AIJREAS

VOLUME 7, ISSUE 10 (2022, OCTOBER)

(ISSN-2455-6300) ONLINE

ANVESHANA’S INTERNATIONAL JOURNAL OF RESEARCH IN ENGINEERING AND APPLIED SCIENCES

DATA AUGMENTATION APPROACHES FOR SATELLITE IMAGE RESOLUTION

Vootla Sridhar Dr. Anupama A Deshpande & Dr. T. SRINIVAS

Page 1-5

Anveshana’s International Journal of Research in Engineering and Applied Sciences (ISSN-2455-6300)ONLINE
Volume - 7 Issue - 10

Paper Title

DATA AUGMENTATION APPROACHES FOR SATELLITE IMAGE RESOLUTION

Abstract

Data augmentation is a well known technique that is frequently used in machine learning tasks to increase the number of training instances and hence decrease model over-fitting. In this paper we propose a data augmentation technique that can further boost the performance of satellite image super resolution tasks. A super-resolution convolutional neural network (SRCNN) was adopted as a state-of-the-art deep learning model to test the proposed data augmentation technique. Different augmentation techniques were studied to investigate their relative importance and accuracy gains. We categorized the augmentation methods into instance based and channel based augmentation methods. The former refers to the standard approach of creating new data instances through applying image transformations to the original images such as adding artificial noise, rotations and translations to training samples, while in the latter we fuse auxiliary channels with each training instance, which helps the model learn useful representations.
KEYWORDS : data augmentation; optical remote sensing image; ship detection; simulated samples; deep learning

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MATHEMATICAL MODEL APPROACH IN CLOUD COMPUTING

Ujenpad Siddeshwar Dr. Vishwajeet Goswami Dr.G.V.Subbarao

Page 6-10

Anveshana’s International Journal of Research in Engineering and Applied Sciences (ISSN-2455-6300)ONLINE
Volume - 7 Issue - 10

Paper Title

MATHEMATICAL MODEL APPROACH IN CLOUD COMPUTING

Abstract

As more and more information on individuals and companies are placed in the cloud, concerns are beginning to grow about just how safe an environment it is. It is better to prevent security threats before they enter into the systems and there is no way how this can be prevented without knowing where they come from. The issue of resource allocation and revenue maximization is also equally important especially when it comes to cloud security. Over the years, there is a significant advance in cloud computing technology. It has grown from being a promising business concept to one of the fast growing sector of the information technology organizations. However, customers are still reluctant to deploy their business in the cloud. Security is one of the key factor which hampers the growth of cloud computing. The exponential growth of the internet users has also lead to a significant growth of threats. Mathematical modelling plays as an important tool in analysing and mitigating possible threats or attacks on cloud computing.
KEYWORDS : cloud security, cloud computing.

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IDLE TIME DETECTION IN MANUFACTURING INDUSTRY-USING MACHINE LEARNING ALGORITHMS

M Naga Rani

Page 11-19

Anveshana’s International Journal of Research in Engineering and Applied Sciences (ISSN-2455-6300)ONLINE
Volume - 7 Issue - 10

Paper Title

IDLE TIME DETECTION IN MANUFACTURING INDUSTRY-USING MACHINE LEARNING ALGORITHMS

Abstract

Our day-to-day commodities truly depend on the industrial sector, which is expanding at a rapid rate along with the growing population. The production of goods needs to be accurate and rapid. Thus, for the present research, idle time detection in manufacturing industry using machine learning algorithms. When put into practice in the real world, predictive maintenance presents a set of challenges for fault detection and prognosis that are often overlooked in studies validated with data from controlled experiments, or numeric simulations. Smart manufacturing is reshaping the manufacturing industry by boosting the integration of information and communication technologies and manufacturing process. As a result, manufacturing companies generate large volumes of machine data which can be potentially used to make data-driven operational decisions using informative computerized algorithms. In the manufacturing domain, it is well-known that the productivity of a production line is constrained by throughput bottlenecks. The operational dynamics of the production system causes the bottlenecks to shift among the production resources between the production runs. Therefore, prediction of the throughput bottlenecks of future production runs allows the production and maintenance engineers to proactively plan for resources to effectively manage the bottlenecks and achieve higher throughput.
KEYWORDS : Machine learning algorithm, smart manufacturing, communication technologies, bottlenecks, operational decisions

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STUDY ON THERAPEUTIC USES FOR CERTAIN CHEMICAL COMPOUNDS USING COMPUTATIONAL AND SPECTROSCOPIC METHODS

M Mallikarjun & Dr. Yashpal

Page 20-27

Anveshana’s International Journal of Research in Engineering and Applied Sciences (ISSN-2455-6300)ONLINE
Vo l- 7 Issue - 10

Paper Title

STUDY ON THERAPEUTIC USES FOR CERTAIN CHEMICAL COMPOUNDS USING COMPUTATIONAL AND SPECTROSCOPIC METHODS

Abstract

In order to investigate the potential therapeutic applications of certain chemical substances, this research article applies computational and spectroscopic methodologies. Researchers may learn more about the pharmacological characteristics and molecular interactions of these chemicals by combining computer modeling with spectroscopic investigation, which may help them find novel medication candidates and improve current treatments. The relevance of these multidisciplinary methods in furthering drug discovery and development is emphasized in the study, which also describes the methodology used and provides case examples of successful implementations.
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A REVIEW OF METAL COMPLEXES IN DRUG INVESTIGATION

Gangadhar A & Dr. Jyotsna

Page 28-30

ANVESHANA’S INTERNATIONAL JOURNAL OF RESEARCH IN ENGINEERING AND APPLIED SCIENCES (ISSN-2455-6300) ONLINE
Volume 7 Issue 10

Paper Title

A REVIEW OF METAL COMPLEXES IN DRUG INVESTIGATION

Abstract

Metal composites provide fresh possibilities for creating structures with specific features. The metabolism of inorganic components is a factor in many illnesses and their treatments. Metal complexes may now be used as medicinal agents better thanks to developments in inorganic chemistry. Metal complexes interact with living things in a different way than non-metals do. These complexes exhibit a wide range of behavior. Biologically active compounds may benefit from metal coordination to increase their activity. The numerous uses of metal complexes in biological systems are reviewed in this essay.
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STATISTICAL ANALYSIS OF URBAN CROSSING ACCIDENTS INVOLVING GRIP RESISTANCE

Biradar Sharath Chandra & Dr. Sirna Santosh kumar

Page 31-37

Anveshana’s International Journal of Research in Engineering and Applied Sciences (ISSN-2455-6300)ONLINE
Vol-7 Issue - 10

Paper Title

STATISTICAL ANALYSIS OF URBAN CROSSING ACCIDENTS INVOLVING GRIP RESISTANCE

Abstract

Linear trend analysis, curvilinear trend analysis, secular trend analysis, and other forms of trend analysis are used to make predictions about the future based on existing trends. In this chapter, we utilized secular trend analysis to objectively forecast the likelihood of future accidents. The trend component of a time series may often be removed by analyzing the secular trend of the time series. Long-term, consistent movement is what we mean when we talk about trends, not short-term fluctuations. Time series analysis approaches such as the auto regressive integrated moving average modelling approach and the dynamic forecasting model were used to time dependent data in the older literatures. Linear (or straight line) trends and non-linear trends are the two main kinds of trends. The long-term upward or downward trend in the data is what we call the secular trend. The analysis of secular trends is limited by the quality of the available information.The strength of a trend is often proportional to the length of time the data was collected for. In a short time, frame, secular trends are less likely to become apparent, and cyclical fluctuations may distort the series\' overall trend. This would make it hard to distinguish between different time series variants. At the very least, two or three full cycles need to have passed before the trend can be calculated. (Bowerman et al 1993). It\'s also important to note that the direction of the trend does not have to be consistent across the whole time to draw conclusions about whether or not the data is indicating an upward or downward tendency.
KEYWORDS : Road safety, Transportation and accidents

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DATA AUGMENTATION APPROACHES FOR SATELLITE IMAGE RESOLUTION

MATHEMATICAL MODEL APPROACH IN CLOUD COMPUTING

IDLE TIME DETECTION IN MANUFACTURING INDUSTRY-USING MACHINE LEARNING ALGORITHMS

STUDY ON THERAPEUTIC USES FOR CERTAIN CHEMICAL COMPOUNDS USING COMPUTATIONAL AND SPECTROSCOPIC METHODS

A REVIEW OF METAL COMPLEXES IN DRUG INVESTIGATION

STATISTICAL ANALYSIS OF URBAN CROSSING ACCIDENTS INVOLVING GRIP RESISTANCE

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