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  • Open Access

    ARTICLE

    A Pattern Classification Model for Vowel Data Using Fuzzy Nearest Neighbor

    Monika Khandelwal1, Ranjeet Kumar Rout1, Saiyed Umer2, Kshira Sagar Sahoo3, NZ Jhanjhi4,*, Mohammad Shorfuzzaman5, Mehedi Masud5

    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3587-3598, 2023, DOI:10.32604/iasc.2023.029785

    Abstract Classification of the patterns is a crucial structure of research and applications. Using fuzzy set theory, classifying the patterns has become of great interest because of its ability to understand the parameters. One of the problems observed in the fuzzification of an unknown pattern is that importance is given only to the known patterns but not to their features. In contrast, features of the patterns play an essential role when their respective patterns overlap. In this paper, an optimal fuzzy nearest neighbor model has been introduced in which a fuzzification process has been carried out for the unknown pattern using… More >

  • Open Access

    ARTICLE

    Sailfish Optimization with Deep Learning Based Oral Cancer Classification Model

    Mesfer Al Duhayyim1,*, Areej A. Malibari2, Sami Dhahbi3, Mohamed K. Nour4, Isra Al-Turaiki5, Marwa Obayya6, Abdullah Mohamed7

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 753-767, 2023, DOI:10.32604/csse.2023.030556

    Abstract Recently, computer aided diagnosis (CAD) model becomes an effective tool for decision making in healthcare sector. The advances in computer vision and artificial intelligence (AI) techniques have resulted in the effective design of CAD models, which enables to detection of the existence of diseases using various imaging modalities. Oral cancer (OC) has commonly occurred in head and neck globally. Earlier identification of OC enables to improve survival rate and reduce mortality rate. Therefore, the design of CAD model for OC detection and classification becomes essential. Therefore, this study introduces a novel Computer Aided Diagnosis for OC using Sailfish Optimization with… More >

  • Open Access

    ARTICLE

    Classification Model for IDS Using Auto Cryptographic Denoising Technique

    N. Karthikeyan2, P. Sivaprakash1,*, S. Karthik2

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 671-685, 2023, DOI:10.32604/csse.2023.029984

    Abstract Intrusion detection systems (IDS) are one of the most promising ways for securing data and networks; In recent decades, IDS has used a variety of categorization algorithms. These classifiers, on the other hand, do not work effectively unless they are combined with additional algorithms that can alter the classifier’s parameters or select the optimal sub-set of features for the problem. Optimizers are used in tandem with classifiers to increase the stability and with efficiency of the classifiers in detecting invasion. These algorithms, on the other hand, have a number of limitations, particularly when used to detect new types of threats.… More >

  • Open Access

    ARTICLE

    Metaheuristic with Deep Learning Enabled Biomedical Bone Age Assessment and Classification Model

    Mesfer Al Duhayyim1,*, Areej A. Malibari2, Marwa Obayya3, Mohamed K. Nour4, Ahmed S. Salama5, Mohamed I. Eldesouki6, Abu Sarwar Zamani7, Mohammed Rizwanullah7

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5473-5489, 2022, DOI:10.32604/cmc.2022.031976

    Abstract The skeletal bone age assessment (BAA) was extremely implemented in development prediction and auxiliary analysis of medicinal issues. X-ray images of hands were detected from the estimation of bone age, whereas the ossification centers of epiphysis and carpal bones are important regions. The typical skeletal BAA approaches remove these regions for predicting the bone age, however, few of them attain suitable efficacy or accuracy. Automatic BAA techniques with deep learning (DL) methods are reached the leading efficiency on manual and typical approaches. Therefore, this study introduces an intellectual skeletal bone age assessment and classification with the use of metaheuristic with… More >

  • Open Access

    ARTICLE

    Hunger Search Optimization with Hybrid Deep Learning Enabled Phishing Detection and Classification Model

    Hadil Shaiba1, Jaber S. Alzahrani2, Majdy M. Eltahir3, Radwa Marzouk4, Heba Mohsen5, Manar Ahmed Hamza6,*

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 6425-6441, 2022, DOI:10.32604/cmc.2022.031625

    Abstract Phishing is one of the simplest ways in cybercrime to hack the reliable data of users such as passwords, account identifiers, bank details, etc. In general, these kinds of cyberattacks are made at users through phone calls, emails, or instant messages. The anti-phishing techniques, currently under use, are mainly based on source code features that need to scrape the webpage content. In third party services, these techniques check the classification procedure of phishing Uniform Resource Locators (URLs). Even though Machine Learning (ML) techniques have been lately utilized in the identification of phishing, they still need to undergo feature engineering since… More >

  • Open Access

    ARTICLE

    A Deep Trash Classification Model on Raspberry Pi 4

    Thien Khai Tran1, Kha Tu Huynh2,*, Dac-Nhuong Le3, Muhammad Arif4, Hoa Minh Dinh1

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2479-2491, 2023, DOI:10.32604/iasc.2023.029078

    Abstract Environmental pollution has had substantial impacts on human life, and trash is one of the main sources of such pollution in most countries. Trash classification from a collection of trash images can limit the overloading of garbage disposal systems and efficiently promote recycling activities; thus, development of such a classification system is topical and urgent. This paper proposed an effective trash classification system that relies on a classification module embedded in a hard-ware setup to classify trash in real time. An image dataset is first augmented to enhance the images before classifying them as either inorganic or organic trash. The… More >

  • Open Access

    ARTICLE

    Gaussian Optimized Deep Learning-based Belief Classification Model for Breast Cancer Detection

    Areej A. Malibari1, Marwa Obayya2, Mohamed K. Nour3, Amal S. Mehanna4, Manar Ahmed Hamza5,*, Abu Sarwar Zamani5, Ishfaq Yaseen5, Abdelwahed Motwakel5

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4123-4138, 2022, DOI:10.32604/cmc.2022.030492

    Abstract With the rapid increase of new cases with an increased mortality rate, cancer is considered the second and most deadly disease globally. Breast cancer is the most widely affected cancer worldwide, with an increased death rate percentage. Due to radiologists’ processing of mammogram images, many computer-aided diagnoses have been developed to detect breast cancer. Early detection of breast cancer will reduce the death rate worldwide. The early diagnosis of breast cancer using the developed computer-aided diagnosis (CAD) systems still needed to be enhanced by incorporating innovative deep learning technologies to improve the accuracy and sensitivity of the detection system with… More >

  • Open Access

    ARTICLE

    Sign Language Recognition and Classification Model to Enhance Quality of Disabled People

    Fadwa Alrowais1, Saud S. Alotaibi2, Sami Dhahbi3,4, Radwa Marzouk5, Abdullah Mohamed6, Anwer Mustafa Hilal7,*

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3419-3432, 2022, DOI:10.32604/cmc.2022.029438

    Abstract Sign language recognition can be considered as an effective solution for disabled people to communicate with others. It helps them in conveying the intended information using sign languages without any challenges. Recent advancements in computer vision and image processing techniques can be leveraged to detect and classify the signs used by disabled people in an effective manner. Metaheuristic optimization algorithms can be designed in a manner such that it fine tunes the hyper parameters, used in Deep Learning (DL) models as the latter considerably impacts the classification results. With this motivation, the current study designs the Optimal Deep Transfer Learning… More >

  • Open Access

    ARTICLE

    Deep Transfer Learning Driven Oral Cancer Detection and Classification Model

    Radwa Marzouk1, Eatedal Alabdulkreem2, Sami Dhahbi3, Mohamed K. Nour4, Mesfer Al Duhayyim5, Mahmoud Othman6, Manar Ahmed Hamza7,*, Abdelwahed Motwakel7, Ishfaq Yaseen7, Mohammed Rizwanullah7

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3905-3920, 2022, DOI:10.32604/cmc.2022.029326

    Abstract Oral cancer is the most commonly occurring ‘head and neck cancers’ across the globe. Most of the oral cancer cases are diagnosed at later stages due to absence of awareness among public. Since earlier identification of disease is essential for improved outcomes, Artificial Intelligence (AI) and Machine Learning (ML) models are used in this regard. In this background, the current study introduces Artificial Intelligence with Deep Transfer Learning driven Oral Cancer detection and Classification Model (AIDTL-OCCM). The primary goal of the proposed AIDTL-OCCM model is to diagnose oral cancer using AI and image processing techniques. The proposed AIDTL-OCCM model involves… More >

  • Open Access

    ARTICLE

    An Imbalanced Dataset and Class Overlapping Classification Model for Big Data

    Mini Prince1,*, P. M. Joe Prathap2

    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1009-1024, 2023, DOI:10.32604/csse.2023.024277

    Abstract Most modern technologies, such as social media, smart cities, and the internet of things (IoT), rely on big data. When big data is used in the real-world applications, two data challenges such as class overlap and class imbalance arises. When dealing with large datasets, most traditional classifiers are stuck in the local optimum problem. As a result, it’s necessary to look into new methods for dealing with large data collections. Several solutions have been proposed for overcoming this issue. The rapid growth of the available data threatens to limit the usefulness of many traditional methods. Methods such as oversampling and… More >

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