Calculation Of Mean Square Error Research Articles

Enhancing online learning: sentiment analysis and collaborative filtering from Twitter social network for personalized recommendations

Online learning presents a major challenge for learners, namely the diversification of courses and information overload. In response to this issue, recommender systems are widely used. Nowadays, social networks have become a global platform where individuals share a multitude of information. For instance, Twitter is a social network where users exchange messages and interact with various communities. These interactions on social networks have created a new dimension in the field of online learning. In this article, we propose a novel approach that combines sentiment analysis of learners’ reviews on social networks with collaborative filtering methods to provide more personalized and relevant course recommendations. To achieve this, we explored different models to analyze the sentiments of tweets related to online courses. Additionally, we used collaborative filtering based on k-nearest neighbors (KNN). Our results demonstrate that integrating sentiment analysis provides more relevant recommendations. This has also been shown based on the calculation of root mean square error (RMSE) compared to a traditional approach. In this study, we demonstrated that by leveraging this information from social networks like Twitter, online learning platforms can enhance the effectiveness of their course recommendations, tailoring them to each individual learner’s needs.

Neural network design for non-Newtonian Fe3O4–blood nanofluid flow modulated by electroosmosis and peristalsis

In this study, we present a novel approach that utilizes the Levenberg–Marquardt algorithm (LMA) based on artificial neural networks (ANNs) to evaluate the flow characteristics of a thermally evolved blood-based nanofluid in the presence of peristalsis and electroosmosis. The Casson fluid model is employed to govern the non-Newtonian characteristics observed in the flow of blood. In addition, the thermal properties of the nanofluidic medium in contact with platelet magnetite nanomaterials are also studied in detail. Further, the effects of thermal radiation, thermal buoyancy force, magnetic field and Joule heating are also given due consideration. The mathematically formulated two-dimensional equations describing the flow of Casson liquid are brought into their dimensionless form under the lubrication theory. A dataset for the proposed ANN models is generated to explore various scenarios of the fluidic model by varying the pertinent parameters using NDSolve in Mathematica. The computational approach utilizing LMA is deployed across three distinct phases of performance assessment, distributing the data into training, testing and validation sets at the proportions of 80%, 10% and 10%, respectively. This implementation involves the utilization of 10 hidden neurons. The utilization of regression analysis for testing, mean-squared error calculation, error histograms and correlation assessment in numerical replications of the ANNs is also examined to verify their capability, accuracy, validity and effectiveness. This study is crucial for understanding the peristaltic blood transportation in small blood vessels of living organisms.

Estimated Sulfur Dioxide Pollutant Concentrations In Medan City Using Ordinary Kriging and Inverse Distance Weighting Approaches

Sulfur dioxide (SO2) pollution is a serious problem that negatively affects air quality and human health. So2 is generated by various human activities, especially the combustion of fossil fuels, as well as from natural sources such as volcanic eruptions. The emission of SO2 in urban areas, including Medan, Indonesia, has raised concerns as it is associated with respiratory diseases and the formation of acid rain. This research aimed to estimate comparative SO2 concentrations in Medan City using geostatistical methods, specifically ordinary kriging and inverse distance weighted (IDW). Air quality monitoring data from the Dinas Lingkungan Hidup Kota Medan were collected during a certain period. The collected data were analyzed and then interpolated using ordinary kriging and IDW methods. Furthermore, the Ordinary Kriging method involves testing the OK assumption, calculation of the experimental semivariogram, calculation of the theoretical semivariogram, structural analysis, and calculation of Root Mean Square Error (RMSE). Meanwhile, the Inverse Distance Weighted method involves calculating the Euclidean distance, determining the weights based on the power parameter, calculating the RMSE value, and estimating the SO2 concentration. The comparison results show that the OK method is more accurate in determining SO2 concentration compared to the IDW method in Medan City. In the estimation of sulfur dioxide (SO2) concentration, the OK method used the best theoretical semivariogram model, namely the exponential model, and in the estimation process of the IDW method, the power parameter 1 was used.

WEaning Age FiNder (WEAN): a tool for estimating weaning age from stable isotope ratios of dentinal collagen

Nitrogen stable isotope ratios (δ15N) of incremental dentine collagen have been extensively used for the study of breastfeeding and weaning practices in ancient populations. The shifts in δ15N values reveal the duration of exclusive breastfeeding, the onset and completion of weaning. Despite the significant progress in sampling precision protocols, the weaning estimation is still performed by visual observation of δ15N individual profiles, a time-consuming, labor-intensive, and error-prone task. To fill this gap, we generated WEAN, a tool that enables automated estimation of weaning age based on δ15N measurements from incremental dentine collagen. WEAN generates a refined age assignment based on regression analysis and calculates the knee/elbow point of the δ15N curvature as the individual’s weaning age. We tested the accuracy of the tool by re-estimating 130 weaning ages from published datasets with the calculation of the root mean square error (RMSE). The results show a strong agreement between the visual observation and the elbow method underlining that an automatic mathematical framework can be used for the accurate estimation of weaning age. The tool can estimate the weaning age of a single or many individuals and produces visually appealing graphics (scatter and line plots) and output files. WEAN introduces a novel and robust method that streamlines the assessment of δ15N values for the exploration of breastfeeding and weaning patterns in antiquity.

Ziv-Zakai Bound for DOAs Estimation

Lower bounds on the mean square error (MSE) play an important role in evaluating the direction-of-arrival (DOA) estimation performance. Among numerous bounds for DOA estimation, the local Cram <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathrm{\acute{e}}$</tex-math></inline-formula> r-Rao bound (CRB) is only tight asymptotically. By contrast, the existing global tight Ziv-Zakai bound (ZZB) is appropriate for evaluating the single source estimation only. In this paper, we derive an explicit ZZB applicable for evaluating hybrid coherent/incoherent multiple sources DOA estimation. It is first shown that, a straightforward generalization of ZZB from single source estimation to multiple sources estimation cannot keep the bound valid in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> performance region. To derive a global tight ZZB, we then introduce order statistics to describe the change of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> distribution of DOAs caused by ordering process during the MSE calculation. The derived ZZB is for the first time formulated as a function of coherent coefficients between coherent sources, and reveals the relationship between the MSE convergency in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> performance region and the number of sources. Moreover, the derived ZZB also provides a unified tight bound for both overdetermined and underdetermined DOA estimation. Simulation results demonstrate the obvious advantages of the derived ZZB over the CRB on evaluating and predicting the estimation performance for multiple sources DOA.

COST 231-Hata Propagation Model Optimization in 1800 MHz Band Based on Magnetic Optimization Algorithm: Application to the City of Limb&amp;#233;

Network planning is essential for the construction and the development of wireless networks. The network planning cannot be possible without an appropriate propagation model which in fact is its foundation. Initially used mainly for mobile radio networks, the optimization of propagation model is becoming essential for efficient deployment of the network in different types of environment, namely rural, suburban and urban especially with the emergence of concepts such as digital terrestrial television, smart cities, Internet of Things (IoT) with wide deployment for different use cases such as smart grid, smart metering of electricity, gas and water. In this paper we use an optimization algorithm that is inspired by the principles of magnetic field theory namely Magnetic Optimization Algorithm (MOA) to tune COST231-Hata propagation model. The dataset used is the result of drive tests carry out on field in the town of Limbe in Cameroon. We take into account the standard K-factor model and then use the MOA algorithm in order to set up a propagation model adapted to the physical environment of a town. The town of Limbe is used as an implementation case, but the proposed method can be used everywhere. The calculation of the root mean square error (RMSE) between the real data from the radio measurements and the prediction data obtained after the implementation of MOA allows the validation of the results. A comparative study between the value of the RMSE obtained by the new model and those obtained by the optimization using linear regression, by the standard COST231-Hata models, and the free space model is also done, this allows us to conclude that the new model obtained using MOA for the city of Limbe is better and more representative of this local environment than the standard COST231-Hata model. The new model obtained can be used for radio planning in the city of Limbé in Cameroon.

Okumura Hata Propagation Model Optimization in 400 MHz Band Based on Differential Evolution Algorithm: Application to the City of Bertoua

Propagation models are the foundation for radio planning in mobile networks. They are widely used during feasibility studies and initial network deployment, or during network extensions, particularly in new cities. They can be used to calculate the power of the signal received by a mobile terminal, evaluate the coverage radius, and calculate the number of cells required to cover a given area. This paper takes into account the standard k factors model and then uses the differential evolution algorithm to set up a propagation model adapted to the physical environment of the Cameroonian cities of Bertoua. Drive tests were made on the LTE TDD network in the city of Bertoua. Differential evolution algorithm is used as the optimization algorithm to deduct a propagation model which fits the environment of the considered town. The calculation of the root mean square error between the actual data from the drive tests and the prediction data from the implemented model allows the validation of the obtained results. A comparative study made between the RMSE value obtained by the new model and those obtained by the Okumura Hata and free space models, allowed us to conclude that the new model obtained is better and more representative of our local environment than the Okumura Hata currently used. The implementation shows that Differential evolution can perform well and solve this kind of optimization problem; the newly obtained models can be used for radio planning in the city of Bertoua in Cameroon.

Development and Comparison of Thermodynamic Equilibrium and Kinetic Approaches for Biomass Pyrolysis Modeling

Biomass pyrolysis is considered as a thermochemical conversion system that is performed under oxygen-depleted conditions. A large body of literature exists in which thermodynamic equilibrium (TE) and kinetic approaches have been applied to predict pyrolysis products. However, the reliability, accuracy and predictive power of both modeling approaches is an area of concern. To address these concerns, in this paper, two new simulation models based on the TE and kinetic approaches are developed using Aspen Plus, to analyze the performance of each approach. Subsequently, the results of two models are compared with modeling and experimental results available in the literature. The comparison shows that, on the one hand, the performance of the TE approach is not satisfactory and cannot be used as an effective way for pyrolysis modeling. On the other hand, the results generated by the new model based on the kinetic approach suggests that this approach is suitable for modeling biomass pyrolysis processes. Calculation of the root mean square error (RMS), to quantify the deviation of the model results from the experiment results, confirms that this kinetic model presents superior agreement with experimental data in comparison with other kinetic models in the literature. The acquired RMS for the developed kinetic method in this paper varies within the span of 1.2 to 3.2 depending on temperature (400–600 °C) and various feedstocks (pine spruce sawdust, bagasse, wood bark, beech wood and paddy straw).

A study on the estimation of COVID-19 case with the adaptive kalman filter (AKF) in USA, Germany, United Kingdom, Italy, France, Russia, Brazil, India, Turkey, Spain, Peru, Colombia, South Africa, Argentina, Iran, Pakistan

In this paper, COVID-19 cumulative cases are estimated with AKF based on total COVID-19 cases between January-September 9, 2020 in USA, Germany, United Kingdom, Italy, France, Russia, Brazil, India, Turkey, Spain, Peru, Colombia, South Africa, Argentina, Iran, Pakistan. The cumulative covid-19 cases time-series was modeled with a stochastic dynamic linear model (DLM). The estimation performance of the models is measured by the calculation of mean square error (MSE) and coefficient of determination ( ). Calculated MSE and values showed that the model and AKF could be used to estimate the number of cases in these countries. In this study, firstly, the cumulative number of cases was estimated. Secondly, using these estimates number of daily cases was calculated. Thirdly, the reproduction number was obtained by using these number of daily cases. The model and estimation method used is suitable. The AKF algorithm uses only the number of cases in the last day. We propose that the model and estimation method under consideration is a convenient tool for calculating the reproduction number depending on time.

Parameter estimation with improved model prediction for over-parametrized nonlinear systems

Parameter estimation is a crucial step in system modeling. In practice, the fundamental models developed by chemical engineers are often complex. There may be parameters that have no or little effect on the model prediction. Also, some parameters may have an impact on model prediction related to other parameters. In this case, it is difficult to estimate all the parameters. The general approach to address this issue is to select a subset of parameters to estimate, and fix the rest at the initial value. Here, a reduced Hessian and statistical criterion-based parameter estimation approach is proposed for improving model prediction. The proposed method considers the influence of the initial parameter values on model prediction. In the process of selecting the optimal subset of transformed parameters based on the mean square error (MSE) of output, the sensitivity matrix is replaced by the reduced Hessian matrix, which can save computational cost of MSE calculation. After obtaining the optimal transformed parameter subset, the optimal initial values are selected based on the statistical criterion. The large difference between the model prediction and the actual model output caused by the arbitrary initial values can be avoided. The numerical results show the effectiveness of the proposed approach.

Application of photoplethysmography signal in diagnosis of cerebrovascular disease and its extraction method based on Wavelet Transform

Effective monitoring of physiological information can effectively control incidence rate and mortality rate for patients with cerebrovascular disease. Among them, near infrared spectroscopy (NIRS) can measure the photoplethysmography (PPG) of cerebral cortex, which provides a new solution for the current detection of cerebrovascular physiological information. There are a lot of characteristic parameters in the pulse wave signal, which can be used to evaluate the cerebrovascular condition. In the time domain, the K value, the main wave width w, the main wave slope k were calculated to evaluate the cerebral vascular sclerosis. And in the frequency domain, the blood pressure of patients can be evaluated by the spectrum amplitude A. Besides, to improve the quality and accuracy of PPG signal parameter calculation, PPG with low noise and distortion is needed to provide support. By analyzing the interference of PPG signal in different frequency bands, the wavelet filtering method is adopted to process the signal. Through the calculation of root mean square error (RMSE) and signal-to-noise ratio (SNR), it is found that this method has excellent extraction effect on PPG signal.

Analysis Comparative Method Shari’a Compliant Asset Pricing Model

The researcher's view of the CAPM model is not in accordance with the Islamic economy because there is an Rf element as an instrument that contains an interest element, so a modified model of CAPM based on sharia is introduced, namely SCAPM. This study aims to analyze the differences in the SCAPM method according to Tomkins &amp; Karim (1987) SCAPM non Rf, Ashker (1987) SCAPMZ, Shaikh (2010) SCAPM NGDP, and Hanif (2011) SCAPMI. The sampling technique used was purposive sampling and obtained 19 samples. Data analysis used is the calculation of Mean Absolute Deviation (MAD), Mean Square Error (MSE), and the coefficient of determination. The results of the calculation of Mean Absolute Deviation (MAD) and Mean Square Error (MSE) explain that there are differences from the SCAPM models without risk free rate, SCAPMZ, SCAPM NGDP, and SCAPMI. Meanwhile, SCAPMI has the best explanatory power than the other four SCAPM models. It is recommended that Islamic and conventional investors use SCAPMZ modeling in predicting stock returns.

Linear-time error calculation for approximate adders

Design space exploration of Low Power Approximate Adders (LPAAs) has become significant as it successfully trades acceptable amounts of accuracy for the power, area, and delay improvements. Evaluating all potential choices in the vast design space over given application requirements and suitable error metrics makes application-oriented design space exploration quite challenging. This entails a need for input-aware, fast and accurate computation of error evaluation metrics such as Mean Squared Error (MSE) and Mean Error (ME). This paper proposes a formal approach that accurately calculates the MSE and ME of LPAAs for any given input pattern with linear time and space complexity. Experimental results exhibit at least 58 times speed up in the MSE calculation over the Monte Carlo sampling methods with 1000 samples. Furthermore, the proposed approach is integrated with an automatic LPAA generation tool to produce LPAAs with superior performance and energy-efficiency compared to their existing counterparts.

Evaluación de la fuente de incertidumbre en la curva de descarga de la microcuenca “Las Flores”, San Sebastián, Manabí, Ecuador.

El presente trabajo de investigación mixta indagó y comparó mediante una evaluación, la incertidumbre asociada a la curva de descarga simulada en la microcuenca Las Flores, en San Sebastián, Manabí, Ecuador, con la finalidad de disminuir los diferentes aspectos de incertidumbre que estaban en cuestionamiento, debido a la falta de datos reales para generar una curva de descarga. Se recopilaron datos mediante 12 aforos realizados por dos métodos diferentes (correntómetro y volumétrico). Se generó la comprobación de la relación entre los datos simulados y datos obtenidos, a través del cálculo de la raíz del error cuadrático medio (RECM), ejecutado mediante los programas Microsoft Excel y Hec-Ras, usados también para verificar los resultados.

Scoping Zygomaticomaxillary Complex Fractures With the Eyes of Virtual Reality: Operative Protocol and Proposal of a Modernized Classification.

Fractures of the zygomaticomaxillary complex (ZMC) represent an extremely heterogeneous group of injuries to the midfacial skeleton. Traditionally, the diagnosis of such fractures was based on 2-dimensional radiograms and, more recently, on volumetric computed tomography (CT) scans, while the treatment was exclusively based on the surgeon's experience. Many classification attempts have been made in the past, but no paper has taken into account the importance of virtual surgical planning (VSP) in proving a modernized classification. The authors propose a classification based on the use of VSP which can guide the surgeon to identify the optimal reduction method and reproduce it in the operating room through the use of navigation. Patients with ZMC fractures were collected to create a study model. The VSP was used to generate 3-dimensional models of fractures. Fractured segments were duplicated and digitally put in the optimal reduction position. Repositioned fragments were overlapped to their original preoperative counterparts and exported to the surgical navigator to be navigated. Planned virtual reduction was overlaid to postoperative CT scan to assess the accuracy of reduction, explored using color maps and the calculation of root mean square error. For all patients, the application of VSP was successfully accomplished. High accuracy was confirmed between the planned virtual reduction and the postoperative CT scan. A 5-item classification based on VSP is proposed. All patients were included in the presented subclasses. The adoption of virtual planning in ZMC fractures allows for an improved study of the displacement of the fracture and might indicate to the surgeons the required maneuvers to achieve optimal reduction. The presented proposal of classification might be an aid to simplify the choice of the most appropriate reduction method and might provide a deeper insight into the morphologic characteristics of fractures.

Tunable color correction for noisy images

Color correction is one of the most essential camera imaging operations that transforms a camera-specific RGB color space to a standard color space, typically the XYZ or the sRGB color space. Linear color correction (LCC) and polynomial color correction (PCC) are two widely used methods; they perform the color space transformation using a color correction matrix. Owing to the use of high-order terms, PCC generally achieves lower colorimetric errors than LCC. However, PCC amplifies noise more severely than LCC. Consequently, for noisy images, there exists a trade-off between LCC and PCC regarding color fidelity and noise amplification. We propose a color correction framework called tunable color correction (TCC) that enables us to tune the color correction matrix between the LCC and the PCC models. We also derive a mean squared error calculation model of PCC that enables us to select the best trade-off balance in the TCC framework. We experimentally demonstrate that TCC effectively balances the trade-off for noisy images and outperforms LCC and PCC. We also generalize TCC to multispectral cases and demonstrate its effectiveness by taking the color correction for an RGB-near-infrared sensor as an example.

Forecasting the cumulative number of confirmed cases of COVID-19 in Italy, UK and USA using fractional nonlinear grey Bernoulli model.

Since the new coronavirus (COVID-19) outbreak spread from China to other countries, it has been a curiosity for how and how long the number of cases will increase. This study aims to forecast the number of confirmed cases of COVID-19 in Italy, the United Kingdom (UK) and the United States of America (USA). In this study, grey model (GM(1,1)), nonlinear grey Bernoulli model (NGBM(1,1)) and fractional nonlinear grey Bernoulli model (FANGBM(1,1)) are compared for the prediction. Therefore, grey prediction models, especially the fractional accumulated grey model, are used for the first time in this topic and it is believed that this study fills the gap in the literature. This model is applied to predict the data for the period 19/03-22/04/2020 (35 days) and forecast the data for the period 23/04-22/05/2020. The number of cases of COVID-19 in these countries are handled cumulatively. The prediction performance of the models is measured by the calculation of root mean square error (RMSE), mean absolute percentage error (MAPE) and R2 values. It is obtained that FANGBM(1,1) gives the highest prediction performance with having the lowest RMSE and MAPE values and the highest R2 values for these countries. Results show that the cumulative number of cases for Italy, UK and USA is forecasted to be about 233000, 189000 and 1160000, respectively, on May 22, 2020 which corresponds to the average daily rate is 0.80%, 1.19% and 1.13%, respectively, from 22/04/2020 to 22/05/2020. The FANGBM(1,1) presents that the cumulative number of cases of COVID-19 increases at a diminishing rate from 23/04/2020 to 22/05/2020 for these countries.

Fast Independent Component Analysis (FastICA) in Separating Vocals and Instruments in the Art of Geguntangan

Geguntangan is pesantian in religious ceremonies in Bali accompanied by gamelan music. The human sense of hearing tends to have limitations, which causes not all vocals mixed with gamelan to be heard clearly. Therefore we need a system that can be used to separate vocals with gamelan in the geguntangan. Separation of sound sources is categorized as Blind Source Separation (BSS) or also called Blind Signal Separation, which means an unknown source. The algorithm used to handle BSS is the Fast Independent Component Analysis (FastICA) algorithm with a focus on separating the sound signal in a wav-format sound file. FastICA algorithm is used for the sound separation process with the value parameter used is Mean Square Error (MSE). From the simulation results show the results of MSE calculations using the mixing matrix [0.3816, 0.8678], [0.8534, -0.5853] obtained the results for the FastICA method, the MSE value is 3.60 x 10-5 for the vocal and 1.71 x 10-6 for the instrument.

A step edge detector based on bilinear transformation

Nowadays, Canny edge detector is considered to be one of the best edge detection approaches for the images with step form. Various overgeneralized versions of these edge detectors have been offered up to now, e.g. Saryazdi edge detector. This paper proposes a new discrete version of edge detection which is obtained from Shen-Castan and Saryazdi filters by using bilinear transformation. Different experimentations are conducted to decide the suitable parameters of the proposed edge detector and to examine its validity. To evaluate the strength of the proposed model, the results are compared to Canny, Sobel, Prewitt, LOG and Saryazdi methods. Finally, by calculation of mean square error (MSE) and peak signal-to-noise ratio (PSNR), the value of PSNR is always equal to or greater than the PSNR value of suggested methods. Moreover, by calculation of Baddeley’s error metric (BEM) on ten test images from the Berkeley Segmentation DataSet (BSDS), we show that the proposed method outperforms the other methods. Therefore, visual and quantitative comparison shows the efficiency and strength of proposed method.

Rancang Bangun Sistem Otomasi Monitoring Level Air Bendungan Untuk Pengendalian Banjir

Seiring perkembangan teknologi permasalahan yang tidak pernah lepas dari masyarakat Indonesia pada umumnya adalah banjir. Banjir adalah fenomena alam yang terjadi karena meluapnya air di selokan, sungai atau danau yang tidak dapat menampung debit air. Banjir terjadi dikarenakan keterlambatan petugas membuka pintu air bendungan. Minimnya petugas pengawas bendungan saat ini mengakibatkan pemantauan bendungan tidak dapat dilakukan dengan maksimal, hal ini menyebabkan terjadinya banjir karena keterlambatan pengaturan pintu air. Pada penelitian ini penulis melakukan perancangan suatu sistem otomasi monitoring level air bendungan untuk pengendalian banjir. Perangkat yang digunakan adalah Raspberry Pi sebagai perangkat yang menerima data ketinggian level air yang di ambil oleh sensor ultrasonik HC-SR04. Hasil uji coba sensor ultrasonik mendapatkan rata-rata dari perhitungan MSE (Mean Square Error) adalah 0,35 cm yang semakin mendekati nilai 0 menunjukkan bahwa hasil akurasi sensor ultrasonik dapat dikatakan akurat, rata-rata error 4,71% sensor menunjukkan akurasi yang sangat baik dengan deviasi sebesar 5,16% yang mendekati rata-rata error menunjukkan rentang data hasil pengujian cukup akurat.