International Journal of Intelligent Systems最新文献_第3页

Optimizing Breast Cancer Detection With an Ensemble Deep Learning Approach 利用集合深度学习方法优化乳腺癌检测

IF 5 2区计算机科学

International Journal of Intelligent Systems Pub Date : 2024-10-20 DOI: 10.1155/2024/5564649

Dilawar Shah, Mohammad Asmat Ullah Khan, Mohammad Abrar, Muhammad Tahir

{"title":"Optimizing Breast Cancer Detection With an Ensemble Deep Learning Approach","authors":"Dilawar Shah, Mohammad Asmat Ullah Khan, Mohammad Abrar, Muhammad Tahir","doi":"10.1155/2024/5564649","DOIUrl":"https://doi.org/10.1155/2024/5564649","url":null,"abstract":"<div>\u0000 <p>In the global fight against breast cancer, the importance of early diagnosis is unparalleled. Early identification not only improves treatment options but also significantly improves survival rates. Our research introduces an innovative ensemble method that synergistically combines the strengths of four state-of-the-art convolutional neural networks (CNNs): EfficientNet, AlexNet, ResNet, and DenseNet. These networks were chosen for their architectural advances and proven efficacy in image classification tasks, particularly in medical imaging. Each network within our ensemble is uniquely optimized: EfficientNet is fine-tuned with customized scaling to address dataset specifics; AlexNet employs a variable dropout mechanism to reduce overfitting; ResNet benefits from learnable weighted skip connections for better gradient flow; and DenseNet uses selective connectivity to balance computational efficiency and feature extraction. This ensembling strategy combines the predictive output of multiple CNNs, each trained with an individually optimized network, to enhance the ensemble’s overall diagnostic performance. This provides higher precision and stability than any model and shows outstanding performance in the early stage of breast cancer with a precision of up to 94.6%, sensitivity of 92.4%, specificity of 96.1%, and area under the curve (AUC) of 98.0%. This ensemble framework indicated a leap in the early diagnosis of breast cancer as it is a powerful tool that combines several state-of-the-art techniques, hence providing better results.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/5564649","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

EWRD: Entropy-Weighted Low-Light Image Enhancement via Reverse Diffusion Model EWRD：通过反向扩散模型进行熵加权弱光图像增强

IF 5 2区计算机科学

International Journal of Intelligent Systems Pub Date : 2024-10-19 DOI: 10.1155/2024/4650233

Yuheng Wu, Guangyuan Wu, Ronghao Liao

{"title":"EWRD: Entropy-Weighted Low-Light Image Enhancement via Reverse Diffusion Model","authors":"Yuheng Wu, Guangyuan Wu, Ronghao Liao","doi":"10.1155/2024/4650233","DOIUrl":"https://doi.org/10.1155/2024/4650233","url":null,"abstract":"<div>\u0000 <p>Low-light enhancement significantly aids vision tasks under poor illumination conditions. Existing methods primarily focus on enhancing severely degraded low-light areas, improving illumination accuracy, or noise suppression, yet they often overlook the loss of additional details and color distortion during calculation. In this paper, we propose an innovative entropy-weighted low-light image enhancement method via the reverse diffusion model, aiming at addressing the limitations of the traditional Retinex decomposition model in preserving local pixel details and handling excessive smoothing issues. This method integrates an entropy-weighting mechanism for improved image quality and entropy, along with a reverse diffusion model to address the detail loss in total variation regularization and refine the enhancement process. Furthermore, we utilize long short-term memory networks for the learning reverse process and the simulation of image degradation, based on a thermodynamics-based nonlinear anisotropic diffusion model. Comparative experiments reveal the superiority of our method over conventional Retinex-based approaches in terms of detail preservation and visual quality. Extensive tests across diverse datasets demonstrate the exceptional performance of our method, evidencing its potential as a robust solution for low-light image enhancement.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/4650233","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

E-Speech: Development of a Dataset for Speech Emotion Recognition and Analysis 电子语音：开发用于语音情感识别和分析的数据集

IF 5 2区计算机科学

International Journal of Intelligent Systems Pub Date : 2024-10-18 DOI: 10.1155/2024/5410080

Wenjin Liu, Jiaqi Shi, Shudong Zhang, Lijuan Zhou, Haoming Liu

{"title":"E-Speech: Development of a Dataset for Speech Emotion Recognition and Analysis","authors":"Wenjin Liu, Jiaqi Shi, Shudong Zhang, Lijuan Zhou, Haoming Liu","doi":"10.1155/2024/5410080","DOIUrl":"https://doi.org/10.1155/2024/5410080","url":null,"abstract":"<div>\u0000 <p>Speech emotion recognition plays a crucial role in analyzing psychological disorders, behavioral decision-making, and human-machine interaction applications. However, the majority of current methods for speech emotion recognition heavily rely on data-driven approaches, and the scarcity of emotion speech datasets limits the progress in research and development of emotion analysis and recognition. To address this issue, this study introduces a new English speech dataset specifically designed for emotion analysis and recognition. This dataset consists of 5503 voices from over 60 English speakers in different emotional states. Furthermore, to enhance emotion analysis and recognition, fast Fourier transform (FFT), short-time Fourier transform (STFT), mel-frequency cepstral coefficients (MFCCs), and continuous wavelet transform (CWT) are employed for feature extraction from the speech data. Utilizing these algorithms, the spectrum images of the speeches are obtained, forming four datasets consisting of different speech feature images. Furthermore, to evaluate the dataset, 16 classification models and 19 detection algorithms are selected. The experimental results demonstrate that the majority of classification and detection models achieve exceptionally high recognition accuracy on this dataset, confirming its effectiveness and utility. The dataset proves to be valuable in advancing research and development in the field of emotion recognition.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/5410080","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Collaborative Attack Sequence Generation Model Based on Multiagent Reinforcement Learning for Intelligent Traffic Signal System 基于多代理强化学习的智能交通信号系统协同攻击序列生成模型

IF 5 2区计算机科学

International Journal of Intelligent Systems Pub Date : 2024-10-18 DOI: 10.1155/2024/4734030

Yalun Wu, Yingxiao Xiang, Thar Baker, Endong Tong, Ye Zhu, Xiaoshu Cui, Zhenguo Zhang, Zhen Han, Jiqiang Liu, Wenjia Niu

{"title":"Collaborative Attack Sequence Generation Model Based on Multiagent Reinforcement Learning for Intelligent Traffic Signal System","authors":"Yalun Wu, Yingxiao Xiang, Thar Baker, Endong Tong, Ye Zhu, Xiaoshu Cui, Zhenguo Zhang, Zhen Han, Jiqiang Liu, Wenjia Niu","doi":"10.1155/2024/4734030","DOIUrl":"https://doi.org/10.1155/2024/4734030","url":null,"abstract":"<div>\u0000 <p>Intelligent traffic signal systems, crucial for intelligent transportation systems, have been widely studied and deployed to enhance vehicle traffic efficiency and reduce air pollution. Unfortunately, intelligent traffic signal systems are at risk of data spoofing attack, causing traffic delays, congestion, and even paralysis. In this paper, we reveal a multivehicle collaborative data spoofing attack to intelligent traffic signal systems and propose a collaborative attack sequence generation model based on multiagent reinforcement learning (RL), aiming to explore efficient and stealthy attacks. Specifically, we first model the spoofing attack based on Partially Observable Markov Decision Process (POMDP) at single and multiple intersections. This involves constructing the state space, action space, and defining a reward function for the attack. Then, based on the attack modeling, we propose an automated approach for generating collaborative attack sequences using the Multi-Actor-Attention-Critic (MAAC) algorithm, a mainstream multiagent RL algorithm. Experiments conducted on the multimodal traffic simulation (VISSIM) platform demonstrate a 15% increase in delay time (DT) and a 40% reduction in attack ratio (AR) compared to the single-vehicle attack, confirming the effectiveness and stealthiness of our collaborative attack.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/4734030","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fast Subpixel Motion Estimation Based on Human Visual System 基于人类视觉系统的快速子像素运动估计

IF 5 2区计算机科学

International Journal of Intelligent Systems Pub Date : 2024-10-18 DOI: 10.1155/2024/6168548

Dadvar Hosseini Avashanagh, Mehdi Nooshyar, Saeed Barghandan, Majid Ghandchi

引用次数: 0

TAE-RWP: Traceable Adversarial Examples With Recoverable Warping Perturbation TAE-RWP：具有可恢复翘曲扰动的可追溯对抗示例

IF 5 2区计算机科学

International Journal of Intelligent Systems Pub Date : 2024-10-17 DOI: 10.1155/2024/6054172

Fan Xing, Xiaoyi Zhou, Hongli Peng, Xuefeng Fan, Wenbao Han, Yuqing Zhang

{"title":"TAE-RWP: Traceable Adversarial Examples With Recoverable Warping Perturbation","authors":"Fan Xing, Xiaoyi Zhou, Hongli Peng, Xuefeng Fan, Wenbao Han, Yuqing Zhang","doi":"10.1155/2024/6054172","DOIUrl":"https://doi.org/10.1155/2024/6054172","url":null,"abstract":"<div>\u0000 <p>Reversible adversarial example (RAE) is an effective cutting-edge technology for protecting the intellectual property (IP) of datasets. However, existing RAE schemes primarily focus on the adversarial and restoration capabilities of adversarial examples (AE), with little attention paid to traceability, which is crucial for IP protection. This oversight leads to the inability to prevent authorized users from redistributing data, thereby posing significant IP security risks. To address this issue, we propose a novel approach named TAE-RWP, wherein adversarial perturbations in AEs are treated as tools for IP verification. To enable the traceability of AEs, we introduce varying degrees of warping to the adversarial perturbations within the AEs of authorized users, utilizing the warping degree as a traceable feature. To further strengthen traceability, we adopt a technique named “random warping” to maintain the resilience of adversarial perturbations against distortions, and employ a strategy named “noise mode” to improve the verification model’s capacity to recognize distortion features. Experimental results indicate that AEs generated by TAE-RWP exhibit remarkable adversarial strength and restoration abilities, while the verification model demonstrates excellence in recognizing distortion features.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/6054172","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142447783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Comprehensive Survey of Animal Identification: Exploring Data Sources, AI Advances, Classification Obstacles and the Role of Taxonomy 动物识别综合调查：探索数据来源、人工智能进展、分类障碍和分类学的作用

IF 5 2区计算机科学

International Journal of Intelligent Systems Pub Date : 2024-10-11 DOI: 10.1155/2024/7033535

Qianqian Zhang, Khandakar Ahmed, Nalin Sharda, Hua Wang

{"title":"A Comprehensive Survey of Animal Identification: Exploring Data Sources, AI Advances, Classification Obstacles and the Role of Taxonomy","authors":"Qianqian Zhang, Khandakar Ahmed, Nalin Sharda, Hua Wang","doi":"10.1155/2024/7033535","DOIUrl":"https://doi.org/10.1155/2024/7033535","url":null,"abstract":"<div>\u0000 <p>With the rapid development of entity recognition technology, animal recognition has gradually become essential in modern society, supporting labour-intensive agriculture and animal husbandry tasks. Severe problems such as maintaining biodiversity can also benefit from animal identification technology. However, certain invasive recognition systems have resulted in permanent harm to animals, while noninvasive identification methods also exhibit certain drawbacks. This paper conducts a systematic literature review (SLR), presenting a comprehensive overview of various animal recognition technologies and their applications. Specifically, it examines methodologies such as deep learning, image processing and acoustic analysis used for different animal characteristics and identification purposes. The contribution of machine learning to animal feature extraction is highlighted, emphasising its significance for animal taxonomy and wild species monitoring. Additionally, this review addresses the challenges and limitations of current technologies, including data scarcity, model accuracy and computational requirements, and suggests opportunities for future research to overcome these obstacles.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/7033535","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142430019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Road Ahead: Emerging Trends, Unresolved Issues, and Concluding Remarks in Generative AI—A Comprehensive Review 未来之路：生成式人工智能的新趋势、未决问题和结语--全面回顾

IF 5 2区计算机科学

International Journal of Intelligent Systems Pub Date : 2024-10-08 DOI: 10.1155/2024/4013195

Balasubramaniam S., Vanajaroselin Chirchi, Seifedine Kadry, Moorthy Agoramoorthy, Gururama Senthilvel P., Satheesh Kumar K., Sivakumar T. A.

{"title":"The Road Ahead: Emerging Trends, Unresolved Issues, and Concluding Remarks in Generative AI—A Comprehensive Review","authors":"Balasubramaniam S., Vanajaroselin Chirchi, Seifedine Kadry, Moorthy Agoramoorthy, Gururama Senthilvel P., Satheesh Kumar K., Sivakumar T. A.","doi":"10.1155/2024/4013195","DOIUrl":"https://doi.org/10.1155/2024/4013195","url":null,"abstract":"<div>\u0000 <p>The field of generative artificial intelligence (AI) is experiencing rapid advancements, impacting a multitude of sectors, from computer vision to healthcare. This paper provides a comprehensive review of generative AI’s evolution, significance, and applications, including the foundational architectures such as generative adversarial networks (GANs), variational autoencoders (VAEs), autoregressive models, flow-based models, and diffusion models. We delve into the impact of generative algorithms on computer vision, natural language processing, artistic creation, and healthcare, demonstrating their revolutionary potential in data augmentation, text and speech synthesis, and medical image interpretation. While the transformative capabilities of generative AI are acknowledged, the paper also examines ethical concerns, most notably the advent of deepfakes, calling for the development of robust detection frameworks and responsible use guidelines. As generative AI continues to evolve, driven by advances in neural network architectures and deep learning methodologies, this paper provides a holistic overview of the current landscape and a roadmap for future research and ethical considerations in generative AI.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/4013195","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142429383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Consistency and Complementarity Jointly Regularized Subspace Support Vector Data Description for Multimodal Data 多模态数据的一致性与互补性联合正则化子空间支持向量数据描述

IF 5 2区计算机科学

International Journal of Intelligent Systems Pub Date : 2024-09-30 DOI: 10.1155/2024/1989706

Chuang Wang, Wenjun Hu, Juan Wang, Pengjiang Qian, Shitong Wang

{"title":"Consistency and Complementarity Jointly Regularized Subspace Support Vector Data Description for Multimodal Data","authors":"Chuang Wang, Wenjun Hu, Juan Wang, Pengjiang Qian, Shitong Wang","doi":"10.1155/2024/1989706","DOIUrl":"https://doi.org/10.1155/2024/1989706","url":null,"abstract":"<div>\u0000 <p>The one-class classification (OCC) problem has always been a popular topic because it is difficult or expensive to obtain abnormal data in many practical applications. Most of OCC methods focused on monomodal data, such as support vector data description (SVDD) and its variants, while we often face multimodal data in reality. The data come from the same task in multimodal learning, and thus, the inherent structures among all modalities should be hold, which is called the consistency principle. However, each modality contains unique information that can be used to repair the incompleteness of other modalities. It is called the complementarity principle. To follow the above two principles, we designed a multimodal graph–regularized term and a sparse projection matrix–regularized term. The former aims to preserve the within-modal structural and between-modal relationships, while the latter aims to richly use the complementarity information hidden in multimodal data. Further, we follow the multimodal subspace (MS) SVDD architecture and use two regularized terms to regularize SVDD. Consequently, a novel OCC method for multimodal data is proposed, called the consistency and complementarity jointly regularized subspace SVDD (CCS-SVDD). Extensive experimental results demonstrate that our approach is more effective and competitive than other algorithms. The source codes are available at https://github.com/wongchuang/CCS_SVDD.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/1989706","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142359988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Intelligent Route Planning Recommendation for Electric Bus Transport 电动巴士运输的智能路线规划建议

IF 5 2区计算机科学

International Journal of Intelligent Systems Pub Date : 2024-09-28 DOI: 10.1155/2024/5947433

Chunjie Zhou, Pengfei Dai, Xin Huang, Fusheng Wang

{"title":"Intelligent Route Planning Recommendation for Electric Bus Transport","authors":"Chunjie Zhou, Pengfei Dai, Xin Huang, Fusheng Wang","doi":"10.1155/2024/5947433","DOIUrl":"https://doi.org/10.1155/2024/5947433","url":null,"abstract":"<div>\u0000 <p>Electric bus transport, a popular mode of public transportation, offers punctual, safe, and comfortable services to passengers through the efficient and effective use of designated road space. The performance of electric bus transport systems depends largely on the design of proper locations of bus stops, with the consideration of passenger demands, waiting time, and traveling time. Optimal electric bus route planning can attract an increasing number of passengers and increase public transit services. Aiming to provide guidance for the electric bus route planning of developing cities, this study proposed an intelligent route planning method to minimize the waiting time and traveling time of passengers, in order to achieve the best comfortable level. In addition, a self-learning anomaly detection method based on reinforcement learning (RL) was proposed to eliminate abnormal data caused by traffic accidents or emergencies. With a large spatiotemporal dataset collected over 3 years from a real electric bus project in Yantai, China, we developed a prototype system and conducted extensive experiments to evaluate the proposed intelligent route planning method. The results showed that the proposed method can reduce the passengers’ waiting time and attract more passengers traveling by electric bus. In addition, the proposed method has achieved optimal route planning recommendation (RPR) subject to 1,872,391 passenger demands on electric bus services; more than 86% of them were accurately predicted, and more than 97% were satisfied with recommendation results.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/5947433","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142359919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0