Analysis of Artificial Neural Networks on Predicting Dementia

The escalating demands and increasing awareness for the environment, resulted in deployment of Photovoltaic (PV) system as a viable option. PV system are widely installed for numerous applications. However, the challenges in tracking the maximum power with intermittent atmospheric condition and varying load is significant. Maximum Power Point Tracking (MPPT) algorithms are employed and based on their convergence speed, control of external variations and oscillation, the output power efficiency, and other significant factors viz. the algorithm complexity and implementation cost, novel MPPT approach are preferable than the conventional approach. This paper presents an artificial intelligence-based optimization controller for MPPT in a PV system under varying load and irradiance conditions. Comparative analysis of Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) based MPPT is simulated and analysed. The PV system consisting of PV array and boost converter with MPPT controller feeds the DC load. The power conversion and panel efficiency are the significant factors to determine the effectiveness of tracking maximum power point. The simulation results show the performance of these controllers on the PV panel output power and the load side output power under changing loads and irradiance. In addition, the comparison of PV panel efficiency of ANN and PSO based MPPT techniques w.r.t changing loads is carried out. Based on the above analysis, PSO based MPPT algorithm marginally outperforms the ANN based MPPT algorithm. Further, the implementation of hybrid MPPT (ANN &PSO) for higher accuracy and tracking capability can be carried out as future work.

Multiple serum tests were performed on archival samples from patients who participated in trials to assess the ProstaScint scan staging ability. Traditional statistical analysis as well as artificial neural network (ANN) analysis were employed to evaluate individual patients and the group as a whole. The results were evaluated so that each factor was tested for prognostic value. Data obtained from serum tests, bone scans, and ProstaScint scans were evaluated by traditional statistical methods and ANN to determine the individual value in clinical staging of prostate cancer. Two hundred seventy-five patients (180 postprostatectomy, 95 intact prostate) with prostate cancer (14 with distant metastases) were available for analysis. Data available included: clinical state (remission or progression), most recent clinical TNM stage, bone scan, and ProstaScint scan. Serum was tested for prostate-specific membrane antigen(PSMA), prostate-specific antigen(PSA), free PSA (fPSA), and complexed PSA (cPSA). Additional calculations included percent free PSA, and percent complexed PSA. Spearman individual statistical assessment for traditional group evaluation revealed no significant factors for T-stage. The free PSA and complex PSA had a significant association with node (N)-status. The distant metastases (M) stage correlated well with the bone scan and clinical stage. ANN analysis revealed no significant T-stage factors. N-stage factors showed a 95% sensitivity and 49% specificity. These factors included the presence or absence of a prostate, PSA serum levels, bone scan, and ProstaScint scans as major associated indicators. ANN analysis of the important variables for M-stage included ProstaScint scan score, and PSA levels (total, percent complexed, percent free, and fPSA). These factors were associated with a 95% sensitivity and 15% specificity level. Two hundred seventy-five patients receiving treatment for prostate cancer were evaluated by ANN and traditional statistical analysis for factors related to stage of disease. ANN revealed that PSA levels, determined by a variety of ways, ProstaScint scan, and bone scan, were significant variables that had prognostic value in determining the likelihood of nodal disease, or distant disease in prostate cancer patients.

Diamond drilling has been widely used in the different civil engineering projects. The prediction of penetration rate in the drilling is especially useful for the feasibility studies. In this study, the predictability of penetration rate for the diamond drilling was investigated from the operational variables and the rock properties such as the uniaxial compressive strength, the tensile strength and the relative abrasiveness. Both the multiple regression and the artificial neural networks (ANN) analysis were used in the study. Very good models were derived from ANN analysis for the prediction of penetration rate. The comparison of ANN models with the regression models indicated that ANN models were much more reliable than the regression models. It is concluded that the penetration rate for the diamond drilling can be reliably estimated from the uniaxial compressive strength, the tensile strength and the relative abrasiveness using the ANN models.

In a previous report, we investigated the capability of commercially available immunohematology tests based on gel technology to add useful information for the diagnosis of autoimmune hemolytic anemia (AIHA). In this report, we analyzed the same casuistic to find useful information on the importance of different immunohematology tests for the AIHA diagnosis, but using the artificial neural network (ANN) analysis. We studied 588 samples with a positive direct antiglobulin test (DAT), of which 52 samples came from patients with AIHA. The samples were analyzed with the ANN using the multilayer perceptron with the backpropagation algorithm. Using the ANN in the observed data set, the predictive value for the presence of AIHAs was 94.7%. The rate of DAT-positive cases that were not AIHA and that were correctly classified was 99.4%. The receiver operating curve area for the model was 0.99. The independent variable importance analysis found that the gel centrifugation test anti-IgG titer was an important contributor to the network performance, but other variables such as the IgG subclasses can also be considered important. The use of the ANN permitted us to identify immunohematology tests that were "hidden" with the common statistical models used previously. This was the case for the IgG subclasses. However, it is very likely that the information given to the network from those tests is quantitative rather than qualitative.

Thyroid nodules with a predominant follicular structure are often diagnosed as indeterminate at fine-needle aspiration biopsy (FNAB). We studied 453 patients with a thyroid nodule diagnosed as indeterminate at FNAB by using a feed-forward artificial neural network (ANN) analysis to integrate cytologic and clinical data, with the goal of subgrouping patients into a high-risk and in a low-risk category. Three hundred seventy-one patients were used to train the network and 82 patients were used to validate the model. The cytologic smears were blindly reviewed and classified in a high-risk and a low-risk subgroup on the basis of standard criteria. Neural network analysis subdivided the 371 lesions of the first series into a high-risk group (cancer rate of approximately 33% at histology) and a low-risk group (cancer rate of 3%). Only cytologic parameters contributed to this classification. Analysis of the receiver operating characteristic (ROC) curves demonstrated that the ANN model discriminated with higher sensitivity and specificity between benign and malignant nodules compared to standard cytologic criteria (p < 0.001). This value did not show degradation when ANN predictions were applied to the validation series of 82 nodules. In conclusion, neural network analysis of cytologic data may be a useful tool to refine the risk of cancer in patients with lesions diagnosed as indeterminate by FNAB.

Modern strategy for the development of plant protection substances includes computer-aided molecular design as a rational approach used for screening, optimization, and the design of new potent agents in plant protection. Coumarins, secondary plant metabolites, and their derivatives demonstrated a wide range of biological activities on different organisms, as well as their applications in agriculture as eco-friendly plant protection agents. Coumarin derivatives have been reported as strong agents against pathogenic species of fungi. 46 Novel coumarin derivatives have been evaluated against four pathogen fungi (Macropomina phaseolina, Sclerotinia sclerotiorum, Fusarium oxysporum, Fusarium culmorum). Observed compounds have shown activity against two fungi, M. phaseolina, S. sclerotiorum. Quantitative structure-activity relationships (QSAR) analysis has been performed on obtained experimental data. Since the quality models were not obtained by multiple linear regression (MLR), the artificial neural networks (ANN) analysis was performed using four descriptors appearing in the best MLR models. For the antifungal activity against S. sclerotiorum, ANN analysis was performed using descriptors: 3D-MoRSE (Mor19v); Moran autocorrelation (MATS7v); relative negative charge (RNCG AM1); and E-States, the sum of (- CH2 -) (SssCH2), while for M. phaseolina: geometrical symmetry (SYMM2); MATS4m; MATS5m; and the sum of (= C&lt;) (SdssC). Nonlinearities of the best ANN model compared with the linear model improved the coefficient of determination to 76.2 % for S. sclerotiorum and 93.6 % for M. phaseolina, and showed a better external predictive ability 92.44 % and 87.81 %, respectively. ANN could be performed for further research of more effective coumarin agents against the pathogen fungi.

Neural network models, which were initially used to model the processes of recognition of graphic images, are now widely used in the field of recognition of multi-parameter objects and in adjusting the parameters of complex systems. Such an application is effective in solving problems of information security management. With the help of neural networks, the tasks of classifying threats, choosing the parameters of protection measures, and adjusting the operating modes of information systems are successfully solved. In the scientific literature, there is a wide variety of approaches and methods for creating artificial neural networks. At the initial stage of designing information security systems, when it is necessary to determine the main parameters of the software being created, it poses a problem for the designer. Miscalculations made at this stage of the design can lead to the unsuccessful completion of the entire project. The article provides a brief comparative analysis of artificial neural networks that differ in the methods of setting (training) the network and in the form of its structure. The applicability, advantages, and disadvantages of such methods for tuning neural networks are characterized. These are the method of backpropagation of an error, genetic algorithm, iterative Widrow-Hoff algorithm with variable step, modified least squares method, and sequential learning method. The differences in the structure for networks designed to solve the problems of adjusting information processes and recognizing multi-parameter objects are shown. To regulate the processes, the structure of the neural network is studied on the example of the fuzzy neural network ANFIS. For the classification problems, the structure of a multilayer perceptron is given, in which the structure of the inner layers reflects the ontological network of the subject area under consideration. The presented results can be used to justify the type of neural network used for a specific task in the field of information security.

Recurrent neural networks-guided vector-valued synchronized key exchange for secure and privacy-preserving communication in Industrial Internet of Things

Research on calculation method of free flow discharge based on artificial neural network and regression analysis

The objective of the study is to investigate the fluid flow and heat transfer characteristics applying Artificial Neural Networks (ANN) analysis in triangular-shaped cavities for the analysis of magnetohydrodynamics (MHD) mixed convection with varying fluid velocity of water/Al2O3 nanofluid. No study has yet been conducted on this geometric configuration incorporating ANN analysis. Therefore, this study analyzes and predicts the complex interactions among fluid flow, heat transfer, and various influencing factors using ANN analysis. The process of finite element analysis was conducted, and the obtained results have been verified by previous literature. The Levenberg-Marquardt backpropagation technique was selected for ANN. Various values of the Richardson number (0.01 ≤ Ri ≤ 5), Hartmann number (0 ≤ Ha ≤ 100), Reynolds number (50 ≤ Re ≤ 200), and solid volume fraction of the nanofluid (ϕ = 1%, 3% and 4%) have been selected. The ANN model incorporates the Gauss-Newton method and the method of damped least squares, making it suitable for tackling complex problems with a high degree of non-linearity and uncertainty. The findings have been shown through the use of streamlines, isotherm plots, Nusselt numbers, and the estimated Nusselt number obtained by ANN. Increasing the solid volume fraction improves the rate of heat transmission for all situations with varying values of Ri, Re, and Ha. The Nusselt number is greater with larger values of the Ri and Re parameters, but it lessens for higher value of Ha. Furthermore, ANN demonstrates exceptional precision, as evidenced by the Mean Squared Error and R values of 1.05200e-6 and 0.999988, respectively.

This work aimed to assess the water quality of the Tuojiang River Basin in recent years to provide a better understanding of its current pollution situation, and the potential pollution risks and causes. Water quality parameters such as dissolved oxygen (DO), ammonia–nitrogen (NH3-N), total phosphorus (TP), the permanganate index (CODMn), five-day biochemical oxygen demand (BOD5), pH, and concentrations of various heavy metals were measured in the Tuojiang River, according to the national standards of the People’s Republic of China. Samples were collected between 2012 to 2018 at 11 national monitoring sites in the Tuojiang River Basin. The overall water pollution situation was evaluated with back propagation artificial neural network (BP-ANN) analysis. The pollution causes were analyzed considering both industrial wastewater discharge in the upper reaches and the current pollution situation. We found potential risks of excessive NH3-N, TP, Cd, Hg, and Pb concentrations in the Tuojiang River Basin. Moreover, corresponding water pollution control suggestions were given.

Artificial neural network (ANN) analysis was used to predict the permeability of selected compounds through Caco-2 cell monolayers. Previously reported models, which were shown to be useful in the prediction of permeability values, use many structural parameters. More complex equations have also been proposed using both linear and non-linear relationships, including ANN analysis and various structural parameters. But proposed models still need to be developed using different neuron patterns for more precise predictions and a better understanding of which factors affect the permeation. To develop a simple and useful model or method for easy prediction is also a general need. Permeability coefficients (log kp) were obtained from various literature sources. Some structural parameters were calculated using computer programs. Multiple linear regression analysis (MLRA) was used to predict Caco-2 cell permeability for the set of 50 compounds (r2 = 0.403). A successful ANN model was developed, and the ANN produced log kp values that correlated well with the experimental ones (r2 = 0.952). The permeability of a compound, famotidine, which has not previously been studied, through the Caco-2 cell monolayer was investigated, and its permeability coefficient determined. It was then possible to compare the experimental data with that predicted using the trained ANN with previously determined Caco-2 cell permeability values and structural parameters of compounds. The model was also tested using literature values. The developed and described ANN model in this publication does not require any experimental parameters; it could potentially provide useful and precise prediction of permeability for new drugs or other penetrants.

The usability of Cerchar abrasivity index for the prediction of UCS and E of Misis Fault Breccia: Regression and artificial neural networks analysis

The present study focuses on the application of artificial neural network (ANN) and Multiple linear Regression (MLR) analysis for developing a model to predict the unconfined compressive strength (UCS) and split tensile strength (STS) of the fiber reinforced clay stabilized with grass ash, fly ash and lime. Unconfined compressive strength and Split tensile strength are the nonlinear functions and becomes difficult for developing a predicting model. Artificial neural networks are the efficient tools for predicting models possessing non linearity and are used in the present study along with regression analysis for predicting both UCS and STS. The data required for the model was obtained by systematic experiments performed on only Kaolin clay, clay mixed with varying percentages of fly ash, grass ash, polypropylene fibers and lime as between 10-20%, 1-4%, 0-1.5% and 0-8% respectively. Further, the optimum values of the various stabilizing materials were determined from the experiments. The effect of stabilization is observed by performing compaction tests, split tensile tests and unconfined compression tests. ANN models are trained using the inputs and targets obtained from the experiments. Performance of ANN and Regression analysis is checked with statistical error of correlation coefficient (R) and both the methods predict the UCS and STS values quite well; but it is observed that ANN can predict both the values of UCS as well as STS simultaneously whereas MLR predicts the values separately. It is also observed that only STS values can be predicted efficiently by MLR.

The water quality of Tasik Kejuruteraan UKM water channel is an issue that should be emphasized because excess water will be discharged to Sungai Langat where this river is the source of water supply for the residents in some part of Klang Valley area. The water quality parameters must comply with the standard assigned by Malaysian’s Department of Environment (DOE). As a first step towards proper monitoring programme, a proper analysis of the water quality should be conducted thoroughly. As such, this study composed of two parts. First, experimental investigation of the water quality of Tasik Kejuruteraan UKM water channel was carried out to assess its environmental quality. Experiment was conducted to determine the Water Quality Index of Tasik Kejuruteraan water channel based upon guidelines as stipulated by DOE. The results indicate that Tasik Kejuruteraan UKM water channel is polluted and proper water quality control should be applied. In the second part of this study, preliminary analysis of artificial neural network (ANN) and neural fuzzy network for water quality control was conducted to determine the suitability of these controllers in water treatment system. Based on the simulations conducted, neural fuzzy network shows better efficacy in the pH control scheme compared with ANN because there is no oscillation detected in the corresponding controlled variable.