Proceedings of the 5th International Conference on Sustainable Information Engineering and Technology最新文献_第4页

Recent advances in large scale whole body MRI image analysis: Imiomics 大规模全身MRI图像分析的最新进展:模拟组学

Proceedings of the 5th International Conference on Sustainable Information Engineering and Technology Pub Date : 2020-11-16 DOI: 10.1145/3427423.3427465

R. Strand, Simon Ekström, Eva Breznik, T. Sjöholm, M. Pilia, L. Lind, F. Malmberg, H. Ahlström, J. Kullberg

{"title":"Recent advances in large scale whole body MRI image analysis: Imiomics","authors":"R. Strand, Simon Ekström, Eva Breznik, T. Sjöholm, M. Pilia, L. Lind, F. Malmberg, H. Ahlström, J. Kullberg","doi":"10.1145/3427423.3427465","DOIUrl":"https://doi.org/10.1145/3427423.3427465","url":null,"abstract":"Due to the massive amount of medical image data being made available, in research and clinical work, computer-aided tools are valuable and have a great potential for a sustainable work situation for physicians and for generating disease understanding. High-end methods in the present era of big data and artifical intelligence are designed to efficiently find patterns in large scale image data. The amount of data is today often too big to be parsed by human experts, and computer-assisted methods often perform at least as well as human experts on well-defined problems where it is possible to quantify performance by a loss function. This paper gives an overview of a computer-assisted method, Imiomics. Imiomics enables statistical analyses of relations between whole body image image data in large cohorts and other non-imaging data, at an unprecedented spatial resolution. Its usefulness in medicine is illustrated by a number of medical applications, and some aspects of technical development that enable the analysis is also presented. We conclude that computer-assisted methods, such as Imiomics, are essential for efficient processing of the huge amount of data in today's medical research and, to some extent, clinical practice.","PeriodicalId":120194,"journal":{"name":"Proceedings of the 5th International Conference on Sustainable Information Engineering and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125686815","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

Prototype of multi-layer personal cardiac monitoring system for data interoperability problem 针对数据互操作性问题的多层个人心脏监测系统原型

Proceedings of the 5th International Conference on Sustainable Information Engineering and Technology Pub Date : 2020-11-16 DOI: 10.1145/3427423.3427442

Eko Sakti Pramukantoro, A. Gofuku

引用次数: 1

Measuring the accuracy of coordinates and elevation of Google earth: how Google earth provide accuracy in location points and elevation 测量Google earth的坐标和高程精度:Google earth如何提供定位点和高程精度

Proceedings of the 5th International Conference on Sustainable Information Engineering and Technology Pub Date : 2020-11-16 DOI: 10.1145/3427423.3427448

B. Supriyanto, F. Ramdani, A. A. Supianto

引用次数: 0

Automated requirement sentences extraction from software requirement specification document 从软件需求说明文档中自动提取需求语句

Proceedings of the 5th International Conference on Sustainable Information Engineering and Technology Pub Date : 2020-11-16 DOI: 10.1145/3427423.3427450

M. Haris, T. A. Kurniawan

引用次数: 5

An efficient adaptive neuro fuzzy inference system for product demand forecasting 一种高效的自适应神经模糊推理系统用于产品需求预测

Proceedings of the 5th International Conference on Sustainable Information Engineering and Technology Pub Date : 2020-11-16 DOI: 10.1145/3427423.3427443

A. Widodo, G. E. Yuliastuti, A. Rizki, W. Mahmudy

引用次数: 1

Evaluate the development of interactive learning media through technology acceptance model 用技术接受模型评价互动式学习媒体的发展

Proceedings of the 5th International Conference on Sustainable Information Engineering and Technology Pub Date : 2020-11-16 DOI: 10.1145/3427423.3427460

Sukirman, Nisaa' Fitriningtyas

{"title":"Evaluate the development of interactive learning media through technology acceptance model","authors":"Sukirman, Nisaa' Fitriningtyas","doi":"10.1145/3427423.3427460","DOIUrl":"https://doi.org/10.1145/3427423.3427460","url":null,"abstract":"An interactive learning media (ILM) has potentially affected to the learning outcomes and may simulate a process or system to be easier to understand. However, the development of an ILM needs to be evaluated to obtain the best result. This article discusses the use of technology acceptance model (TAM) principles to evaluate the development of an ILM that comprises perceived usefulness (PU), perceived ease of use (PEU), attitude toward using (ATU), and intention to use (ITU). Learning contents that put in the developed ILM is about photosynthesis topic. Participants involved in this study consists of 29 students (16 males and 13 females). The data is collected through a questionnaire distributed to users after explore and finished. Statistical analysis was employed to cope with the research objective and assumption. From the analysis and evaluation, it can be concluded that the ILM is useful to use for learning due to the PU value of Cronbach's alpha is 0.694. Meanwhile, the score of Cronbach's alpha for PEU is 0.866 which means easy to use even for a new user who has never used it beforehand.","PeriodicalId":120194,"journal":{"name":"Proceedings of the 5th International Conference on Sustainable Information Engineering and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130090233","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

Improvements of fuzzy C-means clustering performance using particle swarm optimization on student grouping based on learning activity in a digital learning media 基于学习活动的数字学习媒体中模糊c均值聚类性能的粒子群优化

Proceedings of the 5th International Conference on Sustainable Information Engineering and Technology Pub Date : 2020-11-16 DOI: 10.1145/3427423.3427449

A. A. Supianto, Nur Sa'diyah, C. Dewi, R. I. Rokhmawati, Satrio Agung Wicaksono, Hanifah Muslimah Az-zahra, Satrio Hadi Wijoyo, Y. Hayashi, T. Hirashima

{"title":"Improvements of fuzzy C-means clustering performance using particle swarm optimization on student grouping based on learning activity in a digital learning media","authors":"A. A. Supianto, Nur Sa'diyah, C. Dewi, R. I. Rokhmawati, Satrio Agung Wicaksono, Hanifah Muslimah Az-zahra, Satrio Hadi Wijoyo, Y. Hayashi, T. Hirashima","doi":"10.1145/3427423.3427449","DOIUrl":"https://doi.org/10.1145/3427423.3427449","url":null,"abstract":"The field of learning media has been developing rapidly in recent years, especially in an effort to support students' learning process. The amount of recorded learning process data has also significantly increased. The recorded data represents the students' thinking process in building a solution for a problem. The sheer size of the recorded data proves to be quite a challenge in an effort to mine the students' thinking process, especially when done manually. Additionally, to group the recorded data into clusters is also another form of challenge that needs to be faced. In general, the entire process of mining students' thinking patterns aims to utilize the data to gather hidden information which can also be used to give appropriate and proper feedback to the students. This paper aims to employ the Fuzzy C-Means and Particle Swarm Optimization (FCMPSO) method to cluster students based on their learning activity to a digital learning media and compare its performance to original Fuzzy C-Means (FCM) method. Particle Swarm Optimization (PSO) algorithm is proposed to optimize the performance of the FCM algorithm, in which this algorithm is inherently sensitive towards centroid on the initial clustering process that utilizes the Silhouette coefficient as an evaluation method. Based on the experiments that have been done to 12 assignments, each assignment forms a different number of optimal clusters. This shows that each student faces and uses different strategies to solve their assignments. The formed groups are dominated by two major clusters, namely the high-performance students, and the low-performance students. Additionally, the adaptation of PSO to FCM improves the clustering quality significantly based on the observed average Silhouette coefficient.","PeriodicalId":120194,"journal":{"name":"Proceedings of the 5th International Conference on Sustainable Information Engineering and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132631656","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

Speed limiting sign recognition system using histogram of oriented gradients method and K-nearest neighbor classification based on raspberry pi 基于树莓派直方图定向梯度法和k近邻分类的限速标志识别系统

Proceedings of the 5th International Conference on Sustainable Information Engineering and Technology Pub Date : 2020-11-16 DOI: 10.1145/3427423.3427430

Nugraheny Wahyu Try, Fitri Utaminingrum

{"title":"Speed limiting sign recognition system using histogram of oriented gradients method and K-nearest neighbor classification based on raspberry pi","authors":"Nugraheny Wahyu Try, Fitri Utaminingrum","doi":"10.1145/3427423.3427430","DOIUrl":"https://doi.org/10.1145/3427423.3427430","url":null,"abstract":"The dominant transportation vehicle in Indonesia is manual transportation. This type of transportation is controlled by the driver himself. Cases of traffic accidents are increasing due to lack of awareness of driving safety and security. The biggest factor in accidents is human error. One accident caused by human error such as a driver who lost speed control, because he ignored the maximum and minimum speed limiting signs. Therefore, the solution for this problem is creating a warning systems that can be used for recognizing maximum and minimum speed limiting signs. The system uses a raspberry pi camera to capture images then be detected and recognized the speed sign. If the system manages to recognize the signs according to the actual conditions traversed by the driver, it will get notification of speed sign figures in the form of sound from the speakers. The study applied the Histogram of Oriented Gradients (HOG) method to obtain the characteristic feature extraction from the sign, then classify it using the K-Nearest Neighbor (K-NN) method. Classification testing using K-NN consist of 650 training data and 48 test data that are comes from six sign types, there are Maks 20 km/h, Max 25 km/h, Max 30 km/h, Max 40 km/h, Max 50 km/h, Min 20 km/h). The average accuracy values is 97.91% for k=1 and 2. Meanwhile, accuracy of k = 3, 4 and 5 have similar value, that is 95.83%. The average time of computing the system to recognize objects 897 milliseconds. The average result of recognition based on the best k value is 97.91%.","PeriodicalId":120194,"journal":{"name":"Proceedings of the 5th International Conference on Sustainable Information Engineering and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129151766","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

Relay nodes placement for optimal coverage, connectivity, and communication of wireless sensor networks: a PSO-based multi-objective optimization research idea 无线传感器网络覆盖、连接和通信的中继节点布局:基于pso的多目标优化研究思路

Proceedings of the 5th International Conference on Sustainable Information Engineering and Technology Pub Date : 2020-11-16 DOI: 10.1145/3427423.3427452

Kasyful Amron, W. M. Kusumawinahyu, S. Anam, W. Mahmudy

{"title":"Relay nodes placement for optimal coverage, connectivity, and communication of wireless sensor networks: a PSO-based multi-objective optimization research idea","authors":"Kasyful Amron, W. M. Kusumawinahyu, S. Anam, W. Mahmudy","doi":"10.1145/3427423.3427452","DOIUrl":"https://doi.org/10.1145/3427423.3427452","url":null,"abstract":"Designing a Wireless Sensor Networks (WSN) mostly was a great challenge. Shown in previous results, some design approaches lead to problems in its implementation. Deterministic methods face the NP-Hard complex problem. On the other side, heuristic methods sometimes produce a flawed result. With those situations, this research concern with exploring the possibility of a multi-objective optimization (MOO) method. As with the MOO method, some conflicted WSN aspects consider simultaneously. Started with the PSO algorithm, this developing method tries to find the best position of the WSN's relays. Closed neighbor sensor nodes are then will be connected. It is combined with the graph to constructs the best communication link. These steps will be done in a certain number of iterations to enhance fault-tolerance ability. This MOO approached method was implemented to different WSN topologies, with several sensors placed in a simulation area. Used as controls are Steiner Point and Triangular Grid algorithms. The most significant finding is this developing method gave some early potential results that could form future solutions in the multi-objective optimization approach for the WSN designing.","PeriodicalId":120194,"journal":{"name":"Proceedings of the 5th International Conference on Sustainable Information Engineering and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132247415","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

Collaborative optimization networks 协同优化网络

Proceedings of the 5th International Conference on Sustainable Information Engineering and Technology Pub Date : 2020-11-16 DOI: 10.1145/3427423.3427468

A. Ghose

引用次数: 0