Small Error Rate Research Articles

Comprehensive analysis of spliceosome genes and their mutants across 27 cancer types in 9070 patients: clinically relevant outcomes in the context of 3P medicine.

Spliceosome machinery plays important roles in cell biological processes, and its alterations are significantly associated with cancer pathophysiological processes and contribute to the entire healthcare process in the framework of predictive, preventive, and personalized medicine (PPPM/3P medicine). To understand the expression and mutant status of spliceosome genes (SGs) in common malignant tumors and their relationship with clinical characteristics, a pan-cancer analysis of these SGs was performed across 27 cancer types in 9070 patients to discover biomarkers for cancer early diagnosis and prognostic assessment, effectively stratify patients, and improve the survival and prognosis of patients in 3P medical practice. A total of 150 SGs were collected from the KEGG database. The Python and R language were combined to process the transcriptional data of SGs and clinical data of 27 cancer types in The Cancer Genome Atlas (TCGA) database. Mutations of SGs in 27 cancer types were analyzed to identify the most common mutated SGs, as well as survival-related SGs. Different SGs were screened out, and SGs with survival significance in different types of tumors were found. Furthermore, TCGA and GTEx datasets were used to further confirm the expressions of SGs in different tumors. Western blot assay was performed to verify the expression of SNRPB protein in colon cancer and lung adenocarcinoma. Three SGs were screened out to establish the Bagging model for tumor diagnosis. Among 150 SGs, THOC2, PRPF8, SNRNP200, and SF3B1 had the highest mutation rate. The survival time of mutant THOC2 and SF3B1 was better than that of wild type, respectively. The differential expression analysis of 150 SGs between 674 normal tissue samples and 9,163 tumor tissue samples with 27 cancer types of 9070 patients showed that 13 SGs were highly expressed and 1 was low-expressed. For all cancer types, the prognosis (survival time) of the low-expression group of three SGs (SNRPB, LSM7, and HNRNPCL1) was better than the high expression group, respectively (p < 0.05). Cox hazards model showed that male, over 60 years old, clinical stages III-IV, and with highly expressed SNRPB and HNRNPCL1 had a poor prognosis. GEPIA2 website analysis showed that SNRPB and LSM7 were highly expressed in most tumors but not in LAML, showing low expression. Compared with the control group, the expression of SNRPB protein in colon cancer was increased by Western blot (p < 0.05). Enrichment analysis showed that the differential SGs were mainly enriched in RNA splicing and binding. The average error of 10-fold cross-validation of the Bagging model for diagnosed cancer was 0.093, which demonstrates that the Bagging model can effectively diagnose cancer with a small error rate. This study provided the first landscape of spliceosome changes across 27 cancer types in 9070 patients and revealed that spliceosome was related to tumor progression. Spliceosome may play important an important role in cancer biological processes. These findings are the important scientific data to demonstrate the common and specific changes of spliceosome genes across 27 cancer types, which is a valuable biomarker resource to under the common or specific molecular mechanisms among different cancer types and establish biomarkers and therapeutic targets for the common or specific management of different types of cancer patients to benefit the research and practice of 3P medicine in cancers. The online version contains supplementary material available at 10.1007/s13167-022-00279-0.

Application of NIR Spectral Standardization Based on Principal Component Score Evaluation in Wheat Flour Crude Protein Model Sharing

In order to explore spectral standardization methods for spectra collected by different NIR spectrometers, to reduce spectral differences, and to realize model sharing among different instruments, the crude protein content of 154 wheat flour samples was measured using one grating and three Fabry-Perot tunable filter NIR spectrometers in wavelength. At the same wavelength range and wavelength interval, three algorithms, namely, direct standardization (DS), piecewise direct standardization (PDS), and simple linear regression direct standardization (SLRDS), were used to standardize spectra collected by different instruments from the same samples. Spectral standardization error rate (SSER), principal component score error rate (PCSER), and other indicators were employed to analyze the spectral differences between the master and the target spectra, and the effect of model sharing was evaluated using parameters including prediction correlation coefficient (Rp), root mean square error of prediction (RMSEP), and relative prediction deviation (RPD). The results show the following: (1) The difference between spectra can be quantitatively evaluated through analyzing SSER and PCSER. (2) After standardization by the three algorithms, the spectral difference between the three target and the master spectrometers is significantly reduced and the prediction effect of the master model is greatly improved. (3) Among the three algorithms, DS algorithm had the smallest error rate in standardizing spectra from three target spectrometers. After standardization by the DS algorithm, the master model had the best effect. Its prediction accuracy was greatly improved compared with that before standardization. (4) The standard model established based on the S450 spectrometer can be applied to the same spectrometer as the N500 spectrometer with the same resolution and different wavelength ranges, so as to achieve model sharing. Therefore, DS, PDS, and SLRDS algorithms can effectively reduce the spectral differences between different instruments and realize the sharing of NIR calibration models for wheat flour crude protein measurement.

Applying Gamma and Histogram Equalization Algorithms for Improving System-performance of Face Recognition-based CNN

In the last few years, many applications have viewed great development, such as smart city applications, social media, smartphones, security systems, etc. In most of these applications, facial recognition played a major role. The work of these applications begins by locating the face within the image and then recognizing the face. The circumstances surrounding the person at the moment of taking the picture greatly affect the accuracy of these applications, especially the inappropriate lighting. Therefore, the stage of preparing the images is very important in the work. To solve this problem, we proposed a system that combines the use of gamma and Histogram Equalization algorithm (HE) to improve the images before starting to detect the face using the Viola-Jones. Then extract the facial features and identify the person using convolutional neural networks. The proposed system achieved a very small error rate and an accuracy during training that reached 100%. Index Terms— Viola-Jones, HE, AHE, Gamma, CNN.

Circular gap forming device and two-dimensional area calculation for in vitro cell migration study.

Cell wound healing assay is an important experimental technique for the detection of cell migration in vitro. At present, scratch on monolayer cells using a pipette tip is commonly used. However, it is difficult to guarantee the scratch with same width, and only the migration distance of a certain part is calculated. Therefore, the experimental method needs to be optimized. ATRA was used to treat hepa1-6 mouse hepatoma cells. Circular wound with diameter of 0.2cm were formed by a circular gap forming device. The whole cell wound region could be captured under the microscope to observe cell migration. There are almost no crawling cells in the wound region. The migration capacity of hepa1-6 cells was evaluated by calculating the healing area. ATRA could significantly inhibit the migration of hepa1-6 cells. Compared with linear wound, the standard deviation of wound healing rate in the circular cell wound method is smaller. The circular cell wound method can ensure the dynamic observation of the same wound region, and calculate the healing area at the two-dimensional level with small error and high repetition rate. It is reliable and easy to operate, can be widely used in laboratory.

Shafiyyatul Amaliyyah School Student Face Absence Using Principal Component Analysis and K – Nearest Neighbor

Pattern recognition is one of the sciences used to classify things based on quantitative measurements of the main features or properties of an object. Pattern recognition has been widely used in various fields of research. One of the pattern recognition that is often discussed is facial recognition. The face is one of the human biometrics that is often used as the main information of a person. Face recognition is a field of research with many applications in applications such as attendance, population data collection, security systems, and others. The research utilizes feature extraction of PCA (Principal Component Analysis), and K-NN (K – Nearest Neighbor) with variations of the distance formula by applying facial recognition attendance at the Safiatul Amaliyah School. This research is expected to get accurate results in detecting, recognizing, and comparing a person's face with a small error rate. The distance formula with accuracy level is presented with the equation Cityblock &lt; Euclidian &lt; Minkowski &lt; Chebychev. The effect of applying the variation of the distance formula on the performance of the facial attendance recognition model is not too big, but it is better.

Characterization Challenges of a Low Noise Charge Detection ROIC

This article presents the experimentally characterized performance of a low noise and wideband sensor readout integrated circuit (ROIC). The ROIC is designed to detect small amounts of charge generated by a silicon p-i-n detector as a result of particle detection, with very high time resolution and limited power consumption. The architecture of the ROIC permits the analog components of the particle readout to be designed with a reduced bandwidth by implementing the so-called intersymbol interference (ISI) cancellation technique, which improves the noise performance, while reducing the deterministic ISI-induced errors associated with the narrowband circuit; hence, a low error rate (ER) can be maintained. The readout is designed to detect 160 aC charge portions delivered randomly by the detector at a maximum of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4 \times 10^{8}$ </tex-math></inline-formula> events/s with a small average ER while consuming 2.85 mW. Detailed information about the ROIC designed in 65-nm CMOS technology, and the simulated performance, are already reported in a previous publication. This article aims to present the challenges related to the design of the test setup and the obtained experimental results with the first prototype of the ROIC, as well as to discuss the data acquisition process.

KOMPARASI METODE MOVING AVERAGE DAN TREND PROJECTION SEBAGAI ALAT UKUR PERENCANAAN PRODUKSI PADA UD. JAYA ABADI KABUPATEN PROBOLINGGO

The purpose of this research is to find out the comparison of forecasting calculations using the moving average and trend projection methods as a means measuring tool in production planning at UD. Jaya Abadi Probolinggo Regency. The type of research used is quantitative descriptive. Population and sample in this research is UD production data. Jaya Abadi Probolinggo Regerency from 2018 to March 2021. Data analysis using the moving average and trend projection methods which aim to predict future production or the next period. The results showed that the value of MAD (Mean Absolute Deviation) and the MSE (Mean Square Error) moving average values are higher than the MAD and the MSE trend projection, so the company you should use trend projection because when viewed from the results of the comparision of MAD and MSE, this method has a smaller error rate than the moving average method. So it can be concluded that the trend projection method can be used in planning bread production at UD. Jaya Abadi Probolinggo Regency

Estimating the Effects of the COVID-19 Outbreak on the Reductions in Tuberculosis Cases and the Epidemiological Trends in China: A Causal Impact Analysis.

ObjectiveCOVID-19 may have a demonstrable influence on disease patterns. However, it remained unknown how tuberculosis (TB) epidemics are impacted by the COVID-19 outbreak. The purposes of this study are to evaluate the impacts of the COVID-19 outbreak on the decreases in the TB case notifications and to forecast the epidemiological trends in China.MethodsThe monthly TB incidents from January 2005 to December 2020 were taken. Then, we investigated the causal impacts of the COVID-19 pandemic on the TB case reductions using intervention analysis under the Bayesian structural time series (BSTS) method. Next, we split the observed values into different training and testing horizons to validate the forecasting performance of the BSTS method.ResultsThe TB incidence was falling during 2005–2020, with an average annual percentage change of −3.186 (95% confidence interval [CI] −4.083 to −2.281), and showed a peak in March–April and a trough in January–February per year. The BSTS method assessed a monthly average reduction of 14% (95% CI 3.8% to 24%) in the TB case notifications from January–December 2020 owing to COVID-19 (probability of causal effect=99.684%, P=0.003), and this method generated a highly accurate forecast for all the testing horizons considering the small forecasting error rates and estimated a continued downward trend from 2021 to 2035 (annual percentage change =−2.869, 95% CI −3.056 to −2.681).ConclusionCOVID-19 can cause medium- and longer-term consequences for the TB epidemics and the BSTS model has the potential to forecast the epidemiological trends of the TB incidence, which can be recommended as an automated application for public health policymaking in China. Considering the slow downward trend in the TB incidence, additional measures are required to accelerate the progress of the End TB Strategy.

A Novel Luminance-Based Algorithm for Classification of Semi-Dark Images

Image classification of a visual scene based on visibility is significant due to the rise in readily available automated solutions. Currently, there are only two known spectrums of image visibility i.e., dark, and bright. However, normal environments include semi-dark scenarios. Hence, visual extremes that will lead to the accurate extraction of image features should be duly discarded. Fundamentally speaking there are two broad methods to perform visual scene-based image classification, i.e., machine learning (ML) methods and computer vision methods. In ML, the issues of insufficient data, sophisticated hardware and inadequate image classifier training time remain significant problems to be handled. These techniques fail to classify the visual scene-based images with high accuracy. The other alternative is computer vision (CV) methods, which also have major issues. CV methods do provide some basic procedures which may assist in such classification but, to the best of our knowledge, no CV algorithm exists to perform such classification, i.e., these do not account for semi-dark images in the first place. Moreover, these methods do not provide a well-defined protocol to calculate images’ content visibility and thereby classify images. One of the key algorithms for calculation of images’ content visibility is backed by the HSL (hue, saturation, lightness) color model. The HSL color model allows the visibility calculation of a scene by calculating the lightness/luminance of a single pixel. Recognizing the high potential of the HSL color model, we propose a novel framework relying on the simple approach of the statistical manipulation of an entire image’s pixel intensities, represented by HSL color model. The proposed algorithm, namely, Relative Perceived Luminance Classification (RPLC) uses the HSL (hue, saturation, lightness) color model to correctly identify the luminosity values of the entire image. Our findings prove that the proposed method yields high classification accuracy (over 78%) with a small error rate. We show that the computational complexity of RPLC is much less than that of the state-of-the-art ML algorithms.

Sars-Cov-2 Spike protein function prediction using a convolutional neural network ensemble

Introduction: SARS-CoV-2 has become a worldwide pandemic that affects all aspects of life; therefore, numerous organizations and open exploration foundations focus their efforts on research for viable therapeutics. Given past experiences and involvement in SARS, the essential focus has been the Spike protein, considered as the perfect objective for COVID-19 immunotherapies. Most of the vaccines being developed target the spike proteins because this protein covers the virus and helps it invade human cells. Methods: Applications of deep neural network is a quickly expanding field now reaching many areas including proteomics. Results: To be precise, convolutional neural networks have been used for identifying the functional role of amino acid sequences, because of its ability to give nearly accurate results for multi-label classification problems. Here we present a modified convolutional deep learning model that can identify if a given amino acid sequence is a spike protein or not based on the length of the sequence and the function of the protein, that will be done with a short execution time and a relatively small error rate.&#x0D; Conclusion: CNN is an efficient tool at supervised multilabel classification problems

Pengaruh Pelatihan Motivasi Kerja dan Lingkungan Kerja Terhadap Kinerja Guru di Yayasan AL-Muniroh Ujung Pangkah Gresik

the purpose of this study to find out The Influence of training, Work motivation, Work environment on teacher performance at the foundation AL-Muniroh Ujung Pangkah Gresik, using saturated sampling method is a sampling technique when all members of the population are used as samples. This is done because the population is too small and the researcher wants to make generalizations with a small error rate of 76 teacher. The data analysis technique used multiple liniear regression analysis. The result showed that training, work motivation, and work environment partially significant influence on the performance of the AL-Muniroh Ujung Pangkah Gresik Foudation teacher.

KOMPARASI METODE MOVING AVERAGE DAN TREND PROJECTION SEBAGAI ALAT UKUR PERENCANAAN PRODUKSI PADA UD. JAYA ABADI KABUPATEN PROBOLINGGO

The purpose of this research is to find out the comparison of forecasting calculations using the moving average and trend projection methods as a means measuring tool in production planning at UD. Jaya Abadi Probolinggo Regency. The type of research used is quantitative descriptive. Population and sample in this research is UD production data. Jaya Abadi Probolinggo Regerency from 2018 to March 2021. Data analysis using the moving average and trend projection methods which aim to predict future production or the next period. The results showed that the value of MAD (Mean Absolute Deviation) and the MSE (Mean Square Error) moving average values are higher than the MAD and the MSE trend projection, so the company you should use trend projection because when viewed from the results of the comparision of MAD and MSE, this method has a smaller error rate than the moving average method. So it can be concluded that the trend projection method can be used in planning bread production at UD. Jaya Abadi Probolinggo Regency

Analisis Penjadwalan Produksi Dengan Metode MPS di PT. XYZ

In this era in the industrial sector, companies are competing with the other companies to get consumer trust. In addition to quality, product delivery is also in accordance with the predetermined schedule. The problem that exists at PT XYZ is the absence of a master production schedule as a basis for carrying out production activities which results in a lack of labor effectiveness and efficiency of working hours and also causes losses to the company. The method used in this research is forecasting (linear regression, moving average, double exponential smooting), and production scheduling (MPS). The purpose of this study is to analyze future demand and also create a master schedule for the production process. From the results of data processing, it is found that the appropriate forecasting method is linear regression with the smallest error rate. With the number of requests for 12 periods is 64778 units and the master schedule of product production at the beginning of the month is 8638 units and leaves less products to be produced to meet demand in the following month, which is 2947 units because production needs in that month are only 46870 units without overtime for workers .

Investigation of Timing Parameters in Single-Flux-Quantum Circuits Using Low Critical-Current Junctions and Low Bias Voltages

In this study, the trade-off among power consumptions, operating frequencies, and error rates in low-power single-flux-quantum (SFQ) circuit is investigated. The power consumption of SFQ circuits can be reduced by the lowering critical currents ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$I_{\mathrm{c}}$</tex-math></inline-formula> values) of the Josephson junctions (JJs), or the bias voltage ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$V_{\mathrm{b}}$</tex-math></inline-formula> ). The shifts and spreads of timing parameter fluctuations when <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$I_{\mathrm{c}}$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$V_{\mathrm{b}}$</tex-math></inline-formula> were lowered were numerically analyzed, with consideration of thermal noise at 4.2 K. The timing behaviors after <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$I_{\mathrm{c}}$</tex-math></inline-formula> was lowered were relatively simple, being based on the signal-to-noise ratio, whereas those for lowered <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$V_{\mathrm{b}}$</tex-math></inline-formula> values were complicated because of the dynamic behaviors of JJs driven at reduced voltage. The simulation results imply that the operating frequencies decreased because the timing windows narrowed, but the highest energy efficiency was obtained when we lowered <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$I_{\mathrm{c}}$</tex-math></inline-formula> or <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$V_{\mathrm{b}}$</tex-math></inline-formula> to 6.25% of their conventional values, and the energy reduction after lowering of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$V_{\mathrm{b}}$</tex-math></inline-formula> was 26% better than that after lowering of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$I_{\mathrm{c}}$</tex-math></inline-formula> . Moreover, if a small error rate (for example <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$10^{-6}$</tex-math></inline-formula> ) is acceptable, further improvement in energy efficiency would be expected at significantly higher clock frequencies than would be the case if the SFQ circuit must exhibit error-free operation.

Towards Lifespan Automation for Caenorhabditis elegans Based on Deep Learning: Analysing Convolutional and Recurrent Neural Networks for Dead or Live Classification.

The automation of lifespan assays with C. elegans in standard Petri dishes is a challenging problem because there are several problems hindering detection such as occlusions at the plate edges, dirt accumulation, and worm aggregations. Moreover, determining whether a worm is alive or dead can be complex as they barely move during the last few days of their lives. This paper proposes a method combining traditional computer vision techniques with a live/dead C. elegans classifier based on convolutional and recurrent neural networks from low-resolution image sequences. In addition to proposing a new method to automate lifespan, the use of data augmentation techniques is proposed to train the network in the absence of large numbers of samples. The proposed method achieved small error rates (3.54% ± 1.30% per plate) with respect to the manual curve, demonstrating its feasibility.

The Stock Exchange Prediction using Machine Learning Techniques: A Comprehensive and Systematic Literature Review

This literature review identifies and analyzes research topic trends, types of data sets, learning algorithm, methods improvements, and frameworks used in stock exchange prediction. A total of 81 studies were investigated, which were published regarding stock predictions in the period January 2015 to June 2020 which took into account the inclusion and exclusion criteria. The literature review methodology is carried out in three major phases: review planning, implementation, and report preparation, in nine steps from defining systematic review requirements to presentation of results. Estimation or regression, clustering, association, classification, and preprocessing analysis of data sets are the five main focuses revealed in the main study of stock prediction research. The classification method gets a share of 35.80% from related studies, the estimation method is 56.79%, data analytics is 4.94%, the rest is clustering and association is 1.23%. Furthermore, the use of the technical indicator data set is 74.07%, the rest are combinations of datasets. To develop a stock prediction model 48 different methods have been applied, 9 of the most widely applied methods were identified. The best method in terms of accuracy and also small error rate such as SVM, DNN, CNN, RNN, LSTM, bagging ensembles such as RF, boosting ensembles such as XGBoost, ensemble majority vote and the meta-learner approach is ensemble Stacking. Several techniques are proposed to improve prediction accuracy by combining several methods, using boosting algorithms, adding feature selection and using parameter and hyper-parameter optimization.

Analisis Perbandingan Metode Single Exponential Smoothing dan Single Moving Average dalam Peramalan Pemesanan

ACK Fried Chicken (Ayam Crispy Kriuk) is a local fast food business that provides food and beverages. The competition for chicken franchises in Bali and the number of orders made by customers through online applications, namely the Gofood and Grabfood applications on ACK Fried Chicken, has resulted in the importance of implementing the SCM strategy in ACK Fried Chicken, to maintain customer trust and satisfaction. By providing an overview to make estimates in the present for future production, it is necessary to forecast demand which is carried out using the forecasting method. The forecasting method used is the time series method, namely the Single Exponential Smoothing method and the Single Moving Average method. Forecasting using the time series method, namely SES and SMA, the forecasting model is obtained from the actual data used in this case study, the forecasting model using the SMA method is declared very good because it has a MAPE value <10% while the SES method is declared good because it has a MAPE value. = 11% (10-20%). The SMA method has an error rate that is more sophisticated than the SES method. For forecasting order requests in this study, the SMA method is better used because it has the smallest error rate in forecasting.

English

One of the most research discussed topics is the prediction or forecasting of the COVID-19 data using the classical time series (such as exponential smoothing) and machine learning methods. In fact, the classical time series method often produces quite large error rates. In this study, the researchers try to use nonparametric modeling with the kernel method to get better results with the smallest error rate. Furthermore, the results of the kernel method are compared with the results of the classical time series method. As a comparison tool, the researchers use MAPE by paying attention to the smallest MAPE value. The data used in this study are the COVID-19 data in Indonesia in which its variable is the total of deaths per day. After comparing the classical time series method with the kernel method, the obtained better results are the results from the kernel method. In this study, the researchers use five kernel functions, namely the Gaussian, Epanechnikov, Triangular, Biweight, and Triweight. Then, these five kernel functions are compared to find the best function. After the comparison process is done, the triweight kernel function was determined as the best function with the smallest error rate with a MAPE value of 0,9%. S Keywords — Covid-19, Kernel Method, Mean Absolute Percentage Error, Time Series, Triweight Kernel Function.

Real-Time Implementation of a New Multifunction Relay

This paper aims to design and building a New Multifunction Relay (MFR) by using the Arduino MEGA. It is used to provide protection against many abnormal conditions that may occur in the power system. The MFR contains two functions: firstly, are designing an inverse Overcurrent relay, and the other is an Over/Under voltage Relay based on the IEC 60255-151 standard and IEEE 1159 standard respectively. The GUI is designed by using the MATLAB environment to provide a platform of the relay. The proposed MFR showed accuracy results, Low costs, fast response, and very small error rate.

Innovative Artificial Intelligence Approach for Hearing-Loss Symptoms Identification Model Using Machine Learning Techniques

Physicians depend on their insight and experience and on a fundamentally indicative or symptomatic approach to decide on the possible ailment of a patient. However, numerous phases of problem identification and longer strategies can prompt a longer time for consulting and can subsequently cause other patients that require attention to wait for longer. This can bring about pressure and tension concerning those patients. In this study, we focus on developing a decision-support system for diagnosing the symptoms as a result of hearing loss. The model is implemented by utilizing machine learning techniques. The Frequent Pattern Growth (FP-Growth) algorithm is used as a feature transformation method and the multivariate Bernoulli naïve Bayes classification model as the classifier. To find the correlation that exists between the hearing thresholds and symptoms of hearing loss, the FP-Growth and association rule algorithms were first used to experiment with small sample and large sample datasets. The result of these two experiments showed the existence of this relationship, and that the performance of the hybrid of the FP-Growth and naïve Bayes algorithms in identifying hearing-loss symptoms was found to be efficient, with a very small error rate. The average accuracy rate and average error rate for the multivariate Bernoulli model with FP-Growth feature transformation, using five training sets, are 98.25% and 1.73%, respectively.