Laviza Falak Naz¹, Rohail Qamar^2,*, Raheela Asif¹, Muhammad Imran², Saad Ahmed³

Intelligent Automation & Soft Computing, Vol.39, No.5, pp. 855-887, 2024, DOI:10.32604/iasc.2024.055074 - 31 October 2024

Abstract Domain shift is when the data used in training does not match the ones it will be applied to later on under similar conditions. Domain shift will reduce accuracy in results. To prevent this, domain adaptation is done, which adapts the pre-trained model to the target domain. In real scenarios, the availability of labels for target data is rare thus resulting in unsupervised domain adaptation. Herein, we propose an innovative approach where source-free domain adaptation models and Generative Adversarial Networks (GANs) are integrated to improve the performance of computer vision or robotic vision-based systems in… More >

Tamal Kumar Kundu¹, Dinesh Kumar Anguraj^1,*, S. V. Sudha^2,*

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3289-3304, 2023, DOI:10.32604/cmc.2023.039515 - 26 December 2023

Abstract A theoretical methodology is suggested for finding the malaria parasites’ presence with the help of an intelligent hyper-parameter tuned Deep Learning (DL) based malaria parasite detection and classification (HPTDL-MPDC) in the smear images of human peripheral blood. Some existing approaches fail to predict the malaria parasitic features and reduce the prediction accuracy. The trained model initiated in the proposed system for classifying peripheral blood smear images into the non-parasite or parasite classes using the available online dataset. The Adagrad optimizer is stacked with the suggested pre-trained Deep Neural Network (DNN) with the help of the… More >

Sait Can Yucebas^*

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 49-71, 2023, DOI:10.32604/iasc.2023.036871 - 29 April 2023

Abstract The number of studies in the literature that diagnose cancer with machine learning using genome data is quite limited. These studies focus on the prediction performance, and the extraction of genomic factors that cause disease is often overlooked. However, finding underlying genetic causes is very important in terms of early diagnosis, development of diagnostic kits, preventive medicine, etc. The motivation of our study was to diagnose bladder cancer (BCa) based on genetic data and to reveal underlying genetic factors by using machine-learning models. In addition, conducting hyper-parameter optimization to get the best performance from different… More >

Afia Zafar¹, Muhammad Aamir², Nazri Mohd Nawi¹, Ali Arshad³, Saman Riaz³, Abdulrahman Alruban^4,*, Ashit Kumar Dutta⁵, Badr Almutairi⁶, Sultan Almotairi^7,8

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5641-5661, 2023, DOI:10.32604/cmc.2023.033733 - 28 December 2022

Abstract In computer vision, convolutional neural networks have a wide range of uses. Images represent most of today’s data, so it’s important to know how to handle these large amounts of data efficiently. Convolutional neural networks have been shown to solve image processing problems effectively. However, when designing the network structure for a particular problem, you need to adjust the hyperparameters for higher accuracy. This technique is time consuming and requires a lot of work and domain knowledge. Designing a convolutional neural network architecture is a classic NP-hard optimization challenge. On the other hand, different datasets… More >

Anas Al-Ragehi¹, Said Jadid Abdulkadir^1,2,*, Amgad Muneer^1,2, Safwan Sadeq³, Qasem Al-Tashi^4,5

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 2169-2186, 2022, DOI:10.32604/cmc.2022.027885 - 18 May 2022

Abstract Generative Adversarial Networks (GANs) are neural networks that allow models to learn deep representations without requiring a large amount of training data. Semi-Supervised GAN Classifiers are a recent innovation in GANs, where GANs are used to classify generated images into real and fake and multiple classes, similar to a general multi-class classifier. However, GANs have a sophisticated design that can be challenging to train. This is because obtaining the proper set of parameters for all models-generator, discriminator, and classifier is complex. As a result, training a single GAN model for different datasets may not produce… More >

S. Prabu^1,*, B. Thiyaneswaran², M. Sujatha³, C. Nalini⁴, Sujatha Rajkumar⁵

Computer Systems Science and Engineering, Vol.43, No.2, pp. 737-749, 2022, DOI:10.32604/csse.2022.022739 - 20 April 2022

Abstract Diagnosing the cardiovascular disease is one of the biggest medical difficulties in recent years. Coronary cardiovascular (CHD) is a kind of heart and blood vascular disease. Predicting this sort of cardiac illness leads to more precise decisions for cardiac disorders. Implementing Grid Search Optimization (GSO) machine training models is therefore a useful way to forecast the sickness as soon as possible. The state-of-the-art work is the tuning of the hyperparameter together with the selection of the feature by utilizing the model search to minimize the false-negative rate. Three models with a cross-validation approach do the… More >