检索结果-内蒙古大学图书馆

Implementation of ANN and response surface method for dual-fuel CI engine optimization and prediction using water infusion and biofuel

引用

Multimedia Tools and Applications 2025年 1-43页

作者： Pramanik, Sabyasachi Department of Computer Science & Engineering Haldia Institute of Technology Haldia India

In this study, tests were done to see what would happen if hydrogen (H2) and lemon grass oil (LO) were used for a lone-cylinder compression ignition engine as a partial diesel replacement. After starting the trial with pure diesel, the engine was modified to run on two fuels by adding H2 through the inlet pipe and LO as direct injection (DI) gasoline. In this study, the energy fraction of H2 ranged from 5 to 10%. A 3% volume mixture of water (W) and diesel-LO mixtures was used to study how NOX emissions were affected. Using a common back- transmission method, an Artificial Neural Network model was created to forecast the correlation between engine output reactions and input variables (load, LO, and H2). The goal of response surface methodology is for improve the motor input settings for low pollution and high heat efficiency. The trials were designed using response surface methodology (RSM), and the optimization procedure was conducted using RSM. An artificial neural network (ANN) was used to predict the test engine’s emission patterns and performance. The results showed that ANN and RSM were both very good and accurate modeling strategies. When 10% H2 + LO25 was used, the BTE rose to 31%, up from 20% for clean LO and 28% for 12% H2 + LO25 + 2%W means. Compared to clean LO operation, the cylinder pressure raised in 15% H2 mode by up to 63 psi. The measured value for the HRR, under H2 premixed combustion framework was 81 k Joule/CA deg in 10% H2, that is significantly greater than the value for every LO processes. The highest NOX release for 10% H2 + LO 25% was discovered at 690 ppm peak load, while it dropped to 645 ppm in gasoline that had been emulsified in water. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Gasoline

来源：评论

学校读者我要写书评

暂无评论

Text Classification Using Graph Convolutional Networks: A Comprehensive Survey

引用

ACM Computing Surveys 2025年第8期57卷 1-38页

作者： Haider Rizvi, Syed Mustafa Imran, Ramsha Mahmood, Arif The Department of Computer Science Information Technology University Punjab Lahore Pakistan

Text classification is a quintessential and practical problem in natural language processing with applications in diverse domains such as sentiment analysis, fake news detection, medical diagnosis, and document classification. A sizable body of recent works exists where researchers have studied and tackled text classification from different angles with varying degrees of success. Graph convolution network (GCN)-based approaches have gained a lot of traction in this domain over the last decade with many implementations achieving state-of-the-art performance in more recent literature and thus, warranting the need for an updated survey. This work aims to summarize and categorize various GCN-based Text Classification approaches with regard to the architecture and mode of supervision. It identifies their strengths and limitations and compares their performance on various benchmark datasets. We also discuss future research directions and the challenges that exist in this domain. © 2025 Copyright held by the owner/author(s).

关键词： Natural language processing systems

来源：评论

学校读者我要写书评

暂无评论

A novel hybrid method for the registration of deformable medical images using common subregions

引用

Multimedia Tools and Applications 2025年第10期84卷 7401-7424页

作者： Alam, Fakhre Rahman, Sami Ur Ahmad, Jamil Department of Computer Science and Information Technology University of Malakand Dir Pakistan

Deformable image registration is a fundamental technique in medical image analysis and provide physicians with a more complete understanding of patient anatomy and function. Deformable image registration has potentially improved the geometric precision of medical images with large and complex deformations. During the last decade, several types of deformable image registration techniques have been developed with high accuracy and efficiency. However, little work has been done regarding how image registration is performed based on interested common subregions which is still an open question in the field. In this paper, we propose a hybrid method that combines local rigid registration with global deformable registration to address the issue of interested common subregions registration. We first detected and segmented interested common subregions in source and target images, and then registered the obtained interested common subregions with local transformations. The obtained transformation parameters in local registration are then applied on the deformed source image which recovered it according to the coordinates of fixed target image. The fixed target image is then rigidly registered with the recovered source image. Finally, the obtained image from rigid image registration is again registered with fixed target image using deformable registration and a more informative registered image is obtained. The main objective of the proposed work is to address the issue of interested common subregions-based registration in deformable medical images. The proposed deformable image registration method is evaluated on 2D brain MRI images of glioma patients. The evaluation parameters are mean square error (MSE), sum of squared differences (SSD), peak signal to noise ratio (PSNR), cross-correlation CC) and computation time. The overall Analysis of variance (ANOVA) for the mentioned variables are significant (i.e., the value of P © The Author(s), under exclusive licence to Springer S

关键词： Mean square error

来源：评论

学校读者我要写书评

暂无评论

GaitFFDA:Feature Fusion and Dual Attention Gait Recognition Model

引用

Tsinghua science and technology 2025年第1期30卷 345-356页

作者： Zhixiong Wu Yong Cui Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China

Gait recognition has a wide range of application scenarios in the fields of intelligent security and *** recognition currently faces challenges:inadequate feature methods for environmental interferences and insufficient local-global information *** address these issues,we propose a gait recognition model based on feature fusion and dual *** model utilizes the ResNet architecture as the backbone network for fundamental gait features ***,the features from different network layers are passed through the feature pyramid for feature fusion,so that multi-scale local information can be fused into global information,providing a more complete feature *** dual attention module enhances the fused features in multiple dimensions,enabling the model to capture information from different semantics and scale *** model proves effective and competitive results on CASIA-B(NM:95.6%,BG:90.9%,CL:73.7%)and OU-MVLP(88.1%).The results of related ablation experiments show that the model design is effective and has strong competitiveness.

关键词： gait recognition neural network attention mechanism

来源：评论

学校读者我要写书评

暂无评论

Fluid Limits and Optimal Task Assignment Policies for Locally Pooled Service Systems

引用

Performance Evaluation Review 2025年第3期52卷 7-10页

作者： Goldsztajn, Diego Eindhoven University of Technology Mathematics and Computer Science Department Netherlands

Task assignment policies play a central role in many online applications, where service requests or tasks arrive over time and are distributed across parallel servers in a data center or cloud computing platform. The way in which the tasks are distributed across the servers has a tremendous impact on the performance perceived by users and the efficiency of server usage, which has attracted strong interest from our research community. Most of this interest has been directed to the so-called supermarket model, but real-life systems have features that fall outside of this standard framework, raising significant challenges which have been pursued in my doctoral thesis [3]. In particular, my thesis provides novel modeling frameworks and performance analysis techniques that enable the study of interactive online applications with fluctuating demand patterns and networked systems. © 2025 Copyright is held by the owner/author(s).

关键词： Cloud platforms

来源：评论

学校读者我要写书评

暂无评论

MTUNet + + : explainable few-shot medical image classification with generative adversarial network

引用

Multimedia Tools and Applications 2025年第11期84卷 9219-9239页

作者： Titoriya, Ankit Kumar Singh, Maheshwari Prasad Singh, Amit Kumar Department of Computer Science and Engineering National Institute of Technology Patna India

Medical imaging, a cornerstone of disease diagnosis and treatment planning, faces the hurdles of subjective interpretation and reliance on specialized expertise. Deep learning algorithms show improvements in automating medical image analysis, reducing radiologists' burden, and potentially enhancing patient outcomes. However, these algorithms require substantial quantities of high-quality labelled data for effective training and refinement. This paper proposes an innovative approach that harnesses few-shot learning (FSL) and generative adversarial networks (GANs) to overcome conventional methods' limitations in medical image classification. FSL, capable of learning from limited labelled examples, holds promise for scenarios where labelled data is scarce. However, the lack of interpretability in existing FSL models impedes their clinical adoption. To tackle this, this paper proposes a explainable FSL network, "MTUNet + +," which integrates an attention mechanism to emphasize relevant regions in medical images. Furthermore, integrating a generative adversarial network, enhances the performance of MTUNet + + by generating synthetic medical images. Systematically eliminating misleading synthetic images improves the reliability and accuracy of medical image classification. Empirical evaluation on benchmark datasets underscores the effectiveness of the approach, achieving 85.19% and 69.28% accuracy for the HAM10000 and Kvasir datasets, respectively. This paper contributes to advancing AI-driven solutions in clinical practice, facilitating enhanced patient care and streamlined workflows within real-world healthcare settings. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Emotion mining for early suicidal threat detection on both social media and suicide notes using context dynamic masking-based transformer with deep learning

引用

Multimedia Tools and Applications 2025年第13期84卷 11729-11752页

作者： Kodati, Dheeraj Tene, Ramakrishnudu Department of Computer Science and Engineering National Institute of Technology Warangal India

Suicide represents a poignant societal issue deeply entwined with mental well-being. While existing research primarily focuses on identifying suicide-related texts, there is a gap in the advanced detection of mental health states leading to suicidal tendencies. The purpose is to recognize the emotions influencing individuals’ mental states, potentially leading to suicidal behavior. This study seeks to identify specific emotions like anger, depression, anxiety, guilt, fear, stress, and sadness within suicide-related texts sourced from both social media and suicide notes. We introduce a novel suicide severity assessment method emphasizing emotion detection across categories like suicidal ideation, suicide planning, attempted, committed, suicide notes, describing, and cannot dare. By identifying high-intensity negative emotions, our approach enables early detection of potential suicide threats. Existing studies often struggle to capture nuances and semantic relationships, particularly in suicide-related contexts. To address this, we propose a context dynamic masking based on bidirectional long short-term memory along with a multi-head self-attention and convolutional neural network (CoDyn-BMHSA-CNN) model, capturing bidirectional contexts and maintaining variable-length sequences. Our model achieves accuracies of 98.2% and 97.4%, with F1 scores of 95.5% and 93.8% for social media and suicide note datasets, respectively. We perform ablation tests to pinpoint the key components of our proposed models. Furthermore, a thorough comparative analysis evaluates the model’s performance across various contexts and platforms. Experimental results illustrate the superior effectiveness of our model in identifying emotions from text sequences compared to state-of-the-art approaches. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Examining the quality of learned representations in self-supervised medical image analysis: a comprehensive review and empirical study

引用

Multimedia Tools and Applications 2025年第9期84卷 6295-6325页

作者： Pani, Kaliprasad Chawla, Indu Department of Computer Science & IT Jaypee Institute of Information Technology Noida India

Medical Image Analysis (MIA) is integral to healthcare, demanding advanced computational techniques for precise diagnostics and treatment planning. The demand for accurate and interpretable models is imperative in the ever-evolving healthcare landscape. This paper explores the potential of Self-Supervised Learning (SSL), transfer learning and domain adaptation methods in MIA. The study comprehensively reviews SSL-based computational techniques in the context of medical imaging, highlighting their merits and limitations. In an empirical investigation, this study examines the lack of interpretable and explainable component selection in existing SSL approaches for MIA. Unlike prior studies that randomly select SSL components based on their performance on natural images, this paper focuses on identifying components based on the quality of learned representations through various clustering evaluation metrics. Various SSL techniques and backbone combinations were rigorously assessed on diverse medical image datasets. The results of this experiment provided insights into the performance and behavior of SSL methods, paving the way for an explainable and interpretable component selection mechanism for artificial intelligence models in medical imaging. The empirical study reveals the superior performance of BYOL (Bootstrap Your Own Latent) with resnet as the backbone, as indicated by various clustering evaluation metrics such as Silhouette Coefficient (0.6), Davies-Bouldin Index (0.67), and Calinski-Harabasz Index (36.9). The study also emphasizes the benefits of transferring weights from a model trained on a similar dataset instead of a dataset from a different domain. Results indicate that the proposed mechanism expedited convergence, achieving 98.66% training accuracy and 92.48% testing accuracy in 23 epochs, requiring almost half the number of epochs for similar results with ImageNet weights. This research contributes to advancing the understanding of SSL in MIA, providin

关键词： Supervised learning

来源：评论

学校读者我要写书评

暂无评论

Exploring Nonuniform Structure of NSRTDs for Characterizing MACA Rules in the Null-Boundary Condition

引用

Complex Systems 2025年第1期34卷 91-127页

作者： Hazra, Suvadip Banerjee, Som Dalui, Mamata Chakraborty, Bidesh Department of Computer Science and Engineering Indian Institute of Information Technology Karnataka Dharwad58009 India Department of Computer Science and Engineering National Institute of Technology West Bengal Durgapur713209 India Department of Computer Science and Engineering Haldia Institute of Technology West Bengal Haldia721657 India

The cellular automaton (CA), a discrete model, is gaining popularity in simulations and scientific exploration across various domains, including cryptography, error-correcting codes, VLSI design and test pattern generation. This paper examines a two-state three-neighborhood nonuniform finite CA with a specified number of fixed points. We utilize a graph-based tool known as the next state rule minterm transition diagram (NSRTD) to analyze the spatiotemporal behavior of these cellular automata (CAs) across all lengths. We devise methods for synthesizing nonuniform single length cycle multi-attractor CAs (MACAs), a specialized class of irreversible CA with a predefined number of fixed points for any arbitrary length. Despite the method’s exponential worstcase time complexity, it offers the advantage of selecting rules for each CA cell from a set of candidate rules, ensuring the desired number of fixed points. © 2025, Complex Systems Publications, Inc. All rights reserved.

关键词： cellular automata fixed-point attractor length cycle multi-attractor cellular automata MACA next state rule minterm transition diagram NSRTD

来源：评论

学校读者我要写书评

暂无评论

MF-CGAN: Multi-Feature Conditional GAN for Synthetic Data Generation in Internet of Medical Things

引用

IEEE Internet of Things Journal 2025年第10期12卷 13469-13476页

作者： Pandey, Chandrasen Tiwari, Vaibhav Francis, Sharmila A. J. Pal, Vipin Roy, Diptendu Sinha Manipal University Department of Computer Science and Engineering Jaipur India Bennett University School of Computer Science Engineering and Technology Greater Noida India King Khalid University Department of Computer Science and Engineering Rijal Alma Abha Campus Saudi Arabia Netaji Subhash University of Technology Department of Computer Science and Engineering New Delhi India Department of Computer Science and Engineering Meghalaya India

Synthetic data generation via Generative Artificial Intelligence (GenAI) is essential for enhancing cybersecurity and safeguarding privacy in the Internet of Medical Things (IoMT) and healthcare. We introduce Multi-Feature Conditional GAN (MF-CGAN), a Conditional Generative Adversarial Network with multi-feature integration, that generates realistic synthetic data using the WUSTL EHMS 2020 dataset of network traffic and health metrics. MF-CGAN’s architecture integrates conditional variables such as cyberattack types, traffic direction, and status flags, preserving their intricate interdependencies. The generator employs dense layers with batch normalization and Leaky ReLU activations to produce conditional synthetic data;the discriminator evaluates authenticity using the same conditional inputs. Experimental results show that MF-CGAN produces high-fidelity synthetic data, closely mirroring real data characteristics. In addition, we quantitatively demonstrate (MAE=0.012, RMSE=0.035, and classification accuracy up to 99%) that MF-CGAN-generated data effectively supports cybersecurity tasks, indicating its potential to significantly enhance data availability for IoMT. © 2014 IEEE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：