2023 2nd International Conference for Innovation in Technology (INOCON)最新文献_第10页

Medium-Term Load Forecasting Using ANN and RNN in Microgrid Integrating Renewable Energy Source 基于神经网络和RNN的可再生能源微网中期负荷预测

2023 2nd International Conference for Innovation in Technology (INOCON) Pub Date : 2023-03-03 DOI: 10.1109/INOCON57975.2023.10101126

Fanidhar Dewangan, M. Biswal

引用次数: 0

Classification and Prediction of Liver Disease Diagnosis Using Machine Learning Algorithms 使用机器学习算法的肝脏疾病诊断分类和预测

2023 2nd International Conference for Innovation in Technology (INOCON) Pub Date : 2023-03-03 DOI: 10.1109/INOCON57975.2023.10101221

H. Yadav, Rohit Kumar Singhal

{"title":"Classification and Prediction of Liver Disease Diagnosis Using Machine Learning Algorithms","authors":"H. Yadav, Rohit Kumar Singhal","doi":"10.1109/INOCON57975.2023.10101221","DOIUrl":"https://doi.org/10.1109/INOCON57975.2023.10101221","url":null,"abstract":"Liver diseases which is a chronic disease that lasts more than six months are one of the most dangerous and sounding alarms in the health care systems of the world due to the prediction of its enhancement due to several factors such as an increase in the consumption of alcohols, deteriorating polluting the situation in the whole world due to global warming and heavy industrialization and exhaust of toxic gases, contaminated water, and food, drug, primarily poor lifestyle choices lead to continuous increase in the diagnosis of anomalies in the liver of the Patients. The patient’s liver datasets are explored to build classification and prediction models for early diagnosis of liver disease. In an effort to reduce the workload on doctors, machine learning is used to predict disease. This paper explores many historical machine-learning models for liver diseases diagnosis and classification. The comparative evaluation of more than six models suggests the best method for the targeted dataset. Various ensemble techniques and tunning of hyperparameters suggest that these techniques may result in better accuracy but with the increased cost of computing, efficiency makes them irrelevant for real-world applications for offline problems these are the best bet for enhanced accuracy of the model.","PeriodicalId":113637,"journal":{"name":"2023 2nd International Conference for Innovation in Technology (INOCON)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121248262","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

Modeling and Simulation of the Power Train for Fuel Cell Electric Vehicles 燃料电池电动汽车动力系统建模与仿真

2023 2nd International Conference for Innovation in Technology (INOCON) Pub Date : 2023-03-03 DOI: 10.1109/INOCON57975.2023.10101179

Patil Mounica, N. Karuppiah, U. Pranavi

引用次数: 0

An IoT Solutions for Ungulates Attacks in Farmland 针对农田有蹄类动物攻击的物联网解决方案

2023 2nd International Conference for Innovation in Technology (INOCON) Pub Date : 2023-03-03 DOI: 10.1109/INOCON57975.2023.10100983

Ratheesh Raju, T. M. Thasleema

{"title":"An IoT Solutions for Ungulates Attacks in Farmland","authors":"Ratheesh Raju, T. M. Thasleema","doi":"10.1109/INOCON57975.2023.10100983","DOIUrl":"https://doi.org/10.1109/INOCON57975.2023.10100983","url":null,"abstract":"Agriculture is considered to be a significant contributor to the global financial system and human diets. It has been recognized as the country’s primary source of income and employment. So, it’s really important to protect crops from various dangerous hazards, such as diseases, insects, bird and animal attacks, high atmospheric temperature, etc., and also from weak irrigation systems, poor soil quality, weeds management, etc. Specific insect attacks and diseases have long been a primary crop sector concern. Computer vision (CV)-based automatic insect and disease detection methods are used in smart farming systems because of their high cost-effectiveness and efficient automation. This paper gives an overview of the use of Machine Learning (ML), Deep Learning (DL), and the Internet of Things (IoT) in agriculture to protect crops from various dangerous hazards and proposes an automatic Animal-Repelling System (ARS). This study implements a system based on IoT to protect crops from animals. The proposed low-cost agricultural field protection system helps farmers to protect their crops and increase production yield and income.","PeriodicalId":113637,"journal":{"name":"2023 2nd International Conference for Innovation in Technology (INOCON)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116747904","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

Frequency Conversion Speed Control System with Full Rotary Rim Propeller 全回转轮缘螺旋桨变频调速系统

2023 2nd International Conference for Innovation in Technology (INOCON) Pub Date : 2023-03-03 DOI: 10.1109/INOCON57975.2023.10101338

Haoyuan Luo, C. Xu

引用次数: 0

Intelligent System for Vehicles License Plate Recognition Using a Hybrid Model of GAN, CNN and ELM 基于GAN、CNN和ELM混合模型的智能车牌识别系统

2023 2nd International Conference for Innovation in Technology (INOCON) Pub Date : 2023-03-03 DOI: 10.1109/INOCON57975.2023.10101051

B. Nirmala, S. Nithya, R. Vidhiya, K. K. Sunalini, Buddha Hari Kumar, Bhoopathy Varadharajan

{"title":"Intelligent System for Vehicles License Plate Recognition Using a Hybrid Model of GAN, CNN and ELM","authors":"B. Nirmala, S. Nithya, R. Vidhiya, K. K. Sunalini, Buddha Hari Kumar, Bhoopathy Varadharajan","doi":"10.1109/INOCON57975.2023.10101051","DOIUrl":"https://doi.org/10.1109/INOCON57975.2023.10101051","url":null,"abstract":"The scientific community has given license plate recognition systems a lot of consideration. The current methods for vehicle identification need to be improved due to the swift increase in vehicle numbers. In order to lessen reliance on labor, a fully automated system is needed. With the growth of Intelligent Transportation Systems, demand for license plate recognition has increased significantly. License Plate Recognition (LPR) is susceptible to environmental factors such as a complex image background, angle view, and shift in illumination, it is still difficult to correctly recognize the digit letters on license plates. When reading license plates automatically, license plate recognition uses character recognition and image processing to identify the vehicles. The license plate detection and identification subsystems are typically combined into the vehicle license recognition system in order to locate the vehicle and identify the license plate. The Extreme Learning Machine (ELM) is used for categorization, identification, and training. This research suggests a GANCNN-ELM-based technique for detecting vehicle license plates. This method produces an accuracy of about 98.94% which outperforms the GAN-ELM, GAN-SVM, and GAN-CNN models.","PeriodicalId":113637,"journal":{"name":"2023 2nd International Conference for Innovation in Technology (INOCON)","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115775504","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

Transforming College Counseling: An In-House Solution for Student Mental Wellness 转变大学咨询:学生心理健康的内部解决方案

2023 2nd International Conference for Innovation in Technology (INOCON) Pub Date : 2023-03-03 DOI: 10.1109/INOCON57975.2023.10101364

A. Dalvi, Paarth Kapur, Rujuta Ashtekar, Alekhya Gorugantu, Irfan A. Siddavatam

引用次数: 0

Flower Classification Utilisizing Tensor Processing Unit Mechanism 利用张量处理单元机制的花卉分类

2023 2nd International Conference for Innovation in Technology (INOCON) Pub Date : 2023-03-03 DOI: 10.1109/INOCON57975.2023.10101313

Kanwarpartap Singh Gill, Avinash Sharma, Vatsala Anand, Rupesh Gupta

{"title":"Flower Classification Utilisizing Tensor Processing Unit Mechanism","authors":"Kanwarpartap Singh Gill, Avinash Sharma, Vatsala Anand, Rupesh Gupta","doi":"10.1109/INOCON57975.2023.10101313","DOIUrl":"https://doi.org/10.1109/INOCON57975.2023.10101313","url":null,"abstract":"The biodiversity of the species and the potential for visual similarity across the many flower class species, categorizing flowers can be quite a difficult undertaking. The process of classifying flowers is fraught with difficulties, such as blurry, noisy, and poor quality photos, as well as those obscured by plant leaves, stems, and occasionally even insects. With the introduction of deep neural networks, machine learning methods were utilized instead of the conventional handmade features for feature extraction. Because of its quick calculation and efficiency, researchers have shifted their attention to using non-handcrafted features for picture classification tasks. We have discovered several varieties of flowering plants in nature. It is challenging to distinguish and classify the species of flower for education purpose. The identification of objects is expanding across several sectors as a result of the recent development of deep learning in computer vision. In order to get over these issues and constraints, our research created an effective and reliable deep learning flower classifier based on transfer learning and the most advanced convolutional neural networks. According to this study’s suggested model, the Adam optimizer’s accuracy utilising the ResNet50 model is 93 percent.","PeriodicalId":113637,"journal":{"name":"2023 2nd International Conference for Innovation in Technology (INOCON)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122510713","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 Modified UNet based Framework towards Early Detection of Autism using EEG Waves 基于改进UNet的自闭症脑电波早期检测框架

2023 2nd International Conference for Innovation in Technology (INOCON) Pub Date : 2023-03-03 DOI: 10.1109/INOCON57975.2023.10101224

N. Nagashree, M. Chitralekha, S. M. Harsha, S. M. Sai Usha Sree, M. Chinmayee, S. H. Basavaraj

引用次数: 0

Heavy Metal Analysis Platform for Atmospheric Fine Particulate Matter Based on AHP Algorithm 基于AHP算法的大气细颗粒物重金属分析平台

2023 2nd International Conference for Innovation in Technology (INOCON) Pub Date : 2023-03-03 DOI: 10.1109/INOCON57975.2023.10101006

Fang Ying, Huiyuan Zou, L. Fan, Jingyu Liu, Feng Li

引用次数: 0