Yaqiong He, Jinlin Fan^*, Huaiguang Wu

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 995-1032, 2024, DOI:10.32604/cmc.2024.052887

Abstract With the rise of blockchain technology, the security issues of smart contracts have become increasingly critical. Despite the availability of numerous smart contract vulnerability detection tools, many face challenges such as slow updates, usability issues, and limited installation methods. These challenges hinder the adoption and practicality of these tools. This paper examines smart contract vulnerability detection tools from 2016 to 2023, sourced from the Web of Science (WOS) and Google Scholar. By systematically collecting, screening, and synthesizing relevant research, 38 open-source tools that provide installation methods were selected for further investigation. From a developer’s perspective,… More >

Anas AbuKaraki¹, Tawfi Alrawashdeh¹, Sumaya Abusaleh¹, Malek Zakarya Alksasbeh^1,*, Bilal Alqudah¹, Khalid Alemerien², Hamzah Alshamaseen³

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1055-1073, 2024, DOI:10.32604/cmc.2024.051420

Abstract This paper presents a novel multiclass system designed to detect pleural effusion and pulmonary edema on chest X-ray images, addressing the critical need for early detection in healthcare. A new comprehensive dataset was formed by combining 28,309 samples from the ChestX-ray14, PadChest, and CheXpert databases, with 10,287, 6022, and 12,000 samples representing Pleural Effusion, Pulmonary Edema, and Normal cases, respectively. Consequently, the preprocessing step involves applying the Contrast Limited Adaptive Histogram Equalization (CLAHE) method to boost the local contrast of the X-ray samples, then resizing the images to 380 × 380 dimensions, followed by using the data… More >

Fuxi Zhu¹, Jin Xie^2,*, Mohammed Alshahrani³

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 949-971, 2024, DOI:10.32604/cmc.2024.051046

Abstract User representation learning is crucial for capturing different user preferences, but it is also critical challenging because user intentions are latent and dispersed in complex and different patterns of user-generated data, and thus cannot be measured directly. Text-based data models can learn user representations by mining latent semantics, which is beneficial to enhancing the semantic function of user representations. However, these technologies only extract common features in historical records and cannot represent changes in user intentions. However, sequential feature can express the user’s interests and intentions that change time by time. But the sequential recommendation… More >

Ahmad Ababneh¹, Yousef Sanjalawe², Salam Fraihat^3,*, Salam Al-E’mari⁴, Hamzah Alqudah⁵

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1347-1379, 2024, DOI:10.32604/cmc.2024.050600

Abstract This study introduces the Orbit Weighting Scheme (OWS), a novel approach aimed at enhancing the precision and efficiency of Vector Space information retrieval (IR) models, which have traditionally relied on weighting schemes like tf-idf and BM25. These conventional methods often struggle with accurately capturing document relevance, leading to inefficiencies in both retrieval performance and index size management. OWS proposes a dynamic weighting mechanism that evaluates the significance of terms based on their orbital position within the vector space, emphasizing term relationships and distribution patterns overlooked by existing models. Our research focuses on evaluating OWS’s impact… More >

Nidhi Parashar¹, Prashant Johri¹, Arfat Ahmad Khan⁵, Nitin Gaur¹, Seifedine Kadry^2,3,4,*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 389-425, 2024, DOI:10.32604/cmc.2024.050240

Abstract The growing global requirement for food and the need for sustainable farming in an era of a changing climate and scarce resources have inspired substantial crop yield prediction research. Deep learning (DL) and machine learning (ML) models effectively deal with such challenges. This research paper comprehensively analyses recent advancements in crop yield prediction from January 2016 to March 2024. In addition, it analyses the effectiveness of various input parameters considered in crop yield prediction models. We conducted an in-depth search and gathered studies that employed crop modeling and AI-based methods to predict crop yield. The… More >

DIMITRA P. VAGELI^1,2,*, PANAGIOTIS G. DOUKAS³, KERASIA GOUPOU², ANTONIOS D. BENOS², KYRIAKI ASTARA^2,4, KONSTANTINA ZACHAROULI², SOTIRIS SOTIRIOU⁵, MARIA IOANNOU²

Oncology Research, Vol.32, No.8, pp. 1239-1256, 2024, DOI:10.32604/or.2024.052130

Abstract Glioblastoma multiforme (GBM) is an aggressive primary brain tumor characterized by extensive heterogeneity and vascular proliferation. Hypoxic conditions in the tissue microenvironment are considered a pivotal player leading tumor progression. Specifically, hypoxia is known to activate inducible factors, such as hypoxia-inducible factor 1alpha (HIF-1α), which in turn can stimulate tumor neo-angiogenesis through activation of various downward mediators, such as the vascular endothelial growth factor (VEGF). Here, we aimed to explore the role of HIF-1α/VEGF immunophenotypes alone and in combination with other prognostic markers or clinical and image analysis data, as potential biomarkers of GBM prognosis… More >

ANA S. CVETANOVIC^1,2,*, MATTEO LAMBERTINI^3,4, KEVIN PUNIE⁵, GORANA G. MATOVINA BRKO⁶, NIKOLA D. ZIVKOVIC^7,8, MAJA J. POPOVIC^6,9, MARIJANA M. MILOVIC KOVACEVIC¹⁰, LAZAR S. POPOVIC^6,9

Oncology Research, Vol.32, No.8, pp. 1309-1322, 2024, DOI:10.32604/or.2024.049743

Abstract Oncofertility is an extremely significant topic that is increasingly being discussed owing to increased evidence indicating that fertility preservation does not affect the treatment outcomes of patients with cancer but significantly contributes to preserving life quality. The effect of chemotherapy can range from minimal effects to complete ovarian atrophy. Limited data are available on the effects of monoclonal antibodies and targeted therapies on the ovaries and fertility. Temporary ovarian suppression by administering a gonadotropin-releasing hormone agonist (GnRHa) during chemotherapy decreases the gonadotoxic effect of chemotherapy, thereby diminishing the chance of developing premature ovarian insufficiency (POI).… More > Graphic Abstract

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

Intelligent Automation & Soft Computing, Vol.39, No.3, pp. 477-510, 2024, DOI:10.32604/iasc.2024.050726

Abstract Cloud computing has gained significant recognition due to its ability to provide a broad range of online services and applications. Nevertheless, existing commercial cloud computing models demonstrate an appropriate design by concentrating computational assets, such as preservation and server infrastructure, in a limited number of large-scale worldwide data facilities. Optimizing the deployment of virtual machines (VMs) is crucial in this scenario to ensure system dependability, performance, and minimal latency. A significant barrier in the present scenario is the load distribution, particularly when striving for improved energy consumption in a hypothetical grid computing framework. This design… More >

Darakhshan Syed^*, Ghulam Muhammad, Safdar Rizvi

Intelligent Automation & Soft Computing, Vol.39, No.3, pp. 437-476, 2024, DOI:10.32604/iasc.2024.050681

Abstract Cloud Computing has the ability to provide on-demand access to a shared resource pool. It has completely changed the way businesses are managed, implement applications, and provide services. The rise in popularity has led to a significant increase in the user demand for services. However, in cloud environments efficient load balancing is essential to ensure optimal performance and resource utilization. This systematic review targets a detailed description of load balancing techniques including static and dynamic load balancing algorithms. Specifically, metaheuristic-based dynamic load balancing algorithms are identified as the optimal solution in case of increased traffic. More >

Madiha Hameed^1,3, Aneela Zameer^1,*, Muhammad Asif Zahoor Raja²

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2131-2164, 2024, DOI:10.32604/cmes.2024.050124

Abstract The International Skin Imaging Collaboration (ISIC) datasets are pivotal resources for researchers in machine learning for medical image analysis, especially in skin cancer detection. These datasets contain tens of thousands of dermoscopic photographs, each accompanied by gold-standard lesion diagnosis metadata. Annual challenges associated with ISIC datasets have spurred significant advancements, with research papers reporting metrics surpassing those of human experts. Skin cancers are categorized into melanoma and non-melanoma types, with melanoma posing a greater threat due to its rapid potential for metastasis if left untreated. This paper aims to address challenges in skin cancer detection… More >