2017 9th International Conference on Computational Intelligence and Communication Networks (CICN)最新文献

Compact dual band printed planar inverted-F antenna for wireless communications 用于无线通信的紧凑型双频印刷平面倒f天线

2017 9th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2017-11-01 DOI: 10.1109/MICC.2017.8311734

Mangasi Napitupulu, Achmad Munirf

引用次数: 1

Feature analysis of blind and visual signature data collection protocols based on the identification performance 基于识别性能的盲签名和视觉签名数据采集协议特征分析

2017 9th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2017-09-01 DOI: 10.1109/CICN.2017.8319370

Rehab Ibrahem, Meryem Erbilek

引用次数: 0

Augmenting e-commerce product recommendations by analyzing customer personality 通过分析客户个性来增强电子商务产品推荐

2017 9th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2017-09-01 DOI: 10.1109/CICN.2017.8319380

Anwesh Marwade, Nakul Kumar, Shubham Mundada, J. Aghav

{"title":"Augmenting e-commerce product recommendations by analyzing customer personality","authors":"Anwesh Marwade, Nakul Kumar, Shubham Mundada, J. Aghav","doi":"10.1109/CICN.2017.8319380","DOIUrl":"https://doi.org/10.1109/CICN.2017.8319380","url":null,"abstract":"Customer specific personalization has become imperative for e-commerce websites, helping them to convert browsers (visitors) into buyers. The e-commerce industry predominantly uses various machine learning models for product recommendations and analyzing a customer's behavioral patterns, which play a crucial role in exposing customers to new products based on their online behavior. Psychology studies show that if customers are shown products suited to their personality type or complementing their lifestyle, the chances of them buying the said product grow considerably. By incorporating the personality of a customer in a recommendation system, can we achieve increased level of customer-personalization? The answer to this question forms the crux of this paper. With a view to ascertain a customer's personality, we obtain relevant markers from text samples along the five psychological dimensions. We then experiment with various classification models and analyze the effects of different sets of markers on the accuracy. Results demonstrate certain markers contribute more significantly to a personality trait and hence give better classification accuracies. Considering the existence of an ecommerce based conversational bot, we utilize the personality insights to develop a unique recommendation system based on order history and conversational data that the bot-application would gather over time from users.","PeriodicalId":339750,"journal":{"name":"2017 9th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125682978","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: 12

Digital mammogram enhancement based on automatic histogram clipping 基于自动直方图裁剪的数字乳房x光增强

2017 9th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2017-09-01 DOI: 10.1109/CICN.2017.8319351

Bubakari Joda, Z. Dereboylu

{"title":"Digital mammogram enhancement based on automatic histogram clipping","authors":"Bubakari Joda, Z. Dereboylu","doi":"10.1109/CICN.2017.8319351","DOIUrl":"https://doi.org/10.1109/CICN.2017.8319351","url":null,"abstract":"Several studies confirmed the severity of breast cancer as most mortal in women, worldwide. Premature discovery and diagnosis of cancer of breast is of significance importance in the treatment option and increased patients' possible survival opportunity. Image enhancement is one of the frequently applied techniques to curtail lethal rate by providing enhanced image, which would aid early detection and diagnosis of cancer tumor. Image enhancement is applied on the mammogram images to reduce the speckle noise and increase the contrast of the image. In this research work, it is proposed to use a novel Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm which estimates the Clip Limit adaptively by using Otsu's Method in order to enhance mammogram images. Two different threshold calculations are proposed and the proposed methods are compared with a Fuzzy Logic based adaptive clip limit CLAHE method. The experimental images were obtained from mini-MIAS mammogram database. Experiments were carried out for three different breast types; namely fatty, fatty glandular and dense glandular. The subjective test results indicate that to detect breast cancer at its earliest stage, there is need during analysis and diagnosis of the breast cancer to use both of the images obtained with the two proposed methods.","PeriodicalId":339750,"journal":{"name":"2017 9th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116206411","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

Predicting the maximum endurance time for left-side bridge exercise using machine learning methods and hybrid data 使用机器学习方法和混合数据预测左侧桥练习的最大耐力时间

2017 9th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2017-09-01 DOI: 10.1109/CICN.2017.8319387

M. Akay, M. C. Yüksel, F. Abut, F. M. Taş, J. George

{"title":"Predicting the maximum endurance time for left-side bridge exercise using machine learning methods and hybrid data","authors":"M. Akay, M. C. Yüksel, F. Abut, F. M. Taş, J. George","doi":"10.1109/CICN.2017.8319387","DOIUrl":"https://doi.org/10.1109/CICN.2017.8319387","url":null,"abstract":"This study was carried out with the intention to create new models to predict the maximum endurance time for the left-side bridge exercise using machine learning methods and hybrid data. Particularly, four different methods including Multilayer Feed-Forward Artificial Neural Network (MFANN), Generalized Regression Neural Network (GRNN), Radial Basis Function Neural Network (RBFNN) and Single Decision Tree (SDT) have been used for model development. The dataset used to create the prediction models includes physiological, exercise and questionnaire data related to individuals who performed the left-side bridge exercise and completed the Perceived Activity Rating (PAR) and Perceived Functional Ability (PFA) questionnaires. To evaluate the performance of the models, two well-known metrics, namely Root Mean Square Error (RMSE) and Multiple Correlation Coefficient (R) have been used, whereas the generalization errors have been assessed using 10-fold cross validation. The best prediction performance among the models has been obtained by using MFANN along with the predictor variables gender, age, body mass index (BMI), the times to reach a rate of perceived exertion values of 7 and 8 (RPE-7 and RPE-8, respectively) and PAR, producing the lowest RMSE and the highest R with 10.61 seconds (s) and 0.92, respectively.","PeriodicalId":339750,"journal":{"name":"2017 9th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125431059","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

Instant information support and notification system for emergency 紧急情况即时信息支持和通知系统

2017 9th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2017-09-01 DOI: 10.1109/CICN.2017.8319386

Bora Uğurlu, Lütfullah Kaynak

引用次数: 0

Parameter tuning in modeling and simulations by using swarm intelligence optimization algorithms 利用群智能优化算法进行建模和仿真中的参数调整

2017 9th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2017-09-01 DOI: 10.1109/CICN.2017.8319375

R. Tan, Şebnem Bora

{"title":"Parameter tuning in modeling and simulations by using swarm intelligence optimization algorithms","authors":"R. Tan, Şebnem Bora","doi":"10.1109/CICN.2017.8319375","DOIUrl":"https://doi.org/10.1109/CICN.2017.8319375","url":null,"abstract":"Modeling and simulation of real-world environments has in recent times being widely used. The modeling of environments whose examination in particular is difficult and the examination via the model becomes easier. The parameters of the modeled systems and the values they can obtain are quite large, and manual tuning is tedious and requires a lot of effort while it often it is almost impossible to get the desired results. For this reason, there is a need for the parameter space to be set. The studies conducted in recent years were reviewed, it has been observed that there are few studies for parameter tuning problem in modeling and simulations. In this study, work has been done for a solution to be found to the problem of parameter tuning with swarm intelligence optimization algorithms Particle swarm optimization and Firefly algorithms. The performance of these algorithms in the parameter tuning process has been tested on 2 different agent based model studies. The performance of the algorithms has been observed by manually entering the parameters found for the model. According to the obtained results, it has been seen that the Firefly algorithm where the Particle swarm optimization algorithm works faster has better parameter values. With this study, the parameter tuning problem of the models in the different fields were solved.","PeriodicalId":339750,"journal":{"name":"2017 9th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128822943","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

Data analytics using cloud computing 使用云计算进行数据分析

2017 9th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2017-09-01 DOI: 10.1109/CICN.2017.8319361

P. Maheshwari, Alankar Singhal, M. Qadeer

引用次数: 3

Development of novel maximal oxygen uptake prediction models for Turkish college students using machine learning and exercise data 利用机器学习和运动数据为土耳其大学生开发新的最大摄氧量预测模型

2017 9th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2017-09-01 DOI: 10.1109/CICN.2017.8319382

M. Akay, E. Çetin, İmdat Yarım, Özge Bozkurt, M. Özçiloglu

{"title":"Development of novel maximal oxygen uptake prediction models for Turkish college students using machine learning and exercise data","authors":"M. Akay, E. Çetin, İmdat Yarım, Özge Bozkurt, M. Özçiloglu","doi":"10.1109/CICN.2017.8319382","DOIUrl":"https://doi.org/10.1109/CICN.2017.8319382","url":null,"abstract":"Maximal oxygen uptake (VO2max) is the maximum rate of oxygen consumption as measured during maximal exercise. The purpose of this study is to produce new prediction models for Turkish college students by using machine learning methods including Support Vector Machines (SVM), Generalized Regression Neural Networks (GRNN), Radial Basis Function Network (RBFN) and Decision Tree Forest (DTF). The dataset comprises data of 98 subjects and the predictor variables are gender, age, height, weight, maximum heart rate (HRmax), grade, speed and exercise time. Fifteen different VO2max prediction models have been created with the variables listed above. The performance of the prediction models has been calculated by using common metrics such as standard error of estimate (SEE) and multiple correlation coefficient (R). The results show that GRNN based models usually produced much lower SEE's and higher R's than the ones given by SVM, DTF and RBFN based models. On the other hand, the RBFN based models yielded the worst performance with unacceptable error rates. Also, this study shows that the predictor variables grade, speed and time play a significant role in VO2max prediction.","PeriodicalId":339750,"journal":{"name":"2017 9th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128669928","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: 5

DGS-based UWB microstrip BPF and its equivalent circuit 基于dgs的超宽带微带BPF及其等效电路

2017 9th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2017-09-01 DOI: 10.1109/CICN.2017.8319346

Achmad Munirf, Bunga Dwi Wulandari, W. Aditomo, Yogi Prasetio

引用次数: 9