TATIANA KURILO^*, REBECCA D. PENTZ

Oncology Research, Vol.32, No.9, pp. 1401-1406, 2024, DOI:10.32604/or.2024.050266

Abstract Objectives: Rural patients have poor cancer outcomes and clinical trial (CT) enrollment compared to urban patients due to attitudinal, awareness, and healthcare access differential. Knowledge of cancer survival disparities and CT enrollment is important for designing interventions and innovative approaches to address the stated barriers. The study explores the potential disparities in cancer survival rates and clinical trial enrollments in rural and urban breast and lung cancer patients. Our hypotheses are that for both cancer types, urban cancer patients will have longer 5-year survival rates and higher enrollment rates in clinical trials than those in… More >

Islam Zada^1,*, Mohammed Naif Alatawi², Syed Muhammad Saqlain¹, Abdullah Alshahrani³, Adel Alshamran⁴, Kanwal Imran⁵, Hessa Alfraihi⁶

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2917-2939, 2024, DOI:10.32604/cmc.2024.052835

Abstract Malware attacks on Windows machines pose significant cybersecurity threats, necessitating effective detection and prevention mechanisms. Supervised machine learning classifiers have emerged as promising tools for malware detection. However, there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware detection. Addressing this gap can provide valuable insights for enhancing cybersecurity strategies. While numerous studies have explored malware detection using machine learning techniques, there is a lack of systematic comparison of supervised classifiers for Windows malware detection. Understanding the relative effectiveness of these classifiers can inform the selection of… More >

Ain Raal¹, Valeriia Kokitko², Vira Odyntsova², Anne Orav³, Oleh Koshovyi^1,4,*

Phyton-International Journal of Experimental Botany, Vol.93, No.7, pp. 1365-1382, 2024, DOI:10.32604/phyton.2024.053754

Abstract Valeriana officinalis L. is a plant from the Caprifoliaceae family, which is widely distributed in various parts of the world, especially in Europe and Asia. All species of Valeriana are distinguished by their ability to synthesize essential oil, which has a powerful effect on the physiological and mental aspects of the human body. The aim was to study the qualitative and quantitative composition of essential oil from valerian roots, collected in different countries, using the gas chromatography method, and to establish marker compounds for valerian species. 13 samples of commercial roots with rhizomes of V. officinalis from nine… More >

Tianzhe Jiao, Chaopeng Guo, Xiaoyue Feng, Yuming Chen, Jie Song^*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1-35, 2024, DOI:10.32604/cmc.2024.053204

Abstract Multi-modal fusion technology gradually become a fundamental task in many fields, such as autonomous driving, smart healthcare, sentiment analysis, and human-computer interaction. It is rapidly becoming the dominant research due to its powerful perception and judgment capabilities. Under complex scenes, multi-modal fusion technology utilizes the complementary characteristics of multiple data streams to fuse different data types and achieve more accurate predictions. However, achieving outstanding performance is challenging because of equipment performance limitations, missing information, and data noise. This paper comprehensively reviews existing methods based on multi-modal fusion techniques and completes a detailed and in-depth analysis.… More >

Kamlesh Kumar Soothar^1,2, Yuanxiang Chen^1,2,*, Arif Hussain Magsi³, Cong Hu¹, Hussain Shah¹

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2697-2721, 2024, DOI:10.32604/cmc.2024.049607

Abstract Efficient optical network management poses significant importance in backhaul and access network communication for preventing service disruptions and ensuring Quality of Service (QoS) satisfaction. The emerging faults in optical networks introduce challenges that can jeopardize the network with a variety of faults. The existing literature witnessed various partial or inadequate solutions. On the other hand, Machine Learning (ML) has revolutionized as a promising technique for fault detection and prevention. Unlike traditional fault management systems, this research has three-fold contributions. First, this research leverages the ML and Deep Learning (DL) multi-classification system and evaluates their accuracy… More >

Umer Nauman¹, Yuhong Zhang², Zhihui Li³, Tong Zhen^1,3,*

Journal of Intelligent Medicine and Healthcare, Vol.2, pp. 15-34, 2024, DOI:10.32604/jimh.2024.048784

Abstract The convergence of handheld devices and cloud-based computing has transformed how Electronic Health Records (EHRs) are stored in mobile cloud paradigms, offering benefits such as affordability, adaptability, and portability. However, it also introduces challenges regarding network security and data confidentiality, as it aims to exchange EHRs among mobile users while maintaining high levels of security. This study proposes an innovative blockchain-based solution to these issues and presents secure cloud storage for healthcare data. To provide enhanced cryptography, the proposed method combines an enhanced Blowfish encryption method with a new key generation technique called Elephant Herding… More >

Inga Zotova^1,*, Edgars Kirilovs¹, Laura Ziemele²

Journal of Renewable Materials, Vol.12, No.3, pp. 603-613, 2024, DOI:10.32604/jrm.2024.047607

Abstract Nowadays buildings contain innovative materials, materials from local resources, production surpluses and rapidly renewable natural resources. Phase Change Materials (PCM) are one such group of novel materials which reduce building energy consumption. With the wider availability of microencapsulated PCM, there is an opportunity to develop a new type of insulating materials, combinate PCM with traditional insulation materials for latent heat energy storage. These materials are typically flammable and are located on the interior wall finishing yet there has been no detailed assessment of their fire performance. In this research work prototypes of low-density insulating boards… More > Graphic Abstract

Qing Ai^1,2, Hao Tian^2,3,*, Hui Wang^1,*, Qing Lang¹, Xingchun Huang¹, Xinghong Jiang⁴, Qiang Jing⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1797-1827, 2024, DOI:10.32604/cmes.2023.045251

Abstract Structural Health Monitoring (SHM) systems have become a crucial tool for the operational management of long tunnels. For immersed tunnels exposed to both traffic loads and the effects of the marine environment, efficiently identifying abnormal conditions from the extensive unannotated SHM data presents a significant challenge. This study proposed a model-based approach for anomaly detection and conducted validation and comparative analysis of two distinct temporal predictive models using SHM data from a real immersed tunnel. Firstly, a dynamic predictive model-based anomaly detection method is proposed, which utilizes a rolling time window for modeling to achieve… More >

Xiaodong Shi^1,*, Yue Qi¹, Liangzhu Lin¹, Jia Wang¹, Xiaobo Qin², Bei Niu^3,*

Phyton-International Journal of Experimental Botany, Vol.92, No.11, pp. 3149-3162, 2023, DOI:10.32604/phyton.2023.027324

Abstract Tartary buckwheat (Fagopyrum tataricum) is a dual-purpose medicinal and food crop grown for its high contents of functional compounds and abundant nutrients. Although studies have shown the differences of total flavonoid content in Tartary buckwheat at different ploidy levels, the composition of flavonoid and its regulatory mechanisms are largely unknown. In this study, the leaf metabolome and transcriptome of diploid and tetraploid accessions of Tartary buckwheat were analyzed to gain insight into the impact of polyploidization on comparative secondary metabolite composition and molecular regulatory mechanism. Based on a widely targeted metabolomics analysis, a total of 792… More >

Bilal Khan¹, Muhammad Arshad², Sarwar Shah Khan^3,4,*

Journal of Cyber Security, Vol.5, pp. 1-11, 2023, DOI:10.32604/jcs.2023.042501

Abstract The proliferation of maliciously coded documents as file transfers increase has led to a rise in sophisticated attacks. Portable Document Format (PDF) files have emerged as a major attack vector for malware due to their adaptability and wide usage. Detecting malware in PDF files is challenging due to its ability to include various harmful elements such as embedded scripts, exploits, and malicious URLs. This paper presents a comparative analysis of machine learning (ML) techniques, including Naive Bayes (NB), K-Nearest Neighbor (KNN), Average One Dependency Estimator (A1DE), Random Forest (RF), and Support Vector Machine (SVM) for More >