Utilizing Data Mining Research Articles

Measuring the community dream: A social capital lens of capacity building initiatives

ABSTRACT In 2010, San Antonio leaders sought to leverage networks and collective action to create a future in which each resident was responsible to the whole community in helping to reach a shared vision for 2020 to become a world-class city. In doing so, city administrators applied social capital theories to utilize the active networks in a social capital-rich community, which creates a wider capacity for dynamic actions and more opportunity for community practitioners to meet development goals. This case study compiles secondary sources from publicly distributed data sets, utilizing data mining techniques and social network analysis to describe community capacity building outcomes. This case study examines community development outcomes through a social-network analysis of organizational leadership, combined with an examination of diversity, civic engagement, and participation within the decade-long initiative, to provide insights for other leaders seeking to utilize a social capital theory approach to capacity building.

Leveraging transcriptome sequence read archives for virus detection in wild and colony populations of triatomines (Hemiptera: Reduviidae: Triatominae)

Triatomines are infamous as vectors of the parasite Trypanosoma cruzi, the causative agent of Chagas disease. However, climate-driven range expansion and urbanization adaptation of triatomine populations, coupled with their highly diverse feeding strategies (vertebrate haematophagy, kleptohaematophagy, and coprophagy), and has elevated interest in triatomines as potential arboviral vectors. Information on the triatomine virome is scant, with prior records including only eight insect-specific viruses: Triatoma virus (TrV) and Rhodnius prolixus viruses 1–7. Here, we leverage publicly available transcriptome datasets to assess viral diversity in 122 wild and colony kissing bugs representing eight species from six countries. In total, six viruses were detected (including Rhodnius prolixus viruses 4–6), and TrV was detected in almost half of all screened triatomines. This is the first report of TrV in Triatoma brasiliensis and in members of the genus Mepraia (M. gajardoi, M. spinolai, and M. parapatrica), and this effort has vastly expanded the publicly available genomic resources of TrV, adding 39 genome sequences to the single genome sequence currently available in the GenBank database. Furthermore, two additional viruses—Meccus longipennis virus 1 and Drosophila melanogaster Nora virus—are herein reported for the first time from kissing bugs. Meccus longipennis virus 1 was detected in Triatoma infestans from Argentina, Brazil, Chile, and Peru, and Drosophila melanogaster Nora virus was found in T. infestans from Argentina. Our results illustrate the advantage and utility of low-cost transcriptome data mining for the discovery of known and novel arboviruses in triatomines and other potential insect vectors.

Analyzing failure mechanisms and predicting step-like displacement: Rainfall and RWL dynamics in lock-unlock landslides

Lock-unlock landslides have thick sliding zones that store a lot of energy. This makes them start quickly, happen suddenly, and have serious consequences. Therefore, it becomes urgent to study the deformation and failure mechanisms of such landslides and develop rational predictive models. Taking the Jiuxianping landslide as an example, this study investigates the regularity of landslide displacement changes using multi-source data, focusing on the abrupt displacement patterns in the unlock phase. Furthermore, employing Transient Release and Inhalation Method tests combined with Geo-Studio’s SEEP/W and SIGMA/W modules for fluid–solid coupled simulation calculations, the evolution process of landslide failure mechanisms and deformation characteristics is analyzed and discussed. Lastly, utilizing data mining analysis of multi-source data, a hybrid optimized machine learning predictive model is established for model prediction comparison. The study reveals that: (1) The rise in infiltration line elevates pore water pressure, affecting the stability of the sliding zone, leading to “unlock effects” and step-like displacement deformation; (2) Simulation shows that YY208 is closer to the actual situation, located at the far bank position, while YY210 is greatly influenced by the “buoyancy effect”, resulting in a slowdown in deformation velocity; (3) After data preprocessing, overall actual displacement prediction performs better than simulation displacement prediction in terms of Mean Absolute Error, Mean Squared Error and Correlation Coefficient, but noise reduction processing can improve the periodic prediction effect of simulation displacement.

Constructing a Predictive Model for High School Students' Enrolment Results

Nowadays, data-informed decision-making can assist high-school educational decision-making, especially student test scores, and further education data, then these data can be used to inform decision-making in schools and boost overall school performance. We aimed to assess the relationships among high school students' admission scores, academic performance at school, and psychological tests in the school system. The relationships were used to predict the departmental fields and universities that students would select for advanced studies in the future. This research will utilize data mining algorithms, including Naïve Bayes, SVM, and Neural Network classifiers, to extract and analyze hidden patterns in students' academic records and credentials. When the Bayesian Network model was used to predict whether a student's university is public or private, our results showed 78.46% of the predicted results. Predicting the accuracy of the top three departmental fields, the SVM model achieved the highest performance with an accuracy of 69.77% .Given that students' academic performance varies across high schools, each school should develop its customized predictive model using only its students’ data. This will help students believe in and pursue their interests.

Klasterisasi Penggunaan Ban dengan Cost Per Kilometer Terendah pada PT. PL menggunakan Metode K-Means

Established in 1969, PT. Puninar has grown to become one of Indonesia's leading logistics companies. The company provides a broad range of logistics solutions through its subsidiaries, encompassing material management, manufacturing, warehousing, distribution, customs services, freight forwarding, and post-delivery operations. After fuel, tires represent the second-largest expenditure for the company. Proper tire management can reduce maintenance costs, making it a key cost-reduction strategy in the logistics sector. This study utilizes data mining techniques, specifically the K-means method, to analyze and classify tires based on travel distance and the lowest cost per kilometer, enabling monthly cost monitoring. PT. Puninar allocates approximately two billion rupiah per month for tire inventory due to its fleet of over seven hundred trucks. The company currently employs various tire brands, including Bridgestone, Doublecoin, Effiplus, Chengsang, and others, with varying levels of durability.

Symptom mapping and personalized care for depression, anxiety and stress: A data-driven AI approach

BackgroundDepression, anxiety, and stress disorders have significant and widespread impacts worldwide, affecting millions of individuals and their communities. According to the World Health Organization, depression impacts the daily lives of more than 300 million people, making it one of the most important diseases globally. Treatment for these mental disorders (MD) typically involves medication and psychotherapies, but also incorporates technological resources like Artificial Intelligence (AI) to indicate personalized therapies and care. While various AI approaches have been applied in the context of MD in the literature, they often focus solely on aiding diagnosis. ObjectiveThis research proposes an AI approach for mapping symptoms and assisting in the personalized care of depression, anxiety, and stress. MethodsSymptom mapping utilizes data mining (DM) techniques to generate rules representing knowledge extracted from data of 242 patients collected using the Depression, Anxiety, and Stress Scale (DASS-21). This knowledge elucidates how symptoms impact the severity degrees of considered MDs. Subsequently, the generated rules are employed to construct a Fuzzy Inference System (FIS) for inferring the severities of MDs based on patient symptoms and personal data. Results and conclusionsThe results achieved in the DM (accuracy ≥92.98 %, sensibility ≥86.02 %, specificity ≥97.32 %, and kappa statistic ≥87.98 %), indicating consistent patterns, along with the results produced by the FIS, demonstrate the potential of the proposed approach to assist health professionals in rapidly predicting symptoms of depression, anxiety, and stress, thereby facilitating outpatient screening and emergency care. Furthermore, it can improve the association of symptoms, referral to specialized care, therapeutic proposals, and even investigations of other diseases unrelated to MD.

Seismic trend analysis: a data mining approach for pattern prediction

<span lang="EN-US">In the global context, seismic movements represent a constant for the population due to geophysical variability and other factors that make them possible, carrying with them the risk of losing innocent lives. The main purpose of our research is to apply data mining techniques to prevent seismic events of any magnitude to anticipate and mitigate future events. In the development of the research, we applied knowledge discovery database methodology. The clustering analysis results revealed the following: cluster 0 encompassed 45 items, with average magnitude of 0.230, representing 15.5% of the total events. Cluster 1 comprised 56 items with average magnitude of 0.156, equivalent to 19.2% of the total. Cluster 2, the largest, consisted of 94 items with average magnitude of 0.156, constituting 32.3% of the total seismic events. Cluster 3 was composed of 54 items, with average magnitude of 0.155, representing 18.3% of the total. Lastly, cluster 4 included 42 items, with average magnitude of 0.155, representing 14.5% of the total. In conclusion, cluster 3 emerged as the most significant, with 94 events and average magnitude of 0.141, equivalent to 32.3% of the total seismic events. This discovery underscores the need to utilize data mining techniques for earthquake prediction, enabling proactive measures against potential events, which are frequent in various geographic areas.</span>

Revolutionizing Network Intelligence: Innovative Data Mining and Learning Approaches for Knowledge Management in Next-Generation Networks

The Information and Communication (ICT) industry, a global giant among service sectors, is known for both its massive scale and its unforgiving demands. Here, downtime is unacceptable, requiring constant high availability – often at the stringent Sigma Six standard. Redundancy is a common solution, but it comes at a cost. To meet these demands proactively, the ability to predict load and growth becomes crucial. This project aims to develop a prototype, or proof of concept, that utilizes data mining to provide early warnings and growth forecasts for the ICT industry with good accuracy. Big data is key to making discoveries in any data analysis project. Normally, this data comes from real-time system logs. However, for this initial test, I used a dataset called MIT Reality Mining. This dataset is useful because real-world companies, especially in the tech industry (ICT), are often hesitant to share their current information. By using MIT Reality Mining, I could still find trends and potential reasons behind them in the ICT industry. It's important to remember that this is a limited functionality prototype. While it can serve as a guideline for Telcos looking to implement data warehouses, the actual implementation details will need to adapt to the specific needs of each industry.

Unraveling the impact of patent transfers on regional innovation: Empirical insights through the lens of entity relationships

Over the past two decades, the market for patent transfers (PT) has experienced substantial growth in China, driven by the expansion of main relationship types, including inter-firm, industry-university-research (IUR), and individual-to-firm PTs. This growth raises critical questions regarding the influence of these diverse PT types on regional innovation. Utilizing data mining on 1,914,750 PT events, this study constructs panel data models for China's 31 provinces from 2011 to 2020, investigating the presence, dimensions, effect decomposition, and threshold determinations of PTs on regional innovation. Findings reveal heterogeneous impacts of PT types on innovation, with individual-to-firm PT being most effective and IUR PTs the least. Among the dimensions of impact, PT strength demonstrates the most circumscribed promotional effect, predominantly within patent inflows, and particularly hinders IUR PTs due to market frictions. The importance of PT breadth mainly appears in patent outflows, suggesting geographic expansion of PT enhances innovation remuneration, critical for sustaining innovation momentum in hub provinces. PT depth uniformly affects innovation across all PT categories irrespective of patent inflows or outflows, underscoring the significance of diversified technology transfers. A universal observation across PT types is that surpassing regional thresholds of digitalization significantly amplifies the promotion of PT on regional innovation.

Korelasi Perilaku Siswa dengan Tingkat Prestasi Siswa Menggunakan Metode Apriori

Student behavior is the actions of students which are influenced by their attitudes and responsibilities at school. Student behavior is a very important factor in determining student achievement in learning. Students who have personalities that improve skills, knowledge, attitudes, habits, understanding, skills, thinking power and other abilities will more easily increase their concentration in learning, and it will be easier to achieve the students' goals. At SDN 053960 MARYKE there are still students who do not know that student behavior greatly influences their level of achievement. Therefore, it is necessary to educate students from an early age so that students can be more responsible for the rules given by teachers at school, and students must understand that the attitude they carry out at school is assessed in improving their achievement, as well as their presence is very influential. his level of achievement at school. Therefore, there is a need for a solution to overcome the problems that exist at SDN 053960 MARYKE by utilizing data mining to collect data and then it will be processed using the a priori method with variables contained in the correlation between student behavior and student achievement levels. The a priori algorithm is able to determine min support and confidence in these variables will later show the relationship between student behavior and student achievement levels, so that researchers will get the best best rules and be able to produce the latest information..

Enhancing cyber security: a comprehensive approach to the classification and prediction of significant cyber incidents (SCI) through data mining and variational neural network with fox optimization algorithm

The rapid advancement of technology and the proliferation of IoT devices have rendered cyberspace vulnerable, resulting in Significant Cyber Incidents (SCIs). This paper proposes an Enhancing Cyber security: a Comprehensive Approach to the Classification with Prediction of Significant Cyber Incidents through Data Mining with Variation Neural Network with Fox Optimization Algorithm (ECS-SCI-DM-VNN-FOA). The dataset is split into pre-pandemic and post-pandemic SCI subsets, according to the report from the Center for Strategic and International Studies (CSIS). Adaptive Variation Bayesian Filter (AVBF) is utilized to remove the noise from the input data. Then the preprocessed input data is supplied to the Improved Window Adaptive Gray Level Co-Occurrence Matrix (IWAGM) for feature extraction. The proposed approach is implemented in Python and its efficacy is evaluated under some metrics, like accuracy, precision, recall, FI-score, sensitivity, computational time, recall, and RoC. The proposed ECS-SCI-DM-VNN-FOA gives 24.91%, 23.76% and 25.98% high accuracy and 30.45%, 23.67% and 29.32% high precision compared with the existing methods, like classification with prediction of cyber events utilizing data mining with machine learning (CRP-SML), Cyber risk prediction via social media big data analytics along statistical machine learning (PECI-MUIP), Mining user interaction patterns in the dark web to forecast enterprise cyber occurrences (CTPA-CSCS) respectively.

Exploring the Effectiveness of Data Mining Classification Algorithms in Credit Card Fraud Detection

Credit card fraud is a widespread problem that impacts both individuals and companies. Data mining provides a powerful solution to not only detect but also prevent this type of fraud. This research explores this approach by utilizing data mining classification techniques to determine the potential for fraudulent credit card transactions. Data from various sources is collected and processed to extract relevant features. This research will compare 8 classification algorithms, namely Naive Bayes, Decision Trees, Artificial Neural Network, SVM, Linear Regression, Logistic Regression, LDA and Random Forest, in classifying transactions as legitimate or fake. These findings suggest that the combined use of these data mining classification methods offers a powerful tool in combating credit card fraud. To combat the problem of credit card fraud and maintain financial security for both individuals and institutions, this researcher explores the power of data mining. By using potential classification techniques, this research aims to predict fraudulent transactions on credit cards.

College English Smart Classroom Learning Model Utilizing Data Mining Technology

The integration of Internet technology and the collaborative development of smart classrooms is an essential step for colleges and universities to advance English instruction reform. This study utilized data mining (DM) technology to analyze the learning process in college English smart classrooms. The results indicate that the DM algorithm used in this study outperforms the other two algorithms across all metrics. After conducting 15 experiments, the centrality of the DM algorithm in this study reached 0.58, exceeding the ant colony algorithm's centrality of 0.42. The decision tree algorithm exhibited the lowest centrality, reaching a maximum value of only 0.39. Consequently, the methodology utilized in this study demonstrates a significant centrality within the classroom, indicating its suitability for investigating University English smart classroom learning. Hence, implementing a University English smart classroom learning model utilizing DM technology represents the primary approach to achieving intelligent education.

Feasibility Design and Analysis of Process-aware Accounting Information System for Business Management

Accounting Information Systems (AIS), as a fundamental element of Enterprise Information Systems (EIS), are experiencing dual pressures from the enterprise management surroundings and the advancement of accounting control. The current AIS focused on functional applications needs to be revised to meet contemporary enterprise management requirements. This study suggests implementing a Process-Aware Accounting Information System (PA-AIS) that utilizes data mining techniques to enhance managing business processes. The objective is to enhance the accounting effectiveness of organizations and provide valuable insights for building AIS. This paper compares the benefits and drawbacks of standard methods employed in building AIS. It also assesses the potential of process-aware AIS to enhance business Process Management (PM) by considering factors such as enterprise effectiveness, accounting expenses, accounting duration, etc. An analysis assesses the viability of improving the PA-AIS for managing business processes regarding enterprise effectiveness, accounting expenses, accounting duration, etc. The findings indicate that implementing a PA-AIS with enhanced business PM is economically viable and successful. This system saves time, minimizes accounting risks, and enhances management control inside the firm. The implementation of a PA-AIS, along with enhanced business PM, effectively decreases the accounting expenses of firm Y. The initial accounting cost is from 100k to 200k, but the accounting expense after implementing the new system ranges from 80k to 100k. Implementing the new system resulted in a minimum 10-20% improvement in Company Y's accounting efficiency, rationality, and user convenience. The PA-AIS enhances business process administration

A real-world pharmacovigilance study of polatuzumab vedotin based on the FDA adverse event reporting system (FAERS).

Polatuzumab vedotin, the first FDA-approved antibody-drug conjugate (ADC) targeting CD79b, is utilized in the treatment of previously untreated diffuse large B-cell lymphoma (DLBCL) or high-grade B-cell lymphoma (HGBL), as well as relapsed or refractory (R/R) DLBCL. Despite its approval, concerns persist regarding the long-term safety profile of polatuzumab vedotin. This study aims to evaluate the adverse events (AEs) associated with polatuzumab vedotin since its approval in 2019, utilizing data mining strategies applied to the FDA Adverse Event Reporting System (FAERS). Signal detection employed four methodologies, including reporting odds ratio (ROR), proportional reporting ratio (PRR), bayesian confidence propagation neural network (BCPNN), and multi-item gamma poisson shrinker (MGPS), to evaluate and quantify the signals of polatuzumab vedotin-associated AEs. Additionally, subgroup analyses based on patients age, gender, and fatal cases were conducted to investigate AEs occurrences in specific subpopulations. A total of 1,521 reports listing polatuzumab vedotin as a "principal suspect (PS)" drug were collected from the FAERS database. Through concurrent compliance with four algorithms, 19 significant Standardized MedDRA Query (SMQ) AEs and 92 significant Preferred Term (PT) AEs were detected. Subgroup analyses revealed a higher incidence of PTs in male patients compared to female patients, increased likelihood of polatuzumab vedotin-associated AEs in elder patients (>65years), and AEs with a high risk of fatal cases include: blood lactate dehydrogenase increased, cytopenia, and hydronephrosis. The median time to AEs occurrence following polatuzumab vedotin initiation was 18.5 (5∼57.75) days, with 95% of AEs occurred within 162days. This study identified various AEs associated with polatuzumab vedotin, offering critical insights for clinical monitoring and risk identification in patients receiving polatuzumab vedotin therapy.

Developing data-driven O&M policy through sequential pattern mining: A case study

This study focuses on utilizing data mining techniques to extract valuable insights from discrete industrial data, crucial for Operations and Maintenance (O&M) decision-making. We present an innovative methodology integrating data mining with the Plan-Do-Check-Act (PDCA) cycle and Knowledge Discovery in Databases (KDD) principles. Through a manufacturing facility case study, we develop a hybrid opportunistic O&M policy, blending Condition-based Maintenance (CBM) and Time-based Maintenance (TBM) to replace the traditional Failure-based Maintenance (FBM) strategy. Our proposed maintenance policy incorporates delay-time modeling in a machining center’s critical sub-system, the lubrication equipment, proving its cost-effectiveness even amid parameter fluctuations. We assess the financial impact of adopting the Total Productive Maintenance (TPM) approach through Sensitivity Analysis, and compare it with established O&M policies, demonstrating the superior cost-effectiveness of our model.

Comparison of Logistic Regression, Random Forest and Adaboost Algorithms for Diabetes Mellitus Classification

Diabetes mellitus is a chronic disease that affects the way the body regulates sugar (glucose). High blood sugar levels can lead to health complications including heart problems, eye disorders, nerve damage, kidney and blood vessel disorders. It is important for early detection of diabetes by utilizing data mining technology. Data mining has various classification models that can be used to detect diabetes, including logistic regression, random forest and adaboost. The comparison of the three algorithms aims to find out which algorithm is most appropriate in the classification of diabetes. From the results obtained, the random forest algorithm has the best performance in the classification of diabetes mellitus compared to other algorithms.

An Effective Optimization in Education System using Decision Support Systems

For academics, the process of retrieving information from large datasets, known as data mining, has become a captivating area of research. The concept of utilizing data mining techniques to extract information has been in existence for several decades. The dataset was initially designed to be divided into sections and analyzed using classification and clustering methods to explore its intrinsic characteristics. They make their forecasts based on these features. These predictions have been generated in the field of educational data mining for several purposes, such as forecasting student achievement using individual traits and assisting students in identifying suitable professors and courses. These targets have been derived from the analysis of student attrition and retention. Our study is centered around the aims of student attrition and retention. In addition, we have discovered intriguing indicators that contribute to the prediction of students' success, indicating the most competent instructors, and helping them with their choice of courses.

The use of intelligent real-time image acquisition system using data mining technology in news acquisition and editing practice

Aiming to address the timely dissemination of news information, this work explores the clever utilization of data mining (DM) technology and deep learning (DL) algorithms to construct an intelligent real-time news image acquisition system to meet the urgency of news dissemination needs. First, this work introduces an intelligent real-time news image acquisition system and provides a detailed analysis of its principles and advantages. Throughout this process, the crucial role of DM technology in news image classification and automation is emphasized, especially in dealing with rapidly evolving news events. Next, the work establishes an intelligent real-time news image acquisition model based on DL algorithms, which integrates the essence of DM technology. Through this fusion, the research objective is to enhance the performance of the news image acquisition system to achieve higher real-time and accuracy, which is vital for the swift delivery of news information. Finally, this work investigates the application of the intelligent news image acquisition system in network communication to ensure its adaptability to various network communication scenarios while maintaining accuracy and real-time capabilities. The research results demonstrate that the application of DM technology in combination with DL algorithms can effectively meet the practical needs of the news industry, enhancing the automation of news image processing and enabling faster information delivery to the audience. Notably, the AlexNet model employed performs exceptionally well, achieving recognition rates of up to 99.6% after data augmentation or equalization processing, with an accuracy of 90.9% and a high specificity of 93.38%. This indicates outstanding overall classification accuracy and negative class accuracy, even when distinguishing between news and non-news scenarios. These results clearly underscore the connection between DM technology and news acquisition and editing practices, and emphasize its potential to improve the efficiency and accuracy of real-time information dissemination. The research’s contribution and innovation lie in the fusion of DM technology with DL algorithms to build an intelligent real-time news image acquisition system. This fusion enhances the automation and classification performance of news images, thereby improving the real-time and accuracy of news information. Furthermore, the work strongly emphasizes improving the real-time and accuracy of the news image acquisition system to ensure the swift delivery of information, which is of utmost importance in rapidly changing news events.

Shopping Cart Analysis to Support Business Management with the Apriori Algorithm

The increasingly advanced development of the business world has led to increasingly fierce competition. One way to maintain the company's survival is to maintain good relationships with customers. Each market has its own way of increasing sales. Serambi Mart is one of the minimarkets operating in Batusangkar City. This minimarket is relatively new because it has been operating since 2020. Even though it is new, this minimarket is quite busy with consumers visiting for shopping, this is due to Serambi Mart management choosing the right location. Based on observations made, product layout arrangements are still based on subjective management, so there are several products that are not suitable to be compared. The layout seems messy, causing difficulties for consumers in shopping. By utilizing sales transaction data at Serambi Mart. This research uses the Apriori algorithm. The Apriori algorithm is an association rule and looks for relationship patterns between one or more items in data, using Association Rules with Minimum Support of 30% and Minimum Confidence of 50%. Therefore, an application is needed that can help Serambi Mart to get information. One way to get this information is to utilize data mining techniques. By using the Apriori Algorithm method for arranging the product layout at Serambi Mart, it is hoped that it can provide convenience to consumers who shop