K. Anitha¹, S. Srinivasan^2,*

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 233-247, 2022, DOI:10.32604/cmc.2022.026542 - 18 May 2022

Abstract In India’s economy, agriculture has been the most significant contributor. Despite the fact that agriculture’s contribution is decreasing as the world’s population grows, it continues to be the most important source of employment with a little margin of difference. As a result, there is a pressing need to pick up the pace in order to achieve competitive, productive, diverse, and long-term agriculture. Plant disease misinterpretations can result in the incorrect application of pesticides, causing crop harm. As a result, early detection of infections is critical as well as cost-effective for farmers. To diagnose the disease… More >

Yazan Al-Issa¹, Ali Mohammad Alqudah^2,*, Hiam Alquran^3,2, Ahmed Al Issa⁴

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 311-326, 2022, DOI:10.32604/cmc.2022.025750 - 18 May 2022

Abstract Pulmonary diseases are common throughout the world, especially in developing countries. These diseases include chronic obstructive pulmonary diseases, pneumonia, asthma, tuberculosis, fibrosis, and recently COVID-19. In general, pulmonary diseases have a similar footprint on chest radiographs which makes them difficult to discriminate even for expert radiologists. In recent years, many image processing techniques and artificial intelligence models have been developed to quickly and accurately diagnose lung diseases. In this paper, the performance of four popular pretrained models (namely VGG16, DenseNet201, DarkNet19, and XceptionNet) in distinguishing between different pulmonary diseases was analyzed. To the best of… More >

Alaa Khalid Alduraibi^*

Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1295-1309, 2022, DOI:10.32604/iasc.2022.026601 - 03 May 2022

Abstract With technological advancements, deep machine learning can assist doctors in identifying the brain mass or tumor using magnetic resonance imaging (MRI). This work extracts the deep features from 18-pre-trained convolutional neural networks (CNNs) to train the classical classifiers to categorize the brain MRI images. As a result, DenseNet-201, EfficientNet-b0, and DarkNet-53 deep features trained support vector machine (SVM) model shows the best accuracy. Furthermore, the ReliefF method is applied to extract the best features. Then, the fitness function is defined to select the number of nearest neighbors of ReliefF algorithm and feature vector size. Finally, More >

E. Gothai^1,*, Surbhi Bhatia², Aliaa M. Alabdali³, Dilip Kumar Sharma⁴, Bhavana Raj Kondamudi⁵, Pankaj Dadheech⁶

Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1231-1246, 2022, DOI:10.32604/iasc.2022.026264 - 03 May 2022

Abstract At a grocery store, product supply management is critical to its employee's ability to operate productively. To find the right time for updating the item in terms of design/replenishment, real-time data on item availability are required. As a result, the item is consistently accessible on the rack when the client requires it. This study focuses on product display management at a grocery store to determine a particular product and its quantity on the shelves. Deep Learning (DL) is used to determine and identify every item and the store's supervisor compares all identified items with a… More >

Yurananatul Mangalmurti, Naruemon Wattanapongsakorn^*

Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 733-752, 2022, DOI:10.32604/iasc.2022.025073 - 03 May 2022

Abstract This paper presents effective techniques for automatic detection/classification of COVID-19 and other lung diseases using machine learning, including deep learning with convolutional neural networks (CNN) and classical machine learning techniques. We had access to a large number of chest X-ray images to use as input data. The data contains various categories including COVID-19, Pneumonia, Pneumothorax, Atelectasis, and Normal (without disease). In addition, chest X-ray images with many findings (abnormalities and diseases) from the National Institutes of Health (NIH) was also considered. Our deep learning approach used a CNN architecture with VGG16 and VGG19 models which… More >

R. Vidhya^1,*, T. T. Mirnalinee²

Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 911-925, 2022, DOI:10.32604/iasc.2022.025060 - 03 May 2022

Abstract Computer tomography (CT) scan images can provide more helpful diagnosis information regarding the lung cancers. Many machine learning and deep learning algorithms are formulated using CT input scan images for the improvisation in diagnosis and treatment process. But, designing an accurate and intelligent system still remains in darker side of the research side. This paper proposes the novel classification model which works on the principle of fused features and optimized learning network. The proposed framework incorporates the principle of saliency maps as a first tier segmentation, which is then fused with deep convolutional neural networks… More >

Mujeeb Ur Rehman^1,*, Maha Driss^2,3, Abdukodir Khakimov⁴, Sohail Khalid¹

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5681-5697, 2022, DOI:10.32604/cmc.2022.025840 - 21 April 2022

Abstract Lungs are a vital human body organ, and different Obstructive Lung Diseases (OLD) such as asthma, bronchitis, or lung cancer are caused by shortcomings within the lungs. Therefore, early diagnosis of OLD is crucial for such patients suffering from OLD since, after early diagnosis, breathing exercises and medical precautions can effectively improve their health state. A secure non-invasive early diagnosis of OLD is a primordial need, and in this context, digital image processing supported by Artificial Intelligence (AI) techniques is reliable and widely used in the medical field, especially for improving early disease diagnosis. Hence,… More >

K. C. Rajeswari^1,*, R. S. Mohana², S. Manikandan³, S. Beski Prabaharan⁴

Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 65-86, 2022, DOI:10.32604/iasc.2022.022681 - 15 April 2022

Abstract In recent years, Text-to-Speech (TTS) synthesis is taking a new dimension. People prefer voice embedded toys, online buyers are interested in interactive chat application in the form of text-to-speech facility, screen readers for visually challenged people, and many more applications use TTS module. TTSis a system that is capable of converting the arbitrary text input into natural sounding speech. It’s success lies in producing more human like speech sounding more natural. The most importanttechnical aspect of TTS is feature extraction process. Both text and speech features are needed but it is not that easy to… More >

Mirza Adnan Baig^*, Ghulam Ali Mallah, Noor Ahmed Shaikh

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 4201-4213, 2022, DOI:10.32604/cmc.2022.026225 - 29 March 2022

Abstract Precipitation prediction (PP) have become one of the significant research areas of deep learning (DL) and machine vision (MV) techniques are frequently used to predict the weather variables (WV). Since the climate change has left significant impact upon weather variables (WV) and continuously changes are observed in temperature, humidity, cloud patterns and other factors. Although cloud images contain sufficient information to predict the precipitation pattern but due to changes in climate, the complex cloud patterns and rapid shape changing behavior of clouds are difficult to consider for rainfall prediction. Prediction of rainfall would provide more… More >

Rabab Hamed M. Aly^1,*, Aziza I. Hussein², Kamel H. Rahouma³

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3985-3997, 2022, DOI:10.32604/cmc.2022.025862 - 29 March 2022

Abstract This paper aims to design an optimizer followed by a Kawahara filter for optimal classification and prediction of employees’ performance. The algorithm starts by processing data by a modified K-means technique as a hierarchical clustering method to quickly obtain the best features of employees to reach their best performance. The work of this paper consists of two parts. The first part is based on collecting data of employees to calculate and illustrate the performance of each employee. The second part is based on the classification and prediction techniques of the employee performance. This model is More >