2023 IEEE International Conference on Electro Information Technology (eIT)最新文献_第5页

Transfer Learning Based Models for Food Detection Using ResNet-50 基于迁移学习的ResNet-50食品检测模型

2023 IEEE International Conference on Electro Information Technology (eIT) Pub Date : 2023-05-18 DOI: 10.1109/eIT57321.2023.10187288

Biswaranjan Senapati, J. Talburt, Awad Bin Naeem, Venkata Jaipal Reddy Batthula

{"title":"Transfer Learning Based Models for Food Detection Using ResNet-50","authors":"Biswaranjan Senapati, J. Talburt, Awad Bin Naeem, Venkata Jaipal Reddy Batthula","doi":"10.1109/eIT57321.2023.10187288","DOIUrl":"https://doi.org/10.1109/eIT57321.2023.10187288","url":null,"abstract":"Being overweight may be caused by eating too many calories. It is a curable medical condition defined by abnormal fat accumulation in the body. Diabetes, excessive cholesterol, and heart attacks are the most common, although high blood pressure, colon cancer, and prostate cancer are also common. Computer techniques are often utilized to address such difficulties. In this work, we develop a system that detects and identifies food allergies using food photographs. To summaries, powerful computer algorithms such as transfer learning (ResNet50) have been taught to detect food type and validate the identified label in dataset food 101, as well as supply nutrients. The fundamental purpose of this study was to create a single framework capable of managing the difficult process of detecting, localizing, and classifying food allergies. Furthermore, larger weight parameter optimization using Adam and RMS Prop optimizers was attempted to increase their performance on healthy and allergic food image datasets. The Resnet-50 was trained to obtain the greatest mean average accuracy when compared to the other transfer learning meta-architectures. It achieved the best-identifying results by utilizing an Adam optimizer and obtaining 95% accuracy. The suggested technique was discovered to be novel since it detects all food types and then provides the nutrients of that meal from another dataset. In reality, employing the transfer learning technique to successfully diagnose food allergies would assist to prevent the adverse application of issues in diet management.","PeriodicalId":113717,"journal":{"name":"2023 IEEE International Conference on Electro Information Technology (eIT)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130023821","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

Machine Learning Models for PFAS Tracking, Detection and Remediation: A Review PFAS跟踪、检测和修复的机器学习模型综述

2023 IEEE International Conference on Electro Information Technology (eIT) Pub Date : 2023-05-18 DOI: 10.1109/eIT57321.2023.10187291

Nagababu Andraju, G. Curtzwiler, Yun Ji, E. Kozliak, Prakash Ranganathan

引用次数: 0

Adaptive Real-Time Speed Limit Broadcasting for Autonomous Driving applications Using Visible Light Communication 使用可见光通信的自动驾驶应用的自适应实时限速广播

2023 IEEE International Conference on Electro Information Technology (eIT) Pub Date : 2023-05-18 DOI: 10.1109/eIT57321.2023.10187303

Rasha Ghabboun, Diana W. Dawoud, Eman Abu Shabab, Shereen S. Ismail

引用次数: 0

Battery Cell Imbalance and Electric Vehicles Range: Correlation and NMPC-based Balancing Control 电池不平衡与电动汽车续航里程:相关性和基于nmpc的平衡控制

2023 IEEE International Conference on Electro Information Technology (eIT) Pub Date : 2023-05-18 DOI: 10.1109/eIT57321.2023.10187286

Jun Chen, Zhao-Ying Zhou

引用次数: 1

Environment Provisioning and Management for Cybersecurity Education 网络安全教育环境提供与管理

2023 IEEE International Conference on Electro Information Technology (eIT) Pub Date : 2023-05-18 DOI: 10.1109/eIT57321.2023.10187365

J. Ford, David Arnold, J. Saniie

{"title":"Environment Provisioning and Management for Cybersecurity Education","authors":"J. Ford, David Arnold, J. Saniie","doi":"10.1109/eIT57321.2023.10187365","DOIUrl":"https://doi.org/10.1109/eIT57321.2023.10187365","url":null,"abstract":"Hands-on learning environments and cyber ranges are popular tools in cybersecurity education. These resources provide students with practical assessments to strengthen their abilities and can assist in transferring material from the classroom to real-world scenarios. Additionally, virtualization environments, such as Proxmox, provide scalability and network flexibility that can be adapted to newly discovered threats. However, due to the increasing demand for cybersecurity skills and experience, learning environments must support an even greater number of students each term. Manual provisioning and management of environments for large student populations can consume valuable time for the instructor. To address this challenge, we developed an Environment Provisioning and Management Tool for cybersecurity education. Our solution interacts with the exposed Proxmox API to automate the process of user creation, server provisioning, and server destruction for a large set of users. Remote access will be managed by a pfSense firewall. Based on our testing, a six-machine user environment could be provisioned in 14.96 seconds and destroyed in 15.06 seconds.","PeriodicalId":113717,"journal":{"name":"2023 IEEE International Conference on Electro Information Technology (eIT)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126630060","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 Evaluation of Real-time Malware Detection in IoT Devices: Comparison of Machine Learning Algorithms with RapidMiner 物联网设备中实时恶意软件检测的评估:机器学习算法与RapidMiner的比较

2023 IEEE International Conference on Electro Information Technology (eIT) Pub Date : 2023-05-18 DOI: 10.1109/eIT57321.2023.10187265

Minakshi Arya, Shubhavi Arya, Saatvik Arya

引用次数: 0

Towards Blockchain-based Adaptive Trust Management in Wireless Sensor Networks 基于区块链的无线传感器网络自适应信任管理

2023 IEEE International Conference on Electro Information Technology (eIT) Pub Date : 2023-05-18 DOI: 10.1109/eIT57321.2023.10187278

Shereen S. Ismail, Diana W. Dawoud, Tamara Al-Zyoud, H. Reza

{"title":"Towards Blockchain-based Adaptive Trust Management in Wireless Sensor Networks","authors":"Shereen S. Ismail, Diana W. Dawoud, Tamara Al-Zyoud, H. Reza","doi":"10.1109/eIT57321.2023.10187278","DOIUrl":"https://doi.org/10.1109/eIT57321.2023.10187278","url":null,"abstract":"Blockchain is an emerging technology that can be utilized to enhance Wireless Sensor Networks (WSNs) security level in various Internet of Things (IoT) applications. However, the implementation of blockchain in WSNs remains a challenging task due to the high demands for processing and communication. WSNs are prone to different types of cyber-attacks and the sensor nodes can be compromised or become unreliable as they are the primary data sources. To address this issue, it is crucial to incorporate a blockchain-based trust mechanism that guarantees the selection of trusted sources of data-gathering. This paper proposes a lightweight blockchain-based adaptive trust management mechanism that maintains nodes' trust and facilitates the identification of malicious nodes. A trust smart contract is introduced to evaluate the trustworthiness of each sensor node based on its behavior during the network operation using a set of assessment metrics such as node status, Transmitted Signal Strength, Packet Sending Rate, Packet Forwarding Rate, and Forwarding Delay. The proposed mechanism is shown to help mitigate potential cyber-attacks and maintain trustworthiness among sensor nodes.","PeriodicalId":113717,"journal":{"name":"2023 IEEE International Conference on Electro Information Technology (eIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129855718","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

Analysis of Mobile Sensing Applications for Pandemic Monitoring 移动传感在流行病监测中的应用分析

2023 IEEE International Conference on Electro Information Technology (eIT) Pub Date : 2023-05-18 DOI: 10.1109/eIT57321.2023.10187280

S. Tasnim, Daniel Piner

引用次数: 0

Gastrointestinal Disease Diagnosis with Hybrid Model of Capsules and CNNs 胶囊与cnn混合模型诊断胃肠道疾病

2023 IEEE International Conference on Electro Information Technology (eIT) Pub Date : 2023-05-18 DOI: 10.1109/eIT57321.2023.10187250

M. Sarsengeldin, Sanim Imatayeva, Nurmukhamed Abeuov, Myrzakhan Naukhanov, Abdullah Said Erdogan, Debesh Jha, Ulas Bagci

{"title":"Gastrointestinal Disease Diagnosis with Hybrid Model of Capsules and CNNs","authors":"M. Sarsengeldin, Sanim Imatayeva, Nurmukhamed Abeuov, Myrzakhan Naukhanov, Abdullah Said Erdogan, Debesh Jha, Ulas Bagci","doi":"10.1109/eIT57321.2023.10187250","DOIUrl":"https://doi.org/10.1109/eIT57321.2023.10187250","url":null,"abstract":"The Gastrointestinal (GI) tract is responsible for different types of cancer-related mortality worldwide. Regular screening is recommended to detect abnormalities in the GI tract early. However, studies have shown a large number of miss-rates of early GI precursors. This is mostly due to the shortage of experienced physicians and the overall clinical burden. A computer-aided diagnosis system can play a significant role in identifying abnormalities and assisting gastroenterologists during the examination. The main objective of this work is to develop a deep learning-based model for gastrointestinal tract findings classification (pathological findings, anatomical landmarks, polyp removal cases, therapeutic interventions, and the quality of mucosal views) using VGG16 and Capsule Networks. We ex-periment with two commonly available GI endoscopy datasets (Kvasir and HyperKvasir) to achieve this goal. We proposed VGG16+CapsNets-based architecture for the classification of GI abnormalities and findings. For the Kvasir dataset (5 classes), we obtained Matthew's correlation coefficient (MCC) of 89.00%. Similarly, for the HyperKvasir dataset (23 classes), we obtained an MCC of 83.00%. Overall our obtained results are good with the highly imbalanced dataset. Our experimental results on the retrospective dataset showed that the proposed model could act as a benchmark for GI endoscopy image classification tasks.","PeriodicalId":113717,"journal":{"name":"2023 IEEE International Conference on Electro Information Technology (eIT)","volume":"7 10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128766548","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

Machine Learning Approaches for Early Diagnosis of Breast Cancer: A Comparative Study of Performance Evaluation 用于乳腺癌早期诊断的机器学习方法:性能评估的比较研究

2023 IEEE International Conference on Electro Information Technology (eIT) Pub Date : 2023-05-18 DOI: 10.1109/eIT57321.2023.10187257

Rabia Emhamed Al Mamlook, Sujeet Shresth, Tasnim Gharaibeh, A. Almuflih, Wassnaa Al-Mawee, H. Bzizi

{"title":"Machine Learning Approaches for Early Diagnosis of Breast Cancer: A Comparative Study of Performance Evaluation","authors":"Rabia Emhamed Al Mamlook, Sujeet Shresth, Tasnim Gharaibeh, A. Almuflih, Wassnaa Al-Mawee, H. Bzizi","doi":"10.1109/eIT57321.2023.10187257","DOIUrl":"https://doi.org/10.1109/eIT57321.2023.10187257","url":null,"abstract":"Breast cancer is a leading cause of death among women worldwide. Early detection and diagnosis are crucial to improving the chances of survival. This paper presents a study on the diagnosis of breast cancer using various machine-learning approaches. The study includes the performance evaluation of nine different techniques using confusion matrix accuracy for Sensitivity, Specificity, Precision, PME, PPV, NPV, and Model Accuracy. AdaBoost is found to have the highest Sensitivity and PME, while Random Forest and MLP gave the best Specificity and Precision. Logistic Regression is found to be the best model for accuracy with 97.8%, followed by SVM with 96.49%, Random Forest with 95.61%, and KNN & Decision Forest with 94.73%. The proposed approach is found to have the highest accuracy of 97.80% compared to other approaches studied. Our results indicate that the proposed approach using discretization can significantly improve the signal-to-noise ratio in the diagnosis of breast cancer. This approach can accurately predict and diagnose breast cancer using a subset of features.","PeriodicalId":113717,"journal":{"name":"2023 IEEE International Conference on Electro Information Technology (eIT)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127948589","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