检索结果-内蒙古大学图书馆

4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Sara, Umme Das, Utpol Kanti Sikder, Juel Chowdhury, Sudipta Roy Dept. of Computer Science and Engineering National Institute of Textile Engineering and Research Savar Dhaka Bangladesh Dept. of Computer Science and Engineering Rangamati Science and Technology University Rangamati Sadar Rangamati4500 Bangladesh

The uncontrolled growth of skin cells in the epidermis producing the creation of a mass termed a tumor is a dangerous condition known as skin cancer. Current developments in deep learning artificial intelligence have greatly improved image-based diagnosis. In this study, we included a Skin Lesion Cancer feature extractor Convolutional Neural Network (SLC-CNN) model, which is used for both classification with the SVM classifier and segmentation with XGBoost for skin cancer. In our proposed system, a test image of skin cancer is taken and pre-processed for both classification and segmentation purposes. After applying pre-processing, the test image features are extracted using the SLC-CNN feature extractor, which features are used in SVM to classify the types of skin cancer (Benign and Malignant), and based on the classification result, a trained XGBoost model is called to segment the cancer region. We have tested our system using the dermoscopy image collection from the International Skin Imaging Collaboration (ISIC) and built it in Google Colab to best use the GPU. Our suggested approach has gained a segmentation accuracy of 95.25% and a classification accuracy of 99.6%. © 2025 The Authors. Published by Elsevier B.V.

关键词： Lung cancer

来源：评论

学校读者我要写书评

暂无评论

Classification and Structuring of DL-Based Method for Finding Alzheimer's Disease through MRI Images 5

Classification and Structuring of DL-Based Method for Findin...

引用

5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Tripathi, Tripti Kumar, Rakesh Mmmut Dept. of Computer Science and Engineering Gorakhpur India

ISBN: (纸本)9798350383522

Alzheimer's disease (AD) is a type of neurological disorder that progresses over a period and affects a significant number of people worldwide. Early detection of AD is necessary for rapid intervention and improved outcomes for patients. In this paper, we compare the performance of four widely used Deep Convolutional Neural Network (DCNN) architectures: CNN, VGG16, InceptionV3, and DenseNet121. The study focuses on the use of DCNNs for AD identification. Our dataset is collected from the Kaggle website and preprocessed such that the inputs are identical for every model. Several criteria are used to assess the DCNN architecture's performance, including recall, accuracy, precision, and f1-score. By looking at individual architecture's predictive capabilities, we may discover further the benefits and drawbacks of AD detection jobs. Also, transfer learning techniques employ pre-trained weights from large neuroimaging datasets to enhance model performance and generalization abilities. This study contributes to the increasing body of knowledge in AD research by identifying the ideal DCNN architecture for fast and accurate AD diagnosis. © 2024 IEEE.

关键词： AD detection Alzheimer's disease CNN computational efficiency Deep Convolutional Neural Networks DenseNet121 InceptionV3 performance evaluation transfer learning VGG16

来源：评论

学校读者我要写书评

暂无评论

Cyberbullying Detection using Deep Learning Algorithms 3

Cyberbullying Detection using Deep Learning Algorithms

引用

3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Nirmala Devi, K. Jayanthi, P. Rajasekar, Vani Rathinasamy, Rahul Dhandapani, Pradeep Rasil Risam, F. Dept. of Computer Science Kongu Engineering College Erode India

ISBN: (纸本)9798331527549

Social media's widespread use has made it easier for people to communicate openly, but it has also fuelled the growth of cyberbullying, which has seriously harmed victims' emotional and mental health. The intricacy of human language and the variety of abusive behaviours make it difficult to detect cyberbullying. To solve this problem, numerous studies have used deep learning (DL) and machine learning (ML) algorithms over time. However, deep learning (DL) algorithms have transformed text analysis by automatically extracting features and comprehending contextual dependencies in language, while machine learning (ML) techniques are limited in their ability to handle complex and contextual linguistic patterns. Building upon this framework, this study investigates the use of transformer-based models—more especially, BERT—for the detection of cyberbullying. Out of the proposed models, DistlBERT gives the best accuracy, precision, recall, and F1-score of 98.87%,98.87%, 98.87 and 98.86% respectively. © 2025 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

ENHANCING E-HEALTH CYBERSECURITY AND RESILIENCE: SHIFTING FROM MONOLITHIC TO MICROSERVICES ARCHITECTURE

UPB Scientific Bulletin, Series C: Electrical Engineering an...

引用

UPB Scientific Bulletin, Series C: Electrical engineering and computer science 2025年第1期87卷 21-34页

作者： Contașel, Cristian Rughiniș, Răzvan Trancă, Dumitru Cristian Țurcanu, Dinu Dept. of Computer Science and Engineering National University of Science and Technology POLITEHNICA Bucharest Romania Dep. of Software Engineering and Automatics Technical University of Moldova Chișinău Moldova

This study provides an innovative architectural model for e-Health systems that aims to improve cyber resilience while maintaining high availability under fluctuating traffic loads. We examined typical cybersecurity incidents in the field of e-Health, their correlations with architectural defects, and frequent design patterns in currently operational systems. A testing approach based on our research finds those weaknesses and proposes viable fixes. In this paper, a comprehensive support strategy for transitioning from conventional monolithic architectures to microservices is presented. This change makes use of cloud computing's vertical and horizontal scalability to maximize resource utilization while ensuring system reliability. We also discuss deployment ideas for the new microservices, focusing on operational resilience and cybersecurity in e-Health environments. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

Parallelized 0/1 Knapsack Algorithm Optimization in CPU-GPU-Based Heterogeneous System with Algorithm-based Fault Tolerance 18

Parallelized 0/1 Knapsack Algorithm Optimization in CPU-GPU-...

引用

18th International Conference on Ubiquitous Information Management and Communication, IMCOM 2024

作者： Abid, Marzan Binte Shaha, Shuvo Kabir, Upama Arefin, Md. Fahim University of Dhaka Dept. of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350331011

A heterogeneous high-performance computing (HPC) system is an aggregation of CPUs and GPUs through high-speed interconnection. As graphics processing units (GPUs) can conduct rapid mathematical and vector operations due to their hardware properties, GPU-based heterogeneous systems can optimize algorithms that show data parallelism. Since the dynamic programming (DP) solution of the 0/1 knapsack algorithm is inherently data-parallel, this paper optimized the algorithm to maximize the advantages of a GPU-based parallel environment. Furthermore, this research identified that the algorithm was well suited to four distinct GPU-launch approaches. Among them, the "single-cell calculation by single thread with a single kernel launch"approach outperforms others by reducing the complexity from O(n∗w) to O(n). Besides, these HPC systems are susceptible to the slightest of errors. Checkpoint recovery (CP/R) is the most commonly used fault tolerance and recovery system, which suffers from huge checkpoint and recovery overhead. To reduce this overhead in CPU-GPU-based heterogeneous systems, an algorithm-based fault tolerance (ABFT) technique has been proposed for a particular class of algorithm: 0/1 knapsack. The experimental results verify the feasibility of the proposed ABFT technique and demonstrate that the system can recover from failures with minimal overhead. © 2024 IEEE.

关键词： Graphics processing unit

来源：评论

学校读者我要写书评

暂无评论

Spatial Dynamics for Identification of Individuals through Gait and Other Locomotion Activities

Spatial Dynamics for Identification of Individuals through G...

引用

2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Anusha, R. Sanshi, Shridhar Bapuji Institute of Engineering and Technology Dept. of Computer Science and Engineering Davanagere India National Institute of Technology Dept. of Computer Science and Engineering Karnataka Surathkal India

ISBN: (数字)9798350379525

ISBN: (纸本)9798350379525

Gait, the pattern of walking, has been extensively studied and various methods have been developed to use it as a biometric for individual recognition. Despite this, the potential to identify individuals through running videos has not been thoroughly explored. The paper introduces a novel method that expands the feature-based approach for identifying individuals based on their running style. This work focuses on extracting the mutual information and location specific metric from the key gait poses of subjects in the testing and training datasets. Later on, the assignment of a testing sample to the training sample is accomplished using the proposed classification method. The experiments are conducted on KTH, OU-ISIR A, and Weizmann database. The efficiency of this method is demonstrated by the obtained experimental results. © 2024 IEEE.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Exploring Advanced Approaches: A Comprehensive Analysis of Machine Learning and Deep Neural Networks in Spectrum Sensing Applications 4

Exploring Advanced Approaches: A Comprehensive Analysis of M...

引用

4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Karthiga, G. Saravanan, K. Amc Engineering College Dept. of Computer Science and Engineering Karnataka Bengaluru India New Horizon College of Engineering Dept. of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529635

The increasing demand for wireless communication resources has intensified the need for effective spectrum sensing techniques to alleviate the scarcity of available spectral bands. This survey paper presents an indept. exploration and comparative analysis of various spectrum sensing methodologies, focusing on Machine Learning (ML) and Deep Neural Network (DNN) approaches. The study delineates the landscape of traditional spectrum sensing techniques, encompassing energy detection, matched filtering, cyclo-stationary feature detection, and cooperative spectrum sensing. This paper explains sensing using machine learning-based such as Support Vector Machines (SVM), Random Forest, Neural Networks, and Hidden Markov Models (HMM) are systematically elucidated. Moreover, the paper meticulously scrutinizes the advancements in deep learning-based spectrum sensing, encapsulating the utilization of Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Deep Reinforcement Learning (DRL) methodologies. The discussion encompasses their applications, advantages, and limitations in the context of spectrum sensing. © 2024 IEEE.

关键词： Hidden Markov models

来源：评论

学校读者我要写书评

暂无评论

Enhancing Application Placement in Cloud-Fog Environment Based on Multicriteria Decision Making 3

Enhancing Application Placement in Cloud-Fog Environment Bas...

引用

3rd International Conference on Advanced Electrical engineering, ICAEE 2024

作者： Arichi, Bochra Amraoui, Asma Dept. Computer science Tlemcen13000 Algeria Dept. Computer science Tlemcen13000 Algeria

ISBN: (数字)9798350349351

ISBN: (纸本)9798350349351

Fog computing (FC) is a distributed infrastructure computing that extends cloud computing (CC) capabilities to the edge of the network, closer to where data is generated and consumed. This approach responds to the challenges caused by Internet of Things (IoT) applications, which generate massive amounts of data that are costly and time-consuming to send to the cloud for processing. By leveraging fog computing, the cloud's limitations were successfully addressed, such as unpredictable latency. However, this shift introduced a new challenge. One of these challenges is finding the best placement of applications on distributed fog devices. This paper outlines a study aimed at addressing this challenge by utilizing multi-criteria decision-making (MCDM) techniques, including the Analytic Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). This study aims to choose the most appropriate fog nodes for positioning and processing application modules. The simulation was carried out on the iFogSim simulator, and the findings indicate that the suggested technique provides significant improvements in placement time and latency in a fog computing environment. © 2024 IEEE.

关键词： Fog computing

来源：评论

学校读者我要写书评

暂无评论

Performance Analysis of Deep Learning Models in Driver Behavior Recognition

Performance Analysis of Deep Learning Models in Driver Behav...

引用

2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Kapoor, Rameshwari Kuliyal, Rashmi Bhanot, Nilesh Bagla, Piyush Rawat, Ruchira Graphic Era Hill University Dept. of Computer Science and Engineering Uttarakhand Dehradun India University Dept. of Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798331542344

The World Health Organization report states that nearly 1.25 million people lose their lives due to an increasing number of road accidents every year. The majority of accidents are reported due to driver errors, which include distractions like the use of mobile phones and fatigue, which causes drowsiness. This study aims to address these issues by performing a comparative analysis between different deep learning models representing two types of learning mechanisms: Traditional deep learning and transfer learning. This study portrays the use of two datasets to accomplish this task. These two datasets are the Driver Drowsiness Dataset DDD and the State Farm Distracted Drivers Dataset. The purpose of this investigative research is to explore the execution and efficiency differences between three deep learning algorithms, the traditional convolutional neural network, ResNet50, and VGG16, in classifying driver behavior by training these models individually on both datasets. This study also targets developing an alarm system that sounds an alarm based on a threshold by using an ensemble of all the models trained for comparative study and averaging their predictions to classify driver behavior. VGG16 achieved an accuracy of 99.74% on the driver drowsiness dataset, whereas Custom CNN Architecture achieved an accuracy of 98.70% on the driver distraction dataset. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Wavelet Decompositions, Hierarchical Encoding and Convolutional Neural Network Integrated Lossless Audio Codec

引用

Informatica (Slovenia) 2024年第4期48卷 641-652页

作者： Debnath, Asish Mondal, Uttam Kr. Dept. of Computer Science Vidyasagar University West Bengal Midnapore721102 India

In this paper, a lossless audio codec is proposed by leveraging Wavelet transformation, Hierarchical encoding with Convolutional Neural Network architecture. In the first phase, three level 1D wavelet decomposition is applied on the input audio for generating approximation and detail coefficients. In the next phase, the approximation and detail coefficients are transformed into binary streams by utilizing the proposed dynamic hierarchical encoding algorithm. In this encoding technique, coefficients are converted to binary by dynamically accumulating the binary path values. In the subsequent phase, the binary stream is transformed into image patterns and further compressed by reducing the dimensionality by the proposed convolutional neural network(CNN) model. The model’s effectiveness is evaluated against current conventional lossless audio benchmarks and machine learning-based methods. Experiment results demonstrate that the method shows better performance than existing lossless audio techniques. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Wavelet decomposition

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：