2023 4th International Conference on Innovative Trends in Information Technology (ICITIIT)最新文献_第5页

Residential Load Forecasting based on Deep Neural Network 基于深度神经网络的住宅负荷预测

2023 4th International Conference on Innovative Trends in Information Technology (ICITIIT) Pub Date : 2023-02-11 DOI: 10.1109/ICITIIT57246.2023.10068706

K. S. Sudheera, Swetha R, Tejaswini R, Vaishali Meena M, Anu G. Kumar

引用次数: 0

Machine Learning techniques for identifying Cyberbullying on digital networks 识别数字网络上的网络欺凌的机器学习技术

2023 4th International Conference on Innovative Trends in Information Technology (ICITIIT) Pub Date : 2023-02-11 DOI: 10.1109/ICITIIT57246.2023.10068647

N. Gayathri, Prawin R, Ranjith kumar A, M. R.

{"title":"Machine Learning techniques for identifying Cyberbullying on digital networks","authors":"N. Gayathri, Prawin R, Ranjith kumar A, M. R.","doi":"10.1109/ICITIIT57246.2023.10068647","DOIUrl":"https://doi.org/10.1109/ICITIIT57246.2023.10068647","url":null,"abstract":"With the proliferation of virtual entertainment platforms around the world, particularly among young people, digital taunting and enmity have become real and annoying problems that networks must address. Threats can use these levels to attack and weaken others in their networks. To combat digital tormenting, various strategies and tactics have been used or proposed, including early detection and alarms that both detect and protect victims from such attacks. Machine Learning techniques with Artificial Intelligence Framework are being widely used to identify specific linguistic patterns that danger uses to hunt for their victims. The Feeling Analysis (FA) of virtual entertainment content is one of the expanding fields of research in AI. FA makes it possible to gradually identify online harassment and continuously recognize cyberbullying. This study recommends a SA model to identify cyberbullying messages in Facebook web-based entertainment. SVM and MAXENT classifier, controlled AI arrangement tools, are employed in this model. When a higher n-grams language model is applied to such texts in correlation with analogous prior research, the findings of the investigations carried out using this model showed encouraging results. Similar patterns in the results showed that these classifiers preferred execution measures over other classifiers on such remarks.","PeriodicalId":170485,"journal":{"name":"2023 4th International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"14 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131968442","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

Improvement in Breakdown Voltage of Junctionless Power Transistor with Ga2O3 RESURF region Ga2O3 RESURF区无结功率晶体管击穿电压的改进

2023 4th International Conference on Innovative Trends in Information Technology (ICITIIT) Pub Date : 2023-02-11 DOI: 10.1109/ICITIIT57246.2023.10068623

M. R., K. S. Nikhil

引用次数: 0

An Overview of Different Types of Recommendations Systems - A Survey 不同类型推荐系统的概述-一项调查

2023 4th International Conference on Innovative Trends in Information Technology (ICITIIT) Pub Date : 2023-02-11 DOI: 10.1109/ICITIIT57246.2023.10068631

Premkumar Duraisamy, S. Yuvaraj, Yuvaraj Natarajan, V. Niranjani

引用次数: 1

Design of a Crop Disease Detection Model using Multi-parametric Bio-inspired Feature Representation and Ensemble Classification 基于多参数生物特征表示和集成分类的作物病害检测模型设计

2023 4th International Conference on Innovative Trends in Information Technology (ICITIIT) Pub Date : 2023-02-11 DOI: 10.1109/ICITIIT57246.2023.10068649

Snehal A. Lohi, Chinmay Bhatt

{"title":"Design of a Crop Disease Detection Model using Multi-parametric Bio-inspired Feature Representation and Ensemble Classification","authors":"Snehal A. Lohi, Chinmay Bhatt","doi":"10.1109/ICITIIT57246.2023.10068649","DOIUrl":"https://doi.org/10.1109/ICITIIT57246.2023.10068649","url":null,"abstract":"Crop disease detection has become an integral part of smart farming models. To perform this task, various intrusive & non-intrusive models are proposed by researchers. Intrusive models have higher deployment cost, higher complexity & contaminate underlying crops, due to which they are limited to clinical use cases. For non-intrusive methods, it is observed that most of these models are capable of achieving better performance under application-specific datasets, and cannot be scaled for larger datasets. To overcome this limitation, a novel crop disease detection & yield prediction model via multi-parametric bio-inspired feature representation is proposed in this text. The proposed model initially uses a crop-specific adaptive thresholding technique, which assists in efficient segmentation for different crop types. The segmented imagery is processed via multiple feature extraction units, which extract colour, shape, texture & convolutional features. These features are further processed via use of Genetic Algorithm (GA) based feature selection model, that implements feature variance maximization to identify optimal feature sets. The selected feature sets are classified using ensemble classification model that combines Support Vector Machines (SVMs), Multilayer Perceptron (MLP), Linear Regression (LR), Decision Tree (DT), and Naïve Bayes (NB) classifiers. These classifiers were selected based on their accuracy performance under different crop types. It was observed that SVM & LR had better performance for Soybean & Squash crops, MLP & LR had better performance for Potato & Pepper crops, while NB had better accuracy for Apple & Raspberry crops. Due to a combination of these adaptive classifiers, the proposed model is capable of achieving an accuracy of 99.5% across multiple datasets, which makes it highly useful for a wide variety of classification scenarios.","PeriodicalId":170485,"journal":{"name":"2023 4th International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114367487","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 Three-Dimensional Approach for Stock Prediction Using AI/ML Algorithms: A Review & Comparison 使用AI/ML算法进行股票预测的三维方法:综述与比较

2023 4th International Conference on Innovative Trends in Information Technology (ICITIIT) Pub Date : 2023-02-11 DOI: 10.1109/ICITIIT57246.2023.10068584

S. Raju, M. Srikanth, K. Guravaiah, P. Pandiyaan, B. Teja, K. S. Tarun

引用次数: 2