Lilia-Eliana Popescu-Apreutesei, Mihai-Sorin Iosupescu, Sabina Cristiana Necula, Vasile-Daniel Păvăloaia^*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2605-2644, 2025, DOI:10.32604/cmc.2025.064747 - 03 July 2025

Abstract The increasing fluency of advanced language models, such as GPT-3.5, GPT-4, and the recently introduced DeepSeek, challenges the ability to distinguish between human-authored and AI-generated academic writing. This situation is raising significant concerns regarding the integrity and authenticity of academic work. In light of the above, the current research evaluates the effectiveness of Bidirectional Long Short-Term Memory (BiLSTM) networks enhanced with pre-trained GloVe (Global Vectors for Word Representation) embeddings to detect AI-generated scientific abstracts drawn from the AI-GA (Artificial Intelligence Generated Abstracts) dataset. Two core BiLSTM variants were assessed: a single-layer approach and a dual-layer… More >

Kachi Anvesh^1,2, Bharati M. Reshmi^2,3, Shanmugasundaram Hariharan⁴, H. Venkateshwara Reddy⁵, Murugaperumal Krishnamoorthy⁶, Vinay Kukreja⁷, Shih-Yu Chen^8,9,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1485-1517, 2025, DOI:10.32604/cmes.2025.063239 - 30 May 2025

Abstract Automated classification of retinal fundus images is essential for identifying eye diseases, though there is earlier research on applying deep learning models designed especially for detecting tessellation in retinal fundus images. This study classifies 4 classes of retinal fundus images with 3 diseased fundus images and 1 normal fundus image, by creating a refined VGG16 model to categorize fundus pictures into tessellated, normal, myopia, and choroidal neovascularization groups. The approach utilizes a VGG16 architecture that has been altered with unique fully connected layers and regularization using dropouts, along with data augmentation techniques (rotation, flip, and… More >

Zhen Yang¹, Yifan Liu^1,2,*, Fan Feng³, Yi Liu³, Zhenpeng Liu^1,3

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 357-380, 2025, DOI:10.32604/cmc.2025.060709 - 26 March 2025

Abstract Federated learning is a machine learning framework designed to protect privacy by keeping training data on clients’ devices without sharing private data. It trains a global model through collaboration between clients and the server. However, the presence of data heterogeneity can lead to inefficient model training and even reduce the final model’s accuracy and generalization capability. Meanwhile, data scarcity can result in suboptimal cluster distributions for few-shot clients in centralized clustering tasks, and standalone personalization tasks may cause severe overfitting issues. To address these limitations, we introduce a federated learning dual optimization model based on… More >

Erblin Halabaku, Eliot Bytyçi^*

Intelligent Automation & Soft Computing, Vol.39, No.6, pp. 987-1006, 2024, DOI:10.32604/iasc.2024.059429 - 30 December 2024

Abstract Machine learning has emerged as a pivotal tool in deciphering and managing this excess of information in an era of abundant data. This paper presents a comprehensive analysis of machine learning algorithms, focusing on the structure and efficacy of random forests in mitigating overfitting—a prevalent issue in decision tree models. It also introduces a novel approach to enhancing decision tree performance through an optimized pruning method called Adaptive Cross-Validated Alpha CCP (ACV-CCP). This method refines traditional cost complexity pruning by streamlining the selection of the alpha parameter, leveraging cross-validation within the pruning process to achieve More >

S. Gopikha^*, M. Balamurugan

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2727-2742, 2023, DOI:10.32604/csse.2023.028609 - 01 August 2022

Abstract Melanoma is the most lethal malignant tumour, and its prevalence is increasing. Early detection and diagnosis of skin cancer can alert patients to manage precautions and dramatically improve the lives of people. Recently, deep learning has grown increasingly popular in the extraction and categorization of skin cancer features for effective prediction. A deep learning model learns and co-adapts representations and features from training data to the point where it fails to perform well on test data. As a result, overfitting and poor performance occur. To deal with this issue, we proposed a novel Consecutive Layerwise… More >

Zhongyao Du^1,*, Xiaoying Chen¹, Hao Wang², Xuheng Wang¹, Yu Deng¹, Liying Sun¹

Energy Engineering, Vol.119, No.4, pp. 1419-1438, 2022, DOI:10.32604/ee.2022.020283 - 23 May 2022

Abstract To attain the goal of carbon peaking and carbon neutralization, the inevitable choice is the open sharing of power data and connection to the grid of high-permeability renewable energy. However, this approach is hindered by the lack of training data for predicting new grid-connected PV power stations. To overcome this problem, this work uses open and shared power data as input for a short-term PV-power-prediction model based on feature transfer learning to facilitate the generalization of the PV-power-prediction model to multiple PV-power stations. The proposed model integrates a structure model, heat-dissipation conditions, and the loss… More >

Mousa Al-Akhras^1,2,*, Zainab Darwish², Samer Atawneh¹, Mohamed Habib^1,3

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3719-3749, 2022, DOI:10.32604/cmc.2022.025196 - 29 March 2022

Abstract Association rules’ learning is a machine learning method used in finding underlying associations in large datasets. Whether intentionally or unintentionally present, noise in training instances causes overfitting while building the classifier and negatively impacts classification accuracy. This paper uses instance reduction techniques for the datasets before mining the association rules and building the classifier. Instance reduction techniques were originally developed to reduce memory requirements in instance-based learning. This paper utilizes them to remove noise from the dataset before training the association rules classifier. Extensive experiments were conducted to assess the accuracy of association rules with… More >