International Journal of Reasoning-based Intelligent Systems (59 papers in press)

Regular Issues

• A personalized matching method of English learning data packages based on knowledge map  
  by Wei Liu  
  Abstract: In order to improve the retention rate of learning data and the coverage rate of matching results, this paper designs a personalised matching method of English learning data packages based on knowledge map. First, cluster the learning packages information and analyse user interest. Secondly, the knowledge map network is established and the sequence nodes are analysed. Then, the user features and data packages features are decomposed from the relational entities of the knowledge map to obtain independent user features and data packages features; Finally, users’ interests and preferences are optimised by updating the node weights in the knowledge map, and personalised matching is realised according to the similarity between data packets. Experiment shows that this method can control the retention rate of the learning date packages at more than 90% and the matching coverage at more than 95%, which shows that this method can meet the needs of users.
  Keywords: English learning data packages; users; personalised matching; knowledge map; node weight.
  DOI: 10.1504/IJRIS.2023.10057754
   
  
• A Personalized recommendation algorithm of ideological and political education resources based on hybrid collaborative filtering  
  by Shan Zhang 
  Abstract: In order to overcome the problems of large error and long time consuming of existing recommendation methods, this paper proposes a personalised recommendation algorithm of ideological and political education resources based on hybrid collaborative filtering. First, with the help of Oracle, collect ideological and political education resources data to complete the data extraction of ideological and political education resources. Secondly, the data of ideological and political education resources are classified, data removal and data dimension reduction pre-treatment. Finally, the recommended user-resource evaluation matrix of ideological and political education resources is constructed to determine the similarity of the recommended objects of ideological and political education resources. Hybrid collaborative filtering algorithm is introduced to build a personalised recommendation model and realise the personalised recommendation. The experimental results show that the method can reduce the recommendation error and shorten the recommendation time, with the highest recommendation error exceeding 0.03%.
  Keywords: hybrid collaborative filtering; ideological and political education resource data; Oracle; personalised recommendation algorithm.
  DOI: 10.1504/IJRIS.2023.10057768
   
  
• Study of the Crossing Number Associated with Strong Product of Path with Cycle and Triangular Snake Graph  
  by Mhaid Mhdi Alhajjar, Amaresh Chandra Panda, Siva Prasad Behera 
  Abstract: In 2018, Ouyang et al. presented the first efforts related to the crossing number of strong product of the path $P_{m}$ to the cycle $C_{n}$. They proved that $cr(P_{2}$$boxtimes$$C_{n})=n$ for $ngeq3$ together with introducing a general conjecture as follows: $cr(P_{m}$$boxtimes$$C_{n})=(m-1)n$ : $ forall$ $m,ngeq3$ . Here, we prove that Ouyang et al. conjecture is also true for $n=3$ and $mgeq3$ , together with exhibiting an optimal drawing of it. Furthermore, we start to study new case in relation to the strong product of path with triangular snake graph $TS_{n}$ by proving that $cr(P_{2}boxtimes TS_{n})=3lfloordfrac{n}{2}rfloor$ for $ngeq3$ .
  Keywords: Crossing Number; Strong Product; Counting Argument.
  DOI: 10.1504/IJRIS.2023.10057991
   
  
• Study on Intelligent travel route recommendation method based on popularity of interest points  
  by DongMei Ge, Qiang Wu, ZhiZhu Lai 
  Abstract: In order to improve the accuracy of tourist route recommendation and tourist satisfaction, an intelligent route recommendation method based on the popularity of interest points is proposed. From the perspective of popularity of tourist attractions, tourist travel time and scenic spots travel time, the tourist attractions are scored, and the tourist attractions that meet the needs of tourists are mined according to the score results. Design the POI correlation diagram of tourists’ interest points to obtain tourists’ interest preferences from the perspective of time and preference degree. Considering the popularity of POI, UPST-TB algorithm is used to integrate the interest tag data, and combined with the content-based recommendation idea to realise the intelligent recommendation of tourism routes. The experimental results show that the proposed method effectively improves the accuracy and recall rate of tourism route recommendation results, and can recommend scientific and reasonable tourism routes for tourists.
  Keywords: popularity of interest points; tourist routes; intelligent recommendation; directed weighted graph; UPST-TB algorithm.
  DOI: 10.1504/IJRIS.2023.10057992
   
  
• Evaluation of the Shortest Path by Using Bellman Ford's Algorithm in a Fermatean Neutrosophic Environment  
  by Prasanta Kumar Raut, Siva Prasad Behera 
  Abstract: The shortest path problem is an extremely important topic in graph theory with numerous real-life applications in science and technology. In this paper, we discuss an improved edition of Bellman-Ford’s algorithm for evaluating the shortest path on a connected network with respect to fermatean neutrosophic numbers as the vertex weights of the given network, which is the elongation of a neutrosophic number. It is straightforward to evaluate the shortest path when the environment is certain, but in an uncertain environment, it is difficult, so here we used the fermatean neutrosophic number. Finally, we implemented our suggested methodology with a mathematical example and finally studied a comparative analysis with respect to the dynamic programming approach and with different types of existing algorithms, and discussed the advantages of our methods.
  Keywords: Bellman’s algorithm; directed graph network; fermatean neutrosophic numbers; FNNs; score functions; shortest path problem; SPP.
  DOI: 10.1504/IJRIS.2023.10057993
   
  
• A Method for Predicting Learning Achievements in Online Education Based on Behavioral Characteristics  
  by Yan Zhang 
  Abstract: Aiming at the problems of low prediction accuracy, large error in extracting behavioral feature data, and long prediction time in traditional online education learning achievement prediction methods, a behavioral feature based online education learning achievement prediction method is proposed. Firstly, the three-dimensional S-F-T model is used to divide and analyze the students' online education and learning behavior. Then, according to the analysis results, the random forest algorithm is used to calculate the weight of behavior characteristics data, and select the characteristics of students' online education and learning behavior; Finally, a prediction function is constructed through the local self attention mechanism, and the selected network education learning behavior feature data is input as samples into the constructed prediction function, outputting the final prediction result. The test results show that the proposed method has good prediction accuracy, low error in extracting behavioral features, and short prediction time.
  Keywords: Behavioral characteristics; Online education learning; Performance prediction; S-F-T model; Random forest; Local self attention mechanism.
  DOI: 10.1504/IJRIS.2023.10058349
   
  
• Personalized recommendation method of educational resources for ideological and political courses based on data mining  
  by Jun-cheng Wang, Lin Zeng 
  Abstract: In order to improve the teaching quality of ideological and political courses, a personalized recommendation method of educational resources for ideological and political courses based on data mining is proposed in this paper. Firstly, the topological structure for course resources distribution is established, and the data structure features of educational resources are rearranged through sparse and discrete dimension detection. Then, a personalized recommendation model of educational resources is established to control and optimize the recommendation process of teaching resources. The knowledge map model is used to realize the information interaction between users and projects, and the entity embedding and high-order preference dissemination recommendation methods are used to realize the personalized recommendation of educational resources for ideological and political courses. The experimental results show that this method can contributes to high recommendation satisfaction and has a certain application value.
  Keywords: Data mining; Ideological and political courses; Educational resources; Personalized recommendation; Knowledge transfer.
  DOI: 10.1504/IJRIS.2023.10058350
   
  
• Color enhancement processing of interior scene design images based on guided filtering  
  by Shashan Hou 
  Abstract: Aiming at the problems of traditional image color enhancement methods, such as low average gradient, low standard deviation after enhancement and low information entropy, an image color enhancement processing method based on guided filtering was proposed for interior scene design. Firstly, a guided filtering algorithm is used to remove the noise in the interior scene design image, and a gradient domain guided filtering algorithm is used to extract the illumination component. Secondly, the correction weights are linearly stretched to different degrees to obtain adaptive gamma correction weights of different gray levels. Finally, color compensation and enhancement of interior scene design images are achieved through gamma correction. The experimental results show that when the number of images is 6000, the information entropy of the color enhanced image in this method is 921 bits, the image standard deviation reaches 4.83, and the average gradient of the image is 23.8.
  Keywords: Guided filtering; Interior Scene Design Image; Image color enhancement; Gamma correction; Color compensation.
  DOI: 10.1504/IJRIS.2023.10058351
   
  
• A method of swimmer emotion recognition based on sequence annotation model  
  by Yangjun Liu, Junying Yang 
  Abstract: In order to improve the accuracy of swimmer emotion recognition and shorten the time spent on emotion recognition, this paper proposes a swimmer emotion recognition method based on sequence annotation model. Firstly, the original face image of a swimmer is collected, and the collected face image is integrally processed. The original face image is restored through linear equations to obtain a clear face image of the swimmer; Secondly, a face image conversion marker matrix is constructed to match the key points and singular values of the image to extract the features of the swimmer's face image; Then, a sequence annotation model is used to classify the expression features of swimmers; Finally, using fuzzy kernel discriminant analysis technology to complete the swimmer's emotional recognition. Experimental results show that this method can accurately recognize swimmers' facial expressions, with a recognition accuracy of up to 100%.
  Keywords: sequence annotation model; Emotional recognition; Face image; Singular value matching; Swimmers.
  DOI: 10.1504/IJRIS.2023.10058352
   
  
• Retrieval Method of Network Education Resources Based on Associated Data  
  by Guojuan Li 
  Abstract: Aiming at the problems of low retrieval accuracy and long retrieval time in traditional online education resource retrieval methods, a method of online education resource retrieval based on associated data is proposed. First, establish a model of online education resources, obtain online education resource data through web crawlers, and then preprocess the obtained online education resource data through a series of steps. Then, based on the preprocessing results, construct a semantic grid computing model to extract semantic features of online education resources. Finally, based on semantic features, associate the semantic data of online education resources through semantic similarity and semantic correlation, Based on the associated data, a retrieval model for online education resources is established using naive Bayesian methods to obtain the retrieval results. Simulation results show that the proposed method has higher retrieval accuracy and shorter retrieval time for online education resources.
  Keywords: Associated data; Network education; Resource retrieval; Web crawler; Semantic grid; Naive Bayes.
  DOI: 10.1504/IJRIS.2023.10058353
   
  
• A Method of Integrating English Listening and Speaking Distance Teaching Resources Based on Data Deduplication  
  by Wei Liu 
  Abstract: The integration of teaching resources can improve students' English listening and speaking abilities. Therefore, a method of distance teaching resources integration based on data deduplication is proposed. Firstly, construct a teaching feature extraction table and collect English listening and speaking distance teaching resources. Secondly, CAONT is introduced to conduct a round of transformation of distance learning resources for English listening and speaking abilities to achieve safe and deduplication processing of teaching resource data. Finally, after completing the deduplication process, a resource integration function for distance learning of English listening and speaking ability is constructed. Through the constraints of temporal relationships between tasks, the resource integration function for distance learning of English listening and speaking ability is solved to achieve the integration of English teaching resources. The experimental results show that the accuracy rate of integrating English teaching resources can reach 98%.
  Keywords: Data deduplication; CAONT; Temporal relationship constraints; Integration of teaching resources;content of courses.
  DOI: 10.1504/IJRIS.2023.10058354
   
  
• Digital Ideological and Political Education Data Sharing Algorithm Based on Federal Incremental Learning  
  by Aihua Mo 
  Abstract: Traditional sharing methods have problems such as low safety factor, low recall rate, and low efficiency in data sharing. A federated incremental learning based data sharing algorithm for digital ideological and political education is proposed. Based on the data extraction standards of digital ideological and political education, determine the data centre of digital ideological and political education, and determine the dissimilarity of data through Minkowski distance; Using a random mapping method to convert the data into a consistent pattern, and implementing data preprocessing according to the optimal criteria for classification; calculate the aggregation weight of data, enable shared data to be back-propagated and iteratively updated, combine federated learning and incremental learning, build multiple data sharing clients, and add multiple data security keys to achieve secure sharing of digital ideological and political education data. The test results show that the method has good data sharing security and high sharing efficiency.
  Keywords: federal incremental learning; digital ideological and political teaching; data sharing; Minkowski distance; decision tree.
  DOI: 10.1504/IJRIS.2023.10058355
   
  
• A Classification Method of Reader Borrowing Data Information in Modern Library Based on Top-k Query Algorithm  
  by Wei Huang, Jing Ling 
  Abstract: In order to overcome the problems of low accuracy of classification results and long classification time in the traditional classification method of modern library reader borrowing data information, a modern library reader borrowing data information classification method based on top-k query algorithm is proposed. First of all, top-k query algorithm is used to collect library readers’ borrowing data information and preprocess it; then, combining the information gain algorithm and the maximum correlation and minimum redundancy algorithm, the second feature selection is performed for the data information borrowed by readers; finally, the polynomial naive Bayesian model is used to realise the classification of library readers’ borrowing data information. The experimental results show that the classification results using this method are accurate, the classification time is always within 11 s, the classification effect is good, and the application performance is good.
  Keywords: top-k query algorithm; modern library; readers borrow data; information classification; maximum correlation minimum redundancy algorithm; polynomial naive Bayesian model.
  DOI: 10.1504/IJRIS.2023.10058356
   
  
• Automatic classification of library digital resources based on decision tree algorithm  
  by Liping Zhong 
  Abstract: Aiming at the problems of large classification error, low accuracy of feature extraction and long classification time in automatic classification of library digital resources, this paper designs an automatic classification method of library digital resources based on decision tree algorithm. First, determine the maximum and shortest distance between digital resource data and aggregate the collected library digital resources. Then, the feature pyramid network structure is introduced and the channel multiplication method is used to extract the features of library digital resources data. Finally, construct the library digital resource data tree, determine the trunk and the critical degree of the library digital resource branches through entropy calculation, prune the unimportant branches, set up the library digital resource classifier, and realise the final automatic classification research. The test results show that the proposed method can reduce the classification error, and the classification effect is good.
  Keywords: decision tree algorithm; library digital resources; automatic classification; semantic similarity; the shortest distance; channel multiplication.
  DOI: 10.1504/IJRIS.2023.10058357
   
  
• Multi label attribute classification algorithm for big data based on core density estimation  
  by Jian Xie, Dan Chu 
  Abstract: Aiming at the problems of inaccurate core density estimation of tag attributes during the classification process, resulting in large attribute feature extraction errors, low classification accuracy, and poor data balance coefficient, a large data multi tag attribute classification algorithm based on core density estimation is designed. First, the machine learning algorithm is used to extract data attribute features, calculate their expected estimates, and complete feature extraction. Then, the extracted data is processed in a unified dimension, and the mean value processing and Pearson correlation coefficient are used to complete the pre-processing. Finally, according to the core density estimation theory, determine the density of any data in the overall density function, then determine the probability quality function and degradation increment of other characteristic data points, introduce the classification kernel function, and construct the classification model to achieve the final effective classification. The results show that the proposed method can reduce feature extraction error and improve classification accuracy.
  Keywords: core density estimation; big data; multi-label classification; Pearson correlation coefficient; density function; degenerate increment.
  DOI: 10.1504/IJRIS.2023.10058358
   
  
• Solving shortest path using Modified Dijkstra's algorithm with spherical neutrosophic numbers as arc length  
  by Prasanta Kumar Raut, Siva Prasad Behera 
  Abstract: The shortest path problem is a fundamental problem in network analysis, and many algorithms have been developed to solve it efficiently. One of the most widely used algorithms is Dijkstra’s algorithm, which finds the shortest path between two nodes in a graph. Recently, spherical neutrosophic numbers (SNNs) have emerged as a powerful tool for handling uncertain information in network domains. In this study, we propose a modified version of Dijkstra’s algorithm that uses SNNs to handle uncertainty in edge weights in a network. The edge weights are represented as SNNs to account for uncertainty in the distances between vertices, and the algorithm is modified to handle the arithmetic operations of SNNs and update distances and priorities accordingly. Our results show that the algorithm can effectively handle uncertainty in the network domain and find the shortest path between two nodes with high accuracy.
  Keywords: connected network; Dijkstra’s algorithm; shortest path problem; spherical neutrosophic numbers; SNNs.
  DOI: 10.1504/IJRIS.2023.10058359
   
  
• Adaptive Classification of Electronic Music Signals Based on Multiple Machine Learning Models  
  by Shuqing Li 
  Abstract: In order to overcome the problems of low PSNR, low accuracy, and long time consumption in traditional methods, an adaptive classification method of electronic music signals based on multiple machine learning models is designed. Electronic music signals are collected by sensors and filtered by sparse coding. The filtered signal is processed by frame segmentation and windowing, and the time domain and frequency domain characteristics of the signal are extracted. The multi-machine learning model is built by Naive Bayes, support vector machine and decision tree. The signal filtering results are taken as the input vector of the model, and the adaptive classification results of electronic music signals are taken as the output vector of the model to realise the adaptive classification of signals. Experimental results show that the maximum PSNR of this method is 55.66 dB, the classification accuracy is always above 96%, and the average classification time is 867 ms.
  Keywords: multiple machine learning models; electronic music signals; adaptive classification; signal filtering; Naive Bayes; support vector machine; decision tree.
  DOI: 10.1504/IJRIS.2023.10058360
   
  
• Personalized recommendation method of online education learning resources based on collaborative filtering algorithm  
  by Yang Yang 
  Abstract: In this paper, a personalised recommendation method for online education learning resources based on collaborative filtering algorithm is proposed. Firstly, the traditional crawler technology is used to collect resource data, and the implicit crawler technology is introduced to obtain key resources. Then, the automatic encoder extracts the nonlinear features, combines the expected risk minimisation and sample error, defines the empirical risk classification resource data, and completes the pre-treatment of resource data. Finally, a collaborative filtering algorithm is introduced to obtain learners’ individual needs through forgetting factor optimisation, and a collaborative filtering recommendation model is designed. The results show that the recommended error of the proposed method is 0.10%, the recommended time is less than two seconds, and the work efficiency is over 95%, indicating that the method can improve the accuracy and efficiency of resource recommendation.
  Keywords: collaborative filtering algorithm; online education; learning resources; personalised recommendation.
  DOI: 10.1504/IJRIS.2023.10058361
   
  
• Recognition of Foul Actions of Football Players Based on Self Attention Mechanism  
  by Yingdong Song 
  Abstract: In order to solve the problems of low recognition accuracy and long recognition time in traditional methods, a recognition method of foul actions of football players based on self attention mechanism was designed. Firstly, the image of football player is obtained by Kinect device and the image features are extracted. Secondly, the background of action image is separated by background clipping method, and the noise of motion pixel point eight neighbourhood is removed by smoothing noise reduction method. Finally, set a set of key value pairs for foul actions, encode them using the encoder in the self attention mechanism, calculate the probability of foul actions occurring, and decode the dependency relationship of the encoded foul actions to determine whether the action is a foul action and obtain relevant recognition results. The test results show that the proposed method can improve the accuracy of foul action recognition and has a good effect.
  Keywords: self attention mechanism; football players; foul actions; recognition; background clipping method; encoder.
  DOI: 10.1504/IJRIS.2023.10058362
   
  
• Comparison of Bald Eagle Search (BES) algorithm with benchmark meta-heuristic algorithms (PSO, ABC and GWO) applied to robot path planning  
  by Ganapati Kamat, Yogeswarr S, Narahari N, Vineeth Parashivamurthy 
  Abstract: This work focuses on comparing the latest swarm-intelligence-based algorithm, the bald eagle search (BES), with three well-known metaheuristic algorithms
  Keywords: bald-eagle search; BES; particle swarm optimisation; PSO; grey wolf optimisation; GWO; artificial bee colony; ABC; swarm intelligence; path optimisation.
  DOI: 10.1504/IJRIS.2023.10058592
   
  
• Forest fire identification method of UAV remote sensing image based on FCM clustering algorithm  
  by Peiran Li, Yuqing Tan, Wei He, Haifeng Zhang, Zhanlan Xie 
  Abstract: In order to overcome the problems of low recognition accuracy and speed in traditional forest fire recognition methods, the paper proposes a forest fire recognition method based on unmanned aerial vehicle remote sensing images using FCM clustering algorithm. Firstly, the FCM clustering algorithm is used to cluster and segment the target RGB pixels in unmanned aerial vehicle remote sensing images. Secondly, according to the calculation rules of the LPB algorithm, the flame characteristics of forest fires are calculated. Finally, the optimal hyperplane of SVM is used to judge whether the target RGB pixels in the remote sensing image are fire pixels, and the forest fire recognition method can be obtained after traversing all pixels. The experimental results show that the fire location identified by this method is completely consistent with the actual situation, and the recognition rate can reach a maximum of 52 frames/s.
  Keywords: FCM clustering algorithm; UAV remote sensing image; forest fire awareness; flame characteristics.
  DOI: 10.1504/IJRIS.2023.10058888
   
  
• Research on Korean translation error text detection method based on machine vision  
  by Ziyou Zhou 
  Abstract: Due to the differences and complexity between languages,Korean machine translation still has translation errors,ambiguity and discontinuity, such as translation errors, ambiguity and discontinuity.Therefore, this paper proposes a Korean translation error text detection method based on machine vision.The linear array CCD sensor in machine vision is used to collect Korean translation text images. The images are corrected for distortion and processed for grayscale,and enhanced through Gabor filters. The images are segmented into multiple candidate frames for recognizing the Korean translation text regions.Combining the results of text region recognition,a CNN-Attention model is constructed. The model is then used to input the image to be recognized, extract text features and match with knowledge points to output detection results. The experimental results show that the minimum text recognition rate of this method is 94.8%, the average detection rate is 97.1%, and the minimum detection time is 0.3s
  Keywords: Machine vision; Korean translation; Error text detection; Linear array CCD sensor; Gabor filters; CNN-Attention model.
  DOI: 10.1504/IJRIS.2023.10058890
   
  
• Enterprise hidden financial information extraction method based on data source dependency  
  by Jingyi Li 
  Abstract: The significance of hidden financial information extraction research lies in the discovery of corporate financial fraud. In order to address the shortcomings of traditional methods such as low recall and precision, and long time overhead, therefore an enterprise hidden financial information extraction method based on data source dependency is proposed. By determining the redundancy of enterprise financial information through data source dependency relationships, the data source dependency relationships are cleaned, and the cleaned data is input into the RDCNN-CRF model to achieve information label classification. Combined with the label classification results, an ordered long-short-term memory-multi-head attention mechanism neural network model is constructed, and the processed data is input into this model. The model output is the result of extracting hidden financial information. The experimental results show that the mean recall rate and precision rate of the proposed method are 97.92% and 98.07%, and the maximum time consumption is 0.82 s.
  Keywords: data source dependency; enterprise; hidden financial information; information extraction; redundancy; RDCNN-CRF model.
  DOI: 10.1504/IJRIS.2024.10059318
   
  
• A Personalized Recommendation Method for Low-carbon Tourism Routes Based on User Feature Mining  
  by Bing Hou 
  Abstract: In order to overcome the problem of poor user feature mining accuracy and recommendation satisfaction in personalised recommendation methods, a low-carbon tourism route personalised recommendation method based on user feature mining is proposed. Firstly, determine the changes in the cost of low-carbon tourism production behaviour and the demand curve for low-carbon tourism, as well as the characteristics of low-carbon tourism. Secondly, calculate the similarity of low-carbon tourism user features to achieve overall feature mining for low-carbon tourism users. Finally, calculate the user feature entropy and conditional entropy, and analyse the key degree of low-carbon behaviour characteristics of tourism users. Personalise recommendations for low-carbon tourism routes that meet the conditions based on users’ preferred low-carbon tourism attractions. The experimental results indicate that the research method can effectively improve the efficiency of fine-grained user feature mining, and the satisfaction with personalised recommendation of low-carbon tourism routes is high.
  Keywords: user feature mining; low carbon tourism; route planning; personalised recommendation; conditional entropy; route planning.
  DOI: 10.1504/IJRIS.2024.10059319
   
  
• An optimizing method of Japanese character recognition based on improved support vector machine  
  by Yang Wang, Pan Zhao 
  Abstract: Aiming at the problem of low recognition accuracy and long recognition time due to poor feature extraction, a Japanese character recognition method based on improved support vector machine was studied. First, the mean filtering method is used to denoise Japanese text samples, and then Gabor filter is used to extract features, and Fourier transform is used to optimise the extraction speed. Then, the initial text recognition method is constructed by introducing support vector machine and improving the penalty coefficient algorithm. Finally, Japanese character recognition is realised by using the multi-layer perceptron function as the kernel by processing linear indivisible samples with the relaxation term minimisation. The experimental results show that the proposed method can effectively extract Japanese character features with a recognition accuracy of 97.4% and a recognition time of only 1.3 s, which effectively solves the problems of low recognition accuracy and long recognition time.
  Keywords: support vector machine; Japanese characters; identification method; Gabor filter; Fourier transform; penalty coefficient; Kernel function.
  DOI: 10.1504/IJRIS.2024.10059320
   
  
• Evaluation of the shortest path using a ripple-spreading algorithm in an uncertain environment  
  by Prasanta Kumar Raut, Siva Prasad Behera 
  Abstract: The efficient determination of the SP in a connected network is a crucial problem in various domains, such as transportation, communication, and logistics. Traditional approaches often assume crisp values for the parameters, but in real-world scenarios, uncertainties and imprecision are prevalent. This study proposes a novel approach that utilises the ripple-spreading algorithm with triangular fuzzy numbers as parameters to determine the SP in a given network. The ripple-spreading algorithm, known for its ability to propagate information or updates through a network, is adapted to handle uncertainties by incorporating triangular fuzzy numbers. Each connection in the network is described by a triangular fuzzy number, which utilises the triangular fuzzy numbers consisting of lower bounds, modal values, and upper bounds, forming a triangular-shaped membership function. The propagation is performed considering the triangular fuzzy numbers as parameters, accounting for uncertainties and imprecisions.
  Keywords: ripple-spreading algorithm; shortest path; network; triangular fuzzy number; uncertainty.
  DOI: 10.1504/IJRIS.2024.10059321
   
  
• Study on Deep Mining of Network Ideological and Political Resources Based on Weighted Deep Forest  
  by Dan Yin, Na Wang 
  Abstract: Traditional methods for deep mining of ideological and political resources on the internet have problems such as poor comprehensiveness in mining and ineffective fine-grained control. Therefore, a deep mining method of network ideological and political resources based on weighted deep forest is proposed. Firstly, the ideological and political resources are classified and their relationships are structured into a tree-like structure. Then, keyword tables, index tables, and synonym index tables are designed to extract features of resources after indexing and dimensionality reduction. Finally, a multi-granularity scanning structure based on weighted deep forest is constructed to determine the existence status of resources on different network platforms, and resource deep mining is achieved by determining data weight factors. By analysing the experimental results, it can be seen that the method proposed in this article has good comprehensiveness in deep resource extraction, good fine-grained control effect, and good mining effect.
  Keywords: weighted deep forest; network ideological and political resources; deep mining; tree-like structure; extract features; weight factors.
  DOI: 10.1504/IJRIS.2024.10059322
   
  
• Adverse COVID-19 Vaccination-Related Events in India: A Cross-sectional Study Using Machine Learning to Predict Their Severity  
  by Hemangini Mohanty, Santilata Champati, Jyotiranjan Sahoo 
  Abstract: The most effective method of preventing Coronavirus illness is vaccination, despite its status as a global outbreak. In this study, India's COVID-19 vaccination's unfavorable consequences are evaluated and builds a model for predicting the severity of side effects. A cross-sectional study was conducted through an online survey among the Indian population who received at least a single dose of COVID-19 vaccine. Then, data were statistically analyzed, and machine learning tools were used to build a predictive model predicting the severity of side effects. A total of 3222 participants' records were analyzed; for those participants receiving three different Vaccines, i.e., Covaxin, Covishield, and SputnikV. Only 25% experienced mild-to-moderate side effects. The most common side effects recorded were Fever/Chills, headache, feeling pain at the injection site, tiredness and fatigue. The people receiving the first dose (71.93%) had significant side effects compared to the second dose.
  Keywords: SARS-CoV-2 vaccine; COVID-19 vaccine; post-vaccination symptoms; machine learning; predictive model.
  DOI: 10.1504/IJRIS.2024.10059323
   
  
• Content Clustering Based Propagation Feature Extraction for Short Video Media Platforms  
  by Shiming Zhou 
  Abstract: In order to improve the accuracy of short video media platform propagation feature extraction, this paper proposes a content clustering based propagation feature extraction method for short video media platforms. Firstly, the data obtained through data crawling is user online comments, which are used to obtain user comments on short video media platforms; Secondly, preprocess the propagation data of short video media platforms through data cleaning and text segmentation; Then, input the short video, calculate the classification loss for each frame separately and sum it up. Finally, the content clustering method is used to cluster the propagation features of short video media platforms, and the final propagation features of short video media platforms are obtained by solving the propagation feature function. The experimental results show that the proposed method can effectively improve the accuracy of propagation feature extraction and enhance the recall rate of feature extraction.
  Keywords: K-means clustering; Content clustering; Data crawling; Cross entropy loss.
  DOI: 10.1504/IJRIS.2023.10059840
   
  
• Personalized Recommendation Method of Japanese Teaching Resources Based on Collaborative Filtering  
  by Pan Zhao, Yang Wang 
  Abstract: In the existing personalized recommendation methods of Japanese teaching resources, the confidence of resource recommendation is low, and the personalized recommendation effect is poor. Therefore, a personalized recommendation method for Japanese teaching resources based on collaborative filtering is designed. Firstly, the multi-modal interest similarity model and hidden factor depth are used to extract the features of Japanese teaching resources. Then, the scoring matrix and label matrix are constructed, and TF-IDF algorithm is introduced to calculate user preferences, so as to realize user preference mining of Japanese teaching resources. Finally, the Japanese teaching resources recommendation scoring matrix is constructed, similar neighbors with preferences are sought, and personalized recommendation rules are set to realize personalized recommendation of Japanese teaching resources. The experimental results show that the proposed method can improve the confidence of Japanese teaching resource recommendation and improve the poor performance of personalized recommendation.
  Keywords: Collaborative filtering; Japanese language teaching resources; Preference; Scoring matrix; Label matrix; Penalty factor.
  DOI: 10.1504/IJRIS.2023.10059841
   
  
• An online teaching resource recommendation algorithm based on category similarity  
  by Lingyu Chen 
  Abstract: To overcome the problems of poor recall rate, time-consuming resource recommendation list generation, and low ideal loss cumulative gain in traditional online teaching resource recommendation algorithms, a category similarity based online teaching resource recommendation algorithm is proposed. Firstly, segment the user group of online teaching resources and construct a learning user profile of online teaching resources based on dynamic field theory; Secondly, the similarity between user categories and teaching categories is obtained, and the similarity of online teaching resource categories is obtained through sorting; Finally, based on category similarity, an online teaching resource PAF recommender is constructed, taking into account teaching popularity and user loyalty to achieve online teaching resource recommendation. The experimental results show that the online teaching resource recommendation recall rate of this algorithm can reach 99.9%, the ideal cumulative loss gain of teaching resource recommendation is 66.18, and the resource recommendation list generation time is 11 s.
  Keywords: category similarity; teaching resources; recommendation algorithm; dynamics; product adoption forecasting; PAF recommender.
  DOI: 10.1504/IJRIS.2023.10059842
   
  
• Network Intrusion Detection in Smart Grid using Supervised Learning  
  by Jason Marandi, Priyanka Ahlawat 
  Abstract: The emergence of smart grid (SG) as a replacement for the traditional power grid has increased the efficiency and reliability of the power grids. This is due to incorporating communication networks with the traditional power grid. This integration also exposes the grid to vulnerabilities common in communication networks. An intrusion detection system (IDS) is an important tool for providing secure and reliable services in smart grid. Anomaly-based IDS (AIDS) has gained importance for detecting zero-day attacks. Machine learning (ML) is a promising solution as it learns from past events to predict new network threats. Two common ML types are supervised and unsupervised learning. They differ based on how data is presented to them. This paper explores supervised ML approaches using the CICIDS2017 dataset to achieve the best classifier and parameters. The tuned random forest (RF) model achieved an impressive 99.92% accuracy with a 70:30 dataset split for training and testing.
  Keywords: smart grid; SG; intrusion detection system; IDS; signature based detection; anomaly based detection; IDS datasets; machine learning; ML; supervised learning.
  DOI: 10.1504/IJRIS.2023.10060968
   
  
• Multi channel user interface generation method based on conflict degree and collaborative filtering  
  by Ting Zhang 
  Abstract: Aiming at the problems of low recommendation accuracy, low success rate of interactive control and long time of traditional recommendation methods, a multi-channel user interface generation method based on conflict degree and collaborative filtering is proposed. Firstly, the evidence optimality and binary comparison matrix of user behaviour are determined, and the evidence conflict degree of user behaviour is calculated using the weight calculation results, and user behaviour recognition is realised according to the evidence characteristics. Secondly, collaborative filtering is adopted to implement user interface pattern recommendation. Finally, according to the recommended user interface model, a multi-channel user interface generation framework is constructed, including user task decomposition, multi-channel interaction control and interface code determination. Experimental test results show that the maximum accuracy of user interface pattern recommendation using proposed method is 98.36%, the average success rate of multi-channel interaction control is 97.35%, and the minimum time for multi-channel user interface generation is 1.8 s.
  Keywords: conflict degree; collaborative filtering; multi-channel; user interface generation; evidence optimality; multi-channel interaction control.
  DOI: 10.1504/IJRIS.2024.10061315
   
  
• An Abnormal Detection method of Enterprise Financial Accounting Data Based on Bayesian network  
  by Baoyuan Liu 
  Abstract: To improve the effectiveness of anomaly detection in enterprise financial accounting data and reduce the error probability of anomaly detection, this paper proposes a Bayesian network-based anomaly detection method for enterprise financial accounting data to ensure the accuracy and reliability of financial reports. By introducing the nearest neighbour rule and KNN algorithm to calculate the distance between different data attributes, the XGBoot algorithm is used to obtain the optimal balance point and achieve the classification of enterprise financial accounting data; According to the topological structure, the prior knowledge and accounting data characteristics are fitted, the accounting data characteristics are extracted, the interference items of abnormal features are removed by Markov blanket elimination method, the conditional probability of Bayesian network is calculated, and the data anomaly detection is realised to realise the final research. The test results indicate that the false positive rate of abnormal data detected by this method is low, and the recall rate is high, which has certain feasibility.
  Keywords: Bayesian network; enterprise financial accounting data; abnormal detection; nearest neighbour rule; parameter learning.
  DOI: 10.1504/IJRIS.2024.10061316
   
  
• A Modified Algorithm to Solve Minimum Spanning Tree Problem  
  by Siva Behera, Prasanta Kumar Raut 
  Abstract: Here, we give a modified and improved algorithm to find an MST from a given weighted undirected graph. Our algorithm works in two stages. In the first stage, it finds a forest formation involving the edges with minimum weights, and in the second stage, the algorithm converts the forest obtained in the first stage into a spanning tree. Further, we demonstrate our algorithm with one example, discuss the complexity of our algorithm, give its implementation using the Java Applet program, and compare the output with Kruskal’s algorithm. This study demonstrates that the suggested approach is superior to conventional algorithms for handling MST.
  Keywords: array; binary heap; Kruskal’s algorithm; minimum spanning tree; forest; stack.
  DOI: 10.1504/IJRIS.2024.10061376
   
  
• Color Offset Compensation Method of 3D Animation Scene Image Based on Color Difference Interpolation  
  by Xu Lan, Lizhen Jiang 
  Abstract: A colour offset compensation method for 3D animation scene images based on colour difference interpolation is proposed to address the issues of unsatisfactory colour offset compensation and long time in traditional 3D animation scene images. Adopting high-dimensional convolutional fast bilateral filtering algorithm for denoising 3D animation scene images, using an improved gain coefficient greyscale world correction method for correction, converting the image from RGB space to HIS colour space, linearly stretching the saturation component of the image, and achieving colour offset compensation for 3D animation scene images. The simulation results show that after applying the method proposed in this paper for image processing, the highest signal-to-noise ratio is 23.85db, the highest peak signal-to-noise ratio is 28.69db, the denoising running time is always kept below 125ms, the maximum information entropy is 8.30, the maximum contrast is 72.80, the minimum colour difference is 0.521, and the colour offset compensation time is always kept below 90ms. The colour offset compensation effect is good.
  Keywords: colour difference interpolation; 3D animation scene image; colour offset compensation.
  DOI: 10.1504/IJRIS.2024.10061377
   
  
• Speech recognition method of English translation robot based on HMM algorithm  
  by Xiaolin Zhang, Tao Wang, Ling Jiang 
  Abstract: To solve the problems of poor speech feature extraction and high false recognition rate in English translation robot speech recognition, a speech recognition method for English translation robot based on HMM algorithm is proposed. Firstly, the speech signal is collected and processed by noise reduction, reverberation removal, speech segmentation, volume normalisation and speech feature extraction. Then, HMM algorithm is introduced to extract the optimal speech features. Finally, the speech recognition function is constructed, and the speech features are regarded as discrepancy, and the recognition function is optimised through the error term to realise the speech recognition of the English translation robot. The experimental results show that this method can effectively extract the voice features of voice command signals. The average error rate is 1.5%, and the average recognition rate is 98.47%. The identification results of valid information and invalid information obtained are consistent with the actual results.
  Keywords: HMM algorithm; English translation robot; speech recognition; noise reduction; de-reverberation; speech segmentation; volume normalisation; speech feature extraction.
  DOI: 10.1504/IJRIS.2024.10061378
   
  
• A Multi object Tracking Method for Complex Scenes Based on Edge Feature Extraction of Video Images  
  by Haidi Yuan, Wei Li 
  Abstract: In order to improve the accuracy of complex scene tracking and shorten tracking time, this paper proposes a multi object tracking method for complex scenes based on video image edge feature extraction. Firstly, capture video images from complex scenes. Secondly, obtain the edge feature components of video images and extract complex edge features of video images. Then, a single target tracker is designed to construct an affinity matrix and filter out elements that exceed the threshold. Finally, an undirected graph is constructed to match the previously tracked trajectory with the detection results. For trajectory updates on the matching, multi target tracking is achieved by solving the maximum weight clique graph to achieve complex scene multi target tracking. The experimental results show that the accuracy of our method can reach 99.89%, and the tracking time is only 2.6 seconds, indicating that our method can effectively improve the tracking effect.
  Keywords: template matching; sports affinity; maximum weight clique graph; affinity function.
  DOI: 10.1504/IJRIS.2024.10061379
   
  
• Intelligent Management Platform for Enterprise Dynamic Accounting Information Based on Data Mining  
  by Huiying Kang 
  Abstract: In order to promote the improvement of enterprise financial management level, a data mining-based intelligent management platform for enterprise dynamic accounting information is proposed. Firstly, the SOM algorithm is used to fully collect dynamic accounting information of enterprises, and the collected data is used as the management object. Secondly, the association rule method is used to calculate the support and confidence levels, completing the normalisation of accounting information. Finally, based on the normalised accounting information, a quantitative management function for accounting information is constructed to achieve intelligent management of accounting information. The experimental results show that the average accounting information mining time of the platform in this article is around 8 minutes, the maximum access latency is not more than 10 ms, and the accounting information reading time is always less than 2.5 s. The performance of the intelligent management platform has been significantly improved.
  Keywords: data mining; dynamic accounting information; intelligent management platform; SOM algorithm; normalisation processing.
  DOI: 10.1504/IJRIS.2024.10061380
   
  
• Enterprise financial anomaly data detection method based on improved support vector machine  
  by Hao Wang, Huan Wang 
  Abstract: In this paper, a method for detecting financial anomaly data in enterprise based on improved support vector machine is proposed. By analyzing the abnormal financial data of enterprises, the distribution of abnormal financial data is determined, and a multi-channel Text-CNN neural network model is constructed to extract initial abnormal data features. The nonlinear features of abnormal data are adjusted using the least squares method to achieve feature extraction of abnormal financial data of enterprises. Optimize the support vector machine algorithm through differential evolution algorithm to determine the optimal classification population for enterprise financial anomaly data features for global optimization; By constructing a global selection model, achieve the detection of abnormal financial data in enterprises. The test results indicate that the fitting degree of the method proposed in this paper is good, and the detection error is low, indicating a certain degree of feasibility.
  Keywords: Improving support vector machines; Point anomaly; Context exception; Collection anomaly; Least squares method; Differential Evolution Algorithm.
  DOI: 10.1504/IJRIS.2024.10061381
   
  
• Fog Computing Approaches for Sustainable Smart Cities  
  by Parimal Giri, Sanjoy Choudhury, Diptendu Sinha Roy, Bijay Paikaray 
  Abstract: Reverse auction resource allocation and utilisation in a fog environment is an exciting yet challenging problem due to the numerous constraints and needs. The end user presents the resource needed and exposes it to a notable set of qualified expert suppliers in a reverse auction mechanism. This paper provides a combinatorial reserve auction for the fog environment that considers both cost and non-cost attributes, such as the type of QoS criteria, reputation, and other factors, to ensure the success of winning stakeholder cooperatives. To solve this problem, estimate calculation is used, and a polynomial-time solution that comes near to being exact is obtained. In order to increase their benefit, the auction process allows suppliers to disclose accurate data. This strengthens the system since it can keep up with the client’s utility. According to the execution assessment and a relative report utilising various widely used models, the suggested method performs better.
  Keywords: fog computing; CRA; smart cities.
  DOI: 10.1504/IJRIS.2024.10062540
   
  
• AMAA-GMM: Adaptive Mexican Axolotl Algorithm based Enhanced Gaussian Mixture Model to Segment the Cervigram Images  
  by Lalasa Mukku, Jyothi Thomas 
  Abstract: Colposcopy is a crucial imaging technique for finding cervical abnormalities. Colposcopic image evaluation, particularly the accurate delineation of the cervix region, has considerable medical significance. Before segmenting the cervical region, specular reflection removal is an efficient approach. Because, cervical cancer can be found using a visual check with acetic acid, that turns precancerous and cancerous areas white and these could be viewed as signs of abnormalities. Similarly, bright white regions known as specular reflections obstruct the identification of aceto-white areas and should therefore be removed. So, in this paper, specular reflection removal with segmenting the cervix region in a colposcopy image is proposed. The proposed approach consists of two main stages, namely, pre-processing and segmentation. In the pre-processing stage, specular reflections are detected and removed using a swin transformer. After that, cervical regions are segmented using an enhanced Gaussian mixture model (EGMM). For better segmentation accuracy, the best parameters of GMM are chosen via the adaptive Mexican axolotl optimisation (AMAO) algorithm. The performance of the proposed approach is analysed based on accuracy, sensitivity, specificity, Jaccard index, and dice coefficient, and the efficiency of the suggested strategy is compared with various methods.
  Keywords: Gaussian mixture models; machine learning; segmentation; metaheuristics; deep learning; enhanced Gaussian mixture model; EGMM; adaptive Mexican axolotl optimisation; AMAO.
  DOI: 10.1504/IJRIS.2024.10063302
   
  
• Modified VGG19 Transfer Learning Model for Breast Cancer Classification  
  by Sashikanta Prusty, Srikanta Patnaik, Sujit Kumar Dash 
  Abstract: Breast cancer (BC) seems to have become a sign of great concern in everyday life. There have been a lot of research and methods already designed in the last few years but continue to be prone worldwide. To address this issue, a modified version of the visual geometric group-19 (VGG19) model, namely BCNet21 has been proposed here to classify the malignant class from breast mammogram images collected from the MIAS dataset. Furthermore, the performance of our proposed BCNet21 model has been compared with the two most common predefined VGG16 and VGG19 models using the performance metrics and Cohen-Kappa test (k). The result shows that the proposed BCNet21 model outperforms with a higher accuracy of 98.96 % and a kappa score of 86%, compared to the VGG16 and VGG19 models. This concludes that the BCNet21 model is much closer to the near-perfect agreement between actual and predicted breast cancer instances.
  Keywords: breast cancer; BC; deep learning; DL; transfer learning; TL; VGG19; VGG16; kappa score.
  DOI: 10.1504/IJRIS.2024.10063303
   
  
• Unsupervised English-Chinese word translation using various retrieval methods  
  by Cuiping Zou 
  Abstract: Because it is essential for improving the user experience, controlling styles in neural machine translation (NMT) has garnered a lot of interest in recent years. The majority of the earlier research on this subject focused on managing the amount of formality, and it was successful in making some headway in this particular area. The purpose of this study is to tackle each of these difficulties by presenting a new benchmark and strategy. A benchmark for multiway stylistic machine translation (MSMT) is presented, which incorporates a wide variety of styles that span four different language domains. Following that, we offer an approach that we call style activation prompt (StyleAP), which involves extracting prompts from a styled monolingual corpus and does not need any more fine-tuning alterations. Experiments demonstrate that StyleAP is capable of exerting a significant amount of control on the translation style and achieving extraordinary levels of performance.
  Keywords: unsupervised English-Chinese; neural machine translation; NMT; translation induction for Chinese.
  DOI: 10.1504/IJRIS.2024.10067116
   
  
• A Hybrid Model of Fuzzy Logic to Enhance Data Mining Accuracy Incorporating Intra-Concentration and Inter-Separability (I2CS) Loss into Neighborhood Component Analysis  
  by Hemangini Mohanty, Santilata Champati 
  Abstract: Data mining is crucial to discovering meaningful insights and patterns from massive datasets. However, the accuracy and efficiency of data mining algorithms are often challenged by the curse of dimensionality and the complexity of real-world data. In this article, we propose a novel approach to enhance the accuracy of data mining by enriching the concept of intra-concentration and inter-separability (I2CS) loss into neighbourhood component analysis (NCA). NCA is a dimensionality reduction technique that focuses on preserving local neighbourhood information, thus improving classification accuracy. Fuzzy logic, on the other hand, provides a flexible framework to handle uncertainty and vagueness in data, enabling more nuanced decision-making. By integrating fuzzy C-means clustering with I2CS-NCA, we aim to leverage the complementary strengths of both approaches to enhance the accuracy and robustness of data mining algorithms. Also, the experimental results show that the proposed model gives the highest accuracy.
  Keywords: I2CS loss; neighbourhood component analysis; NCA; fuzzy C-means clustering; random forest.
  DOI: 10.1504/IJRIS.2024.10067117
   
  
• Ensemble of Transfer Learning With Convolutional Neural Networks for Writer Recognition in Historical Documents  
  by Radmila Jankovic Babic, Alessia Amelio, Ivo R. Draganov, Marijana Cosovic 
  Abstract: In the cultural heritage domain, writer recognition has become a challenging classification task still explored for historical documents, due to the presence of different types of noise in the documents, i.e. ink bleed-through, ink corrosion, stains on paper or parchment, difficulty in the character discrimination, elements different from the text, such as images, etc. that limit the effectiveness of existing techniques. To further advance in terms of robustness of classification and experimental setting, we propose a new deep learning model which ensembles pre-trained Convolutional Neural Networks for writer recognition. Specifically, the ensemble is composed of three pre-trained Inception-ResNet-v2 models with different hyperparameter values. Results obtained on the benchmark ICDAR 2019 dataset of handwritten historical documents prove that the proposed approach is very promising in recognizing the handwritten characters of different writers, also when compared with other deep learning models.
  Keywords: Convolutional Neural Networks; Writer recognition; Cultural heritage; Historical documents; Ensemble learning; Artificial neural networks; Document analysis; Deep learning; Transfer learning.
  DOI: 10.1504/IJRIS.2024.10067482
   
  
• Information fusion method on hexagonal fuzzy number based Multi-Criteria Decision Making problems  
  by Lakshmana Gomathi Nayagam Velu, Bharanidharan R 
  Abstract: Recieving the information from the experts are crucial stage in fuzzy multicriteria decision making (MCDM) problems. Different types of fuzzy numbers are used in fuzzy MCDM problems. Moreover, Hexagonal fuzzy numbers is widely used in fuzzy MCDM problems because of its convenience on piecewise linearity. The major drawback of fuzzy MCDM problems is non-availability of information for some alternatives with respect to some criteria while collecting information from the experts. To overcome this, researchers found some methodologies which are known as information fusion/infusion methods. In this paper, we have proposed two infusion methods based on score functions and similarity measures and studied infusion fusion algorithms by giving illustrative numerical examples. Further, due to the needfulness, a new similarity measure on Hexagonal fuzzy numbers have been introduced and used in the infusion method.
  Keywords: Hexagonal fuzzy numbers; Information Fusion; Missing data MCDM; Similarity measure on HXFN.
  DOI: 10.1504/IJRIS.2024.10068105
   
  
• Rice Plant Nutrient Deficiency Classification Using Deep Learning Techniques  
  by D. Sindhujah, R. Shoba Rani 
  Abstract: Every day, half of the world’s population eats rice. The World Bank predicts that by 2025, the demand for rice consumption will have increased by 51%. Mineral deficiency is one of the variables that impact rice yield. Plants need a variety of minerals and nutrients to flourish, especially while they are in the process of blooming or developing fruit. Critical plant growth disorders, which impact agricultural productivity, are caused by nutrient deficiencies. As soon as farmers see signs of nutrient inadequacy in their plants, they may use effective nutrient management measures to remedy the situation. New possibilities in non-destructive field-based analysis for nutritional deficiencies have emerged with computer vision and deep learning algorithms. In this research, we presented a ResNet50 model that has been fine-tuned to identify nutritional deficits in rice images. Our suggested model is combined with the ADAM optimiser and the softmax classifier to get the best possible outcome. Using our model, we will determine whether the rice plant is deficient in nitrogen, phosphorus, and potassium. Our findings show that our model outperforms the competition with an accuracy of 94.34%.
  Keywords: image augmentation; ResNet50; ADAM optimiser; softmax classifier; critical plant growth disorders; deep learning algorithms; nutrient inadequacy; agricultural productivity.
  DOI: 10.1504/IJRIS.2024.10068106
   
  
• Optimizing Feature Selection in Educational Data Sets Using an Enhanced Teaching-Learning Based Optimization Algorithm  
  by George Amalarethinam, A. Emima 
  Abstract: Educational data mining (EDM) is an emerging study topic that helps schools improve student performance. Selecting only relevant data reduces model input parameters with feature selection. It reduces dimensionality by selecting a subset of features and removing incorrect, superfluous, or noisy ones. It improves learning accuracy, computational cost, and model interpretability. This impacts the accuracy of performance models used to assess student outcomes. Most optimisation methods, including the genetic algorithm, must optimise many governing parameters for greater performance. Optimisation approaches using wrapper feature selection (WFS) improve classifier prediction. The proposed ETLBO algorithm with WFS techniques uses the Euclidean distance formula to assess fitness value and popular control parameters to select the optimal feature subset. The algorithm above is used on the educational dataset. Classification algorithms evaluate the best features from TLBO ETLBO algorithms: 4 algorithms classify performance metrics: GNB, LR, SVM, and K-nearest neighbour. Experimental results suggest that the ELTBO algorithm’s best feature subset improves classification accuracy for GNB, LR, SVM, and KNN compared to TLBO.
  Keywords: classification algorithms; feature selection; FS; optimisation technique; Euclidean distance; enhanced teacher learner based optimisation; ETLBO; teacher learner based optimisation.
  DOI: 10.1504/IJRIS.2024.10068107
   
  
• Advancing Healthcare Intelligent Systems: The Critical Role of Paternity Benefits in Modern Caregiving  
  by Swapna Ashmi, P.R.L. Rajavenkatesan 
  Abstract: The Maternity Benefit Act of 1961 ensures that women are entitled to receive payment for maternity leave and leave in the event of a miscarriage. It is also important to note that Indian law has not appropriately recognised paternity leave. Fathers can take paternity leave after the birth of their child or after miscarriage, adoption, or similar circumstances. The legislation regarding paternity leave in India was officially passed in 2017. The execution of this law needs improvement, as dads’ paternity leave rights are not regulated. Gender-neutral policy guidelines matter in a global economy. Fathers’ contributions to their spouses and children’s well-being make paid parental leave crucial. The study examines how paternity benefits affect children’s development and growth. The study also compared India’s paternity leave policy to many others. Healthcare analysis and kid well-being are also examined. It was given to 317 people from diverse fields. The study evaluates the importance of paternity benefit enforcement in India based on 250-member replies. MS Office was used to draft and organise the research, while Python was used to process and compare data.
  Keywords: childcare; advancing healthcare; intelligent systems; equality and fatherhood; gender-neutral; maternity leave; miscarriage and paternity leave.
  DOI: 10.1504/IJRIS.2024.10068108
   
  
• Leveraging Social Capital and SIoT for Sustainable Entrepreneurship Development  
  by K.M. Ashifa, Mehdi Safaei, HINA Zahoor, Rehab El Gamil, NASIR MUSTAFA 
  Abstract: The current research examines the combined effect of integrating social internet of things technology in entrepreneurial skill development programs for the Irula tribal community, Tamil Nadu, toward socio-economic upliftment. LAS and SCAM were adopted to collect data at the household level of 538 households, besides gathering qualitative information through purposive collection through focused group discussion and an in-depth interview of 60 participants. Quantitative results, as shown by paired t-tests and CR analyses, recorded significant increases in social capital and entrepreneurial skills following intervention. In-depth interviews, FGDs, and workshops brought rich qualitative insights into improved networking, innovation, and decision-making. Increasing communities’ cohesion and resilience resulted in enhanced livelihood
  Keywords: social internet of things; SioT; tribal development; indigenous knowledge; community health; entrepreneurial skills; government interventions; livelihood assessment schedule; LAS.
  DOI: 10.1504/IJRIS.2024.10068109
   
  
• Ancient Epigraphical Monuments' Convolution Neural Network-Based Skeletonized Structural Angularmorphing Character Identification Intelligent Systems  
  by P. Selvakumar 
  Abstract: Tamil is one of the oldest languages, and it is based on several proofs from ancient Kiladhi epigraphic monuments. Tamil texts have various structural styles and projections identified from monuments like palm lead characters, vattezhuthu, and stone inscriptions. By projecting Tamil characters in various angles, the text style may vary due to structural representation, leading the actual character style to differentiate from the old style. Thus, recognition of the specific projection of the old character leads to more features on the dimension level to get the Tamil character and classification. Consider skeletonized structural angular morphing (S2AM) based on a CNN-identified Tamil character from ancient epigraphic monuments for optimum identification. Epigrammatic images will be pre-processed using Gaussian filters, then SMS will glide the character region using CED. Use the skeletonized angular projection to discover text structural components and extract angular information. The selected features will be trained with a DFCNN to
  Keywords: Script Systems Identifying; Tamil character detection; edge detection skeletonization; Character Identification Intelligent Systems; Canny Edge Detection (CED); Deep Features Convolution Neural Networ.
  DOI: 10.1504/IJRIS.2025.10068690
   
  
• Embracing Creativity and Encouraging Teacher Satisfaction at Intelligent Systems  
  by Neenet Baby Manjaly, S.A.Vignesh Karthik, H. Lekha, V. Ameena Babu, Gayathri Joshi 
  Abstract: Many firms can now achieve high employee performance thanks to self-motivated working cultures. Employee behavior and job satisfaction at the organizational climate level have been extensively studied. Academics need job dedication and satisfaction to boost productivity, student advancement, retention, and cognitive and personal growth. Academic independence, creativity, professional commitment, and job joy are examined in this study. This research will examine the relationship between these factors. The study tested work-life balance theories for Chennai's private professional teachers. Data was collected using a self-administered questionnaire, and 353 were analyzed. The model's validity and reliability were assessed using multivariate statistics. Data was analyzed using structural equation modeling for normalcy, reliability, and discriminant validity. Results demonstrated that employment independence boosts creativity, dedication, and satisfaction. All components boost job happiness. Freedom at work and job commitment facilitated creativity, supporting the mediation hypothesis. The results also showed that job dedication mediates flexibility at work and
  Keywords: Embracing Creativity; Encouraging Teacher; Freedom at Work; Job Commitment; Intelligent Systems; Job Satisfaction; Employment Independence; Teaching Profession.
  DOI: 10.1504/IJRIS.2025.10068691
   
  
• Integrating Yoga and Nutrition: A Complementary Therapies for Addressing Obesity in Clinical Practice  
  by Madhavi Chekatla, Amiya Bhaumik, Mohammad Gousuddin, Vijaykumar Chekatla 
  Abstract: Yoga's ability to treat modern health disorders like coronary heart disease, COPD, obesity, diabetes, hypertension, and mental stress has been studied more recently. Yoga improves body, mind, and breath through physical postures, breathing exercises, and meditation. An integrated approach combining yoga and diet may help combat obesity. A nutrient-dense diet and frequent yoga practice can help manage stress and weight, two significant contributors in overeating and obesity, according to one study. Over a six- to twelve-month intervention, 96 youngsters' weight and BMI were measured. Correlation analyses were used to investigate the relationships between food, yoga, and lifestyle factors, while the Shapiro-Wilk test assessed normality. Data was statistically analyzed using SPSS 21.0. The 70
  Keywords: Obesity and Diabetes; Yoga and Nutritious; Body Mass Index; Body weight and Body Mass Index (BMI); Chronic Health Conditions; Chronic Obstructive Pulmonary Disease; Hypertension; and Mental Stress.
  DOI: 10.1504/IJRIS.2025.10068826
   
  
• Enhancing Critical Thinking Skills through Generative AI Models: Mechanisms and Educational Impacts  
  by Vincent Raj, Eronimus Jeslin Renjith, S.Silvia Priscila, C.Sathish Kumar, S. Suman Rajest 
  Abstract: Enhancing Critical Thinking Skills has even been considered to revolutionize the future of artificial intelligence and has such huge impacts across sectors, especially education. This study looks at the way generative AI models enhance critical thinking in learners based on recent studies; it debates applying them to actual education and their influence. The study adopted a mixed-method approach. One would be to carry out an analysis of learners’ performance using a quantitative method and also obtain a subjective assessment of the development of learners’ critical thinking skills. The results show that generative AI improves scholastic performance; personalised learning tools raise critical thinking scores from 50-80 to 70-100. The conclusion shows that models increase engagement and positive attitudes towards enhanced learning outcomes. AI integration into higher education faces various hurdles, including privacy issues over higher education data and educator training. The study also discusses how educators and governments might use focused
  Keywords: Generative AI; Critical Thinking; Educational Technology; Personalized Learning; Interactive Learning; AI in Education; Pedagogical Strategies; Cognitive Development.
  DOI: 10.1504/IJRIS.2025.10068827
   
  
• Intelligent Techniques for Evaluating Organizational Agility via Contingency Theory in Dynamic Environments  
  by Sivakoti Reddy Manukonda, Seema Bhakuni, Vinayak Anil Bhat, Rameshwaran Byloppilly, Rishi Shukla, Jayesh Solanki 
  Abstract: The contingency approach to management holds that effective management depends on the context of a situation. This paper discusses how the contingency approach works in different managerial settings, focussing on environmental variables like organisational size, task structure, and leadership style that may affect manager effectiveness. This study will combine an in-depth literature review, interview and survey data, and statistical analysis to understand the contingency strategy in practice. Management approaches combined with these environmental variables improved organisational performance, adaptation, and resilience. With organised contingency practices, larger organisations perform better, adaptive management tactics help difficult tasks, and democratic leadership styles work better in varied situations. Regression models and the coefficient of correlation show strong positive correlations supporting these links. Since the contingency technique usually requires ongoing adjustment and integration of various factors into a system, it also highlights its obstacles and limitations from the study. Long-term contingency practices and their effects or
  Keywords: Contingency Approach; Managerial Effectiveness; Organizational Performance; Leadership Style; Environmental Variables; Task Structure; Organizational Size; Adaptability.
  DOI: 10.1504/IJRIS.2025.10068931
   
  
• Reinforcement Learning-Driven Collective Intelligence for Prioritized Spectrum Reservation in Cognitive Radio Network  
  by Meetu Nag, Bhanu Pratap 
  Abstract: In the realm of cognitive radio networks, research aims to enhance spectrum usage by enabling access for more users through different spectrum allocation policies. The dynamic and rapid changes in the communication environment pose challenges in making correct decision for spectrum allocation. To facilitate dynamic spectrum allocation, intelligence is integrated into the cognitive system to analyze environmental parameters, various known parameters have to be analyzed to know about the nature of the radio node. In this paper a novel method is discussed for spectrum allocation by involving a multiple decision system that works on priority-based allocation approach. This system collects environmental data for decision-making, ensuring efficient service in this adaptive communication scenario.
  Keywords: Reinforcement Learning; Collective Intelligence; Spectrum Reservation in Cognitive Radio Network; Spectrum Sensing; Cognitive Radio Network.
  DOI: 10.1504/IJRIS.2025.10069324
   
  
• Predicting Chronic Obstructive Pulmonary Disease (COPD) using Machine Learning with Bio-Inspired Hyperparameter Optimization  
  by Yalin Song 
  Abstract: Chronic obstructive pulmonary disease (COPD) is a prevalent respiratory condition for which early detection is crucial to effective patient management. With LGBM and DTC as the foundational models, this study explores the predictive capability of ML approaches for COPD. Two bio-inspired optimizers, the TSA and ROA, were employed to enhance their performance. These optimizers mimic the collective behavior of biological systems, such as tunicates’ foraging patterns and jellyfish’s pulsating movements, to achieve optimal solutions within the model training process. Relevant features are extracted from patient data, potentially including demographics, medical history, lung function tests, and lifestyle factors. Among the metrics used to evaluate the performance of the optimized models are their accuracy and precision. The DTTS model’s excellent performance shows how well the DTC model predicts COPD. The greatest accuracy and precision scores of 0.907 and 0.911 support its COPD prediction accuracy. These findings demonstrate the DTTS model’s reliability and
  Keywords: Chronic Obstructive Pulmonary; Decision Tree Classification (DTC); Light Gradient Boosting Classification (LGBM); Rhizostoma Optimization Algorithm (ROA); Tunicate Swarm Algorithm (TSA); Machine Learn.
  DOI: 10.1504/IJRIS.2025.10070424
   
  
• Efficient deep mood-based Hindustani raga music recommendation using facial emotion expressions  
  by Yogesh Prabhakar Pingle, Lakshmappa K. Ragha 
  Abstract: Music recommendation is considered as a solution, and the performance is degraded with prediction error. A novel approach for music recommendation based on facial emotions with the objective of extracting better feature information without loss is required. In this paper, an efficient cross-dense network model with multi-pooling is used to detect basic emotions from the face image. The complex cross-dense connections are provided for the extraction of most discriminate feature information. After recognising the emotion from the face, a new attention-based deep collaborative filtering recommendation system is proposed, with a list of Hindustani raga music to improve users moods. The proposed framework is invoked with the Facial Expression Recognition 2013 (FER-2013) dataset, and the recommendation is provided for happy and sad emotions from the ragas. The performance is compared with existing deep learning-based approaches. The proposed approach improves accuracy, precision and recall by 0.9972, 0.9896, and 0.9906.
  Keywords: facial emotion recognition; CrossDenseNet; multi-pooling layer; AttentionNet; collaborative recommendation; Hindustani music.
  DOI: 10.1504/IJRIS.2025.10070903