2017 Palestinian International Conference on Information and Communication Technology (PICICT)最新文献

Precision Agriculture for Greenhouses Using a Wireless Sensor Network 利用无线传感器网络实现温室精准农业

2017 Palestinian International Conference on Information and Communication Technology (PICICT) Pub Date : 2017-09-18 DOI: 10.1109/PICICT.2017.20

Yousef E. M. Hamouda, Basel H. Y. Elhabil

{"title":"Precision Agriculture for Greenhouses Using a Wireless Sensor Network","authors":"Yousef E. M. Hamouda, Basel H. Y. Elhabil","doi":"10.1109/PICICT.2017.20","DOIUrl":"https://doi.org/10.1109/PICICT.2017.20","url":null,"abstract":"Agriculture is one of the most crucial needs of the life since it supports human and animals with food supplies and benefits the human employment and the national economy. Wireless Sensor Network (WSN) has recently applied in precision agriculture to improve the crop yields and apply the agricultural resources at right time and place. In this paper, Greenhouse Smart Management System (GSMS) using WSNs is designed and developed to automatically control, manage and monitor the agricultural parameters and activities inside the greenhouses. The ambient relative humidity and temperature of the greenhouse are measured using sensor nodes. When the sensed parameters exceed threshold values, the irrigation and cooling activities are triggered by activating the fan and water pump devices. GSMS includes also an algorithm to compute the period of irrigation and coolingaccording to the measured agricultural parameters. Hardware and software for the proposed GSMS are developed in this paper. The results show that the GSMS can save the agricultural resources and improve the crop yields, compared with other traditional schemes.","PeriodicalId":259869,"journal":{"name":"2017 Palestinian International Conference on Information and Communication Technology (PICICT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114998380","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: 30

Automatic Arabic Text Summarization for Large Scale Multiple Documents Using Genetic Algorithm and MapReduce 基于遗传算法和MapReduce的大规模多文档自动阿拉伯语文本摘要

2017 Palestinian International Conference on Information and Communication Technology (PICICT) Pub Date : 2017-05-08 DOI: 10.1109/PICICT.2017.32

R. Baraka, Sulaiman N. Al Breem

{"title":"Automatic Arabic Text Summarization for Large Scale Multiple Documents Using Genetic Algorithm and MapReduce","authors":"R. Baraka, Sulaiman N. Al Breem","doi":"10.1109/PICICT.2017.32","DOIUrl":"https://doi.org/10.1109/PICICT.2017.32","url":null,"abstract":"Multi document summarization focuses on extracting the most significant information from a collection of textual documents. Most summarization techniques require the data to be centralized, which may not be feasible in many cases due to computational and storage limitations. The huge increase of data emerging by the progress of technology and the various sources makes automatic text summarization of large scale of data a challenging task. We propose an approach for automatic text summarization of large scale Arabic multiple documents using Genetic algorithm and MapReduce parallel programming model. The approach insures scalability, speed and accuracy in summary generation. It eliminates sentence redundancy and increases readability and cohesion factors between the sentences of summaries. The experiments resulted in acceptable precision and recall scores. This indicates that the system successfully identifies the most important sentences. In Addition to all to that, the approach provided up to 10x speedup score, which is faster than on a single machine. Therefore, it can deal with large-scale datasets successfully. Finally, the efficiency score of the proposed approach indicates that the large data set utilizes the available resources up to 62%.","PeriodicalId":259869,"journal":{"name":"2017 Palestinian International Conference on Information and Communication Technology (PICICT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129212191","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

A New Set of Features for Detecting Router Advertisement Flooding Attacks 一组检测路由器通告泛洪攻击的新特征

2017 Palestinian International Conference on Information and Communication Technology (PICICT) Pub Date : 2017-05-08 DOI: 10.1109/PICICT.2017.19

Omar E. Elejla, B. Belaton, Mohammed Anbar, Issa M. Smadi

引用次数: 9

Review on Detection Techniques against DDoS Attacks on a Software-Defined Networking Controller 基于软件定义网络控制器的DDoS攻击检测技术综述

2017 Palestinian International Conference on Information and Communication Technology (PICICT) Pub Date : 2017-05-08 DOI: 10.1109/PICICT.2017.26

Haider Dhia Zubaydi, Mohammed Anbar, Chong Yung Wey

引用次数: 24

Arabic Opinion Mining Using Distributed Representations of Documents 使用分布式文档表示的阿拉伯意见挖掘

2017 Palestinian International Conference on Information and Communication Technology (PICICT) Pub Date : 2017-05-08 DOI: 10.1109/PICICT.2017.15

A. El-Halees

{"title":"Arabic Opinion Mining Using Distributed Representations of Documents","authors":"A. El-Halees","doi":"10.1109/PICICT.2017.15","DOIUrl":"https://doi.org/10.1109/PICICT.2017.15","url":null,"abstract":"Nowadays, many people express their opinions using user generated contains such as social media, forums and reviews. Opinion mining is a field of study that extracts sentiments from user generated contents. Because of the complexity of the Arabic language, extracting those opinions are challenging. Better representation of reviews can help to improve extraction of opinions. The traditional way of representing opinion documents is using Bag-of-Words where the word is presented in fixed-length. The problem of this presentation is that it loses the order of the word and it ignores grammatical structure and lexicon-dependent. To overcome these limitations, distributed representations can be employed. It is based on learning vector representations of words, which also called \"word embeddings\". It can make the performance of natural language processing tasks have better performance with the help of learning algorithms. This representation uses neural networks and makes the learned vectors explicitly encode many linguistic patterns. In this study, we used distributed representations for Arabic opinion mining and compare it with Bag of Words (BOW) representation. We applied them on four benchmark datasets. Then, we used four machine learning methods which are Support Vector Machine, Logistic Regression and Random Forest. Using f-measure metric, we found that, in all datasets and all methods we used in our experiment, the distributed representations have better performance than bag-of-words representation.","PeriodicalId":259869,"journal":{"name":"2017 Palestinian International Conference on Information and Communication Technology (PICICT)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132002904","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: 11

WikiDocsAligner: An Off-the-Shelf Wikipedia Documents Alignment Tool WikiDocsAligner:一个现成的维基百科文档对齐工具

2017 Palestinian International Conference on Information and Communication Technology (PICICT) Pub Date : 2017-05-01 DOI: 10.1109/PICICT.2017.27

Motaz Saad, B. Alijla

引用次数: 4

Absolute Exponential Companding to Reduced PAPR for FBMC/OQAM FBMC/OQAM的绝对指数扩展到减小PAPR

2017 Palestinian International Conference on Information and Communication Technology (PICICT) Pub Date : 2017-05-01 DOI: 10.1109/PICICT.2017.17

Imad A. Shaheen, A. Zekry, F. Newagy, Reem Ibrahim

引用次数: 8

Systematic Approach Based on Best Practices to Develop Requirements Engineering (RE) Guideline in an Organization 基于最佳实践的系统方法在组织中开发需求工程(RE)指南

2017 Palestinian International Conference on Information and Communication Technology (PICICT) Pub Date : 2017-05-01 DOI: 10.1109/PICICT.2017.31

A. Haron, S. Sahibuddin, Haizan Yusoff, M. H. Osman

引用次数: 1

Effect of People around User to WLAN Indoor Positioning System Accuracy 用户周围人群对无线局域网室内定位系统精度的影响

2017 Palestinian International Conference on Information and Communication Technology (PICICT) Pub Date : 2017-05-01 DOI: 10.1109/PICICT.2017.21

Firdaus, Noor Azurati Ahmad, S. Sahibuddin

引用次数: 5

Predicting User Entries by Using Data Mining Algorithms 使用数据挖掘算法预测用户条目

2017 Palestinian International Conference on Information and Communication Technology (PICICT) Pub Date : 2017-05-01 DOI: 10.1109/PICICT.2017.24

Basel A. Alhaj, Ashraf Y. A. Maghari

{"title":"Predicting User Entries by Using Data Mining Algorithms","authors":"Basel A. Alhaj, Ashraf Y. A. Maghari","doi":"10.1109/PICICT.2017.24","DOIUrl":"https://doi.org/10.1109/PICICT.2017.24","url":null,"abstract":"The information systems are widely spread in most official institutions, and become certified in all areas of our life such as education, health and entertainment. Usability is one of the most important factors, which encourages users to deal with these systems or refuse it. Data mining is the process of finding correlations or patterns among dozens of fields in large relational databases. In this paper we analyze the stored data in database of Palestinian Government decisions system in order to study the relationship between some attributes. Accordingly, we can find patterns that help us to make the system more user-friendly by offering suggestions to the users during data entry process. Naive Bayes, Rule Induction, K-NN, and Decision Tree methods are applied to the stored data in order to produce a prediction model that predicts entries to the user during the entry process, which can make the entry system more user-friendly. The experiment result shows the Naïve Bayes is the best model among the other techniques by achieving the highest accuracy of 68.41%. Future efforts can apply this model in the Government decisions system of Palestinian Ministers Council in Gaza.","PeriodicalId":259869,"journal":{"name":"2017 Palestinian International Conference on Information and Communication Technology (PICICT)","volume":"70 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":"129507423","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