Decrease In Error Rate Research Articles

A-182 Applications of artificial intelligence optical character recognition in laboratory clerical functions offsetting staffing shortages and error reduction

Abstract Background The clinical laboratory profession has been dealing with a decade long, and now critical retention and staffing problem. The average national turnover rate for laboratory technicians is 17%, and the highest turnover rate exhibited for phlebotomists is 36%. While turnover rates have an impact on a laboratory, or hospitals budget, more importantly they have a substantial impact on quality and error rates, particularly in the pre-analytical stage of testing; with studies showing up to 65.09% of errors impacting the pre-analytical phase. Methods In an effort to remedy this high error rate, a meta-analysis was performed on paper order entry and clerical pre-analytical error, in parallel with performance and accuracy data of advanced optical character recognition driven by artificial intelligence and machine learning (AI OCR). Results AI OCR studies demonstrated an untrained accuracy percentage, to include handwriting of 91.08%, and when trained utilizing both relational and hierarchal logic data bases accuracy increased to 98.4%. The rate at which AI OCR processed and classified medical information was 3-4 pages per second. It is estimated that 99.9%-100% accuracy can be achieved with additional form field training and mapping. The level of accuracy and efficiency for laboratory paper order-entry utilizing AI OCR can also be increased with the addition of natural language processing and data recognition algorithms when combined with logic databases containing information such as medical record numbers, ICD-10 codes, and laboratory test compendiums. Conclusions The performance of this technology demonstrates a strong application to reduce the burden of clerical paper order entry, while addressing the 30.8% dissatisfaction rate regarding manual entry processes among lab professionals surveyed. Moreover, AI OCR clerical data classification, extraction and entry processes augmented with human verification steps examined, demonstrated overall accuracy of 99.99%. The application of AI OCR in laboratory pre-analytical processes has the potential to improve test order accuracy and efficiency, ultimately impacting the quality of patient care and operational costs for healthcare facilities. When examined through the lens of staff retention, studies show that 80% of laboratory employees feel that a workplace with the necessary tools and technology positively impacts their job satisfaction. While organizations investing in technologies that drive both job efficiency and satisfaction see a 125% return on investment through productivity. Demonstrating that the application of sophisticated technologies that both increase productivity and decrease error rates have a substantial impact on job satisfaction and ultimately retention. Although laboratory clerical data and order entry processes were examined for this meta-analysis, the aggregate data compiled showed that the application of AI OCR in clerical processes could also have a substantial impact on the 25.9% annual turnover rate for hospitals as a whole in the United States.

Attention improvement to transcranial alternating current stimulation at gamma frequency over the right frontoparietal network: a preliminary report.

Applying transcranial alternating current stimulation (tACS) at 40 Hz to the frontal and parietal regions, either unilaterally (left or right) or bilaterally, can improve cognitive dysfunctions. This study aimed to explore the influence of tACS at gamma frequency over right fronto-parietal (FP) region on attention. The analysis is based on retrospective data from a clinical intervention. We administered test of variables of attention (TOVA; visual mode) to 44 participants with various neuropsychiatric diagnoses before and after 12 sessions of tACS treatment. Alternating currents at 2.0 mA were delivered to the electrode positions F4 and P4, following the 10-20 EEG convention, for 20 mins in each session. We observed significant improvement across 3 indices of the TOVA, including reduction of variability in reaction time (p = 0.0002), increase in d-Prime (separability of targets and non-targets; p = 0.0157), and decrease in commission error rate (p = 0.0116). The mean RT and omission error rate largely remained unchanged. Artificial injection of tACS at 40 Hz over right FP network may improve attention function, especially in the domains of consistency in performance, target/non-target discrimination, and inhibitory control.

Construction of University Football Basic Teaching and Training System Based on Object Detection and Tracking Algorithm

Football, often referred to as soccer outside the UK and Europe, is a highly popular activity on many university campuses. As a result, football training programs are offered across many institutions, training students at a range of skill levels. Therefore, this paper investigates a deep learning (DL)-based algorithm for recognizing and tracking targets in sports training videos. The study specifically addresses those videos tailored to identifying small targets, by generating new multi-scale features and modifying anchor point generation rules. Experimental results demonstrate the algorithm's strong performance in tracking football targets. Compared to Histogram of Oriented Gradients, the DL-based model achieved a 29.58% increase in accuracy and a 39.68% decrease in error rates when recognizing football player movement features. This algorithm accurately locates the edge contours of a football player's movements, meaning that universities can, and should, actively reform football teaching and training to enhance teaching effectiveness by utilizing this powerful algorithm.

Enhancing Quantum Key Distribution Protocols for Extended Range and Reduced Error

Abstract this paper proposes an optimized Quantum Key Distribution (QKD) protocol using entanglement swapping techniques to extend transmission range and improve error correction. Additionally, integrates an advanced error correction technique which is Low Density Parity Check (LDPC) and multi-hop quantum repeaters for more enhancement of the protocol performance. Hybrid Quantum Classical Error Correction Methods is applied ensuring compatibility and optimal performance and to manage the increased complexity. Simulations prove that 25% improvement in transmission distance with entanglement swapping. 50% improvement with advanced error correction and a 100% improvement with multi-hop quantum repeaters compared to existing protocols. These discoveries are supported by both theoretical analysis and simulation results, indicating significant decreases in error rates and extensions in maximum transmission distances. Comparative analysis made with existing protocols and that demonstrated the superiority of proposed approach in terms of extended secure communication distance, higher key generation rate and improved resilience to attacks.

Improved BioCyc Operon Prediction: Revisiting the Operon Prediction Problem.

Operon prediction is a valuable component of microbial-genome annotation because operon organization can yield inferences about gene function, and because knowledge of operon structure can aid the interpretation of gene expression data. We present a number of improvements to the existing Pathway Tools operon predictor based mostly on 7 new features that we hypothesized would increase its performance. The new features include shared Gene Ontology biological process terms, similarity of codon usage and GC content, correlated gene expression, and shared protein complex. We evaluated the proposed 7 new features and found that the addition of 6 of them improved the performance of the operon predictor from 79.55% to 83.49%, a decrease in error rate of 19.3%. When gene expression data was not included, the accuracy decreased to 82.547, still an improvement of 14.7%. One of the proposed features as well as a previously used feature had no effect and were removed. Although some of the new features had strong predictive value individually, when combined with the other features they did not have a large impact on predictive accuracy, suggesting that they were not independent from the other features.

Hybrid electronic record: An error reduction strategy for diverse medical prescription formats.

This project is part of a broader effort to develop a new electronic registry for ophthalmology in the KwaZulu-Natal (KZN) province in South Africa. The registry should include a clinical decision support system that reduces the potential for human error and should be applicable for our diversity of hospitals, whether electronic health record (EHR) or paper-based. Post-operative prescriptions of consecutive cataract surgery discharges were included for 2019 and 2020. Comparisons were facilitated by the four chosen state hospitals in KZN each having a different system for prescribing medications: Electronic, tick sheet, ink stamp and handwritten health records. Error types were compared to hospital systems to identify easily-correctable errors. Potential error remedies were sought by a four-step process. There were 1307 individual errors in 1661 prescriptions, categorised into 20 error types. Increasing levels of technology did not decrease error rates but did decrease the variety of error types. High technology scripts had the most errors but when easily correctable errors were removed, EHRs had the lowest error rates and handwritten the highest. Increasing technology, by itself, does not seem to reduce prescription error. Technology does, however, seem to decrease the variability of potential error types, which make many of the errors simpler to correct.Contribution:Regular audits are an effective tool to greatly reduce prescription errors, and the higher the technology level, the more effective these audit interventions become. This advantage can be transferred to paper-based notes by utilising a hybrid electronic registry to print the formal medical record.

Separable contextual graph neural networks to identify tailgating-oriented traffic congestion

Identifying largescale traffic congestion with thousands of robots is a complicated task involving structured-data graphical modeling, and it has recently gained more research attention. This study aims to model traffic metrics that exhibit negative correlations in specific spatiotemporal contexts of automated material handling systems in semiconductor manufacturing. Numerous studies have focused on detecting vehicle congestion using multivariate time-series data. However, they faced challenges regarding the understanding of the complex graph structural joint spatiotemporal dynamics of the traffic rails. Existing methods fail to distinguish between congestion caused by high traffic volumes and nondisruptive delivery robot operations, such as parking and hoisting. This study introduces a novel approach to detect tailgating-oriented congestion using separable contextual graph neural networks (SC-GNNs). The proposed method employs deviation-based anomaly detection with attentive GNNs to capture spatial correlations and temporal dependencies. SC-GNNs were proposed for dual metrics with distinct characteristics, and dynamic thresholds were applied for each GNN to ensure reliable and interpretable anomaly detection. To demonstrate the outperformance of the proposed method, we conducted simulation experiments via a high-fidelity simulator similar to actual semiconductor fabrication. The experimental results showed that our method achieved both a 36% reduction in the false positive rate and a 32% decrease in the equal error rate compared to the best performance of existing state-of-the-art anomaly detection models. Overall, the proposed approach significantly improved the accuracy and interpretability of anomaly detection in multivariate time series, particularly effective in the context of identifying anomalies related to traffic congestion tailgating.

Exploring the Impact of Online Translation on Writing Revision among Chinese Non-English Major Students

The present study examined the efficacy of online translation as an auxiliary revision tool for Chinese non-English majors, assessing its influence on writing performance and the students’ perceptions of its role in the revision process. This study employed writing task, questionnaire survey, and semi-structured interview to examine the efficacy of online translation as an auxiliary revision tool for Chinese non-English majors, assessing its influence on writing performance and the students’ perceptions of its role in the revision process. A total of 94 Chinese non-English majors who learn English as a foreign language (EFL) participated in the study. The results indicate that compared with those who revise their texts independently, those Chinese non-English majors who refer to online translation in the process of revision, made significantly more changes at the below-clause and clause and above levels in terms of revision domain, with increased additions and substitutions. Additionally, having access to online translation in the process of revision, Chinese non-English majors significantly increased their text length in final drafts, with decreased error rate, more low-frequency and sophisticated words, and varied lexical choices. Besides, virtually most of the participants use online translation frequently in English writing activities with a generally positive attitude towards the integration of online translation into English writing revision. For English teachers, they can consider allowing students to use online translation in the process of revision and giving students sufficient guidance on how best to realize its fullest potential. For Chinese non-English majors, they’d better keep improving their self-learning ability and double-check the online translation output by using other resources.

Optimizing Treatment Quality in Head and Neck Cancer Therapy: Impact of Adaptive Intensity-Modulated Radiotherapy on Plan Quality and Tumor Volume Changes

Objective: This research analyses whether volumetric changes and PTV shifting affect adaptive intensity-modulated radiotherapy (IMRT) plan quality and head and neck cancer therapy. Methods: The research was carried out at the Sohag Cancer Centre in Egypt, including a sample of 36 individuals diagnosed with head and neck squamous cell carcinoma (HNSCC). Patients were chosen on the basis of the following criteria: locally progressed and node-positive malignancy, oropharyngeal cancer, oral cavity cancer, and unknown primary. The research used dynamic intensity-modulated radiation therapy (D-IMRT) to establish treatment plans and Siemens SOMATOM DEFINITION CT scanners to acquire 3D anatomical images. A range of 1.64 to 2.12 Gy was observed for the median dosage per fraction. Using the conformance index (CI) and the homogeneity index, the quality of the plan was assessed (HI). The threshold for statistical significance was set at 0.05. Results: The study analysed patients with unilateral and bilateral HNSCC tumors, with a majority having bilateral tumors. The findings demonstrated that the tumor PTV was significantly reduced as a consequence of the suggested adaptive radiation techniques. Both the homogeneity index and the conformity index were used to evaluate the plan's quality (HI). The maximal homogeneity index (HI) was reached after 7 and 14 fractions, respectively. The positive correlation between PTV reduction and CI after 7 fractions, while a negative correlation found after fractions 14 and 21. It was also shown that HI is positively correlated with PTV changes after 7 and 14. Conclusion: The use of adaptive radiotherapy in intensity-modulated radiation therapy (IMRT) treatment planning improves plan quality and decreases error rates due to tumor margin movement. Following radiation fractions, PTV shifting affects the homogeneity index (HI).

Study on the Equivalent Stiffness of a Local Resonance Metamaterial Concrete Unit Cell

This paper addresses the pressing scientific problem of accurately predicting the equivalent stiffness of local resonance metamaterial concrete unit cells. Existing theoretical models often fail to capture the nuanced dynamics of these complex systems, resulting in suboptimal predictions and hindering advancements in engineering applications. To address this deficit, this paper proposes a novel two-dimensional theoretical vibration model that incorporates shear stiffness, a crucial yet often overlooked parameter in previous formulations. Motivated by the need for improved predictive accuracy, this paper rigorously validates a new theoretical model through numerical simulations, considering variations in material parameters and geometric dimensions. The analysis reveals several key findings: firstly, the equivalent stiffness increases with elastic modulus while the error rate decreases, holding geometric parameters and Poisson’s ratio constant. Secondly, under fixed geometric parameters and coating elastic modulus, the equivalent stiffness rises with an increasing Poisson’s ratio, accompanied by a decrease in error rate. Importantly, this paper demonstrates that the proposed model exhibits the lowest error rate across all parameter conditions, facilitating superior prediction of equivalent stiffness. This advancement holds significant implications for the design and optimization of metamaterial structures in various engineering applications for vibration isolation, with promising enhancements of performance and efficiency.

A Review on Possibilities of Artificial Intelligence in construction Industry

Abstract: At present if we see construction industries, they are facing lot of problems which finally create project delay. The progression of the construction industry is strictly limited by the countless complex challenges it faces such as cost and time overruns, health and safety, productivity and labour scarcities. Working on these complex problems of construction industries is not easy in many aspects but if these problems are countered with the help of AI results will be robust. Therefore, many research efforts in the Architecture, Engineering, and Construction community have recently tried introducing AI into building asset management processes. AI is also able to make the process of decision making faster, decrease error rates, and increase computational efficiency. Among the different AI techniques, machine learning, pattern recognition, and deep learning have recently acquired considerable attention and are establishing themselves as a new class of intelligent methods for use in structural engineering. A present review of existing literature on AI applications in the construction industry such as activity monitoring, risk management, resource and waste optimization was conducted. Additionally, the opportunities and challenges of AI applications in construction were identified and presented in this study. This study focuses on AI and its application in construction industry.

Abstract 337: Balanced-strand sequencing for highly efficient duplex variant calling in circulating tumor DNA

Abstract Several approaches for the detection of circulating tumor DNA (ctDNA) are anchored in the accurate identification of cancer-specific single nucleotide variants (SNVs) in plasma. Such approaches are limited in ultra-low tumor fraction (TF) contexts, such as early cancer detection or the identification of postoperative minimal residual disease, where ctDNA SNVs are difficult to distinguish against background sequencing error. ctDNA detection in these critical low TF contexts is further limited by the sparse amount of cell free DNA (cfDNA) in plasma (Zviran et al., 2020). To reduce single-stranded artifact and base calling errors, duplex sequencing can be used to jointly analyze corresponding Watson and Crick strands to detect true, double-stranded variants. Recent work has demonstrated that duplex sequencing can decrease error rates to 10−7 (Hoang et al., 2016) and can be deployed in the whole-genome sequencing (WGS) setting (Cheng et al., 2023). A significant limitation for existing duplex techniques is the need to analytically pair matching DNA strands through exhaustive sequencing, resulting in inefficient use of scarce input material, particularly in plasma cfDNA. These techniques typically capture 1-5% of sequenced molecules, restricting duplex coverage depth. We present balanced-strand sequencing, a PCR-free approach that leverages the hydrogen bonds of complementary DNA to carry matched native strands directly into sequencing, enabling massively scalable duplex sequencing. The approach relies on Ultima Genomics sequencing, where DNA denaturation is not required prior to clonal amplification. In balanced strand sequencing, double-stranded DNA is partitioned and clonally amplified on sequencing beads through emulsion-PCR. Each double-stranded DNA molecule contributes to a single sequencing read, allowing for a linear increase in duplex recovery with increasing sequencing depth. The approach does not require redundant sequencing and therefore maximizes unique molecule throughput. We applied balanced strand sequencing to cfDNA, input as low as 5 ng, and achieved 7-35x whole-genome duplex coverage, roughly 20-fold higher than our prior duplex WGS. We further developed a machine-learning guided single read mutation calling framework, which when combined with duplex error suppression, reduced error rates to 10−7 and enabled accurate ctDNA detection at concentrations <10−5. Use of our analysis framework in APOBEC-mutant bladder cancer plasma samples allowed us to identify APOBEC signatures in plasma and platinum exposure signatures indicative of prior chemotherapy treatment. Altogether, balanced strand sequencing radically enhanced duplex efficiency and has broad applications across cancer. In cfDNA applications, balanced strand sequencing enhances error-correction in tumor-informed ctDNA detection and paves the way for non-tumor informed WGS ctDNA detection. Citation Format: Adam Widman, Alexandre P. Cheng, Aaron Sossin, Srinivas Rajagopalan, Majd Al Assaad, Sunil Deochand, Zoe Steinsnyder, Dina Manaa, Melissa Marton, Catherine Reeves, Itai Rusinek, Eti Meiri, Omer Barad, Zohar Shipony, Shlomit Gilad, Ariel Jaimovich, Michael Sigouros, Jyothi Manohar, Abigail King, David Wilkes, John Otilano, Olivier Elemento, Bishoy M. Faltas, Juan M. Mosquera, Dan A. Landau. Balanced-strand sequencing for highly efficient duplex variant calling in circulating tumor DNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 337.

Evaluating Machine Learning-Based Approaches in Land Subsidence Susceptibility Mapping

Land subsidence (LS) due to natural and human-driven forces (e.g., earthquakes and overexploitation of groundwater) has detrimental and irreversible impacts on the environmental, economic, and social aspects of human life. Thus, LS hazard mapping, monitoring, and prediction are important for scientists and decision-makers. This study evaluated the performance of seven machine learning approaches (MLAs), comprising six classification approaches and one regression approach, namely (1) classification and regression trees (CARTs), (2) boosted regression tree (BRT), (3) Bayesian linear regression (BLR), (4) support vector machine (SVM), (5) random forest (RF), (6) logistic regression (LogR), and (7) multiple linear regression (MLR), in generating LS susceptibility maps and predicting LS in two case studies (Semnan Plain and Kashmar Plain in Iran) with varying intrinsic characteristics and available data points. Multiple input variables (slope, aspect, groundwater drawdown, distance from the river, distance from the fault, lithology, land use, topographic wetness index (TWI), and normalized difference vegetation index (NDVI)), were used as predictors. BRT outperformed the other classification approaches in both case studies, with accuracy rates of 75% and 74% for Semnan and Kashmar plains, respectively. The MLR approach yielded a Mean Square Error (MSE) of 0.25 for Semnan plain and 0.32 for Kashmar plain. According to the BRT approach, the variables playing the most significant role in LS in Semnan Plain were groundwater drawdown (20.31%), distance from the river (17.11%), land use (14.98%), NDVI (12.75%), and lithology (11.93%). Moreover, the three most important factors in LS in Kashmar Plain were groundwater drawdown (35.31%), distance from the river (23.1%), and land use (12.98%). The results suggest that the BRT method is not significantly affected by data set size, but increasing the number of training set data points in MLR results in a decreased error rate.

Binding of Precursors to Replicator Assemblies Can Improve Replication Fidelity and Mediate Error Correction.

Copying information is vital for life's propagation. Current life forms maintain a low error rate in replication, using complex machinery to prevent and correct errors. However, primitive life had to deal with higher error rates, limiting its ability to evolve. Discovering mechanisms to reduce errors would alleviate this constraint. Here, we introduce a new mechanism that decreases error rates and corrects errors in synthetic self-replicating systems driven by self-assembly. Previous work showed that macrocycle replication occurs through the accumulation of precursor material on the sides of the fibrous replicator assemblies. Stochastic simulations now reveal that selective precursor binding to the fiber surface enhances replication fidelity and error correction. Centrifugation experiments show that replicator fibers can exhibit the necessary selectivity in precursor binding. Our results suggest that synthetic replicator systems are more evolvable than previously thought, encouraging further evolution-focused experiments.

Binding of Precursors to Replicator Assemblies Can Improve Replication Fidelity and Mediate Error Correction

AbstractCopying information is vital for life‘s propagation. Current life forms maintain a low error rate in replication, using complex machinery to prevent and correct errors. However, primitive life had to deal with higher error rates, limiting its ability to evolve. Discovering mechanisms to reduce errors would alleviate this constraint. Here, we introduce a new mechanism that decreases error rates and corrects errors in synthetic self‐replicating systems driven by self‐assembly. Previous work showed that macrocycle replication occurs through the accumulation of precursor material on the sides of the fibrous replicator assemblies. Stochastic simulations now reveal that selective precursor binding to the fiber surface enhances replication fidelity and error correction. Centrifugation experiments show that replicator fibers can exhibit the necessary selectivity in precursor binding. Our results suggest that synthetic replicator systems are more evolvable than previously thought, encouraging further evolution‐focused experiments.

Hybrid approach of type-2 fuzzy inference system and PSO in asthma disease

This research work presents a hybrid approach combining a type-2 fuzzy inference system with particle swarm optimization (PSO) to develop a type-2 fuzzy optimized inference system, specifically tailored for asthma patient data. Addressing the inherent uncertainty in medical diagnostics, this model enhances traditional type-1 fuzzy logic by incorporating ambiguity into linguistic variables and utilizing type-2 fuzzy if-then rules. The system is trained to minimize diagnostic error in asthma disease identification. Applied to a dataset comprising eight medical entities from asthma patients, the model demonstrates substantial accuracy improvements. Numerical computations validate the system, showing a decrease in error rate from 1.445 to 0.03, indicating a significant enhancement in diagnostic precision. These results underscore the potential of our model in medical diagnostic problems, providing a novel and effective tool for tackling the complexities of asthma diagnosis.

Image Object and Scene Recognition Based on Improved Convolutional Neural Network

In recent years, due to the continuous optimization of network structure and the emergence of large-scale data, Convolutional neural network has made breakthroughs in a series of applications of computer vision. Based on this, the Convolutional neural network is improved and optimized. The improved convolutional neural network is introduced into image Object detection and scene recognition, and image object detection is carried out by combining sliding window fusion and Convolutional neural network. The image scene recognition model is constructed by using potential object area recognition and Convolutional neural network Transfer learning. Using different data sets to verify the algorithm, the research results show that in Group1 and Group2, the error rate of the multi column convolutional neural network fused by sliding window is reduced by about 25% compared with the single column convolutional neural network. As the group with the smallest decrease in error rate, Group3 also achieved a 9% decrease in error rate. The fitness rate of object detection algorithm is gradually stable after 7 runs, reaching about 9.8%, and its operation effect is obviously better than other algorithms. The multi column convolutional neural network fused by sliding window is more adaptive to the training data set, and gets better recognition effect in the algorithm operation. However, the image scene recognition model based on potential object area recognition algorithm and Convolutional neural network has good convergence. The average recognition time for image scenes is 1.5356s. The recognition speed is fast and stable, which can effectively solve the problem of multi-scale image scene recognition.

Effect of yoga on cognitive function of hypertensive personnel in India’s Central Industrial Security Force

ABSTRACT Background: The Central Industrial Security Force (CISF) protects India’s strategic assets, a role burdened with stress that may affect cognitive functions in hypertensive personnel. This study investigates yoga’s potential to enhance cognitive abilities in these individuals. Methods: In our study, 88 male CISF personnel at the Indian Space Research Organisation, Bangalore, aged 25­60 years, were divided into yoga and control groups. The yoga group underwent a 3-month regimen, daily 1-h sessions for 5 days each week. These sessions included a combination of loosening exercises, breathing practices, asanas, pranayama, and relaxation techniques. Cognitive evaluations were performed using Psychological Experimental Building Language software, encompassing the digit span, Stroop task, and Tower of London tests. Results: Postintervention, the yoga group marginally improved in the digit forward task without significant difference (p = 0.12), while in the digit backward task, they showed a slight decline compared to the control group’s pronounced decrease (p = 0.03). In the Stroop task, across all conditions-reaction time for the congruent condition (RTC), neutral condition (RTN), and incongruent condition (RTI)-the yoga group demonstrated either stable or slightly increased reaction times. In contrast, the control group exhibited significant increases in reaction times across these conditions. Significant differences were observed across conditions (RTC: p < 0.001, r = −0.67; RTN: p < 0.001, r = −0.65; RTI: p < 0.001, r = −0.70). Post-intervention, the yoga group demonstrated improved accuracy across all conditions: congruent (96%), incongruent (80%), and neutral (92%). Post-intervention, the yoga group significantly reduced their errors compared to the control group, as indicated by a notable decrease in error rate (p < 0.001, r = -0.68). Postintervention, the yoga group exhibited enhanced planning abilities (p = 0.0038, r = −0.36). Conclusion: Our study highlights the potential of yoga-based interventions in boosting cognitive abilities among hypertensive members of India’s CISF. However, a deeper exploration is essential to comprehensively understand yoga’s cognitive impact.

Performance Analysis of the Ada-Boost Algorithm For Classification of Hypertension Risk With Clinical Imbalanced Dataset

High blood pressure, another name for hypertension, is a condition in which the pressure inside the blood arteries keeps rising. One of the most exciting fields of study is data mining, which is being developed to increase its acceptance in organizations, particularly in the health sector [5]. Nowadays, the AdaBoost method is a crucial feature classification approach in machine learning. AdaBoost assigns weights to the samples during the training phase that increase as the error rate rises and reduce as the error rate decreases. The classification process on the hypertension risk using clinical Healthy Family Index dataset, obtained of 8 attributes. Requires two partitions, 70% training data and 30% testing data. Binary classifications are applied “Normal” and “Hypertension”. The most important features of the 10 attributes are chosen based on the ranking value of the ReliefF feature selection algorithm, with the 5 best-ranked attributes. ReliefF feature selection can distinguish between classes and identify the most relevant feature with a value exceeding a specified threshold. The high confusion matrix results also show that the AdaBoost algorithm applied in this study has good performance. This is also supported by pre-processing data which is carefully processed in order to not affect the final classification result. The initial preprocessing step is to data cleaning, missing value handling, data normalization, feature selection using relief-F, imbalanced dataset handling using oversampling technique, classification process with adaBoost algorithm, test and score and confusion matrix.

Individual tree inventory based on uncrewed aerial vehicle data: how to utilise stand-wise field measurements of diameter for calibration?

Uncrewed aerial vehicles (UAV) have great potential for use in forest inventories, but in practice they can be expensive for relatively small inventory areas as a large number of field measurements are needed for model construction. One proposed solution is to transfer previously constructed models to a new inventory area and to calibrate these with a small number of local field measurements. Our objective was to compare calibration of general models and the construction of new models to determine the best approach for UAV-based forest inventories. Our material included field measurements and UAV-based laser scanning data, from which individual trees were automatically identified. A general mixed-effects model for diameter at breast height (DBH) had been formulated earlier based on data from a geographically wider area. It was calibrated to the study area with field measurements from 2–10 randomly selected calibration trees. The calibrated diameters were used to calculate the diameter of a basal area median tree (DGM), tree volumes, and the volume of all trees at plot-level. Next, new DBH-models were formulated based on the 2–10 randomly selected trees and calibrated with plot-level random effects estimated during model construction. Finally, plot-specific height-diameter regression models were formulated by randomly selecting 10 trees from each plot. Calibration reduced the prediction errors of all variables. An increase in the number of calibration trees decreased error rates by 1–6% depending on the variable. Calibrated predictions from the general mixed-effects model were similar to the separately formulated mixed-effects models and plot-specific regression models.