scholarly journals Segmentation of Melanocytic Lesion Images Using Gamma Correction with Clustering of Keypoint Descriptors

Diagnostics ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1366
Author(s):  
Damilola Okuboyejo ◽  
Oludayo O. Olugbara
Keyword(s):  
Skin Lesions ◽  
Diagnostic Process ◽  
Melanocytic Lesion ◽  
Gamma Correction ◽  
Intrinsic Factors ◽  
Skin Imaging ◽  
Benchmark Datasets ◽  
And Performance ◽  
Sensitivity Specificity ◽  
Performance Results

The early detection of skin cancer, especially through the examination of lesions with malignant characteristics, has been reported to significantly decrease the potential fatalities. Segmentation of the regions that contain the actual lesions is one of the most widely used steps for achieving an automated diagnostic process of skin lesions. However, accurate segmentation of skin lesions has proven to be a challenging task in medical imaging because of the intrinsic factors such as the existence of undesirable artifacts and the complexity surrounding the seamless acquisition of lesion images. In this paper, we have introduced a novel algorithm based on gamma correction with clustering of keypoint descriptors for accurate segmentation of lesion areas in dermoscopy images. The algorithm was tested on dermoscopy images acquired from the publicly available dataset of Pedro Hispano hospital to achieve compelling equidistant sensitivity, specificity, and accuracy scores of 87.29%, 99.54%, and 96.02%, respectively. Moreover, the validation of the algorithm on a subset of heavily noised skin lesion images collected from the public dataset of International Skin Imaging Collaboration has yielded the equidistant sensitivity, specificity, and accuracy scores of 80.59%, 100.00%, and 94.98%, respectively. The performance results are propitious when compared to those obtained with existing modern algorithms using the same standard benchmark datasets and performance evaluation indices.

Segmentation of Tissue-Injured Melanoma Convolution Neural Networks

2021 ◽  
Vol 18 (4) ◽  
pp. 1256-1262
Author(s):  
C. Hemalatha ◽  
S. Satheesh ◽  
N. Kamal ◽  
C. Devi ◽  
A. Vinothkumar ◽  
...  
Keyword(s):  
Neural Networks ◽  
Skin Lesions ◽  
Convolution Neural Networks ◽  
Skin Imaging ◽  
The Past ◽  
Training Samples ◽  
Basic Work ◽  
Benchmark Datasets ◽  
Variable Success ◽  
Ann Classification

In global dermatological conditions, skin lesions are significant. Curable early in the diagnosis, only skin lesions can be accurately identified by highly trained dermatologists. Around 21 million patients are diagnosed with this disease and more than 10.12 million deaths worldwide. This paper presents basic work for the detection and ensuing purpose of the CNN to dermoscopic images of skin lesions with cancerous inclination. The models proposed are trained and evaluated in the 2018 International Skin Imaging Collaboration challenge, comprising 2100 training samples and 750 test samples, on normal benchmark datasets. Skin-injured images were mainly segment based on person thresholds for channel intensity. The images were added to CNN to extract features. The extracted characteristics were then used to classify the associated ANN classification. In the past, many approaches have been used to diagnose subjects with variable success levels. The methodology described in this paper showed associated accuracy of 97.13% in comparison to the previous best of ninety seven.

scholarly journals Models versus Datasets: Reducing Bias through Building a Comprehensive IDS Benchmark

2021 ◽  
Vol 13 (12) ◽  
pp. 318
Author(s):  
Rasheed Ahmad ◽  
Izzat Alsmadi ◽  
Wasim Alhamdani ◽  
Lo’ai Tawalbeh
Keyword(s):  
Deep Learning ◽  
Classification Model ◽  
Intrusion Detection Systems ◽  
Complex Nature ◽  
Learning Approaches ◽  
Learning Models ◽  
Detection Systems ◽  
Benchmark Datasets ◽  
And Performance ◽  
Performance Results

Today, deep learning approaches are widely used to build Intrusion Detection Systems for securing IoT environments. However, the models’ hidden and complex nature raises various concerns, such as trusting the model output and understanding why the model made certain decisions. Researchers generally publish their proposed model’s settings and performance results based on a specific dataset and a classification model but do not report the proposed model’s output and findings. Similarly, many researchers suggest an IDS solution by focusing only on a single benchmark dataset and classifier. Such solutions are prone to generating inaccurate and biased results. This paper overcomes these limitations in previous work by analyzing various benchmark datasets and various individual and hybrid deep learning classifiers towards finding the best IDS solution for IoT that is efficient, lightweight, and comprehensive in detecting network anomalies. We also showed the model’s localized predictions and analyzed the top contributing features impacting the global performance of deep learning models. This paper aims to extract the aggregate knowledge from various datasets and classifiers and analyze the commonalities to avoid any possible bias in results and increase the trust and transparency of deep learning models. We believe this paper’s findings will help future researchers build a comprehensive IDS based on well-performing classifiers and utilize the aggregated knowledge and the minimum set of significantly contributing features.

The Relation Between Financial Incentives, Motivation, and Performance

2017 ◽  
Vol 16 (2) ◽  
pp. 61-76 ◽  
Author(s):  
Anaïs Thibault Landry ◽  
Marylène Gagné ◽  
Jacques Forest ◽  
Sylvie Guerrero ◽  
Michel Séguin ◽  
...  
Keyword(s):  
Financial Incentives ◽  
Work Performance ◽  
Autonomous Motivation ◽  
Compensation Plans ◽  
Work Settings ◽  
Path Analyses ◽  
Three Samples ◽  
And Performance ◽  
Performance Results

Abstract. To this day, researchers are debating the adequacy of using financial incentives to bolster performance in work settings. Our goal was to contribute to current understanding by considering the moderating role of distributive justice in the relation between financial incentives, motivation, and performance. Based on self-determination theory, we hypothesized that when bonuses are fairly distributed, using financial incentives makes employees feel more competent and autonomous, which in turn fosters greater autonomous motivation and lower controlled motivation, and better work performance. Results from path analyses in three samples supported our hypotheses, suggesting that the effect of financial incentives is contextual, and that compensation plans using financial incentives and bonuses can be effective when properly managed.

scholarly journals Two Sides of the Same Coin. University-Industry Collaboration and Open Innovation as Enhancers of Firm Performance

2021 ◽  
Vol 13 (7) ◽  
pp. 3866
Author(s):  
Joana Costa ◽  
Ana Rita Neves ◽  
João Reis
Keyword(s):  
Firm Performance ◽  
Open Innovation ◽  
Primary Data ◽  
Company Performance ◽  
Innovation Ecosystem ◽  
Industry Collaboration ◽  
And Performance ◽  
Performance Results ◽  
The Impact ◽  
University Industry

Open innovation is proved to be determinant in the rationalization of sustainable innovation ecosystems. Firms, universities, governments, user communities and the overall environment are called to contribute to this dynamic process. This study aims to contribute to a better understanding of the impact of open innovation on firms’ performance and to empirically assess whether university-industry collaborations are complementary or substitutes for this activity. Primary data were collected from a survey encompassing 908 firms, and then combined with performance indicators from SABI (Spanish and Portuguese business information). Econometric estimations were run to evaluate the role of open innovation and university-industry collaboration in the firm innovative propensity and performance. Results highlight the importance of diversity in collaborations with the academia and inbound open innovation strategy as enhancers of firm performance. The two activities reinforce each other. By testing the impact of open innovation practices on company performance, the need for heterogeneity in terms of contact type and university is also demonstrated. Findings cast light on the need to reformulate existing policy packages, reinforcing the ties with academia as well as the promotion of open innovation strategies. The connection to the innovation ecosystem needs to be further encouraged as well as the promotion of persistent connections with the knowledge sources in an open and multilateral framework.

scholarly journals Extended haplotype-phasing of long-read de novo genome assemblies using Hi-C

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Zev N. Kronenberg ◽  
Arang Rhie ◽  
Sergey Koren ◽  
Gregory T. Concepcion ◽  
Paul Peluso ◽  
...  
Keyword(s):  
Zebra Finch ◽  
Cultured Cells ◽  
De Novo ◽  
Single Cells ◽  
Variant Calling ◽  
Chromatin Interaction ◽  
Extended Haplotype ◽  
Benchmark Datasets ◽  
And Performance ◽  
Genome Assemblies

AbstractHaplotype-resolved genome assemblies are important for understanding how combinations of variants impact phenotypes. To date, these assemblies have been best created with complex protocols, such as cultured cells that contain a single-haplotype (haploid) genome, single cells where haplotypes are separated, or co-sequencing of parental genomes in a trio-based approach. These approaches are impractical in most situations. To address this issue, we present FALCON-Phase, a phasing tool that uses ultra-long-range Hi-C chromatin interaction data to extend phase blocks of partially-phased diploid assembles to chromosome or scaffold scale. FALCON-Phase uses the inherent phasing information in Hi-C reads, skipping variant calling, and reduces the computational complexity of phasing. Our method is validated on three benchmark datasets generated as part of the Vertebrate Genomes Project (VGP), including human, cow, and zebra finch, for which high-quality, fully haplotype-resolved assemblies are available using the trio-based approach. FALCON-Phase is accurate without having parental data and performance is better in samples with higher heterozygosity. For cow and zebra finch the accuracy is 97% compared to 80–91% for human. FALCON-Phase is applicable to any draft assembly that contains long primary contigs and phased associate contigs.

DESIGN, PREPRODUCTION, AND PERFORMANCE OF THE CDF RUN IIB SILICON DETECTOR

2005 ◽  
Vol 20 (16) ◽  
pp. 3811-3814
Author(s):  
◽  
PAUL LUJAN
Keyword(s):  
Building Block ◽  
Silicon Detector ◽  
Radiation Hardness ◽  
Control Signals ◽  
Lightweight Structure ◽  
And Performance ◽  
Performance Results ◽  
And Control ◽  
Radiation Hard ◽  
Detector Design

A new silicon detector was designed by the CDF collaboration for Run IIb of the Tevatron at Fermilab. The main building block of the new detector is a "supermodule" or "stave", an innovative, compact and lightweight structure of several readout hybrids and sensors with a bus cable running directly underneath the sensors to carry power, data, and control signals to and from the hybrids. The hybrids use a new, radiation-hard readout chip, the SVX4 chip. A number of SVX4 chips, readout hybrids, sensors, and supermodules were produced and tested in preproduction. The performance (including radiation-hardness) and yield of these components met or exceeded all design goals. The detector design goals, solutions, and performance results are presented.

scholarly journals Popularity and performance of bioinformatics software: the case of gene set analysis

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Chengshu Xie ◽  
Shaurya Jauhari ◽  
Antonio Mora
Keyword(s):  
Comprehensive Evaluation ◽  
Gene Set Analysis ◽  
Tool Selection ◽  
Functional Interpretation ◽  
Selection Procedures ◽  
Gene Set ◽  
And Performance ◽  
Reported Data ◽  
Performance Results

Abstract Background Gene Set Analysis (GSA) is arguably the method of choice for the functional interpretation of omics results. The following paper explores the popularity and the performance of all the GSA methodologies and software published during the 20 years since its inception. "Popularity" is estimated according to each paper's citation counts, while "performance" is based on a comprehensive evaluation of the validation strategies used by papers in the field, as well as the consolidated results from the existing benchmark studies. Results Regarding popularity, data is collected into an online open database ("GSARefDB") which allows browsing bibliographic and method-descriptive information from 503 GSA paper references; regarding performance, we introduce a repository of jupyter workflows and shiny apps for automated benchmarking of GSA methods (“GSA-BenchmarKING”). After comparing popularity versus performance, results show discrepancies between the most popular and the best performing GSA methods. Conclusions The above-mentioned results call our attention towards the nature of the tool selection procedures followed by researchers and raise doubts regarding the quality of the functional interpretation of biological datasets in current biomedical studies. Suggestions for the future of the functional interpretation field are made, including strategies for education and discussion of GSA tools, better validation and benchmarking practices, reproducibility, and functional re-analysis of previously reported data.

On improving the training of aircraft engineers

2021 ◽  
Vol 20 (5) ◽  
pp. 865-885
Author(s):  
Leonid B. SOBOLEV
Keyword(s):  
Aviation Industry ◽  
Information Protection ◽  
Global Trends ◽  
Space Industry ◽  
The Poor ◽  
Military Aviation ◽  
And Performance ◽  
Performance Results ◽  
The Military ◽  
The U.S

Subject. The article continues the discussion about the method of training aircraft engineers to work in the military and civil segments of aviation and rocket-and-space industry. Objectives. The purpose is to improve the training of Russian engineers to work in the competitive market environment, on the basis of the analysis of experience in training the aviation engineers in leading foreign technical universities. Methods. The study rests on the comparative analysis of implementation of major projects in the military and civil segments of aviation in the U.S. and Russia, as well as programs for training aircraft engineers in both countries. Results. The analysis shows that the duration of modern large military aviation projects in both countries is the same (the comparison of cost is impossible, due to information protection in Russia), while in the civil segment of the aviation industry, Russia's lagging behind is significant both in terms of the duration of projects and performance results. One of the reasons is in the poor training of aircraft engineers to work in the competitive environment. Conclusions. It is crucial to reform Russian aviation universities in terms of conformity to global trends in multidisciplinarity and differentiation of financing and research base.

Secretly Pruned Convolutional Codes: Security Analysis and Performance Results

2016 ◽  
Vol 11 (7) ◽  
pp. 1500-1514 ◽  
Author(s):  
Nicholas Kolokotronis ◽  
Alexandros Katsiotis ◽  
Nicholas Kalouptsidis
Keyword(s):  
Security Analysis ◽  
Convolutional Codes ◽  
And Performance ◽  
Performance Results
Sign in / Sign up

Export Citation Format

Share Document