Recent Advances in Computer Science and Communications - Volume 16, Issue 6, 2023

In recent years, the increasing interest in ontologies resulted in the developing and publishing of many ontologies in the same or different domains. When users try to reuse the existing ontologies in their applications, they may encounter problems determining the most appropriate ontology based on their needs. Ontology evaluation is a process that helps users to examine the quality of the ontology based on different attributes. Many accessible and usable tools for ontology evaluation have been studied in the literature. However, finding an efficient ontology evaluation tool, following ontology specifications and their requirements (advantages/disadvantages), is still missing, limiting the researchers from determining possible future research. This paper aims to help new researchers and practitioners identify appropriate ontology evaluation tools based on their requirements and provide guidelines for future research directions on the same topic. This paper provides a detailed description of the different types of ontologies and classifies the available ontology evaluation tools into two categories, namely domain dependant ontology evaluation tools and domain-independent ontology evaluation tools.

Cloud is based on the underlying technology of virtualization. Here, the physical servers are divided into multiple virtual servers. Through the technology of virtualization, each virtual server contains virtual machines. Live virtual machine migration is to have migration time and inactive time with minimal duration. Various machine-learning approaches have been investigated and research gaps were identified to enhance the security features during the migration process. Moreover, a secure virtual machine live migration is proposed using Advanced Metric Encryption (AME). Considering the duration of live migration in data centers as well as ensuring the security aspects, the proposed model has been tested and evaluated.

A suite of standards known as the Semantic Web is transforming the Internet into a semantic graph rather than a graph of hypertext links. This paper will describe how various ideas and initiatives in artificial intelligence knowledge representation influenced its design. We begin with the seminal work by Alan Turing and Alonzo Church that led to the definition of Turing Machines, enabled digital computing, and provided the mathematical theory of computation, which has been one of the determining factors for Artificial Intelligence knowledge representation. We then provide a brief history of artificial intelligence knowledge representation, starting with groundbreaking researchers, such as Newell and Simon, then to the first "AI boom" driven primarily by rule-based expert systems, followed by major initiatives such as Cyc and the DARPA Knowledge Sharing Initiative. We will discuss how innovations from these initiatives affected standards that, in turn, led to the suite of standards known as the Semantic Web. We conclude with a brief overview of the most important issues currently facing those who wish to see widespread adoption of Semantic Web technology in the industry.

At present, image recognition technology first classifies images and outputs category information through the neural network. The next step involves the search. Before retrieval, the feature database needs to be established, followed by one-to-one correspondence. This method is tedious, time-consuming and has low accuracy. In computer vision research, researchers have proposed various image recognition methods to be applied in various fields and made many research achievements. However, at present, the accuracy, stability and time efficiency cannot meet the needs of practical work. In terms of UAV image recognition, high accuracy and low consumption are required. Previous methods require huge databases, which increases the consumption of UAVs. Taking aerial transmission and line images as the research object, this paper proposes a method of image recognition based on chaotic synchronization. Firstly, the image is used as a function to construct a dynamic system, and the function structure and parameters are adjusted to realize chaos synchronization. In this process, different types of images are identified. At the same time, we research this dynamic system characteristics and realize the mechanism of image recognition. Compared with other methods, the self-built aerial image data set for bird's nest identification, iron frame identification and insulator identification has the characteristics of a high identification rate and less calculation time. It is preliminarily proven that the method of synchronous image recognition is practical, and also worthy of further research, verification and analysis..

Objective: Big Data processing is a demanding task, and several big data processing frameworks have emerged in recent decades. The performance of these frameworks is greatly dependent on resource management models. Methods: YARN is one of such models which acts as a resource management layer and provides computational resources for execution engines (Spark, MapReduce, storm, etc.) through its schedulers. The most important aspect of resource management is job scheduling. Results: In this paper, we first present the design goal of YARN real-life schedulers (FIFO, Capacity, and Fair) for the MapReduce engine. Later, we discuss the scheduling issues of the Hadoop MapReduce cluster. Conclusion: Many efforts have been carried out in the literature to address issues of data locality, heterogeneity, straggling, skew mitigation, stragglers and fairness in Hadoop MapReduce scheduling. Lastly, we present the taxonomy of different scheduling algorithms available in the literature based on some factors like environment, scope, approach, objective and addressed issues.

Refining the quality of a noisy image is essential for many image applications. Various median filter variants have been introduced to suppress various noises, but they have their own limitations when detecting noise and restoring noise-free images. Denoising convolutional neural networks (DnCNNs), primarily developed for Gaussian noise removal, are influential nonlinear mapping models in image processing. After alterations in training data, they can be used to suppress other noise with outstanding results. This article suggests a frequency median filter method combined with deep learning for color images polluted by Salt and Pepper (SnP) noise. The analysis presented in this paper has primarily used a frequency median filter to suppress impulse noise wherein the restored value for the center pixel is evaluated by the frequency median rather than the traditional median. After which, the pretrained denoising convolutional neural network is hired to suppress the remaining noise and attain the output image finally. With a visual comparative study, simulation results on the taken test images show that the proposed method surpasses de-noising methods in terms of PSNR, SSIM, NMSE, Entropy, IEF, NCC, PCC and Running Time.

Background: The trend of the stock market prediction has always been challenging and confusing for investors. There is tremendous growth in stock market prediction with the advancement of technology, machine learning, data science, and big data. The media and entertainment sector is one of the diverse sectors in the stock market. In the Indian stock market, Sensex and Nifty are the two indexes. The 2019 pandemic forced the movie theatres to shut down. As a result, distributors and film directors were not able to release their movies in theatres, and production was also stopped. Consequently, during the lockdown, people spent more time at home watching electronic media, resulting in a higher degree of media consumption. Objectives: The objective of the research is to predict the performance of the media and entertainment companies stock prices using machine-learning techniques. Investors will be benefited by maximizing the profit and minimizing the loss. Methods: The proposed stock prediction system is used to predict the stock values and find the accuracy of linear regression and logistic regression in machine learning algorithms for data science. Results: The experiments are conducted for the media and entertainment stock price data using Machine-learning algorithms. Media stock prices are considered as the input dataset. The model has been developed using the daily frequency of stock prices with different attributes. Conclusion: Thus, the media and entertainment stocks are predicted using linear regression and logistic regression. Using the above techniques, stock prices are predicted accurately to maximize profits and minimize the loss for the investors.