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The application of big data analytics in financial decision-making has become pivotal in addressing the complexities of modern financial markets. With the growing availability of high-dimensional and high-frequency data, traditional investment strategies often fail to capture dynamic market behaviors and multi-scale dependencies. Conventional methods, grounded in static models and linear assumptions, lack the flexibility and robustness required for optimizing financial decision-making in volatile and interconnected markets. This paper aligns with the scope of computational advancements in financial systems, introducing an Adaptive Investment Optimization Model (AIOM) that integrates deep learning, stochastic modeling, and reinforcement learning to enhance investment strategies. By leveraging multi-scale feature extraction, dynamic market state estimation, and a reinforcement learning-based optimization engine, the model achieves superior adaptability and precision. Our novel Market-Aware Optimization Framework (MOF) further refines portfolio management by dynamically adjusting allocations based on predictive market signals and advanced risk measures, such as Conditional Value at Risk (CVaR) and drawdown control. Experimental results demonstrate significant improvements in portfolio returns and risk management compared to traditional methods. This work exemplifies the potential of computational innovations in transforming financial decision-making, offering robust solutions for real-time, adaptive investment optimization.

This study investigates the application of big data analytics (BDA) and its impact on consulting firms’ competitiveness. Descriptive statistics, ordinary least square regression, and moderated regression analysis were used to analyze survey data obtained from 118 business and management consultants in Nigeria. The robustness of the result was evaluated using structural equation modeling. Result shows that the application level of BDA by consulting firms is generally moderate. BDA application has a significant positive impact on organizational competitiveness, although the strength of the relationship is weak. Further, data quality significantly moderates the relationship between BDA and organizational competitiveness. The study concludes that the application of BDA can enhance the competitiveness of consulting firms. However, the extent to which such benefit is realized is dependent on the quality of data applied in the analysis. The study contributes to knowledge by providing empirical evidence that the deployment of BDA can be a source of competitive advantage for consulting firms. The study adds to literature on management accounting in the digital economy and the application of big data to business and management consulting.

Due to the rapid advancement of computational power and the Internet of Things (IoT) together with recent developments in digital-twin and cyber-physical systems, the integration of big data analytics techniques (BDAT) (e.g., data mining, machine learning (ML), deep learning, data farming, etc.) into traditional simulation methodologies, along with the enhancement of past simulation optimization techniques, has given rise to an emerging field known as simulation learning and optimization (SLO). Unlike simulation optimization, SLO is a data-driven fusion approach that integrates conventional simulation modeling methodologies, simulation optimization techniques, and learning-based big data analytics (BDA) to enhance the ability to address stochastic and dynamic decision-making problems in the real-world complex system and quickly find the best outcomes for the decision support in both real-time and nonreal-time analyses to achieve the data-driven digital twin capability. Although some literature in the past has mentioned similar applications and concepts, no paper provides a structural explanation and comprehensive review of relevant literature on SLO from both methodological and applied perspectives. Consequently, in the first part of this paper, we conduct a literature review on a novel SLO methodology emerging from the fusion of traditional simulation methodology, simulation optimization algorithms, and BDAT. In the second part of this paper, we review the applications of SLO in various contexts, with detailed discussions on manufacturing, maintenance, redundancy allocation, hybrid energy systems, humanitarian logistics, and healthcare systems. Lastly, we investigate potential research directions for SLO in this new era of big data and artificial intelligence (AI).

Recent advances in simulation optimization research and explosive growth in computing power have made it possible to optimize complex stochastic systems that are otherwise intractable. In the first part of this paper, we classify simulation optimization techniques into four categories based on how the search is conducted. We provide tutorial expositions on representative methods from each category, with a focus in recent developments, and compare the strengths and limitations of each category. In the second part of this paper, we review applications of simulation optimization in various contexts, with detailed discussions on health care, logistics, and manufacturing systems. Finally, we explore the potential of simulation optimization in the new era. Specifically, we discuss how simulation optimization can benefit from cloud computing and high-performance computing, its integration with big data analytics, and the value of simulation optimization to help address challenges in engineering design of complex systems.

Data clustering is a thoroughly studied data mining issue. As the amount of information being analyzed grows exponentially, there are several problems with clustering diagnostic large datasets like the monitoring, microbiology, and end results (SEER) carcinoma feature sets. These traditional clustering methods are severely constrained in terms of speed, productivity, and adaptability. This paper summarizes the most modern distributed clustering algorithms, organized according to the computing platforms used to process vast volumes of data. The purpose of this work was to offer an optimized distributed clustering strategy for reducing the algorithm’s total execution time. We obtained, preprocessed, and analyzed clinical SEER data on liver cancer, respiratory cancer, human immunodeficiency virus (HIV)-related lymphoma, and lung cancer for large-scale data clustering analysis. Three major contributions and their effects were covered in this paper: To begin, three current Pyspark distributed clustering algorithms were evaluated on SEER clinical data using a simulated New York cancer dataset. Second, systemic inflammatory response syndrome (SIRS) model inference was done and described using three SEER cancer datasets. Third, employing lung cancer data, we suggested an optimized distributed bisecting $k$-means method. We have shown the outcomes of our suggested optimized distributed clustering technique, demonstrating the performance enhancement.

Infertility is becoming a public health issue in almost all countries. Assisted Reproductive Technology (ART) is considered as a method of last resort for treating infertility. The treatment of ART is highly expensive and painful, and also the probability of success is low since the success is affected by a large number of variables. Researchers are now trying to identify patterns comprising significant variables, their impact on success, and the interdependence of different variables to enumerate the status of the patient and to support the doctors and biologists to prescribe treatment to improve the probability of success of ART. Machine learning technique is a tool that is used by various researchers in the field of ART to identify the interlink between the variables. The objective of this review paper is to find the appliance of machine learning techniques in ART and to find further enrichment needed for future research. From the literature, it is found that some research works were done using machine learning techniques to predict ART outcome. On analyzing the reviews qualitatively and quantitatively, it is understood that various classifiers are used for ART outcome prediction but they are trained using limited amount of static data collected from fertility centers. The exact prediction of ART outcome may be improved by training the classifier with large amount of dynamic data. But building such a classifier is difficult by the already existing techniques. This may be made possible by introducing Big Data Analytics in ART.

People’s need for healthcare capacity has become increasingly critical as the elderly population continues to grow in most communities. Approximately 25–47% of seniors fall annually, and early detection of poor balance can significantly reduce their risk. Automated fall detection with big data analytics is key to maintaining the safety of the elderly in smart cities. Visible image systems (VIS) in smart buildings, on the other hand, visible image systems (VIS) in smart buildings may compromise the privacy of seniors by enabling technologies for intelligent big data analytics (IBDA). Thermal imaging (TI) is less obtrusive than visual imaging and can be used in combination with machine vision to perform a wide range of IBDAs. In this study, we present a novel two-step method for detecting falls in TI frames using deep learning (DL). As the first step, tracking tools are used to locate people’s locations. A novel modified deep transfer learning (TL) technique is used to classify the trajectory created by the tracking approach for people who are at risk of falling. Fall detection by the IBDA will be connected to the Internet of medical things (IoMT) and used as smart technology in the process of big data-assisted pervasive surveillance and health analytics. According to an analysis of the publicly available thermal fall dataset, our method outperforms traditional fall detection methods, with an average error of less than 3%. Additionally, IoMT platforms facilitate data processing, real-time monitoring and healthcare management. Our smart scheme for using big data analytics to enable intelligent decisions is compatible with the various spaces and provides a comfortable and safe environment for current and future elderly people.

Feature Selection (FS) is an important preprocessing step in data analytics. It is used to select a subset of the original feature set such that the selected subset does not affect the classification performance significantly. Its objective is to remove irrelevant and redundant features from the original dataset. FS can be done either in offline mode or in online mode. The basic assumption in the former mode is that the entire dataset has been available for the FS algorithm; and the FS algorithm takes multiple epochs to select optimal feature subset that gives good accuracy. In contrast, the FS algorithms in online mode take input data one instance at a time and accumulate knowledge by learning each one of them. In online mode each instance of the original dataset is considered as training and testing sample as well. The offline FS algorithms require long time periods, if the data to be processed is large such as Big data. Whereas online FS algorithms will take only one epoch to learn the entire data and can produce the results swiftly which is highly desirable in the case of Big data. This paper deals with the online FS problem and provides a novel Feature Selection algorithm which uses the Sparse Gradient method to build a sparse classifier. In this proposed method, an online classifier is built and maintained throughout the learning process and feature weights, which are limited to a particular boundary limit, are reduced in a step by step decrement process. This method creates sparsity in the classifier. Effectively, the built classifier is used to select optimal feature subset from the incoming data. As this method reduces the weights in the classifier in step by step manner, only those important features which have value higher than the boundary survive from this repeated decrement process. The resultant optimal feature subset is formed using these non-zero weighted features. Most significantly, this particular method can be used with any learning algorithm. To show its applicability with different learning algorithms, various online feature selection models have been built using Learning Vector Quantization, Radial Basis Function Networks and Adaptive Resonance Theory MAP. In all these models, the proposed Sparse Gradient method is used. The encouraging results shows the effectiveness of the proposed method with different learning algorithms in medium and large sized benchmark datasets.

For the problem of class-imbalance in the operation monitoring data of wind turbine (WT) pitch connecting bolts, an improved Borderline-SMOTE oversampling method based on “two-step decision” with adaptive selection of synthetic instances (TSDAS-SMOTE) is proposed. Then, TSDAS-SMOTE is combined with XGBoost to construct a WT pitch connection bolt fault detection model. TSDAS-SMOTE generates new samples by “two-step decision making” to avoid the problem of class–class boundary blurring that Borderline-SMOTE tends to cause when oversampling. First, the nearest neighbor sample characteristics are perceived by the fault class samples in the first decision step. If the characteristics of this fault class sample are different from the characteristics of all its nearest neighbor samples, the fault class sample is identified as interference and filtered. Second, the faulty class samples in the boundary zone are extracted as synthetic instances to generate new samples adaptively. Finally, the normal class samples in the boundary zone are used to perceive the unqualified new generated samples in the boundary zone based on the minimum Euclidean distance characteristics, and these unqualified samples are eliminated. For the second step of decision making, since the first step decision removes some of the newly generated samples, the remaining fault class samples without interference samples and boundary zone samples are used as synthetic instances to continue adaptively generating new samples. Thus, a balanced data set with clear class–class boundary zone is obtained, which is then used to train a WT pitch connection bolt fault detection model based on the XGBoost algorithm. The experimental results show that compared with six popular oversampling methods such as Borderline-SMOTE, Cluster-SMOTE, $K$-means-SMOTE, etc., the fault detection model constructed by the proposed oversampling method is better than the compared fault detection models in terms of missed alarm rate (MAR) and false alarm rate (FAR). Therefore, it can well achieve the fault detection of large WT pitch connection bolts.

Energy is now seen as a significant resource that develops abundant on the world economy, with short supply and development. A study found that renewable energy systems are needed to prevent shortages. Hence, all the focus in this study to decrease electricity consumption and reduce the overall completion times for a regular console in green technology networks was an efficient and scalable production genomic solution. A Renewable green energy resources smart city (RGER-SC) framework is proposed that used a multi-target evolutionary algorithm was hybridized to be effective and calculated arithmetically in this study. This work deals with fostering renewable energy incorporation by adjusting federal charges to increase the energy accounting practitioners. Besides, this report analyses the timely generation of delay-tolerant demands and the maintenance of district heating at network infrastructure. In comparison, capacity differentials between consumers and information centres are considered and evaluated using the Renewable green energy resources smart city (RGER-SC) framework for energy conservation and controlled task management at an industrial level.

Big data analysis of human behavior can provide the basis and support for the application of various scenarios. Using sensors for human behavior analysis is an effective means of identification method, which is very valuable for research. To address the problems of low recognition accuracy, low recognition efficiency of traditional human behavior recognition (HBR) algorithms in complex scenes, in this paper, we propose an HBR algorithm for Mobile Big data analytics in wireless sensor network using improved transfer learning. First, different wireless sensors are fused to obtain human behavior mobile big data, and then by analyzing the importance of human behavior features (HBF), the dynamic change parameters of HBF extraction threshold are calculated. Second, combined with the dynamic change parameters of threshold, the HBF of complex scenes are extracted. Finally, the best classification function of human behavior in complex scenes is obtained by using the classification function of HBF in complex scenes. Human behavior in complex scenes is classified according to the HBF in the feature set. The HBR algorithm is designed by using the improved transfer learning network to realize the recognition of human behavior in complex scenes. The results show that the proposed algorithm can accurately recognize up to 22 HBF points, and can control the HBR time within 2 s. The human behavior false recognition rate of miscellaneous scenes is less than 10%. The recognition speed is above 10/s, and the recall rate can reach more than 98%, which improves the HBR ability of complex scenes.

In time series modeling, one problem is to identify a small number of influential factors to explain variations in the variable of interest. With a vast number of possible factors available, suitable features need to be identified to yield multi-factor models with good explanatory power. In this paper, we propose a novel subset selection method which makes use of the properties in the frequency domain environment. The proposed system ensures key patterns in the target variable be sought and suitable factors be selected based on frequency peaks in common. It can perform well even when the number of factors is significantly greater than the sample size. Moreover, a very important feature of the proposed system is the capability of handling factors with different timeframes, which is lacking in existing methods. We demonstrate the system via several examples with dataset from finance, economic, road traffic and air pollution.

In the current data-driven digital economy, organizations attempt to harness big data power to make their decisions better. The big data analytics assist them not only to identify new opportunities but extract knowledge and obtain better performance. Despite a huge investment in big data analytics initiatives, the majority of organizations have failed to successfully exploit their power. Although big data analytics have received considerable research attention, a little has been done on how organizations implement strategies in order to integrate the different dimensions of big data analytics; hence, a roadmap is required to navigate these technological initiatives. This paper is also an attempt to overcome this challenge by developing a comprehensive big data analytics maturity model to help managers evaluate their existing capabilities and formulate an appropriate strategy for further progress. A mixed-method was applied in this research using a qualitative meta-synthesis approach. For this purpose, first, a systematic literature review was conducted to identify the capabilities and practices of big data analytics maturity. Then the proposed key capabilities and practices were assessed and prioritized based on the opinions of experts using the quantitative survey method. Finally, considering the architecture of the big data analytics maturity model, the capabilities were assigned to maturity levels according to their priority of implementation using a focus group. The proposed model is comprised of four main capabilities, nine key dimensions (KDs) and five maturity levels based on the capability maturity model integration (CMMI) architecture. A questionnaire and a focus group were used to present the big data maturity model. The capabilities and KDs, as well as their implementation order and weight in the proposed maturity model are presented as a roadmap for implementing big data analytics effectively. The proposed model enables organizations to assess their current big data analytics capabilities and navigate them to select appropriate strategies for their improvement. Due to its nature, it allows managers to find their strong and weak points and identify investment priorities. This study provides a comprehensive maturity model using a meta-synthesis which has not been used in this field so far. The proposed model is both descriptive and prescriptive and has a significant theoretical contribution to big data researches. The paper provides a mechanism to benchmark big data analytics projects and develop an appropriate strategy in terms of progress.

This paper examines big data analytics implications on the central banking financial system’s technological progress. A digital technological progress framework and model is established to analyze the economy’s aggregate supply via covering the monetary policy, big data analytics, pollutants emissions as independent variables and the economy’s aggregate demand as a moderating variable in a modified extensive growth theory framework and model to compute the productivity indicators and the total factor productivity (TFP) as the central banking technological progress that combined the mentioned variables qualities contribution. Besides, data analytics positive and negative externalities that include data analytics shortcomings as unpriced undesirable output in the form of cybersecurity and pollutants’ emissions among other proxies are internalized in the framework and the model to integrate the digital technology innovation with digital technology shortcomings and climate change. This revised extensive theory framework and model is a remarkable technique comprehensive of the technological progress matters and sustainable economic development and is considered one of the most important sustainable development and long-run economic growth proportions in the central banking financial system functions to manage the economy’s aggregate supply and demand that unnoticed by previous studies.

In Ethiopia, healthcare service delivery faces various challenges, specifically in relation to prescribing the right medicine to the right patient at the right time. Thus, patients are exposed to challenges ranging from staying on treatment plans longer than necessary, leaving treatment too early and dying of complications. This paper aims to explore the trends, challenges and opportunities of applying big data analytics (BDA) in precision medicine in other locations around the globe and taking lessons for Ethiopia through a systematic review of 14 peer-reviewed articles from three popular databases. The findings indicate that cancer, epilepsy and systemic diseases altogether are areas currently getting big attention. The challenges are attributed to the nature of health data, failure in collaboration of data sharing, ethical and legal issues, interoperability of systems, poor knowledge skills and culture, and poor infrastructure. In addition to the adoption of modern technologies (new and experimental methods, cloud computing, Internet of Things, social networks, etc.), Ethiopia’s government initiative to promote private technological firms could be an opportunity to use BDA in precision medicine in Ethiopia.

Understanding huge amounts of data with a wide variety of data kinds is referred to as big data analytics. “Human, Machine and Material” development strategy will result in an enormous amount of data. The management department may enhance its potential to process big data by assessing and analysing current network big data issues. As a consequence, it considerably plays a role in minimising resource costs and consumption in every sector. Every sector can effortlessly transition into the following information and digitalisation phase of development. Big data will aid in tackling challenges and enhancing knowledge across various sectors. Although, the efficiency of big data analytics is still questioned by some challenges. The challenges that arise in big data analytics are storage, data quality, lack of data science professionals, data accumulation and data validation. Therefore, this discusses the term “Big data analytics” by configuring its applications, tools, Machine Learning (ML) models and challenges in existing approaches. A comprehensive analysis of over 58 research papers, covering various aspects of big data analytics across multiple domains including healthcare, education, agriculture, multimedia and travel is presented in this study. The main objective of this survey is to contribute to advancing knowledge, facilitating informed decision-making and guiding future research efforts in the dynamic and rapidly evolving landscape of big data analytics. Through meticulous paper selection, a diverse representation of the latest advancements in big data analytics techniques was curated. Each domain underwent a thorough review, elucidating methodologies, tools, datasets and performance measures. Further, the general steps involved in big data analytics techniques are outlined by providing a foundational understanding. Key areas of analysis include chronological review, algorithms utilised, tools and datasets employed and performance evaluation measures. By addressing these aspects, the study offers valuable insights into the evolution, methodologies and performance of big data analytics techniques across diverse domains. Additionally, it identifies research gaps and challenges, paving the way for future research to address critical issues such as data interoperability, privacy concerns and scalability. This study serves as a comprehensive resource for researchers, practitioners and policymakers, contributing to advancing knowledge and facilitating informed decision-making in the rapidly evolving landscape of big data analytics.

Multiple emerging technologies both threaten grocers and offer them attractive opportunities to enhance their value propositions, improve processes, reduce costs, and therefore generate competitive advantages. Among the variety of technological innovations and considering the scarcity of resources, it is unclear which technologies to focus on and where to implement them in the value chain. To develop the most probable technology forecast that addresses the application of emerging technologies in the grocery value chain within the current decade, we conduct a two-stage Delphi study. Our results suggest a high relevance of almost all technologies. The panel is only skeptical about three specific projections. As a consequence, grocers are advised to build up knowledge regarding the application of these technologies in the most promising areas of their value chain.

The concept of big data (BD) has been coupled with disaster management to improve the crisis response during pandemic and epidemic. BD has transformed every aspect and approach of handling the unorganized set of data files and converting the same into a piece of more structured information. The constant inflow of unstructured data shows the research lacuna, especially during a pandemic. This study is an effort to develop a pandemic disaster management approach based on BD. BD text analytics potential is immense in effective pandemic disaster management via visualization, explanation, and data analysis. To seize the understanding of using BD toward disaster management, we have taken a comprehensive approach in place of fragmented view by using BD text analytics approach to comprehend the various relationships about disaster management theory. The study’s findings indicate that it is essential to understand all the pandemic disaster management performed in the past and improve the future crisis response using BD. Though worldwide, all the communities face big chaos and have little help reaching a potential solution.

Data-driven technologies changed the way how service innovation is conducted in organisations. The literature discusses the potential of Data-Driven Service Innovation (DDSI) processes, but it is not clear yet what tool support for DDSI looks like. This structured literature review examines the tool support for each phase of DDSI processes. We found clear differences between the DDSI phases and different tools for each phase. In the first phase, tools with batch processing capability are employed for methods like text mining and sentiment analysis, helping to capture evolving customer behaviour and trends to increase the speed of innovation rate. In the second phase, immersive technologies, real-time sentiment analysis and stream analytics are used for the validation of service development processes to increase the likelihood of market success. In the third phase, AI tools with the capability to continuously learn from emerging data and Big Data Analytics tools are combined.

The recent research evolution on big data has brought exciting aspiration to mathematicians, computer scientists and business professionals alike. However, the lack of a sound mathematical foundation presents itself as a real challenge amidst the swarm of big data marketing activities. This paper intends to propose a possible mathematical theory as a foundation for big data research. Specifically, we propose the concept of the adjective “big” as a mathematical operator, furthermore, the concept of so-called “big” logically and naturally fits the concept of being “linguistics variable” as per fuzzy logic research community for decades. The consequence of adopting such a mathematical modeling can be profoundly considered as an abstraction of the technologies, systems, tools for data management and processing that transforms data into big data. In addition, the concept of infinity of the big data is based on the theory of calculus and the set theory. Furthermore, the concept of relativity of the big data, as we find out, is based on the operations of the fuzzy subsets theory. The proposed approach in this paper, we hope, can facilitate and open up more opportunities for big data research and developments on big data analytics, business analytics, big data intelligence, big data computing as well as big data science.