MHANet: A hybrid attention mechanism for retinal diseases classification

With the increase of patients with retinopathy, retinopathy recognition has become a research hotspot. In this article, we describe the etiology and symptoms of three kinds of retinal diseases, including drusen(DRUSEN), choroidal neovascularization(CNV) and diabetic macular edema(DME). In addition, we also propose a hybrid attention mechanism to classify and recognize different types of retinopathy images. In particular, the hybrid attention mechanism proposed in this paper includes parallel spatial attention mechanism and channel attention mechanism. It can extract the key features in the channel dimension and spatial dimension of retinopathy images, and reduce the negative impact of background information on classification results. The experimental results show that the hybrid attention mechanism proposed in this paper can better assist the network to focus on extracting thr fetures of the retinopathy area and enhance the adaptability to the differences of different data sets. Finally, the hybrid attention mechanism achieved 96.5% and 99.76% classification accuracy on two public OCT data sets of retinopathy, respectively.

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ADAPTIVE FUZZY LOGIC-BASED FRAMEWORK FOR HANDLING IMPRECISION AND UNCERTAINTY IN CLASSIFICATION OF BIOINFORMATICS DATASETS

International Journal of Computational Methods ◽

10.1142/s0219876211002496 ◽

2011 ◽

Vol 08 (03) ◽

pp. 513-534

Author(s):

TAREK HELMY ◽

ZEHASHEEM RASHEED ◽

MOHAMED AL-MULHEM

Keyword(s):

Fuzzy Logic ◽

Classification Accuracy ◽

Empirical Studies ◽

Data Sets ◽

Training Algorithms ◽

Classification Framework ◽

Different Types ◽

Adaptive Fuzzy Logic ◽

And Performance ◽

The Impact

Classification in the emerging field of bioinformatics is a challenging task, because the information about different diseases is either insufficient or lacking in authenticity as data is collected from different types of medical equipments. In addition, the limitation of human expertise in manual diagnoses leads to incorrect diagnoses. Moreover, the information gathered from various sources is subject to imprecision and uncertainty. Imprecision arises when the data is not validated by experts. This paper presents an adaptive Type-2 Fuzzy Logic System-based (FLS) classification framework for multivariate data to diagnose different types of diseases. This framework is capable of handling imprecision and uncertainty, and its classification accuracy and performance are measured by using University of California Irvine (UCI), well-known medical data sets. The results are compared with the most common existing classifiers in both computer science and statistics literatures. This classification is performed based on the nature of inputs (e.g., singleton or nonsingleton) and on whether uncertainty is present in the system or absent. Empirical results have shown that our proposed FLS classification framework outperforms earlier implemented models with better classification accuracy. In addition, we conducted empirical studies on this classifier regarding the impact of various parameters of FLS such as training algorithms and defuzzification methods.

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Investigating the Impact of Noneffortful Responses on Individual-Level Scores: Can the Effort-Moderated IRT Model Serve as a Solution?

Applied Psychological Measurement ◽

10.1177/01466216211013896 ◽

2021 ◽

pp. 014662162110138

Author(s):

Joseph A. Rios ◽

James Soland

Keyword(s):

Classification Accuracy ◽

Negative Impact ◽

Parameter Recovery ◽

Individual Level ◽

Irt Model ◽

Ability Estimates ◽

Misclassification Rates ◽

Individual Scores ◽

3Pl Model ◽

The Impact

Suboptimal effort is a major threat to valid score-based inferences. While the effects of such behavior have been frequently examined in the context of mean group comparisons, minimal research has considered its effects on individual score use (e.g., identifying students for remediation). Focusing on the latter context, this study addressed two related questions via simulation and applied analyses. First, we investigated how much including noneffortful responses in scoring using a three-parameter logistic (3PL) model affects person parameter recovery and classification accuracy for noneffortful responders. Second, we explored whether improvements in these individual-level inferences were observed when employing the Effort Moderated IRT (EM-IRT) model under conditions in which its assumptions were met and violated. Results demonstrated that including 10% noneffortful responses in scoring led to average bias in ability estimates and misclassification rates by as much as 0.15 SDs and 7%, respectively. These results were mitigated when employing the EM-IRT model, particularly when model assumptions were met. However, once model assumptions were violated, the EM-IRT model’s performance deteriorated, though still outperforming the 3PL model. Thus, findings from this study show that (a) including noneffortful responses when using individual scores can lead to potential unfounded inferences and potential score misuse, and (b) the negative impact that noneffortful responding has on person ability estimates and classification accuracy can be mitigated by employing the EM-IRT model, particularly when its assumptions are met.

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Potential advantages and limitations of using information fusion in media forensics—a discussion on the example of detecting face morphing attacks

EURASIP Journal on Information Security ◽

10.1186/s13635-021-00123-4 ◽

2021 ◽

Vol 2021 (1) ◽

Author(s):

Christian Kraetzer ◽

Andrey Makrushin ◽

Jana Dittmann ◽

Mario Hildebrandt

Keyword(s):

Information Fusion ◽

Classification Accuracy ◽

Negative Impact ◽

Field Application ◽

Attack Detection ◽

Majority Voting ◽

Forensic Sciences ◽

List Type ◽

Media Forensics ◽

Shafer Theory

AbstractInformation fusion, i.e., the combination of expert systems, has a huge potential to improve the accuracy of pattern recognition systems. During the last decades, various application fields started to use different fusion concepts extensively. The forensic sciences are still hesitant if it comes to blindly applying information fusion. Here, a potentially negative impact on the classification accuracy, if wrongly used or parameterized, as well as the increased complexity (and the inherently higher costs for plausibility validation) of fusion is in conflict with the fundamental requirements for forensics.The goals of this paper are to explain the reasons for this reluctance to accept such a potentially very beneficial technique and to illustrate the practical issues arising when applying fusion. For those practical discussions the exemplary application scenario of morphing attack detection (MAD) is selected with the goal to facilitate the understanding between the media forensics community and forensic practitioners.As general contributions, it is illustrated why the naive assumption that fusion would make the detection more reliable can fail in practice, i.e., why fusion behaves in a field application sometimes differently than in the lab. As a result, the constraints and limitations of the application of fusion are discussed and its impact to (media) forensics is reflected upon.As technical contributions, the current state of the art of MAD is expanded by: The introduction of the likelihood-based fusion and an fusion ensemble composition experiment to extend the set of methods (majority voting, sum-rule, and Dempster-Shafer Theory of evidence) used previously The direct comparison of the two evaluation scenarios “MAD in document issuing” and “MAD in identity verification” using a realistic and some less restrictive evaluation setups A thorough analysis and discussion of the detection performance issues and the reasons why fusion in a majority of the test cases discussed here leads to worse classification accuracy than the best individual classifier

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Cancer-related fatigue: an overview

British Journal of Nursing ◽

10.12968/bjon.2021.30.4.s36 ◽

2021 ◽

Vol 30 (4) ◽

pp. S36-S43

Author(s):

Mohammed Al Maqbali

Keyword(s):

Negative Impact ◽

Management Strategies ◽

Background Information ◽

Clinical Settings ◽

Measurement Scales ◽

Major Life ◽

Patients With Cancer ◽

Cancer Related Fatigue ◽

Diagnosis Of Cancer ◽

Life Stressor

A diagnosis of cancer is a major life stressor that can affect the physiological, psychological and physical state of the person concerned. Fatigue is a particularly common and troubling symptom that has a negative impact on quality of life throughout all phases of treatment and stages of the illness. The aim of this review is to provide background information on cancer-related fatigue. This review discusses cancer-related fatigue (CRF) in terms of the definition, prevalence, risk factors, aetiology, and the measurement scales used. The differences between definitions of symptoms and relevant theories will be explored and discussed to help explain the variety of instruments used in its measurement. The prevalence of fatigue will be assessed by looking critically at the evidence of fatigue and the factors that affect it. Potential treatment and management strategies for CRF will also be discussed. Finally, there will be an overview of the instruments used to measure fatigue. This review also provides important evidence for measuring and managing CRF that can help nurses to understand fatigue among patients with cancer. Assessing CRF should be routinely undertaken in clinical settings to help identify the proper interventions, treatments and management to reduce fatigue among cancer patients.

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Threat or Corrective? Assessing the Impact of Populist Parties in Government on the Qualities of Democracy: A 19-Country Comparison

Government and Opposition ◽

10.1017/gov.2021.21 ◽

2021 ◽

pp. 1-21

Author(s):

Davide Vittori

Keyword(s):

Negative Impact ◽

Quality Of Democracy ◽

Clear Cut ◽

Liberal Democratic ◽

Country Comparison ◽

Democratic Regimes ◽

Different Types ◽

The Impact

Abstract Scholars have long debated whether populism harms or improves the quality of democracy. This article contributes to this debate by focusing on the impact of populist parties in government. In particular, it inquires: (1) whether populists in government are more likely than non-populists to negatively affect the quality of democracies; (2) whether the role of populists in government matters; and (3) which type of populism is expected to negatively affect the quality of liberal-democratic regimes. The results find strong evidence that the role of populists in government affects several qualities of democracy. While robust, the findings related to (2) are less clear-cut than those pertaining to (1). Finally, regardless of their role in government, different types of populism have different impacts on the qualities of democracy. The results show that exclusionary populist parties in government tend to have more of a negative impact than other forms of populism.

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Assessment of the mercury contamination of landfilled and recovered foundry waste – a case study

Open Chemistry ◽

10.1515/chem-2021-0043 ◽

2021 ◽

Vol 19 (1) ◽

pp. 462-470

Author(s):

Marta Bożym ◽

Beata Klojzy-Karczmarczyk

Keyword(s):

Negative Impact ◽

Mercury Content ◽

Mercury Contamination ◽

Reference Level ◽

Organic Carbon Content ◽

Foundry Sands ◽

Industrial Sites ◽

Different Types ◽

Main Component

Abstract Environmental pollution by mercury is a local problem in Poland and concerns mainly industrial sites. Foundry waste are usually characterized by low mercury content compared to other heavy metals. Spent foundry sands with low content of Hg are the main component of foundry waste. However, Hg may be present in foundry dust, which may also be landfilled. Due to Hg toxicity, even a minimal content may have a negative impact on biota. This study focuses on assessing the mercury content of landfilled foundry waste (LFW), to assess its toxicity. Currently tested waste is recovered and reused as a road aggregate. The results were compared with the mercury content of local soils as the reference level. Waste samples were taken from foundry landfill. The mercury content, fractional composition, organic matter (OM) and total organic carbon content, pH and elementary composition of waste were analysed. It was found that the mercury content in LFW was very low, at the level of natural content in soils and did not pose a threat to the environment. The statistical analysis shows that mercury was not associated with OM of the waste, in contrast to soils, probably due to different types of OM in both materials.

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A Multi-Branch Feature Fusion Strategy Based on an Attention Mechanism for Remote Sensing Image Scene Classification

Remote Sensing ◽

10.3390/rs13101950 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1950

Author(s):

Cuiping Shi ◽

Xin Zhao ◽

Liguo Wang

Keyword(s):

Remote Sensing ◽

Feature Extraction ◽

Classification Accuracy ◽

Feature Fusion ◽

State Of The Art ◽

Rapid Development ◽

Remote Sensing Image ◽

Classification Performance ◽

Attention Mechanism ◽

Scene Classification

In recent years, with the rapid development of computer vision, increasing attention has been paid to remote sensing image scene classification. To improve the classification performance, many studies have increased the depth of convolutional neural networks (CNNs) and expanded the width of the network to extract more deep features, thereby increasing the complexity of the model. To solve this problem, in this paper, we propose a lightweight convolutional neural network based on attention-oriented multi-branch feature fusion (AMB-CNN) for remote sensing image scene classification. Firstly, we propose two convolution combination modules for feature extraction, through which the deep features of images can be fully extracted with multi convolution cooperation. Then, the weights of the feature are calculated, and the extracted deep features are sent to the attention mechanism for further feature extraction. Next, all of the extracted features are fused by multiple branches. Finally, depth separable convolution and asymmetric convolution are implemented to greatly reduce the number of parameters. The experimental results show that, compared with some state-of-the-art methods, the proposed method still has a great advantage in classification accuracy with very few parameters.

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An Interoperability Platform Enabling Reuse of Electronic Health Records for Signal Verification Studies

BioMed Research International ◽

10.1155/2016/6741418 ◽

2016 ◽

Vol 2016 ◽

pp. 1-18 ◽

Cited By ~ 5

Author(s):

Mustafa Yuksel ◽

Suat Gonul ◽

Gokce Banu Laleci Erturkmen ◽

Ali Anil Sinaci ◽

Paolo Invernizzi ◽

...

Keyword(s):

Real Life ◽

Case Series ◽

Background Information ◽

Data Sets ◽

Local Data ◽

Lombardy Region ◽

Common Information ◽

Spontaneous Reports ◽

Wide Range ◽

Common Information Model

Depending mostly on voluntarily sent spontaneous reports, pharmacovigilance studies are hampered by low quantity and quality of patient data. Our objective is to improve postmarket safety studies by enabling safety analysts to seamlessly access a wide range of EHR sources for collecting deidentified medical data sets of selected patient populations and tracing the reported incidents back to original EHRs. We have developed an ontological framework where EHR sources and target clinical research systems can continue using their own local data models, interfaces, and terminology systems, while structural interoperability and Semantic Interoperability are handled through rule-based reasoning on formal representations of different models and terminology systems maintained in the SALUS Semantic Resource Set. SALUS Common Information Model at the core of this set acts as the common mediator. We demonstrate the capabilities of our framework through one of the SALUS safety analysis tools, namely, the Case Series Characterization Tool, which have been deployed on top of regional EHR Data Warehouse of the Lombardy Region containing about 1 billion records from 16 million patients and validated by several pharmacovigilance researchers with real-life cases. The results confirm significant improvements in signal detection and evaluation compared to traditional methods with the missing background information.

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Estimating the Borrowing Behavior of French and German Firms. An Econometric Analysis / Verschuldungsverhalten französischer und deutscher Unternehmen. Eine ökonometrische Analyse

Jahrbücher für Nationalökonomie und Statistik ◽

10.1515/jbnst-2001-5-610 ◽

2001 ◽

Vol 221 (5-6) ◽

Author(s):

Elizabeth Kremp ◽

Elmar Stöß

Keyword(s):

Firm Growth ◽

Negative Impact ◽

Positive Impact ◽

Size Class ◽

Monetary Transmission ◽

Data Sets ◽

Pecking Order ◽

Trends Over Time ◽

Deutsche Bundesbank ◽

The Cost

SummaryThis paper investigates the borrowing behavior of 2,900 French and 1,300 German firms over the 1987-95 period. Both samples based on data sets of the Banque de France and the Deutsche Bundesbank not only include large but also small and medium-sized enterprises. Applying GMM techniques, we estimate identical debt equations for the two total samples and by size class. Despite the large differences between the two countries in term of debt trends over time and size class the main result is the similarity of a few determinants between France and Germany. E.g. we find that firm growth has a positive impact on borrowing according to the theory of signalling whereas the negative correlation of profit and debt supports pecking order approach and the cost of finance has a negative impact on leverage, too. Additionally, the study can provide some insights for the monetary transmission mechanism in both EMU member countries.

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“De terughaalactie geld alleen voor de hierboven afgebelede pizza’s geldig.”

Tijdschrift voor Communicatiewetenschappen ◽

10.5117/2015.043.003.292 ◽

2015 ◽

Vol 43 (3) ◽

Author(s):

Mariet Raedts ◽

Irene Roozen

Keyword(s):

Negative Impact ◽

Purchase Intentions ◽

The Other ◽

Product Recalls ◽

Negative Consequences ◽

Product Recall ◽

Brand Evaluations ◽

Different Types

Consumers’ responses to product recalls with language errors Consumers’ responses to product recalls with language errors Product recall notices not only warn consumers for faulty products, they also limit the damage which may be caused to the company. But what happens when the product recall notice itself contains errors? This study investigated the effects of three different types of language errors: typographical errors, verb errors and sentence errors. Four versions of a product recall were created. The control condition contained no errors. The other three versions contained either five typos, five grammatical conjugation errors or five poorly formed sentences. Participants (N = 710) were randomly assigned to one of the four conditions. Results indicate that participants who detected the errors, had lower attitudes towards the advertisement and the company than participants in the control condition and participants who failed to detect the errors. Poorly formed sentences also had a negative impact on consumers’ brand evaluations and their future product purchase intentions. Hence, language errors in product recall notices can have negative consequences for companies.

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