2023 International Conference on Electrical, Computer and Communication Engineering (ECCE)最新文献_第8页

Numerical Analysis of Coronary Stent Alloy Materials During Radial Expansion 冠状动脉支架合金材料径向膨胀的数值分析

2023 International Conference on Electrical, Computer and Communication Engineering (ECCE) Pub Date : 2023-02-23 DOI: 10.1109/ECCE57851.2023.10101627

Samanta Paul, Torikul Islam Palash, Amit Dutta Roy, Arup Kumar Debnath

{"title":"Numerical Analysis of Coronary Stent Alloy Materials During Radial Expansion","authors":"Samanta Paul, Torikul Islam Palash, Amit Dutta Roy, Arup Kumar Debnath","doi":"10.1109/ECCE57851.2023.10101627","DOIUrl":"https://doi.org/10.1109/ECCE57851.2023.10101627","url":null,"abstract":"Atherosclerosis has been increasing rapidly in the past couple of decades. To treat this pathology, a stent with an expandable balloon has been mostly utilized among all other techniques. However, the performance of the stent is predominantly contingent upon the materials that are utilized for stent development. Though stainless steel and Co-Cr are used the most in stent manufacturing, there has been the development of new prospective biomaterials, mostly alloys, for coronary stent deployment. In this paper, four different alloys have been selected as those materials are considered to have better properties. The behavior of disparate alloy materials such as Co-Cr, AZ31, WE43 as well as Fe-Mn-Si unprecedentedly in stent development is studied through Finite Element Simulation in COMSOL Multiphysics. For the simulation, Palmaz- Schatz model is used and performance properties of the stent after simulation including stress distribution, dogboning, foreshortening along with recoil are evaluated. With the results of the simulation, it is perceived that WE43 is exhibiting better performance during and after stent expansion than other material alloys. It has lesser Von misses stress (400 MPa) with the lowest dogboning (0.48) and medium ranged foreshortening (-0.29) compared to other material alloys. AZ31 is observed to have shown closer results to WE43 due to their similar mechanical properties. Therefore, WE43 along with AZ31- these Mg alloys can be used as stent materials alongside commercially used materials because of their better performances, with the requirement of testing it experimentally beforehand.","PeriodicalId":131537,"journal":{"name":"2023 International Conference on Electrical, Computer and Communication Engineering (ECCE)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127206843","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

Intelligent training system for snowboard big air 滑雪板大气流智能训练系统

2023 International Conference on Electrical, Computer and Communication Engineering (ECCE) Pub Date : 2023-02-23 DOI: 10.1109/ECCE57851.2023.10101605

Liang Jiang, Bo Huo, Qing Sun, Xue Chen, Xianzhi Gao

引用次数: 0

Dynamic Hand Gesture Recognition using Sequence of Human Joint Relative Angles 基于人体关节相对角度序列的动态手势识别

2023 International Conference on Electrical, Computer and Communication Engineering (ECCE) Pub Date : 2023-02-23 DOI: 10.1109/ECCE57851.2023.10101606

S. Ishrak, Muhaimin Bin Munir, M. H. Kabir

引用次数: 0

Interpretable Multi Labeled Bengali Toxic Comments Classification using Deep Learning 基于深度学习的可解释多标签孟加拉语有毒评论分类

2023 International Conference on Electrical, Computer and Communication Engineering (ECCE) Pub Date : 2023-02-23 DOI: 10.1109/ECCE57851.2023.10101588

Tanveer Ahmed Belal, G. M. Shahariar, M. H. Kabir

{"title":"Interpretable Multi Labeled Bengali Toxic Comments Classification using Deep Learning","authors":"Tanveer Ahmed Belal, G. M. Shahariar, M. H. Kabir","doi":"10.1109/ECCE57851.2023.10101588","DOIUrl":"https://doi.org/10.1109/ECCE57851.2023.10101588","url":null,"abstract":"This paper presents a deep learning-based pipeline for categorizing Bengali toxic comments, in which at first a binary classification model is used to determine whether a comment is toxic or not, and then a multi-label classifier is employed to determine which toxicity type the comment belongs to. For this purpose, we have prepared a manually labeled dataset consisting of 16,073 instances among which 8,488 are Toxic and any toxic comment may correspond to one or more of the six toxic categories - vulgar, hate, religious, threat, troll, and insult simulta-neously. Long Short Term Memory (LSTM) with BERT Embedding achieved 89.42% accuracy for the binary classification task while as a multi-label classifier, a combination of Convolutional Neural Network and Bi-directional Long Short Term Memory (CNN-BiLSTM) with attention mechanism achieved 78.92% accuracy and 0.86 as weighted F1-score. To explain the predictions and interpret the word feature importance during classification by the proposed models, we utilized Local Interpretable Model-Agnostic Explanations (LIME) framework. We have made our dataset public and can be accessed at - https://github.com/deepu099cse/Multi-Labeled-Bengali-Toxic-Comments-Classification","PeriodicalId":131537,"journal":{"name":"2023 International Conference on Electrical, Computer and Communication Engineering (ECCE)","volume":"2007 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134618713","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

A Decentralized Secure Blockchain-based Privacy-Preserving Healthcare Clouds and Applications 基于区块链的分散安全隐私保护医疗云和应用程序

2023 International Conference on Electrical, Computer and Communication Engineering (ECCE) Pub Date : 2023-02-23 DOI: 10.1109/ECCE57851.2023.10101540

Md. Anwar Hussen Wadud, Anichur Rahman, Md. Jahidul Islam, T. M. Amir-Ul-Haque Bhuiyan, Md. Jobaer Hossain, Rakib Hossen

{"title":"A Decentralized Secure Blockchain-based Privacy-Preserving Healthcare Clouds and Applications","authors":"Md. Anwar Hussen Wadud, Anichur Rahman, Md. Jahidul Islam, T. M. Amir-Ul-Haque Bhuiyan, Md. Jobaer Hossain, Rakib Hossen","doi":"10.1109/ECCE57851.2023.10101540","DOIUrl":"https://doi.org/10.1109/ECCE57851.2023.10101540","url":null,"abstract":"In recent times, Blockchain (BC) technology has been one of the fastest-growing technologies poised to perform a significant role in the near future. In the age of Cloud Computing (CC) solutions, the Internet of Things (IoT), and big data analytics, BC guarantees data safety, transaction security, and self-regulation development. CC solutions initiated its role in the health industry due to its flexibility and efficient energy consumption paradigm. Nevertheless, CC operations have an issue with preserving confidential data and sharing patients' critical medical data with other healthcare facilities. BC provides solutions in regard to cloud protection and privacy issues of such decentralization features coupled with information protection and privacy, while the cloud contributes towards the resolution of BC's measurability and efficiency difficulties, thereby introducing the idea of an innovative BC-Cloud integration to track and preserve the electronic health records of patients in a reliable manner. In this paper, we present a BC-cloud combination for the electronic healthcare service to provide healthcare officials with the impulses following the rise of this most delinquent model, propose an arrangement of surviving structures, and their applicability for more reliable medical healthcare services. We then evaluate the improvement stages and co-operations and focus on the analysis difficulties for the combined BC-cloud structure, potential solutions, and prospective analysis objectives. This paper's outcome will benefit the health service enterprise to create and improve data administration systems to address patient concerns reliably.","PeriodicalId":131537,"journal":{"name":"2023 International Conference on Electrical, Computer and Communication Engineering (ECCE)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132257608","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: 3

A Statistical Investigation of Spatial Consistency and Human Blockage Consideration based mmWave Channel Modeling for 5G Back-Haul Networks 基于空间一致性和人为阻塞考虑的5G回程网络毫米波信道建模统计研究

2023 International Conference on Electrical, Computer and Communication Engineering (ECCE) Pub Date : 2023-02-23 DOI: 10.1109/ECCE57851.2023.10100745

Md. Rahat Imrose, Md. Tarek Hassan, Md. Rajibul Hassen, M. Mowla

引用次数: 0

Recognition of Leukemia Sub-types Using Transfer Learning and Extraction of Distinguishable Features Using an Effective Machine Learning Approach 使用迁移学习识别白血病亚型和使用有效的机器学习方法提取可区分特征

2023 International Conference on Electrical, Computer and Communication Engineering (ECCE) Pub Date : 2023-02-23 DOI: 10.1109/ECCE57851.2023.10101490

Tahsen Islam Sajon, Maria Chowdhury, Azmain Yakin Srizon, Md. Farukuzzaman Faruk, S. M. Mahedy Hasan, Abu Sayeed, A. F. M. Minhazur Rahman

{"title":"Recognition of Leukemia Sub-types Using Transfer Learning and Extraction of Distinguishable Features Using an Effective Machine Learning Approach","authors":"Tahsen Islam Sajon, Maria Chowdhury, Azmain Yakin Srizon, Md. Farukuzzaman Faruk, S. M. Mahedy Hasan, Abu Sayeed, A. F. M. Minhazur Rahman","doi":"10.1109/ECCE57851.2023.10101490","DOIUrl":"https://doi.org/10.1109/ECCE57851.2023.10101490","url":null,"abstract":"Although extensive research has been conducted on leukemia, the disease still accounts for more than 350,000 fatalities annually. Automated Leukemia diagnosis may alter the situation because actions can be taken immediately; as a result, accurate detection of Leukemia has been a subject of interest for researchers. As statistics grow and expand, the need for precise leukemia identification continues to increase. In this study, we investigated a dataset of leukemia that used the WHO classification scheme. We developed a modified DenseNet201 design that achieved an overall accuracy of 99.69% without relying on data augmentation. Additionally, we identified and validated key features for leukemia classification by utilizing three feature extraction approaches (i.e., hu moments, haralick texture and parameter-free threshold adjacency statistics) and several machine learning classifiers (i.e., Gaussian Process, Support Vector Machine, K-Nearest Neighbor or KNN, Extra Trees Classifier, and Logistic regression) that outperformed earlier feature extraction-based techniques.","PeriodicalId":131537,"journal":{"name":"2023 International Conference on Electrical, Computer and Communication Engineering (ECCE)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130989353","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

Optimization strategies for Micro-grid energy management and scheduling systems by Sine cosine Algorithm 基于正弦余弦算法的微电网能量管理与调度系统优化策略

2023 International Conference on Electrical, Computer and Communication Engineering (ECCE) Pub Date : 2023-02-23 DOI: 10.1109/ECCE57851.2023.10101663

Mohammad A. Islam, Md. Abu Zardar, M. Shafiullah, Awatif Nadia

{"title":"Optimization strategies for Micro-grid energy management and scheduling systems by Sine cosine Algorithm","authors":"Mohammad A. Islam, Md. Abu Zardar, M. Shafiullah, Awatif Nadia","doi":"10.1109/ECCE57851.2023.10101663","DOIUrl":"https://doi.org/10.1109/ECCE57851.2023.10101663","url":null,"abstract":"Energy is an essential factor for power generation, where a community microgrid seeks to integrate renewable energy sources such as solar, wind, tidal, hydropower, and bioenergy into a distribution network along with an energy storage system. This is not only for the security of national growth, but also to minimize electricity costs and availability. The source of renewable energy has no predicted schedule for synchronization of power generation, supporting battery as energy storage for emergency supply or backup. In this paper, predictive scheduling of battery energy considering the cost of degradation due to charging and discharging cycles is proposed. According to the economy, day ahead planning has required a technique for solving the cost efficiency. In this paper, planned has developed by sine-cosine algorithm, which belongs to mathematical trigonometric base populations sector meta-heuristic technique. The sine-cosine algorithm works on the principle of trigonometric mathematical solution to search a target location within an area evaluation.","PeriodicalId":131537,"journal":{"name":"2023 International Conference on Electrical, Computer and Communication Engineering (ECCE)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126165420","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

SkinNet-8: An Efficient CNN Architecture for Classifying Skin Cancer on an Imbalanced Dataset SkinNet-8:一个在不平衡数据集上对皮肤癌进行分类的高效CNN架构

2023 International Conference on Electrical, Computer and Communication Engineering (ECCE) Pub Date : 2023-02-23 DOI: 10.1109/ECCE57851.2023.10101527

Nur Mohammad Fahad, S. Sakib, Mohaimenul Azam Khan Raiaan, Md. Saddam Hossain Mukta

{"title":"SkinNet-8: An Efficient CNN Architecture for Classifying Skin Cancer on an Imbalanced Dataset","authors":"Nur Mohammad Fahad, S. Sakib, Mohaimenul Azam Khan Raiaan, Md. Saddam Hossain Mukta","doi":"10.1109/ECCE57851.2023.10101527","DOIUrl":"https://doi.org/10.1109/ECCE57851.2023.10101527","url":null,"abstract":"Skin cancer is a fatal disease that has become the leading cause of death worldwide in recent years, although it is curable if diagnosed early. Early skin cancer detection significantly improves patients' chances of survival and reduces mortality. In this research, we conduct experiments on a high imbalance dermoscopic ISIC 2020 dataset. The primary objective of this study is to develop a shallow CNN architecture to complete the classification task effectively, requiring fewer computational resources without compromising accuracy. We have used pre-processing techniques to remove image noise and truncation and augmentation techniques to balance the dataset before fitting it into the model. Multiple performance measurement metrics were utilized to establish the overall performance. Our proposed model yields a remarkable test accuracy of 98.81%. We compare our models' performance with different transfer learning (TL) models to assess the faster convergence rate. The proposed model demonstrated its robustness by outperforming the other TL models in terms of accuracy within a short processing time. It is reasonable to assume that our proposed system will reliably aid dermatologists in diagnosing skin cancer patients early and increasing survival rates.","PeriodicalId":131537,"journal":{"name":"2023 International Conference on Electrical, Computer and Communication Engineering (ECCE)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127158747","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

AI-based Consumers' Preference Prediction Using a Research-grade BCI and a Commercial-grade BCI for Neuromarketing: A Systematic Comparison 神经营销中基于ai的研究级脑机接口与商业级脑机接口的消费者偏好预测:系统比较

2023 International Conference on Electrical, Computer and Communication Engineering (ECCE) Pub Date : 2023-02-23 DOI: 10.1109/ECCE57851.2023.10101563

Farhan Ishtiaque, Fazla Rabbi Mashrur, Mohammad Tohidul Islam Miya, Khandoker Mahmudur Rahman, R. Vaidyanathan, S. Anwar, F. Sarker, K. Mamun

{"title":"AI-based Consumers' Preference Prediction Using a Research-grade BCI and a Commercial-grade BCI for Neuromarketing: A Systematic Comparison","authors":"Farhan Ishtiaque, Fazla Rabbi Mashrur, Mohammad Tohidul Islam Miya, Khandoker Mahmudur Rahman, R. Vaidyanathan, S. Anwar, F. Sarker, K. Mamun","doi":"10.1109/ECCE57851.2023.10101563","DOIUrl":"https://doi.org/10.1109/ECCE57851.2023.10101563","url":null,"abstract":"In Neuromarketing, BCI technology is used to analyze how a consumer behaves in response to a marketing stimulus, to evaluate the stimuli itself. Traditionally it can be achieved by different marketing research techniques such as questioner-based surveys, interviews, field surveys, etc. But since these procedures are time-consuming and prone to human error, neuromarketing promises a more advanced, automated, and accurate solution. Most of the neuromarketing solutions use research-grade EEG devices to analyze consumer preferences, but their effectiveness using consumer-grade EEG devices is unknown. In this study, we designed an experiment to compare a research-grade EEG device with a consumer-grade EEG device for predicting consumer preference stated as affective attitude (AA) and purchase intention (PI). We determined what type of setup, processing, and algorithm brings out the best result using the two devices. EEG signals were collected while the participants were shown pictures of different products in two different setups After that several signal-processing techniques were applied to remove artifacts and multi-domain features were extracted. 50 features were selected using Recursing Feature Elimination techniques. SMOTE was used to balance out the data. After that SVM classifier was used to classify Positive and Negative consumer preferences. With the first setup, we managed to achieve 82.4 % and 85.23 % accuracy for predicting purchase intention and affective attitude respectively with the research-grade EEG device whereas we achieved 75.43% and 79.5% accuracy with the commercial-grade EEG device. With the second setup, it's 78.75% and 83.75% using the research-grade EEG device whereas it's 75% and 82.97% using the commercial-grade EEG device for purchase intention and affective attitude respectively.","PeriodicalId":131537,"journal":{"name":"2023 International Conference on Electrical, Computer and Communication Engineering (ECCE)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117132079","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