Execution Time Research Articles

Multicenter Analysis of Emergency Patient Severity through Local Model Evaluation Client Selection: Optimizing Client Selection Based on Local Model Evaluation

In multi-institutional emergency room settings, the early identification of high-risk patients is crucial for effective severity management. This necessitates the development of advanced models capable of accurately predicting patient severity based on initial conditions. However, collecting and analyzing large-scale data for high-performance predictive models is challenging due to privacy and data security concerns in integrating data from multiple emergency rooms. To address this, our work applies federated learning (FL) techniques, maintaining privacy without centralizing data. Medical data, which are often non-independent and identically distributed (non-IID), pose challenges for existing FL, where random client selection can impact overall FL performance. Therefore, we introduce a new client selection mechanism based on local model evaluation (LMECS), enhancing performance and practicality. This approach shows that the proposed FL model can achieve comparable performance to centralized models and maintain data privacy. The execution time was reduced by up to 27% compared to the existing FL algorithm. In addition, compared to the average performance of local models without FL, our LMECS improved the AUC by 2% and achieved up to 23% performance improvement compared to the existing FL algorithm. This work presents the potential for effective patient severity management in multi-institutional emergency rooms using FL without data movement, offering an innovative approach that satisfies both medical data privacy and efficient utilization.

Leveraging Real-World Data in Safety Signal Assessment.

TransCelerate BioPharma surveyed its member biopharmaceutical companies to understand current practices and identify opportunities to complement safety signal assessment with rapid real-world data (RWD) analysis. A voluntary 30-question questionnaire regarding the use of RWD in safety signal assessment was disseminated to subject matter experts at all TransCelerate member companies in July 2022. Responses were blinded, aggregated, summarized, and presented. Eighteen of 20 member companies provided responses to the questionnaire. Sixteen (89%) companies reported actively leveraging RWD in their signal assessment processes. Of 18 respondent companies, 8 (44%) routinely use rapid approaches to RWD analysis, 7 (39%) utilize rapid RWD analysis non-routinely or in a pilot setting, 2 (11%) are considering using rapid RWD analysis, and 1 (6%) has no plans to use rapid RWD analysis for their signal assessment. Most companies reported that RWD adds context to and improves quality of signal assessments. To conduct RWD analysis for signal assessment, 16 of 17 (94%) respondent companies utilize or plan to utilize internally available data, 8 (47%) utilize both internal and external data, and 3 (18%) utilize data networks. Respondents identified key challenges to rapidly performing RWD analyses, including data access/availability, time for analysis execution, and uncertainties regarding acceptance of minimal or non-protocolized approaches by health authorities. Biopharmaceutical companies reported that they see value in the use of rapid RWD analyses for complementing signal assessments. Future work is recommended to offer a framework and process for use of rapid use of RWD analyses in signal assessment.

PSA-FL-CDM: A Novel Federated Learning-Based Consensus Model for Post-Stroke Assessment

The rapid development of Internet of Things (IoT) technologies and the potential benefits of employing the vast datasets generated by IoT devices, including wearable sensors and camera systems, has ushered in a new era of opportunities for enhancing smart rehabilitation in various healthcare systems. Maintaining patient privacy is paramount in healthcare while providing smart insights and recommendations. This study proposed the adoption of federated learning to develop a scalable AI model for post-stroke assessment while protecting patients’ privacy. This research compares the centralized (PSA-MNMF) model performance with the proposed scalable federated PSA-FL-CDM model for sensor- and camera-based datasets. The computational time indicates that the federated PSA-FL-CDM model significantly reduces the execution time and attains comparable performance while preserving the patient’s privacy. Impact Statement—This research introduces groundbreaking contributions to stroke assessment by successfully implementing federated learning for the first time in this domain and applying consensus models in each node. It enables collaborative model training among multiple nodes or clients while ensuring the privacy of raw data. The study explores eight different clustering methods independently on each node, revolutionizing data organization based on similarities in stroke assessment. Additionally, the research applies the centralized PSA-MNMF consensus clustering technique to each client, resulting in more accurate and robust clustering solutions. By utilizing the FedAvg federated learning algorithm strategy, locally trained models are combined to create a global model that captures the collective knowledge of all participants. Comparative performance measurements and computational time analyses are conducted, facilitating a fair evaluation between centralized and federated learning models in stroke assessment. Moreover, the research extends beyond a single type of database by conducting experiments on two distinct datasets, wearable and camera-based, broadening the understanding of the proposed methods across different data modalities. These contributions develop stroke assessment methodologies, enabling efficient collaboration and accurate consensus clustering models and maintaining data privacy.

A Performance Analysis of Hybrid and Columnar Cloud Databases for Efficient Schema Design in Distributed Data Warehouse as a Service

A Data Warehouse (DW) is a centralized database that stores large volumes of historical data for analysis and reporting. In a world where enterprise data grows exponentially, new architectures are being investigated to overcome the deficiencies of traditional Database Management Systems (DBMSs), driving a shift towards more modern, cloud-based solutions that provide resources such as distributed processing, columnar storage, and horizontal scalability without the overhead of physical hardware management, i.e., a Database as a Service (DBaaS). Choosing the appropriate class of DBMS is a critical decision for organizations, and there are important differences that impact data volume and query performance (e.g., architecture, data models, and storage) to support analytics in a distributed cloud environment efficiently. In this sense, we carry out an experimental evaluation to analyze the performance of several DBaaS and the impact of data modeling, specifically the usage of a partially normalized Star Schema and a fully denormalized Flat Table Schema, to further comprehend their behavior in different configurations and designs in terms of data schema, storage form, memory availability, and cluster size. The analysis is done in two volumes of data generated by a well-established benchmark, comparing the performance of the DW in terms of average execution time, memory usage, data volume, and loading time. Our results provide guidelines for efficient DW design, showing, for example, that the denormalization of the schema does not guarantee improved performance, as solutions performed differently depending on its architecture. We also show that a Hybrid Processing (HTAP) NewSQL solution can outperform solutions that support only Online Analytical Processing (OLAP) in terms of overall execution time, but that the performance of each query is deeply influenced by its selectivity and by the number of join functions.

Correction to: Minimization of VANET execution time based on joint task offloading and resource allocation

Correction to: Minimization of VANET execution time based on joint task offloading and resource allocation

Understanding and Therapeutic Application of Immune Response in Major Histocompatibility Complex (MHC) Diversity Using Multimodal Artificial Intelligence

Human Leukocyte Antigen (HLA) is like a device that monitors the internal environment of the body. T lymphocytes immediately recognize the HLA molecules that are expressed on the surface of the cells of the different individual, attacking it defeats microorganisms that is one of the causes of rejection in organ transplants performed between people with unmatched HLA types. Over 2850 and 3580 different polymorphisms have been reported for HLA-A and HLA-B respectively, around the world. HLA genes are associated with the risk of developing a variety of diseases, including autoimmune diseases, and play an important role in pathological conditions. By using a deep learning method called multi-task learning to simultaneously predict the gene sequences of multiple HLA genes, it is possible to improve accuracy and shorten execution time. Some new systems use a model called convolutional neural network (CNNs) in deep learning, which uses neural networks consisting of many layers and can learn complex correlations between SNP information and HLA gene sequences based on reference data for HLA imputation, which serves as training data. The learned model can output predicted values of HLA gene sequences with high accuracy using SNP information as input. To investigate which part of the input information surrounding the HLA gene is used to make learning predictions, predictions were made using not only a small number of nearby SNP information but also many SNP information distributed over a wider area by visualizing the learning information of the model. While conventional methods are strong at learning using nearly SNP information and not good at learning using SNP information located at distant locations, some new systems are thought that prediction accuracy may have improved because this problem was overcome. HLA genes are involved in the onset of a variety of diseases and are attracting attention. As an important area from the perspective of elucidating pathological conditions and realizing personalized medicine. The applied multi-task learning to two different HLA imputation reference panels—a Japanese panel (n = 1118) and type I diabetes genetics consortium panel (n = 5122). Through 10-fold cross-validation on these panels, the multi-task learning achieved higher imputation accuracy than conventional methods, especially for imputing low-frequency and rare HLA alleles. The increased prediction accuracy of HLA gene sequences is expected to increase the reliability of HLA analysis, including integrated analysis between different racial populations, and is expected to greatly contribute to the identification of HLA gene sequences associated with diseases and further elucidation of pathological conditions.

Multi-objective discrete Cuckoo search algorithm for optimization of bag-of-tasks scheduling in fog computing environment

Internet of Things (IoT) applications are mostly time-sensitive and require reliable executions. The majority of IoT applications experience low quality of service (QoS) because of the intrinsic delay in utilizing the far-distant cloud infrastructures. To experience better QoS, fog computing as a complementary facility for cloud infrastructure is extended near the IoT devices. The bag of tasks (BoTs) IoT applications include a number of independent tasks each of which needs a certain amount of processing time so it is favorable to experience the lowest total execution time, makespan, for all applications. In addition, reliable execution guarantees the business continuity of IoT applications. Therefore, this paper presents a fog computing scheduler for the execution of BoT IoT applications in the fog platforms to minimize makespan and maximize the reliability execution at the same time. To this end, a new reliability model is presented. Then, the BoTs scheduling issue is formulated into a multi-objective discrete optimization problem with makespan and reliability optimality viewpoints. To solve this combinatorial problem, a multi-objective discrete cuckoo search algorithm (MoDCSA) is extended. The proposed MoDCSA optimization algorithm takes the benefit of several discrete operators each of which is smartly invoked. The quality of gained results from simulations of extensive scenarios verifies the effectiveness of the proposed MoDCSA scheduling algorithm which has 2.27%, 3.89%, 5.45%, 5.40%, and 4.55% improvement in terms of makespan and 1.99%, 5.11%, 6.39%, 7.95%, and 7.78% improvement in terms of reliability against MoBLA, MoGA, MoGWO, MoSA, and MoGSA comparative algorithms respectively.

Artificial intelligence for early-stage detection of chronic kidney disease

Early-stage detection of chronic kidney disease (CKD) is crucial in research to enable timely intervention, enhance understanding of disease progression, reduce healthcare costs and support public health initiatives. The traditional approaches on early-stage chronic kidney disease detection often suffer from slow convergence and not integrate advanced technologies, impacting their effectiveness. Additionally, security and privacy concerns related to patient data are ineffectively addressed. To overcome these issues, this research incorporates novel optimized artificial intelligence-based approaches. The main aim is to enhance detection process through enhanced hybrid mud ring network (EHMRN), a novel detection technique combining light gradient boosting machine and MobileNet, involving extensive data collection, including a large dataset of 100,000 instances. The introduced network is optimized through the mud ring optimization to attain enhanced performance. Incorporating spark ensures secure cloud-based storage, enhancing privacy and compliance with healthcare data regulations. This approach represents a significant advancement in primary stage detection more effectively and promptly. The results show that the introduced approach outperforms traditional approaches in terms of accuracy (99.96%), F1-score (99.91%), precision (100%), specificity (99.98%), recall (100%) and execution time (0.09 s).

Multi-objective optimal reconfiguration of distribution networks using a novel meta-heuristic algorithm

Reconfiguration strategies are used to reduce power losses and increase the reliability of the distribution systems. Since the optimal reconfiguration problem is a multi-objective optimization problem with non-convex functions and constraints, meta-heuristic algorithms are the most suitable choice for the problem-solving approach. One of the new meta-heuristic algorithms that exhibits excellent performance in solving multi-objective problems is the wild mice colony (WMC) algorithm, which is implemented based on aggressive and mating strategies of wild mice. In this paper, the distribution network reconfiguration problem is solved to reduce power losses, improve reliability, and increase the voltage profile of network buses using the WMC algorithm. In addition, the obtained results are compared with conventional multi-objective algorithms. The optimal reconfiguration problem is applied to the IEEE 33-bus and 69-bus test systems. The comparative study confirms the superior performance of the proposed algorithm in terms of convergence speed, execution time, and the final solution.

Deep convolutional neural network for Lampung character recognition

Recognition of document based, and handwritten characters has recently emerged as highly relevant field of study in the field of digital image processing. The ability to read and write Lampung script is a crucial competency as it helps preserve the language, which is a part of Indonesian culture. This research utilizes data obtained from classified documents and handwritten samples, categorized into eight types. To recognize Lampung characters, deep convolutional neural network (DCNN) architecture is proposed. The novelty of this architecture lies in optimizing document-based and handwritten character recognition to achieve the best performance in terms of accuracy and execution time. The proposed architecture will be compared to principal component analysis (PCA) combined with support vector machine (SVM) to evaluate its results. Experimental results using the DCNN architecture show an average accuracy of 99.3% and an execution time of 283 seconds for all data, while PCA and SVM exhibit an average accuracy of 92.9%. Furthermore, the recognition results for all data from documents and handwritten samples yield satisfactory accuracy of 98.6%. These results make the DCNN architecture suitable for use in recognizing Lampung characters and are expected to make it easier for Lampung people to recognize Lampung character.

An insight into the effect of different parameters for optimizing parallel reservoir simulation performance

Parallel simulation significantly reduces runtime in oil reservoir simulations, enabling higher-resolution analysis and a better understanding of reservoir behavior. This study comprehensively evaluates how simulation time, hardware configuration, and various physical parameters impact the efficiency, scalability, and performance metrics such as run time reduction (RTR) and number of optimal cores (NOC) in parallel simulations. This study introduced the reduced simulation time (RST) concept, an innovative approach that enhances parallel simulation efficiency, and dynamic load balance. In this approach, simulations were performed using only 5% of the total simulation time. The findings reveal that in 80% of the models, switching to RST mode has no effect on NOC but significantly reduces overall execution time in most cases. In complex models, this time reduction can be as high as 60% compared to serial mode. In the remaining 20%, the number of cores in the total simulation time (TST) mode may be higher than in the RST mode, but this difference can be disregarded due to limited hardware resources and no change in RTR.

Multi-priority scheduling algorithm for scientific workflows in cloud

The public cloud environment has emerged as a promising platform for exe-cuting scientific workflows. These executions involve leasing virtual machines (VMs) from public services for the duration of the workflow. The structure of the workflows significantly impacts the performance of any proposed scheduling approach. A task within a workflow cannot begin its execution before receiving all the required data from its preceding tasks. In this paper, we introduce a multi-priority scheduling approach for executing workflow tasks in the cloud. The key component of the proposed approach is a mechanism that logically or-ders and groups workflow tasks based on their data dependencies and locality. Using the proposed approach, the number of available VMs influences the num-ber of groups (partitions) obtained. Based on the locality of each group’s tasks, the priority of each group is determined to reduce the overall execution delay and improve VM utilization. As the results demonstrate, the proposed approach achieves a significant reduction in both execution costs and time in most scenar-ios

Feature-consistent coplane-pair correspondence- and fusion-based point cloud registration

It is an important and challenging task to register two point clouds, and the estimated registration solution can be applied in 3D vision. In this paper, an outlier removal method is first proposed to delete redundant coplane-pair correspondences for constructing three feature-consistent coplane-pair correspondence subsets. Next, Rodrigues’ formula and a scoring-based method are adopted to solve the representative registration solution of each correspondence subset. Then, a robust fusion method is proposed to fuse the three representative solutions as the final registration solution. Based on typical testing datasets, comprehensive experimental results demonstrated that with good registration accuracy, our registration algorithm achieves significant execution time reduction effect when compared with the state-of-the-art methods.

A pre-trained language model for emergency department intervention prediction using routine physiological data and clinical narratives

IntroductionThe urgency and complexity of emergency room (ER) settings require precise and swift decision-making processes for patient care. Ensuring the timely execution of critical examinations and interventions is vital for reducing diagnostic errors, but the literature highlights a need for innovative approaches to optimize diagnostic accuracy and patient outcomes. In response, our study endeavors to create predictive models for timely examinations and interventions by leveraging the patient’s symptoms and vital signs recorded during triage, and in so doing, augment traditional diagnostic methodologies. MethodsFocusing on four key areas—medication dispensing, vital interventions, laboratory testing, and emergency radiology exams, the study employed Natural Language Processing (NLP) and seven advanced machine learning techniques. The research was centered around the innovative use of BioClinicalBERT, a state-of-the-art NLP framework. ResultsBioClinicalBERT emerged as the superior model, outperforming others in predictive accuracy. The integration of physiological data with patient narrative symptoms demonstrated greater effectiveness compared to models based solely on textual data. The robustness of our approach was confirmed by an Area Under the Receiver Operating Characteristic curve (AUROC) score of 0.9. ConclusionThe findings of our study underscore the feasibility of establishing a decision support system for emergency patients, targeting timely interventions and examinations based on a nuanced analysis of symptoms. By using an advanced natural language processing technique, our approach shows promise for enhancing diagnostic accuracy. However, the current model is not yet fully mature for direct implementation into daily clinical practice. Recognizing the imperative nature of precision in the ER environment, future research endeavors must focus on refining and expanding predictive models to include detailed timely examinations and interventions. Although the progress achieved in this study represents an encouraging step towards a more innovative and technology-driven paradigm in emergency care, full clinical integration warrants further exploration and validation.

Comparison of Automation Testing On Card Printer Project Using Playwright And Selenium Tools

The quality of the software is greatly determined by the testing phase, which involves various test cases that can be conducted through manual testing and automation testing. Manual testing is performed manually without using automation scripts, whereas automation testing is conducted using automation scripts. ABC is a company that operates globally in the field of access control, with the Card Printer being one of the menus used in access control. In the development process of this software, both manual and automation testing phases are carried out. The automation testing process employs the Selenium tool, which has proven to be time-consuming and poses challenges when running numerous test cases. This research aims to develop automation testing using Playwright to address the long execution time issue encountered with Selenium. The research utilizes the Card Printer project in the development of automation testing and adopts the Agile methodology. The result of developing automation testing using Playwright was successfully applied to 12 test cases. Additionally, the time analysis between Playwright and Selenium showed that Playwright has a total execution time of 4.9 minutes, which is faster compared to Selenium's total execution time of 8.3 minutes. With faster execution times, Playwright can be considered a tool in the development of automation testing.

Organized Optimization Integration Validation Model for Internet of Things (IoT)-Based Real-Time Applications

Emerging technology like the Internet of Things (IoT) has great potential for use in real time in many areas, including healthcare, agriculture, logistics, manufacturing, and environmental surveillance. Many obstacles exist alongside the most popular IoT applications and services. The quality of representation, modeling, and resource projection is enhanced through interactive devices/interfaces when IoT is integrated with real-time applications. The architecture has become the most significant obstacle due to the absence of standards for IoT technology. Essential considerations while building IoT architecture include safety, capacity, privacy, data processing, variation, and resource management. High levels of complexity minimization necessitate active application pursuits with variable execution times and resource management demands. This article introduces the Organized Optimization Integration Validation Model (O2IVM) to address these issues. This model exploits k-means clustering to identify complexities over different IoT application integrations. The harmonized service levels are grouped as a single entity to prevent additional complexity demands. In this clustering, the centroids avoid lags of validation due to non-optimized classifications. Organized integration cases are managed using centroid deviation knowledge to reduce complexity lags. This clustering balances integration levels, non-complex processing, and time-lagging integrations from different real-time levels. Therefore, the cluster is dissolved and reformed for further integration-level improvements. The volatile (non-clustered/grouped) integrations are utilized in the consecutive centroid changes for learning. The proposed model’s performance is validated using the metrics of execution time, complexity, and time lag.

Word frequency statistics based on MapReduce on serverless platforms

This paper investigates the application of serverless computing in conjunction with the MapReduce framework, particularly in machine learning (ML) tasks. The MapReduce programming model has been widely used to process large-scale datasets by simplifying parallel and distributed data processing. This study explores how the combination of these two technologies can provide more efficient and cost-effective ML solutions. Through a detailed analysis of serverless environments and the MapReduce framework, this paper shows how the combination can advance the fields of cloud computing and machine learning. The experimental part includes the implementation of Map-Reduce model on a serverless platform, exploring the impact of different parameter settings on performance and improving efficiency by optimizing the data processing flow. In addition, the paper analyzes the use of memory and CPU resources and derives the relationship between dataset size, memory consumption and processor configuration and execution time. Through these experiments and analyses, this paper provides an empirical basis and theoretical support for the optimization of cloud computing frameworks.

SRCNN: Stacked-Residual Convolutional Neural Network for Improving Human Activity Classification Based on Micro-Doppler Signatures of FMCW Radar

Current methods for daily human activity classification primarily rely on optical images from cameras or wearable sensors. Despite their high detection reliability, camera-based approaches suffer from several drawbacks, such as low-light conditions, limited range, and privacy concerns. To address these limitations, this article proposes the use of a frequency-modulated continuous wave radar sensor for activity recognition. A stacked-residual convolutional neural network (SRCNN) is introduced to classify daily human activities based on the micro-Doppler features of returned radar signals. The model employs a two-layer stacked-residual structure to reuse former features, thereby improving the classification accuracy. The model is fine-tuned with different hyperparameters to find a trade-off between classification accuracy and inference time. Evaluations are conducted through training and testing on both simulated and measured datasets. As a result, the SRCNN model with six stacked-residual blocks and 64 filters achieves the best performance, with accuracies exceeding 95% and 99% at 0 dB and 10 dB, respectively. Remarkably, the proposed model outperforms several state-of-the-art CNN models in terms of classification accuracy and execution time on the same datasets.

Advanced differential evolution for gender-aware English speech emotion recognition

Speech emotion recognition (SER) technology involves feature extraction and prediction models. However, recognition efficiency tends to decrease because of gender differences and the large number of extracted features. Consequently, this paper introduces a SER system based on gender. First, gender and emotion features are extracted from speech signals to develop gender recognition and emotion classification models. Second, according to gender differences, distinct emotion recognition models are established for male and female speakers. The gender of speakers is determined before executing the corresponding emotion model. Third, the accuracy of these emotion models is enhanced by utilizing an advanced differential evolution algorithm (ADE) to select optimal features. ADE incorporates new difference vectors, mutation operators, and position learning, which effectively balance global and local searches. A new position repairing method is proposed to address gender differences. Finally, experiments on four English datasets demonstrate that ADE is superior to comparison algorithms in recognition accuracy, recall, precision, F1-score, the number of used features and execution time. The findings highlight the significance of gender in refining emotion models, while mel-frequency cepstral coefficients are important factors in gender differences.

A methodology to correlate river inflows in state sampling Monte Carlo simulation

This paper proposes a methodology to sample inflow states of run-of-the-river hydropower plants that are spatially correlated, preserving the statistical dependence among them, in Non-Sequential Monte Carlo Simulation (MCS). The technique used to correlate the inflows of hydro plants in the Brazilian synthetic series generation model, which is based on the correlation matrix and Cholesky Decomposition, is adapted to be used in the sampling stage. Nataf transformation is used for addressing non-normality of the inflow data. The solution method is such that no assumption about the data probability distribution is necessary. Although the methodology is applied to inflows in this paper, it can be applied to any other quantity, such as winds. A case study is proposed to evaluate the correlation of the sampled inflows and the impact on the reliability indices. Besides, it is investigated the effect when inflows states are clustered to reduce the number of states in the system for better computational efficiency. Case study shows that the reliability indices obtained with the proposed methodology are within the confidence interval of the indices calculated by the Sequential MCS with an execution time as fast as the Non-Sequential one, while the non-consideration of correlation causes very significant errors. Simulation with 50 clusters produces equally good results with a reduction of 75 % of the execution time. Sensitivity analysis showed that halving the number of clusters maintains the accuracy of the results, while 10 clusters results in overestimating system reliability. Different degrees of correlation have been simulated, showing that the higher the correlation among the hydro plants, the less reliable the system becomes.