2023 2nd International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies (ViTECoN)最新文献_第3页

An approach for Credit-Scoring using Swarm intelligence for feature selection and PSO trained ANN based classification 一种使用群智能进行特征选择和基于粒子群算法训练的人工神经网络分类的信用评分方法

2023 2nd International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies (ViTECoN) Pub Date : 2023-05-05 DOI: 10.1109/ViTECoN58111.2023.10157909

I. Singh, Nikhil Mishra, Anshul Joshi, Nishchal Agarwal

{"title":"An approach for Credit-Scoring using Swarm intelligence for feature selection and PSO trained ANN based classification","authors":"I. Singh, Nikhil Mishra, Anshul Joshi, Nishchal Agarwal","doi":"10.1109/ViTECoN58111.2023.10157909","DOIUrl":"https://doi.org/10.1109/ViTECoN58111.2023.10157909","url":null,"abstract":"As the financial system expanded, the credit scoring process changed in a way that has attracted more interest from scholars and businesses. Artificial intelligence technology based on predictive classification has changed how credit scores are calculated. These decision-making processes are taken with the help of various data mining algorithms to predict if the client is part of a suspicious group which is more likely to cause losses. In our proposed model (SIFS-PNN), we have used swarm intelligence-based algorithms to find relevant data along with a fine-tuned artificial neural network to successfully classify, whether or not, a client is a part of a sensitive group of credit lines. To do this, we will pick features using various swarm intelligence algorithms and fine-tune our ANN using Particle Swarm Optimization algorithm to perform credit classification. We also performed a comparative study with multiple previous researches to show how the suggested approach has outperformed previous results","PeriodicalId":407488,"journal":{"name":"2023 2nd International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies (ViTECoN)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114722738","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

Using DLT for Textile Fabric Inspection: A Novel Network for Detecting Fabric Defects DLT用于纺织品检测:一种检测织物缺陷的新网络

2023 2nd International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies (ViTECoN) Pub Date : 2023-05-05 DOI: 10.1109/ViTECoN58111.2023.10157629

Gandhimathinathan A, A. B, Balaji R, D. T, Sabarisrinivas K R

引用次数: 0

Parallelization Algorithm of Computer Vision for Autonomous Vehicles 自动驾驶汽车计算机视觉并行化算法

2023 2nd International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies (ViTECoN) Pub Date : 2023-05-05 DOI: 10.1109/ViTECoN58111.2023.10157031

Kaniskaa Ms, R. Manimegalai, N. Sk

引用次数: 0

Efficient Machine Learning Model for Cardiac Disease Prediction 用于心脏病预测的高效机器学习模型

2023 2nd International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies (ViTECoN) Pub Date : 2023-05-05 DOI: 10.1109/ViTECoN58111.2023.10157382

Tanishq Soni, Mudita Uppal, D. Gupta, Gifty Gupta

引用次数: 1

Breast Cancer Prediction Using Unsupervised Learning Technique K-Means Clustering Algorithm 基于无监督学习技术的K-Means聚类算法的乳腺癌预测

2023 2nd International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies (ViTECoN) Pub Date : 2023-05-05 DOI: 10.1109/ViTECoN58111.2023.10157765

Soumyalatha Naveen, Nachiketh V. Kashyap, Varun P Kulkarni, Sandeep A, M. S. Chakradhar

{"title":"Breast Cancer Prediction Using Unsupervised Learning Technique K-Means Clustering Algorithm","authors":"Soumyalatha Naveen, Nachiketh V. Kashyap, Varun P Kulkarni, Sandeep A, M. S. Chakradhar","doi":"10.1109/ViTECoN58111.2023.10157765","DOIUrl":"https://doi.org/10.1109/ViTECoN58111.2023.10157765","url":null,"abstract":"Breast cancer is a highly deadly and common cancer that is spreading over the world and claiming many lives. Breast cancer develops in the glandular tissue of the breast in the lining of epithelial cells of ducts or (15%) lobules. These tissues heal with time. These in situ tumours may develop over time and infect the breast cells' immediate environs (stage 1), impact the lymph nodes, and finally totally spread throughout the body's organs (stage 3). There were 685,000 deaths worldwide in 2020 and a later rise in the number of malignant women. Cancer was a common disease that affected 7.8 million of the female population. The k-means algorithm is a popular data clustering algorithm. As the main analytical routine in data mining, the techniques of the clustering algorithm will impact the clustering outcome directly. This paper examines the shortcomings of the standard k-means algorithm and discusses them. This paper reviews existing methods for selecting the number of clusters for the algorithm. However, one of its drawbacks is the requirement that the number of clusters, K, be specified before the algorithm is applied. Therefore, we obtained an accuracy of 85% after executing the program, and we were able to depict the difference between a benign and a malignant tumour. And we were able to see the centroid between the two tumours.","PeriodicalId":407488,"journal":{"name":"2023 2nd International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies (ViTECoN)","volume":"322 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115840530","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

Performance Analysis of Real and Complex Wavelet Transform Techniques used for Wavelet-Based Image Denoising 用于小波图像去噪的实小波变换和复小波变换性能分析

2023 2nd International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies (ViTECoN) Pub Date : 2023-05-05 DOI: 10.1109/ViTECoN58111.2023.10157293

Lakshmi Sai Niharika Vulchi, G. Aakash, D. N. Kumar, H. Valiveti

{"title":"Performance Analysis of Real and Complex Wavelet Transform Techniques used for Wavelet-Based Image Denoising","authors":"Lakshmi Sai Niharika Vulchi, G. Aakash, D. N. Kumar, H. Valiveti","doi":"10.1109/ViTECoN58111.2023.10157293","DOIUrl":"https://doi.org/10.1109/ViTECoN58111.2023.10157293","url":null,"abstract":"Image de noising is a principal technique majorly used for original image restoration, segmentation and image classification. It is basically used to refine the images by eliminating noise embedded. In the current work, authors present a denoising technique based on Wavelet Domain Filtering. Denoising of images after domain transform helps in separating the noise and data components. The discrete wavelet transform and dual tree complex wavelet transforms work on the analysis and synthesis filter banks to filter and further segment the noisy input signal to low frequency and high frequency components constituting data artifacts and noise respectively. The progressive decomposition of data to a particular number of levels finally results in a noise-free output after filtering, considering a particular threshold. A comparative analysis of thresholding techniques is presented and evaluated for the parameters Signal to Noise Ratio (SNR) and lowest Root Mean Square Error Value (RMSE). The simulation results indicate superior performance of dual tree complex wavelet transform(DTCWT) when compared to the discrete wavelet transform.","PeriodicalId":407488,"journal":{"name":"2023 2nd International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies (ViTECoN)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115937265","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

Accident Prevention For Autonomous Vehicle 自动驾驶汽车的事故预防

2023 2nd International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies (ViTECoN) Pub Date : 2023-05-05 DOI: 10.1109/ViTECoN58111.2023.10157414

Muralidhar P, Sai Prashanth A, Pavan Kumar K, R. C, R. M.

{"title":"Accident Prevention For Autonomous Vehicle","authors":"Muralidhar P, Sai Prashanth A, Pavan Kumar K, R. C, R. M.","doi":"10.1109/ViTECoN58111.2023.10157414","DOIUrl":"https://doi.org/10.1109/ViTECoN58111.2023.10157414","url":null,"abstract":"Autonomous vehicles, also known as self-driving cars, are a rapidly developing technology that promises to revolutionize the way we travel. These vehicles use a combination of sensors, cameras, and machine learning algorithms to perform navigation, obstacle avoidance, and decision-making tasks. This advanced technology allows vehicles to operate without human intervention, making them a safer and more efficient alternative to traditional vehicles. One of the key benefits of autonomous vehicles is their potential to reduce the number of accidents caused by human error. According to statistics, the majority of car accidents are caused by factors such as distracted driving, driving under the influence, and speeding. Autonomous vehicles have the potential to eliminate these human errors, making the roads safe for everyone. In addition to improving road safety, autonomous vehicles can also improve traffic flow. By eliminating the need for human drivers, autonomous vehicles can reduce congestion, improve travel times, and reduce emissions. This can have a significant impact on the environment and the economy, as people will be able to travel more efficiently and get to their destinations faster. Autonomous vehicles can also provide increased mobility to people who cannot drive, such as the elderly and people with disabilities. This can improve their quality of life and give them greater independence. However, there are also challenges associated with the deployment of autonomous vehicles. These include technical issues such as ensuring that the technology is safe and reliable, regulatory issues such as obtaining government approval, and societal issues such as public acceptance and trust. This model works when the vehicle is in traffic and also while parking.","PeriodicalId":407488,"journal":{"name":"2023 2nd International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies (ViTECoN)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131456134","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

Forest Fire Detection using Deep Learning Techniques 使用深度学习技术进行森林火灾探测

2023 2nd International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies (ViTECoN) Pub Date : 2023-05-05 DOI: 10.1109/ViTECoN58111.2023.10157262

Ishaan Dawar, Soumyo Deep Gupta, Rashika Singh, Yash Kothari, Shirshendu Layek

{"title":"Forest Fire Detection using Deep Learning Techniques","authors":"Ishaan Dawar, Soumyo Deep Gupta, Rashika Singh, Yash Kothari, Shirshendu Layek","doi":"10.1109/ViTECoN58111.2023.10157262","DOIUrl":"https://doi.org/10.1109/ViTECoN58111.2023.10157262","url":null,"abstract":"Forest fires pose a serious threat to both people and the environment. Half of the world's forests are predicted to be destroyed by forest fires by 2030. They are a reason of serious concern since both natural and man-made disasters endanger land, plants, and human as well as animal life which exist in those habitats. To prevent substantial disasters, a quick decision-making mechanism is needed for early detection to save the environment from these fast-spreading forest fires. The latest detection mechanisms make use of artificial intelligence for early forest fire detection. This work proposes a convolutional neural network-based image identification technique for detecting forest fires. The work uses a publicly available satellite image dataset for detecting forest fires, which resolves the classification problem of recognizing images with and without fire. The work proposes the use of deep learning-based CNN models like VGGNet, LeNet5, AlexNet, and Xception and a CNN based model. Several performances criterions are used to evaluate the performance of the suggested methods which are used for identifying fire and no fire and are compared to among the applied CNN models. Where Mobilenet has the lowest accuracy, and the CNN model has the best accuracy. Considering the availability of this new forest fire detection dataset, the results of the suggested approach for classifying forest fires are encouraging.","PeriodicalId":407488,"journal":{"name":"2023 2nd International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies (ViTECoN)","volume":"7 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131759170","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

An Unmanned Automatic Coconut Tree Climber and Harvester Using IoT 使用物联网的无人自动椰子树攀登机和收割机

2023 2nd International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies (ViTECoN) Pub Date : 2023-05-05 DOI: 10.1109/ViTECoN58111.2023.10157666

M. Nirmala, K. Akilan, D. Ishvarya, S. Ramkumar

{"title":"An Unmanned Automatic Coconut Tree Climber and Harvester Using IoT","authors":"M. Nirmala, K. Akilan, D. Ishvarya, S. Ramkumar","doi":"10.1109/ViTECoN58111.2023.10157666","DOIUrl":"https://doi.org/10.1109/ViTECoN58111.2023.10157666","url":null,"abstract":"In addition to coconut juice or water, there is a diverse range of products that can be made from the coconut palm, including various food items as well as soaps and cosmetics. India and other developing nations, there is a severe lack of workers to climb coconut trees and collect coconuts. In India approximately five billion coconuts are harvested annually, and coconuts are extremely important to the economics of many places of countries. Tamil Nadu, Kerala, and Karnataka are important coconut harvesting regions in India. Most coconuts are obtained through climbing the tree by manpower. The demand of the skilled labors, more labor cost and unpredictable accident are disadvantages of human based coconut harvesting method. The proposed paper consists of a cutting-edge robotic climber and harvester for coconut trees harvester equipped with a unique Robotic Arm (RA) and a cutting tool that can be affixed to a frame resembling a coconut tree is available. The device can be wirelessly operated by an individual on the ground or via the Blynk mobile app on a smartphone. The proposed paper aims to create a cost-effective and straightforward prototype tool for harvesting coconuts by climbing and gathering them from the trees. The effectiveness of proposed paper is analyzed by using MATLAB Simulink.","PeriodicalId":407488,"journal":{"name":"2023 2nd International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies (ViTECoN)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131078005","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

IoT based Fire Detection and Warning System for Surveillance Applications using Deep Learning Model 基于物联网的火灾探测和预警系统，用于使用深度学习模型的监视应用

2023 2nd International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies (ViTECoN) Pub Date : 2023-05-05 DOI: 10.1109/ViTECoN58111.2023.10157176

Sushmitha E C, Alageswaran R, Ezhilarasie R

{"title":"IoT based Fire Detection and Warning System for Surveillance Applications using Deep Learning Model","authors":"Sushmitha E C, Alageswaran R, Ezhilarasie R","doi":"10.1109/ViTECoN58111.2023.10157176","DOIUrl":"https://doi.org/10.1109/ViTECoN58111.2023.10157176","url":null,"abstract":"Fire is a natural and destructive phenomenon that can cause significant damage to property and loss of life. Various devices, sensors, and methods have already been proposed and implemented to mitigate the effects of fire by detecting it in its early stages. Early detection helps to stop fire spread, saves lives, and mitigates the destruction of the infrastructure. Traditional sensors such as smoke, heat, flame, infrared, ultraviolet light radiation, and gas have been used for fire detection. These sensors are inefficient and suffer from low sensitivity, difficult deployment in large-scale applications, high false alarms, and detection delays. This paper proposes a Deep Learning model with an IoT system for monitoring, detecting, and Warning of fire in various places like forests, apartments, Industrial buildings, etc. A pre-trained Convolutional Neural Network namely MobileNet is used in Deep Learning as the base model. Transfer learning is used to develop a FireNet architecture for fire detection tasks. Deep Learning on IoT devices provides speed and accuracy in real-time detection.","PeriodicalId":407488,"journal":{"name":"2023 2nd International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies (ViTECoN)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133545377","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