2022 4th International Conference on Artificial Intelligence and Speech Technology (AIST)最新文献_第3页

Demystifying the use of Machine Learning in Fog Computing 揭开机器学习在雾计算中的应用面纱

2022 4th International Conference on Artificial Intelligence and Speech Technology (AIST) Pub Date : 2022-12-09 DOI: 10.1109/AIST55798.2022.10065153

Revika Anand, Mitali Jain, Naina Yadav, Bhawna Narwal, Arunima Jaiswal

引用次数: 0

Vision Enhancement of Single Foggy Image using CNN 基于CNN的单幅雾天图像视觉增强

2022 4th International Conference on Artificial Intelligence and Speech Technology (AIST) Pub Date : 2022-12-09 DOI: 10.1109/AIST55798.2022.10064907

Pooja Pandey, Rashmi Gupta, Nidhi Goel

引用次数: 0

Akshi: An Assistance system for visually challenged using Machine Learning Akshi:一个使用机器学习的视觉障碍辅助系统

2022 4th International Conference on Artificial Intelligence and Speech Technology (AIST) Pub Date : 2022-12-09 DOI: 10.1109/AIST55798.2022.10064996

Aakash Jain, Ritik Verma, Gurtej Singh Khokhar, Madhulika Bhadauria

{"title":"Akshi: An Assistance system for visually challenged using Machine Learning","authors":"Aakash Jain, Ritik Verma, Gurtej Singh Khokhar, Madhulika Bhadauria","doi":"10.1109/AIST55798.2022.10064996","DOIUrl":"https://doi.org/10.1109/AIST55798.2022.10064996","url":null,"abstract":"This work focuses on is emotion recognition. Emotion shows crucial data about human communication. It’s general to utilize face expressions to convey feelings throughout a discussion, and personal communication is only possible through facial expressions. The goal of this study is for offering a machine learning-based emotion recognition structure for people who are impaired visually. We present a CNN-based solution approach to manage this challenge, for training and testing we used FER2013 database which consisted of 7 facial expression and a total of 35,685 images out of which we selected 3 facial expression happy, sad, and neutral comprising of 21264 images and achieved an accuracy of 81%.It has some limitations that it needs a person to operate and sometimes mix up of expressions so gives wrong results. Likewise, CNN we also implemented Transfer learning model i.e., Mobile Net for facial expression detection with similar dataset and achieved an accuracy of around 80%. To improvise our overall accuracy firstly we and modified the CNN design and achieved an overall accuracy of 91.65% which was superior to previous two implementations. The primary utility of the model is to help visually impaired people for better communication.","PeriodicalId":360351,"journal":{"name":"2022 4th International Conference on Artificial Intelligence and Speech Technology (AIST)","volume":"156 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121525057","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

Deep-Learning Based Hybrid Model For The Classification of Lung Diseases 基于深度学习的肺部疾病分类混合模型

2022 4th International Conference on Artificial Intelligence and Speech Technology (AIST) Pub Date : 2022-12-09 DOI: 10.1109/AIST55798.2022.10065198

Aakanksha Gupta, Ashwni Kumar

引用次数: 0

Comparative analysis of pre-trained Convolution Neural Network Techniques for tomato leaf disease detection 预训练卷积神经网络技术在番茄叶病检测中的比较分析

2022 4th International Conference on Artificial Intelligence and Speech Technology (AIST) Pub Date : 2022-12-09 DOI: 10.1109/AIST55798.2022.10064924

Gaurikaa Kathpalia, Revika Anand, Arunima Jaiswal, Bhawna Narwal

引用次数: 0

OPABP-Optimizing Parameters, to Improve Accuracy in Bug Prediction using Machine Learning opabp参数优化，利用机器学习提高Bug预测的准确性

2022 4th International Conference on Artificial Intelligence and Speech Technology (AIST) Pub Date : 2022-12-09 DOI: 10.1109/AIST55798.2022.10064852

Nidhi Srivastava, Manisha Agarwal, Sapna Arora, Tripti Lamba

{"title":"OPABP-Optimizing Parameters, to Improve Accuracy in Bug Prediction using Machine Learning","authors":"Nidhi Srivastava, Manisha Agarwal, Sapna Arora, Tripti Lamba","doi":"10.1109/AIST55798.2022.10064852","DOIUrl":"https://doi.org/10.1109/AIST55798.2022.10064852","url":null,"abstract":"Predicting a bug and attaining a successful application is critical in today's scenario during the development phase of a program. This can only be accomplished by foreseeing some of the shortcomings in the early stages of development, resulting in software that is dependable, efficient, and of high quality. A challenging aspect is to develop a sophisticated model capable to determine the error and producing effective software. A few ML methods are utilized to achieve this, and they produce accuracy with both trained and test datasets. The novelty of this approach is to demonstrate the applicability of machine learning algorithms namely Neural Network, SVM, Decision Tree and Cubist in using different performance metrics i.e. R, R square, Root Mean Square Error, Accuracy and obtaining the optimal outcome-based algorithm for a Bug report on diversion dataset from PROMISE repository. Findings reveal that SVM is giving significantly higher accuracy among all the algorithms in the ANT dataset and integrates the existing work on detecting a bug in software by providing information about various aforementioned methods in bug prediction The proposed work is highlighting the accuracy obtained by the current approaches that are significant for research scholars and solution providers.","PeriodicalId":360351,"journal":{"name":"2022 4th International Conference on Artificial Intelligence and Speech Technology (AIST)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128198615","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

Sentiment Analysis of Stock Prices and News Headlines Using the MCDM Framework 基于MCDM框架的股票价格和新闻标题情绪分析

2022 4th International Conference on Artificial Intelligence and Speech Technology (AIST) Pub Date : 2022-12-09 DOI: 10.1109/AIST55798.2022.10065221

Neha Punetha, Goonjan Jain

引用次数: 3

Predictive Maintenance System Design for Infant Intensive Phototherapy Lamp 婴儿强化光疗灯预测性维护系统设计

2022 4th International Conference on Artificial Intelligence and Speech Technology (AIST) Pub Date : 2022-12-09 DOI: 10.1109/AIST55798.2022.10065161

V. Vijean, Razi Ahmad, Wan Amiza Amneera Wan Ahmad, R. Santiagoo, Abdul Ghapar Ahmad, Syakirah Afiza Mohammed

{"title":"Predictive Maintenance System Design for Infant Intensive Phototherapy Lamp","authors":"V. Vijean, Razi Ahmad, Wan Amiza Amneera Wan Ahmad, R. Santiagoo, Abdul Ghapar Ahmad, Syakirah Afiza Mohammed","doi":"10.1109/AIST55798.2022.10065161","DOIUrl":"https://doi.org/10.1109/AIST55798.2022.10065161","url":null,"abstract":"Planned-Preventive maintenance (PPM) is an essential part of clinical engineering to ensure correct functionality of the medial equipment. PPM involves the extension of equipment’s life and reducing failure by performing selective substitution of its components in contrast to the \"fix it when it fails\" concept. However, this strategy often leads to un-necessary downtime and increased costs, especially in hospital environment. Therefore, a maintenance system for predictive preventive maintenance that can monitor the usage of medical equipment is much preferred option. In this regards, a predictive maintenance system design is proposed that focuses on the LED Infant Intensive Phototherapy Lamp. In order to improve the weakness arise from the schedule Planned-Preventive Maintenance (PPM), the predictive maintenance system will be real time performance based in which the performance of the LED Infant Intensive Phototherapy Lamp will be monitored. The purpose of this monitoring system is to ensure that the light intensity, which is measured in irradiance level, can be delivered in sufficient amount for the baby with jaundice. In order to monitor the performances of LED infant intensive phototherapy lamp, a cloud based webpage has been implemented for real time monitoring of LED infant intensive phototherapy lamp which can be accessed by authorized personals.","PeriodicalId":360351,"journal":{"name":"2022 4th International Conference on Artificial Intelligence and Speech Technology (AIST)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122508528","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

Multimodal Machine Translation for Sanskrit-Hindi: An Empirical Analysis 梵语-印地语多模态机器翻译的实证分析

2022 4th International Conference on Artificial Intelligence and Speech Technology (AIST) Pub Date : 2022-12-09 DOI: 10.1109/AIST55798.2022.10064790

N. Sethi, A. Dev, Poonam Bansal

{"title":"Multimodal Machine Translation for Sanskrit-Hindi: An Empirical Analysis","authors":"N. Sethi, A. Dev, Poonam Bansal","doi":"10.1109/AIST55798.2022.10064790","DOIUrl":"https://doi.org/10.1109/AIST55798.2022.10064790","url":null,"abstract":"Due to its extensive use in ancient Indian religious scriptures, Sanskrit is among the oldest indigenous languages and is rightfully referred to as the language of the gods. However, it is losing favour in contemporary India. Sanskrit is not widely used in current times due in large part to the lack of resources for translation into and out of it. In recent years, machine translation (MT) has improved above and beyond the norm and is now typically performed utilising supervised learning approaches. Due to the paucity of comparable corpora for Sanskrit, new research in the unsupervised MT domain appears to have promise for Sanskrit. With the aid of manually created parallel corpora for the Sanskrit-Hindi language pair, an analysis is conducted between various modelling techniques of building a machine translation system, namely Statistical and Neural, in order to bridge the gap between Sanskrit and its contemporary successor Hindi. In order to provide a fresh viewpoint on the area as a whole, the primary benefits and drawbacks of statistical and neural machine translation has been examined in this work. Our results suggest that Neural machine translation modelling technique performs better than Statistical machine translation.","PeriodicalId":360351,"journal":{"name":"2022 4th International Conference on Artificial Intelligence and Speech Technology (AIST)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126682783","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

Binary Classification of Pulmonary Nodules using Long Short-Term Memory (LSTM) 基于长短期记忆的肺结节二分类

2022 4th International Conference on Artificial Intelligence and Speech Technology (AIST) Pub Date : 2022-12-09 DOI: 10.1109/AIST55798.2022.10065049

Smridhi Gupta, Arushi Garg, Vidhi Bishnoi, Nidhi Goel

引用次数: 1