Fixed-length interval estimation of population sizes: sequential adaptive Monte Carlo mark–recapture–mark sampling

Mark–recapture sampling schemes are conventional approaches for population size (N) estimation. In this paper, we mainly focus on providing fixed-length confidence interval estimation methodologies for N under a mark–recapture–mark sampling scheme, where, during the resampling phase, non-marked items are marked before they are released back in the population. Using a Monte Carlo method, the interval estimates for N are obtained through a purely sequential procedure with an adaptive stopping rule. Such an adaptive decision criterion enables the user to "learn” with the subsequent marked and newly tagged items. The method is then compared with a recently developed accelerated sequential procedure in terms of coverage probability and expected number of captured items during the resampling stage. To illustrate, we explain how the proposed procedure could be applied to estimate the number of infected COVID-19 individuals in a near-closed population. In addition, we present a numeric application inspired on the problem of estimating the population size of endangered monkeys of the Atlantic forest in Brazil.

Performance enhancement of eulerian video magnification

In this study, a linear and phase-based Eulerian video magnification (EVM) methods are developed to minimize magnified noises and processing time. The developed approaches utilize the Lanczos resampling algorithm to reduce the frames' size of the source video so that the size of the processed data is significantly reduced. Then spatial decomposition is applied to the resized frames. Subsequently, temporal filters with specific cut-off frequencies are also used to filter only the desired frequencies to be amplified and then add them to the decomposed frames. The magnified frames are processed by a wavelet denoising algorithm to locate distributed noise over the different frequency bands and then remove it. The resulted denoised-magnified frames are resized up and then reconstructed by the spatial synthesis process. The experiments show the superiority and effectiveness of the developed EVM approaches compared to the conventional ones and other related approaches in terms of the execution time and the quality of the magnified video. The developed EVM approach can be used in several applications such as the detection of human vital signs without contact so that it is very useful to avoid infection in several diseases such as Covid-19. Furthermore, it can be used in detection of human mood and lying detection, detection and localization of material and liquid variations. © 2023 University of Bahrain. All rights reserved.

Evaluation of AI Techniques for Detecting Deceptive Reviews in Cyberspace: A Study of Pre- and Post-COVID-19 Trends

With cyberspace's continuous evolution, online reviews play a crucial role in determining business success in various sectors, ranging from restaurants and hotels to e-commerce applications. Typically, a favorable review for a specific product draws in more consumers and results in a significant boost in sales. Unfortunately, a few businesses are using deceptive methods to improve their online reputation by using fake reviews of competitors. As a result, detecting fake reviews has become a difficult and ever-changing research field. Verbal characteristics extracted from review text, as well as nonverbal features such as the reviewer's engagement metrics, the IP address of the device, and so on, play an important role in detecting fake reviews. This article examines and compares various machine learning techniques for detecting deceptive reviews on various online platforms such as e-commerce websites such as Amazon and online review websites such as Yelp, among others. © 2023 IEEE.

THE STOCHASTIC APPROXIMATION METHOD FOR SEMI-RECURSIVE MULTIVARIATE KERNEL-TYPE REGRESSION ESTIMATION

In this research paper, we elaborate an extension of the semi-recursive kernel-type regression function estimator. We investigate the asymptotic properties of this estimator and compare them with non-recursive Nadaraya Watson regression estimator. From this perspective, we first calculate the bias and the variance of the proposed estimator which strongly depend on the choice of three parameters, namely the stepsizes (βn) and (γn) as well as the bandwidth (hn) chosen using one of the best methods of bandwidth selection, the bootstrap approach compared to the plug-in method. An appropriate choice of those parameters yields that, under some conditions, the MSE (Mean Squared Error) of the proposed estimator can be smaller than that of Nadaraya Watson's estimator. We corroborate our theoretical results through simulations studies and by considering two real dataset applications, the French Hospital Data of COVID-19 epidemic as well as the Plasmodium Falciparum Parasite Load (PL). © 2022 Ukrainian National Academy of Sciences. All rights reserved.

Borderline SARS-CoV-2 patients: the trace behind.

BACKGROUND: Effective and precise SARS-CoV-2 detection assays are crucial for maintaining regular hospital routines and identifying infected hospital employees and infected patients before hospital admission. Inconclusive PCR test results of potentially infectious borderline SARS-CoV-2 patients can confuse clinicians and delay appropriate infection control. OBJECTIVES AND STUDY DESIGN: In this retrospective study, we followed up borderline SARS-CoV-2 patients who were tested (from the second sample with the same method) at the Clinical Department of Clinical Microbiology. We aimed to determine the positivity conversion ratio within 7 days after inconclusive PCR test results. RESULTS: Out of 247 borderline patients, who were resampled and retested in the same laboratory, 60 patients (29.4%) showed conversion of the borderline viral load (inconclusive RT-PCR test) to a positive RT-PCR test result. CONCLUSIONS: Our results highlight the need for retesting of borderline patients with inconclusive SARS-CoV-2 results. Follow-up testing of inconclusive PCR results within 7 days can identify additional positive results and reduce the potential risk of intrahospital transmission.

Comparison of models' performance for predicting depression under COVID-19

The prediction on depression with respect to the effect of safety behaviour during COVID-19 has been seldom investigated previously. Furthermore, the effect of balance of data based on regenerating methods is hardly ever discussed. In this paper, the performance of prediction is investigated with data collected across 26 countries across the world in consideration of the effect on the variables of potential affecting factors. Specifically, the data was retrieved from the open-source dataset conducted by IGHI, at imperial college London, containing 384,250 valid individuals with measurement of age, gender, country, covid status, employment status and behaviour score. Five machine-learning methods, namely logistic regression, MLR, RF, SVM and k-NN, were used for comparison of the performance metric by different statistical measurements. Based on the six chosen latent factors, RF is evaluated as an optimal model with the highest F1 score (0.787) and G-mean (0.503) without using re-sampling methods. Linear SVM, on the other hand, has the highest specificity (0.998) with original data. Furthermore, although there is an increase in sensitivity, using oversampling and undersampling procedure reduce the prediction accuracy to a nearly random value (0.5). Overall, RF without re-sampling method is considered to be the comparatively best model for its highest sensitivity, precision, F1-score and G-mean among all five data analysis algorithms;especially for minimizing the false positive rate such that all patients with depression are successfully identified. These results shed light on the choice of models when applied on prediction of depression status under different scenario. © COPYRIGHT SPIE.

Identifying Protein Features and Pathways Responsible for Toxicity Using Machine Learning and Tox21: Implications for Predictive Toxicology

Humans are exposed to numerous compounds daily, some of which have adverse effects on health. Computational approaches for modeling toxicological data in conjunction with machine learning algorithms have gained popularity over the last few years. Machine learning approaches have been used to predict toxicity-related biological activities using chemical structure descriptors. However, toxicity-related proteomic features have not been fully investigated. In this study, we construct a computational pipeline using machine learning models for predicting the most important protein features responsible for the toxicity of compounds taken from the Tox21 dataset that is implemented within the multiscale Computational Analysis of Novel Drug Opportunities (CANDO) therapeutic discovery platform. Tox21 is a highly imbalanced dataset consisting of twelve in vitro assays, seven from the nuclear receptor (NR) signaling pathway and five from the stress response (SR) pathway, for more than 10,000 compounds. For the machine learning model, we employed a random forest with the combination of Synthetic Minority Oversampling Technique (SMOTE) and the Edited Nearest Neighbor (ENN) method (SMOTE+ENN), which is a resampling method to balance the activity class distribution. Within the NR and SR pathways, the activity of the aryl hydrocarbon receptor (NR-AhR) and the mitochondrial membrane potential (SR-MMP) were two of the top-performing twelve toxicity endpoints with AUCROCs of 0.90 and 0.92, respectively. The top extracted features for evaluating compound toxicity were analyzed for enrichment to highlight the implicated biological pathways and proteins. We validated our enrichment results for the activity of the AhR using a thorough literature search. Our case study showed that the selected enriched pathways and proteins from our computational pipeline are not only correlated with AhR toxicity but also form a cascading upstream/downstream arrangement. Our work elucidates significant relationships between protein and compound interactions computed using CANDO and the associated biological pathways to which the proteins belong for twelve toxicity endpoints. This novel study uses machine learning not only to predict and understand toxicity but also elucidates therapeutic mechanisms at a proteomic level for a variety of toxicity endpoints.

Analyzing the Impact of Resampling Approaches on Chest X-Ray Images for COVID-19 Identification in a Local Hierarchical Classification Scenario

Researchers dealing with real-world data - such as in the healthcare domain - tend to face class imbalance issues. More specifically, publicly available datasets containing Chest X-Ray (CXR) of Pneumonia diseases (including COVID-19) usually have an imbalanced class distribution. This dataset imbalance causes automatic diagnosis systems to classify majority classes with much more accuracy than the minority ones. Several resampling algorithms were proposed in the past to deal with the class imbalance issue. Hierarchical classifiers have also been proposed to increase the predictive performance of classifiers, but there is little research in the literature verifying if using existing resampling algorithms with hierarchical classifiers are a good alternative to improve classification performance. This work proposes an experimental classification schema to investigate the effectiveness of using resampling algorithms in the identification of COVID-19 and other types of Pneumonia through CXR images. The proposed schema uses resampling algorithms to rebalance the class distribution, in a Local Hierarchical Classification scenario. The experimental evaluation, which is supported by inferential statistical analysis, showed that using specific resampling algorithms with Local Hierarchical Classifiers brings a statistically significant increase to the macro-averaged F1-Score, and improves the predictive performance for the minority classes.

Effective Decision Making: Data Envelopment Analysis for Efficiency Evaluation in the Cloud Computing Marketplaces

Assessing business performance is a critical issue for practicing managers, and business performance has always been of interest to managers and researchers. In recent years, the world has experienced a rapid growth in the cloud computing service sector thanks to its benefits to business organizations and economic development. Therefore, the performance efficiency of this sector has been concerned as one of the keys in today’s economic environment. This study aimed to assess the performance efficiency of cloud computing service providers in the United States of America, one of the biggest global markets in terms of cloud computing, by applying the data envelopment analysis models. The efficiency of cloud computing providers was evaluated based on the assumption of the non-cooperative game among cloud computing providers in which providers selfishly choose the best strategy to maximize their payoff with three stages. In the first stage, the performance of these providers over the past period was measured by a super slack-based measure. In the second stage, the performance in the future period was predicted by the new data envelopment analysis model: the past–present–future model based on resampling. In the last stage, the efficiency improvement was investigated by adopting the Malmquist productivity index. The findings of this study indicated that the percentage of inefficient providers would increase from 10% in the period from 2017 to 2020 to 20% for 2021 and 2024. Moreover, 30% of providers showed a regress in performance efficiency over the research period of 2017 to 2024. The results of this study provide an insight picture to the decision-makers, and this research will fill the gap in the literature as the first study that measures and predicts the performance efficiency of cloud computing service providers, which will provide a helpful reference for future studies.

Measuring the Effectiveness of Adaptive Random Forest for Handling Concept Drift in Big Data Streams.

We are living in the age of big data, a majority of which is stream data. The real-time processing of this data requires careful consideration from different perspectives. Concept drift is a change in the data's underlying distribution, a significant issue, especially when learning from data streams. It requires learners to be adaptive to dynamic changes. Random forest is an ensemble approach that is widely used in classical non-streaming settings of machine learning applications. At the same time, the Adaptive Random Forest (ARF) is a stream learning algorithm that showed promising results in terms of its accuracy and ability to deal with various types of drift. The incoming instances' continuity allows for their binomial distribution to be approximated to a Poisson(1) distribution. In this study, we propose a mechanism to increase such streaming algorithms' efficiency by focusing on resampling. Our measure, resampling effectiveness (ρ), fuses the two most essential aspects in online learning; accuracy and execution time. We use six different synthetic data sets, each having a different type of drift, to empirically select the parameter λ of the Poisson distribution that yields the best value for ρ. By comparing the standard ARF with its tuned variations, we show that ARF performance can be enhanced by tackling this important aspect. Finally, we present three case studies from different contexts to test our proposed enhancement method and demonstrate its effectiveness in processing large data sets: (a) Amazon customer reviews (written in English), (b) hotel reviews (in Arabic), and (c) real-time aspect-based sentiment analysis of COVID-19-related tweets in the United States during April 2020. Results indicate that our proposed method of enhancement exhibited considerable improvement in most of the situations.