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

Face Gesture Based Virtual Mouse Using Mediapipe 使用Mediapipe的基于面部手势的虚拟鼠标

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126453

Akula Kumar Raja, Chidakash Sugandhi, Gorantla Nymish, Nama Sai Havish, Manazhy Rashmi

引用次数: 0

Strategies for Improving Object Detection in Real-Time Projects that use Deep Learning Technology 在使用深度学习技术的实时项目中改进目标检测的策略

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126449

Niloofar Abed, Ramu Murugan

{"title":"Strategies for Improving Object Detection in Real-Time Projects that use Deep Learning Technology","authors":"Niloofar Abed, Ramu Murugan","doi":"10.1109/I2CT57861.2023.10126449","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126449","url":null,"abstract":"The popularity and prevalence of devices equipped with object detection technology and controllable via the Internet of Things (IoT) have increased, especially in the post-Corona era. The development of neural networks and artificial intelligence by combining them with IoT systems has achieved acceptable satisfaction among users in adverse conditions by reducing the need for manpower and increasing productivity. Therefore, the scope of using such mechanisms has expanded in most fields, from self-driving vehicles to agricultural crops. Beginners will be confronted with a massive amount of complex information as a result of the design and application of such technologies in interdisciplinary fields. Due to the popularity of using the You Only Look Once (YOLO) object detection algorithm, this article provided a guideline as a traffic light subject classification and, offers suggested solutions and exclusive approches to increase the accuracy of object detection in real-time projects with a practical application attitude for the enthusiasts and developers particularly in object detection scenarios by employing YOLO.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115886182","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

Relative Study of Intelligent Control Techniques to Maintain Variable Pitch-Angle of the Wind Turbine 风电机组变俯仰角智能控制技术的相关研究

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126335

V. Khatavkar, Snehal Andhale, Panchshila Pillewar, Utkarsh Alset

{"title":"Relative Study of Intelligent Control Techniques to Maintain Variable Pitch-Angle of the Wind Turbine","authors":"V. Khatavkar, Snehal Andhale, Panchshila Pillewar, Utkarsh Alset","doi":"10.1109/I2CT57861.2023.10126335","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126335","url":null,"abstract":"The wind turbine requires a robust and time-responsive system to control the pitch–angle (Pit–Ang) of the mechanical actuator. If the response of speed is very efficient then, the controller can act according to the prescribed logic and its mechanical mechanism can work faster with its response time. In this paper, real-time Data from IMD (Indian Meteorological Department) is used for the relative study of the model of wind turbine created in MATLAB / Simulink® environment using Fuzzy Logic Toolbox™. The principle of wind turbine used here is to supply a controlled input to the system and after synthesis, these rules in the form of signal are transferred to the plant which has a drive train and pitch actuator. The output responses of the proposed controller are compared amongst proportional– integral–derivative (PID), fuzzy, and adaptive fuzzy–PID Controllers. The simulation results seen between the adaptive fuzzy– based PID controller surpasses the expected results by Tr = 95.454%, Ts = 61.409% and negligible overshoot as compared to open–looped and conventional responsive controller.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":"28 9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115634459","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 Analysis of Machine Learning Algorithms to Predict Cardiovascular Disease 预测心血管疾病的机器学习算法性能分析

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126428

Hridya V Ramesh, Rahul Krishnan Pathinarupothi

{"title":"Performance Analysis of Machine Learning Algorithms to Predict Cardiovascular Disease","authors":"Hridya V Ramesh, Rahul Krishnan Pathinarupothi","doi":"10.1109/I2CT57861.2023.10126428","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126428","url":null,"abstract":"Globally the rate of heart disease has increased drastically due to unhealthy eating habits and reduced physical activities. It has become one of the significant causes of death worldwide. As per the reports of the world health organization(WHO), 31% of all deaths worldwide are caused by cardiovascular diseases. This demands the development of a system capable of early detection of cardiovascular diseases at an affordable cost. With this as the objective, multiple machine learning algorithms have been selected to evaluate their performance in the early detection of cardiovascular diseases. This work utilizes available data sets of an individual’s vital parameters, demographic data, and exercise parameters for predicting cardiovascular diseases. An extensive evaluation is performed to identify the best-suited supervised machine learning classifier that could predict cardiovascular diseases using the available datasets. This research work details the nine different classification algorithms utilized for this analysis. For each algorithm, the F1-score, precision, recall, accuracy, and Area Under the Receiver Operating Characteristics (AUROC) values for each model have been determined and compared with the rest of the algorithms. The results show that random forest and gradient boosting models outperform others and demonstrate an F1-Score of 0.88 and an AUROC value of 0.92, respectively. This showcases that doctors could utilize this technique for the early identification of cardiovascular diseases. This will provide the opportunity to offer adequate medical treatments early, thus saving lives.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116332368","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

Forest Fire Detection using Convolutional Neural Network Model 基于卷积神经网络模型的森林火灾探测

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126370

Shubham Sah, S. Prakash, S. Meena

引用次数: 0

Design of an Efficient Bioinspired Model for Optimizing Robotic Arm Movements via Ensemble Learning Operations 基于集成学习操作优化机械臂运动的高效仿生模型设计

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126406

Prachi V. Karlekar, Swapna Choudhary, Atul Deshmukh, Harish Banote

{"title":"Design of an Efficient Bioinspired Model for Optimizing Robotic Arm Movements via Ensemble Learning Operations","authors":"Prachi V. Karlekar, Swapna Choudhary, Atul Deshmukh, Harish Banote","doi":"10.1109/I2CT57861.2023.10126406","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126406","url":null,"abstract":"Robotic arm movements are highly dependent on design and deployment of sensors & actuation devices & their duty cycles. Optimizing current-level duty cycles for these devices can reduce the power consumption, and maximize the efficiency of control for different device operations. Existing duty cycle control models for robotic arms are highly complex, or have lower efficiency levels. To overcome these issues, this text proposes design of an efficient bioinspired model for optimizing robotic arm movements via ensemble learning operations. The arm is built using Arduino controller along with stepper motors, which assist in controlled movements for different arm operations. The proposed model uses Mayfly Optimization (MO) in order to identify duty cycles of different arm components for different movement types. The MO Model uses delay, energy and jitter parameters in order to estimate a fitness function that is optimized in order to identify arm movement sets. These movement sets are classified into performance-aware movements via a combination of Naïve Bayes (NB), k Nearest Neighbours (kNN), Support Vector Machine (SVM), Logistic Regression (LR), and Multilayer Perceptron (MLP) classifiers. Due to which the model is able to reduce the delay needed for control the arms by 8.3%, reduce the energy needed for control operations by 2.9%, and reduce the control jitter by 4.5% under real-time scenarios.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116748604","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

Hybrid Machine Learning Model for Lie-Detection 用于测谎的混合机器学习模型

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126460

Rupali J Dhabarde, D. V. Kodawade, Sheetal Zalte

引用次数: 0

Enhancing Mango Fruit Disease Severity Assessment with CNN and SVM-Based Classification 基于CNN和svm分类增强芒果果实病害严重程度评估

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126397

D. Banerjee, V. Kukreja, S. Hariharan, Vishal Jain

{"title":"Enhancing Mango Fruit Disease Severity Assessment with CNN and SVM-Based Classification","authors":"D. Banerjee, V. Kukreja, S. Hariharan, Vishal Jain","doi":"10.1109/I2CT57861.2023.10126397","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126397","url":null,"abstract":"The mango leaf powdery mildew disease poses a serious threat to mango production society globally by significantly lowering yield and quality. For timely intervention and efficient management, early disease detection and classification are important. In this research and education area, a novel hybrid approach utilizes Convolutional Neural Networks (CNN) and Support Vector Machines to identify the mango leaf powdery mildew disease based on four severity levels (SVM). Three phases make up the proposed approach: data structure, CNN-selected attributes, and SVM classification. We collect and preprocess images of mango leaves during the data organization step, and in the CNN - attributes selection phase, we apply a CNN model for feature extraction and selection. For the mango leaf powdery mildew dataset, we improve the CNN model to find the most relevant features for the classification task. The SVM - classification step includes training an SVM model on the obtained features and refining the hyperparameters via k-fold cross-validation. The proposed CNN and SVM hybrid multi-classification model for mango leaf powdery mildew disease achieved an overall accuracy of 89.29%. A dataset of 2559 images with 4 severity levels was utilized. The model works well overall, as a macro-average F1-score of 90.10, the weighted average F1-score's minimal value of 53.85%. The model is less successful in predicting instances for classes with smaller support proportions, as shown by the micro-average F1-score, which is 89.29% and is lower overall than the macro-average F1-score.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":"164 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123135445","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

Optimized Recognition Of CAPTCHA Through Attention Models 通过注意模型优化验证码识别

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126193

Raghavendra A Hallyal, S. C, P. Desai, M. M

{"title":"Optimized Recognition Of CAPTCHA Through Attention Models","authors":"Raghavendra A Hallyal, S. C, P. Desai, M. M","doi":"10.1109/I2CT57861.2023.10126193","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126193","url":null,"abstract":"Information retrieval from the CAPTCHA is a crucial part, this CAPTCHA always contains some unwanted information along with required information, so attention technique comes in handy to select such useful information discarding the unwanted part. The attention concept has become a very important part in the field of deep learning which uses Natural Language Processing(NLP) and Computer Vision(CV). Attention mechanism is rigorously used in OCR based applications which requires generating of selected information rather than every information available. Our work includes implementation of general, global and local Attention mechanisms used with two different models which were transfer learning model and the parameter search model. As OCR with attention technique is computationally costly it is required to optimize the entire process so we suggest optimized retrieval of information from CAPTCHA using parameter search algorithm. This retrieval includes using weights that reduced the training time from 4.03 minutes to 3.33 minutes and the number of training images which were used for training were reduced than before. We obtained the highest accuracy of 87.34% for general attention with parameter search model and local attention model with parameter search model proved to have less computation and less training time than the general attention with parameter search model.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123330931","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

Weighted Pooling RoBERTa for Effective Text Emotion Detection 有效文本情感检测的加权池RoBERTa

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126396

Meenu Mathew, J. Prakash

引用次数: 0