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检索条件"任意字段=3rd International Conference on Advanced Computer Science and Information Technology, AST 2011"

共 3954 条记录，以下是1-10 订阅

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A Review of Memory Management Mechanisms Based on Hot Page Monitoring 3

A Review of Memory Management Mechanisms Based on Hot Page M...

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3rd international conference on Artificial Intelligence and computer information technology, AICIT 2024

作者： Yue, Lei Wu, Tao Shen, Yang Zhang, Jin School of Computer and Communication Engineering Changsha University Of Science and Technology Changsha China College of Computer Science National University of Defense Technology Changsha China

ISBN: (纸本)9798350365443

'Memory hot pages' usually refer to memory pages that are frequently accessed within a period of time. Using faster memory pages for these hot pages can optimize application performance. Using 'fast memory' for hot pages involves data placement and migration issues on heterogeneous memory systems, and the synthesis and splitting of huge pages in ordinary systems. The premise for implementing these operations is to detect hot pages. This article briefly describes several advanced memory hot page detection methods and the principles of the corresponding memory management mechanism, and discusses multiple aspects such as hot page detection accuracy, overhead, and performance improvement of the corresponding memory management mechanism. © 2024 IEEE.

关键词： Memory management

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Hyperspectral Images for Crop Prediction using Machine and Deep Learning Models 3

Hyperspectral Images for Crop Prediction using Machine and D...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Mahalakshmi, R. Kumutha, K. Sakthivanitha, M. Lakshmi, R. Bagavathi Sirajudeen, Mohamed Padmanabhan, Sankar Vels Institute of Science Technology & Advanced Studies Department of Computer Applications-PG Tamil Nadu Chennai India Vels Institute of Science Technology Advanced Studies Department of Computer Applications-PG Tamil Nadu Chennai India Vels Institute of Science Technology & Advanced Studies Department of Computer Application Tamil Nadu Chennai India Vels Institute of Science Technology & Advanced Studies Department of Information Technology Tamil Nadu Chennai India Nilgiri College of Arts and Science Pg and Research Department of Computer Science Tami Nadu The Nilgiris India Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509675

Crop classification is essential for local and national governments to make informed agricultural decisions. Remote sensing technology has made it possible to employ high-resolution hyperspectral images (HSIs) for land cover classification for decades. Machine Learning (ML) and Deep Learning (DL) are becoming more popular for HSI categorization due to advances in processing capacity and technology. HSIs can accurately identify crop types due to their narrow and continuous spectral band reflection. This study compares traditional machine learning (ML) models like Support Vector Machines (SVM) and Random Forest (RF) to deep learning (DL) models like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) for hyperspectral image classification (HSI). The HSI categorization was evaluated using performance criteria such as accuracy, precision, recall, and the F1-Score. CNN achieved the highest accuracy of 96%, demonstrating that it is the most reliable solution for complex HSI classification tasks because it successfully captures spatial and spectral data. © 2025 IEEE.

关键词： Classification Deep Learning(DL) Hyperspectral Images(HSI) Machine Learning(ML)

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DeepKet- Quantum Space-Efficient Word Embedding Layer for Steganalysis 3

DeepKet- Quantum Space-Efficient Word Embedding Layer for St...

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3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Roshan Ahmed, N. Shridevi, S. Vellore Institute of Technology School of Computer Science and Engineering Chennai India Vellore Institute of Technology Center for Advanced Data Science Chennai India

ISBN: (纸本)9798350372120

Text-based Statistical steganography is one of the most non-human detectable methods of embedding hidden messages in plain text format which is useful in concealing information. Steganalysis is its counter, the process of detecting if a text has any encrypted data in it. This paper applies quantum computing to create DeepKet Embedding, which optimizes the space requirements for word embeddings similar to Word2Vec. DeepKet is benchmarked against existing embedding layers and a significant size reduction is achieved while maintaining accuracy for steganalysis. © 2024 IEEE.

关键词： Embeddings

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Blockchain Security Using Q-Pragmatic-Sensitive Feature Analysis to Preserve Privacy 3

Blockchain Security Using Q-Pragmatic-Sensitive Feature Anal...

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3rd IEEE international conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Punithavathi, S. Jeyalaksshmi, S. Vels Institute Of Science Technology & Advanced Studies Department Of Computer Science Chennai India School Of Computing Science Vels Institute Of Science Technology & Advanced Studies Department Of Information Technology Chennai India

ISBN: (纸本)9798350318609

Healthcare data contains large amounts of personal information and is privacy sensitive. The healthcare sector needs privacy protection to keep private and sensitive information safe from unauthorized access. Security problems are raised by fundamental sharing difficulties involving personal information setting to securely forecast sensitive medical data using blockchain technology to address this issue. To solve the problem issue, propose a quasi-sensitive identification of blockchain security using tried-and-true partition fold padding encryption (PFPE) to improve the security of healthcare transactions. The Q-Pragmatic-Sensitive Feature Analysis (QPSFA) determines the terms related to sensitive privacy features. Finally, the Provable Fold Padding Encryption, and the authentication procedure is completed by using an Access Verification-based role to verify it. To ensure that, in a blockchain, the private data are cleansed and placed into data blocks. This suggests that the proposed system outperforms high accuracy up to 90%, as well security up to 94 % high insecurity compared to previous approaches regarding sensitivity and specificity rate performance and prediction accuracy. © 2024 IEEE.

关键词： Blockchain

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TCPNet: A 3D Point Cloud Classification Model Based on the advanced CNN and Transformer 3

TCPNet: A 3D Point Cloud Classification Model Based on the A...

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3rd international conference on Artificial Intelligence and computer information technology, AICIT 2024

作者： Wu, Jinmeng Ma, Jingwei Hong, Hanyu Hao, Yanbin Ma, Lei Wang, Lei Wuhan Institute of Technology Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan China School of Information Science and Technology University of Science and Technology of China Anhui China

ISBN: (纸本)9798350365443

Transformer and Convolutional Neural Network (CNN) are currently two important models in the field of deep learning. Among them, Transformer has strong global perception ability but weak local perception ability, and CNN precisely compensates for this. CNN has strong local perception ability, so in order to combine the advantages of both, we propose a 3D classification model TCPNet based on Transformer and CNN. We first use Transformer to extract the global features of the point cloud, and then concatenate a CNN network to enhance the detail perception ability, so that the network can better understand the 3D point cloud. The experimental results on the ModelNet40 and ScanObjectNN datasets show that our method can effectively achieve 3D classification tasks. On ModelNet40, an accuracy of 93.6% can be achieved. On the most difficult variant PB-T50-RS of ScanObjectNN, an accuracy of 83.93% can be achieved. © 2024 IEEE.

关键词： Convolutional neural networks

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Countering Deepfakes using an Improved advanced CNN and its Ensemble with Pretrained Models 3

Countering Deepfakes using an Improved Advanced CNN and its ...

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3rd international conference on Electrical, Electronics, information and Communication Technologies, ICEEICT 2024

作者： Mathur, Abeer Tejpal, Moulik Bhargava, Kshitiz Natarajan, Krishnaraj Singh, Manvendra Vellore Institute of Technology School of Computer Science & Engineering Department of Software Systems Vellore India Vellore Institute of Technology School of Computer Science & Engineering Department of Database Systems Vellore India

ISBN: (纸本)9798350369083

The extensive spread of DeepFake images on the internet has emerged as a significant challenge, with applications ranging from harmless entertainment to harmful acts like blackmail, misinformation, and spreading false propaganda. To tackle this issue, this paper introduces a sophisticated DeepFake detection model designed to identify and mitigate the increase of these deceptive images. The model architecture integrates an ensemble approach, combining the strengths of two pre-trained Convolutional Neural Network (CNN) models - MobileNet and Xception - with a novel CNN architecture, the advanced CNN (ACNN). This rigorous validation process enabled the model to achieve a high accuracy rate of 97.89% in detecting DeepFakes. The successful implementation of this ensemble CNN approach demonstrates its effectiveness in distinguishing between real and fabricated imagery with high precision. This research makes a substantial contribution to the field of digital image forensics, offering a reliable tool for stakeholders across various sectors to identify and counteract the spread of DeepFake images online. © 2024 IEEE.

关键词： Generative adversarial networks

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AI-Powered Healthcare with advanced Graph Convolutional Neural Networks and SVM for Diagnosis 3

AI-Powered Healthcare with Advanced Graph Convolutional Neur...

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3rd international conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Mahendran, A. Nalla, Sridhar Babu Trivedi, Lalit Mohan Mohankumar, S. Williams, D.R. Mohan Renuka, Manne Sona College of Arts and Science Department of Computer Science Salem India Sr University Department of Computer Science and Engineering Hyderabad India Moradabad Institute of Technology Department of Ash Mathematics Moradabad India Erode Sengunthar Engineering College Department of Computer Science and Engineering Perundurai India College of Computing and Information Sciences University of Technology and Applied Science-Sohar Department of Information Technology Sohar Oman Vidya Jyothi Institute of Technology Department of Electronics and Communication Engineering Hyderabad India

ISBN: (纸本)9798331508456

Medical treatment costs are rising for many reasons. These include an increasing world population, an aging population, greater healthcare needs, more diseases, and inflation. With AI's emergence in navigation, voice recognition, image lesion segmentation, ride-sharing, and smartphone personal assistants, the healthcare industry has quickly adopted it. Initial preprocessing includes standardization, missing values, and discarding anomalies. Next, the model is trained using the obtained features. Spearman correlation, a rank-based nonparametric statistic, can quantify monotonic correlation strength and direction. Even with missing values, the Graph Convolutional Network (GCN) performs well in model training. The suggested model outperformed state-of-the-art alternatives with 93.48% accuracy. It outperformed TransLSTM and GraphSAGE on complex datasets. This research stresses the system's ability to improve healthcare forecast accuracy through preprocessing, feature extraction, and GCN model training. Results show that AI can improve healthcare analytics despite lacking data. © 2025 IEEE.

关键词： Patient treatment

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Multi-Branch information Network for advanced Image Captioning 3

Multi-Branch Information Network for Advanced Image Captioni...

引用

3rd international conference on Artificial Intelligence, Internet of Things and Cloud Computing technology, AIoTC 2024

作者： Song, Caixia School of Computer Science and Engineering Tianjin University of Technology Tianjin China

ISBN: (纸本)9798331530280

Understanding images from different perspectives and performing advanced semantic consistency transformation across modalities are key elements in generating natural language descriptions that are relevant and coherent with the current image captions. Insufficient understanding or errors in interpreting visual features of images can lead to inconsistent descriptions or generate hallucination problems. Moreover, current techniques often employ attention mechanisms to achieve semantic alignment in different representation spaces while overlooking the underlying heterogeneity of multimodal data. In this paper, we propose a Multi-Branch information Network for advanced Image Captioning that integrates comprehensive visual semantic understanding and advanced semantic consistency transformation into a single image captioning model architecture. Technically, we first utilize a cross-modal retrieval model to obtain fine-grained contextual descriptions for each image region, aiming to compensate for and alleviate hallucination. Next, we introduce a novel mapping network that reconstructs visual information in the textual space and achieves consistency transformation between visual and textual modalities within the textual domain. Empirical evidences show that, ours outperforms the latest approaches on the COCO dataset and achieves the best CIDEr score of 138.4% in the Karpathy test set. © 2024 IEEE.

关键词： computer architecture cross-modal gap Encoding Feature extraction Image captioning Transformers

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Guardians of Truth: Exploring Techniques for Veracity Identification in social media during an Infodemic 3

Guardians of Truth: Exploring Techniques for Veracity Identi...

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3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Suthanthiradevi, P. Revathy, G. Sathish Kumar, P. Karthikeyan, B. Muthu, Lakshmi.V SRM institute of Science and Technology Data Science and Business Systems School of Computing India Vels Institute of science Technology and Advanced studies Department of Computer science and engineering India Rajalakshmi institute of technology Computer and Communication Engineering India SASTRA Deemed University School of Computing India St. Joseph's Institute of Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350372120

With the recent advent of technology, social networks are accessible 24/7 using mobile devices. During covid-19 pandemic the propagation of misinformation are mostly related to the disease, its cures and prevention. With Twitter as the medium this information was spreaded to millions of people who lack domain specific knowledge. This can lead to bloster fear and direct damage to the people. This research focuses on identifying the veracity of the Twitter posts behind pandemic situation. The authors have proposed a Transformer Model using Bat Algorithm for identifying the fake news. This model is trained and evaluated using infodemic metadata. Our experimental results shows by which proposed model accurately detected the fake news and achieved 96.5% of f1-score. © 2024 IEEE.

关键词： Social networking (online)

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Integrating ICT for Enhanced Air Quality and Community Satisfaction in Smart Villages 3

Integrating ICT for Enhanced Air Quality and Community Satis...

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3rd international conference on Creative Communication and Innovative technology, ICCIT 2024

作者： Utamajaya, Joy Nashar Prabowo, Harjanto Ramadhan, Arief Octavia, Tanty Bina Nusantara University Computer Science Department Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350367492

This study investigates the role of information and Communication technology (ICT) in enhancing air quality and community satisfaction within an innovative village framework. By implementing advanced ICT-based public services, the village aims to streamline access to essential services and improve information dissemination related to environmental health. The research evaluates community satisfaction with these intelligent government initiatives and their effectiveness in addressing public service needs and air quality enhancement. Data was collected through comprehensive questionnaires and direct observations of the E-Government application system. The analysis utilized the community satisfaction index to measure user perceptions and employed both percentage and descriptive analysis to compare innovative government services' planned and actual implementations. Findings indicate that while overall satisfaction with intelligent government services is high, there are areas for improvement in implementing these technologies to maximize their potential impact on air quality and community well-being. © 2024 IEEE.

关键词： Community Satisfaction ICT Public Services Smart Government

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