Intelligent Information Service Research Articles

Development Status of Intelligent Traffic Management System

The intelligent traffic management system is the most important part of the intelligent traffic system. The research and analysis of the development status of the intelligent traffic management system can provide a research basis for its future development. This paper studies the three subsystems of the intelligent traffic management system: the intelligent traffic monitoring system, the By comparing the research methods used by scholars in different time periods for intelligent information service systems and signal control systems, it is found that with the application of technologies such as the Internet of Vehicles, big data, Internet of Things, and deep learning, the level of intelligent traffic management system management is constantly rising.

Enhanced computer network security assessment through employing an integrated LogTODIM-TOPSIS technique under interval neutrosophic sets

In the context of the development of the new era, computer network technology has become an indispensable and important technological means in people’s daily work and life. Through network technology, information resources can be collected, integrated, processed, and applied, thereby improving information analysis and mining capabilities, constructing big data environments for various industries, providing convenient and fast intelligent information services, and promoting social transformation and development. However, in practical development, network security issues seriously affect information security and social stability, and computer viruses and hackers pose a huge threat to computer systems. The computer network security evaluation is the MAGDM problems. Recently, the Logarithmic TODIM (LogTODIM) and TOPSIS technique has been utilized to cope with MAGDM issues. The interval neutrosophic sets (INSs) are utilized as a technique for characterizing uncertain information during the computer network security evaluation. In this paper, the interval neutrosophic number Logarithmic TODIM-TOPSIS (INN-LogTODIM-TOPSIS) technique is conducted to solve the MAGDM under INSs. Finally, a numerical case study for computer network security evaluation is utilized to validate the proposed technique. The prime contributions of this paper are put forward: (1) The entropy technique based on score values and accuracy value are conducted to obtain weight information under INSs; (2) an integrated INN-LogTODIM-TOPSIS technique is conducted to put forward the MAGDM issue; (3) An illustrative example for computer network security evaluation has been accomplished to put forward the INN-LogTODIM-TOPSIS technique.

Information Service Innovation in College Libraries Based on Big Data Analysis

Abstract This paper first proposes a real-time recommendation technique based on user images and utilizes multi-dimensional small data fusion for user image construction, which in turn gives a personalized wisdom service recommendation structure. Secondly, user long and short-term preference fusion is carried out by introducing a self-attention mechanism and GRU network, and then personalized retrieval recommendation based on user long and short-term preference is realized. Finally, an investigation is carried out to determine the effectiveness of the personalized wisdom information service model through empirical analysis. The results show that the mean value of literature and history in the user portrait category III is 1.553, the change in the borrowing of books in the I major category decreased by 31.94% from 2018 to 2022, and the average satisfaction of users for the personalized recommendation service is 83.52%. This shows that the personalized intelligent information service model of the college library can realize the innovation of library information service and further improve the satisfaction of users for the college libraries.

Retracted: Integrated Design and Development of Intelligent Scenic Area Rural Tourism Information Service Based on Hybrid Cloud.

[This retracts the article DOI: 10.1155/2022/5316304.].

자동적 처분에 대한 통제 수단으로서의 알고리즘 영향평가

The Framework Act on Administration, scheduled to take effect in March 2021, provides an explicit basis for fully automated administrative decisions by introducing the concept of ‘automatic disposition’ in Article 20. Traditionally, the ‘automation of administration’ was significant as an auxiliary administrative procedure consisting of automatic mechanical devices such as traffic signal transmission, tax, and utility bill calculation. Moreover, since automated administrative decisions using artificial intelligence (AI) technology are also included in this automatic disposition, there is room for legal acceptance of automated administrative decisions based on artificial intelligence algorithms that exceed the level of conventional computerization or partial automation. However, it is still a long way from implementing a fully automated administrative decision system based on current law and having technical and institutional safety devices for it. On the other hand, the Framework Act on Intelligent Informatization, which took effect in December 2020, introduced a new ‘social impact assessment’ for intelligent information services that have far-reach effects on citizens’ lives. According to Article 56 of the Act, the main goal of this impact assessment system is to investigate and evaluate the impact of intelligent information services on society, economy, culture, etc., disclose the results, reflect them in policies, and directly recommend appropriate measures. Since the scope of intelligent information services subject to evaluation is not limited to the private sector, it should be considered that intelligent information services in the administrative sector can also be subject to social impact assessment in this article. It is reasonable to assume that automatic administrative services by artificial intelligence system belong to a wide range of intelligent information services prescribed by law, and that automatic disposition issued as a fully automated system based on intelligent information technology can also be subject to social impact assessment. Although no provisions have been found in law to assess the impact of automated administrative decisions, the government of Canada has implemented the “Directive on Automated Decision-Making” since April 2019, which systematically regulates the automated administrative decision-making system. The requirement that constitutes an important axis of this directive is the ‘algorithmic impact assessment'. For an automated administrative decision-making system that bears relatively stricter legality and public responsibility than private companies' services, it is necessary to process and operate an impact assessment that reviews safety, transparency, and solidity in advance. In this paper, referring to the Canadian case, as a control system of the algorithm-based administrative decision-making system, I propose the institutionalization of the “algorithmic impact assessment” in consideration of the connection with the social impact assessment under the current law.

Dynamics modeling and optimal control for multi-information diffusion in Social Internet of Things

As an ingenious convergence between the Internet of Things and social networks, the Social Internet of Things (SIoT) can provide effective and intelligent information services and has become one of the main platforms for people to spread and share information. Nevertheless, SIoT is characterized by high openness and autonomy, multiple kinds of information can spread rapidly, freely and cooperatively in SIoT, which makes it challenging to accurately reveal the characteristics of the information diffusion process and effectively control its diffusion. To this end, with the aim of exploring multi-information cooperative diffusion processes in SIoT, we first develop a dynamics model for multi-information cooperative diffusion based on the system dynamics theory in this paper. Subsequently, the characteristics and laws of the dynamical evolution process of multi-information cooperative diffusion are theoretically investigated, and the diffusion trend is predicted. On this basis, to further control the multi-information cooperative diffusion process efficiently, we propose two control strategies for information diffusion with control objectives, develop an optimal control system for the multi-information cooperative diffusion process, and propose the corresponding optimal control method. The optimal solution distribution of the control strategy satisfying the control system constraints and the control budget constraints is solved using the optimal control theory. Finally, extensive simulation experiments based on real dataset from Twitter validate the correctness and effectiveness of the proposed model, strategy and method.

Software Escalation Prediction Based on Deep Learning in the Cognitive Internet of Vehicles

In the Cognitive Internet of Vehicles (CIoV), vehicles, road side units (RSU) and other key nodes have been equipped with more and more software to support intelligent transportation system (ITS), vehicle automatic control and intelligent road information services. Additionally, technological innovation forces the software in the CIoV to update and upgrade in time. However, escalation is critical to the safety, stability, and maintenance cost of transportation systems. It can be assumed that when the intelligent services supporting CIoV can realize self-perception and escalation, the cognitive ability and coordination ability of the entire CIoV will be greatly improved. To address this, we first propose a deep learning-based method for Software Escalation Prediction (SEP) in CIoV. Specifically, the pretraining mechanism of transformers in the field of natural language processing is combined with software upgrade-related events to dynamically model software sequence activities. To capture the event association in the software activities, we use graph modeling software’s state log and utilize a graph neural network (GNN) to learn the complex life activity rule of software. Finally, the above characteristics are deeply integrated. The proposed method has a 6%–8% improvement over the RoBERTa methods.

Intelligent Information Service System of Smart Library Based on Virtual Reality and Eye Movement Technology

An innovative system provides intelligent services in the library to both users and the terminal. Compared to the core digital reading room, they can make wise judgments on the retrieval and use of information assets. The implementation of succeeding value management based on the latest technological tools is required for learning to provide knowledge services and fulfil its role as convergence is capable of reacting to varied data needs. The major obstacles to digital libraries are lack of planning and software, import restrictions on equipment, inadequately skilled staff, lack of standards, and a refusal to cooperate. This paper introduces the Eye Movement Technology (EMT) for the intelligent information service system of a smart library (SL) based on virtual reality (VR) strengths as an automated library management system for cost savings and increased output. EMT technology that can identify a person’s appearance and monitor something to look at in real-time is recognized as wearable technology. Visual field position and the gaze vectors for each eye are converted into a data stream that includes the focus origin. Most of the library’s operations can be managed by librarians; in a word, this system allows them to keep track of all of their books’ transactions. The research in EMT-SL technology in the intelligent library system has developed to the point that a wider audience can use it. As a result, eye-tracking in reading rooms and information science research are expected to rise in the future; with the advancement of VR, the computer-generated modelling of images is experienced through special electrical devices, and people can be visible in different climates using traditional VR and augmented reality.

OES-Fed: a federated learning framework in vehicular network based on noise data filtering.

The Internet of Vehicles (IoV) is an interactive network providing intelligent traffic management, intelligent dynamic information service, and intelligent vehicle control to running vehicles. One of the main problems in the IoV is the reluctance of vehicles to share local data resulting in the cloud server not being able to acquire a sufficient amount of data to build accurate machine learning (ML) models. In addition, communication efficiency and ML model accuracy in the IoV are affected by noise data caused by violent shaking and obscuration of in-vehicle cameras. Therefore we propose a new Outlier Detection and Exponential Smoothing federated learning (OES-Fed) framework to overcome these problems. More specifically, we filter the noise data of the local ML model in the IoV from the current perspective and historical perspective. The noise data filtering is implemented by combining data outlier, K-means, Kalman filter and exponential smoothing algorithms. The experimental results of the three datasets show that the OES-Fed framework proposed in this article achieved higher accuracy, lower loss, and better area under the curve (AUC). The OES-Fed framework we propose can better filter noise data, providing an important domain reference for starting field of federated learning in the IoV.

Cloud Computing Database and Travel Smart Platform Design Based on LSTM Algorithm

Information technology has played a key role in the development of the tourism planning service industry and has now become an important foundation for the survival and rapid development of the industry. In this context, due to the fast updating and popularization of information technology, it has greatly promoted the development of the tourism industry. In order to meet the current public demand for tourism information, this paper integrates cloud computing, VR technology, and big data analysis technology to build a smart platform for intelligent perception tourism information services. The system can obtain tourism information through mobile Internet terminals. Among them, the database of the smart tourism planning platform is the most important module. Aiming at the many difficulties in adapting data encryption in cloud storage applications, this article designs an adaptive CloudCrypt data encryption system based on cloud computing technology and proposes dynamic JavaScript dynamic analysis and automatic identification of data technology, through adaptive different cloud applications to obtain data encryption protection. CloudCrypt is suitable for typical cloud applications, such as mail and storage. The entry cost of the system is extremely low; it can fully guarantee the security of the tourism information platform database and can integrate wireless sensor networks into the tourism information platform. The network system composed of sensor nodes activates detection, calculation, and communication modules through wireless communication and has the advantages of low cost, low power consumption, and fast networking speed. In this paper, through the construction of integrated wireless sensor technology and cloud computing database, it is applied to the construction of a tourism smart platform, thereby promoting the development of smart travel technology.

Machine learning-based cloud IOT platform for intelligent tourism information services

Thanks to the continuous development of the tourism industry, the application of Internet of things (IoT) begins to boom among modern tourists. Smart tourism utilizes Internet of things to facilitate the information dissemination and exchange, making IoT technology an indispensable part of next-generation travelling tool kits. In this paper, based on visitor selection behavior, we develop a hybrid intelligent categorization approach. Particularly, our proposed method of categorization could help tourists decide whether or not to visit a sightseeing attraction in a minutes. Cross-validation testing and performance assessment show that our designed platform can work effectively and efficiently by considering a variety of factors.

Integrated Design and Development of Intelligent Scenic Area Rural Tourism Information Service Based on Hybrid Cloud.

Although the “Internet+” technologies (big data and cloud computing) have been implemented in many industries, each industry involved in rural tourism economic information services has its own database, and there are still vast economic information resources that have not been exploited. Z travel agency through rural tourism enterprise third-party information services and mobile context-awareness-based Z travel has achieved good economic and social benefits by deep value mining and innovative application of the existing data of the enterprise through the third-party information service of rural tourism enterprises and mobile context-aware travel recommendation service. It clearly demonstrates that, in order to maximise the benefits of economic data, rural tourist businesses should focus not only on the application of new technologies and methodologies but also on the core of demand and data-driven and thoroughly investigate the potential value of current data. This paper mainly analyzes the problems related to how rural tourism can be upgraded under the smart tourism platform, with the aim of improving the development of China's rural tourism industry with the help of an integrated smart tourism platform, and proposes a hybrid cloud-based integrated system of smart scenic rural tourism information services, which can meet the actual use needs of rural tourism, with good shared service effect and platform application performance, and promote the development of rural tourism and resource utilization rate.

Intelligent Tourism Information Service Model considering Tourist Experience in the Environment of Internet of Things.

Due to the narrow range of route search and the single choice of the optimal route during the peak period of tourists' tourism, resulting in a long walking time and relatively high route cost, an intelligent tourism information service model considering tourists' experience is proposed in the environment of Internet of things. Firstly, the overall architecture of the Internet of things environment is designed to obtain the characteristic data of the carrying capacity of scenic spots, including tourism resources, psychology, ecology, and economic carrying capacity, based on which the tourist perception experience model is constructed; different service functions such as in-depth mining of Web content, personalized information push, and data information conversion under the environment of Internet of things and the construction of intelligent tourism information service model are realized. The experimental results show that the passenger flow prediction error of the designed model is small and has a good prediction effect of tourist attraction selection demand.

Guest Editorial: Intelligent information processing and services in media convergence

Guest Editorial: Intelligent information processing and services in media convergence

A ship navigation information service system for the Arctic Northeast Passage using 3D GIS based on big Earth data

ABSTRACT Research on Arctic passages has mainly focused on navigation policies, sea ice extraction models, and navigation of Arctic sea routes. It is difficult to quantitatively address the specific problems encountered by ships sailing in the Arctic in real time through traditional manual approaches. Additionally, existing sea ice information service systems focus on data sharing and lack online calculation and analysis capabilities, making it difficult for decision-makers to derive valuable information from massive amounts of data. To improve navigation analysis through intelligent information service, we built an advanced Ship Navigation Information Service System (SNISS) using a 3D geographic information system (GIS) based on big Earth data. The SNISS includes two main features: (1) heuristic algorithms were developed to identify the optimal navigation route of the Arctic Northeast Passage (NEP) from a macroscale perspective for the past 10 years to the next 100 years, and (2) for key sea straits along the NEP, online local sea-ice images can be retrieved to provide a fully automatic sea ice data processing workflow, solving the problems of poor flexibility and low availability of real sea ice remote sensing data extraction. This work can potentially enhance the safety of shipping navigation along the NEP.

Adapting intelligent information services in libraries: a case of smart AI chatbots

PurposeThe recent COVID-19 pandemic affects all segments of the population in all possible fields of life. To encounter this critical situation and to safeguard the health of the library users & library staff, the library services have been forced to adapt and accept the “new normal” and give a greater use and reliance on virtual space than ever before with physical spaces. This paper aims to demonstrate an artificial intelligence (AI)-driven solution that can be practically implemented in the form of a “InfoBot” or “Chatbot” to fulfil user needs 24/7 with minimal or without human intervention.Design/methodology/approachTo give a general overview of the use of AI Chatbot in libraries and its multitasking features followed by the practical implementation, a versatile AIchatbot service, Engati is used as a target bot with its basic free plan.FindingsThe study findings reveal that AI Chatbots offer a dependable solution for the libraries to initiate virtual assistance, thus augmenting the reference service while adding a newer dimension to virtual reference service.Social implicationsInnovative and intelligent information service would be overcoming the time and location barriers reaffirming the concept of “library without walls” while falling in line with the five laws of library science as propounded by Dr S.R. Ranganathan, the father of library science in India.Originality/valueThough the implementation of bots and chatbots is not a purely novel concept to explore, no such research has been conducted focusing particularly on its exceptional benefits in the library in reference to the recent pandemic.

Intelligent Digital Human Agent Service With Deep Learning Based-Face Recognition

This study proposes a framework for an intelligent agent information service using digital human and deep learning technology. The framework can recognize the identity of individuals using facial features and provide personalized services through a digital human. The personalized service is defined by a relevance graph based on personal data collected in advance. The proposed system can continuously evolve to recommend customized services using relevance graphs and dynamic data processing, gradually become more intelligent using additionally collected data. Moreover, it uses animation keyframe interpolation for natural and seamless digital human interaction and provides visual effects that are synchronized based on specific information collected for the intuitive service. The proposed system was tested on a school domain for two months, and a statistical domain feedback system based on a mathematical model that predicts service usage per unit time was developed using the recorded information. Additionally, we evaluate our system through user experience surveys.

Research on Library Personalized Push System Based on Big Data Decision

Big Data is the product of the rapid development and application of information technology, and it is also the inevitable trend of the development and application of intelligent technology. In the environment of big data, by giving full play to the advantages of big data decision-making system and actively constructing library personalized push system, we will break the foundation of Library Service Space and time, fully guarantee the information system and service ability of the library. In the process of operation, libraries should make full use of big data resources and accurately identify the service demand, reading behavior and individual characteristics of library users, innovate the Information Mode and personalized service method, continuously improve the user’s reading experience, improve the user relationship, thus form the accurate, personalized and intelligent book information service system.

ALBERT over Match-LSTM Network for Intelligent Questions Classification in Chinese

This paper introduces a series of experiments with an ALBERT over match-LSTM network on the top of pre-trained word vectors, for accurate classification of intelligent question answering and thus the guarantee of precise information service. To improve the performance of data classification, a short text classification method based on an ALBERT and match-LSTM model was proposed to overcome the limitations of the classification process, such as few vocabularies, sparse features, large amount of data, lots of noise and poor normalization. In the model, Jieba word segmentation tools and agricultural dictionary were selected to text segmentation, GloVe algorithm was then adopted to expand the text characteristic and weighted word vector according to the text of key vector, bi-directional gated recurrent unit was applied to catch the context feature information and multi-convolutional neural networks were finally established to gain local multidimensional characteristics of text. Batch normalization, Dropout, Global Average Pooling and Global Max Pooling were utilized to solve overfitting problem. The results showed that the model could classify questions accurately, with a precision of 96.8%. Compared with other classification models, such as multi-SVM model and CNN model, ALBERT+match-LSTM had obvious advantages in classification performance in intelligent Agri-tech information service.

Guest Editorial Software Defined Internet of Vehicles

Internet of Vehicles (IoV) is a large interactive network composed of information such as vehicle location, speed and route. Vehicles can collect their own environment and state information through GPS, RFID, sensors, camera image processing, and other devices. They can transmit their various information to the central processing unit through Internet technology. These large amounts of vehicle information can be analyzed and processed through computer technology to calculate the optimal route for different vehicles, and report road conditions in time and schedule signal light cycles. Internet of Vehicles (IoV) is a large-scale system network for wireless communication and information exchange between vehicles and people, vehicles and roads, vehicles and the Internet, which is based on intra-vehicle network, inter-vehicle network and vehicle mobile Internet, in accordance with the agreed communication protocols and data interaction standards. The system realizes the integrated network of intelligent traffic management, intelligent dynamic information service, and intelligent vehicle control by “filtering and cleaning” massive data and processing data on the platform. Internet of Vehicles (IoV) system utilizes advanced IoT technology, cloud computing, and big data to make the system fully aware of roads and traffic. It enables all vehicles to collect information through their own environment and state, and upload all kinds of information to the Internet big data platform. The central processing unit collects, analyzes, and processes a large amount of uploaded information. The system will control every vehicle involved in the traffic and control every road in real time to provide users with traffic efficiency and safety.