Нейромережеві моделі управління процесом функціонування систем захисту інформації

Nowadays, The Indian stock market is very promising and every trader wants to take advantage of this market. So, prediction plays an important role to predict the value of Nifty 50 in the stock market. Nifty 50 comes under the futures and options (F&O) segment in the market. Machine learning tools are used for better prediction purposes. In this paper, we have presented four neural network models (Feedforward neural network, generalized regression neural network, radial basis neural network, and exact radial basis neural network) and all are based on machine learning. The research work proposed four model to predict the accuracy of Nifty 50 value by comparing it with a minimum error rate. The data set samples of last nine years, Jan, 2013 to June, 2021 collected from the Yahoo Finance URL, has been used in order to carried out the research work. Total four features as initial opening value, day high, day low, and total volumes traded on that particular day are considered as input parameters and the closing value of the day taken as an output parameter for the Nifty 50 value of Stock Market. The experimental results reveals that the best model to predict the Nifty 50 stock market is radial basis neural network model provides 98.17% accuracy in contrast to the rest of the three models.

The problem of self-collisions of manipulators with several links installed on a robot can arise when they work together in one zone. To prevent self-collisions, it is necessary to develop methods for their detection and their subsequent inclusion in control algorithms. This paper proposes an approach for determining the occurrence of self-collisions of manipulators using the Artificial Neural Networks approach. In contrast to the regression problem, this article proposes a classification approach. The effectiveness of the proposed approach was tested on robots with multilink manipulators “Ichtiandr” and SAR-401 and their simulators. Self-collision detection using the proposed method is much faster than using the traditional approach of solving the inverse kinematics problem without loss of accuracy. The problem was solved by constructing various Artificial Neural Networks and then checking the accuracy of the solution. A comparative analysis of Artificial Neural Networks was carried out and as a result, the Artificial Neural Networks approach showing the best accuracy was selected. The problem was solved for a robot with two manipulators. The resulting solution can be extended to a larger number of manipulators installed on the robot.

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

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.

این مطالعه به منظور بررسی پیش بینی شاخص های عملکرد تولیدی در مرغان تخم گذار با استفاده از شبکه های عصبی مصنوعی و رگرسیون غیرخطی چندگانه انجام شد. بررسی بر روی اطلاعات چهار دوره متوالی پرورش در یک واحد پرورش مرغ تخم گذار صورت گرفت. روش های داده-کاوی شامل رگرسیون خطی و غیر خطی، شبکه عصبی پرسپترون سه لایه، شبکه عصبی پرسپترون چهار لایه و شبکه عصبی با تابع پایه ای شعاعی بود. در این مدل ها از متغیرهای سن گله، میزان خوراک مصرفی و فصل تولید به عنوان متغیر پیشگو و شاخص های عملکرد تولیدی شامل درصد تخم-گذاری، وزن توده ای تخم مرغ تولیدی و ضریب تبدیل غذایی به عنوان متغیر پاسخ استفاده شد. نتایج نهایی رگرسیون های خطی نشان داد که برای تمامی متغیرهای وابسته مورد مطالعه متغیر مستقل سن گله معنی دار می باشد. بنابراین رگرسیون غیر خطی شاخص های عملکرد تولیدی در مقابل سن برای مقایسه با شبکه های عصبی مختلف مورد بررسی قرار گرفت و برای مقایسه کلیه مدل ها از ضریب تعیین (R2) و میانگین قدر مطلق خطا (MAE) استفاده شد. نتایج نشان داد بین شبکه های عصبی مصنوعی مختلف مورد مطالعه، شبکه با تابع پایه ای شعاعی بهتر از سایر مدل های در پیش‌بینی شاخص های عملکرد تولیدی مرغان تخم گذار عمل می کند.

In the current article, an experimental investigation has been implemented of flow and heat transfer characteristics in a parabolic trough solar collector (PTSC) using both nano-fluids and artificial neural networks modeling. Water was used as a standard working fluid in order to compare with two different types of nano-fluid namely, nano-CuO /H2O and nano-TiO2/ H2O, both with a volume concentration of 0.02. The performance of the PTSC system was eval-uated using three main indicators: outlet water temperature, useful energy and thermal efficiency under the influence of mass flowrate ranging from 30 to 80 Lt/hr. In parallel, an artificial neural network (ANN) has been proposed to predict the thermal efficiency of PTSC depending on the experimental re-sults. An Artificial Neural Network (ANN) model consists of four inputs, one output parameter and two hidden layers, two neural network models (4-2-2-1) and (4-9-9-1) were built. The experimental results show that CuO/ H2O and TiO2/H2O have higher thermal performance than water. Overall, it was veri-fied that the maximum increase in thermal efficiency of TiO2/H2O and CuO/H2O compared to water was 7.12% and 19.2%, respectively. On the oth-er hand, the results of the model 4-9-9-1 of ANN provide a higher reliability and accuracy for predicting the Thermal efficiency than the model 4-2-2-1. The results revealed that the agreement in the thermal efficiency between the ANN analysis and the experimental results about of 91% and RMSE 3.951 for 4-9-9-1 and 86% and RMSE 5.278 for 4-2-21.

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.

Comparative study of artificial neural networks and multiple regression analysis for predicting hoisting times of tower cranes

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.

Dementia, a chronic neurological disorder characterized by memory loss and impaired cognitive ability, has affected tens of millions of people globally and projections estimate this number to rise in the coming decade. Despite its widespread impact, dementia remains one of many diseases with no true cure or definitive method of diagnosis. This study aims to explore the question of how artificial neural networks can be used to predict dementia. Analyzing current research on neural networks for disease diagnosis highlights a notable gap, known as the "black box conundrum," indicating the challenge of tracing steps leading to a given output. Another gap in the literature is the absence of comparative data on factors influencing successful neural network algorithms. Conducting a comprehensive cross-continental comparative analysis across North America, Asia, and Europe, and utilizing an experimental design with the UCI ML Repository’s Parkinson’s Database, this study specifically aims to fill the identified gaps. The findings reveal key elements for successful neural networks: a data sample exceeding 100, clinically intelligible data values, and a backpropagation model. In the experimental phase, a comparison between a backpropagation model and logistic regression, paired with Shapley value analysis, elucidated a 94.87% accuracy for the former, surpassing the latter's 92.3%. These findings extend beyond dementia, providing valuable insights for neural network applications in all healthcare settings. Future research endeavors should focus on refining and optimizing neural networks with the ultimate goal of achieving 100% accuracy and maximized efficiency.

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

In this paper, a sensitivity analysis of artificial neural networks (NNs) is presented and employed for estimating the patch load resistance of plate girders subjected to patch loading. To evaluate the accuracy of the proposed NN model, the results are compared with the previously proposed empirical models, so that we can estimate the resistance of plate girders subjected to patch loading. The empirical models are calibrated, for improving the formulae, with experimental data set which was collected from the corresponding literature. NNs models are later trained and validated through using the existing experimental data. In this process several NNs architectures are taken into account. A set of good NNs models are selected and then analyzed regarding their robustness when confronted with the test data set and regarding their ability to reproduce the effect of uncertainty on the data. A sensitivity analysis is conducted herein in order to investigate the effect of variability in material and geometrical properties of plate girders. Thereafter, several estimates measuring the efficiency and the quality of the NN model and the calibrated models are obtained and discussed.