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Anti-leakage method of network sensitive information data based on homomorphic encryption 基于同态加密的网络敏感信息数据防泄漏方法
IF 3 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-01-01 DOI: 10.1515/jisys-2022-0281
Junlong Shi, Xiaofeng Zhao
Abstract With the development of artificial intelligence, people begin to pay attention to the protection of sensitive information and data. Therefore, a homomorphic encryption framework based on effective integer vector is proposed and applied to deep learning to protect the privacy of users in binary convolutional neural network model. The conclusion shows that the model can achieve high accuracy. The training is 93.75% in MNIST dataset and 89.24% in original dataset. Because of the confidentiality of data, the training accuracy of the training set is only 86.77%. After increasing the training period, the accuracy began to converge to about 300 cycles, and finally reached about 86.39%. In addition, after taking the absolute value of the elements in the encryption matrix, the training accuracy of the model is 88.79%, and the test accuracy is 85.12%. The improved model is also compared with the traditional model. This model can reduce the storage consumption in the model calculation process, effectively improve the calculation speed, and have little impact on the accuracy. Specifically, the speed of the improved model is 58 times that of the traditional CNN model, and the storage consumption is 1/32 of that of the traditional CNN model. Therefore, homomorphic encryption can be applied to information encryption under the background of big data, and the privacy of the neural network can be realized.
随着人工智能的发展,人们开始关注敏感信息和数据的保护。为此,提出了一种基于有效整数向量的同态加密框架,并将其应用于深度学习中,以保护二元卷积神经网络模型中用户的隐私。结果表明,该模型能够达到较高的精度。MNIST数据集的训练率为93.75%,原始数据集的训练率为89.24%。由于数据的保密性,训练集的训练准确率仅为86.77%。增加训练周期后,准确率开始收敛到300次左右,最终达到86.39%左右。此外,取加密矩阵中元素的绝对值后,该模型的训练准确率为88.79%,测试准确率为85.12%。并将改进后的模型与传统模型进行了比较。该模型可以减少模型计算过程中的存储消耗,有效提高计算速度,对精度影响较小。具体来说,改进模型的速度是传统CNN模型的58倍,存储消耗是传统CNN模型的1/32。因此,可以将同态加密应用于大数据背景下的信息加密,实现神经网络的隐私性。
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引用次数: 0
A new method for writer identification based on historical documents 一种基于历史文献的作者鉴定新方法
IF 3 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-01-01 DOI: 10.1515/jisys-2022-0244
A. Gattal, Chawki Djeddi, Faycel Abbas, I. Siddiqi, Bouderah Brahim
Abstract Identifying the writer of a handwritten document has remained an interesting pattern classification problem for document examiners, forensic experts, and paleographers. While mature identification systems have been developed for handwriting in contemporary documents, the problem remains challenging from the viewpoint of historical manuscripts. Design and development of expert systems that can identify the writer of a questioned manuscript or retrieve samples belonging to a given writer can greatly help the paleographers in their practices. In this context, the current study exploits the textural information in handwriting to characterize writer from historical documents. More specifically, we employ oBIF(oriented Basic Image Features) and hinge features and introduce a novel moment-based matching method to compare the feature vectors extracted from writing samples. Classification is based on minimization of a similarity criterion using the proposed moment distance. A comprehensive series of experiments using the International Conference on Document Analysis and Recognition 2017 historical writer identification dataset reported promising results and validated the ideas put forward in this study.
识别手写文件的作者一直是文件审查员、法医专家和古文字学家的一个有趣的模式分类问题。虽然成熟的识别系统已经发展为当代文件中的笔迹,但从历史手稿的角度来看,这个问题仍然具有挑战性。专家系统的设计和开发可以识别被质疑手稿的作者或检索属于给定作者的样本,这可以极大地帮助古文字学家进行实践。在此背景下,本研究利用笔迹的纹理信息来刻画历史文献中的作者。更具体地说,我们采用oBIF(面向基本图像特征)和铰链特征,并引入了一种新的基于矩的匹配方法来比较从书写样本中提取的特征向量。分类是基于最小化的相似性标准使用提出的矩距离。利用2017年国际文献分析与识别会议的历史作者识别数据集进行的一系列综合实验报告了令人鼓舞的结果,并验证了本研究提出的想法。
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引用次数: 1
A BiLSTM-attention-based point-of-interest recommendation algorithm 基于bilstm的兴趣点推荐算法
Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-01-01 DOI: 10.1515/jisys-2023-0033
Aichuan Li, Fuzhi Liu
Abstract Aiming at the problem that users’ check-in interest preferences in social networks have complex time dependences, which leads to inaccurate point-of-interest (POI) recommendations, a location-based POI recommendation model using deep learning for social network big data is proposed. First, the original data are fed into an embedding layer of the model for dense vector representation and to obtain the user’s check-in sequence (UCS) and space-time interval information. Then, the UCS and spatiotemporal interval information are sent into a bidirectional long-term memory model for detailed analysis, where the UCS and location sequence representation are updated using a self-attention mechanism. Finally, candidate POIs are compared with the user’s preferences, and a POI sequence with three consecutive recommended locations is generated. The experimental analysis shows that the model performs best when the Huber loss function is used and the number of training iterations is set to 200. In the Foursquare dataset, Recall@20 and NDCG@20 reach 0.418 and 0.143, and in the Gowalla dataset, the corresponding values are 0.387 and 0.148.
摘要针对社交网络用户签到兴趣偏好具有复杂的时间依赖性,导致POI推荐不准确的问题,提出了一种基于位置的深度学习社交网络大数据POI推荐模型。首先,将原始数据输入到模型的嵌入层进行密集向量表示,得到用户签入序列和时空间隔信息;然后,将UCS和时空间隔信息发送到双向长期记忆模型中进行详细分析,在双向长期记忆模型中,UCS和位置序列表示使用自注意机制进行更新。最后,将候选POI与用户的偏好进行比较,并生成具有三个连续推荐位置的POI序列。实验分析表明,当使用Huber损失函数并将训练迭代次数设置为200次时,该模型表现最佳。在Foursquare数据集中,Recall@20和NDCG@20分别达到0.418和0.143,在Gowalla数据集中,对应的值分别为0.387和0.148。
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引用次数: 0
Waste material classification using performance evaluation of deep learning models 利用深度学习模型的性能评价进行废弃物分类
Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-01-01 DOI: 10.1515/jisys-2023-0064
Israa Badr Al-Mashhadani
Abstract Waste classification is the issue of sorting rubbish into valuable categories for efficient waste management. Problems arise from issues such as individual ignorance or inactivity and more overt issues like pollution in the environment, lack of resources, or a malfunctioning system. Education, established behaviors, an improved infrastructure, technology, and legislative incentives to promote effective trash sorting and management are all necessary for a solution to be implemented. For solid waste management and recycling efforts to be successful, waste materials must be sorted appropriately. This study evaluates the effectiveness of several deep learning (DL) models for the challenge of waste material classification. The focus will be on finding the best DL technique for solid waste classification. This study extensively compares several DL architectures (Resnet50, GoogleNet, InceptionV3, and Xception). Images of various types of trash are amassed and cleaned up to form a dataset. Accuracy, precision, recall, and F 1 score are only a few measures used to assess the performance of the many DL models trained and tested on this dataset. ResNet50 showed impressive performance in waste material classification, with 95% accuracy, 95.4% precision, 95% recall, and 94.8% in the F 1 score, with only two incorrect categories in the glass class. All classes are correctly classified with an F 1 score of 100% due to Inception V3’s remarkable accuracy, precision, recall, and F 1 score. Xception’s classification accuracy was excellent (100%), with a few difficulties in the glass and trash categories. With a good 90.78% precision, 100% recall, and 89.81% F 1 score, GoogleNet performed admirably. This study highlights the significance of using models based on DL for categorizing trash. The results open the way for enhanced trash sorting and recycling operations, contributing to an economically and ecologically friendly future.
垃圾分类是将垃圾分类成有价值的类别,以便进行有效的垃圾管理。问题来自个人的无知或不作为,以及更明显的问题,如环境污染、资源缺乏或系统故障。教育、既定行为、改善基础设施、技术和立法激励措施,以促进有效的垃圾分类和管理,都是实施解决方案的必要条件。要使固体废物管理和回收工作取得成功,必须对废物进行适当分类。本研究评估了几种深度学习(DL)模型对废物分类挑战的有效性。重点将是寻找固体废物分类的最佳DL技术。这项研究广泛地比较了几种深度学习架构(Resnet50, GoogleNet, InceptionV3和Xception)。各种类型的垃圾图像被收集和清理,形成一个数据集。准确性、精密度、召回率和f1分数只是用来评估在该数据集上训练和测试的许多深度学习模型的性能的几个指标。ResNet50在废物分类方面表现出色,准确率为95%,精密度为95.4%,召回率为95%,f1得分为94.8%,玻璃类中只有两个分类不正确。由于Inception V3出色的准确率、精密度、召回率和f1分数,所有类都被正确分类,并获得了100%的f1分数。Xception的分类准确率非常好(100%),在玻璃和垃圾类别中有一些困难。GoogleNet的准确率为90.78%,召回率为100%,f1得分为89.81%,表现令人钦佩。本研究强调了使用基于深度学习的模型进行垃圾分类的重要性。研究结果为加强垃圾分类和回收操作开辟了道路,为经济和生态友好的未来做出了贡献。
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引用次数: 0
Detecting biased user-product ratings for online products using opinion mining 使用意见挖掘检测在线产品的有偏见的用户产品评级
IF 3 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-01-01 DOI: 10.1515/jisys-2022-9030
A. Chopra, V. S. Dixit
Abstract Collaborative filtering recommender system (CFRS) plays a vital role in today’s e-commerce industry. CFRSs collect ratings from the users and predict recommendations for the targeted product. Conventionally, CFRS uses the user-product ratings to make recommendations. Often these user-product ratings are biased. The higher ratings are called push ratings (PRs) and the lower ratings are called nuke ratings (NRs). PRs and NRs are injected by factitious users with an intention either to aggravate or degrade the recommendations of a product. Hence, it is necessary to investigate PRs or NRs and discard them. In this work, opinion mining approach is applied on textual reviews that are given by users for a product to detect the PRs and NRs. The work also examines the effect of PRs and NRs on the performance of CFRS by evaluating various measures such as precision, recall, F-measure and accuracy.
摘要协同过滤推荐系统(CFRS)在当今的电子商务行业中起着至关重要的作用。cfrs收集用户的评分,并预测目标产品的推荐。通常,CFRS使用用户-产品评级来提出建议。通常这些用户-产品评级是有偏见的。较高的额定值被称为推力额定值(pr),较低的额定值被称为核额定值(nr)。pr和nr是由人为用户注入的,目的是加重或降低产品的推荐。因此,有必要调查pr或nr并丢弃它们。在这项工作中,将意见挖掘方法应用于用户对产品给出的文本评论中,以检测pr和nr。该研究还通过评估精确度、召回率、f值和准确性等各种指标,考察了pr和nr对CFRS性能的影响。
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引用次数: 0
Evaluation and analysis of teaching quality of university teachers using machine learning algorithms 基于机器学习算法的高校教师教学质量评价与分析
IF 3 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-01-01 DOI: 10.1515/jisys-2022-0204
Ying Zhong
Abstract In order to better improve the teaching quality of university teachers, an effective method should be adopted for evaluation and analysis. This work studied the machine learning algorithms and selected the support vector machine (SVM) algorithm to evaluate teaching quality. First, the principles of selecting evaluation indexes were briefly introduced, and 16 evaluation indexes were selected from different aspects. Then, the SVM algorithm was used for evaluation. A genetic algorithm (GA)-SVM algorithm was designed and experimentally analyzed. It was found that the training time and testing time of the GA-SVM algorithm were 23.21 and 7.25 ms, both of which were shorter than the SVM algorithm. In the evaluation of teaching quality, the evaluation value of the GA-SVM algorithm was closer to the actual value, indicating that the evaluation result was more accurate. The average accuracy of the GA-SVM algorithm was 11.64% higher than that of the SVM algorithm (98.36 vs 86.72%). The experimental results verify that the GA-SVM algorithm can have a good application in evaluating and analyzing teaching quality in universities with its advantages in efficiency and accuracy.
摘要为了更好地提高高校教师的教学质量,需要采取有效的方法对高校教师的教学质量进行评价和分析。本工作研究了机器学习算法,选择支持向量机(SVM)算法来评价教学质量。首先,简要介绍了评价指标的选取原则,从不同方面选取了16个评价指标。然后,使用SVM算法进行评价。设计了一种遗传算法-支持向量机算法,并进行了实验分析。结果表明,GA-SVM算法的训练时间为23.21 ms,测试时间为7.25 ms,均短于SVM算法。在教学质量评价中,GA-SVM算法的评价值更接近实际值,说明评价结果更准确。GA-SVM算法的平均准确率比SVM算法高11.64% (98.36 vs 86.72%)。实验结果验证了GA-SVM算法以其高效、准确的优势在高校教学质量评价与分析中具有良好的应用前景。
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引用次数: 0
Development of an intelligent controller for sports training system based on FPGA 基于FPGA的运动训练系统智能控制器的研制
IF 3 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-01-01 DOI: 10.1515/jisys-2022-0260
Yaser M. Abid, N. Kaittan, M. Mahdi, B. I. Bakri, A. Omran, M. Altaee, Sura Khalil Abid
Abstract Training, sports equipment, and facilities are the main aspects of sports advancement. Countries are investing heavily in the training of athletes, especially in table tennis. Athletes require basic equipment for exercises, but most athletes cannot afford the high cost; hence, the necessity for developing a low-cost automated system has increased. To enhance the quality of the athletes’ training, the proposed research focuses on using the enormous developments in artificial intelligence by developing an automated training system that can maintain the training duration and intensity whenever necessary. In this research, an intelligent controller has been designed to simulate training patterns of table tennis. The intelligent controller will control the system that sends the table tennis balls’ intensity, speed, and duration. The system will detect the hand sign that has been previously assigned to different speeds using an image detection method and will work accordingly by accelerating the speed using pulse width modulation techniques. Simply showing the athletes’ hand sign to the system will trigger the artificial intelligent camera to identify it, sending the tennis ball at the assigned speed. The artificial intelligence of the proposed device showed promising results in detecting hand signs with minimum errors in training sessions and intensity. The image detection accuracy collected from the intelligent controller during training was 90.05%. Furthermore, the proposed system has a minimal material cost and can be easily installed and used.
训练、运动器材和设施是体育进步的主要方面。各国正在大力投资于运动员的训练,特别是在乒乓球方面。运动员需要基本的运动设备,但大多数运动员负担不起高昂的费用;因此,开发低成本自动化系统的必要性增加了。为了提高运动员的训练质量,本研究的重点是利用人工智能的巨大发展,开发一种可以随时保持训练时间和强度的自动化训练系统。在本研究中,设计了一个智能控制器来模拟乒乓球的训练模式。智能控制器将控制发送乒乓球的强度、速度和持续时间的系统。该系统将使用图像检测方法检测先前分配给不同速度的手势,并使用脉冲宽度调制技术加速相应的速度。只需向系统显示运动员的手势,就会触发人工智能摄像头进行识别,并以指定的速度发送网球。该设备的人工智能在检测手势方面显示出良好的效果,在训练课程和强度方面的错误最小。训练时从智能控制器采集的图像检测准确率为90.05%。此外,该系统的材料成本最低,易于安装和使用。
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引用次数: 0
Automatic adaptive weighted fusion of features-based approach for plant disease identification 基于特征自适应加权融合的植物病害识别方法
IF 3 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-01-01 DOI: 10.1515/jisys-2022-0247
Kirti, N. Rajpal, V. P. Vishwakarma
Abstract With the rapid expansion in plant disease detection, there has been a progressive increase in the demand for more accurate systems. In this work, we propose a new method combining color information, edge information, and textural information to identify diseases in 14 different plants. A novel 3-branch architecture is proposed containing the color information branch, an edge information branch, and a textural information branch extracting the textural information with the help of the central difference convolution network (CDCN). ResNet-18 was chosen as the base architecture of the deep neural network (DNN). Unlike the traditional DNNs, the weights adjust automatically during the training phase and provide the best of all the ratios. The experiments were performed to determine individual and combinational features’ contribution to the classification process. Experimental results of the PlantVillage database with 38 classes show that the proposed method has higher accuracy, i.e., 99.23%, than the existing feature fusion methods for plant disease identification.
摘要随着植物病害检测的迅速发展,对更精确的系统的需求也在不断增加。在这项工作中,我们提出了一种结合颜色信息、边缘信息和纹理信息的方法来识别14种不同植物的疾病。提出了一种新的包含颜色信息分支、边缘信息分支和纹理信息分支的三分支结构,利用中心差分卷积网络(CDCN)提取纹理信息。选择ResNet-18作为深度神经网络(DNN)的基础架构。与传统的深度神经网络不同,权重在训练阶段自动调整,并提供所有比例中的最佳比例。进行实验以确定单个和组合特征对分类过程的贡献。PlantVillage数据库38个分类的实验结果表明,与现有的特征融合方法相比,该方法具有更高的植物病害识别准确率,达到99.23%。
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引用次数: 1
Predicting medicine demand using deep learning techniques: A review 使用深度学习技术预测药品需求:综述
IF 3 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-01-01 DOI: 10.1515/jisys-2022-0297
Bashaer Abdurahman Mousa, Belal Al-Khateeb
Abstract The supply and storage of drugs are critical components of the medical industry and distribution. The shelf life of most medications is predetermined. When medicines are supplied in large quantities it is exceeding actual need, and long-term drug storage results. If demand is lower than necessary, this has an impact on consumer happiness and medicine marketing. Therefore, it is necessary to find a way to predict the actual quantity required for the organization’s needs to avoid material spoilage and storage problems. A mathematical prediction model is required to assist any management in achieving the required availability of medicines for customers and safe storage of medicines. Artificial intelligence applications and predictive modeling have used machine learning (ML) and deep learning algorithms to build prediction models. This model allows for the optimization of inventory levels, thus reducing costs and potentially increasing sales. Various measures, such as mean squared error, mean absolute squared error, root mean squared error, and others, are used to evaluate the prediction model. This study aims to review ML and deep learning approaches of forecasting to obtain the highest accuracy in the process of forecasting future demand for pharmaceuticals. Because of the lack of data, they could not use complex models for prediction. Even when there is a long history of accessible demand data, these problems still exist because the old data may not be very useful when it changes the market climate.
药品的供应和储存是医疗行业和分销的关键组成部分。大多数药物的保质期是预先确定的。当大量供应的药品超过实际需要时,就会导致药品长期储存。如果需求低于必要水平,这将影响消费者的幸福感和药品营销。因此,有必要找到一种方法来预测组织需要的实际数量,以避免材料损坏和储存问题。需要一个数学预测模型来协助任何管理人员实现客户所需的药品供应和药品的安全储存。人工智能应用和预测建模已经使用机器学习(ML)和深度学习算法来构建预测模型。这种模式允许优化库存水平,从而降低成本并潜在地增加销售。各种度量,如均方误差、平均绝对平方误差、均方根平方误差等,用于评估预测模型。本研究旨在回顾机器学习和深度学习的预测方法,以在预测未来药品需求的过程中获得最高的准确性。由于缺乏数据,他们无法使用复杂的模型进行预测。即使有很长一段可访问的需求数据历史,这些问题仍然存在,因为旧数据在改变市场环境时可能不是很有用。
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引用次数: 0
Computer technology of multisensor data fusion based on FWA–BP network 基于FWA-BP网络的多传感器数据融合计算机技术
IF 3 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-01-01 DOI: 10.1515/jisys-2022-0278
Xiaowei Hai
Abstract Due to the diversity and complexity of data information, traditional data fusion methods cannot effectively fuse multidimensional data, which affects the effective application of data. To achieve accurate and efficient fusion of multidimensional data, this experiment used back propagation (BP) neural network and fireworks algorithm (FWA) to establish the FWA–BP multidimensional data processing model, and a case study of PM2.5 concentration prediction was carried out by using the model. In the PM2.5 concentration prediction results, the trend between the FWA–BP prediction curve and the real curve was basically consistent, and the prediction deviation was less than 10. The average mean absolute error and root mean square error of FWA–BP network model in different samples were 3.7 and 4.3%, respectively. The correlation coefficient R value of FWA–BP network model was 0.963, which is higher than other network models. The results showed that FWA–BP network model could continuously optimize when predicting PM2.5 concentration, so as to avoid falling into local optimum prematurely. At the same time, the prediction accuracy is better with the improvement in the correlation coefficient between real and predicted value, which means, in computer technology of multisensor data fusion, this method can be applied better.
摘要由于数据信息的多样性和复杂性,传统的数据融合方法无法有效融合多维数据,影响了数据的有效应用。为实现多维数据的准确高效融合,本实验采用反向传播(BP)神经网络和烟花算法(FWA)建立了FWA - BP多维数据处理模型,并利用该模型对PM2.5浓度预测进行了案例研究。在PM2.5浓度预测结果中,FWA-BP预测曲线与实际曲线趋势基本一致,预测偏差小于10。FWA-BP网络模型在不同样本中的平均绝对误差和均方根误差分别为3.7和4.3%。FWA-BP网络模型的相关系数R值为0.963,高于其他网络模型。结果表明,FWA-BP网络模型在预测PM2.5浓度时可以持续优化,避免过早陷入局部最优。同时,随着预测值与实测值之间相关系数的提高,预测精度得到了提高,这意味着该方法在多传感器数据融合的计算机技术中可以得到更好的应用。
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引用次数: 0
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Journal of Intelligent Systems
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