Dataset Research Articles

Semi-supervised spectral clustering using shared nearest neighbour for data with different shape and density

<p>In the absence of supervisory information in spectral clustering algorithms, it is difficult to construct suitable similarity graphs for data with complex shapes and varying densities. To address this issue, this paper proposes a Semi-supervised Spectral Clustering algorithm based on shared nearest neighbor. The proposed algorithm combines the idea of semi-supervised clustering, adding Shared Nearest Neighbor information to the calculation of the distance matrix, and using pairwise constraint information to find the relationship between two data points, while providing a portion of supervised information. Comparative experiments were conducted on artificial data sets and University of California Irvine machine learning repository datasets. The experimental results show that the proposed algorithm achieves better clustering results compared to traditional K-means and spectral clustering algorithms.</p>

Next-gen security in IIoT: integrating intrusion detection systems with machine learning for industry 4.0 resilience

In the dynamic landscape of Industry 4.0, characterized by the integration of smart technologies and the industrial internet of things (IIoT), ensuring robust security measures is imperative. This paper explores advanced security solutions tailored for the IIoT, focusing on the integration of intrusion detection systems (IDS) with advanced machine learning (ML) and deep learning (DL) techniques. In this paper, we present a novel intrusion detection model to fortify to fortify Industry 4.0 systems against evolving cyber threats by leveraging ML an DL algorithms for dynamic adaptation. To evaluate the performances and effectiveness of our proposed model, we use the improved Coburg intrusion detection data sets (CIDDS) and BoT-IoT datasets, showcasing notable performance attributes with an exceptional 99.99% accuracy, high recall, and precision scores. The model demonstrates computational efficiency, with rapid learning and detection phases. This research contributes to advancing next-gen security solutions for Industry 4.0, offering a promising approach to tackle contemporary cyber.

Fast Parameterization of Planar Domains for Isogeometric Analysis via Generalization of Deep Neural Network

One prominent step in isogeometric analysis (IGA) is known as domain parameterization, that is, finding a parametric spline representation for a computational domain. Typically, domain parameterization is divided into two separate steps: identifying an appropriate boundary correspondence and then parameterizing the interior region. However, this separation significantly degrades the quality of the parameterization. To attain high-quality parameterization, it is necessary to optimize both the boundary correspondence and the interior mapping simultaneously, referred to as integral parameterization. In a prior research, an integral parameterization approach for planar domains based on neural networks was introduced. One limitation of this approach is that the neural network has no ability of generalization, that is, a network has to be trained to obtain a parameterization for each specific computational domain. In this article, we propose an efficient enhancement over this work, and we train a network which has the capacity of generalization—once the network is trained, a parameterization can be immediately obtained for each specific computational via evaluating the network. The new network greatly speeds up the parameterization process by two orders of magnitudes. We evaluate the performance of the new network on the MPEG data set and a self-design data set, and experimental results demonstrate the superiority of our algorithm compared to state-of-the-art parameterization methods.

Deep learning for audio signal-based tempo classification scenarios

This article explains how to determine the tempo of the kendhang, an Indonesian traditional melodic instrument. This research presents novelty as technological research related to gamelan instruments, which has rarely been achieved thus far, through the introduction of kendhang tempo types through the sounds produced, with the hope of creating an automatic system that can recognize the kendhang tempo during a gamelan performance. The testing in this work will categorize the tempo of kendhang into three categories: slow, medium, and fast, utilizing one of the two scenario models proposed, mel frequency cepstral coefficients (MFCC) and convolutional neural network (CNN) in the first scenario, and mel spectrogram and CNN in the second. Kendhang's original audio data, which was captured in real time and later enhanced, makes up the data set. The model 1 scenario, which entails feature extraction using MFCC and classification using the CNN classification approach, is the best scenario in this research, based on the experimental results. When compared to the other suggested modeling scenarios, model 1 has a level of 97%, an average accuracy, and a gain value of 96.67%, making it a solid assistant in terms of kendhang's good tempo recognition accuracy.

Enterotoxin-related genes PPFIA4 and SCN3B promote colorectal cancer development and progression.

To identify the role of enterotoxin-related genes in colorectal cancer (CRC) development and progression. Upregulated differentially expressed genes shared by three out of five Gene Expression Omnibus (GEO) data sets were included to screen the key enterotoxin-induced oncogenes (EIOGs) according to criteria oncogene definition, enrichment, and protein-protein interaction (PPI) network analysis, followed by prognosis survival, immune infiltration, and protential drugs analyses was performed via integration of RNA-sequencing data and The Cancer Genome Atlas-derived clinical profiles. We screened nine common key EIOGs from at least three GEO data sets. A Cox proportional hazards regression models verified that more alive cases, decreased overall survival, and highest 4-year survival prediction in CRC patients with high-risk score. Protein tyrosine phosphatase receptor type F polypeptide-interacting protein alpha-4 (PPFIA4), STY11, SCN3B, and SPTBN5 were shared in the same PPI network. Immune infiltration results showed that SCN3B and synaptotagmin 11 expression were obviously associated with B cell, macrophage, myeloid dendritic cell, neutrophils, and T cell CD4+ and CD8+ in both colon adenocarcinoma and rectal adenocarcinoma. CHIR-99021, MLN4924, and YK4-279 were identified as the potential drugs for treatment. Finally, upregulated EIOGs genes PPFIA4 and SCN3B were found in colon adenocarcinoma and PPFIA4 and SCN3B were proved to promote cell proliferation and migration in vitro. We demonstrated here that EIOGs promoting a malignancy phenotype was related with poor survival and prognosis in CRC, which might be served as novel therapeutic targets in CRC management.

Feature selection techniques for microarray dataset: a review

For many researchers working on feature selection techniques, finding an appropriate feature from the microarray dataset has turned into a bottleneck. Researchers often create feature selection approaches and algorithms with the goal of improving accuracy in microarray datasets. The main goal of this study is to present a variety of contemporary feature selection techniques, such as Filter, Wrapper, and Embedded methods proposed for microarray datasets to work on multi-class classification problems and different approaches to enhance the performance of learning algorithms, to address the imbalance issue in the data set, and to support research efforts on microarray dataset. This study is based on Critical Review Questions (CRQ) constructed using feature election methods described in the review methodology and applied to a microarray dataset. We discussed the analysed findings and future prospects of feature selection strategies for multi-class classification issues using microarray datasets, as well as prospective ways to speed up computing environment

Brain magnetic resonance imaging image classification for Alzheimer's disease and its hardware acceleration

<span lang="EN-US">Alzheimer's is a progressive neurodegenerative disorder and is considered the sixth leading cause of death after cancer and heart attack. Early detection and diagnosis provide individuals to go through a wider variety of clinical trials and get multiple medical benefits. Research on the application of deep learning and machine learning to the early detection of Alzheimer's disease has recently gained considerable attention. In this paper, we propose a deep learning classification framework to classify the individual with different progression stages of Alzheimer's disease such as mild cognitive impairment (MCI) and cognitive normal (CN). The dataset from Alzheimer’s disease neuroimaging initiative (ADNI) is considered in this paper which is a multisite having collection of Neuroimaging data for researchers. Structural magnetic resonance imaging (MRI) images are considered from the ADNI data set and feature extraction is done using a 2D discrete wavelet transform. 97% of data reduction is achieved during data pre-processing. The algorithm is trained and validated. The algorithm is accelerated in Nvidia Tx2 graphics processing unit (GPU) to get the better throughput. The result shows our algorithm outperforms the other deep learning algorithms with 91.56% accuracy. </span>

Causes of Acquired Methemoglobinemia – a Retrospective Study at a Large Academic Hospital

Methemoglobinemia is a potentially life-threatening condition caused by the formation of methemoglobin, a form of hemoglobin that cannot bind oxygen. While there are some rare congenital causes of methemoglobinemia, most cases are acquired from the effects of specific drugs or environmental exposures. In this retrospective study, we analyzed a large data set of whole blood samples analyzed for methemoglobin at an academic medical center in Midwestern United States that provides both pediatric and adult services. For a 14 year timeframe (May 2009- June 2023), we performed detailed chart analysis of all patients with a methemoglobin concentration of 3.1% or higher. For an earlier 13 year timeframe (January 1996-April 2009), we performed chart review for all patients with a methemoglobin concentration of 10.0% or higher. For the 2009-2023 data, dapsone was the most frequent cause of methemoglobinemia (methemoglobin 3.1% or higher) in both pediatric (73.3%, 115 clinical encounters, 105 unique patients) and adult (65.3%, 195 clinical encounters, 190 unique patients) populations. Inhaled nitric oxide as medical therapy was the next most frequent cause in both pediatric (18.1%) and adult (13.2%) populations. Causes associated with two or more unique episodes with methemoglobin concentrations of 10.0% and higher included the following: dapsone (n=40 episodes), benzocaine (n=10), recreational use of amyl or isobutyl nitrite (n=3), suicide attempt with sodium nitrite (n=3 with 1 fatality; all 3 cases within last 3 years), food contaminated with nitrates (n=2), and sepsis (n=2). A total of 18 patients received treatment with methylene blue including 5 cases associated with benzocaine and all of the cases associated with amyl nitrite, isobutyl nitrite, sodium nitrite, and contaminated food. Only 3 patients with dapsone-associated methemoglobinemia received methylene blue, reflecting primary management by dose reduction or discontinuation of drug. Overall, our data reinforce previous studies showing dapsone, inhaled nitric oxide, and nitrites as common agents causing methemoglobinemia in a patient population seen at a medical center. Our data also are consistent with recent epidemiology trends showing increase in suicide attempts using sodium nitrite.

Aircraft routing using dynamic programming and reinforcement learning: A customer-centric approach

Over the past few decades, the airline industry has evolved into a sophisticated business, with conventional objectives such as operational efficiency, effective fleet assignment, flight punctuality, and equitable crew scheduling being the primary focus. Nevertheless, given the growing accessibility to customer feedback (either through public forums or private data), it is crucial to make business decisions that cater to the customers’ requirements in addition to conventional goals. Within aircraft operations, there is a series of decisions to be made, such as flight scheduling, fleet assignment, aircraft routing, and crew scheduling. After designating a fleet type to each flight leg, airlines ascertain the sequence of flight legs, or strings, to be operated by individual aircrafts, while complying with obligatory maintenance checks. This constitutes the aircraft routing problem.We introduce a novel approach to aircraft routing problem, by infusing traditional methodologies with insightful customer feedback to create a more responsive and customer-centric model. Unlike conventional techniques of constructing routes, in our work, we construct maintenance feasible strings. Traditional aircraft routing methodologies typically concentrate on minimizing propagated delay. However, by integrating customer feedback, more effective prioritization to minimize delays for flight legs with higher customer dissatisfaction can be achieved. We propose an optimization model that strives to minimize propagated delay while prioritizing flights based on customer feedback, thus resulting in customer-aware routes. To solve the proposed optimization model, we design an iterative alternating optimization scheme where feasible strings are first constructed from the pool of available flight legs using a string generation procedure, followed by solving an integer linear optimization problem over only the feasible set of strings obtained in the first stage. To construct feasible strings of flights at each iteration, we use two different methods. The first method is based on a dynamic programming approach which is useful for mean delay information. To handle practical cases where delays are uncertain, we propose a reinforcement learning (RL) approach to construct the strings. We provide experiments of our proposed methods on synthetic and real data sets and demonstrate the effectiveness of our RL model in comparison to dynamic programming based model. Our experiments affirm that routing decisions informed by customer feedback prioritize minimizing delays for specific flight legs, resulting in routes that are inherently customer-friendly.

A benchmark of health insurance fraud detection using machine learning techniques

Health insurance fraud is a complex problem that also has a significant financial impact. Recently, with the availability of large volumes of data and the evolution of computing power, machine learning techniques have become the preferred method for fraud detection. However, the main difficulty facing researchers in this field is the lack of real data sets and the absence of reliable fraud labels. Most published studies use aggregated provider-level or simulated data to test fraud detection algorithms, which may not deliver accurate results. The present study aims to provide a more accurate assessment of fraud detection methods by using real detailed health insurance claims data to compare six of the most common supervised classification algorithms including neural networks and the use of two categorical feature preparation methods. The study was conducted under the guidance of insurance experts, who provided the fraud label inference rules and reviewed the results. A comprehensive description of the benchmarking process and an interpretation of the results are provided in this paper. The results show that supervised classification can be used effectively to detect health insurance fraud, improving detection accuracy by a factor of 4.2 (84% recall for a positive rate of 20%).<p> </p>

Analysis of the emotional coloring of text using machine and deep learning methods

The presented scientific article is a comprehensive study of machine learning and deep learning methods in the context of emotion recognition in text data. The main goal of the study is to conduct a comprehensive analysis and comparison of various machine learning and deep learning methods to classify emotions in text. During the work, special attention was paid to the analysis of traditional machine learning algorithms, such as multinomial naive Bayes (MNB), multilayer perceptron (MLP), and support vector machine (SVM), as well as the use of deep learning methods based on long short-term memory (LSTM). The experimental part of the study involves the analysis of different data sets covering a variety of text styles and contexts. The experimental results are analyzed in detail, identifying the advantages and limitations of each method. The article provides practical recommendations for choosing the optimal method depending on the specific tasks and context of the application. The data obtained is important for the development of intelligent systems that can effectively adapt to the emotional aspects of interaction with users. Overall, this work makes a significant contribution to the field of emotion recognition in text and provides a basis for further research in this area.

Comparison of Analgesic Effects of Continuous Femoral Nerve Block, Femoral Triangle Block, and Adductor Block After Total Knee Arthroplasty: A Randomized Clinical Trial.

This study aimed to compare the analgesic effects of continuous femoral nerve block (FNB), femoral triangle block (FTB), and adductor canal block (ACB) following total knee arthroplasty (TKA). The goal was to identify the most effective nerve block technique among these. Patients undergoing TKA were randomly assigned to 1 of 3 groups: FNB, FTB, or ACB. Nerve blocks were administered preoperatively, with catheters placed for patient-controlled nerve analgesia (PCNA). The primary end point was the Numeric Rating Scale (NRS) score at movement at 24 hours postsurgery. Secondary end points included NRS scores at rest and movement, quadriceps strength, Timed Up and Go (TUG) test performance, range of motion, effective PCNA utilization, and opioid consumption at various postsurgery time points. Of the 94 valid data sets analyzed (FNB: 31, FTB: 31, ACB: 32), significant differences were observed in the primary end point (H=7.003, P =0.03). Post hoc analysis with Bonferroni correction showed that the FNB group had a significantly lower median pain score (3 [2 to 4]) compared with the ACB group (4 [3 to 5], Bonferroni-adjusted P =0.03). Regarding secondary end points, both the FNB and FTB groups had significantly lower NRS scores than the ACB group at various time points after surgery. Quadriceps strength and TUG completion were better in the FTB and ACB groups. There were no statistically significant differences among the groups for the other end points. Continuous FTB provides postoperative analgesia comparable to FNB but with the advantage of significantly less impact on quadriceps muscle strength, a benefit not seen with FNB. Both FTB and ACB are effective in preserving quadriceps strength postoperatively.

Development of the fuzzy grid partition methods in generating fuzzy rules for the classification of data set

The main weakness of complex and sizeable fuzzy rule systems is the complexity of data interpretation in terms of classification. Classification interpretation can be affected by reducing rules and removing important rules for several reasons. Based on the results of experiments using the fuzzy grid partition (FGP) approach for high-dimensional data, the difficulty in generating many fuzzy rules still increases exponentially as the number of characteristics increases. The solution to this problem is a hybrid method that combines the advantages of the rough set method and the FGP method, which is called the fuzzy grid partition rough set (FGPRS) method. In the Irish data, the rough set approach reduces the number of characteristics and objects so that data with excessive values can be minimized, and the fuzzy rules produced using the FGP method are more concise. The number of fuzzy rules produced using the FGPRS method at K=2 is 50%; at K=K+1, it is reduced by 66.7% and at K=2 K, it is reduced by 75%. Based on the findings of the data collection classification test, the FGPRS method has a classification accuracy rate of 83.33%, and all data can be classified.

A cost analysis of upscaling access to continuity of midwifery carer: Population-based microsimulation in Queensland, Australia

ObjectiveTo quantify the economic impact of upscaling access to continuity of midwifery carer, compared with current standard maternity care, from the perspective of the public health care system. MethodsWe created a static microsimulation model based on a whole-of-population linked administrative data set containing all public hospital births in one Australian state (Queensland) between July 2017 to June 2018 (n = 37,701). This model was weighted to represent projected State-level births between July 2023 and June 2031. Woman and infant health service costs (inpatient, outpatient and emergency department) during pregnancy and birth were summed. The base model represented current standard maternity care and a counterfactual model represented two hypothetical scenarios where 50% or 65% of women giving birth would access continuity of midwifery carer. Costs were reported in 2021/22 AUD. ResultsThe estimated cost savings to Queensland public hospital funders per pregnancy were $336 in 2023/24 and $546 with 50% access. With 65% access, the cost savings were estimated to be $534 per pregnancy in 2023/24 and $839 in 2030/31. A total State-level annual cost saving of $12 million in 2023/24 and $19 million in 2030/31 was estimated with 50% access. With 65% access, total State-level annual cost savings were estimated to be $19 million in 2023/24 and $30 million in 2030/31. ConclusionEnabling most childbearing women in Australia to access continuity of midwifery carer would realise significant cost savings for the public health care system by reducing the rate of operative birth.

Classification of Diabetes Data Set from Iraq via Different Machine Learning Techniques

Classification of Diabetes Data Set from Iraq via Different Machine Learning Techniques

Coarsened Mixtures of Hierarchical Skew Normal Kernels for Flow and Mass Cytometry Analyses

Cytometry is the standard multi-parameter assay for measuring single cell phenotype and functionality. It is commonly used for quantifying the relative frequencies of cell subsets in blood and disaggregated tissues. A typical analysis of cytometry data involves cell classification—that is, the identification of cell subgroups in the sample—and comparisons of the cell subgroups across samples or conditions. While modern experiments often necessitate the collection and processing of samples in multiple batches, analysis of cytometry data across batches is challenging because differences across samples may occur due to either true biological variation or technical reasons such as antibody lot effects or instrument optics across batches. Thus a critical step in comparative analyses of multi-sample cytometry data—yet missing in existing automated methods for analyzing such data—is cross-sample calibration, whose goal is to align corresponding cell subsets across multiple samples in the presence of technical variations, so that biological variations can be meaningfully compared. We introduce a Bayesian nonparametric hierarchical modeling approach for accomplishing both calibration and cell classification simultaneously in a unified probabilistic manner. Three important features of our method make it particularly effective for analyzing multi-sample cytometry data: a nonparametric mixture avoids prespecifying the number of cell clusters; a hierarchical skew normal kernel that allows flexibility in the shapes of the cell subsets and cross-sample variation in their locations; and finally the “coarsening” strategy makes inference robust to departures from the model not captured by the skew normal kernels. We demonstrate the merits of our approach in simulated examples and carry out a case study in the analysis of a multi-sample cytometry data set. We provide an R package for our method.

Sparse Group Penalties for bi-level variable selection.

Many data sets exhibit a natural group structure due to contextual similarities or high correlations of variables, such as lipid markers that are interrelated based on biochemical principles. Knowledge of such groupings can be used through bi-level selection methods to identify relevant feature groups and highlight their predictive members. One of the best known approaches of this kind combines the classical Least Absolute Shrinkage and Selection Operator (LASSO) with the Group LASSO, resulting in the Sparse Group LASSO. We propose the Sparse Group Penalty (SGP) framework, which allows for a flexible combination of different SGL-style shrinkage conditions. Analogous to SGL, we investigated the combination of the Smoothly Clipped Absolute Deviation (SCAD), the Minimax Concave Penalty (MCP) and the Exponential Penalty (EP) with their group versions, resulting in the Sparse Group SCAD, the Sparse Group MCP, and the novel Sparse Group EP (SGE). Those shrinkage operators provide refined control of the effect of group formation on the selection process through a tuning parameter. In simulation studies, SGPs were compared with other bi-level selection methods (Group Bridge, composite MCP, and Group Exponential LASSO) for variable and group selection evaluated with the Matthews correlation coefficient. We demonstrated the advantages of the new SGE in identifying parsimonious models, but also identified scenarios that highlight the limitations of the approach. The performance of the techniques was further investigated in a real-world use case for the selection of regulated lipids in a randomized clinical trial.

Baseline physiological data from anesthetized pigs in a VX intoxication model

Over the past fifty years, swine models have been used for organophosphorus intoxication studies. Among these studies and others on the swine model in general, some physiological data, especially cholinesterase activity highly impacted by organophosphorus compounds like nerve agent VX, still need to be completed. To support and compare our model to others, we have published the experimental protocol, the physiological values of 31 juvenile anesthetized pigs, and the 6h-follow-up of six supplementary anesthetized control animals and 7 VX-intoxicated pigs. We reported hemodynamics and respiratory parameters, blood levels in several biochemical parameters, blood gas, and complete blood count and compared them to the literature. We also focused on tissue and blood cholinesterase activities and detailed them for acetylcholinesterase and butyrylcholinesterase. After establishing a broad physiological data set consistent with the literature, we reported several cardio-respiratory parameters that seem more affected by an organophosphate intoxication, like heart rate, arterial blood pressure, cardiac output, and respiratory rate. Within the blood, oxygen saturation (SpO2), lactatemia, base excess, and glycemia can also be measured and associated with the other parameters to evaluate the life-threatening status.This swine model is currently used to develop and evaluate medical countermeasures against organophosphate nerve agent intoxications.

Phishing Website Detection

Abstract: Phishing is an online threat where an attacker impersonates an authentic and trustworthy organization to obtain sensitive information from a victim. One example of such is trolling, which has long been considered a problem. However, recent advances in phishing detection, such as machine learning-based methods, have assisted in combatting these attacks. Therefore, this paper develops and compares four models for investigating the efficiency of using machine learning to detect phishing domains. It also compares the most accurate model of the four with existing solutions in the literature. The work carried out in this study is an update in the previous systematic literature surveys with more focus on the latest trends in phishing detection techniques. This study enhances readers' understanding of different types of phishing website detection techniques, the data sets used, and the comparative performance of algorithms used. Our findings show that the model based on the K means clustering is the most accurate of the other four techniques and outperforms other solutions in the literature.

Deepfake Audio Detection System

Abstract: To ensure the authenticity of audio material, it is important to establish reliable detection tools that can trace the spread and use of deepfake technologies. This study focuses on the methodology of deepfake audio identification via Mel Frequency Cepstral Coefficients (MFCC) as features with Random Forest as a classifier. By extracting MFCC features from audio clips and using them in a Random Forest model, one can learn unique spectral properties that can help distinguish deepfakes from authentic audio. The Random Forest algorithm, famous for its quality of being able to work well in an ensemble learning paradigm, is utilized to identify patterns that are representative of deepfake manipulation. To ensure the efficiency and reliability of this method, it was tested on a large number of different data sets that included both genuine and fake voice samples. To ensure robustness and generalization, cross-validation techniques are employed, restricting model predictions to the range of 0 to 1 and providing informative error messages for effective diagnosis. Thus, it is an important scientific study helping to develop and strengthen methods for identifying and eliminating threats in the area of artificial sound activity.