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  • 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 - 31 March 2023

    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… 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 - 15 March 2023

    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

    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 - 09 February 2023

    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… 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 - 06 February 2023

    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… More >

  • Open Access

    ARTICLE

    Multi-label Emotion Classification of COVID–19 Tweets with Deep Learning and Topic Modelling

    K. Anuratha1,*, M. Parvathy2

    Computer Systems Science and Engineering, Vol.45, No.3, pp. 3005-3021, 2023, DOI:10.32604/csse.2023.031553 - 21 December 2022

    Abstract The COVID-19 pandemic has become one of the severe diseases in recent years. As it majorly affects the common livelihood of people across the universe, it is essential for administrators and healthcare professionals to be aware of the views of the community so as to monitor the severity of the spread of the outbreak. The public opinions are been shared enormously in microblogging media like twitter and is considered as one of the popular sources to collect public opinions in any topic like politics, sports, entertainment etc., This work presents a combination of Intensity Based… More >

  • Open Access

    ARTICLE

    An Efficient Hybrid Model for Arabic Text Recognition

    Hicham Lamtougui1,*, Hicham El Moubtahij2, Hassan Fouadi1, Khalid Satori1

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2871-2888, 2023, DOI:10.32604/cmc.2023.032550 - 31 October 2022

    Abstract In recent years, Deep Learning models have become indispensable in several fields such as computer vision, automatic object recognition, and automatic natural language processing. The implementation of a robust and efficient handwritten text recognition system remains a challenge for the research community in this field, especially for the Arabic language, which, compared to other languages, has a dearth of published works. In this work, we presented an efficient and new system for offline Arabic handwritten text recognition. Our new approach is based on the combination of a Convolutional Neural Network (CNN) and a Bidirectional Long-Term More >

  • Open Access

    ARTICLE

    A Construction of Object Detection Model for Acute Myeloid Leukemia

    K. Venkatesh1,*, S. Pasupathy1, S. P. Raja2

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 543-560, 2023, DOI:10.32604/iasc.2023.030701 - 29 September 2022

    Abstract The evolution of bone marrow morphology is necessary in Acute Myeloid Leukemia (AML) prediction. It takes an enormous number of times to analyze with the standardization and inter-observer variability. Here, we proposed a novel AML detection model using a Deep Convolutional Neural Network (D-CNN). The proposed Faster R-CNN (Faster Region-Based CNN) models are trained with Morphological Dataset. The proposed Faster R-CNN model is trained using the augmented dataset. For overcoming the Imbalanced Data problem, data augmentation techniques are imposed. The Faster R-CNN performance was compared with existing transfer learning techniques. The results show that the More >

  • Open Access

    ARTICLE

    Masked Face Recognition Using MobileNet V2 with Transfer Learning

    Ratnesh Kumar Shukla1,*, Arvind Kumar Tiwari2

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 293-309, 2023, DOI:10.32604/csse.2023.027986 - 16 August 2022

    Abstract Corona virus (COVID-19) is once in a life time calamity that has resulted in thousands of deaths and security concerns. People are using face masks on a regular basis to protect themselves and to help reduce corona virus transmission. During the on-going coronavirus outbreak, one of the major priorities for researchers is to discover effective solution. As important parts of the face are obscured, face identification and verification becomes exceedingly difficult. The suggested method is a transfer learning using MobileNet V2 based technology that uses deep feature such as feature extraction and deep learning model,… More >

  • Open Access

    ARTICLE

    Hybrid Deep Learning Method for Diagnosis of Cucurbita Leaf Diseases

    V. Nirmala1,*, B. Gomathy2

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2585-2601, 2023, DOI:10.32604/csse.2023.027512 - 01 August 2022

    Abstract In agricultural engineering, the main challenge is on methodologies used for disease detection. The manual methods depend on the experience of the personal. Due to large variation in environmental condition, disease diagnosis and classification becomes a challenging task. Apart from the disease, the leaves are affected by climate changes which is hard for the image processing method to discriminate the disease from the other background. In Cucurbita gourd family, the disease severity examination of leaf samples through computer vision, and deep learning methodologies have gained popularity in recent years. In this paper, a hybrid method More >

  • Open Access

    ARTICLE

    Detecting Deepfake Images Using Deep Learning Techniques and Explainable AI Methods

    Wahidul Hasan Abir1, Faria Rahman Khanam1, Kazi Nabiul Alam1, Myriam Hadjouni2, Hela Elmannai3, Sami Bourouis4, Rajesh Dey5, Mohammad Monirujjaman Khan1,*

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2151-2169, 2023, DOI:10.32604/iasc.2023.029653 - 19 July 2022

    Abstract Nowadays, deepfake is wreaking havoc on society. Deepfake content is created with the help of artificial intelligence and machine learning to replace one person’s likeness with another person in pictures or recorded videos. Although visual media manipulations are not new, the introduction of deepfakes has marked a breakthrough in creating fake media and information. These manipulated pictures and videos will undoubtedly have an enormous societal impact. Deepfake uses the latest technology like Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) to construct automated methods for creating fake content that is becoming increasingly difficult… More >

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