Veena Dillshad¹, Muhammad Attique Khan^2,*, Muhammad Nazir¹, Jawad Ahmad², Dina Abdulaziz AlHammadi³, Taha Houda², Hee-Chan Cho⁴, Byoungchol Chang^5,*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063851

Abstract Globally, skin cancer is a prevalent form of malignancy, and its early and accurate diagnosis is critical for patient survival. Clinical evaluation of skin lesions is essential, but several challenges, such as long waiting times and subjective interpretations, make this task difficult. The recent advancement of deep learning in healthcare has shown much success in diagnosing and classifying skin cancer and has assisted dermatologists in clinics. Deep learning improves the speed and precision of skin cancer diagnosis, leading to earlier prediction and treatment. In this work, we proposed a novel deep architecture for skin cancer… More >

Ahmet Emre Ergün, Aytuğ Onan^*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063826

Abstract Large Language Models (LLMs) have significantly advanced human-computer interaction by improving natural language understanding and generation. However, their vulnerability to adversarial prompts–carefully designed inputs that manipulate model outputs–presents substantial challenges. This paper introduces a classification-based approach to detect adversarial prompts by utilizing both prompt features and prompt response features. Eleven machine learning models were evaluated based on key metrics such as accuracy, precision, recall, and F1-score. The results show that the Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) cascade model delivers the best performance, especially when using prompt features, achieving an accuracy of over 97% in… More >

Tehreem Fatima¹, Kewen Xia^1,*, Wenbiao Yang², Qurat Ul Ain¹, Poornima Lankani Perera¹

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063686

Abstract The rising prevalence of diabetes in modern society underscores the urgent need for precise and efficient diagnostic tools to support early intervention and treatment. However, the inherent limitations of existing datasets, including significant class imbalances and inadequate sample diversity, pose challenges to the accurate prediction and classification of diabetes. Addressing these issues, this study proposes an innovative diabetes prediction framework that integrates a hybrid Convolutional Neural Network-Bidirectional Gated Recurrent Unit (CNN-BiGRU) model for classification with Adaptive Synthetic Sampling (ADASYN) for data augmentation. ADASYN was employed to generate synthetic yet representative data samples, effectively mitigating class… More >

Hayder Faeq Alhashimi¹, Mhd Nour Hindia¹, Kaharudin Dimyati^1,*, Effariza Binti Hanafi¹, Feras Zen Alden², Faizan Qamar³, Quang Ngoc Nguyen^4,5,*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062867

Abstract The forthcoming 6G wireless networks have great potential for establishing AI-based networks that can enhance end-to-end connection and manage massive data of real-time networks. Artificial Intelligence (AI) advancements have contributed to the development of several innovative technologies by providing sophisticated specific AI mathematical models such as machine learning models, deep learning models, and hybrid models. Furthermore, intelligent resource management allows for self-configuration and autonomous decision-making capabilities of AI methods, which in turn improves the performance of 6G networks. Hence, 6G networks rely substantially on AI methods to manage resources. This paper comprehensively surveys the recent… More >

Praveen Kumar Sekharamantry^1,2,*, Farid Melgani¹, Roberto Delfiore³, Stefano Lusardi³

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062686

Abstract Recent advances in computer vision and artificial intelligence (AI) have made real-time people counting systems extremely reliable, with experts in crowd control, occupancy supervision, and security. To improve the accuracy of people counting at entry and exit points, the current study proposes a deep learning model that combines You Only Look Once (YOLOv8) for object detection, ByteTrack for multi-object tracking, and a unique method for vector-based movement analysis. The system determines if a person has entered or exited by analyzing their movement concerning a predetermined boundary line. Two different logical strategies are used to record… More >

Muhammad Hameed Siddiqi^1,*, Menwa Alshammeri¹, Jawad Khan^2,*, Muhammad Faheem Khan³, Asfandyar Khan⁴, Madallah Alruwaili¹, Yousef Alhwaiti¹, Saad Alanazi¹, Irshad Ahmad⁵

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062340

Abstract As legal cases grow in complexity and volume worldwide, integrating machine learning and artificial intelligence into judicial systems has become a pivotal research focus. This study introduces a comprehensive framework for verdict recommendation that synergizes rule-based methods with deep learning techniques specifically tailored to the legal domain. The proposed framework comprises three core modules: legal feature extraction, semantic similarity assessment, and verdict recommendation. For legal feature extraction, a rule-based approach leverages Black’s Law Dictionary and WordNet Synsets to construct feature vectors from judicial texts. Semantic similarity between cases is evaluated using a hybrid method that… More >

MEI HUANG, ZHENYU ZHANG, YUQI LUO, YUQI WU, DAN PAN, YU ZHOU^*, XIAOBO LUO, YUCHEN JIANG^*

BIOCELL, DOI:10.32604/biocell.2025.062918

Abstract This review explores the pivotal role of circadian rhythm regulators, particularly the PER genes, in Oral Squamous Cell Carcinoma (OSCC). As key constituents of the biological clock, PERs exhibit a downregulated expression pattern in OSCC, and the expression levels of PERs in OSCC patients are correlated with a favorable prognosis. PERs impact the occurrence and development of OSCC through multiple pathways. In the regulation of cell proliferation, they can function not only through cell cycle regulation but also via metabolic pathways. For example, PER1 can interact with receptors for activated C kinase 1 (RACK1) and… More >

Nan Li^1,2,*, Jingxiong Dong², Liang Tao³, Liang Huang³

Energy Engineering, DOI:10.32604/ee.2025.062667

Abstract With the continuous expansion and increasing complexity of power system scales, the binary classification for transient stability assessment in power systems can no longer meet the safety requirements of power system control and regulation. Therefore, this paper proposes a multi-class transient stability assessment model based on an improved Transformer. The model is designed with a dual-tower encoder structure: one encoder focuses on the time dependency of data, while the other focuses on the dynamic correlations between variables. Feature extraction is conducted from both time and variable perspectives to ensure the completeness of the feature extraction… More >

Dandan Wang^*, Jiaxin Chen, Yanwen Zhang

Phyton-International Journal of Experimental Botany, DOI:10.32604/phyton.2025.063894

Abstract Taxus cuspidata, a rare species of the Taxus genus, and its wild resources are under severe threat. The development of cultivated species has become an important strategy to replace wild species. The objective of this work was to elucidate the differences in secondary metabolite accumulation, particularly in the paclitaxel biosynthesis pathway, between wild and cultivated species. This study employed liquid chromatography-tandem mass spectrometry (LC-MS/MS) and RNA sequencing (RNA-Seq) technologies to conduct integrated metabolomic and transcriptomic analyses of wild and cultivated species of T. cuspidata. The results showed that the content of paclitaxel in cultivated species was significantly higher… More >

Ashish Rayal¹, Priya Dogra¹, Sabri T. M. Thabet^2,3,4,*, Imed Kedim⁵, Miguel Vivas-Cortez^6,*

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.060989

Abstract The Rössler attractor model is an important model that provides valuable insights into the behavior of chaotic systems in real life and is applicable in understanding weather patterns, biological systems, and secure communications. So, this work aims to present the numerical performances of the nonlinear fractional Rössler attractor system under Caputo derivatives by designing the numerical framework based on Ultraspherical wavelets. The Caputo fractional Rössler attractor model is simulated into two categories, (i) Asymmetric and (ii) Symmetric. The Ultraspherical wavelets basis with suitable collocation grids is implemented for comprehensive error analysis in the solutions of More >