2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS)最新文献_第4页

Image Captioning with Audio Reinforcement using RNN and CNN 使用RNN和CNN的音频强化图像字幕

2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS) Pub Date : 2023-06-14 DOI: 10.1109/ICSCSS57650.2023.10169692

Rohan Patankar, Harshik Sethi, Aditi Sadhukha, Nishant Banjade, Ashwini Mathur

引用次数: 1

Scrap Management using E-com Online Machine Learning Iscrap Algorithm 利用E-com在线机器学习报废算法进行报废管理

2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS) Pub Date : 2023-06-14 DOI: 10.1109/ICSCSS57650.2023.10169827

S. Hammed, M.P. Karthikeyan, R.Pranesh Raj, C. Preethi, K. Haripriya, A. Kathiravan

{"title":"Scrap Management using E-com Online Machine Learning Iscrap Algorithm","authors":"S. Hammed, M.P. Karthikeyan, R.Pranesh Raj, C. Preethi, K. Haripriya, A. Kathiravan","doi":"10.1109/ICSCSS57650.2023.10169827","DOIUrl":"https://doi.org/10.1109/ICSCSS57650.2023.10169827","url":null,"abstract":"The scrap recycler app is an online application where users can sell the scrap materials that are available in the house. Scrap materials are referred to as waste, unused products like broken items, plastics, waste metals, papers, glass etc. Scrap is defined as a material that has no economic value only the basic materials that are available in that scrap can be recovered and recycled.This application serves as a bridge to sell those waste scraps online. The main motto is to create an application that will be used to sell waste scraps and reduce the scraps that are available in the house and increase the recycling process and develop a sustainable environment. By using this application users can directly post the scrap materials online and vendors can be able to see the available scrap materials to be purchased and it is send to recycling stations. This application helps the vendors to easily make profit and it collects the scraps efficiently from the users, by this transportation costs can be managed. Only verified vendors can buy the scrap from the customers so that customer trust is developed and the vendors can mainly focus on customer satisfaction. This study provides a new perspective on selling those available scraps that are available in houses and small organizations using machine learning. The main objective of creating this algorithm is to reduce the usage of waste and reduce the land pollution. The algorithm is going to encounter challenges in sorting scraps and segregating waste, as well as disposing of waste. The machine learning is also used to detect the scraps so that the app will able to detect the scraps and indicate to the vendors. As a consequence, the algorithm is capable of resolving it. The software application is free and is available on the Android platform. The application is user-friendly with a basic structure and easily able to sell scraps online. This software application is developed using MYSQL, android studio, and kotlin.","PeriodicalId":217957,"journal":{"name":"2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124072705","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

Applications of Machine Learning in Cloud Forensics 机器学习在云取证中的应用

2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS) Pub Date : 2023-06-14 DOI: 10.1109/ICSCSS57650.2023.10169719

Malluri Reddy Theertani, Neha Valeti, Darshini Solanki, K. A. Kumar

{"title":"Applications of Machine Learning in Cloud Forensics","authors":"Malluri Reddy Theertani, Neha Valeti, Darshini Solanki, K. A. Kumar","doi":"10.1109/ICSCSS57650.2023.10169719","DOIUrl":"https://doi.org/10.1109/ICSCSS57650.2023.10169719","url":null,"abstract":"Cloud computing has recently experienced rapid expansion, making it a major target for cybercrimes. Though cloud computing has many advantages, there are also substantial security issues related to confidentiality, integrity, privacy, and availability. Being a relatively new discipline, cloud forensics faces numerous challenges and issues with interpreting and analyzing data. Forensic investigators and law enforcement confront numerous difficulties in data collection, data protection, and evidence access. Several sophisticated models have been proposed in recent years aiming to accelerate the entire investigation process or address several issues that arise frequently in forensic investigations. This review paper seeks to comprehend the significance of various machine learning models which would help cloud forensics advance significantly. It also provides a detail information on cloud forensics as well as the research trends that have been going on in this field over the past few years. A detailed comparative analysis of different approaches of machine learning on development of cloud forensics has been presented in this research study.","PeriodicalId":217957,"journal":{"name":"2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124081281","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 Thorough Assessment on Orthogonal Frequency Division Multiplexing (OFDM) based Wireless Communication: Challenges and Interpretation 基于正交频分复用(OFDM)的无线通信的全面评估:挑战与解释

2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS) Pub Date : 2023-06-14 DOI: 10.1109/ICSCSS57650.2023.10169766

Subhra Paul, D. Vetrithangam

{"title":"A Thorough Assessment on Orthogonal Frequency Division Multiplexing (OFDM) based Wireless Communication: Challenges and Interpretation","authors":"Subhra Paul, D. Vetrithangam","doi":"10.1109/ICSCSS57650.2023.10169766","DOIUrl":"https://doi.org/10.1109/ICSCSS57650.2023.10169766","url":null,"abstract":"Analysis of existing research on wireless communication states that, in the present world, Orthogonal Frequency Division Multiplexing (OFDM) is a promising technique for enabling data transmission in wireless networks due to its resource management scheme. In a specific wireless network, the available bandwidth is distributed among various channels by covering sub-carrier bands so that bandwidth and frequency can be properly utilized. For sensor device and Nano-sensor device level communication in a wireless environment, OFDM can have a great impact on forwarding data packets efficiently with less attenuation. For simultaneous multiple transmission using a shareable (bandwidth) medium, OFDM performs comparatively better throughput. A better data rate can be achieved during the data transmission process using multi-carrier communication, which is introduced in the OFDM technique. Although OFDM is one of the key communication methods for wireless networks, there are some major drawbacks to this technique. The problem statement of this research paper is to identify the major drawbacks of OFDM-based communication as well as introduce some remedial mechanisms to overcome those drawbacks. Channel fading, synchronization, reliability, and interference are the key challenges in OFDM based wireless communication. The main objective of this research is to find out key challenges of this OFDM based wireless communication field and to introduce some guidelines to handle those challenges efficiently. Additionally, a comparative analysis of OFDM-based wireless communication is provided in this paper to differentiate between our research contribution and existing research work in this field.","PeriodicalId":217957,"journal":{"name":"2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125797467","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

Atmospheric Image Correction and Removal of Cloud Cover for Satellite Images 卫星图像的大气图像校正和云量去除

2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS) Pub Date : 2023-06-14 DOI: 10.1109/ICSCSS57650.2023.10169542

Vaishnavi Kharat, Sanyukta Khatdeo, Harshada Kothe, Rutuja Kshirsagar, M. Dixit, M. Balan

{"title":"Atmospheric Image Correction and Removal of Cloud Cover for Satellite Images","authors":"Vaishnavi Kharat, Sanyukta Khatdeo, Harshada Kothe, Rutuja Kshirsagar, M. Dixit, M. Balan","doi":"10.1109/ICSCSS57650.2023.10169542","DOIUrl":"https://doi.org/10.1109/ICSCSS57650.2023.10169542","url":null,"abstract":"Accurate atmospheric correction is crucial for surface analysis and environmental monitoring. Adverse weather conditions, such as clouds and haze, often degrade the contrast and fidelity of outdoor images, leading to uncertainties in surface observations. Cloud removal techniques in computer vision and image processing, including recent approaches like Generative Adversarial Networks (GANs), Image Inpainting, and Image Fusion, have been developed to address this challenge. However, these techniques face various challenges, such as limited availability of diverse training data, artifacts introduced in the resulting images, and computational complexity. This paper focuses on the removal of cloud cover and haze in multiband satellite images using the S2 Cloudless dataset and the Dark Channel Prior (DCP) algorithm. An approach is proposed to reconstruct clouded areas and generate cloud-free spatio-temporally continuous images for in-depth time-series analysis. The objective is to enhance the utility of the data by restoring the polluted areas in cloudy satellite imagery. The methods are evaluated using metrics such as Mean Square Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Average Mean Pixel Value to assess the performance of both the input and output images. The results demonstrate the effectiveness of the methods in improving the quality and utility of cloud-covered satellite imagery, overcoming the challenges posed by limited training data, artifacts, and computational complexity.","PeriodicalId":217957,"journal":{"name":"2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125078250","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 Performance Analysis of Different Detection Techniques of Human Eye Pupils 人眼瞳孔不同检测技术的性能分析

2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS) Pub Date : 2023-06-14 DOI: 10.1109/ICSCSS57650.2023.10169155

D. Gokulavani, J. Mohana

引用次数: 0

Preserving Security of Crypto Transactions with Machine Learning Methodologies 用机器学习方法保护加密交易的安全性

2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS) Pub Date : 2023-06-14 DOI: 10.1109/ICSCSS57650.2023.10169192

Atul Kumar, Ishu Sharma

引用次数: 0

Formal Techniques to Verify Functionality of Digital Memory Decoder 验证数字记忆解码器功能的形式化技术

2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS) Pub Date : 2023-06-14 DOI: 10.1109/ICSCSS57650.2023.10169287

Gaurav Kasana, Sonam Rewari

引用次数: 0

Data Analytics for Disaster Management Response using Sentiments 使用情感进行灾害管理响应的数据分析

2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS) Pub Date : 2023-06-14 DOI: 10.1109/ICSCSS57650.2023.10169707

Supriya Kamoji, Nair Dhananjay Jayaprakash, Chacko Bibin Biju, George Anna Benoy

引用次数: 0

Brain Tumor Detection using RCNN and MobileNet 利用RCNN和MobileNet进行脑肿瘤检测

2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS) Pub Date : 2023-06-14 DOI: 10.1109/ICSCSS57650.2023.10169423

B. Swamy, P. Dedeepya, J. C. Sekhar, V. Pratap, Koneru Ananya, S. Sindhura

{"title":"Brain Tumor Detection using RCNN and MobileNet","authors":"B. Swamy, P. Dedeepya, J. C. Sekhar, V. Pratap, Koneru Ananya, S. Sindhura","doi":"10.1109/ICSCSS57650.2023.10169423","DOIUrl":"https://doi.org/10.1109/ICSCSS57650.2023.10169423","url":null,"abstract":"This study examines the identification and categorization of brain tumors by analyzing medical images. Based on their origin, it is said that brain tumors, which are an uncontrolled cell growth within the brain, can be classified into two broad categories. One is primary and the other is secondary. The main tumor detection technique is Magnetic Resonance Imaging (MRI), although it is only effective for tumors larger than 3 mm. Segmentation is frequently utilized for precise analysis of micro-sized cancers less than 3mm. To extract the features and perform classification, a method is suggested in this research which applies deep learning techniques such as Recurrent Convolutional Neural Networks and the Gray Level Co-occurrence Matrix. The improved K-means clustering technique is opted for image segmentation. The trials used a Kaggle dataset with 2870 training sets and 394 testing sets of MR images. The outcomes demonstrate that the suggested method outperforms earlier techniques like BP and U-Net. The study investigates the use of the MobileNet algorithm for classifying brain tumors in addition to the suggested technique, which yielded equivalent results. Overall, reliable and effective brain tumor detection and classification appear to be possible because of the combination of deep learning approaches with effective designs.","PeriodicalId":217957,"journal":{"name":"2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127883188","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