This research proposes an efficient parallel graph partitioning algorithm for the big data environment, aiming to solve the bottlenecks of traditional clustering techniques in terms of processing speed and scalability. The algorithm adopts a multi-level graph partitioning framework, decomposing the network information processing task into multiple levels, gradually simplifying the graph structure and backtracking refinement, thereby significantly reducing the computational complexity while ensuring the partitioning quality. The algorithm focuses on balancing the node cohesion within partitions and the edge cutting cost of inter-partition communication. By constructing a global objective function, it minimizes the number of edges across partitions and the workload differences among various sub-graphs, thereby achieving a more balanced partitioning result. The research results show that this algorithm achieves a resource utilization rate of 0.95. In the Hadoop cluster environment, 95% of the computing resources are effectively used for actual task processing, which is significantly higher than that of the competing algorithms. The energy efficiency ratio reaches 0.98, indicating that the number of tasks completed per unit of energy consumption is close to the optimal level, which is superior to the 0.78 to 0.67 range of existing methods, reflecting the advantages of this algorithm in green computing. The load imbalance rate is only 0.00395, and the point weight imbalance rate is 0.00141, which are much lower values than those of the comparison algorithm. This indicates that the algorithm achieves a high degree of balance in task allocation and node weight distribution, effectively avoiding resource waste and performance bottlenecks.

The Internet of Medical Things (IoMT) consists of interconnected devices and applications that enable real-time collection, transmission, and analysis of medical data for healthcare applications. This study utilizes medical data from the publicly available BCICIV2a dataset rather than data collected directly from individuals or medical institutions. With advancements in neuroinformatics and intelligent computing, the classification of electroencephalography (EEG) signals has become increasingly important, particularly for detecting and predicting epilepsy. However, existing EEG classification methods often suffer from low accuracy, high computational complexity, and slow processing. To address these challenges, this study proposes an EEG classification approach utilizing a Backpropagation Neural Network (BPNN) enhanced with Bayesian optimization. This method enhances the identification and prediction of epileptic seizures by utilizing IoT-enabled EEG data. Performance evaluation on the BCICIV2a dataset demonstrates that the proposed model achieves an accuracy of 93.21%, outperforming conventional techniques. The results indicate that this approach enhances efficiency and accuracy in EEG signal processing, contributing to real-time medical diagnostics. The integration of IoMT with advanced neural networks represents a significant advancement in medical informatics and telemedicine, providing promising directions for future research and clinical applications.

The zero trust systems help to improve the overall security of computer networks, while one of the main challenges of zero trust systems is the construction and optimization of continuous authentication models. Aiming at the shortcomings of the existing authentication performance, this paper proposes a continuous authentication model based on an improved flower pollination algorithm (FPA) and extreme gradient boosting (XGBoost) to improve the accuracy of authentication. The model first uses multiple strategies to optimize FPA to obtain MSFPA; then MSFPA is applied to XGBoost for hyper-parameter tuning to obtain MSFPA-XGBoost; and finally, MSFPA-XGBoost is applied to the user's continuous authentication. Among these approaches, MSFPA incorporates chaotic mapping to enhance the initial population. It also utilizes an adaptive transformation probability strategy to dynamically strike a balance between global and local search. Furthermore, it refines the search equation to optimize both global and local search. Additionally, MSFPA employs a pollen cross-boundary correction strategy and incorporates the concept of cross-mutation to augment the algorithm's exploratory capabilities. The experimental results demonstrate that, in the context of parameter optimization tasks, MSFPA exhibits superior performance compared to other optimization algorithms, specifically in terms of search accuracy and convergence speed. In addition, in terms of continuous identity verification effect, compared with each classification model, MSFPA-XGBoost improves the Accuracy, Recall, Precision, F1-Score, and AUC metrics by an average of 1.84%, 2.49%, 2.33%, 2.39%, and 3.11%, which indicates that the proposed model enhances the accuracy of continuous authentication and is more effectively applicable within zero-trust systems.

The September 2023 (Vol. 31, No. 3) issue of CIT. Journal of Computing and Information Technology brings four papers from the areas of telecommunications, power load forecasting, computer vision, and social networks.

In this study, we propose a segmentation model based on convolutional neural networks (CNNs) to address image segmentation challenges in computer vision. Prior to designing the model, the activation function and other modules of the convolutional neural network were optimized to meet specific requirements. The segmentation task was transformed into binary classification problem to simplify network calculations and improve efficiency. Additionally, the model utilized a mask map obtained from the semantic segmentation model to aid in instance segmentation. Class activation technology was introduced to extract feature mapping maps. The corresponding thermal maps were obtained to achieve target instance segmentation. To further validate the effectiveness of the segmentation model, simulation experiments were conducted on semantic segmentation and instance segmentation respectively. The results show that the accuracy of the basic semantic segmentation model reached 87.58%, while the average accuracy of the entire class of the optimized instance segmentation model reached 97.9%. Therefore, the research and design of image segmentation models demonstrate high accuracy and good robustness.

With the March 2023 (Vol. 31, No. 1) issue, CIT. Journal of Computing and Information Technology enters its 31st year of continuous publishing. The current issue brings four papers from the areas of machine vision, image analysis, natural language processing and mental health modeling.

This study proposes a nature-based system survivability model. The model was simulated, and its performance was evaluated for the mobile ad hoc wireless networks. The survivability model was used to enable mobile wireless distributed systems to keep on delivering packets during their stated missions in a timely manner in the presence of attacks. A prey-predator communal defence algorithm was developed and fused with the Ad hoc On-demand Distance Vector (AODV) protocol. The mathematical equations for the proposed model were formulated using the Lotka-Volterra theory of ecology. The model deployed a security mechanism for intrusion detection in three vulnerable sections of the AODV protocol. The model simulation was performed using MATLAB for the mathematical model evaluation and using OMNET++ for protocol performance testing. The MATLAB simulation results, which used empirical and field data, have established that the adapted Lotka-Volterra-based equations adequately represent network defense using the communal algorithm. Using the number of active nodes as a measure of throughput after attack (with a maximum throughput of 250 units), the proposed model had a throughput of 230 units while under attack and the intrusion was nullified within 2 seconds. The OMNET++ results for protocol simulation that use throughput, delivery ratio, network delay, and load as performance metrics with the OMNET++ embedded datasets showed good performance of the model, which was better than the existing conventional survivability systems. The comparison of the proposed model with the existing model is also presented. The study concludes that the proposed communal defence model was effective in protecting the entire routing layer (layer 2) of the AODV protocol when exposed to diverse forms of intrusion attacks.

Online short video platform (OSVP) can disseminate information in an intuitive and three-dimensional (3D) way, and achieve commercial value in a mobile, socialized, life-oriented manner. To fully understand the information dissemination mode of short videos, it is of practical significance to explore user behaviors and their interactive relationship. Therefore, this paper carries out a behavior analysis on user interaction on OSVP. Firstly, an OSVP user interaction model was established based on three aspects: activity, attention, and interactivity. Next, user preference for short videos was examined through cluster analysis and evaluation. Finally, the authors summarized the behavior features of OSVP user interaction and investigated user interaction to decide which videos should be pushed to users. The average utility and average cost of user interaction behavior were compared, indicators of degree centrality were summarized, and the validity of the proposed algorithm was verified by experimental results.

The purpose of this study was to implement a reliable model for bathing water quality prediction using the Cogent Confabulation classifier and to compare it with other well-known classifiers. This study is a continuation of a previously published work and focuses on the areas of Kaštela Bay and the Brač Channel, located in the Republic of Croatia. The Cogent Confabulation classifier is a thorough and simple method for data classification based on the cogency measure for observed classes. To implement the model, we used data sets constructed of remote sensing data (band values) and in situ measurements presenting ground-truth bathing water quality. Satellite data was retrieved from the Sentinel-3 OLCI satellite and it was atmospherically corrected based on the characteristics and specifications of band wavelengths. The results showed that the Random Forest, K-Nearest Neighbour, and Decision Tree classifiers outperformed the Cogent Confabulation classifier. However, results showed that the Cogent Confabulation classifier achieved better results compared to classifiers based on Bayesian theory. Additionally, a qualitative analysis of the four best classifiers was conducted using spatial maps created in the QGIS tool.

The four papers that make up the September 2021 issue (Vol. 29, No. 3) of CIT. Journal of Computing and Information Technology cover the areas of countering malicious activities in social networks, predictions in intelligent agriculture, use of artificial intelligence in the judiciary, and modeling of intelligent logistics.