Mohamed Ezz¹, Meshrif Alruily^1,*, Ayman Mohamed Mostafa^2,*, Alaa S. Alaerjan¹, Bader Aldughayfiq², Hisham Allahem², Abdulaziz Shehab²

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-28, 2026, DOI:10.32604/cmc.2025.063189 - 10 November 2025

Abstract Automated essay scoring (AES) systems have gained significant importance in educational settings, offering a scalable, efficient, and objective method for evaluating student essays. However, developing AES systems for Arabic poses distinct challenges due to the language’s complex morphology, diglossia, and the scarcity of annotated datasets. This paper presents a hybrid approach to Arabic AES by combining text-based, vector-based, and embedding-based similarity measures to improve essay scoring accuracy while minimizing the training data required. Using a large Arabic essay dataset categorized into thematic groups, the study conducted four experiments to evaluate the impact of feature selection,… More >

Sandra Pérez-Álvarez^1,*, Eduardo Fidel Héctor-Ardisana², Eduardo Sandoval Castro³, Erick H. Ochoa-Chaparro⁴, Luisa Patricia Uranga-Valencia¹

Phyton-International Journal of Experimental Botany, Vol.94, No.12, pp. 3749-3803, 2025, DOI:10.32604/phyton.2025.072135 - 29 December 2025

Abstract Grape (Vitis vinifera L.) cultivation has progressed from early domestication and clonal propagation to modern, data-driven breeding that is reshaping viticulture and wine quality. Yet climatic and biotic constraints still impose heavy losses—downy mildew can reduce yields by ≈75% in humid regions and gray mold by 20–50%—sustaining the need for resistant cultivars. Producer selection, interspecific crossing, and formal improvement programs have generated ~10,000 varieties, although only a few dozen dominate global acreage. Conventional breeding has delivered fungus-resistant “PIWI” cultivars that retain ≥85% of the V. vinifera genome; in Austria, national PIWI varieties are gaining acceptance for combined… More >

Muhammad Saad Farooqui¹, Aizaz Ahmad Khattak², Bakri Hossain Awaji³, Nazik Alturki⁴, Noha Alnazzawi⁵, Muhammad Hanif^6,*, Muhammad Shahbaz Khan²

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4395-4439, 2025, DOI:10.32604/cmes.2025.072023 - 23 December 2025

Abstract Quality of Service (QoS) assurance in programmable IoT and 5G networks is increasingly threatened by cyberattacks such as Distributed Denial of Service (DDoS), spoofing, and botnet intrusions. This paper presents AutoSHARC, a feedback-driven, explainable intrusion detection framework that integrates Boruta and LightGBM–SHAP feature selection with a lightweight CNN–Attention–GRU classifier. AutoSHARC employs a two-stage feature selection pipeline to identify the most informative features from high-dimensional IoT traffic and reduces 46 features to 30 highly informative ones, followed by post-hoc SHAP-guided retraining to refine feature importance, forming a feedback loop where only the most impactful attributes are More >

Hongrong Wang^1,2, Xinjian Li^3,4, Xingjing She¹, Wenjian Ma^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2039-2055, 2025, DOI:10.32604/cmes.2025.072572 - 26 November 2025

Abstract In modern complex systems, real-time regression prediction plays a vital role in performance evaluation and risk warning. Nevertheless, existing methods still face challenges in maintaining stability and predictive accuracy under complex conditions. To address these limitations, this study proposes an online prediction approach that integrates event tracking sensitivity analysis with machine learning. Specifically, a real-time event tracking sensitivity analysis method is employed to capture and quantify the impact of key events on system outputs. On this basis, a mutual-information–based self-extraction mechanism is introduced to construct prior weights, which are then incorporated into a LightGBM prediction More >

Andrea Panunzio¹, Rossella Orlando¹, Federico Greco^2,3, Giovanni Mazzucato⁴, Floriana Luigina Rizzo¹, Serena Domenica D’Elia¹, Antonio Benito Porcaro⁵, Alessandro Antonelli⁵, Alessandro Tafuri^1,6,*

Canadian Journal of Urology, Vol.32, No.5, pp. 375-384, 2025, DOI:10.32604/cju.2025.066140 - 30 October 2025

Abstract Background: The Barthel Index (BI) measures the level of patient independence in activities of daily living. This review aims to summarize current evidence on the use of the BI in urology, highlighting its potential as a tool for assessing patients prior to surgery. Materials and methods: A comprehensive search of PubMed, Scopus, and Web of Science databases was conducted for studies evaluating the BI in patients undergoing urologic surgery, following Systematic Review and Meta-analyses (PRISMA) guidelines. The BI was investigated both as a descriptor of baseline or postoperative health status and a prognostic indicator. A qualitative… More >

Hajer Hafaiedh^1,2, Adel Mahjoub³, Yahia Saoudi⁴, Anouar Benamor², Okba Taouali^5,*, Kamel Zidi⁶, Wad Ghaban⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 571-596, 2025, DOI:10.32604/cmes.2025.071642 - 30 October 2025

Abstract Several optimization methods, such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), are used to select the most suitable Static Synchronous Compensator (STATCOM) technology for the optimal operation of the power system, as well as to determine its optimal location and size to minimize power losses. An IEEE 14 bus system, integrating three wind turbines based on Squirrel Cage Induction Generators (SCIGs), is used to test the applicability of the proposed algorithms. The results demonstrate that these algorithms are capable of selecting the most appropriate technology while optimally sizing and locating the STATCOM to More >

Seungyeon Baek^1,#, Jueun Jeon^2,#, Byeonghui Jeong¹, Young-Sik Jeong^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 1213-1236, 2025, DOI:10.32604/cmes.2025.070679 - 30 October 2025

Abstract With the advent of the sixth-generation wireless technology, the importance of using artificial intelligence of things (AIoT) devices is increasing to enhance efficiency. As massive volumes of data are collected and stored in these AIoT environments, each device becomes a potential attack target, leading to increased security vulnerabilities. Therefore, intrusion detection studies have been conducted to detect malicious network traffic. However, existing studies have been biased toward conducting in-depth analyses of individual packets to improve accuracy or applying flow-based statistical information to ensure real-time performance. Effectively responding to complex and multifaceted threats in large-scale AIoT… More >

Nimra Bari¹, Tahir Saleem², Munam Shah³, Abdulmohsen Algarni⁴, Asma Patel^5,*, Insaf Ullah^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 1167-1187, 2025, DOI:10.32604/cmes.2025.070311 - 30 October 2025

Abstract Today, phishing is an online attack designed to obtain sensitive information such as credit card and bank account numbers, passwords, and usernames. We can find several anti-phishing solutions, such as heuristic detection, virtual similarity detection, black and white lists, and machine learning (ML). However, phishing attempts remain a problem, and establishing an effective anti-phishing strategy is a work in progress. Furthermore, while most anti-phishing solutions achieve the highest levels of accuracy on a given dataset, their methods suffer from an increased number of false positives. These methods are ineffective against zero-hour attacks. Phishing sites with… More >

Hui Qi¹, Jingyi Lian², Congjun Rao^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 995-1028, 2025, DOI:10.32604/cmes.2025.069722 - 30 October 2025

Abstract For the past few years, the prevalence of cardiovascular disease has been showing a year-on-year increase, with a death rate of 2/5. Coronary heart disease (CHD) rates have increased 41% since 1990, which is the number one disease endangering human health in the world today. The risk indicators of CHD are complicated, so selecting effective methods to screen the risk characteristics can make the risk prediction more efficient. In this paper, we present a comprehensive analysis of CHD risk indicators from both data and algorithmic levels, propose a method for CHD risk indicator identification based… More >

Bibhuprasad Sahu¹, Amrutanshu Panigrahi², Abhilash Pati², Ashis Kumar Pati³, Janmejaya Mishra⁴, Naim Ahmad^5,*, Salman Arafath Mohammed⁶, Saurav Mallik^7,8,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 1029-1054, 2025, DOI:10.32604/cmes.2025.069618 - 30 October 2025

Abstract Metaheuristic optimization methods are iterative search processes that aim to efficiently solve complex optimization problems. These basically find the solution space very efficiently, often without utilizing the gradient information, and are inspired by the bio-inspired and socially motivated heuristics. Metaheuristic optimization algorithms are increasingly applied to complex feature selection problems in high-dimensional medical datasets. Among these, Teaching-Learning-Based optimization (TLBO) has proven effective for continuous design tasks by balancing exploration and exploitation phases. However, its binary version (BTLBO) suffers from limited exploitation ability, often converging prematurely or getting trapped in local optima, particularly when applied to… More >