Asian Journal of Research in Computer Science

Unimodal biometrics system (UBS) drawbacks include noisy data, intra-class variance, inter-class similarities, non-universality, which all affect the system's classification performance. Intramodal fingerprint fusion can overcome the limitations imposed by UBS when features are fused at the feature level as it is a good approach to boost the performance of the biometric system. However, feature level fusion leads to high dimensionality of feature space which can be overcame by Feature Selection (FS). FS improves the performance of classification by selecting only relevant and useful information from extracted feature sets being an optimization problem. Artificial Bee Colony (ABC) is an optimizing algorithm that has been frequently used in solving FS problems because of its simple concept, use of few control parameters, easy implementation and good exploration characteristics. ABC was proposed for optimized feature selection prior to the classification of Fingerprint Intramodal Biometric System (FIBS). Performance evaluation of ABC-based FIBS showed the system had a Sensitivity of 97.69% and RA of 96.76%. The developed ABC optimized feature selection reduced the high dimensionality of features space prior to classification tasks thereby increasing sensitivity and recognition accuracy of FIBS.

Aims/ Objectives: The research presented in the following application aims to prove use of Ternary Maths for calculating machines and to simplify the process of calculating In it we will try to justify the use of triplets and describe how it works.

An earlier research presented in “Logical Principles in Ternary Mathematics” [1,2,3] shows that we can transit from one expression of a number such as a "component form" to another, e.g a decimal, or still another, that is it’s vector form [4]. The aim of our further research is to explain why we associate Triplets of numbers in such choice {-1,0,1} and not the numbers 1,2,3 for example, or a set {1,2,3} The explanation seems obvious as a set of decimal numbers consists of 10 entries not 3 At the same time we have to prove that the mentioned set of triplets is a unique and the only one to be used as a Ternary Set or a base, as we might call it, for our calculating machines.

Coronavirus has become a significant concern for the whole world. It has had a substantial influence on our social and economic life. The infection rate is rapidly increasing at every moment throughout the world. At present, predicting coronavirus has become one of the challenging issues for us. As the pace of COVID-19 detection increases, so does the death rate. This research predicts the number of coronavirus detection and deaths using Fbprophet, a tool designed to assist in performing time series forecasting at a large scale. Two major affected countries, India and Japan, have been taken into consideration in our approach.  Using the prophet model, a prediction is performed on the number of total cases, new cases, total deaths and new deaths. This model works considerably well, and it has given a satisfactory result that may help the authority in taking early and appropriate decisions depending on the predicted COVID situation.

Prediction is vital in our daily lives, as it is used in various ways, such as learning, adapting, predicting, and classifying. The prediction of parameters capacity of RNNs is very high; it provides more accurate results than the conventional statistical methods for prediction. The impact of a hierarchy of recurrent neural networks on Predicting process is studied in this paper. A recurrent network takes the hidden state of the previous layer as input and generates as output the hidden state of the current layer. Some of deep Learning algorithms can be utilized in as prediction tools in video analysis, musical information retrieval and time series applications. Recurrent networks may process examples simultaneously, maintaining a state or memory that recreates an arbitrarily long background window. Long Short-Term Memory (LSTM) and Bidirectional RNN (BRNN) are examples of recurrent networks. This paper aims to give a comprehensive assessment of predictions based on RNN. Additionally, each paper presents all relevant facts, such as dataset, method, architecture, and the accuracy of the predictions they deliver.

In the last few days, data and the internet have become increasingly growing, occurring in big data. For these problems, there are many software frameworks used to increase the performance of the distributed system. This software is used for available ample data storage. One of the most beneficial software frameworks used to utilize data in distributed systems is Hadoop. This software creates machine clustering and formatting the work between them. Hadoop consists of two major components: Hadoop Distributed File System (HDFS) and Map Reduce (MR). By Hadoop, we can process, count, and distribute each word in a large file and know the number of affecting for each of them. The HDFS is designed to effectively store and transmit colossal data sets to high-bandwidth user applications. The differences between this and other file systems provided are relevant. HDFS is intended for low-cost hardware and is exceptionally tolerant to defects. Thousands of computers in a vast cluster both have directly associated storage functions and user programmers. The resource scales with demand while being cost-effective in all sizes by distributing storage and calculation through numerous servers. Depending on the above characteristics of the HDFS, many researchers worked in this field trying to enhance the performance and efficiency of the addressed file system to be one of the most active cloud systems. This paper offers an adequate study to review the essential investigations as a trend beneficial for researchers wishing to operate in such a system. The basic ideas and features of the investigated experiments were taken into account to have a robust comparison, which simplifies the selection for future researchers in this subject.

According to many authors, this paper will explain what Hadoop is and its architectures, how it works, and its performance analysis in a distributed systems. In addition, assessing each Writing and compare with each other.