2022 IEEE World Conference on Applied Intelligence and Computing (AIC)最新文献_第7页

Indoor Real-Time Location System for Efficient Location Tracking Using IoT 利用物联网实现高效位置跟踪的室内实时定位系统

2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848912

Sunny Verma, Tej Raj, K. Joshi, Preeti Raturi, Harishchander Anandaram, Ashulekha Gupta

{"title":"Indoor Real-Time Location System for Efficient Location Tracking Using IoT","authors":"Sunny Verma, Tej Raj, K. Joshi, Preeti Raturi, Harishchander Anandaram, Ashulekha Gupta","doi":"10.1109/AIC55036.2022.9848912","DOIUrl":"https://doi.org/10.1109/AIC55036.2022.9848912","url":null,"abstract":"Researchers have broadened the focus of RFID technology development because to the growing need for low-cost edge devices to bridge the physical-digital gap. In addition to item identification, researchers have explored the use of RFID tags for low-power wireless sensing, localization, and activity inference. A security system utilized today is not powerful enough to provide a real-time alert after identifying a problem. Movement detection is a technology for detecting a change in the environment relative to an object. Sensor-based applications can be used to watch activity and receive alerts when movement is detected, which solves the issue and saves time and money. RFID technology is used to track the location of people or things in an interior setting in real time. A reader and several tags, each of which can house a number of sensors, make up an RFID system. In this research, we present the iLocate framework for the IoT, a recently developed real-time locating framework using dynamic RFID for resource the board in indoor settings. iLocate used ubidots to assist with a broad range RFID organization. Our exploratory findings and the actual project have both shown the prevalence. Location tracking indoors will be more effective using this iLocate framework.","PeriodicalId":433590,"journal":{"name":"2022 IEEE World Conference on Applied Intelligence and Computing (AIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129813711","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

Trend Analysis in Landsat Based Scheme for Burned Area over Northwestern Region of India 基于Landsat方案的印度西北地区燃烧面积趋势分析

2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848968

Gurjeetpal Bawa, Akashdeep Sharma, H. Kumar, S. Kaushal

{"title":"Trend Analysis in Landsat Based Scheme for Burned Area over Northwestern Region of India","authors":"Gurjeetpal Bawa, Akashdeep Sharma, H. Kumar, S. Kaushal","doi":"10.1109/AIC55036.2022.9848968","DOIUrl":"https://doi.org/10.1109/AIC55036.2022.9848968","url":null,"abstract":"Crop residue burning exhibits a significant problem to air pollution witnessed by the Northwestern region of India during the Kharif and Rabi seasons. Kharif and Rabi refer to the crop pattern adopted by Asian countries following the monsoon season. After Harvesting crops, stubble formed as a byproduct is disposed of and burned on the ground. Consequently, raised a need to take action corresponds to fire incidents with changing frequency, acuteness, and different dimensions that need large image collection and processing of huge data. The paper investigates the potential of Satellite images in Spatial and Temporal Resolution in Burned areas in Haryana, India. The work presents the trend analysis over Burned Area assessment based on Landsat satellite data. A detailed analysis was carried out and found that some districts have more burning activity. Top burned areas in districts were selected and compared. Hence, a Scheme based on Landsat data can provide insight and help identify the affected area for investigation by stakeholders like Government, R&D Department, Agencies, etc.","PeriodicalId":433590,"journal":{"name":"2022 IEEE World Conference on Applied Intelligence and Computing (AIC)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123030438","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

Indoor Home Scene Recognition through Instance Segmentation Using a Combination of Neural Networks 结合神经网络的实例分割室内家庭场景识别

2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848982

Amlan Basu, Keerati Kaewrak, L. Petropoulakis, G. Di Caterina, J. Soraghan

引用次数: 0

Efficient Recognition and Classification of Stuttered Word from Speech Signal using Deep Learning Technique 利用深度学习技术从语音信号中高效识别和分类口吃词

2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848868

Kalpana Murugan, Nikhil Kumar Cherukuri, Sai Subhash Donthu

{"title":"Efficient Recognition and Classification of Stuttered Word from Speech Signal using Deep Learning Technique","authors":"Kalpana Murugan, Nikhil Kumar Cherukuri, Sai Subhash Donthu","doi":"10.1109/AIC55036.2022.9848868","DOIUrl":"https://doi.org/10.1109/AIC55036.2022.9848868","url":null,"abstract":"Fluency is a metric that assesses how well a speaker communicates with another person while presenting the information. Stuttering is one of the fluency problems that have a significant impact on speech recognition. The fluency of a speech is disrupted by involuntary word repetitions and prolongations, as well as external and internal noises. The objective of this study is to improve stuttered speech and create a better speech recognition system that decimates involuntary prolongations of sounds and repetitions of syllables or words. To get a good-quality speech signal, we propose a method in which a stuttered voice signal is analyzed using the classification algorithm called Convolutional Neural Network (CNN). For conversion of data into recognized speech, the approach is to save the input audio (speech signal of a person) with help of a microphone, then eradicate the external noises and stammers, extract features, and finally classify the speech data. The algorithm’s performance is compared using several filters such as Median Filter, Gaussian Filter, Gabor Filter, and Kalman Filter with the measures such as Mean Square Error (MSE), Signal to Noise ratio (SNR), Cross-correlation (CC), Mean Absolute Error (MAE), and Peak Signal to Noise ratio (PSNR). As per the experimental observations, the proposed scheme outperforms the established methods in terms of maintaining the overall speech signal intelligibility of the stuttered speech signal by identifying the stuttered word and removing the repetitions or prolongations. The Kalman filter performs better when compared to other used filters for analysis in terms of pre-processing level.","PeriodicalId":433590,"journal":{"name":"2022 IEEE World Conference on Applied Intelligence and Computing (AIC)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115295946","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

A Concurrent Intelligent Natural Language Understanding Model for an Automated Inquiry System 面向自动查询系统的并发智能自然语言理解模型

2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848883

G. Sunilkumar, S. S, Steven Frederick Gilbert, C. S.

{"title":"A Concurrent Intelligent Natural Language Understanding Model for an Automated Inquiry System","authors":"G. Sunilkumar, S. S, Steven Frederick Gilbert, C. S.","doi":"10.1109/AIC55036.2022.9848883","DOIUrl":"https://doi.org/10.1109/AIC55036.2022.9848883","url":null,"abstract":"The work is intended to tackle a vital field that lies at the intersection of speech processing and natural language processing: Spoken Language Understanding (SLU). Its idea is to understand the essence of machine-directed human speech in order to facilitate its further processing and take on board its cognitive impact. The proposed system is CIDIS -Concurrent Intelligent Model for Dialogue Act Classification, Intent Detection and Slot Filling, that uses a deep concurrent multi-task paradigm to perform the three fundamental tasks of the SLU domain: Dialogue Act Classification, Intent Detection and Slot Filling. Since the model is orchestrated in a multi-task fashion, every task interacts with the other to have a global understanding of the input query. It follows an intelligent encoding strategy involving concatenation of the query’s BERT and CharCNN embedding to handle all possible edge cases and ambiguities involved in human speech queries. This intelligent encoding is passed through a Stacked BiLSTM architecture followed by task-specific attention layers. The three supplementary outputs are in turn fed to the final module that generates the expected query response in real-time based on the dialogue act, intent and slot. The developed models are evaluated against standard benchmark datasets like ATIS, TRAINS and FRAMES and the achieved state-of-the-art performances are eventually tabulated.","PeriodicalId":433590,"journal":{"name":"2022 IEEE World Conference on Applied Intelligence and Computing (AIC)","volume":"169 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131576877","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

Search Engine for Assorted Media in Chat Applications 搜索引擎的分类媒体在聊天应用程序

2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848901

Aditya Pandey, Ishita Jaiswal, S. Pandey

{"title":"Search Engine for Assorted Media in Chat Applications","authors":"Aditya Pandey, Ishita Jaiswal, S. Pandey","doi":"10.1109/AIC55036.2022.9848901","DOIUrl":"https://doi.org/10.1109/AIC55036.2022.9848901","url":null,"abstract":"The mobile industry has come across many revolutionizing advancements in its technologies over the past three decades, making mobile phones an integral part of everyone’s daily lives. With the exponential advent of this technology to handle work on chat applications for prolonged hours, there has been a great increase in the interconnectivity of different sections of society, both economically and demographically. Existing chat applications provide in-built search engines that are competent in handling text searches but cannot search for different types of media, both visual and audible, which may be present in the chat. This paper proposes a novel approach that allows chat applications to use an inbuilt media search engine that performs searches for all the disparate media that the chat holds, using keywords. The machine learning model detects the objects from the media files and maps those objects’ keywords to the list of images. These keywords may be any of the objects that can be detected in those media files. Say, a user searches for the keyword ‘Table’ in the search engine, and he gets all the images having tables. This feature saves time for the user as no manual work is required to search for any media exchanged in the chat by scrolling and searching in case of many media files. This idea blooms out from within the feedback that the real-world audience has provided when asked for their expectations from a “perfect” chat application. The entire study associated with this paper conforms with the problem statement and guarantees the user a more comfortable and helpful experience while using the proposed feature. The proposed method uses TensorFlow-Lite and Google Machine Learning (ML) Kit’s Image Labelling APIs to detect the keywords that together characterize the media present in the chat. This method is found to be performing accurately for all types of media (especially photos) when manually tested with real-world data.","PeriodicalId":433590,"journal":{"name":"2022 IEEE World Conference on Applied Intelligence and Computing (AIC)","volume":"2015 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127326956","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

Review on Automatic Covid-19 Detection from Chest CT Images using Artificial Intelligence 基于人工智能的胸部CT图像自动检测新冠肺炎研究进展

2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848891

Dhanshri B Mali, S. A. Patil

引用次数: 0

Cassava plant disease detection with imbalanced dataset using transfer learning 基于迁移学习的不平衡数据集木薯病害检测

2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848882

Riya Yadav, Manish Pandey, S. Sahu

引用次数: 2

A comparative Study of Handwritten Devanagari Script Character Recognition Techniques 手写体梵文文字字符识别技术比较研究

2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848911

Ranadeep Dey, P. Gawade, Ria Sigtia, Shrushti Naikare, Atharva Gadre, Diptee Chikmurge

引用次数: 0

Identification of NPPs Transients Using Transductive Semi-supervised Learning Algorithm 基于转换半监督学习算法的核反应堆瞬态辨识

2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848910

K. Moshkbar-Bakhshayesh

{"title":"Identification of NPPs Transients Using Transductive Semi-supervised Learning Algorithm","authors":"K. Moshkbar-Bakhshayesh","doi":"10.1109/AIC55036.2022.9848910","DOIUrl":"https://doi.org/10.1109/AIC55036.2022.9848910","url":null,"abstract":"In this study, an identifier for NPPs transients based on semi-supervised learning (SSL) algorithm is developed. Modular identifier using transudative support vector machine (TSVM) model classifies the type of transients. This identifier versus unsupervised learning algorithms has the advantage of using the collected information. Moreover, the proposed identifier theoretically can measure the proximity between labeled and unlabeled patterns making it probably more efficient than supervised techniques. The developed identifier is examined by the Iris flower dataset as a benchmark test problem. Transients of the Bushehr nuclear power plant (BNPP) are studied as a case study. Results show good performance of the identifier. Recognition of unknown transients as don’t know, identification of transients in presence of noise, distinctive identification of transients, and training of the identifier by independent features are advantages of the proposed identifier. SVM is a supervised classifier that can find auto-correlation and detect cross-correlation of input data. SSL is trained on labeled and unlabeled patterns and makes it possible to measure similarity between new transients and trained ones.","PeriodicalId":433590,"journal":{"name":"2022 IEEE World Conference on Applied Intelligence and Computing (AIC)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121585747","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