检索结果-内蒙古大学图书馆

12th International Conference on computer Communication and Informatics, ICCCI 2022

作者： Tyagi, Amit Kumar Agarwal, Deepshikha Sreenath, N School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Tamilnadu Chennai600127 India Department of Information Technology Iiit Lucknow India Department of Computer Science and Engineering Puducherry Technological University Puducherry India

ISBN: (纸本)9781665480352

The Internet of Things (IoT) has developed from just an idea or concept to real-time market use-case. Automotive industry is one of the pioneers to adapt to the technology in its budding stage. They are not only focussing on the vehicle's internal features like the traditional self-driven vehicles but have developed a broader-field of view by focussing on communication with other vehicles. The exchange of large volume of data can pose a threat to the user's security. This calls for the need for implementation of multilevel cyber security countermeasures in order to prevent vulnerability to hacking. The use of blockchain has however been one the most useful and advanced technology developed to protect data, i.e., preserve user's personal information/ user's privacy (during communication). This paper mainly deals with the implementation of securing the information regarding next generation intelligent vehicles. © 2022 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Cloud resource management using adaptive firefly algorithm and artificial neural network

引用

International Journal of Cloud Computing 2022年第5-6期11卷 480-491页

作者： Manigandan, S.K. Manjula, S. Nagaraju, V. Ramya, D. TapasBapu, B.R. Department of Computer Science and Engineering VelTech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Avadi Chennai India Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai India Department of Information Technology Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India Department of Electronics and Communication Engineering S.A. Engineering College Chennai India

Cloud computing can be characterised in basic terms as one of the stages for giving conveyed figuring assets. Registering assets may incorporate capacity, data transfer capacity, memory space, handling components, etc. These assets are leased to the clients utilising the compensation per-utilised model. The interest for the assets may not be static, yet they can be mentioned on-request because of the developing web office. The clients of the cloud want to have a limited reaction time and cost yet then again, the cloud supplier centres on accomplishing proficient cloud assets distribution and limiting the support costs. Resource management is a practice of provisioning and managing the cloud resources efficiently. It also provides the techniques to provision the resources, schedule the jobs, and balance the loads. This proposal provides a resource management technique for efficient provisioning of resources and the scheduling of jobs on the static and dynamic cloudlet requests. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Neural networks

来源：评论

学校读者我要写书评

暂无评论

Machine Learning based Classifier Models for Detection of Celestial Objects 3

Machine Learning based Classifier Models for Detection of Ce...

引用

3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Sharma, Vikrant Goel, Sandeep Jain, Anuj Kumar Vajpayee, Amit Bhandari, Rahul Tiwari, Raj Gaurang GraphicEra Hill University Uttarakhand Dehradun India Panipat Institute of Engineering & Technology Department of Information Technology Haryana India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Punjab Mohali India Chandigarh University Department of Computer Science & Engineering Punjab Mohali India

ISBN: (纸本)9798350338607

The classification of celestial objects such as stars, galaxies, and quasars is one of astronomy's most difficult and fundamental problems. Due to the technological advancement of telescopes and observatories, the classification of large volumes of data must be automated. Various machine learning techniques are currently employed for accurate classification. In this paper, a comparison of the efficacy of various classification algorithms is presented. XGBoost seems to be the most effective. © 2023 IEEE.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

FRCD: Future Ready Career-Duck a Machine Learning-Driven Career Guidance for Students

FRCD: Future Ready Career-Duck a Machine Learning-Driven Car...

引用

Silchar Subsection Conference (SILCON), IEEE

作者： Priyanshu Paritosh Vedant Yadav Dev Gupta Ujjwal Biswas Department of Mechatronics and Automation Engineering Indian Institute of Information Technology Bhagalpur Bhagalpur India Department of Computer Science and Engineering Indian Institute of Information Technology Bhagalpur Bhagalpur India

ISBN: (数字)9798331540821

ISBN: (纸本)9798331540838

In today's society, finding a suitable career is crucial. High school students frequently need to fully understand the notion of labor due to a lack of experience and practical knowledge in various disciplines. This renders students particularly vulnerable to external influences while deciding on a career path. As a result, students must carefully consider high-paying careers to ensure that they align with their interests and skills. Otherwise, students may end up in jobs they dislike or are not suited to. In response to this issue, we intend to develop a machine-learning framework capable of predicting career pathways. Students will receive career recommendation from our model based on their performance and potential. Students can confidently follow their chosen fields if they do well academically; if not, they can choose to change to other fields that better suit them. There are challenges in determining the metrics and improving prediction accuracy to quantify students' career paths and performance in teaching-learning. Best on the test results, the proposed model has a higher prediction accuracy. Also, this method is both reliable and portable, and it may be used to predict career paths in real time.

关键词： Measurement Accuracy engineering profession Education Machine learning Predictive models Real-time systems Planning Reliability Usability

来源：评论

学校读者我要写书评

暂无评论

Real-Time Monitoring of Glacial Changes Using Artificial Intelligence 4

Real-Time Monitoring of Glacial Changes Using Artificial Int...

引用

4th International Conference on Computation, Automation and Knowledge Management, ICCAKM 2023

作者： Babu, K. Vijayalakshmi, G. Umadevi, P. Patel, Neeraj Gopalakrishnan, S. Srm Institute of Science & Technology Kattankulathur India Prince Shri Venkateshwara Padmavathy Engineering College Ece Chennai India Kpr Institute of Engineering and Technology Computer Science and Business Systems India Ies Institute of Technology and Management Ies University Bhopal India Department of Information Technology Kelambakkam India

ISBN: (纸本)9798350393248

Because of the fast changes that climate change is bringing about in glacial environments, real-time monitoring is essential to comprehending and reducing its consequences. In order to observe glacial change in real time, this research study provides a novel method that makes use of artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT). We start by providing a brief overview of the problem's urgency and the shortcomings of the conventional monitoring techniques. The study then goes on to talk about the main elements of our AI and ML-based system, which include data integration from IoT devices and distant sensors, machine learning algorithms, and analysis of satellite images. Researchers can more accurately understand the intricate dynamics of glacier behaviour thanks to the automated detection of glacial movement, changes in ice thickness, and patterns of meltwater made possible by AI and ML models. As we dig further, the article looks at creating an approachable interface that would make real-time glacier data accessible to scholars, decision-makers, and the general public. It also emphasises how early warning systems may help reduce the risks associated with glaciers and provide guidance for plans aimed at adapting to climate change. This multi-faceted approach, from the simplicity of AI and ML implementation to its intricate data analysis capabilities, in conjunction with IoT, represents a significant advancement in our ability to monitor and respond to glacial changes in real-time. © 2023 IEEE.

关键词： Global warming

来源：评论

学校读者我要写书评

暂无评论

Cyberbullying in Digital Era: History, Trends, Limitations, Recommended Solutions for Future

Cyberbullying in Digital Era: History, Trends, Limitations, ...

引用

2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Nair, Meghna Manoj Fernandez, Terrance Frederick Tyagi, Amit Kumar Vellore Institute of Technology School of Computer Science and Engineering Chennai Campus Tamilnadu Chennai600127 India Saveetha School of Engineering Department of Information Security TN Chennai India National Institute of Fashion Technology New Delhi Department of Fashion Technology Delhi India

ISBN: (纸本)9798350348217

The digital and virtual world has taken a toll on the lives of the vast majority of the population across the globe. Even though it has resulted in numerous technological advancements and has enhanced the scope of socialising and staying connected with people from different parts of the earth, it has a variety of drawbacks and negative aspects which seem to dwindle the affected person both physically and mentally. One such adversity is the reality of cyberbullying, which millions of people who extensively use digital platforms and social media are exposed to. Cyberbullying is a very broad field of adversity and pressing concern in the twenty-first century, especially since it's the digital era. Cyberbullying occurs in varied forms and affects people in numerous ways with different intensities. It includes cracking into an individual's profile, posting unnecessary comments to defame people, malicious use of social media platforms, etc. Techniques and algorithms to bring a halt to cyberbullying are one of the primary areas researchers and programmers need to focus on. In the earlier stages of the digital era, cyberbullying was considered to be an event of the occurrence of a mishap due to peer pressure. As virtual platforms evolved massively, cyberbullying has taken the spotlight resulting in the harassment of naive individuals. The fact that there's an absence of face-to-face confrontation has complemented the cruel aims and deeds of cyberbullies. Cyberbullies have initiated the use of technology in the disguise of a weapon to humiliate and hamper the growth and success of people out there. Another trend that has been closely observed is the conversion transition and huge jump from physical bullying to cyberbullying wherein, social media and similar digital tenets which were meant to play a positive role in the lives of people started amplifying the negativities of virtual bullying. This chapter talks about how cyberbullying has evolved over the decades, the limitati

关键词： Social networking (online)

来源：评论

学校读者我要写书评

暂无评论

Ensuring Cloud Tenant Data Security and OptimizingDDoS Detection Efficiency through Trust-Based Filtering 23

Ensuring Cloud Tenant Data Security and OptimizingDDoS Detec...

引用

Proceedings of the 5th International Conference on information Management & Machine Intelligence

作者： Ankit Agarwal Saurabh Gupta G Swamy G Prasadu Vandana Ahuja Ajay Kumar Badhan Udit Mamodiya Department of Information Technology Institute of Technology & Management India Department of Computer Science and Engineering Institute of Technology & Management India Department of Computer Science and Engineering Vignana Bharthi Institute of Technology India Department of IT Sreenidhi Institute of Science and Technology India Department of CSE Swami Vivekanand Institute of Engineering and Technology India Department of Computer Science and Engineering Lovely Professional University India Poornima University India

ISBN: (纸本)9798400709418

Tenant data security has grown critical as more and more commercial enterprises use cloud services for data processing and storage. The present study endeavors to tackle the dual problems of preserving tenant data in the cloud and enhancing the effectiveness of detecting Distributed Denial of Service (DDoS) attacks. We provide a brand-new strategy called Trust-Based Filtering that incorporates trust measurements into the current cloud security framework. Our approach for data security is centered on access controls, encryption, and ongoing monitoring. Sensitive information is prevented from being accessed by unauthorized parties even in the case of a breach thanks to encrypted data storage. Based on trust scores, access controls are dynamically modified to grant genuine tenants more access privileges while limiting dubious entities. The dynamic trust model used by the Trust-Based Filtering system assesses cloud tenants' behavior by looking at their past interactions with the cloud environment.

关键词： Cloud Environment

来源：评论

学校读者我要写书评

暂无评论

Intelligent Task Offloading in IoT Devices Using Deep Reinforcement Learning Approaches

Intelligent Task Offloading in IoT Devices Using Deep Reinfo...

引用

Omni-layer Intelligent Systems (COINS), International Conference on

作者： Megha Sharma Abhinav Tomar Abhishek Hazra Department of Computer Science and Engineering Netaji Subhas University of Technology New Delhi India Department of Computer Science and Engineering Indian Institute of Information Technology Sri City India

ISBN: (数字)9798350349597

ISBN: (纸本)9798350349603

The proliferation of smart devices has led to a rapid expansion of Internet of Things (IoT) applications. Consequently, a substantial volume of unprocessed data is produced, necessitating processing and storage. Individual IoT devices alone are insufficient to manage significant volumes of data. Fog computing is an essential technology that enables the proximity of cloud services to the end-user. Efficient task offloading techniques are necessary to balance the demand due to the unpredictable nature of tasks with user's Quality of Service (QoS) needs. To address the issue effectively, two different model-free off-policy i.e. Deep Q-Network (DQN) and on-policy i.e. Asynchronous Advantage Actor-Critic (A3C) Deep Reinforcement Learning (DRL) techniques are described as optimising resource utilisation by maximising the reward. Ultimately, extensive experiments are carried out to verify and assess the efficiency and effectiveness of the proposed methods.

关键词： Performance evaluation Privacy Quality of service Deep reinforcement learning Internet of Things Security Resource management

来源：评论

学校读者我要写书评

暂无评论

Advanced Deep Learning Framework for Accurate Detection and Classification of Bone Marrow Malignancies using YOLOv8

Advanced Deep Learning Framework for Accurate Detection and ...

引用

Augmented Intelligence and Sustainable Systems (ICAISS), International Conference on

作者： V. Kumari Isukapalli S. Gopalakrishnan R. Dinesh Kumar Department of Computer Science and Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India Department of Information Technology Hindustan Institute of Technology and Science Chennai Tamil Nadu India Department of Computer Science and Engineering Bhoj Reddy Engineering College for Women Hyderabad Telangana India

ISBN: (数字)9798331507244

ISBN: (纸本)9798331507251

The diagnosis of bone marrow malignancies, including leukemia, lymphoma, and myeloma, has so far primarily depended on subtle variations in cellular morphology and tissue features, which pose a formidable challenge. To tackle these tipping points, the study proposes an advanced deep learning framework that exploits the features of the YOLOv8 model architecture for accurate detection and classification of malignancies in microscopic images of bone marrow. The modules proposed in the framework encompass three critical components: detection, classification, and evaluation. Detection, which is the very backbone of the YOLOv8 model, performs the task of real-time detection of regions of interest (ROIs) containing malignant cells using state-of-the-art CNN architecture. Then ROIs identified by these models are passed on to the classification module, where they will be used to classify the cells into malignancies versus non-malignancies. Using robust augmentation techniques like rotational transformations as well as edge detection allows the model to be robust via establishing orientation invariance and highlighting important structural features. Next, feature selection methods are applied, mainly SelectKBest and the Chi-square test to enhance computational efficiency by rejecting the least diagnostically important features. The proposed framework shows great promise, yielding an average AUC value of 0.9490±0.0124 across validation folds. A precision-recall curve and ROC analyses confirm the high sensitivity and specificity of the framework, hence minimizing false positives and false negatives. Thus, its capability for accurate and efficient detection of malignant tissues enables real-time use in clinics. With its capability of enhancing diagnostic accuracy and circumventing observer bias, the proposed system endows pathologists with a radical support tool for timely intervention.

关键词： Deep learning Accuracy Microscopy Morphology computer architecture Sensitivity and specificity Observers Bones Feature extraction Real-time systems

来源：评论

学校读者我要写书评

暂无评论

Compound Multi-Branch Feature Fusion for Image Deraindrop

Compound Multi-Branch Feature Fusion for Image Deraindrop

引用

IEEE International Conference on Image Processing

作者： Chi-Mao Fan Tsung-Jung Liu Kuan-Hsien Liu Department of Electrical Engineering and Graduate Institute of Communication Engineering National Chung Hsing University Taiwan Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taiwan

Image restoration is a challenging and ill-posed problem which also has been a long-standing issue. In this paper, we proposed a multi-branch restoration model inspired from the Human Visual System (i.e., Retinal Ganglion Cells) for image deraindrop. The experiments show that the proposed multi-branch architecture, called CMFNet, has state-of-the-art performance results. The source code and pretrained models are available at https://***/FanChiMao/CMFNet. And the interactive demonstration of the proposed deraindrop model can be accessed at https://***/dXaeNg.

关键词：

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：