检索结果-内蒙古大学图书馆

18th IEEE India Council International Conference, INDICON 2021

作者： Veetil, Iswarya Kannoth Gopalakrishnan, E.A. Sowmya, V. Soman, K.P. Centre for Computational Engineering and Networking Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9781665441759

Parkinson's Disease (PD) is a progressive brain disorder cased by dopmainergic neuronal loss and mainly affects the Substantia Nigra located in the mid brain region. The increasing availability of public datasets has driven the development of advanced machine learning algorithms as a tool to assist in the classification and initial risk assessment of patients with PD. This work provides an analysis of five major deep learning architectures with the aim of refinement of Magnetic Resonance Imaging (MRI) based diagnosis of PD, evaluated using multiple performance indices. Three of the five architectures considered show a significant increase in performance in comparison to existing work without hyper-parameter tuning and can aid researchers in selecting a Deep Neural Network (DNN) model as an MRI based classification model for PD. The results support and demonstrate the scope for the use of Artificial Intelligence (AI) as a decision support system. © 2021 IEEE.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Grapheme to Phoneme Mapping for Tamil Language 5th

Grapheme to Phoneme Mapping for Tamil Language

引用

5th International Conference on Advances in Computing and Data Sciences, ICACDS 2021

作者： Anusha, M. Geerthana Govind, D. Menon, Vijay Krishna Center for Computational Engineering and Networking Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9783030882433

In this paper, a Deep learning-based approach is analyzed to improve the accuracy of the Language Modelling of a system that is a part of ASR. The main intent is to build an End to End Neural Network Language Model. Generally, ASR works in three different modes. The modes are Acoustic Modeling, Lexicons, and Language Modeling. In ASR, G2P models are vital in the case of analyzing the Out Of Vocabulary words. Mostly these G2P models were fabricated using the traditional approaches such as Bi-gram, n-gram language models. The traditional approaches are deceptive in aiding the meanings, long term dependencies of the words and are also impotent in rendering the phonemes of new words. Thus Seq2Seq G2P models exceed the state of the art of the traditional approach. As we all know, the ASR (Speech to Text) system is widely used in AI applications such as voice dialing, system control, navigation, etc. The method which is applied here is the Statistical based one. A method other than the Rule-based approach is trialed out. A suitable neural network of LSTM and BI-LSTM is proposed which enhances the accuracy of the system. © 2021, Springer Nature Switzerland AG.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Analysis of Text-Semantics via Efficient Word Embedding using Variational Mode Decomposition 35

Analysis of Text-Semantics via Efficient Word Embedding usin...

引用

35th Pacific Asia Conference on Language, Information and Computation, PACLIC 2021

作者： Ramakrishnan, Rohith Vadakedath, Anirudh Krishna, U. Vamsi Premjith, B. Soman, K.P. Centre for Computational Engineering and Networking Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India

In this paper, we propose a novel method which establishes a newborn relation between Signal Processing and Natural Language Processing (NLP) method via Variational Mode Decomposition (VMD). Unlike the modern Neural Network approaches for NLP which are complex and often masked from the end user, our approach involving Term Frequency - Inverse Document Frequency (TFIDF) aided with VMD dials down the complexity retaining the performance with transparency. The performance in terms of Machine Learning based approaches and semantic relationships of words along with the methodology of the above mentioned approach is analyzed and discussed in this paper. © Proceedings of the 35th Pacific Asia Conference on Language, Information and Computation, PACLIC 2021.

关键词： Variational mode decomposition

来源：评论

学校读者我要写书评

暂无评论

Comparative Study of Bert Models and Roberta in Transformer based Question Answering

Comparative Study of Bert Models and Roberta in Transformer ...

引用

Intelligent Technologies (CONIT), International Conference on

作者： N Akhila Sanjanasri J. P Soman K. P Center for Computational Engineering and Networking (CEN) Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham Coimbatore India

Using deep learning technique, transformer-based self-supervised pre-trained models have revolutionised the idea of transfer learning in natural language processing (NLP). The self-attention mechanism makes transformers more prevalent in transfer learning across broad range of NLP tasks. In this study, seven prominent models are compared., including the Bertbase- uncased, Distilbert-base-cased, Distilbert-base-uncased, and Roberta models, in terms of their effectiveness (using 3 epochs). Bert base cased, Bert-medium-squad2-distilled, and Electra-basesquad2 on the Stanford Question Answering Dataset were utilised models with two epochs (SQuAD). The analysis shows that in three epochs, Roberta models provide the most accuracy, while Distilbert-base cased models provide the maximum accuracy when compared to Bert-base uncased, Distilbert-base uncased, and Distilbert base cased models. While Electra-base-squad2 performs better than Bert base cased and Bert-medium-squad2- distilled in cases with two epoches. Although all Bert and Roberta models take a long time to run, increasing accuracy requires more time to train the dataset.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Quantum Teleportation and EntanglementBased Quantum Blind Signature Protocol for Quantum Secure Communication in Security Service Bases 5

Quantum Teleportation and EntanglementBased Quantum Blind Si...

引用

5th International Conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Sumathy, G. Suresh, A. Udendhran, R. Maheshwari, A. Arunprasath, S. Department of Computational Intelligence Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Networking and Communications Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology SRM Nagar Chengalpattu Tamil NaduKattankulathur Chennai603203 India

ISBN: (纸本)9798400709418

Quantum teleportation and entanglement-based quantum blind signature protocol were promising new technologies for secure communication between security service bases. They were secure against a variety of attacks, including eavesdropping, signatory disavowal, verifier denial, and forgery. The protocol worked by using quantum entanglement to create a shared secret state between the security service bases. This shared secret state was then used to generate a quantum signature for each message sent between the bases. The signature was then verified by the receiving base using the shared secret state. Compared to classical blind signature schemes, the main advantages of the quantum entanglement-based quantum blind signature protocol were its information-theoretic security and reduced complexity and noise. However, this protocol also faced some challenges, such as preparing and storing entangled states, performing quantum operations and measurements, and ensuring the trustworthiness of the third party. Despite these challenges, the quantum entanglement-based quantum blind signature protocol was a promising new technology for secure communication between security service like military bases. It had the potential to revolutionize the way that military bases communicated with each other, making it possible to send secure messages without revealing the content or identity of the messages. © 2023 ACM.

关键词： Secure communication

来源：评论

学校读者我要写书评

暂无评论

Performance Improvement of Deep Residual Skip Convolution Neural Network for Atrial Fibrillation Classification 8th

Performance Improvement of Deep Residual Skip Convolution Ne...

引用

8th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2020

作者： Sanjana K Sowmya, V. Gopalakrishnan, E.A. Soman, K.P. Amrita School of Engineering Center for Computational Engineering and Networking Amrita Vishwa Vidyapeetham CoimbatoreTamilNadu India

ISBN: (纸本)9789811557873

Atrial fibrillation is a life-threatening cardiac disease which requires a long and tedious process of detection. So, the detection of atrial fibrillation has gained great importance. One of the most reliable ways to detect cardiac disease is through analysis of ECG signal. In this paper, we show that the performance of a deep residual skip convolution neural network-based approach for automatic detection of atrial fibrillation can be improved by hyperparameter tuning. For the present work, atrial fibrillation dataset from the 2017 PhysioNet/CinC Challenge is used. The proposed method obtained an overall accuracy of 96.08% and weighted average F1 score of 0.96, a recall of 0.96 and a precision of 0.96. The main advantage of the present work is the improved accuracy achieved using a lighter model which is trained for a lesser number of epochs. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Industry 4.0: IoT and Cloud Computing Enabled Smart Waste Management System

Industry 4.0: IoT and Cloud Computing Enabled Smart Waste Ma...

引用

2023 IEEE International Conference on Electrical, Electronics, Communication and Computers, ELEXCOM 2023

作者： Monsang, Kh. Gainner Kirola, Madhu Joshi, Kapil Kumar, Rajiv Anandaram, Harish Chander Gupta, Richa Uttaranchal Institute of Technology Uttaranchal University Deptt. of Computer Science and Engineering Dehradun248001 India Centre for Computational Engineering and Networking Amrita School of Artificial Intelligence Coimbatore248001 India Graphic Era Hill University Deptt. of Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350305111

Waste management system is the practice of controlling garbage from the moment it is produced until its disposal. In other words it can be termed as a life cycle of garbage. The main objective behind waste management is to lessen the harmful effects of garbage on the environment and people's health. A substantial percentage of waste management is devoted to managing municipal solid waste, which is generated by business, residential, and industrial activity. Waste inevitably results from everything we undertake. As a result of the population expansion, waste management has evolved into one of the most difficult challenges facing our current generation. Overflowing trash cans are now a common sight;burning trash and dumping it outside has become a regular ritual. These factors have made a significant impact on the environment and the planet as a whole. In order to address this problem;we require an efficient and dynamic waste management system. In this research study, we presented a smart waste management system by integrating industry 4.0 enabled technologies such as (IoT) Internet of Things and cloud computing into the current waste management system. © 2023 IEEE.

关键词： Industry 4.0

来源：评论

学校读者我要写书评

暂无评论

A fusion based approach for uneven illumination correction in retinal images using Gaussian Filtering method

引用

Procedia Computer Science 2025年 260卷 991-999页

作者： Suchithra M Soman K P Dept of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore-641112 India Center for Computational Engineering and Networking Amrita School of Artificial Intelligence Amrita Vishwa Vidyapeetham Coimbatore-641112 India

Enhancing the poor-quality retinal images is one of the inevitable pre-processing steps in retinal image analysis for the accurate diagnosis and treatment of different eye-related issues such as diabetic retinopathy, macular degeneration, glaucoma, etc. This paper presents a systematic approach to address this challenge by proportionally adjusting the red, green, and blue channels to ensure consistent luminance correction, enhancement, and color preservation. The core of our approach involves generating a luminance scaling matrix using a Gaussian low-pass filter applied to the red channel. This matrix is then used to correct the luminance proportion in each channel, resulting in new luminance-enhanced channels. We then applied Contrast Limited Adaptive Histogram Equalization(CLAHE) for enhancing the structural properties of the luminance corrected retina image. Our approach is effectively validated on two publically available datasets Diaretdb0 and Diaretdb1. We have tested the effectiveness of our proposed method using different evaluation metrics such as entropy, standard deviation, Natural Image Quality Evaluator(NIQE) and Perception-based Image Quality Evaluator(PIQE) measures and also confirms that our approach significantly improves the quality of retinal images, making them more suitable for accurate analysis and diagnosis.

关键词： Uneven illumination Gaussian Filtering image enhancement CLAHE

来源：评论

学校读者我要写书评

暂无评论

Analysis of Heart Disease Prediction using Various Machine Learning Techniques: A Review Study

Analysis of Heart Disease Prediction using Various Machine L...

引用

2023 IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2023

作者： Joshi, Kapil Reddy, G. Abhishek Kumar, Sachin Anandaram, Harishchander Gupta, Ashulekha Gupta, Himanshu Uttaranchal Institute of Technology Uttaranchal University Department of Cse Dehradun India Ms Ramaiah Institute of Technology Electronics and Communication India Amrita School of Engineering Centre for Computational Engineering and Networking Coimbatore India Department of Management Studies Uttarakhand Dehradun India College of Engineering Roorkee Department of Cse Roorkee India

ISBN: (纸本)9781665474924

Heart-related illnesses, often known as Cardiovascular diseases (CVDs) have over the past few decades become the major cause of death worldwide and the most dangerous ailment in both India and the rest of the world. The medicinal services industry contains large a measure of knowledge. This large measure of information, the infection is frequently anticipated, distinguished, or might be relieved. A serious risk to humanity is caused by infections such as cardiovascular disease, cancerous development, tumour, and so forth. Within this article, We attempt to focus on coronary illness forecast, utilizing AI methods, coronary illness is regularly anticipated. The knowledge likes pulse, hypertension, diabetes, cigarette smoking is used as information, and these highlights are then shown as a forecast. The calculations like Random Forest, KNN, and choice tree are use. In this paper, we predict the complete analysis of heart disease. The precision of the prototype is to investigate using each computation. Because of the model for predicting the infection of daringness, action is conducted at that point with greater precision. The use of machine learning algorithms by researchers is speeding up the creation of software that can help physicians with the diagnosis and prognosis of heart ailment. The main objective of this work is to forecast a patient's heart state using machine learning techniques. © 2023 IEEE.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Quantum Teleportation and EntanglementBased Quantum Blind Signature Protocol for Quantum Secure Communication in Security Service Bases 23

Quantum Teleportation and EntanglementBased Quantum Blind Si...

引用

Proceedings of the 5th International Conference on Information Management & Machine Intelligence

作者： Sumathy G Suresh A Udendhran R Maheshwari A Arun Prasath Selvaraj Department of Computational Intelligence SRM Institute of Science and Technology India Networking and Communications SRM Institute of Science and Technology India Department of Computational Intelligence SRM IST India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology India

ISBN: (纸本)9798400709418

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：