Percentage Of Precision Research Articles

Real-Time Multiface Mask Automatic Detection System in Classroom Learning using YOLOv4 Deep Learning

During the Covid-19 pandemic, students were required to wear masks in classroom learning. However, students often do not use masks, so they are prone to transmission of Covid-19. For this reason, this study proposes the development of a real-time multi-face mask automatic detection system in classroom learning using YOLOv4 deep learning. Experimental results on 22 samples of students who collected real-time/live video data every 3 minutes for 20 scenarios proved that the proposed system was successful in detecting objects wearing masks (PM) and not wearing masks (TPM) with the average percentage of precision was 95.63% for PM and 97.33% for TPM, the average percentage of recall was 61.61% for PM and 60.23% for TPM, and the average percentage of F-measure was 74.55% for PM and 74.00% for TPM. This results indicate an effective, valid and accurate proposed system for monitoring the use of masks in classroom learning easily and automatically.

Impact of Inaccurate Clinical Coding on Financial Outcome: A Study in a local hospital in Najran, Saudi Arabia.

Coding in medical procedures is crucial for patients, and errors made by hospital administration during the coding process can have an impact on both the financial results and the course of therapy. The present study aims to assess the accuracy of diagnostic and procedural codes as recorded by the hospital's coders and to also evaluate their impact on the hospital's revenue. In a local hospital in Najran, Saudi Arabia, a cross-sectional observational analysis was conducted on patients with a clinical coder. The percentage of precision and error following the re-coding of cases was calculated using a statistical analysis. Primary diagnosis was incorrectly coded in 57 (26 per cent) records, and secondary diagnosis was incorrectly coded in 21 (9.9 per cent) records. Inaccurate medical labelling has been seen in emergency rooms, operating rooms, and gynaecology facilities. The percentage of records with the most incorrect coding was found to be 16 (7.5 per cent) in the emergency room, 10 (4.7 per cent) in the surgical clinic, and 5 (2.3 per cent) in the gynaecology/OBS clinic. Six (2.8 per cent) records in the private clinic had inaccurate secondary diagnoses, followed by four (1.9 per cent) and two (1 per cent) records in nephrology. The percentage of inaccurate clinical codes in primary diagnoses reached (26.8 per cent) and the percentage of incorrect clinical codes in secondary diagnoses reached (9.9 per cent).

Implementation of Efficient Machine Learning Techniques for Prediction of Cardiac Disease using SMOTE

The leading cause of mortality in the world for the past few decades has been cardiovascular disease (CVD) or cardiac arrest, which is caused by conditions that affect the Heart. With Machine Learning (ML), predictive models are refined and adapted to test health industries in a more efficient way. ML algorithms will helpful to make an early prediction for those who may need to make a change to their lifestyle and receive appropriate medical treatment. Our research presents supervised ML classifiers capable of detecting significant features using machine learning techniques to predict Heart disease. An over sampling method is presented here for learning from unbalanced datasets. Several classification techniques are employed with different features in our prediction model. Performance metrics are analyzed to determine their effectiveness when applied to the UCI heart dataset using ML techniques by SMOTE oversampling; A maximum of 96.6 percentage of accuracy, 90 percentage of sensitivity, and 100 percentage of specificity and precision were achieved by the Random Forest method.

Comparison of the Results of the K-Nearest Neighbor (KNN) and Naïve Bayes Methods in the Classification of ISPA Diseases (Case Study: RSUD Fauziah Bireuen)

Acute Respiratory Infection or commonly called (ARI) is a disease caused by bacteria or viruses. (ARI) can attack all ages, especially children. This study aims to compare the accuracy of classification in (ARI) disease. The data used is data from patients affected by (ARI) disease at Fauziah Bireuen Hospital. K-Nearest Neighbors and Naïve Bayes can be used in the classification of (ARI) diseases. Measurement of accuracy using Confusion Matrix in the K-Nearest Neighbors method with the Eulidean Distance approach in the case of (ARI) disease classification obtained a percentage of precision of 91%, recall 84% and accuracy of 88%. While the Naïve Bayes method obtained a percentage of precision of 95%, recall 78% and accuracy of 86%. The results of the accuracy comparison of the two methods show that the K-Nearest Neighbors method has a higher accuracy rate than the Naïve Bayes method.

Melanoma Skin Cancer and Nevus Mole Classification using Intensity Value Estimation with Convolutional Neural Network

Melanoma skin cancer is one of the most dangerous and life-threatening cancer. Exposure to ultraviolet rays may damage the skin cell's DNA, which causes melanoma skin cancer. However, it is difficult to detect and classify melanoma and nevus mole at the immature stages. In this work, an automatic deep learning system is developed based on the intensity value estimation with a convolutional neural network model (CNN) to detect and classify melanoma and nevus mole more accurately. Since intensity levels are the most distinctive features for object or region of interest identification, the high-intensity pixel values are selected from the extracted lesion images. Incorporating those high-intensity features into the CNN improves the overall performance than the state-of-the-art methods for detecting melanoma skin cancer. To evaluate the system, we used 5-fold cross-validation. Experimental results show that a superior percentage of accuracy (92.58%), Sensitivity (93.76%), Specificity (91.56%), and Precision (90.68%) are achieved.

Investigating the Use of E-Dictionaries as Strategy to Improve Speaking Skill through Practical Activities of Precise Phonemes Realization: Case Study of EFL Undergraduate Students of Haripur University, Abatabad University, & Hazara University Mansehra, Pakistan

Reassessing the operationalization of e-dictionaries, the last epoch of English teaching-learning has revolutionized direct motivational currents and precise phoneme realization in undergraduates. Instructors have experienced incredible progress in improving speaking accuracy. E-dictionaries predispose undergraduates to imitate and emulate the accurate enunciation of mispronounced lexicons. Reconsidering the rapid evolution and practicing correct patterns of stereotypically distorted pronunciation have been reformed through operating E-dictionaries to rectify poor articulation. Instructors have reflected their insights regarding their prospective objectives of enhancing undergraduates’ accurate pronunciation, inflicting prompt spoken consciousness by integrating E-dictionaries into EFL classrooms for practical activities. The tenacity of the contemporary study was to determine the impact, influence, and efficacy of e-dictionary integration as an instructional strategy to measure altercations and accuracies during speech sound production. A randomization strategy and Praat quantitative data analysis were applied to determine the variation in the preference poll percentage of precision during the phonetic analysis of phoneme vocalization. The study has been categorically designed to monitor changes in English learners’ speaking, refining inaccurate pronunciation from 3.5% to 6.8% out of 10% through an Integration Model Application. The findings revealed substantial disparities during articulations. The results revealed significant differences in undergraduates’ precise realization of phonemes that improve excellence in enunciation, which leads to improvement in speaking faculty. After the integration of e-dictionaries, undergraduates recognized the distinction and amelioration of their conversations. To Investigate the use of e-dictionaries as strategy to improve speaking skill has helped to realize precise phonemes during practical activities.

Quantification of 7 cannabinoids in cannabis oil using GC-MS: Method development, validation and application to therapeutic preparations in Friuli Venezia Giulia region, Italy

The use of therapeutic cannabis preparations in Friuli Venezia Giulia is increasingly expanding. Even if cannabis oil finds its applications in several disorders affecting adults and children, it is not yet a standardized product and, to ensure the quality of the preparation, a quantitative analysis must be carried out before dispensing it to patients. Gas chromatography coupled to mass spectrometry (GC-MS) is a frequently used technique for quantification of cannabinoids, the active compounds of C. sativa. In this context, we developed a GC-MS method for the simultaneous quantification of 7 cannabinoids (CBD, CBDA, CBG, CBN, THCA, THCV and Δ9-THC) that is not time and sample consuming: 10 μL of cannabis oil were used for the sample preparation, that consists in derivatization of analytes through silylation. Calibration curves were built from 0.2 to 2 μg/mL. The percentage of accuracy and precision did not exceed the values recommended by validation guidelines. The limit of detection was 0.01 μg/mL; whereas the lower limit of quantification was 0.2 μg/mL. There was no carry over. The proposed GC-MS method showed good sensitivity, specificity, linearity, accuracy, precision and applicability to therapeutic preparations.

A Validated HPLC-Diode Array Detection Method for Therapeutic Drug Monitoring of Thiopurines in Pediatric Patients: From Bench to Bedside.

Thiopurine drugs are part of the therapeutic armamentarium for pediatric patients suffering from inflammatory bowel disease (IBD) and acute lymphoblastic leukemia (ALL). The therapeutic drug monitoring of these drugs, consisting of measurements of the thiopurine metabolites thioguanine nucleotides (TGN) and methylmercaptopurine nucleotides (MMPN) are used to optimize the effectiveness of treatment and prevent adverse effects. In this context, we developed and validated a high-performance liquid chromatography-diode array detection (HPLC-DAD) method for the simultaneous quantification of thiopurine metabolites according to the most recent International Council for Harmonisation (ICH) guidelines. The calibration curves were built in the clinically relevant range of concentrations for TGN of 300-12,000 nM and for MMPN of 3000-60,000 nM. The limit of detection and the lower limit of quantification were 100 and 300 nM for TGN and 900 and 3000 nM for MMPN, respectively. The percentage of inter-day accuracy and precision (CV%) varied between 85 and 104% and 1.6 and 13.8%. Stability was demonstrated for both of the metabolites for at least 50 days at -20 °C. The proposed HPLC-DAD method showed an appropriate selectivity, specificity, linearity, accuracy, precision and good applicability to samples from patients with IBD and ALL undergoing thiopurine treatment.

The value of artificial intelligence for detection and grading of prostate cancer in human prostatectomy specimens: a validation study

BackgroundThe Gleason grading system is an important clinical practice for diagnosing prostate cancer in pathology images. However, this analysis results in significant variability among pathologists, hence creating possible negative clinical impacts. Artificial intelligence methods can be an important support for the pathologist, improving Gleason grade classifications. Consequently, our purpose is to construct and evaluate the potential of a Convolutional Neural Network (CNN) to classify Gleason patterns.MethodsThe methodology included 6982 image patches with cancer, extracted from radical prostatectomy specimens previously analyzed by an expert uropathologist. A CNN was constructed to accurately classify the corresponding Gleason. The evaluation was carried out by computing the corresponding 3 classes confusion matrix; thus, calculating the percentage of precision, sensitivity, and specificity, as well as the overall accuracy. Additionally, k-fold three-way cross-validation was performed to enhance evaluation, allowing better interpretation and avoiding possible bias.ResultsThe overall accuracy reached 98% for the training and validation stage, and 94% for the test phase. Considering the test samples, the true positive ratio between pathologist and computer method was 85%, 93%, and 96% for specific Gleason patterns. Finally, precision, sensitivity, and specificity reached values up to 97%.ConclusionThe CNN model presented and evaluated has shown high accuracy for specifically pattern neighbors and critical Gleason patterns. The outcomes are in line and complement others in the literature. The promising results surpassed current inter-pathologist congruence in classical reports, evidencing the potential of this novel technology in daily clinical aspects.

Comparative Analysis of Methods K-Nearest Neighbor, Support Vector Machine and Decision Tree on Prediction Model of Turnover Intention

This study analyzed the comparison of methods on machine learning technique to predict turnover intention, turnover intention refers to intention or possibility of an employee to leave a company or the job that he is currently working on. The analysis with comparing the K-Nearest Neighbor, Support Vector Machine and Decision Tree methods, in an effort to predict turnover intention and reduce the risks of turnover intention in employee. The dataset used is taken from the Kaggle dataset, the dataset file is in the form of human resource (HR) data records with 311 data records with 24 features used out of 36 features. The dataset is obtained by using the K-Nearest Neighbor, Support Vector Machine and Decision Tree methods to calculate the accuracy, precision and sensitivity with a confusion matrix, the results of accuracy, precision and sensitivity from those three methods are compared and the method with the highest average percentage of accuracy, precision and sensitivity will be used as a prediction model.

N-Gram Feature for Comparison of Machine Learning Methods on Sentiment in Financial News Headlines


 
 
 Sentiment analysis is currently widely used in natural language processing or information retrieval applications. Sentiment analysis analysis can provide information related to outstanding financial news headlines and provide input to the company. Positive sentiment will also have a good impact on the development of the company, but negative sentiment will damage the company's reputation. This will affect the company's development. This study compares machine learning methods on financial news headlines with n-gram feature extraction. The purpose of this study was to obtain the best method for classifying the headline sentiment of the company's financial news. The machine learning methods compared are Multinomial Naïve Bayes, Logistic Regression, Support Vector Machine, multi-layer perceptron (MLP), Stochastic Gradient Descent, and Decision Trees. The results show that the best method is logistic regression with a percentage of f1-measure, precision, and recal of 73.94 %, 73.94 %, and 74.63 %. This shows that the n-gram and machine learning features have successfully carried out sentiment analysis. 
 
 

BERTFOREX: Cascading Model for Forex Market Forecasting Using Fundamental and Technical Indicator Data Based on BERT

Foreign exchange (Forex) rate forecasting is presently pursued by many researchers as it plays an important role in financial technology and business. The challenge of Forex research lies in its characteristics, fluctuation, non-linearity, and random walk phenomena. Several related studies generate forecasting signals using fundamental data (FD) and technical indicator data (TI) to support Forex. FD is an indicator of country economic conditions, while TI shows the price pattern-based signal. However, these two indicators pose two major limitations on their deployment. First, modeling a sequential neural network causes gradient vanishing and information loss. Second, FD exerts a significant impact on currency price upon its quarterly update and release. The second limitation is known as FD releasing problem. Moreover, Forex forecasting using FD and TI is typically conducted in an equal aggregation manner, resulting in inaccurate prediction due to unequal data changing frequency. In this work, we propose a cascading model for forex market forecasting using FD and TI based on BERT (BERTFOREX). The processing step of BERTFOREX are as follows: 1) BERT is applied to FD for extracting the hidden patterns over the designated period. 2) These extracted hidden patterns of FD are aggregated as additional weights to TI since FD frequency changes slower than that of TI. 3) BERT is again applied to extract the aggregated pattern within TI and FD over other influencing days. 4) To demonstrate the efficiency of BERTFOREX, the aggregated pattern is fed into a simple neural network for forecasting. The proposed method outperforms other methods in terms of percentage of correct signals, sensitivity, specificity, precision, and negative predictive value.

Prediction of Patients' Illness Based on Average Temperature and Rainfall In Az-Zainiyah Clinic Using Backpropagation Method

The purpose of this study is to predict the type of disease in students based on the average temperature and rainfall at the Az-Zainiyah clinic using the backpropagation method. With this research, we hope that the prediction/estimation process on the type of patient's disease using the backpropagation artificial neural network method provides a solution with precise and accurate prediction results based on data on many patients, average temperature, and previous rainfall. This research is carried out through a process of predicting the type of disease by collecting time-series data from patient visit reports. The raw data obtained are examined for completeness and quality of the data. Then, the data was analyzed and applied to an artificial neural network method to predict the type of patient's disease based on many patients, average temperature, and rainfall. Furthermore, the artificial neural network will be optimized by the backpropagation algorithm. From this study, we find that the percentage of precision obtained in the experimental type of pharyngitis/sore throat disease in November 2020 with an average precision percentage is 68.92%, the best precision percentage is 90.25%, and the worst precision percentage 47.59%. In December 2020, the average precision percentage is 41.46%, with the best precision percentage being 65.55%, and the worst precision percentage 4.92%. In the type of dermatitis/itching disease in November 2020, the best precision percentage is 98.81%, the average precision percentage is 68.31%, and the worst precision percentage is -21.57 %. For December 2020, the average precision percentage is -63.27%, the best precision percentage is 48.37%, and the worst precision percentage is -183.85%.

A Novel Approach for Outlier Detection in Multivariate Data

Outlier detection is a challenging task especially when outliers are defined by rare combinations of multiple variables. In this paper, we develop and evaluate a new method for the detection of outliers in multivariate data that relies on Principal Components Analysis (PCA) and three-sigma limits. The proposed approach employs PCA to effectively perform dimension reduction by regenerating variables, i.e., fitted points from the original observations. The observations lying outside the three-sigma limits are identified as the outliers. This proposed method has been successfully employed to two real life and several artificially generated datasets. The performance of the proposed method is compared with some of the existing methods using different performance evaluation criteria including the percentage of correct classification, precision, recall, and F-measure. The supremacy of the proposed method is confirmed by abovementioned criteria and datasets. The F-measure for the first real life dataset is the highest, i.e., 0.6667 for the proposed method and 0.3333 and 0.4000 for the two existing approaches. Similarly, for the second real dataset, this measure is 0.8000 for the proposed approach and 0.5263 and 0.6315 for the two existing approaches. It is also observed by the simulation experiments that the performance of the proposed approach got better with increasing sample size.

Line Detection and Monitoring System on Woodball Sport

In most sports today, the decisions taken by referees are supported by the use of electronic technology. The detection and monitoring system of the woodball lines serves the same function, helping the referee to take decisions and modernise woodball sports. The modern sport of wood balls depends entirely on out decisions. The line detection technology helps inform the judge whether the ball is OB (out of Bounds), while the monitoring system notifies players, judges, and the crowd whether or not the gating is effective. But the referee's manual assistance is always necessary to start the game after a good gating, since the player cannot touch the wood ball the whole time. Finally, the percentage of precision and error was achieved.

IMPLEMENTASI DEEP LEARNING MENGGUNAKAN CNN UNTUK SISTEM PENGENALAN WAJAH

Face recognition system is generally divided into two stages, face detection system, which is a pre-processing step followed by a facial recognition system. This step will quickly be done by humans but it takes a long time for the computer. This ability of humans is what researchers want to duplicate in the last few years as biometric technology in computer vision to create a model of face recognition in computer. Deep learning becomes a spotlight in developing machine learning, the reason because deep learning has reached an extraordinary result in computer vision. Based on that, the author came up with an idea to create a face recognition system by implementing deep learning using the CNN method and applying library open face. The result of this research is applying deep learning with the CNN method to classification process that resulting percentage of precision of 96%, recall percentage of 100%, and accuracy percentage of 99.8%.

Accuracy Improvement of Age Prediction Model Based on AutoML

Recommendations from websites are sometimes not suitable enough to fit the needs of different groups of people. One element in considering the recommended algorithm is the age range of users. To approach the goal of improving recommended algorithms in general, age prediction based on machine learning became the target of this research. We started by using AutoML from Google Cloud as the training tool and proportionally choosing 4,908 facial images of Asian people, which were categorized into six groups: images of the age below 10; images of the age from 10 to 20; images of the age from 20 to 30; images of the age from 30 to 40; images of the age from 40 to 50; images of the age from 50 to 60; and images of the age above 60. From the first training, we demonstrated 64.89 percentage of precision with the confidence threshold value of 0.5. After changing the threshold to 0.71, we optimized the result to 69.83 percentage of precision. By observing the result given by the deployment from Cloud training to an Android device, we found that the model differentiated Asian people aged from 10 to 40 most precisely, while it was relatively weak to distinguish people of the age from 50 to 60 and people of the age above 60. These results show an intelligent work of the prediction model on sifting people by ages and the strong relationship between the precision and the quantity of corresponding data. Its focus on distinguishing young people is suitable for our goal as young people are the main force of Internet users. By adjusting the composition of the dataset, we are looking for broader usage in other fields.

Application Of Naïve Bayes to Predict the Potential of Rain in Ternate City

The amount of rainfall that occurs cannot be determined with certainty, but it can be predicted or estimated. In predicting the potential for rain, data mining techniques can be used by classifying data using the naive Bayes method. Naïve Bayes algorithm is a classification method using probability and statistical methods. The purpose of this study is how to implement the naive Bayes method to predict the potential for rain in Ternate City, and be able to calculate the accuracy of the Naive Bayes method from system created. The highest calculation results with new data with a total of 400 training data and 30 test data, obtained 30 correct data with 100% precision, 100% recall and 100% accuracy and the lowest calculation results with new data with a total of 500 training data and 50 test data, obtained 38 correct data and 12 incorrect data with a percentage of precision 61.29%, recall 100% and accuracy 76%.

DETEKSI TEPI UNTUK VALIDASI MODEL TIGA DIMENSI TULANG PANGGUL PADA PERENCANAAN DESAIN DAN FABRIKASI IMPLAN

In the process of implant design and fabrication planning, the anatomical background of human bone is an important aspect to determine the accuracy of implant design. To help in understanding the human bone anatomy, replicas of human bone can be reconstructed using three-dimensional (3D) printing technology. This study aims to validate the digital model of human bone and the imaging data of human bone in order to have an appropriate recommendation for designing the total hip replacement (THR). Achieved data from computed tomography scanning (CT-Scan) modality was deciphered to build the digital 3D model human bone. The bone and the digital 3D model of bone were captured to be two-dimensional(2D) data in the same dimension and processed using the algorithm of edge detection. The images from the edge detection process were processed for finding the similarity of the images. The percentage of similarity, precision, and accuracy between the bone and the digital 3D model have been validated and can be used as a reference in the THR designs.

The Precision and Accuracy of English Verbs Choice in Recount Text Written by the Students of CEC Denpasar: An Analysis of Metalanguage

<p>This writing entitled “The Precision and Accuracy of English Verbs Choice in Recount Text Written by The Students of CEC Denpasar: An Analysis of Metalanguage” was conducted in order to be able to find out the percentage of precision and accuracy of English verbs choice in recount text written by the students of CEC Denpasar, analyze the lexico-semantic error, and describe the factors that cause this error. The data in this study were taken directly from recount texts written by the students of CEC Denpasar, interview, and questionnaire. Those data were collected by using a test, interview, and questionnaire method. The theory applied in this writing is Metalanguage from NSM theory proposed by Wierzbicka (1996). However, in describing the error, the analysis was done through configuration only by the qualitative method. The result shows that out of twenty-six verbs, twelve chosen verbs were less precise. Through Metalanguage, those errors can be described configurationally. Moreover, the difference between the error and the recommendation verbs can be explained, therefore, the difference can be shown. There were two factors that cause this error. They are external (teacher, method, strategy, technique used in teaching) and internal (motivation, potency, competence, and learners’ language background) factors.</p>