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An Improved Sentiment Analysis Approach to Detect Radical Content on Twitter 一种改进的情感分析方法来检测Twitter上的激进内容
Int. J. Inf. Technol. Web Eng. Pub Date : 2021-10-01 DOI: 10.4018/ijitwe.2021100103
Kamel Ahsene Djaballah, K. Boukhalfa, Omar Boussaïd, Yassine Ramdane
{"title":"An Improved Sentiment Analysis Approach to Detect Radical Content on Twitter","authors":"Kamel Ahsene Djaballah, K. Boukhalfa, Omar Boussaïd, Yassine Ramdane","doi":"10.4018/ijitwe.2021100103","DOIUrl":"https://doi.org/10.4018/ijitwe.2021100103","url":null,"abstract":"Social networks are used by terrorist groups and people who support them to propagate their ideas, ideologies, or doctrines and share their views on terrorism. To analyze tweets related to terrorism, several studies have been proposed in the literature. Some works rely on data mining algorithms; others use lexicon-based or machine learning sentiment analysis. Some recent works adopt other methods that combine multi-techniques. This paper proposes an improved approach for sentiment analysis of radical content related to terrorist activity on Twitter. Unlike other solutions, the proposed approach focuses on using a dictionary of weighted terms, the Word2vec method, and trigrams, with a classification based on fuzzy logic. The authors have conducted experiments with 600 manually annotated tweets and 200,000 automatically collected tweets in English and Arabic to evaluate this approach. The experimental results revealed that the new technique provides between 75% to 78% of precision for radicality detection and 61% to 64% to detect radicality degrees.","PeriodicalId":222340,"journal":{"name":"Int. J. Inf. Technol. Web Eng.","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121737685","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 Long Short-Term Memory Neural Network for Daily NO2 Concentration Forecasting 基于长短期记忆神经网络的二氧化氮日浓度预测
Int. J. Inf. Technol. Web Eng. Pub Date : 2021-10-01 DOI: 10.4018/ijitwe.2021100102
Bingchun Liu, Xiaogang Yu, Qingshan Wang, Shijie Zhao, Lei Zhang
{"title":"A Long Short-Term Memory Neural Network for Daily NO2 Concentration Forecasting","authors":"Bingchun Liu, Xiaogang Yu, Qingshan Wang, Shijie Zhao, Lei Zhang","doi":"10.4018/ijitwe.2021100102","DOIUrl":"https://doi.org/10.4018/ijitwe.2021100102","url":null,"abstract":"NO2 pollution has caused serious impact on people's production and life, and the management task is very difficult. Accurate prediction of NO2 concentration is of great significance for air pollution management. In this paper, a NO2 concentration prediction model based on long short-term memory neural network (LSTM) is constructed with daily NO2 concentration in Beijing as the prediction target and atmospheric pollutants and meteorological factors as the input indicators. Firstly, the parameters and architecture of the model are adjusted to obtain the optimal prediction model. Secondly, three different sets of input indicators are built on the basis of the optimal prediction model to enter the model learning. Finally, the impact of different input indicators on the accuracy of the model is judged. The results show that the LSTM model has high application value in NO2 concentration prediction. The maximum temperature and O3 among the three input indicators improve the prediction accuracy while the NO2 historical low-frequency data reduce the prediction accuracy.","PeriodicalId":222340,"journal":{"name":"Int. J. Inf. Technol. Web Eng.","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130418679","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 Robust Lightweight Data Security Model for Cloud Data Access and Storage 面向云数据访问和存储的健壮轻量级数据安全模型
Int. J. Inf. Technol. Web Eng. Pub Date : 2021-07-01 DOI: 10.4018/IJITWE.2021070103
Pajany Murugaiyan, G. Zayaraz
{"title":"A Robust Lightweight Data Security Model for Cloud Data Access and Storage","authors":"Pajany Murugaiyan, G. Zayaraz","doi":"10.4018/IJITWE.2021070103","DOIUrl":"https://doi.org/10.4018/IJITWE.2021070103","url":null,"abstract":"In this paper, an efficient lightweight cloud-based data security model (LCDS) is proposed for building a secured cloud database with the assistance of intelligent rules, data storage, information collection, and security techniques. The major intention of this study is to introduce a new encryption algorithm to secure intellectual data, proposing a new data aggregation algorithm for effective data storage and improved security, developing an intelligent data merging algorithm for accessing encrypted and original datasets. The major benefit of the proposed model is that it is fast in the encryption process at the time of data storage and reduced decryption time during data retrieval. In this work, the authors proposed an enhanced version of the hybrid crypto algorithm (HCA) for cloud data access and storage. The proposed system provides secured storage for storing data within the cloud.","PeriodicalId":222340,"journal":{"name":"Int. J. Inf. Technol. Web Eng.","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133664674","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
A Detector and Evaluation Framework of Abnormal Bidding Behavior Based on Supplier Portrait 基于供应商画像的异常投标行为检测与评价框架
Int. J. Inf. Technol. Web Eng. Pub Date : 2021-04-01 DOI: 10.4018/IJITWE.2021040104
Ma Xinqiang, Xuewei Li, Zhong Baoquan, Huang Yi, Y. Gu, Maonian Wu, Yong Liu, Mingyi Zhang
{"title":"A Detector and Evaluation Framework of Abnormal Bidding Behavior Based on Supplier Portrait","authors":"Ma Xinqiang, Xuewei Li, Zhong Baoquan, Huang Yi, Y. Gu, Maonian Wu, Yong Liu, Mingyi Zhang","doi":"10.4018/IJITWE.2021040104","DOIUrl":"https://doi.org/10.4018/IJITWE.2021040104","url":null,"abstract":"In a large number of bidding supplier groups, it is difficult to accurately find suppliers with unreasonable bidding behavior. In order to solve the problem of precise positioning of massive abnormal bidding behavior groups of diverse and widely distributed suppliers, the authors design a detector framework of abnormal bidding behavior based on supplier portrait. This paper mainly focuses on three abnormal bidding behaviors which harmful to the tenderers—“affiliated operation,” “subcontracting behavior,” and “colluding behavior.” Based on the bidding behavior records of suppliers, this paper establishes supplier portraits in four dimensions. In order to solve the problem that the detection algorithm under the unlabeled data is difficult to verify, this research establishes a new evaluation framework based on the bid base price formula and benefit map database of the supplier. The experiment verifies that the framework can effectively detect most suppliers with abnormal bidding behavior and can significantly change the benchmark price after eliminating abnormal suppliers.","PeriodicalId":222340,"journal":{"name":"Int. J. Inf. Technol. Web Eng.","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122882877","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
An Integer Linear Programming-Based Method for the Extraction of Ontology Alignment 基于整数线性规划的本体对齐提取方法
Int. J. Inf. Technol. Web Eng. Pub Date : 2021-04-01 DOI: 10.4018/IJITWE.2021040102
Naima El Ghandour, Moussa Benaissa, Yahia Lebbah
{"title":"An Integer Linear Programming-Based Method for the Extraction of Ontology Alignment","authors":"Naima El Ghandour, Moussa Benaissa, Yahia Lebbah","doi":"10.4018/IJITWE.2021040102","DOIUrl":"https://doi.org/10.4018/IJITWE.2021040102","url":null,"abstract":"The Semantic Web uses ontologies to cope with the data heterogeneity problem. However, ontologies become themselves heterogeneous; this heterogeneity may occur at the syntactic, terminological, conceptual, and semantic levels. To solve this problem, alignments between entities of ontologies must be identified. This process is called ontology matching. In this paper, the authors propose a new method to extract alignment with multiple cardinalities using integer linear programming techniques. The authors conducted a series of experiments and compared them with currently used methods. The obtained results show the efficiency of the proposed method.","PeriodicalId":222340,"journal":{"name":"Int. J. Inf. Technol. Web Eng.","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129175545","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
Towards Efficient Big Data Storage With MapReduce Deduplication System 利用MapReduce重复数据删除系统实现高效大数据存储
Int. J. Inf. Technol. Web Eng. Pub Date : 2021-04-01 DOI: 10.4018/IJITWE.2021040103
C. Joe, J. S. Raj, S. Smys
{"title":"Towards Efficient Big Data Storage With MapReduce Deduplication System","authors":"C. Joe, J. S. Raj, S. Smys","doi":"10.4018/IJITWE.2021040103","DOIUrl":"https://doi.org/10.4018/IJITWE.2021040103","url":null,"abstract":"In the big data era, there is a high requirement for data storage and processing. The conventional approach faces a great challenge, and de-duplication is an excellent approach to reduce the storage space and computational time. Many existing approaches take much time to pinpoint the similar data. MapReduce de-duplication system is proposed to attain high duplication ratio. MapReduce is the parallel processing approach that helps to process large number of files in less time. The proposed system uses two threshold two divisor with switch algorithm for chunking. Switch is the average parameter used by TTTD-S to minimize the chunk size variance. Hashing using SHA-3 and fractal tree indexing is used here. In fractal index tree, read and write takes place at the same time. Data size after de-duplication, de-duplication ratio, throughput, hash time, chunk time, and de-duplication time are the parameters used. The performance of the system is tested by college scorecard and ZCTA dataset. The experimental results show that the proposed system can lessen the duplicity and processing time.","PeriodicalId":222340,"journal":{"name":"Int. J. Inf. Technol. Web Eng.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123837289","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
Integrity of E-Health Record Ensured With Context-Based Merkle Tree Through Temporal Shadow in Blockchain b区块链中基于上下文的Merkle树通过时间阴影确保电子健康记录完整性
Int. J. Inf. Technol. Web Eng. Pub Date : 2020-10-01 DOI: 10.4018/ijitwe.2020100105
R. Charanya, R. Saravanaguru
{"title":"Integrity of E-Health Record Ensured With Context-Based Merkle Tree Through Temporal Shadow in Blockchain","authors":"R. Charanya, R. Saravanaguru","doi":"10.4018/ijitwe.2020100105","DOIUrl":"https://doi.org/10.4018/ijitwe.2020100105","url":null,"abstract":"The patient's health record is sensitive and confidential information. The sharing of health information is a first venture to make health services more productive and improve the quality of healthcare services. Decentralized online ledgers with blockchain-based platforms were already proposed and in use to address the interoperability and privacy issues. However, other challenges remain, in particular, scalability, usability, and accessibility as core technical challenges. The paper focuses on ensuring the integrity of the health record with context-based Merkle tree (CBMT) through temporal shadow. In this system, two ledgers were used to ensure the integrity of eHealth records like general public ledger (GPL) and personalized micro ledger (PML). The context-based Merkle tree (CBMT) is used to aggregates all the transactions at a particular time. The context means it depends on time, location, and identity. This is ensured without the help of a third party.","PeriodicalId":222340,"journal":{"name":"Int. J. Inf. Technol. Web Eng.","volume":"103 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121016478","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
Learning Models for Concept Extraction From Images With Drug Labels for a Unified Knowledge Base Utilizing NLP and IoT Tasks 基于NLP和物联网任务的统一知识库的药物标签图像概念提取学习模型
Int. J. Inf. Technol. Web Eng. Pub Date : 2020-07-01 DOI: 10.4018/ijitwe.2020070102
Sukumar Rajendran, P. Jayagopal
{"title":"Learning Models for Concept Extraction From Images With Drug Labels for a Unified Knowledge Base Utilizing NLP and IoT Tasks","authors":"Sukumar Rajendran, P. Jayagopal","doi":"10.4018/ijitwe.2020070102","DOIUrl":"https://doi.org/10.4018/ijitwe.2020070102","url":null,"abstract":"The evolution of humankind is through the exchange of information and extraction of knowledge from available information. The process of exchange of the information differs by the probability of the medium through which the information is exchanged. The Internet of things (IoT) contains millions of devices with sensors simultaneously transferring real time information to devices as rapid streams of data that need to be processed on the go. This leads to the need for development of effective and efficient approaches for segregating data based on class, relatedness, and differences in the information. The extraction of text from images is performed through tesseract irrespective of the language. SCIBERT models to extract scientific information and evaluating on a suite of tasks specially in classifying drugs based on free data (tweets, images, etc.). The images and text-based semantic similarity analysis provide similar drugs grouped together by composition or manufacturer.","PeriodicalId":222340,"journal":{"name":"Int. J. Inf. Technol. Web Eng.","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116154259","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
TBHM: A Secure Threshold-Based Encryption Combined With Homomorphic Properties for Communicating Health Records TBHM:用于通信健康记录的基于安全阈值的加密与同态属性相结合
Int. J. Inf. Technol. Web Eng. Pub Date : 2020-07-01 DOI: 10.4018/ijitwe.2020070101
L. Gupta, A. Samad, H. Garg
{"title":"TBHM: A Secure Threshold-Based Encryption Combined With Homomorphic Properties for Communicating Health Records","authors":"L. Gupta, A. Samad, H. Garg","doi":"10.4018/ijitwe.2020070101","DOIUrl":"https://doi.org/10.4018/ijitwe.2020070101","url":null,"abstract":"Healthcare today is one of the most promising, prevailing, and sensitive sectors where patient information like prescriptions, health records, etc., are kept on the cloud to provide high quality on-demand services for enhancing e-health services by reducing the burden of data storage and maintenance to providing information independent of location and time. The major issue with healthcare organization is to provide protected sharing of healthcare data from the cloud to the decision makers, medical practitioners, data analysts, and insurance firms by maintaining confidentiality and integrity. This article proposes a novel and secure threshold based encryption scheme combined with homomorphic properties (TBHM) for accessing cloud based health information. Homomorphic encryption completely eliminates the possibility of any kind of attack as data cannot be accessed using any type of key. The experimental results report superiority of TBHM scheme over state of art in terms throughput, file encryption/decryption time, key generation time, error rate, latency time, and security overheads.","PeriodicalId":222340,"journal":{"name":"Int. J. Inf. Technol. Web Eng.","volume":"33 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126423913","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
Analysis of Crime Data Using Neighbourhood Rough Sets 利用邻域粗糙集分析犯罪数据
Int. J. Inf. Technol. Web Eng. Pub Date : 2020-07-01 DOI: 10.4018/ijitwe.2020070104
Lydia J. Gnanasigamani, H. Seetha
{"title":"Analysis of Crime Data Using Neighbourhood Rough Sets","authors":"Lydia J. Gnanasigamani, H. Seetha","doi":"10.4018/ijitwe.2020070104","DOIUrl":"https://doi.org/10.4018/ijitwe.2020070104","url":null,"abstract":"Crime analysis has been carried out to find out patterns and associations in crime incidents. A few of the different latitudes that research has been carried out are the prediction of crime rate, sociological impacts of crime, the contribution of socio-economic factors to the crime and finding the places where the frequency of crime is unusually high. GIS and spatial information have evolved as an inherent part of the crime data as the information is made public by the policing agencies. ‘Crime mapping' refers to mapping a crime to a particular place. Geography or the spatial information of crime plays an important role in the analysis of crime. Previous research have documented the spatial importance in identifying the hotspots and showing crime distribution in a particular geography. This work intends to identify the similarity between regions in the geographical area using Rough Set methodology. By doing so, we can prepare similar crime-fighting strategies for the neighbours and alleviate the crime.","PeriodicalId":222340,"journal":{"name":"Int. J. Inf. Technol. Web Eng.","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114317127","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
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