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 study introduces a novel hybrid… More >

Abidhan Bardhan^1,*, Raushan Kumar Singh², Mohammed Alatiyyah³, Sulaiman Abdullah Alateyah^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1521-1555, 2024, DOI:10.32604/cmes.2023.044467

Abstract This research proposes a highly effective soft computing paradigm for estimating the compressive strength (CS) of metakaolin-contained cemented materials. The proposed approach is a combination of an enhanced grey wolf optimizer (EGWO) and an extreme learning machine (ELM). EGWO is an augmented form of the classic grey wolf optimizer (GWO). Compared to standard GWO, EGWO has a better hunting mechanism and produces an optimal performance. The EGWO was used to optimize the ELM structure and a hybrid model, ELM-EGWO, was built. To train and validate the proposed ELM-EGWO model, a sum of 361 experimental results featuring five influencing factors was… More >

Weisi Chen^1,*, Walayat Hussain^2,*, Francesco Cauteruccio³, Xu Zhang¹

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 187-224, 2024, DOI:10.32604/cmes.2023.031388

Abstract Financial time series prediction, whether for classification or regression, has been a heated research topic over the last decade. While traditional machine learning algorithms have experienced mediocre results, deep learning has largely contributed to the elevation of the prediction performance. Currently, the most up-to-date review of advanced machine learning techniques for financial time series prediction is still lacking, making it challenging for finance domain experts and relevant practitioners to determine which model potentially performs better, what techniques and components are involved, and how the model can be designed and implemented. This review article provides an overview of techniques, components and… More > Graphic Abstract

Yinghua Song^1,2, Hairong Lyu^1,2, Wei Zhang^1,2,*

Journal on Big Data, Vol.5, pp. 19-40, 2023, DOI:10.32604/jbd.2023.038249

Abstract A precise and timely forecast of short-term rail transit passenger flow provides data support for traffic management and operation, assisting rail operators in efficiently allocating resources and timely relieving pressure on passenger safety and operation. First, the passenger flow sequence models in the study are broken down using VMD for noise reduction. The objective environment features are then added to the characteristic factors that affect the passenger flow. The target station serves as an additional spatial feature and is mined concurrently using the KNN algorithm. It is shown that the hybrid model VMD-CLSMT has a higher prediction accuracy, by setting… More >

Baydaa Abdul Kareem^1,2, Salah L. Zubaidi^2,3, Nadhir Al-Ansari^4,*, Yousif Raad Muhsen^2,5

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 1-41, 2024, DOI:10.32604/cmes.2023.027954

Abstract Forecasting river flow is crucial for optimal planning, management, and sustainability using freshwater resources. Many machine learning (ML) approaches have been enhanced to improve streamflow prediction. Hybrid techniques have been viewed as a viable method for enhancing the accuracy of univariate streamflow estimation when compared to standalone approaches. Current researchers have also emphasised using hybrid models to improve forecast accuracy. Accordingly, this paper conducts an updated literature review of applications of hybrid models in estimating streamflow over the last five years, summarising data preprocessing, univariate machine learning modelling strategy, advantages and disadvantages of standalone ML techniques, hybrid models, and performance… More > Graphic Abstract

Nguyen Minh Trieu, Nguyen Truong Thinh^*

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2381-2396, 2023, DOI:10.32604/csse.2023.040030

Abstract Nowadays, wood identification is made by experts using hand lenses, wood atlases, and field manuals which take a lot of cost and time for the training process. The quantity and species must be strictly set up, and accurate identification of the wood species must be made during exploitation to monitor trade and enforce regulations to stop illegal logging. With the development of science, wood identification should be supported with technology to enhance the perception of fairness of trade. An automatic wood identification system and a dataset of 50 commercial wood species from Asia are established, namely, wood anatomical images collected… More >

Volkan Kaya¹, İsmail Akgül¹, Özge Zencir Tanır^2,*

Phyton-International Journal of Experimental Botany, Vol.92, No.9, pp. 2519-2534, 2023, DOI:10.32604/phyton.2023.029811

Abstract Classification and monitoring of microalgae species in aquatic ecosystems are important for understanding population dynamics. However, manual classification of algae is a time-consuming method and requires a lot of effort with expertise due to the large number of families and genera in its classification. The recognition of microalgae species has become an increasingly important research area in image recognition in recent years. In this study, machine learning and deep learning methods were proposed to classify images of 12 different microalgae species in order to successfully classify algae cells. 8 Different novel models (MobileNetV3Small-Lr, MobileNetV3SmallRf, MobileNetV3Small-Xg, MobileNetV3Large-Lr, MobileNetV3Large-Rf, MobileNetV3Large-Xg, MobileNetV3Small-Improved and… More >

Saleh Naif Almuayqil¹, Reham Arnous^2,*, Noha Sakr³, Magdy M. Fadel³

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5177-5192, 2023, DOI:10.32604/cmc.2023.038625

Abstract Skin segmentation participates significantly in various biomedical applications, such as skin cancer identification and skin lesion detection. This paper presents a novel framework for segmenting the skin. The framework contains two main stages: The first stage is for removing different types of noises from the dermoscopic images, such as hair, speckle, and impulse noise, and the second stage is for segmentation of the dermoscopic images using an attention residual U-shaped Network (U-Net). The framework uses variational Autoencoders (VAEs) for removing the hair noises, the Generative Adversarial Denoising Network (DGAN-Net), the Denoising U-shaped U-Net (D-U-NET), and Batch Renormalization U-Net (Br-U-NET) for… More >

Yuxin Chen¹, Weixun Yong¹, Chuanqi Li², Jian Zhou^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2507-2526, 2023, DOI:10.32604/cmes.2023.025714

Abstract After the excavation of the roadway, the original stress balance is destroyed, resulting in the redistribution of stress and the formation of an excavation damaged zone (EDZ) around the roadway. The thickness of EDZ is the key basis for roadway stability discrimination and support structure design, and it is of great engineering significance to accurately predict the thickness of EDZ. Considering the advantages of machine learning (ML) in dealing with high-dimensional, nonlinear problems, a hybrid prediction model based on the random forest (RF) algorithm is developed in this paper. The model used the dragonfly algorithm (DA) to optimize two hyperparameters… More >

Hicham Lamtougui^1,*, Hicham El Moubtahij², Hassan Fouadi¹, Khalid Satori¹

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2871-2888, 2023, DOI:10.32604/cmc.2023.032550

Abstract In recent years, Deep Learning models have become indispensable in several fields such as computer vision, automatic object recognition, and automatic natural language processing. The implementation of a robust and efficient handwritten text recognition system remains a challenge for the research community in this field, especially for the Arabic language, which, compared to other languages, has a dearth of published works. In this work, we presented an efficient and new system for offline Arabic handwritten text recognition. Our new approach is based on the combination of a Convolutional Neural Network (CNN) and a Bidirectional Long-Term Memory (BLSTM) followed by a… More >