Takanobu Hirosawa^*, Taro Shimizu

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 3919-3944, 2025, DOI:10.32604/cmc.2025.063803 - 19 May 2025

Abstract Artificial Intelligence (AI) is fundamentally transforming medical diagnostics, driving advancements that enhance accuracy, efficiency, and personalized patient care. This narrative review explores AI integration across various diagnostic domains, emphasizing its role in improving clinical decision-making. The evolution of medical diagnostics from traditional observational methods to sophisticated imaging, laboratory tests, and molecular diagnostics lays the foundation for understanding AI’s impact. Modern diagnostics are inherently complex, influenced by multifactorial disease presentations, patient variability, cognitive biases, and systemic factors like data overload and interdisciplinary collaboration. AI-enhanced clinical decision support systems utilize both knowledge-based and non-knowledge-based approaches, employing machine… More >

Aamir Farooq¹, Zhengrong Xiang^1,*, Wen-Jer Chang^2,*, Muhammad Shamrooz Aslam³

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 3623-3674, 2025, DOI:10.32604/cmc.2025.063665 - 19 May 2025

Abstract Formation control in multi-agent systems has become a critical area of interest due to its wide-ranging applications in robotics, autonomous transportation, and surveillance. While various studies have explored distributed cooperative control, this review focuses on the theoretical foundations and recent developments in formation control strategies. The paper categorizes and analyzes key formation types, including formation maintenance, group or cluster formation, bipartite formations, event-triggered formations, finite-time convergence, and constrained formations. A significant portion of the review addresses formation control under constrained dynamics, presenting both model-based and model-free approaches that consider practical limitations such as actuator bounds,… More >

Yong Zheng^*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4315-4328, 2025, DOI:10.32604/cmc.2025.063498 - 19 May 2025

Abstract A recommender system is a tool designed to suggest relevant items to users based on their preferences and behaviors. Collaborative filtering, a popular technique within recommender systems, predicts user interests by analyzing patterns in interactions and similarities between users, leveraging past behavior data to make personalized recommendations. Despite its popularity, collaborative filtering faces notable challenges, and one of them is the issue of grey-sheep users who have unusual tastes in the system. Surprisingly, existing research has not extensively explored outlier detection techniques to address the grey-sheep problem. To fill this research gap, this study conducts… More >

Ziyang Wang, Yuanzhen Feng^*, Zhengxin Wang, Cong Zheng

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4835-4853, 2025, DOI:10.32604/cmc.2025.063179 - 19 May 2025

Abstract Continuous control protocols are extensively utilized in traditional MASs, in which information needs to be transmitted among agents consecutively, therefore resulting in excessive consumption of limited resources. To decrease the control cost, based on ISC, several LFC problems are investigated for second-order MASs without and with time delay, respectively. Firstly, an intermittent sampled controller is designed, and a sufficient and necessary condition is derived, under which state errors between the leader and all the followers approach zero asymptotically. Considering that time delay is inevitable, a new protocol is proposed to deal with the time-delay situation.… More >

Jixiang Wang¹, Jing Wei², Siqi Chen¹, Haiyang Yu^1,3,4, Yilong Ren^1,3,4,*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4167-4192, 2025, DOI:10.32604/cmc.2025.062503 - 19 May 2025

Abstract The real-time path optimization for heterogeneous vehicle fleets in large-scale road networks presents significant challenges due to conflicting traffic demands and imbalanced resource allocation. While existing vehicle-to-infrastructure coordination frameworks partially address congestion mitigation, they often neglect priority-aware optimization and exhibit algorithmic bias toward dominant vehicle classes—critical limitations in mixed-priority scenarios involving emergency vehicles. To bridge this gap, this study proposes a preference game-theoretic coordination framework with adaptive strategy transfer protocol, explicitly balancing system-wide efficiency (measured by network throughput) with priority vehicle rights protection (quantified via time-sensitive utility functions). The approach innovatively combines (1) a multi-vehicle… More >

Shrabani Sutradhar^1,2, Rajesh Bose³, Sudipta Majumder¹, Arfat Ahmad Khan^4,*, Sandip Roy³, Fasee Ullah⁵, Deepak Prashar^6,7

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 3975-4029, 2025, DOI:10.32604/cmc.2025.061965 - 19 May 2025

Abstract Cloud-based setups are intertwined with the Internet of Things and advanced, and technologies such as blockchain revolutionize conventional healthcare infrastructure. This digitization has major advantages, mainly enhancing the security barriers of the green tree infrastructure. In this study, we conducted a systematic review of over 150 articles that focused exclusively on blockchain-based healthcare systems, security vulnerabilities, cyberattacks, and system limitations. In addition, we considered several solutions proposed by thousands of researchers worldwide. Our results mostly delineate sustained threats and security concerns in blockchain-based medical health infrastructures for data management, transmission, and processing. Here, we describe… More >

Zeyang Zhou^1,*, Zachary James Ryan¹, Utkarsh Sharma², Tran Tien Anh³, Shashi Mehrotra⁴, Angelo Greco⁵, Jason West⁶, Mukesh Prasad^1,*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4795-4813, 2025, DOI:10.32604/cmc.2025.060291 - 19 May 2025

Abstract Accurate capacity and State of Charge (SOC) estimation are crucial for ensuring the safety and longevity of lithium-ion batteries in electric vehicles. This study examines ten machine learning architectures, Including Deep Belief Network (DBN), Bidirectional Recurrent Neural Network (BiDirRNN), Gated Recurrent Unit (GRU), and others using the NASA B0005 dataset of 591,458 instances. Results indicate that DBN excels in capacity estimation, achieving orders-of-magnitude lower error values and explaining over 99.97% of the predicted variable’s variance. When computational efficiency is paramount, the Deep Neural Network (DNN) offers a strong alternative, delivering near-competitive accuracy with significantly reduced… More >

Song Gao, Shixin Liu^*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5623-5641, 2025, DOI:10.32604/cmc.2025.058334 - 19 May 2025

Abstract With the development of economic globalization, distributed manufacturing is becoming more and more prevalent. Recently, integrated scheduling of distributed production and assembly has captured much concern. This research studies a distributed flexible job shop scheduling problem with assembly operations. Firstly, a mixed integer programming model is formulated to minimize the maximum completion time. Secondly, a Q-learning-assisted co-evolutionary algorithm is presented to solve the model: (1) Multiple populations are developed to seek required decisions simultaneously; (2) An encoding and decoding method based on problem features is applied to represent individuals; (3) A hybrid approach of heuristic… More >

Zhiyuan Zhang¹, Yongjun Wu¹, Xiqin Li¹, Minghui Song¹, Guangwu Zhang², Ziren Wang^3,*, Wei Li³

Energy Engineering, Vol.122, No.5, pp. 1919-1948, 2025, DOI:10.32604/ee.2025.063178 - 25 April 2025

Abstract The park-level integrated energy system (PIES) is essential for achieving carbon neutrality by managing multi-energy supply and demand while enhancing renewable energy integration. However, current carbon trading mechanisms lack sufficient incentives for emission reductions, and traditional optimization algorithms often face challenges with convergence and local optima in complex PIES scheduling. To address these issues, this paper introduces a low-carbon dispatch strategy that combines a reward-penalty tiered carbon trading model with P2G-CCS integration, hydrogen utilization, and the Secretary Bird Optimization Algorithm (SBOA). Key innovations include: (1) A dynamic reward-penalty carbon trading mechanism with coefficients (μ = 0.2,… More >

Arpita Johri^1,2,*, Varnita Verma³, Mainak Basu^1,*

Energy Engineering, Vol.122, No.5, pp. 1887-1918, 2025, DOI:10.32604/ee.2025.062355 - 25 April 2025

Abstract The globe faces an urgent need to close the energy demand-supply gap. Addressing this difficulty requires constructing a Hybrid Renewable Energy System (HRES), which has proven to be the most appropriate solution. HRES allows for integrating two or more renewable energy resources, successfully addressing the issue of intermittent availability of non-conventional energy resources. Optimization is critical for improving the HRES’s performance parameters during implementation. This study focuses on HRES using solar and biomass as renewable energy supplies and appropriate energy storage technologies. However, energy fluctuations present a problem with the power quality of HRES. To… More > Graphic Abstract