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检索条件"任意字段=5th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2004"

共 181 条记录，以下是11-20 订阅

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automated Detection of Encryption Algorithms Using AI Techniques 5

Automated Detection of Encryption Algorithms Using AI Techni...

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5th international conference on IoT Based Control Networks and intelligent Systems, ICICNIS 2024

作者： Chidambaranathan, Senthilnathan Santhanaraj, M. Ajitha, E. Syed Fiaz, A.S. Suchithra, B. Kathirvel, T. Virtusa Corporation NJ United States Kongunadu College of Engineering and Technology Department of IT Tamil Nadu Trichy India St. Joseph's Institute of Technology Computer Science and Engineering India Sona College of Technology Department of Information Technology Salem India Sri Eshwar College of Engineering Department of CSE Coimbatore India

ISBN: (纸本)9798331518097

In today's digital era, cryptographic algorithms play a vital role in safeguarding sensitive information, maintaining confidentiality, and defending against cyberattacks. With the growing variety of encryption methods and modes, identifying the specific algorithm used to generate a ciphertext has become increasingly challenging. To address this, we propose a machine learning solution that automatically identifies cryptographic algorithms using the Random Forest classifier. the system processes a vast dataset called 'encrypted-data,' which contains 39 different algorithms and modes, by extracting essential features like byte frequency and entropy from the ciphertexts. these features form a 257-dimensional Feature vector used for classification. Random Forest models are trained with these data. During testing, models are loaded is used to predict the encryption algorithm for a given ciphertext. Our method offers a scalable and efficient approach to cryptographic identification, surpassing traditional techniques. © 2024 IEEE.

关键词： Encryption algorithms

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A Novel Approach to Optimizing Spark: Detecting Misconfigurations for Enhanced Efficiency 5

A Novel Approach to Optimizing Spark: Detecting Misconfigura...

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5th international conference on IoT Based Control Networks and intelligent Systems, ICICNIS 2024

作者： Sanjana, S. Rachana, V. Umesh, Uday Rao, Suraj Jayashree, R. PES University Department of Computer Science and Engineering Bengaluru India Bengaluru India

ISBN: (纸本)9798331518097

Configuration errors constitute 60% of contemporary system failures in distributed computing platforms. these errors lead to data breaches, system crashes, or silent failures requiring extensive technical support and knowledge of the application architecture. this research work introduces a dataset that can be used to detect misconfigurations in a Spark cluster. the dataset encompasses detailed system performance metrics such as CPU usage and memory allocation that were recorded by running CPU intensive and memory intensive Spark jobs under various configuration settings to capture performance variations. Analysis of this dataset provides insights into optimizing the values of Spark cluster configurations and mitigating the impact of configuration errors. By using machine learning techniques on this dataset, it is possible to construct automated methods for proactively detecting and mitigating misconfigurations, which improves the stability and efficiency of distributed computing environments. © 2024 IEEE.

关键词： Storage allocation (computer)

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Exploring data Symbion EI Deep learning and Model Sharing Modules 25th

Exploring Data Symbion EI Deep Learning and Model Sharing Mo...

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25th international conference on intelligent data engineering and automated learning

作者： Huszcza, Rafael Mendes, Amanda Lopes, Jeferson Borges, Eduardo N. Lucca, Giancarlo B. Guilherme, Pablo D. B. Pereira, Leandro A. Univ Fed Rio Grande FURG Ctr Ciencias Computac C3 Rio Grande Brazil Univ Catolica Pelotas Ctr Ciencias Sociais & Tecnol Mestrado Engenharia Eletron & Computacao Pelotas RS Brazil Univ Estadual Parana UNESPAR Ctr Educ Humanidades Ciencias Biolog Paranagua Brazil Inst Fed Parana IFPR Eixo Tecnolog Meio Ambiente Paranagua Brazil

ISBN: (纸本)9783031777370;9783031777387

Considering the growing volume of available information, storing and managing data is an increasing challenge. Leveraging these raw datasets to extract meaningful knowledge and make informed decisions requires data mining methods, especially when combined with Machine learning algorithms to uncover hidden patterns in data. Considering this context, this work describes a project to contribute to datadriven initiatives by seamlessly integrating a data repository system with Machine learning (ML) tools. this integration allows users to implement pipelines within a single system-from storing and managing datasets to training and deploying ML models, making easier, more efficient, and intuitive data science workflow accessible to all, not just computer scientists. Furthermore, the system aims to enhance knowledge sharing among researchers and practitioners by enabling users to share datasets and trained machine learning models within the platform. this work highlights the system's key features, particularly the Machine learning module, which includes classic ML algorithms for tabular data and the newly introduced Deep learning algorithms for image classification. the new Model Sharing module also emphasizes promoting collaboration by allowing users to share and manage trained models effectively.

关键词： data Repository Machine learning Deep learning Model sharing data Symbion EI

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An automated Face Mask Detection Using Ensemble learning Approach 5

An Automated Face Mask Detection Using Ensemble Learning App...

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5th international conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Kumar, B. Kiran Mounika, Guggilam Harish, R.V.S. Nagarjuna, Valeti St. Ann's College of Engineering & Technology Embedded Systems Andhra Pradesh Chirala523187 India St. Ann's College of Engineering & Technology Andhra Pradesh Chirala523187 India Kallam Haranadhareddy Institute of Technology Dept of Cse NH-16 Andhra Pradesh Chowdavaram India

ISBN: (纸本)9798350379945

the COVID-19 pandemic and other infectious diseases are spreading quickly, which has brought attention to how essential face masks are in stopping the spread of respiratory disorders. To ensure compliance with mask-wearing rules, automatic face mask detection devices are becoming increasingly important. the application of automated Convolutional Neural Networks (ACNN) in conjunction with Internet of things (IoT) devices for an automated Face Mask Detection is the main emphasis of this research. the suggested method correctly detects people wearing or not wearing face masks in real-time video and picture data by leveraging ACNN, a deep-learning architecture created for image segmentation. A wide range of labeled photos of people wearing and not wearing masks are used to train the FCNN. the model is ideal for real-world applications in public areas, healthcare facilities, and educational institutions due to its high accuracy and efficiency. Real-time monitoring and notifications are made possible by the integration of IoT devices, which improves the operation of the system. In order to collect images and videos data, IoT sensors and cameras are positioned strategically in specific regions. A central processing unit (CPU) that executes the trained FCNN model is connected to these devices. Using email, SMS, or a mobile app, the system instantly notifies the appropriate authorities or persons when it finds someone not donning a face mask. © 2024 IEEE.

关键词： Convolutional neural networks

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PlantLDNet : Disease Detection in Leaf Images Using Deep learning Models 5

PlantLDNet : Disease Detection in Leaf Images Using Deep Lea...

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5th international conference on intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Nikhileswar, Gurram Sai Pampana, Surya Sai Jani Anbarasi, L. Bhavana, Chintalapudi Likhitha Nanditha, Rayanki Lakshmi Krishna, Kodela Chaithanya School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350385649

automated disease diagnosis in the agricultural field has benefited from the recent advances in big data, deep learning, and computer vision across a wide range of applications. However, intelligent disease prediction is crucial for safely maintaining a good agricultural production. Moreover, unclear disease prognosis is caused by the degree of leaf disease inhomogeneity, the complexity of the background-which includes the low contrast with the surrounding area and the potential for shadows with a similar intensity. this research provides a custom Convolutional Neural Network (CNN) model called PlantLDNet that has hyperparameters configured optimum to distinguish between leaf images showing disease and those without it, hence avoiding the problems associated with vanishing gradient. A comparative study of the proposed custom CNN model PlantLDNet and earlier research works was conducted using the following metrics: precision, recall, accuracy, F1 score, loss accuracy, receiver operating characteristic (ROC) curves, and Area Under Curve (AUC) measure. the comparison findings show that the proposed PlantLDNet model has the highest classification prediction effectiveness (99.85%) with less computational complexity. It also has an elevated high F1-score of 93% when trained for Early Bright, 98% for Healthy, and 92% for Late Bright. © 2024 IEEE.

关键词： Convolutional neural networks

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Advancing Android Security: Leveraging Stacking Ensemble and Bioinspired Feature Selection for Efficient Malware Detection 5

Advancing Android Security: Leveraging Stacking Ensemble and...

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5th IEEE international conference for Emerging Technology, INCET 2024

作者： Jyothsna, V. Mokshitha, Peddesugari Khulud, Shaik Reddy, Lakshmireddy gari Premanath Reddy, Nare Jagannath Pydala, Bhasha Tirupati India Dept. of Information Technology Sree Vidyanikethan Engineering College Tirupati India Department of Data Science Mohan Babu University Tirupati India

ISBN: (纸本)9798350361155

Nowadays, the use of smartphones and related applications is increasing rapidly due to their "easy to use" features, the functionality of many applications, and the constant development of software and hardware of smart devices. Research in this field shows that 4.5 billion people will have mobile phones by 2024. the most used smartphone operating system among these devices is Android. Android accounts for 75.5% of the market. Because Android is so widely used, it is more vulnerable to viruses and malware, making it an ideal target for hackers. Android anti-malware technology, which can quickly detect and classify different malware to create a rapid response plan, has become popular in recent years. the usefulness of combining deep learning and machine learning to offer automated and self-learning services has been shown by numerous applications;nevertheless, the shortage of malware samples has been identified as a barrier to the development of deep learning-based solutions. To solve this problem, this work introduces new machine-learning techniques that can classify malware into different groups and identify malware attacks. the proposed method used the Drebin dataset divided into hazardous and non-hazardous substances. © 2024 IEEE.

关键词： Smartphones

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Enhancing the Scalability of Knowledge Discovery Services for Massive data Mining on Clouds 5

Enhancing the Scalability of Knowledge Discovery Services fo...

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5th international conference on IoT Based Control Networks and intelligent Systems, ICICNIS 2024

作者： Sajjan, Vijayakumar Kumar, Jayam Pradeep Kalvala, Vikram Domala, Narsaiah Kumar, B. Kiran Pavan Kumar Hota, C.P. Malla Reddy University Department of CSE Hyderabad India Chaitanya Deemed to Be University Department of ECE Hyderabad India MLR Institute of Technology Department of ECE Hyderabad India Shri Vishnu Engineering College for Women Department of Artificial Intelligence Bhimavaram India

ISBN: (纸本)9798331518097

the traditional data mining techniques do not work well when it comes to the big data and the tremendous growth rate of the digital data also becomes a major challenge to the discovery of knowledge. Cloud computing offers an ideal solution to computationally and storage intensive tasks with scalable and on-demand services that can process big data at short notice. this paper reviews the current models that employ cloud structure for distributed data mining as well as models like CosDic and the data Mining Cloud Framework (DMCF). the way of processing large datasets is also represented well by the Map Reduce model where it is possible to implement the data processing in parallel on several nodes. Despite the fact that cloud-based systems are very useful when it comes to the management of large amounts of data, challenges such as privacy, data security and data coordination still persist. these innovative methodologies include Edge Computing and Federated learning that are anticipated to solve the mentioned issues by enhancing the speed of data processing and its safety. thus, scalable cloud data mining to meet growing needs of knowledge discovery in big data contexts will have to develop new privacy protection mechanisms, integration of cloud models, and complex algorithms. © 2024 IEEE.

关键词： Cloud platforms

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Alzheimer's Disease Classification Using CUDA-Accelerated DenseNet201 and VGG16 5

Alzheimer's Disease Classification Using CUDA-Accelerated De...

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5th international conference on IoT Based Control Networks and intelligent Systems, ICICNIS 2024

作者： Munugala, Shravya Marathe, Atharva Prashant Vijayakumar, Rahul Kumaran, K. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331518097

automated classification of Alzheimer's disease through MRI analysis presents significant computational challenges due to complex deep learning architectures and largescale image processing requirements. this paper introduces a GPU-accelerated multi-branch neural network framework that combines modified DenseNet201 and VGG16 architectures for efficient Alzheimer's disease stage classification. the proposed model employs parallel feature extraction pathways, with each branch independently processing MRI inputs before feature fusion. CUDA optimization techniques are implemented across the processing pipeline, including data loading, augmentation, and model training phases, resulting in significantly reduced computational overhead. the framework classifies brain MRI scans into four categories: Non-Demented, Very Mild Demented, Mild Demented, and Moderate Demented. Experimental results demonstrate that the proposed hybrid architecture achieves 98% classification accuracy while substantially reducing training time through GPU acceleration. the model's performance is further enhanced through adaptive learning rate scheduling and selective layer fine-tuning. this work establishes an efficient approach for deploying complex neural architectures in clinical neuroimaging applications where computational resources and processing time are critical constraints. © 2024 IEEE.

关键词： Neurodegenerative diseases

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AI for automated thoracic Disease Assessment from X-Ray Imaging: a Review 5

AI for Automated Thoracic Disease Assessment from X-Ray Imag...

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5th international IEEE Novel intelligent and Leading Emerging Sciences conference, NILES 2023

作者： Ali, Hadeel M. El-Metwally, Shereen M. Wahed, Manal Abdel Cairo University Nile University Systems and Biomedical Engineering Giza Egypt Cairo University Systems and Biomedical Engineering Giza Egypt

ISBN: (纸本)9798350381030

With the increasing availability of digital X-ray imaging, artificial intelligence (AI) has emerged as a promising tool for automating the assessment of thoracic diseases. the objective of this study is to systematically review the artificial intelligence (AI) and deep learning methods proposed for the automated assessment of thoracic diseases from chest X-ray images. A thorough search of the relevant literature was conducted, and studies that met the inclusion criteria were critically reviewed. Information on the datasets, model architectures, evaluation metrics, and results was extracted. Convolutional neural networks are prevalent, achieving a state-of-the-art classification performance. Recent studies have explored more complex tasks such as disease localization, segmentation, and report generation. the multitask and multimodal approaches are promising. Challenges related to the data, evaluations, and clinical adoption were identified. this study prevails that there is a significant progress in using deep learning for automated chest X-ray analysis. Further research is needed to validate these models in real-world settings and to facilitate their integration into clinical workflows. © 2023 IEEE.

关键词： Deep learning

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Vehicle Trajectory-Based Intersection Classification Using Deep Transfer learning 5

Vehicle Trajectory-Based Intersection Classification Using D...

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5th international conference on intelligent Computing, Communication, Networking and Services, ICCNS 2024

作者： Kased, Abanoub Rabee, Rana Fahmy, Akram Mohamed, Hussein Yacoub, Marco Elhenawy, Mohammed Ashqar, Huthaifa I. Alsobeh, Anas Masoud, Mahmoud Hassan, Abdallah A. Minia University Faculty of Engineering Computers and Systems Department Minia Egypt Australia Arab American University Civil Engineering Department Jenin Palestine School of Computing Southern Illinois University Carbondale CarbondaleIL United States Center for Smart Mobility and Logistics King Fahd University of Petroleum and Minerals Department of Information Systems and Operations Management Dhahran Saudi Arabia

ISBN: (纸本)9798350354690

this document is a model and instructions for M-TEX. the accelerated expansion of global road networks necessitates the availability of accurate and up-to-date cartographic data for connected and automated vehicles. Conventional mapping techniques, such as satellite imagery and field surveys, are inher-ently time-consuming and labor-intensive. this study proposes a novel approach that makes use of the ubiquity of GPS-embedded devices to facilitate the efficient updating of road maps. We present a classifier that identifies the types of intersections from vehicle trajectories, which represents a crucial initial step in the generation of automated maps. the methodology employs five distinct convolutional deep neural network architectures for the detection and classification of road intersection types. the experimental results, which were obtained using three real-world vehicle trajectory datasets, demonstrate classification accuracy levels that range from 84 % to 91 %. It is noteworthy that these results were achieved using less than 10% of the data that is typically required for comparable accuracy levels, which highlights the efficiency of the feature extraction approach that was employed. this research contributes to the advancement of intelligent transportation systems by offering a more efficient and scalable method for road network mapping and updating. © 2024 IEEE.

关键词： Convolutional neural networks

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