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  • Open Access

    ARTICLE

    Improved Ant Lion Optimizer with Deep Learning Driven Arabic Hate Speech Detection

    Abdelwahed Motwakel1,*, Badriyya B. Al-onazi2, Jaber S. Alzahrani3, Sana Alazwari4, Mahmoud Othman5, Abu Sarwar Zamani1, Ishfaq Yaseen1, Amgad Atta Abdelmageed1

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3321-3338, 2023, DOI:10.32604/csse.2023.033901

    Abstract Arabic is the world’s first language, categorized by its rich and complicated grammatical formats. Furthermore, the Arabic morphology can be perplexing because nearly 10,000 roots and 900 patterns were the basis for verbs and nouns. The Arabic language consists of distinct variations utilized in a community and particular situations. Social media sites are a medium for expressing opinions and social phenomena like racism, hatred, offensive language, and all kinds of verbal violence. Such conduct does not impact particular nations, communities, or groups only, extending beyond such areas into people’s everyday lives. This study introduces an Improved Ant Lion Optimizer with… More >

  • Open Access

    ARTICLE

    Parameter Tuned Machine Learning Based Emotion Recognition on Arabic Twitter Data

    Ibrahim M. Alwayle1, Badriyya B. Al-onazi2, Jaber S. Alzahrani3, Khaled M. Alalayah1, Khadija M. Alaidarous1, Ibrahim Abdulrab Ahmed4, Mahmoud Othman5, Abdelwahed Motwakel6,*

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3423-3438, 2023, DOI:10.32604/csse.2023.033834

    Abstract Arabic is one of the most spoken languages across the globe. However, there are fewer studies concerning Sentiment Analysis (SA) in Arabic. In recent years, the detected sentiments and emotions expressed in tweets have received significant interest. The substantial role played by the Arab region in international politics and the global economy has urged the need to examine the sentiments and emotions in the Arabic language. Two common models are available: Machine Learning and lexicon-based approaches to address emotion classification problems. With this motivation, the current research article develops a Teaching and Learning Optimization with Machine Learning Based Emotion Recognition… More >

  • Open Access

    ARTICLE

    Energy Based Random Repeat Trust Computation in Delay Tolerant Network

    S. Dheenathayalan*, B. Paramasivan

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 2845-2859, 2023, DOI:10.32604/csse.2023.033326

    Abstract As the use of mobile devices continues to rise, trust administration will significantly improve security in routing the guaranteed quality of service (QoS) supply in Mobile Ad Hoc Networks (MANET) due to the mobility of the nodes. There is no continuance of network communication between nodes in a delay-tolerant network (DTN). DTN is designed to complete recurring connections between nodes. This approach proposes a dynamic source routing protocol (DSR) based on a feed-forward neural network (FFNN) and energy-based random repetition trust calculation in DTN. If another node is looking for a node that swerved off of its path in this… More >

  • Open Access

    ARTICLE

    Deep Learning Algorithm for Detection of Protein Remote Homology

    Fahriye Gemci1,*, Turgay Ibrikci2, Ulus Cevik3

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3703-3713, 2023, DOI:10.32604/csse.2023.032706

    Abstract The study aims to find a successful solution by using computer algorithms to detect remote homologous proteins, which is a significant problem in the bioinformatics field. In this experimental study, structural classification of proteins (SCOP) 1.53, SCOP benchmark, and the newly created SCOP protein database from the structural classification of proteins—extended (SCOPe) 2.07 were used to detect remote homolog proteins. N-gram method and then Term Frequency-Inverse Document Frequency (TF-IDF) weighting were performed to extract features of the protein sequences taken from these databases. Next, a smoothing process on the obtained features was performed to avoid misclassification. Finally, the proteins with… More >

  • Open Access

    ARTICLE

    Classification of Gastric Lesions Using Gabor Block Local Binary Patterns

    Muhammad Tahir1,*, Farhan Riaz2, Imran Usman1,3, Mohamed Ibrahim Habib1

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 4007-4022, 2023, DOI:10.32604/csse.2023.032359

    Abstract The identification of cancer tissues in Gastroenterology imaging poses novel challenges to the computer vision community in designing generic decision support systems. This generic nature demands the image descriptors to be invariant to illumination gradients, scaling, homogeneous illumination, and rotation. In this article, we devise a novel feature extraction methodology, which explores the effectiveness of Gabor filters coupled with Block Local Binary Patterns in designing such descriptors. We effectively exploit the illumination invariance properties of Block Local Binary Patterns and the inherent capability of convolutional neural networks to construct novel rotation, scale and illumination invariant features. The invariance characteristics of… More >

  • Open Access

    ARTICLE

    Fusing Satellite Images Using ABC Optimizing Algorithm

    Nguyen Hai Minh1, Nguyen Tu Trung2,*, Tran Thi Ngan2, Tran Manh Tuan2

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3901-3909, 2023, DOI:10.32604/csse.2023.032311

    Abstract Fusing satellite (remote sensing) images is an interesting topic in processing satellite images. The result image is achieved through fusing information from spectral and panchromatic images for sharpening. In this paper, a new algorithm based on based the Artificial bee colony (ABC) algorithm with peak signal-to-noise ratio (PSNR) index optimization is proposed to fusing remote sensing images in this paper. Firstly, Wavelet transform is used to split the input images into components over the high and low frequency domains. Then, two fusing rules are used for obtaining the fused images. The first rule is “the high frequency components are fused… More >

  • Open Access

    ARTICLE

    Automatic Sentimental Analysis by Firefly with Levy and Multilayer Perceptron

    D. Elangovan1,*, V. Subedha2

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 2797-2808, 2023, DOI:10.32604/csse.2023.031988

    Abstract The field of sentiment analysis (SA) has grown in tandem with the aid of social networking platforms to exchange opinions and ideas. Many people share their views and ideas around the world through social media like Facebook and Twitter. The goal of opinion mining, commonly referred to as sentiment analysis, is to categorise and forecast a target’s opinion. Depending on if they provide a positive or negative perspective on a given topic, text documents or sentences can be classified. When compared to sentiment analysis, text categorization may appear to be a simple process, but number of challenges have prompted numerous… More >

  • Open Access

    ARTICLE

    Modelling a Fused Deep Network Model for Pneumonia Prediction

    M. A. Ramitha*, N. Mohanasundaram, R. Santhosh

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 2725-2739, 2023, DOI:10.32604/csse.2023.030504

    Abstract Deep Learning (DL) is known for its golden standard computing paradigm in the learning community. However, it turns out to be an extensively utilized computing approach in the ML field. Therefore, attaining superior outcomes over cognitive tasks based on human performance. The primary benefit of DL is its competency in learning massive data. The DL-based technologies have grown faster and are widely adopted to handle the conventional approaches resourcefully. Specifically, various DL approaches outperform the conventional ML approaches in real-time applications. Indeed, various research works are reviewed to understand the significance of the individual DL models and some computational complexity… More >

  • Open Access

    ARTICLE

    Image Fusion Based on NSCT and Sparse Representation for Remote Sensing Data

    N. A. Lawrance*, T. S. Shiny Angel

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3439-3455, 2023, DOI:10.32604/csse.2023.030311

    Abstract The practice of integrating images from two or more sensors collected from the same area or object is known as image fusion. The goal is to extract more spatial and spectral information from the resulting fused image than from the component images. The images must be fused to improve the spatial and spectral quality of both panchromatic and multispectral images. This study provides a novel picture fusion technique that employs L0 smoothening Filter, Non-subsampled Contour let Transform (NSCT) and Sparse Representation (SR) followed by the Max absolute rule (MAR). The fusion approach is as follows: first, the multispectral and panchromatic… More >

  • Open Access

    ARTICLE

    Anomaly Detection and Classification in Streaming PMU Data in Smart Grids

    A. L. Amutha1, R. Annie Uthra1,*, J. Preetha Roselyn2, R. Golda Brunet3

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3387-3401, 2023, DOI:10.32604/csse.2023.029904

    Abstract The invention of Phasor Measurement Units (PMUs) produce synchronized phasor measurements with high resolution real time monitoring and control of power system in smart grids that make possible. PMUs are used in transmitting data to Phasor Data Concentrators (PDC) placed in control centers for monitoring purpose. A primary concern of system operators in control centers is maintaining safe and efficient operation of the power grid. This can be achieved by continuous monitoring of the PMU data that contains both normal and abnormal data. The normal data indicates the normal behavior of the grid whereas the abnormal data indicates fault or… More >

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