Journal of Computer Networks and Communications
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Acceptance rate7%
Submission to final decision144 days
Acceptance to publication16 days
CiteScore8.900
Journal Citation Indicator0.500
Impact Factor2.0

A Custom Backbone UNet Framework with DCGAN Augmentation for Efficient Segmentation of Leaf Spot Diseases in Jasmine Plant

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Journal of Computer Networks and Communications publishes original research and review articles that investigate both the theoretical and practical aspects of computer networks and communications.

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Journal of Computer Networks and Communications maintains an Editorial Board of practicing researchers from around the world, to ensure manuscripts are handled by editors who are experts in the field of study.

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Review Article

Using QoE Metric as a Decision Criterion in Multimedia Heterogeneous Network Optimization: Challenges and Research Perspectives

This article explores the growing importance of the QoE (Quality of Experience) metric as a fundamental criterion in the optimization of heterogeneous multimedia networks. We explore the benefits of using QoE, such as improving user experience, efficient resource management, and adaptation to network conditions. However, this paradigm presents technical challenges, including accurately measuring QoE and managing the complexity of heterogeneous networks. To address these challenges, we highlight promising research prospects, such as the development of advanced algorithms, real-time measurement of QoE, and the integration of machine learning. Ultimately, the use of QoE in the optimization of heterogeneous multimedia networks contributes to a substantial improvement in the quality of multimedia services offered to end users.

Review Article

A Systematic Review of Blockchain Technology Assisted with Artificial Intelligence Technology for Networks and Communication Systems

Blockchain is a very secure, authentic, and distributed technology and is very prominent in areas such as edge computation, cloud computation, and Internet-of-things. Artificial intelligence assists in the completion of activities efficiently and effectively by providing intelligence, analytics, and predicting capabilities. There is an obvious convergence between the two technologies. Artificial intelligence systems can utilize blockchain to establish trust in communication channels, ensuring that messages are securely transmitted and received without the need for a centralized intermediary. By leveraging blockchain, artificial intelligence systems can maintain an immutable record of communications, ensuring transparency and preventing unauthorized modifications. The integration of blockchain and artificial intelligence technologies can enhance the security, transparency, and privacy of communication systems. By leveraging blockchain’s decentralized nature and artificial intelligence’s analytical capabilities, secure and trustworthy communication channels can be established, benefiting various domains such as finance, healthcare, and supply chain. Overall, the integration of blockchain and artificial intelligence has the potential to offer several benefits, and as these technologies continue to evolve, new and innovative applications will continue to emerge.

Research Article

Development of an AI-Enabled Q-Agent for Making Data Offloading Decisions in a Multi-RAT Wireless Network

Data offloading is considered as a potential candidate for alleviating congestion on wireless networks and for improving user experience. However, due to the stochastic nature of the wireless networks, it is important to take optimal actions under different conditions such that the user experience is enhanced and congestion on heavy-loaded radio access technologies (RATs) is reduced by offloading data through lower loaded RATs. Since artificial intelligence (AI)-based techniques can learn optimal actions and adapt to different conditions, in this work, we develop an AI-enabled Q-agent for making data offloading decisions in a multi-RAT wireless network. We employ a model-free Q-learning algorithm for training of the Q-agent. We use stochastic geometry as a tool for estimating the average data rate offered by the network in a given region by considering the effect of interference. We use the Markov process for modeling users’ mobility, that is, estimating the probability that a user is currently located in a region given its previous location. The user equipment (UE) plays the role of a Q-agent responsible for taking sequence of actions such that the long-term discounted cost for using network service is minimized. Q-agent performance has been evaluated and compared with the existing data offloading policies. The results suggest that the existing policies offer the best performance under specific situations. However, the Q-agent has learned to take near-optimal actions under different conditions. Thus, the Q-agent offers performance which is close to the best under different conditions.

Research Article

Maximum Entropy Principle Based on Bank Customer Account Validation Using the Spark Method

Bank customer validation is carried out with the aim of providing a series of services to users of a bank and financial institutions. It is necessary to perform various analytical methods for user’s accounts due to the high volume of banking data. This research works in the field of money laundering detection from real bank data. Banking data analysis is a complex process that involves information gathered from various sources, mainly in terms of personality, such as bills or bank account transactions which have qualitative characteristics such as the testimony of eyewitnesses. Operational or research activities can be greatly improved if supported by proprietary techniques and tools, due to the vast nature of this information. The application of data mining operations with the aim of discovering new knowledge of banking data with an intelligent approach is considered in this research. The approach of this research is to use the spiking neural network (SNN) with a group of sparks to detect money laundering, but due to the weakness in accurately identifying the characteristics of money laundering, the maximum entropy principle (MEP) method is also used. This approach will have a mapping from clustering and feature extraction to classification for accurate detection. Based on the analysis and simulation, it is observed that the proposed approach SNN-MFP has 87% accuracy and is 84.71% more functional than the classical method of using only the SNN. In this analysis, it is observed that in real banking data from Mellat Bank, Iran, in its third and fourth data, with a comprehensive analysis and reaching different outputs, there have been two money laundering cases.

Research Article

Cooperative Game-Based Resource Allocation Scheme for Heterogeneous Networks with eICIC Technology

Heterogeneous network (HetNet) is considered to be the most promising approach for increasing communication capacity. However, HetNet control problems are difficult due to their intertier interference. Recently, the enhanced intercell interference coordination (eICIC) technology is introduced to offer several benefits, including a more equitable traffic load distribution across the macro and embedded small cells. In this paper, we design a new resource allocation scheme for the eICIC-based HetNet. Our proposed scheme is formulated as a joint cooperative game to handle conflicting requirements. By adopting the ideas of Kalai and Smorodinsky solution (KSS), multicriteria Kalai and Smorodinsky solution (MCKSS), and sequential Raiffa solution (SRS), we develop a hybrid control algorithm for an adaptive resource sharing between different base stations. To effectively adjust the eICIC fraction rates, the concepts of MCKSS and SRS are applied in an interactive manner. For mobile devices in the HetNet, the assigned resource is distributed by using the idea of KSS. The key insight of our algorithm is to translate the originally competitive problem into a hierarchical cooperative problem to reach a socially optimal outcome. The main novelty of our approach is its flexibility to reach a reciprocal consensus under dynamic HetNet environments. Exhaustive system simulations illustrate the performance gains along different dimensions, such as system throughput, device payoff, and fairness among devices. The superiority of our proposed scheme is fully demonstrated in comparison with three other existing eICIC control protocols.

Research Article

SCWOMP Recovery Algorithm for 5G MIMO Communication Symbol Detection

In order to solve the problem of small capacity and high energy consumption in China’s 5G communication technology system, the research proposes that based on the segmented weakly orthogonal matching pursuit (SWOMP) algorithm, it is combined with the compressed sensing matching pursuit algorithm to form a segmented backtracking weak selection positive algorithm and Cross Match Tracking (SCWOMP) algorithm. First, the sparseness of MIMO system technology and its transmission structure is analyzed. Then, the new model is built after comparing with other algorithms, and the problem of overestimating the low recovery probability in the calculation process is improved by the backtracking of the algorithm and the improvement of the angle of the atomic column selection, so as to reduce the number of iterations and improve the performance of the algorithm. The results show that, in the performance comparison of different sampling points under different compressed sensing recovery algorithms, the recovery probability of the SCWOMP algorithm is the best, and when the number of sampling points is 80, although the fixed step size of the SCWOMP algorithm is different, there is recovery. The probability has a maximum value, close to 1. Then, the improved compressed sensing recovery algorithm is simulated and analyzed. When the pruning coefficient is 0.5 and the number of sampling points is 80, the reconstruction rate has a maximum value, and when other algorithms reach the maximum reconstruction rate, the number of sampling points (M) is significantly greater than that of the SCWOMP algorithm. An increase in the rate of reduction of the reconstruction probability of the SCWOMP algorithm is significantly lower than that of other algorithms; when sparsity is equal to 70, the reconstruction probability becomes 0, indicating that SCWOMP has a wider reconfigurable range and has a significant performance effect. This shows that the proposed SCWOMP algorithm has the best detection performance for 5G communication symbol detection, which can effectively increase the capacity of the system and better promote technology.

Journal of Computer Networks and Communications
 Journal metrics
See full report
Acceptance rate7%
Submission to final decision144 days
Acceptance to publication16 days
CiteScore8.900
Journal Citation Indicator0.500
Impact Factor2.0
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