Large Volumes Of Data Research Articles

AI-Based Risk Assessments in Forensic Auditing: Benefits, Challenges and Future Implications

Forensic auditing is a critical component of ensuring financial integrity and detecting fraud within organizations. Traditional methods of risk assessment in forensic auditing often rely on manual processes, which can be time-consuming, labour-intensive, and prone to human error. In recent years, the integration of artificial intelligence (AI) techniques has revolutionized the field, offering more efficient and accurate risk assessment capabilities. This abstract explores the role of AI-based risk assessment in forensic auditing, highlighting its benefits, challenges, and future implications. AI-based risk assessment leverages advanced algorithms and machine learning models to analyse large volumes of financial data, identify patterns, anomalies, and potential red flags indicative of fraudulent activities. By automating repetitive tasks such as data collection, classification, and analysis, AI streamlines the auditing process, enabling forensic auditors to focus on interpreting results and making informed decisions. One of the primary advantages of AI-based risk assessment is its ability to detect complex fraud schemes that may go unnoticed by traditional methods. Machine learning algorithms can detect subtle deviations from expected behaviour, flagging transactions or activities that exhibit unusual patterns or characteristics. Moreover, AI systems can adapt and learn from new data, continuously improving their detection capabilities over time. Another benefit of AI-based risk assessment is its scalability and efficiency. With the increasing volume and complexity of financial transactions, manual auditing processes struggle to keep pace. AI, on the other hand, can analyse vast datasets in a fraction of the time it would take a human auditor, allowing organizations to conduct more comprehensive and timely audits.

Advanced Hybrid Techniques for Cyberattack Detection and Defense in IoT Networks

ABSTRACTThe Internet of Things (IoT) represents a vast network of devices connected to the Internet, making it easier for users to connect to modern technology. However, the complexity of these networks and the large volume of data pose significant challenges in protecting them from persistent cyberattacks, such as distributed denial‐of‐service (DDoS) attacks and spoofing. It has become necessary to use intrusion detection systems and protect these networks. Existing intrusion detection systems for IoT networks face many problems and limitations, including high false alarm rates and delayed detection. Also, the datasets used for training may be outdated or sparse, which reduces the model's accuracy, and mechanisms may not be used to defend the network when any intrusion is detected. To address these limitations, a new hybrid deep learning and machine learning methodology is proposed that contributes to detecting DDoS and spoofing attacks, reducing false alarms, and then implementing the necessary defensive measures. In proposed hybrid methodology consists of three stages: the first stage is to propose a hybrid method for feature selection consisting of techniques (correlation coefficient and sequential feature selector); the second stage is to propose a hybrid model by integrating deep learning neural networks with a machine learning classifier (cascaded long short‐term memory [LSTM] and Naive Bayes classifier); and in the third stage, improving network defense mechanisms and blocking ports after detecting threats and maintaining network integrity. In training and evaluating the performance of the proposed methodology, three datasets (CIC‐DDoS2019, CIC‐IoT2023, and CIC‐IoV2024) were used, and these data were also balanced to obtain effective results. The accuracy of 99.91%, 99.88%, and 99.77% was obtained. Also, a cross‐validation technique was used with the test data to ensure no overfitting. The proposed methodology has proven its high performance in detecting attacks, as it provides a powerful solution to enhance the security of IoT networks and protect them from cyberattacks, as it can be applied in many fields and to other attacks.

Classifying Magnetic Reconnection Regions Using k‐Means Clustering: Applications to Energy Partition

AbstractMagnetic reconnection is a fundamental plasma process which facilitates the conversion of magnetic energy to particle energies. This local process both contributes to and is affected by a larger system, being dependent on plasma conditions and transporting energy around the system, such as Earth's magnetosphere. When studying the reconnection process with in situ spacecraft data, it can be difficult to determine where spacecraft are in relation to the reconnection structure. In this work, we use k‐means clustering, an unsupervised machine learning technique, to identify regions in a 2.5‐D PIC simulation of symmetric magnetic reconnection with conditions comparable to those observed in Earth's magnetotail. This allows energy flux densities to be attributed to these regions. The ion enthalpy flux density is the most dominant form of energy flux density in the outflows, agreeing with previous studies. Poynting flux density may be dominant at some points in the outflows and is only half that of the Poynting flux density in the separatrices. The proportion of outflowing particle energy flux decreases as guide field increases. We find that k‐means is beneficial for analyzing data and comparing between simulations and in situ data. This demonstrates an approach which may be applied to large volumes of data to determine statistically different regions within phenomena in simulations and could be extended to in situ observations, applicable to future multi‐point missions.

Adapting the GPT engine for proactive customer insight extraction in product development

Understanding the perceptions of consumers is widely recognized as a critical element in the innovation of new products. Traditional techniques used by companies to collect essential consumer insights have largely remained unchanged. Common practices like interviews and surveys have distinct limitations. Interviews may not always capture the precise needs of consumers due to communication barriers, and surveys tend to encourage incremental changes rather than radical innovations. Service industries face further complexity as they navigate customer feedback on the more intangible aspects of service quality. This study highlights the pioneering use of GPT-3.5 Turbo, a tool known for its exceptional ability to delve into the nuances of conversational context and process data in a chat-centric manner, thereby enhancing the extraction of Voice of the Customer (VoC). Its capability to handle large volumes of data in multiple languages leads to a more thorough and inclusive VoC analysis. The study links these technological advancements with Lean Six Sigma 4.0, suggesting that incorporating GPT-3.5 Turbo could significantly improve the customer-focused strategies in the current industrial landscape. This breakthrough in VoC analysis suggests possibilities for more perceptive, immediate data-driven approaches in customer service, and lays a stronger groundwork for decisions in product evolution and process enhancement. The paper concludes by urging further investigation to confirm these initial results and to explore the ethical aspects of employing such advanced natural language processing technologies.

Anomaly Detection and Approximate Similarity Searches of Transients in Real-time Data Streams

We present Lightcurve Anomaly Identification and Similarity Search (LAISS), an automated pipeline to detect anomalous astrophysical transients in real-time data streams. We deploy our anomaly detection model on the nightly Zwicky Transient Facility (ZTF) Alert Stream via the ANTARES broker, identifying a manageable ∼1–5 candidates per night for expert vetting and coordinating follow-up observations. Our method leverages statistical light-curve and contextual host galaxy features within a random forest classifier, tagging transients of rare classes (spectroscopic anomalies), of uncommon host galaxy environments (contextual anomalies), and of peculiar or interaction-powered phenomena (behavioral anomalies). Moreover, we demonstrate the power of a low-latency (∼ms) approximate similarity search method to find transient analogs with similar light-curve evolution and host galaxy environments. We use analogs for data-driven discovery, characterization, (re)classification, and imputation in retrospective and real-time searches. To date, we have identified ∼50 previously known and previously missed rare transients from real-time and retrospective searches, including but not limited to superluminous supernovae (SLSNe), tidal disruption events, SNe IIn, SNe IIb, SNe I-CSM, SNe Ia-91bg-like, SNe Ib, SNe Ic, SNe Ic-BL, and M31 novae. Lastly, we report the discovery of 325 total transients, all observed between 2018 and 2021 and absent from public catalogs (∼1% of all ZTF Astronomical Transient reports to the Transient Name Server through 2021). These methods enable a systematic approach to finding the “needle in the haystack” in large-volume data streams. Because of its integration with the ANTARES broker, LAISS is built to detect exciting transients in Rubin data.

Evaluation of the Impact of Artificial Intelligence on the Systems Audit Process

The paper analyzes the impact of artificial intelligence (AI) in systems auditing, fastening on process optimization through the use of advanced technologies similar as intelligent independent systems. A comprehensive literature review was conducted to understand the operation of AI in checkups, revealing that the integration of these technologies has increased inspection delicacy by over to 93. Specific ways similar as cross-validation (CV), support vector machines (SVMs), and artificial neural networks (ANNs) were employed, demonstrating their effectiveness in perfecting delicacy, receptivity, and particularly in anomaly discovery, with results of 87, 90, and 93 independently. The findings emphasize the need to address the ethical and sequestration pitfalls that accompany the use of AI in checkups, given that while these technologies ameliorate effectiveness and delicacy, they also pose significant challenges in terms of ethical and security data running. In this environment, it's recommended that associations invest in training their staff in the use of AI tools, as well as establish clear programs to insure ethics and sequestration. In addition, it emphasizes the significance of continuing to probe and develop new AI operations that will further ameliorate the effects of system checkups in an ever-changing digital terrain. The perpetration of AI not only optimizes processes but also provides a significant competitive advantage by enabling more accurate discovery of irregularities and patterns in large volumes of data. In summary, AI represents a revolution in the field of systems auditing, offering opportunities to improve accuracy and efficiency, although it is crucial to proactively manage the associated ethical and privacy challenges.

Computational linguistics and natural language processing techniques for semantic field extraction in Arabic online news

The research aimed to extract semantic fields from Arabic online news and advance Natural Language Processing (NLP) applications in understanding and managing news information effectively. It provides a comprehensive approach to processing and analyzing large volumes of Arabic news data by integrating semantic field analysis, NLP, and computational linguistics. Using quantitative methods, Arabic news articles were collected and processed with Python, a popular programming language in data analysis, and applied various NLP techniques and machine learning models to accurately extract semantic fields. The primary objective was to evaluate the effectiveness of different classification models in categorizing Arabic news and to identify the most suitable model for semantic field extraction. The research evaluated five classification models: Naive Bayes, Support Vector Machine (SVM), Logistic Regression, Random Forest, and Gradient Boosting. Among these, SVM achieves the highest overall accuracy of 90%. Specifically, SVM demonstrated exceptional performance in categorizing sports-related news, with a 99% probability and an F1-Score of 98%. However, it faced challenges in categorizing health and science news, achieving a lower F1-Score of 79%. Overall, the study demonstrated the effectiveness of computational methods, particularly SVM, in classifying Arabic news and extracting semantic fields, thereby advancing NLP and computational linguistics. The findings highlighted the potential of SVM for accurate news analysis and the need for further enhancement of NLP techniques to address multilingual and domain-specific challenges.

Pluto: Sample Selection for Robust Anomaly Detection on Polluted Log Data

Log anomaly detection, critical in identifying system failures and preempting security breaches, finds irregular patterns within large volumes of log data. Modern log anomaly detectors rely on training deep learning models on clean anomaly-free log data. However, such clean log data requires expensive and tedious human labeling. In this paper, we thus propose a robust log anomaly detection framework, PlutoNOSPACE, that automatically selects a clean representative sample subset of the polluted log sequence data to train a Transformer-based anomaly detection model. Pluto features three innovations. First, due to localized concentrations of anomalies inherent in the embedding space of log data, Pluto partitions the sequence embedding space generated by the model into regions that then allow it to identify and discard regions that are highly polluted by our pollution level estimation scheme, based on our pollution quantification via Gaussian mixture modeling. Second, for the remaining more slightly polluted regions, we select samples that maximally purify the eigenvector spectrum, which can be transformed into the NP-hard facility location problem; allowing us to leverage its greedy solution with a (1-(1/e)) approximation guarantee in optimality. Third, by iteratively alternating between the above subset selection, a model re-training on the latest subset, and a subset filtering using dynamic training artifacts generated by the latest model, the data selected is progressively refined. The final sample set is used to retrain the final anomaly detection model. Our experiments on four real-world log benchmark datasets demonstrate that by retaining 77.7% (BGL) to 96.6% (ThunderBird) of the normal sequences while effectively removing 90.3% (BGL) to 100.0% (ThunderBird, HDFS) of the anomalies, Pluto provides a significant absolute F-1 improvement up to 68.86% (2.16% → 71.02%) compared to the state-of-the-art sample selection methods. The implementation of this work is available at https://github.com/LeiMa0324/Pluto-SIGMOD25.

Анализ методов многопараметрической оптимизации для механической обработки деталей

In the modern world, digitalization has become an integral part of various sectors of the economy, including manufacturing enterprises. However, for the efficient operation and development of the production process, it is necessary not only to be able to use the latest technologies, but also to be able to analyze and optimize the available data. One of the most effective methods of data analysis and management is multicriteria analysis. This method allows you to take into account many factors when making decisions, which is important for production processes where there are many variables and constraints. At the same time, large volumes of data collected and stored by the enterprise help improve the quality of decisions made and increase the efficiency of the production process. Data analysis allows you to identify patterns, predict the behavior of processes, and subsequently optimize their operation.

Architecture of an automated program complex based on a multiple kernel svm classifier for analyzing malicious executable files

Subject matter. This article presents the development and architecture of an automated program complex designed to identify and analyze malicious executable files using a classifier based on a multiple kernel support vector machine (SVM). Goal. The aim of the work is to create an automated system that enhances the accuracy and efficiency of malware detection by combining static and dynamic analysis into a single framework capable of processing large volumes of data with optimal time expenditure. Tasks. To achieve this goal, tasks were carried out that included developing a program complex that automates the collection of static and dynamic data from executable files using tools like IDA Pro, IDAPython, and Drakvuf; integrating a multiple kernel SVM classifier to analyze the collected heterogeneous data; validating the system's effectiveness based on a substantial dataset containing 1,389 executable samples; and demonstrating the system's scalability and practical applicability in real-world conditions. Methods. The methods involved a hybrid approach that combines static analysis – extracting byte code, disassembled instructions, and control flow graphs using IDA Pro and IDAPython – with dynamic analysis, which entails monitoring real-time behavior using Drakvuf. The multiple kernel SVM classifier integrates different data representations using various kernels, allowing for both linear and nonlinear relationships to be considered in the classification process. Results. The results of the study show that the system achieves a high level of accuracy and completeness, as evidenced by key performance metrics such as an F-score of 0.93 and ROC AUC and PR AUC values. The automated program complex reduces the analysis time of a single file from an average of 11 minutes to approximately 5 minutes, effectively doubling the throughput compared to previous methods. This significant reduction in processing time is critically important for deployment in environments where rapid and accurate malware detection is necessary. Furthermore, the system's scalability allows for efficient processing of large data volumes, making it suitable for real-world applications. Conclusions. In conclusion, the automated program complex developed in this study demonstrates significant improvements in the accuracy and efficiency of malware detection. By integrating multiple kernel SVM classification with static and dynamic analysis, the system shows potential for real-time malware detection and analysis. Its scalability and practical applicability indicate that it could become an important tool in combating modern cyber threats, providing organizations with an effective means to enhance their cybersecurity.

Modelos de evaluación del desempeño laboral: Una revisión sistemática de literatura.

The quality and success of an organization are inherently linked to employee performance, making periodic performance evaluation essential. This article analyzes the most commonly used existing models of performance evaluation at the organizational level. Traditional performance evaluation models, such as competency assessment, 360-degree feedback, and critical incident evaluation, have advantages and disadvantages. For example, competency assessment allows measuring skills and behaviors, while 360-degree feedback offers a comprehensive view by considering multiple perspectives. However, many performance evaluation models are affected by biases and subjectivity in results, requiring a significant investment of time and resources. On the other hand, artificial intelligence is transforming the landscape of performance evaluation. Technological advances enable the development of more objective and efficient systems for assessing employee performance. Additionally, artificial intelligence can analyze large volumes of data to identify trends and patterns in performance.

Fractal-based supervised approach for dimensionality reduction of hyperspectral images

Dimensionality reduction is one of the most challenging and crucial issues apart from data mining, security, and scalability, which have retained much traction due to the ever-growing need to analyze the large volumes of data generated daily. Fractal Dimension (FD) has been successfully used to characterize data sets and has found relevant applications in dimension reduction. This paper presents an application of the FD Reduction (FDR) Algorithm on geospatial hyperspectral data, examining its usefulness for data sets with a relatively high embedding dimension. We examine the algorithm at two levels. First is the conventional FDR approach (unsupervised) at the image level. Alternatively, we propose a pixel-level supervised approach for band reduction based on time-series complexity analysis. Techniques for determining an optimal intrinsic dimension for the dataset using these two techniques are examined. We also develop a parallel GPU-based implementation for the unsupervised image-level FDR algorithm, reducing the run-time by nearly 10 times. Furthermore, both approaches use a support vector machine classifier to compare the classification performance of the original and reduced image obtained.

Development of the Combined Operations Method to Improve the Efficiency of Block Encryption

Real-time information protection requires the implementation of special methods aimed at providing reliable and fast encryption algorithms to protect personal and corporate information from unauthorized access. With the growth of data volumes and the speed of their processing, the importance of effective encryption methods increases significantly. One of the common, reliable and well-known encryption algorithms is AES (Advanced Encryption Standard), also known as Rijndael, which is a symmetric block cipher. AES has high efficiency and cryptoresistance and is suitable for processing large volumes of data. The reliability and speed of encryption and decryption using the AES algorithm depends on the size of the key and the data. This paper proposes to improve algorithm of symmetric block encryption AES to provide faster data processing. The possibility of combining mathematical operations that have a similar principle of processing elements is shown. This approach made it possible to reduce the processing time for data encryption and decryption compared to known implementations. A comparative analysis of the practical implementation of the standard and optimized AES cryptoalgorithms has been carried out. The general principles of the proposed method are to transform all two-dimensional arrays into one-dimensional arrays, add auxiliary tables for ShiftRows and MixColumns operations, and combine operations with similar element processing principles. The simulation results showed that the modified implementation of the AES algorithm demonstrates a reduction in processing time of up to 50% when encrypting and up to 75% when decrypting data compared to known results

Federated learning: A cutting-edge survey of the latest advancements and applications

Robust machine learning (ML) models can be developed by leveraging large volumes of data and distributing the computational tasks across numerous devices or servers. Federated learning (FL) is a technique in the realm of ML that facilitates this goal by utilizing cloud infrastructure to enable collaborative model training among a network of decentralized devices. Beyond distributing the computational load, FL targets the resolution of privacy issues and the reduction of communication costs simultaneously. To protect user privacy, FL requires users to send model updates rather than transmitting large quantities of raw and potentially confidential data. Specifically, individuals train ML models locally using their own data and then upload the results in the form of weights and gradients to the cloud for aggregation into the global model. This strategy is also advantageous in environments with limited bandwidth or high communication costs, as it prevents the transmission of large data volumes. With the increasing volume of data and rising privacy concerns, alongside the emergence of large-scale ML models like Large Language Models (LLMs), FL presents itself as a timely and relevant solution. It is therefore essential to review current FL algorithms to guide future research that meets the rapidly evolving ML demands. This survey provides a comprehensive analysis and comparison of the most recent FL algorithms, evaluating them on various fronts including mathematical frameworks, privacy protection, resource allocation, and applications. Beyond summarizing existing FL methods, this survey identifies potential gaps, open areas, and future challenges based on the performance reports and algorithms used in recent studies. This survey enables researchers to readily identify existing limitations in the FL field for further exploration.

Leveraging State-of-the-Art AI Algorithms in Personalized Oncology: From Transcriptomics to Treatment

Background: Continuous breakthroughs in computational algorithms have positioned AI-based models as some of the most sophisticated technologies in the healthcare system. AI shows dynamic contributions in advancing various medical fields involving data interpretation and monitoring, imaging screening and diagnosis, and treatment response and survival prediction. Despite advances in clinical oncology, more effort must be employed to tailor therapeutic plans based on each patient’s unique transcriptomic profile within the precision/personalized oncology frame. Furthermore, the standard analysis method is not compatible with the comprehensive deciphering of significant data streams, thus precluding the prediction of accurate treatment options. Methodology: We proposed a novel approach that includes obtaining different tumour tissues and preparing RNA samples for comprehensive transcriptomic interpretation using specifically trained, programmed, and optimized AI-based models for extracting large data volumes, refining, and analyzing them. Next, the transcriptomic results will be scanned against an expansive drug library to predict the response of each target to the tested drugs. The obtained target-drug combination/s will be then validated using in vitro and in vivo experimental models. Finally, the best treatment combination option/s will be introduced to the patient. We also provided a comprehensive review discussing AI models’ recent innovations and implementations to aid in molecular diagnosis and treatment planning. Results: The expected transcriptomic analysis generated by the AI-based algorithms will provide an inclusive genomic profile for each patient, containing statistical and bioinformatics analyses, identification of the dysregulated pathways, detection of the targeted genes, and recognition of molecular biomarkers. Subjecting these results to the prediction and pairing AI-based processes will result in statistical graphs presenting each target’s likely response rate to various treatment options. Different in vitro and in vivo investigations will further validate the selection of the target drug/s pairs. Conclusions: Leveraging AI models will provide more rigorous manipulation of large-scale datasets on specific cancer care paths. Such a strategy would shape treatment according to each patient’s demand, thus fortifying the avenue of personalized/precision medicine. Undoubtedly, this will assist in improving the oncology domain and alleviate the burden of clinicians in the coming decade.

Requirements and their implementation for an intelligent decision-making system for the identification and classification of objects of cultural value

The article considers the problem of development and analysis of requirements and ways of their implementation for an intelligent decision support system for identification and classification of objects of cultural value. The work analyzes both general standards of software development and specialized standards relating to intelligent systems, modern methodologies and practical instructions are also taken into account. The work defines and analyzes such system requirements as: speed of processing large volumes of data, accuracy of identification, integration with other information systems and databases, data protection and information security, scaling, adaptation to new types of data and object behavior models, interface user, processing and analysis of various types of data, cross-platform and forecasting based on available data to support decision-making. The article also defines the complexity and complexity of the approach to solving the given task, the prospects and ways of further research of this subject area are determined.

Electric load forecasting under false data injection attacks via denoising deep learning and generative adversarial networks

AbstractAccurate electric load forecasting at various time periods is considered a necessary challenge for electricity consumers and generators to maximize their economic efficiency in energy markets. Hence, the accuracy and effectiveness of existing electric load forecasting approaches depends on the data quality. Nowadays, with the implementation of modern power systems and Internet of Things technology, forecasting models are faced with a large volume of data, which puts the security and health of data at risk due to the use of numerous measuring devices and the threat of cyber‐attackers. In this study, a cyber‐resilient hybrid deep learning‐based model is developed that accurately forecasts electric load in short‐term and long‐term time horizons. The architecture of the proposed model systematically integrates stacked multilayer denoising autoencoder (SMDAE) and generative adversarial network (GAN) and is called SMDAE‐GAN. In the proposed framework, SMDAE layer is used to pre‐process and remove real fs and intentional anomalies in data, and GAN layer is also utilized to forecast electric load values. The effectiveness of the SMDAE‐GAN structure is studied using realistic electrical load data monitored in the distribution network of Tabriz, Iran, and meteorological data measured in weather station there. Compared with other conventional load forecasting approaches, the developed framework has the highest accuracy in both cases of using normal data with real‐world noise and damaged data under false data injection attacks.

Analysis of technologies and algorithms for management of computing resources in video data processing

Managing computational resources in video data processing involves planning, distributing, monitoring, and controlling various types of computational resources such as computing power, network resources, data storage, etc., to ensure their effective use in processing large volumes of video data. This process includes a complex of various tasks for processing, analyzing, and modifying data, which requires significant computational power. Video data are large in size and require a lot of storage space, which can lead to problems such as data transmission delays and data loss. Special hardware and software tools, which can be quite expensive and complex to configure, are usually required for processing video data. This paper provides a comprehensive analysis of existing methods and means for solving the tasks of distributing and managing computational resources in the modern systems of video data processing. Existing modern algorithms and technologies, including those based on artificial intelligence, are examined in detail, identifying their main advantages and disadvantages.

Social media usage and sustainable performance in manufacturing supply chains: exploring dynamic capabilities

Due to the global impact of COVID-19, businesses and supply chains worldwide have been disrupted, leading to challenges for manufacturing firms and customers. Social media usage (SMU) is a vital and valuable tool that builds customer-centric supply chains by effectively communicating large volumes of valuable data. This study examines the influence of social media usage on the development of dynamic capabilities, including supply chain sensing capabilities (SCSC), knowledge sharing capability (KSC), and social capital (SC), which are crucial for constructing agile and resilient supply chains that can effectively respond to dynamic and turbulent environments, ultimately enhancing sustainable business performance. To assess the proposed investigation model, data was gathered through a structured questionnaire administered to supply chain members currently engaged with manufacturing firms. The evaluation of the proposed hypotheses is carried out through the application of structural equation modeling (SEM). The results of this study offer empirical evidence supporting the proposition that SMU within manufacturing firms promotes the development of sensing capabilities, knowledge-sharing capability, and social capital. Furthermore, this study tests the moderation role of environmental dynamism in the relationship between social media use and sustainable business performance. This underscores the pivotal role played by SMU in facilitating the adaptation and responsiveness of the supply chain to dynamic and uncertain environments, thereby contributing to the long-term sustainability and competitiveness of organizations.

Named entity recognition and its role in unstructured data analysis

In today's digital world, where vast amounts of unstructured data are generated every day, the ability to efficiently process this information is key for many industries. Unstructured data, which includes text files, emails, video, audio, images, and other forms of media, is the bulk of digital data and requires specialized tools to analyze it. Natural Language Processing (NLP) and Named Entity Recognition (NER) are two key technologies that enable the transformation of unstructured data into structured information that can be used for a variety of applications. Natural Language Processing enables machines to understand, interpret, manipulate and generate human language, opening up possibilities for deep analysis of textual data. This includes identifying key words, phrases, themes, and emotional nuances in texts. NER, as an important component of Natural Language Processing, specializes in identifying and classifying named entities in the text into certain categories, such as names of persons, organizations, locations, dates, times, and others. This allows you to automate the processes of sorting, categorizing and analyzing information. However, working with Natural Language Processing and Named Entity Recognition faces a number of challenges. The large volume and variety of data make it difficult to collect, store and analyze it. Lack of standardization can lead to problems with interoperability and integration of different data sources. In addition, there are challenges related to the recognition of named entities, in particular, distinguishing between the same names belonging to different persons and understanding the context in which the names are used. Despite these challenges, the outlook for Natural Language Processing and Named Entity Recognition looks bright, with continued innovations in artificial intelligence and machine learning promising to improve the accuracy and efficiency of these technologies in the future.