检索结果-内蒙古大学图书馆

17th International Conference on Open Source Systems and Technologies, ICOSST 2023

作者： Shah, Aqib Raza Mahboob, Tahira Yaseen, Hira Mehmood, Muhammad Qasim of the Punjab MicroNano Lab Department of Electrical Engineering Lahore Pakistan of the Punjab Department of Computer and Software Engineering Lahore Pakistan

ISBN: (纸本)9798350381320

A comprehensive assessment of blood cell components serves as a pivotal step in recognizing specific health issues in individuals. The analysis of life-Threatening conditions like myeloma and thrombocytopenia underscores the quantification of platelets, erythrocytes (RBCs), and leukocytes (WBCs). Conventional methods, comprising automated analyzers, and manual counting were frequently employed, but they are laborious, lengthy, costly, and require the skills of numerous medical professionals. To mitigate the aforementioned slow and expensive techniques, this study utilizes a deep learning-driven system for object recognition and categorization, employing the You Only Look Once (YOLO) algorithm to count blood cells. The Tiny YOLO model was trained on the BCCD dataset, utilizing a customized architecture designed specifically for detecting the different components of blood cells. Following this, a Convolutional Neural Network (CNN) was utilized to classify the identified WBCs. © 2023 IEEE.

关键词： Object recognition

来源：评论

学校读者我要写书评

暂无评论

Effects of Data Augmentation Techniques on Classification Performance in Knee MRIs 5

Effects of Data Augmentation Techniques on Classification Pe...

引用

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

作者： Kucuk, Elif Nur Gur, Aybars Celal Bayar University Department of Software Engineering Manisa Turkey Department of Computer Engineering Ege University Izmir Turkey

ISBN: (纸本)9798350337525

The role of the amount of data used in increasing the effectiveness of deep learning models is very important. Due to the insufficient publicly available data in some sub-fields of health, data augmentation is vital. This study proposes an approach to improve the experimental work process in deep learning-based injury detection using data augmentation techniques on knee Magnetic Resonance (MR) images. The study is also one of the few in the body of literature to examine the impact of data augmentation in hard tissues. The effect of data augmentation on classification performance is tested using various transfer learning models, and the highest success rates in this study are determined for three classes. Forecasting achievements: the accuracy is 89.98% in the abnormal, 80.35% in the anterior cruciate ligament, and 76.66% in the meniscus classes. As a result of the experiments, it has been seen that the AutoAugment architecture works faster and generally gives more successful results than other augmentation methods. © 2023 IEEE.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Comparison of Leading Language Parsers - ANTLR, JavaCC, SableCC, Tree-sitter, Yacc, Bison 13

Comparison of Leading Language Parsers - ANTLR, JavaCC, Sabl...

引用

13th International Conference on software Technology and engineering, ICSTE 2023

作者： Latif, Afshan Azam, Farooque Anwar, Muhammad Waseem Zafar, Amina College of Electrical and Mechanical Engineering Department of Computer and Software Engineering Islamabad Pakistan Malardalen University Department of Innovation Design and Engineering Vasteras Sweden

ISBN: (纸本)9798350371475

software engineering applications in domains like embedded systems and health care have increased exponentially during the last few years. Developing, analyzing, and customization of languages is one of the core software engineering aspects. This usually involves lexical, syntactical, and semantic operations, technically termed parsing. For this, several parsers have been introduced in state-of-the-art. However, due to diverse features, selecting a parser for a particular operation during software engineering applications is always problematic. In this article, we identified six leading parsers (i.e., ANTLR, JavaCC, SableCC, Tree-sitter, Yacc, and Bison) from the state-of-the-art. Furthermore, we also identified significant parser features to perform meaningful comparative analysis. Results indicate that ANTLR and JavaCC provide enhanced parsing features, such as the parsing algorithm and the extended grammar notation. However, JavaCC is suitable for simple grammar definition, whereas ANTLR allows specifying complex grammar with multiple alternative paths. The findings of this article are highly beneficial for researchers and practitioners while selecting the right parser to perform specific software engineering tasks. © 2023 IEEE.

关键词： Embedded systems

来源：评论

学校读者我要写书评

暂无评论

Facial Emotion Recognition using Deep Learning (FERDL) 25

Facial Emotion Recognition using Deep Learning (FERDL)

引用

25th International Multi Topic Conference, INMIC 2023

作者： Ramzan, Hafiz Arslan Sohaib, Ahmed Ramzan, Sadia Kalsum, Tehmina Aqeel, Muhammad Majeed, Mohsin Khwaja Fareed University of Engineering and Information Technology Institute of Computer and Software Engineering Rahim Yar Khan Pakistan National University of Modern Languages Department of Computer Science Multan Pakistan University of Engineering & Technology Department of Software Engineering Taxila Pakistan

ISBN: (纸本)9798350317701

Facial Emotion Recognition is one of the in-demand and rapidly growing research topics in the domain of computer Vision (CV) and artificial intelligence (AI). The ability to identify or detect human emotions from real-time facial expressions (FEs) has vast conceivable applications in different domains, such as sentiment analysis, human-computer interaction, human resource management, security, and human psychology. In this paper, a Convolutional Neural Network (CNN) based deep learning model is trained with haar-cascade classifier to recognize the real-time FEs. The suggested model is specially trained to categorize the FEs into one of the seven emotion categories, namely six basic emotions (sad, happy, angry, surprised, disgusted, fear) and a neutral emotion. It includes several convolutional layers, as well as fully connected neurons followed by max-pooling layers, and soft-max activation function with the corresponding seven classes. ReLU activation functions along with various kernels to enhance filtering depth, and extraction of facial features. FER-2013 dataset is used for experimentation purpose. To improve the classification performance and model accuracy, a data augmentation technique is used for rescaling and horizontal flipping. The proposed model outperforms the previous related works by achieving a validation accuracy of 71.96% and training accuracy above 90%, with fewer epochs. © 2023 IEEE.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

Improving Transfer Learning Performance for Abnormality Detection in Brain MRI Images Using Feature Optimization Techniques 27

Improving Transfer Learning Performance for Abnormality Dete...

引用

27th International Conference on Soft Computing and Measurements, SCM 2024

作者： Nizamli, Yasser A. Filatov, Anton Yu. Saint Petersburg Electrotechnical University 'LETI' Department of Software Engineering and Computer Applications Saint Petersburg Russia

ISBN: (纸本)9798350363708

Accurate and early diagnosis of brain tumors plays a vital role in determining appropriate treatment and increasing survival rates. Manual diagnosis using MRI images is often prone to errors and can be a tedious and time-consuming procedure. As a result, automated computer-aided systems that utilize artificial intelligence and machine learning techniques are widely used. However, accurately diagnosing the type of brain tumor remains a significant and complex challenge for today's medical imaging research community. In this paper, we present an enhanced transfer learning approach that aims to differentiate brain tumor types using MRI scans while achieving state-of-the-art performance. In the proposed approach, the pre-trained VGG-19 network with fixed weights is used to map MRI scans to a high-level numerical representation. The extracted features are then passed to a support vector machine algorithm to classify brain tumors into one of three major categories: glioma, meningioma, and pituitary. To further improve the performance of the model, the effectiveness of utilizing feature optimization techniques based on the radial basis similarity function and L1-penalty logistic regression was investigated. For evaluation, the benchmark Figshare dataset containing 3064 MRI images was used after a series of processing operations. The proposed system achieved a high overall accuracy of 98.53%, outperforming other analogues. © 2024 IEEE.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Countering Deepfakes using an Improved Advanced CNN and its Ensemble with Pretrained Models 3

Countering Deepfakes using an Improved Advanced CNN and its ...

引用

3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Mathur, Abeer Tejpal, Moulik Bhargava, Kshitiz Natarajan, Krishnaraj Singh, Manvendra Vellore Institute of Technology School of Computer Science & Engineering Department of Software Systems Vellore India Vellore Institute of Technology School of Computer Science & Engineering Department of Database Systems Vellore India

ISBN: (纸本)9798350369083

The extensive spread of DeepFake images on the internet has emerged as a significant challenge, with applications ranging from harmless entertainment to harmful acts like blackmail, misinformation, and spreading false propaganda. To tackle this issue, this paper introduces a sophisticated DeepFake detection model designed to identify and mitigate the increase of these deceptive images. The model architecture integrates an ensemble approach, combining the strengths of two pre-trained Convolutional Neural Network (CNN) models - MobileNet and Xception - with a novel CNN architecture, the Advanced CNN (ACNN). This rigorous validation process enabled the model to achieve a high accuracy rate of 97.89% in detecting DeepFakes. The successful implementation of this ensemble CNN approach demonstrates its effectiveness in distinguishing between real and fabricated imagery with high precision. This research makes a substantial contribution to the field of digital image forensics, offering a reliable tool for stakeholders across various sectors to identify and counteract the spread of DeepFake images online. © 2024 IEEE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Investigating Visual Guide Cues in VR: Impacts of Virtual Humans and Symbol-Based Navigation on Real-World Performance and Experience

Investigating Visual Guide Cues in VR: Impacts of Virtual Hu...

引用

2025 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2025

作者： Khan, Omar Nguyen, Anh Nam, Hyeongil Kim, Kangsoo University of Calgary Department of Computer Science Canada University of Calgary Department of Electrical and Software Engineering Canada

ISBN: (纸本)9798331514846

Virtual experiences can significantly influence our perception and behavior in the real world, shaping how we interact with and navigate physical environments. In this paper, we examine the impact of learning navigation routes in an immersive virtual environment (IVE) on navigation performance and user experience in a corresponding real-world indoor setting. We developed a guide system with two distinct audiovisual representations: a human agent guide and a symbol-based guide. A preliminary user study (N = 10) was conducted to evaluate the system. While no significant differences were observed between the two guide conditions, the findings reveal valuable insights into user-perceived confidence and enjoyment during real-world navigation tasks. Contrary to our expectations, the symbol-based guide elicited slightly higher positive scores compared to the human agent guide. We discuss these findings and outline directions for future research. © 2025 IEEE.

关键词： Virtual environments

来源：评论

学校读者我要写书评

暂无评论

Mutual Retinex: Combining Transformer and CNN for Image Enhancement

引用

IEEE Transactions on Emerging Topics in Computational Intelligence 2024年第3期8卷 2240-2252页

作者： Jiang, Kui Wang, Qiong An, Zhaoyi Wang, Zheng Zhang, Cong Lin, Chia-Wen Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Wuhan University National Engineering Research Center for Multimedia Software School of Computer Science Wuhan430072 China Wuhan Polytechnic University School of Mathematics and Computer Science Wuhan430024 China National Tsing Hua University Department of Electrical Engineering Institute of Communications Engineering Hsinchu30013 Taiwan

Images captured in low-light or underwater environments are often accompanied by significant degradation, which can negatively impact the quality and performance of downstream tasks. While convolutional neural networks (CNNs) and Transformer architectures have made significant progress in computer vision tasks, there are few efforts to harmonize them into a more concise framework for enhancing such images. To this end, this study proposes to aggregate the individual capability of self-attention (SA) and CNNs for accurate perturbation removal while preserving background contents. Based on this, we carry forward a Retinex-based framework, dubbed as Mutual Retinex, where a two-branch structure is designed to characterize the specific knowledge of reflectance and illumination components while removing the perturbation. To maximize its potential, Mutual Retinex is equipped with a new mutual learning mechanism, involving an elaborately designed mutual representation module (MRM). In MRM, the complementary information between reflectance and illumination components are encoded and used to refine each other. Through the complementary learning via the mutual representation, the enhanced results generated by our model exhibit superior color consistency and naturalness. Extensive experiments have shown the significant superiority of our mutual learning based method over thirteen competitors on the low-light task and ten methods on the underwater image enhancement task. In particular, our proposed Mutual Retinex respectively surpasses the state-of-the-art method MIRNet-v2 by 0.90 dB and 2.46 dB in PSNR on the LOL 1000 and FIVEK datasets, while with only 19.8% model parameters. © 2017 IEEE.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Hopping-Aware Cluster Header Capability for Sensor Relocation in Mobile IoT Networks

引用

Intelligent Automation & Soft Computing 2023年第5期36卷 1613-1625页

作者： Moonseong Kim Jaeyoung Park Young-Joon Kim Woochan Lee Department of IT Convergence Software Seoul Theological UniversityBucheon14754Korea Department of Computer Engineering Hongik UniversitySeoul04066Korea Department of Electronic Engineering Gachon UniversitySeongnam13120Korea Department of Electrical Engineering Incheon National UniversityIncheon22012Korea

Mobile sensor nodes such as hopping sensors are of critical importance in data ***,the occurrence of sensing holes is unavoidable due to the energy limitation of the ***,it is evident that the relocation of mobile sensors is the most desirable method to recover the sensing *** previous research conducted by the authors so far demonstrated the most realistic hopping sensor relocation scheme,which is suitable for the distributed *** previous studies,the cluster header plays an essential role in detecting the sensing hole and requesting the neighboring cluster to recover the sensing hole that occurred in the sensor ***,the limitations of the cluster header in the previously proposed relocation protocol are not fully *** the cluster header jumps more frequently than non-header nodes,its energy con-sumption is relatively high compared to other ***,it is most likely to lead to header node failure and can lead to data loss on the *** this paper,the jumping ability and energy consumption of the cluster header are seriously *** ability to replace cluster headers in case of failure is also *** results show that the data collection time can be further increased,which demonstrates the validity of the proposed algorithms.

关键词： Hopping sensor mobileinternetofthings relocationprotocol header node numericalsimulation

来源：评论

学校读者我要写书评

暂无评论

Enhancing Ropes for Collaborative Text Editing

Enhancing Ropes for Collaborative Text Editing

引用

2023 Congress in computer Science, computer engineering, and Applied Computing, CSCE 2023

作者： Sandal, Semih Ovatman, Tolga Aselsan Inc Software Engineering Department Ankara Turkey Istanbul Technical University Department of Computer Engineering Istanbul Turkey

ISBN: (纸本)9798350327595

Rope is a tree-based data structure providing performance enhancements in string concatenation operations in applications such as text editors. Even though collaborative text editing has become one of the major applications in cloud environments, utilizing ropes in collaborative text editing is rarely discussed in current literature. In this study, we enhance the Rope data structure for collaborative text editing. Firstly we discuss possible issues that Ropes may suffer in collaborative text editing and propose enhancements that adapt Ropes to such environments. During this adaptation we pay special attention to keep Ropes' immutable nature for the insert operations and balance the tree by following AVL-tree-style and Red-Black-Tree-style methods. We also provide a performance comparison of those balancing approaches. In our results, it can be seen that the AVL-Rope and Red-Black-Rope are slower in the concatenation operation due to balancing, but faster in index search operation and insert operation than the unbalanced Rope. © 2023 IEEE.

关键词： Balanced trees Collaborative text editing Rope data structure String processing

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：