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International Journal of Intelligent Systems serves as a forum for individuals interested in tapping into the vast theories based on intelligent systems construction.
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Chief Editor, Professor Jin Li, is based at Guangzhou University, China. His research interests include trust and dependable artificial intelligence, cloud computing, and blockchain.
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More articlesA Genetic Algorithm with Lower Neighborhood Search for the Three-Dimensional Multiorder Open-Size Rectangular Packing Problem
This paper addresses the multiorder open-dimension three-dimensional rectangular packing problem (3D-MOSB-ODRPP), which involves packing rectangular items from multiple orders into a single, size-adjustable container. We propose a novel metaheuristic approach combining a genetic algorithm with the Gurobi solver. The algorithm incorporates a lower neighborhood search strategy and is underpinned by a mathematical model representing the multiorder open-dimension packing scenario. Extensive experiments validate the effectiveness of the proposed approach. The LNSGA algorithm outperforms Gurobi and the traditional genetic algorithm in solution quality and computational efficiency. For small-scale instances, LNSGA achieves optimal values in most cases. LNSGA demonstrates significant optimization improvements over Gurobi and the genetic algorithm for large-scale instances. The superior performance is attributed to the effective integration of the lower neighborhood search mechanism and the Gurobi solver. This study offers valuable insights for optimizing the packing process in e-commerce warehousing and logistics operations.
Dynamics and Control Strategies for SLBRS Model of Computer Viruses Based on Complex Networks
The proliferation of computer viruses has escalated in recent years, posing threats not only to individuals’ safety and property but also to societal well-being. Consequently, effectively curtailing virus spread has become an urgent imperative. To address this issue, our paper introduces a new virus propagation model and associated control strategy. First, diverging from conventional approaches in network virus literature, we propose a susceptible-latent-breaking-out-recovered-susceptible (SLBRS) virus propagation model tailored to the topological characteristics of scale-free networks, thus comprehensively incorporating network structure’s impact on virus propagation. Second, we analyze the model’s foundational properties, derive the basic reproduction number, and demonstrate the existence and global asymptotic stability of disease-free equilibrium. Finally, leveraging global stability of the model at the disease-free equilibrium, we integrate the target immunization strategy (TIS) and the acquaintance immunization strategy (AIS) to devise an optimal control strategy. The paper’s findings offer fresh insights into disease-free equilibrium existence and stability, furnishing a more dependable approach to curbing network virus dissemination. The simulation results demonstrate the persistent presence of network viruses in the absence of control measures and the instability of the disease-free equilibrium. However, effective control is achieved after implementing immunization measures.
Optimal Gasoline Price Predictions: Leveraging the ANFIS Regression Model
This study presents an in-depth analysis of gasoline price forecasting using the adaptive network-based fuzzy inference system (ANFIS), with an emphasis on its implications for policy-making and strategic decisions in the energy sector. The model leverages a comprehensive dataset from the U.S. Energy Information Administration, spanning over 30 years of historical price data from 1993 to 2023, along with relevant temporal features. By combining the strengths of fuzzy logic and neural networks, the ANFIS approach can effectively capture the complex, nonlinear relationships present in the data, enabling reliable price predictions. The dataset’s preprocessing involved decomposing the date into year, month, and day components to enhance the model’s input features. Our methodology entailed a systematic approach to ANFIS regression, including data preparation, model training with the inclusion of the previous week’s prices as an additional feature, and rigorous performance evaluation using MSE, RMSE, and correlation coefficients. The results indicate that incorporating previous prices significantly enhances the model’s accuracy, as reflected by improved scores and correlation metrics. The findings have significant implications for the energy sector, where stakeholders can leverage the ANFIS model’s insights for strategic decision-making. Accurate gasoline price forecasts are instrumental in devising pricing strategies, managing risks associated with price volatility, and guiding policy formulation. The model’s predictive capability enables energy companies to optimize resource allocation, plan for future investments, and maintain competitive advantage in a market influenced by fluctuating prices. Moreover, policymakers can utilize these predictions to assess the impact of energy policies on market prices and consumer behavior, ensuring that regulatory measures align with market dynamics and sustainability goals. In addition to the ANFIS model, we also employed Vector Autoregression (VAR) and Autoregressive Integrated Moving Average (ARIMA) models to validate our approach and provide a comprehensive understanding of time series forecasting within the energy sector. Notably, the ANFIS model achieves a score of 0.9970 and a robust correlation of 0.9985, demonstrating its ability to accurately forecast gasoline prices based on historical data and features. The integration of these traditional techniques with advanced ANFIS modeling offers a robust framework for accurate and reliable gasoline price prediction, which is vital for informed policy-making and strategic planning in the energy industry.
A Novel Approach to Optimizing Convolutional Neural Networks for Improved Digital Image Segmentation
To divide a digital image into individual parts that share similar characteristics is known as digital image segmentation, and it is a vital research subject in the field of computer vision. Object recognition, medical imaging, surveillance, and video processing are just a few of the many real-world contexts where this study could prove useful. While digital image segmentation research has come a long way, there are still certain obstacles to overcome. Segmentation algorithms frequently encounter challenges in achieving both accuracy and efficiency when confronted with intricate settings, noisy pictures, or fluctuating lighting conditions. The absence of established evaluation standards adds complexity to the process of performing equitable comparisons among different segmentation methodologies. Due to the subjective nature of photo segmentation, attaining consistent results among specialists can be challenging. The integration of machine learning and deep neural networks into segmentation algorithms has introduced new challenges, including the need for large amounts of annotated data and the interpretability of the outcomes. Given these challenges, the objective of this study is to enhance the segmentation model. To this end, this research suggests a model of convolutional neural networks that is optimal for digital picture segmentation. The model is based on a dense convolution neural network, and it incorporates a transfer learning technique to significantly boost the model’s robustness and the quality of picture segmentation. The model’s adaptability to new datasets is improved by the incorporation of a transfer learning method. As demonstrated by experimental results on two publicly available datasets, the suggested methodology considerably enhances the resilience of digital picture segmentation.
Optimal Maintenance Decision Method for a Sensor Network Based on Belief Rule Base considering Attribute Correlation
Optimal maintenance decision for a sensor network aims to intelligently determine the optimal repair time. The accuracy of the optimal maintenance decision method directly affects the reliability and safety of the sensor network. This paper develops a new optimal maintenance decision method based on belief rule base considering attribute correlation (BRB-c), which is designed to address three challenges: the lack of observation data, complex system mechanisms, and characteristic correlation. This method consists of two sections: the health state assessment model and the health state prediction model. Firstly, the former is accomplished through a BRB-c-based health assessment model that considers characteristic correlation. Subsequently, based on the current health state, a Wiener process is used to predict the health state of the sensor network. After predicting the health state, experts are then required to establish the minimum threshold, which in turn determines the optimal maintenance time. To demonstrate the proposed method is effective, a case study for the wireless sensor network (WSN) of oil storage tank was conducted. The experimental data were collected from an actual storage tank sensor network in Hainan Province, China. The experimental results validate the accuracy of the developed optimal maintenance decision model, confirming its capability to efficiently predict the optimal maintenance time.
Incorporating Adaptive Sparse Graph Convolutional Neural Networks for Segmentation of Organs at Risk in Radiotherapy
Precisely segmenting the organs at risk (OARs) in computed tomography (CT) plays an important role in radiotherapy’s treatment planning, aiding in the protection of critical tissues during irradiation. Renowned deep convolutional neural networks (DCNNs) and prevailing transformer-based architectures are widely utilized to accomplish the segmentation task, showcasing advantages in capturing local and contextual characteristics. Graph convolutional networks (GCNs) are another specialized model designed for processing the nongrid dataset, e.g., citation relationship. The DCNNs and GCNs are considered as two distinct models applicable to the grid and nongrid datasets, respectively. Motivated by the recently developed dynamic-channel GCN (DCGCN) that attempts to leverage the graph structure to enhance the feature extracted by the DCNNs, this paper proposes a novel architecture termed adaptive sparse GCN (ASGCN) to mitigate the inherent limitations in DCGCN from the aspect of node’s representation and adjacency matrix’s construction. For the node’s representation, the global average pooling used in the DCGCN is replaced by the learning mechanism to accommodate the segmentation task. For the adjacency matrix, an adaptive regularization strategy is leveraged to penalize the coefficient in the adjacency matrix, resulting in a sparse one that can better exploit the relationships between nodes. Rigorous experiments on multiple OARs’ segmentation tasks of the head and neck demonstrate that the proposed ASGCN can effectively improve the segmentation accuracy. Comparison between the proposed method and other prevalent architectures further confirms the superiority of the ASGCN.