Application Of Big Data Analytics Research Articles

Application of big data analysis in water pollution monitoring

Contemporarily, the quick development of an abundant amount of big data analysis technologies has brought great convenience to individuals' everyday existence. In terms of environmental protection, especially water pollution monitoring, this technological progress is particularly critical. As the global demand for clean water resources grows and global industrialization intensifies pollution of the water environment, the adoption of advanced data analysis technologies has become critical. Among the vast array of machine learning architectures, three particularly stand out due to their significance and widespread adoption: the artificial neural network (ANN), which serves as a foundational pillar in understanding complex data patterns; the multi-layer perceptron neural network (MLPNN), a sophisticated evolution that allows for deeper computations and learning; and the adaptive neuro-fuzzy inference system (ANFIS), which brilliantly combines neural and fuzzy logic principles for intricate problem solving. These models not only have high accuracy due to their wide application, but they still have their own limitations. This article aims to introduce the methods, basic principles, and application scenarios of these models. In addition, this article also compares the advantages and limitations of these machine learning models, thereby providing some new ideas for future improvements and innovations in model algorithms, application scenarios, and integration.

Investigation of Artificial Intelligence Algorithms in Robot Object Recognition Systems Under the Background of Big Data

In the long history of human beings, with the continuous exploration and research of natural phenomena and social life, many scientific fields have emerged, and robots are the product of this technological development to a certain stage. At present, there are hundreds of different types of robots applied in production and daily life in the world, which have achieved significant economic benefits. However, its technical issues have gradually emerged. For example, the shortcomings in visual perception and other aspects cannot be effectively addressed. Object recognition is not precise enough, and information resources cannot be effectively utilized to achieve control functions. These are the main factors that constrain the further progress and improvement of robots. The emergence of big data and Artificial Intelligence (AI) has brought unprecedented opportunities to robots. Especially, the application of big data analysis in intelligent manufacturing and smart city construction is becoming increasingly widespread, thus providing new solutions for robot services. They not only enable people to quickly and accurately grasp a large amount of valuable knowledge, but also better tap into the enormous potential contained in human intelligence, which largely drives the robot industry towards intelligence. By summarizing the existing research results, this paper explored the development trend of robot object recognition systems, and focused on its key technologies, the feature matching-based pattern recognition and acceleration strategy-based detection efficiency improvement. In response to the current problems, corresponding solutions were proposed and comparative experiments were designed. This proved that the anti-interference detection accuracy of the robot object recognition system based on big data and AI algorithm improved by about 12.48%, thus hoping to provide reference for future robot system development.

Application of Big Data Analysis in Identifying Problems in Financial Reporting of BYD Company

With the advancement of technology, big data has been applied to various industries and fields. Its emergence has also sparked a revolution in accounting information processing and transmission technology. In the era of big data, the environment for corporate financial management is changing. For the new energy vehicle industry, the government's subsidy intensity for related enterprises has been continuously shrinking since 2017. In addition, the limited capabilities of enterprises themselves have brought enormous financial pressure to new energy vehicle enterprises. Therefore, for new energy vehicle enterprises, it is particularly important to avoid financial risks and enhance financial stability in this period. Therefore, this article takes BYD Company as a case study and uses big data analysis methods to identify the problems in cash flow, operational capacity, and debt repayment ability in financial reports, and proposes several improvement measures to adapt to the requirements of enterprise development in the new era of big data.

A study on big data analytics and innovation: From technological and business cycle perspectives

In today's rapidly changing business landscape, organizations increasingly invest in different technologies to enhance their innovation capabilities. Among the technological investment, a notable development is the applications of big data analytics (BDA), which plays a pivotal role in supporting firms' decision-making processes. Big data technologies are important factors that could help both exploratory and exploitative innovation, which could affect the efforts to combat climate change and ease the shift to green energy. However, studies that comprehensively examine BDA's impact on innovation capability and technological cycle remain scarce. This study therefore investigates the impact of BDA on innovation capability, technological cycle, and firm performance. It develops a conceptual model, validated using CB-SEM, through responses from 356 firms. It is found that both innovation capability and firm performance are significantly influenced by big data technology. This study highlights that BDA helps to address the pressing challenges of climate change mitigation and the transition to cleaner and more sustainable energy sources. However, our results are based on managerial perceptions in a single country. To enhance generalizability, future studies could employ a more objective approach and explore different contexts. Multidimensional constructs, moderating factors, and rival models could also be considered in future studies.

Big Data Analytics: Techniques, Tools, and Applications in Various Industries


 
 
 Big data analytics has emerged as a powerful tool for extracting valuable insights from large and complex datasets across diverse industries. This article provides a comprehensive review of the techniques, tools, and applications of big data analytics in various industries. Through qualitative analysis and extensive literature review, this study explores the key methods and technologies used in processing and analyzing massive volumes of data to uncover hidden patterns, trends, and correlations. The article discusses the significance of big data analytics in enhancing decision-making processes, optimizing operations, and gaining competitive advantages in today's data-driven economy. Furthermore, it examines the challenges and opportunities associated with implementing big data analytics solutions across different sectors. By examining real-world case studies and industry examples, this research highlights the practical applications of big data analytics in areas such as healthcare, finance, manufacturing, retail, and marketing. The findings underscore the transformative impact of big data analytics on improving business performance, customer engagement, and innovation across diverse domains. Overall, this article aims to contribute to the understanding of big data analytics techniques and their wide-ranging applications, providing insights for researchers, practitioners, and decision-makers in leveraging data-driven approaches to drive organizational success.
 
 

Exploring the dynamics of bigdata adoption in the Indian food industry with fuzzy analytical hierarchical process

PurposeThe main aim of this study is to identify and prioritize the factors that influence the adoption of big data analytics (BDA) within the supply chain (SC) of the food industry in India.Design/methodology/approachThe study is carried out in two distinct phases. In the first phase, barriers hindering BDA adoption in the Indian food industry are identified. Subsequently, the second phase rates/prioritizes these barriers using multicriteria methodologies such as the “analytical hierarchical process” (AHP) and the “fuzzy analytical hierarchical process” (FAHP). Fifteen barriers have been identified, collectively influencing the BDA adoption in the SC of the Indian food industry.FindingsThe findings suggest that the lack of data security, availability of skilled IT professionals, and uncertainty about return on investments (ROI) are the top three apprehensions of the consultants and managers regarding the BDA adoption in the Indian food industry SC.Research limitations/implicationsThis research has identified several reasons for the adoption of bigdata analytics in the supply chain management of foods in India. This study has also highlighted that big data analytics applications need specific skillsets, and there is a shortage of critical skills in this industry. Therefore, the technical skills of the employees need to be enhanced by their organizations. Also, utilizing similar services offered by other external agencies could help organizations potentially save time and resources for their in-house teams with a faster turnaround.Originality/valueThe present study will provide vital information to companies regarding roadblocks in BDA adoption in the Indian food industry SC and motivate academicians to explore this area further.

Research on Learning Social Networks and Collaboration in Education Using Big Data Analytics and Machine Learning

This paper explores the application of big data analytics and machine learning in education, particularly in the context of learning social networks and collaborative research. By systematically reviewing existing literature and conducting theoretical analysis, this study delves into how big data and machine learning technologies can support and optimize learning behaviors and collaboration patterns within educational social networks. Using literature review and theoretical deduction methods, the research analyzes the potential applications of big data analytics in educational data processing, learning behavior prediction, and personalized learning recommendations. Additionally, it examines the contributions of machine learning algorithms in identifying and optimizing node relationships, information dissemination paths, and collaboration efficiency within learning social networks. By comparing domestic and international research outcomes and considering current social trends and policy directions, several theoretical models and analytical frameworks are proposed to provide theoretical support and reference for future educational research. The results indicate that big data analytics and machine learning technologies not only enhance the efficiency of educational data processing, but also significantly improve information flow and collaboration quality within learning social networks, thereby promoting the development of personalized and collaborative learning. This theoretical analysis offers new perspectives and approaches for further exploring the applications of big data and machine learning in the education sector.

Multimodal Imaging in Oncology: Challenges and Future Directions

Multimodal imaging, a vital tool in oncology, combines various imaging techniques such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) to offer valuable insights for tumor detection, diagnosis, staging, and treatment planning. Despite its advantages, challenges persist in image registration, fusion, optimization of algorithms, hardware advancements, and the integration of artificial intelligence (AI) and computer vision techniques. This paper delves into the current state of multimodal imaging in oncology, addresses the challenges faced, explores potential solutions, and underscores future developments to augment diagnostic and therapeutic capacities, including the application of AI, computer vision, and big data analytics to enhance the efficiency and effectiveness of multimodal imaging in cancer diagnosis and treatment. Additionally, it highlights how AI-powered algorithms can automate image analysis, assist in tumor segmentation, and provide predictive insights, ultimately improving the precision and speed of diagnosis and treatment planning in oncology.

Evaluation of a software positioning tool to support SMEs in adoption of big data analytics

Big data analytics has been widely adopted by large companies to achieve measurable benefits including increased profitability, customer demand forecasting, cheaper development of products, and improved stock control. Small and medium sized enterprises (SMEs) are the backbone of the global economy, comprising of 90 % of businesses worldwide. However, only 10 % SMEs have adopted big data analytics despite the competitive advantage they could achieve. Previous research has analysed the barriers to adoption and a strategic framework has been developed to help SMEs adopt big data analytics. The framework was converted into a scoring tool which has been applied to multiple case studies of SMEs in the UK. This paper documents the process of evaluating the framework based on the structured feedback from a focus group composed of experienced practitioners. The results of the evaluation are presented with a discussion on the results, and the paper concludes with recommendations to improve the scoring tool based on the proposed framework. The research demonstrates that this positioning tool is beneficial for SMEs to achieve competitive advantages by increasing the application of business intelligence and big data analytics.

Cloud-Based Infrastructure and DevOps for Energy Fault Detection in Smart Buildings

We have designed a real-world smart building energy fault detection (SBFD) system on a cloud-based Databricks workspace, a high-performance computing (HPC) environment for big-data-intensive applications powered by Apache Spark. By avoiding a Smart Building Diagnostics as a Service approach and keeping a tightly centralized design, the rapid development and deployment of the cloud-based SBFD system was achieved within one calendar year. Thanks to Databricks’ built-in scheduling interface, a continuous pipeline of real-time ingestion, integration, cleaning, and analytics workflows capable of energy consumption prediction and anomaly detection was implemented and deployed in the cloud. The system currently provides fault detection in the form of predictions and anomaly detection for 96 buildings on an active military installation. The system’s various jobs all converge within 14 min on average. It facilitates the seamless interaction between our workspace and a cloud data lake storage provided for secure and automated initial ingestion of raw data provided by a third party via the Secure File Transfer Protocol (SFTP) and BLOB (Binary Large Objects) file system secure protocol drivers. With a powerful Python binding to the Apache Spark distributed computing framework, PySpark, these actions were coded into collaborative notebooks and chained into the aforementioned pipeline. The pipeline was successfully managed and configured throughout the lifetime of the project and is continuing to meet our needs in deployment. In this paper, we outline the general architecture and how it differs from previous smart building diagnostics initiatives, present details surrounding the underlying technology stack of our data pipeline, and enumerate some of the necessary configuration steps required to maintain and develop this big data analytics application in the cloud.

Review On E-Commerce Application with Leveraged Cryptosystems and Big Data

The fast improvements in technology and the growing desire for safe and easy online purchasing have caused a dramatic shift in the e-commerce market in recent years. The merging of big data analysis and encryption, two potent instruments that are transforming how companies run and customers interact in the digital marketplace, is at the core of this change. In e-commerce applications, cryptography, the study of secure communication, is essential for maintaining transaction integrity, protecting user privacy, and securing sensitive data. Businesses may optimize pricing strategies, improve auction efficiency, and obtain important consumer insights by practicing big data analysis, which is the art of gleaning insights from large volumes of bidding data. This in-depth analysis explores the diverse applications of big data analysis and cryptography in the e-commerce industry, delving into their complex worlds. We look at the underlying ideas behind these technologies, the range of applications they can be used for, and how they affect the entire e-commerce ecosystem

Big Data Analysis in Consumer Behavior: Evidence from the Retail, Healthcare, and Financial Services Industries

As a matter of fact, big data analytics has quickly become one of the cornerstones of various industries such as retail, healthcare, and financial services. This study delves into the applications of big data analytics within various sectors, exploring how models such as AISAS, PDCA, and customer segmentation can assist organizations in crafting successful data-driven strategies. Based on case studies, it is found that retailers utilizing the AISAS model can significantly increase customer engagement and optimize marketing strategies, while healthcare organizations applying PDCA through big data analytics improve patient outcomes and operational efficiencies. Financial services using customer segmentation models experience improved customer satisfaction and profitability. Big data analytics enable more customized, efficient decision-making processes. This extensive review explores how big data analytics can provide tailored solutions to complex industry problems. The findings from this investigation can assist businesses across multiple sectors understand both its practical benefits and possible downsides when implemented into operations.

Examining the Dimensions of Adopting Natural Language Processing and Big Data Analytics Applications in Firms

Big data analytics (BDA) is an advanced analytic technique used with very large and diverse sets of data from different sources. Natural language processing (NLP) is a technology that interfaces with different fields such as computer science, linguistics, and human-computer interactions. Over the past few years, there is a growing number of firms, which are using different BDA and NLP applications in their businesses. Only a few of the research have investigated different dimensions of NLP and BDA and their impacts on the overall organizational performance. There is a growing interest among researchers and practitioners in understanding the consequences for firms that adopt BDA and NLP applications. In this context, the aim of this article is to determine the factors for the usage of BDA and NLP applications in business. With the help of dynamic capability view theory and existing literature, a theoretical model was developed conceptually. Later, the model was validated using structural equation modeling approach considering 1287 samples from 23 firms, primarily based in Asia and Europe, which use NLP and BDA applications. The article finds that NLP and BDA applications help the firms to improve their operational efficiency, which in turn improves the overall firm performance.

Research and Practice of the Course Ideological and Political Education in the Case-Driven “Principles and Applications of Big Data Analysis” Course

Taking the case driven “Principles and Applications of Big Data Analysis” course ideological and political education as the research object, we analyze the current problems of ideological and political education in science and engineering courses in universities, and propose an overall design framework for course ideological and political education. The principles and criteria for case selection were provided, while typical cases were provided for the four ideological and political elements of science and technology: Serving the country, scientific thinking, engineering ethics, and craftsmanship spirit. The specific implementation methods of case discussions were also introduced in detail. Through a questionnaire survey, it was found that case driven curriculum ideological and political education has a positive impact on students’ learning effectiveness and ideological and political literacy. This study provides new ideas and methods for the ideological and political education of science and engineering courses in universities, and has certain significance for the practice of ideological and political education in science and engineering courses in universities.

The Application of Big Data Analysis in Elderly Oriented Renovation Space Design

In order to understand the application of big data analysis in the design of elderly-oriented renovation spaces, an application study of big data analysis in the design of elderly-oriented renovation spaces has been proposed. The rapid development of the economy has led to the prosperity and spatial expansion of cities. At the same time, social issues such as land scarcity, traffic congestion, environmental pollution, and the uneven allocation of urban spatial resources have led to social polarization, marginalization of the interests of vulnerable groups, and seriously affected the physical and mental health of residents. With the intensification of the aging trend of the world population, aging has become a major global strategic issue. How to create a truly suitable spatial environment for elderly living has become a question worth considering. This article uses methods such as questionnaire surveys and in-depth interviews to obtain a large amount of data, analyze the current situation of the living space of the elderly in Changchun City, the physiological and psychological needs, activity characteristics, social interaction characteristics, etc. of the elderly group, and then propose suggestions for the planning and design of the living space of healthy urban elderly.

A model for the application of big data analytics in cost prediction and control in construction projects

Abstract Construction project cost control is an important aspect of project management. This paper aims to provide an effective cost control management mode for the construction industry by studying the application of big data technology in cost prediction and control management of construction projects. The standard particle swarm algorithm is optimized by establishing a particle object element model, performing iterative and hierarchical updating of particles, and introducing the Tent mapping strategy. Then it is used in the optimization of the Extreme Learning Machine algorithm to design a construction project cost prediction and control model based on APSO-LLM. The index system of the construction project cost prediction and control model is constructed, and the convergence effect, prediction effect, and case application of the model are analyzed. The APSO-LLM model enhances the convergence speed and search accuracy, boosts the optimal fitness by 0.037, and maintains a construction cost prediction error below 3%. The study proves the APSO-LLM model’s applicability, accuracy, and good prediction effect in cost prediction and construction project control.

Application of Big Data Analysis in Model Construction to Prevent Athlete Injury in Training

Abstract Sports injury is a hot issue in the field of exercise science and sports medicine and a practical problem that needs to be solved urgently. Based on big data analysis, this paper proposes an Ada-RF prediction model that integrates the fusion of the Adaboost algorithm and Random Forest algorithm by studying five machine learning algorithms: logistic regression, k-nearest neighbor, plain Bayes, Adaboost algorithm, and Random Forest and evaluates experiments through the model evaluation criteria. The influence factors of injury risk in adolescent male basketball players were explored in terms of demographic information, training load, subjective perceived health, and assessment of athletic quality. By screening the important factors as independent variables, the risk of athlete injury and illness as dependent variables were applied to the model constructed in this paper and the models built by five traditional machine learning classification algorithms. The prediction effects of multiple models are compared. The experimental results show that the accuracy, recall, specificity, F1 score, sensitivity, and AUC value of the Ada-RF model constructed in this paper are 0.869, 0.885, 0.755, 0.683, 0.754, and 0.789, respectively, which indicate that the Ada-RF integrated model outperforms the single prediction model, and can be used for the early prevention and early treatment of athletes’ injuries and illnesses. It shows that the Ada-RF integrated model outperforms the single prediction model and can provide scientific and accurate auxiliary information for the early prevention and treatment of injuries and diseases in athletes.

Application of Big-Data Analysis and QFD Based Quality Management Model on Powertrain Electronic Control Software Development

Application of Big-Data Analysis and QFD Based Quality Management Model on Powertrain Electronic Control Software Development

Analysis of Weather Prediction, Resource Management, and Land Optimization on the Application of Big Data Analytics in Agricultural Land Utilization in Agrarian Areas of West Java

This research investigates the impact of Weather Prediction, Resource Management, and Land Optimization on the adoption of Big Data Analytics in agricultural land utilization within the agrarian region of West Java. Employing a quantitative approach, the study integrates measurement model analysis, structural equation modeling, demographic profiling, and model fit assessment to comprehensively explore the intricate dynamics of technological adoption in agriculture. Results indicate that Land Optimization, Resource Management, and Weather Prediction significantly influence the adoption of Big Data Analytics. Demographic factors such as gender, age, education, and farming experience demonstrate varying correlations with key variables. The model exhibits strong fit, and approximately 60.2% of the variance in Big Data Analytics adoption is explained by the combined influence of the identified factors. This study contributes nuanced insights to inform policy and practice for sustainable and technology-driven agriculture in West Java.

Big Data Analysis in Consumer Behavior: Evidence from Social Media and Mobile Payment

Recently, along with the continuous progress of science and technology, big data analytics has started to be applied in all works of life. From the perspective of consumer behavior studies, big data analytics is a tool that can effectively predict future consumer behavior. Consumer behavior studies is a discipline that focuses on understanding the behavior and motivations of consumers during the process of purchasing products or services. In the past, researchers mainly relied on traditional research methods such as questionnaire surveys and field observations to understand consumer behavior and needs. When the Internet era arrives, the amount of data generated by consumers has exploded, providing vast opportunities for the application of big data analytics. This article provides a brief overview of the application of big data analytics in analyzing consumer behavior in social media and mobile payment, and briefly reveals the conclusions drawn from these analyses. It aims to provide a small contribution to further understanding the application of big data analytics in the field of consumer behavior studies.