International Journal of Computer Applications in Technology (26 papers in press)

Regular Issues

• FPGA implementation and Multisim simulation of a new four-dimensional two-scroll hyperchaotic system with coexisting attractors  
  by Sundarapandian Vaidyanathan, Esteban Tlelo-Cuautle, Khaled Benkouider, Aceng Sambas, Ciro Fabian Bermudez-Marquez, Samy Abdelwahab Safaan 
  Abstract: Field-programmable gate array (FPGA) design of a new four-dimensional two-scroll hyperchaotic system is investigated in this work. A detailed system modelling of the new system with a hyperchaotic attractor begins this work with phase plots, which is followed by a bifurcation study of the new system. Special dynamic properties such as multistability and symmetry are also investigated for the new system. Using Multisim software, a circuit model is designed and simulated for the new hyperchaotic system. FPGA design and Multisim simulation of the new system enable practical applications in science and engineering. The implementation of the FPGA design in this work is carried out by applying two numerical schemes, viz. Forward Euler and Trapezoidal methods. Experimental attractors observed in the oscilloscope show good match with the Matlab signal plots.The FPGA hardware resources are detailed for both numerical methods.
  Keywords: hyperchaos; bifurcation; symmetry; phase plots; hyperchaotic system;rnparameters; stability; multistability; circuit model; FPGA implementation.
  
  
• Improving hybrid-layer convolutional neural network system for lung cancer nodule classification using enhanced weight optimisation algorithm  
  by Vikul Pawar, P. Premchand 
  Abstract: In recent times, lung cancer is evolving as a highly life-threatening disease for human beings. According to the WHO, lung cancer disease is the second largest cause of deaths as compared to all other types of cancer. The prevailing available technology is striving to get more exposure in the field of medical science using Computer Assisted Diagnosis (CAD), where image processing is playing a crucial role for detecting the cancerous nodules in computer tomographic images. Augmenting the machine learning techniques with image processing algorithms is becoming a more comprehensive examination of cancer disease in proposed CAD systems. This paper is describes a heuristic approach for lung cancer nodule detection, and the proposed model predominantly consists of the following tasks, which are image enhancement, segmenting ROI (Region of Interest), features extraction, and nodule classification. In pre-processing, primarily the Adaptive Median Filter (AMF) filtering method is applied to eliminate the speckle noise from input CT images of Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): in the LIDC-IDRI dataset, the quality of input image is improved by applying Histogram Equalization (HE) technique with Contrast-Limited Adaptive (CLA) approach. Secondly, in the successive stage the Improved Level-Set (ILS) algorithm is used to segment the ROI. Furthermore, the third step of the projected work is applied to extract the definite learnable texture features and statistical features from the segmented ROI. The extracted features in the subsequent stage of classification are applied to Hybrid-Layer Convolutional Neural Network (HL-CNN) architecture to classify the lung cancer nodule as either benign or malignant. Principally this research is carried out by contributing to each stage of it, where the novel concept of the improved Hybrid-Layer Convolutional Neural Network (HL-CNN) is employed by optimising and selecting the optimal weight using the Enhanced Cat Swarm Optimisation (ECSO) algorithm. The experimental result of the proposed HL-CNN using the weight optimisation algorithm ECSO is achieved an accuracy of 93%, which is comparatively efficient with respect to existing models such as DBN, SVM, CNN, WOA, MFO, and CSO. Moreover, the proposed model conclusively gives a decision on the detected nodule as either benign or malignant.
  Keywords: Computer Assisted Diagnosis (CAD); Computer Vision; Cancer Diagnosis; Image Classification; Image Enhancement; Image Segmentation; Feature Extraction.
  
  
• Prediction model for total amount of coke oven gas generation based on FCM-RBF  
  by Lili Feng, Jun Peng, Zhaojun Huang 
  Abstract: The rational use of Coke Oven Gas (COG) is of great significance to improve the economic efficiency of enterprises. In this paper, a COG generation prediction model based on fuzzy C-mean clustering (FCM) and radial basis function (RBF) neural network is proposed to address the problems such as the difficulty of accurate modelling of COG generation process and the difficulty of real-time flow prediction. Firstly, the coke oven production process is analysed and correlation analysis is used to select the influencing factors. Secondly, the FCM is used to classify the working conditions of the coke oven, and the appropriate number of working conditions is selected through experiments. Finally, the prediction models under different working conditions are established separately by using RBF. The experiments were carried out using actual industrial production data, and the experimental results showed that the model could provide guidance reference for the dispatchers.
  Keywords: coking oven process; fuzzy C-means clustering; prediction model; radial basis function neural network.
  
  
• Research on machine reading comprehension for BERT and its variant neural network models  
  by Yanfeng Wang, Ning Ma, Wenrong Lv 
  Abstract: Currently, machine reading comprehension model is mainly models primarily rely on the basis of LSTM network networks with the gate mechanism. In the present this study, we employ BERT and its variant pre training-trained language model are used models to conduct research and experiment experimentation on the DuReader dataset. It is found We find that improved mask methods enhanced masking techniques, such as full -word mask masking and dynamic mask masking, can notably significantly enhance the model's performance of the model in machine reading comprehension tasks. Therefore, the ROUGE-L and BLEU-4 values of Consequently, the best RoBERTRoBERTa-wwm-ext model on the test set are achieves ROUGE-L and BLEU-4 scores of 51.02% and 48.14% separately, on the test set, respectively, which are 19.12% and 8.94% higher than the benchmark model. In addition, in view of the problem that the, Moreover, addressing the issue of suboptimal model performance is not optimal when the data dealing with large-scale is large data and the relatively dispersed effective information is relatively dispersed, this paper adopts employs a three-step preprocessing of approach for the dataset.
  Keywords: machine reading comprehension; BERT pre-training language model; masking mode.
  DOI: 10.1504/IJCAT.2024.10063844
   
  
• An unsupervised video summarisation method based on temporal convolutional networks  
  by Ke Jin, Haoran Li, Hui Li, Qichuang Liu, Rong Chen, Shikai Guo 
  Abstract: Video summarisation automatically select a sparse subset of video frames that best represent the semantic content of the input video. Previous work mainly used Long Short-Term Memory (LSTM) networks to learn how to assess the importance of each video frame and then select appropriate frames to compose a video summary. However, these models still represent shortcomings, such as limited memory capacity in handling long-term dependencies, and extended training time. Therefore, we present a deep video summarisation model, named TCN-SUM, which centres around Bi-TCN and models the frame sequence. TCN-SUM consists of three modules: frame selection module incorporates both Bidirectional Temporal Convolutional Network (Bi-TCN) and self-attention to model inter-frame dependencies, video reconstruction module reconstructs the original video based on the summary, and discriminator module measures the similarity between the original and reconstructed videos. Experimental studies on two benchmark datasets demonstrates that TCN-SUM outperforms state-of the- art techniques, achieving superior performance in unsupervised approaches and showing competitiveness compared to supervised methods.
  Keywords: Bi-TCN; video summarisation; self-attention; LSTM.
  DOI: 10.1504/IJCAT.2024.10067536
   
  
• Artificial intelligence-based drug structure extraction and representation in chemistry documents  
  by Xin Xu, Jiaheng Pan, Dazhou Li 
  Abstract: The feature extraction and representation of chemical bond linear molecular structure from chemistry publication images is of great significance to rediscover the properties of chemical structure, but the rule-based method and the existing deep learning methods are facing the problem of low recognition rate. This paper presents ChemRAL, an automatic conversion model for chemical molecular images and identifiers. ChemRAL employs an encoder-decoder architecture with a ResNet residual network for image feature extraction, and an attention-based LSTM long-term memory network for converting molecular structure images into chemical identifiers. Comparative evaluations demonstrate that the ChemRAL model outperforms existing methods in terms of cross entropy loss and accuracy of the longest common subsequence. The conducted experiments have successfully showcased the advancements achieved by the ChemRAL model. The findings unequivocally indicate that ChemRAL not only enhances the precision and effectiveness of molecular image feature extraction and representation but also provides a significant benchmark.
  Keywords: artificial intelligence; drug discovery; drug structure extraction and representation; image characteristics extraction; drug information representation; LSTM.
  DOI: 10.1504/IJCAT.2024.10068749
   
  
• Assisted English Oral Teaching with Mouth Recognition Technology  
  by Shaoli Xiong, Rui Cong, Siew Eng Lin, Chunyan Ruan 
  Abstract: Deep learning-based speech emotion analysis has been widely applied in English oral teaching. Due to the close relationship between oral pronunciation and mouth shape, mouth recognition has received increasing attention to assist speech recognition. To effectively improve the effectiveness of English oral teaching, this paper constructs an efficient emotion analysis model by introducing human mouth-shape features. Specifically, we first use the Dlib tool to locate 68 facial landmarks and crop mouth-associated landmark information. To improve the extraction of mouth landmark features, we introduce the spatiotemporal graph convolutional network which can effectively mine spatial and temporal features from landmark information. In addition, to effectively model local and global dependencies, we utilise Focal-Transformer for speech feature extraction. To verify the effectiveness of our proposed model, we conducted extensive comparative experiments on two publicly available multimodal sentiment analysis datasets and the self-built English oral teaching dataset. All the experimental results confirm that our proposed model obtains a higher performance compared to other deep models.
  Keywords: deep learning; mouth recognition; English oral teaching; multimodal emotion analysis; facial landmarks; Focal-Transformer.
  DOI: 10.1504/IJCAT.2024.10068792
   
  
• Building heating management based on deep learning to strengthen intelligent building design  
  by Yipeng Wang, ShiLin Zhang 
  Abstract: Building heating systems based on deep networks plays an important role in intelligent urban construction, providing a comfortable environment for residents. Accurate heating prediction can help operation and maintenance personnel grasp the energy demand of buildings in advance, avoiding unnecessary energy waste. To improve the long-term dependence between multiple variables, LSTM-based networks have been widely used in building heating prediction tasks. However, local dependencies cannot be utilized, and the model requires a large amount of computation. Therefore, this paper proposes a new Swin Transformer-based model for building heating system prediction. In addition, We use convolution operations for local dependency modeling. We conducted extensive experiments on our self-built dataset. All experimental results show that the proposed model achieves higher performance than LSTM and Transformer models.
  Keywords: building heating system; deep learning; sparse self-attention; LSTM; Transformer.
  DOI: 10.1504/IJCAT.2024.10068871
   
  
• Text swin transformer: a new transformer model for enterprise management text classification  
  by Bo Yuan, Yang Wang, Jing Chi 
  Abstract: With the rapid development of Internet technology and information technology, enterprises have generated a large amount of data in business operations, business management, and other processes Behind these data lies extremely important knowledge closely related to the enterprise, which can provide decision-making support for enterprise management Therefore, how to discover useful knowledge from massive textual data, make data truly become the wealth of enterprises, and serve the decision-making and development of enterprises, has become a challenging task. Inspired by this, this paper revises the Swin Transformer model that has achieved widely successful in computer vision tasks and proposes a Text Swin Transformer (TST) model for enterprise management text data classification tasks Specifically, we revised the window attention, shifted window attention and patch merging strategies to text window attention, text shift window attention and window scaling strategies to meet the needs of text classification tasks.
  Keywords: enterprise management text classification; text swin transformer; window attention; shift window attention.
  DOI: 10.1504/IJCAT.2024.10069181
   
  
• Enhancing Chinese teachers information literacy in international education through big data technology  
  by Yu Lu 
  Abstract: With the development of Chinese international education, the improvement of information literacy of Chinese teachers has become the focus of attention This paper first explores the ways and means of Human-Computer Interaction to explore the use of Big Data technology to improve the information literacy of Chinese teachers in the context of Chinese international education Then, a large amount of data related to Chinese teachers is collected and analyzed The results show that BD technology can effectively supplement the search for relevant information for Chinese teachers, and the human-computer information interaction efficiency can be compressed to less than 90ms compared with before in the context of Chinese international education, the application of BD technology can provide an effective information literacy improvement path for Chinese teachers Through HCI, teachers can learn and train according to individual needs to better adapt to and cope with the challenges of Chinese international
  Keywords: Chinese international education; big data technology; Chinese teachers; information literacy; human-computer interaction.
  DOI: 10.1504/IJCAT.2024.10069361
   
  
• Advancing financial security: integrating AI and blockchain for cloud network protection in supply chain financing  
  by Weishuang Xu, Daming Li 
  Abstract: This paper investigates the integration of Artificial Intelligence (AI) into cloud network security protocols, recognizing its capacity to analyze vast datasets and detect patterns effectively. By leveraging AI, cloud network security can be fortified, thereby enhancing risk identification and control within enterprise supply chain financing, a domain where efficient data collection and integration remain pivotal challenges. The study explores the application of blockchain technology (BT) in enterprise supply chain systems, demonstrating its potential to enable real-time monitoring and management of the financing process. The research findings indicate substantial improvements in risk identification (0.99) and security performance (1.22), affirming the feasibility of integrating IoT-based BT into enterprise supply chain financing for enhanced risk management and security.
  Keywords: corporate supply chain; risk identification and control; internet of things; blockchain; electricity system trading.
  DOI: 10.1504/IJCAT.2025.10069456
   
  
• Development of intelligent data collection and management system based on internet of things big data crawler technology  
  by Jin Chen, Yao Li, Xia Hua, Long Lu 
  Abstract: This paper used web crawler technology to develop intelligent data collection and management system. This paper first analyzed the basic principles of system design and the structural requirements of the system according to the system requirements, and then evaluated the overall results of the intelligent data collection and management system. The process and principle of information collection using the IoT Big Data (BD) crawler technology were introduced in detail. Finally, the information collection effect of the system was verified by experiments. The experimental results showed that the data acquisition accuracy of the system was high, accounting for more than 90%. The system had high usability and efficiency; users were satisfied with the system, and data collection and management could be carried out well.
  Keywords: intelligent data collection; data management; big data of the internet of things; big data crawler technology.
  DOI: 10.1504/IJCAT.2025.10069544
   
  
• Visual evaluation of tourism ecological and environmental protection green management in the context of sustainable development  
  by Kun Zheng, Lijing Zhang 
  Abstract: The tourism industry has developed well in the past few years. However, due to the further spread of the COVID-19 in recent years, the development of tourism industry in various regions is relatively poor. At the same time, some behaviors of tourists in tourism have also brought a greater burden to the ecological environment of various regions. This is mainly due to the differences of the ecological environment between different regions, which leads to some habitual or other uncivilized behaviors that would cause a destructive blow to the local ecological environment. However, with the increasing scale of the tourism industry, people’s economic level and material conditions are getting better and better, and more and more people want to visit the cultural scenery of different regions, which leads to the increasingly obvious impact of the tourism industry on the ecological environment of different regions.
  Keywords: tourism ecology; green management; sustainable development; visualisation analysis.
  DOI: 10.1504/IJCAT.2025.10069655
   
  
• Classification method for online teaching resources by integrating conceptual similarity and random forest  
  by Jie Zhang, Kexuan Zong 
  Abstract: Aiming to achieve efficient and accurate classification of online educational resources, a classification method for online teaching resources that integrates concept similarity and random forest is proposed. Firstly, the K-means algorithm is used to reprocess teaching resources and eliminate duplicate resources; Secondly, convert the keywords in the deduplicated teaching resources into vectors in high-dimensional space, and evaluate the similarity between concepts through vectors; Finally, based on the similarity calculation results, the BERT word embedding model is used to convert teaching resource texts into numerical feature vectors, set the parameters of the random forest, and adjust the parameters of the random forest through cross validation and other methods. The optimized model is used to obtain classification results. The experimental results show that the log loss of the proposed method is less than 0.05, the highest MCC value is 0.80, indicating that the resource classification effect of this method is good.
  Keywords: conceptual similarity; random forest; online teaching resources; resource classification; K-means algorithm; BERT model.
  DOI: 10.1504/IJCAT.2025.10070214
   
  
• Semantic retrieval method for learning resources in educational forms based on feedback algorithm  
  by Zhiheng Liu 
  Abstract: To improve the accuracy of resource retrieval methods and shorten retrieval time. The paper proposes a semantic retrieval method for learning resources in educational forms based on feedback algorithms. Firstly, design a learning resource corpus in the form of education, calculate the weights of key informational anchors, and use Link state adaptation (LSA) to perform Singular Value Decomposition (SVD) decomposition on the knowledge base matrix to obtain the semantic index of learning resources; Then, using implicit feedback information to calculate confidence and obtain semantic information of learning resources; Finally, determine the index weights, use the feedback mechanism of knowledge to adjust the search results, and complete the feedback update of the search results. The results show that the accuracy of the method proposed in this paper can reach 99.9%, with a time variation of 3.2-5.6 seconds, high retrieval efficiency, and strong anti-interference ability.
  Keywords: feedback algorithm; learning resources; natural language processing; educational form; implicit feedback information; keyword weight.
  DOI: 10.1504/IJCAT.2024.10070215
   
  
• Study on classification and search method of college English electronic resources based on Top-k query algorithm  
  by Huanxia Deng 
  Abstract: This paper proposes a college English electronic resource classification search method based on Top-k query algorithm.Firstly,determine the query mode of the Top-k query algorithm;Secondly, the preprocessing of electronic resources is completed through data cleaning and denoising;Thirdly, in order to extract significant information features from electronic resource data, a combination of mutual information gain method and minimum redundancy maximum correlation (mRMR) criterion is adopted.Finally, a modified version of the polynomial Bayesian classifier, especially the polynomial Bayesian classifier with Laplacian smoothing, was used to classify college English electronic resources. Subsequently,an enhanced information retrieval model was established,which combined a hash based indexing mechanism to achieve efficient classification and search of college English electronic resources. Experiments show that the classification accuracy of the proposed method is between 90%~98%,and the search waiting time can be kept within 23s, with high classification accuracy and short search waiting time, which has a good application effect.
  Keywords: Top-k query algorithm; electronic resources; categorized search; polynomial naïve Bayes; Hashing algorithm.
  DOI: 10.1504/IJCAT.2024.10070232
   
  
• An optimisation and application of artificial intelligence models based on deep learning in personalised recommendation of English education  
  by Peiqing Wang 
  Abstract: To diminish the mean square error in personalized English education recommendations and enhance user contentment, a research methodology is introduced for refining and deploying deep learning-based artificial intelligence models within the context of personalized English education recommendations. Initially, leveraging the similarity measurement outcomes of English education resources, the K-means algorithm is employed for the clustering of English education resources. Subsequently, deep learning artificial intelligence models are utilized to unearth latent features inherent in users and English teaching resources, and convolutional neural network models are applied to forecast ratings for English education resources. Ultimately, a dynamic collaborative filtering algorithm is harnessed to generate personalized English education recommendations predicated on the predicted scores. Experimental findings reveal that the peak mean square error of the personalized recommendation approach delineated in this article remains below 0.02, with user satisfaction peaking at 98%
  Keywords: deep learning; artificial intelligence models; English education; personalised recommendations.
  DOI: 10.1504/IJCAT.2025.10070442
   
  
• Evaluation method of English MOOC teaching quality based on big data mining algorithm  
  by Jing Han, Jing Ma 
  Abstract: In order to improve the accuracy of English MOOC teaching quality evaluation, an English MOOC teaching quality evaluation method based on big data mining algorithm is proposed. By crawling MOOC platform data and combining TF-IDF model in big data mining algorithm with Zipfs law, relevant indicators affecting the quality of English MOOC teaching are mined from course reviews. In order to improve the efficiency and accuracy of BT-SVM multi-class classification algorithm evaluation, Euclidean distance is used to improve the evaluation of English MOOC teaching quality. The results show that the R2 -value of the proposed method is above 0.9, the F1-score can reach 0.98 and the maximum evaluation time is 6.10 s, indicating that the proposed method has high evaluation accuracy and efficiency.
  Keywords: teaching quality evaluation; TF-IDF model; Zipf's law; binary tree support vector machine multi class classification algorithm.
  DOI: 10.1504/IJCAT.2025.10070443
   
  
• Deep learning-based method for detecting student concentration in online learning of English courses  
  by Xiaohui Zeng 
  Abstract: To enhance the precision of facial recognition and attentiveness monitoring among students engaged in online English courses, a novel approach leveraging deep learning techniques for assessing student focus during virtual instruction has been developed. Initially, the method employs an image local optimization preserving photography technique to amalgamate reconstructed facial data into a localized objective function, subsequently segmenting the online learning facial imagery. Subsequently, facial features are extracted using LBP-TOP and fed into a convolutional neural network's SoftMax classifier within the deep learning framework to facilitate facial recognition in the online educational context. Lastly, by consolidating the attentiveness score derived from facial expressions with the score obtained from facial recognition, a comprehensive attentiveness score is computed to gauge student concentration levels. Experimental outcomes indicate that the proposed methodology maintains a facial recognition accuracy exceeding 93%, with the pinnacle of concentration detection accuracy achieving 98.4%.
  Keywords: deep learning; English courses; online learning; student concentration detection.
  DOI: 10.1504/IJCAT.2025.10070444
   
  
• Visual learning quality evaluation of online teaching platform based on MOOC learning behaviour big data  
  by Zhi Zhang 
  Abstract: To enhance the precision and efficacy of assessing visual learning quality on online educational platforms, a methodology leveraging MOOC-derived big data on learning behaviors has been developed. Initially, a text feature vector that accounts for word significance is constructed through the computation of word frequency and inverse document frequency. Subsequently, by integrating the vectorized representation of the DOM tree structure, predictions are made regarding the attribute tags associated with learners' behavioral categories on the platform. Lastly, the significance of these behavioral category attribute tags is scrutinized, and a logical regression model, incorporating the sigmoid function and regularization techniques, is employed to conduct the visual learning quality assessment. Experimental outcomes reveal that the proposed approach boasts a prediction accuracy exceeding 90%, with a peak average processing time of merely 19.6 seconds, demonstrating its effective applicability.
  Keywords: MOOC learning behaviour; online teaching platform; visual learning; quality evaluation.
  DOI: 10.1504/IJCAT.2025.10070445
   
  
• Logistic regression analysis of college students' mobile learning attitude and its influencing factors  
  by Quan Lin 
  Abstract: In order to improve the quality of students' mobile learning, this study carried out a logistic regression analysis of College Students' mobile learning attitude and its influencing factors. Firstly, the university students' mobile learning attitude is analyzed theoretically, and then the influencing factor structure model is constructed by selecting five indicators: family, peer, media form, media content and learning support service. Based on this, the questionnaire design is completed. Finally, based on the obtained survey data, the Logistic regression model is constructed to complete the regression analysis of the influencing factors of students' mobile learning attitude. The empirical results show that the five factors can significantly affect college students' mobile learning attitude, and the order of importance is media content>peer>media form>family>learning support services.
  Keywords: mobile learning; students' learning attitude; influencing factors; logistic regression model.
  DOI: 10.1504/IJCAT.2025.10070446
   
  
• Application of ARCS learning motivation model in evaluating the effectiveness of gamified teaching  
  by Jianlei Zhang 
  Abstract: In order to improve the volatility and half confidence of evaluation results and optimize the effectiveness of gamified teaching, this paper explores the application of ARCS learning motivation model in evaluating the effectiveness of gamified teaching. Firstly, collect and classify evaluation data from both online and offline perspectives; Then, using the ARCS learning motivation model, construct an evaluation index system, analyze the reliability and weight of the indicators, and calculate the priority of the indicators based on the Cronbach's alpha coefficient; Finally, combined with fractal evaluation algorithm, the comprehensive score of the evaluation object is calculated. The experiment showed that the volatility of the evaluation results obtained by this method remained below 0.15%, and the split-half reliability remained between 0.8 and 1.0, indicating that the evaluation results obtained after applying this method were more reliable.
  Keywords: ARCS model; effect evaluation; indicator system; Lambert coefficient; fractal evaluation algorithm.
  DOI: 10.1504/IJCAT.2025.10070447
   
  
• Semantic retrieval method for online and offline collaborative education and teaching resources based on Bayesian networks  
  by Yanling Xu 
  Abstract: In order to solve the problems of high omission rate and poor consistency of retrieval results in semantic retrieval of teaching resources. This paper designs a semantic retrieval method of online and offline collaborative education and teaching resources based on Bayesian network. Firstly, the doc2vec model is used to characterize the semantic characteristics of resources. Secondly, the similarity between semantic features and index vectors is calculated to extract semantic features. Then, by assigning weights to semantic feature vectors through IDF values, a keyword balanced binary tree is constructed to sort semantic feature vectors. Finally, the Bayesian network retrieval model is constructed and the retrieval results are output. The results show that the missing detection rate is less than 1%, the retrieval consistency is higher than 0.9, and the highest false detection rate is only 0.12%, indicating that the retrieval effect of the proposed method has certain advantages.
  Keywords: Bayesian network; teaching resources; semantic retrieval; Doc2Vec model; IDF value; balanced binary tree.
  DOI: 10.1504/IJCAT.2025.10070448
   
  
• Quantitative evaluation methods for teaching quality of gamified teaching in English classrooms  
  by Ying Song 
  Abstract: To augment the impact of interactive games in English instruction, this research develops a numerical approach for gauging the merit of such pedagogical strategies. Initially, an assessment framework is devised, informed by the CIPP model. Following this, the Analytic Hierarchy Process and the Coefficient of Variation technique are engaged to deduce the subjective and objective weights assigned to the assessment metrics. In the final stage, the set pair analysis methodology is applied to establish the degrees of similarity, dissimilarity, and inverse relationships among the assessment entities, postulating their interconnections across diverse indicators. Utilizing the metrics of similarity, dissimilarity, and set pair interconnections, a composite index is formulated for each entity under review, enabling their classification and culminating in a thorough evaluation of the efficacy of English classroom game-based learning. The experimental results show that the Cronbach's Alpha coefficient of this method remains above 0.950, and the discriminability remains above 0.95.
  Keywords: English classroom; gamified teaching; teaching quality; quantitative evaluation; indicator system; set pair analysis method.
  DOI: 10.1504/IJCAT.2025.10070449
   
  
• Data security risks of implementing mobile cloud computing in higher education institutions: a conceptual paper  
  by Abeer Qashou, Nurhidayah Binti Bahar, Hazura Mohammad 
  Abstract: Implementing Mobile Cloud Computing (MCC) can significantly benefit higher education institutions (HEIs). However, using MCC technology can also result in various security and privacy concerns. The global higher education sector is currently facing many security threats and cyber-attacks. These threats and attacks are causing concerns for HEIs, and the result may be a reluctance to adopt MCC technology. This paper aims to investigate the numerous data security risks that could emerge when HEIs implement MCC. To ensure the successful implementation of MCC in HEIs focusing on data security risks, a conceptual research framework has been suggested based on the Diffusion of Innovation (DOI) model and TOE (Technology- Organization- Environment) framework. Furthermore, some strategies formulated from this model are designed to address the identified data security risks.
  Keywords: security; data security risks; cloud computing; mobile cloud computing; higher education.
  DOI: 10.1504/IJCAT.2025.10070572
   
  
• Integrating language models with sensor data for enhanced plant health monitoring and query response  
  by Param Ahir, Nikunj Tahilramani 
  Abstract: The integration of environmental sensors with large language models, such as chatGPT, led to an advancement in the domain of plant health monitoring. This study investigated the correlation between the simulation of environmental sensor data, such as soil moisture, temperature, and humidity, and the use of language models to improve plant care. By simulating sensor data, we aim to closely approximate the complexity of the natural environment, acknowledging the inherent challenges and limitations. This provides better adaptability between various plant species and different environmental circumstances. By integrating language models, people can participate in more authentic conversations and receive customised recommendations. This study examines the potential of language models and the Internet of Things to enhance the effectiveness of plant health monitoring systems. The results show that additional knowledge acquired through such a large language model can help in utilising sensor data in an efficient manner.
  Keywords: large language models; internet of things; plant health monitoring; artificial intelligence.
  DOI: 10.1504/IJCAT.2025.10070596