Template-Type: ReDIF-Article 1.0 Author-Name: Yuanyuan Zhang Author-X-Name-First: Yuanyuan Author-X-Name-Last: Zhang Title: A comprehensive evaluation method of Chinese online teaching effect based on cluster analysis Abstract: In order to improve the accuracy of evaluation results, a comprehensive evaluation method for the effectiveness of Chinese online teaching based on cluster analysis is proposed. Firstly, the AHP is used to select evaluation indicators, and the weighted average method is applied to quantify the evaluation indicators. Secondly, the K-means algorithm is used to comprehensively evaluate the teaching effectiveness, calculate the average value of all sample points within each cluster to locate the cluster centre, and iteratively update the centre position. Finally, repeat the calculation of the distance from each point to the nearest cluster centre, select new cluster centres, ensure that the distance between the initial cluster centres is as far as possible, and use the optimised K-means algorithm to achieve teaching effectiveness evaluation. The experimental results show that the proposed method has a low mean square error, indicating that its evaluation results are relatively accurate. Journal: Int. J. of Information Technology and Management Pages: 15-29 Issue: 1 Volume: 25 Year: 2026 Keywords: cluster analysis; online teaching; effect evaluation; k-means algorithm. File-URL: http://www.inderscience.com/link.php?id=152446 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijitma:v:25:y:2026:i:1:p:15-29 Template-Type: ReDIF-Article 1.0 Author-Name: Chang Liu Author-X-Name-First: Chang Author-X-Name-Last: Liu Title: Accurate recommendation method for enterprise product network marketing information under the background of big data Abstract: Aiming to achieve personalised and precise recommendation of marketing information, a method for accurate recommendation of enterprise product network marketing information under the background of big data is proposed. Firstly, collect user information data and preprocess the data to construct a user profile that comprehensively describes user interests and preferences based on the data processing results. Secondly, a collaborative filtering algorithm based on users and items is adopted for predicting user preferences. Finally, the three-dimensional features of marketing information are obtained through the serial parallel convolutional gate valve recurrent neural network in deep learning, and combined with user profiles and preference prediction results, the matching between users and marketing information is achieved, thereby realising personalised recommendation of marketing information. The experimental results show that the proposed method has high recommendation accuracy, high user satisfaction, and high data processing efficiency, indicating its good application effect. Journal: Int. J. of Information Technology and Management Pages: 1-14 Issue: 1 Volume: 25 Year: 2026 Keywords: big data; online marketing; information recommendation; user profile; collaborative filtering. File-URL: http://www.inderscience.com/link.php?id=152447 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijitma:v:25:y:2026:i:1:p:1-14 Template-Type: ReDIF-Article 1.0 Author-Name: Xian Yang Author-X-Name-First: Xian Author-X-Name-Last: Yang Author-Name: Jue Huang Author-X-Name-First: Jue Author-X-Name-Last: Huang Author-Name: Yun Zhao Author-X-Name-First: Yun Author-X-Name-Last: Zhao Author-Name: Hui Tong Author-X-Name-First: Hui Author-X-Name-Last: Tong Author-Name: Shibing Chen Author-X-Name-First: Shibing Author-X-Name-Last: Chen Author-Name: Yuxin Lu Author-X-Name-First: Yuxin Author-X-Name-Last: Lu Author-Name: Wei Cao Author-X-Name-First: Wei Author-X-Name-Last: Cao Title: A balanced scheduling method for multi-threaded tasks based on two-level parallelism between clusters and big data clustering Abstract: To improve the efficiency of task scheduling and enhance the negative load-balancing effect of tasks, this paper proposes a multi-threaded task-balancing scheduling method based on two-level parallelism between clusters and big data clustering. Firstly, use fuzzy C-means clustering to group task data into multiple clusters based on feature similarity. Then, build a multi-threaded task model that allows tasks to be executed in parallel on multiple threads, achieving two-level parallel processing between and within clusters. Finally, by determining task priority and hierarchical sorting, a task scheduling manager is designed to achieve balanced task scheduling. The experiment shows that the maximum standard deviation of the load in this method is 0.05, and the maximum over-time task ratio is 0.015, indicating that this method has strong load-balancing ability and can achieve real-time processing of tasks. Journal: Int. J. of Information Technology and Management Pages: 30-45 Issue: 1 Volume: 25 Year: 2026 Keywords: multi-threaded model; task scheduling; balanced scheduling; fuzzy C-means clustering; task scheduling manager. File-URL: http://www.inderscience.com/link.php?id=152448 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijitma:v:25:y:2026:i:1:p:30-45 Template-Type: ReDIF-Article 1.0 Author-Name: Yiming Shen Author-X-Name-First: Yiming Author-X-Name-Last: Shen Author-Name: Jingyi Qiu Author-X-Name-First: Jingyi Author-X-Name-Last: Qiu Author-Name: Jie Mei Author-X-Name-First: Jie Author-X-Name-Last: Mei Author-Name: Xin Sun Author-X-Name-First: Xin Author-X-Name-Last: Sun Author-Name: Luyao Qu Author-X-Name-First: Luyao Author-X-Name-Last: Qu Title: Supply chain information sharing and collaborative innovation based on social network analysis Abstract: In order to improve the completeness of supply chain information sharing and shorten the sharing time, a supply chain information sharing and collaborative innovation method based on social network analysis is proposed. Firstly, through dynamic programming and cost function optimisation, the optimal cluster of supply chain information is divided. Secondly, generate anonymous sequences of supply chain information through social network analysis and dynamic grouping strategies. Once again, establish a node reputation evaluation system that combines direct and indirect reputation to achieve secure sharing of supply chain information. Ultimately, leveraging social network analysis, the supply chain's information sharing mechanism is refined, fostering synergistic collaboration between resources and competencies. Additionally, network governance structures are crafted to enhance collaborative innovation within the supply chain. Empirical outcomes reveal that the proposed approach in this study achieves a 0.97 completeness rate for supply chain information sharing, concurrently reducing the duration required for information dissemination. Journal: Int. J. of Information Technology and Management Pages: 46-60 Issue: 1 Volume: 25 Year: 2026 Keywords: social network analysis; SNA; supply chain; information sharing; collaborative innovation. File-URL: http://www.inderscience.com/link.php?id=152450 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijitma:v:25:y:2026:i:1:p:46-60 Template-Type: ReDIF-Article 1.0 Author-Name: Xiaoyang Li Author-X-Name-First: Xiaoyang Author-X-Name-Last: Li Title: Eliminating duplicate values of enterprise financial big data based on dynamic grid generation technology Abstract: To improve the spatial reduction rate of the processed dataset and adjust the rand coefficient, this paper designs a method for removing duplicate values in enterprise financial big databased on dynamic grid generation technology. Firstly, denoising of enterprise financial big data is implemented through fast orthogonal wavelet transform. Secondly, based on dynamic grid generation technology, the fusion correlation features of enterprise financial data are constructed, and the correlation degree between data is calculated. Finally, use similarity clustering algorithms to cluster data with high correlation. For highly similar data in the same cluster, retain one record and exclude other identical data entries. The experimental results show that after applying this method, the spatial reduction rate of the dataset ranges from 9.61% to 15.55%, and the highest adjusted rand coefficient of the dataset can reach 0.997, indicating that this method effectively achieves the design expectations. Journal: Int. J. of Information Technology and Management Pages: 61-72 Issue: 1 Volume: 25 Year: 2026 Keywords: enterprise financial data; data duplicate value; elimination process; dynamic grid generation technology; fusion of associated features; similar clustering algorithm. File-URL: http://www.inderscience.com/link.php?id=152451 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijitma:v:25:y:2026:i:1:p:61-72 Template-Type: ReDIF-Article 1.0 Author-Name: P. Remya Krishnan Author-X-Name-First: P. Remya Author-X-Name-Last: Krishnan Title: A novel leader election mechanism for distributed systems using dynamic priority scores Abstract: Efficient coordination in distributed systems relies on selecting a suitable leader node, a process often challenged by asynchronous operations and node failures. Traditional leader election algorithms struggle with scalability and fault tolerance, necessitating more adaptive approaches to address these challenges. This paper introduces priority enhanced quorum consensus (PEQC), a novel leader election mechanism that integrates dynamically computed priority scores with quorum-based decision making. By utilising priority-based selection, PEQC ensures the election of the most suitable node while addressing node downtime and enhancing system responsiveness. A comparative analysis with existing algorithms demonstrates PEQC's superior performance in reducing election overhead, improving fault recovery, and ensuring fair leadership distribution. Journal: Int. J. of Information Technology and Management Pages: 73-88 Issue: 1 Volume: 25 Year: 2026 Keywords: leader election; distributed systems; dynamic priority; quorum consensus; scalability; fault tolerance. File-URL: http://www.inderscience.com/link.php?id=152453 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijitma:v:25:y:2026:i:1:p:73-88