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Search Results (231)
  • Open Access

    ARTICLE

    Real Time Brain Tumor Prediction Using Adaptive Neuro Fuzzy Technique

    Duraimurugan Nagendiran1,*, S. P. Chokkalingam2

    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 983-996, 2022, DOI:10.32604/iasc.2022.023982

    Abstract Uncontrollable growth of cells may lead to brain tumors and may cause permanent damages to the brain or even death. To make early diagnosis and treatment, identifying the position and size of tumors is identified as a tedious and troublesome problem among the existing computer-aided diagnosis systems. Moreover, the progression of tumors may vary among the patients with respect to shape, location, and volume. Therefore, to effectively classify and diagnose the brain tumor images according to severity stages follows the sequence of processing such as pre-processing, segmentation, feature extraction, and classification techniques to carrying out the appropriate treatment. To enhance… More >

  • Open Access

    ARTICLE

    Identification of Bio-Markers for Cancer Classification Using Ensemble Approach and Genetic Algorithm

    K. Poongodi1,*, A. Sabari2

    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 939-953, 2022, DOI:10.32604/iasc.2022.023038

    Abstract The microarray gene expression data has a large number of genes with different expression levels. Analyzing and classifying datasets with entire gene space is quite difficult because there are only a few genes that are informative. The identification of bio-marker genes is significant because it improves the diagnosis of cancer disease and personalized medicine is suggested accordingly. Initially, the parallelized minimum redundancy and maximum relevance ensemble (mRMRe) is employed to select top m informative genes. The selected genes are then fed into the Genetic Algorithm (GA) that selects the optimal set of genes heuristically, which uses Mahalanobis Distance (MD) as… More >

  • Open Access

    ARTICLE

    Feature Selection Based on IoT Aware QDA Node Authentication in 5G Networks

    M. P. Haripriya*, P. Venkadesh

    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 825-836, 2022, DOI:10.32604/iasc.2022.022940

    Abstract The coming generation in mobile networks is the fifth generation (5G), which appears to be the promoter of the upcoming digital world. 5G is defined by a single piece of cellular access technology or a combination of advanced access technologies. Rather, 5G is a true network assembler that provides consistent support for a slew of novel network topologies. Prior generations provide as a suitable starting point and give support for the security architecture for 5G security. Through authentication and cryptography techniques, many works have tackled the security issues in 3G and 4G networks in an effective manner. However, security of… More >

  • Open Access

    ARTICLE

    Mathematical Modelling of Quantum Kernel Method for Biomedical Data Analysis

    Mahmoud Ragab1,2,3, Ehab Bahauden Ashary4, Maha Farouk S. Sabir5, Adel A. Bahaddad5, Romany F. Mansour6,*

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5441-5457, 2022, DOI:10.32604/cmc.2022.024545

    Abstract This study presents a novel method to detect the medical application based on Quantum Computing (QC) and a few Machine Learning (ML) systems. QC has a primary advantage i.e., it uses the impact of quantum parallelism to provide the consequences of prime factorization issue in a matter of seconds. So, this model is suggested for medical application only by recent researchers. A novel strategy i.e., Quantum Kernel Method (QKM) is proposed in this paper for data prediction. In this QKM process, Linear Tunicate Swarm Algorithm (LTSA), the optimization technique is used to calculate the loss function initially and is aimed… More >

  • Open Access

    ARTICLE

    Metaheuristic Optimization Algorithm for Signals Classification of Electroencephalography Channels

    Marwa M. Eid1,*, Fawaz Alassery2, Abdelhameed Ibrahim3, Mohamed Saber4

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4627-4641, 2022, DOI:10.32604/cmc.2022.024043

    Abstract Digital signal processing of electroencephalography (EEG) data is now widely utilized in various applications, including motor imagery classification, seizure detection and prediction, emotion classification, mental task classification, drug impact identification and sleep state classification. With the increasing number of recorded EEG channels, it has become clear that effective channel selection algorithms are required for various applications. Guided Whale Optimization Method (Guided WOA), a suggested feature selection algorithm based on Stochastic Fractal Search (SFS) technique, evaluates the chosen subset of channels. This may be used to select the optimum EEG channels for use in Brain-Computer Interfaces (BCIs), the method for identifying… More >

  • Open Access

    ARTICLE

    Optimized Ensemble Algorithm for Predicting Metamaterial Antenna Parameters

    El-Sayed M. El-kenawy1,2, Abdelhameed Ibrahim3,*, Seyedali Mirjalili4,5, Yu-Dong Zhang6, Shaima Elnazer7,8, Rokaia M. Zaki9,10

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4989-5003, 2022, DOI:10.32604/cmc.2022.023884

    Abstract Metamaterial Antenna is a subclass of antennas that makes use of metamaterial to improve performance. Metamaterial antennas can overcome the bandwidth constraint associated with tiny antennas. Machine learning is receiving a lot of interest in optimizing solutions in a variety of areas. Machine learning methods are already a significant component of ongoing research and are anticipated to play a critical role in today's technology. The accuracy of the forecast is mostly determined by the model used. The purpose of this article is to provide an optimal ensemble model for predicting the bandwidth and gain of the Metamaterial Antenna. Support Vector… More >

  • Open Access

    ARTICLE

    Enhancing Parkinson's Disease Prediction Using Machine Learning and Feature Selection Methods

    Faisal Saeed1,2,*, Mohammad Al-Sarem1,3, Muhannad Al-Mohaimeed1, Abdelhamid Emara1,4, Wadii Boulila1,5, Mohammed Alasli1, Fahad Ghabban1

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5639-5658, 2022, DOI:10.32604/cmc.2022.023124

    Abstract Several millions of people suffer from Parkinson's disease globally. Parkinson's affects about 1% of people over 60 and its symptoms increase with age. The voice may be affected and patients experience abnormalities in speech that might not be noticed by listeners, but which could be analyzed using recorded speech signals. With the huge advancements of technology, the medical data has increased dramatically, and therefore, there is a need to apply data mining and machine learning methods to extract new knowledge from this data. Several classification methods were used to analyze medical data sets and diagnostic problems, such as Parkinson's Disease… More >

  • Open Access

    ARTICLE

    Android Malware Detection Based on Feature Selection and Weight Measurement

    Huizhong Sun1, Guosheng Xu1,*, Zhimin Wu2, Ruijie Quan3

    Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 585-600, 2022, DOI:10.32604/iasc.2022.023874

    Abstract With the rapid development of Android devices, Android is currently one of the most popular mobile operating systems. However, it is also believed to be an entry point of many attack vectors. The existing Android malware detection method does not fare well when dealing with complex and intelligent malware applications, especially those based on feature detection systems which have become increasingly elusive. Therefore, we propose a novel feature selection algorithm called frequency differential selection (FDS) and weight measurement for Android malware detection. The purpose is to solve the shortcomings of the existing feature selection algorithms in detection and to filter… More >

  • Open Access

    ARTICLE

    Tissue specific prediction of N6-methyladenine sites based on an ensemble of multi-input hybrid neural network

    CANGZHI JIA1, DONG JIN1, XIN WANG1, QI ZHAO2,*

    BIOCELL, Vol.46, No.4, pp. 1105-1121, 2022, DOI:10.32604/biocell.2022.016655

    Abstract N6-Methyladenine is a dynamic and reversible post translational modification, which plays an essential role in various biological processes. Because of the current inability to identify m6A-containing mRNAs, computational approaches have been developed to identify m6A sites in DNA sequences. Aiming to improve prediction performance, we introduced a novel ensemble computational approach based on three hybrid deep neural networks, including a convolutional neural network, a capsule network, and a bidirectional gated recurrent unit (BiGRU) with the self-attention mechanism, to identify m6A sites in four tissues of three species. Across a total of 11 datasets, we selected different feature subsets, after optimized… More >

  • Open Access

    ARTICLE

    An Improved DeepNN with Feature Ranking for Covid-19 Detection

    Noha E. El-Attar1,*, Sahar F. Sabbeh1,2, Heba Fasihuddin2, Wael A. Awad3

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2249-2269, 2022, DOI:10.32604/cmc.2022.022673

    Abstract The outbreak of Covid-19 has taken the lives of many patients so far. The symptoms of COVID-19 include muscle pains, loss of taste and smell, coughs, fever, and sore throat, which can lead to severe cases of breathing difficulties, organ failure, and death. Thus, the early detection of the virus is very crucial. COVID-19 can be detected using clinical tests, making us need to know the most important symptoms/features that can enhance the decision process. In this work, we propose a modified multilayer perceptron (MLP) with feature selection (MLPFS) to predict the positive COVID-19 cases based on symptoms and features… More >

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