54 research outputs found
Combined measures to control the COVID-19 pandemic in Wuhan, Hubei, China: A narrative review
No full textCoronavirus disease 2019 (COVID-19) is an emerging disease caused by the coronavirus, SARS-CoV-2, which leads to severe respiratory infections in humans. COVID-19 was first reported in December 2019 in Wuhan city, a populated area of the Hubei province in China. As of now, Wuhan and other cities nearby have become safe places for locals. The rapid control of the spread of COVID-19 infection was made possible due to several interventions and measures that were undertaken in Wuhan. This narrative review study was designed to evaluate the emerging literature on the combined measures taken to control the COVID-19 pandemic in Wuhan city. Science Direct, Springer, Web of Science, and the PubMed data repositories were searched for studies published between December 1, 2019, and June 07, 2020. The referred ”preferred reporting items for systematic reviews and meta-analyses” (PRISMA) protocol was used to conduct this narrative review. A total of 330 research studies were found as a result of the initial search based on exclusion and inclusion criteria, and 30 articles were chosen on final evaluation. It was discovered that the combined measures to control the spread of COVID-19 in Wuhan included cordon sanitaire, social distancing, universal symptom surveys, quarantine strategies, and transport restrictions. Based on the recommendations presented in this review study, existing policies with regard to combined measures and public health policies can be enforced by other countries to implement a rapid control procedure to control the spread of the COVID-19 pandemic.</p
Drupal core 8 caching mechanism for scalability improvement of web services
No full textWeb services is a growing technology to meet the multiple and concurrent users’ access requirements. Web services’ performance degrades when concurrent users face the delay in receiving replies from services. In an attempt to address the limitation of web services technology, Drupal 8 has been recently introduced. Drupal 8 enables the fast caching of dynamic web pages, and keeps several other features which aid in reducing the response time to users. Results show that Drupal 8 has more impacts in the academic setting for ongoing research on enhancing the scalability of web services.</p
Optimisasi Hyperparameter BiLSTM Menggunakan Bayesian Optimization untuk Prediksi Harga Saham
No full textThe accuracy of deep learning models in predicting dynamic and non-linear stock market data highly depends on selecting optimal hyperparameters. However, finding optimal hyperparameters can be costly in terms of the model's objective function, as it requires testing all possible combinations of hyperparameter configurations. This research aims to find the optimal hyperparameter configuration for the BiLSTM model using Bayesian Optimization. The study was conducted using three blue-chip stocks from different sectors, namely BBCA, BYAN, and TLKM, with two scenarios of search iterations. The test results show that Bayesian Optimization was able to find the optimal hyperparameter configuration for the BiLSTM model, with the best MAPE values for each stock: BBCA 1.2092%, BYAN 2.0609%, and TLKM 1.2027%. Compared to previous research on Grid Search-BiLSTM, the use of Bayesian Optimization-BiLSTM resulted in lower MAPE values
A Neural Network-Based Application to Identify Cubic Structures in Multi Component Crystalline Materials Using X-Ray Diffraction Data.
No full textOne of the crystalline materials structures is cubic. An
experimental study has been done about developing a scheme to identify the cubic structure types in single or multi component materials. This scheme is using fingerprints created from the calculation of quadratic Miller indices ratios and matches it with the ratio of the sin20 values from the diffracted data of material obtained by X-Ray Diffraction (XRD) method. These manual matching processes are complicated and sometimes are tedious because the diffracted data are complex and may have more than
one fingerprint inside. This paper proposes an application of multi-layered back-propagation neural network in matching the fingerprints with the diffracted data of crystalline material to quickly and correctly identify its cubic structure component types
Implementation of the CNN-LSTM Hybrid Model in Predicting Bitcoin Price Fluctuations
No full textDigital financial systems of today face formidable obstacles from the extreme price volatility and unpredictability of Bitcoin. Data cleaning, Min-Max normalization, and sequence creation with a sliding window were performed on the daily BTC-USD historical data received from Yahoo Finance from 2020 to 2024 before implementing a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model in this study. The CNN layers are responsible for extracting local patterns with a limited time horizon, whereas the LSTM layers are responsible for capturing the time series' long-term relationships. The experimental findings show that the CNN-LSTM model outperforms the CNN and LSTM in terms of predictive ability, with an RMSE of 2,202.717, an MAE of 1,553.202, and a MAPE of 2.244%, which translates to an accuracy of about 97.756%. These results provide useful information for adaptive trading techniques and digital asset risk management based on artificial intelligence, and they prove that the hybrid method is successful in dealing with complicated, non-linear, and unpredictable trends in the cryptocurrency market
New Approach: Customer Segmentation using RFM Model and Demand Classification
No full textThis research introduces an integrated data mining framework that combines RFM (Recency, Frequency, Monetary) analysis with demand pattern classification—encompassing Smooth, Erratic, Intermittent, and Lumpy categories—to refine customer segmentation strategies. While RFM effectively captures transactional behavior, its scope remains insufficient as it overlooks demand variability and intermittency, which critically influence purchasing dynamics and inventory planning. By incorporating demand classification, this model addresses behavioral dimensions beyond conventional transactional metrics, thereby enhancing segmentation precision and strategic relevance. Customer clustering employs the K-Means algorithm, with cluster optimization validated through Elbow Method and Silhouette Index analyses, yielding five distinct segments: Ideal, Interest, Improve, Inconsistent, and Inactive. Subsequently, Customer Lifetime Value (CLV) is computed by weighting RFM and demand parameters via Analytic Hierarchy Process (AHP), with Consistency Index and Consistency Ratio assessments ensuring methodological rigor. Results are synthesized within an interactive dashboard, facilitating data-driven decision-making in retention strategies, inventory optimization, profitability enhancement, and sustainable business development
Ontology-Based Regression Testing: A Systematic Literature Review
No full textWeb systems evolve by adding new functionalities or modifying them to meet users’ requirements. Web systems require retesting to ensure that existing functionalities are according to users’ expectations. Retesting a web system is challenging due to high cost and time consumption. Existing ‘systematic literature review’ (SLR) studies do not comprehensively present the ontology-based regression testing approaches. Therefore, this study focuses on ontology-based regression testing approaches because ontologies have been a growing research solution in regression testing. Following this, a systematic search of studies was performed using the “Preferred Reporting Items for Systematic Reviews and Meta-Analyses” (PRISMA) guidelines. A total of 24 peer-reviewed studies covering ontologies (semantic and inference rules) and regression testing, published between 2007 and 2019, were selected. The results showed that mainly ontology-based regression testing approaches were published in 2011–2012 and 2019 because ontology got momentum in research in other fields of study during these years. Furthermore, seven challenges to ontology-driven regression testing approaches are reported in the selected studies. Cost and validation are the main challenges examined in the research studies. The scalability of regression testing approaches has been identified as a common problem for ontology-based and other benchmark regression testing approaches. This SLR presents that the safety of critical systems is a possible future research direction to prevent human life risks
Performance anomaly detection in web services:An RNN-based approach using dynamic quality of service features
No full textPerformance anomaly detection is the process of identifying occurrences that do not conform to expected behavior or correlate with other incidents or events in time series data. Anomaly detection has been applied to areas such as fraud detection, intrusion detection systems, and network systems. In this paper, we propose an anomaly detection framework that uses dynamic features of quality of service that are collected in a simulated setup. Three variants of recurrent neural networks-SimpleRNN, long short term memory, and gated recurrent unit are evaluated. The results reveal that the proposed method effectively detects anomalies in web services with high accuracy. The performance of the proposed anomaly detection framework is superior to that of existing approaches using maximum accuracy and detection rate metrics.</p
An ontology based test case prioritization approach in regression testing
No full textRegression testing is a widely studied research area, with the aim of meeting the quality challenges of software systems. To achieve a software system of good quality, we face high consumption of resources during testing. To overcome this challenge, test case prioritization (TCP) as a sub-type of regression testing is continuously investigated to achieve the testing objectives. This study provides an insight into proposing the ontology-based TCP (OTCP) approach, aimed at reducing the consumption of resources for the quality improvement and maintenance of software systems. The proposed approach uses software metrics to examine the behavior of classes of software systems. It uses Binary Logistic Regression (BLR) and AdaBoostM1 classifiers to verify correct predictions of the faulty and non-faulty classes of software systems. Reference ontology is used to match the code metrics and class attributes. We investigated five Java programs for the evaluation of the proposed approach, which was used to achieve code metrics. This study has resulted in an average percentage of fault detected (APFD) value of 94.80%, which is higher when compared to other TCP approaches. In future works, large sized programs in different languages can be used to evaluate the scalability of the proposed OTCP approach.</p
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