2017 IEEE 4th International Conference on Soft Computing & Machine Intelligence (ISCMI)最新文献

Exploring the application and usability of NFC for promoting self-learning on energy consumption of household electronic appliances 探索近距离通信技术在促进家电能耗自我学习中的应用和可用性

2017 IEEE 4th International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2017-11-23 DOI: 10.1109/ISCMI.2017.8279617

Vishamlall Ramrecha, Girish Bekaroo, A. Santokhee, Suraj Juddoo

{"title":"Exploring the application and usability of NFC for promoting self-learning on energy consumption of household electronic appliances","authors":"Vishamlall Ramrecha, Girish Bekaroo, A. Santokhee, Suraj Juddoo","doi":"10.1109/ISCMI.2017.8279617","DOIUrl":"https://doi.org/10.1109/ISCMI.2017.8279617","url":null,"abstract":"During the past decade, the significant increase in the adoption of consumer electronics has caused a rise in energy demand within the residential and household sectors globally. Since these electronics are dependent on electricity, the impact of these sectors on the environment is also deteriorating and it becomes important to take remedial action. For this, various websites and mobile applications have emerged that provide information to household users on energy consumption of devices and as well as reduction mechanisms. However, since these platforms are limited in various ways in their endeavor to promote self-learning on energy consumption reduction, awareness still remains an important barrier thus giving rise to the need for further investigation on innovative technologies and platforms. Even though Near Field Communication (NFC) could potentially be used, limited work has been conducted in relation to energy consumption of consumer electronics. As such, this paper delves into the application and usability of NFC for promoting self-learning on energy consumption of household electronic appliances through an Android based application called NFC Energy Tracker (NET).","PeriodicalId":119111,"journal":{"name":"2017 IEEE 4th International Conference on Soft Computing & Machine Intelligence (ISCMI)","volume":"859 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131240844","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

JarPi: A low-cost raspberry pi based personal assistant for small-scale fishermen JarPi:一个低成本的基于树莓派的小型渔民个人助理

2017 IEEE 4th International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2017-11-23 DOI: 10.1109/ISCMI.2017.8279618

Megh Hitesh Vora, Girish Bekaroo, A. Santokhee, Suraj Juddoo, Divesh Roopowa

引用次数: 2

Fruitify: Nutritionally augmenting fruits through markerless-based augmented reality 水果:通过无标记的增强现实技术增强水果的营养

2017 IEEE 4th International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2017-11-23 DOI: 10.1109/ISCMI.2017.8279616

Annu Kulpy, Girish Bekaroo

{"title":"Fruitify: Nutritionally augmenting fruits through markerless-based augmented reality","authors":"Annu Kulpy, Girish Bekaroo","doi":"10.1109/ISCMI.2017.8279616","DOIUrl":"https://doi.org/10.1109/ISCMI.2017.8279616","url":null,"abstract":"In the past few decades, a significant decrease in fruit consumption around the world has resulted in a hiking rate of cardiovascular diseases and obesity among youngsters. In order to address this issue, healthy eating is being recommended. However, awareness on nutritional information on fruits remain an important challenge that still needs to be addressed even though various sources in the form of books, websites and mobile applications are already available. This is also potentially due to the limited interaction and engagement with such sources. One technology that has shown to improve engagement, enhance understanding and provide a unique learning experience is Augmented Reality. However, limited work has been undertaken to provide nutritional information on fruits via this technology. As such, this paper investigates the application of AR to nutritionally augment fruits through a proposed prototype called Fruitify, before discussing the usability tests performed on the application and involving end users. As key findings, a system usability scale score of 82.1% was obtainedwhere participants expressed strong intention to utilize the tool again in the future.","PeriodicalId":119111,"journal":{"name":"2017 IEEE 4th International Conference on Soft Computing & Machine Intelligence (ISCMI)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129872761","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: 6

Waveform learning based on a genetic algorithm and its application to signal integrity improvement 基于遗传算法的波形学习及其在信号完整性改进中的应用

2017 IEEE 4th International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2017-11-01 DOI: 10.1109/ISCMI.2017.8279615

M. Yasunaga, I. Yoshihara

引用次数: 1

New kinetostatic criterion for robot parametric optimization 机器人参数优化的新动静力准则

2017 IEEE 4th International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2017-11-01 DOI: 10.1109/ISCMI.2017.8279599

M. Svejda

引用次数: 1

Use of reliability engineering concepts in machine learning for classification 可靠性工程概念在机器学习分类中的应用

2017 IEEE 4th International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2017-11-01 DOI: 10.1109/ISCMI.2017.8279593

Ziauddin Ursani, D. Corne

引用次数: 6

An enhanced clustering analysis based on glowworm swarm optimization 基于萤火虫群优化的增强聚类分析

2017 IEEE 4th International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2017-11-01 DOI: 10.1109/ISCMI.2017.8279595

R. Isimeto, C. Yinka-banjo, C. Uwadia, Daniel C. Alienyi

{"title":"An enhanced clustering analysis based on glowworm swarm optimization","authors":"R. Isimeto, C. Yinka-banjo, C. Uwadia, Daniel C. Alienyi","doi":"10.1109/ISCMI.2017.8279595","DOIUrl":"https://doi.org/10.1109/ISCMI.2017.8279595","url":null,"abstract":"Data clustering has always been an important aspect of data mining. Extracting clusters from data could be very difficult especially when the features are large and the classes not clearly partitioned, hence the need for high-quality clustering techniques. The major shortcoming of various clustering techniques is that the number of clusters must be stated before the clustering starts. A recent successful work in clustering is the Clustering analysis based on Glowworm Swarm Optimization (CGSO) algorithm. CGSO uses the multimodal search capacity of the Glowworm Swarm Optimization (GSO) algorithm to automatically figure out clusters within a data set without prior knowledge about the number of clusters. However, the sensor range — one of the parameters of the CGSO algorithm and a determinant of the number of clusters as well as the cluster quality — is in fact obtained by trial and error, which is clearly an inefficient approach. Consequently, this paper proposes the Modified Clustering analysis based on Glowworm Swarm Optimization (CGSOm) algorithm. The CGSOm extends the CGSO by incorporating a mechanism that determines the sensor range efficiently and automatically, modifying the glowworm initialization method and introducing a function that measures the cluster error during the iteration phase. The proposed algorithm was tested on artificial and real-world data sets. Experimental results show that for most data sets, the proposed CGSOm algorithm gives better clustering quality results of entropy and purity values when compared with the original CGSO algorithm and four standard clustering algorithms commonly used in the literature. The results reveal that the CGSOm yields better quality clusters.","PeriodicalId":119111,"journal":{"name":"2017 IEEE 4th International Conference on Soft Computing & Machine Intelligence (ISCMI)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121589353","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: 6

A simple text detection in document images using classification-based techniques 使用基于分类的技术在文档图像中进行简单的文本检测

2017 IEEE 4th International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2017-11-01 DOI: 10.1109/ISCMI.2017.8279610

Khanabhorn Kawattikul, P. Chomphuwiset

引用次数: 2

A segmentation technology for multivariate contextual time series 多变量上下文时间序列的分割技术

2017 IEEE 4th International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2017-11-01 DOI: 10.1109/ISCMI.2017.8279600

Hui-juan Zhang, Jia-Cheng Huang

引用次数: 0

Quality assessment of large scale dimensionality reduction methods 大规模降维方法的质量评价

2017 IEEE 4th International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2017-11-01 DOI: 10.1109/ISCMI.2017.8279588

Ntombikayise Banda, A. Engelbrecht

引用次数: 1