2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)最新文献_第5页

Detecting Parkinson's Disease using Machine Learning 利用机器学习检测帕金森病

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10083581

N. R, Vinora A, S AlwynRajiv, Ajitha E, Sivakarthi G

{"title":"Detecting Parkinson's Disease using Machine Learning","authors":"N. R, Vinora A, S AlwynRajiv, Ajitha E, Sivakarthi G","doi":"10.1109/ICECONF57129.2023.10083581","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10083581","url":null,"abstract":"SubstantiaNigra, a part of the Mesencephalon, is the primary site of dopaminergic neuron loss in Parkinson's disease (PD), which is currently incurable (midbrain). All PD medications address symptoms; they do not prevent or postpone the degeneration of dopaminergic neurons. Machine learning techniques are applicable here since tremor detection and classification are crucial for the diagnosis and treatment of PD patients. This is one of the most common movement disorders seen in clinical practice, and it is frequently categorized according to etiological or behavioral traits. Another critical challenge is identifying and evaluating PD-related gait patterns, such as gait start and freezing, which are defining PD symptoms. Given that 90% of Parkinson's disease patients have vocal impairment, it is crucial to analyze speech data to discriminate between healthy individuals and those with the condition. This study, which presents a succinct assessment of the current state-of-the-art and some potential for future research, was inspired by the ongoing PD management initiative.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121749929","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 Novel and Robust Breast Cancer classification based on Histopathological Images using Naive Bayes Classifier 基于组织病理图像的一种新颖稳健的乳腺癌分类方法——朴素贝叶斯分类器

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10083855

S. G, Ramkumar G

{"title":"A Novel and Robust Breast Cancer classification based on Histopathological Images using Naive Bayes Classifier","authors":"S. G, Ramkumar G","doi":"10.1109/ICECONF57129.2023.10083855","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10083855","url":null,"abstract":"One of the most significant problems facing public health today, breast cancer is regarded as the primary reason for cancer-related mortality among females all over the world. Early detection of this condition can significantly help in boosting the likelihood of the patient surviving the illness. In this regard, the gold standard diagnostic procedure is the biopsy, which entails the collection of tissue samples for subsequent examination under the microscope. However, the histological examination of breast cancer is not a simple process, requires a significant amount of effort, and can result in a significant amount of disagreement among pathologists. Pathologists may therefore benefit from the assistance that an automatic diagnostic system can provide in terms of improving the efficiency of diagnostic procedures. In this study, we identified cases of breast cancer by employing the Naive Bayes (NB) method. The implementations of this machine learning method could most certainly help with breast cancer control efforts for identifying, predicting, and preventing the disease, as well as planning for health care.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","volume":"176 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133984754","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: 2

Price Prediction of Bitcoin and Ethereum - A Machine Learning Approach 比特币和以太坊的价格预测——一种机器学习方法

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10084003

S. B, Tamil Iniyal T, Thasmiya J, Taariq Ziyaadh J, Sri Ranjani, Teju Thomas S

引用次数: 1

A Novel Ai Framework for Personalisation and Customization of Product Prices through Bigdata Analytics 通过大数据分析实现产品价格个性化和定制的新型Ai框架

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10083978

Sudharson D, Aman Kumar Dubey, Arun Kumar B, S. J, Neha F, Kavinaya S K

引用次数: 1

Forest Fire Detection: A Comparative Analysis of Deep Learning Algorithms 森林火灾探测:深度学习算法的比较分析

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10084329

M. Karthi, R. Priscilla, Subhashini G, Niroshini Infantia C, Abijith G R, Vinisha J

{"title":"Forest Fire Detection: A Comparative Analysis of Deep Learning Algorithms","authors":"M. Karthi, R. Priscilla, Subhashini G, Niroshini Infantia C, Abijith G R, Vinisha J","doi":"10.1109/ICECONF57129.2023.10084329","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10084329","url":null,"abstract":"Fires are a serious hazard to the planet, from spreading cities to impenetrable jungles. These must be avoided by implementing fire detection systems, however, the high cost, specialized connectivity, misfires, and lack of reliability of current facilities-based detection systems have functioned as hurdles. In this paper, we use Deep Learning to make a step toward detecting fire in videos. Deep learning is a novel concept built on artificial neural networks where it has excelled in a number of fields. We intend to resolve the inadequacies of current methods and develop a precise and perceptive system that can identify firesas quickly as practicableand function in a range of environments, saving a tremendous amount of time and resources. the recommended commended a fire detection is an approach based on Convolutional Neural Networks-CNN and the YOLOV3 method to accomplish well ad suitablefire picture detection, which is compatible with fire detection through dataset training. existing fire-detection systems in smart cities can be changed with computer vision techniques to build fire safety in the community using digital innovations. In this work, we created a fire detector that can quickly and precisely recognize even the smallest sparks and sound an alarm if a fire starts over eight seconds. An improved You Only Look Once (YOLOv3) network was used to create a revolutionary convolutional neural network that can detect fire zones.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123085715","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

Breast Cancer Detection Mammogram Imagesusing Convolution Neural Network 基于卷积神经网络的乳腺癌乳房x线图像检测

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10083530

S. V, G. Vadivu

{"title":"Breast Cancer Detection Mammogram Imagesusing Convolution Neural Network","authors":"S. V, G. Vadivu","doi":"10.1109/ICECONF57129.2023.10083530","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10083530","url":null,"abstract":"One in eight women globally develop breast cancer. By identifying the cancer of the breast tissue cells, it is diagnosed.Utilizing various algorithms and methodologies, modern medical image processing systems examine histopathology images that have been recorded by a microscope.Medical imaging and pathology tools are being processed using machine learning techniques.Computer-aided methods are used to achieve better outcomes than manual pathological detection systems since manually identifying a cancer cell is a laborious operation and entails human mistake. Transfer learning and fine-tuning can also be used to get the most out of a CNN that has already been trained. The first is to develop simple models or adapt existing ones to reduce the time investment and the number of training instances.In deep learning, this is typically accomplished by first extracting features with the assistance of a convolutional neural network (CNN), and then categorizing data with the assistance of a fully connected network. The field of medical imaging makes extensive use of the technique of deep learning because it does not necessitate prior knowledge in a field that is related to it. Within the scope of this investigation, we trained a convolutional neural network to generate forecasts that had an accuracy of up to 88.86%.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122188970","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

Dynamic Host Configuration Protocol Attacks and its Detection Using Python Scripts 动态主机配置协议攻击及其使用Python脚本检测

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10084265

Pratik Shrestha, Tshering Dolkar Sherpa

引用次数: 1

Automated Fare Collection System for Public Transport using Intelligent IoT based System 基于智能物联网系统的公共交通自动收费系统

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10083627

Kanwarpartap Singh Gill, Avinash Sharma, Vatsala Anand, Sheifali Gupta

{"title":"Automated Fare Collection System for Public Transport using Intelligent IoT based System","authors":"Kanwarpartap Singh Gill, Avinash Sharma, Vatsala Anand, Sheifali Gupta","doi":"10.1109/ICECONF57129.2023.10083627","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10083627","url":null,"abstract":"Unfortunately, in many nations, public transportation is difficult for the visually impaired to utilise and access. In the case of buses, for example, the visually impaired have difficulties identifying and calculating the arrival of buses at bus stations. Unlike regular persons who travel alone, blind people require constant guidance. This may have an impact on their ability to function as contributing members of society. Furthermore, the difficulties for the visually impaired to use public transit will isolate them and make it impossible for them to live a normal life. In this research, a solution is presented for public transportation automated fare collection that is not only simple to use but also accessible to those with visual impairments. This system comprises an RFID tag to swipe against an RFID reader installed in the public transport, IR sensors to detect the number of co-passengers traveling along with a passenger, a control unit to record boarding of passenger and using GPS to determine distance and an LCD to show price based on that distance. A speaker is also installed into the system for providing audio instructions to a visually impaired individual for boarding and de-boarding of the transport.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124829470","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

Supervised Machine Learning Strategy for detection of covid19 patients 用于检测covid - 19患者的监督机器学习策略

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10083602

I. S. Hephzi Punithavathi, K. Deepa, Cheruku Poorna Venkata Srinivasa Rao, S. Gopal, P. Rajasekar, Ashok Kumar

引用次数: 0

Leaf Disease Identification using ResNet 利用ResNet进行叶片病害鉴定

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10083963

P. Sirenjeevi, J. M. Karthick, K. Agalya, R. Srikanth, T. Elangovan, R. Nareshkumar

引用次数: 2