2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)最新文献_第10页

Human Activity Recognition with Privacy Preserving using Deep Learning Algorithms 使用深度学习算法保护隐私的人类活动识别

2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP) Pub Date : 2022-02-12 DOI: 10.1109/AISP53593.2022.9760596

K. Kumar, J. Harikiran, B. S. Chandana

{"title":"Human Activity Recognition with Privacy Preserving using Deep Learning Algorithms","authors":"K. Kumar, J. Harikiran, B. S. Chandana","doi":"10.1109/AISP53593.2022.9760596","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760596","url":null,"abstract":"Human activity recognition is an extensively researched topic in the field of computer vision. Recognizing human activities without revealing a person’s identity is one such use case. To solve this, we propose a practical method for human activity recognition (HAR) while maintaining anonymity. It captures and distributes data from a variety of sources while respecting the privacy of the individuals concerned. At the core of our approach is (DBN-RGMAA) based on deep neural networks, which are not only more accurate but can also be deployed in real-time video surveillance systems. Hence, this work presents a deep learning-based scheme for privacy-preserving human activities. Initially, for extracting the features from raw video data, a Deep Belief Network (DBN) is used. To increase the HAR identification rate, Hybrid Deep Fuzzy Hashing Algorithm (HDFHA) is employed to capture dependencies between two actions. Finally, the privacy model enhances the privacy of humans while permitting a highly accurate approach towards action recognition by the Recursive Genetic Micro-Aggregation Approach (RGMAA). The implementation is executed and the performances are evaluated by Accuracy, Precision, Recall, and F1 Score. A dataset named HMDB51 is used for empirical study. Our experiments using the Python data science platform reveal that the OPA-PPAR outperforms existing methods.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":"64 3 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78042055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Crack identification from concrete structure images using deep transfer learning 基于深度迁移学习的混凝土结构图像裂缝识别

2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP) Pub Date : 2022-02-12 DOI: 10.1109/AISP53593.2022.9760670

Amena Qadri Syed, J. Jothi, K. Anusree

引用次数: 0

Classification of Hand Movements via EMG using Machine Learning Methods for Prosthesis 基于机器学习方法的假肢手运动肌电图分类

2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP) Pub Date : 2022-02-12 DOI: 10.1109/AISP53593.2022.9760543

M. Karuna, S. R. Guntur

引用次数: 0

Word Translation using Cross-Lingual Word Embedding: Case of Sanskrit to Hindi Translation 跨语言词嵌入的词翻译:以梵语到印地语的翻译为例

2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP) Pub Date : 2022-02-12 DOI: 10.1109/AISP53593.2022.9760564

Rashi Kumar, V. Sahula

{"title":"Word Translation using Cross-Lingual Word Embedding: Case of Sanskrit to Hindi Translation","authors":"Rashi Kumar, V. Sahula","doi":"10.1109/AISP53593.2022.9760564","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760564","url":null,"abstract":"Sanskrit is a low resource language for which large parallel data sets are not available. Large parallel data sets are required for Machine Translation. Cross-Lingual word embedding helps to learn the meaning of words across languages in a shared vector space. In the present work, we propose a translation technique between Sanskrit and Hindi words without a parallel corpus-base. Here, fastText pre-trained word embedding for Sanskrit and Hindi are used and are aligned in the same vector space using Singular Value Decomposition and a Quasi bilingual dictionary. A Quasi bilingual dictionary is generated from similar character string words in the monolingual word embeddings of both languages. Translations for the test dictionary are evaluated on the various retrieval methods e.g. Nearest neighbor, Inverted Sofmax approach, and Cross-domain Similarity Local Scaling, in order to address the issue of hubness that arises due to the high dimensional space of the vector embeddings. The results are compared with the other Unsupervised approaches at 1, 10, and 20 neighbors. While computing the Cosine similarity, we observed that the similarity between the expected and the translated target words is either close to unity or equal to unity for the cases that were even not included in the Quasi bilingual dictionary that was used to generate the orthogonal mapping. A test dictionary was developed from the Wikipedia Sanskrit-Hindi Shabdkosh to test the translation accuracy of the system. The proposed method is being extended for sentence translation.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":"10 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75491016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multiscale Discrete Wavelet Transform based Efficient Energy Detection for Wideband Spectrum Sensing 基于多尺度离散小波变换的宽带频谱感知高效能量检测

2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP) Pub Date : 2022-02-12 DOI: 10.1109/AISP53593.2022.9760625

Biji Rose, B. Arunadevi

引用次数: 0

Block chain Based Framework for Document Verification 基于区块链的文档验证框架

2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP) Pub Date : 2022-02-12 DOI: 10.1109/AISP53593.2022.9760651

Mrs.Latha S S, M. N, Mrs.Anusha Shettar

{"title":"Block chain Based Framework for Document Verification","authors":"Mrs.Latha S S, M. N, Mrs.Anusha Shettar","doi":"10.1109/AISP53593.2022.9760651","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760651","url":null,"abstract":"Document Verification using Blockchain Technology has a huge scope. With increasing documents generated every year, there is no systematic and simple way to verify the documents. This system could be used to the governments, organizations, employers and basically anybody who wants to verify that the given document is not forged. This could be used to verify all kinds of immutable records ranging from attendance records, birth certificates, graduation and academic credentials. The proposed system could be used by the government to construct a decentralized network to store and maintain record. This is also the best way to ensure that the documents exist in the state of their creation, that they are not tampered with by anyone. Motivated by this, we propose to develop a decentralized blockchain system using Ethereum that will serve as an application to authenticate the documents. An application will be installed to local systems in which the users will verify the documents. These local systems also known as “Nodes” or “Blocks”.Once the documents are added to blocks forming the blockchain, complex calculations are performed to find the unique hash for that particular document.This concept can be implemented through decentralized applications deployed on the blockchain. The blockchain that is intended to be used for the deployment process is the Ropsten Ethereum Network. Thus, the immutability of documents can be maintained, while providing a simple, yet secure way for authenticating/verifying documents.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":"46 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83154646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Language Effect on Speaker Gender Classification Using Deep Learning 深度学习对说话人性别分类的影响

2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP) Pub Date : 2022-02-12 DOI: 10.1109/AISP53593.2022.9760599

Adal A. Alashban, Y. Alotaibi

引用次数: 1

Computer Assisted Diagnosis of Breast Cancer Using Histopathology Images and Convolutional Neural Networks 使用组织病理学图像和卷积神经网络的乳腺癌计算机辅助诊断

2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP) Pub Date : 2022-02-12 DOI: 10.1109/AISP53593.2022.9760669

Chinnapapakkagari Sreenivasa Vikranth, B. Jagadeesh, Kanna Rakesh, Doriginti Mohammad, S. Krishna, Remya Ajai A S

{"title":"Computer Assisted Diagnosis of Breast Cancer Using Histopathology Images and Convolutional Neural Networks","authors":"Chinnapapakkagari Sreenivasa Vikranth, B. Jagadeesh, Kanna Rakesh, Doriginti Mohammad, S. Krishna, Remya Ajai A S","doi":"10.1109/AISP53593.2022.9760669","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760669","url":null,"abstract":"In recent years, breast cancer has become one of the most prevalent kinds of cancer. Breast Ultrasound, Diagnostic Mammogram, Magnetic Resonance Imaging (MRI), and other imaging modalities are routinely used to diagnose breast cancer. Doctors make final judgments about treatments, drugs, and other matters based on biopsy results, which are regarded the standard diagnostic approach for cancer. However, this is a time-consuming process that also necessitates extensive pathologist training and expertise. Each pathology lab receives around 300-500 slides per day. This overburdens the pathologists and increases the misdiagnosis rate in the biopsy results. In order to provide timely error free results to the patients, the research community focuses more on the development of Computer Aided Diagnosis (CAD) System to assist pathologists to diagnose cancer. Recent developments in Deep Learning techniques made the CAD systems more effective in detecting breast cancer at an early stage with a great accuracy. In this paper, we present a CAD system that recognises histopathology images to diagnose breast cancer using a Convolutional Neural Network (CNN). DenseNet201, ResNet50 and MobileNetV2 are used in this work. These are trained and tested using the openly available BreakHis and BACH datasets. The datasets are subjected to binary and multi-class classifications. Accuracy, Precision, Recall, F1 Score, and AUC are all performance measures that are used to evaluate the model’s performance. For Binary classification, the model built using MobileNetV2 with Sigmoid as activation function displayed a higher accuracy of 97% - 98% and in the case of multi-class classification, again the model built using MobileNetV2 with Softmax as activation function displayed a higher accuracy of 91% - 92% for both Magnifican Independant (MI) and Magnification Dependant (MD) cases.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":"26 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82631265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

CNN based Static Hand Gesture Recognition using RGB-D Data 基于CNN的静态手势识别使用RGB-D数据

2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP) Pub Date : 2022-02-12 DOI: 10.1109/AISP53593.2022.9760658

N. C. Dayananda Kumar, K. Suresh, R. Dinesh

{"title":"CNN based Static Hand Gesture Recognition using RGB-D Data","authors":"N. C. Dayananda Kumar, K. Suresh, R. Dinesh","doi":"10.1109/AISP53593.2022.9760658","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760658","url":null,"abstract":"Hand gesture recognition refers to identification of various hand postures which interprets the signs of non verbal communication. It finds various applications like Sign Language Recognition (SLR), Human Computer Interaction (HCI) for robotics control, 3D modeling etc., Efficiently recognizing the hand gestures in various complex background scenarios is still a challenging problem. This issue can be effectively addressed by using depth data as a additional cue along with RGB image. Depth refers to the distance between camera sensor and image scene, hence depth cues can be used in suppressing the complex backgrounds which are far away from the hand region. Depth can also be effectively used to handle the illumination issues. In this paper, we propose a two stage approach where first stage involves k-means algorithm based depth clustering and removal of the background region. In the later stage, the foreground filtered depth map is fused with RGB and the resultant RGB-D data is used for gesture recognition using Convolutional Neural Network (CNN) classification model. Experiments are conducted on OUHANDS datasets and the results are compared with well known existing methods. Experimental result shows that accuracy of 87.57 % can be achieved on OUHANDS test dataset using the proposed method.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":"5 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83480308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

A New Dynamic Method of Multiprocessor Scheduling using Modified Crow Search Optimization 一种基于改进乌鸦搜索优化的多处理器动态调度新方法

2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP) Pub Date : 2022-02-12 DOI: 10.1109/AISP53593.2022.9760642

Ronali Madhusmita Sahoo, S. Padhy, Kumar Debasis

引用次数: 0