2017 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)最新文献_第3页

Enhancing the accuracy of firefly algorithm by using the reproduction mechanism 利用繁殖机制提高萤火虫算法的精度

2017 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2017-06-01 DOI: 10.1109/SNPD.2017.8048202

Nasrin Evazzadeh Mohammadiyan, A. Ghaedi

{"title":"Enhancing the accuracy of firefly algorithm by using the reproduction mechanism","authors":"Nasrin Evazzadeh Mohammadiyan, A. Ghaedi","doi":"10.1109/SNPD.2017.8048202","DOIUrl":"https://doi.org/10.1109/SNPD.2017.8048202","url":null,"abstract":"Up to now, different algorithms based on evolutionary processes have been provided, which each has its own strengths and weaknesses. A physical phenomenon has many and complex variables and assumptions, and a number of these assumptions should be ignored in order to have a simpler modeling. Generally speaking, full modeling of a natural process through evolutionary algorithms is impossible, therefore, researchers consider special requirements for the natural and biological modeling processes, and they begin modeling, considering the assumptions limits. Evolutionary algorithms for modeling, need simple conditions and assumptions, and this simplification can take away a modeling from its actual state. Firefly algorithm is one of the evolutionary algorithms that is presented on the basis of social behavior and optical pulses between the insects. Lack of sufficient breeding of fireflies is one of the drawbacks that is not considered in the algorithm, and this factor has a negative impact on the convergence of the algorithm. Unlike the firefly algorithm, the weed algorithm, is a reproduction mechanism among plants, and the number of each plant's offspring are considered in accordance with its fitness, and in this study, this mechanism is used in firefly algorithm. The results show that the proposed algorithm has better accuracy and convergence, at least in comparison with firefly and bat algorithms.","PeriodicalId":186094,"journal":{"name":"2017 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124560500","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

Analysis of motivation of convention participants and proposal of attendance management system 会议参与者动机分析及考勤管理系统的提出

2017 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2017-06-01 DOI: 10.1109/SNPD.2017.8022787

T. Matsuo, Hidekazu Iwamoto

引用次数: 4

A novel passenger hotspots searching algorithm for taxis in urban area 城市出租车乘客热点搜索算法

2017 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2017-06-01 DOI: 10.1109/SNPD.2017.8022719

Yuhan Dong, Siyuan Qian, Kai Zhang, Yongzhi Zhai

引用次数: 7

Recognizing ADLs of one person household based on non-intrusive environmental sensing 基于非侵入式环境感知的一人家庭ADLs识别

2017 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2017-06-01 DOI: 10.1109/SNPD.2017.8022765

L. Niu, S. Saiki, Masahide Nakamura

{"title":"Recognizing ADLs of one person household based on non-intrusive environmental sensing","authors":"L. Niu, S. Saiki, Masahide Nakamura","doi":"10.1109/SNPD.2017.8022765","DOIUrl":"https://doi.org/10.1109/SNPD.2017.8022765","url":null,"abstract":"Pervasive sensing technologies are promising for increasing one-person households (OPH), where the sensors monitor and assist the resident to maintain healthy life rhythm. Towards the practical use, the recognition of activities of daily living (ADL) is an important step. Many studies of the ADL recognition have been conducted so far, for real-life and human-centric applications such as eldercare and healthcare. However, most existing methods have limitations in deployment cost, privacy exposure, and inconvenience for residents. To cope with the limitations, this paper presents a new indoor ADL recognition system especially for OPH. To minimize the deployment cost as well as the intrusions to user and house, we exploit an IoT-based environment-sensing device, called Autonomous Sensor Box (SensorBox) which can autonomously measure 7 kinds of environment attributes. We apply machine-learning techniques to the collected data, and predicts 7 kinds of ADLs. We conduct an experiment within an actual apartment of a single user. The result shows that the proposed system achieves the average accuracy of ADL recognition with more than 88%, by carefully developing the features of environment attributes.","PeriodicalId":186094,"journal":{"name":"2017 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121403059","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

Simplify methodology for developing VR applications based on shared VR materials 简化基于共享VR材料开发VR应用程序的方法

2017 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2017-06-01 DOI: 10.1109/SNPD.2017.8022722

Benjamas Panyangam, Suttipong Surak, Sangapong Panyakeaw

引用次数: 0

A user interface utilizing various contents in difficult environment 在困难的环境中利用各种内容的用户界面

2017 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2017-06-01 DOI: 10.1109/SNPD.2017.8022783

A. Sada, Naoshi Sakamoto

引用次数: 0

Histogram of Accumulated Changing Gradient Orientation (HACGO) for saliency navigated action recognition 基于累积变化梯度方向直方图的显著性导航动作识别

2017 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2017-06-01 DOI: 10.1109/SNPD.2017.8022726

Hnin Mya Aye, Sai Maung Maung Zaw

{"title":"Histogram of Accumulated Changing Gradient Orientation (HACGO) for saliency navigated action recognition","authors":"Hnin Mya Aye, Sai Maung Maung Zaw","doi":"10.1109/SNPD.2017.8022726","DOIUrl":"https://doi.org/10.1109/SNPD.2017.8022726","url":null,"abstract":"Action recognition has been an active research area in computer vision community during the recent years. However, it is still a challenging task due to the difficulties mainly resulted from the background clutter, illumination changes, large intra-class variation and noise. In this paper, we aim to develop an action recognition approach by navigating focus of attention (action region) with saliency detection and introducing a feature descriptor, namely Histogram of Accumulated Changing Gradient Orientation (HACGO). We firstly detect saliency in each video frame by computing pattern and color distinctness to localize action region. Then, we extract appearance and motion features using proposed HACGO, and existing HOG and HOF feature descriptors. Finally, a multi-class SVM classifier is applied to recognize different actions. The experiments were conducted on the standard UCF Sports action dataset. As experimental results, our action recognition approach achieved high recognition accuracy with a new combination of feature descriptors.","PeriodicalId":186094,"journal":{"name":"2017 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129399703","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

Developing personalized security information service using open data 利用开放数据开发个性化安全信息服务

2017 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2017-06-01 DOI: 10.1109/SNPD.2017.8022763

Takuhiro Kagawa, S. Saiki, Masahide Nakamura

引用次数: 3

Security vulnerability assessment for software version upgrade 软件版本升级安全漏洞评估

2017 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2017-06-01 DOI: 10.1109/SNPD.2017.8022734

Sirikwan Treetippayaruk, T. Senivongse

引用次数: 11

Similarity measure of human body movement through 3D chaincode 基于三维链码的人体运动相似度测量

2017 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2017-06-01 DOI: 10.1109/SNPD.2017.8022785

Pham Truong Hong Ngan, T. Hochin, Hiroki Nomiya

引用次数: 5