On the Use of Generative Deep Learning Approaches for Generating Hidden Test Scripts

2021 ◽  
Vol 31 (10) ◽  
pp. 1447-1468
Author(s):  
Mert Oz ◽  
Caner Kaya ◽  
Erdi Olmezogullari ◽  
Mehmet S. Aktas
Keyword(s):  
Web Application ◽  
Web Applications ◽  
Short Term Memory ◽  
Learning Approaches ◽  
Generation Task ◽  
Clickstream Data ◽  
Test Script ◽  
Proposed Model ◽  
Action Sequences ◽  
Lstm Network

With the advent of web 2.0, web application architectures have been evolved, and their complexity has grown enormously. Due to the complexity, testing of web applications is getting time-consuming and intensive process. In today’s web applications, users can achieve the same goal by performing different actions. To ensure that the entire system is safe and robust, developers try to test all possible user action sequences in the testing phase. Since the space of all the possibilities is enormous, covering all user action sequences can be impossible. To automate the test script generation task and reduce the space of the possible user action sequences, we propose a novel method based on long short-term memory (LSTM) network for generating test scripts from user clickstream data. The experiment results clearly show that generated hidden test sequences are user-like sequences, and the process of generating test scripts with the proposed model is less time-consuming than writing them manually.

An Intelligent Approach to Detect Fake News Using Artificial Intelligence Technique

2021 ◽  
Vol 13 (2) ◽  
pp. 1-12
Author(s):  
Sumit Das ◽  
Manas Kumar Sanyal ◽  
Sarbajyoti Mallik
Keyword(s):  
Artificial Intelligence ◽  
Web Application ◽  
Short Term Memory ◽  
Real Data ◽  
Fake News ◽  
Artificial Intelligence Technique ◽  
News Reports ◽  
Proposed Model ◽  
Long Short Term Memory ◽  
Intelligent Approach

There is a lot of fake news roaming around various mediums, which misleads people. It is a big issue in this advanced intelligent era, and there is a need to find some solution to this kind of situation. This article proposes an approach that analyzes fake and real news. This analysis is focused on sentiment, significance, and novelty, which are a few characteristics of this news. The ability to manipulate daily information mathematically and statistically is allowed by expressing news reports as numbers and metadata. The objective of this article is to analyze and filter out the fake news that makes trouble. The proposed model is amalgamated with the web application; users can get real data and fake data by using this application. The authors have used the AI (artificial intelligence) algorithms, specifically logistic regression and LSTM (long short-term memory), so that the application works well. The results of the proposed model are compared with existing models.

scholarly journals Tourism Demand Forecasting Based on an LSTM Network and Its Variants

Algorithms ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 243
Author(s):  
Shun-Chieh Hsieh
Keyword(s):  
Adaptive Learning ◽  
Time Series Data ◽  
Short Term Memory ◽  
Demand Forecasting ◽  
Series Data ◽  
Learning Approaches ◽  
Tourism Demand ◽  
Long Short Term Memory ◽  
Lstm Network ◽  
Gated Recurrent Unit

The need for accurate tourism demand forecasting is widely recognized. The unreliability of traditional methods makes tourism demand forecasting still challenging. Using deep learning approaches, this study aims to adapt Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit networks (GRU), which are straightforward and efficient, to improve Taiwan’s tourism demand forecasting. The networks are able to seize the dependence of visitor arrival time series data. The Adam optimization algorithm with adaptive learning rate is used to optimize the basic setup of the models. The results show that the proposed models outperform previous studies undertaken during the Severe Acute Respiratory Syndrome (SARS) events of 2002–2003. This article also examines the effects of the current COVID-19 outbreak to tourist arrivals to Taiwan. The results show that the use of the LSTM network and its variants can perform satisfactorily for tourism demand forecasting.

scholarly journals Optimizing the Procurement of IaaS Reservation Contracts via Workload Predicting and Integer Programming

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Huamin Zhu ◽  
Jun Luo ◽  
Hongyao Deng
Keyword(s):  
Business Cycle ◽  
Web Application ◽  
Web Applications ◽  
Short Term Memory ◽  
Resource Planning ◽  
Support Vector ◽  
Cloud Infrastructure ◽  
Great Opportunity ◽  
The Future ◽  
Future Business

Cloud-based web applications are proliferating fast. Owing to the elastic capacity and diverse pricing schemes, cloud Infrastructure-as-a-Service (IaaS) offers great opportunity for web application providers to optimize resource cost. However, such optimization activities are confronting the challenges posed by the uncertainty of future demand and the increasing reservation contracts. This work investigates the problem of how to minimize IaaS rental cost associated with hosting web applications, while meeting the demand in the future business cycle. First, an integer liner program model is developed to optimize reservation-contract procurement, in which reserved and on-demand resources are planned for multiple provisioning stages as well as a long-term plan, e.g., twelve stages in an annual plan. Then, a Long Short-Term Memory (LSTM) based algorithm is designed to predict the workload in the future business cycle. In addition, the approaches for determining virtual instance capacity and the baseline workload of planning time slot are also presented. Finally, the experimental prediction results show the LSTM-based algorithm gains an advantage over several popular models, such as the Holter–Winters, the Seasonal Autoregressive Integrated Moving Average (SARIMA), and the Support Vector Regression (SVR). The simulations of resource planning show that the provisioning scheme based on our reservation-optimization model obtains significant cost savings than other typical provisioning schemes, while satisfying the demands.

scholarly journals A Context-Aware Location Recommendation System for Tourists Using Hierarchical LSTM Model

2020 ◽  
Vol 12 (10) ◽  
pp. 4107
Author(s):  
Wafa Shafqat ◽  
Yung-Cheol Byun
Keyword(s):  
Recommendation System ◽  
Short Term Memory ◽  
Contextual Information ◽  
Optimal Size ◽  
Effective Parameters ◽  
Travel History ◽  
Proposed Model ◽  
Processing Information ◽  
Lstm Network ◽  
Gated Recurrent Unit

The significance of contextual data has been recognized by analysts and specialists in numerous disciplines such as customization, data recovery, ubiquitous and versatile processing, information mining, and management. While a generous research has just been performed in the zone of recommender frameworks, by far most of the existing approaches center on prescribing the most relevant items to customers. It usually neglects extra-contextual information, for example time, area, climate or the popularity of different locations. Therefore, we proposed a deep long-short term memory (LSTM) based context-enriched hierarchical model. This proposed model had two levels of hierarchy and each level comprised of a deep LSTM network. In each level, the task of the LSTM was different. At the first level, LSTM learned from user travel history and predicted the next location probabilities. A contextual learning unit was active between these two levels. This unit extracted maximum possible contexts related to a location, the user and its environment such as weather, climate and risks. This unit also estimated other effective parameters such as the popularity of a location. To avoid feature congestion, XGBoost was used to rank feature importance. The features with no importance were discarded. At the second level, another LSTM framework was used to learn these contextual features embedded with location probabilities and resulted into top ranked places. The performance of the proposed approach was elevated with an accuracy of 97.2%, followed by gated recurrent unit (GRU) (96.4%) and then Bidirectional LSTM (94.2%). We also performed experiments to find the optimal size of travel history for effective recommendations.

scholarly journals Short-Term Prediction of Bus Passenger Flow Based on a Hybrid Optimized LSTM Network

2019 ◽  
Vol 8 (9) ◽  
pp. 366 ◽  
Author(s):  
Yong Han ◽  
Cheng Wang ◽  
Yibin Ren ◽  
Shukang Wang ◽  
Huangcheng Zheng ◽  
...  
Keyword(s):  
High Efficiency ◽  
Short Term Memory ◽  
Spatial Scales ◽  
Optimization Procedure ◽  
Stochastic Gradient Descent ◽  
Short Term ◽  
Passenger Flow ◽  
Proposed Model ◽  
Gradient Descent Algorithm ◽  
Lstm Network

The accurate prediction of bus passenger flow is the key to public transport management and the smart city. A long short-term memory network, a deep learning method for modeling sequences, is an efficient way to capture the time dependency of passenger flow. In recent years, an increasing number of researchers have sought to apply the LSTM model to passenger flow prediction. However, few of them pay attention to the optimization procedure during model training. In this article, we propose a hybrid, optimized LSTM network based on Nesterov accelerated adaptive moment estimation (Nadam) and the stochastic gradient descent algorithm (SGD). This method trains the model with high efficiency and accuracy, solving the problems of inefficient training and misconvergence that exist in complex models. We employ a hybrid optimized LSTM network to predict the actual passenger flow in Qingdao, China and compare the prediction results with those obtained by non-hybrid LSTM models and conventional methods. In particular, the proposed model brings about a 4%–20% extra performance improvements compared with those of non-hybrid LSTM models. We have also tried combinations of other optimization algorithms and applications in different models, finding that optimizing LSTM by switching Nadam to SGD is the best choice. The sensitivity of the model to its parameters is also explored, which provides guidance for applying this model to bus passenger flow data modelling. The good performance of the proposed model in different temporal and spatial scales shows that it is more robust and effective, which can provide insightful support and guidance for dynamic bus scheduling and regional coordination scheduling.

scholarly journals Deep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins

2021 ◽  
Author(s):  
Farshid Rahmani ◽  
Chaopeng Shen ◽  
Samantha Oliver ◽  
Kathryn Lawson ◽  
Alison Appling
Keyword(s):  
Short Term Memory ◽  
Network Models ◽  
Stream Temperature ◽  
Data Availability ◽  
Training Data ◽  
Training Dataset ◽  
Learning Approaches ◽  
Input Selection ◽  
Daily Sampling ◽  
Lstm Network

Basin-centric long short-term memory (LSTM) network models have recently been shown to be an exceptionally powerful tool for simulating stream temperature (Ts, temperature measured in rivers), among other hydrological variables. However, spatial extrapolation is a well-known challenge to modeling Ts and it is uncertain how an LSTM-based daily Ts model will perform in unmonitored or dammed basins. Here we compiled a new benchmark dataset consisting of >400 basins for across the contiguous United States in different data availability groups (DAG, meaning the daily sampling frequency) with or without major dams and study how to assemble suitable training datasets for predictions in monitored or unmonitored situations. For temporal generalization, CONUS-median best root-mean-square error (RMSE) values for sites with extensive (99%), intermediate (60%), scarce (10%) and absent (0%, unmonitored) data for training were 0.75, 0.83, 0.88, and 1.59°C, representing the state of the art. For prediction in unmonitored basins (PUB), LSTM’s results surpassed those reported in the literature. Even for unmonitored basins with major reservoirs, we obtained a median RMSE of 1.492°C and an R2 of 0.966. The most suitable training set was the matching DAG that the basin could be grouped into, e.g., the 60% DAG for a basin with 61% data availability. However, for PUB, a training dataset including all basins with data is preferred. An input-selection ensemble moderately mitigated attribute overfitting. Our results suggest there are influential latent processes not sufficiently described by the inputs (e.g., geology, wetland covers), but temporal fluctuations are well predictable, and LSTM appears to be the more accurate Ts modeling tool when sufficient training data are available.

scholarly journals ENHANCED ONLINE BOOK STORE MODEL: ADOPTING THE PROGRESSIVE WEB APPLICATION (PWA) TECHNOLOGY

2020 ◽  
Vol 9 (9) ◽  
pp. 1-8
Keyword(s):  
Web Application ◽  
Web Applications ◽  
Research Work ◽  
Business Owners ◽  
Proposed Model ◽  
Book Store ◽  
Architectural Framework ◽  
Mobile Development ◽  
Online Book

From the physical book store to the online bookstore, business owners find a way to meet the demands of their prospective customers. The daily advancement in technology has brought about a huge change the operation of e-commerce. The development of the Progressive Web Applications (PWA) by Google has caused a revolution in mobile development. Using an online bookstore as a case study, this research work presents a PWA architectural framework that can be adopted by any e-commerce applications. This was achieved after a systematic review of existing online bookstore models was carried out – identifying the gaps which will serve as strengths for the proposed model. Also, the emerging technology of PWA was critically reviewed to solidify the proposed model. Adoption of the model will avoid current issues faced the world of mobile development especially code fragmentation. However, exploring the payment gateways and modules will help solidify the model.

User-Based Load Visualization of Categorical Forecasted Smart Meter Data Using LSTM Network

2020 ◽  
Vol 11 (1) ◽  
pp. 30-50
Author(s):  
Ajay Kumar ◽  
Parveen Poon Terang ◽  
Vikram Bali
Keyword(s):  
Power Systems ◽  
Web Application ◽  
Short Term Memory ◽  
Essential Feature ◽  
Load Forecasting ◽  
Smart Meter ◽  
Short Term ◽  
Model Framework ◽  
Term Basis ◽  
Lstm Network

Electrical load forecasting is an essential feature in power systems planning, operation and control. The non-linearity and non-stationary nature of the data, however, poses a challenge in terms of accuracy. This article explores a deep learning technique, a long short-term memory recurrent neural network-based framework to tackle this tricky issue. The proposed machine learning model framework is tested on real time residential smart meter data showing promising results. A web application has also been developed to allow consumers to have access to greater levels of information and facilitate decision-making at their end. The performance of the proposed model is also comprehensively compared to other methods in the field of load forecasting showing more accurate results for the function of forecasting of load on short term basis.

scholarly journals Forecasting the spread of COVID-19 using LSTM network

2021 ◽  
Vol 22 (S6) ◽  
Author(s):  
Shiu Kumar ◽  
Ronesh Sharma ◽  
Tatsuhiko Tsunoda ◽  
Thirumananseri Kumarevel ◽  
Alok Sharma
Keyword(s):  
Short Term Memory ◽  
Forecasting Model ◽  
Policy Makers ◽  
Business Partners ◽  
Global Pandemic ◽  
Proposed Model ◽  
Travel Restrictions ◽  
The Government ◽  
Lstm Network ◽  
Novel Coronavirus

Abstract Background The novel coronavirus (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2, and within a few months, it has become a global pandemic. This forced many affected countries to take stringent measures such as complete lockdown, shutting down businesses and trade, as well as travel restrictions, which has had a tremendous economic impact. Therefore, having knowledge and foresight about how a country might be able to contain the spread of COVID-19 will be of paramount importance to the government, policy makers, business partners and entrepreneurs. To help social and administrative decision making, a model that will be able to forecast when a country might be able to contain the spread of COVID-19 is needed. Results The results obtained using our long short-term memory (LSTM) network-based model are promising as we validate our prediction model using New Zealand’s data since they have been able to contain the spread of COVID-19 and bring the daily new cases tally to zero. Our proposed forecasting model was able to correctly predict the dates within which New Zealand was able to contain the spread of COVID-19. Similarly, the proposed model has been used to forecast the dates when other countries would be able to contain the spread of COVID-19. Conclusion The forecasted dates are only a prediction based on the existing situation. However, these forecasted dates can be used to guide actions and make informed decisions that will be practically beneficial in influencing the real future. The current forecasting trend shows that more stringent actions/restrictions need to be implemented for most of the countries as the forecasting model shows they will take over three months before they can possibly contain the spread of COVID-19.

scholarly journals Predicting COVID-19 cases using bidirectional LSTM on multivariate time series

2021 ◽  
Author(s):  
Ahmed Ben Said ◽  
Abdelkarim Erradi ◽  
Hussein Ahmed Aly ◽  
Abdelmonem Mohamed
Keyword(s):  
Time Series ◽  
Clustering Algorithm ◽  
Short Term Memory ◽  
Multivariate Time Series ◽  
Health Sector ◽  
Cumulative Number ◽  
Disease Propagation ◽  
Proposed Model ◽  
Bidirectional Lstm ◽  
Lstm Network

AbstractTo assist policymakers in making adequate decisions to stop the spread of the COVID-19 pandemic, accurate forecasting of the disease propagation is of paramount importance. This paper presents a deep learning approach to forecast the cumulative number of COVID-19 cases using bidirectional Long Short-Term Memory (Bi-LSTM) network applied to multivariate time series. Unlike other forecasting techniques, our proposed approach first groups the countries having similar demographic and socioeconomic aspects and health sector indicators using K-means clustering algorithm. The cumulative case data of the clustered countries enriched with data related to the lockdown measures are fed to the bidirectional LSTM to train the forecasting model. We validate the effectiveness of the proposed approach by studying the disease outbreak in Qatar and the proposed model prediction from December 1st until December 31st, 2020. The quantitative evaluation shows that the proposed technique outperforms state-of-art forecasting approaches.

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