Journal of Computer Engineering & Information TechnologyISSN : 2324-9307

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Commentary, J Comput Eng Inf Technol Vol: 11 Issue: 8

Intelligent Target Detection in Complex Scene Image

Harish Kumar*

Department of Computer Engineering, University of Viswa Bharati, Santiniketan, West Bengal, India

*Corresponding Author:Harish Kumar
Department of Computer Engineering, University of Viswa Bharati, Santiniketan, West Bengal, India
Email: [email protected]

Received date: 05 July, 2022, Manuscript No. JCEIT-22-61682;
Editor assigned date: 07 July, 2022; PreQC No. JCEIT-22-61682 (PQ);
Reviewed date: 18 July, 2022, QC No. JCEIT-22-61682;
Revised date: 08 August, 2022, Manuscript No. JCEIT-22-61682 (R);
Published date: 23 August, 2022, DOI: 10.4172/jceit.1000247.

Citation: Kumar H (2022) Intelligent Target Detection in Complex Scene Image. J Comput Eng Inf Technol 11:08

Keywords: Cloud Computing

Description

In order to solve the problem of tourism information overload caused by the rapid development of tourism and the Internet era, the author proposes a tourist attraction recommendation model based on deep learning. Convolutional Neural Network (CNN) is used to extract the sentiment of text comments; the Pearson similarity formula is used to calculate similar user groups and the mean absolute error (MAE) is used to evaluate the resulting error. Compare with traditional collaborative filtering methods. Experimental results show that: the MAE value is smaller than the MAE value of the collaborative filtering method, indicating that considering tourists’ behavioral information, contextual information, and emotional factors in comments can effectively improve the accuracy of recommendation, as the data volume of the test set increased from 250 to 2000; although there was an increase in the MAE value, the overall trend showed a downward trend, indicating that the quality of the model can be more fully verified when the data volume is large. The model proposed by the author can effectively reduce the prediction error and improve the efficiency of tourist attractions recommendation. The rapid growth of information technology, the need for deepening of the information technology industry and information technology has become increasingly strong. In the data age, which is overloaded with data, users can not quickly find the data they are interested in. Therefore, the tourism consulting industry has been designed to meet the needs of consumers and provide customer satisfaction. One-person travel advertising has been widely used because it integrates referrals into the tourism industry, allowing consumers to make informed personal decisions, and provide their favorite and most accurate products. Research costs. The design recommendations are effective based on an analysis of user data behavior and provide users with the quality of the model as they see fit. Recommendations can be divided by data: user behavior data, data usage data, content data, and social network data. In recent years, many scientists have proposed hybrid technology-approved and restricted-based technology to improve the technology performance of feedback and to ensure the use of multiple restrictions.

Development of Cloud Computing

As personal tourism referral technology has become a hotspot for research and industry research, the author evaluates referral technology, including information on the behavior of consumers, consumer information and content and social network information years. At the same time, the latest advances in related activities have been studied, highlighting the use of technology that can improve performance and limitations-based approval high-performance technology that meets various options. Currently, the most widely used training models in the field of communication include multilayer sensors, automatic encoders, repeat neural networks, circulatory neural networks, and interactive interactions alk different versions. The rapid development of information technology such as the Internet, big data and cloud computing, people have expanded to big data in the environment around. Big data contains a lot of information and knowledge, which allows people to access big data in a short amount of time. At the same time, however, the negative impact of cracked data is that “broadcast data” is problematic because it is difficult for users to get still content. Importance of hard data when encountering large data. In terms of data filtering technology, the conventional system solution of data overload by providing users with personal quality content has become an important technology in various areas of application and is focused on research. Technically, there are two consensus points: a combination filter based on consensus and a consensus based concept. The first is based on the relationship of user impact and the latter focuses on the appropriate ranking by the characteristics of the content. In recent years, through in-depth research, the system has become increasingly important to many scientists’ ability to study and represent the negative effects of consumer products and provide essential products of consumers and products. Although in-depth training works very well to facilitate the study of the secrets of users and products, and to advance the level of research and application of recommendations, it is difficult to obtain get enough information of users to choose from in a variety of different, low starting cold and Other problems, the problem still exists. At the same time, model approval determines everything on its own in determining consumer preferences, and it is not possible to model product relationships in chronological order. Because he could not determine the status of his system. Learning-improvement strategies have led to advances in game and robotics management, new breakthroughs in science in the age of intelligence and new ways to explore research in a commentary. Reinforcement learning combined with deep learning methods has the ability to process large-scale data and discover and extract low-level features, so as to achieve specific goals more accurately. As an Interactive Recommendation (IR) method, the recommendation model based on reinforcement learning can update the recommendation strategy by interacting with users in real time and obtaining real feedback from users, compared with traditional static methods; it is more in line with realistic recommendation scenarios. At the same time, since reinforcement learning problems are usually normalized as Markov Decision Process (MDP), such models have the natural characteristics of modeling user behavior sequences, which can fully characterize the sequence features and capture users’ dynamic preferences. In addition, the setting of the exploration mechanism can enable the agent to fully explore the state and action space, which improves the diversity of recommendation results to a certain extent; finally, since this type of model often maximizes the cumulative revenue of the recommendation system, that is, the long-term feedback of users, updating the recommendation strategy as an optimization goal can improve the long-term satisfaction of users to a certain extent.

Reinforcement Learning

Reinforcement learning can dynamically obtain user behavior information, incorporating the latest preference information in real time, more and more reinforcement learning is currently being used in news, e-commerce, medical and other fields. Among them, tourism is one of the entertainment items involved in people’s life, and there are few research studies on the recommendation of tourist attractions. The inverse reinforcement learning is applied to the recommendation of tourist attractions, using the user’s past selection order of attractions and the current scene context to understand the user’s preferences and establishes a preference learning model that takes into account the timing of commodity consumption, then further use the inverse reinforcement learning method for tourist attraction recommendation. Recommendations for tourism always ignore information about tourists, characteristics of tourists, and tourists, and make predictions based on a survey of tourists and tourist destinations. The advantages of tour recommendations can be improved by identifying features and descriptive information only using a collapsible neural network. In order to differentiate between different features in the video, the author proposes an approved multimodal algorithm based on a multimodal in-depth study to obtain a wide range of data added by video. A combination of neural frequencies is used to provide users and video recordings, and long-term and short-term memory is used to generate historical data to provide users movies correctly. The battery and finally improve the accuracy of the instructions. The author has developed models of travel recommendations as the rupture of the neural network interferes with excessive travel information. In the past, recommendations for tourists were rarely considered in the recommendations for tourists, but these recommendations contain the notion of tourism, which is the basis of reason important for others to measure. The authors combine the connections of neural networks and filtering methods to obtain and distribute features through the connections of neural networks and estimate user rating using the coax filtering method-filtering. Among them, decomposing the role in the agreement would be to alleviate the problem of data thinning of the disrupted neural network sharing method, which can preserve the shared knowledge and personal experience advantage of the shared filter method. For the most commonly used recommendations, the key concept of integrated filters is to calculate the compatibility of users or products to complete the agreement. A consumer sharing algorithm filter algorithm counts the similarities of users, creates proximity user groups, and then provides product satisfaction to the user group close to each other; the product-based filter approval algorithm takes into account the similarity of the products; the standard-based filter approval algorithm combines some smart models, train, and test data so that the user can get the desired information better when making the recommendations.

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