Smita Kapse¹, Ganesh Yenurkar^1,*, Vincent Omollo Nyangaresi^2,3,*, Gunjan Balpande¹, Shravani Kale¹, Manthan Jadhav¹, Sahil Lawankar¹, Vikrant Jaunjale¹

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 935-976, 2025, DOI:10.32604/cmc.2025.061981 - 26 March 2025

Abstract In today’s fast-paced world, many elderly individuals struggle to adhere to their medication schedules, especially those with memory-related conditions like Alzheimer’s disease, leading to serious health risks, hospitalizations, and increased healthcare costs. Traditional reminder systems often fail due to a lack of personalization and real-time intervention. To address this critical challenge, we introduce MediServe, an advanced IoT-enabled medication management system that seamlessly integrates deep learning techniques to provide a personalized, secure, and adaptive solution. MediServe features a smart medication box equipped with biometric authentication, such as fingerprint recognition, ensuring authorized access to prescribed medication while… More >

Tien-Wen Sung¹, Wei Li¹, Chao-Yang Lee^2,*, Yuzhen Chen¹, Qingjun Fang¹

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 407-434, 2025, DOI:10.32604/cmc.2025.061694 - 26 March 2025

Abstract To transmit customer power data collected by smart meters (SMs) to utility companies, data must first be transmitted to the corresponding data aggregation point (DAP) of the SM. The number of DAPs installed and the installation location greatly impact the whole network. For the traditional DAP placement algorithm, the number of DAPs must be set in advance, but determining the best number of DAPs is difficult, which undoubtedly reduces the overall performance of the network. Moreover, the excessive gap between the loads of different DAPs is also an important factor affecting the quality of the… More >

Chuanyun Xu^1,#, Die Hu^1,#, Yang Zhang^1,*, Shuaiye Huang¹, Yisha Sun¹, Gang Li²

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 559-574, 2025, DOI:10.32604/cmc.2025.061579 - 26 March 2025

Abstract Detecting abnormal cervical cells is crucial for early identification and timely treatment of cervical cancer. However, this task is challenging due to the morphological similarities between abnormal and normal cells and the significant variations in cell size. Pathologists often refer to surrounding cells to identify abnormalities. To emulate this slide examination behavior, this study proposes a Multi-Scale Feature Fusion Network (MSFF-Net) for detecting cervical abnormal cells. MSFF-Net employs a Cross-Scale Pooling Model (CSPM) to effectively capture diverse features and contextual information, ranging from local details to the overall structure. Additionally, a Multi-Scale Fusion Attention (MSFA)… More >

Josua Käser¹, Thomas Nagy¹, Patrick Stirnemann¹, Thomas Hanne^2,*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 201-217, 2025, DOI:10.32604/cmc.2025.061527 - 26 March 2025

Abstract We analyze the suitability of existing pre-trained transformer-based language models (PLMs) for abstractive text summarization on German technical healthcare texts. The study focuses on the multilingual capabilities of these models and their ability to perform the task of abstractive text summarization in the healthcare field. The research hypothesis was that large language models could perform high-quality abstractive text summarization on German technical healthcare texts, even if the model is not specifically trained in that language. Through experiments, the research questions explore the performance of transformer language models in dealing with complex syntax constructs, the difference… More >

Yasser Alharbi¹, Sarwar Shah Khan^2,*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1275-1290, 2025, DOI:10.32604/cmc.2025.061490 - 26 March 2025

Abstract Sentiment analysis plays an important role in distilling and clarifying content from movie reviews, aiding the audience in understanding universal views towards the movie. However, the abundance of reviews and the risk of encountering spoilers pose challenges for efficient sentiment analysis, particularly in Arabic content. This study proposed a Stochastic Gradient Descent (SGD) machine learning (ML) model tailored for sentiment analysis in Arabic and English movie reviews. SGD allows for flexible model complexity adjustments, which can adapt well to the Involvement of Arabic language data. This adaptability ensures that the model can capture the nuances… More >

Stellar Choi¹, HeeAe Ko², KyungRok Bae³, HyunSook Lee², HaeJong Joo⁴, Woong Choi^5,*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 335-355, 2025, DOI:10.32604/cmc.2025.061243 - 26 March 2025

Abstract Previous research utilizing Cartoon Generative Adversarial Network (CartoonGAN) has encountered limitations in managing intricate outlines and accurately representing lighting effects, particularly in complex scenes requiring detailed shading and contrast. This paper presents a novel Enhanced Pixel Integration (EPI) technique designed to improve the visual quality of images generated by CartoonGAN. Rather than modifying the core model, the EPI approach employs post-processing adjustments that enhance images without significant computational overhead. In this method, images produced by CartoonGAN are converted from Red-Green-Blue (RGB) to Hue-Saturation-Value (HSV) format, allowing for precise adjustments in hue, saturation, and brightness, thereby… More >

Ra-Yeon Choi¹, Yeji Song², Minsoo Jang¹, Taekyung Kim³, Jinhyun Ahn^4,*, Dong-Hyuk Im^5,*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 785-801, 2025, DOI:10.32604/cmc.2025.061185 - 26 March 2025

Abstract Smart contracts are self-executing programs on blockchains that manage complex business logic with transparency and integrity. However, their immutability after deployment makes programming errors particularly critical, as such errors can be exploited to compromise blockchain security. Existing vulnerability detection methods often rely on fixed rules or target specific vulnerabilities, limiting their scalability and adaptability to diverse smart contract scenarios. Furthermore, natural language processing approaches for source code analysis frequently fail to capture program flow, which is essential for identifying structural vulnerabilities. To address these limitations, we propose a novel model that integrates textual and structural… More >

Xiang Zhang^1,2,*, Shenyan Han^1,2, Wenbin Huang^1,2, Daoyong Fu^1,2

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1149-1171, 2025, DOI:10.32604/cmc.2025.060876 - 26 March 2025

Abstract Generative image steganography is a technique that directly generates stego images from secret information. Unlike traditional methods, it theoretically resists steganalysis because there is no cover image. Currently, the existing generative image steganography methods generally have good steganography performance, but there is still potential room for enhancing both the quality of stego images and the accuracy of secret information extraction. Therefore, this paper proposes a generative image steganography algorithm based on attribute feature transformation and invertible mapping rule. Firstly, the reference image is disentangled by a content and an attribute encoder to obtain content features… More >

Borui Wang, Dongyue Gao^*, Haiyang Gu, Mengke Ding, Zhanjun Wu

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 455-473, 2025, DOI:10.32604/cmc.2025.060617 - 26 March 2025

Abstract Fatigue damage is a primary contributor to the failure of composite structures, underscoring the critical importance of monitoring its progression to ensure structural safety. This paper introduces an innovative approach to fatigue damage monitoring in composite structures, leveraging a hybrid methodology that integrates the Whale Optimization Algorithm (WOA)-Backpropagation (BP) neural network with an ultrasonic guided wave feature selection algorithm. Initially, a network of piezoelectric ceramic sensors is employed to transmit and capture ultrasonic-guided waves, thereby establishing a signal space that correlates with the structural condition. Subsequently, the Relief-F algorithm is applied for signal feature extraction,… More >

Chayut Bunterngchit¹, Laith H. Baniata², Mohammad H. Baniata³, Ashraf ALDabbas⁴, Mohannad A. Khair⁵, Thanaphon Chearanai⁶, Sangwoo Kang^2,*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 517-536, 2025, DOI:10.32604/cmc.2025.060368 - 26 March 2025

Abstract Stroke is a leading cause of death and disability worldwide, significantly impairing motor and cognitive functions. Effective rehabilitation is often hindered by the heterogeneity of stroke lesions, variability in recovery patterns, and the complexity of electroencephalography (EEG) signals, which are often contaminated by artifacts. Accurate classification of motor imagery (MI) tasks, involving the mental simulation of movements, is crucial for assessing rehabilitation strategies but is challenged by overlapping neural signatures and patient-specific variability. To address these challenges, this study introduces a graph-attentive convolutional long short-term memory (LSTM) network (GACL-Net), a novel hybrid deep learning model… More >