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Bhargavi Konda. "The impact of data preprocessing on data mining outcomes." World Journal of Advanced Research and Reviews 15, no. 3 (2022): 540–44. https://doi.org/10.30574/wjarr.2022.15.3.0931.
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Data preprocessing is a vital initial step during knowledge discovery because it determines the success of data mining projects. A dataset's quality and representation stand as the primary element because any presence of redundant, irrelevant, too noisy, or unreliable information will severely disrupt the knowledge discovery process. The preprocessing phase first converts unstructured data into an analytical format alongside solutions for data inconsistencies, errors, and missing values to maintain data mining result integrity. The preprocessing corrects data quality problems and arranges data properly, improving data mining model accuracy, efficiency, and interpretability. The data mining pipeline requires data preprocessing as its essential foundation since it provides multiple techniques to convert raw data into an effective analytical format. Data mining depends heavily on preprocessing operations because they guarantee proper analysis results through accurate correction of errors and optimal data structure development and absent data point management.
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Matthew, N. O. Sadiku, K. Suman Guddi, and M. Musa Sarhan. "Big Data in Cybersecurity: A Primer." Journal of Scientific and Engineering Research 8, no. 9 (2021): 6–13. https://doi.org/10.5281/zenodo.10612875.
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<strong>Abstract</strong> Big data refers to mining usable information from the massive amounts of data. It is becoming a focal point of cybersecurity. Cybersecurity has become a big data problem due to the size and complexity of the data and due to the fact that sophistication of threats has increased dramatically. While businesses and government agencies take advantage of big data analytics to improve operations, cyber criminals are mining the same data for unethical reasons. Traditional protection tools used for data mining and cyber-attack prevention are insufficient for several companies. Modern cybersecurity solutions are mostly driven by big data. Intelligent big data analytics allows data specialists to develop a predictive model. This paper is a primer on big data in cybersecurity.
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Gonnade, Priyanka, and Sonali Ridhorkar. "Data Driven Decision making Framework for Businesses." Journal of Neonatal Surgery 14, no. 6S (2025): 648–60. https://doi.org/10.52783/jns.v14.2301.
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Digital technologies have revolutionized the way businesses are built and managed, requiring the development of new solutions and a diverse set of applications. Massive volumes of data are now easily accessible and database capacity has risen tremendously and data collection methods have altered. As a result, while mining big data, issues with regression, the analytical process, and the complexity of the large data all arise.To cope with the aforementioned issues a data analysis framework is proposed for various business decision-making processes which collects the data and saves in Hadoop, a java-based data management system that allows enormous amounts of data to be handled in parallel clusters without failure. Generally, node failures are not concentrated in data storage management systems. Consequently, data mining techniques are used in the design phase to obtain pertinent and essential vital information. Thus, the proposed framework efficiently provides data analytics for various decision making processes with improved accuracy.
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Dandi Sudrajat and Nur Alamsyah. "Penerapan Data Mining Menganalisa Pola Pembelian Sayur Hidroponik Sawargaloka Hydrofarm Metode Apriori." SABER : Jurnal Teknik Informatika, Sains dan Ilmu Komunikasi 2, no. 1 (2023): 200–210. http://dx.doi.org/10.59841/saber.v2i1.690.
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The aim of this research is to apply an a priori algorithm to determine vegetable purchasing patterns and analyze the results in order to control vegetable stocks at Sawargaloka Hydroponic Hydrofarm. The need for quality and safe food supplies is increasing along with population growth, where plants are grown without using land, but using nutrient solutions that are rich in important substances, the application of data mining using the Apriori method can provide valuable insight into the purchasing patterns of hydroponic vegetables by customers. By understanding these patterns, companies can improve marketing strategies, plan production more efficiently, and provide product recommendations to customers. The results of analytical research using the Apriori method on hydroponic vegetable purchase data at Sawargaloka Hydrofarm, it can be concluded that the application of data mining has great potential in identifying significant purchasing patternsa
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Bawiskar, Saurav. "Smart Profitable Solutions with Recommendation Framework." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (2022): 4099–105. http://dx.doi.org/10.22214/ijraset.2022.44835.
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Abstract: Discovering the frequent patterns in transactional databases is one of the crucial functionalities of apriori algorithm. Apriori algorithm is an algorithm which works on the principle of association rule mining. It is a dynamic and skillful algorithm used for discovering frequent patterns in a database, hence proving out to be efficient and important in data mining. Apriori algorithm finds associations between different sets of data. Every different set of data has a collective number of items and is called a transaction. The accomplishment of apriori is the set of rules that expose us how often any particular item or a set of items is contained in a set of data. In our proposed system, to provide efficiency, our basic aim is to implement apriori algorithm by setting up a threshold value and a varying support count which will act as a filter for our recommendation data. We can adjust the threshold value in order to increase or decrease the accuracy of the system. We have used apriori algorithm keeping in mind, its application in retailing industry and its capability of computing and handling large datasets and especially for the purpose of market basket analysis. The use of apriori algorithm along with analytical tools can provide insights into data and help the user in management and decision making provided that the user feeds the system in a correct way. Our aim is to provide user with recommendations which would ultimately help them in improving their business operations.
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Klebanov, A. F., A. V. Bondarenko, Yu L. Zhukovsky, and D. A. Klebanov. "Establishing remote control centers of a mining operation: strategic prerequisites and implementation stages." Mining Industry Journal (Gornay Promishlennost), no. 4/2024 (August 23, 2024): 174–83. http://dx.doi.org/10.30686/1609-9192-2024-4-174-183.
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The article proposes the following plan to implement a project to create a remote control center of a mining company: (1) creation of infrastructural and technological conditions for remote control of equipment and mining operations; (2) organization of the control center that is located at a significant distance from the mining operations and successively transfer to it the functions of planning, monitoring, control and dispatching; (3) development of methodological and regulatory support for mining operations with the use of robotic equipment and transition to remote control and autonomous mining technologies. It is shown that the necessary condition for effective execution of the project is the development and industrial implementation of digital platform solutions for integration, end-to-end optimization, centralized data collection and analysis, control and monitoring of the complete management cycle of mining production. Arguments are provided for the expediency of organizing dedicated service management companies (based on IT companies, i.e. developers and/or integrators of digital mining technologies) for remote management of the Intelligent Mining Enterprise. The necessity of creating analytical centers to support decision making for optimization of mining production processes (as one of the key sub-stages of the project) on the basis of leading research organizations and Universities of mining profile is justified. Goals and objectives of the Remote Analytical Center are formulated using the case of the Digital Mining Production Laboratory at the Empress Catherine II St. Petersburg Mining University. It is stated that creation of analytical centers for decision support will contribute to training of qualified academic staff and accelerate the transformation processes of the Russian higher education.
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Oursatyev, Oleksii A. "Data Research in Industrial Data Mining Projects in the Big Data Generation Era." Control Systems and Computers, no. 3 (303) (2023): 33–53. http://dx.doi.org/10.15407/csc.2023.03.033.
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Introduction. The review material is based mainly on business intelligence (BI) solutions designed for tasks with corporate data. But all the main aspects of working with data discussed in the work are also used on data processing platforms (Data Science Platform). Many BI vendors have expanded the capabilities of their systems to perform more advanced analytics, including Data Science. They added the phrase “Data Science” to their marketing research, and the term “advanced analytics” lost some popularity in relation to corporate data. The Data Science Platform provides a comprehensive set of tools for use by advanced users who traditionally work with data. Capabilities that allow you to connect to multi-structured data across different types of storage platforms, both on-premises and in the cloud, and the infrastructure architecture of a modern BI analytics platform enable high-performance workloads, including business intelligence. It uses distributed architecture, massively parallel processing, data virtualization, in-memory computing, etc. The combination of traditional relational data processing with calculations on the well-known Apache Hadoop software infrastructure, which integrates a number of components of the Hadoop ecosystem (Apache Hive, HBase, Spark, Solr, etc.) with the necessary target functions, allows you to create a fully functional platform for storing and processing structured and non-structures data. Purpose. A review of data processing problems and an analysis of the use of world-class mathematical apparatus and tools for obtaining knowledge from information were carried out. Methods. The paper describes the use of Data Mining methods in big data processing tasks, as well as methods of business, recommendation and predictive analytics. Result. The study suggests that machine learning-enhanced master data management (MDM), data quality, data preparation, and data catalogs will converge into a single, modern Enterprise Information Management (EIM) platform applicable to most new analytics projects. The results of the analysis of the process of identifying useful data can be useful to researchers and developers of modern platforms for processing and researching data in various spheres of society. Conclusion. A review of data processing problems and an analysis of the use of world-class mathematical apparatus and tools for obtaining knowledge from information were carried out. It is shown that a high-quality solution to the problems of working with first-level data indicated in this review will be provided by data research in modern analytical platforms. Successful penetration into their essence at the level of obtaining knowledge using machine learning and artificial intelligence algorithms will make it possible to predict future results in managed objects (processes) and make informed decisions.
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Siswono, Siswono. "Peran Business Intelligence dalam Solusi Bisnis." ComTech: Computer, Mathematics and Engineering Applications 4, no. 2 (2013): 812. http://dx.doi.org/10.21512/comtech.v4i2.2518.
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The purpose of this study is to give examine the use of Business Intelligence as a critical technology solutions in the decision making by management. Business Intelligence application is able to address the needs of organizations in improving problem analytical skills encountered in making decisions with the ability tocollect, store, analyze and provide access to data, as well as dovarious activities such as statistical analysis, forecasting, and data mining.
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Goncharenko, S. N., and A. B. Avdeev. "Problem-oriented information analytical system development for management, planningand mining enterprise production activities optimization." Issues of radio electronics, no. 11 (November 20, 2019): 82–86. http://dx.doi.org/10.21778/2218-5453-2019-11-82-86.
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The paper considers the scenario analysis tool in the form initial scenarios set simulation and optimal option selection enterprise development on the basis information analytical system – integrated planning system (IPS) is considered. The IPS practical application field as a tool of simulation modeling for optimization the enterprise activity in terms the main technical and economic indicators, strategic planning and production system development, valuation and structuring assets is shown. Such tasks in the IPS framework will improve the plan recosting speed and planning accuracy, model multiple enterprise development scenarios, taking into account changes in macroeconomic indicators and solve the task choosing the optimal one, which will significantly affect the quality and speed of management decisions. This system will allow data consolidation and integration with other information and industrial enterprise analytical systems. The IPS contains applications for creating and viewing reports, as well as data preparation and analysis functionality, tools for developing and editing program code, analytical solutions for studying data structure and building analytical models, including scenario models, and is a complete analytical platform that provides a secure multi-user environment for simultaneous access to data. The system provides a specialized data warehouse section, the logical and physical structure which is designed to create a special report or a group reports for a specific section the subject area.
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Zhang, Yang, Yourong Chen, Kelei Miao, Tiaojuan Ren, Changchun Yang, and Meng Han. "A Novel Data-Driven Evaluation Framework for Fork after Withholding Attack in Blockchain Systems." Sensors 22, no. 23 (2022): 9125. http://dx.doi.org/10.3390/s22239125.
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In the blockchain system, mining pools are popular for miners to work collectively and obtain more revenue. Nowadays, there are consensus attacks that threaten the efficiency and security of mining pools. As a new type of consensus attack, the Fork After Withholding (FAW) attack can cause huge economic losses to mining pools. Currently, there are a few evaluation tools for FAW attacks, but it is still difficult to evaluate the FAW attack protection capability of target mining pools. To address the above problem, this paper proposes a novel evaluation framework for FAW attack protection of the target mining pools in blockchain systems. In this framework, we establish the revenue model for mining pools, including honest consensus revenue, block withholding revenue, successful fork revenue, and consensus cost. We also establish the revenue functions of target mining pools and other mining pools, respectively. In particular, we propose an efficient computing power allocation optimization algorithm (CPAOA) for FAW attacks against multiple target mining pools. We propose a model-solving algorithm based on improved Aquila optimization by improving the selection mechanism in different optimization stages, which can increase the convergence speed of the model solution and help find the optimal solution in computing power allocation. Furthermore, to greatly reduce the possibility of falling into local optimal solutions, we propose a solution update mechanism that combines the idea of scout bees in an artificial bee colony optimization algorithm and the constraint of allocating computing power. The experimental results show that the framework can effectively evaluate the revenue of various mining pools. CPAOA can quickly and accurately allocate the computing power of FAW attacks according to the computing power of the target mining pool. Thus, the proposed evaluation framework can effectively help evaluate the FAW attack protection capability of multiple target mining pools and ensure the security of the blockchain system.
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Bhandari, Adarsh. "Analyzation and Comparison of Cloud Computing and Data Mining Techniques: Big Data and Impact of Blockchain." International Journal for Research in Applied Science and Engineering Technology 9, no. 11 (2021): 712–21. http://dx.doi.org/10.22214/ijraset.2021.38888.
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Abstract: With the rapid escalation of data driven solutions, companies are integrating huge data from multiple sources in order to gain fruitful results. To handle this tremendous volume of data we need cloud based architecture to store and manage this data. Cloud computing has emerged as a significant infrastructure that promises to reduce the need for maintaining costly computing facilities by organizations and scale up the products. Even today heavy applications are deployed on cloud and managed specially at AWS eliminating the need for error prone manual operations. This paper demonstrates about certain cloud computing tools and techniques present to handle big data and processes involved while extracting this data till model deployment and also distinction among their usage. It will also demonstrate, how big data analytics and cloud computing will change methods that will later drive the industry. Additionally, a study is presented later in the paper about management of blockchain generated big data on cloud and making analytical decision. Furthermore, the impact of blockchain in cloud computing and big data analytics has been employed in this paper. Keywords: Cloud Computing, Big Data, Amazon Web Services (AWS), Google Cloud Platform (GCP), SaaS, PaaS, IaaS.
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Balashov, A. M. "Issues related to implementation of big data analytical systems and other digitalization achievements to improve the business efficiency of mining companies." Mining Industry Journal (Gornay Promishlennost), no. 3/2024 (July 10, 2024): 139–42. http://dx.doi.org/10.30686/1609-9192-2024-3-139-142.
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Currently, digitalization and widespread adoption of digital technologies are significantly changing people's activities in many areas. Digital technologies provide automation of business processes, data management, analytics, they support strategic decision-making and dictate the need to introduce new approaches to doing business in order to increase its efficiency and profitability, as well as to ensure sustainability of companies' development in modern conditions. It needs to be especially mentioned how the big data processing and analysis technologies and other Industry 4.0 achievements are introduced in the mining industry. The use of big data analytical systems in modern production, including the mining industry, provides an integrated approach to processing and analyzing a large amount of information. It also provides organizations with significant advantages reflected at various levels of management and strategic decision-making. The prospects for implementation and development of these digital solutions currently look very encouraging. Effective management of these processes provides companies with significant opportunities and advantages, allowing them to increase competitiveness, optimize the use of resources and increase the efficiency of their business as a whole.
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Mehdizade, M. "BIG DATA ANALYTICS." Sciences of Europe, no. 142 (June 9, 2024): 77–81. https://doi.org/10.5281/zenodo.11535739.
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In the information era, enormous amounts of data have become available on hand to decision makers. Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be studied and provided in order to handle and extract value and knowledge from these datasets. Furthermore, decision makers need to be able to gain valuable insights from such varied and rapidly changing data, ranging from daily transactions to customer interactions and social network data. Such value can be provided using big data analytics, which is the application of advanced analytics techniques on big data. This paper aims to analyze some of the different analytics methods and tools which can be applied to big data, as well as the opportunities provided by the application of big data analytics in various decision domains.
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Jørs, Erik, Omar Huici, Margrethe Smith, and Chris Kuylenstierne. "SS08-01 INTERVENTIONS TO REDUCE MERCURY USE AND POISONINGS AMONG SMALL-SCALE MINERS: PRACTICAL EXAMPLES FROM BOLIVIA AND OTHER PARTS OF THE WORLD." Occupational Medicine 74, Supplement_1 (2024): 0. http://dx.doi.org/10.1093/occmed/kqae023.0086.
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Abstract Introduction Mercury pollution from small-scale mining is a global problem affecting not only miners’ health but also the health of people living close by or even far from the mining sites. Several methods are applied to reduce mercury use e.g. by banning and substitution. In Bolivia, prevention of poisoning is promoted not only by introducing mercury-free gold mining techniques, but also by strengthening leadership and organisational training, and training in alternative income generating activities like pesticide-free agriculture. Changes in society are hard to achieve by only focusing on technical solutions. Materials and Methods Data gathered among miners during the last four years by interviews, focus group discussions and questionnaires are analysed and presented using sound statistical and analytical methods. Results The results from the last three years of intervention in Bolivia among male miners and the actual status of the intervention among female miners and children are presented. In addition, experiences from other parts of the world with the introduction of mercury-free gold mining and effect on health are discussed and compared with the results from Bolivia. Conclusions The preliminary results show an economically sound outcome by introducing mercury-free gold mining techniques, but low sustainability if miners do not take ownership of the process or are not well organized in their miners’ societies. Therefore, changing habits takes more than only introducing technical solutions.
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Shustov, O., A. Adamchuk, P. Kravchenko, V. Symonenko, and A. Shustova. "Improvement of technological solutions for reclamation of Morozivskyi lignite open pit mine." Collection of Research Papers of the National Mining University 79 (December 30, 2024): 121–32. https://doi.org/10.33271/crpnmu/79.121.
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Purpose. Substantiation of the parameters of mining reclamation of the sides of the split and howling trenches of the flooded Morozivskyi lignite open pit mine. Methods. In solving the problems of the study, methods of analytical analysis, graph analytics, engineering calculations, statistical processing, and comparative analysis of data to improve the technological schemes of mining and technical reclamation of the sides of the mine workings of the flooded Morozivskyi lignite open pit mine were used. Findings. The main stages of land restoration are defined, which are as follows: alignment of slopes of the cut and exit trench at angles ensuring their long-term stability; terracing of aligned slopes; surface layout of dumps; application of fertile soil layer from the warehouse to the surface of terraces and industrial site. For the first time, parameters of mining reclamation were substantiated, namely, the volumes of excavation and filling of workings when straightening the slopes of the sides with bulldozers during top-down work, the width of the transport berm, the angles of the slopes of ledges, which are 18-22 degrees and the distance of movement of rocks with a bulldozer. The originality. The dependence of the removal volume in the block on the average area of its intersection is established. Based on this dependence, it is concluded that the removal volumes and the intersection areas, in turn, depend on the width of the transport berms, which affects the transportation distance. At the same time, the width of the transport platform during the operation of dump trucks is at least 12 m. Practical implementation. The technological scheme of mining and technical reclamation has been improved, and the sequence of work on sure sides of the lignite open pit mine, which is at the liquidation stage, has been shown. These technological solutions can be used in implementing projects to eliminate mining enterprises to extract lignite. The recommendations on the reclamation sequence will be useful in the case of the resumption of lignite rock mining with minimal impact on the environment.
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Aboura, Khalid, Miroljub Kljajić, and Ali Eskandarian. "The need for simulation in complex industrial systems." Organizacija 45, no. 5 (2012): 219–27. http://dx.doi.org/10.2478/v10051-012-0022-4.
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We discuss the concept of simulation and its application in the resolution of problems in complex industrial systems. Most problems of serious scale, be it an inventory problem, a production and distribution problem, a management of resources or process improvement, all real world problems require a mix of generic, data algorithmic and Ad-hoc solutions making the best of available information. We describe two projects in which analytical solutions were applied or contemplated. The first case study uses linear programming in the optimal allocation of advertising resources by a major internet service provider. The second study, in a series of projects, analyses options for the expansion of the production and distribution network of mining products, as part of a sensitive strategic business review. Using the examples, we make the case for the need of simulation in complex industrial problems where analytical solutions may be attempted but where the size and complexity of the problem forces a Monte Carlo approach.
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Wibisono, Keris Aji, and Umar Ma'ruf. "The Law Enforcement Against The Crime Of Illegal Mining." Law Development Journal 3, no. 2 (2021): 424. http://dx.doi.org/10.30659/ldj.3.2.424-430.
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This study aims to determine law enforcement against the criminal act of illegal mining in the jurisdiction of the Central Kalimantan Regional Police and the obstacles that arise in law enforcement of the criminal act of illegal mining in the jurisdiction of the Central Kalimantan Regional Police and their solutions. The approach method used is sociological juridical, descriptive analytical research specifications, types and sources of data using primary and secondary data, data collection methods are field studies and literature studies, while the data analysis method uses qualitative analysis. The results of the study indicate that law enforcement for the criminal act of illegal mining in the jurisdiction of the Central Kalimantan Regional Police is carried out through preventive and repressive efforts. Repressive efforts are carried out with outreach activities to the community at the Polres and Polsek levels, while repressive efforts through a series of investigative actions. There are several obstacles in the law enforcement process, namely the presence of irresponsible individuals, limited facilities and infrastructure and a lack of legal awareness from the community.
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Ryndina, Svetlana V. "Import substitution of software for intelligent process analysis in Russia." Research Result. Economic Research 10, no. 4 (2024): 102–10. https://doi.org/10.18413/2409-1634-2024-10-4-0-9.
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The article presents the findings of a study investigating the extent to which software for intelligent process analysis can be considered an import substitute. This section provides a concise overview of the evolution of the concept of identifying patterns of activity based on data analytics. This category of software has been the subject of investigation by Russian technology companies, who have developed solutions for organisations seeking to identify their current processes, analyse and evaluate their effectiveness, risks, errors and failures. The objective is to evaluate the capabilities of process mining systems and to assess the potential for their dissemination and integration within Russian organisations. The article presents a classification of tasks that can be solved by a Process Mining System. The infrastructural prerequisites for the successful implementation of a system of this nature are delineated. Primarily, these include the availability of digital services that accompany the organisation's processes and collect raw data about them for subsequent consolidation, preprocessing and transfer to the Process Mining System for analytical research. A review of the functionality of domestic solutions is conducted, and an evaluation of the competitiveness of Process Mining systems developed by Russian companies is provided. The findings of the analysis may be employed to encourage organisations to adopt domestic intelligent process analysis systems, as awareness of the efficacy and suitability of such systems in addressing pressing business issues grows. Furthermore, the research enables an evaluation of the preparedness to implement such systems in a business context and the necessity for Process Mining systems in accordance with the digital maturity level of the organisation.
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Chang, Dongping, Pengcheng Xu, Minjie Li, and Wencong Lu. "OCPMDM 2.0: An intelligent solution for materials data mining." Chemometrics and Intelligent Laboratory Systems 243 (December 2023): 105022. http://dx.doi.org/10.1016/j.chemolab.2023.105022.
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Binti Muhammad Zahruddin, Nursyuhadah Alghazali, Nur Diyana Kamarudin, Ruzanna Mat Jusoh, Nur Aisyah Abdul Fataf, and Rahmat Hidayat. "Case Study: Using Data Mining to Predict Student Performance Based on Demographic Attributes." JOIV : International Journal on Informatics Visualization 7, no. 4 (2023): 2460. http://dx.doi.org/10.62527/joiv.7.4.2454.
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This study predicts student performance at Universiti Pertahanan Nasional Malaysia (UPNM) based on their socio-demographic profile; it also determines how a prediction algorithm can be used to classify the student data for the most significant demographic attributes. The analytical pattern in academic results per batch has been identified using demographic attributes and the student's grades to improve short-term and long-term learning and teaching plans. Understanding the likely outcome of the education process based on predictions can help UPNM lecturers enhance the achievements of the subsequent batch of students by modifying the factors contributing to the prior success. This study identifies and predicts student performance using data mining and classification techniques such as decision trees, neural networks, and k-nearest neighbors. This frequently adopted method comprises data selection and preparation, cleansing, incorporating previous knowledge datasets, and interpreting precise solutions. This study presents the simplified output from each data mining method to facilitate a better understanding of the result and determine the best data mining method. The results show that the critical attributes influencing student performance are gender, age, and student status. The Neural Networks method has the lowest Root of the Mean of the Square of Errors (RMSE) for accuracy measurement. In contrast, the decision tree method has the highest RMSE, which indicates that the decision tree method has a lower performance accuracy. Moreover, the correlation coefficient for the k-nearest neighbor has been recorded as less than one.
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Binti Muhammad Zahruddin, Nursyuhadah Alghazali, Nur Diyana Kamarudin, Ruzanna Mat Jusoh, Nur Aisyah Abdul Fataf, and Rahmat Hidayat. "Case Study: Using Data Mining to Predict Student Performance Based on Demographic Attributes." JOIV : International Journal on Informatics Visualization 7, no. 4 (2023): 2460. http://dx.doi.org/10.30630/joiv.7.4.2454.
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This study predicts student performance at Universiti Pertahanan Nasional Malaysia (UPNM) based on their socio-demographic profile; it also determines how a prediction algorithm can be used to classify the student data for the most significant demographic attributes. The analytical pattern in academic results per batch has been identified using demographic attributes and the student's grades to improve short-term and long-term learning and teaching plans. Understanding the likely outcome of the education process based on predictions can help UPNM lecturers enhance the achievements of the subsequent batch of students by modifying the factors contributing to the prior success. This study identifies and predicts student performance using data mining and classification techniques such as decision trees, neural networks, and k-nearest neighbors. This frequently adopted method comprises data selection and preparation, cleansing, incorporating previous knowledge datasets, and interpreting precise solutions. This study presents the simplified output from each data mining method to facilitate a better understanding of the result and determine the best data mining method. The results show that the critical attributes influencing student performance are gender, age, and student status. The Neural Networks method has the lowest Root of the Mean of the Square of Errors (RMSE) for accuracy measurement. In contrast, the decision tree method has the highest RMSE, which indicates that the decision tree method has a lower performance accuracy. Moreover, the correlation coefficient for the k-nearest neighbor has been recorded as less than one.
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Binti Muhammad Zahruddin, Nursyuhadah Alghazali, Nur Diyana Kamarudin, Ruzanna Mat Jusoh, Nur Aisyah Abdul Fataf, and Rahmat Hidayat. "Case Study: Using Data Mining to Predict Student Performance Based on Demographic Attributes." JOIV : International Journal on Informatics Visualization 7, no. 4 (2023): 2460. http://dx.doi.org/10.30630/joiv.7.4.02454.
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This study predicts student performance at Universiti Pertahanan Nasional Malaysia (UPNM) based on their socio-demographic profile; it also determines how a prediction algorithm can be used to classify the student data for the most significant demographic attributes. The analytical pattern in academic results per batch has been identified using demographic attributes and the student's grades to improve short-term and long-term learning and teaching plans. Understanding the likely outcome of the education process based on predictions can help UPNM lecturers enhance the achievements of the subsequent batch of students by modifying the factors contributing to the prior success. This study identifies and predicts student performance using data mining and classification techniques such as decision trees, neural networks, and k-nearest neighbors. This frequently adopted method comprises data selection and preparation, cleansing, incorporating previous knowledge datasets, and interpreting precise solutions. This study presents the simplified output from each data mining method to facilitate a better understanding of the result and determine the best data mining method. The results show that the critical attributes influencing student performance are gender, age, and student status. The Neural Networks method has the lowest Root of the Mean of the Square of Errors (RMSE) for accuracy measurement. In contrast, the decision tree method has the highest RMSE, which indicates that the decision tree method has a lower performance accuracy. Moreover, the correlation coefficient for the k-nearest neighbor has been recorded as less than one.
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Ekerete, Idongesit, Matias Garcia-Constantino, Christopher Nugent, Paul McCullagh, and James McLaughlin. "Data Mining and Fusion Framework for In-Home Monitoring Applications." Sensors 23, no. 21 (2023): 8661. http://dx.doi.org/10.3390/s23218661.
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Sensor Data Fusion (SDT) algorithms and models have been widely used in diverse applications. One of the main challenges of SDT includes how to deal with heterogeneous and complex datasets with different formats. The present work utilised both homogenous and heterogeneous datasets to propose a novel SDT framework. It compares data mining-based fusion software packages such as RapidMiner Studio, Anaconda, Weka, and Orange, and proposes a data fusion framework suitable for in-home applications. A total of 574 privacy-friendly (binary) images and 1722 datasets gleaned from thermal and Radar sensing solutions, respectively, were fused using the software packages on instances of homogeneous and heterogeneous data aggregation. Experimental results indicated that the proposed fusion framework achieved an average Classification Accuracy of 84.7% and 95.7% on homogeneous and heterogeneous datasets, respectively, with the help of data mining and machine learning models such as Naïve Bayes, Decision Tree, Neural Network, Random Forest, Stochastic Gradient Descent, Support Vector Machine, and CN2 Induction. Further evaluation of the Sensor Data Fusion framework based on cross-validation of features indicated average values of 94.4% for Classification Accuracy, 95.7% for Precision, and 96.4% for Recall. The novelty of the proposed framework includes cost and timesaving advantages for data labelling and preparation, and feature extraction.
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Devda, Pareshkumar, Suraj Shah, and Maurvi Vasavada. "ANALYTICAL CRM FOR GOOGLE EDGE - DATA MINING FRAMEWORK WITH REFERENCE TO PHARMACEUTICALS INDUSTRY IN INDIA." International Journal of Management, Public Policy and Research 2, no. 1 (2023): 72–93. http://dx.doi.org/10.55829/ijmpr.v2i1.108.
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As well as increasing the utilization of analytical CRM software over time, as you collect more and more valuable data, you'll also gain more benefits over time by using analytical CRM. It’s vital role to the business strategy before you buy and introduce a program and make sure that the sort of CRM software solutions that you simply choose is that the best choice to maximize your sales volume and boost your business. Many businesses have recognized the importance of implementing new technological trends to assist them make decisions and satisfy their customers.
 Research Objective
 Research objectives explain what your study's goals are and why you are conducting it. They serve to focus your research by providing an overview of your project's methodology and goals.
 Your research paper's introduction should include your objectives after the problem statement. They ought to:
 
 Identify the project's depth and scope.
 Add to the planning of your research
 Explain how your project will advance our knowledge.
 
 Design enables researchers to fine-tune research methods appropriate for the subject matter.
 Research Methodology
 The term "research methodology" simply refers to the actual "how" of any given piece of research. More specifically, it pertains to how a researcher systematically designs a study to guarantee valid and reliable results that address the research aims and objectives
 Due to the nature of CRM and data mining research, which makes it challenging to confine to particular disciplines, the pertinent materials are dispersed throughout numerous journals. For data mining research in CRM, business intelligence and knowledge discovery are the most popular academic fields. Consequently, to compile a thorough bibliography of the academic literature on CRM and Data Mining, the following online journal databases were searched.
 Data Analysis
 In quantitative research, collect data and use statistical analyses in SPSS. Using Regression method, find out whether data demonstrate support for research predictions. Inconsistencies and errors are examples of dirty data. These data can originate from any stage of the research process, such as poor research design, insufficient measurement materials, or incorrect data entry.
 Social Implication
 CRM analytics offers you insights approximately your clients and the way properly your income and customer support groups are attaining them. CRM analytics enables you display your customer support efforts, validate your client data, examine your clients' conduct and generate higher leads.
 Originality/Value
 Customer techniques entails analyzing the prevailing and capability consumer primarily based totally and become aware of which sorts of segment are maximum suitable. This look at believes whether or not a macro, micro, or one-to-tone segmentation method is suitable is a selection for a commercial enterprise to make.
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Mohamad, Masurah, Suraya Masrom, Khairulliza Ahmad Salleh, Lathifah Alfat, Muhammad Nasucha, and Nur Uddin. "Web mining and sentiment analysis of COVID-19 discourse in online forum communities." Indonesian Journal of Electrical Engineering and Computer Science 34, no. 2 (2024): 1280. http://dx.doi.org/10.11591/ijeecs.v34.i2.pp1280-1287.
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Recently, various discussions, solutions, data, and methods related to coronavirus disease 2019 (COVID-19) have been posted in online forum communities. Although a vast amount of posting on COVID-19 analytical projects are available in the online forum communities, much of them remain untapped due to limited overview and profiling that focuses on COVID-19 analytic techniques. Thus, it is quite challenging for information diggers and researchers to distinguish the recent trends and challenges of COVID-19 analytic for initiating different and critical studies to fight against the coronavirus. This paper presents the findings of a study that executed a web mining process on COVID-19 data analytical projects from the Stack Overflow and GitHub online community platforms for data scientists. This study provides an insight on what activities can be conducted by novice researchers and others who are interested in data analysis, especially in sentiment analysis. The classification results via Naïve Bayes (NB), support vector machine (SVM) and logistic regression (LR) have returned high accuracy, indicating that the constructed model is efficient in classifying the sentiment data of COVID-19. The findings reported in this paper not only enhance the understanding of COVID-19 related content and analysis but also provides promising framework that can be applied in diverse contexts and domains.
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Mohamad, Masurah, Suraya Masrom, Khairulliza Ahmad Salleh, Lathifah Alfat, Muhammad Nasucha, and Nur Uddin. "Web mining and sentiment analysis of COVID-19 discourse in online forum communities." Indonesian Journal of Electrical Engineering and Computer Science 34, no. 2 (2024): 1280–87. https://doi.org/10.11591/ijeecs.v34.i2.pp1280-1287.
Full textAbstract:
Recently, various discussions, solutions, data, and methods related to coronavirus disease 2019 (COVID-19) have been posted in online forum communities. Although a vast amount of posting on COVID-19 analytical projects are available in the online forum communities, much of them remain untapped due to limited overview and profiling that focuses on COVID-19 analytic techniques. Thus, it is quite challenging for information diggers and researchers to distinguish the recent trends and challenges of COVID-19 analytic for initiating different and critical studies to fight against the coronavirus. This paper presents the findings of a study that executed a web mining process on COVID-19 data analytical projects from the Stack Overflow and GitHub online community platforms for data scientists. This study provides an insight on what activities can be conducted by novice researchers and others who are interested in data analysis, especially in sentiment analysis. The classification results via Naïve Bayes (NB), support vector machine (SVM) and logistic regression (LR) have returned high accuracy, indicating that the constructed model is efficient in classifying the sentiment data of COVID-19. The findings reported in this paper not only enhance the understanding of COVID-19 related content and analysis but also provides promising framework that can be applied in diverse contexts and domains.
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Hu, Hua. "The Design and Implementation of Tourism Monitoring Analysis System." Advanced Materials Research 926-930 (May 2014): 4126–29. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.4126.
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Abstract: In this paper, the domestic tourism industry and tourism development of information technology on the basis of the Tourism Information System on the background of the analysis，analytical focus on building a tourism information，monitoring and analysis of several important technology, which leads to the new Tourist Information system discussed in detail．In this paper, the tourism process monitoring analysis and mining of various principles and related technology solutions are discussed，by reference to the theory of GIS technology and computer technology in data mining and CRM theory in modem logistics industry, research has highlighted the tourism business focus Tourism monitoring of the RTMS system，citing the relevant functional requirements and system—level framework to achieve all the details．Finally，we outlook the future of travel information system．
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Stupar, Danijela Ignjatović, Vukan Ogrizović, Janez Rošer, and Goran Vižintin. "Analytical and Numerical Solution for Better Positioning in Mines with Potential Extending Application in Space Mining." Minerals 12, no. 5 (2022): 640. http://dx.doi.org/10.3390/min12050640.
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Application of new technologies and operational methodologies in mining sector targets to obtain a beneficial outcome in the long term. Instrumentation and monitoring systems for shafts, underground tunneling, storing faculties, etc. are often automated. Implemented systems provide data of mines state, integrated enhanced protection, and early warning solutions. Navigation and positioning in mines are deemed to be unstable in parts of mining tunnels when the external reference points are very far apart, thus significantly increasing the error of the internal network. This paper demonstrates a simulation of an innovative analytical and numerical solution for better positioning in the mines, yielding to increased accuracy of the control points, while reducing the time needed for performing measurements. Based on real tunnel dimensions, different control network configurations are tested. Statistical analysis of simulated environments and virtual measurements, created by combining various instrumentation, confirms cm-level positioning accuracy. The innovative approach to a mine control network design is based on involving fixed-length bars in the network design, gaining in shorter measurements sessions, but keeping homogeneous accuracy throughout the network. The concept is tested on 27 simulated network configurations, combining network points distribution and measurement accuracy of distances and angles. Obtained results and statistical analysis prove that consistent cm-level accuracy can be expected within the network. Extending the concept to space mining, which is becoming an attractive destination for chasing the rare-earth elements (REEs), this methodology will be a spin-off for space exploration mainly applicable in the Lunar lava tube positioning, which are the most secure place to settle the new human life.
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Wisiak, Katja, Michel Jakić, and Philipp Hartlieb. "Application of Ultra-Wide Band Sensors in Mining." Sensors 23, no. 1 (2022): 300. http://dx.doi.org/10.3390/s23010300.
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Ultra-wideband (UWB) sensors are a radio frequency technology that use wireless communication between devices to precisely determine the position. The most recent applications focus on locating and sensor data collecting on mobile phones, car keys and other similar devices. However, this technology is still not being utilized in the mining sector. To overcome this gap, this perspective offers implementation options and solutions. Additionally, it evaluated the benefits and drawbacks of using ultra-wideband for mining. The measurements provided were made using QORVO two-way ranging sensors, and these were compared to theoretical and existing technological solutions. To ensure the optimal use of UWB sensors, a special emphasis was placed on certain influencing factors, such as ways of locating via UWB and factors affecting measurement accuracies, such as the line of sight, multipath propagation, the effect of shielding and the ideal measurement setup. A conducted experiment showed that the most accurate results are obtained when there is no multipath propagation and the arriving signal travels directly from the transmitter to the receiver.
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Henao-Céspedes, Vladimir, and Garcés-Gómez Yeison Alberto. "Remote sensing in the analysis between forest cover and COVID-19 cases in Colombia." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 1 (2024): 732–40. https://doi.org/10.11591/ijece.v14i1.pp732-740.
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This article explores the relationship between forest cover and coronavirus disease 2019 (COVID-19) cases in Colombia using remote sensing techniques and data analysis. The study focuses on the CORINE land cover methodology's five main land cover categories: artificial territory, agricultural territories, forests and semi-natural areas, humid areas, and water surfaces. The research methodology involves several phases of the unified method of analytical solutions for data mining (ASUM-DM). Data on COVID-19 cases and forest cover are collected from the Colombian National Institute of Health and Advanced Land Observation Satellite (ALOS PALSAR), respectively. Land cover data is processed using QGIS software. The results indicate an inverse relationship between forest cover and COVID-19 cases, as evidenced by Pearson's index ρ of -0.439 (p-value <0.012). In addition, a negative correlation is observed between case density and forests and semi-natural areas, one of the land cover categories. The findings of this study suggest that higher forest cover is associated with lower numbers of COVID-19 cases in Colombia. The results could potentially inform government organizations and policymakers in implementing strategies and policies for forest conservation and the inclusion of green areas in densely populated urban areas.
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Arno, Veronika, Eva Kolesnichenko, and Ekaterina Mikkel'sen. "COMPARATIVE ANALYSIS OF PRECIOUS METALS MINING IN MUNICIPAL DISTRICTS MAGADAN REGION IN 2022-2024." MOSCOW ECONOMIC JOURNAL 10, no. 4 (2025): 367–85. https://doi.org/10.55186/2413046x_2025_10_4_116.
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The article provides a comprehensive comparative analysis of the dynamics of gold and silver production in the municipal districts of the Magadan region for the period 2022-2024. The relevance of the research is due to the strategic importance of the mining industry for the socio-economic development of the region, as well as the need to find effective solutions in the context of depletion of a number of deposits and changes in the structure of the mineral resource base. The purpose of the work is to identify current trends, features and factors affecting the volume and structure of precious metal mining in various municipal districts of the Magadan region, as well as to assess the impact of investment projects and innovative technologies on the development of the industry. The research methods used are statistical and comparative analysis of official data on gold and silver production, structural comparison of the shares of ore and placer deposits, as well as expert assessment of investment programs and implemented technological solutions. To expand the analytical base, we used up-to-date data from international sources, including forecasts of world prices for precious metals and reviews of foreign practices in modernizing the mining industry. The study revealed multidirectional trends in the dynamics of production in the municipal districts: steady growth was noted in the Tenkinsky and Severo-Evensky districts due to the development of new ore deposits and modernization of production facilities; in other districts there is stagnation or decrease in production volumes due to the depletion of placer reserves. It has been established that the share of ore gold in the total mining structure continues to increase, and silver is almost entirely extracted from ore sources. The implementation of major investment projects and the introduction of modern technologies contribute to the stabilization of the industry, productivity growth and the creation of new jobs. As a conclusion, the need for further development of new deposits, the active introduction of innovative technological solutions and the diversification of the region's economy is emphasized in order to ensure the long-term sustainability and competitiveness of the mining industry in the Magadan region in a changing global environment.
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Tulkinbekov, Khikmatullo, and Deok-Hwan Kim. "Data Modifications in Blockchain Architecture for Big-Data Processing." Sensors 23, no. 21 (2023): 8762. http://dx.doi.org/10.3390/s23218762.
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Due to the immutability of blockchain, the integration with big-data systems creates limitations on redundancy, scalability, cost, and latency. Additionally, large amounts of invaluable data result in the waste of energy and storage resources. As a result, the demand for data deletion possibilities in blockchain has risen over the last decade. Although several prior studies have introduced methods to address data modification features in blockchain, most of the proposed systems need shorter deletion delays and security requirements. This study proposes a novel blockchain architecture called Unlichain that provides data-modification features within public blockchain architecture. To achieve this goal, Unlichain employed a new indexing technique that defines the deletion time for predefined lifetime data. The indexing technique also enables the deletion possibility for unknown lifetime data. Unlichain employs a new metadata verification consensus among full and meta nodes to avoid delays and extra storage usage. Moreover, Unlichain motivates network nodes to include more transactions in a new block, which motivates nodes to scan for expired data during block mining. The evaluations proved that Unlichain architecture successfully enables instant data deletion while the existing solutions suffer from block dependency issues. Additionally, storage usage is reduced by up to 10%.
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Mikhnenko, Pavel. "Transformation of the largest Russian companies’ business vocabulary in annual reports: Data Mining." Upravlenets 13, no. 5 (2022): 17–33. http://dx.doi.org/10.29141/2218-5003-2022-13-5-2.
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One of the promising areas of business analysis is the development of new methods and tools for accounting of nonfinancial and non-numeric information. There is a significant number of theoretical and practical solutions in this field; however, the issues of the transformation dynamics of companies’ business vocabulary need to be studied more extensively. The article aims to identify and interpret latent information reflecting strategic guidelines and conditions for the economic development of Russian enterprises. The methodology of the study is based on the concepts of narrative economics and multimodal business analytics, which is a system of scientific-practical methods for analyzing the activities of economic entities through the use of data from heterogeneous sources. The Data Mining methods and tools for analyzing and systematizing large volumes of textual information were used. The data for research were retrieved from the annual reports of the largest Russian companies for 2018–2020. Among the main indicators of the business vocabulary transformation considered in the paper are the occurrence of unique key tokens (UKTs) and the dynamics of its change, as well as the main contexts of UKTs relevant to the problem of development. The findings indicate noticeable changes in the vocabulary of Russian companies’ annual reports, such as a decline in covering formal aspects of economic activity and a growing debate on the development in the presence of risk. It is shown that these trends were most clearly manifested in the reports of metallurgical and energy enterprises. The research results can serve as a basis for enhancing the analytical and predictive effectiveness of modern business analysis
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Carbaja, Eliu, Mariana Diniz, Roberto Rodriguez-Pacheco, and André Cavalcante. "Contaminant transport model in transient and unsaturated conditions applied to laboratory column test with tailings." Soils and Rocks 45, no. 2 (2022): 1–15. http://dx.doi.org/10.28927/sr.2022.076021.
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Mining is an important economic activity in the modern world. However, despite the generated benefits, mining produces tremendous volumes of tailings, an environmental liability with numerous adverse effects. Researches about contaminant transport in tailings dam are important to assess the degree of contamination and to propose preventive or remedial measures. In geotechnical practice, the flow of solutes is generally characterized by numerical solution of the Richards equation to describe water movement followed by advection-dispersion equation to describe contaminant movement. This study aimed to model and simulate contaminant transport in a laboratory column test, using a new analytical formulation and mathematical codes, through tailings in transient unsaturated conditions. The analytical solution for the Richards equation was used to simulate the variation in the volumetric water content and to determine the transient contaminant plume using the advection-dispersion equation subsequently. The models were used to calibrate experimental data from hydraulic characterization and contamination tests. Finally, the normalized contaminant plume (cw/c0) was simulated as a function of time and space. Comparisons with experimental data showed that the analytical formulations adequately expressed the process of contaminant infiltration through the unsaturated porous medium. The formulations offered effectively and are configured as a new approach to solve various contamination problems in transient unsaturated conditions, providing insights into many complex processes that occur in the lab tests and requires far less computational effort compared with current programs to modeling the solute transport using numerical solutions, as the versatile commercial Software HYDRUS.
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Velikanov, V. S., I. A. Grishin, O. A. Lukashuk, V. V. Davydova, and A. D. Lukashuk. "Promising technical solutions to improve the efficiency of releasing mineral materials from bin storages." Mining Industry Journal (Gornay Promishlennost), no. 3/2023 (July 1, 2023): 102–7. http://dx.doi.org/10.30686/1609-9192-2023-3-102-107.
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The efficiency of mineral processing is determined by the correct selection strategy for equipment and technology in the production process. In the present-day context, when it becomes necessary to increase the production capacity of existing mining and processing plants, enhancing the production capacity cannot be considered in isolation from the potential performance of the specific process equipment. When addressing the challenges associated with improving the efficiency of mining and processing equipment, special attention needs to be paid to streamlining its interaction within a unified process flowchart. It is generally known, that the bin storages at mining and processing facilities are intended to receive rock mass and secure batching and uniform supply of loose and lumpy materials from the bin to the downstream technological equipment. The bin storage parameters have to be defined with account of the properties of the materials to be used. The aim of this study is to develop an optimal design of the bin-and-feeder system that would ensure the material path close to the brachistochrone curve. Based on the presented analytical dependences, the main characteristics of releasing the mineral raw material from the bin-and-feeder systems have been established for the conditions of mining and processing plants. The received data can be used in further research to provide a mathematical model of material flow, as well as in the finding an optimal design of the bin. A complex approach that included a system scientific analysis and generalization of previously published studies was used in addressing the tasks set. An approach is suggested to develop a promising design of the bin-and-feeder system to deliver the mineral raw materials based on using the guides that would ensure the material flow as close to the brachistochrone curve as possible. This approach can be implemented in the bin-and-feeder systems for ore processing plants in order to reduce the energy consumption for the transportation and feeding of the mineral raw materials.
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Tavana, Madjid, Akram Shaabani, Francisco Javier Santos-Arteaga, and Iman Raeesi Vanani. "A Review of Uncertain Decision-Making Methods in Energy Management Using Text Mining and Data Analytics." Energies 13, no. 15 (2020): 3947. http://dx.doi.org/10.3390/en13153947.
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The managerial and environmental studies conducted in the energy research area reflect its substantial importance, particularly when optimizing and modifying consumption patterns, transitioning to renewable sources away from fossil ones, and designing plans and systems. The aim of this study is to provide a systematic review of the literature allowing us to identify which research subjects have been prioritized in the fields of energy and sustainability in recent years, determine the potential reasons explaining these trends, and categorize the techniques applied to analyze the uncertainty faced by decision-makers. We review articles published in highly ranked journals through the period 2003–2020 and apply text analytics to cluster their main characteristics; that is, we rely on pre-processing and text mining techniques. We analyze the title, abstract, keywords, and research methodology of the articles through clustering and topic modeling and illustrate what methods and fields constitute the main focus of researchers. We demonstrate the substantial importance of fuzzy-related methods and decision-making techniques such as the Analytical Hierarchy Process and Technique for Order Preferences by Similarity to Ideal Solutions (TOPSIS). We also show that subjects such as renewable energy, energy planning, sustainable energy, energy policy, and wind energy have gained relevance among researchers in recent years.
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Kim, Tae Yeun, and Hyoung Ju Kim. "Opinion Mining-Based Term Extraction Sentiment Classification Modeling." Mobile Information Systems 2022 (April 27, 2022): 1–17. http://dx.doi.org/10.1155/2022/5593147.
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The spread of social media has accelerated the formation and dissemination of user review data, which contain subjective opinions of users on products, in an e-commerce environment. Because these reviews significantly influence other users, opinion mining has garnered substantial attention in analyzing the positive and negative opinions of users and deriving solutions based on these analytical results. Terms that include sentimental information and used in user reviews serve as the most crucial element in sentimental classification. In this regard, it is crucial to distinguish the most influential terms in user reviews. This study proposed a document-level sentiment classification model based on the collection and application of user reviews generated in an e-commerce environment. Here, a term information extraction method was applied to the proposed model to select core terms, classify the selected terms according to parts of speech (POS), determine terms that can increase information power and influence, and adopt these terms in opinion mining research, based on SVM, SVM+, and SVM+MTL techniques. The results obtained from evaluating the proposed model indicate that it exhibited excellent sentiment analysis performance. The proposed model is expected to be effectively utilized in providing enhanced services for users and increasing competitiveness in the e-commerce environment.
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Amanowicz, Marek, and Damian Jankowski. "Detection and Classification of Malicious Flows in Software-Defined Networks Using Data Mining Techniques." Sensors 21, no. 9 (2021): 2972. http://dx.doi.org/10.3390/s21092972.
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The increasing availability of mobile devices and applications, the progress in virtualisation technologies, and advances in the development of cloud-based distributed data centres have significantly stimulated the growing interest in the use of software-defined networks (SDNs) for both wired and wireless applications. Standards-based software abstraction between the network control plane and the underlying data forwarding plane, including both physical and virtual devices, provides an opportunity to significantly increase network security. In this paper, to secure SDNs against intruders’ actions, we propose a comprehensive system that exploits the advantages of SDNs’ native features and implements data mining to detect and classify malicious flows in the SDN data plane. The architecture of the system and its mechanisms are described, with an emphasis on flow rule generation and flow classification. The concept was verified in the SDN testbed environment that reflects typical SDN flows. The experiments confirmed that the system can be successfully implemented in SDNs to mitigate threats caused by different malicious activities of intruders. The results show that our combination of data mining techniques provides better detection and classification of malicious flows than other solutions.
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Sheshadri, Thejasvi, Karthik Reddy, Jaladi Santosh Rupa, et al. "Analysing the Intersection of Education and Data Science: Enhancing Learning Outcomes through Information Systems -An Analytical Study." Indian Journal of Information Sources and Services 15, no. 1 (2025): 12–19. https://doi.org/10.51983/ijiss-2025.ijiss.15.1.03.
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The current research will investigate the relationship between education and data science, concerning information systems in an attempt to establish how learning can be enhanced. In other words, current data trends in educational sectors can help optimize the existing approaches to learning processes, address students' needs, and stimulate their interest and motivation. The study focuses on how information systems can be used in capturing, processing, and using education information for purposes of decision-making. These systems make it possible for teachers to track students' performance in real-time, analyze the students' learning profile, and even forecast their performance shortly hence designing instructions befitting the students. It also explores different fields of data science including passive and active learning, predictive analysis, and data mining to analyze their effectiveness in improving curriculum and assessment approaches and other learning processes. Furthermore, the research examines the difficulties of implementing data science in education frameworks; data protection and technology, and the training of teachers and faculty, among them. Based on a review of the literature and analysis of empirical literature, this paper establishes best practices for the implementation of information systems in education. Consequently, the areas of data science highlighted here indicate that the positive outcomes in terms of effective organizational resource management, increase in students' retention and increased learning performance are possible if the data science is applied correctly. Additionally, the study highlights the need for integration of technology-based solutions with learning objectives, in ways that technological solutions do not supplant conventional pedagogical practices but supplement them. The study provides a list of recommendations to policymakers, educators, and educational technologists about how data science and Information systems can be utilized for designing and developing adaptive student-cantered learning contexts. The study adds to the existing literature on the application of data science in education and provides valuable implementable strategies to enhance learning in the current emerging technology and result-driven academic environment.
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Ahmed, Khan Anwar, Shama Siddiqui, and Indrakshi Dey. "Enhancing Health Risk Prediction in Internet of Medical Things: Leveraging Association Rule Mining." JUCS - Journal of Universal Computer Science 30, no. (8) (2024): 1068–88. https://doi.org/10.3897/jucs.115261.
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Due to rapid advancements in the field of Internet of Medical Things (IoMT), a continuous influx of health data is being generated at a large scale. The primary objective of IoMT solutions is to transmit critical health data from patients to remote locations in real-time. Apart from remote patient monitoring, the extensive collection of health data offers opportunities for uncovering noteworthy patterns and potential risks associated with future diseases. This study introduces a novel risk prediction approach, namely Association Rule Mining for Risk Prediction (ARMR), which integrates an IoMT framework with the emerging machine learning technique known as Association Rule Mining (ARM). The proposed scheme employs a dataset obtained from various hospitals. The findings demonstrate that ARMR effectively extracts rules to identify a patient's risk of heart disease by considering demographic, physiological, and lifestyle data. Moreover, intriguing, and unexpected patterns and associations in the disease data can be identified, aiding medical professionals in guiding diagnosis and treatment decisions more efficiently.
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Shcherban, Pavel, Alexander Gapchich, Aleksey Zhdanov, and Olga Letunovskaya. "Optimization of excess brines disposal methods at potash mining and processing plants." Chemical Industry and Chemical Engineering Quarterly, no. 00 (2022): 24. http://dx.doi.org/10.2298/ciceq211228024s.
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The paper analyzes the positive and negative aspects of various technological solutions of the liquid brines use during the development of polymineral potash ore deposits, considers the problem of determining the choice of the optimal approach by taking into account geological, technical, environmental, and financial factors. The study of the issues of utilization and reduction of the liquid brines components of discharges in the production of potash fertilizers, the simultaneous reduction of valuable components loss with liquid discharges, and, due to this, increasing the production of potash fertilizers, and also the usage in the technology of mine brines, are an urgent and important scientific and engineering challenge of the potash industry. Technologically, several alternative solutions are possible to reduce the number of liquid by-products placed in sludge storages. A set of analytical methods was used in the work, including statistical data processing, modeling, pre-design studies of technological solutions, and assessment of economic costs. Excess brines of potash mining and processing plants are liquid waste obtained during the production of potashfertilizers - MOP ? SOP. The accumulation of excess brines in sludge storage facilities is estimated at millions of cubic meters per year. The expansion of the sludge storage facilities area and the construction of dams are only a temporary solution and associated with risks in design, construction, and operation of hydraulic structures, increasing the risks of brine leakage into open and underground water basins. This makes it necessary to use other methods of brine disposal. Depending on the nature of the processed polymineral potash ores, several methods can be used in combination for the the disposal of excess brines at once: backfiling, osmosis, injection into deep horizons, multistage evaporation. The most optimal combination of brine reduction technologies for potassium-magnesium processing plants raw materials is the following : 60% is disposed by usage of vacuum evaporation units , 20 % - by injecting excessive brines into deep absorbing horizons, 10-20% should be used for backfilling or production of additional products.
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Terroso-Saenz, Fernando, Andres Muñoz, and José Cecilia. "QUADRIVEN: A Framework for Qualitative Taxi Demand Prediction Based on Time-Variant Online Social Network Data Analysis." Sensors 19, no. 22 (2019): 4882. http://dx.doi.org/10.3390/s19224882.
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Road traffic pollution is one of the key factors affecting urban air quality. There is a consensus in the community that the efficient use of public transport is the most effective solution. In that sense, much effort has been made in the data mining discipline to come up with solutions able to anticipate taxi demands in a city. This helps to optimize the trips made by such an important urban means of transport. However, most of the existing solutions in the literature define the taxi demand prediction as a regression problem based on historical taxi records. This causes serious limitations with respect to the required data to operate and the interpretability of the prediction outcome. In this paper, we introduce QUADRIVEN (QUalitative tAxi Demand pRediction based on tIme-Variant onlinE social Network data analysis), a novel approach to deal with the taxi demand prediction problem based on human-generated data widely available on online social networks. The result of the prediction is defined on the basis of categorical labels that allow obtaining a semantically-enriched output. Finally, this proposal was tested with different models in a large urban area, showing quite promising results with an F1 score above 0.8.
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Gemina, Dwi, Endang Silaningsih, Titiek Tjahja Andari, Tini Kartini, and Siti Julaiha. "The Effect Of Service Quality And Price On Customer Satisfaction Of Mineral And Coal Mining Consulting Services At Pt Allsys Solutions." International Journal of Science, Technology & Management 4, no. 5 (2023): 1139–48. http://dx.doi.org/10.46729/ijstm.v4i5.939.
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The rapid development of business in the era of globalization causes competition between companies to be tighter, so various efforts must be made in order to attract the attention of consumers from competitors. This study aims to determine and analyze service quality and price both simultaneously and partially on PT Allsys Solutions customer satisfaction. The sampling amounted to 92 consumers with a saturated sampling technique in which all members of the population were sampled. This questionnaire is tested with validity tests, reliability tests and also classical assumption tests. The results of the test are valid, reliable and can be used for regression data. The analytical methods used in this study are descriptive and verifiative methods with a quantitative approach. The results showed that the variables of service quality and price had a direct and positive effect on PT Allsys Solutions customer satisfaction. The result of the R (square) determination test is 40.61% while the remaining 59.39%. The relationship between service quality and price has a strong relationship with the result of a correlation coefficient of sort. 0.637.
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Заернюк, В. М. "The state and level of development of human capital in the mining industry of Russia." Management of Education, no. 6(64) (June 15, 2023): 218–27. http://dx.doi.org/10.25726/u8593-3238-6217-t.
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Отечественная система экономики в последние годы претерпела значительные структурные и содержательные реформы. В данной работе автор обосновывает необходимость гибкого подхода к управлению инвестициями при развитии человеческого капитала в горнодобывающей отрасли. Исследование проводилось на основе обобщения и систематизации статистических и аналитических данных Федеральной службы статистики, Университетской информационной системы Россия. Проведена оценка качественных и количественных статистических показателей, характеризующих состояние и уровень развития человеческого капитала горнодобывающей отрасли российской промышленно¬сти. Рассмотрены наиболее актуальные виды инвестиций в развитие человеческого капитала в горнодобывающей отрасли. Выявлены проблемные зоны развития человеческого капитала в горнодобывающей отрасли. Выявлены актуальные формы инвестирования в развитие человеческого капитала в горнодобывающей промышленности. Предлагается повысить качество подготовки кадров для горнодобывающей отрасли России, необходимых в новых социально-экономических условиях и требующих принятия инновационных решений. In recent years, the domestic economic system has undergone significant structural and substantive reforms. In this paper, the author substantiates the need for a flexible approach to investment management in the development of human capital in the mining industry. The study was conducted on the basis of generalization and systematization of statistical and analytical data of the Federal Statistics Service, the University Information System of Russia. An assessment of qualitative and quantitative statistical indicators characterizing the state and level of development of human capital in the mining industry of the Russian industry is carried out. The most relevant types of investments in the development of human capital in the mining industry are considered. The problem areas of human capital development in the mining industry are identified. The actual forms of investment in the development of human capital in the mining industry are revealed. It is proposed to improve the quality of training for the mining industry in Russia, which is necessary in the new socio-economic conditions and requires innovative solutions.
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AL-Jumaili, Ahmed Hadi Ali, Ravie Chandren Muniyandi, Mohammad Kamrul Hasan, Johnny Koh Siaw Paw, and Mandeep Jit Singh. "Big Data Analytics Using Cloud Computing Based Frameworks for Power Management Systems: Status, Constraints, and Future Recommendations." Sensors 23, no. 6 (2023): 2952. http://dx.doi.org/10.3390/s23062952.
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Traditional parallel computing for power management systems has prime challenges such as execution time, computational complexity, and efficiency like process time and delays in power system condition monitoring, particularly consumer power consumption, weather data, and power generation for detecting and predicting data mining in the centralized parallel processing and diagnosis. Due to these constraints, data management has become a critical research consideration and bottleneck. To cope with these constraints, cloud computing-based methodologies have been introduced for managing data efficiently in power management systems. This paper reviews the concept of cloud computing architecture that can meet the multi-level real-time requirements to improve monitoring and performance which is designed for different application scenarios for power system monitoring. Then, cloud computing solutions are discussed under the background of big data, and emerging parallel programming models such as Hadoop, Spark, and Storm are briefly described to analyze the advancement, constraints, and innovations. The key performance metrics of cloud computing applications such as core data sampling, modeling, and analyzing the competitiveness of big data was modeled by applying related hypotheses. Finally, it introduces a new design concept with cloud computing and eventually some recommendations focusing on cloud computing infrastructure, and methods for managing real-time big data in the power management system that solve the data mining challenges.
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Perdana, Alim, and Nur Budi Mulyono. "PURCHASING STRATEGIES IN THE KRALJIC PORTFOLIO MATRIX – A CASE STUDY IN OPEN PIT COAL MINING." Indonesian Mining Professionals Journal 3, no. 1 (2021): 45–58. http://dx.doi.org/10.36986/impj.v3i1.41.
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Kraljic matrix (or Kraljic model) is a method used to segment the purchases or suppliers of a company by dividing them into four quadrants, based on the complexity (or risk) of the supply market (such as monopoly situations, barriers to entry, technological innovation) and the importance of the purchases or suppliers (determined by the impact that they have on the profitability of the company). This quandrant allows the company to define the optimal purchasing strategies for each of the four types of purchases or suppliers. In coal mining company, hundred thousand goods, part number or SKUs are purchased by corporate in fulfilling its mining operations requirement. However, the commodities generally purchased by open pit coal mining company are classified into 6 (six) classes which are fuel, maintenance of mobile equipment, blasting material, tyre, lubricants, and others. With the complexity of dealing with suppliers, it is mandatory for developing purchasing strategies as part of managing of supply chain. Mining operations and profitability of coal mining company shall depend on the total cost of ownership in purchasing the abovementioned commodities. This Kraljic Portfolio Model (1983) will assist coal mining company in applying purchasing strategies based on the class or quadrant which has been developed. Objective of this research is to develop purchasing strategies by empirically quantifying using data from a comprehensive survey among purchasing professionals in coal mining industry. Kraljic Portfolio Matrix is developed with 2 (two) stages of questioner. First questioner is to assess the importance level of each attribute in the dimension of purchasing activity by using Analytical Hierarchy Process. Second questioner is to assess every commodity’s scoring against each supply attribute. Subsequently, the matrix is developed by using SPSS (Statistical Product and Service Solutions) software. This research successfully classifies purchasing commodity in the appropriate quadrant of Kraljic Portfolio Matrix. By classifying the commodities purchased by coal mining company in the right quadrant of Kraljic Portfolio Matrix, the company will be able to implement the right purchasing strategies which will be different in one quadrant and another.
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Sharma, Shukla, Ludovic Koehl, Pascal Bruniaux, Xianyi Zeng, and Zhujun Wang. "Development of an Intelligent Data-Driven System to Recommend Personalized Fashion Design Solutions." Sensors 21, no. 12 (2021): 4239. http://dx.doi.org/10.3390/s21124239.
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In the context of fashion/textile innovations towards Industry 4.0, a variety of digital technologies, such as 3D garment CAD, have been proposed to automate, optimize design and manufacturing processes in the organizations of involved enterprises and supply chains as well as services such as marketing and sales. However, the current digital solutions rarely deal with key elements used in the fashion industry, including professional knowledge, as well as fashion and functional requirements of the customer and their relations with product technical parameters. Especially, product design plays an essential role in the whole fashion supply chain and should be paid more attention to in the process of digitalization and intelligentization of fashion companies. In this context, we originally developed an interactive fashion and garment design system by systematically integrating a number of data-driven services of garment design recommendation, 3D virtual garment fitting visualization, design knowledge base, and design parameters adjustment. This system enables close interactions between the designer, consumer, and manufacturer around the virtual product corresponding to each design solution. In this way, the complexity of the product design process can drastically be reduced by directly integrating the consumer’s perception and professional designer’s knowledge into the garment computer-aided design (CAD) environment. Furthermore, for a specific consumer profile, the related computations (design solution recommendation and design parameters adjustment) are performed by using a number of intelligent algorithms (BIRCH, adaptive Random Forest algorithms, and association mining) and matching with a formalized design knowledge base. The proposed interactive design system has been implemented and then exposed through the REST API, for designing garments meeting the consumer’s personalized fashion requirements by repeatedly running the cycle of design recommendation—virtual garment fitting—online evaluation of designer and consumer—design parameters adjustment—design knowledge base creation, and updating. The effectiveness of the proposed system has been validated through a business case of personalized men’s shirt design.
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Balaniuk, Remis, Olga Isupova, and Steven Reece. "Mining and Tailings Dam Detection in Satellite Imagery Using Deep Learning." Sensors 20, no. 23 (2020): 6936. http://dx.doi.org/10.3390/s20236936.
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This work explores the combination of free cloud computing, free open-source software, and deep learning methods to analyze a real, large-scale problem: the automatic country-wide identification and classification of surface mines and mining tailings dams in Brazil. Locations of officially registered mines and dams were obtained from the Brazilian government open data resource. Multispectral Sentinel-2 satellite imagery, obtained and processed at the Google Earth Engine platform, was used to train and test deep neural networks using the TensorFlow 2 application programming interface (API) and Google Colaboratory (Colab) platform. Fully convolutional neural networks were used in an innovative way to search for unregistered ore mines and tailing dams in large areas of the Brazilian territory. The efficacy of the approach is demonstrated by the discovery of 263 mines that do not have an official mining concession. This exploratory work highlights the potential of a set of new technologies, freely available, for the construction of low cost data science tools that have high social impact. At the same time, it discusses and seeks to suggest practical solutions for the complex and serious problem of illegal mining and the proliferation of tailings dams, which pose high risks to the population and the environment, especially in developing countries.
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Fan, Yong, Litang Hu, Hongliang Wang, and Xin Liu. "Machine Learning Methods for Improved Understanding of a Pumping Test in Heterogeneous Aquifers." Water 12, no. 5 (2020): 1342. http://dx.doi.org/10.3390/w12051342.
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Pumping tests are very important means for investigating aquifer properties; however, interpreting the data using common analytical solutions become invalid in complex aquifer systems. The paper aims to explore the potential of machine learning methods in retrieving the pumping tests information in a field site in the Democratic Republic of Congo. A newly planned mining site with a pumping test of three pumping wells and 28 observation wells over one month was chosen to analyze the significance of machine learning methods in the pumping test analysis. Widely used machine learning methods, including correlation, cluster, time-series analysis, artificial neural network (ANN), support vector machine (SVR), random forest (RF) method, and linear regression, are all used in this study. Correlation and cluster analyses among wells provide visual pictures of possible hydraulic connections. The pathway with the best permeability ranges from the depth of 250 m to 350 m. Time-series analysis perfectly captured changes of drawdowns within the three pumping wells. The RF method is found to have the higher accuracy and the lower sensitivity to model parameters than ANN and SVR methods. The coupling of the linear regressive model and analytical solutions is applied to estimate hydraulic conductivities. The results found that ML methods can significantly and effectively improve our understanding of pumping tests by revealing inherent information hidden in those tests.
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Nurekenov, I. S., and Z. M. Nazarova. "INFORMATION TECHNOLOGY: A CATALYST FOR THE TRANSFORMATION OF MANAGEMENT ACCOUNTING IN MINING ENTERPRISES." Beneficium, no. 3 (2024): 25–34. http://dx.doi.org/10.34680/beneficium.2024.3(52).25-34.
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The article analyzes the impact of information technology (IT) on the transformation of management accounting in the mining industry. The relevance of the problem is due to the need to improve the efficiency and competitiveness of enterprises in the face of increasing complexity of business processes and data volumes. The aim of the study is to explore the integration of IT solutions to overcome common challenges, such as task complexity, high labor intensity, lack of analytical information, and high costs.The main objectives include analyzing modern IT trends, such as cloud computing, artificial intelligence, big data, and the Internet of Things, and their im-pact on management accounting. A comprehensive analysis methodology was applied, including literature review, case analysis, and statistical modeling. The main findings indicate that IT usage facilitates the automation of routine operations, improves data quality, and maintains a high level of analytics for managerial decision-making. The practical significance of the study lies in the fact that IT implementation contributes to cost optimization, risk reduction associated with the human factor, and improved production performance. Future research prospects include further develop-ment of IT infrastructureto ensure sustainable industry development. The integration of such technologies will enable mining enterprises to respond more effectively to changes in the exter-nal environment and internal challenges, thereby enhancing overall competitiveness and stability. The analysis also shows that the application of IT technologies can significantly improve resource management and operations, which is particularly important in the context of global competition and environmental sustainability.