2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)最新文献_第2页

Fast and Accurate Genomic Minisatellites Disclosure 快速和准确的基因组微型卫星披露

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303664

Reza Behboodi, Mahmoud Naghibzadeh, Mostafa Nouri-Baygi

{"title":"Fast and Accurate Genomic Minisatellites Disclosure","authors":"Reza Behboodi, Mahmoud Naghibzadeh, Mostafa Nouri-Baygi","doi":"10.1109/ICCKE50421.2020.9303664","DOIUrl":"https://doi.org/10.1109/ICCKE50421.2020.9303664","url":null,"abstract":"Minisatellites are genomic sequences comprised of short monomers which are successively repeated many times in the same direction. They are highly variable sequences in terms of their monomer building blocks, number of repeats, and their location on the genomic sequences. This mutability has made them excellent genetic markers for both linkage analysis and forensic science. This paper presents an accurate and highly efficient computer method for identifying all minisatellites in a given DNA, gene, or genomic sequence. It is based on a new indexing method which is intelligently used in such a way that does not use any main memory or secondary storage for storing search-keys. Furthermore, for each search-key value, which is not stored, a pointer points to a list occurrences of the search-key value in the sequence. Potential minisatellites are detected from these lists and actual ones are recognized. The software is capable of discovering all minisatellites of very large sequences such as human genome with 3.2 Giga base pairs in a very short time. The minimum number of repeats of the motif is set to be 3. An advantage of the software is in the detection of overlapped minisatellites that some state of the art software cannot detect. With respect to the time, looking for minisatellites with the proposed approach would be faster than TRF, Mreps and Dot2dot.","PeriodicalId":402043,"journal":{"name":"2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"143 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128265404","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

Persian OCR with Cascaded Convolutional Neural Networks Supported by Language Model 语言模型支持的级联卷积神经网络波斯语OCR

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303691

M. PourReza, R. Derakhshan, S. Bibak, M. Fallah, H. Fayyazi, M. Sabokrou

{"title":"Persian OCR with Cascaded Convolutional Neural Networks Supported by Language Model","authors":"M. PourReza, R. Derakhshan, S. Bibak, M. Fallah, H. Fayyazi, M. Sabokrou","doi":"10.1109/ICCKE50421.2020.9303691","DOIUrl":"https://doi.org/10.1109/ICCKE50421.2020.9303691","url":null,"abstract":"Persian1OCR is a difficult task because of some specific features of Persian writing style, like different styles of letters in different places of the word and similarity of letters to each other. Recognizing sub-words instead of individual letters can reduce these difficulties. In this manner sub-word segmentation is the critical task of pre-process step. In this paper, a cascaded Convolutional Neural Network is utilized to convert sub-word images into text. A large dictionary of Persian sub-word images with different font styles is used as training data and an Auto-Encoder enriches the features needed for constructing the cascade classifier structure. The initial classifier learns the overall structure of sub-word images that its training data is the result of applying k-means clustering on the huge sub-word image dataset. The later classifier finds the exact text equivalent of the sub-word image. A word segmentation method forms the words based on extracted sub-words. This method use contour distances as a measure for distinguishing words from sub-words. The initial OCR result is improved using Natural Language Processing techniques. Two fast search structures in word dictionaries with the help of a language model build the post-processing module and substitute the misspelled extracted words with the best alternative. Comparison results with Tesseract OCR engine shows the superiority of the algorithm.","PeriodicalId":402043,"journal":{"name":"2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130523979","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

ACS-SOP: A new solution method to the Set Orienteering Problem ACS-SOP:一种求解集合定向问题的新方法

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303697

Maryam Asadi Golmankhaneh, M. Keshtkaran

引用次数: 0

Reliability Estimating By Demographic Matrix in Item-based Recommender Systems 基于人口统计矩阵的商品推荐系统可靠性估计

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303704

Seyedeh Niusha Motevallian, S. Hasheminejad, Hedieh Ahmadi

{"title":"Reliability Estimating By Demographic Matrix in Item-based Recommender Systems","authors":"Seyedeh Niusha Motevallian, S. Hasheminejad, Hedieh Ahmadi","doi":"10.1109/ICCKE50421.2020.9303704","DOIUrl":"https://doi.org/10.1109/ICCKE50421.2020.9303704","url":null,"abstract":"Nowadays, with the growth of communication between both users and websites, recommender systems have gained significant essential. These systems filter information to find out the user's interests and make personalized recommendations for them. Currently, it is important to provide high-reliability recommendations, because if the recommendations are unreliable, the system may lose the user at the very beginning. In this paper, a Demographic Matrix of users is proposed, then for estimating the reliability of predictions, we combined it with similarity or entropy matrix between items. Finally, we evaluated our approach by comparing it to some other reliability estimation algorithms by MAE (Mean Absolute Error). The slope of a regression line helps to determine how quickly our MAE change by the increase of reliability values, and in this way, we calculated the impact of our method on MAE reduction. The experiments on MovieLens dataset show that the proposed reliability estimation algorithm, due to its massive impact on MAE reduction, is significantly better than other algorithms.","PeriodicalId":402043,"journal":{"name":"2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"135 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121617723","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

Efficient Fuzzy based UAV Positioning in IoT Environment Data Collection 物联网环境数据采集中基于模糊的高效无人机定位

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303618

Afshin Alaghehband, Marzieh Ziyainezhad, M. J. Sobouti, S. Seno, A. Mohajerzadeh

{"title":"Efficient Fuzzy based UAV Positioning in IoT Environment Data Collection","authors":"Afshin Alaghehband, Marzieh Ziyainezhad, M. J. Sobouti, S. Seno, A. Mohajerzadeh","doi":"10.1109/ICCKE50421.2020.9303618","DOIUrl":"https://doi.org/10.1109/ICCKE50421.2020.9303618","url":null,"abstract":"Wireless networks can be considered one of the key features of Internet of Things (IoT), and the rise of IoT, has helped wireless networks to be improved and developed immensely. This improvement has increased QoS, data rate, transmit range, etc. The use of Unmanned Aerial Vehicles (UAVs) as Flying Base Stations (FBS) are growing in wireless networks specially in IoT environments. FBSs are a cost-effective and rapid way that can be used in areas which are difficult to access or there are no chances to build a fixed BS. The inherent characteristics of drones such as high mobility and presence of Line of Sight (LoS) in their connection has increased their efficiency when used as FBS, but one of the main challenges is the optimal placement of drones as BS in such a way that full coverage of sensors and actuators is provided to guarantee the demanded service. In this paper, the efficient placement of drones as BSs is modeled in the form of an optimization placement (OP) problem. The objective of this approach is to minimize the number of required UAVs and the distance of the drones from the cluster points while the sensor positions are accessible, keeping in mind the limitations of backhaul. We are using fuzzy-based clustering to find the candidate cluster heads. Finally the results shows that a proper parameter of fuzzy-based clustering algorithm can significantly improve the results of the optimization problem.","PeriodicalId":402043,"journal":{"name":"2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"185 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131404398","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

A hybrid friend-based recommendation system using the combination of Meta-heuristic Invasive weed and genetic algorithms 一种结合元启发式入侵杂草和遗传算法的基于朋友的混合推荐系统

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303619

A. Rezaee, Navid Abravan

{"title":"A hybrid friend-based recommendation system using the combination of Meta-heuristic Invasive weed and genetic algorithms","authors":"A. Rezaee, Navid Abravan","doi":"10.1109/ICCKE50421.2020.9303619","DOIUrl":"https://doi.org/10.1109/ICCKE50421.2020.9303619","url":null,"abstract":"One of the most important goals of researchers in designing recommender systems is to increase the accuracy of recommender models. The main purpose of this study is to combine weed algorithm and genetics to increase the efficiency of clustering, which increases the accuracy of clustering for analyzing suggestions in the recommender system. Clustering is based on three similarity criteria KMEAN, JACCARD and MINKOWSKI and in addition, it has been implemented with the mentioned algorithms. Using clustering method considering the mutation of genetic algorithm and using weed algorithm has led to the emergence of an efficient system that for this purpose, genetic generators have been used to sample data in the problem space instead of random generators, This selection has led to an increase in the accuracy of the algorithm due to the more uniform coverage of the problem space and the increase in the variety of problem searches. The Recommender system on the standard MovieLense data set is tested and its error is 0.02, which has a more minimal error than other algorithms (genetics and weeds).","PeriodicalId":402043,"journal":{"name":"2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125922598","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

A Novel Approach for Developing Emergency Evacuation Plans 制定紧急疏散计划的新方法

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303700

Hanan Daher, Samaneh Hoseindoost, B. Zamani, A. Fatemi

{"title":"A Novel Approach for Developing Emergency Evacuation Plans","authors":"Hanan Daher, Samaneh Hoseindoost, B. Zamani, A. Fatemi","doi":"10.1109/ICCKE50421.2020.9303700","DOIUrl":"https://doi.org/10.1109/ICCKE50421.2020.9303700","url":null,"abstract":"In case of a disaster, planning for pedestrian evacuation from buildings is a major issue since it threatens human lives. To cope with this problem, evacuation plans are developed to ensure efficient evacuation in minimum time. These plans can be very sophisticated according to the complexity of the evacuation environment. This advocates the use of architectures such as Multi-Agent Systems (MAS) to develop the evacuation plans before happening of a real accident. Since developing an evacuation plan using MAS requires considerable effort, finding more efficient approaches is still an open problem. This paper introduces a new approach, based on the model-driven principles, to support developing evacuation plans. The approach includes utilizing a graphical editor for designing evacuation models, automatic generation of the evacuation plan code, as well as running the generated code on a MAS platform. We evaluated our approach using a case study. The results show that our approach provides elevated speed, less effort, high abstraction level, and more flexibility and productivity in developing emergency evacuation plans.","PeriodicalId":402043,"journal":{"name":"2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125019540","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

Uplink Massive MIMO Detector Based on Vector Approximate Message Passing 基于矢量近似消息传递的上行海量MIMO检测器

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303611

Seyyed Akbar Hodudi Atigh, J. Pourrostam, Behzad Mozaffary Tazeh kand

引用次数: 1

Unsupervised Feature Selection based on Constructing Virtual Cluster’s Representative 基于构造虚拟聚类代表的无监督特征选择

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303633

Mohsen Rahmanian, E. Mansoori, Mohammad Taheri

{"title":"Unsupervised Feature Selection based on Constructing Virtual Cluster’s Representative","authors":"Mohsen Rahmanian, E. Mansoori, Mohammad Taheri","doi":"10.1109/ICCKE50421.2020.9303633","DOIUrl":"https://doi.org/10.1109/ICCKE50421.2020.9303633","url":null,"abstract":"The data readability, complexity reduction of learning algorithms and increase predictability are the most important reasons for using feature selection methods, especially when there exist lots of features. In recent years, unsupervised feature selection techniques are well explored. In this paper, we proposed an unsupervised feature selection algorithm using multivariate-symmetrical-uncertainty based feature clustering, Feature Selection-based Virtual Feature Representative (FSVFR). The main idea of FSVFR is as follows: First, it selects the cluster centers based on the similarity density of the neighbors of each feature; after assigning the features to the clusters, the virtual representative is generated in such a way that contains maximum common information with cluster’s members and minimum similarity with other representatives. These steps continues until there is no more change in the representatives. Second, a feature that has the most common information in each cluster is selected as its representative. The experimental results on benchmark datasets demonstrate the effectiveness of our approaches as compared to the two common methods.","PeriodicalId":402043,"journal":{"name":"2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123208706","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

Behavior Analysis of Random Power Graphs for Optimal PMU Placement in Smart Grids 智能电网PMU优化配置的随机功率图行为分析

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303632

M. Shahraeini, Ahad Alvandi, Shahla Khormali

引用次数: 4