J. Prabakaran^1,*, P. Selvaraj²

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2141-2157, 2023, DOI:10.32604/csse.2023.034210

Abstract In healthcare systems, the Internet of Things (IoT) innovation and development approached new ways to evaluate patient data. A cloud-based platform tends to process data generated by IoT medical devices instead of high storage, and computational hardware. In this paper, an intelligent healthcare system has been proposed for the prediction and severity analysis of lung disease from chest computer tomography (CT) images of patients with pneumonia, Covid-19, tuberculosis (TB), and cancer. Firstly, the CT images are captured and transmitted to the fog node through IoT devices. In the fog node, the image gets modified into a convenient and efficient format… More >

E. Bharath^1,*, T. Rajagopalan²

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1417-1433, 2023, DOI:10.32604/iasc.2023.032102

Abstract Parkinson’s disease (PD) is a neurodegenerative disease cause by a deficiency of dopamine. Investigators have identified the voice as the underlying symptom of PD. Advanced vocal disorder studies provide adequate treatment and support for accurate PD detection. Machine learning (ML) models have recently helped to solve problems in the classification of chronic diseases. This work aims to analyze the effect of selecting features on ML efficiency on a voice-based PD detection system. It includes PD classification models of Random forest, decision Tree, neural network, logistic regression and support vector machine. The feature selection is made by RF mean-decrease in accuracy… More >

Abishek Mahesh¹, Deeptimaan Banerjee¹, Ahona Saha¹, Manas Ranjan Prusty^2,*, A. Balasundaram²

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5967-5982, 2023, DOI:10.32604/cmc.2023.033920

Abstract A brain tumor is the uncharacteristic progression of tissues in the brain. These are very deadly, and if it is not diagnosed at an early stage, it might shorten the affected patient’s life span. Hence, their classification and detection play a critical role in treatment. Traditional Brain tumor detection is done by biopsy which is quite challenging. It is usually not preferred at an early stage of the disease. The detection involves Magnetic Resonance Imaging (MRI), which is essential for evaluating the tumor. This paper aims to identify and detect brain tumors based on their location in the brain. In… More >

R. S. Sandhya Devi^1,*, V. R. Vijay Kumar², P. Sivakumar³

Computer Systems Science and Engineering, Vol.45, No.2, pp. 2249-2263, 2023, DOI:10.32604/csse.2023.032231

Abstract Plant disease classification and prevention of spreading of the disease at earlier stages based on visual leaves symptoms and Pest recognition through deep learning-based image classification is in the forefront of research. To perform the investigation on Plant and pest classification, Transfer Learning (TL) approach is used on EfficientNet-V2. TL requires limited labelled data and shorter training time. However, the limitation of TL is the pre-trained model network’s topology is static and the knowledge acquired is detrimentally overwriting the old parameters. EfficientNet-V2 is a Convolutional Neural Network (CNN) model with significant high speed learning rates across variable sized datasets. The… More >

Kanupriya Mittal^*, V. Mary Anita Rajam

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1907-1921, 2023, DOI:10.32604/iasc.2023.029037

Abstract An automated retinal disease detection system has long been in existence and it provides a safe, no-contact and cost-effective solution for detecting this disease. This paper presents a game theory-based dynamic weighted ensemble of a feature extraction-based machine learning model and a deep transfer learning model for automatic retinal disease detection. The feature extraction-based machine learning model uses Gaussian kernel-based fuzzy rough sets for reduction of features, and XGBoost classifier for the classification. The transfer learning model uses VGG16 or ResNet50 or Inception-ResNet-v2. A novel ensemble classifier based on the game theory approach is proposed for the fusion of the… More >

G. Ignisha Rajathi¹, R. Ramesh Kumar², D. Ravikumar³, T. Joel⁴, Seifedine Kadry^4,5, Chang-Won Jeong⁶, Yunyoung Nam^7,*

Computer Systems Science and Engineering, Vol.44, No.2, pp. 1793-1806, 2023, DOI:10.32604/csse.2023.024674

Abstract Recently, Internet of Medical Things (IoMT) has gained considerable attention to provide improved healthcare services to patients. Since earlier diagnosis of brain tumor (BT) using medical imaging becomes an essential task, automated IoMT and cloud enabled BT diagnosis model can be devised using recent deep learning models. With this motivation, this paper introduces a novel IoMT and cloud enabled BT diagnosis model, named IoMTC-HDBT. The IoMTC-HDBT model comprises the data acquisition process by the use of IoMT devices which captures the magnetic resonance imaging (MRI) brain images and transmit them to the cloud server. Besides, adaptive window filtering (AWF) based… More >

Maqsood Ahmad^1,2, Noorhaniza Wahid¹, Rahayu A Hamid¹, Saima Sadiq², Arif Mehmood³, Gyu Sang Choi^4,*

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5041-5058, 2022, DOI:10.32604/cmc.2022.026077

Abstract Mental health signifies the emotional, social, and psychological well-being of a person. It also affects the way of thinking, feeling, and situation handling of a person. Stable mental health helps in working with full potential in all stages of life from childhood to adulthood therefore it is of significant importance to find out the onset of the mental disease in order to maintain balance in life. Mental health problems are rising globally and constituting a burden on healthcare systems. Early diagnosis can help the professionals in the treatment that may lead to complications if they remain untreated. The machine learning… More >

Muhammad Asif Saleem¹, Muhammad Aamir^{1,2, * ,*}, Rosziati Ibrahim¹, Norhalina Senan¹, Tahir Alyas³

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 6053-6067, 2022, DOI:10.32604/cmc.2022.022215

Abstract Plant disease classification based on digital pictures is challenging. Machine learning approaches and plant image categorization technologies such as deep learning have been utilized to recognize, identify, and diagnose plant diseases in the previous decade. Increasing the yield quantity and quality of rice forming is an important cause for the paddy production countries. However, some diseases that are blocking the improvement in paddy production are considered as an ominous threat. Convolution Neural Network (CNN) has shown a remarkable performance in solving the early detection of paddy leaf diseases based on its images in the fast-growing era of science and technology.… More >

K. Sathya^1,*, M. Rajalakshmi²

Computer Systems Science and Engineering, Vol.42, No.1, pp. 33-47, 2022, DOI:10.32604/csse.2022.022206

Abstract In the field of agriculture, the development of an early warning diagnostic system is essential for timely detection and accurate diagnosis of diseases in rice plants. This research focuses on identifying the plant diseases and detecting them promptly through the advancements in the field of computer vision. The images obtained from in-field farms are typically with less visual information. However, there is a significant impact on the classification accuracy in the disease diagnosis due to the lack of high-resolution crop images. We propose a novel Reconstructed Disease Aware–Convolutional Neural Network (RDA-CNN), inspired by recent CNN architectures, that integrates image super… More >

S. Kusuma^*, Dr. Jothi K. R

Computer Systems Science and Engineering, Vol.41, No.3, pp. 1273-1289, 2022, DOI:10.32604/csse.2022.021741

Abstract One of the severe health problems and the most common types of heart disease (HD) is Coronary heart disease (CHD). Due to the lack of a healthy lifestyle, HD would cause frequent mortality worldwide. If the heart attack occurs without any symptoms, it cannot be cured by an intelligent detection system. An effective diagnosis and detection of CHD should prevent human casualties. Moreover, intelligent systems employ clinical-based decision support approaches to assist physicians in providing another option for diagnosing and detecting HD. This paper aims to introduce a heart disease prediction model including phases like (i) Feature extraction, (ii) Feature… More >