International Journal of Information Technologies and Systems Approach最新文献_第4页

Attention-Based Time Sequence and Distance Contexts Gated Recurrent Unit for Personalized POI Recommendation 基于注意力的时间序列和距离上下文门控递归单元的个性化POI推荐

IF 0.8

International Journal of Information Technologies and Systems Approach Pub Date : 2023-07-10 DOI: 10.4018/ijitsa.325790

Yanli Jia

引用次数: 0

Trend-Aware Data Imputation Based on Generative Adversarial Network for Time Series 基于时间序列生成对抗网络的趋势感知数据推断

IF 0.8

International Journal of Information Technologies and Systems Approach Pub Date : 2023-06-27 DOI: 10.4018/ijitsa.325212

Han Li, Zhenxiong Liu, Jixiang Niu, Zhongguo Yang, Sikandar Ali

{"title":"Trend-Aware Data Imputation Based on Generative Adversarial Network for Time Series","authors":"Han Li, Zhenxiong Liu, Jixiang Niu, Zhongguo Yang, Sikandar Ali","doi":"10.4018/ijitsa.325212","DOIUrl":"https://doi.org/10.4018/ijitsa.325212","url":null,"abstract":"To solve the problems of generative adversarial network (GAN)-based imputation method for time series, which are ignoring the implied trends in data and using multi-stage training that may lead to high training complexity, this article proposes a trend-aware data imputation method based on GAN (TrendGAN). It implements an end-to-end training using de-noising auto-encoder (DAE). It also uses bidirectional gated recurrent unit (Bi-GRU) in the generator model to consider the bi-directional characteristics and supplement the features lost by de-noising auto-encoder and improves the discriminator's ability using Bi-GRU and hint vector. The authors conducted experiments on four real datasets. The results showed that all components introduced into the method contribute to enhancing the imputation accuracy, and the MSE values of TrendGAN are much lower than those of baseline methods when dealing with time series with random and continuous missing patterns. That is, TrendGAN is suitable for data imputation in complex scenarios with two missing patterns coexist, such as electric power and transportation.","PeriodicalId":52019,"journal":{"name":"International Journal of Information Technologies and Systems Approach","volume":" ","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43887165","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

Sentiment Classification of Social Network Text Based on AT-BiLSTM Model in a Big Data Environment 大数据环境下基于AT-BiLSTM模型的社交网络文本情感分类

International Journal of Information Technologies and Systems Approach Pub Date : 2023-06-21 DOI: 10.4018/ijitsa.324808

Jinjun Liu

引用次数: 0

Design of Healthcare Lighting in Medical Centers Based on Power Carrier Communication 基于电力载波通信的医疗中心医疗照明设计

IF 0.8

International Journal of Information Technologies and Systems Approach Pub Date : 2023-06-15 DOI: 10.4018/ijitsa.324748

Yan Huang, Yongfeng Zhang

引用次数: 0

Dynamic Interaction and Visualization Design of Database Information Based on Artificial Intelligence 基于人工智能的数据库信息动态交互与可视化设计

IF 0.8

International Journal of Information Technologies and Systems Approach Pub Date : 2023-06-15 DOI: 10.4018/ijitsa.324749

Yin Fan

{"title":"Dynamic Interaction and Visualization Design of Database Information Based on Artificial Intelligence","authors":"Yin Fan","doi":"10.4018/ijitsa.324749","DOIUrl":"https://doi.org/10.4018/ijitsa.324749","url":null,"abstract":"With the explosive growth of data, people's demand for data analysis has become more intense. Although modern technology can collect a large amount of data, the collected original data is often useless and contains little information. How to extract useful information from massive amounts of data has become an urgent problem. Driven by artificial intelligence (AI) technology and personalized consumption demand of users, this article puts forward a dynamic interactive and visualization algorithm of e-business database information based on an improved collaborative filtering (CF) algorithm to help enterprises more efficiently mine the required potential customer groups from massive customer data and log data. Experiment results prove the effectiveness of the model and algorithm. Data mining (DM) technology is applied to the user access control model in this model. First, the maximum forward reference sequence of mobile e-business groups is mined by data technology. Then a user access control model is established according to this sequence to control user access so enterprises can formulate reasonable marketing strategies based on this knowledge.","PeriodicalId":52019,"journal":{"name":"International Journal of Information Technologies and Systems Approach","volume":"1 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70460154","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

Unmanned Bicycle Balance Control Based on Tunicate Swarm Algorithm Optimized BP Neural Network PID 基于迭代群算法优化BP神经网络PID的无人自行车平衡控制

IF 0.8

International Journal of Information Technologies and Systems Approach Pub Date : 2023-06-13 DOI: 10.4018/ijitsa.324718

Yun Li, Yufei Wu, Xiaohui Zhang, Xinglin Tan, Wei Zhou

{"title":"Unmanned Bicycle Balance Control Based on Tunicate Swarm Algorithm Optimized BP Neural Network PID","authors":"Yun Li, Yufei Wu, Xiaohui Zhang, Xinglin Tan, Wei Zhou","doi":"10.4018/ijitsa.324718","DOIUrl":"https://doi.org/10.4018/ijitsa.324718","url":null,"abstract":"In this study, the authors introduce a novel approach that leverages the tunicate swarm algorithm (TSA) to optimize proportional-integral-derivative (PID) controller based on a back propagation (BP) neural network. The core objective of the approach is to manage and counteract uncertainties and disturbance that may jeopardize the balance and stability of self-driving bicycles in operation. By using the self-learning capabilities of BP neural networks, the controller can dynamically adjust PID parameters in real time. This enables an enhanced robustness and reliability during operation. Further bolstering the efficiency of our controller, the authors use the TSA to optimize the initial weights of a neural network. This effectively mitigates the commonly associated with slow convergence and being entrapped in local minima. Through simulation and experimentation, the findings reveal that the TSA-optimized BP neural network PID controller dramatically improves dynamic performance and robustness. It also proficiently manages changes in the environment such as wind and ground bumps. Therefore, the proposed controller design offers an effective solution to the balancing problem of self-driving bicycles and paves the way for a promising future in designing versatile controllers with broad application potential.","PeriodicalId":52019,"journal":{"name":"International Journal of Information Technologies and Systems Approach","volume":" ","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49283169","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

Design of Graphic Design Assistant System Based on Artificial Intelligence 基于人工智能的平面设计辅助系统设计

IF 0.8

International Journal of Information Technologies and Systems Approach Pub Date : 2023-06-13 DOI: 10.4018/ijitsa.324761

Yanqi Liu

{"title":"Design of Graphic Design Assistant System Based on Artificial Intelligence","authors":"Yanqi Liu","doi":"10.4018/ijitsa.324761","DOIUrl":"https://doi.org/10.4018/ijitsa.324761","url":null,"abstract":"With the development of technology, graphic design tools are becoming more and more perfect, which allows graphic designers to realize their dream designs, achieve more special effects, and thus expand their conceptual choices. The application of various new technologies in graphic design can promote the development of the graphic design industry. The emergence of artificial intelligence (AI) has broken through the layout design in traditional graphic design. In this article, the author proposes the creation of a graphic design assistant system based on AI drawing on the deep learning (DL) theory. According to the DL theory, the image is segmented by the class variance. The voxelized image matrix of a two-dimensional (2D) model is input into a convolution-automatic encoder (CAE) as input data. The input data first pass through the convolution layer of the CAE, which mainly completes the mapping of features. The research results show that the average aesthetic evaluation of the system design works in this study is higher than that of CAD software and PS software, and the total average score is as high as 8.788, which shows that the system design works in this study are more in line with the requirements of professional understanding.","PeriodicalId":52019,"journal":{"name":"International Journal of Information Technologies and Systems Approach","volume":"303 3","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41298456","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

Machine Learning-Assisted Diagnosis Model for Chronic Obstructive Pulmonary Disease 慢性阻塞性肺病的机器学习辅助诊断模型

IF 0.8

International Journal of Information Technologies and Systems Approach Pub Date : 2023-06-13 DOI: 10.4018/ijitsa.324760

Yongfu Yu, Nannan Du, Zhongteng Zhang, Weihong Huang, Min Li

{"title":"Machine Learning-Assisted Diagnosis Model for Chronic Obstructive Pulmonary Disease","authors":"Yongfu Yu, Nannan Du, Zhongteng Zhang, Weihong Huang, Min Li","doi":"10.4018/ijitsa.324760","DOIUrl":"https://doi.org/10.4018/ijitsa.324760","url":null,"abstract":"Chronic obstructive pulmonary disease (COPD) is a long-term, irreversible, and progressive respiratory disease that often leads to lung function decline. Pulmonary function tests (PFTs) provide valuable information for diagnosing COPD; however, they are underutilised in clinical practice, with only a subset of test values being used for decision making. The final clinical diagnosis requires combining PFT results with patient information, symptoms, and other tests, such as imaging and blood analysis. This study aims to comprehensively utilise all the testing information in PFTs to assist in the diagnosis of COPD. Various machine learning models, such as logistic regression, support vector machine (SVM), k-nearest neighbour (KNN), random forest, decision tree, and XGBoost, have been employed to establish COPD diagnosis assistance models. The XGBoost model, trained with features extracted by the group LASSO algorithm, achieved the best performance, with an area under the receiver operating characteristic curve (ROC) of 0.90, 88.6% accuracy, and 98.5% sensitivity. This model can assist doctors in the clinical diagnosis and early prediction of COPD.","PeriodicalId":52019,"journal":{"name":"International Journal of Information Technologies and Systems Approach","volume":" ","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49379462","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

Condition Monitoring and Analysis Method of Smart Substation Equipment Based on Deep Learning in Power Internet of Things 电力物联网中基于深度学习的智能变电站设备状态监测与分析方法

IF 0.8

International Journal of Information Technologies and Systems Approach Pub Date : 2023-06-09 DOI: 10.4018/ijitsa.324519

Lishuo Zhang, Zhu-xing Ma, Hao Gu, Zi-zhong Xin, Pengcheng Han

引用次数: 0

BTCBMA Online Education Course Recommendation Algorithm Based on Learners' Learning Quality 基于学习者学习质量的BTCBMA在线教育课程推荐算法

IF 0.8

International Journal of Information Technologies and Systems Approach Pub Date : 2023-06-09 DOI: 10.4018/ijitsa.324101

Yanli Jia

{"title":"BTCBMA Online Education Course Recommendation Algorithm Based on Learners' Learning Quality","authors":"Yanli Jia","doi":"10.4018/ijitsa.324101","DOIUrl":"https://doi.org/10.4018/ijitsa.324101","url":null,"abstract":"To address the problems of existing online education curriculum recommendation methods such as low recommendation accuracy, an online education course recommendation algorithm (BTCBMA) considering learner learning quality is proposed. Firstly, the BERT model is combined with the TextCNN model to implement the preliminary extraction of text features. Secondly, the convolution neural networks and BiLSTM networks are used to capture deep features and temporal features in data. Finally, a multi-head attention mechanism is used to extract key information from learner interaction sequences, review texts, and curriculum multiple attributes. Experiments demonstrate that the accuracy, precision, recall, and F1 values of the proposed online course recommendation method in the MOOC dataset are 0.224, 0.241, 0.237, and 0.239, respectively, while in the CN dataset are 0.217, 0.239, 0.227, and 0.233, respectively, and the performance of the proposed method in online education course recommendation is significantly superior to the compared methods. For learners in online learning systems, the proposed method can effectively recommend high-quality courses, which is of great significance for improving the learning quality and learning efficiency of learners.","PeriodicalId":52019,"journal":{"name":"International Journal of Information Technologies and Systems Approach","volume":" ","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47637876","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