A novel unsupervised ensemble framework using concept-based linguistic methods and machine learning for twitter sentiment analysis

The underwater environment is gaining importance due to its role in enhancing the economy of the world and improving relationships between different countries across the world. There are several applications for underwater imaging, which are affected by the underwater environment. The review and bibliometric analysis provide a systematic understanding of various applications and problems faced by different underwater imaging techniques. It provides potential directions for future research as it provides an insight into the efficiency and sustainability of the proposed solutions for underwater imaging problems. The review consists of identifying relevant published articles from SCOPUS. The literature review included papers from underwater image denoising, detection, recognition, restoration, generation, dehazing, deblurring, quality assessment, classification, compression, and image processing. Analysis of network, recognition of pivot research topics, correlation and pattern combinations of accepted and recent research were identified with the help of bibliometric software. It also helped in identifying key authors, journals, influential institutions, and impactful keywords. The systematic reviews and bibliometric analysis exhibits and recognize the current and potential underwater image processing research interest for the future. The review and bibliometric analysis are considered relevant as they exhibit an organized and systematic underwater image processing study which could help in future research. A research framework is proposed for systematically organizing the main underwater techniques and their relationships to maximize the benefits offered in different underwater imaging fields. The study will help researchers and engineers in developing practical strategic plans for underwater environment cleaning operations.

Image mining applications for underwater environment management - A review and research agenda

Due to the explosive growth of massive consumption data, the traditional marketing model of enterprises has become stretched. The rapid advancement of data mining technology has given new impetus to the innovation of the marketing strategies of enterprises, promoting the progressive transformation of enterprises from traditional passive marketing to precise and refined marketing. Data mining technology’s basic task is to analyze data, acquire insight into cause and effect, and then predict the future. As a result, the technology is coupled with the enterprises’ marketing activities, and a precise marketing model is built using big data consumption. This will aid in the comprehensive and three-dimensional description of customers, as well as the analysis of potential customers’ attributes, to give a scientific basis for the formulation and implementation of precise data-driven decision-making for enterprises. As a result, our study enhances and optimizes the K-means algorithm in combination with the artificial bee colony algorithm aiming at fixing the issue that the K-means algorithm is sensitive to cluster center initialization and improving the enterprise precision marketing model’s clustering performance. In the precise marketing scenario of the telecom business, the improved K-means clustering model is utilized to realize the analysis and prediction of telecom customers, as well as to carry out precise marketing based on the predicted findings. Finally, the optimized K-means clustering algorithm can objectively and comprehensively reflect the characteristics of telecom customer value segmentation, efficiently mining future clients and preventing blind marketing by enterprises, based on the model's actual verification results. Simultaneously, it provides substantial data support for telecom enterprises’ resource planning, as well as pointing out the next step in increasing market share.

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Enterprise Precision Marketing Effectiveness Model Based on Data Mining Technology

In the later stage of industrialization or postindustrialization era, the driving force of human social and economic development mainly comes from technological innovation, cultural creativity, and the development of creative industries, high-tech industries, and service industries. The cultural and creative industries meet the needs of the postindustrial era and the era of knowledge economy. This shows the importance of culture, knowledge, and technology in the rapid economic development. This paper introduces data mining technology and obtains a data mining method system suitable for this study. It mines and analyzes data related to cultural and creative industries and rural tourism industries. It finally obtained that the degree of integration of the two industries in the past six years was 0.498, 0.496, 0.487, 0.498, 0.499, and 0.5, respectively.

https://downloads.hindawi.com/journals/wcmc/2022/2351906.pdf

An Integrated Development Model of Cultural and Creative Industry and Rural Tourism Industry Based on Data Mining

: Detection and analysis of software vulnerabilities is a very important consideration. For this reason, software security vulnerabilities that have been identified for many years are listed and tried to be classified. Today, this process, performed manually by experts, takes time and is costly. Many methods have been proposed for the reporting and classification of software security vulnerabilities. Today, for this purpose, the Common Vulnerability Scoring System is officially used. The scoring system is constantly updated to cover the different security vulnerabilities included in the system, along with the changing security perception and newly developed technologies. Different versions of the scoring system are used with vulnerability reports. In order to add new versions of the published scoring system to the old vulnerability reports, all analyzes must be done manually backwards in accordance with the new security framework. This is a situation that requires a lot of resources, time and expert skill. For this reason, there are large deficiencies in the values of vulnerability scoring systems in the database. The aim of this study is to estimate missing security metrics of vulnerability reports using natural language processing and machine learning algorithms. For this purpose, a model using term frequency inverse document frequency and K-Nearest Neighbors algorithms is proposed. In addition, the obtained data was presented to the use of researchers as a new database. The results obtained are quite promising. A publicly available database was chosen as the data set that all researchers accepted as a reference. This approach facilitates the evaluation and analysis of our model. This study was performed with the largest dataset size available from this database to the best of our knowledge and is one of the limited studies on the latest version of the official scoring system published for classification of software security vulnerabilities. Due to the mentioned issues, our study is a comprehensive and original study in the field.

/pdf/estimating-missing-security-vectors-in-nvd-database-security-eby3wvn6.pdf

Estimating Missing Security Vectors in NVD Database Security Reports

Increasing progress in numerous research fields and information technologies, led to an increase in the publication of research papers. Therefore, researchers take a lot of time to find interesting research papers that are close to their field of specialization. Consequently, in this paper we have proposed documents classification approach that can cluster the text documents of research papers into the meaningful categories in which contain a similar scientific field. Our presented approach based on essential focus and scopes of the target categories, where each of these categories includes many topics. Accordingly, we extract word tokens from these topics that relate to a specific category, separately. The frequency of word tokens in documents impacts on weight of document that calculated by using a numerical statistic of term frequency-inverse document frequency (TF-IDF). The proposed approach uses title, abstract, and keywords of the paper, in addition to the categories topics to perform the classification process. Subsequently, documents are classified and clustered into the primary categories based on the highest measure of cosine similarity between category weight and documents weights.

/pdf/text-documents-clustering-using-data-mining-techniques-4tyidp40ap.pdf

Text documents clustering using data mining techniques

Distributed Denial of services (DDoS) attack is one of the most dangerous attacks that targeted servers. The main consequence of this attack is to prevent users from getting their legitimate services by bringing down targeted victim. CICFlowMeter tool generates bi-directional flows from packets. Each flow generates 83 of different features. The research focuses on 8 features which are active min (f1), active mean (f2), active max (f3), active std (f4), idle min (f5), idle mean (f6), idle max (f7), and idle std (f8). CICFlowMeter tool has several problems that affected on the detection accuracy of DDoS attacks. The idle and active based feature of Shannon entropy and sequential probability ratio test (SE-SPRT) approach was implemented in this research. The problems of original CICFlowMeter were presented, and the differences between original and revised version of CICFlowMeter tool were explored. The DARPA database and confusion matrix were used to evaluate the detection technique and present the comparison between two versions of CICFlowMeter. The detection method detected neptune and smurf attacks and had higher accuracy, f1-score, sensitivity, specificity, and precision when revised version of CICFlowMeter used to generate flows. However, the detection method failed to detect neptune attack and had higher miss-rate, lower accuracy, lower f1-score, and lower specificity, and lower precision when original version used in generating flows.

Basheer Husham Ali

Papers

Text documents clustering using data mining techniques

DDoS Detection Using Active and Idle Features of Revised CICFlowMeter and Statistical Approaches