Both amplitude and latency of single-trial EEG/MEG recordings provide valuable information regarding functionality of the human brain. In this article, we provided a data-driven graph and network-based framework for mining information from multi-trial event-related brain recordings. In the first part, we provide the general outline of the proposed methodological approach. In the second part, we provide a more detailed illustration, and present the obtained results on every step of the algorithmic procedure. To justify the proposed framework instead of presenting the analytic data mining and graph-based steps, we address the problem of response variability, a prerequisite to reliable estimates for both the amplitude and latency on specific N/P components linked to the nature of the stimuli. The major question addressed in this study is the selection of representative single-trials with the aim of uncovering a less noisey averaged waveform elicited from the stimuli. This graph and network-based algorithmic procedure increases the signal-to-noise (SNR) of the brain response, a key pre-processing step to reveal significant and reliable amplitude and latency at a specific time after the onset of the stimulus and with the right polarity (N or P). We demonstrated the whole approach using electroencephalography (EEG) auditory mismatch negativity (MMN) recordings from 42 young healthy controls. The method is novel, fast and data-driven succeeding first to reveal the true waveform elicited by MMN on different conditions (frequency, intensity, duration, etc.). The proposed graph-oriented algorithmic pipeline increased the SNR of the characteristic waveforms and the reliability of amplitude and latency within the adopted cohort. We also demonstrated how different EEG reference schemes (REST vs. average) can influence amplitude-latency estimation. Simulation results revealed robust amplitude-latency estimations under different SNR and amplitude-latency variations with the proposed algorithm.

The dataset consists of three folders:Systems: Three public event logs: Sepsis Cases, RTFMS, and BPIC 2012.Logs: Clean and noisy logs derived from the base systems.From each base log, we created samples of seven sizes (1000, 2000, 4000, 10000, 20000, 40000, 100000 traces) using sampling with replacement, yielding 21 clean logs.Noise was then added using $\snip$ across seven intensity levels (0.1%, 0.2%, 0.4%, 1.0%, 2.0%, 4.0%, 10.0%) and five noise types (absence, insertion, ordering, substitution, mixed). Percentages refer to the number of trace-level injections.Each configuration was repeated five times, producing 3,675 noisy logs and a total of 3,696 logs.Models: Contains discovered models for all clean logs and a random subset of noisy logs (incomplete), using the Alpha, Heuristics, and Inductive miners.

The evaluation of urban noise suitability is crucial for urban environmental management. Efficient and cost-effective methods for obtaining noise distribution data are of great interest. This study introduces various machine learning methods and applies the Random Forest algorithm, which performed best, to investigate noise suitability in the central urban area of Nanchang City. The findings are as follows:1.Machine learning algorithms can be effectively used for urban noise evaluation. The optimized model accurately reflects the noise suitability levels in Nanchang City.2.The feature importance ranking reveals that population spatial distribution has the most significant impact on urban noise, followed by distance to water bodies and road network density. These three features significantly influence the assessment of urban noise suitability and should be prioritized in noise control measures.3.The weakly suitable noise areas in Nanchang’s central urban region are primarily concentrated on the east bank of the Ganjiang River, making this a key area for noise management. Overall, the Unsuitable, Slightly suitable, Moderately suitable, Relatively suitable, and Height suitable areas constitute 9.38%, 16.03%, 28.02%, 33.31%, and 13.25% of the central urban area, respectively.4.The SHAP model identifies the top three features in terms of importance, showing that different values of feature variables have varying impacts on noise suitability.This study employs data mining concepts and machine learning techniques to provide an accurate and objective assessment of urban noise levels. The results offer scientific decision-making support for urban spatial planning and noise mitigation measures, ensuring the healthy and sustainable development of the urban environment.

Graph representation learning—especially via graph neural networks (GNNs)—has demonstrated considerable promise in modeling intricate interaction systems, such as social networks and molecular structures. However, the deployment of GNN-based frameworks in industrial settings remains challenging due to the inherent complexity and noise in real-world graph data. This dissertation systematically addresses these challenges by advancing novel methodologies to improve the comprehensiveness and robustness of graph representation learning, with a dual focus on resolving data complexity and denoising across diverse graph-learning scenarios. In addressing graph data denoising, we design auxiliary self-supervised optimization objectives that disentangle noisy topological structures and misinformation while preserving the representational sufficiency of critical graph features. These tasks operate synergistically with primary learning objectives to enhance robustness against data corruption. The efficacy of these techniques is demonstrated through their application to real-world opioid prescription time series data for predicting potential opioid over-prescription. To mitigate data complexity, the study investigates two complementary approaches: (1) multimodal fusion, which employs attentive integration of graph data with features from other modalities, and (2) hierarchical substructure mining, which extracts semantic patterns at multiple granularities to enhance model generalization in demanding contexts. Finally, the dissertation explores the adaptability of graph data in a range of practical applications, including E-commerce demand forecasting and recommendations, to further enhance prediction and reasoning capabilities.

The coronavirus disease 2019 pandemic has impacted and changed consumer behavior because of a prolonged quarantine and lockdown. This study proposed a theoretical framework to explore and define the influencing factors of online consumer purchasing behavior (OCPB) based on electronic word-of-mouth (e-WOM) data mining and analysis. Data pertaining to e-WOM were crawled from smartphone product reviews from the two most popular online shopping platforms in China, Jingdong.com and Taobao.com. Data processing aimed to filter noise and translate unstructured data from complex text reviews into structured data. The machine learning based K-means clustering method was utilized to cluster the influencing factors of OCPB. Comparing the clustering results and Kotler’s five products level, the influencing factors of OCPB were clustered around four categories: perceived emergency context, product, innovation, and function attributes. This study contributes to OCPB research by data mining and analysis that can adequately identify the influencing factors based on e-WOM. The definition and explanation of these categories may have important implications for both OCPB and e-commerce.

Background

Any set of related activities that are executed in a repeatable manner and with a defined goal can be seen as process.

Process analytic approaches allow organizations to support the practice of Business Process Management and continuous improvement by leveraging all process-related data to extract knowledge, improve process performance and support managerial-decision making across the organization.

For organisations interested in continuous improvement, such datasets allow data-driven approach for identifying performance bottlenecks, reducing costs, extracting insights and optimizing the utilization of available resources. Understanding the properties of ‘current deployed process’ (whose execution trace is available), is critical to knowing whether it is worth investing in improvements, where performance problems exist, and how much variation there is in the process across the instances and what are the root-causes.

What is Process Mining (PM) ?

→ process of extracting valuable information from event logs/databases that are generated by processes.

Two topics are important i) process discovery where a process model describing the control flow is inferred from the data and ii) of conformance checking which deals with verifying that the behavior in the event log adheres to a set of business rules, e.g., defined as a process model. Rhese two use cases focus on the control-flow perspective,

Why Process Mining ?

→ identifying hidden nodes and bottlenecks in business processes.

About the Dataset

A synthetic event log with 100,000 traces and 900,000 events that was generated by simulating a simple artificial process model. There are three data attributes in the event log: Priority, Nurse, and Type. Some paths in the model are recorded infrequently based on the value of these attributes.

Noise is added by randomly adding one additional event to an increasing number of traces. CPN Tools (http://cpntools.org) was used to generate the event log and inject the noise. The amount of noise can be controlled with the constant 'noise'.

Smaller dataset:

The files test0 to test5 represent process traces and maybe used for debugging and sanity check purposes

The global market for noise control systems in mining is experiencing robust growth, driven by stringent government regulations aimed at protecting worker health and reducing environmental impact. The increasing adoption of automated mining techniques and the expansion of mining operations worldwide are further contributing to market expansion. While precise market sizing data was not fully provided, considering a plausible CAGR (let's assume a conservative 5% based on industry trends and the inherent need for safety improvements in mining), a 2025 market value of $500 million is reasonable. This figure projects substantial growth throughout the forecast period (2025-2033), with segments like internal noise control systems likely outpacing external systems due to the higher priority placed on worker safety. The North American and Asia-Pacific regions, characterized by significant mining activity and stricter regulations, are expected to dominate the market share. However, increasing awareness and regulatory pressure in other regions, such as Europe and South America, will fuel growth in these areas as well. Challenges remain, however. High initial investment costs associated with implementing noise control systems can be a barrier to entry for smaller mining companies, particularly in developing economies. Furthermore, the effectiveness of these systems can be dependent on proper installation and maintenance, requiring specialized expertise and ongoing investment. This factor, along with the need for ongoing research and development to improve system efficiency and durability, represents a potential restraint on market growth. Nevertheless, the long-term prospects for the mining noise control systems market remain positive, fueled by a growing focus on sustainable and responsible mining practices. Key players in the market are focusing on innovation, developing more efficient and cost-effective solutions to address these challenges and tap into the expanding global demand.

Acoustic noise data in the frequency range of 0-2 kHz have been recorded in the framework of IP21 cruise (campaign MANGAN2021) of the MiningImpact project (https://miningimpact.geomar.de) during its independent scientific monitoring of the polymetallic nodule collector trials of PATANIA-II (conducted by Global Sea Resources, GSR). Two ocean-bottom hydrophones (GMR_HYA-173 and GMR_HYA-174) were deployed at the trial sites in the BEL and GER working areas in about 50 m distance from the Patania-II test field. Details of the monitoring program and sensor array layout can be found in the IP21 cruise report (doi:10.25928/hw7d-fs42). The soundscape at in total four sites (two in area GER and two in area BEL) was recorded using a single channel hydrophone originally designed for seismic and seismological exploration. Data are the original field recordings in mseed format (https://ds.iris.edu/ds/nodes/dmc/data/formats/miniseed) a standard seismological data format for data exchange and readable with a broad range of programmes or codes.

The Noise Control System for Mining market is an essential segment of the mining industry, addressing a critical challenge: excessive noise exposure that can adversely affect both worker health and productivity. As mining operations become more complex and demands for mineral extraction rise, the implementation of e

The proposed dataset contains 1669 labeled audio files from the following Picidae species doing 3 types of birdsongs: call, drumming and song. Audio data are organized in thirteen folders: twelve containing the labeled audios of each birdsong type from every Picidae specie listed in what follows and one folder containing the background sound samples. 1- DendrocoposLeucotos - call.2- DendrocoposLeucotos - drumming.3- DendrocoposMajor - call.4- DendrocoposMajor - drumming.5- DendrocoposMedius - call.6- DendrocoposMedius - song.7- DendrocoposMinor - call.8- DendrocoposMinor - drumming.9- DryocopusMartius - call.10- DryocopusMartius - drumming.11- JynxTorquilla - song.12- PicusViridis - song.13- Silence (or background noise).Licence type: CC-BY-SA

Heuristics Miner

Malaria is the leading cause of death in the African region. Data mining can help extract valuable knowledge from available data in the healthcare sector. This makes it possible to train models to predict patient health faster than in clinical trials. Implementations of various machine learning algorithms such as K-Nearest Neighbors, Bayes Theorem, Logistic Regression, Support Vector Machines, and Multinomial Naïve Bayes (MNB), etc., has been applied to malaria datasets in public hospitals, but there are still limitations in modeling using the Naive Bayes multinomial algorithm. This study applies the MNB model to explore the relationship between 15 relevant attributes of public hospitals data. The goal is to examine how the dependency between attributes affects the performance of the classifier. MNB creates transparent and reliable graphical representation between attributes with the ability to predict new situations. The model (MNB) has 97% accuracy. It is concluded that this model outperforms the GNB classifier which has 100% accuracy and the RF which also has 100% accuracy. Methods Prior to collection of data, the researcher was be guided by all ethical training certification on data collection, right to confidentiality and privacy reserved called Institutional Review Board (IRB). Data was be collected from the manual archive of the Hospitals purposively selected using stratified sampling technique, transform the data to electronic form and store in MYSQL database called malaria. Each patient file was extracted and review for signs and symptoms of malaria then check for laboratory confirmation result from diagnosis. The data was be divided into two tables: the first table was called data1 which contain data for use in phase 1 of the classification, while the second table data2 which contains data for use in phase 2 of the classification. Data Source Collection Malaria incidence data set is obtained from Public hospitals from 2017 to 2021. These are the data used for modeling and analysis. Also, putting in mind the geographical location and socio-economic factors inclusive which are available for patients inhabiting those areas. Naive Bayes (Multinomial) is the model used to analyze the collected data for malaria disease prediction and grading accordingly. Data Preprocessing: Data preprocessing shall be done to remove noise and outlier. Transformation: The data shall be transformed from analog to electronic record. Data Partitioning The data which shall be collected will be divided into two portions; one portion of the data shall be extracted as a training set, while the other portion will be used for testing. The training portion shall be taken from a table stored in a database and will be called data which is training set1, while the training portion taking from another table store in a database is shall be called data which is training set2. The dataset was split into two parts: a sample containing 70% of the training data and 30% for the purpose of this research. Then, using MNB classification algorithms implemented in Python, the models were trained on the training sample. On the 30% remaining data, the resulting models were tested, and the results were compared with the other Machine Learning models using the standard metrics. Classification and prediction: Base on the nature of variable in the dataset, this study will use Naïve Bayes (Multinomial) classification techniques; Classification phase 1 and Classification phase 2. The operation of the framework is illustrated as follows: i. Data collection and preprocessing shall be done. ii. Preprocess data shall be stored in a training set 1 and training set 2. These datasets shall be used during classification. iii. Test data set is shall be stored in database test data set. iv. Part of the test data set must be compared for classification using classifier 1 and the remaining part must be classified with classifier 2 as follows: Classifier phase 1: It classify into positive or negative classes. If the patient is having malaria, then the patient is classified as positive (P), while a patient is classified as negative (N) if the patient does not have malaria.
Classifier phase 2: It classify only data set that has been classified as positive by classifier 1, and then further classify them into complicated and uncomplicated class label. The classifier will also capture data on environmental factors, genetics, gender and age, cultural and socio-economic variables. The system will be designed such that the core parameters as a determining factor should supply their value.

Discover the booming fixed noise monitoring system market, projected to reach $1045 million by 2025 with a 3.9% CAGR. Explore market trends, regional analysis, key players (Bruel & Kjaer, 3M, Extech), and applications across mining, wind energy, and more. Get insights into future growth opportunities in this vital sector.

Flowback Noise Reduction Systems Market Outlook

According to our latest research, the global Flowback Noise Reduction Systems market size reached USD 1.24 billion in 2024, with a robust year-on-year growth trajectory. The market is projected to expand at a CAGR of 7.2% during the forecast period, reaching a forecasted market size of USD 2.32 billion by 2033. This strong growth is primarily driven by increasing environmental regulations, heightened health and safety standards, and the rising adoption of advanced noise mitigation technologies in oil & gas, mining, and construction sectors.

One of the primary growth factors propelling the Flowback Noise Reduction Systems market is the intensification of global environmental regulations targeting noise pollution, especially in industrial zones and near residential areas. Governments and regulatory bodies across the globe have enacted stringent noise control laws, compelling industries to adopt innovative noise reduction solutions. The oil & gas sector, in particular, has witnessed a significant increase in operational activities, leading to higher demand for flowback noise reduction systems to comply with occupational health and safety norms. Furthermore, the rising public awareness regarding the adverse effects of industrial noise on communities and ecosystems has prompted companies to invest in state-of-the-art noise reduction technologies, ensuring both regulatory compliance and social responsibility.

Technological advancements in noise reduction systems have further accelerated market growth. The integration of smart sensors, real-time monitoring, and data analytics into both active and hybrid noise reduction systems has enabled more precise and efficient noise mitigation during flowback operations. These innovations not only enhance the effectiveness of noise suppression but also facilitate predictive maintenance and remote diagnostics, reducing downtime and operational costs. Additionally, the shift towards digitalization and automation in industrial processes has created new opportunities for manufacturers to develop modular, scalable, and customizable noise reduction solutions tailored to specific operational needs. This technological evolution is particularly evident in the adoption of hybrid systems, which combine the best features of active and passive noise reduction for superior performance.

Another significant growth driver is the expansion of oil & gas exploration and production activities, especially in emerging markets. The increasing demand for energy and raw materials has led to a surge in onshore and offshore drilling projects, where flowback operations are critical. These activities often take place in environmentally sensitive or densely populated areas, intensifying the need for effective noise reduction systems to minimize the impact on local communities and wildlife. Moreover, the construction and mining sectors are also adopting advanced noise reduction solutions to address similar challenges, further broadening the market base. The ability of modern flowback noise reduction systems to operate efficiently in harsh and remote environments adds to their appeal across multiple end-user industries.

Regionally, North America continues to dominate the global Flowback Noise Reduction Systems market, driven by the presence of major oil & gas companies, stringent regulatory frameworks, and high adoption rates of advanced technologies. Europe follows closely, benefiting from robust environmental policies and significant investments in industrial noise control measures. The Asia Pacific region is emerging as a lucrative market, fueled by rapid industrialization, urbanization, and increasing awareness of environmental and occupational health issues. Latin America and the Middle East & Africa are also witnessing steady growth, supported by expanding energy and mining sectors. This regional diversity underscores the global relevance and adaptability of flowback noise reduction systems across varying regulatory and operational landscapes.

Prod

Chronic pain (CP) lasts for more than 3 months, causing prolonged physical and mental burdens to patients. According to the US Centers for Disease Control and Prevention, CP contributes to more than 500 billion US dollars yearly in direct medical cost plus the associated productivity loss. CP is complex in etiology and can occur anywhere in the body, making it difficult to treat and manage. There is a pressing need for research to better summarize the common health issues faced by consumers living with CP and their experience in accessing over-the-counter analgesics or therapeutic devices. Modern online shopping platforms offer a broad array of opportunities for the secondary use of consumer-generated data in CP research. In this study, we performed an exploratory data mining study that analyzed CP-related Amazon product reviews. Our descriptive analyses characterized the review language, the reviewed products, the representative topics, and the network of comorbidities mentioned in the reviews. The results indicated that most of the reviews were concise yet rich in terms of representing the various health issues faced by people with CP. Despite the noise in the online reviews, we see potential in leveraging the data to capture certain consumer-reported outcomes or to identify shortcomings of the available products.

The global market for Noise Monitoring Devices is poised for significant expansion, estimated to reach approximately $1.2 billion in 2025, with a projected Compound Annual Growth Rate (CAGR) of 6.8% over the forecast period from 2025 to 2033. This growth is propelled by increasing regulatory mandates concerning workplace noise exposure across various industries, a heightened awareness of occupational health and safety, and the growing adoption of smart and connected devices that offer real-time data analysis and remote monitoring capabilities. Key drivers include stringent government regulations in developed economies and emerging markets alike, pushing industries to invest in compliant and effective noise monitoring solutions. Furthermore, the rising industrialization and infrastructure development in regions like Asia Pacific and the Middle East are creating substantial demand for these devices. Technological advancements, such as the integration of IoT capabilities and AI-driven analytics for predictive maintenance and pattern recognition in noise pollution, are further fueling market growth and innovation. The noise monitoring devices market is characterized by its diverse applications across sectors such as construction, mining, transportation, and oil & gas, each with unique noise-related challenges and regulatory frameworks. While Wi-Fi and Cellular connectivity are gaining traction for their flexibility and real-time data transmission, traditional Ethernet and USB Cable connections remain vital for robust, fixed installations. However, the market faces certain restraints, including the high initial cost of advanced monitoring systems, particularly for smaller enterprises, and the need for specialized training to operate and interpret data from complex devices. Despite these challenges, the trend towards miniaturization, enhanced portability, and user-friendly interfaces is making these devices more accessible. Leading companies like FLIR Systems, Pulsar Instruments, and 3M are actively investing in research and development to introduce sophisticated, cost-effective solutions that meet the evolving demands of industries and regulatory bodies worldwide, ensuring a robust market trajectory.

The global Noise Reduction System market is poised for significant expansion, projected to reach an estimated market size of approximately USD 15,000 million by 2025, with a robust Compound Annual Growth Rate (CAGR) of 5.5% anticipated through 2033. This upward trajectory is primarily fueled by increasingly stringent environmental regulations worldwide, mandating lower noise pollution levels across industrial and urban landscapes. The escalating demand for enhanced worker safety and productivity in noisy industrial environments, coupled with a growing consumer awareness and preference for quieter living and working spaces, are also key drivers. Furthermore, advancements in material science and acoustic engineering are leading to the development of more efficient, cost-effective, and aesthetically pleasing noise reduction solutions, further stimulating market adoption. The market segmentation reveals a diverse range of applications, with the Oil and Gas Industrial, Mining, and Construction Industrial sectors emerging as major consumers of noise reduction systems due to the inherently loud nature of their operations. The Automobile Industrial segment also presents substantial growth opportunities as manufacturers focus on improving cabin acoustics and reducing external noise pollution. Geographically, the Asia Pacific region is expected to lead market growth, driven by rapid industrialization and significant infrastructure development in countries like China and India. North America and Europe, with their mature economies and established regulatory frameworks, will continue to be substantial markets, while emerging economies in South America and the Middle East & Africa are anticipated to witness steady growth. The market is characterized by a competitive landscape with established players focusing on product innovation, strategic partnerships, and expanding their global reach to cater to the evolving demands for acoustic management solutions.

The Mine Noise Monitoring Solutions market has emerged as a critical component in the mining industry, addressing the growing concern of noise pollution and its impact on both worker health and environmental compliance. With the rise in regulatory requirements and a heightened awareness of workplace safety, mining c

Noise Monitoring Station Market Outlook

According to our latest research, the global Noise Monitoring Station market size in 2024 stands at USD 1.12 billion, reflecting a robust demand driven by urbanization, regulatory mandates, and growing environmental awareness. The market is experiencing a healthy compound annual growth rate (CAGR) of 6.8% and is forecasted to reach USD 2.18 billion by 2033. This growth is primarily propelled by the increasing implementation of noise regulations, expansion of smart city projects, and the heightened focus on occupational health and safety across industrial and urban sectors.

The Noise Monitoring Station market is witnessing significant momentum due to the rapid urbanization and industrialization observed across both emerging and developed economies. Urban expansion, combined with infrastructure development such as highways, airports, and railways, has led to a notable increase in noise pollution levels, necessitating the deployment of sophisticated noise monitoring solutions. Governments and regulatory bodies worldwide are intensifying their efforts to enforce stringent noise control standards, which in turn is fueling market growth. Additionally, the adoption of smart city initiatives, where environmental monitoring is a key component, is further accelerating the integration of advanced noise monitoring stations in urban landscapes.

Technological advancements are another critical driver for the Noise Monitoring Station market. The integration of IoT, wireless connectivity, and cloud-based analytics has revolutionized noise monitoring by enabling real-time data collection, remote access, and predictive analytics. These innovations have enhanced the operational efficiency and reliability of both permanent and portable noise monitoring stations. Furthermore, the development of user-friendly software interfaces and mobile applications has facilitated broader adoption across various end-user sectors, including government agencies, transportation authorities, and industrial enterprises. The emphasis on data-driven decision-making is compelling organizations to invest in comprehensive noise monitoring solutions that offer actionable insights and compliance support.

Environmental and occupational health concerns are also propelling the demand for noise monitoring stations. Prolonged exposure to high noise levels has been linked to adverse health effects such as hearing loss, stress, and cardiovascular issues, prompting both regulatory bodies and private organizations to prioritize noise management. The construction, mining, and transportation sectors, in particular, are under increased scrutiny to monitor and mitigate noise emissions. This growing awareness, coupled with the rising incidence of noise-related complaints in urban areas, is contributing to the sustained expansion of the Noise Monitoring Station market. As a result, manufacturers are focusing on developing more accurate, durable, and cost-effective monitoring solutions to cater to the diverse needs of end-users.

In the realm of smart city development, the Smart City Noise Monitoring Node is emerging as a pivotal component in managing urban noise pollution. These nodes are designed to seamlessly integrate with existing smart city infrastructure, providing real-time noise data that can be used to enhance urban planning and improve quality of life for residents. By leveraging advanced sensors and IoT connectivity, Smart City Noise Monitoring Nodes enable city planners to identify noise hotspots, assess the impact of traffic and construction, and implement targeted noise reduction strategies. This technology not only supports regulatory compliance but also fosters community engagement by addressing noise-related concerns in a proactive manner. As cities continue to grow and evolve, the deployment of Smart City Noise Monitoring Nodes is becoming increasingly essential to ensure sustainable and livable urban environments.

From a regional perspective, Asia Pacific is emerging as the fastest-growing market, driven by rapid urbanization, infrastructure development, and the implementation of noise control regulations in countries such as China, India, and Japan. North America and Europe continue to dominate the market owing to established regulatory frameworks and high adoption rates of adv