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Wireless sensor networks (WSNs) face significant challenges related to dependability, integrity, and confidentiality due to their widespread deployment. Intrusion detection emerges as a pivotal active defense technology to fortify the security of WSNs. Given the unique characteristics of WSNs, achieving a balance between precise data transmission and the limited energy resources of sensors, as well as between effective detection and optimal utilization of network resources, becomes imperative. In this research, we propose a fog computing-based intrusion detection system (IDS) tailored for a smart power grid environment. The primary focus of this article is to elucidate the application of IDS within the context of a smart grid. We present a novel stacked model based on ensemble learning algorithms, adept at accurately delineating the interconnections among fog nodes susceptible to cyber-attacks. To validate the efficacy of our approach, we conduct a series of comparative experiments utilizing the KDD-Cup-99 dataset with cross-validation. Our findings reveal that the Fog-IDS and Stacking-based approaches significantly enhance the performance of the IDS. Through simulations, we substantiate that our proposed approach effectively analyzes fog nodes using the deployed IDS, enabling the prediction of energy requirements at refill stations. Achieving an impressive accuracy rate exceeding 99.837%, MCC rate of 99.73%, and F1-score rate of 99.827%, our study underscores the robustness and reliability of the Fog-IDS and Stacking-based methods in augmenting IDS performance in WSNs. [ABSTRACT FROM AUTHOR]

Mobile Passport is an application that can be used as a digital service for people in Indonesia to apply for a new passport and an official online passport replacement from the Directorate General of Immigration replacing APAPO (Online Passport Service Application). User reviews of the Mobile Passport application are the output of big data generated as a result of the Internet of Things. The problem formulation in this research is how the implementation of the Naive Bayes text mining classifier algorithm can analyze the reviews contained in the Mobile Passport application as well as the accuracy, precision and recall values. This research uses the KDD (Knowledge Discovery and database) method which consists of data selection, data preprocessing, transformation, data mining, and evaluation using the R Studio tool. The resulting knowledge and information from this process is used as a useful knowledge base in decision making. The Naive Bayes classifier algorithm method in this research is used because of its reliability in handling data quickly and accurate predictions based on class probabilities, thus enabling research to obtain consistent and reliable results. [ABSTRACT FROM AUTHOR]

Bali is one of the provinces with the most popular destinations for tourists. However, there are obstacles in developing tourist destinations in the province of Bali in terms of absorbing more tourist visits. Tripadvisor, the world's largest tourism information platform. In order to improve its service to users, Tripadvisor conducts online reviews to obtain ratings based on travel experience. The purpose of this study is to determine clustering and accuracy in tourist visits to tourist destinations in the province of Bali. Clustering is done using 3 clusters using the KDD method. The first process is data selection, then data processing which consists of several stages, first deleting rows of empty data, then cleaning duplicate data and the final result is 5261 clean data then data transformation, so that data can be read by python, The next process is data mining, this process uses the K-Means clustering algorithm which produces 3 clusters with cluster 1 being medium with 1495 data, high cluster 2 with 2315 data, and low cluster 3 with 1451 data. The Davies Boldin Index is used to evaluate the K-Algorithm means clustering, the result is 0.3 where the value is very good because it is not minus and the value is close to zero.

Market research is an indispensable part of an organization's ability to understand market dynamics. Over the past 20 years, data collection and analysis through Knowledge Discovery through Databases (KDD) has arisen to supplement the traditional methods of surveys and focus groups. Market Basket Analysis is a discipline of KDD that identifies associations between commonly purchased items. As social media use has grown, link shortening companies help users share links in a constrained space environment and, in exchange, collect data about each user when a link is clicked. This research applies market basket analysis techniques with graph mining to shortened web link data to identify communities of co-visited websites to help analysts better understand web traffic for a geographic area during a time range. Patterns within clusters of web domains regarding hardware platforms, operating systems, or referral sources are then identified and used to gain a better understanding of a geographic area. [ABSTRACT FROM AUTHOR]

Com o objetivo de fornecer um panorama geral da Revista de Educação Pública (REP), o trabalho traz uma análise de sua comunidade por meio das redes de colaboração e contribuições. O periódico foi criado em 1992; o acervo analisado conta com 598 artigos publicados por 861 autores. Para a realização das meta-análises foi considerado o processo de KDD e conceitos de Teoria dos Grafos. Os resultados obtidos contemplam estatísticas gerais, análises das redes de colaboração, termos em destaque e autores considerados influentes. Assim, o trabalho fornece perspectivas e contribuições deste importante acervo.

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Contexto: Se propone un conjunto de opciones que ayudan a determinar el método más adecuado para subsanar en bases de datos de tamaño apreciable, condiciones iniciales de datos faltantes y que serán utilizadas en procesos de investigación. Metodología: El presente artículo aborda una propuesta para el desarrollo y manejo de bases de datos robustas como el caso de registros financieros, enfocándose desde el proceso knowledge discovery in databases (KDD). Resultados: Se desarrolla y prueba una metodología utilizando tres técnicas de imputación en una base de datos construida a partir de 1 253 280 registros financieros de 2238 empresas y que representan siete años de su actividad económica en la localidad de Chapinero, en la ciudad de Bogotá D. C. Conclusiones: Se realiza un comparativo de los métodos de imputación como factor determinante para la elección del método de imputación y consolidación de la base para su posterior uso. Financiamiento: Fundación Universitaria Los Libertadores.

The company produces sales data every day. Over time, the data increases, and the amount becomes very large. The data is only stored without understanding the benefits that exist from these data due to limitations in proper knowledge in analyzing the data, especially transaction data. Sale. To overcome these problems, a study focused on reprocessing sales transaction data in 2018 with a data mining technique approach using the Knowledge Discovery in Database (KDD) concept using the association method and apriori algorithm and a supporting application, namely RapidMiner. This study aims to help companies find customer buying habits or patterns based on 2018 sales transaction data. The results of this study produce 316 association rules where the best rules are generated on record 309 with PRO 889 & PRO 868 PRO 869 rules.

WeTV is an online streaming application widely used by Indonesia’s people as an entertainment medium while at home. This application has been downloaded more than 50 million times on the official Google Play Store website. The number of users who use it makes the reviews of this application abundant as well. Large numbers of reviews are very difficult to read manually, sentiment analysis is needed to classify reviews into positive and negative classes. This study uses a support vector machine algorithm with a linear kernel to classify review data from the WeTV application. KDD was used as a method to complete this research. In the analysis process to obtain information, 4 scenarios were carried out, with the division in the first scenario consisting of 60% training data and 40% test data, the second scenario consisting of 70% training data and 30% test data, the third scenario 80% training data and 20% test data, and the last scenario 90% training data and 10% test data. The highest test results of 85% were obtained from the second scenario with the distribution of training data of 70% and 30% of test data, the third with the distribution of training data of 80% and 20% of test data, and the fourth with the distribution of training data of 90% and test 10% data. The confusion matrix is used as an evaluation of the model that has been made, the results show an accuracy in the first scenario of 83%, with a precision value of 83%, recall 89%, and an f1-score of 86%. The accuracy in the second scenario is 85%, precision is 86%, recall is 89%, f1-score is 87%, accuracy in the third scenario is 85%, precision is 85%, recall is 90%, and f1-score is 88%. And the fourth scenario gets an accuracy of 85%, precision 86%, recall 90%, and f1-score 90%.

The analysis of sports data and the possibility of using machine learning in the prediction of sports results is an increasingly popular topic of research and application. The main problem, apart from choosing the right algorithm, is to obtain data that allow for effective prediction. The article presents a comprehensive KDD (Knowledge Discovery in Databases) approach that allows for the appropriate preparation of data for sports prediction on sports data. The first part of the article covers the subject of KDD and sports data. The next section presents an approach to developing a dataset on top football leagues. The developed datasets are the main purpose of the article and have been made publicly available to the research community. In the latter part of the article, an experiment with the results based on heterogeneous groups of classifiers and the developed datasets is presented. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge of the increasing number of traffic accidents resulting from population growth and increased vehicle usage, leading to significant economic and environmental impacts. To address this issue, this paper proposes an improved model for traffic accident management that employs Knowledge Discovery in Databases (KDD) and extensive data analysis techniques to extract the main factors contributing to car accidents. A case study of traffic accidents in Jordan is conducted, utilizing actual data from the Department of Statistics and the Public Security Directorate Records between 2016 to 2020 to identify the primary factors contributing to road accidents and their effects on various types of injuries. The study identifies 11 factors that contribute to car accidents, with driver error being the primary factor for increasing the number of accidents and injuries. Additionally, minor car accidents are found to cause more injuries based on the analysis of the proposed approach. The findings provide a solid foundation for developing an accurate and precise scheme to reduce or eliminate the number of road accidents, which is essential for transitioning to smart cities. Using big data and KDD techniques could significantly improve current traffic accident management practices, informing the development of new policies and regulations to improve road safety and reduce economic and environmental impacts. This paper presents a promising solution for addressing traffic accident management, contributing to safer and sustainable urban environments. [ABSTRACT FROM AUTHOR]

Association rule methods are among the most used approaches for Knowledge Discovery in Databases (KDD), as they allow discovering and extracting hidden meaningful relationships between attributes or items in large datasets in the form of rules. Algorithms to extract these rules require considerable time and large memory spaces. This paper presents an algorithm that decomposes this complex problem into subproblems and processes items by category according to their support. Very frequent items and fairly frequent items are studied together. To evaluate the performance of the proposed algorithm, it was compared with Eclat and LCMFreq on two actual transactional databases. The experimental results showed that the proposed algorithm was faster in execution time and demonstrated its efficiency in memory consumption. [ABSTRACT FROM AUTHOR]

In recent years, the world has dramatically moved toward using the internet of things (IoT), and the IoT has become a hot research field. Among various aspects of IoT, real-time cyber-threat protection is one of the most crucial elements due to the increasing number of cyber-attacks. However, current IoT devices often offer minimal security features and are vulnerable to cyber-attacks. Therefore, it is crucial to develop tools to detect such attacks in real time. This paper presents a new and intelligent network intrusion detection system named APAE that is based on an asymmetric parallel auto-encoder and is able to detect various attacks in IoT networks. The encoder part of APAE has a lightweight architecture that contains two encoders in parallel, each one having three successive layers of convolutional filters. The first encoder is for extracting local features using standard convolutional layers and a positional attention module. The second encoder also extracts the long-range information using dilated convolutional layers and a channel attention module. The decoder part of APAE is different from its encoder and has eight successive transposed convolution layers. The proposed APAE approach has a lightweight and suitable architecture for real-time attack detection and provides very good generalization performance even after training using very limited training records. The efficacy of the APAE has been evaluated using three popular public datasets named UNSW-NB15, CICIDS2017, and KDDCup99, and the results showed the superiority of the proposed model over the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

People are increasingly open to using online education mainly to break the distance and time barriers of presential education. This type of education is sustainable at all levels, and its relevance has increased even more during the pandemic. Consequently, educational institutions are saving large volumes of data containing relevant information about their operations, but they do not know why students succeed or fail. The Knowledge Discovery in Databases (KDD) process could support this challenge by extracting innovative models to identify the main patterns and factors that could affect the success of their students in online education programs. This work uses the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology to analyze data from the Distance Education Center of the Universidad Católica del Norte (DEC-UCN) from 2000 to 2018. CRISP-DM was chosen because it represents a proven process that integrates multiple methodologies to provide an effective meta-process for data knowledge projects. DEC-UCN is one of the first centers to implement online learning in Chile, and this study analyses 18,610 records in this period. The study applies data mining, the most critical KDD phase, to find hidden data patterns to identify the variables associated with students' success in online learning (e-learning) programs. This study found that the main variables explaining student success in e-learning programs are age, gender, degree study, educational level, and locality. [ABSTRACT FROM AUTHOR]

Hashtag feminism is a recent phenomenon, however, increasingly recurrent in the network society, connected by the Internet. One of the examples of this type of activism began in 2019 when a young woman named Mariana Ferrer used her social media to report that she had suffered sexual violence. Since then, her case has become increasingly known and several hashtags have been created to refer to the case, such as #JusticePorMariFerrer. Engagement with the hashtag reached a large number of engagements in November 2020, when the acquittal of the accused was reported and the term 'wrongful rape' was used by the portal that shared more details of the process. The indignation at what happened was so great that it invaded the streets of Brazil, with protests and demonstrations. Thus, the objective of this study is to identify the structure and aspects of the representation of feminism by hashtag, the semantic contexts, and common sense knowledge that permeate this type of manifestation. For this analysis, the KDD methodology was used, extracting 198,009 tweets with the hashtag #JustiçaPorMariFerrer in November 2020. Among the findings, the categories that reveal the discourse of rejection in relation to the system and digital solidarity stand out. Through the categories, it is possible to perceive that there is a distrust of official institutions and that women create connective solidarity networks. [ABSTRACT FROM AUTHOR]

Crime has a big impact in both the human lives and the society’s growth, which needs to be addressed and controlled. Machine learning algorithms as the fanciest technology to assist decision makers in policy making has proven its reliability in showing unseen patterns in crime. This research aims to examine the capability of trees and ensemble trees in classifying crime through model development. Experiments were done to enhance the capability of the ensembles in both classification and regression. Feature extraction like synthetic minority oversampling technique was applied in order to address the problem in the imbalanced data. Different metrics relevant to classification and regression were considered in evaluating the performance of each model used. With the use of different metrics, Gradient boosted tree was found to have better classification capability in crime dataset after outperforming decision tree and random forest in both classification and regression problem. Furthermore, random forest was also found to have a promising capability in classification by regression. Therefore, it is highly recommended that this ensemble algorithm be further examined and considered in developing model in other datasets.

Big Data has changed how enterprises and people manage knowledge and make decisions. However, when talking about Big Data, so many times there are different definitions about what it is and what it is used for, as there are many interpretations and disagreements. For these reasons, we have reviewed the literature to compile and provide a possible solution to the existing discrepancies between the terms Data Analysis, Data Mining, Knowledge Discovery in Databases, and Big Data. In addition, we have gathered the patterns used in Data Mining, the different phases of Knowledge Discovery in Databases, and some definitions of Big Data according to some important companies and organisations. Moreover, Big Data has challenges that sometimes are the same as its own characteristics. These characteristics are known as the Vs. Nonetheless, depending on the author, these Vs can be more or less, from 3 to 5, or even 7. Furthermore, the 4Vs or 5Vs are not the same every time. Therefore, in this survey, we reviewed the literature to explain how many Vs have been detected and explained according to different existing problems. In addition, we detected 7Vs, three of which had subtypes. [ABSTRACT FROM AUTHOR]

Recent development of cognitive computing driven evolutionary techniques improve the overall quality of service and user experience in wireless communication network. This Paper consists of a feature selection method based on improvement of Crow Search Algorithm which has been used in Intrusion Detection System to limit the size of the dataset with which the system is working with and getting better results. Since IDS deals with a large data, the crucial task of IDS is to keep efficient features which represents the whole data and there is no duplicity and irrelevancy. The previous model that was proposed used the crow search algorithm in the intrusion detection system (CSA-IDS) as a model to find the optimal feature's subset and random forest as a judgement on features that are produced by the CSA-IDS. The KDD and UNSW datasets are used to evaluate the earlier proposed model. The proposed model achieved an accuracy of 99.84% for attack detection using UNSW datasets. Similarly, R2L and U2R attacks have detected accuracy of 99.97% for NSL-KDD dataset. The development of proposed model improve the overall communication services and feature selection in wireless communication network. The outcome proves that the subset of features that are obtained by using CSA-IDS fetches higher accuracy rate using a smaller number of features. [ABSTRACT FROM AUTHOR]

The spatiotemporal model consists of stationary and non-stationary data, respectively known as the Generalized Space–Time Autoregressive (GSTAR) model and the Generalized Space–Time Autoregressive Integrated (GSTARI) model. The application of this model in forecasting climate with rainfall variables is also influenced by exogenous variables such as humidity, and often the assumption of error is not constant. Therefore, this study aims to design a spatiotemporal model with the addition of exogenous variables and to overcome the non-constant error variance. The proposed model is named GSTARI-X-ARCH. The model is used to predict climate phenomena in West Java, obtained from National Aeronautics and Space Administration Prediction of Worldwide Energy Resources (NASA POWER) data. Climate data are big data, so we used knowledge discovery in databases (KDD) in this study. The pre-processing step is collecting and cleaning data. Then, the data mining process with the GSTARI-X-ARCH model follows the Box–Jenkins procedure: model identification, parameter estimation, and diagnostic checking. Finally, the post-processing step for visualization and interpretation of forecast results was conducted. This research is expected to contribute to developing the spatiotemporal model and forecast results as recommendations to the relevant agencies. [ABSTRACT FROM AUTHOR]