Integrated Approach of Brain Disorder Analysis by Using Deep Learning Based on DNA Sequence

In order to research brain problems using MRI,PET,and CT neuroimaging,a correct understanding of brain function is required.This has been considered in earlier times with the support of traditional algorithms.Deep learning process has also been widely considered in these genomics data processing system.In this research,brain disorder illness incliding Alzheimer’s disease,Schizophrenia and Parkinson’s diseaseis is analyzed owing to misdetection of disorders in neuroimaging data examined by means fo traditional methods.Moeover,deep learning approach is incorporated here for classification purpose of brain disorder with the aid of Deep Belief Networks(DBN).Images are stored in a secured manner by using DNA sequence based on JPEG Zig Zag Encryption algorithm(DBNJZZ)approach.The suggested approach is executed and tested by using the performance metric measure such as accuracy,root mean square error,Mean absolute error and mean absolute percentage error.Proposed DBNJZZ gives better performance than previously available methods.

Output Linearization of Single-Input Single-Output Fuzzy System to Improve Accuracy and Performance

For fuzzy systems to be implemented effectively,the fuzzy membership function(MF)is essential.A fuzzy system(FS)that implements precise input and output MFs is presented to enhance the performance and accuracy of single-input single-output(SISO)FSs and introduce the most applicable input and output MFs protocol to linearize the fuzzy system’s output.Utilizing a variety of non-linear techniques,a SISO FS is simulated.The results of FS experiments conducted in comparable conditions are then compared.The simulated results and the results of the experimental setup agree fairly well.The findings of the suggested model demonstrate that the relative error is abated to a sufficient range(≤±10%)and that the mean absolute percentage error(MPAE)is reduced by around 66.2%.The proposed strategy to reduceMAPE using an FS improves the system’s performance and control accuracy.By using the best input and output MFs protocol,the energy and financial efficiency of every SISO FS can be improved with very little tuning of MFs.The proposed fuzzy system performed far better than other modern days approaches available in the literature.

Interval Type-2 Fuzzy Model for Intelligent Fire Intensity Detection Algorithm with Decision Making in Low-Power Devices

Local markets in East Africa have been destroyed by raging fires,leading to the loss of life and property in the nearby communities.Electrical circuits,arson,and neglected charcoal stoves are the major causes of these fires.Previous methods,i.e.,satellites,are expensive to maintain and cause unnecessary delays.Also,unit-smoke detectors are highly prone to false alerts.In this paper,an Interval Type-2 TSK fuzzy model for an intelligent lightweight fire intensity detection algorithm with decision-making in low-power devices is proposed using a sparse inference rules approach.A free open–source MATLAB/Simulink fuzzy toolbox integrated into MATLAB 2018a is used to investigate the performance of the Interval Type-2 fuzzy model.Two crisp input parameters,namely:FIT and FIG��are used.Results show that the Interval Type-2 model achieved an accuracy value of FIO�=98.2%,MAE=1.3010,MSE=1.6938 and RMSE=1.3015 using regression analysis.The study shall assist the firefighting personnel in fully understanding and mitigating the current level of fire danger.As a result,the proposed solution can be fully implemented in low-cost,low-power fire detection systems to monitor the state of fire with improved accuracy and reduced false alerts.Through informed decision-making in low-cost fire detection devices,early warning notifications can be provided to aid in the rapid evacuation of people,thereby improving fire safety surveillance,management,and protection for the market community.

A new polar motion prediction method combined with the difference between polar motion series

After the first Earth Orientation Parameters Prediction Comparison Campaign(1 st EOP PCC),the traditional method using least-squares extrapolation and autoregressive(LS+AR)models was considered as one of the polar motion prediction methods with higher accuracy.The traditional method predicts individual polar motion series separately,which has a single input data and limited improvement in prediction accuracy.To address this problem,this paper proposes a new method for predicting polar motion by combining the difference between polar motion series.The X,Y,and Y-X series were predicted separately using LS+AR models.Then,the new forecast value of X series is obtained by combining the forecast value of Y series with that of Y-X series;the new forecast value of Y series is obtained by combining the forecast value of X series with that of Y-X series.The hindcast experimental comparison results from January 1,2011 to April 4,2021 show that the new method achieves a maximum improvement of 12.95%and 14.96%over the traditional method in the X and Y directions,respectively.The new method has obvious advantages compared with the differential method.This study tests the stability and superiority of the new method and provides a new idea for the research of polar motion prediction.

Forecasting Inflation Rate of Zambia Using Holt’s Exponential Smoothing

In this paper, the Holt’s exponential smoothing and Auto-Regressive Integrated Moving Average (ARIMA) models were used to forecast inflation rate of Zambia using the monthly consumer price index (CPI) data from May 2010 to May 2014. Results show that the ARIMA ((12), 1, 0) is an adequate model which best fits the CPI time series data and is therefore suitable for forecasting CPI and subsequently the inflation rate. However, the choice of the Holt’s exponential smoothing is as good as an ARIMA model considering the smaller deviations in the mean absolute percentage error and mean square error. Moreover, the Holt’s exponential smoothing model is less complicated since you do not require specialised software to implement it as is the case for ARIMA models. The forecasted inflation rate for April and May, 2015 is 7.0 and 6.6 respectively.

An Improved Rate Control Method Based on Modification at Scene Change for H.264

This paper presents an improved rate control method for H.264. First, the scene changes are detected by the average absolute difference of the brightness histograms between the adjacent frames. Then, the bit allocation and quantization parameters are adjusted, using a certain threshold. In addition, the calculation of the mean absolute difference （MAD） is modified in an alternative way, which makes the rate distortion optimization （RDO） more accurate. Extensive simulation results show that the proposed method, compared with G012, can improve the average peak signal-to-noise ratio （PSNR） and moderate the image quality.

Performance of Dexcom G5 and FreeStyle Libre sensors tested simultaneously in people with type 1 or 2 diabetes and advanced chronic kidney disease

BACKGROUND Advanced chronic kidney disease(CKD) is a common complication for people with type 1 and 2 diabetes and can often lead to glucose instability. Continuous glucose monitoring(CGM) helps users monitor and stabilize their glucose levels. To date, CGM and intermittent scanning CGM are only approved for people with diabetes but not for those with advanced CKD.AIM To compare the performance of Dexcom G5 and FreeStyle Libre sensors in adults with type 1 or 2 diabetes and advanced CKD.METHODS This was a non-randomized clinical trial that took place in two outpatient clinics in western Sweden. All patients with type 1 or 2 diabetes and an estimated glomerular filtration rate(eGFR) of < 30 mL/min per 1.73 m^(2) were invited to participate. Forty patients(full analysis set = 33) carried the Dexcom G5 sensor for 7 d and FreeStyle Libre sensor for 14 d simultaneously. For referencing capillary blood glucose(SMBG) was measured with a high accuracy glucose meter(HemoCue®) during the study period. At the end of the study, all patients were asked to answer a questionnaire on their experience using the sensors.RESULTS The mean age was 64.1(range 41-77) years, hemoglobin A1 c was 7.0% [standard deviation(SD) 3.2], and diabetes duration was 28.5(SD 14.7) years. A total of 27.5% of the study population was on hemodialysis and 22.5% on peritoneal dialysis. The mean absolute relative difference for Dexcom G5 vs SMBG was significantly lower than that for FreeStyle Libre vs SMBG [15.2%(SD 12.2) vs 20.9%(SD 8.6)], with a mean difference of 5.72 [95% confidence interval(CI): 2.11-9.32;P = 0.0036]. The mean absolute difference was also significantly lower for Dexcom G5 than for FreeStyle Libre, 1.21 mmol/L(SD 0.78) and 1.76 mmol/L(SD 0.78), with a mean diffrenec of 0.55(95%CI: 0.27-0.83;P = 0.0004).The mean difference(MD) was-0.107 mmol/L and-1.10 mmol/L(P = 0.0002), respectively. In all, 66% of FreeStyle Libre values were in the no risk zone on the surveillance error grid compared to 82% of Dexcom G5 values.CONCLUSION Dexcom G5 produces more accurate sensor values than FreeStyle Libre in people with diabetes and advanced CKD and is likely safe to be used by those with advanced CKD.

Application of Hidden Markov Models in Stock Forecasting

In this paper,we tested our methodology on the stocks of four representative companies:Apple,Comcast Corporation(CMCST),Google,and Qualcomm.We compared their performance to several stocks using the hidden Markov model(HMM)and forecasts using mean absolute percentage error(MAPE).For simplicity,we considered four main features in these stocks:open,close,high,and low prices.When using the HMM for forecasting,the HMM has the best prediction for the daily low stock price and daily high stock price of Apple and CMCST,respectively.By calculating the MAPE for the four data sets of Google,the close price has the largest prediction error,while the open price has the smallest prediction error.The HMM has the largest prediction error and the smallest prediction error for Qualcomm’s daily low stock price and daily high stock price,respectively.

On the Mean Absolute Geodesic Curvature of Closed Curves in 2-Dimensional Space Forms

In this paper, we establish some formulas on closed curves in 2-dimensionalspace forms. Mean absolute geodesic curvature is introduced to describe the average curving of aclosed curve. In this sense, a closed curve could be compared with a geodesic circle that is theboundary of a convex geodesic circular disk containing the closed curve. The comparison can be usedto show some properties of space forms only on themselves.

Computer Experiments on Rapidly Rotating Plane Couette Flow

The turbulence in plane Couette flow subjected to system rotation is investigated. The anti-cyclonic rotation rate is well above the range in which roll-cells occurand close to the upper bound, beyond which no stationary turbulent states of motionexist. The mean velocity profile exhibits a linear region over 80% of the cross-section, inwhich the mean absolute vorticity is driven to zero. Viscous effects still prevail in narrow regions next to the walls, whereas the quasi-homogeneous central core exhibitsabnormal anisotropies of the Reynolds stress tensor, the vorticity tensor and the energy dissipation rate tensor. In spite of the distinctly higher turbulence level observed,a 13% drag reduction is found. This paradoxical finding is ascribed to configurationalchanges in the turbulence field brought about by the system rotation.

Tile selection method based on error minimization for photomosaic image creation

Photomosaic images are composite images composed of many small images called tiles.In its overall visual effect,a photomosaic image is similar to the target image,and photomosaics are also called“montage art”.Noisy blocks and the loss of local information are the major obstacles in most methods or programs that create photomosaic images.To solve these problems and generate a photomosaic image in this study,we propose a tile selection method based on error minimization.A photomosaic image can be generated by partitioning the target image in a rectangular pattern,selecting appropriate tile images,and then adding them with a weight coefficient.Based on the principles of montage art,the quality of the generated photomosaic image can be evaluated by both global and local error.Under the proposed framework,via an error function analysis,the results show that selecting a tile image using a global minimum distance minimizes both the global error and the local error simultaneously.Moreover,the weight coefficient of the image superposition can be used to adjust the ratio of the global and local errors.Finally,to verify the proposed method,we built a new photomosaic creation dataset during this study.The experimental results show that the proposed method achieves a low mean absolute error and that the generated photomosaic images have a more artistic effect than do the existing approaches.

Reconstructing Missing Hourly Real-Time Precipitation Data Using a Novel Intermittent Sliding Window Period Technique for Automatic Weather Station Data

Precipitation is the most discontinuous atmospheric parameter because of its temporal and spatial variability. Precipitation observations at automatic weather stations（AWSs） show different patterns over different time periods. This paper aims to reconstruct missing data by finding the time periods when precipitation patterns are similar, with a method called the intermittent sliding window period（ISWP） technique—a novel approach to reconstructing the majority of non-continuous missing real-time precipitation data. The ISWP technique is applied to a 1-yr precipitation dataset（January 2015 to January 2016）, with a temporal resolution of 1 h, collected at 11 AWSs run by the Indian Meteorological Department in the capital region of Delhi. The acquired dataset has missing precipitation data amounting to 13.66%, of which 90.6% are reconstructed successfully. Furthermore, some traditional estimation algorithms are applied to the reconstructed dataset to estimate the remaining missing values on an hourly basis. The results show that the interpolation of the reconstructed dataset using the ISWP technique exhibits high quality compared with interpolation of the raw dataset. By adopting the ISWP technique, the root-mean-square errors（RMSEs）in the estimation of missing rainfall data—based on the arithmetic mean, multiple linear regression, linear regression,and moving average methods—are reduced by 4.2%, 55.47%, 19.44%, and 9.64%, respectively. However, adopting the ISWP technique with the inverse distance weighted method increases the RMSE by 0.07%, due to the fact that the reconstructed data add a more diverse relation to its neighboring AWSs.

An adaptive fast search algorithm for block motion estimation in H.264

Motion estimation is an important issue in H.264 video coding systems because it occupies a large amount of encoding time.In this paper,a novel search algorithm which utilizes an adaptive hexagon and small diamond search (AHSDS) is proposed to enhance search speed.The search pattern is chosen according to the motion strength of the current block.When the block is in active motion,the hexagon search provides an efficient search means;when the block is inactive,the small diamond search is adopted.Simulation results showed that our approach can speed up the search process with little effect on distortion performance compared with other adaptive approaches.