Emad Abd Al Rahman¹, Nur Intan Raihana Ruhaiyem^1,*, Majed Bouchahma², Kamarul Imran Musa³

Intelligent Automation & Soft Computing, Vol., , DOI:10.32604/iasc.2023.032580

Abstract This study offers a framework for a breast cancer computer-aided treatment prediction (CATP) system. The rising death rate among women due to breast cancer is a worldwide health concern that can only be addressed by early diagnosis and frequent screening. Mammography has been the most utilized breast imaging technique to date. Radiologists have begun to use computer-aided detection and diagnosis (CAD) systems to improve the accuracy of breast cancer diagnosis by minimizing human errors. Despite the progress of artificial intelligence (AI) in the medical field, this study indicates that systems that can anticipate a treatment plan once a patient has… More >

D. Vidyabharathi^1,*, V. Mohanraj²

Intelligent Automation & Soft Computing, Vol., , DOI:10.32604/iasc.2023.032255

Abstract For training the present Neural Network (NN) models, the standard technique is to utilize decaying Learning Rates (LR). While the majority of these techniques commence with a large LR, they will decay multiple times over time. Decaying has been proved to enhance generalization as well as optimization. Other parameters, such as the network’s size, the number of hidden layers, dropouts to avoid overfitting, batch size, and so on, are solely based on heuristics. This work has proposed Adaptive Teaching Learning Based (ATLB) Heuristic to identify the optimal hyperparameters for diverse networks. Here we consider three architectures Recurrent Neural Networks (RNN),… More >

A. Selvi^*, S. Thilagamani

Intelligent Automation & Soft Computing, Vol., , DOI:10.32604/iasc.2022.029850

Abstract Mammography is considered a significant image for accurate breast cancer detection. Content-based image retrieval (CBIR) contributes to classifying the query mammography image and retrieves similar mammographic images from the database. This CBIR system helps a physician to give better treatment. Local features must be described with the input images to retrieve similar images. Existing methods are inefficient and inaccurate by failing in local features analysis. Hence, efficient digital mammography image retrieval needs to be implemented. This paper proposed reliable recovery of the mammographic image from the database, which requires the removal of noise using Kalman filter and scale-invariant feature transform… More >

Hosam Alhakami^1,*, Abdullah Baz², Mohammad Al-shareef³, Rajeev Kumar⁴, Alka Agrawal⁵, Raees Ahmad Khan⁵

Intelligent Automation & Soft Computing, Vol., , DOI:10.32604/iasc.2023.021560

Abstract Recent transformation of Saudi Arabian healthcare sector into a revenue producing one has signaled several advancements in healthcare in the country. Transforming healthcare management into Smart hospital systems is one of them. Secure hospital management systems which are breach-proof only can be termed as effective smart hospital systems. Given the perspective of Saudi Vision-2030, many practitioners are trying to achieve a cost-effective hospital management system by using smart ideas. In this row, the proposed framework posits the main objectives for creating smart hospital management systems that can only be acknowledged by managing the security of healthcare data and medical practices.… More >

M. S. Vivekanandan¹, T. Jesudas^2,*

Intelligent Automation & Soft Computing, Vol., , DOI:10.32604/iasc.2023.032301

Abstract In an urban city, the daily challenges of managing cleanliness are the primary aspect of routine life, which requires a large number of resources, the manual process of labour, and budget. Street cleaning techniques include street sweepers going away to different metropolitan areas, manually verifying if the street required cleaning taking action. This research presents novel street garbage recognizing robotic navigation techniques by detecting the city’s street-level images and multi-level segmentation. For the large volume of the process, the deep learning-based methods can be better to achieve a high level of classification, object detection, and accuracy than other learning algorithms.… More >

J. Aran Glenn^1,*, Srinivasan Alavandar²

Intelligent Automation & Soft Computing, Vol., , DOI:10.32604/iasc.2023.033545

Abstract Nowadays, researchers are becoming increasingly concerned about developing a highly efficient emission free transportation and energy generation system for addressing the pressing issue of environmental crisis in the form of pollution and climate change. The introduction of Electric Vehicles (EVs) solves the challenge of emission-free transportation while the necessity for decarbonized energy production is fulfilled by the installation and expansion of solar-powered Photovoltaic (PV) systems. Hence, this paper focuses on designing an effective PV based EV charging system that aids in stepping towards the achievement of a pollution free future. For overcoming the inherent intermittency associated with PV, a novel… More >

S. Amutha^*, J. Raja Sekar

Intelligent Automation & Soft Computing, Vol., , DOI:10.32604/iasc.2023.028903

Abstract In this digital era, Cardio Vascular Disease (CVD) has become the leading cause of death which has led to the mortality of 17.9 million lives each year. Earlier Diagnosis of the people who are at higher risk of CVDs helps them to receive proper treatment and helps prevent deaths. It becomes inevitable to propose a solution to predict the CVD with high accuracy. A system for predicting Cardio Vascular Disease using Deep Neural Network with Binarized Butterfly Optimization Algorithm (DNN–BBoA) is proposed. The BBoA is incorporated to select the best features. The optimal features are fed to the deep neural… More >