Amerah Alabrah^*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3897-3912, 2024, DOI:10.32604/cmc.2024.048528

Abstract The emergence of digital networks and the wide adoption of information on internet platforms have given rise to threats against users’ private information. Many intruders actively seek such private data either for sale or other inappropriate purposes. Similarly, national and international organizations have country-level and company-level private information that could be accessed by different network attacks. Therefore, the need for a Network Intruder Detection System (NIDS) becomes essential for protecting these networks and organizations. In the evolution of NIDS, Artificial Intelligence (AI) assisted tools and methods have been widely adopted to provide effective solutions. However,… More >

Xiu Liu, Liang Gu^*, Xin Gong, Long An, Xurui Gao, Juying Wu

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4045-4061, 2024, DOI:10.32604/cmc.2024.048442

Abstract With the popularisation of intelligent power, power devices have different shapes, numbers and specifications. This means that the power data has distributional variability, the model learning process cannot achieve sufficient extraction of data features, which seriously affects the accuracy and performance of anomaly detection. Therefore, this paper proposes a deep learning-based anomaly detection model for power data, which integrates a data alignment enhancement technique based on random sampling and an adaptive feature fusion method leveraging dimension reduction. Aiming at the distribution variability of power data, this paper developed a sliding window-based data adjustment method for… More >

Xiaoqin Ma^1,2, Jun Wang¹, Wenchang Yu¹, Qinli Zhang^1,2,*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2063-2083, 2024, DOI:10.32604/cmc.2024.049147

Abstract The presence of numerous uncertainties in hybrid decision information systems (HDISs) renders attribute reduction a formidable task. Currently available attribute reduction algorithms, including those based on Pawlak attribute importance, Skowron discernibility matrix, and information entropy, struggle to effectively manages multiple uncertainties simultaneously in HDISs like the precise measurement of disparities between nominal attribute values, and attributes with fuzzy boundaries and abnormal values. In order to address the aforementioned issues, this paper delves into the study of attribute reduction within HDISs. First of all, a novel metric based on the decision attribute is introduced to solve… More >

K. Umapathi^1,*, S. Shobana¹, Anand Nayyar², Judith Justin³, R. Vanithamani³, Miguel Villagómez Galindo⁴, Mushtaq Ahmad Ansari⁵, Hitesh Panchal^6,*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1875-1901, 2024, DOI:10.32604/cmc.2024.047961

Abstract Breast cancer detection heavily relies on medical imaging, particularly ultrasound, for early diagnosis and effective treatment. This research addresses the challenges associated with computer-aided diagnosis (CAD) of breast cancer from ultrasound images. The primary challenge is accurately distinguishing between malignant and benign tumors, complicated by factors such as speckle noise, variable image quality, and the need for precise segmentation and classification. The main objective of the research paper is to develop an advanced methodology for breast ultrasound image classification, focusing on speckle noise reduction, precise segmentation, feature extraction, and machine learning-based classification. A unique approach… More >

YI HU^1,#, ZHONGYANG REN^2,#, ZHENGZHONG ZHAO¹, YONGJIA HUANG³, WANTING HUANG³, JIE LIU^3,*, LING DING^3,*

BIOCELL, Vol.48, No.5, pp. 745-758, 2024, DOI:10.32604/biocell.2024.049140

Abstract Background: Acute pancreatitis (AP), known for its rapid onset and significant incidence and mortality rates, presents a clinical challenge due to the limited availability of effective treatments and preventive measures. Anemarsaponin B (ASB) has emerged as a potential therapeutic agent, demonstrating capabilities in reducing immune inflammation, positioning it as a promising candidate for AP treatment. Methods: We investigated the effects of ASB on AP in mice, induced by caerulein and lipopolysaccharide (LPS). Peripheral blood samples were collected 24 h post-induction with caerulein to assess of key biomarkers including lipase, amylase, TNF-α, IL-1β, IL-6, SOD, and… More >

Fayez Alfayez^*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1539-1560, 2024, DOI:10.32604/cmc.2024.046443

Abstract This paper emphasizes a faster digital processing time while presenting an accurate method for identifying spine fractures in X-ray pictures. The study focuses on efficiency by utilizing many methods that include picture segmentation, feature reduction, and image classification. Two important elements are investigated to reduce the classification time: Using feature reduction software and leveraging the capabilities of sophisticated digital processing hardware. The researchers use different algorithms for picture enhancement, including the Wiener and Kalman filters, and they look into two background correction techniques. The article presents a technique for extracting textural features and evaluates three… More >

Muhammad Armghan Latif¹, Zohaib Mushtaq², Saad Arif³, Sara Rehman⁴, Muhammad Farrukh Qureshi⁵, Nagwan Abdel Samee⁶, Maali Alabdulhafith^6,*, Yeong Hyeon Gu⁷, Mohammed A. Al-masni⁷

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4225-4241, 2024, DOI:10.32604/cmc.2024.047621

Abstract Thyroid disorders represent a significant global health challenge with hypothyroidism and hyperthyroidism as two common conditions arising from dysfunction in the thyroid gland. Accurate and timely diagnosis of these disorders is crucial for effective treatment and patient care. This research introduces a comprehensive approach to improve the accuracy of thyroid disorder diagnosis through the integration of ensemble stacking and advanced feature selection techniques. Sequential forward feature selection, sequential backward feature elimination, and bidirectional feature elimination are investigated in this study. In ensemble learning, random forest, adaptive boosting, and bagging classifiers are employed. The effectiveness of… More >

Ninis Hadi Haryanti^1,*, Eka Suarso¹, Tetti N. Manik¹, Suryajaya¹, Nurlita Sari¹, Darminto²

Journal of Renewable Materials, Vol.12, No.2, pp. 203-214, 2024, DOI:10.32604/jrm.2023.043549

Abstract Nipa palm is one of the non-wood plants rich in lignocellulosic content. In this study, palm fronds were converted into activated carbon, and their physical, chemical, and morphological properties were characterized. The resulting activated carbon was then applied as an adsorbent of Fe metal in peat water. The carbonization process was carried out for 60 min, followed by sintering at 400°C for 5 h with a particle size of 200 mesh. KOH and H₃PO₄ were used in the chemical activation process for 24 h. KOH-activated carbon contained 6.13% of moisture, 4.55% of ash, 17.02% of volatile… More > Graphic Abstract

Mostafa Diba^*, Hossein Khosravi

Intelligent Automation & Soft Computing, Vol.38, No.3, pp. 315-332, 2023, DOI:10.32604/iasc.2023.046590

Abstract Convolutional neural networks continually evolve to enhance accuracy in addressing various problems, leading to an increase in computational cost and model size. This paper introduces a novel approach for pruning face recognition models based on convolutional neural networks. The proposed method identifies and removes inefficient filters based on the information volume in feature maps. In each layer, some feature maps lack useful information, and there exists a correlation between certain feature maps. Filters associated with these two types of feature maps impose additional computational costs on the model. By eliminating filters related to these categories… More >

Deng Qin¹, Xing Du², Tian Li^1,*, Jiye Zhang¹

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 2155-2173, 2024, DOI:10.32604/cmes.2023.044460

Abstract Reducing the aerodynamic drag and noise levels of high-speed pantographs is important for promoting environmentally friendly, energy efficient and rapid advances in train technology. Using computational fluid dynamics theory and the K-FWH acoustic equation, a numerical simulation is conducted to investigate the aerodynamic characteristics of high-speed pantographs. A component optimization method is proposed as a possible solution to the problem of aerodynamic drag and noise in high-speed pantographs. The results of the study indicate that the panhead, base and insulator are the main contributors to aerodynamic drag and noise in high-speed pantographs. Therefore, a gradual… More >