Journal of Soft Computing Paradigm - June 2020
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TOTAL DOCUMENTS

63
(FIVE YEARS 63)

H-INDEX

6
(FIVE YEARS 6)

Published By Inventive Research Organization

2582-2640
Updated Wednesday, 20 October 2021

2021 ◽  
Vol 3 (3) ◽  
pp. 234-248
Author(s):  
N. Bhalaji

In recent days, we face workload and time series issue in cloud computing. This leads to wastage of network, computing and resources. To overcome this issue we have used integrated deep learning approach in our proposed work. Accurate prediction of workload and resource allocation with time series enhances the performance of the network. Initially the standard deviation is reduced by applying logarithmic operation and then powerful filters are adopted to remove the extreme points and noise interference. Further the time series is predicted by integrated deep learning method. This method accurately predicts the workload and sequence of resource along with time series. Then the obtained data is standardized by a Min-Max scalar and the quality of the network is preserved by incorporating network model. Finally our proposed method is compared with other currently used methods and the results are obtained.


2021 ◽  
Vol 3 (3) ◽  
pp. 218-233
Author(s):  
R. Dhaya

In recent years, there has been an increasing research interest in image de-noising due to an emphasis on sparse representation. When sparse representation theory is compared to transform domain-based image de-noising, the former indicates that the images have more information. It contains structural characteristics that are quite similar to the structure of dictionary-based atoms. This structure and the dictionary-based method is highly unsuccessful. However, image representation assumes that the noise lack such a feature. The dual-tree complex wavelet transform incorporates an increase in transform data density to reduce the effects of sparse data. This technique has been developed to decrease the image noise by selecting the best-predicted threshold value derived from wavelet coefficients. For our experiment, Discrete Cosine Transform (DCT) and Complex Wavelet Transform (CWT) are used to examine how the suggested technique compares the conventional DCT and CWT on sets of realistic images. As for image quality measures, DT-CWT has leveraged superior results. In terms of processing time, DT-CWT gave better results with a wider PSNR range. Further, the proposed model is tested with a standard digital image named Lena and multimedia sensor images for the denoising algorithm. The suggested denoising technique has delivered minimal effect on the MSE value.


2021 ◽  
Vol 3 (3) ◽  
pp. 205-217
Author(s):  
Hari Krishnan Andi

In recent years, there has been an increase in demand for machine learning and AI-assisted trading. To extract abnormal profits from the bitcoin market, the machine learning and artificial intelligence (AI) assisted trading process has been used. Each day, the data gets saved for the specified amount of time. These approaches produce great results when integrated with cutting-edge algorithms. The results of algorithms and architectural structures drive the development of cryptocurrency market. The unprecedented increase in market capitalization has enabled the cryptocurrency to flourish in 2017. Currently, the market accommodates totally 1500 cryptocurrencies, all of which are actively trading. It is always possible to mine the cryptocurrency and use it to pay for online purchases. The proposed research study is more focused on leveraging the accurate forecast of bitcoin prices via the normalization of a particular dataset. With the use of LSTM machine learning, this dataset has been trained to deploy a more accurate forecast of the bitcoin price. Furthermore, this research work has evaluated different machine learning methods and found that the suggested work delivers better results. Based on the resultant findings, the accuracy, recall, precision, and sensitivity of the test has been calculated.


2021 ◽  
Vol 3 (3) ◽  
pp. 192-204
Author(s):  
R. Rajesh Sharma

Transformers are one of the primary device required for an AC (Alternating Current) distribution system which works on the principle of mutual induction without any rotating parts. There are two types of transformers are utilized in the distribution systems namely step up transformer and step down transformer. The step up transformers are need to be placed at some regular distances for reducing the line losses happening over the electrical transmission systems. Similarly the step down transformers are placed near to the destinations for regulating the electricity power for the commercial usage. Certain regular check-ups are must for a distribution transformer for increasing its operational life time. The proposed work is designed to regularize such health check-ups using IoT sensors for making a centralized remote monitoring system.


2021 ◽  
Vol 3 (3) ◽  
pp. 177-191
Author(s):  
R. Kanthavel

In recent days Internet of Things (IOT) has grown up dramatically. It has wide range of applications. One of its applications is Health care system. IOT helps in managing and optimizing of healthcare system. Though it helps in all ways it also brings security problem in account. There is lot of privacy issues aroused due to IOT. In some cases it leads to risk the patient’s life. To overcome this issue we need an architecture named Internet of Medical Things (IOMT). In this paper we have discussed the problems faced by healthcare system and the authentication approaches used by Internet of Medical Things. Machine learning approaches are used to improvise the system performance.


2021 ◽  
Vol 3 (3) ◽  
pp. 163-176
Author(s):  
H. James Deva Koresh

Water process stations are very common nowadays, that can be noticed everywhere from a small house to very big industrial area. The main objective of the water process stations are to reduce the hardness of the drinking water. In order to attain such a clear drinking water, the process station will work over several stages like sediment filter, carbon filter and RO membrane. Usually all these stages will be taken into account by the process stations on the feed water irrespective on its hardness measurement. The control strategy imposed in the paper verifies the hardness of the feed water at the very first step to avoid several stages for making the process simpler and faster. In the same way, at the stage of each filtering process huge amount of water will be wasted in the traditional process station. Due to the implementation of an efficient control strategy such wastages can also be minimized in the proposed work. The experimental study performed based on the proposed methodology explores the amount of water saved during the clear water processes as well as the time requirement for processing the feed water.


2021 ◽  
Vol 3 (3) ◽  
pp. 149-162
Author(s):  
G Ranganathan ◽  
Jennifer S Raj

This paper has proposed a hybrid electric vehicle that uses intelligent energy management strategy to decrease the energy consumption of the vehicle. Here, the total energy consumption of the vehicle is initially modelled and further investigated to reduce the amount of energy used to be identified as a sum of electrical energy provided by consumed fuels and on-board batteries. In particular, an intelligent controller is proposed in this work to execute its ability to decrease the total amount of energy consumed and improve the energy efficiency of the vehicle. A fuzzy system is utilized in an account supervisory controller to decide the appropriate mode of operation for the system. The part of the proposed work involves development of optimal control strategies by using neuro-fuzzy logic. In order to obtain optimal performance, the controllers are used to regulate vehicle subsystems and set points. The biggest advantage of this work is the reduction in energy consumption and their ability to execute the operation online. Simulink/MATLAB is used to simulate and validate the performance of the proposed work under various conditions and under several dataset values.


2021 ◽  
Vol 3 (3) ◽  
pp. 135-148
Author(s):  
Nayana Shetty

For the purpose of high performance computation, several machines are developed at an exascale level. These machines can perform at least one exaflop calculations per second, which corresponds to a billion billon or 108. The universe and nature can be understood in a better manner while addressing certain challenging computational issues by using these machines. However, certain obstacles are faced by these machines. As huge quantity of components is encompassed in the exascale machines, frequent failure may be experienced and also the resilience may be challenging. High progress rate must be maintained for the applications by incorporating certain form of fault tolerance in the system. Power management has to be performed by incorporating the system in a parallel manner. All layers inclusive of fault tolerance layer must adhere to the power limitation in the system. Huge energy bills may be expected on installation of exascale machines due to the high power consumption. For various fault tolerance models, the energy profile must be analyzed. Parallel recovery, message-logging, and restart or checkpoint fault tolerance models for rollback recovery are evaluated in this paper. For execution with failure, the most energy efficient solution is provided by parallel recovery when programs with various programming models are used. The execution is performed faster with parallel recovery when compared to the other techniques. An analytical model is used for exploring these models and their behavior at extreme scales.


2021 ◽  
Vol 3 (2) ◽  
pp. 123-134
Author(s):  
Pasumpon Pandian A.

One of the most common applications of deep learning algorithms is sentiment analysis. This study delivers a better performing and efficient automated feature extraction technique when compared to previous approaches. Traditional methodologies like surface approach will use the complicated manual feature extraction process, which forms the fundamental aspect of feature driven advancements. These methodologies serve as a strong baseline to determine the predictability of the features, and it will also serve as the perfect platform for integrating the deep learning techniques. The proposed research work has introduced a deep learning technique, which can be incorporated with feature-extraction. Moreover, this research work includes three crucial parts. The first step is the development of sentiment classifiers with deep learning, which can be used as the baseline for comparing the performance. This is followed by the use of ensemble techniques and information merger to obtain the final set of sources. As the third step, a combination of ensembles is introduced to categorize various models along with the proposed model. Finally experimental analysis is carried out and the performance is recorded to determine the best model with respect to the deep learning baseline.


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