检索结果-内蒙古大学图书馆

2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Gnanabaskaran, A.G. Kavinilavu, S. Kowshika, V. Logesh, A.S. Nivitha, K. K. S. Rangasamy College of Technology Computer Science and Engineering Tiruchengode637 215 India Vellore Institute of Technology Computer Science and Engineering Vellore632014 India

ISBN: (纸本)9798350359688

Cyberbullying has become a pervasive issue in online communities, necessitating the development of robust detection methods to mitigate its harmful effects. In this paper, we propose an innovative approach to cyberbullying detection inspired by the complex mating behaviors of Satin Bower Birds. Our algorithm leverages ensemble learning techniques to combine the strengths of multiple classifiers, enhancing the accuracy and reliability of detection. We present a comprehensive methodology that integrates bio-inspired optimization principles with machine learning frameworks, facilitating the extraction of diverse features from user interactions and content. Through extensive experimentation on real-world datasets, we demonstrate the effectiveness of our ensemble-based Satin Bower Bird algorithm in accurately identifying instances of cyberbullying behavior. The results highlight the potential of bio-inspired algorithms to address social challenges in online environments and underscore the importance of interdisciplinary research in advancing cyberbullying prevention efforts. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Resilience-Centric Trust Evaluation in Cloud Computing: A Comparative Assessment of Cloud Trust Evaluation Methods Using the RAA Metric

Resilience-Centric Trust Evaluation in Cloud Computing: A Co...

引用

2024 International Conference on Smart Electronics and Communication Systems, ISENSE 2024

作者： John, Jomina John Singh, K. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore632014 India

ISBN: (数字)9798331528126

ISBN: (纸本)9798331528126

In cloud computing environments, trust evaluation models are critical for assessing the reliability and security of cloud service providers. The accuracy of trust evaluations may be impacted by the failure of traditional trust measurements to take resilience under dynamic settings into consideration. In order to assess the flexibility and resilience of trust assessment models in cloud computing, this research presents a new metric called the Resilience-Aware Assessment. The Risk Assessment offers a thorough assessment of trust models in a variety of cloud environments by taking into account elements including system performance during high stress, reaction to malevolent activity, and recovery time following interruptions. In order to compare various trust evaluation models already in use, this study evaluates them using the RAA in addition to other traditional criteria like accuracy, scalability, and convergence time. The findings show that the RAA metric, especially in situations with uncertainty and volatility, successfully draws attention to trust model strengths and shortcomings that are missed by traditional metrics. The integration of the RAA metric into multi-criteria decision-making frameworks for cloud service selection is also covered in the article, offering a more adaptable and robust method for evaluating trust. Future research will examine how the RAA measure can be used to improve autonomous trust management systems in edge-cloud contexts as well as how it can be applied in real-time cloud orchestration. © 2024 IEEE.

关键词： Cloud security

来源：评论

学校读者我要写书评

暂无评论

Covid-19 crowd detection and alert system using image processing

Covid-19 crowd detection and alert system using image proces...

引用

2023 International Conference on Advances in Intelligent Computing and Applications, AICAPS 2023

作者： Lodha, Nitin Singh Gahlaut, Harshvardhan Vellore Institute of Technology Dept. of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350333817

In this paper, we aim to help in identifying the people that are violating social distancing norms set by the government (necessary during the COVID-19 pandemic in public places), by providing an efficient real-time deep learning-based framework to automate the process of monitoring the social distancing via object detection and tracking approaches. Our system is divided into two subsystems: one that deals with crowd detection and control, and the other that sends information to the police authorities. Our system technologies, including as IoT, image processing, web cams, BLE, OpenCV, and Cloud, are being considered for inclusion in the proposed framework. The image processing is divided into two sections, the first of which is the extraction of frames from real-time movies, and the second of which is the processing of the frame to determine the number of individuals in the crowd. Even in a crowd, dissemination may be restricted if people adhere to social distancing standards. As a result, the image processing model primarily targets the number of people who do not adhere to social distancing norms and stand too close together. © 2023 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Modified Convolutional Neural Network Algorithm for Categorization of Solar PV Panel Soiling 2

Modified Convolutional Neural Network Algorithm for Categori...

引用

2nd International Conference on Smart Technologies for Power and Renewable Energy, SPECon 2024

作者： Chaki, Abhyudoy Bhatia, Rachna Mishra, Pallavi Singh, R. Raja Rajasekar, N. Vellore Institute of Technology School of Computer Science Vellore India Vellore Institute of Technology School of Advanced Sciences Dept. of Mathematics Vellore India Vellore Institute of Technology Automotive Research Center Vellore India Vellore Institute of Technology School of Electrical Engineering Vellore India

ISBN: (纸本)9798350383041

The identification and categorization of soiling using artificial intelligence is essential for improving solar photovoltaic (PV) system efficiency. It is possible to use focused and effective cleaning techniques on solar panels by classifying and comprehending the particular types of pollutants. This research proposes a modified convolutional neural network (CNN) approach to efficiently classify soiling on solar PV panels. In contrast to the conventional method, the suggested CNN model is created using simple images rather than images plus time series data. As a result, processing speed is increased. Additionally, the suggested model achieved 96.91 percent accuracy. This success establishes our suggested methodology as a ground-breaking development in soiling type detection, providing improved accuracy and a unique strategy in contrast to current approaches. The trained and tested model is deployed on the web using the Flask. Flask is a powerful and flexible microweb framework for Python, Finally, a comparison is made between the existing method and the proposed method. © 2024 IEEE.

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

Enhanced Cloud Data Security with Lightweight Chaotic Map and Quantum Key Distribution

Enhanced Cloud Data Security with Lightweight Chaotic Map an...

引用

2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Devi, M. Varshana Aasha, G. Vimal, P. Gowtham, K. Nivitha, K. K. S. Rangasamy College of Technology Computer Science and Engineering Tiruchengode637 215 India Vellore Institute of Technology Computer Science and Engineering Vellore632014 India

ISBN: (纸本)9798350359688

In today's digital landscape, ensuring the security of data transmitted and stored in the cloud is paramount. Traditional encryption methods face challenges in scalability and susceptibility to quantum threats. This abstract presents a novel approach to cloud data security leveraging lightweight chaos encryption and quantum key distribution (QKD). The proposed mechanism combines the dynamic properties of chaos encryption, which offers high entropy and resistance to conventional attacks, with the quantum-level security provided by QKD. By integrating chaos-based encryption techniques with QKD protocols, our system achieves enhanced security for data communications in cloud environments. Furthermore, the lightweight nature of chaos encryption ensures efficient utilization of computational resources, making it suitable for cloud-based applications. This abstract outlines the theoretical framework and operational principles of the proposed security mechanism, highlighting its potential to address the evolving challenges of data security in cloud computing. © 2024 IEEE.

关键词： Cloud security

来源：评论

学校读者我要写书评

暂无评论

MAC AI - Multilingual & Automatic Conversational AI 4

MAC AI - Multilingual & Automatic Conversational AI

引用

4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Sheik Imam, S. Rhithik Raj, K. Senthil Pandian, M. Deepa Nivethika, S. Jenath, M. Vellore Institute of Technology School of Civil Engineering TamilNadu Chennai India Vellore Institute of Technology School of Computer Science and Engineering TamilNadu Chennai India Srm Institute of Science and Technology Kattankulathur India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508845

MAC AI (Multilingual & Automatic Conversational Artificial Intelligence) is a cutting-edge chatbot engineered to transform global communication. Combining real-time language translation with speech-to-text capabilities, MAC AI excels in breaking down linguistic barriers using advanced Natural Language Processing (NLP) techniques. Its integration of speech recognition allows for seamless processing and translation of spoken language into various target languages. Furthermore, MAC AI's sophisticated NLP algorithms enable contextually accurate interactions, making it an essential tool in international customer service, commerce, and cross-cultural communication. By leveraging the latest AI technologies, MAC AI represents a significant advancement in making technology more accessible and efficient for diverse linguistic needs, redefining the landscape of AI-driven communication. © 2024 IEEE.

关键词： Linguistics

来源：评论

学校读者我要写书评

暂无评论

Neoteric Furtherance of Artificial Intelligence in Kidney Disease - a Purview

Neoteric Furtherance of Artificial Intelligence in Kidney Di...

引用

2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems, AICERA/ICIS 2023

作者： Aiysha Sameena, V. Rajashekaran, Rajkumar School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore India

ISBN: (纸本)9798350303452

Artificial intelligence cites the brainpower exhibited by the contraption, as antagonistic to spontaneous brainpower disposed of by personage conviction. Artificial Intelligence is machine-demonstrated intelligence and human thoughts, which include psychology, philosophy, computer science, information technology, and linguistics, which is opposed to natural thoughts and intelligence. AI is widely used in the medical field. Such as early detection of disease, diagnosis, management, and treatment. As compared to other fields, kidney disease diagnosis and treatment remains a global health issue. Kidney disease diagnosis and treatment is still a dare to us. Artificial Intelligence can input patients' positions;suitable decisions can output and augment the possibilities in kidney disease management. Here, this paper gives a comprehensive study of the scope of artificial intelligence / current studies of AI in kidney disease. © 2023 IEEE.

关键词： Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Optimization of Data Structures and Trade-Offs with Concurrency Control in Multithread Software Structures Using Artificial Intelligence 5

Optimization of Data Structures and Trade-Offs with Concurre...

引用

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

作者： Cherukuri, Bangar Raju Arulkumar, V. Germantown MD United States School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore India

ISBN: (纸本)9798350383522

The optimization of data structures and trade-offs is a complex problem. This is simplified and provides solutions through the aid of artificial intelligence. There are various synchronization problems occur due to the concurrent access to the data structures. This affects the overall performance of the system. To overcome various challenges, deep learning with optimization algorithms is implemented. This helps to achieve the potential areas for optimization in multithread software by evaluating the historical patterns and performance information. Trade-offs are defined as the choices that are initiated in the optimization process for data structures in concurrency. Deep learning helps to analyze the impacts of these trade-offs by developing training models on the historical data. A large amount of data is retrieved through the deep learning techniques. This includes extracting image data for CNN and time series data for RNN and LSTM. The reduction of using data compression techniques helps in obtaining optimal performance parameters. The various forms of threats are neglected through data loading and preprocessing stages. The concurrency is essential due to the usage of multiple threats working on the neural networks. This is further accessed through synchronizing access to shared data structures. They must be properly done because excess synchronization leads to the origin of bottlenecks. A balance between data structure optimization and concurrency control must be maintained properly. Excessive concurrency control may reduce the performance. The execution speed of the CNN is enhanced through the aid of hardware accelerators. Thus the fine-tuning of trade-offs and creation of software are done through training and testing to obtain increased use of multi-core processors. © 2024 IEEE.

关键词： Artificial intelligence Concurrency Data structures Deep learning Multi-core processors Multithread software Trade-offs

来源：评论

学校读者我要写书评

暂无评论

Slag and Porosity Defective Region Identification in Welding Images Using computer Vision Techniques

引用

Defect and Diffusion Forum 2023年 428卷 143-148页

作者： Rajalakshmi, Ratnavel Sivasankaran, S. Shinow, Abhinav Basil Abhimannan, Giridharan Boopathy, C. School of Computer Science & Engineering Vellore Institute of Technology Chennai India HCL Technologies Ltd Chennai India School of Mechanical Engineering Vellore Institute of Technology Chennai India ACB Technologies Chennai India

The process of welding is prone to many defects and these defects can cause the formation of many defective regions. It is necessary to identify the regions of defects as these may cause problems and breakages. In this work, we have proposed a method to detect and identify the defects that are commonly seen in seam welds. Manually identifying the detects is not only error prone and time consuming, most of the defects are not visible to the human eyes. In recent days, X-ray images of weld seam are used for this purpose. In this paper we have applied computer vision techniques and proposed an image processing pipeline to generate a binary segmentation of the image to identify the regions of slag and porosity defect seen in weld seams. From the experimental results on the publicly available dataset, GDX-ray images, it could be observed that, there is a significant improvement in detecting various defects with the proposed approach. © 2023 Trans Tech Publications Ltd, Switzerland.

关键词： Defects

来源：评论

学校读者我要写书评

暂无评论

Unification of Billing and Employee Management through Integrated E-Commerce for Enhanced Pharmacy Services

Unification of Billing and Employee Management through Integ...

引用

2023 IEEE technology and engineering Management Conference - Asia Pacific, TEMSCON-ASPAC 2023

作者： Sangaraju, Sai Harsha Varma Mathur, Preksha Jindal, Sumit Kumar Vellore Institute of Technology School of Computer Science and Engineering Tamilnadu Vellore India Vellore Institute of Technology School of Electronics Engineering Tamilnadu Vellore India

ISBN: (纸本)9798350384659

The present research endeavors to introduce a groundbreaking web application, which serves as a unified billing management system specifically designed for pharmacies. The current state of pharmacy management systems is characterized by a fragmented approach, where separate software solutions are utilized for employee management and e-commerce functionalities. This fragmented approach has led to various inefficiencies and a subsequent rise in operational costs. One significant constraint of existing systems is found within the arduous process of managing inventory. The proposed solution aims to mitigate the issue by incorporating an advanced search capability that encompasses various filters. This feature enables the swift identification of medicines, thereby ensuring the precise fulfillment of prescriptions. The present system has been meticulously developed with the primary objective of identifying expired pharmaceutical products. This innovative feature serves to bolster the overall safety of medication by effectively thwarting the inadvertent inclusion of expired medicines in customer orders. Moreover, the web application integrates sophisticated deep-learning techniques for the purpose of inventory management, facilitating the option of either manual data entry or image-based identification. In summary, the present study introduces a transformative solution for the management of pharmacies, effectively tackling the drawbacks associated with fragmented software systems. The application presented in this study aims to provide a secure and efficient platform for pharmacies by integrating billing, inventory, and customer interactions. Additionally, the application utilizes deep learning techniques to facilitate inventory updates. This research paper explores the potential benefits of this unified approach and discusses how it can contribute to a streamlined and enhanced future for pharmaceutical management. © 2023 IEEE.

关键词： Personnel

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：