Exploratory Data Analysis (EDA) is a field of data analysis used to visually represent the knowledge embedded deep in the given data set. The technique is widely used to generate inferences from a given data set. Data set of current pandemic, the COVID-19 is widely made available by the standard dataset repository. EDA can be applied to these standard dataset to generate inferences. In this paper, data visualization technique is applied to the dataset and is used to formulate patterns for better insights on the effects of the pandemic with respect to the variables/ labels given in the dataset. A Web application tool called Jupyter Notebook is used to generate graphs using python language as it consists of libraries which are used for the process of EDA and the visualization is depicted for the attributes showing higher correlation. Based on the graphs obtained, we can draw conclusions from the current situation based on the data available, understand why a certain variable is increasing/decreasing with respect to another and what can be done to improve the drawbacks found.

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Using Exploratory Data Analysis for Generating Inferences on the Correlation of COVID-19 cases

Aspect Oriented Software Development (AOSD) is a promising methodology for efficiently capturing the crosscutting functionalities (concerns) into modular units called aspects, thereby reducing maintainability, and increasing reusability and ease of software evolution. Since inheritance of classes and aspects plays a vital role in defining the units of encapsulation, it is essential that the impact of introducing inheritance in AOSD need to be quantitatively captured using core design level metrics and mapped onto higher level quality attributes. In this research work, a new set of metrics have been proposed to quantify the impact of using multi-level inheritance hierarchies in an Aspect Oriented software. A model that captures the reusability using inheritance of aspects has been used for defining and applying the proposed metrics and later relates them onto higher level quality attributes. Further, the proposed metrics and model have been applied to AspectJ versions of an SOA based case study application. Based on the measurement, it was inferred that aspectization has improved the higher level quality attributes of reusability, modularity and maintainability of the case study over its versions.

Quantitative Assessment of Inheritance Hierarchies for Aspect Oriented Software Development using a proposed Aspect Inheritance Reusability Model

Node straggling is most impactful thing to improve the Quality of Service (QoS) in distributed as well as cloud environment. On the other hand, the reason for which node discharges are caused is complicated. The precedents work mainly focus on detecting Stragglers, optimizing the level of programming and analysis of the root cause. Without order to help users automate their services, these approaches cannot provide valuable information. We in proposed new approach, a general method that incorporates framework and characteristics of the system for the analysis of root cause for detection of stragglers in the big data system, which basically reduce the process execution time and generate job failure issues during execution. We proposed an framework for straggler node detection from distributed environment using machine learning algorithm based on large scale job performed log data. After execution of machine learning algorithm system will predict straggler nodes list and dynamically eliminate such nodes from execution list. Q-Learning based reinforcement learning algorithm initially proposed for execution and validate the system result in multi node environments.

Identification of Straggler Node in Distributed Environment Using Soft Computing Algorithms

Four Rs Framework for the development of a digital twin: The implementation of Representation with a FDM manufacturing machine

Exploratory Data Analysis (EDA) is a data analysis technique that can be used to visually represent the knowledge embedded deep in the given data set. The application of this technique can be used in medical data processing for the betterment of the offered services of healthcare providers. In women, breast cancer has become prevalent and requires proper methods to identify the possibility of occurrence at an early stage for treatment and cure. In this context, visualization technique of EDA can be applied to the existing datasets for learning and prediction. In this research work, the analysis aims on finding predominant features which would be helpful in predicting whether the tumor is benign or malignant. To achieve this goal various graphical techniques are used for a better visual understanding. Based on the results obtained it can be found that EDA plays a significant role in describing the data using statistical and visualization analysis without making any speculations about the content. With this dataset and EDA approach we have achieved a clear understanding about the significant features needed to predict if the tumor is benign or malignant.

Application of Exploratory Data Analysis to Generate Inferences on the Occurrence of Breast Cancer using a Sample Dataset

Preventive Maintenance is the new buzzword to upkeep an enterprise application in a real world scenario. It has also become a necessity to predict the faults that could occur between the different transfer nodes of the enterprise application. The features of preventive maintenance which includes methodical study, estimation, the notion of time, the diagnosis of faults and so on can be analyzed using well designed and structured methods of relevance to the application domain. In this scenario, the technologies which are focused are diagnosis of faults and activities, prediction of the states and monitoring the conditions. There is a large amount of data which is being used for preventive maintenance. The flow of packets between transfer nodes can be stored and this data can be used for purpose of training and testing. Further, machine learning algorithms can be implemented based on the features present in the historical data. The advantage on the study of preventive maintenance is that it can aid in reduction of cost which is the primary motive of each organization existing toady. By doing so the results can be applied to various organizations to resolve failures that could possibly occur in the future well in advance. This paper explains the application of machine learning algorithms for the detection of fault in transfer noted using preventive maintenance in an organization.

Preventive Maintenance for Fault Detection in Transfer Nodes using Machine Learning

In this research paper, we are going to see the profound scientific use of computer technology applied in the fields of AI and Machine Learning primarily focused on Image Processing and Pattern recognition. Techniques such as ours are widely used to recognize real life objects including human faces etc. Thus, using such techniques, we can recognize a person from pictures. Using face recognition modules from python's huge collection of libraries, we are able to train the model to recognize people while wearing masks. Since when masks are worn, half of the facial features are lost, therefore developing a technique to recognize faces in such way is crucial. This specific technology of face detection is used in biometrics, video surveillance, etc. Therefore it's at utmost importance to increase the security as well as efficiency whilst making the recognition faster.

Facial Recognition using the OpenCV Libraries of Python for the Pictures of Human Faces Wearing Face Masks during the COVID-19 Pandemic

Aspect Oriented Programming (AOP) is a software development methodology that aims to improve the modularity by encapsulating the cross-cutting concerns into modular units called aspects. Inheritance of classes and aspects play a vital role while redefining the units of encapsulation. In this research, the impact of using multi-level inheritance in Aspect Oriented Soft-ware is quantitatively evaluated, with an extended and validated metric, namely the Weighted Average Depth of Inheritance. The metric has been applied on two equivalent OO and AO versions of a multi-level inheritance based Banking application case study which was developed using Java and AspectJ programming languages respectively. Based on the measurement it was found that the AspectJ version exhibits reduced cognitive complexity of computational intelligence compared to its equivalent Java version.

Investigating the Complexity of Computational Intelligence using the Levels of Inheritance in an AOP based Software

Digital Image Processing is an important field of computer science significantly focused on providing solutions to extract patterns and features from the given set of images. Image processing algorithms can very useful and can provide information such as edge detections in the given set of welded images. This technique to identify the edges can play a role in determining the quality of welding and thereby reducing the cost of maintenance. Edge detection algorithms such as Canny, Prewitt and Sobel are very efficient in identifying the spread of edges in the given greyscale welded images. In this research, the edge detections of the given set of greyscale welded images are compared with the edge detections of a quality welding images. The types of welding were considered for the comparison, namely, ARC and MIG welding of steel joints. Based on the results, it was found that samples obtained from the laboratory tests were have a quality of only 25% match in comparison with reference good welds.

Application of Digital Image Processing Techniques in Determining the Quality of ARC and MIG Welding of Steel Joints

Abstract: Human emotions are the mental state of feelings and are spontaneous. There is no clear connection between emotions and facial expressions and there is significant variability making facial recognition a challenging research area. Emotion recognition becomes a popular topic of deep learning and provides more application fields in our daily life. The primary idea of our project is to process the input imagesof human facial emotion to train the pre-trained models on datasets. In recent years, Machine Learning (ML) and Neural Networks (NNs) have been used for emotion recognition. In this project, a Convolutional Neural Network (CNN) is used toextract features from images to detect emotions. The numerical result of the algorithm will show a probabilistic result of each labeled class.

S Senthil Velan

Papers

Preventive Maintenance for Fault Detection in Transfer Nodes using Machine Learning

Facial Recognition using the OpenCV Libraries of Python for the Pictures of Human Faces Wearing Face Masks during the COVID-19 Pandemic

Investigating the Complexity of Computational Intelligence using the Levels of Inheritance in an AOP based Software

Application of Digital Image Processing Techniques in Determining the Quality of ARC and MIG Welding of Steel Joints

Human Emotion Detection Using Deep Learning