Patterns In Data Research Articles

Attention-enhanced conditional-diffusion-based data synthesis for data augmentation in machine fault diagnosis

Data scarcity and class imbalance are pervasive challenges in machine fault diagnosis, impeding the development and broad adaptation of accurate and reliable deep-learning-based fault detection systems. To address these issues, we propose a novel attention-enhanced conditioning-guided diffusion-based approach for synthesizing additional training data. By generating noisy motor-current signals and employing a diffusion process to reduce noise levels, we obtain synthetic data that closely resembles the statistical properties and patterns of real-world data. The quality of the synthesized samples is assessed using a four-layer, pre-trained convolutional neural network, achieving an impressive classification rate of 96.39%. This demonstrates the effectiveness of our attention-enhanced conditioned diffusion-based model in synthesizing critical features for fault detection, making it well-suited for the introduced fault detection task. To further investigate the quality of the synthesized data samples, we implement a specific training strategy that uses the generated samples as training data. The trained diffusion model generates samples per class, which are then used to train a simple four-layer convolutional neural network for fault classification. Remarkably, the model achieves an accuracy of 98.96% when evaluated with real inverter signals, indicating that the generated samples effectively capture the distribution of real data. For further evaluation, multiple scenarios are created confirming the value of the additionally synthesized samples for real-world bearing fault detection. Our approach presents a well-suited solution to the data scarcity and class imbalance problem in machine fault diagnosis. The proposed method enhances the accuracy and reliability of fault diagnosis systems, offering valuable insights for various real-world condition monitoring applications.

Real-Time Violence Detection in Surveillance Videos Using Deep Learning Approach

Abstract: In order to tackle the ongoing problem of keeping fruits fresh, this research presents an automated freshness detection. The ever-growing presence of surveillance cameras has undoubtedly enhanced public safety. However, manually reviewing vast quantities of video footage for violence detection remains a tedious and error-prone task. This paper proposes a deep learningbased approach for anomaly detection in surveillance videos, with a specific focus on identifying violent activities. Traditional surveillance methods often lack the sophistication to distinguish between normal behaviour and potentially threatening actions. Deep learning offers a significant advantage by automating the identification of deviations from established behavioural patterns in video data. This automation enables real-time analysis of footage, potentially signifying the occurrence of violent incidents and allowing for a swifter response from security personnel. This research delves into the application of deep learning models for violence detection in surveillance footage. We begin by discussing the limitations inherent in conventional methods, such as motion detection and manual review, which struggle to capture the nuances of human behaviour. Subsequently, we explore the advantages that deep learning-based approaches bring to the table, including their ability to learn complex patterns from large datasets and identify subtle anomalies that might escape human observation.

Application of big data and artificial intelligence inmental health prediction and intervention

In this article, we delve into applying Convolutional Neural Networks (CNNs) and big data in predicting and intervening in mental health issues, emphasizing the potential for early detection and personalized treatment. By analyzing patterns in social media data, CNNs can identify indicators of mental health conditions, offering insights for tailored interventions. The discussion highlights the importance of addressing privacy, data security, and algorithmic bias to ensure ethical implementation. Future directions include enhancing predictive accuracy, expanding AI applications in therapy, fostering interdisciplinary collaborations, developing ethical frameworks, and engaging the public. Embracing these technologies in mental health care promises significant advancements but necessitates careful consideration of ethical imperatives to maximize benefits while safeguarding patient welfare.

Decoding Immuno-Competence: A Novel Analysis of Complete Blood Cell Count Data in COVID-19 Outcomes

Background: While ‘immuno-competence’ is a well-known term, it lacks an operational definition. To address this omission, this study explored whether the temporal and structured data of the complete blood cell count (CBC) can rapidly estimate immuno-competence. To this end, one or more ratios that included data on all monocytes, lymphocytes and neutrophils were investigated. Materials and methods: Longitudinal CBC data collected from 101 COVID-19 patients (291 observations) were analyzed. Dynamics were estimated with several approaches, which included non-structured (the classic CBC format) and structured data. Structured data were assessed as complex ratios that capture multicellular interactions among leukocytes. In comparing survivors with non-survivors, the hypothesis that immuno-competence may exhibit feedback-like (oscillatory or cyclic) responses was tested. Results: While non-structured data did not distinguish survivors from non-survivors, structured data revealed immunological and statistical differences between outcomes: while survivors exhibited oscillatory data patterns, non-survivors did not. In survivors, many variables (including IL-6, hemoglobin and several complex indicators) showed values above or below the levels observed on day 1 of the hospitalization period, displaying L-shaped data distributions (positive kurtosis). In contrast, non-survivors did not exhibit kurtosis. Three immunologically defined data subsets included only survivors. Because information was based on visual patterns generated in real time, this method can, potentially, provide information rapidly. Discussion: The hypothesis that immuno-competence expresses feedback-like loops when immunological data are structured was not rejected. This function seemed to be impaired in immuno-suppressed individuals. While this method rapidly informs, it is only a guide that, to be confirmed, requires additional tests. Despite this limitation, the fact that three protective (survival-associated) immunological data subsets were observed since day 1 supports many clinical decisions, including the early and personalized prognosis and identification of targets that immunomodulatory therapies could pursue. Because it extracts more information from the same data, structured data may replace the century-old format of the CBC.

Evidence for a constituent order boost in structural priming

The study investigates the role of constituent order in structural priming. We report the results from a PO/DO priming experiment in German, in which we experimentally manipulated verb position in primes and targets. Significant structural priming effects occurred irrespective of whether verb position was the same in prime and target or not. However, additional similarity in constituent order was able to boost structural priming effects, with significantly stronger priming when the verb occurred in the same position in prime and target. We argue that existing one-stage and two-stage accounts of formulation struggle to account for the entire data pattern and propose an alternative account of formulation which can explain our results.

Deciphering Ferroptosis: From Molecular Pathways to Machine Learning-Guided Therapeutic Innovation.

Ferroptosis is a unique form of cell death reliant on iron and lipid peroxidation. It disrupts redox balance, causing cell death by damaging the plasma membrane, with inducers acting through enzymatic pathways or transport systems. In cancer treatment, suppressing ferroptosis or circumventing it holds significant promise. Beyond cancer, ferroptosis affects aging, organs, metabolism, and nervous system. Understanding ferroptosis mechanisms holds promise for uncovering novel therapeutic strategies across a spectrum of diseases. However, detection and regulation of this regulated cell death are still mired with challenges. The dearth of cell, tissue, or organ-specific biomarkers muted the pharmacological use of ferroptosis. This review covers recent studies on ferroptosis, detailing its properties, key genes, metabolic pathways, and regulatory networks, emphasizing the interaction between cellular signaling and ferroptotic cell death. It also summarizes recent findings on ferroptosis inducers, inhibitors, and regulators, highlighting their potential therapeutic applications across diseases. The review addresses challenges in utilizing ferroptosis therapeutically and explores the use of machine learning to uncover complex patterns in ferroptosis-related data, aiding in the discovery of biomarkers, predictive models, and therapeutic targets. Finally, it discusses emerging research areas and the importance of continued investigation to harness the full therapeutic potential of targeting ferroptosis.

A comparative analysis and prediction of the economic growth of Pakistan using machine learning models

This article investigates a comparative analysis of machine learning models for Pakistan's Gross Domestic Product (GDP), an important indicator of the nation's economic development. GDP is crucial to assess well-versed decisions. Since machine learning techniques are more sophisticated, much interest has been developed in predicting GDP to handle complex data patterns and enhance prediction accuracy. In this study, we evaluated the performance of a variety of machine learning algorithms like Auto-Regressive Integrated Moving Average (ARIMA), double exponential smoothing, Multilayer Perceptron (MLP), Neural Network Auto-Regressive (NNAR), and hybrid machine learning models on data from 1960 to 2022. The MLP used in Artificial Neural Networks (ANNs) outperforms based on the outcomes. This comparative analysis provides insights into the most suitable model for accurate prediction of Pakistani GDP for the years 2023 to 2032. This article provides a detailed analysis of various machine learning models used to predict Pakistan's GDP accurately. GDP prediction is an essential indicator of a nation's economic development and is crucial in making informed decisions. With the advancements in machine learning techniques, there has been a growing interest in predicting GDP due to their efficiency in handling complex data patterns and improving prediction accuracy. 

Machine Learning in Healthcare: A Review

Machine learning a new and very sophisticated technology has been developed as a large trend in the market nowadays. Machine learning is an everywhere phenomenon which is applied in different areas. It’s an irreplaceable factor in many sectors such as business, science and security and so on. Implementation of machine learning technology in healthcare is emerging as a crucial trend. The potential presented by the machine learning technologies for the healthcare field is simply stunning. Machine learning technologies are making a huge influence in healthcare. ML, a sort of technology which attempts to enhance the velocity and precision of physicians work. Machine learning algorithms are used to detect hidden patterns in medical data and provides great prediction skills. In this research, we have to examine various methods used to create effective decision assistance for medical applications. This work contributes to minimizing the gap of generating effective decision support systems for healthcare applications. Keywords: Healthcare, Machine Learning, ubiquitous, medical applications

Анализ климата и сельскохозяйственных факторов для прогнозирования урожайности основных зерновых культур в Эфиопии: визуальный анализ (1995-2021 гг.)

<p>This paper serves as a foundational exploration into predicting the yields of important cereal crops in Ethiopia. We use a visual analytic approach to identify patterns in annual temperature, precipitation, area harvested, production, and yield data from 1995 to 2021 by integrating climate factors and agricultural practices. By examining these Agricultural variables, we hope to build links between shifting climatic conditions, agricultural decisions, and crop production, providing indispensable insights for stakeholders, farmers, and policymakers.</p>

The use of artificial intelligence in the diagnosis and detection of complications of diabetes

Introduction: Diabetes poses a significant global health challenge, impacting patient well-being and longevity. Despite advances in diagnosis and treatment, the prevalence of diabetes continues to rise, with projections indicating a substantial increase in affected individuals in the coming years. The complications of diabetes, including cardiovascular disease, retinopathy, nephropathy, and neuropathy, underscore the importance of early detection and management. In this context, artificial intelligence (AI) offers promising opportunities to revolutionize diabetes care, enabling faster diagnostics, more effective treatment strategies.
 Description of the State of Knowledge: Artificial intelligence (AI) has emerged as a transformative force in healthcare, leveraging machine learning and deep learning algorithms to analyze vast amounts of medical data. These algorithms enable more accurate diagnosis, prediction of disease onset, and early detection of complications associated with diabetes. Machine learning models, including support vector machines and neural networks, have shown promise in identifying diabetes risk factors and predicting disease progression. Deep learning techniques, with their ability to analyze complex data patterns, offer further insights into diabetes diagnosis. Additionally, fuzzy cognitive maps provide a framework for decision-making based on patient data, enhancing early detection efforts.
 Summary: Artificial intelligence holds immense potential to transform diabetes care, offering solutions for early detection, personalized treatment, and improved patient outcomes. By harnessing the power of AI algorithms, healthcare providers can enhance diagnostic accuracy, predict disease progression, and implement targeted interventions.

Employing machine learning to enhance fracture recovery insights through gait analysis.

This study aimed to explore the potential of gait analysis coupled with supervised machine learning models as a predictive tool for assessing post-injury complications such as infection, malunion, or hardware irritation among individuals with lower extremity fractures. We prospectively identified participants with lower extremity fractures at a tertiary academic center. These participants underwent gait analysis with a chest-mounted inertial measurement unit device. Using customized software, the raw gait data were preprocessed, emphasizing 12 essential gait variables. The data were standardized, and several machine learning models, including XGBoost, logistic regression, support vector machine, LightGBM, and Random Forest, were trained, tested, and evaluated. Special attention was given to class imbalance, addressed using the synthetic minority oversampling technique (SMOTE). Additionally, we introduced a novel methodology to compute the post-injury recovery rate for gait variables, which operates independently of the time difference between the gait analyses of different participants. XGBoost was identified as the optimal model both before and after the application of SMOTE. Before using SMOTE, the model achieved an average test area under the ROC curve (AUC) of 0.90, with a 95% confidence interval (CI) of [0.79, 1.00], and an average test accuracy of 86%, with a 95% CI of [75%, 97%]. Through feature importance analysis, a pivotal role was attributed to the duration between the occurrence of the injury and the initial gait analysis. Data patterns over time revealed early aggressive physiological compensations, followed by stabilization phases, underscoring the importance of prompt gait analysis. χ2 analysis indicated a statistically significant higher readmission rate among participants with underlying medical conditions (p = 0.04). Although the complication rate was also higher in this group, the association did not reach statistical significance (p = 0.06), suggesting a more pronounced impact of medical conditions on readmission rates rather than on complications. This study highlights the transformative potential of integrating advanced machine learning techniques like XGBoost with gait analysis for orthopedic care. The findings underscore a shift toward a data-informed, proactive approach in orthopedics, enhancing patient outcomes through early detection and intervention. The χ2 analysis added crucial insights into the broader clinical implications, advocating for a comprehensive treatment strategy that accounts for the patient's overall health profile. The research paves the way for personalized, predictive medical care in orthopedics, emphasizing the importance of timely and tailored patient assessments.

Real-Time Monitoring of Wind Turbine Bearing Using Simple Neural Network on Raspberry Pi

Wind turbines are a crucial part of renewable energy generation, and their reliable and efficient operation is paramount in ensuring clean energy availability. However, the bearings in wind turbines are subjected to high stress and loads, resulting in faults that can lead to costly downtime and repairs. Fault detection in real time is critical to minimize downtime and reduce maintenance costs. In this work, a simple neural network model was designed and implemented on a Raspberry Pi for the real-time detection of wind turbine bearing faults. The model was trained to accurately identify complex patterns in raw sensor data of healthy and faulty bearings. By splitting the data into smaller segments, the model can quickly analyze each segment and generate predictions at high speed. Additionally, simplified algorithms were developed to analyze the segments with minimum latency. The proposed system can efficiently process the sensor data and performs rapid analysis and prediction within 0.06 milliseconds per data segment. The experimental results demonstrate that the model achieves a 99.8% accuracy in detecting wind turbine bearing faults within milliseconds of their occurrence. The model’s ability to generate real-time predictions and to provide an overall assessment of the bearing’s health can significantly reduce maintenance costs and increase the availability and efficiency of wind turbines.

Nonresponse data in sexual well-being among breast reconstruction patients-who are we overlooking?

Missing data can affect the representativeness and accuracy of survey results, and sexual health-related surveys are especially at a higher risk of nonresponse due to their sensitive nature and stigma. The purpose of this study was to evaluate the proportion of patients who do not complete the BREAST-Q Sexual Well-being relative to other BREAST-Q modules and compare responders versus nonresponders of Sexual Well-being. We secondarily examined variables associated with Sexual Well-being at 1-year. A retrospective analysis of patients who underwent breast reconstruction from January 2018 to December 2021 and completed any of the BREAST-Q modules postoperatively at 1-year was performed. The 2941 patients were included. Of the four BREAST-Q domains, Sexual Well-being had the highest rate of nonresponse (47%). Patients who were separated (vs. married, OR = 0.69), whose primary language was not English (vs. English, OR = 0.60), and had Medicaid insurance (vs. commercial, OR = 0.67) were significantly less likely to complete the Sexual Well-being. Postmenopausal patients were significantly more likely to complete the survey than premenopausal patients. Lastly, autologous reconstruction patients were 2.93 times more likely to respond than implant-based reconstruction patients (p < 0.001) while delayed (vs. immediate, OR = 0.70, p = 0.022) and unilateral (vs. bilateral, OR = 0.80, p = 0.008) reconstruction patients were less likely to respond. History of psychiatric diagnosis, aromatase inhibitors, and immediate breast reconstruction were significantly associated with lower Sexual Well-being at 1-year. Sexual Well-being is the least frequently completed BREAST-Q domain, and there are demographic and clinical differences between responders and nonresponders. We encourage providers to recognize patterns in nonresponse data for Sexual-Well-being to ensure that certain patient population's sexual health concerns are not overlooked.

Profiling effects of filtering noise labels on learning performance

Deciding whether a label in a dataset is noisy or not depends on factors such as filtering levels, data patterns, relabeling policies, and pre-defined regulations. By focusing on filtering acceptance levels (FAL), a decision support model (named DSM-ENL) is proposed and implemented on real datasets that are granulated, dealing with noise label detection and profiling effects of 50 FALs (51 %–100 %) on noise rate (NoR), classification accuracy (CA), and area under ROC curve (AUC). A case used to demonstrate the DSM-ENL is illustrated, followed by experiments on 27 unsupervised and 29 supervised granulated datasets using five classifiers with 70 %–30 % training and testing criteria. The findings revealed that: (1) the best CAs among classifiers vary with FALs; (2) NoR, CA, and AUC oscillate as FAL increases; (3) lower FAL boundaries (from 0.51 to 0.60) are more likely to lead to higher CAs; and (4) correlation of coefficient among dataset characteristics, FAL, NoR, CA, and AUC are not high compared to that of CA and NoR and that of CA and AUC. The research demonstrates the value of profiling the effects of FALs applied for noise-label datasets on learning performance.

Survey-Reported Coverage in 2019-2022 and Implications for Unwinding Medicaid Continuous Eligibility

Policy changes and the COVID-19 pandemic affected health coverage rates, and the "unwinding" of Medicaid's continuous coverage provision in 2023 and 2024 may cause widespread coverage loss. Recent coverage patterns in national survey and administrative data can inform these issues. To assess national and state changes in survey-based Medicaid, private insurance, and uninsured rates between 2019 and 2022, as well as how these changes compare with administrative Medicaid enrollment totals. This cross-sectional study analyzes nationally representative survey data for all US residents in the American Community Survey (ACS) from 2019 to 2022 compared with administrative data on Medicaid and the Children's Health Insurance Program from the Centers for Medicare & Medicaid Services (CMS). Data analysis was conducted between June 2023 and January 2024. The COVID-19 pandemic, the Medicaid continuous coverage provision, and policy efforts to increase Marketplace coverage. Medicaid coverage (self-reported [ACS] and administratively recorded [CMS]), survey-reported uninsured, Medicare, and private insurance status. A nationally representative sample consisted of 12 506 584 US residents of all ages (survey-weighted 59.7% aged 19-64 years and 50.6% female). CMS statistics showed an increase in Medicaid coverage of 5.2 percentage points as a share of the population from 2019 to 2022. However, changes in the uninsured rate and survey-reported Medicaid were smaller: -1.2 (95% CI, -1.3 to -1.2) percentage points and 1.3 (95% CI, 1.2-1.4) percentage points, respectively. There was a 3.9 percentage point increase in the ACS's "undercount" of Medicaid enrollment, compared with CMS data, from 2019 to 2022. This undercount was larger among children than adults but smaller in states that recently expanded Medicaid. Rates of additional forms of coverage (such as private insurance) among those in Medicaid also grew during this time. In this cross-sectional study, the uninsured rate declined considerably from 2019 to 2022 but was just one-fourth as large as the growth in administrative Medicaid enrollment under the pandemic continuous coverage provision. Survey-based Medicaid growth was far smaller than administrative growth. This suggests that many people who remained enrolled in Medicaid during the pandemic did not realize that their coverage had continued. These findings have implications for projecting uninsured changes during unwinding, as well as the effect of continuous coverage policies on continuity of care.

E-nose analysis of milk to detect the inclusion of hydroponic barley forage in the buffalo diet

This study explores the use of an electronic nose (E-nose), equipped with ten metal oxide semiconductor thin-film sensors, to detect raw milk samples from buffaloes fed hydroponic barley forage as a substitute for maize silage. 108 samples were collected on three different days, from three groups of twelve Italian Mediterranean buffaloes each. The control group (C) was fed a diet based on maize silage, which in the other groups was replaced by hydroponic forage at 50 % (LH group) and 100 % (HH group). The data pattern from the E-nose sensor responses data was analysed using linear discriminant analysis (LDA) and principal component analysis (PCA) as pattern recognition methods, to investigate the discrimination capability of the sensor array. Confusion matrix, obtained from LDA, correctly classified C, LH and HH milk at 90 %. Solid-phase microextraction with gas chromatography–mass spectrometry revealed that C samples had the highest total level of volatile organic compounds, particularly ketones, volatile fatty acids and esters, while HH had the lowest quantity of volatile fatty acids and higher amounts of p-cresol, 1-octen-3-ol and dimethyl sulfone. This has resulted in a greater response intensity of most E-nose sensors for C, such as W1S, W2S, W3S and W6S, which react to high concentrations of broad-range and aliphatic compounds. E-nose proves effective in rapidly identifying hydroponic forage in buffalo diets.

Auto encoders with Cellular Automata for Anomaly Detection

This work combines auto encoders with cellular automata (CA) to present a novel hybrid strategy for anomaly identification. For feature learning, auto encoders are used to identify spatial patterns in the input data. Simultaneously, temporal and geographical dependencies are captured by CA, which improves the model's capacity to identify complicated anomalies. Training on spatially altered data, the auto encoder-CA hybrid model makes use of CA's temporal evolution to reveal dynamic patterns. Reconstruction errors between the input data and its decoded representation are computed to identify anomalies. A comprehensive framework for anomaly identification is provided by the synergy between spatial-temporal analysis of CA and auto encoder-based feature learning. Performance is optimized by fine-tuning the model's parameters, which include the auto encoder architecture and CA setup. The hybrid model's dynamic adaptation ensures robustness over time by accommodating changing data distributions. Evaluation measures show how well the suggested method captures abnormalities that appear in both temporal and geographical dimensions. A promising method for identifying abnormalities in complicated datasets with detailed spatial and temporal patterns is presented by this novel combination of auto encoders and cellular automata. The proposed method is evaluated with various parameters like reconstruction error, precision , recall, F1 score and Area Under the Receiver Operating Characteristic (ROC-AUC). The average accuracy is reported as 97.36% which is promising when compared with baseline methods.

Enhancing hydrological modeling with transformers: a case study for 24-h streamflow prediction

Abstract In this paper, we address the critical task of 24-h streamflow forecasting using advanced deep-learning models, with a primary focus on the Transformer architecture which has seen limited application in this specific task. We compare the performance of five different models, including Persistence, long short-term memory (LSTM), Seq2Seq, GRU, and Transformer, across four distinct regions. The evaluation is based on three performance metrics: Nash–Sutcliffe Efficiency (NSE), Pearson's r, and normalized root mean square error (NRMSE). Additionally, we investigate the impact of two data extension methods: zero-padding and persistence, on the model's predictive capabilities. Our findings highlight the Transformer's superiority in capturing complex temporal dependencies and patterns in the streamflow data, outperforming all other models in terms of both accuracy and reliability. Specifically, the Transformer model demonstrated a substantial improvement in NSE scores by up to 20% compared to other models. The study's insights emphasize the significance of leveraging advanced deep learning techniques, such as the Transformer, in hydrological modeling and streamflow forecasting for effective water resource management and flood prediction.

Empirical Approach to Modelling and Forecasting Inflation using ARIMA Model: Evidence from Tanzania

This study investigates the dynamics of the inflation rate (INFL) in Tanzania spanning from 1990 to 2021 using advanced time series analysis techniques. The dataset sourced from the World Bank online database serves as the basis for analytical exploration. Initially, the Autoregressive Integrated Moving Average (ARIMA) model is applied to understand underlying patterns in the INFL data. Transitioning to Seasonal ARIMA (SARIMA) modeling encounters challenges due to the absence of pronounced seasonality or clear trends. Unit root tests, including the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests, assess the stationarity of the data. Following the identification of non-stationarity, differencing is employed to achieve stationarity. Estimation of ARIMA models (ARIMA (1,1,1) and ARIMA (2,1,1)) is conducted, with diagnostic checks confirming the suitability of the ARIMA (1,1,1) model. The study contributes to the understanding of inflation dynamics and facilitates evidence-based economic policymaking in Tanzania.

Space as a scaffold for rotational generalisation of abstract concepts

Learning invariances allows us to generalise. In the visual modality, invariant representations allow us to recognise objects despite translations or rotations in physical space. However, how we learn the invariances that allow us to generalise abstract patterns of sensory data (‘concepts’) is a longstanding puzzle. Here, we study how humans generalise relational patterns in stimulation sequences that are defined by either transitions on a nonspatial two-dimensional feature manifold, or by transitions in physical space. We measure rotational generalisation, i.e., the ability to recognise concepts even when their corresponding transition vectors are rotated. We find that humans naturally generalise to rotated exemplars when stimuli are defined in physical space, but not when they are defined as positions on a nonspatial feature manifold. However, if participants are first pre-trained to map auditory or visual features to spatial locations, then rotational generalisation becomes possible even in nonspatial domains. These results imply that space acts as a scaffold for learning more abstract conceptual invariances.