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检索条件"任意字段=International Conference on Computer Technology and Development"

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A two-dimensional convolutional gated recurrent unit based method for classifying malicious encrypted traffic 3

A two-dimensional convolutional gated recurrent unit based m...

引用

3rd IEEE international conference on Electronic technology, Communication and Information, ICETCI 2023

作者： Jianhua, Chen Zhaohui, Liu Xin, Deng Xinyue, Lu University of South China School of Computer Science Hengyang China University of South China College of Innovation and Entrepreneurship Hengyang China Northeastern University at Qinhuangdao School of Computer and Communication Engineering Qinhuangdao China

ISBN: (纸本)9798350398410

With the development of traffic encryption technology, more and more malicious traffic adopts encryption technology to hide their activities. To address the problems of existing deep learning models such as low training efficiency and poor real-time classification due to deeper hierarchy, this paper proposes a malicious encrypted traffic classification method based on Two-Dimensional Convolution Gate Recurrent Unit (TDCGRU). Firstly, the spatial features of individual packets are extracted using two-dimensional convolution;then the time series features between packets at the traffic level are extracted using the gated recurrent unit, so that the spatio-temporal features are extracted at the packet level and the flow level of the traffic respectively. Based on the public datasets CTU-13 and CICAndMal2017, the experimental results show that the accuracy of the method in this paper reaches 99.2%, and compared with the BGRUA model, the classification delay and training time are saved by 11.2% and 12%, respectively, while improving the classification effect. © 2023 IEEE.

关键词： Convolution

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AI-Powered Personalised Learning Platforms for EFL Learning: Preliminary Results 16

AI-Powered Personalised Learning Platforms for EFL Learning:...

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16th international conference on computer Supported Education, CSEDU 2024

作者： Folgieri, Raffaella Gil, Marisa Bait, Miriam Lucchiari, Claudio Department of Philosophy "Piero Martinetti" Università degli Studi di Milano via Festa del Perdono 7 Milan Italy Computer Architecture Department UPC Barcelona TECH Barcelona Spain

ISBN: (纸本)9789897586972

Artificial intelligence (AI) has been increasingly integrated into the field of education, including personalised learning platforms. However, concerns have been raised about the potential of AI to replace human teachers and the impact on student agency and autonomy. In this research, we discuss the development of an AI-powered platform as a helper, not a substitution, for self-directed personal and professional growth. The present study investigates the effectiveness of an AI-powered personalised learning platform in enhancing self-directed learning and personal and professional growth. We also explored the role of human teaching and the ethical considerations of AI in education. A mixed-methods approach was used, including surveys, interviews, and qualitative analysis of participant feedback. The participants were randomly assigned to either an AI group or the traditional learning group. Findings suggest that the AI-powered personalised learning platform that we used is a promising approach for enhancing self-directed learning and personal and professional growth. However, it is important to note that these are just preliminary findings, and further research is needed to confirm our results and to understand the mechanisms by which a specific use of AI in education may lead to positive effects. Copyright © 2024 by SCITEPRESS – Science and technology Publications, Lda.

关键词： E-learning

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A Conceptualized Study of ML Classifiers in Solid Waste Management

A Conceptualized Study of ML Classifiers in Solid Waste Mana...

引用

2024 international conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Nair, Prabha Shreeraj Srivastav, Akanksha Aadil, Mir Department of Information Technology & M. Tech Integrated Noida Institute Of Engineering and Technology Uttar Pradesh Greater Noida India Department of Computer Science and Engineering Tula's Institute Dehradun India Bangalore India

ISBN: (纸本)9798350355338

The phenomenon of population development and the subsequent acceleration of urbanization has resulted in a significant upsurge in the generation of municipal solid garbage. Consequently, scholars and experts have endeavoured to employ sophisticated technology in order to address this pressing issue. In recent years, there has been a progressive integration of machine learning (ML) algorithms into the field of municipal solid waste management (MSWM) with the aim of facilitating sustainable environmental growth. ML algorithms have demonstrated their effectiveness in modelling intricate nonlinear systems. This study involved the assessment and analysis of over 200 papers that were published throughout the timeframe of two decades, namely from 2016 to 2023. This study provides a comprehensive overview of the utilization of machine learning algorithms throughout the entirety of MSWM, encompassing trash creation, collection and transportation, and ultimate disposal stages. This thorough study aims to address the gaps and identify future directions in the application of ML in MSWM. The findings of this review offer both theoretical and practical guidelines for future research in this field. © 2024 IEEE.

关键词： Adversarial machine learning

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Vision Transformer-based Model for Gastric Cancer Detection and Classification using Weakly Annotated Histopathological Images 26

Vision Transformer-based Model for Gastric Cancer Detection ...

引用

26th international conference on Advanced Communications technology, ICACT 2024

作者： Theodore Armand, Tagne Poupi Bhattacharjee, Subrata Kim, Hyun-Joong Hussain, Ali Ali, Sikandar Choi, Heung-Kook Kim, Hee-Cheol Institute of Digital Anti-Aging Healthcare Inje University Gimhae50834 Korea Republic of Inje University Department of Computer Engineering U-AHRC Gimhae50834 Korea Republic of

ISBN: (纸本)9791188428120

Gastric Cancer (GC) is the fifth most diagnosed cancer worldwide. An early diagnosis is a hope for patients suffering from GC. A biopsy is a procedure that helps detect abnormal and suspicious areas to determine whether cancer cells are in the stomach. Tissue samples collected through biopsy are stained using Hematoxylin and Eosin (H&E) and digitalized through scanning to produce a whole slide image (WSI) needed for further analysis. Recently, most prognostics have proven effective using artificial intelligence techniques combined with related computer aid detection systems. This research used a vision transformer to detect and classify gastric cancer from weakly annotated tissue images. After acquiring normal and cancer histopathological samples, we applied the vision transformer (ViT) model for binary classification. We generalized our approach by performing region-based prediction on unannotated tissue samples. The proposed approach will ease diagnosis and support pathologists in decision-making. © 2024 Global IT Research Institute - GIRI.

关键词： Biopsy

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Interim Analysis Using Machine Learning 1

Interim Analysis Using Machine Learning

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1st IEEE international conference on Electronics, Computing, Communication and Control technology, ICECCC 2024

作者： Vennila, J. Prakash, V.S. Kotian, Himani Chitra, K. Sebastian, Liji Vijayan, Keerthi Basker, P. Esther Jenitha, K. Karnatka Manipal India Kristu Jayanti College Department of Computer Science Karnataka Bengaluru India Department of Community Medicine Karnataka India CMR Institute Of Technology Department of Electrical and Electronics Engineering Karnataka Bangalore India Periyar University Department of Statistics Karnataka Salem560077 India Kristu Jayanti College Department of Statistics Karnataka560077 India Kristu Jayanti College Department of Statistics Karnataka Bengaluru India Chandigarh University Punjab 140413 India Loyola College Department of Statistics Tamil Nadu Chennai34 India

ISBN: (纸本)9798350371802

The purpose of this article is to recalculate sample size by adjusting the initial design about trial results and taking uncertainty in power estimations into consideration throughout study preparation. This article examines two primary methods: Pocock and O'Brien/Fleming using R programming. Interim data analysis must be done far in advance to provide enough power and minimize the risk of having Type I errors. These results suggest that robust performance indicators have enabled improved comprehension of the study's development, notably increased control, more efficient use of resources (e.g., monitoring time), quicker enrolment, shorter operational timelines, and less expensive costs. © 2024 IEEE.

关键词： Machine learning

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Maximizing Efficiency in Landfill Operations Using IoT and Random Forest Algorithms for Green Revolution

Maximizing Efficiency in Landfill Operations Using IoT and R...

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2024 international conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Solainayagi, P. Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350375237

This paper proposes a new approach to waste management that is more sustainable and eco-friendly. Important landfill factors including temperature, humidity, gas emissions, and waste accumulation may be monitored in real-time with this system, which combines the Random Forest algorithm with Internet of Things (IoT) technology. The IoT sensor has the potential to provide real-time data interchange and monitoring, while the Random Forest algorithm a machine learning approach might provide effective pattern identification and decision-making. The use of Random Forest in predictive modeling method for the creation of early warning systems that can combat potential issues such gas leaks, overfilling, and operational inefficiencies before they ever happen. Future possibilities and consequences of this technology convergence may also be covered in the paper, with an emphasis on the important factors to consider for the effective and scalable deployment of waste management systems. Privacy and security of data, development of infrastructure, cost-effectiveness, and involvement of stakeholders are all factors that may be taken into account. A performance indicator would be a prediction accuracy of more than 95% on the validation and test datasets. Wind turbines and solar panels, which produce power from waste gas, may be more easily integrated into landfill operations using this technology, significantly reducing the environmental impact of landfill operations. © 2024 IEEE.

关键词： Internet of things

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LLDSNet: Limited labeled data segmentation network for COVID-19 CT lesion segmentation

LLDSNet: Limited labeled data segmentation network for COVID...

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2022 international conference on Electronic Information Engineering and computer Science, EIECS 2022

作者： Liu, Jingyao Qu, Feng Shi, Weili Yang, Chen Jiang, Zhengang School of Computer Science and Technology Changchun University of Science and Technology Changchun China School of Computer and Information Engineering Chuzhou University Chuzhou China Zhongshan Institute of Changchun University of Science and Technology Zhongshan China Bone and Joint Surgery The First Hospital of Jilin University Changchun China

ISBN: (纸本)9781510663312

With the COVID-19 outbreak in 2019, the world is facing a major crisis and people's health is at serious risk. Accurate segmentation of lesions in CT images can help doctors understand disease infections, prescribe the right medicine and control patients' conditions. Fast and accurate diagnosis not only can make the limited medical resources get reasonable allocation, but also can control the spread of disease, and computer-aided diagnosis can achieve this purpose, so this paper proposes a deep learning segmentation network LLDSNet based on a small amount of data, which is divided into two modules: contextual feature-aware module (CFAM) and shape edge detection module (SEDM). Due to the different morphology of lesions in different CT, lesions with dispersion, small lesion area and background area imbalance, lesion area and normal area boundary blurred, etc. The problem of lesion segmentation in COVID-19 poses a major challenge. The CFAM can effectively extract the overall and local features, and the SEDM can accurately find the edges of the lesion area to segment the lesions in this area. The hybrid loss function is used to avoid the class imbalance problem and improve the overall network performance. It is demonstrated that LLDSNet dice achieves 0.696 for a small number of data sets, and the best performance compared to five currently popular segmentation networks. © 2023 SPIE.

关键词： COVID-19

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A Comprehensive Framework for Integrating RPA into Logistics Systems 15

A Comprehensive Framework for Integrating RPA into Logistics...

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15th international conference on Emerging Ubiquitous Systems and Pervasive Networks / 14th international conference on Current and Future Trends of Information and Communication Technologies in Healthcare, EUSPN/ICTH 2024

作者： Nalgozhina, Nurgul Smagul, Diana Yesmurzayeva, Aknur Daineko, Yevgeniya International Information Technology University Manas st. 34/1 Almaty050000 Kazakhstan

In the era of digital transformation, Robotic Process Automation (RPA) has emerged as a pivotal technology for enhancing operational efficiency and sustainability in logistics. The primary goal of this paper is to explore the potential of RPA in optimizing logistics processes, specifically focusing on its impact on energy efficiency, cost reduction, and overall operational performance. The research aims to identify and address the challenges and opportunities presented by RPA, offering a structured framework for its implementation in large-scale logistics operations. By automating routine and repetitive tasks, RPA reduces human error, accelerates process execution, and significantly lowers energy consumption in both warehouse and transportation logistics. The integration of RPA with existing logistics systems, such as warehouse management systems and transportation management systems, facilitates seamless operations and real-time data analysis. The scientific novelty of the study lies in the development of a comprehensive framework for integrating RPA into logistics systems, highlighting not only the immediate operational benefits but also long-term sustainability and efficiency improvements. This paper also provides insights into the best practices associated with RPA deployment in logistics, supported by case studies and quantitative analysis. © 2024 The Authors.

关键词： Automation Integration Energy Efficiency Logistics Robotic Process Automation Warehouse Management

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Enhancing User Experience and Design in Agricultural Information Portals: A Study of Websites for Indian Farmers

Enhancing User Experience and Design in Agricultural Informa...

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2024 international conference on Artificial Intelligence and Emerging technology, Global AI Summit 2024

作者： Sharma, Mradula Garg, Sahil Srivastava, Ashutosh Srivastava, Divya Verma, Madhushi Bhardwaj, Arpit School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9798350379716

Agriculture plays a significant role in India's economy, not only in terms of its GDP share but also in employment, rural development, and food security. As per the 2022-2023 economic survey, the gross value added (GVA) of agriculture in Indian economy is 18.3%. The economic survey also shows that there has been a decline of this percentage from 35% to 18% in the last 20 years. This is an alarming situation! India has got a very good agricultural land but due to lack of awareness in this field its contribution in GVA is decreasing. The present article took a small initiative to present a study of various agriculture information portals that are although designed to help farmers, but are they actually helpful? This question is taken over in this article as it discusses the various issues like, technological literacy of Indian Farmers, user friendly website design, accessibility in rural areas, language differences, and etc. Total ten (10) government agricultural websites are analyzed and we suggest some changes/improvements that these websites can implement for a better user experience to reach out to more farmers and deliver better information regarding agricultural issues. © 2024 IEEE.

关键词： Agricultural economics

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The New Frontier in Media: AI-Driven Content Creation for Ad-Supported TV using Generative Adversarial Network 7

The New Frontier in Media: AI-Driven Content Creation for Ad...

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7th international conference of computer and Informatics Engineering, IC2IE 2024

作者： Ramagundam, Shashishekhar Karne, Niharika Comcast Software Development and Engineering in Content Discovery and Ai United States Troy University Masters in Computer Science AL. Department of Computer Science Montgomery Campus United States Dt Amtrak Sr. Principal Systems Engineer in C and Rs Platform Services United States University of South Alabama Masters in Electrical Engineering Department of Electrical Engineering United States

ISBN: (纸本)9798331505530

The use of Artificial intelligence (AI) technology in the content creation to produce creative aspects like editing, audience analysis, creating ideas, writing copy, etc. The major aim is to streamline and automate the process of content creation and convert it into a more efficient and effective way. There is a lack of transparency in the AI content production process and hence it does not exactly mimic human activities, which includes fantasizing and picking up new abilities. AI content creation requires more creativity while investigating with a specific goal in mind. Although, AI faces many challenges since it constantly expands its base knowledge. One of the biggest ethical problems is the probability that AI content is used to deceive or influence people. Hence, to overcome these difficulties a machine learning-based Artificial Intelligent content creation framework is generated for content creation in Ad-supported TV. Here, an effective model Generative Adversarial Network (GAN) introduced to deliver enterprise data, customize descriptions, and adjust content based on consumer behaviors. This developed model produces material that is closely related to the preferences of the user. AI content creation increases efficiency and productivity by saving time and money. It also leads to the development of better content. Finally, the experimental analysis is performed to find the effectiveness of the developed deep learning-based AI content creation framework via various metrics. © 2024 IEEE.

关键词： Generative adversarial networks

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