Forthcoming and Online First Articles
International Journal of Information and Communication Technology
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International Journal of Information and Communication Technology (82 papers in press)
Abstract: Augmentative and alternative communication, the mechanisms that may complement or substitute formal language in certain situations, tends to lead to technological assistance in English as a foreign language (EFL). In this context, this paper discusses the mobile AAC concept, which can enhance EFL teaching performance. The article proposes a hybrid approach to EFL using big data analytics and mobile AAC (EFLAAC) to facilitate smooth access for mobile learners to educational digital content on various mobile devices. The compression and decompression program in the new framework compress the file during the streaming without changing the quality. A systematic subjective analysis has been carried out with dataset 0f 250. The proposed model is evaluated and delivers high learning results from multimedia instructional videos using pre and post-test questionnaires. EFLAAC attains 94.9% of accuracy, which is comparatively good than other approaches.
Keywords: EFL teaching; mobile augmentative; alternative communication; big data analytics; digital learning.
Abstract: Due to the low monitoring accuracy and duration of the traditional cellular mobile network security infringement monitoring system, a computerised cellular mobile network intelligent blank monitoring system is proposed. It connects the blank detection module to the scanner according to the data attributes to scan the blanks in the mobile cellular network. During the tracking of cyberspace signals, control the data space of the system session. Mobile cells of cellular networks introduce machine intelligence data processing learning algorithms hidden in the data. Experimental results show that ML-based cellular mobile network vulnerability detection (VD-MCN) can effectively improve system control accuracy and cellular network security space control efficiency, but there are still some things that are ignored to improve the development efficiency of MCN, and developers often only care about themselves Whether the corresponding functions can be realised in the process of code reuse, but the lack of understanding, inspection and testing of the reuse code, and the integration of these, can achieve our expected results.
Keywords: machine learning; ML; wireless communication; network security vulnerability; intelligent monitoring; mobile cellular network; MCN.
A collection method of motion video motion track based on fuzzy clustering algorithm
by Honglan Yang, Guan He
Abstract: In order to overcome the problems of low spatial accuracy, poor noise reduction ability and high spatial distortion rate of traditional methods, a motion video motion trajectory collection method based on fuzzy clustering algorithm is proposed. The fuzzy clustering algorithm is used to pre-process the motion video to improve the compression ratio of the motion video. The motion point accumulation method is used to extract the image sequences in the motion video, the motion targets are extracted, and the trajectory points of the motion targets are labeled. Establish a new motion trajectory range, and realise the collection of the motion trajectory within the marked range. The experimental results shows that the minimum noise value of the proposed method is only 0.02dB and 0.03dB, the spatial distortion rate of designed method is lower, it has better anti-noise effect, and more accurate motion trajectory acquisition results.
Keywords: moving target extraction; track point annotation; motion trajectory; descriptor; integral invariant.
Facial expression recognition of aerobics athletes based on CNN and HOG dual channel feature fusion
by Shitao Wang, Jing Li
Abstract: The problem of low feature extraction accuracy and low recognition accuracy in facial expression recognition of aerobics athletes is presented. Propose a recognition method for fusion CNN and HOG dual channel features. The basic principle of the HOG is analysed, and the facial expression image of aerobics athletes is processed by grey level with the help of local binary mode. The pixel gradient intensity value in each small image is obtained, and all the intensity values are fused. Lagrange formula is used to transform high-dimensional features, support vector machine is used to classify facial expression images, and feature points are used as CNN, to process feature points according to network input and regularisation regression is used to realise facial expression recognition of aerobics athletes. The experimental results show that the accuracy of feature extraction is 97% and the recognition accuracy is always higher than 90%.
Keywords: CNN; HOG; local binary mode; pixel gradient intensity value; facial expression.
Feature extraction method of football fouls based on deep learning algorithm
by Weicheng Ma, Yanfei Lv
Abstract: In order to overcome the problems of abnormal detection and low accuracy in the process of football foul feature extraction, this paper proposes a football foul feature extraction method based on deep learning algorithm to accurately identify the fouls in the process of normal competition. In this method, the background is eliminated by the difference between the input image and the background image, so as to obtain the effective detection target. According to the characteristics of football competition, the human motion tracking algorithm is proposed. Through the template representation, candidate target representation, similarity measurement calculation and search strategy, the dynamic target is tracked in real-time, and its dynamic information is obtained. Finally, the star skeleton feature is used to extract the football foul action feature, and the image feature is transformed into available data to realise the data extraction of action feature. The experimental results show that the proposed method can detect the target with low accuracy.
Keywords: deep learning; human motion; action recognition; mean shift algorithm; background subtraction.
Research on reliability analysis of catenary model based on the fusion particle swarm least square support vector machine algorithm
by Haigang Zhang, Xuan Chen, Piao Liu, Decheng Zhao, Bulai Wang, Jinbai Zou, Minglai Yang
Abstract: The reliability of contact network is always an important part of the reliability analysis of traction power supply system. In this paper, combined with the failure rate data of the main parts of the contact network, the small sample data is expanded by Bootstrap non-parametric regeneration sampling method using the Fused Particle Swarm Least Squares Support Vector Machine (PSOLSSVM) algorithm to provide training set data for particle swarm optimisation. Mann and Schuer and Fertig fit and test the two-parameter Weibull distribution of the model based on MATLAB. Select the key components such as load cable and insulator, and combine the data to establish a model to characterise the overall reliability of the contact network. Based on the established model, the relevant parameters of each main part are estimated separately, which accords with the actual situation.
Keywords: Weibull distribution; least squares support vector machine; parameter fitting; bootstrap.
Potato late blight disease detection using convolutional neural network
by Mominul Islam, Md. Ashraful Islam, Ahsan Habib
Abstract: This paper proposes a convolutional neural network-based deep learning model to classify and detect the infectious potato leaves suffering from late blight disease. The proposed model has two classifiers - the potato leaf classifier and the late blight disease classifier. Both healthy and diseased plant leaf images taken from the plantVillage dataset and real-time images are used to train, validate and test the classifiers. A total of 4,680 and 1,470 plant leaf images are used for the two classifiers, respectively. The potato leaf classification accuracy of the proposed model is 97.12%. The proposed CNN model also provides an accuracy of 98.62% while identifying late blight disease. The ten-fold cross-validation technique is used to observe the performance of the proposed late blight classifier and then compared with other cutting-edge approaches. In observation, it has been shown that the proposed technique outperformed many other existing techniques.
Keywords: late blight; convolutional neural network; deep learning; image processing; image augmentation.
Classification of existing mobile cross-platform approaches and proposal of decision support criteria
by Ayoub Korchi, Mohamed Karim Khachouch, Younes Lakhrissi, Nisrine El Marzouki, Aniss Moumen, Mohammed El Mohajir
Abstract: The smartphone market has known an exponential growth since 2007, with the apparition of the first Apple phone. Nowadays, developing an application that targets all existing mobile platforms, becomes a tedious task for developers, due to the diversity of mobile platforms, their tools. For that, cross-platform approach with its various sub-approaches has shown its strength in reducing projects cost and time respecting the slogan develop once and deploy everywhere. This paper aims to compare the mobile app developments approaches and suggests a decisional framework to choose the adequate one to get an application respecting the clients need with a low cost and time. This frameworks criteria have a huge impact on the eventual cost, time, and success of an application building. If developers fail to match an app demands to the right development approach, it can turn their project into a certain failure.
Keywords: OS; development; cross-platform approaches; MDA.
Information technologies of the Russian-Cuban GNSS service
by Ilia Bezrukov, Vladislav Yakovlev, Dmitry Marshalov, Yuri Bondarenko, Alexander Salnikov, Omar Pons Rodriguez
Abstract: We present the hardware and software implementation of the information technologies for the GNSS service of the Russian-Cuban co-located geodetic station. The service is equipped with geodetic and meteorological instruments with data acquisition and transmission systems and allows to conduct high-precision observations of the GPS and GLONASS global navigation satellite systems (GNSS) as well as meteorological measurements. The coordinates of the station are refined while processing GNSS observations and according to the change in coordinates, the movement of Cuban tectonic plates is estimated. Information technologies of the GNSS service is one of the key elements required for conducting regular automated geodetic and meteorological measurements. Information technologies is a group of computing and networking equipment that provides preliminary processing and transfer of observations to the data processing centre at the Institute of Applied Astronomy in St. Petersburg. It also allows the remote monitoring, control, and maintenance of GNSS service scientific instruments.
Keywords: remote control and monitoring; virtual private network; VPN; informational security; GNSS service; geodesy; applied geophysics.
A PRI estimation and signal deinterleaving method based on density-based clustering
by Lei Wang, Zhiyong Zhang, Tianyu Li, Tianhe Zhang
Abstract: In the existing statistics-based PRI estimation method, it is difficult to improve the PRI estimation accuracy due to the contradiction between the width of the statistical interval and the PRI extraction accuracy. In order to improve the accuracy of PRI estimation, a radar signal PRI estimation and deinterleaving method based on the density-based clustering is proposed in this paper. The dense area of the time of arrival (TOA) difference sequence near the true PRI value is extracted out by density-based clustering, take the intra-class mean value as the PRI estimation value and the intra-class point dispersion interval length as the PRI jitter amplitude. Combined with the sequence searching method with dynamic tolerance, the pulse sequence with a large number of pulses and small PRI jitter is preferentially extracted, which can improve the accuracy of signal deinterleaving. The simulation results show that the proposed method can significantly improve the accuracy of PRI estimation and the success rate of signal deinterleaving in the case of PRI jitter and false pulse interference.
Keywords: radar emitters; radar signals; pulse repetition interval; PRI; PRI estimation; signal deinterleaving; density-based clustering; DBSCAN; time of arrival; TOA; PRI jitter.
Intelligent recommendation method for personalised tourist attractions based on cloud computing technology
by Changchun Guan, Jinhua Luo
Abstract: In order to overcome the problems of poor recommendation results and long travel time of traditional personalised tourist attractions recommendation methods, this paper proposes an intelligent personalised tourist attractions recommendation method based on cloud computing technology. The method constructs user interest model based on knowledge map vectorisation and user interest vectorisation. In the algorithm recommendation module, based on Hadoop cloud platform, Maple-Duce is parallelised, and the Bayesian network is used to predict the users preference for items. The probability is presented to show the possibility of the users preference for items, and the user is recommended according to the probability, so as to complete the personalised intelligent recommendation design for tourist attractions. The experimental results show that the personalised tourist attractions intelligent recommendation method has the highest recommendation accuracy up to 99%, and reduces the travel time, the minimum time is 430 min, which is feasible to some extent.
Keywords: cloud computing technology; individualisation; tourist attractions; intelligent recommendation.
Research on operational effectiveness of air and missile defence in maritime stronghold based on queuing theory
by Wenfei Zhao, Kenan Teng, Jian Chen, Yan Wang
Abstract: For the complexity and uncertainty of the enemy situation faced by the defence decision-making in the maritime stronghold, this study adopts the queuing theory to study the process of air and missile defence operations in the maritime stronghold, quantitatively analyses the state transfer probability of the stochastic service system for air and missile defence operations in the maritime stronghold, constructs a mixed system queuing model of multiple service stations arriving in batches, and gives the evaluation model of operational effectiveness for air and missile defence. Finally, the validity of the model and algorithm is verified by arithmetic simulation.
Keywords: maritime stronghold; air and missile defence; queuing theory; operational effectiveness.
Group popular travel route recommendation method based on dynamic clustering
by Yanhua Guo
Abstract: This paper proposes a new group popular travel route recommendation method based on dynamic clustering. Based on the recommended pattern graph, the decreasing function of tourist interest and the score matrix of interest preference are calculated. And the dynamic clustering method is used to construct the dynamic mining model of group passenger preference data to obtain the tourist preference data. Based on the preference data, the hot areas of tourist routes are divided, and the Markov model is used to calculate the transfer probability of tourist routes, and the final result of tourist route recommendation is obtained. Experimental results show that, compared with traditional recommendation methods, the proposed method has higher recommendation accuracy and efficiency, and the highest recommendation accuracy and efficiency can reach 97% and 98%. Therefore, the proposed method is more effective.
Keywords: Dynamic clustering; popular group; tourist route; recommendation method.
Algorithm for interference filtering of Wi-Fi gesture recognition
by Wei Han, Jing Yu
Abstract: Nowadays, On the one hand, with the continuous improvement of computer technology, human-computer interaction becomes more and more important in people's life. Among them, gesture, as an intuitive human language, has become an important way of human-computer interaction. On the other hand, as an important branch of mobile network, WLAN has gradually developed into an irreplaceable technology in indoor communication. This paper based on the relevant knowledge of the wireless channel state information (CSI), put forward under the environment of Wi-Fi, with the help of fertility carrier amplitude information provided by the CSI for fine-grained gesture recognition. Because of interference, a gesture recognition system based on Wi-Fi accuracy and robustness to ascend, therefore this paper proposes a Wi-Fi gesture recognition interference Filter algorithm, using Butterworth low-pass Filter and principal component analysis (PCA), combining to the CSI of raw data denoising processing, filtering CSI noise in the raw data. On the basis of the recognition and machine learning the results verify the robustness and accuracy of the algorithm.
Keywords: channel state information; CSI; noise and interference filtering; gesture; recognition; machine learning.
Colossal pattern extraction using optimised length constraints based on differential evolutionary arithmetic optimisation algorithm
by T. Sreenivasula Reddy, R. Sathya, Mallikharjunarao Nuka
Abstract: Extracting large amounts of information and knowledge from a large database is a trivial task. Existing bulk item mining algorithms for an extensive database are systematic and mathematically expensive and cannot be used for large-scale mining with interruptions. In this paper, the problem of mining the colossal patterns (CPs) is solved by using optimised length constraints (LCs). First, we describe the minimum LC and maximum LC problems and connect them to the optimal LC by identifying the optimal threshold values. Here, the differential evolutionary arithmetic optimisation algorithm (DAOA) is used to find the optimal threshold values of the constraints and extract the colossal patterns. The effectiveness of the proposed algorithm is proven by various experiments using different biological datasets.
Keywords: biological databases; colossal patterns; differential evolutionary arithmetic optimisation algorithm; DAOA; massive data itemset; optimised length constraints.
Design of a new type of logistics handling robot based on STM32
by Xinliang Cheng, Jinyun Jiang, WanXin Fu, Shiyi Ying, Xiaoliang Jiang
Abstract: In order to improve the stability of the logistics handling robot and the accuracy of grasping logistics materials, this paper proposes a new type of logistics handling robot, which is designed by using the combination of PID closed-loop control algorithm and visual recognition technology. In the part of robot software, we use the PID closed-loop control algorithm to make real-time control of the robot, so that it can accurately patrol the line, and use the raspberry Pi call recognition algorithm (opencv) to complete the identification of two-dimensional code, bar code and logistics materials. In the structural part, we improved the five-degree-of-freedom articulated manipulator to realise three-dimensional grasping and ensure fast and accurate grasping and placing of logistics materials. In order to verify the performance of the robot, we design a mini logistics warehouse environment to simulate the real experimental scene. The experimental results show that the designed logistics handling robot can accurately locate and place logistics materials, and complete the handling task according to the task requirements.
Keywords: logistics handling robot; PID; recognition; articulated manipulator; STM32; image.
Assessment and insurance of cyber risks as tools for ensuring information security of an organisation (on the example of Russia)
by Dmitry R. Sergeev, Oksana N. Suslyakova, Gulnaz F. Galieva, Elena E. Kukina, Olga Yu. Frantsisko
Abstract: The purpose of the study is to reveal the specifics of cyber risks as a source of reducing the information security of organisation, as well as to develop a methodological approach to the formation of tools for managing cyber risks based on their assessment and insurance. The authors analysed the dynamics of the growth of cyber risks in relation to small, medium and large organisations, and also assessed the possible scale of damage from their occurrence in the global economy. The article offers a methodological approach to the analysis of risk factors that affect the amount of possible damage and the likelihood of cyber risk. The authors formed an algorithm for managing the organisations cyber risks based on their assessment. The analysis of advanced foreign experience allowed the authors to determine the directions of its adaptation in the process of modernisation and improvement of the mechanism of cyber risk insurance.
Keywords: cyber risk; cyber incident; digital economy; cyber risk insurance; information security.
Securing BYOD environment from social and mobility related threats: the case of Nigerian banking sector
by Lizzy Oluwatoyin Ofusori, Prabhakar Rontala Subramaniam
Abstract: Globally, bring your own device (BYOD) is gradually gaining popularity in work environments and businesses. The benefits of BYOD, which include increased productivity, flexibility, and efficiency, have necessitated all sectors to adopt this trend to maximise the benefits. The banking sector, in particular, has been at the forefront of the adoption of BYOD, and employees are now enjoying the benefits. However, despite BYOD benefits, security threats and privacy invasion have been a major concern for individuals and organisations. Ofusori et al. (2018) have classified these threats into technical, social and mobility threats. Thus, this paper investigates the influence of social and mobility threats as it relates to BYOD phenomenon in the banking sector. Data was collected from Nigerian bank employees, and the study found that there are overlapping threats due to the influence of social threats over mobility threats. This study addresses the overlap security threats.
Keywords: security vulnerabilities; security measures; security practices; bring your own device; BYOD; mobile devices.
Online education big data mining method based on association rules
by Na Zhang
Abstract: In order to solve the problems of slow mining speed, high noise and poor data correlation in the existing online education big data mining methods, an online education big data mining method based on association rules is designed. Firstly, the recursive distance of the big data centre is determined, and the online education big data is extracted according to the calculation of fuzzy membership. Secondly, the covariance matrix is used to remove the noise in online education big data and reduce the dimension. Finally, calculate the confidence and support of online education big data association rules, determine the association strength between online education big data in the set, and complete data mining. The experimental results show that the mining speed of this method is significantly improved, the longest time is no more than 4 s, and the data mining is highly correlated.
Keywords: association rules; online education; big data; Euclidean distance; uniformity.
Analysing the Algerian social movement through Twitter
by Meriem Laifa, Djamila Mohdeb, Mouhoub Belazzoug
Abstract: Technology has altered collective actions guidance resulting in a new regulatory frame for action. For the sake of being successful in a social movement, people plan and advertise in advance to encourage and gather greater participation to strengthen the influence of crowds. For this, social media offers exceptional opportunities to organise masses of people into actions with lower participation expenses, and to foster new repositories of information and actions that go beyond communities offline. While most contemporary social movements have been studied from different perspectives, the Algerian social movement (i.e., Hirak) was overlooked in the literature. This paper presents a distinctive foundation for understanding the Algerian Hirak through analysing Twitter data. The used approach is established mainly at the intersection of sociology and data analysis, with the intention to generate an improved discernment of this movement. Promising future research directions are also discussed in this paper.
Keywords: social movements; social media; Algerian Hirak; natural language processing; Twitter; Algerian Social Movement.
Received signal strength-based power map generation in a 2-D obstructed wireless sensor network
by Mrinmoy Sen, Indrajit Banerjee, Tuhina Samanta
Abstract: This paper analyses the effect of received signal strength (RSS) in efficient deployment, in presence of obstacles. We consider RSS based power values, so that x-y plane represents the spatial coordinates within a target field and z coordinates denote power values over the field. We plot the power values on the x-y coordinates, addressed as power map, having some peaks and falls: the peaks represent strong signals and the falls represent weak signals at the co-ordinates. It is intuitive that locations with strong signals are more suitable for communications. The falls in the power strength indicates that more sensor nodes are to be put for successful communications. We validate the proposed scheme via simulations as well as small-scale indoor and outdoor experiments with XBee sensor motes. We propose an algorithm to estimate the received power and analyse the estimated results with the results generated through the hardware test-bed.
Keywords: noisy channel; node deployment; power map; channel frequency; obstructed network.
Research on dynamic bidirectional security authentication of user identity in wireless sensor network based on improved des algorithm
by Xiaodong Mao, Haiyan Li, Shixin Sun
Abstract: In order to overcome the problems of time-consuming and weak attack defence of traditional authentication methods, this paper proposes a new dynamic bidirectional authentication method for user identity in wireless sensor network based on DES algorithm. This method uses DES algorithm to complete the user identity information encryption at the client-side through three steps: initial transposition, encryption operation and final transposition of identity information. In view of the server transmission to obtain the challenge value, combined with the one-time password DES to distinguish the authenticity of the server, the user identity authenticity verification is completed. Through the client-side and server-side legitimacy verification, the dynamic bidirectional security authentication of user identity is realised. The experimental results show that the proposed method is efficient, and the security of identity authentication is stronger, which provides theoretical support for the research of related fields.
Keywords: wireless sensor network; DES; user identity; bidirectional; authentication.
Which people are loyal followers of influencers? An exploratory study
by Javier A. Sánchez-Torres, Juan Sebastían Roldan-Gallego, Francisco-Javier Arroyo-Cañada, Ana María Argila-Irurita
Abstract: Influencers are tools implemented in digital marketing as a communication mechanism between the brand and its target; however, there are few studies that observe the relationship between the personality of the follower and their attitude towards the influencer. The objective of this study is to explore whether personality traits influence positive attitudes towards influencers. An empirical study was carried out in Spain and Colombia with a sample of 381 individuals and cause-effect relationships were analysed using the partial least squares methodology. The results show that extroversion and disordered personality traits are related to positive attitudes towards influencers and there could be some differences between genders, specifically men with a calm personality and women with a sympathetic personality
Keywords: influencers; personality; followers; social network analysis; internet marketing; digital marketing; partial least squares methodology; extroversion; disordered personality; calm personality; sympathetic personality.
An automatic correction system of singing intonation based on deep learning
by Hui Tang
Abstract: In order to solve the problems of low accuracy and slow correction speed in traditional singing intonation correction system, an automatic singing intonation correction system based on deep learning is proposed. In the hardware, floating-point DSP and TDSP-TF984 chip are selected as the core chips of automatic correction processor of singing intonation. The data input module and parameter calculation module of singing intonation are designed to improve the singing intonation data collector. In the software, the group delay estimation method is used to collect the singing intonation signal, and the deep learning algorithm is used to decompose the false component of the singing intonation signal, and the autocorrelation function and characteristic distribution operator of the singing intonation signal are obtained to realise the singing intonation signal correction. The experimental results show that the highest accuracy of the proposed system is about 97.8%, and the shortest correction time is about 1 s.
Keywords: deep learning; singing intonation; automatic correction; signal extraction; autocorrelation function.
A wireless sensor network node redeployment method based on improved leapfrog algorithm
by Bin Zhang, Xinhua Wang
Abstract: In order to overcome the problems of large errors and low average coverage of nodes in traditional node redeployment methods, a node redeployment method based on improved frog jump algorithm is designed in this paper. The output node path of wireless sensor network is determined by constructing the distribution node deployment model, then the leapfrog algorithm is improved by introducing virtual force algorithm, and the physical model of node deployment area is established, so as to optimise the node deployment process by using gravity and repulsion force, and redeploy static and dynamic nodes. Experiments show that the minimum node redeployment error of this method is only 0.01. When the energy of some nodes is exhausted, it still has relatively good coverage quality performance, and the average coverage rate reaches 75%, which proves that it not only ensures the network coverage quality, but also reduces the number of working nodes.
Keywords: improved leapfrog algorithm; wireless sensor network; node redeployment; node path.
Study on a method for capturing basketball player's layup motion based on grey level co-occurrence matrix
by Luojing Wang
Abstract: In the process of basketball players layup motion capture, the image blur leads to high layup motion capture error. Therefore, a basketball player layup motion capture method based on grey level co-occurrence matrix is proposed. The hue, saturation and brightness components of basketball players layup action image are unified greyed, and the fuzzy information in the image is transformed into different greyscale information; The Pearson correlation coefficient is used to analyse the correlation between each component after greying, and the grey information of fuzzy image is filtered by establishing grey co-occurrence matrix; By analysing the change of positioning coordinates of basketball players layup action in three-dimensional space, the core area of action capture is determined, and the key point position capture results are aggregated to realise the capture of basketball players layup action. The results show that the accuracy of the proposed method can reach 97.14%.
Keywords: grey level co-occurrence matrix; motion capture; grey processing; Pearson correlation coefficient; blur image; positioning coordinates.
Study on route selection method of scenic spot tourism based on GIS spatial model
by Yaqi Shi
Abstract: In order to overcome the problems of low coverage and high cost in traditional route selection methods for scenic spots, this paper proposes a new route selection method for scenic spots based on GIS spatial model. In the GIS spatial model, the weight scores of influencing factors are calculated. According to the weight calculation results, the weight values of various influencing factors are jointly superimposed on the grid of the GIS spatial model, the cumulative value of each grid is calculated, and then the weighted distance function of the scenic sightseeing route selection is established, to calculate the minimum distance from the grid to the scenic spot, so as to complete the route selection for scenic sightseeing. The experimental results show that the proposed method can improve the coverage of route selection and reduce the cost of route selection and the maximum coverage of route selection reaches 98%.
Keywords: GIS spatial model; sightseeing and tourism; route selection method; weighted distance function.
An efficient retrieval of relational database information based on knowledge graph
by Hongqin Zhu
Abstract: In order to optimise the effect of database information retrieval and overcome the problem of high index cost of traditional retrieval methods, an efficient retrieval design method of relational database based on knowledge graph is designed in this paper. Firstly, the distribution model of database information retrieval nodes is constructed to analyse the data structure, and the semantic characteristics of stored data of retrieval nodes are fused. Then the knowledge graph model analysis method is used to retrieve the database node attribute clustering and adaptive scheduling to realise the design of efficient information retrieval model for the database. The experimental results show that the index distance cost of this method is always below 5 x 1,010 byte, and the index distance cost is always below 40 s, which has certain application value.
Keywords: relational database; knowledge graph; indexing structure; semantic features; data graph; key words; information retrieval.
A deep mining method of enterprise financial risk data based on improved support vector machine
by Hui Sun, Li Fang
Abstract: Aiming at the problems of low accuracy of enterprise financial risk data mining, large amount of redundant data and low accuracy of data mining in traditional methods, an enterprise financial risk data depth mining method based on improved support vector machine is proposed. Design the deep mining route of financial risk data, fuzzy transform the financial risk data, and complete the preprocessing of enterprise financial risk data. According to the transformation results, by improving the linear separability and nonlinear separability of data in support vector machine, based on the improved support vector machine, the optimal hyperplane in these two cases is obtained to realise the classification of enterprise financial risk data. On this basis, RFID module is designed to complete the in-depth mining of enterprise financial risk data. The experimental results show that the data mining accuracy of the proposed method is 88%.
Keywords: improved support vector machine; financial risk; data mining; linearly separable; linearly inseparable.
A secure and integrated ontology-based fusion using multi-agent system
by Tarek Salah Sobh
Abstract: This study aims to handle ontology-based fusion and use multi-agent systems to obtain information fusion from multiple sources/sensors in a secure and integrated manner. Therefore, our objective is to produce a secure and integrated ontology-based fusion framework by using multi-agent. The agent system gets different props from using ontologies such as interoperability, reusability, and support. Here, fusion levels vary from the signal level that is low to the high knowledge level. Securing a multi-agent platform was introduced through a security system called 'SMASP'. The performance results show that the framework is almost idle while the user is composing the query. The workload is low on CPU and memory. This framework receives multiple data sources through cloudlet. Ontologies support a secure multi-agent system with different operations such as reasoner agents and query agents. Using the cloudlet architecture gives the flexibility to overcome intensive computing and sensitivity to latency.
Keywords: information fusion; multiple data sources; integrated framework; ontology; reasoning; cloudlet; agent security.
An efficient single unit for virtual-machine placement in cloud data centres
by Salam Ismaeel, Ali Miri, Ayman Al-Khazraji
Abstract: There are numerous energy minimisation plans are adopted in todays data centres (DCs). The highest important ones are those that depend on switching off unused physical machines (PMs). This is usually done by optimal distribution and/or reallocating of virtual machines (VMs) on the selected servers. While maintaining the quality of service (QoS) to ensure the performance of a DC. In this work, a novel server machine condition index (MCI) has been proposed, which includes all resources related to servers available in the DC using a single unit. The MCI represents a dynamic tool to compare services, increase effectiveness, reflect PM adequation, and ensure the optimal management of heterogeneous DC resources. The MCI will be used to convert the multi-objective VM allocation optimisation problem into a single-objective problem. This work will identify the MCI components and the way that can be used as a cloud resource unit, and modified VMP algorithms.
Keywords: power consumption; virtual-machine placement; cloud data centre; closed loop system; sustainable energy systems; task scheduling.
Community detection of trajectory data for location-based facility recommendation system
by B.A. Sabarish, R. Karthi, T. Gireesh Kumar
Abstract: Trajectory contains spatial-data generated from traces of moving objects like people, animals, etc. Community generated from trajectories portrays common behaviour. Trajectory clustering based on community-detection involves region-graph generation and community-detection. In region-graph generation, Trajectories are projected to spatial grid to transform GPS representation into string representation. Sequential graph is generated from string representation. Edge-based similarity is calculated between trajectories to create an adjacency matrix representing relationship and represent entire region. In community-detection phase, region-graph is divided into communities using various algorithms and validated using modularity values. Based on analysis, Louvain, fast-greedy, leading-eigenvector, and edge-Betweenness algorithms provide the optimum modularity value for better community detection. Analysing the community can be used as a pre-processing step in identifying location for location-based services (LBS), including hotspots, delay-tolerant-networks, and mobile antenna placements for better coverage. Design and capacity planning of the network based on the size and pattern of the community improves quality of LBS.
Keywords: trajectory; community; delay tolerant networks; quality of service; clustering; representation.
Information integration method of English teaching resources based on artificial intelligence
by Jin Guo
Abstract: In this paper, an information integration method of English teaching resources based on artificial intelligence is proposed. The generalised fuzzy C-means clustering algorithm was used to construct the Arduino device image dataset, and the convolutional neural network model of Arduino device was designed. The image data input model in TFRecord format was designed, and multiple Arduino device resource feature maps were output through convolution, pooling and other operations to establish the English teaching resource library and complete the information integration of English teaching resources. The experimental results show that this method has fast convergence speed, with a recognition success rate of 96.7% and can improve the academic performance to more than 90 points, and the actual evaluation value of it in the 5th and 6th academic year is close to 1. Therefore, it can improve the information integration efficiency of English teaching resources and English performance.
Keywords: artificial intelligence; English teaching; resource information integration; Arduino device; convolution neural network; clustering algorithm.
Research on reversible information hiding in image encryption domain based on multilayer perceptron
by Zhiqiang Yue, Weijia Chai
Abstract: To solve the problems of large mean square error, low peak signal-to-noise (PSNR) ratio and low embedding rate of traditional methods, a reversible information hiding in image encryption domain based on multilayer perceptron is proposed. Image blocks are encrypted sequentially, and the encrypted information is embedded into the image to complete the construction of the image encryption domain. Reversible information in the image encryption domain is extracted, the weight of the extracted information is calculated by multilayer perceptron, and the sender and receiver models are built according to the calculation results. Reversible information hiding in the image encryption domain is realised by using these two models. Experimental results show that the maximum and minimum mean square error of the encrypted image and the original image are 0.254 and 0.482 respectively, the maximum and minimum PSNR ratio are 56dB and 50dB respectively, and embedding rate is always above 91%.
Keywords: multilayer perceptron; images; encrypted domain; reversible information hiding; dimensionality reduction; sender model; receiver model.
A key feature mining method of online teaching behavior based on k-kernel decomposition
by Wei Wang
Abstract: In view of the poor effect of online teaching behaviour key feature mining, an online teaching behaviour key feature mining method based on k-kernel decomposition is designed. Firstly, the adjacent data of the key features of network teaching behaviour are interpolated to determine the key features, and the singular distance function is normalised to complete the feature preprocessing; Then, the key characteristics of network teaching behaviour are transformed into weighted network, and the key characteristics are divided according to the centrality of nodes; Finally, the online behaviour feature k-kernel after classification is assigned, the feature k-kernel value index after assignment is calculated, the correlation of feature data is calculated, the probability of feature data belonging to k-clustering is determined, and the key feature mining of network teaching behaviour is completed. The results show that the mining effect of this method is good.
Keywords: K-kernel decomposition; weighted network; online teaching behaviour; key feature mining.
A colour transfer method of interior design based on machine learning
by Tiesheng Liu
Abstract: In order to overcome the problems of large colour range, low structure similarity and poor objective evaluation index in the process of interior design colour transfer, this paper proposes a new method of interior design colour transfer based on machine learning. In this method, K-means algorithm is introduced to eliminate the uneven brightness area of the target image. The target image is processed by initial clustering, and the iterative threshold segmentation method is used to obtain the final clustering accurate target image. Combined with machine learning, the corresponding samples are selected on the two images to finish colouring, and the colour transfer of interior design is realised by referring to the coloured sample block. The experimental results show that the colour degree of the proposed method is maintained between 33 and 45, the structural similarity is always above 95%, and the comprehensive objective evaluation index value is close to 100%.
Keywords: machine learning; interior design; colour transfer; K-means algorithm.
Research on an online teaching platform for college music course based on internet of things technology
by Qingquan Zhu
Abstract: Aiming at the problems of long time-consuming and high resource integration error in traditional music teaching platform, this paper proposes to design online teaching platform of music course in colleges and universities with the help of internet of things technology. This platform consists for user online access module, music teaching content management module and music score online evaluation module; the user online access module is responsible for user login and registration; the music teaching content management module integrates music teaching resource data, backs up music teaching resource data, and creates online virtual learning classroom through the internet of things. The online evaluation model of university music course is constructed by technology, and the online teaching platform of university music course is designed. The experimental results show that: the time of uploading course resources is always less than 4 s, and the minimum error of teaching resources integration is about 0.01%.
Keywords: internet of things technology; online teaching; user online access module; music teaching content management module; online evaluation module of music score.
Multi-feature fusion friend recommendation algorithm based on complex network
by Kan Pan, Hailong Chen, Qian Liu, Jian Wang, Yingming Pu, Chunlin Yin, Zheng Yang, Na Zhao
Abstract: At present, one of the problems of friend recommendation algorithms used in most social networks is that these networks often rely on a single index for recommendation. To solve this problem, multi-feature fusion (MFF) algorithm, a social network friend recommendation algorithm based on complex network theory, is proposed. The recommendation algorithm works by firstly divides the existing social networks into different communities. The importance of nodes in a social network is then calculated through the fusion of nodes importance information. Lastly, by integrating node importance information, friend number information and the shortest path information, features are comprehensively evaluated so as to generate final friend recommendation list. Simulation shows that, with the increase of network nodes, the MFF algorithm outperforms common friend (CF) algorithm and friend similarity (FS) algorithm over all evaluation indicators including P value, R value and F value.
Keywords: complex network; social network; friend recommendation; node importance; multi-feature.
A rapid elimination of communication signal interference in complex electromagnetic environment
by Cao Chai
Abstract: To solve the problems of low recognition accuracy, high noise amplitude and long time consuming of traditional methods, a rapid elimination method of communication signal interference in complex electromagnetic environment is studied. Short-time Fourier transform (STFT) algorithm is used to calculate the time spectrum of mixed signals in complex electromagnetic environment. According to frequency and amplitude characteristics, fuzzy minimum-maximum neural network (FMNN) is used to classify and identify the interference signals. According to the transverse filter, least mean square (LMS) algorithm is constructed to calculate the tap weight coefficient and filter coefficient, which is combined with the filter coefficient to separate the normal communication signal from the interference signal to achieve the rapid elimination of interference. Experimental results show that the maximum recognition accuracy of the proposed method is 97%, the signal noise amplitude is between 3 and 10dB, and the average time of interference elimination is 0.73s.
Keywords: complex electromagnetic environment; communication signal; time spectrum; FMNN; LMS algorithm; low recognition accuracy; long time consuming.
Study on recommendation of personalized learning resources based on deep reinforcement learning
by Zilong Li, Hongdong Wang
Abstract: In order to overcome the problems of the traditional network personalised learning resource recommendation methods, such as low recommendation accuracy, poor recommendation quality and poor F1 comprehensive evaluation index, a network personalised learning resource recommendation method based on deep reinforcement learning was proposed. This method uses web crawler technology to capture learning resource data. Based on this, a deep reinforcement learning strategy model is built, and the recommended trajectory of network personalised learning resources is divided into independent states. The correlation degree between different network personalised learning resource variables is measured, and the objective function of personalised learning resource recommendation is constructed by combining resource keywords and track segmentation results to complete the resource recommendation. The experiment proves that the accuracy rate of the personalised resources recommended in this paper is above 70%, and the comprehensive evaluation index obviously reaches the highest 99%, which improves the resource recommendation effect.
Keywords: deep reinforcement learning; network; personalised learning resources; recommended.
Content and opinion-enhanced neural model for opinion sentence classification of Chinese microblog comments
by Yan Xiang, Junjun Guo, Yuxin Huang, Zhengtao Yu
Abstract: Opinion sentence classification of Chinese microblog comments aims to recognise those comments with opinions about the specific microblog content, which is the basis of internet public opinion analysis and opinion mining. However, existing opinion sentence classification methods do not consider whether the opinion sentences point at concerned objects or not. To address these issues, we propose a novel neural model which combines the microblog content relevance-enhanced module and the opinion representation-enhanced module. In the first module, we propose a mutual attention operation that enables the model to extract better features representing microblog content. In the second module, we employ sentiment word embedding and self-attention operations to enhance the ability of the model to extract the opinion features. We evaluate our model using a Chinese microblog comment dataset. Experimental results show that the accuracy of the proposed model is 2%-5% higher than that of the baseline models, which shows that the proposed content relevance-enhancement and attention mechanism are beneficial to this task.
Keywords: opinion sentence; classification; microblog; neural model; attention operation.
A secret sharing scheme based on integer decomposition and hexagonal structure
by Zender Rouia, Noui Lemnouar, Abdessemed Mohamed Rida
Abstract: Security is a major challenge in storage and transmission of digital data. Secret sharing scheme is a fundamental primitive used in multiparty computations, access control and key management, which is based here on two concepts, namely: hexagonal structure and integer decomposition. Use of hexagonal structure is common in biological modelling. For integer decomposition, the oldest known method is Fermats factorisation, while for the proposed decomposition, the factorisation uniqueness of positive integer into two factors is exploited. Experimental results obtained from the applied scheme to digital images reveal interesting properties; this scheme turns out to be lossless, ideal, flexible, extensible, and even can detect and identify cheater; in sum, it has a good security.
Keywords: secret sharing; quasi-square decomposition; bio-inspired hexagonal structure; isoperimetry.
Research on fast mining algorithm for multi-feature fuzzy association data based on compressed matrix
by Yibing Han, Zhanlei Shang
Abstract: In order to overcome the low mining accuracy and efficiency of traditional multi-feature fuzzy association data mining algorithms, a new fast multi-feature fuzzy association data mining algorithm based on compressed matrix is proposed in this paper. The compressed matrix structure is used to compress the fuzzy correlation data and generate the learning and training module. The average weighting method is used to extract fuzzy features, and the rule information of association data is integrated according to the mining mechanism to obtain the weighted confidence of association rules of fuzzy data. After data weighting, the optimal solution of fuzzy association rules is finally obtained, and the fast mining of fuzzy association data is completed. The experimental results show that the algorithm has accurate data mining effect, the execution speed of the algorithm is fast, and the maximum mining time is only 5.7 s.
Keywords: compression matrix; data support degree; membership degree; association rules; data mining.
Cost-effective cryptographic architecture in quantum dot cellular automata for secured nano-communication
by S. Senthilnathan, S. Kumaravel
Abstract: Quantum dot cellular automata (QCA) provide rapid computational efficiency, high density and low power consumption, which is an alternative for CMOS technology. In digital world, cryptography is an important feature to protect digital data. To ensure the data protection in nano-communication, a QCA-based cryptographic architecture is proposed in this article. In the proposed design, the encryption and decryption is done with the help of random keys which is produced by the pseudo random number generator (PRNG). In this paper, architectural component of cryptographic architecture includes XOR block, 1 to 4 de-multiplexer and PRNG, which are realised using QCA. Finally, an integration of the individual components through clock zone-based crossover, lead to the generation of a novel cryptographic architecture. This design achieves low cost compared to the existing literature, as it uses minimum number of majority gate and inverters with clock zone-based crossover.
Keywords: quantum dot cellular automata; QCA; clock zone-based crossover; CZBC; cryptographic architecture; pseudo random number generator; PRNG; demultiplexer; nano-router.
Diabetic retinopathy detection using curvelet and retina analyser
by Manas Saha, Biswa Nath Chatterji
Abstract: The diabetic retinopathy (DR) is a clinical disorder of retina caused due to diabetes mellitus. This work presents an automated detection of DR images using curvelet and retina analyser. Like Fourier transform, curvelet is a mathematical transform. It is deployed here to trace the directional field of the curve singularities of the retina images. This helps to segment the retinal vasculature of the fundus images. The change in retinal morphology like length, diameter, tortuosity due to the ophthalmoscopic changes are computed by retina analyser. Feedforward neural network (FNN) is implemented to detect DR images with sensitivity: 79%, specificity: 94% and accuracy: 88% which is better than the contemporary works. The proposed system is a smart integration of three modules - curvelet, retina analyser and FNN. It is simple, less time consuming and easily implementable. In future the same system can be extended to detect exact stage of DR.
Keywords: diabetic retinopathy; retinal vasculature; tortuosity; optic fundus; single layer perceptron.
Study on enterprise financial information management system based on big data analysis.
by Li Zhang
Abstract: In order to improve the accuracy of enterprise financial information management and reduce management time, this paper proposes to design an intelligent enterprise financial information management system. Stm32f103zet6 single chip microcomputer was selected in this hardware, and TC1782 is the main controller; in the software, this system login module, authority management module, financial subject information module and financial database module are designed; in the financial information database management module, the confidence of data is determined with the help of big data analysis method, and the effective financial information data is defined through the fuzzy theory in big data analysis to complete this design. The comparison shows that the proposed system can increase this accuracy of financial data management, and the data processing time is short.
Keywords: big data analysis; financial information; Stm32f103zet6; Tc1782 microcontroller; authority management module; financial information database management module.
Research on accurate estimation of energy consumption of new energy vehicles based on improved Kalman filter
by Fangling Zhang
Abstract: Because the previous traditional methods have a series of obstacles in data acquisition, such as low accuracy, large error and long calculation time, an accurate estimation method of energy consumption of new energy vehicles based on improved Kalman filter is proposed. Taking TC275 chip as the core, a set of energy vehicle energy consumption data acquisition architecture is designed to filter the collected data. After changing the estimation calculation method, finally, the latest consumption estimates result is obtained by using the Kalman filter. The result is 88%, the maximum is 93%, the average energy consumption estimation error rate is 6.8%, and the estimation time fluctuates between 0.3 s and 0.7 s.
Keywords: improved Kalman filtering; new energy vehicles; accurate energy consumption estimation; data acquisition architecture; filtering.
Research on tracking and decomposing method of aerobics movement based on machine learning
by Ningning Zuo, Jian Liu
Abstract: In order to overcome the low success rate of tracking and decomposing traditional aerobics movement tracking and decomposition method, this paper proposes a method of aerobics action tracking and decomposition based on machine learning. In this method, a multi-layer pyramid structure is built to segment the aerobics video image, and the key frame and the frame with the highest energy value are detected to obtain the binary image. This paper constructs a standardised tracking target model, uses convolution filter to detect multi-objective feature vectors in the image, uses binary classifier to build machine learning framework, and realises aerobics action tracking decomposition under the vector constraints of impact strength, spatial and frequency domain vectors. The experimental results show that this method can effectively deal with the interference from the external environment in partial sequence tracking, and compared with the traditional method, this method has higher success rate of tracking and decomposition, and has reliability.
Keywords: machine learning; aerobics; tracking and decomposition; feature extraction.
A visual effect enhancement method of print advertisement based on Sobel operator filtering
by Fang Wang, Huaxi Chen
Abstract: Aiming at the problems of long enhancement time, low image entropy and poor visual effects of traditional methods, a method for enhancing the visual effects of plane advertisements based on Sobel operator filtering is proposed. Through DCT coefficient and target contour extraction algorithm, the edge contour of the flat advertisement image is accurately extracted, and the hyperspectral sensor is used to sparse the contour extraction result to achieve the image denoising effect. Finally, the Sobel operator filter is used to remove part of the interference in the image background, highlight the small targets in the image, and supplement the missing points in the image to achieve the enhancement of the visual effect of the print advertisement. Experimental results show that the proposed method can achieve image enhancement in a shorter time, and the image entropy value is relatively high, indicating that the method can retain a large amount of image detail information, and the subjective visual effect is better, in line with human visual standards, and sufficient verify the effectiveness of the method.
Keywords: Sobel operator filter; print advertisement; visual effect enhancement; DCT coefficient; target contour extraction algorithm.
Recognition method of whole body rotation in alpine skiing based on long short time memory network
by Fang Yu
Abstract: In order to solve the problem of low recognition performance in traditional motion recognition methods, a whole body rotation motion recognition method for alpine skiing based on long and short time memory network is proposed. This paper analyses the relationship between snowboard running route and athletes centre of gravity track, and determines the data change of whole body rotation in alpine skiing; it divides the initial rotation data into sections through swab algorithm to complete the whole body rotation data collection; uses long-term and long-term memory network to classify the whole body rotation movement of alpine skiing, and constructs the recognition model of acceleration rotation action The whole body rotation movement recognition of alpine skiing. The experimental results show that the accuracy, recognition rate and recall rate of the proposed method are all higher than 96%, and the recognition time is short.
Keywords: long- and short-term memory network; alpine skiing; whole body; turning movement.
Research on classification method of painting features based on stochastic forest algorithm
by Hai Wang
Abstract: In order to solve the problems of low classification accuracy and long time consuming of feature extraction in traditional painting feature classification methods, a new method based on stochastic forest algorithm is proposed. The colour feature of painting is converted into HSV component, and the original LBP value of the painting image is converted into 59 dimension feature vector by uniform mode to extract the painting texture feature. The wavelet transform method is used to obtain the high and low frequency band signal of painting features, and the noise reduction of painting features is completed. The similarity coefficient is determined by stochastic forest algorithm, and the similarity matrix of painting features is obtained to complete the classification of painting features. The experimental results show that the accuracy of the classification method can reach 98% and the time is less than 2 s.
Keywords: stochastic forest algorithm; painting features; feature classification; uniform model; LBP value.
Research on singing breath correction system based on improved deep learning
by Pengjiang Yu, Hui Tang
Abstract: The traditional system has some problems, such as low correction accuracy and long correction time. This paper designs a singing breath correction system based on improved deep learning. The rectification content is extracted by the singing audio extraction module, and the sample data is stored by the singing breath sample storage module. The improved deep learning algorithm is used to select a small part of the stored samples to input at the lowest level of the neural network. Combined with the probability density function, the distribution probability between the original audio signal and the input sample signal of the filter is determined, and the output of the audio signal after noise processing is processed to correct the singing breath. The experimental results show that the accuracy of the system is about 96%.
Keywords: improved deep learning; singing breath correction; filter; probability density function.
Study on fast collection method of massive marketing data based on crawler technology
by Shuiying Hu
Abstract: In this paper, a fast collection method of massive marketing data based on crawler technology is proposed. According to the variable characteristics of marketing data, the normal distribution model is used to extract the features of marketing data, and the fusion algorithm is used to fuse the features of marketing data; setup the network node of marketing data collection, determine the collection location of marketing data, take the fused data as the initial URL, and join the crawler queue to judge the marketing data whose similarity meets the collection requirements, and crawl the data whose similarity meets the requirements again to complete the rapid collection of massive marketing data. The experimental results show that the proposed method takes less than 10 seconds to collect the experimental sample data, and the error is less than 5%.
Keywords: crawler technology; marketing data; fast acquisition; similarity; mass data.
An intelligent detection method of local feature points in computer vision image
by Yongliang Feng
Abstract: In order to solve the problems of traditional image feature point detection methods, such as low efficiency and low detection effect of image feature points, this paper proposes an intelligent detection method of local feature points in computer vision image. The Hessian matrix is used to obtain the localised feature points of computer vision image, and the image scrambling method is used to obtain the localised corner points of computer vision image. The scale space of computer vision image is constructed to realise the feature extraction of image localisation points, determine the direction of image localisation feature points, and realise the intelligent detection of computer vision image localisation feature points according to the requirements of computer vision. The results show that this method can improve the detection effect of image feature points and shorten the intelligent detection time of local feature points.
Keywords: computer vision; Hessian matrix; scale space; localised feature points.
Tracking and decomposition of throwing and jumping movements in high level figure skating based on deep learning
by Xue Bai
Abstract: In order to overcome the problems of high average noise and poor decomposition accuracy of throwing jump in traditional motion tracking decomposition methods, this paper proposes a new high-level figure skating throwing jump motion tracking decomposition method based on deep learning. The average depth of the key frame of the throwing action image is calculated, and the average depth is input into the depth learning neural network for training. According to the training results, the depth image is regularised to track the throwing action. According to the tracking results, the AHP judgment matrix is given, and the target trajectory characteristics of figure skating throwing jump are obtained, and the decomposition of high-level figure skating throwing jump is completed. The experimental results show that the mean noise of the designed method is 0.05dB, and the decomposition ability is higher.
Keywords: deep learning; high level figure skating; throwing jump action; tracking decomposition.
A detection method of similar segments of music based on multi-feature fusion
by Yu Zhou
Abstract: In order to improve the problem of high rejection rate of similar music fragments, this paper proposes a similar music segment detection method based on multi feature fusion. Firstly, music audio signal is decomposed into perceptual subspace to express musics audio signal features in each subspace, and scale vector and parameter matrix are used to save atomic and molecular information of the signal. Then, the music beat histogram is extracted by discrete wavelet transform to obtain its dynamic and static characteristics. On this basis, isolated words in similar music fragments are identified according to the results of multi-feature fusion, and then similar music fragments are detected by the classification of similar feature sets. The experimental results show that the semitones rejection rate of this method is between 1.9% and 3.5%, and the accuracy of music signal connection is between 92% and 98%.
Keywords: similar music segment; similarity detection; audio signal features; multi feature fusion; beat histogram; isolated words; feature set.
Energy consumption prediction system for intelligent building based on fuzzy petri net
by Min Wang
Abstract: This paper presents a new energy consumption prediction system of intelligent building based on fuzzy Petri net. When designing the database, the method improves the coverage of the database based on fuzzy petri net, and completes the design of the intelligent building energy consumption prediction system through system software design, overall architecture design, system data flow design and database design. In the database, SPSS software is used to analyse the relationship between the influencing factors and building energy consumption, according to the analysis results, the building energy consumption is predicted by artificial neural network, and the design of intelligent building energy consumption prediction system is realised. The experimental results show that the database coverage of the proposed method is more than 90%, the prediction accuracy is up to 95%, and the prediction time is always less than 0.5 s.
Keywords: fuzzy petri net; building energy consumption; prediction system design; system database; SPSS software.
Research on mass data storage and retrieval method of relational database based on KID cognitive model
by Haiyan Xu
Abstract: In order to solve the problems of high space occupation rate and low retrieval security existing in traditional methods, this paper designs a massive data storage and retrieval method based on KID cognitive model of relational database. First, the singular value of the information flow of the data with similar attributes in the database is decomposed, and the data with similar attributes is removed, and then the mass data is stored according to the mapping relationship between the data. Then the KID cognitive model is used to transform the data mode, and the safe retrieval coefficient is obtained by determining the threshold value of the core point of the data to realise the safe retrieval of the data. Experimental results show that the space occupancy rate of this method is always less than 10%, and the safety factor of data retrieval is the highest about 0.95.
Keywords: Relational database; kid cognitive model; singular value decomposition; massive data storage; secure retrieval.
An information management system of land resources based on UAV remote sensing
by Kexue Liu, Lingyu Xia, Jianbo Xu
Abstract: The traditional system cannot effectively extract the information of land resources, which leads to the problems of low efficiency of system management and low utilisation rate of land resources. This paper designs an information management system of land resources based on UAV remote sensing. Through the feature extraction module, land use approval management module, document information management module, office business management module and information integrated management module, the system function module is established, and the information management of land resources is carried out in combination with data structure and operation process. UAV remote sensing is used to extract land resources image and make land use thematic map to realise the information management of land resources. Through comparison, we can see that the highest management efficiency of the system can reach 98.74%, and the utilisation rate of land resources is more than 95%.
Keywords: land resources; information management; feature extraction; land use; B/S structure; UAV remote sensing technology; thematic map of land use.
A data tamper-proof method of cloud computing platform based on blockchain
by Dongying Gao, Renjie Su, Helin Zhang, Jie Fu, Hang Zhang
Abstract: In order to solve the problems existing in traditional methods such as low tamper-proof success rate, high tamper-proof time and high resource occupation, a data tamper-proof method of cloud computing platform based on blockchain is designed. The differences of data features are obtained according to the number of data spatial dimensions to determine the importance of data features. According to the importance of data features, on the basis of cloud computing platform data denoising processing, the data feature set is constructed to complete feature extraction. Combined with the result of feature extraction, blockchain is used to construct block structure, and the tamper-proof algorithm of cloud computing platform data is designed to ensure the data security of cloud computing platform. The experimental results show that the tamper-proof method designed has a high success rate, low tamper-proof time, low resource occupation, and good practical application effect.
Keywords: blockchain; cloud computing; data tamper-proofing; platform data; realtime.
Study on detection of attacking nodes in power communication network based on non-parametric CUSUM algorithm
by Mengxiang Liang, Wenlong Yao, Changqi Wei
Abstract: In order to improve the detection accuracy and time-consuming of traditional attack node detection methods, the paper proposes a new method for power communication network attack node detection based on non-parametric CUSUM algorithm. First, according to the similarity evaluation results, the suspicious nodes in the power communication network are collectively processed. Secondly, in order to make the node sequence meet the calculation requirements of the non-parametric CUSUM algorithm, the node sequence is preprocessed. Finally, the non-parametric CUSUM algorithm is used to calculate the threshold of the attacking node detection, and the attacking node decision function is constructed to complete the detection of the attacking node. Through experimental verification, it is found that the detection method proposed in this study can effectively detect attacking nodes, the detection accuracy is basically maintained above 95%, and the maximum detection time does not exceed 4 s.
Keywords: non-parametric CUSUM algorithm; power communication network; attack node detection.
Emotion recognition algorithm of basketball players based on deep learning
by Limin Zhou, Cong Zhang, Miao Wang
Abstract: Aiming at the problems of traditional methods of emotion recognition accuracy, long recognition time and low recognition rate, a basketball player emotion recognition algorithm based on deep learning is proposed. Based on the Emotic dataset, a basketball remote mobilisation emotion recognition dataset is constructed to realise emotion classification. The LBP method is used to extract the facial expression features in the dataset, and the KDIsomap algorithm is used to perform nonlinear dimensionality reduction on the features according to the feature extraction results. According to the deep learning algorithm, the SVM classifier is combined with the KNN classification to form an SVM-KNN classifier to recognise the emotions of basketball players. Experimental results show that the shortest recognition time of the proposed algorithm is only 4.38 s, the highest recognition accuracy rate reaches 94.2%, and the recognition rate is high, indicating that the algorithm has a certain effectiveness.
Keywords: deep learning; facial expression feature extraction; emotion recognition; dimensionality reduction method.
Network covert channel data security detection method based on threshold re-encryption
by Yuanyuan Li, Jidong Sha
Abstract: In order to solve the problems of long detection time and high detection error existing in traditional methods, this paper proposes a data security detection method of network covert channel based on threshold re-encryption. First, design the channel data collection terminal, then combine the artificial neural network technology to realise the classification of data results, and finally use the threshold re-encryption method for data encryption and decryption processing, so as to realise the safe detection of network covert channel data. The experimental results show that the detection time of the proposed method is shorter and is always less than 2.0 s, the comprehensiveness coefficient of the data detection results is as high as 10, and the detection error is always less than 5%, fully verifying that the proposed method can effectively carry out network covert channel data Safety inspection.
Keywords: threshold re-encryption; network covert channel; data security detection; data collection terminal; artificial intelligence.
Service fault diagnosis method of information operation and maintenance platform based on Gaussian kernel function
by Ruohan Sun, Kai Yun, Meihui Hu, Jinping Cao, Shu Cao
Abstract: In order to solve the problems of low diagnosis accuracy and long diagnosis time of existing information operation and maintenance platform service fault diagnosis methods, this paper proposes a service fault diagnosis method of information operation and maintenance platform based on Gaussian kernel function. Through the analysis of service fault data density of information operation and maintenance platform, the service fault feature extraction of information operation and maintenance platform is completed. The key noise is extracted and the fault data is denoised. Using the kernel density of Gaussian kernel function to estimate the kernel density of fault data, the feature data of the nearest distance of fault data is obtained, and the service fault diagnosis of information operation and maintenance platform is completed. The experimental results show that the proposed method has the highest accuracy of 97%, and the diagnosis time is less than 0.2 s.
Keywords: Gaussian kernel function; information operation and maintenance platform; fault diagnosis; average distance; kernel density.
An accuracy detection system of lyrics singing based on Gaussian mixture model
by Jianshu Wang
Abstract: In order to solve the problems of low detection accuracy, long time consuming and high system resource occupancy in traditional lyrics accuracy detection system, a lyrics accuracy detection system based on Gaussian mixture model is designed. In the lyric data pre-processing module, through clustering the similarity of the lyric data; in the lyric audio data feature fusion module, the accuracy of the lyric singing is analysed; in the lyric singing accuracy detection module, with the help of Gaussian mixture model, the Gaussian distribution probability low density function of the lyric singing accuracy is determined to complete the lyric singing accuracy detection. The experimental results show that: the system designed in this paper has the highest accuracy of about 95%, the shortest detection time is about 3S, and the system has the lowest resource share of about 10%.
Keywords: Gaussian mixture model; GMM; lyrics singing; accuracy detection; audio data features; Gaussian distribution probability low density function.
Upper limb movement trajectory recognition of basketball players based on machine learning
by Miao Wang, Limin Zhou, Cong Zhang
Abstract: In order to overcome the problems of low accuracy of action classification and poor denoising effect of action signals in traditional motion trajectory recognition methods, the paper proposes a method for recognition of upper limb motion trajectories of basketball players based on machine learning. Determine the characteristic change curve of the upper limb movement trajectory to extract the movement trajectory characteristic. The wavelet transform method is used for signal denoising, and the support vector machine method in machine learning is used to design the movement trajectory recognition classifier to realise the movement trajectory recognition of the upper limbs of basketball players. The experimental results show that the method in this paper can effectively remove the noise in the upper-limb motion signal, improve the accuracy and recognition effect of upper-limb motion trajectory recognition, and has certain practical value in the recognition of basketball players posture and motion.
Keywords: machine learning; support vector machine; wavelet transform; feature extraction.
Research on deep mining model of online learning data based on multiscale clustering
by Lijuan Liu
Abstract: Online learning data mining model does not consider the nonlinearity of data, and has the problems of low recall, precision and MMR. SVM is used to classify the online learning data, and hierarchical interpolation method is used to suppress the noise in the online learning data. This paper uses the multi-scale clustering algorithm to recursively process the data association rules, constructs the online learning data deep mining model through the regression classification tree, and solves the optimal solution of the deep mining model with the help of similar recursive function to complete the online learning data deep mining. The results show that: this method has a high fitting degree with the ideal hierarchical result distribution, and the precision, recall and MRR of data mining are better than the traditional methods.
Keywords: multi scale clustering; SVM; inverse distance weighting method; layered interpolation method; regression classification tree; mining model.
Study on improved personalised music recommendation method based on label information and recurrent neural network
by Yali Zhang
Abstract: In order to improve the problems of low accuracy and time-consuming of traditional personalised music recommendation methods, a personalised music recommendation method based on label information and recurrent neural network is proposed in this paper. Firstly, the music label information is extracted, and the music label information is clustered according to the label similarity; secondly, the music label information clustering results are decomposed by tensor, and all tensor decomposition data are fused to generate the target user recommendation list. Finally, the recurrent neural network is used to select personalised music from the user recommendation list and recommend it to users. The experimental results show that the simulation results show that the accuracy of personalised music recommendation is always more than 93%, and the recommendation time is always less than 0.6 s.
Keywords: label information; recurrent neural network; individualisation; music recommendation; similarity calculation; tensor decomposition.
Multi-link similar data mining and cleaning method based on Bayesian algorithm
by Kaiku Wang, Congkuan Huang, Yang Yang
Abstract: Aiming at the problem of low precision and large cleaning error in multi-link similar data mining, a multi-link similar data mining cleaning method based on Bayesian algorithm is proposed in this paper. On the basis of extracting multi-link data, the data is preprocessed and the similarity between the data is calculated. The data with high similarity is input into the Bayesian network, and the data cleaning process is completed according to the maximum likelihood value of the data. Experimental results show that the mining accuracy of the proposed method for similar data can reach 98.51%, and the cleaning error is about 1.22%, indicating that the proposed method can more effectively complete the mining and cleaning of similar data in multi-links.
Keywords: Bayesian algorithm; multi-link similar data; data mining; directed graph; maximum likelihood.
A feature extraction method of English learning behaviour data based on improved maximum expectation clustering
by Rui Yang
Abstract: Because the traditional methods do not consider the problem of data granularity control, the feature extraction accuracy of English learning behaviour data is not high, the data feature extraction results are not comprehensive and the extraction time is long. A feature extraction method of English learning behaviour data based on improved maximum expectation clustering is proposed. A data feature mining model is established, that is, adaptive mining of frequent data; control the granularity of use records of English learning activities. The improved maximum expectation clustering algorithm is used to obtain the posterior probability of the density branch of the sample to be processed to realise feature extraction. The results show that this method can effectively improve the feature extraction accuracy, the highest is about 89%, and the extraction time is less than 2.0S.
Keywords: improved maximum expectation clustering; English learning behaviour; feature extraction; feature mining; objective function; feature mining model.
Prediction method of tourism destination selection behavior based on nearest neighbour decision tree
by Qun Shang
Abstract: Aiming at the problems of large feature extraction error and poor prediction accuracy in tourism destination selection behaviour prediction method, a tourism destination selection behaviour prediction method based on nearest neighbour decision tree is proposed. With the help of bilinear function, the abstract tourism destination selection behaviour feature data is linearised, the freedom of linear feature data is limited, and the tourism feature is extracted through the scoring matrix; Set the characteristic data matrix, fix the characteristic data in a specific area, and determine the data weight through cosine similarity algorithm; According to the nearest neighbour algorithm, the maximum attribute value of the selection behaviour data is determined, the tourism destination selection behaviour prediction decision tree is constructed, and the selection error is corrected with the help of the correction function to complete the behaviour prediction. The results show that the accuracy of the proposed method is 97%.
Keywords: nearest neighbour decision tree; tourist destinations; select behaviour prediction; correction function; maximum attribute value.
Real time recommendation method of online education resources based on improved decision tree algorithm
by Shufang Xiao, Jue Liu, Ping Xu
Abstract: In order to improve the accuracy of online education resources recommendation and reduce the recommendation time, this paper introduces the improved decision tree algorithm to design a real-time recommendation method of online education resources. Calculate the information gain rate of online educational resources and collect the dataset of educational resources to obtain the attribute information of online educational resources; the specific attribute branches of users are matched according to the information gain rate, and the pruning structure of incomplete data feature attributes of online education resources is constructed by discretisation method; C4.5 algorithm is used to improve the discreteness of decision tree algorithm, and the improved decision tree is used to classify online education resources; the real-time path of online resource recommendation is simulated to realise real-time resource recommendation. The results show that this method can improve the real-time recommendation effect of educational resources.
Keywords: improved decision tree algorithm; C4.5 algorithm; data entropy; knowledge association map.
Multimedia teaching resource allocation method for distance online education based on packet cluster mapping
by Yiming Qu, I-Hua Chen, Shangjie Meng
Abstract: In order to overcome the problems of low resource utilisation and long allocation time existing in traditional teaching resource allocation methods, this paper proposes a new multimedia teaching resource allocation method based on packet cluster mapping. This paper uses the mining algorithm of time series to mine the multimedia teaching resources of distance online education, extracts the characteristics of multimedia teaching resources of distance online education, and uses the Naive Bayesian classification algorithm to classify the types of multimedia teaching resources of distance online education. According to the results of resource partition, the objective function of resource allocation is constructed by using the packet cluster mapping technology to complete the resource allocation of multimedia teaching in distance online education. The simulation results show that this method has higher resource utilisation and shorter allocation time.
Keywords: distance online education; multimedia teaching; resource allocation; mining algorithm; packet cluster mapping.
A Huffman based short message service compression technique using adjacent distance array
by Pranta Sarker, Mir Lutfur Rahman
Abstract: The short message service (SMS) is a wireless medium of transmission that allows you to send brief text messages. Cell phone devices have an uttermost SMS capacity of 1,120 bits in the traditional system. Moreover, the conventional SMS employs seven bits for each character, allowing the highest 160 characters for an SMS text message to be transmitted. This research demonstrated that an SMS message could contain more than 200 characters by representing around five bits each, introducing a data structure, namely, adjacent distance array (ADA) using the Huffman principle. Allowing the concept of lossless data compression technique, the proposed method of the research generates characters codeword utilising the standard Huffman. However, the ADA encodes the message by putting the ASCII value distances of all characters, and decoding performs by avoiding the whole Huffman tree traverse, which is the pivotal contribution of the research to develop an effective SMS compression technique for personal digital assistants (PDAs). The encoding and decoding processes have been discussed and contrasted with the conventional SMS text message system, where our proposed ADA technique performs outstandingly better from every aspect discovered after evaluating all outcomes.
Keywords: data compression; SMS compression; Huffman coding; data structure; adjacent distance array; ADA.
Fuzzy-based weighted fair queue scheduling technique for internet of things networks
by Harpreet Kaur, Manoj Kumar, Sukhpreet Kaur Sidhu, Sukhwinder Singh Sran
Abstract: In an IoT enabled network, a variety of devices are interconnected to each other and communicate by using ultra low power communication technology known as time slotted channel hopping mechanism. To transmit information accurately, efficiently and in collision free manner, a scheduling window is implemented for the devices deployed in the network. The priority scheduling is one of the solutions implemented recently in which nodes have high priority to transmit data first. Such algorithms can block low priority communication channels indefinitely that may leave some events unreported. In order to achieve fairness, efficiency in scheduling, we used fuzzy-based weighted fair queue scheduling algorithm. So, the fuzzy-based weighted fair queue scheduling suppresses the unfairness in the scheduling mechanism for communication paths having little information. The weighted fair queue scheduling algorithm belongs to a class of scheduling algorithms that are used in network schedulers. To implement this algorithm and compare the performance with the existing technique, the MATLAB platform is used. The results reveal the improvements in network throughput, end-to-end delay, energy consumption rate, network lifetime, congestion rate and packet loss ratio as compared to existing work in the similar scenarios.
Keywords: fair queue scheduling; priority scheduling; data transmissions; fuzzy logic; sensors.
Research on WLAN scenario optimisation policy based on IoT smart campus
by Yu-qin Wu, Wei Feng, Shun-bin Li
Abstract: Analyse the development history of IoT wireless technology, and further analyse the need for dedicated low power wide area technology in IoT systems. Because of the internet of things (IoT), which connects various network-embedded devices to the internet, this paper studies the problems in WLAN of college students dormitory, as well as introducing and analysing the optimal way of the short-distance WLAN technology in IoT. After reviewing methods to optimise WLAN optimisation, WLAN scenarios for the student dormitory are studied in depth and the optimisation scheme of wireless local area networks based on IoT short-range technology is proposed. Targeted network optimisation strategies are elaborated. Comparisons revealed that the optimisation resulted in significant improvement in terms of signal strength, latency, packet loss, throughput, propagation speed, and roaming success rate. The network became more stable, with better performance and user experience. Besides, the new network solved quite many previous issues and achieved desired results.
Keywords: internet of things; IoT; wireless local area network; WLAN; scene optimisation; policy.
Signal priority control method for emergency vehicles at intersections based on wireless communication technology
by Gaili Xu, Jialin Yao
Abstract: In order to give priority to control emergency vehicle signals at intersections, a priority control method for emergency vehicle signals at intersections is proposed based on wireless communication technology. The setting method of vehicle detector and emergency vehicle detector is analysed, and the non-delay model of emergency vehicle passing through the intersection is established to solve the phase and green time problems of emergency vehicle. Combined with wireless communication technology, the determination process of priority signal of emergency vehicle under fixed cycle conditions is elaborated in detail. The experimental results show that the queuing time of the designed French ordinary cars is significantly shortened, the average number of stops can be ignored, and the average delay time is shorter than that of the traditional method, which provides theoretical and practical basis for further guaranteeing the implementation of urban emergency rescue green channel and enriching the content of urban emergency rescue decision support method.
Keywords: wireless communication technology; intersection; emergency vehicle; signal control.
An interference recognition algorithm for auditing engineering drawings based on optimised CNN
by Jing Zhang, Jie Li, Ailv Zhu, Zining Li
Abstract: Engineering drawing is one of the main objects of audit work. At present, the use of computer automatic identification technology to digitise engineering drawings has become the main method to solve the low efficiency of traditional audit work. However, few reports on solutions for sample attacks based on engineering drawings. An interference recognition algorithm for auditing engineering drawings based on optimised convolutional neural networks (CNN) is proposed in this paper. The purpose is to be able to proactively identify whether the engineering drawings contain adversarial samples before carrying out the audit of engineering drawings. The specific methods are as follows: ten types of images that may be embedded in engineering drawings are considered firstly; Then, the LeNet and VGG networks were improved for these ten types of images, respectively. And the network model with high recognition accuracy against sample images is trained and generated. Finally, compared with the other three networks, the experimental results show that the accuracy of IVGG is improved on average 4.44% than the other three methods.
Keywords: engineering drawing; interference; active recognition; convolutional neural networks.
An optimised LSTM algorithm for short-term load forecasting
by Ziqiang Zhang, Zhiru Li, Liang Yan
Abstract: Load forecasting is a basic work of power dispatching, planning and other departments, and has always attracted attention from inside and outside the industry. The purpose of this is to make the extracted features nonlinear and to be able to learn more complex knowledge. The improved LSTM network is introduced to make a short-term forecast of wind power load. The dataset used in this experiment is the foreign GEFcom2014-Load wind power dataset. Because the dimension units of the 24-dimensional impact indicators in the dataset are different, in order to eliminate the dimensional impact between the indicators, the data is normalised in the experiment by adopting the min-max standardisation method. The experimental results show that the training error, validation error, and test error of the improved method are all reduced by 1%, 18%, and 16% compared with the second, third, and fourth groups.
Keywords: load forecast; power dispatch; planning; short-term; LSTM.
Special Issue on: Human Computer Interaction for Speech and Augmentative Communication
by Hua Sun, Hongxia Hou, Wenxia Song, Hongjuan Hu, Na Liu, Achyut Shankar
Abstract: A predictive brain-computer interface is a way to monitor EEG signals in humans currently under the experiment to understand the capability and get proper feedback about the digital education systems. The use of dynamically processed and collected data in a feedback system is unfeasible. Substantial processing delays are caused by a large volume of data utilised by the modern higher educational ideas. An artificial intelligence assisted brain-computer interface feedback system (AI-BCIFS) for ugmentative and alternative communication is proposed to improve feedback analysis based on the EEG signals. AI-BCIFS method is proposed to avoid an unwanted and improper understanding of feedback in the higher education systems. An expanded optimisation methodology is introduced based on the feedback analysis, and the enhanced seeking feedback protocol (ESFP) has been developed to describe automatic recognition and storage. The experimental studies show that AI-BCIFAC is preferable to the existing approaches in terms of accuracy.
Keywords: brain-computer interface; BCI; higher education; signal; augmentation.
A deep learning approach in brain-computer interaction for augmentative and alternative communication
by Yubin Liu
Abstract: The electroencephalography classification is the primary aspect of brain-computer systems. The changes there are two main concerns. Firstly, conventional approaches do not use multimodal knowledge to their fullest degree. Second, it is almost unlikely to obtain the rule-based EEG repositories as genetic information processing is complex, and metadata accuracy is expensive. In this sense, researchers suggest a new approach named deep learning-based brain-computer interaction (DLBCI) for augmentative and alternative communication to profound transfer learning to address these issues. Initially, these model perceptual activities based on EEG signals, using the EEG input images characterisation, which is intended to retain a standardised description of multimodal ECG signals. Secondly, this model develops a deep-scope transmission of knowledge through joint operations, including an opponents infrastructure and an incredibly unique transfer function. The proposed framework for EEG classification problems, such as strength and precision, has many economic benefits in experimentation.
Keywords: deep learning; brain-computer interaction; augmentative and alternative communication; multimodal signal.
Improving college ideological and political education based on deep learning
by Youwu Zhang, Yongquan Yan, R. Lakshmana Kumar, Sapna Juneja
Abstract: The rapid development of information and technology results in the involvement of technology channels like communication devices, and simultaneously it acts as a vital part of life. It emerged as a significant concern in the students educational progress both physically and mentally. It is essential to maintain the teaching quality in the teaching field, and more concentration is needed for the college ideological political education. For successive enhancement, a novel multimedia assisted ideological and political education system using deep learning techniques (MIPE-DLT) is introduced. The model analyses the characteristics and the capability of higher education students in gathering information and realising the effects of propagating novelties in ideological and political education. The proper flow of protocols has been executed in implementing multimedia techniques towards ideological and political education. It bridges the gap efficiently with a higher accuracy rate and processing rate. Compared with previous techniques, the MIPE-DLT achieves a high-order performance ratio with a minimal delay rate.
Keywords: multimedia; political; ideology; education; college learning; quality; teaching.