Utilizing Data Mining Research Articles

Voltage-Gated Proton Channel Hv1 Regulates Neuroinflammation and Dopaminergic Neurodegeneration in Parkinson's Disease Models.

Although the precise mechanisms for neurodegeneration in Parkinson's disease (PD) are unknown, evidence suggests that neuroinflammation is a critical factor in the pathogenic process. Here, we sought to determine whether the voltage-gated proton channel, Hv1 (HVCN1), which is expressed in microglia and regulates NADPH oxidase, is associated with dopaminergic neurodegeneration. We utilized data mining to evaluate the mRNA expression of HVCN1 in the brains of PD patients and controls and uncovered increased expression of the gene encoding Hv1, HVCN1, in the brains of PD patients compared to controls, specifically in male PD patients. In an acute 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP; 4 × 16 mg/kg) mouse model of PD, Hvcn1 gene expression was increased 2-fold in the striatum. MPTP administration to wild-type (WT) mice resulted in a ~65% loss of tyrosine hydroxylase positive neurons (TH+) in the substantia nigra (SN), while a ~39% loss was observed in Hv1 knockout (KO) mice. Comparable neuroprotective effects of Hv1 deficiency were found in a repeated-dose LPS model. Neuroprotection was associated with decreased pro-inflammatory cytokine levels and pro-oxidant factors in both neurotoxicant animal models. These in vivo results were confirmed in primary microglial cultures, with LPS treatment increasing Hvcn1 mRNA levels and Hv1 KO microglia failing to exhibit the LPS-mediated inflammatory response. Conditioned media from Hv1 KO microglia treated with LPS resulted in an attenuated loss of cultured dopamine neuron cell viability compared to WT microglia. Taken together, these data suggest that Hv1 is upregulated and mediates microglial pro-inflammatory cytokine production in parkinsonian models and therefore represents a novel target for neuroprotection.

Data Mining Inspired Localized Resistivity in Global MHD Simulations of the Magnetosphere

AbstractRecent advances in reconstructing Earth's magnetic field and associated currents by utilizing data mining of in situ magnetometer observations in the magnetosphere based on geomagnetic indices and solar wind parameters have proven remarkably accurate at reproducing observed ion diffusion regions. We investigate the effect of placing regions of localized resistivity in global simulations of the magnetosphere at specific locations inspired by the data mining results for the substorm occurring on 6 July 2017. When explicit resistivity is included, the simulation forms an x‐line at the same time and location as the Magnetospheric Multiscale Mission observation of an ion diffusion region at 15:35 UT on that day. Without this explicit resistivity, reconnection forms later in the substorm and far too close to Earth (≳−15RE), a common problem with global simulations of Earth's magnetosphere. A consequence of reconnection taking place farther down the tail due to localized resistivity is that the reconnection outflows transport magnetic flux Earthward and thus prevent the current sheet from thinning enough for reconnection to take place nearer Earth. As these flows rebound tailward from the inner magnetosphere, they can temporarily and locally (in the dawn‐dusk direction) stretch the magnetic field allowing for small scale x‐lines to form in the near Earth region. Due to the narrow cross‐tail extent of these x‐lines (≲5RE) and their short lifespan (≲5 min), they would be difficult to observe with in situ measurements. Future work will explore time‐dependent resistivity using 5 min cadence data mining reconstructions.

The state of lead scoring models and their impact on sales performance.

Although lead scoring is an essential component of lead management, there is a lack of a comprehensive literature review and a classification framework dedicated to it. Lead scoring is an effective and efficient way of measuring the quality of leads. In addition, as a critical Information Technology tool, a proper lead scoring model acts as an alleviator to weaken the conflicts between sales and marketing functions. Yet, little is known regarding lead scoring models and their impact on sales performance. Lead scoring models are commonly categorized into two classes: traditional and predictive. While the former primarily relies on the experience and knowledge of salespeople and marketers, the latter utilizes data mining models and machine learning algorithms to support the scoring process. This study aims to review and analyze the existing literature on lead scoring models and their impact on sales performance. A systematic literature review was conducted to examine lead scoring models. A total of 44 studies have met the criteria and were included for analysis. Fourteen metrics were identified to measure the impact of lead scoring models on sales performance. With the increased use of data mining and machine learning techniques in the fourth industrial revolution, predictive lead scoring models are expected to replace traditional lead scoring models as they positively impact sales performance. Despite the relative cost of implementing and maintaining predictive lead scoring models, it is still beneficial to supersede traditional lead scoring models, given the higher effectiveness and efficiency of predictive lead scoring models. This study reveals that classification is the most popular data mining model, while decision tree and logistic regression are the most applied algorithms among all the predictive lead scoring models. This study contributes by systematizing and recommending which machine learning method (i.e., supervised and/or unsupervised) shall be used to build predictive lead scoring models based on the integrity of different types of data sources. Additionally, this study offers both theoretical and practical research directions in the lead scoring field.

Utilization of data mining and machine learning in digital and electronic payments in banks

The purpose of the paper is to study and measure the usability level of new technologies such as machine learning (ML) and data mining (DM) in the banking sector. An overview of the current situation in the international markets and a deeper look at the Albanian one is given. The research methodology includes qualitative and quantitative methods. This mixed approach offers the possibility of obtaining detailed answers and allows statistical analysis to draw patterns or trends related to the subject of the study. This research topic is important since the studies in this field in the regional context are few and this topic has not been extensively studied before. New technologies and their advantages are of great importance to the banking sector. This study identifies prevalent ML approaches currently and is expected to be used in the near future. An online survey is done on a network of professionals in the banking and financial sector. The findings show that the impact of ML and DM in these organizations, currently is at an average level. The results of this study can be a useful indicator for the stakeholders of the banking sector to implement new digital technologies such as ML and DM.

Intelligent Model for Customer Churn Prediction using Deep Learning Optimization Algorithms

Business intelligence (BI) mentions to the technical and procedural structure which gathers, supplies, and examines the data formed by company action. BI is a wide term that includes descriptive analytics, procedure analysis, data mining, and performance benchmarking. Customer churn is a general problem across businesses from several sectors. Companies are working always for improving their supposed quality by way of providing timely and quality service to its customer. Customer churn is developed most initial challenges which several firms were facing currently. Many churn prediction techniques and methods were presented before in literature for predicting customer churn from the domains like telecom, finance, banking, and so on. Researchers are also working on customer churn prediction (CCP) from e-commerce utilizing data mining and machine learning (ML) approaches. This manuscript focuses on the development of Stacked Deep Learning with Wind Driven Optimization based Business Intelligence for Customer Churn Prediction model. The proposed model is considered an intelligent system that applies golden sine algorithm (GSA) based feature selection approach to derive a set of features. In addition, the stacked gated recurrent unit (SGRU) model is applied for the prediction of customer churns.

A Systematic Review of Data Mining Studies in Parenting Research

This study aims to provide a comprehensive review of the application of data mining methods in parenting research and to explore the insights offered by data mining algorithms in this domain. In this study, 31 academic parenting research studies, which utilized data mining techniques based on empirical data, are finally selected and thoroughly. They were subsequently analysed, in terms of research inquiries, sources of datasets, and methodological approaches, as well as systematically categorized. Consequently, seven primary research directions in the field of parenting research using data mining methods were outlined. This review also delves into the sources of datasets in extant studies, and discussed the added value that data mining methods bring to the field of parenting research, pointing towards potential directions for future research.

STUDY OF BIG DATA BASED PROBLEMS FOR DATA ANONYMIZATION

In order to defeat typical attacks like the similarity attack, the probabilistic inference attack, and others that are possible with anonymized data, we have studied different techniques. To anonymize the data set and disseminate the anonymized data set on a distributed environment without endangering data privacy, a privacy-preserving distributed framework is suggested in most of the techniques. It is possible to achieve a better balance between privacy and data utility, and the data utility is demonstrated in terms of conventional measures. The privacy-preserved data set is also subjected to the application of several classifiers in order to measure the utility of the data When sharing and processing data in a distributed setting or with the Internet of Things, data privacy is a crucial requirement. High communication and computational costs are involved in collaborative privacy-preserving data mining based on secured multiparty computation. Data protection against identity revelation is achieved by the use of data anonymization, a promising technology in the field of privacy-preserving data mining. Anonymization faces significant difficulties, including information loss and frequent attacks that may be made on the anonymized data. Utilizing data mining techniques, data anonymization has recently demonstrated a considerable increase in data value. Still, the methods now in use are ineffective for dealing with attacks. Therefore, a clustering-based anonymization approach that is resistant to similarity attacks and attacks based on inference is suggested in this study. On the Hadoop Distributed File System, the anonymized data is dispersed. The technique creates a better balance between utility and privacy.

Data Analytics and Predictive Modeling for Toll Violation Prevention

This study aims to utilize data analytics and predictive modeling techniques to prevent toll violations. By analyzing various sources of data such as travel datasets, unstructured data, and financial data, the study will develop algorithms that can accurately predict toll violations and identify potential fraud patterns. The research, which utilized unstructured data, points to the utility of data mining which would greatly benefit traffic research, particularly African-based studies, that suffer from data inadequacy. The study will also explore the use of machine learning algorithms to decipher the large textual data contained in transportation research papers. Additionally, the study will investigate the accuracy and performance of different predictive algorithms, such as linear regression, analysis of variance, and artificial neural networks, in predicting toll violations based on various factors such as category, purpose, total distance, and speed of travel. Furthermore, the study will examine the forensic aspects of big data in order to enhance the capabilities of forensic investigators in case of a network attack. In today’s rapidly changing world, the significance of accurate toll violation prevention using data analytics and predictive modeling techniques cannot be overstated. In today’s rapidly changing world, the significance of accurate toll violation prevention using data analytics and predictive modeling techniques cannot be overstated. By leveraging data analytics and predictive modeling, this study aims to develop an effective toll violation prevention system

Sex-Specific Human, Agent, and Environment-Related Interactions in Traumatic Brain Injury: Utility of The Haddon Matrix

<h3>Research Objectives</h3> To explore sex differences in the Host, the Agent, and the Environment utilizing computational analysis of traumatic brain injury (TBI) events. <h3>Design</h3> The retrospective population-based study utilized province-wide population-based decade-long health administrative data collected through the routine operation of the publicly funded health system in Ontario, Canada. <h3>Setting</h3> We used a previously established cohort of patients discharged between the fiscal years 2007/2008 and 2015/2016 from acute care and from the emergency department (ED) with diagnoses of TBI. <h3>Participants</h3> We drew upon health records of 235,003 unique patients (57% male) with TBI and the same number of reference patients without TBI, who were individually matched based on sex, age, place of residence, and income level, to organize factors associated with the TBI event. We dissected complex and multidimensional TBI event data by exploiting data mining and information visualization methods in male and female patients separately. We adopted concepts of the Haddon Matrix (Human, Agent, Environment) to organize injury event factors and compare TBI events of male and female patients. <h3>Interventions</h3> Not applicable. <h3>Main Outcome Measures</h3> Sex differences in the health status of the Host, the Agent, and the Environment at the TBI event phase. <h3>Results</h3> Based on perceived similarities, the computational technique detected 35 injury event-related factors, allowing observation of a set of definable patterns within the Host, the Agent, and the Environment. We uncovered significant sex-based differences in Host, Agent, and Environment patterns. We found that the Host's health status is of great importance to injury severity, and the Agent and the Environment have implications for external causes of injury. <h3>Conclusions</h3> Study findings support the utility of computational data mining in examining patterns of information within the TBI event. <h3>Author(s) Disclosures</h3> Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health (NIH) under Award Number R21HD089106 and the National Institute Of Neurological Disorders And Stroke of the NIH under Award Number R01NS117921 and partially by Canada Research Chair Programs.

Classification of Salt Quality based on Salt-Forming Composition using Random Forest

Salt is part of the chemical that can be used and needed by humans in the field of consumption or industry. The formation of salt can be done in several ways, namely with seawater or lake water that is evaporated to produce salt crystals or through the process of mining rock salt. The results of the salt obtained will have a different composition depending on the process of formation, the difference in composition can affect the quality of the salt produced, so not all salt results are suitable for consumption. Generally, the salt quality classification process is still done manually, but this method takes a long time and is less effective. So, to overcome this problem, this research utilizes data mining science in classifying salt quality automatically using the Machine Learning algorithm, namely Random Forest. The data used in this study is a salt dataset with 7 attributes and 4 target classes totaling 349 data where the data is divided into training data and test data using k-fold cross validation with different k-fold values, namely 5-fold, 10-fold, and 20-fold. folds. The test results obtained indicate that the value of k = 10 has the best performance by achieving an AUC value of 96.1%, then for the classification accuracy is 87.7%, f1 score is 87.6%, precision is 87.7% and recall is 87.7%.

Data Mining in Social Sciences: A Decision Tree Application Using Social and Political Concepts

Abstract In this paper, we investigated the utility of data mining to classify individuals into predefined categories of a target variable, based on their social and political attitude. Data collected for a social psychology study conducted in Greece in 1994 were used for this purpose. We established the theoretical background of our analysis through explanatory factor analysis. We ran the decision tree algorithm CHAID in order to build a predictive model that classifies the study participants in terms of their attitude toward physical and symbolic violence. The CHAID algorithm provided a decision tree that was easily interpreted, and which revealed meaningful predictive patterns. CHAID algorithm showed satisfactory predictive ability and promising alternatives to social psychology data analysis. To the best of our knowledge, there is no other evidence in the literature that the decision tree algorithms can be used to identify latent variables.

An Exploration of Architectural Design Factors with a Consideration of Natural Aspects Based on Web Crawling and Text Mining

Architectural construction is responsible for the consumption of large amounts of resources, so the optimization of architectural design and evaluation is significant for sustainable global development. Most architectural assessments focus on energy conservation, novel materials and eco-friendly strategies, but without agreed indicators and criteria. Since the consideration of natural aspects is somewhat fuzzy and vague, this study utilized data mining technology to explore the major factors related to relationships between buildings and nature. By employing the popular technique of web crawling, this study collected 38,320 architectural descriptions from the “Archdaily”, including descriptions of 11 types of buildings, four of which were taken as typical research representatives. The 100 most frequent words were used to create a word cloud. Using Python script, all of the text was refined and processed with the word2vec model, thereby allowing to conduct Agglomerative Hierarchical Clustering (AHC). The frequency of words related to natural aspects were analyzed within 15 architectural design elements. Different building types in different areas have obvious similarities in terms of design elements, so it is feasible to adopt the same evaluation factors for the building evaluation systems of different regions. This paper mainly focuses on improving the accuracy and validity of assessment by providing basic evaluation indicators that could enhance connections between design and evaluation progress, stimulating the improvement of building environmental performance.

APPLICATION OF DATA MINING FOR PREDICTION OF LONG COVID ON COVID-19 SURVIVAL WITH FEATURE SELECTION AND NAÏVE BAYES METHOD

Since it was declared a global pandemic in March 2020, Corona Virus Disease (Covid-19) has become the world's attention, a lot of research has focused on all things related to Covid-19. Covid-19 is an infectious disease caused by the acute respiratory syndrome Corona virus 2 (SARS-CoV-2). Several studies have shown that the symptoms of COVID-19 persist for a long period of time even though they have been declared cured of Covid-19, this is known as Long Covid. Complaints that are often experienced by patients who progress to Long Covid are fatigue, headaches, coughs, runny noses, sleep disturbances and even shortness of breath. Several risk factors for the occurrence of Long Covid include age, gender, patient congenital disease, condition during acute infection, ethnicity and the patient's Body Mass Index (BMI). To anticipate the occurrence of long covid, it is necessary to have a risk prediction system for the occurrence of long covid in covid-19 patients, this aims to anticipate and prepare for early handling and prevention efforts for covid-19 patients who are at risk of experiencing long covid. Prediction of the risk of long covid can be done by classifying long covid risk factors by utilizing data mining. The purpose of this study is to classify symptom data and patient history, so that data patterns can be obtained that can be used as predictions to estimate the risk of the occurrence of Long covid-19 in Covid-19 survivors. This study uses the Naïve Bayes classification method by classifying data based on the Long covid risk factor and the feature selection information gain method which is used as a technique in attribute selection to optimize the nave Bayes algorithm. The results of this study have a real contribution to the development of science and technology. The concept of the resulting prediction data pattern can be used as a reference in developing early detection of the risk of the occurrence of Long Covid.

Comparison of Correlated Algorithm Accuracy Naive Bayes Classifier and Naive Bayes Classifier for Classification of heart failure

Heart failure (ARF) is a health problem that has relatively high mortality and morbidity rates in developed or developing countries, including Indonesia. In 2016, WHO stated that 17.5 million people died from cardiovascular disease, while in 2008, HF disease represented 31% of patient deaths worldwide. One of the new breakthroughs for early diagnosis is utilizing data mining techniques. In this study, the Correlated Naive Bayes Classifier (C-NBC) and Naive Bayes Classifier (NBC) algorithms are used to obtaining the best accuracy results so that they can be used for the Heart Failure dataset. Based on the results of the tests that have been carried out, it shows that the Correlated Naive Bayes Classifier (C-NBC) algorithm accuracy of 80.6% obtains higher accuracy than the Naive Bayes Classifier (NBC) algorithm of 67.5%. With the results of this study, the use of the Correlated Naive Bayes Classifier (C-NBC) algorithm can be used to diagnose patients with heart failure (heart failure) because it has a high level of accuracy and is categorized as Good Classification.

Application of data mining algorithms to study data trends for corneal transplantation

To utilize data mining for analysis of corneal transplantations (CT) in Florida from 2005-2014, segmented by demographics, geography, and transplantation technique. A retrospective, database study was performed utilizing data queried from the Healthcare and Cost Utilization Project using Current Procedural Terminology codes for lamellar keratoplasty (ALK), endothelial keratoplasty (EK), and penetrating keratoplasty (PKP). Payer status, ethnic group, age, gender, and geography (urban versus rural) was extracted from each surgical encounter and reconfigured to provide a "clean", congruous dataset for statistical analysis. This Institutional Review Board-approved study did not utilize identifiable patient information; thus, individual informed consent was not required. From 2005-2014, CT (n=28,607) represented less than 1% of the total ambulatory surgeries (n=12,695,932) performed in Florida. EK volume increased while PKP and ALK volume decreased, year-over-year. Statistical significance was found between transplantation technique by sex (P<0.001) and ethnic group (P<0.001). The largest sex discrepancy was EK (59% female, 41% male). White patients underwent relatively fewer PKP than EK (71% vs. 83% of totals), while Black patients underwent relatively more PKP than EK (14% vs 6% of totals). Statistical significance was found between techniques by payer (P<0.001). Medicare was the most common payer for all techniques, but ALK and PKP had higher percentages of private insurance and self-pay. No statistical significance was found between techniques by geographic location. Corneal edema (22.4%), endothelial dystrophy (17.5%), and bullous keratopathy (10.9%) were erroneously coded as indications for ALK. Corneal scars (2.5%) and corneal opacity (1.7%) were erroneously coded as indications for EK. CT rates in Florida appear to overrepresent the female sex and underrepresent ethnic minorities, with propensities between PKP and African Americans, EK and female patients, and EK and Medicare reimbursement. Our study further confirms the utility of data mining for providing efficient, detailed, and practical insights into ophthalmology procedures, while highlighting the intrinsic challenges of large datasets.

PENERAPAN ALGORITMA APRIORI DALAM MENINGKATKAN PRODUKSI BUDIDAYA PERIKANAN MENGGUNAKAN ASSOCIATION RULE

Aquaculture production in North Sumatra needs to be increased in order to meet the increasing demand for aquaculture in the area from time to time. In order to optimize aquaculture production, recommendations are made for what types of aquaculture are most in demand by the community so that the North Sumatra Marine and Fisheries Service can prepare fishery supplies optimally. Recommendations for the provision of fish are carried out by utilizing data mining technology with an association rule algorithm. Processed data is fish sales history data. The result of this data processing is the combination of fish data that is most in demand by the community with a minimum amount of support of 40% to 3 itemset. Furthermore, the a priori algorithm is implemented at the marine and fisheries service to determine associations. By utilizing the results of this a priori algorithm analysis, the Department of Marine and Fisheries of North Sumatra can find out what types of aquaculture are the priorities for increasing production so that they can meet the needs of the community.

The digital spectator activities of Twitter users on the issue of the anti-terrorism bill in the Philippines

Throughout history, activism has evolved with the rise of digital technologies. Individuals and groups can now conduct political activities on social media platforms. Existing studies about hashtag activism typically centered on race and gender, which are usually contextualized in the West. This study addresses the insufficient research about online activism related to policymaking in the Philippine context and focuses on online activism towards the issue of the Anti-Terrorism Bill (ATB) of 2020 that concerns potential harm to freedom of speech, expression, or of the press. This research was inspired by George and Leidner’s (2019) Hierarchy of Digital Activism, notably the Digital Spectator activism. The researchers utilized data mining approach to collect data sets scraped from Twitter. The data was acquired by using TWINT and extracting Twitter’s visible measure of interaction such as likes, retweets, and replies. These data were used to analyze Twitter users’ online activism during the heightened discussion of ATB that showed negative views of the bill using the topmost hashtag, #junkterrorbillnow and the five dominant themes: law-making process, human security, the role of social media, dialogue, and related issues. Findings also showed a link between #junkterrorbillnow and the dominant themes, and the digital spectator activities.

A discrete-event simulation tool for airport deicing activities: Dallas-Fort Worth International Airport

Aircraft deicing/anti-icing fluids (ADFs) are applied to remove and prevent icing on aircraft during taxi and takeoff. The Dallas-Fort Worth (DFW) International Airport uses deicing pads for deicing activities that collect and contain the spent deicing fluids for proper treatment or disposal. Local waterways receive ADF as “drip and shear” during the aircraft taxi on the runway and then takeoff. The glycol-based ADF serves as a nutrient for bacteria that grow exponentially, deplete dissolved oxygen (DO) from receiving waterways, and subsequently kill aquatic life. In this paper, we present a data-driven discrete-event simulation modeling process developed in collaboration with DFW Airport to assess aircraft assignment strategies to deicing pad locations by monitoring impact on DO. Our process consists of the following phases: (1) Data Collection, (2) Probability Distribution Modeling, and (3) State Transition Modeling. Both Phases (2) and (3) utilized data mining approaches, including treed regression and variable selection via false discovery rate. Detailed implementation of these phases is described for the DFW Airport case study, and the DFW Airport deicing activities simulation tool framework is presented. The actual data and simulation results were compared in terms of the DO levels in airport receiving waterways to verify the model validity after implementing the proposed model for DFW. Thus, the proposed model can be implemented by airports to control and minimize the adverse environmental effects resulting from deicing activities by optimizing the aircraft assignment to the pad locations.

Malware and Malware Detection Techniques: A Survey

Abstract: Malicious software is a kind of software or codes which took some: private data, information from the PC framework, its tasks is to do only malicious objectives to the PC framework, without authorization of the PC clients. The effect of malicious software are worsen to the client. Malicious software i.e malwares are programs that are made to mischief, hinder or harm PCs, organizations and different assets related with it. Malwares are moved in PCs without the information on its proprietor. Presently malicious program is a serious threat. It is created to harm the PC framework and some of them are spread over the associated framework in the organization or web association. Analysts are making great efforts in malware framework field with compelling malware detection techniques to safeguard PC framework. Two essential methodologies have been proposed for it for example signature-based and heuristic-based detection. These methodologies distinguish known malware precisely yet can't distinguish the new, obscure malware. Recently various analysts have proposed malware identification framework utilizing data mining and machine learning strategies to distinguish between obscure and non – obscure malwares. In this paper, an detailed examination has been led on the present status of malware infection and work done for finding it. Keywords: PC framework, malicious software, heuristic-based , signature – based , zero -day malware , obscure malware

Comparative Study of K-Means Clustering Algorithm and K-Medoids Clustering in Student Data Clustering

Universities as educational institutions have very large amounts of academic data which may not be used properly. The data needs to be analyzed to produce information that can map the distribution of students. Student academic data processing utilizes data mining processes using clustering techniques, K-Means and K-Medoids. This study aims to implement and analyze the comparison of which algorithm is more optimal based on the cluster validation test with the Davies Bouldin Index. The data used are academic data of UIN Sunan Kalijaga students in the 2013-2015 batch. In the k-Means process, the best number of clusters is 5 with a DBI value of 0.781. In the k-Medoids process, the best number of clusters is 3 with a DBI value of 0.929. Based on the value of the DBI validation test, the k-Means algorithm is more optimal than the k-Medoids. So that the cluster of students with the highest average GPA of 3,325 is 401 students.