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

Analysis for Determining Best Machine learning Algorithm for Classification of Heart Diseases 确定心脏病分类最佳机器学习算法的分析

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126151

Y. Kale, S. Rathkanthiwar, Sarvadnya Rajurkar, Himanshu Parate, Anshul Ninawe, Aditya Bharti

引用次数: 0

Automated Handwriting Machine 自动书写机

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126330

KG Deekshitha, Chaitanya Patange, M. Harshitha, Pavitra Y.J

引用次数: 0

DDoS Detection Using Hybrid Deep Neural Network Approaches 基于混合深度神经网络的DDoS检测

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126434

Vanlalruata Hnamte, J. Hussain

引用次数: 0

Empirical evaluation of Amazon fine food reviews using Text Mining 使用文本挖掘对亚马逊美食评论进行实证评价

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126349

K. Harsha, S. Yuva Nitya, Sravani Kota, K. Satyanarayana, Jaya Lakshmi

{"title":"Empirical evaluation of Amazon fine food reviews using Text Mining","authors":"K. Harsha, S. Yuva Nitya, Sravani Kota, K. Satyanarayana, Jaya Lakshmi","doi":"10.1109/I2CT57861.2023.10126349","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126349","url":null,"abstract":"Approximately 1.6 million individuals use the e-commerce website “amazon” to buy things from a variety of categories, including food. Reviewing products by consumers who have already purchased them is beneficial to those who are considering doing so, however reviews can be either positive or negative. The buyer finds it difficult to read through such many evaluations before making a purchase, but machine learning ideas and training models make it possible. Our objective is to categorize the reviews based on the attributes that are present in the dataset in order to address issues like these. Redundancy is present in data when it is presented to us in its raw form. So, since evaluations with a score of 3 are regarded as impartial, we delete them along with redundancy. After that, we use the NLP tool kit (a column in the data set) to preprocess the text by removing any stop words (such as in, as, is, on, and punctuation), and we lowercase each letter. The suggested approach renders the text into machine-understandable language using word embedding techniques. Text processing is necessary because customer reviews written in language that is understood by humans cannot be read by machines. The data must be in a machine-readable language in order to apply any classification technique. We separate the data into train and test set after the preprocessing is complete. After the training is complete, we use this model on a test set of data to determine its accuracy. Next, we utilize classification methods like logistic regression and XG Boost to see how accurate our model is. This study’s conclusion involves using the model we developed to predict the review based on previous reviews. In this project, we build a model, feed it with existing reviews, apply it to upcoming reviews, and then forecast if the product is good or not. For this work we have taken the data set from Kaggle.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125348162","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

Monkeypox Detection from Various Types of Poxes: A Deep Learning Approach 从各种类型的痘中检测猴痘:一种深度学习方法

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126223

Anik Pramanik, Fayazunnesa Chowdhury, Salma Sultana, Md. Mahbubur Rahman, Md. Hasan Imam Bijoy, Md. Sadekur Rahman

{"title":"Monkeypox Detection from Various Types of Poxes: A Deep Learning Approach","authors":"Anik Pramanik, Fayazunnesa Chowdhury, Salma Sultana, Md. Mahbubur Rahman, Md. Hasan Imam Bijoy, Md. Sadekur Rahman","doi":"10.1109/I2CT57861.2023.10126223","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126223","url":null,"abstract":"According to World Health Organization (WHO) statistics, monkeypox has been identified as an epidemic in 127 nations so far, and it is spreading quickly over the globe. While the rashes and skin lesions associated with monkeypox usually mimic those of other poxes, including chickenpox and measles. Due to these similarities, it could be challenging for medical professionals to identify monkeypox based just on the appearance of lesions and rashes. Because monkeypox was uncommon before in the current outbreak, healthcare professionals lack knowledge in this area. But the scientific community has demonstrated a rising interest in implementing Artificial Intelligence in Monkeypox prediction and detection from digital skin images as a result of the success of image processing approaches in COVID-19 detection. In this study, we have applied three cutting-edge deep learning models which are InceptionV3, MobileNetV3, and DenseNet201, referred to as transfer learning models, to detect monkeypox on skin images using the publicly available Monkeypox Skin Image Dataset 2022 with four classes. According to our research, transfer learning models can detect monkeypox with a top 93.59% accuracy for the InceptionNet-V3 pre-trained model from three implemented algorithms on digitized skin images. For further research, larger training images are required to train those deep learning models to achieve a higher vigorous detection rate.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116398527","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

"Talking Books" : A Sinhala Abstractive Text Summarization Approach for Sinhala Textbooks “会说话的书”:僧伽罗语教科书的抽象文本摘要方法

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126205

Bhagya Rathnayake, Kalpani Manathunga, D. Kasthurirathna

{"title":"\"Talking Books\" : A Sinhala Abstractive Text Summarization Approach for Sinhala Textbooks","authors":"Bhagya Rathnayake, Kalpani Manathunga, D. Kasthurirathna","doi":"10.1109/I2CT57861.2023.10126205","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126205","url":null,"abstract":"The ability for books to talk would be an exciting concept, and this research discussion paves the path for an identical approach. The research objectives discussed in this paper address several burning problems, solve them and adapt them to future technological enhancements from a Sri Lankan context. Burning problems include reducing printing costs for textbooks, addressing students’ health, promoting green technology, and identifying a suitable summarising approach to the native language, Sinhala resulting in students’ learning ease. Other symptoms for the betterment indicate paths taken to reduce the weight of school bags carried by students, reduce paper usage by the government on printing textbooks, and spread technological awareness to teenagers regarding e-Learning. Textbooks issued by the government will be digitized and centralized into a single system that the government officials themselves can administer. The paper discusses limited hindsight literature and proposes 2 new algorithms for abstractive and extractive summarization for Sinhala text. The 2 algorithms are compared against one another in terms of performance, efficiency, precision and accuracy. Experts in the education domain have verified the derived summary of both algorithms. The deliverable artefacts are the mobile application, a RESTful auto-summarization plugin service, and new data sets extracted to train the GPT-3 models.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116438696","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

Power Quality Enhancements of AC Grid Using Luo Converter with GWO Based MPPT 基于GWO的MPPT技术提高交流电网电能质量

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126160

L. Chitra, K. S.

{"title":"Power Quality Enhancements of AC Grid Using Luo Converter with GWO Based MPPT","authors":"L. Chitra, K. S.","doi":"10.1109/I2CT57861.2023.10126160","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126160","url":null,"abstract":"The Hybrid Renewable Energy System (HRES), which consists of multiple solar panels and wind turbines along with energy storage device, has been used in this work to satisfy the requirements of power consumers. Because of its multiple benefits like cheap maintenance, economic benefits, and fuel independence, photovoltaic power generation has gotten a lot of attention in recent decades. However, getting stable and enhanced power from the PV is the most difficult task and so the highly efficient LUO converter is developed in this work which generates a stable output voltage from an unregulated dc source. In order to optimize the performance of LUO converter and to get supreme power output, GWO based MPPT algorithm is used since it is the most adaptive methodology that is not dependent on system information. On the other hand, Wind Energy Conversion System (WECS) contribution to global energy supply has been steadily increasing. To enhance the wind power generation, a DFIG is utilized, also a PI controller has been assigned which controls the power delivered to the inverter through the PWM rectifier. Grid synchronization is achieved by employing a PI controller which analogize the real and reactive power and delivers it output to the 3ϕ inverter through the PWM generator. MATLAB/Simulink is used to simulate the overall system in this work.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122641194","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

Embedded System Software Reliability Estimation During New Product Development 新产品开发中的嵌入式系统软件可靠性评估

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126401

Ashutosh Biswal, Ramesh S, Ranjith Kumar Sreenilayam

引用次数: 0

TransFAS: Transformer-based network for Face Anti-Spoofing using Token Guided Inspection transas:基于变压器的基于令牌引导检测的人脸防欺骗网络

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126455

Dipra Chaudhry, Harshi Goel, Bindu Verma

{"title":"TransFAS: Transformer-based network for Face Anti-Spoofing using Token Guided Inspection","authors":"Dipra Chaudhry, Harshi Goel, Bindu Verma","doi":"10.1109/I2CT57861.2023.10126455","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126455","url":null,"abstract":"Face IDs are becoming the most acceptable modality used for authentication purposes in many recognition systems. This makes it crucial for the recognition and authentication systems to carry out a spoof detection operation before performing facial recognition. The Face Anti-Spoofing (FAS) systems handle the task of identifying fakes. Traditionally, Convolutional Neural Networks (CNNs) have been used to detect spoofs. But, CNNs have certain limitations. One such limitation is that they are not very efficient in extracting the relative placement of different objects. In this paper, we propose a novel TransFAS system. It is based on Video Vision Transformer (VVT). The system takes a bunch of frames at a time and then extracts tokens from them. These tokens are flattened and then loaded with positional information to store the relative placement of each entity in a token. These embedded tokens are passed on to the Transformer Encoder. In the transformer encoder, work is done in different layers. Its final output is a prediction of whether the input sample is live or spoof (print attack, replay attack or 3D Mask attack). Our model is trained on Replay-Attack and 3DMAD datasets. Results show that our model performs better than most of the existing models.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128682089","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

Neurodegenerative Disease Detection using Deep Convolutional GANs and CNN 基于深度卷积gan和CNN的神经退行性疾病检测

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126492

Tushar Deshpande, Khushi Chavan, Priya Gandhi, Ramchandra S. Mangrulkar

{"title":"Neurodegenerative Disease Detection using Deep Convolutional GANs and CNN","authors":"Tushar Deshpande, Khushi Chavan, Priya Gandhi, Ramchandra S. Mangrulkar","doi":"10.1109/I2CT57861.2023.10126492","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126492","url":null,"abstract":"Over the past ten years, advances in deep machine learning techniques, high-speed computing infrastructure development, and an improved understanding of deep learning algorithms have created new opportunities for advanced analysis of neuroimaging data. Neuroscientists can now use the data from neuroimaging to diagnose neurodegenerative diseases. Yet, due to the similarities in disease characteristics, it is challenging to identify such disorders from neuroimaging data accurately. The reason for such results is the current or inevitable limited availability of neuroimaging data. Thus, this paper suggests an unsupervised generative modeling technique using Deep Convolutional Adversarial Networks to produce synthetic images (DCGANs). This method uses the ADNI dataset, which contains data for four neurodegenerative diseases, namely: Alzheimer’s Disease(AD), Mild Cognitive Impairment(MCI), Early Mild Cognitive Impairment(EMCI), Late Mild Cognitive Impairment(LMCI), and subsequently uses DCGAN on the small quantity of data, so increasing the dataset’s size and variety by utilizing GAN. To outperform the conventional deep learning techniques, the artificial images, and the original dataset images are combined and trained into a convolutional neural network (CNN).","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128965321","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