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Improvement in Performance of image Classification based on Apache Spark 2

Improvement in Performance of Image Classification based on ...

引用

2nd Asian conference on Innovation in Technology, ASIANCON 2022

作者： Sunil, K. Sivagamasundari, G. Pes University Department of Computer Science and Engineering Bangalore India

ISBN: (数字)9781665468510

ISBN: (纸本)9781665468510

Apache Spark is a widely used efficient distributed computing framework in the field of Big data for data processing and analytics at a large scale. There is wide demand from organizations to apply deep learning technologies to their existing big data analysis pipelines which will reduce the cost of maintaining additional computational resources. To classify large scale image data is a hot topic. For image classification, the classic Convolution neural network (CNN) model has been widely used as a standard deep learning algorithm. This paper focuses on implementation and demonstrates the execution of combination of Apache Spark and Convolution neural network algorithm that will provide significant improvement in performance for the image classification model. The paper aims to reduce overheads involved in this approach to provide better performance by the usage of novel opensource frameworks and bring together a unified pipeline for the same. Improvements in various performance metrics that are obtained from our experimental setup are presented in this work accordingly. © 2022 IEEE.

关键词： data Analytics

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2024 IEEE International conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

2024 IEEE International Conference on Interdisciplinary Appr...

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2nd IEEE International conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

ISBN: (纸本)9798350360523

The proceedings contain 376 papers. The topics discussed include: EIDA: an effective image deraining approach based on upscaling method;performance analysis and implementation of LSTM and GRU based on synthetic data;wideband frequency reconfigurable dielectric resonator antenna with defected ground plane for 5G millimeter wave application;water and land surface detection from sentinel C band dual polarimetric SAR satellite imagery;intelligent optimizing and computing for sustainable insurance business and sustainable development;performance analysis of InSb source-based heterojunctionless nanowire TFET for low-power application: design and simulation;and classification of cyberattack detection in network traffic using machine learning techniques.

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ADAPTIVE META-LEARNING VIA data-DEPEndENT PAC-BAYES BOUndS 2

ADAPTIVE META-LEARNING VIA DATA-DEPENDENT PAC-BAYES BOUNDS

引用

2nd conference on Lifelong Learning Agents (CoLLAs)

作者： Friedman, Lior Meir, Ron Technion Israel Inst Technol Viterbi Fac Elect & Comp Engn IL-3200003 Haifa Israel

Meta-learning aims to extract common knowledge from similar training tasks in order to facilitate efficient and effective learning on future tasks. Several recent works have extended PAC-Bayes generalization error bounds to the meta-learning setting. By doing so, prior knowledge can be incorporated in the form of a distribution over hypotheses that is expected to lead to low error on new tasks that are similar to those that have been previously observed. In this work, we develop novel bounds for the generalization error on test tasks based on recent data-dependent bounds and provide a novel algorithm for adapting prior knowledge to downstream tasks in a potentially more effective manner. We demonstrate the effectiveness of our algorithm numerically for few-shot image classification tasks with deep neural networks and show a significant reduction in generalization error without any additional adaptation data.

关键词： Deep neural networks

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Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances 2

Machine Learning and Deep Learning Approaches for Brain Dise...

引用

2nd International conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Dinesh Kumar, R.T. Mamrith, U. Joy Vimal Raj, F.D. Gokula Krishnan, K. K S Rangasamy College of Technology Department of Information Technology Tamilnadu India

ISBN: (纸本)9798350349221

Justification It is often challenging to identity and classify mental cancers, such as glioblastoma multi shaped in appealing reverberation (MR) images, due to their distinctly varied sign characteristics. For cerebrum growth X-ray tests, a robust division strategy was developed and tested. Methods Measurable techniques and basic restrictions are insufficient to adequately divide the many components of GBM, such as rot, edema, and local difference upgrading. Most voxel-based methods are unable to achieve satisfactory results in larger informative indices, and strategies based on generative or discriminative models are naturally limited in their applicability, such as limited sample set learning and mobility. These two activities aimed to demonstrate the intricate relationship between behavior and cognition as well as to understand and examine brain disorders by collecting and analyzing large. These two projects aimed to demonstrate the complex interplay between behavior and cognition as well as to understand and study brain diseases through the collection and analysis of massive volumes of data. Maintaining, analyzing, and disseminating the growing neuroimaging datasets proved to be somewhat challenging. Multimodal MR images are computationally segmented into super pixels in order to facilitate inspection and enhance the representativeness of the example. We then used staggered Gabor wavelet channels to extract highlights from the super pixels. Given the elements, two models were created to overcome the inadequacies of previous generative models: a grey level co-occurrence matrix (GLCM) model and a liking metric model for growths. Improved patient outcomes and possibly cheaper healthcare costs can result from early detection and intervention, among other possible benefits. © 2024 IEEE.

关键词： image segmentation

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Smart watch activity recognition using plot image analysis 2

Smart watch activity recognition using plot image analysis

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2nd IEEE conference on Information Technology and data Science, CITDS 2022

作者： Alexan, Alexandru Alexan, Anca Oniga, Stefan Technical University of Cluj-Napoca North University Center Baia Mare Electric Electronic and Computer Engineering Romania

ISBN: (数字)9781665496537

ISBN: (纸本)9781665496537

Nowadays, many of us wear multiple devices capable of acquiring and storing data related to our everyday activities. Since the computing power of mobile battery-operated devices slowly increases and the power optimizations allow for more and more continuous use, these devices are capable of not only monitoring our activity but analyzing the activity as well. Of these devices, the smartwatch is probably the most inconspicuous, and due to its widespread use, we have used accelerometer data gathered from a smartwatch to identify common user activities by using image generated plots and image recognition machine learning. By leveraging *** *** machine learning framework we have managed to obtain a decent recognition rate. © 2022 IEEE.

关键词： image analysis

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Design of Dance Movement Recognition Algorithm Based on 3D Motion Capture data 2

Design of Dance Movement Recognition Algorithm Based on 3D M...

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2nd International conference on 3D Immersion, Interaction and Multi-Sensory Experiences, ICDIIME 2023

作者： Du, Linlin Weifang Engineering Vocational Collage Weifang Shandong262500 China

ISBN: (纸本)9798350323818

Although the traditional photography, audio recording and video recording methods are easy to collect and make, they can't record the dancers' body movements in detail, let alone make scientific analysis and research on them and apply them to choreography. This paper proposes a dance movement Recognition technology based on 3D motion capture data. After fully considering the characteristics of dance movements, the HOG (Histogram of Oriented Gradient) features extracted from dance videos are used to represent dance movements. In this paper, the image with HOG feature extraction has included the edge feature information of the target in all the images in the video segment after accumulation operation. The time series HMM (Hidden Markov model) is used to study the spatial information of adjacent joint points in the human skeleton model. Experiments show that the correct recognition rate of the proposed method can reach 97.797% when the number of nodes is 20. Experiments show that compared with one-dimensional Markov model method, the method proposed in this paper has better recognition effect and stronger anti-interference ability. © 2023 IEEE.

关键词： Hidden Markov models

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Classification and Evaluation of Indian Faces Using Machine Learning Models 2

Classification and Evaluation of Indian Faces Using Machine ...

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2nd IEEE International conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Kumar, Madala Veerendra Harish Chowdary, Pullela Kwatra, Chetna Vaid Nandeesh, Talari Pawan, Kommu Venkata Lovely Professional University Department of Computer Science and Engineering Punjab Phagwara India Lovely Professional University Department of Computer Science and Engineering Punjab Phagwara Pakistan

ISBN: (纸本)9798350360523

In a country like India whose citizens carry a diversity of facial features and appearances widely varying from each other this paper can address one of the solutions that can be considered accountable to classify and distinguish the differences among each other. Facial classification is crucial for computer vision applications and Machine learning programs such as image retrieval, audit systems, biometrics, and security mechanisms. The purpose of this paper is to express our findings and understanding of Indian face classification based on states using Python. Our approach to the idea is a Machine Learning model that is trained on a compact dataset of Indian faces labeled with their respective states. The model has achieved results of 42%,40%,42%, and 30% respectively on benchmarks of accuracy, precision, recall, and f1 score. The approach to this project has key phases of data collection and a Novel dataset creation, cumulating data, extracting features, and feeding the results as input to SVM and Logistic regression models to classify the data. This work contributes to further research and development of innovative applications that can benefit the field of research. © 2024 IEEE.

关键词： image classification

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MRI image-based Advanced Transfer Learning and Machine Learning Architectures for Classifying Alzheimer's Disease 2

MRI Image-based Advanced Transfer Learning and Machine Learn...

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2nd International conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI)

作者： Das, Tulip Nayak, Chinmaya Kumar Pattnayak, Parthasarathi Sri Sri Univ Fac Emerging Technol Cuttack India KIIT Deemed Be Univ Sch Comp Applicat Bhubaneswar India

ISBN: (纸本)9798331540661;9798331540678

Alzheimer's Disease (AD) is classified as a nerve disorder of the brain characterized by the irreversible degeneration of neurons responsible for computational functions and memory in humans. Exploratory investigations have been devoted to diverse Machine Learning methodologies in the context of AD diagnosis via brain images, such as Magnetic Resonance Imaging. There are several disadvantages to Deep Neural Network models, such as their dependence on large volumes of trained data and their need for a suitable optimisation technique. Here, an attempt is made to tackle these concerns by employing Deep Transfer Learning models. Specifically, previously trained current Convolutional Neural Network models used that have already been trained using large standard benchmark datasets of real-world photos, including Xception, RESNET, Inception, and VGG. Retraining the entirely connected layer with an insignificant number of MRI images ensues. Additionally, the data is augmented in order to facilitate learning from unbalanced datasets, thereby, significantly enhancing the performance of TL models.

关键词： Alzheimer's Disease Deep Transfer Learning Magnetic Resonance Imaging Dementia Optimization

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Fingerprint Restoration and Identification using Pix2Pix cGAN and Triplet Loss 2

Fingerprint Restoration and Identification using Pix2Pix cGA...

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2nd IEEE International conference for Women in Innovation, Technology and Entrepreneurship, ICWITE 2024

作者： Ojha, Shubhangi Kumar, Niraj Akgec Computer Science and Engineering Uttar Pradesh Ghaziabad India Varanasi Computer Science and Engineering Varanasi India

ISBN: (纸本)9798350383287

Fingerprint restoration and identification is a critical biometric authentication method with security and digital identity verification applications. Its accuracy relies on image quality and identification algorithms. Real-world fingerprint images often suffer from noise and distortions. This work presents a novel approach using a Pix2Pix Conditional GAN (CGAN) for fingerprint restoration. We evaluated the method using the triplet loss function by measuring Euclidean distances between anchor, positive, and negative embeddings. Rigorous testing across various image qualities enhances network performance. Combining CGAN with the Siamese triplet network, shown in this study, leads to substantial improvements in fingerprint restoration and identification results. We achieved the best accuracy of 86% in fingerprint identification while preserving the quality of the dataset designed for this work. This innovation holds potential for real-world applications, notably in crime scene investigation and forensic sciences, where recovering and enhancing fingerprint data are crucial. © 2024 IEEE.

关键词： Restoration

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Integration of Depth Normal Consistency and Depth Map Refinement for MVS reconstruction 17th

Integration of Depth Normal Consistency and Depth Map Refine...

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17th Chinese conference on image and Graphics Technologies and Applications, IGTA 2022

作者： Yang, Lifang Bai, Zhengyao Liu, Huijie School of Information Science and Engineering Yunnan University Kunming650500 China

ISBN: (纸本)9789811950957

To address the problem of incomplete Multi-view Stereo (MVS) reconstruction, the initial depth and loss function of the depth residual iterative network are investigated, and a new multi-view stereo reconstruction network integrating depth normal consistency and depth map thinning is presented. Firstly, downsampling the input image to create an image pyramid and extracting a feature map from the image pyramid;Then, constructing a cost volume from the 2D feature map, adding the depth normal consistency to the initial cost volume to optimize the depth map. On the DTU data set, the network is tested and compared to traditional reconstruction approaches and MVS networks based on deep learning. The experimental results show that the proposed MVS reconstruction network was produced the better results in completeness and increased the quality of MVS reconstruction. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Stereo image processing

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