Precise Values Research Articles

A Framework for Prediction of Type II Diabetes through Ensemble Stacking Model

In order to prevent long term complications of diabetes its early diagnosis is crucial. With Increasing advances in Artifical Intelligence (AI) and Machine Learning(ML) researchers are increasingly focusing on using them for early diagnosis of diseases.AI and ML has significant potential for early prediction of type 2 diabetes. In this article we have described a ML based framework for prediction of type 2 diabetes -Improved Ensemble Learning with Dimensionality Reduction Model (IELDR) and discussed its result. An IELDR algorithm is an Auto encoder-based feature extraction method with ensemble learning. The experiments were carried out using the LS_diabetes dataset. LS_diabetes dataset containing 374 records with 35 features related to lifestyle and stress. Accuracy, precision, specificity, sensitivity, f1 score, roc and Mathew correlation coefficient (MCC) were measured. After this results were tested and validated using Diabetes_2019 dataset and PIMA diabetes dataset. The IELDR model showed results in terms accuracy, precision, specificity, sensitivity, f1 score, roc and Mathew correlation coefficient (MCC) of 98.67%, 95.24%, 100%, 98.18%, 97.56%, 99.09% and 0.97 respectively. In comparison with PIMA diabetes dataset, LS_diabetes dataset showed an accuracy, precision, sensitivity, specificity, f1-score,roc and mcc value by 17.96%,13.15% 40.22%,5.59%,28.38%,22.09% and 0.4 respectively. The IELDR model achieved the best result on the LS_diabetes dataset showed an accuracy, sensitivity, roc and mcc value improved by 1.82%, 1.58%, 3.01%and 0.04 % compared to the Diabetes_2019 dataset .This proposed IELDR system predicts the risk of type 2 diabetes in a healthy person based on the person’s current lifestyle pattern. This system can be helpful for early prediction of type2 diabetes.

Except-Extremes Ranked Set Sampling for Estimating the Population Variance with Two Applications of Real Data Sets

The ranked set sampling (RSS) procedure was initially established by McIntyre (1952) for estimating the mean of forage and pasture yield as more precise than simple random sampling (SRS). Recently, Aldrabseh and Ismail (2023) suggested the except extreme RSS (EERSS) approach as a modification to RSS for estimating the population mean. In this paper, a new estimator of the population variance is proposed using the EERSS method. The mean squared error and bias equations of the new estimator are derived. When the underlying distribution is non-symmetric, a simulation study is conducted to evaluate the suggested estimator relative to SRS and RSS, based on the same number of measured units, in terms of the relative precision and bias values for several sample sizes. For symmetric distributions, the exact values of the bias and relative precision of the EERSS variance estimator are evaluated. Two real datasets are utilized to illustrate the performance of the suggested variance estimator. It is found that the EERSS variance estimator is more efficient than the SRS estimator and more precise than RSS in most cases, especially for small set sizes.

The Gromov–Wasserstein Distance Between Spheres

AbstractThe Gromov–Wasserstein distance—a generalization of the usual Wasserstein distance—permits comparing probability measures defined on possibly different metric spaces. Recently, this notion of distance has found several applications in Data Science and in Machine Learning. With the goal of aiding both the interpretability of dissimilarity measures computed through the Gromov–Wasserstein distance and the assessment of the approximation quality of computational techniques designed to estimate the Gromov–Wasserstein distance, we determine the precise value of a certain variant of the Gromov–Wasserstein distance between unit spheres of different dimensions. Indeed, we consider a two-parameter family $$\{d_{{{\text {GW}}}p,q}\}_{p,q=1}^{\infty }$$ { d GW p , q } p , q = 1 ∞ of Gromov–Wasserstein distances between metric measure spaces. By exploiting a suitable interaction between specific values of the parameters p and q and the metric of the underlying spaces, we are able to determine the exact value of the distance $$d_{{{\text {GW}}}4,2}$$ d GW 4 , 2 between all pairs of unit spheres of different dimensions endowed with their Euclidean distance and their uniform measure.

Automated anomaly detection for categorical data by repurposing a form filling recommender system

Data quality is crucial in modern software systems, like data-driven decision support systems. However, data quality is affected by data anomalies, which represent instances that deviate from most of the data. These anomalies affect the reliability and trustworthiness of software systems, and may propagate and cause more issues. Although many anomaly detection approaches have been proposed, they mainly focus on numerical data. Moreover, the few approaches targeting anomaly detection for categorical data do not yield consistent results across datasets. In this paper, we propose a novel anomaly detection approach for categorical data named LAFF-AD (LAFF-based Anomaly Detection), which takes advantage of the learning ability of a state-of-the-art form filling tool (LAFF) to perform value inference on suspicious data. LAFF-AD runs a variant of LAFF that predicts the possible values of a suspicious categorical field in the suspicious instance. LAFF-AD then compares the output of LAFF to the recorded values in the suspicious instance, and uses a heuristic-based strategy to detect categorical data anomalies. We evaluated LAFF-AD by assessing its effectiveness and efficiency on six datasets. Our experimental results show that LAFF-AD can accurately determine a high range of data anomalies, with recall values between 0.6 and 1 and a precision value of at least 0.808. Furthermore, LAFF-AD is efficient, taking at most 7000 s and 735 ms to perform training and prediction, respectively.

Graph-based, dynamics-preserving reduction of (bio)chemical systems.

Complex dynamical systems are often governed by equations containing many unknown parameters whose precise values may or may not be important for the system's dynamics. In particular, for chemical and biochemical systems, there may be some reactions or subsystems that are inessential to understanding the bifurcation structure and consequent behavior of a model, such as oscillations, multistationarity and patterning. Due to the size, complexity and parametric uncertainties of many (bio)chemical models, a dynamics-preserving reduction scheme that is able to isolate the necessary contributors to particular dynamical behaviors would be useful. In this contribution, we describe model reduction methods for mass-action (bio)chemical models based on the preservation of instability-generating subnetworks known as critical fragments. These methods focus on structural conditions for instabilities and so are parameter-independent. We apply these results to an existing model for the control of the synthesis of the NO-detoxifying enzyme Hmp in Escherichia coli that displays bistability.

An integrated framework for driving risk evaluation that combines lane-changing detection and an attention-based prediction model

Objective In recent years, the increase in traffic accidents has emerged as a significant social issue that poses a serious threat to public safety. The objective of this study is to predict risky driving scenarios to improve road safety. Methods On the basis of data collected from naturalistic driving real-vehicle experiments, a comprehensive framework for identifying and analyzing risky driving scenarios, which combines an integrated lane-changing detection model and an attention-based long short-term memory (LSTM) prediction model, is proposed. The performance of the 4 machine learning methods on the CULane data set is compared in terms of model running time and running speed as evaluation metrics, and the ultrafast network with the best performance is selected as the method for lane line detection. We compared the performance of LSTM and attention-based LSTM on the basis of the prediction accuracy, recall, precision, and F1 value and selected the better model (attention-based LSTM) for risky scenario prediction. Furthermore, Shapley additive explanation analysis (SHAP) is used to understand and interpret the prediction results of the model. Results In terms of algorithm efficiency, the running time of the ultrafast lane detection network only requires 4.1 ms, and the average detection speed reaches 131 fps. For prediction performance, the accuracy rate of attention-based LSTM reaches 96%, the precision rate is 98%, the recall rate is 96%, and the F1 value is 97%. Conclusions The improved attention-based LSTM model is significantly better than the LSTM model in terms of convergence speed and prediction accuracy and can accurately identify risky scenarios that occur during driving. The importance of factors varies by risky scenario. The characteristics of the yaw rate, speed stability, vehicle speed, acceleration, and lane change significantly influence the driving risk, among which lane change has the greatest impact. This study can provide real-time risky scenario prediction, warnings, and scientific decision guidance for drivers.

Regional multi-station real-time time transfer using an undifferenced multi-GNSS network solution

Real-time time transfer is the basis for the time synchronization and establishment and maintenance of time scales. In this contribution, we present a regional multi-station real-time time transfer approach, in which observations from multi-global navigation satellite system (GNSS) received at regional multi-station are integrated to conduct an undifferenced network solution. Consequently, the simultaneous estimation of receiver clock offsets for multiple stations becomes possible, enabling the execution of real-time time transfer. One week of observation data from five multi-GNSS Experiment (MGEX) stations located in Europe are selected to generate four links (distances from 919.7 to 2153.4 km), and four schemes are designed, i.e., GPS-only, BDS-3-only, Galileo-only, and GPS/BDS-3/Galileo (GCE) solutions, respectively. Experiment results show that the mean number of observations, time dilution of precision (TDOP) values, and standard deviation (STD) of clock offset difference time series between the Center for Orbit Determination in Europe (CODE) and the estimated solutions for four time links are 90, 76, 72, 239 and 0.34, 0.38, 0.39, 0.21 and 0.039, 0.050, 0.043, 0.036 ns for GPS-only, BDS-3-only, Galileo-only, and GCE solutions, respectively. When the averaging time is shorter than 7680 s, the frequency stability of GCE solutions shows the best performance. The mean frequency stability of four time links at 7680 s is 6.59 × 10–15,7.25 × 10–15, 6.88 × 10–15 and 5.96 × 10–15 for GPS-only, BDS-3-only, Galileo-only, and GCE solutions, respectively. The GCE solution shows the best performance among the four schemes in terms of the number of observations, TDOP, STD, and frequency stability. The experiment results demonstrate that this approach is suitable for regional multi-station real-time time transfer.

Bayesian optimized multimodal deep hybrid learning approach for tomato leaf disease classification

Manual identification of tomato leaf diseases is a time-consuming and laborious process that may lead to inaccurate results without professional assistance. Therefore, an automated, early, and precise leaf disease recognition system is essential for farmers to ensure the quality and quantity of tomato production by providing timely interventions to mitigate disease spread. In this study, we have proposed seven robust Bayesian optimized deep hybrid learning models leveraging the synergy between deep learning and machine learning for the automated classification of ten types of tomato leaves (nine diseased and one healthy). We customized the popular Convolutional Neural Network (CNN) algorithm for automatic feature extraction due to its ability to capture spatial hierarchies of features directly from raw data and classical machine learning techniques [Random Forest (RF), XGBoost, GaussianNB (GNB), Support Vector Machines (SVM), Multinomial Logistic Regression (MLR), K-Nearest Neighbor (KNN)], and stacking for classifications. Additionally, the study incorported a Boruta feature filtering layer to capture the statistically significant features. The standard, research-oriented PlantVillage dataset was used for the performance testing, which facilitates benchmarking against prior research and enables meaningful comparisons of classification performance across different approaches. We utilized a variety of statistical classification metrics to demonstrate the robustness of our models. Using the CNN-Stacking model, this study achieved the highest classification performance among the seven hybrid models. On an unseen dataset, this model achieved average precision, recall, f1-score, mcc, and accuracy values of 98.527%, 98.533%, 98.527%, 98.525%, and 98.268%, respectively. Our study requires only 0.174 s of testing time to correctly identify noisy, blurry, and transformed images. This indicates our approach's time efficiency and generalizability in images captured under challenging lighting conditions and with complex backgrounds. Based on the comparative analysis, our approach is superior and computationally inexpensive compared to the existing studies. This work will aid in developing a smartphone app to offer farmers a real-time disease diagnosis tool and management strategies.

Predictive analytics in customer behavior: Anticipating trends and preferences

In order to effectively manage their customers, businesses need to thoroughly analyze the costs and advantages associated with various alternative expenditures and investments and determine the most effective way to allocate resources to marketing and sales activities over time. Those in charge of making decisions will reap the benefits of decision support models that estimate the value of the customer portfolio and tie expenses to customers' purchasing behavior. In the current work, various machine learning algorithms such as Decision Tree (DT), Random Forest (RT), Logistic Regression (LR), Support Vector Machines (SVM), and gradient boosting are used to predict customer behavior. The evaluation criteria considered in the work include precision, recall, F1-Score, and ROC-AUC. The accuracy values obtained for DT, RT, LR, SVM, and gradient boosting are 0.787, 0.806, 0.826, 0.826, and 0.823, respectively. The results emphasize RT and LR's good performance, while the values of 0.620, 1, 0.766, and 0.878 for the precision, recall, F1-score, and ROC-AUC score outperform the rest. The novelty of this work lies in employing a comprehensive set of machine learning algorithms to predict customer behavior, with a particular emphasis on the superior performance of RF and LR models, as demonstrated by their high precision, recall, F1-score, and ROC-AUC values.

Benchmark dose determining airborne crystalline silica particles based on A549 lung-cell line survival in an in vitro study

Crystalline silica has emerged as a prominent occupational toxicant over extended periods, leading to the development of lung disease and cancer. The objective of this investigation is to establish a benchmark dose (BMD) for crystalline silica micro and nanoparticles based on the dehydrogenase activity of the A549 lung-cell line. The impact of exposure to crystalline silica micro-particles (C–SiO2 MPs) and crystalline silica nanoparticles (C–SiO2 NPs) on A549 epithelial lung cells was examined for durations of 24 and 72 h to evaluate cell viability using the MTT (3-(4, 5-dimethylthiazolyl-2)-2, 5-diphenyltetrazolium bromide) assay. The determination of dose-response and BMD was carried out through the BMD software v 3.2. The findings reveal a dose-dependent relationship between cell viability and both C–SiO2 MPs and -NPs. The BMDL values for 24 h treatment of C–SiO2 MPs and -NPs were determined to be 2.26 and 0.97 µg/ml, respectively, based on exponential models. Correspondingly, these values were found to be 1.17 and 0.85 µg/ml for the 72 h treatment. This investigation underscores the significance of particle size as a contributing factor in assessing occupational health risks. Moreover, the utilization of BMDL can facilitate the determination of more precise values for occupational exposures by considering various parameters associated with particle presence.

A transfer learning based patent transfer prediction model: Discussion about the condition of China and U.S.

In the same patent classification, there are significant differences in the patent transfer quantity for different countries. For the countries with small patent transfer quantities, the performance of the patent transfer prediction is worse than that of high-transfer countries. To improve the performance of the patent prediction model learned for the low-transfer countries, we propose a transfer learning based patent transfer prediction model, for which the high-transfer countries are the source domain and the low-transfer countries are the target domain. In the model, the textual feature of each patent text is embedded using BERT-CNN with the parameters fine-tuned on the target domain from the source domain. The textual features are optimized to minimize the domain loss of Maximum Mean Discrepancy between source and target domain. What is more, we extract graph feature vectors by embedding the patent node in a three-layer heterogeneous network which consists of patent nodes, International Patent Classification nodes, consumer nodes, consumer location nodes and their relations. Finally, the patent text vectors, structural feature vectors, and graph feature vectors are spliced together to predict target domain patents transfer or not. The performance of the model is verified by some comparative experiments. The experimental results show that the proposed model outperforms significantly the baseline models in the values of Precision, Recall, and F1 of patent data improve by 6.47%, 6.54%, and 6.58% on average, respectively.

Deep learning-based sow posture classifier using colour and depth images

Assessing sow posture is essential for understanding their physiological condition and helping farmers improve herd productivity. Deep learning-based techniques have proven effective for image interpretation, offering a better alternative to traditional image processing methods. However, distinguishing transitional postures such as sitting and kneeling is challenging with only conventional top-view RGB images. This study aimed to develop and compare different deep learning-based sow posture classifiers using different architectures and image types. Using Kinect v.2 cameras, RGB and depth images were collected from 9 sows housed individually in farrowing crates. A total of 26,362 images were manually labelled by posture: “standing”, “kneeling”, “sitting”, “ventral recumbency” and “lateral recumbency”. Different deep learning algorithms were developed to detect sow postures from three types of images: colour (RGB), depth (depth image transformed into greyscale), and fused (colour-depth composite images). Results indicated that the ResNet-18 model presented the best results and that including depth information improved the performance of all models tested. Depth and fused models achieved higher accuracies than the models using only RGB images. The best model used only depth images as input and presented an accuracy of 98.3 %. The mean precision and recall values were 97.04 % and 97.32 %, respectively (F1-score = 97.2 %). The study shows improved posture classification using depth images. Future research can improve model accuracy and speed by expanding the database, exploring fused methods and computational models, considering different breeds of sows, and incorporating more postures. These models can be integrated into computer vision systems to automatically characterise sow behavior.

Evaluating the Contribution of Model Complexity in Predicting Robustness in Synthetic Genetic Circuits.

The design-build-test-learn workflow is pivotal in synthetic biology as it seeks to broaden access to diverse levels of expertise and enhance circuit complexity through recent advancements in automation. The design of complex circuits depends on developing precise models and parameter values for predicting the circuit performance and noise resilience. However, obtaining characterized parameters under diverse experimental conditions is a significant challenge, often requiring substantial time, funding, and expertise. This work compares five computational models of three different genetic circuit implementations of the same logic function to evaluate their relative predictive capabilities. The primary focus is on determining whether simpler models can yield conclusions similar to those of more complex ones and whether certain models offer greater analytical benefits. These models explore the influence of noise, parametrization, and model complexity on predictions of synthetic circuit performance through simulation. The findings suggest that when developing a new circuit without characterized parts or an existing design, any model can effectively predict the optimal implementation by facilitating qualitative comparison of designs' failure probabilities (e.g., higher or lower). However, when characterized parts are available and accurate quantitative differences in failure probabilities are desired, employing a more precise model with characterized parts becomes necessary, albeit requiring additional effort.

The Stupid, the Ridiculous, the Camp: How Goat Simulator's “Messy” Design Facilitates Queer Play

This article identifies the queer potentialities of Goat Simulator by exploring how the gameplay mechanics can be understood as “stupid” and “Camp.” Moving away from representational notions of queerness, I argue that queering a game requires an alternative way to create and play. Often, the normative method of designing a videogame prioritizes technological precision and purposeful gameplay experiences. However, Goat Simulator does not fix its errors, nor does it have a cohesive narrative, instead it incorporates brokenness and goallessness as core gameplay mechanics. Goat Simulator embraces messiness as an alternative—and arguably queer—way to design and play a videogame. I conclude by arguing the importance of brokenness and stupidity as a useful pushback against normative game design and gameplay values of precision, perfection, and seriousness.

Modelling of Joule-Thomson cooling effect using a modified shift-DeepONet method for predicting hydrate onset during CO2 sequestration

Carbon Storage in underground depleted oil and gas reservoirs play a key role in reducing anthropogenic Carbon Dioxide (CO2) emissions around the world. Injection of CO2 at high pressure into a low pressure depleted gas formations containing methane (CH4) leads to Joule-Thompson (J-T) cooling, possibly resulting in injection blocking precipitation of CO2 or CH4 hydrates. Here, we present a deep neural operator-based approach to model the J-T effects during CO2 injection in depleted reservoirs to demonstrate the effectiveness of Deep Operator Networks (DeepONets) in modelling piece-wise functions with sharp changes in gradient for cases of varying permeabilities. We propose a modified version of the shift-DeepONet model using non-linear constructors in the neural network architecture to accurately model temperature and pressure profiles in space and time. The modified shift-DeepONet model achieves a higher accuracy in modelling temperature for permeability in the range of 1.01mD to 101mD with three to up to ten times improvement in reducing Relative L2 (RL2) norm and with a testing RL2 norm of 3.8 × 10−4 compared to 2.97 × 10−3 for the Vanilla DeepONet. Using the temperature and pressure profiles, estimates are made for hydrate onset in space and time. The proposed modified shift-DeepONet method achieves high accuracy in hydrate classification with precision values > 0.99, recall values > 0.86 and F1 scores >0.92 in classification of CO2 or CH4 hydrate onset during CO2 injection into depleted gas reservoirs.

Parameter identification of Johnson–Cook constitutive model based on genetic algorithm and simulation analysis for 304 stainless steel

Addressing the significant discrepancy between actual experimental cutting force and its predicted values derived from traditional constitutive model parameter identification methods, a reverse identification research of the Johnson–Cook (J-C) constitutive model for 304 stainless steel was conducted via genetic algorithm. Considering actual cutting zone and the influence of feed motion on the rake (flank) angle, an unequal division shear zone model was established to implement the theoretical calculation for shear zone stress. Through cutting experiments, the spindle speed was negatively correlated with the cutting force at first, and then became positively correlated; The empirical formula (EXP model) for turning force was corrected, revealing that the EXP model was unable to provide optimal predicted values for cutting force. The influence of the J-C constitutive parameter C on the cutting morphology was firstly investigated through simulation analysis, and determined an appropriate value for C, then obtained the precise values for the other four constitutive parameters by genetic algorithm. Moreover, the simulated values of cutting force in JC1 model (obtained from the Split Hopkinson Pressure Bar test) and JCM model (the improved model using genetic algorithm) were obtained by three-dimensional (3-D) simulation via FEM software. The results indicated that, the maximum error between actual experimental cutting force and its simulated values (by JCM model) was 14.8%, with an average error of 6.38%. These results outperformed the JC1 and EXP models, suggesting that the JCM model identified via genetic algorithm was more reliable.

Application value of domestic single-port robot in nephron sparing surgery

Objective: To explore the application value of the domestic precision ®single-port robotic system in nephron sparing surgery. Methods: The clinical data of patients with renal masses underwent nephron sparing surgery using the domestic precision ®single-port robotic system at the PLA General Hospital, Gulou Hospital Affiliated to Nanjing University School of Medicine, Zhongnan Hospital of Wuhan University and the First Affiliated Hospital of Nanchang University from September to November 2023 were retrospectively included. Perioperative clinical data, pathological examination results, and postoperative complications were summarized. Results: A total of 12 patients were included, including 8 males and 4 females, with 26-75 (56±16) years. Body mass index (BMI) was (25.1±2.7) kg/m2. There were 6 cases on the left side and 6 case on the right side. The surgical approach was transabdominal in 9 cases and retroperitoneal in 3 case. The maximum diameter of the lesions was (2.7±0.7) cm, the warm ischemia time [M (Q1, Q3)] was 19 (15, 26) minutes, the surgical time was 180 (149, 216) minutes, and the intraoperative blood loss was 50 (28, 100) ml. Postoperative visual analogue scale (VAS) was (2.9±1.5) points. Postoperative pathology revealed malignant renal clear cell carcinoma in 9 cases, with nuclear grading of 3 cases for Grade 1, 3 cases for Grade 2, and 3 cases for Grade 3. Eight cases of pathological TNM staging were pT1aN0M0 and 1 case was pT3aN0M0, with no cancer at the resection margin. Three cases showed benign renal vascular smooth muscle lipoma. There were no postoperative blood transfusions and no complications such as fever, urine leakage and poor wound healing. Conclusion: The prliminary experience reveals that the domestic precision ®single-port laparoscopic robotic system has good clinical application value in urological nephron sparing surgery.

Integrating Fuzzy C-Means Clustering and Explainable AI for Robust Galaxy Classification

The classification of galaxies has significantly advanced using machine learning techniques, offering deeper insights into the universe. This study focuses on the typology of galaxies using data from the Galaxy Zoo project, where classifications are based on the opinions of non-expert volunteers, introducing a degree of uncertainty. The objective of this study is to integrate Fuzzy C-Means (FCM) clustering with explainability methods to achieve a precise and interpretable model for galaxy classification. We applied FCM to manage this uncertainty and group galaxies based on their morphological characteristics. Additionally, we used explainability methods, specifically SHAP (SHapley Additive exPlanations) values and LIME (Local Interpretable Model-Agnostic Explanations), to interpret and explain the key factors influencing the classification. The results show that using FCM allows for accurate classification while managing data uncertainty, with high precision values that meet the expectations of the study. Additionally, SHAP values and LIME provide a clear understanding of the most influential features in each cluster. This method enhances our classification and understanding of galaxies and is extendable to environmental studies on Earth, offering tools for environmental management and protection. The presented methodology highlights the importance of integrating FCM and XAI techniques to address complex problems with uncertain data.

Fully‐Optimized Quantum Metrology: Framework, Tools, and Applications

AbstractThis tutorial introduces a systematic approach for addressing the key question of quantum metrology: For a generic task of sensing an unknown parameter, what is the ultimate precision given a constrained set of admissible strategies. The approach outputs the maximal attainable precision (in terms of the maximum of quantum Fisher information) as a semidefinite program and optimal strategies as feasible solutions thereof. Remarkably, the approach can identify the optimal precision for different sets of strategies, including parallel, sequential, quantum SWITCH‐enhanced, causally superposed, and generic indefinite‐causal‐order strategies. The tutorial consists of a pedagogic introduction to the background and mathematical tools of optimal quantum metrology, a detailed derivation of the main approach, and various concrete examples. As shown in the tutorial, applications of the approach include, but are not limited to, strict hierarchy of strategies in noisy quantum metrology, memory effect in non‐Markovian metrology, and designing optimal strategies. Compared with traditional approaches, the approach here yields the exact value of the optimal precision, offering more accurate criteria for experiments and practical applications. It also allows for the comparison between conventional strategies and the recently discovered causally‐indefinite strategies, serving as a powerful tool for exploring this new area of quantum metrology.

Preliminary analysis of acoustic detection of the Red-throated Caracara in northern Costa Rica

The noisy Red-Throated Caracara (Ibycter americanus) is a species whose population has inexplicably declined across much of its range and is now rare in the Pacific and Caribbean slopes of Costa Rica. Advances in automatic acoustic detection have transformed bird ecology, allowing researchers to analyze bird populations using pattern matching algorithms, machine learning, and random forest models. Although these studies are limited in the country, it represents an area with great interdisciplinary potential for technological advances. This study focused on the use of Pattern Matching to detect the presence of the Red-Throated Caracara in northern Costa Rica using a large number of sound recordings and its validation with metrics such as Accuracy, Precision Negative predictive value, Sensitivity, Specificity and Unweighted average recall. The results showed a moderate performance of the model by obtaining accuracy and precision values of 0.71 compared to the values obtained in other investigations in which the reported model was used. Therefore, we suggest exploring new techniques and methods to improve the detection of the species, considering the particular acoustic structure, the repertoire of sounds of the species and similarities with vocalizations of other species. This similarity could indicate a supposed anti-predator defense behavior by “imitating” the sounds of other species with which it shares habitat. To optimize this acoustic detection, we recommend using complementary techniques such as noise filters that improve the quality and precision of the data.