EGOR V. BATOROV^1,2,*, ALISA D. INESHINA², TATIANA A. ARISTOVA³, VERA V. DENISOVA³, SVETLANA A. SIZIKOVA³, DARIA S. BATOROVA³, GALINA Y. USHAKOVA³, EKATERINA Y. SHEVELA¹, ELENA R. CHERNYKH¹

Oncology Research, Vol.32, No.10, pp. 1575-1587, 2024, DOI:10.32604/or.2024.047893

Abstract Background: Immune checkpoint ligand-receptor interactions appear to be associated with multiple myeloma (MM) progression. Simultaneously, previous studies showed the possibility of PD-1 and TIM-3 expression on T cells upon stimulation with common γ-chain family cytokines in vitro and during homeostatic proliferation. The aim of the present work was to study the impact of homeostatic proliferation on the expansion of certain T cell subsets up-regulating PD-1 and TIM-3 checkpoint molecules. Methods: The expression of CD25, CD122, CD127 common γ-chain cytokine receptors, phosphorylated signal transducer and activator of transcription-5 (pSTAT5) and eomesodermin (EOMES) was comparatively assessed with flow… More >

Robertas Damasevicius^1,*, Faheem Mahmood², Yaseen Zaman³, Sobia Dastgeer², Sajid Khan²

Computer Systems Science and Engineering, Vol.48, No.5, pp. 1349-1366, 2024, DOI:10.32604/csse.2024.050359

Abstract Plant diseases must be identified as soon as possible since they have an impact on the growth of the corresponding species. Consequently, the identification of leaf diseases is essential in this field of agriculture. Diseases brought on by bacteria, viruses, and fungi are a significant factor in reduced crop yields. Numerous machine learning models have been applied in the identification of plant diseases, however, with the recent developments in deep learning, this field of study seems to hold huge potential for improved accuracy. This study presents an effective method that uses image processing and deep… More >

Qian Liu^1,#, Hui Zhang^1,#, Juan Gao², Meiping Sha¹, Lijun Shen¹, Xianfeng Cheng^3,*, Hao Chen^4,*

Psycho-Oncologie, Vol.18, No.3, pp. 213-221, 2024, DOI:10.32604/po.2024.052607

Abstract Background: Keratinocyte carcinoma (KC) is a common malignancy characterized by a high recurrence rate and considerable psychological distress. The incidence of KC is increasing in China, raising concerns about its psychological consequences and adverse effects on quality of life. Demographic and clinical factors are thought to influence mental health outcomes in these patients. Nonetheless, data on the prevalence of anxiety in Chinese patients with KC and the factors associated with this anxiety are notably lacking. Therefore, a comprehensive investigation into the anxiety of patients with KC is imperative. Objective: This study aimed to investigate the… More >

Shih-Lin Lin^*, Bo-Chen Lin

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3555-3572, 2024, DOI:10.32604/cmc.2024.055456

Abstract This paper explores the application of Model Predictive Control (MPC) to enhance safety and efficiency in autonomous vehicle (AV) navigation through optimized path planning. The evolution of AV technology has progressed rapidly, moving from basic driver-assistance systems (Level 1) to fully autonomous capabilities (Level 5). Central to this advancement are two key functionalities: Lane-Change Maneuvers (LCM) and Adaptive Cruise Control (ACC). In this study, a detailed simulation environment is created to replicate the road network between Nantun and Wuri on National Freeway No. 1 in Taiwan. The MPC controller is deployed to optimize vehicle trajectories,… More >

Huiling Yu¹, Yanqiu Hang², Shen Shi¹, Kangning Wu¹, Yizhuo Zhang^1,*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4859-4874, 2024, DOI:10.32604/cmc.2024.055403

Abstract Electrolysis tanks are used to smelt metals based on electrochemical principles, and the short-circuiting of the pole plates in the tanks in the production process will lead to high temperatures, thus affecting normal production. Aiming at the problems of time-consuming and poor accuracy of existing infrared methods for high-temperature detection of dense pole plates in electrolysis tanks, an infrared dense pole plate anomalous target detection network YOLOv5-RMF based on You Only Look Once version 5 (YOLOv5) is proposed. Firstly, we modified the Real-Time Enhanced Super-Resolution Generative Adversarial Network (Real-ESRGAN) by changing the U-shaped network (U-Net)… More >

Weiqiang Jin^1,#, Xingwu Tian^1,#, Bohang Shi¹, Biao Zhao^1,*, Haibin Duan², Hao Wu³

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3523-3553, 2024, DOI:10.32604/cmc.2024.055125

Abstract The UAV pursuit-evasion problem focuses on the efficient tracking and capture of evading targets using unmanned aerial vehicles (UAVs), which is pivotal in public safety applications, particularly in scenarios involving intrusion monitoring and interception. To address the challenges of data acquisition, real-world deployment, and the limited intelligence of existing algorithms in UAV pursuit-evasion tasks, we propose an innovative swarm intelligence-based UAV pursuit-evasion control framework, namely “Boids Model-based DRL Approach for Pursuit and Escape” (Boids-PE), which synergizes the strengths of swarm intelligence from bio-inspired algorithms and deep reinforcement learning (DRL). The Boids model, which simulates collective… More >

Aniqua Nusrat Zereen¹, Abir Das², Jia Uddin^3,*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3463-3484, 2024, DOI:10.32604/cmc.2024.054886

Abstract Machine fault diagnostics are essential for industrial operations, and advancements in machine learning have significantly advanced these systems by providing accurate predictions and expedited solutions. Machine learning models, especially those utilizing complex algorithms like deep learning, have demonstrated major potential in extracting important information from large operational datasets. Despite their efficiency, machine learning models face challenges, making Explainable AI (XAI) crucial for improving their understandability and fine-tuning. The importance of feature contribution and selection using XAI in the diagnosis of machine faults is examined in this study. The technique is applied to evaluate different machine-learning More >

Xiangkui Jiang^*, Zihao Wang, Chao Dong

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4303-4323, 2024, DOI:10.32604/cmc.2024.054640

Abstract

For the problem of slow search and tortuous paths in the Rapidly Exploring Random Tree (RRT) algorithm, a feedback-biased sampling RRT, called FS-RRT, is proposed based on RRT. Firstly, to improve the sampling efficiency of RRT to shorten the search time, the search area of the random tree is restricted to improve the sampling efficiency. Secondly, to obtain better information about obstacles to shorten the path length, a feedback-biased sampling strategy is used instead of the traditional random sampling, the collision of the expanding node with an obstacle generates feedback information so that the next

… More >

Wen-Tsai Sung¹, Indra Griha Tofik Isa^1,2, Sung-Jung Hsiao^3,*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3733-3759, 2024, DOI:10.32604/cmc.2024.053634

Abstract Mango fruit is one of the main fruit commodities that contributes to Taiwan’s income. The implementation of technology is an alternative to increasing the quality and quantity of mango plantation product productivity. In this study, a Wireless Sensor Networks (“WSNs”)-based intelligent mango plantation monitoring system will be developed that implements deep reinforcement learning (DRL) technology in carrying out prediction tasks based on three classifications: “optimal,” “sub-optimal,” or “not-optimal” conditions based on three parameters including humidity, temperature, and soil moisture. The key idea is how to provide a precise decision-making mechanism in the real-time monitoring system.… More >

Ahmad Alzu’bi^1,*, Amjad Albashayreh², Abdelrahman Abuarqoub³, Mai A. M. Alfawair⁴

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3785-3802, 2024, DOI:10.32604/cmc.2024.052599

Abstract In the era of the Internet of Things (IoT), the proliferation of connected devices has raised security concerns, increasing the risk of intrusions into diverse systems. Despite the convenience and efficiency offered by IoT technology, the growing number of IoT devices escalates the likelihood of attacks, emphasizing the need for robust security tools to automatically detect and explain threats. This paper introduces a deep learning methodology for detecting and classifying distributed denial of service (DDoS) attacks, addressing a significant security concern within IoT environments. An effective procedure of deep transfer learning is applied to utilize More >