Future Computing and Informatics Journal最新文献_第7页

Fuzzy clustering based transition region extraction for image segmentation 基于模糊聚类的图像分割过渡区域提取

Future Computing and Informatics Journal Pub Date : 2018-08-01 DOI: 10.1016/J.JESTCH.2018.05.012

Priyadarsan Parida

引用次数: 19

A survey on opinion summarization techniques for social media 社交媒体意见汇总技术调查

Future Computing and Informatics Journal Pub Date : 2018-06-01 DOI: 10.1016/j.fcij.2017.12.002

Mohammed Elsaid Moussa, Ensaf Hussein Mohamed, Mohamed Hassan Haggag

引用次数: 39

Pulsar selection using fuzzy knn classifier 基于模糊已知分类器的脉冲星选择

Future Computing and Informatics Journal Pub Date : 2018-06-01 DOI: 10.1016/j.fcij.2017.11.001

Taha M. Mohamed

引用次数: 26

Privacy-preserving data aggregation in resource-constrained sensor nodes in Internet of Things: A review 物联网中资源受限传感器节点的隐私保护数据聚合研究进展

Future Computing and Informatics Journal Pub Date : 2018-06-01 DOI: 10.1016/j.fcij.2017.11.004

Inayat Ali, Eraj Khan, Sonia Sabir

{"title":"Privacy-preserving data aggregation in resource-constrained sensor nodes in Internet of Things: A review","authors":"Inayat Ali, Eraj Khan, Sonia Sabir","doi":"10.1016/j.fcij.2017.11.004","DOIUrl":"10.1016/j.fcij.2017.11.004","url":null,"abstract":"<div><p>Privacy problems are lethal and getting more attention than any other issue with the notion of the Internet of Things (IoT). Since IoT has many application areas including smart home, smart grids, smart healthcare system, smart and intelligent transportation and many more. Most of these applications are fueled by the resource-constrained sensor network, such as Smart healthcare system is powered by Wireless Body Area Network (WBAN) and Smart home and weather monitoring systems are fueled by Wireless Sensor Networks (WSN). In the mentioned application areas sensor node life is a very important aspect of these technologies as it explicitly effects the network life and performance. Data aggregation techniques are used to increase sensor node life by decreasing communication overhead. However, when the data is aggregated at intermediate nodes to reduce communication overhead, data privacy problems becomes more vulnerable. Different Privacy-Preserving Data Aggregation (PPDA) techniques have been proposed to ensure data privacy during data aggregation in resource-constrained sensor nodes. We provide a review and comparative analysis of the state of the art PPDA techniques in this paper. The comparative analysis is based on Computation Cost, Communication overhead, Privacy Level, resistance against malicious aggregator, sensor node life and energy consumption by the sensor node. We have studied the most recent techniques and provide in-depth analysis of the minute steps involved in these techniques. To the best of our knowledge, this survey is the most recent and comprehensive study of PPDA techniques.</p></div>","PeriodicalId":100561,"journal":{"name":"Future Computing and Informatics Journal","volume":"3 1","pages":"Pages 41-50"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.fcij.2017.11.004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72792690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 17

Overcoming business process reengineering obstacles using ontology-based knowledge map methodology 利用基于本体的知识地图方法克服业务流程再造障碍

Future Computing and Informatics Journal Pub Date : 2018-06-01 DOI: 10.1016/j.fcij.2017.10.006

Mahmoud AbdEllatif , Marwa Salah Farhan , Naglaa Saeed Shehata

{"title":"Overcoming business process reengineering obstacles using ontology-based knowledge map methodology","authors":"Mahmoud AbdEllatif , Marwa Salah Farhan , Naglaa Saeed Shehata","doi":"10.1016/j.fcij.2017.10.006","DOIUrl":"10.1016/j.fcij.2017.10.006","url":null,"abstract":"<div><p>Business process reengineering (BPR) is identified as one of the most important solutions for organizational improvements in all performance measures of business processes. However, high failure rates 70% is reported about using it the most important reason that caused the failure is the focus on the process itself; regardless of the surrounding environment, and the knowledge of the organization. The other reasons are due to the lack of tools to determine the causes of the inconsistencies and inefficiencies.</p><p>This paper proposes Process Reengineering Ontology-based knowledge Map Methodology (PROM) to reduce the failure ratio, solve BPR problems, and overcome their difficulties. Using an organizational ontology to show the structure and environment surrounding to organization's processes, using knowledge maps as an inference that succeeds to identify and find out the causes that lead to contradictions and inefficiencies, and using Analytical hierarchy processing to identify and prioritize processes of the business to be re-designed. Through the proposed methodology, all organizational processes are completely analyzed. Moreover, Analytical Hierarchy Processing technique is used to show the most important processes with high priority to be reengineered first then it is easy to discover any errors occurred during reengineering process through knowledge map so BPR is done successfully. Finally, Apply the proposed methodology to inventory management shows how processes reengineering are done successfully and helping the organization to achieve its objectives.</p></div>","PeriodicalId":100561,"journal":{"name":"Future Computing and Informatics Journal","volume":"3 1","pages":"Pages 7-28"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.fcij.2017.10.006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88160323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 50

Attribute selection using fuzzy roughset based customized similarity measure for lung cancer microarray gene expression data 基于模糊粗糙集自定义相似性度量的肺癌微阵列基因表达数据属性选择

Future Computing and Informatics Journal Pub Date : 2018-06-01 DOI: 10.1016/j.fcij.2018.02.002

C. Arunkumar , S. Ramakrishnan

{"title":"Attribute selection using fuzzy roughset based customized similarity measure for lung cancer microarray gene expression data","authors":"C. Arunkumar , S. Ramakrishnan","doi":"10.1016/j.fcij.2018.02.002","DOIUrl":"10.1016/j.fcij.2018.02.002","url":null,"abstract":"<div><p>Microarray gene expression data plays a prominent role in feature selection that helps in diagnosis and treatment of a wide variety of diseases. Microarray gene expression data contains redundant feature genes of high dimensionality and smaller training and testing samples. This paper proposes a customized similarity measure using fuzzy rough quick reduct algorithm for attribute selection. Information Gain based entropy is used to reduce the dimensionality in the first stage and the proposed fuzzy rough quick reduct method that defines a customized similarity measure for selecting the minimum number of informative genes and removing the redundant genes is employed at the second stage. The proposed method is evaluated using leukemia, lung and ovarian cancer gene expression datasets on a random forest classifier. The proposed method produces 97.22%, 99.45% and 99.6% classifier accuracy on leukemia, lung and ovarian cancer gene expression datasets respectively. The research study is carried out using the R open source software package. The proposed method shows substantial improvement in the performance with respect to various statistical parameters like classification accuracy, precision, recall, f-measure and region of characteristic compared to available methods in literature.</p></div>","PeriodicalId":100561,"journal":{"name":"Future Computing and Informatics Journal","volume":"3 1","pages":"Pages 131-142"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.fcij.2018.02.002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90018455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 29

HCIDL: Human-computer interface description language for multi-target, multimodal, plastic user interfaces HCIDL:多目标、多模态、可塑用户界面的人机界面描述语言

Future Computing and Informatics Journal Pub Date : 2018-06-01 DOI: 10.1016/j.fcij.2018.02.001

Lamia Gaouar , Abdelkrim Benamar , Olivier Le Goaer , Frédérique Biennier

{"title":"HCIDL: Human-computer interface description language for multi-target, multimodal, plastic user interfaces","authors":"Lamia Gaouar , Abdelkrim Benamar , Olivier Le Goaer , Frédérique Biennier","doi":"10.1016/j.fcij.2018.02.001","DOIUrl":"10.1016/j.fcij.2018.02.001","url":null,"abstract":"<div><p>From the human-computer interface perspectives, the challenges to be faced are related to the consideration of new, multiple interactions, and the diversity of devices. The large panel of interactions (touching, shaking, voice dictation, positioning …) and the diversification of interaction devices can be seen as a factor of flexibility albeit introducing incidental complexity. Our work is part of the field of user interface description languages. After an analysis of the scientific context of our work, this paper introduces HCIDL, a modelling language staged in a model-driven engineering approach. Among the properties related to human-computer interface, our proposition is intended for modelling multi-target, multimodal, plastic interaction interfaces using user interface description languages. By combining plasticity and multimodality, HCIDL improves usability of user interfaces through adaptive behaviour by providing end-users with an interaction-set adapted to input/output of terminals and, an optimum layout.</p></div>","PeriodicalId":100561,"journal":{"name":"Future Computing and Informatics Journal","volume":"3 1","pages":"Pages 110-130"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.fcij.2018.02.001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81338293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

Simultaneous ranking and selection of keystroke dynamics features through a novel multi-objective binary bat algorithm 通过一种新颖的多目标二进制bat算法对击键动力学特征进行同步排序和选择

Future Computing and Informatics Journal Pub Date : 2018-06-01 DOI: 10.1016/j.fcij.2017.11.005

Taha M. Mohamed , Hossam M. Moftah

引用次数: 9

Patient symptoms elicitation process for breast cancer medical expert systems: A semantic web and natural language parsing approach 乳腺癌医学专家系统的患者症状提取过程:语义网和自然语言解析方法

Future Computing and Informatics Journal Pub Date : 2018-06-01 DOI: 10.1016/j.fcij.2017.11.003

O.N. Oyelade , A.A. Obiniyi , S.B. Junaidu , S.A. Adewuyi

{"title":"Patient symptoms elicitation process for breast cancer medical expert systems: A semantic web and natural language parsing approach","authors":"O.N. Oyelade , A.A. Obiniyi , S.B. Junaidu , S.A. Adewuyi","doi":"10.1016/j.fcij.2017.11.003","DOIUrl":"10.1016/j.fcij.2017.11.003","url":null,"abstract":"<div><p>Information gathering from patient by clinicians during diagnostic procedures may sometimes require some skills to adequately collect required information that will be sufficient for the procedure. A situation where this information gathering may proof difficult in when a diagnostic decision making support system (DDSS) will have to gather such information from patient before carrying out the diagnostic procedure. Research has proven that it is more challenging to ensure user or patient inputs, in their raw form, maps into the list of acceptable medical terms for diagnostic tasks. This paper therefore proposes a formalized input generating model that addresses this shortcoming through the creation of an inference process, breast cancer lexicon, rule set and natural language processing (NLP). We developed an input generation algorithm which uses the python natural language processing capability in first filtering and generation the first pre-input collection. Furthermore, this algorithm then feeds in the pre-input word collection as input into the inference engine which has in its memory the rule set and ontology-based lexicon developed. Finally, this generates a list of acceptable tokens that will be sent into the medical expert system or DDSS for the diagnosing breast cancer. This proposed model was tested on a breast cancer based DDSS earlier designed by this authors, and result shows that the inference support of this model generates additional input of about 64% compared to when the patient's input where sent in as input in is state.</p></div>","PeriodicalId":100561,"journal":{"name":"Future Computing and Informatics Journal","volume":"3 1","pages":"Pages 72-81"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.fcij.2017.11.003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75474134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 13

Classification using deep learning neural networks for brain tumors 使用深度学习神经网络对脑肿瘤进行分类

Future Computing and Informatics Journal Pub Date : 2018-06-01 DOI: 10.1016/j.fcij.2017.12.001

Heba Mohsen , El-Sayed A. El-Dahshan , El-Sayed M. El-Horbaty , Abdel-Badeeh M. Salem

引用次数: 627