Reviewerly/exHarmony huggingface dataset:
Enhancing the Credit Card Fraud Detection Using Decision Tree and Adaptive Boosting Techniques

The recent technology enhancement in the mobile and e-commerce field includes a huge number of online transaction, which results an increased number of fraud transaction and made a notable financial loss, for the individuals and banking sectors. Significant number of fraud transactions are made with credit cards. So, it is essential to develop a mechanism that ensures security and integrity of credit card transactions. In this article the main aim is to detect such fraud transactions using several machine learning algorithms such as Decision Tree & Adaptive Boosting. Due to high imbalance in dataset, the Synthetic Minority Oversampling Technique (SMOTE) is used to balance the data and Decision tree algorithm for classification. The Decision tree algorithm is used with the Adaptive Boosting technique to increase their quality of binary classification. The results are compared using the accuracy, precision and recall.
Content-Based Long Text Documents Classification Using Bayesian Approach for a Resource-Poor Language Urdu

Due to the emerging technologies like hand-held devices, fast and reliable internet services, everyday millions of text documents are being produced by online libraries, blogs, and news forums. To reuse and extract useful information from these enormous amount of documents, these documents must be process and arranged into labels automatically. Text classification is an important task of text mining where a document is labeled based on its content. Long text classification is more challenging than short text classification because of high dimensional feature space, irrelevant features require more time and resources for processing. Further, Urdu text processing is more challenging because of its complex language features like rich morphological font and lack of public datasets. To overcome the above challenges, in this study, we attempt to investigate the potential of six machine learning classifiers based on Bayesian theorem in the context of long text document classification of Urdu. For systematic performance comparison, we perform experiments on three datasets of small, medium and large size. Performance measures such as F-measure, Root Mean Square Error (RMSE), and time have been considered to evaluate the model’s performance. Usually Naïve Bayes is considered an efficient classifier but our experimental results show that DMNBtext is the most fast, reliable and accurate classifier than five Bayesian and three non-Bayesian models.
AI Big Data Multi-dimensional Intelligent Pension Model Recommendation Algorithm Empowered by Artificial Intelligence

The application of artificial intelligence technology has brought about great changes. Artificial intelligence technology has facilitated a change in the understanding of elderly care service models, elderly care centers and elderly care management. This paper aims to study the recommendation algorithm of AI big data multi-dimensional intelligent elderly care model empowered by artificial intelligence. This paper describes the integration of elderly care services from the aspects of elderly care service content and work quality. By collecting and analyzing key customer information on the elderly care service platform, empowering behavioral information and shaping the image of elderly users. The user chart depicts the individual needs and preferences of elderly customers, and on this basis, combined with the proposed algorithm, elderly customers can be recommended elderly care services that meet their needs and preferences. Experiments have shown that the recommendation accuracy of the algorithm constructed in this paper is as high as 90%, and the number of users who have been taught within 1 h is more than 90%, which is easy to operate.
Multi-modal Knowledge Graph Convolutional Network for Recommendation

As one of the classic algorithms for recommender systems, collaborative filtering faces the problem of data sparsity and cold start. To address these issues, researchers propose knowledge graph-based recommendation, which uses the attributes of items to build a knowledge network. But most works ignore the multimodal information of items (such as images, etc.). In this paper, we combine multimodal knowledge graph and graph convolutional network to propose Multimodal Knowledge Graph Convolutional Network (MKGCN). MKGCN can make full use of the multimodal information of items to enhance recommendation, adjust the neighborhood sampling strategy to capture relatively important neighborhood information, and capture higher-order information by expanding the receptive field. We conduct extensive experiments on movie and music datasets, and the results show that our MKGCN model outperforms the baselines.
A Voting Classifier for Mortality Prediction Post-Thoracic Surgery

Thoracic surgery involves the surgical treatment of vital organs inside the thoracic cavity to treat conditions of the lungs, heart, trachea, diaphragm, etc. Such procedures are primarily classified as high-risk, and chances of mortality are increasingly high. Due to this challenge, post-surgical risk assessment is of crucial relevance. This study used an ensemble machine learning approach based on a voting mechanism using different base learners. We evaluated the performance of the voting methods with classification metrics, including accuracy, precision, recall, F-1 score, and AUC-ROC. The soft voting classifier showed better performance with 97.73% accuracy. Our study demonstrates the predictive power of machine learning algorithms in analyzing and predicting outcomes after thoracic surgery.
Cloud Computing Information System Security Monitoring Under Artificial Intelligence Technology

Cloud computing (CC) represents the trend of IT field towards the road of intensification, scale and specialization, and is a profound change that is happening in the IT field. It greatly improves the efficiency of using various resources, but also brings great impact and challenges to the security and privacy protection of users’ information assets. In this paper, we take a dam cloud computing information system as an example, use Revit as a platform, combine Visual Studio and C# language, secondary development of Revit, and establish a dam safety monitoring BIM IS. The monitoring data management module completes the interaction between Revit and database through Visual Studio and C# language, and adopts two ways of monitoring instrument 3D model and command button to realize the association between each monitoring item and monitoring data; the monitoring data analysis module embeds the mathematical model established by the research into the system to realize the modeling prediction of dam safety monitoring data. The research results show that the development of the dam cloud platform safety monitoring BIM IS with the functions of dam structure management, monitoring instrument management, monitoring data management and monitoring data analysis is of positive significance to improve the efficiency of dam safety monitoring and promote the development of dam safety monitoring technology.
Information Collection System of Learning City Based on Big Data Technology

City is a highly concentrated regional type of human society and an important part of society. Under the influence of lifelong education, lifelong learning and learning society, the concept of Learning City(LC) also came into being and continues to develop, indicating that the development of the city has ushered in a new stage and a new height. Based on BDT, this paper designs the LC information acquisition system, briefly analyzes the characteristics of BDT and data acquisition and processing technology, takes urban traffic information as an example, analyzes the LC traffic information acquisition system, and verifies the effectiveness of the LC information acquisition system based on BDT proposed in this paper.
Building Fall Safety Early Warning System Based on Directed Weighted Network

Directed Weighted Network (DWN) are ubiquitous in nature. It is of great practical significance to evaluate and analyze the importance of their nodes. With the continuous maturity of the network research field, hypernetworks have gradually entered the research field of scholars. At this stage, China's construction safety management has gradually changed from post emergency treatment to pre-warning and prevention. In order to improve the level of construction safety early warning, this paper studies and analyzes the building fall safety early warning system (SEWS) based on DWN. This paper briefly analyzes the establishment principle of the early warning index system of building falling safety accidents, and constructs the early warning system of building falling safety based on the DWN; Finally, through the experimental test and analysis, the test results obtained from the early warning model of building falling accidents are compared with the sample data. The results show that the test results of the early warning model of falling accidents are highly consistent with the conclusions of the expert group, that is, based on the DWN, the knowledge, experience and thinking mode of safety experts to judge the safety situation are well obtained and learned, The effectiveness and feasibility of building fall SEWS based on DWN are verified.
Visual Intelligent Recognition System Based on Visual Thinking

With globalization, technological growth and the rapid popularization of the Internet, the marketing atmosphere faced by enterprises is undergoing tremendous changes. For the relevant marketing enterprises, the actual efficiency of information response will have a significant impact on the comprehensive competitiveness. Therefore, this paper studies and analyzes the enterprise marketing strategy (MS), combination optimization system based on data analysis algorithm (DAA); This paper analyzes the application of data analysis technology in Combinatorial Marketing and the mining of marketing data, puts forward the optimization strategy of Combinatorial Marketing for the optimal project solution, and discusses the construction of marketing mix; Finally, taking enterprise a as an example, the accuracy of the data mining technology of the DAA proposed in this paper is compared with that of the traditional algorithm for the analysis of marketing factors, as well as the data comparison of the monthly average value of business income, cost expenditure and promotion profit of enterprise a under different marketing models. The results show that the accuracy of the DAA proposed in this paper is much higher than that of the traditional algorithm. After the optimization of the MS combination of enterprise a, The average monthly income and marketing business of the enterprise are much higher than the profits under the traditional marketing methods in the past, which verifies the accuracy of the data mining technology analysis algorithm. It can also be seen that the DAA proposed in this paper is very helpful to the enterprise marketing and plays an important role in the long-term stable development of the enterprise.
Skeleton-Based Human Activity Recognition Using Bidirectional LSTM

Human activity recognition using 3D skeleton data has drawn the attention of research community. It is an interesting and well-sought-after computer vision problem whose prime objective is to determine actions performed by a human in a video or an image. The research work investigating human activity recognition in the 3D skeleton are still very limited. In this paper, we employ bidirectional long short-term memory (Bi-LSTM) deep learning model that utilizes skeleton information for modeling the dynamics in sequential data. The proposed model has been evaluated on Stony Brook University’s (SBU) dataset having two-person interactions with 8 different actions. Results demonstrate that the proposed method perform better than feature-based machine learning and state-of-the-art methods.
Assessing the Importance of Global Relationships for Source Code Analysis Using Graph Neural Networks

Representing the source code as a sequence of tokens does not capture long-distance dependencies and inter-project dependencies. In this study, we analyze to which extent inter-project (global) relationships can be used in machine learning tasks related to source code analysis. Our findings show that information implicitly stored in inter-project relationships can be used to select the next called function among candidates with an accuracy of 92%. We demonstrate that source code embeddings achieve the best performance on transfer learning tasks when they are computed with graph neural networks in a multitask mode.
Early Warning of Linguistic Achievement Pattern Based on DNA-GA Algorithm

With the continuous improvement and development of China’s education system, the education management system has been applied to all colleges and universities in China. In recent years, it undertakes the management and storage of educational information in Colleges and universities, and produces a large number of educational data information. This paper summarizes the basic principle of DNA-GA algorithm, analyzes the advantages of DNA-GA algorithm, discusses the difficulties of DNA-GA algorithm, puts forward the language ability test scale, and tests the linguistic performance of college students. The results show that the current situation of 50 college students Take a linguistic test is above the medium level, and the overall performance of academic performance is normal distribution.
Interactive Translation System of Intelligent Fuzzy Decision Tree Algorithm (IFDTA)

Interactive machine translation is a technology that uses machine translation and human–computer interaction to improve the translation efficiency between natural languages. Although the current interactive machine translation has achieved some success, there are still some weaknesses. Based on this, this paper introduces the IFDTA into the interactive translation (IT) system, discusses the interactive machine translation method based on word graph and the application of the IFDTA in the IT system, overcomes the weakness of the current interactive machine translation, so as to improve the ability of the IT system and reduce the cost of user translation; The experimental results show that the translation efficiency of the system implemented in this paper is significantly improved on different corpora. Taking Parliamentary records as an example, the average translation time of the decision tree algorithm is 78.34 s, which is 22.83% shorter than the traditional IT. The addition of phrase translation item table can provide translators with more references of word translation items, effectively reduce the translation time delayed due to unknown words in the translation process and improve the translation efficiency.
HIM: An End-to-End Hierarchical Interaction Model for Aspect Sentiment Triplet Extraction

Aspect Sentiment Triplet Extraction (ASTE) is an emerging task of fine-grained sentiment analysis, which aims to extract aspect terms, associated opinion terms, and sentiment polarities in the form of triplets. Thus, ASTE involves two groups of subtasks: aspect/opinion term extraction and aspect-opinion-pair sentiment classification. Due to the high correlations of subtasks, three categories of joint methods have been proposed, including <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">end-to-end tagging-based methods</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cascaded span-based methods</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sequence-to-sequence generation-based methods</i> . These methods basically learn either a shared feature space or a shared sentence encoder to capture interactions across all subtasks by parameter sharing. However, they fail to learn deep and mutual interactive features for ASTE. In this work, we present a novel tagging scheme to cast ASTE as a unified boundary-words relation classification problem. Subsequently, we propose an end-to-end Hierarchical Interaction Model (HIM), exploiting deep and mutual interactions across subtasks mainly with two interaction modules. The first-level interaction module primarily leverages multi-task learning models to capture implicit subtask interactions. Then, the second-level interaction module, namely Gated Interaction Network (GIN), adopts a novel gated control mechanism and a newly-designed Conditional BiLSTM (Cond-BiLSTM) network to capture explicit subtask interactions. Moreover, to refine the unreliable outputs of the first-level module, we develop a General word-Pair Relationship Learning (G-PRL) component. With the task-shared features as input, G-PRL further facilitates interactions between term extraction and pair classification. We conduct experiments on two benchmarks and achieve promising results. Extensive analyses demonstrate the effectiveness and flexibility of our work.
Research on Blockchain Technology Applications for Digital Currency

Blockchain, a focus of enterprises and governments for the latest decade, has been widely used on a range of economic and social area, especially for digital currency. The special attributes and technique advantages of blockchain contribute to the current digitalization. Therefore, this paper researches on the application of blockchain on digital currency, which is enhanced by two technical supports, four mechanism designs and application layers. Technical support can help to strengthen the credibility and security of digital currency in blockchain system. The four mechanism designs also support the integration between blockchain and digital currency, which makes the application on currency more secured, unchangeable and more convenient. As well, in the paper, benefits and drawbacks are also studied like high criminal rate with lack of regulation and wider economic gap between developing and developed societies, which need to be further considered in the road of currency digitalization.
Extracting Knowledge from Pharmaceutical Package Inserts

This work aims to present and describe the methodology used for the automatic Information Extraction from pharmaceutical package inserts based on Named Entities Recognition and their relationships. For this, an Artificial Intelligence model was used, built from a hybrid approach based on Conditional Random Fields (CRF) and Local Grammar (LG), called CRF + LG. The results obtained were, in F1 measure, 94.85% in the extraction of entities from the ABS category (diseases and their symptoms) and 68.63% in the extraction of entities from the TNG category (drugs). The results showed that the initiative presented in this work could help health professionals make decisions about therapies and drug prescriptions and clear patients’ doubts. In addition, the time and human effort to perform Information Extraction from pharmaceutical package inserts are considerably reduced.
Design of English Automatic Translation System Based on Machine Intelligent Improved GLR Algorithm

The traditional English translation system is mainly based on words and sentences to explain the translation, but the accuracy of the traditional system is relatively low, it cannot express the content of the translation well, and the machine translation system is deficient in English words and sentence patterns to some extent. Therefore, this paper adopts an intelligent recognition model of English translation based on improved GLR algorithm to design an English translation system, which is based on artificial neural network to simulate, analyze and process the machine translation process, etc. The main purpose of creating an intelligent recognition model for English translation is to analyze the sentence types, structural features and semantic characteristics of the translation process through the neural network algorithm, which can systematically collect and process English signals.
Construction of Fuzzy Control Evaluation System for Regional Economy Based on VIKOR Algorithm

With the continuous improvement of economic globalization and the level of science and technology, the acceleration of regional integration and the increasingly close pace of regional urbanization, the fuzzy control model is of great significance to the development of regional economy and makes the development of regional economy more reasonable. It calculates a clear and definite function expression by mathematical method, which has the advantages of small calculation, easy realization and easy understanding. In this paper, a comprehensive evaluation system of regional economy based on fuzzy mathematical model and object-oriented is established under the framework of VIKOR. Based on VIKOR algorithm, the fuzzy control evaluation system of regional economy is designed in this paper. Firstly, the concept and characteristics of regional economy are introduced, then the application characteristics of VIKOR algorithm are studied, and then the performance of the system is evaluated by software simulation. The experimental results show that the fuzzy control evaluation index has good performance in the system response time and step response time, which indicates that the fuzzy control evaluation system optimized by the algorithm can meet the actual needs of users.
V-Recover: Virtual Machine Recovery When Live Migration Fails

Live migration is a critical technology used in cloud infrastructures to transfer running virtual machines (VMs). When live migration fails, as it often does, it is critical that any VMs in transit are not lost. There are two primary live migration techniques – pre-copy and post-copy. Pre-copy transfers a VM's memory to the destination before its virtual CPUs are transferred, whereas post-copy does the reverse. Both pre-copy and post-copy will lose the VM if the source machine fails during migration. Additionally, post-copy can lose the VM if the destination machine or network fail since the VM's memory and execution state are split across the source and destination machines. We present V-Recover, an approach to recover a VM when the source, destination, or network fails during live migration. V-Recover consists of two techniques: (1) a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">forward incremental checkpointing</i> (FIC) mechanism to handle source machine failure during both pre-copy and post-copy, and (2) a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">reverse incremental checkpointing</i> (RIC) mechanism to handle destination or network failure during post-copy. We present the design, implementation, and evaluation of V-Recover in the KVM/QEMU virtualization platform. Our evaluations show that V-Recover effectively recovers a VM upon migration failure with acceptable overheads on migration metrics and application performance.