Optimal Placement Algorithms for Virtual Machines

Cloud computing provides a computing platform for the users to meet their demands in an efficient, cost-effective way. Virtualization technologies are used in the clouds to aid the efficient usage of hardware. Virtual machines (VMs) are utilized to satisfy the user needs and are placed on physical machines (PMs) of the cloud for effective usage of hardware resources and electricity in the cloud. Optimizing the number of PMs used helps in cutting down the power consumption by a substantial amount. In this paper, we present an optimal technique to map virtual machines to physical machines (nodes) such that the number of required nodes is minimized. We provide two approaches based on linear programming and quadratic programming techniques that significantly improve over the existing theoretical bounds and efficiently solve the problem of virtual machine (VM) placement in data centers

Status of Textile Engineering College Libraries in Haryana, India

The purpose of the study is to estimate the performance of the textile engineering institute libraries of Haryana, India, and determine what collections and services are provided. A survey was conducted through a questionnaire circulated among the librarians of textile engineering institutes. The paper discusses the collection and worth of various services rendered by these libraries

Analysis & Numerical Simulation of Indian Food Image Classification Using Convolutional Neural Network

Recognition of Indian food can be assumed to be a fine-grained visual task owing to recognition property of various food classes. It is therefore important to provide an optimized approach to segmentation and classification for different applications based on food recognition. Food computation mainly utilizes a computer science approach which needs food data from various data outlets like real-time images, social flat-forms, food journaling, food datasets etc, for different modalities. In order to consider Indian food images for a number of applications we need a proper analysis of food images with state-of-art-techniques. The appropriate segmentation and classification methods are required to forecast the relevant and upgraded analysis. As accurate segmentation lead to proper recognition and identification, in essence we have considered segmentation of food items from images. Considering the basic convolution neural network (CNN) model, there are edge and shape constraints that influence the outcome of segmentation on the edge side. Approaches that can solve the problem of edges need to be developed; an edge-adaptive As we have solved the problem of food segmentation with CNN, we also have difficulty in classifying food, which has been an important area for various types of applications. Food analysis is the primary component of health-related applications and is needed in our day to day life. It has the proficiency to directly predict the score function from image pixels, input layer to produce the tensor outputs and convolution layer is used for self- learning kernel through back-propagation. In this method, feature extraction and Max-Pooling is considered with multiple layers, and outputs are obtained using softmax functionality. The proposed implementation tests 92.89% accuracy by considering some data from yummly dataset and by own prepared dataset. Consequently, it is seen that some more improvement is needed in food image classification. We therefore consider the segmented feature of EA-CNN and concatenated it with the feature of our custom Inception-V3 to provide an optimized classification. It enhances the capacity of important features for further classification process. In extension we have considered south Indian food classes, with our own collected food image dataset and got 96.27% accuracy. The obtained accuracy for the considered dataset is very well in comparison with our foregoing method and state-of-the-art techniques.

Numerical Simulation and Design of COVID-19 Forecasting Framework Using Efficient Data Analytics Methodologies

The COVID-19 pandemic hit globally in December 2019 when a certain virus strain from Wuhan, China started proliferating throughout the world. By the end of March 2020, lockdowns and curfews were imposed all over the world halting trade, commerce, education, and various other essential activities. It has been nearly a year since the WHO declared a pandemic but there is still a consistent rise of the cases even with the administration of various types of vaccines and preventive measure. One of the main struggles that the healthcare workers face is to find out the how the virus is spreading amongst a community. The knowledge of this can be used to stop the spread of virus. This is a very important step towards getting things back into momentum to restore activities globally. Many attempts have been made under epidemiology to study the spread of COVID and many mathematical models have emerged as a result that can help with this. A popular model that is used for estimating the effective reproduction number (Rt) has the shortcoming that it cannot simultaneously forecast the future number of cases. This work explores an extension of another model, the SIR-model, in which the model parameters are fitted to recorded data. This makes the model adaptive, opening up the possibilities for estimating the Rt daily and making predictions of future number of confirmed cases. The paper use this adaptive SIR-model (aSIR) to estimate the Rt and create forecasts of new cases in India. The paper purpose is to determine how precise aSIR-models are at estimating the Rt (when compared with FHM’s model). It will also analyze how accurate aSIR-models are at simultaneously forecasting the future spread of Covid-19 in India. The coronavirus spread can be mathematically modelled using factors such as the number of susceptible people, exposed people, infected people, asymptotic people and the number of recovered people. The Khan-Atangana system is an integer-order coronavirus model that uses the above-mentioned factors. Since the coronavirus model depends on the initial conditions, the Khan-Atangana model uses the Atangana-Baleanu operate as it has a non-variant and non-local kernel. Instead, we replace the equations with fractional-order derivatives using the Grünwald-Letnikov derivative. The fractional order derivatives need to be fed with initial conditions and are useful to determine the spread due to their non-local nature. This project proposes to solve these fractional-order derivatives using numerical methods and analyse the stability of this epidemiological model

A top-down approach to classify enzyme functional classes and sub-classes using random forest

Advancements in sequencing technologies have witnessed an exponential rise in the number of newly found enzymes. Enzymes are proteins that catalyze bio-chemical reactions and play an important role in metabolic pathways. Commonly, function of such enzymes is determined by experiments that can be time consuming and costly. Hence, a need for a computing method is felt that can distinguish protein enzyme sequences from those of non-enzymes and reliably predict the function of the former. To address this problem, approaches that cluster enzymes based on their sequence and structural similarity have been presented. But, these approaches are known to fail for proteins that perform the same function and are dissimilar in their sequence and structure. In this article, we present a supervised machine learning model to predict the function class and sub-class of enzymes based on a set of 73 sequence-derived features. The functional classes are as defined by International Union of Biochemistry and Molecular Biology. Using an efficient data mining algorithm called random forest, we construct a top-down three layer model where the top layer classifies a query protein sequence as an enzyme or non-enzyme, the second layer predicts the main function class and bottom layer further predicts the sub-function class. The model reported overall classification accuracy of 94.87% for the first level, 87.7% for the second, and 84.25% for the bottom level. Our results compare very well with existing methods, and in many cases report better performance. Using feature selection methods, we have shown the biological relevance of a few of the top rank attributes

Design Assessment and Simulation of PCA Based Image Difference Detection and Segmentation for Satellite Images Using Machine Learning

It is possible to define the quantity of temporal effects by employing multitemporal data sets to discover changes in nature or in the status of any object based on observations taken at various points in time. It's not uncommon to come across a variety of different methods for spotting changes in data. These methods can be categorized under a single umbrella term.  There are two primary areas of study: supervised and unsupervised change detection. In this study, the goal is to identify the changes in land cover.  Covers a specific area in Kayseri using unsupervised change detection algorithms and Landsat satellite pictures from various years have been gleaned through the use of remote sensing. In the meantime, image differencing is taking place.  The method will be applied to the photographs using the image-enhancing process. In the next step, Principal Component Analysis (PCA) is employed.  The difference image will be analyzed using Component Analysis. To find out which locations have and which do not. As a first step, a procedure must be in place.  We've finished registering images one after the other. Consequently, the photos are being linked together. After then, it's back to black and white.  Three non-overlapping portions of the difference image have been created. This can be done using the principal component analysis method.  From the eigenvector space, we may get to the fundamental components. As a last point, the major feature vector space fuzzy C-Means Clustering is used to divide the component into two clusters, and then a change detection technique is carried out. As the world's population grew, farmland expansion and unplanned land encroachment intensified, resulting in uncontrolled deforestation around the globe. This project uses unsupervised learning algorithm K-means clustering. In a cost-effective manner that can be employed by officials, companies as well as private groups, to assist in fighting illicit deforestation and analysis of satellite database

Cyber-physical Systems (CPS) Security: State of the Art and Research Opportunities for Information Systems Academics

Attacks on cyber-physical systems (CPS) continue to grow in frequency. However, cybersecurity academics and practitioners have so far focused primarily on computer systems and networks rather than CPS. Given the alarming frequency with which cybercriminals attack CPS and the unique cyber-physical relationship in CPS, we propose that CPS security needs go beyond what purely computer and network security requires. Thus, we require more focused research on cybersecurity based on the cyber-physical relationship between various CPS components. In this paper, we stock of the current state of CPS security and identify research opportunities for information systems (IS) academics