A Comparative Performance Evaluation of Classification Algorithms for Clinical Decision Support Systems

Classification algorithms are widely taken into account for clinical decision support systems. However, it is not always straightforward to understand the behavior of such algorithms on a multiple disease prediction task. When a new classifier is introduced, we, in most cases, will ask ourselves whether the classifier performs well on a particular clinical dataset or not. The decision to utilize classifiers mostly relies upon the type of data and classification task, thus making it often made arbitrarily. In this study, a comparative evaluation of a wide-array classifier pertaining to six different families, i.e., tree, ensemble, neural, probability, discriminant, and rule-based classifiers are dealt with. A number of real-world publicly datasets ranging from different diseases are taken into account in the experiment in order to demonstrate the generalizability of the classifiers in multiple disease prediction. A total of 25 classifiers, 14 datasets, and three different resampling techniques are explored. This study reveals that the classifier that is likely to become the best performer is the conditional inference tree forest (cforest), followed by linear discriminant analysis, generalize linear model, random forest, and Gaussian process classifier. This work contributes to existing literature regarding a thorough benchmark of classification algorithms for multiple diseases prediction

Ensemble learning for intrusion detection systems: A systematic mapping study and cross-benchmark evaluation

Intrusion detection systems (IDSs) are intrinsically linked to a comprehensive solution of cyberattacks prevention instruments. To achieve a higher detection rate, the ability to design an improved detection framework is sought after, particularly when utilizing ensemble learners. Designing an ensemble often lies in two main challenges such as the choice of available base classifiers and combiner methods. This paper performs an overview of how ensemble learners are exploited in IDSs by means of systematic mapping study. We collected and analyzed 124 prominent publications from the existing literature. The selected publications were then mapped into several categories such as years of publications, publication venues, datasets used, ensemble methods, and IDS techniques. Furthermore, this study reports and analyzes an empirical investigation of a new classifier ensemble approach, called stack of ensemble (SoE) for anomaly-based IDS. The SoE is an ensemble classifier that adopts parallel architecture to combine three individual ensemble learners such as random forest, gradient boosting machine, and extreme gradient boosting machine in a homogeneous manner. The performance significance among classification algorithms is statistically examined in terms of their Matthews correlation coefficients, accuracies, false positive rates, and area under ROC curve metrics. Our study fills the gap in current literature concerning an up-to-date systematic mapping study, not to mention an extensive empirical evaluation of the recent advances of ensemble learning techniques applied to IDSs. (C) 2020 Elsevier Inc. All rights reserved

A Stacking-Based Deep Neural Network Approach for Effective Network Anomaly Detection

An anomaly-based intrusion detection system (A-IDS) provides a critical aspect in a modern computing infrastructure since new types of attacks can be discovered. It prevalently utilizes several machine learning algorithms (ML) for detecting and classifying network traffic. To date, lots of algorithms have been proposed to improve the detection performance of A-IDS, either using individual or ensemble learners. In particular, ensemble learners have shown remarkable performance over individual learners in many applications, including in cybersecurity domain. However, most existing works still suffer from unsatisfactory results due to improper ensemble design. The aim of this study is to emphasize the effectiveness of stacking ensemble-based model for A-IDS, where deep learning (e.g., deep neural network [DNN]) is used as base learner model. The effectiveness of the proposed model and base DNN model are benchmarked empirically in terms of several performance metrics, i.e., Matthew's correlation coefficient, accuracy, and false alarm rate. The results indicate that the proposed model is superior to the base DNN model as well as other existing ML algorithms found in the literature

Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals

Vibration measurement and monitoring are essential in a wide variety of applications. Vibration measurements are critical for diagnosing industrial machinery malfunctions because they provide information about the condition of the rotating equipment. Vibration analysis is considered the most effective method for predictive maintenance because it is used to troubleshoot instantaneous faults as well as periodic maintenance. Numerous studies conducted in this vein have been published in a variety of outlets. This review documents data-driven and recently published deep learning techniques for vibration-based condition monitoring. Numerous studies were obtained from two reputable indexing databases, Web of Science and Scopus. Following a thorough review, 59 studies were selected for synthesis. The selected studies are then systematically discussed to provide researchers with an in-depth view of deep learning-based fault diagnosis methods based on vibration signals. Additionally, a few remarks regarding future research directions are made, including graph-based neural networks, physics-informed ML, and a transformer convolutional network-based fault diagnosis method

A Multimodal Deep Learning-Based Fault Detection Model for a Plastic Injection Molding Process

The authors of this work propose a deep learning-based fault detection model that can be implemented in the field of plastic injection molding. Compared to conventional approaches to fault detection in this domain, recent deep learning approaches prove useful for on-site problems involving complex underlying dynamics with a large number of variables. In addition, the advent of advanced sensors that generate data types in multiple modalities prompts the need for multimodal learning with deep neural networks to detect faults. This process is able to facilitate information from various modalities in an end-to-end learning fashion. The proposed deep learning-based approach opts for an early fusion scheme, in which the low-level feature representations of modalities are combined. A case study involving real-world data, obtained from a car parts company and related to a car window side molding process, validates that the proposed model outperforms late fusion methods and conventional models in solving the problem

The importance of critically short telomere in myelodysplastic syndrome

A few critically short telomeres trigger genomic instability regardless of average telomere length (TL). Recently, the telomere shortest length assay (TeSLA) was developed to detect critically short telomeres and measure absolute telomeres. Using TeSLA with the internally labeled biotin probe, we measured the TL of bone marrow (BM) aspirates from 52 patients with myelodysplastic syndrome (MDS). A percentage of shortest telomeres (< 1.0 kb (ShTL1.0)) were calculated. ShTL1.0 was correlated to IPSS-R risk (spearman’s rho = 0.35 and p = 0.0196), and ShTL1.0 and BM blast (2.61% in < 5% blast, 4.15% in 5–10% blast, and 6.80% in 10–20% blast, respectively, p = 0.0332). Interestingly, MDS patients with a shortest TL ≥ 0.787 kb at the time of diagnosis showed better overall survival (OS) and progression-free survival (PFS) than patients with a shortest TL < 0.787 kb in the multivariate analyses (HR = 0.13 and 0.30, p = 0.011 and 0.048 for OS and PFS, respectively). Our results clearly show the presence and abundance of critically short telomeres in MDS patients. These pathologic telomeres are associated with IPSS-R which is a validated prognostic scoring system in MDS. Furthermore, they are independent prognostic factors for OS in MDS patients. Future prospective studies are needed to validate our results.Highlights Telomere length (TL) has been reported to be important in myelodysplastic syndrome (MDS).A novel TeSLA method demonstrated the presence and abundance of extremely short telomeres (<1.0kb) in MDS.Critically short TL rather than an average TL is associated with the IPSS-R and BM blast in MDS.The shortest TL is an independent prognostic factor for PFS and OS.Short TL should be incorporated into the risk scoring system in MDS in the future.This work was supported by the Ministry of Science and ICT(MSIT) of the Republic of Korea and the National Research Foundation of Korea (NRF-2020R1A3B3079653)

Methionine deprivation suppresses triple-negative breast cancer metastasis in vitro and in vivo

Nutrient deprivation strategies have been proposed as an adjuvant therapy for cancer cells due to their increased metabolic demand. We examined the specific inhibitory effects of amino acid deprivation on the metastatic phenotypes of the human triple-negative breast cancer (TNBC) cell lines MDA-MB-231 and Hs 578T, as well as the orthotopic 4T1 mouse TNBC tumor model. Among the 10 essential amino acids tested, methionine deprivation elicited the strongest inhibitory effects on the migration and invasion of these cancer cells. Methionine deprivation reduced the phosphorylation of focal adhesion kinase, as well as the activity and mRNA expression of matrix metalloproteinases MMP-2 and MMP-9, two major markers of metastasis, while increasing the mRNA expression of tissue inhibitor of metalloproteinase 1 in MDA-MB-231 cells. Furthermore, methionine restriction downregulated the metastasis-related factor urokinase plasminogen activatior and upregulated plasminogen activator inhibitor 1 mRNA expression. Animals on the methionine-deprived diet showed lower lung metastasis rates compared to mice on the control diet. Taken together, these results suggest that methionine restriction could provide a potential nutritional strategy for more effective cancer therapy

Variation block-based genomics method for crop plants

BACKGROUND: In contrast with wild species, cultivated crop genomes consist of reshuffled recombination blocks, which occurred by crossing and selection processes. Accordingly, recombination block-based genomics analysis can be an effective approach for the screening of target loci for agricultural traits. RESULTS: We propose the variation block method, which is a three-step process for recombination block detection and comparison. The first step is to detect variations by comparing the short-read DNA sequences of the cultivar to the reference genome of the target crop. Next, sequence blocks with variation patterns are examined and defined. The boundaries between the variation-containing sequence blocks are regarded as recombination sites. All the assumed recombination sites in the cultivar set are used to split the genomes, and the resulting sequence regions are termed variation blocks. Finally, the genomes are compared using the variation blocks. The variation block method identified recurring recombination blocks accurately and successfully represented block-level diversities in the publicly available genomes of 31 soybean and 23 rice accessions. The practicality of this approach was demonstrated by the identification of a putative locus determining soybean hilum color. CONCLUSIONS: We suggest that the variation block method is an efficient genomics method for the recombination block-level comparison of crop genomes. We expect that this method will facilitate the development of crop genomics by bringing genomics technologies to the field of crop breeding

Variation block-based genomics method for crop plants

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.Abstract Background In contrast with wild species, cultivated crop genomes consist of reshuffled recombination blocks, which occurred by crossing and selection processes. Accordingly, recombination block-based genomics analysis can be an effective approach for the screening of target loci for agricultural traits. Results We propose the variation block method, which is a three-step process for recombination block detection and comparison. The first step is to detect variations by comparing the short-read DNA sequences of the cultivar to the reference genome of the target crop. Next, sequence blocks with variation patterns are examined and defined. The boundaries between the variation-containing sequence blocks are regarded as recombination sites. All the assumed recombination sites in the cultivar set are used to split the genomes, and the resulting sequence regions are termed variation blocks. Finally, the genomes are compared using the variation blocks. The variation block method identified recurring recombination blocks accurately and successfully represented block-level diversities in the publicly available genomes of 31 soybean and 23 rice accessions. The practicality of this approach was demonstrated by the identification of a putative locus determining soybean hilum color. Conclusions We suggest that the variation block method is an efficient genomics method for the recombination block-level comparison of crop genomes. We expect that this method will facilitate the development of crop genomics by bringing genomics technologies to the field of crop breeding