2017 5th International Conference on Information and Communication Technology (ICoIC7)最新文献_第5页

A combination of individual model on memory-based group recommender system to the books domain 将个体模型结合到图书领域的基于记忆的群组推荐系统

2017 5th International Conference on Information and Communication Technology (ICoIC7) Pub Date : 2017-05-01 DOI: 10.1109/ICOICT.2017.8074655

Hafidz Shohihuddin Ahmad, Dade Nurjanah, Rita Rismala

{"title":"A combination of individual model on memory-based group recommender system to the books domain","authors":"Hafidz Shohihuddin Ahmad, Dade Nurjanah, Rita Rismala","doi":"10.1109/ICOICT.2017.8074655","DOIUrl":"https://doi.org/10.1109/ICOICT.2017.8074655","url":null,"abstract":"A recommender system has an important role in decision support by identifying items that will be useful to users in various domains. Development of recommender systems does not only focus on individual users but also groups in various domains. In this paper we present group recommender systems with a collaborative filtering method. Collaborative filtering is a widely used method for recommender systems, easily found in previous studies, that finds similar preferences on the basis of other users' explicit feedback for recommendations. The problem with recommendations for groups using collaborative filtering still is finding the best aggregation technique for group recommender systems. Aggregation techniques in group recommender systems are used to construct an individual model. There are several aggregation techniques that we discuss in this research, such as average, least misery and most happiness. Observations of the recommendation give us the conclusion that the use of least-misery techniques results in the best recommendations according to F1. Furthermore, giving more weight to group neighborhood can also improve the recommendation.","PeriodicalId":244500,"journal":{"name":"2017 5th International Conference on Information and Communication Technology (ICoIC7)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115800313","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: 7

Stock price forecasting using artificial neural network: (Case Study: PT. Telkom Indonesia) 基于人工神经网络的股价预测(以印尼电信公司为例)

2017 5th International Conference on Information and Communication Technology (ICoIC7) Pub Date : 2017-05-01 DOI: 10.1109/ICOICT.2017.8074673

Adetya Prastyo, D. Junaedi, M. D. Sulistiyo

{"title":"Stock price forecasting using artificial neural network: (Case Study: PT. Telkom Indonesia)","authors":"Adetya Prastyo, D. Junaedi, M. D. Sulistiyo","doi":"10.1109/ICOICT.2017.8074673","DOIUrl":"https://doi.org/10.1109/ICOICT.2017.8074673","url":null,"abstract":"Investment in stocks is one of the best alternatives for investing the assets, because with a stake of exposure to risk of inflation is smaller when compared to the savings. But the problem is the difficulty prospective shareholders in stock options because they do not know the stock price predictions for the future. This resulted in the growing level of loss if there are errors in determining the decisions taken in regard to these shares. To solve these problems, required future stock price prediction by using the method of forecasting. Forecasting is done by using Artificial Neural Network (ANN) and for the training, Backpropagation algorithm is used. In this study, stock price prediction using neural network with backpropagation algorithm. ANN is used due to have the ability to perform activities based on past data, where the data of the past will be studied so as to have the ability to give a decision on the data that has never been studied. By using Backpropagation algorithms, network architectures are trained to get the best architecture. After the training, the best architecture that is obtained is 8: 9: 1. Then, the test carried out on test data using the best network architecture and found that the mean squared error (MSE) is equal to 0.1830.","PeriodicalId":244500,"journal":{"name":"2017 5th International Conference on Information and Communication Technology (ICoIC7)","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123261651","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: 4

Automobile radar co-channel interference modeling, simulation and outage analysis 汽车雷达同信道干扰建模、仿真及中断分析

2017 5th International Conference on Information and Communication Technology (ICoIC7) Pub Date : 2017-05-01 DOI: 10.1109/ICOICT.2017.8074707

N. Hettiarachchi, S. Kandeepan, R. Evans

引用次数: 2

Malay speech corpus of telecommunication call center preparation for ASR 电信呼叫中心马来语语料库的ASR准备

2017 5th International Conference on Information and Communication Technology (ICoIC7) Pub Date : 2017-05-01 DOI: 10.1109/ICOICT.2017.8074675

M. Draman, D. C. Tee, Z. Lambak, M. R. Yahya, M. I. M. Yusoff, S. Ibrahim, S. Saidon, N. A. Haris, T. Tan

引用次数: 9

A new approach to reducing execution time in real-time computation 实时计算中减少执行时间的一种新方法

2017 5th International Conference on Information and Communication Technology (ICoIC7) Pub Date : 2017-05-01 DOI: 10.1109/ICOICT.2017.8074685

F. A. Yulianto, Kuspriyanto

引用次数: 4

Video minor stroke extraction using learning vector quantization 基于学习向量量化的视频小笔划提取

2017 5th International Conference on Information and Communication Technology (ICoIC7) Pub Date : 2017-05-01 DOI: 10.1109/ICOICT.2017.8074687

Aviv Yuniar Rahman, S. Sumpeno, M. Purnomo

{"title":"Video minor stroke extraction using learning vector quantization","authors":"Aviv Yuniar Rahman, S. Sumpeno, M. Purnomo","doi":"10.1109/ICOICT.2017.8074687","DOIUrl":"https://doi.org/10.1109/ICOICT.2017.8074687","url":null,"abstract":"Video is very beneficial for health consultations, monitoring, disease identification, customer satisfaction survey efforts on fitness, health information in youtube. However, if the video health tips from youtube are untrue, the information will harm the person. Additionally, it will create the emotion youtube users who are looking for health information. Negative emotions cause effects blood pressure to rise. The impact of high blood pressure is such as stroke. In many developing countries found stroke, especially minor stroke disease which can attack several times a week. If it is not treated immediately, it will result in more severe disability. Handling stroke detection should be begun when the patient was thought to be affected. If it is done early, stroke treatment will reduce damage to organs caused. It requires early detection and treatment of stroke rehabilitation automatically. Therefore we propose the video object extraction minor stroke. The results of the segmentation process can be used to improve the detection performance of stroke in future studies. In addition, segmentation is used to maximize the prediction process of healing and rehabilitation of stroke patients in subsequent studies. This study uses a minor stroke extracted video object using LVQ which has been modified. Tests are using a variation of the constant (K) from 0.1 to 5. The results for the best accuracy are with a value 68.76 % of K = 4.3.","PeriodicalId":244500,"journal":{"name":"2017 5th International Conference on Information and Communication Technology (ICoIC7)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125113284","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: 15

Development of a vehicle and pedestrian simulation environment with M.I.S.O. fuzzy logic controlled intelligent traffic light system 基于M.I.S.O.模糊逻辑控制的智能交通灯系统车辆与行人仿真环境的开发

2017 5th International Conference on Information and Communication Technology (ICoIC7) Pub Date : 2017-05-01 DOI: 10.1109/ICOICT.2017.8074643

Kimberly Ann C. Basconcillo, Diuse Josiah B. Benitez, Elfred Alver S. Cantuba, Renz Erwin L. Enriquez, Chester Robert I. Falcon, Kanny Krizzy D. Serrano, E. Guevara, Angelo R. dela Cruz, R. R. Vicerra

{"title":"Development of a vehicle and pedestrian simulation environment with M.I.S.O. fuzzy logic controlled intelligent traffic light system","authors":"Kimberly Ann C. Basconcillo, Diuse Josiah B. Benitez, Elfred Alver S. Cantuba, Renz Erwin L. Enriquez, Chester Robert I. Falcon, Kanny Krizzy D. Serrano, E. Guevara, Angelo R. dela Cruz, R. R. Vicerra","doi":"10.1109/ICOICT.2017.8074643","DOIUrl":"https://doi.org/10.1109/ICOICT.2017.8074643","url":null,"abstract":"Pre-timed traffic light system, which is currently used, is no longer sufficient at handling vehicular congestion. Various intelligent traffic control signals are present to replace the currently used system. These systems only focused on the parameters of the vehicles. Most intersections contain traffic signals for pedestrians and vehicles, pedestrian lanes must also be considered in the decision making of the intelligent traffic signals. Cameras were used to estimate the number of pedestrian and vehicle at an intersection. A fuzzy logic controller is used to receive and process the information for the decision making of the traffic light system. NetLogo — a multi-agent programmable modeling environment was used to create a traffic model and a simulation environment to simulate and test the developed fuzzy logic system. Results shows that the fuzzy logic system poses a lower rate of car congestion compared to the pre timed system, and this is tested for 3 sets of data including a different time. A lesser number of vehicles congestion will result to less waiting time at a traffic intersection. The developed fuzzy logic based adaptive traffic light system has proven the effectiveness of reducing the congestion and waiting time of vehicles.","PeriodicalId":244500,"journal":{"name":"2017 5th International Conference on Information and Communication Technology (ICoIC7)","volume":"1492 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113995004","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: 4

Towards malware detection in routers with C500-toolkit 基于C500-toolkit的路由器恶意软件检测

2017 5th International Conference on Information and Communication Technology (ICoIC7) Pub Date : 2017-05-01 DOI: 10.1109/ICOICT.2017.8074691

Nghi Tran, Ngoc-Binh Nguyen, Quoc-Dung Ngo, Van-Hoang Le

引用次数: 9

Smart cane location guide for blind using GPS 使用GPS的盲人智能手杖定位指南

2017 5th International Conference on Information and Communication Technology (ICoIC7) Pub Date : 2017-05-01 DOI: 10.1109/ICOICT.2017.8074697

Gita Hapsari, Giva Andriana Mutiara, Dicky Tiara Kusumah

引用次数: 11

Prediction and anomaly detection of rainfall using evolving neural network to support planting calender in soreang (Bandung) 基于进化神经网络的万隆种植压延机降水预测与异常检测

2017 5th International Conference on Information and Communication Technology (ICoIC7) Pub Date : 2017-05-01 DOI: 10.1109/ICOICT.2017.8074671

Gunawansyah, Thee Houw Liong, Adiwijaya

{"title":"Prediction and anomaly detection of rainfall using evolving neural network to support planting calender in soreang (Bandung)","authors":"Gunawansyah, Thee Houw Liong, Adiwijaya","doi":"10.1109/ICOICT.2017.8074671","DOIUrl":"https://doi.org/10.1109/ICOICT.2017.8074671","url":null,"abstract":"As an agricultural country and located around the equator line, Indonesia geographical position between two continents and oceans is very sensitive to regional and global atmospheric circulations as sunspot, cosmic rays, Indian Ocean Dipole(IOD) and Southern Oscillation Index(SOI). The disruption of one circulation can affect climate and weather. The extreme anomaly climate is very influential in the agriculture field because in many regions rainfall is the main source of water to meet the needs of agriculture, so that agricultural activities always depend on the rainfall. As a consequence, agricultural activities in this area, especially rice plants should always pay attention and adjust the pattern of rainfall. Because of that, to determine the best beginning of growing season based on prediction and anomaly detection of rainfall used local, regional and global variables that influences rainfall is very important. So, losses due to precipitation anomalies in planting time or growing season until harvest can be minimized because farmers can adjust the planting starting time with the change of the rainfall and the attention to anomalies of rainfall. Artificial Neural Network (ANN) is widely used for predictions because of its accuracy and data error tolerance but have some limited. Therefore this study proposes ENN which used ANN and Genetic Algorithm(GA) to optimize and find the best weights and biases. From three scenarios, one hidden layer in ANN architecture was sufficient and ENN had good performance in different dataset. The rainfall prediction result used all data (January-December) from 1999–2013 had the accuracy of 84.6%, 66.02% for dry season (April-September) and 79.7% for wet season (October-March). Based on the prediction and anomaly detection in this research, in Soreang region the first week of January, April and October 2014 were confirmed for the starting time of planting in 2014.","PeriodicalId":244500,"journal":{"name":"2017 5th International Conference on Information and Communication Technology (ICoIC7)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130499856","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: 8