2023 3rd International Conference on Intelligent Technologies (CONIT)最新文献_第2页

ChatGPT: Its Applications and Limitations ChatGPT:它的应用和限制

2023 3rd International Conference on Intelligent Technologies (CONIT) Pub Date : 2023-06-23 DOI: 10.1109/CONIT59222.2023.10205621

Md. Naseef-Ur-Rahman Chowdhury, Ahshanul Haque

引用次数: 1

Soft-Switching Bridge-less Ćuk Converter based EV Charger for 2/3-Wheeler 软开关无桥Ćuk转换器为基础的电动汽车充电器2 - 3-惠勒

2023 3rd International Conference on Intelligent Technologies (CONIT) Pub Date : 2023-06-23 DOI: 10.1109/CONIT59222.2023.10205860

Singudasu Anupam Sandeep, Pinkymol K.P.

{"title":"Soft-Switching Bridge-less Ćuk Converter based EV Charger for 2/3-Wheeler","authors":"Singudasu Anupam Sandeep, Pinkymol K.P.","doi":"10.1109/CONIT59222.2023.10205860","DOIUrl":"https://doi.org/10.1109/CONIT59222.2023.10205860","url":null,"abstract":"The functioning of the Diode Bridge Rectifier (DBR) fed charger does not abide by the international regulations such as the IEC 6100-3-2 standard. In this paper different types of topologies for bridgeless Ćuk converter are being analyzed and based on that a finest bridgeless Ćuk converter is proposed for electric vehicle charger. The proposed bridgeless Ćuk converter enhances the overall efficiency by avoiding one switch and two passive elements. To overcome the violation of the recommended guidelines of IEC 6100-3-2 standard for output inductor, the single loop voltage control is replaced with double loop controller having an inner current loop control and outer output voltage control. The proposed converter topology is operated in DCM mode to have natural benefit of zero current switching. Furthermore, to reduce the switching losses of the new topology, soft-switching technique has been implemented.On the other hand, electric vehicle battery charging, is done with the help of flyback DC-DC converter with constant current and constant voltage control regimes. This CC-CV method safeguard the battery from overloading. The whole proposed topology is designed and implemented using MATLAB Simulink. The enhanced charger performance is demonstrated by comparing the proposed charger with the typical diode bridge rectifier fed charger.","PeriodicalId":377623,"journal":{"name":"2023 3rd International Conference on Intelligent Technologies (CONIT)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128515350","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

Analysis and Early Diagnosing Tool for Learning Disability using Machine Learning Models 基于机器学习模型的学习障碍分析与早期诊断工具

2023 3rd International Conference on Intelligent Technologies (CONIT) Pub Date : 2023-06-23 DOI: 10.1109/CONIT59222.2023.10205649

Nandha D Anand, Sanitha Lakshmi K Das, Rajalakshmi V R

{"title":"Analysis and Early Diagnosing Tool for Learning Disability using Machine Learning Models","authors":"Nandha D Anand, Sanitha Lakshmi K Das, Rajalakshmi V R","doi":"10.1109/CONIT59222.2023.10205649","DOIUrl":"https://doi.org/10.1109/CONIT59222.2023.10205649","url":null,"abstract":"The learning deficit is one particular type of neurological disorder that can affect a kid's mental abilities, word identification, ability to write and read, as well as their capacity for problem-solving. These disabilities are known as Particular Learning Disabilities because they primarily influence individuals' academic performance, particularly reading (dyslexia), writing (dysgraphia), and trouble with mathematical (dyscalculia) (SLD). These pupils must be discovered at an early stage so that, with the right assistance, they can gain sufficient experience with a particular task and hone their disability-related skills. The testing scale tool has been suggested for use in diagnosing and identifying SLD. The suggested tool enables the student who may have SLD to participate in the quiz. Depending on the type of test, some individual questions are repeated. The machine learning algorithms CNN and Random Forest receives the test results as input after the test is over. The algorithms predict children with learning impairments based on student grades and the amount of time spent by the kids. The suggested tool is used to create a user-friendly, integrated system for diagnosing reading, writing, and math impairments. It also suggests to parents and instructors the best methods and instructional activities.","PeriodicalId":377623,"journal":{"name":"2023 3rd International Conference on Intelligent Technologies (CONIT)","volume":"26 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128271385","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 Recognizer and Parser for Basic Sentences in Telugu using CYK Algorithm 基于CYK算法的泰卢固语基本句识别与解析器

2023 3rd International Conference on Intelligent Technologies (CONIT) Pub Date : 2023-06-23 DOI: 10.1109/CONIT59222.2023.10205628

S. Varshini, Gottimukkala Sarayu Varma, S. M.

{"title":"A Recognizer and Parser for Basic Sentences in Telugu using CYK Algorithm","authors":"S. Varshini, Gottimukkala Sarayu Varma, S. M.","doi":"10.1109/CONIT59222.2023.10205628","DOIUrl":"https://doi.org/10.1109/CONIT59222.2023.10205628","url":null,"abstract":"The scientific and technical field of computational linguistics seeks to comprehend spoken and written language from a computational standpoint. The way of describing rules and semantics in linguistics paved the beginning of natural language processing research for various languages spoken in the world. Over 700 languages are spoken in India alone, out of an estimated 7,000 spoken worldwide. Telugu is one of the most predominantly spoken languages in the states of Andhra Pradesh and Telangana. This proposed work presents a syntactical parsing technique on some basic sentences in Telugu. The Cocke-Younger-Kasami algorithm has been implemented to parse these basic sentences and also infer their grammatical structure. At present, there are very few language processing tools for Indian languages. Hence, an effort has been made to efficiently parse a few simple sentences in Telugu. The syntactical parser that has been developed acts as a recognizer and parser which can not only recognize and parse the grammatically correct sentences but can also recognize the grammatically incorrect sentences. This recognizer cum parser is then evaluated using the performance metrics like accuracy, precision and recall.","PeriodicalId":377623,"journal":{"name":"2023 3rd International Conference on Intelligent Technologies (CONIT)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128373125","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: 3

Design and Performance Analysis of Voltage-Controlled Oscillator 压控振荡器的设计与性能分析

2023 3rd International Conference on Intelligent Technologies (CONIT) Pub Date : 2023-06-23 DOI: 10.1109/CONIT59222.2023.10205948

Aastha Soni, R. Dhiman, R. Chandel

引用次数: 0

A Transition of Face Recognition to Mask Face Recognition Using Improvised Attention DL Model 基于临时注意深度学习模型的人脸识别向掩模人脸识别的过渡

2023 3rd International Conference on Intelligent Technologies (CONIT) Pub Date : 2023-06-23 DOI: 10.1109/CONIT59222.2023.10205598

Himani Trivedi, Mahesh M. Goyani

{"title":"A Transition of Face Recognition to Mask Face Recognition Using Improvised Attention DL Model","authors":"Himani Trivedi, Mahesh M. Goyani","doi":"10.1109/CONIT59222.2023.10205598","DOIUrl":"https://doi.org/10.1109/CONIT59222.2023.10205598","url":null,"abstract":"With the emergence of the communicable fatal Covid-19 virus and increased level of air pollution, every individual is adapting to wear a facial mask now a days, due to which the existing Face Recognition Models are experiencing reduction in accuracy rate. The study in this paper is aimed to analyze the rate of drop in test accuracy for the task of face recognition from normal face recognition to the recognition of masked faces. Also, it targets to analyze the behavior of Convolution Block Attention mechanism with 10 different deep learning architectures as trunk branch. This paper discusses a novel idea to generate a sample space for training by various combinations of original face images, simulated masked faces and augmentation, from the original 2 datasets which improves the test accuracy of the models. The combination of InceptionV3+CBAM proves to be the best model with the peak accuracy of 88.78% & 88.62% and least model’s inference time of 512 & 923 milliseconds on Yale and Casia subset datset respectively among all other 9 implemented attention models. Moreover the proposed implementation of InceptionV3+CBAM also succeeds to achieve better accuracy as compared to existing models by using proposed set of dataset (Original face Images+ Simulated Mask faces+Augmentation) type for training.","PeriodicalId":377623,"journal":{"name":"2023 3rd International Conference on Intelligent Technologies (CONIT)","volume":"126 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130372070","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

Fire Detection using Transfer Learning and Pre-Trained Model 基于迁移学习和预训练模型的火灾探测

2023 3rd International Conference on Intelligent Technologies (CONIT) Pub Date : 2023-06-23 DOI: 10.1109/CONIT59222.2023.10205560

Prachi Pednekar, Abheet Srivastava, Anil S. Jadhav

{"title":"Fire Detection using Transfer Learning and Pre-Trained Model","authors":"Prachi Pednekar, Abheet Srivastava, Anil S. Jadhav","doi":"10.1109/CONIT59222.2023.10205560","DOIUrl":"https://doi.org/10.1109/CONIT59222.2023.10205560","url":null,"abstract":"In recent years, there has been a rise in the occurrence of fire outbreaks, posing a significant threat to both natural resources and human lives. Consequently, the significance of fire detection in safeguarding human life and property has become increasingly crucial. Various studies have employed sensors and machine learning algorithms for fire detection, with CNN emerging as one of the most promising approaches in this field. The aim of this research paper is to explore the efficacy of using pre-trained CNN models, including VGG16, VGG19, MobileNet, and InceptionV3, through transfer learning to enhance the accuracy of the fire detection classification task. The models were trained on a dataset of 600 images and tested on 250 images, collected from various online sources and sorted into Fire and Non-Fire categories. Our findings indicate that the use of transfer learning in fire detection task yields higher accuracy than the base model. Especially, the modified VGG16, VGG19, MobileNet, and InceptionV3 models achieved 98%, 96%, 85%, and 75% accuracy respectively. This emphasizes the significance of leveraging pre-trained models through transfer learning techniques, which can substantially enhance the performance of the fire detection classification task, even when using a small dataset.","PeriodicalId":377623,"journal":{"name":"2023 3rd International Conference on Intelligent Technologies (CONIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130570239","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

Performance Assessment of Various Machine Learning Classification Methods for Classifying the Landcover Using Landsat 8 OLI 基于Landsat 8 OLI的不同机器学习分类方法在土地覆盖分类中的性能评价

2023 3rd International Conference on Intelligent Technologies (CONIT) Pub Date : 2023-06-23 DOI: 10.1109/CONIT59222.2023.10205822

Auchithya Sajan, Dhanya M

{"title":"Performance Assessment of Various Machine Learning Classification Methods for Classifying the Landcover Using Landsat 8 OLI","authors":"Auchithya Sajan, Dhanya M","doi":"10.1109/CONIT59222.2023.10205822","DOIUrl":"https://doi.org/10.1109/CONIT59222.2023.10205822","url":null,"abstract":"Classification is a technique used for categorizing different features associated with the land cover. It is a very important and visually distinguishable method for understanding the land cover and land use pattern in any area. Classifying images is a part of every study that includes change detection. Various methods are used for classification. Classical, conventional and parametric methods of classification are always time consuming and chances for error are also high. Supervised and unsupervised classification techniques are useful inorder to classify the images into distinguishable forms. For fast and accurate classification it is very vital to build a model that can classify the satellite image. In this study two Machine Learning techniques are used to perform LULC classification and the accuracy of both techniques are compared and a performance assessment is done. For attaining the best classification results it is important to provide good training samples, correct data sets, the best algorithm for classification, etc. So here we use Landsat 8 Operational Land Imager (OLI) dataset and uniform training sites for all three classification techniques. Maximum Likelihood Classification (MLC) is done in ArcGIS software, Naive Bayes classification (NBC), and Random Forest Classification (RFC) is done in Google Earth Engine using the same training samples. All three classifiers worked well. To evaluate the accuracy of each classifier, the Kappa coefficient was calculated for each technique by forming a confusion matrix. The accuracy assessment gave a promising result that Random Forest is comparatively the best technique with an accuracy of 94.86% while NBC and MLC also gave satisfactory accuracy values. All three classification techniques applied had certain limitations and were not 100% accurate hence study can be elaborated to find more classifying techniques and their accuracy in order to get the most reliable classification technique.","PeriodicalId":377623,"journal":{"name":"2023 3rd International Conference on Intelligent Technologies (CONIT)","volume":"330 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123323146","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

Action Recognition of Skateboarding Tricks – Ollie and Kickflip Using Neural Network 用神经网络识别滑板动作-奥利和踢翻

2023 3rd International Conference on Intelligent Technologies (CONIT) Pub Date : 2023-06-23 DOI: 10.1109/CONIT59222.2023.10205934

S. Shilaskar, S. Bhatlawande, Harshal Dhande, Shivpriya Deshmukh, Jayesh B. Deshmukh, Manoj Dohale

{"title":"Action Recognition of Skateboarding Tricks – Ollie and Kickflip Using Neural Network","authors":"S. Shilaskar, S. Bhatlawande, Harshal Dhande, Shivpriya Deshmukh, Jayesh B. Deshmukh, Manoj Dohale","doi":"10.1109/CONIT59222.2023.10205934","DOIUrl":"https://doi.org/10.1109/CONIT59222.2023.10205934","url":null,"abstract":"This research project aimed to develop a computer-based system using deep learning techniques to accurately detect and recognize skateboarding tricks, with a focus on ollies and kickflips. A deep learning architecture that combined RCNN, Mobile Net with Bi-directional LSTM, and CNN was proposed and implemented on a dataset of 222 skateboarding trick videos categorized into two subdirectories - Ollie and Kickflip. The proposed models were trained to precisely identify the different skateboarding motions, and the results of the experiment showed how well the recommended deep learning model classified ollies and kickflips. The trial results revealed that the algorithms were highly effective in guiding viewers through the process of scoring skateboarding films. Based on their accuracy, precision, recall, F1-Score, and AUC, the three deep learning models CNN, CRNN, and Mobile Net with Bidirectional LSTM were assessed. The results showed that CRNN had the highest accuracy and AUC of 79% and 86 respectively, while Mobile Net with Bidirectional LSTM had the highest recall and F1-Score.","PeriodicalId":377623,"journal":{"name":"2023 3rd International Conference on Intelligent Technologies (CONIT)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121192306","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

Design of Wideband Antenna for Brain Imaging System 脑成像系统宽带天线的设计

2023 3rd International Conference on Intelligent Technologies (CONIT) Pub Date : 2023-06-23 DOI: 10.1109/CONIT59222.2023.10205869

Bini Palas P, Saketh Charan S, Ravi Shankar J, Rahimunnissa K

引用次数: 0