2022 International Conference on Innovative Trends in Information Technology (ICITIIT)最新文献_第4页

An IoT based Intelligent Transport and Road Safety System 基于物联网的智能交通与道路安全系统

2022 International Conference on Innovative Trends in Information Technology (ICITIIT) Pub Date : 2022-02-12 DOI: 10.1109/ICITIIT54346.2022.9744248

P. Sharmila, J. Nandhini, K. Anuratha, Soshya Joshi

引用次数: 0

Real Time Analysis of Diabetic Retinopathy Lesions by Employing Deep Learning and Machine Learning Algorithms using Color Fundus Data 基于眼底颜色数据的深度学习和机器学习算法的糖尿病视网膜病变实时分析

2022 International Conference on Innovative Trends in Information Technology (ICITIIT) Pub Date : 2022-02-12 DOI: 10.1109/ICITIIT54346.2022.9744228

Siddharth Gupta, A. Panwar, Akanksha Kapruwan, Nisha Chaube, Manav Chauhan

{"title":"Real Time Analysis of Diabetic Retinopathy Lesions by Employing Deep Learning and Machine Learning Algorithms using Color Fundus Data","authors":"Siddharth Gupta, A. Panwar, Akanksha Kapruwan, Nisha Chaube, Manav Chauhan","doi":"10.1109/ICITIIT54346.2022.9744228","DOIUrl":"https://doi.org/10.1109/ICITIIT54346.2022.9744228","url":null,"abstract":"Diabetes is a rapidly spreading illness that has devastating consequences on human organs such as kidney, lungs, heart, eyes, etc. Diabetic Retinopathy (DR) is a condition caused by abiding diabetes that damages small vessels carrying blood and tissues in the eyes. The condition is characterized by the creation of inflated formations in the retinal region known as Micro-aneurysms, which if ignored can result in irreversible damage to the eye's blood vessels, eventually leading to blindness. In the early stages of the disease, such clinical manifestations do not appear. As a result, regular and timely checkups are foremost important. However, manual identification of diabetic retinopathy is time intensive and prone to human mistake. In the stated research, the color fundus dataset scans after processing are passed to multiple Deep Learning (DL) models employed to learn characteristics. These models trained on millions of different images from thousands of classes. Finally, several machine learning classifiers were used to classify lesions using the collected characteristics. The extracted result shows very eye catching performance. This enables experts to create architecture that fully address the problem of classifying unidentified scans into the right class or category.","PeriodicalId":184353,"journal":{"name":"2022 International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132561707","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: 7

Analyzing the Cognitive Process Dimension and Rate of Learning to Identify the Slow Learners in e-Learning 分析网络学习中认知过程维度和学习速度识别慢学习者

2022 International Conference on Innovative Trends in Information Technology (ICITIIT) Pub Date : 2022-02-12 DOI: 10.1109/ICITIIT54346.2022.9744144

B. Joseph, Sajimon Abraham

{"title":"Analyzing the Cognitive Process Dimension and Rate of Learning to Identify the Slow Learners in e-Learning","authors":"B. Joseph, Sajimon Abraham","doi":"10.1109/ICITIIT54346.2022.9744144","DOIUrl":"https://doi.org/10.1109/ICITIIT54346.2022.9744144","url":null,"abstract":"The advancement of Internet technology has expanded the horizon of face-to-face classroom learning environments to an open, borderless learning space that is no longer curbed to the walls of a classroom. E-Learning encompasses all forms of electronically supported teaching and learning. Asynchronous e-Learning has the potential to be customized to the unique needs of each learner. Despite the possible benefits of e-Learning, the experience of educators confirms that there are many students who have lower rates of learning and require special attention and assistance in digital learning. These slow learners, as with classroom learning, also constitute a noticeable part of the student community in the e-Learning environment. Over the past decade, rapid developments in the field of big data and data analytics have offered opportunities to discover useful insights from massive volumes of educational data. In this paper, the authors have explored the possibilities in identifying and supporting slow learners in e-Learning, which will bring learning satisfaction and academic improvement. Data mining of log files from a Learning Management System (LMS) can have the power to support, challenge, and reshape current educational practices in e-Learning. The potentials of Machine Learning (ML) and Educational Data mining techniques can be employed to classify these learners based on the rate of learning and assessments conducted. An intelligent personalized remedial instruction system that addresses each learner's learning necessities and preferences will help slow learners to reach their optimum levels in the e-Learning situation and will ensure the best quality of education.","PeriodicalId":184353,"journal":{"name":"2022 International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125028015","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

Quantum Key Agreement simulation using pattern-based encoding 使用基于模式编码的量子密钥协议仿真

2022 International Conference on Innovative Trends in Information Technology (ICITIIT) Pub Date : 2022-02-12 DOI: 10.1109/ICITIIT54346.2022.9744215

Revathi Ganesan, Dilip Kothari

引用次数: 1

[ICITIIT 2022 Front cover] [ICITIIT 2022封面]

2022 International Conference on Innovative Trends in Information Technology (ICITIIT) Pub Date : 2022-02-12 DOI: 10.1109/icitiit54346.2022.9744190

引用次数: 0

Ensemble Method for User Activity classification in Ambient Assisted Living 环境辅助生活中用户活动分类的集成方法

2022 International Conference on Innovative Trends in Information Technology (ICITIIT) Pub Date : 2022-02-12 DOI: 10.1109/ICITIIT54346.2022.9744194

G. S. Madhan Kumar, S. P. Shiva Prakash, K. Krinkin

{"title":"Ensemble Method for User Activity classification in Ambient Assisted Living","authors":"G. S. Madhan Kumar, S. P. Shiva Prakash, K. Krinkin","doi":"10.1109/ICITIIT54346.2022.9744194","DOIUrl":"https://doi.org/10.1109/ICITIIT54346.2022.9744194","url":null,"abstract":"Artificial Intelligence(AI) has become a global plat-form that allows objects in IoT to Interact and perform computations. The wide range of application areas of IoT are Smart Cities, Smart grids, Smart Supply chain and Ambient Assisted Living(AAL). These applications have challenges like tolerance to uncertainty,adaptiveness to the changing environment and improved trust among users. Thus, machine learning algorithms improve the performance of smart objects in various environment. The AAL environment deploys heterogeneous devices and sensors to capture various activities carried out through the daily by the individuals who resides in the smart home. In this work, an ensemble method using k-Nearest Neighbor(KNN), Decision Tree(DT) and Logistic Regression(LR)is proposed by investigating the performance of existing conventional supervised machine learning algorithms and selecting best model by considering the sensors features and improves the performance metrics. The work is evaluated using the benchmark ARAS (Activity Recognition with Ambient Sensing) dataset. The results are analysed using different parameters. The comparative analysis show that the proposed ensemble method gives accuracy of 76.28%.","PeriodicalId":184353,"journal":{"name":"2022 International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"26 11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130632608","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

The Ramification of Single Event Transient effect on Efficient Charge Recovery Logic circuit 单事件瞬态效应对高效电荷恢复逻辑电路的影响

2022 International Conference on Innovative Trends in Information Technology (ICITIIT) Pub Date : 2022-02-12 DOI: 10.1109/ICITIIT54346.2022.9744208

Amanda Sara Philip, Sreekala K.S.

引用次数: 0

A Statistical Study and Analysis to Identify the Importance of Open-source Software 开源软件重要性的统计研究与分析

2022 International Conference on Innovative Trends in Information Technology (ICITIIT) Pub Date : 2022-02-12 DOI: 10.1109/ICITIIT54346.2022.9744176

M. Himansh, V. Manikandan

引用次数: 0

Study on the Behaviour of Mel Frequency Cepstral Coffecient Algorithm for Different Windows Mel频率倒谱系数算法在不同窗口下的行为研究

2022 International Conference on Innovative Trends in Information Technology (ICITIIT) Pub Date : 2022-02-12 DOI: 10.1109/ICITIIT54346.2022.9744231

Vimal W

引用次数: 0

Applications of Computer Vision and Machine Learning in Agriculture: A State-of-the-Art Glimpse 计算机视觉和机器学习在农业中的应用:最新的一瞥

2022 International Conference on Innovative Trends in Information Technology (ICITIIT) Pub Date : 2022-02-12 DOI: 10.1109/ICITIIT54346.2022.9744150

Md. Tarek Habib, D. Raza, Md. Mohaiminul Islam, Debasish Bhattacharjee Victor, Md. Ariful Islam Arif

引用次数: 5