Supriya Gupta^*, Aakanksha Sharaff, Naresh Kumar Nagwani

Computer Systems Science and Engineering, Vol.45, No.3, pp. 2333-2349, 2023, DOI:10.32604/csse.2023.030385

Abstract Text Summarization models facilitate biomedical clinicians and researchers in acquiring informative data from enormous domain-specific literature within less time and effort. Evaluating and selecting the most informative sentences from biomedical articles is always challenging. This study aims to develop a dual-mode biomedical text summarization model to achieve enhanced coverage and information. The research also includes checking the fitment of appropriate graph ranking techniques for improved performance of the summarization model. The input biomedical text is mapped as a graph where meaningful sentences are evaluated as the central node and the critical associations between them. The proposed framework utilizes the top… More >

Huong Thi Thanh Ngo¹, Nguyen Duc Dam¹, Quynh-Anh Thi Bui¹, Nadhir Al-Ansari^2,*, Romulus Costache^3,4,*, Hang Ha⁵, Quynh Duy Bui⁵, Sy Hung Mai⁶, Indra Prakash⁷, Binh Thai Pham¹

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 2219-2241, 2023, DOI:10.32604/cmes.2023.022566

Abstract Flash floods are one of the most dangerous natural disasters, especially in hilly terrain, causing loss of life, property, and infrastructures and sudden disruption of traffic. These types of floods are mostly associated with landslides and erosion of roads within a short time. Most of Vietnam is hilly and mountainous; thus, the problem due to flash flood is severe and requires systematic studies to correctly identify flood susceptible areas for proper landuse planning and traffic management. In this study, three Machine Learning (ML) methods namely Deep Learning Neural Network (DL), Correlation-based Feature Weighted Naive Bayes (CFWNB), and Adaboost (AB-CFWNB) were… More >

Farhad Soleimanian Gharehchopogh^1,*, Benyamin Abdollahzadeh¹, Bahman Arasteh²

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 1981-2006, 2023, DOI:10.32604/cmes.2023.024172

Abstract Travelling Salesman Problem (TSP) is a discrete hybrid optimization problem considered NP-hard. TSP aims to discover the shortest Hamilton route that visits each city precisely once and then returns to the starting point, making it the shortest route feasible. This paper employed a Farmland Fertility Algorithm (FFA) inspired by agricultural land fertility and a hyper-heuristic technique based on the Modified Choice Function (MCF). The neighborhood search operator can use this strategy to automatically select the best heuristic method for making the best decision. Lin-Kernighan (LK) local search has been incorporated to increase the efficiency and performance of this suggested approach.… More >

Muhammad Junaid Umer¹, Muhammad Sharif¹, Majed Alhaisoni², Usman Tariq³, Ye Jin Kim⁴, Byoungchol Chang^5,*

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1001-1016, 2023, DOI:10.32604/csse.2023.030463

Abstract Breast cancer (BC) is a most spreading and deadly cancerous malady which is mostly diagnosed in middle-aged women worldwide and effecting beyond a half-million people every year. The BC positive newly diagnosed cases in 2018 reached 2.1 million around the world with a death rate of 11.6% of total cases. Early diagnosis and detection of breast cancer disease with proper treatment may reduce the number of deaths. The gold standard for BC detection is biopsy analysis which needs an expert for correct diagnosis. Manual diagnosis of BC is a complex and challenging task. This work proposed a deep learning-based (DL)… More >

Mahima Dahiya^*, Nitin Nitin

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4183-4201, 2023, DOI:10.32604/cmc.2023.032430

Abstract Intrusion detection is critical to guaranteeing the safety of the data in the network. Even though, since Internet commerce has grown at a breakneck pace, network traffic kinds are rising daily, and network behavior characteristics are becoming increasingly complicated, posing significant hurdles to intrusion detection. The challenges in terms of false positives, false negatives, low detection accuracy, high running time, adversarial attacks, uncertain attacks, etc. lead to insecure Intrusion Detection System (IDS). To offset the existing challenge, the work has developed a secure Data Mining Intrusion detection system (DataMIDS) framework using Functional Perturbation (FP) feature selection and Bengio Nesterov Momentum-based… More >

Changyong Chu^1,2,*, Chengfang Yin¹, Shuo Shi¹, Shaohui Su¹, Chang Chen¹

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.2, pp. 1413-1434, 2023, DOI:10.32604/cmes.2022.022395

Abstract To enhance the efficiency of system modeling and optimization in the conceptual design stage of satellite parameters, a system modeling and optimization method based on System Modeling Language and Co-evolutionary Algorithm is proposed. At first, the objectives of satellite mission and optimization problems are clarified, and a design matrix of discipline structure is constructed to process the coupling relationship of design variables and constraints of the orbit, payload, power and quality disciplines. In order to solve the problem of increasing non-linearity and coupling between these disciplines while using a standard collaborative optimization algorithm, an improved genetic algorithm is proposed and… More > Graphic Abstract

Y. Nishio^1,*, B. Janssens¹, K. Limam², J. van Beeck³

FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.3, pp. 743-753, 2023, DOI:10.32604/fdmp.2022.021848

Abstract A LES model is proposed to predict the dispersion of particles in the atmosphere in the context of Chemical, Biological, Radiological and Nuclear (CBRN) applications. The code relies on the Finite Element Method (FEM) for both the fluid and the dispersed solid phases. Starting from the Navier-Stokes equations and a general description of the FEM strategy, the Streamline Upwind Petrov-Galerkin (SUPG) method is formulated putting some emphasis on the related assembly matrix and stabilization coefficients. Then, the Variational Multiscale Method (VMS) is presented together with a detailed illustration of its algorithm and hierarchy of computational steps. It is demonstrated that… More >

K. Venkatesh^1,*, S. Pasupathy¹, S. P. Raja²

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 543-560, 2023, DOI:10.32604/iasc.2023.030701

Abstract The evolution of bone marrow morphology is necessary in Acute Myeloid Leukemia (AML) prediction. It takes an enormous number of times to analyze with the standardization and inter-observer variability. Here, we proposed a novel AML detection model using a Deep Convolutional Neural Network (D-CNN). The proposed Faster R-CNN (Faster Region-Based CNN) models are trained with Morphological Dataset. The proposed Faster R-CNN model is trained using the augmented dataset. For overcoming the Imbalanced Data problem, data augmentation techniques are imposed. The Faster R-CNN performance was compared with existing transfer learning techniques. The results show that the Faster R-CNN performance was significant… More >

Chao-Hsi Huang¹, Tsung-Shun Hsieh^2,3, Hsiao-Ting Chien⁴, Ehsan Eftekhari-Zadeh^5,*, Saba Amiri⁶

International Journal of Mental Health Promotion, Vol.24, No.6, pp. 825-841, 2022, DOI:10.32604/ijmhp.2022.021899

Abstract In addition to the effect that the COVID-19 pandemic has had on the physical and mental health of individuals, it has also led to a change in the mental and emotional state of many employees. Especially among businesses and private companies, which faced many restrictions due to the special conditions of the pandemic. Therefore, the present study aimed to design an artificial neural network with MLP technique to analyze the relationship between demographic variables, resilience, COVID-19 and burnout in start-ups in Iran. The research method was quantitative. Managers and employees of start-ups formed the statistical population of the study, based… More >

Jieren Cheng^1,3, Hua Li^2,*, Dengbo Li³, Shuai Hua², Victor S. Sheng⁴

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1941-1957, 2023, DOI:10.32604/cmc.2023.032757

Abstract Image semantic segmentation is an important branch of computer vision of a wide variety of practical applications such as medical image analysis, autonomous driving, virtual or augmented reality, etc. In recent years, due to the remarkable performance of transformer and multilayer perceptron (MLP) in computer vision, which is equivalent to convolutional neural network (CNN), there has been a substantial amount of image semantic segmentation works aimed at developing different types of deep learning architecture. This survey aims to provide a comprehensive overview of deep learning methods in the field of general image semantic segmentation. Firstly, the commonly used image segmentation… More >