New Approach For Accurate Prediction Research Articles

Meta-Heuristic Optimization of LSTM-Based Deep Network for Boosting the Prediction of Monkeypox Cases

Recent technologies such as artificial intelligence, machine learning, and big data are essential for supporting healthcare monitoring systems, particularly for monitoring Monkeypox confirmed cases. Infected and uninfected cases around the world have contributed to a growing dataset, which is publicly available and can be used by artificial intelligence and machine learning to predict the confirmed cases of Monkeypox at an early stage. Motivated by this, we propose in this paper a new approach for accurate prediction of the Monkeypox confirmed cases based on an optimized Long Short-Term Memory (LSTM) deep network. To fine-tune the hyper-parameters of the LSTM-based deep network, we employed the Al-Biruni Earth Radius (BER) optimization algorithm; thus, the proposed approach is denoted by BER-LSTM. Experimental results show the effectiveness of the proposed approach when assessed using various evaluation criteria, such as Mean Bias Error, which is recorded as (0.06) using BER-LSTM. To prove the superiority of the proposed approach, six different machine learning models are included in the conducted experiments. In addition, four different optimization algorithms are considered for comparison purposes. The results of this comparison confirmed the superiority of the proposed approach. On the other hand, several statistical tests are applied to analyze the stability and significance of the proposed approach. These tests include one-way Analysis of Variance (ANOVA), Wilcoxon, and regression tests. The results of these tests emphasize the robustness, significance, and efficiency of the proposed approach.

Enhancing wind direction prediction of South Africa wind energy hotspots with Bayesian mixture modeling

Wind energy production depends not only on wind speed but also on wind direction. Thus, predicting and estimating the wind direction for sites accurately will enhance measuring the wind energy potential. The uncertain nature of wind direction can be presented through probability distributions and Bayesian analysis can improve the modeling of the wind direction using the contribution of the prior knowledge to update the empirical shreds of evidence. This must align with the nature of the empirical evidence as to whether the data are skew or multimodal or not. So far mixtures of von Mises within the directional statistics domain, are used for modeling wind direction to capture the multimodality nature present in the data. In this paper, due to the skewed and multimodal patterns of wind direction on different sites of the locations understudy, a mixture of multimodal skewed von Mises is proposed for wind direction. Furthermore, a Bayesian analysis is presented to take into account the uncertainty inherent in the proposed wind direction model. A simulation study is conducted to evaluate the performance of the proposed Bayesian model. This proposed model is fitted to datasets of wind direction of Marion island and two wind farms in South Africa and show the superiority of the approach. The posterior predictive distribution is applied to forecast the wind direction on a wind farm. It is concluded that the proposed model offers an accurate prediction by means of credible intervals. The mean wind direction of Marion island in 2017 obtained from 1079 observations was 5.0242 (in radian) while using our proposed method the predicted mean wind direction and its corresponding 95% credible interval based on 100 generated samples from the posterior predictive distribution are obtained 5.0171 and (4.7442, 5.2900). Therefore, our results open a new approach for accurate prediction of wind direction implementing a Bayesian approach via mixture of skew circular distributions.

UMPred-FRL: A New Approach for Accurate Prediction of Umami Peptides Using Feature Representation Learning.

Umami ingredients have been identified as important factors in food seasoning and production. Traditional experimental methods for characterizing peptides exhibiting umami sensory properties (umami peptides) are time-consuming, laborious, and costly. As a result, it is preferable to develop computational tools for the large-scale identification of available sequences in order to identify novel peptides with umami sensory properties. Although a computational tool has been developed for this purpose, its predictive performance is still insufficient. In this study, we use a feature representation learning approach to create a novel machine-learning meta-predictor called UMPred-FRL for improved umami peptide identification. We combined six well-known machine learning algorithms (extremely randomized trees, k-nearest neighbor, logistic regression, partial least squares, random forest, and support vector machine) with seven different feature encodings (amino acid composition, amphiphilic pseudo-amino acid composition, dipeptide composition, composition-transition-distribution, and pseudo-amino acid composition) to develop the final meta-predictor. Extensive experimental results demonstrated that UMPred-FRL was effective and achieved more accurate performance on the benchmark dataset compared to its baseline models, and consistently outperformed the existing method on the independent test dataset. Finally, to aid in the high-throughput identification of umami peptides, the UMPred-FRL web server was established and made freely available online. It is expected that UMPred-FRL will be a powerful tool for the cost-effective large-scale screening of candidate peptides with potential umami sensory properties.

Porpoise: a new approach for accurate prediction of RNA pseudouridine sites.

Pseudouridine is a ubiquitous RNA modification type present in eukaryotes and prokaryotes, which plays a vital role in various biological processes. Almost all kinds of RNAs are subject to this modification. However, it remains a great challenge to identify pseudouridine sites via experimental approaches, requiring expensive and time-consuming experimental research. Therefore, computational approaches that can be used to perform accurate in silico identification of pseudouridine sites from the large amount of RNA sequence data are highly desirable and can aid in the functional elucidation of this critical modification. Here, we propose a new computational approach, termed Porpoise, to accurately identify pseudouridine sites from RNA sequence data. Porpoise builds upon a comprehensive evaluation of 18 frequently used feature encoding schemes based on the selection of four types of features, including binary features, pseudo k-tuple composition, nucleotide chemical property and position-specific trinucleotide propensity based on single-strand (PSTNPss). The selected features are fed into the stacked ensemble learning framework to enable the construction of an effective stacked model. Both cross-validation tests on the benchmark dataset and independent tests show that Porpoise achieves superior predictive performance than several state-of-the-art approaches. The application of model interpretation tools demonstrates the importance of PSTNPs for the performance of the trained models. This new method is anticipated to facilitate community-wide efforts to identify putative pseudouridine sites and formulate novel testable biological hypothesis.

A New Approach for Accurate Prediction of Subharmonic Oscillation in Switching Regulators—Part II: Case Studies

This part of the paper illustrates the use of the expression derived in Part I for predicting subharmonic oscillation in switching regulators. Six case studies with different control schemes from the recent literature are used to validate the novel approach and the new derived expression, which is benchmarked for these case studies against results obtained using the discrete-time modeling approach or Floquet theory combined with Filippov method. Numerical simulations from the circuit-level switched models, implemented in PSIM software, are used to demonstrate the correctness of the derived closed-form condition. A procedure is presented to apply the approach of the paper to power converters with nonlinear sources, such as photovoltaic (PV) arrays or fuel cells. Experimental measurements from a boost converter prototype for PV applications are also provided to validate the theoretical predictions and the numerical simulations showing a remarkable agreement.

A New Approach for Accurate Prediction of Subharmonic Oscillation in Switching Regulators—Part I: Mathematical Derivations

This part of the paper takes a new look at the stability of the fundamental periodic behavior and the associated subharmonic oscillation boundary in dc–dc switching regulators with fixed-frequency pulse width modulation. After revisiting different approaches applied to a unified reduced-order model of switching regulators, the work presents a novel way of obtaining such a boundary without any simplification nor order reduction. A new simple closed-form condition is then obtained using the new approach, which is valid for different strategies with both trailing edge and leading edge modulation schemes. The critical condition is obtained from the steady-state response using an asymptotic approach without resorting to frequency-domain Fourier analysis or using the monodromy or the Jacobian matrix of the discrete-time model. Unlike the method based on the Fourier series expansion, the proposed method does not require the use of any transform and, most importantly, can be applied to bilinear switching regulators. As a byproduct of the proposed method, a singularity problem that is encountered in steady state, when the system involves integrators in the feedback loop, is addressed and a solution is developed to achieve a closed-form expression in the presence of such integrating feedback loops.

A New Approach for Accurate Prediction of Liquid Loading of Directional Gas Wells in Transition Flow or Turbulent Flow

Current common models for calculating continuous liquid-carrying critical gas velocity are established based on vertical wells and laminar flow without considering the influence of deviation angle and Reynolds number on liquid-carrying. With the increase of the directional well in transition flow or turbulent flow, the current common models cannot accurately predict the critical gas velocity of these wells. So we built a new model to predict continuous liquid-carrying critical gas velocity for directional well in transition flow or turbulent flow. It is shown from sensitivity analysis that the correction coefficient is mainly influenced by Reynolds number and deviation angle. With the increase of Reynolds number, the critical liquid-carrying gas velocity increases first and then decreases. And with the increase of deviation angle, the critical liquid-carrying gas velocity gradually decreases. It is indicated from the case calculation analysis that the calculation error of this new model is less than 10%, where accuracy is much higher than those of current common models. It is demonstrated that the continuous liquid-carrying critical gas velocity of directional well in transition flow or turbulent flow can be predicted accurately by using this new model.

New Approach for Accurate Prediction of Eddy Current Losses in Laminated Material in the Presence of Skin Effect With 2-D FEA

Two-dimensional finite element (FE) models normally assume the laminated core as a non-conductive material, and therefore, the damping effect of the eddy currents is ignored in the field solution. Besides, the use of integrated iron loss models in FE software could result in overestimated eddy current losses at high frequencies if the skin effect is ignored. This paper presents a simple way to accurately predict eddy current losses in laminations with FE analysis. An experimental setup along with the simulation models is used to demonstrate the validity of the method. The utility of this method in loss separation and identification of loss coefficients in the presence of skin effect is also illustrated. This method is also applied to a synchronous permanent magnet (PM) machine with sinusoidal current.

A New Approach for Accurate Prediction of the Phase Behavior of the Complex Mixtures of Acid Gases, Hydrocarbons, Water, and Methanol in the Petroleum Industry

In this work a new approach for accurate prediction of the vapor-liquid equilibrium of the complex mixtures of water, methanol, acid gases (H2S, CO2), and hydrocarbons considering the hydrogen bonding association, hydrolytic dissociation, and acid gas solvation effects is presented. The overall average absolute deviation between the predicted and measured compositions regarding 330 sour gas mixtures is about 3.22%. The proposed CPA/electrolyte model is quite reliable over wide ranges of temperatures and sour gas concentrations and can be employed for accurate design of sour natural treatment and flow assurance systems in oil and gas industries.

A new approach for accurate prediction of toxicity of amino compounds

Many amino compounds are highly toxic to living organisms and mammals. However, there is the increase of production and the consumption of these compounds annually as well as there is a need to design new amino compounds. A simple method is introduced to predict the toxicity of amino derivatives of organic compounds without using any computer codes, which can be done by prediction of the values of LD50 (lethal dose, 50 %) in rats via oral. It is based on the existence of some molecular moieties that may increase or decrease toxicity of amino derivatives. The predicted results of the new simple model for 58 aliphatic amines and anilines are more reliable than those values obtained by quantitative structure–toxicity relationship methods, where their computed data were available. The predicted results of the new method are also compared with experimental data for further 112 amino derivatives, which show good to excellent agreement.

Implementing Profile Permittivity Reconstruction Technique for Dielectric Tube in a Rectangular Waveguide

In this article a new approach for accurate prediction of scattering parameters of lossy dielectric materials is suggested. It is based on reconstructing complex permittivity profile of the test cell placed within rectangular waveguide and numerical determination of proper scattering parameters using algorithm Kao or its modification. Both propagating and evanescent modes excited in the test cell consisting of the dielectric tube filled with lossy materials are investigated to validate resonance and non-resonance phenomena. Comparison with earlier reported results shows a good agreement. The advantage of this method is the simplicity of its realization and accuracy in prediction of scattering parameters measured in practice.

A New Approach for Accurate Prediction of Loading in Gas Wells Under Different Flowina Conditions

Summary Several authors have introduced various mathematical equations to calculate the critical flow rate necessary to keep gas wells unloaded. The most widely used equation is that of Turner et al.1 However, Turner's equation required empirical adjustment with different ranges of data which made the application rather questionable. In this paper we present a new approach for calculating the critical flow rate necessary to keep gas wells unloaded. This approach still adopts Turner's basic concepts, but considers different flow conditions that result in different flow regimes. Hence it explains the previous discrepancies of Turner's equation (the droplet model) with different data ranges, and presents a new set of equations that eliminates the need for empirical adjustment and better matches actual data records.