Oluwaseun Peter Ige^1,2, Keng Hoon Gan^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1847-1865, 2024, DOI:10.32604/cmes.2024.053373 - 27 September 2024

Abstract Feature selection is a crucial technique in text classification for improving the efficiency and effectiveness of classifiers or machine learning techniques by reducing the dataset’s dimensionality. This involves eliminating irrelevant, redundant, and noisy features to streamline the classification process. Various methods, from single feature selection techniques to ensemble filter-wrapper methods, have been used in the literature. Metaheuristic algorithms have become popular due to their ability to handle optimization complexity and the continuous influx of text documents. Feature selection is inherently multi-objective, balancing the enhancement of feature relevance, accuracy, and the reduction of redundant features. This… More >

Ye-Seul Kil^1,#, Yu-Ran Jeon^1,#, Sun-Jin Lee¹, Il-Gu Lee^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1473-1493, 2024, DOI:10.32604/cmes.2024.052637 - 27 September 2024

Abstract With the rise of remote work and the digital industry, advanced cyberattacks have become more diverse and complex in terms of attack types and characteristics, rendering them difficult to detect with conventional intrusion detection methods. Signature-based intrusion detection methods can be used to detect attacks; however, they cannot detect new malware. Endpoint detection and response (EDR) tools are attracting attention as a means of detecting attacks on endpoints in real-time to overcome the limitations of signature-based intrusion detection techniques. However, EDR tools are restricted by the continuous generation of unnecessary logs, resulting in poor detection… More >

Daekook Kang¹, Ramya Lakshmanaraj², Samayan Narayanamoorthy², Navaneethakrishnan Suganthi Keerthana Devi², Samayan Kalaiselvan³, Ranganathan Saraswathy⁴, Dragan Pamucar^5,6,7,*, Vladimir Simic^8,9

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4947-4972, 2024, DOI:10.32604/cmc.2024.055260 - 12 September 2024

Abstract This socialized environment among educated and developed people causes them to focus more on their appearance and health, which turns them towards medical-related treatments, leading us to discuss anti-aging treatment methods for each age group, particularly for urban people who are interested in this. Some anti-aging therapies are used to address the alterations brought on by aging in human life without the need for surgery or negative effects. Five anti-aging therapies such as microdermabrasion or dermabrasion, laser resurfacing anti-aging skin treatments, chemical peels, dermal fillers for aged skin, and botox injections are considered in this… More >

Aniqua Nusrat Zereen¹, Abir Das², Jia Uddin^3,*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3463-3484, 2024, DOI:10.32604/cmc.2024.054886 - 12 September 2024

Abstract Machine fault diagnostics are essential for industrial operations, and advancements in machine learning have significantly advanced these systems by providing accurate predictions and expedited solutions. Machine learning models, especially those utilizing complex algorithms like deep learning, have demonstrated major potential in extracting important information from large operational datasets. Despite their efficiency, machine learning models face challenges, making Explainable AI (XAI) crucial for improving their understandability and fine-tuning. The importance of feature contribution and selection using XAI in the diagnosis of machine faults is examined in this study. The technique is applied to evaluate different machine-learning More >

Pouyan Razmjouei¹, Elaheh Moharamkhani², Mohamad Hasanvand³, Maryam Daneshfar⁴, Mohammad Shokouhifar^5,*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3855-3880, 2024, DOI:10.32604/cmc.2024.054460 - 12 September 2024

Abstract This study investigates the application of deep learning, ensemble learning, metaheuristic optimization, and image processing techniques for detecting lung and colon cancers, aiming to enhance treatment efficacy and improve survival rates. We introduce a metaheuristic-driven two-stage ensemble deep learning model for efficient lung/colon cancer classification. The diagnosis of lung and colon cancers is attempted using several unique indicators by different versions of deep Convolutional Neural Networks (CNNs) in feature extraction and model constructions, and utilizing the power of various Machine Learning (ML) algorithms for final classification. Specifically, we consider different scenarios consisting of two-class colon… More >

Cong Shen^1,*, Wei Zhang^1,2,*, Tanping Zhou^1,2, Yiming Zhang¹, Lingling Zhang³

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4729-4748, 2024, DOI:10.32604/cmc.2024.054434 - 12 September 2024

Abstract With the increasing awareness of privacy protection and the improvement of relevant laws, federal learning has gradually become a new choice for cross-agency and cross-device machine learning. In order to solve the problems of privacy leakage, high computational overhead and high traffic in some federated learning schemes, this paper proposes a multiplicative double privacy mask algorithm which is convenient for homomorphic addition aggregation. The combination of homomorphic encryption and secret sharing ensures that the server cannot compromise user privacy from the private gradient uploaded by the participants. At the same time, the proposed TQRR (Top-Q-Random-R) More >

Turki Ali Alghamdi, Saud S. Alotaibi^*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4047-4064, 2024, DOI:10.32604/cmc.2024.052796 - 12 September 2024

Abstract Internet of Things (IoTs) provides better solutions in various fields, namely healthcare, smart transportation, home, etc. Recognizing Denial of Service (DoS) outbreaks in IoT platforms is significant in certifying the accessibility and integrity of IoT systems. Deep learning (DL) models outperform in detecting complex, non-linear relationships, allowing them to effectually severe slight deviations from normal IoT activities that may designate a DoS outbreak. The uninterrupted observation and real-time detection actions of DL participate in accurate and rapid detection, permitting proactive reduction events to be executed, hence securing the IoT network’s safety and functionality. Subsequently, this… More >

Kaveh Heidary^1,*, Venkata Atluri¹, John Bland²

Journal of Cyber Security, Vol.6, pp. 69-87, 2024, DOI:10.32604/jcs.2024.051196 - 28 August 2024

Abstract This paper investigates the impact of reducing feature-vector dimensionality on the performance of machine learning (ML) models. Dimensionality reduction and feature selection techniques can improve computational efficiency, accuracy, robustness, transparency, and interpretability of ML models. In high-dimensional data, where features outnumber training instances, redundant or irrelevant features introduce noise, hindering model generalization and accuracy. This study explores the effects of dimensionality reduction methods on binary classifier performance using network traffic data for cybersecurity applications. The paper examines how dimensionality reduction techniques influence classifier operation and performance across diverse performance metrics for seven ML models. Four… More >

ROSSANA RONCATO^1,2, JERRY POLESEL³, FEDERICA TOSI^4,5,*, ELENA PERUZZI^1,*, ERIKA BRUGUGNOLI⁶, CLAUDIA LAURIA PANTANO⁷, MARIA FURFARO⁸, FILIPPO DI GIROLAMO^9,10, ALESSANDRO NANI¹¹, ARIANNA PANI⁴, NOEMI MILAN¹, ELENA DE MATTIA¹, ANDREA SARTORE-BIANCHI^4,5, ERIKA CECCHIN¹

Oncology Research, Vol.32, No.9, pp. 1407-1422, 2024, DOI:10.32604/or.2024.049181 - 23 August 2024

Abstract Objectives: Treatment of metastatic colorectal cancer (mCRC) includes resection of liver metastases (LM), however, no validated biomarker identifies patients most likely to benefit from this procedure. This meta-analysis aimed to assess the impact of the most relevant molecular alterations in cancer-related genes of CRC (i.e., RAS, BRAF, SMAD4, PIK3CA) as prognostic markers of survival and disease recurrence in patients with mCRC surgically treated by LM resection. Methods: A systematic literature review was performed to identify studies reporting data regarding survival and/or recurrence in patients that underwent complete liver resection for CRC LM, stratified according to… More > Graphic Abstract

Yuwei Xu, Baokang Zhao^*, Huan Zhou, Jinshu Su

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 609-629, 2024, DOI:10.32604/cmes.2024.053462 - 20 August 2024

Abstract The rapid expansion of artificial intelligence (AI) applications has raised significant concerns about user privacy, prompting the development of privacy-preserving machine learning (ML) paradigms such as federated learning (FL). FL enables the distributed training of ML models, keeping data on local devices and thus addressing the privacy concerns of users. However, challenges arise from the heterogeneous nature of mobile client devices, partial engagement of training, and non-independent identically distributed (non-IID) data distribution, leading to performance degradation and optimization objective bias in FL training. With the development of 5G/6G networks and the integration of cloud computing… More >