检索结果-内蒙古大学图书馆

26th ACM/IEEE International Conference on Model Driven engineering Languages and Systems, MODELS 2023

作者： Shrestha, Sohil Lal Boll, Alexander Chowdhury, Shafiul Azam Kehrer, Timo Csallner, Christoph University of Texas at Arlington Computer Science & Eng. Dept. ArlingtonTX76019 United States University of Bern Software Engineering Group Bern3012 Switzerland

ISBN: (纸本)9798350324808

Having readily available corpora is crucial for performing replication, reproduction, extension, and verification studies of existing research tools and techniques. MAT-LAB/Simulink is a de-facto standard tool in several safety-critical industries for system modeling and analysis, compiling models to code, and deploying code to embedded hardware. There is no commonly used corpus for large-scale model change studies because there is no readily available corpus. EvoSL is the first large corpus of Simulink projects that includes model and project changes and allows redistribution. EvoSL is available under a permissive open-source license and contains its collection and analysis tools. Using a subset of EvoSL, we replicated a case study of model changes on a single closed-source industrial project. © 2023 IEEE.

关键词： Simulink

来源：评论

学校读者我要写书评

暂无评论

Explainable Multiclass Blood Cell Classification: Combining Custom CNNs with SHAP

Explainable Multiclass Blood Cell Classification: Combining ...

引用

International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Md. Ismiel Hossen Abir Shantanu Dey Anik Hasibul Islam Peyal Dept. of Computer Science & Engineering International Standard University Dhaka Bangladesh Dept. of Software Engineering Daffodil International University Birulia Savar Dhaka Bangladesh Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Blood cell classification is a crucial task in medical diagnostics. This work can identify different blood cell types that play essential roles in the immune system and overall body function. This research introduces a custom Convolutional Neural Network (CNN) model with SHAP for explainable multiclass classification of blood cell images. We classify eight types of blood cells—basophil, eosinophil, erythroblast, immunoglobulin (Ig), lymphocyte, monocyte, neutrophil, and platelet. An ablation study was conducted to optimize the CNN architecture. We compared its performance with eleven transfer learning models, including VGG16, ResNet101, and EfficientNet variants. Our model achieved 92.60% accuracy while maintaining lower complexity. The use of SHAP provides transparency by highlighting crucial features in the model’s decision-making process.

关键词： Training Accuracy Microprocessors Transfer learning Lymphocytes computer architecture Convolutional neural networks Medical diagnosis Blood Testing

来源：评论

学校读者我要写书评

暂无评论

Embodied AI in Mobile Robots: Coverage Path Planning with Large Language Models

arXiv

引用

arXiv 2024年

作者： Kong, Xiangrui Zhang, Wenxiao Hong, Jin Braunl, Thomas Dept. of Electrical Electronic and Computer Engineering The University of Western Australia Perth Australia Dept. of Computer Science and Software Engineering The University of Western Australia Perth Australia

In recent years, Large Language Models (LLMs) have demonstrated remarkable capabilities in understanding and solving mathematical problems, leading to advancements in various fields. We propose an LLM-embodied path planning framework for mobile agents, focusing on solving high-level coverage path planning issues and low-level control. Our proposed multilayer architecture uses prompted LLMs in the path planning phase and integrates them with the mobile agents' low-level actuators. To evaluate the performance of various LLMs, we propose a coverage-weighted path planning metric to assess the performance of the embodied models. Our experiments show that the proposed framework improves LLMs' spatial inference abilities. We demonstrate that the proposed multi-layer framework significantly enhances the efficiency and accuracy of these tasks by leveraging the natural language understanding and generative capabilities of LLMs. Our experiments show that this framework can improve LLMs' 2D plane reasoning abilities and complete coverage path planning tasks. We also tested three LLM kernels: gpt-4o, gemini-1.5-flash, and claude- 3.5-sonnet. The experimental results show that claude-3.5 can complete the coverage planning task in different scenarios, and its indicators are better than those of the other models. © 2024, CC BY-NC-SA.

关键词： Motion planning

来源：评论

学校读者我要写书评

暂无评论

Identifying COVID-19 through X-ray and CT scan images using Machine Learning 18

Identifying COVID-19 through X-ray and CT scan images using ...

引用

18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Shehzadi, Maham Arshad, Usama Abideen, Zainul Ali, Raja Hashim Khan, Ammad Aslam Ijaz, Ali Zeeshan BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Virtual University of Pakistan Dept. of Biological Sciences Rawalpindi Pakistan National University of Computer & Emerging Sciences Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350328172

In the current global scenario, marked by the COVID-19 pandemic, it has become imperative for healthcare systems around the globe to swiftly and accurately diagnose the disease. This is where cutting-edge approaches such as machine learning (ML) come into play, specifically, ML-based identification of COVID-19 from chest X-ray and CT-Scan imagery. Our research collective is devotedly working towards devising a robust, accurate ML model capable of deciphering these images to identify COVID-19 effectively. This involves the application of a variety of AI algorithms and vast data resources. Our work is grounded on a large, annotated image dataset that aids in training and refining the model. Our findings indicate that ML methodologies can discern COVID-19 from chest X-ray and CT scan images with a mean accuracy of 90%. Such a tool holds promise in aiding medical practitioners in prompt and precise patient diagnosis, thereby improving patient prognosis. The most notable contribution of our study lies in the creation of an ML model that can accurately detect COVID-19 in chest X-ray and CT-Scan images, potentially aiding in early disease detection and management. © 2023 IEEE.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

AMN: Attention-based Multimodal Network for Android Malware Classification

AMN: Attention-based Multimodal Network for Android Malware ...

引用

IEEE Conference on Cybernetics and Intelligent Systems

作者： Yi Yang Huyu Wu Yanzhe Wang Peng Wang Dept. Software Engineering Sichuan University Chengdu China Dept. Computer Science Sichuan University Chengdu China

ISBN: (数字)9798350364194

ISBN: (纸本)9798350364200

With the rapid evolution of malware, android malware classification has become increasingly important and remains an active area of research. Traditional machine learning methods rely heavily on hand-crafted features based on expert domain knowledge, making the feature engineering process time-consuming. In recent years, deep learning-based Android malware classification has attracted considerable attention, inspired by the remarkable success of deep learning techniques in the fields of vision and natural language processing. Deep learning methods replace the manual process of feature engineering with a framework capable of simultaneous feature extraction and classification directly from raw data. However, existing approaches do not fully exploit hybrid and multimodal features, which limits their ability to consider multiple disparate sources of information during classification. In this paper, we present attention-based multimodal network(AMN), a multimodal approach to malware classification based on the attention mechanism. AMN combines two modalities of data: 1) grayscale images generated by malware binary files, and 2) network traffic feature sequences. The model employs a cross-modal attention mechanism that combines the features of both modalities by compensating for their limitations. Furthermore, a transfer learning method is proposed to improve the generalization performance of the model, especially in scenarios with limited sample size. The proposed solution surpasses the state-of-the-art precision, achieving 97.73%, along with a recall of 96.51%, as demonstrated through experiments conducted on the CICInvesAndMal2017 dataset. Additionally, to show the effectiveness of our model, ablation experiments were conducted.

关键词： Deep learning Attention mechanisms Mechatronics Transfer learning Random access memory Telecommunication traffic Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Using LLMs for Annotation and ML Methods for Comparative Analysis of Large-Scale Turkish Sentiment Datasets

Using LLMs for Annotation and ML Methods for Comparative Ana...

引用

International Conference on computer Science and engineering (UBMK)

作者： Ercan Ezin Rukiye Savran Kiziltepe Murat Karakus Dept. of Computer Engineering Harran University Şanlıurfa Türkiye Dept. of Software Engineering Karadeniz Tech. University Trabzon Türkiye Dept. of Software Engineering Ankara University Ankara Türkiye

ISBN: (数字)9798350365887

ISBN: (纸本)9798350365894

With the increasing prevalence of e-commerce plat-forms, understanding customer sentiments expressed in product reviews is crucial for assessing platform and product performance. However, traditional sentiment analysis (SA) models often struggle with low-resource languages such as Turkish, particularly due to the language's agglutinative nature and morphological complexity. To address this, several public datasets are now available online. However, these datasets are often annotated using rule-based methods, resulting in poor accuracy for Machine Learning (ML) models. This study addresses these challenges by employing two large Turkish datasets, TRSAv1 and VSCR, and improving their sentiment annotations through advanced Large Language Models (LLMs) such as ChatGPT4o-mini. The reannotation significantly enhanced the performance of traditional ML models-Logistic Regression, Naive Bayes, Random Forest, and Support Vector Machine-especially in the neutral and negative sentiment classes, as evidenced by increased precision, recall, and F1-scores. The findings underscore the importance of thorough data preparation and demonstrate that utilizing LLMs for sentiment annotation in Turkish can lead to more reliable SA, providing a stronger foundation for developing advanced SA tailored to low-resource languages. This study is the first to employ recent LLM for relabelling Turkish customer reviews, highlighting the potential for improved accuracy in SA for low-resource languages.

关键词： Analytical models Accuracy Systematics Annotations Reviews Computational modeling Transfer learning Transformers Vectors Electronic commerce

来源：评论

学校读者我要写书评

暂无评论

SafeEmbodAI: a Safety Framework for Mobile Robots in Embodied AI Systems

arXiv

引用

arXiv 2024年

作者： Zhang, Wenxiao Kong, Xiangrui Braunl, Thomas Hong, Jin B. Dept. of Computer Science and Software Engineering The University of Western Australia Perth Australia Dept. of Electrical Electronic and Computer Engineering The University of Western Australia Perth Australia

Embodied AI systems, including AI-powered robots that autonomously interact with the physical world, stand to be significantly advanced by Large Language Models (LLMs), which enable robots to better understand complex language commands and perform advanced tasks with enhanced comprehension and adaptability, highlighting their potential to improve embodied AI capabilities. However, this advancement also introduces safety challenges, particularly in robotic navigation tasks. Improper safety management can lead to failures in complex environments and make the system vulnerable to malicious command injections, resulting in unsafe behaviours such as detours or collisions. To address these issues, we propose SafeEmbodAI, a safety framework for integrating mobile robots into embodied AI systems. SafeEmbodAI incorporates secure prompting, state management, and safety validation mechanisms to secure and assist LLMs in reasoning through multi-modal data and validating responses. We designed a metric to evaluate mission-oriented exploration, and evaluations in simulated environments demonstrate that our framework effectively mitigates threats from malicious commands and improves performance in various environment settings, ensuring the safety of embodied AI systems. Notably, In complex environments with mixed obstacles, our method demonstrates a significant performance increase of 267% compared to the baseline in attack scenarios, highlighting its robustness in challenging conditions. © 2024, CC BY.

关键词： Mobile robots

来源：评论

学校读者我要写书评

暂无评论

Enhancing Women's Safety Through Customized Smartphone Sensor Data: A Human Activity Recognition System 9

Enhancing Women's Safety Through Customized Smartphone Senso...

引用

9th IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2023

作者： Islam, Yamina Nowrin, Sanjida Faqih, Majed Ibrahim Sultana, Fozia Bahadur, Erfanul Hoque Masum, Abdul Kadar International Islamic University Chittagong Dept. of Computer Science and Engineering Chattogram Bangladesh Signal Corps Institute Director of Research and Development Saudi Arabia Hiperdyne Artificial Intelligence Engineer Tokyo Japan Daffodil International University Dept. of Software Engineering Dhaka Bangladesh

ISBN: (纸本)9798350319651

Human activity recognition (HAR) research using smartphone sensor technology has changed people's daily lives and opened the door for numerous exciting applications due to its extensive applications in addressing real-world humancentric issues. These studies, however, mostly concentrated on a woman's daily activities and behaviours when she is attacked, such as running, walking, sleeping, eating, drinking, lying, straight punching, front kicking, and so forth. The article offers a method to foresee human behaviour using a deep learning model and evaluates the efficiency of the approach using real-world data in response to this. We offer an LSTM structure for detecting the intensity of HAR in order to track activity patterns and build a neural network with binary classification skills to evaluate the situation of women. The proposed model uses the dataset to identify human activity, which records 30 volunteers' body movements while they engage in 18 physical activities. Feature engineering is used to extract additional variables from the data in addition to that. Our proposed model achieves a 90.89% accuracy. © 2023 IEEE.

关键词： Smartphones

来源：评论

学校读者我要写书评

暂无评论

Comparing Emotion Detection Methods in Online Classrooms: YOLO Models, Multimodal LLM, and Human Baseline

Comparing Emotion Detection Methods in Online Classrooms: YO...

引用

IEEE Education engineering (EDUCON)

作者： Medha Mohan Ambali Parambil Salah Bouktif Munkhjargal Gochoo Fady Alnajjar Dept. Computer Science and Software Engineering United Arab Emirates University Al Ain UAE

ISBN: (数字)9798331539498

ISBN: (纸本)9798331539504

The COVID-19 pandemic has transformed learning environments, challenging educators to understand students' behaviors during the online mode of learning, in particular emotions associated with students' attention during virtual classrooms. As learning transitions between physical and virtual spaces, the ability to interpret student attention and engagement has become complex. In response to this challenge, our research investigates the use of GPT-4o, a multimodal large language model, for identifying student emotions by analyzing images in diverse learning settings. The study involved analyzing online classroom images featuring 149 faces, utilizing three distinct approaches: a computer vision model (YOLO), the multimodal LLM (GPT-4o), and a human-annotated baseline. The analysis systematically categorized facial expressions into eight emotional categories: Happy, Sad, Angry, Neutral, Contempt, Disgust, Fear, and Surprise. The findings indicate that multimodal LLMs can effectively detect student emotions, achieving an average accuracy of 93.8%, which aligns with the human baseline accuracy of 97.0%. In contrast, YOLO models maintained an average accuracy of 81.9%, performing well for basic emotions but struggling with subtle expressions. This research contributes to enhancing educational practices by providing valuable insights regarding the application of multimodal LLMs to assist educators in comprehending student emotions within both physical and digital classroom settings.

关键词： YOLO Emotion recognition Privacy Accuracy Annotations Pandemics Scalability Large language models Real-time systems Faces

来源：评论

学校读者我要写书评

暂无评论

Optimized Crop Detection Using IoT and Deep Neural Networks 5

Optimized Crop Detection Using IoT and Deep Neural Networks

引用

5th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2023

作者： Choudhury, Tanupriya Rohini, A. Mahdi, Hussain Falih Uttarakhnad Dehradun248007 India Graphic Era Hill University Dehradun248002 India Dept. of Computer Science & Engg Visakhapatnam531162 India University of Diyala Department of Computer and Software Engineering Baquba Iraq

ISBN: (纸本)9798350337525

The advancement of agriculture in food production is an increasing concern. It is one of the major challenges in contemporary farming. The development methods of soil farming have increased the fabrication of crops and food. It emphasizes the vital role of sustainability in soil. Different technologies were used to grow and flourish in soil to supply the nutrition's. It buffers and delicate the plant roots from fluctuations in temperature. Hydroponically grown fruits can produce rich nutrition's like traditional grown fruits. Proposed research has focused on the IoT in Hydroponics for Growing Tomato Plants. The Nutrient Film Technique (NFT) is needed for the present farmers for monitor and managing the crop system. For managing and monitoring the crop to execute the method scientifically and take the decision about the crops in time. The integration of IoT has given the solutions to solve the contemporary problems of calcium deficiency, lack of airflow around plants and so on. This proposal has developed a smart hydroponic system with technology enabled by IoT system. © 2023 IEEE.

关键词： Crops

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：