What attracts vehicle consumers’ buying:A Saaty scale-based VIKOR (SSC-VIKOR) approach from after-sales textual perspective?

Purpose: The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint. Design/methodology/approach: A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naïve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint. Findings: The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior. Research limitations/implications: The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation. Originality/value: Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective

Sentiment Analysis Based on the BERT Model : Attitudes Towards Politicians Using Media Data

The latest analysis methods of sentiments borrowed from computational linguistics are relevant in the age of big data, which is difficult to process through traditional content analysis. These methods have made it possible to analyze information over a long period, which allows us to trace the dynamics of the relationship to a particular object over time and large-scale comparative studies of texts. The authors demonstrate the applicability of sentiment analysis based on transformer models to the study of the temporal model of attitudes towards well-known politicians (2001-2021) on the example of text analysis of multilingual online publications. To do this, the authors used the targeted-BERT method for automated directed analysis of sentiments, obtained quality indicators F1-score 0.799 and 0.741 for Ukrainian and Russian models, respectively. The authors tested the dependence of mediatization of politicians on the country's political hierarchy, confirmed hypotheses about the attitude to their power (more significant criticism of the Ukrainian media and gradual loyalty to the Russian media) and foreign politicians (dominance of negative tone in both media with a growing trend for Ukrainian media).Non peer reviewe

An automated classification system based on the strings of trojan and virus families

Classifying malware correctly is an important research issue for anti-malware software producers. This paper presents an effective and efficient malware classification technique based on string information using several wellknown classification algorithms. In our testing we extracted the printable strings from 1367 samples, including unpacked trojans and viruses and clean files. Information describing the printable strings contained in each sample was input to various classification algorithms, including treebased classifiers, a nearest neighbour algorithm, statistical algorithms and AdaBoost. Using k-fold cross validation on the unpacked malware and clean files, we achieved a classification accuracy of 97%. Our results reveal that strings from library code (rather than malicious code itself) can be utilised to distinguish different malware families.<br /

Large-Scale Legal Reasoning with Rules and Databases

Traditionally, computational knowledge representation and reasoning focused its attention on rich domains such as the law. The main underlying assumption of traditional legal knowledge representation and reasoning is that knowledge and data are both available in main memory. However, in the era of big data, where large amounts of data are generated daily, an increasing rangeof scientific disciplines, as well as business and human activities, are becoming data-driven. This chapter summarises existing research on legal representation and reasoning in order to uncover technical challenges associated both with the integration of rules and databases and with the main concepts of the big data landscape. We expect these challenges lead naturally to future research directions towards achieving large scale legal reasoning with rules and databases

The properties of the stellar populations in ULIRGs II: the star formation histories and evolution

This is the second of two papers presenting a detailed long-slit spectroscopic study of the stellar populations in a sample of 36 ULIRGs. In the previous paper we presented the sample, the data and the spectral synthesis modelling while in this paper, we carry out a detailed analysis of the modelling results. We find that the star formation histories of ULIRGs are complex, with at least two epochs of star formation activity and that the charcteristic timescale of the star formation acivity is <100Myr. These results are consistent with models that predict an epoch of enhanced star formation coinciding with the first pass of the merging nuclei, along with a further, more intense, episode of star formation occurring as the nuclei finally merge together. It is also found that the young stellar populations (YSPs) tend to be younger and more reddened in the nuclear regions of the galaxies. This is in good agreement with the merger simulations, which predict that the bulk of the star formation activity in the final stages of mergers will occur in the nuclear regions of the merging galaxies. In addition, our results show that ULIRGs have total stellar masses that are similar to, or smaller than, the break of the galaxy mass function (m* = 1.4 x 10^{11} Msolar). Finally, we find no significant differences between the ages of the YSP in ULIRGs with and without optically detected Seyfert nuclei, nor between those with warm and cool mid- to far-IR colours. While this results do not entirely rule out the idea that cool ULIRGs with HII/LINER spectra evolve into warm ULIRGs with Seyfert-like spectra, it is clear that the AGN activity in local Seyfert-like ULIRGs has not been triggered a substantial period (>=100 Myr) after the major merger-induced starbursts in the nuclear regions.Comment: Accepted for publication in MNRAS. The paper contains 16 pages, 6 figures and 7 table

Automatic Hoax Detection System

Hoaxes are non malicious viruses. They live on deceiving human's perception by conveying false claims as truth. Throughout history, hoaxes have actually able to influence a lot of people to the extent of tarnishing the victim's image and credibility. Moreover, wrong and misleading information has always been a distortion to a human's growth. Some hoaxes were created in a way that they can even obtain personal data by convincing the victims that those data were required for official purposes. Hoaxes are different from spams in a way that they masquerade themselves through the address of those related either directly or indirectly to us. Most of the time, they appear as a forwarded message and sometimes from legit companies such as PayPal. Having known the threat that this non malicious brought, it is important for us to address this problem seriously by providing an automatic hoax detection system as the solution to this matter. Consciousness and Awareness are definitely the first step to be taken for this matte

Machine learning model for prediction and visualization of HIV index testing in northern Tanzania

A Project Report Submitted in Partial Fulfilment of the Requirements for the Degree of Master of Science in Embedded and Mobile Systems of the Nelson Mandela African Institution of Science and TechnologyInfection with the human immunodeficiency virus and acquired immunodeficiency syndrome (HIV/AIDS) continue to pose a threat to Tanzanian society. Various tactics have been used to improve the number of persons who are aware of their HIV status. Index testing stands out among these methods as the most effective way to count the number of HIV contacts who may be at risk of catching HIV from HIV-positive individuals. The current HIV index testing, however, is manual, which presents a number of difficulties, including inaccuracies, is time consuming, and is expensive to operate. In order to forecast and depict HIV index testing, this study presents the findings of the machine-learning model. The software development procedure was in accordance with agile software development principles. The regions of Kilimanjaro, Arusha, and Manyara in Tanzania are where the data was gathered which consisted of 11 features and 6346 samples. The dataset was then separated into training sets with 5075 samples each and testing sets with 1270 samples (80/20). The datasets were subjected to the methods Random Forest (RF), XGBoost, and Artificial Neural Networks (ANN). Random forest MAE (1.1261), XGBoost MAE (1.2340), and ANN MAE (1.1268) were the three results obtained. Random forest algorithms had the lowest mean absolute errors (MAE). Therefore, RF appearing to have the highest performance when compared to the other two algorithms. In comparison to men (17.4%), data visualization reveals that females are more likely to test for HIV and to name their partners (82.6%). Additionally, there were higher instances of persons listing and mentioning their partners in the Kilimanjaro region. This work helped us realize the importance of machine learning in predicting and visualizing HIV index tests in general. The created model can help decision-makers build a viable intervention to stop the spread of HIV and AIDS in our communities. The report suggests that health centers in other areas employ this concept to make their work more straightforward

Application of machine learning algorithm to measure a firm's performance.

Machine learning techniques are an emerging field in today’s world. The objective of this thesis was to use machine learning methodology to measure a company’s per-formance by using forecasting techniques in financial statements. This information can be useful for investors, managers, and analysts. The financial statement data collected were from 250 companies from the United States of America. The method-ology that was applied was Long Short-Term Memory. The forecasting method used was time-series forecasting. The software used for running the code was Juypter. The conclusion of the study shows that machine learning algorithms can be applied for forecasting firm performance. The program shows the results for the future predic-tion of the performance of companies

Learned Spatio-Temporal Texture Descriptors for RGB-D Human Action Recognition

Due to the recent arrival of Kinect, action recognition with depth images has attracted researchers' wide attentions and various descriptors have been proposed, where Local Binary Patterns (LBP) texture descriptors possess the properties of appearance invariance. However, the LBP and its variants are most artificially-designed, demanding engineers' strong prior knowledge and not discriminative enough for recognition tasks. To this end, this paper develops compact spatio-temporal texture descriptors, i.e. 3D-compact LBP (3D-CLBP) and local depth patterns (3D-CLDP), for color and depth videos in the light of compact binary face descriptor learning in face recognition. Extensive experiments performed on three standard datasets, 3D Online Action, MSR Action Pairs and MSR Daily Activity 3D, demonstrate that our method is superior to most comparative methods in respects of performance and can capture spatial-temporal texture cues in videos