2020 IEEE 10th International Conference on System Engineering and Technology (ICSET)最新文献

Fast k-Means Clustering Algorithm for Malaria Detection in Thick Blood Smear 基于快速k均值聚类算法的厚血涂片疟疾检测

2020 IEEE 10th International Conference on System Engineering and Technology (ICSET) Pub Date : 2020-11-09 DOI: 10.1109/ICSET51301.2020.9265380

T. A. Aris, A. Nasir, L. C. Chin, H. Jaafar, Z. Mohamed

{"title":"Fast k-Means Clustering Algorithm for Malaria Detection in Thick Blood Smear","authors":"T. A. Aris, A. Nasir, L. C. Chin, H. Jaafar, Z. Mohamed","doi":"10.1109/ICSET51301.2020.9265380","DOIUrl":"https://doi.org/10.1109/ICSET51301.2020.9265380","url":null,"abstract":"Lots of people all over the world is threaten by a popular blood infection illness that is called as malaria. According to this fact, immediate diagnosis tests are essential to avoid the malaria parasites from expanding in every part of the body. Malaria detection is based on parasitic count process on thick blood smear samples. Anyhow, this mechanism consist the chances of misinterpretation of parasites on behalf to human flaws. Thus, this research objective is to investigate the segmentation performance for improving malaria detection in thick blood smear images through fast k-means clustering algorithm on various color models. In this research, fast k-means clustering is used because of its advantage which is no need to retrain cluster center that causes time taken to train the image cluster centers is reduce. Meanwhile, different color models have been utilized in order to identify the most relevant color model that obviously highlight the parasites. Five varied color models namely RGB, XYZ, HSV, YUV and CMY are selected and 15 color components namely R, G, B, X, Y, Z, H, S, V, Y, U, V, C, M and Y component have been derived with the aim to discover which color component is the topnotch for malaria parasites detection. In general, around 100 thick blood smear images have been tested in this study and the outcomes reveal that the best segmentation performance is segmentation through R component of RGB with 99.81% accuracy.","PeriodicalId":299530,"journal":{"name":"2020 IEEE 10th International Conference on System Engineering and Technology (ICSET)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127175847","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

Detection of Palm Oil Health Through Multispectral UAV Platform 基于多光谱无人机平台的棕榈油健康检测

2020 IEEE 10th International Conference on System Engineering and Technology (ICSET) Pub Date : 2020-11-09 DOI: 10.1109/ICSET51301.2020.9265374

Carl Marlyana Rafezall, N. Darwin, M. Ariff, Z. Majid

{"title":"Detection of Palm Oil Health Through Multispectral UAV Platform","authors":"Carl Marlyana Rafezall, N. Darwin, M. Ariff, Z. Majid","doi":"10.1109/ICSET51301.2020.9265374","DOIUrl":"https://doi.org/10.1109/ICSET51301.2020.9265374","url":null,"abstract":"In 2018, 19.5 million tonnes of crude palm oil from a planted area of 5.8 million have been produced in Malaysia. RM67.5 billion has been earned from palm oil export which contributes to one of Malaysia's gross domestic products (GDP). Unfortunately, palm oil cultivation is suffering from the destructive disease known as Ganoderma Boninense which causes the basal stem rot (BSR) disease. The objectives of this study are to provide a low-cost monitoring method for palm oil mapping and to analyse the health condition of palm oil trees through the spectral reflectance curve. The implementation of Unmanned Aerial Vehicles (UAV) that attach with the multispectral camera was used as the platform in data acquisition to monitor the condition of palm oil health. The multispectral UAV can determine the plant health to identify the early stage of the disease by extracting the palm oil leaf spectral reflectance curve. The range of Normalized Difference Vegetation Index (NDVI) of 0 to 1 is for good condition, while 0 to −1 is for dead or extremely stressed condition of palm oil. The result of spectral reflectance curve from multispectral camera was compared and analysed with the reading from spectroradiometer observation. Besides, flight planning and camera calibration were done before the data acquisition process. Ground control points (GCP) were established using GNSS method at the survey area for the geo-referencing process. The area of the study is located at the palm oil plantation of Sime Darby Plantation, Kulai. The outcome of this study is to produce a Normalized Difference Vegetation Index (NDVI) map as the final output.","PeriodicalId":299530,"journal":{"name":"2020 IEEE 10th International Conference on System Engineering and Technology (ICSET)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115205805","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 Niche Particle Swarm Optimization-Perks and Perspectives 一个利基粒子群优化-津贴和前景

2020 IEEE 10th International Conference on System Engineering and Technology (ICSET) Pub Date : 2020-11-09 DOI: 10.1109/ICSET51301.2020.9265384

Z. Arfeen, M. Abdullah, M. Shehzad, Saleh Altbawi, Muhammad Ashafaq Khan Jiskani, Muhammad Asif Imran YiRan

引用次数: 5

Classification of Motor Imagery EEG Signals Using Machine Learning 基于机器学习的运动图像脑电信号分类

2020 IEEE 10th International Conference on System Engineering and Technology (ICSET) Pub Date : 2020-11-09 DOI: 10.1109/ICSET51301.2020.9265364

Amr Abdeltawab, A. Ahmad

{"title":"Classification of Motor Imagery EEG Signals Using Machine Learning","authors":"Amr Abdeltawab, A. Ahmad","doi":"10.1109/ICSET51301.2020.9265364","DOIUrl":"https://doi.org/10.1109/ICSET51301.2020.9265364","url":null,"abstract":"Brain Computer Interface (BCI) is a term that was first introduced by Jacques Vidal in the 1970s when he created a system that can determine the human eye gaze direction, making the system able to determine the direction a person want to go or move something to using scalp-recorded visual evoked potential (VEP) over the visual cortex. Ever since that time, many researchers where captivated by the huge potential and list of possibilities that can be achieved if simply a digital machine can interpret human thoughts. In this work, we explore electroencephalography (EEG) signal classification, specifically for motor imagery (MI) tasks. Classification of MI tasks can be carried out by using machine learning and deep learning models, yet there is a trade between accuracy and computation time that needs to be maintained. The objective is to create a machine learning model that can be optimized for real-time classification while having a relatively acceptable classification accuracy. The proposed model relies on common spatial patter (CSP) for feature extraction as well as linear discriminant analysis (LDA) for classification. With simple pre-processing stage and a proper selection of data for training the model proved to have a balanced accuracy of +80% while maintaining small run-time (milliseconds) that is opted for real-time classifications","PeriodicalId":299530,"journal":{"name":"2020 IEEE 10th International Conference on System Engineering and Technology (ICSET)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121678241","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

Spatial Mapping and Analysis of Tuberculosis Cases in Kuala Lumpur, Malaysia 马来西亚吉隆坡肺结核病例的空间测绘与分析

2020 IEEE 10th International Conference on System Engineering and Technology (ICSET) Pub Date : 2020-11-09 DOI: 10.1109/ICSET51301.2020.9265385

Norshuhada Aniza Rajab, N. Hashim, Abdul Rauf Abdul Rasam

{"title":"Spatial Mapping and Analysis of Tuberculosis Cases in Kuala Lumpur, Malaysia","authors":"Norshuhada Aniza Rajab, N. Hashim, Abdul Rauf Abdul Rasam","doi":"10.1109/ICSET51301.2020.9265385","DOIUrl":"https://doi.org/10.1109/ICSET51301.2020.9265385","url":null,"abstract":"Tuberculosis (TB) is one of the serious health problems in Malaysia, especially in a crowded area. This disease has become increasingly important as public health concerns with the development and progress that has been achieved in this country. Due to high number of cases reported each year, Malaysia should take precautionary actions to reduce the number of cases. Spatial analysis of TB cases can be performed using geospatial approach such as Geographical Information System (GIS). The aim of this study is to analyse the spatial distribution and identify spatial pattern of TB cases in Kuala Lumpur (KL), Malaysia. The high-risk area and spatial of the TB outbreak in KL were mapped using hot spot analysis. Spatial statistical methods were also used with Average Nearest Neighbor Distance and Spatial Autocorrelation (Moran I). The relationships between demographic distributions was analysed and the outbreak distribution pattern was also identified. The result shows that the main clustering area of tuberculosis cases in Kuala Lumpur is concentrated in Bandar Tun Razak. This main township is not only known as a crowded area, but it is also surrounded by a rapid physical urban development.","PeriodicalId":299530,"journal":{"name":"2020 IEEE 10th International Conference on System Engineering and Technology (ICSET)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124095634","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

Unmanned Aerial Vehicles for Post-Disaster Communication Networks 灾后通信网络无人机

2020 IEEE 10th International Conference on System Engineering and Technology (ICSET) Pub Date : 2020-11-09 DOI: 10.1109/ICSET51301.2020.9265369

Abdu Saif, K. Dimyati, K. Noordin, N. Shah, Qazwan Abdullah (غزوان عبد الله محمد طربوش), Fadhil Mukhlif

引用次数: 24

Finger Vein Segmentation from Infrared Images Using Spectral Clustering: An Approach for User Indentification 基于光谱聚类的红外图像手指静脉分割:一种用户识别方法

2020 IEEE 10th International Conference on System Engineering and Technology (ICSET) Pub Date : 2020-11-09 DOI: 10.1109/ICSET51301.2020.9265399

Zenin J. Vásqucz-Villar, Juan José Choquehuanca Zevallos, Jimmy Ludeña-Choez, Efraín Mayhua-López

引用次数: 4

Singular Value Determination for IR-UWB Radar Sensor-Based Human Motion Detection 基于IR-UWB雷达传感器的人体运动检测奇异值确定

2020 IEEE 10th International Conference on System Engineering and Technology (ICSET) Pub Date : 2020-11-09 DOI: 10.1109/ICSET51301.2020.9265147

T. J. Daim, R. A. Lee

引用次数: 3

Particle Filter based Single Shot MultiBox Detector for Human Moving Prediction 基于粒子滤波的单镜头多盒检测人体运动预测

2020 IEEE 10th International Conference on System Engineering and Technology (ICSET) Pub Date : 2020-11-09 DOI: 10.1109/ICSET51301.2020.9265355

D. Maharani, C. Machbub, L. Yulianti, P. Rusmin

引用次数: 5

Simulation of Drone Controller using Reinforcement Learning AI with Hyperparameter Optimization 基于超参数优化的强化学习AI无人机控制器仿真

2020 IEEE 10th International Conference on System Engineering and Technology (ICSET) Pub Date : 2020-11-09 DOI: 10.1109/ICSET51301.2020.9265381

Mohamad Hafiz Abu Bakar, Abu Ubaidah bin Shamsudin, R. A. Rahim

{"title":"Simulation of Drone Controller using Reinforcement Learning AI with Hyperparameter Optimization","authors":"Mohamad Hafiz Abu Bakar, Abu Ubaidah bin Shamsudin, R. A. Rahim","doi":"10.1109/ICSET51301.2020.9265381","DOIUrl":"https://doi.org/10.1109/ICSET51301.2020.9265381","url":null,"abstract":"Drone is one of the latest drone technologies that grows with multiple applications; one of the critical applications is for fire-fighting drones such as water hose carrying for firefighting. One of the main challenges of the drone technologies is the non-linear dynamic movement caused by a variety of fire conditions. One solution is to use a nonlinear controller such as Reinforcement Learning. In this paper, Reinforcement Learning has been applied as their key control system to improve the conventional approach, which is the agent (drone) that will interact with the environment without need of the controller for the flying process. This paper is introduced an optimization method for the hyperparameter in order to achieve a better reward. In addition, we only concentrate on the learning rate (alpha) and potential reward factor discount (gamma) for optimization in this paper. From this optimization, the better performance and response from our result by using alpha = 0.1 & gamma = 0.8 with reward produced 6100 and it takes 49 seconds in the learning process.","PeriodicalId":299530,"journal":{"name":"2020 IEEE 10th International Conference on System Engineering and Technology (ICSET)","volume":"132 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131475880","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