2023 IEEE 8th International Conference for Convergence in Technology (I2CT)最新文献_第5页

Kannada Textual Error Correction Using T5 Model 基于T5模型的卡纳达语文本纠错

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126228

Sushmitha Ramaneedi, P. Pati

{"title":"Kannada Textual Error Correction Using T5 Model","authors":"Sushmitha Ramaneedi, P. Pati","doi":"10.1109/I2CT57861.2023.10126228","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126228","url":null,"abstract":"Error creeps into text in various ways. Typing error may come due to either mis-typing or due to poor language expertise. Similarly, recognition technologies while converting textual images and speech into text may generate error due to their limitations. Irrespective of the channel of error induction, presence of error poses a huge challenge for downstream consumption of such textual content. Additionally, error present in Indian language textual documents come with their own set of issues. This necessitates focused study on the textual errors in Indian language documents and the various technologies which may be employed to eliminate them.This work proposes to employ mT5, a very popular deep learning based multi-lingual language model, to eliminate errors present in Kannada, an Indian Language, text. A pretrained model of mT5 is enhanced with transfer learning for a Kannada dataset. The ability of the enhanced mT5 model to reduce error is studied at various levels of noise. Character Error Rate (CER) is employed as the metric. It’s observed that the enhanced mT5 model is effectively able to reduce noise by 12% for input text with 25% CER.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129875693","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

Detection of Monkeypox Based on Improved Darknet19 基于改进Darknet19的猴痘检测

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126170

Dr. Prabira Kumar Sethy, J. Nayak, M. Bhargavi, Himanshu Sekhar Maharana, S. Behera, S. Rathore

引用次数: 0

Autograding of Programming Skills 编程技能的自动升级

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126211

N. Narmada, P. Pati

引用次数: 1

Greenhouse Monitoring and Controlling using Cloud-Based Android Application 基于云的Android应用的温室监测与控制

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126283

Tarun Kumar, Samli, Dilip Kumar

{"title":"Greenhouse Monitoring and Controlling using Cloud-Based Android Application","authors":"Tarun Kumar, Samli, Dilip Kumar","doi":"10.1109/I2CT57861.2023.10126283","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126283","url":null,"abstract":"Wireless sensor network (WSN) technology is the amalgamation of numerous sensors exploited for monitoring and controlling the physical climate conditions. The integration of WSN technology in greenhouse farming has offered a new direction towards the production of crops. Greenhouse presents a protected environment for the plants/crops and facilitates the farmers in boosting their production. The paper presents a real-time greenhouse monitoring and controlling system. Humidity-Temperature sensor (DHT11), Soil Moisture sensor and Light sensor (LDR) are explored to measure the climate parameters accurately. To collect the real-time monitoring data from these sensors and transmit the data to the Firebase Cloud, the Wi-Fi module ESP8266 is attached to Arduino UNO. Further, an android application is developed for monitoring and controlling climate parameters via smart devices. The proposed model is efficient in terms of services and cost. The proposed automatic greenhouse monitoring and control system can facilitate the farmers in monitoring and controlling the climate conditions automatically as well as manually if the need arises. The developed application is user-friendly and can be efficiently utilized by farmers.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127371611","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

Effect of Transformer oil on Silicon Rubber Nano-Micro Composites 变压器油对硅橡胶纳米微复合材料的影响

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126365

Shireesha Gorre, Palash Mishra, M. Agrawal, A. Paramane, S. Chatterjee

引用次数: 0

MRI image based Ensemble Voting Classifier for Alzheimer's Disease Classification with Explainable AI Technique 基于MRI图像的可解释AI阿尔茨海默病分类集成投票分类器

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126269

Uppin Rashmi, Tripty Singh, Sateesh Ambesange

{"title":"MRI image based Ensemble Voting Classifier for Alzheimer's Disease Classification with Explainable AI Technique","authors":"Uppin Rashmi, Tripty Singh, Sateesh Ambesange","doi":"10.1109/I2CT57861.2023.10126269","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126269","url":null,"abstract":"Alzheimer's is one of the causes of dementia, which causes memory loss, problem-solving disability, speaking, and a lot more difficulties in day-to-day life. Generally, dementia is a loss of memory, problem-solving ability, language fluency, and other thinking abilities that severely affect day-to-day life. Alzheimer's creates a huge impact on family life, the economy, and finally, the country as a whole is affected. According to statistics every 3 seconds, one person develops dementia in the world and the estimates say that by 2030, 78 million people will be affected, and by 2050 139 million people will have dementia. Estimates say that the economic impact due to dementia by 2030 in the US will be $2.8 Trillion which causes a huge loss and needs to be avoided.Alzheimer's can be diagnosed at various stages, with different datasets like Magnetic Resonance Imaging (MRI) Test images, Speech Tests, Symptoms, genes, and other data. Several models are developed to diagnose, but doctors expect proper insights about results apart from diagnosis, so the paper explains the results using various explainable methods like SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME).Data Sets used are MRI Features data extracted with generic information, Cross-sectional MRI data, and Longitudinal MRI Data. The step-by-step data processing includes data balancing using SMOTEENN, and then data transferred, using Quantile Transformer and PCA dimension reduction technique for 6 features, and Meta machine learning model, first level six key machine learning methods and finally voting classifier with hyperparameter tuning to get performance, 97.6 %, Precision 95.8%, recall 97.9% and finally F1 Score 96.8%.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131354929","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

2S (Superposition Coding, Successive Interference Cancellation) Operations in NOMA Technology for 5G Networks: Review and Implementation 5G网络NOMA技术中的2S(叠加编码，连续干扰消除)操作:回顾与实现

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126268

Kartik Patel, Sanjay Dasrao Deshmukh

{"title":"2S (Superposition Coding, Successive Interference Cancellation) Operations in NOMA Technology for 5G Networks: Review and Implementation","authors":"Kartik Patel, Sanjay Dasrao Deshmukh","doi":"10.1109/I2CT57861.2023.10126268","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126268","url":null,"abstract":"With ever increasing demand and progress of telecommunication networks (4G, 5G and beyond) in industrial automation, smart cities, internet of things and vehicular communication, some of the key goals or challenges that need to be talked are capacity enhancement, better data rate, increased spectral efficiency, reduced latency and better Quality of Service (QoS) and Quality of Experience (QoE) to mobile users. The limitations of the 3G and 4G networks lies in the capacity because of the Orthogonal Multiple Access (OMA) they use in time, frequency, and code domain. To overcome this limitation 5G NR (New Radio) is developed by 3GPP. The multiple access technique used in 5G network is Non-Orthogonal Multiple Access (NOMA). This paper presents results of review and basic implementation of NOMA operations like superposition coding, Successive Interference Cancellation (SIC) and effect of it on bit error rate (BER) curve for two users and three users with different power weights. Authors have found that with large difference in the power weights assigned to user there is significant difference in the BER of users after decoding, also by changing the power weights assigned to users BER can be reduced at lower values of SNR.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126294487","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

Object Detection and Recognition System Using Deep Learning Method 基于深度学习方法的目标检测与识别系统

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126316

Yashal Railkar, Aditi Nasikkar, Sakshi Pawar, P. Patil, Rohini. G. Pise

{"title":"Object Detection and Recognition System Using Deep Learning Method","authors":"Yashal Railkar, Aditi Nasikkar, Sakshi Pawar, P. Patil, Rohini. G. Pise","doi":"10.1109/I2CT57861.2023.10126316","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126316","url":null,"abstract":"Object detection has been studied by many researchers for important applications in the industry like detecting a road object for self-driving cars, medical research for detecting particular diseases, gesture control, etc. Object detection and recognition is incredibly very important wrt security purposes. As computers and models can work 24/7 it can watch for video surveillance in secure areas. Humans can quickly detect or make out what items are there in photos and photographs, where these images and pictures are located, and how they interact with systems when they see them. [1]. Object identification and tracking is a key challenge in CV systems and interactions, such as visual surveillance and human computer vision systems. Human visual systems are quick and precise, allowing them to handle complicated activities such as driving. Computers will be able to drive automobiles using improvised and speedy errorfree object identification algorithms, yet they will require specialized sensors and auxiliary gadgets to relay real-time scenarios. [1]Using exact object recognition and picture classification approaches, strategies, and methodologies, it is critical and essential for deciding autonomous driving in metropolitan situations. Many big companies are currently working on this and achieving their goals day by day. In this report a object detection system has been proposed which can detect various objects, in fact it can detect almost any object wrt. training given to the model. Proposed methodology for object detection in the report is You Look Only Once (YOLO).","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126405880","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: 2

Remotely monitored Web based Smart Hydroponics System for Crop Yield Prediction using IoT 利用物联网远程监控基于Web的作物产量预测智能水培系统

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126337

V. Mamatha, J. Kavitha

{"title":"Remotely monitored Web based Smart Hydroponics System for Crop Yield Prediction using IoT","authors":"V. Mamatha, J. Kavitha","doi":"10.1109/I2CT57861.2023.10126337","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126337","url":null,"abstract":"Manures, pesticides, agricultural chemicals, small and fragmented land holdings, and other problems plague agriculture in developing nations like India. Consumers is also demanding a healthier diet that is high in quality and free of agricultural chemicals and pesticides. The above mentioned difficulties and demands are met by using the system of hydroponics, which can be organic. This kind of agriculture could result in a high yield if properly controlled and monitored. A smart agriculture system based on web application is proposed for remote monitoring by combining an appropriate IoT platform with the necessary sensor network. The proposed system controls the necessary conditions for the plant to grow hydroponically, and cultivators can remotely control agriculture using IoT. Various sensors are deployed in the field to collect parameters such as temperature, humidity, pH and water content. The sensor data that has been collected and the external input data such as the District Name, Crop Name, Area in acres and the Type of Hydroponics system is then sent to microcontroller, which in turn processes the data and then acts on it. The data that has been collected is sent to the cloud, processed and the notifications are delivered to the farmers. The proposed web application provides the farmers with an estimate of how much crop yield will be produced based on the given sensor and user input. The crop yield prediction is provided in tones and is estimated using Random Forest algorithm.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122341458","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

A Transpose-SELDNet for Polyphonic Sound Event Localization and Detection 基于转置seldnet的复音事件定位与检测

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126251

S. V, S. Koolagudi

{"title":"A Transpose-SELDNet for Polyphonic Sound Event Localization and Detection","authors":"S. V, S. Koolagudi","doi":"10.1109/I2CT57861.2023.10126251","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126251","url":null,"abstract":"Human beings have the ability to identify a particular event occurring in a surrounding based on sound cues even when no visual scenes are presented. Sound events are the auditory cues that are present in a surrounding. Sound event detection (SED) is the process of determining the beginning and end of sound events as well as a textual label for the event. The term sound source localization (SSL) refers to the process of identifying the spatial location of a sound occurrence in addition to the SED. The integrated task of SED and SSL is known as Sound Event Localization and Detection (SELD). In this proposed work, three different deep learning architectures are explored to perform SELD. The three deep learning architectures are SELDNet, D-SELDNet (Depthwise Convolution), and T-SELDNet (Transpose Convolution). Two sets of features are used to perform SED and Direction-of-Arrival (DOA) estimation tasks in this work. D-SELDNet uses a Depthwise convolution layer which helps reduce the model’s complexity in terms of computation time. T-SELDNet uses Transpose Convolution, which helps in learning better discriminative features by retaining the input size and not losing necessary information from the input. The proposed method is evaluated on the First-order Ambisonic (FOA) array format of the TAU-NIGENS Spatial Sound Events 2020 dataset. An improvement has been observed as compared to the existing SELD systems with the proposed T-SELDNet.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125275701","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