Classification Model of Web Application Attacks

2021 ◽  
Author(s):  
Hafidh Fadhil ◽  
Arif Rahman Hakim
Keyword(s):  
Web Application ◽  
Classification Model

scholarly journals Cloud based Agile Methodology Test Automation for Web Application by Using Tanh Activated Clustering and Classification Model (TACC) in Machine Learning

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 05) ◽  
pp. 1137-1157
Author(s):  
V. Vamsi Krishna ◽  
G. Gopinath
Keyword(s):  
Web Application ◽  
Web Applications ◽  
Cost Effective ◽  
Daily Basis ◽  
Automatic Testing ◽  
Classification Model ◽  
Test Case ◽  
Test Automation ◽  
The Status ◽  
Clustering And Classification

Automatic functional tests are a long-standing issue in software development projects, and they are still carried out manually. The Selenium testing framework has gained popularity as an active community and standard environment for automated assessment of web applications. As a result, the trend setting of web services is evolving on a daily basis, and there is a need to improve automatic testing. The study involves to make the system to understand the experiences of previous test cases and apply new cases to predict the status of test case using Tanh activated Clustering and Classification model (TACC). The primary goal is to improve the model's clustering and classification output. The outcomes show that the TACC model has increased performance and demonstrated that automated testing results can be predicted, which is cost effective and reduces manual effort to a greater extent.

scholarly journals ORFhunteR: an accurate approach for the automatic identification and annotation of open reading frames in human mRNA molecules

2021 ◽  
Author(s):  
Vasily V. Grinev ◽  
Mikalai M. Yatskou ◽  
Victor V. Skakun ◽  
Maryna K. Chepeleva ◽  
Petr V. Nazarov
Keyword(s):  
Single Molecule ◽  
Web Application ◽  
R Package ◽  
Nucleotide Sequences ◽  
Open Reading Frames ◽  
Classification Model ◽  
Automatic Identification ◽  
Large Set ◽  
Link Type ◽  
Reading Frames

AbstractMotivationModern methods of whole transcriptome sequencing accurately recover nucleotide sequences of RNA molecules present in cells and allow for determining their quantitative abundances. The coding potential of such molecules can be estimated using open reading frames (ORF) finding algorithms, implemented in a number of software packages. However, these algorithms show somewhat limited accuracy, are intended for single-molecule analysis and do not allow selecting proper ORFs in the case of long mRNAs containing multiple ORF candidates.ResultsWe developed a computational approach, corresponding machine learning model and a package, dedicated to automatic identification of the ORFs in large sets of human mRNA molecules. It is based on vectorization of nucleotide sequences into features, followed by classification using a random forest. The predictive model was validated on sets of human mRNA molecules from the NCBI RefSeq and Ensembl databases and demonstrated almost 95% accuracy in detecting true ORFs. The developed methods and pre-trained classification model were implemented in a powerful ORFhunteR computational tool that performs an automatic identification of true ORFs among large set of human mRNA molecules.Availability and implementationThe developed open-source R package ORFhunteR is available for the community at GitHub repository (https://github.com/rfctbio-bsu/ORFhunteR), from Bioconductor (https://bioconductor.org/packages/devel/bioc/html/ORFhunteR.html) and as a web application (http://orfhunter.bsu.by).

Urban Region Function Mining Service Based on Social Media Text Analysis

2021 ◽  
Vol 31 (04) ◽  
pp. 563-586
Author(s):  
Yanchun Sun ◽  
Hang Yin ◽  
Jiu Wen ◽  
Zhiyu Sun
Keyword(s):  
Social Media ◽  
Text Analysis ◽  
Human Activities ◽  
Web Application ◽  
Classification Model ◽  
Urban Region ◽  
Urban Regions ◽  
Social Media Text ◽  
Urban Function ◽  
High Level

Urban region functions are the types of potential activities in an urban region, such as residence, commerce, transportation, entertainment, etc. A service which mines urban region functions is of great value for various applications, including urban planning and transportation management, etc. Many studies have been carried out to dig out different regions’ functions, but few studies are based on social media text analysis. Considering that the semantic information embedded in social media texts is very useful to infer an urban region’s main functions, we design a service which extracts human activities using Sina Weibo ( www.weibo.com ; the largest microblog system in Chinese, similar to Twitter) with location information and further describes a region’s main functions with a function vector based on the human activities. First, we predefine a variety of human activities to get the related activities corresponding to each Weibo post using an urban function classification model. Second, urban regions’ function vectors are generated, with which we can easily do some high-level work such as similar place recommendation. At last, with the function vectors generated, we develop a Web application for urban region function querying. We also conduct a case study among the urban regions in Beijing, and the experiment results demonstrate the feasibility of our method.

scholarly journals Assessing the potential for deep learning and computer vision to identify bumble bee species from images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Brian J. Spiesman ◽  
Claudio Gratton ◽  
Richard G. Hatfield ◽  
William H. Hsu ◽  
Sarina Jepsen ◽  
...  
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Web Application ◽  
Morphological Variability ◽  
Error Rates ◽  
Species Level ◽  
Bumble Bees ◽  
Classification Model ◽  
Automated Identification ◽  
Speed Accuracy

AbstractPollinators are undergoing a global decline. Although vital to pollinator conservation and ecological research, species-level identification is expensive, time consuming, and requires specialized taxonomic training. However, deep learning and computer vision are providing ways to open this methodological bottleneck through automated identification from images. Focusing on bumble bees, we compare four convolutional neural network classification models to evaluate prediction speed, accuracy, and the potential of this technology for automated bee identification. We gathered over 89,000 images of bumble bees, representing 36 species in North America, to train the ResNet, Wide ResNet, InceptionV3, and MnasNet models. Among these models, InceptionV3 presented a good balance of accuracy (91.6%) and average speed (3.34 ms). Species-level error rates were generally smaller for species represented by more training images. However, error rates also depended on the level of morphological variability among individuals within a species and similarity to other species. Continued development of this technology for automatic species identification and monitoring has the potential to be transformative for the fields of ecology and conservation. To this end, we present BeeMachine, a web application that allows anyone to use our classification model to identify bumble bees in their own images.

scholarly journals Development of a cross-platform application for leprosy screening based on artificial intelligence. (Preprint)

2020 ◽  
Author(s):  
Márcio Luís Moreira De Souza ◽  
Gabriel Ayres Lopes ◽  
Alexandre Castelo Branco ◽  
Jessica K Fairley ◽  
Lucia Alves De Oliveira Fraga
Keyword(s):  
Artificial Intelligence ◽  
Confidence Intervals ◽  
Health Professionals ◽  
Web Application ◽  
Cloud Service ◽  
Accurate Method ◽  
Classification Model ◽  
Mato Grosso ◽  
Unified Health System

BACKGROUND According to WHO, to achieve targets for control of leprosy by 2030, it will require disease elimination and interruption of transmission at the national or regional level. India and Brazil have reported the highest leprosy burden over the decades, revealing the need for strategies and tools to help health professionals correctly manage and control the disease. OBJECTIVE The objective of this study is to assess the quality of leprosy data in Brazil by SINAN (Information System for Notifiable Diseases) and build a web application to increase the accessibility of an accurate method of classifying leprosy treatment for health professionals, especially for communities further away from the country's major diagnostic centers. METHODS Leprosy data were extracted from the SINAN database, carefully cleaned, and used to build artificial intelligence (AI) decision model based on Random Forest (RF) algorithm to predict operational classification in Paucibacillary (PB) or Multibacillary (MB). It used the software: i) Python to extract and clean the data; ii) R to train and test the AI model via cross-validation. To allow broad access, we deployed the final RF classification model in a web application that integrates the cloud service, Microsoft Azure, with a friendly layout built in Bubble.io. It used data available on the IBGE (Brazilian Institute of Geography and Statistics) and the DATASUS (Department of Informatics of the Unified Health System). RESULTS We mapped the dispersion of leprosy incidence in Brazil, 2014 to 2018, and noticed a high number of cases in central Brazil in 2014 that became even higher in 2018, in the state of Mato Grosso. Some municipalities showed discrepancies in the 80% range. We considered inconsistency the fact of not matching a set of standards for leprosy classification, according to WHO. Of a total of 21,047 discrepancies detected, the main was considered the operational rating that does not match the clinical form. After data processing, we identified a total of 77,628 cases with missing data. Regarding the quality of the AI model applied, the sensitivity was 93.97%, and the specificity was 87.09%. In most states of Brazil, human and machine confidence intervals intersect. CONCLUSIONS The proposed APP was able to recognize patterns in leprosy cases registered in the SINAN database and classify new patients as PB or MB, reducing the probability of oversight by health professionals. The collection and notification of data on leprosy in Brazil seem to lack specific validation to increase the quality of the data for implementations via AI. The AI model implemented in this work presented relatively large confidence intervals of accuracy that varied from across Brazilian states. This distortion is possibly due to the quality of the data that fed the classification model.

scholarly journals Web Radio Automation for Audio Stream Management in the Era of Big Data

Information ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 205 ◽  
Author(s):  
Nikolaos Vryzas ◽  
Nikolaos Tsipas ◽  
Charalampos Dimoulas
Keyword(s):  
Digital Media ◽  
Speaker Recognition ◽  
Web Application ◽  
Clustering Algorithms ◽  
Radio Station ◽  
Classification Model ◽  
Speaker Diarization ◽  
Stream Management ◽  
Media Ecosystem ◽  
Audio Data

Radio is evolving in a changing digital media ecosystem. Audio-on-demand has shaped the landscape of big unstructured audio data available online. In this paper, a framework for knowledge extraction is introduced, to improve discoverability and enrichment of the provided content. A web application for live radio production and streaming is developed. The application offers typical live mixing and broadcasting functionality, while performing real-time annotation as a background process by logging user operation events. For the needs of a typical radio station, a supervised speaker classification model is trained for the recognition of 24 known speakers. The model is based on a convolutional neural network (CNN) architecture. Since not all speakers are known in radio shows, a CNN-based speaker diarization method is also proposed. The trained model is used for the extraction of fixed-size identity d-vectors. Several clustering algorithms are evaluated, having the d-vectors as input. The supervised speaker recognition model for 24 speakers scores an accuracy of 88.34%, while unsupervised speaker diarization scores a maximum accuracy of 87.22%, as tested on an audio file with speech segments from three unknown speakers. The results are considered encouraging regarding the applicability of the proposed methodology.

scholarly journals PSAP: Improving Accuracy of Students' Final Grade Prediction using ID3 and C4.5

2019 ◽  
Vol 6 (2) ◽  
pp. 125-133
Author(s):  
Ismail Yusuf Panessai ◽  
Muhammad Modi Lakulu ◽  
Mohd Hishamuddin Abdul Rahman ◽  
Noor Anida Zaria Mohd Noor ◽  
Nor Syazwani Mat Salleh ◽  
...  
Keyword(s):  
Student Performance ◽  
Web Application ◽  
Confusion Matrix ◽  
Classification Model ◽  
Web Based ◽  
Final Grade ◽  
Grade Prediction ◽  
Qualitative Approaches ◽  
Improving Accuracy ◽  
The Web

PSAP: Improving Accuracy of Students' Final Grade Prediction using ID3 and C4.5 This study was aimed to increase the performance of the Predicting Student Academic Performance (PSAP) system, and the outcome is to develop a web application that can be used to analyze student performance during present semester. Development of the web-based application was based on the evolutionary prototyping model. The study also analyses the accuracy of the classifier that is constructed for the prediction features in the web application. Qualitative approaches by user evaluation questionnaire were used for this study. A number of few personnel expert users which are lecturers from Universiti Pendidikan Sultan Idris were chosen as respondents. Each respondent is instructed to answer a total of 27 questions regarding respondent’s background and web application design. The accuracy of the classifier for the prediction features is tested by using the confusion matrix by using the test set of 24 rows. The findings showed the views of respondents on the aspects of interface design, functionality, navigation, and reliability of the web-based application that is developed. The result also showed that accuracy for the classifier constructed by using ID3 classification model (C4.5) is 79.18% and the highest compared to Naïve Bayes and Generalized Linear classification model.

scholarly journals Assessment of Deep Convolutional Neural Network Models for Species Identification of Forensically-Important Fly Maggots Based on Images of Posterior Spiracles

2022 ◽  
Author(s):  
Darlin Apasrawirote ◽  
Pharinya Boonchai ◽  
Paisarn Muneesawang ◽  
Wannacha Nakhonkam ◽  
Nophawan Bunchu
Keyword(s):  
Web Application ◽  
Confusion Matrix ◽  
Digital Camera ◽  
Network Models ◽  
Classification Model ◽  
Neural Network Models ◽  
Species Classification ◽  
Difficult Time ◽  
Novel Method ◽  
Level Identification

Abstract Forensic entomology is the branch of forensic science that is related to using arthropod specimens found in legal issues. Fly maggots are one of crucial pieces of evidence that can be used for estimating post-mortem intervals worldwide. However, the species-level identification of fly maggots is difficult, time consuming, and requires specialized taxonomic training. In this work, a novel method for the identification of different forensically-important fly species is proposed using convolutional neural networks (CNNs). The data used for the experiment were obtained from a digital camera connected to a compound microscope. We compared the performance of four widely used models that vary in complexity of architecture to evaluate tradeoffs in accuracy and speed for species classification including ResNet-101, Densenet161, Vgg19_bn, and AlexNet. In the validation step, all of the studied models provided 100% accuracy for identifying maggots of 4 species including Chrysomya megacephala (Diptera: Calliphoridae), Chrysomya (Achoetandrus) rufifacies (Diptera: Calliphoridae), Lucilia cuprina (Diptera: Calliphoridae), and Musca domestica (Diptera: Muscidae) based on images of posterior spiracles. However, AlexNet showed the fastest speed to process the identification model and presented a good balance between performance and speed. Therefore, the AlexNet model was selected for the testing step. The results of the confusion matrix of AlexNet showed that misclassification was found between C. megacephala and C. (Achoetandrus) rufifacies as well as between C. megacephala and L. cuprina. No misclassification was found for M. domestica. In addition, we created a web-application platform called thefly.ai to help users identify species of fly maggots in their own images using our classification model. The results from this study can be applied to identify further species by using other types of images. This model can also be used in the development of identification features in mobile applications. This study is a crucial step for integrating information from biology and AI-technology to develop a novel platform for use in forensic investigation.

Development of an Automated Solution for Large Scale Health Service Feedback: Using NLP and Topic Modelling techniques (Preprint)

2021 ◽  
Author(s):  
George Alexander ◽  
Mohammed Bahja ◽  
Gibran F Butt
Keyword(s):  
Lived Experience ◽  
Language Processing ◽  
Web Application ◽  
Large Scale ◽  
Service Providers ◽  
Classification Model ◽  
Free Text ◽  
Analysis Tool ◽  
Patient Feedback ◽  
Health And Social Care

UNSTRUCTURED Obtaining patient feedback is an essential mechanism for healthcare service providers to assess their quality and effectiveness. Unlike assessments of clinical outcomes, feedback from patients offers insights into their lived experience. The Department of Health and Social Care in England via NHS Digital operates a patient feedback web service through which patients can leave feedback of their experiences into structured and free-text report forms. Free-text feedback compared to structured questionnaires may be less biased by the feedback collector thus more representative; however, it is harder to analyse in large quantities and challenging to derive meaningful, quantitative outcomes for better representation of the general public feedback. This study details the development of a text analysis tool that utilises contemporary natural language processing (NLP) and machine learning models to analyse free-text clinical service reviews to develop a robust classification model, and interactive visualisation web application based on a Vue.js application with NodeJS, working with a C# serverless API and SQL server all hosted on Microsoft Azure Platform, which facilitates exploration of the data, designed for the use by all stakeholders. Of the 11,103 possible clinical services that could be reviewed across England, 2030 different services had received a combined total of 51,845 reviews between 1/10/2017 and 31/10/2019; these were included for analysis. Dominant topics were identified for the entire corpus and then negative and positive sentiment topics in turn. Reviews containing high and low sentiment topics occurred more frequently than less polarised topics. Time series analysis can identify trends in topic and sentiment occurrence frequency across the study period. This tool automates the analysis of large volumes of free text specific to medical services, and the web application summarises the results and presents them in an accessible and interactive format. Such a tool has the potential to considerably reduce administrative burden and increase user uptake.

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