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A multi-feature-based intelligent redundancy elimination scheme for cloud-assisted health systems

CAAI Transactions on Intelligence Technology 2024年第2期9卷 491-510页

作者： Ling Xiao Beiji Zou Xiaoyan Kui Chengzhang Zhu Wensheng Zhang Xuebing Yang Bob Zhang School of Computer Science and Engineering Central South UniversityChangshaChina Hunan Engineering Research Center of Machine Vision and Intelligent Medicine Central South UniversityChangshaChina The College of Literature and Journalism Central South UniversityChangshaChina Institute of Automation Chinese Academy of SciencesBeijingChina Department of Computer and Information Science University of MacaoMacaoChina

Redundancy elimination techniques are extensively investigated to reduce storage overheads for cloud-assisted health *** eliminates the redundancy of duplicate blocks by storing one physical instance referenced by multiple *** compression is usually regarded as a complementary technique to deduplication to further remove the redundancy of similar blocks,but our observations indicate that this is disobedient when data have sparse duplicate *** addition,there are many overlapped deltas in the resemblance detection process of post-deduplication delta compression,which hinders the efficiency of delta compression and the index phase of resemblance detection inquires abundant non-similar blocks,resulting in inefficient system ***,a multi-feature-based redundancy elimination scheme,called MFRE,is proposed to solve these *** similarity feature and temporal locality feature are excavated to assist redundancy elimination where the similarity feature well expresses the duplicate ***,similarity-based dynamic post-deduplication delta compression and temporal locality-based dynamic delta compression discover more similar base blocks to minimise overlapped deltas and improve compression ***,the clustering method based on block-relationship and the feature index strategy based on bloom filters reduce IO overheads and improve system *** demonstrate that the proposed method,compared to the state-of-the-art method,improves the compression ratio and system throughput by 9.68%and 50%,respectively.

关键词： big data cloud computing compression data compression medical applications performance evaluation

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OpenCV and its applications in Artificial Intelligent Systems 5

OpenCV and its Applications in Artificial Intelligent System...

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5th International Conference on Intelligent Computing, Communication, Networking and Services, ICCNS 2024

作者： Odeh, Ayman Odeh, Nada College Of Engineering Al Ain University Department of Software Engineering Al Ain United Arab Emirates College Of Information Technology UAEU Department of Computer Science and Software Engineering Al Ain United Arab Emirates

ISBN: (纸本)9798350354690

This paper explores the utilization of OpenCV (Open-Source Computer vision Library) in artificial intelligence (AI) systems, elucidating its pivotal role in advancing various applications across diverse domains. OpenCV, renowned for its comprehensive functionalities and real-time processing capabilities, has become instrumental in tasks such as object detection (OD), facial recognition (FR), and image processing (IP). However, despite its widespread adoption, there remains a need for a thorough examination of its integration within AI frameworks and the optimization of its functionalities. Through a comprehensive analysis, this study delineates the advantages, limitations, and potential areas for improvement of OpenCV in AI applications. Case studies exemplifying its efficacy in OD, FR, and autonomous systems further elucidate its practical implications. By delving into these aspects, this paper not only underscores the significance of OpenCV in AI but also provides valuable insights for researchers, developers, and practitioners aiming to leverage its capabilities in their endeavors. This study collectively underscores the significant role of OpenCV in enhancing the capabilities of AI systems. © 2024 IEEE.

关键词： Adversarial machine learning

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Comparative Performance Analysis of Edge-AI Devices in Deep Learning applications 19

Comparative Performance Analysis of Edge-AI Devices in Deep ...

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IEEE 19th Conference on Industrial Electronics and applications (ICIEA)

作者： Samsuri, Muhammad Hafiz Yuen, Shang Li Lau, Phooi Yee Wong, Chin Wee Kamarudin, Nur Afiqah Hussin, Zarina Talib, Muhammad Syukri Mohd Hon, Hock Woon MIMOS Berhad Adv Intelligence Lab Kuala Lumpur Malaysia

ISBN: (纸本)9798350360875;9798350360868

The fusion of edge computing and artificial intelligence, known as Edge AI, represents a paradigm shift that facilitates the direct execution of AI algorithms on edge devices. As these devices become increasingly powerful, their role in developing and deploying AI systems becomes more significant. By eliminating the need to transmit and analyze data at remote machines, Edge AI applications can significantly reduce latency and enhance efficiency by processing data closer to the source. In this study, we thoroughly investigate the performance of our object classification model deployed in a vision inspection system on four types of edge devices (Jetson AGX Orin, Jetson Orin Nano, NUC, and Raspberry Pi). Our object classification models are trained using proprietary industrial datasets provided by industry partners. These models, in FP32, are converted into lower precision processing, being INT8, to evaluate the accuracy variation between FP32 and INT8 precision, and inference speed for different edge devices. In our experiments, we identified that the average accuracy deviation for INT8 models is -2.78%, with some models exhibiting variations exceeding - 10.95%. Most devices have an average inference speed less than 100 ms per image (as requested by industrial partners), except the Raspberry Pi, which records more than 2 seconds of inferencing an image. Intel NUC consumes 107 W, which is averagely comparable with a server PC, while AGX Orin, Orin Nano, and Raspberry Pi consume less than 20 W of power. The outcomes of our evaluations offer valuable insights for selecting appropriate devices for specific scenarios. These detailed observations on the strengths and limitations of different edge devices can guide future research and advancements in Edge AI technology.

关键词： Edge AI machine Learning Visual Inspection Jetson AGX Orin Jetson Orin Nano INT8 Precision Intel NUC Raspberry Pi

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Detection and Segmentation of Grape Bunch by Integrating Channel Attention and Large Kernel Attention 2

Detection and Segmentation of Grape Bunch by Integrating Cha...

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2nd International Conference on image processing, Computer vision and machine Learning, ICICML 2023

作者： Huang, Zhitao Li, Guo Guangxi Normal University School of Computer Science and Engineering Guilin China

ISBN: (纸本)9798350331417

Detecting and segmenting fruits in an orchard environment is a vital technique in multiple applications of precision agriculture, such as automated harvesting and yield estimation. This study aims to improve the accuracy and robustness of detectors for detecting and segmenting fruits on trees by integrating detection and segmentation models based on channel and large kernel attention. The proposed Triplet-Large Kernel Attention (TLKA) module inherits the advantages of channel and large kernel attention. It was integrated with YOLOv7 to achieve real-time object detection and segmentation. Several experiments were conducted to verify the effectiveness of the proposed TLKA module. These included comparative attention mechanisms from small to large input image scales, comparative analysis of different attention mechanisms through Grad-CAM visualization, and test experiments with integrated comparisons on mid-term fruits (immature, intermediate, and mature) under three different light conditions (morning, noon, and afternoon). The proposed TLKA module achieved higher accuracy than comparative attention at different input scales. Finally, the proposed model was used to predict the yield of both grapes. The TLKA-YOLOv7 outperformed all other investigated models in terms of grape bunch detection and segmentation and obtained more competitive results in yield prediction. © 2023 IEEE.

关键词： attention mechanism component computer vision deep learning detection YOLO

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Deep Learning Based Gender Identification Using Ear images

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TRAITEMENT DU SIGNAL 2023年第4期40卷 1629-1639页

作者： Kilic, Safak Dogan, Yahya Kayseri Univ Dept Software Engn TR-38100 Kayseri Turkiye Siirt Univ Dept Comp Engn TR-56100 Siirt Turkiye

The classification of an individual as male or female is a significant issue with several practical implications. In recent years, automatic gender identification has garnered considerable interest because of its potential applications in e-commerce and the accumulation of demographic data. Recent observations indicate that models based on deep learning have attained remarkable success in a variety of problem domains. In this study, our aim is to establish an end-to-end model that capitalizes on the strengths of competing convolutional neural network (CNN) and vision transformer (ViT) models. To accomplish this, we propose a novel approach that combines the MobileNetV2 model, which is recognized for having fewer parameters than other CNN models, with the ViT model. Through rigorous evaluations, we have compared our proposed model with other recent studies using the accuracy metric. Our model attained state-of-the-art performance with a remarkable score of 96.66% on the EarVN1.0 dataset, yielding impressive results. In addition, we provide t-SNE results that demonstrate our model's superior learning representation. Notably, the results show a more effective disentanglement of classes.

关键词： deep learning gender identification ear images convolutional neural network (CNN) biometric identification image processing machine learning facial recognition

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Computer vision Based Hybrid Classroom Attention Monitoring

Computer Vision Based Hybrid Classroom Attention Monitoring

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2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Rawat, Saniya Rodrigues, Malivia Sheregar, Prateeksha Wagaskar, Kalpita Ajinkya Tripathy, Amiya Kumar Mumbai India

ISBN: (纸本)9798350382693

This research presents a novel computer vision-based attention monitoring system designed for both online and offline contexts. Leveraging advanced image processing and machine learning algorithms, the system analyzes human gaze patterns, eye movements, and facial expressions to accurately gauge attention levels. In online scenarios, the system employs real-time webcam-based gaze tracking and facial recognition to provide immediate insights into user engagement during activities like video conferencing and virtual meetings. For offline analysis, recorded video footage is retrospectively examined, facilitating applications in education, workplace productivity, and user experience assessments. Privacy considerations are addressed through the implementation of privacy-preserving techniques. Experimental results demonstrate the system's efficacy in monitoring attention dynamics across diverse settings, contributing to a deeper understanding of human attention in various domains. © 2024 IEEE.

关键词： Video conferencing

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Deep Learning Hybrid Technique for Generation of image Caption

Deep Learning Hybrid Technique for Generation of Image Capti...

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2024 International Conference on Signal processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Rakshith, N. Manoj Gowda, B.K. Preetham, N. Tejas, M. Baig, Mohammed Ifraz PES college of enginnering mandya Dept. of Information Science and Engineering Mandya India

ISBN: (纸本)9798331540685

image captioning is a fascinating and demanding work with applications in many different fields, including image retrieval, organizing and finding user-interested images, etc. It has enormous potential to replace the tedious process of creating captions for photos, and it works particularly well with massive amounts of picture data. Deep neural network-based techniques have recently shown significant success in the domains of language synthesis, machine translation, and computer vision. In this research, we present a paradigm based on encoder-decoders that may produce grammatically acceptable image captions. This model uses LSTM as the decoder and VGG16 Hybrid Places 1365 as the encoder. Model is evaluated using all standard metrics such as BLEU. Experimental results indicate that the proposed model obtained a BLEU-1 score 0.603774, BLEU-2 score 0.388514, BLUE-3 score 0.244706 on Flickr8k dataset. By comparison with the state-of-the-art techniques, the suggested strategy produced a noteworthy performance. We also present the outcomes of caption creation from real sample photographs, which supports the validity of the suggested method, in order to assess the model's effectiveness even further. © 2024 IEEE.

关键词： Decoding

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IMPLICIT CHANNEL LEARNING FOR machine LEARNING applications IN 6G WIRELESS NETWORKS

IMPLICIT CHANNEL LEARNING FOR MACHINE LEARNING APPLICATIONS ...

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IEEE International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Elbir, Ahmet M. Shi, Wei Mishra, Kumar Vijay Papazafeiropoulos, Anastasios K. Chatzinotas, Symeon Univ Luxembourg Interdisciplinary Ctr Secur Reliabil & Trust Luxembourg Luxembourg Carleton Univ Sch Informat Technol Ottawa ON Canada US DEVCOM Army Res Lab Adelphi MD USA Univ Hertfordshire Hatfield Herts England

ISBN: (纸本)9798350302615

With the deployment of the fifth generation (5G) wireless systems gathering momentum across the world, possible technologies for 6G are under active research discussions. In particular, the role of machine learning (ML) in 6G is expected to enhance and aid emerging applications such as virtual and augmented reality, vehicular autonomy computer vision and internet of everything. This will result in large segments of wireless data traffic comprising image, video and speech. The ML algorithms process these for classification/recognition/estimation through the learning models located on cloud servers. This requires wireless transmission of data from edge devices to the cloud server. Channel estimation, handled separately from recognition step, is critical for accurate learning performance. Toward combining the learning for both channel and the ML data, we introduce implicit channel learning to perform the ML tasks without estimating the wireless channel. Here, the ML models are trained with channel-corrupted datasets in place of nominal data. Without channel estimation, the proposed approach exhibits approximately 60% improvement in image and speech classification tasks for diverse scenarios such as millimeter wave and IEEE 802.11p vehicular channels.

关键词： machine learning channel estimation artificial intelligence wireless communications

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International Conference on Internet of Everything and Quantum Information processing, IEQIP 2023

International Conference on Internet of Everything and Quant...

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International Conference on Internet of Everything and Quantum Information processing, IEQIP 2023

ISBN: (纸本)9783031619281

The proceedings contain 31 papers. The special focus in this conference is on Internet of Everything and Quantum Information processing. The topics include: Revolutionizing Agriculture: A Mobile App for Rapid Plant Disease Prediction and Sustainable Food Security;EMG Based Human machine Integration for IoT Based Instruments;medrack: Bridging Trust and Technology for Safer Drug Supply Chain Using Ethereum and IoT;a Review on Tuberculosis Pattern Detection Based on Various machine Learning Techniques;sensor Based Hand Gesture Identification for Human machine Interface;an Improved Detection System Using Genetic Algorithm and Decision Tree;a Detailed Analysis of Colorectal Polyp Segmentation with U-Network;a Review on Internet of Things (IoT): Parkinson’s Disease Monitoring Device;machine Learning-Based Prediction of Temperature Rise in Squirrel Cage Induction Motor (SCIM);quantum Many-Body Problems: Quantum machine Learning applications;Experimental Study on the Impact of Airborne Dust Deposition on PV Modules Using Internet of Things;bidirectional Converter with Time Utilization-Based Tariff Investigation and IoT Monitoring of Charging Parameters Based on G2V and V2G Operations;predictive Analysis of Telecom Customer Churn Using machine Learning Techniques;baker’s Map Based Chaotic image Encryption in Military Surveillance Systems;Cyber Security Investigation of GPS-Spoofing Attack in Military UAV Networks;ioT Based Enhanced Safety Monitoring System for Underground Coal Mines Using LoRa Technology;ioT Based Hydroponic System for Sustainable Organic Farming;predicting Stride Length from Acceleration Signals Using Lightweight machine Learning Algorithms;unveiling Hate: Multimodal Perspectives and Knowledge Graphs;vision-Based Toddler Activity Recognition: Challenges and applications;automated W-Sitting Posture Detection in Toddlers.

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Building Detection Using Very High Resolution SAR images with Multi-Direction Based on Weighted-Morphological Indexes 12

Building Detection Using Very High Resolution SAR Images wit...

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12th Iranian/2nd International Conference on machine vision and image processing, MVIP 2022

作者： Amjadipour, Fateme Ghassemian, Hassan Imani, Maryam Tarbiat Modares University Image Processing and Information Analysis Lab Tehran Iran

ISBN: (纸本)9781665412162

Today, technological advancement in production of radar images can be seen with high spatial resolution and also the availability of these images' significant growth in interpretation and processing of high-resolution radar images. The building extraction from urban areas is one of the most challenging applications in VHR SAR image, which is used to estimate the population and urban development. Detection of individual buildings in the urban context is highly considered by researchers due to complexity of interpreting radar images in these fields. On the other hand, one of the main issues in the complexity of the scatters received from buildings is change in direction of the building relative to the horizon, which is correlated with the look angle. Other influential parameters are geometric distortions, which include layover and shadow effects. In some cases, the effect of shadow is an auxiliary parameter in detection of these targets that increases accuracy of the detection. In this paper, we intend to extract the building from high spatial resolution SAR images using fuzzy fusion of two morphological indicators, SI and DI, which represent the shadow and bright area, respectively. Due to the effect of SAR imaging geometry on ground targets, different sizes and directions of structural elements were applied to the image. The use of indicators weights with different sizes is proposed in this work. The Detection Ratio of experiment of TerraSAR-X image has a result of 95.3%. © 2022 IEEE.

关键词： Morphology

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