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Search Results (21)
  • Open Access


    Developing a Breast Cancer Resistance Protein Substrate Prediction System Using Deep Features and LDA

    Mehdi Hassan1,2, Safdar Ali3, Jin Young Kim2,*, Muhammad Sanaullah4, Hani Alquhayz5, Khushbakht Safdar6

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1643-1663, 2023, DOI:10.32604/cmc.2023.038578

    Abstract Breast cancer resistance protein (BCRP) is an important resistance protein that significantly impacts anticancer drug discovery, treatment, and rehabilitation. Early identification of BCRP substrates is quite a challenging task. This study aims to predict early substrate structure, which can help to optimize anticancer drug development and clinical diagnosis. For this study, a novel intelligent approach-based methodology is developed by modifying the ResNet101 model using transfer learning (TL) for automatic deep feature (DF) extraction followed by classification with linear discriminant analysis algorithm (TLRNDF-LDA). This study utilized structural fingerprints, which are exploited by DF contrary to conventional molecular descriptors. The proposed in… More >

  • Open Access


    Facial Expression Recognition Model Depending on Optimized Support Vector Machine

    Amel Ali Alhussan1, Fatma M. Talaat2, El-Sayed M. El-kenawy3, Abdelaziz A. Abdelhamid4,5, Abdelhameed Ibrahim6, Doaa Sami Khafaga1,*, Mona Alnaggar7

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 499-515, 2023, DOI:10.32604/cmc.2023.039368

    Abstract In computer vision, emotion recognition using facial expression images is considered an important research issue. Deep learning advances in recent years have aided in attaining improved results in this issue. According to recent studies, multiple facial expressions may be included in facial photographs representing a particular type of emotion. It is feasible and useful to convert face photos into collections of visual words and carry out global expression recognition. The main contribution of this paper is to propose a facial expression recognition model (FERM) depending on an optimized Support Vector Machine (SVM). To test the performance of the proposed model… More >

  • Open Access


    Construction of Intelligent Recommendation Retrieval Model of FuJian Intangible Cultural Heritage Digital Archives Resources

    Xueqing Liao*

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 677-690, 2023, DOI:10.32604/iasc.2023.037219

    Abstract In order to improve the consistency between the recommended retrieval results and user needs, improve the recommendation efficiency, and reduce the average absolute deviation of resource retrieval, a design method of intelligent recommendation retrieval model for Fujian intangible cultural heritage digital archive resources based on knowledge atlas is proposed. The TG-LDA (Tag-granularity LDA) model is proposed on the basis of the standard LDA (Linear Discriminant Analysis) model. The model is used to mine archive resource topics. The Pearson correlation coefficient is used to measure the relevance between topics. Based on the measurement results, the FastText deep learning model is used… More >

  • Open Access


    Cyberbullying Detection and Recognition with Type Determination Based on Machine Learning

    Khalid M. O. Nahar1,*, Mohammad Alauthman2, Saud Yonbawi3, Ammar Almomani4,5

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5307-5319, 2023, DOI:10.32604/cmc.2023.031848

    Abstract Social media networks are becoming essential to our daily activities, and many issues are due to this great involvement in our lives. Cyberbullying is a social media network issue, a global crisis affecting the victims and society as a whole. It results from a misunderstanding regarding freedom of speech. In this work, we proposed a methodology for detecting such behaviors (bullying, harassment, and hate-related texts) using supervised machine learning algorithms (SVM, Naïve Bayes, Logistic regression, and random forest) and for predicting a topic associated with these text data using unsupervised natural language processing, such as latent Dirichlet allocation. In addition,… More >

  • Open Access


    Wrapper Based Linear Discriminant Analysis (LDA) for Intrusion Detection in IIoT

    B. Yasotha1,*, T. Sasikala2, M. Krishnamurthy3

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1625-1640, 2023, DOI:10.32604/csse.2023.025669

    Abstract The internet has become a part of every human life. Also, various devices that are connected through the internet are increasing. Nowadays, the Industrial Internet of things (IIoT) is an evolutionary technology interconnecting various industries in digital platforms to facilitate their development. Moreover, IIoT is being used in various industrial fields such as logistics, manufacturing, metals and mining, gas and oil, transportation, aviation, and energy utilities. It is mandatory that various industrial fields require highly reliable security and preventive measures against cyber-attacks. Intrusion detection is defined as the detection in the network of security threats targeting privacy information and sensitive… More >

  • Open Access


    A Quasi-Newton Neural Network Based Efficient Intrusion Detection System for Wireless Sensor Network

    A. Gautami1,*, J. Shanthini2, S. Karthik3

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 427-443, 2023, DOI:10.32604/csse.2023.026688

    Abstract In Wireless Sensor Networks (WSN), attacks mostly aim in limiting or eliminating the capability of the network to do its normal function. Detecting this misbehaviour is a demanding issue. And so far the prevailing research methods show poor performance. AQN3 centred efficient Intrusion Detection Systems (IDS) is proposed in WSN to ameliorate the performance. The proposed system encompasses Data Gathering (DG) in WSN as well as Intrusion Detection (ID) phases. In DG, the Sensor Nodes (SN) is formed as clusters in the WSN and the Distance-based Fruit Fly Fuzzy c-means (DFFF) algorithm chooses the Cluster Head (CH). Then, the data… More >

  • Open Access


    Anti-cancer effects of sitagliptin, vildagliptin, and exendin-4 on triple-negative breast cancer cells via mitochondrial modulation


    BIOCELL, Vol.46, No.12, pp. 2645-2657, 2022, DOI:10.32604/biocell.2022.021754

    Abstract Triple-negative breast cancer (TNBC) cell line MDA-MB-231 is known for Warburg metabolism and defects in mitochondria. On the other hand, dipeptidyl peptidase-IV (DPP-IV) inhibitors such as sitagliptin and vildagliptin and GLP-1 agonist exendin-4 are known to improve mitochondrial functions as well as biogenesis, but no study has evaluated the influence of these drugs on mitochondrial biogenesis on metastatic breast cancer cell line. We have recently reported anticancer effects of 5-aminoimidazole-4-carboxamide riboside on MDA-MB-231 cells via activation of AMP-dependent kinase (AMPK), which activates the downstream transcription factors PGC-1α, PGC-1β, or FOXO1 for mitochondrial biogenesis; above-mentioned incretin-based therapies are also known to… More >

  • Open Access


    Shrinkage Linear with Quadratic Gaussian Discriminant Analysis for Big Data Classification

    R. S. Latha1, K. Venkatachalam2, Jehad F. Al-Amri3, Mohamed Abouhawwash4,5,*

    Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1803-1818, 2022, DOI:10.32604/iasc.2022.024539

    Abstract Generation of massive data is increasing in big data industries due to the evolution of modern technologies. The big data industries include data source from sensors, Internet of Things, digital and social media. In particular, these big data systems consist of data extraction, preprocessing, integration, analysis, and visualization mechanism. The data encountered from the sources are redundant, incomplete and conflict. Moreover, in real time applications, it is a tedious process for the interpretation of all the data from different sources. In this paper, the gathered data are preprocessed to handle the issues such as redundant, incomplete and conflict. For that,… More >

  • Open Access


    Incremental Linear Discriminant Analysis Dimensionality Reduction and 3D Dynamic Hierarchical Clustering WSNs

    G. Divya Mohana Priya1,*, M. Karthikeyan1, K. Murugan2

    Computer Systems Science and Engineering, Vol.43, No.2, pp. 471-486, 2022, DOI:10.32604/csse.2022.021023

    Abstract Optimizing the sensor energy is one of the most important concern in Three-Dimensional (3D) Wireless Sensor Networks (WSNs). An improved dynamic hierarchical clustering has been used in previous works that computes optimum clusters count and thus, the total consumption of energy is optimal. However, the computational complexity will be increased due to data dimension, and this leads to increase in delay in network data transmission and reception. For solving the above-mentioned issues, an efficient dimensionality reduction model based on Incremental Linear Discriminant Analysis (ILDA) is proposed for 3D hierarchical clustering WSNs. The major objective of the proposed work is to… More >

  • Open Access


    Parkinson's Detection Using RNN-Graph-LSTM with Optimization Based on Speech Signals

    Ahmed S. Almasoud1, Taiseer Abdalla Elfadil Eisa2, Fahd N. Al-Wesabi3,4, Abubakar Elsafi5, Mesfer Al Duhayyim6, Ishfaq Yaseen7, Manar Ahmed Hamza7,*, Abdelwahed Motwakel7

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 871-886, 2022, DOI:10.32604/cmc.2022.024596

    Abstract Early detection of Parkinson's Disease (PD) using the PD patients’ voice changes would avoid the intervention before the identification of physical symptoms. Various machine learning algorithms were developed to detect PD detection. Nevertheless, these ML methods are lack in generalization and reduced classification performance due to subject overlap. To overcome these issues, this proposed work apply graph long short term memory (GLSTM) model to classify the dynamic features of the PD patient speech signal. The proposed classification model has been further improved by implementing the recurrent neural network (RNN) in batch normalization layer of GLSTM and optimized with adaptive moment… More >

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