Farhad Mortezapour Shiri^*, Thinagaran Perumal, Norwati Mustapha, Raihani Mohamed

Journal on Artificial Intelligence, Vol.6, pp. 301-360, 2024, DOI:10.32604/jai.2024.054314 - 20 November 2024

Abstract Deep learning (DL) has emerged as a powerful subset of machine learning (ML) and artificial intelligence (AI), outperforming traditional ML methods, especially in handling unstructured and large datasets. Its impact spans across various domains, including speech recognition, healthcare, autonomous vehicles, cybersecurity, predictive analytics, and more. However, the complexity and dynamic nature of real-world problems present challenges in designing effective deep learning models. Consequently, several deep learning models have been developed to address different problems and applications. In this article, we conduct a comprehensive survey of various deep learning models, including Convolutional Neural Network (CNN), Recurrent… More >

Yadong Xu¹, Weixing Hong², Mohammad Noori^3,6,*, Wael A. Altabey^4,*, Ahmed Silik⁵, Nabeel S.D. Farhan²

Structural Durability & Health Monitoring, Vol.18, No.6, pp. 763-783, 2024, DOI:10.32604/sdhm.2024.053763 - 20 September 2024

Abstract This study introduces an innovative “Big Model” strategy to enhance Bridge Structural Health Monitoring (SHM) using a Convolutional Neural Network (CNN), time-frequency analysis, and fine element analysis. Leveraging ensemble methods, collaborative learning, and distributed computing, the approach effectively manages the complexity and scale of large-scale bridge data. The CNN employs transfer learning, fine-tuning, and continuous monitoring to optimize models for adaptive and accurate structural health assessments, focusing on extracting meaningful features through time-frequency analysis. By integrating Finite Element Analysis, time-frequency analysis, and CNNs, the strategy provides a comprehensive understanding of bridge health. Utilizing diverse sensor More >

Md. Abdur Rahim¹, Fahmid Al Farid², Abu Saleh Musa Miah³, Arpa Kar Puza¹, Md. Nur Alam⁴, Md. Najmul Hossain⁵, Sarina Mansor², Hezerul Abdul Karim^2,6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1689-1710, 2024, DOI:10.32604/cmes.2024.048714 - 20 May 2024

Abstract Handwriting is a unique and significant human feature that distinguishes them from one another. There are many researchers have endeavored to develop writing recognition systems utilizing specific signatures or symbols for person identification through verification. However, such systems are susceptible to forgery, posing security risks. In response to these challenges, we propose an innovative hybrid technique for individual identification based on independent handwriting, eliminating the reliance on specific signatures or symbols. In response to these challenges, we propose an innovative hybrid technique for individual identification based on independent handwriting, eliminating the reliance on specific signatures… More >

Li Wang^1,2,3,*, Mingshan Xia^1,2,*, Hao Hu¹, Jianfang Li^1,2, Fengyao Hou^1,2, Gang Chen^1,2,3

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2991-3006, 2024, DOI:10.32604/cmc.2024.048637 - 15 May 2024

Abstract With the rapid development of the mobile communication and the Internet, the previous web anomaly detection and identification models were built relying on security experts’ empirical knowledge and attack features. Although this approach can achieve higher detection performance, it requires huge human labor and resources to maintain the feature library. In contrast, semantic feature engineering can dynamically discover new semantic features and optimize feature selection by automatically analyzing the semantic information contained in the data itself, thus reducing dependence on prior knowledge. However, current semantic features still have the problem of semantic expression singularity, as… More >

Syeda Shamaila Zareen^1,*, Guangmin Sun^1,*, Mahwish Kundi², Syed Furqan Qadri³, Salman Qadri⁴

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1497-1519, 2024, DOI:10.32604/cmc.2024.047418 - 25 April 2024

Abstract Skin cancer diagnosis is difficult due to lesion presentation variability. Conventional methods struggle to manually extract features and capture lesions spatial and temporal variations. This study introduces a deep learning-based Convolutional and Recurrent Neural Network (CNN-RNN) model with a ResNet-50 architecture which used as the feature extractor to enhance skin cancer classification. Leveraging synergistic spatial feature extraction and temporal sequence learning, the model demonstrates robust performance on a dataset of 9000 skin lesion photos from nine cancer types. Using pre-trained ResNet-50 for spatial data extraction and Long Short-Term Memory (LSTM) for temporal dependencies, the model More >

D. Dorathy Prema Kavitha¹, L. Francis Raj¹, Sandeep Kautish^2,#, Abdulaziz S. Almazyad³, Karam M. Sallam⁴, Ali Wagdy Mohamed^5,6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 801-816, 2024, DOI:10.32604/cmes.2023.030902 - 30 December 2023

Abstract The intuitive fuzzy set has found important application in decision-making and machine learning. To enrich and utilize the intuitive fuzzy set, this study designed and developed a deep neural network-based glaucoma eye detection using fuzzy difference equations in the domain where the retinal images converge. Retinal image detections are categorized as normal eye recognition, suspected glaucomatous eye recognition, and glaucomatous eye recognition. Fuzzy degrees associated with weighted values are calculated to determine the level of concentration between the fuzzy partition and the retinal images. The proposed model was used to diagnose glaucoma using retinal images… More >

Asif Rahim¹, Yanru Zhong², Tariq Ahmad^3,*, Sadique Ahmad^4,*, Mohammed A. ElAffendi⁴

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1947-1976, 2023, DOI:10.32604/cmc.2023.039340 - 30 August 2023

Abstract Authorship verification is a crucial task in digital forensic investigations, where it is often necessary to determine whether a specific individual wrote a particular piece of text. Convolutional Neural Networks (CNNs) have shown promise in solving this problem, but their performance highly depends on the choice of hyperparameters. In this paper, we explore the effectiveness of hyperparameter tuning in improving the performance of CNNs for authorship verification. We conduct experiments using a Hyper Tuned CNN model with three popular optimization algorithms: Adaptive Moment Estimation (ADAM), Stochastic Gradient Descent (SGD), and Root Mean Squared Propagation (RMSPROP).… More >

Arfa Siddiqu, Abdul Basit^*, Waheed Noor, Muhammad Asfandyar Khan, M. Saeed H. Kakar, Azam Khan

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 875-885, 2023, DOI:10.32604/iasc.2023.036426 - 29 April 2023

Abstract The optical character recognition for the right to left and cursive languages such as Arabic is challenging and received little attention from researchers in the past compared to the other Latin languages. Moreover, the absence of a standard publicly available dataset for several low-resource languages, including the Pashto language remained a hurdle in the advancement of language processing. Realizing that, a clean dataset is the fundamental and core requirement of character recognition, this research begins with dataset generation and aims at a system capable of complete language understanding. Keeping in view the complete and full… More >

V. Banupriya^1,*, S. Anusuya²

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5597-5613, 2023, DOI:10.32604/cmc.2023.038696 - 29 April 2023

Abstract In the modern world, one of the most severe eye infections brought on by diabetes is known as diabetic retinopathy (DR), which will result in retinal damage, and, thus, lead to blindness. Diabetic retinopathy (DR) can be well treated with early diagnosis. Retinal fundus images of humans are used to screen for lesions in the retina. However, detecting DR in the early stages is challenging due to the minimal symptoms. Furthermore, the occurrence of diseases linked to vascular anomalies brought on by DR aids in diagnosing the condition. Nevertheless, the resources required for manually identifying… More >

Sobia Nawaz¹, Sidra Rasheed², Wania Sami³, Lal Hussain^4,5,*, Amjad Aldweesh^6,*, Elsayed Tag eldin⁷, Umair Ahmad Salaria^8,9, Mohammad Shahbaz Khan¹⁰

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5213-5228, 2023, DOI:10.32604/cmc.2023.037543 - 29 April 2023

Abstract This study is designed to develop Artificial Intelligence (AI) based analysis tool that could accurately detect COVID-19 lung infections based on portable chest x-rays (CXRs). The frontline physicians and radiologists suffer from grand challenges for COVID-19 pandemic due to the suboptimal image quality and the large volume of CXRs. In this study, AI-based analysis tools were developed that can precisely classify COVID-19 lung infection. Publicly available datasets of COVID-19 (N = 1525), non-COVID-19 normal (N = 1525), viral pneumonia (N = 1342) and bacterial pneumonia (N = 2521) from the Italian Society of Medical and… More >