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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]
  • Computational linguistic material for Vietnamese speech processing:
           Appling in Vietnamese text-to-speech

    • Authors: Đồng Văn Phạm
      Pages: 49 - 54
      Abstract: The motivation of this paper is to propose a set of best-quality linguistic materials for Vietnamese speech processing, which can be used for Vietnamese TTS and ASR problems. This proposed material includes: (1) a pronunciation dictionary, which adapts from X-SAMPA,  (2) a rule-based grapheme to phoneme for Vietnamese. In order to test and evaluate, we have built a Vietnamese TTS system based on the Merlin engine, using the above materials, and evaluating the quality of speech and the accuracy of pronunciation. The results show that the applicability of these materials is favorable for further research and development on Vietnamese speech processing.
      PubDate: 2023-01-04
      DOI: 10.26483/ijarcs.v13i6.6935
      Issue No: Vol. 13, No. 6 (2023)
  • An ANN Model for Early Prediction of Diabetes

    • Authors: Amit Mishra
      Pages: 1 - 11
      Abstract: Diabetes a metabolic disease with the botanical name diabetes mellitus is diagnosed in a person who has high sugar levels in the bloodstream which could be either because of the cells not reacting to the insulin that is created or the pancreas does not deliver insulin by any stretch of the imagination. According to research (World Health Organization, Geneva 2014), Worldwide 194 million people have already been diagnosed with diabetes and this rate is expanding quickly and is estimated to achieve 333 million by 2025. In Africa, more than 5 million people are already diagnosed with diabetes, and it has been estimated that by 2025 diabetes patients in the continent would be up to 15 million. (KMV Narayan). Ogbera also reported that in Nigeria patients confirmed diabetes up to 158 million, and as such, the need to study the prediction of diabetes is very paramount. In this way, there is an incredible need to concentrate on the prediction of diabetes. So that precautions can be taken to control this deadly disease. This research work wishes to present the prediction of diabetes using ANN. The model used in this work considered a group of fifteen factors and identified the factors that are very influential in the diagnosis of diabetes using regression analysis so as to achieve better accuracy of prediction.
      PubDate: 2022-12-20
      DOI: 10.26483/ijarcs.v13i6.6916
      Issue No: Vol. 13, No. 6 (2022)

    • Authors: Santosh Saklani, Dr. Anshul Kalia, Dr. Sumesh Sood
      Pages: 12 - 25
      Abstract: Software quality prediction is the Machine Learning (ML) based technique in which ML models are trained using historical data. Output from these quality models can be used by software experts in the early phase of software development for improving the quality of software by controlling the various quality attributes like maintainability, reliability, security issues of software etc.  In this study a systematic review of studies from 2005 to 2021 is performed.  Studies that use ML techniques and source code metrics for Software Quality Prediction (SQP) are included for review. Study assesses the commonly used machine learning techniques and source code metric for SQP. Commonly used datasets, feature selection techniques and commonly used performance measures in software quality prediction are also assessed. In this paper   53 primary studies are selected for systematic review. Results of this study prove that Bayesian Learning (BL), Regression, Ensemble Learning (EL), Decision Tree (DT) and Support Vector Machine (SVM) are most commonly ML techniques used for quality prediction which comprises 58%, 52%, 41%, 32%, and 32% of the overall studies respectively. It is also assessed that NASA, PROMISE, Apache, Mozilla Firefox and Eclipse are the most commonly used datasets for training and testing the SQP models. LOC, CC, CBO, RFC, WMC, LCOM, DIT and NOC are among the most commonly used source code metrics in SQP. Based on the results from the selected studies it is concluded that ML techniques and source code metrics   have the ability to improve the overall quality of the software.
      PubDate: 2022-12-20
      DOI: 10.26483/ijarcs.v13i6.6918
      Issue No: Vol. 13, No. 6 (2022)

    • Authors: Tewodrose Tilahun, Binyam Bayou, Workineh Negasa
      Pages: 26 - 32
      Abstract: Teaching programming in an efficient and effective method has been the most difficult subject in the world of computing for the last couple of decades. Aside from a lack of laboratory equipment, the most prevalent issues are the lengthy time it takes to understand a problem, devising algorithms, writing coding, syntax, and semantic complexity of the programming language. Because of the wide range of students' backgrounds, misconceptions about the course, traditional classroom teaching methodology, and the limited allotted time available to cover the course, it is extremely difficult for a teacher to go beyond the fundamental concepts that impede the development of students' problem-solving abilities. The major goal of thisstudy is to assist students to become better programmers by comprehending code and implementing applications for real-world problems.
      PubDate: 2022-12-20
      DOI: 10.26483/ijarcs.v13i6.6934
      Issue No: Vol. 13, No. 6 (2022)
  • Stock market forecasting using Continuous Wavelet Transform and Long
           Short-Term Memory neural networks

      Pages: 33 - 39
      Abstract: The analysis and exploitation of complex and large-volume data requires new approaches, and modeling it in time series is a very successful technique. A characteristic time series is the one that defines the dynamic financial market and its asset prices. This research presents a novel forecasting methodology, which uses the Continuous Wavelet Transform for the definition of representative elements that define a time series, and a recurrent neural network architecture for the forecast of prices of financial stocks related by the item of income in the short and medium time term. The proposed model, inspired by the Continuous Wavelet Transform and Neural Networks of the "Long short-term memory" type, uses the most representative coefficients of the Wavelet transform based on the time series in the time domain, for the prediction of future prices of stocks in short prospective periods. The results show a very successful projection using this methodology. Future research will analyze the interrelationship presented by the price time series of the same stock market section, in the domain of Wavelets, and how it affects the stock market forecast.
      PubDate: 2022-12-21
      DOI: 10.26483/ijarcs.v13i6.6919
      Issue No: Vol. 13, No. 6 (2022)
  • Deep Learning Architechture for classification of Breast cancer Cells in
           Fluorescence Microscopy Images

    • Authors: Aruna Kumari Kakumani
      Pages: 40 - 44
      Abstract: Biological cell classification plays a significant role in the field of biomedical research. Cell classification is useful in different biomedical applications like identification of a normal and abnormal cell, cancer cell recognition, behovioural studies of cells to different drugs etc. Automated cell classification techniquies would assist the radiologist for the disease diagnoses and to grasp the severity of the disease based on the intricate intracellular structures of the cells. In this work a deep learning architechutre based on EfficientNet is designed for automatic classification of human breast cancer cells from fluorescence microscopy images. More specifically transfer learning is employed to take the advantage of the pretrained model and further improvising the performance of the network by fine tuning several of last layers for learning the specific classification task. The proposed deep learning architechture is evaluated on human breast cancer cells, which gave 98.15% accuracy, precision, recall and F1 score. Comparitive analysis of the proposed architechture with the standard architechures is also performed to assert the efficacy of our model.
      PubDate: 2022-12-21
      DOI: 10.26483/ijarcs.v13i6.6921
      Issue No: Vol. 13, No. 6 (2022)

    • Authors: Vaishnavi J. Deshmukh, Dr. Asha Ambhaikar
      Pages: 45 - 48
      Abstract: As time flows, the quantity of information, in particular textual content information will increase exponentially. Along with the information, our know-how of Machine Learning additionally will increase and the computing electricity permits us to teach very complicated and big fashions faster. Fake information has been accumulating loads of interest international recently. The results may be political, economic, organizational, or maybe personal. This paper discusses the one-of-a-kind evaluation of datasets and classifiers technique that's powerful for implementation of Deep gaining knowledge of and system gaining knowledge of that allows you to remedy the problem. Secondary cause of this evaluation on this paper is a faux information detection version that uses n-gram evaluation and system gaining knowledge of strategies. We look at and evaluate one-of-a-kind functions extraction strategies and 3 one-of-a-kind system category datasets offer a mechanism for re-searchers to cope with excessive effect questions that might in any other case be prohibitively steeply-priced and time-ingesting to study.
      PubDate: 2022-12-20
      DOI: 10.26483/ijarcs.v13i6.6944
      Issue No: Vol. 13, No. 6 (2022)
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