析方法进行比较,提出了感应电动机的故障检测方法。与所有现有的信号信息分析方法相比,小波变换方法被证明具有更好的准确性。所提出的研究工作已在MATLAB / SIMULINK中进行了仿真,该方法有助于有效地检测电机的健康状态和故障状态。
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引用次数: 23
Location-based Orientation Context Dependent Recommender System for Users 基于位置的用户定向上下文相关推荐系统
Pub Date : 2021-04-10 DOI: 10.36548/JTCSST.2021.1.002
Vijesh Joe C, Jennifer S. Raj
As the technology revolving around IoT sensors develops in a rapid manner, the subsequent social networks that are essential for the growth of the system will be utilized as a means to filter the objects that are preferred by the consumers. The ultimate purpose of the system is to give the customers personalized recommendations based on their preference. Similarly, the location and orientation will also play a crucial role in identifying the preference of the customer is a more efficient manner. Almost all social networks make use of location information to provide better services to the users based on the research performed. Hence there is a need for developing a recommender system that is dependent on location. In this paper, we have incorporated a recommender system that makes use of recommender algorithm that is personalized to take into consideration the context of the user. The preference of the user is analysed with the help of IoT smart devices like the smart watches, Google home, smart phones, ipads etc. The user preferences are obtained from these devices and will enable the recommender system to gauge the best resources. The results based on evaluation are compared with that of the content-based recommender algorithm and collaborative filtering to enable the recommendation engine’s power.
随着围绕物联网传感器的技术快速发展,对系统发展至关重要的后续社交网络将被用作过滤消费者偏好对象的手段。该系统的最终目的是根据顾客的喜好给他们个性化的推荐。同样,位置和方向也将发挥至关重要的作用,以确定顾客的偏好是一种更有效的方式。根据所做的研究,几乎所有的社交网络都利用位置信息为用户提供更好的服务。因此,有必要开发一个依赖于位置的推荐系统。在本文中,我们整合了一个推荐系统,该系统利用个性化的推荐算法来考虑用户的上下文。通过物联网智能设备,如智能手表、谷歌家居、智能手机、ipad等,分析用户的偏好。从这些设备中获得用户偏好,并将使推荐系统能够衡量最佳资源。将基于评价的推荐结果与基于内容的推荐算法和协同过滤的推荐结果进行比较,以发挥推荐引擎的强大功能。
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引用次数: 35
Incorporation of post-consumer pizza boxes in the recovered fiber stream: Impacts of grease on finished product quality 在回收纤维流中加入消费后披萨盒:油脂对成品质量的影响
Pub Date : 2021-04-01 DOI: 10.32964/tj20.3.161
Ashoka Ghosh, P. Hart, Adele Panek, T. Nguyen, Meredith Dooley
Grease and cheese contamination of used pizza boxes has led to misunderstanding and controversy about the recyclability of pizza boxes. Some collection facilities accept pizza boxes while others do not. The purpose of this study is to determine whether typical grease or cheese contamination levels associated with pizza boxes impact finished product quality. Grease (from vegetable oil) and cheese are essentially hydrophobic and in sufficiently high concentration could interfere with interfiber bonding, resulting in paper strength loss.Findings from this study will be used to determine the viability of recycling pizza boxes at current and future concentrations in old corrugated containers (OCC) recovered fiber streams. These findings will also be used to inform the acceptability of pizza boxes in the recycle stream and educate consumers about acceptable levels of grease or cheese residue found on these recycled boxes.
用过的披萨盒被油脂和奶酪污染导致了对披萨盒可回收性的误解和争议。一些收集设施接受披萨盒,而另一些则不接受。本研究的目的是确定与披萨盒相关的典型油脂或奶酪污染水平是否会影响成品质量。油脂(来自植物油)和奶酪本质上是疏水性的,如果浓度足够高,可能会干扰纤维间的粘合,导致纸张强度损失。本研究的结果将用于确定当前和未来在旧瓦楞容器(OCC)回收纤维流中回收披萨盒的可行性。这些发现还将用于告知比萨盒在回收流中的可接受性,并教育消费者这些回收盒上发现的油脂或奶酪残留物的可接受水平。
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引用次数: 0
Black liquor evaporator upgrades— life cycle cost analysis 黑液蒸发器升级-全生命周期成本分析
Pub Date : 2021-04-01 DOI: 10.32964/tj20.3.208
J. Cantrell
Black liquor evaporation is generally the most energy intensive unit operation in a pulp and paper manufacturing facility. The black liquor evaporators can represent a third or more of the total mill steam usage, followed by the paper machine and digester. Evaporator steam economy is defined as the unit mass of steam required to evaporate a unit mass of water from black liquor (i.e., lb/lb or kg/kg.) The economy is determined by the number of effects in an evaporator train and the system configuration. Older systems use four to six effects, most of which are the long tube vertical rising film type. Newer systems may be designed with seven or even eight effects using falling film and forced circulation crystallization technology for high product solids. The median age of all North American evaporator systems is 44 years. Roughly 25% of the current North American operating systems are 54 years or older. Older systems require more periodic maintenance and have a higher risk of unplanned downtime. Also, older systems have chronic issues with persistent liquor and vapor leaks, shell wall thinning, corrosion, and plugged tubes. Often these issues worsen to the point of requiring rebuild or replacement. When considering the age, technology, and lower efficiency of older systems, a major rebuild or new system may be warranted. The intent of this paper is to review the current state of black liquor evaporator systems in North America and present a basic method for determining whether a major rebuild or new installation is warranted using total life cycle cost analysis (LCCA).
黑液蒸发通常是纸浆和造纸设备中最耗能的单元操作。黑液蒸发器可以代表工厂总蒸汽使用量的三分之一或更多,其次是造纸机和消化器。蒸发器蒸汽经济性定义为从黑液中蒸发单位质量的水所需的单位质量的蒸汽(即lb/lb或kg/kg)。经济性是由蒸发器串中的效应数和系统配置决定的。旧的系统使用四到六种效果,其中大部分是长管垂直上升膜类型。较新的系统可以设计为七种甚至八种效果,使用降膜和强制循环结晶技术用于高产品固体。所有北美蒸发器系统的平均使用年限为44年。目前北美大约有25%的操作系统已经使用了54年或更久。旧的系统需要更多的定期维护,并且有更高的意外停机风险。此外,老旧的系统存在持续的液体和蒸汽泄漏、壳壁变薄、腐蚀和管道堵塞等长期问题。通常这些问题恶化到需要重建或更换的地步。当考虑到旧系统的年龄、技术和较低的效率时,可能需要进行重大重建或新系统。本文的目的是回顾北美黑液蒸发器系统的现状,并提出一种基本方法来确定是否需要使用全生命周期成本分析(LCCA)进行重大改造或新安装。
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引用次数: 0
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