International Journal of Intelligent Systems最新文献

The rapid advancement in unmanned aerial vehicle (UAV) technology has marked a transformative shift in various industries, with logistics distribution service being one of the prime sectors reaping the benefits. UAVs offer substantial benefits in speed, cost, and reach, promising to revolutionize logistics, especially in remote areas. On the one hand, they are poised to meet demands for quick and versatile delivery options. On the other hand, their deployment comes with challenges. Weather variabilities such as rainfall, wind speed, and the need for safe take-off intervals can compromise UAV safety and operation. Conventional route optimization often overlooks these dynamic factors, resulting in inefficient or unworkable delivery routes. The repeated time-consuming calculations are caused by repeated trials when making UAV group distribution plans. Recognizing these gaps, this study proposes a data representation to effectively transform the flight flyable area of UAVs into a time-varying network that maintains spatiotemporal connectivity and establishes a mathematical model that represents the complexities of UAV group distribution. Then, a multistage dynamic optimization algorithm specifically tailored for large-scale time-varying network distribution route search is designed to obtain the stable and optimal solution. Subsequent experimental validations on actual case datasets have confirmed the correctness, effectiveness, and adaptability of the algorithm. Benchmarking against traditional CPLEX methods demonstrated that the algorithm not only rivals the best solutions but does so with a 38.8 times increase in computational speed. When pitted against the shortest path Dijkstra and A^∗ algorithms, the method consistently outperformed, delivering solutions up to 3.5 times faster in large-scale applications. Moreover, the parameter sensitivity analysis is performed on the algorithm by adjusting the safe flight thresholds of rainfall and wind speed parameters and revealed that the performance of the algorithm has a strong positive correlation with the size of the time-varying network.

Breast cancer (BC) remains a major global health problem designed for early diagnosis and requires innovative solutions. Mammography is the most common method of detecting breast abnormalities, but it is difficult to interpret the mammogram due to the complexities of the breast tissue and tumor characteristics. The EfficientViewNet model is designed to overcome false predictions of BC. The model consists of two pathways designed to analyze breast mass characteristics from craniocaudal (CC) and mediolateral oblique (MLO) views. These pathways comprehensively analyze the characteristics of breast tumors from each view. The proposed study possesses several significant strengths, with a high F1 score and recall of 0.99. It shows the robust discriminatory ability of the proposed model compared to other state-of-the-art models. The study also explored the effects of different learning rates on the model’s training dynamics. It showed that the widely used stepwise reduction strategy of the learning rate played a key role in the convergence and performance of the model. It enabled fast early progress and careful fine-tuning of the learning rate as the model nears optimum. The model opens the door to achieving a high level of patient outcomes through a very rigorous methodology.

We investigated the fusion of the Intelligent Internet of Medical Things (IIoMT) with depression management, aiming to autonomously identify, monitor, and offer accurate advice without direct professional intervention. Addressing pivotal questions regarding IIoMT’s role in depression identification, its correlation with stress and anxiety, the impact of machine learning (ML) and deep learning (DL) on depressive disorders, and the challenges and potential prospects of integrating depression management with IIoMT, this research offers significant contributions. It integrates artificial intelligence (AI) and Internet of Things (IoT) paradigms to expand depression studies, highlighting data science modeling’s practical application for intelligent service delivery in real-world settings, emphasizing the benefits of data science within IoT. Furthermore, it outlines an IIoMT architecture for gathering, analyzing, and preempting depressive disorders, employing advanced analytics to enhance application intelligence. The study also identifies current challenges, future research trajectories, and potential solutions within this domain, contributing to the scientific understanding and application of IIoMT in depression management. It evaluates 168 closely related articles from various databases, including Web of Science (WoS) and Google Scholar, after the rejection of repeated articles and books. The research shows that there is 48% growth in research articles, mainly focusing on symptoms, detection, and classification. Similarly, most research is being conducted in the United States of America, and the trend is increasing in other countries around the globe. These results suggest the essence of automated detection, monitoring, and suggestions for handling depression.

This study addresses the pressing need for improved lung cancer diagnosis and treatment by leveraging computational methods and omics data analysis. Lung cancer remains a leading cause of cancer-related deaths globally, highlighting the urgency for more effective diagnostic and therapeutic approaches. Current diagnostic methods, such as imaging and biopsies, suffer from limitations in sensitivity, specificity, and accessibility, often due to factors such as poor data quality, small sample sizes, and variability in data sources. These limitations highlight the necessity for the development of advanced noninvasive techniques. Computational methods utilizing omics data have shown promise in overcoming these challenges by comprehensively understanding the molecular pathways involved in lung cancer. We propose a novel approach that utilizes RNA-Seq data and employs LASSO regression with attention mechanisms to identify lung cancer biomarkers. Our results demonstrate the effectiveness of this approach in identifying potential biomarkers for lung cancer, including well-known genes such as TP53, EGFR, KRAS, ALK, and PIK3CA, validating the model’s ability to uncover key genes associated with lung cancer development and progression. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses revealed significant associations of the identified genes with critical biological processes and pathways, including protein synthesis, folding, cell adhesion, gene regulation, and immune responses. The PPI network analysis, constructed using the STRING database and Cytoscape application, highlighted a highly interconnected interaction landscape, with central hub genes playing pivotal roles in lung cancer progression. RPSA emerged as a crucial hub gene, consistently identified across different centrality measures. This study sheds light on the potential of computational methods and omics data analysis in improving lung cancer diagnosis and treatment, offering new insights for future research directions and personalized medicine strategies.

The two-degree-of-freedom rotation stage serves as a crucial component in ground unmanned platforms, and its servo performance is pivotal to the platform’s overall functionality. To enhance the servo performance of the two-degree-of-freedom rotation stage, we proposed a novel adaptive control approach: SSA–GA optimization of RBFNN integration into PID control. This method leverages the SSA–GA algorithm to optimize the parameters within the RBFNN, which is then seamlessly integrated into the PID control framework. This integration enables precise control of the two-degree-of-freedom rotation stage, overcoming the limitations of traditional PID controllers. By simulating various real-world situations such as step, noise, sinusoidal, transient excitation, impulse and modelling errors, it is demonstrated that the proposed control method achieves significant improvement in terms of control accuracy, fast response and robustness. It offers a more effective and reliable method for controlling the two-degree-of-freedom rotation stage, addressing the challenges posed by various interfering factors. Meanwhile, through comprehensive comparisons with various optimization algorithms, we have proven that SSA–GA has the shortest optimization time. Consequently, the proposed method exhibits excellent application potential and broad prospects for future applications.

Renewable energy forecasting is crucial for pollution prevention, management, and long-term sustainability. In response to the challenges associated with energy forecasting, the simultaneous deployment of several data-processing approaches has been used in a variety of studies in order to improve the energy–time-series analysis, finding that, when combined with the wavelet analysis, deep learning techniques can achieve high accuracy in energy forecasting applications. Consequently, we investigate the implementation of various wavelets within the structure of a long short-term memory neural network (LSTM), resulting in the new LSTM wavelet (LSTMW) neural network. In addition, and as an improvement phase, we modeled the uncertainty and incorporated it into the forecast so that systemic biases and deviations could be accounted for (LSTMW with luster: LSTMWL). The models were evaluated using data from six renewable power generation plants in Chile. When compared to other approaches, experimental results show that our method provides a prediction error within an acceptable range, achieving a coefficient of determination (R²) between 0.73 and 0.98 across different test scenarios, and a consistent alignment between forecasted and observed values, particularly during the first 3 prediction steps.

This article develops a new penalty-based aggregation operator known as the penalty-based induced ordered weighted averaging (P-IOWA) operator which is an extension of penalty-based ordered weighted averaging (P-OWA) operator. Our goal is to figure out how the induced variable realigns penalties when gathering information. We extend the P-OWA and P-IOWA operators with the different means such as generalized mean and quasi-arithmetic mean. This article also includes different families of P-OWA and P-IOWA operators. The value of these new operators is demonstrated through a case study centered on investment matters. This study evaluates the economic and governance performance of seven South Asian nations utilizing nine indicators from 2021 data. The research examines 5 economic indicators including GDP growth, exports and imports (% of GDP), inflation, and labor force metrics, alongside 4 governance indicators focusing on corruption control, government effectiveness, and political stability. We use min–max normalization to standardize the varied values, which originally ranged from 0.5% to 77.7% across various metrics. Following this, the normalized inverse penalty method is used to derive optimal weights for these indicators, tackling the task of combining multidimensional data. Subsequently, we implement and evaluate various penalty-based aggregation methodologies on the normalized data, each offering a distinct approach to penalizing outliers and balancing indicator weights. The study compares the results obtained from these operators to assess their impact on country rankings and overall performance evaluation. This approach allows for a comprehensive comparison of countries’ performances, integrating both economic and governance dimensions into a single, quantifiable framework.

With the rapid development of image generation techniques, it becomes much more difficult to distinguish high-quality computer-generated (CG) images from photographic (PG) images, challenging the authenticity and credibility of digital images. Therefore, distinguishing CG images from PG images has become an important research problem in image forensics, and it is crucial to develop reliable methods to detect CG images in practical scenarios. In this paper, we proposed a forensics contrastive learning (FCL) framework to adaptively learn intrinsic forensics features for the general and robust detection of CG images. The data augmentation module is specially designed for CG image forensics, which reduces the interference of forensic-irrelevant information and enhances discrimination features between CG and PG images in both the spatial and frequency domains. Instance-wise contrastive loss and patch-wise contrastive loss are simultaneously applied to capture critical discrepancies between CG and PG images from global and local views. Extensive experiments on different public datasets and common postprocessing operations demonstrate that our approach can achieve significantly better generalization and robustness than the state-of-the-art approaches. This manuscript was submitted as a pre-print in the following link https://papers.ssrn.com/-sol3/papers.cfm?abstract_id=4778441.

Quantum information processing introduces novel approaches for classical data encoding to encompass the complex patterns of input data of practical computational challenges using basic principles of quantum mechanics. The classification of diabetes is an example of a problem that can be efficiently resolved by using quantum unitary operations and the variational quantum classifier (VQC). This study demonstrates the effects of the number of qubits, types of feature maps, optimizers’ class, and the number of layers in the parametrized circuit, and the number of learnable parameters in ansatz influences the effectiveness of the VQC. In total, 76 variants of VQC are analyzed for four and eight qubits’ cases and their results are compared with six classical machine learning models to predict diabetes. Three different types of feature maps (Pauli, Z, and ZZ) are implemented during analysis in addition to three different optimizers (COBYLA, SPSA and SLSQP). Experiments are performed using the PIMA Indian Diabetes Dataset (PIDD). The results conclude that VQC with six layers embedded with an error correction scaling factor of 0.01 and having ZZ feature map and COBYLA optimizer outperforms other quantum variants. The optimal proposed model attained the accuracy of 0.85 and 0.80 for eight and four qubits’ cases, respectively. In addition, the final quantum model among 76 variants was compared with six classical machine learning models. The results suggest that the proposed VQC model has outperformed four classical models including SVM, random forest (RF), decision tree (DT), and linear regression (LR).

Mobile edge computing (MEC) servers integrated with multi-unmanned aerial vehicles (multi-UAVs) present a new system the multi-UAV-assisted MEC system. This system relies on the mobility of the UAVs to reduce the transmission distance between the servers and mobile users, thereby enhancing service quality and minimizing the overall energy consumption. Achieving optimal UAV deployment and precise task scheduling is crucial for improved coverage and service quality in this system. This problem is framed as a nonconvex optimization problem known as joint task scheduling and deployment optimization. Recently, an optimization technique based on a dual-layer framework: Upper layer optimization and lower layer optimization have been proposed to tackle this problem and achieved superior performance compared to the alternative methods. In this framework, the lower layer was responsible for task scheduling optimization, while the upper layer was designed to assist in optimizing UAV deployment and thus achieving improved coverage and enhanced task scheduling for mobile users, thereby minimizing the total energy consumption. However, further refinement of upper layer optimization is needed to improve the deployment process. In this study, the upper layer undergoes enhancement through key modifications: First, random selection of the solutions is replaced with sequential selection to maintain the unique characteristics of each individual throughout the optimization process, fostering both exploration and exploitation. Second, a selection of recently reported metaheuristic algorithms, such as spider wasp optimizer (SWO), generalized normal distribution optimization (GNDO), and gradient-based optimizer (GBO), are adapted to optimize UAV deployments. Both improved upper layer and lower layer optimization led to the development of novel, more effective optimization approaches, including IToGBOTaS, IToGNDOTaS, and IToSWOTaS. These techniques are evaluated using nine instances with a variety of mobile tasks ranging from 100 to 900 to test their stability and then compared to different optimization techniques to measure their effectiveness. This comparison is based on several statistical information to determine the superiority and difference between their outcomes. The results reveal that IToGBOTaS and IToSWOTaS exhibit slightly superior performance compared to all other algorithms, showcasing their competitiveness and efficacy in addressing the optimization challenges of the multi-UAV-assisted MEC system.