检索结果-内蒙古大学图书馆

Designing an improved deep learning-based model for COVID-19 recognition in chest X-ray images: a knowledge distillation approach

引用

Iran Journal of computer Science 2024年第2期7卷 177-187页

作者： BabaAhmadi, AmirReza Khalafi, Sahar ShariatPanahi, Masoud Ayati, Moosa School of Mechanical Engineering College of Engineering University of Tehran Tehran Iran Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran

The COVID-19 pandemic has had a significant impact on society, necessitating accurate identification and suitable medical treatment. This study addresses two primary objectives: reducing computational costs for running the model on embedded devices, mobile devices, and conventional computers and improving the model's performance relative to existing methods for high-performance and accurate medical recognition. To achieve these goals, we utilized two neural networks, VGG19 and ResNet50V2, for improved feature extraction from the dataset. Consequently, the semantic features supplied by these networks were combined in a fully connected classifier layer to achieve satisfactory classification results for normal and COVID-19 cases. In addition, MobileNetV2, which is effective on mobile and embedded devices, was adopted to reduce computational demands. Furthermore, knowledge distillation was used to transfer information from the teacher networks (ResNet50V2 and VGG19) to the student network (MobileNetV2), improving its performance in COVID-19 identification. Pre-trained networks and fivefold cross-validation were used to evaluate the proposed method. The model achieved an accuracy of 98.8% and an F1 score of 99.1% in detecting infectious and normal cases. These results demonstrate the superior performance of our approach. The student model is suitable for conventional computers, embedded systems, and clinical experts' cell phones, with acceptable accuracy and F1 score using cross-validation. Our method provides a cost-effective solution for COVID-19 identification, enabling wider accessibility and accurate diagnosis. Moreover, the proposed method outperforms previous works by improving accuracy, F1 score, and other related metrics by at least 1%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

关键词： Chest X-ray images COVID-19 Deep learning Knowledge distillation Medical image analysis Teacher–student model

来源：评论

学校读者我要写书评

暂无评论

Exhibiting the Explicit Aspects in Twitter Sentimental Data

Exhibiting the Explicit Aspects in Twitter Sentimental Data

引用

2023 International Conference on Data Science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Jayanthi, S. Arumugam, S.S. Saveetha School of Engineering Department of Computer Science & Engineering India

ISBN: (纸本)9798350348910

The evolution of social networks has resulted in medium to express feelings and thoughts of an individual to global audience, thanks to the rapid expansion of the Internet. Many individual utilize textual content, images, audio, and videos to convey their emotions and perspectives. However, the sheer volume of text-based communication on social media can be overwhelming. Each second, an enormous quantity of unstructured information is created online through these platforms. To comprehend human psychology in real-time, this data needs to be processed promptly, and one way to achieve this is through sentiment analysis. Sentiment analysis identifies the polarity of texts, determining whether the author holds a opinion of a negative, favourable, or neutral nature product, service, person, or location. In certain applications, sentiment analysis alone may be insufficient, necessitating the inclusion of emotion detection to accurately determine condition of one's mind or heart. Sentiment analysis and emotion detection from textual data are both covered in depth in this work, with explicitly revealed insights into the distinct stages of sentiment analysis and the corresponding emotion models. © 2023 IEEE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Knowledge‐based deep learning system for classifying Alzheimer's disease for multi‐task learning

引用

CAAI Transactions on Intelligence Technology 2024年第4期9卷 805-820页

作者： Amol Dattatray Dhaygude Gaurav Kumar Ameta Ihtiram Raza Khan Pavitar Parkash Singh Renato R.Maaliw III Natrayan Lakshmaiya Mohammad Shabaz Muhammad Attique Khan Hany S.Hussein Hammam Alshazly Department of Data Science University of WashingtonSeattleWashingtonUSA Department of Computer Science&Engineering Parul Institute of TechnologyParul UniversityVadodaraGujaratIndia Department of Computer Science Jamia HamdardDelhiIndia Department of Management Lovely Professional UniversityPhagwaraIndia 5 College of Engineering Southern Luzon State UniversityLucbanQuezonPhilippines Department of Mechanical Engineering Saveetha School of EngineeringSIMATSChennaiTamil NaduIndia Model Institute of Engineering and Technology JammuJ&KIndia Department of Computer Science HITEC UniversityTaxilaPakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Electrical Engineering Department College of EngineeringKing Khalid UniversityAbhaSaudi Arabia Electrical Engineering Department Aswan UniversityAswanEgypt Faculty of Computers and Information South Valley UniversityQenaEgypt

Deep learning has recently become a viable approach for classifying Alzheimer's disease(AD)in medical ***,existing models struggle to efficiently extract features from medical images and may squander additional information resources for illness *** address these issues,a deep three‐dimensional convolutional neural network incorporating multi‐task learning and attention mechanisms is *** upgraded primary C3D network is utilised to create rougher low‐level feature *** introduces a new convolution block that focuses on the structural aspects of the magnetORCID:ic resonance imaging image and another block that extracts attention weights unique to certain pixel positions in the feature map and multiplies them with the feature map ***,several fully connected layers are used to achieve multi‐task learning,generating three outputs,including the primary classification *** other two outputs employ backpropagation during training to improve the primary classification *** findings show that the authors’proposed method outperforms current approaches for classifying AD,achieving enhanced classification accuracy and other in-dicators on the Alzheimer's disease Neuroimaging Initiative *** authors demonstrate promise for future disease classification studies.

关键词： classification deep learning

来源：评论

学校读者我要写书评

暂无评论

Predictive Insights for Enhancing Customer Retention in Banking: Integrating Power BI and Random Forest 3rd

Predictive Insights for Enhancing Customer Retention in Bank...

引用

3rd International Conference on Machine Vision and Augmented Intelligence, MAI 2023

作者： Sumanasri, A. Vyshnavi, M. Manogna, R. Nikhila Keerthi, Kelam Kesavarshini, A. Pande, Sagar Dhanraj School of Computer Science Engineering VIT-AP University Andhra Pradesh Amaravati India Department of Computer Science and Engineering School of Engineering and Technology Pimpri Chinchwad University Maharashtra Pune India

ISBN: (纸本)9789819743582

The main goal of the study is to solve the problem of losing customers or clients in various financial organizations, especially in the banking sector. It investigates the use of Random Forest algorithm and the Power BI dashboard which helps to forecast churn and improve retention strategies. Historical data—transactions, demographics, and usage patterns—is collected and a thorough analyzation is done with the help of Power BI to provide a dynamic dashboard that provides insights in real-time. Combining data analytics technologies and predictive skills with their retention initiatives can help banks increase their efficacy and efficiency. Economic loss and operational difficulties are brought on by customer attrition. The outcome of this study suggests a method for forecasting customer attrition in banks that combines Power BI’s amazing data visualization techniques with Random Forest’s highly accurate predictive skills. The Random Forest algorithm integrates multiple decision trees to make predictions in a precise and expandable way. Moreover, it has the capability of handling large data sets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sales

来源：评论

学校读者我要写书评

暂无评论

Virtual Twin Analytics: Advancing IoT Security through AI-Powered Software informatics 1

Virtual Twin Analytics: Advancing IoT Security through AI-Po...

引用

1st International Conference on Software, Systems and Information Technology, SSITCON 2024

作者： Nanthini, S. Jain, Upma Arora, Renuka Bhatia, Rishu Sutaria, Kruti Patil, Harshal Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Jagannath University Department of Computer Science and Engineering Haryana India Ganga Institute of Technology and Management Department of Electronics and Communication Engineering Haryana India Parul Institute of Engineering and Technology Faculty of Engineering and Technology Gujarat India School of Computer Science and Engineering Uttar Pradesh India

ISBN: (纸本)9798350352931

The Internet has grown in importance and impact over the years, causing people to become more reliant on it. The Internet has evolved into a major vector for cybercrime because to its ever-increasing user base. Over the last decade, the number of these computing systems - including desktops, laptops, smartphones, and the Internet of Things (IoT) - has skyrocketed. Among them, cell phones are practically integral to modern life. The popularity of web-based assaults has skyrocketed with the exponential growth in the number of individuals using the Internet. These web-based assaults are increasingly being combatted by security corporations. Unfortunately, new forms of these assaults are appearing all the time, making it hard for older security measures to stay up. Artificial intelligence (AI) is a source of optimism in the current cybersecurity landscape, offering a potential solution to the ever-changing digital dangers. The fast development of AI over the last decade has given rise to this optimism, because it is now impacting the expansion of every industry. With AI bringing so many advantages in every field, online security is one sector that just cannot afford to ignore it. This planned effort's work represents an advance in that direction. Critical online security issues have been the focus of this proposed work's study, which aims to address these issues using AI. Web security issues for desktop and mobile devices have been addressed in the proposed work. The planned work's contributions to online security are as follows: The 'MalCrawler' web crawler is a targeted tool for finding and exploring the web. This crawler makes it easy to gather websites, particularly ones that are harmful. It does a better job of collecting dangerous websites than a typical crawler. Additionally, it is built to circumvent the evasion strategies used by rogue websites. The crawler's ability to gather webpages - particularly dangerous ones - in order to provide datasets for ML-based an

关键词： Web crawler

来源：评论

学校读者我要写书评

暂无评论

A GMM-CGABC Based Energy Replenishment Scheme for On-Demand Wireless Rechargeable Sensor Networks 1

A GMM-CGABC Based Energy Replenishment Scheme for On-Demand ...

引用

1st International Symposium on Parallel Computing and Distributed Systems, PCDS 2024

作者： Mouli, S.Sudhamsu Veeraiah, T. Mahesh, K. Singh, M.P. Ecole Centrale School of Engineering Mahindra University Department of Computer Science and Engineering Hyderabad India National Institute of Technology Department of Computer Science and Engineering Patna India

ISBN: (纸本)9798350349658

Wireless Sensor Networks (WSNs) are constrained by the limited energy capacity of Sensor Nodes (SNs), which hinders their perpetual operation. The advent of Wireless Energy Transfer (WET) technology has emerged as a promising solution to this challenge, enabling the creation of Wireless Rechargeable Sensor Networks (WRSNs). Recent research has shown growing interest in integrating Wireless Mobile Chargers (WMCs) into WRSNs to replenish the energy reserves of critical SNs through WET. A key aspect of WET involves using one or more WMCs to recharge SNs, with optimizing the charging schedule (i.e., the sequence in which SNs are charged) crucial in on-demand WRSNs. In addition, spatiotemporal constraints imposed by the requesting SNs must be considered when devising the WMC's charging schedule. To address these challenges, this work proposes the integration of a Gaussian Mixture Model with a Chaotic Gaussian Artificial Bee Colony (GMM-CGABC) to develop an efficient recharge schedule that revitalizes SNs. This model is designed to optimize SN deployment, maximize network coverage, minimize energy consumption, and extend network longevity. The proposed method determines the optimal charging path for WMCs within each cluster to minimize Average Charging Latency (ACL) while incorporating a novel strategy for fitness function evaluation. Extensive simulations demonstrate that the proposed system outperforms state-of-the-art algorithms, including First Come, First Served (FCFS), Nearest Job Next with Preemption (NJNP), and the Gravitational Search Algorithm (GSA), in terms of charging latency. © 2024 IEEE.

关键词： Inductive power transmission

来源：评论

学校读者我要写书评

暂无评论

GShuttle:Optimizing Memory Access Efficiency for Graph Convolu-tional Neural Network Accelerators

引用

Journal of computer Science & Technology 2023年第1期38卷 115-127页

作者：李家军王可郑皓 Ahmed Louri School of Astronautics Beihang UniversityBeijing 100191China Department of Electrical and Computer Engineering University of North Carolina at CharlotteCharlotteNC 28223 U.S.A. Department of Electrical and Computer Engineering University of Central FloridaOrlandoFL 32816U.S.A. Department of Electrical and Computer Engineering George Washington UniversityWashingtonDC 20052U.S.A.

Graph convolutional neural networks(GCNs)have emerged as an effective approach to extending deep learning for graph data analytics,but they are computationally challenging given the irregular graphs and the large num-ber of nodes in a *** involve chain sparse-dense matrix multiplications with six loops,which results in a large de-sign space for GCN *** work on GCN acceleration either employs limited loop optimization techniques,or determines the design variables based on random sampling,which can hardly exploit data reuse efficiently,thus degrading system *** overcome this limitation,this paper proposes GShuttle,a GCN acceleration scheme that maximizes memory access efficiency to achieve high performance and energy *** systematically explores loop opti-mization techniques for GCN acceleration,and quantitatively analyzes the design objectives(e.g.,required DRAM access-es and SRAM accesses)by analytical calculation based on multiple design *** further employs two ap-proaches,pruned search space sweeping and greedy search,to find the optimal design variables under certain design *** demonstrated the efficacy of GShuttle by evaluation on five widely used graph *** experimental simulations show that GShuttle reduces the number of DRAM accesses by a factor of 1.5 and saves energy by a factor of 1.7 compared with the state-of-the-art approaches.

关键词： graph convolutional neural network memory access neural network accelerator

来源：评论

学校读者我要写书评

暂无评论

Deep Learning-based Classification of Infant Cries for Parental Assistance: A Study using MFCC and CNN Technique 3

Deep Learning-based Classification of Infant Cries for Paren...

引用

3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Mala, B.M. Darandale, Smitha Sandeep Gitam School of Technology Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331509675

Worldwide Approximate count of 130 million babies were born per annum. Maintaining newborn babies is a great difficulty, mainly for first-time parents. However, intimations from experienced parents, books, and videos help to a certain extent but not significantly because of difficulty understanding the meaning of Infant cries. Researchers have proposed several techniques using machine learning and artificial intelligence. In this work, we try to leverage deep learning-based techniques such as convolutional neural networks (CNN) to identify the infant crying. Infant crying is further classified into four classes (inclusive of pain, hunger, sleepiness, and wet diaper). The MFCC (Mel-Frequency Cepstral Coefficients) technique is used for feature extraction, as it efficiently captures audio characteristics from crying sounds. In this study, will compare the various existing machine learning models with our proposed model. Among the models evaluated, MFCC combined with Convolutional Neural Networks (CNN) achieves the highest accuracy of 95.5%, for classifying the infant cry and reasoning. © 2025 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Amazon Web Services Cloud Compliance Automation with Open Policy Agent 4

Amazon Web Services Cloud Compliance Automation with Open Po...

引用

4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Paul, Alen Manoj, Rishi Udhayakumar, S. Amrita School of Computing Department of Computer Science and Engineering India

ISBN: (纸本)9798350385793

The security challenges posed by Infrastructure as code (IaC) are outgrowing established security procedures in an era of rapidly adopting cloud computing and DevOps methodologies. Using security principles to be integrated into the CI/CD pipeline for continuous validation and remediation, this research work proposes a DevSecOps approach to enable cloud environment IaC security. The proposed architecture evaluates CloudFormation Template file to the specified security policy by utilizing Open Policy Agent (OPA). Any violations are flagged by OPA, which may force the deployment process to stop. By incorporating security and compliance considerations into the development pipeline, this method guarantees that vulnerabilities are kept out of production systems. Organizations can improve the overall security posture of their cloud systems by proactively identifying and remediating security problems by implementing a DevSecOps approach. By ensuring that IaC deployments adhere to specified security policies based on the compliance requirements, OPA's continuous security validation reduces the possibility of misconfigurations and security flaws. © 2024 IEEE.

关键词： Cloud security

来源：评论

学校读者我要写书评

暂无评论

Probabilistic Feamework for Modeling and Analyzing Complex Networks 15

Probabilistic Feamework for Modeling and Analyzing Complex N...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Nair, Lekshmi S Cheriyan, Jo Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India SAINTGITS College of Engineering Department of Computer Science and Engineering Kottayam India

ISBN: (纸本)9798350370249

Complex networks, characterised by intricate structures arising from the relationships and interactions among their constituent elements, play a pivotal role in various domains such as social networks, biological systems, and technological infrastructures. The evolution and dynamics of complex networks are analysed for various applications such as link prediction, information diffusion, and recommendation systems. Recent research has focused on the study of structural motifs and the probabilistic analysis of motifs to capture the dynamics of the network. The motifs, considered building blocks of the larger network structure, are recurrent patterns in the network. This study focuses on the different probabilistic models built on triads, which are sets of three interconnected nodes. We investigate six different probability distributions, including Poisson, Gamma, Exponential, Power Law, Gaussian, Lorentzian, and Gaussian Mixture Models (GMMs) that can be applied in the complex network to capture the insight of the connectivity pattern. The applications of this research extend to refining network models, improving simulations, and enhancing our understanding of the likelihood of observing distinct patterns within complex social networks. © 2024 IEEE.

关键词： Poisson distribution

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：