Data Analysis Technology Research Articles

Role of Cooperative in Promoting Women’s Empowerment

Women empowerment is the process of enhancement of women development through different initiatives, cooperative is the unity of people, basically for economic enhancement of local people. The cooperative collects the small amount money from local people basically from women and they provide to the needy people among the members of cooperative. Main aim of this article is to analyzed the role of cooperative to enhance the women empowerment of study area, this article will be useful to researcher, students, teachers and other related people who are directly and indirectly working in cooperative and women empowerment field. Quantitative research methods, descriptive and analytical research design has been applied with deductive approach. Both primary and secondary data are used. Data are analyzed using the latest data analysis tool and technology. The cooperative is playing vital role to empowerment of women through the economic enhancement.

Research on the Application of PID Control Algorithm and Fuzzy Control Theory in the Field of Temperature Control

In the field of industrial automation control systems, PID control algorithms are widely used in various control fields such as temperature, speed and position. Among them, fuzzy PID control can improve control accuracy and stability, can significantly improve product quality and production efficiency, in food processing, chemical engineering, pharmaceutical and textile industries have a wide range of applications. Therefore, this paper comprehensively discusses the theory of fuzzy PID control algorithm and its application in the field of temperature control. The advantages of fuzzy PID control in complex industrial environments are emphasized through a comparative analysis with other temperature control methods as well as traditional linear PID control. The research indicates that by optimizing the control algorithm and hardware design, the fuzzy PID control algorithm can reduce the system overshoot and steady state error, thus improving the control accuracy and response speed. At the same time, the introduction of multivariable PID controllers or the use of advanced multivariable control strategies can achieve coordinated control of multiple variables, thereby improving the performance of the entire system. In addition, the integration of advanced energy management system and data analysis technology can comprehensively monitor and optimize the management of energy consumption, further improving the energy efficiency of the system

Advancing Genetic Engineering through AI: Sequencing and Editing Innovations

As an important part of modern biotechnology, genetic engineering is widely used in fields such as disease treatment, agricultural improvement, and environmental protection. Gene sequencing technology, especially next-generation sequencing technology, provides a powerful tool for studying biological genetic information. However, with the rapid growth of genomic data, how to efficiently and accurately analyze and apply this huge data has become a major challenge facing genetic engineering. In recent years, artificial intelligence (AI) technology, especially deep learning, has been widely used in the automated processing of large-scale data and has shown great potential in genetic engineering. AI technology not only shows advantages in gene editing optimization, genetic variation detection and genome association analysis, but also significantly improves the efficiency and accuracy of genetic data analysis. Although AI has brought many conveniences in genetic engineering, challenges such as technology transparency, data quality issues, and ethics and privacy protection still need to be solved. This article explores the application of artificial intelligence in genetic engineering sequencing and data analysis, analyzes how AI can improve the efficiency and accuracy of genetic data analysis, and discusses the potential contribution of AI in gene editing and precision medicine. As AI continues to develop, it is expected to play an increasingly important role in fields such as genomics, gene editing, and precision medicine, and provide more effective strategies for future disease treatment

EXPLORING THE PLIGHT OF THE HONEYBEE: USING DATA SENSORS AND CODAP TO SUPPORT EMERGING BILINGUAL LEARNERS IN REASONING ABOUT BIG STATISTICAL IDEAS

Children require access to high-quality statistics education to develop the skills to participate in a technological and data-reliant workforce. This study consisted of a five-lesson integrated STEM unit designed to develop the statistical literacy of 62 6th-grade (11–12 years old) emerging bilingual (EB) learners. Learning was situated in the study of the honeybee, utilising innovative technologies to gather data and support data visualisation and analysis. Lesson study was used to design lessons targeting understandings of distribution, centre, variability, data comparison, informal measures of association and informal inference. This paper reports on the data comparison lesson. It reveals the influential role of digital technologies in highlighting the relevance of statistics in understanding societal issues and developing students’ statistical agency. This qualitative study also revealed that the development of statistical understanding was supported by the use of inclusive pedagogies guided by the principles of universal design and the incorporation of data analysis technologies.

Analysis of OPC Data Using Federated Learning: An Evaluation of Performance and Privacy

This study examines the benefits of applying federated learning (FL) technology to OPC (Operational Performance Control) systems within industrial automation and data analysis processes. FL enables each production facility to process its data locally while only transmitting model parameters to a central server, thereby preserving data privacy. This approach provides significant advantages in industrial environments, particularly concerning data privacy and communication costs. The study evaluates FL's potential to ensure data privacy, reduce communication costs, improve efficiency in training time, and deliver high performance in predictive maintenance and quality estimation. Model performance was analyzed using accuracy, F1 score, precision, and loss metrics; the results demonstrated that FL achieved a 90% accuracy rate, offering competitive performance compared to centralized modeling. In predictive maintenance and quality analysis specifically, FL achieved 85-88% accuracy while reducing network data load by 65%. These findings validate that FL provides a secure, cost-effective, and efficient solution for industrial data analysis processes by eliminating the need for centralized data collection. In conclusion, FL and OPC integration supports data privacy, cost savings, and communication efficiency in industrial processes. The study highlights that FL could become a prevalent technology in industrial data analysis, establishing a new standard particularly in digital manufacturing processes.

Data-Driven and Sustainable Transportation Route Optimization in Green Logistics Supply Chain

In the green logistics supply chain, transportation route optimization faces urgent problems such as high energy consumption and environmental pollution. This paper aims to achieve sustainable optimization of transportation routes through a data-driven approach. First, a large amount of transportation-related data is collected, including vehicle operation information, cargo characteristics, road conditions and meteorological factors. Big data analysis technology is used to clean and extract features from the data, and a transportation demand forecasting model is constructed. Then, a new optimization model is designed using the particle swarm optimization algorithm, with the goal of minimizing transportation costs and carbon dioxide emissions. In actual application cases, by optimizing the transportation routes of a logistics company, the results showed that the lowest transportation cost was 1,512 dollars and the lowest carbon dioxide emissions were 1.2 tons. Data-driven transportation route optimization not only improves logistics efficiency, but also promotes environmental sustainable development.

Risk management of engineering projects installation using Analytic Hierarchy Process

The electromechanical installation project in the city park is confronted with numerous risks, prominently involving uncertainty and human factors, which are critical to the project’s overall success. This study carefully identified and categorized the project risks into a detailed hierarchical structure, employing the Analytic Hierarchy Process (AHP) to systematically quantify their relative importance. The findings revealed that uncertainty factors bear a significantly greater influence on the overall project risks compared to human factors. Notably, the research highlighted an inverse relationship between the reduction of uncertainty and the effectiveness of human intervention, thus offering a novel perspective on managing project risks. This study's technical innovation lies in the creation of a comprehensive risk assessment framework, which enables project managers to accurately identify, evaluate, and devise effective control strategies tailored to the specific risks identified. Future research endeavors will focus on harnessing advanced data analysis technologies and artificial intelligence algorithms to enhance the risk assessment process further, aiming to improve decision-making accuracy, optimize resource allocation, and ultimately boost project success rates.

The Impact of Big Data on the Fashion Industry

This study explores the impact of big data on the fashion industry. Through analysis of market trend forecasting, supply chain management, personalized design and customization, optimization of marketing strategies, acceleration of market responsiveness, enhancement of market competitiveness, and improvement of consumer experience, the potential applications of big data in the fashion sector are further elucidated. The research reveals that big data can effectively boost overall operational efficiency in the fashion industry, enabling companies to achieve precise marketing, optimize inventory management, shorten time-to-market for products, reduce operational costs, and enhance market competitiveness. However, the application of big data in the fashion industry also presents challenges such as data security and privacy protection, risks of data breaches, security of data storage and transmission, and barriers to data analysis technology. Therefore, when utilizing big data technologies, businesses must strengthen data security measures, enhance data analysis capabilities, ensure the lawful use of data, and strive for sustainable development.

Challenges and Prospects of Sensing Technology for the Promotion of Tele-Physiotherapy: A Narrative Review

Significant developments in sensing technology have had many impacts, enhancing monitoring and assessment accuracy across diverse fields. In the field of physical therapy, sensing, which plays a pivotal role in tele-physiotherapy, rapidly expanded amid the COVID-19 pandemic. Its primary objective is to monitor biological signals and patient movements at remote locations. To further enhance the effectiveness and the scope of tele-physiotherapy, it is essential to further develop sensing and data analysis technologies. However, there are usability and analysis issues that have limited its use. The development of these technologies will not only enhance the accuracy of deep learning by AI through the acquisition of big data, but also has the potential to elucidate movement characteristics associated with movement disorders or pathological conditions. Furthermore, improving sensing technologies can broaden applications extending beyond tele-physiotherapy to impact daily life. Looking forward, it holds promise for improving our understanding of disease prognosis and progression.

Measuring and Forecasting the Development Concept of the “Green” Macrosystem Using Data Analysis Technologies

A research framework is formed by the semantics of the “green” macrosystem, supported by a methodological approach, data analysis, and forecasting, with a focus on the dynamics of transition to a qualitatively new state. The purpose of the work is to develop conceptual provisions and methodological tools for assessing the implementation of the concept of a “green” macrosystem. Applying methods of system analysis, content analysis, formalization, statistical analysis (technologies of knowledge discovery in databases and time series analysis), and discriminant analysis contributed to achieving the goal. As a result of the research, the categorical apparatus of a “green” economy was clarified by outlining narrow and broad approaches to defining the “green” macrosystem; the author’s method of assessing the implementation of the concept of the “green” macrosystem was modified by expanding the list of factors of greening the economic system, transforming the condition of the “green” corridor to calculate the developed dynamic coefficient of transition to the concept of the “green” macrosystem (DCGM), and adapting the method to the macrosystem level; the regularities of a transition to the concept of a “green” macrosystem were revealed. The novelty of the study lies in the proposal of an integral DCGM indicator, which avoids the problems of normalization, weighting, and loss of relevant data, incorporates the determinants of the “green” economy (natural resources and pollution), and relies on available data. The formulated provisions develop the theoretical basis on which to transform the macrosystem to the “green” concept and can be taken into account in the implementation’s framework of strategic planning documents for the greening of production and economic systems.

Investment Market Trend Forecasting and Strategy Optimization based on Big Data Analytics

The financial market is an important part of the global economy, and there exists a certain special internal regularity in its changes. With the rapid development of big data technology, the huge amount of data in the financial market provides new opportunities for the prediction of investment trends and strategy optimization. Based on big data analytics technology, combined with machine learning models, the study explores how to construct investment market trend prediction models through effective data preprocessing and feature engineering, and evaluates and compares the performance of different prediction models. How to accurately predict the direction of the financial market will be an important reference value for relevant practitioners as well as researchers. Traditional financial time series research mainly focuses on linear trend fitting based on statistical knowledge, however, the trend of the financial market is not limited to pure mathematical and rational function curves, the financial market is affected by a combination of various factors from all walks of life. Big data analytics has significant application potential in the investment market, providing investors with more scientific and accurate decision support.

Applied body-fluid analysis by wearable devices.

Wearable sensors are a recent paradigm in healthcare, enabling continuous, decentralized, and non- or minimally invasive monitoring of health and disease. Continuous measurements yield information-rich time series of physiological data that are holistic and clinically meaningful. Although most wearable sensors were initially restricted to biophysical measurements, the next generation of wearable devices is now emerging that enable biochemical monitoring of both small and large molecules in a variety of body fluids, such as sweat, breath, saliva, tears and interstitial fluid. Rapidly evolving data analysis and decision-making technologies through artificial intelligence has accelerated the application of wearables around the world. Although recent pilot trials have demonstrated the clinical applicability of these wearable devices, their widespread adoption will require large-scale validation across various conditions, ethical consideration and sociocultural acceptance. Successful translation of wearable devices from laboratory prototypes into clinical tools will further require a comprehensive transitional environment involving all stakeholders. The wearable device platforms must gain acceptance among different user groups, add clinical value for various medical indications, be eligible for reimbursements and contribute to public health initiatives. In this Perspective, we review state-of-the-art wearable devices for body-fluid analysis and their translation into clinical applications, and provide insight into their clinical purpose.

Deep learning-driven multi-omics sequential diagnosis with Hybrid-OmniSeq: Unraveling breast cancer complexity

Breast cancer results from an uncontrolled growth of breast tissue. Many methods of diagnosis are using multi-omics data to better understand the complexity of breast cancer. The new strategy laid out in this work, called “Hybrid-OmniSeq,” is a deep learning-based multi-omics data analysis technology that uses molecular subtypes of breast cancer gene to increase the precision and effectiveness of breast cancer diagnosis. For preprocessing, the BC-VM procedure is utilized, and for molecular subtype analysis, the BC-MSA procedure is utilized. The implementation of Deep Neural Network (DNN) technology in conjunction with Sequential Forward Floating Selection (SFFS) and Truncated Singular Value Decomposition (TSVD) entropy enable adaptive learning from multi-omics gene data. Five machine learning classifiers are used for classification purpose. Hybrid-OmniSeq uses a variety of machine learning classifiers in a thorough analytical process to achieve remarkable diagnostic accuracy. Deep Learning-based multi-omics sequential approach was evaluated using METABRIC RNA-seq data sets of intrinsic subtypes of breast cancer. According to test results, Logistic Regression (LR) had ER (Estrogen Receptor) status values of 94.51%, ER status values of 96.33%, and HER2 (Human Epidermal growth factor Receptor) status values of 92.3%; Random Forest (RF) had ER status values of 93.77%, ER status values of 95.23%, and HER2 status values of 93.4%. LR and RF increase the cancer detection accuracy for all subtypes when compared to alternative machine learning classifiers or the majority voting method, providing a comprehensive understanding of the underlying causes of breast cancer.

IoT-Based System for Real-Time Fall Risk Assessment and Health Monitoring

Real-time fall risk assessment and continuous health monitoring are critical components in enhancing elderly care and preventing fall-related injuries. This study presents an IoT-based system designed to provide real-time fall risk assessment and monitor health parameters using wearable sensors. The system integrates the MPU6050 sensor with IoT technology for efficient data collection and analysis. A Random Forest algorithm is employed to process the complex health data, offering precise and reliable fall detection models. The algorithm demonstrates high sensitivity, precision, and accuracy, making it well-suited for processing sensor data to detect falls. The study identifies the waist as the optimal sensor placement, achieving up to 97.9% accuracy, 95.0% precision, and 95.4% sensitivity in detecting mild falls while standing. The wrist sensor performs well in detecting sudden falls, while the leg sensor shows lower accuracy, highlighting challenges in identifying specific fall types. Model validation with Support Vector Machine (SVM) and Random Forest (RF) reveals that the RF model outperforms the SVM, confirming its superiority for fall detection tasks. The system's adaptability and potential for personalized risk assessment promise significant improvements in fall prevention strategies. These findings highlight potential applications that go beyond elderly care, involving at-risk individuals in future research, including those with neurological disorders, sports injuries, or disabilities.

Метафоры визуализации в задачах разведочного анализа гетерогенных данных

The subject of the study is the construction and application of visual models using the concept of visualization metaphors in the context of exploratory analysis of heterogeneous data. This study considers improved variants of the previously proposed visualization metaphors that can be used as a basis for building visual models. A technology for exploratory analysis of heterogeneous data based on the joint use of different visualization metaphors is proposed. The process of visual data exploration at the stage of exploratory analysis using the proposed technology is demonstrated to be iterative and multiscenary, contingent upon the analysis goals. The software tool developed to implement the proposed technology is described, along with its additional functionality to calculate and export quantitative characteristics of the visual model. The software tool is then considered in the context of exploratory analysis of a synthetic data set. The future direction of the proposed approach to the construction of visual models, the technology of exploratory data analysis and the software tool for its support are determined.

Integrating computing techniques in preserving Makassar’s Pakarena dance heritage

This interdisciplinary study bridges the cultural heritage of the Pakarena dance, a key element of Makassar ethnicity, with contemporary computing and informatics to examine its social implications, particularly in terms of identity formation, community transmission, and narrative preservation. Emphasizing the dance’s crucial role within the Makassar community, the research employs digital technologies for comprehensive data collection, analysis, and dissemination. Utilizing a qualitative framework supplemented by digital ethnography, the methodology includes in-depth interviews and participant observation, enriched with advanced data analysis and virtual reality (VR) presentations. This innovative approach facilitates the digital capture of the narratives and experiences of dance practitioners, cultural experts, and community members, ensuring the preservation of the dance’s cultural narrative and expression. The study reveals that the Pakarena dance is not only a bearer of the Makassar community’s history and traditions but also a platform for individual creativity and cultural identity, adapting while preserving its core in the face of societal shifts. The findings highlight the potential of computing and informatics in cultural preservation, suggesting new methods for documenting, analyzing, and promoting intangible cultural heritage. The study advocates for the use of technology to enhance and perpetuate cultural heritage, especially for younger generations, in our increasingly digital era.

Implicaciones de la inteligencia artificial en la metodología de investigación

This systematic review explores how advanced data analysis technologies are transforming scientific research methodology. Sources describing the transformation of various stages of the research process by these tools, from hypothesis generation to results interpretation, were examined. These technologies offer unprecedented possibilities to enhance research. They allow for the identification of complex patterns, such as subtle trends in genomic data or unexpected correlations in sociological studies, which might go unnoticed with traditional methods. Additionally, they automate repetitive tasks, freeing up time for more in-depth analysis. Significant ethical and practical challenges are highlighted. Algorithmic biases, for example, could perpetuate existing prejudices in training data, affecting the validity of results in fields such as medical or socioeconomic research. Concerns also arise regarding the privacy of study participants and the risk of exclusion for groups underrepresented in the data. The review concludes that adopting a holistic and responsible approach is imperative. In practice, this involves developing robust ethical frameworks, fostering collaboration between disciplines such as computer science, ethics, and social sciences, investing in specialized education, promoting transparency in algorithmic processes, and strengthening global cooperation through responsible data sharing and the development of common research standards.

A Comprehensive review on technological breakthroughs in precision agriculture: IoT and emerging data analytics

Rapid population expansion has led to a corresponding rise in the demand for sustenance. Researchers have found that traditional agricultural practices are insufficient to meet the demands of commodities, and their inefficiency poses the most pressing obstacle to addressing the growing global food demand. Precision agriculture (PA) is an advanced hierarchy farming system supported by multidisciplinary technologies such as specialized sensors, communication protocols, algorithms, and management tools, helping mitigate the problems of conventional farming by ensuring maximum production and minimum wastage. Given the rapid evolution of the aforementioned multidisciplinary technologies, this review paper analyzed 24337 research documents from 1938 to April 2024 using bibliographical software from the Scopus dataset. Internet of Things (IoT), Agriculture Robots (AR), and Artificial Intelligence (AI) are currently driving ongoing research, with frequency occurrences of 12.245, 8.259, and 7.791, highlighting the trend towards interconnected farming systems and data-driven automated systems. Bibliographical evidence indicates the current utilization of AI, AR, and IoT for accurate assessments like crop yield prediction, disease and weed detection, and soil analysis. Additionally, China is the most productive country in terms of publication, while the United States leads in terms of patents. This review paper also explores emerging trends that could guide future research, including blockchain technology, big data analysis, computing paradigms, and drone technology. Subsequently, a PA framework has been suggested to facilitate innovation in this field, followed by the open issues, highlighting the ongoing concerns related to insufficient infrastructure, integration, cost, and security measures, with the aim to engage all stakeholders.

Research on the Application of Artificial Intelligence in Mental Health Literacy Service for Middle School Students

Artificial Intelligence (AI) is applied to improve the mental health literacy of middle school students. It can effectively improve the efficiency, effectiveness and feasibility of this service. Through the current big data analysis and machine autonomous learning technology, AI can analyze the characteristics of students' behavior patterns and physiological data by training the model used. This paper examines how artificial intelligence can improve the mental health literacy of middle school students. It begins by analyzing and interpreting three key factors that influence their understanding of mental health. Firstly, it analyzes and interprets the three factors that affect the mental health literacy of middle school students. Then from these three factors, the author discusses how artificial intelligence can be applied to enhance the mental health literacy of middle school students. That is to say, it puts forward the new application of artificial intelligence technology in mental health counseling, emotional recognition, mental health assessment, media publicity and education. For example, the use of artificial intelligence to develop virtual psychological teachers, through language processing analysis and emotional recognition to provide online counseling services; It develops emotion recognition function through video and speech analysis of artificial intelligence, and uses digital signal processing means. Through the comprehensive analysis of a large number of data, the mental health evaluation system is gradually intellectualized, so that the evaluation model standard is more targeted.

An Accurate Construction Method of E-commerce User Profile Based on Artificial Intelligence Algorithm and Big Data Analysis

User’s basic attributes, behavior characteristics, value attributes, social attributes, interest attributes, psychological attributes, and other factors will lead to poor user experience, information overload, interference, and other negative effects. In order to develop more accurate marketing strategies, optimize user experience, and improve the conversion rate and user satisfaction of e-commerce platforms, an accurate construction method of e-commerce user profile based on artificial intelligence algorithm and big data analysis is proposed. Based on big data analysis technology, the basic attributes, behavior characteristics, value attributes, social attributes, interest attributes, and psychological attributes of e-commerce users are collected and integrated from multiple dimensions. The improved sequential pattern mining algorithm (PBWL) is applied to mine the frequent sequential pattern in the e-commerce user behavior, and to reveal the user’s behavior habit. The comprehensive attribute representation of e-commerce users is obtained by combining the LINE network model and the convolutional neural network. The firefly K-means clustering algorithm is used to cluster the e-commerce users, group the users based on the similarity of user attribute information, create different types of user clusters, and achieve the accurate construction of an e-commerce user profile. The experimental results show that this method can build an accurate e-commerce user profile and provide strong support for personalized recommendation and precision marketing of e-commerce platforms. This method can dig deeply into the behavior habits of e-commerce users and accurately reflect their interest preferences and consumption characteristics. This method can quickly and stably cluster e-commerce users, and the clustering effect of user profiles is optimal. This method can also divide the data into meaningful groups according to the user’s consumption behavior, and reveal the characteristics and values of different groups.