Weiqiang Jin^1,2, Ningwei Wang¹, Lei Zhang³, Xingwu Tian¹, Bohang Shi¹, Biao Zhao^1,*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 3961-4018, 2025, DOI:10.32604/cmc.2025.067857 - 30 July 2025

Abstract With the growing adoption of Artifical Intelligence (AI), AI-driven autonomous techniques and automation systems have seen widespread applications, become pivotal in enhancing operational efficiency and task automation across various aspects of human living. Over the past decade, AI-driven automation has advanced from simple rule-based systems to sophisticated multi-agent hybrid architectures. These technologies not only increase productivity but also enable more scalable and adaptable solutions, proving particularly beneficial in industries such as healthcare, finance, and customer service. However, the absence of a unified review for categorization, benchmarking, and ethical risk assessment hinders the AI-driven automation progress.… More >

B. Gomathi^1,*, R. Manimegalai¹, Srivatsan Santhanam², Atreya Biswas³

Computer Systems Science and Engineering, Vol.48, No.6, pp. 1749-1765, 2024, DOI:10.32604/csse.2024.056659 - 22 November 2024

Abstract In enterprise operations, maintaining manual rules for enterprise processes can be expensive, time-consuming, and dependent on specialized domain knowledge in that enterprise domain. Recently, rule-generation has been automated in enterprises, particularly through Machine Learning, to streamline routine tasks. Typically, these machine models are black boxes where the reasons for the decisions are not always transparent, and the end users need to verify the model proposals as a part of the user acceptance testing to trust it. In such scenarios, rules excel over Machine Learning models as the end-users can verify the rules and have more… More >

A. K. S. Saranya^1,*, T. Jaya²

Computer Systems Science and Engineering, Vol.45, No.1, pp. 545-562, 2023, DOI:10.32604/csse.2023.029975 - 16 August 2022

Abstract Heartbeat detection stays central to cardiovascular an electrocardiogram (ECG) is used to help with disease diagnosis and management. Existing Convolutional Neural Network (CNN)-based methods suffer from the less generalization problem thus; the effectiveness and robustness of the traditional heartbeat detector methods cannot be guaranteed. In contrast, this work proposes a heartbeat detector Krill based Deep Neural Network Stacked Auto Encoders (KDNN-SAE) that computes the disease before the exact heart rate by combining features from multiple ECG Signals. Heartbeats are classified independently and multiple signals are fused to estimate life threatening conditions earlier without any error… More >

Bilal Khan¹, Danish Shehzad¹, Numan Shafi¹, Ga-Young Kim^2,*, Muhammad Umar Aftab¹

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1671-1690, 2022, DOI:10.32604/cmc.2022.024918 - 18 May 2022

Abstract Nowadays multiple wireless communication systems operate in industrial environments side by side. In such an environment performance of one wireless network can be degraded by the collocated hostile wireless network having higher transmission power or higher carrier sensing threshold. Unlike the previous research works which considered IEEE 802.15.4 for the Industrial Wireless communication systems (iWCS) this paper examines the coexistence of IEEE 802.11 based iWCS used for delay-stringent communication in process automation and gWLAN (general-purpose WLAN) used for non-real time communication. In this paper, we present a Markov chain-based performance model that described the transmission… More >