Iyad Katib¹, Mahmoud Ragab^2,*

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3749-3765, 2023, DOI:10.32604/csse.2023.036796

Abstract The concept of smart healthcare has seen a gradual increase with the expansion of information technology. Smart healthcare will use a new generation of information technologies, like artificial intelligence, the Internet of Things (IoT), cloud computing, and big data, to transform the conventional medical system in an all-around way, making healthcare highly effective, more personalized, and more convenient. This work designs a new Heap Based Optimization with Deep Quantum Neural Network (HBO-DQNN) model for decision-making in smart healthcare applications. The presented HBO-DQNN model majorly focuses on identifying and classifying healthcare data. In the presented HBO-DQNN model, three stages of operations… More >

Nabil Almalki^1,2, Mrim M. Alnfiai^1,3, Fahd N. Al-Wesabi⁴, Mesfer Alduhayyem⁵, Anwer Mustafa Hilal^6,*, Manar Ahmed Hamza⁶

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6719-6736, 2023, DOI:10.32604/cmc.2023.034417

Abstract Mobile communication and the Internet of Things (IoT) technologies have recently been established to collect data from human beings and the environment. The data collected can be leveraged to provide intelligent services through different applications. It is an extreme challenge to monitor disabled people from remote locations. It is because day-to-day events like falls heavily result in accidents. For a person with disabilities, a fall event is an important cause of mortality and post-traumatic complications. Therefore, detecting the fall events of disabled persons in smart homes at early stages is essential to provide the necessary support and increase their survival… More >

Mesfer Al Duhayyim^1,*, Saud S. Alotaibi², Shaha Al-Otaibi³, Fahd N. Al-Wesabi⁴, Mahmoud Othman⁵, Ishfaq Yaseen⁶, Mohammed Rizwanullah⁶, Abdelwahed Motwakel⁶

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3315-3332, 2023, DOI:10.32604/cmc.2023.033250

Abstract Proper waste management models using recent technologies like computer vision, machine learning (ML), and deep learning (DL) are needed to effectively handle the massive quantity of increasing waste. Therefore, waste classification becomes a crucial topic which helps to categorize waste into hazardous or non-hazardous ones and thereby assist in the decision making of the waste management process. This study concentrates on the design of hazardous waste detection and classification using ensemble learning (HWDC-EL) technique to reduce toxicity and improve human health. The goal of the HWDC-EL technique is to detect the multiple classes of wastes, particularly hazardous and non-hazardous wastes.… More >

Nidhi Agarwal^1,2, M. Gokilavani³, S. Nagarajan⁴, S. Saranya⁵, Hadeel Alsolai⁶, Sami Dhahbi^7,*, Amira Sayed Abdelaziz⁸

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 141-152, 2023, DOI:10.32604/cmc.2023.030074

Abstract In recent times, wireless sensor network (WSN) finds their suitability in several application areas, ranging from military to commercial ones. Since nodes in WSN are placed arbitrarily in the target field, node localization (NL) becomes essential where the positioning of the nodes can be determined by the aid of anchor nodes. The goal of any NL scheme is to improve the localization accuracy and reduce the localization error rate. With this motivation, this study focuses on the design of Intelligent Aquila Optimization Algorithm Based Node Localization Scheme (IAOAB-NLS) for WSN. The presented IAOAB-NLS model makes use of anchor nodes to… More >

Manar Ahmed Hamza^1,*, Hadeel Alsolai², Jaber S. Alzahrani³, Mohammad Alamgeer^4,5, Mohamed Mahmoud Sayed⁶, Abu Sarwar Zamani¹, Ishfaq Yaseen¹, Abdelwahed Motwakel¹

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 6563-6577, 2022, DOI:10.32604/cmc.2022.031541

Abstract Intelligent Transportation System (ITS) is one of the revolutionary technologies in smart cities that helps in reducing traffic congestion and enhancing traffic quality. With the help of big data and communication technologies, ITS offers real-time investigation and highly-effective traffic management. Traffic Flow Prediction (TFP) is a vital element in smart city management and is used to forecast the upcoming traffic conditions on transportation network based on past data. Neural Network (NN) and Machine Learning (ML) models are widely utilized in resolving real-time issues since these methods are capable of dealing with adaptive data over a period of time. Deep Learning… More >

Thavavel Vaiyapuri¹, S. Srinivasan², Mohamed Yacin Sikkandar³, T. S. Balaji^4,5, Seifedine Kadry⁶, Maytham N. Meqdad⁷, Yunyoung Nam^8,*

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5543-5557, 2022, DOI:10.32604/cmc.2022.023919

Abstract In past decades, retinal diseases have become more common and affect people of all age grounds over the globe. For examining retinal eye disease, an artificial intelligence (AI) based multilabel classification model is needed for automated diagnosis. To analyze the retinal malady, the system proposes a multiclass and multi-label arrangement method. Therefore, the classification frameworks based on features are explicitly described by ophthalmologists under the application of domain knowledge, which tends to be time-consuming, vulnerable generalization ability, and unfeasible in massive datasets. Therefore, the automated diagnosis of multi-retinal diseases becomes essential, which can be solved by the deep learning (DL)… More >

Nasser Ali Aljarallah^1,2, Ashit Kumar Dutta^3,*, Majed Alsanea⁴, Abdul Rahaman Wahab Sait⁵

Computer Systems Science and Engineering, Vol.44, No.2, pp. 1853-1868, 2023, DOI:10.32604/csse.2023.028755

Abstract A learning management system (LMS) is a software or web based application, commonly utilized for planning, designing, and assessing a particular learning procedure. Generally, the LMS offers a method of creating and delivering content to the instructor, monitoring students’ involvement, and validating their outcomes. Since mental health issues become common among studies in higher education globally, it is needed to properly determine it to improve mental stability. This article develops a new seven spot lady bird feature selection with optimal sparse autoencoder (SSLBFS-OSAE) model to assess students’ mental health on LMS. The major aim of the SSLBFS-OSAE model is to… More >

Ashit Kumar Dutta^1,*, Yasser Albagory², Manal Al Faraj¹, Majed Alsanea³, Abdul Rahaman Wahab Sait⁴

Computer Systems Science and Engineering, Vol.44, No.2, pp. 1419-1432, 2023, DOI:10.32604/csse.2023.027377

Abstract Accurate soil prediction is a vital parameter involved to decide appropriate crop, which is commonly carried out by the farmers. Designing an automated soil prediction tool helps to considerably improve the efficacy of the farmers. At the same time, fuzzy logic (FL) approaches can be used for the design of predictive models, particularly, Fuzzy Cognitive Maps (FCMs) have involved the concept of uncertainty representation and cognitive mapping. In other words, the FCM is an integration of the recurrent neural network (RNN) and FL involved in the knowledge engineering phase. In this aspect, this paper introduces effective fuzzy cognitive maps with… More >

Ahmed S. Almasoud^*

Computer Systems Science and Engineering, Vol.44, No.2, pp. 1485-1498, 2023, DOI:10.32604/csse.2023.025190

Abstract The latest advancements in computer vision and deep learning (DL) techniques pave the way to design novel tools for the detection and monitoring of forest fires. In this view, this paper presents an intelligent wild forest fire detection and alarming system using deep learning (IWFFDA-DL) model. The proposed IWFFDA-DL technique aims to identify forest fires at earlier stages through integrated sensors. The proposed IWFFDA-DL system includes an Integrated sensor system (ISS) combining an array of sensors that acts as the major input source that helps to forecast the fire. Then, the attention based convolution neural network with bidirectional long short… More >

Kingsley A. Ogudo¹, R. Surendran^2,*, Osamah Ibrahim Khalaf³

Computer Systems Science and Engineering, Vol.44, No.1, pp. 693-707, 2023, DOI:10.32604/csse.2023.024154

Abstract Skin lesions have become a critical illness worldwide, and the earlier identification of skin lesions using dermoscopic images can raise the survival rate. Classification of the skin lesion from those dermoscopic images will be a tedious task. The accuracy of the classification of skin lesions is improved by the use of deep learning models. Recently, convolutional neural networks (CNN) have been established in this domain, and their techniques are extremely established for feature extraction, leading to enhanced classification. With this motivation, this study focuses on the design of artificial intelligence (AI) based solutions, particularly deep learning (DL) algorithms, to distinguish… More >