2023 3rd International Conference on Intelligent Technologies (CONIT)最新文献_第7页

A Generalized Grayscale Image Processing Framework for Retinal Fundus Images 视网膜眼底图像的广义灰度图像处理框架

2023 3rd International Conference on Intelligent Technologies (CONIT) Pub Date : 2023-06-23 DOI: 10.1109/CONIT59222.2023.10205834

Siddhesh Yerramneni, Kotta Sai Vara Nitya, Sirikrishna Nalluri, Rajiv Senapati

{"title":"A Generalized Grayscale Image Processing Framework for Retinal Fundus Images","authors":"Siddhesh Yerramneni, Kotta Sai Vara Nitya, Sirikrishna Nalluri, Rajiv Senapati","doi":"10.1109/CONIT59222.2023.10205834","DOIUrl":"https://doi.org/10.1109/CONIT59222.2023.10205834","url":null,"abstract":"Diabetic Retinopathy (DR) is a debilitating ocular complication of diabetes that results from prolonged exposure of the retina to elevated levels of blood glucose. This exposure can lead to progressive microvascular changes and neuronal injury, resulting in a spectrum of visual impairments ranging from mild vision changes to severe vision loss and blindness. DR typically manifests as structural changes in the blood vessels of the retina, including capillary non-perfusion, microaneurysms, retinal hemorrhages, and new vessel formation. DR is challenging to diagnose and treat due to the gradual onset of symptoms and the lack of early warning signs. Therefore, regular eye exams are critical for early detection and management of DR. A human ophthalmologist would take a significant amount of time, based on their ability and experience, to go through the fundus image and diagnose DR. Despite advancements in DR management, it remains a significant public health issue, and further research is essential to improve the understanding of DR in order to overcome the existing complications. This paper proposes a solution for improving retinal fundus images by creating more precise computerized image analysis medical diagnosis with fewer computational requirements as the images are grayscaled so that irrespective of the imaging apparatus the features of the images are enhanced without loss of information. The results of the proposed framework are assessed using entropy, contrast improvement index and structural similarity index measure.","PeriodicalId":377623,"journal":{"name":"2023 3rd International Conference on Intelligent Technologies (CONIT)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132267712","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 Mental Health Performance Assessment using Support Vector Machine 基于支持向量机的心理健康绩效评估

2023 3rd International Conference on Intelligent Technologies (CONIT) Pub Date : 2023-06-23 DOI: 10.1109/CONIT59222.2023.10205772

Ravita Chahar, A. Dubey, S. Narang

引用次数: 0

A Compact MIMO Antenna for GPS/PCS/Bluetooth Wireless Applications 一种适用于GPS/ pc /蓝牙无线应用的小型MIMO天线

2023 3rd International Conference on Intelligent Technologies (CONIT) Pub Date : 2023-06-23 DOI: 10.1109/CONIT59222.2023.10205651

Dr. V N Koteswara, Rao Devana, Phani Ponnapalli, Dr. Asa Jyothi, G. Professor, G. S. Reddy, S. C. Sekhar, Dr. N. Radha

引用次数: 0

Privacy-Preserving sensitive data on Medical diagnosis using Federated Learning and Homomorphic Re-encryption 使用联邦学习和同态重加密保护医疗诊断敏感数据的隐私

2023 3rd International Conference on Intelligent Technologies (CONIT) Pub Date : 2023-06-23 DOI: 10.1109/CONIT59222.2023.10205836

Jahnavi Dasari, Telugu Sai Joshith, Duddupudi Daya Lokesh, Sanjipogu Sandeep Kumar, Ganesh Kumar Mahato, Swarnendu Kumar Chakraborty

{"title":"Privacy-Preserving sensitive data on Medical diagnosis using Federated Learning and Homomorphic Re-encryption","authors":"Jahnavi Dasari, Telugu Sai Joshith, Duddupudi Daya Lokesh, Sanjipogu Sandeep Kumar, Ganesh Kumar Mahato, Swarnendu Kumar Chakraborty","doi":"10.1109/CONIT59222.2023.10205836","DOIUrl":"https://doi.org/10.1109/CONIT59222.2023.10205836","url":null,"abstract":"Federated learning is an emerging technique that allows multiple participants to train a shared model without exchanging their private data. This approach is particularly useful for IoT applications, where data is often collected locally and stored in distributed devices like edge nodes. Privacy concerns arise when using IoT devices to record medical data for distributed learning. Techniques such as differential privacy and federated learning can help ensure data security while preserving privacy. Encryption and secure multi-party computation can also be used to securely share and compute data. By training a model on a combination of data from these devices. In the medical research, federated learning can be used to train models on data from multiple healthcare devices such as wearables, smart medical sensors, and electronic health records. By training models on local data, healthcare providers can improve the accuracy of diagnosis and treatment while protecting patient privacy. Security analysis can involve evaluating the potential vulnerabilities and risks of the system and identifying measures to protect against them. Experimental results can involve testing the performance of the system in terms of accuracy, convergence speed, and other metrics, and comparing it to other methods.","PeriodicalId":377623,"journal":{"name":"2023 3rd International Conference on Intelligent Technologies (CONIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126366709","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

WebAssembly Beyond the Web: A Review for the Edge-Cloud Continuum WebAssembly超越Web:对边缘云连续体的回顾

2023 3rd International Conference on Intelligent Technologies (CONIT) Pub Date : 2023-06-23 DOI: 10.1109/CONIT59222.2023.10205816

Sangeeta Kakati, M. Brorsson

{"title":"WebAssembly Beyond the Web: A Review for the Edge-Cloud Continuum","authors":"Sangeeta Kakati, M. Brorsson","doi":"10.1109/CONIT59222.2023.10205816","DOIUrl":"https://doi.org/10.1109/CONIT59222.2023.10205816","url":null,"abstract":"The cloud computing environment has changed over the past years, transitioning from a centralized architecture including big data centers to a dispersed and heterogeneous architecture that incorporates edge followed by device and processing units. This transformation calls for a cross-platform, interoperable solution, a feature that WebAssembly (Wasm) offers. Wasm can be used as a compact and effective representation of server-less functions or micro-services deployment at the cloud edge. In heterogeneous edge settings, where various hardware and software systems might be employed, this is especially crucial. Developers can create applications that can operate on any Wasm-compatible device without spending time worrying about platform-specific challenges by using a common runtime environment.In this survey, we indicate the main challenges and opportunities for Wasm runtimes in the edge-cloud continuum, such as performance optimisation, security, and interoperability with other programming languages and platforms. We provide a comprehensive overview of the current landscape of Wasm outside the web, including possible standardization efforts and best practices for using these runtimes, thus serving as a valuable resource for researchers and practitioners in the field.","PeriodicalId":377623,"journal":{"name":"2023 3rd International Conference on Intelligent Technologies (CONIT)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126088743","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

Threshold Segmentation Bridge Crack Detection Algorithm Based on Deep Learning 基于深度学习的阈值分割桥式裂纹检测算法

2023 3rd International Conference on Intelligent Technologies (CONIT) Pub Date : 2023-06-23 DOI: 10.1109/CONIT59222.2023.10205945

Liyong Guo

{"title":"Threshold Segmentation Bridge Crack Detection Algorithm Based on Deep Learning","authors":"Liyong Guo","doi":"10.1109/CONIT59222.2023.10205945","DOIUrl":"https://doi.org/10.1109/CONIT59222.2023.10205945","url":null,"abstract":"A crack segmentation and width measurement method based on deep learning and Zernike orthogonal moments was proposed. In response to the shortcomings of poor applicability of traditional methods, deep learning algorithms are combined with traditional algorithms based on digital image processing. Based on the qualitative classification ability of deep learning, the detection of cracks in images is divided into three levels: \"judgment of presence\", \"automatic sketching\", and \"width measurement\". Adopting multiple scales of deep learning to narrow the scope of cracks, and on the basis of traditional methods for preliminary segmentation of cracks, deep learning is used again to screen the preliminary segmented cracks, greatly improving the accuracy of crack segmentation and noise resistance in complex environments. In terms of width measurement, a method based on Zernike orthogonal moments for measuring the width of small cracks within 5 pixels is proposed to address the shortcomings of traditional \"few pixels\" methods, which have large measurement errors for small cracks within 5 pixels. The method directly uses the grayscale information of cracks to calculate the width of cracks and improves the accuracy of width measurement for small cracks in images.","PeriodicalId":377623,"journal":{"name":"2023 3rd International Conference on Intelligent Technologies (CONIT)","volume":"18 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116071306","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 Enhancement using Adaptive Notch Filter in Grid Distribution System 自适应陷波滤波器在电网配电系统中的应用

2023 3rd International Conference on Intelligent Technologies (CONIT) Pub Date : 2023-06-23 DOI: 10.1109/CONIT59222.2023.10205688

C. Singh, G. Kumar, Himanshu Kumar, Ankita Arora

引用次数: 0

Classification of Lung Diseases using Deep Learning Techniques: A Comparative Study of Classification Algorithms 基于深度学习技术的肺部疾病分类:分类算法的比较研究

2023 3rd International Conference on Intelligent Technologies (CONIT) Pub Date : 2023-06-23 DOI: 10.1109/CONIT59222.2023.10205940

Vanshika Gupta, Abhishek Singhal, Aniket Tripathi

{"title":"Classification of Lung Diseases using Deep Learning Techniques: A Comparative Study of Classification Algorithms","authors":"Vanshika Gupta, Abhishek Singhal, Aniket Tripathi","doi":"10.1109/CONIT59222.2023.10205940","DOIUrl":"https://doi.org/10.1109/CONIT59222.2023.10205940","url":null,"abstract":"The significant health impact of lung diseases hampers the life of an individual and his/her family. It is crucial to ensure that everyone lives a healthy life, hence early detection of lung diseases is encouraged at an early stage. As several lung illnesses reduce the life span of people, they are not able to live a healthy life. There are errors in many detection algorithms, so a better algorithm is required to detect such diseases. In this paper, we have discussed lung diseases and how to recognize them. The two primary techniques for identifying lung illness are therefore image processing and deep learning. Deep learning is increasingly emphasized as the preferable method with convolutional neural networks. We further discussed various machine learning algorithms and compared their results with the newly designed algorithm of a convolutional neural network with an autoencoder. There are several approaches described in the literature for classifying medical images. This paper aims to develop a useful tool that will assist medical practitioners in quickly determining if a patient has a lung disease or is at risk of contracting one; by analyzing lung images and examining disease development risk factors with the use of an autoencoder.","PeriodicalId":377623,"journal":{"name":"2023 3rd International Conference on Intelligent Technologies (CONIT)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125205658","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 Fuzzy Logic-Based Control Strategy for Power Balance and Enhance Power Quality in a Grid-Tied PV System 一种基于模糊逻辑的并网光伏系统功率平衡与提高电能质量控制策略

2023 3rd International Conference on Intelligent Technologies (CONIT) Pub Date : 2023-06-23 DOI: 10.1109/CONIT59222.2023.10205873

S. Farook, Malligunta Kiran Kumar, A. Sekhar, K. Balaji, Nanda Kumar Reddy

引用次数: 0

Sentiment Analysis at Document level of Telugu Data from Multi-Domains 多域泰卢固语数据文档级情感分析

2023 3rd International Conference on Intelligent Technologies (CONIT) Pub Date : 2023-06-23 DOI: 10.1109/CONIT59222.2023.10205691

Katipally Vighneshwar Reddy, Sachin Kumar S, KP Soman

{"title":"Sentiment Analysis at Document level of Telugu Data from Multi-Domains","authors":"Katipally Vighneshwar Reddy, Sachin Kumar S, KP Soman","doi":"10.1109/CONIT59222.2023.10205691","DOIUrl":"https://doi.org/10.1109/CONIT59222.2023.10205691","url":null,"abstract":"Finding common threads of optimism or negative in text is the goal of sentiment analysis. In the business world, it is used to learn about a product's reception, a customer's identity, and their expectations of a firm by monitoring the tone of online conversations on forums like Reddit and Twitter. Sentiment analysis in various languages has arisen as a separate topic of NLP as businesses seek for client feedback in previously untapped markets. The stakes could not be greater, since Telugu is a Dravidian language spoken by almost 82 million people. Little effort, such as annotated data and software, is put towards supporting Telugu, hence the language is often overlooked. To better understand the sentiment of the data, we conducted our research using the \"Sentirama\" dataset and, as you'll see in the paper, we used a variety of Machine Learning models (including SVM-linear, SVM-quadratic, SVM-polynomial, Random Forest, Naive Bayes, and KNN) and Featured concepts (including word2vec+ (CBOW or skip-gram), TF-IDF, and Fastext). To find the best Deep-learning model for Telugu sentiment analysis, we also tried out LSTM, Bidirectional-LSTM, and 1D-CNN.","PeriodicalId":377623,"journal":{"name":"2023 3rd International Conference on Intelligent Technologies (CONIT)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128138731","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