Mehmood Ahmed^1,3,*, Noraini B. Ibrahim¹, Wasif Nisar², Adeel Ahmed³, Muhammad Junaid^3,*, Emmanuel Soriano Flores⁴, Divya Anand⁴

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1399-1422, 2024, DOI:10.32604/cmc.2023.046648

Abstract Accurate software cost estimation in Global Software Development (GSD) remains challenging due to reliance on historical data and expert judgments. Traditional models, such as the Constructive Cost Model (COCOMO II), rely heavily on historical and accurate data. In addition, expert judgment is required to set many input parameters, which can introduce subjectivity and variability in the estimation process. Consequently, there is a need to improve the current GSD models to mitigate reliance on historical data, subjectivity in expert judgment, inadequate consideration of GSD-based cost drivers and limited integration of modern technologies with cost overruns. This… More >

Xinyu Hu, Defeng Kong^*, Xiyang Liu, Junwei Zhang, Daode Zhang

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 915-933, 2024, DOI:10.32604/cmc.2023.046376

Abstract Printed Circuit Board (PCB) surface tiny defect detection is a difficult task in the integrated circuit industry, especially since the detection of tiny defects on PCB boards with large-size complex circuits has become one of the bottlenecks. To improve the performance of PCB surface tiny defects detection, a PCB tiny defects detection model based on an improved attention residual network (YOLOX-AttResNet) is proposed. First, the unsupervised clustering performance of the K-means algorithm is exploited to optimize the channel weights for subsequent operations by feeding the feature mapping into the SENet (Squeeze and Excitation Network) attention… More >

Zhihua Liu, Shengquan Liu^*, Jian Zhang

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 411-433, 2024, DOI:10.32604/cmc.2023.046237

Abstract Network intrusion detection systems (NIDS) based on deep learning have continued to make significant advances. However, the following challenges remain: on the one hand, simply applying only Temporal Convolutional Networks (TCNs) can lead to models that ignore the impact of network traffic features at different scales on the detection performance. On the other hand, some intrusion detection methods consider multi-scale information of traffic data, but considering only forward network traffic information can lead to deficiencies in capturing multi-scale temporal features. To address both of these issues, we propose a hybrid Convolutional Neural Network that supports… More >

Zahid Farooq Khan¹, Muhammad Ramzan^1,*, Mudassar Raza¹, Muhammad Attique Khan^2,3, Khalid Iqbal⁴, Taerang Kim⁵, Jae-Hyuk Cha⁵

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1207-1225, 2024, DOI:10.32604/cmc.2023.045491

Abstract Accurate detection and classification of artifacts within the gastrointestinal (GI) tract frames remain a significant challenge in medical image processing. Medical science combined with artificial intelligence is advancing to automate the diagnosis and treatment of numerous diseases. Key to this is the development of robust algorithms for image classification and detection, crucial in designing sophisticated systems for diagnosis and treatment. This study makes a small contribution to endoscopic image classification. The proposed approach involves multiple operations, including extracting deep features from endoscopy images using pre-trained neural networks such as Darknet-53 and Xception. Additionally, feature optimization… More >

Kolli Ramujee^1,*, Pooja Sadula¹, Golla Madhu², Sandeep Kautish³, Abdulaziz S. Almazyad⁴, Guojiang Xiong⁵, Ali Wagdy Mohamed^6,7,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1455-1486, 2024, DOI:10.32604/cmes.2023.043384

Abstract Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental problems. Its attributes as a non-toxic, low-carbon, and economical substitute for conventional cement concrete, coupled with its elevated compressive strength and reduced shrinkage properties, position it as a pivotal material for diverse applications spanning from architectural structures to transportation infrastructure. In this context, this study sets out the task of using machine learning (ML) algorithms to increase the accuracy and interpretability of predicting the compressive strength of geopolymer concrete in the civil engineering field. To achieve this goal,… More >

Yuchen Zhou¹, Hongtao Huo¹, Zhiwen Hou¹, Lingbin Bu¹, Yifan Wang¹, Jingyi Mao¹, Xiaojun Lv², Fanliang Bu^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 537-563, 2024, DOI:10.32604/cmes.2023.044895

Abstract Graph Convolutional Neural Networks (GCNs) have been widely used in various fields due to their powerful capabilities in processing graph-structured data. However, GCNs encounter significant challenges when applied to scale-free graphs with power-law distributions, resulting in substantial distortions. Moreover, most of the existing GCN models are shallow structures, which restricts their ability to capture dependencies among distant nodes and more refined high-order node features in scale-free graphs with hierarchical structures. To more broadly and precisely apply GCNs to real-world graphs exhibiting scale-free or hierarchical structures and utilize multi-level aggregation of GCNs for capturing high-level information… More >

Mohammed Zakariah¹, Salman A. AlQahtani^2,*, Abdulaziz M. Alawwad¹, Abdullilah A. Alotaibi³

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 4025-4054, 2023, DOI:10.32604/cmc.2023.043752

Abstract Modern networks are at risk from a variety of threats as a result of the enormous growth in internet-based traffic. By consuming time and resources, intrusive traffic hampers the efficient operation of network infrastructure. An effective strategy for preventing, detecting, and mitigating intrusion incidents will increase productivity. A crucial element of secure network traffic is Intrusion Detection System (IDS). An IDS system may be host-based or network-based to monitor intrusive network activity. Finding unusual internet traffic has become a severe security risk for intelligent devices. These systems are negatively impacted by several attacks, which are… More >

Manas Ranjan Prusty¹, Rishi Dinesh², Hariket Sukesh Kumar Sheth², Alapati Lakshmi Viswanath², Sandeep Kumar Satapathy^2,3,*

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3077-3094, 2023, DOI:10.32604/cmc.2023.042718

Abstract This paper presents a novel computerized technique for the segmentation of nuclei in hematoxylin and eosin (H&E) stained histopathology images. The purpose of this study is to overcome the challenges faced in automated nuclei segmentation due to the diversity of nuclei structures that arise from differences in tissue types and staining protocols, as well as the segmentation of variable-sized and overlapping nuclei. To this extent, the approach proposed in this study uses an ensemble of the UNet architecture with various Convolutional Neural Networks (CNN) architectures as encoder backbones, along with stain normalization and test time… More >

Shaik Thaseentaj, S. Sudhakar Ilango^*

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3593-3618, 2023, DOI:10.32604/cmc.2023.042496

Abstract The South Indian mango industry is confronting severe threats due to various leaf diseases, which significantly impact the yield and quality of the crop. The management and prevention of these diseases depend mainly on their early identification and accurate classification. The central objective of this research is to propose and examine the application of Deep Convolutional Neural Networks (CNNs) as a potential solution for the precise detection and categorization of diseases impacting the leaves of South Indian mango trees. Our study collected a rich dataset of leaf images representing different disease classes, including Anthracnose, Powdery… More >

Doaa Sami Khafaga¹, Zongming Lv², Imran Khan^3,4, Shebnam M. Sefat⁵, Amel Ali Alhussan^1,*

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3723-3740, 2023, DOI:10.32604/cmc.2023.039956

Abstract With the capacities of self-learning, acquainted capacities, high-speed looking for ideal arrangements, solid nonlinear fitting, and mapping self-assertively complex nonlinear relations, neural systems have made incredible advances and accomplished broad application over the final half-century. As one of the foremost conspicuous methods for fake insights, neural systems are growing toward high computational speed and moo control utilization. Due to the inborn impediments of electronic gadgets, it may be troublesome for electronic-implemented neural systems to make the strides these two exhibitions encourage. Optical neural systems can combine optoelectronic procedures and neural organization models to provide ways… More >