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检索条件"任意字段=International Conference on Information Technology - Coding and Computing"

共 195250 条记录，以下是4781-4790 订阅

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Exploring Machine learning applications in cyber attacks detection

Exploring Machine learning applications in cyber attacks det...

引用

2024 international conference on Electrical, Electronics and computing Technologies, ICEECT 2024

作者： Rai, Rina Mishra, Aditi Singh, Divya Kumar, Gaurav Kumar, Anuj Arya, Bhupal Madan Mohan Malaviya University of Technology Department of Computer Science Gorakhpur India Parul University Vadodara Department of Computer Science Gujarat Vadodara India University of Lucknow Roorkee Institute of Technology Department of Computer Science Lucknow India Uttaranchal Institute of Technology Department of Computer Science and Engineering Dehradun India Babasaheb Bhimrao Ambedkar University Department of Information Technology Lucknow India RIT Department of Computer Science and Engineering Roorkee India

ISBN: (纸本)9798350378092

Attackers and hackers employ many tactic known as phishing, cyber-attacks, dos attacks DOS attacks, man in middle attacks etc to damage trustworthy URLs as well as important websites. In this research, we discussed various cyber threats, the interrelation of cyber threats, and machine learning types of phishing attacks various techniques to detect cyber phishing by using machine learning, and we have also compared various phishing attacks with a few machine learning techniques with the help of a classification technique like supervised learning in which we apply SVM, random forest and extreme learning machine, the NSL-knowledge discovery is used in the evaluation of intrusion detection method also we have compared the accuracy in which precision-recall is used to find the accuracy and to calculate the performance and we concluded that ELM method give best result in terms of accuracy. © 2024 IEEE.

关键词： Phishing

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Video Content Analysis to Distinguish Between Violence and Nonviolence Using ResNet101v2 and LSTM

Video Content Analysis to Distinguish Between Violence and N...

引用

2024 international conference on Advances in computing Research on Science Engineering and technology, ACROSET 2024

作者： Sharma, Neeraj Sikarwar, Chandni Ahuja, Kamlesh Information Technology Mahakal Institute of Technology Ujjain India

ISBN: (纸本)9798350388800

Video content analysis is the most important field in which video classification techniques provide a good result to differentiate 'Violence' with 'Nonviolence. Although essential, the traditional ResNet50 model is not as successful in detecting this series compared to VGG19 and InceptionV3 with a validation accuracy of 83.87%. To alleviate this problem, LSTM networks were introduced to make video data more susceptible for sequential processing and trained with the same spatial CNN vanish network reaching an accuracy of 97.54%, but a validation accuracy of slightly lower: only 80.65%. This indicates that some over fitting also occurred in this approach as well which suggests dubious reliability on our LSTMs;A new hybrid method to utilize the spatial natural of image using ResNet101v2 and temporal sensing nature with LSTM was suggested in this paper. This hybrid achieved 99.18% training accuracy and a validation of only 77.42%. The difference highlights the difficulty of training data that generalizes well (to new, unseen examples) and improves performance) did Receive Memory Warning () the results highlight the continued hurdle in computational video analysis: designing models that balance high predictive accuracy with their ability to generalize across disparate datasets. © 2024 IEEE.

关键词： Video analysis

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Automatic Arrhythmia Diagnosis System Based on CNN-LSTM 3

Automatic Arrhythmia Diagnosis System Based on CNN-LSTM

引用

3rd international conference on computing, Communication, Perception and Quantum technology, CCPQT 2024

作者： Li, Ying Li, Yonghong Xia, Chuanqian Wang, Peng Wang, Yushui Zhang, Jun School of Electronic Engineering Chengdu University of Information Technology Chengdu China

ISBN: (纸本)9798331528386

Electrocardiograms (ECG) are essential tools for diagnosing cardiovascular diseases, yet traditional ECG identification relies on the experiential interpretation by doctors, which is time-consuming and subject to subjective influence. In light of the limitations of ECG, this study introduces an automatic ECG signal analysis system based on Convolutional Neural Networks (CNN) and Long Short-Term Memory networks (LSTM). This system is capable of learning from and recognizing complex patterns in vast ECG datasets. Initially, the study utilizes four public ECG databases, and the data are denoised, sliced, resampling and combined. Subsequently, a CNN-LSTM model is applied to perform seven-category classification of the ECG signals. In the end, the CNN-LSTM model showed a 98.20% recall rate, 98.30% accuracy, and 98.78% accuracy. Additionally, the fully trained model has been successfully deployed on a Raspberry Pi, enabling online applications and real-time diagnostic capabilities. Simultaneously, this study also developed a wearable ECG monitoring vest based on the *** vest is capable of real-time ECG signal acquisition and transmits the data to the Raspberry Pi via Bluetooth technology, facilitating immediate detection of cardiac anomalies. © 2024 IEEE.

关键词： Convolutional neural networks

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Database Management and Data Mining 5

Database Management and Data Mining

引用

5th international conference on Computer information Science and Artificial Intelligence, CISAI 2022

作者： Qiao, Zhiying College of Arts Media and Technology Chiang Mai University Chiang Mai50200 Thailand

ISBN: (纸本)9781510662520

Today, driven by new technologies such as cloud computing, big data, the Internet of Things and artificial intelligence, the era of artificial intelligence has arrived. "Digital transformation" has become the only way for enterprises to seek breakthroughs and innovations. As the carrier of information, the applications of data are fundamental. This paper discusses the database construction, data mining, analysis methods and the application of optimizing the database. Each of these applications will be discussed in details. © 2023 SPIE.

关键词： Data mining

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Cloud computing and its Emerging Trends on Big Data Analytics 4

Cloud Computing and its Emerging Trends on Big Data Analytic...

引用

4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Thakur, Shikher Jha, Shambhu Kumar Amity University Amity Institute of Information Technology Noida-125 India

ISBN: (纸本)9798350300093

Cloud computing and big data analytics are two rapidly growing fields in technology industry. The way that organizations store, handle, and analyze their data has been changed by cloud computing. Big Data Analytics has become an essential part of utilizing the potential of cloud computing as a result of the growing volume and complexity of data. This research study explores the emerging trends in big data analytics within the context of cloud computing and examines the fundamentals of cloud computing and how it has altered the field of big data analytics. This study analyzes the effects of different developing cloud computing technologies on big data analytics, including serverless computing, multi -cloud computing, and edge computing. This study also discusses about the opportunities and problems associated with using cloud computing for big data analytics, such as security, scalability, and cost effectiveness. At the end of the seminar, participants will have a thorough understanding of how cloud computing and Big Data Analytics relate to one another as well as knowledge of the most recent developments in this quickly developing sector. © 2023 IEEE.

关键词： Big data

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Review of DDoS Attack Detection in Big Data with Cloud using Machine Learning 2

Review of DDoS Attack Detection in Big Data with Cloud using...

引用

2nd international conference on Automation, computing and Renewable Systems, ICACRS 2023

作者： Radhika, P. Kamalakkannan, S. Kavitha, P. Department of Computer Science School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Pallavaram Chennai India Department of Information Technology School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Pallavaram Chennai India Department of Computer Applications School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Pallavaram Chennai India

ISBN: (纸本)9798350340235

Cloud computing (CC) is a massive breakthrough in information technology (IT) that provides end users to access flexible andvirtualizedsources at affordable infrastructure cost and management. One of the most significant technologies in the big-data era is the CC Data Centres (DCs). The Distributed Denial of Service (DDoS) attacks are one of the most serious issues when it comes to the privacy of DC. DDoS attacks using Transmission Control Protocol (TCP) traffic are taken into consideration, which are becoming more prevalent but challenging to identify. The DDoS attack is the focus of this study, along with the technique used to prevent it and lessen the vulnerability of the big data server side. The system entails the delivery of packets in the form of DDoS attacks to cloud-based websites and even addresses the real-time prediction of software layer DDoS attacks using various Machine Learning (ML) and Deep Learning (DL) techniques. As a result, it stands apart among numerous hosts. Additionally, the objective of this paper was to offer a succinct introduction to attack detection approaches for early researchers working on cloud-based big data applications. As a result, these approaches are categorised according to how they function, their strengths and shortcomings are reviewed, and finally, several research papers that used each method are examined. © 2023 IEEE.

关键词： Big Data Cloud computing Deep learning Distributed Denial of Service attacks Machine Learning

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EcoCardio: Affordable IoT-Powered Portable ECG Monitoring for Seamless Health Insights 4

EcoCardio: Affordable IoT-Powered Portable ECG Monitoring fo...

引用

4th international conference on Ubiquitous computing and Intelligent information Systems, ICUIS 2024

作者： Mohandas, R. Patan, Muzeeb Khan Zeeshan, G Ahmed Kumar, B. Siva Rajeshwari, M. Raja Balaram, Allam Chennai Institute of Technology Department of Ece Chennai India College of Engineering and Technology University of Technology and Applied Sciences Department of Eee Muscat Oman Moinabad Telangana State Ranga Reddy India Department of Management Studies AP India DR.M.G.R Educational and research university Chennai India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad500043 India

ISBN: (纸本)9798331529635

Electrocardiograph (ECG) monitoring systems are crucial tools in diagnosing and monitoring a wide range of heart conditions, including arrhythmias and myocardial infarctions. Traditional ECG systems, while effective, often require patients to be tethered to bulky equipment, limiting their mobility and comfort. The advent of IoT technology has opened new possibilities for enhancing the functionality and accessibility of ECG monitoring systems. This system utilizes core components such as the AD8232 ECG sensor, ESP32 Arduino module, EEPROM for data storage, an LCD for real-time display, and the Blynk App for remote monitoring. Key performance metrics include a high accuracy rate of 98.1%, a processing time of 200 ms, and a data transmission latency of 100 ms, ensuring reliable and timely monitoring. By leveraging Wi-Fi and Bluetooth connectivity, the system provides real-time data transmission to cloud-based platforms, facilitating immediate access and analysis by healthcare providers. This IoT-enabled ECG monitoring system significantly enhances patient care, enabling early detection of cardiac anomalies and reducing the need for frequent hospital visits. © 2024 IEEE.

关键词： Remote patient monitoring

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Cloud-Driven Machine Learning with AWS: A Comprehensive Review of Services 2

Cloud-Driven Machine Learning with AWS: A Comprehensive Revi...

引用

2nd IEEE international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, ICIITCEE 2024

作者： Ravindranathan, Manya K. Sendil Vadivu, D. Rajagopalan, Narendran National Institute of Technology Puducherry Department of Computer Science and Engineering Karaikal India

ISBN: (纸本)9798350306415

With the rise of cloud computing, people now have 24/7 online access to IT resources, which is a transformational force. Amazon Web Services (AWS) is a market leader in the field of cloud service providers, providing a broad range of platform, software, and infrastructure services. In this review article, we explore the domain of cloud-driven Machine Learning (ML) with an emphasis on the extensive resources and support provided by AWS. The ability of the AWS ecosystem to provide scalable and effective ML applications is highlighted in this study. It looks at the range of AWS services created to make building, training, and deploying cloud-based ML applications easier. Our analysis includes an AWS Machine Learning as a Service (MLaaS) product categorization, illuminating the range of resources and options available to ML practitioners. We also take into account findings from comparable studies that have used AWS services for a range of purposes. In conclusion, this article offers a thorough introduction for individuals wishing to explore the world of cloud- driven ML using AWS. It provides insightful information on AWS services, their uses, and how they are influencing ML in the cloud. © 2024 IEEE.

关键词： AWS AWS Free Tier Services Cloud computing Distributed computing Machine Learning as a Service

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ORGANiser 3

ORGANiser

引用

3rd international conference on Advanced computing Technologies and Applications, ICACTA 2023

作者： Thaker, Ruchit Saxena, Kumkum Adappa, Vinisha Raghuvanshi, Rishabh Thadomal Shahani Engineering College Department of Information Technology Maharashtra Mumbai India

ISBN: (纸本)9798350348347

With regards to listing patients, organ matching, procurement, transportation and transplantation, current organ procurement and transplantation systems provide various requirements and obstacles as well as technological, clinical, ethical, and legal limitations. Therefore, a complete system for donating organs and its process of transplantation, is needed to provide a just and effective procedure that will improve patient experience and confidence. The objective is to develop a website that will automate and secure the organ donation procedure while safeguarding private patient and donor health information to obviate any chance of manipulation. © 2023 IEEE.

关键词： Smart contract

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Communication-Aware Optimization of Microservice Scheduling on Edge computing 7th

Communication-Aware Optimization of Microservice Scheduling...

引用

7th international conference on Innovative computing, IC 2024

作者： Park, Hokun Yu, Heonchang Korea University Seoul Korea Republic of

ISBN: (纸本)9789819741816

Edge computing is well suited to environments that send massive data over the network, such as the Internet of Things. Kubernetes is used to manage services reliably in geographically distributed edge environments. However, the default Kubernetes placement policy does not consider the edge environment, which may lead to the degradation of service. In this paper, we propose a placement and replacement method that considers the edge environment. Compared to the default placement policy, proposed method improves the service performance by reducing the data transfer delay and the number of replacements. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Internet of things

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