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  Subjects -> ELECTRONICS (Total: 207 journals)
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International Journal of Advanced Research in Computer Science and Electronics Engineering
Number of Followers: 14  

  This is an Open Access Journal Open Access journal
ISSN (Print) 2277-971X - ISSN (Online) 2277-9043
Published by Shri Pannalal Research Institute of Technology Homepage  [1 journal]
  • No Reference Image Quality Algorithm based on Human-Eye Sensitive
           Conception for Contrast-Distortion Evaluation

    • Authors: Hasan Thabit Rashid, Mohammed Hussain Naji
      Pages: 1 - 4
      Abstract: No reference image quality assessment IQAs algorithms are widely used for finding distortions in images without comparing them to references. Image' areas with no details may indicate a brightness saturation change, as well as the presence of noise in the background, which are more visible types of distortions. Thus, the design of such IQA should take into account the human visual conception. This paper proposes a no-reference image quality evaluation algorithm that takes into account the finest edge detection process and entropy deployment in regard to human visual sensitive HVS to quantify brightness saturation variations and noise in pixels. Statistic objective metrics for correlation coefficient which person PCC, spearman rank order SROCC, Root Mean Squared Error RMSE are used in the objective evaluation in corresponding with the subjective evaluation. The proposed algorithm is tested and significantly has well correlation PCC > 0.87, SROCC > 0.91, RMSE < 0.34. The findings of this research could be used to improve the performance of no-reference HVS-based IQA algorithms currently in use.
      PubDate: 2022-06-21
      DOI: 10.26483/ijarcs.v13i3.6819
      Issue No: Vol. 13, No. 3 (2022)
  • A Brief Review about Biometrics Systems in Modern Context

    • Authors: Hector Caballero, Leopoldo Gil-Antonio, Erika Lopez
      Pages: 5 - 10
      Abstract: Authentication systems employ various access mechanisms for the data validation process.Nowadays there are many proposals focused on solving the traditional problems that exist to validate the records of installations in public or private places.With the appearance of COVID-19, the use of technology has intensified to avoid contact with physical devices and achieve successful access.This article shows a compilation of work dedicated to methods and techniques for access to facilities by reading modern biometric systems.
      PubDate: 2022-06-21
      DOI: 10.26483/ijarcs.v13i3.6829
      Issue No: Vol. 13, No. 3 (2022)

    • Authors: Vandana Killari, Ratan Kumar Sajja; Satya Avinash Nimmakayala, Varun Ippili
      Pages: 11 - 14
      Abstract: American Sign Language is used by mute and deaf people so that they can interact with the people around them. It is used by approximately 2,50,000-5,00,000 Americans (and some Canadians) of all ages. Over the period of time, many have proposed different methods for recognition of ASL. Sign language Recognition is a complex technical problem due to the difficulty of visual analysis of hand motions and the highly structured nature of sign language. Hence the accuracy is not achieved. To enhance this accuracy, the proposed system compares different machine learning classification algorithms using Lenet5 architecture for feature extraction. Lenet5 is one of the architectures of Convolution Neural Network (CNN). The proposed system uses machine learning algorithms like Neural Network, Decision Tree Classifier, K-Nearest Neighbour and Support Vector Machine. There is a raise in accuracy using Neural Network algorithm when compared to other machine learning algorithms. The proposed solution was tested on data samples from ASL data sets and achieved an overall accuracy of 99.99% using Neural Network.
      PubDate: 2022-06-21
      DOI: 10.26483/ijarcs.v13i3.6828
      Issue No: Vol. 13, No. 3 (2022)
  • Data Mining in Disease Prediction

    • Authors: Archana Thakur
      Pages: 15 - 17
      Abstract: Enhancement in information technology has led to design of many applications in the field of crop disease recognition. Disease recognition applications generate voluminous data. The disease related data can be processed using data mining techniques to predict various diseases. Data mining is a field of analysing, extracting data for furnishing new knowledge which represents the relationship between different patterns of data. Some of the data mining methods include classification, clustering, prediction and association rule mining.  In the present work data mining is used for disease prediction.
      PubDate: 2022-06-21
      DOI: 10.26483/ijarcs.v13i3.6832
      Issue No: Vol. 13, No. 3 (2022)

    • Authors: Tamilsenthil S, Dr.Kangaiammal A
      Pages: 18 - 25
      Abstract: Distributed cloud computing handles a large number of tasks and provides many dynamic virtualized resources that aim to share as a service through the internet. While handling a large volume of tasks, task execution times, throughput, and makespan are the most significant metrics in practical scenarios.  So, the scheduling task is essential to achieve accuracy and correctness on task completion. A novel technique called Multivariate Piecewise Regressive African Buffalo Optimization-based Resource Aware Task Scheduling (MPRABO-RATS) is introduced for improving the task scheduling efficiency and minimizing time consumption.  First, the cloud user dynamically generates numerous heterogeneous tasks in the cloud environments. After receiving the tasks, the task scheduler in the cloud server finds the resource-optimized virtual machine using the Multivariate Piecewise Regressive African Buffalo Optimization technique. The proposed optimization technique uses the Multivariate Piecewise Regression function for analyzing the different resources availablity such as CPU Time, Memory, Bandwidth, and Energy before the task scheduling. Initially, the population of the virtual machine is defined. After that, the fitness is measured using Multivariate Piecewise Regression. Based on the fitness estimation, the resource-efficient virtual machine is determined. Finally, the task scheduler assigns the tasks to the resource-optimized virtual machine with higher efficiency. Experimental evaluation is carried out in the CloudSim simulator on the factors such as task scheduling Efficiency, Throughput, Makespan, and Memory Consumption with respect to a number of tasks. The observed results indicate that the MPRABO-RATS technique offers an efficient solution in terms of achieving higher task scheduling Efficiency, Throughput, and Minimizing the Makespan as well as Memory Consumption than the conventional scheduling techniques
      PubDate: 2022-06-29
      DOI: 10.26483/ijarcs.v13i3.6834
      Issue No: Vol. 13, No. 3 (2022)

    • Authors: N. Sreevidya, Rekulapally Sushma Sai; Prathani Akshit, Bhavani Medi
      Pages: 26 - 29
      Abstract: As the use of artificial intelligence increases, this technology is more advanced than ever. Artificial intelligence is integrated into the components of life. What was unthinkable 10 years ago is now a casual act, with fluent conversations with computers. This was made possible by concepts of machine learning and speech recognition. That is, you can only run and complete user-designed quests. Plus, get started with audiobooks, SIM tracking, and Google Meet right away. Even if we combine voice technology with human-like conversations, There is no need to talk to our devices and have a conversation to complete our task of choice. The real focus should be on unspoken commands in completing the user’s task as quickly as possible. Voice UI is getting popular as it makes the work easy and when it is integrated with a device it will become a good user product. This is why we developed a personalized user-designated model that fulfills all the features and provides a good experience
      PubDate: 2022-07-02
      DOI: 10.26483/ijarcs.v13i3.6833
      Issue No: Vol. 13, No. 3 (2022)
  • Edibility detection of mushroom using Logistic Regression and PCA

    • Authors: Sai Charan Gangu, Madhu Nitesh Bandi, Dr Sangeeta Viswanadham; Chintala Chandrasekhar Sivaji, Toyaka Sai Kiran
      Pages: 30 - 34
      Abstract: Mushroom is found to be one of the best nutritional foods with high proteins, vitamins and minerals. Only some of the mushroom varieties were found to be edible. Some of them are dangerous to consume. To distinguish between the edible and poisonous mushrooms, we use machine learning algorithms to classify them. Classification is performed using various machine learning classifiers and Logistic regression showed better results compared to other algorithms. A survey of various algorithms resulted in KNN giving an accuracy of 100% at k=1 using 800 samples. A change k value is leading to a decrease in accuracy. By using hybrid algorithms (i.e., using two or more algorithms) which includes a combination of dimensionality reduction techniques such as Linear Discriminant Analysis(LDA) and Principal Component Analysis(PCA) along with existing classifiers better performance is achieved. Logistic Regression along with Principal Component Analysis is used to increase the accuracy. The results are shown in form of bar plots.
      PubDate: 2022-07-02
      DOI: 10.26483/ijarcs.v13i3.6830
      Issue No: Vol. 13, No. 3 (2022)

    • Authors: Dr. Subhani Shaik, Dr. K. Vijayalakshmi; K Gowri Priya, V. Vismitha, J. Saiteja
      Pages: 35 - 41
      Abstract: The COVID-19 epidemic has been causing chaos on the society, rendering it in ruins. WHO predicts that in March 2020, the world has taken a significant toll on people, leading to a lot of distortion in their lifestyle' Mental health has gone for a toss and overlooked. People in India do not have the privilege to expect support and deal with their mental health. It is high time to bridge this gap, and we have attempted to do using unconventional approaches like the booming technologies. The proposed model is validated against other baseline techniques like naive-bayes, gradient-boosting, xgboost, catboost, lightgbm and optimization techniques like simulated annealing using svm. The proposed method outperforms other baseline techniques for attaining better accuracy. They are particularly suited to predicting psychological problems. For implementation purposes, choose features like age, family history, seek_help, employment, and a few other features. The proposed model is evaluated with a COVID-19 dataset based on various performance matrices to show its effectiveness.
      PubDate: 2022-07-05
      DOI: 10.26483/ijarcs.v13i3.6838
      Issue No: Vol. 13, No. 3 (2022)
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