2022 IEEE 5th International Conference on Knowledge Innovation and Invention (ICKII )最新文献_第6页

IoBCT: A Brain Computer Interface using EEG Signals for Controlling IoT Devices IoBCT:利用脑电图信号控制物联网设备的脑机接口

2022 IEEE 5th International Conference on Knowledge Innovation and Invention (ICKII ) Pub Date : 2022-07-22 DOI: 10.1109/ICKII55100.2022.9983557

Eyhab Al-Masri, Ankit Singh, Alireza Souri

{"title":"IoBCT: A Brain Computer Interface using EEG Signals for Controlling IoT Devices","authors":"Eyhab Al-Masri, Ankit Singh, Alireza Souri","doi":"10.1109/ICKII55100.2022.9983557","DOIUrl":"https://doi.org/10.1109/ICKII55100.2022.9983557","url":null,"abstract":"For people with motor disabilities, completing simple tasks or processes such as turning on lights or directly controlling smart home devices can be tedious and requires considerable thought and effort. Unfortunately, recent advancements in the IoT and AI, which aim to simplify and enhance device interaction, have not been equally accessible to people with motor disabilities. As a result, individuals with severe motor disabilities caused by various conditions such as Spinal Cord Injury (SCI) or Anthropomorphic Lateral Sclerosis (ALS) may be unable to effectively interact with IoT devices or complete tasks without significant effort. To solve this challenge, in this research work, we present a novel brain-computer interface (BCI) framework called the Internet of Brain-Controlled Things (IoBCT) that enables an individual to interact or communicate with IoT devices directly and effectively. Our IoBCT framework uses human brain signals for BCI operations and an optimization methodology for effectively communicating with IoT devices using brain waves. Our experiments demonstrate the effectiveness and feasibility of employing EEG signals for controlling IoT devices with an accuracy rate of 95%.","PeriodicalId":352222,"journal":{"name":"2022 IEEE 5th International Conference on Knowledge Innovation and Invention (ICKII )","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117102747","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

Assessing the Acceleration Sensing in the Flight of a Micro Aerial Vehicle 微型飞行器飞行中的加速度感知评估

2022 IEEE 5th International Conference on Knowledge Innovation and Invention (ICKII ) Pub Date : 2022-07-22 DOI: 10.1109/ICKII55100.2022.9983566

E. Lau, Gouda Chun-Cheng Peng, T. Y. Yeh, J. Lau

引用次数: 0

Implementation of Green Coffee Bean Quality Classification Using Slim-CNN in Edge Computing 边缘计算中利用Slim-CNN实现绿咖啡豆品质分类

2022 IEEE 5th International Conference on Knowledge Innovation and Invention (ICKII ) Pub Date : 2022-07-22 DOI: 10.1109/ICKII55100.2022.9983596

Yan-Feng Wang, Chuan-Chung Cheng, J. Tsai

引用次数: 1

Potential of Using Computer Vision to Predict Graphics for Learning-by-doing 利用计算机视觉预测图形的潜力，边做边学

2022 IEEE 5th International Conference on Knowledge Innovation and Invention (ICKII ) Pub Date : 2022-07-22 DOI: 10.1109/ICKII55100.2022.9983585

Shaofu Li

引用次数: 0

Computational Forecast of PM2.5 Pollution Based on Gas Emission and Traffic Volume Observations 基于气体排放和交通量观测的PM2.5污染计算预报

2022 IEEE 5th International Conference on Knowledge Innovation and Invention (ICKII ) Pub Date : 2022-07-22 DOI: 10.1109/ICKII55100.2022.9983556

Chien-Hung Fan, Sucharita Khuntia, Sue-Yuan Fan, Po-Hsiang Juan, Getaneh Berie Tarekegn, Jen-Wen Chang, Bing Zhang, L. Tai

引用次数: 0

Low-Power 14GHz LC-VCO in 180nm CMOS Technology 基于180nm CMOS技术的低功耗14GHz LC-VCO

2022 IEEE 5th International Conference on Knowledge Innovation and Invention (ICKII ) Pub Date : 2022-07-22 DOI: 10.1109/ICKII55100.2022.9983526

M. Chung, Pin-Rui Huang, Y. Kuo

引用次数: 0

Refined Attention Module for WSI Cancer Diagnosis 改进的WSI肿瘤诊断关注模块

2022 IEEE 5th International Conference on Knowledge Innovation and Invention (ICKII ) Pub Date : 2022-07-22 DOI: 10.1109/ICKII55100.2022.9983555

T. S. Sheikh, Jee Yeon Kim, Migyung Cho

{"title":"Refined Attention Module for WSI Cancer Diagnosis","authors":"T. S. Sheikh, Jee Yeon Kim, Migyung Cho","doi":"10.1109/ICKII55100.2022.9983555","DOIUrl":"https://doi.org/10.1109/ICKII55100.2022.9983555","url":null,"abstract":"In clinical pathology, the accurate and precise classification of cancer is one of the critical challenges due to the complex pattern of cancer cells. This study proposes an effective attention module to highlight the most important parts of cancer whole slide images (WSIs). Our attention module improves the significance of learnable features and overcomes the noisy features while training. Conventional attention modules use only feature extraction capability to learn information but we merge noisy removal capability within our attention module to leverage and randomly discard the noise during the training of the model which enhances the performance of the WSI classification task. We evaluated the performance of our module on our biopsy needle WSIs dataset, named bnWSIs. Our dataset contains a total of 24,613 labeled patches extracted from 21 WSIs. The dataset is split into two types of classification categories, with different variants of magnifications, and classes. The key is to improve the existing state-of-the-art (SoTA) performance by using the attention module, For binary classification, the achieved accuracies are improved up to 7%, whereas in multi-class classification are 6% with three magnification levels.","PeriodicalId":352222,"journal":{"name":"2022 IEEE 5th International Conference on Knowledge Innovation and Invention (ICKII )","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128716205","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

Development of Machine Learning Based Fraudulent Website Detection Scheme 基于机器学习的欺诈网站检测方案的开发

2022 IEEE 5th International Conference on Knowledge Innovation and Invention (ICKII ) Pub Date : 2022-07-22 DOI: 10.1109/ICKII55100.2022.9983523

Shyh-Wei Chen, Po-Hsiang Chen, Ching-Tsorng Tsai, Chia-Hui Liu

{"title":"Development of Machine Learning Based Fraudulent Website Detection Scheme","authors":"Shyh-Wei Chen, Po-Hsiang Chen, Ching-Tsorng Tsai, Chia-Hui Liu","doi":"10.1109/ICKII55100.2022.9983523","DOIUrl":"https://doi.org/10.1109/ICKII55100.2022.9983523","url":null,"abstract":"The development of mobile computing and e-commerce has greatly changed traditional transactions and grown online shopping. People are buying and selling goods on websites or social platforms. However, there are many malicious and counterfeit products on fraudulent websites to deceive consumers and make high improper profits. Due to the obvious increase in the number of such fraudulent websites, it is difficult to identify and detect these websites by manual inspection. In order to solve this problem, we propose an intelligent detection mechanism by using a machine learning approach to classify fraudulent websites. We use data set containing 300 legitimate websites, 300 fraudulent websites, and 15 features for training. In two machine learning algorithms, Random Forest and Deep Neural Networks, we divided the training set and the test set in a ratio of 8:2. Finally, the prediction is compared with the previous research results. The experimental results show that the RF accuracy of the random forest algorithm is 99.3% which is better than other deep neural networks algorithms.","PeriodicalId":352222,"journal":{"name":"2022 IEEE 5th International Conference on Knowledge Innovation and Invention (ICKII )","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127634449","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

Speed Control of Dual Sensorless Interior Permanent-Magnet Synchronous Motors Based on the Sliding Mode Observer Principle 基于滑模观测器原理的双无传感器内置永磁同步电动机速度控制

2022 IEEE 5th International Conference on Knowledge Innovation and Invention (ICKII ) Pub Date : 2022-07-22 DOI: 10.1109/ICKII55100.2022.9983588

Hong-Xin Liao, Shu-Yu Du, Jun-Kun Lu, Fred Cheng, Seng-Chi Chen

引用次数: 1

Resource Optimization for Log Shipper and Preprocessing Pipeline in a Large-Scale Logging System 大型测井系统中运日志者资源优化及预处理管道

2022 IEEE 5th International Conference on Knowledge Innovation and Invention (ICKII ) Pub Date : 2022-07-22 DOI: 10.1109/ICKII55100.2022.9983590

Thanarit Lertwuthikarn, V. C. Barroso, K. Akkarajitsakul

{"title":"Resource Optimization for Log Shipper and Preprocessing Pipeline in a Large-Scale Logging System","authors":"Thanarit Lertwuthikarn, V. C. Barroso, K. Akkarajitsakul","doi":"10.1109/ICKII55100.2022.9983590","DOIUrl":"https://doi.org/10.1109/ICKII55100.2022.9983590","url":null,"abstract":"In resource management, resource optimization is a usual technique to proceed for most professional organizations in order to reduce expenses and to dispose unnecessary resource usages. The European Organization for Nuclear Research (CERN) intends to implement a logging system based on AI for A Large Ion Collider Experiment detector, or ALICE. This system has been being implemented by using the Elasticsearch, Kibana, Beats, and Logstash also called ELK Stack which gives us the capability for the logs aggregation from systems and applications. Log data are collected from involved servers at CERN called First Level Processors (FLPs) nodes by Beats. These nodes run a large number of services when tasks are executed and generate a large volume of log data. Filebeat is used as a log shipper to transfer the data to Logstash, a server-side preprocessing pipeline. When Filebeat and Logstash are working together, there are many configurable factors affecting their efficiency. We then apply a factorial experiment to identify the significant factors and their correlation. These parameters are also optimized to find the best possible values of their configurations. Then, the resource usage can be minimized while a suitable performance of the system is maintained. The results of this study show that we can increase the efficiency of the system thanks to the adjusted values of the parameters. This can be used as a guideline for tuning some configurable parameters to optimize resource usage when there is a large amount of log data to be handled.","PeriodicalId":352222,"journal":{"name":"2022 IEEE 5th International Conference on Knowledge Innovation and Invention (ICKII )","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126249527","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