P. S. Arthy^1,*, A. Kavitha²

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2959-2971, 2023, DOI:10.32604/iasc.2023.032511 - 15 March 2023

Abstract With the advent of Machine and Deep Learning algorithms, medical image diagnosis has a new perception of diagnosis and clinical treatment. Regrettably, medical images are more susceptible to capturing noises despite the peak in intelligent imaging techniques. However, the presence of noise images degrades both the diagnosis and clinical treatment processes. The existing intelligent methods suffer from the deficiency in handling the diverse range of noise in the versatile medical images. This paper proposes a novel deep learning network which learns from the substantial extent of noise in medical data samples to alleviate this challenge.… More >

Shijun Fu^1,*, Hongji Fu²

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1051-1071, 2023, DOI:10.32604/cmc.2023.034495 - 06 February 2023

Abstract This paper is to explore the problems of intelligent connected vehicles (ICVs) autonomous driving decision-making under a 5G-V2X structured road environment. Through literature review and interviews with autonomous driving practitioners, this paper firstly puts forward a logical framework for designing a cerebrum-like autonomous driving system. Secondly, situated on this framework, it builds a hierarchical finite state machine (HFSM) model as well as a TOPSIS-GRA algorithm for making ICV autonomous driving decisions by employing a data fusion approach between the entropy weight method (EWM) and analytic hierarchy process method (AHP) and by employing a model fusion… More >

Oleg P. Dimitriev^*

Journal of Renewable Materials, Vol.11, No.4, pp. 1555-1600, 2023, DOI:10.32604/jrm.2023.024772 - 01 December 2022

Abstract Thermal energy, i.e., the electromagnetic energy in the infrared range that originates from the direct solar radiation, outgoing terrestrial radiation, waste heat from combustion of fuels, heat-emitting electrical devices, decay of radioactive isotopes, organic putrefaction and fermentation, human body heat, and so on, constitutes a huge energy flux circulating on the earth surface. However, most energy converters designed for the conversion of electromagnetic energy into electricity, such as photovoltaic cells, are mainly focused on using a narrow part of the solar energy lying in the visible spectrum, while thermomechanical engines that are fueled by heat… More > Graphic Abstract

Ping-Yueh Chang¹, Fu-I Chou¹, Po-Yuan Yang^2,*, Shao-Hsien Chen³

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 23-38, 2023, DOI:10.32604/iasc.2023.031609 - 29 September 2022

Abstract This paper proposes a hybrid multi-object optimization method integrating a uniform design, an adaptive network-based fuzzy inference system (ANFIS), and a multi-objective particle swarm optimizer (MOPSO) to optimize the rigid tapping parameters and minimize the synchronization errors and cycle times of computer numerical control (CNC) machines. First, rigid tapping parameters and uniform (including 41-level and 19-level) layouts were adopted to collect representative data for modeling. Next, ANFIS was used to build the model for the collected 41-level and 19-level uniform layout experiment data. In tapping center machines, the synchronization errors and cycle times are important… More >

Atif Ikram^1,2,*, Masita Abdul Jalil¹, Amir Bin Ngah¹, Saqib Raza⁶, Ahmad Salman Khan³, Yasir Mahmood^3,4, Nazri Kama⁴, Azri Azmi⁴, Assad Alzayed⁵

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1871-1886, 2023, DOI:10.32604/cmc.2023.030818 - 22 September 2022

Abstract Software maintenance is the process of fixing, modifying, and improving software deliverables after they are delivered to the client. Clients can benefit from offshore software maintenance outsourcing (OSMO) in different ways, including time savings, cost savings, and improving the software quality and value. One of the hardest challenges for the OSMO vendor is to choose a suitable project among several clients’ projects. The goal of the current study is to recommend a machine learning-based decision support system that OSMO vendors can utilize to forecast or assess the project of OSMO clients. The projects belong to… More >

Anwar R Shaheen^*, Sundar Santhosh Kumar

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2963-2978, 2023, DOI:10.32604/iasc.2023.025780 - 17 August 2022

Abstract Cloud computing plays a significant role in Information Technology (IT) industry to deliver scalable resources as a service. One of the most important factor to increase the performance of the cloud server is maximizing the resource utilization in task scheduling. The main advantage of this scheduling is to maximize the performance and minimize the time loss. Various researchers examined numerous scheduling methods to achieve Quality of Service (QoS) and to reduce execution time. However, it had disadvantages in terms of low throughput and high response time. Hence, this study aimed to schedule the task efficiently… More >

Saleem Mustafa¹, Arfan Jaffar¹, Muhammad Waseem Iqbal^2,*, Asma Abubakar², Abdullah S. Alshahrani³, Ahmed Alghamdi⁴

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2205-2218, 2023, DOI:10.32604/iasc.2023.029549 - 19 July 2022

Abstract Melanoma is of the lethal and rare types of skin cancer. It is curable at an initial stage and the patient can survive easily. It is very difficult to screen all skin lesion patients due to costly treatment. Clinicians are requiring a correct method for the right treatment for dermoscopic clinical features such as lesion borders, pigment networks, and the color of melanoma. These challenges are required an automated system to classify the clinical features of melanoma and non-melanoma disease. The trained clinicians can overcome the issues such as low contrast, lesions varying in size,… More >

R. Mangalagowri^1,*, Revathi Venkataraman²

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1565-1580, 2023, DOI:10.32604/iasc.2023.026360 - 19 July 2022

Abstract Cloud computing involves remote server deployments with public network infrastructures that allow clients to access computational resources. Virtual Machines (VMs) are supplied on requests and launched without interactions from service providers. Intruders can target these servers and establish malicious connections on VMs for carrying out attacks on other clustered VMs. The existing system has issues with execution time and false-positive rates. Hence, the overall system performance is degraded considerably. The proposed approach is designed to eliminate Cross-VM side attacks and VM escape and hide the server’s position so that the opponent cannot track the target… More >

N. Jagadeeswari^1,*, V. Mohan Raj²

Computer Systems Science and Engineering, Vol.44, No.1, pp. 929-943, 2023, DOI:10.32604/csse.2023.024945 - 01 June 2022

Abstract Virtualization is the backbone of cloud computing, which is a developing and widely used paradigm. By finding and merging identical memory pages, memory deduplication improves memory efficiency in virtualized systems. Kernel Same Page Merging (KSM) is a Linux service for memory pages sharing in virtualized environments. Memory deduplication is vulnerable to a memory disclosure attack, which uses covert channel establishment to reveal the contents of other colocated virtual machines. To avoid a memory disclosure attack, sharing of identical pages within a single user’s virtual machine is permitted, but sharing of contents between different users is More >

Qi Wang¹, Zhaoying Liu¹, Ting Zhang^1,*, Shanshan Tu¹, Yujian Li², Muhammad Waqas³

Journal on Artificial Intelligence, Vol.4, No.1, pp. 37-48, 2022, DOI:10.32604/jai.2022.027875 - 16 May 2022

Abstract Deep kernel mapping support vector machines have achieved good results in numerous tasks by mapping features from a low-dimensional space to a high-dimensional space and then using support vector machines for classification. However, the depth kernel mapping support vector machine does not take into account the connection of different dimensional spaces and increases the model parameters. To further improve the recognition capability of deep kernel mapping support vector machines while reducing the number of model parameters, this paper proposes a framework of Lightweight Deep Convolutional Cross-Connected Kernel Mapping Support Vector Machines (LC-CKMSVM). The framework consists More >