Big Data Algorithms Research Articles

Big Data Dilemma: The Psychological Impact on Adolescents

Big data, characterized by its vast volume, has revolutionized the landscape of information analytics across multiple domains. While recording digital footprints that users generate online, the big data learns the preferences of each user and thus can form precise user images, which helps marketing for major companies. By implementing content recommendation mechanism based on the big data, users, including a great number of adolescents, are constantly impacted by it. This research mainly investigates big data algorithms' influence on adolescent’s psychological state. By examining big data's impact on adolescent’s stress levels and cognitive growth, this study reveals both positive and negative effects on their psychological health, including increased engagement, improved social skill, alongside with potential risk of social media addiction, decreased self-confidence and higher level of anxiety. Improper usage can worsen psychological issues, while proper implementation allows early detection and intervention. This study integrates and summarizes current understanding big data and adolescent psychological health, discusses potential effects on adolescents and proposes future challenges with solutions.

Typology of Big Data Algorithms for Solving Computing Problems in Transport

Typology of Big Data Algorithms for Solving Computing Problems in Transport

A Fault Monitoring System for Mechanical and Electrical Equipment of Subway Vehicles Based on Big Data Algorithms

A Fault Monitoring System for Mechanical and Electrical Equipment of Subway Vehicles Based on Big Data Algorithms

Corporate Accounting Management Risks Integrating Improved Association Rules and Data Mining

With the development of the times, enterprises need to face more data in operational decision-making. Traditional data analysis strategies cannot handle the growing amount of data well, and the accuracy of analysis will also decrease when faced with uneven data types. The research uses a corporate accounting management risk analysis technology that combines big data algorithms and improved clustering algorithms. This method combines big data processing ideas with a clustering algorithm that incorporates improved weighting parameters. The results show that on the data sets DS1, DS2, and DS3, the NMI values of the GMM algorithm are all 0; while the NMI values of the MCM algorithm correspond to 0.9291, 0.9088 and 0.8881 respectively. At the same time, the Macro-F1 values of the Verify2 algorithm correspond to 0.9979, 0.9501, and 0.9375 respectively, and the recognition accuracy of the data remains above 85%. In the running time comparison, when the number of samples in the data set reaches 5,000, the calculation time of the Verify2 algorithm remains within 5 seconds. In terms of practical application results, the study selected the profitability risk indicators of 40 companies for analysis. After conducting risk ratings, it can be seen that companies No. 5, 6, 7, and 39 have the highest risk levels, and companies No. 33 and 34 have the highest risk levels. The lowest level. After conducting risk assessments on the 40 selected listed companies, the risk level of net asset income of each company remained at level 5, and the risk level of earnings per share remained at level 3. The above results show that this technology has good performance in terms of calculation accuracy and calculation time, can assess enterprise risks, and can provide data support for enterprise operation decisions.

Exploring the Frontiers of Data Analysis: A Comprehensive Review

The process of assessing, cleansing, transforming, and interpreting data to find trends, patterns, or insights that might guide choices and help manage problems is known as data analysis. Data analysis is a leading light on the cutting edge of contemporary research, revealing the path of knowledge across many areas. It includes the methodical examination of data to find trends, patterns, and insights that are helpful for the analytical and creative processes. This review also examines how data analysis is developing, emphasizing new approaches, paradigms, viewpoints, and graphical data displays. In addition to the significant improvements brought about by artificial intelligence, deep learning, and machine learning, it emphasizes statistical inference, exploratory data analysis, and data pretreatment. One of the main ideas behind this review was to use a systematic literature review approach along with meta-analysis techniques to look for new developments and trends in how data is analyzed in a lot of different areas. The article also addresses how data visualization could improve comprehension and dissemination of the results. In promoting responsible data use and legal rules, it also looks at how analytics affects society, the law, and ethical issues. The evaluation underscores the diverse disciplines that employ data analysis, underscoring the need for interdisciplinary coherence and comprehensible algorithms. Finally, this thorough research offers recommendations for analyzing and determining the boundaries of data analysis, in addition to offering insightful viewpoints and opinions that are helpful for academics, professionals, and decision-makers. If we just stay up to speed with the latest advancements and strive to be more, we can utilize data analysis to its fullest potential for complicated problems and positively impact society. Keywords: Data mining, big data analytics, machine learning, algorithms, and data analysis and statistics

Criteria for ensuring the reliability of data in analytical activities

The article analyses the criteria for ensuring data reliability in analytical activities, particularly in economic research, based on a large amount of data that requires careful analysis and processing. The study uses an integrated approach to identify the degree of interrelationships between variables and their dependencies and identify hidden influence factors. The article describes the need to use methods of correlation, regression factor analysis, verification, unification, and analysis of possible deviations of calculations to ensure the objectivity and reliability of economic indicators. The main criteria for ensuring data reliability are defined. It is established that applying an integrated methodological approach allows for obtaining reliable and accurate economic research results, contributing to adopting effective management decisions. It is noted that research requires the development of a systematic approach to ensuring data reliability. It includes three areas: statistical methods of quantitative analysis of cause-and-effect relationships, the objectivity of economic indicators, and criteria for the reliability of indicators of the information field of research. The proposed methods aim to obtain reasonable and balanced results, which is critical for making effective decisions in the digitalization of the economy. Modern methods for detecting, evaluating, and correcting errors in data can significantly improve the reliability of economic conclusions and recommendations. In the digital economy, the development and implementation of cutting-edge technologies, such as machine learning and artificial intelligence, play a pivotal role in ensuring higher data quality. This underscores the relevance of the research. Further studies could be directed toward developing and enhancing methodological approaches to data analysis, including exploring new statistical methods and algorithms for processing big data. Additionally, research on integrating different analysis methods to study complex economic processes and forecast future trends holds promise. Keywords: data reliability, analytical activity, economic research, objectivity of economic indicators, evaluation criteria, information field.

Optimization of Online Advertising Decision Combination Based on the Attribution of Consumer Behavior Big Data

In the field of internet advertising, associated products based on various big data algorithms are widely presented on consumers’ browsing pages in order to increase their purchasing interest. With the help of computer big data processing, online advertisers can better grasp the shopping needs of consumers. This study proposes a chance constrained programming optimization model (CC-PR) for budget allocation of advertisers in a dynamic and complex consumer behavior context, based on the premise of analyzing consumers’ shopping journey from the perspective of the attribution of consumer behavior effects. It models different online behaviors generated by consumers’ interest transfer at different shopping stages. The characteristic of this method is to simulate changes in consumer psychology, overcome the difficulty of predicting multi-stage behavior of consumers, and extract a model of dynamic generalizable laws of consumer psychology. It dynamically adjusts advertising strategies based on changes in consumer tendencies at different stages. In order to verify the effectiveness of the proposed strategy, this paper introduces batch data from large shopping platforms and compares our method with the baseline method through experiments. The results show that the CC-PR method proposed in this paper has superior performance. A large amount of consumer behavior data can help advertisers make more accurate advertising decisions, and the method proposed in this article is also a good example of the application of computer big data processing in the field of online advertising.

Performance Comparison of Cryptographic Algorithms for Big Data Stored in Cloud

Performance Comparison of Cryptographic Algorithms for Big Data Stored in Cloud

Research on fault-controlled hydrocarbon accumulation in rifted lacustrine basins based on structural geological modeling: a case study of the Banqiao area in Bohai Bay Basin, China

The Banqiao area in the Bohai Bay Basin has experienced three stages of extensional deformation, leading to the formation of numerous fault-bound traps. Faults, acting as boundary conditions for these traps, play a crucial role in hydrocarbon accumulation. In this study, we conducted a 3D structural modeling of the area using high-resolution 3D seismic data and established a fault-reservoir database based on previous research. Our findings reveal four levels of faults in the Banqiao area: basin-controlling faults, boundary faults, derivative master faults, and secondary adjusting faults. The structural units can be categorized into subsag areas, slope areas, stress tran-sition zones, bifurcation and main incised fault zones, and southern block areas. The segmented growth of the main boundary faults controls the evolution of the subsags, with the subsidence center gradually shifting eastward from Rift Phase I to Rift Phase II, aligning with the distribution of source rocks. Fault-bound traps in the Banqiao area include single faults, intersecting faults, and side faults. Faults primarily act as barriers to lateral hydrocarbon migration during the process of hydrocarbon accumulation, while also providing pathways to a lesser extent. By integrating the fault-reservoir database with the fault system classification, we identified four types of fault-controlled hydro-carbon accumulation models: like-dipping fault barrier model, oppositely-dipping fault barrier model, intersecting fault barrier model, and reactivation-controlled secondary hydrocarbon ac-cumulation model. This structural geological model effectively demonstrates the spatial configura-tion of faults and their role in hydrocarbon accumulation in the Banqiao area. The fault control mechanisms presented in the model can also be applied to other blocks in the Bohai Bay Basin, laying a foundation for future petroleum exploration in continental rifted basins and facilitating the ap-plication of big data algorithms in various geoscientific research fields.

Machine learning-based field geological mapping: A new exploration of geological survey data acquisition strategy

Geological mapping, as the fundamental core of geological research and investigation, provides indispensable basic understanding and exploration data for mineral prospecting, disaster prevention and control, and other related fields. The emergence of big data algorithms and models, such as machine learning and deep learning, has brought new assistance to the task of geological mapping. By utilizing these advanced technologies, long-term accumulated exploratory data has been deeply mined and analyzed, significantly enriching the information obtained through conventional mapping methods and laying a solid foundation for subsequent research. However, challenges faced by machine learning in the field of geological mapping are gradually being recognized: the vast demand for data volume, interference from irrelevant information, the “black box” problem, limitations in computing power, algorithm applicability, and data security, among others. To address these challenges, this study takes the Duolong mineral district, Tibet, China, a region with a high degree of exploration, as an example, assuming it to be a blank area without mapping, and attempts to integrate the light gradient boosting machine (LightGBM) algorithm into the field geological mapping process. Firstly, based on remote sensing data, the distribution of alteration was identified to design the initial mapping route. Subsequently, geological units along the route were labeled, and the model was trained using geochemical sampling point data and remote sensing data as feature inputs. During the model prediction stage, the probability distribution obtained through the Softmax function was utilized to guide the subsequent design and planning of field mapping routes. After five iterations, based on field mapping that covers 20% of the entire area, 90% of the lithological units were successfully predicted. This study explores an effective combination of machine learning algorithms with field geological mapping that establishes a new method for field geological mapping based on machine learning. It not only improves the efficiency and accuracy of mapping but also provides a new strategy for balancing geological work with structured data acquisition.

China's Universities Network Security Current Situation and Solutions

With the development of new technologies such as the Internet, artificial intelligence and big data, more convenience has been brought to Chinese colleges and universities, but it is followed by threats in network security. Due to the maturity of big data algorithms, it is easy to leak personal information and cause campus network security accidents while bringing convenience to college students. China's university network has experienced great development, but the current situation is not optimistic. There are many problems such as not paying attention to the ideology, heavy construction and light application. This paper focuses on the system construction, safety education and professional construction of these problems.

Personalized Services Based on Big Data Algorithms—taking Netease Cloud as an Example

Personalized Services Based on Big Data Algorithms—taking Netease Cloud as an Example

Is information evaluated subjectively? Social media has changed the way users search for medical information.

In recent years, social media platforms, such as TikTok and RedBook, have emerged as important channels through which users access and share medical information. Additionally, an increasing number of healthcare professionals have created social media accounts through which to disseminate medical knowledge. This paper explores why users obtain their medical information from social media and how the signals transmitted by social platforms affect use behaviours. We combined the elaboration likelihood model and signal theories to construct a comprehensive model for this study. We used simple random sampling to investigate users' behaviours related to social media usage. A total of 351 valid questionnaires were completed by people in Mainland China. The participants were enthusiastic about social media platforms and had searched for health-related information on social media in the past three months. We analysed the data using partial least squares structural equation modelling to investigate the influence of two pathways and two signals (objective and subjective judgement pathways and positive and negative signals) on social media use behaviours. When seeking medical information on social media, users tend to rely on subjective judgment rather than objective judgment, although both are influential. Furthermore, in the current era, in which marketing methods involving big data algorithms and artificial intelligence prevail, negative signals, such as information overload, have a more pronounced impact than positive signals. This study demonstrates that the subjective judgment path has a greater impact on users than the objective judgment path. Platforms are encouraged to focus more on users' emotional needs. The paper also discusses the negative impact of information overload on users, sounding an alarm for enterprises to control their use of homogeneous information resulting from the excessive use of big data algorithms.

Construction of an Intelligent Mental Health Assessment System Based on Big Data Algorithms

Construction of an Intelligent Mental Health Assessment System Based on Big Data Algorithms

Enterprise Performance Management and Evaluation Incentive Model for Blockchain Technology and Big Data Algorithms

Enterprise Performance Management and Evaluation Incentive Model for Blockchain Technology and Big Data Algorithms

Using the method of image decomposition based on topological features for processing satellite images

The problem of interpretation of spatial data on satellite images is considered in the article. It is proposed to use the decomposition of images by topological features to highlight objects of interest, global and detailed structures on satellite images. The description of the method and the features of its implementation for creating a software product with effective algorithms for processing big data are given. The functionality of the developed software, which includes the classification of objects on satellite images, segmentation, binarization, noise removal is described. It is shown that these algorithms are built on a single theoretical basis in the form of a topological decomposition. Examples of using the program for segmentation and binarization of satellite images from urban neighborhoods are demonstrated.

A STUDY IN THE INFLUENCE OF SOCIAL MEDIA ON COMMUNICATIVE BEHAVIOR IN ALL WALKS OF LIFE

As technology and big data algorithms advance, more social media emerge and grow popular, attracting many individuals. Almost everyone relies on social media platforms like Weibo, WeChat, TikTok, and others for communication and socializing, but young people rarely realize how much their communication habits have changed. In our daily lives, communication is vital, and the means of communication are constantly linked to results, therefore they should be given more attention. Some difficulties caused by this new communication have been found. Thus, fixing them appears crucial. This study aims To investigate the improvement of communicative behavior using social media. By exploring how social media change the way of communication from different angles, this research aims to provide valuable insights for educators, mental health professionals, and policymakers seeking to distinguish the advantage and disadvantage brought by social media among people and raise some possible solutions to make full use of this type of platform. The result showed the effective communication, social interaction, and conflict management are the main aspects of communication behavior improvement using social media. This research contributes to both industry and academia by updating theoretical knowledge of the how the use of social media can improve the communication effectiveness to All Walks of Life in social science studies.

The Enlightenment of Evolutionary Medicine

Darwin’s evolution theory serves as the basis to understand biology and medicine. In addition to fossil evidence used by Darwin and his contemporaries, advances in genomics allowed the tracing of gene sequences. Understanding human evolution further allowed us to re-evaluate our body’s maladaptation due to rapid environmental and cultural changes, including anxiety, fever, pain, vomiting as well as menstrual blood loss, increases in reproductive tissue cancers, and dystocia in human females. The talk will discuss the relationship between evolution and acute infectious diseases, including HIV resistance, and why the new coronavirus is not man-made. We will further discuss the relationship between evolution and chronic diseases, including why many modern people are obese and our increased risk of diabetes. Cancer development can be explained as somatic cell evolution, starting from cancer stem cell mutations to developing resistance against cancer therapies. Applying evolutionary concepts in medical research, one can discover new hormones and receptors based on their co-evolution, and the development of bionics based on unique characteristics evolved in diverse organisms. Beyond biology, Darwin’s theory explains the evolution of languages and memes such as currencies and religions. During future evolution, the application of artificial intelligence and big data algorithms could guide our “free will”.

Визуализация графовой модели медицинских препаратов и их классов с учетом несовместимости

Currently, the world is actively undergoing digital transformation, which is associated with the introduction of digital technologies in various fields. Methods for predicting and combating various types of diseases are being developed more and more actively. A separate place in this issue is given to pharmacology and reducing the risks of poly-pharmacotherapy. Works in this area have become more and more active in the last three years, and relate primarily to the problems of experimental medicine - the search for new drugs or classification of patients by images. In this connection, the development of algorithms for processing big data in the field of clinical pharmacology, including regulatory documents, instructions for the use of drugs, reliable medical resources for machine learning and building a knowledge base focused on practical application in real medical practice, is becoming increasingly important. One of the considered approaches for solving the problems described above is the search for an effective vector representation of the found concepts (word embedding) to build models of recommendations for the practical use of drugs based on the diagnosis and models for identifying their compatibility. The article presents a software tool for visualizing a graph model of medicines and their classes, considering their incompatibility. The problem of incompatibility of medicines is being investigated. The considered database of medicines, obtained from various sources, and the advantages of its use are described. The article presents the results of the developed prototype of a software tool that allows you to visualize a complete graph of relationships between drugs, a graph of incompatible drugs for a selected drug, and a graph of compatibility of two drugs. Graph model visualization provides a visual representation of complex relationships and allows researchers and pharmacologists to better understand interactions between drugs. The software offer the necessary tools to make informed decisions when prescribing medications and improve safety when using medicines.

Analysis of Shopping Addiction Behavior of Douyin Users under the Background of New Media

As a product of new media technology, live streaming commerce has emerged as a popular consumption trend. With its interactive nature, the variety of live streaming commerce formats continues to expand. Notably, food-related live streaming commerce has gained significant popularity among consumers. Increasingly, people are engaging in addictive behaviors, purchasing food products through these platforms. This paper focuses on examining the addictive behavior of new media users in food commercial live streaming, using Douyin as a case study. The research delves into the relationship between users addictive behavior and the various types of live streaming commerce, the interactive dynamics between users and anchors, as well as the role of big data algorithms used by new media platforms. By exploring these relationships, the study sheds light on the captivating aspects of food commerce live streaming and its impact on consumer behavior.