Elumalaivasan Poongavanam^1,*, Padmanathan Kasinathan², Kulothungan Kanagasabai³

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 249-265, 2023, DOI:10.32604/iasc.2023.030101

Abstract The nation deserves to learn what India’s future energy demand will be in order to plan and implement an energy policy. This energy demand will have to be fulfilled by an adequate mix of existing energy sources, considering the constraints imposed by future economic and social changes in the direction of a more sustainable world. Forecasting energy demand, on the other hand, is a tricky task because it is influenced by numerous micro-variables. As a result, an macro model with only a few factors that may be predicted globally, rather than a detailed analysis for each of these variables, is… More >

Kandasamy Sellamuthu^*, Vishnu Kumar Kaliappan

Computer Systems Science and Engineering, Vol.45, No.1, pp. 715-736, 2023, DOI:10.32604/csse.2023.029017

Abstract Pesticides have become more necessary in modern agricultural production. However, these pesticides have an unforeseeable long-term impact on people's wellbeing as well as the ecosystem. Due to a shortage of basic pesticide exposure awareness, farmers typically utilize pesticides extremely close to harvesting. Pesticide residues within foods, particularly fruits as well as veggies, are a significant issue among farmers, merchants, and particularly consumers. The residual concentrations were far lower than these maximal allowable limits, with only a few surpassing the restrictions for such pesticides in food. There is an obligation to provide a warning about this amount of pesticide use in… More >

Chaithra^1,*, G. M. Lingaraju², S. Jagannatha³

Computer Systems Science and Engineering, Vol.45, No.1, pp. 803-817, 2023, DOI:10.32604/csse.2023.028770

Abstract In the data retrieval process of the Data recommendation system, the matching prediction and similarity identification take place a major role in the ontology. In that, there are several methods to improve the retrieving process with improved accuracy and to reduce the searching time. Since, in the data recommendation system, this type of data searching becomes complex to search for the best matching for given query data and fails in the accuracy of the query recommendation process. To improve the performance of data validation, this paper proposed a novel model of data similarity estimation and clustering method to retrieve the… More >

Masayuki Honda¹, Kenichi Tatsumi^2,*, Masaki Nakagawa³

CMES-Computer Modeling in Engineering & Sciences, Vol.133, No.3, pp. 557-577, 2022, DOI:10.32604/cmes.2022.020623

Abstract A model to predict photosynthetic carbon assimilation rate (A) with high accuracy is important for forecasting crop yield and productivity. Long short-term memory (LSTM), a neural network suitable for time-series data, enables prediction with high accuracy but requires mesophyll variables. In addition, for practical use, it is desirable to have a technique that can predict A from easily available information. In this study, we propose a BLSTMaugmented LSTM (BALSTM) model, which utilizes bi-directional LSTM (BLSTM) to indirectly reproduce the mesophyll variables required for LSTM. The most significant feature of the proposed model is that its hybrid architecture uses only three… More >

S. Girirajan, A. Pandian^*

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1987-2001, 2023, DOI:10.32604/iasc.2023.028090

Abstract Speech enhancement is the task of taking a noisy speech input and producing an enhanced speech output. In recent years, the need for speech enhancement has been increased due to challenges that occurred in various applications such as hearing aids, Automatic Speech Recognition (ASR), and mobile speech communication systems. Most of the Speech Enhancement research work has been carried out for English, Chinese, and other European languages. Only a few research works involve speech enhancement in Indian regional Languages. In this paper, we propose a two-fold architecture to perform speech enhancement for Tamil speech signal based on convolutional recurrent neural… More >

G. Kavipriya^*, D. Manjula

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1675-1689, 2023, DOI:10.32604/iasc.2023.027670

Abstract Drug-target interactions prediction (DTIP) remains an important requirement in the field of drug discovery and human medicine. The identification of interaction among the drug compound and target protein plays an essential process in the drug discovery process. It is a lengthier and complex process for predicting the drug target interaction (DTI) utilizing experimental approaches. To resolve these issues, computational intelligence based DTIP techniques were developed to offer an efficient predictive model with low cost. The recently developed deep learning (DL) models can be employed for the design of effective predictive approaches for DTIP. With this motivation, this paper presents a… More >

M. Mohan^1,*, P. C. Kishore Raja², P. Velmurugan³, A. Kulothungan⁴

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 1151-1163, 2023, DOI:10.32604/iasc.2023.026255

Abstract Prediction of stock market value is highly risky because it is based on the concept of Time Series forecasting system that can be used for investments in a safe environment with minimized chances of loss. The proposed model uses a real time dataset of fifteen Stocks as input into the system and based on the data, predicts or forecast future stock prices of different companies belonging to different sectors. The dataset includes approximately fifteen companies from different sectors and forecasts their results based on which the user can decide whether to invest in the particular company or not; the forecasting… More >

S. Hariprasad^1,*, T. Deepa¹, N. Bharathiraja²

Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1425-1440, 2022, DOI:10.32604/iasc.2022.022856

Abstract The Internet of Things (IoT) is a global information and communication technology which aims to connect any type of device to the internet at any time and in any location. Nowadays billions of IoT devices are connected to the world, this leads to easily cause vulnerability to IoT devices. The increasing of users in different IoT-related applications leads to more data attacks is happening in the IoT networks after the fog layer. To detect and reduce the attacks the deep learning model is used. In this article, a hybrid sample selected recurrent neural network-extreme learning machine (hybrid SSRNN-ELM) algorithm that… More >

Pranav Pushkar¹, Christo Ananth², Preeti Nagrath¹, Jehad F. Al-Amri⁵, Vividha¹, Anand Nayyar^3,4,*

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1601-1619, 2022, DOI:10.32604/cmc.2022.026205

Abstract The study of viruses and their genetics has been an opportunity as well as a challenge for the scientific community. The recent ongoing SARS-Cov2 (Severe Acute Respiratory Syndrome) pandemic proved the unpreparedness for these situations. Not only the countermeasures for the effect caused by virus need to be tackled but the mutation taking place in the very genome of the virus is needed to be kept in check frequently. One major way to find out more information about such pathogens is by extracting the genetic data of such viruses. Though genetic data of viruses have been cultured and stored as… More >

Nasebah Almufadi¹, Ali Mustafa Qamar^1,2,*

Computer Systems Science and Engineering, Vol.43, No.3, pp. 1255-1270, 2022, DOI:10.32604/csse.2022.025029

Abstract Currently, mobile communication is one of the widely used means of communication. Nevertheless, it is quite challenging for a telecommunication company to attract new customers. The recent concept of mobile number portability has also aggravated the problem of customer churn. Companies need to identify beforehand the customers, who could potentially churn out to the competitors. In the telecommunication industry, such identification could be done based on call detail records. This research presents an extensive experimental study based on various deep learning models, such as the 1D convolutional neural network (CNN) model along with the recurrent neural network (RNN) and deep… More >