P. Nalayini^1,*, R. Arun Prakash²

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2033-2047, 2023, DOI:10.32604/csse.2023.028269 - 01 August 2022

Abstract Fog computing is a promising technology that has been emerged to handle the growth of smart devices as well as the popularity of latency-sensitive and location-awareness Internet of Things (IoT) services. After the emergence of IoT-based services, the industry of internet-based devices has grown. The number of these devices has raised from millions to billions, and it is expected to increase further in the near future. Thus, additional challenges will be added to the traditional centralized cloud-based architecture as it will not be able to handle that growth and to support all connected devices in… More >

Rehab Alanazi^*, Ahamed Aljuhani

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2361-2378, 2023, DOI:10.32604/csse.2023.026712 - 01 August 2022

Abstract The evolution of the Internet of Things (IoT) has empowered modern industries with the capability to implement large-scale IoT ecosystems, such as the Industrial Internet of Things (IIoT). The IIoT is vulnerable to a diverse range of cyberattacks that can be exploited by intruders and cause substantial reputational and financial harm to organizations. To preserve the confidentiality, integrity, and availability of IIoT networks, an anomaly-based intrusion detection system (IDS) can be used to provide secure, reliable, and efficient IIoT ecosystems. In this paper, we propose an anomaly-based IDS for IIoT networks as an effective security… More >

V. Thanikachalam^1,*, M. G. Kavitha², V. Sivamurugan¹

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2129-2145, 2023, DOI:10.32604/csse.2023.026527 - 01 August 2022

Abstract In recent times, Internet of Things (IoT) and Deep Learning (DL) models have revolutionized the diagnostic procedures of Diabetic Retinopathy (DR) in its early stages that can save the patient from vision loss. At the same time, the recent advancements made in Machine Learning (ML) and DL models help in developing Computer Aided Diagnosis (CAD) models for DR recognition and grading. In this background, the current research works designs and develops an IoT-enabled Effective Neutrosophic based Segmentation with Optimal Deep Belief Network (ODBN) model i.e., NS-ODBN model for diagnosis of DR. The presented model involves… More >

Abeer Abdulaziz AlArfaj, Hanan Ahmed Hosni Mahmoud^*

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2527-2540, 2023, DOI:10.32604/iasc.2023.030168 - 19 July 2022

Abstract The detection of heart disease is a problematic task in medical research. This diagnosis utilizes a thorough analysis of the clinical tests from the patient’s medical history. The massive advances in deep learning models pursue the development of intelligent computerized systems that aid medical professionals to detect the disease type with the internet of things support. Therefore, in this paper, we propose a deep learning model for elderly patients to aid and enhance the diagnosis of heart disease. The proposed model utilizes a deeper neural architecture with multiple perceptron layers with regularization learning techniques. The… More >

Thien Khai Tran¹, Kha Tu Huynh^2,*, Dac-Nhuong Le³, Muhammad Arif⁴, Hoa Minh Dinh¹

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2479-2491, 2023, DOI:10.32604/iasc.2023.029078 - 19 July 2022

Abstract Environmental pollution has had substantial impacts on human life, and trash is one of the main sources of such pollution in most countries. Trash classification from a collection of trash images can limit the overloading of garbage disposal systems and efficiently promote recycling activities; thus, development of such a classification system is topical and urgent. This paper proposed an effective trash classification system that relies on a classification module embedded in a hard-ware setup to classify trash in real time. An image dataset is first augmented to enhance the images before classifying them as either… More >

Nithya Rekha Sivakumar^*

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2383-2398, 2023, DOI:10.32604/iasc.2023.028574 - 19 July 2022

Abstract Internet of things (IoT) has brought a greater transformation in healthcare sector thereby improving patient care, minimizing treatment costs. The present method employs classical mechanisms for extracting features and a regression model for prediction. These methods have failed to consider the pollution aspects involved during COVID 19 prediction. Utilizing Ensemble Deep Learning and Framingham Feature Extraction (FFE) techniques, a smart healthcare system is introduced for COVID-19 pandemic disease diagnosis. The Collected feature or data via predictive mechanisms to form pollution maps. Those maps are used to implement real-time countermeasures, such as storing the extracted data… More >

Vivekraj Mannayee^1,*, Thirumalai Ramanathan²

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1545-1563, 2023, DOI:10.32604/iasc.2023.027675 - 19 July 2022

Abstract Blockchain has recently sparked interest in both the technological and business firms. The Internet of Things's (IoT) core principle emerged due to the connectivity of several new technologies, including wireless technology, the Internet, embedded automation systems, and micro-electromechanical devices. Manufacturing environments and operations have been successfully converted by implementing recent advanced technology like Cloud Computing (CC), Cyber-Physical System (CSP), Information and Communication Technologies (ICT) and Enterprise Model, and other technological innovations into the fourth industrial revolution referred to as Industry 4.0. Data management is defined as the process of accumulation in order to make better… More >

R. Gopi¹, R. Sheeba², K. Anguraj³, T. Chelladurai⁴, Haya Mesfer Alshahrani⁵, Nadhem Nemri^6,*, Tarek Lamoudan⁷

Computer Systems Science and Engineering, Vol.44, No.2, pp. 1567-1582, 2023, DOI:10.32604/csse.2023.025216 - 15 June 2022

Abstract Rapid increase in the large quantity of industrial data, Industry 4.0/5.0 poses several challenging issues such as heterogeneous data generation, data sensing and collection, real-time data processing, and high request arrival rates. The classical intrusion detection system (IDS) is not a practical solution to the Industry 4.0 environment owing to the resource limitations and complexity. To resolve these issues, this paper designs a new Chaotic Cuckoo Search Optimization Algorithm (CCSOA) with optimal wavelet kernel extreme learning machine (OWKELM) named CCSOA-OWKELM technique for IDS on the Industry 4.0 platform. The CCSOA-OWKELM technique focuses on the design… More >

Feslin Anish Mon Solomon^1,*, Godfrey Winster Sathianesan², R. Ramesh³

Computer Systems Science and Engineering, Vol.44, No.2, pp. 1125-1142, 2023, DOI:10.32604/csse.2023.024292 - 15 June 2022

Abstract Internet of Things (IoT) is a popular social network in which devices are virtually connected for communicating and sharing information. This is applied greatly in business enterprises and government sectors for delivering the services to their customers, clients and citizens. But, the interaction is successful only based on the trust that each device has on another. Thus trust is very much essential for a social network. As Internet of Things have access over sensitive information, it urges to many threats that lead data management to risk. This issue is addressed by trust management that help… More >

Dae-Young Kim, Seokhoon Kim^*

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 727-737, 2023, DOI:10.32604/iasc.2023.027563 - 06 June 2022

Abstract As the Internet of Things (IoT) advances, machine-type devices are densely deployed and massive networks such as ultra-dense networks (UDNs) are formed. Various devices attend to the network to transmit data using machine-type communication (MTC), whereby numerous, various are generated. MTC devices generally have resource constraints and use wireless communication. In this kind of network, data aggregation is a key function to provide transmission efficiency. It can reduce the number of transmitted data in the network, and this leads to energy saving and reducing transmission delays. In order to effectively operate data aggregation in UDNs, More >