Journal of Intelligent Systems最新文献_第2页

A systematic literature review of undiscovered vulnerabilities and tools in smart contract technology 对智能合约技术中未被发现的漏洞和工具进行系统的文献综述

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Journal of Intelligent Systems Pub Date : 2023-01-01 DOI: 10.1515/jisys-2023-0038

Oualid Zaazaa, Hanan El Bakkali

引用次数: 0

Analyzing SQL payloads using logistic regression in a big data environment 在大数据环境中使用逻辑回归分析SQL有效负载

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Journal of Intelligent Systems Pub Date : 2023-01-01 DOI: 10.1515/jisys-2023-0063

O. Shareef, Rehab Flaih Hasan, Ammar Hatem Farhan

{"title":"Analyzing SQL payloads using logistic regression in a big data environment","authors":"O. Shareef, Rehab Flaih Hasan, Ammar Hatem Farhan","doi":"10.1515/jisys-2023-0063","DOIUrl":"https://doi.org/10.1515/jisys-2023-0063","url":null,"abstract":"Abstract Protecting big data from attacks on large organizations is essential because of how vital such data are to organizations and individuals. Moreover, such data can be put at risk when attackers gain unauthorized access to information and use it in illegal ways. One of the most common such attacks is the structured query language injection attack (SQLIA). This attack is a vulnerability attack that allows attackers to illegally access a database quickly and easily by manipulating structured query language (SQL) queries, especially when dealing with a big data environment. To address these risks, this study aims to build an approach that acts as a middle protection layer between the client and database server layers and reduces the time consumed to classify the SQL payload sent from the user layer. The proposed method involves training a model by using a machine learning (ML) technique for logistic regression with the Spark ML library that handles big data. An experiment was conducted using the SQLI dataset. Results show that the proposed approach achieved an accuracy of 99.04, a precision of 98.87, a recall of 99.89, and an F-score of 99.04. The time taken to identify and prevent SQLIA is 0.05 s. Our approach can protect the data by using the middle layer. Moreover, using the Spark ML library with ML algorithms gives better accuracy and shortens the time required to determine the type of request sent from the user layer.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"137 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86671255","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

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

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Journal of Intelligent Systems Pub Date : 2023-01-01 DOI: 10.1515/jisys-2022-0281

Junlong Shi, Xiaofeng Zhao

{"title":"Anti-leakage method of network sensitive information data based on homomorphic encryption","authors":"Junlong Shi, Xiaofeng Zhao","doi":"10.1515/jisys-2022-0281","DOIUrl":"https://doi.org/10.1515/jisys-2022-0281","url":null,"abstract":"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.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"29 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86712507","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

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

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Journal of Intelligent Systems Pub Date : 2023-01-01 DOI: 10.1515/jisys-2022-0244

A. Gattal, Chawki Djeddi, Faycel Abbas, I. Siddiqi, Bouderah Brahim

{"title":"A new method for writer identification based on historical documents","authors":"A. Gattal, Chawki Djeddi, Faycel Abbas, I. Siddiqi, Bouderah Brahim","doi":"10.1515/jisys-2022-0244","DOIUrl":"https://doi.org/10.1515/jisys-2022-0244","url":null,"abstract":"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.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"87 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81093418","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: 1

Reinforcement learning with Gaussian process regression using variational free energy 基于变分自由能的高斯过程回归强化学习

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Journal of Intelligent Systems Pub Date : 2023-01-01 DOI: 10.1515/jisys-2022-0205

Kiseki Kameda, F. Tanaka

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

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Journal of Intelligent Systems 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

{"title":"Development of an intelligent controller for sports training system based on FPGA","authors":"Yaser M. Abid, N. Kaittan, M. Mahdi, B. I. Bakri, A. Omran, M. Altaee, Sura Khalil Abid","doi":"10.1515/jisys-2022-0260","DOIUrl":"https://doi.org/10.1515/jisys-2022-0260","url":null,"abstract":"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.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"34 10 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82780818","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

On numerical characterizations of the topological reduction of incomplete information systems based on evidence theory 基于证据理论的不完全信息系统拓扑约简的数值表征

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Journal of Intelligent Systems Pub Date : 2023-01-01 DOI: 10.1515/jisys-2022-0214

Changqing Li, Yanlan Zhang

引用次数: 0

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

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Journal of Intelligent Systems Pub Date : 2023-01-01 DOI: 10.1515/jisys-2022-0297

Bashaer Abdurahman Mousa, Belal Al-Khateeb

{"title":"Predicting medicine demand using deep learning techniques: A review","authors":"Bashaer Abdurahman Mousa, Belal Al-Khateeb","doi":"10.1515/jisys-2022-0297","DOIUrl":"https://doi.org/10.1515/jisys-2022-0297","url":null,"abstract":"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.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"48 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90279097","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

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

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Journal of Intelligent Systems Pub Date : 2023-01-01 DOI: 10.1515/jisys-2022-0278

Xiaowei Hai

{"title":"Computer technology of multisensor data fusion based on FWA–BP network","authors":"Xiaowei Hai","doi":"10.1515/jisys-2022-0278","DOIUrl":"https://doi.org/10.1515/jisys-2022-0278","url":null,"abstract":"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.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"13 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85088144","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

Application study of ant colony algorithm for network data transmission path scheduling optimization 蚁群算法在网络数据传输路径调度优化中的应用研究

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Journal of Intelligent Systems Pub Date : 2023-01-01 DOI: 10.1515/jisys-2022-0277

Peng Xiao

{"title":"Application study of ant colony algorithm for network data transmission path scheduling optimization","authors":"Peng Xiao","doi":"10.1515/jisys-2022-0277","DOIUrl":"https://doi.org/10.1515/jisys-2022-0277","url":null,"abstract":"Abstract With the rapid development of the information age, the traditional data center network management can no longer meet the rapid expansion of network data traffic needs. Therefore, the research uses the biological ant colony foraging behavior to find the optimal path of network traffic scheduling, and introduces pheromone and heuristic functions to improve the convergence and stability of the algorithm. In order to find the light load path more accurately, the strategy redefines the heuristic function according to the number of large streams on the link and the real-time load. At the same time, in order to reduce the delay, the strategy defines the optimal path determination rule according to the path delay and real-time load. The experiments show that under the link load balancing strategy based on ant colony algorithm, the link utilization ratio is 4.6% higher than that of ECMP, while the traffic delay is reduced, and the delay deviation fluctuates within ±2 ms. The proposed network data transmission scheduling strategy can better solve the problems in traffic scheduling, and effectively improve network throughput and traffic transmission quality.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"10 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86173934","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