Large Amount Of Data Research Articles

DEVELOPMENT OF A METHODOLOGY FOR DATA NORMALISATION AND AGGREGATION TO ENHANCE SECURITY LEVELS IN INTERNET OF THINGS INTERACTIONS

The number of interacting devices is increasing every day, and with this constant innovation, serious security challenges arise. The concept of the Internet of Things is being actively applied in both domestic and industrial settings. Researchers are increasingly highlighting the challenges and importance of network security. Data preprocessing plays an important role in security by transforming the input data corresponding to algorithmic criteria and thereby contributing to the prediction accuracy. The data preprocessing process is determined by many factors, including the processing algorithm, the data, and the application. Moreover, in Internet of Things interactions, data normalisation and aggregation can significantly improve security and reduce the amount of data used further decision making. This paper discusses the challenges of data normalisation and aggregation in the IoT to handle large amounts of data generated by multiple connected IoT devices. A secure data normalisation and aggregation method promotes successful minimised data transfer over the network and provides scalability to meet the increasing demands of IoT deployment. The proposed work presents approaches used in data aggregation protocols that address interference, fault tolerance, security and mobility issues. A local aggregation approach using the run-length encoding algorithm is presented. The proposed technique consists of data acquisition, data preprocessing, data normalisation and data aggregation steps. Data normalisation was performed via the Z-score algorithm, and the LEACH algorithm was used for data aggregation. In the experimental study, the percentage of faulty nodes reached 35%. The performance of the proposed solution was 0.82. The results demonstrate a reduction in resource consumption while maintaining the value and integrity of the data.

ЖACAНДЫ ИНТEЛЛEКТ ПEН AҚПAРAТТЫҚ ТEХНOЛOГИЯЛAРДЫҢ ИНТEГРAЦИЯCЫ: ҚAЗІРГІ ЖAҒДAЙЫ ЖӘНE ДAМУ ПEРCПEКТИВAЛAРЫ

The integration of artificial intelligence (AI) and information technology (IT) plays a key role in today's digital world, leading to revolutionary changes in various industries such as healthcare, finance, manufacturing and transportation. In this article, we will review the current state of AI and IT integration, discuss the main advantages of such integration, and identify prospects for its further development. Artificial intelligence allows you to analyze a large amount of data, which is important for analyzing the academic performance of each individual and forming an individual learning plan taking into account specific gaps in each individual area or topic. To date, it is difficult to imagine an enterprise that does not use or has not attempted to use AI in its activities. Examples of enterprises actively integrating intelligent solutions in their activities are the oil and gas industry, the energy sector, aircraft manufacturing and many others. In each of these areas, AI can significantly reduce the number of emergencies, increasing the operational reliability and safety of technological equipment and machinery.

Application of logistic regression to detect an attack on financial monitoring

This paper discusses the application of logistic regression to detect attacks on financial monitoring. Financial organizations are facing increasing security threats, which requires effective methods to detect fraudulent activities. Logistic regression, with its binary classification capability and interpretability of results, is a powerful tool for transaction analysis. The paper also emphasizes the features of this method, including its applicability to large amounts of data and opportunities for model development. Thus, the use of logistic regression is a promising solution for securing financial transactions and identifying potential threats.

Utilization Of Artificial Intelligence And Big Data In Disaster Management: Future Risk Reduction Strategies

Disasters are events that significantly impact people's lives and living spaces globally. Natural disasters can arise from various causes, such as climate change, geological movements, weather events, and human factors. The damage caused by these disasters can affect millions of people and negatively impact societies economically, socially, and environmentally. Disaster management has emerged as a multidisciplinary field aimed at minimizing the damage caused by disasters and making communities more resilient to them. Traditional disaster management strategies include emergency planning, crisis management, pre-disaster preparation, and rapid response during disasters. However, these strategies generally reflect a reactive approach and rely on human resources and existing infrastructure. This article aims to examine the role and impact of innovative technologies such as artificial intelligence and big data in the field of disaster management. While artificial intelligence is known for its ability to analyze complex datasets, discover patterns and relationships, optimize decision-making processes, and predict future events, big data provides the ability to process large amounts of data quickly and efficiently, transforming them into meaningful information. These technologies play a significant role in pre-disaster preparation, crisis management during disasters, and post-disaster recovery processes. The article discusses how artificial intelligence and big data technologies can be used in disaster management, how these technologies can be integrated into disaster risk reduction strategies, and how their effectiveness can be assessed. In conclusion, the integration of artificial intelligence and big data technologies into disaster management offers a more effective and efficient approach to dealing with disasters and can make significant contributions to making communities more resilient to disasters. This article aims to provide a guide to understanding the current state of disaster management and developing more effective strategies.

P-TIMA: a framework of T witter threat intelligence mining and analysis based on a prompt-learning NER model

Abstract Open-source information platforms such as Twitter continuously provide the latest threat intelligence, including new vulnerabilities and in-the-wild exploitations of advanced persistent threat (APT) groups. Automated extraction of threat intelligence from Twitter has become crucial for defenders to access up-to-date threat knowledge. However, existing studies mainly rely on supervised learning methods to extract threat intelligence knowledge, such as entities, which require a large amount of annotated data. This paper presents Threat Intelligence Mining and Analysis based on Prompt Learning (P-TIMA), a framework specifically crafted for extracting and analyzing threat intelligence from Twitter. P-TIMA employs our innovative few-shot entity recognition method, SecEntPrompt (SEP), built on prompt learning, to extract vulnerability intelligence from Twitter. Additionally, P-TIMA analyzes and profiles the overarching vulnerability intelligence obtained from Twitter, along with in-the-wild exploitation intelligence of APT groups. The SEP improves the average entity recognition F1 score by 3.62-4.40 compared with the best-performing comparison model and outperforms the method based on the large language model on recognition performance and inference time. To validate our framework, we apply P-TIMA to extract vulnerability-related threat intelligence from real Twitter data. Through case studies, we then analyze trends in vulnerability threats and the exploitation capabilities of APT groups. In conclusion, our framework provides a more efficient and accurate method for extracting threat intelligence from Twitter, enabling defenders to stay up-to-date with the latest threat trends and helping them improve their defense strategies against cyber attacks.

Improved Road Extraction Models through Semi-Supervised Learning with ACCT

Improving the performance and reducing the training cost of road extraction models in the absence of samples is important for updating road maps. Despite the success of recent road extraction models on standard datasets, they often fail to perform when applied to new datasets or real-world scenarios where labeled samples are not available. In this paper, our focus diverges from the typical quest to pinpoint the optimal road extraction model or evaluate generalization prowess across models. Instead, we propose a method called Asymmetric Consistent Co-Training (ACCT) to train existing road extraction models faster and make them perform better in new scenarios lacking samples. ACCT uses two models with different structures and a supervision module to enhance accuracy through mutual learning. Labeled and unlabeled images are processed by both models to generate road maps from different perspectives. The supervision module ensures consistency between predictions by computing losses based on labeling status. ACCT iteratively adjusts parameters using unlabeled data, improving generalization. Empirical evaluations show that ACCT improves IoU by 2.79% to 10.26% using only 1/8 of the labeled data compared to fully supervised methods. It also reduces parameters by over 49% compared to state-of-the-art semi-supervised methods while maintaining similar accuracy. These results highlight the potential of leveraging large amounts of unlabeled data to enhance road extraction models as data acquisition technology advances.

Blockchain-Based Privacy Preservation for the Internet of Medical Things: A Literature Review

The Internet of Medical Things (IoMT) is a rapidly expanding network comprising medical devices, sensors, and software that collect and exchange patient health data. Today, the IoMT has the potential to revolutionize healthcare by offering more personalized care to patients and improving the efficiency of healthcare delivery. However, the IoMT also introduces significant privacy concerns, particularly regarding data privacy. IoMT devices often collect and store large amounts of data about patients’ health. These data could be used to track patients’ movements, monitor their health habits, and even predict their future health risks. This extensive data collection and surveillance could be a major invasion of patient privacy. Thus, privacy-preserving research in an IoMT context is an important area of research that aims to mitigate these privacy issues. This review paper comprehensively applies the PRISMA methodology to analyze, review, classify, and compare current approaches of preserving patient data privacy within IoMT blockchain-based healthcare environments.

Exploration of potential drug targets for Glaucoma by plasma proteome screening

BackgroundGlaucoma is the leading cause of irreversible blindness. However, the current available treatment methods are still unsatisfactory. Therefore, the exploration of new drug targets for the treatment of glaucoma is of paramount importance. MethodsWe conducted two-sample Mendelian randomization (MR) using plasma protein quantitative trait loci (pQTL) data from two datasets (n = 734, n = 4907) and their instrumental variables to investigate the causal relationship between plasma proteins and glaucoma. The analysis was validated by replacing the exposure and outcome cohorts. Additionally, we utilized protein-protein interaction networks to assess the associations between these potential drug targets and existing drug targets. ResultsThrough two-sample Mendelian randomization analysis, we identified causal relationships between Glaucoma and the following proteins: AZU1, OBP2B, ENPP5, INPP5B, KREMEN1, LYPLAL1, and PTPRJ. External validation confirmed the protective effect of LYPLAL1 on Glaucoma, while ENPP5, KREMEN1, and PTPRJ increased the risk of Glaucoma. Reverse MR and Steiger filtering did not indicate any reverse causal associations of the aforementioned proteins with Glaucoma. ConclusionOur study demonstrates a causal impact of ENPP5, KREMEN1, PTPRJ, and LYPLAL1 on the risk of Glaucoma. These findings suggest that these four proteins may serve as promising drug targets for Glaucoma treatment. SignificanceCurrently, the pharmacological treatment of glaucoma primarily focuses on lowering intraocular pressure, which has its limitations. Targeted therapy is a personalized treatment approach that aims to inhibit or block the development and progression of diseases such as cancer and inflammation by selectively acting on specific biomolecules or signaling pathways. Our research employs a two-sample Mendelian randomization (MR) method, integrating a large amount of GWAS and pQTL data to perform MR analysis. This has enabled us to explore several plasma proteins as potential drug targets for glaucoma, providing direction and a research foundation for future investigations into glaucoma drug targets.

Bus arrival and departure time updates in the Greater Sydney Area

Public Transport Authorities generate large quantities of data as part of their daily operations, including vehicle positions, arrival times, and dynamic routing options. This information is essential feedback into the system for planning routes and timetables, modelling passenger demand growth and evaluating operational performance. The General Transit Feed Specification (GTFS) is a data format that allows public transport data to be consumed by a wide variety of software applications. There are barriers to widespread consumption of GTFS data related to location-specific data extensions, non-human-readable formats and error cleaning. This paper describes a flexible dataset of actual bus arrivals and departure times created with a pipeline for GTFS realtime feeds designed to address these challenges. The paper describes the pipeline, verifies the quality of the data and presents an output of 25 months of actual bus arrival and departure times for Sydney, Australia. We conclude by discussing relevance to researchers and practitioners of the pipeline outputs in general and the sample data specifically.

An Experimental and Clinical Physiological Signal Dataset for Automated Pain Recognition

Access to large amounts of data is essential for successful machine learning research. However, there is insufficient data for many applications, as data collection is often challenging and time-consuming. The same applies to automated pain recognition, where algorithms aim to learn associations between a level of pain and behavioural or physiological responses. Although machine learning models have shown promise in improving the current gold standard of pain monitoring (self-reports) only a handful of datasets are freely accessible to researchers. This paper presents the PainMonit Dataset for automated pain detection using physiological data. The dataset consists of two parts, as pain can be perceived differently depending on its underlying cause. (1) Pain was triggered by heat stimuli in an experimental study during which nine physiological sensor modalities (BVP, 2×EDA, skin temperature, ECG, EMG, IBI, HR, respiration) were recorded from 55 healthy subjects. (2) Eight modalities (2×BVP, 2×EDA, EMG, skin temperature, respiration, grip) were recorded from 49 participants to assess their pain during a physiotherapy session.

Design of software system architecture for reading and publishing amateur literary works

The article discusses the functionality and key requirements for a web application for reading and publishing amateur literary works. Against the backdrop of current changes in the book industry caused by the development of technology and the emergence of self-publishing, the need to create platforms that provide authors with the opportunity to publish content independently is emphasized. One of the main problems that authors face when publishing is the loss of the original formatting of their works when transferring them to text editors on web platforms. This paper proposes a solution to this problem by developing a web application with the function of importing the content of text files while preserving their original formatting. The article also contains a comparative analysis of popular web-based platforms for publishing works, such as Archive of Our Own, Wattpad, and Arkush, which helped to highlight the advantages and disadvantages of each of them. In particular, the difficulties associated with storing and processing unstructured data, such as images and text files, were investigated. To solve the problem of storing such data, a comparative analysis of three approaches was conducted: storing data in relational databases, local file systems, and cloud storage. Taking into account the results of this analysis, the web application under development was chosen to integrate with the Amazon Web Services cloud provider and use Amazon S3 cloud storage, which provides scalability, reliability, and convenience in managing large amounts of unstructured data. It was also decided to use the Amazon CloudFront CDN service to improve the speed of data access and increase overall system performance. In addition, the article describes the choice of a technology stack for implementing the web application, which includes Java, Spring Framework, MySQL, ORM Hibernate for the server side and HTML, CSS, React.js for the client side. The article highlights important aspects of designing a web application architecture aimed at scalability and resilience to large amounts of data and an increase in the number of active users, and provides a basis for further system improvement.

Translation Consistent Semi-supervised Segmentation for 3D Medical Images.

3D medical image segmentation methods have been successful, but their dependence on large amounts of voxel-level annotated data is a disadvantage that needs to be addressed given the high cost to obtain such annotation. Semi-supervised learning (SSL) solves this issue by training models with a large unlabelled and a small labelled dataset. The most successful SSL approaches are based on consistency learning that minimises the distance between model responses obtained from perturbed views of the unlabelled data. These perturbations usually keep the spatial input context between views fairly consistent, which may cause the model to learn segmentation patterns from the spatial input contexts instead of the foreground objects. In this paper, we introduce the Translation Consistent Co-training (TraCoCo) which is a consistency learning SSL method that perturbs the input data views by varying their spatial input context, allowing the model to learn segmentation patterns from foreground objects. Furthermore, we propose a new Confident Regional Cross entropy (CRC) loss, which improves training convergence and keeps the robustness to co-training pseudo-labelling mistakes. Our method yields state-of-the-art (SOTA) results for several 3D data benchmarks, such as the Left Atrium (LA), Pancreas-CT (Pancreas), and Brain Tumor Segmentation (BraTS19). Our method also attains best results on a 2D-slice benchmark, namely the Automated Cardiac Diagnosis Challenge (ACDC), further demonstrating its effectiveness. Our code, training logs and checkpoints are available at https://github.com/yyliu01/ TraCoCo.

A Novel Framework for Multimodal Brain Tumor Detection with Scarce Labels.

Brain tumor detection has advanced significantly with the development of deep learning technology. Although multimodal data, such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT), has potential advantages in diagnostics, most existing studies rely solely on a single modality. This is because common fusion methods may lead to the loss of critical information when attempting multimodal fusion. Therefore, effectively integrating multimodal data has become a significant challenge. Additionally, medical image analysis requires large amounts of annotated data, and labeling images is a resourceintensive task that demands experienced professionals to spend a considerable amount of time. To address these challenges, this paper introduces a new unsupervised learning framework named Double-SimCLR. This framework builds on the foundation of contrastive learning and features a dual-branch structure, enabling direct and simultaneous processing of MRI and CT images for multimodal feature fusion. Given the "weak feature" characteristics of CT images (e.g., low soft tissue contrast and low resolution), we incorporated adaptive weight masking technology to enhance CT feature extraction. Moreover, we introduced a multimodal attention mechanism, which ensures that the model focuses on salient information, thereby elevating the precision and robustness of brain tumor detection. Even without substantial labeled data, experimental results demonstrate that Double-SimCLR achieves 93.458% accuracy, 92.463% precision, and a 93.058% F1-score, outperforming state-of-the-art (SOTA) models by 2.871%, 2.643%, and 3.098%, respectively.

A Practical In Silico Method for Predicting Compound Brain Concentration-Time Profiles: Combination of PK Modeling and Machine Learning.

Given the aging populations in advanced countries globally, many pharmaceutical companies have focused on developing central nervous system (CNS) drugs. However, due to the blood-brain barrier, drugs do not easily reach the target area in the brain. Although conventional screening methods for drug discovery involve the measurement of (unbound fraction of drug) brain-to-plasma partition coefficients, it is difficult to consider nonequilibrium between plasma and brain compound concentration-time profiles. To truly understand the pharmacokinetics/pharmacodynamics of CNS drugs, compound concentration-time profiles in the brain are necessary; however, such analyses are costly and time-consuming and require a significant number of animals. Therefore, in this study, we attempted to develop an in silico prediction method that does not require a large amount of experimental data by combining modeling and simulation (M&S) with machine learning (ML). First, we constructed a hybrid model linking plasma concentration-time profile to the brain compartment that takes into account the transit time and brain distribution of each compound. Using mouse plasma and brain time experimental values for 103 compounds, we determined the brain kinetic parameters of the hybrid model for each compound; this case was defined as scenario I (a positive control experiment) and included the full brain concentration-time profile data. Next, we built an ML model using chemical structure descriptors as explanatory variables and rate parameters as the target variable, and we then input the predicted values from 5-fold cross-validation (CV) into the hybrid model; this case was defined as scenario II, in which no brain compound concentration-time profile data exist. Finally, for scenario III, assuming that the brain concentration is obtained at only one time point, we used the brain kinetic parameters from the result of the 5-fold CV in scenario II as the initial values for the hybrid model and performed parameter refitting against the observed brain concentration at that time point. As a result, the RMSE/R2-values of the brain compound concentration-time profiles over time were 0.445/0.517 in scenario II and 0.246/0.805 in scenario III, indicating the method provides high accuracy and suggesting that it is a practical method for predicting brain compound concentration-time profiles.

Knowledge graph of agricultural engineering technology based on large language model

Agriculture is an industry that has evolved alongside human evolution and has faithfully fulfilled its core mission of food supply. With the reduction of rural labor, the progress of artificial intelligence and the development of Internet of Things technology, it is hoped that the efficiency and productivity of the agricultural industry can be improved. Recently, with the development of information and intelligent technology, agricultural production and management have been significantly enhanced. However, there is still a considerable challenge in effectively integrating the vast amount of fragmented information for downstream applications. An agricultural knowledge graph (AGKG) will serve as the foundation for achieving these goals. Knowledge graphs can be general or domain-specific, and are the basis for many applications, such as search engines, online question-and-answer services, and knowledge inference. Therefore, there are many knowledge graphs, including Wikidata and DBpedia, for accessing structured knowledge. Although some general knowledge graphs contain some entities and relationships related to agriculture, there are no domain-specific knowledge graphs specifically for agricultural applications. Therefore, this paper proposes an agricultural knowledge graph (AGKG) for automatically integrating large amounts of agricultural data from the Internet. By applying natural language processing and deep learning technologies, AGKG can automatically identify agricultural entities from unstructured text and connect them to form a knowledge graph. In addition, we have described the typical scenarios of our AGKG and validated it through real-world applications such as agricultural entity retrieval and agricultural question-answering.

Semi-Supervised Interior Decoration Style Classification with Contrastive Mutual Learning

Precisely identifying interior decoration styles holds substantial significance in directing interior decoration practices. Nevertheless, constructing accurate models for the automatic classification of interior decoration styles remains challenging due to the scarcity of expert annotations. To address this problem, we propose a novel pseudo-label-guided contrastive mutual learning framework (PCML) for semi-supervised interior decoration style classification by harnessing large amounts of unlabeled data. Specifically, PCML introduces two distinct subnetworks and selectively utilizes the diversified pseudo-labels generated by each for mutual supervision, thereby mitigating the issue of confirmation bias. For labeled images, the inconsistent pseudo-labels generated by the two subnetworks are employed to identify images that are prone to misclassification. We then devise an inconsistency-aware relearning (ICR) regularization model to perform a review training process. For unlabeled images, we introduce a class-aware contrastive learning (CCL) regularization to learn their discriminative feature representations using the corresponding pseudo-labels. Since the use of distinct subnetworks reduces the risk of both models producing identical erroneous pseudo-labels, CCL can reduce the possibility of noise data sampling to enhance the effectiveness of contrastive learning. The performance of PCML is evaluated on five interior decoration style image datasets. For the average AUC, accuracy, sensitivity, specificity, precision, and F1 scores, PCML obtains improvements of 1.67%, 1.72%, 3.65%, 1.0%, 4.61%, and 4.66% in comparison with the state-of-the-art method, demonstrating the superiority of our method.

Вплив штучного інтелекту на права людини та загальні рекомендації для сталого втілення

The relevance of the topic is due to the development of artificial intelligence (AI), one of the most important technological trends of our time, which has a significant impact on human rights and various aspects of human life. AI is developing extremely fast. This creates new challenges for human rights that need to be addressed immediately. It is important to understand how technology can change our society and what measures should be taken to protect human rights. The article explores the impact of AI development on human rights, emphasizing both positive and negative aspects of technological progress. The author analyzes how AI can threaten the right to privacy through the collecting and processing of large amounts of data, which can lead to a loss of control over personal information. The author considers the impact of AI on access to information, where algorithms can create information bubbles, limiting the variety of information received. Attention is drawn to possible restrictions on freedom of expression through algorithmic censorship of content, which may lead to restrictions on freedom of speech. The use of AI in the judicial system may affect the fairness of court decisions, especially if the algorithms have biases or errors. The author traces the impact of automation on the labor market and the risks of job losses and the need to find a balance between technological progress and employment. In the healthcare sector, AI can both improve diagnosis and treatment and create new challenges due to possible algorithmic errors and unequal access to medical technologies. In education, the use of AI opens up new opportunities but also creates barriers to access to knowledge, especially for vulnerable groups. The author emphasizes the importance of taking human rights into account in the context of climate risks. The author raises the issues of discrimination and restriction of freedom of movement due to biased algorithms. The author emphasizes the need to develop regulatory mechanisms to protect human rights in the context of the rapid development of AI, ensuring a balance between innovation and human rights protection, and provides a number of clear recommendations.

Evolutionary Insights from Association Rule Mining of Co-Occurring Mutations in Influenza Hemagglutinin and Neuraminidase.

Seasonal influenza viruses continuously evolve via antigenic drift. This leads to recurring epidemics, globally significant mortality rates, and the need for annually updated vaccines. Co-occurring mutations in hemagglutinin (HA) and neuraminidase (NA) are suggested to have synergistic interactions where mutations can increase the chances of immune escape and viral fitness. Association rule mining was used to identify temporal relationships of co-occurring HA-NA mutations of influenza virus A/H3N2 and its role in antigenic evolution. A total of 64 clusters were found. These included well-known mutations responsible for antigenic drift, as well as previously undiscovered groups. A majority (41/64) were associated with known antigenic sites, and 38/64 involved mutations across both HA and NA. The emergence and disappearance of N-glycosylation sites in the pattern of N-X-[S/T] were also identified, which are crucial post-translational processes to maintain protein stability and functional balance (e.g., emergence of NA:339ASP and disappearance of HA:187ASP). Our study offers an alternative approach to the existing mutual-information and phylogenetic methods used to identify co-occurring mutations, enabling faster processing of large amounts of data. Our approach can facilitate the prediction of critical mutations given their occurrence in a previous season, facilitating vaccine development for the next flu season and leading to better preparation for future pandemics.

Forecasting staffing needs in general education: HR-practices and staffing planning model for metropolitan schools

Introduction. The development of human capital is crucial for preparing an effective response of the Russian society to the great challenges related to ensuring sustainable economic growth, development and implementation of new promising technologies, increasing labour productivity, and improving the quality of life of citizens. The key role here is played by the state of the education sector, on which the population's mastering of competences, knowledge and skills demanded by the economy and the labour market directly depends. Ensuring the training of highly qualified specialists considering the changing staffing needs of society and trends in the development of the digital economy refers to the urgent problem of management and forecasting of staffing in the Russian education system. Materials and Methods. The methodological basis of the study was formed by reports of international organizations, national agencies in the field of education, reports of working groups, as well as domestic and foreign studies in the field of forecasting the staffing status; official web pages of federal and regional executive authorities that carry out management and control in the field of education; websites of educational organizations of general and higher education; information from open data sources. Qualitative analysis of data using general scientific methods of cognition was carried out on the obtained set of data. Results. The analysed solutions have limitations that make it difficult to directly transfer them to the staff forecasting model for Moscow schools. They do not consider the peculiarities of Moscow education, for example, the directions of the Moscow education development strategy, specifics of the nomenclature of positions and certification of managerial staff, city projects of pre-professional education, optimization of administrative staff, etc. When creating a staff forecasting service for Moscow schools, it is necessary to consider the advantages of platform solutions that provide an integrated interdisciplinary approach using large amounts of data. At the same time, the forecast should consider the peculiarities of the Moscow education system that can affect the staff composition of schools. Discussion and Conclusions. Based on the analysis, a model solution for forecasting the staffing status of educational organizations in the metropolitan education system has been developed, including methodological approaches to evaluation; directions for assessing the dynamics of changes in the staffing status of schools; the structure of the staffing composition of Moscow schools; the structure of primary data; and a set of indicators.

Implementasi Algoritma K-Means Guna Pengelompokkan Data Penjualan Berdasarkan Pembelian

Information technology can help solve problems faced by humans by facilitating performance. Information technology and information systems are difficult to separate in the business world. Data mining is the core of the KDD process, which involves inferring algorithms that explore data, developing models and finding previously unknown patterns. KDD is often referred to as knowledge discovery in databases. The KDD process generally consists of 5 stages, namely data selection, pre-processing/cleaning, transformation, data mining and interpretation/evaluation. K-Means is a clustering algorithm in data mining to be able to produce groups of large amounts of data with a point-based partition method with fast and efficient computing time. Clustering is the process of dividing objects from a data set into several homogeneous clusters. The main purpose of the cluster method is to group a number of data/objects into clusters (groups) so that each cluster will contain data that is as similar as possible. This study aims to provide real solutions to UD. Martua in order to know which items are selling well and which items are not selling well so that the object can know which items need to be added to the stock and which items need to be reduced. The method used in this study is the K-Means method with stages, namely data selection, pre-processing, data transformation, information extraction and evaluation of results. The data consists of 30 item data, there are 8 as members of C1 and are best-selling items and 22 as members of C2 and are not selling items. The conclusion that can be obtained from this study is that the K-Means method can group items at UD. Martua. This study shows that the implementation of the K-Means method with the support of the RapidMiner application is effective in grouping item data at UD. Martua.