Journal of Big Data最新文献

The construction industry substantially contributes to the economic growth of a country. However, it records a large number of workplace injuries and fatalities annually due to its hesitant adoption of automated safety monitoring systems. To address this critical concern, this study presents a real-time monitoring approach that uses the Internet of Things and ensemble learning. This study leverages wearable sensor technology, such as photoplethysmography and electroencephalography sensors, to continuously track the physiological parameters of construction workers. The sensor data is processed using an ensemble learning approach called the ChronoEnsemble Fatigue Analysis System (CEFAS), comprising deep autoregressive and temporal fusion transformer models, to accurately predict potential physical and mental fatigue. Comprehensive evaluation metrics, including mean square error, mean absolute scaled error, and symmetric mean absolute percentage error, demonstrated the superior prediction accuracy and reliability of the proposed model compared to standalone models. The ensemble learning model exhibited remarkable precision in predicting physical and mental fatigue, as evidenced by the mean square errors of 0.0008 and 0.0033, respectively. The proposed model promptly recognizes potential hazards and irregularities, considerably enhancing worker safety and reducing on-site risks.

This paper presents a comprehensive approach to harmonizing lunar calendars across different global regions, addressing the long-standing challenge of variations in new crescent Moon sightings that mark the beginning of lunar months. We propose a machine learning (ML)-based framework to predict the visibility of the new crescent Moon, representing a significant advancement toward a globally unified lunar calendar. Our study utilized a dataset covering various countries globally, making it the first to analyze all 12 lunar months over a span of 13 years. We applied a wide array of ML algorithms and techniques. These techniques included feature selection, hyperparameter tuning, ensemble learning, and region-based clustering, all aimed at maximizing the model’s performance. The overall results reveal that the gradient boosting (GB) model surpasses all other models, achieving the highest F1 score of 0.882469 and an area under the curve (AUC) of 0.901009. However, with selected features identified through the ANOVA F-test and optimized parameters, the Extra Trees model exhibited the best performance with an F1 score of 0.887872, and an AUC of 0.906242. We expanded our analysis to explore ensemble models, aiming to understand how a combination of models might boost predictive accuracy. The Ensemble Model exhibited a slight improvement, with an F1 score of 0.888058 and an AUC of 0.907482. Additionally, the geographical segmentation of the dataset enhanced predictive performance in certain areas, such as Africa and Asia. In conclusion, ML techniques can provide efficient and reliable tool for predicting the new crescent Moon visibility that would support the decisions of marking the beginning of new lunar months.

The k-Nearest Neighbors (kNN) method, established in 1951, has since evolved into a pivotal tool in data mining, recommendation systems, and Internet of Things (IoT), among other areas. This paper presents a comprehensive review and performance analysis of modifications made to enhance the exact kNN techniques, particularly focusing on kNN Search and kNN Join for high-dimensional data. We delve deep into 31 kNN search methods and 12 kNN join methods, providing a methodological overview and analytical insight into each, emphasizing their strengths, limitations, and applicability. An important feature of our study is the provision of the source code for each of the kNN methods discussed, fostering ease of experimentation and comparative analysis for readers. Motivated by the rising significance of kNN in high-dimensional spaces and a recognized gap in comprehensive surveys on exact kNN techniques, our work seeks to bridge this gap. Additionally, we outline existing challenges and present potential directions for future research in the domain of kNN techniques, offering a holistic guide that amalgamates, compares, and dissects existing methodologies in a coherent manner.

Graphical Abstract

Objective

This paper addresses the Graeco-Latin square design (GLSD) under neutrosophic statistics. In this work, we propose a novel approach for analyzing Graeco-Latin square designs using uncertain observations.

Method

This approach involves the determination of a neutrosophic ANOVA and the determination of the neutrosophic hypotheses and decision rule.

Results

The performance of the proposed design is evaluated using the numerical examples and simulation study.

Conclusion

Based on the results observed, it can be concluded that the GLSD under neutrosophic statistics performs better than the GLSD under classical statistics in the presence of uncertainty.

With the growing complexity of data structures, which include high-dimensional and multilabel datasets, the significance of feature selection has become more emphasized. Multilabel feature selection endeavors to identify a subset of features that concurrently exhibit relevance across multiple labels. Owing to the impracticality of performing exhaustive searches to obtain the optimal feature subset, conventional approaches in multilabel feature selection often resort to a heuristic search process. In this context, memetic multilabel feature selection has received considerable attention because of its superior search capability; the fitness of the feature subset created by the stochastic search is further enhanced through a refinement process predicated on the employed multilabel feature filter. Thus, it is imperative to employ an effective refinement process that frequently succeeds in improving the target feature subset to maximize the benefits of hybridization. However, the refinement process in conventional memetic multilabel feature selection often overlooks potential biases in feature scores and compatibility issues between the multilabel feature filter and the subsequent learner. Consequently, conventional methods may not effectively identify the optimal feature subset in complex multilabel datasets. In this study, we propose a new memetic multilabel feature selection method that addresses these limitations by incorporating the pruning of features and labels into the refinement process. The effectiveness of the proposed method was demonstrated through experiments on 14 multilabel datasets.

Prediction processes in areas ranging from climate and disease spread to disasters and air pollution rely heavily on spatial–temporal data. Understanding and forecasting the distribution patterns of disease cases and climate change phenomena has become a focal point of researchers around the world. Machine learning models for prediction can generally be classified into 2: based on previous patterns such as LSTM and based on causal factors such as Naive Bayes and other classifiers. The main drawback of models such as Naive Bayes is that it does not have the ability to predict future trends because it only make predictionsin the present time. In this study, we propose a novel approach that makes the Naive Bayes classifier capable of predicting future classification. The process of expanding the dimension of the feature matrix based on historical data from several previous time periods is performed to obtain a long-term classification prediction model using Naive Bayes. The case studies used are the prediction of the distribution of the annual number of dengue fever cases in Bandung City and the distribution of monthly rainfall in Java Island, Indonesia. Through rigorous testing, we demonstrate the effectiveness of this Time-Based Feature Expansion approach in Naive Bayes in accurately predicting the distribution of annual dengue fever cases in 30 sub-districts in Bandung City and monthly rainfall in Java Island, Indonesia with with both accuracy and F1-score reaching more than 97%.

Graphical Abstract

The convergence of artificial intelligence (AI), big data (DB), and Internet of Things (IoT) in Society 5.0, has given rise to Marketing 5.0, revolutionizing personalized customer experiences. In this study, a systematic literature review was conducted to examine the integration of predictive modelling and sentiment analysis within the Marketing 5.0 domain. Unlike previous research, this study addresses both aspects within a single context, emphasizing the need for a sentiment-based predictive approach to the buyers’ journey. This review explores how predictive and sentiment models enhance customer experience, inform business decisions, and optimize marketing processes. This study contributes to the literature by identifying areas of improvement in predictive modelling and emphasizes the role of a sentiment-based approach in Marketing 5.0. The sentiment-based model assists businesses in understanding customer preferences, offering personalized products, and enabling customers to receive relevant advertisements during their purchase journey. The paper’s structure covers the evolution of traditional marketing to digital marketing, AI’s role in digital marketing, predictive modelling in marketing, and the significance of analyzing customer sentiments in their reviews. The Prisma-P methodology, research questions, and suggestions for future work and limitations provide a comprehensive overview of the scope and contributions of this review.

As the number and cleverness of cyber-attacks keep increasing rapidly, it's more important than ever to have good ways to detect and prevent them. Recognizing cyber threats quickly and accurately is crucial because they can cause severe damage to individuals and businesses. This paper takes a close look at how we can use artificial intelligence (AI), including machine learning (ML) and deep learning (DL), alongside metaheuristic algorithms to detect cyber-attacks better. We've thoroughly examined over sixty recent studies to measure how effective these AI tools are at identifying and fighting a wide range of cyber threats. Our research includes a diverse array of cyberattacks such as malware attacks, network intrusions, spam, and others, showing that ML and DL methods, together with metaheuristic algorithms, significantly improve how well we can find and respond to cyber threats. We compare these AI methods to find out what they're good at and where they could improve, especially as we face new and changing cyber-attacks. This paper presents a straightforward framework for assessing AI Methods in cyber threat detection. Given the increasing complexity of cyber threats, enhancing AI methods and regularly ensuring strong protection is critical. We evaluate the effectiveness and the limitations of current ML and DL proposed models, in addition to the metaheuristic algorithms. Recognizing these limitations is vital for guiding future enhancements. We're pushing for smart and flexible solutions that can adapt to new challenges. The findings from our research suggest that the future of protecting against cyber-attacks will rely on continuously updating AI methods to stay ahead of hackers' latest tricks.

Physical fitness is one of the most important traits a person could have for health longevity. Conducting regular exercise is fundamental to maintaining physical fitness, but with the caveat of occurring injury if not done properly. Several algorithms exists to automatically monitor and evaluate exercise using the user’s pose. However, it is not an easy task to accurately monitor and evaluate exercise poses automatically. Moreover, there are limited number of datasets exists in this area. In our work, we attempt to construct a neural network model that could be used to evaluate exercise poses based on key points extracted from exercise video frames. First, we collected several images consists of different exercise poses. We utilize the the OpenPose library to extract key points from exercise video datasets and LSTM neural network to learn exercise patterns. The result of our experiment has shown that the methods used are quite effective for exercise types of push-up, sit-up, squat, and plank. The neural-network model achieved more than 90% accuracy for the four exercise types.

Short-term traffic flow forecasting is a hot issue in the field of intelligent transportation. The research field of traffic forecasting has evolved greatly in past decades. With the rapid development of deep learning and neural networks, a series of effective methods have been proposed to address the short-term traffic flow forecasting problem, which makes it possible to examine and forecast traffic situations more accurately than ever. Different from linear based methods, deep learning based methods achieve traffic flow forecasting by exploring the complex nonlinear relationships in traffic flow. Most existing methods always use a single framework for feature extraction and forecasting only. These approaches treat all traffic flow equally and consider them contain same attribute. However, the traffic flow from different time spots or roads may contain distinct attributes information (such as congested and uncongested). A simple single framework usually ignore the different attributes embedded in different distributions of data. This would decrease the accuracy of traffic forecasting. To tackle these issues, we propose an adaptive composite framework, named Long-Short-Combination (LSC). In the proposed method, two data forecasting modules(L and S) are designed for short-term traffic flow with different attributes respectively. Furthermore, we also integrate an attribute forecasting module (C) to forecast the traffic attributes for each time point in future time series. The proposed framework has been assessed on real-world datasets. The experimental results demonstrate that the proposed model has excellent forecasting performance.