2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA)最新文献_第3页

Score Level based Fusion Method for Multimodal Biometric Recognition using Palmprint and Iris 基于分数水平的掌纹和虹膜多模态生物识别融合方法

2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA) Pub Date : 2020-07-01 DOI: 10.1109/ACCTHPA49271.2020.9213216

Chandny Ramachandran, D. Sankar

{"title":"Score Level based Fusion Method for Multimodal Biometric Recognition using Palmprint and Iris","authors":"Chandny Ramachandran, D. Sankar","doi":"10.1109/ACCTHPA49271.2020.9213216","DOIUrl":"https://doi.org/10.1109/ACCTHPA49271.2020.9213216","url":null,"abstract":"Digital technology is always in our lives and hence security using biometrics has become inevitable in areas like banking, logical access control, law and enforcement etc. Person recognition using more than one biometric trait is known as multimodal biometric recognition. In this work, images of palmprint and iris are chosen as the traits for performing multimodal recognition. From these images the Features from these images were extracted using Log-Gabor transform, Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP). Distance measures such as Hamming distance, Chi-Square distance and Euclidean distance were employed for generating matching scores. A weighted sum score level fusion technique was applied for combining the scores generated from iris and palmprint images. The performance of the system was evaluated by plotting the Detection Error Trade off (DET) curves and Receiver Operating Characteristic (ROC) curves. The proposed system proved that multimodal recognition performed best than unimodal with a recognition accuracy of 92.23% by employing HOG as the feature descriptor. It was also found that less error rates to the system was achieved with Log Gabor filter.","PeriodicalId":191794,"journal":{"name":"2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125131765","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

Comparison of deep neural networks for reference evapotranspiration prediction using minimal meteorological data 基于最小气象数据的参考蒸散发深度神经网络预测比较

2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA) Pub Date : 2020-07-01 DOI: 10.1109/ACCTHPA49271.2020.9213201

M. Sowmya, M. B. Santosh Kumar, Sooraj K. Ambat

{"title":"Comparison of deep neural networks for reference evapotranspiration prediction using minimal meteorological data","authors":"M. Sowmya, M. B. Santosh Kumar, Sooraj K. Ambat","doi":"10.1109/ACCTHPA49271.2020.9213201","DOIUrl":"https://doi.org/10.1109/ACCTHPA49271.2020.9213201","url":null,"abstract":"Evapotranspiration, the process of water loss through surface evaporation and plant transpiration, is hugely influenced by numerous meteorological variables in different climatic zones. A precise and accurate prediction of reference-evapotranspiration (ET0), a crucial factor in the crop evapotranspiration estimation, facilitates efficient management of agricultural water supplies. This paper proposes an ET0 prediction method, employing minimal meteorological data, as well as exploring the potential of deep learning to learn the time series data pattern. In this study, four variants of a deep neural network model were developed using different feature combinations of two datasets of California Irrigation Management Information System (CIMIS) weather stations in California, USA for ET0 modeling and evaluated their predictive performance. The results showed that among the four deep neural network model variants (DnnV1, DnnV2, DnnV3, and DnnV6), the two input deep neural network, DnnV2 (RMSE =0.36 Millimeter/day and 0.52 Millimeter/day, R2=0.94 and 0.94) showed a comparable performance to the six input neural network, DnnV6 (RMSE =0.3 Millimeter/day and 0.43 Millimeter/day, R2=0.96 and 0.96). However, by considering the minimalism factor in the selection of input variables, we recommend DnnV2 for ET0 modeling.","PeriodicalId":191794,"journal":{"name":"2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131494491","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: 11

ACCTHPA 2020 Cover Page ACCTHPA 2020封面页

2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA) Pub Date : 2020-07-01 DOI: 10.1109/accthpa49271.2020.9213220

引用次数: 0

Comprehensive Study on Sentiment Analysis: Types, Approaches, Recent Applications, Tools and APIs 情感分析的综合研究:类型、方法、最新应用、工具和api

2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA) Pub Date : 2020-07-01 DOI: 10.1109/ACCTHPA49271.2020.9213209

Binju Saju, Siji Jose, Amal Antony

引用次数: 5

A Hybrid Feature Selection Approach Using Artificial Bee Colony and Genetic Algorithm 基于人工蜂群和遗传算法的混合特征选择方法

2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA) Pub Date : 2020-07-01 DOI: 10.1109/ACCTHPA49271.2020.9213197

M. Bindu, M. Sabu

引用次数: 3

A Hybrid Approach Using Ant Colony Optimization and Binary Particle Swarm Optimization (ACO: BPSO) for Energy Efficient Multi-path Routing in MANET 基于蚁群优化和二元粒子群优化(ACO: BPSO)的多路径路由算法

2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA) Pub Date : 2020-07-01 DOI: 10.1109/ACCTHPA49271.2020.9213196

Valanto Alappatt, P. M. Joe Prathap

引用次数: 5

Ear-biometrics for human identification 人耳生物识别技术

2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA) Pub Date : 2020-07-01 DOI: 10.1109/ACCTHPA49271.2020.9213190

Shruti Nikose, Hemant Kumar Meena

引用次数: 0

Multimodal MRI Based Classification and Prediction of Alzheimer’s Disease Using Random Forest Ensemble 基于随机森林集合的多模态MRI阿尔茨海默病分类与预测

2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA) Pub Date : 2020-07-01 DOI: 10.1109/ACCTHPA49271.2020.9213211

A. Thushara, C. Ushadevi Amma, Ansamma John, R. Saju

{"title":"Multimodal MRI Based Classification and Prediction of Alzheimer’s Disease Using Random Forest Ensemble","authors":"A. Thushara, C. Ushadevi Amma, Ansamma John, R. Saju","doi":"10.1109/ACCTHPA49271.2020.9213211","DOIUrl":"https://doi.org/10.1109/ACCTHPA49271.2020.9213211","url":null,"abstract":"Alzheimer’s disease (AD) is a neurodegenerative disorder that affects millions of people worldwide and it accounts for a significant decrease in the quality of life of patients and their families. Currently, available treatment options for AD is merely palliative and no drugs are available for the inexorable progression of the disorder that is diagnosed during the later stage of the disease. So the early diagnosis of AD is an optimal strategy in formulating the treatment plan. Neuroimaging modalities like Magnetic Resonance Imaging (MRI), resting-state functional Magnetic resonance imaging (rs-fMRI), Diffusion Tensor Imaging (DTI) and Positron emission tomography (PET) are used to diagnose the structural and functional alteration caused by AD. For the past few years, machine learning methods are widely used to analyze the neuroimaging data acquired from MRI imaging modalities for the diagnosis and prediction of neurological disorder. In this work, the random forest classification algorithm is used to classify and predict Alzheimer’s disease. The data set that is used in this study is TADPOLE data set, which has been acquired from Alzheimer’s neuroimaging Initiative (ADNI). In this work, the multiclass classification that distinguishes the different level of Alzheimer’s disease has achieved an accuracy comparable to current research in the prediction of AD.","PeriodicalId":191794,"journal":{"name":"2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127857835","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: 8

Optimal Path Length for Octagon-Cell Interconnection Networks: Fundamental Theorems with Proofs 八角形单元互连网络的最优路径长度:基本定理及其证明

2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA) Pub Date : 2020-07-01 DOI: 10.1109/ACCTHPA49271.2020.9213221

Sanjukta Mohanty, P. Behera

{"title":"Optimal Path Length for Octagon-Cell Interconnection Networks: Fundamental Theorems with Proofs","authors":"Sanjukta Mohanty, P. Behera","doi":"10.1109/ACCTHPA49271.2020.9213221","DOIUrl":"https://doi.org/10.1109/ACCTHPA49271.2020.9213221","url":null,"abstract":"Interconnection Networks are widely used in many applications such as parallel and massively parallel systems, industrial applications, VLSI circuits, electronic devices and telephone switches etc. The realization of data communication in parallel system is possible all the way through interconnection networks. The whole performance of the system depends on the type of interconnection networks used. Interconnection networks with embed ability by some other interconnection structure, high resilience, simple routing, constant node degree, and scalability are highly desirable. We had introduced Octagon-Cell Networks (OCN), in the past research, which is a new interconnection network. In this research, we introduce and prove the fundamental theorems for finding the optimal path length between any pair of source and destination nodes in Octagon-Cell Network of any depth. The basic objective of these theorems (excluding Horizontal and Vertical Moves) to calculate the optimal path length, is to check whether any intermediate node exists along the optimal path, for which its line number or serial number matches with the line number or the serial number of destination node respectively. We also introduce the theorems to find the total number of horizontal lines, which is the maximum line number of a node, as well the total number of vertical lines, which is the maximum serial number of a node in Octagon-Cell Network of any depth. The theorems are proved mathematically and are tested successfully on all possible cases.","PeriodicalId":191794,"journal":{"name":"2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123478162","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

Morphological Operators on Hypergraphs for Colour Image Processing 用于彩色图像处理的超图形态学算子

2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA) Pub Date : 2020-07-01 DOI: 10.1109/ACCTHPA49271.2020.9213191

V. Bino Sebastian, Nuja M Unnikrishnan, Neenu Sebastian, Rosebell Paul

引用次数: 2