Forthcoming and Online First Articles

International Journal of Reasoning-based Intelligent Systems

International Journal of Reasoning-based Intelligent Systems (IJRIS)

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International Journal of Reasoning-based Intelligent Systems (33 papers in press)

Regular Issues

  • Study on privacy node encryption method for wireless sensor networks based on edge computing   Order a copy of this article
    by Yun Wei, Lingnan Zhou 
    Abstract: Aiming at the problem of poor encryption effect of the existing wireless sensor network privacy node encryption method, a wireless sensor network privacy node encryption method is designed based on edge computing. Firstly, the wireless sensor network structure and protocol stack structure are clarified, the composition structure of privacy nodes and the set of neighbour nodes are determined, and then the privacy node sensitivity is obtained through the adjacency matrix, and the privacy node location of wireless sensor network is realised by the trilateral positioning method. Finally, the edge computing method is introduced to aggregate the number of edge nodes of privacy nodes, complete the division of privacy node datasets, set terminal encryption keys for them, and realise wireless sensor network privacy node encryption. The experimental results show that the positioning error of this method is up to 0.9%, which can effectively improve the positioning accuracy of privacy nodes and improve the security of the network.
    Keywords: edge calculation; privacy node; encryption; differential privacy; undirected graph; encryption key.
    DOI: 10.1504/IJRIS.2023.10050215
     
  • A decision tree based method for detecting middle school students' behavior characteristics in online English learning   Order a copy of this article
    by Feifei Wang, Fengxiang Zhang 
    Abstract: In order to solve the problem of low accuracy and long detection time caused by poor feature extraction effect of online English learning students’ behavioural characteristics detection. This paper proposes a method of online English learning students’ behaviour characteristics detection based on decision tree. Firstly, the concept and structure of decision tree are analysed, and the classification steps are designed. Secondly, weighted principal component analysis was used to extract the behaviour characteristics of students. Then, the characteristic data is standardised. Finally, the C4.5 decision tree algorithm is used to construct a student behaviour feature detection model to detect students’ behaviour characteristics in online English learning. The experimental results show that the feature detection rate of the proposed method is as high as 99.5%, the accuracy is 96.2%, and the detection time is 19.8 s. Therefore, the feature detection effect of the proposed method is good, the accuracy is high, and the detection time is effectively shortened.
    Keywords: decision tree algorithm; weighted principal component analysis; WPCA; online English learning; student behaviour characteristics; behaviour characteristic detection.
    DOI: 10.1504/IJRIS.2023.10050216
     
  • Cluster Quality Analysis Based on SVD, PCA based k-means and NMF Techniques: An Online Survey Data   Order a copy of this article
    by Hemangini Mohanty, Santilata Champati, B.L. Padmasani Barik, Anita Panda 
    Abstract: With the increase in computerisation in every field, a huge amount of data is collected from everywhere. Therefore, extracting useful information has become a necessary task in the present era. Data mining helps to extract the information and uncover the relationship among the data. Clustering is an unsupervised technique used for partitioning objects into several groups and discover the hidden relationship among the data. There are many techniques used for clustering. In this article, a comparative study and analysis of three famous clustering techniques are done: principal component analysis (PCA), singular value decomposition (SVD) and non-negative matrix factorisation (NMF) for the clustering of a database. The database collected through a set of questionnaire surveys related to day-to-day activities. Then a comparison of their natural clustering ability is being done. Also, the use of normalised mutual information (NMI) and purity as two cluster quality evaluation measures are explored. Then an attempt is made to show that the approximated data matrix contains how much amount of information from the original data matrix. Next, to verify the accuracy of the variance covered by the approximated data matrix, the Frobenius norm is used. At last, the results are compared with the variance covered by using singular values, and a detailed analysis of each data matrix is explained.
    Keywords: clustering; k-means; non-negative matrix factorisation; NMF; normalised mutual information; NMI; principal component analysis; PCA; purity.
    DOI: 10.1504/IJRIS.2023.10050583
     
  • Location distribution detection of urban drainage pipeline based on deep learning feature   Order a copy of this article
    by Weishan Chen, Zhigang Zhou 
    Abstract: In order to improve the accuracy and efficiency of drainage pipeline location distribution detection, a new urban drainage pipeline location distribution detection method based on depth learning feature is proposed in this paper. Firstly, the main contents of drainage pipeline location data are analysed, and the drainage pipeline data are collected by acoustic detection method. Secondly, the dual tree complex wavelet method is used to extract the location distribution characteristics of urban drainage pipelines. Finally, the deep convolution neural network is used to train the location distribution characteristics to complete the detection results of urban drainage pipeline location distribution. The experimental results show that compared with the traditional detection methods, the detection accuracy of this method is higher and the time is shorter.
    Keywords: deep learning features; urban drainage belt; position distribution detection.
    DOI: 10.1504/IJRIS.2023.10050584
     
  • A method for personalized music recommendation based on emotional multi-label   Order a copy of this article
    by Yuan Luo, Qiuji Chen 
    Abstract: In this paper, a personalised music recommendation method based on emotion multi-label was proposed. First of all, the analysis of music emotion and music emotional label, then, the principal component analysis method is used to reduce the dimension to process the music features and complete the preprocessing. Secondly, construct the music emotion multi-label, and combine the cosine method to calculate the emotional multi-label similarity. Finally, the interest degree of emotional multi-label is calculated to obtain the user’s interest degree of music resources, and the personalised recommendation method is optimised to realise the personalised recommendation of music. Experimental results show that the average coverage rate of personalised music recommendation of the proposed method is as high as 99.5%, the accuracy is 98.3%, and the recommendation time of 500 music items is only 18.9s. Therefore, the recommendation effect of the proposed method is good, the accuracy of personalised music recommendation is improved, and the recommendation time is shortened.
    Keywords: sentiment multi-label; principal component analysis; TF-IDF method; cosine method; music personalised recommendation.
    DOI: 10.1504/IJRIS.2023.10050585
     
  • A Recognition method of basketball players' shooting action based on Gaussian mapping   Order a copy of this article
    by Zhu Xia 
    Abstract: In order to overcome the problem of low recognition accuracy of traditional action recognition methods, this paper proposes a basketball player shooting action recognition method based on Gaussian mapping. Firstly, the basketball shooting image is preprocessed by block initialization and denoising to improve the quality of the image. Secondly, based on the image preprocessing results, Gaussian mapping is used to extract the target features of shooting action image. Finally, according to the target characteristics, the multi-level feature decomposition and fuzzy processing of the image are carried out to realise the shooting action recognition. Experiments show that the designed method has high accuracy and recall rate, the maximum recognition accuracy reaches 96%, and the recognition time is short and the number of false recognition frames is less, which shows that the designed method has high practical application performance.
    Keywords: Gaussian mapping; basketball; shooting action; action recognition; feature extraction.
    DOI: 10.1504/IJRIS.2023.10050586
     
  • A rapid recognition of athlete's human posture based on SVM decision tree   Order a copy of this article
    by Nianhui Wang, Qingxue Li 
    Abstract: In order to solve the problems of low recall rate of human posture data collection results, low recognition rate and long recognition time in traditional recognition methods, a rapid recognition method of athlete’s human posture based on SVM decision tree was proposed. The Kinect sensor is used to collect the athlete’s human posture data, and the mixed Gaussian background modelling method is used to segment the collected athlete’s human posture image. Scale normalisation is performed on the segmented images, and a star model is used to extract the pose features of athletes’ bodies. According to the characteristics of human posture, the SVM decision tree is used to classify and identify the human posture of athletes. The experimental results show that the maximum recall rate of this method is 98%, the minimum value is 93%, the recognition rate is above 97.2%, and the average recognition time is 0.62.
    Keywords: SVM decision tree; athlete; human posture; rapid recognition; scale normalisation; star model.
    DOI: 10.1504/IJRIS.2023.10050587
     
  • Application of fuzzy logic in evaluating the authenticity of hadith and narrators   Order a copy of this article
    by Musaab Zarog 
    Abstract: Hadith in Islam is the Arabic word used to represent the traditions and sayings of the prophet. These traditions are normally categorised into four levels depending on their degree of authenticity. This accepted ranking system ignores the categories that might fall between these four ranks. Traditionally, hadith scholars apply a multi-criterion to conclude the degree of authenticity, which mainly depends on their training, experience, and various information reported about the hadith narrators. The research aims to use an arithmetic decision-making approach to judge the degree of validity and degree of authenticity of hadith, considering all factors utilised by hadith scholars such as; the reliability and accuracy of the narrator as well as the chain of transmission. The proposed approach will also consider different opinions in the literature about narrators’ accuracy, reliability, and memorisation capabilities. The arithmetic method was applied to three hadiths ranking: true, good, and weak, and then the weighted arithmetic mean were calculated for each of them which represent the degree of validity of each hadith. The obtained results agree to a high degree with judgment reported by hadith scholars.
    Keywords: artificial intelligence; natural language processing; decision making; authentication; decision making; Sharia; deep learning; Islamic sciences.
    DOI: 10.1504/IJRIS.2023.10051253
     
  • Identification and Detection of Brain Tumour Using Deep Learning-based Classification MRI Applied Using Neural Network and Machine Learning Algorithm   Order a copy of this article
    by Biswaranjan Mishra, Kakita Gopal, Srikant Patnaik, Bijay Paikaray 
    Abstract: Human brain is considered as the most sophisticated part of the body and used to consist of several neurons and biological components. The normal brain is usually functioning at an aspect of 93% for a healthy human. A brain tumour is a common disease nowadays and this disease usually leads to the accumulation of aberrant cells in certain brain tissues. Which may cause the formation of dump cells in the brain. One of the most valuable approaches is the MRI images which can identify the various stages for the detection of the tumour. Here a variety of feature extraction and classification techniques are available and MRI pictures are used to identify brain tumours. Here in this paper, the convolutional neural network approach is discussed where the high-accuracy image classification technique for early tumour detection is used.
    Keywords: MRI; human brain; tumour; CNN; and Glioma; RNN.
    DOI: 10.1504/IJRIS.2023.10051697
     
  • Entity Extraction Based on the Combination of Information Entropy and TF-IDF   Order a copy of this article
    by Hankiz Yilahun, Askar Hamdulla 
    Abstract: Traditional knowledge graph entity extraction methods require expert knowledge and a large number of artificial features. Furthermore, deficiencies exist in the accuracy and efficiency of keyword extraction based on methods such as TF-IDF. Thus, this study proposes a Chinese entity extraction method based on the combination of information entropy and TF-IDF. First, the text is preprocessed, which involves operations such as sentence segmentation, word segmentation, removal of stop words, and POS tagging, to detect keywords based on POS. Secondly, the word frequency is analysed to determine feature word weight, and the TF-IDF algorithm is used to compare the importance of keywords. Finally, information entropy is used to improve the TF-IDF algorithm to provide entity knowledge for the construction of the knowledge graph. The entity extraction method and optimisation scheme proposed in this study can help users extract domain entities and provide better entity resources for the construction of knowledge graphs.
    Keywords: entity extraction; improved TF-IDF; information entropy; knowledge graph.
    DOI: 10.1504/IJRIS.2023.10051698
     
  • A Recommendation method for online learning resources of mathematics courses based on feature graph clustering   Order a copy of this article
    by Zhixia Duan, Na Zhao 
    Abstract: Due to the low comprehensiveness of the traditional method to the analysis of learners’ needs, the degree of fitting between the recommended learning resources and the actual needs of learners is low. To solve this problem, a recommendation method for online learning resources of mathematics courses based on feature graph clustering is proposed. Construct learner feature map from cognitive level and learning preference, and analyse their attribute characteristics, behaviour characteristics and learning characteristics. Then on the basis of clustering processing, resources with the same clustering characteristics are matched as the recommendation target. The test results show that the satisfaction of the recommendation results of this method is always stable at more than 90.0%, the maximum and minimum F1-score values are 0.52 and 0.46, respectively, and it has high stability, which is obviously better than the traditional method.
    Keywords: online learning resources; cognitive level; learning preferences; characteristic atlas; feature similarity; clustering processing; fitting degree.
    DOI: 10.1504/IJRIS.2023.10051721
     
  • A fast identification method of UAV inspection image target for substation equipment   Order a copy of this article
    by Wen Kang, You Li, DeKai Liu, JieXue Zheng, Yisheng Yu 
    Abstract: Aiming at the problems of low accuracy, long time and poor recognition effect of traditional methods, a fast identification method of UAV inspection image target for substation equipment is proposed. Firstly, Gabor filter is used to extract the target features of UAV patrol image of substation equipment, and the UAV patrol image is segmented. Then, the binary image is processed, and the constraint criterion of image reconstruction is established to realise image reconstruction. Finally, all the image blocks are normalised, and the target model is constructed on the basis of prior knowledge to match and find the corresponding target, so as to realise the rapid recognition of the image target of UAV patrol inspection of substation equipment. The experimental results show that the target recognition accuracy of this method is always higher than 70%, and the minimum recognition time is only 3.26s. The target recognition effect is good.
    Keywords: substation equipment; UAV patrol inspection; image recognition; Gabor filter; image reconstruction; prior knowledge; image segmentation; binarisation.
    DOI: 10.1504/IJRIS.2023.10051722
     
  • The fast multi threshold color image segmentation based on decision rough set   Order a copy of this article
    by Yanting Cao 
    Abstract: In order to improve the speed and quality of multi-threshold colour image segmentation, a new multi-threshold colour image segmentation method based on decision rough set is proposed. Firstly, the RGB colour space is constructed, and the average brightness value is used to calculate the position segmentation points of the global histogram of the multi-threshold image. Then, the output membership value is obtained by decision rough set, and the multi-threshold image colour channel compensation and attenuation information are determined. Finally, the Gauss function is used to determine the segmentation position of each channel of the multi threshold image, and the fast multi-threshold colour image segmentation is completed. The experimental results show that the segmentation coefficient of this method is 0.868, the maximum PSNR value is 0.982 and the minimum MSE value is 0.003.
    Keywords: multi-threshold colour image; RGB colour space; histogram equalisation; decision rough set; image segmentation.
    DOI: 10.1504/IJRIS.2023.10051723
     
  • The control of lower limb rehabilitation robot with multi-pose based on man-machine coupling and fuzzy PID   Order a copy of this article
    by Xiaomei Kang 
    Abstract: In order to accurately control the joint displacement and joint angle of the lower limb rehabilitation robot, a control method for lower limb rehabilitation robot with multi-pose based on man-machine coupling and fuzzy PID is proposed. According to the theory of lower limb rehabilitation medicine, the human lower limb model is constructed. The FSR membrane pressure sensor is selected to obtain the man-machine contact force. Based on the man-machine coupling theory, the interaction requirements between the patient and the lower limb rehabilitation robot are analysed. The fuzzy PID controller is designed to control the lower limb rehabilitation robot with multi-pose on the basis of fuzzy rules to help the patient complete the rehabilitation training. The experimental results show that the proposed method can accurately control the joint displacement and joint angle of the lower limb rehabilitation robot, and has good positioning accuracy.
    Keywords: man-machine coupling; fuzzy PID controller; lower limb rehabilitation robot with multi-posture; kinematic analysis; FSR membrane pressure sensor.
    DOI: 10.1504/IJRIS.2023.10051724
     
  • The Individualized Travel Route Selection Based on Dynamic Transfer Graph   Order a copy of this article
    by Xiao-Qin Geng, Yun-Duo Wang 
    Abstract: In order to solve the problems of low selection accuracy, high cost and low tourist satisfaction in traditional methods, this paper proposes an individualised travel route selection method based on dynamic transfer graph. The granular time set is introduced to map the binary set of any event in the scenic spot to the triplet, so as to realise the construction of the dynamic transfer graph of the scenic spot. TF-IDF technology is used to calculate the fitting degree between tourists’ demand and scenic spots, so as to preliminarily select scenic spots’ tourism routes, complete the secondary screening of scenic spots’ tourism routes according to the dynamic transfer map, and realise individualised travel route selection. The test results show that the maximum selection accuracy of the proposed method is 98.3 and the minimum is 95.6%, the cost is low, and the user satisfaction is the highest, reaching 9.7.
    Keywords: dynamic transfer graph; personalisation; travel route; granular time set; dynamic transfer cycle; TF-IDF technology.
    DOI: 10.1504/IJRIS.2023.10051778
     
  • A sleep scheduling algorithm of redundant nodes in power communication sensor networks based on relative local density   Order a copy of this article
    by Binyuan Yan 
    Abstract: In order to overcome the problems of low network coverage and short network node survival time in the traditional node sleep scheduling algorithm, a redundant node sleep scheduling algorithm based on relative local density is proposed in this paper. The composition of power communication sensor network is analysed, and the energy receiving model of redundant nodes in power communication sensor network is constructed. The sleep scheduling of network redundant nodes is realised according to the relative local density redundant node sleep algorithm. The results show that when the number of nodes is 120, the network performance of this method is 5.18 and the network coverage can reach 99.93%. At the same time, the survival time of sensor nodes under this method is longer, this method can prolong the network life cycle, and the sleep scheduling effect of redundant nodes is better.
    Keywords: node sleep scheduling algorithm; energy consumption model; relative local density; network life cycle; relative local density.
    DOI: 10.1504/IJRIS.2023.10051779
     
  • A balanced allocation method of learning resources in smart classroom based on regional clustering   Order a copy of this article
    by Guangquan Zhou, Yaqi He, Pengwei Li 
    Abstract: In view of the problems of balanced allocation and large error in traditional methods, this paper designs a balanced allocation method of learning resources in smart classroom based on regional clustering. First, the resource data is mapped to the cloud computing network, and the weighted undirected graph is used to complete the data collection. Then, the distribution characteristics of resources are extracted according to the continuity of resource data. Finally, using the regional clustering algorithm, after determining the data core points, according to the distance between the various resource data and the core points, the objective function is used to construct the balanced allocation algorithm. Experimental results show that the allocation balance coefficient of this method is always kept at about 0.9 and the allocation error is kept at about 1%, which indicates that this method can improve the effect of balanced allocation of learning resources.
    Keywords: regional clustering; smart classroom; allocation of learning resources; naive Bayesian algorithm; decision chart; local density.
    DOI: 10.1504/IJRIS.2023.10051981
     
  • Study on weak signal enhancement method in wireless communication under electromagnetic interference environment   Order a copy of this article
    by Cao Chai 
    Abstract: In order to overcome the problems of high bit error rate and high signal enhancement time in weak signal enhancement methods, this paper proposes a weak signal enhancement method in wireless communication under electromagnetic interference environment. Firstly, the transmission characteristics of electromagnetic wave are analysed and the attenuation results of electromagnetic wave signal transmission are determined. Then, the bandwidth of weak signal is determined by band-pass sampling theorem, and the key characteristics of weak signal are determined by time-domain characteristic analysis. Finally, the amplitude modulation coefficient is used to adjust the frequency offset and initial phase through a low-pass filter to enhance the weak signal in wireless communication. The experimental results show that the lowest bit error rate of this method is about 1%, the weak signal enhancement time is no more than 2.6 s, and the weak signal enhancement effect is good.
    Keywords: electromagnetic interference; wireless communication; weak signal enhancement; band-pass sampling theorem; discrete signal; maximum likelihood estimation.
    DOI: 10.1504/IJRIS.2023.10052085
     
  • An adaptive fusion method of multi-mode human-computer interaction information in intelligent warehouse   Order a copy of this article
    by Shengbo Sun, Hao Wang, Chong Li, Yi Wang, Bing Li 
    Abstract: In order to obtain comprehensive multi-mode human-computer interaction information and enhance image definition, the adaptive fusion method of multi-mode human-computer interaction information in intelligent warehouse is studied. The canonical correlation analysis (CCA) fusion method of double width learning is established. The multi-mode HMI image information is introduced. Combined with the enhancement characteristics of dimensionality reduction modes at each layer, the enhanced nonlinear fusion dimensionality reduction characteristics are obtained through the fusion node layer, and the adaptive fusion results of multi-mode human-computer interaction information are output at the output layer. The experimental results show that when Gaussian and Poisson noise are added, this method can still adaptively fuse the multi-mode human-computer interaction image information, highlight the image details and improve the image definition. In different dimensions of human-computer interaction image information, it has rich image information and less visual information loss.
    Keywords: intelligent warehouse; multimodal; human-computer interaction information; adaptive fusion; width learning; correlation analysis.
    DOI: 10.1504/IJRIS.2023.10052086
     
  • A Personalized recommendation method of pop music based on machine learning   Order a copy of this article
    by Honghao Yu 
    Abstract: In order to enhance the satisfaction of pop music personalised recommendation and improve the accuracy and efficiency of pop music personalised recommendation, a pop music personalised recommendation method based on machine learning is proposed. Firstly, the relevant theories of machine learning and short-term and long-term memory artificial neural networks are studied, and then the popular music word vector is extracted by using softmax function, and the collaborative filtering algorithm with weighting factor is introduced to calculate the similarity of popular music word vector. Finally, based on the LSTM network, a pop music personalised recommendation model is constructed to realise pop music personalised recommendation. Experiments show that the method proposed in this paper has a personalised recommendation satisfaction index of 97.8% for pop music, the recommendation time is only 19.4s, and the average value of MAPE and RMSE are only 0.037 and 0.039 respectively. The recommendation accuracy, satisfaction and efficiency are high, and the design purpose can be achieved.
    Keywords: machine learning; Word2Vec; LSTM network; popular music; personalised recommendation.
    DOI: 10.1504/IJRIS.2023.10052087
     
  • Study on behavior anomaly detection method of English online learning based on feature extraction   Order a copy of this article
    by Feng Wei 
    Abstract: There are many problems in abnormal detection of online English learning behaviour, such as large error and high detection time. Therefore, a detection method based on feature extraction is proposed. Firstly, frequent pattern mining method is used to collect learners’ behaviour data, and the data is collected and preprocessed. Then, the classification constraints are set by support vector machine to complete the data classification. Finally, the sequence minimum eigenvalue method is used to train the abnormal data, extract the high frequency features of the abnormal data, establish the anomaly detection model, and realise the anomaly detection. Experimental results show that the highest detection error of this method is 1.2%, and the highest time cost is 1.8 s. Therefore, this method can effectively reduce the detection error and time cost, and is feasible.
    Keywords: feature extraction; English online learning behaviour; anomaly detection; threshold; K-means clustering; Lagrange function.
    DOI: 10.1504/IJRIS.2023.10052088
     
  • Recognition method of football players' shooting action based on Bayesian classification   Order a copy of this article
    by Xiaofang Zhao 
    Abstract: Aiming at the problem of low accuracy and poor real-time performance of existing algorithms in the process of football players’ shooting action recognition, a football players’ shooting action recognition method based on Bayesian classification is proposed. Firstly, Gaussian mixture model is constructed to extract the characteristics of shooting action. Secondly, the Gaussian parameters are estimated to obtain the optimal state sequence, which provides a basic reference for football players’ shooting action recognition. Finally, based on the marking of football players’ shooting action behaviour, the recognition of football players’ shooting action based on Bayesian classification is realised. Experiments show that the designed Bayesian classification method can accurately identify the shooting action of football players, and has good real-time performance. This shows that the design method can provide basic basis and theoretical guarantee for football players’ action recognition, and has certain practical application performance.
    Keywords: Bayesian method; motion recognition; football sport; athlete movement.
    DOI: 10.1504/IJRIS.2023.10052089
     
  • Research on enterprise financial investment risk prediction method based on binary tree clustering   Order a copy of this article
    by Qian Cao 
    Abstract: In order to overcome the problems of poor accuracy of enterprise financial investment risk prediction and poor fitting effect of operating profit margin, this paper proposes a risk prediction method of enterprise financial investment based on binary tree clustering. Firstly, binary tree clustering is used to classify financial data; secondly, the binary tree is used to obtain the data probability density function, and then the maximum likelihood estimation method is used to solve the density objective function; finally, the investment risk prediction results are obtained through the expectation maximisation method to realise the financial investment risk prediction. The experimental results show that the prediction accuracy of this method is as high as 99.06%, and the fitting effect of operating profit margin is good, which shows that this method can improve the prediction accuracy of enterprise financial investment risk.
    Keywords: binary tree clustering; Bayes theorem; covariance matrix; financial investment risk.
    DOI: 10.1504/IJRIS.2023.10052090
     
  • Intelligent Recommendation Method of Personalized Tour Route Based on Association Rules   Order a copy of this article
    by Yumei Jing 
    Abstract: In this paper, an intelligent recommendation method of personalised tourism routes based on association rules was proposed. Firstly, the membership matrix is constructed to mine tourist attractions, and the scope of tourist attractions is determined by attribute clustering. Secondly, the association rule algorithm is used to extract the features of scenic spots, tourists and tourist interest points to complete the personalised classification of tourist routes. Finally, the similarity of tourist routes is calculated by dynamic and static attributes, and the maximum probability scenic spots are output intelligently. The personalised recommendation method of tourist routes is optimised to realise personalised intelligent recommendation of tourist routes. The simulation results show that the proposed method has 98.5% accuracy, 97% recall rate and only 6s recommendation time. Therefore, the proposed method improves the performance of the intelligent recommendation method and has practicability.
    Keywords: association rules; personalised recommendation; artificial intelligence; travel route selection.
    DOI: 10.1504/IJRIS.2023.10052091
     
  • A Recognition method of learning behavior in English online classroom based on feature data mining   Order a copy of this article
    by Lu Shi, Xiaoran Di 
    Abstract: This paper proposes a recognition method of learning behaviour in English online classroom based on feature data mining. Firstly, with the support of fractal theory, the adjacent search method is used to extract the edge of learning behaviour image, and then the data clustering method is used to reduce the dynamic change range of data caused by edge extraction and improve the degree of data standardisation. Finally, the optimal characteristics of learning behaviour are obtained by Drosophila optimisation algorithm, then the learning behaviour recognition of English online classroom is realised by mining characteristic data. Simulation results show that this method has the highest accuracy of 98% and the comprehensiveness of recognition of different types of learning behaviour can reach 0.95. This method retains the details of behaviour image as much as possible to make it more practical.
    Keywords: feature data mining; behaviour identification; proximity search method; fruit fly optimisation algorithm; data clustering.
    DOI: 10.1504/IJRIS.2023.10052092
     
  • Image detail enhancement of two-dimensional animation scene based on dual domain decomposition   Order a copy of this article
    by Ben Ma 
    Abstract: In order to improve the performance of traditional image detail enhancement methods in distortion, real-time and detail information error, a new two-dimensional animation scene image detail enhancement method based on dual domain decomposition is proposed in this paper. Firstly, the two-dimensional animation scene image is divided into basic image domain and image detail domain by double domain decomposition. Then, the basic image domain is reconstructed and the image detail domain is denoised. Finally, the image detail enhancement is realised by fusing the results of information reconstruction and denoising. The simulation results show that the enhancement process of this method takes at least 11 minutes, and the maximum local standard deviation can reach 0.975, which is closer to 1.000, indicating that this method has the advantages of low distortion, high real-time performance and small detail information error.
    Keywords: two dimensional animation scene image; dual domain decomposition; basic image domain; image detail domain; information reconstruction; denoising; Information fusion; detail enhancement.
    DOI: 10.1504/IJRIS.2023.10052093
     
  • Face recognition algorithm of sprinters based on sliding data camera measurement   Order a copy of this article
    by Yujie Fan 
    Abstract: In order to solve the problems of low accuracy of face key point recognition and large noise error in recognition, a sprinter face recognition algorithm based on sliding data camera measurement is designed. Firstly, the sliding data camera measurements is imported, the camera calibration method is used to obtain the 3D points and plane projection in the athlete’s running scene, which are collected into the same coordinate system through the conversion matrix to extract the taxiing data; Then, the unclear coordinate points are replaced by convolution kernel, and the image noise points are removed by bilateral filter. Finally, multiple key point coordinates are designed in the face image, and face recognition is realised by sparse approximation of the key points and the most matched feature points in the face image combined with greedy algorithm. The results show that the proposed algorithm can recognise the six key points of human face, and the noise reduction error is less than 1.3%, which achieves the expected goal and has practical application value.
    Keywords: taxi data camera measurement; sprinter; face recognition; 3D points; bilateral filtering.
    DOI: 10.1504/IJRIS.2023.10052094
     
  • Speech unlocking information recognition method of intelligent electronic lock based on acoustic spectrum characteristics   Order a copy of this article
    by Zhifeng Lu, Lingling Ma 
    Abstract: In order to overcome the problems of low recognition accuracy and long recognition time in traditional speech unlocking information recognition methods, a speech unlocking information recognition method of intelligent electronic lock based on acoustic spectrum characteristics is proposed. Firstly, the voice unlocking information of intelligent electronic lock is collected by digital filter, and the unlocking information is enhanced by spectral subtraction. Secondly, the acoustic spectrum of unlocking information after enhanced processing is calculated, and the characteristics of voice unlocking information of intelligent electronic lock are extracted based on the calculated acoustic spectrum. Finally, the short-time autocorrelation function is used to detect the unlocking information endpoint and recognise the voice unlocking information of intelligent electronic lock. The simulation results show that the recognition time of this method is short, the missing recognition rate is low, and the recognition accuracy is high, and the shortest recognition time is only 2.5 s.
    Keywords: acoustic spectrum characteristics; intelligent electronic lock; voice unlocking; spectral subtraction; short-time autocorrelation function.
    DOI: 10.1504/IJRIS.2023.10052189
     
  • Vulnerability assessment method of network topology structure based on maximum likelihood function   Order a copy of this article
    by Qing Li 
    Abstract: In order to improve the accuracy of network vulnerability assessment results, reduce the node loss rate, a network topology vulnerability assessment method based on maximum likelihood function is proposed. The network characteristics and network topology are analysed. The reliability of the network topology is estimated by using the maximum likelihood function according to the analysis results. An assessment index set is constructed according to the reliability assessment results, and each assessment index in the assessment index set is standardised. At the same time, a fuzzy assessment matrix and a judgement scale matrix of network topology vulnerability are constructed, and the weight of each assessment index is calculated. Establish a network topology vulnerability assessment model to achieve vulnerability assessment. The experimental results show that the minimum assessment time of the proposed method is 1.93 s, the node loss rate is always lower than 2%, and the assessment accuracy is high.
    Keywords: maximum likelihood function; network topology; vulnerability assessment; constraints; normalisation.
    DOI: 10.1504/IJRIS.2023.10052190
     
  • An Efficient data transmission method of IOT terminal based on cloud and fog hybrid computing   Order a copy of this article
    by Benjie Wei  
    Abstract: In order to overcome the problems of long transmission delay and small throughput in the terminal data transmission method, this paper proposes an efficient terminal data transmission method of the internet of things based on cloud and fog hybrid computing. Firstly, the architecture of IOT terminal is analysed to determine the random selection coefficient of coding. Secondly, the coding information is solved by Gauss elimination method. Then, CSI modulation is used to remove the terminal interference data. Finally, the channel allocation probability is determined, and the transmission channel allocation is realised through the cloud and fog hybrid calculation method, so as to realise the efficient transmission of the terminal data of the internet of things. The experimental results show that the transmission delay of the design method is always controlled below 3 s, and the network throughput is always higher than 50 MB s
    Keywords: cloud and fog mixing calculation; IOT terminal data; efficient transmission; code; CSI modulation.
    DOI: 10.1504/IJRIS.2023.10052191
     
  • A narrative review on the characterization of automated human emotion detection systems using biomedical sensors and machine intelligence   Order a copy of this article
    by Stobak Dutta, Brojo Mishra, Anirban Mitra, Amartya Chakraborty 
    Abstract: In our day-to-day life, emotion plays an essential role in decision-making and human interaction. For many years, psychologists have been trying to develop many emotional models to explain the human emotional or affective states. Automated emotion recognition is a popular research problem increasingly utilised in marketing, education, health sector, and human-robot interaction. There are different ways of emotion recognition, namely uni-modal and multi-modal solutions. However, it depends upon the purpose for which it is to be used. The primary focus of this paper is to provide a detailed review of the existing literature in this domain with the help of three verticals. Initially, the standard databases used for elicitation of human emotions are discussed in brief. Next, the different sensing approaches used to gather physiological signals are discussed. Finally, a thorough review of the state-of-the-art is given, with reference to the emotional states from the circumplex model of valence-arousal plane.
    Keywords: human emotions; emotion perception; electroencephalography; EEG; galvanic skin response; GSR; electromyographic signal; EMG; skin temperature measurements; SKT.
    DOI: 10.1504/IJRIS.2023.10052192
     
  • Detection method of e-commerce cluster consumption behavior based on data feature mining   Order a copy of this article
    by Ming Yang 
    Abstract: In order to effectively improve the accuracy and efficiency of e-commerce cluster consumption behaviour detection, an e-commerce cluster consumption behaviour detection method based on data feature mining is proposed. Analyse the concept and process of data feature mining, analyse the e-commerce cluster consumption behaviour, and extract the characteristics of the e-commerce cluster consumption behaviour data with multiple characteristics. The Laplace feature mapping method is used to pre-process the extracted data features of e-commerce cluster consumption behaviour, the cyclic neural network structure is used to classify the data of e-commerce cluster consumption behaviour, and the data feature mining method is used to construct the detection model of e-commerce cluster consumption behaviour, so as to realise the detection of e-commerce cluster consumption behaviour. Experimental results show that the proposed method can effectively improve the detection accuracy and efficiency of e-commerce cluster consumption behaviour.
    Keywords: data feature mining; cyclic neural network; Laplace feature mapping; e-commerce clustering; consumer behaviour detection.
    DOI: 10.1504/IJRIS.2023.10052333
     
  • Machine Learning Approach to Roof Fall Risks Classification in UG Mines using Adaboost and XGboost Incorporating Transfer Learning Technique   Order a copy of this article
    by Jitendra Pramanik, Bijay Paikaray, Singam Jayanthu, Abhaya Kumar Samal 
    Abstract: Roof stability in underground coal mines is critical in commanding mine productivity as well as miners’ safety. From this perspective, it is a distinctive challenge to provide a safe working environment along with uncompromised productivity and uninterrupted mining operations. Tested over time, machine learning techniques have evolved as a trusted tool in delivering successful outcomes and in providing trustworthy solutions to many real-life problems in various domains of application that can be safely extended to be adopted in this context. The prime objective of this paper is to propose a transfer learning technique-based approach to classify the occurrence of sudden roof fall based on the available roof sag data. The potency of AdaBoost classification algorithms like decision tree, Gaussian Na
    Keywords: transfer learning techniques; roof fall classification; machine learning techniques; AdaBoost classification; XGBoost.
    DOI: 10.1504/IJRIS.2023.10052334