Non-verbal intelligence outperforms selective attention in a visual short-term memory test

Short-term memory is a dynamic psychological process that operates within a network in which non-verbal intelligence and attentional domains are connected. However, no consensus has been reached about which process has the greatest effect on this memory ability, which was the main objective of the present study. A sample of 1448 Brazilian participants (mean age = 26.62 years, standard deviation = 9.97 years; 53.9% females) were collectively tested on pen-and-paper standardized and validated measures of selective (ROTAS-C), alternating (ROTAS-A), and divided (ROTAS-D) attention. They also performed the R1 Non-verbal Intelligence Test and a visual short-term memory test (Memória Visual de Curto Prazo [MEMORE] test). The statistical analyses consisted of a data mining procedure, in which exhaustive automatic selection screening was performed. The results were compared with Corrected Akaike Information Criteria. The linear model met the classic assumptions of ordinary least squares and only included main effects of selective attention (standardized β = 0.39) and non-verbal intelligence (standardized β = 0.37) as main predictors (F2,39 = 7.01, p < 0.01, adjusted R2 = 24%). The results are discussed within a cognitive psychology framework.

3D MODELING AND PRODUCTION SCHEDULING OF KHEWRA SALT MINES

In this study, AutoCAD based 3D Modelling of production scheduling, visualization of mining, and geological features in Khewra Salt Mines are showing. Unmanned Aerial Vehicle (UAV), photogrammetry and GIS Softwares are used to generate 3D surface modelling of Khewra Salt Mining Area. Khewra Salt Mines is the oldest and largest mine of sub-continent in the Salt Range with huge salt reserves from industrial quality to piece grade. Being a state-of-the-art mine consisting of 17 levels, 70 chambers with hundreds of tunnels, a layman pattern of development and manual mining procedure is followed with handy-made planes and maps. Hundreds of levels and cross-section maps were unified to a single 3D Model, presenting all mining features like tunnels, chambers, levels, inclines, and geological deposition of different salt seams with their thickness and qualities, overburden, and surface feature. The quantity of salt excavated since the beginning of mining is calculated for corroboration, and the remaining amounts of different qualities of salt are determined from the model. 3D topographic Modelling can also be used for area, volume calculations, and planning of remedial actions for rainwater inundations inside the mine.

Utilizing the Available Open-Source Remotely Sensed Data in Assessing the Wildfire Ignition and Spread Capacities of Vegetated Surfaces in Romania

We bring a practical and comprehensive GIS-based framework to utilize freely available remotely sensed datasets to assess wildfire ignition probability and spreading capacities of vegetated landscapes. The study area consists of the country-level scale of the Romanian territory, characterized by a diversity of vegetated landscapes threatened by climate change. We utilize the Wildfire Ignition Probability/Wildfire Spreading Capacity Index (WIPI/WSCI). WIPI/WSCI models rely on a multi-criteria data mining procedure assessing the study area’s social, environmental, geophysical, and fuel properties based on open access remotely sensed data. We utilized the Receiver Operating Characteristic (ROC) analysis to weigh each indexing criterion’s impact factor and assess the model’s overall sensitivity. Introducing ROC analysis at an earlier stage of the workflow elevated the final Area Under the Curve (AUC) of WIPI from 0.705 to 0.778 and WSCI from 0.586 to 0.802. The modeling results enable discussion on the vulnerability of protected areas and the exposure of man-made structures to wildfire risk. Our study shows that within the wildland–urban interface of Bucharest’s metropolitan area, there is a remarkable building stock of healthcare, residential and educational functions, which are significantly exposed and vulnerable to wildfire spreading risk.

COMPARATIVE ANALYSIS OF CRIME TYPE PREDICTION

Crime analysis and prediction is a logical approach for analysing and identifying different patterns, relationships and trends in crime. The system can predict the area which have high probability for crime occurrence and indicate crime prone areas. It will be useful for the law enforcement agencies to speed up the process of solving crimes with the increasing computerized systems and with the help of crime data analysts. Here we have an approach between computer science and criminal justice to develop a data mining procedure that can help solve crimes faster. Analysis of police data createawareness that lets officers track criminal activities, predict the incidents, effectively deploy resources and solve cases faster. In this paper crime analysis is done by k-means,Spectral clustering and Agglomerative clustering on the crime dataset.

A Practical Approach to Assess the Wildfire Ignition and Spreading Capacities of Vegetated Areas at Landscape-scale

We bring a practical and comprehensive GIS-based framework to utilize freely available remote sensed datasets to assess wildfire ignition probability and spreading capacities of vegetated landscapes. The study area consists of the country-level scale of the Romanian territory, characterized by a diversity of vegetated landscapes threatened by the consequences of climate change. We utilize the Wildfire Ignition Probability/ Wildfire Spreading Capacity Index (WIPI/ WSCI). WIPI/ WSCI models rely on a multi-criteria data mining procedure assessing the social, environmental, geophysical, and fuel properties of the study area based on open access remote sensed data. We utilized the Receiver Operating Characteristic (ROC) analysis to weigh each indexing criterion's impact factor and assess the model's overall sensitivity. Introducing ROC analysis at an earlier stage of the workflow elevated the final Area Under the Curve (AUC) of WIPI from 0.705 to 0.778 and WSCI from 0.586 to 0.802. The modeling results enable discussion on the vulnerability of protected areas and the exposure of man-made structures to wildfire risk. Our study shows that within the wildland-urban interface of Bucharest's metropolitan area, there is a remarkable building stock like healthcare, residential and educational that are significantly exposed to wildfire spreading the risk.

Mass Spectral Filtering by Mass-Remainder Analysis (MARA) at High Resolution and Its Application to Metabolite Profiling of Flavonoids

Flavonoids represent an important class of secondary metabolites because of their potential health benefits and functions in plants. We propose a novel method for the comprehensive flavonoid filtering and screening based on direct infusion mass spectrometry (DIMS) analysis. The recently invented data mining procedure, the multi-step mass-remainder analysis (M-MARA) technique is applied for the effective mass spectral filtering of the peak rich spectra of natural herb extracts. In addition, our flavonoid-filtering algorithm facilitates the determination of the elemental composition. M-MARA flavonoid-filtering uses simple mathematical and logical operations and thus, it can easily be implemented in a regular spreadsheet software. A huge benefit of our method is the high speed and the low demand for computing power and memory that enables the real time application even for tandem mass spectrometric analysis. Our novel method was applied for the electrospray ionization (ESI) DIMS spectra of various herb extract, and the filtered mass spectral data were subjected to chemometrics analysis using principal component analysis (PCA).

The Sentiment Analysis Model of Services Providers’ Feedback

The purpose of this paper is to develop a hybrid model Ukrainian language sentiment analyzer, which should improve the accuracy of the mood definition to expand the Ukrainian language among the instruments on the market. The object of research is the processes of determining the language of the text and predicting its sentiment score. The subject of the study is Ukrainian comments posted by Google Maps users. The following text categories are taken into account: food, hotels, museums, and shops. The new method was built as an ensemble of support vector machine, logistic regression, and XGBoost, in combination with a rule-based algorithm. The practical use of the algorithm makes it possible to analyze the Ukrainian text in accordance with the category with the visualization of the research results. The accuracy of the proposed method is bigger than 0.88 in the worst case. The mining procedure of the positive and negative sides of service providers based on users’ feedback is developed. It allows electronics business to make improvements based on frequent positive and negative words.

Discovering the sustainable hotel brand personality on TripAdvisor

Purpose This paper aims to explore a hotel brand personality performance through electronic word-of-mouth. A complementary attribute is designed and tested in addition to the already existing five dimensions from the brand personality scale, denominated sustainability. Design/methodology/approach A sample of 16,175 reviews from the rating session of three hotel properties behind a brand was retrieved from TripAdvisor for a data mining procedure. A complementary list of associated words was considered in addition to the 42 personality traits of Aaker’s model, and a brief inventory was developed based on the 17 sustainable development goals (SDGs) to compose the sustainability dimension. Findings This study registered sincerity as the most representative dimension in its results, and ruggedness as the lowest. This is evidence that the latter is not suitable for representing a brand personality scale for hotels and could be replaced by sustainability. Research limitations/implications Despite the relevant findings, new surveys and tests are recommended to provide better support to the new proposed dimension. Practical implications This investigation enables hotel managers to work more effectively on their brand strategies based on sustainability-oriented brand personality, which could deliver economic, social and environmental benefits to the world by influencing consumption behavior in association with the SDGs. Originality/value This study differs from existing literature by attempting to fill a gap on the limitations of studies focused on linking brand personality to sustainability, and using data mining to reach this goal.

Novel Methods for Crop Prediction based on Influencing Parameters in Indian Regions using Data Mining

Agriculture plays a crucial role for the production of food in Indian regions. Indian regions mainly produces crops like rice, wheat, maize and many other types of crop. There are several factors required for the productivity of any harvest, but we know that soil, climate, pesticides, Fertilizers and ground water is most influencing essential factor for the productivity of any harvest. Let us consider soil which is the key element as it provides nutrients for proper development and growth of crops. Secondly, climate is also having major role in agriculture as crop growth depends on rainfall, humidity, temperature etc. Thirdly, Pesticides is widely used to control pest and prevents the damage of crops. Fourthly, Fertilizers can improve the quality of crops. Finally, ground water which will enrich nutrients in soil. The current preparation centers around different information mining procedures utilized in various regions of India and anticipate future harvest along with reasonable information mining procedure saw during the period(1920-2019).The parameters considered for the examination were soil, atmosphere, water thickness, pesticides and composts and Crop informational collection. The Classification calculations utilized in preparation were Adaptive boosting classification, Excess tree classification, neural based classification, Multiple Process classification, Decision making classification, K-closest neighbors, Bayesian theory classification, decision Forest classification, support group machine, and Randomized Gradient Classification. By using the techniques mentioned above we can improve the harvest prediction using information mining techniques which in turn help the farmers to take better decisions in future and it can be used in other technologies like image analyzing etc. The Experimental results show predicted crop, suitable algorithm and algorithm accuracy in that particular state of India respectively.

Understanding Daily Travel Patterns of Subway Users – An Example From the Beijing Subway

The daily travel patterns (DTPs) present short-term and timely characteristics of the users’ travel behaviour, and they are helpful for subway planners to better understand the travel choices and regularity of subway users (SUs) in details. While several well-known subway travel patterns have been detected, such as commuting modes and shopping modes, specific features of many patterns are still confused or omitted. Now, based on the automatic fare collection (AFC) system, a data-mining procedure to recognize DTPs of all SUs has become possible and effective. In this study, DTPs are identified by the station sequences (SSs), which are modelled from smart card transaction data of the AFC system. The data-mining procedure is applied to a large weekly sample from the Beijing Subway to understand DTPs. The results show that more than 93% SUs of the Beijing Subway travel in 7 DTPs, which are remarkably stable in share and distribution. Different DTPs have their own unique characteristics in terms of time distribution, activity duration and repeatability, which provide a wealth of information to calibrate different types of users and characterize their travel patterns.