Library book intelligent recommendation system based on artificial intelligence

2021 ◽  
pp. 1-6
Author(s):  
Lu Pang
Keyword(s):  
Artificial Intelligence ◽  
Recommendation System ◽  
Absolute Error ◽  
Recall Rate ◽  
Data Set ◽  
Evaluation Data ◽  
Artificial Intelligence Technology ◽  
Collaborative Filtering Algorithm ◽  
Book Evaluation ◽  
Evaluation Matrix

In order to improve the accuracy of intelligent recommendation of library books, an intelligent recommendation system of library books based on artificial intelligence was designed. The system uses artificial intelligence technology to clean up and normalize the data, automatically extracts the user’s historical evaluation data of books, divides the whole user space into several similar user clusters through the similar user clustering module, constructs the user book evaluation matrix according to the historical evaluation data, and uses the hybrid collaborative filtering algorithm which integrates user based and project-based to predict each user a book evaluation matrix of similar user clusters was used to realize the intelligent recommendation of library books, and the recommendation results were displayed to users through the user interface module. The results show that the average absolute error and root mean square error of the system are always the lowest, and the recommendation accuracy is high. When the control parameter is 0.4, the best intelligent book recommendation effect can be obtained; the recommended recall rate is not affected by the sparse density of the data set, and the stability is strong.

scholarly journals Application of Artificial Intelligence in Diagnosis of Craniopharyngioma

2022 ◽  
Vol 12 ◽  
Author(s):  
Caijie Qin ◽  
Wenxing Hu ◽  
Xinsheng Wang ◽  
Xibo Ma
Keyword(s):  
Artificial Intelligence ◽  
Clinical Diagnosis ◽  
Great Influence ◽  
Imbalanced Data ◽  
Learning Model ◽  
Future Research ◽  
Data Set ◽  
Congenital Brain Tumor ◽  
Pituitary Dysfunction ◽  
Artificial Intelligence Technology

Craniopharyngioma is a congenital brain tumor with clinical characteristics of hypothalamic-pituitary dysfunction, increased intracranial pressure, and visual field disorder, among other injuries. Its clinical diagnosis mainly depends on radiological examinations (such as Computed Tomography, Magnetic Resonance Imaging). However, assessing numerous radiological images manually is a challenging task, and the experience of doctors has a great influence on the diagnosis result. The development of artificial intelligence has brought about a great transformation in the clinical diagnosis of craniopharyngioma. This study reviewed the application of artificial intelligence technology in the clinical diagnosis of craniopharyngioma from the aspects of differential classification, prediction of tissue invasion and gene mutation, prognosis prediction, and so on. Based on the reviews, the technical route of intelligent diagnosis based on the traditional machine learning model and deep learning model were further proposed. Additionally, in terms of the limitations and possibilities of the development of artificial intelligence in craniopharyngioma diagnosis, this study discussed the attentions required in future research, including few-shot learning, imbalanced data set, semi-supervised models, and multi-omics fusion.

Semantic Tags and Neutral Network Based Personalized Advertisement Recommendation System for Movies

2012 ◽  
Vol 488-489 ◽  
pp. 1727-1731
Author(s):  
Wei Du ◽  
Jun Liang Chen
Keyword(s):  
Recommendation System ◽  
Rapid Development ◽  
Internet Technology ◽  
Neutral Network ◽  
User Preference ◽  
Simulation Experiments ◽  
Data Set ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm ◽  
Personalized Advertisement

With the rapid development of information especially internet technology, people have to choose the most suitable goods without any experience, so the recommendation system is seriously required. Yet no research on advertisement recommendation system for movie play is presented. Regarding this problem, the paper introduces the theory of semantic computing and annotates the semantic tags from the movie slices and the candidate advertisements, the potential preferences on them are predicted with neutral network model trained by some data set predefined. The user preference model and the predicting workflow are described in detail. Finally, the MovieLens dataset is employed to validate the validity of the system designed. The results of simulation experiments prove that the technology proposed can not only satisfy the requirement of matched advertisement recommendation but also outperform the traditional collaborative filtering algorithm.

scholarly journals Personalized News Recommendation and Simulation Based on Improved Collaborative Filtering Algorithm

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Kunni Han
Keyword(s):  
Collaborative Filtering ◽  
Nearest Neighbor ◽  
Data Preprocessing ◽  
Recall Rate ◽  
Personalized Recommendation ◽  
Recommendation Algorithm ◽  
Data Set ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm ◽  
Recommendation Accuracy

Faced with massive amounts of online news, it is often difficult for the public to quickly locate the news they are interested in. The personalized recommendation technology can dig out the user’s interest points according to the user’s behavior habits, thereby recommending the news that may be of interest to the user. In this paper, improvements are made to the data preprocessing stage and the nearest neighbor collection stage of the collaborative filtering algorithm. In the data preprocessing stage, the user-item rating matrix is filled to alleviate its sparsity. The label factor and time factor are introduced to make the constructed user preference model have a better expression effect. In the stage of finding the nearest neighbor set, the collaborative filtering algorithm is combined with the dichotomous K-means algorithm, the user cluster matching the target user is selected as the search range of the nearest neighbor set, and the similarity measurement formula is improved. In order to verify the effectiveness of the algorithm proposed in this paper, this paper selects a simulated data set to test the performance of the proposed algorithm in terms of the average absolute error of recommendation, recommendation accuracy, and recall rate and compares it with the user-based collaborative filtering recommendation algorithm. In the simulation data set, the algorithm in this paper is superior to the traditional algorithm in most users. The algorithm in this paper decomposes the sparse matrix to reduce the impact of data sparsity on the traditional recommendation algorithm, thereby improving the recommendation accuracy and recall rate of the recommendation algorithm and reducing the recommendation error.

scholarly journals CONSTRUCTION OF ENTERPRISE ECONOMIC DECISION RECOMMENDATION SYSTEM BASED ON COMBINED ASSOCIATION ANALYSIS MODEL

2018 ◽  
Vol 48 (4) ◽  
pp. 249-254
Author(s):  
S. J. ZHAO
Keyword(s):  
Decision Making ◽  
Association Analysis ◽  
Recommendation System ◽  
Recall Rate ◽  
Test Method ◽  
Analysis Model ◽  
Economic Decision ◽  
Economic Decision Making ◽  
Depth Study ◽  
Collaborative Filtering Algorithm

With the development of the information age and the network economy, enterprises need more and more decisions, and the difficulty and complexity of decision-making are constantly improving. The traditional centralized decision-making is no longer in line with the requirements of current social and economic development. Therefore, based on the in-depth study of conventional algorithms, this paper constructs a portfolio association analysis model for enterprise economic decision-making recommendation system, and uses the off-line test method to test the construction model. The test results show that the accuracy and recall rate calculated by the combination algorithm are higher than the common collaborative filtering algorithm. The enterprise economic decision recommendation system realizes the integration of economic mathematical model, operational research method and decision-making means, greatly improving the enterprise. The accuracy of scientifically feasible decision-making has improved the efficiency of business operations to a certain extent.

Design and Application of Handicraft Recommendation System Based on Improved Hybrid Algorithm

2022 ◽  
Author(s):  
Yong Yang ◽  
Young Chun ko
Keyword(s):  
Collaborative Filtering ◽  
Hybrid Algorithm ◽  
Recommendation System ◽  
Sparse Matrix ◽  
Rapid Development ◽  
Data Set ◽  
Improve Accuracy ◽  
Collaborative Filtering Algorithm ◽  
Candidate Set ◽  
Design And Application

With the rapid development of online e-commerce, traditional collaborative filtering algorithms have the disadvantages of data set reduction and sparse matrix filling cannot meet the requirements of users. This paper takes handicrafts as an example to propose the design and application of handicraft recommendation system based on an improved hybrid algorithm. Based on the theory of e-commerce system, through the traditional collaborative filtering algorithm of users, the personalized e-commerce system of hybrid algorithm is designed and analyzed. The personalized e-commerce system based on hybrid algorithm is further proposed. The component model of the business recommendation system and the specific steps of the improved hybrid algorithm based on user information are given. Finally, an experimental analysis of the improved hybrid algorithm is carried out. The results show that the algorithm can effectively improve the effectiveness and exemption of recommending handicrafts. What’s more, it can reduce the user item ratings of candidate set and improve accuracy of the forecast recommendation.

scholarly journals A Shadow Capture Deep Neural Network for Underwater Forward-Looking Sonar Image Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Taowen Xiao ◽  
Zijian Cai ◽  
Cong Lin ◽  
Qiong Chen
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Wireless Communication ◽  
Lightweight Design ◽  
Object Identification ◽  
Feature Representation ◽  
Data Set ◽  
Evaluation Standard ◽  
Artificial Intelligence Technology ◽  
Underwater Wireless Communication

Image sonar is a widely used wireless communication technology for detecting underwater objects, but the detection process often leads to increased difficulty in object identification due to the lack of equipment resolution. In view of the remarkable results achieved by artificial intelligence techniques in the field of underwater wireless communication research, we propose an object detection method based on convolutional neural network (CNN) and shadow information capture to improve the object recognition and localization effect of underwater sonar images by making full use of the shadow information of the object. We design a Shadow Capture Module (SCM) that can capture the shadow information in the feature map and utilize them. SCM is compatible with CNN models that have a small increase in parameters and a certain degree of portability, and it can effectively alleviate the recognition difficulties caused by the lack of device resolution through referencing shadow features. Through extensive experiments on the underwater sonar data set provided by Pengcheng Lab, the proposed method can effectively improve the feature representation of the CNN model and enhance the difference between class and class features. Under the main evaluation standard of PASCAL VOC 2012, the proposed method improved from an average accuracy (mAP) of 69.61% to 75.73% at an IOU threshold of 0.7, which exceeds many existing conventional deep learning models, while the lightweight design of our proposed module is more helpful for the implementation of artificial intelligence technology in the field of underwater wireless communication.

scholarly journals A Study of Automatic Judgment of Food Color and Cooking Conditions with Artificial Intelligence Technology

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1128
Author(s):  
Chern-Sheng Lin ◽  
Yu-Ching Pan ◽  
Yu-Xin Kuo ◽  
Ching-Kun Chen ◽  
Chuen-Lin Tien
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Color Space ◽  
Color Difference ◽  
Color Space Conversion ◽  
Before And After ◽  
Artificial Intelligence Technology ◽  
Artificial Intelligence Algorithm ◽  
Time Image ◽  
Cooking Conditions

In this study, the machine vision and artificial intelligence algorithms were used to rapidly check the degree of cooking of foods and avoid the over-cooking of foods. Using a smart induction cooker for heating, the image processing program automatically recognizes the color of the food before and after cooking. The new cooking parameters were used to identify the cooking conditions of the food when it is undercooked, cooked, and overcooked. In the research, the camera was used in combination with the software for development, and the real-time image processing technology was used to obtain the information of the color of the food, and through calculation parameters, the cooking status of the food was monitored. In the second year, using the color space conversion, a novel algorithm, and artificial intelligence, the foreground segmentation was used to separate the vegetables from the background, and the cooking ripeness, cooking unevenness, oil glossiness, and sauce absorption were calculated. The image color difference and the distribution were used to judge the cooking conditions of the food, so that the cooking system can identify whether or not to adopt partial tumbling, or to end a cooking operation. A novel artificial intelligence algorithm is used in the relative field, and the error rate can be reduced to 3%. This work will significantly help researchers working in the advanced cooking devices.

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