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Author(s):  
Kalva Sindhu Priya

Abstract: In the present scenario, it is quite aware that almost every field is moving into machine based automation right from fundamentals to master level systems. Among them, Machine Learning (ML) is one of the important tool which is most similar to Artificial Intelligence (AI) by allowing some well known data or past experience in order to improve automatically or estimate the behavior or status of the given data through various algorithms. Modeling a system or data through Machine Learning is important and advantageous as it helps in the development of later and newer versions. Today most of the information technology giants such as Facebook, Uber, Google maps made Machine learning as a critical part of their ongoing operations for the better view of users. In this paper, various available algorithms in ML is given briefly and out of all the existing different algorithms, Linear Regression algorithm is used to predict a new set of values by taking older data as reference. However, a detailed predicted model is discussed clearly by building a code with the help of Machine Learning and Deep Learning tool in MATLAB/ SIMULINK. Keywords: Machine Learning (ML), Linear Regression algorithm, Curve fitting, Root Mean Squared Error


2021 ◽  
Vol 21 (4) ◽  
pp. 317-322
Author(s):  
Tatiana Krutsevich ◽  
Natalia Panhelova ◽  
Sergii Trachuk ◽  
Viktor Kuibida ◽  
Roman Pidleteychuk ◽  
...  

Research purpose: is a substantiation and modeling of appropriate norms of physical readiness of youth for service in the army. Materials and Methods. The expert group included 21 specialists (whose field of activity is physical education, special training, physical training in the security and defense forces). The following research methods were used to solve the problem posed in the work: theoretical analysis, comparison, systematization and generalization of materials of scientific, historical, methodical literature and guiding documents; expert evaluation (Delphi and analysis of Saati hierarchies); methods of mathematical statistics. Results. It is determined that even simple statistical methods in combination with expert information when choosing promising solutions often give better results than accurate calculations with a focus on averages. A comprehensive approach using peer review (Delphi), a method of modern theory of hierarchical systems Saati allowed to determine the structure of the projected model of physical fitness of young people for military service. The structural interconnected components of the predicted model with the corresponding weighting factors are the level of formation and development of general and special physical qualities (ρ1 = 0.411), the level of formation and development of special physical qualities (ρ2 = 0.235), the level of acquisition of military applied motor skills (ρ3 = 0.216), the state of the cardiovascular system (ρ4 = 0.138). Conclusions. The presented structural predictable model of integrated assessment of physical readiness of youth for service in the army allows to define limits of levels of components and to estimate their level of formation in points from 1 to 12, and also to correct the maintenance of means of physical training depending on such components which lag behind a proper norm.


Author(s):  
Ahmad Jafari ◽  
Ramin Mazaheri Nezhad Fard ◽  
Sima Shahabi ◽  
Farid Abbasi ◽  
Golshid Javdani Shahedin ◽  
...  

Background and Objectives: Silver nanoparticles (Ag-NPs) are potent antimicrobial agents, which have recently been used in dentistry. The aim of the current study was to optimize antimicrobial activity of Ag-NPs used in preparing irre- versible hydrocolloid impressions against three microorganisms of Escherichia coli, Streptococcus mutans and Candida albicans. Materials and Methods: After assessing antimicrobial activity of the compound using disk diffusion method, three parame- ters of concentration of Ag-NPs (250-1000 ppm), ratio of hydrocolloid impression material powder to water (0.30-0.50) and time of mixing (20.0-60.0 s), affecting antimicrobial activity of irreversible hydrocolloid impression materials against the three microorganisms, were optimized. This combined process was successfully modeled and optimized using Box-Behnken design with response surface methodology (RSM). Decreases in colony number of E. coli, S. mutans and C. albicans were proposed as responses. Results: Qualitative antimicrobial assessments respectively showed average zone of inhibition (ZOI) of 3.7 mm for E. coli, 3.5 mm for S. mutans and 4 mm for C. albicans. For all responses, when the mixing duration and powder-to-water ratio increased, the circumstances (mixing duration of 59.38 s, powder-to-water ratio of 0.4 and Ag-NP concentration of 992 response) increased. Results showed that in optimum ppm, the proportion of decreases in colony numbers was maximum (89.03% for E. coli, 87.08% for S. mutans and 74.54% for C. albicans). Regression analysis illustrated a good fit of the ex- perimental data to the predicted model as high correlation coefficients validated that the predicted model was well fitted with data. Values of R2Adj with R2Pred were associated to the accuracy of this model in all responses. Conclusion: Disinfection efficiency dramatically increased with increasing of Ag-NP concentration, powder-to-water ratio and mixing time.


Geofluids ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Daiming Hu ◽  
Bülent Tezkan ◽  
Mingxin Yue ◽  
Xiaodong Yang ◽  
Xiaoping Wu ◽  
...  

Water inrush in tunneling poses serious harm to safe construction, causing economic losses and casualties. The prediction of water hazards before tunnel excavations becomes an urgent task for governments or enterprises to ensure security. The three-dimensional (3D) direct current (DC) resistivity method is widely used in the forward-probing of tunnels because of its low cost and highly sensitive response to water-bearing structures. However, the different sizes of the tunnel will distort the distribution of the potential field, which causes an inaccurate prediction of water-bearing structures in front of the tunnels. Some studies have pointed out that the tunnel effect must be considered in the quantitative interpretation of the data. However, there is rarely a predicted model considering the tunnel effect to be reported in geophysical literature. We developed a predicted model algorithm by considering the tunnel effect for forward-probing in tunnels. The algorithm is proven to be feasible using a slab analytic model. By simulating a large number of models with different tunnel sizes, we propose an equation, which considers the tunnel effect and can predict the water-bearing structures ahead of the tunnel face. The Monte Carlo method is used to evaluate the quality of the predicted model by simulating and comparing 10,000 random models. The results show that the proposed method is accurate to forecast the water-rich structures with small errors.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Peng Qin ◽  
Hongping Hu ◽  
Zhengmin Yang

AbstractGrasshopper optimization algorithm (GOA) proposed in 2017 mimics the behavior of grasshopper swarms in nature for solving optimization problems. In the basic GOA, the influence of the gravity force on the updated position of every grasshopper is not considered, which possibly causes GOA to have the slower convergence speed. Based on this, the improved GOA (IGOA) is obtained by the two updated ways of the position of every grasshopper in this paper. One is that the gravity force is introduced into the updated position of every grasshopper in the basic GOA. And the other is that the velocity is introduced into the updated position of every grasshopper and the new position are obtained from the sum of the current position and the velocity. Then every grasshopper adopts its suitable way of the updated position on the basis of the probability. Finally, IGOA is firstly performed on the 23 classical benchmark functions and then is combined with BP neural network to establish the predicted model IGOA-BPNN by optimizing the parameters of BP neural network for predicting the closing prices of the Shanghai Stock Exchange Index and the air quality index (AQI) of Taiyuan, Shanxi Province. The experimental results show that IGOA is superior to the compared algorithms in term of the average values and the predicted model IGOA-BPNN has the minimal predicted errors. Therefore, the proposed IGOA is an effective and efficient algorithm for optimization.


2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Sahar Qazi ◽  
Khalid Raza

Abstract Ovarian cancer is the third leading cause of cancer-related deaths in India. Epigenetics mechanisms seemingly plays an important role in ovarian cancer. This paper highlights the crucial epigenetic changes that occur in POTEE that get hypomethylated in ovarian cancer. We utilized the POTEE paralog mRNA sequence to identify major motifs and also performed its enrichment analysis. We identified 6 motifs of varying lengths, out of which only three motifs, including CTTCCAGCAGATGTGGATCA, GGAACTGCC, and CGCCACATGCAGGC were most likely to be present in the nucleotide sequence of POTEE. By enrichment and occurrences identification analyses, we rectified the best match motif as CTTCCAGCAGATGT. Since there is no experimentally verified structure of POTEE paralog, thus, we predicted the POTEE structure using an automated workflow for template-based modeling using the power of a deep neural network. Additionally, to validate our predicted model we used AlphaFold predicted POTEE structure and observed that the residual stretch starting from 237-958 had a very high confidence per residue. Furthermore, POTEE predicted model stability was evaluated using replica exchange molecular dynamic simulation for 50 ns. Our network-based epigenetic analysis discerns only 10 highly significant, direct, and physical associators of POTEE. Our finding aims to provide new insights about the POTEE paralog.


Author(s):  
V. Akash Kumar ◽  
Vijaya Mishra ◽  
Monika Arora

The inhibition of healthy cells creating improper controlling process of the human body system indicates the occurrence of growth of cancerous cells. The cluster of such cells leads to the development of tumor. The observation of this type of abnormal skin pigmentation is done using an effective tool called Dermoscopy. However, these dermatoscopic images possess a great challenge for diagnosis. Considering the characteristics of dermatoscopic images, transfer learning is an appropriate approach of automatically classifying the images based on the respective categories. An automatic identification of skin cancer not only saves human life but also helps in detecting its growth at an earlier stage which saves medical practitioner’s effort and time. A newly predicted model has been proposed for classifying the skin cancer as benign or malignant by DCNN with transfer learning and its pre-trained models such as VGG 16, VGG 19, ResNet 50, ResNet 101, and Inception V3. The proposed methodology aims at examining the efficiency of pre-trained models and transfer learning approach for the classification tasks and opens new dimensions of research in the field of medicines using imaging technique which can be implementable in real-time applications.


2021 ◽  
Vol 13 (19) ◽  
pp. 10999
Author(s):  
Abdullah Faisal Alshalif ◽  
J. M. Irwan ◽  
Husnul Azan Tajarudin ◽  
N. Othman ◽  
A. A. Al-Gheethi ◽  
...  

Foamed concrete bricks (FCB) have high levels of porosity to sequestrate atmospheric CO2 in the form of calcium carbonate CaCO3 via acceleration of carbonation depth. The effect of density and curing conditions on CO2 sequestration in FCB was investigated in this research to optimize carbonation depth. Statistical analysis using 2k factorial and response surface methodology (RSM) comprising 11 runs and eight additional runs was used to optimize the carbonation depth of FCB for 28 days (d). The main factors selected for the carbonation studies include density, temperature and CO2 concentration. The curing of the FCB was performed in the chamber. The results indicated that all factors significantly affected the carbonation depth of FCB. The optimum carbonation depth was 9.7 mm, which was determined at conditions; 1300 kg/m3, 40 °C, and 20% of CO2 concentration after 28 d. Analysis of variance (ANOVA) and residual plots demonstrated the accuracy of the regression equation with a predicted R2 of 89.43%, which confirms the reliability of the predicted model.


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