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  • Open Access

    ARTICLE

    Fuzzy Difference Equations in Diagnoses of Glaucoma from Retinal Images Using Deep Learning

    D. Dorathy Prema Kavitha1, L. Francis Raj1, Sandeep Kautish2,#, Abdulaziz S. Almazyad3, Karam M. Sallam4, Ali Wagdy Mohamed5,6,*

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

    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 and involved utilizing the Convolutional… More >

  • Open Access

    ARTICLE

    Hyper-Tuned Convolutional Neural Networks for Authorship Verification in Digital Forensic Investigations

    Asif Rahim1, Yanru Zhong2, Tariq Ahmad3,*, Sadique Ahmad4,*, Mohammed A. ElAffendi4

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

    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). The model is trained and… More >

  • Open Access

    ARTICLE

    Baseline Isolated Printed Text Image Database for Pashto Script Recognition

    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

    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 autonomous recognition of the cursive… More >

  • Open Access

    ARTICLE

    Strategy for Rapid Diabetic Retinopathy Exposure Based on Enhanced Feature Extraction Processing

    V. Banupriya1,*, S. Anusuya2

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

    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 the lesions are high. Similarly,… More >

  • Open Access

    ARTICLE

    Deep Learning ResNet101 Deep Features of Portable Chest X-Ray Accurately Classify COVID-19 Lung Infection

    Sobia Nawaz1, Sidra Rasheed2, Wania Sami3, Lal Hussain4,5,*, Amjad Aldweesh6,*, Elsayed Tag eldin7, Umair Ahmad Salaria8,9, Mohammad Shahbaz Khan10

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

    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 Interventional Radiology (SIRM), Radiopaedia, The… More >

  • Open Access

    ARTICLE

    Deep Learning for Multivariate Prediction of Building Energy Performance of Residential Buildings

    Ibrahim Aliyu1, Tai-Won Um2, Sang-Joon Lee3, Chang Gyoon Lim4,*, Jinsul Kim1,*

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5947-5964, 2023, DOI:10.32604/cmc.2023.037202

    Abstract In the quest to minimize energy waste, the energy performance of buildings (EPB) has been a focus because building appliances, such as heating, ventilation, and air conditioning, consume the highest energy. Therefore, effective design and planning for estimating heating load (HL) and cooling load (CL) for energy saving have become paramount. In this vein, efforts have been made to predict the HL and CL using a univariate approach. However, this approach necessitates two models for learning HL and CL, requiring more computational time. Moreover, the one-dimensional (1D) convolutional neural network (CNN) has gained popularity due to its nominal computational complexity,… More >

  • Open Access

    ARTICLE

    An Innovative Bispectral Deep Learning Method for Protein Family Classification

    Isam Abu-Qasmieh, Amjed Al Fahoum*, Hiam Alquran, Ala’a Zyout

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3971-3991, 2023, DOI:10.32604/cmc.2023.037431

    Abstract Proteins are essential for many biological functions. For example, folding amino acid chains reveals their functionalities by maintaining tissue structure, physiology, and homeostasis. Note that quantifiable protein characteristics are vital for improving therapies and precision medicine. The automatic inference of a protein’s properties from its amino acid sequence is called “basic structure”. Nevertheless, it remains a critical unsolved challenge in bioinformatics, although with recent technological advances and the investigation of protein sequence data. Inferring protein function from amino acid sequences is crucial in biology. This study considers using raw sequencing to explain biological facts using a large corpus of protein… More >

  • Open Access

    ARTICLE

    Enhanced Deep Learning for Detecting Suspicious Fall Event in Video Data

    Madhuri Agrawal*, Shikha Agrawal

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2653-2667, 2023, DOI:10.32604/iasc.2023.033493

    Abstract

    Suspicious fall events are particularly significant hazards for the safety of patients and elders. Recently, suspicious fall event detection has become a robust research case in real-time monitoring. This paper aims to detect suspicious fall events during video monitoring of multiple people in different moving backgrounds in an indoor environment; it is further proposed to use a deep learning method known as Long Short Term Memory (LSTM) by introducing visual attention-guided mechanism along with a bi-directional LSTM model. This method contributes essential information on the temporal and spatial locations of ‘suspicious fall’ events in learning the video frame in both… More >

  • Open Access

    ARTICLE

    A General Linguistic Steganalysis Framework Using Multi-Task Learning

    Lingyun Xiang1,*, Rong Wang1, Yuhang Liu1, Yangfan Liu1, Lina Tan2,3

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 2383-2399, 2023, DOI:10.32604/csse.2023.037067

    Abstract Prevailing linguistic steganalysis approaches focus on learning sensitive features to distinguish a particular category of steganographic texts from non-steganographic texts, by performing binary classification. While it remains an unsolved problem and poses a significant threat to the security of cyberspace when various categories of non-steganographic or steganographic texts coexist. In this paper, we propose a general linguistic steganalysis framework named LS-MTL, which introduces the idea of multi-task learning to deal with the classification of various categories of steganographic and non-steganographic texts. LS-MTL captures sensitive linguistic features from multiple related linguistic steganalysis tasks and can concurrently handle diverse tasks with a… More >

  • Open Access

    ARTICLE

    Firefly-CDDL: A Firefly-Based Algorithm for Cyberbullying Detection Based on Deep Learning

    Monirah Al-Ajlan*, Mourad Ykhlef

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 19-34, 2023, DOI:10.32604/cmc.2023.033753

    Abstract There are several ethical issues that have arisen in recent years due to the ubiquity of the Internet and the popularity of social media and community platforms. Among them is cyberbullying, which is defined as any violent intentional action that is repeatedly conducted by individuals or groups using online channels against victims who are not able to react effectively. An alarmingly high percentage of people, especially teenagers, have reported being cyberbullied in recent years. A variety of approaches have been developed to detect cyberbullying, but they require time-consuming feature extraction and selection processes. Moreover, no approach to date has examined… More >

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